Association Between High Levels of Gynoid Fat and the Increase of Bone Mineral Density in Women

Climacteric

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Association between high levels of gynoid fat and the increase of bone mineral density in women

S. Aedo, J. E. Blümel, R. M. Carrillo-Larco, M. S. Vallejo, G. Aedo, G. G. Gómez & I. Campodónico

To cite this article: S. Aedo, J. E. Blümel, R. M. Carrillo-Larco, M. S. Vallejo, G. Aedo, G. G. Gómez & I. Campodónico (2019): Association between high levels of gynoid fat and the increase of bone mineral density in women, Climacteric, DOI: 10.1080/13697137.2019.1679112 To link to this article: https://doi.org/10.1080/13697137.2019.1679112

Published online: 18 Nov 2019.

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ORIGINAL ARTICLE Association between high levels of gynoid fat and the increase of bone mineral density in women

S. Aedoa ,J.E.Blumel€ b , R. M. Carrillo-Larcoc , M. S. Vallejod , G. Aedoe ,G.G.Gomez a and I. Campodonico d aEscuela de Medicina, Facultad de Medicina, Universidad Finis Terrae, Santiago, Chile; bDepartamento de Medicina Interna Sur, Facultad de Medicina, Universidad de Chile, Santiago, Chile; cDepartment of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, UK; dClınica Quilın, Facultad de Medicina, Universidad de Chile, Santiago, Chile; eDepartment of Obstetrics and Gynecology, Facultad de Medicina, Universidad de Chile, Santiago, Chile

ABSTRACT ARTICLE HISTORY Introduction: In women, bone mineral density (BMD) is related to age, estrogenic action, and appen- Received 6 May 2019 dicular skeletal muscle mass (ASMM). The gynoid fat distribution is linked to estrogenic action. Revised 29 August 2019 Objective: This study aimed to assess whether an increase of gynoid fat is associated with high BMD Accepted 2 October 2019 independent of age and ASMM. Published online 18 Novem- ber 2019 Methods: An observational study was performed in women aged between 20 and 79 years. Fat mass, ASMM, and BMD were measured with dual-energy X-ray absorptiometry. The binned scatterplots and KEYWORDS multivariate linear regression models were used to study the relationship between hip BMD and age, Body fat; gynoid fat; bone height, android fat, gynoid fat, and ASMM. mineral density; women Results: Of 673 women invited, 596 accepted to participate. Their mean age was 55.4 ± 12.8 years, weight 63.4 ± 9.4 kg, height 1.61 ± 0.06 m, body mass index 24.54 ± 3.59 kg/m2, average hip BMD 0.914 ± 0.122 g/cm2, android fat 2.12 ± 0.83 kg, gynoid fat 4.54 ± 1.07 kg, and ASMM 15.15 ± 1.97 kg. The final regression model included age (linear coefficient 0.004; 95% confidence interval [CI]: 0.005 to 0.003; p < 0.001), ASMM (linear coefficient 0.013; 95% CI: 0.009 to 0.018; p < 0.001), and gynoid fat (linear coefficient 0.013; 95% CI: 0.005 to 0.022; p < 0.002). Conclusion: Gynoid fat is associated with BMD in the hip independently of age and ASMM.

Introduction on the effect of the distribution of body fat on BMD1. This leaves room to complement the available evidence with new In women, bone mineral density (BMD) is related to age, studies from different ethnic and socio-demographic back- estrogenic action, and appendicular skeletal muscle mass grounds that distinguish between android and gynoid fat. (ASMM)1–5. Also, compared to men, women have higher per- The aim of this study is to assess whether an increase in centage body fat and deposit it in a different pattern, with gynoid fat is associated with high BMD independent of age relatively more adipose tissue in the hips and thighs. This and ASMM in women. ‘female’ fat distribution seems at least in part due to an estradiol-induced increase of anti-lipolytic a2-adrenergic receptors in gluteofemoral subcutaneous depots6. The gynoid fat distri- Methods and subjects bution is thus linked to estrogenic action and therefore an Study participants increase of gynoid fat should be associated with a high BMD independent of age and ASMM. In fact, gynoid fat was posi- This retrospective observational study included women aged tively associated with better trabecular bone in postmeno- 20–79 years. Participants, who attended a preventive health pausal Korean women7. In another study, conducted in control in Santiago, Chile between January 2015 and postmenopausal Caucasian women, gynoid fat correlated posi- October 2016, were randomly selected. tively with BMD8.Shaoet al., studying Chinese women, Exclusion criteria included: women who used medications pointed out that the reduction of the android-to-gynoid fat likely to have an effect on muscle mass or bone (e.g. levo- ratio during menopause may have vital clinical significance in thyroxine, corticosteroids, hormone replacement therapy, decreasing postmenopausal osteoporosis9. The Healthy and oral contraceptives); women who were on dedicated Postmenopausal Thai Women study showed that higher dietary or exercise programs; and women with non-commu- gynoid adiposity was associated with higher BMD10. nicable diseases, such as polycystic ovarian syndrome, and Notwithstanding the foregoing and given the cross-talk chronic pulmonary, cardiac, hepatic, or renal diseases. between gynoid fat and android fat, there is no consensus Furthermore, women with limb deformities and disorders

CONTACT J. E. Blumel€ [email protected] Departamento Medicina Sur, Facultad de Medicina, Universidad de Chile, Orquıdeas 1068, Dpto 302, PO BOX 7510258, Providencia, Santiago de Chile, Chile ß 2019 International Menopause Society 2 S. AEDO ET AL. of the nervous and musculoskeletal systems were A preliminary linear regression model was fitted including also excluded. age, height, android fat, gynoid fat, and ASMM as predic- tors14; the dependent variable (predicted or outcome) was average hip BMD. A stepwise backward-selection approach Instruments was followed to select the final predictors in the regression Questionnaires and clinical examination were conducted fol- model. The final model included predictors for which linear 16 lowing standardized procedures and by trained clinicians to coefficients showed p < 0.05 . measure: age (years), weight (kg), height (m), average hip The following information was estimated from the final BMD (includes the average BMD between the right and left model: log likelihood, Akaike Information Criterion, Bayesian 2 hips; g/cm2), android fat (kg), gynoid fat (kg), and Information Criterion, and adjusted-R . Also, for each pre- ASMM (kg). dictor in the final model, the linear coefficients and standar- Dual-energy X-ray absorptiometry (Lunar Corporation, dized linear coefficients were computed. In addition, the Madison, WI, USA) was performed on all study participants to adequate compliance of the following assumptions for ordin- measure whole and regional body composition. Percentage of ary least squares regression was assessed: normality of resid- fat mass, lean mass, and bone mineral content were measured. uals, homoscedasticity, multicollinearity, functional form 15,16 In addition, the BMD in both femoral necks was recorded. (linearity), model specification, and independence . The Determination of BMD in the vertebral spine was not used, to normality of residuals was evaluated with graphic methods avoid any interference caused by osteoarthritis in the spine. (p-norm, q-norm, and kernel density estimation on histo- The information retrieved included bone mineral content, min- gram) and tests for normality of a random variable based on 17–19 eral density, T-score, and Z-score, and was analyzed with the the quantile-mean covariance . The error variance con- software provided by the manufacturer (version 4.7e). We cal- stant (homoscedasticity) was assessed by plotting the resid- culated the appendicular lean mass as the sum of lean mass in uals versus fitted (predicted) values and the Breusch–Pagan the arms and legs11. Calibration for the measurement of BMD test (null hypothesis: variance of the residuals is homoge- 20 was performed using a phantom spine made of calcium neous) . The variance inflation factor for each predictor in 16 hydroxyapatite and embedded in a lucite block. Scans of the the final model was studied to avoid multicollinearity . phantom spine occurred every other day according to the When we estimate linear regressions, we assume that the manufacturer’s guidelines. The BMD values obtained from the relationship between the response variable and the predic- calibration were stable over the entire study period (mean tors is linear. The linearity assumption of the regression 0.991 g/cm2; coefficient of variation 0.08%). between the response and the predictors was established 21 The android and gynoid regions were defined using the through an augmented component-plus-residual plot .A software provided by the manufacturer. The ‘android region’ model specification error can occur when one or more rele- has a lower boundary at the pelvis cut and an upper bound- vant variables are omitted from the model or one or more ary above the pelvis cut at 20% of the distance between the irrelevant variables are included in the model. The link 22,23 24 pelvis and the neck cuts. The lateral boundaries are the arm test and the Ramsey reset test were using to assess cuts. The ‘gynoid region’ has an upper boundary between the specification error. Finally, the assumption of independ- the upper part of the greater trochanters and a lower ent errors was evaluated with the Durbin–Watson test and 25,26 boundary defined at a distance equal to twice the height of Durbin’s alternative test . the android region. The lateral boundaries are the outer leg 12 cuts . All scans were performed by the same operator while Ethical considerations the subjects were wearing light indoor clothing and no removable metal objects. The study was approved by the CEGEP (Centro de Estudios Ginecologicos y Preventivos, Santiago, Chile) and is in com- plete agreement with the Declaration of Helsinki. All patients Statistical analysis provided written informed consent. The analysis was conducted with Stata/SE 16.0 for Windows (StataCorp LLC, College Station, TX, USA). Quantitative varia- Results bles were described according to their distribution and the corresponding central tendency and dispersion estimates For this study, 673 women were invited, and 596 (88.5%) were computed13. accepted to participate. All variables were symmetrically dis- Binned scatterplots are a non-parametric method of plot- tributed. The mean ± standard deviation of the variables of ting the conditional expectation function (which describes interest was: age 55.36 ± 12.77 years, weight 63.36 ± 9.41 kg, the average dependent value for each independent value)14. height 1.61 ± 0.06 m, body mass index 24.54 ± 3.59 kg/m2,aver- Therefore, binned scatterplots inform about the functional age hip BMD 0.914 ± 0.122 g/cm2, android fat 2.12 ± 0.83 kg, form of variables and their standard errors. The binned scat- gynoid fat 4.54 ± 1.07 kg, and ASMM 15.15 ± 1.97 kg. terplots were used to study the relationship between aver- The binned scatterplots between average hip BMD and age hip BMD and the variables of age, height, weight, the variables of age, height, weight, android fat, gynoid fat, android fat, gynoid fat, and ASMM, and the corresponding and ASMM suggested that there is a linear relationship Pearson’s correlation coefficients were also computed15. among these variables (Figure 1). Pearson’s correlation CLIMACTERIC 3

Figure 1. Binned scatterplots between average hip bone mineral density and the variables of age, weight, height, android fat, gynoid fat, and appendicular skeletal muscle mass in 596 women who attended a preventive health control in Santiago, Chile, during January 2015 and October 2016. y ¼ predictor for simple linear regression using ordinary least squares. coefficients are presented in Table 1. Of note, the correlation 0.004; 95% confidence interval [CI]: 0.005 to 0.003; between average hip BMD and age, height, weight, android p < 0.001), ASMM (linear coefficient 0.013; 95% CI: 0.009 to fat, gynoid fat, and ASMM was highly significant (p < 0.01). 0.018; p < 0.001), and gynoid fat (linear coefficient 0.013; Weight is not suitable for incorporation into a linear 95% CI: 0.005 to 0.022; p ¼ 0.002). Android fat showed a very regression model15,16 because gynoid fat and android fat are close positive correlation with gynoid fat and was automatic- a linear combination for weight (Pearson coefficient >0.8). ally excluded from the final model. The linear coefficient of The stepwise backward-selection approach yielded a final the intercept in the final model was 0.880 (95% CI: 0.800 to linear regression model including age (linear coefficient 0.959; p < 0.01). The final model had the following 4 S. AEDO ET AL.

Table 1. Pearson’s correlation coefficient between average hip bone mineral factors not considered but mentioned in the study density and the variables of age, height, android fat, gynoid fat, appendicular limitations. skeletal muscle mass, and weight in 596 women who attended a preventive health control in Santiago, Chile, during January 2015 and October 2016. The final regression model indicates that ASMM is associ- Pearson’s correlation ated with better bone density, which could be explained by Variable coefficient the cross-talk between muscle and bone. Numerous studies Average hip bone mineral density support the concept of a bone–muscle unit, where constant a Age 0.46 cross-talk between the two tissues takes place, involving Height 0.11a Android fat 0.16a molecules released by the skeletal muscle secretome, which Gynoid fat 0.18a affect bone, and osteokines secreted by the osteoblasts and a Appendicular skeletal muscle mass 0.30 2 Weight 0.27a osteocytes, which, in turn, impact muscle cells . Also, the Age association between BMD and muscle can be influenced by Height 0.11a a estrogen, since the muscle has estrogen receptors that seem Android fat 0.17 3 Gynoid fat 0.01 to play a significant role in the apoptosis of muscle cells . Appendicular skeletal muscle mass 0.12a Another result is the negative effect of age on BMD. Weight 0.07 Aging progressively triggers greater cellular senescence, a Height 4 Android fat 0.03 process that is expressed in bone as osteoporosis . Estradiol Gynoid fat 0.19a attenuates this process by increasing osteogenic differenti- a Appendicular skeletal muscle mass 0.55 ation through promoting increased expression of SATB2, a Weight 0.30a Android fat protein that regulates cellular transcription and which is cru- Gynoid fat 0.74a cial for osteogenic differentiation of bone stem cells and a Appendicular skeletal muscle mass 0.28 therefore for bone quality5. Weight 0.85a Gynoid fat In the absence of detailed information about menopause Appendicular skeletal muscle mass 0.32a status, some epidemiologic studies consider age alone as a a Weight 0.82 crude proxy for menopause status27. In this study with age ap < 0.01. incorporated, aging and the menopausal status were linked to hypoestrogenism. Although several of the factors associated with low bone mass in women are linked to hypoestro- characteristics: log likelihood ¼ 506.82, Akaike Information genism, we cannot consider it the most relevant factor in Criterion ¼1005.64, Bayesian Information Criterion ¼ bone metabolism and in the risk of osteoporosis, since many 988.08, and adjusted-R2 ¼ 0.28. The standardized linear other conditions can modify bone mass. Proof of this is the coefficients for the final model were as follows: age ¼0.43, broad list of diseases that can cause secondary osteopor- ASMM ¼ 0.22, and gynoid fat ¼ 0.12. osis28. However, hypoestrogenism is another factor to con- The final regression model residuals showed a normal dis- sider when assessing the risks of osteoporosis in a woman, tribution on the plots: p-norm, q-norm, and kernel density and the important thing is that it is a modifiable factor and estimation on a histogram. Hypothesis Test based on the there is solid evidence that shows the positive results of quantile-mean covariance showed p-value of 0.1, which does menopausal hormone therapy in the prevention of osteopor- not reject the null hypothesis of normality. Plots of the final otic fractures29. model residuals versus fitted values did not show any pat- The principal result indicates that gynoid fat, independent terns. The Breusch–Pagan test yielded a p-value of 0.13. The of age and ASMM, predicts BMD of the hip. This result sup- variance inflation factor for each predictor in the final model ports gynoid fat being associated with better BMD, consist- 7–9 was age ¼ 1.02, ASMM ¼ 1.14, and gynoid fat ¼ 1.12. ent with the evidence observed . BMD has been described The condition number for all predictors in the final model as associated, inter alia, with ethnicity, sex, age, body pheno- 30 was 23.76. In the final model, the augmented component- type, estrogen, calcium, and lifestyle factors . The positive plus-residual plot showed a linear fit for each predictor. The association between gynoid fat and bone mineral density link test showed a correct specification for the final model. can be explained not only by the positive relationship The Ramsey reset test showed a p-value of 0.59, the between gynoid fat and estrogenic effect, but also other fac- Durbin–Watson test a p-value of 2.0002, and Durbin’s alter- tors related to greater gynoid fat such as lifestyle and phys- native test a p-value of 0.81. ical exercise. The results of this study have limitations due to the lack of incorporation of variables that could have influenced Discussion bone mass, such as physical activity, menopause status, age at menopause, smoking, alcohol consumption, parity, lifetime The results of univariate analysis (binned scatter plots and duration of lactation, level of physical activity, personal his- Pearson’s correlation coefficients) support that BMD is related tory of fracture, family history of fracture osteoporosis, vita- to age, gynoid fat, and ASMM. However, significant correl- min D status, and intake of supplements. This study is an ation was found between BMD of the hip and height and initial contribution to a causal model of the factors that android fat. This could be a product of confounding factors, affect BMD in women, and therefore further studies should among which are highlighted gynoid fat, ASMM, and other be performed to establish causal relations. CLIMACTERIC 5

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