Adrenal limb thickness is associated with metabolism profiles in patients with diabetes: A cross-sectional study.

BACKGROUND: The association between adrenal size and metabolic profiles in patients with diabetes mellitus (DM) is unclear. This study was conducted to determine whether the adrenal thickness measured by computed tomography (CT) is correlated with the metabolic profiles of patients with DM. METHODS: This was a cross-sectional study including 588 Chinese hospitalized patients with DM without comorbidities or medications known to affect adrenal morphology or hormone secretion. Adrenal limb thickness was measured on unenhanced chest CT. Participants were stratified into tertiles according to their total adrenal limb thickness. Linear and logistic regression models were used to estimate the correlations. RESULTS: After adjustment for sex and age, the adrenal thickness was positively associated with body mass index (BMI), waist circumference (WC), urinary albumin/creatinine ratio, and 24-h urinary free cortisol (UFC) and negatively correlated with high-density lipoprotein cholesterol. The sequential equation model (SEM) suggested UFC partially mediated the effect of adrenal limb thickness on WC by 12%. Adrenal thickness, but not UFC, was associated with a higher risk of existing hypertension (odds ratio [OR] = 3.78, 95% confidence interval [CI] 1.58, 9.02) and hyperlipidemia (OR = 2.76, 95% CI 1.03, 7.38), independent of age, gender, BMI, and WC. CONCLUSIONS: The adrenal thickness is independently associated with BMI, WC, cortisol levels, urinary albumin/creatinine ratio, hypertension, and dyslipidemia but not glycemic parameters in patients with diabetes. Our study encourages further studies to investigate the role of adrenal physiology in patients with diabetes.

Bariatric surgery reduces colorectal cancer incidence in obese individuals: Systematic review and meta-analysis.

BACKGROUND: Colorectal cancer ranks third in global cancer prevalence and stands as the second leading cause of cancer-related mortalities. With obesity recognized as a pivotal risk factor for colorectal cancer, the potential protective role of bariatric surgery, especially laparoscopic Roux-en-Y gastric bypass and laparoscopic sleeve gastrectomy, has garnered attention. AIM: To investigate the Roux-en-Y gastric bypass (RYGB) vs sleeve gastrectomy (SG) effect on colorectal cancer incidence in obese individuals. METHODS: A systematic review and meta-analysis of the literature was conducted following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses. Seventeen studies with a total of 12497322 patients were included. The primary outcome was the relative risk (RR) of developing colorectal cancer in obese patients who underwent weight loss surgery compared to those who did not. Secondary outcomes included determining the RR for colon and rectal cancer separately and subgroup analyses by gender and type of weight loss surgery. RESULTS: The meta-analysis revealed a 54% reduction in colorectal cancer risk in morbidly obese patients who underwent bariatric surgery compared to those who did not. A significant 46% reduction in colorectal cancer risk was observed among female patients. However, no significant differences were found in the meta-analysis for various types of bariatric surgery, such as SG and RYGB. CONCLUSION: This meta-analysis reveals weight loss surgery, regardless of type, reduces colorectal cancer risk, especially in women, as indicated by RR and hazard ratio assessments. Further validation is essential.

Review: Machine learning in precision pharmacotherapy of type 2 diabetes-A promising future or a glimpse of hope?

Precision pharmacotherapy of diabetes requires judicious selection of the optimal therapeutic agent for individual patients. Artificial intelligence (AI), a swiftly expanding discipline, holds substantial potential to transform current practices in diabetes diagnosis and management. This manuscript provides a comprehensive review of contemporary research investigating drug responses in patient subgroups, stratified via either supervised or unsupervised machine learning approaches. The prevalent algorithmic workflow for investigating drug responses using machine learning involves cohort selection, data processing, predictor selection, development and validation of machine learning methods, subgroup allocation, and subsequent analysis of drug response. Despite the promising feature, current research does not yet provide sufficient evidence to implement machine learning algorithms into routine clinical practice, due to a lack of simplicity, validation, or demonstrated efficacy. Nevertheless, we anticipate that the evolving evidence base will increasingly substantiate the role of machine learning in molding precision pharmacotherapy for diabetes.