Quantitative Imaging of Body Fat Distribution in the Era of Deep Learning

Adiposity modifies the association between heart failure risk and glucose metabolic disorder in older individuals: a community-based prospective cohort study

BACKGROUND

Glucose metabolic disorder is associated with the risk of heart failure (HF). Adiposity is a comorbidity that is inextricably linked with abnormal glucose metabolism in older individuals. However, the effect of adiposity on the association between glucose metabolic disorder and HF risk, and the underlying mechanism remain unclear.

METHODS

A total of 13,251 participants aged ≥ 60 years from a cohort study were categorized into euglycemia, prediabetes, uncontrolled diabetes, and well-controlled diabetes. Adiposity was assessed using body mass index (BMI), waist-to-hip ratio (WHR), and visceral fat area (VFA). Adiposity-associated metabolic activities were evaluated using adiponectin-to-leptin ratio (ALR), homeostatic model assessment of insulin resistance (HOMA-IR), and triglyceride-glucose index (TyG). The first occurrence of HF served as the outcome during the follow-up period.

RESULTS

A total of 1,138 participants developed HF over the course of an average follow-up period of 10.9 years. The rate of incident HF occurrence was higher in prediabetes, uncontrolled diabetes, and well-controlled diabetes participants compared to that in euglycemia participants. However, the high rates were significantly attenuated by BMI, VFA, and WHR. For WHR in particular, the hazard ratio for incident HF was 1.18 (95% confidence interval (CI): 1.03, 1.35, Padj.=0.017) in prediabetes, 1.59 (95% CI: 1.34, 1.90, Padj.<0.001) in uncontrolled diabetes, and 1.10 (95% CI: 0.85, 1.43, Padj.=0.466) in well-controlled diabetes. The population attributable risk percentage for central obesity classified by WHR for incident HF was 30.3% in euglycemia, 50.0% in prediabetes, 48.5% in uncontrolled diabetes, and 54.4% in well-controlled diabetes. Adiposity measures, especially WHR, showed a significant interaction with glucose metabolic disorder in incident HF (all Padj.<0.001). ALR was negatively associated and HOMA-IR and TyG were positively associated with BMI, WHR, VFA, and incident HF (all Padj.<0.05). ALR, HOMA-IR, and TyG mediated the associations for BMI, WHR and VFA with incident HF (all Padj.<0.05).

CONCLUSIONS

Adiposity attenuated the association of glucose metabolic disorder with incident HF. The results also showed that WHR may be an appropriate indicator for evaluating adiposity in older individuals. Adiposity-associated metabolic activities may have a bridging role in the process of adiposity attenuating the association between glucose metabolic disorder and incident HF.

TRIAL REGISTRATION

retrospectively registered number: ChiCTR-EOC-17,013,598.

Sex-Specific Computed Tomography Abdominal Fat and Skeletal Muscle Characteristics in Type 2 Diabetic Retinopathy Patients With/Without Comorbid Diabetic Kidney Disease

RATIONALE AND OBJECTIVES

To investigate differences in sex-specific computed tomography abdominal fat and skeletal muscle (SM) characteristics between type 2 diabetic retinopathy (DR) patients with and without diabetic kidney disease (DKD).

MATERIALS AND METHODS

This retrospective study included type 2 diabetes mellitus DR patients with/without DKD between January 2019 and July 2021. Visceral adipose tissue (VAT), subcutaneous adipose tissue, perirenal adipose tissue (PAT), intramuscular adipose tissue, and SM areas were measured. Univariate and multivariate logistic regression analyses were used to analyze risk factors for DKD. Correlation and multiple linear regression analyses were used to clarify the association between computed tomography abdominal fat, SM characteristics, and cystatin C.

RESULTS

Two hundred and forty-one patients were enrolled and divided into DR with DKD group (n = 142) and DR without DKD group (n = 99). In men, hypertension (OR: 5.21; 95%CI: 1.93-14.05; p = 0.001), diastolic pressure (OR: 1.07; 95%CI: 1.01-1.12; p = 0.011), hemoglobin (OR: 0.94; 95%CI: 0.92-0.97; p < 0.001) and PAT attenuation value (OR: 1.09; 95%CI: 1.01-1.17; p = 0.026) were independent risk factors for DKD progression in DR patients, while the VAT index (VATI) (OR: 1.03; 95%CI: 1.01-1.05; p = 0.014) was an independent risk factor for female patients. Multiple linear regression analysis revealed significant correlations between hypertension (β = 0.22, p = 0.002) and hemoglobin (β = -0.53, p < 0.001) with cystatin C in men, and a significant correlation between VATI and cystatin C (β = 0.35, p = 0.037) in women after adjustment for confounders.

CONCLUSION

Female DR patients with elevated VAT level may suffer from a higher risk of DKD than that in male patients.

A Combined Region- and Pixel-Based Deep Learning Approach for Quantifying Abdominal Adipose Tissue in Adolescents Using Dixon Magnetic Resonance Imaging

BACKGROUND

The development of adipose tissue during adolescence may provide valuable insights into obesity-associated diseases. We propose an automated convolutional neural network (CNN) approach using Dixon-based magnetic resonance imaging (MRI) to quantity abdominal subcutaneous adipose tissue (SAT) and visceral adipose tissue (VAT) in children and adolescents.

METHODS

474 abdominal Dixon MRI scans of 136 young healthy volunteers (aged 8-18) were included in this study. For each scan, an axial fat-only Dixon image located at the L2-L3 disc space and another image at the L4-L5 disc space were selected for quantification. For each image, an outer and an inner region around the abdomen wall, as well as SAT and VAT pixel masks, were generated by expert readers as reference standards. A standard U-Net CNN architecture was then used to train two models: one for region segmentation and one for fat pixel classification. The performance was evaluated using the dice similarity coefficient (DSC) with fivefold cross-validation, and by Pearson correlation and the Student's t-test against the reference standards.

RESULTS

For the DSC results, means and standard deviations of the outer region, inner region, SAT, and VAT comparisons were 0.974 ± 0.026, 0.997 ± 0.003, 0.981 ± 0.025, and 0.932 ± 0.047, respectively. Pearson coefficients were 1.000 for both outer and inner regions, and 1.000 and 0.982 for SAT and VAT comparisons, respectively (all p = NS).

CONCLUSION

These results show that our method not only provides excellent agreement with the reference SAT and VAT measurements, but also accurate abdominal wall region segmentation. The proposed combined region- and pixel-based CNN approach provides automated abdominal wall segmentation as well as SAT and VAT quantification with Dixon MRI and enables objective longitudinal assessment of adipose tissues in children during adolescence.