N-lactoyl amino acids are potential biomarkers for insulin resistance and diabetic complications

Abstract

Aims: N-lactoyl amino acids (Lac-AA) are emerging as crucial players in metabolic research, with potential implications for disease mechanisms and therapeutic interventions. This study exploress the role of Lac-AA in insulin resistance, type 2 diabetes (T2D), and its complications.

Materials and methods: A cross-sectional study was conducted using data from 2918 participants from Qatar Biobank. After quality control, 2907 individuals were retained and randomly divided into discovery (n = 1990) and validation (n = 917) cohorts. Untargeted metabolomics was employed to profile serum metabolites, and analysis was focused on three Lac-AA species. Participants were stratified into insulin-sensitive, insulin-resistant, T2D without complications and T2D with complications. Associations with clinical traits were assessed using linear regression and Spearman correlation. Diagnostic performance was evaluated using Receiver Operating Characteristic (ROC) analysis in an independent cohort (n = 60). One-sample Mendelian randomisation was performed to assess causality between genetic predisposition to T2D and Lac-AA levels. Network analysis explored metabolic pathways linked to Lac-AA.

Results: Lac-AA levels were significantly higher in individuals with insulin resistance and diabetic complications. These findings were robustly replicated in the validation cohort. These metabolites showed strong positive correlations with markers of poor glycaemic control independent of metformin use. ROC analysis demonstrated that Lac-AA could discriminate between insulin-resistant and insulin-sensitive individuals. Mendelian randomisation analysis indicated a potential causal association between genetic risk for T2D and increased Lac-AA, particularly in patients with complications, supporting their role as downstream biomarkers of metabolic disease severity. Gaussian graphical model analysis revealed Lac-AA as central nodes in metabolic networks, showing strong associations with mitochondrial dysfunction biomarkers.

Conclusions: Lac-AA may serve as integrative biomarkers of metabolic dysfunction and diabetic complications. Further longitudinal and interventional studies are needed to clarify their mechanistic roles and clinical utility.

Keywords: Mendelian randomisation; N‐lactoyl amino acids; insulin resistance; metabolomics; type 2 diabetes.

FIGURE 1

Study design depicting the characterisation of participants.

FIGURE 2

Lac‐AA levels are elevated in the insulin resistant individuals and T2D patients with complications. Violin plots showing the linear regression results comparing the levels of Lac‐Phe, Lac‐Tyr, and Lac‐Val between phenotypes of interest. (A–C) Comparing levels of Lac‐AA between all non‐diabetic general population (Control, n = 1601) and all type 2 diabetic individuals (T2D, n = 389) from the discovery cohort. (D–F) Comparing the levels of metabolites after stratifying controls into insulin sensitive (IS, n = 893) and insulin resistant (IR, n = 708), and T2D into T2D without complications (T2D‐NC, n = 163) and T2D with complications (T2D‐C, n = 226). Similarly, (G–I) comparing levels between non‐diabetic population (Control, n = 738), and T2D (n = 179) from the validation cohort. (J–L) Comparing levels of Lac‐AAs after stratifying controls into insulin sensitive (IS, n = 393) and insulin resistant (IR, n = 345), and T2D into T2D without complications (T2D‐NC, n = 75) and T2D with complications (T2D‐C, n = 104). *p < 0.05, **p < 0.01, ***p < 0.001, and ****p < 0.0001.

FIGURE 3

ROC curves showing the diagnostic performance of the three studied metabolites in differentiating insulin‐resistant and insulin‐sensitive individuals in an independent cohort (n = 60). (A) Lac‐Phe, (B) Lac‐Tyr, and (C) Lac‐Val. The area under the curve (AUC) values/effect size and p‐values indicate the ability of each Lac‐AA to discriminate between insulin‐resistant and insulin‐sensitive individuals.

FIGURE 4

Association of elevated Lac‐AA levels with clinical traits in IR and T2D is independent of metformin use, Spearman correlation analysis examining the relationships between Lac‐Phe, Lac‐Tyr, Lac‐Val, and various clinical traits, divided into two panels: Panel (A) includes all participants from the general population, while panel (B) excludes metformin users. Positive correlations are represented in blue, while negative correlations are shown in red. The size of the circles reflects correlation coefficient. Significance was defined as * (p‐value ≤0.05), ** (p‐value ≤0.01), *** (p‐value ≤0.001).

FIGURE 5

Metabolomics network analysis reveals metabolic pathways associated with Lac‐AA in T2D. A Gaussian graphical model (GGM) representing the partial correlations between Lac‐AA and T2D‐associated metabolites in the general cohort is shown. Red colour indicates positive correlation. Blue colour indicates negative correlation. Each node corresponds to a metabolite, and the edges indicate partial correlations, with the thickness of the edges reflecting the strength of these correlations.