European Journal of Internal Medicine 24 (2013) 824–831

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Original article

Regional fat distribution and cardiometabolic risk in healthy postmenopausal women Melpomeni Peppa a,⁎, Chrysi Koliaki a, Dimitrios I. Hadjidakis a, Efstathios Garoflos a, Athanasios Papaefstathiou a, Nicholas Katsilambros b, Sotirios A. Raptis c,d, George D. Dimitriadis c a

Endocrine Unit, Second Department of Internal Medicine-Propaedeutic, Research Institute and Diabetes Center, Attikon University Hospital, 1 Rimini Street, 12462 Haidari, Athens, Greece Experimental Research Laboratory “N.S. Christeas”, Athens University Medical School, 15B Agiou Thoma Street, 11527 Athens, Greece Second Department of Internal Medicine-Propaedeutic, Research Institute and Diabetes Center, Attikon University Hospital, 1 Rimini Street, 12462 Haidari, Athens, Greece d Hellenic National Diabetes Center for the Prevention, Research and Treatment of Diabetes Mellitus and its Complications (H.N.D.C.), 3 Ploutarchou Street, 10675 Athens, Greece b c

a r t i c l e

i n f o

Article history: Received 10 December 2012 Received in revised form 23 May 2013 Accepted 3 July 2013 Available online 26 October 2013 Keywords: Dual-energy X-ray absorptiometry Regional fat distribution Central adiposity Peripheral adiposity Cardiometabolic risk factors Postmenopausal women

a b s t r a c t Background: Regional fat distribution is an important determinant of cardiometabolic risk after menopause. The aim of the present study was to investigate the association between indices of fat distribution obtained by Dualenergy X-ray Absorptiometry (DXA) and representative cardiometabolic risk factors in a cohort of healthy postmenopausal women. Methods: In this cross-sectional study, cardiometabolic risk factors were correlated with a variety of central and peripheral fat depots obtained by DXA, in a total of 150 postmenopausal women, free of diabetes and cardiovascular disease (age 54 ± 7 years, BMI 29.6 ± 5.8 kg/m2, mean ± 1 SD). Results: After adjusting for age and total adiposity, DXA-derived indices of central and peripheral fat distribution displayed opposite associations (positive versus negative) with the examined cardiometabolic risk factors. In multivariate regression analysis, thoracic fat mass % was an independent predictor of blood pressure, HOMA index and triglycerides, abdominal fat mass % was an independent predictor of high sensitivity C-reactive protein, and abdominal-to-gluteofemoral fat ratio was an independent predictor of high density lipoprotein cholesterol. An index of peripheral fat distribution, gluteofemoral fat mass %, proved to be the most important determinant of metabolic syndrome (Odds Ratio 0.76, 95% confidence intervals 0.67–0.87, p b 0.001), independent of total and central adiposity. Conclusion: DXA-derived indices of regional fat distribution such as thoracic, abdominal and gluteofemoral fat, correlate significantly with cardiometabolic risk factors in healthy postmenopausal women, and may serve as clinically useful tools for evaluating cardiometabolic risk after menopause. © 2013 European Federation of Internal Medicine. Published by Elsevier B.V. All rights reserved.

1. Introduction Menopause is a condition in women's life, characterized by loss of protective estrogens and subsequent excess of androgens, leading to various metabolic abnormalities and increased cardiovascular risk [1–4]. Altered body composition during menopause transition, consisting of upper body fat accumulation, decreased peripheral fat deposition and ectopic fat storage, is considered the major mediator of menopause-related cardiometabolic morbidity and mortality [5]. In accordance with early clinical observations of Vague and Björntorp, a large body of evidence has demonstrated that central rather than peripheral adiposity is strongly associated with the adverse health consequences of obesity [6–8]. Abdominal, particularly visceral, adiposity is causally involved in the pathogenesis of metabolic syndrome [9], while the

⁎ Corresponding author at: Endocrine Unit, 2nd Department of Internal MedicinePropaedeutic, Research Institute and Diabetes Center, Attikon University Hospital, Athens University Medical School, 1 Rimini Street, 12462 Haidari, Greece. Tel.: +30 2105832525; fax: +30 2105326454. E-mail address: [email protected] (M. Peppa).

propensity to store fat subcutaneously in the gluteal and femoral regions appears to be cardioprotective [10]. As a result, the detailed evaluation of regional fat distribution in postmenopausal women – regardless of their overall adiposity – is considered an important screening tool, in order to discriminate between high risk and low risk phenotypes of fat distribution [5]. Several methods can be used to assess total and regional adiposity in postmenopausal women [11]. Anthropometric measurements such as Body Mass Index (BMI), waist circumference, waist-to-hip ratio, waist-to-thigh ratio and skinfold thickness, have been widely used in the epidemiological setting, because they are simple, quick and inexpensive. However, they cannot accurately quantify or localize fat mass, and they are also subject to considerable intra- and inter-observer variability [11,12]. Direct measurements of regional fat mass can be accurately performed with computed tomography (CT) and magnetic resonance imaging (MRI), which are currently considered as “gold standard” methods [13,14]. However, their routine use in clinical research and practice is limited because of inaccessibility to equipment, relatively high cost and exposure to ionizing radiation particularly for CT. Dual energy X-ray absorptiometry (DXA) represents an alternative method of

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estimating both total and regional fat masses, and its use has important advantages over both CT/MRI and anthropometry [15,16]. Compared to CT and MRI, the major strengths of DXA include better feasibility, lower cost and minimal radiation exposure, while relative to anthropometric methods, DXA is significantly superior in terms of accuracy and reproducibility. In addition, DXA software provides the Regions of Interest (ROI) program, which allows the operator to determine manually several anatomic subregions and obtain accurate estimates of total fat mass localized in these subregions. This enables a thorough assessment of regional fat distribution in the study population and provides useful information about several fat depots. Although many studies have used DXA to assess fat distribution in postmenopausal women [11,17], none of them have taken so far full advantage of the method's potential to investigate simultaneously several upper and lower body fat depots by more than one manually-defined subregions. The aim of this cross-sectional study was to introduce practical DXAderived indices of regional fat distribution in a large number of Caucasian obese and non-obese postmenopausal women, and investigate their association with representative cardiometabolic risk factors such as hypertension, insulin resistance, dyslipidemia and subclinical inflammation, as well as the presence of metabolic syndrome.

blood samples were collected for the biochemical measurements. Fasting plasma glucose was measured with an automated analyzer of spectrophotometry, using the method of glucose oxidase (BT T.A.R.G.A. 3000 Plus, Biotecnica Instruments S.p.A., Rome, Italy), while fasting plasma insulin was measured with the immunoradiometric assay (Wallac Wizard automatic gamma counter). HOMA index was estimated from the formula [fasting glucose (mmol/l) × fasting insulin (μIU/ml)] / 22.5 [21]. Serum urea, creatinine, uric acid, total, LDL and HDL cholesterol, triglycerides and liver transaminases were measured with standard enzymatic colorimetric techniques. Serum levels of hs-CRP were assessed with immunonephelometry. The definition of metabolic syndrome (MS) was based on USA National Cholesterol Education Program's Adult Treatment Panel III criteria (NCEP-ATP III), as revised by the American Heart Association, requiring at least three of the following [22]: elevated waist circumference (N88 cm for women), hypertriglyceridemia (≥150 mg/dl or 1.7 mmol/l), low HDL cholesterol (b50 mg/dl or 1.3 mmol/l for women), high blood pressure (≥130/85 mm Hg) and high fasting glucose levels (≥110 mg/dl, or 6.1 mmol/l).

2. Materials and methods

Whole-body and regional body composition was assessed with DXA (QDR bone densitometer with fan-beam technology, software version for Windows XP 12.3, Hologic Discovery-W, Bedford, MA, USA). Body composition scans were performed in a supine position, with an average time of measurement approximately 7 min. To ensure proper function of QDR system, daily quality control procedures were performed and a step phantom calibration was performed on a weekly basis. The present study has used DXA program of subregions, which allows a manual determination of specific regions of interest based on concrete anatomic landmarks. As illustrated in Fig. 1, the four subregions evaluated in this study were determined as following: the thoracic subregion was defined as a quadrilateral area extending from the sternal end of the clavicles to the lower edge of T12 thoracic vertebra (R1). The abdominal subregion was defined as a quadrilateral area extending from the upper edge of L2 lumbar vertebra to the horizontal pelvis line (top of the pelvis) (R2). The gluteofemoral subregion was defined as a quadrilateral area extending from the horizontal pelvis line to the level of the knee joints (R3), and finally, the femoral subregion was defined as a quadrilateral area extending from the inferior border of the ischial tuberosity to the level of the knee joints (R4). For all subregions, the lateral edges followed the lateral boundaries of subcutaneous tissue. All subregions were delineated by the same trained researcher. In a subgroup of the study population (n = 15), detailed instructions were given to a second researcher, who determined again the same subregions so that an inter-observer reproducibility could be calculated. Based on the above subregions and the conventional indices provided by DXA analysis, we assessed central fat distribution with the following five indices:

2.1. Subjects We studied a total of 150 healthy postmenopausal women who visited consecutively the Endocrine Clinic of our hospital for their annual osteoporosis screening between September 2009 and May 2011. The postmenopausal state was defined by reporting more than a year since the last menstruation and having follicle stimulating hormone levels above 30 U/l. All women were euthyroid, non-smokers and free of known diabetes and cardiovascular disease. Furthermore, they were not using any type of hormone replacement treatment (HRT), and they were not treated with medications that could potentially interfere with body composition or cardiometabolic parameters (statins, antihypertensive agents, corticosteroids, antidepressants). All women reported a relatively stable body weight (±5%) within the previous 6 months. The study protocol was approved by our institutional Biomedical Research Ethics Committee, and a written informed consent was obtained from all participants before the initiation of the study. 2.2. Anthropometric and biochemical measurements All anthropometric variables were measured in duplicate by the same trained physician, after women had removed their shoes and heavy clothing. Body weight was measured to the nearest 0.1 kg on a calibrated electronic scale, and height was measured using a standard wallmounted stadiometer at the nearest 0.5 cm. BMI was calculated as body weight in kilograms divided by height in meters squared (kg/m2). Women with BMI N 27 were defined as obese (OB) and those with BMI ≤ 27 were defined as non-obese (NO), since the cut-off point of 27 has been proposed as an appropriate threshold for identifying obesity among white postmenopausal women [18,19]. Waist, hip, mid-thigh and mid-arm circumferences were obtained using a flexible steel metric tape according to standard procedures. The present study is primarily focused on five prespecified cardiometabolic risk markers: mean blood pressure (MBP), Homeostasis Model Assessment Index for Insulin Resistance (HOMA index), high density lipoprotein cholesterol levels (HDL), triglycerides and high sensitivity C-reactive protein (hs-CRP). Systolic and diastolic blood pressures were measured with an aneroid sphygmomanometer in women resting for at least 10 min in a sitting position, and the average of three consecutive measurements at 5-min intervals was used as final values. MBP was estimated from the formula MBP = diastolic + 1/3 × (systolic − diastolic) [20]. After an overnight fast of 12 h, venous

2.3. DXA-derived indices of regional adiposity

(i) Thoracic Fat Mass % (ThFM%), reflecting fat deposition in the thoracic region, relative to total fat mass (ii) Abdominal Fat Mass % (AbFM%), reflecting fat deposition in the abdominal region, relative to total fat mass (iii) Trunk Fat Mass % (TrFM%), reflecting fat deposition in the overall region of chest, abdomen and pelvis, relative to total fat mass (iv) Abdominal-to-Gluteofemoral Fat Ratio (AbFM/GFM), derived by dividing AbFM to gluteofemoral fat mass (GFM) (v) Trunk-to-Legs Fat Ratio (TrFM/LFM), derived by dividing TrFM to fat mass of the legs (LFM). Accordingly, peripheral fat distribution was assessed with the following indices:

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(a)

(b)

sternal end of the clavicles

T12

L2 upper iliac crest

inferior border of ischial tuberosity knee joints

Fig. 1. (a) The anatomic landmarks for defining the thoracic (R1), abdominal (R2), gluteofemoral (R3) and femoral (R4) region by DXA analysis. (b) Print out of a DXA body composition scan displaying the selected four subregions. R1 (thoracic): sternal end of the clavicles–T12 (thoracic) vertebra. R2 (abdominal): L2 (lumbar) vertebra–upper iliac crest. R3 (gluteofemoral): upper iliac crest–knee joints. R4 (femoral): inferior border of ischial tuberosity–knee joints.

(i) Arm Fat Mass % (ArFM%), reflecting fat deposition in the upper extremities, relative to total fat mass (ii) Leg Fat Mass % (LFM%), reflecting fat deposition in the lower extremities, relative to total fat mass (iii) Femoral Fat Mass % (FFM%), reflecting fat deposition in the femoral region, relative to total fat mass (iv) Gluteofemoral Fat Mass % (GFM%), reflecting fat deposition in the gluteofemoral region, relative to total fat mass. In the present study, regional fat indices were normalized to total body fat mass, in order to take into account the parameter of lean body mass, which could potentially exert an effect on cardiometabolic risk factors through mechanisms of sarcopenia. The intra-observer reproducibility for the above mentioned subregions, expressed as coefficient of variation (CV), was 2.1% for ThFM, 3.5% for AbFM, 0.4% for GFM and 1% for FFM (n = 30). The interobserver reproducibility for the same subregions was 6, 5.2, 1.2 and 2.1%, respectively (n = 15). The in vivo reproducibility of DXA method was assessed by performing duplicate body composition scans with 1min interval in 10 subjects after repositioning. The estimated CVs for total fat mass (FM), TrFM and LFM were 1.6, 1.2 and 2.8%, respectively.

as mean values ± standard deviation, skewed data are presented as median values and interquartile ranges (25th–75th percentile), and categorical data are given as absolute and relative frequencies. The descriptive characteristics of NO and OB subgroups were compared with t-test for independent samples or Mann–Whitney U test for quantitative variables, and Chi-square test for categorical data. Correlations between anthropometric and DXA indices of fat distribution were examined with Pearson bivariate correlation. Partial correlation analyses were used to examine the associations of DXA indices with cardiometabolic variables after controlling for confounders such as age and total FM%. The comparison of DXA indices between women with and without MS, was performed with univariate Analysis of Variance (ANOVA), after adjusting for age and total FM%, and F-values served as measures of effect size. Stepwise multivariate linear and logistic regression analysis was applied to examine the independent associations of DXA indices of fat distribution with cardiometabolic variables and the presence of MS, respectively. It was assumed that the stability of the regression models was not significantly disturbed by multicollinearity, if tolerance value was ≥0.1 or the variance inflation factor was ≤10. P-values (twotailed) were considered statistically significant at the level of 0.05. The statistical analysis of the data was performed using the SPSS Statistical Package (version 19.0, SPSS, Chicago, USA).

2.4. Statistical analysis 3. Results All numerical data were assessed for normality of their distribution with the non-parametric one-sample Kolmogorov–Smirnov test. All DXA indices of regional fat distribution and the examined cardiometabolic variables were normally distributed, except for hs-CRP for which logarithmically transformed values were used in all analyses. Quantitative variables which were normally distributed are expressed

Table 1 presents the clinical and biochemical characteristics of the study participants according to their obesity status. Based on average values of BMI and waist circumference in the total cohort, our study population was mainly consisted of overweight–obese postmenopausal women (n = 90, 60%) with a relatively large waist circumference. The

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Table 1 Clinical and biochemical characteristics of the studied subjects according to their obesity status. Descriptive variables

Total cohort (n = 150)

NO (n = 60)

OB (n = 90)

p-Value (NO vs. OB)

Clinical data Age (years) Duration of menopausea (years) Body mass index (kg/m2) Waist circumference (cm) Waist-to-hip ratio (WHR) Mid-arm circumference (cm) Mid-thigh circumference (cm) MBP (mm Hg)

54 ± 7 4 [1–9] 29.6 ± 5.8 93 ± 12 0.88 ± 0.06 29 ± 4 52 ± 6 93 ± 11

53 ± 7 3 [1–8] 24 ± 2 83 ± 7 0.86 ± 0.06 26 ± 2 48 ± 3 88 ± 9

55 ± 6 4 [1–9] 33 ± 5 100 ± 10 0.9 ± 0.06 31 ± 3 54 ± 6 96 ± 11

0.08 0.4 b0.001 b0.001 b0.001 b0.001 b0.001 b0.001

Laboratory data Urea (mg/dl) Creatinine (mg/dl) Uric Acid (mg/dl) FPG (mg/dl) FPI (μIU/ml) HOMA index Total cholesterol (mg/dl) LDL cholesterol (mg/dl) HDL cholesterol (mg/dl) Triglycerides (mg/dl) SGOT (U/l) SGPT (U/l)a γ-GT (U/l)a Hs-CRPa (mg/l) Metabolic syndrome, n (%)

33 ± 8 0.87 ± 0.1 4.4 ± 1 97 ± 13 11 ± 5 2.6 ± 1.2 226 ± 38 148 ± 34 63 ± 14 101 ± 42 19 ± 6 18 [15–24] 15 [13–20] 2.9 [1.1–4.2] 39 (26)

32 ± 9 0.88 ± 0.1 3.9 ± 0.9 95 ± 12 8.9 ± 4 2.1 ± 0.9 221 ± 40 145 ± 36 69 ± 15 95 ± 49 19 ± 7 16 [12–20] 14 [12–17] 1.5 [0.5–2.97] 4 (6.7)

34 ± 8 0.87 ± 0.1 4.7 ± 1 99 ± 13 12 ± 5 2.9 ± 1.3 235 ± 35 151 ± 31 59 ± 13 104 ± 37 19 ± 6 20 [17–28] 16 [13–22] 3.2 [1.7–5.6] 35 (38.9)

0.2 0.7 b0.001 0.1 b0.001 b0.001 0.03 0.4 b0.001 0.2 0.9 b0.001 0.003 b0.001 b0.001

Data are presented as mean values ± standard deviation (SD), unless otherwise indicated. NO: Non-obese; OB: Obese; MBP: Mean Blood Pressure; FPG: Fasting Plasma Glucose; FPI: Fasting Plasma Insulin; HOMA: Homeostasis Model Assessment for Insulin Resistance; LDL: Low Density Lipoprotein; HDL: High Density Lipoprotein; SGOT: aspartate aminotransferase; SGPT: alanine aminotransferase; γGT: gamma glutamyltransferase; hs-CRP: high sensitivity Creactive protein. a Since data about duration of menopause, SGPT, γGT and hs-CRP were not normally distributed, they are presented as median values plus their interquartile range [25th–75th percentile].

OB women of our study displayed higher anthropometric indices of total and regional adiposity as well as a higher prevalence of MS abnormalities than NO women. 3.1. Correlations between anthropometric and DXA indices of fat distribution Total FM measured by DXA was highly correlated with BMI (r = 0.95, p b 0.001). All DXA-derived indices of central fat distribution were strongly correlated with anthropometric indices of central adiposity (r from 0.43 to 0.92, p b 0.001). The strongest correlate of waist circumference was TrFM (r = 0.92, p b 0.001), and the strongest correlate of WHR was TrFM/LFM ratio (r = 0.71, p b 0.001). Accordingly, all DXA-derived indices of peripheral fat distribution were highly correlated with anthropometric indices of peripheral adiposity (r from 0.65 to 0.93, p b 0.001). Among these indices, the strongest correlate of hip circumference was GFM (r = 0.93, p b 0.001), of mid-thigh circumference FFM (r = 0.92, p b 0.001), and of mid-arm circumference ArFM (r = 0.86, p b 0.001). 3.2. Correlation of DXA indices of fat distribution with cardiometabolic risk factors Table 2 provides the results of partial correlation analysis between DXA-derived indices of central and peripheral fat distribution and selected cardiometabolic variables, after controlling for age and total FM%. All examined indices of central adiposity were significantly correlated with MBP (r from 0.16 to 0.21, p ≤ 0.05), HOMA index (r from 0.34 to 0.39, p ≤ 0.001), HDL cholesterol (r from −0.22 to −0.35, p ≤ 0.01), and triglyceride levels (r from 0.29 to 0.38, p ≤ 0.001), independent of age and total adiposity. ThFM% displayed the strongest correlation with HOMA index (r = 0.39, p b 0.001), while AbFM% displayed the strongest correlation with hs-CRP (r = 0.25, p b 0.01). In addition, TrFM/

LFM ratio showed significant correlations with all cardiometabolic variables (p b 0.05), while AbFM/GFM ratio had the strongest inverse correlation with HDL cholesterol (r = −0.35, p b 0.001). Both ratios displayed stronger correlations with cardiometabolic variables than TrFM% or AbFM% alone. The correlations between central fat indices and cardiometabolic parameters were attenuated but remained statistically significant, when an additional partial correlation was performed controlling for peripheral fat (data not shown). Contrary to indices of central fat distribution, most indices of peripheral fat distribution displayed significant negative correlations with cardiometabolic risk factors, independent of total adiposity (Table 2). More specifically, LFM%, FFM% and GFM% were negatively correlated with HOMA index (r from −0.32 to −0.37, p b 0.001) and triglyceride levels (r from −0.32 to −0.37, p b 0.001), and positively correlated with HDL cholesterol (r from 0.24 to 0.29, p ≤ 0.01). ArFM% was not correlated with any cardiometabolic variable (p N 0.05), independent of total FM%. No significant correlations were found between indices of peripheral adiposity and hs-CRP levels after adjusting for total adiposity. After controlling for central fat, all correlations of peripheral fat indices with cardiometabolic variables lost their statistical significance (data not shown). When the above correlations were performed separately in NO and OB women of our study, similar patterns were observed, but the associations were more pronounced in the OB group. However, even among NO women, there were significant correlations of DXA indices with cardiometabolic variables especially with triglyceride levels (Table 2). 3.3. Association of DXA indices of fat distribution with MS Regarding the prevalence of MS components in our study population, nearly 67% of studied women had an abnormal waist circumference, 39.3% had increased blood pressure, 41.8% had increased fasting glucose, 12.8% had elevated triglycerides and 18.7% had low HDL

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Table 2 Correlation of DXA indices of fat distribution with cardiometabolic variables, after controlling for age and total FM%. DXA indices of fat distribution Central fat ThFM% AbFM% TrFM% AbFM/GFM TrFM/LFM Peripheral fat ArFM% LFM% FFM% GFM%

MBP

HOMA

0.21⁎⁎ (0.06/0.29⁎⁎) 0.16⁎⁎⁎ (0.06/0.16) 0.18⁎⁎⁎ (0.07/0.21⁎⁎⁎) 0.18⁎⁎⁎ (0.1/0.18) 0.19⁎⁎⁎ (0.08/0.21⁎⁎⁎)

0.09 (−0.001/0.14) −0.18⁎⁎⁎ (−0.05/−0.22⁎⁎⁎) −0.12 (−0.1/−0.12) −0.15 (−0.1/−0.15)

HDL

Triglycerides

0.39⁎ (0.26/0.42⁎) 0.34⁎ (0.3⁎⁎⁎/0.29⁎⁎) 0.35⁎ (0.26/0.35⁎) 0.38⁎ (0.26/0.39⁎) 0.38⁎ (0.25/0.4⁎)

−0.22⁎⁎ (−0.12/−0.2) −0.32⁎ (−0.21/−0.26⁎⁎) −0.28⁎ (−0.16/−0.27⁎⁎) −0.35⁎ (−0.2/−0.33⁎⁎) −0.27⁎ (−0.13/−0.27⁎⁎)

0.16 (0.05/0.19) −0.37⁎ (−0.26/−0.37⁎) −0.32⁎ (−0.23/−0.33⁎⁎) −0.35⁎ (−0.13/−0.41⁎)

−0.11 (0.001/−0.18) 0.28⁎ (0.14/0.28⁎⁎) 0.24⁎⁎ (0.12/0.28⁎⁎) 0.29⁎ (0.15/0.33⁎⁎)

Hs-CRP

0.38⁎ (0.44⁎/0.35⁎) 0.29⁎ (0.32⁎⁎⁎/0.3⁎⁎) 0.35⁎ (0.36⁎⁎/0.36⁎) 0.33⁎ (0.35⁎⁎/0.35⁎) 0.34⁎ (0.34⁎⁎/0.37⁎)

0.08 (0.31⁎⁎⁎/0.07) 0.25⁎⁎ (0.37⁎⁎/0.24⁎⁎⁎) 0.17⁎⁎⁎ (0.29⁎⁎⁎/0.19) 0.21⁎⁎⁎ (0.31⁎⁎⁎/0.21⁎⁎⁎) 0.19⁎⁎⁎ (0.25/0.23⁎⁎⁎)

0.15 (0.12/0.17) −0.37⁎ (−0.37⁎⁎/−0.38⁎) −0.32⁎ (−0.33⁎⁎/−0.31⁎⁎) −0.32⁎ (−0.32⁎⁎⁎/−0.33⁎⁎)

−0.07 (−0.17/0.01) −0.13 (−0.2/−0.18) −0.08 (−0.19/−0.12) −0.01 (−0.1/−0.06)

Data are presented as partial correlation coefficients for the total study population. Separate coefficients for NO and OB subgroups are provided within the parentheses (NO/OB). For skewed data such as hs-CRP, partial correlations were performed with its logarithmically transformed values. MBP: Mean Blood Pressure; HOMA: Homeostasis Model Assessment for Insulin Resistance; HDL: High Density Lipoprotein; hs-CRP: high-sensitivity C-reactive protein. ThFM: Thoracic Fat Mass; AbFM: Abdominal Fat Mass; TrFM: Trunk Fat Mass; AbFM/GFM: Abdominal-to-Gluteofemoral Fat Ratio; TrFM/LFM: Trunk-to-Legs Fat Ratio; ArFM: Arm Fat Mass; LFM: Leg Fat Mass; FFM: Femoral Fat Mass; GFM: Gluteofemoral Fat Mass; FM: Fat Mass; NO: Non-obese; OB: Obese. ⁎ p ≤ 0.001. ⁎⁎ p ≤ 0.01. ⁎⁎⁎ p ≤ 0.05.

cholesterol levels. The criteria for MS were met by a total of 39 women, 4 NO and 35 OB. As shown in Table 3, the comparison of DXA indices between women with and without MS after adjusting for age and total FM%, revealed that women with MS displayed significantly higher ThFM%, AbFM%, TrFM%, TrFM/LFM and AbFM/GFM ratios, and significantly lower LFM%, FFM% and GFM%, compared to women without MS. Based on F-values, TrFM/LFM ratio and GFM% were the indices of central and peripheral fat distribution respectively, displaying the most significant differences between positive and negative women for MS. ArFM%, despite being a peripheral fat depot, was positively associated with the presence of MS. A strong positive correlation was also observed between absolute ArFM and indices of central adiposity such as TrFM (r = 0.85), AbFM (r = 0.83) and ThFM (r = 0.89) (p b 0.001 for all).

which had the strongest correlation with each cardiometabolic variable and the presence of MS, after adjusting for total FM%. As shown in Table 4, ThFM% was an independent predictor of MBP, HOMA index and triglycerides, AbFM% was an independent predictor of hs-CRP, and AbFM/GFM ratio was an independent predictor of HDL cholesterol.

Table 4 Stepwise multivariate regression analysis for the association of DXA indices with cardiometabolic variables and the presence of MS. R2 of the model (adjusted)

Independent Variables Dependent variable: MBPa FM% ThFM% Age (years)

0.22

Standardized beta coefficient

p-Value

0.239 0.226 0.192

0.005 0.008 0.01

0.467

b0.001

Dependent variable: HDL cholesterol AbFM/GFM ratio 0.15

−0.388

b0.001

Dependent variable: Triglyceridesd ThFM% 0.16

0.404

b0.001

0.002 0.002

3.4. Multivariate regression models The results of stepwise multivariate regression for the association between DXA parameters and cardiometabolic variables as well as MS, are presented in Table 4. The variables included in the models were age, total FM% and DXA indices of central and peripheral fat distribution,

Dependent variable: HOMA indexb ThFM% 0.21 c

e

Table 3 Comparison of DXA indices of central and peripheral fat distribution between women with and without MS, after adjusting for age and total FM%. DXA Indices

MS (+) N = 39

MS (−) N = 111

p-Value

F-value

Central fat indices ThFM% AbFM% TrFM% AbFM/GFM ratio TrFM/LFM ratio

21.5 ± 0.6 10.5 ± 0.3 47.9 ± 0.8 0.26 ± 0.009 1.41 ± 0.05

19.0 9.5 44.4 0.21 1.15

± 0.3 ± 0.2 ± 0.5 ± 0.005 ± 0.03

b0.001 0.003 b0.001 b0.001 b0.001

14.8 9.3 13.7 17.2 19.1

Peripheral fat indices ArFM% LFM% FFM% GFM%

13.0 ± 0.3 35.8 ± 0.9 21.2 ± 0.5 42.0 ± 0.6

12.0 40.1 23.7 45.2

± 0.2 ± 0.5 ± 0.3 ± 0.4

0.01 b0.001 b0.001 b0.001

6.6 16.9 16.2 18.5

Data are presented as mean values ± standard error of the mean (SEM). Age and total FM% have been used as covariates. MS: Metabolic Syndrome; ThFM: Thoracic Fat Mass; AbFM: Abdominal Fat Mass; TrFM: Trunk Fat Mass; AbFM/GFM: Abdominal-to-Gluteofemoral Fat Ratio; TrFM/LFM: Trunkto-Legs Fat Ratio; ArFM: Arm Fat Mass; LFM: Leg Fat Mass; FFM: Femoral Fat Mass; GFM: Gluteofemoral Fat Mass; FM: Fat Mass.

Dependent variable: hs-CRP FM% AbFM%

0.2

0.273 0.266

Dependent variable: MSf

Odds ratio (OR)

95% Confidence intervals

FM% GFM%

1.17 0.76

1.1–1.3 0.7–0.9

p-Value 0.001 b0.001

Since hs-CRP was not normally distributed, its logarithmically transformed value was used as the dependent variable. MBP: Mean Blood Pressure; HOMA: Homeostasis Model Assessment for Insulin Resistance; HDL: High Density Lipoprotein; hs-CRP: high-sensitivity C-reactive protein; MS: Metabolic Syndrome. ThFM: Thoracic Fat Mass; AbFM: Abdominal Fat Mass; AbFM/GFM: Abdominal-toGluteofemoral Fat Ratio; TrFM/LFM: Trunk-to-Legs Fat Ratio; LFM: Leg Fat Mass; GFM: Gluteofemoral Fat Mass; FM: Fat Mass. a Variables included in the model were age, total FM%, ThFM% and LFM%. b Variables included in the model were age, total FM%, ThFM% and LFM%. c Variables included in the model were age, total FM%, AbFM/GFM ratio and GFM%. d Variables included in the model were age, total FM%, ThFM% and LFM%. e Variables included in the model were age, total FM% and AbFM%. f Variables included in the model were age, total FM%, ThFM%, TrFM/LFM ratio and GFM%.

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When indices of total, central and peripheral adiposity were simultaneously included in multivariate regression for predicting MS, an index of peripheral fat distribution, GFM%, was the most important determinant of MS in our study population (p b 0.001). In order to test whether models with regional fat indices are superior to models with BMI in terms of predicting cardiometabolic risk factors, all the above regression analyses were repeated using models with age and BMI, as independent variables. Compared to models with DXA regional fat indices, models with BMI could explain a greater percentage of the variance in MBP and hs-CRP (R2 = 0.28 and 0.30 compared to 0.22 and 0.20, respectively), but they explained a far smaller percentage of variance in HOMA index and HDL cholesterol (R2 = 0.14 and 0.05 compared to 0.21 and 0.15, respectively), while they could not explain any of the variance in triglyceride levels at all. 4. Discussion Our data in a considerable number of healthy postmenopausal women have shown that independent of total adiposity, DXA-derived indices of central fat distribution displayed significant positive correlations with markers of hypertension, insulin resistance, dyslipidemia and inflammation, as well as with the presence of MS as a clustering of cardiometabolic risk factors. On the contrary, DXA-derived indices of peripheral adiposity exhibited significant negative correlations with cardiometabolic risk factors and MS. Although the observed associations were more pronounced in OB women, these were also evident in the NO group. These findings indicate a strong relationship between regional adiposity and cardiometabolic risk in postmenopausal women, independent of their body weight. Considering that the majority of postmenopausal women are advised to undergo annual DXA scans for measuring their bone mineral density [23], it seems convenient to combine the annual osteoporosis monitoring with an additional evaluation of total and regional adiposity. The most common indices of DXA to assess central fat distribution are TrFM and AbFM, which are both associated with increased cardiometabolic risk, independent of total adiposity [24,25]. More specifically in postmenopausal women, TrFM and AbFM have been proposed as strong and independent predictors of insulin resistance, dyslipidemia and many other cardiometabolic abnormalities [26,27]. TrFM incorporates the total amount of fat, which is localized in the thoracic, abdominal and pelvic regions of the body, without differentiating between visceral and subcutaneous depots. Similar to TrFM, AbFM represents both intra-abdominal and subcutaneous abdominal fat, and can be estimated by using several methods [15,28,29]. In our study, we defined the abdominal region as a quadrilateral area extending between L2 lumbar vertebra and the upper part of the pelvis, since the horizontal pelvis line is a prominent anatomic landmark, which ensures adequate reproducibility. In the present study, we divided the overall region of trunk into thoracic and abdominal subregions, which reflect upper and lower trunks respectively. In line with previous observations of our group [30], we found that this discrimination is clinically meaningful, because fat deposition in the thoracic and abdominal compartments correlated better with specific cardiometabolic parameters compared to TrFM. More specifically, AbFM% displayed the strongest correlation with hs-CRP levels independent of total FM%, while ThFM% was the strongest independent correlate of blood pressure, insulin resistance and triglycerides among all examined indices of central adiposity. To our knowledge, our study is the first to suggest the measurement of ThFM% as part of a detailed assessment of central adiposity in healthy postmenopausal women. Although it has been suggested that DXA estimates of thoracic composition may not be precise enough due to the presence of ribs within this anatomical region [31], the present study has shown that ThFM% displayed much more significant correlations with cardiometabolic variables, compared to the widely used TrFM% or AbFM%. Although the exact nature and composition of ThFM remain unclear, it becomes

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evident from our study that ThFM constitutes a metabolically distinct upper body fat depot, which is adversely associated with several cardiometabolic risk factors in healthy postmenopausal women, and thus it merits further investigation. This study has also evaluated two ratios of central-to-peripheral fat distribution, namely TrFM/LFM and AbFM/GFM. These ratios were found to correlate better with cardiometabolic variables compared to TrFM% or AbFM% alone. Furthermore, it was shown that TrFM/LFM ratio had the strongest association with MS among all central fat indices, while AbFM/GFM ratio was an independent predictor of HDL cholesterol. These findings are consistent with previous studies, suggesting that TrFM/LFM and other similarly defined centrality indices such as android/gynoid fat ratio are more accurate predictors of cardiovascular risk factors than central indices alone unadjusted for peripheral adiposity [32–36]. As far as peripheral fat distribution is concerned, most studies in postmenopausal women have used LFM measured by DXA, or thigh subcutaneous adipose tissue assessed by CT, to assess peripheral adiposity, and they have demonstrated significant favorable correlations with insulin resistance, glucose homeostasis, lipid parameters and markers of atherosclerosis, independent of total FM [6,10,24,26,37]. Considering that the beneficial metabolic effects of peripheral fat have been mainly documented for subcutaneous fat located in the region of hips and thighs, we provided for the first time an alternative and anatomically more precise index of peripheral adiposity, namely GFM%, based on clear-cut anatomic landmarks such as the iliac crest and the knee joints. In the present study, we were able to reproduce for GFM the same favorable associations with cardiometabolic risk factors as those observed by other investigators for LFM. In addition, we found that GFM% was even better associated with MS, compared to the conventional index LFM%. We have also investigated the relationship between fat mass in the upper extremities (ArFM%) and cardiometabolic risk factors. Despite being a peripheral fat depot, ArFM% was positively associated with the presence of MS, independent of total FM%. The strong positive correlations of ArFM with central fat indices in our study may suggest that increased central fat accumulation could mediate the positive relationship between ArFM and cardiometabolic risk in postmenopausal women. Our findings agree with a previous study, reporting a negative effect of ArFM on cardiometabolic variables, mediated by central fat distribution [26]. In pathophysiological terms, it has been suggested that upper body non-visceral fat depots, such as ArFM and ThFM described in our study, are composed of large and lipolytically active fat cells, which contribute the majority of free fatty acid flux to the circulation and thus promote systemic insulin resistance to a greater extent than visceral fat depots, which are mainly involved in hepatic insulin resistance [38]. Based on this rationale, we support that ArFM may actually reflect a central rather than peripheral pattern of adiposity in terms of its cardiometabolic implications, due to its upper body localization and high basal lipolytic activity. Our study showed that DXA indices of central and peripheral fat distribution display opposite patterns of associations with cardiometabolic risk factors, confirming existing evidence. A large number of studies pertaining to postmenopausal women have shown contrasting effects of central and peripheral fat on the progression of atherosclerosis [6], arterial stiffness [24], plasma lipase activities [39], insulin resistance and lipids [10,26]. Some data also suggest that peripheral fat is a more significant determinant of cardiometabolic risk in postmenopausal women [6] than central fat. This notion was confirmed by our finding that only peripheral fat distribution, expressed as GFM%, was an important and independent determinant of MS in the multivariate model for predicting MS. To the best of our knowledge, this is the first study in which a comprehensive evaluation of regional fat distribution is performed, in a relatively large group of healthy postmenopausal women, by means of multiple DXA-derived fat depots. Using a great variety of easy-toobtain and reproducible indices of central and peripheral fat distribution,

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our study correlated regional adiposity with representative markers of cardiometabolic risk, and attempted to identify the most informative DXA parameters. However, certain limitations should be acknowledged. First, cross-sectional correlations do not prove causality. In addition, an important consideration is that DXA technique does not allow separate quantification of visceral and subcutaneous fat in the trunk, or subcutaneous and intramuscular fat in the extremities. It should be also acknowledged that the lack of data on physical activity and dietary intake does not permit to explore the effect of these variables on the observed relationships. Being aware of these limitations, we suggest that upper body fat wherever it is deposited (arms, thorax, abdomen) is adversely associated with cardiometabolic variables, whereas lower body fat (leg, femoral, gluteofemoral) is beneficial. In conclusion, our data have shown that DXA-derived indices of central and peripheral fat distribution correlate significantly and in an opposite way with MS in healthy postmenopausal women, independent of total adiposity. An index of peripheral fat (GFM%) emerged as more important determinant of MS than indices of central fat. Beyond the conventional indices of DXA, we introduced and proposed novel, reproducible and easy-to-define indices of regional fat distribution, such as ThFM%, AbFM% and GFM%, which were strongly and independently correlated with cardiometabolic risk factors. DXA-derived parameters of fat distribution may thus serve as clinically useful tools for evaluating fat distribution and the associated cardiometabolic risk in women after menopause. Learning points • DXA-derived indices of central and peripheral fat distribution correlate significantly and in an opposite way with blood pressure, insulin resistance, lipids and metabolic syndrome in obese and non-obese healthy postmenopausal women, independent of total adiposity. • Upper body fat wherever it is deposited (arms, thorax, abdomen) is adversely associated with cardiometabolic variables, whereas lower body fat (leg, femoral, gluteofemoral) is beneficial in postmenopausal women. • Thoracic fat mass may constitute a metabolically distinct upper body fat depot, which is adversely associated with multiple cardiometabolic risk factors in postmenopausal women, and thus merits further investigation. • Arm fat mass may reflect a central rather than peripheral pattern of fat distribution in terms of cardiometabolic implications in postmenopausal women, due to its upper body localization and high basal lipolytic activity. • Indices of peripheral fat distribution, such as gluteofemoral fat, emerge as more important determinants of metabolic syndrome than indices of central fat in postmenopausal women. • Considering that the majority of postmenopausal women are advised to undergo annual DXA scans for measuring their bone mineral density, it seems convenient to combine the annual osteoporosis screening with an additional evaluation of total and regional adiposity using indices such as thoracic, abdominal and gluteofemoral fat. Conflict of interests The authors have no potential conflicts of interest related to this manuscript to declare. Acknowledgments We are grateful to the Research Nurse Coordinator Mrs Vasilia Fragaki and the technician Ms Stefania Tsouknida, for their valuable contribution to this project. Furthermore, the authors, especially CK, would like to thankfully acknowledge the substantial contribution of the State Scholarships Foundation of Greece, as well as the Public

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