Multiple studies have demonstrated the effects of type 2 diabetes (T2D) on various human diseases; however, most of these were observational epidemiological studies that suffered from many potential biases including reported confounding and reverse causations. In this article, we investigated whether cancer and vascular disease can be affected by T2D-related traits, including fasting plasma glucose (FPG), 2-h postprandial glucose (2h-PG), and glycated hemoglobin A1c (HbA1c) levels, by using Mendelian randomization (MR). The summary statistics for FPG, 2h-PG, and HbA1c level were obtained through meta-analyses of large-scale genome-wide association studies that included data from 133,010 nondiabetic individuals from collaborating Meta-analysis of Glucose and Insulin Related Traits Consortium studies. Thereafter, based on the statistical assumptions for MR analyses, the most reliable approaches including inverse-variance-weighted (IVW), MR-Egger, MR-Egger with a simulation extrapolation (SIMEX), weighted median, and MR-pleiotropy residual sum and outlier (MR-PRESSO) methods were applied to identify traits affected by FPG, 2h-PG, and HbAlc. We found that coronary artery disease is affected by FPG, as per the IVW [log odds ratio (logOR): 0.21; P = 0.012], MR-Egger (SIMEX) (logOR: 0.22; P = 0.014), MR-PRESSO (logOR: 0.18; P = 0.045), and weighted median (logOR: 0.29; P < 0.001) methods but not as per the MR-Egger (logOR: 0.13; P = 0.426) approach. Furthermore, low-density lipoprotein cholesterol levels are affected by HbA1c, as per the IVW [beta (B): 0.23; P = 0.015), MR-Egger (B: 0.45; P = 0.046), MR-Egger (SIMEX) (B: 0.27; P = 0.007), MR-PRESSO (B; 0.14; P = 0.010), and the weighted median (B: 0.15; P = 0.012] methods. Further studies of the associated biological mechanisms are required to validate and understand the disease-specific differences identified in the TD2-related causal effects of each trait.

While smoking is widely acknowledged to be a social activity, limited evidence exists on the extent to which friends influence each other during worksite-based tobacco cessation interventions. Drawing on data from adult smokers (N = 1823) in a large, cluster randomized controlled trial in worksites in Thailand, this study examines the presence of social spillovers in the decision to abstain from smoking. We leverage a unique aspect of social network structure in these data-the existence of non-overlapping friendship networks-to address the challenge of isolating the effects of peers on smoking behavior from the confounding effects of endogenous friend selection and bidirectional peer influence. We find that individuals with workplace friends who have abstained from smoking during the trial are significantly more likely to abstain themselves. Instrumental variables estimates suggest that abstinence after 3 and 12 months increases 26 and 32 percentage points, respectively, for each additional workplace friend who abstains. These findings highlight the potential for workplace interventions to use existing social networks to magnify the effect of individual-level behavior change, particularly in low- and middle-income countries where tobacco cessation support tends to be limited.

Models of plant-plant interactions underpin our understanding of species coexistence, invasive plant impacts, and plant community responses to climate change. In recent studies, models of competitive interactions failed predictive tests, thereby casting doubt on results of many past studies. We believe these model failures owe at least partly to heterogeneity in unmodeled factors (e.g., nutrients, soil pathogens) that affect both target plants and neighboring competitors. Such heterogeneity is ubiquitous, and models that do not account for it will suffer omitted variable bias. We used instrumental variables analysis to test for and correct omitted variable bias in studies that followed common protocols for measuring plant competition. In an observational study, omitted variables caused competition to seem like mutualism. In a quasi-experiment that partially controlled competitor abundances with seeding, omitted variables caused competition to seem about 35% weaker than it really was, even though the experiment occurred in an abandoned agricultural field where environmental heterogeneity was expected to be relatively low. Despite decades of research, consistently accurate estimates of competitive interactions remain elusive. The most foolproof way around this problem is true experiments that avoid omitted variable bias by completely controlling competitor abundances, but such experiments are rare.

Instrumental variable (IV) analyses are a common causal inference technique used in the absence of randomized data. Combination Antiretroviral Therapy (cART) was first introduced in 1996 and calendar periods have been used as a proxy for cART use. However, cART use misclassification can bias IV analyses.
We aim to highlight the differences in the effects of antiretroviral therapy on clinical outcomes between the applications of traditional and adapted IV analysis techniques.
This study includes children with perinatal human immunodeficiency virus (HIV-1) infection followed from 1988 to 2009. We describe an application of traditional and adapted IV analysis techniques. Noncompliance adjustments were applied to correct the misclassification of cART-use. Weighting the inverse probability of calendar era, the selected covariates were performed to control for variables that may be related to both the IV and outcome.
During 48,380 person-days, 78 HIV-positive children progressed to an initial stage-3- defining diagnosis or death. The Intention to Treat (ITT) rate ratio (RR) of stage-3-defining diagnosis or death comparing the pre-cART and cART eras was estimated at 2·67 (95% confidence interval (CI): 1·.47, 4·84). The IV estimator was used to adjust for cART use misclassification, yielding an IV RR of 5·42 (95% CI: 2·99, 9·83). Weighting analyses did not markedly alter the results.
cART use decreased progression to stage-3-defining diagnosis or death. The use of noncompliance adjustments for cART misclassification in IV analyses may provide more robust evidence of cART\'s effectiveness than traditional ITT analysis.

The metabolic state of the body can be a major determinant of bone health. We used a Mendelian randomization approach to identify metabolites causally associated with bone mass to better understand the biological mechanisms of osteoporosis. We tested bone phenotypes (femoral neck, total hip, and lumbar spine bone mineral density [BMD]) for association with 280 fasting blood metabolites in 6055 women from TwinsUK cohort with genomewide genotyping scans. Causal associations between metabolites and bone phenotypes were further assessed in a bidirectional Mendelian randomization study using genetic markers/scores as instrumental variables. Significant associations were replicated in 624 participants from the Hong Kong Osteoporosis Study (HKOS). Fifteen metabolites showed direct associations with bone phenotypes after adjusting for covariates and multiple testing. Using genetic instruments, four of these metabolites were found to be causally associated with hip or spine BMD. These included androsterone sulfate, epiandrosterone sulfate, 5alpha-androstan-3beta17beta-diol disulfate (encoded by CYP3A5), and 4-androsten-3beta17beta-diol disulfate (encoded by SULT2A1). In the HKOS population, all four metabolites showed significant associations with hip and spine BMD in the expected directions. No causal reverse association between BMD and any of the metabolites were found. In the first metabolome-genomewide Mendelian randomization study of human bone mineral density, we identified four novel biomarkers causally associated with BMD. Our findings reveal novel biological pathways involved in the pathogenesis of osteoporosis. © 2017 American Society for Bone and Mineral Research.

BACKGROUND: Epidemiological studies suggest a potential role for obesity and determinants of adult stature in prostate cancer risk and mortality, but the relationships described in the literature are complex. To address uncertainty over the causal nature of previous observational findings, we investigated associations of height- and adiposity-related genetic variants with prostate cancer risk and mortality.
METHODS: We conducted a case-control study based on 20,848 prostate cancers and 20,214 controls of European ancestry from 22 studies in the PRACTICAL consortium. We constructed genetic risk scores that summed each man\'s number of height and BMI increasing alleles across multiple single nucleotide polymorphisms robustly associated with each phenotype from published genome-wide association studies.
RESULTS: The genetic risk scores explained 6.31 and 1.46% of the variability in height and BMI, respectively. There was only weak evidence that genetic variants previously associated with increased BMI were associated with a lower prostate cancer risk (odds ratio per standard deviation increase in BMI genetic score 0.98; 95% CI 0.96, 1.00; p = 0.07). Genetic variants associated with increased height were not associated with prostate cancer incidence (OR 0.99; 95% CI 0.97, 1.01; p = 0.23), but were associated with an increase (OR 1.13; 95 % CI 1.08, 1.20) in prostate cancer mortality among low-grade disease (p heterogeneity, low vs. high grade <0.001). Genetic variants associated with increased BMI were associated with an increase (OR 1.08; 95 % CI 1.03, 1.14) in all-cause mortality among men with low-grade disease (p heterogeneity = 0.03).
CONCLUSIONS: We found little evidence of a substantial effect of genetically elevated height or BMI on prostate cancer risk, suggesting that previously reported observational associations may reflect common environmental determinants of height or BMI and prostate cancer risk. Genetically elevated height and BMI were associated with increased mortality (prostate cancer-specific and all-cause, respectively) in men with low-grade disease, a potentially informative but novel finding that requires replication.

BACKGROUND: Economic interventions are increasingly recognised as a mechanism to address perinatal health outcomes among disadvantaged groups. In the US, the earned income tax credit (EITC) is the largest poverty alleviation programme. Little is known about its effects on perinatal health among recipients and their children. We exploit quasi-random variation in the size of EITC payments to examine the effects of income on perinatal health.
METHODS: The study sample includes women surveyed in the 1979 National Longitudinal Survey of Youth (n = 2985) and their children born during 1986-2000 (n = 4683). Outcome variables include utilisation of prenatal and postnatal care, use of alcohol and tobacco during pregnancy, term birth, birthweight, and breast-feeding status. We first examine the health effects of both household income and EITC payment size using multivariable linear regressions. We then employ instrumental variables analysis to estimate the causal effect of income on perinatal health, using EITC payment size as an instrument for household income.
RESULTS: We find that EITC payment size is associated with better levels of several indicators of perinatal health. Instrumental variables analysis, however, does not reveal a causal association between household income and these health measures.
CONCLUSIONS: Our findings suggest that associations between income and perinatal health may be confounded by unobserved characteristics, but that EITC income improves perinatal health. Future studies should continue to explore the impacts of economic interventions on perinatal health outcomes, and investigate how different forms of income transfers may have different impacts.