Chronic kidney diseases are associated with changes in cardiometabolic risk (CMR) factors in which body composition parameters have been used as sensitive predictors. This study aimed to explore the associations of anthropometric indicators, body fat (BF), body mass index (BMI) and waist circumference (WC) with estimated glomerular filtration rate (eGFR) in an adult healthy Chinese population.
A cross-sectional study was conducted for the subjects undergoing annual health examinations. The associations of subjects with body composition parameters were analyzed using the cutoff values of BMI, BF and WC in accordance with the criteria for Asian or Taiwanese population by gender.
A total of 3473 subjects, aged 30–45 years, who received physical examinations in 2007 were analyzed. The levels of CMR factors were significantly higher in males than in females. eGFR was negatively associated with BMI but positively related to BF. The additional roles of BMI and WC were observed in the subjects who were categorized according to BF. Females with normal weight obese were associated with increased eGFR, whereas a higher eGFR was found in males with low/normal BF and BMI or normal WC.
Our data provided evidence that anthropometric parameters were associated with changes of eGFR in relatively healthy adults. Higher eGFR was observed in females with normal weight obese in whom hyperfiltration may be suspected, and this finding deserves further studies.
Chronic kidney disease (CKD) is a risk factor for cardiovascular disease (CVD) and is associated with an increase in all-cause mortality.1 The etiology of the increased risk of CVD in CKD is unknown but may, in part, be due to shared risk factors, including diabetes, hypertension, obesity, lipid abnormalities and smoking.2 CKD is commonly not detected until an advanced stage for most patients.3 Overweight and obesity are major risk factors for renal dysfunction.4,5 Previous studies have shown that obesity is a risk factor for a diminished glomerular filtration rate (GFR), and body mass index (BMI) is a strong predictor for developing CKD. BMI is a widely used indicator of overall obesity; however, waist circumference (WC) and waist-to-hip ratio (WHR) as indices of visceral obesity have been reported to be even more sensitive predictors of late-stage renal disease.6
Moreover, different fat compartments may be associated with differential metabolic risk.7 In particular, the visceral adipose tissue (VAT) compartment may be a unique pathogenic fat depot.8,9 VAT has been termed an endocrine organ that secretes adipocytokines and vasoactive substances influencing the risk of developing specific features of metabolic syndrome (MS).8,10 MS is a combination of medical disorders that increase the risk of developing many diseases, including chronic renal disease and so on. Central adiposity is a key feature of MS, reflecting the fact that the syndrome's prevalence is driven by the strong relationship between WC and increasing adiposity.10,11
Obesity indices related to cardiometabolic risk (CMR) factors include BMI, body fat percentage (BF%), WC, waist-to-height ratio (WHtR) and waist-to-hip ratio (WHR).12,13 WC, WHtR and WHR reflect the CMR represented by abdominal obesity.14,15 However, results of previous studies showed that high BF was associated with increased CMR despite normal body weight and with increases in risk factors for CVDs in the subjects with low- and normal-BMI.16 An increasing number of studies indicate that the degree of central fat distribution may be more closely related to metabolic risks than BMI. Measurement of the degree of central fat distribution thus appears to be important for the early detection of subsequent health risks, even among subjects of normal weight.17
Not only obese and overweight subjects but also lean subjects with central fat distribution were at a high risk of diminished GFR.18 A recent study evaluating the associations between WC, WHtR, BMI and renal function also demonstrated that both obese and very thin subjects have a higher risk of renal impairment.19 BMI and WC were found to be associated with reduced estimated GFR (eGFR) in Japanese cohort studies.20,21 Central obesity was positively related to WC in some cross-sectional studies, including Asian populations.22 WC contributed to an increased risk for the Taiwanese population with CKD in the cohort study23 but not for Southeast Asian subjects.24 The discrepancies of these studies were found because of the differences in cutoff values of anthropometric parameters and methodologies.
Given the fact that body composition is associated with many physiopathyological conditions and studies have increasingly emphasized the importance of BF in determining CMR status, we hypothesized that body composition would be associated with renal function and that fat tissue measurement may contribute to the detection of renal impairment. Therefore, this study aimed to explore the association of anthropometric indicators, BF, BMI and WC, with eGFR in an adult healthy Chinese population in Taiwan.
Subjects and methods
Study design and population
In this retrospective cross-sectional study, we included the subjects living in Northern Taiwan who underwent annual heath examinations at the Health Examination Center of Linkou Chang Gung Memorial Hospital between May and September 2007. Participants who were aged⩾18 years and who agreed to undergo a body composition examination using the Inbody 3.0 eight-polar tactile electrode system (BIA instrument, Biospace Co. Ltd., Seoul, Korea) were included in our study. Exclusion criteria were (1) subjects who had used hypolipidemic drugs within the past 6 weeks before enrollment; (2) subjects without 12-h fasting before examining/testing; (3) pregnant women; (4) subjects who had been diagnosed with chronic diseases, including thyroid diseases, liver cirrhosis, chronic active hepatitis, pituitary disease, chronic glomerulonephritis, renal insufficiency, renal failure (eGFR<15 ml/min/1.73 m2) or who were under active thyroid medication; and (5) subjects who did not complete the questionnaire and/or did not respond to the questions about medical, medication and smoking history. Informed consent was obtained from all participants. The Institutional Review Board of Chang Gung Memorial Hospital approved this study before initiation.
Trained examiners performed anthropometric measurements for all participants. Blood pressure (BP) was measured by an automatic sphygmomanometer (Welch Allyn, Skaneateles Falls, NY, USA), and measurements were repeated 2–3 times after at least 5 min of rest if results were higher than normal. Height and weight were measured using an automatic scale (Super-View Medical HW-686, Hualien County, Taiwan). BMI was calculated using the following equation: BMI=body weight (kg)/the square of body height (m2). WC was measured using a tap positioned in a horizontal plane around the abdomen and at midpoint between the lower border of the rib cage and the iliac crest. BF% was measured using a bioelectrical impedance analysis (InBody 3.0 model; Biospace), and all subjects were told not to perform any physical exercise or consume alcohol for at least 24 h before examination.
All blood samples were collected in the morning after 12 h of fasting. Extensive serum measurements were performed, including fasting plasma glucose (FPG), total cholesterol (TChol), triglyceride (TG), high-density lipoprotein cholesterol (HDL-C) and creatinine (Scr) measurements. FPG was measured using the hexokinase method. TChol and TG levels were measured using an enzymatic colorimetric test. HDL-C was measured using the selective-inhibition method. Urinary protein excretion was evaluated by a dipstick urine test (Siemens/Bayer, Multistix-10SG, Wales, UK) and graded as negative, trace, 1+(25 mg/dl), 2+(75 mg/dl), 3+(150 mg/dl) or 4+(500 mg/dl).
Criteria of measurement cutoffs
BMI cutoff values for public health of Asian populations were used in the present study. The normal range of BMI is between 18.5 and 22.99 kg/m2; a BMI⩾of 23 indicates an increased risk for health.12,25 Categories of BF% were based on the standardized cutoff values followed by the Chinese Taipei Association for the study of obesity (http://ctaso.tmu.edu.tw/main.php), a low/normal (L/N) or high BF% was defined as a BF%⩽of 23% or>23% for males and⩽27 or>27 for females. The cutoffs for WC for abdominal obesity are⩾90 cm for men (that is, normal<90 cm, high⩾90 cm) and⩾80 cm for women (that is, normal<80 cm, high⩾80 cm) according to the Asian-specific cutoff points of the International Diabetes Federation criteria.26 The mean arterial pressure (MAP) was approximated by the direct measurements of systolic and diastolic BPs according to the following equation: MAP=1/3 (systolic BP)+2/3 (diastolic BP). Modified MDRD (Modification of Diet in Renal Disease) equations for Chinese patients with CKD27 were applied to measure eGFR as follows: 175 × (Scr)−1.234 × (Age)−0.179 × 0.79 (if female).
According to the definition of Kidney Disease Outcomes Quality Initiative (K/DOQI),28 CKD was defined as an eGFR<60 ml/min/1.73 m2 (CKD⩾stages 3). Proteinurea was defined as having a grade of⩾1+in repeated measures. Diagnostic criteria for MS were established according to the 2004 Taiwan Ministry of Health criteria adapted from the Asian modification of the US National Cholesterol Education Program (NCEP) criteria.29 A participant diagnosed with MS was defined as fulfilling⩾3 of the following criteria: (a) high BP (systolic BP⩾130 mm Hg, diasystolic BP⩾85 mm Hg); (b) high serum TG (TG⩾150 mg/dl); (c) increased HDL-C (<40 mg/dl for males and<50 mg/dl for female); (d) hyperglycemia (FBG⩾100 mg/dl); and (e) abdominal obesity (modified WC cutoffs for Asian populations were used).
The continuous variables were presented as median and interquartile range (the range between the 25th and 75th percentile) because of non-normal distribution. The categorical variables were expressed by count and percentage. The differences between the L/N BF% group and the high BF% group were examined using the Mann–Whitney U-test. The Chi-square test or Fisher’s exact test were used to determine the differences for category variables. Simple and multiple linear regression models were applied to evaluate the estimates and associations with eGFR. The SAS software package, version 9.2 (SAS Institute Inc., Cary, NC, USA) was used for statistical analysis. All statistic assessments were evaluated at a two-sided α level of 0.05.
Characteristics of CMR factors
A total of 3473 subjects aged⩾18 years were enrolled in this study, including 1803 males (51.9%) and 1670 females (46.1%) with a median age of 36.0 years (interquartile range: 33–40 years). There were significant differences in demographic and CMR factors by gender (Table 1). Compared with females, males were older and had higher levels of BMI, WHtR, MAP, FPG, TChol and TG. In addition, the prevalence of smoking, MS and CKD was higher in males. In contrast, the levels of BF%, HDL-C and eGFR were higher in females compared with males. With the exception of those identified with CKD, the results indicated that most subjects were healthy with the cardiometabolic factors falling within normal ranges and suggested that females were more likely than males to be healthier.
Relationships between BF, CMR factors and eGFR
All subjects were stratified by gender and further divided into two groups with L/N BF% or high BF%. Comparisons of CMR factor burden and CKD between the two groups are shown in Table 2. Male subjects classified as having L/N BF and high BF were 65.0% (n=1172) and 35.0% (n=631), respectively. Male subjects with high BF were older and had significantly elevated levels of BMI, WHtR, MAP, FPG, TChol and TG but lower HDL-C compared with those with L/N BF% (all P-values were<0.001). A lower proportion of current smokers and higher prevalence rates of MS and CKD were observed in the participants with high BF%; however, no significant difference in either eGFR or proteinuria values was found.
Similar patterns were observed in the CMR risk factor for female subjects. Those with high BF also had significantly greater levels of CMR risk factors but lower HDL-C. The prevalence of MS was significantly higher among those with high BF. No difference was observed in age, smoking status, proteinuria value or CKD prevalence.
Association of anthropometric parameters with eGFR
As summarized in Table 3, BMI was significantly negatively associated with eGFR for both genders (males, β=−0.491; females, β=−0.519, P<0.01); whereas BF demonstrated significantly positive relationships with eGFR in both genders (males, β=0.324; females, β=0.423, P<0.01), WC only showed relatively lower relation for females (β=0.094, P<0.05).
The associations of BF, BMI and WC with eGFR in both genders were then stratified according to cutoff values (Table 4). A higher percentage of females compared with that of males had a L/N BMI (71.6% vs 39.5%). BMI was significantly negatively associated with eGFR for both genders with either L/N BF (P<0.01) or high BF (P<0.01). BF revealed significantly positive relationships with eGFR for both genders with L/N BMI (P<0.01) or overweight BMI (P<0.01). A significant relationship between WC and eGFR was detected in females with L/N BF (β=0.126, P<0.01) and males with L/N BF (β=−0.190, P<0.01) and overweight BMI (β=−0.116, P<0.01).
Association of BF with eGFR in males and females grouped by BMI and WC
As indicated in Table 5, a relatively higher proportion of males than of females was overweight with high BF (34.1% vs 27.5%). The proportions of subjects who had a high WC and high BF were 18.1% for males and 15.0% for females. In males with L/N BF, a significantly higher eGFR was associated with L/N than overweight BMI (82.3 vs 79.7 ml/min/1.73 m2, P<0.001) and with normal than high WC (81.5 vs 79.4 ml/min/1.73 m2, P<0.05). In contrast, significant differences in eGFR were observed in females with high BF. The results indicated that females with a normal weight obese had a higher eGFR compared with the females with high BF and BMI⩾23 (95.9 vs 88.5 ml/min/1.73 m2, P<0.001). Similarly, a higher eGFR was observed in the females with high BF and normal WC (94.9 vs 87.0 ml/min/1.73 m2, P<0.001). However, the prevalence of CKD was low among the subjects in this study, and no significant relationship with BF, BMI or WC was identified.
While interpreting the findings of this study, it must be borne in mind that the population in this study consisted of relatively healthy adults aged 30–45 years. The results of this study reported the association of body composition in terms of BF, BMI and WC with CMRs and eGFR. A higher prevalence of high BF was observed in females compared with males (54.6% vs 35.0%). Despite most of the subjects being without disease and the median values of CMR factors remaining within normal ranges, we observed significantly greater levels of these risk factors in the high BF group than the L/N BF group for both genders. The effects of high BF were more profound in males than in females as indicated by higher prevalence of MS and CKD, as well as lower median eGFR estimates.
The associations between BMI and BF with eGFR were significant and evident. Even if the CMR factors were within normal ranges, BMI was always negatively associated with eGFR. On the other hand, BF was positively associated with eGFR in spite of the data indicating that people with high BF had elevated levels of CMR factors. These results also suggest that fat tissue measurement may contribute to the detection of renal impairment in healthy adults. Compared with BMI and BF, WC was a less sensitive parameter for detecting the association with eGFR in this study. The most likely reason was the majority of subjects with normal WC (77.9% of males and 95.9% of females, Table 5), which also contributed to a generally normal range of WHtR.
In a recent study investigating the associations between BF and risk factors for CVD and MS in a population similar to this study,30 BF was suggested to be an accurate predictor for CVD and MS, particularly in women with normal WC and L/N BMI. Similar to these findings, our results indicated that high BF was positively associated with CMR factors in both genders. Moreover, our observations of females with normal weight obese (Table 5) having higher eGFR were in line with the positive association between BF and eGFR (Tables 3 and 4), suggesting that the association of high BF with increased values of CMR factors is likely to contribute to the risk of glomerular hyperfiltration.31 The causes of glomerular hyperfiltration, a compensatory response to a reduction in functioning renal mass and an absolute increase in glomerular filtration rate, including MS, obesity, overweight, high-protein diets, sleep apnea (associated with MS) and some other physiological states or diseases, lead to renal dynamic changes and have a crucial role in the initiation of consequent progression of CKD.31,32
Our findings were in agreement with a recent study reporting that the subjects with normal BMI but high BF (normal weight obesity) were associated with high prevalence of CMRs compared with those with normal BMI and normal BF in Korean middle-aged healthy adults.33 However, the study included subjects undergoing treatment for cardiometabolic diseases (that is, hypertension and diabetes) and used different cutoffs of BMI (25 kg/m2) and BF (25% for men and 30% for women), leading to difficulties in comparing our findings. Differences in the association of eGFR with BF between males and females may reflect a different pathophysiology related to cardiometabolic burden between genders. For example, in the report using the US third National Health and Nutrition Examination Survey (NHANES III) population,34 subjects with normal weight obesity had a higher prevalence of CMRs. Particularly, women with normal weight obesity were strongly associated with an increased risk of cardiovascular mortality.
High BMI is related to higher mortality risk, and an U-shaped relationship between BMI and all-cause mortality is widely accepted.35 Therefore, even if the relation between anthropometric parameters and eGFR is not fully understood, there is no doubt about the association of higher eGFR with normal BMI and WC. The optimal cutoff points for WHtR were close to 0.5 in East Asians.36 The median values of the studied subjects with high BF were 0.5 for both genders, suggesting that they were generally overweight or obese but without central obesity. Nevertheless, the impact of WC on eGFR was different between genders. Among the subjects with normal WC, higher eGFR was observed in males with L/N BF in contrast to females with high BF (Table 5). In addition, our findings of a positive association between WC (as an index of central obesity) and eGFR in the females with L/N BF and a negative association with eGFR in males with L/N or overweight BMI (Table 4) argue an important role for visceral vs subcutaneous fat in relation to renal function.
Adipose tissue is recognized as an active endocrine organ that releases several bioactive mediators.37 Adipose tissue inside the abdominal muscular wall is defined as VAT and SAT (subcutaneous adipose tissue) as adipose tissue outside the abdominal muscular wall. SAT releases 2–3 times more leptin than VAT,38 whereas VAT secretes more factors associated with inflammation.39 Therefore, SAT may only be protective in obese individuals by providing a nonpathological energy storage depot. Although the relationships between VAT, SAT and cardiometabolic pathogenesis still lack direct evidence, paracrine and perhaps endocrine factors may contribute to the differential effects of VAT and SAT. In addition to the volume of VAT or SAT, the association of quality and distribution of BF with CMR factors should also deserve further evaluation.40
The strengths and limitations associated with this study need to be addressed. These subjects were primarily middle-aged and relatively healthy, allowing assessment of relations between fat compartments and risk factors in the absence of significant comorbidity. Therefore, the results can only be referenced for healthy adults aged 30–40 years. Some limitations warrant mention. First, we measured the CMR factors on a single occasion, which could have led to outcomes biasing our results. Second, the study was a cross-sectional design in nature; hence, a casual or pathophysiological relationship between anthropometric measures and renal function cannot be inferred. Third, the definition of CKD was limited to a single measurement of serum creatinine level on one occasion, not measured during a period of 3 months or longer as defined. Fourth, the quality or distribution of BF could not be differentiated as bioelectrical impedance analysis only measures total fat mass, leading to a limited extent of interpretation of results. Fifth, many uncertainties associated with changes in eGFR, such as diet, physical activity and the use of Chinese herbal medicine, were not evaluated.
Taken together, our data provided the evidence that anthropometric parameters were associated with changes in eGFR. Our data suggest that high BF is associated with higher levels of CMR factors for healthy adults of both genders aged 30–45 years. BF was positively associated with eGFR for females with normal weight obese. Higher eGFR was observed in males with L/N BF and normal BMI or WC. Accordingly, our results indicated that BF exhibited an important role for eGFR in both genders. However, further studies are required to define the prevalence and importance of changes in GFR with or without clinical impairment for preventing the progression to CKD.
Go AS, Chertow GM, Fan D, McCulloch CE, Hsu CY . Chronic kidney disease and the risks of death, cardiovascular events, and hospitalization. N Engl J Med 2004; 351: 1296–1305.
Fox CS, Larson MG, Leip EP, Culleton B, Wilson PW, Levy D . Predictors of new-onset kidney disease in a community-based population. JAMA 2004; 291: 844–850.
Whaley-Connell AT, Sowers JR, Stevens LA, McFarlane SI, Shlipak MG, Norris KC et al. CKD in the United States: Kidney Early Evaluation Program (KEEP) and National Health and Nutrition Examination Survey (NHANES) 1999-2004. Am J Kidney Dis 2008; 51 (Suppl 2): S13–S20.
Hsu CY, McCulloch CE, Iribarren C, Darbinian J, Go AS . Body mass index and risk for end-stage renal disease. Ann Int Med 2006; 144: 21–28.
Foster MC, Hwang SJ, Larson MG, Lichtman JH, Parikh NI, Vasan RS et al. Overweight, obesity, and the development of stage 3 CKD: the Framingham Heart Study. Am J Kidney Dis 2008; 52: 39–48.
Ritz E . Metabolic syndrome and kidney disease. Blood Purif 2008; 26: 59–62.
Poirier P, Despres JP . Waist circumference, visceral obesity, and cardiovascular risk. J Cardiopulm Rehabil 2003; 23: 161–169.
Klein S . The case of visceral fat: argument for the defense. J Clin Invest 2004; 113: 1530–1532.
Kanaya AM, Harris T, Goodpaster BH, Tylavsky F, Cummings SR, Health A et al. Adipocytokines attenuate the association between visceral adiposity and diabetes in older adults. Diabetes Care 2004; 27: 1375–1380.
Katzmarzyk PT, Leon AS, Wilmore JH, Skinner JS, Rao DC, Rankinen T et al. Targeting the metabolic syndrome with exercise: evidence from the HERITAGE Family Study. Med Sci Sports Exerc 2003; 35: 1703–1709.
Pollex RL, Hegele RA . Genetic determinants of the metabolic syndrome. Nat Clin Pract Cardiovasc Med 2006; 3: 482–489.
WHO/IASO/IOTF The Asia-Pacific perspective: redefining obesity and its treatment: Health Communications Australia Pty Ltd, 2000.
Obesity: preventing and managing the global epidemic. Report of a WHO consultation. World Health Organ Tech Rep Ser 2000; 894: i–xii. 1-253.
Ashwell M, Gibson S . Waist to height ratio is a simple and effective obesity screening tool for cardiovascular risk factors: analysis of data from the British National Diet And Nutrition Survey of adults aged 19-64 years. Obes Facts 2009; 2: 97–103.
Browning LM, Hsieh SD, Ashwell M . A systematic review of waist-to-height ratio as a screening tool for the prediction of cardiovascular disease and diabetes: 0.5 could be a suitable global boundary value. Nutr Res Rev 2010; 23: 247–269.
Deurenberg-Yap M, Chew SK, Deurenberg P . Elevated body fat percentage and cardiovascular risks at low body mass index levels among Singaporean Chinese, Malays and Indians. Obes Rev 2002; 3: 209–215.
Ruderman N, Chisholm D, Pi-Sunyer X, Schneider S . The metabolically obese, normal-weight individual revisited. Diabetes 1998; 47: 699–713.
Pinto-Sietsma SJ, Navis G, Janssen WM, de Zeeuw D, Gans RO, de Jong PE et al. A central body fat distribution is related to renal function impairment, even in lean subjects. Am J Kidney Dis 2003; 41: 733–741.
Dittmann K, Hannemann A, Wallaschofski H, Rettig R, Stracke S, Volzke H et al. U-shaped association between central body fat and the urinary albumin-to-creatinine ratio and microalbuminuria. BMC Nephrol 2013; 14: 87.
Watanabe H, Obata H, Watanabe T, Sasaki S, Nagai K, Aizawa Y . Metabolic syndrome and risk of development of chronic kidney disease: the Niigata preventive medicine study. Diabetes Metab Res Rev 2010; 26: 26–32.
Miyatake N, Shikata K, Makino H, Numata T . Decreasing abdominal circumference is associated with improving estimated glomerular filtration rate (eGFR) with lifestyle modification in Japanese men: a pilot study. Acta Med Okayama. 2011; 65: 363–367.
Chen W, Chen W, Wang H, Dong X, Liu Q, Mao H et al. Prevalence and risk factors associated with chronic kidney disease in an adult population from southern China. Nephrol Dial Transplant 2009; 24: 1205–1212.
Sun F, Tao Q, Zhan S . Metabolic syndrome and the development of chronic kidney disease among 118 924 non-diabetic Taiwanese in a retrospective cohort. Nephrology 2010; 15: 84–92.
Kitiyakara C, Yamwong S, Cheepudomwit S, Domrongkitchaiporn S, Unkurapinun N, Pakpeankitvatana V et al. The metabolic syndrome and chronic kidney disease in a Southeast Asian cohort. Kidney Int 2007; 71: 693–700.
Choo V . WHO reassesses appropriate body-mass index for Asian populations. Lancet 2002; 360: 235.
American Diabetes Association. Standards of medical care in diabetes. Diabetes Care 2004; 27 (Suppl 1): S15–S35.
Ma YC, Zuo L, Chen JH, Luo Q, Yu XQ, Li Y et al. Modified glomerular filtration rate estimating equation for Chinese patients with chronic kidney disease. J Am Soc Nephrol 2006; 17: 2937–2944.
National Kidney Foundation. K/DOQI clinical practice guidelines for chronic kidney disease: evaluation, classification, and stratification. Am J Kidney Dis 2002; 39 (Suppl 1): S1–S266.
National Cholesterol Education Program Expert Panel. Third report of the National Cholesterol Education Program (NCEP) Expert Panel on Detection, Evaluation, and Treatment of High Blood Cholesterol in Adults (Adult Treatment Panel III) final report. Circulation. 2002; 106: 3143-421.
Chuang HH, Li WC, Sheu BF, Liao SC, Chen JY, Chang KC et al. Correlation between body composition and risk factors for cardiovascular disease and metabolic syndrome. Biofactors 2012; 38: 284–291.
Helal I, Fick-Brosnahan GM, Reed-Gitomer B, Schrier RW . Glomerular hyperfiltration: definitions, mechanisms and clinical implications. Nat Rev Nephrol 2012; 8: 293–300.
Levine DZ . Can rodent models of diabetic kidney disease clarify the significance of early hyperfiltration?: recognizing clinical and experimental uncertainties. Clin Sci 2008; 114: 109–118.
Kim JY, Han SH, Yang BM . Implication of high-body-fat percentage on CMR in middle-aged, healthy, normal-weight adults. Obesity 2012; 21: 1571–1577.
Romero-Corral A, Somers VK, Sierra-Johnson J, Korenfeld Y, Boarin S, Korinek J et al. Normal weight obesity: a risk factor for cardiometabolic dysregulation and cardiovascular mortality. Eur Heart J 2010; 31: 737–746.
Prospective Studies Collaboration, Prospective Studies Collaboration Whitlock G, Prospective Studies Collaboration Lewington S, Prospective Studies Collaboration Sherliker P, Prospective Studies Collaboration Clarke R, Prospective Studies Collaboration Emberson J et al. Body-mass index and cause-specific mortality in 900 000 adults: collaborative analyses of 57 prospective studies. Lancet 2009; 373: 1083–1096.
Ashwell M, Hsieh SD . Six reasons why the waist-to-height ratio is a rapid and effective global indicator for health risks of obesity and how its use could simplify the international public health message on obesity. Int J Food Sci Nutr 2005; 56: 303–307.
Rabe K, Lehrke M, Parhofer KG, Broedl UC . Adipokines and insulin resistance. Mol Med 2008; 14: 741–751.
Tritos NA, Mantzoros CS . Leptin: its role in obesity and beyond. Diabetologia 1997; 40: 1371–1379.
Yang X, Smith U . Adipose tissue distribution and risk of metabolic disease: does thiazolidinedione-induced adipose tissue redistribution provide a clue to the answer? Diabetologia 2007; 50: 1127–1139.
Rosenquist KJ, Pedley A, Massaro JM, Therkelsen KE, Murabito JM, Hoffmann U et al. Visceral and subcutaneous fat quality and CMR. JACC Cardiovasc Imaging 2013; 6: 762–771.
The authors declare no conflict of interest.
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Tsai, Y., Ho, C., Chen, J. et al. Impact of body composition on estimated glomerular filtration rate in relatively healthy adults in Taiwan. Eur J Clin Nutr 69, 34–39 (2015). https://doi.org/10.1038/ejcn.2014.66
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