1460-P: Prevalence of Chronic Kidney Disease Associated with Type 1 Diabetes in the U.S.
Bibliographic record
Abstract
Background: Chronic kidney disease (CKD) is associated with an increased risk of kidney failure, cardiovascular events, and mortality in patients with type 1 diabetes (T1D). However, the prevalence of CKD associated with T1D in the US is unclear. The aim of this study was to estimate the prevalence of CKD in people with T1D in the US using data collected in the National Health Examinations Survey (NHANES). Methods: People with T1D were identified from NHANES using an established algorithm (Mosslemi et al. Cardiovasc Endocrinol Metab. 2020). Adults aged ≥18 years with CKD in this group were identified by single measurements of estimated glomerular filtration rate (eGFR) ≤60 mL/min/1.73 m2 or urinary albumin-to-creatinine ratio (UACR) ≥30 mg/g. The NHANES data were used to create corresponding weighted variables to represent the US Results: During 2015 to 2018, diabetes was identified in 1647 of 19,225 adults surveyed in NHANES. Among those with diabetes, CKD was present in 20 out of 47 people with T1D with evaluable eGFR and UACR, corresponding to an unweighted estimate of 43%. CKD status was uncertain in 7 patients with T1D as their eGFR and/or UACR measurements were not available (unweighted estimate). In the CKD and T1D group, 59% were male and 60% were non-Hispanic White (weighted characteristics). Mean eGFR (SD) was 57 (4) mL/min/1.73 m2 and median UACR (IQR) was 89 (8-875) mg/g (weighted characteristics). Since the weighted overall number of adult people with T1D in the US was estimated at 1,202,739 (95% CI: 681,820-1,723,657), the corresponding number with T1D and CKD with evaluable eGFR and UACR was 258,196 (95% CI: 71,189-445,203), corresponding to a weighted estimate of 21%. Conclusions: CKD was common in people with T1D and evaluable eGFR and UACR based on recent US data. Since the absolute number in NHANES was small, these prevalence estimates should be interpreted cautiously and validated in other cohorts. Disclosure P.Rossing: Other Relationship; Abbott Diagnostics, AstraZeneca, Bayer Inc., Boehringer Ingelheim Inc., Novo Nordisk, Merck KGaA, Gilead Sciences, Inc., Sanofi. P.Groop: Advisory Panel; Boehringer-Ingelheim, Bayer Inc., Speaker's Bureau; AstraZeneca, Boehringer-Ingelheim, Bayer Inc., Merck Sharp & Dohme Corp., Medscape, Nestlé Health Science. R.Singh: None. R.Lawatscheck: Employee; Bayer Inc. K.R.Tuttle: Consultant; Lilly, AstraZeneca, Gilead Sciences, Inc., Research Support; Bayer Inc., Boehringer Ingelheim (Canada) Ltd., Novo Nordisk, Goldfinch Bio, Inc., Traveere Pharmaceuticals. Funding Bayer AG
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".