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Association between MRI-based visceral adipose tissues and metabolic abnormality in a Chinese population: a cross-sectional study

Association between MRI-based visceral adipose tissues and metabolic abnormality in a Chinese... Background: Previous studies have indicated that the deposition of abdominal adipose tissue was associated with the abnormalities of cardiometabolic components. The aim of this study was to examine the relationship of visceral adipose tissue ( VAT ), subcutaneous adipose tissue (SAT ) and metabolic status and the different effects between males and females. Methods: The 1388 eligible subjects were recruited in a baseline survey of metabolic syndrome in China, from two communities in Hangzhou and Chengdu. Areas of abdominal VAT and SAT were measured by magnetic resonance imaging (MRI). Serum total triglycerides ( TG), high-density lipoprotein cholesterol (HDL-C) were measured by an auto- mated biochemical analyzer. Metabolic abnormality (MA) was defined more than one abnormal metabolic compo - nents, which was based on the definition of metabolic syndrome (IDF 2005). Multiple logistic regression was used to calculate the odds ratios (ORs) and 95% confidence intervals (95%CI). Predictive value was assessed by area under the curve (AUC), net reclassification improvement (NRI), and integrated discrimination improvement (IDI), respectively. Results: Their mean age was 53.8 years (SD: 7.1 years), the mean body mass index (BMI) was 23.7 kg/m , and 44.8% of the subjects were male. Both male and female with MA had higher VAT levels compared to subjects with normal metabolism (MN), and male had higher SAT levels than female (P < 0.05). Higher VAT was significantly associated with MA with ORs in the fourth quartile (Q4) of 6.537 (95% CI = 3.394–12.591) for male and 3.364 (95% CI = 1.898–5.962) for female (P for trend < 0.05). In female, VAT could increase the risk of metabolic abnormalities, but SAT could increase the risk of MA in the second and fourth quartiles (Q2 and Q4) only at BMI > 24 kg/m . In male, VAT improved the predic- tive value of MA compared to BMI and waist circumference ( WC), the AUC was 0.727 (95% CI = 0.687–0.767), the NRI *Correspondence: lqg3713@163.com; zhoujq27@yahoo.com; zhuym@zju.edu.cn Xuhui Zhang, Qiannan Chen and Xiaohui Sun have contributed equally to this work Department of Epidemiology and Biostatistics, School of Public Health, Zhejiang University, Hangzhou 310058, Zhejiang, China Department of Endocrinology and Metabolism, West China Hospital, Sichuan University, Chengdu 610000, Sichuan Province, China Department of Endocrinology, Sir Run Run Shaw Hospital Affiliated to School of Medicine, Zhejiang University, Hangzhou 310058, Zhejiang, China Full list of author information is available at the end of the article © The Author(s) 2022. 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The Creative Commons Public Domain Dedication waiver (http:// creat iveco mmons. org/ publi cdoma in/ zero/1. 0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data. Zhang et al. Nutrition & Metabolism (2022) 19:16 Page 2 of 8 was 0.139 (95% CI = 0.070–0.208) and 0.106 (95% CI = 0.038–0.173), and the IDI was 0.074 (95% CI = 0.053–0.095) and 0.046 (95% CI = 0.026–0.066). Similar results were found in female. Conclusions: In male, VAT and SAT could increase the risk of metabolic abnormalities both at BMI < 24 kg/m and at BMI ≥ 24 kg/m . In female, VAT could increase the risk of metabolic abnormalities but SAT could increase the risk of MA in the second and fourth quartiles (Q2 and Q4) only at BMI > 24 kg/m . Deposition of abdominal adipose tissue was associated with metabolic abnormalities. VAT improved the predictive power of MA. Keywords: Obesity, Subcutaneous adipose tissue (SAT ), Visceral adipose tissue ( VAT ), Body mass index (BMI), Metabolic abnormality (MA) Introduction sex. Therefore, we examined the relationship between Obesity, especially central obesity, is a well-established SAT, VAT and metabolic abnormality in male and female risk factor for a several diseases, such as dyslipidemia, separately. type 2 diabetes (T2DM), cardiovascular diseases (CVD), and all-cause mortality [1, 2]. Body mass index (BMI) and Materials and methods waist circumference (WC) were widely used to evaluate Subjects obesity. However, BMI does not fully characterize adipos- These subjects were recruited in a baseline investigation ity, which is limited by age, sex and race specific BMI in of metabolic syndrome investigation in China in 2010. body fat [3]. Although WC reflected central obesity and The participants were recruited if they were ≥ 18  years is readily available, it does not adequately reflect actual old, detailed information has been described in our pre- body adipose tissue distribution and therefore fail to dis- vious study [15]. In this study, a subpopulation from tinguish between abdominal subcutaneous adipose tissue two communities in Hangzhou (n = 1170) and Chengdu (SAT) and visceral adipose tissue (VAT), and SAT and (n = 761) was included. Subjects were excluded if they VAT have different metabolic consequences [4]. Studies had (1) severe chronic diseases including cardiovascular on the effects of SAT on metabolic abnormality are still diseases (including angina, cerebral infarction), cancers, inconclusive and even contradictive [1, 5–7]. Some stud- renal dysfunction and other chronic wasting diseases and ies have found SAT to be a beneficial fat depot for type 2 (2) missing anthropometric information, SAT and VAT diabetes and metabolic syndrome [7, 8], however, others data. A total of 1388 eligible subjects were eventually have not found a significant correlation between SAT and included. some components of metabolic abnormalities [5, 6, 9]. In This study was approved by the institutional Review addition, because of sex-difference in fat accumulation Board of Zhejiang University, China. All participants between male and female, the extent to which it affects provided their written informed consents. metabolic abnormalities across sexes is unclear. Some studies have found a causal relationship between higher SAT and VAT measurements VAT and cardiometabolic risk factors, with a greater Abdominal adipose tissue was measured by magnetic effect on female [10]. While other studies indicated that resonance imaging (MRI) using a whole-body imaging the absolute risk contribution of VAT to metabolic fac- system (SMT-100, Shimadzu, Japan) with TR-500 and tors is greater in male than in female [11], and various TE-200 of SE. MRI scans were performed at the inter- findings also emphasize the sex differences in regional fat face of the umbilicus (approximately the lower edge of distribution. L4) with the subject in the supine position. The area of One possible reason for these sophisticated associa- subcutaneous and visceral adipose tissue was calculated tions is the different fat distribution in different ethnic using sliceOmatic software (version 4.2). groups [12, 13]. In addition, the way in which SAT and VAT are measured and the adjustment for confounding Covariant assessment factors are also possible causes. Currently, techniques With a standard questionnaire, the information includ- to accurately assess regional adipose depots include ing age, sex, smoking (current, former, and never), alco- computed tomography (CT) and magnetic resonance hol drinking behaviors (never, moderate, and heavy), imaging (MRI) and so on [14]. MRI-based adipose tis- menstrual history (for female) and disease history were sue measurements that can directly quantify abdominal collected. Current smoking was defined as smoking at fat compartments and without radiation. In the Chinese least one cigarette per day and lasting for one year. Pre- population, limited studies have explored the effects of vious smoking was considered to have quit smoking for MRI-measured SAT, VAT on metabolic disorders by at least one year. They were classified as heavy drinkers, Zhang  et al. Nutrition & Metabolism (2022) 19:16 Page 3 of 8 moderate drinkers and never drinkers according to the were used to compare categorical variables. The subjects frequency of alcohol consumption: more than 3 times a were divided into four groups by quartiles of SAT and week was classified as heavy drinking. A local nurse or VAT, with the first quartile (Q1) as the reference group. investigator asked the subjects if they have any diseases The ORs and 95%CIs for each quartile using multiple such as hypertension and if they use medication. Anthro- logistic regression, adjusted for age, BMI (for overall), pometric variables were collected by trained investigators smoke, drink, menstrual history (for female). A two- following a standard protocol [15] and included weight, tailed P < 0.05 was considered statistically significant. height, waist circumference (WC), systolic blood pres- Two packages of “Predict ABEL” and “pROC” were used sure (SBP), and diastolic blood pressure (DBP). The pro - to calculate the net reclassification improvement (NRI), tocols were briefly described below. BMI was calculated integrated discrimination improvement (IDI), area under as weight (kg) divided by the square of height (m). Height curve (AUC) and so on. The software IBM SPSS Statistics and weight were measured when the subjects wore light version 25.0 and R 3.6.3 were used to analyze the data. clothing and without shoes. WC was measured at the midpoint between the iliac crest and lowest rib. Blood Results pressure was investigated in a seated position with a mer- The baseline characteristics of subjects cury sphygmomanometer. SBP and DBP were measured The baseline characteristics by sex are summarized in as the average of three repeat measurements with an Table  1. Of the 1388 subjects, 622 (44.8%) were male, interval of at least 30 s. 766 (55.2%) were female. Their mean age was 53.8  years The overnight fasting blood samples were collected for (SD = 7.1). The median VAT for male was 91.0 cm 2 2 2 each subject. Total triglycerides (TG), total cholesterol (55.1–127.4 cm ) higher than 60.4 cm (43.3–79.6 cm ) (TC), high-density lipoprotein cholesterol (HDL-C), low- for female (P < 0.05), and the median SAT for male was 2 2 2 density lipoprotein cholesterol (LDL-C) were measured 123.2 cm (98.1–149.8 c m ) lower than 178.2 c m (139.1– by biochemical auto-analyzers (Hitachi 7060, Tokyo, 221.6 cm ) for female (P < 0.05). Compared to female, Japan). Fasting plasma glucose (FPG) was analyzed using male had higher BMI, WC, WHR, VAT, SBP, DBP, FPG, the glucose oxidase method with a Beckman Glucose OGTT-2  h, TG, and higher prevalence of metabolic Analyzer (Beckman Instruments, Irvine, CA, USA). A 2 h abnormality; however, female had higher TC and HDL-C oral glucose tolerance test (OGTT-2h) was performed as (all the P values < 0.05). a routine procedure for the subjects, except for patients with previously diagnosed diabetes. Levels of SAT, VAT in different metabolic status stratified The metabolic abnormality component was defined by sex and BMI according to the 2005 International Diabetes Federation The levels of SAT and VAT in metabolic abnormalities (IDF) criteria for metabolic syndrome [16], including stratified by sex and BMI are presented in Table  2. In elevated TG ≥ 1.7  mmol/L, low HDL-C < 1.03  mmol/L male, the median SAT for MA was 130.6 cm (106.2– 2 2 2 (in male), < 1.29  mmol/L (in female); elevated 159.5 cm ) higher than 110.8 cm (80.3–141.3 cm ) for FPG ≥ 5.6  mmol/L or a history of diabetes, or used MN group. The median VAT for MA was 110.5 cm 2 2 2 antidiabetic drugs; elevated SBP ≥ 130  mmHg, or (75.2–136.8 cm ) higher than 64.5 c m (32.1–101.2 c m ) DBP ≥ 85  mmHg or used antihypertensive drugs. Meta- for MN group (all the P values < 0.05). Similar results bolic abnormality (MA) was defined as more than one were found in female (Table 2). abnormal metabolic component, and metabolic normal- When stratified by levels of BMI at 24  kg/m , subjects ity (MN) was defined as zero or only one abnormal meta - with MA had significantly higher levels of SAT and VAT bolic component [15]. According to the BMI standard for than MN group in male. In female, only VAT was rela- Chinese adults proposed by the China Working Group tively high in MA, and the difference was statistically sig - on Obesity (WGOC), a BMI threshold of 24  kg/m [17] nificant (P < 0.05). was used to explore the correlation between SAT, VAT and MA in different Chinese populations at normal and The associations of different levels of SAT, VAT abnormal BMI. with metabolic abnormality stratified by sex and BMI Table  3 shows the associations of SAT, VAT with meta- Statistical analysis bolic abnormality stratified by sex and BMI after adjusted Continuous variables are presented as mean ± stand- for age, BMI (for overall), smoke, drink, and menstrual ard deviation (SD) or median and inter-quartile range history (for female). In male and female, VAT was sig- (IQR). Categorical variables were shown as numbers nificantly correlated with the higher risk of MA (P for (%). Student’s t-test or the Wilcoxon rank-sum test was trend < 0.05). Compared with the reference group for used to compare continuous variables. Chi-square tests the first quartile (Q1), the ORs in fourth quartile (Q4) Zhang et al. Nutrition & Metabolism (2022) 19:16 Page 4 of 8 Table 1 Characteristics of the subjects stratified by sex Characteristics Total (n = 1388) Male (n = 622) Female (n = 766) P Age (years) 53.8 ± 7.1 53.6 ± 7.1 53.9 ± 7.1 0.539 BMI (kg/m ) 23.70 ± 2.99 24.18 ± 2.99 23.31 ± 2.93 < 0.001 WC (cm) 79.3 ± 9.0 83.3 ± 8.4 76.0 ± 8.1 < 0.001 WHR 0.87 ± 0.07 0.90 ± 0.06 0.84 ± 0.06 < 0.001 SAT area (cm ) 148.5 (112.9–194.7) 123.2 (98.1–149.8) 178.2 (139.1–221.6) < 0.001 VAT area (cm ) 69.5 (45.5–107.2) 91.0 (55.1–127.4) 60.4 (43.3–79.6) < 0.001 SBP (mmHg) 120.5 ± 15.8 123.7 ± 15.4 117.8 ± 15.6 < 0.001 DBP (mmHg) 79.5 ± 9.8 82.4 ± 9.8 77.2 ± 9.2 < 0.001 FPG (mmol/L) 5.12 ± 1.17 5.23 ± 1.41 5.03 ± 0.92 0.003 OGTT-2 h (mmol/L) 6.65 ± 3.30 6.97 ± 3.93 6.38 ± 2.65 0.002 TC (mmol/L) 5.28 ± 1.08 5.13 ± 1.00 5.40 ± 1.13 < 0.001 TG (mmol/L) 1.30 (0.90–1.87) 1.47 (1.00–2.19) 1.20 (0.85–1.70) < 0.001 HDL-C (mmol/L) 1.48 ± 0.36 1.36 ± 0.33 1.58 ± 0.36 < 0.001 LDL-C (mmol/L) 2.59 ± 0.67 2.57 ± 0.67 2.61 ± 0.66 0.273 MA (n, %) 667 (48.1%) 357 (57.4) 310 (40.5) < 0.001 Data are presented as means ± standard deviation or medians (inter-quartile ranges) or n (percentage). BMI body mass index, WC waist circumference, WHR waist-to- hip ratio, SAT subcutaneous adipose tissue, VAT visceral adipose tissue, SBP systolic blood pressure, DBP diastolic blood pressure, FPG fasting plasma glucose, OGTT-2h 2 h post oral glucose tolerance test, TC total cholesterol, TG triglyceride, HDL-C high density lipoprotein cholesterol, LDL-C low density lipoprotein cholesterol, MA metabolic abnormality, which was defined as metabolic abnormal components ≥ 2, which were based on the definition of metabolic syndrome (IDF 2005) Table 2 Levels of SAT, VAT in different metabolic status stratified by sex and BMI Male Female MN MA P MN MA P Overall SAT 110.8 (80.3–141.3) 130.6 (106.2–159.5) < 0.001 170.3 (133.9–205.9) 191.0 (148.8–239.3) < 0.001 VAT 64.5 (32.1–101.2) 110.5 (75.2–136.8) < 0.001 53.4 (36.0–68.8) 75.2 (55.9–107.2) < 0.001 BMI < 24 kg/m SAT 90.7 (66.92–112.2) 106.5 (82.7–121.4) 0.001 154.5 (125.9–183.1) 155.2 (126.7–192.6) 0.378 VAT 42.1 (22.8–67.8) 77.10 (53.90–104.05) < 0.001 48.9 (34.2–62.6) 59.9 (46.6–78.3) < 0.001 BMI ≥ 24 kg/m SAT 139.5 (116.1–161.7) 148.0 (125.6–180.9) 0.004 212.5 (180.8–250.6) 223.6 (184.3–265.0) 0.066 VAT 97.8 (69.4–122.9) 126.6 (101.7–155.8) < 0.001 66.4 (47.0–92.7) 94.0 (68.0–123.1) < 0.001 Data are presented as medians (inter-quartile ranges) BMI body mass index, MN metabolic normality, which was defined as abnormally metabolic components ≤ 1, MA metabolic abnormality, which was defined as metabolic abnormal components ≥ 2 2 2 were 6.537 (95% CI = 3.394–12.591) for male and 3.364 BMI < 24  kg/m and BMI ≥ 24  kg/m (P for trend < 0.05). (95% CI = 1.898–5.962) for female, respectively. How- In female, SAT could increase the risk of MA only when ever, there was no association between SAT and MA BMI ≥ 24  kg/m . Additional File 1: Table  S2 show the when BMI was not grouped. Since there are relatively relationship between SAT, VAT and metabolic compo- few Q3 and Q4 males with metabolic abnormality when nents, indicating that SAT may be a protective factor for BMI < 24  kg/m , so we put Q3 and Q4 males together for high BS (blood sugar) in female, with an OR for Q4 was analysis. 0.383 (0.185–0.792) (P for trend < 0.05). When stratified by BMI level of 24  kg/m , VAT was found to be significantly associated with MA in both The predictive abilities of VAT and SAT for metabolic male and female. However, for SAT, different effects abnormality were found between males and females. In male, SAT Table  4 describes the predictive abilities of VAT and were consistently associated with the risk of MA for both SAT for metabolic abnormality. In male, the AUC Zhang  et al. Nutrition & Metabolism (2022) 19:16 Page 5 of 8 Table 3 The relationships between SAT, VAT and metabolic abnormality stratified by sex and BMI Male Female n % OR (95%CI) n % OR (95%CI) Overall SAT Q1 60 38.7 ref 60 31.4 Ref Q2 91 58.7 1.458 (0.878–2.421) 70 36.6 0.833 (0.518–1.338) Q3 97 62.6 1.344 (0.762–2.371) 76 39.4 0.667 (0.403–1.103) Q4 109 70.3 1.391 (0.707–2.735) 104 54.5 0.576 (0.319–1.040) P for trend 0.445 0.05 VAT Q1 45 29 ref 40 20.9 ref Q2 85 54.5 2.530 (1.512–4.232) 64 33.3 1.495 (0.914–2.444) Q3 105 67.3 3.939 (2.199–7.053) 77 40.1 1.565 (0.946–2.589) Q4 122 78.7 6.537 (3.394–12.591) 129 67.5 3.364 (1.898–5.962) P for trend < 0.001 < 0.001 BMI < 24 kg/m SAT Q1 50 38.8 ref 47 30.1 ref Q2 54 56.8 2.062 (1.177–3.613) 49 30.8 0.911 (0.549–1.511) Q3 and Q4 32 56.1 2.121 (1.103–4.078) 31 29.5 0.896 (0.510–1.575) Q4 20 47.6 1.631 (0.781–3.407) P for trend 0.009 0.463 VAT Q1 36 28.1 ref 29 19.5 Ref Q2 52 59.1 3.505 (1.945–6.314) 46 31.1 1.631 (0.936–2.845) Q3 and Q4 48 71.6 6.026 (3.079–11.795) 40 33.6 1.770 (0.988–3.168) Q4 32 69.6 7.422 (3.422–16.095) P for trend < 0.001 < 0.001 BMI ≥ 24 kg/m SAT Q1 10 38.5 ref 13 37.1 Ref Q2 37 61.7 2.516 (0.949–6.672) 21 65.6 4.753 (1.531–14.755) Q3 70 64.8 2.823 (1.132–7.039) 45 51.1 2.474 (0.968–6.323) Q4 104 71.7 3.862 (1.573–9.484) 84 56.4 2.502 (1.021–6.129) P for trend 0.005 0.350 VAT Q1 9 33.3 ref 11 26.2 ref Q2 33 48.5 1.703 (0.656–4.420) 18 40.9 2.185 (0.803–5.944) Q3 71 64 3.244 (1.305–8.064) 37 50.7 2.576 (1.024–6.478) Q4 108 81.2 7.836 (3.086–19.893) 97 66.9 4.607 (1.909–11.118) P for trend < 0.001 < 0.001 Data are presented as OR (95%CI). The "n" was the case of MA, and "%" means the proportion of MA in the subgroups BMI body mass index. The ORs was adjusted for age, BMI (for overall), smoke, drink, and menstrual history (for female). Male: SAT:Q1 (< 98.1), Q2 (98.1−), Q3 (123.2−), Q4 (149.8−); VAT:Q1 (< 55.1), Q2 (55.1−), Q3 (91.00−), Q4 (127.4−); Female:SAT:Q1 (< 139.1), Q2 (139.1−),Q3 (178.2−),Q4 (221.6−); VAT:Q1 (< 43.0), Q2 (43.0−), Q3 (60.4−), Q4 (79.6−) MA compared with BMI or WC, with NRIs (95%CI) of of VAT was 0.727 (95%CI = 0.687–0.767), signifi - 0.139 (0.070, 0.208) and 0.106 (0.038, 0.173), respec- cantly higher than BMI (0.658, 95%CI = 0.614–0.701) tively; and the IDIs (95%CI) were 0.074 (0.053, 0.095) and WC (0.688, 95%CI = 0.646–0.730) (all the P val- and 0.046 (0.026, 0.066), respectively. But SAT was less ues < 0.05). VAT could improve the predictive value of Zhang et al. Nutrition & Metabolism (2022) 19:16 Page 6 of 8 Table 4 The predictive values on metabolic abnormality in BMI, WC, SAT and VAT AUC (95%CI) Z P NRI (95%CI) * P IDI (95%CI)* P Male BMI 0.658 (0.614–0.701) WC 0.688 (0.646–0.730) SAT 0.639 (0.594–0.683) 1.160 0.246 0.070 (− 0.004, 0.144) 0.062 − 0.022 (− 0.036, − 0.007) 0.003 SAT* 3.012 0.003 − 0.029 (− 0.102, 0.044) 0.442 − 0.050 (− 0.066, − 0.034) < 0.001 VAT 0.727 (0.687–0.767) − 3.864 < 0.001 0.139 (0.070, 0.208) < 0.001 0.074 (0.053, 0.095) < 0.001 VAT* − 2.458 0.014 0.106 (0.038, 0.173) 0.003 0.046 (0.026, 0.066) < 0.001 Female BMI 0.666 (0.627–0.705) WC 0.693 (0.655–0.732) SAT 0.602 (0.560–0.643) 3.895 < 0.001 − 0.057 (− 0.125, 0.012) 0.106 − 0.052 (− 0.067, − 0.037) < 0.001 SAT* 5.095 < 0.001 − 0.118 (− 0.188, − 0.048) 0.001 − 0.074 (− 0.092, − 0.056) < 0.001 VAT 0.712 (0.674–0.749) − 2.562 0.010 0.112 (0.037, 0.188) 0.004 0.050 (0.028, 0.072) < 0.001 VAT* − 1.115 0.265 0.042 (− 0.031, 0.114) 0.261 0.028 (0.007, 0.049) 0.008 *The predictive values in VAT and SAT compared to WC AUC area under curve, NRI net reclassification improvement, IDI integrated discriminationimprovement predictive of metabolic abnormalities than WC and activity of VAT and its accompanying inflammatory BMI. response also contribute to abnormal lipogenesis, glu- Similar results were found in female (Table  4), with an cose homeostasis, and vascular health [23, 24]. Thus, a AUC of 0.712 (95%CI = 0.674–0.749) for VAT, signifi - higher VAT may increase the risk of developing meta- cantly higher than BMI (0.666, 95%CI = 0.627–0.705) and bolic abnormalities. With regards to the contribution of WC (0.693, 95%CI = 0.655–0.732) (all the P values < 0.05). VAT in different sex, inconclusive results were reported Compared with BMI and WC, VAT improved the predic- [10, 11, 25–27]. Several Caucasian studies have shown tive value. that VAT is more strongly associated with type 2 diabe- tes, hypertension and hyperlipidemia in female [10, 25, Discussion 28]. In our Additional file  1: Table  S3, we observed that In this cross-sectional study, we found that higher VAT, the effect of VAT on high TG and low-HDL was higher in but not SAT, was associated with the risk of MA when male, indicating that VAT may have more striking effect BMI was used as a covariate. However, after BMI strati- on lipid metabolism in male than female. The possible fication, SAT and VAT in men could increase the risk of reason maybe that only a limited number of confounders MA at all levels of BMI. For women, SAT could increase were adjusted, which may have affected the results. An the risk of MA in the second and fourth quartiles (Q2 expanded study of the Chinese population is necessary to and Q4) only at BMI > 24  kg/m . Compared with BMI determine the gender differences in the contribution of and WC, VAT improved the predictive power of MA. VAT. In general, the relationship between VAT and meta- Deposition of abdominal adipose tissue was associated bolic outcomes is relatively stable, which may be related with the risk of MA. to multiple biological effects of VAT. In fact, there are some differences between SAT and SAT is known to have adverse effects on a variety of VAT in anatomy, cytology, molecular, physiology, clini- metabolic risk factors and may have unique pathogenic cal and so on [18]. The VAT is considered to be the more properties independent of BMI [1, 6, 25, 29], and the pathogenic adipose tissue compartment compared to the effects of different levels of SAT on cardiometabolic fac - SAT [19]. This may be related to the biological function tors are inconsistent [1, 6, 13, 19, 25, 30]. Consistent with of VAT, a metabolically active organ that includes more previous studies [30–32], our study (See Additional file  1: non-adipocytes, including macrophages, immune cells, Tables S1, S2) showed that higher SAT was not associ- preadipocytes and fibroblasts, and can secrete amounts ated with hypertension, higher TG, and lower HDL-C of inflammation mediators to induce metabolic disor - risk after adjustment for age, smoke, drink, and menstrual ders [18, 20–22]. And in our Additional file  1: Table  S1, history (for women), and SAT may be a protective factor We found that VAT was positively associated with both for blood sugar. Several studies with European or African high TG and low HDL-C. In addition, the high lipolytic populations have found independent associations of SAT Zhang  et al. Nutrition & Metabolism (2022) 19:16 Page 7 of 8 Abbreviations with high blood pressure (H-BP) and HDL-C [1, 14, 26], SAT: Subcutaneous adipose tissue; VAT: Visceral adipose tissue; MRI: Nuclear suggesting that SAT has different effects in different eth - magnetic resonance imaging; BMI: Body mass index; WC: Waist circumference; nic groups. A possible explanation for this sex difference WHR: Waist-to-hip ratio; SBP: Systolic blood pressure; DBP: Diastolic blood pressure; FPG: Fasting plasma glucose; OGTT-2h: 2-Hour post oral glucose in SAT is the different sex steroid hormone profiles, as tolerance test; TC: Total cholesterol; TG: Triglyceride; HDL-C: High density lipo- these sex hormones are important in regulating adipose protein cholesterol; LDL-C: Low density lipoprotein cholesterol; MA: Metabolic tissue distribution and energy metabolism [33, 34]. There abnormality; MN: Metabolic normality. are also several hypotheses for the protective effect of SAT to explain this observation. One is that smaller adipo- Supplementary Information cytes, SAT are more sensitive to insulin and have a greater The online version contains supplementary material available at https:// doi. org/ 10. 1186/ s12986- 022- 00651-x. capacity to absorb fatty acids and triglycerides and there- fore can act as a powerful buffer to prevent excess fat from Additional file 1. Supplementary Tables. entering non-adipose tissue [35]. On the other hand, SAT can secrete more favorable adipokines such as adiponec- tin, with antidiabetics and antiatherogenic properties Acknowledgements We also would like to thank all the participants and investigators that took [18, 23]. Therefore, the different effects of SAT on meta - part in this study. bolic outcomes may be related to its biological functions. Since SAT has different effects on metabolic components Authors’ contributions Conceptualization, YZ; Data curation, QC; Formal analysis, QC and XS; Funding in different sexes, it may result in a less stable correlation acquisition, XZ and YZ; Investigation, QL and JZ; Project administration, XZ and between SAT and metabolic abnormality. YZ; Supervision, YZ; Writing—original draft, XZ and QC; Writing—review and Previous studies have shown that baseline and changes editing, YZ, QW, and ZC. All authors read and approved the final manuscript. in VAT were independent predictors of future dyslipi- Funding demia, but BMI and SAT were not associated with future This work was supported by the grants from National Key Research and development of atherosclerotic dyslipidemia [36]. This Development Program of China (2017YFC0907004), Hangzhou Science and Technology Project (20171226Y27), and Zhejiang Health Science and technol- result is consistent to our study that VAT is a better pre- ogy Project (2021KY268), Key Medical Discipline of Hangzhou (Disinfection dictor for MA compared with BMI and WC. and Vector Biological Control). The funder has no role in the design of the There are some advantages in our study. Areas of SAT study, collection, analysis, and interpretation of data. and VAT were measured using MRI, which is the gold Availability of data and materials standard method of determining abdominal adipose tis- The datasets used and/or analyzed during the current study are available from sue [37]. The data, including anthropometric and ques - the corresponding author on reasonable request. tionnaire-based information, were collected by trained health professionals, and the biochemical measurements Declarations followed the standard protocols. Our study also has some Ethics approval and consent to participate limitations. First, we cannot infer a causal relationship The study was approved by the institutional review board at Zhejiang between the adipose indices and the metabolic abnor- University, Zhejiang, China. All participants were given their written informed consents. mality because of the cross-sectional design. Second, this study included limited confounding factors, such as Consent for publication not including regional fat distribution, such as deep SAT Not applicable. and superficial SAT, and medication use, which may have Competing interests biased the results. Thirdly, the sample size of this study The authors declare that they have no competing interests. was relatively small. Finally, our data were based on only Author details one single ethnic group, thus the results may not be Hangzhou Center for Disease Control and Prevention, Hangzhou 310051, applied to other ethnicities. Zhejiang, China. Affiliated Hangzhou Center of Disease Control and Preven- tion, Zhejiang University School of Public Health, Hangzhou 310051, Zhejiang, China. Basic Discipline of Chinese and Western Integrative, School of Public Conclusions Health, Zhejiang Chinese Medical University, Hangzhou 310053, Zhejiang, In male, VAT and SAT could increase the risk of meta- China. Department of Epidemiology and Biostatistics, School of Public bolic abnormalities both at BMI < 24  kg/m and at Health, Zhejiang Chinese Medical University, Hangzhou 310053, Zhejiang, China. Department of Epidemiology and Biostatistics, School of Public BMI ≥ 24  kg/m . In female, VAT could increase the risk Health, Zhejiang University, Hangzhou 310058, Zhejiang, China. Depar tment of metabolic abnormalities but SAT could increase the of Endocrinology and Metabolism, West China Hospital, Sichuan University, risk of MA in the second and fourth quartiles (Q2 and Chengdu 610000, Sichuan Province, China. Department of Endocrinology, Sir Run Run Shaw Hospital Affiliated to School of Medicine, Zhejiang University, Q4) only at BMI > 24  kg/m . Deposition of abdominal Hangzhou 310058, Zhejiang, China. Department of Respiratory Diseases, Sir adipose tissue was associated with metabolic abnormali- Run Run Shaw Hospital Affiliated to School of Medicine, Zhejiang Univer - ties. VAT improved the predictive power of MA. sity, Hangzhou 310058, Zhejiang, China. Department of Pathology, School of Medicine, Zhejiang University, Hangzhou 310058, Zhejiang, China. Zhang et al. Nutrition & Metabolism (2022) 19:16 Page 8 of 8 Received: 16 October 2021 Accepted: 17 February 2022 21. Despres JP, Lemieux I, Bergeron J, Pibarot P, Mathieu P, Larose E, et al. Abdominal obesity and the metabolic syndrome: contribution to global cardiometabolic risk. Arterioscler Thromb Vasc Biol. 2008;28(6):1039–49. 22. Misra A, Vikram NK. Clinical and pathophysiological consequences of abdominal adiposity and abdominal adipose tissue depots. Nutrition (Burbank, Los Angeles County, Calif ). 2003;19(5):457–66. References 23. Chait A, den Hartigh LJ. Adipose tissue distribution, inflammation and its 1. Abraham TM, Pedley A, Massaro JM, Hoffmann U, Fox CS. Association metabolic consequences, including diabetes and cardiovascular disease. between visceral and subcutaneous adipose depots and incident cardio- Front Cardiovasc Med. 2020;7:22. vascular disease risk factors. Circulation. 2015;132(17):1639–47. 24. Després JP, Lemieux I. Abdominal obesity and metabolic syndrome. 2. Rothney MP, Catapano AL, Xia J, Wacker WK, Tidone C, Grigore L, et al. Nature. 2006;444(7121):881–7. Abdominal visceral fat measurement using dual-energy X-ray: asso- 25. Fox CS, Massaro JM, Hoffmann U, Pou KM, Maurovich-Horvat P, Liu CY, ciation with cardiometabolic risk factors. Obesity (Silver Spring, Md). et al. Abdominal visceral and subcutaneous adipose tissue compart- 2013;21(9):1798–802. ments: association with metabolic risk factors in the Framingham Heart 3. Jackson AS, Stanforth PR, Gagnon J, Rankinen T, Leon AS, Rao DC, et al. Study. Circulation. 2007;116(1):39–48. The effect of sex, age and race on estimating percentage body fat from 26. Rønn PF, Andersen GS, Lauritzen T, Christensen DL, Aadahl M, Carstensen body mass index: The Heritage Family Study. Int J Obes Relat Metab B, et al. Abdominal visceral and subcutaneous adipose tissue and associa- Disord J Int Assoc Study Obes. 2002;26(6):789–96. tions with cardiometabolic risk in Inuit, Africans and Europeans: a cross- 4. Keum N, Lee DH, Kim R, Greenwood DC, Giovannucci EL. Visceral adipos- sectional study. BMJ Open. 2020;10(9):e038071. ity and colorectal adenomas: dose-response meta-analysis of observa- 27. Bidulescu A, Liu J, Hickson DA, Hairston KG, Fox ER, Arnett DK, et al. Gen- tional studies. Ann Oncol. 2015;26(6):1101–9. der differences in the association of visceral and subcutaneous adiposity 5. Chen P, Hou X, Hu G, Wei L, Jiao L, Wang H, et al. Abdominal subcutane- with adiponectin in African Americans: the Jackson Heart Study. BMC ous adipose tissue: a favorable adipose depot for diabetes? Cardiovasc Cardiovasc Disord. 2013;13:9. Diabetol. 2018;17(1):93. 28. Borel AL, Nazare JA, Smith J, Aschner P, Barter P, Van Gaal L, et al. Visceral, 6. Liu J, Fox CS, Hickson DA, May WD, Hairston KG, Carr JJ, et al. Impact of subcutaneous abdominal adiposity and liver fat content distribution abdominal visceral and subcutaneous adipose tissue on cardiometa- in normal glucose tolerance, impaired fasting glucose and/or impaired bolic risk factors: the Jackson Heart Study. J Clin Endocrinol Metab. glucose tolerance. Int J Obes (Lond). 2015;39(3):495–501. 2010;95(12):5419–26. 29. Oka R, Miura K, Sakurai M, Nakamura K, Yagi K, Miyamoto S, et al. 7. Kwon H, Kim D, Kim JS. Body fat distribution and the risk of incident met- Impacts of visceral adipose tissue and subcutaneous adipose tissue on abolic syndrome: a longitudinal cohort study. Sci Rep. 2017;7(1):10955. metabolic risk factors in middle-aged Japanese. Obesity (Silver Spring). 8. Porter SA, Massaro JM, Hoffmann U, Vasan RS, O’Donnel CJ, Fox CS. 2010;18(1):153–60. Abdominal subcutaneous adipose tissue: a protective fat depot? Diabe- 30. Matsha TE, Ismail S, Speelman A, Hon GM, Davids S, Erasmus RT, et al. tes Care. 2009;32(6):1068–75. Visceral and subcutaneous adipose tissue association with metabolic 9. Hoyer D, Boyko EJ, McNeely MJ, Leonetti DL, Kahn SE, Fujimoto syndrome and its components in a South African population. Clin Nutr WY. Subcutaneous thigh fat area is unrelated to risk of type 2 dia- ESPEN. 2019;32:76–81. betes in a prospective study of Japanese Americans. Diabetologia. 31. Koh H, Hayashi T, Sato KK, Harita N, Maeda I, Nishizawa Y, et al. Visceral 2011;54(11):2795–800. adiposity, not abdominal subcutaneous fat area, is associated with 10. Karlsson T, Rask-Andersen M, Pan G, Höglund J, Wadelius C, Ek WE, et al. high blood pressure in Japanese men: the Ohtori study. Hypertens Res. Contribution of genetics to visceral adiposity and its relation to cardio- 2011;34(5):565–72. vascular and metabolic disease. Nat Med. 2019;25(9):1390–5. 32. Zhao X, Gang X, Liu Y, Sun C, Han Q, Wang G. Using metabolomic profiles 11. Chen Y, Zhang Z, Wang J, Sun H, Zhao X, Cheng X, et al. Sex differences as biomarkers for insulin resistance in childhood obesity: a systematic in the association of abdominal adipose tissue and anthropometric data review. J Diabetes Res. 2016;2016:8160545. with untreated hypertension in a Chinese population. Biol Sex Differ. 33. Palmer BF, Clegg DJ. The sexual dimorphism of obesity. Mol Cell Endo- 2020;11(1):38. crinol. 2015;402:113–9. 12. Lesser IA, Gasevic D, Lear SA. The effect of body fat distribution on ethnic 34. Karastergiou K. The interplay between sex, ethnicity, and adipose tissue differences in cardiometabolic risk factors of Chinese and Europeans. characteristics. Curr Obes Rep. 2015;4(2):269–78. Appl Physiol Nutr Metab. 2013;38(7):701–6. 35. Kim S, Cho B, Lee H, Choi K, Hwang SS, Kim D, et al. Distribution of 13. Rønn PF, Andersen GS, Lauritzen T, Christensen DL, Aadahl M, Carstensen abdominal visceral and subcutaneous adipose tissue and metabolic B, et al. Ethnic differences in anthropometric measures and abdominal fat syndrome in a Korean population. Diabetes Care. 2011;34(2):504–6. distribution: a cross-sectional pooled study in Inuit, Africans and Europe- 36. Hwang YC, Fujimoto WY, Hayashi T, Kahn SE, Leonetti DL, Boyko EJ. ans. J Epidemiol Community Health. 2017;71(6):536–43. Increased visceral adipose tissue is an independent predictor for future 14. Bertoli S, Leone A, Vignati L, Spadafranca A, Bedogni G, Vanzulli A, et al. development of atherogenic dyslipidemia. J Clin Endocrinol Metab. Metabolic correlates of subcutaneous and visceral abdominal fat meas- 2016;101(2):678–85. ured by ultrasonography: a comparison with waist circumference. Nutr J. 37. Maislin G, Ahmed MM, Gooneratne N, Thorne-Fitzgerald M, Kim C, Teff K, 2016;15:2. et al. Single slice vs. volumetric MR assessment of visceral adipose tissue: 15. Zheng R, Yang M, Bao Y, Li H, Shan Z, Zhang B, et al. Prevalence and reliability and validity among the overweight and obese. Obesity (Silver determinants of metabolic health in subjects with obesity in Chinese Spring). 2012;20(10):2124–32. population. Int J Environ Res Public Health. 2015;12(11):13662–77. 16. Alberti KGMM, Zimmet P, Shaw J. The metabolic syndrome—a new worldwide definition. The Lancet. 2005;366(9491):1059–62. Publisher’s Note 17. Liu Y, Liu J, Gao Y, Zheng D, Pan W, Nie M, et al. The body composition in Springer Nature remains neutral with regard to jurisdictional claims in pub- early pregnancy is associated with the risk of development of gestational lished maps and institutional affiliations. diabetes mellitus late during the second trimester. Diabetes Metab Syndr Obes Targets Ther. 2020;13:2367–74. 18. Ibrahim MM. Subcutaneous and visceral adipose tissue: structural and functional differences. Obes Rev. 2010;11(1):11–8. 19. Tang L, Zhang F, Tong N. The association of visceral adipose tissue and subcutaneous adipose tissue with metabolic risk factors in a large popu- lation of Chinese adults. Clin Endocrinol (Oxf ). 2016;85(1):46–53. 20. Kahn CR, Wang G, Lee KY. Altered adipose tissue and adipocyte function in the pathogenesis of metabolic syndrome. J Clin Investig. 2019;129(10):3990–4000. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Nutrition & Metabolism Springer Journals

Association between MRI-based visceral adipose tissues and metabolic abnormality in a Chinese population: a cross-sectional study

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10.1186/s12986-022-00651-x
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Abstract

Background: Previous studies have indicated that the deposition of abdominal adipose tissue was associated with the abnormalities of cardiometabolic components. The aim of this study was to examine the relationship of visceral adipose tissue ( VAT ), subcutaneous adipose tissue (SAT ) and metabolic status and the different effects between males and females. Methods: The 1388 eligible subjects were recruited in a baseline survey of metabolic syndrome in China, from two communities in Hangzhou and Chengdu. Areas of abdominal VAT and SAT were measured by magnetic resonance imaging (MRI). Serum total triglycerides ( TG), high-density lipoprotein cholesterol (HDL-C) were measured by an auto- mated biochemical analyzer. Metabolic abnormality (MA) was defined more than one abnormal metabolic compo - nents, which was based on the definition of metabolic syndrome (IDF 2005). Multiple logistic regression was used to calculate the odds ratios (ORs) and 95% confidence intervals (95%CI). Predictive value was assessed by area under the curve (AUC), net reclassification improvement (NRI), and integrated discrimination improvement (IDI), respectively. Results: Their mean age was 53.8 years (SD: 7.1 years), the mean body mass index (BMI) was 23.7 kg/m , and 44.8% of the subjects were male. Both male and female with MA had higher VAT levels compared to subjects with normal metabolism (MN), and male had higher SAT levels than female (P < 0.05). Higher VAT was significantly associated with MA with ORs in the fourth quartile (Q4) of 6.537 (95% CI = 3.394–12.591) for male and 3.364 (95% CI = 1.898–5.962) for female (P for trend < 0.05). In female, VAT could increase the risk of metabolic abnormalities, but SAT could increase the risk of MA in the second and fourth quartiles (Q2 and Q4) only at BMI > 24 kg/m . In male, VAT improved the predic- tive value of MA compared to BMI and waist circumference ( WC), the AUC was 0.727 (95% CI = 0.687–0.767), the NRI *Correspondence: lqg3713@163.com; zhoujq27@yahoo.com; zhuym@zju.edu.cn Xuhui Zhang, Qiannan Chen and Xiaohui Sun have contributed equally to this work Department of Epidemiology and Biostatistics, School of Public Health, Zhejiang University, Hangzhou 310058, Zhejiang, China Department of Endocrinology and Metabolism, West China Hospital, Sichuan University, Chengdu 610000, Sichuan Province, China Department of Endocrinology, Sir Run Run Shaw Hospital Affiliated to School of Medicine, Zhejiang University, Hangzhou 310058, Zhejiang, China Full list of author information is available at the end of the article © The Author(s) 2022. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http:// creat iveco mmons. org/ licen ses/ by/4. 0/. The Creative Commons Public Domain Dedication waiver (http:// creat iveco mmons. org/ publi cdoma in/ zero/1. 0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data. Zhang et al. Nutrition & Metabolism (2022) 19:16 Page 2 of 8 was 0.139 (95% CI = 0.070–0.208) and 0.106 (95% CI = 0.038–0.173), and the IDI was 0.074 (95% CI = 0.053–0.095) and 0.046 (95% CI = 0.026–0.066). Similar results were found in female. Conclusions: In male, VAT and SAT could increase the risk of metabolic abnormalities both at BMI < 24 kg/m and at BMI ≥ 24 kg/m . In female, VAT could increase the risk of metabolic abnormalities but SAT could increase the risk of MA in the second and fourth quartiles (Q2 and Q4) only at BMI > 24 kg/m . Deposition of abdominal adipose tissue was associated with metabolic abnormalities. VAT improved the predictive power of MA. Keywords: Obesity, Subcutaneous adipose tissue (SAT ), Visceral adipose tissue ( VAT ), Body mass index (BMI), Metabolic abnormality (MA) Introduction sex. Therefore, we examined the relationship between Obesity, especially central obesity, is a well-established SAT, VAT and metabolic abnormality in male and female risk factor for a several diseases, such as dyslipidemia, separately. type 2 diabetes (T2DM), cardiovascular diseases (CVD), and all-cause mortality [1, 2]. Body mass index (BMI) and Materials and methods waist circumference (WC) were widely used to evaluate Subjects obesity. However, BMI does not fully characterize adipos- These subjects were recruited in a baseline investigation ity, which is limited by age, sex and race specific BMI in of metabolic syndrome investigation in China in 2010. body fat [3]. Although WC reflected central obesity and The participants were recruited if they were ≥ 18  years is readily available, it does not adequately reflect actual old, detailed information has been described in our pre- body adipose tissue distribution and therefore fail to dis- vious study [15]. In this study, a subpopulation from tinguish between abdominal subcutaneous adipose tissue two communities in Hangzhou (n = 1170) and Chengdu (SAT) and visceral adipose tissue (VAT), and SAT and (n = 761) was included. Subjects were excluded if they VAT have different metabolic consequences [4]. Studies had (1) severe chronic diseases including cardiovascular on the effects of SAT on metabolic abnormality are still diseases (including angina, cerebral infarction), cancers, inconclusive and even contradictive [1, 5–7]. Some stud- renal dysfunction and other chronic wasting diseases and ies have found SAT to be a beneficial fat depot for type 2 (2) missing anthropometric information, SAT and VAT diabetes and metabolic syndrome [7, 8], however, others data. A total of 1388 eligible subjects were eventually have not found a significant correlation between SAT and included. some components of metabolic abnormalities [5, 6, 9]. In This study was approved by the institutional Review addition, because of sex-difference in fat accumulation Board of Zhejiang University, China. All participants between male and female, the extent to which it affects provided their written informed consents. metabolic abnormalities across sexes is unclear. Some studies have found a causal relationship between higher SAT and VAT measurements VAT and cardiometabolic risk factors, with a greater Abdominal adipose tissue was measured by magnetic effect on female [10]. While other studies indicated that resonance imaging (MRI) using a whole-body imaging the absolute risk contribution of VAT to metabolic fac- system (SMT-100, Shimadzu, Japan) with TR-500 and tors is greater in male than in female [11], and various TE-200 of SE. MRI scans were performed at the inter- findings also emphasize the sex differences in regional fat face of the umbilicus (approximately the lower edge of distribution. L4) with the subject in the supine position. The area of One possible reason for these sophisticated associa- subcutaneous and visceral adipose tissue was calculated tions is the different fat distribution in different ethnic using sliceOmatic software (version 4.2). groups [12, 13]. In addition, the way in which SAT and VAT are measured and the adjustment for confounding Covariant assessment factors are also possible causes. Currently, techniques With a standard questionnaire, the information includ- to accurately assess regional adipose depots include ing age, sex, smoking (current, former, and never), alco- computed tomography (CT) and magnetic resonance hol drinking behaviors (never, moderate, and heavy), imaging (MRI) and so on [14]. MRI-based adipose tis- menstrual history (for female) and disease history were sue measurements that can directly quantify abdominal collected. Current smoking was defined as smoking at fat compartments and without radiation. In the Chinese least one cigarette per day and lasting for one year. Pre- population, limited studies have explored the effects of vious smoking was considered to have quit smoking for MRI-measured SAT, VAT on metabolic disorders by at least one year. They were classified as heavy drinkers, Zhang  et al. Nutrition & Metabolism (2022) 19:16 Page 3 of 8 moderate drinkers and never drinkers according to the were used to compare categorical variables. The subjects frequency of alcohol consumption: more than 3 times a were divided into four groups by quartiles of SAT and week was classified as heavy drinking. A local nurse or VAT, with the first quartile (Q1) as the reference group. investigator asked the subjects if they have any diseases The ORs and 95%CIs for each quartile using multiple such as hypertension and if they use medication. Anthro- logistic regression, adjusted for age, BMI (for overall), pometric variables were collected by trained investigators smoke, drink, menstrual history (for female). A two- following a standard protocol [15] and included weight, tailed P < 0.05 was considered statistically significant. height, waist circumference (WC), systolic blood pres- Two packages of “Predict ABEL” and “pROC” were used sure (SBP), and diastolic blood pressure (DBP). The pro - to calculate the net reclassification improvement (NRI), tocols were briefly described below. BMI was calculated integrated discrimination improvement (IDI), area under as weight (kg) divided by the square of height (m). Height curve (AUC) and so on. The software IBM SPSS Statistics and weight were measured when the subjects wore light version 25.0 and R 3.6.3 were used to analyze the data. clothing and without shoes. WC was measured at the midpoint between the iliac crest and lowest rib. Blood Results pressure was investigated in a seated position with a mer- The baseline characteristics of subjects cury sphygmomanometer. SBP and DBP were measured The baseline characteristics by sex are summarized in as the average of three repeat measurements with an Table  1. Of the 1388 subjects, 622 (44.8%) were male, interval of at least 30 s. 766 (55.2%) were female. Their mean age was 53.8  years The overnight fasting blood samples were collected for (SD = 7.1). The median VAT for male was 91.0 cm 2 2 2 each subject. Total triglycerides (TG), total cholesterol (55.1–127.4 cm ) higher than 60.4 cm (43.3–79.6 cm ) (TC), high-density lipoprotein cholesterol (HDL-C), low- for female (P < 0.05), and the median SAT for male was 2 2 2 density lipoprotein cholesterol (LDL-C) were measured 123.2 cm (98.1–149.8 c m ) lower than 178.2 c m (139.1– by biochemical auto-analyzers (Hitachi 7060, Tokyo, 221.6 cm ) for female (P < 0.05). Compared to female, Japan). Fasting plasma glucose (FPG) was analyzed using male had higher BMI, WC, WHR, VAT, SBP, DBP, FPG, the glucose oxidase method with a Beckman Glucose OGTT-2  h, TG, and higher prevalence of metabolic Analyzer (Beckman Instruments, Irvine, CA, USA). A 2 h abnormality; however, female had higher TC and HDL-C oral glucose tolerance test (OGTT-2h) was performed as (all the P values < 0.05). a routine procedure for the subjects, except for patients with previously diagnosed diabetes. Levels of SAT, VAT in different metabolic status stratified The metabolic abnormality component was defined by sex and BMI according to the 2005 International Diabetes Federation The levels of SAT and VAT in metabolic abnormalities (IDF) criteria for metabolic syndrome [16], including stratified by sex and BMI are presented in Table  2. In elevated TG ≥ 1.7  mmol/L, low HDL-C < 1.03  mmol/L male, the median SAT for MA was 130.6 cm (106.2– 2 2 2 (in male), < 1.29  mmol/L (in female); elevated 159.5 cm ) higher than 110.8 cm (80.3–141.3 cm ) for FPG ≥ 5.6  mmol/L or a history of diabetes, or used MN group. The median VAT for MA was 110.5 cm 2 2 2 antidiabetic drugs; elevated SBP ≥ 130  mmHg, or (75.2–136.8 cm ) higher than 64.5 c m (32.1–101.2 c m ) DBP ≥ 85  mmHg or used antihypertensive drugs. Meta- for MN group (all the P values < 0.05). Similar results bolic abnormality (MA) was defined as more than one were found in female (Table 2). abnormal metabolic component, and metabolic normal- When stratified by levels of BMI at 24  kg/m , subjects ity (MN) was defined as zero or only one abnormal meta - with MA had significantly higher levels of SAT and VAT bolic component [15]. According to the BMI standard for than MN group in male. In female, only VAT was rela- Chinese adults proposed by the China Working Group tively high in MA, and the difference was statistically sig - on Obesity (WGOC), a BMI threshold of 24  kg/m [17] nificant (P < 0.05). was used to explore the correlation between SAT, VAT and MA in different Chinese populations at normal and The associations of different levels of SAT, VAT abnormal BMI. with metabolic abnormality stratified by sex and BMI Table  3 shows the associations of SAT, VAT with meta- Statistical analysis bolic abnormality stratified by sex and BMI after adjusted Continuous variables are presented as mean ± stand- for age, BMI (for overall), smoke, drink, and menstrual ard deviation (SD) or median and inter-quartile range history (for female). In male and female, VAT was sig- (IQR). Categorical variables were shown as numbers nificantly correlated with the higher risk of MA (P for (%). Student’s t-test or the Wilcoxon rank-sum test was trend < 0.05). Compared with the reference group for used to compare continuous variables. Chi-square tests the first quartile (Q1), the ORs in fourth quartile (Q4) Zhang et al. Nutrition & Metabolism (2022) 19:16 Page 4 of 8 Table 1 Characteristics of the subjects stratified by sex Characteristics Total (n = 1388) Male (n = 622) Female (n = 766) P Age (years) 53.8 ± 7.1 53.6 ± 7.1 53.9 ± 7.1 0.539 BMI (kg/m ) 23.70 ± 2.99 24.18 ± 2.99 23.31 ± 2.93 < 0.001 WC (cm) 79.3 ± 9.0 83.3 ± 8.4 76.0 ± 8.1 < 0.001 WHR 0.87 ± 0.07 0.90 ± 0.06 0.84 ± 0.06 < 0.001 SAT area (cm ) 148.5 (112.9–194.7) 123.2 (98.1–149.8) 178.2 (139.1–221.6) < 0.001 VAT area (cm ) 69.5 (45.5–107.2) 91.0 (55.1–127.4) 60.4 (43.3–79.6) < 0.001 SBP (mmHg) 120.5 ± 15.8 123.7 ± 15.4 117.8 ± 15.6 < 0.001 DBP (mmHg) 79.5 ± 9.8 82.4 ± 9.8 77.2 ± 9.2 < 0.001 FPG (mmol/L) 5.12 ± 1.17 5.23 ± 1.41 5.03 ± 0.92 0.003 OGTT-2 h (mmol/L) 6.65 ± 3.30 6.97 ± 3.93 6.38 ± 2.65 0.002 TC (mmol/L) 5.28 ± 1.08 5.13 ± 1.00 5.40 ± 1.13 < 0.001 TG (mmol/L) 1.30 (0.90–1.87) 1.47 (1.00–2.19) 1.20 (0.85–1.70) < 0.001 HDL-C (mmol/L) 1.48 ± 0.36 1.36 ± 0.33 1.58 ± 0.36 < 0.001 LDL-C (mmol/L) 2.59 ± 0.67 2.57 ± 0.67 2.61 ± 0.66 0.273 MA (n, %) 667 (48.1%) 357 (57.4) 310 (40.5) < 0.001 Data are presented as means ± standard deviation or medians (inter-quartile ranges) or n (percentage). BMI body mass index, WC waist circumference, WHR waist-to- hip ratio, SAT subcutaneous adipose tissue, VAT visceral adipose tissue, SBP systolic blood pressure, DBP diastolic blood pressure, FPG fasting plasma glucose, OGTT-2h 2 h post oral glucose tolerance test, TC total cholesterol, TG triglyceride, HDL-C high density lipoprotein cholesterol, LDL-C low density lipoprotein cholesterol, MA metabolic abnormality, which was defined as metabolic abnormal components ≥ 2, which were based on the definition of metabolic syndrome (IDF 2005) Table 2 Levels of SAT, VAT in different metabolic status stratified by sex and BMI Male Female MN MA P MN MA P Overall SAT 110.8 (80.3–141.3) 130.6 (106.2–159.5) < 0.001 170.3 (133.9–205.9) 191.0 (148.8–239.3) < 0.001 VAT 64.5 (32.1–101.2) 110.5 (75.2–136.8) < 0.001 53.4 (36.0–68.8) 75.2 (55.9–107.2) < 0.001 BMI < 24 kg/m SAT 90.7 (66.92–112.2) 106.5 (82.7–121.4) 0.001 154.5 (125.9–183.1) 155.2 (126.7–192.6) 0.378 VAT 42.1 (22.8–67.8) 77.10 (53.90–104.05) < 0.001 48.9 (34.2–62.6) 59.9 (46.6–78.3) < 0.001 BMI ≥ 24 kg/m SAT 139.5 (116.1–161.7) 148.0 (125.6–180.9) 0.004 212.5 (180.8–250.6) 223.6 (184.3–265.0) 0.066 VAT 97.8 (69.4–122.9) 126.6 (101.7–155.8) < 0.001 66.4 (47.0–92.7) 94.0 (68.0–123.1) < 0.001 Data are presented as medians (inter-quartile ranges) BMI body mass index, MN metabolic normality, which was defined as abnormally metabolic components ≤ 1, MA metabolic abnormality, which was defined as metabolic abnormal components ≥ 2 2 2 were 6.537 (95% CI = 3.394–12.591) for male and 3.364 BMI < 24  kg/m and BMI ≥ 24  kg/m (P for trend < 0.05). (95% CI = 1.898–5.962) for female, respectively. How- In female, SAT could increase the risk of MA only when ever, there was no association between SAT and MA BMI ≥ 24  kg/m . Additional File 1: Table  S2 show the when BMI was not grouped. Since there are relatively relationship between SAT, VAT and metabolic compo- few Q3 and Q4 males with metabolic abnormality when nents, indicating that SAT may be a protective factor for BMI < 24  kg/m , so we put Q3 and Q4 males together for high BS (blood sugar) in female, with an OR for Q4 was analysis. 0.383 (0.185–0.792) (P for trend < 0.05). When stratified by BMI level of 24  kg/m , VAT was found to be significantly associated with MA in both The predictive abilities of VAT and SAT for metabolic male and female. However, for SAT, different effects abnormality were found between males and females. In male, SAT Table  4 describes the predictive abilities of VAT and were consistently associated with the risk of MA for both SAT for metabolic abnormality. In male, the AUC Zhang  et al. Nutrition & Metabolism (2022) 19:16 Page 5 of 8 Table 3 The relationships between SAT, VAT and metabolic abnormality stratified by sex and BMI Male Female n % OR (95%CI) n % OR (95%CI) Overall SAT Q1 60 38.7 ref 60 31.4 Ref Q2 91 58.7 1.458 (0.878–2.421) 70 36.6 0.833 (0.518–1.338) Q3 97 62.6 1.344 (0.762–2.371) 76 39.4 0.667 (0.403–1.103) Q4 109 70.3 1.391 (0.707–2.735) 104 54.5 0.576 (0.319–1.040) P for trend 0.445 0.05 VAT Q1 45 29 ref 40 20.9 ref Q2 85 54.5 2.530 (1.512–4.232) 64 33.3 1.495 (0.914–2.444) Q3 105 67.3 3.939 (2.199–7.053) 77 40.1 1.565 (0.946–2.589) Q4 122 78.7 6.537 (3.394–12.591) 129 67.5 3.364 (1.898–5.962) P for trend < 0.001 < 0.001 BMI < 24 kg/m SAT Q1 50 38.8 ref 47 30.1 ref Q2 54 56.8 2.062 (1.177–3.613) 49 30.8 0.911 (0.549–1.511) Q3 and Q4 32 56.1 2.121 (1.103–4.078) 31 29.5 0.896 (0.510–1.575) Q4 20 47.6 1.631 (0.781–3.407) P for trend 0.009 0.463 VAT Q1 36 28.1 ref 29 19.5 Ref Q2 52 59.1 3.505 (1.945–6.314) 46 31.1 1.631 (0.936–2.845) Q3 and Q4 48 71.6 6.026 (3.079–11.795) 40 33.6 1.770 (0.988–3.168) Q4 32 69.6 7.422 (3.422–16.095) P for trend < 0.001 < 0.001 BMI ≥ 24 kg/m SAT Q1 10 38.5 ref 13 37.1 Ref Q2 37 61.7 2.516 (0.949–6.672) 21 65.6 4.753 (1.531–14.755) Q3 70 64.8 2.823 (1.132–7.039) 45 51.1 2.474 (0.968–6.323) Q4 104 71.7 3.862 (1.573–9.484) 84 56.4 2.502 (1.021–6.129) P for trend 0.005 0.350 VAT Q1 9 33.3 ref 11 26.2 ref Q2 33 48.5 1.703 (0.656–4.420) 18 40.9 2.185 (0.803–5.944) Q3 71 64 3.244 (1.305–8.064) 37 50.7 2.576 (1.024–6.478) Q4 108 81.2 7.836 (3.086–19.893) 97 66.9 4.607 (1.909–11.118) P for trend < 0.001 < 0.001 Data are presented as OR (95%CI). The "n" was the case of MA, and "%" means the proportion of MA in the subgroups BMI body mass index. The ORs was adjusted for age, BMI (for overall), smoke, drink, and menstrual history (for female). Male: SAT:Q1 (< 98.1), Q2 (98.1−), Q3 (123.2−), Q4 (149.8−); VAT:Q1 (< 55.1), Q2 (55.1−), Q3 (91.00−), Q4 (127.4−); Female:SAT:Q1 (< 139.1), Q2 (139.1−),Q3 (178.2−),Q4 (221.6−); VAT:Q1 (< 43.0), Q2 (43.0−), Q3 (60.4−), Q4 (79.6−) MA compared with BMI or WC, with NRIs (95%CI) of of VAT was 0.727 (95%CI = 0.687–0.767), signifi - 0.139 (0.070, 0.208) and 0.106 (0.038, 0.173), respec- cantly higher than BMI (0.658, 95%CI = 0.614–0.701) tively; and the IDIs (95%CI) were 0.074 (0.053, 0.095) and WC (0.688, 95%CI = 0.646–0.730) (all the P val- and 0.046 (0.026, 0.066), respectively. But SAT was less ues < 0.05). VAT could improve the predictive value of Zhang et al. Nutrition & Metabolism (2022) 19:16 Page 6 of 8 Table 4 The predictive values on metabolic abnormality in BMI, WC, SAT and VAT AUC (95%CI) Z P NRI (95%CI) * P IDI (95%CI)* P Male BMI 0.658 (0.614–0.701) WC 0.688 (0.646–0.730) SAT 0.639 (0.594–0.683) 1.160 0.246 0.070 (− 0.004, 0.144) 0.062 − 0.022 (− 0.036, − 0.007) 0.003 SAT* 3.012 0.003 − 0.029 (− 0.102, 0.044) 0.442 − 0.050 (− 0.066, − 0.034) < 0.001 VAT 0.727 (0.687–0.767) − 3.864 < 0.001 0.139 (0.070, 0.208) < 0.001 0.074 (0.053, 0.095) < 0.001 VAT* − 2.458 0.014 0.106 (0.038, 0.173) 0.003 0.046 (0.026, 0.066) < 0.001 Female BMI 0.666 (0.627–0.705) WC 0.693 (0.655–0.732) SAT 0.602 (0.560–0.643) 3.895 < 0.001 − 0.057 (− 0.125, 0.012) 0.106 − 0.052 (− 0.067, − 0.037) < 0.001 SAT* 5.095 < 0.001 − 0.118 (− 0.188, − 0.048) 0.001 − 0.074 (− 0.092, − 0.056) < 0.001 VAT 0.712 (0.674–0.749) − 2.562 0.010 0.112 (0.037, 0.188) 0.004 0.050 (0.028, 0.072) < 0.001 VAT* − 1.115 0.265 0.042 (− 0.031, 0.114) 0.261 0.028 (0.007, 0.049) 0.008 *The predictive values in VAT and SAT compared to WC AUC area under curve, NRI net reclassification improvement, IDI integrated discriminationimprovement predictive of metabolic abnormalities than WC and activity of VAT and its accompanying inflammatory BMI. response also contribute to abnormal lipogenesis, glu- Similar results were found in female (Table  4), with an cose homeostasis, and vascular health [23, 24]. Thus, a AUC of 0.712 (95%CI = 0.674–0.749) for VAT, signifi - higher VAT may increase the risk of developing meta- cantly higher than BMI (0.666, 95%CI = 0.627–0.705) and bolic abnormalities. With regards to the contribution of WC (0.693, 95%CI = 0.655–0.732) (all the P values < 0.05). VAT in different sex, inconclusive results were reported Compared with BMI and WC, VAT improved the predic- [10, 11, 25–27]. Several Caucasian studies have shown tive value. that VAT is more strongly associated with type 2 diabe- tes, hypertension and hyperlipidemia in female [10, 25, Discussion 28]. In our Additional file  1: Table  S3, we observed that In this cross-sectional study, we found that higher VAT, the effect of VAT on high TG and low-HDL was higher in but not SAT, was associated with the risk of MA when male, indicating that VAT may have more striking effect BMI was used as a covariate. However, after BMI strati- on lipid metabolism in male than female. The possible fication, SAT and VAT in men could increase the risk of reason maybe that only a limited number of confounders MA at all levels of BMI. For women, SAT could increase were adjusted, which may have affected the results. An the risk of MA in the second and fourth quartiles (Q2 expanded study of the Chinese population is necessary to and Q4) only at BMI > 24  kg/m . Compared with BMI determine the gender differences in the contribution of and WC, VAT improved the predictive power of MA. VAT. In general, the relationship between VAT and meta- Deposition of abdominal adipose tissue was associated bolic outcomes is relatively stable, which may be related with the risk of MA. to multiple biological effects of VAT. In fact, there are some differences between SAT and SAT is known to have adverse effects on a variety of VAT in anatomy, cytology, molecular, physiology, clini- metabolic risk factors and may have unique pathogenic cal and so on [18]. The VAT is considered to be the more properties independent of BMI [1, 6, 25, 29], and the pathogenic adipose tissue compartment compared to the effects of different levels of SAT on cardiometabolic fac - SAT [19]. This may be related to the biological function tors are inconsistent [1, 6, 13, 19, 25, 30]. Consistent with of VAT, a metabolically active organ that includes more previous studies [30–32], our study (See Additional file  1: non-adipocytes, including macrophages, immune cells, Tables S1, S2) showed that higher SAT was not associ- preadipocytes and fibroblasts, and can secrete amounts ated with hypertension, higher TG, and lower HDL-C of inflammation mediators to induce metabolic disor - risk after adjustment for age, smoke, drink, and menstrual ders [18, 20–22]. And in our Additional file  1: Table  S1, history (for women), and SAT may be a protective factor We found that VAT was positively associated with both for blood sugar. Several studies with European or African high TG and low HDL-C. In addition, the high lipolytic populations have found independent associations of SAT Zhang  et al. Nutrition & Metabolism (2022) 19:16 Page 7 of 8 Abbreviations with high blood pressure (H-BP) and HDL-C [1, 14, 26], SAT: Subcutaneous adipose tissue; VAT: Visceral adipose tissue; MRI: Nuclear suggesting that SAT has different effects in different eth - magnetic resonance imaging; BMI: Body mass index; WC: Waist circumference; nic groups. A possible explanation for this sex difference WHR: Waist-to-hip ratio; SBP: Systolic blood pressure; DBP: Diastolic blood pressure; FPG: Fasting plasma glucose; OGTT-2h: 2-Hour post oral glucose in SAT is the different sex steroid hormone profiles, as tolerance test; TC: Total cholesterol; TG: Triglyceride; HDL-C: High density lipo- these sex hormones are important in regulating adipose protein cholesterol; LDL-C: Low density lipoprotein cholesterol; MA: Metabolic tissue distribution and energy metabolism [33, 34]. There abnormality; MN: Metabolic normality. are also several hypotheses for the protective effect of SAT to explain this observation. One is that smaller adipo- Supplementary Information cytes, SAT are more sensitive to insulin and have a greater The online version contains supplementary material available at https:// doi. org/ 10. 1186/ s12986- 022- 00651-x. capacity to absorb fatty acids and triglycerides and there- fore can act as a powerful buffer to prevent excess fat from Additional file 1. Supplementary Tables. entering non-adipose tissue [35]. On the other hand, SAT can secrete more favorable adipokines such as adiponec- tin, with antidiabetics and antiatherogenic properties Acknowledgements We also would like to thank all the participants and investigators that took [18, 23]. Therefore, the different effects of SAT on meta - part in this study. bolic outcomes may be related to its biological functions. Since SAT has different effects on metabolic components Authors’ contributions Conceptualization, YZ; Data curation, QC; Formal analysis, QC and XS; Funding in different sexes, it may result in a less stable correlation acquisition, XZ and YZ; Investigation, QL and JZ; Project administration, XZ and between SAT and metabolic abnormality. YZ; Supervision, YZ; Writing—original draft, XZ and QC; Writing—review and Previous studies have shown that baseline and changes editing, YZ, QW, and ZC. All authors read and approved the final manuscript. in VAT were independent predictors of future dyslipi- Funding demia, but BMI and SAT were not associated with future This work was supported by the grants from National Key Research and development of atherosclerotic dyslipidemia [36]. This Development Program of China (2017YFC0907004), Hangzhou Science and Technology Project (20171226Y27), and Zhejiang Health Science and technol- result is consistent to our study that VAT is a better pre- ogy Project (2021KY268), Key Medical Discipline of Hangzhou (Disinfection dictor for MA compared with BMI and WC. and Vector Biological Control). The funder has no role in the design of the There are some advantages in our study. Areas of SAT study, collection, analysis, and interpretation of data. and VAT were measured using MRI, which is the gold Availability of data and materials standard method of determining abdominal adipose tis- The datasets used and/or analyzed during the current study are available from sue [37]. The data, including anthropometric and ques - the corresponding author on reasonable request. tionnaire-based information, were collected by trained health professionals, and the biochemical measurements Declarations followed the standard protocols. Our study also has some Ethics approval and consent to participate limitations. First, we cannot infer a causal relationship The study was approved by the institutional review board at Zhejiang between the adipose indices and the metabolic abnor- University, Zhejiang, China. All participants were given their written informed consents. mality because of the cross-sectional design. Second, this study included limited confounding factors, such as Consent for publication not including regional fat distribution, such as deep SAT Not applicable. and superficial SAT, and medication use, which may have Competing interests biased the results. Thirdly, the sample size of this study The authors declare that they have no competing interests. was relatively small. Finally, our data were based on only Author details one single ethnic group, thus the results may not be Hangzhou Center for Disease Control and Prevention, Hangzhou 310051, applied to other ethnicities. Zhejiang, China. Affiliated Hangzhou Center of Disease Control and Preven- tion, Zhejiang University School of Public Health, Hangzhou 310051, Zhejiang, China. Basic Discipline of Chinese and Western Integrative, School of Public Conclusions Health, Zhejiang Chinese Medical University, Hangzhou 310053, Zhejiang, In male, VAT and SAT could increase the risk of meta- China. Department of Epidemiology and Biostatistics, School of Public bolic abnormalities both at BMI < 24  kg/m and at Health, Zhejiang Chinese Medical University, Hangzhou 310053, Zhejiang, China. Department of Epidemiology and Biostatistics, School of Public BMI ≥ 24  kg/m . In female, VAT could increase the risk Health, Zhejiang University, Hangzhou 310058, Zhejiang, China. Depar tment of metabolic abnormalities but SAT could increase the of Endocrinology and Metabolism, West China Hospital, Sichuan University, risk of MA in the second and fourth quartiles (Q2 and Chengdu 610000, Sichuan Province, China. Department of Endocrinology, Sir Run Run Shaw Hospital Affiliated to School of Medicine, Zhejiang University, Q4) only at BMI > 24  kg/m . Deposition of abdominal Hangzhou 310058, Zhejiang, China. Department of Respiratory Diseases, Sir adipose tissue was associated with metabolic abnormali- Run Run Shaw Hospital Affiliated to School of Medicine, Zhejiang Univer - ties. VAT improved the predictive power of MA. sity, Hangzhou 310058, Zhejiang, China. Department of Pathology, School of Medicine, Zhejiang University, Hangzhou 310058, Zhejiang, China. Zhang et al. Nutrition & Metabolism (2022) 19:16 Page 8 of 8 Received: 16 October 2021 Accepted: 17 February 2022 21. Despres JP, Lemieux I, Bergeron J, Pibarot P, Mathieu P, Larose E, et al. Abdominal obesity and the metabolic syndrome: contribution to global cardiometabolic risk. Arterioscler Thromb Vasc Biol. 2008;28(6):1039–49. 22. Misra A, Vikram NK. Clinical and pathophysiological consequences of abdominal adiposity and abdominal adipose tissue depots. Nutrition (Burbank, Los Angeles County, Calif ). 2003;19(5):457–66. References 23. Chait A, den Hartigh LJ. Adipose tissue distribution, inflammation and its 1. Abraham TM, Pedley A, Massaro JM, Hoffmann U, Fox CS. Association metabolic consequences, including diabetes and cardiovascular disease. between visceral and subcutaneous adipose depots and incident cardio- Front Cardiovasc Med. 2020;7:22. vascular disease risk factors. Circulation. 2015;132(17):1639–47. 24. Després JP, Lemieux I. Abdominal obesity and metabolic syndrome. 2. Rothney MP, Catapano AL, Xia J, Wacker WK, Tidone C, Grigore L, et al. Nature. 2006;444(7121):881–7. Abdominal visceral fat measurement using dual-energy X-ray: asso- 25. Fox CS, Massaro JM, Hoffmann U, Pou KM, Maurovich-Horvat P, Liu CY, ciation with cardiometabolic risk factors. Obesity (Silver Spring, Md). et al. Abdominal visceral and subcutaneous adipose tissue compart- 2013;21(9):1798–802. ments: association with metabolic risk factors in the Framingham Heart 3. Jackson AS, Stanforth PR, Gagnon J, Rankinen T, Leon AS, Rao DC, et al. Study. Circulation. 2007;116(1):39–48. The effect of sex, age and race on estimating percentage body fat from 26. Rønn PF, Andersen GS, Lauritzen T, Christensen DL, Aadahl M, Carstensen body mass index: The Heritage Family Study. Int J Obes Relat Metab B, et al. Abdominal visceral and subcutaneous adipose tissue and associa- Disord J Int Assoc Study Obes. 2002;26(6):789–96. tions with cardiometabolic risk in Inuit, Africans and Europeans: a cross- 4. Keum N, Lee DH, Kim R, Greenwood DC, Giovannucci EL. Visceral adipos- sectional study. BMJ Open. 2020;10(9):e038071. ity and colorectal adenomas: dose-response meta-analysis of observa- 27. Bidulescu A, Liu J, Hickson DA, Hairston KG, Fox ER, Arnett DK, et al. Gen- tional studies. Ann Oncol. 2015;26(6):1101–9. der differences in the association of visceral and subcutaneous adiposity 5. Chen P, Hou X, Hu G, Wei L, Jiao L, Wang H, et al. Abdominal subcutane- with adiponectin in African Americans: the Jackson Heart Study. BMC ous adipose tissue: a favorable adipose depot for diabetes? Cardiovasc Cardiovasc Disord. 2013;13:9. Diabetol. 2018;17(1):93. 28. Borel AL, Nazare JA, Smith J, Aschner P, Barter P, Van Gaal L, et al. Visceral, 6. Liu J, Fox CS, Hickson DA, May WD, Hairston KG, Carr JJ, et al. Impact of subcutaneous abdominal adiposity and liver fat content distribution abdominal visceral and subcutaneous adipose tissue on cardiometa- in normal glucose tolerance, impaired fasting glucose and/or impaired bolic risk factors: the Jackson Heart Study. J Clin Endocrinol Metab. glucose tolerance. Int J Obes (Lond). 2015;39(3):495–501. 2010;95(12):5419–26. 29. Oka R, Miura K, Sakurai M, Nakamura K, Yagi K, Miyamoto S, et al. 7. Kwon H, Kim D, Kim JS. Body fat distribution and the risk of incident met- Impacts of visceral adipose tissue and subcutaneous adipose tissue on abolic syndrome: a longitudinal cohort study. Sci Rep. 2017;7(1):10955. metabolic risk factors in middle-aged Japanese. Obesity (Silver Spring). 8. Porter SA, Massaro JM, Hoffmann U, Vasan RS, O’Donnel CJ, Fox CS. 2010;18(1):153–60. Abdominal subcutaneous adipose tissue: a protective fat depot? Diabe- 30. Matsha TE, Ismail S, Speelman A, Hon GM, Davids S, Erasmus RT, et al. tes Care. 2009;32(6):1068–75. Visceral and subcutaneous adipose tissue association with metabolic 9. Hoyer D, Boyko EJ, McNeely MJ, Leonetti DL, Kahn SE, Fujimoto syndrome and its components in a South African population. Clin Nutr WY. Subcutaneous thigh fat area is unrelated to risk of type 2 dia- ESPEN. 2019;32:76–81. betes in a prospective study of Japanese Americans. Diabetologia. 31. Koh H, Hayashi T, Sato KK, Harita N, Maeda I, Nishizawa Y, et al. Visceral 2011;54(11):2795–800. adiposity, not abdominal subcutaneous fat area, is associated with 10. Karlsson T, Rask-Andersen M, Pan G, Höglund J, Wadelius C, Ek WE, et al. high blood pressure in Japanese men: the Ohtori study. Hypertens Res. Contribution of genetics to visceral adiposity and its relation to cardio- 2011;34(5):565–72. vascular and metabolic disease. Nat Med. 2019;25(9):1390–5. 32. Zhao X, Gang X, Liu Y, Sun C, Han Q, Wang G. Using metabolomic profiles 11. Chen Y, Zhang Z, Wang J, Sun H, Zhao X, Cheng X, et al. Sex differences as biomarkers for insulin resistance in childhood obesity: a systematic in the association of abdominal adipose tissue and anthropometric data review. J Diabetes Res. 2016;2016:8160545. with untreated hypertension in a Chinese population. Biol Sex Differ. 33. Palmer BF, Clegg DJ. The sexual dimorphism of obesity. Mol Cell Endo- 2020;11(1):38. crinol. 2015;402:113–9. 12. Lesser IA, Gasevic D, Lear SA. The effect of body fat distribution on ethnic 34. Karastergiou K. The interplay between sex, ethnicity, and adipose tissue differences in cardiometabolic risk factors of Chinese and Europeans. characteristics. Curr Obes Rep. 2015;4(2):269–78. Appl Physiol Nutr Metab. 2013;38(7):701–6. 35. Kim S, Cho B, Lee H, Choi K, Hwang SS, Kim D, et al. Distribution of 13. Rønn PF, Andersen GS, Lauritzen T, Christensen DL, Aadahl M, Carstensen abdominal visceral and subcutaneous adipose tissue and metabolic B, et al. Ethnic differences in anthropometric measures and abdominal fat syndrome in a Korean population. Diabetes Care. 2011;34(2):504–6. distribution: a cross-sectional pooled study in Inuit, Africans and Europe- 36. Hwang YC, Fujimoto WY, Hayashi T, Kahn SE, Leonetti DL, Boyko EJ. ans. J Epidemiol Community Health. 2017;71(6):536–43. Increased visceral adipose tissue is an independent predictor for future 14. Bertoli S, Leone A, Vignati L, Spadafranca A, Bedogni G, Vanzulli A, et al. development of atherogenic dyslipidemia. J Clin Endocrinol Metab. Metabolic correlates of subcutaneous and visceral abdominal fat meas- 2016;101(2):678–85. ured by ultrasonography: a comparison with waist circumference. Nutr J. 37. Maislin G, Ahmed MM, Gooneratne N, Thorne-Fitzgerald M, Kim C, Teff K, 2016;15:2. et al. Single slice vs. volumetric MR assessment of visceral adipose tissue: 15. Zheng R, Yang M, Bao Y, Li H, Shan Z, Zhang B, et al. Prevalence and reliability and validity among the overweight and obese. Obesity (Silver determinants of metabolic health in subjects with obesity in Chinese Spring). 2012;20(10):2124–32. population. Int J Environ Res Public Health. 2015;12(11):13662–77. 16. Alberti KGMM, Zimmet P, Shaw J. The metabolic syndrome—a new worldwide definition. The Lancet. 2005;366(9491):1059–62. Publisher’s Note 17. Liu Y, Liu J, Gao Y, Zheng D, Pan W, Nie M, et al. The body composition in Springer Nature remains neutral with regard to jurisdictional claims in pub- early pregnancy is associated with the risk of development of gestational lished maps and institutional affiliations. diabetes mellitus late during the second trimester. Diabetes Metab Syndr Obes Targets Ther. 2020;13:2367–74. 18. Ibrahim MM. Subcutaneous and visceral adipose tissue: structural and functional differences. Obes Rev. 2010;11(1):11–8. 19. Tang L, Zhang F, Tong N. The association of visceral adipose tissue and subcutaneous adipose tissue with metabolic risk factors in a large popu- lation of Chinese adults. Clin Endocrinol (Oxf ). 2016;85(1):46–53. 20. Kahn CR, Wang G, Lee KY. Altered adipose tissue and adipocyte function in the pathogenesis of metabolic syndrome. J Clin Investig. 2019;129(10):3990–4000.

Journal

Nutrition & MetabolismSpringer Journals

Published: Mar 5, 2022

Keywords: Obesity; Subcutaneous adipose tissue (SAT); Visceral adipose tissue (VAT); Body mass index (BMI); Metabolic abnormality (MA)

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