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Background: Serum lipids were found to be correlated with chronic kidney disease and cardiovascular disease. Here, we aimed to research the potential causal associations between five serum lipid parameters and the risk of diabetic nephropathy using several Mendelian Randomization methods. Methods: Genetic data was obtained from the UK Biobank datasets. Causal effects were estimated using multiple MR methods. Heterogeneity and pleiotropy tests were performed. Results: MR analysis revealed that HDL-C and TG exhibited causal associations with diabetic nephropathy (P<0.05). Similar trends were not observed for other lipid parameters. Conclusions: Our research has suggested links between HDL-C, TG and diabetic nephropathy. The findings could contribute to further elucidation of the disease etiology. Strengths and limitations of this study: This article only uses Mendel randomization method to analyze the relationship between blood lipids and diabetes nephropathy, which is more convincing when combined with population data. [ABSTRACT FROM AUTHOR]

The instrumental variable method is widely used in the health and social sciences for identification and estimation of causal effects in the presence of potential unmeasured confounding. To improve efficiency, multiple instruments are routinely used, raising concerns about bias due to possible violation of the instrumental variable assumptions. To address such concerns, we introduce a new class of G-estimators that are guaranteed to remain consistent and asymptotically normal for the causal effect of interest provided that a set of at least |$\gamma$| out of |$K$| candidate instruments are valid, for |$\gamma \leqslant K$| set by the analyst ex ante without necessarily knowing the identities of the valid and invalid instruments. We provide formal semiparametric efficiency theory supporting our results. Simulation studies and applications to UK Biobank data demonstrate the superior empirical performance of the proposed estimators compared with competing methods. [ABSTRACT FROM AUTHOR]

Background Epidemiological studies demonstrated that adverse in utero environment was associated with increased risk of offspring high blood pressure, by using birthweight as the proxy of maternal intrauterine exposure; however, the nature of such association remains less understood. Methods With maternal/fetal-specific summary statistics of birthweight (n  =   297 356 for own birthweight and n  =   210 248 for offspring birthweight) and summary statistics of blood pressure [i.e. systolic blood pressure (SBP), diastolic blood pressure (DBP) and pulse pressure (PP)] (n  =   757 601), we evaluated the genetic correlation between fetal-specific birthweight and blood pressure using cross-trait linkage disequilibrium score regression, and next detected pleiotropic genes for them with a pleiotropy mapping method called mixture-adjusted intersect-union pleiotropy test. Furthermore, we conducted a genetic risk score (GRS)-based Mendelian randomization analysis in parent-offspring pairs (n  =   6031) of the UK Biobank cohort, to assess the causal relation between maternal-specific GRS and blood pressure conditioning on fetal genotypes. Results We found fetal-specific birthweight had a negative genetic correlation with DBP (⁠ ρ ^ g  = −0.174, P  =   1.68 × 10–10), SBP (⁠ ρ ^ g  = −0.198, P  =   8.09 × 10–12), and PP (⁠ ρ ^ g  = −0.152, P  =   6.04 × 10–8), and detected 143, 137 and 135 pleiotropic genes shared between fetal-specific birthweight and PP, SBP and DBP, respectively. These genes often exhibited opposite genetic effects, and were more likely to be differentially expressed in pancreas, liver, heart, brain, whole blood and muscle skeletal tissues. A causal negative association of maternal-specific birthweight was identified with SBP (P  =   2.20 × 10–2) and PP (P  =   7.67 × 10–3) but not DBP (P  =   0.396) in mother-offspring pairs, after accounting for the influence of fetal-specific GRS; and the two significant relations were robust against the horizontal pleiotropy of instruments and the confounding influence of gestational duration and preterm birth. However, these causal associations could not be detected in father-offspring pairs. Conclusions This study revealed common genetic components underlying birthweight and blood pressure, and provided important insight into aetiology and early prevention of high blood pressure. [ABSTRACT FROM AUTHOR]

Alzheimer's disease (AD) is a severe public health issue in the world. Magnetic Resonance Imaging (MRI) offers a way to study brain differences between AD patients and healthy individuals through feature extraction and comparison. However, in most previous works, the extracted features were not aimed to be causal, hindering biological understanding and interpretation. In order to extract causal features, we propose using instrumental variable (IV) regression with genetic variants as IVs. Specifically, we propose Deep Feature Extraction via Instrumental Variable Regression (DeepFEIVR), which uses a nonlinear neural network to extract causal features from three‐dimensional neuroimages to predict an outcome (eg, AD status in our application) while maintaining a linear relationship between the extracted features and IVs. DeepFEIVR not only can handle high dimensional individual‐level data for model building, but also is applicable to GWAS summary data to test associations of the extracted features with the outcome in subsequent analysis. In addition, we propose an extension of DeepFEIVR, called DeepFEIVR‐CA, for covariate adjustment (CA). We apply DeepFEIVR and DeepFEIVR‐CA to the Alzheimer's Disease Neuroimaging Initiative (ADNI) individual‐level data as training data for model building, then apply to the UK Biobank neuroimaging and the International Genomics of Alzheimer's Project (IGAP) AD GWAS summary data, showcasing how the extracted causal features are related to AD and various brain endophenotypes. [ABSTRACT FROM AUTHOR]

Introduction: Previous research has reported that the gut microbiota performs an essential role in sleep through the microbiome–gut–brain axis. However, the causal association between gut microbiota and sleep remains undetermined. Methods: We performed a two-sample, bidirectional Mendelian randomization (MR) analysis using genome-wide association study summary data of gut microbiota and self-reported sleep traits from the MiBioGen consortium and UK Biobank to investigate causal relationships between 119 bacterial genera and seven sleep-associated traits. We calculated effect estimates by using the inverse-variance weighted (as the main method), maximum likelihood, simple model, weighted model, weighted median, and MR-Egger methods, whereas heterogeneity and pleiotropy were detected and measured by the MR pleiotropy residual sum and outlier method, Cochran’s Q statistics, and MR-Egger regression. Results: In forward MR analysis, inverse-variance weighted estimates concluded that the genetic forecasts of relative abundance of 42 bacterial genera had causal effects on sleep-associated traits. In the reverse MR analysis, sleep-associated traits had a causal effect on 39 bacterial genera, 13 of which overlapped with the bacterial genera in the forward MR analysis. Discussion: In conclusion, our research indicates that gut microbiota may be involved in the regulation of sleep, and conversely, changes in sleep-associated traits may also alter the abundance of gut microbiota. These findings suggest an underlying reciprocal causal association between gut microbiota and sleep. [ABSTRACT FROM AUTHOR]

Background An increasing proportion of people have a body mass index (BMI) classified as overweight or obese and published studies disagree whether this will be beneficial or detrimental to health. We applied and evaluated two intergenerational instrumental variable methods to estimate the average causal effect of BMI on mortality in a cohort with many deaths: the parents of UK Biobank participants. Methods In Cox regression models, parental BMI was instrumented by offspring BMI using an 'offspring as instrument' (OAI) estimation and by offspring BMI-related genetic variants in a 'proxy-genotype Mendelian randomization' (PGMR) estimation. Results Complete-case analyses were performed in parents of 233 361 UK Biobank participants with full phenotypic, genotypic and covariate data. The PGMR method suggested that higher BMI increased mortality with hazard ratios per kg/m2 of 1.02 (95% CI: 1.01, 1.04) for mothers and 1.04 (95% CI: 1.02, 1.05) for fathers. The OAI method gave considerably higher estimates, which varied according to the parent–offspring pairing between 1.08 (95% CI: 1.06, 1.10; mother–son) and 1.23 (95% CI: 1.16, 1.29; father–daughter). Conclusion Both methods supported a causal role of higher BMI increasing mortality, although caution is required regarding the immediate causal interpretation of these exact values. Evidence of instrument invalidity from measured covariates was limited for the OAI method and minimal for the PGMR method. The methods are complementary for interrogating the average putative causal effects because the biases are expected to differ between them. [ABSTRACT FROM AUTHOR]

Background: Critical care beds are a limited resource, yet research indicates that recommendations for postoperative critical care admission based on patient-level risk stratification are not followed. It is unclear how prioritisation decisions are made in real-world settings and the effect of this prioritisation on outcomes., Methods: This was a prespecified analysis of an observational cohort study of adult patients undergoing inpatient surgery, conducted in 274 hospitals across the UK and Australasia during 2017. The primary outcome was postoperative morbidity at day 7. Logistic regression models were used to evaluate the relationship between critical care admission and patient and health system factors. The causal effect of critical care admission on outcome was estimated using variation in critical care occupancy as a natural experiment in an instrumental variable analysis., Results: A total of 19,491 patients from 248 hospitals were eligible for analysis, of whom 2107 were directly admitted to critical care postoperatively. Postoperative morbidity occurred in 2829/19,491 (15%) patients. Increasing surgical risk was associated with critical care admission, as was increased availability of critical care beds (odds ratio (95%CI) 1.04 (1.01-1.06), p = 0.002) per available bed; however, the probability of admission varied significantly between hospitals (median odds ratio 3.05). There was no evidence of a difference in postoperative morbidity with critical care admission (odds ratio (95%CI) 0.91 (0.57-1.45), p = 0.710)., Discussion: Postoperative critical care admission is variable and related to bed availability. Statistical methods that adjust for unobserved confounding lowered the estimates of harm previously reported to have been associated with postoperative critical care admission. Our findings provide a rationale for a clinical trial which would evaluate any potential benefits for postoperative critical care admission for patients in whom there is no absolute indication for admission., (© 2024 The Author(s). Anaesthesia published by John Wiley & Sons Ltd on behalf of Association of Anaesthetists.)

Background: In epidemiological studies, it has been proven that the occurrence of type 2 diabetes mellitus (T2DM) is related to an increased risk of infectious diseases. However, it is still unclear whether the relationship is casual. Methods: We employed a two-sample Mendelian randomization (MR) to clarify the causal effect of T2DM on high-frequency infectious diseases: sepsis, skin and soft tissue infections (SSTIs), urinary tract infections (UTIs), pneumonia, and genito-urinary infection (GUI) in pregnancy. And then, we analyzed the genome-wide association study (GWAS) meta-analysis of European-descent individuals and conducted T2DM-related single-nucleotide polymorphisms (SNPs) as instrumental variables (IVs) that were associated with genome-wide significance (p < 5 × 10–8). MR estimates were obtained using the inverse variance-weighted (IVW), the MR-Egger regression, the simple mode (SM), weighted median, and weighted mode. Results: The UK Biobank (UKB) cohort (n > 500,000) provided data for GWASs on infectious diseases. MR analysis showed little evidence of a causal relationship of T2DM with five mentioned infections' (sepsis, SSTI, UTI, pneumonia, and GUI in pregnancy) susceptibility [odds ratio (OR) = 0.99999, p = 0.916; OR = 0.99986, p = 0.233; OR = 0.99973, p = 0.224; OR = 0.99997, p = 0.686; OR, 1.00002, p = 0.766]. Sensitivity analysis showed similar results, indicating the robustness of causality. There were no heterogeneity and pleiotropic bias. Conclusion: T2DM would not be causally associated with high-frequency infectious diseases (including sepsis, SSTI, UTI, pneumonia, and GUI in pregnancy). [ABSTRACT FROM AUTHOR]

Background: Mendelian Randomization (MR) studies show conflicting causal associations of genetically predicted serum urate with cardiovascular risk factors (i.e., hypertension, diabetes, lipid profile, and kidney function). This study aimed to robustly investigate a causal relationship between urate and cardiovascular risk factors considering single nucleotide polymorphisms (SNPs) as instrumental variables using two-sample MR and various sensitivity analyses. Methods: Data on SNP-urate associations were taken from the Global Urate Genetics Consortium and data on SNP-cardiovascular risk factor associations were taken from various consortia/UK Biobank. SNPs were selected by statistically and biologically driven approaches as instrumental variables. Various sensitivity analyses were performed using different MR methods including inverse variance weighted, MR-Egger, weighted median/mode, MR-PRESSO, and the contamination mixture method. Results: The statistically driven approach showed significant causal effects of urate on HDL-C and triglycerides using four of the six MR methods, i.e., every 1 mg/dl increase in genetically predicted urate was associated with 0.047 to 0.103 SD decrease in HDL-C and 0.034 to 0.207 SD increase in triglycerides. The biologically driven approach to selection of SNPs from ABCG2, SLC2A9, SLC17A1, SLC22A11 , and SLC22A12 showed consistent causal effects of urate on HDL-C from all methods with 0.038 to 0.057 SD decrease in HDL-C per 1 mg/dl increase of urate, and no evidence of horizontal pleiotropy was detected. Conclusion: Our study suggests a significant and robust causal effect of genetically predicted urate on HDL-C. This finding may explain a small proportion (7%) of the association between increased urate and cardiovascular disease but points to urate being a novel cardiac risk factor. [ABSTRACT FROM AUTHOR]

In order to design efficient interventions aimed to improve public health, policy makers need to be provided with reliable information of the health burden of different risk factors. For this purpose, we are interested in the proportion of cases that could be prevented had some harmful exposure been eliminated from the population, i.e. the attributable fraction. The attributable fraction is a causal measure; thus, to estimate the attributable fraction from observational data, we have to make appropriate adjustment for confounding. However, some confounders may be unobserved, or even unknown to the investigator. A possible solution to this problem is to use instrumental variable analysis. In this work, we present how the attributable fraction can be estimated with instrumental variable methods based on the two-stage estimator or the G-estimator. One situation when the problem of unmeasuredconfounding may be particularly severe is when assessing the effect of low educational qualifications on coronary heart disease. By using Mendelian randomization, a special case of instrumental variable analysis, it has been claimed that low educational qualifications is a causal risk factor for coronary heart disease. We use Mendelian randomization to estimate the causal risk ratio and causal odds ratio of low educational qualifications as a risk factor for coronary heart disease with data from the UK Biobank. We compare the two-stage and G-estimator as well as the attributable fraction based on the two estimators. The plausibility of drawing causal conclusion in this analysis is thoroughly discussed and alternative genetic instrumental variables are tested. [ABSTRACT FROM AUTHOR]

Background: Previous observational studies have suggested that coffee intake may be associated with a reduction in cancer risk. Mendelian randomization (MR) studies can help clarify whether the observed associations are likely to be causal. Here we evaluated whether coffee intake is associated with: (i) overall risk of being diagnosed with/dying from any cancer; and (ii) risk of individual cancers.Methods: We identified 46 155 cases (of which 6998 were fatal) and 270 342 controls of White British ancestry from the UK Biobank cohort (UKB), based on ICD10 diagnoses. Individuals with benign tumours were excluded. Coffee intake was self-reported and recorded based on cup/day consumption. We conducted both observational and summary data MR analyses.Results: There was no observational association between coffee intake and overall cancer risk [odds ratio (OR) per one cup/day increase = 0.99, 95% confidence interval (CI) 0.98, 1.00] or cancer death (OR = 1.01, 0.99, 1.03); the estimated OR from MR is 1.01 (0.94, 1.08) for overall cancer risk and 1.11 (0.95, 1.31) for cancer death. The relationship between coffee intake and individual cancer risks were consistent with a null effect, with most cancers showing little or no associations with coffee. Meta-analysis of our MR findings with publicly available summary data on various cancers do not support a strong causal relationship between coffee and risk of breast, ovarian, lung or prostate cancer, upon correction for multiple testing.Conclusions: Taken together, coffee intake is not associated with overall risk of being diagnosed with or dying from cancer in UKB. For individual cancers, our findings were not statistically inconsistent with earlier observational studies, although for these we were unable to rule out a small effect on specific types of cancer. [ABSTRACT FROM AUTHOR]

Associations between education and oral health have frequently been reported, but until now there has been no causal evidence. Exploiting exogenous variation in the duration of schooling due to 1947 and 1972 reforms in mandatory schooling in the United Kingdom, we examined the causal relationship between education and tooth loss in older age. We conducted a cross-sectional study using data from waves 3 (2006–2007), 5 (2010–2011), and 7 (2014–2015) of the English Longitudinal Study of Ageing. We used a 2-stage least squares instrumental variable approach and included 5,667 respondents (average age = 67.8 years; 44.4% were men) in the analyses, of whom 819 (14.5%) had no teeth. The schooling reforms increased the duration of education by an average of 0.624 years (95% confidence interval: 0.412, 0.835). For respondents born within ±6 years of the pivotal cohorts, a 1-year increment of education causally reduced the probability of edentulism by 9.1 (95% confidence interval: 1.5, 16.8) percentage points. The effects were stronger for the 1947 reform than for the 1972 reform. Results were robust to broadening of the cohort bandwidth and functional form of the cohort trend. The findings suggest that investment in education produces improved oral health later in life. [ABSTRACT FROM AUTHOR]

Background: Maternal vitamin D status in pregnancy has been postulated to have important effects on intrauterine development. UVB radiation is not commonly measured but is the prime determinant of circulating 25-hydroxyvitamin-D [25-(OH)D] and is highly dependent on regional weather including cloud cover, ozone and sunshine hours.Methods: Using linear regression we described the relationship between estimated ambient-erythemal ultraviolet (eUV) exposure in Oxford (1990-95) and total hours of sunshine and month in order to forecast eUV in nearby regions, whilst adjusting for regional variations in weather. The forecast was validated with empirical data collected from Cornwall and then predicted for the Avon region. Total 98-day prenatal ambient-eUV was then predicted in 355 expectant mothers in the Avon Longitudinal Study of Parents and Children (ALSPAC) cohort and its relationship with maternal vitamin D status was determined.Results: Estimated ambient-eUV was strongly associated with measured ambient-eUV (r(2) = 0.989) with a near 1:1 prediction for the validation data set [beta = 0.99, 95% confidence interval (CI) 0.913, 1.067 r(2) = 0.980]; strong seasonal associations were observed between eUV in the last trimester of pregnancy and maternal serum 25-(OH)D concentrations (r(2) = 0.40).Conclusion: This technique of prediction could be applied to existing cohorts allowing the relationship between maternal vitamin D status and the health of the offspring to be studied via instrumental variable analysis. [ABSTRACT FROM AUTHOR]

• Causal endophenotype inference with correlated SNPs boosts power over MR. • Multivariable instrumental variable regression accounts for horizontal pleiotropy. • Novel IWAS analogue to MR-Egger adjusts for remaining directional pleiotropy. • Integration of GWAS summary data for multiple heritable phenotypes. • Implicating genetically regulated MRI-derived brain measures in Alzheimer's Disease. Recent evidence suggests the existence of many undiscovered heritable brain phenotypes involved in Alzheimer's Disease (AD) pathogenesis. This finding necessitates methods for the discovery of causal brain changes in AD that integrate Magnetic Resonance Imaging measures and genotypic data. However, existing approaches for causal inference in this setting, such as the univariate Imaging Wide Association Study (UV-IWAS), suffer from inconsistent effect estimation and inflated Type I errors in the presence of genetic pleiotropy, the phenomenon in which a variant affects multiple causal intermediate risk phenotypes. In this study, we implement a multivariate extension to the IWAS model, namely MV-IWAS, to consistently estimate and test for the causal effects of multiple brain imaging endophenotypes from the Alzheimer's Disease Neuroimaging Initiative (ADNI) in the presence of pleiotropic and possibly correlated SNPs. We further extend MV-IWAS to incorporate variant-specific direct effects on AD, analogous to the existing Egger regression Mendelian Randomization approach, which allows for testing of remaining pleiotropy after adjusting for multiple intermediate pathways. We propose a convenient approach for implementing MV-IWAS that solely relies on publicly available GWAS summary data and a reference panel. Through simulations with either individual-level or summary data, we demonstrate the well controlled Type I errors and superior power of MV-IWAS over UV-IWAS in the presence of pleiotropic SNPs. We apply the summary statistic based tests to 1578 heritable imaging derived phenotypes (IDPs) from the UK Biobank. MV-IWAS detected numerous IDPs as possible false positives by UV-IWAS while uncovering many additional causal neuroimaging phenotypes in AD which are strongly supported by the existing literature. [ABSTRACT FROM AUTHOR]

Mendelian randomization is the use of genetic variants to assess the effect of intervening on a risk factor using observational data. We consider the scenario in which there is a pharmacomimetic (i.e., treatment-mimicking) genetic variant that can be used as a proxy for a particular pharmacological treatment that changes the level of the risk factor. If the association of the pharmacomimetic genetic variant with the risk factor is stronger in one subgroup of the population, then we may expect the effect of the treatment to be stronger in that subgroup. We test for gene-gene interactions in the associations of variants with a modifiable risk factor, where one genetic variant is treated as pharmacomimetic and the other as an effect modifier, to find genetic subgroups of the population with different predicted response to treatment. If individual genetic variants that are strong effect modifiers cannot be found, moderating variants can be combined using a random forest of interaction trees method into a polygenic response score, analogous to a polygenic risk score for risk prediction. We illustrate the application of the method to investigate effect heterogeneity in the effect of statins on low-density lipoprotein cholesterol., (© 2020 The Authors. Genetic Epidemiology published by Wiley Periodicals LLC.)