Conceived and designed the experiments: SMK JWY HYA SYK KHL SL. Performed the experiments: SMK SL. Analyzed the data: HS SHC KSP HCJ. Contributed reagents/materials/analysis tools: SMK JWY HS. Wrote the paper: SMK SL.
The authors have declared that no competing interests exist.
Fat accumulation in android compartments may confer increased metabolic risk. The incremental utility of measuring regional fat deposition in association with metabolic syndrome (MS) has not been well described particularly in an elderly population.
As part of the Korean Longitudinal Study on Health and Aging, which is a community-based cohort study of people aged more than 65 years, subjects (287 male, 75.9±8.6 years and 278 female, 76.0±8.8 years) with regional body composition data using Dual energy X-ray absorptiometry for android/gynoid area, computed tomography for visceral/subcutaneous adipose tissue (VAT/SAT), and cardiometabolic markers including adiponectin and high-sensitivity CRP were enrolled. We investigated the relationship between regional body composition and MS in multivariate regression models. Mean VAT and SAT area was 131.4±65.5 cm2 and 126.9±55.2 cm2 in men (P = 0.045) and 120.0±46.7 cm2 and 211.8±65.9 cm2 in women (P<0.01). Mean android and gynoid fat amount was 1.8±0.8 kg and 2.5±0.8 kg in men and 2.0±0.6 kg and 3.3±0.8 kg in women, respectively (both P<0.01). VAT area and android fat amount was strongly correlated with most metabolic risk factors compared to SAT or gynoid fat. Furthermore, android fat amount was significantly associated with clustering of MS components after adjustment for multiple parameters including age, gender, adiponectin, hsCRP, a surrogate marker of insulin resistance, whole body fat mass and VAT area.
Our findings are consistent with the hypothesized role of android fat as a pathogenic fat depot in the MS. Measurement of android fat may provide a more complete understanding of metabolic risk associated with variations in fat distribution.
Obesity is a heterogeneous disorder characterized by multi-factorial etiology. Obese individuals vary in their body fat distribution, their metabolic profile and the degree of associated cardiovascular and metabolic risks. There is substantial evidence providing that fat distribution is a better predictor of cardiovascular disease than the degree of obesity
In a different context, truncal fat depot can be partitioned into upper body (android or central) and lower body (gynoid or peripheral) area. Empirically, android or central fat deposition is known to be more associated with cardiometabolic risk than gynoid or peripheral fat deposition. Many studies with simple anthropometric measurements such as waist circumference or waist-to-hip ratio have given more weight to the central adiposity
Metabolic syndrome (MS) increases cardiovascular morbidity and mortality, and all cause of mortality
Thus, assessment of fat distribution may be important in the clinical evaluation of cardiometabolic risks. However, there has been no comprehensive study on fat distribution related risks particularly in elderly Asian populations whose physical and metabolic characteristics differ from those of Caucasians. We evaluated the association between clustering of components constituting MS and the whole and regional body composition measured by comprehensive methods including DXA and CT in a community-based cohort study of elderly men and women. The effects of metabolic or inflammatory markers were also evaluated.
This study was part of the Korean Longitudinal Study on Health and Aging (KLoSHA), which is a cohort that began in 2005 and consisted of 1000 Korean subjects aged over 65 years (439 men and 561 women) recruited from Seongnam city, one of the satellites of Seoul Metropolitan district. The study population and part of the method of measurements for the cohort have been published previously
The current study subjects were from the KLoSHA. Of the original 1000 KLoSHA subjects, we randomly selected 600 participants (60% of the KLoSHA subjects) for assessment of body composition. Of these 600 subjects, 21 declined the DXA or CT scans and 14 were unable to undergo the examination due to their poor physical condition. In total, 565 participants (94.2% of 600 selected subjects) who underwent DXA/CT scans for body composition evaluation were enrolled in the current analysis. Pertinent demographic and other characteristics of the selected subjects were similar to the cohort population. Among study participants, 39.1% (n = 221) were found to have diabetes: 17.5% (n = 99) were previously on antidiabetic medication and 21.6% (n = 122) were diagnosed with diabetes by 75 g standard OGTT which was performed as s study screening procedure. Smoking and alcohol status was divided into three categories; current smoker, ex-smoker, or never smoker, and current drinker, ex-drinker, or never drinker, respectively. Current drinker was defined as a person consuming more than 4 drinks/week (50 g/day of ethanol). Physical activity was divided into two categories; none or regular exercise. Regular exercise was defined as exercising more than three times a week (each session should be at least 30 min long). The homeostasis model assessment of the insulin resistance (HOMA-IR) was calculated as reported previously
DXA measures were recorded using a bone densitometer (Lunar, GE Medical systems, Madison, WI). DXA is quantified by body tissue absorption of photons that are emitted at two energy levels to resolve body weight into bone mineral, lean and fat soft tissue masses. In vivo precision for body composition measurements using DXA was proven previously
The regions of interest (ROI) for regional body composition were defined using the software provided by the manufacturer (
Trunk ROI (T): from the pelvis cut (lower boundary) to the neck cut (upper boundary).
Android fat distribution ROI (A): from the pelvis cut (lower boundary) to above the pelvis cut by 20% of the distance between the pelvis and neck cuts (upper boundary).
Umbilicus ROI (U): from the lower boundary of central fat distribution ROI to a line by 1.5 times the height of the android fat distribution ROI (lower boundary).
Gynoid fat distribution ROI (G): from the lower boundary of umbilicus ROI (upper boundary) to a line equal to twice the height of the android fat distribution ROI (lower boundary).
CT scans were obtained using a 64–detector (Brilliance; Philips Medical Systems, Cleveland, Ohio). All patients were placed in the supine position and were scanned from L3-4 to L5-S1 intervetebral disc level. The tube voltage was 120 kVp for 64 detector row scanner. Effective tube current-time product generally ranged between 20–50 mAs. The images were reconstructed with 5 mm thickness with 5 mm-intervals. One slice obtained at the level of umbilicus were selected and the amount of the total abdominal fat were calculated by measuring the area of the pixels whose attenuation values ranged from −190 to −30 Hounsfield unit (HU) using a commercially available software (Rapidia, version 2.8, Infinitt Co., Seoul)
Detailed information about the cardiac CT angiography protocol was described previously
All data are presented as the mean and SD or n and %, and were analyzed using SPSS Windows version 11.0 (SPSS Inc., Chicago, IL). The demographic and laboratory characteristics of subjects were compared using Student's t test or a Chi-square test according to the presence of MS. Correlations between variables were analyzed using Pearson's correlation. Multiple regression analysis was used to determine the independent effect of body composition parameters on clustering of five components of MS. P<0.05 was considered significant.
Anthropometric, body composition, and metabolic characteristics of the study population stratified by sex are provided in
No MS (n = 268) | MS (n = 297) | P-value | |||
Mean | SD | Mean | SD | ||
Age (years) | 72.5 | 6.9 | 73.6 | 7.4 | 0.067 |
Male (n, %) | 159 | 59.3% | 128 | 43.1% | <0.001 |
SBP (mmHg) | 128.9 | 17.7 | 136.6 | 16.6 | <0.001 |
DBP (mmHg) | 81.6 | 11.0 | 85.0 | 10.4 | <0.001 |
BMI (kg/m2) | 23.1 | 3.0 | 25.5 | 2.9 | <0.001 |
Waist circumference (cm) | 82.8 | 9.0 | 90.3 | 7.9 | <0.001 |
Smoking | 0.037 | ||||
Current smoker (n, %) | 37 | 13.8% | 32 | 10.8% | |
Ex-smoker (n, %) | 89 | 33.2% | 76 | 25.6% | |
Never smoker (n, %) | 142 | 53.0% | 189 | 63.6% | |
Alcohol | 0.008 | ||||
Current drinker (n, %) | 89 | 33.5% | 66 | 22.3% | |
Ex-drinker (n, %) | 41 | 15.4% | 44 | 14.9% | |
Never drinker (n, %) | 136 | 51.1% | 186 | 62.8% | |
Regular exercise (n, %) | 166 | 62.6% | 160 | 54.4% | 0.049 |
Medication | |||||
Antihypertensive medication | 89 | 33.2% | 160 | 53.9% | <0.001 |
Antidiabetic medication | 26 | 9.7% | 73 | 23.6% | <0.001 |
Lipid lowering medication | 30 | 11.2% | 40 | 13.5% | 0.445 |
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Whole body muscle mass (kg) | 37.1 | 7.2 | 36.9 | 37.1 | 0.801 |
Whole body fat mass (kg) | 18.5 | 7.4 | 24.3 | 18.5 | <0.001 |
Android fat mass (kg) | 1.6 | 0.7 | 2.1 | 1.6 | <0.001 |
Gynoid fat mass (kg) | 2.6 | 0.9 | 3.2 | 2.6 | <0.001 |
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Visceral adipose tissue (cm2) | 103.9 | 52.9 | 149.8 | 55.4 | <0.001 |
Subcutaneous adipose tissue (cm2) | 134.2 | 68.4 | 189.4 | 66.8 | <0.001 |
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Coronary artery stenosis (%) | 20.1 | 21.6 | 25.6 | 25.2 | <0.001 |
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Triglycerides (mg/dL) | 104.2 | 41.4 | 171.6 | 107.8 | <0.001 |
HDL-cholesterol (mg/dL) | 51.1 | 12.0 | 40.5 | 10.4 | <0.001 |
LDL-cholesterol (mg/dL) | 129.2 | 33.9 | 127.4 | 35.5 | 0.532 |
Fasting glucose (mg/dL) | 104.9 | 20.0 | 117.5 | 29.0 | <0.001 |
Fasting insulin (µIU/mL) | 4.3 | 2.1 | 6.0 | 3.8 | <0.001 |
HOMA-IR |
1.1 | 0.6 | 1.8 | 1.2 | <0.001 |
A1C (%) | 5.9 | 0.7 | 6.3 | 1.0 | <0.001 |
Adiponectin (µg/mL) | 10.1 | 6.1 | 7.4 | 5.1 | <0.001 |
hsCRP (mg/dL) | 0.3 | 0.8 | 0.2 | 0.5 | 0.413 |
*HOMA-IR; homeostasis model assessment for insulin resistance.
Of the study population of 565 elderly people (73.0±7.2 years of age), 47.4% (n = 268) fulfilled the criteria of MS. Participants with or without MS were similar in age, but more women had MS than men. Systolic and diastolic blood pressure, BMI, and waist circumference were significantly higher in participants with MS compared to without MS. In terms of specific adiposity measurements, whole body fat mass, total android and gynoid tissue, android and gynoid fat amount measured by DXA, and VAT and SAT quantified by CT scan were all greater in participants with MS compared to without MS. The concentrations of triglycerides, and HDL-cholesterol, fasting glucose and insulin, and A1C levels, and HOMA-IR were significantly higher in participants with MS than without MS. Circulating adiponectin levels were significantly lower in participants with MS, whereas hsCRP level was not significantly different between two groups. In terms of lifestyle habits, the proportion of subjects with cigarette smoking and alcohol consumption were significantly higher in MS. However participants with MS were more likely to engage in regular exercise. Past medical history of coronary heart disease (i.e. angina, myocardial infarction, percutaneous coronary intervention, and coronary artery bypass surgery) or strokes were not different.
VAT | SAT | Android fat | Gynoid fat | |
Age (years) | −0.078 | −0.111 | −0.128* | −0.167* |
BMI (kg/m2) | 0.675 |
0.649 |
0.773 |
0.697 |
Waist circumference (cm) | 0.598 |
0.438 |
0.661 |
0.450 |
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Whole body muscle mass (kg) | 0.314 |
−0.288 |
0.169* | −0.111* |
Whole body fat mass (kg) | 0.696 |
0.809 |
0.927 |
0.945 |
Android fat mass (kg) | 0.813 |
0.684 |
1 | 0.797 |
Gynoid fat mass (kg) | 0.568 |
0.794 |
0.797 |
1 |
Android/gynoid fat ratio | 0.624 |
0.163 |
0.594 |
0.032 |
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VAT (cm2) | 1 | 0.442 |
0.813 |
0.568 |
SAT (cm2) | 0.442 |
1 | 0.684 |
0.794 |
VAT/SAT | 0.544 |
−0.413 |
0.159 |
−0.137* |
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Coronary artery stenosis | 0.225 |
−0.098 | 0.201 |
0.033 |
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Triglycerides (mg/dL) | 0.211 |
0.169* | 0.238 |
0.147* |
HDL-cholesterol (mg/dL) | −0.284 |
−0.049 | −0.224 |
−0.079 |
LDL-cholesterol (mg/dL) | 0.036 | 0.108 | 0.063 | 0.112 |
Fasting glucose (mg/dL) | 0.207 |
0.074 | 0.205 |
0.035 |
Fasting insulin (µIU/mL) | 0.488 |
0.414 |
0.478 |
0.391 |
HOMA-IR | 0.514 |
0.400 |
0.509 |
0.362 |
A1C (%) | 0.205 |
0.120 | 0.244 |
0.067 |
Adiponectin (µg/mL) | −0.346 |
−0.110 | −0.276 |
−0.092 |
hsCRP (mg/dL) | 0.075 | −0.057 | 0.023 | 0.006 |
**Correlation is significant at the 0.01 level (2-tailed).
There was a negative correlation between age and android and gynoid fat amount (both P<0.01). BMI and waist circumference were highly correlated with VAT and SAT, and android and gynoid fat amount (all P<0.01). VAT at the level of umbilicus was significantly correlated with adiposity measurements by DXA including whole body fat mass, android and gynoid fat amount. However, the correlation coefficient was significantly greater between VAT and android fat than between VAT and gynoid fat (P<0.05). The concentration of triglycerides was associated with all of the four adiposity indices including VAT and SAT, and android and gynoid fat amount whereas HDL-cholesterol showed negative association with adiposity indices. Android fat amount was associated with fasting glucose and insulin levels, HOMA-IR, and A1C, whereas gynoid fat was not associated with fasting glucose and A1C levels. Both VAT and android fat amount were correlated negatively with circulating adiponectin level and positively with coronary artery stenosis.
Indices of adiposity including BMI, whole body fat mass, android and gynoid fat amount, VAT and SAT area were associated with the five components of MS (
β coefficient | t | P-value | |
Model 1: Age, gender, smoking, exercise, BMI, hsCRP, LDL-cholesterol, adiponectin, HOMA-IR, and whole body fat mass adjusted | |||
Age (years) | 0.150 | 4.136 | <0.001 |
Gender (male vs. female) | 0.210 | 4.229 | <0.001 |
BMI (kg/m2) | 0.211 | 3.429 | 0.001 |
hsCRP (≥ 2.5 mg/l vs. <2.5 mg/l) | 0.187 | 3.012 | 0.034 |
Adiponectin (µg/mL) | −0.225 | −6.002 | <0.001 |
HOMA-IR | 0.200 | 4.850 | <0.001 |
Whole body fat mass (kg) | 0.114 | 1809 | 0.071 |
Model 2: Model 1+VAT | |||
Age (years) | 0.121 | 2.743 | 0.006 |
Gender (male vs. female) | 0.276 | 4.527 | <0.001 |
BMI (kg/m2) | 0.143 | 1.869 | 0.062 |
hsCRP (≥ 2.5 mg/l vs. <2.5 mg/l) | 0.156 | 2.891 | 0.041 |
Adiponectin (µg/mL) | −0.226 | −4.852 | <0.001 |
HOMA-IR | 0.178 | 3.412 | 0.001 |
VAT (cm2) | 0.172 | 2.493 | 0.013 |
Model 3: Model 1+android fat | |||
Age (years) | 0.143 | 3.965 | <0.001 |
Gender (male vs. female) | 0.275 | 5.204 | <0.001 |
BMI (kg/m2) | 0.207 | 3.399 | 0.001 |
hsCRP (≥ 2.5 mg/l vs. <2.5 mg/l) | 0.142 | 2.528 | 0.063 |
Adiponectin (µg/mL) | −0.194 | −5.094 | <0.001 |
HOMA-IR | 0.173 | 4.153 | <0.001 |
Whole body fat mass (kg) | −0.243 | −1.976 | 0.049 |
Android fat (kg) | 0.384 | 3.381 | 0.001 |
Model 4: Model 1+VAT+android fat | |||
Age (years) | 0.119 | 2.712 | 0.007 |
Gender (male vs. female) | 0.317 | 5.032 | <0.001 |
BMI (kg/m2) | 0.151 | 1.976 | 0.049 |
Adiponectin (µg/mL) | −0.203 | −4.298 | <0.001 |
HOMA-IR | 0.159 | 3.043 | 0.003 |
Android fat (kg) | 0.378 | 2.404 | 0.017 |
HOMA-IR: homeostasis model assessment for insulin resistance.
β coefficient | t | P-value | |
Model 1: Age, gender, smoking, exercise, BMI, hsCRP, LDL-cholesterol, adiponectin, HOMA-IR, and whole body fat mass adjusted | |||
Age (years) | 0.237 | 4.308 | <0.001 |
Gender (male vs. female) | 0.106 | 1.929 | 0.055 |
hsCRP (≥ 2.5 mg/l vs. <2.5 mg/l) | 0.187 | 3.012 | 0.034 |
Adiponectin (µg/mL) | −0.152 | −2.009 | 0.046 |
Whole body fat mass (kg) | 0.114 | 1.809 | 0.071 |
Model 2: Model 1+VAT | |||
Age (years) | 0.262 | 3.726 | <0.001 |
Gender (male vs. female) | 0.109 | 1.927 | 0.089 |
hsCRP (≥ 2.5 mg/l vs. <2.5 mg/l) | 0.156 | 2.891 | 0.041 |
Adiponectin (µg/mL) | −0.226 | −4.852 | <0.001 |
VAT (cm2) | 0.162 | 2.321 | 0.018 |
Model 3: Model 1+android fat | |||
Age (years) | 0.237 | 4.288 | <0.001 |
Gender (male vs. female) | 0.105 | 1.884 | 0.060 |
hsCRP (≥ 2.5 mg/l vs. <2.5 mg/l) | 0.142 | 2.528 | 0.056 |
Adiponectin (µg/mL) | −0.294 | −5.094 | <0.001 |
Android fat (kg) | 0.159 | 2.312 | 0.026 |
Model 4: Model 1+VAT+android fat | |||
Age (years) | 0.247 | 3.472 | 0.001 |
Gender (male vs. female) | 0.123 | 1.993 | 0.042 |
hsCRP (≥ 2.5 mg/l vs. <2.5 mg/l) | 0.102 | 1.528 | 0.063 |
Adiponectin (µg/mL) | −0.158 | −2.087 | 0.038 |
HOMA-IR: homeostasis model assessment for insulin resistance.
Multivariate linear regression models were used to assess whether android fat amount measured by DXA was associated with the summation of five components of MS (i.e. central obesity, hypertension, high triglyceride and low HDL-cholesterol, dysglycemia) controlling for VAT quantified by CT. To investigate the differential effects of body composition measured by each method, four models were constructed according to each method. In Model 1, age, gender, smoking status, exercise habit, BMI, hsCRP (≥ 2.5 mg/l vs. <2.5 mg/l), LDL-cholesterol, adiponectin, HOMA-IR, and whole body fat mass were selected as independent variables. In Model 2, VAT area was added as an independent variable. In Model 3, android fat was further added to Model 1 as an independent variable. Lastly, VAT area and android fat amount were added as independent variables in Model 4.
In model 1, age, female gender, BMI, hsCRP and HOMA-IR were positively associated with clustering of MS components, whereas adiponectin was negatively associated. Adjusting for VAT resulted in a positive association of MS with age, female gender, hsCRP, HOMA-IR, and VAT, and a negative association with adiponectin (Model 2). Association with BMI was attenuated after including VAT in the model. Adjusting for android fat with MS, age, gender, BMI, HOMA-IR, and android fat were positively associated with MS, and negatively associated with adiponectin (Model 3). Finally, adjusting for both VAT and android fat in Model 4 yielded a consistent and unchanged positive association of android fat with MS, whereas an association with VAT was attenuated. When the combined VAT area between L3-4 and L5-S1 was used instead of a single level of VAT (992.3±48.7 cm2 in men and 1469.4±53.7 cm2 in women, P<0.001), this merged VAT area was associated with MS with a borderline significance (
We further investigated the association between android fat/VAT and coronary artery stenosis. In univariate analysis, android fat and VAT were significantly associated with the degree of coronary artery stenosis. After adjusting for the risk factors previously used in
In this study with community-based elderly population, of the various body compositions examined using advanced techniques, android fat and VAT were significantly associated with clustering of five components of MS in multivariate linear regression analysis adjusted for various factors. When android fat and VAT were both included in the regression model, only android fat remained to be associated with clustering of MS components. The results suggest that android fat is strongly associated with MS in the elderly population even after adjusting for VAT.
Abdominal obesity is well recognized as a major risk factor of cardiovascular disease and type 2 diabetes
Alternatively, more accurate methods used to measure regional fat depot are DXA and CT. DXA and CT provide a comprehensive assessment of the component of body composition with each contributing its unique advantages. CT can distinguish between visceral and subcutaneous fat, and has been useful in measuring fat or muscle distribution at specific regions
In contrast, DXA has the ability to accurately identify where fat or muscle is distributed throughout the body with high precision
In the current study, adiponectin levels were negatively and hsCRP levels were positively associated with MS with at least borderline significance except for hsCRP in model 4, where both VAT and android fat were included as covariates in the regression model. The close relationship between hsCRP and VAT/android fat may have attenuated the association between hsCRP and MS.
Mechanistically and theoretically, fat deposition in android area is suggested to have deleterious effects on the heart function, energy metabolism and development of atherosclerosis. However, studies on android fat depot are limited
Of note, in this study, android fat was more closely associated with a clustering of metabolic abnormalities than visceral fat. There is no clear answer for this but several explanations can be postulated. First, android area defined in this study includes liver, pancreas and lower part of the heart. Many studies have shown that fat accumulation in these structures have more detrimental metabolic impacts through direct and indirect mechanisms
Second, the android fat represents whole fat amount in the upper abdomen area while VAT measurement was performed at a single umbilicus level. This different methodology may possibly contribute to greater association between metabolic impairments and android fat than VAT. This interpretation is supported by the borderline significance of VAT in the association with MS when combined VAT area was used instead of a single level of VAT. A recent study also showed that the whole fat amount between L1–L5 vertebra showed a stronger relationship with insulin resistance than that of the single L3 level
In this study, both android fat amount and VAT were associated with coronary artery stenosis. Android fat is closely related with VAT because of their proximity and correlation with various cardiovascular risk factors. The attenuated associations of both variables without statistical significance in the regression model where android fat and VAT were simultaneously included may be due to a shared systemic effect as a result of shared risk factors for the development of atherosclerosis.
This study has several strengths. First, DXA with its advanced technology was used to measure regional fat depot. Second, the subjects were recruited from a well-defined population, which represented a single ethnic group and were older than 65 years. Third, the regression analysis was adjusted for important factors including whole body fat mass, insulin resistance, and biochemical markers including adiponectin and hsCRP that might affect MS.
This study also has several limitations. First, since our study is limited by its cross-sectional nature, it is impossible to confirm clinically meaningful role of android fat depot. Therefore, further studies are needed to determine a predictive role of android fat for a clustering of cardiometabolic risk factors and subsequent incidence of cardiovascular diseases. Second, this is a single cohort study with a small number of subjects and the results are confined to this specific cohort.
Of the various body compositions examined using advanced techniques, android fat measured by DXA was significantly associated with clustering of five components of MS even after accounting for various factors including visceral adiposity. It would be interesting to apply this concept of body composition phenotypes to health risks in light of race/ethnic and age variability in metabolic susceptibility to obesity and MS. Further studies are necessary to determine whether the information gathered in the present study is generalizable to other populations and also to validate the practicality and implication of using android fat/DXA in predicting for cardiovascular diseases.
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