A new method for estimating the probability of causal relationships from observational data: Application to the study of the short-term effects of air pollution on cardiovascular and respiratory disease.

In this paper we investigate which airborne pollutants have a short-term causal effect on cardiovascular and respiratory disease using the Ancestral Probabilities (AP) procedure, a novel Bayesian approach for deriving the probabilities of causal relationships from observational data. The results are largely consistent with EPA assessments of causality, however, in a few cases AP suggests that some pollutants thought to cause cardiovascular or respiratory disease are associated due purely to confounding. The AP procedure utilizes maximal ancestral graph (MAG) models to represent and assign probabilities to causal relationships while accounting for latent confounding. The algorithm does so locally by marginalizing over models with and without causal features of interest. Before applying AP to real data, we evaluate it in a simulation study and investigate the benefits of providing background knowledge. Overall, the results suggest that AP is an effective tool for causal discovery.

Association and Interaction of Genetics and Area-Level Socioeconomic Factors on the Prevalence of Type 2 Diabetes and Obesity.

OBJECTIVE: Quantify the impact of genetic and socioeconomic factors on risk of type 2 diabetes (T2D) and obesity. RESEARCH DESIGN AND METHODS: Among participants in the Mass General Brigham Biobank (MGBB) and UK Biobank (UKB), we used logistic regression models to calculate cross-sectional odds of T2D and obesity using 1) polygenic risk scores for T2D and BMI and 2) area-level socioeconomic risk (educational attainment) measures. The primary analysis included 26,737 participants of European genetic ancestry in MGBB with replication in UKB (N = 223,843), as well as in participants of non-European ancestry (MGBB N = 3,468; UKB N = 7,459). RESULTS: The area-level socioeconomic measure most strongly associated with both T2D and obesity was percent without a college degree, and associations with disease prevalence were independent of genetic risk (P < 0.001 for each). Moving from lowest to highest quintiles of combined genetic and socioeconomic burden more than tripled T2D (3.1% to 22.2%) and obesity (20.9% to 69.0%) prevalence. Favorable socioeconomic risk was associated with lower disease prevalence, even in those with highest genetic risk (T2D 13.0% vs. 22.2%, obesity 53.6% vs. 69.0% in lowest vs. highest socioeconomic risk quintiles). Additive effects of genetic and socioeconomic factors accounted for 13.2% and 16.7% of T2D and obesity prevalence, respectively, explained by these models. Findings were replicated in independent European and non-European ancestral populations. CONCLUSIONS: Genetic and socioeconomic factors significantly interact to increase risk of T2D and obesity. Favorable area-level socioeconomic status was associated with an almost 50% lower T2D prevalence in those with high genetic risk.

LDmat: efficiently queryable compression of linkage disequilibrium matrices.

MOTIVATION: Linkage disequilibrium (LD) matrices derived from large populations are widely used in population genetics in fine-mapping, LD score regression, and linear mixed models for Genome-wide Association Studies (GWAS). However, these matrices can reach large sizes when they are derived from millions of individuals; hence, moving, sharing and extracting granular information from this large amount of data can be cumbersome. RESULTS: We sought to address the need for compressing and easily querying large LD matrices by developing LDmat. LDmat is a standalone tool to compress large LD matrices in an HDF5 file format and query these compressed matrices. It can extract submatrices corresponding to a sub-region of the genome, a list of select loci, and loci within a minor allele frequency range. LDmat can also rebuild the original file formats from the compressed files. AVAILABILITY AND IMPLEMENTATION: LDmat is implemented in python, and can be installed on Unix systems with the command 'pip install ldmat'. It can also be accessed through https://github.com/G2Lab/ldmat and https://pypi.org/project/ldmat/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

The impact of socioeconomic status on subsequent neurological outcomes in multiple sclerosis.

BACKGROUND: To examine whether lower neighborhood-level and individual-level indicators of socioeconomic status (SES) are associated with subsequently worse neurological disability in people with MS (pwMS). METHODS: In a multi-center study using prospectively collected data from discovery cohorts (University of Pittsburgh, N=1316) and replication cohorts (Columbia University, N=488), we calculated a neighborhood SES indicator, area deprivation index (ADI), based on participants' residence at enrollment, and we derived an individual SES indicator based on participants' household income. Patient-reported neurological outcomes included the Multiple Sclerosis Rating Scale-Revised (MSRS-R), Patient-Determined Disease Steps (PDDS), and Patient-Reported Outcomes Measurement Information System (PROMIS) Physical Function scores from 2018 to 2020. We performed covariate-adjusted regression analyses in each cohort and then random-effects meta-analyses. RESULTS: Higher ADI (lower SES) in 2015 was associated with subsequently worse neurological outcomes during 2018-2020 (discovery: MSRS-R, ß=0.62, 95%CI [0.36,0.89], p<0.001; PDDS, ß=0.11, 95%CI [0.02,0.20], p=0.02 | replication: MSRS-R, ß=0.46, 95%CI [0.21,0.72], p<0.001; PDDS, ß=0.12, 95%CI [0.03,0.21], p=0.009, PROMIS, ß=-0.60, 95%CI [-1.12,-0.08], p=0.025). Lower neighborhood percent with college education (MSRS-R, ß=-7.31, 95%CI [-8.99,-5.64], p<0.001; PDDS, ß=-1.62, 95%CI [-2.20,-1.05], p<0.001; PROMIS, ß=9.31, 95%CI [5.73,12.89], p<0.001), neighborhood median household income (MSRS-R, ß=-3.80e-05, 95%CI [-5.05e-05,-2.56e-05], p<0.001; PDDS, ß=-8.58e-06, 95%CI [-1.28e-05,-4.32e-06], p<0.001; PROMIS, ß=2.55e-05, 95%CI [5.96e-07,5.05e-05], p=0.045), and neighborhood median home value (MSRS-R, ß=-6.50e-06, 95%CI [-8.16e-06,-4.84e-06], p<0.001; PDDS, ß=-1.54e-06, 95%CI [-2.11e-06,-9.65e-07], p<0.001; PROMIS, ß=4.98e-06, 95%CI [1.81e-06,8.14e-06], p=0.002) drove the association between higher ADI and subsequently worse neurological disability (in joint analyses). Neighborhood percent of population with Medicaid, but not private insurance, significantly mediated the observed covariate-adjusted associations. Higher individual-level household income bracket was associated with better neurological outcomes in joint analyses (MSRS-R: R=-0.39, p<0.001; PDDS: R=-0.35, p<0.001; PROMIS: R=0.37, p<0.001), independent of ADI. CONCLUSIONS: Lower neighborhood SES is associated with subsequently worse neurological outcomes in pwMS. Future testing of targeted intervention through public policies that improve SES are warranted.

Identifying Communities at Risk for COVID-19-Related Burden Across 500 US Cities and Within New York City: Unsupervised Learning of the Coprevalence of Health Indicators.

BACKGROUND: Although it is well-known that older individuals with certain comorbidities are at the highest risk for complications related to COVID-19 including hospitalization and death, we lack tools to identify communities at the highest risk with fine-grained spatial resolution. Information collected at a county level obscures local risk and complex interactions between clinical comorbidities, the built environment, population factors, and other social determinants of health. OBJECTIVE: This study aims to develop a COVID-19 community risk score that summarizes complex disease prevalence together with age and sex, and compares the score to different social determinants of health indicators and built environment measures derived from satellite images using deep learning. METHODS: We developed a robust COVID-19 community risk score (COVID-19 risk score) that summarizes the complex disease co-occurrences (using data for 2019) for individual census tracts with unsupervised learning, selected on the basis of their association with risk for COVID-19 complications such as death. We mapped the COVID-19 risk score to corresponding zip codes in New York City and associated the score with COVID-19-related death. We further modeled the variance of the COVID-19 risk score using satellite imagery and social determinants of health. RESULTS: Using 2019 chronic disease data, the COVID-19 risk score described 85% of the variation in the co-occurrence of 15 diseases and health behaviors that are risk factors for COVID-19 complications among ~28,000 census tract neighborhoods (median population size of tracts 4091). The COVID-19 risk score was associated with a 40% greater risk for COVID-19-related death across New York City (April and September 2020) for a 1 SD change in the score (risk ratio for 1 SD change in COVID-19 risk score 1.4; P<.001) at the zip code level. Satellite imagery coupled with social determinants of health explain nearly 90% of the variance in the COVID-19 risk score in the United States in census tracts (r2=0.87). CONCLUSIONS: The COVID-19 risk score localizes risk at the census tract level and was able to predict COVID-19-related mortality in New York City. The built environment explained significant variations in the score, suggesting risk models could be enhanced with satellite imagery.

Comparisons of Polyexposure, Polygenic, and Clinical Risk Scores in Risk Prediction of Type 2 Diabetes.

OBJECTIVE: To establish a polyexposure score (PXS) for type 2 diabetes (T2D) incorporating 12 nongenetic exposures and examine whether a PXS and/or a polygenic risk score (PGS) improves diabetes prediction beyond traditional clinical risk factors. RESEARCH DESIGN AND METHODS: We identified 356,621 unrelated individuals from the UK Biobank of White British ancestry with no prior diagnosis of T2D and normal HbA1c levels. Using self-reported and hospital admission information, we deployed a machine learning procedure to select the most predictive and robust factors out of 111 nongenetically ascertained exposure and lifestyle variables for the PXS in prospective T2D. We computed the clinical risk score (CRS) and PGS by taking a weighted sum of eight established clinical risk factors and >6 million single nucleotide polymorphisms, respectively. RESULTS: In the study population, 7,513 had incident T2D. The C-statistics for the PGS, PXS, and CRS models were 0.709, 0.762, and 0.839, respectively. Individuals in the top 10% of PGS, PXS, and CRS had 2.00-, 5.90-, and 9.97-fold greater risk, respectively, compared to the remaining population. Addition of PGS and PXS to CRS improved T2D classification accuracy, with a continuous net reclassification index of 15.2% and 30.1% for cases, respectively, and 7.3% and 16.9% for controls, respectively. CONCLUSIONS: For T2D, the PXS provides modest incremental predictive value over established clinical risk factors. However, the concept of PXS merits further consideration in T2D risk stratification and is likely to be useful in other chronic disease risk prediction models.

Geospatial Analysis of Individual and Community-Level Socioeconomic Factors Impacting SARS-CoV-2 Prevalence and Outcomes.

Background: The SARS-CoV-2 pandemic has disproportionately affected racial and ethnic minority communities across the United States. We sought to disentangle individual and census tract-level sociodemographic and economic factors associated with these disparities. Methods and Findings: All adults tested for SARS-CoV-2 between February 1 and June 21, 2020 were geocoded to a census tract based on their address; hospital employees and individuals with invalid addresses were excluded. Individual (age, sex, race/ethnicity, preferred language, insurance) and census tract-level (demographics, insurance, income, education, employment, occupation, household crowding and occupancy, built home environment, and transportation) variables were analyzed using linear mixed models predicting infection, hospitalization, and death from SARS-CoV-2.Among 57,865 individuals, per capita testing rates, individual (older age, male sex, non-White race, non-English preferred language, and non-private insurance), and census tract-level (increased population density, higher household occupancy, and lower education) measures were associated with likelihood of infection. Among those infected, individual age, sex, race, language, and insurance, and census tract-level measures of lower education, more multi-family homes, and extreme household crowding were associated with increased likelihood of hospitalization, while higher per capita testing rates were associated with decreased likelihood. Only individual-level variables (older age, male sex, Medicare insurance) were associated with increased mortality among those hospitalized. Conclusions: This study of the first wave of the SARS-CoV-2 pandemic in a major U.S. city presents the cascade of outcomes following SARS-CoV-2 infection within a large, multi-ethnic cohort. SARS-CoV-2 infection and hospitalization rates, but not death rates among those hospitalized, are related to census tract-level socioeconomic characteristics including lower educational attainment and higher household crowding and occupancy, but not neighborhood measures of race, independent of individual factors.

Author Correction: Repurposing large health insurance claims data to estimate genetic and environmental contributions in 560 phenotypes.

In the version of this article initially published, in Fig. 4b, the shared environmental variance (c2) values for all MaTCH functional domains except 'all traits' were erroneously estimated because of a coding error. Figure 4 has been revised to include corrected c2 estimates in the data in panel b as well as the number of phenotypes in CaTCH and MaTCH functional domains in the y axes of panels a and b; the Fig. 4 legend and the description of Fig. 4b in the Results section have also been revised to describe these changes. In addition, the erroneous term 'depravity index', appearing throughout the article's main text, Fig. 1, Supplementary Fig. 10 and the Supplementary Note, should have read 'deprivation index'. The errors have been corrected in the HTML and PDF versions of the article. Images of the original figure are shown in the correction notice.

Repurposing large health insurance claims data to estimate genetic and environmental contributions in 560 phenotypes.

We analysed a large health insurance dataset to assess the genetic and environmental contributions of 560 disease-related phenotypes in 56,396 twin pairs and 724,513 sibling pairs out of 44,859,462 individuals that live in the United States. We estimated the contribution of environmental risk factors (socioeconomic status (SES), air pollution and climate) in each phenotype. Mean heritability (h2 = 0.311) and shared environmental variance (c2 = 0.088) were higher than variance attributed to specific environmental factors such as zip-code-level SES (varSES = 0.002), daily air quality (varAQI = 0.0004), and average temperature (vartemp = 0.001) overall, as well as for individual phenotypes. We found significant heritability and shared environment for a number of comorbidities (h2 = 0.433, c2 = 0.241) and average monthly cost (h2 = 0.290, c2 = 0.302). All results are available using our Claims Analysis of Twin Correlation and Heritability (CaTCH) web application.

An efficient synthesis and in vitro antibacterial evaluation of ruthenium-quinolinol complexes.

A series of ruthenium-quinolinol complexes were synthesized using a simple and effective pathway and their in vitro antibacterial activity against various resistant gram-positive and gram-negative bacteria were evaluated. It was established that compound [(η6-pcymene)RuCl(κ2-O,N-5,7-dibromo-HyQ)]·Cl (3b) & [(η6-pcymene)RuCl(κ2-O,N-5,7-dibromo-HyQ)]·Cl (3e) were significantly active against Staphylococcus aureus, Escherichia coli, Bacillus subtilis &Salmonella sp. The structures of the new compounds were elucidated by the analysis of spectroscopic data. The stability of complex [(η6-pcymene)RuCl(κ2-O,N-5,7-dibromo-HyQ)]·Cl (3b) was measured by UV spectroscopy & time dependent NMR spectroscopy. Compound 3b also shows remarkable fluorescence.