Association Between Indices of Body Composition and Metabolically Unhealthy Phenotype in China: A Cross-Sectional Study

Evaluation for Performance of Body Composition Index Based on Quantitative Computed Tomography in the Prediction of Metabolic Syndrome

Objective: We aimed to evaluate the performance of predicting metabolic syndrome (MS) using body composition indices obtained by quantitative computed tomography (QCT). Methods: In this cross-sectional study, data were collected from 4745 adults who underwent QCT examinations at a Chongqing teaching hospital between July 2020 and March 2022. Visceral adipose tissue (VAT), subcutaneous adipose tissue (SAT), total abdominal fat (TAT), abdominal muscle tissue (AMT), and liver fat content (LFC) were measured at the L2-L3 disc level using specialized software, and the skeletal muscle index (SMI) were calculated. The correlations between body composition indicators were analyzed using the Pearson correlation analysis. Receiver operating characteristic (ROC) curve analysis and area under the curve (AUC) were used to assess these indicators' predictive potential for MS. Results: VAT and TAT exhibited the best predictive ability for MS, with AUCs of 0.797 [95% confidence interval (CI): 0.779-0.815] and 0.794 (95% CI: 0.775-0.812) in males, and 0.811 (95% CI: 0.785-0.836) and 0.802 (95% CI: 0.774-0.830) in females. The AUCs for VAT and TAT were the same but significantly higher than body mass index and other body composition measures. SAT also demonstrated good predictive power in females [AUC = 0.725 (95%CI: 0.692-0.759)] but fair power in males [AUC = 0.6673 (95%CI: 0.650-0.696)]. LFC showed average predictive ability, AMT showed average predictive ability in males but poor ability in females, and SMI had no predictive ability. Correlation analysis revealed a strong correlation between VAT and TAT (males: r = 0.95, females: r = 0.89). SAT was strongly correlated with TAT only in females (r = 0.89). In the male group, the optimal thresholds for VAT and TAT were 207.6 and 318.7 cm2, respectively; in the female group, the optimal thresholds for VAT and TAT were 128.0 and 269.4 cm2, respectively. Conclusions: VAT and TAT are the best predictors of MS. SAT and LFC can also be acceptable to make predictions, whereas AMT can only make predictions of MS in males.

Lower skeletal muscle mass is associated with diabetes and insulin resistance: A cross-sectional study

AIMS

The association between skeletal muscle mass and diabetes incidence/insulin resistance/glycated hemoglobin (HbA1C) is unknown. The aim of this study was to investigate such association in clinically apparently healthy males and females.

METHODS

A cross-sectional study of 372,399 Korean males and females who completed bioelectrical impedance analysis (BIA) in a health-screening programme was performed. Skeletal muscle index was used as an indicator of skeletal muscle mass. Skeletal muscle index (%) [appendicular skeletal muscle mass (kg)/body weight (kg)X100] was estimated using BIA. The study outcomes were diabetes incidence, homoeostasis model assessment of insulin resistance (HOMA-IR), and HbA1C.

RESULTS

The mean age of study participants was 38.92 ± 8.54 years. Multiple logistic regression analysis revealed a significant negative association between Skeletal muscle index and diabetes incidence/HOMA-IR/HbA1C after adjusting for various confounding factors. Odds ratios (95% confidence interval (CI)) of diabetes incidence in Q2, Q3, and Q4 compared to the lowest quantile (Q1) were 0.95 (0.85-1.05), 0.88 (0.78-0.99), and 0.79 (0.69-0.9), respectively. Beta coefficients (95% CI) of HOMA-IR in Q2, Q3, and Q3 with Q1 were 0.05 (0.03-0.07), -0.06 (-0.09∼-0.04), and -0.19 (-0.22∼-0.16), respectively. Beta coefficients (95% CI) of HbA1C in Q2, Q3, and Q4 with Q1 were 0.02 (0.01-0.03), -0.001 (-0.01∼0.01), and -0.02 (-0.03∼-0.01), respectively.

CONCLUSIONS

This study demonstrated negative associations of skeletal muscle mass with diabetes incidence, insulin resistance, and HbA1C levels in healthy adults.

Gender-specific data-driven adiposity subtypes using deep-learning-based abdominal CT segmentation

OBJECTIVE

The aim of this study was to quantify abdominal adiposity and generate data-driven adiposity subtypes with different diabetes risks.

METHODS

A total of 3817 participants from the Pinggu Metabolic Disease Study were recruited. A deep-learning-based recognition model on abdominal computed tomography (CT) images (A-CT model) was developed and validated in 100 randomly selected cases. The volumes and proportions of subcutaneous fat, visceral fat, liver fat, and muscle fat were automatically recognized in all cases. K-means clustering was used to identify subgroups using the proportions of the four fat components.

RESULTS

The Dice indices among the measurements assessed by the A-CT model and manual evaluation to detect liver fat, muscle fat, and subcutaneous fat areas were 0.96, 0.95, and 0.92, respectively. Three subtypes were generated separately in men and women: visceral fat dominant type (VFD); subcutaneous fat dominant type (SFD); and intermuscular fat dominant type (MFD). Compared with the SFD group, the MFD group had similar diabetes risk, and the VFD group had a 60% higher diabetes risk when age and BMI were adjusted for in men. The adjusted odds ratio for diabetes was 1.92 (95% CI: 1.32-2.78) in the MFD group and 6.14 (95% CI: 4.18-9.03) in the VFD group in women.

CONCLUSIONS

This study identified gender-specific abdominal adiposity subgroups, which may help clinicians to distinguish diabetes risk quickly and automatically.

Body fat and muscle were associated with metabolically unhealthy phenotypes in normal weight and overweight/obesity in Yi people: A cross-sectional study in Southwest China

This study aimed to determine the association between the absolute mass, distribution, and relative ratio of body fat and muscle with the metabolically unhealthy (MU) phenotypes in normal weight and overweight/obesity in Yi people in China. The cross-sectional data from the Yi Migrants Study was used, which included 3,053 Yi people aged 20-80 years from the rural and urban sets. Participants were classified according to body mass index and metabolic status. Body composition including body fat percentage (BFP), fat mass index (FMI), visceral fat grade (VFG), muscle mass index (MMI), and muscle/fat ratio (M/F) were measured by bioelectrical impedance analysis. Restricted cubic spline and logistics regression models were used to test the associations between body composition parameters with MU phenotypes. Receiver-operating characteristic curves (ROC) were used to analyze the predictive value of MU phenotypes. Among the normal weight and overweight/obesity, 26.31% (497/1,889) and 52.15% (607/1,164) were metabolically unhealthy. Stratified by BMI, covariance analysis showed higher body fat (BFP, FMI, and VFG) and MMI in MU participants than in healthy participants. BFP, FMI, VFG, and MMI were positively associated with MU phenotypes both in normal weight and overweight/obesity after adjustment. M/F was significantly lower than MU participants and was negatively associated with MU phenotypes. BFP, FMI, VFG, and M/F could better predict MU phenotypes than BMI. We concluded that BFP, FMI, and VFG were positively associated with MU phenotypes, while M/F was negatively associated with MU phenotypes across the BMI categories in Yi people. Body fat and muscle measurement could be a valuable approach for obesity management.