ArticleBMC medical research methodology2010
Adaptive regression modeling of biomarkers of potential harm in a population of U.S. adult cigarette smokers and nonsmokers.
Article in BMC medical research methodology, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
4 citing papers in PubMed.
- The Association of White Blood Cells and Air Pollutants-A Population-Based Study.International journal of environmental research and public health · 2021Observational
- Influence of smoking on levels of urinary 8-iso Prostaglandin F2α.Toxicology reports · 2019Article
- Total Exposure Study Analysis consortium: a cross-sectional study of tobacco exposures.BMC public health · 2015Article
- Menthol's potential effects on nicotine dependence: a tobacco industry perspective.Tobacco control · 2011Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThis article describes the data mining analysis of a clinical exposure study of 3585 adult smokers and 1077 nonsmokers. The analysis focused on developing models for four biomarkers of potential harm (BOPH): white blood cell count (WBC), 24 h urine 8-epi-prostaglandin F2alpha (EPI8), 24 h urine 11-dehydro-thromboxane B2 (DEH11), and high-density lipoprotein cholesterol (HDL).
methodsRandom Forest was used for initial variable selection and Multivariate Adaptive Regression Spline was used for developing the final statistical models
resultsThe analysis resulted in the generation of models that predict each of the BOPH as function of selected variables from the smokers and nonsmokers. The statistically significant variables in the models were: platelet count, hemoglobin, C-reactive protein, triglycerides, race and biomarkers of exposure to cigarette smoke for WBC (R-squared = 0.29); creatinine clearance, liver enzymes, weight, vitamin use and biomarkers of exposure for EPI8 (R-squared = 0.41); creatinine clearance, urine creatinine excretion, liver enzymes, use of Non-steroidal antiinflammatory drugs, vitamins and biomarkers of exposure for DEH11 (R-squared = 0.29); and triglycerides, weight, age, sex, alcohol consumption and biomarkers of exposure for HDL (R-squared = 0.39).
conclusionsLevels of WBC, EPI8, DEH11 and HDL were statistically associated with biomarkers of exposure to cigarette smoking and demographics and life style factors. All of the predictors together explain 29%-41% of the variability in the BOPH.
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