Quantified Metabolomics and Lipidomics Profiles Reveal Serum Metabolic Alterations and Distinguished Metabolites of Seven Chronic Metabolic Diseases.

The co-occurrence of multiple chronic metabolic diseases is highly prevalent, posing a huge health threat. Clarifying the metabolic associations between them, as well as identifying metabolites which allow discrimination between diseases, will provide new biological insights into their co-occurrence. Herein, we utilized targeted serum metabolomics and lipidomics covering over 700 metabolites to characterize metabolic alterations and associations related to seven chronic metabolic diseases (obesity, hypertension, hyperuricemia, hyperglycemia, hypercholesterolemia, hypertriglyceridemia, fatty liver) from 1626 participants. We identified 454 metabolites were shared among at least two chronic metabolic diseases, accounting for 73.3% of all 619 significant metabolite-disease associations. We found amino acids, lactic acid, 2-hydroxybutyric acid, triacylglycerols (TGs), and diacylglycerols (DGs) showed connectivity across multiple chronic metabolic diseases. Many carnitines were specifically associated with hyperuricemia. The hypercholesterolemia group showed obvious lipid metabolism disorder. Using logistic regression models, we further identified distinguished metabolites of seven chronic metabolic diseases, which exhibited satisfactory area under curve (AUC) values ranging from 0.848 to 1 in discovery and validation sets. Overall, quantitative metabolome and lipidome data sets revealed widespread and interconnected metabolic disorders among seven chronic metabolic diseases. The distinguished metabolites are useful for diagnosing chronic metabolic diseases and provide a reference value for further clinical intervention and management based on metabolomics strategy.

The triglyceride glucose index trajectory is associated with hypertension: a retrospective longitudinal cohort study.

BACKGROUND: Previous studies have found that the triglyceride glucose index (TyG index) trajectories are associated with cardiovascular diseases. However, the association between the patterns of TyG index trajectories and risk for hypertension has not been investigated. In a longitudinal general population, we aimed to identify distinct TyG index trajectories over 12 years and describe their association with incidence of hypertension. METHOD: Of the 15,056 adults retrospectively recruited from the Physical Examination Center of the Second Affiliated Hospital of Dalian Medical University in northeast of China from 2011 to 2022. TyG index was calculated as ln (fasting TG [mg/dL] × FPG [mg/dL]/2) and the TyG index trajectories were developed using group-based trajectory modelling. Cox regression analysis was accomplished to assess the association between TyG index and incidence of hypertension. RESULTS: The median age of the population was 38 years, and 7352 (48.83%) of the participants were men. Three distinct TyG index trajectories were identified: "low increasing" (N = 7241), "moderate increasing" (N = 6448), and "high stable" (N = 1367). Using "low increasing" trajectory as a reference, "moderate increasing" and "high stable" trajectory were associated with increased risk of hypertension (HR = 2.45; 95% CI 2.25-2.67 and HR = 3.88; 95% CI 3.48-4.33). After adjusting for baseline sex, age, diabetes, smoking, systolic blood pressure, diastolic blood pressure, BMI, cholesterol, high density lipoprotein cholesterol, low density lipoprotein cholesterol, blood glucose, triglyceride, urea, uric acid, and glomerular filtration rate, the HR were slightly attenuate in "moderate increasing" and "high stable" trajectories to 1.38 (95% CI 1.23-1.54) and 1.69 (95% CI 1.40-2.02) respectively. Meanwhile, similar results were observed in multiple sensitivity analyses. The HR of the "moderate increasing" and "high stable" trajectory groups were 2.63 (95% CI 2.30-3.00) and 4.66 (95% CI 3.66-5.93) in female, and 1.66 (95% CI 1.48-1.86) and 2.33 (95% CI 2.04-2.66) in male. CONCLUSIONS: Elevated TyG index at baseline and long-term TyG index trajectories were associated with the risk of hypertension. Early identification of increasing TyG index could provide insights for preventing hypertension later in life.

Construction and Validation of a Model for Predicting Impaired Fasting Glucose Based on More Than 4000 General Population.

Purpose: Impaired fasting glucose (IFG) is associated with an increased risk of multiple diseases. Therefore, the early identification and intervention of IFG are particularly significant. Our study aims to construct and validate a clinical and laboratory-based nomogram (CLN) model for predicting IFG risk. Patients and Methods: This cross-sectional study collected information on health check-up subjects. Risk predictors were screened mainly by the LASSO regression analysis and were applied to construct the CLN model. Furthermore, we showed examples of applications. Then, the accuracy of the CLN model was evaluated by the receiver operating characteristic (ROC) curve, the area under the ROC curve (AUC) values, and the calibration curve of the CLN model in the training set and validation set, respectively. The decision curve analysis (DCA) was used to estimate the level of clinical benefit. Furthermore, the performance of the CLN model was evaluated in the independent validation dataset. Results: In the model development dataset, 2340 subjects were randomly assigned to the training set (N = 1638) and validation set (N = 702). Six predictors significantly associated with IFG were screened and used in the construction of the CLN model, a subject was randomly selected, and the risk of developing IFG was predicted to be 83.6% by using the CLN model. The AUC values of the CLN model were 0.783 in the training set and 0.789 in the validation set. The calibration curve demonstrated good concordance. DCA showed that the CLN model has good clinical application. We further performed independent validation (N = 1875), showed an AUC of 0.801, with the good agreement and clinical diagnostic value. Conclusion: We developed and validated the CLN model that could predict the risk of IFG in the general population. It not only facilitates the diagnosis and treatment of IFG but also helps to reduce the medical and economic burdens of IFG-related diseases.