Dataset Information

Different Indicators of Adiposity and Fat Distribution and Cardiometabolic Risk Factors in Patients with Type 2 Diabetes.

ABSTRACT:

Objective

This study aimed to evaluate the effect of adiposity and fat distribution on the odds of elevated cardiovascular risk factors among adults with type 2 diabetes mellitus.

Methods

The present cross-sectional study included 2,427 adults with type 2 diabetes mellitus. Body fat was assessed by dual-energy x-ray absorptiometry. Multivariate-adjusted logistic regression was used to estimate effects of adiposity parameters on elevated hemoglobin A_1c (HbA_1c , ≥7.0%), hypertension (blood pressure ≥140/90 mmHg), and elevated low-density lipoprotein (LDL) cholesterol (≥2.6 mmol/L).

Results

The multivariable-adjusted odds ratio (OR) for elevated HbA_1c was 0.82 (95% CI: 0.70-0.96) for each SD increase in leg fat mass. The multivariable-adjusted OR for hypertension was 1.15 (95% CI: 1.00-1.32) for each SD increase in android fat mass. Multivariable-adjusted ORs for elevated LDL cholesterol ranged from 1.16 (95% CI: 1.00-1.35) to 1.27 (95% CI: 1.06-1.51) for each SD increase in arm and android fat mass and percentage of total, truncal, arm, and android fat. Each SD increase in BMI, truncal-to-leg fat ratio, and android-to-gynoid fat ratio was significantly associated with increased risks of elevated HbA_1c , hypertension, and elevated LDL cholesterol.

Conclusions

Subcutaneous fat in the lower body was associated with a more favorable glycemic profile, but not blood pressure or lipid profile, whereas central adiposity was associated with poor control of cardiovascular risk factors among patients with type 2 diabetes mellitus.

SUBMITTER: Ding L

PROVIDER: S-EPMC9115840 | biostudies-literature | 2021 May

REPOSITORIES: biostudies-literature

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Publications

Different Indicators of Adiposity and Fat Distribution and Cardiometabolic Risk Factors in Patients with Type 2 Diabetes.

Ding Li L Fan Yuxin Y He Jing J Wang Ruodan R He Qing Q Cui Jingqiu J Ma Zhongshu Z Zheng Fangqiu F Gao Hua H Dai Chenlin C Wei Hongyan H Li Jun J Cao Yuming Y Hu Gang G Liu Ming M

Obesity (Silver Spring, Md.) 20210501 5

<h4>Objective</h4>This study aimed to evaluate the effect of adiposity and fat distribution on the odds of elevated cardiovascular risk factors among adults with type 2 diabetes mellitus.<h4>Methods</h4>The present cross-sectional study included 2,427 adults with type 2 diabetes mellitus. Body fat was assessed by dual-energy x-ray absorptiometry. Multivariate-adjusted logistic regression was used to estimate effects of adiposity parameters on elevated hemoglobin A<sub>1c</sub> (HbA<sub>1c</sub> ...[more]

PMID: 33899339

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Project description:BackgroundCardiometabolic multimorbidity (CMM) has emerged as a prominent public health concern. Hypertensive patients are prone to develop comorbidities. Moreover, the accumulation of visceral adipose tissue is the main cause for the development of cardiometabolic diseases. The cardiometabolic index (CMI), lipid accumulation product (LAP), visceral adiposity index (VAI), and Chinese visceral adiposity index (CVAI) not only assess adipose tissue mass but also reflect adipose tissue dysfunction. So far, no study has been reported to evaluate the association of CMI, LAP, VAI, and CVAI with CMM risk in hypertensive patients. Therefore, this study aimed to assess the association between these adiposity indicators and the risk of CMM among Chinese hypertensive patients.MethodsIn this cross-sectional study, a total of 229,287 hypertensive patients aged 35 years and older were included from the National Basic Public Health Service Project. All participants underwent a face-to-face questionnaire survey, physical examination, and the collection of fasting venous blood samples. Multivariable logistic regression models were performed to estimate odds ratios (ORs) and 95% confidence intervals (CIs). Receiver operating characteristic curve was utilized to evaluate the identification ability for CMM.ResultsAfter adjusting for confounders, each 1-standard deviation increase in CMI, LAP, VAI, and CVAI was associated with a 14%, 8%, 12%, and 54% increased risk of CMM, respectively. When comparing the highest quartile of these indicators with the lowest quartile, individuals in the highest quartile of CMM, LAP, VAI, and CVAI had a 1.39-fold (95% CI 1.30, 1.48), 1.28-fold (95% CI 1.19, 1.37), 1.37-fold (95% CI 1.29, 1.46), and 2.56-fold (95% CI 2.34, 2.79) increased risk of CMM after adjusting for potential confounders. Notably, a nonlinear association was observed for CMI, LAP, and VAI with the risk of CMM (all P nonlinearity < 0.001). CVAI exhibited the highest area under the receiver operating characteristic curve (AUC) among all the included adiposity indices in this analysis.ConclusionThis study indicated the significant positive association of CMI, LAP, VAI, and CVAI with the risk of CMM in hypertensive patients. Among these indicators, CVAI demonstrated the most robust performance in predicting CMM risk and may serve as a valuable tool for identifying CMM risk in Chinese hypertensive patients.

Project description:BackgroundWe aimed to assess the associations between adiposity distribution and cardiometabolic risk factors among overweight and obese adults in China, and to demonstrate the sex differences in these associations.MethodsA total of 1221 participants (455 males and 766 females) were included in this study. Percentage of body fat (PBF) of the whole body and regional areas, including arm, thigh, trunk, android, and gynoid, were measured by the dual-energy X-ray absorptiometry method. Central adiposity was measured by waist circumference. Clustered cardiometabolic risk was defined as the presence of two or more of the six cardiometabolic risk factors, namely, high triglyceride, low high density lipoprotein, elevated glucose, elevated blood pressure, elevated high sensitivity C-reactive protein, and low adiponectin. Linear regression models and multivariate logistic regression models were used to assess the associations between whole body or regional PBF and cardiometabolic risk factors.ResultsIn females, except arm adiposity, other regional fat (thigh, trunk, android, gynoid) and whole-body PBF are significantly associated with clustered cardiometabolic risk, adjusting for age, smoking, alcohol drinking, physical activity, and whole-body PBF. One-SD increase in Z scores of the thigh and gynoid PBF were significantly associated with 80 and 78% lower odds of clustered cardiometabolic risk (OR: 0.20, 95%CI: 0.12-0.35 and OR: 0.22, 95%CI: 0.12-0.41). Trunk, android and whole-body PBF were significantly associated with higher odds of clustered risk with OR of 1.90 (95%CI:1.02-3.55), 2.91 (95%CI: 1.75-4.85), and 2.01 (95%CI: 1.47-2.76), respectively. While in males, one-SD increase in the thigh and gynoid PBF are associated with 94% (OR: 0.06, 95%CI: 0.02-0.23) and 83% lower odds (OR: 0.17, 95%CI: 0.05-0.57) of clustered cardiometabolic risk, respectively. Android and whole-body PBF were associated with higher odds of clustered cardiometabolic risk (OR: 3.39, 95%CI: 1.42-8.09 and OR: 2.45, 95%CI: 1.53-3.92), but the association for trunk PBF was not statistically significant (OR: 1.16, 95%CI: 0.42-3.19).ConclusionsAdiposity distribution plays an important role in the clustered cardiometabolic risk in participants with overweight and obese and sex differences were observed in these associations. In general, central obesity (measured by android PBF) could be the best anthropometric measurement for screening people at risk for CVD risk factors for both men and women. Upper body fat tends to be more detrimental to cardiometabolic health in women than in men, whereas lower body fat is relatively more protective in men than in women.

Project description:Background and aimsNonalcoholic fatty liver disease is a major risk factor for cardiometabolic disease in adults. The burden of liver fat and associated cardiometabolic risk factors in healthy children is unknown. In a population-based prospective cohort study among 3,170 10-year-old children, we assessed whether both liver fat accumulation across the full range and nonalcoholic fatty liver disease are associated with cardiometabolic risk factors already in childhood.Approach and resultsLiver fat fraction was measured by magnetic resonance imaging, and nonalcoholic fatty liver disease was defined as liver fat fraction ≥5.0%. We measured body mass index, blood pressure, and insulin, glucose, lipids, and C-reactive protein concentrations. Cardiometabolic clustering was defined as having three or more risk factors out of high visceral fat mass, high blood pressure, low high-density-lipoprotein cholesterol or high triglycerides, and high insulin concentrations. Nonalcoholic fatty liver disease prevalences were 1.0%, 9.1%, and 25.0% among children who were normal weight, overweight, and obese, respectively. Both higher liver fat within the normal range (<5.0% liver fat) and nonalcoholic fatty liver disease were associated with higher blood pressure, insulin resistance, total cholesterol, triglycerides, and C-reactive protein concentrations (P values < 0.05). As compared with children with <2.0% liver fat, children with ≥5.0% liver fat had the highest odds of cardiometabolic clustering (odds ratio 24.43 [95% confidence interval 12.25, 48.60]). The associations remained similar after adjustment for body mass index and tended to be stronger in children who were overweight and obese.ConclusionsHigher liver fat is, across the full range and independently of body mass index, associated with an adverse cardiometabolic risk profile already in childhood. Future preventive strategies focused on improving cardiometabolic outcomes in later life may need to target liver fat development in childhood.

Project description:ImportanceBody fat distribution, usually measured using waist-to-hip ratio (WHR), is an important contributor to cardiometabolic disease independent of body mass index (BMI). Whether mechanisms that increase WHR via lower gluteofemoral (hip) or via higher abdominal (waist) fat distribution affect cardiometabolic risk is unknown.ObjectiveTo identify genetic variants associated with higher WHR specifically via lower gluteofemoral or higher abdominal fat distribution and estimate their association with cardiometabolic risk.Design, setting, and participantsGenome-wide association studies (GWAS) for WHR combined data from the UK Biobank cohort and summary statistics from previous GWAS (data collection: 2006-2018). Specific polygenic scores for higher WHR via lower gluteofemoral or via higher abdominal fat distribution were derived using WHR-associated genetic variants showing specific association with hip or waist circumference. Associations of polygenic scores with outcomes were estimated in 3 population-based cohorts, a case-cohort study, and summary statistics from 6 GWAS (data collection: 1991-2018).ExposuresMore than 2.4 million common genetic variants (GWAS); polygenic scores for higher WHR (follow-up analyses).Main outcomes and measuresBMI-adjusted WHR and unadjusted WHR (GWAS); compartmental fat mass measured by dual-energy x-ray absorptiometry, systolic and diastolic blood pressure, low-density lipoprotein cholesterol, triglycerides, fasting glucose, fasting insulin, type 2 diabetes, and coronary disease risk (follow-up analyses).ResultsAmong 452 302 UK Biobank participants of European ancestry, the mean (SD) age was 57 (8) years and the mean (SD) WHR was 0.87 (0.09). In genome-wide analyses, 202 independent genetic variants were associated with higher BMI-adjusted WHR (n = 660 648) and unadjusted WHR (n = 663 598). In dual-energy x-ray absorptiometry analyses (n = 18 330), the hip- and waist-specific polygenic scores for higher WHR were specifically associated with lower gluteofemoral and higher abdominal fat, respectively. In follow-up analyses (n = 636 607), both polygenic scores were associated with higher blood pressure and triglyceride levels and higher risk of diabetes (waist-specific score: odds ratio [OR], 1.57 [95% CI, 1.34-1.83], absolute risk increase per 1000 participant-years [ARI], 4.4 [95% CI, 2.7-6.5], P < .001; hip-specific score: OR, 2.54 [95% CI, 2.17-2.96], ARI, 12.0 [95% CI, 9.1-15.3], P < .001) and coronary disease (waist-specific score: OR, 1.60 [95% CI, 1.39-1.84], ARI, 2.3 [95% CI, 1.5-3.3], P < .001; hip-specific score: OR, 1.76 [95% CI, 1.53-2.02], ARI, 3.0 [95% CI, 2.1-4.0], P < .001), per 1-SD increase in BMI-adjusted WHR.Conclusions and relevanceDistinct genetic mechanisms may be linked to gluteofemoral and abdominal fat distribution that are the basis for the calculation of the WHR. These findings may improve risk assessment and treatment of diabetes and coronary disease.

Dataset Information

Different Indicators of Adiposity and Fat Distribution and Cardiometabolic Risk Factors in Patients with Type 2 Diabetes.

Objective

Methods

Results

Conclusions

Publications

Different Indicators of Adiposity and Fat Distribution and Cardiometabolic Risk Factors in Patients with Type 2 Diabetes.

Similar Datasets

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