The Association of Dyslipidemia With Chronic Lymphocytic Leukemia: A Population-Based Study
Bibliographic record
Abstract
Background: Metabolic syndrome (MetS) is a risk factor for development of cancer. Because aberrant lipid metabolism is a pathogenic feature of chronic lymphocytic leukemia (CLL), our objective was to determine if CLL patients have a higher prevalence of MetS preceding diagnosis and to determine the impact of lipid-lowering medications on survival. Methods: We conducted a population-based case-control study in Ontario, Canada, using administrative databases of adults age 66 years and older to compare the prevalence of MetS preceding CLL with age- and sex-matched control subjects. Logistic regression was used to study the association between MetS and its components to CLL. The Kaplan-Meier method and Cox Regression were used to investigate survival. All statistical tests were two-sided. Results: We identified 2124 persons with CLL and 7935 control subjects from January 1, 2000, to December 31, 2005, with follow-up until March 31, 2014, three years from the date of last contact with the health care system, or death. The mean age was 75.6 years, 20.2% had diabetes, 35.8% had hypertension, and 17.6% had dyslipidemia. In multivariable analysis, dyslipidemia (odds ratio [OR] = 1.26, 95% confidence interval [CI] = 1.11 to 1.44, P < .001) and hypertension (OR = 1.12, 95% CI = 1.01 to 1.25, P = .03) were associated with the development of CLL, whereas MetS and diabetes were not. Lipid-lowering medication was associated with a statistically significant improved survival in patients with CLL (HR = 0.53, 95% CI = 0.47 to 0.61, P < .001). Conclusions: We demonstrate a higher prevalence of dyslipidemia preceding a diagnosis of CLL compared with control subjects, supporting preclinical data. Lipid-lowering medications appear to confer a survival advantage in CLL. Prospective studies are needed to confirm these results and test their potential as therapeutic applications.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".