Association between goiter and type 2 diabetes in a Chinese population: a cross-sectional study using CHNS data

The comorbidity of multiple diseases is receiving growing attention, and the coexistence of goiter and type 2 diabetes mellitus (T2DM) is an important one. Thus, this study aims to utilize the widely representative China Health and Nutrition Survey (CHNS) database for relevant investigations.

This study adopted a pooled cross-sectional design, assessing the goiter-T2DM association using data from ten survey waves of the CHNS (1989-2018). Adjusted covariates included demographics (age, gender, education), anthropometry (BMI), lifestyle (smoking), and clinical conditions (hypertension). Univariate and multivariate logistic regression models were used to evaluate the association of goiter with T2DM. Additionally, multivariable-adjusted Receiver Operating Characteristic (ROC) curves and smooth curves (via Generalized Additive Models, GAM) were constructed to investigate the association between goiter and T2DM. The ROC analysis was employed to evaluate the combined predictive value of the multivariable model, including goiter and all adjusted covariates, for T2DM risk.

Among 63,848 eligible participant-wave observations, 2,109 were identified as T2DM and 155 as goiter. After adjusting for covariates, a positive association between goiter and increased risk of T2DM was observed (odds ratio [OR]= 2.62, 95% confidence interval [CI]: 1.29-4.81). The multivariable model (goiter plus covariates) yielded an AUC of 0.809, representing the collective discriminatory power rather than goiter's independent performance. Given the cross-sectional design, these results reflect statistical associations and should be interpreted with caution regarding clinical utility.

This study identifies a positive cross-sectional association between goiter and T2DM. Given the study's observational nature, causal or predictive inferences are precluded, highlighting the need for longitudinal research to validate this relationship and explore potential mechanisms.