Population-Attributable Fractions of Modifiable Lifestyle Factors for CKD and Mortality in Individuals With Type 2 Diabetes: A Cohort Study
Bibliographic record
Abstract
BACKGROUND: We quantified the impact of lifestyle and dietary modifications on chronic kidney disease (CKD) by estimating population-attributable fractions (PAFs). STUDY DESIGN: Observational cohort study. SETTING & PARTICIPANTS: Middle-aged adults with type 2 diabetes but without severe albuminuria from the Ongoing Telmisartan Alone and in Combination With Ramipril Global Endpoint Trial (ONTARGET; n=6,916). FACTORS: Modifiable lifestyle/dietary risk factors, such as physical activity, size of social network, alcohol intake, tobacco use, diet, and intake of various food items. OUTCOMES: The primary outcome was CKD, ascertained as moderate to severe albuminuria or ≥5% annual decline in estimated glomerular filtration rate (eGFR) after 5.5 years. The competing risk for death was considered. PAF was defined as the proportional reduction in CKD or mortality (within 5.5 years) that would occur if exposure to a risk factor was changed to an optimal level. RESULTS: At baseline, median urinary albumin-creatinine ratio and eGFR were 6.6 (IQR, 2.9-25.0) mg/mmol and 71.5 (IQR, 58.1-85.9) mL/min/1.73m(2), respectively. After 5.5 years, 704 (32.5%) participants developed albuminuria, 1,194 (55.2%) had a ≥5% annual eGFR decline, 267 (12.3%) had both, and 1,022 (14.8%) had died. Being physically active every day has PAFs of 5.1% (95% CI, 0.5%-9.6%) for CKD and 12.3% (95% CI, 4.9%-19.1%) for death. Among food items, increasing vegetable intake would have the largest impact on population health. Considering diet, weight, physical activity, tobacco use, and size of social network, exposure to less than optimum levels gives PAFs of 13.3% (95% CI, 5.5%-20.9%) for CKD and 37.5% (95% CI, 27.8%-46.7%) for death. For the 17.8 million middle-aged Americans with diabetes, improving 1 of these lifestyle behaviors to the optimal range could reduce the incidence or progression of CKD after 5.5 years by 274,000 and the number of deaths within 5.5 years by 405,000. LIMITATIONS: Ascertainment of changes in kidney measures does not precisely match the definitions for incidence or progression of CKD. CONCLUSIONS: Healthy lifestyle and diet are associated with less CKD and mortality and may have a substantial impact on population kidney health.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".