ArticleBMJ public health2026
Quantitative metabolomics and machine learning identify differential metabolomic biomarkers across normoglycaemia, pre-diabetes and diabetes in a Qinghai Plateau population.
Article in BMJ public health, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Objective: To identify biomarkers for pre-diabetes mellitus (PreDM) and diabetes mellitus (DM) in high-altitude Tibetan populations through integrated analysis of body fat composition (BFC), haematologic parameters, and metabolomics. Methods: 904 Tibetan participants were included. T-tests and Wilcoxon tests assessed differences in 21 blood parameters, 15 BFC measures, 7 anthropometrics, 332 metabolites among Control, PreDM and DM. Key biomarkers were identified using least absolute shrinkage and selection operator (LASSO), random forest (RF) and extreme gradient boosting (XGBoost) models. Diagnostic performance was evaluated using logistic regression, reporting precision, F1-score, sensitivity, specificity and the area under the receiver operating characteristic curve (AUROC). Results: Preliminary analysis showed significant differences in 302 metabolites, 6 anthropometric, 14 biochemical and 15 BFC indicators between disease and control groups (p<0.05). Based on LASSO, RF and XGBoost methods, the biomarkers including body mass index, waist, visceral fat level (VFL), blood cholesterol, cholesteryl esters-to-total lipids ratio in low-density lipoprotein 6 (LDL6), phospholipid-to-total lipids ratio in intermediate-density lipoprotein (IDL) (IDPLp), polyunsaturated fatty acid/total fatty acid (PUFAp), triglycerides in LDL1, free cholesterol to total lipids ratio in LDL6, hypersensitive C reactive protein (hsCRP), red blood cell distribution width and LDL1 particle number showed differences between Control and PreDM; body mass index, waist, systolic blood pressure (BP), diastolic BP, hsCRP, VFL and particle number in IDL (IDPN) showed differences between Control and DM; IDPN, triglycerides-to-total lipids ratio in LDL3, cholesteryl esters-to-total lipids ratio in LDL1, and 3-hydroxybutyrate showed differences between PreDM and DM. Selected biomarkers demonstrated high diagnostic performance. AUROCs were 0.919 (0.883 to 0.954) for PreDM versus Control, 0.980 (0.943 to 1.000) for DM versus Control, 0.694 (0.597 to 0.783) for DM versus PreDM. Conclusions: This study identified metabolic biomarkers (eg, IDPLp, PUFAp, hsCRP, VFL) that distinguish normoglycaemia from dysglycaemia in high-altitude Tibetans, but not preDM from DM. These findings provide insights into high-altitude metabolic dysregulation and lay a foundation for future altitude-specific screening.
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