Identifying serum amino acid as biomarkers of gestational diabetes mellitus in second-trimester among Chinese pregnant women: a machine learning-based cross-sectional study.

BACKGROUND

Levels of plasma branched-chain and aromatic amino acids in pregnancy have been associated with gestational diabetes mellitus (GDM), but the metabolic role of serum amino acid (AA) profiles in its pathogenesis remains insufficiently elucidated.

OBJECTIVE

This study evaluated the diagnostic potential of second-trimester serum AA profiles, including Cys, Met, Val, Lys, Cit, Tau, Asp, Ile and Ala, for distinguishing GDM patients from healthy controls.

METHODS

A total of 189 women with GDM and 189 healthy women at 24-28 weeks of gestation were enrolled in the study, recruited from 2019 to 2022. Serum levels of 21 amino acids were precisely measured using automatic amino acid analyzer. Three machine learning methods were employed to select the most significant variables. Generalized linear models (GLMs) were established to evaluate the association between Serum AAs and GDM.

RESULTS

Serum cysteine (Cys) and lysine (Lys) were inversely associated with GDM risk, whereas methionine (Met) and citrulline (Cit) showed positive associations. Notably, Met demonstrated an inverted U-shaped relationship, with an inflection at 239.9 µmol/L. The adjusted model achieved higher discrimination than the crude model. Sensitivity and subgroup analyses confirmed robust associations for Cys, while associations for Met, Lys, and Cit varied by pre-pregnancy body mass index (BMI).

CONCLUSIONS

Mid-pregnancy serum AAs, particularly Cys, Lys, Met, and Cit, were associated with GDM risk. These findings highlighted the heterogeneity of GDM metabolic signatures and support AAs as potential biomarkers for diagnosis of GDM.