Qi Fu

Aims/hypothesis Type 1 diabetes is an autoimmune disease with a strong genetic component. Nevertheless, the genetic underpinnings linking type 1 diabetes susceptibility to both insulin secretion and insulin resistance across diverse ancestries have yet to be fully characterised. Methods We performed a multi-ancestry type 1 diabetes genome-wide association study meta-analysis comprising 26,198 cases and 36,733 control individuals to identify risk regions across diverse ancestral populations. We further evaluated their associations with clinical traits, integrated epigenetic profiles, mapped candidate genes to define tissue and cell type specificity, and conducted pathway enrichment analyses to elucidate the genetic links between type 1 diabetes and both insulin secretion and insulin resistance. Results We observed 69 non-HLA regions for type 1 diabetes risk, including three novel genome-wide association study signals: rs2179781 in AHI1 (OR 1.074, p=1.19×10⁻⁸), rs10117059 near TRAF1 (OR 1.074, p=3.95×10⁻⁸) and rs7184802 near ADCY7 (OR 1.089, p=4.12×10⁻⁸). Notably, over one-third (26/69) of these variants were associated with insulin secretion or resistance indices. Using Bayesian fine-mapping integrated with three complementary annotation strategies, we prioritised 136 putative functional variants. Integrative analysis leveraging Roadmap Epigenomics data revealed that these variants exhibited elevated regulatory potential, as evidenced by DNase I hypersensitivity, characteristic H3 histone modifications and active chromatin states in key metabolic tissues (pancreatic islets, liver, skeletal muscle and adipose). Computational mapping and annotation of these variants implicated 447 candidate genes, which showed metabolic tissue-specific expression, and were enriched in pathways related to insulin secretion and resistance (p=5.41×10⁻⁴ and 2.10×10⁻⁵, respectively), a pattern not evident for HLA risk genes. Subsequent functional characterisation identified ADCY7, regulated by functional variant rs1872691, as the probable causal gene at the novel 16q12.1 locus. In pancreatic beta cells, proinflammatory cytokines downregulated Adcy7 expression and Adcy7 perturbation bidirectionally modulated both insulin secretion and cytokine-induced apoptosis. Conversely, Adcy7 knockdown in adipocytes augmented lipogenesis and enhanced insulin sensitivity. Conclusions/interpretation Our findings support the existence of a shared non-HLA genetic architecture that may link type 1 diabetes susceptibility to both impaired insulin secretion and insulin resistance, providing preliminary genetic evidence for their convergent pathogenic mechanisms. Graphical Abstract

Background: Homeostasis model assessment of insulin resistance (HOMA-IR) mainly reflects basal IR, whereas dynamic measures such as the Matsuda index are less practical. Methods: This cross-sectional study evaluated eight non-insulin-based metabolic indices [the metabolic score for insulin resistance [METS-IR], triglyceride-glucose body mass index [TyG-BMI], TyG, TyG-WC, LAP, TG/HDL-C, VAI, CMI] as IR surrogates in 710 Chinese adults without diabetes (aged 20–60 years). IR was separately defined using two criteria (1): HOMA-IR above the 75th percentile, and (2) Matsuda index below the 25th percentile, in normal-weight participants with normal glucose tolerance. Correlations, restricted cubic splines, and receiver operating characteristic (ROC) analyses were performed, including separate sex- and 10-year age-stratified subgroup analyses. Results: METS-IR and TyG-BMI showed moderate correlations with both HOMA-IR and the Matsuda index. METS-IR, TyG, TyG-BMI, and TyG-WC were linearly associated with IR. In ROC analyses, overall AUCs for METS-IR and TyG-BMI were 0.776 and 0.770 for HOMA-IR-defined IR and 0.768 and 0.765 for Matsuda index-defined IR, indicating moderate discrimination. At cut-offs of 35.54 for METS-IR and 211.37 for TyG-BMI (which were consistent across both IR definitions), sensitivities were 66.1–68.9%, and specificities were 75.1–78.4%. Under the HOMA-IR definition, METS-IR had a significantly larger AUC than six indices, but not TyG-BMI. Under the Matsuda definition, its AUC did not differ from those of TyG-BMI, TyG-WC, LAP, or CMI. Rankings varied across subgroups. Conclusion: METS-IR and TyG-BMI demonstrated comparable and favorable performance, serving as practical, insulin-independent initial screening markers for identifying elevated IR risk in Chinese young and middle-aged adults without diabetes. Rather than replacing insulin measurements, these indices offer valuable risk stratification tools in primary care or epidemiological settings where insulin assays are routinely unavailable.

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Type 2 diabetes (T2D) is characterized by pancreatic islet β-cell dysfunction and systemic insulin resistance, with meta-inflammation playing a critical role in disease progression. As the major type of immune cell population in islets, both resident and recruited macrophages are important regulators of the islet immune microenvironment under physiological and T2D conditions. Exercise is an effective strategy for treating T2D, yet its impacts on islet inflammation and β-cell dysfunction remain elusive. Here, we established a mouse model of exercise intervention in obesity-associated T2D by combining high-fat diet (HFD) feeding with treadmill running. Notably, exercise markedly improves glucose tolerance and insulin sensitivity, accompanied by substantial mitigation of HFD-induced β-cell dysfunction, islet hypertrophy, and alterations in β-cell subpopulations. Exercise also reduces intra-islet infiltration of CD45+ immune cells and dampens pro-inflammatory gene expression, indicating robust attenuation of islet inflammation. Using untargeted plasma proteomics, we identified the secreted protein acidic and rich in cysteine (SPARC) as a circulating factor, whose suppression is associated with exercise-linked islet protection under HFD conditions. Mechanistically, our data support a model in which SPARC contributes to β-cell dysfunction, at least in part, through macrophage inflammasome-related signaling. Further analysis of a human cohort demonstrates that circulating SPARC protein levels are markedly elevated in patients with T2D, exhibiting a significant negative correlation with parameters indicative of insulin sensitivity and β-cell function, and a positive correlation with insulin resistance. Together, this work provides a systemic characterization of the effects of exercise intervention on islet homeostasis and β-cell function, and highlights SPARC as a candidate immuno-metabolic node for T2D intervention.

Aims/hypothesis: A recent study has suggested eight clusters of genetic variants associated with type 2 diabetes. We aimed to characterise metabolite associations for these eight clusters. Methods: We constructed type 2 diabetes overall and cluster-partitioned polygenic risk scores (PRSs) in 10,015 Finnish men with 979 named plasma metabolites measured in Metabolon HD4 mass spectrometry platform. We evaluated metabolite–PRS associations using linear regression. We also performed a mediation analysis to examine whether metabolites statistically accounted for part of the association between genetic risk and incident type 2 diabetes that developed in a mean of 13.6 years’ follow-up. Results: We identified 337 metabolites significantly associated with type 2 diabetes genetic risk, including 242 exclusive to cluster-partitioned PRSs. Of the significant metabolites, 26 exhibited significantly heterogeneous associations across clusters. We identified significant enrichment for 33 metabolic pathways among the cluster-associated metabolites. Notably, metabolites for the two pancreatic beta cell-related clusters exhibited enrichment in distinct pathways: the beta cell + proinsulin (PI) cluster in fructose, mannose and galactose metabolism; and the beta cell − PI cluster in branched-chain amino acid metabolism. Mediation analysis suggested that >50% of the associated metabolites showed patterns statistically consistent with a mediating role in the associations between PRSs and incident type 2 diabetes. Conclusions/interpretation: This study underscores the value of type 2 diabetes clustering and highlights metabolic heterogeneity across clusters. The findings have the potential to guide personalised interventions. Data availability: The datasets generated during and/or analysed in the current study are available in dbGaP (accession ID: phs000743.v4.p1 and phs004033.v1.p1).

OBJECTIVE: We aimed to identify plasma metabolites significantly associated with existing type 2 diabetes (T2D), further characterize their associations with T2D-related laboratory measures and lifestyle-related risk factors, and evaluate their potential mediating and predictive roles for incident T2D. RESEARCH DESIGN AND METHODS: We performed metabolomic profiling in 10,163 Finnish men in the Metabolic Syndrome in Men (METSIM) study at baseline. Of these participants, 1,412 had prevalent T2D and 1,234 developed incident T2D during an average follow-up period of 13.6 years. We tested for associations of 979 named metabolites with prevalent T2D. For significant metabolites, we further evaluated their associations with 24 T2D-related laboratory measures of five groups and five T2D lifestyle-related risk factors. We performed an exploratory mediation analysis to examine the mediation effects of metabolites on incident T2D for the five risk factors. We evaluated metabolite associations with incident T2D and built exploratory metabolite predictive models for incident T2D. RESULTS: We identified 193 plasma metabolites significantly associated with prevalent T2D at baseline, including 71 previously unreported. Of these 193 metabolites, 88 were associated with incident T2D. In participants with prevalent T2D, the 193 metabolites showed associations with zero to 24 (mean 10.2) T2D-related laboratory measures. Eighty-one metabolites partially mediated the associations of the five risk factors with T2D onset under standard mediation assumptions. The significant metabolites showed improved predictive ability for future T2D. CONCLUSIONS: This study identifies T2D plasma metabolic biomarkers for further investigation in women and other populations. The findings enhance our understanding of T2D biology.

Vascular endothelial cells (ECs) play pivotal roles in maintaining metabolic tissue homeostasis, and EC dysfunction is associated with obesity and metabolic disorders. The mammalian ER stress sensor IRE1α kinase/RNase responds to metabolic cues, but it remains unclear whether endothelial IRE1α is implicated in controlling systemic metabolism. Here we show that genetic depletion of IRE1α in ECs leads to maladaptation of pancreatic islets under obesity-associated metabolic stress. We find that in high-fat diet-fed male mice, loss of IRE1α in ECs has no significant impact upon adiposity, but unexpectedly results in glucose intolerance with impaired insulin secretion, accompanied by blunted intra-islet angiogenesis and compensatory islet growth. Mechanistically, IRE1α RNase decays the mRNA encoding the endogenous anti-angiogenic factor thrombospondin-1 (THBS1/TSP1) in islet ECs. These findings thus uncover a critical role of the endothelial IRE1α suppression of THBS1 in governing the vascular support that enables the functional adaptation of islets to metabolic stress.