Biologically Informed Polygenic Scores for Brain Insulin Receptor Network Are Associated with Cardiometabolic Risk Markers and Diabetes in Women
Bibliographic record
Abstract
BACKGRUOUND: To investigate associations between variations in the co-expression-based brain insulin receptor polygenic score and cardiometabolic risk factors and diabetes mellitus. METHODS: This cross-sectional study included 1,573 participants from the Helsinki Birth Cohort Study. Biologically informed expression-based polygenic risk scores for the insulin receptor gene network were calculated for the hippocampal (hePRS-IR) and the mesocorticolimbic (mePRS-IR) regions. Cardiometabolic markers included body composition, waist circumference, circulating lipids, insulin-like growth factor 1 (IGF-1), and insulin-like growth factor-binding protein 1 and 3 (IGFBP-1 and -3). Glucose and insulin levels were measured during a standardized 2-hour 75 g oral glucose tolerance test and impaired glucose regulation status was defined by the World Health Organization 2019 criteria. Analyzes were adjusted for population stratification, age, smoking, alcohol consumption, socioeconomic status, chronic diseases, birth weight, and leisure-time physical activity. RESULTS: Multinomial logistic regression indicated that one standard deviation increase in hePRS-IR was associated with increased risk of diabetes mellitus in all participants (adjusted relative risk ratio, 1.17; 95% confidence interval, 1.01 to 1.35). In women, higher hePRS-IR was associated with greater waist circumference and higher body fat percentage, levels of glucose, insulin, total cholesterol, low-density lipoprotein cholesterol, triglycerides, apolipoprotein B, insulin, and IGFBP-1 (all P≤0.02). The mePRS-IR was associated with decreased IGF-1 level in women (P=0.02). No associations were detected in men and studied outcomes. CONCLUSION: hePRS-IR is associated with sex-specific differences in cardiometabolic risk factor profiles including impaired glucose regulation, abnormal metabolic markers, and unfavorable body composition in women.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".