A GENETIC RISK SCORE OF EARLY-ONSET TYPE 2 DIABETES PREDICTS EARLY DEATH AND BENEFITS OF INTENSIVE GLYCEMIC CONTROL IN ADVANCE TRIAL
Bibliographic record
Abstract
Objective: Context: Early-onset type 2 diabetes (T2D) increases rapidly worldwide, alongside obesity, and carries an excess risk of microvascular complications and early death compared to when it started later in life. We have developed a multi-polygenic risk score (multiPRS) that predicts the risk of major micro- and macrovascular complications of T2D. We showed that individuals with high multiPRS scores for macrovascular events benefited more from tight blood pressure control while those with high multiPRS scores for microvascular events benefited from glycemic control. Early-onset T2D is genetically determined. Objective: To develop a genetic risk score that identifies individuals who are more susceptible to early-onset T2D and assess whether they could benefit from tight glycemic control. Design and method: We developed a genetic risk score (GRS) composed of 22 SNPs associated with early-onset T2D. We analysed 545 participants of European descent of ADVANCE with the highest GRS values and 526 subjects with the lowest GRS for early-onset T2D. Results: The median age at diagnosis of T2D was 54.9 in the high GRS and 64.6 in the low GRS group. Compared to participants with low GRS, those with high GRS were slightly heavier with a BMI of 30.8 (5.1 SD) vs 29.6 (4.9 SD), p = 7.2 x 10-5, they received more antidiabetic drugs (n= 1.5 (0.8) vs 1.2 (0.8), p = 1.2 x 10-7) and they died 5.7 years earlier (70.8 vs 76.5 years old p = 9 x 10-7). Contrary to the low GRS group, intensive glycemic control reduced mortality significantly in the high GRS group. Combination of intensive blood pressure and glycemia control led to a relative risk reduction of 52% (p = 0.028) and a number needed to treat of only 10 in the high GRS group while no significant effects were observed in the low GRS group. Conclusions: This novel GRS can identify individuals at risk of early-onset T2D who can benefit from tight glycemic control while its combination with blood pressure control is the most effective in reducing cardiorenal outcomes and death rate.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".