A high burden of diabetes and ankle brachial index abnormalities exists in Mexican Americans in South Texas.

Ethnic differences exist in the United States in the interrelated problems of diabetes (DM), peripheral arterial disease (PAD), and leg amputations. The purpose of this study was to determine the prevalence and risk factor associations for subclinical PAD in a population sample of Mexican Americans using the ankle brachial (ABI) index. The ABI-High (higher of the two ankle pressures/highest brachial pressure) and ABI-Low (lower of the two ankle pressures/highest brachial pressure) were calculated to define PAD. Toe brachial index (TBI) was also calculated. 746 participants were included with an age of 53.4 ± 0.9 years, 28.3 % had diabetes mellitus (DM), 12.6 % were smokers, and 51.2 % had hypertension (HTN). Using ABI-High ≤ 0.9, the prevalence of PAD was 2.7 %. This rose to 12.7 % when an ABI-Low ≤ 0.9 was used; 4.0 % of the population had an ABI-High > 1.4. The prevalence of TBI < 0.7 was 3.9 %. DM was a significant risk factor for ABI-High ≤ 0.9 and ABI-High > 1.4, and TBI < 0.7. Increased age, HTN, smoking was associated with ABI-High ≤ 0.9, while being male was associated with ABI-High > 1.4. Increased age, smoking, and lower education were all associated with abnormal TBI. Despite relatively younger mean age than other studied Hispanic cohorts, the present population has a high burden of ABI abnormalities. DM was a consistent risk factor for PAD. These abnormalities indicate an important underlying substrate of vascular and metabolic disease that may predispose this population to the development of symptomatic PAD and incident amputations.

A prognostic molecular signature of hepatic steatosis is spatially heterogeneous and dynamic in human liver.

Metabolic dysfunction-associated steatotic liver disease (MASLD) prevalence is increasing in parallel with an obesity pandemic, calling for novel strategies for prevention and treatment. We defined a circulating proteome of human MASLD across ≈7000 proteins in ≈5000 individuals from diverse, at-risk populations across the metabolic health spectrum, demonstrating reproducible diagnostic performance and specifying both known and novel metabolic pathways relevant to MASLD (central carbon and amino acid metabolism, hepatocyte regeneration, inflammation, fibrosis, insulin sensitivity). A parsimonious proteomic signature of MASLD was associated with a protection from MASLD and its related multi-system metabolic consequences in >26000 free-living individuals, with an additive effect to polygenic risk. The MASLD proteome was encoded by genes that demonstrated transcriptional enrichment in liver, with spatial transcriptional activity in areas of steatosis in human liver biopsy and dynamicity for select targets in human liver across stages of steatosis. We replicated several top relations from proteomics and spatial tissue transcriptomics in a humanized "liver-on-a-chip" model of MASLD, highlighting the power of a full translational approach to discovery in MASLD. Collectively, these results underscore utility of blood-based proteomics as a dynamic "liquid biopsy" of human liver relevant to clinical biomarker and mechanistic applications.

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.

A Comprehensive Program to Improve Treatment of Precancerous Cervical Lesions in the Rio Grande Valley of Texas.

OBJECTIVE: To assess the impact of a multicomponent intervention in women with cervical dysplasia who were treated with loop electrosurgical excision procedure (LEEP), as well as the time between colposcopy and treatment. DESIGN: Retrospective cohort study. INTERVENTION: Clinic participation in a multicomponent cervical cancer prevention program that included community outreach, patient in-reach, and navigation, as well as provider capacity building with in-person training and ongoing telementoring through Project ECHO. MAIN OUTCOME MEASURES: Medical records were reviewed to evaluate women with cervical dysplasia undergoing treatment with LEEP within 90 days of colposcopy, as well as time between colposcopy and treatment. Baseline data from year 1 were compared with each subsequent year of implementation. Additional variables examined included patient's age, history of abnormal screening results, and percentage of families living below poverty line based on county of residence, parity, and clinic site. We performed logistic regression and multiple linear regression analyses to assess the programmatic impact in the outcomes of interest by year of program implementation. RESULTS: A total of 290 women were included in the study. The proportion of women undergoing treatment within 90 days of colposcopy increased from 76.2% at baseline to 91.3% in year 3 and 92.9% in year 4 of program implementation. The odds of undergoing treatment within 90 days were 5.11 times higher in year 4 of program implementation than at baseline. The mean time between colposcopy and LEEP decreased from 62 days at baseline to 45 days by year 4 of program implementation. CONCLUSIONS: Implementation of our multicomponent cervical cancer prevention program increased the proportion of women undergoing LEEP within 90 days of colposcopy and decreased the time between colposcopy and LEEP. This program has the potential to support cervical cancer prevention efforts and could be implemented in other low-resource settings.

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.

Addressing cervical cancer disparities in Texas: Expansion of a community-based prevention initiative for medically underserved populations.

Although cervical cancer is preventable, significant disparities exist in access to screening and prevention services. In medically underserved areas (MUAs) of Texas, these rates are 55% higher compared to the remainder of the US. In 2019, we expanded a multicomponent, comprehensive program to improve cervical cancer prevention in partnership with 13 clinics and mobile vans in MUAs of Texas. Our multicomponent intervention program consists of community education and patient navigation coupled with a training/mentoring program for local medical providers to perform diagnostic procedures and treatment for patients with abnormal screening results. Hands-on training courses to learn these skills are coupled with biweekly telementoring conferences using Project ECHO® (Extension for Community Healthcare Outcomes). This program was implemented in 2015 and expanded to other MUAs in Texas in 2019. From March 2019 to August 2022, 75,842 individuals were educated about cervical cancer screening and HPV vaccination. A total of 44,781 women underwent screening for cervical cancer, and 2,216 underwent colposcopy and 264 underwent LEEP. High-grade cervical dysplasia was diagnosed in 658 individuals and invasive cervical cancer in 33 individuals. We trained 22 providers to perform colposcopy and/or LEEP. In addition, 78 Project ECHO telementoring sessions were held with an average of 42 attendees per session, with 72 individual patient cases discussed. Our comprehensive community-based prevention initiative for medically underserved populations has led to a significant number of individuals undergoing cervical cancer screening in MUAs, as well as improved access to colposcopy and LEEP services.

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.

A Novel Bayesian Spatial-Temporal Approach to Quantify SARS-CoV-2 Testing Disparities for Small Area Estimation.

Objectives. To propose a novel Bayesian spatial-temporal approach to identify and quantify severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) testing disparities for small area estimation. Methods. In step 1, we used a Bayesian inseparable space-time model framework to estimate the testing positivity rate (TPR) at geographically granular areas of the census block groups (CBGs). In step 2, we adopted a rank-based approach to compare the estimated TPR and the testing rate to identify areas with testing deficiency and quantify the number of needed tests. We used weekly SARS-CoV-2 infection and testing surveillance data from Cameron County, Texas, between March 2020 and February 2022 to demonstrate the usefulness of our proposed approach. Results. We identified the CBGs that had experienced substantial testing deficiency, quantified the number of tests that should have been conducted in these areas, and evaluated the short- and long-term testing disparities. Conclusions. Our proposed analytical framework offers policymakers and public health practitioners a tool for understanding SARS-CoV-2 testing disparities in geographically small communities. It could also aid COVID-19 response planning and inform intervention programs to improve goal setting and strategy implementation in SARS-CoV-2 testing uptake. (Am J Public Health. 2023;113(1):40-48. https://doi.org/10.2105/AJPH.2022.307127).

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.