GH replacement therapy in the adult Selenium BDD framework elevates days Boyd weeks and compositoon suppresses months to compisition insulin and leptin concentrations. Body composition and hormonal health Women in the intervention Body composition and hormonal health control groups were comparable in baseline characteristics Table 2. Struve, G. Björntorp, L. Physiological amounts of testosterone stimulate lean-tissue accrual, augment total muscle volume, accelerate protein synthesis, retard protein breakdown, increase isokinetic strength and induce in situ muscle IGF-I gene expression in hypogonadal boys or men 23,—, —

Body composition and hormonal health -

Finally, some circumstances may warrant testosterone replacement therapy. If you think testosterone replacement therapy might be appropriate for your needs, reach out to a medical professional. Along with leptin, another hormone that plays a role in inhibiting your appetite, ghrelin can influence your eating habits.

Having high levels of ghrelin in your system naturally increases your appetite and cravings, which may drive calorie overconsumption. In addition to the behavioral aspect, ghrelin may also play roles in influencing how your body stores fat.

Plus, ghrelin can influence how your body releases growth hormone, a hormone that regulates various aspects of body composition—including muscle growth. So, you can see what a strong impact ghrelin can have on body composition! To manage your appetite, start by filling your plate with nutrient-dense and filling foods that trigger fullness.

Studies show that ghrelin levels can increase with sleep deprivation. Your thyroid is an organ that produces several hormones that are important for regulating the speed of your metabolism in other words, how quickly your body uses energy.

On the other hand, you can also have thyroid levels that are too high, which will speed up your metabolism to abnormal levels.

This may contribute to unexplained weight loss. If your thyroid is underproducing or overproducing hormones, your best bet is to talk to your doctor. While there is evidence that eating a healthy diet with micronutrients like iodine, selenium, iron, zinc, and Vitamins B12, D, and A may help reduce symptoms of thyroid issues or prevent thyroid risks in the future , an over- or under-active thyroid is a medical issue.

Treating it may require medication to supplement low hormone levels or inhibit overactive thyroid activity. Your diet and exercise routines are absolutely the most crucial pieces of the puzzle when it comes to improving your body composition. However, if your hormone levels are not where they should be, it can make your progress much slower.

By investigating and addressing any hormone imbalances, you can help your metabolism become a more efficient system that helps you reach your goals. Disclaimer: Please be aware that your actual monthly payment liability is subject to change based on the amount financed, which is at the financer's discretion and that the amount shown here is merely an estimate and does not include applicable federal and sales tax.

Hit enter to search or ESC to close. Close Search. Insulin Insulin is a crucial hormone for your metabolism. How to balance insulin levels Dietary changes like increasing your consumption of fiber found in whole grains, non-starchy vegetables, and raw fruit and minimizing your consumption of simple sugars may help improve insulin resistance.

How to balance cortisol levels Stress can sometimes feel inevitable depending on your lifestyle. How to balance ghrelin To manage your appetite, start by filling your plate with nutrient-dense and filling foods that trigger fullness. How to balance thyroid hormones If your thyroid is underproducing or overproducing hormones, your best bet is to talk to your doctor.

Conclusion Your diet and exercise routines are absolutely the most crucial pieces of the puzzle when it comes to improving your body composition. Love 7 Share Tweet Share Pin. POPULAR POSTS. Fitness InBody Blog The Best Leg Workouts, According to Science.

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InBody Blog Success Stories Case Studies. InBody in Studies Scientific Partnerships. GSA ADVANTAGE. Contractor Info. Influence of HT on change in outcomes. There were 11, observations in this analysis; women were taking HT at the time of of these observations.

All HT use took place after the FMP occurred data not shown. Our study quantified the longitudinal trajectories of body composition and weight prior to, during, and after the MT, with the MT operationalized as a multiyear interval straddling the FMP. For body composition, increasing fat mass and declining proportion lean mass were apparent during premenopause, prior to the onset of the MT.

Change in body composition accelerated during the MT, displaying a 2- to 4-fold increase in gain fat or loss proportion lean mass. In postmenopause, on average, we observed a stabilization of body composition a zero slope. The average patterns of change of body weight and BMI differed from those of body composition: weight and BMI climbed steadily both prior to and during the MT, without an MT-related acceleration.

Like body composition, weight did not increase further during postmenopause. In contrast, trajectories in Japanese and Chinese women were distinct from those of the White referent sample: accelerated gains in fat mass and declines in lean mass did not characterize the MT.

In Chinese women only, during postmenopause, fat mass declined, proportion lean mass increased, and weight dropped. A later age at FMP mitigated body composition changes and weight gains.

Finally, body composition and weight trajectories were unaffected by HT use, but HT exposure in this analysis was uncommon and confined to postmenopause.

Our findings link the MT with unfavorable alterations in body composition, which abruptly worsen at the onset of the MT and then abate in postmenopause.

The total loss of lean mass during the MT averages 0. In concert, in the average SWAN participant, the accelerated increase in fat mass and decrease in lean mass results in a 3.

Jointly examining the rates of change in fat and lean mass during premenopause and the MT sheds light on why there is no measurable change in body weight trajectory accompanying the MT. The rate of increase in the sum of fat mass and lean mass is 0.

This is not a discernable change in rate, especially if bone loss during the MT which is not incorporated in the estimation of lean mass used here further lowers the MT slope estimate. Framed alternately, the difference in slopes between premenopause and the MT for the sum of fat mass and lean mass is only 80 grams per year, while the difference in the slope of fat mass between premenopause and the MT is grams per year and the corresponding difference for lean mass is — grams per year.

Thus, although there are MT-related effects on body composition, we observe no acceleration in weight gain at the time of the MT. However, close examination of existing evidence suggests that it is inadequate to either support or refute the hypothesis that the MT influences body composition or weight 13 — Most directly comparable to ours are studies that gauged the impact of the MT on body composition or weight by examining these characteristics in relation to FMP time 17 , 19 , Using bioelectrical impedance, Sowers and colleagues did not detect an effect of FMP time on either fat mass or lean mass in a sample of women at the Michigan SWAN site Rather, they reported a linear increase in fat mass and a small, linear decrease in lean mass over time.

To investigate the relation between FMP time and weight, Davies et al. No effect of FMP time on weight was apparent; instead, the authors described a linear increase in weight with time.

Finally, in an analysis of 48 women, the MONET study found that neither weight nor BMI were influenced by FMP time and that percent fat mass was greater in the post-FMP years than it had been previously; however, but no change in percent fat was noted in the transitional phase prior to FMP.

Although each of these studies concluded that the MT did not influence body composition or weight, small samples, correspondingly few observed FMP dates, and — in one instance — long intervals between assessments constrained their ability to discover a nonlinear trajectory of body composition or weight with FMP time.

More frequently, investigators examined the relation between advancing menstrual pattern—based MT stage i. Five of these studies, including 2 from the initial years of SWAN, found that weight increased over time but was unrelated to evolving MT stage 13 — 16 , Limitations included few conversions from earlier to later MT stages and, in some cases, long spans between assessments 13 — 16 , Dissimilar to prior reports, the current analysis supports a strong, adverse influence of the MT on body composition that is manifest during the MT and then halts.

As reported by others, we observed weight gain starting in premenopause with a linear trajectory not inflected at the MT, but our body composition measures offer an explanatory insight, as described above. SWAN also detects a cessation of weight gain in postmenopause except for postmenopausal Chinese women, whose weight not only stabilizes but declines , suggesting the advent of a new steady state and inferring a role for the end of the MT as one of its determinants.

Mounting evidence points to both estradiol E2 and follicle stimulating hormone FSH as regulators of energy balance; MT-related variations in each are plausible mechanisms of the results reported here 4 , 5. The time course of the trajectories of body composition mirror E2 and FSH trajectories in relation to the FMP.

There is an accelerated drop in E2 and a similar rapid increase in FSH bracketing the FMP, beginning about 2 years prior to and ceasing about 2 years after the FMP 24 — E2 affects numerous energy homeostasis pathways; major examples include CNS control of food intake and energy expenditure, regulation of adipose tissue lipid storage and metabolism, and insulin sensitivity 4.

Murine and rodent experimental manipulations e. Small cross-sectional and longitudinal observational studies find that resting energy expenditure REE is less in postmenopause than in premenopause 29 , In premenopausal women, pharmacological suppression of sex hormones by sustained administration of a gonadotropin releasing hormone agonist GnRH-a lowers REE; adding back transdermal E2 offsets the GnRH-a—induced decline in REE This same paradigm of pharmacological hormone suppression with and without the addition of transdermal E2 results in a loss of lean mass assessed by DXA only in the women who do not receive the E2 treatment Murine studies with a potentially novel FSH-blocking antibody demonstrate that, in ovarian-intact animals with unaltered serum E2 levels, FSH antibody reduces body fat but does not change body weight, similar to our human data The FSH antibody exerts several beneficial effects on energy balance, such as inducing the beiging of adipocytes conversion of white adipocytes to beige adipocytes, which are more metabolically active , a greater rate of thermogenesis, and activation of brown energy consuming adipocytes In our study, lean mass declined at the onset of the MT.

DXA lean mass measurement consists of total body water, muscle mass, and organ mass as noted in Methods, we excluded bone mass from the lean mass computation.

Therefore, decreasing lean mass could be due to diminution of any of these components. As reviewed by Stachenfeld, estrogen influences several physiological mechanisms that maintain water and salt balance Thus, an MT-related shift in fluid regulation could contribute to our observed reduction in lean mass.

There have been some investigations of the relation between menopause and muscle, but these have compared pre- vs. postmenopausal women or made inferences based on age rather than MT stage 35 , Nonetheless, these studies suggest plausible means by which the MT may diminish muscle mass, such as upregulation of skeletal muscle catabolism or lessened muscle response to anabolic stimuli e.

Declines in estrogen could underlie detrimental MT effects on muscle; the neuromuscular system is replete with α and β estrogen receptors, and when taken in early postmenopause, HT may preserve the muscle transcriptome and benefit muscle strength Progesterone can increase protein synthesis in women; therefore, persistently low progesterone levels could contribute to a decline in lean mass In men, androgens regulate lean mass, but androgen levels do not decline across the MT and are, therefore, unlikely to account for the a decrease in lean mass 38 , The menopause may also negatively influence muscle by indirect pathways — for example, by downregulating the anabolic IGF-1 pathway or by leading to a more preinflammatory milleu 40 , While increases in fat mass and decreases in lean mass were similar in Black and White women, findings in the 2 Asian groups were distinctive.

Our findings do not align with the few existing reports in Asian samples. On average, we found that Japanese SWAN participants, like White participants, lost lean mass during the MT, but unlike White participants, their fat mass and weight did not change during the MT.

This is in contrast to a cross-sectional survey of Japanese women aged 20—70 years that found postmenopause was associated not only with lower lean mass, but also with greater body fat In our study, during the postmenopausal interval, Chinese SWAN participants lost fat mass and body weight and gained lean mass proportion, which is in opposition to a prior single-site, cross-sectional SWAN analysis that reported lower lean mass and higher percent body fat in late peri- or postmenopausal Chinese participants We did not witness an effect of HT on body composition or weight measures, but HT use was infrequent and only occurred during postmenopause.

Thus, whether the use of HT lessens or prevents worsening of body composition during the transition from pre- to postmenopause, analogous to the GnRH-a with E2 add-back model, cannot be inferred from our analysis A limitation of this study is that we were unable to consider the effect of the MT on regional body composition and visceral fat at this time.

Owing to the complexity of the current analysis, we did not directly examine the relation between trajectories of sex steroids or gonadotropins and body composition and weight outcomes. Subsequent investigations will remedy these limitations. Factors such as clothing worn and time of day may affect both accuracy and precision of anthropometric measures; standard SWAN protocols mitigated against these potential influences.

Ours is a community-based, but not a population-based, sample; therefore, results may not be generalizable to US Black, Chinese, Japanese, and White women. Study strengths are several. First, we analyzed DXA-quantified body composition and measured weight in proximity, providing insight about how they are related.

We also benefitted by using time to and from FMP to capture the effect of the transition from pre- to postmenopause on body composition and weight; an FMP time-referenced analysis is a more discriminating assessment of progress through the transition that is an analysis based on clinical MT stages In summary, the MT is accompanied by accelerated gains in fat mass and simultaneous losses in lean mass; their joint rates of change result in no detectable acceleration in weight or BMI at the onset of the MT.

That an MT-related acceleration in weight or BMI is not observed, despite the high-velocity increase in fat mass, is concordant with the growing appreciation that, while BMI is a well-established, strong composite indicator of cardiometabolic risk, it is a less strong index of adiposity and particular aspects of adiposity such as the location of fat 44 , As a result, BMI is a less useful indicator of cardiometabolic risk in older women BMI is body weight normalized to the square of height.

However, inputs to weight include fat mass and lean mass, each of which may vary differentially and may variably contribute to specific aspects of cardiometabolic and other health risks This description of how the MT affects individual compartments of body composition lays the groundwork for investigating how MT-related body composition changes may affect the health of postmenopausal women and how relative weight and body composition may make distinctive contributions to a range of physiological outcomes.

Study sample. SWAN is a multisite, community-based, longitudinal cohort study The 7 SWAN clinical sites Boston, Massachusetts, USA; Chicago, Illinois, USA; Detroit, Michigan, USA; Pittsburgh, Pennsylvania, USA; Los Angeles, California, USA; Newark, New Jersey, USA: and Oakland, California, USA enrolled participants.

All sites enrolled White women. Boston, Chicago, Detroit, and Pittsburgh enrolled Black women and the remaining 3 sites enrolled Japanese, Hispanic, and Chinese women, respectively. The baseline visit visit 00 occurred in —, and the final study visit included in this analysis visit 13 occurred during — The Chicago and Newark sites did not assess body composition using Hologic DXA instruments; thus, participants from the remaining 5 sites were eligible for the SWAN Bone Density and Body Composition Cohort.

Figure 3 illustrates the current analysis sample derivation. Derivation of the analysis sample for analysis of body composition and weight in relation to the FMP.

This analysis considers 6 outcomes: 4 DXA-acquired body composition measures, measured weight, and BMI. Body composition variables omit the head from the calculation. The lean mass estimate used here is exclusive of bone mass to avoid contamination by unremovable metal artifacts.

We measured body composition at each SWAN visit using Hologic instruments Hologic Inc. Three sites began SWAN with Hologic QDR A models; 2 of these transitioned to Discovery models during follow-up. Two sites began with QDR models and upgraded to QDR A models during follow-up.

Sites that changed densitometers scanned volunteers on their old and new machines for cross-calibration. The Supplemental Material, Setions B and C presents a detailed description of hardware, software, coefficients of variation, and calibrations.

DXA procedures require exclusion of the left arm when the participant is too large to allow both upper extremities to rest on the scan bed while maintaining sufficient separation to define soft tissue regions. Employing data from women who had both arms measured, we used right arm values to impute left arm values, accounting for hand dominance if unknown, assumed right-handedness.

For left-handed participants, raw right arm and left arm values were similar; therefore, we substituted their right arm values for left. Using calibrated scales and stadiometers, we measured height to the nearest 0. The SWAN protocol asked participants to come for visits in the morning in the fasting state.

For physical measures, they wore hospital gowns and removed shoes. Primary predictor. The primary exposure was the number of months before or after the FMP at the time of the body composition, weight, or height measurement FMP time.

We computed FMP time using month and year of the FMP and month and year of each DXA or anthropometry. SWAN defined FMP date as the last menstrual bleeding date immediately prior to the first visit when the participant was postmenopausal.

The FMP date can only be identified after the woman has completed 12 months of amenorrhea. Other predictors. For each of these predictors, except for HT use, information was available for each participant at every visit. For HT, 56 values were missing, which represents 0.

Observations missing HT were excluded from the longitudinal models. Baseline normalization allows comparison of slopes rates of increase or decrease among outcomes because the units of slope are percent change per year.

Step 1 LOESS plots suggested piece-wise linear trajectories with 3 segments and 2 changes in slope, or knots, for all outcomes Figure 1. In steps 2 and 3, we used mixed effects linear regression to fit piece-wise linear growth curves to repeated measurements of baseline-normalized values of each of the 6 outcomes in separate models as functions of FMP time, using linear splines with 2 fixed knots.

See below for knot selection and for formal testing of whether the slopes in each of the postulated 3 segments were different from zero and different from each other. To account for within-woman correlation between repeated observations, we included random effects for the intercept and 3 slopes allowing intercept and slope to vary from woman to woman.

In step 2, we tested model adequacy and appropriateness of knot locations by running null models with only random effects and no fixed effects. We evaluated knot selection by examining the change in the explained proportion of within-woman variance pseudo R 2 when each of the 2 knots were varied in 6-month intervals around the candidate knot locations suggested by the LOESS plots.

For each of the 4 DXA outcomes, unexplained variance was minimized by knot locations at FMP minus 2 years and FMP plus 1. Knot locations at FMP minus 1 year and FMP plus 3 years minimized unexplained variance for weight and BMI. Thus, all outcome trajectories were modeled as being composed of 3 linear segments with knots anchored to the FMP date.

The model also adjusted for SWAN study site as a fixed effect on intercept and each of 3 slopes and time-varying HT use as fixed effect on intercept. We ran analyses in SAS version 9. Study approval. Each site obtained IRB approval and participants provided written informed consent.

Names and locations of IRBs follow. UCLA: The Office of the Human Research Protection Program OHRPP ; Kaiser: IRB for the Protection of Human Subjects Northern California Kaiser Permanente; Michigan: IRB-University of Michigan Health Sciences and Behavioral Sciences HSBS ; Pittsburgh: University of Pittsburgh IRB; Massachusetts General Hospital: Partners Human Research Committee.

Of course, you Body composition and hormonal health consult your medical provider before implementing any of Dry mouth suggestions! Insulin is hormonao crucial hormone comlosition your metabolism. It helps your body to Anti-obesity education digest and store glucose, a hexlth of hormonsl. However, Bodh insulin is such a Body composition and hormonal health player in your metabolism, this also means that having irregular insulin levels can negatively impact your body compositionnot to mention your health as a whole. If you are consistently eating a diet that is high in fat and sugar, your body needs to release more and more insuli n to bring sugar from your bloodstream into your cells for energy. Over time, these high insulin levels can make your cells less responsive to insulin, a state that is referred to as insulin resistance.