Yanhua Zhu

Objective Obesity is a heterogeneous condition that leads to diverse cardiovascular and metabolic outcomes. This study aimed to identify the primary visceral fats contributing to metabolically unhealthy obesity and to investigate the characteristics of fat distribution associated with different obesity-related complications. Methods A retrospective analysis was conducted on fat-selective magnetic resonance images (MRI) from 319 individuals with a BMI ≥24 kg/m². Participants were categorized into four groups: solely obesity, T2DM, hypertension, and dyslipidemia, to examine differences in the distribution of hepatic, pancreatic, preperitoneal, mesenteric, and perirenal fat (PrFT). Correlation analyses were performed to elucidate relationships between visceral fat deposits and obesity-related characteristics. Logistic regression identified key fat deposition sites associated with common obesity-related complications. Additionally, the limitations of single-site fat measurements in capturing the heterogeneity of obesity were examined. Results PrFT exhibited the strongest correlation with blood pressure (r = 0.225 ~ 0.306, all p < 0.001) among all visceral fats, and the hypertensive individuals with obesity presented the highest PrFT. Hepatic fat showed the highest association with glucose metabolism (r = 0.188 ~ 0.407 all p < 0.01), as evidenced by higher hepatic fat content in the T2DM group compared to other groups. Risk of metabolic syndrome increased by 3.06-fold (95% CI:1.35–6.93, p = 0.007) and 6.79-fold (95% CI:2.45–18.83, p < 0.001) with moderate and severe fatty steatosis compared to those without hepatic steatosis. A 2.24-fold (95% CI:1.27–3.97, p = 0.006) increase in metabolic syndrome likelihood was observed for each 1 cm increment in PrFT. Conclusions Besides hepatic fat, perirenal fat is also a key determinant for metabolic syndrome. Patients with various metabolic abnormalities present distinct patterns of visceral fat distribution, which could be simply profiled by perirenal and hepatic fat quantification.

Type 2 diabetes (T2D) is a complex metabolic disease characterized by chronic hyperglycemia due to insulin resistance and inadequate insulin secretion. Beyond the classically implicated organs, emerging evidence highlights the gut as a central player in T2D pathophysiology through its interactions with metabolic organs. The gut hosts trillions of microbes and enteroendocrine cells that influence inflammation, energy homeostasis, and hormone regulation. Disruptions in gut homeostasis (dysbiosis and increased permeability) have been linked to obesity, insulin resistance, and β-cell dysfunction, suggesting multifaceted “Gut-X axes” contribute to T2D development. We aimed to comprehensively review the evidence for gut-mediated crosstalk with the pancreas, endocrine system, liver, and kidneys in T2D. Key molecular mechanisms (incretins, bile acids, short-chain fatty acids, endotoxins, etc.) were examined to construct an integrated model of how gut-derived signals modulate metabolic and inflammatory pathways across organs. We also discuss clinical implications of targeting Gut-X axes and identify knowledge gaps and future research directions. A literature search (2015–2025) was conducted in PubMed, Scopus, and Web of Science, following PRISMA guidelines (Preferred Reporting Items for Systematic Reviews). Over 150 high-impact publications (original research and review articles from Nature, Cell, Gut, Diabetologia, Lancet Diabetes & Endocrinology, etc.) were screened. Data on gut microbiota, enteroendocrine hormones, inflammatory mediators, and organ-specific outcomes in T2D were extracted. The GRADE framework was used informally to prioritize high-quality evidence (e.g., human trials and meta-analyses) in formulating conclusions. T2D involves perturbations in multiple Gut-X axes. This review first outlines gut homeostasis and T2D pathogenesis, then dissects each axis: (1) Gut–Pancreas Axis: how incretin hormones (GLP-1 and GIP) and microbial metabolites affect insulin/glucagon secretion and β-cell health; (2) Gut–Endocrine Axis: enteroendocrine signals (e.g., PYY and ghrelin) and neural pathways that link the gut with appetite regulation, adipose tissue, and systemic metabolism; (3) Gut–Liver Axis: the role of microbiota-modified bile acids (FXR/TGR5 pathways) and bacterial endotoxins in non-alcoholic fatty liver disease (NAFLD) and hepatic insulin resistance; (4) Gut–Kidney Axis: how gut-derived toxins and nutrient handling intersect with diabetic kidney disease and how incretin-based and SGLT2 inhibitor therapies leverage gut–kidney communication. Shared mechanisms (microbial SCFAs improving insulin sensitivity, LPS driving inflammation via TLR4, and aryl hydrocarbon receptor ligands modulating immunity) are synthesized into a unified model. An integrated understanding of Gut-X axes reveals new opportunities for treating and preventing T2D. Modulating the gut microbiome and its metabolites (through diet, pharmaceuticals, or microbiota therapies) can improve glycemic control and ameliorate complications by simultaneously influencing pancreatic islet function, hepatic metabolism, and systemic inflammation. However, translating these insights into clinical practice requires addressing gaps with robust human studies. This review provides a state-of-the-art synthesis for researchers and clinicians, underlining the gut as a nexus for multi-organ metabolic regulation in T2D and a fertile target for next-generation therapies.

Background Ectopic fat deposition, involving lipid infiltration within organs and fat accumulating surrounding organs, plays a crucial role in the development of metabolic abnormalities in obesity. Current imaging measurements of obesity primarily focus on lipid infiltration within liver, neglecting fat deposition in other areas. This study aims to explore the methods of measuring and correlating different types of abdominal ectopic fat deposition in obese patients using magnetic resonance imaging (MRI) and ultrasound techniques, and to investigate the relationship between these fat parameters and obesity-related metabolic markers. Methods Abdominal ectopic fat deposition including liver fat content, mesenteric fat thickness (MFT), perirenal fat thickness (PrFT) and preperitoneal fat thickness (PFT) were measured in 220 overweight/obese patients using both MRI and ultrasound techniques. Correlation analysis validated the concordance of fat parameters at specific sites between the two imaging methods and identified the cutoff values of hepatic attenuation coefficient (AC) for diagnosis of liver steatosis. Additionally, we investigated the correlation between fat parameters by both methods and obesity-related metabolic markers. Results Ultrasonic measurement of PrFT and hepatic AC both had high correlation with PrFT (r = 0.829, p < 0.001) and hepatic Proton-density fat fraction (PDFF, r = 0.822, p < 0.001) measured via MR. Hepatic AC cutoff values for diagnosing mild, moderate, and severe fatty liver were 0.705 dB/cm/MHz (AUC = 0.922), 0.755 dB/cm/MHz (AUC = 0.923), and 0.875 dB/cm/MHz (AUC = 0.890) respectively. Hepatic AC correlated significantly with AST and ALT (r = 0.477 ~ 0.533, p < 0.001). MFT measured by ultrasound were positively associated with glycated hemoglobin (r = 0.324 ~ 0.371, p < 0.001) and serum triglyceride levels (r = 0.303 ~ 0.353, p < 0.001). PrFT measured by both methods showed significant positive correlations with serum creatinine levels (r = 0.305 ~ 0.308, p < 0.001). Conclusions Both MRI and ultrasound demonstrate metabolic correlations in quantifying mesenteric, hepatic, and perirenal fat. In addition to assessment of liver fat content, the measurements of ectopic fat deposition by MRI or ultrasound are a simple and crucial way for comprehensive fat evaluation in individuals with overweight/obesity.

Background: Chronic inflammation in type 2 diabetes mellitus (T2DM) is an essential contributor to the development of diabetic retinopathy (DR). The monocyte–to–high-density lipoprotein cholesterol (HDL-C) ratio (MHR) is a novel and simple measure related to inflammatory and oxidative stress status. However, little is known regarding the role of the MHR in evaluating the development of DR.Methods: A total of 771 patients with T2DM and 607 healthy controls were enrolled in this cross-sectional study. MHR determination and eye examination were performed. The association of MHR with the prevalence of DR in T2DM patients was analyzed.Results: The MHR in patients with DR was significantly higher than that in both non-DR diabetic patients (P < 0.05) and healthy controls (P < 0.01). No significance was observed in the MHR of different DR severity grades. Moreover, the MHR was similar between patients with non-macular oedema and those with macular oedema. Logistic regression analysis demonstrated that MHR was independently associated with the prevalence of DR in diabetic patients [odds ratio (OR) = 1.438, 95% confidence interval (CI): 1.249–1.655, P < 0.01]. After additional stratification by HbA1c level and diabetic duration, the MHR was still independently associated with the prevalence of DR.Conclusions: Our study suggests that the MHR can be used as a marker to indicate the prevalence of DR in patients with T2DM.

Purpose: The aim of this study was to investigate the association between high-normal thyrotropin (TSH) levels and the prevalence of non-alcoholic fatty liver disease (NAFLD) in euthyroid patients with T2DM. Methods: A total of 2289 euthyroid adults with T2DM were included in this cross-sectional study conducted at the Third Affiliated Hospital of Sun Yat-sen University from January 2016 to December 2018. NAFLD was diagnosed by abdominal ultrasound. Thyroid function parameters, including the levels of TSH, free triiodothyronine (FT3) and free thyroxine (FT4), were analyzed. The patients were stratified by quartiles (Q1-4) of TSH levels. Multivariate logistic regression models were used to evaluate the association between the quartiles of TSH levels and the risk of NAFLD in euthyroid adults with T2DM. Results: There were 940 (41.1%) euthyroid adults with T2DM who were diagnosed with NAFLD. The subjects were divided according to the thyroid function parameter quartiles. The prevalence of NAFLD increased with increasing TSH level quartiles (Q1 to Q4: 34.8%, 37.5%, 44.9% and 47.0%, P< 0.01) but not with increasing FT3 or FT4 level quartiles. In the multivariate logistic regression model, compared with the lowest TSH level quartile (Q1), the highest TSH level quartile (Q4) (OR=1.610, 95% CI=1.131– 2.289, P=0.008) was independently associated with an increased risk of NAFLD in euthyroid adults with T2DM after adjusting for multiple confounders. After additional stratification by the level of glycosylated haemoglobin (HbA1c) and body mass index (BMI), the highest TSH level quartile was still independently associated with an increased risk of NAFLD in euthyroid patients with T2DM who had an HbA1c level≥ 7% or a BMI< 28 kg/m². Conclusion: High-normal serum TSH levels are significantly associated with the presence of NAFLD in T2DM patients with euthyroid function, which provide novel insight for treating NAFLD.

Platinum-based chemotherapy as first-line treatment for lung cancers encounters insufficient selectivity, severe side effects and drug resistance in clinics. In this study, we developed an amphiphilic prodrug of cisplatin-poly(ethylene glycol)-block-polycaprolactone and demonstrated that the prodrug formed micellar nanoparticles, NP_Pt(IV), with an average diameter of ∼100 nm. NP_Pt(IV) released platinum in response to the intracellular acidic and reductive environment, and in turn induced significant anti-proliferative activity in lung cancer cells. More importantly, NP_Pt(IV) exhibited a prominent inhibitory effect on CD133⁺ lung cancer stem cells (CSCs) and suppressed tumor growth in vivo. Unlike cisplatin treatment which eventually enriches CSCs, NP_Pt(IV) treatment prevents the accumulation of CD133⁺ lung CSCs in tumors. Therefore, NP_Pt(IV) simutaneously targeting CSCs and non-CSCs might represent a superior strategy to improve conventional anticancer therapy directed predominantly to tumor bulk populations.