Healthy lifestyles are associated with alleviating the single-nucleotide polymorphism-based genetic risks of ischaemic stroke, intracerebral haemorrhage and myocardial infarction.

BACKGROUND: Both genetic and lifestyle factors contribute to myocardial infarction (MI) and stroke, including ischaemic stroke (IS) and intracerebral haemorrhage (ICH). We explored how and the extent to which a healthy lifestyle, by considering a comprehensive list, could counteract the genetic risk of those diseases, respectively. METHODS: 315 044 participants free of stroke and MI at baseline were identified from the UK Biobank. Genetic risk scores (GRS) for those diseases were constructed separately and categorised as low, intermediate and high by tertile. Lifestyle risk scores (LRS) were constructed separately using smoking, alcohol intake, physical activity, dietary patterns and sleep patterns. Similarly, participants were categorised into low, intermediate and high LRS. The data were analysed using Cox proportional hazard models. RESULTS: Over a median follow-up of 12.8 years, 4642, 1046 and 9485 participants developed IS, ICH and MI, respectively. Compared with participants with low levels of GRS and LRS, the HRs of those with high levels of GRS and LRS were 3.45 (95% CI 2.71 to 4.41), 2.32 (95% CI 1.40 to 3.85) and 4.89 (95% CI 4.16 to 5.75) for IS, ICH and MI, respectively. Moreover, among participants with high GRS, the standardised 14-year rates of IS events were 4.40% (95% CI 3.45% to 5.36%) among those with high LRS. In contrast, it is only 1.78% (95% CI 1.63% to 1.94%) among those with low LRS. Similarly for MI, the high LRS group had standardised rates of 8.60% (95% CI 7.38% to 9.81%), compared with 3.34% (95% CI 3.12% to 3.56%) in low LRS. Among the high genetic risk group of ICH, the rate is reduced by about half compared low LRS to high LRS, although the rate was low for both (0.36% (95% CI 0.31% to 0.42%) and 0.71% (95% CI 0.36% to 1.05%), respectively). CONCLUSION: Healthy lifestyles were substantially associated with a reduction in the risk of IS, ICH and MI and attenuated the genetic risk of IS, ICH and MI by at least half, respectively.

Sleep Pattern, Genetic Susceptibility, and Abdominal Aortic Aneurysm in UK Biobank Participants: Large-Scale Cohort Study.

Background: Abdominal aortic aneurysm (AAA) is an important cause of cardiovascular mortality. Objectives: The authors aimed to explore the associations between sleep patterns and genetic susceptibility to AAA. Methods: We included 344,855 UK Biobank study participants free of AAA at baseline. A sleep pattern was defined by chronotype, sleep duration, insomnia, snoring, and daytime sleepiness, and an overall sleep score was constructed with a range from 0 to 5, where a high score denotes a healthy sleep pattern. Polygenic risk score based on 22 single nucleotide polymorphisms was categorized into tertiles and used to evaluate the genetic risk for AAA. Cox proportional hazards regression models were used to assess the association between sleep, genetic factors, and the incidence of AAA. Results: During a median of 12.59 years of follow-up, 1,622 incident AAA cases were identified. The HR per 1-point increase in the sleep score was 0.91 (95% CI: 0.86-0.96) for AAA. Unhealthy sleep patterns, defined as a sleep score ranging from 0 to 3, were found to be associated with a higher risk of AAA for the intermediate (HR: 1.18, 95% CI: 1.06-1.31) and poor sleep patterns (HR: 1.40, 95% CI: 1.13-1.73), respectively, compared to the healthy pattern. Participants with poor sleep patterns and high genetic risks had a 2.5-fold higher risk of AAA than those with healthy sleep patterns and low genetic risk. Conclusions: In this large prospective study, healthy sleep patterns were associated with a lower risk of AAA among participants with low, intermediate, or high genetic risk.

Single extracellular vesicle surface protein-based blood assay identifies potential biomarkers for detection and screening of five cancers.

Extracellular vesicles (EVs) and EV proteins are promising biomarkers for cancer liquid biopsy. Herein, we designed a case-control study involving 100 controls and 100 patients with esophageal, stomach, colorectal, liver, or lung cancer to identify common and type-specific biomarkers of plasma-derived EV surface proteins for the five cancers. EV surface proteins were profiled using a sequencing-based proximity barcoding assay. In this study, five differentially expressed proteins (DEPs) and eight differentially expressed protein combinations (DEPCs) showed promising performance (area under curve, AUC > 0.900) in pan-cancer identification [e.g., TENM2 (AUC = 0.982), CD36 (AUC = 0.974), and CD36-ITGA1 (AUC = 0.971)]. Our classification model could properly discriminate between cancer patients and controls using DEPs (AUC = 0.981) or DEPCs (AUC = 0.965). When distinguishing one cancer from the other four, the accuracy of the classification model using DEPCs (85-92%) was higher than that using DEPs (78-84%). We validated the performance in an additional 14 cancer patients and 14 controls, and achieved an AUC value of 0.786 for DEPs and 0.622 for DEPCs, highlighting the necessity to recruit a larger cohort for further validation. When clustering EVs into subpopulations, we detected cluster-specific proteins highly expressed in immune-related tissues. In the context of colorectal cancer, we identified heterogeneous EV clusters enriched in cancer patients, correlating with tumor initiation and progression. These findings provide epidemiological and molecular evidence for the clinical application of EV proteins in cancer prediction, while also illuminating their functional roles in cancer physiopathology.

Host Genetic Factors, Comorbidities and the Risk of Severe COVID-19.

BACKGROUND: Coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), was varied in disease symptoms. We aim to explore the effect of host genetic factors and comorbidities on severe COVID-19 risk. METHODS: A total of 20,320 COVID-19 patients in the UK Biobank cohort were included. Genome-wide association analysis (GWAS) was used to identify host genetic factors in the progression of COVID-19 and a polygenic risk score (PRS) consisted of 86 SNPs was constructed to summarize genetic susceptibility. Colocalization analysis and Logistic regression model were used to assess the association of host genetic factors and comorbidities with COVID-19 severity. All cases were randomly split into training and validation set (1:1). Four algorithms were used to develop predictive models and predict COVID-19 severity. Demographic characteristics, comorbidities and PRS were included in the model to predict the risk of severe COVID-19. The area under the receiver operating characteristic curve (AUROC) was applied to assess the models' performance. RESULTS: We detected an association with rs73064425 at locus 3p21.31 reached the genome-wide level in GWAS (odds ratio: 1.55, 95% confidence interval: 1.36-1.78). Colocalization analysis found that two genes (SLC6A20 and LZTFL1) may affect the progression of COVID-19. In the predictive model, logistic regression models were selected due to simplicity and high performance. Predictive model consisting of demographic characteristics, comorbidities and genetic factors could precisely predict the patient's progression (AUROC = 82.1%, 95% CI 80.6-83.7%). Nearly 20% of severe COVID-19 events could be attributed to genetic risk. CONCLUSION: In this study, we identified two 3p21.31 genes as genetic susceptibility loci in patients with severe COVID-19. The predictive model includes demographic characteristics, comorbidities and genetic factors is useful to identify individuals who are predisposed to develop subsequent critical conditions among COVID-19 patients.

Investigating the Effect of Metabolic Phenotypes on Health Events in Alcoholic and Nonalcoholic Fatty Liver Disease.

Background and Aims: Metabolic dysfunction and obesity commonly coexist with both alcoholic and nonalcoholic fatty liver disease (AFLD and NAFLD). The association of AFLD and NAFLD with incident diseases in individuals with different metabolic phenotypes are unclear. Methods: UK Biobank study participants were screened for the presence of fatty liver at baseline. Body mass index and metabolic dysfunction were used to define metabolic phenotypes. Cox regression model was performed to examine the associations of AFLD and NAFLD with incident significant liver diseases (SLDs), cardiovascular diseases (CVDs), chronic kidney diseases (CKDs), and cancers, respectively. Results: A total of 43,974 AFLD and 103,248 NAFLD cases were identified. Both AFLD and NAFLD were associated with an increased risk of diseases of interest. The effects were amplified by obesity and metabolic abnormalities and modified by metabolic phenotypes. Compared to individuals free of fatty liver and with phenotype of metabolically healthy-normal weight, AFLD [hazard ratio (HR) 3.27; 95% CI: 1.95-5.47)] and NAFLD (HR 2.25; 95% CI: 1.28-3.94) cases with phenotype of metabolically obese-normal weight had the greatest risk of SLDs. For CVDs, CKDs, and cancer, the greatest risks were detected in AFLD and NAFLD cases with phenotype of metabolically obese-overweight/obesity. In this subpopulation, AFLD and NAFLD conferred a 2.75-fold (95% CI: 2.32-3.25) and 4.02-fold 95% CI: (3.64-4.43) increased risk of CVDs, 4.37-fold 95% CI: (3.38-5.64) and 6.55-fold 95% CI: (5.73-7.48) increased risk of CKDs, and 1.19-fold 95% CI: (1.08-1.27) and 1.21-fold 95% CI: (1.14-1.28) increased risk of cancers, respectively. Conclusions: Metabolic phenotypes modified the association of AFLD and NAFLD with intrahepatic and extrahepatic diseases.

Investigating the Association Between Seven Sleep Traits and Nonalcoholic Fatty Liver Disease: Observational and Mendelian Randomization Study.

Background and Aim: Aberrant sleep parameters are associated with the risk of nonalcoholic fatty liver disease (NAFLD). However, existing information is inconsistent among studies and involves reverse causation. Therefore, we aimed to investigate the observational associations and causations between sleep traits and NAFLD. Methods: We performed multivariable regression to assess observational associations of seven sleep traits (sleep duration, easiness of getting up in the morning, chronotype, nap during day, snoring, insomnia, and narcolepsy), and NAFLD in the UK Biobank (1,029 NAFLD). The Cox proportional hazards model was applied to derive hazard ratios and 95% confidence intervals (CIs). Furthermore, a bidirectional two-sample Mendelian randomization (MR) approach was used to explore the causal relationships between sleep traits and NAFLD. Results: In the multivariable regression model adjusted for potential confounders, getting up in the morning not at all easy (HR, 1.51; 95% CI, 1.27-1.78) and usually insomnia (HR, 1.46; 95% CI, 1.21-1.75) were associated with the risk of NAFLD. Furthermore, the easiness of getting up in the morning and insomnia showed a dose-response association with NAFLD (Ptrend <0.05). MR analysis found consistent causal effects of NAFLD on easiness of getting up in the morning (OR, 0.995; 95% CI, 0.990-0.999; p = 0.033) and insomnia (OR, 1.006; 95% CI, 1.001-1.011; p = 0.024). These results were robust to weak instrument bias, pleiotropy, and heterogeneity. Conclusions: Findings showed consistent evidence of observational analyses and MR analyses that trouble getting up in the morning and insomnia were associated with an increased risk of NAFLD. Bidirectional MR demonstrated causal effects of NAFLD on sleep traits.

Phenome-Wide Association Analysis Reveals Novel Links Between Genetically Determined Levels of Liver Enzymes and Disease Phenotypes.

Serum liver enzymes (alanine aminotransferase [ALT], aspartate aminotransferase [AST], λ-glutamyl transferase [GGT] and alkaline phosphatase [ALP]) are the leading biomarkers to measure liver injury, and they have been reported to be associated with several intrahepatic and extrahepatic diseases in observational studies. We conducted a phenome-wide association study (PheWAS) to identify disease phenotypes associated with genetically predicted liver enzymes based on the UK Biobank cohort. Univariable and multivariable Mendelian randomization (MR) analyses were performed to obtain the causal estimates of associations that detected in PheWAS. Our PheWAS identified 40 out of 1,376 pairs (16, 17, three and four pairs for ALT, AST, GGT and ALP, respectively) of genotype-phenotype associations reaching statistical significance at the 5% false discovery rate threshold. A total of 34 links were further validated in Mendelian randomization analyses. Most of the disease phenotypes that associated with genetically determined ALT level were liver-related, including primary liver cancer and alcoholic liver damage. The disease outcomes associated with genetically determined AST involved a wide range of phenotypic categories including endocrine/metabolic diseases, digestive diseases, and neurological disorder. Genetically predicted GGT level was associated with the risk of other chronic non-alcoholic liver disease, abnormal results of function study of liver, and cholelithiasis. Genetically determined ALP level was associated with pulmonary heart disease, phlebitis and thrombophlebitis of lower extremities, and hypercholesterolemia. Our findings reveal novel links between liver enzymes and disease phenotypes providing insights into the full understanding of the biological roles of liver enzymes. Supplementary Information: The online version contains supplementary material available at 10.1007/s43657-021-00033-y.

The Health Impact of MAFLD, a Novel Disease Cluster of NAFLD, Is Amplified by the Integrated Effect of Fatty Liver Disease-Related Genetic Variants.

BACKGROUND & AIMS: Metabolic dysfunction-associated fatty liver disease (MAFLD) is a newly proposed disease category that derived from non-alcoholic fatty liver disease. The impact of MAFLD on health events has not been investigated. METHODS: UK Biobank participants were diagnosed for whether MAFLD presented at baseline. Five genetic variants (PNPLA3 rs738409 C/G, TM6SF2 rs58542926 C/T, GCKR rs1260326 T/C, MBOAT7 rs641738 C/T, and HSD17B13 rs72613567 T/TA) were integrated into a genetic risk score (GRS). Cox proportional hazard model was used to examine the association of MAFLD with incident diseases. RESULTS: A total of 160 979 (38.0%, 95% confidence interval [CI] 37.9%, 38.2%) participants out of 423 252 were diagnosed as MAFLD. Compared with participants without MAFLD, MAFLD cases had multivariate adjusted hazard ratio (HR) for liver cancer of 1.59 (95% CI, 1.28, 1.98), cirrhosis of 2.77 (2.29, 3.36), other liver diseases of 2.09 (1.95, 2.24), cardiovascular diseases of 1.39 (1.34, 1.44), renal diseases of 1.56 (1.48, 1.65), and cancers of 1.07 (1.05, 1.10). The impact of MAFLD, especially on hepatic events, was amplified by high GRS, of which the genetic variations in PNPLA3, TM6SF2, and MBOAT7 play the principal roles. MAFLD case with normal body weight is also associated with an increased risk of hepatic outcomes, but the genetic factor seems do not influence the risk in this subpopulation. CONCLUSIONS: MAFLD is independently associated with an increased risk of both intrahepatic and extrahepatic events. Fatty liver disease related genetic variants amplify the effect of MAFLD on disease outcomes.

Metabolic dysfunction-associated fatty liver disease and the risk of 24 specific cancers.

BACKGROUND: Metabolic dysfunction-associated fatty liver disease (MAFLD) is a significant health issue closely associated with multiple metabolic dysfunctions. The association between MAFLD and cancer risk is yet unknown. METHODS: UK Biobank study participants were diagnosed for the presence of MAFLD at baseline. A multivariable Cox regression model was performed to examine the associations of MAFLD with incident events in 24 specific cancers. RESULTS: We included 352,911 individuals (37.2% with MAFLD), among whom 23,345 developed cancers. Compared with non-MAFLD, MAFLD was significantly associated with 10 of the 24 examined cancers, including corpus uteri (hazard ratio [HR] = 2.36, 95% CI 1.99-2.80), gallbladder (2.20, 1.14-4.23), liver (1.81, 1.43-2.28), kidney (1.77, 1.49-2.11), thyroid (1.69, 1.20-2.38), esophagus (1.48, 1.25-1.76), pancreas (1.31, 1.10-1.56), bladder (1.26, 1.11-1.43), breast (1.19, 1.11-1.27), and colorectal and anus cancers (1.14, 1.06-1.23). The associations of MAFLD with liver, esophageal, pancreatic, colorectal and anal and bladder cancers and malignant melanoma were strengthened in males, and associations with kidney, thyroid, and lung cancers were increased in females. The associations of MAFLD with the risk of liver, kidney, and thyroid cancers remained significant after further adjusting for the waist circumference or body mass index and the number of metabolic syndrome components based on the main models. The risk-increasing allele of PNPLA3 rs738409 significantly amplified the association of MAFLD with the risk of liver and kidney cancers. CONCLUSION: MAFLD is associated with an increased risk of a set of cancers, but the effect substantially varies by site. MAFLD deserves higher priority in the current scheme of cancer prevention.

Global Burden and Trends in Incidence, Mortality, and Disability of Stomach Cancer From 1990 to 2017.

INTRODUCTION: Stomach cancer is a serious global public health problem. The current burden of stomach cancer and its trends across time and location need to be understood to develop effective preventive strategies. METHODS: Data were obtained from the Global Burden of Disease study. The burden of stomach cancer and variations in time and geographical regions were assessed according to the age-standardized rate and estimated annual percentage change (EAPC) of the incidence and mortality rate between 1991 and 2017. We also investigated the associations between the relevant rates and sociodemographic index (SDI). RESULTS: Overall, the age-standardized incidence rate (EAPC = -1.36, 95% confidence interval [CI]: -1.47 to -1.25), age-standardized mortality rate (EAPC = -2.2, 95% CI: -2.29 to -2.12), and age-standardized disability-adjusted life years rate (EAPC = -2.52, 95% CI: -2.63 to -2.43) decreased worldwide from 1990 to 2017. This trend varied across different countries and regions and according to sex and age. SDI had a significant negative correlation with the age-standardized mortality rate (P < 0.01, r = -0.28) and age-standardized disability-adjusted life years rate (P < 0.01, r = -0.31). Similar negative correlations were observed between SDI and the EAPC. DISCUSSION: The observed correlation between SDI and disease burden suggests that strategically implementing the screening and eradication of Helicobacter pylori, improving the medical level in countries with low SDI, and promoting the implementation of tobacco cessation policies would help reduce the disease burden of stomach cancer.

Genetic predisposition, lifestyle risk, and obesity associate with the progression of nonalcoholic fatty liver disease.

BACKGROUND: Nonalcoholic fatty liver disease (NAFLD) is prevalent worldwide. We aim to identify the factors promoting NAFLD progression. METHODS: UK Biobank study participants were diagnosed for whether NAFLD presented at baseline. Cox regression model was used to examine the association of risk factors with incident diseases (significant liver diseases [SLDs], type 2 diabetes [T2D], cardiovascular diseases [CVDs], chronic kidney diseases [CKDs], and cancers) among NAFLD cases. RESULTS: Of 78 283 individuals, 35 159 (44.9%) were females, and the mean (SD) age was 57.56 (7.90) years. Compared with participants had both low genetic and lifestyle risk, individuals with both high genetic and lifestyle risk had a hazard ratio of 1.64 (95% CI 1.32-2.03) for SLDs, 1.16 (1.08-1.24) for T2D, 1.25 (1.13-1.37) for CVDs, 1.33 (1.18-1.49) for CKDs, and 1.13 (1.05-1.22) for cancers. Compared with participants who were non-obese and had low genetic risk, those with obesity and high genetic risk had an 75% (95% CI 38-123%), 147% (128-167%), 46% (33-61%), and 76% (56-99%) increased risk for developing SLDs, T2D, CVDs, and CKDs, respectively. The population-attributable fractions suggested that lifestyle risk and obesity contributed more to the progression of NAFLD than genetic risk. CONCLUSION: Adhering to a healthy lifestyle and avoiding obesity are important to prevent NAFLD progression.

Tumor infiltrating lymphocyte signature is associated with single nucleotide polymorphisms and predicts survival in esophageal squamous cell carcinoma patients.

PURPOSE: Esophageal cancer is the sixth leading cause of cancer-related death worldwide, and is associated with a poor prognosis. Stromal tumor infiltrating lymphocytes (sTIL) and certain single nucleotide polymorphisms (SNPs) have been found to be predictive of patient survival. In this study, we explored the association between SNPs and sTIL regarding the predictability of disease-free survival in patients with esophageal squamous cell carcinoma (ESCC). MATERIALS AND METHODS: We collected 969 pathologically confirmed ESCC patients from 2010 to 2013 and genotyped 101 SNPs from 59 genes. The number of sTIL for each patient was determined using an automatic algorithm. A Kruskal-Wallis test was used to determine the association between genotype and sTIL. The genotypes and clinical factors related to survival were analyzed using a Kaplan-Meier curve, Cox proportional hazards model, and log-rank test. RESULTS: The median age of the patients was 67 (42-85 years), there was a median follow-up of 851.5 days and 586 patients died. The univariable analysis showed that 10 of the 101 SNPs were associated with sTIL. Six SNPs were also associated with disease-free survival. A multivariable analysis revealed that sTIL, rs1801131, rs25487, and rs8030672 were independent prognostic markers for ESCC patients. The model combining SNPs, clinical characteristics and sTIL outperformed the model with clinical characteristics alone for predicting outcomes in ESCC patients. CONCLUSION: We discovered 10 SNPs associated with sTIL in ESCC and we built a model of sTIL, SNPs and clinical characteristics with improved prediction of survival in ESCC patients.