Association analysis between circulating methylmalonic acid and cognitive performance: a population-based cross-sectional study.

Background: Cognitive impairment is one of the common manifestations of abnormal development or dysfunction of the nervous system. Methylmalonic acid (MMA) is a dicarboxylic acid in the propionate metabolism pathway involving vitamin B12 (B12), it is also one of the commonly used biomarkers in human B12 testing. The relationship between MMA and cognition is not yet fully elucidated.

Objective: A population-based cross-sectional study was performed to assess the correlation between circulating MMA and cognitive performance.

Methods: This cross-sectional study finally included 4,464 individuals aged 60 years and older who participated in the National Health and Nutrition Examination Survey (NHANES) from 1999 to 2002 and from 2011 to 2014. In addition to cognitive score [Digit Symbol Substitution Test (DSST)] and circulating MMA levels, covariates included sex, age, race, education, marital status, family poverty-to-income ratio (PIR), BMI, smoking, drinking, serum B12, serum folate, and red blood cell folate. In the statistical analysis, one-way ANOVA, Kruskal-Wallis test, Mann-Whitney test, and Pearson's chi-squared test were used to compare the differences between different groups. Non-linear relationships were analyzed using a restricted cubic spline model. Pearson and Spearman correlation analyses were used to assess associations. The regression model was conducted using a multiple linear regression model.

Results: A total of 4,464 participants were finally included, with a mean age of 70.05 years (SD: 7.2), and 2,215 males (49.6%). In Spearman correlation analysis, there was a significant negative correlation between serum MMA levels and cognitive levels (ρ = -0.12, < 0.001). The results of univariate linear regression analysis showed a very significant negative correlation between square roots (sqrt) of MMA and cognitive scores ( = -0.47, < 0.001). It remained a significant negative correlation between the sqrt of MMA and cognitive scores after controlling for the various covariates, with values of -0.13, -0.13, and -0.14, respectively. The results of the stratified analysis indicated that some covariates may affect the stability of the model. The sensitivity analysis results showed that the correlation between the sqrt of MMA and cognitive scores was no longer statistically significant after adjusting for the covariate homocysteine (HCY), or removing patients with hypertension or chronic kidney disease (CKD).

Conclusions: In the general population aged 60 and above, there was a significant negative correlation between circulating MMA and cognition, with HCY, hypertension, and CKD identified as important influencing factors.