Deciphering CT texture features of human visceral fat to evaluate metabolic disorders and surgery-induced weight loss effects.

BACKGROUND: Metabolic syndrome (MetS) is highly related to the excessive accumulation of visceral adipose tissue (VAT). Quantitative measurements of VAT are commonly applied in clinical practice for measurement of metabolic risks; however, it remains largely unknown whether the texture of VAT can evaluate visceral adiposity, stratify MetS and predict surgery-induced weight loss effects. METHODS: 675 Chinese adult volunteers and 63 obese patients (with bariatric surgery) were enrolled. Texture features were extracted from VATs of the computed tomography (CT) scans and machine learning was applied to identify significant imaging biomarkers associated with metabolic-related traits. FINDINGS: Combined with sex, ten VAT texture features achieved areas under the curve (AUCs) of 0.872, 0.888, 0.961, and 0.947 for predicting the prevalence of insulin resistance, MetS, central obesity, and visceral obesity, respectively. A novel imaging biomarker, RunEntropy, was identified to be significantly associated with major metabolic outcomes and a 3.5-year follow-up in 338 volunteers demonstrated its long-term effectiveness. More importantly, the preoperative imaging biomarkers yielded high AUCs and accuracies for estimation of surgery responses, including the percentage of excess weight loss (%EWL) (0.867 and 74.6%), postoperative BMI group (0.930 and 76.1%), postoperative insulin resistance (0.947 and 88.9%), and excess visceral fat loss (the proportion of visceral fat reduced over 50%; 0.928 and 84.1%). INTERPRETATION: This study shows that the texture features of VAT have significant clinical implications in evaluating metabolic disorders and predicting surgery-induced weight loss effects. FUNDING: The complete list of funders can be found in the Acknowledgement section.

The impact of shift work on glycemic characteristics assessed by CGM and its association with metabolic indices in non-diabetic subjects.

AIM: To explore the glycemic characteristics of non-diabetic shift workers and associations with metabolic indices. METHODS: In this cross-sectional study, 450 non-diabetic males, including 238 shift workers, aged 23-58 years, were recruited after a screening oral glucose tolerance test. Blood samples and anthropometric data were collected. Hundred and fifty of them finished a continuous glucose monitoring for 3-7 days. RESULTS: Compared to daytime workers, shift workers presented with larger WHR (p < 0.001), higher HOMA-IR (p < 0.001), higher hs-CRP level (p < 0.001) and worse lipid profiles. In glycemic characteristics, shift workers with normal glucose regulation had a similar mean blood glucose (MBG), daytime MBG, percentage of time of hyperglycemia, hypoglycemia, euglycemia, and fluctuation parameters, including standard deviation of blood glucose (SDBG), mean amplitude of glycemic excursions (MAGE) and mean of daily differences (p > 0.05, respectively), while they had a higher nighttime MBG (p = 0.026) and blood glucose (BG) at 3 a.m. (p = 0.015). For subjects with impaired glucose regulation, both groups had no difference in any clinical characteristics or glycemic parameters (p > 0.05, respectively). Further regression analysis revealed the association between MBG/SDBG/MAGE/nighttime MBG/BG at 3 a.m. and age/WHR/hs-CRP/TC. CONCLUSION: For non-diabetic shift workers, the glycemic characteristic was the elevated nighttime glycemia, presented as higher nighttime MBG and BG at 3 a.m. And both metrics were closely associated with central obesity. Elevated nighttime glycemia was an early signal of glucose metabolism disorder in shift workers.