Relation of Low Glomerular Filtration Rate to Metabolic Disorders in Individuals without Diabetes and with Normoalbuminuria
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Microalbuminuria increases cardiovascular risk and is considered a metabolic disorder. Low glomerular filtration rate is also associated with increased cardiovascular risk, but the relation of low glomerular filtration rate to metabolic disorders is not well understood. DESIGN, SETTING, PARTICIPANTS, & MEASUREMENTS: Designed as a cross-sectional, epidemiologic study, the Insulin Resistance Atherosclerosis Study was conducted in four centers: San Antonio (Texas), San Luis Valley (Colorado), and Oakland and Los Angeles (California). The Modification of Diet in Renal Disease equation was used to classify individuals without diabetes and with normoalbuminuria (n = 856; age 40 to 69 yr) by the presence or absence of low glomerular filtration rate (<60 ml/min per 1.73 m(2)). A direct marker of insulin resistance, the insulin sensitivity index, was measured by the frequently sampled intravenous glucose tolerance test. RESULTS: Low glomerular filtration rate was related to hypertension and the metabolic syndrome. Low glomerular filtration rate was associated with fasting insulin concentration and insulin sensitivity index. Low glomerular filtration rate was also associated with insulin concentration after adjustment for potential determinants of glomerular filtration rate but was not associated with insulin sensitivity index. CONCLUSIONS: Low glomerular filtration rate is associated with increased insulin concentration in individuals without diabetes and with normoalbuminuria. Longitudinal analyses are needed to determine whether insulin concentration (insulin resistance) precedes the deterioration of renal function.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".