A causal analysis of the relationship between exposure to sunlight and colorectal cancer risk: A Mendelian randomization study.

Several observational studies have found that exposure to sunlight reduces the risk of colorectal cancer (CRC). However, sun exposure remains ambiguous in its relationship to CRC. We carried out a Mendelian randomization (MR) study to explore the potential associations between them. We examined the exposure to sunlight summary statistics of the UK Biobank Consortium using a 2-sample MR analysis. Using data from the FinnGen consortium, we derived summary statistics for CRC. We conducted our analysis with various methods, incorporating inverse variance weighted (IVW) along with 4 other approaches. A Cochran Q statistic was used to measure the heterogeneity of instrumental variables (IVs). We screened 133 single nucleotide polymorphisms (SNPs) (time spent outdoors in summer), 41 SNPs (time spent outdoors in winter), and 35 SNPs (frequency of solarium/sunlamp use) representing sunlight exposure for MR analysis. All selected SNPs had an F-statistic >20, indicating that IVs did not weakly bias the results. The summer outdoor activity trait exhibited significant heterogeneity (Cochran Q statistic = 183.795, P = .002 < 0.05), but we found no horizontal polymorphisms or significant heterogeneity for the other exposure traits. According to IVW estimates, no causal association exists between time spent outdoors in summer and CRC (Odds Ratio, OR = 0.735, 95% confidence interval, CI = 0.494-1.017, P = .128 > 0.017). No causal relationship existed between time spent outdoors in winter and CRC, as indicated by Bonferroni-corrected adjusted p-values. The OR was 0.877 with a 95% CI of 0.334-2.299, and the P value was .789, more significant than the significance threshold of 0.017. The solarium/sunlamp use frequency was not associated with CRC (OR = 1.567, 95%CI = 0.243-10.119, P = .637 > .017). Also, an IVW with random effects was applied to determine the causal relationship between summer outdoor time and CRC. No causal association between summer outdoor time and CRC was found (OR = 0.735, 95% CI = 0.494-1.017, P = .128 > .017). Additionally, 4 additional analyses yielded similar results. The findings of our study suggest that exposure to sunlight may reduce CRC risk, but the causal relationship remains unsolved. There is no evidence to suggest that exposure to sunlight prevents CRC. Randomized, controlled trials are needed to determine whether sunlight exposure protects against CRC.

Investigating the causal association between branched-chain amino acids and Alzheimer's disease: A bidirectional Mendelian randomized study.

Background: There are many metabolic pathway abnormalities in Alzheimer's disease (AD). Several studies have linked branched-chain amino acid (BCAA) metabolism disorders with AD but have not obtained consistent results. The purpose of this study is to explore the causal association between BCAA concentration and the risk of AD. Methods: A bidirectional Mendelian randomized (MR) study was applied to explore the causal effect between BCAA level and the risk of AD. Genetic instrumental variables from the genome-wide association study (GWAS) of serum BCAA levels [total BCAAs (115,047 participants), valine (115,048 participants), leucine (115,074 participants), and isoleucine (115,075 participants)] from the UK Biobank and AD (21,982 AD cases and 41,944 controls) from the International Genomics of Alzheimer's Project were applied to explore the causal effect through the inverse variance-weighted (IVW) method, MR-Egger, and weighted median, accompanied by multiple pluripotency and heterogeneity tests. Results: The forward MR analysis showed that there was no causal effect of total BCAAs (OR: 1.067, 95% CI: 0.838-1.358; p = 0.838), valine (OR: 1.106, 95% CI: 0.917-1.333; p = 0.292), leucine (OR: 1.096, 95% CI: 0.861-1.396; p = 0.659), and isoleucine (OR: 1.457, 95% CI: 1.024-2.742; p = 0.037) levels on the risk of AD. The reverse analysis showed that AD was related to reduced levels of total BCAAs (OR: 0.979, 95% CI: 0.989-0.990; p < 0.001), valine (OR: 0.977, 95% CI: 0.963-0.991; p = 0.001), leucine (OR: 0.983, 95% CI: 0.973-0.994; p = 0.002), and isoleucine (OR: 0.982, 95% CI: 0.971-0.992; p = 0.001). Conclusion: We provide robust evidence that AD was associated with a decreased level of BCAAs, which can serve as a marker for early diagnosis of AD.

Clinical scoring model based on age, NIHSS, and stroke-history predicts outcome 3 months after acute ischemic stroke.

Background: The clinical nomogram is a popular decision-making tool that can be used to predict patient outcomes, bringing benefits to clinicians and patients in clinical decision-making. This study established a simple and effective clinical prediction model to predict the 3-month prognosis of acute ischemic stroke (AIS), and based on the predicted results, improved clinical decision-making and improved patient outcomes. Methods: From 18 December 2021 to 8 January 2022, a total of 146 hospitalized patients with AIS confirmed by brain MR were collected, of which 132 eligible participants constituted a prospective study cohort. The least absolute shrinkage and selection operator (LASSO) regression was applied to a nomogram model development dataset to select features associated with poor prognosis in AIS for inclusion in the logistic regression of our risk scoring system. On this basis, the nomogram was drawn, evaluated for discriminative power, calibration, and clinical benefit, and validated internally by bootstrap. Finally, the optimal cutoff point for each independent risk factor and nomogram was calculated using the Youden index. Results: A total of 132 patients were included in this study, including 85 men and 47 women. Good outcome was found in 94 (71.212%) patients and bad outcome in 38 (28.788%) patients during the follow-up period. A total of eight (6.061%) deaths were reported over this period, of whom five (3.788%) died during hospitalization. Five factors affecting the 3-month prognosis of AIS were screened by LASSO regression, namely, age, hospital stay, previous stroke, atrial fibrillation, and NIHSS. Further multivariate logistic regression revealed three independent risk factors affecting patient outcomes, namely, age, previous stroke, and NIHSS. The area under the curve of the nomogram was 0.880, and the 95% confidence interval was 0.818-0.943, suggesting that the nomogram model has good discriminative power. The p-value for the calibration curve is 0.925, indicating that the nomogram model is well-calibrated. According to the decision curve analysis results, when the threshold probability is >0.01, the net benefit obtained by the nomogram is the largest. The concordance index for 1,000 bootstrapping calculations is 0.869. The age cutoff for predicting poor patient outcomes using the Youden index was 76.5 years (specificity 0.777 and sensitivity 0.684), the cutoff for the NIHSS was 7.5 (specificity 0.936, sensitivity 0.421), and the cutoff for total nomogram score was 68.8 (sensitivity 81.6% and specificity 79.8%). Conclusion: The nomogram model established in this study had good discrimination, calibration, and clinical benefits. A nomogram composed of age, previous stroke, and NIHSS might predict the prognosis of stroke after AIS. It might intuitively and individually predict the risk of poor prognosis in 3 months of AIS and provide a reference basis for screening the treatment plan of patients.