Neil M Davies

Associations of Anti-Hypertensive Treatments with Alzheimer's Disease, Vascular Dementia, and Other Dementias

We investigated whether angiotensin II receptor blockers (ARBs) and angiotensin converting enzyme inhibitors (ACE-Is) are more strongly associated with Alzheimers disease (AD), vascular dementia (VaD), and other dementias, than other anti-hypertensive drugs. We conducted a nested case-control analysis within the UK general practice research database, with prospectively recorded anti-hypertensive prescribing data. We sampled cases aged ≥60 years and diagnosed between 1997-2008 (5,797 with AD, 2,186 with VaD, 1,214 with unspecified/other dementia) which were matched to up to four controls by age, general practice and gender. We computed odds-ratios and dose response effects for AD, vascular and unspecified/other dementia, comparing those prescribed ARBs or ACE-Is for at least six months with patients prescribed other anti-hypertensives. We controlled for matching factors, co-morbidities, smoking status, an area measure of socioeconomic status, consultation rate and blood pressure and accounted for reverse causality by introducing time-lags of up to eight years prior to diagnosis/index date. Patients diagnosed with AD, vascular and unspecified/other dementia had fewer prescriptions for ARBs and ACE-Is. Inverse associations with AD were strongest for ARBs (odds-ratio; 0.47, 95%CI, 0.37-0.58) compared with ACE-Is (odds-ratio; 0.76, 95%CI, 0.69-0.84) (p(difference) < 0.001). Associations of ARBs with AD were stronger than for vascular dementia (p(difference) = 0.01) and unspecified/other dementia (p(difference) = 0.23). There were inverse dose-response relationships between ARBs and ACE-Is with AD (both p(trend) < 0.01). The inverse association of ACE-Is with AD diminished when using longer time lags but the ARB-AD association persisted. Patients with AD were around half as likely to be prescribed ARBs. Further randomized controlled trial evidence is required to rigorously test these findings.

Smoking cessation treatment and risk of depression, suicide, and self harm in the Clinical Practice Research Datalink: prospective cohort study

Objective To compare the risk of suicide, self harm, and depression in patients prescribed varenicline or bupropion with those prescribed nicotine replacement therapy. Design Prospective cohort study within the Clinical Practice Research Datalink. Setting 349 general practices in England. Participants 119 546 men and women aged 18 years and over who used a smoking cessation product between 1 September 2006 and 31 October 2011. There were 81 545 users of nicotine replacement products (68.2% of all users of smoking cessation medicines), 6741 bupropion (5.6%), and 31 260 varenicline (26.2%) users. Main outcome measures Outcomes were treated depression and fatal and non-fatal self harm within three months of the first smoking cessation prescription, determined from linkage with mortality data from the Office for National Statistics (for suicide) and Hospital Episode Statistics data (for hospital admissions relating to non-fatal self harm). Hazard ratios or risk differences were estimated using Cox multivariable regression models, propensity score matching, and instrumental variable analysis using physicians’ prescribing preferences as an instrument. Sensitivity analyses were performed for outcomes at six and nine months. Results We detected 92 cases of fatal and non-fatal self harm (326.5 events per 100 000 person years) and 1094 primary care records of treated depression (6963.3 per 100 000 person years). Cox regression analyses showed no evidence that patients prescribed varenicline had higher risks of fatal or non-fatal self harm (hazard ratio 0.88, 95% confidence interval 0.52 to 1.49) or treated depression (0.75, 0.65 to 0.87) compared with those prescribed nicotine replacement therapy. There was no evidence that patients prescribed bupropion had a higher risk of fatal or non-fatal self harm (0.83, 0.30 to 2.31) or of treated depression (0.63, 0.46 to 0.87) compared with patients prescribed nicotine replacement therapy. Similar findings were obtained using propensity score methods and instrumental variable analyses. Conclusions There is no evidence of an increased risk of suicidal behaviour in patients prescribed varenicline or bupropion compared with those prescribed nicotine replacement therapy. These findings should be reassuring for users and prescribers of smoking cessation medicines.

Mendelian randomization in health research: Using appropriate genetic variants and avoiding biased estimates

Highlights • We model potential biases that may arise in Mendelian randomization analysis.• Genetic variants should robustly associate with exposures in independent samples.• If not, Mendelian randomization can suggest causality despite no true associations.

Is infant weight associated with childhood blood pressure? Analysis of the Promotion of Breastfeeding Intervention Trial (PROBIT) cohort

BACKGROUND Weight gain during infancy may programme later health outcomes, but examination of this hypothesis requires appropriate lifecourse methods and detailed weight gain measures during childhood. We examined associations between weight gain in infancy and early childhood and blood pressure at the age of 6.5 years in healthy children born at term. METHODS We carried out an observational analysis of data from a cluster-randomized breastfeeding promotion trial in Belarus. Of 17 046 infants enrolled between June 1996 and December 1997, 13 889 (81.5%) had systolic and diastolic blood pressure measured at 6.5 years; 10 495 children with complete data were analysed. A random-effects linear spline model with three knot points was used to estimate each individuals birthweight and weight gain from birth to 3 months, 3 months to 1 year and 1-5 years. Path analysis was used to separate direct effects from those mediated through subsequent weight gain. RESULTS In boys, after controlling for confounders and prior weight gain, the change in systolic blood pressure per z-score increase in weight gain was 0.09 mmHg [95% confidence interval (95% CI) -0.14 to 0.31] for birthweight; 0.41 mmHg (95% CI 0.19-0.64) for birth to 3 months; 0.69 mmHg (95% CI 0.47-0.92) for 3 months to 1 year and 0.82 mmHg (95% CI 0.58-1.06) for 1-5 years. Most of the associations between weight gain and blood pressure were mediated through weight at the age of 6.5 years. Findings for girls and diastolic blood pressure were similar. CONCLUSIONS Children who gained weight faster than their peers, particularly at later ages, had higher blood pressure at the age of 6.5 years, with no association between birthweight and blood pressure.

Two-sample Mendelian randomisation: avoiding the downsides of a powerful, widely applicable but potentially fallible technique

Two-sample Mendelian randomization: avoiding the downsides of a powerful, widely applicable but potentially fallible technique Fernando Pires Hartwig*, Neil Martin Davies, Gibran Hemani and George Davey Smith Postgraduate Program in Epidemiology, Federal University of Pelotas, Pelotas, Brazil, Medical Research Council Integrative Epidemiology Unit at the University of Bristol and School of Social and Community Medicine, University of Bristol, Bristol, UK

Bias due to participant overlap in two-sample Mendelian randomization

Mendelian randomization analyses are often performed using summarized data. The causal estimate from a one‐sample analysis (in which data are taken from a single data source) with weak instrumental variables is biased in the direction of the observational association between the risk factor and outcome, whereas the estimate from a two‐sample analysis (in which data on the risk factor and outcome are taken from non‐overlapping datasets) is less biased and any bias is in the direction of the null. When using genetic consortia that have partially overlapping sets of participants, the direction and extent of bias are uncertain. In this paper, we perform simulation studies to investigate the magnitude of bias and Type 1 error rate inflation arising from sample overlap. We consider both a continuous outcome and a case‐control setting with a binary outcome. For a continuous outcome, bias due to sample overlap is a linear function of the proportion of overlap between the samples. So, in the case of a null causal effect, if the relative bias of the one‐sample instrumental variable estimate is 10% (corresponding to an F parameter of 10), then the relative bias with 50% sample overlap is 5%, and with 30% sample overlap is 3%. In a case‐control setting, if risk factor measurements are only included for the control participants, unbiased estimates are obtained even in a one‐sample setting. However, if risk factor data on both control and case participants are used, then bias is similar with a binary outcome as with a continuous outcome. Consortia releasing publicly available data on the associations of genetic variants with continuous risk factors should provide estimates that exclude case participants from case‐control samples.

Instrumental variable analysis with a nonlinear exposure-outcome relationship

Background: Instrumental variable methods can estimate the causal effect of an exposure on an outcome using observational data. Many instrumental variable methods assume that the exposure–outcome relation is linear, but in practice this assumption is often in doubt, or perhaps the shape of the relation is a target for investigation. We investigate this issue in the context of Mendelian randomization, the use of genetic variants as instrumental variables. Methods: Using simulations, we demonstrate the performance of a simple linear instrumental variable method when the true shape of the exposure–outcome relation is not linear. We also present a novel method for estimating the effect of the exposure on the outcome within strata of the exposure distribution. This enables the estimation of localized average causal effects within quantile groups of the exposure or as a continuous function of the exposure using a sliding window approach. Results: Our simulations suggest that linear instrumental variable estimates approximate a population-averaged causal effect. This is the average difference in the outcome if the exposure for every individual in the population is increased by a fixed amount. Estimates of localized average causal effects reveal the shape of the exposure–outcome relation for a variety of models. These methods are used to investigate the relations between body mass index and a range of cardiovascular risk factors. Conclusions: Nonlinear exposure–outcome relations should not be a barrier to instrumental variable analyses. When the exposure–outcome relation is not linear, either a population-averaged causal effect or the shape of the exposure–outcome relation can be estimated.

Associations of growth trajectories in infancy and early childhood with later childhood outcomes

BACKGROUND Weight and length at birth (which represent fetal growth) and weight and length or height gain during childhood (which potentially represent catch-up growth) may be related to later health outcomes. However, methods for the assessment of such relations are complex and underdeveloped. OBJECTIVES We aimed to describe childhood weight and length or height trajectories and to relate these to later outcomes by using rash at age 6.5 y as an example. DESIGN The data came from a prospective cohort study in Belarus in 10,494 children born in 31 hospitals that participated in a cluster randomized trial of breastfeeding promotion. Weight and length or height were measured at birth, at scheduled clinic visits up to 1 y, and at 6.5 y; intermediate measures were obtained from routine child health records. Linear spline multilevel models for weight and length or height were used to estimate each childs deviance from average birth weight, birth length, weight, and length or height gain velocity in each time period. Logistic regression was used to relate the outcome (parental report of rash at 6.5 y) to these weight and length or height estimates. RESULTS The best-fitting splines for length or height and weight had knots at 3 and 12 mo, with another knot at 34 mo for height. The only relation between weight and length or height and reported rash was a positive association with weight gain velocity between 12 and 34 mo (odds ratio per SD increase in weight gain velocity: 1.11; 95% CI: 1.01, 1.22). CONCLUSION Advantages of multilevel models include no restriction to measures at arbitrary times or to individuals with complete data and allowance for measurement error. This trial was registered at isrctn.org as ISRCTN37687716.

The many weak instruments problem and Mendelian randomization

Instrumental variable estimates of causal effects can be biased when using many instruments that are only weakly associated with the exposure. We describe several techniques to reduce this bias and estimate corrected standard errors. We present our findings using a simulation study and an empirical application. For the latter, we estimate the effect of height on lung function, using genetic variants as instruments for height. Our simulation study demonstrates that, using many weak individual variants, two-stage least squares (2SLS) is biased, whereas the limited information maximum likelihood (LIML) and the continuously updating estimator (CUE) are unbiased and have accurate rejection frequencies when standard errors are corrected for the presence of many weak instruments. Our illustrative empirical example uses data on 3631 children from England. We used 180 genetic variants as instruments and compared conventional ordinary least squares estimates with results for the 2SLS, LIML, and CUE instrumental variable estimators using the individual height variants. We further compare these with instrumental variable estimates using an unweighted or weighted allele score as single instruments. In conclusion, the allele scores and CUE gave consistent estimates of the causal effect. In our empirical example, estimates using the allele score were more efficient. CUE with corrected standard errors, however, provides a useful additional statistical tool in applications with many weak instruments. The CUE may be preferred over an allele score if the population weights for the allele score are unknown or when the causal effects of multiple risk factors are estimated jointly.

MR-PheWAS: hypothesis prioritization among potential causal effects of body mass index on many outcomes, using Mendelian randomization.

Observational cohort studies can provide rich datasets with a diverse range of phenotypic variables. However, hypothesis-driven epidemiological analyses by definition only test particular hypotheses chosen by researchers. Furthermore, observational analyses may not provide robust evidence of causality, as they are susceptible to confounding, reverse causation and measurement error. Using body mass index (BMI) as an exemplar, we demonstrate a novel extension to the phenome-wide association study (pheWAS) approach, using automated screening with genotypic instruments to screen for causal associations amongst any number of phenotypic outcomes. We used a sample of 8,121 children from the ALSPAC dataset, and tested the linear association of a BMI-associated allele score with 172 phenotypic outcomes (with variable sample sizes). We also performed an instrumental variable analysis to estimate the causal effect of BMI on each phenotype. We found 21 of the 172 outcomes were associated with the allele score at an unadjusted p < 0.05 threshold, and use Bonferroni corrections, permutation testing and estimates of the false discovery rate to consider the strength of results given the number of tests performed. The most strongly associated outcomes included leptin, lipid profile, and blood pressure. We also found novel evidence of effects of BMI on a global self-worth score.