Eric Harshfield

Evaluating the Sustained Health Impact of Household Chlorination of Drinking Water in Rural Haiti

The Jolivert Safe Water for Families program has sold sodium hypochlorite solution (chlorine) and conducted household visits in rural Haiti since 2002. To assess the impact of the program on diarrheal disease, in 2010 we conducted a survey and water quality testing in 201 program participants and 425 control households selected at random. Fifty-six percent of participants (versus 10% of controls) had free chlorine residuals between 0.2 and 2.0 mg/L, indicating correct water treatment. Using intention-to-treat analysis, we found that significantly fewer children < 5 in participant households had an episode of diarrhea in the previous 48 hours (32% versus 52%; P < 0.001) with 59% reduced odds (odds ratio = 0.41, 95% confidence interval = 0.21–0.79). Treatment-on-treated estimates of the odds of diarrhea indicated larger program effects for participants who met more stringent verifications of participation. Diarrheal disease reduction in this long-term program was comparable with that seen in short-term randomized, controlled interventions, suggesting that household chlorination can be an effective long-term water treatment strategy.

Mendelian randomization to assess causal effects of blood lipids on coronary heart disease: lessons from the past and applications to the future

Purpose of reviewMendelian randomization is a technique for judging the causal impact of a risk factor on an outcome from observational data using genetic variants. Although evidence from Mendelian randomization for the effects of major lipids and lipoproteins on coronary heart disease (CHD) risk has been around for a relatively long time, new data resources and new methodological approaches have given fresh insight into these relationships. The lessons from these analyses are likely to be highly relevant when it comes to lipidomics and the analyses of lipid subspecies whose biology is less well understood. Recent findingsAlthough analyses of low-density lipoprotein cholesterol and lipoprotein(a) are unambiguous as there are genetic variants that associate exclusively with these risk factors and have well understood biology, analyses for triglycerides, and high-density lipoprotein cholesterol (HDL-c) are less clear. For example, a subset of genetic variants having specific associations with HDL-c are not associated with CHD risk, but an allele score including all variants associated with HDL-c does associate with CHD risk. Recently developed methods, such as multivariable Mendelian randomization, Mendelian randomization-Egger, and a weighted median method, suggest that the relationship between HDL-c and CHD risk is null, thus confirming experimental evidence. SummaryRobust methods for Mendelian randomization have important utility for understanding the causal relationships between major lipids and CHD risk, and are likely to play an important role in judging the causal relevance of lipid subspecies and other metabolites measured on high-dimensional phenotyping platforms.

Association of hypertension and hyperglycaemia with socioeconomic contexts in resource-poor settings: the Bangladesh Demographic and Health Survey

BACKGROUND Cardiovascular diseases and risk factors are disproportionally concentrated among the socioeconomically disadvantaged in high-income countries; however, this relationship is not well-understood or documented in resource-limited countries. METHODS We analysed data from the 2011 Bangladesh Demographic and Health Survey to estimate age-, sex- and location-adjusted differences in blood pressure and blood glucose outcomes by categories of a standardized wealth index and education levels. Body mass index (BMI) was examined as a secondary outcome and also assessed as a potential confounder. RESULTS There was strong evidence that the prevalence of hypertension was higher among Bangladeshi women than among men (33.6% vs 19.6%, P < 0.001), whereas the overall prevalence of hyperglycaemia was 7.1% with no evidence of sex differences. The likelihood of having hypertension was more than double for individuals in the highest vs lowest wealth quintile [odds ratio (OR) for men: 2.82, 95% confidence interval (CI): 2.32-3.44; OR for women: 2.25, 95% CI: 1.90-2.67], and for individuals with the highest level of education attained vs those with no education (OR for men: 2.55, 95% CI: 2.06-3.16; OR for women: 1.42, 95% CI: 0.99-2.03). Likewise, the likelihood of having hyperglycaemia was more than four times higher in the wealthiest compared with the poorest individuals (OR for men: 6.48, 95% CI: 5.11-8.22; OR for women: 4.77, 95% CI: 3.72-6.12), and in individuals with the highest level of education attained vs those with no education (OR for men: 4.68, 95% CI: 3.56-6.15; OR for women: 5.02, 95% CI: 3.30-7.64). There were no appreciable differences in these trends when stratified by geographical location. BMI did not attenuate these associations and exhibited similarly positive associations with education and wealth. CONCLUSIONS Increasing levels of wealth and educational attainment were associated with an increased likelihood of having hypertension and hyperglycaemia in Bangladesh.

ProGeM: a framework for the prioritization of candidate causal genes at molecular quantitative trait loci

Abstract Quantitative trait locus (QTL) mapping of molecular phenotypes such as metabolites, lipids and proteins through genome-wide association studies represents a powerful means of highlighting molecular mechanisms relevant to human diseases. However, a major challenge of this approach is to identify the causal gene(s) at the observed QTLs. Here, we present a framework for the ‘Prioritization of candidate causal Genes at Molecular QTLs’ (ProGeM), which incorporates biological domain-specific annotation data alongside genome annotation data from multiple repositories. We assessed the performance of ProGeM using a reference set of 227 previously reported and extensively curated metabolite QTLs. For 98% of these loci, the expert-curated gene was one of the candidate causal genes prioritized by ProGeM. Benchmarking analyses revealed that 69% of the causal candidates were nearest to the sentinel variant at the investigated molecular QTLs, indicating that genomic proximity is the most reliable indicator of ‘true positive’ causal genes. In contrast, cis-gene expression QTL data led to three false positive candidate causal gene assignments for every one true positive assignment. We provide evidence that these conclusions also apply to other molecular phenotypes, suggesting that ProGeM is a powerful and versatile tool for annotating molecular QTLs. ProGeM is freely available via GitHub.

From lipid locus to drug target through human genomics

In the last decade, over 175 genetic loci have robustly been associated to levels of major circulating blood lipids. Most loci are specific to one or two lipids, whereas some (SUGP1, ZPR1, TRIB1, HERPUD1, and FADS1) are associated to all. While exposing the polygenic architecture of circulating lipids and the underpinnings of dyslipidaemia, these genome-wide association studies (GWAS) have provided further evidence of the critical role that lipids play in coronary heart disease (CHD) risk, as indicated by the 2.7-fold enrichment for macrophage gene expression in atherosclerotic plaques and the association of 25 loci (such as PCSK9, APOB, ABCG5-G8, KCNK5, LPL, HMGCR, NPC1L1, CETP, TRIB1, ABO, PMAIP1-MC4R, and LDLR) with CHD. These GWAS also confirmed known and commonly used therapeutic targets, including HMGCR (statins), PCSK9 (antibodies), and NPC1L1 (ezetimibe). As we head into the post-GWAS era, we offer suggestions for how to move forward beyond genetic risk loci, towards refining the biology behind the associations and identifying causal genes and therapeutic targets. Deep phenotyping through lipidomics and metabolomics will refine and increase the resolution to find causal and druggable targets, and studies aimed at demonstrating gene transcriptional and regulatory effects of lipid associated loci will further aid in identifying these targets. Thus, we argue the need for deeply phenotyped, large genetic association studies to reduce costs and failures and increase the efficiency of the drug discovery pipeline. We conjecture that in the next decade a paradigm shift will tip the balance towards a data-driven approach to therapeutic target development and the application of precision medicine where human genomics takes centre stage.