A collection of 1,108 case-parent trios ascertained through an isolated, non-syndromic cleft lip with or without cleft palate (CL/P) was used to replicate the findings from a genome-wide association study (GWAS) conducted by Beaty et al. (2010) where four different genes/regions were identified as influencing risk to CL/P. Tagging SNPs for 33 different genes were genotyped (1,269 SNPs). All four of the genes originally identified as showing genome-wide significance (IRF6, ABCA4 and MAF, plus the 8q24 region) were confirmed in this independent sample of trios (who were primarily of European and Southeast Asian ancestry). In addition, eight genes classified as ‘second tier’ hits in the original study (PAX7, THADA, COL8A1/FILIP1L, DCAF4L2, GADD45G, NTN1, RBFOX3 and FOXE1) showed evidence of linkage and association in this replication sample. Meta-analysis between the original GWAS trios and these replication trios showed PAX7, COL8A1/FILIP1L and NTN1 achieved genome-wide significance. Tests for gene-environment interaction between these 33 genes and maternal smoking found evidence for interaction with two additional genes: GRID2 and ELAVL2 among European mothers (who had a higher rate of smoking than Asian mothers). Formal tests for gene-gene interaction (epistasis) failed to show evidence of statistical interaction in any simple fashion. This study confirms that many different genes influence risk to CL/P.

OBJECTIVEWe performed a systematic review to identify which genetic variants predict response to diabetes medications.RESEARCH DESIGN AND METHODSWe performed a search of electronic databases (PubMed, EMBASE, and Cochrane Database) and a manual search to identify original, longitudinal studies of the effect of diabetes medications on incident diabetes, HbA1c, fasting glucose, and postprandial glucose in prediabetes or type 2 diabetes by genetic variation. Two investigators reviewed titles, abstracts, and articles independently. Two investigators abstracted data sequentially and evaluated study quality independently. Quality evaluations were based on the Strengthening the Reporting of Genetic Association Studies guidelines and Human Genome Epidemiology Network guidance.RESULTSOf 7,279 citations, we included 34 articles (N = 10,407) evaluating metformin (n = 14), sulfonylureas (n = 4), repaglinide (n = 8), pioglitazone (n = 3), rosiglitazone (n = 4), and acarbose (n = 4). Studies were not standalone randomized controlled trials, and most evaluated patients with diabetes. Significant medication–gene interactions for glycemic outcomes included 1) metformin and the SLC22A1, SLC22A2, SLC47A1, PRKAB2, PRKAA2, PRKAA1, and STK11 loci; 2) sulfonylureas and the CYP2C9 and TCF7L2 loci; 3) repaglinide and the KCNJ11, SLC30A8, NEUROD1/BETA2, UCP2, and PAX4 loci; 4) pioglitazone and the PPARG2 and PTPRD loci; 5) rosiglitazone and the KCNQ1 and RBP4 loci; and 5) acarbose and the PPARA, HNF4A, LIPC, and PPARGC1A loci. Data were insufficient for meta-analysis.CONCLUSIONSWe found evidence of pharmacogenetic interactions for metformin, sulfonylureas, repaglinide, thiazolidinediones, and acarbose consistent with their pharmacokinetics and pharmacodynamics. While high-quality controlled studies with prespecified analyses are still lacking, our results bring the promise of personalized medicine in diabetes one step closer to fruition.

Inorganic arsenic exposure is ubiquitous and both exposure and inter-individual differences in its metabolism have been associated with cardiometabolic risk. The association between arsenic exposure and arsenic metabolism with metabolic syndrome and its individual components, however, is relatively unknown. We used poisson regression with robust variance to evaluate the association between baseline arsenic exposure (urine arsenic levels) and metabolism (relative percentage of arsenic species over their sum) with incident metabolic syndrome and its individual components (elevated waist circumference, elevated triglycerides, reduced HDL, hypertension, elevated fasting plasma glucose) in 1,047 participants from the Strong Heart Family Study, a prospective family-based cohort in American Indian communities (baseline visits in 1998-1999 and 2001-2003, follow-up visits in 2001-2003 and 2006-2009). 32% of participants developed metabolic syndrome over follow-up. An IQR increase in arsenic exposure was associated with 1.19 (95% CI: 1.01, 1.41) greater risk for elevated fasting plasma glucose but not with other individual components or overall metabolic syndrome. Arsenic metabolism, specifically lower MMA% and higher DMA% was associated with higher risk of overall metabolic syndrome and elevated waist circumference, but not with any other component. These findings support there is a contrasting and independent association between arsenic exposure and arsenic metabolism with metabolic outcomes which may contribute to overall diabetes risk.

Background:High arsenic exposure has been related to diabetes, but at low-moderate levels the evidence is mixed. Arsenic metabolism, which is partly genetically controlled and may rely on certain B vitamins, plays a role in arsenic toxicity.Objective:We evaluated the prospective association of arsenic exposure and metabolism with type 2 diabetes and insulin resistance.Methods:We included 1,838 American Indian men and women free of diabetes (median age, 36 y). Arsenic exposure was assessed as the sum of inorganic arsenic (iAs), monomethylarsonate (MMA), and dimethylarsinate (DMA) urine concentrations (ΣAs). Arsenic metabolism was evaluated by the proportions of iAs, MMA, and DMA over their sum (iAs%, MMA%, and DMA%). Homeostasis model assessment for insulin resistance (HOMA2-IR) was measured at baseline and follow-up visits. Incident diabetes was evaluated at follow-up.Results:Median ΣAs, iAs%, MMA%, and DMA% was 4.4μg/g creatinine, 9.5%, 14.4%, and 75.6%, respectively. Over 10,327 person-years of follow-up, 252 participants developed diabetes. Median HOMA2-IR at baseline was 1.5. The fully adjusted hazard ratio [95% confidence interval (CI)] for incident diabetes per an interquartile range increase in ΣAs was 1.57 (95% CI: 1.18, 2.08) in participants without prediabetes at baseline. Arsenic metabolism was not associated with incident diabetes. ΣAs was positively associated with HOMA2-IR at baseline but negatively with HOMA2-IR at follow-up. Increased MMA% was associated with lower HOMA2-IR when either iAs% or DMA% decreased. The association of arsenic metabolism with HOMA2-IR differed by B-vitamin intake and AS3MT genetics variants.Conclusions:Among participants without baseline prediabetes, arsenic exposure was associated with incident diabetes. Low MMA% was cross-sectional and prospectively associated with higher HOMA2-IR. Research is needed to confirm possible interactions of arsenic metabolism with B vitamins and AS3MT variants on diabetes risk. https://doi.org/10.1289/EHP2566

Background:Metabolism of inorganic arsenic (iAs) is subject to inter-individual variability, which is explained partly by genetic determinants.Objectives:We investigated the association of genetic variants with arsenic species and principal components of arsenic species in the Strong Heart Family Study (SHFS).Methods:We examined variants previously associated with cardiometabolic traits (~ 200,000 from Illumina Cardio MetaboChip) or arsenic metabolism and toxicity (670) among 2,428 American Indian participants in the SHFS. Urine arsenic species were measured by high performance liquid chromatography–inductively coupled plasma mass spectrometry (HPLC-ICP-MS), and percent arsenic species [iAs, monomethylarsonate (MMA), and dimethylarsinate (DMA), divided by their sum × 100] were logit transformed. We created two orthogonal principal components that summarized iAs, MMA, and DMA and were also phenotypes for genetic analyses. Linear regression was performed for each phenotype, dependent on allele dosage of the variant. Models accounted for familial relatedness and were adjusted for age, sex, total arsenic levels, and population stratification. Single nucleotide polymorphism (SNP) associations were stratified by study site and were meta-analyzed. Bonferroni correction was used to account for multiple testing.Results:Variants at 10q24 were statistically significant for all percent arsenic species and principal components of arsenic species. The index SNP for iAs%, MMA%, and DMA% (rs12768205) and for the principal components (rs3740394, rs3740393) were located near AS3MT, whose gene product catalyzes methylation of iAs to MMA and DMA. Among the candidate arsenic variant associations, functional SNPs in AS3MT and 10q24 were most significant (p < 9.33 × 10–5).Conclusions:This hypothesis-driven association study supports the role of common variants in arsenic metabolism, particularly AS3MT and 10q24.Citation:Balakrishnan P, Vaidya D, Franceschini N, Voruganti VS, Gribble MO, Haack K, Laston S, Umans JG, Francesconi KA, Goessler W, North KE, Lee E, Yracheta J, Best LG, MacCluer JW, Kent J Jr., Cole SA, Navas-Acien A. 2017. Association of cardiometabolic genes with arsenic metabolism biomarkers in American Indian communities: the Strong Heart Family Study (SHFS). Environ Health Perspect 125:15–22; http://dx.doi.org/10.1289/EHP251

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Antihypertensive medications complicate studies of blood pressure (BP) natural history; BP if untreated (“underlying BP”) needs to be estimated. Our objectives were to compare validity of five missing data imputation methods to estimate underlying BP and longitudinal associations of underlying BP and age. We simulated BP treatment in untreated hypertensive participants from Atherosclerosis Risk in Communities (ARIC) in visits 1–5 (1987–2013) using matched treated hypertensive participants. The underlying BP was imputed: #1, set as missing; #2, add 10 mmHg for systolic, 5 mmHg for diastolic; #3, add medication class-specific constant; #4, truncated normal regression; and #5, truncated normal regression including prior visit data. Longitudinal associations were estimated using linear mixed models of imputed underlying BP for simulated treated and measured BP for untreated participants. Method 3 was the best-performing for systolic BP; lowest relative bias (5.3% for intercept at age 50, 0% for age coefficient) and average deviation from expected (0.04 to -1.79). Method 2 performed best for diastolic BP; lowest relative bias (0.6% intercept at age 50, 33.3% age <60, 9.1% age 60+) and average deviation (-1.25 to -1.68). Methods 4 and 5 were comparable or slightly inferior. In conclusion, constant addition methods yielded valid and precise underlying BP and longitudinal associations.

Cadmium is an environmental pollutant that has been associated with cardiovascular disease in populations, but the relationship of cadmium with hypertension has been inconsistent. We studied the association between urinary cadmium concentrations, a measure of total body burden, and blood pressure in American Indians, a U.S. population with above national average cadmium burden. Urinary cadmium (Cd) was measured using inductively coupled plasma mass spectrometry, and adjusted for urinary creatinine concentration. Among 3,714 middle-aged American Indian participants of the Strong Heart Study (mean age 56 years, 41% male, 67% ever-smokers, 23% taking anti-hypertensive medications), urinary Cd ranged from 0.01 to 78.48 μg/g creatinine (geometric mean=0.94 μg/g) and it was correlated with smoking pack-year among ever-smokers (r2=0.16, P<0.0001). Participants who were smokers were on average light smokers (mean 10.8 pack-years), and urinary Cd was similarly elevated in light- and never-smokers (geometric means of 0.88 μg/g creatinine for both categories). Log-transformed urinary Cd was significantly associated with higher systolic blood pressure in models adjusted for age, sex, geographic area, body mass index, smoking (ever vs. never, and cumulative pack-years) and kidney function (mean blood pressure difference by lnCd concentration [β]=1.64, P=0.002). These associations were present among light- and never-smokers (β=2.03, P=0.002, n=2,627), although not significant among never-smokers (β=1.22, P=0.18, n=1,260). Cd was also associated with diastolic blood pressure among light- and never-smokers (β=0.94, P=0.004). These findings suggest there is a relationship between cadmium body burden and increased blood pressure in American Indians, a population with increased cardiovascular disease risk.