Aims/hypothesis: Type 2 diabetes is a chronic condition that is caused by hyperglycaemia. Our aim was to characterise the metabolomics to find their association with the glycaemic spectrum and find a causal relationship between metabolites and type 2 diabetes. Methods: As part of the Innovative Medicines Initiative - Diabetes Research on Patient Stratification (IMI-DIRECT) consortium, 3000 plasma samples were measured with the Biocrates AbsoluteIDQ p150 Kit and Metabolon analytics. A total of 911 metabolites (132 targeted metabolomics, 779 untargeted metabolomics) passed the quality control. Multivariable linear and logistic regression analysis estimates were calculated from the concentration/peak areas of each metabolite as an explanatory variable and the glycaemic status as a dependent variable. This analysis was adjusted for age, sex, BMI, study centre in the basic model, and additionally for alcohol, smoking, BP, fasting HDL-cholesterol and fasting triacylglycerol in the full model. Statistical significance was Bonferroni corrected throughout. Beyond associations, we investigated the mediation effect and causal effects for which causal mediation test and two-sample Mendelian randomisation (2SMR) methods were used, respectively. Results: In the targeted metabolomics, we observed four (15), 34 (99) and 50 (108) metabolites (number of metabolites observed in untargeted metabolomics appear in parentheses) that were significantly different when comparing normal glucose regulation vs impaired glucose regulation/prediabetes, normal glucose regulation vs type 2 diabetes, and impaired glucose regulation vs type 2 diabetes, respectively. Significant metabolites were mainly branched-chain amino acids (BCAAs), with some derivatised BCAAs, lipids, xenobiotics and a few unknowns. Metabolites such as lysophosphatidylcholine a C17:0, sum of hexoses, amino acids from BCAA metabolism (including leucine, isoleucine, valine, N-lactoylvaline, N-lactoylleucine and formiminoglutamate) and lactate, as well as an unknown metabolite (X-24295), were associated with HbA_1c progression rate and were significant mediators of type 2 diabetes from baseline to 18 and 48 months of follow-up. 2SMR was used to estimate the causal effect of an exposure on an outcome using summary statistics from UK Biobank genome-wide association studies. We found that type 2 diabetes had a causal effect on the levels of three metabolites (hexose, glutamate and caproate [fatty acid (FA) 6:0]), whereas lipids such as specific phosphatidylcholines (PCs) (namely PC aa C36:2, PC aa C36:5, PC ae C36:3 and PC ae C34:3) as well as the two n-3 fatty acids stearidonate (18:4n3) and docosapentaenoate (22:5n3) potentially had a causal role in the development of type 2 diabetes. Conclusions/interpretation: Our findings identify known BCAAs and lipids, along with novel N-lactoyl-amino acid metabolites, significantly associated with prediabetes and diabetes, that mediate the effect of diabetes from baseline to follow-up (18 and 48 months). Causal inference using genetic variants shows the role of lipid metabolism and n-3 fatty acids as being causal for metabolite-to-type 2 diabetes whereas the sum of hexoses is causal for type 2 diabetes-to-metabolite. Identified metabolite markers are useful for stratifying individuals based on their risk progression and should enable targeted interventions. Graphical

Background: Altered plasma N-glycosylation is increasingly recognized as a contributor to metabolic dysregulation. This study aimed to investigate the role of plasma N-glycans in glucose metabolism and the progression from normoglycemia to prediabetes and type 2 diabetes (T2D). Methods: We analyzed longitudinal data from 473 participants in the Cooperative Health Research in the Region of Augsburg (KORA) cohort over 7 years. N-glycan profiles were measured using hydrophilic interaction ultrahigh-performance liquid chromatography with fluorescence detection (HILIC-UHPLC-FLR). Glycan associations with incident prediabetes/T2D and related traits, such as body mass index (BMI), fasting glucose, homeostasis model assessment of insulin resistance (HOMA-IR) were evaluated using longitudinal models based on N-glycan measurements obtained at F4 and FF4. Classification performance at FF4 was assessed using machine learning models interpreted with SHapley Additive exPlanation (SHAP) values. Mendelian randomization (MR) and glycan quantitative trait loci (glycan-QTL) analyses were conducted to explore causality and genetic determinants. Results: During follow-up, 231 individuals progressed to prediabetes/T2D, while 242 remained normoglycemic. Nineteen glycans were associated with diabetes progression in the basic model; 12 remained significant after full adjustment. Glycans such as GP18 and GP32 were also linked to metabolic traits. A glycan-clinical model achieved high classification accuracy (AUC = 0.895). MR supported causal roles for GP18, GP19, and S1. Glycan-QTL analysis revealed SNPs and genes (FUT8, ST3GAL4) are associated with key glycans. Conclusions: Plasma N-glycans are diagnostic of early glycemic deterioration and supported by genetic and causal evidence, highlighting their potential as biomarkers for diabetes risk stratification. Graphic

Aims: A data‐driven cluster analysis in a cohort of European individuals with type 2 diabetes (T2D) has previously identified four subgroups based on clinical characteristics. In the current study, we performed a comprehensive statistical assessment to (1) replicate the above‐mentioned original clusters; (2) derive de novo T2D subphenotypes in the Kooperative Gesundheitsforschung in der Region Augsburg (KORA) cohort and (3) describe underlying genetic risk and diabetes complications. Methods: We used data from n = 301 individuals with T2D from KORA FF4 study (Southern Germany). Original cluster replication was assessed forcing k = 4 clusters using three different hyperparameter combinations. De novo clusters were derived by open k ‐means analysis. Stability of de novo clusters was assessed by assignment congruence over different variable sets and Jaccard indices. Distribution of polygenic risk scores and diabetes complications in the respective clusters were described as an indication of underlying heterogeneity. Results: Original clusters did not replicate well, indicated by substantially different assignment frequencies and cluster characteristics between the original and current sample. De novo clustering using k = 3 clusters and including high sensitivity C‐reactive protein in the variable set showed high stability (all Jaccard indices >0.75). The three de novo clusters ( n = 96, n = 172, n = 33, respectively) adequately captured heterogeneity within the sample and showed different distributions of polygenic risk scores and diabetes complications, that is, cluster 1 was characterized by insulin resistance with high neuropathy prevalence, cluster 2 was defined as age‐related diabetes and cluster 3 showed highest risk of genetic and obesity‐related diabetes. Conclusion: T2D subphenotyping based on its sample's own clinical characteristics leads to stable categorization and adequately reflects T2D heterogeneity.

Aims: This study investigated the role of plasma proteins in obesity to identify predictive biomarkers and explore underlying biological mechanisms. Methods: In the Cooperative Health Research in the Region of Augsburg (KORA) FF4 study, 809 proteins were measured in 2045 individuals (564 obese and 1481 non‐obese). Multivariate logistic regression adjusted for confounders (basic and full models) was used to identify obesity‐associated proteins. Priority‐Lasso was applied for feature selection, followed by machine learning models (support vector machine [SVM], random forest [RF], k‐nearest neighbour [KNN] and adaptive boosting [Adaboost]) for prediction. Correlation and enrichment analyses were performed to elucidate relationships between protein biomarkers, obesity risk factors and perturbed pathways. Mendelian randomisation (MR) assessed causal links between proteins and obesity. Results: A total of 16 proteins were identified as significantly associated with obesity through multivariable logistic regression in the basic model and subsequent Priority‐Lasso analysis. Enrichment analyses highlighted immune response, lipid metabolism and inflammation regulation were linked to obesity. Machine learning models demonstrated robust predictive performance with area under the curves (AUC) of 0.820 (SVM), 0.805 (RF), 0.791 (KNN) and 0.819 (Adaboost). All 16 proteins correlated with obesity‐related risk factors such as blood pressure and lipid levels. MR analysis identified AFM, CRP and CFH as causal and potentially modifiable proteins. Conclusions: The protein signatures identified in our study showed promising predictive potential for obesity. These findings warrant further investigation to evaluate their clinical applicability, offering insights into obesity prevention and treatment strategies.

Background/Objectives: Metabolic syndrome (MetS) is a heterogeneous cluster of cardiometabolic risk factors whose underlying molecular heterogeneity remains incompletely understood. Methods: Targeted metabolomics (388 metabolites) and untargeted proteomics (842 proteins) were analyzed in the KORA FF4 study (n = 1921). Multivariable regression, LASSO, and Multi-Omics Factor Analysis (MOFA) were applied to identify MetS-associated signatures. As a secondary analysis, soft-clustering archetypal analysis based on 22 clinical variables was performed within the MetS subgroup (n = 582) to identify extreme clinical subphenotypes. Results: A total of 117 metabolites and 70 proteins were associated with MetS, including five metabolites and eight proteins shared across all MetS traits. Key metabolomic features comprised diacylglycerols, triglyceride score, hexoses, and branched-chain amino acids. Prominent proteins included APOC4, APOE, APOB, APOD, APOF, adiponectin, LBP, and SHBG, reflecting alterations in lipid metabolism, inflammation, and metabolic regulation. MOFA identified a latent factor strongly linked to MetS, integrating signatures of lipid metabolism, insulin resistance, and inflammation. Archetypal analysis identified extreme MetS profiles and associated with metabolites and proteins shared across all Mets. Conclusions: Integrated multi-omics analysis revealed MetS-associated molecular signatures characterized primarily by dysregulated lipid metabolism, insulin resistance, and chronic low-grade inflammation. Secondary archetypal analysis highlighted clinical heterogeneity at the extreme boundaries of MetS while supporting a shared molecular foundation. These findings highlight the value of combining clinical, metabolomics and proteomics to improve characterization of MetS heterogeneity and inform future precision risk stratification.