Abstract
BACKGROUND: The Triglyceride-Glucose (TyG) index has become a reliable indicator for evaluating the level of insulin resistance, a pivotal factor in both metabolic and neurodegenerative disorders. Serum neurofilament light chain (sNfL) serves as a responsive biomarker for detecting neuroaxonal injury. Despite this, the interplay between the TyG index and sNfL levels has not been sufficiently investigated. The aim of this research is to scrutinize the correlation between TyG index and sNfL levels across a substantial, population-based cohort. METHODS: Our study involved an examination of the dataset from the 2013-2014 round of the National Health and Nutrition Examination Survey (NHANES), encompassing a total of 2029 enrolled subjects. The TyG index was calculated using fasting triglycerides and glucose levels. Multivariable linear regression models were conducted to evaluate the relationship between TyG index and sNfL levels, adjusting for potential confounders such as age, sex, race, BMI, hypertension, stroke, congestive heart failure, alcohol consumption and NHHR (Non-High-Density Lipoprotein Cholesterol to High-Density Lipoprotein Cholesterol Ratio). Nonlinear associations were investigated using regression models based on restricted cubic splines (RCS). RESULTS: Both the unadjusted and adjusted regression analyses revealed a substantial positive correlation between the TyG index and ln-sNfL levels. After accounting for all covariates, each unit increase in the TyG index was associated with a 0.15 (95% CI: 0.02-0.27, p = 0.04) increase in ln-sNfL levels. RCS analysis revealed a nonlinear relationship, with a threshold around a TyG index value of 9.63, beyond which ln-sNfL levels increased more rapidly. The association was consistent across subgroups. CONCLUSION: Our study links higher TyG index with increased sNfL levels, indicating insulin resistance's role in neuroaxonal injury. The nonlinear relationship implies a heightened risk of neurodegeneration beyond a certain insulin resistance threshold. This underscores the need for early metabolic interventions to prevent neurodegenerative processes.