Sociobiome - Individual and neighborhood socioeconomic status influence the gut microbiome in a multi-ethnic population in the US.

Lower socioeconomic status (SES) is related to increased incidence and mortality due to chronic diseases in adults. Association between SES variables and gut microbiome variation has been observed in adults at the population level, suggesting that biological mechanisms may underlie the SES associations; however, there is a need for larger studies that consider individual- and neighborhood-level measures of SES in racially diverse populations. In 825 participants from a multi-ethnic cohort, we investigated how SES shapes the gut microbiome. We determined the relationship of a range of individual- and neighborhood-level SES indicators with the gut microbiome. Individual education level and occupation were self-reported by questionnaire. Geocoding was applied to link participants' addresses with neighborhood census tract socioeconomic indicators, including average income and social deprivation in the census tract. Gut microbiome was measured using 16SV4 region rRNA gene sequencing of stool samples. We compared α-diversity, ß-diversity, and taxonomic and functional pathway abundance by SES. Lower SES was significantly associated with greater α-diversity and compositional differences among groups, as measured by ß-diversity. Several taxa related to low SES were identified, especially an increasing abundance of Prevotella copri and Catenibacterium sp000437715, and decreasing abundance of Dysosmobacter welbionis in terms of their high log-fold change differences. In addition, nativity and race/ethnicity have emerged as ecosocial factors that also influence the gut microbiota. Together, these results showed that lower SES was strongly associated with compositional and taxonomic measures of the gut microbiome, and may contribute to shaping the gut microbiota.

A microbial causal mediation analytic tool for health disparity and applications in body mass index.

BACKGROUND: Emerging evidence suggests the potential mediating role of microbiome in health disparities. However, no analytic framework can be directly used to analyze microbiome as a mediator between health disparity and clinical outcome, due to the non-manipulable nature of the exposure and the unique structure of microbiome data, including high dimensionality, sparsity, and compositionality. METHODS: Considering the modifiable and quantitative features of the microbiome, we propose a microbial causal mediation model framework, SparseMCMM_HD, to uncover the mediating role of microbiome in health disparities, by depicting a plausible path from a non-manipulable exposure (e.g., ethnicity or region) to the outcome through the microbiome. The proposed SparseMCMM_HD rigorously defines and quantifies the manipulable disparity measure that would be eliminated by equalizing microbiome profiles between comparison and reference groups and innovatively and successfully extends the existing microbial mediation methods, which are originally proposed under potential outcome or counterfactual outcome study design, to address health disparities. RESULTS: Through three body mass index (BMI) studies selected from the curatedMetagenomicData 3.4.2 package and the American gut project: China vs. USA, China vs. UK, and Asian or Pacific Islander (API) vs. Caucasian, we exhibit the utility of the proposed SparseMCMM_HD framework for investigating the microbiome's contributions in health disparities. Specifically, BMI exhibits disparities and microbial community diversities are significantly distinctive between reference and comparison groups in all three applications. By employing SparseMCMM_HD, we illustrate that microbiome plays a crucial role in explaining the disparities in BMI between ethnicities or regions. 20.63%, 33.09%, and 25.71% of the overall disparity in BMI in China-USA, China-UK, and API-Caucasian comparisons, respectively, would be eliminated if the between-group microbiome profiles were equalized; and 15, 18, and 16 species are identified to play the mediating role respectively. CONCLUSIONS: The proposed SparseMCMM_HD is an effective and validated tool to elucidate the mediating role of microbiome in health disparity. Three BMI applications shed light on the utility of microbiome in reducing BMI disparity by manipulating microbial profiles. Video Abstract.

Risk-Stratified Screening for Colorectal Cancer Using Genetic and Environmental Risk Factors: A Cost-Effectiveness Analysis Based on Real-World Data.

BACKGROUND & AIMS: Previous studies on the cost-effectiveness of personalized colorectal cancer (CRC) screening were based on hypothetical performance of CRC risk prediction and did not consider the association with competing causes of death. In this study, we estimated the cost-effectiveness of risk-stratified screening using real-world data for CRC risk and competing causes of death. METHODS: Risk predictions for CRC and competing causes of death from a large community-based cohort were used to stratify individuals into risk groups. A microsimulation model was used to optimize colonoscopy screening for each risk group by varying the start age (40-60 years), end age (70-85 years), and screening interval (5-15 years). The outcomes included personalized screening ages and intervals and cost-effectiveness compared with uniform colonoscopy screening (ages 45-75, every 10 years). Key assumptions were varied in sensitivity analyses. RESULTS: Risk-stratified screening resulted in substantially different screening recommendations, ranging from a one-time colonoscopy at age 60 for low-risk individuals to a colonoscopy every 5 years from ages 40 to 85 for high-risk individuals. Nevertheless, on a population level, risk-stratified screening would increase net quality-adjusted life years gained (QALYG) by only 0.7% at equal costs to uniform screening or reduce average costs by 1.2% for equal QALYG. The benefit of risk-stratified screening improved when it was assumed to increase participation or costs less per genetic test. CONCLUSIONS: Personalized screening for CRC, accounting for competing causes of death risk, could result in highly tailored individual screening programs. However, average improvements across the population in QALYG and cost-effectiveness compared with uniform screening are small.

A microbial causal mediation analytic tool for health disparity and applications in body mass index.

Background: Emerging evidence suggests the potential mediating role of microbiome in health disparities. However, no analytic framework is available to analyze microbiome as a mediator between health disparity and clinical outcome, due to the unique structure of microbiome data, including high dimensionality, sparsity, and compositionality. Methods: Considering the modifiable and quantitative features of microbiome, we propose a microbial causal mediation model framework, SparseMCMM_HD, to uncover the mediating role of microbiome in health disparities, by depicting a plausible path from a non-manipulable exposure (e.g. race or region) to a continuous outcome through microbiome. The proposed SparseMCMM_HD rigorously defines and quantifies the manipulable disparity measure that would be eliminated by equalizing microbiome profiles between comparison and reference groups. Moreover, two tests checking the impact of microbiome on health disparity are proposed. Results: Through three body mass index (BMI) studies selected from the curatedMetagenomicData 3.4.2 package and the American gut project: China vs. USA, China vs. UK, and Asian or Pacific Islander (API) vs. Caucasian, we exhibit the utility of the proposed SparseMCMM_HD framework for investigating microbiome’s contributions in health disparities. Specifically, BMI exhibits disparities and microbial community diversities are significantly distinctive between the reference and comparison groups in all three applications. By employing SparseMCMM_HD, we illustrate that microbiome plays a crucial role in explaining the disparities in BMI between races or regions. 11.99%, 12.90%, and 7.4% of the overall disparity in BMI in China-USA, China-UK, and API-Caucasian comparisons, respectively, would be eliminated if the between-group microbiome profiles were equalized; and 15, 21, and 12 species are identified to play the mediating role respectively. Conclusions: The proposed SparseMCMM_HD is an effective and validated tool to elucidate the mediating role of microbiome in health disparity. Three BMI applications shed light on the utility of microbiome in reducing BMI disparity by manipulating microbial profiles.

Risk factors for head and neck cancer in more and less developed countries: Analysis from the INHANCE consortium.

OBJECTIVE: We analyzed the pooled case-control data from the International Head and Neck Cancer Epidemiology (INHANCE) consortium to compare cigarette smoking and alcohol consumption risk factors for head and neck cancer between less developed and more developed countries. SUBJECTS AND METHODS: The location of each study was categorized as either a less developed or more developed country. We compared the risk of overall head and neck cancer and cancer of specific anatomic subsites associated with cigarette smoking and alcohol consumption. Additionally, age and sex distribution between categories was compared. RESULTS: The odds ratios for head and neck cancer sites associated with smoking duration differed between less developed and more developed countries. Smoking greater than 20 years conferred a higher risk for oral cavity and laryngeal cancer in more developed countries, whereas the risk was greater for oropharynx and hypopharynx cancer in less developed countries. Alcohol consumed for more than 20 years conferred a higher risk for oropharynx, hypopharynx, and larynx cancer in less developed countries. The proportion of cases that were young (<45 years) or female differed by country type for some HNC subsites. CONCLUSION: These findings suggest the degree of industrialization and economic development affects the relationship between smoking and alcohol with head and neck cancer.

Assessment of a Polygenic Risk Score for Colorectal Cancer to Predict Risk of Lynch Syndrome Colorectal Cancer.

It was not known whether the polygenic risk scores (PRSs) that predict colorectal cancer could predict colorectal cancer for people with inherited pathogenic variants in DNA mismatch repair genes-people with Lynch syndrome. We tested a PRS comprising 107 established single-nucleotide polymorphisms associated with colorectal cancer in European populations for 826 European-descent carriers of pathogenic variants in DNA mismatch repair genes (293 MLH1, 314 MSH2, 126 MSH6, 71 PMS2, and 22 EPCAM) from the Colon Cancer Family Registry, of whom 504 had colorectal cancer. There was no evidence of an association between the PRS and colorectal cancer risk, irrespective of which DNA mismatch repair gene was mutated, or sex (all 2-sided P > .05). The hazard ratio per standard deviation of the PRS for colorectal cancer was 0.97 (95% confidence interval = 0.88 to 1.06; 2-sided P = .51). Whereas PRSs are predictive of colorectal cancer in the general population, they do not predict Lynch syndrome colorectal cancer.

Global estimates of mortality associated with long-term exposure to outdoor fine particulate matter.

Exposure to ambient fine particulate matter (PM2.5) is a major global health concern. Quantitative estimates of attributable mortality are based on disease-specific hazard ratio models that incorporate risk information from multiple PM2.5 sources (outdoor and indoor air pollution from use of solid fuels and secondhand and active smoking), requiring assumptions about equivalent exposure and toxicity. We relax these contentious assumptions by constructing a PM2.5-mortality hazard ratio function based only on cohort studies of outdoor air pollution that covers the global exposure range. We modeled the shape of the association between PM2.5 and nonaccidental mortality using data from 41 cohorts from 16 countries-the Global Exposure Mortality Model (GEMM). We then constructed GEMMs for five specific causes of death examined by the global burden of disease (GBD). The GEMM predicts 8.9 million [95% confidence interval (CI): 7.5-10.3] deaths in 2015, a figure 30% larger than that predicted by the sum of deaths among the five specific causes (6.9; 95% CI: 4.9-8.5) and 120% larger than the risk function used in the GBD (4.0; 95% CI: 3.3-4.8). Differences between the GEMM and GBD risk functions are larger for a 20% reduction in concentrations, with the GEMM predicting 220% higher excess deaths. These results suggest that PM2.5 exposure may be related to additional causes of death than the five considered by the GBD and that incorporation of risk information from other, nonoutdoor, particle sources leads to underestimation of disease burden, especially at higher concentrations.

Retrospective benzene exposure assessment for a multi-center case-cohort study of benzene-exposed workers in China.

Quality of exposure assessment has been shown to be related to the ability to detect risk of lymphohematopoietic disorders in epidemiological investigations of benzene, especially at low levels of exposure. We set out to build a statistical model for reconstructing exposure levels for 2898 subjects from 501 factories that were part of a nested case-cohort study within the NCI-CAPM cohort of more than 110,000 workers. We used a hierarchical model to allow for clustering of measurements by factory, workshop, job, and date. To calibrate the model we used historical routine monitoring data. Measurements below the limit of detection were accommodated by constructing a censored data likelihood. Potential non-linear and industry-specific time-trends and predictor effects were incorporated using regression splines and random effects. A partial validation of predicted exposures in 2004/2005 was performed through comparison with full-shift measurements from an exposure survey in facilities that were still open. Median cumulative exposure to benzene at age 50 for subjects that ever held an exposed job (n=1175) was 509 mg/m(3) years. Direct comparison of model estimates with measured full-shift personal exposure in the 2004/2005 survey showed moderate correlation and a potential downward bias at low (<1 mg/m(3)) exposure estimates. The modeling framework enabled us to deal with the data complexities generally found in studies using historical exposure data in a comprehensive way and we therefore expect to be able to investigate effects at relatively low exposure levels.

Detailed exposure assessment for a molecular epidemiology study of benzene in two shoe factories in China.

OBJECTIVES: We carried out a detailed exposure assessment of benzene and toluene in two shoe factories in Tianjin, China. Our goal was to identify workers with a broad range of benzene exposures, for an epidemiologic study relating exposure to early biologic effects. METHODS: A comprehensive exposure survey program was initiated. Over a period of 16 months, 2783 personal solvent exposure samples were collected in two workplaces from 250 workers. Mixed-effects models were used to identify factors affecting exposure. Principal component analyses (PCA) and subsequent regression analyses on the scores of the identified principal components were used to relate potential co-exposures to various exposure sources present in the workplace. RESULTS: The mean benzene exposure level was 21.86 p.p.m. (10th-90th percentiles 5.23-50.63 p.p.m.) in the smaller shoe factory (factory A) and 3.46 p.p.m. (10th-90th percentiles 0.20-7.00 p.p.m.) in the larger shoe factory (factory B). Within-factory exposure levels differed among job titles and were higher for subjects directly involved in handling glues. In contrast, mean toluene levels were relatively similar in the two factories (factory A, 9.52 p.p.m.; factory B, 15.88 p.p.m.). A seasonal trend was identified for both benzene and toluene in factory B. This could be explained in part by changes in air movement and ventilation patterns occurring during the year. A seasonal trend was not present in the smaller shoe factory, where general ventilation was absent. Supplemental analysis showed that exposure levels to other hydrocarbons were low (< or =5 p.p.m.), less than 5% of their respective ACGIH threshold limit values, and generally comparable in the two factories. PCA showed that co-exposures in factory B could largely be explained by glue sources that were used in distinct areas in the workplace. CONCLUSIONS: We demonstrated the occurrence of a broad range of benzene exposure levels in two shoe manufacturing factories in Tianjin, China. Benzene and toluene exposures were determined in part by the degree of contact with glues, the benzene and toluene content of each glue, air movement and ventilation patterns. The availability of long-term monthly personal monitoring data provides an excellent opportunity to estimate individual exposures at different times during the 1 yr period of observation.

Whole blood cryopreservation in epidemiological studies.

Standardized and cost-effective biological sample collection, processing, and storage procedures are needed in large-scale epidemiological studies to provide material for testing a broad range of etiological hypotheses. One component of sample collection in the Prostate, Lung, Colorectal and Ovarian Cancer Screening Trial involves shipment of blood in acid-citrate-dextrose anticoagulant to a central processing laboratory, where 10% DMSO is added, and whole blood aliquots are cryopreserved. A single technician is able to routinely process 50-60 samples/day. Tests conducted to evaluate potential uses of cryopreserved whole blood showed successful EBV transformation (>90%, up to 20 months of storage). In addition, lymphocytes maintained good viability and stable T-cell:B-cell ratios, and T cells maintained the capacity to proliferate in response to solid phase anti-CD3/CD28 plus interleukin 2. Whole blood cryopreservation is a cost-effective approach to large-scale storage of viable cells in epidemiological studies.