Genetic markers as instrumental variables

Child health and its effect on adult social capital accumulation

Although studies have demonstrated important effects of poor health in childhood on stocks of human and health capital, little research has tested economic theories to investigate the effect of child health on social capital in adulthood. Studies on the influence of child health on adult social capital are mixed and have not used sibling fixed effects models to account for unmeasured family and genetic characteristics, that are likely to be important. Using the Add-Health sample, health in childhood was assessed as self-rated health, the occurrence of a physical health condition or mental health condition, while social capital in adulthood was measured as volunteering, religious service attendance, team sports participation, number of friends, social isolation, and social support. We used sibling fixed effects models, which attenuated several associations to non-significance. In sibling fixed effects models there was significant positive effects of greater self-rated health on participation in team sports and social support, and negative effect of mental health in childhood on social isolation in adulthood. These results suggest that children with poor health require additional supports to build and maintain their stock of social capital and highlight further potential benefits to efforts that address poor child health.

Causal effect of obesity on the probability of employment in women in Turkey

This study aims to investigate the causal effect of obesity on the employment probability of women in Turkey via the instrumental variable approach by using data from the 2018 Turkey and Demographic Health Survey. Obesity prevalence in the area of living and the overweight status of the oldest child, which are the most common types of instruments seen in the literature, are used as instruments. Consistent with the OLS result, the 2SLS estimate indicates that obese women are statistically significantly less likely to be employed when the overweight status of the child is used as an instrument. On the other hand, the statistically significant effect of obesity disappears when the area level obesity prevalence is used as the instrument. In addition, obesity in women decreases the likelihood of employment in the services sector and full-time jobs and employment with social security. Although the results of this study do not indicate the source of the effect found, the presence of a causal relationship for women should not be ignored when the economic burden of obesity for Turkey is considered.

The relationship between physical activity, cognitive function and health care use: A mediation analysis

Physical activity is known to provide substantial health benefits and subsequently reduce health care use among older people, but little is known about how much of this effect is due to improved cognitive function as opposed to physical improvements in health. We study the direct and indirect effect of physical activity on health care use using the word recall task as a measure of cognitive function in a mediation framework. We use data from eight waves of the US Health and Retirement Study (HRS) (2004 - 2018) of people aged 65 and older and exploit genetic variations between individuals as an instrumental variable (IV) for cognitive function, a local health care supply measure as IV for health care use, and neighbourhood physical activity as IV for individual physical activity in our simultaneous three-equation model. We find small but negative direct and indirect effects of physical activity through improved cognitive function on the probability to see a GP and being admitted to a hospital, as well as the number of GP visits and the hospital length of stay. Improved cognitive function explains between 5% to 17% of the total effect of physical activity on the reduction in health care use.

Boys with overweight status lagged behind girls with overweight status in reading: evidence from mendelian randomization

OBJECTIVES

We investigated the relationship between childhood weight status and academic achievement across sexes and different school subjects in Norway.

STUDY DESIGN AND SETTING

We used data from the Norwegian Mother, Father and Child Cohort Study (MoBa), which includes genetic data (N = 13,648, 8-year-old children). We employed within-family mendelian randomization, using a body mass index (BMI) polygenic risk score as an instrument to address unobserved heterogeneity.

RESULTS

Contrary to most previous findings, we observed that overweight status (including obesity) has more detrimental effects on reading achievement in boys than in girls; the test scores of boys with overweight were about a standard deviation lower than those of normal weight boys, and the negative effects on reading achievement became stronger in the later grade.

CONCLUSION

Previous obesity prevention studies have mainly targeted girls, based on the assumption that the obesity penalty is greater for girls. Our findings highlight that particular attention to boys with overweight may help reduce the existing gender gap in academic achievement.

Average Causal Effect Estimation Via Instrumental Variables: the No Simultaneous Heterogeneity Assumption

BACKGROUND

Instrumental variables (IVs) can be used to provide evidence as to whether a treatment has a causal effect on an outcome . Even if the instrument satisfies the three core IV assumptions of relevance, independence, and exclusion restriction, further assumptions are required to identify the average causal effect (ACE) of on . Sufficient assumptions for this include homogeneity in the causal effect of on ; homogeneity in the association of with ; and no effect modification.

METHODS

We describe the no simultaneous heterogeneity assumption, which requires the heterogeneity in the - causal effect to be mean independent of (i.e., uncorrelated with) both and heterogeneity in the - association. This happens, for example, if there are no common modifiers of the - effect and the - association, and the - effect is additive linear. We illustrate the assumption of no simultaneous heterogeneity using simulations and by re-examining selected published studies.

RESULTS

Under no simultaneous heterogeneity, the Wald estimand equals the ACE even if both homogeneity assumptions and no effect modification (which we demonstrate to be special cases of-and therefore stronger than-no simultaneous heterogeneity) are violated.

CONCLUSIONS

The assumption of no simultaneous heterogeneity is sufficient for identifying the ACE using IVs. Since this assumption is weaker than existing assumptions for ACE identification, doing so may be more plausible than previously anticipated.

Is Asian flushing syndrome a disadvantage in the labor market?

A large fraction of people in East Asia are incapable of digesting alcohol because of a genetic deficiency. This study examines whether the variation in alcohol tolerance contributes to inequality in the labor market. We conduct our original surveys in Japan, Taiwan, and Korea with the measurement of respondents' degree of alcohol tolerance by a bio-marker test. We find that alcohol-tolerant men consume significantly more alcohol, but their earnings and hours worked do not differ from those of alcohol-intolerant men. Despite a prevalent view that drinking alcohol is indispensable to establish good relationships with colleagues and business partners, our results suggest that there is no systematic impact of alcohol tolerance on labor market outcomes.

Genetic nurture in educational attainment

Health is strongly and positively correlated with education, which is one of many reasons to better understand the determinants of education. In this paper, we test for a specific type of family influence on education: genetic nurture. Specifically, we test whether a person's educational attainment is correlated with their sibling's polygenic score (PGS) for education, controlling for their own PGS. Models estimated using data from the National Longitudinal Survey of Adolescent to Adult Health (Add Health) yield strong evidence of genetic nurture; a two-standard deviation increase in a sibling's genetic predisposition to higher education is associated with a 13.6% point increase in the probability that the respondent has a college degree. Evidence of genetic nurture is robust to alternative measures of educational attainment and different measures of the polygenic score. An exploration of mechanisms suggests that omission of parental PGS explains no more than half of the estimated effect, and that the magnitude of the genetic nurture varies with the characteristics of the sibling.

An integrated multi-omics analysis of sleep-disordered breathing traits implicates P2XR4 purinergic signaling

Sleep Disordered Breathing (SDB) is a common disease associated with increased risk for cardiometabolic, cardiovascular, and cognitive diseases. How SDB affects the molecular environment is still poorly understood. We study the association of three SDB measures with gene expression measured using RNA-seq in multiple blood tissues from the Multi-Ethnic Study of Atherosclerosis. We develop genetic instrumental variables for the associated transcripts as polygenic risk scores (tPRS), then generalize and validate the tPRS in the Women's Health Initiative. We measure the associations of the validated tPRS with SDB and serum metabolites in Hispanic Community Health Study/Study of Latinos. Here we find differential gene expression by blood cell type in relation to SDB traits and link P2XR4 expression to average oxyhemoglobin saturation during sleep and butyrylcarnitine (C4) levels. These findings can be used to develop interventions to alleviate the effect of SDB on the human molecular environment.

Maternal genetic risk for depression and child human capital

We here address the causal relationship between the maternal genetic risk for depression and child human capital using UK birth-cohort data. We find that an increase of one standard deviation (SD) in the maternal polygenic risk score for depression reduces their children's cognitive and non-cognitive skill scores by 5 to 7% of a SD throughout adolescence. Our results are robust to a battery of sensitivity tests addressing, among others, concerns about pleiotropy and dynastic effects. Our Gelbach decomposition analysis suggests that the strongest mediator is genetic nurture (through maternal depression itself), with genetic inheritance playing only a marginal role.

Mendelian randomization as a tool to gain insights into the mosaic causes of autoimmune diseases

Autoimmune diseases (ADs) are a broad range of disorders which are characterized by long-term inflammation and tissue damage arising from an immune response against one's own tissues. It is now widely accepted that the causes of ADs include environmental factors, genetic susceptibility and immune dysregulation. However, the exact etiology of ADs has not been fully elucidated to date. Because observational studies are plagued by confounding factors and reverse causality, no firm conclusions can be drawn about the etiology of ADs. Over the years, Mendelian randomization (MR) analysis has come into focus, offering unique perspectives and insights into the etiology of ADs and promising the discovery of potential therapeutic interventions. In MR analysis, genetic variation (alleles are randomly dispensed during meiosis, usually irrespective of environmental or lifestyle factors) is used instead of modifiable exposure to explore the link between exposure factors and disease or other outcomes. Therefore, MR analysis can provide a valuable method for exploring the causal relationship between different risk factors and ADs when its inherent assumptions and limitations are fully considered. This review summarized the recent findings of MR in major ADs, including systemic lupus erythematosus (SLE), rheumatoid arthritis (RA), multiple sclerosis (MS), and type 1 diabetes mellitus (T1DM), focused on the effects of different risk factors on ADs risks. In addition, we also discussed the opportunities and challenges of MR methods in ADs research.

Simultaneous Maximum Likelihood Estimation for Piecewise Linear Instrumental Variable Models

Analysis of instrumental variables is an effective approach to dealing with endogenous variables and unmeasured confounding issue in causal inference. We propose using the piecewise linear model to fit the relationship between the continuous instrumental variable and the continuous explanatory variable, as well as the relationship between the continuous explanatory variable and the outcome variable, which generalizes the traditional linear instrumental variable models. The two-stage least square and limited information maximum likelihood methods are used for the simultaneous estimation of the regression coefficients and the threshold parameters. Furthermore, we study the limiting distribution of the estimators in the correctly specified and misspecified models and provide a robust estimation of the variance-covariance matrix. We illustrate the finite sample properties of the estimation in terms of the Monte Carlo biases, standard errors, and coverage probabilities via the simulated data. Our proposed model is applied to an education-salary data, which investigates the causal effect of children's years of schooling on estimated hourly wage with father's years of schooling as the instrumental variable.

Alcohol Consumption and Mild Cognitive Impairment: A Mendelian Randomization Study from Rural China

Alcohol consumption has been associated with the risk of mild cognitive impairment (MCI) in observational studies. The result is inconsistent and whether the association is causal remains unknown. To examine the causal effect of alcohol consumption on MCI in rural China, this study used a cross-sectional dataset that included 1966 observations collected in rural China, of which 235 observations’ genotyping were collected. All participants accepted the MCI evaluation using Mini-Cog and were asked about the participants’ alcohol consumption behavior. The causal effect of alcohol consumption on MCI was investigated by Mendelian randomization (MR) of genetic variation in the aldehyde dehydrogenase 2 (ALDH2 rs671) gene. The risk of MCI in Chinese rural areas was 43%. Alcohol consumption was causally associated with a higher risk of MCI under MR design. Parameter estimates of drinking or not (b = 0.271, p = 0.007, 95% CI = 0.073 to 0.469), drinking frequency during the past 30 days (b = 0.016, p = 0.003, 95% CI = 0.005 to 0.027), and the weekly ethanol consumption (b = 0.132, p = 0.004, 95% CI = 0.042 to 0.223) were all positive and statistically significant at the 5% level. In conclusion, there was a high risk of MCI in rural China, and alcohol consumption was causally associated with a higher risk of MCI.

eQTLs as causal instruments for the reconstruction of hormone linked gene networks

Hormones act within in highly dynamic systems and much of the phenotypic response to variation in hormone levels is mediated by changes in gene expression. The increase in the number and power of large genetic association studies has led to the identification of hormone linked genetic variants. However, the biological mechanisms underpinning the majority of these loci are poorly understood. The advent of affordable, high throughput next generation sequencing and readily available transcriptomic databases has shown that many of these genetic variants also associate with variation in gene expression levels as expression Quantitative Trait Loci (eQTLs). In addition to further dissecting complex genetic variation, eQTLs have been applied as tools for causal inference. Many hormone networks are driven by transcription factors, and many of these genes can be linked to eQTLs. In this mini-review, we demonstrate how causal inference and gene networks can be used to describe the impact of hormone linked genetic variation upon the transcriptome within an endocrinology context.

Does better education mitigate risky health behavior? A mendelian randomization study

Education and risky health behaviors are strongly negatively correlated. Education may affect health behaviors by enabling healthier choices through higher disposable income, increasing information about the harmful effects of risky health behaviors, or altering time preferences. Alternatively, the observed negative correlation may stem from reverse causality or unobserved confounders. Based on the data from the Cardiovascular Risk in Young Finns Study linked to register-based information on educational attainment and family background, this paper identifies the causal effect of education on risky health behaviors. To examine causal effects, we used a genetic score as an instrument for years of education. We found that individuals with higher education allocated more attention to healthy habits. In terms of health behaviors, highly educated people were less likely to smoke. Some model specifications also indicated that the highly educated consumed more fruit and vegetables, but the results were imprecise in this regard. No causal effect was found between education and abusive drinking. In brief, inference based on genetic instruments showed that higher education leads to better choices in some but not all dimensions of health behaviors.

Understanding the assumptions underlying Mendelian randomization

With the rapidly increasing availability of large genetic data sets in recent years, Mendelian Randomization (MR) has quickly gained popularity as a novel secondary analysis method. Leveraging genetic variants as instrumental variables, MR can be used to estimate the causal effects of one phenotype on another even when experimental research is not feasible, and therefore has the potential to be highly informative. It is dependent on strong assumptions however, often producing biased results if these are not met. It is therefore imperative that these assumptions are well-understood by researchers aiming to use MR, in order to evaluate their validity in the context of their analyses and data. The aim of this perspective is therefore to further elucidate these assumptions and the role they play in MR, as well as how different kinds of data can be used to further support them.

Health Economic Aspects of Childhood Excess Weight: A Structured Review

An economic perspective is crucial to understand the broad consequences of childhood excess weight (CEW). These can manifest in the form of elevated health care and societal costs, impaired health status, or inefficiencies in the allocation of resources targeted at its prevention, management, or treatment. Although existing systematic reviews provide summaries of distinct economic research strands covering CEW, they have a restricted focus that overlooks relevant evidence. The overarching aim of this structured review was to update and enhance recent key reviews of four strands of economic evidence in this area, namely, (1) economic costs associated with CEW, (2) health utilities associated with CEW, (3) economic evaluations of interventions targeting CEW, and (4) economic determinants and broader consequences of CEW. Our de novo searches identified six additional studies for the first research strand, five studies for the second, thirty-one for the third, and two for the fourth. Most studies were conducted in a small number of high-income countries. Our review highlights knowledge gaps across all the research strands. Evidence from this structured review can act as data input into future economic evaluations in this area and highlights areas where future economic research should be targeted.

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.

Interaction-based Mendelian randomization with measured and unmeasured gene-by-covariate interactions

Studies leveraging gene-environment (GxE) interactions within Mendelian randomization (MR) analyses have prompted the emergence of two similar methodologies: MR-GxE and MR-GENIUS. Such methods are attractive in allowing for pleiotropic bias to be corrected when using individual instruments. Specifically, MR-GxE requires an interaction to be explicitly identified, while MR-GENIUS does not. We critically examine the assumptions of MR-GxE and MR-GENIUS in the absence of a pre-defined covariate, and propose sensitivity analyses to evaluate their performance. Finally, we explore the effect of body mass index (BMI) upon systolic blood pressure (SBP) using data from the UK Biobank, finding evidence of a positive effect of BMI on SBP. We find both approaches share similar assumptions, though differences between the approaches lend themselves to differing research settings. Where a suitable gene-by-covariate interaction is observed MR-GxE can produce unbiased causal effect estimates. MR-GENIUS can circumvent the need to identify interactions, but as a consequence relies on either the MR-GxE assumptions holding globally, or additional information with respect to the distribution of pleiotropic effects in the absence of an explicitly defined interaction covariate.

Birth weight, adult weight, and cardiovascular biomarkers: Evidence from the Cardiovascular Young Finns Study

This study quantifies the causal effect of birth weight on cardiovascular biomarkers in adulthood using the Cardiovascular Risk in Young Finns Study (YFS). We apply a multivariable Mendelian randomization (MVMR) method that provides a novel approach to improve inference in causal analysis based on a mediation framework. The results show that birth weight is linked to triglyceride levels (β = -0.294; 95% CI [-0.591, 0.003]) but not to low-density lipoprotein (LDL) cholesterol levels (β = 0.007; 95% CI [-0.168, 0.183]). The total effect of birth weight on triglyceride levels is partly offset by a mediation pathway linking birth weight to adult BMI (β = 0.111; 95% CI [-0.013, 0.234]). The negative total effect is consistent with the fetal programming hypothesis. The positive indirect effect via adult BMI highlights the persistence of body weight throughout a person's life and the adverse effects of high BMI on health. The results are consistent with previous findings that both low birth weight and weight gain increase health risks in adulthood.

Genetic risks, adolescent health, and schooling attainment

We provide new evidence on the effect of adolescent health behaviors/outcomes (obesity, depression, smoking, and attention deficit hyperactivity disorder [ADHD]) on schooling attainment using the National Longitudinal Study of Adolescent to Adult Health. We take two different approaches to deal with omitted variable bias and reverse causality. Our first approach attends to the issue of reverse causality by estimating the effect of health polygenic scores (PGSs) on schooling. Second, we estimate the effect of adolescent health using sibling fixed-effects models that control for unmeasured genetic and family factors shared by siblings. We use the PGSs as additional controls in the sibling fixed-effects models to reduce concerns about residual confounding from sibling-specific genetic differences. We find consistent evidence across both approaches that being genetically predisposed to smoking and smoking regularly in adolescence reduces schooling attainment. Estimates for depression are more imprecise, but also suggest that a high genetic risk of depression and adolescent depression reduce schooling attainment. We find mixed evidence for ADHD. Our estimates suggest that having a high genetic risk for ADHD reduces grades of schooling, but we do not find any statistically significant negative effects of ADHD. Finally, we find no consistent evidence for a detrimental effect of obesity on schooling attainment.

Birth weight and adult income: An examination of mediation through adult height and body mass

This paper examines the causal links between early human endowments and socioeconomic outcomes in adulthood. We use a genotyped longitudinal survey (Cardiovascular Risk in Young Finns Study) that is linked to the administrative registers of Statistics Finland. We focus on the effect of birth weight on income via two anthropometric mediators: body mass index (BMI) and height in adulthood. We find that (i) the genetic instruments for birth weight, adult height, and adult BMI are statistically powerful; (ii) there is a robust total effect of birth weight on income for men but not for women; (iii) the total effect of birth weight on income for men is partly mediated via height but not via BMI; and (iv) the share of the total effect mediated via height is substantial, of approximately 56%.

The relationship between body mass index and income: Using genetic variants from HUNT as instrumental variables

Several studies have estimated effects of body mass index (BMI) on labour market outcomes, and these studies have mixed findings. A significant challenge has been to adequately control for omitted variables, selection, reverse causality, and measurement error. We examine the impact of BMI on income using genetic variants as instrumental variables for BMI. Individual-level pre-tax income from tax records was merged with health survey data containing measured height and weight, and data on genetic variants. The analyses were stratified by sex and a variety of methods were used to explore the sensitivity and validity of the instrumental variable (IV) strategy. For females we found that BMI had a negative effect on the logarithm of income. The effect estimated from the IV models (-0.02) was larger than the effect estimated from naïve ordinary least squares regressions (-0.01). For males, the coefficients for the effect of BMI on income were imprecise, and both positive and negative coefficients were estimated depending on the estimation method. Our results suggest that females are susceptible to reduced income levels following increased BMI.

Causal Effect of Adiposity Measures on Blood Pressure Traits in 2 Urban Swedish Cohorts: A Mendelian Randomization Study

Background Different adiposity traits may be causally related to hypertension in different ways. By using genetic variants as randomly allocated proxies for studying the effect of modifying adiposity traits, the Mendelian randomization approach can be used to investigate this. Methods and Results In this study, we used 4 different genetic risk scores (GRS; GRS-BMI₅₆₅, GRS-WHR₃₂₄, GRS-VAT₂₀₈, GRS-BF₈₁) including hundreds of single nucleotide polymorphisms associated with body mass index, waist-to-hip ratio, visceral adipose tissue, and body fat, respectively. These were applied as instrumental variables in Mendelian randomization analyses. Two Swedish urban-based cohort studies, the Malmö Diet and Cancer, and the Malmö Preventive 795Projects were used to obtain genetic association estimates with blood pressure (BP). In both the Malmö Preventive Projects and Malmö Diet and Cancer studies, except for that for body fat, all of the genetic risk scores were significantly associated with systolic BP and diastolic BP, but with different magnitudes. In particular, in both cohorts, each standard deviation increase in the genetic risk score made up by the 324 single nucleotide polymorphisms associated with waist-to-hip ratio was associated with doubling of the likelihood of hypertension prevalence at baseline. However, only the genetic risk score made up by the 565 SNPs associated with body mass index was significantly associated with hypertension incidence during 23.6±4.3 years of follow-up in the Malmö Preventive Project. Conclusions We support a causal link between genetically mediated adiposity, especially waist-to-hip ratio and body mass index, and BP traits including hypertension prevalence and, for the first time to our knowledge, hypertension incidence. The differences in magnitude between these associations might suggest different mechanisms by which different adiposity affects BP/hypertension and consequently may indicate that tailored interventions are needed to reduce cardiovascular risk.

Two-Sample Mendelian Randomization Analysis of Associations Between Periodontal Disease and Risk of Cancer

Background

Observational studies indicate that periodontal disease may increase the risk of colorectal, lung, and pancreatic cancers. Using a 2-sample Mendelian randomization (MR) analysis, we assessed whether a genetic predisposition index for periodontal disease was associated with colorectal, lung, or pancreatic cancer risks.

Methods

Our primary instrument included single nucleotide polymorphisms with strong genome-wide association study evidence for associations with chronic, aggressive, and/or severe periodontal disease (rs729876, rs1537415, rs2738058, rs12461706, rs16870060, rs2521634, rs3826782, and rs7762544). We used summary-level genetic data for colorectal cancer (n = 58 131 cases; Genetics and Epidemiology of Colorectal Cancer Consortium, Colon Cancer Family Registry, and Colorectal Transdisciplinary Study), lung cancer (n = 18 082 cases; International Lung Cancer Consortium), and pancreatic cancer (n = 9254 cases; Pancreatic Cancer Consortia). Four MR approaches were employed for this analysis: random-effects inverse-variance weighted (primary analyses), Mendelian Randomization-Pleiotropy RESidual Sum and Outlier, simple median, and weighted median. We conducted secondary analyses to determine if associations varied by cancer subtype (colorectal cancer location, lung cancer histology), sex (colorectal and pancreatic cancers), or smoking history (lung and pancreatic cancer). All statistical tests were 2-sided.

Results

The genetic predisposition index for chronic or aggressive periodontitis was statistically significantly associated with a 3% increased risk of colorectal cancer (per unit increase in genetic index of periodontal disease; P = .03), 3% increased risk of colon cancer (P = .02), 4% increased risk of proximal colon cancer (P = .01), and 3% increased risk of colorectal cancer among females (P = .04); however, it was not statistically significantly associated with the risk of lung cancer or pancreatic cancer, overall or within most subgroups.

Conclusions

Genetic predisposition to periodontitis may be associated with colorectal cancer risk. Further research should determine whether increased periodontitis prevention and increased cancer surveillance of patients with periodontitis is warranted.

Mendelian randomization: estimation of inpatient hospital costs attributable to obesity

BACKGROUND

Mendelian Randomization is a type of instrumental variable (IV) analysis that uses inherited genetic variants as instruments to estimate causal effects attributable to genetic factors. This study aims to estimate the impact of obesity on annual inpatient healthcare costs in the UK using linked data from the UK Biobank and Hospital Episode Statistics (HES).

METHODS

UK Biobank data for 482,127 subjects was linked with HES inpatient admission records, and costs were assigned to episodes of care. A two-stage least squares (TSLS) IV model and a TSLS two-part cost model were compared to a naïve regression of inpatient healthcare costs on body mass index (BMI).

RESULTS

The naïve analysis of annual cost on continuous BMI predicted an annual cost of £21.61 [95% CI £20.33 - £22.89] greater cost per unit increase in BMI. The TSLS IV model predicted an annual cost of £14.36 [95% CI £0.31 - £28.42] greater cost per unit increase in BMI. Modelled with a binary obesity variable, the naïve analysis predicted that obese subjects incurred £205.53 [95% CI £191.45 - £219.60] greater costs than non-obese subjects. The TSLS model predicted a cost £201.58 [95% CI £4.32 - £398.84] greater for obese subjects compared to non-obese subjects.

CONCLUSIONS

The IV models provide evidence for a causal relationship between obesity and higher inpatient healthcare costs. Compared to the naïve models, the binary IV model found a slightly smaller marginal effect of obesity, and the continuous IV model found a slightly smaller marginal effect of a single unit increase in BMI.

Metabolic phenotyping and cardiovascular disease: an overview of evidence from epidemiological settings

Metabolomics, the comprehensive measurement of low-molecular-weight molecules in biological fluids used for metabolic phenotyping, has emerged as a promising tool to better understand pathways underlying cardiovascular disease (CVD) and to improve cardiovascular risk stratification. Here, we present the main methodologies for metabolic phenotyping, the methodological steps to analyse these data in epidemiological settings and the associated challenges. We discuss evidence from epidemiological studies linking metabolites to coronary heart disease and stroke. These studies indicate the systemic nature of CVD and identify associated metabolic pathways such as gut microbial cometabolism, branched-chain amino acids, glycerophospholipid and cholesterol metabolism, as well as activation of inflammatory processes. Integration of metabolomic with genomic data can provide new evidence for involved biochemical pathways and potential for causality using Mendelian randomisation. The clinical utility of metabolic biomarkers for cardiovascular risk stratification in healthy individuals has not yet been established. As sample sizes with high-dimensional molecular data increase in epidemiological settings, integration of metabolomic data across studies and platforms with other molecular data will lead to new understanding of the metabolic processes underlying CVD and contribute to identification of potentially novel preventive and pharmacological targets. Metabolic phenotyping offers a powerful tool in the characterisation of the molecular signatures of CVD, paving the way to new mechanistic understanding and therapies, as well as improving risk prediction of CVD patients. However, there are still challenges to face in order to contribute to clinically important improvements in CVD.

Common health conditions in childhood and adolescence, school absence, and educational attainment: Mendelian randomization study

Good health is positively related to children's educational outcomes, but relationships may not be causal. Demonstrating a causal influence would strongly support childhood and adolescent health as important for education policy. We applied genetic causal inference methods to assess the causal relationship of common health conditions at age 10 (primary/elementary school) and 13 (mid-secondary/mid-high school) with educational attainment at 16 and school absence at 14-16. Participants were 6113 children from the Avon Longitudinal Study of Parents and Children (ALSPAC). Exposures were symptoms of attention-deficit hyperactivity disorder (ADHD), autism spectrum disorder (ASD), depression, asthma, migraines and BMI. Genetic liability for these conditions and BMI was indexed by polygenic scores. In non-genetic, multivariate-adjusted models, all health conditions except asthma and migraines were associated with poorer attainment and greater school absence. School absence substantially mediated effects of BMI (39.9% for BMI at 13) and migraines (72.0% at 10), on attainment with more modest mediation for emotional and neurodevelopmental conditions. In genetic models, a unit increase in standardized BMI at 10 predicted a 0.19 S.D. decrease (95% CI: 0.11, 0.28) in attainment at 16, equivalent to around a 1/3 grade lower in all subjects, and 8.7% more school absence (95% CI:1.8%,16.1%). Associations were similar at 13. Genetic liability for ADHD predicted lower attainment but not more absence. Triangulation across multiple approaches supports a causal, negative influence on educational outcomes of BMI and ADHD, but not of ASD, depression, asthma or migraine. Higher BMI in childhood and adolescence may causally impair educational outcomes.

The impact of childhood obesity on human capital in high-income countries: A systematic review

Current evidence of the impact of childhood obesity on human capital development does not point in a consistent direction, and its interpretation is challenging. We carried out a systematic review of studies from high-income countries that used robust causal inference approaches to assess the impact of childhood overweight and obesity on outcomes typically linked to human capital development in economics. Global Health, Medline and EconLit were used to search for peer-reviewed papers. Three reviewers independently assessed study quality using the Newcastle-Ottawa Scale. Nineteen papers representing 22 studies met the inclusion criteria. Included studies were categorized based on three components of human capital: cognitive performance (n = 18), measured through test scores; educational attainment (n = 3), through grade progression and college completion; and labour market outcomes (n = 1), through wages. We find that childhood overweight and obesity hinder education outcomes, with effects mostly observed at older ages of exposure measurement (12+ years). Girls with overweight and obesity experienced larger negative effects and more often than boys. Future research should elucidate the pathways through which childhood obesity impacts human capital development, to support policies that may mitigate those impacts, thus averting social costs that are currently widespread, increasing and unaccounted for.

The Interplay between Maternal Smoking and Genes in Offspring Birth Weight

It is well-established that both the child's genetic endowments as well as maternal smoking during pregnancy impact offspring birth weight. In this paper we move beyond the nature versus nurture debate by investigating the interaction between genetic endowments and this critical prenatal environmental exposure - maternal smoking - in determining birth weight. We draw on longitudinal data from the Avon Longitudinal Study of Parents and Children (ALSPAC) study and replicate our results using data from the UK Biobank. Genetic endowments of the children are proxied with a polygenic score that is constructed based on the results of the most recent genome-wide association study of birth weight. We instrument the maternal decision to smoke during pregnancy with a genetic variant (rs1051730) located in the nicotine receptor gene CHRNA3. This genetic variant is associated with the number of cigarettes consumed daily, and we present evidence that this is plausibly the only channel through which the maternal genetic variant affects the child's birth weight. Additionally, we deal with the misreporting of maternal smoking by using measures of cotinine, a biomarker of nicotine, collected from the mother's urine during their pregnancy. We confirm earlier findings that genetic endowments as well as maternal smoking during pregnancy significantly affects the child's birth weight. However, we do not find evidence of meaningful interactions between genetic endowments and an adverse fetal environment, suggesting that the child's genetic predisposition cannot cushion the damaging effects of maternal smoking.

Causal associations of body mass index and waist-to-hip ratio with cardiometabolic traits among Chinese children: A Mendelian randomization study

BACKGROUND AND AIMS

Body mass index (BMI) and waist-to-hip ratio (WHR) have been reported to be causally associated with cardiometabolic diseases in adults in European populations. However, this causality was less explored in East Asian populations and in children. Our study aimed to explore and compare the causal associations of general obesity (measured by BMI) and central obesity (measured by WHR) with cardiometabolic traits.

METHODS AND RESULTS

We performed a Mendelian randomization (MR) analysis in 2030 unrelated children from two independent case-control studies in Beijing, China. BMI-associated single nucleotide polymorphisms (SNPs) and WHR-SNPs identified by previous genome-wide association studies were used as genetic instruments to examine the casual associations of BMI and WHR with cardiometabolic traits, including glycemic traits, blood lipids, and blood pressure. Each 1-SD increase in BMI and WHR were significantly associated with 0.111 mmol/L and 0.110 mmol/L increase in log-transformed fasting insulin (FINS), 0.049 and 0.060 increase in log-transformed HOMA-β, 0.112 and 0.108 increase in log-transformed HOMA-IR, 0.009 mmol/L and 0.015 mmol/L increase in log-transformed triglyceride, and 15.527 mmHg and 7.277 mmHg increase in systolic blood pressure, respectively (all P < 0.05). The receiver operating characteristic curves showed that WHR had a stronger effect on FINS, HOMA-β, HOMA-IR, and triglyceride than BMI (all P < 0.05).

CONCLUSIONS

Using the MR method, we found that the genetic predisposition to higher BMI or WHR was associated with altered cardiometabolic traits in Chinese children. When compared with general obesity, central obesity might have stronger effects on glycemic traits and blood lipids among children.

The impact of BMI on mental health: Further evidence from genetic markers

We estimate the effect of BMI on mental health for young adults and elderly individuals using data from the National Longitudinal Study of Adolescent Health and the Health & Retirement Study. To tackle confounding due to unobserved factors, we exploit variation in a polygenic score (PGS) for BMI within two related econometric methods that differ in the assumptions they employ. First, we use the BMI PGS as an IV and adjust for PGSs for other factors (depression and educational attainment) that may invalidate this IV. We find a large statistically significant effect of BMI on mental health for the elderly: a 5 kg/m2 increase in BMI (a difference equivalent to moving from overweight to obese) increases the probability of depression by 29 %. In contrast, for young adults the IV estimates are statistically and economically insignificant. We show that IV estimates likely have to be interpreted as identifying a weighted average of effects of BMI on mental health mostly for compliers on the upper quantiles of the BMI distribution. Second, we use the BMI PGS as an "imperfect" IV and estimate an upper bound on the average treatment effect for the population. The estimated upper bounds are consistent with the conclusions from the IV estimates.

Not only in my genes: The effects of peers' genotype on obesity

We use data from three waves of Add Health to study the short- and long-run effects of high school peers' genetic predisposition to high BMI-measured by grade-mates' average BMI polygenic scores-on adolescent and adult obesity in the U.S. We find that, in the short-run, a one standard deviation increase in peers' average BMI polygenic scores raises the probability of obesity for females by 2.8% points, about half the size of the effect induced by a one standard deviation increase in one's own polygenic score. No significant effect is found for males. In the long-run, however, the social-genetic effect fades away, while the effect of one's own genetic risk for BMI increases substantially. We suggest that mechanisms explaining the short-run effect for females include changes in nutrition habits and a distorted perception of body size.

A comparison of robust Mendelian randomization methods using summary data

The number of Mendelian randomization (MR) analyses including large numbers of genetic variants is rapidly increasing. This is due to the proliferation of genome-wide association studies, and the desire to obtain more precise estimates of causal effects. Since it is unlikely that all genetic variants will be valid instrumental variables, several robust methods have been proposed. We compare nine robust methods for MR based on summary data that can be implemented using standard statistical software. Methods were compared in three ways: by reviewing their theoretical properties, in an extensive simulation study, and in an empirical example. In the simulation study, the best method, judged by mean squared error was the contamination mixture method. This method had well-controlled Type 1 error rates with up to 50% invalid instruments across a range of scenarios. Other methods performed well according to different metrics. Outlier-robust methods had the narrowest confidence intervals in the empirical example. With isolated exceptions, all methods performed badly when over 50% of the variants were invalid instruments. Our recommendation for investigators is to perform a variety of robust methods that operate in different ways and rely on different assumptions for valid inferences to assess the reliability of MR analyses.

Posttraumatic Stress Disorder and Inflammation: Untangling Issues of Bidirectionality

Posttraumatic stress disorder (PTSD) has increasingly been linked to heightened systemic inflammation. It matters whether this association is causal (and either bidirectional or unidirectional) or correlational. Investigators have hypothesized that chronic systemic low-grade inflammation may contribute to greater risk of developing PTSD after experiencing trauma and/or serve as a mechanism linking PTSD to adverse physical health outcomes. However, if the PTSD-inflammation relation is correlational, it may not warrant further research aimed at understanding inflammation as a PTSD risk factor or as a pathway linking PTSD with poor health. In this review, we first assess the longitudinal evidence related to PTSD and inflammation to understand more clearly the directionality and causal nature of this relation. Overall, few longitudinal studies rigorously assess the direction of the PTSD-inflammation relation. Some of the evidence indicates that elevated inflammation assessed pretrauma or in the acute aftermath of trauma increases risk for developing PTSD. Fewer studies evaluate the influence of PTSD on subsequent inflammation levels, and findings are mixed. Sample characteristics and study designs, and also the type of inflammation-related measure, vary widely across studies. Based on current evidence, we then recommend several statistical and study design approaches that may help untangle issues of bidirectionality and aid in determining the direction of causality between PTSD and inflammation. Last, we conclude with future research directions and consider potential implications for interventions or treatment approaches based on this growing body of literature.

The medical care costs of obesity and severe obesity in youth: An instrumental variables approach

This paper is the first to use the method of instrumental variables to estimate the impact of obesity and severe obesity in youth. on U.S. medical care costs. We examine data from the Medical Expenditure Panel Survey for 2001-2015 and instrument for child BMI using the BMI of the child's biological mother. Instrumental variables estimates indicate that obesity in youth raises annual medical care costs by $907 (in 2015 dollars) or 92%, which is considerably higher than previous estimates of the association of youth obesity with medical costs. We find that obesity in youth significantly raises costs in all major categories of medical care: outpatient doctor visits, inpatient hospital stays, and prescription drugs. The costs of youth obesity are borne almost entirely by third-party payers, which is consistent with substantial externalities of youth obesity, which in turn represents an economic rationale for government intervention.

What is creating the height premium? New evidence from a Mendelian randomization analysis in China

This study uses a Mendelian randomization approach to resolve the difficulties of identifying the causal relationship between height and earnings by using a unique sample of 3,427 respondents from mainland China with sociodemographic information linked to individual genotyping data. Exploiting genetic variations to create instrumental variables for observed height, we find that while OLS regressions yield that an additional centimeter in height is associated with a 10–13% increase in one’s annual earnings, IV estimates reveal only an insubstantial causal effect of height. Further analyses suggest that the observed height premium is likely to pick up the impacts of several cognitive/noncognitive skills on earnings confounded in previous studies, such as mental health, risk preference, and personality factors. Our study is the first empirical study that employs genetic IVs in developing countries, and our results contribute to the recent debate on the mechanism of height premium.

Education leads to a more physically active lifestyle: Evidence based on Mendelian randomization

Physical inactivity is a major health risk worldwide. Observational studies suggest that higher education is positively related to physical activity, but it is not clear whether this relationship constitutes a causal effect. Using participants (N = 1651) drawn from the Cardiovascular Risk in Young Finns Study linked to nationwide administrative data from Statistics Finland, this study examined whether educational attainment, measured by years of education, is related to adulthood physical activity in terms of overall physical activity, weekly hours of intensive activity, total steps per day, and aerobic steps per day. We employed ordinary least squares (OLS) models and extended the analysis using an instrumental variables approach (Mendelian randomization, MR) with a genetic risk score as an instrument for years of education. Based on the MR results, it was found that years of education is positively related to physical activity. On average, one additional year of education leads to a 0.62-unit higher overall physical activity (P < .01), 0.26 more hours of weekly intensive activity (P < .05), 560 more steps per day (P < .10), and 390 more aerobic steps per day (P < .09). The findings indicate that education may be a factor leading to higher leisure-time physical activity and thus promoting global health.

Application of Mendelian Randomization to Investigate the Association of Body Mass Index with Health Care Costs

Causal effect estimates for the association of obesity with health care costs can be biased by reversed causation and omitted variables. In this study, we use genetic variants as instrumental variables to overcome these limitations, a method that is often called Mendelian randomization (MR). We describe the assumptions, available methods, and potential pitfalls of using genetic information and how to address them. We estimate the effect of body mass index (BMI) on total health care costs using data from a German observational study and from published large-scale data. In a meta-analysis of several MR approaches, we find that models using genetic instruments identify additional annual costs of €280 for a 1-unit increase in BMI. This is more than 3 times higher than estimates from linear regression without instrumental variables (€75). We found little evidence of a nonlinear relationship between BMI and health care costs. Our results suggest that the use of genetic instruments can be a powerful tool for estimating causal effects in health economic evaluation that might be superior to other types of instruments where there is a strong association with a modifiable risk factor.

Mendelian Randomization analysis of the causal effect of adiposity on hospital costs

Estimates of the marginal effect of measures of adiposity such as body mass index (BMI) on healthcare costs are important for the formulation and evaluation of policies targeting adverse weight profiles. Most estimates of this association are affected by endogeneity bias. We use a novel identification strategy exploiting Mendelian Randomization - random germline genetic variation modelled using instrumental variables - to identify the causal effect of BMI on inpatient hospital costs. Using data on over 300,000 individuals, the effect size per person per marginal unit of BMI per year varied according to specification, including £21.22 (95% confidence interval (CI): £14.35-£28.07) for conventional inverse variance weighted models to £18.85 (95% CI: £9.05-£28.65) for penalized weighted median models. Effect sizes from Mendelian Randomization models were larger in most cases than non-instrumental variable multivariable adjusted estimates (£13.47, 95% CI: £12.51-£14.43). There was little evidence of non-linearity. Within-family estimates, intended to address dynastic biases, were imprecise.

Precision Medicine and Cardiovascular Health: Insights from Mendelian Randomization Analyses

Cardiovascular disease (CVD) is considered a primary driver of global mortality and is estimated to be responsible for approximately 17.9 million deaths annually. Consequently, a substantial body of research related to CVD has developed, with an emphasis on identifying strategies for the prevention and effective treatment of CVD. In this review, we critically examine the existing CVD literature, and specifically highlight the contribution of Mendelian randomization analyses in CVD research. Throughout this review, we assess the extent to which research findings agree across a range of studies of differing design within a triangulation framework. If differing study designs are subject to non-overlapping sources of bias, consistent findings limit the extent to which results are merely an artefact of study design. Consequently, broad agreement across differing studies can be viewed as providing more robust causal evidence in contrast to limiting the scope of the review to a single specific study design. Utilising the triangulation approach, we highlight emerging patterns in research findings, and explore the potential of identified risk factors as targets for precision medicine and novel interventions.

Reporting and guidelines for mendelian randomization analysis: A systematic review of oncological studies

BACKGROUND

Mendelian randomization (MR) analyses have been increasingly used to seek evidence of causal associations. This systematic review aims at characterizing and evaluating the reporting of MR analyses in oncological studies.

METHODS

The PubMed database was searched to identify MR cancer studies until December 31, 2017. Two of the authors independently selected and evaluated reporting quality of the studies. Reporting quality in MR studies before 2016 and in 2016/17 was compared.

RESULTS

Cancer studies with MR analyses in 2016 and 2017 accounted for 55.8% of the total number of studies identified. In the 77 eligible articles, 39 (50.6%) did not report subjects' characteristics, 53 (68.8%) did not conduct power estimation, 40 (51.9%) did not state all of the first three MR assumptions (i.e., genetic instrument is associated with exposure, is not associated with confounders, and acts on outcome only through exposure), and 31 (40.3%) did not exclude SNPs that diverged from Hardy-Weinberg equilibrium. More studies estimated power in 2016/2017 than before 2016 (p = 0.028).

CONCLUSIONS

Some MR cancer studies did not sufficiently report essential information, posing obstacles for critical appraisal. This study proposes for MR analysis a guideline/checklist for future publications in cancer and other biomedical research.

How do cardiovascular diseases harm labor force participation? Evidence of nationally representative survey data from Japan, a super-aged society

OBJECTIVE

To evaluate how cardiovascular diseases harm labor force participation (LFP) among the Japanese population and verify the validity of plasma biomarkers as instrumental variables of cardiovascular diseases after adjusting for a broad set of confounders including dietary intake.

DESIGN

Using nationally representative repeated cross-sectional surveys in Japan, the Comprehensive Survey of Living Conditions and National Health and Nutrition Survey, with plasma biomarkers as instrumental variables for quasi-randomization.

SETTING

Onset of cardiovascular diseases in those receiving regular treatment for hypertension, intracerebral hemorrhage, intracerebral infarction, angina pectoris, myocardial infarction, or other types of cardiovascular diseases.

PARTICIPANTS

A total of 65,615 persons aged ≥ 20 years (35,037 women and 30,578 men) who completed a survey conducted every three years from 1995 through 2013.

MAIN OUTCOME MEASURES

Respondent employment and weekly working hours during each survey year.

RESULTS

Cardiovascular diseases significantly and remarkably reduced the probability of working by 15.4% (95% CI: -30.6% to -0.2%). The reduction in working probability was detected for women only. Respondents aged ≥ 40 years were less likely to work once diagnosed and the reduction was enlarged for those aged ≥ 65 years, while those aged < 40 years appeared to be unaffected. Probability of engaging in manual labor significantly decreased once diagnosed; however, no impact was found for cognitive occupations. Among employed respondents, the adverse effects of cardiovascular diseases decreased working hours by five hours per week. Validity of the biomarker instrumental variables was generally verified.

CONCLUSIONS

A vicious circle is suggested between LFP and unfavorable health. However, the effects vary across age, sex, and occupation type, even after adjusting for causal effects, which could cause a downward bias in LFP impact.

ATTRIBUTES

cardiovascular disease, labor force participation, instrumental variable method as quasi-randomization, plasma biomarker, Comprehensive Survey of Living Conditions, National Health and Nutrition Survey.

Epidemiology, genetic epidemiology and Mendelian randomisation: more need than ever to attend to detail

In the current era, with increasing availability of results from genetic association studies, finding genetic instruments for inferring causality in observational epidemiology has become apparently simple. Mendelian randomisation (MR) analyses are hence growing in popularity and, in particular, methods that can incorporate multiple instruments are being rapidly developed for these applications. Such analyses have enormous potential, but they all rely on strong, different, and inherently untestable assumptions. These have to be clearly stated and carefully justified for every application in order to avoid conclusions that cannot be replicated. In this article, we review the instrumental variable assumptions and discuss the popular linear additive structural model. We advocate the use of tests for the null hypothesis of ‘no causal effect’ and calculation of the bounds for a causal effect, whenever possible, as these do not rely on parametric modelling assumptions. We clarify the difference between a randomised trial and an MR study and we comment on the importance of validating instruments, especially when considering them for joint use in an analysis. We urge researchers to stand by their convictions, if satisfied that the relevant assumptions hold, and to interpret their results causally since that is the only reason for performing an MR analysis in the first place.

Pleiotropy-robust Mendelian randomization

Background

The potential of Mendelian randomization studies is rapidly expanding due to: (i) the growing power of genome-wide association study (GWAS) meta-analyses to detect genetic variants associated with several exposures; and (ii) the increasing availability of these genetic variants in large-scale surveys. However, without a proper biological understanding of the pleiotropic working of genetic variants, a fundamental assumption of Mendelian randomization (the exclusion restriction) can always be contested.

Methods

We build upon and synthesize recent advances in the literature on instrumental variables (IVs) estimation that test and relax the exclusion restriction. Our pleiotropy-robust Mendelian randomization (PRMR) method first estimates the degree of pleiotropy, and in turn corrects for it. If (i) a subsample exists for which the genetic variants do not affect the exposure; (ii) the selection into this subsample is not a joint consequence of the IV and the outcome; (iii) pleiotropic effects are homogeneous, PRMR obtains unbiased estimates of causal effects.

Results

Simulations show that existing MR methods produce biased estimators for realistic forms of pleiotropy. Under the aforementioned assumptions, PRMR produces unbiased estimators. We illustrate the practical use of PRMR by estimating the causal effect of: (i) tobacco exposure on body mass index (BMI); (ii) prostate cancer on self-reported health; and (iii) educational attainment on BMI in the UK Biobank data.

Conclusions

PRMR allows for instrumental variables that violate the exclusion restriction due to pleiotropy, and it corrects for pleiotropy in the estimation of the causal effect. If the degree of pleiotropy is unknown, PRMR can still be used as a sensitivity analysis.

Health endowment and later-life outcomes in the labour market: Evidence using genetic risk scores and reduced-form models

This paper examines the relationship between health endowment and later-life outcomes in the labour market. The analysis is based on reduced-form models in which labour market outcomes are regressed on genetic variants related to the increased risk of cardiovascular diseases. We use linked Finnish data that have many strengths. Genetic risk scores constitute exogenous measures for health endowment, and accurate administrative tax records on earnings, employment and social income transfers provide a comprehensive account of an individual's long-term performance in the labour market. The results show that although the direction of an effect is generally consistent with theoretical reasoning, the effects of health endowment on outcomes are statistically weak, and the hypothesis of no effect can be rejected only in one case: genetic endowment related to obesity influences male earnings and employment in prime age. Due to the sample size (N = 1651), the results should be interpreted with caution and should be confirmed in larger samples and in other institutional settings.

"Mendelian Randomization" Approach in Economic Assessment of Health Conditions

The increased prevalence of non-communicable chronic diseases (NCDs) is reflected in the rising economic burden of health conditions. Observational studies conducted in health economics research are detecting associations of NCDs or related risk factors with economic measures like health insurance, economic inequalities, accessibility of jobs, education, annual income, health expenditure, etc. The inferences of such relationships do not prove causation and are limited to associations which are many times influenced by confounding factors and reverse causation. Mendelian randomization (MR) approach is a useful method for exploring causal relations between modifiable risk factors and measures of health economics. The application of MR in economic assessment of health conditions has been started and is producing fruitful results.