Back

MTHFR C677T polymorphism and promoter methylation in schizophrenia patients with type 2 diabetes mellitus: evidence from a Han Chinese cohort

Yang, C.; Li, R.; Wang, X.; Li, K.; Yuan, F.; Jia, X.; Zhang, R.; Zheng, J.

2026-04-13 psychiatry and clinical psychology
10.64898/2026.04.09.26350471 medRxiv
Show abstract

Schizophrenia (SCZ) and type 2 diabetes mellitus (T2DM) are common comorbid disorders that severely impair patient prognosis and quality of life. This study aimed to explore the association between the methylenetetrahydrofolate reductase (MTHFR) C677T gene polymorphism and MTHFR promoter methylation in patients with comorbid SCZ and T2DM. A total of 120 participants were enrolled from Liaocheng Fourth Peoples Hospital between January 2025 and June 2025, comprising 30 subjects in each of the four groups: SCZ group, T2DM group, SCZ-T2DM comorbid (SCZ+T2DM) group, and healthy control (CTL) group. Corresponding primers were designed for genetic analysis, and methylation-specific PCR (MSP) was performed to detect the methylation level of the MTHFR promoter. Genotype distribution of the MTHFR C677T polymorphism was consistent with Hardy-Weinberg equilibrium (HWE) (p>0.05). The C677T polymorphism was significantly associated with an elevated risk of SCZ and T2DM comorbidity (p<0.05). Notably, the methylation rate of the MTHFR promoter in the SCZ+T2DM group (95.00%) was not significantly higher than that in the CTL group (90.00%) (p>0.05). In conclusion, the MTHFR gene may serve as a susceptibility gene for SCZ-T2DM comorbidity, whereas MTHFR promoter methylation is not associated with the pathogenesis of this comorbid condition. These results indicate that genetic variation in MTHFR, rather than promoter methylation, contributes critically to the comorbidity of SCZ and T2DM in the Han Chinese population. Our findings may provide novel molecular insights into their shared pathophysiology and inform future clinical strategies for patients with this complex phenotype.

Matching journals

The top 9 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.