Case finding of pre-diabetes and evaluation of the association of dietary patterns with glycaemic levels in Chinese adults with pre-diabetes in primary care

Abstract

Population surveys found a high prevalence of undiagnosed pre-diabetes (pre-DM) and type 2 diabetes mellitus (T2DM) in Hong Kong. Most people with pre-DM eventually progress to T2DM. Pre-DM is associated with elevated health risks even before the onset of T2DM.

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), for case finding of pre-DM and DM in a Chinese primary care (PC) 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 PC clinics. Each participant completed a questionnaire to provide data on the models’ predictors and attended a blood test on HbA1c and oral glucose tolerance test (OGTT) between April 2021 and January 2022. The LR model was converted to an additive risk-scoring algorithm for clinical application. The models’ sensitivities were 0.69 (ML), 0.72 (LR) and 0.77 (LR-risk-scoring algorithm) in this PC population. The models and the algorithm had area under the receiver operating characteristic curves (AUROCs) >0.7, suggesting satisfactory external discrimination. The discrimination was highest among participants with a lower pre-test probability of DM, e.g. those aged 18-44 years.

The pre-DM and DM risks estimated by the models were lower than the observed incidence in the PC study population. Thus, recalibration was explored by seven methods, including recalibrating, revising and extending methods. Recalibrating the LR model’s regression constant improved its accuracy (calibration intercept: -0.01, slope: 0.69), while extensive methods did not improve the accuracy further. All recalibrated models had similar AUROCs to those of the original.

The cohort study included 287 Chinese overweight/obese adults aged 40-60 with pre-DM. Each participant completed a 24-hour diet 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 levels (β=0.210, p<0.001). Late eating (>20% daily calorie intake at evening meals) was associated with higher HbA1c levels (β=0.167, p=0.004) that was partially mediated by total caloric intake (z-value=2.220, p=0.013). The longitudinal data of 222 participants showed that a decrease in total calorie intake (β=0.39, p=0.040) and rectifying late eating (β= -0.44, p=0.026) were associated with reductions in HbA1c levels, independent of the RCT intervention allocation and changes in BMI and quantities of individual nutrient intake.

This study confirmed the validity of two local risk models for case finding of pre-DM in PC. Reducing total calorie intake should be the principal dietary modification for adults with pre-DM. Rectifying late eating is a promising additional strategy to lower glycaemic level. The study findings provide evidence to support case finding of and simple dietary modifications for adults with pre-DM to attenuate the rising prevalence of T2DM.