Background: Acute myocardial infarction (AMI) is a leading cause of death and disability. Diabetes is an important risk factor and a common comorbidity in AMI patients. The higher mortality risk of diabetes-AMI relative to nondiabetes-AMI indicates a need for specific treatment to improve clinical outcome. However, the global metabolic dysregulation of AMI complicated with diabetes is still unclear. We aim to systematically interrogate changes in the metabolic microenvironment immediate to AMI episodes in the absence or presence of diabetes. Methods: In this work, quantitative metabolomics was used to investigate plasma metabolic differences between diabetes-AMI (n=59) and nondiabetes-AMI (n=59) patients. A diverse array of perturbed metabolic pathways involving carbohydrate metabolism, lipid metabolism, glycolysis, tricarboxylic acid cycle, and amino acid metabolism emerged. Results: In all, our omics-oriented approach defined a metabolic signature of afflicted mitochondrial function aggravated by concurrent diabetes in AMI patients. In particular, our analyses uncovered N-lactoyl-phenylalanine and lysophosphatidylcholines as key functional metabolites that skewed the metabolic picture of diabetes-AMI relative to nondiabetes-AMI. N-lactoyl-phenylalanine was strongly associated with metabolic indicators reflective of mitochondrial overload and negatively correlated with HbA1c (glycosylated hemoglobin, type A1C) specifically in hyperglycemic AMI, suggestive of its central role in glucose utilization and mitochondrial energy production instrumental to the clinical outcome of diabetes-AMI. Reductions in lysophosphatidylcholines, which were negatively correlated with blood glucose and inflammatory markers, might further compromise glucose expenditure and aggravate inflammation leading to poorer prognosis in diabetes-AMI. Conclusions: As circulating metabolite levels are amenable to therapeutic intervention, such shifts in metabolic signatures provide new clues and potential therapeutic targets specific to the treatment of diabetes-AMI.

S-Adenosylmethionine (SAM) and S-adenosylhomocysteine (SAH) are relevant to a variety of diseases. Previous reports that quantified SAM and SAH were based on HPLC or LC–MS/MS. No antibody against SAM has been generated, and the antibody against SAH cannot be used with blood samples. Immunoassays have not been used to measure SAM and SAH. In this study, ELISA was used to measure blood SAM and SAH levels. Specific antibodies against SAM were produced for the first time using a stable analog as the antigen. The monoclonal antibodies against SAM and SAH were characterized. No cross-reactivity was detected for the analyzed analogs. For the anti-SAM antibodies, the ELISA sensitivity was ~2 nM, and the affinity was 7.29 × 1010 L/mol. For the anti-SAH antibodies, the sensitivity was ~15 nM, and the affinity was 2.79 × 108 L/mol. Using high-quality antibodies against SAM and SAH, immunoassays for the detection of SAM and SAH levels in blood and tissue samples were developed. Clinical investigations using immunoassays to measure SAM, SAH and the methylation index (MI) in normal and diseased samples indicated that (1) the SAM level is age and gender dependent; (2) the SAM level is associated with the severity of liver diseases, inflammatory reactions and other diseases; and (3) the methylation index (MI) is significantly reduced in many diseases and may serve as a screening biomarker to identify potentially unfavorable health conditions. It is possible to generate antibodies against active small biomolecules with weak immunogenicity, such as SAM and SAH, using traditional hybridoma technology. The antigens and antibodies described here will contribute to the development of immunoassays to measure SAM, SAH and related molecules. These assays enable the MI to be measured specifically, accurately, easily and quickly without costly equipment. This preliminary study indicates that the MI could be an effective indicator of general health, except under conditions that may alter the value of the MI, such as special diets and medications.

Growing evidence indicate that chronic diseases have distinct clusters, each potentially influencing intrinsic capacity (IC) through unique pathological pathways. This raises two critical questions: (1) How do different multimorbidity clusters preferentially impact specific IC domains? (2) Which multimorbidity cluster would have the highest risk of mortality in individuals with IC impairment? We used data from the China Health and Retirement Longitudinal Study (CHARLS), a nationally representative cohort study in China. Multimorbidity was defined as the presence of two or more chronic conditions. IC impairment was assessed across six domains: cognition, locomotion, vitality, psychology, hearing and vision. We identified multimorbidity clusters using latent class analysis (LCA). Logistic regression was used to evaluate the association between identified multimorbidity clusters and domain specific IC decline, while Kaplan-Meier analysis and cox regression analysis were used to assess the mortality risk. Among the 4333 participants with multimorbidity, 48.6% were male, with mean age of 59.5 years. LCA method identified four distinct multimorbidity clusters: arthritis-metabolic (24.8%), stomach-arthritis (29.4%), respiratory (16.5%), metabolic-vascular (29.4%) clusters. Multimorbidity cluster-specific patterns of IC impairment revealed markedly elevated risks of decline in the cognitive, psychological, hearing, and vitality domains within the respiratory cluster. Similarly, the stomach-arthritis cluster was associated with significantly higher risks of impairment in the psychological, visual, hearing, and vitality domains. In contrast, the arthritis-metabolic cluster demonstrated significantly increased risks specifically in the cognitive and psychological domains. Survival analysis revealed significant mortality differences across multimorbidity clusters (p < 0.001). After multivariate adjustment, intrinsic capacity impairment remained significantly associated with increased mortality in the respiratory cluster (HR = 1.74, 95%CI:1.06-2.87, p = 0.029), demonstrating pattern-dependent prognostic value of IC. Our findings revealed a significant heterogeneity in IC impairment patterns across different multimorbidity clusters, showing cluster-specific IC impairment and cluster-dependent risk of mortality according to IC impairment.