Genetically determined hypoalbuminemia as a risk factor for hypertension: instrumental variable analysis.
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ABSTRACT:
SUBMITTER: Choi JW
PROVIDER: S-EPMC8163734 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:In this paper, we discuss causal inference on the efficacy of a treatment or medication on a time-to-event outcome with competing risks. Although the treatment group can be randomized, there can be confoundings between the compliance and the outcome. Unmeasured confoundings may exist even after adjustment for measured covariates. Instrumental variable methods are commonly used to yield consistent estimations of causal parameters in the presence of unmeasured confoundings. On the basis of a semiparametric additive hazard model for the subdistribution hazard, we propose an instrumental variable estimator to yield consistent estimation of efficacy in the presence of unmeasured confoundings for competing risk settings. We derived the asymptotic properties for the proposed estimator. The estimator is shown to be well performed under finite sample size according to simulation results. We applied our method to a real transplant data example and showed that the unmeasured confoundings lead to significant bias in the estimation of the effect (about 50% attenuated). Copyright © 2017 John Wiley & Sons, Ltd.
| S-EPMC5479873 | biostudies-literature
Project description:Background:Instrumental variable analysis, for example with physicians' prescribing preferences as an instrument for medications issued in primary care, is an increasingly popular method in the field of pharmacoepidemiology. Existing power calculators for studies using instrumental variable analysis, such as Mendelian randomization power calculators, do not allow for the structure of research questions in this field. This is because the analysis in pharmacoepidemiology will typically have stronger instruments and detect larger causal effects than in other fields. Consequently, there is a need for dedicated power calculators for pharmacoepidemiological research. Methods and Results:The formula for calculating the power of a study using instrumental variable analysis in the context of pharmacoepidemiology is derived before being validated by a simulation study. The formula is applicable for studies using a single binary instrument to analyse the causal effect of a binary exposure on a continuous outcome. An online calculator, as well as packages in both R and Stata, are provided for the implementation of the formula by others. Conclusions:The statistical power of instrumental variable analysis in pharmacoepidemiological studies to detect a clinically meaningful treatment effect is an important consideration. Research questions in this field have distinct structures that must be accounted for when calculating power. The formula presented differs from existing instrumental variable power formulae due to its parametrization, which is designed specifically for ease of use by pharmacoepidemiologists.
| S-EPMC5837396 | biostudies-literature
Project description:OBJECTIVES:To assess the incremental effects of adding extra antihypertensive drugs from a new class to a patient's regimen. DESIGN:Instrumental variable analysis of data from SPRINT (Systolic Blood Pressure Intervention Trial). To account for confounding by indication-when treatments seem less effective if they are administered to sicker patients-randomization status was used as the instrumental variable. Patients' randomization status was either intensive (systolic blood pressure target <120 mm Hg) or standard (systolic blood pressure target <140 mm Hg) treatment. Results from instrumental variable models were compared with those from standard multivariable models. SETTING:Secondary data analysis of a randomized clinical trial conducted at 102 sites in 2010-15. PARTICIPANTS:9092 SPRINT participants with hypertension and increased cardiovascular risk but no history of diabetes or stroke. MAIN OUTCOMES MEASURES:Systolic blood pressure, major cardiovascular events, and serious adverse events. RESULTS:In standard multivariable models not adjusted for confounding by indication, addition of an antihypertensive drug from a new class was associated with modestly lower systolic blood pressure (-1.3 mm Hg, 95% confidence interval -1.6 to -1.0) and no change in major cardiovascular events (absolute risk of events per 1000 patient years, 0.5, 95% confidence interval -1.5 to 2.3). In instrumental variable models, the addition of an antihypertensive drug from a new class led to clinically important reductions in systolic blood pressure (-14.4 mm Hg, -15.6 to -13.3) and fewer major cardiovascular events (absolute risk -6.2, -10.9 to -1.3). Incremental reductions in systolic blood pressure remained large and similar in magnitude for patients already taking drugs from zero, one, two, or three or more drug classes. This finding was consistent across all subgroups of patients. The addition of another antihypertensive drug class was not associated with adverse events in either standard or instrumental variable models. CONCLUSIONS:After adjustment for confounding by indication, the addition of a new antihypertensive drug class led to large reductions in systolic blood pressure and major cardiovascular events among patients at high risk for cardiovascular events but without diabetes. Effects on systolic blood pressure persisted across all levels of baseline drug use and all subgroups of patients.
| S-EPMC5736968 | biostudies-literature
Project description:Identifying the effects of maternal risk factors during pregnancy on infant and child health is an area of tremendous research interest. However, policymakers are primarily interested in unraveling the causal effects of prenatal risk factors, not their associations with child health, which may be confounded by several unobserved factors. In this article, we evaluate the utility of genetic variants in three genes that have unequivocal evidence of being related to three major risk factors-CHRNA3 for smoking, ADH1B for alcohol use, and FTO for obesity-as instrumental variables for identifying the causal effects of such factors during pregnancy. Using two independent datasets, we find that these variants are overall predictive of the risk factors and are not systematically related to observed confounders, suggesting that they may be useful instruments. We also find some suggestive evidence that genetic effects are stronger during than before pregnancy. We provide an empirical example illustrating the use of these genetic variants as instruments to evaluate the effects of risk factors on birth weight. Finally, we offer suggestions for researchers contemplating the use of these variants as instruments.
| S-EPMC3690512 | biostudies-literature
Project description:ImportanceLow academic achievement (AA) in childhood and adolescence is associated with increased substance use. Empirical evidence, using longitudinal epidemiologic data, may provide support for interventions to improve AA as a means to reduce risk of drug abuse (DA).ObjectiveTo clarify the nature of the association between adolescent AA and risk of DA by using instrumental variable and co-relative analysis designs.Design, setting, and participantsThis study, assessing nationwide data from individuals born in Sweden between 1971 and 1982, used instrumental variable and co-relative analyses of the association between AA and DA. The instrument was month of birth. Co-relative analyses were conducted in pairs of cousins (263 222 pairs), full siblings (154 295), and monozygotic twins (1623) discordant for AA, with raw results fitted to a genetic model. The AA-DA association was modeled using Cox regression. Data analysis was conducted from October 2017 to January 2018.ExposuresAcademic achievement assessed at 16 years of age (for instrumental variable analyses), and estimated discordance in AA in pairs of monozygotic twins (for co-relative analyses).Main outcomes and measuresDrug abuse registration in national medical, criminal, or pharmacy registries.ResultsThis instrumental variable analysis included 934 462 participants (478 341 males and 456 121 females) with a mean (SD) age of 34.7 (4.3) years at a mean follow-up of 19 years. Earlier month of birth was associated with a linear effect on AA, with the regression coefficient per month equaling -0.0225 SDs (95% CI, -0.0231 to -0.0219). Controlling for AA, month of birth had no association with risk of DA (hazard ratio [HR], 1.000; 95% CI, 0.997-1.004). Lower AA had a significant association with risk of subsequent DA registration (HR per SD, 2.33; 95% CI, 2.30-2.35). Instrumental variable analysis produced a substantial but modestly attenuated association (HR, 2.04; 95% CI, 1.75-2.33). Controlling for modest associations between month of birth and parental educational status and DA risk reduced the association to a HR of 1.92 (95% CI, 1.67-2.22). The genetic model applied to the results of co-relative analyses fitted the observed data well and estimated the AA-DA association in monozygotic twins discordant for AA to equal a HR of 1.79 (95% CI, 1.64-1.92).Conclusions and relevanceTwo different methodological approaches with divergent assumptions both produced results consistent with the hypothesis that the significant association observed between AA at 16 years of age and risk of DA into middle adulthood may be causal. These results provide empirical support for efforts to improve AA as a means to reduce risk of DA.
| S-EPMC6237552 | biostudies-literature
Project description:BackgroundEpidemiological studies have shown inverse association between intelligence and coronary artery disease (CAD) risk, but the underlying mechanisms remain unclear.MethodsBased on 242 SNPs independently associated with intelligence, we calculated the genetic intelligence score (gIQ) for participants from 10 CAD case-control studies (n = 34,083) and UK Biobank (n = 427,306). From UK Biobank, we extracted phenotypes including body mass index (BMI), type 2 diabetes (T2D), smoking, hypertension, HDL cholesterol, LDL cholesterol, measured intelligence score, and education attainment. To estimate the effects of gIQ on CAD and its related risk factors, regression analyses was applied. Next, we studied the mediatory roles of measured intelligence and educational attainment. Lastly, Mendelian randomization was performed to validate the findings.ResultsIn CAD case-control studies, one standard deviation (SD) increase of gIQ was related to a 5% decrease of CAD risk (odds ratio [OR] of 0.95; 95% confidence interval [CI] 0.93 to 0.98; P = 4.93e-5), which was validated in UK Biobank (OR = 0.97; 95% CI 0.96 to 0.99; P = 6.4e-4). In UK Biobank, we also found significant inverse correlations between gIQ and risk factors of CAD including smoking, BMI, T2D, hypertension, and a positive correlation with HDL cholesterol. The association signals between gIQ and CAD as well as its risk factors got largely attenuated after the adjustment of measured intelligence and educational attainment. The causal role of intelligence in mediating CAD risk was confirmed by Mendelian randomization analyses.ConclusionGenetic components of intelligence affect measured intelligence and educational attainment, which subsequently affect the prevalence of CAD via a series of unfavorable risk factor profiles.
| S-EPMC7862508 | biostudies-literature
Project description:BackgroundSocioeconomic status (SES) is considered to be associated with the prevalence of Alzheimer's disease (AD). However, the causal association remain unclear. Here, we determining whether income has a causal protective effect on the risk of developing AD using Mendelian randomization (MR).MethodsSingle-nucleotide polymorphisms (SNPs) that are strongly associated with household income levels (P<5×10-8) from the UK Biobank (UKB) (n=286,301) were selected as instrumental variables for this study. Confounding instruments were removed through data set browsing. Selected SNPs were then harmonized with results from an AD genome-wide meta-analysis (71,880 cases, 383,378 controls) including both case-control and proxy cases. The analysis was conducted using MR methods, and multiple sensitivity analyses were applied for testing of potential bias.ResultsAfter confounding instrument removal and clumping, 9 SNPs associated with household income level identified by the UKB were left for the MR analysis. Our results demonstrated that higher household income level was causally related with a lower risk of AD (odds ratio 0.78, 95% confidence interval: 0.69-0.89; P<0.001). Multiple sensitivity analyses suggested no obvious evidence for heterogeneity or pleiotropy of the results.ConclusionsUnder MR assumptions, our results suggest robust evidence of a causal association between household income and AD risk, which may provide potential prevention strategies for this devastating disease.
| S-EPMC8421944 | biostudies-literature
Project description:BACKGROUND:Instrumental variable methods can estimate the causal effect of an exposure on an outcome using observational data. Many instrumental variable methods assume that the exposure-outcome relation is linear, but in practice this assumption is often in doubt, or perhaps the shape of the relation is a target for investigation. We investigate this issue in the context of Mendelian randomization, the use of genetic variants as instrumental variables. METHODS:Using simulations, we demonstrate the performance of a simple linear instrumental variable method when the true shape of the exposure-outcome relation is not linear. We also present a novel method for estimating the effect of the exposure on the outcome within strata of the exposure distribution. This enables the estimation of localized average causal effects within quantile groups of the exposure or as a continuous function of the exposure using a sliding window approach. RESULTS:Our simulations suggest that linear instrumental variable estimates approximate a population-averaged causal effect. This is the average difference in the outcome if the exposure for every individual in the population is increased by a fixed amount. Estimates of localized average causal effects reveal the shape of the exposure-outcome relation for a variety of models. These methods are used to investigate the relations between body mass index and a range of cardiovascular risk factors. CONCLUSIONS:Nonlinear exposure-outcome relations should not be a barrier to instrumental variable analyses. When the exposure-outcome relation is not linear, either a population-averaged causal effect or the shape of the exposure-outcome relation can be estimated.
| S-EPMC4222800 | biostudies-literature
Project description:ObjectivesObservational studies suggested an inverse association between physical activity and periodontitis. However, observational studies might be subject to unobserved confounding and reverse causation bias. We conducted an instrumental variable study to strengthen the evidence on the relationship between physical activity and periodontitis.Materials and methodsWe used genetic variants associated with self-reported and accelerometer-assessed physical activity in 377,234 and 91,084 UK Biobank participants, respectively, as instruments. For these instruments, genetic associations with periodontitis were obtained from 17,353 cases and 28,210 controls in the GeneLifestyle Interactions in Dental Endpoints consortium.ResultsWe found no evidence for effects of self-reported moderate-to-vigorous physical activity, self-reported vigorous physical activity, accelerometry "average accelerations," and "fraction of accelerations > 425 milli-gravities" on periodontitis. For example, the odds ratio for self-reported moderate-to-vigorous physical activity was 1.07 (95% credible interval: 0.87; 1.34) in Causal Analysis using Summary Effect Estimates. We conducted sensitivity analyses to rule out weak instrument bias and correlated horizontal pleiotropy.ConclusionsThe study does not support an effect of physical activity on the risk of periodontitis.Clinical relevanceThis study provides little evidence that recommending physical activity would help prevent periodontitis.
| S-EPMC10415453 | biostudies-literature
Project description:A strong association between the proportion of indigenous South American Mapuche ancestry and the risk of gallbladder cancer (GBC) has been reported in observational studies. Chileans show the highest incidence of GBC worldwide, and the Mapuche are the largest indigenous people in Chile. We set out to assess the confounding-free effect of the individual proportion of Mapuche ancestry on GBC risk and to investigate the mediating effects of gallstone disease and body mass index (BMI) on this association. Genetic markers of Mapuche ancestry were selected based on the informativeness for assignment measure, and then used as instrumental variables in two-sample Mendelian randomization analyses and complementary sensitivity analyses. Results suggested a putatively causal effect of Mapuche ancestry on GBC risk (inverse variance-weighted (IVW) risk increase of 0.8% per 1% increase in Mapuche ancestry proportion, 95% CI 0.4% to 1.2%, p = 6.7 × 10-5) and also on gallstone disease (3.6% IVW risk increase, 95% CI 3.1% to 4.0%), pointing to a mediating effect of gallstones on the association between Mapuche ancestry and GBC. In contrast, the proportion of Mapuche ancestry showed a negative effect on BMI (IVW estimate -0.006 kg/m2, 95% CI -0.009 to -0.003). The results presented here may have significant implications for GBC prevention and are important for future admixture mapping studies. Given that the association between the individual proportion of Mapuche ancestry and GBC risk previously noted in observational studies appears to be free of confounding, primary and secondary prevention strategies that consider genetic ancestry could be particularly efficient.
| S-EPMC10452561 | biostudies-literature