Associations Between Type 2 Diabetes Subtypes and Complications: Analysis of the Malaysia National Diabetes Registry
Bibliographic record
Abstract
Background: The aim of the study was to investigate type 2 diabetes (DM2) subtypes and associations with complications in a multiethnic Asian population. Methods: Analytical cohort (n = 60,946), extracted from 2019 Malaysia National Diabetes Registry, included 63.8% Malay, 16.7% Chinese, 11.3% Indian, and 11.3% other. A K-means cluster analysis was performed with complete data on six variables: age, DM2 duration, body mass index, metabolic syndrome severity, triglyceride-glucose index, and glycated hemoglobin . Separate Cox regression models and time-to-event analysis (from DM2 diagnosis) assessed the hazard ratio (HR) and time-to-complications, adjusting for sex, age, and ethnicity. Results: Four clusters emerged: mild age-related diabetes (MARD) in 21,059 (35.6%), severe insulin-deficient diabetes (SIDD) in 11,751 (19.3%), mild obesity-related diabetes (MOD) in 14,700 (24.1%), and severe insulin-resistant diabetes (SIRD) in 13,436 (22.0%). Each cluster was compared to MARD. SIDD had later-onset and lowest HR for chronic kidney disease (CKD) (HR 0.25 (0.24 - 0.26)), retinopathy (HR 0.28 (0.27 - 0.30)), cerebrovascular disease (HR 0.57 (0.47 - 0.69)), and ischemic heart disease (HR 0.83 (0.76 - 0.91)). MOD had lowest HR (0.53 (0.34 - 0.84)) for limb amputations, and low HR for CKD, retinopathy, and cerebrovascular disease. SIRD had highest HR (1.43 (1.13 - 1.81)) for foot ulcers, and low HR (0.59 (0.56 - 0.63)) for retinopathy and CKD (HR 0.77 (0.74 - 0.80)). Known severe CKD cases were excluded from National Diabetes Registry. Conclusions: DM2 subtypes associate differently with complications in Malaysia, similar to patterns found in European cohorts. DM2 subtypes complications, particularly for advanced CKD, are affected by registry-related selection bias and deserve further longitudinal investigation. J Endocrinol Metab. 2024;14(1):1-12 doi: https://doi.org/10.14740/jem879
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".