Aims/hypothesis
Research using data-driven cluster analysis has proposed five novel subgroups of diabetes based on six measured variables in individuals with newly diagnosed diabetes. Our aim was (1) ...to validate the existence of differing clusters within type 2 diabetes, and (2) to compare the cluster method with an alternative strategy based on traditional methods to predict diabetes outcomes.
Methods
We used data from the Swedish National Diabetes Register and included 114,231 individuals with newly diagnosed type 2 diabetes.
k
-means clustering was used to identify clusters based on nine continuous variables (age at diagnosis, HbA
1c
, BMI, systolic and diastolic BP, LDL- and HDL-cholesterol, triacylglycerol and eGFR). The elbow method was used to determine the optimal number of clusters and Cox regression models were used to evaluate mortality risk and risk of CVD events. The prediction models were compared using concordance statistics.
Results
The elbow plot, with values of
k
ranging from 1 to 10, showed a smooth curve without any clear cut-off points, making the optimal value of
k
unclear. The appearance of the plot was very similar to the elbow plot made from a simulated dataset consisting only of one cluster. In prediction models for mortality, concordance was 0.63 (95% CI 0.63, 0.64) for two clusters, 0.66 (95% CI 0.65, 0.66) for four clusters, 0.77 (95% CI 0.76, 0.77) for the ordinary Cox model and 0.78 (95% CI 0.77, 0.78) for the Cox model with smoothing splines. In prediction models for CVD events, the concordance was 0.64 (95% CI 0.63, 0.65) for two clusters, 0.66 (95% CI 0.65, 0.67) for four clusters, 0.77 (95% CI 0.77, 0.78) for the ordinary Cox model and 0.78 (95% CI 0.77, 0.78) for the Cox model with splines for all variables.
Conclusions/interpretation
This nationwide observational study found no evidence supporting the existence of a specific number of distinct clusters within type 2 diabetes. The results from this study suggest that a prediction model approach using simple clinical features to predict risk of diabetes complications would be more useful than a cluster sub-stratification.
Graphical abstract
The study aimed to identify the most predictive factors for the development of type 2 diabetes. Using an XGboost classification model, we projected type 2 diabetes incidence over a 10-year horizon. ...We deliberately minimized the selection of baseline factors to fully exploit the rich dataset from the UK Biobank. The predictive value of features was assessed using shap values, with model performance evaluated via Receiver Operating Characteristic Area Under the Curve, sensitivity, and specificity. Data from the UK Biobank, encompassing a vast population with comprehensive demographic and health data, was employed. The study enrolled 450,000 participants aged 40-69, excluding those with pre-existing diabetes. Among 448,277 participants, 12,148 developed type 2 diabetes within a decade. HbA1c emerged as the foremost predictor, followed by BMI, waist circumference, blood glucose, family history of diabetes, gamma-glutamyl transferase, waist-hip ratio, HDL cholesterol, age, and urate. Our XGboost model achieved a Receiver Operating Characteristic Area Under the Curve of 0.9 for 10-year type 2 diabetes prediction, with a reduced 10-feature model achieving 0.88. Easily measurable biological factors surpassed traditional risk factors like diet, physical activity, and socioeconomic status in predicting type 2 diabetes. Furthermore, high prediction accuracy could be maintained using just the top 10 biological factors, with additional ones offering marginal improvements. These findings underscore the significance of biological markers in type 2 diabetes prediction.
Major prospective randomized clinical safety trials have demonstrated beneficial effects of treatment with glucagon-like peptide-1 receptor agonists (GLP-1RA) and sodium-glucose co-transporter-2 ...inhibitors (SGLT-2i) in people with type 2 diabetes and elevated cardiovascular risk, and recent clinical treatment guidelines therefore promote early use of these classes of pharmacological agents. In this Swedish nationwide observational study, we compared cardiorenal outcomes and safety of new treatment with GLP-1RA and SGLT-2i in people with type 2 diabetes.
We linked data from national Swedish databases to capture patient characteristics and outcomes and used propensity-score based matching to account for differences between the two groups. The treatments were compared using Cox regression models.
We identified 9648 participants starting GLP-1RA and 12,097 starting SGLT-2i with median follow-up times 1.7 and 1.1 years, respectively. The proportion of patients with a history of MACE were 15.8%, and 17.0% in patients treated with GLP-1RA and SGLT-2i, respectively. The mean age was 61 years with 7.6 years duration of diabetes. Mean HbA1c were 8.3% (67.6 mmol/mol) and 8.3% (67.2 mmol/mol), and mean BMI 33.3 and 32.5 kg/m
in patients treated with GLP-1RA or SGLT-2i, respectively. The cumulative mortality risk was non-significantly lower in the group treated with SGLT-2i, HR 0.78 (95% CI 0.61-1.01), as were incident heart failure outcomes, but the risks of cardiovascular or renal outcomes did not differ. The risks of stroke and peripheral artery disease were higher in the SGLT-2i group relative to GLP-1RA, with HR 1.44 (95% CI 0.99-2.08) and 1.68 (95% CI 1.04-2.72), respectively.
This observational study suggests that treatment with GLP-1RA and SGLT-2i result in very similar cardiorenal outcomes. In the short term, treatment with GLP-1RA seem to be associated with lower risks of stroke and peripheral artery disease, whereas SGLT-2i seem to be nominally associated with lower risk of heart failure and total mortality.
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