Supporting data for “Case Finding of Pre-Diabetes and Evaluation of the Association of Dietary Patterns with Glycaemic Levels in Chinese People with Pre-Diabetes in Primary Care"

This PhD study first aimed to validate two locally developed non-laboratory-based multivariable risk models, developed using logistic regression (LR) and machine learning (ML) methods, for case finding of pre-DM and DM in a Chinese primary care population. It then evaluated the associations of dietary intake and patterns on glycaemic levels among a cohort of Chinese overweight/obese adults with pre-DM who were participants of an RCT on lifestyle interventions on glycaemic levels.

The validation study was a cross-sectional study on 919 Chinese adults aged 18-84 without a prior diagnosis of DM recruited from primary care clinics. Each participant completed a questionnaire to provide data on the risk models’ predictors and attended a blood test on HbA1c and OGTT between April 2021 and January 2022. The LR model was converted to an additive risk-scoring algorithm for easy clinical application. The sensitivities of the models were 0.69 (ML), 0.72 (LR) and 0.77 (LR-risk-scoring algorithm) in this (external) primary care population. All prediction models and the scoring algorithm had area under the receiver operating characteristic curves (AUROCs) >0.7, suggesting satisfactory external discriminatory ability. The discriminatory powers were highest among participants with a lower pre-test probability of DM, e.g. those aged 18-44 years.

However, the risks of pre-DM and DM estimated by the models were lower than the observed incidence in the primary care study population. Thus, recalibration was explored on the data. Simple recalibration of the LR model’s regression constant significantly improved the model accuracy, while extensive updating recalibration methods did not improve the accuracy any further. All recalibrated models had similar AUROCs to those of the original.

The cohort study included 287 overweight/obese Chinese adults aged 40-60 with pre-DM. Each participant completed a 24-hour diet history recall and attended a blood test on HbA1c and OGTT at baseline and 12-month follow-up between October 2021 and September 2023. The baseline data showed that total daily caloric intake was positively associated with HbA1c level. Late eating (>20% total daily calorie intake) was associated with higher HbA1c level that was partially mediated by total caloric intake. The 12-month longitudinal data of 222 participants with complete baseline and follow-up measures showed that a decrease in total daily calorie intake and rectifying late eating pattern were associated with a reduction in HbA1c level, independent of the changes in BMI, quantities of individual nutrient intake and the RCT arm allocation.

This study confirmed the validity of two local non-laboratory-based risk models for case finding of pre-DM in primary care. Restriction of total calorie intake should be the principal dietary modification for people with pre-DM. Rectifying late eating patterns is a promising additional strategy to lower glycaemic level. The study findings provide evidence to support screening and simple dietary modifications for people with pre-DM, which may attenuate the rising prevalence of T2DM.