Jul 2026· International Journal of Molecular Sciences· Vol 27, pp. 6721· 0 citations· 38 references
Medicine
TL;DR
These exploratory findings suggest that the transition from prediabetes to diabetes may be accompanied by alterations in amino acid, lipid, and energy metabolism, which represent candidate metabolites that require validation in larger independent cohorts using targeted metabolomics.
Abstract
Type 2 diabetes (T2D) and prediabetes represent a progressive glycemic continuum associated with multi-pathway metabolic deterioration that often precedes clinical diagnosis. Early identification of molecular alterations underlying this transition is critical for prevention strategies. Untargeted gas chromatography–mass spectrometry (GC–MS) metabolomics combined with multivariate statistical analysis (PCA) was applied to serum samples from 188 participants stratified into Control (n = 48), Prediabetes (n = 113), and Diabetes (n = 27) groups according to ADA/WHO diagnostic criteria. Progressive metabolic alterations were observed across the glycemic continuum. Several amino acid-, lipid-, and organic acid-related features showed nominal differences between the study groups. However, none of the detected metabolomic features remained statistically significant after false discovery rate (FDR) correction, indicating that these findings should be considered exploratory. Diabetes was associated with widespread downregulation of amino acid-related features, long-chain and complex lipid species, and small organic acids relative to both control and prediabetes groups. PCA (PC1 = 35.9%) showed progressive metabolic stratification primarily driven by hyperglycemia, dyslipidemia, and blood pressure elevation. These exploratory findings suggest that the transition from prediabetes to diabetes may be accompanied by alterations in amino acid, lipid, and energy metabolism. The identified metabolomic features represent candidate metabolites that require validation in larger independent cohorts using targeted metabolomics.
INTRODUCTION
Obesity is highly prevalent in schizophrenia (SCZ) and contributes to unfavorable clinical and cognitive outcomes, yet its underlying metabolic mechanisms remain unclear. We applied non-targeted metabolomics to characterize body mass index (BMI) related metabolic signatures in chronic SCZ.
METHODS
A total of 347 chronic SCZ patients were stratified into high BMI (n = 187) and low BMI (n = 160) groups. The Positive and Negative Symptom Scale (PANSS) and the Repeatable Battery for the Assessment of Neuropsychological Status (RBANS) scales were used to evaluate psychopathology and cognition. Fasting serum samples were collected and analyzed using non-targeted metabolomics via ultra-high-performance liquid chromatography-high-resolution mass spectrometry. Multivariate and univariate statistical approaches, pathway enrichment analyses, receiver operating characteristic (ROC) analyses, and linear regression models were applied.
RESULTS
Fifty-eight metabolites differed significantly between high and low BMI groups (primarily involving 12 amino acids and 8 fatty acids). Pathway analyses revealed significant perturbations in Arginine biosynthesis (p < 0.001), Nicotinate and nicotinamide metabolism (p = 0.008) and Phenylalanine metabolism (p = 0.037). Panels combining differential amino acids and fatty acids demonstrated moderate discriminative performance for high BMI status (AUC > 0.7). Moreover, C-Mannosyl Valine and C-Mannosyl Phenylalanine were associated with cognitive impairment in the low BMI group (all p < 0.05), whereas 2-Aminoheptanoic acid were associated with negative symptoms severity in the high BMI group (p < 0.001).
CONCLUSION
Distinct metabolic profiles characterize BMI heterogeneity in SCZ and are differentially associated with cognitive impairment and negative symptoms.
Background Pre-diabetes is a critical, reversible intermediate metabolic state bridging normal glucose tolerance and overt type 2 diabetes mellitus (T2DM). Early clinical detection is essential for timely intervention, yet single-biomarker screenings often fail to capture the systemic metabolic and inflammatory alterations characteristic of this state. This study aimed to identify independent clinical and biochemical risk factors for pre-diabetes and to construct and validate a robust, low-cost multivariable predictive model leveraging routine health examination data, with a specific focus on dissecting the underlying molecular and biochemical pathways. Methods A retrospective cross-sectional analysis was performed on clinical registry data from 2,376 adult participants (1,242 pre-diabetes patients and 1,134 normoglycemic controls) who underwent health examinations in 2023. Comprehensive demographic, anthropometric, and biochemical profiles were analyzed. Multivariable logistic regression was utilized to isolate independent risk and protective factors. Receiver Operating Characteristic (ROC) curve analysis evaluated the discriminative capacity of individual parameters versus the integrated multivariable model. Results Univariate analyses demonstrated widespread systemic alterations across metabolic, inflammatory, hepatic, and lipid domains in pre-diabetic patients (P<0.05). Multivariable logistic regression identified gender(OR = 1.635, 95% CI: 1.177–2.274, P = 0.0034), age (OR = 1.105, 95% CI: 1.093–1.117, P<0.001), BMI (OR = 1.12, 95% CI: 1.079–1.163, P<0.001), MPV (OR = 1.103, 95% CI: 1.009–1.205, P = 0.0306), total protein (OR = 1.233, 95% CI: 1.076–1.416, P = 0.0029), γ-GT(OR = 1.007, 95% CI: 1.004–1.012, P<0.001), UA(OR = 1.002, 95% CI: 1.001–1.003, P = 0.0047), and apolipoprotein A1 (OR = 2.736, 95% CI: 1.200–6.273, P = 0.017) as independent risk factors. Serum creatinine, and high-density lipoprotein cholesterol (HDL-C) were identified as independent protective factors. While single parameters showed poor diagnostic capacity (AUCs <0.76), after bootstrap internal validation, the 8-parameter model achieved a bias-corrected AUC of 0.806, indicating good discriminative ability and robust generalizability. Conclusion Pre-diabetes is associated with a complex, interconnected network of subclinical inflammation, hepatic stress, dyslipidemia, and altered skeletal muscle dynamics. Integrating these routine biomarkers into a unified predictive model offers a highly accessible, biologically sound, and cost-effective strategy for early diabetes screening in primary care settings.
Guitao Ruan, Xiaoliang Guo, Weizheng Zhang et al.· Frontiers in Endocrinology· 0 citations
Age-related hearing loss (ARHL) is a progressive neurodegenerative disorder for which reliable biomarkers are lacking. This study aimed to identify serum metabolic markers associated with ARHL that may improve early screening and diagnostic performance. A total of 300 subjects, including 125 healthy controls and 175 patients with ARHL from three independent centers, were enrolled. Untargeted and targeted metabolomics based on liquid chromatography–quadrupole time-of-flight mass spectrometry were performed to characterize metabolic alterations. Multivariable regression analysis was used to examine metabolite–phenotype associations, and logistic regression combined with receiver operating characteristic curve analysis evaluated diagnostic performance. Ten candidate metabolites were initially identified. Stepwise regression analysis demonstrated that d-glutamine (OR 1.81, 95% CI 1.29–2.53), sphingomyelin (d18:1/20:0) (OR 1.24, 95% CI 1.12–1.37) and N6-methyladenosine (OR 1.22, 95% CI 1.09–1.42) were independently associated with ARHL after controlling for age, sex, body mass index, smoking status, triglycerides and high-density lipoprotein cholesterol. The validation cohort confirmed that the biomarker panel exhibited strong diagnostic potential for ARHL (AUC 0.8523, 95% CI 0.7429–0.9704). These findings identify and validate a serum metabolomic signature for ARHL, providing new insight into early detection and clinical assessment of ARHL.
Hongshun Wang, Hai-rong Shi, Hao Zhang et al.· Biochemistry and Biophysics...· 0 citations
Early metabolic disturbances in young-onset Type 2 Diabetes Mellitus (T2DM) are frequently overlooked by conventional glycemic indices, necessitating sensitive molecular markers of early dysfunction. Amino acids, as key regulators of metabolic flux, redox homeostasis and insulin signaling represent attractive biomarker candidates. We employed a rapid, derivatization free four minutes LC-ESI-HRMS method for quantifying 20 amino acids and three amino acid related metabolites from only 10 μL of fasting plasma. The method was applied to young onset T2DM patients (<40 years; n = 28) and healthy controls (n = 22). Eight metabolites were significantly altered in young-onset T2DM, with increased methionine and tryptophan levels and a marked reduction in cysteine. Pathway analysis revealed disruptions across 14 metabolic pathways, predominantly affecting aromatic and sulfur amino acid metabolism. Integrated univariate and multivariate analysis identified a seven amino acid panel glycine, cysteine, glutamic acid, valine, methionine, phenylalanine and tryptophan) representing 10 out of 14 perturbed pathways. This panel demonstrated strong diagnostic performance (AUC 100; sensitivity 100%; specificity 100%), confirmed by logistic regression (AUC 100%, CI 93-100%, P < 0.0001). This amino acid signature provides mechanistic insight into early metabolic dysregulation and shows promise for early young-onset T2DM risk stratification pending validation.
Summary Background Parkinson's disease (PD) remains challenging to diagnose at early stages owing to subtle and heterogeneous clinical manifestations and the lack of reliable biomarkers. Metabolomics offers a powerful approach to capture disease-related biochemical alterations that reflect underlying pathophysiology. This study aimed to identify robust plasma metabolic signatures for PD diagnosis and to elucidate metabolic alterations associated with clinical severity. Methods We performed untargeted plasma metabolomic profiling using ultra-high performance liquid chromatography-tandem mass spectrometry in a large Chinese population comprising two independent early-stage PD groups, one of which consisted of drug-naïve, de novo patients. Integrative statistical analyses, pathway enrichment, and machine learning-based diagnostic modelling were applied to identify discriminative metabolites and characterise disease- and treatment-related metabolic changes. Findings In the two case–control datasets, 111 metabolites were consistently altered in early-stage PD, among which 12-hydroxyeicosatetraenoic acid, spermine, and niacinamide emerged as key differential metabolites. Pathway enrichment analysis highlighted sphingolipid metabolism as a major dysregulated pathway in early-stage PD. Using machine learning-based models, a classification model based on six metabolites achieved strong performance (area under the receiver operating characteristic curve [AUC] = 0.976), while individual metabolites also demonstrated good discriminative ability, with the highest AUC reaching 0.916. We further observed that antiparkinsonian medication was significantly associated with metabolic alterations in tyrosine, tryptophan, and polyamine pathways. In addition, gut microbiota-derived metabolites, particularly phenylacetylglutamine and p-Cresol glucuronide, were markedly elevated in PD and associated with both motor and non-motor symptom severity, suggesting a potential contribution to clinical heterogeneity. Interpretation These findings indicate reproducible plasma metabolic differences associated with early PD and suggest the potential diagnostic value of internally validated classifiers for disease diagnosis. Alterations in gut microbiota-derived metabolites correlate with clinical severity, highlighting the need for further mechanistic and translational research. Funding This study was supported by the National Natural Science Foundation of China (82271281 and 82471267), the Science and Technology Major Project of Hunan Provincial Science and Technology Department (2021SK1010), and the National Key Research and Development Program of China (2021YFC2501204).
