Epidemiology of 40 blood biomarkers of one-carbon metabolism, vitamin status, inflammation, and renal and endothelial function among cancer-free older adults

Abstract

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.

Consumption of a light meal affects serum concentrations of one-carbon metabolites and B-vitamins. A clinical intervention study

“…In the present study we observed higher fasting serum concentrations of methionine, sarcosine and betaine in men when compared to women, which is partly in line with observations in a larger study in cancer-free older adults (42) . The higher fasting serum concentrations of trimethyllysine and 3-methylhistidine observed in our male participants may be a consequence of the larger muscle mass in men, since both methylated amino acids are found in muscle proteins (40) .…”

Tracking TRYCAT: A Critical Appraisal of Kynurenine Pathway Quantifications in Blood

“…A sedentary lifestyle will therefore unquestionably affect kynurenine metabolization (Alme et al, 2021). Previous work showing that most KYN metabolites correlate with BMI (Zahed et al, 2021) indicates that lifestyle influences tryptophan metabolism also at the level of food intake. The amount and type of alimentation strongly define both the essential amino acid's bioavailability and its metabolization.…”

“…According to Zahed et al ( Zahed et al, 2021 ), most TRYCAT remain relatively unaffected by smoking, which only appears to decrease QUIN, AA, and 3-HAA ( Zahed et al, 2021 ). This may however be consequent to unassessed gender bias as Naz et al (2019) demonstrate KYN and KA (but not TRP) levels to be lower in male smokers vs. non-smokers but remain unaffected in female smokers.…”

“…), which may in themselves impact TRYCAT levels. Specifically, all KYN metabolites increase with BMI and with age (except xanthurenic acid (XA) and 3-hydroxyanthranilic acid (3HAA)), and most are higher in males ( Pertovaara et al, 2006 ; Zahed et al, 2021 ).…”

Design and methodological considerations for biomarker discovery and validation in the Integrative Analysis of Lung Cancer Etiology and Risk (INTEGRAL) Program

“…We are currently establishing a legal and technical infrastructure that will allow investigators outside of the LC3 consortium to request permission to remotely access and analyze the data in a secure computing environment. Available data will include the variables listed in Table 4 , the metabolomics biomarkers measured in the first project of the LC3, 36 and eventually the proteomics biomarkers following the publication of the validation phase of the project.…”