Associations between elevated kidney and liver biomarker ratios, metabolic syndrome and all-cause and coronary heart disease (CHD) mortality: analysis of the U.S. National Health and Nutrition Examination Survey (NHANES)
Bibliographic record
Abstract
BACKGROUND: We examined the relationship between ratios of select biomarkers of kidney and liver function on all-cause and coronary heart disease (CHD) mortality, both in isolation, and in combination with metabolic syndrome (MetS), among adults (20 + years, n = 10,604). METHODS: Data was derived from the U.S. National Health and Nutrition Examination Survey (1999-2016) including public-use linked mortality follow-up files through December 31, 2015. RESULTS: Select biomarker ratios of kidney (UACR or albuminuria and BUN-CR) and liver (AST-ALT and GGT-ALP) function in isolation and in combination with MetS were associated with all-cause and CHD mortality. Compared to individuals with neither elevated biomarker ratios nor MetS (HR = 1.00, referent), increased risk of all-cause mortality was observed in the following groups: MetS with elevated UACR (HR, 95% CI = 2.57, 1.99-3.33), MetS with elevated AST-ALT (HR = 2.22, 1.61-3.07), elevated UACR without MetS (HR = 2.12, 1.65-2.72), and elevated AST-ALT without MetS (HR = 1.71, 1.35-2.18); no other biomarker ratios were associated with all-cause mortality. For cause-specific deaths, elevated risk of CHD mortality was associated with MetS with elevated UACR (HR = 1.67, 1.05-2.67), MetS with elevated AST-ALT (HR = 2.80, 1.62-4.86), and elevated BUN-CR without MetS (HR = 2.12, 1.12-4.04); no other biomarker ratios were associated with CHD mortality. CONCLUSION: Future longitudinal studies are necessary to examine the utility of these biomarker ratios in risk stratification for chronic disease management.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".