The association of cancer-preventive lifestyle with colonoscopy screening use in border Hispanic adults along the Texas-Mexico border.

PURPOSE: The relationship between engaging in two domains of cancer-preventive behaviors, lifestyle behaviors and colonoscopy screening, is unknown in Hispanic adults. Accordingly, the study examined the association between lifestyle and colonoscopy screening in Hispanic adults along the Texas-Mexico border, where there is suboptimal colorectal cancer prevention. METHODS: Lifestyle behavior adherence and compliance with colonoscopy screening schedules were assessed using 2013-2023 data from the Cameron County Hispanic Cohorta population-based sample of Hispanic adults living along the Texas-Mexico border. The 2018 World Cancer Research Fund scoring system characterized healthy lifestyle engagement. Multivariable logistic regression quantified the association between lifestyle behaviors and colonoscopy screening. RESULTS: Among 914 Hispanic adults, there was a mean adherence score of 2.5 out of 7 for recommended behaviors. Only 33.0% (95% CI 25.64-41.39%) were up-to-date with colonoscopy. Complete adherence to fruit and vegetable (AOR [adjusted odds ratio] 5.2, 95% CI 1.68-16.30; p = 0.004), fiber (AOR 2.2, 95% CI 1.06-4.37; p = 0.04), and ultra-processed foods (AOR 2.8, 95% CI 1.30-6.21; p = 0.01) consumption recommendations were associated with up-to-date colonoscopy screening. Having insurance versus being uninsured (AOR 10.8, 95% CI 3.83-30.62; p < 0.001) and having local medical care versus in Mexico (AOR 7.0, 95% CI 2.26-21.43; p < 0.001) were associated with up-to-date colonoscopy. CONCLUSIONS: Adherence to dietary lifestyle recommendations was associated with being up-to-date with colonoscopy screenings. Those with poor dietary behavior are at risk for low-colonoscopy use. Improving lifestyle behaviors may complement colonoscopy promotion interventions. Healthcare accessibility influences up-to-date colonoscopy prevalence. Our findings can inform cancer prevention strategies for the Hispanic population.

A Laplacian regularized graph neural network for predictive modeling of multiple chronic conditions.

BACKGROUND AND GOALS: One of the biggest difficulties facing healthcare systems today is the prevalence of multiple chronic diseases (MCC). Mortality and the development of new chronic illnesses are more likely in those with MCC. Pre-existing diseases and risk factors specific to the patient have an impact on the complex stochastic process that guides the evolution of MCC. This study's goal is to use a brand-new Graph Neural Network (GNN) model to examine the connections between specific chronic illnesses, patient-level risk factors, and pre-existing conditions. METHODS: We propose a graph neural network model to analyze the relationship between five chronic conditions (diabetes, obesity, cognitive impairment, hyperlipidemia, and hypertension). The proposed model adds a graph Laplacian regularization term to the loss function, which aims to improve the parameter learning process and accuracy of the GNN based on the graph structure. For validation, we used historical data from the Cameron County Hispanic Cohort (CCHC). RESULTS: Evaluating the Laplacian regularized GNN on data from 600 patients, we expanded our analysis from two chronic conditions to five chronic conditions. The proposed model consistently surpassed a baseline GNN model, achieving an average accuracy of ≥89% across all combinations. In contrast, the performance of the standard model declined more markedly with the addition of more chronic conditions. The Laplacian regularization provided consistent predictions for adjacent nodes, beneficial in cases with shared attributes among nodes. CONCLUSIONS: The incorporation of Laplacian regularization in our GNN model is essential, resulting in enhanced node categorization and better predictive performance by harnessing the graph structure. This study underscores the significance of considering graph structure when designing neural networks for graph data. Future research might further explore and refine this regularization method for various tasks using graph-structured data.

Evaluation of a Targeted COVID-19 Community Outreach Intervention: Case Report for Precision Public Health.

BACKGROUND: Cameron County, a low-income south Texas-Mexico border county marked by severe health disparities, was consistently among the top counties with the highest COVID-19 mortality in Texas at the onset of the pandemic. The disparity in COVID-19 burden within Texas counties revealed the need for effective interventions to address the specific needs of local health departments and their communities. Publicly available COVID-19 surveillance data were not sufficiently timely or granular to deliver such targeted interventions. An agency-academic collaboration in Cameron used novel geographic information science methods to produce granular COVID-19 surveillance data. These data were used to strategically target an educational outreach intervention named "Boots on the Ground" (BOG) in the City of Brownsville (COB). OBJECTIVE: This study aimed to evaluate the impact of a spatially targeted community intervention on daily COVID-19 test counts. METHODS: The agency-academic collaboration between the COB and UTHealth Houston led to the creation of weekly COVID-19 epidemiological reports at the census tract level. These reports guided the selection of census tracts to deliver targeted BOG between April 21 and June 8, 2020. Recordkeeping of the targeted BOG tracts and the intervention dates, along with COVID-19 daily testing counts per census tract, provided data for intervention evaluation. An interrupted time series design was used to evaluate the impact on COVID-19 test counts 2 weeks before and after targeted BOG. A piecewise Poisson regression analysis was used to quantify the slope (sustained) and intercept (immediate) change between pre- and post-BOG COVID-19 daily test count trends. Additional analysis of COB tracts that did not receive targeted BOG was conducted for comparison purposes. RESULTS: During the intervention period, 18 of the 48 COB census tracts received targeted BOG. Among these, a significant change in the slope between pre- and post-BOG daily test counts was observed in 5 tracts, 80% (n=4) of which had a positive slope change. A positive slope change implied a significant increase in daily COVID-19 test counts 2 weeks after targeted BOG compared to the testing trend observed 2 weeks before intervention. In an additional analysis of the 30 census tracts that did not receive targeted BOG, significant slope changes were observed in 10 tracts, of which positive slope changes were only observed in 20% (n=2). In summary, we found that BOG-targeted tracts had mostly positive daily COVID-19 test count slope changes, whereas untargeted tracts had mostly negative daily COVID-19 test count slope changes. CONCLUSIONS: Evaluation of spatially targeted community interventions is necessary to strengthen the evidence base of this important approach for local emergency preparedness. This report highlights how an academic-agency collaboration established and evaluated the impact of a real-time, targeted intervention delivering precision public health to a small community.

Challenges and strategies for recruitment of minorities to clinical research and trials.

Minority populations are largely absent from clinical research trials. The neglect of these populations has become increasingly apparent, with escalating cancer burdens and chronic disease. The challenges to recruitment of minorities in the United States are multiple including trust or lack thereof. Keys to successful recruitment are responding to community issues, its history, beliefs, and its social and economic pressures. The strategy we have used in many low-income, sometimes remote, communities is to recruit staff from the same community and train them in the required basic research methods. They are the first line of communication. After our arrival in the Texas Rio Grande Valley in 2001, we applied these principles learned over years of global research, to studies of chronic diseases. Beginning in 2004, we recruited and trained a team of local women who enrolled in a cohort of over five thousand Mexican Americans from randomly selected households. This cohort is being followed, and the team has remained, acquiring not only advanced skills (ultrasound, FibroScan, retinal photos, measures of cognition, etc.) but capacity to derive key health information. Currently, we are participating in multiple funded studies, including an NIH clinical trial, liver disease, obesity, and diabetes using multiomics aimed at developing precision medicine approaches to chronic disease prevention and treatment.

Endothelial Dysfunction and Cardiometabolic Risk Factors in Mexican American Adults: The Cameron County Hispanic Cohort.

Endothelial dysfunction assessed by impaired brachial flow-mediated dilation (FMD) predicts incident cardiovascular disease (CVD). We have previously shown that clustering of diabetes mellitus, obesity, and metabolic syndrome in young Hispanic patients was associated with subclinical atherosclerosis. This study aimed to assess determinants of impaired FMD response (%FMD), an earlier marker of atherosclerosis, in a population-based sample of asymptomatic Mexican Americans. Cardiometabolic biomarkers and FMD were obtained from 960 Cameron County Hispanic Cohort participants. Gender-specific median values of %FMD were used to categorize participants into those with %FMD below or above the median. The sample was further stratified into those younger and older than 55 years. Survey-weighted logistic regression analyses were conducted to evaluate the effects of cardiometabolic biomarkers on the %FMD groups. The low %FMD group was significantly older, had higher visceral adipose tissue, systolic blood pressure, or plasma glucose, and had metabolic syndrome compared with those in the high %FMD group. Multivariable-adjusted age-stratified logistic regression analyses showed that in older participants, male gender (odds ratio [OR] = 2.4 [1.4 to 4.2]) and having hypertension (OR = 2.3 [1.3 to 4.3]) or prediabetes mellitus (OR = 3.4 [1.5 to 7.5]) remained significantly associated with odds of low %FMD. In younger participants, high low-density lipoprotein (OR = 2.8 [1.6 to 4.9]) or having the metabolic syndrome (OR = 1.9 [1.1 to 3.6]) were significantly associated with odds of low %FMD. In conclusion, we found age-dependent associations between cardiometabolic biomarkers and an FMD response below the gender-specific median in a sample composed of Mexican Americans without previous CVD. Targeting specific risk factors by age may mitigate progression to incident CVD in this high-risk racial disparity group.

Sex-specific associations between adipokine profiles and carotid-intima media thickness in the Cameron County Hispanic Cohort (CCHC).

BACKGROUND: Adipokines are hormones secreted from adipose tissue and are associated with cardiometabolic diseases (CMD). Functional differences between adipokines (leptin, adiponectin, and resistin) are known, but inconsistently reported associations with CMD and lack of studies in Hispanic populations are research gaps. We investigated the relationship between subclinical atherosclerosis and multiple adipokine measures. METHODS: Cross-sectional data from the Cameron County Hispanic Cohort (N = 624; mean age = 50; Female = 70.8%) were utilized to assess associations between adipokines [continuous measures of adiponectin, leptin, resistin, leptin-to-adiponectin ratio (LAR), and adiponectin-resistin index (ARI)] and early atherosclerosis [carotid-intima media thickness (cIMT)]. We adjusted for sex, age, body mass index (BMI), smoking status, cytokines, fasting blood glucose levels, blood pressure, lipid levels, and medication usage in the fully adjusted linear regression model. We conducted sexes-combined and sex-stratified analyses to account for sex-specificity and additionally tested whether stratification of participants by their metabolic status (metabolically elevated risk for CMD as defined by having two or more of the following conditions: hypertension, dyslipidemia, insulin resistance, and inflammation vs. not) influenced the relationship between adipokines and cIMT. RESULTS: In the fully adjusted analyses, adiponectin, leptin, and LAR displayed significant interaction by sex (p < 0.1). Male-specific associations were between cIMT and LAR [ß(SE) = 0.060 (0.016), p = 2.52 × 10-4], and female-specific associations were between cIMT and adiponectin [ß(SE) = 0.010 (0.005), p = 0.043] and ARI [ß(SE) = - 0.011 (0.005), p = 0.036]. When stratified by metabolic health status, the male-specific positive association between LAR and cIMT was more evident among the metabolically healthy group [ß(SE) = 0.127 (0.015), p = 4.70 × 10-10] (p for interaction by metabolic health < 0.1). However, the female-specific associations between adiponectin and cIMT and ARI and cIMT were observed only among the metabolically elevated risk group [ß(SE) = 0.014 (0.005), p = 0.012 for adiponectin; ß(SE) = - 0.015 (0.006), p = 0.013 for ARI; p for interaction by metabolic health < 0.1]. CONCLUSION: Associations between adipokines and cIMT were sex-specific, and metabolic health status influenced the relationships between adipokines and cIMT. These heterogeneities by sex and metabolic health affirm the complex relationships between adipokines and atherosclerosis.

The power of TOPMed imputation for the discovery of Latino-enriched rare variants associated with type 2 diabetes.

AIMS/HYPOTHESIS: The Latino population has been systematically underrepresented in large-scale genetic analyses, and previous studies have relied on the imputation of ungenotyped variants based on the 1000 Genomes (1000G) imputation panel, which results in suboptimal capture of low-frequency or Latino-enriched variants. The National Heart, Lung, and Blood Institute (NHLBI) Trans-Omics for Precision Medicine (TOPMed) released the largest multi-ancestry genotype reference panel representing a unique opportunity to analyse rare genetic variations in the Latino population. We hypothesise that a more comprehensive analysis of low/rare variation using the TOPMed panel would improve our knowledge of the genetics of type 2 diabetes in the Latino population. METHODS: We evaluated the TOPMed imputation performance using genotyping array and whole-exome sequence data in six Latino cohorts. To evaluate the ability of TOPMed imputation to increase the number of identified loci, we performed a Latino type 2 diabetes genome-wide association study (GWAS) meta-analysis in 8150 individuals with type 2 diabetes and 10,735 control individuals and replicated the results in six additional cohorts including whole-genome sequence data from the All of Us cohort. RESULTS: Compared with imputation with 1000G, the TOPMed panel improved the identification of rare and low-frequency variants. We identified 26 genome-wide significant signals including a novel variant (minor allele frequency 1.7%; OR 1.37, p=3.4 × 10-9). A Latino-tailored polygenic score constructed from our data and GWAS data from East Asian and European populations improved the prediction accuracy in a Latino target dataset, explaining up to 7.6% of the type 2 diabetes risk variance. CONCLUSIONS/INTERPRETATION: Our results demonstrate the utility of TOPMed imputation for identifying low-frequency variants in understudied populations, leading to the discovery of novel disease associations and the improvement of polygenic scores. DATA AVAILABILITY: Full summary statistics are available through the Common Metabolic Diseases Knowledge Portal ( https://t2d.hugeamp.org/downloads.html ) and through the GWAS catalog ( https://www.ebi.ac.uk/gwas/ , accession ID: GCST90255648). Polygenic score (PS) weights for each ancestry are available via the PGS catalog ( https://www.pgscatalog.org , publication ID: PGP000445, scores IDs: PGS003443, PGS003444 and PGS003445).

Population-Based Mini-Mental State Examination Norms in Adults of Mexican Heritage in the Cameron County Hispanic Cohort.

BACKGROUND: Accurately identifying cognitive changes in Mexican American (MA) adults using the Mini-Mental State Examination (MMSE) requires knowledge of population-based norms for the MMSE, a scale which has widespread use in research settings. OBJECTIVE: To describe the distribution of MMSE scores in a large cohort of MA adults, assess the impact of MMSE requirements on their clinical trial eligibility, and explore which factors are most strongly associated with their MMSE scores. METHODS: Visits between 2004-2021 in the Cameron County Hispanic Cohort were analyzed. Eligible participants were ≥18 years old and of Mexican descent. MMSE distributions before and after stratification by age and years of education (YOE) were assessed, as was the proportion of trial-aged (50-85- year-old) participants with MMSE <24, a minimum MMSE cutoff most frequently used in Alzheimer's disease (AD) clinical trials. As a secondary analysis, random forest models were constructed to estimate the relative association of the MMSE with potentially relevant variables. RESULTS: The mean age of the sample set (n = 3,404) was 44.4 (SD, 16.0) years old and 64.5% female. Median MMSE was 28 (IQR, 28-29). The percentage of trial-aged participants (n = 1,267) with MMSE <24 was 18.6% overall and 54.3% among the subset with 0-4 YOE (n = 230). The five variables most associated with the MMSE in the study sample were education, age, exercise, C-reactive protein, and anxiety. CONCLUSION: The minimum MMSE cutoffs in most phase III prodromal-to-mild AD trials would exclude a significant proportion of trial-aged participants in this MA cohort, including over half of those with 0-4 YOE.

Prevalence and factors associated with liver fibrosis among first-degree relatives of Mexican Americans with hepatocellular carcinoma.

BACKGROUND AND AIMS: Whether hepatocellular carcinoma (HCC) increases the familial risk for hepatic fibrosis has not been thoroughly explored, particularly in Mexican Americans who are disproportionately affected by obesity and metabolic syndrome. We evaluated the risk of significant hepatic fibrosis in first-degree relatives of Mexican American adults with HCC. METHODS: We performed a cross-sectional analysis of a prospective cohort of Mexican American probands with HCC and first-degree relatives enrolled in the Hispanic Liver Cancer Cohort study. We evaluated the prevalence of hepatic fibrosis in first-degree relatives, defined by liver stiffness measurement (LSM) >= 7.0 kPa with transient elastography (TE). Secondary outcomes included the prevalence of definite hepatic steatosis, defined by controlled attenuation parameter >=288 dB/m. RESULTS: We identified 70 probands diagnosed with HCC; 47% were female and the mean age was 62 years (±13 years). Among 112 first-degree relatives with a mean age of 43 years (±14 years), 19 (17%) had significant fibrosis and 47 (42%) had definite hepatic steatosis, respectively. The prevalence of significant fibrosis was 20% in first-degree relatives 40 years of age or older. Regression analysis revealed that diabetes (OR 3.2, 95% CI: 1.1-9.2, p = 0.03) and aspartate aminotransferase >=30 units/L (OR 4.0, 95% CI: 1.4-11.7, p = 0.01) were predictors of significant fibrosis in first-degree relatives. CONCLUSIONS: Using a well-phenotyped familial cohort, we found that the prevalence of significant fibrosis and definite hepatic steatosis are high in first-degree relatives of Mexican Americans with HCC, particularly those with diabetes, suggesting that this population may benefit from screening for liver disease.

Data Mining Framework for Discovering and Clustering Phenotypes of Atypical Diabetes.

CONTEXT: Some individuals present with forms of diabetes that are "atypical" (AD), which do not conform to typical features of either type 1 diabetes (T1D) or type 2 diabetes (T2D). These forms of AD display a range of phenotypic characteristics that likely reflect different endotypes based on unique etiologies or pathogenic processes. OBJECTIVE: To develop an analytical approach to identify and cluster phenotypes of AD. METHODS: We developed Discover Atypical Diabetes (DiscoverAD), a data mining framework, to identify and cluster phenotypes of AD. DiscoverAD was trained against characteristics of manually classified patients with AD among 278 adults with diabetes within the Cameron County Hispanic Cohort (CCHC) (Study A). We then tested DiscoverAD in a separate population of 758 multiethnic children with T1D within the Texas Children's Hospital Registry for New-Onset Type 1 Diabetes (TCHRNO-1) (Study B). RESULTS: We identified an AD frequency of 11.5% in the CCHC (Study A) and 5.3% in the pediatric TCHRNO-1 (Study B). Cluster analysis identified 4 distinct groups of AD in Study A: cluster 1, positive for the 65 kDa glutamate decarboxylase autoantibody (GAD65Ab), adult-onset, long disease duration, preserved beta-cell function, no insulin treatment; cluster 2, GAD65Ab negative, diagnosed at age ≤21 years; cluster 3, GAD65Ab negative, adult-onset, poor beta-cell function, lacking central obesity; cluster 4, diabetic ketoacidosis (DKA)-prone participants lacking a typical T1D phenotype. Applying DiscoverAD to the pediatric patients with T1D in Study B revealed 2 distinct groups of AD: cluster 1, autoantibody negative, poor beta-cell function, lower body mass index (BMI); cluster 2, autoantibody positive, higher BMI, higher incidence of DKA. CONCLUSION: DiscoverAD can be adapted to different datasets to identify and define phenotypes of participants with AD based on available clinical variables.

Genetic variants associated with circulating liver injury markers in Mexican Americans, a population at risk for non-alcoholic fatty liver disease.

Objective: Mexican Americans are disproportionally affected by non-alcoholic fatty liver disease (NAFLD), liver fibrosis and hepatocellular carcinoma. Noninvasive means to identify those in this population at high risk for these diseases are urgently needed. Approach: The Cameron County Hispanic Cohort (CCHC) is a population-based cohort with high rates of obesity (51%), type 2 diabetes (28%) and NAFLD (49%). In a subgroup of 564 CCHC subjects, we evaluated 339 genetic variants previously reported to be associated with liver injury markers aspartate aminotransferase (AST) and alanine aminotransferase (ALT) in United Kingdom and Japanese cohorts. Results: Association was confirmed for 86 variants. Among them, 27 had higher effect allele frequency in the CCHC than in the United Kingdom and Japanese cohorts, and 16 had stronger associations with AST and ALT than rs738409 (PNPLA3). These included rs17710008 (MYCT1), rs2519093 (ABO), rs1801690 (APOH), rs10409243 (S1PR2), rs1800759 (LOC100507053) and rs2491441 (RGL1), which were also associated with steatosis and/or liver fibrosis measured by vibration-controlled transient elastography. Main contributors to advanced fibrosis risk were rs11240351 (CNTN2), rs1800759 (LOC100507053), rs738409 (PNPLA3) and rs1801690 (APOH), with advanced fibrosis detected in 37.5% of subjects with 3 of these 4 variants [AOR = 11.6 (95% CI) = 3.8-35.3]. AST- and ALT-associated variants implicated distinct pathways (ethanol and galactose degradation versus antigen presentation and B cell development). Finally, 8 variants, including rs62292950 (DNAJC13), were associated with gut microbiome changes. Conclusion: These genotype-phenotype findings may have utility in risk modeling and disease prevention in this high-risk population.

Genetic pleiotropy underpinning adiposity and inflammation in self-identified Hispanic/Latino populations.

BACKGROUND: Concurrent variation in adiposity and inflammation suggests potential shared functional pathways and pleiotropic disease underpinning. Yet, exploration of pleiotropy in the context of adiposity-inflammation has been scarce, and none has included self-identified Hispanic/Latino populations. Given the high level of ancestral diversity in Hispanic American population, genetic studies may reveal variants that are infrequent/monomorphic in more homogeneous populations. METHODS: Using multi-trait Adaptive Sum of Powered Score (aSPU) method, we examined individual and shared genetic effects underlying inflammatory (CRP) and adiposity-related traits (Body Mass Index [BMI]), and central adiposity (Waist to Hip Ratio [WHR]) in HLA participating in the Population Architecture Using Genomics and Epidemiology (PAGE) cohort (N = 35,871) with replication of effects in the Cameron County Hispanic Cohort (CCHC) which consists of Mexican American individuals. RESULTS: Of the > 16 million SNPs tested, variants representing 7 independent loci were found to illustrate significant association with multiple traits. Two out of 7 variants were replicated at statistically significant level in multi-trait analyses in CCHC. The lead variant on APOE (rs439401) and rs11208712 were found to harbor multi-trait associations with adiposity and inflammation. CONCLUSIONS: Results from this study demonstrate the importance of considering pleiotropy for improving our understanding of the etiology of the various metabolic pathways that regulate cardiovascular disease development.

Association of Puberty Stage and Weight Status with Cardiometabolic Risk in Children and Adolescents Living on the Texas-Mexico Border.

Background: This retrospective cohort study aimed to examine the interaction effect between puberty stage and weight status on individual and clustering of cardiometabolic risk factors (CMRFs) among Mexican American children and adolescents. A total of 333 children and adolescents (aged 8-18 years) enrolled in the Cameron County Hispanic Cohort (CCHC) from 2014 to 2020 were included in the study. Methods: CCHC is a longitudinal, randomly recruited cohort based on the United States Census tracts/blocks of Mexican Americans living on the Texas-Mexico border. Individual CMRFs, including high blood pressure, central obesity, hypertriglyceridemia, low high-density lipoprotein cholesterol, and insulin resistance (IR) were assessed. Clustering of CMRFs is defined as the presence of three or more individual CMRFs. Puberty stages were assessed using the Tanner criteria. Multivariable logistic regressions were conducted to assess the association of puberty, weight status, and the interaction of the two main exposures with individual and clustering of CMRFs. Results: We observed that weight status had a dominant effect on all CMRF measures. The effect was especially prominent on central obesity and clustering of CMRFs. There were 95.4% of children with central obesity and 98.4% of those with clustering of CMRF were either overweight or obese. Entering puberty was associated with an increased risk of having IR [Tanner stage 2 vs. 1: odds ratio (OR) = 3.25, 95% confidence interval (95% CI) 1.28-8.27; Tanner stage 3 vs. 1: OR = 3.50, 95% CI 1.45-8.46] and hypertriglyceridemia (Tanner stage 2 vs. 1: OR = 2.67, 95% CI 1.11-6.45). However, the effects were not observed among those reaching the end of puberty (Tanner stage 4 and 5). Conclusions: A significant interaction effect between weight status and puberty was not detected on any individual CMRF and in the clustering of CMRFs. Other factors positively associated with individual CMRFs, especially IR, were being female and having a family history of diabetes.

Gut Microbiome Alterations Associated with Diabetes in Mexican Americans in South Texas.

Mexican Americans have a high prevalence of diabetes and burden of diabetes-related complications, highlighting the need for novel preventive strategies and noninvasive predictors of diabetes risk tailored to this population. Changes in the gut microbiome have the potential to predict diabetes. Here, we aimed to identify alterations in the gut microbiome associated with diabetes in the high-risk population of Mexican Americans in South Texas. Stool samples were collected from 216 subjects from the population-based Cameron County Hispanic Cohort. Among them, 75 had type 2 diabetes. Taxonomic and functional profiling of the stool samples were assessed by 16S and shotgun metagenomic sequencing, and the influence of genetic factors was explored. The gut microbiome of subjects with diabetes was enriched with proinflammatory Proteobacteria members (Enterobacteriaceae, Escherichia-Shigella) and depleted of butyrate-producing Clostridiales members (Faecalibacterium prausnitzii, Peptostreptococcaceae, and Clostridium sensu stricto 1). The accompanying metagenomic changes in subjects with diabetes suggested dysregulated amino acid metabolism, reduced galacturonate and glucuronate catabolism (correlating with Faecalibacterium prausnitzii abundance), and enriched heme biosynthesis (correlating with Enterobacteriaceae abundance). Polymorphism rs7129790 near MMP27 was strongly associated with high Proteobacteria abundance and was more frequent in this cohort and in individuals of Mexican ancestry than in Europeans. In conclusion, Mexican Americans in South Texas with diabetes display distinct gut microbiome and metagenomic signatures. These signatures may have utility in risk modeling and disease prevention in this high-risk population. IMPORTANCE The gut microbiome composition varies across ethnicities and geographical locations, yet studies on diabetes-associated microbiome changes specific to high-risk Mexican Americans are lacking. Here, we aimed to identify specific alterations associated with diabetes in this population, as well as host genetic factors that may explain increased disease susceptibility in this ethnic group. Using samples from a population-based cohort of Mexican Americans with a high prevalence of obesity and diabetes, we confirmed findings from studies on other ethnicities that suggested promotion of a chronic proinflammatory environment, loss of butyrate production, and compromised intestinal barrier integrity. High abundance of proinflammatory Proteobacteria was associated with a polymorphism that was more frequent in this cohort and in individuals of Mexican ancestry than in Europeans. Validation of microbiome-based risk models for diabetes should be evaluated in prospective cohort studies.

Novel diabetes gene discovery through comprehensive characterization and integrative analysis of longitudinal gene expression changes.

Type 2 diabetes is a complex, systemic disease affected by both genetic and environmental factors. Previous research has identified genetic variants associated with type 2 diabetes risk; however, gene regulatory changes underlying progression to metabolic dysfunction are still largely unknown. We investigated RNA expression changes that occur during diabetes progression using a two-stage approach. In our discovery stage, we compared changes in gene expression using two longitudinally collected blood samples from subjects whose fasting blood glucose transitioned to a level consistent with type 2 diabetes diagnosis between the time points against those who did not with a novel analytical network approach. Our network methodology identified 17 networks, one of which was significantly associated with transition status. This 822-gene network harbors many genes novel to the type 2 diabetes literature but is also significantly enriched for genes previously associated with type 2 diabetes. In the validation stage, we queried associations of genetically determined expression with diabetes-related traits in a large biobank with linked electronic health records. We observed a significant enrichment of genes in our identified network whose genetically determined expression is associated with type 2 diabetes and other metabolic traits and validated 31 genes that are not near previously reported type 2 diabetes loci. Finally, we provide additional functional support, which suggests that the genes in this network are regulated by enhancers that operate in human pancreatic islet cells. We present an innovative and systematic approach that identified and validated key gene expression changes associated with type 2 diabetes transition status and demonstrated their translational relevance in a large clinical resource.

Gut microbiome features associated with liver fibrosis in Hispanics, a population at high risk for fatty liver disease.

BACKGROUND AND AIMS: Hispanics are disproportionately affected by NAFLD, liver fibrosis, cirrhosis, and HCC. Preventive strategies and noninvasive means to identify those in this population at high risk for liver fibrosis, are urgently needed. We aimed to characterize the gut microbiome signatures and related biological functions associated with liver fibrosis in Hispanics and identify environmental and genetic factors affecting them. APPROACH AND RESULTS: Subjects of the population-based Cameron County Hispanic Cohort (CCHC; n = 217) were screened by vibration-controlled transient elastography (FibroScan). Among them, 144 (66.7%) had steatosis and 28 (13.0%) had liver fibrosis. The gut microbiome of subjects with liver fibrosis was enriched with immunogenic commensals (e.g., Prevotella copri, Holdemanella, Clostridiaceae 1) and depleted of Bacteroides caccae, Parabacteroides distasonis, Enterobacter, and Marinifilaceae. The liver fibrosis-associated metagenome was characterized by changes in the urea cycle, L-citrulline biosynthesis and creatinine degradation pathways, and altered synthesis of B vitamins and lipoic acid. These metagenomic changes strongly correlated with the depletion of Parabacteroides distasonis and enrichment of Prevotella and Holdemanella. Liver fibrosis was also associated with depletion of bacterial pathways related to L-fucose biosynthesis. Alcohol consumption, even moderate, was associated with high Prevotella abundance. The single-nucleotide polymorphisms rs3769502 and rs7573751 in the NCK adaptor protein 2 (NCK2) gene positively associated with high Prevotella abundance. CONCLUSION: Hispanics with liver fibrosis display microbiome profiles and associated functional changes that may promote oxidative stress and a proinflammatory environment. These microbiome signatures, together with NCK2 polymorphisms, may have utility in risk modeling and disease prevention in this high-risk population.

Real-time geospatial analysis identifies gaps in COVID-19 vaccination in a minority population.

COVID-19 vaccination is being rapidly rolled out in the US and many other countries, and it is crucial to provide fast and accurate assessment of vaccination coverage and vaccination gaps to make strategic adjustments promoting vaccine coverage. We reported the effective use of real-time geospatial analysis to identify barriers and gaps in COVID-19 vaccination in a minority population living in South Texas on the US-Mexico Border, to inform vaccination campaign strategies. We developed 4 rank-based approaches to evaluate the vaccination gap at the census tract level, which considered both population vulnerability and vaccination priority and eligibility. We identified areas with the highest vaccination gaps using different assessment approaches. Real-time geospatial analysis to identify vaccination gaps is critical to rapidly increase vaccination uptake, and to reach herd immunity in the vulnerable and the vaccine hesitant groups. Our results assisted the City of Brownsville Public Health Department in adjusting real-time targeting of vaccination, gathering coverage assessment, and deploying services to areas identified as high vaccination gap. The analyses and responses can be adopted in other locations.

Resistin levels decrease as insulin resistance increases in a Mexican-American cohort.

AIMS: Links between resistin, insulin resistance (IR), and resistin-stimulated cytokine signaling remain unknown in Mexican-Americans. A Mexican-American cohort was examined to determine (1) relationships between circulating resistin and IR, (2) resistin's associations with cytokines and demographic and anthropometric variables, and (3) similar measurements with other adipokines. METHODS: For cross sectional analyses, 953 adults (367 males and 586 females) in the Cameron County Hispanic Cohort (CCHC) were stratified into three groups: normal glucose tolerance, prediabetes, and diabetes mellitus. Differences in resistin and other adipokine levels were examined using linear regression via unadjusted model (Model 1), model adjusted for cytokines (Model 2), and model further adjusted for demographic and anthropometric variables (Model 3). RESULTS: HOMA-IR increased with worsening glucose tolerance (p < 0.0001). In all models, resistin significantly decreased as glucose tolerance deteriorated. Model 3 resistin was positively associated with IL-1ß (p = 0.0252) and IL-8 (p < 0.0001), inversely associated with TNF-α (p = 0.0352), but nonsignificantly associated with IL-6 (p = 0.8671). Model 3 leptin was significantly lower in diabetes mellitus compared to other groups (p < 0.005) and positively associated with female sex (p < 0.0001), age (p = 0.024), and BMI (p < 0.0001), without significant cytokine associations. Adiponectin displayed no significant associations with glucose tolerance, but was significantly associated with sex, BMI, and lipids (Model 3). CONCLUSIONS: Resistin unexpectedly decreased as IR increased while supporting evidence of a resistin-stimulated cytokine pathway in this Mexican-American cohort. Leptin fell with elevated IR after adjusting for cytokines, demographic and anthropometric variables. Adiponectin nonsignificantly decreased as IR increased while showing significant associations with sex, BMI, and lipids.

Somatic Mutations in Circulating Cell-Free DNA and Risk for Hepatocellular Carcinoma in Hispanics.

Hispanics are disproportionally affected by liver fibrosis and hepatocellular carcinoma (HCC). Advanced liver fibrosis is a major risk factor for HCC development. We aimed at identifying somatic mutations in plasma cell-free DNA (cfDNA) of Hispanics with HCC and Hispanics with advanced liver fibrosis but no HCC. Targeted sequencing of over 262 cancer-associated genes identified nonsynonymous mutations in 22 of the 27 HCC patients. Mutations were detected in known HCC-associated genes (e.g., CTNNB1, TP53, NFE2L2, and ARID1A). No difference in cfDNA concentrations was observed between patients with mutations and those without detectable mutations. HCC patients with higher cfDNA concentrations or higher number of mutations had a shorter overall survival (p < 0.001 and p = 0.045). Nonsynonymous mutations were also identified in 17 of the 51 subjects with advanced liver fibrosis. KMT2C was the most commonly mutated gene. Nine genes were mutated in both subjects with advanced fibrosis and HCC patients. Again, no significant difference in cfDNA concentrations was observed between subjects with mutations and those without detectable mutations. Furthermore, higher cfDNA concentrations and higher number of mutations correlated with a death outcome in subjects with advanced fibrosis. In conclusion, cfDNA features are promising non-invasive markers for HCC risk prediction and overall survival.