Awareness, Treatment, and Control of LDL Cholesterol Are Lower Among U.S. Adults With Undiagnosed Diabetes Versus Diagnosed Diabetes
Bibliographic record
Abstract
OBJECTIVE: Diabetes is often undiagnosed, resulting in incorrect risk stratification for lipid-lowering therapy. We conducted a cross-sectional analysis of the National Health and Nutrition Examination Survey (NHANES) 2005-2010 to determine the prevalence, awareness, treatment, and control of elevated LDL cholesterol (LDL-C) among U.S. adults with undiagnosed diabetes. RESEARCH DESIGN AND METHODS: Fasting NHANES participants 20 years of age or older who had 10-year Framingham coronary heart disease (CHD) risk scores <20% and were free of CHD or other CHD risk equivalents (n = 5,528) were categorized as having normal glucose, impaired fasting glucose, undiagnosed diabetes, or diagnosed diabetes. High LDL-C was defined by the 2004 Adult Treatment Panel (ATP) III guidelines. RESULTS: The prevalence of diagnosed and of undiagnosed diabetes was 8 and 4%, respectively. Mean LDL-C was 102 ± 2 mg/dL among those with diagnosed diabetes and 117 ± 3 mg/dL for those with undiagnosed diabetes (P < 0.001). The prevalence of high LDL-C was similar among individuals with undiagnosed (81%) and diagnosed (77%) diabetes. Among individuals with undiagnosed diabetes and high LDL-C, 38% were aware, 27% were treated, and 16% met the ATP III LDL-C goal for diabetes. In contrast, among individuals with diagnosed diabetes and high LDL-C, 70% were aware, 61% were treated, and 36% met the ATP III goal. Subjects with undiagnosed diabetes remained less likely to have controlled LDL-C after multivariable adjustment (prevalence ratio, 0.42; 95% CI, 0.23-0.80). CONCLUSIONS: Improved screening for diabetes and reducing the prevalence of undiagnosed diabetes may identify individuals requiring more intensive LDL-C reduction.
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 | 0.001 | 0.003 |
| 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) | 0.002 | 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".