Incident Hypertension in Young Adults with a Modest eGFR Reduction
Bibliographic record
Abstract
Background: Hypertension (HTN) is a common, modifiable risk factors for cardiovascular (CV) disease with a rising occurrence in young adults (18-39). Advanced chronic kidney disease is well established as a risk factor for HTN, however, uncertainty remains regarding the relationship between modest eGFR declines (between 60 to 100 ml/min) and HTN. Methods: A retrospective cohort study of 8.7 million individuals (3.6 million aged 18-39 years) using linked provincial healthcare datasets from Ontario, Canada from January 2008 to March 2021. Cox models were conducted to examine the association of categorized eGFR (50-120 mL/min/1.73m2) and incident HTN, stratified by age (18-39, 40-49, 50-65 years). Results: Among our cohort (8.7 million individuals, mean age 41.3, mean eGFR 104.2, median follow-up 9.2 years), a step wise increase in blood pressure was observed elevations in blood pressure observed as early as eGFR<90 in young adults (eg. at eGFR 70-80, ages 18-30: 10.5 events per 1000 person-years(p-y), HR 1.31 (1.27-1.40); ages 40-49: 20.4/1000p-y, HR 1.07 (1.05-1.09); ages 50-65: 31.9/1000p-y, HR 1.03 (1.02-1.04) (figure 1). In addition, HTN was detected in patients with mild to negligeable albuminuria with increasing incidence as GFR declined. Among young adults who developed HTN, CV events were higher, and this increased with modest eGFR reductions. Conclusion: HTN in young adults is associated with modest reductions in eGFR (<70-80) and a higher risk of CV consequences warranting early identification, monitoring, and management.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".