Genetic Evidence for a Normal-Weight “Metabolically Obese” Phenotype Linking Insulin Resistance, Hypertension, Coronary Artery Disease, and Type 2 Diabetes
Bibliographic record
Abstract
The mechanisms that predispose to hypertension, coronary artery disease (CAD), and type 2 diabetes (T2D) in individuals of normal weight are poorly understood. In contrast, in monogenic primary lipodystrophy-a reduction in subcutaneous adipose tissue-it is clear that it is adipose dysfunction that causes severe insulin resistance (IR), hypertension, CAD, and T2D. We aimed to test the hypothesis that common alleles associated with IR also influence the wider clinical and biochemical profile of monogenic IR. We selected 19 common genetic variants associated with fasting insulin-based measures of IR. We used hierarchical clustering and results from genome-wide association studies of eight nondisease outcomes of monogenic IR to group these variants. We analyzed genetic risk scores against disease outcomes, including 12,171 T2D cases, 40,365 CAD cases, and 69,828 individuals with blood pressure measurements. Hierarchical clustering identified 11 variants associated with a metabolic profile consistent with a common, subtle form of lipodystrophy. A genetic risk score consisting of these 11 IR risk alleles was associated with higher triglycerides (β = 0.018; P = 4 × 10(-29)), lower HDL cholesterol (β = -0.020; P = 7 × 10(-37)), greater hepatic steatosis (β = 0.021; P = 3 × 10(-4)), higher alanine transaminase (β = 0.002; P = 3 × 10(-5)), lower sex-hormone-binding globulin (β = -0.010; P = 9 × 10(-13)), and lower adiponectin (β = -0.015; P = 2 × 10(-26)). The same risk alleles were associated with lower BMI (per-allele β = -0.008; P = 7 × 10(-8)) and increased visceral-to-subcutaneous adipose tissue ratio (β = -0.015; P = 6 × 10(-7)). Individuals carrying ≥17 fasting insulin-raising alleles (5.5% population) were slimmer (0.30 kg/m(2)) but at increased risk of T2D (odds ratio [OR] 1.46; per-allele P = 5 × 10(-13)), CAD (OR 1.12; per-allele P = 1 × 10(-5)), and increased blood pressure (systolic and diastolic blood pressure of 1.21 mmHg [per-allele P = 2 × 10(-5)] and 0.67 mmHg [per-allele P = 2 × 10(-4)], respectively) compared with individuals carrying ≤9 risk alleles (5.5% population). Our results provide genetic evidence for a link between the three diseases of the "metabolic syndrome" and point to reduced subcutaneous adiposity as a central mechanism.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".