Using Mendelian randomization to assess and develop clinical interventions: limitations and benefits

Exploring the relationship between anal fistula and colorectal cancer based on Mendelian randomization and bioinformatics

The association between anal fistula patients and colorectal cancer, as well as the potential pathophysiological mechanisms, remains unclear. To explore the relationship between anal fistula and colorectal cancer and its potential mechanisms. Analysis of GEO and TCGA databases. Disease-related genes were also referenced from Coremine Medical, GeneCard and OMIM. Core hub genes were identified through protein-protein interaction analysis by intersecting differentially expressed genes from the datasets with disease data. On one hand, a prognostic model was developed using genes and its prognostic role was validated. On the other hand, the optimal diagnostic genes were selected through machine learning. Mendelian randomization (MR) analysis was conducted to explore the potential causal link between anal fistula and colorectal cancer. Thirteen core genes were identified (TMEM121B, PDGFRA, MID2, WNT10B, HOXD13, BARX1, SIX2, MMP1, SNAL1, CDKN2A, ITGB3, TIMP1, CALB2). Functional enrichment analysis revealed that the intersecting genes between anal fistula and colorectal cancer were associated with extracellular matrix components, signalling pathways, cell growth, protein modification, as well as important roles in cellular activities, tissue and organ development, and biological function maintenance. These genes were also involved in pathways related to Wnt signalling and colorectal cancer development. Prognostic analysis and immune infiltration analysis indicated a close relationship between core hub genes and the prognosis and immune infiltration in colorectal cancer. Machine learning showed that core genes played an essential role in the diagnostic differentiation of colorectal cancer. MR results suggested no causal relationship between anal fistula and colorectal cancer. This study identified shared core genes between anal fistula and colorectal cancer, involved in various pathways related to tumour development. These genes play crucial roles in prognosis and diagnosis.

Television Viewing Time, Overweight, Obesity, and Severe COVID-19: A Brief Report From UK Biobank

BACKGROUND

Overweight and obesity are well-established risk factors for COVID-19 severity; however, less is known about the role of sedentary behaviors such as television (TV) viewing. The purpose of this brief report was to determine whether lower TV viewing time may mitigate the risk of severe COVID-19 in individuals with excess weight.

METHODS

We analyzed 329,751 UK Biobank participants to investigate the independent and combined associations of BMI and self-reported TV viewing time with odds of severe COVID-19 (inpatient COVID-19 or COVID-19 death).

RESULTS

Between March 16 and December 8, 2020, there were 1648 instances of severe COVID-19. Per 1-unit (hours per day) increase in TV viewing time, the odds of severe COVID-19 increased by 5% (adjusted odds ratio = 1.05, 95% confidence interval = 1.02-1.08). Compared with normal-weight individuals with low (≤1 h/d) TV viewing time, the odds ratios for overweight individuals with low and high (≥4 h/d) TV viewing time were 1.17 (0.89-1.55) and 1.66 (1.31-2.11), respectively. For individuals with obesity, the respective ORs for low and high TV viewing time were 2.18 (1.61-2.95) and 2.14 (1.69-2.73).

CONCLUSION

Higher TV viewing time was associated with higher odds of severe COVID-19 independent of BMI and moderate to vigorous physical activity. Additionally, low TV viewing time may partly attenuate the elevated odds associated with overweight, but not obesity.

The Effect of Circulating Zinc, Selenium, Copper and Vitamin K1 on COVID-19 Outcomes: A Mendelian Randomization Study

Background & Aims: Previous results from observational, interventional studies and in vitro experiments suggest that certain micronutrients possess anti-viral and immunomodulatory activities. In particular, it has been hypothesized that zinc, selenium, copper and vitamin K1 have strong potential for prophylaxis and treatment of COVID-19. We aimed to test whether genetically predicted Zn, Se, Cu or vitamin K1 levels have a causal effect on COVID-19 related outcomes, including risk of infection, hospitalization and critical illness. Methods: We employed a two-sample Mendelian Randomization (MR) analysis. Our genetic variants derived from European-ancestry GWAS reflected circulating levels of Zn, Cu, Se in red blood cells as well as Se and vitamin K1 in serum/plasma. For the COVID-19 outcome GWAS, we used infection, hospitalization or critical illness. Our inverse-variance weighted (IVW) MR analysis was complemented by sensitivity analyses including a more liberal selection of variants at a genome-wide sub-significant threshold, MR-Egger and weighted median/mode tests. Results: Circulating micronutrient levels show limited evidence of association with COVID-19 infection, with the odds ratio [OR] ranging from 0.97 (95% CI: 0.87-1.08, p-value = 0.55) for zinc to 1.07 (95% CI: 1.00-1.14, p-value = 0.06)-i.e., no beneficial effect for copper was observed per 1 SD increase in exposure. Similarly minimal evidence was obtained for the hospitalization and critical illness outcomes with OR from 0.98 (95% CI: 0.87-1.09, p-value = 0.66) for vitamin K1 to 1.07 (95% CI: 0.88-1.29, p-value = 0.49) for copper, and from 0.93 (95% CI: 0.72-1.19, p-value = 0.55) for vitamin K1 to 1.21 (95% CI: 0.79-1.86, p-value = 0.39) for zinc, respectively. Conclusions: This study does not provide evidence that supplementation with zinc, selenium, copper or vitamin K1 can prevent SARS-CoV-2 infection, critical illness or hospitalization for COVID-19.

Evaluating the role of alcohol consumption in breast and ovarian cancer susceptibility using population-based cohort studies and two-sample Mendelian randomization analyses

Alcohol consumption is correlated positively with risk for breast cancer in observational studies, but observational studies are subject to reverse causation and confounding. The association with epithelial ovarian cancer (EOC) is unclear. We performed both observational Cox regression and two-sample Mendelian randomization (MR) analyses using data from various European cohort studies (observational) and publicly available cancer consortia (MR). These estimates were compared to World Cancer Research Fund (WCRF) findings. In our observational analyses, the multivariable-adjusted hazard ratios (HR) for a one standard drink/day increase was 1.06 (95% confidence interval [CI]; 1.04, 1.08) for breast cancer and 1.00 (0.92, 1.08) for EOC, both of which were consistent with previous WCRF findings. MR ORs per genetically predicted one standard drink/day increase estimated via 34 SNPs using MR-PRESSO were 1.00 (0.93, 1.08) for breast cancer and 0.95 (0.85, 1.06) for EOC. Stratification by EOC subtype or estrogen receptor status in breast cancers made no meaningful difference to the results. For breast cancer, the CIs for the genetically derived estimates include the point-estimate from observational studies so are not inconsistent with a small increase in risk. Our data provide additional evidence that alcohol intake is unlikely to have anything other than a very small effect on risk of EOC.

Refractive Error Has Minimal Influence on the Risk of Age-Related Macular Degeneration: A Mendelian Randomization Study

PURPOSE

To test the hypothesis that refractive errors such as myopia and hyperopia cause an increased risk of age-related macular degeneration (AMD) and to quantify the degree of risk.

DESIGN

Two-sample Mendelian randomization analysis of data from a genome-wide association study.

PARTICIPANTS

As instrumental variables for refractive error, 126 genome-wide significant genetic variants identified by the Consortium for Refractive Error and Myopia and 23andMe Inc. were chosen. The association with refractive error for the 126 variants was obtained from a published study for a sample of 95,505 European ancestry participants from UK Biobank. Association with AMD for the 126 genetic variants was determined from a genome-wide association study (GWAS) published by the International Age-related Macular Degeneration Genomics consortium of 33,526 (16,144 cases and 17,832 controls) European ancestry participants.

METHODS

Two-sample Mendelian randomization (MR) analysis was used to assess the causal role of refractive error on AMD risk, using the 126 genetic variants associated with refractive error as instrumental variables, under the assumption that the relationship between refractive error and AMD risk is linear.

MAIN OUTCOME MEASUREMENT

the risk AMD was caused by a 1-diopter (D) change in refractive error.

RESULTS

MR analysis suggested that refractive error had very limited influence on the risk of AMD. Specifically, 1 D more hyperopic refractive error was associated with an odds ratio (OR) of 1.080 (95% confidence interval [CI], 1.021-1.142; P = 0.007) increased risk of AMD. MR-Egger, MR pleiotropy residual sum and outlier, weighted median, and Phenoscanner-based sensitivity analyses detected minimal evidence to suggest that this result was biased by horizontal pleiotropy.

CONCLUSIONS

Under the assumption of a linear relationship between refractive error and the risk of AMD, myopia and hyperopia only minimally influence the causal risk for AMD. Thus, inconsistently reported strong associations between refractive error and AMD are likely to be the result of noncausal factors such as stochastic variation, confounding, or selection bias.

What do hypnotics cost hospitals and healthcare?

Hypnotics (sleeping pills) are prescribed widely, but the economic costs of the harm they have caused have been largely unrecognized. Randomized clinical trials have observed that hypnotics increase the incidence of infections. Likewise, hypnotics increase the incidence of major depression and cause emergency admissions for overdoses and deaths.  Epidemiologically, hypnotic use is associated with cancer, falls, automobile accidents, and markedly increased overall mortality.  This article considers the costs to hospitals and healthcare payers of hypnotic-induced infections and other severe consequences of hypnotic use. These are a probable cause of excessive hospital admissions, prolonged lengths of stay at increased costs, and increased readmissions. Accurate information is scanty, for in-hospital hypnotic benefits and risks have scarcely been studied -- certainly not the economic costs of inpatient adverse effects.  Healthcare costs of outpatient adverse effects likewise need evaluation. In one example, use of hypnotics among depressed patients was strongly associated with higher healthcare costs and more short-term disability. A best estimate is that U.S. costs of hypnotic harms to healthcare systems are on the order of $55 billion, but conceivably might be as low as $10 billion or as high as $100 billion. More research is needed to more accurately assess unnecessary and excessive hypnotics costs to providers and insurers, as well as financial and health damages to the patients themselves.

Mapping of 79 loci for 83 plasma protein biomarkers in cardiovascular disease

Recent advances in highly multiplexed immunoassays have allowed systematic large-scale measurement of hundreds of plasma proteins in large cohort studies. In combination with genotyping, such studies offer the prospect to 1) identify mechanisms involved with regulation of protein expression in plasma, and 2) determine whether the plasma proteins are likely to be causally implicated in disease. We report here the results of genome-wide association (GWA) studies of 83 proteins considered relevant to cardiovascular disease (CVD), measured in 3,394 individuals with multiple CVD risk factors. We identified 79 genome-wide significant (p<5e-8) association signals, 55 of which replicated at P<0.0007 in separate validation studies (n = 2,639 individuals). Using automated text mining, manual curation, and network-based methods incorporating information on expression quantitative trait loci (eQTL), we propose plausible causal mechanisms for 25 trans-acting loci, including a potential post-translational regulation of stem cell factor by matrix metalloproteinase 9 and receptor-ligand pairs such as RANK-RANK ligand. Using public GWA study data, we further evaluate all 79 loci for their causal effect on coronary artery disease, and highlight several potentially causal associations. Overall, a majority of the plasma proteins studied showed evidence of regulation at the genetic level. Our results enable future studies of the causal architecture of human disease, which in turn should aid discovery of new drug targets.

Several proteins that circulate in blood have been linked to cardiovascular disease through the use of classic epidemiology and correlation studies. If individuals with higher risk of disease have higher levels of a protein, the protein may be associated with disease. However, this does not necessarily mean that the protein causes disease; it may merely be an innocent bystander or a consequence of the disease process. To establish whether a protein causes disease, a genetic approach, insensitive to reverse causation, can be used. Instead of correlating the levels of the protein itself, gene variants that regulate the protein levels are used in the analysis. This approach requires prior knowledge of which genetic variants are linked to individual proteins. Therefore we completed a map of how common genetic variants affect the blood concentration levels of 83 proteins that have been implicated in cardiovascular disease. By using this map of cause-to-effect findings, we gained insights into the regulation of a majority of the proteins under study and how they relate to risk of coronary artery disease. This study provides a map of genetic regulation of important cardiovascular plasma proteins, insights into their upstream regulatory environment, as well as novel leads for cardiovascular drug development.

Mendelian Randomization: New Applications in the Coming Age of Hypothesis-Free Causality

Mendelian randomization (MR) is an approach that uses genetic variants associated with a modifiable exposure or biological intermediate to estimate the causal relationship between these variables and a medically relevant outcome. Although it was initially developed to examine the relationship between modifiable exposures/biomarkers and disease, its use has expanded to encompass applications in molecular epidemiology, systems biology, pharmacogenomics, and many other areas. The purpose of this review is to introduce MR, the principles behind the approach, and its limitations. We consider some of the new applications of the methodology, including informing drug development, and comment on some promising extensions, including two-step, two-sample, and bidirectional MR. We show how these new methods can be combined to efficiently examine causality in complex biological networks and provide a new framework to data mine high-dimensional studies as we transition into the age of hypothesis-free causality.