Conclusions
We showed that one in five individuals from our population will progress to HbA1c-defined type 2 diabetes within 5 years after their first HbA1c-defined pre-diabetes diagnosis, and that one in nine will initiate glucose-lowering treatment within the same period. In addition to age, sex, metabolic factors and pre-existing comorbidities, we found that self-rated health, lifestyle, and existence of a social network are important predictors of the progression to type 2 diabetes. Although we could identify individuals with pre-diabetes who were at high risk, the AUCts were modest at only 73 (95% CI 71 to 74) for HbA1c-defined type 2 diabetes and 79 (95% CI 78 to 81) for glucose-lowering treatment initiation.
Comparison to other studies
HbA1c levels above the lower limit for pre-diabetes have been shown to increase the risk of future type 2 diabetes compared with normal levels of HbA1c,1 2 27 32 33 but many individuals with pre-diabetes never progress to overt diabetes. In the Whitehall II cohort (26.4% women, mean age 61.6 years, mean HbA1c 42 mmol/mol, and mean BMI 24.6 kg/m234), an observed 14% of individuals with pre-diabetes (HbA1c 39–47 mmol/mol (5.7%–6.4%)) developed diabetes (HbA1c ≥48 mmol/mol (6.5%)) within 5 years.34 The Whitehall II cohort was much younger than our study population (mean 61.6 years vs median 69.9 years) and included fewer women (26.4% vs 51.9% women in our study). The Whitehall II finding of 14% developing diabetes is close to the observed 12% of individuals reaching an HbA1c level ≥48 mmol/mol (6.5%) within 5 years of follow-up in our study; however, our median follow-up time was shorter (median 2.7 years of follow-up in our study vs median 6.7 years in Whitehall II). In the Diabetes Prevention Program Outcomes Study (DPPOS; 68% women, mean age 51 years, mean HbA1c 41 mmol/mol, mean BMI 34 kg/m2),11 32 an estimated 35% of individuals with pre-diabetes defined as elevated fasting plasma glucose (FPG; 5.3–6.9 mmol/l) or abnormal 2-hour plasma glucose (2hPG; 7.8–11.0 mmol/l) developed diabetes (FPG ≥7.0 mmol/l or 2hPG ≥11.0 mmol/l) within 5 years. As only 26% of the DPPOS participants with diabetes according to glucose criteria also had HbA1c levels ≥48 mmol/mol (6.5%),32 we could not make a direct comparison with our study34; however, our estimates (19% for diabetes defined by HbA1c ≥48 mmol/mol (6.5%) and 11% for glucose-lowering treatment initiation) were markedly lower. Compared with our study population, the DPPOS included more women (68% in DPPOS vs 52% in our study) and a lower baseline HbA1c value (mean 41 mmol/mol in DPPOS vs median 43 mmol/mol in our study), with both variables predicting lower diabetes progression risk. On the other hand, DPPOS participants had a substantially higher BMI (mean BMI 34.7 kg/m2 in DPPOS vs median 26.7 kg/m2 in our study) and were markedly younger (mean age 51.1 years in DPPOS vs median 69.6 years in our study), with both factors increasing the risk of diabetes in our models.11
In a review, Jonas et al emphasized the current lack of evidence concerning diabetes screening and pre-diabetes interventions available from trials based on HbA1c values.35 They highlighted the need for further research on factors associated with risk of progression from pre-diabetes to overt diabetes.35 In addition to some important and previously known predictors of developing type 2 diabetes—younger age at onset of pre-diabetes (often associated with more obesity and a more severe pre-diabetes phenotype22), male sex, high BMI, and pre-existing comorbidities—we also found self-rated health, self-reported doctor’s advice regarding lifestyle problems, and measures of lack of a strong social network to be important predictors for diabetes. Mental well-being and the perception of having a supportive social network may be important factors in successful changes of poor health behavior. Moreover, perceived loneliness was recently found to be a strong independent predictor of incident type 2 diabetes, independent of living alone, socioeconomic factors, and lifestyle factors.36 Mechanisms are unclear, but loneliness may associate with dysregulation in cortisol responses and heightened inflammation.36
Our models indicated that higher versus lower HbA1c at time of first pre-diabetes detection was associated with a strongly increased risk of future type 2 diabetes. This observation corroborates our current understanding of the pathophysiology of type 2 diabetes, with gradual exhaustion of beta cell capacity over time to compensate for insulin resistance, followed by an increase in blood glucose in the years immediately prior to a diabetes diagnosis.37
In an American study33 assessing the performance of HbA1c in predicting long-term diabetes (glucose-lowering treatment, FPG ≥7 mmol/l, HbA1c ≥48 mmol/mol (6.5%), or self-reported diabetes), prediction models with and without HbA1c as a predictor were compared for individuals without diabetes. They reported AUCs of 66 (95% CI 63 to 68) for a model including only HbA1c, age, and sex, to 86 (95% CI 84 to 89) for a model in which fasting laboratory tests and clinical visits were added. These estimates are similar to ours (AUCt 73 (95% CI 71 to 74) for HbA1c ≥48 mmol/mol (6.5%) and AUCt 79 (95% CI 78 to 81) for glucose-lowering treatment initiation). However, our main models containing input from multiple predictors showed only slightly better discrimination than minimum models including just age and sex.
Study limitations
Ideally, individuals with pre-diabetes should be identified soon after their HbA1c levels increase to the pre-diabetes range. While we aimed to identify individuals with incident pre-diabetes, the sensitivity analysis showed that one in six might have had prior indications of pre-diabetes (more than 1 year prior to the pre-diabetes index date). Other individuals may have had undiagnosed pre-diabetes prior to study inclusion. As the median HbA1c at study inclusion was in the lower end of the pre-diabetes interval (median 43 mmol/mol (IQR 42–44 mmol/mol) or 6.1% (6.0%–6.2%)), we believe they were generally included early in the course of pre-diabetes. Still, individuals with neither HbA1c measurements nor glucose-lowering treatment or hospital-diagnosed diabetes, and individuals with type 2 diabetes based on glucose definitions who were treated only with lifestyle interventions, were not captured in our data. This could have resulted in an underestimation of type 2 diabetes risk in our study.
Another limitation is that our study cohort was based on individuals who responded to the Danish National Health Survey. The response rate for the survey was 55%–60%, and it varied along sociodemographic groups.17 As individuals from higher sociodemographic groups were more likely to respond than those from lower sociodemographic groups, this may have led to an underestimate of the risks, and may limit the generalizability of our results. Although we aimed to include individuals as soon as they crossed the line from normal HbA1c values to pre-diabetes, increasing HbA1c levels are positively associated with increasing age on the population level,37 and our population-based pre-diabetes cohort was rather old (median age 69.6 years) compared with other pre-diabetes cohorts.11 34 Importantly, we corrected our estimates for the competing risk of death, and our prediction models also included age as a predictor per se; however, the high average age may have limited the comparability of our results with other cohorts.
We included a wide range of potential diabetes predictors (demographic variables, HbA1c measures, prescription drug use, comorbidities, socioeconomic variables, and self-reported lifestyle and health indicators), but data on other potential predictors10 and other variable selection strategies may have improved the model validity. We included ethnic origin as a potential predictor for developing diabetes,14 32 38 yet, the vast majority (95%) of our individuals were Caucasian, and model performance might not be generalizable to other ethnic groups. Unfortunately, we did not have access to other biomarkers than HbA1c in our data set, and could thus not include, for example, glucose levels, lipids, or estimates of insulin resistance and beta cell function in our models. We also missed clinical details on, for example, blood pressure, waist circumference, and family history of diabetes. These covariates are rather easily available in everyday clinical practice, and could further improve the prediction model for use in routine care.
Both HbA1c testing and the initiation of glucose-lowering treatment rely on clinical decisions influenced by potential predictors. This may have affected the variable selection and overestimated the importance of well-known risk factors. Another concern is that external model performance was estimated by split-sample validation, and this possibly overestimated the external validity. Before our models become useful for clinical work, they require additional validation along with model impact studies.39 40 Overall, our models provide a snapshot of the current risk of progression from pre-diabetes to diabetes for a specific individual, and can thus identify individuals at high risk of progressing, thereby helping to target high-risk groups for preventive interventions in routine care. Before our models can also inform about the risk of diabetes progression under certain preventive interventions or treatment strategies, these interventions should be included in the models, and thus be part of any baseline risk assessment. We have included all relevant information in the online supplemental material and encourage others to validate and calibrate our models in other settings.
Although we have identified individuals with pre-diabetes who are at high risk of later progression to type 2 diabetes in a real-world setting, the models’ discrimination should be further improved. Additional biomarkers41 and substratification using new pre-diabetes phenotypes and genetic risk scores42 may lead to improved prediction models in the future. Knowing individual-level risks for progression from pre-diabetes to type 2 diabetes is crucial to effectively target preventive interventions.