Causal Effects Of Risk Factors Research Articles

SUMMER: a Mendelian randomization interactive serverto systematically evaluate the causal effects of risk factors and circulating biomarkers on pan-cancer survival.

Genome-wide association studies (GWASs) underlying case-control design have uncovered hundreds of genetic loci involved in tumorigenesis and provided rich resources for identifying risk factors and biomarkers associated with cancer susceptibility. However, the application of GWAS in determining the genetic architecture of cancer survival remains unestablished. Here, we systematically evaluated genetic effects at the genome-wide level on cancer survival that included overall survival (OS) and cancer-specific survival (CSS), leveraging data deposited in the UK Biobank cohort of a total of 19 628 incident patients across 17 cancer types. Furthermore, we assessed the causal effects of risk factors and circulating biomarkers on cancer prognosis via a Mendelian randomization (MR) analytic framework, which integrated cancer survival GWAS dataset, along with phenome-wide association study (PheWAS) and blood genome-wide gene expression/DNA methylation quantitative trait loci (eQTL/meQTL) datasets. On average, more than 10 traits, 700 genes, and 4,500 CpG sites were prone to cancer prognosis. Finally, we developed a user-friendly online database, SUrvival related cancer Multi-omics database via MEndelian Randomization (SUMMER; http://njmu-edu.cn:3838/SUMMER/), to help users query, browse, and download cancer survival results. In conclusion, SUMMER provides an important resource to assist the research community in understanding the genetic mechanisms of cancer survival.

Causal effects of psychosocial factors on chronic back pain: a bidirectional Mendelian randomisation study.

Risk factors for chronic back pain (CBP) may share underlying genetic factors, making them difficult to study using conventional methods. We conducted a bi-directional Mendelian randomisation (MR) study to examine the causal effects of risk factors (education, smoking, alcohol consumption, physical activity, sleep and depression) on CBP and the causal effect of CBP on the same risk factors. Genetic instruments for risk factors and CBP were obtained from the largest published genome-wide association studies (GWAS) of risk factor traits conducted in individuals of European ancestry. We used inverse weighted variance meta-analysis (IVW), Causal Analysis Using Summary Effect (CAUSE) and sensitivity analyses to examine evidence for causal associations. We interpreted exposure-outcome associations as being consistent with a causal relationship if results with IVW or CAUSE were statistically significant after accounting for multiple statistical testing (p < 0.003), and the direction and magnitude of effect estimates were concordant between IVW, CAUSE, and sensitivity analyses. We found evidence for statistically significant causal associations between greater education (OR per 4.2years of schooling = 0.54), ever smoking (OR = 1.27), greater alcohol consumption (OR = 1.29 per consumption category increase) and major depressive disorder (OR = 1.41) and risk of CBP. Conversely, we found evidence for significant causal associations between CBP and greater alcohol consumption (OR = 1.19) and between CBP and smoking (OR = 1.21). Other relationships did not meet our pre-defined criteria for causal association. Fewer years of schooling, smoking, greater alcohol consumption, and major depressive disorder increase the risk of CBP. CBP increases the risk of greater alcohol consumption and smoking.

On the interpretability of machine learning methods in crash frequency modeling and crash modification factor development

Machine learning (ML) model interpretability has attracted much attention recently given the promising performance of ML methods in crash frequency studies. Extracting accurate relationship between risk factors and crash frequency is important for understanding the causal effects of risk factors and developing safety countermeasures. However, there is no study that comprehensively summarizes ML model interpretation methods and provides guidance for safety researchers and practitioners. This research aims to fill this gap. Model-based and post-hoc ML interpretation methods are critically evaluated and compared to study their suitability in crash frequency modeling. These methods include classification and regression tree (CART), multivariate adaptive regression splines (MARS), Local Interpretable Model-agnostic Explanations (LIME), Local Sensitivity Analysis (LSA), Partial Dependence Plots (PDP), Global Sensitivity Analysis (GSA), and SHapley Additive exPlanations (SHAP). Model-based interpretation methods cannot reveal the detailed interaction relationships among risk factors. LIME can only be used to analyze the effects of a risk factor at the prediction level. LSA and PDP assume that different risk factors are independently distributed. Both GSA and SHAP can account for the potential correlation among risk factors. However, only SHAP can visualize the detailed relationships between crash outcomes and risk factors. This study also demonstrates the potential and benefits of using ML and SHAP to derive Crash Modification Factors (CMF). Finally, it is emphasized that statistical and ML models may not directly differentiate causation from correlation. Understanding the differences between them is critical for developing reliable safety countermeasures.

Type 2 diabetes mellitus increases risk of erectile dysfunction independent of obesity and dyslipidemia: A Mendelian randomization study.

The causal effects of individual risk factors of metabolic syndrome on erectile dysfunction (ED) are still unclear. To evaluate the causal effect of risk factors of metabolic syndrome on ED through Mendelian randomization (MR). Data for risk factors were obtained from multiple databases with 173,082-757,601 individuals, and that for ED were collected from a genome-wide association study including 223,805 Europeans. We performed univariate MR analysis using inverse-variance weighted, MR-Egger, weighted-median, weighted mode methods and multivariable MR analysis to evaluate the total and direct causal effects. The univariable MR supported that type 2 diabetes mellitus (odds ratios [OR]=1.14, 95% confidence intervals [CI]: 1.08-1.21, p<0.001) and body mass index (BMI) (OR=1.27, 95% CI: 1.12-1.44, p<0.001) were associated with ED. After excluding the SNPs associated with BMI and other risk factors, the results of multivariable MR for T2D (OR=1.15, 95% CI: 1.05-1.25, p=0.001) remained consistent. However, the results of multivariable MR provided limited evidence for the causality between BMI and ED (OR=1.06, 95% CI: 0.88-1.29, p=0.532). For systolic blood pressure and lipid components (low-density lipoprotein, high-density lipoprotein and triglycerides), both univariable and multivariable MR failed to offer sufficient evidence to confirm their causal effect on ED. T2D showed a direct causal effect on ED independent of obesity and dyslipidemia.

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

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

Gallstones, Body Mass Index, C-Reactive Protein, and Gallbladder Cancer: Mendelian Randomization Analysis of Chilean and European Genotype Data.

Gallbladder cancer (GBC) is a neglected disease with substantial geographical variability: Chile shows the highest incidence worldwide, while GBC is relatively rare in Europe. Here, we investigate the causal effects of risk factors considered in current GBC prevention programs as well as C-reactive protein (CRP) level as a marker of chronic inflammation. We applied two-sample Mendelian randomization (MR) using publicly available data and our own data from a retrospective Chilean and a prospective European study. Causality was assessed by inverse variance weighted (IVW), MR-Egger regression, and weighted median estimates complemented with sensitivity analyses on potential heterogeneity and pleiotropy, two-step MR, and mediation analysis. We found evidence for a causal effect of gallstone disease on GBC risk in Chileans (P=9×10-5 ) and Europeans (P=9×10-5 ). A genetically elevated body mass index (BMI) increased GBC risk in Chileans (P=0.03), while higher CRP concentrations increased GBC risk in Europeans (P=4.1×10-6 ). European results suggest causal effects of BMI on gallstone disease (P=0.008); public Chilean data were not, however, available to enable assessment of the mediation effects among causal GBC risk factors. Two risk factors considered in the current Chilean program for GBC prevention are causally linked to GBC risk: gallstones and BMI. For Europeans, BMI showed a causal effect on gallstone risk, which was itself causally linked to GBC risk.

Causal Associations Between Modifiable Risk Factors and the Alzheimer's Phenome.

The purpose of this study was to infer causal relationships between 22 previously reported risk factors for Alzheimer's disease (AD) and the "AD phenome": AD, AD age of onset (AAOS), hippocampal volume, cortical surface area and thickness, cerebrospinal fluid (CSF) levels of amyloid-β (Aβ42 ), tau, and ptau181 , and the neuropathological burden of neuritic plaques, neurofibrillary tangles (NFTs), and vascular brain injury (VBI). Polygenic risk scores (PRS) for the 22 risk factors were computed in 26,431 AD cases/controls and the association with AD was evaluated using logistic regression. Two-sample Mendelian randomization (MR) was used to infer the causal effect of risk factors on the AD phenome. PRS for increased education and diastolic blood pressure were associated with reduced risk for AD. MR indicated that only education was causally associated with reduced risk of AD, delayed AAOS, and increased cortical surface area and thickness. Total- and LDL-cholesterol levels were causally associated with increased neuritic plaque burden, although the effects were driven by single nucleotide polymorphisms (SNPs) within the APOE locus. Diastolic blood pressure and pulse pressure are causally associated with increased risk of VBI. Furthermore, total cholesterol was associated with decreased hippocampal volume; smoking initiation with decreased cortical thickness; type 2 diabetes with an earlier AAOS; and sleep duration with increased cortical thickness. Our comprehensive examination of the genetic evidence for the causal relationships between previously reported risk factors in AD using PRS and MR supports a causal role for education, blood pressure, cholesterol levels, smoking, and diabetes with the AD phenome. ANN NEUROL 2021;89:54-65.

Modifiable Factors for Coronary Artery Disease, Myocardial Infarction, and Stroke: A Mendelian Randomisation Analysis (P15-006-19)

ObjectivesWe aimed to examine the causal associations of potentially risk factors, including lifestyle/dietary, cardiometabolic, and inflammatory factors, with risks of coronary artery disease (CAD), myocardial infarction (MI), and stroke. MethodsWe used single nucleotide polymorphisms (SNPs) associated with risk factors as instrumental variables to test the causal effect of risk factors on cardiovascular diseases using summary-level data from consortia. ResultsA total of 53 potentially risk factors were included. A Bonferroni corrected threshold of P = 0.00094 was considered to be significant. Genetically predicted higher BMI (odds ratio: 1.43, confidence interval: 1.26 to1.63 per 1 kg/m2, P = 7.64 × 10−8), smoking initiation (1.62, 1.42 to1.85 per year, P = 2.72 × 10−12), vitamin E (3.22, 1.81 to 4.63, P = 1.33 × 10−6). sIL-6R (1.63, 1.22 to 2.05, P = 0.0002), LDL (1.60, 1.46 to 1.75, P = 8.78 × 10−23), TC (1.48, 1.34 to 1.64, P = 1.14 × 10−13), TG (1.27, 1.13 to 1.43, P = 7.32 × 10−5), SBP (1.03, 1.02 to 1.05, P = 6.8 × 10−5), asthma (1.07, 1.05 to 1.09, P = 3.94 × 10−12), T2DM (1.12, 1.08 to 1.17, P = 4.37 × 10−8) were associated with higher risk of CAD; whilst, genetically predicted education (0.64, 0.55 to 0.75 per year, P = 2.68 × 10−8), and HDL (0.84, 0.76 to 0.92, P = 0.0003) were associated with lower risk of CAD. Similar results were observed for MI. In addition, atrial firillation (1.24, 1.18 to 1.30 P = 2.75 × 10−17), carotid artery plaque (1.24, 1.10 to 1.41, P = 0.0006), asthma (1.04 to 1.02, 1.06, P = 6.81 × 10−5), and T2DM (1.09, 1.04 to 1.14, P = 0.0002) were associated with higher risk of stroke per unit increase in log odds. We further observed suggestive associations of morning person, iron, fasting glucose, insulin, HbA1c, uric acid, and pulse pressure with CAD and suggestive associations of education, intelligence, Hcy, HDL, and pulse pressure with stroke. ConclusionsOur results identified several modifiable factors as treatment targets for prevention of CAD, MI, or stroke. Funding SourcesNone. Supporting Tables, Images and/or Graphs▪▪

PO-068 Atlas of causal risk factors for epithelial ovarian cancer risk: a mendelian randomization analysis in up to 66 450 women

IntroductionThough observational epidemiological studies have suggested associations between various modifiable risk factors and epithelial ovarian cancer (EOC) incidence, there is a need to establish whether these associations reflect true causal effects of risk factors on EOC or merely reflect residual confounding, reverse causation, or other forms of bias inherent to conventional epidemiological designs. Mendelian randomization (MR) is an analytical approach that uses randomly assigned genetic variation to proxy phenotypes to obtain more reliable estimates of causal effects in observational studies.Material and methodsWe used MR to test the causal effects of 25 traits hypothesised to influence ovarian cancer (3 anthropometric traits, 4 metabolic markers, 4 reproductive factors, 3 medical conditions, 4 dietary or behavioural traits, and 7 hormones) on risk of total and histosubtype-specific EOC in up to 25 509 cases and 40 941 controls from the Ovarian Cancer Association Consortium. We employed inverse-variance weighted models as primary analyses and MR-Egger, a weighted median estimate, and mode estimators as sensitivity analyses to examine violations of MR assumptions.Results and discussionsBody mass index (BMI) was the strongest risk factor for total EOC (1-SD increase: OR 1.23 [95%CI:1.07–1.23];p=0.003). For high-grade serous ovarian cancer, the strongest associations were for circulating leptin (1 unit increase in log10 leptin: OR 2.90 [95%CI:1.45–5.76];p=0.002) and BMI (OR 1.26 [95% CI:1.06–1.50];p=0.01). Endometrioid ovarian cancer was most strongly associated with age at menopause (per year increase in onset: OR 1.09 [95%CI:1.02–1.16];p=0.007), circulating estradiol (10% increase in log10 estradiol: OR 1.07 [95%CI:1.01–1.14];p=0.02), BMI (OR 1.48 [95%CI:1.07–2.06];p=0.02), and genetic liability to polycystic ovary syndrome (OR 0.90 [95%CI:0.82–0.99];p=0.03). Earlier age at menarche increased risk of mucinous ovarian carcinoma (per year earlier onset: OR 1.31 [95%CI:1.02–1.67];p=0.03). Height increased risk of clear cell carcinoma (1-SD increase: OR 1.35 [95%CI:1.15–1.61];p=0.0003). We also found evidence that ZKSCAN5 and FSHB loci influenced risk of endometrioid and mucinous subtypes, implicating sex hormone pathways.ConclusionOur comprehensive examination of possible etiological drivers of ovarian carcinogenesis provides support for a causal role of various modifiable factors in risk of ovarian cancer and suggests distinct etiologies across ovarian cancer histosubtypes.

Dissecting Causal Pathways Using Mendelian Randomization with Summarized Genetic Data: Application to Age at Menarche and Risk of Breast Cancer.

Mendelian randomization is the use of genetic variants as instrumental variables to estimate causal effects of risk factors on outcomes. The total causal effect of a risk factor is the change in the outcome resulting from intervening on the risk factor. This total causal effect may potentially encompass multiple mediating mechanisms. For a proposed mediator, the direct effect of the risk factor is the change in the outcome resulting from a change in the risk factor, keeping the mediator constant. A difference between the total effect and the direct effect indicates that the causal pathway from the risk factor to the outcome acts at least in part via the mediator (an indirect effect). Here, we show that Mendelian randomization estimates of total and direct effects can be obtained using summarized data on genetic associations with the risk factor, mediator, and outcome, potentially from different data sources. We perform simulations to test the validity of this approach when there is unmeasured confounding and/or bidirectional effects between the risk factor and mediator. We illustrate this method using the relationship between age at menarche and risk of breast cancer, with body mass index (BMI) as a potential mediator. We show an inverse direct causal effect of age at menarche on risk of breast cancer (independent of BMI), and a positive indirect effect via BMI. In conclusion, multivariable Mendelian randomization using summarized genetic data provides a rapid and accessible analytic strategy that can be undertaken using publicly available data to better understand causal mechanisms.

Beyond Mendelian randomization: how to interpret evidence of shared genetic predictors

ObjectiveMendelian randomization is a popular technique for assessing and estimating the causal effects of risk factors. If genetic variants which are instrumental variables for a risk factor are shown to be additionally associated with a disease outcome, then the risk factor is a cause of the disease. However, in many cases, the instrumental variable assumptions are not plausible, or are in doubt. In this paper, we provide a theoretical classification of scenarios in which a causal conclusion is justified or not justified, and discuss the interpretation of causal effect estimates. ResultsA list of guidelines based on the ‘Bradford Hill criteria’ for judging the plausibility of a causal finding from an applied Mendelian randomization study is provided. We also give a framework for performing and interpreting investigations performed in the style of Mendelian randomization, but where the choice of genetic variants is statistically, rather than biologically motivated. Such analyses should not be assigned the same evidential weight as a Mendelian randomization investigation. ConclusionWe discuss the role of such investigations (in the style of Mendelian randomization), and what they add to our understanding of potential causal mechanisms. If the genetic variants are selected solely according to statistical criteria, and the biological roles of genetic variants are not investigated, this may be little more than what can be learned from a well-designed classical observational study.

Using published data in Mendelian randomization: a blueprint for efficient identification of causal risk factors.

Finding individual-level data for adequately-powered Mendelian randomization analyses may be problematic. As publicly-available summarized data on genetic associations with disease outcomes from large consortia are becoming more abundant, use of published data is an attractive analysis strategy for obtaining precise estimates of the causal effects of risk factors on outcomes. We detail the necessary steps for conducting Mendelian randomization investigations using published data, and present novel statistical methods for combining data on the associations of multiple (correlated or uncorrelated) genetic variants with the risk factor and outcome into a single causal effect estimate. A two-sample analysis strategy may be employed, in which evidence on the gene-risk factor and gene-outcome associations are taken from different data sources. These approaches allow the efficient identification of risk factors that are suitable targets for clinical intervention from published data, although the ability to assess the assumptions necessary for causal inference is diminished. Methods and guidance are illustrated using the example of the causal effect of serum calcium levels on fasting glucose concentrations. The estimated causal effect of a 1 standard deviation (0.13 mmol/L) increase in calcium levels on fasting glucose (mM) using a single lead variant from the CASR gene region is 0.044 (95 % credible interval −0.002, 0.100). In contrast, using our method to account for the correlation between variants, the corresponding estimate using 17 genetic variants is 0.022 (95 % credible interval 0.009, 0.035), a more clearly positive causal effect. Electronic supplementary materialThe online version of this article (doi:10.1007/s10654-015-0011-z) contains supplementary material, which is available to authorized users.

Adolescent outcomes and opportunities in a Canadian province: looking at siblings and neighbors.

BackgroundWell-organized administrative data with large numbers of cases (building on linked files from several government departments) and a population registry facilitate new studies of population health and child development. Analyses of family relationships and a number of outcomes--educational achievement, health, teen pregnancy, and receipt of income assistance--are relatively easy to conduct using several birth cohorts. Looking both at means/proportions and at sibling correlations enriches our study of opportunity and well-being in late adolescence. With observational research possibly exaggerating the causal effects of risk factors, sibling comparisons involving individuals sharing both many family characteristics and many genes help deal with such criticisms.MethodsThis paper uses a rich dataset from one Canadian province (Manitoba) covering a wide range of geographical areas (cities to rural regions). Influences on opportunity and well-being are analyzed looking at both means/proportions and sibling correlations. We measure a variety of outcomes that may reflect different causal influences. A creative application of linear programming advances the use of data on residential location.ResultsPredicting educational achievement using available variables was much easier than predicting adolescent health status (R-square of .200 versus R-square of .043). Low levels of educational achievement, high levels of teenage pregnancy, and high sibling correlations outside Winnipeg and within Winnipeg’s lower income areas highlight inequalities across socioeconomic and geographic backgrounds. Stratifying our analyses by different variables, such as income quintiles, reveals differences in means and correlations within outcomes and across groups. Particular events--changes in mother’s marital status and in place of residence--were associated with less favorable outcomes in late adolescence.ConclusionOur findings suggest a paradox: Canadian developmental outcomes through late adolescence appear quite similar to those in the United States, even though intergenerational mobility in Canada is closer to mobility in the Nordic countries than to that in the United States.

Adolescent Outcomes and Opportunities in a Canadian Province: Looking at Siblings and Neighbors

BACKGROUND: Well-organized administrative data with large numbers of cases (building on linked files from several government departments) and a population registry facilitate new studies of population health and child development. Analyses of family relationships and a number of outcomes -- educational achievement, health, teen pregnancy, and receipt of income assistance -- are relatively easy to conduct using several birth cohorts. Looking both at means/proportions and at sibling correlations enriches our study of opportunity and well-being in late adolescence. With observational research possibly exaggerating the causal effects of risk factors, sibling comparisons involving individuals sharing both many family characteristics and many genes help deal with such criticisms. METHODS: This paper uses a rich dataset from one Canadian province (Manitoba) covering a wide range of geographical areas (cities to rural regions). Influences on opportunity and well-being are analyzed looking at both means/proportions and sibling correlations. We measure a variety of outcomes that may reflect different causal influences. A creative application of linear programming advances the use of data on residential location. RESULTS: Predicting educational achievement using available variables was much easier than predicting adolescent health status (R-square of .200 versus R-square of .043). Low levels of educational achievement, high levels of teenage pregnancy, and high sibling correlations outside Winnipeg and within Winnipeg's lower income areas highlight inequalities across socioeconomic and geographic backgrounds. Stratifying our analyses by different variables, such as income quintiles, reveals differences in means and correlations within outcomes and across groups. Particular events -- changes in mother's marital status and in place of residence -- were associated with less favorable outcomes in late adolescence. CONCLUSION: Our findings suggest a paradox: Canadian developmental outcomes through late adolescence appear quite similar to those in the United States, even though intergenerational mobility in Canada is closer to mobility in the Nordic countries than to that in the United States.

Life Study: a UK-wide birth cohort study of environment, development, health, and wellbeing

BackgroundHealth and social policies relevant to improving the lives of children draw on understanding of early life developmental trajectories and the social and material environments in which children are born and grow up. These policies draw on information from the UK's unique birth cohorts (comprising cohorts from 1946, 1958, 1970, and 2000 that have been assessed repeatedly to the present day) and other longitudinal studies. The importance of intergenerational and intragenerational effects on child health and development in the UK is increasingly recognised. A cross-disciplinary approach to the lifecourse is needed to meet this pressing scientific public health and policy challenge, which is sensitive to social, gender, and ethnic inequalities and incorporates biomedical, clinical, and social sciences from the outset. We will create a longitudinal data resource to address questions and hypotheses relevant to improving the lives of children, both now and in their futures. Five major research themes will be explored: inequalities, diversity, and social mobility; early life antecedents of school readiness and later educational performance; developmental origins of health and ill-health in childhood; social, emotional, and behavioural development: the interplay between infant and parent; and neighbourhoods and environment: effects on child and family. MethodsA multidisciplinary scientific leadership team is responsible for the detailed scientific design and protocol. Methods used to support protocol development and delivery include systematic review; process models; consultation with academics, policy makers, and parents; development of an ethics and governance framework; data simulations; and power calculations. The study has been designed to provide sufficient statistical power to address key scientific hypotheses and to provide nationally representative statistical information. It consists of two components: mothers and partners recruited during pregnancy through selected maternity units and those recruited after birth through probability-based sampling of the birth register. These sampling methods allow the larger component of pregnant mothers to be geographically clustered and ethnically diverse while increasing the precision of estimates for nationally representative measures. Mothers will be invited to attend a Life Study centre local to their maternity unit once during pregnancy and twice during the first year of life; fathers or partners will also be seen antenatally. Biological samples will be taken at the visits and at birth. Parents and babies in the probability sample will be contacted twice in the first year. Up to 90 000 children will be recruited with whom contact will be maintained through childhood and into adult life. An achieved sample size of 50 000 by 12 months would provide adequate power to detect moderately strong causal effects of risk factors (environmental, psychosocial, genetic) that are moderately common (eg, prevalence ≥10%) on outcomes of public health importance (eg, prevalence ≥2%, including asthma, obesity, autism spectrum disorders, and learning difficulties). Ethics and information governance approvals have been obtained and an ethics framework developed. Data enhancements that are under development address infection and immunity, the role of fathers or partners, and home-based environmental exposures. InterpretationLife Study offers an opportunity to develop and test our understanding of social and biological mechanisms operating through the lifecourse and to identify translational opportunities for policy and practice relevant to children's lives. FundingLife Study is supported by the Economic and Social Research Council, the Medical Research Council, and University College London. It is part of the Birth Cohort Facility Project, which receives funding from the government's Large Facilities Capital Fund.