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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".