Running head: LEAN BODY MASS IN MENOPAUSE Association of Lean Body Mass to Menopausal Symptoms in the Study of Women’s Health Across the Nation A DISSERTATION Submitted to the Faculty of the School of Graduate Studies and Research of California University of Pennsylvania in partial fulfillment of the requirements for the degree of Doctor of Health Science (DHSc) in Health Sciences and Exercise Leadership By Rosanne Woods Research Adviser, Dr. Rebecca Hess California, Pennsylvania 2018 CALIFORNIA UNIVERSITY of PENNSYLVANIA CALIFORNIA, PA LEAN BODY MASS IN MENOPAUSE DISSERTATION APPROVAL Health Science and Exercise Leadership We hereby approve the Dissertation of Rosanne Woods Candidate for the degree of Doctor of Health Sciences (DHSc) LEAN BODY MASS IN MENOPAUSE ACKNOWLEDGEMENTS This has been an amazing process from which I have grown as a student of academics, a researcher and as a person. For that I would like to thank my research advisor, Dr. Rebecca Hess, for her direction, support, encouragement and expertise in preparing my dissertation. Additionally, I would like to thank my research committee members, Dr. Marc Federico and Dr. Carol Biddington for your guidance and support on this journey and Dr. Melissa Sovak for her expertise and guidance on the statistical analysis. I would also like thank Dr. Jeffery Hatton for having my back and going to bat for me. Without the support I received from all of you, I would not have accomplished my dream. And lastly, I am so very grateful to my family. To my husband, who continues to be my biggest fan and my greatest source of strength, your unconditional support and unwavering confidence in me motivates me every day. To my babies, Alee, Jake, Joe, Ben and Sam, who didn’t see much of their Momma some days, thank you for never complaining and for allowing me the time to get my work done. And to Brodie & Leah who helped both financially, and with love, support and encouragement, I thank you. This would not have been possible without the grace and love of all of you. I am truly blessed and appreciate and love every one of you more than you know. LEAN BODY MASS IN MENOPAUSE TABLE OF CONTENTS Page List of Figures ...................................................................................................................... i List of Tables ...................................................................................................................... ii Abstract .............................................................................................................................. iii Introduction ..........................................................................................................................1 Methods................................................................................................................................6 Research Design.......................................................................................................6 Participants ...............................................................................................................7 Instruments ...............................................................................................................8 Procedures ..............................................................................................................12 Data Analysis .........................................................................................................14 Results ................................................................................................................................16 SWAN Study Data .................................................................................................16 Study Sample Characteristics ................................................................................17 Hypotheses Testing ................................................................................................21 Predicted Probabilities ...........................................................................................23 Discussion ..........................................................................................................................24 Conclusion .........................................................................................................................30 LEAN BODY MASS IN MENOPAUSE Future Directions for Research ..........................................................................................31 References ..........................................................................................................................32 Appendix A – Review of the Literature .............................................................................45 Appendix B – Problem Statement......................................................................................88 Appendix C – Additional Methodology Appendix C1 – Informed Consent .........................................................................91 Appendix C2 – IRB Review Request Approval ..................................................104 Appendix C3 – Certificates of IRB and SAS Training ........................................106 References ........................................................................................................................109 LEAN BODY MASS IN MENOPAUSE LIST OF FIGURES Page Figure 1 Timeline of SWAN participant visits ........................................................14 Figure 2 Association of body composition variables at baseline grouped by VMS with symptoms = 1 represents ‘any’ and symptoms = 0, ‘none’ .......................19 Figure 3 Correlation of Mean SMI to Mean FMI over time ....................................20 Figure 4 Mean SMI, FMI and FFMI at each visit grouped by symptoms ...............21 Figure 5 Predicted probability of symptoms occurring at given levels of SMI (adjusted for FMI) in women with no VMS at baseline .................................................23 Figure 6 Menopausal status of study cohort by visit ................................................24 Figure 7 Characteristics of remaining cohort ...........................................................25 Figure 8 Symptoms and deleterious effects of menopause ......................................49 Figure 9 Relationship between MetS components and cardiometabolic complications ....................................................................................................................54 Figure10 Menopause-related changes in muscle mass and its impact on functional status ....................................................................................................................57 LEAN BODY MASS IN MENOPAUSE LIST OF TABLES Page Table 1 Baseline characteristics by symptom status .....................................................18 Table 2 Association of VMS to LBM ...........................................................................22 LEAN BODY MASS IN MENOPAUSE Abstract Vasomotor symptoms (VMS) are experienced by most women as they transition through menopause, but their etiology is incompletely understood as is their relationship to body composition. While the association of VMS to body fat, body weight, and body mass index has been broadly researched, little is known about the role of lean body mass. The purpose of this research was to examine the association of lean body mass (LBM) to the development of VMS as women transition through menopause. Data from 2,533 participants in the longitudinal Study of Women’s Health Across the Nation (SWAN) who provided bioelectrical impedance (BIA) measurements over five visits, was accessed and examined. Women who reported no VMS at baseline were modelled for concurrent association of skeletal muscle mass and fat free mass with VMS, and additionally for percent change since prior visit and percent change since baseline. Adjusted models included covariates of fat mass, age, race/ethnicity, education, and menopausal status. VMS with concurrent LBM was significant in unadjusted (p <.0001) and adjusted models (p = .036). Percent change since prior visit and since baseline models were significant (adjusted p = .003, p <.001; unadjusted p = .009, p = .001) and overall mean association remained significant (p = .007, p = .023). Associations at each visit were not significant in logistic regression. Predicted probabilities of VMS showed a negative correlation to lean body mass for all visits and overall regression analysis. These results suggest that maintaining higher levels of LBM as women progress through menopause may protect against the development of VMS. Keywords: Lean body mass – vasomotor symptoms – hot flashes – night sweats skeletal muscle mass – fat free mass LEAN BODY MASS IN MENOPAUSE 1 Association of Lean Body Mass to Menopausal Symptoms in the Study of Women’s Health Across the Nation Menopause is a significant event in many women’s lives as it marks the end of the natural reproductive life. For most women, menopause will occur between the ages of 40 and 58 years with the average being 51 years (Zapantis & Santoro, 2003). The Stages of Reproductive Aging Workshop +10 (Harlow et al., 2012) classifies the transition through menopause into five stages. Early menopause (variable) and late menopausal (1-3 years) both precede the final menstrual period (FMP). The FMP marks the start of the postmenopausal stages, early postmenopause (1-2 years), postmenopause (3-6 years), and late postmenopause (remaining lifespan). The menopause transition is often accompanied by a variety of bothersome symptoms (vasomotor, psychological, psychosocial, urogenital) that are uniquely experienced and impact quality of life for menopausal women. About 80-96% of women experience mild to severe physical or physiological menopause-related complaints as they approach menopause due to declining estrogen levels (Moilanen et al., 2012) which may include hot flashes and night sweats, depression, irritability, sleep disorders, increased abdominal fat mass, increased prevalence of metabolic syndrome, and increased risk of cardiovascular disease (Stefanska et al., 2015). Hot flashes and night sweats are the most common symptoms of menopause and are collectively referred to as vasomotor symptoms (VMS) due to the dilation of blood vessels during an episode. They are reported as feelings of intense warmth along with sweating, flushing (in the face, neck and chest), and chills (Gallichio et al., 2014). It is reported that 60-80% of women will experience VMS at some point during the menopausal transition (Thurston & Joffe, 2011). LEAN BODY MASS IN MENOPAUSE 2 Recent research has been focused on examining the correlation of obesity, BMI, and adiposity to the presence and burden of VMS (Da Fonseca et al., 2013; Gallichio et al., 2014; Gjelsvik et al., 2011; Herber-Gast, et al., 2013; Kazlauskaite et al., 2015; Thurston, Chang, Mancuso, & Matthews, 2013). It was originally hypothesized that body fat protected against VMS because of the aromatization of androgens to estrogens in fat tissue (Kershaw & Flier, 2004), but early studies showed an association of higher BMI and body fat with greater VMS reporting (Freeman et al., 2001; Thurston et al., 2009; Thurston et al., 2008a). Further research has shown that higher adiposity is a risk factor for VMS in early postmenopause but is a protective factor in late menopause (Abdulnour et al, 2012; Thurston et al., 2013; Thurston et al, 2008b). Thurston et al. (2009) examined the relationship between body fat change to VMS over time in 1,659 women aged 47-59 and found that body fat gains were associated with greater hot flush reporting during the menopausal transition. To date, several risk factors for VMS have been identified including race/ethnicity, obesity, smoking, and diet (Thurston & Joffe, 2011), however, no studies have been conducted on the association of lean body mass (LBM) to VMS or other menopausal symptoms. As they transition through menopause, women will simultaneously experience a decrease in basal metabolic rate and a loss of lean muscle tissue which increases the risk of weight gain and obesity (Lovejoy, 2009). Loss of muscle mass and function normally associated with aging is referred to as sarcopenia. In women, sarcopenia develops and is highly prevalent during menopause, and is primarily due to an imbalance between muscle protein synthesis and breakdown, contributed to by an increase in oxidative stress, proinflammation markers, and hormonal changes (Messier et al., 2011). Oxidative stress is LEAN BODY MASS IN MENOPAUSE 3 caused by the imbalance between the production of reactive oxygen species (free radicals) and antioxidant defenses resulting in tissue damage and its increase during menopause is attributed to aging (Mittal & Kant, 2009). Sarcopenic obesity, which refers to elevated body fat mass combined with reduced muscle mass, has also been proposed as characterizing age-related changes in body composition (Roubenoff, 2004). Evidence indicates that muscle strength and quality (ratio of muscle strength to mass) may be negatively associated with the severity of menopausal symptoms due to declining levels of sex hormones and the resulting increase in oxidative stress (Lee & Lee, 2013). The authors also suggested that there is a vicious cycle of increased oxidative stress during menopause that may induce the loss of muscle strength, and in turn, the loss of muscle strength increases oxidative stress (Lee & Lee, 2013). Postmenopausal women have been shown to have significantly higher oxidative stress blood marker levels and lower antioxidant capacity relative to premenopausal women (Signorelli et al., 2006) which is thought to be correlated to their higher body weight (Mittal & Kant, 2009). Body weight gain at midlife is frequently reported by women; studies have shown that an increase in subcutaneous adipose tissue is related to age whereas the increase in visceral and total body fat is related to the abrupt decline in estrogen (Abdulnour et al., 2012; Lovejoy et al., 2008). This accumulation of abdominal fat in postmenopausal women appears to be a critical factor in the development of insulin resistance, which in turn is a major risk factor for type 2 diabetes (Lobo et al., 2014). In their longitudinal study, Kabat et al. (2014), found that BMI, waist circumference, and the waist circumference to height ratio robustly predicted change in cardiometabolic risk in postmenopausal women with the strongest associations seen among increased triglycerides, LEAN BODY MASS IN MENOPAUSE 4 glucose, and reduced high-density lipoprotein (HDL) cholesterol. For postmenopausal women, chronic systemic inflammation, oxidative stress, abdominal visceral adipose tissue, dyslipidemia, sarcopenia, and a sedentary lifestyle are all risk factors for metabolic syndrome (Mendoza et al., 2016). A review by Maltais et al. (2009) found that the menopausal transition is associated with a decline in estrogen, growth hormone, IGF-1, and DHEA, a decrease in muscle protein synthesis, and an increase in catabolic factors such as the pro-inflammatory cytokines, and tumor necrosis factor alpha (TNF-α) or interleukine 6 (IL-6). The authors concluded that low physical activity, inadequate protein intake, and elevated oxidative stress are the greatest contributors of sarcopenia in postmenopausal women (Maltais et al., 2009). A recent study by Takamura et al. (2017) found that weight-adjusted LBM and skeletal muscle area were protective against weight-associated insulin resistance and metabolic abnormalities. As skeletal muscle also has estrogen receptors and estradiol plays a role in the glucose metabolism of skeletal muscles (Lemoine et al., 2003), the decrease in estrogen production in menopausal women further impacts glucose metabolism. Thus, women with lower muscle mass and fewer estrogen receptors are at greater risk for metabolic complications (Ou et al., 2017). Additionally, Abildgaard et al. (2013) found that decreased LBM is the most important contributor to changes in metabolism for postmenopausal women as it correlates to low whole-body fat oxidation and energy expenditure which in turn are associated with high visceral fat mass and low insulin resistance. Maintaining adequate levels of muscle mass as women transition into menopause may play a role in minimizing the risks of sarcopenic obesity and protect against the LEAN BODY MASS IN MENOPAUSE 5 development of deleterious metabolic conditions commonly associated with menopause. Exercise has also been considered as a potential mitigating factor for the presence and severity of menopausal symptoms, but the research is limited with focus on low- to moderate-intensity cardiovascular exercise (Bailey et al., 2016; Daley et al., 2014; Luoto et al., 2012; Sternfeld et al., 2104; Zhang et al., 2014) where the evidence has shown mixed results. Research regarding resistance training is very limited (Alvarez & Campillo, 2013; Boganha et al., 2012; Conceição et al., 2013; Correa et al., 2015; Leite et al., 2010), but has shown a positive correlation with some metabolic markers and quality of life. However, little is known regarding the role of LBM and its influence on menopausal symptoms throughout the transition period. The purpose of this research was to examine the association between relative levels of lean body mass and the development of VMS as women transition through menopause using the public data set from the Study of Women’s Health Across the Nation (SWAN) (Sutton-Tyrell et al., 2018a-f). As the transition into menopause is uniquely stressful and perceived symptoms can be impacted by many factors, the role of hip/waist circumference, self-reported quality of life, and physical activity levels were also considered. The main hypothesis tested was that lower LBM is associated with an increased prevalence of reported VMS of hot flashes and night sweats. LEAN BODY MASS IN MENOPAUSE 6 Methods This section will describe the design and methods used for the current research study and provide a brief review of the methods used for the Study of Women’s Health Across the Nation (SWAN). The data collected from the SWAN study, forming part of a free public use data set, was accessed for statistical analysis and interpretation in the current study. Research Design This study conducted a secondary analysis of existing data utilizing the public dataset from the SWAN study participants to identify, analyze, and describe factors contributing to the development of VMS over time. LBM (relative to height) was compared over the span of the study as the participants moved through menopause to the development/presence of VMS. Statistical comparisons of variables, including body weight, waist circumference, bodyfat percent, and LBM was made between those who developed VMS and those who did not. The SWAN study was a U.S. seven-site, longitudinal cohort study of five racial/ethnic groups of women aged 42 to 52 years at enrollment in 1995 to 1997 who were pre- or early perimenopausal. Data was still being collected at the 15th visit in 2016. This analysis was limited to data collected at five of these sites (Pittsburgh, PA, Boston, MA, Detroit, MI, Los Angeles, CA, and Oakland, CA) which collected additional data from dual-energy x-ray absorptiometry (DXA) scans for bone mineral density and body composition as part of the bone sub study conducted annually for 10 years and then biennially twice (visits 11 and 12) by 2010-2011 on 2,335 LEAN BODY MASS IN MENOPAUSE 7 participants. Examination and analysis of this public data set demonstrates a heightened ability to generalize the results of the current study to those women who fit within the parameters of the SWAN study. As published on the SWAN study website (www.swanstudy.org), the “SWAN Public Use Datasets provide access to longitudinal data describing the physical, biological, psychological, and social changes that occur during the menopausal transition. Data collected from 3,302 SWAN participants from Baseline through the 10th Annual Follow-Up visit are currently freely available to the public. SWAN researchers have published over 300 manuscripts covering a wide range of topics related to menopause and women’s health. Through the utilization of this public database, the current study conducted an original analysis on the data collected through DXA scans at the Boston, Detroit, Pittsburgh, Los Angeles, and Oakland sites on participants in the bone cohort not previously considered by other researchers. Participants’ Data Complete details of the SWAN design and recruitment procedures are reported elsewhere in the original study report (Sowers et al., 2000), however, a summary is provided here. Baseline eligibility criteria included being aged 42 to 52 years, having a uterus and at least one ovary, not being pregnant or lactating, not using oral contraceptives or hormone therapy in the previous three months, and having at least one menstrual cycle in the preceding months. Participants self-identified as AfricanAmerican (28%), Caucasian (47%), Chinese (8%), Hispanic (8%), or Japanese (9%). LEAN BODY MASS IN MENOPAUSE 8 For the SWAN study, a total of 202,985 sampling units of households, telephone numbers, or individual names of women were screened through either random digit dialing or list sampling frames for potential participation in the SWAN cross sectional study. Of these, 34,985 met the criteria and were defined as eligible and 16,065 completed the interview (cross sectional phase of the study), for an overall response rate of 46.6%. Of these, 6,521 women were cohort-eligible and asked to participate in the SWAN Longitudinal Study; a total of 3,306 women entered the cohort, for an overall response rate of 50.6%. Of 2,413 participants at the five SWAN bone study sites, 2,335 were enrolled in the bone cohort at baseline. Their data formed the subject database for this study. Procedures included annual examinations, questionnaires, blood draw, and DXA Scan. Written informed consent was obtained from each participant and the study was approved by the institutional review boards at each site (Appendix C1). Approval for this study was granted by the Institutional Review Board at California University of Pennsylvania (Appendix C2). Instruments The current study analyzed body mass data collected during the SWAN study at the five U.S. sites utilizing dual energy x-ray absorptiometry – Boston, MA, Detroit, MI, Los Angeles, CA, Pittsburgh, PA, and Oakland, CA. This data is available for free public use and was used to determine the association of LBM to the development of VMS. Body composition. Lean body mass was measured by dual energy x-ray absorptiometry (DXA) using Hologic Instruments (Hologic Inc., Waltham, MA, USA). Three sites (Boston, MA, Detroit, MI, and Los Angeles, CA) used Hologic model 4500A LEAN BODY MASS IN MENOPAUSE 9 throughout the study period, and two sites (Pittsburgh, PA and Oakland, CA) upgraded from the 2000 to 4500A model during follow-up but cross-calibrated the machines (Solomon et al., 2015). Each DXA laboratory measured a Hologic anthropomorphic spine phantom daily, and a phantom was circulated among laboratories for crosscalibration. Phantom measurements were analyzed by Synarc, Inc. (Waltham, MA, USA) with calibration regression coefficients used by the study’s coordinating center to adjust DXA measurements for minor temporal or geographic variations in densitometer performance. Additional quality-control measures included review of every scan image by a local site investigator and central review or a random subset of 5% of all scans and all problem scans by Synarc, Inc. (Sowers et al., 2006; Solomon et al., 2015). DXA measures fat tissue mass and lean tissue mass with very good precision (coefficient of variation) of 4.7% and 1.5% respectively (Svendsen, Hassager & Christiansen, 1995). Vasomotor symptoms. Hot flashes and night sweats were assessed via questionnaire at each SWAN visit. In the end of the month survey, women responded to two questions that separately asked them to record how often hot flashes and night sweats were experienced in the two weeks prior to the annual visit (not at all, 1-5 days, 6-8 days, 9-13 days, everyday) at each of the 12 annual visits. To measure the accuracy of recall for VMS among SWAN participants, Crawford et al. (2008) analyzed data from a sub cohort of 880 women recruited into the Daily Hormone Study, with daily urine collection for an entire menstrual cycle concurrent with completion of a daily symptom diary, and compared the entries to responses in the end-of-month survey (as was completed by all SWAN participants) over a three-year period. The authors found the overall sensitivity of the two-week recall as compared to daily recall was high, ranging from 78% to 84%, LEAN BODY MASS IN MENOPAUSE 10 and that the specificity was also high, ranging from 85% to 89% suggesting that this method of data collection is acceptable for use in research. Anthropometric measures. Height (m) and weight (kg) were measured in light clothing, without shoes, using a standard protocol with a stadiometer for height and a balance beam scale for weight. Waist circumference (cm) was measured at the level of the natural waist or the narrowest part of the torso from the anterior aspect. (Gold et al., 2017; Sowers et al., 2006). Covariates. All covariates were selected on the basis of previously documented associations with VMS (Gold et al., 2006) and body composition (Sowers et al., 2006), and included age, educational level (less than high school, high school, some college, college, or post baccalaureate degree), race/ethnicity (self-designated Caucasian, African American, Japanese, or Chinese), physical activity, quality of life, and menopausal transition stage. Race/ethnicity and educational level were self-reported in the SWAN screening interview. SWAN participants were assessed for menopausal status assignment based on annual reports about menstrual bleeding and its regularity. Premenopause was identified as no decreased regularity in menstrual bleeding during the last year. Other classifications were early perimenopause (decreased menses regularity in the three months before interview), late perimenopause (no menses for 3-11 months), and postmenopause (no menses for 12 or more months) (Thurston et al., 2013). Surgical menopause was defined by report of either hysterectomy or oophorectomy, and hormone therapy (HT) use was reported as use of HT during the year (Thurston et al., 2013). Physical activity was assessed using an adaptation of the Kaiser Physical Activity Survey (Sternfeld, Ainsworth & Quesenberry, 1999). This survey is a self-administered LEAN BODY MASS IN MENOPAUSE 11 questionnaire with high to very high established test-retest reliability (r = 0.79 to 0.91, p <.01) and low to moderate validity (r = 0.28 to 0.64, p <.01) against activity records, accelerometer recordings, and maximal oxygen consumption among White women (Ainsworth et al., 2000) and concurrent validity among racial/ethnic minority women in terms of body mass index and socioeconomic factors (Sternfeld et al., 1999). Originally adapted from the Baecke questionnaire (Baecke, Burema & Fritjers, 1982), the version of the Kaiser Physical Activity Survey used in SWAN consists of 38 questions with primarily Likert-scale responses about physical activity in various domains, including sports/exercise, household/caregiving, and daily routine (defined as walking or biking for transportation and hours of television viewing) which are reverse-scored. The Medical Outcomes Short-Form 36 (SF-36) was used to assess health related quality of life (HRQL) using the original coding algorithm in which raw scores are transformed to a 0 to 100 range. Scoring generates total scores for the mental health dimension and physical health dimension in which higher scores indicate a higher quality of life. The SF-36 is a generic HRQL measure yielding eight subscales, of which SWAN used five that are considered the components of the physical dimension: bodily pain, role limitations due to physical health, role limitations due to emotional problems, social functioning, and vitality (Avis et al., 2003). Jenkinson, Wright and Coulter (1994) found the internal consistency of the SF-36 to have a Cronbach’s alpha of >.8 for each of the scales except social functioning (alpha = .76) and high internal reliability (alpha >.7) for all except social functioning (alpha >.5), which is considered acceptable as it has fewer items in the domain. The authors also showed Kruskal-Wallis tests indicated clear linear LEAN BODY MASS IN MENOPAUSE 12 trends for all seven dimensions being strongly associated (p <0.0001) with patient reported overall general health (Jenkinson et al., 1994). Procedures This section details the procedures followed in the SWAN study and further outlines the procedure followed for this study. After approval from the California University of Pennsylvania’s Institutional Review Board (IRB) (Appendix C2), the current study examined data obtained from participants in the SWAN study. Multiple variables within the existing public dataset available from the SWAN Study website (https://www.swanstudy.org) including age, height, weight, menopausal status, education level, race/ethnicity, LBM, fat mass, waist circumference, VMS (hot flashes, night sweats), quality of life, and physical activity were accessed for each of the five sites (Boston, Detroit, Pittsburgh, Los Angeles and Oakland) designated in the SWAN study as participating in the bone cohort (with additional data collected using dual energy x-ray absorptiometry), and downloaded for analysis. SWAN study. A detailed description of the SWAN study design has been previously published (Sowers et al., 2000). Each of the five sites selected for the current study adhered to the presiding Institutional Review Board’s guidelines. The initial crosssectional study consisted of a 15- to 20-minute telephone interview (or face-to-face interview in instances where no telephone number could be associated with a sampled respondent). This interview was administered to 16,065 women aged 40-55 years who were randomly selected from sampling frames established at each site and included a verbal consent for interview. Women then provided information on sociodemographic, LEAN BODY MASS IN MENOPAUSE 13 medical and menstrual history, symptoms, and psychosocial factors. Sites used a variety of sampling frames and recruitment strategies to recruit a community-based sample of local women belonging to four ethnic groups. Eligible women meeting the inclusion criteria were invited to join the cohort and were seen within three months of the initial survey for their baseline assessment, and a written informed consent was obtained. Assessments consisted of questionnaires regarding medical history, medication, menstrual history, lifestyle, psycho-social factors, physical and psychological symptoms, and health-related quality of life, as well as blood and urine specimen collection and physical measures. Questions were administered orally by trained staff or were part of a paper-and-pencil form. For women enrolled in the bone cohort, body composition was determined from dual energy x-ray absorptiometry (DXA) performed by trained and certified technicians following a standardized protocol. Whole-body scan data at each of the five sites was measured as kilograms of lean mass and fat mass. After enrollment in 1996-1998, eligible participants in the SWAN bone cohort, representing a potential maximum of 2,335 for this study, were followed with annual in-person clinic visits that included signed, written informed consent (Appendix C1). All measures described above were repeated annually for 10 visits on the anniversary of the baseline assessment and then biennially for visits 11 and 12. The timeline of the ongoing data collection which has continued to the 15th visit is shown below in Figure 1(www.swanstudy.org). However, only datasets up to and including the 10th visit, are currently available as part of the public dataset. LEAN BODY MASS IN MENOPAUSE 14 Figure 1. Timeline of data collection. Retrieved from www.swanstudy.org Data Analysis The following general aims were investigated: • LBM is associated with physical activity levels in postmenopausal women • The association of LBM with the cardiovascular risk factors for postmenopausal women of increased waist circumference and body mass index • The correlation between development of VMS during the menopausal transition with LBM • The association between LBM and self-reported quality of life Specific hypotheses of the study included: Hypothesis 1 (H1) - Lower concurrent LBM will be associated with greater concurrent incident reporting of VMS Hypothesis 2 (H2) - In longitudinal analyses, lower LBM over time, since baseline, will be associated with greater incident reporting of VMS Hypothesis 3 (H3) - In longitudinal analyses, lower LBM over time, since last annual visit, will be associated with greater incident reporting of VMS LEAN BODY MASS IN MENOPAUSE 15 Characteristics of the sample were described by means (standard deviation) and frequency (%). At baseline, two VMS groups – any or none – were compared for group differences in, and associations among, demographics (age, race/ethnicity, education), quality of life (SF-36 score), physical activity score, and clinical characteristics (weight, waist circumference, menopausal status, LBM ), and VMS was estimated using chi square test ( ) for categorical variables, and Kruskal-Wallis test for continuous variables. A scatter plot matrix was used to examine linear correlations among variables. Analysis of incident VMS included only women reporting no VMS in the prior two weeks at baseline. A participant’s data was censored and omitted at initiation of hormone therapy, at hysterectomy or bilateral oophorectomy, or at her last visit and visits concurrent with pregnancy or breastfeeding. To assess H1, incident VMS was modeled as a function of concurrent LBM using logistic regression analysis. To address H2 regarding long term change in LBM, the model was expanded to add within-woman percent change in LBM since baseline and to address H3, regarding recent change in LBM, the model was expanded to add withinwoman percent change in LBM since prior visit (approximately one year earlier). The overall association between LBM and VMS was estimated in binary logistic regression models. Statistical analyses were one-tailed with an alpha level of 0.05and conducted using SAS University Edition (© 2012-2018, SAS Institute Inc., Cary, NC). 16 LEAN BODY MASS IN MENOPAUSE Results Swan Study Data At the time of data access and retrieval for this study, the SWAN data collected via Dual Energy X-ray Scans was unavailable for public use. Therefore, this analysis was limited to the use of body composition data collected at visits six through 10 at each of the seven SWAN sites (Pittsburgh, PA, Boston, MA, Chicago, IL, Detroit, MI, Los Angeles, CA, Newark, NJ, and Oakland, CA) using bioelectrical impedance analysis (BIA) and included 2,533 women. BIA is based on measurement of the transmission speed of a one-quarter volt electrical pulse between electrodes attached at the feet and electrodes attached across the knuckles of the hand. Because fat-free mass is comprised of water, proteins, and electrolytes, conductivity is greater in fat-free mass than in fat mass (Lukaski, Johnson, Bolonchuk, & Lykken, 1985). Resistance and reactance are used to estimate total body water, and by extension, fat mass and lean mass, with the latter including bone (Boulier, Fricker, Thomasset, & Apfelbaum, 1990). The validity and predictive value of BIA in menopausal women has been confirmed by a recent study by Tanaka et al., (2015). The main finding of the study was that the SEE (3.6%), the existence of systematic error (r = 0.447, p <.05), and the slope and the intercept of the regression line between measurement values by DXA and BIA were not significantly different for menopausal or premenopausal women. The authors determined that the predictive accuracy of BIA for postmenopausal women would not be inferior to that for premenopausal women. Skeletal muscle mass was calculated by the method of Janssen et al. (2000), who subsequently indexed skeletal muscle mass to height for a skeletal muscle index (SMI = skeletal muscle mass (kg) / height (m2)). Fat free mass, total body water, LEAN BODY MASS IN MENOPAUSE 17 and percent body fat were all provided by RJL Systems and validated using NHANES III data (Chumlea et al., 2002). Two new variables referred to as fat mass index (FMI) and fat free mass index (FFMI) were constructed from available data to create a measure of total body fat mass and a measure of total LBM, both on the same scale as the skeletal muscle mass index (SMI) and used the same formula where FMI = total body fat (kg) / (m2) and FFMI = fat free mass (kg) / (m2). Data regarding scores on the modified Kaiser Physical Activity Survey were missing from available data sets for visits six through 10 and were therefore not included as a covariate in this study. Study Sample Characteristics At baseline (visit 6) there were 2,533 participants remaining in the SWAN study who were on average 52 years old and comprised of 30% African-American, 49% Caucasian, with the remainder of the sample consisting of Hispanic (11%), Chinese (9%) and Japanese (2%) women (Table 1). Approximately 53% of participants reported having night sweats and/or hot flashes (VMS) in the two weeks prior to visit 6. Differences in group (none/any VMS) demographics at baseline were found for race (p <.0001), education levels (p <.0007), and menopausal status (p <.0001). Additionally, group differences were detected for all body composition measures except for SMI (p = .1298). Quality of life scores were not significantly different between the groups. At baseline, African-American participants (64%) were more likely to report VMS and women who were either late perimenopausal or postmenopausal (61%) and with less than a college degree (57%) were also more likely to report VMS. Those 18 LEAN BODY MASS IN MENOPAUSE reporting VMS were significantly higher in body weight (p < .0001) and in all measures of bodyfat (% body fat, total body fat (kg), fat mass index, hip waist ratio) with p <.0001. Those reporting VMS also showed significant differences for measures of fat free mass (FFMI, p = .0003; FFM, p = .0002; skeletal muscle mass, p = .0423). Only women reporting symptoms at baseline were included in analyses for VMS with LBM (n=1179). Table 1 Baseline Characteristics by symptom status, SWAN Presence of Vasomotor Symptoms (VMS) None (% n ) Any (% n ) p n 1179 (47%) 1354 (53%) Age, yr (mean ± SD) 52.0 ± 2.7 52.0 ± 2.6 Race/Ethnicity (n ) <.0001 African-American 272 (36%) 494 (64%) Chinese 118 (52%) 107 (48%) Hispanic 144 (54%) 122 (46%) Japanese 26 (67%) 13 (33%) Caucasian 619 (50%) 622 (50%) Education (n ) <.0007 ≤ High School 225 (44%) 286 (56%) Some College 349 (42%) 481 (58%) ≥College Graduate 595 (50%) 584 (50%) Menopausal Status (n ) <.0001 Post menopausal 430 (42%) 591 (58%) Late Perimenopausal 77 (32%) 163 (68%) Early Perimenopausal 368 (50%) 372 (50%) Premenopausal 54 (68%) 26 (33%) Quality of Life Score (mean ± SD) 7.5 ± 1.7 7.4 ± 1.7 .0910 Body Composition (mean ± SD) Weight (kg) 2 BMI (kg/m ) Body Fat (%) Total Body Fat (kg) Fat Mass Index (TBF/m2) Fat Free Mass (kg) Fat Free Mass Index (kg/m2) Skeletal Muscle Mass (kg) Skeletal Muscle Index (kg/m2) Waist Hip Ratio (%) 74.3 ± 20.7 28.1 ± 7.3 36.8 ± 8.0 28.3 ± 13.2 10.75 ± 4.9 45.1 ± 7.4 17.1 ± 2.4 20.6 ± 3.3 7.8 ± 1.1 0.81 ± 0.07 78.4 ± 20.7 29.6 ± 7.3 39.1 ± 7.6 31.9 ± 13.9 12.03 ± 5.11 46.3 ± 7.6 17.5 ± 2.5 20.9 ± 3.3 7.9 ± 1.1 0.83 ± 0.07 <.0001 <.0001 <.0001 <.0001 <.0001 .0002 .0003 .0423 .1298 <.0001 Note. SWAN = Study of Women's Health Across the Nation; BMI = body mass index; TBF = total body fat; FFM = fat free mass; SMM = skeletal muscle mass **Data are presented as number (percentage), unless specified otherwise. Numbers may not add up to reflect total n due to missing values at baseline. LEAN BODY MASS IN MENOPAUSE 19 Correlation analyses. The continuous variables SMI, FMI, and FFMI at baseline are summarized according to symptom group (0 = none, 1 = any) in Figure 2. Figure 2. Association of body composition variables at baseline grouped by VMS with symptoms = 1 represents any, and symptoms = 0 represents none. SMI = skeletal muscle mass, FMI = fat mass Index, FFMI = fat free mass index SMI showed a strong positive correlation to FFMI at baseline both for symptoms = none (r0 (864) = 0.931, p <.0001) and symptoms = any (r1 (1143) = 0.933, p <.0001), and a LEAN BODY MASS IN MENOPAUSE 20 moderate positive correlation to FMI (r0 (864) = 0.567, p <.0001) (r1 (1143) = 0.579, p <.0001). FMI showed a strong positive correlation to FFMI at baseline for both groups (r0 (864) = 0.820, p <.0001; r1 (1143) = 0.826, p <.0001). These results indicate that the continuous variables are all positively correlated and specifically that there are no significant differences in these relationships between the groups at baseline. Association of means. Pearson correlation (Figure 33) of mean SMI to mean FMI (calculated on a participant basis over visits 6 – 10) was moderately strong for ‘none’ (r0 (405) = 0.648, p <.0001) and relatively weak for ‘any’ with (r1 (611) = 0.559, p <.0001). Mean FFMI was strongly correlated to mean FMI in both groups (r0 (405) = 0.860, p <.0001; r1 (611) = 0.829, p <.0001). Mean SMI was very strongly correlated to both symptom groups for mean FFMI (r0 (405) = 0.942, p <.0001; r1 (611) = 0.923, p <.0001). SMI, as nested within FFMI, continued to be very strong as expected and the relationship of FFMI to FMI remained consistent over time. Deviation from baseline correlations for SMI to FMI indicates the importance of time. Figure 3. Correlation of Mean SMI to Mean FMI over visits 6-10 by symptom group SMI = Skeletal Muscle Index, FMI = Fat Mass Index LEAN BODY MASS IN MENOPAUSE 21 The average (mean) for variables SMI, FMI, and FFMI at each visit, depicted in Figure 4, indicate the trend and association over the five-year time frame between the two groups – any vs. none VMS. Figure 4. Mean SMI, FMI, FFMI at each visit grouped by symptoms SMI = skeletal muscle index, FMI = fat mass index, FFMI = fat free mass index Hypothesis testing. Models were developed to address the three hypotheses and are presented as odds ratios and 95% confidence intervals for incident VMS in Table 2. Adjusted models included time-varying covariates of FMI, age, and menopausal status and single-time variates of race/ethnicity and education. Quality of life scores were not significant in any models and were not considered in further analyses. For H1 (VMS associations with concurrent LBM) results are summarized in Model 1. LBM is significant in both the unadjusted (p <.0001) and adjusted (p = .036) analyses. For H2 (VMS associations with LBM over time since baseline), within woman percentage LEAN BODY MASS IN MENOPAUSE 22 change since baseline was added to Model 1 and results are presented as Model 2. Again, LBM showed significance in both unadjusted (p = .001) and adjusted (p <.001) modelling. For H3, (VMS associations with LBM over time since prior visit) within women percentage change since prior visit was added to Model 1 and the results are summarized as Model 3. LBM remained significant in unadjusted (p = .009) and adjusted (p = .003) models respectively. Additional analyses. Participant means for SMI and FFMI (calculated over visit 6-10) had significant associations for VMS (p = .007, p = .023) while still considering mean FMI (over visit 6-10) as reported in Model 4. FMI was significant in adjusted models 1 (p =.0003), 2 (p <.0001), 3 (p <.0001), and 4 (p =.0073). Race/ethnicity was significant in Models 1 (p <.0001) and 2 (p =.0139) but failed to reach significance for LEAN BODY MASS IN MENOPAUSE 23 Model 3 or 4. Menopausal status was significant (p =.0052) in Model 4 only. Age was not significant in any model. In mixed regression models (Proc Mixed, SAS) of VMS and LBM repeated for a fixed factor of time (symptoms as random effect), results were significant (p < .0001) for least squares means at visit (time) 6, 7, 8, 9, and 10. Additionally, significance (p <.0001) was found for the difference of least square means (Tukey-Kramer method) at visit 6 (time effect 6-7, 6-8, 6-9, 6-10), and for time effect visit 8-10 (p = .007) (data not shown). Further, estimated predicted probabilities were examined to determine the likelihood of LBM to predict the development of VMS. Predicted probabilities. Given the binary nature of the outcome variable (VMS), binary logistic regression was used to estimate the predicted probabilities of symptoms occurring specifically in relation to SMI (with FFMI as covariate and FMI constant at the mean). Figure 5 illustrates the probabilities estimated for given levels of SMI at each of the four visits following baseline, including the average (mean) over time, for women reporting no VMS at visit 6 (n = 1179). The probability of reporting VMS shows a negative correlation to increasing levels of SMI with overall association decreasing from 71% to 37% as SMI increases from 4 to 14 (kg/m2). Figure 5. Predicted probability of symptoms occurring at given levels of SMI (adjusted for FFMI and FMI) in women with no VMS at baseline LEAN BODY MASS IN MENOPAUSE 24 Discussion This study is among the first to examine the relationship of LBM to VMS longitudinally. Using the data for this large, multiethnic sample of mid-age women from the SWAN study, we found that participants with higher relative levels of LBM were less likely to develop VMS as they transitioned through menopause. This effect was found to be independent of sociodemographic factors and levels of bodyfat. Over the duration of the SWAN study period (visits 6-10), the average age of the participants increased from 52.0 to 56.6 years and the number of women who were postmenopausal increased from 40% (n = 430) at visit 6 to 72% (n = 733) at visit 10 (Figure 6). VMS are most likely to be experienced by women in the late perimenopausal and postmenopausal period (Thurston & Joffe, 2011), thus the time frame used for this research was able to document the movement through this important transition for the majority of the cohort. Figure 6. Menopausal status of study cohort by visit (numbers do not add to 100% due to surgical menopause or HT use) LEAN BODY MASS IN MENOPAUSE 25 The increasing levels of significance for the three models measuring VMS with concurrent LBM, percent change since prior visit, and percent change since baseline, suggests that the effect of time on the likelihood of VMS is considerable. At individual visits after baseline, with only those women reporting VMS = none at visit 6, simple models showed that the effect of LBM was not significant. It is only after the women had continued to progress through to visit 10 with no symptoms that the effect of time became significant, suggesting that it is the maintaining of higher levels of LBM that may offer protection against the probability of VMS. In fact, as shown in Figure 4, the mean SMI and mean FFMI levels per visit were inverted at baseline (VMS = any show slightly higher mean levels than VMS = none). However, by visit 7, the VMS = none group show consistently higher levels of lean mass than VMS = any and the groups begin to significantly diverge by visit 10. After the cohort was regrouped for analysis at visit 6, the percentage of women in the VMS = none group remained fairly steady around 60%. Figure 7 depicts this shift in the VMS groups from visit 6 (n = 2005) through to visit 10 (n = 800) which suggests that the data is not skewed by the characteristics of the remaining women in the cohort. Figure 7. Percentage of women by VMS group at each visit LEAN BODY MASS IN MENOPAUSE 26 To understand why lean mass may potentially act as a predictor of VMS, previous research on both body fat and oxidative stress must be considered. Considerable recent research has focused on the association of BMI and bodyfat with VMS and has suggested a positive correlation between increasing BMI and the presence of VMS (da Fonseca et al., 2013; Gallichio et al., 2014; Gjelsvik et al., 2011; Herber-Gast et al., 2013; Kazlauskaite et al., 2015; Thurston et al., 2013). However, BMI is considered a poor predictor of body mass as it is merely a measure of excess weight and does not distinguish between body fat mass and fat free mass (Akindele, Phillips, & Igumbor, 2016). This study examined the individual components of body composition and found lean mass, determined by both fat free mass and skeletal muscle mass (both indexed to height), to have a significant effect on the likelihood of developing VMS over time while still considering the potential impact of fat mass as illustrated by the significant gap in fat mass seen between groups in Figure 4. A recent cross-sectional study of 758 women found that trunk lean mass was an independent protective factor for moderate to severe menopausal symptoms and that VMS were independently related to higher BMI and fat mass (Zhou et al., 2018). Other research has shown substantial effects of adiposity on the magnitude of hormone changes experienced during menopause (Wildman & Sowers, 2011), but the underlying mechanisms of the relationship between body composition and VMS are not entirely clear due to the incomplete understanding of the physiology of VMS (Moilanen et al., 2012). Evidence is emerging on the role of oxidative stress in menopause and its relationship to muscle mass. Lipoperoxide (LPO) levels are considered a measure of oxidative stress and in postmenopausal women are found to be significantly higher than LEAN BODY MASS IN MENOPAUSE 27 in premenopausal women suggesting that the depletion of estrogen is a risk factor for oxidative stress (Sanchez-Rodriguez et al., 2012). A recent study showed that the loss of muscle mass in menopause, due to declining estrogen levels, was negatively associated with oxidative stress (LPO), but that skeletal muscle mass was positively associated with serum uric acid which offered a protective role against oxidative stress due to its capacity to clear reactive oxygen species (Zacarias-Flores et al., 2018). Cagnacci et al. (2015) found that increased oxidative stress also impaired the ability of free oxygen radical defenses (FORD) in menopausal women, and that VMS are negatively associated with FORD. Together, these results suggest that declining estrogen in menopause contributes to loss of muscle mass which simultaneously increases oxidative stress and decreases antioxidant levels potentially leading to higher probability of VMS. The results of our study add to the evidence that women with lower levels of LBM are more likely to experience VMS. LBM and skeletal muscle have also been found to be protective against weight-associated insulin resistance and metabolic abnormalities - conditions which plague menopausal women (Fukushima et al., 2016; Takamura et al., 2017). Research on resistance training as an intervention for menopausal women has shown positive correlations to metabolic markers and quality of life as well as increases in muscle mass (Alvarez & Campillo, 2013; Boganha et al., 2012; Conceicao et al., 2013; Correa et al., 2015; Leite et al., 2010). Martins et al. (2018) were able to show, in a randomized controlled study, that high intensity interval body weight training was as effective as combined aerobic and resistance training in changes to muscle mass, physical performance, inflammatory markers, and metabolic health in postmenopausal women. This type of training is shown to be highly efficient and time saving helping to overcome LEAN BODY MASS IN MENOPAUSE 28 barriers of lack of time or equipment and can also be configured for a lower motor skill level compared to traditional high intensity interval (HIIT) training (Martins et al., 2018). However, research on the impact of resistance training and increased muscle mass, most specifically on VMS, is missing and the lack of longitudinal studies has left a gap in the research. As the results from this study show, it may be the maintenance of lean mass over time that proves optimal for prevention of VMS, thus further studies in this area are needed. The women in the SWAN study were between ages 42-52 when they were recruited in 1996-97. The American College of Sports Medicine (ACSM) conducted the National Health Interview Survey which analyzed resistance training trends over the period of 1998-2004 (Kruger, Carlson, & Kohl, 2006). The study showed that women in the age group 45-64 years increased their participation in weight training from 12.3% of respondents to 17.6%, and while this was a significant increase, it was still well below the national health objective for 2010 which was 30%. The current North American culture finds that younger women today are more accepting of resistance training as an important piece in maintaining their health (Hurley et al., 2018). This may translate into future generations of women who are more likely to carry higher levels of lean mass into midlife and potentially experience fewer menopausal symptoms. For those who did not have the benefit of this culture shift and faced barriers to exercise in the past, a study by Diniz et al. (2015) found that postmenopausal women who spent more than 150 minutes per week in moderate-vigorous physical activity showed higher levels of lean body and leg mass which suggests that for women currently entering menopause, adding strength training to their exercise regimen could still provide benefits. LEAN BODY MASS IN MENOPAUSE 29 Certain limitations need considered when interpreting results of the current study. The use of BIA measurement involves several assumptions, and while skeletal muscle mass was calculated using a validated equation by Janssen et al. (2000) and fat mass and fat free mass were calculated with validated equations by Chumlea et al. (2002), this allows for potentially differing interpretation of data supplied by the internal BIA system. Additionally, VMS were assessed through responses to two questions regarding number of days experiencing hot flashes or night sweats in the previous two weeks. This data yielded limited information and is subject to recall bias although this population was found to have high specificity and high sensitivity regarding VMS recall (Crawford et al., 2008). Although frequently used in this type of epidemiological study, future research should attempt to consider more objective measures of VMS. Multiple statistical comparisons were made during this study. As a result, some observed associations may have occurred by chance or represent other uncontrolled variables. Moreover, odds ratios presented here should not be interpreted as measures of relative risk because VMS are not a rare outcome and the risk may be overestimated. LEAN BODY MASS IN MENOPAUSE 30 Conclusion This study was the first to examine the longitudinal association of LBM to VMS, providing new evidence that lean mass may provide protection against the development of VMS as women transition through menopause. Using the longitudinal SWAN database that encompasses a large, multiethnic sample of women from across the United States, these findings are particularly relevant. Given the current backlash against hormone replacement therapy as a means to mitigate symptoms, these results provide for the possibility of symptom prevention through exercise. Importantly, the results of this study suggest that the greatest contributing factor to mitigating symptoms was maintaining LBM throughout the menopausal transition. This finding implies the need to encourage all women, especially premenopausal to post-menopausal, to incorporate resistance/strength training into their exercise programs on a regular and consistent basis, and to continue with this type of training as long as they are healthy. LEAN BODY MASS IN MENOPAUSE 31 Future Directions for Research Further and more complete understanding of the etiology of VMS and the underlying mechanisms will allow continued development of methods for managing bothersome, and sometimes debilitating, symptoms during this transitional time in a woman’s life. Additional longitudinal studies are needed to support and expand on the benefits of lean mass for postmenopausal women which could also provide additional benefits related to managing the impact of metabolic syndrome and insulin resistance frequently seen in this population. Specifically focusing on research involving increased lean mass through exercise, in early menopause or during child bearing years, could offer untold opportunities for educating future generations on symptom management and prevention. LEAN BODY MASS IN MENOPAUSE 32 References Abdulnour, J., Doucet, E., Brochu, M., Lavoie, J-M., Stychar, I., Rabasa-Lhoret, R., & Prud’homme, D. (2012). The effect of the menopausal transition on body composition and cardiometabolic risk factors: A Montreal-Ottawa New Emerging Team group study. Menopause, 19(7), 760-767. https://doi.org/10.1097/gme.0b013e318240f6f3 Abildgaard, J., Pedersen, A.T., Green, C.J., Harder-Lauridsen, N.M., Solomon, T.P., Thomsen, C., …Lindegaard, B. (2013). Menopause is associated with decreased whole-body fat oxidation during exercise. American Journal of Physiology Endocrinology and Metabolism, 304, E1227-E1236. https://doi.org/10.1152/ajpendo.00492.2012 Akindele, M.O., Phillips, J.S., & Igumbor, E.U. (2016). The relationship between bodyfat percentage and body mass index in overweight and obese individuals in an urban African setting. Journal of Public Health in Africa, 7(11), 515. https://doi.org/10.4081/jphia.2016.515 Alvarez, C. & Campillo, R. (2013). Effect of a low intensity strength training program on overweight/obese and premenopausal/menopausal women. Brazilian Journal of Kinanthropometry and Human Performance, 15(4), 427-436. https://doi.org/10.5007/1980-0037v15n4p427 Avis, N.E., Ory, M., Matthews, K.A., Schocken, M., Bromberger, J., & Colvin, A. (2003). Health-related quality of life in a multi-ethnic sample of middle-aged LEAN BODY MASS IN MENOPAUSE 33 women: Study of Women’s Health Across the Nation (SWAN). Medical Care, 41(11), 1262-1276. http://www,jstor.org/stable/3768415 Baecke, J.A.H., Burema, J., & Frijters, J.E.R. (1982). A short questionnaire for the measurement of habitual physical activity in epidemiological studies. American Journal of Clinical Nutrition, 36, 936-942. Retrieved from: www.ajcn.nutrition.org Bailey, T. G., Cable, N. T., Aziz, N., Atkinson, G., Cuthbertson, D. J., Low, D. A., & Jones, H. (2016). Exercise training reduces the acute physiological severity of post‐menopausal hot flushes. The Journal of Physiology, 594(3), 657-667. https://doi.org/10.1113/JP271456 Bonganha, V., Modeneze, D.M., Madruga, V.A., & Vilarta, R. (2012). Effects of resistance training (RT) on body composition, muscle strength and quality of life (QoL) in postmenopause life. Archives of Gerontology and Geriatrics, 54, 361365. https://doi.org/10.1016/j.archger.2011.04.006 Boulier, A., Fricker, J., Thomasset, A.L., & Apfelbaum, M. (1990). Fat free mass estimation by the two-electrode impedance method. American Journal of Clinical Nutrition, 52, 581-585. Retrieved from www.ajcn.nutrition.org Cagnacci, A., Palma, F., Romani, C., Xhouli, A., Bellafronte, M., & Di Carlo, C. (2015). Are climacteric symptoms associated with risk factors of cardiovascular disease in pre-menopausal women. Gynecological Endocrinology, 31(5), 359-362. https://doi.org/10.1031/09513590.2014.998188 LEAN BODY MASS IN MENOPAUSE 34 Chumlea, W.C., Guo, S.S., Kuczmarcki, R.J., Flegal, K.M., Johnson, C.L., Heymsfield, S.B., … Hubbard, V.S. (2002). Body composition estimates from NHANES III bioelectrical impedance data. International Journal of Obesity & Related Metabolic Disorders, 26(12), 1596-1611. https://doi.org/10.1038/sj.ijo.0802167 Conceição, M.S., Bonganha, V., Vechin, F.C., de Barros Berton, R.P., Lixandrão, M.E., Nogueira, F.R.D., …Libardi, C.A. (2013). Sixteen weeks of resistance training can decrease the risk of metabolic syndrome in healthy postmenopausal women. Clinical Interventions in Aging, 8, 1221-1228. https://doi.org/10.2147/CIA.544245 Correa, C.S., Teixeira, B.C., Cobos, R.C.R., Macedo, R.C.O., Kruger, R.L., Carteri, R.B.K.… Reischak-Oliveira, A. (2015). High-volume resistance training reduces postprandial lipaemia in postmenopausal women. Journal of Sport Sciences, 33(18), 1890-1901. https://doi.org/10.1080/02640414.2015.1017732 Crawford, S.L., Avis, N.E., Gold, E., Johnston, J., Kelsey, J., Santoro, N., …Sternfeld, B. (2008). Sensitivity and specificity of recalled vasomotor symptoms in a multiethnic cohort. American Journal of Epidemiology, 168(12), 1452-1459. https://doi.org/10.1093/aje/kwn279 Da Fonseca, A.M., Bagnoli, V.R., Souza, M.A., Azevedo, R.S., De Barros Couto Junior, E., Soares Junior, J.M., & Baracat, E.C. (2013). Impact of age and body mass on the intensity of menopausal symptoms in 5968 Brazilian women. Gynecological Endocrinology, 29(2), 116-118. https://doi.org/10.3109/09513590.2012.730570 LEAN BODY MASS IN MENOPAUSE 35 Daley, A. J., Thomas, A., Roalfe, A. K., Stokes Lampard, H., Coleman, S., Rees, M., ... MacArthur, C. (2015). The effectiveness of exercise as treatment for vasomotor menopausal symptoms: Randomised controlled trial. BJOG: An International Journal of Obstetrics and Gynaecology, 122(4), 565-575. https://doi.org/10.1111/14710528.13193 Diniz, T.A., Christofaro, D.G.D., dos Santos, V.R., Viezel, J., Buonani, C., Rossi, F.E., & Frietas Junior, I.F. (2015). Practice of moderate physical activity can attenuate the loss of lean body mass in menopausal women. Motricidade, 11(1), 151-159. https://dx.doi.org/10.6063/motricidade.3727 Freeman, E.W., Sammel, M.D., Grisso J.A., Battistini, M., Garcia-Espagna, B., & Hollander, L. (2001). Hot flashes in the late reproductive years: Risk factors for African American and Caucasian women. Journal of Women’s Health & GenderBased Medicine, 10(1), 67-76. https://doi.org/10.1089/152460901750067133 Fukushima, Y., Kurose, S., Shinno, H., Thu, H.C., Takao, N., Tsutsami, H., & Kimura, Y. (2016). Importance of lean muscle maintenance to improve insulin resistance by body weight reduction in female patients with obesity. Diabetes & Metabolism Journal, 40, 147-153. https://doi.org/10.4093/dmj.2016.40.2.147 Gallichio, L., Miller, S.R., Kiefer, J., Greene, T., Zacur, H.A., & Flaws, J.A. (2014). Change in body mass index, weight, and hot flashes: A longitudinal analysis from the midlife women’s health study. Journal of Women’s Health, 23(3). https://doi.org/10.1089/jwh.2013.4526 LEAN BODY MASS IN MENOPAUSE 36 Gjelsvik, B., Rosvold, E.O., Dalen, I., & Hunskaar, S. (2011). Symptom prevalence during menopause and factors associated with symptoms and menopausal age: Results from the Norwegian Hoardland Women’s Cohort. Maturitas, 70, 383390. https://doi.org/10.1016/j.maturitas.2011.09.011 Gold, E.B., Colvin, A., Avis, N., Bromberger, J., Greendale, G.A., Sternfeld, B., & Matthews, K. (2006). Longitudinal analysis of the association between vasomotor symptoms and race/ethnicity across the menopausal transition: Study of Women’s Health Across the Nation. American Journal of Public Health, 96(7), 1226-1235. https://doi.org/10.2105/AJPH.2005.066936 Gold, E.B., Crawford, S.L., Shelton, J.F., Tepper, P.G., Crandall, C.J., Greendale, G.A., …Avis, N.E. (2016). Longitudinal analysis of changes in weight and waist circumference in relation to incident vasomotor symptoms: The Study of Women’s Health Across the Nation (SWAN). Menopause, 24(1), 9-26. https://doi.org/10.1097/GME.0000000000000723 Harlow, S.D., Gass, M., Hall, J.E., Lobo, R., Maki, P., Rebar, R.W., …de Villiers, T.J. (2012). Executive summary of the Stages of Reproductive Aging Workshop +10: Addressing the unfinished agenda of staging reproductive aging. Climacteric, 15, 105-114. https://doi.org/10.3109/13697137.2011.650656 Herber-Gast, G-C.M., Mishra, G.D., van der Schouw, Y.T., Brown, W.J., & Dobson, A.J. (2013). Risk factors for night sweats and hot flushes in midlife: Results from a prospective cohort study. Menopause, 20(9), 953-959. https://doi.org/10.1097/gme.0b013e3182844a7c LEAN BODY MASS IN MENOPAUSE 37 Hurley, K.S., Flippin, K.J., Blom, L.C., Bolin, J.E., Hoover, D.L., & Judge, L.W. (2018). Practices, perceived benefits, and barriers to resistance training among women enrolled in college. International Journal of Exercise Science, 11(5), 226-238. Retrieved from https://digitalcommons.wku.edu/ijes/ Janssen, I., Heymsfield, S.B., Baumgartner, R.N., & Ross, R. (2000). Estimation of skeletal muscle mass by bioelectrical impedance analysis. Journal of Applied Physiology, 89(2), 465-471. https://doi.org/10.1152/jappl.2000.89.2.465 Jenkinson, C., Wright, L., & Coulter, A. (1994). Criterion validity and reliability of the SF-36 in a population sample. Quality of Life Research, 3(1), 7-12. http://www.jstor.org/stable/4034552 Kabat, G.C., Heo, M., Van Horn, L.V., Kazlauskaite, R., Getaneh, A., Ard, J., …Rohan, T.E. (2014). Longitudinal association of anthropometric measures of adiposity with cardiometabolic risk factors in postmenopausal women. Annals of Epidemiology, 24, 896-902. https://doi.org/10.1016/j.annepidem.2014.10.007 Kazlauskaite, R., Innola, P., Karavolos, K., Dugan, S.A., Avery, E.F., …Powell, L.H. (2015). Abdominal adiposity changes in white and black midlife women: The Study of Women’s Health Across the Nation. Obesity, 23(12), 2340-2343. https://doi.org/10.1002/oby.21350 Kershaw, E.E., & Flier, J.S. (2004). Adipose tissue as an endocrine organ. Journal of Clinical Endocrinology & Metabolism, 89(6), 2548-2556. https://doi.org/10.1210/jc.2004-0395 LEAN BODY MASS IN MENOPAUSE 38 Kruger, J., Carlson, S., & Kohl, H. (2006). Trends in Strength Training – United States, 1998-2004. Centers for Disease Control and Prevention, Atlanta. Retrieved from https://www.cdc.gov/mmwr/preview/mmwrhtml/mm5528a1.htm Lee, J-Y., & Lee, D-C. (2013). Muscle strength and quality are associated with severity of menopausal symptoms in peri-and post-menopausal women. Maturitas, 76, 88-94. https://doi.org/10.1016/j.maturitas.2013.06.007 Leite, R.D., Prestes, J., Pereira, G.B., Shiguemoto, G.E., & Perez, S.E.A. (2010). Menopause: Highlighting the effects of resistance training. International Journal of Sports Medicine 31, 761-767. https://doi.org/ 10.1055/s-0030-1263117 Lemoine, S., Granier, P., Tiffoche, C., Rannou-Bekono, F., Thieulant, M.I., & Delamarche, P. (2003). Estrogen receptor alpha mRNA in human skeletal muscles. Medicine and Science in Sports and Exercise, 35, 439-443. https://doi.org/10.1249/01.MSS.0000053654.14410.78 Lobo, R.A., Davis, S.R., De Villiers, T.J., Gompel, A., Henderson, V.W., Hodis, H.N., …Baber, R.J. (2014). Prevention of diseases after menopause. Climacteric, 17, 540-556. https://doi.org/10.3109/13697137.2014.933411 Lovejoy, J.C. (2009). Weight gain in women at midlife: The influence of menopause. Obesity Management, 5, 52-56. https://doi.org/10.1059/obe.2009.0203 Lovejoy, J.C., Champagne, C.M., de Jong, L., Xie, H., & Smith, S.R. (2008). Increased visceral fat and decreased energy expenditure during the menopausal transition. International Journal of Obesity (London), 32, 949-958. https://doi.org/10.1038/ijo.2008.25 LEAN BODY MASS IN MENOPAUSE 39 Lukaski, H.C., Johnson, P.E., Bolonchik, W.W., & Lykken, G.I. (1985). Assessment of fat-free mass using bioelectrical impedance measurements of the human body. American Journal of Clinical Nutrition, 41, 810-817. Retrieved from www.ajcn.nutrition.org. Luoto, R., Moilanen, J., Heinonen, R., Mikkola, T., Raitanen, J., Tomas, E., ... Nygård, C. (2012). Effect of aerobic training on hot flushes and quality of life - A randomized controlled trial. Annals of Medicine, 44(6), 616-626. https://doi.org/10.3109/07853890.2011.583674 Maltais, M.L., Desroches, J., & Dionne, I.J. (2009). Changes in muscle mass and strength after menopause. Journal of Musculoskeletal Neuronal Interactions, 9(4), 186197. Retrieved from http://www.ismni.org/jmni/ Martins, F.M., Souza, A.P., Nunes, P.R.P., Michelin, M.A., Murta, E.F.C., …, Orsatti, F.L. (2018). High-intensity body weight training is comparable to combined training in changes in muscle mass, physical performance, inflammatory markers and metabolic health in postmenopausal women at high risk for type 2 diabetes mellitus: A randomized controlled clinical trial. Experimental Gerontology, 107, 108-115. https://doi.org/10.1016/j.exger.2018.02.016 Mendoza, N., Teresa, C-D., Cano, A., Godoy, D., Hita-Contreras, F., Lapotka, M., …Sánchez-Borrega, R. (2016). Benefits of physical exercise in postmenopausal women. Maturitas, 93, 83-88. https://doi.org/10.1016/j.maturitas.2016.04.017 Messier, V., Rabasa-Lhoret, R., Barbat-Artigas, S., Elisha, B., Karelis, A.D., & AubertinLeheudre, M. (2011). Menopause and sarcopenia: A potential role for sex LEAN BODY MASS IN MENOPAUSE 40 hormones. Maturitas, 68, 331-336. https://doi.org/10.1016/j.maturitas.2011.01.014 Mittal, P.C. & Kant, R. (2009). Correlation of increased oxidative stress to body weight in disease-free postmenopausal women. Clinical Biochemistry, 42(2009), 10071011. https://doi.org/10.1016/j.clinbiochem.2009.03.019 Moilanen, J.M., Aalto, A-M., Raitanen, J., Hemminki, E., Aro, A.R., & Luoto, R. (2012). Physical activity and change in quality of life during menopause – An 8-year follow-up study. Health and Quality of Life Outcomes 10(8). https://doi.org/10.1186/1477-7525-10-8 Ou, Y-C., Chuang, H-H., Li, W-C., Tzeng, I-S., & Chen, J-Y. (2017). Gender difference in the association between lower muscle mass and metabolic syndrome independent of insulin resistance in a middle-aged and elderly Taiwanese population. Archives of Gerontology and Geriatrics, 72, 12-18. https://doi.org/10.1016/j.archger.2017.04.006 Roubenoff, R. (2004). Sarcopenic obesity: The confluence of two epidemics. Obesity Research, 12, 887-888. https://doi.org/10.1038/oby.2004.107 Sánchez-Rodriguez, M.A., Zacarías-Flores, M., Arronte-Rosales, A., Correa-Muñoz, E., & Mendoza-Núñez, V.M. (2012). Menopause as risk factor for oxidative stress. Menopause, 19(3), 361-367. https://doi.org/10.1097/gme.0b013e318229977d Signorelli, S.S., Neri, S., Sciacchitano, S., Di Pino, L., Pia Costa, M., Marchese, G., Celotta, …Caschetto, S. (2006). Behavior of some indicators of oxidative stress in LEAN BODY MASS IN MENOPAUSE 41 postmenopausal and fertile women. Maturitas, 53, 77-82. https://doi.org/10.1016/j.maturitas.2005.03.001 Solomon, D.H., Diem, S.J., Ruppert, K., Lian, Y.J., Liu, C-C, Wohlfart, A., …Finkelstein, J.S. (2015). Bone mineral density changes among women initiating proton pump or H2 receptor antagonists: A SWAN cohort study. Journal of Bone and Mineral Research, 30(2), 232-239. https://doi.org/10.1002/jbmr.2344 Sowers, M.F., Crawford, S.L., Sternfeld, B., Morganstein, D., Gold, E.B., Greendale, G.A., …Kelsey, J. (2000). Design, survey sampling and recruitment methods of SWAN: A multi-center, multi-ethnic community-based cohort of women and the menopausal transition. In: R.A. Lobos & J. Kelsey (Eds.), Menopause: Biology and Pathobiology (pp175-188). San Diego: Academic Press Sowers, M.R., Jannausch, M., McConnell, D., Little, R., Greendale, G.A., Finkelstein, J.S., …Ettinger, B. (2006). Hormone predictors of bone mineral density changes during the menopausal transition. Journal of Clinical Endocrinology & Metabolism, 91(4), 1261-1267. https://doi.org/10.1210/jc.2005-1836 Stefanska, A., Bergmann, K., & Sypniewska, G. (2015). Metabolic syndrome and menopause: Pathophysiology, clinical and diagnostic significance. Advances in Clinical Chemistry, 72, 1-75. https://doi.org/10.1016/bs.acc.2015.07.001 Sternfeld, B., Ainsworth, B.E., & Quesenberry, C.P. (1999). Physical activity patterns in a diverse population of women. Preventative Medicine, 28, 313-323. https://doi.org/10.1006/pmed.1998.0470 LEAN BODY MASS IN MENOPAUSE 42 Sternfeld, B., Guthrie, K. A., Ensrud, K. E., LaCroix, A. Z., Larson, J. C., Dunn, A. L., ... Caan, B. J. (2014). Efficacy of exercise for menopausal symptoms: A randomized controlled trial. Menopause, 21(4), 330-338. https://doi.org/10.1097/GME.0b013e31829e4089 Sutton-Tyrell, K., Selzer, F., Sowers, MF.R., Neer, R., Powell, L., Gold, E., … McKinlay, S. (2018a). Study of Women’s Health Across the Nation (SWAN), 1995-1997: Cross-Sectional Screener Dataset. Ann Arbor, MI: Inter-university Consortium for Political and Social Research [distributor]. https://doi.org/10.3886/ICPSR04368.v4 Sutton-Tyrell, K., Selzer, F., Sowers, MF.R., Finkelstein, J., Powell, L., Gold, E.B., … Matthews, K. (2018b) Study of Women’s Health Across the Nation (SWAN), 2002-2004: Visit 06 Dataset. Ann Arbor, MI: Inter-university Consortium for Political and Social Research [distributor]. https://doi.org/10.3886/ICPSR31181.v2 Sutton-Tyrell, K., Selzer, F., Sowers, MF.R., Finkelstein, J., Powell, L., Gold, E.B., … Matthews, K. (2018c). Study of Women’s Health Across the Nation (SWAN), 2003-2005: Visit 07 Dataset. Ann Arbor, MI: Inter-university Consortium for Political and Social Research [distributor]. https://doi.org/10.3886/ICPSR31901.v2 Sutton-Tyrell, K., Selzer, F., Sowers, MF.R., Finkelstein, J., Powell, L., Gold, E.B., … Brooks, M.M. (2018d). Study of Women’s Health Across the Nation (SWAN), 2004-2006: Visit 08 Dataset. Ann Arbor, MI: Inter-university Consortium for LEAN BODY MASS IN MENOPAUSE 43 Political and Social Research [distributor]. https://doi.org/10.3886/ICPSR32122.v2 Sutton-Tyrell, K., Selzer, F., Sowers, MF. R., Finkelstein, J., Powell, L., Gold, E.B., … Brooks, M.M. (2018e). Study of Women’s Health Across the Nation (SWAN), 2005-2007: Visit 09 Dataset. Ann Arbor, MI: Inter-university Consortium for Political and Social Research [distributor]. https://doi.org/10.3886/ICPSR32721.v2 Sutton-Tyrrell, K., Selzer, F., Sowers, MF.R., Finkelstein, J., Powell, L., Gold, E.B., … Brooks, M.M. (2018f). Study of Women’s Health Across the Nation (SWAN), 2006-2008: Visit 10 Dataset. Ann Arbor, MI: Inter-university Consortium for Political and Social Research [distributor]. https://doi.org/10.3886/ICPSR32961.v2 Svendsen, O.L., Hassager, C., & Christiansen, C. (1995). Age- and menopauseassociated variations in body composition and fat distribution in healthy women as measured by dual-energy x-ray absorptiometry. Metabolism, 44(3), 369-373. https://doi.org/10.1016/0026-0495(95)90168-X Tanaka, N.I., Hanawa, S., Murakami, H., Cao, Z-B., Tanimoto, M., Kiyoshi, Motohiko, M. (2015). Accuracy of segmental bioelectrical impedance analysis for predicting body composition in pre- and postmenopausal women. Journal of Clinical Densitometry, 18(2), 252-259. https://doi.org/10.1016/j.jocd.2014.07.002 Takamura, T., Kita, Y., Nakagen, M., Sakurai, M., Isobe, Y., …Kaneko, S. (2017). Weight-adjusted lean body mass and calf circumference are protective against LEAN BODY MASS IN MENOPAUSE 44 obesity-associated insulin resistance and metabolic abnormalities. Heliyon, 3. https://doi.org/10.1016/j.heliyon.2017.e00347 Thurston, R.C., Sowers, M.R., Chang, Y., Sternfeld, B., Gold, E.B., Chang, Y., …Matthews, K.A. (2008a). Adiposity and reporting of vasomotor symptoms among midlife women: The Study of Women’s Health Across the Nation. American Journal of Epidemiology, 167(1), 78-85. https://doi.org/10.1093/aje/kwm244 Thurston, R.C., Sowers, M.R., Sutton-Tyrrell, K., Everson-Rose, S.A., Lewis, T.T., …Matthews, K.A. (2008b). Abdominal adiposity and hot flashes among midlife women. Menopause, 15(3), 429-434. https://doi.org/10.1097/gme.0b013e31815879cf Thurston, R.C., Sowers, M.R., Sternfeld, B., Gold, E.B., Bromberger, J., …Matthews, K.A. (2009). Gains in bodyfat and vasomotor symptoms reporting over the menopausal transition. American Journal of Epidemiology, 170(6), 766-774. https://doi.org/10.1093/aje/kwp203 Thurston, R.C. & Joffe, H. (2011). Vasomotor symptoms and menopause: Findings from the Study of Women’s Health Across the Nation. Obstetrics & Gynecology Clinics of North America, 38(3), 489-501. https://doi.org/10.1016/j.oge.2011.05.006 Thurston, R.C., Chang, Y., Mancuso, P., & Matthews, K.A. (2013). Adipokines, adiposity, and vasomotor symptoms during the menopause transition: Findings LEAN BODY MASS IN MENOPAUSE 45 from the Study of Women’s Health Across the Nation. Fertility and Sterility, 100(3), 793-800. https://doi.org/10.1016/j.fertnstert.2013.05.005 Wildman R.P. & Sowers, MF R. (2011). Adiposity and the menopausal transition. Obstetrics & Gynecology Clinics of North America, 38, 411-454. https://doi.org/10.1016/j.ogc.2011.05.003 Zacarías-Flores, M., Sánchez-Rodríguez, M.A., García-Anaya, O.D., Correa-Muñoz, E., & Mendoza-Núñez, V.M. (2018). Relationship between oxidative stress and muscle loss in early postmenopause: An exploratory study. Endocrinología, Diabetes y Nutrición, 65(6), 328-334. https://doi.org/10.1016/j.endien.2018.01.006 Zapantis, G & Santoro, N. (2003). The menopausal transition: Characteristics and management. Best Practice and Research Clinical Endocrinology & Metabolism 17, 33-52. https://doi.org/10.1053/ybeem.2003.236 Zhang, J., Chen, G., Lu, W., Yan, X., Zhu, S., Dai, Y., ... Bai, W. (2014). Effects of physical exercise on health-related quality of life and blood lipids in perimenopausal women: A randomized placebo-controlled trial. Menopause, 21(12), 1269-1276. https://doi.org/10.1097/GME.0000000000000264 Zhou, Y., Zheng, Y., Li, C., Hu, J., Zhou, Y., Geng, L. & Tao, M. (2018). Association of body composition with menopausal symptoms in (peri-)menopausal women. Climacteric, 21(2), 179-183. https://doi.org/10.1080/13697137.2018.1428295 LEAN BODY MASS IN MENOPAUSE Appendix A Review of the Literature 46 LEAN BODY MASS IN MENOPAUSE 47 Menopause is said to have occurred after twelve months of amenorrhea due to the cessation of ovarian follicular function and decreasing estrogen levels (Greendale, Lee & Arriola, 1999). Fluctuating estrogen levels in the years preceding menopause create an impact on female physiology and women begin to experience a variety of physical and psychological symptoms. These symptoms include hot flashes, nights sweats, sleep disturbances, depression, irritability, headache, and urogenital issues, all of which can disrupt daily functioning (Woods & Mitchell, 2011). These symptoms can persist over the course of 20 years and can severely impact quality of life (Gjelsvik, Rosvold, Straand, Dalen & Hunskaar, 2011; Huang et al., 2010; Sternfeld & Dugan, 2011). The aging of the large baby boom cohort with a combination of low fertility and increasing life expectancy has led to an aging female population in Canada (Statistics Canada, 2016). This large cohort, born between 1946 and 1965, was 45 to 64 years old in 2010: They made up about 28% of the overall female population in that year, representing almost five million women (Statistics Canada, 2016). Given the potential long-term duration of symptoms impacting this large at-risk population, there is a growing need to find nonpharmacological treatments for menopause symptoms. Exercise has been suggested as a low-cost alternative that may also provide additional health benefits. Additionally, the role of muscle mass and its impact on symptoms and the development of menopauserelated diseases is incompletely understood. This literature review will examine the pathophysiology and clinical data regarding menopause, hormone therapy and the association to disease, then more closely review the current literature on the effects of exercise on menopause symptom management and the role of muscle mass in menopausal women. LEAN BODY MASS IN MENOPAUSE 48 Menopause Pathophysiology and Reproductive Aging Menopause is a significant event in most women’s lives as it marks the end of the natural reproductive life. For most women, menopause will occur between the ages of 40 and 58 years with the average being 51 years (Zapantis & Santoro, 2003). The timing of menopause globally is relatively constant; however, both the nature and severity of symptoms varies substantially between women from different ethnicities and geographical locations for reasons that are not completely understood (Roberts & Hickey, 2016). The Stages of Reproductive Aging Workshop +10 (Harlow et al., 2012) classifies the transition through menopause into five stages through early menopause to late post menopause. The first two stages are early menopausal (variable length of time) and late menopausal (1-3 years) which both precede the final menstrual period (FMP). The menstrual cycle is beginning to vary in length and hormone levels can see extreme fluctuations. Symptoms, including vasomotor symptoms, are likely to occur in the late menopausal stage. The FMP marks the start of the postmenopausal stages. The first two years of post-menopause, known as early post menopause, see a continued decline in estradiol that will eventually stabilize toward the end of this stage. Symptoms are most likely to occur during this stage and throughout the next stage of late post-menopause which can last from 3-6 years. Late post menopause is the time when further hormonal LEAN BODY MASS IN MENOPAUSE 49 changes are limited, and the effects of somatic aging become of more concern leading to urogenital symptoms (Harlow et al., 2012). Epidemiology and Prevalence of Symptoms The most frequent symptoms during menopause are hot flushes, night sweats, depression, irritability and sleep disorders (Skrzypulec, Dabrowska, & Drosdzol, 2010). About 80-96% of women experience mild to severe physical or physiological menopause-related complaints as they approach menopause due to declining estrogen levels (Moilanen et al., 2012). A recent systematic review suggested that women with more negative attitudes towards menopause in general report more symptoms during the menopausal transition (Ayers, Forshaw, & Hunter, 2010). A longitudinal cohort study found that smoking was significantly associated with more symptoms and an earlier menopause age, while other life style factors, like body mass index (BMI) or physical activity (PA) before menopause, have no significant correlation with the prevalence or burden of menopausal symptoms (Gjelsvik, et al., 2011). Other changes that occur during the menopause transition are changes in body composition with an increase in abdominal fat mass, as well as associated alterations in cardiometabolic risks due to hormone-related decreases in energy expenditure and fat oxidation (Lovejoy, Champagne, de Jonge, Xie & Smith, 2008). Duval et al. (2013), found during the fiveyear longitudinal Montreal-Ottawa New Emerging Team (MONET) study, that menopause is accompanied by a decrease in energy expenditure (EE) which is mainly due to a decrease in physical activity EE and a shift to a more sedentary lifestyle. The physiological complexity of the menopause transition and its many effects on the female LEAN BODY MASS IN MENOPAUSE 50 body, as depicted in Figure 8, is not completely understood and warrants continued research in this area. Figure 8. Symptoms and deleterious effects of menopause. LDL, low density lipoprotein; VLDL, very low-density lipoprotein; TGL, triglycerides; HDL, high density lipoprotein; IL-6, Interleukin 6; IL-1, Interleukin 1; TNF, tumor necrosis factor. Adapted from R.D. Leite, J. Prestes, G.B. Pereira, G.E. Shiguemoto, and S.E.A. Perez, 2010, Menopause: Highlighting the effects of resistance training. International Journal of Sports Medicine, 31, p. 762. Physiology of Vasomotor Symptoms Hot flashes and night sweats are the most common symptoms of menopause (collectively referred to as vasomotor symptoms) and are reported as feelings of intense warmth along with sweating, flushing (in the face, neck and chest) and chills (Gallichio et al., 2014). The median duration of vasomotor symptoms (VMS) is about four years for most women, however, some women experience them for as long as 20 years (Feldman, Voda, & Gronseth, 1985). Research has suggested that lower levels of estrogen appear to LEAN BODY MASS IN MENOPAUSE 51 narrow the thermoneutral zone – the internal temperature zone between sweating and shivering (Freedman, 2014). Women will sweat more easily when their core temperature rises if they have a narrow thermoneutral zone, but if all woman will experience a decline in estrogen and not all women have hot flashes, this must not be the only mechanism to trigger them (Kronenberg, 2010). Freedman (2014) suggested that an increase in brain norepinephrine, which is also known to reduce the thermoneutral zone, in conjunction with a decrease in estrogen, is part of the etiology of hot flashes. His theory hypothesized that hot flashes are triggered by elevations in core body temperature acting within a greatly reduced thermoneutral zone in menopausal woman (Freedman, 2014). Treatments for Menopausal Symptoms Per the most recent position paper of the North American Menopause Society (NAMS, 2015), surveys suggest that 50-80% of menopausal women use non-hormonal therapies for vasomotor symptoms. The paper recommends cognitive behavioral therapy, clinical hypnosis, along with low- dose paroxetine salt, and FDA approved selective serotonin reuptake inhibitors and serotonin-norepinephrine reuptake inhibitors as the only methods supported by level 1 evidence (Oxford Centre for Evidence-Based Medicine, 2011). Exercise, while considered important to other domains of health and may prove to be important in the treatment of other menopausal symptoms, is lacking in clinical evidence to support use for VMS management (Daley, Stokes-Lampard, Thomas & Macarthur, 2014). LEAN BODY MASS IN MENOPAUSE 52 Measurement of Menopausal Symptoms Although there are numerous variations available, the most frequently used instruments for measuring menopause symptoms are the Menopause Specific Quality of Life (MENQOL), Greene Climacteric Scale and the SF-36. The MENQOL is a validated questionnaire for the assessment of degree of bother for menopausal women’s symptoms and an effective instrument, although more research is needed to confirm validity in different cultures and countries (Jenabi et al., 2015). The Greene Climacteric Scale is a self-reported questionnaire that measures degree of bother for 21 menopause specific physical and psychological symptoms (Greene, 2008) and the SF-36 is a 36-item shortform designed for general use in clinical practice and research, health policy evaluation and general population surveys (Jenabi et al., 2015). The Menopause Symptoms’ Severity Inventory (MSSI-38) is a newer instrument that measures severity of symptoms by evaluating both frequency and intensity of symptoms, but this instrument is not yet validated for clinical samples (Pimenta et al., 2012). The Kupperman Index is a numerical index that also scores 11 menopausal symptoms but does not address quality of life or symptom burden. The Women’s Health Questionnaire (WHQ) was specifically developed to measure the perceptions of a range of physical and emotional symptoms in middle-aged women allowing perceived quality of life to be measured. It is important to have specific instruments that are capable of measuring symptoms and health related quality of life in menopausal women because perceived severity of symptoms and overall burden may vary significantly between women and uniquely impact quality of life. LEAN BODY MASS IN MENOPAUSE 53 At least one study was able to objectively measure severity of hot flashes. Bailey et al. (2016) used tube-lined jacket and trousers to cover the entire body and exposed participants to a mild heat stress by perfusing heated water through the suit. Heart rate, core body temperature, blood pressure and local sweat rate were all recorded at baseline and after 16 weeks of intervention. This method of physiological measurement requires a significant lab setting and highly trained technicians but provides valuable research data that is not always accurately captured through self-report diaries or survey instruments which can be subject to recall bias. Hormone Therapy Contraindications In 2002, the National Institutes of Health put an early stop to randomized control trials, called the Women’s Health Initiative (WHI), testing the association of menopausal hormone therapy (MHT) with decreased risk of coronary heart disease due to implications that there was an increased risk of breast cancer, coronary heart disease, stroke and pulmonary embolism in women with an intact uterus (Rossouw, Manson, Kaunitz & Anderson, 2013). Prescriptions for MHT dropped dramatically after the findings received wide spread attention and continued to decline up to 2010 (Ettinger et al., 2012). In the most recent update to the study’s findings, the current recommendation is that short term, low dose MHT (oral or transdermal) can be used for treatment of moderate to severe VMS in healthy women with an intact uterus soon after menopause onset (Ettinger et al., 2012). LEAN BODY MASS IN MENOPAUSE 54 Association of Menopause with Health Conditions Metabolic Syndrome The menopausal transition is accompanied by metabolic changes and the increasing prevalence of metabolic syndrome (MetS) which is defined by the cooccurrence of abdominal obesity, hypertension, dyslipidemia and insulin resistance (Stefanska, Bergmann, & Sypniewska, 2015). MetS is similarly associated with development of type 2 diabetes and cardiovascular disease (O’Neill & O’Driscoll, 2015). Globally it is estimated that 30-55% of post-menopausal women fulfill the diagnostic criteria for MetS (Stefanska et al., 2015). This high incidence of MetS is induced by several factors including sedentary lifestyle, diminished exercise capacity, weight gain and central obesity, and is facilitated by low levels of estrogens, dyslipidemia, low glucose tolerance, elevated blood pressure and an increase in the pro-inflammatory state (Wang et al., 2014). There is still debate as to whether the increased incidence of MetS is due to the hormonal changes which accompany menopause or normal aging; however, a recent large population-based study showed that chronological age and menopausal status are independently associated with cardiovascular risk factors (de Kat et al., 2017). As they transition through menopause, women will experience a decrease in basal metabolic rate and a loss of lean muscle tissue which increases the risk of weight gain and obesity (Lovejoy, 2009). Weight gain at midlife is frequently reported by women; studies have shown that an increase in subcutaneous adipose tissue is related to age whereas the increase in visceral and total body fat is related to the abrupt decline in estrogen (Abdulnour et al., 2012, Lovejoy et al., 2008). This accumulation of abdominal LEAN BODY MASS IN MENOPAUSE 55 fat in postmenopausal women appears to be a critical factor in the development of insulin resistance, which in turn is a major risk factor for type 2 diabetes (Lobo et al., 2014). In their longitudinal study, Kabat et al. (2014), found that BMI, waist circumference and the waist circumference to height ratio robustly predicted change in cardiometabolic risk in post-menopausal women with the strongest associations seen for increased triglycerides, glucose, and reduced high-density lipoprotein (HDL) cholesterol. For postmenopausal women, chronic systemic inflammation, oxidative stress, abdominal visceral adipose tissue, dyslipidemia, sarcopenia, and a sedentary lifestyle are all risk factors for MetS (Mendoza et al. 2016). The complicated interactions between these risk factors are illustrated in Figure 9. However, both aerobic and strength training have been shown to partially counteract these changes by increasing anti-inflammatory responses, antioxidant enzyme expression, and insulin sensitivity, and by reducing adipose tissue (Glouzon et al, 2015). Figure 9. Relationship between MetS components and cardiometabolic complications. Adapted from Stefanska, A., Bergmann, K., & Sypniewska, G. (2015). Metabolic Syndrome and Menopause: Pathophysiology, clinical and diagnostic significance. Advances in Clinical Chemistry, 72(1) p. 9 LEAN BODY MASS IN MENOPAUSE 56 In the Dose-Response to Exercise in Women (DREW) control study, authors found a dose-response relationship between weekly exercise energy expenditure, prevalence of MetS and overall cardiometabolic risk factors in a large, randomized population of postmenopausal women (Earnest et al., 2013). The results of this study indicated that exercise training at or above (100% or 150%) the public recommendations for physical activity improved the components of MetS in postmenopausal women and that even at a reduced level of activity (50%), small improvements were recorded (Earnest et al., 2013). Cardiovascular Disease In their systematic review and meta-analysis, Muka et al. (2016) concluded that women who experience VMS and other menopausal symptom are at a greater risk for developing cardiovascular disease. The cardiovascular risk factors are generally considered to include: total cholesterol levels, hypercholesterolemia, systolic and diastolic blood pressure, hypertension, and BMI (Stefanska et al., 2015). In a populationbased study involving 5,523 women, Gast et al., (2008) found that women with vasomotor complaints have an unfavorable cardiovascular risk profile with increased cholesterol levels, diastolic and systolic blood pressures and BMI. Many studies to date have reviewed the association between VMS and cardiovascular disease, however, a 2012 study examined whether menopausal symptoms other than VMS were associated with biochemical and biophysical risk factors (Cagnacci et al., 2012). The data seemed to indicate that menopausal symptoms may represent stressful events associated with endocrine modifications, such as an increase in cortisol, leading to insulin resistance and LEAN BODY MASS IN MENOPAUSE 57 to an increased metabolic risk for cardiovascular disease (Cagnacci et al., 2012). A more recent study by Cagnacci et al. (2015) found that the symptoms of menopause may be associated with biochemical risk factors which begin to appear early in menopause and are also related to cardiovascular disease. Using multivariate analysis, the study compared the scores of 590 participants on Greene’s climacteric scale to cardiovascular risk factors which included blood pressure, fasting glucose, fasting lipids. Sarcopenia Loss of muscle mass and function normally associated with aging is referred to as sarcopenia. In women, sarcopenia develops and is highly prevalent during menopause, and is thought to be related to the decline in estrogen with subsequent detrimental effects on skeletal mass and strength (Lee & Lee, 2013). A review by Messier et al. (2011) concluded that after the fifth decade of life, declining estrogen may play a role in declining muscle mass in postmenopausal women, however, sarcopenia is complicated in that it involves hormonal, biological, nutritional and physical activity mechanisms. Figure 10 represents the complex association of menopause related health changes and sarcopenia. LEAN BODY MASS IN MENOPAUSE 58 Figure 10. Menopause-related changes on muscle mass and its impact on functional status. Adapted from Messier, V., Rabasa-Lhoret, R., Barbat-Artigas, S., Elisha, B., Karelis, A.D., Aubertin-Leheudre, M. (2011). Menopause and sarcopenia: A potential role for sex hormones (2011). Maturitas 68, pg. 334. A recent cross-sectional study of 148 women assessed biomarkers of metabolic risk through blood tests and used Kupperman Index scores to measure symptom severity then correlated those scores with muscle mass determined by Dual-energy X-ray Absorptiometry and hand grip strength measured with isometric dynamometry (Lee & Lee, 2013). Muscle quality was calculated as the ratio of strength to muscle mass in the upper extremities. The authors found that muscle strength and quality (but not muscle mass) may be related to the severity of menopausal symptoms due to declining sex hormones and the resulting increase in oxidative stress (Lee & Lee, 2013). The authors also suggest that there is a vicious cycle of increased oxidative stress during menopause that may induce the loss of muscle strength, and the loss of muscle strength, in turn, increases oxidative stress (Lee & Lee, 2013). Postmenopausal women have been shown LEAN BODY MASS IN MENOPAUSE 59 to have significantly higher oxidative stress blood marker levels and lower antioxidant capacity relative to premenopausal women (Signorelli et al, 2006) which is thought to be correlated to their higher body weight (Mittal & Kant, 2009). A review by Maltais et al. (2009) found that the menopausal transition is associated with a decline in estrogen, growth hormone, IGF-1, and DHEA, a decrease in muscle protein synthesis, and an increase in catabolic factors such as inflammation. The authors concluded that low physical activity, inadequate protein intake, and elevated oxidative stress are the greatest contributors of sarcopenia in postmenopausal women and suggest that resistance training may be the key strategy to maintain muscle mass (Maltais et al., 2009). Interestingly, untrained obese women with low muscle strength (as measured by hand grip strength) were shown to have lower fasting glucose, lower fasting insulin, lower diastolic blood pressure and insulin sensitivity significantly higher than those with normal muscle strength, although both groups had similar lean body mass (BarbatArtigas et al., 2012). Glouzan et al. (2015) also determined that muscle mass in overweight-to-obese postmenopausal women is correlated with insulin resistance in a study involving forty-eight sedentary women who followed a six-month mixed exercise intervention. However, the implication is not that resistance training is not beneficial, but that genetically determined lean body mass may be related to an android pattern of body fat, and that the benefits of exercise-induced increases in lean body mass on insulin sensitivity would outweigh any adverse effects of genetic predisposition of lean body mass. In considering the prevention of sarcopenia and interventions directed at menopausal women, methods of identifying at-risk women become important. A large LEAN BODY MASS IN MENOPAUSE 60 cross-sectional study of 2,152 women positively correlated leukocyte counts (measure of inflammation) with higher risk of sarcopenia in postmenopausal women noting that higher leukocyte counts are also associated with cardiovascular disease, type 2 diabetes and MetS (Chung et al., 2016). The importance of muscle strength in everyday life would suggest the need to find effective strategies to prevent its loss. Evidence suggests that resistance exercise and proper nutrition are important to maintain or prevent the accelerated loss of muscle strength due to aging and potentially, menopausal status (Maltais et al., 2009) Role of Physical Activity During Menopause Benefits of Physical Activity Physical activity (PA), including regular exercise, has a range of health benefits including improved cardiovascular function, reduced blood pressure, reduced blood triglyceride levels and improved blood glucose metabolism, reduced abdominal fat, and improved weight control (World Health Organization, 2010). The mechanism through which PA may be beneficial for menopausal symptoms is not well understood, however, Dugan and Sternfeld (2011) proposed several possible mechanisms which include neuroendocrine, thermoregulation, body composition, and psychological effects. These mechanisms have been examined in various studies. A recent randomized controlled trial examined the effects of a long term (+12 months) mixed exercise program in 158 postmenopausal women and found positive LEAN BODY MASS IN MENOPAUSE 61 changes in basal metabolic rate, skeletal muscle mass, percent body fat and other anthropometric and body composition variables with no significant interactive effects between exercise and menopausal symptoms (Aragão et al., 2014). In another randomized controlled trial of 325 postmenopausal women, a greater reduction in waist circumference occurred in participants with higher habitual physical activity combined with aerobic training than those with a lower habitual physical activity (Swift et al., 2012). Physical activity has also been shown to be effective in attenuating symptoms in the emotional and psychological aspects of menopause as measured by health-related quality of life (Kishida & Elavsky, 2015; Moratalla-Cecilia et al., 2016; Skrzypulec et al., 2010; Sternfeld & Dugan, 2011). In a cross-sectional study, menopausal women who were physically active reported less severe symptoms in the areas of mood, energy and concentration (Haimov-Kochman et al., 2013). This study also suggested that women who exercised regularly felt better and had fewer complaints in a dose-dependent manner, in that the more frequent the exercise, the less severe the reported symptoms (Haimov-Kochman et al., 2013). In their longitudinal study, Kishida and Elavsky (2015) found that menopausal women who engaged in physical activity experienced greater positive affect and enhanced coping efficacy which helped to reduce the symptom burden and improve quality of life. Additionally, in an eight-year follow up study of 1,165 women aged 4564, global quality of life improvement was correlated with stable or increased levels of physical activity (Moilanen et al., 2012). Evidence suggests that regular physical activity is beneficial to menopausal women and using data from a six-month randomized LEAN BODY MASS IN MENOPAUSE 62 controlled trial (n=151), Kolu et al. (2015) found that it is also cost-effective for improving cardiorespiratory fitness, lean muscle mass, and QALY (quality adjusted life years) in relation to exercise cost (gym membership, trainer fees, etc.), health care cost and productivity cost. Barriers to Participation in Physical Activity Unfortunately, as women age, PA tends to decline, with Statistics Canada reporting that in 2012/2013 only 18% of women aged 40-59 years were meeting the recommended guidelines of 150 minutes of moderate to vigorous intensity aerobic physical activity per week (Statistics Canada, 2016). A Canadian study compared the quality of life in postmenopausal women of those achieving the recommended amount of PA versus those who did not and found that women meeting the public health guidelines had higher scores in health-related quality of life and satisfaction with life and reported fewer symptoms of depression and anxiety (Vallance et al., 2010). Another study of 2,606 Finnish women also reported that women who meet the public recommendation for leisure time physical activity had better menopausal and global quality of life (Mansikkamaki et al., 2015). The increasingly sedentary lifestyle among menopausal women is often ascribed to their perceived barriers to exercise. Common barriers for middle-aged women considering initiation of exercise are lack of time, safety concerns about exercising outdoors, weather, and not having a family member or friend to exercise with (Grindler & Santoro, 2015). The three most common barriers interfering with adherence to exercise LEAN BODY MASS IN MENOPAUSE 63 were disruptions in daily structure, competing demands on time, and self-sacrifice (Grindler & Santoro, 2015; McArthur, Dumas, Woodend, Beach & Stacey, 2014). Interestingly, McArthur et al. (2014) did not find that experiencing menopausal symptoms itself, was considered a factor in exercise adherence but that the majority of factors were related to the psychosocial aspects of this mid-life stage. A cross-sectional study conducted in Australia found that positive perceptions about the benefits of exercise, self-efficacy for exercise, and physical and mental well-being were the key factors that predict postmenopausal women’s perceptions of barriers (McGuire et al., 2016). A prospective cohort study of 216 women followed for five years showed the most frequently cited barriers to exercise were lack of time and lack of motivation at baseline, but at the five-year mark the lack of time barrier was no longer a significant factor (Ball et al., 2016). The authors suggest the change over time correlated to the age of the women as most were near retirement age at baseline which may have led to more flexibility in schedules of the participants. Ball et al. (2016) also found that women with a higher BMI at baseline were more likely to reduce their BMI over time and those at normal BMI at baseline were more likely to gain weight although there was no statistically significant difference in changes to physical activity. Menopausal women’s adherence to regular exercise is the result of a complex interplay of social, emotional, environmental, and psychological factors (McArthur et al., 2014). As the presence of symptoms itself does not appear to be a barrier, women should be encouraged to make exercise a priority through assessment of the barriers and enabling factors that are unique to them. Most women already believe that they “should” be more active so the challenge is to translate that into motivation for action as opposed LEAN BODY MASS IN MENOPAUSE 64 to it being perceived as another demand on their time and then manifests internally as a lack of ability to follow through. Exercise Training for Symptom Management Cardiovascular (Aerobic) Training As noted above, physical activity is a beneficial and cost-effective method to help women cope with the symptoms of menopause. The evidence regarding the effectiveness of exercise to ameliorate symptoms, however, has been contradictory and there is a clear delineation in the literature between VMS and other symptom types. First, we will examine the studies which did not find exercise effective for VMS. A 2014 random control trial of 157 postmenopausal women reported reductions in most symptoms, excluding VMS, with improved blood lipid profiles after 12 weeks of walking with strides three times per week (Zhang et al., 2014). The study authors developed a 16item questionnaire that, together with the modified Kupperman Index, was completed by participants at weeks 4, 8, and 12. The study found significantly lower (P < 0.05) scores at week 12 for the Kupperman Index, weight, BMI, waist circumference, triglycerides and total cholesterol as compared to baseline and controls. A second random control trial of 248 women was conducted as one arm of a 3 x 2 factorial trial called the Msflash study. The exercise intervention consisted of individual, facility-based aerobic training (treadmill, elliptical or bike) three times per week for 12 weeks. Target heart rate was monitored throughout with a goal of 50-60% of heart rate reserve for the first month and LEAN BODY MASS IN MENOPAUSE 65 60-70% for the remainder of the intervention. VMS frequency and bother were recorded in daily diaries for seven days at baseline, and weeks six and twelve. Sleep quality and sleep disturbances, insomnia symptoms, depressive symptoms and anxiety were measured at baseline and week 12 through validated generalized self-report surveys. The authors found that the 12 weeks of aerobic exercise did not result in a statistically significant reduction in VMS but led to small improvements in sleep quality, insomnia and depression (Sternfeld et al., 2014). A further random control trial involved 261 postmenopausal women randomized to one of two exercise groups or control for a 24-week study. Participants were given an initial goal of progressing towards 30 minutes of moderate intensity exercise on at least three days per week. The definition of moderate intensity was an increase in breathing rate, an increase in heart rate to the level where they could feel their pulse and an increase in body temperature and they were given examples of what types of activities would constitute moderate intensity. For weeks 12-24, participants were encouraged to increase activity to 30 minutes on 3-5 days per week. Both exercise groups received counselling; one group also received a DVD of case studies on women with hot flashes and their motivation to exercise, while the other group were invited to take part in three exercise support groups. The primary outcome was the frequency of hot flashes and was measured at baseline and 6 months using the hot flush rating scale. All participants were asked to complete prospective seven-day hot flush/night sweat diaries at weeks 7, 14, and 21 recording both frequency and severity, and exercise groups were also asked to complete 7-day prospective exercise logs that detailed amount and type of exercise completed. A random 50% sample of all participants were asked to wear heart rate LEAN BODY MASS IN MENOPAUSE 66 monitors for five consecutive days at baseline and at 6 months to provide objective data on physical activity. A total of 50% of the hot flush diaries were completed and returned and a total of 47% of the exercise diaries were completed and returned with the most common type of exercise being walking, aerobics classes and swimming. The authors concluded that 30 mins of moderate intensity exercise three days per week did not result in significantly fewer hot flashes/night sweats at six-month or 12-month follow ups (Daley et al., 2015). A 2014 Cochrane Review found only five eligible studies reviewing the effect of exercise on VMS (Daley et al., 2014). Studies that had been included in the previous edition of this review were excluded based on having participants at baseline who were not symptomatic. The five included studies for this review were all parallel-group randomized controlled trials and included interventions of walking versus yoga versus control, aerobics versus MHT, walking plus soy versus soy versus control, cardiovascular conditioning versus yoga versus control, and walking plus education versus control plus education. All studies assessed VMS by self-report with one using the Women’s Health Questionnaire, another using the Greene Climacteric Scale, and two using the Kupperman Index. Overall, the studies were considered to be of poor quality due to limitations in study design, inconsistency and imprecision. The authors therefore concluded that there was insufficient evidence to determine the effectiveness of exercise on vasomotor symptoms (Daley et al., 2014). In contrast to these, several studies have shown benefits of exercise on VMS and other symptom domains. A longitudinal study randomized 176 postmenopausal women in Finland to six months of an aerobic training intervention or control. The participants LEAN BODY MASS IN MENOPAUSE 67 were asked to include unsupervised aerobic training for 50 minutes four times per week. Instructions were given to exercise at rates of perceived exertion corresponding to 13-16 on a scale from 6-20 (approximately 64%- 80% of maximal heart rate). At least two sessions had to include walking and the other two could include walking, Nordic walking, jogging, cycling, swimming, skiing, aerobics or other gymnastic exercise. Participants wore heart rate monitors during training and received feedback every two weeks from an instructor based on data transferred from the monitors. Hot flushes were the primary outcome as measured by the WHQ and secondary outcomes of all other reported symptoms and their perceived disturbance level were also collected. Both primary and secondary outcome data was captured using mobile phone questionnaires twice per day thus minimizing recall bias (telephone-based questionnaire was not validated but questions used were dichotomous). The study authors concluded that aerobic training for six months reduced prevalence for all symptoms except vaginal dryness including a statistically significant decrease in night sweats, mood swings, and irritability (Moilanen et al., 2012b). Additionally, in a four-year follow up to this study, 95 of the women agreed to participate in anthropometric measurements, wear an accelerometer to record PA and complete a 2-km walk test. Participants completed a questionnaire and kept a one-week diary on physical, menopausal symptoms and sleep quality. The study showed that participants in the intervention group had positive long-term effects of exercise training on hot flash scores relative to the non-exercise group (Mansikkamaki et al., 2016). Supporting these studies, Luoto et al. (2012) also reported from the same study that six LEAN BODY MASS IN MENOPAUSE 68 months of aerobic exercise training resulted in decreased frequency of hot flashes and improved quality of life in the 176 participants. To date, the frequency, duration, and severity of VMS have been measured through subjective recall diaries which are subject to bias. However, as discussed previously, a recent preference control trial was the first to objectively measure the effect of exercise on the severity of hot flashes. The authors used a tube lined jacket and pants in a small study involving 21 postmenopausal women to quantify the acute physiological thermoregulatory and cerebrovascular changes that occur during hot flashes prior to and following a 16-week exercise intervention. The intervention consisted of supervised weekly exercise sessions which progressed from 30 min of moderate intensity aerobic exercise (30% heart rate reserve (HRR)) three times per week to 4-5 times per week at 45 min per session at 60% HRR by week 12. At baseline and follow-up, participants completed a seven-day hot flush frequency and severity diary as well as the physiological hot flush assessment. The authors observed that 16 weeks of moderate intensity exercise improved cardiorespiratory fitness and attenuated cutaneous vasodilation, sweating, and reductions in cerebral blood flow during hot flashes thus reducing the severity of hot flushes (Bailey et al., 2016). In their previous work, Bailey et al. (2015) found that exercise training improves thermoregulatory control and enhances the function of the cerebral and cutaneous circulations to passive heating alongside improvements in the frequency of menopausal hot flushes. More specifically, they found that exercise training mediated a reduction in resting core temperature that enabled earlier onset of sweating and cutaneous vasodilation, improved sensitivity of sweating and attenuated reductions in cerebral blood LEAN BODY MASS IN MENOPAUSE 69 flow during passive heating. It has also been hypothesized that exercise may have ameliorating effects on vasomotor symptoms by increasing the presence of hypothalamic and peripheral ß-endorphin production which may help stabilize the thermoregulatory center and diminish the risk of hot flushes (Moilanen et al., 2012a) but these mechanisms will require further invasive investigation by clinical technicians with substantial, research-validated equipment. The above noted studies all used moderate intensity continuous training (CT) as the method of cardiovascular exercise for the intervention. There is however, emerging evidence regarding the use of high intensity training (HIT) as a more efficient and effective method of exercise for postmenopausal women. High Intensity Training (HIT) Protocol Klonizakis et el. (2014) conducted a two-arm, parallel-group, randomized trial involving 22 postmenopausal women. The purpose of the trial was to compare the effects of a low-volume HIT protocol and a higher-volume moderate intensity CT protocol on cardiopulmonary function and macrovascular endothelial function (measured via brachial artery flow-mediated dilation (FMD)). Baseline data was collected for macrovascular function (ultrasound of brachial artery, resting blood pressure, resting heart rate), microvascular function (laser Doppler flowmetry of forearm), and cardiopulmonary function (VO2peak) and all tests were repeated at two-week follow up. The exercise protocol consisted of six sessions over two weeks on alternating days with all sessions completed on an electronically braked cycle ergometer. For the HIT group, LEAN BODY MASS IN MENOPAUSE 70 training consisted of 10 x 1-minute intervals at 100% of peak power, interspersed with 1 minute of light active recovery and workload was increased as tolerated by participants. The CT group performed 40 minutes of continuous cycling at an intensity of 65% peak power. All exercise sessions were supervised by an exercise physiologist. The results of the study suggest that the low-volume HIT protocol promotes rapid increases in cardiopulmonary function but not vascular function in postmenopausal women (Klonizakis et al., 2014). This type of protocol may therefore provide an alternative training strategy for those with limited time available for exercise. Three studies have examined the effects of HIT on anthropometric measures and body composition. Grossman and Payne (2016) conducted a small, randomized controlled pilot study with 18 postmenopausal women to compare the effects of shortduration, HIT and traditional exercise on body weight, body composition, and anthropometric measures. Data was collected at baseline, week 6 and week 12 and included height, weight, measures of waist, hips, abdomen, right bicep and right thigh, bodyfat % (DEXA Scan) and demographic information. The participants were randomized into either Standard Condition (SC) or HIT. The SC group were instructed to walk at an intensity level of 65% of their age-predicted maximal heart rate. For the first week participants were instructed to walk 10 mins each day for five days per week and 5 mins were added to each day for every subsequent week until they had reached 200 mins per week (week 6) and this was maintained for weeks 7-12. Exercise intensity was measured using a heart rate monitor and calorie wristband device. The HIT group received the “10-min Trainer” DVD and were instructed to exercise 10 mins per day for five days each week for the entire 12-week intervention. The DVD set included five LEAN BODY MASS IN MENOPAUSE 71 different groups of exercises ‘total body’, ‘cardio’, ‘lower body’, ‘abs’, and ‘yoga flex’ which were completed in random order, one each day. The DVD exercises were 1 minute in length, with 10 different exercises within each segment. The results showed both groups achieved decreases in four of five anthropometric measures with both groups seeing increases in the bicep measure. Additionally, both groups experienced decreases in body weight, body fat %, fat mass and BMI as well as increases in fat free mass. There was a greater increase in fat free mass and a greater decrease in body fat % in the HIT group but the results were not statistically significant (Grossman & Payne, 2014). This may have been due to the small sample size and the short duration of the intervention. This study was the first to compare HIT and CT in postmenopausal women and provides a foundation for future study in this area. A systematic review and meta-analysis by Weston, Wisloff and Coombes (2014) found that HIT significantly increased cardiorespiratory fitness by almost double (19.4% vs 10.3%) over moderate-intensity CT in patients with lifestyle-induced cardiometabolic disease. As women are at a greater risk for cardiometabolic disease as they transition through menopause, there is an increased need to explore this modality of training for this population. Mandrup et al. (2017) recently conducted a three-month parallel group study with 79 healthy, non-obese late premenopausal (n=40) and postmenopausal (n=39) women. The aim of the study was to investigate the effect of a well-controlled highintensity aerobic training program on risk factors for type II diabetes and cardiovascular disease between late premenopausal and postmenopausal (age difference of 4 years). Data collected at baseline and three months included height, weight, BMI, waist circumference, body composition (DEXA scan), blood samples, blood pressure, resting 72 LEAN BODY MASS IN MENOPAUSE heart rate, oral glucose tolerance test, and VO2-max. The intervention consisted of three months of high-intensity spinning conducted three times per week for one hour. Two of the weekly sessions were conducted in the research exercise facility by instructors from the research group and one session took place in a local fitness center. The intensity of the training sessions increased gradually during the 3-month period. Participants wore heart rate monitors for all sessions. The results indicate that 3-months of high intensity training induced substantial beneficial effects on aerobic fitness and a number of risk factors and that postmenopausal women experienced the same positive adaptations to training as premenopausal women (Mandrup et al., 2017). Both groups had decreases in BMI, weight, waist circumference, fat mass, fat percentage and lean body mass increased in both groups. Both groups also showed reductions in total cholesterol and fasting insulin, however, the postmenopausal group had a better response to the intervention as they had lower fasting glucose (27% vs 3%). The postmenopausal group also had significantly lower systolic BP while the premenopausal women had a slight elevation (nonsignificant) suggesting high intensity exercise helped to normalize vascular function for postmenopausal women. The results of this study show the importance of recommending physical activity to postmenopausal women to mitigate risk factors beyond weight loss. Physical activity has been shown to be important in the psychological wellbeing of menopausal women alongside the physical adaptations experienced. Freese et al. (2014) conducted a small, randomized controlled trial of 47 women pre-and postmenopausal, who were considered to be at risk of developing MetS. The purpose of the study was to determine whether six weeks of sprint interval training (SIT) is LEAN BODY MASS IN MENOPAUSE 73 associated with changes in mood and perceived health in women at risk for MetS. Participants were randomized to either six weeks of SIT or control and all participants met weekly with a nutritionist to ensure weight remained stable throughout the trial. Participant characteristics of age, weight, height, BMI, waist circumference, and bodyfat were all recorded at baseline and after the 6-week intervention. Mood and perceived health was measured using the 30 item Profile of Mood States – Brief (POMS-B) questionnaire both at baseline and again at six weeks. The SIT group completed three exercise session per week for six weeks. Training sessions were completed in the laboratory under supervision and consisted of repeated 30 second all-out cycling sprints (4-8 sprints per session) against a fixed resistance of 0.09 kg per kilogram of fat free mass. Participants completed four sprints per session for the first two weeks and then the number of sprints was increased by one sprint per session in subsequent weeks until completing eight sprints per session in week six. The results of this study show that six weeks of HIT led to significant increases in role-physical scores (ES=0.64) and vitality (ES=0.52) suggesting increased feelings of vitality and perceptions of having fewer physical limitations and that HIT does not produce mood disturbances (Freese et al., 2014). The conclusion we can draw from the research on HIT would suggest that the low-volume nature makes for an attractive option for postmenopausal women and has shown to be effective in producing positive physiological and psychological outcomes. Resistance Training Given the impact of sarcopenia in menopause and the resulting decrease in muscle strength, resistance training is an important factor for women to maintain physical LEAN BODY MASS IN MENOPAUSE 74 functioning through aging. The most common muscle associated changes in menopause include: increase in connective tissue and intramuscular fat, decrease of type II fibers, and decrease of estrogen receptors (Maltais et al., 2009). Since muscle loss is influenced by several factors such as age, low levels of physical activity and inflammatory processes the impact of menopause specifically on muscle wasting mechanisms remains unclear (Maltais et al., 2009). Resistance training has been growing in popularity due to its efficacy and positive results with several pathologies including osteopenia, sarcopenia, diabetes, and cardiovascular disease, however, it is still not routinely prescribed to prevent or reduce the symptoms and effects of menopause (Leite et al., 2010). The literature on this modality of exercise is sparse but there have been a few significant studies. A small, randomized controlled trial assigned 20 postmenopausal women to a resistance training program or control group. Blood samples, blood pressure and anthropometric measures were taken at baseline and at week 16. Maximal strength tests were also conducted at baseline and again at week 16. Subjects performed three training sessions per week on alternate days. During the first 8-week period they performed leg press, leg extension, leg curl, bench press, latissimus pulldown, lateral raise, triceps pushdown, arm curl and abdominal crunches. The training consisted of three sets of ten repetitions maxima, with 60 seconds’ rest between sets. For the second 8-week period, the same exercises were performed but with eight repetition maxima and a 90-second rest. This protocol is characterized by high volume/low intensity in the first 8 weeks followed by low volume/high intensity for the second 8 weeks. Workloads were increased by 1 kg for lower body and 0.5 kg for upper body for each repetition over the LEAN BODY MASS IN MENOPAUSE 75 established protocol participants were able to perform in the previous week. Study authors found that the 16 weeks of training reduced the risk of metabolic syndrome as measured by the metabolic syndrome z-score (P=0.0162). Results also showed significant decreases in fasting blood glucose (-13.97%), body fat % (-6.75%) and body fat mass (-7.06%) and showed increases in lean body mass (+2.46%) and muscle strength (leg press +41.29% & bench press +27.23%) when compared to baseline (Conceiçao et al, 2013). Alvarez and Campillo (2013) conducted a parallel-group study comparing overweight and obese premenopausal women and overweight and obese postmenopausal women. Thirty-five women were divided into these four groups: 8 overweight premenopausal, 9 obese premenopausal, 8 overweight postmenopausal, 10 obese postmenopausal. Data was collected at baseline and weeks 4 and 8 including body weight, height, BMI, waist circumference and skinfolds. Additionally, at baseline and end of week 8, maximal strength (1RM) was assessed for squat, bent row, shoulder press and bicep curl. Subjects attended 24-40-minute supervised sessions twice weekly for the 8 weeks. During weeks 1-3, subjects completed three sets for each of the four exercises with 20%1RM. For weeks 4-6, subjects completed four sets for each exercise at 25% 1RM and for the final two weeks, subjects completed five sets for each exercise at 30% 1RM. Repetitions in each set were to failure (voluntary exhaustion) with a rest period of one minute between sets and exercises. This type of protocol is considered low volume/high intensity. The authors found this eight-week program resulted in significant reductions in body weight, BMI, waist circumference and skin fold measurements in all LEAN BODY MASS IN MENOPAUSE 76 groups with no significant between group differences. Additionally, all four groups experienced significant increases in maximal strength on all exercises. A smaller, non-random controlled study of 32 women looked to evaluate the effect of 16 weeks of resistance training on the subjective perception of QoL and secondary outcomes of anthropometric measures (height, weight, BMI, body fat %, skin folds) and muscle strength (1RM for bench press, leg press & leg curl). Both primary and secondary outcomes were measured at baseline and week 16. Quality of life was measured using the World Health Organization questionnaire (WHOQOL-Bref). The resistance training program lasted 16 weeks and was divided into two stages (E1 & E2). E1 consisted of alternating upper and lower body exercises for three sets of ten reps with a rest period of one minute between sets. E2 consisted of alternating upper and lower exercises for three sets of eight reps and 90 secs of rest between sets. The work load for all exercises was 70-85% of 1 RM and exercises included were leg extension, leg curl, bench press, lat pulldown, arm curl, triceps extension, leg press, sit-ups, side plank and calf raise (for abdominal and calf exercises, three sets of 15 reps were used for entire 16week program). Participants performed three, weekly, supervised, non-consecutive sessions for 60 mins. The authors found that the 16-week program resulted in significant changes in muscle strength indicators (P = 0.001), but there were no significant differences in QoL (except the energy facet of physical domain) or body composition measures (Boganha et al, 2012). In determining the optimal training protocol for postmenopausal women, Correa et al. (2015) is the first study to compare the effects of low volume resistance training (LVRT) to high volume resistance training (HVRT) on postmenopausal women. The LEAN BODY MASS IN MENOPAUSE 77 objective of their study was to compare the effects of 11 weeks of LVRT and HVRT on postprandial lipaemia, EE, basal metabolic rate (BMR), post exercise oxygen consumption (EPOC), muscle strength and muscle thickness. Thirty-six women were randomly divided into HVRT, LVRT and control group. Both at baseline and post intervention, data was collected including anthropometric measures, VO2peak test, leg extension 1 RM test, muscle thickness (B-mode ultrasound), dietary records for three days preceding pre/post training test, BMR (indirect calorimetry), EE (ergo spirometry) and oral fat tolerance test, and blood samples. Participants completed 11 weeks of training performing eight exercises (bench press, bicep curl, triceps extension, one arm row, leg press, leg extension, leg curl, & crunch) three times per week. Both LVRT and HVRT groups were required to perform 15 max reps with 40 secs of rest between sets and exercises. The LVRT group performed one set of the eight exercises ( ̴ 15 mins) and the HVRT group performed three sets ( ̴ 45 mins). The major finding of this study was that 11 weeks of HVRT led to decreases in baseline triglycerides and the total serum triglycerides and both resting fat oxidation and muscle thickness were significantly increased post training. These results are the first to suggest that resistance training in postmenopausal women may help reduce the risk of coronary heart disease (Correa et al., 2015). The largest and most recent study was a randomized controlled trial with 80 postmenopausal women aged 40-65 years looking to investigate the influence of a 12week resistance training program on perceived QoL. Researchers collected anthropometric data (height, weight, BMI) and sociodemographic information at baseline and participants completed the SF-36 for QoL measures at both baseline and post LEAN BODY MASS IN MENOPAUSE 78 intervention. The exercise group participated in training sessions three times per week for 12 weeks. The exercise intervention included warming-up exercises, walking, stretching, strengthening exercises with an elastic band, and a cool down. The results show that the 12 weeks of resistance training resulted in a positive change in all domains of the SF-36 and statistically significant improvement in QoL scores in the domains of vitality (P = 0.0046) and mental health (P = 0.0052) (Dabrowska et al, 2016). While these studies show promising results for the effectiveness of resistance training for postmenopausal women, the research in this area is still lacking and to date there have been no large randomized controlled trials measuring the effects of low volume-high intensity resistance training on menopausal symptoms. Yoga & Pilates Interventions Recent focus on the impact of stress on quality of life and menopausal symptoms has led to studies examining the efficacy of complementary therapies. Reed et al. (2014) conducted a randomized controlled factorial study comparing yoga and aerobic exercise interventions combined with a placebo-controlled omega-3 supplement with 355 women aged 40-62 years. The purpose of the study was to determine the efficacy of three nonhormonal therapies for the improvement of menopause-related quality of life in women with VMS. Data was collected at baseline and at 12 weeks using the MENQOL and secondary outcomes measures included demographic information, weight, height, BMI, and additional questionnaires (7-item Insomnia Severity Index, Pittsburgh Sleep Quality Index, 8-item Patient Health Questionnaire for depressive symptoms, and 7-item LEAN BODY MASS IN MENOPAUSE 79 Generalized Anxiety Disorder scale). Frequency and severity of VMS were recorded retrospectively in daily diaries. The yoga intervention group completed 90 minutes instructed sessions and daily home practice of 20 minutes was expected. The exercise intervention group completed three individual cardiovascular sessions per week at a local facility that were supervised by trainers. Participants exercised 40-60 minutes per session at a target heart rate of 50-60% of HRR for month one and 60-70% HRR for second month. Participants also randomly received omega-3 capsules or placebo. The authors found that relative to usual activity, only the yoga group experienced small (nonsignificant) improvement on MENQOL scores. The exercise group improved scores in the physical function domain, but it was not enough to improve overall MENQOL scores (Reed et al., 2014). A second random control trial of 88 postmenopausal women compared control to stretching exercises to hatha yoga for 12 weeks to investigate the psychophysiological effects of Hatha Yoga regular practice. Saliva, blood samples and questionnaires (Lipp Stress Symptom Inventory (LSSI), MRS, WHOQOL-brief, Beck Depression Inventory (BDI), & State and Trait Anxiety Inventories (STAI)) were collected at baseline and 12 weeks. The yoga and exercise group attended 75 minutes of supervised activity twice per week for twelve weeks. Both groups were instructed by a single, certified Hatha Yoga instructor. Yoga consisted of 45 minutes of asanas followed by breathing (10 mins) relaxation (10 mins) and meditation (10 mins). Exercises consisted of stretching for the shoulder girdle and cervical muscles and lower legs. The main findings of the study were that menopause symptoms, stress symptoms and depression are reduced and quality of life increased after 12 weeks of yoga (Jorge et al., 2016). LEAN BODY MASS IN MENOPAUSE 80 Similar to yoga as a complementary therapy, Pilates has been the subject of research for postmenopausal women as well. Lee et al. (2016) designed a randomized controlled trial with 74 postmenopausal women to investigate the effects of an 8-week Pilates exercise program on menopausal symptoms and lumbar strength and flexibility. Baseline data included a 44-item menopausal symptom questionnaire, lumbar strength (lumbar extension machine), sit-and-reach, and truck lift tests. All tests were repeated at the end of the 8-week intervention. The Pilates group completed a series of exercises adopted from Pilates Academy International for approximately 60 minutes (location and supervision not indicated in study). The results found the intervention resulted in decreased VMS, psychological and physical symptoms compared to controls and significant increases in both lumbar strength and flexibility (Lee et al., 2016). Complementary therapies like yoga and Pilates have the potential to impact the psychosocial component of menopausal symptoms but more high-quality research is needed to confirm the importance of adding this protocol to the exercise prescription for menopausal women. Exercise, in its various forms, has been shown to ameliorate some menopausal symptoms or at the very least, alter the psychological impact on the perceived quality of life in postmenopausal women. However, the development of symptoms in some women but not others, given that all women experience a decline in estrogen, suggests that other factors are at play. The role of muscle mass and oxidative stress has been considered as a possible mechanism in menopausal symptoms and menopausal-related diseases, specifically MetS. For postmenopausal women, chronic systemic inflammation, oxidative stress, abdominal visceral adipose tissue, dyslipidemia, sarcopenia, and a sedentary lifestyle are LEAN BODY MASS IN MENOPAUSE 81 all risk factors for metabolic syndrome (Mendoza et al., 2016). The impact of obesity and muscle mass as possible risk factors for both MetS and VMS must be examined. Impact of Body Composition on Postmenopausal Women Obesity as a Risk Factor for MetS Abdominal obesity is a known risk factor for metabolic and cardiovascular diseases (Berrington de Gonzalez, Hartge, & Cerhan, 2010; Chan et al., 1994; Flint et al., 2010). Women transitioning through menopause frequently experience weight gain but studies across differing populations suggest weight gain is influenced by age (Lobo et al., 2014) and the abrupt decline of estrogen during menopause is characterized by increased subcutaneous abdominal and visceral fat. This results in fat distribution shifting from a gynoid to an android pattern and an increase in total body fat which has been documented across ethnicities and in non-obese as well as obese women (Abdulnour et al., 2012; Kazlauskaite et al., 2015). A small longitudinal study by Franklin, Ploutz-Snyder and Kanaley (2009) was the first to use magnetic resonance imaging (MRI) to measure total abdominal, subcutaneous, and visceral fat in premenopausal women and then repeat the measures 8 years later when they had entered postmenopause. The study found that the absolute volume of visceral fat increased which may contribute to the increase in cardiovascular risk after menopause. Total abdominal fat mass increased by 35% (with no change in body weight) and was due to changes in both subcutaneous and visceral fat mass and possibly a decrease in abdominal muscle mass (Franklin et al., 2009). LEAN BODY MASS IN MENOPAUSE 82 Abdominal fat mass measured anthropometrically has also been shown to influence cardiometabolic risk factors. Kabat et al. (2014) used data from a subsample of women who participated in the Women’s Health Initiative Study (n=2672) and compared fasting blood samples and anthropometric measurements taken at multiple times over 12.8 years of follow-up. Blood samples provided measures of cardiometabolic risk including blood glucose, insulin, total cholesterol, low-density lipoprotein cholesterol, high-density lipoprotein cholesterol (HDL-C) and triglycerides. The purpose of the study was to examine the association of change in BMI, waist-to-hip ratio (WHR), waist circumference (WC), and waist circumference-to-height ratio (WCHtR) to the change in markers of cardiometabolic risk (Kabat et al., 2014). The authors found that an increase in BMI, WC and WCHtR strongly predicted change in serum triglycerides and glucose, and reduced HDL-C. Obesity and VMS Vasomotor symptoms and MetS share a common nominator of sympathetic overdrive (Tuomikoski & Savolainen-Peltonen, 2017). Menopausal women exhibiting VMS may be at risk for MetS and conversely, MetS may exacerbate sympathetic overdrive thus worsening VMS. Obesity is a mediator both in VMS and MetS and thus has been the subject of much research. Gold et al. (2017) examined data from the Study of Women’s Health Across the Nation (SWAN) from 1,546 participants who reported no VMS at baseline. SWAN is a prospective cohort study that enrolled 3,302 women in 1996-1997 and have recorded follow up data for over 15 years. The current study looked to analyze whether concurrent BMI or waist circumference and/or changes in weight or LEAN BODY MASS IN MENOPAUSE 83 waist circumference predicted incident VMS using discrete survival analyses, adjusting for covariates. The authors found that concurrent BMI and waist circumference were positively related to incident VMS (Gold et al., 2017). In contrast, Gallichio et al. (2014) analyzed data from an ongoing 5-year cohort study of 631 women to examine the associations between BMI, change in BMI, and change in weight with hot flashes and determined there was no relationship between the variables. Thurston et al. (2009) examined data from the SWAN study that included 1,659 women assessed annually from 2002-2006. For this longitudinal study measures of body fat (bioelectrical impedance), reproductive hormones (fasting blood sample) and VMS (self-report) were analyzed. The authors found that gains in body fat over three years were associated with increased reporting of hot flashes (Thurston et al., 2009). This result echoed their previous findings of 461 participants from the SWAN study which examined the association of abdominal adiposity, measured via computed tomography (CT), and hot flashes (Thurston el al., 2008). This study also found that increased abdominal adiposity is associated with increased odds of hot flashes. In a third study, Thurston et al. (2008) examined sample data from 1,776 women in the SWAN study who completed bioelectrical impedance analysis for assessment of body composition at the sixth annual study visit (2002-2004). The results of this study indicated that a higher percentage of bodyfat was associated with increased odds of reporting VMS (odds ratio = 1.27). Finally, in a 2013 longitudinal study of 536 participants from the SWAN study, Thurston et al. (2013) were the first to test the relationship between adipokines, adiposity and VMS from data collected over an 8-year period. The authors found that an adverse adipokine profile was associated with more VMS in early stages of menopause but not later stages. LEAN BODY MASS IN MENOPAUSE 84 Association of Lean Body Mass As menopause leads to a significant decrease in circulating levels of estrogen, it has been hypothesized that reduced estrogen levels may also influence substrate utilization (Abildgaard et al., 2013). Decreased fat oxidation and mitochondrial dysfunction are important factors preceding obesity and type 2 diabetes (Kelley, He, Menshikova, & Ritov, 2002), thus a change in substrate utilization could explain the changed metabolic profile after menopause (Abildgaard et al., 2013). Further, menopause has been shown to be associated with changes in skeletal muscle mass (Douchi et al, 2002) and muscle mass has proven to play a critical role in whole-body metabolism. Muscle mass is the most abundant insulin-sensitive tissue (Baron et al., 1988) and is responsible for up to one third of the oxygen consumption at rest (Zurlo et al., 1990). Abildgaard et al. (2013) conducted a cross sectional study of 41 healthy women and found that menopause is associated with lower whole-body fat oxidation and energy expenditure during exercise. Additionally, the authors found that decreased LBM seems to be the most important contributor to the observed changes in metabolism. In a recent study, Takamura et al. (2017) tested the hypothesis that preserved muscle mass is protective against obesity-associated insulin resistance and metabolic abnormalities. A total 195 healthy, non-diabetic subjects (88 male: 107 female) participated in fasting blood samples, anthropometric measures, bioelectrical impedance and CT of specific skeletal muscles. The authors found that weight-adjusted lean body mass is protective against weight-associated insulin resistance and metabolic abnormalities (Takamura et al., 2017). Another recent cross-sectional study examined LEAN BODY MASS IN MENOPAUSE 85 the relationship between muscle mass and metabolic syndrome in a community-based sample of 394 (138 male: 256 female) mid aged participants (Ou et al., 2016). The authors concluded that relative lower levels of skeletal muscle mass (mass/weight) had a higher risk of MetS, particularly in females. Lee and Lee (2013) conducted a cross-sectional study investigating the relationship between Kupperman Index scores for symptom severity and muscle strength and quality in 148 menopausal women. Biomarkers of metabolic risk factors were assessed along with muscle mass (measured with dual-energy x-ray absorptiometry), and muscle strength (measured with isometric dynamometry). Muscle quality was calculated as the ratio of strength to muscle mass in upper extremities. The authors found that the severity of menopausal symptoms correlated with muscle strength and quality (Lee & Lee, 2013). Although the precise mechanism to explain the association is unclear, the authors suggest decreasing levels of all the sex hormones (estrogen, testosterone and DHEAs) play a role as well as oxidative stress. Role of Oxidative Stress Oxidative stress (OS) is characterized by the imbalance between the reactive oxygen species produced and the effective action of the antioxidant system (SanchezRodriguez et al., 2016). Oxidative damage contributes to loss of muscle strength and a decline in physical activity, due to the loss of muscle strength, induces oxidative stress creating a vicious cycle. MetS is also known to be related to aggravated OS in postmenopausal women due to hyperinsulinemia and the lack of control of glycaemia LEAN BODY MASS IN MENOPAUSE 86 (Park, Kim, Lee & Park, 2011). Furthermore, menopausal women with VMS have increased oxidative stress, and symptomatic women have lower antioxidant activities as estrogen is lost as a protective factor (Van Der Schouw & Grobbee, 2005). In a crosssectional study of 94 premenopausal and 93 postmenopausal, Sanchez-Rodriguez et al. (2012) found that menopause is a risk factor for oxidative stress by examining lipoprotein levels suggesting that decreasing levels of estrogen contributes to increases in oxidative stress. In a cross-sectional study of 50 postmenopausal women, Cagnacci et al. (2015) evaluated the relationship between menopausal symptoms and oxidative stress and found that abdominal adiposity and hypoestrogenism increase oxidative stress. The authors also found that VMS markedly reduce antioxidant defenses. However, Bonaccorsi et al. (2015) conducted a cross-sectional study on 245 peri-and postmenopausal women and found no association between VMS and oxidative stress levels. The discrepancies between studies may be due to differences in the potential markers of oxidative stress measured in each study. The role of muscle mass and its relationship to oxidative stress in menopausal women is complex and incompletely understood. Further research is needed to examine the longitudinal impact of lean body mass on oxidative stress and menopausal symptoms. LEAN BODY MASS IN MENOPAUSE 87 Conclusion Menopause is the period in a woman’s life that is characterized by hormonal changes that lead to a variety of symptoms that may compromise quality of life. Hot flashes, night sweats, depression, irritability, sleep disorders, increases in abdominal fat mass, increased prevalence of metabolic syndrome and increased risk of cardiovascular disease will affect all women to some degree and may last for long periods of time (Stefanska et al., 2015). While hormone therapy is available and recommended for severe symptoms (NAMS, 2015), the majority of women prefer to use non-hormonal treatments, and exercise is a cost-effective method. Regular physical activity has been shown in the research to ameliorate symptoms in the emotional and psychological aspects of menopause, and positively affect perceived ability to cope as well as quality of life (Elavsky & Kishida., 2015; Haimov-Kochman et al., 2013; Moilanen et al., 2012; Moratalla-Cecilia et al., 2016; Skrzypulec et al., 2010; Sternfeld & Dugan, 2011). The research is limited in this area with focus on low-moderate intensity cardiovascular exercise (Bailey et al., 2016; Daley et al., 2014; Luoto et al., 2012; Sternfeld et al., 2104; Zhang et al., 2014) where the evidence has shown mixed results. Research regarding resistance training is very limited (Alvarez & Campillo, 2013; Boganha et al., 2012; Conceição et al., 2013; Correa et al., 2015; Leite et al., 2010), but has shown a positive correlation with some metabolic markers and quality of life. Additionally, Abildgaard et al. (2013) found that decreased lean body mass correlates to low whole- body fat oxidation and energy expenditure which in turn are associated with high visceral fat mass and low insulin resistance. Obesity is a risk factor for metabolic syndrome and women LEAN BODY MASS IN MENOPAUSE 88 transitioning through menopause are known to experience an increase in abdominal adiposity (Abdulnour et al., 2012; Franklin et al., 2009; Kabat et al., 2014; Kazlauskaite et al., 2015). Obesity is also a mediator for both VMS and MetS and it has been demonstrated that there is a positive relationship between BMI and reporting of VMS among menopausal women (Gold et al., 2017; Thurston et al., 2009; Thurston et al., 2008; Thurston et al., 2013). Menopause has also been shown to be negatively associated with changes in muscle mass (Abildgaard et al., 2013; Baron et al., 1988; Douchi et al., 2002) but recent studies indicate that preserved muscle mass is protective against metabolic abnormalities characteristic of MetS (Lee & Lee, 2013; Ou et al., 2016; Takamura et al., 2017). Loss of muscle mass and increased oxidative stress in menopause are also related to the development of MetS (Park et al., 2011; SanchezRodriguez et al., 2016; Sanchez-Rodriguez et al., 2012) and there is mixed evidence regarding association of oxidative stress to VMS (Bonaccorsi et al., 2015; Cagnacci et al., 2015). There is a complicated relationship whereby menopause increases oxidative stress, oxidative stress leads to MetS, and MetS is associated with VMS. As lean body mass protects against MetS, then there is potentially an association between lean body mass and the development of VMS that is worthy of exploration. LEAN BODY MASS IN MENOPAUSE Appendix B Problem Statement 89 LEAN BODY MASS IN MENOPAUSE 90 Menopause is said to have occurred after twelve months of amenorrhea due to the cessation of ovarian follicular function and decreasing estrogen levels (Greendale, Lee & Arriola, 1999). The transition through menopause can last up to 20 years and is accompanied by a variety of bothersome vasomotor, psychological, psychosocial, and urogenital symptoms (Gjelsvik et al., 2011; Huang et al. 2010; Sternfeld & Dugan, 2011) that are uniquely experienced and impact quality of life for menopausal women. Muscle loss (sarcopenia) during menopause is of great concern and is primarily due to an imbalance between muscle protein synthesis and breakdown contributed to by an increase in oxidative stress, pro-inflammation markers and hormonal changes (Lee & Lee, 2013; Messier et al., 2011). There is also an overall reduction in blood flow and oxygen delivery with age (Nyberg et al., 2015), due to structural alterations in the vasculature, reduction in muscle mass and quality, increased muscle sympathetic outflow and balance alterations in the vasodilators/vasoconstrictors (Proctor & Parker, 2006) which could potentially impact vasomotor symptoms. Thus, higher levels of muscle mass during menopause may potentially protect against the deleterious effects of sarcopenia, mitigate vascular disturbances and possibly ameliorate the symptom burden experienced by women leading to improved quality of life. The purpose of the research was to examine the association of relative lean body mass to the development of menopausal symptoms over time and the potential correlation to perceived quality of life through a quantitative descriptive design. This study will be conducted utilizing the existing dataset from a previous national study (Study of Women’s Health Across the Nation) to identify, analyze, and describe these factors. LEAN BODY MASS IN MENOPAUSE Appendix C Additional Methodology 91 LEAN BODY MASS IN MENOPAUSE Appendix C1 Informed Consent Consent Form - Massachusetts General Hospital SWAN study (Visit 15) 92 Partners HealthCare System Research Consent Form Subject Identification General Template Version Date: October 2014 Protocol Title: The Study of Women's Health Across the Nation: Annual evaluation (VISIT 15) Principal Investigator: Joel S. Finkelstein, M.D. Site Principal Investigator: Description of Subject Population: Pre- or perimenopausal women, aged 42-52 at baseline About this consent form Please read this form carefully. It tells you important information about a research study. A member of our research team will also talk to you about taking part in this research study. People who agree to take part in research studies are called “subjects.” This term will be used throughout this consent form. Partners HealthCare System is made up of Partners hospitals, health care providers, and researchers. In the rest of this consent form, we refer to the Partners system simply as “Partners.” If you have any questions about the research or about this form, please ask us. Taking part in this research study is up to you. If you decide to take part in this research study, you must sign this form to show that you want to take part. We will give you a signed copy of this form to keep. Why is this research study being done? We would like permission to enroll you as a participant in a research study. This research is part of a multi-center study called SWAN sponsored by the National Institutes of Health Page 1 of 12 Partners HealthCare System Research Consent Form Subject Identification General Template Version Date: October 2014 (NIH) and is being conducted at Massachusetts General Hospital and 6 other teaching hospitals across the United States. The overall goal of the study is to investigate a variety of aspects related to women's health at mid-life in multiple ethnic groups. Topics to be studied include symptomatology, psychosocial issues, sexuality, health care utilization, racial differences in bone density and rates of bone loss, potential genes related to osteoporosis, new blood tests for ovarian function, and risk factors for arteriosclerosis and heart disease. You have already participated in the first year of data collection for SWAN and may have participated in multiple follow-up visits to this study; this form describes the procedures for this year’s follow-up visit in the continuation of the study. How long will I take part in this research study? This is a longitudinal study that has been ongoing since 1996 and is scheduled to last for an additional 2 to 3 years. What will happen in this research study? You may be given the choice of having the blood and urine test at home but the remaining tests (listed below) must be performed at Massachusetts General Hospital. No treatments are involved in this study and your participation will not affect your ability to receive any form of therapy that your own physician might recommend. All the tests are non-invasive, and the entire study visit should take about 4 to 5 hours. Hospitalization is not required in this study. During some visits, not all of the above tests may be done. In addition, you have the option of declining any portion of the protocol that you do not feel like doing. You will be asked to sign the consent form each year you are in the study. If you agree to continue to participate in this study, you will be asked to participate in: 1) Physical Measures: the research assistant on the study will take measurements of your blood pressure, height, weight, waist and hip sizes. 2) Blood Draw: The research assistant will take a blood sample from a vein in your arm. The blood sample will be used to measure your levels of blood fats (e.g. total cholesterol, LDL, and HDL), glucose (e.g. blood sugar), insulin (a hormone that controls your sugar level), thyroid stimulating hormone, blood clotting factors, reproductive hormones (such as FSH, estradiol, estrone, testosterone, and DHEA-S), risk factors for heart disease (e.g. C-Reactive Protein, PAI-1, Lp(a), and markers of ovarian aging (Mullerian Inhibiting Substance and inhibin B). In any given year, not all of these tests may be performed. In addition, some blood will be saved for possible future measurements of factors that help regulate bone metabolism (e.g. Vitamin D and sclerostin), factors that help regulate body weight (such as leptin), or other factors that have not yet been determined. However, the additional lab tests may not be performed for a considerable period of time. Page 2 of 12 Partners HealthCare System Research Consent Form Subject Identification General Template Version Date: October 2014 3) Cognitive Assessment: We will administer a Cognitive Functioning Evaluation, which will measure activities of the mind, such as thinking, reasoning, and remembering 4) Physical function assessment: You will be asked to perform a set of physical functioning tests including: a) measurement of the time it takes you to rise from sitting in a chair to a standing position 5 times, b) measurement of the time it takes you to walk 40 feet, c) measurement of your grip strength by squeezing an instrument called a dynamometer 3 times with each hand, d) measurement of the time it takes you to walk 4 meters, and e) seeing if you can stand with your feet in different positions for at least 10 seconds. These tests will take about 10 minutes. 5) Questionnaires: You will have interviewer administered and self-administered questionnaires that include items about your medical history, medications, alternative medicine usage, menstrual history, reproductive history, sleep quality, health care utilization, symptoms, mood, social support, sexual activity, physical activity, childhood trauma, and other lifestyle factors. These questionnaires should take about 60 minutes to complete. You will have the option of skipping any questions that you choose not to answer. The questionnaires include some questions that may be considered sensitive. The questionnaires will be stored in locked files and your name will not be included with your questionnaires. The questionnaires will be assigned with an ID number and the code that links the ID numbers to specific individuals will be stored in a separate, locked file. The questionnaire that includes items related to your sexual activity will be selfadministered and then placed immediately into a sealed envelope so that the research assistant will not see the responses. To ensure high quality data, a small number of randomly-selected interviews may be audio taped for later review. 6) Body Composition by Bioelectrical Impedance: You will have your body composition (i.e. amount of fat and muscle) measured by a technique called bioelectrical impedance. The bioelectrical impedance test requires that a small clip called an electrode is attached to your skin through which an electrical current is passed for a few seconds. The electrical current is very weak, and you will not be able to feel it. 7) Collection of a urine sample: The urine sample will be used to measure markers of bone breakdown. Page 3 of 12 Partners HealthCare System Research Consent Form Subject Identification General Template Version Date: October 2014 8) Bone Density and Vertebral Morphometry by DXA: Bone density of your spine, hip, and total body will be measured by a technique called DXA. Bone density tests reflect the strength of your bones and your risk of having a broken bone (fracture). The vertebral morphometry test assesses the shape of your bones to check for possible broken bones in your back. Vertebral morphometry test may not be done at this particular visit. DXA tests require you to lie on a cushioned table without moving and are painless. The DXA and vertebral morphometry take 20 minutes to complete. 9) Monofilament Testing: We will do monofilament testing, which is a simple, noninvasive screening test to identify people who have lost protective sensation and who are at risk of developing ulcers. You will be asked to complete a self-administered questionnaire that includes questions about foot sensation including pain, numbness and temperature sensitivity. We will also administer monofilament testing, which involves evaluating touch-pressure sensation using a nylon thread on your feet. The test is painless. 10) Vaginal Sample Collection: We will ask if you would be willing to participate in SWAN’s vaginal health study that should take about 15-20 minutes. Many women after menopause have vaginal and bladder problems such as vaginal dryness or irritation, frequent or an urgent need to urinate, or pain with sex. In SWAN, we want to gain a better understanding of what may cause some of these bothersome symptoms. We are looking for women with and without vaginal or bladder problems. We will be studying two different things: (1) the pH (acidity) of the vagina and (2) the types of normal bacteria that are also important in keeping the vagina healthy. To participate, we would like you to self-collect two samples from your vagina in a private space. We provide you collection kits with instructions that you take into the bathroom by yourself. The kits have soft swabs (like long q-tips) that you will insert (one by one) about 2 inches (the length of your little finger) into your vagina. SWAN will reimburse you $50 for these two self-collections and for completing a one-page questionnaire. I would like to participate in the optional vaginal sample collection procedure at this time: Page 4 of 12 Partners HealthCare System Research Consent Form Subject Identification General Template Version Date: October 2014 Yes No Initials___________ 11) Actigraphy: We will ask if you would be willing to participate in additional testing that involves measuring your sleep and physical activity over a span of 7 nights (sleep) and 8 days (activity). We will ask you to wear a watch that monitors your sleep and an accelerometer that you would wear around your waist during the day. You will also need to record additional sleep and physical activity information in a diary throughout the 7 days. You will be asked to return the monitors and diaries to the SWAN study office in a pre-stamped envelope. You will not be financially responsible for lost or broken equipment. If you complete this protocol, you will be provided with your results. I would like to participate in the optional actigraphy procedure at this time: Yes No Initials___________ 12) Xtreme CT scan: We will ask you to participate in additional testing to measure your inner bone structure by an imaging study called an Xtreme CT scan. The Xtreme CT scan requires that you put your arm and leg in a machine and hold still for several minutes. The Xtreme CT scan does not require any injections and is painless. I would like to participate in the option Xtreme CT scan at this time: Yes No Initials___________ We would like to store some of your samples and health information for future research related to women’s health. We will label your samples and health information with a code instead of your name. The key to the code connects your name to your samples and health information. The study doctor will keep the key to the code in a password protected computer/locked file. Do you agree to let us store your samples and health information for future research related to women’s health? Page 5 of 12 Partners HealthCare System Research Consent Form Subject Identification General Template Version Date: October 2014 Yes No Initials___________ A notation that you are taking part in this research study may be made in your electronic medical record. Information from the research that relates to your general medical care may be included in the record (for example, list of allergies, results of standard blood tests done at the hospital labs). What are the risks and possible discomforts from being in this research study? The risks from this study are minimal. Blood drawing may cause a small amount of pain. In addition, a temporary bruise or “black and blue” mark may develop. Rarely, people faint after blood drawing. Very rarely, the vein in which the needle has been inserted may become inflamed or infected, which can be treated. As a result of your participation in this study, you will be exposed to radiation from the DEXA and Vertebral Morphometry, scans used to evaluate the composition of your bones. The total amount of radiation exposure from all of the bone scans is less than 10% of the annual natural background radiation from the earth and sky. There are no known health risks associated with such a dose. However, if you are pregnant there might be risk of harm from any radiation on your unborn child. If you are a woman of childbearing potential, you will have a pregnancy test performed free of charge immediately before undergoing the annual bone density tests. The bioelectrical impedance test involves attaching electrodes to your skin and then passing a very low level of electricity through your body for a few seconds. You will not feel anything and there is no known danger to the test. These tests involve no significant risk to you. If you receive the Xtreme CT scan, the amount of radiation exposure you will receive from is equal to a whole-body exposure of 0.01 milliSieverts (mSv). A mSv is a unit of radiation dose. This amount of radiation is about the same as you would normally get in 1 day from natural background sources from the earth and sky. Please note that this radiation is not necessary for your medical care and is for research purposes only. Scientists disagree on whether radiation doses at these low levels are harmful. A possible effect that could occur at doses used in this study is a slight increase in the risk of developing cancer later in life. If you are pregnant or breast feeding, you may not be able to participate in this research study. What are the possible benefits from being in this research study? Page 6 of 12 Partners HealthCare System Research Consent Form Subject Identification General Template Version Date: October 2014 By participating in this study, you will receive free information about your blood pressure, cholesterol levels, physical activity and a thorough evaluation of your bone density (which will help predict your future risk of breaking bones). Furthermore, others may benefit in the future from the information derived from this study. The physicians in this study will not be providing you with any medical care. However, the results of your blood pressure, cholesterol panel, thyroid test, estrogen level (the major hormone made by the ovaries), FSH level (a test often used to determine if a woman is pre- or postmenopausal), and bone density tests will be sent to you each year that these tests are performed. You will be informed about any abnormal test results that are felt to be clinically important and, if necessary, one of the study physicians will communicate this information directly to your doctor. If you do not have a doctor, you will be referred either to a doctor in your area or to the Physicians Referral Service at the MGH. If any test results require immediate attention, this information will be telephoned immediately to you or your physician. Can I still get medical care within Partners if I don’t take part in this research study, or if I stop taking part? Yes. Your decision won’t change the medical care you get within Partners now or in the future. There will be no penalty, and you won’t lose any benefits you receive now or have a right to receive. Taking part in this research study is up to you. You can decide not to take part. If you decide to take part now, you can change your mind and drop out later. We will tell you if we learn new information that could make you change your mind about taking part in this research study. What should I do if I want to stop taking part in the study? If you take part in this research study, and want to drop out, you should tell us. We will make sure that you stop the study safely. We will also talk to you about follow-up care, if needed. Page 7 of 12 Partners HealthCare System Research Consent Form Subject Identification General Template Version Date: October 2014 Also, it is possible that we will have to ask you to drop out of the study before you finish it. If this happens, we will tell you why. We will also help arrange other care for you, if needed. Will I be paid to take part in this research study? You will be paid $135 if you come to the MGH SWAN center and complete the interviews, physical measures, blood draw, and bone density tests. In addition, you will be reimbursed an additional $15 for transportation each time you come to the MGH. If you have a home SWAN visit, which will include the interviews, some physical measures, and a blood draw, you will be paid $75. If you complete your SWAN interviews over the telephone but do not have the bone density tests, physical measures, or blood tests, you will be paid $50. If you complete the optional vaginal swab collection protocol, you will be compensated an additional $50. What will I have to pay for if I take part in this research study? Neither you nor your insurance company will be charged for any of the procedures that are performed as part of this research study. What happens if I am injured as a result of taking part in this research study? We will offer you the care needed to treat any injury that directly results from taking part in this research study. We reserve the right to bill your insurance company or other third parties, if appropriate, for the care you get for the injury. We will try to have these costs paid for, but you may be responsible for some of them. For example, if the care is billed to your insurer, you will be responsible for payment of any deductibles and co-payments required by your insurer. Injuries sometimes happen in research even when no one is at fault. There are no plans to pay you or give you other compensation for an injury, should one occur. However, you are not giving up any of your legal rights by signing this form. If you think you have been injured or have experienced a medical problem as a result of taking part in this research study, tell the person in charge of this study as soon as possible. The researcher's name and phone number are listed in the next section of this consent form. Page 8 of 12 Partners HealthCare System Research Consent Form Subject Identification General Template Version Date: October 2014 If I have questions or concerns about this research study, whom can I call? You can call us with your questions or concerns. Our telephone numbers are listed below. Ask questions as often as you want. Dr. Joel Finkelstein is the person in charge of this research study. You can call him at (617) 726-3296 Monday through Friday from 9am-5pm. You can also call Dr. Burnett-Bowie at 617-724-5594 Monday through Friday from 9am5pm or 24 hours a day by calling 617-726-2066 with questions about this research study. If you have questions about the scheduling of appointments or study visits, call the study coordinator, Karin Darakananda at (617) 724-2033. If you want to speak with someone not directly involved in this research study, please contact the Partners Human Research Committee office. You can call them at 617-4244100. You can talk to them about: ▪ ▪ ▪ Your rights as a research subject Your concerns about the research A complaint about the research Also, if you feel pressured to take part in this research study, or to continue with it, they want to know and can help. If I take part in this research study, how will you protect my privacy? During this research, identifiable information about your health will be collected. In the rest of this section, we refer to this information simply as “health information.” In general, under federal law, health information is private. However, there are exceptions to this rule, and you should know who may be able to see, use, and share your health information for research and why they may need to do so. Page 9 of 12 Partners HealthCare System Research Consent Form Subject Identification General Template Version Date: October 2014 In this study, we may collect health information about you from: • • Past, present, and future medical records Research procedures, including research office visits, tests, interviews, and questionnaires Who may see, use, and share your identifiable health information and why they may need to do so: • • • • • • • • • • • Partners research staff involved in this study The sponsor(s) of this study, and the people or groups it hires to help perform this research Other researchers and medical centers that are part of this study and their ethics boards A group that oversees the data (study information) and safety of this research Non-research staff within Partners who need this information to do their jobs (such as for treatment, payment (billing), or health care operations) The Partners ethics board that oversees the research and the Partners research quality improvement programs. People from organizations that provide independent accreditation and oversight of hospitals and research People or groups that we hire to do work for us, such as data storage companies, insurers, and lawyers Federal and state agencies (such as the Food and Drug Administration, the Department of Health and Human Services, the National Institutes of Health, and other US or foreign government bodies that oversee or review research) Public health and safety authorities (for example, if we learn information that could mean harm to you or others, we may need to report this, as required by law) Other: Some people or groups who get your health information might not have to follow the same privacy rules that we follow and might use or share your health information without your permission in ways that are not described in this form. For example, we understand that the sponsor of this study may use your health information to perform additional research on various products or conditions, to obtain regulatory approval of its products, to propose new products, and to oversee and improve its products’ performance. We share your health information only when we must, and we ask anyone who receives it from us to take measures to protect your privacy. The sponsor has agreed that it will not Page 10 of 12 Partners HealthCare System Research Consent Form Subject Identification General Template Version Date: October 2014 contact you without your permission and will not use or share your information for any mailing or marketing list. However, once your information is shared outside Partners, we cannot control all the ways that others use or share it and cannot promise that it will remain private. Because research is an ongoing process, we cannot give you an exact date when we will either destroy or stop using or sharing your health information. The results of this research study may be published in a medical book or journal or used to teach others. However, your name or other identifying information will not be used for these purposes without your specific permission. Your Privacy Rights You have the right not to sign this form that allows us to use and share your health information for research; however, if you don’t sign it, you can’t take part in this research study. You have the right to withdraw your permission for us to use or share your health information for this research study. If you want to withdraw your permission, you must notify the person in charge of this research study in writing. Once permission is withdrawn, you cannot continue to take part in the study. If you withdraw your permission, we will not be able to take back information that has already been used or shared with others. You have the right to see and get a copy of your health information that is used or shared for treatment or for payment. To ask for this information, please contact the person in charge of this research study. You may only get such information after the research is finished. Page 11 of 12 Partners HealthCare System Research Consent Form Subject Identification General Template Version Date: October 2014 Informed Consent and Authorization Statement of Person Giving Informed Consent and Authorization ▪ ▪ ▪ ▪ I have read this consent form. This research study has been explained to me, including risks and possible benefits (if any), other possible treatments or procedures, and other important things about the study. I have had the opportunity to ask questions. I understand the information given to me. Signature of Subject: I give my consent to take part in this research study and agree to allow my health information to be used and shared as described above. Subject Date Time (optional) Signature of Study Doctor or Person Obtaining Consent: Statement of Study Doctor or Person Obtaining Consent • • I have explained the research to the study subject. I have answered all questions about this research study to the best of my ability. Study Doctor or Person Obtaining Consent Date Consent Form Version: 07/08/2016 Page 12 of 12 Time (optional) Appendix C2 IRB Review Request Approval Appendix C3 Certificates of IRB and SAS Training Certification for Canadian Researchers References Abdulnour, J., Doucet, E., Brochu, M., Lavoie, J-M., Stychar, I., Rabasa-Lhoret, R., & Prud’homme, D. (2012). The effect of the menopausal transition on body composition and cardiometabolic risk factors: A Montreal-Ottawa New Emerging Team group study. Menopause, 19(7), 760-767. https://doi.org/10.1097/gme.0b013e318240f6f3 Abildgaard, J., Pedersen, A.T., Green, C.J., Harder-Lauridsen, N.M., Solomon, T.P., Thomsen, C., …& Lindegaard, B. (2013). Menopause is associated with decreased whole-body fat oxidation during exercise. American Journal of Physiology - Endocrinology and Metabolism, 304, E1227-E1236. https://doi.org/10.1152/ajpendo.00492.2012 Ainsworth, B. E., Sternfeld, B., Richardson, M.T., & Jackson, K. (2000) Evaluation of the Kaiser Physical Activity Survey in women. Medicine and Science in Sports & Exercise, 32(7), 1327–1338. Retrieved from http://www.acsm.org Akindele, M.O., Phillips, J.S., & Igumbor, E.U. (2016). The relationship between bodyfat percentage and body mass index in overweight and obese individuals in an urban African setting. Journal of Public Health in Africa, 7(11), 515. https://doi.org/10.4081/jphia.2016.515 Alvarez, C. & Ramirez Campillo, R. (2013). Effect of a low intensity strength training program on overweight/obese and premenopausal/menopausal women. Brazilian Journal of Kinanthropometry and Human Performance, 15(4), 427-436. https://doi.org/10.5007/1980-0037v15n4p427 Aragão, F.R., Abrantes, C.G., Gabriel, R.E., Sousa, M.F., Castelo-Branco, C., & Moreira, M.H. (2014). Effects of a 12-month multi-component exercise program on the body composition of postmenopausal women. Climacteric, 17, 155-162. https://doi.org/10.3109/13697137.2013.819328 Avis, N.E., Ory, M., Matthews, K.A., Schocken, M., Bromberger, J., & Colvin, A. (2003). Health-related quality of life in a multi-ethnic sample of middle-aged women: Study of Women’s Health Across the Nation (SWAN). Medical Care, 41(11), 1262-1276. http://www,jstor.org/stable/3768415 Ayers, B., Forshaw, M., & Hunter, M.S. (2010). The impact of attitudes towards the menopause on women’s symptom experience: A systematic review. Maturitas, 65, 28-36. https://doi.org/ 10.1016/j.maturitas.2009.10.016 Baecke, J.A.H., Burema, J., & Frijters, J.E.R. (1982). A short questionnaire for the measurement of habitual physical activity in epidemiological studies. American Journal of Clinical Nutrition, 36, 936-942. Retrieved from: www.ajcn.nutrition.org Ball, C., Abdelmoneim, S.S., Huang, R., Eifert-Rain, S., Mantovani, F., Wilansky, S., Mulvagh, S.L. (2016). Changes in exercise patterns in menopausal women at lowintermediate risk for cardiovascular disease: A prospective survey study. Journal of Women’s Health, 25(10), 1014-1020. https://doi.org/10.1089/jwh.2015.5347 Bailey, T. G., Cable, N. T., Aziz, N., Atkinson, G., Cuthbertson, D. J., Low, D. A., & Jones, H. (2016). Exercise training reduces the acute physiological severity of post‐menopausal hot flushes. The Journal of Physiology, 594(3), 657-667. https://doi.org/10.1113/JP271456 Barbat-Artigas, S., Filion, M-E., Ringuet, M-E., Aubertin-Leheudre, M., & Karelis, A.D. (2012). Relationship between low muscle strength and metabolic risk factors in obese postmenopausal women: A pilot study. Canadian Journal of Diabetes, 36, 269-274. https://doi.org/10.1016/j.cjd.2012.08.001 Baron, A.D., Brechtel, G., Wallace, P., & Edelman, S.V. (1988). Rates and tissue sites of non-insulin- and insulin-mediated glucose uptake in humans. American Journal of Physiology: Endocrinology and Metabolism, 255(6). E769-E774. Retrieved from http://www.physiology/journal/ajpendo Berrington de Gonzalez, A., Hartge, P., & Cerhan, J.R. (2010). Body mass index and mortality among 1.46 million white adults. New England Journal of Medicine, 363(23), 2211-2219. https://doi.org/10.1056/NEJMoa1000367 Bonaccorsi, G., Romani, A., Cremonini, E., Bergamini, C.M., Castaldini, M.C., Fila, E., …Cervellati, C. (2015). Oxidative stress and menopause-related hot flashes may be independent events. Taiwanese Journal of Obstetrics & Gynecology, 54, 290293. https://doi.org/10.1016/j.tjog.2014.09.009 Bonganha, V., Modeneze, D.M., Madruga, V.A., & Vilarta, R. (2012). Effects of resistance training (RT) on body composition, muscle strength and quality of life (QoL) in postmenopause life. Archives of Gerontology and Geriatrics, 54, 361365. https://doi.org/10.1016/j.archger.2011.04.006 Boulier, A., Fricker, J., Thomasset, A.L., & Apfelbaum, M. (1990). Fat free mass estimation by the two-electrode impedance method. American Journal of Clinical Nutrition, 52, 581-585. Retrieved from www.ajcn.nutrition.org Cagnacci, A., Cannoletta, M., Palma, F., Zanin, R., Xholli, A. & Volpe, A. (2012). Menopausal symptoms and risk factors for cardiovascular disease in postmenopause. Climacteric, 15, 157-162. https://doi.org/10.3109/13697137.2011.617852 Cagnacci, A., Cannoletta, M., Palma, F., Bellaforte, M., Romani, C., & Palmieri, B. (2015). Relation between oxidative stress and climacteric symptoms in early postmenopausal women. Climacteric, 18, 631-636. https://doi.org/10.3109/13697137.2014.999659 Cagnacci, A., Palma, F., Romani, C., Xhouli, A., Bellafronte, M., & Di Carlo, C. (2015). Are climacteric complaints associated with risk factors of cardiovascular disease in peri-menopausal women? Gynecological Endocrinology, 31(5), 359-362. https://doi.org/10.3109/09513590.2014.998188 Chan, J.M., Rimm, E.B., Colditz, G.A., Stampfer, M.J., & Willett, W.C. (1994). Obesity, fat distribution, and weight gain as risk factors for clinical diabetes in men. Diabetes Care, 17(9), 961-969. Retrieved from http://care.diabetesjournals.org/ Chumlea, W.C., Guo, S.S., Kuczmarcki, R.J., Flegal, K.M., Johnson, C.L., Heymsfield, S.B., … Hubbard, V.S. (2002). Body composition estimates from NHANES III bioelectrical impedance data. International Journal of Obesity & Related Metabolic Disorders, 26(12), 1596-1611. https://doi.org/10.1038/sj.ijo.0802167 Chung, T-H., Shim, J-Y., & Lee, Y-J. (2016). Association between leukocyte count and sarcopenia in postmenopausal women: The Korean National Health and Nutrition Examination Survey. Maturitas, 84, 89-93. https://doi.org/10.1016/j.maturitas.2015.11.011 Conceição, M.S., Bonganha, V., Vechin, F.C., de Barros Berton, R.P., Lixandrão, M.E., Nogueira, F.R.D., …Libardi, C.A. (2013). Sixteen weeks of resistance training can decrease the risk of metabolic syndrome in healthy postmenopausal women. Clinical Interventions in Aging, 8, 1221-1228. https://doi.org/10.2147/CIA.544245 Correa, C.S., Teixeira, B.C., Cobos, R.C.R., Macedo, R.C.O., Kruger, R.L., Carteri, R.B.K.… Reischak-Oliveira, A. (2015). High-volume resistance training reduces postprandial lipaemia in postmenopausal women. Journal of Sport Sciences, 33(18), 1890-1901. https://doi.org/10.1080/02640414.2015.1017732 Crawford, S.L., Avis, N.E., Gold, E., Johnston, J., Kelsey, J., Santoro, N., …Sternfeld, B. (2008). Sensitivity and specificity of recalled vasomotor symptoms in a multiethnic cohort. American Journal of Epidemiology, 168(12), 1452-1459. https://doi.org/10.1093/aje/kwn279 Dąbrowska, J., Rutkowska, M., Dąbrowska Galas, M., & Michalski, B. A. (2016). Twelve-week exercise training and the quality of life in menopausal women clinical trial. Menopausal Review / Przeglad Menopauzalny, 15(1), 20-25. https://doi.org/10.5114/pm.2016.58769 Da Fonseca, A.M., Bagnoli, V.R., Souza, M.A., Azevedo, R.S., De Barros Couto Junior, E., Soares Junior, J.M., & Baracat, E.C. (2013). Impact of age and body mass on the intensity of menopausal symptoms in 5968 Brazilian women. Gynecological Endocrinology, 29(2), 116-118. https://doi.org/10.3109/09513590.2012.730570 Daley, A. J., Thomas, A., Roalfe, A. K., Stokes Lampard, H., Coleman, S., Rees, M., & ... MacArthur, C. (2015). The effectiveness of exercise as treatment for vasomotor menopausal symptoms: Randomised controlled trial. BJOG: An International Journal of Obstetrics and Gynaecology, 122(4), 565-575. https://doi.org/10.1111/14710528.13193 Daley, A., Stokes Lampard, H., Thomas, A., & MacArthur, C. (2014). Exercise for vasomotor menopausal symptoms (Review). Cochrane Database of Systematic Reviews 2014, (11), 1-41. https://doi.org/ 10.1002/14651858.CD006108.pub4 de Kat, A.C., Dam, V., Onland-Moret, N.C., Eijkemans, M.J.C., Broekmans, F.J.M., & van der Schouw, Y.T. (2017). Unraveling the associations of age and menopause with cardiovascular risk factors in a large population-based study. BMC Medicine, 15(2) https://doi.org/10.1186/s12916-016-0762-8 Diniz, T.A., Christofaro, D.G.D., dos Santos, V.R., Viezel, J., Buonani, C., Rossi, F.E., & Frietas Junior, I.F. (2015). Practice of moderate physical activity can attenuate the loss of lean body mass in menopausal women. Motricidade, 11(1), 151-159. https://dx.doi.org/10.6063/motricidade.3727 Douchi, T., Yamamoto, S., Yoshimitsu, N., Andoh, T., Matsuo, T., & Nagata, Y. (2002). Relative contribution of aging and menopause to changes in lean and fat mass in segmental regions. Maturitas, 42(4), 301-306. https://doi.org/10.1016/S03785122(02)00161-5 Duval, K., Prud’homme, D., Rabasa-Lhoret, R., Strychar, I., Brochu, M., Lavoie, J-M., & Doucet, E. (2013). Effects of the menopausal transition on energy expenditure: A MONET group study. European Journal of Clinical Nutrition, 67, 407-411. https://doi.org/10.1038/ejcn.2013.33 Ernest, C.P., Johannsen, N.M., Swift, D.L., Lavie, C.J., Blair, S.N., & Church, T.S. (2013). Dose effect of cardiorespiratory exercise on metabolic syndrome in postmenopausal women. American Journal of Cardiology, 111, 1805-1811. https://doi.org/10.1016/j.amjcard.2013.02.037 Ettinger, B., Wang, S.M., Leslie, R.S., Patel, B.V., Boulware, M.J., Mann, M.E., & McBride, M. (2012). Evolution of postmenopausal hormone therapy between 2002 and 2009. Menopause, 19, 610-615. https://doi.org/ 10.1097/gme.0b013e31823a3e5d Feldman, B.M., Voda, A., & Gronseth, E. (1985). The prevalence of hot flash and associated variables among perimenopausal women. Research in Nursing & Health, 8(3), 261-268. https://doi.org/10.1002/nurs.4770080308 Flint, A.J., Hu, F.B., Glynn, R.J., Caspardi, H., Manson, J.E., & Willett, W.C. (2010). Excess weight and the risk of incident coronary heart disease among men and women. Obesity, 18(2), 377-383. https://doi.org/10.1038/oby.2009.223 Franklin, R.M., Ploutz-Snyder, L., & Kanaley, J.A. (2009). Longitudinal changes in abdominal fat distribution with menopause. Metabolism: Clinical and Experimental, 58, 311-315. https://doi.org/10.1016/j.metabol.2008.09.030 Freeman, E.W., Sammel, M.D., Grisso J.A., Battistini, M., Garcia-Espagna, B., & Hollander, L. (2001). Hot flashes in the late reproductive years: Risk factors for African American and Caucasian women. Journal of Women’s Health & GenderBased Medicine, 10(1), 67-76. https://doi.org/10.1089/152460901750067133 Freedman, R. (2014). Menopausal hot flashes: Mechanisms, endocrinology, treatment. Journal of Steroid Biochemistry and Molecular Biology, 142, 115-120. https://doi.org/10.1016/j.jsbmb.2013.08.010 Freese, E.C., Acitelli, R.M., Gist, N.H., Cureton, K.J., Evans, E.M., & O’Connor, P.J. (2014). Effect of six weeks of sprint interval training on mood and perceived health in women at risk for metabolic syndrome. Journal of Sport & Exercise Psychology, 36, 610-618. https://doi.org/10.1123/jsep.2014-0083 Fukushima, Y., Kurose, S., Shinno, H., Thu, H.C., Takao, N., Tsutsami, H., & Kimura, Y. (2016). Importance of lean muscle maintenance to improve insulin resistance by body weight reduction in female patients with obesity. Diabetes & Metabolism Journal, 40, 147-153. https://doi.org/10.4093/dmj.2016.40.2.147 Gallichio, L., Miller, S.R., Kiefer, J., Greene, T., Zacur, H.A., & Flaws, J.A. (2014). Change in body mass index, weight, and hot flashes: A longitudinal analysis from the midlife women’s health study. Journal of Women’s Health, 23(3). https://doi.org/10.1089/jwh.2013.4526 Gast, G-C.M., Grobbee, D.E., Pop, V.J.M., Keyzer, J.J., Wijnands-van Gent, C.J.M., Samsioe, G.N., ...& van der Schouw, Y.T. (2008). Menopausal complaints are associated with cardiovascular risk factors. Hypertension, 51, 1492-1498. https://doi.org/10.1161/HYPERTENSIONAHA.107.106526 Gjelsvik, B., Rosvold, E.O., Dalen, I., & Hunskaar, S. (2011). Symptom prevalence during menopause and factors associated with symptoms and menopausal age: Results from the Norwegian Hoardland Women’s Cohort. Maturitas, 70, 383390. https://doi.org/10.1016/j.maturitas.2011.09.011 Glouzon, B.K.J., Barsalani, R., Lagacé, J-C., & Dionne, I.J. (2015). Climacteric, 18(6). 846-851. https://doi.org/10.3109/13697137.2015.1083002 Gold, E.B., Colvin, A., Avis, N., Bromberger, J., Greendale, G.A., Sternfeld, B., & Matthews, K. (2006). Longitudinal analysis of the association between vasomotor symptoms and race/ethnicity across the menopausal transition: Study of Women’s Health Across the Nation. American Journal of Public Health, 96(7), 1226-1235. https://doi.org/10.2105/AJPH.2005.066936 Gold, E.B., Crawford, S.L., Shelton, J.F., Tepper, P.G., Crandall, C.J., Greendale, G.A., …Avis, N.E. (2016). Longitudinal analysis of changes in weight and waist circumference in relation to incident vasomotor symptoms: The Study of Women’s Health Across the Nation (SWAN). Menopause, 24(1), 9-26. https://doi.org/10.1097/GME.0000000000000723 Greendale, G.A., Lee, N.P., & Arriola, E.R. (1999). The menopause. The Lancet 353, 571-580. Retrieved from http://www.thelancet.com Grindler, N.M. & Santoro, N.F. (2015). Menopause and exercise. Menopause, 22(12). 1351-1358. https://doi.org/10.1097/GME.0000000000000536 Grossman, J. C., & Payne, E. K. (2016). A randomized comparison study regarding the impact of short duration, high intensity exercise and traditional exercise on anthropometric and body composition measurement changes in postmenopausal women: A pilot study. Post Reproductive Health, 22(1), 1419. https://doi.org/10.1177/2053369115623899 Haimov-Kochman, R., Constantini, N., & Brzezinski, A. (2013). Regular exercise is the most significant lifestyle parameter associated with the severity of climacteric symptoms: A cross sectional study. European Journal of Obstetrics & Gynecology and Reproductive Biology, 170, 229-234. https://doi.org/10.1016/j.ejogrb.2013.06.018 Harlow, S.D., Gass, M., Hall, J.E., Lobo, R., Maki, P., Rebar, R.W., …de Villiers, T.J. (2012). Executive summary of the Stages of Reproductive Aging Workshop + 10: Addressing the unfinished agenda of staging reproductive aging. Climacteric, 15, 105-114. https://doi.org/10.3109/13697137.2011.650656 Herber-Gast, G-C.M., Mishra, G.D., van der Schouw, Y.T., Brown, W.J., & Dobson, A.J. (2013). Risk factors for night sweats and hot flushes in midlife: Results from a prospective cohort study. Menopause, 20(9), 953-959. https://doi.org/10.1097/gme.0b013e3182844a7c Huang, A. J., Subak, L. L., Wing, R., West, D. S., Hernandez, A. L., Macer, J., & Grady, D. (2010). An intensive behavioral weight loss intervention and hot flushes in women. Archives of Internal Medicine, 170(13), 1161-1167. https://doi.org/10.1001/archinternmed.2010.162 Hurley, K.S., Flippin, K.J., Blom, L.C., Bolin, J.E., Hoover, D.L., & Judge, L.W. (2018). Practices, perceived benefits, and barriers to resistance training among women enrolled in college. International Journal of Exercise Science, 11(5), 226-238. Retrieved from https://digitalcommons.wku.edu/ijes/ Janssen, I., Heymsfield, S.B., Baumgartner, R.N., & Ross, R. (2000). Estimation of skeletal muscle mass by bioelectrical impedance analysis. Journal of Applied Physiology, 89(2), 465-471. https://doi.org/10.1152/jappl.2000.89.2.465 Jenkinson, C., Wright, L., & Coulter, A. (1994). Criterion validity and reliability of the SF-36 in a population sample. Quality of Life Research, 3(1), 7-12. http://www.jstor.org/stable/4034552 Jinabi, E., Shobein, F., Hazavehei, S.M.M., & Roshanaei. (2015). Assessment of questionnaire measuring quality of life in menopausal women: A systematic review. Oman Medical Journal, 30(3), 151-156. https://doi.org/10.5001/omj.2015.34 Jorge, M. P., Santaella, D. F., Pontes, I. M., Shiramizu, V. K., Nascimento, E. B., Cabral, A., ... Ribeiro, A. M. (2016). Hatha yoga practice decreases menopause symptoms and improves quality of life: A randomized controlled trial. Complementary Therapies in Medicine, 26, 128-135. https://doi.org/ 10.1016/j.ctim.2016.03.014 Kabat, G.C., Heo, M., Van Horn, L.V., Kazlauskaite, R., Getaneh, A., Ard, J., …& Rohan, T.E. (2014). Longitudinal association of anthropometric measures of adiposity with cardiometabolic risk factors in postmenopausal women. Annals of Epidemiology, 24, 896-902. https://doi.org/10.1016/j.annepidem.2014.10.007 Karastergiou, K. & Mohamed-Ali, V. (2010). The autocrine and paracrine roles of adipokines. Molecular and Cellular Endocrinology, 318(1-2), 69-78. https://doi.org/10.1016/j.mce.2009.11.011 Kazlauskaite, R., Innola, P., Karavolos, K., Dugan, S.A., Avery, E.F., …Powell, L.H. (2015). Abdominal adiposity change in white and black midlife women: The Study of Women’s Health Across the Nation. Obesity, 23(12), 2340-2343. https://doi.org/10.1002/oby.21350 Kelley, D.E., He, J., Menshikova, E.V. & Ritov, V.B. (2002). Dysfunction of mitochondria in human skeletal muscle in type 2 diabetes. Diabetes, 51(10), 2944-2950. https://doi.org/10.2337/diabetes.51.10.2944 Kershaw, E.E., & Flier, J.S. (2004). Adipose tissue as an endocrine organ. Journal of Clinical Endocrinology & Metabolism, 89(6), 2548-2556. https://doi.org/10.1210/jc.2004-0395 Kishida, M., & Elavsky, S. (2015). Daily physical activity enhances resilient resources for symptom management in middle-aged women. Health Psychology, 34(7), 756-764. https://doi.org/10.1037/hea0000190 Klonizakis, M., Moss, J., Gilbert, S., Broom, D., Foster, J., & Tew, G. A. (2014). Low volume high intensity interval training rapidly improves cardiopulmonary function in postmenopausal women. Menopause 21(10), 1099-1105. https://doi.org/10.1097/GME.0000000000000208 Kolu, P., Raitanen, J., Nygård, C., Tomás, E., & Luoto, R. (2015). Cost effectiveness of physical activity among women with menopause symptoms: Findings from a randomised controlled trial. Plos One, 10(8), e0135099. https://doi.org/10.1371/journal.pone.0135099 Kronenberg, F. (2010) Menopausal hot flashes: A review of physiology and biosociocultural perspective on methods of assessment. The Journal of Nutrition, 140, 13805-13855. https://doi.org/10.3945/jn.109.120840 Kruger, J., Carlson, S., & Kohl, H. (2006). Trends in Strength Training – United States, 1998-2004. Centers for Disease Control and Prevention, Atlanta. Retrieved from https://www.cdc.gov/mmwr/preview/mmwrhtml/mm5528a1.htm Lee, H., Caguicla, J.M.C., Park, S., Kwak, D.J., Won, D-Y., Park, Y., … Kim, M. (2016). Effects of 8-week Pilates exercise program on menopausal symptoms and lumbar strength and flexibility in postmenopausal women. Journal of Exercise Rehabilitation, 12(3), 247-251. https://doi.org/10.12965/jer.1632630.315 Lee, J-Y., & Lee, D-C. (2013). Muscle strength and quality are associated with severity of menopausal symptoms in peri-and post-menopausal women. Maturitas, 76, 88-94. https://doi.org/10.1016/j.maturitas.2013.06.007 Leite, R.D., Prestes, J., Pereira, G.B., Shiguemoto, G.E., & Perez, S.E.A. (2010). Menopause: Highlighting the effects of resistance training. International Journal of Sports Medicine 31, 761-767. https://doi.org/ 10.1055/s-0030-1263117 Lemoine, S., Granier, P., Tiffoche, C., Rannou-Bekono, F., Thieulant, M.I., & Delamarche, P. (2003). Estrogen receptor alpha mRNA in human skeletal muscles. Medicine and Science in Sports and Exercise, 35, 439-443. https://doi.org/10.1249/01.MSS.0000053654.14410.78 Lobo, R.A., Davis, S.R., De Villiers, T.J., Gompel, A., Henderson, V.W., Hodis, H.N., …Baber, R.J. (2014). Prevention of diseases after menopause. Climacteric, 17, 540-556. https://doi.org/10.3109/13697137.2014.933411 Lovejoy, J.C. (2009). Weight gain in women at midlife: The influence of menopause. Obesity Management, 5, 52-56. https://doi.org/10.1059/obe.2009.0203 Lovejoy, J.C., Champagne, C.M., de Jong, L., Xie, H., & Smith, S.R. (2008). Increased visceral fat and decreased energy expenditure during the menopausal transition. International Journal of Obesity (London), 32, 949-958. https://doi.org/ 10.1038/ijo.2008.25 Lukaski, H.C., Johnson, P.E., Bolonchik, W.W., & Lykken, G.I. (1985). Assessment of fat-free mass using bioelectrical impedance measurements of the human body. American Journal of Clinical Nutrition, 41, 810-817. Retrieved from www.ajcn.nutrition.org. Luoto, R., Moilanen, J., Heinonen, R., Mikkola, T., Raitanen, J., Tomas, E., ...& Nygård, C. (2012). Effect of aerobic training on hot flushes and quality of life - A randomized controlled trial. Annals of Medicine, 44(6), 616-626. https://doi.org/10.3109/07853890.2011.583674 Maltais, M.L., Desroches, J., & Dionne, I.J. (2009). Changes in muscle mass and strength after menopause. Journal of Musculoskeletal Neuronal Interactions, 9(4), 186197. Retrieved from www.ismni.org.jmni Mandrup, C.M., Egelund, J., Nyberg, M., Slingsbury, M.H.L., Andersen, C., Løgstrup, S., …Hellsten, Y. (2017). Effects of high-intensity training on cardiovascular risk factors in premenopausal and postmenopausal women. American Journal of Obstetrics & Gynecology. Advance online publication. https://doi.org/10.1016/j.ajog.2016.12.017 Mansikkamäki, K., Nygård, C., Raitanen, J., Kukkonen Harjula, K., Tomás, E., Rutanen, R., & Luoto, R. (2016). Hot flushes among aging women: A 4-year follow up study to a randomised controlled exercise trial. Maturitas, 88, 84-89. https://doi.org/10.1016/j.maturitas.2016.03.010 Mansikkamaki, K., Raitanen, J., Malila, N., Sarkeala, T., Mannisto, Fredman, J., …& Luoto, R. (2015). Physical activity and menopause-related quality of life- A population-based cross-sectional study. Maturitas, 80, 69-74. https://doi.org/10.1016/j.maturitas.2014.09.009 Mansikkamäki, K., Raitanen, J., Nygård, C., Heinonen, R., Mikkola, T., Eija Tomás, & Luoto, R. (2012). Sleep quality and aerobic training among menopausal women A randomized controlled trial. Maturitas, 72, 339-345. https://doi.org/10.1016/j.maturitas.2012.05.003 Martins, F.M., Souza, A.P., Nunes, P.R.P., Michelin, M.A., Murta, …, Orsatti, F.L. (2018). High-intensity body weight training is comparable to combined training in changes in muscle mass, physical performance, inflammatory markers and metabolic health in postmenopausal women at high risk for type 2 diabetes mellitus: A randomized controlled clinical trial. Experimental Gerontology, 107, 108-115. https://doi.org/10.1016/j.exger.2018.02.016 McArthur, D., Dumas, A., Woodend, K., Beach, S., & Stacey, D. (2014). Factors influencing adherence to regular exercise in middle-aged women: A qualitative study to inform clinical practice. BMC Women’s Health, 14(49). www.biomedcentral.com/1472-6874/14/49 McGuire, A., Seib, C., & Anderson, D. (2016). Factors predicting barriers to exercise in midlife Australian women. Maturitas, 87, 61-66. https://doi.org/10.1016/j.maturitas.2016.02.010 Mendoza, N., Teresa, C-D., Cano, A., Godoy, D., Hita-Contreras, F., Lapotka, M., …Sánchez-Borrega, R. (2016). Benefits of physical exercise in postmenopausal women. Maturitas, 93, 83-88. https://doi.org/10.1016/j.maturitas.2016.04.017 Messier, V., Rabasa-Lhoret, R., Barbat-Artigas, S., Elisha, B., Karelis, A.D., AubertinLeheudre, M. (2011). Menopause and sarcopenia: A potential role for sex hormones. Maturitas, 68, 331-336. https://doi.org/10.1016/j.maturitas.2011.01.014 Mittal, P.C. & Kant, R. (2009). Correlation of increased oxidative stress to body weight gain disease-free postmenopausal women. Clinical Biochemistry, 42(2009), 1007-1011. https://doi.org/10.1016/j.clinbiochem.2009.03.019 Moilanen, J.M., Aalto, A-M., Raitanen, J., Hemminki, E., Aro, A.R., Luoto, R. (2012a). Physical activity and change in quality of life during menopause – An 8-year follow-up study. Health and Quality of Life Outcomes 10(8). https://doi.org/10.1186/1477-7525-10-8 Moilanen, J., Mikkola, T., Raitanen, J., Heinonen, R., Tomas, E., Nygård, C., & Luoto, R. (2012b). Effect of aerobic training on menopausal symptoms - A randomized controlled trial. Menopause (10723714), 19(6), 691-696. https://doi.org/10.1097/gme.0b013e31823cc5f7 Moratalla-Cecilia, N., Soriano-Maldonado, A., Ruiz-Cabello, P., Fernández, M.M., Gregorio-Arenas, E., Aranda, P., Aparicio, V.A. (2016). Association of physical fitness with health-related quality of life in early postmenopause. Quality of Life Research, 25, 2675-2681. https://doi.org/10.1007/s11136-016-1294-6 Muka, T., Oliver-Williams, C., Colpani, V., Kunutsor, S., Chowdhury, S., Chowdhury, R., …Franco, O.H. (2016). Association of vasomotor and other menopausal symptoms with risk of cardiovascular disease: A systematic review and metaanalysis. PLoS ONE, 11(6), e0157417. https://doi.org/10.1371/journal.pone.0157417 Nyberg, M., Seidelin, K., Andersen, T.R., Overby, N.N., Hellsten, Y., & Bangsbo, J. (2014). Biomarkers of vascular function in premenopausal and recent postmenopausal women of similar age: Effect of exercise training. American Journal of Physiology – Regulatory, Integrative and Comparative Physiology, 306, R510-R517. https://doi.org/10.1152/ajpregu.00539.2013 O’Neill, S. & O’Driscoll, L. (2015). Metabolic syndrome: A closer look at the growing epidemic and its associated pathologies. Obesity Review, 16. 1-12. https://doi.org/10.111/obr.12229 OCEBM Levels of Evidence Working Group*. “The Oxford Levels of Evidence 2”. Oxford Centre for Evidence-Based Medicine. http://www.cebm.net/index.aspx?o=5653 * OCEBM Levels of Evidence Working Group = Jeremy Howick, Iain Chalmers (James Lind Library), Paul Glasziou, Trish Greenhalgh, Carl Heneghan, Alessandro Liberati, Ivan Moschetti, Bob Phillips, Hazel Thornton, Olive Goddard and Mary Hodgkinson Ou, Y-C, Chuang, H-H, Li, W-C, Tzeng, I-S, & Chen, J-Y. (2017). Gender difference in the association between lower muscle mass and metabolic syndrome independent of insulin resistance in a middle-aged and elderly Taiwanese population. Archives of Gerontology and Geriatrics, 72, 12-18. https://doi.org/10.1016/j.archger.2017.04.006 Park, S.H., Kim, J.Y., Lee, J.H., & Park, H.Y. (2011). Elevated oxidized low-density lipoprotein concentrations in postmenopausal women with metabolic syndrome. Clinica Chimica Acta, 412(5-6), 435-440. https://doi.org/10.1016/j.cca.2010.11.017 Pimenta, F., Leal, I., Maroco, J. & Ramos, C. (2012). Menopause Symptoms’ Severity Inventory (MSSI-38): Assessing the frequency and intensity of symptoms. Climacteric, 15, 143-152. https://doi.org/10.3109/13697137.2011.590617 Reed, S. D., Guthrie, K. A., Newton, K. M., Anderson, G. L., Booth Laforce, C., Caan, B., ... Lacroix, A. Z. (2014). Menopausal quality of life: RCT of yoga, exercise, and omega 3 supplements. American Journal of Obstetrics & Gynecology, 210(3), 244.e1244.e11. https://doi.org/ 10.1016/j.ajog.2013.11.016 Roberts, H., & Hickey, M. (2016). Managing the menopause: An update. Maturitas, 86, 53-58. https://doi.org/ 10.1016/j.maturitas.2016.01.007 Rossouw, J.E., Manson, J.E., Kaunitz, A.M., & Anderson, G.L. (2013). Lessons learned from the Women’s Health Initiative trials of menopausal hormone therapy. Obstetrics & Gynecology, 121(1), 172-176. https://doi.org/ 10.1097/AOG.0b013e31827a08c8 Roubenoff, R. (2004). Sarcopenic obesity: The confluence of two epidemics. Obesity Research, 12, 887-888. https://doi.org/10.1038/oby.2004.107 Sánchez-Rodríguez, M.A., Zacarías-Flores, M., Arronte-Rosales, A., Correa-Muñoz, E., & Mendoza-Núñez, V.M. (2012). Menopause as risk factor for oxidative stress. Menopause, 19(3), 361-367. https://doi.org/10.1097/gme.0b013e318229977d Sánchez-Rodríguez, M.A., Zacarías-Flores, M., Castrejón-Delgado, L., Ruiz-Rodríguez, A.K., & Mendoza-Núñez, V.M. (2016). Effects of hormone therapy on oxidative stress in postmenopausal women with metabolic syndrome. International Journal of Molecular Sciences, 17, 1388-1403. https://doi.org/10.3390/ijms17091388 Signorelli, S.S., Neri, S., Sciacchitano, S., Di Pino, L., Pia Costa, M., Marchese, G., Celotta, …Caschetto, S. (2006). Behavior of some indicators of oxidative stress in postmenopausal and fertile women. Maturitas, 53, 77-82. https://doi.org/10.1016/j.maturitas.2005.03.001 Skrzypulec, V., Dabrowska, J., & Drosdzol, A. (2010). The influence of physical activity level on climacteric symptoms in menopausal women. Climacteric, 13, 355-361. https://doi.org/10.3109/13697131003597019 Solomon, D.H., Diem, S.J., Ruppert, K., Lian, Y.J., Liu, C-C, Wohlfart, A., …Finkelstein, J.S. (2015). Bone mineral density changes among women initiating proton pump or H2 receptor antagonists: A SWAN cohort study. Journal of Bone and Mineral Research, 30(2), 232-239. https://doi.org/10.1002/jbmr.2344 Sowers, M.F., Crawford, S.L., Sternfeld, B., Morganstein, D., Gold, E.B., Greendale, G.A., …Kelsey, J. (2000). Design, survey sampling and recruitment methods of SWAN: A multi-center, multi-ethnic community-based cohort of women and the menopausal transition. In: R.A. Lobos & J. Kelsey (Eds.), Menopause: Biology and Pathobiology (pp175-188). San Diego: Academic Press Sowers, M.R., Jannausch, M., McConnell, D., Little, R., Greendale, G.A., Finkelstein, J.S., …Ettinger, B. (2006). Hormone predictors of bone mineral density changes during the menopausal transition. Journal of Clinical Endocrinology & Metabolism, 91(4), 1261-1267. https://doi.org/10.1210/jc.2005-1836 Statistics Canada, 2016. Retrieved from http://www.statcan.gc.ca/dailyquotidien/150218/dq150218c-eng.pdf Stefanska, A., Bergmann, K., & Sypniewska, G. (2015). Metabolic syndrome and menopause: Pathophysiology, clinical and diagnostic significance. Advances in Clinical Chemistry, 72. 1-75. https://doi.org/10.1016/bs.acc.2015.07.001 Sternfeld, B., Ainsworth, B.E., & Quesenberry, C.P. (1999). Physical activity patterns in a diverse population of women. Preventative Medicine, 28, 313-323. https://doi.org/10.1006/pmed.1998.0470 Sternfeld, B., & Dugan, S. (2011). Physical activity and health during the menopausal transition. Obstetrics and Gynecology Clinics of North America, 38(3), 537-566. https://doi.org/10.1016/j.ogc.2011.05.008 Sternfeld, B., Guthrie, K. A., Ensrud, K. E., LaCroix, A. Z., Larson, J. C., Dunn, A. L., ... Caan, B. J. (2014). Efficacy of exercise for menopausal symptoms: A randomized controlled trial. Menopause, 21(4), 330-338. https://doi.org/10.1097/GME.0b013e31829e4089 Sutton-Tyrell, K., Selzer, F., Sowers, MF.R., Neer, R., Powell, L., Gold, E., … McKinlay, S. (2018a). Study of Women’s Health Across the Nation (SWAN), 1995-1997: Cross-Sectional Screener Dataset. Ann Arbor, MI: Inter-university Consortium for Political and Social Research [distributor]. https://doi.org/10.3886/ICPSR04368.v4 Sutton-Tyrell, K., Selzer, F., Sowers, MF.R., Finkelstein, J., Powell, L., Gold, E.B., … Matthews, K. (2018b) Study of Women’s Health Across the Nation (SWAN), 2002-2004: Visit 06 Dataset. Ann Arbor, MI: Inter-university Consortium for Political and Social Research [distributor]. https://doi.org/10.3886/ICPSR31181.v2 Sutton-Tyrell, K., Selzer, F., Sowers, MF.R., Finkelstein, J., Powell, L., Gold, E.B., … Matthews, K. (2018c). Study of Women’s Health Across the Nation (SWAN), 2003-2005: Visit 07 Dataset. Ann Arbor, MI: Inter-university Consortium for Political and Social Research [distributor]. https://doi.org/10.3886/ICPSR31901.v2 Sutton-Tyrell, K., Selzer, F., Sowers, MF.R., Finkelstein, J., Powell, L., Gold, E.B., … Brooks, M.M. (2018d). Study of Women’s Health Across the Nation (SWAN), 2004-2006: Visit 08 Dataset. Ann Arbor, MI: Inter-university Consortium for Political and Social Research [distributor]. https://doi.org/10.3886/ICPSR32122.v2 Sutton-Tyrell, K., Selzer, F., Sowers, MF. R., Finkelstein, J., Powell, L., Gold, E.B., … Brooks, M.M. (2018e). Study of Women’s Health Across the Nation (SWAN), 2005-2007: Visit 09 Dataset. Ann Arbor, MI: Inter-university Consortium for Political and Social Research [distributor]. https://doi.org/10.3886/ICPSR32721.v2 Sutton-Tyrrell, K., Selzer, F., Sowers, MF.R., Finkelstein, J., Powell, L., Gold, E.B., … Brooks, M.M. (2018f). Study of Women’s Health Across the Nation (SWAN), 2006-2008: Visit 10 Dataset. Ann Arbor, MI: Inter-university Consortium for Political and Social Research [distributor]. https://doi.org/10.3886/ICPSR32961.v2 Svendsen, O.L., Hassager, C., & Christiansen, C. (1995). Age- and menopauseassociated variations in body composition and fat distribution in healthy women as measured by dual-energy x-ray absorptiometry. Metabolism, 44(3), 369-373. https://doi.org/10.1016/0026-0495(95)90168-X Swift, D.L., Johannsen, N.M., Tudor-Locke, C., Earnest, C.P., Johnson, W.D., Blair, S.N., …Church, T.S. (2012). Exercise training and habitual physical activity. American Journal of Preventive Medicine, 43(6), 629-635. https://doi.org/10.1016/j.amepre.2012.08.024 Takamura, T., Kita, Y., Nakagen, M., Sakuri, M., Isobe, Y., Takeshita, Y., …Kaneko, S. (2017). Weight-adjusted lean body mass and calf circumference are protective against obesity-associated insulin resistance and metabolic abnormalities. Heliyon, 3(7), 1-25. https://doi.org/10.1016/j.heliyon.2017.e00347 Tanaka, N.I., Hanawa, S., Murakami, H., Cao, Z-B., Tanimoto, M., Kiyoshi, Motohiko, M. (2015). Accuracy of segmental bioelectrical impedance analysis for predicting body composition in pre- and postmenopausal women. Journal of Clinical Densitometry, 18(2), 252-259. https://doi.org/10.1016/j.jocd.2014.07.002 The North American Menopause Society (2015). Nonhormonal management of menopause-Associated vasomotor symptoms. Menopause, 22(11). https://doi.org/10.1097/GME.0000000000000546 Thurston, R.C., Sowers, M.R., Chang, Y., Sternfeld, B., Gold, E.B., Johnston, J.M. & Matthews, K.A. (2008a). Adiposity and reporting of vasomotor symptoms among midlife women: The Study of Women’s Health Across the Nation. American Journal of Epidemiology, 167(1), 78-85. https://doi.org/10.1093/aje/kwm244 Thurston, R.C., Sowers, M.R., Sutton-Tyrrell, K., Everson-Rose, S.A., Lewis, T.T., Edmundowicz, D. & Matthews, K.A. (2008b). Abdominal adiposity and hot flashes among midlife women. Menopause, 15(3), 429-434. https://doi.org/10.1097/gme.0b013e31815879cf Thurston, R.C., Sowers, M.R., Sternfeld, B., Gold, E.B., Bromberger, J., Chang, Y., …Matthews, K.A. (2009). Gains in bodyfat and vasomotor symptoms reporting over the menopausal transition. American Journal of Epidemiology, 170(6), 766774. https://doi.org/10.1093/aje/kwp203 Thurston, R.C. & Joffe, H. (2011). Vasomotor symptoms and menopause: Findings from the Study of Women’s Health Across the Nation. Obstetrics & Gynecology Clinics of North America, 38(3), 489-501. https://doi.org/10.1016/j.oge.2011.05.006 Thurston, R.C., Chang, Y., Mancuso, P., & Matthews, K.A. (2013). Adipokines, adiposity, and vasomotor symptoms during the menopause transition: Findings from the Study of Women’s Health Across the Nation. Fertility and Sterility, 100(3), 793-800. https://doi.org/10.1016/j.fertnstert.2013.05.005 Vallance, J.K., Murray, T.C., Johnson, S.T., & Elavsky, S. (2010). Quality of life and psychosocial health in postmenopausal women achieving public health guidelines for physical activities. Menopause, 17(1), 64-71. https://doi.org/10.1097/gme.0b013e3181b6690c Van Der Schouw, Y.T. & Grobbee, D.E. (2005). Menopausal complaints, oestrogens, and heart disease risk: An explanation for discrepant findings on the benefits of post-menopausal hormone therapy. European Heart Journal, 26(14), 1358-1361. https://doi.org/10.1093/eurheartj/ehi297 Wang, C-H., Chung, M-H., Chan, P., Tsai, J-C., & Chen, F-C. (2014). Effects of endurance exercise training on risk components for metabolic syndrome, interleukin-6, and the exercise capacity of postmenopausal women. Geriatric Nursing, 35. 212-218. https://doi.org/10.1016/j.gerinurse.2014.02.001 Weston, K.S., Wisloff, U., & Coombes, J.S. (2014). High-intensity interval training in patients with lifestyle-induced cardiometabolic disease: A systematic review and meta-analysis. British Journal of Sports Medicine, 48, 1227-1234. https://doi.org/10.1136/bjsports-2013-092576 Wildman, R.P. & Sowers, MF R. (2011). Adiposity and the menopausal transition. Obstetrics & Gynecology Clinics of North America, 38, 411-454. https://doi.org/10.1016/j.ogc.2011.05.003 Woods, N.F., & Mitchell, E.S. (2011). Symptom interference with work and relationships during the menopausal transition and early postmenopause: Observations from the Seattle Midlife Women’s Health Study. Menopause, 18, 654-661. https://doi.org/10.1097/gme.0b013e3318205bd76 World Health Organization, (2010). Global recommendations on physical activity for health. Geneva. Retrieved from www.who.int Zacarías-Flores, M., Sánchez-Rodríguez, M.A., García-Anaya, O.D., Correa-Muñoz, E., & Mendoza-Núñez, V.M. (2018). Relationship between oxidative stress and muscle loss in early postmenopause: An exploratory study. Endocrinología, Diabetes y Nutrición, 65(6), 328-334. https://doi.org/10.1016/j.endien.2018.01.006 Zapantis, G & Santoro, N. (2003). The menopausal transition: Characteristics and management. Best Practice and Research Clinical Endocrinology & Metabolism 17, 33-52. https://doi.org/10.1053/ybeem.2003.236 Zhang, J., Chen, G., Lu, W., Yan, X., Zhu, S., Dai, Y., ... Bai, W. (2014). Effects of physical exercise on health-related quality of life and blood lipids in perimenopausal women: A randomized placebo-controlled trial. Menopause, 21(12), 1269-1276. https://doi.org/10.1097/GME.0000000000000264 Zurlo, F., Larson, K., Bogardus, C., & Ravussin, E. (1990). Skeletal muscle metabolism is a major determinant or resting energy expenditure. Journal of Clinical Investigation, 86(5), 1423-1427. https://doi.org/10.1172/JCI114857