Multivariate Bayesian network learning results.

<p><b>(A)</b> Bayesian network of all included variables estimated from data with the Hill-climbing (HC) algorithm, averaged over 1000 bootstrap replicates. An edge between two variables indicates an association independent of all other variables in the network. Edge thickness corresponds to the strength of association, measured by edge confidence (proportion of times an edge was present in 1000 bootstrap sample networks). Node size corresponds to the number of edge connections (i.e., number of independent associations with other variables) <b>(B)</b> Strength of association to CRC risk by <i>KRAS</i> and <i>BRAF</i> mutation status for each biomarker and SNP. Abbreviations: BMI: Body mass index, CRC: Colorectal cancer, DMG: Dimethylglycine, eGFR: estimated glomerular filtration rate, PA: Physical activity.</p

Replication of the <i>CTH</i> rs1021737 SNPs associations in The Cancer Genome Atlas (TCGA).

<p>Replication of the <i>CTH</i> rs1021737 SNPs associations in The Cancer Genome Atlas (TCGA).</p

Characteristics of the 59,654 individuals with 122,940 health examinations in the Västerbotten Intervention Project and the Vorarlberg Health Monitoring and Prevention Programme by age of measurement [31–34].

<p>Characteristics of the 59,654 individuals with 122,940 health examinations in the Västerbotten Intervention Project and the Vorarlberg Health Monitoring and Prevention Programme by age of measurement [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0197830#pone.0197830.ref031" target="_blank">31</a>–<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0197830#pone.0197830.ref034" target="_blank">34</a>].</p

Beta (β) and 95% confidence intervals (CI) from linear regression with baseline plasma triglyceride level as exposure and plasma glucose change as outcome, by age (baseline- end of follow-up) and tertile of baseline BMI.

<p>All analyses were adjusted for baseline smoking status and baseline level of glucose, BMI, and cholesterol. Triglycerides, and annual glucose change as outcome, were log-transformed and entered into the model on their Z transformed scale, standardized by sex and cohort. Each analysis excluded individuals with values more extreme than ±3 standard deviations of the baseline level of triglycerides or glucose or of change in glucose level. The number of individuals in each tertile analysis was: 30–40 y, 2282–2376; 40–50 y, 4082–4217; 50–60 y, 4674–4893. The range of cohort- and sex-specific BMI tertile cut-points were for T1-2: 30 y, 20.6–23.3 kg/m²; 40 y, 21.8–24.2 kg/m², 50 y, 23.2–24.8 kg/m²; and for T2-3; 30 y, 23.2–25.8 kg/m², 40 y, 24.9–26.9 kg/m², 50 y, 26.6–27.6 kg/m². Abbreviations: BMI, body mass index; CI, confidence interval; y, years.</p

One-carbon metabolism biomarkers and genetic variants in relation to colorectal cancer risk by <i>KRAS</i> and <i>BRAF</i> mutation status

<div><p>Disturbances in one-carbon metabolism, intracellular reactions involved in nucleotide synthesis and methylation, likely increase the risk of colorectal cancer (CRC). However, results have been inconsistent. To explore whether this inconsistency could be explained by intertumoral heterogeneity, we evaluated a comprehensive panel of one-carbon metabolism biomarkers and some single nucleotide polymorphisms (SNPs) in relation to the risk of molecular subtypes of CRC defined by mutations in the <i>KRAS</i> and <i>BRAF</i> oncogenes. This nested case-control study included 488 CRC cases and 947 matched controls from two population-based cohorts in the Northern Sweden Health and Disease Study. We analyzed 14 biomarkers and 17 SNPs in prediagnostic blood and determined <i>KRAS</i> and <i>BRAF</i> mutation status in tumor tissue. In a multivariate network analysis, no variable displayed a strong association with the risk of specific CRC subtypes. A non-synonymous SNP in the <i>CTH</i> gene, rs1021737, had a stronger association compared with other variables. In subsequent univariate analyses, participants with variant rs1021737 genotype had a decreased risk of <i>KRAS</i>-mutated CRC (OR per allele = 0.72, 95% CI = 0.50, 1.05), and an increased risk of <i>BRAF</i>-mutated CRC (OR per allele = 1.56, 95% CI = 1.07, 2.30), with weak evidence for heterogeneity (P_{heterogeneity} = 0.01). This subtype-specific SNP association was not replicated in a case-case analysis of 533 CRC cases from The Cancer Genome Atlas (P = 0.85). In conclusion, we found no support for clear subtype-specific roles of one-carbon metabolism biomarkers and SNPs in CRC development, making differences in CRC molecular subtype distributions an unlikely explanation for the varying results on the role of one-carbon metabolism in CRC development across previous studies. Further investigation of the <i>CTH</i> gene in colorectal carcinogenesis with regards to <i>KRAS</i> and <i>BRAF</i> mutations or other molecular characteristics of the tumor may be warranted.</p></div

Study design.

<p>Illustrating the selection of participants based on the availability of one-carbon metabolism data in CRC cases and matched controls, and availability of <i>BRAF</i> and <i>KRAS</i> mutation status data in the CRC cases. *Other than non-melanoma skin cancer. †High methionine sulfoxide, indicates sample degradation. Abbreviations: CRC: Colorectal cancer, VIP: Västerbotten Intervention Programme, MSP: Mammography Screening Project.</p

Median (percentile 25–75) 10-year^a changes of metabolic factors by age, sex and cohort.

<p>Median (percentile 25–75) 10-year<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0197830#t002fn002" target="_blank">^a</a> changes of metabolic factors by age, sex and cohort.</p

Results in Fig 1A when using body mass index per 5 kg/m² increment as exposure and absolute unit changes (95% confidence intervals) of metabolic factors over ten years as outcomes^a.

<p>Results in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0197830#pone.0197830.g001" target="_blank">Fig 1A</a> when using body mass index per 5 kg/m² increment as exposure and absolute unit changes (95% confidence intervals) of metabolic factors over ten years as outcomes<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0197830#t003fn001" target="_blank">^a</a>.</p

Beta (β) and 95% confidence intervals (CI) from linear regression with baseline A) body mass index, B) mid-blood pressure, C) glucose, D) total cholesterol, and E) triglycerides as exposure, and change in a metabolic factor as outcome, by age (baseline-end of follow-up).

<p>Analyses were adjusted for baseline smoking status and baseline level of the outcome metabolic factor and body mass index (except in A). Analyses of cholesterol and triglycerides as exposures were additionally mutually adjusted for baseline level of the counterpart factor. All metabolic factors, and annual change of the outcome metabolic factor, were log-transformed and entered into the model on their Z transformed scale, standardized by sex and cohort. Each analysis excluded individuals with values more extreme than ±3 standard deviations of the exposure, outcome, or baseline level of the outcome metabolic factor. The number of individuals in each analysis differed depending on completeness of variables and on exclusions and was: 30–40 years, 5253–8388; 40–50 years, 12 442–17 137; 50–60 years, 13 345–16 694. Abbreviation; BP, blood pressure; CI, confidence interval; y, years.</p

Comparison between odds ratios (ORs) for CRC risk by <i>KRAS</i>/<i>BRAF</i> mutation status for all one-carbon metabolism variables using complete-case models and selection-bias-adjusted IPW models.

<p>The 45-degree line represents values for which the complete case ORs are equal to the IPW ORs. The broken line represents a fitted regression line.</p