Imbalances of blood biomarkers are associated with disease, and biomarkers may also vary non-pathologically across population groups. We described variation in concentrations of biomarkers of one-carbon metabolism, vitamin status, inflammation including tryptophan metabolism, and endothelial and renal function among cancer-free older adults. We analyzed 5167 cancer-free controls aged 40–80 years from 20 cohorts in the Lung Cancer Cohort Consortium (LC3). Centralized biochemical analyses of 40 biomarkers in plasma or serum were performed. We fit multivariable linear mixed effects models to quantify variation in standardized biomarker log-concentrations across four factors: age, sex, smoking status, and body mass index (BMI). Differences in most biomarkers across most factors were small, with 93% (186/200) of analyses showing an estimated difference lower than 0.25 standard-deviations, although most were statistically significant due to large sample size. The largest difference was for creatinine by sex, which was − 0.91 standard-deviations lower in women than men (95%CI − 0.98; − 0.84). The largest difference by age was for total cysteine (0.40 standard-deviation increase per 10-year increase, 95%CI 0.36; 0.43), and by BMI was for C-reactive protein (0.38 standard-deviation increase per 5-kg/m2 increase, 95%CI 0.34; 0.41). For 31 of 40 markers, the mean difference between current and never smokers was larger than between former and never smokers. A statistically significant (p < 0.05) association with time since smoking cessation was observed for 8 markers, including C-reactive protein, kynurenine, choline, and total homocysteine. We conclude that most blood biomarkers show small variations across demographic characteristics. Patterns by smoking status point to normalization of multiple physiological processes after smoking cessation.
The Integrative Analysis of Lung Cancer Etiology and Risk (INTEGRAL) program is an NCI-funded initiative with an objective to develop tools to optimize lung cancer screening. Here, we describe the rationale and design for the Risk Biomarker and Nodule Malignancy projects within INTEGRAL.
The overarching goal of these projects is to systematically investigate circulating protein markers to include on a panel for use (i) pre-LDCT, to identify people likely to benefit from screening, and (ii) post-LDCT, to differentiate benign versus malignant nodules. To identify informative proteins, the Risk Biomarker project measured 1,161 proteins in a nested-case control study within 2 prospective cohorts (n=252 lung cancer cases and 252 controls) and replicated associations for a subset of proteins in 4 cohorts (n=479 cases and 479 controls). Eligible participants had any history of smoking and cases were diagnosed within 3 years of blood draw. The Nodule Malignancy project measured 1,077 proteins among participants with a heavy smoking history within 4 LDCT screening studies (n=425 cases within 5 years of blood draw, 398 benign-nodule controls, and 430 nodule-free controls).
The INTEGRAL panel will enable absolute quantification of 21 proteins. We will evaluate its lung cancer discriminative performance in the Risk Biomarker project using a case-cohort study including 14 cohorts (n=1,696 cases and 2,926 subcohort representatives), and in the Nodule Malignancy project within 5 LDCT screening studies (n=675 cases, 648 benign-nodule controls, and 680 nodule-free controls). Future progress to advance lung cancer early detection biomarkers will require carefully designed validation, translational, and comparative studies.
PURPOSE Differences in the age at diagnosis for lung, colon, breast, and prostate cancers have been reported between low- and middle-income countries (LMICs) and high-income countries (HICs). However, this may be influenced by differences in the population age distributions across countries. We aimed to compare the median age at diagnosis for these cancers after adjusting for population age differences. METHODS We analyzed data from the Cancer Incidence in 5 Continents (CI5) Volume XI database. It includes information on cancer diagnoses during 2008 to 2012 from cancer registries in 66 countries. We calculated crude median ages at diagnosis for each cancer in each country, and then performed indirect standardization using the age-specific UN world population estimate to remove the influence of population age structure. RESULTS Overall, the adjustment for population age structure tended to increase the median ages at diagnosis in LMICs which have younger populations, and decrease them in HICs which have older populations. After standardization, differences between the youngest and oldest median ages of diagnosis across cancer sites were: 11 years for lung cancer (youngest median age observed was 61 in Bulgaria v 71 in Bahrain), 10 years for colon cancer (59 in Iran v 69 New Zealand), 10 years for breast (49 in Algeria v 59 Iceland), and 8 years for prostate cancer (65 in USA v 73 in the Philippines). LMICs had younger ages at diagnosis for colon cancer but older ages at diagnosis for prostate cancer as compared with HICs. Countries with higher smoking prevalence had younger ages at lung cancer diagnosis ( P value Pearson correlation = 0.0025). CONCLUSION For lung, colon, breast, and prostate cancers, the differences across countries in the median age at diagnosis range from 8 to 11 years after adjusting for population age distribution. These differences likely reflect population-level variation in risk factors and screening.
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