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Colorectal cancer prognosis is dependent on stage, and measures to improve early detection are urgently needed. Using prospectively collected plasma samples from the population-based Northern Sweden Health and Disease Study, we evaluated protein biomarkers in relation to colorectal cancer risk. Applying a two-tiered approach, we analyzed 160 proteins in matched sequential samples from 58 incident colorectal cancer case–control pairs. Twenty-one proteins selected from both this discovery phase and the literature were then analyzed in a validation set of 450 case–control pairs. Odds ratios were estimated by conditional logistic regression. LASSO regression and ROC analysis were used for multi-marker analyses. In the main validation analysis, no proteins retained statistical significance. However, exploratory subgroup analyses showed associations between FGF-21 and colon cancer risk (multivariable OR per 1 SD: 1.23 95% CI 1.03–1.47) as well as between PPY and rectal cancer risk (multivariable OR per 1 SD: 1.47 95% CI 1.12–1.92). Adding protein markers to basic risk predictive models increased performance modestly. Our results highlight the challenge of developing biomarkers that are effective in the asymptomatic, prediagnostic window of opportunity for early detection of colorectal cancer. Distinguishing between cancer subtypes may improve prediction accuracy. However, single biomarkers or small panels may not be sufficient for effective precision screening.

Metabolic syndrome (MetS), a set of metabolic risk factors including obesity, dysglycemia, and dyslipidemia, is associated with increased colorectal cancer (CRC) risk. A putative biological mechanism is chronic, low-grade inflammation, both a feature of MetS and a CRC risk factor. However, excess body fat also induces a proinflammatory state and increases CRC risk. In order to explore the relationship between MetS, body size, inflammation, and CRC, we studied large panels of inflammatory and cancer biomarkers. We included 138 participants from the Västerbotten Intervention Programme with repeated sampling occasions, 10 years apart. Plasma samples were analyzed for 178 protein markers by proximity extension assay. To identify associations between plasma protein levels and MetS components, linear mixed models were fitted for each protein. Twelve proteins were associated with at least one MetS component, six of which were associated with MetS score. MetS alone was not related to any protein. Instead, BMI displayed by far the strongest associations with the biomarkers. One of the 12 MetS score-related proteins (FGF-21), also associated with BMI, was associated with an increased CRC risk (OR 1.71, 95% CI 1.19–2.47). We conclude that overweight and obesity, acting through both inflammation and other mechanisms, likely explain the MetS-CRC connection.

Inflammation-related mechanisms may contribute to the link between diet and cancer. We sought to investigate the inflammatory impact of diet on cancer risk using the Dietary inflammatory index (DII) and an adapted Mediterranean diet score (MDS). This population-based, prospective cohort study used self-reported dietary data from the Västerbotten Intervention Programme, including 100,881 participants, of whom 35,393 had repeated measures. Associations between dietary patterns and cancer risk were evaluated using Cox proportional hazards regression. We also used restricted cubic splines to test for potential non-linear associations. A total of 9,250 incident cancer cases were diagnosed during a median follow-up of 15 years. The two dietary patterns were moderately correlated to each other and had similar associations with cancer risk, predominantly lung cancer in men (DII per tertile decrease: Hazard ratio (HR) 0.81 (0.66-0.99), MDS per tertile increase: HR 0.86 (0.72-1.03)), and gastric cancer in men (DII: 0.73 (0.53-0.99), MDS: 0.73 (0.56-0.96)). Associations were, in general, found to be linear. We found no longitudinal association between 10-year change in diet and cancer risk. We confirm small, but consistent and statistically significant associations between a more anti-inflammatory or healthier diet and reduced risk of cancer, including a lower risk of lung and gastric cancer in men. The dietary indexes produced similar associations with respect to the risk of cancer.

Background Autism spectrum disorder (ASD) evolves from an interplay between genetic and environmental factors during prenatal development. Since identifying maternal biomarkers associated with ASD risk in offspring during early pregnancy might result in new strategies for intervention, we investigated maternal metabolic biomarkers in relation to occurrence of ASD in offspring using both univariate logistic regression and multivariate network analysis. Methods Serum samples from 100 women with an offspring diagnosed with ASD and 100 matched control women with typically developing offspring were collected at week 14 of pregnancy. Concentrations of 62 metabolic biomarkers were determined, including amino acids, vitamins (A, B, D, E, and K), and biomarkers related to folate (vitamin B 9 ) metabolism, lifestyle factors, as well as C-reactive protein (CRP), the kynurenine-tryptophan ratio (KTR), and neopterin as markers of inflammation and immune activation. Results We found weak evidence for a positive association between higher maternal serum concentrations of folate and increased occurrence of ASD (OR per 1 SD increase: 1.70, 95% CI 1.22–2.37, FDR adjusted P = 0.07). Multivariate network analysis confirmed expected internal biochemical relations between the biomarkers. Neither inflammation markers nor vitamin D 3 levels, all hypothesized to be involved in ASD etiology, displayed associations with ASD occurrence in the offspring. Conclusions Our findings suggest that high maternal serum folate status during early pregnancy may be associated with the occurrence of ASD in offspring. No inference about physiological mechanisms behind this observation can be made at the present time because blood folate levels may have complex relations with nutritional intake, the cellular folate status and status of other B-vitamins. Therefore, further investigations, which may clarify the potential role and mechanisms of maternal blood folate status in ASD risk and the interplay with other potential risk factors, in larger materials are warranted.

Body fatness increases the risk of colorectal cancer (CRC). Insulin resistance and altered adipokines are potential mechanisms, but previous biomarker studies have been inconsistent. Intertumoral heterogeneity might provide an explanation. We investigated insulin, C-peptide, adiponectin, and leptin in relation to CRC molecular subtypes using a nested case-control design (1010 cases, 1010 matched controls, median 12.3 years from baseline to CRC diagnosis) from the population-based Northern Sweden Health and Disease Study. Repeated samples were available from 518 participants. Risks of CRC and subtypes, defined by tumor BRAF and KRAS mutations and microsatellite instability (MSI) status, were estimated using conditional logistic regression and linear mixed models. Higher C-peptide and lower adiponectin were associated with increased CRC risk (odds ratios per standard deviation increase (95% CI): 1.11 (1.01, 1.23) and 0.91 (0.83, 1.00), respectively), though weakened when adjusted for body mass index. Insulin and leptin were not associated with CRC risk. Within-individual time trajectories were similar in cases and controls, and no subtype-specific relationships were identified (all P heterogeneity > 0.1). Adiponectin was weakly inversely associated with the risk of KRAS -mutated (P = 0.08) but not BRAF -mutated or KRAS/BRAF -wildtype CRC, consistent with the one previous study. These findings contribute to an increased understanding of the complex role of body size in CRC.

Obesity is suggested to underlie development of other metabolic aberrations, but longitudinal relationships between metabolic factors at various ages has not been studied in detail. Data from 27,379 men and 32,275 women with in total 122,940 health examinations in the Västerbotten Intervention Project, Sweden and the Vorarlberg Health Monitoring and Prevention Programme, Austria were used to investigate body mass index (BMI), mid-blood pressure, and fasting levels of glucose, triglycerides, and total cholesterol at baseline in relation to 10-year changes of these factors and weight. We included paired examinations performed 10±2 years apart and used them for longitudinal analysis with linear regression of changes between the ages 30 and 40, 40 and 50, or 50 and 60 years. Higher levels of BMI were associated with increases in glucose and mid-blood pressure as well as triglycerides levels, and, to a lesser extent, decreases in cholesterol levels. For instance, per 5 kg/m2 higher BMI at age 40, glucose at age 50 increased by 0.24 mmol/l (95%CI: 0.22-0.26) and mid-blood pressure increased by 1.54 mm Hg (95%CI: 1.35-1.74). The strongest association observed was between BMI at age 30 and mid-blood pressure, which was 2.12 mm Hg (95% CI: 1.79-2.45) increase over ten years per 5 kg/m2 higher BMI level. This association was observed at an age when blood pressure levels on average remained stable. Other associations than those with BMI at baseline were much weaker. However, triglyceride levels were associated with future glucose changes among individuals with elevated BMI, particularly in the two older age groups. BMI was most indicative of long-term changes in metabolic factors, and the strongest impact was observed for increases in blood pressure between 30 and 40 years of age. Our study supports that lifestyle interventions preventing metabolic aberrations should focus on avoiding weight increases.

Colorectal cancer (CRC) is a major cause of deaths worldwide but has a good prognosis if detected early. The need for efficient, preferable non‐ or minimally invasive, inexpensive screening tools is therefore critical. We analyzed 12 miRNAs in pre‐ and postdiagnostic plasma samples to evaluate their potential as CRC screening markers. We used a unique study design with two overlapping cohorts, allowing analysis of pre‐ and postdiagnostic samples from 58 patients with CRC and matched healthy controls. Plasma concentrations of miR‐15b, ‐16, ‐18a, ‐19a, 21, ‐22, ‐25, ‐26a, ‐29c, ‐142‐5p, ‐150, and ‐192 were measured by semi‐quantitative real‐time PCR. Concentrations of miR‐18a, ‐21, ‐22, and ‐25 in plasma from patients with CRC were significantly altered compared to healthy controls. Combined as a multimarker panel, they detected CRC with an AUC of 0.93. Furthermore, levels of these three miRNAs also showed different levels in the prediagnostic case samples close to diagnosis. Only miR‐21‐levels were elevated several years before diagnosis. Plasma levels of miR‐18a, ‐21, ‐22, and ‐25 show promise as screening biomarkers for CRC. However, based on our unique analysis of prediagnostic and postdiagnostic samples from the same patients, we conclude that circulating miRNAs elevated at diagnosis may not automatically be suitable for CRC screening, if the increase occurs too close to clinical diagnosis. Increased plasma levels of miR18a, ‐21, ‐22, and ‐25 significantly identified patients with CRC, and when combined as a multimarker panel detected both early and late CRC stages with high sensitivity and specificity. Blood levels of miRNAs can detect both early‐ and late‐ stage CRC, and our data suggest potential usefulness in a prediagnostic screening setting. Prediagnostic samples can contribute important information in the design of optimal miRNA panels for clinical use.

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 KRAS and BRAF 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 KRAS and BRAF 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 CTH gene, rs1021737, had a stronger association compared with other variables. In subsequent univariate analyses, participants with variant rs1021737 genotype had a decreased risk of KRAS-mutated CRC (OR per allele = 0.72, 95% CI = 0.50, 1.05), and an increased risk of BRAF-mutated CRC (OR per allele = 1.56, 95% CI = 1.07, 2.30), with weak evidence for heterogeneity (Pheterogeneity = 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 CTH gene in colorectal carcinogenesis with regards to KRAS and BRAF mutations or other molecular characteristics of the tumor may be warranted.

Characteristics of the participants are provided in table 1.Table 1 Characteristics of colorectal cancer cases and matched control participants, all of whom provided repeated blood samples at 10-year intervals Time period prior to diagnosis of cases >10 years <5 years Cases Controls Cases Controls N  60  60  60  60  Males (%) 34 (57) 34 (57) 34 (57) 34 (57) Age (years) 50.0 (40.3 – 50.2) 50.0 (40.2 – 50.2) 59.8 (50.3 – 60.1) 59.9 (50.2 – 60.1) BMI 25.5 (23.6 – 27.0) 24.6 (22.8 – 28.1) 26.1 (24.4 – 28.9) 25.5 (23.3 – 29.0) Ever smoker, N (%) 36 (60) 34 (57) 38 (63) 35 (58) Alcohol intake (g/day) 3.0 (0.9 – 6.3) 3.4 (1.6 – 6.7) 4.6 (1.7 – 8.1) 3.2 (0.9 – 6.3) No physical activity* (%) 28 (47) 23 (38) 27 (45) 25 (42) Ghrelin† (pg/mL) 556 (323 – 772) 494 (370 – 695) 527 (338 – 758) 504 (340 – 623)  

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