Plasma Endothelin of Sibpairs: Variability, Inheritance, and Linkage to Obesity in Essential Hypertension
Bibliographic record
Abstract
P178 A polymorphism of the endothelin (ET)-1 gene has been associated with blood pressure (BP) of obese individuals in large population studies. We explored the variability of plasma ET in essential hypertensive sibpairs (n=14 pairs, 13 with two sibs, 1 with three sibs) in relationship to their body mass index (BMI). ET levels were measured on ad libitum Na intake (AL), after a 24 hr Na load (300 mEq iv +160 mEq po, HI) and after 24 hr of Na deprivation (10 mEq po + furosemide 120 mg, LO). BP was 147±3/90±2 mmHg and BMI 35.1±1.4, with 75% of patients exceeding 30 Kg/m 2 . ET was 4.4±0.3, 4.1±0.2, and 4.3±0.3 fmol/ml for AL, HI and LO, respectively, all higher than 20 controls (3.3±0.3, ps <0.04). One way ANOVA disclosed that ET variability within pairs was significantly less than that between pairs. This was true for each diet and for all diets combined, with maximal statistical significance after the fixed Na load (F=18.9, p <0.0001). The sibpair variance of plasma ET(AL) exhibited a negative correlation with BMI (r=-0.51, p<0.01) but did not correlate with the sibpair variance for BMI. In an ANCOVA with family ID as main effect and BMI as the regressor covariate, individual ET(AL) was explained by family ID, independent of BMI (Model: R 2 =0.79, F=3.2, p<0.02; Family ID: p<0.03; BMI: ns). Our data suggest that inheritance plays a major role in determining plasma ET of essential hypertensive patients, while salt-balance has a small additional effect. The concordance of ET levels in sibpairs increases with the magnitude of obesity, but this effect of BMI is explained by family ID in the covariate analysis. These results suggest that there is a link between the inheritance of obesity and ET in hypertension, consistent with observations in population studies.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".