BMI as a Modifiable Risk Factor for Type 2 Diabetes: Refining and Understanding Causal Estimates Using Mendelian Randomization

A Two-Sample Mendelian Randomization Study of Neuroticism and Sleep Bruxism

Sleep bruxism (SB) affects a considerable part of the population and is associated with neuroticism, stress, and anxiety in various studies. However, the causal mechanisms between neuroticism and SB have not been examined. Understanding the reasons for SB is important as understanding bruxism may allow improved comprehensive management of the disorders and comorbidities related to it. Previous studies on the association of risk factors to SB have provided important symptomatic insight but were mainly questionnaire based or limited in sample size and could not adequately assess causal relationships. The aim of this study was to elaborate the possible causal relationship of neuroticism as a risk factor for SB through a Mendelian randomization (MR) approach by combining questionnaires, registry data, and genetic information in large scale. We performed a two-sample MR study using instrumental genetic variants of neuroticism, including neuroticism subcategories, in the UK Biobank (n = 380,506) and outcome data of probable SB using FinnGen (n [cases/controls] = 12,297/364,980). We discovered a causal effect from neuroticism to SB (odds ratio [OR] = 1.38 [1.10-1.74], P = 0.0057). A phenotype sensitive to stress and adversity had the strongest effect (OR = 1.59 [1.17-2.15], P = 0.0028). Sensitivity analyses across MR methods supported a causal relationship, and we did not observe pleiotropy between neuroticism and SB (MR-Egger intercept, P = 0.87). Our findings are in line with earlier observational studies that connect stress and SB. Furthermore, our results provide evidence that neurotic traits increase the risk of probable SB.

Based on systematic druggable genome-wide Mendelian randomization identifies therapeutic targets for diabetes

Purpose

Diabetes and its complications cause a heavy burden of disease worldwide. In recent years, Mendelian randomization (MR) has been widely used to discover the pathogenesis and epidemiology of diseases, as well as to discover new therapeutic targets. Therefore, based on systematic "druggable" genomics, we aim to identify new therapeutic targets for diabetes and analyze its pathophysiological mechanisms to promote its new therapeutic strategies.

Material and method

We used double sample MR to integrate the identified druggable genomics to evaluate the causal effect of quantitative trait loci (eQTLs) expressed by druggable genes in blood on type 1 and 2 diabetes (T1DM and T2DM). Repeat the study using different data sources on diabetes and its complications to verify the identified genes. Not only that, we also use Bayesian co-localization analysis to evaluate the posterior probabilities of different causal variations, shared causal variations, and co-localization probabilities to examine the possibility of genetic confounding. Finally, using diabetes markers with available genome-wide association studies data, we evaluated the causal relationship between established diabetes markers to explore possible mechanisms.

Result

Overall, a total of 4,477 unique druggable genes have been gathered. After filtering using methods such as Bonferroni significance (P<1.90e-05), the MR Steiger directionality test, Bayesian co-localization analysis, and validation with different datasets, Finally, 7 potential druggable genes that may affect the results of T1DM and 7 potential druggable genes that may affect the results of T2DM were identified. Reverse MR suggests that C4B may play a bidirectional role in the pathogenesis of T1DM, and none of the other 13 target genes have a reverse causal relationship. And the 7 target genes in T2DM may each affect the biomarkers of T2DM to mediate the pathogenesis of T2DM.

Conclusion

This study provides genetic evidence supporting the potential therapeutic benefits of targeting seven druggable genes, namely MAP3K13, KCNJ11, REG4, KIF11, CCNE2, PEAK1, and NRBP1, for T2DM treatment. Similarly, targeting seven druggable genes, namely ERBB3, C4B, CD69, PTPN22, IL27, ATP2A1, and LT-β, has The potential therapeutic benefits of T1DM treatment. This will provide new ideas for the treatment of diabetes and also help to determine the priority of drug development for diabetes.

Mendelian randomization: causal inference leveraging genetic data

Mendelian randomization (MR) leverages genetic information to examine the causal relationship between phenotypes allowing for the presence of unmeasured confounders. MR has been widely applied to unresolved questions in epidemiology, making use of summary statistics from genome-wide association studies on an increasing number of human traits. However, an understanding of essential concepts is necessary for the appropriate application and interpretation of MR. This review aims to provide a non-technical overview of MR and demonstrate its relevance to psychiatric research. We begin with the origins of MR and the reasons for its recent expansion, followed by an overview of its statistical methodology. We then describe the limitations of MR, and how these are being addressed by recent methodological advances. We showcase the practical use of MR in psychiatry through three illustrative examples - the connection between cannabis use and psychosis, the link between intelligence and schizophrenia, and the search for modifiable risk factors for depression. The review concludes with a discussion of the prospects of MR, focusing on the integration of multi-omics data and its extension to delineating complex causal networks.

Genetic Analysis of Obstructive Sleep Apnea and Its Relationship with Severe COVID-19

Rationale: Although patients with obstructive sleep apnea (OSA) have a higher risk for coronavirus disease (COVID-19) hospitalization, the causal relationship has remained unexplored. Objectives: To understand the causal relationship between OSA and COVID-19 by leveraging data from vaccination and electronic health records, genetic risk factors from genome-wide association studies, and Mendelian randomization. Methods: We elucidated genetic risk factors for OSA using FinnGen (total N = 377,277), performing genome-wide association. We used the associated variants as instruments for univariate and multivariate Mendelian randomization (MR) analyses and computed absolute risk reduction against COVID-19 hospitalization with or without vaccination. Results: We identified nine novel loci for OSA and replicated our findings in the Million Veteran Program. Furthermore, MR analysis showed that OSA was a causal risk factor for severe COVID-19 (P = 9.41 × 10-4). Probabilistic modeling showed that the strongest genetic risk factor for OSA at the FTO locus reflected a signal of higher body mass index (BMI), whereas BMI-independent association was seen with the earlier reported SLC9A4 locus and a MECOM locus, which is a transcriptional regulator with 210-fold enrichment in the Finnish population. Similarly, multivariate MR analysis showed that the causality for severe COVID-19 was driven by BMI (multivariate MR P = 5.97 × 10-6, β = 0.47). Finally, vaccination reduced the risk for COVID-19 hospitalization more in the patients with OSA than in the non-OSA controls, with respective absolute risk reductions of 13.3% versus 6.3%. Conclusions: Our analysis identified novel genetic risk factors for OSA and showed that OSA is a causal risk factor for severe COVID-19. The effect is predominantly explained by higher BMI and suggests BMI-dependent effects at the level of individual variants and at the level of comorbid causality.

Causal relationship between serum metabolites and juvenile idiopathic arthritis: a mendelian randomization study

BACKGROUND

Juvenile Idiopathic Arthritis (JIA) is a condition that occurs when individuals under the age of 16 develop arthritis that lasts for more than six weeks, and the cause is unknown. The development of JIA may be linked to serum metabolites. Nevertheless, the association between JIA pathogenesis and serum metabolites is unclear, and there are discrepancies in the findings across studies.

METHODS

In this research, the association between JIA in humans and 486 serum metabolites was assessed using genetic variation data and genome-wide association study. The identification of causal relationships was accomplished through the application of univariate Mendelian randomization (MR) analysis. Various statistical methods, including inverse variance weighted and MR-Egger, were applied to achieve this objective. To ensure that the findings from the MR analysis were trustworthy, a number of assessments were carried out. To ensure the accuracy of the obtained results, a range of techniques were utilised including the Cochran Q test, examination of the MR-Egger intercept, implementation of the leave-one-out strategy, and regression analysis of linkage disequilibrium scores. In order to identify the specific metabolic pathways associated with JIA, our primary objective was to perform pathway enrichment analysis using the Kyoto Encyclopedia of Genes and Genomes.

RESULTS

Two-sample summary data MR analyses and sensitivity analyses showed that five metabolites were significantly causally associated with JIA, including two risk factors-kynurenine (odds ratio [OR]: 16.39, 95% confidence interval [CI]: 2.07-129.63, p = 5.11 × 10- 6) and linolenate (OR: 16.48, 95% CI: 1.32-206.22, p = 0.030)-and three protective factors-3-dehydrocarnitine (OR: 0.32, 95% CI: 0.14-0.72, p = 0.007), levulinate (4-oxovalerate) (OR: 0.40, 95% CI: 0.20-0.80, p = 0.010), and X-14,208 (phenylalanylserine) (OR: 0.68, 95% CI: 0.51-0.92, p = 0.010). Furthermore, seven metabolic pathways, including α-linolenic acid metabolism and pantothenate and CoA biosynthesis, are potentially associated with the onset and progression of JIA.

CONCLUSION

Five serum metabolites, including kynurenine and 3-dehydrocarnitine, may be causally associated with JIA. These results provide a theoretical framework for developing effective JIA prevention and screening strategies.

Causal relationship between eosinophilic esophagitis and inflammatory bowel disease: a bidirectional two-sample Mendelian randomization study

Background

Eosinophilic esophagitis (EoE) and inflammatory bowel diseases (IBDs), including Crohn's disease (CD) and ulcerative colitis (UC), are immune-mediated gastrointestinal diseases with overlapped pathogenesis and are sometimes concurrently diagnosed, but their causal relationship remains unclear. We investigated the causal relationship between EoE and IBD and its subtypes via a two-sample bidirectional Mendelian randomization (MR) approach.

Methods

MR analyses were performed using summary data of a genome-wide association study (GWAS) on individuals of European ancestry. Independent single-nucleotide polymorphisms correlated with EoE (from a GWAS meta-analysis containing 1,930 cases and 13,634 controls) and IBD (from FinnGen GWASs containing 9,083 IBD, 2,033 CD, and 5,931 UC cases, and GWASs of IBD genetic consortium containing 12,882 IBD, 6,968 UC, and 5,956 CD cases) were selected as instruments. We applied the inverse variance weighted (IVW) method as the primary analysis followed by several sensitivity analyses. For the forward MR study, estimates from IVW methods were subsequently meta-analyzed using a random-effect model.

Results

Our results suggested a causal effect of EoE on IBD [pooled odds ratio (OR), 1.07; 95% confidence interval (CI), 1.02-1.13] and EoE on UC (pooled OR, 1.09, 95% CI, 1.04-1.14). No causal link between EoE and CD was observed (pooled OR, 1.05; 95% CI, 0.96-1.16). The reverse MR analyses revealed no causal effect of IBD (and its subtypes) on EoE. Sensitivity analyses confirmed the robustness of primary results.

Conclusions

Our findings provided evidence of a suggestive causal effect of EoE on IBD (specifically on UC) in the European population. Increased awareness of concurrent or subsequent IBD in patients with EoE is called for. Still, the present evidence is not adequate enough and ought to be validated by further investigations.

Phenome-wide Mendelian randomisation analysis of 378,142 cases reveals risk factors for eight common cancers

For many cancers there are only a few well-established risk factors. Here, we use summary data from genome-wide association studies (GWAS) in a Mendelian randomisation (MR) phenome-wide association study (PheWAS) to identify potentially causal relationships for over 3,000 traits. Our outcome datasets comprise 378,142 cases across breast, prostate, colorectal, lung, endometrial, oesophageal, renal, and ovarian cancers, as well as 485,715 controls. We complement this analysis by systematically mining the literature space for supporting evidence. In addition to providing supporting evidence for well-established risk factors (smoking, alcohol, obesity, lack of physical activity), we also find sex steroid hormones, plasma lipids, and telomere length as determinants of cancer risk. A number of the molecular factors we identify may prove to be potential biomarkers. Our analysis, which highlights aetiological similarities and differences in common cancers, should aid public health prevention strategies to reduce cancer burden. We provide a R/Shiny app to visualise findings.

Causal relationship between dietary salt intake and dementia risk: Mendelian randomization study

OBJECTIVE

Observational research has indicated a potential link between dietary salt intake and susceptibility to dementia. However, it is important to note that these types of studies are prone to the issues of reverse causation and residual confounding. Therefore, we conducted a two-sample Mendelian randomization (MR) study to explore the causality.

METHOD

To explore the causal relationship between them, this Mendelian randomization (MR) study incorporated summary statistics of dietary salt intake and dementia. We estimated the causality between salt intake and the risk of overall dementia and various subtypes of dementia, including Alzheimer's disease (AD), Vascular dementia (VaD), and Lewy body dementia (LBD). The inverse variance-weighted (IVW) method was the major MR analysis. To conduct sensitivity analyses, we employed various MR methods, the pleiotropy residual sum and outlier (MR-PRESSO) method, and the leave-one-out approach. The MR-Egger intercept and Cochran's Q test were conducted to test pleiotropy and heterogeneity respectively.

RESULTS

A suggestive association was observed for genetically predicted higher dietary salt intake and increased risk of overall dementia in the European ancestry [odds ratio (OR): 1.542; 95% confidence interval (95% CI): 1.095-2.169; P = 0.013]. The causal relationship between dietary salt intake and overall dementia is robust with respect to the choice of statistical methods and is validated through extensive sensitivity analyses that guard against various model assumption violations. Meanwhile, no clear heterogeneity or pleiotropy was identified. However, we failed to detect a causal effect of dietary salt intake on the risk of various dementia subtypes.

CONCLUSION

The results of this research present strong evidence that established a significant association between dietary salt intake and the likelihood of developing dementia. These findings reinforce the notion that the amount of dietary salt intake plays a crucial role in determining the risk of acquiring this cognitive condition. By establishing a definitive correlation, this study highlights the importance of reducing salt consumption as a preventive measure against dementia.

Exploring the causal relationship between gut microbiota and multiple myeloma risk based on Mendelian randomization and biological annotation

Introduction

The microbial genome-wide association studies (mbGWAS) have highlighted significant host-microbiome interactions based on microbiome heritability. However, establishing causal relationships between particular microbiota and multiple myeloma (MM) remains challenging due to limited sample sizes.

Methods

Gut microbiota data from a GWAS with 18,340 participants and MM summary statistics from 456,348 individuals. The inverse variance-weighted (IVW) method was used as the main bidirectional Mendelian randomization (MR) analysis. To assess the robustness of our results, we further performed supplementary analyses, including MR pleiotropy residual sum and outlier (MR-PRESSO) test, MR-Egger, Weighted median, Simple mode, and Weighted mode. Moreover, a backward MR analysis was conducted to investigate the potential for reverse causation. Finally, gene and gene-set-based analyses were then conducted to explore the common biological factors connecting gut microbiota and MM.

Results

We discovered that 10 gut microbial taxa were causally related to MM risk. Among them, family Acidaminococcaceae, Bacteroidales family S24-7, family Porphyromonadaceae, genus Eubacterium ruminantium group, genus Parabacteroides, and genus Turicibacter were positively correlated with MM. Conversely, class Verrucomicrobia, family Verrucomicrobiaceae, genus Akkermansia, and order Verrucomicrobiales were negatively correlated with MM. The heterogeneity test revealed no Heterogeneity. MR-Egger and MR-PRESSO tests showed no significant horizontal pleiotropy. Importantly, leave-one-out analysis confirmed the robustness of MR results. In the backward MR analysis, no statistically significant associations were discovered between MM and 10 gut microbiota taxa. Lastly, we identified novel host-microbiome shared genes (AUTS2, CDK2, ERBB3, IKZF4, PMEL, SUOX, and RAB5B) that are associated with immunoregulation and prognosis in MM through biological annotation.

Discussion

Overall, this study provides evidence supporting a potential causal relationship between gut microbiota and MM risk, while also revealing novel host-microbiome shared genes relevant to MM immunoregulation and clinical prognosis.

Gut microbiota in combination with blood metabolites reveals characteristics of the disease cluster of coronary artery disease and cognitive impairment: a Mendelian randomization study

Background

The coexistence of coronary artery disease (CAD) and cognitive impairment has become a common clinical phenomenon. However, there is currently limited research on the etiology of this disease cluster, discovery of biomarkers, and identification of precise intervention targets.

Methods

We explored the causal connections between gut microbiota, blood metabolites, and the disease cluster of CAD combined with cognitive impairment through two-sample Mendelian randomization (TSMR). Additionally, we determine the gut microbiota and blood metabolites with the strongest causal associations using Bayesian model averaging multivariate Mendelian randomization (MR-BMA) analysis. Furthermore, we will investigate the mediating role of blood metabolites through a two-step Mendelian randomization design.

Results

We identified gut microbiota that had significant causal associations with cognitive impairment. Additionally, we also discovered blood metabolites that exhibited significant causal associations with both CAD and cognitive impairment. According to the MR-BMA results, the free cholesterol to total lipids ratio in large very low density lipoprotein (VLDL) was identified as the key blood metabolite significantly associated with CAD. Similarly, the cholesteryl esters to total lipids ratio in small VLDL emerged as the primary blood metabolite with a significant causal association with dementia with lewy bodies (DLB). For the two-step Mendelian randomization analysis, we identified blood metabolites that could potentially mediate the association between genus Butyricicoccus and CAD in the potential causal links.

Conclusion

Our study utilized Mendelian randomization (MR) to identify the gut microbiota features and blood metabolites characteristics associated with the disease cluster of CAD combined with cognitive impairment. These findings will provide a meaningful reference for the identification of biomarkers for the disease cluster of CAD combined with cognitive impairment as well as the discovery of targets for intervention to address the problems in the clinic.

Smokeless Tobacco Use and Its Association with Type 2 Diabetes: A Case Control Study

BACKGROUND

Type 2 diabetes mellitus (T2DM) is a prevalent metabolic disorder characterized by hyperglycemia and insulin resistance. Its incidence is increasing globally, with a significant impact on public health. Smokeless tobacco (SLT) is a form of tobacco consumption that has been associated with various health risks, including potential effects on glucose homeostasis. This case-control study aimed to investigate the association between SLT use and T2DM. Additionally, the study aimed to assess the relationship of age, gender, socioeconomic status (SES), body mass index (BMI), family history of diabetes, physical activity, and periodontal status with T2DM participants.

METHODS

The study was conducted over 24 months and included 82 T2DM cases and 164 non-diabetic controls. Demographic data, tobacco use, medical history, oral hygiene habits, BMI, and periodontal status were collected through a self-administered questionnaire and interviews. Statistical analyses were performed using Statistical Package for Social Sciences (SPSS) for Windows 26.0 (SPSS, Inc. Chicago, Illinois).

RESULTS

The majority of T2DM cases were in the age group of 31-50 years, and there was a significant association between gender and T2DM, with more males being diabetic. There was no significant association between SES and diabetes. Obesity was found to be a significant risk factor for T2DM. Among SLT users, gutkha was the most commonly used product. SLT use was significantly associated with T2DM. Family history of diabetes and physical inactivity were also significantly associated with diabetes.

CONCLUSION

The study suggests that SLT use is a risk factor for T2DM and may be associated with increased diabetes risk. Further research is warranted to understand the underlying mechanisms and potential interventions to reduce the impact of SLT on diabetes risk.

Causal relationship between body mass index, type 2 diabetes and bone mineral density: Mendelian randomization

OBJECTIVE

To reveal the relationship between Body Mass Index(BMI), type 2 diabetes, and bone mineral density(BMD) using a mendelian randomization (MR) approach.

METHODS

GWAS data on BMI, type 2 diabetes, and BMD were selected from the IEU GWAS database at the University of Bristol.Univariable, multivariable, and mediated MR analyses were used to explore the relationship between BMI, type 2 diabetes, and BMD. beta(β) values were given, and three methods, including inverse variance weighting, MR-Egger regression, and weighted median, were used in this analysis.

RESULTS

Univariable mendelian randomization (UVMR) results showed that BMI and type 2 diabetes were positively associated with BMD. However, the association between BMI and BMD was insignificant in the multivariable Mendelian randomization (MVMR) analysis, while that between type 2 diabetes and BMD remained significant. Mediated MR analysis indicated that type 2 diabetes mediated the regulation of BMD by BMI.

CONCLUSION

This study provides evidence supporting a positive causal association between BMI, type 2 diabetes, and BMD. Type 2 diabetes acts as a mediator in the regulation of BMD by BMI, indicating that both BMI and type 2 diabetes exert a protective influence on BMD.

The interactions between host genome and gut microbiome increase the risk of psychiatric disorders: Mendelian randomization and biological annotation

BACKGROUND

The correlation between human gut microbiota and psychiatric diseases has long been recognized. Based on the heritability of the microbiome, genome-wide association studies on human genome and gut microbiome (mbGWAS) have revealed important host-microbiome interactions. However, establishing causal relationships between specific gut microbiome features and psychological conditions remains challenging due to insufficient sample sizes of previous studies of mbGWAS.

METHODS

Cross-cohort meta-analysis (via METAL) and multi-trait analysis (via MTAG) were used to enhance the statistical power of mbGWAS for identifying genetic variants and genes. Using two large mbGWAS studies (7,738 and 5,959 participants respectively) and12 disease-specific studies from the Psychiatric Genomics Consortium (PGC), we performed bidirectional two-sample mendelian randomization (MR) analyses between microbial features and psychiatric diseases (up to 500,199 individuals). Additionally, we conducted downstream gene- and gene-set-based analyses to investigate the shared biology linking gut microbiota and psychiatric diseases.

RESULTS

METAL and MTAG conducted in mbGWAS could boost power for gene prioritization and MR analysis. Increases in the number of lead SNPs and mapped genes were witnessed in 13/15 species and 5/10 genera after using METAL, and MTAG analysis gained an increase in sample size equivalent to expanding the original samples from 7% to 63%. Following METAL use, we identified a positive association between Bacteroides faecis and ADHD (OR, 1.09; 95 %CI, 1.02-1.16; P = 0.008). Bacteroides eggerthii and Bacteroides thetaiotaomicron were observed to be positively associated with PTSD (OR, 1.11; 95 %CI, 1.03-1.20; P = 0.007; OR, 1.11; 95 %CI, 1.01-1.23; P = 0.03). These findings remained stable across statistical models and sensitivity analyses. No genetic liabilities to psychiatric diseases may alter the abundance of gut microorganisms.Using biological annotation, we identified that those genes contributing to microbiomes (e.g., GRIN2A and RBFOX1) are expressed and enriched in human brain tissues.

CONCLUSIONS

Our statistical genetics strategy helps to enhance the power of mbGWAS, and our genetic findings offer new insights into biological pleiotropy and causal relationship between microbiota and psychiatric diseases.

Efficacy of metformin targets on cardiometabolic health in the general population and non-diabetic individuals: a Mendelian randomization study

BACKGROUND

Metformin shows beneficial effects on cardiometabolic health in diabetic individuals. However, the beneficial effects in the general population, especially in non-diabetic individuals are unclear. We aim to estimate the effects of perturbation of seven metformin targets on cardiometabolic health using Mendelian randomization (MR).

METHODS

Genetic variants close to metformin-targeted genes associated with expression of the corresponding genes and glycated haemoglobin (HbA1c) level were used to proxy therapeutic effects of seven metformin-related drug targets. Eight cardiometabolic phenotypes under metformin trials were selected as outcomes (average N = 466,947). MR estimates representing the weighted average effects of the seven effects of metformin targets on the eight outcomes were generated. One-sample MR was applied to estimate the averaged and target-specific effects in 338,425 non-diabetic individuals in UK Biobank.

FINDINGS

Genetically proxied averaged effects of five metformin targets, equivalent to a 0.62% reduction of HbA1c level, was associated with 37.8% lower risk of coronary artery disease (CAD) (odds ratio [OR] = 0.62, 95% confidence interval [CI] = 0.46-0.84), lower levels of body mass index (BMI) (β = -0.22, 95% CI = -0.35 to -0.09), systolic blood pressure (SBP) (β = -0.19, 95% CI = -0.28 to -0.09) and diastolic blood pressure (DBP) levels (β = -0.29, 95% CI = -0.39 to -0.19). One-sample MR suggested that the seven metformin targets showed averaged and target-specific beneficial effects on BMI, SBP and DBP in non-diabetic individuals.

INTERPRETATION

This study showed that perturbation of seven metformin targets has beneficial effects on BMI and blood pressure in non-diabetic individuals. Clinical trials are needed to investigate whether similar effects can be achieved with metformin medications.

FUNDING

Funding information is provided in the Acknowledgements.

Systematic comparison of Mendelian randomisation studies and randomised controlled trials using electronic databases

OBJECTIVE

To scope the potential for (semi)-automated triangulation of Mendelian randomisation (MR) and randomised controlled trials (RCTs) evidence since the two methods have distinct assumptions that make comparisons between their results invaluable.

METHODS

We mined ClinicalTrials.Gov, PubMed and EpigraphDB databases and carried out a series of 26 manual literature comparisons among 54 MR and 77 RCT publications.

RESULTS

We found that only 13% of completed RCTs identified in ClinicalTrials.Gov submitted their results to the database. Similarly low coverage was revealed for Semantic Medline (SemMedDB) semantic triples derived from MR and RCT publications -36% and 12%, respectively. Among intervention types that can be mimicked by MR, only trials of pharmaceutical interventions could be automatically matched to MR results due to insufficient annotation with Medical Subject Headings ontology. A manual survey of the literature highlighted the potential for triangulation across a number of exposure/outcome pairs if these challenges can be addressed.

CONCLUSIONS

We conclude that careful triangulation of MR with RCT evidence should involve consideration of similarity of phenotypes across study designs, intervention intensity and duration, study population demography and health status, comparator group, intervention goal and quality of evidence.

Deciphering the causal relationship between blood pressure and regional white matter integrity: A two-sample Mendelian randomization study

Elevated arterial blood pressure (BP) is a common risk factor for cerebrovascular and cardiovascular diseases, but no causal relationship has been established between BP and cerebral white matter (WM) integrity. In this study, we performed a two-sample Mendelian randomization (MR) analysis with individual-level data by defining two nonoverlapping sets of European ancestry individuals (genetics-exposure set: N = 203,111; mean age = 56.71 years, genetics-outcome set: N = 16,156; mean age = 54.61 years) from UK Biobank to evaluate the causal effects of BP on regional WM integrity, measured by fractional anisotropy of diffusion tensor imaging. Two BP traits: systolic and diastolic blood pressure were used as exposures. Genetic variant was carefully selected as instrumental variable (IV) under the MR analysis assumptions. We existing large-scale genome-wide association study summary data for validation. The main method used was a generalized version of inverse-variance weight method while other MR methods were also applied for consistent findings. Two additional MR analyses were performed to exclude the possibility of reverse causality. We found significantly negative causal effects (FDR-adjusted p < .05; every 10 mmHg increase in BP leads to a decrease in FA value by .4% ~ 2%) of BP traits on a union set of 17 WM tracts, including brain regions related to cognitive function and memory. Our study extended the previous findings of association to causation for regional WM integrity, providing insights into the pathological processes of elevated BP that might chronically alter the brain microstructure in different regions.

Guidelines for performing Mendelian randomization investigations: update for summer 2023

This paper provides guidelines for performing Mendelian randomization investigations. It is aimed at practitioners seeking to undertake analyses and write up their findings, and at journal editors and reviewers seeking to assess Mendelian randomization manuscripts. The guidelines are divided into ten sections: motivation and scope, data sources, choice of genetic variants, variant harmonization, primary analysis, supplementary and sensitivity analyses (one section on robust statistical methods and one on other approaches), extensions and additional analyses, data presentation, and interpretation. These guidelines will be updated based on feedback from the community and advances in the field. Updates will be made periodically as needed, and at least every 24 months.

Genetic association of mosaic loss of chromosome Y with prostate cancer in men of European and East Asian ancestries: a Mendelian randomization study

Background: Genomic instability is a significant hallmark of aging and has a major impact on aging biology. Mosaic loss of chromosome Y (mLOY) in blood cells is a common chromosomal abnormality in aging men and is considered an indicator of genomic instability. Previous studies have indicated a connection between mLOY and prostate cancer risk, but the causal relationship has not been fully established. Methods: To determine the causal effect of mLOY on prostate cancer, we conducted a Mendelian Randomization (MR) study in two ancestral groups. We utilized 125 and 42 mLOY-associated variants as instrumental variables (IVs) in European and East Asian GWAS of prostate cancer, respectively. Summary-level data on prostate cancer was obtained from the PRACTICAL consortium (79,148 cases and 61,106 controls of European ancestry) and the Biobank Japan consortium (5,408 cases and 103,939 controls of East Asian ancestry). A single population was used to assess the causal relationship in East Asian ancestry. Our main method for obtaining MR results was inverse-variance weighted (IVW), and we conducted sensitivity analyses to confirm the robustness of our results. Finally, we combined the estimates from both sources using a fixed-effects meta-analysis. Results: Our MR analysis using the IVW method showed that a one-unit increase in genetically predicted mLOY was associated with an increased risk of prostate cancer in the PRACTICAL consortium (OR = 1.09%, 95% CI: 1.05-1.13, p = 1.2 × 10^-5), but not in the Biobank Japan consortium (OR = 1.13%, 95% CI: 0.88-1.45, p = 0.34). Sensitivity analyses robustly indicated increased odds ratios for prostate cancer with every one-unit increase in genetically predicted mLOY for the PRACTICAL consortium. Furthermore, mLOY was found to be associated with prostate cancer risk in a meta-analysis of both sources (OR = 1.09%, 95% CI: 1.05-1.13, p = 8.0 × 10^-6). Conclusion: Our MR study provides strong evidence that higher mLOY increases the risk of prostate cancer. Preventing mLOY may be a means of reducing the risk of developing prostate cancer.

Educational attainment, structural brain reserve and Alzheimer's disease: a Mendelian randomization analysis

Higher educational attainment is observationally associated with lower risk of Alzheimer's disease. However, the biological mechanisms underpinning this association remain unclear. The protective effect of education on Alzheimer's disease may be mediated via increased brain reserve. We used two-sample Mendelian randomization to explore putative causal relationships between educational attainment, structural brain reserve as proxied by MRI phenotypes and Alzheimer's disease. Summary statistics were obtained from genome-wide association studies of educational attainment (n = 1 131 881), late-onset Alzheimer's disease (35 274 cases, 59 163 controls) and 15 measures of grey or white matter macro- or micro-structure derived from structural or diffusion MRI (nmax = 33 211). We conducted univariable Mendelian randomization analyses to investigate bidirectional associations between (i) educational attainment and Alzheimer's disease; (ii) educational attainment and imaging-derived phenotypes; and (iii) imaging-derived phenotypes and Alzheimer's disease. Multivariable Mendelian randomization was used to assess whether brain structure phenotypes mediated the effect of education on Alzheimer's disease risk. Genetically proxied educational attainment was inversely associated with Alzheimer's disease (odds ratio per standard deviation increase in genetically predicted years of schooling = 0.70, 95% confidence interval 0.60, 0.80). There were positive associations between genetically predicted educational attainment and four cortical metrics (standard deviation units change in imaging phenotype per one standard deviation increase in genetically predicted years of schooling): surface area 0.30 (95% confidence interval 0.20, 0.40); volume 0.29 (95% confidence interval 0.20, 0.37); intrinsic curvature 0.18 (95% confidence interval 0.11, 0.25); local gyrification index 0.21 (95% confidence interval 0.11, 0.31)]; and inverse associations with cortical intracellular volume fraction [-0.09 (95% confidence interval -0.15, -0.03)] and white matter hyperintensities volume [-0.14 (95% confidence interval -0.23, -0.05)]. Genetically proxied levels of surface area, cortical volume and intrinsic curvature were positively associated with educational attainment [standard deviation units change in years of schooling per one standard deviation increase in respective genetically predicted imaging phenotype: 0.13 (95% confidence interval 0.10, 0.16); 0.15 (95% confidence interval 0.11, 0.19) and 0.12 (95% confidence interval 0.04, 0.19)]. We found no evidence of associations between genetically predicted imaging-derived phenotypes and Alzheimer's disease. The inverse association of genetically predicted educational attainment with Alzheimer's disease did not attenuate after adjusting for imaging-derived phenotypes in multivariable analyses. Our results provide support for a protective causal effect of educational attainment on Alzheimer's disease risk, as well as potential bidirectional causal relationships between education and brain macro- and micro-structure. However, we did not find evidence that these structural markers affect risk of Alzheimer's disease. The protective effect of education on Alzheimer's disease may be mediated via other measures of brain reserve not included in the present study, or by alternative mechanisms.

Risk factors for eight common cancers revealed from a phenome-wide Mendelian randomisation analysis of 378,142 cases and 485,715 controls

For many cancers there are few well-established risk factors. Summary data from genome-wide association studies (GWAS) can be used in a Mendelian randomisation (MR) phenome-wide association study (PheWAS) to identify causal relationships. We performed a MR-PheWAS of breast, prostate, colorectal, lung, endometrial, oesophageal, renal, and ovarian cancers, comprising 378,142 cases and 485,715 controls. To derive a more comprehensive insight into disease aetiology we systematically mined the literature space for supporting evidence. We evaluated causal relationships for over 3,000 potential risk factors. In addition to identifying well-established risk factors (smoking, alcohol, obesity, lack of physical activity), we provide evidence for specific factors, including dietary intake, sex steroid hormones, plasma lipids and telomere length as determinants of cancer risk. We also implicate molecular factors including plasma levels of IL-18, LAG-3, IGF-1, CT-1, and PRDX1 as risk factors. Our analyses highlight the importance of risk factors that are common to many cancer types but also reveal aetiological differences. A number of the molecular factors we identify have the potential to be biomarkers. Our findings should aid public health prevention strategies to reduce cancer burden. We provide a R/Shiny app (https://mrcancer.shinyapps.io/mrcan/) to visualise findings.

Distinct metabolic features of genetic liability to type 2 diabetes and coronary artery disease: a reverse Mendelian randomization study

BACKGROUND

Type 2 diabetes (T2D) and coronary artery disease (CAD) both have known genetic determinants, but the mechanisms through which their associated genetic variants lead to disease onset remain poorly understood.

METHODS

We used large-scale metabolomics data in a two-sample reverse Mendelian randomization (MR) framework to estimate effects of genetic liability to T2D and CAD on 249 circulating metabolites in the UK Biobank (N = 118,466). We examined the potential for medication use to distort effect estimates by conducting age-stratified metabolite analyses.

FINDINGS

Using inverse variance weighted (IVW) models, higher genetic liability to T2D was estimated to decrease high-density lipoprotein cholesterol (HDL-C) and low-density lipoprotein cholesterol (LDL-C) (e.g.

, HDL-C

 -0.05 SD; 95% CI -0.07 to -0.03, per doubling of liability), whilst increasing all triglyceride groups and branched chain amino acids (BCAAs). IVW estimates for CAD liability suggested an effect on reducing HDL-C as well as raising very-low density lipoprotein cholesterol (VLDL-C) and LDL-C. In pleiotropy-robust models, T2D liability was still estimated to increase BCAAs, but several estimates for higher CAD liability reversed and supported decreased LDL-C and apolipoprotein-B. Estimated effects of CAD liability differed substantially by age for non-HDL-C traits, with higher CAD liability lowering LDL-C only at older ages when statin use was common.

INTERPRETATION

Overall, our results support largely distinct metabolic features of genetic liability to T2D and CAD, illustrating both challenges and opportunities for preventing these commonly co-occurring diseases.

FUNDING

Wellcome Trust [218495/Z/19/Z], UK MRC [MC_UU_00011/1; MC_UU_00011/4], the University of Bristol, Diabetes UK [17/0005587], World Cancer Research Fund [IIG_2019_2009].

Risk factors for eight common cancers revealed from a phenome-wide Mendelian randomisation analysis of 378,142 cases and 485,715 controls

For many cancers there are few well-established risk factors. Summary data from genome-wide association studies (GWAS) can be used in a Mendelian randomisation (MR) phenome-wide association study (PheWAS) to identify causal relationships. We performed a MR-PheWAS of breast, prostate, colorectal, lung, endometrial, oesophageal, renal, and ovarian cancers, comprising 378,142 cases and 485,715 controls. To derive a more comprehensive insight into disease aetiology we systematically mined the literature space for supporting evidence. We evaluated causal relationships for over 3,000 potential risk factors. In addition to identifying well-established risk factors (smoking, alcohol, obesity, lack of physical activity), we provide evidence for specific factors, including dietary intake, sex steroid hormones, plasma lipids and telomere length as determinants of cancer risk. We also implicate molecular factors including plasma levels of IL-18, LAG-3, IGF-1, CT-1, and PRDX1 as risk factors. Our analyses highlight the importance of risk factors that are common to many cancer types but also reveal aetiological differences. A number of the molecular factors we identify have the potential to be biomarkers. Our findings should aid public health prevention strategies to reduce cancer burden. We provide a R/Shiny app (https://mrcancer.shinyapps.io/mrcan/) to visualise findings.

Obesity, overweight and hyperglycemia among primary school children in a low-middle income country with a multiethnic population

Objective

The number of children with cardiovascular risk factors is increasing steadily. However, limited data are available on the prevalence of overweight, obesity, and hyperglycemia among children in low-middle-income countries with multiethnic populations. Therefore, we assessed these factors in a school-based survey in Suriname, a low-middle-income country.

Methods

We invited pupils of 5th and 6th grade visiting the primary school to participate in this survey. We used a questionnaire and face to face interviews, and conducted measurements to collect data on biological factors (ethnicity, sex, length, weight, waist circumference, and fasting blood glucose levels), behavior (frequency of physical activity, breakfast, bedtime, screentime), consumption (fruit and vegetables, snack, dairy products) and social factors (parental education, living area).

Results

Overall, the percentage of children with overweight was 13.9%, obesity 13.3% and for elevated fasting blood glucose level (> 6 mmol/L) 4.5%. In the investigated group of individuals, obesity and overweight were associated with sex (girls showed a lower OR of 0.54 [95%CI: 0.39-0.75] for obesity), ethnicity (Javanese 2.1, 1.5-3.0 for overweight and 5.0, 3.1-8.2 for obesity, Maroon 2.2, 1.2-4.1 and Mixed ethnicity 1.7, 1.1-2.6, for obesity compared to Hindustani), behavior (Skip Breakfast: 1.4, 1.2-1.7, physical activity: 0.8, 0.7-0.9) and maternal education level (high 1.7, 1.0-2.7). Children with elevated fasting blood glucose levels showed an association with obesity (1.8, 1.2-2.7) and waist circumference (1.02, 1.01-1.03).

Conclusion

The results show that there is a high prevalence for overweight, obesity and elevated fasting blood glucose among children in Suriname. Furthermore, during childhood ethnicity is associated with obesity and overweight. We suggest that the modifiable risk factors such as BMI, WC, behavior, consumption are interesting for early intervention in children in a developing country.

The effect of maternal BMI, smoking and alcohol on congenital heart diseases: a Mendelian randomisation study

BACKGROUND

Congenital heart diseases (CHDs) remain a significant cause of infant morbidity and mortality. Epidemiological studies have explored maternal risk factors for offspring CHDs, but few have used genetic epidemiology methods to improve causal inference.

METHODS

Three birth cohorts, including 65,510 mother/offspring pairs (N = 562 CHD cases) were included. We used Mendelian randomisation (MR) analyses to explore the effects of genetically predicted maternal body mass index (BMI), smoking and alcohol on offspring CHDs. We generated genetic risk scores (GRS) using summary data from large-scale genome-wide association studies (GWAS) and validated the strength and relevance of the genetic instrument for exposure levels during pregnancy. Logistic regression was used to estimate the odds ratio (OR) of CHD per 1 standard deviation (SD) higher GRS. Results for the three cohorts were combined using random-effects meta-analyses. We performed several sensitivity analyses including multivariable MR to check the robustness of our findings.

RESULTS

The GRSs associated with the exposures during pregnancy in all three cohorts. The associations of the GRS for maternal BMI with offspring CHD (pooled OR (95% confidence interval) per 1SD higher GRS: 0.95 (0.88, 1.03)), lifetime smoking (pooled OR: 1.01 (0.93, 1.09)) and alcoholic drinks per week (pooled OR: 1.06 (0.98, 1.15)) were close to the null. Sensitivity analyses yielded similar results.

CONCLUSIONS

Our results do not provide robust evidence of an effect of maternal BMI, smoking or alcohol on offspring CHDs. However, results were imprecise. Our findings need to be replicated, and highlight the need for more and larger studies with maternal and offspring genotype and offspring CHD data.

Causal associations between obstructive sleep apnea and COVID-19: A bidirectional Mendelian randomization study

BACKGROUNDS

The COVID-19 pandemic has caused significant impact on human health. Whether obstructive sleep apnea (OSA) increases the risk of COVID-19 remains unclear. We sought to clarify this issue using two-sample Mendelian randomization (TSMR) analysis in large cohorts.

METHODS

Bidirectional two-sample Mendelian randomization (MR) was used to evaluate the potential causality between OSA and COVID-19 by selecting single-nucleotide polymorphisms (SNPs) as instrumental variables (IVs) from genome-wide association studies (GWAS). The inverse-variance weighted (IVW) method was selected as the main approach for data analysis to estimate the possible causal effects. Alternative methods such as MR-Egger, the MR pleiotropy residual sum and outlier (MR-PRESSO), and leave-one-out analysis methods were implemented as sensitivity analysis approaches to ensure the robustness of the results.

RESULTS

All forward MR analyses consistently indicated the absence of a causal relationship between OSA and any COVID-19 phenotype. In the reverse MR analysis, the IVW mode demonstrated that severe respiratory confirmed COVID-19 was correlated with a 4.9% higher risk of OSA (OR, 1.049; 95%CI, 1.018-1.081; P = 0.002), consistent in MR-PRESSO (OR = 1.049, 95%CI 1.018-1.081, P = 0.004), weighted median (OR = 1.048, 95%CI 1.003-1.095, P = 0.035), and MR-Egger (OR = 1.083, 95%CI 1.012-1.190, P = 0.041) methods.

CONCLUSIONS

There is no significant evidence supporting a causal association between OSA and any COVID phenotype, while we identified potential evidence for a causal effect of severe COVID-19 on an increased risk of OSA.

Diabetes and physical activity: A prospective cohort study

Diabetes is on the rise as the worldwide population ages. While physical activity can help protect against diabetes, ageing is commonly associated with reduced physical activity. This study aimed to examine if physical activity differs by diabetes status in mid-aged adults, how this association changes over time, and whether physical activity-related sociodemographic factors and health indicators differ in those with and without diabetes. Data came from four waves of the How Areas in Brisbane Influence HealTh and AcTivity (HABITAT), a longitudinal study of mid-age adults living in Brisbane, Australia. Random effects/Expectation-maximisation (RE-EM) regression trees were used to identify factors affecting physical activity among those with and without diabetes, both separately and combined. At study entry, those with diabetes had a higher median age of 58 years (95% CI: 57-60) and a lower median physical activity of 699 MET.min/week (95% CI: 599-799) than people without diabetes (53 years (95% CI: 53-53) and 849 MET.min/week (95% CI: 799-899)). However, the strongest factors influencing physical activity were BMI and gender, not diabetes status. It is vital to promote physical activity among adults, in particular among those with high BMI and women, as well as those with and at high risk of diseases like diabetes.

Genetically predicted phosphate and cardiovascular disease: A Mendelian randomization study

Background

Extensive epidemiological studies have highlighted the correlation between serum phosphate and cardiovascular diseases. The present study aims to determine whether genetically predicted serum phosphate is causally associated with the distinct subtypes of cardiovascular events through the use of Mendelian randomization (MR) analysis.

Methods

Independent and strongly correlated single-nucleotide polymorphisms (SNPs) for serum phosphate were extracted from publicly available genome-wide association studies. Summary statistics of cardiovascular diseases were derived from large-scale consortiums, including HERMES and FinnGen biobank. MR-Egger, weighted median, inverse variance weighted, pleiotropy residual sum and outlier (MR-PRESSO) methods and MR using robust adjusted profile score (MR-RAPS) were employed to analyze causality. The sensitivity analyses comprised heterogeneity, horizontal pleiotropy, and leave-one-out approaches; these were used to ensure the stability of the results.

Results

Our study demonstrated that increased genetically predicted serum phosphate is causally associated with a higher risk of valvular heart disease (VHD) [For VHD including rheumatic fever: odds ratio (OR) = 2.45; 95% confidence interval (CI), 1.52-3.94; p = 0.0002; for non-rheumatic VHD: OR = 6.58; 95% CI, 2.50-17.32; p = 0.0001]. However, no causal association was detected between serum phosphate and other common cardiovascular diseases (including coronary heart disease, heart failure, atrial fibrillation, and essential hypertension).

Conclusions

The results indicate strong causality between serum phosphate and valvular heart disease. Serum phosphate-lowering therapy within the physiological range may represent a novel therapeutic method for valvular heart disease.

Birthweight, BMI in adulthood and latent autoimmune diabetes in adults: a Mendelian randomisation study

AIMS/HYPOTHESIS

Observational studies have found an increased risk of latent autoimmune diabetes in adults (LADA) associated with low birthweight and adult overweight/obese status. We aimed to investigate whether these associations are causal, using a two-sample Mendelian randomisation (MR) design. In addition, we compared results for LADA and type 2 diabetes.

METHODS

We identified 43 SNPs acting through the fetal genome as instrumental variables (IVs) for own birthweight from a genome-wide association study (GWAS) of the Early Growth Genetics Consortium (EGG) and the UK Biobank. We identified 820 SNPs as IVs for adult BMI from a GWAS of the UK Biobank and the Genetic Investigation of ANthropometric Traits consortium (GIANT). Summary statistics for the associations between IVs and LADA were extracted from the only GWAS involving 2634 cases and 5947 population controls. We used the inverse-variance weighted (IVW) estimator as our primary analysis, supplemented by a series of sensitivity analyses.

RESULTS

Genetically determined own birthweight was inversely associated with LADA (OR per SD [~500 g] decrease in birthweight 1.68 [95% CI 1.01, 2.82]). In contrast, genetically predicted BMI in adulthood was positively associated with LADA (OR per SD [~4.8 kg/m2] increase in BMI 1.40 [95% CI 1.14, 1.71]). Robust results were obtained in a range of sensitivity analyses using other MR estimators or excluding some IVs. With respect to type 2 diabetes, the association with birthweight was not stronger than in LADA while the association with adult BMI was stronger than in LADA.

CONCLUSIONS/ INTERPRETATION

This study provides genetic support for a causal link between low birthweight, adult overweight/obese status and LADA.

Genetics of Type 2 Diabetes: Past, Present, and Future

Diabetes has reached epidemic proportions worldwide. Currently, approximately 537 million adults (20–79 years) have diabetes, and the total number of people with diabetes is continuously increasing. Diabetes includes several subtypes. About 80% of all cases of diabetes are type 2 diabetes (T2D). T2D is a polygenic disease with an inheritance ranging from 30 to 70%. Genetic and environment/lifestyle factors, especially obesity and sedentary lifestyle, increase the risk of T2D. In this review, we discuss how studies on the genetics of diabetes started, how they expanded when genome-wide association studies and exome and whole-genome sequencing became available, and the current challenges in genetic studies of diabetes. T2D is heterogeneous with respect to clinical presentation, disease course, and response to treatment, and has several subgroups which differ in pathophysiology and risk of micro- and macrovascular complications. Currently, genetic studies of T2D focus on these subgroups to find the best diagnoses and treatments for these patients according to the principles of precision medicine.

Connecting Genomics and Proteomics to Identify Protein Biomarkers for Adult and Youth-Onset Type 2 Diabetes: A Two-Sample Mendelian Randomization Study

Type 2 diabetes shows an increasing prevalence in both adults and children. Identification of biomarkers for both youth and adult-onset type 2 diabetes is crucial for development of screening tools or drug targets. In this study, using two-sample Mendelian randomization (MR), we identified 22 circulating proteins causally linked to adult type 2 diabetes and 11 proteins with suggestive evidence for association with youth-onset type 2 diabetes. Among these, colocalization analysis further supported a role in type 2 diabetes for C-type mannose receptor 2 (MR odds ratio [OR] 0.85 [95% CI 0.79-0.92] per genetically predicted SD increase in protein level), MANS domain containing 4 (MR OR 0.90 [95% CI 0.88-0.92]), sodium/potassium-transporting ATPase subunit β2 (MR OR 1.10 [95% CI 1.06-1.15]), endoplasmic reticulum oxidoreductase 1β (MR OR 1.09 [95% CI 1.05-1.14]), spermatogenesis-associated protein 20 (MR OR 1.12 [95% CI 1.06-1.18]), haptoglobin (MR OR 0.96 [95% CI 0.94-0.98]), and α1-3-N-acetylgalactosaminyltransferase and α1-3-galactosyltransferase (MR OR 1.04 [95% CI 1.03-1.05]). Our findings support a causal role in type 2 diabetes for a set of circulating proteins, which represent promising type 2 diabetes drug targets.

Targeted metabolomics analysis of amino acids and acylcarnitines as risk markers for diabetes by LC-MS/MS technique

Diabetes is a common chronic disease affecting millions of people worldwide. It underlies various complications and imposes many costs on individuals and society. Discovering early diagnostic biomarkers takes excellent insight into preventive plans and the best use of interventions. Therefore, in the present study, we aimed to evaluate the association between the level of amino acids and acylcarnitines and diabetes to develop diabetes predictive models. Using the targeted LC-MS/MS technique, we analyzed fasting plasma samples of 206 cases and 206 controls that were matched by age, sex, and BMI. The association between metabolites and diabetes was evaluated using univariate and multivariate regression analysis with adjustment for systolic and diastolic blood pressure and lipid profile. To deal with multiple comparisons, factor analysis was used. Participants' average age and BMI were 61.6 years, 28.9 kg/m2, and 55% were female. After adjustment, Factor 3 (tyrosine, valine, leucine, methionine, tryptophan, phenylalanine), 5 (C3DC, C5, C5OH, C5:1), 6 (C14OH, C16OH, C18OH, C18:1OH), 8 (C2, C4OH, C8:1), 10 (alanine, proline) and 11 (glutamic acid, C18:2OH) were positively associated with diabetes. Inline, factor 9 (C4DC, serine, glycine, threonine) and 12 (citrulline, ornithine) showed a reverse trend. Some amino acids and acylcarnitines were found as potential risk markers for diabetes incidents that reflected the disturbances in the several metabolic pathways among the diabetic population and could be targeted to prevent, diagnose, and treat diabetes.

Separating the direct effects of traits on atherosclerotic cardiovascular disease from those mediated by type 2 diabetes

AIMS/HYPOTHESIS

Type 2 diabetes and atherosclerotic CVD share many risk factors. This study aimed to systematically assess a broad range of continuous traits to separate their direct effects on coronary and peripheral artery disease from those mediated by type 2 diabetes.

METHODS

Our main analysis was a two-step Mendelian randomisation for mediation to quantify the extent to which the associations observed between continuous traits and liability to atherosclerotic CVD were mediated by liability to type 2 diabetes. To support this analysis, we performed several univariate Mendelian randomisation analyses to examine the associations between our continuous traits, liability to type 2 diabetes and liability to atherosclerotic CVD.

RESULTS

Eight traits were eligible for the two-step Mendelian randomisation with liability to coronary artery disease as the outcome and we found similar direct and total effects in most cases. Exceptions included fasting insulin and hip circumference where the proportion mediated by liability to type 2 diabetes was estimated as 56% and 52%, respectively. Six traits were eligible for the analysis with liability to peripheral artery disease as the outcome. Again, we found limited evidence to support mediation by liability to type 2 diabetes for all traits apart from fasting insulin (proportion mediated: 70%).

CONCLUSIONS/INTERPRETATION

Most traits were found to affect liability to atherosclerotic CVD independently of their relationship with liability to type 2 diabetes. These traits are therefore important for understanding atherosclerotic CVD risk regardless of an individual's liability to type 2 diabetes.

Eosinophilia and Lung Cancer: Analysis From Real-World Data and Mendelian Randomization Study

Background and Objective

Growing evidence added to the results from observational studies of lung cancer patients exhibiting eosinophilia. However, whether eosinophils contributed to tumor immune surveillance or neoplastic evolution was unknown. This study aimed to analyze the causal association between eosinophilia and lung cancer.

Methods

The causal effect of eosinophil count on lung cancer from a genome-wide association study (GWAS) was investigated using the two-sample Mendelian randomization (MR) method. Secondary results according to different histological subtypes of lung cancer were also implemented. Meanwhile, we compared the measured levels of blood eosinophil counts among different subtypes of lung cancer from real-world data.

Results

The median absolute eosinophilic count (unit: 10⁹/L) [median (min, max): Lung adenocarcinoma 0.7 (0.5, 15); Squamous cell lung cancer 0.7 (0.5, 1.3); Small cell lung cancer 0.7 (0.6, 1.3); p = 0.96] and the median eosinophil to leukocyte ratio [median (min, max): Lung adenocarcinoma 8.7% (2.1, 42.2%); Squamous cell lung cancer 9.3% (4.1, 17.7%); Small cell lung cancer 8.9% (5.1, 24.1%); p = 0.91] were similar among different histological subtypes of lung cancer. MR methods indicated that eosinophilia may provide 28% higher risk for squamous cell lung cancer in East Asian [Weighted median method: odds ratio (OR) = 1.28, 95% CI: 1.04–1.57, p = 0.02].

Conclusion

Our study suggested that eosinophilia may be a potential causal risk factor in the progression of squamous cell lung cancer in East Asian.

Lesão do manguito rotador e obesidade: Uma avaliação demográfica e metabólica

Objective  To analyze the relationship between the presence and severity of rotator cuff (RC) injury with obesity and the time of exposure to obesity. Secondarily, to evaluate the relationship and prevalence of demographic and metabolic factors in obese individuals with RC injury.

Methods  This is a cross-sectional study with 235 obese patients (body mass index [BMI] ≥ 30 kg/m ² ). Demographic data (age and gender), metabolic data (hypertension, diabetes mellitus, lipid profile, and time of exposure to obesity), physical examination (weight, height, waist circumference, and clinical tests), and musculoskeletal ultrasound examination were used to analyze the results.

Results  There was no evidence of an association between RC injury and BMI ( p  = 0.82), time of exposure to obesity ( p  = 0.29), or abdominal circumference ( p  = 0.52). In the subgroup with injury, age ( p  < 0.001), presence of diabetes mellitus ( p  = 0.013), hypertension ( p  < 0.001), level of high-density lipoprotein (HDL) ( p  = 0.026), and time of exposure to obesity ( p  < 0.001) were significantly greater compared to the subgroup without injury. In the search for other parameters independently associated with RC injury, associations were observed with age ( p  = 0.0003) and hypertension ( p  = 0.004).

Conclusion  We did not evidence an association between obesity and the time of exposure to it with the occurrence and severity of RC injury. However, individuals with injury had a longer time of exposure to obesity and prevalence of metabolic disorders than individuals without RC injury. In addition, our findings suggest an association between systemic arterial hypertension (SAH) and advanced age with RC injury.

Disease consequences of higher adiposity uncoupled from its adverse metabolic effects using Mendelian randomisation

Background

Some individuals living with obesity may be relatively metabolically healthy, whilst others suffer from multiple conditions that may be linked to adverse metabolic effects or other factors. The extent to which the adverse metabolic component of obesity contributes to disease compared to the non-metabolic components is often uncertain. We aimed to use Mendelian randomisation (MR) and specific genetic variants to separately test the causal roles of higher adiposity with and without its adverse metabolic effects on diseases.

Methods

We selected 37 chronic diseases associated with obesity and genetic variants associated with different aspects of excess weight. These genetic variants included those associated with metabolically 'favourable adiposity' (FA) and 'unfavourable adiposity' (UFA) that are both associated with higher adiposity but with opposite effects on metabolic risk. We used these variants and two sample MR to test the effects on the chronic diseases.

Results

MR identified two sets of diseases. First, 11 conditions where the metabolic effect of higher adiposity is the likely primary cause of the disease. Here, MR with the FA and UFA genetics showed opposing effects on risk of disease: coronary artery disease, peripheral artery disease, hypertension, stroke, type 2 diabetes, polycystic ovary syndrome, heart failure, atrial fibrillation, chronic kidney disease, renal cancer, and gout. Second, 9 conditions where the non-metabolic effects of excess weight (e.g. mechanical effect) are likely a cause. Here, MR with the FA genetics, despite leading to lower metabolic risk, and MR with the UFA genetics, both indicated higher disease risk: osteoarthritis, rheumatoid arthritis, osteoporosis, gastro-oesophageal reflux disease, gallstones, adult-onset asthma, psoriasis, deep vein thrombosis, and venous thromboembolism.

Conclusions

Our results assist in understanding the consequences of higher adiposity uncoupled from its adverse metabolic effects, including the risks to individuals with high body mass index who may be relatively metabolically healthy.

Funding

Diabetes UK, UK Medical Research Council, World Cancer Research Fund, National Cancer Institute.

Mendelian Randomization: Concepts and Scope

Mendelian randomization (MR) is a method of studying the causal effects of modifiable exposures (i.e., potential risk factors) on health, social, and economic outcomes using genetic variants associated with the specific exposures of interest. MR provides a more robust understanding of the influence of these exposures on outcomes because germline genetic variants are randomly inherited from parents to offspring and, as a result, should not be related to potential confounding factors that influence exposure-outcome associations. The genetic variant can therefore be used as a tool to link the proposed risk factor and outcome, and to estimate this effect with less confounding and bias than conventional epidemiological approaches. We describe the scope of MR, highlighting the range of applications being made possible as genetic data sets and resources become larger and more freely available. We outline the MR approach in detail, covering concepts, assumptions, and estimation methods. We cover some common misconceptions, provide strategies for overcoming violation of assumptions, and discuss future prospects for extending the clinical applicability, methodological innovations, robustness, and generalizability of MR findings.

Mendelian randomization analysis of the causal impact of body mass index and waist-hip ratio on rates of hospital admission

We analyze how measures of adiposity - body mass index (BMI) and waist hip ratio (WHR) - causally influence rates of hospital admission. Conventional analyses of this relationship are susceptible to omitted variable bias from variables that jointly influence both hospital admission and adipose status. We implement a novel quasi-Poisson instrumental variable model in a Mendelian randomization framework, identifying causal effects from random perturbations to germline genetic variation. We estimate the individual and joint effects of BMI, WHR, and WHR adjusted for BMI. We also implement multivariable instrumental variable methods in which the causal effect of one exposure is estimated conditionally on the causal effect of another exposure. Data on 310,471 participants and over 550,000 inpatient admissions in the UK Biobank were used to perform one-sample and two-sample Mendelian randomization analyses. The results supported a causal role of adiposity on hospital admissions, with consistency across all estimates and sensitivity analyses. Point estimates were generally larger than estimates from comparable observational specifications. We observed an attenuation of the BMI effect when adjusting for WHR in the multivariable Mendelian randomization analyses, suggesting that an adverse fat distribution, rather than a higher BMI itself, may drive the relationship between adiposity and risk of hospital admission.

Simultaneous estimation of bi-directional causal effects and heritable confounding from GWAS summary statistics

Mendelian Randomisation (MR) is an increasingly popular approach that estimates the causal effect of risk factors on complex human traits. While it has seen several extensions that relax its basic assumptions, most suffer from two major limitations; their under-exploitation of genome-wide markers, and sensitivity to the presence of a heritable confounder of the exposure-outcome relationship. To overcome these limitations, we propose a Latent Heritable Confounder MR (LHC-MR) method applicable to association summary statistics, which estimates bi-directional causal effects, direct heritabilities, and confounder effects while accounting for sample overlap. We demonstrate that LHC-MR outperforms several existing MR methods in a wide range of simulation settings and apply it to summary statistics of 13 complex traits. Besides several concordant results with other MR methods, LHC-MR unravels new mechanisms (how disease diagnosis might lead to improved lifestyle) and reveals new causal effects (e.g. HDL cholesterol being protective against high systolic blood pressure), hidden from standard MR methods due to a heritable confounder of opposite effect direction.

Impact of Telehealth Care among Adults Living with Type 2 Diabetes in Primary Care: A Systematic Review and Meta-Analysis of Randomised Controlled Trials

Primary health care physicians are increasingly offering telehealth services to patients not only for its cost and time saving advantages but for the additional benefits telehealth can provide for patients with type 2 diabetes (T2D) such as improved self-management behaviours. To support the development of telehealth based T2D clinical care models in primary health care settings, a narrative synthesis and meta-analysis of randomised controlled trial studies was completed for 29 studies that evaluated the effect of one or more types of telehealth interventions on HbA1c levels compared to usual care alone. Results from the random effects meta-analysis demonstrated that telehealth interventions had a stronger influence on HbA1c compared to usual care with a mean difference in HbA1c \% -0.18 (CI -0.35, -0.01), p = 0.04. Results from the subgroup meta-analysis demonstrated that telehealth interventions, when grouped by type of telemonitoring (mHealth and telephone communication), all have a stronger effect on lowering HbA1c levels; however, none of these findings were significant. Key findings from this review demonstrate that telehealth interventions that address T2D self-management behaviours and have higher levels of health care provider engagement, have greater effects on lowering HbA1c levels compared to usual care alone.

The "Common Soil Hypothesis" Revisited-Risk Factors for Type 2 Diabetes and Cardiovascular Disease

The prevalence and the incidence of type 2 diabetes (T2D), representing >90% of all cases of diabetes, are increasing rapidly worldwide. Identification of individuals at high risk of developing diabetes is of great importance, as early interventions might delay or even prevent full-blown disease. T2D is a complex disease caused by multiple genetic variants in interaction with lifestyle and environmental factors. Cardiovascular disease (CVD) is the major cause of morbidity and mortality. Detailed understanding of molecular mechanisms underlying in CVD events is still largely missing. Several risk factors are shared between T2D and CVD, including obesity, insulin resistance, dyslipidemia, and hyperglycemia. CVD can precede the development of T2D, and T2D is a major risk factor for CVD, suggesting that both conditions have common genetic and environmental antecedents and that they share “common soil”. We analyzed the relationship between the risk factors for T2D and CVD based on genetics and population-based studies with emphasis on Mendelian randomization studies.

Genome-Wide Association Study Identifies a Functional SIDT2 Variant Associated With HDL-C (High-Density Lipoprotein Cholesterol) Levels and Premature Coronary Artery Disease

Objective

Low HDL-C (high-density lipoprotein cholesterol) is the most frequent dyslipidemia in Mexicans, but few studies have examined the underlying genetic basis. Our purpose was to identify genetic variants associated with HDL-C levels and cardiovascular risk in the Mexican population.

Approach and Results

A genome-wide association studies for HDL-C levels in 2335 Mexicans, identified four loci associated with genome-wide significance: CETP, ABCA1, LIPC, and SIDT2. The SIDT2 missense Val636Ile variant was associated with HDL-C levels and was replicated in 3 independent cohorts (P=5.9×10−18 in the conjoint analysis). The SIDT2/Val636Ile variant is more frequent in Native American and derived populations than in other ethnic groups. This variant was also associated with increased ApoA1 and glycerophospholipid serum levels, decreased LDL-C (low-density lipoprotein cholesterol) and ApoB levels, and a lower risk of premature CAD. Because SIDT2 was previously identified as a protein involved in sterol transport, we tested whether the SIDT2/Ile636 protein affected this function using an in vitro site-directed mutagenesis approach. The SIDT2/Ile636 protein showed increased uptake of the cholesterol analog dehydroergosterol, suggesting this variant affects function. Finally, liver transcriptome data from humans and the Hybrid Mouse Diversity Panel are consistent with the involvement of SIDT2 in lipid and lipoprotein metabolism.

Conclusions

This is the first genome-wide association study for HDL-C levels seeking associations with coronary artery disease in the Mexican population. Our findings provide new insight into the genetic architecture of HDL-C and highlight SIDT2 as a new player in cholesterol and lipoprotein metabolism in humans.

Genetic determinants of increased body mass index mediate the effect of smoking on increased risk for type 2 diabetes but not coronary artery disease

Clinical observations have linked tobacco smoking with increased type 2 diabetes risk. Mendelian randomization analysis has recently suggested smoking may be a causal risk factor for type 2 diabetes. However, this association could be mediated by additional risk factors correlated with smoking behavior, which have not been investigated. We hypothesized that body mass index (BMI) could help to explain the association between smoking and diabetes risk. First, we confirmed that genetic determinants of smoking initiation increased risk for type 2 diabetes (OR 1.21, 95% CI: 1.15–1.27, P = 1 × 10⁻¹²) and coronary artery disease (CAD; OR 1.21, 95% CI: 1.16–1.26, P = 2 × 10⁻²⁰). Additionally, 2-fold increased smoking risk was positively associated with increased BMI (~0.8 kg/m², 95% CI: 0.54–0.98 kg/m², P = 1.8 × 10⁻¹¹). Multivariable Mendelian randomization analyses showed that BMI accounted for nearly all the risk smoking exerted on type 2 diabetes (OR 1.06, 95% CI: 1.01–1.11, P = 0.03). In contrast, the independent effect of smoking on increased CAD risk persisted (OR 1.12, 95% CI: 1.08–1.17, P = 3 × 10⁻⁸). Causal mediation analyses agreed with these estimates. Furthermore, analysis using individual-level data from the Million Veteran Program independently replicated the association of smoking behavior with CAD (OR 1.24, 95% CI: 1.12–1.37, P = 2 × 10⁻⁵), but not type 2 diabetes (OR 0.98, 95% CI: 0.89–1.08, P = 0.69), after controlling for BMI. Our findings support a model whereby genetic determinants of smoking increase type 2 diabetes risk indirectly through their relationship with obesity. Smokers should be advised to stop smoking to limit type 2 diabetes and CAD risk. Therapeutic efforts should consider pathophysiology relating smoking and obesity.

Evaluating marginal genetic correlation of associated loci for complex diseases and traits between European and East Asian populations

Genome-wide association studies (GWASs) have successfully identified a large amount of single-nucleotide polymorphisms associated with many complex phenotypes in diverse populations. However, a comprehensive understanding of the genetic correlation of associated loci of phenotypes across populations remains lacking and the extent to which associations discovered in one population can be generalized to other populations or can be utilized for trans-ethnic genetic prediction is also unclear. By leveraging summary statistics, we proposed MAGIC to evaluate the trans-ethnic marginal genetic correlation (r_m) of per-allele effect sizes for associated SNPs (P < 5E-8) under the framework of measurement error models. We confirmed the methodological advantage of MAGIC over general approaches through simulations and demonstrated its utility by analyzing 34 GWAS summary statistics of phenotypes from the East Asian (N_max = 254,373) and European (N_max = 1,220,901) populations. Among these phenotypes, r_m was estimated to range from 0.584 (se = 0.140) for breast cancer to 0.949 (se = 0.035) for age of menarche, with an average of 0.835 (se = 0.045). We also uncovered that the trans-ethnic genetic prediction accuracy for phenotypes in the target population would substantially become low when using associated SNPs identified in non-target populations, indicating that associations discovered in the one population cannot be simply generalized to another population and that the accuracy of trans-ethnic phenotype prediction is generally dissatisfactory. Overall, our study provides in-depth insight into trans-ethnic genetic correlation and prediction for complex phenotypes across diverse populations.

A genome-wide association study identifies 5 loci associated with frozen shoulder and implicates diabetes as a causal risk factor

Frozen shoulder is a painful condition that often requires surgery and affects up to 5% of individuals aged 40-60 years. Little is known about the causes of the condition, but diabetes is a strong risk factor. To begin to understand the biological mechanisms involved, we aimed to identify genetic variants associated with frozen shoulder and to use Mendelian randomization to test the causal role of diabetes. We performed a genome-wide association study (GWAS) of frozen shoulder in the UK Biobank using data from 10,104 cases identified from inpatient, surgical and primary care codes. We used data from FinnGen for replication and meta-analysis. We used one-sample and two-sample Mendelian randomization approaches to test for a causal association of diabetes with frozen shoulder. We identified five genome-wide significant loci. The most significant locus (lead SNP rs28971325; OR = 1.20, [95% CI: 1.16-1.24], p = 5x10-29) contained WNT7B. This variant was also associated with Dupuytren's disease (OR = 2.31 [2.24, 2.39], p<1x10-300) as were a further two of the frozen shoulder associated variants. The Mendelian randomization results provided evidence that type 1 diabetes is a causal risk factor for frozen shoulder (OR = 1.03 [1.02-1.05], p = 3x10-6). There was no evidence that obesity was causally associated with frozen shoulder, suggesting that diabetes influences risk of the condition through glycemic rather than mechanical effects. We have identified genetic loci associated with frozen shoulder. There is a large overlap with Dupuytren's disease associated loci. Diabetes is a likely causal risk factor. Our results provide evidence of biological mechanisms involved in this common painful condition.

Atrial fibrillation and left atrial size and function: a Mendelian randomization study

Atrial fibrillation (AF) patients have enlarged left atria (LA), but prior studies suggested enlarged atria as both cause and consequence of AF. The aim of this study is to investigate the causal association between AF and LA size and function. In the UK Biobank, all individuals with contoured cardiovascular magnetic resonance data were selected. LA maximal volume (LA max), LA minimal volume (LA min), LA stroke volume and LA ejection fraction were measured and indexed to body surface area (BSA). Two-sample Mendelian randomization analyses were performed using 84 of the known genetic variants associated with AF to assess the association with all LA size and function in individuals without prevalent AF. A total of 4274 individuals (mean age 62.0 ± 7.5 years, 53.2% women) were included. Mendelian randomization analyses estimated a causal effect between genetically determined AF and BSA-indexed LA max, LA min, and LA ejection fraction, but not between AF and LA stroke volume. Leave-one-out analyses showed that the causal associations were attenuated after exclusion of rs67249485, located near PITX2 gene. Our results suggest that AF causally increases LA size and decreases LA ejection fraction. The AF risk allele of rs67249485, located near the PITX2 gene, contributes strongly to these associations.

A robust two-sample transcriptome-wide Mendelian randomization method integrating GWAS with multi-tissue eQTL summary statistics

By treating genetic variants as instrumental variables (IVs), two-sample Mendelian randomization (MR) methods detect genetically regulated risk exposures for complex diseases using only summary statistics. When considering gene expression as exposure in transcriptome-wide MR (TWMR) analyses, the eQTLs (expression-quantitative-trait-loci) may have pleiotropic effects or be correlated with variants that have effects on disease not via expression, and the presence of those invalid IVs would lead to biased inference. Moreover, the number of eQTLs as IVs for a gene is generally limited, making the detection of invalid IVs challenging. We propose a method, "MR-MtRobin," for accurate TWMR inference in the presence of invalid IVs. By leveraging multi-tissue eQTL data in a mixed model, the proposed method makes identifiable the IV-specific random effects due to pleiotropy from estimation errors of eQTL summary statistics, and can provide accurate inference on the dependence (fixed effects) between eQTL and GWAS (genome-wide association study) effects in the presence of invalid IVs. Moreover, our method can improve power and precision in inference by selecting cross-tissue eQTLs as IVs that have improved consistency of effects across eQTL and GWAS data. We applied MR-MtRobin to detect genes associated with schizophrenia risk by integrating summary-level data from the Psychiatric Genomics Consortium and the Genotype-Tissue Expression project (V8).

Polygenic Mendelian Randomization

Many exposures considered in Mendelian randomization (MR) studies are polygenic in that they are influenced by thousands of genetic variants. By using many single-nucleotide polymorphisms (SNPs) as instrumental variables, more variation in the exposure is explained, increasing the precision of MR. Furthermore, methods can be designed that relax the assumptions of MR, especially concerning direct pleiotropic effects on the outcome. This article reviews the concepts and assumptions underlying the commonly used polygenic MR methods. Using a polygenic score as an instrument is equivalent to a weighted mean of individual SNP results, and the other fundamental averages, median and mode, may also be used to estimate causal effects. Outlier detection is useful for identifying pleiotropic SNPs to be excluded from analysis. Bayesian approaches are available to incorporate prior beliefs about pleiotropy. These methods each entail different assumptions, and together provide a set of sensitivity analyses to help triangulate evidence about causality.