The Impact of Community Engagement in the Design and Implementation of the Flint Registry.

BACKGROUND: The Flint water crisis (FWC) was a public health tragedy caused by crumbling infrastructure, subverted democracy, and indifference toward a predominantly poor and Black community that resulted in lead-in-water exposure, Legionnaires' disease, and emotional and health-related trauma. Through the cooperation of community partners, the Flint Registry (FR) was conceived to track long-term health and improve public health via service connections. OBJECTIVES: This study sought to share the FR's community-partnered, multi-tiered engagement strategy and determine the efficacy of this strategy to engage the community and reach Flint residents. METHODS: Community engagement and impact were measured by collecting and describing feedback from the community engagement strategies and by comparing the demographics of the enrollees recruited through community-engaged recruitment (CER) and non-CER methods. Enroll-ees indicated how they heard about the FR; CER involved direct interaction with a community member. RESULTS: Community engagement strategies incorporated approximately 1,200 people and 7 funded organizations, impacting 22 key areas of FR design and implementation. More than 50% of enrollees heard about the FR through CER methods. They were, on average, more likely to be younger, female, Black/African American, and living outside of Flint during the FWC. CONCLUSIONS: Community engagement elevated voices of those impacted by the FWC. CER methods were as effective as non-CER methods. Although there were no differences in screened measures of social vulnerability, there were in age, gender, and race. CER methods may increase participation and build trust in populations which historically are hesitant to participate in public health efforts.

Transcriptomic and metabolomic correlates of increased colonic permeability in post-infection irritable bowel syndrome.

BACKGROUND AND AIM: Post-infection irritable bowel syndrome (PI-IBS) is well-known epidemiologically; however, its physiological and molecular characteristics are not well studied. We aimed to determine the physiological phenotypes, colonic transcriptome, fecal microbiome, and metabolome in PI-IBS. METHODS: Fifty-one Rome III Campylobacter PI-IBS patients and 39 healthy volunteers (HV) were enrolled. Participants completed questionnaires, in vivo intestinal permeability, gastrointestinal transit, and rectal sensation. Fecal samples were collected for shotgun metagenomics, untargeted metabolomics, and sigmoid colonic biopsies for bulk RNAseq. Differential gene expression, differences in microbiota composition and metabolite abundance were determined. Gene and metabolite clusters were identified via weighted gene correlation network analysis and correlations with clinical and physiological parameters determined. RESULTS: PI-IBS (59% F, 46±2 yrs.) and HV (64% F, 42±2 yrs.) demographics were comparable. Mean IBS-symptom severity score was 227; 94% were non-constipation. 2-24h lactulose excretion was increased in PI-IBS, suggesting increased colonic permeability (4.4±0.5 mg vs. 2.6±0.3 mg, p=0.01). Colonic transit and sensory thresholds were similar between the two groups. Overall, expression of 2036 mucosal genes and 223 fecal metabolites were different, with changes more prominent in females. Fecal N-acetylputrescine was increased in PI-IBS and associated with colonic permeability, worse diarrhea, and negatively correlated with abundance of Collinsella aerofaciens. Histamine and N-acetyl histamine positively associated with 2-24 hr lactulose excretion. Eight weighted gene coexpression modules significantly correlated with phenotypes (sex, stool frequency, colonic permeability, transit). CONCLUSIONS: Campylobacter PI-IBS patients demonstrate higher colonic permeability which associated with changes in polyamine and histamine metabolites. Female patients demonstrated greater molecular changes.

Reported Incidence of Infections Caused by Pathogens Transmitted Commonly Through Food: Impact of Increased Use of Culture-Independent Diagnostic Tests - Foodborne Diseases Active Surveillance Network, 1996-2023.

Reducing foodborne disease incidence is a public health priority. This report summarizes preliminary 2023 Foodborne Diseases Active Surveillance Network (FoodNet) data and highlights efforts to increase the representativeness of FoodNet. During 2023, incidences of domestically acquired campylobacteriosis, Shiga toxin-producing Escherichia coli infection, yersiniosis, vibriosis, and cyclosporiasis increased, whereas those of listeriosis, salmonellosis, and shigellosis remained stable compared with incidences during 2016-2018, the baseline used for tracking progress towards federal disease reduction goals. During 2023, the incidence and percentage of infections diagnosed by culture-independent diagnostic tests (CIDTs) reported to FoodNet continued to increase, and the percentage of cases that yielded an isolate decreased, affecting observed trends in incidence. Because CIDTs allow for diagnosis of infections that previously would have gone undetected, lack of progress toward disease reduction goals might reflect changing diagnostic practices rather than an actual increase in incidence. Continued surveillance is needed to monitor the impact of changing diagnostic practices on disease trends, and targeted prevention efforts are needed to meet disease reduction goals. During 2023, FoodNet expanded its catchment area for the first time since 2004. This expansion improved the representativeness of the FoodNet catchment area, the ability of FoodNet to monitor trends in disease incidence, and the generalizability of FoodNet data.

Protocol for performing metabolic pathway-based subtyping of breast tumors.

Here, we present a protocol for analyzing the global metabolic landscape in breast tumors for the purpose of metabolism-based patient stratification. We describe steps for analyzing 1,454 metabolic genes representing 90 metabolic pathways and subjecting them to an algorithm that calculates the deregulation score of 90 pathways in each tumor sample, thus converting gene-level information into pathway-level information. We then detail procedures for performing clustering analysis to identify metabolic subtypes and using machine learning to develop a signature representing each subtype. For complete details on the use and execution of this protocol, please refer to Iqbal et al.1.

Outbreak of Human Trichinellosis - Arizona, Minnesota, and South Dakota, 2022.

Trichinellosis is a parasitic zoonotic disease transmitted through the consumption of meat from animals infected with Trichinella spp. nematodes. In North America, human trichinellosis is rare and is most commonly acquired through consumption of wild game meat. In July 2022, a hospitalized patient with suspected trichinellosis was reported to the Minnesota Department of Health. One week before symptom onset, the patient and eight other persons shared a meal that included bear meat that had been frozen for 45 days before being grilled and served rare with vegetables that had been cooked with the meat. Investigation identified six trichinellosis cases, including two in persons who consumed only the vegetables. Motile Trichinella larvae were found in remaining bear meat that had been frozen for >15 weeks. Molecular testing identified larvae from the bear meat as Trichinella nativa, a freeze-resistant species. Persons who consume meat from wild game animals should be aware that that adequate cooking is the only reliable way to kill Trichinella parasites and that infected meat can cross-contaminate other foods.

Outbreak of Human Trichinellosis - Arizona, Minnesota, and South Dakota, 2022.

Trichinellosis is a parasitic zoonotic disease transmitted through the consumption of meat from animals infected with Trichinella spp. nematodes. In North America, human trichinellosis is rare and is most commonly acquired through consumption of wild game meat. In July 2022, a hospitalized patient with suspected trichinellosis was reported to the Minnesota Department of Health. One week before symptom onset, the patient and eight other persons shared a meal that included bear meat that had been frozen for 45 days before being grilled and served rare with vegetables that had been cooked with the meat. Investigation identified six trichinellosis cases, including two in persons who consumed only the vegetables. Motile Trichinella larvae were found in remaining bear meat that had been frozen for >15 weeks. Molecular testing identified larvae from the bear meat as Trichinella nativa, a freeze-resistant species. Persons who consume meat from wild game animals should be aware that that adequate cooking is the only reliable way to kill Trichinella parasites and that infected meat can cross-contaminate other foods.

Multi-ancestry polygenic mechanisms of type 2 diabetes.

Type 2 diabetes (T2D) is a multifactorial disease with substantial genetic risk, for which the underlying biological mechanisms are not fully understood. In this study, we identified multi-ancestry T2D genetic clusters by analyzing genetic data from diverse populations in 37 published T2D genome-wide association studies representing more than 1.4 million individuals. We implemented soft clustering with 650 T2D-associated genetic variants and 110 T2D-related traits, capturing known and novel T2D clusters with distinct cardiometabolic trait associations across two independent biobanks representing diverse genetic ancestral populations (African, n = 21,906; Admixed American, n = 14,410; East Asian, n =2,422; European, n = 90,093; and South Asian, n = 1,262). The 12 genetic clusters were enriched for specific single-cell regulatory regions. Several of the polygenic scores derived from the clusters differed in distribution among ancestry groups, including a significantly higher proportion of lipodystrophy-related polygenic risk in East Asian ancestry. T2D risk was equivalent at a body mass index (BMI) of 30 kg m-2 in the European subpopulation and 24.2 (22.9-25.5) kg m-2 in the East Asian subpopulation; after adjusting for cluster-specific genetic risk, the equivalent BMI threshold increased to 28.5 (27.1-30.0) kg m-2 in the East Asian group. Thus, these multi-ancestry T2D genetic clusters encompass a broader range of biological mechanisms and provide preliminary insights to explain ancestry-associated differences in T2D risk profiles.

Integrative analysis of multimodal patient data identifies personalized predictors of tuberculosis treatment prognosis.

Tuberculosis (TB) afflicted 10.6 million people in 2021, and its global burden is increasing due to multidrug-resistant TB (MDR-TB) and extensively resistant TB (XDR-TB). Here, we analyze multi-domain information from 5,060 TB patients spanning 10 countries with high burden of MDR-TB from the NIAID TB Portals database to determine predictors of TB treatment outcome. Our analysis revealed significant associations between radiological, microbiological, therapeutic, and demographic data modalities. Our machine learning model, built with 203 features across modalities outperforms models built using each modality alone in predicting treatment outcomes, with an accuracy of 83% and area under the curve of 0.84. Notably, our analysis revealed that the drug regimens Bedaquiline-Clofazimine-Cycloserine-Levofloxacin-Linezolid and Bedaquiline-Clofazimine-Linezolid-Moxifloxacin were associated with treatment success and failure, respectively, for MDR non-XDR-TB. Drug combinations predicted to be synergistic by the INDIGO algorithm performed better than antagonistic combinations. Our prioritized set of features predictive of treatment outcomes can ultimately guide the personalized clinical management of TB.

Defining the roles of local precipitation and anthropogenic water sources in driving the abundance of Aedes aegypti, an emerging disease vector in urban, arid landscapes.

Understanding drivers of disease vectors' population dynamics is a pressing challenge. For short-lived organisms like mosquitoes, landscape-scale models must account for their highly local and rapid life cycles. Aedes aegypti, a vector of multiple emerging diseases, has become abundant in desert population centers where water from precipitation could be a limiting factor. To explain this apparent paradox, we examined Ae. aegypti abundances at > 660 trapping locations per year for 3 years in the urbanized Maricopa County (metropolitan Phoenix), Arizona, USA. We created daily precipitation layers from weather station data using a kriging algorithm, and connected localized daily precipitation to numbers of mosquitoes trapped at each location on subsequent days. Precipitation events occurring in either of two critical developmental periods for mosquitoes were correlated to suppressed subsequent adult female presence and abundance. LASSO models supported these analyses for female presence but not abundance. Precipitation may explain 72% of Ae. aegypti presence and 90% of abundance, with anthropogenic water sources supporting mosquitoes during long, precipitation-free periods. The method of using kriging and weather station data may be generally applicable to the study of various ecological processes and patterns, and lead to insights into microclimates associated with a variety of organisms' life cycles.

Polycystic Ovary Syndrome Physiologic Pathways Implicated Through Clustering of Genetic Loci.

CONTEXT: Polycystic ovary syndrome (PCOS) is a heterogeneous disorder, with disease loci identified from genome-wide association studies (GWAS) having largely unknown relationships to disease pathogenesis. OBJECTIVE: This work aimed to group PCOS GWAS loci into genetic clusters associated with disease pathophysiology. METHODS: Cluster analysis was performed for 60 PCOS-associated genetic variants and 49 traits using GWAS summary statistics. Cluster-specific PCOS partitioned polygenic scores (pPS) were generated and tested for association with clinical phenotypes in the Mass General Brigham Biobank (MGBB, N = 62 252). Associations with clinical outcomes (type 2 diabetes [T2D], coronary artery disease [CAD], and female reproductive traits) were assessed using both GWAS-based pPS (DIAMANTE, N = 898,130, CARDIOGRAM/UKBB, N = 547 261) and individual-level pPS in MGBB. RESULTS: Four PCOS genetic clusters were identified with top loci indicated as following: (i) cluster 1/obesity/insulin resistance (FTO); (ii) cluster 2/hormonal/menstrual cycle changes (FSHB); (iii) cluster 3/blood markers/inflammation (ATXN2/SH2B3); (iv) cluster 4/metabolic changes (MAF, SLC38A11). Cluster pPS were associated with distinct clinical traits: Cluster 1 with increased body mass index (P = 6.6 × 10-29); cluster 2 with increased age of menarche (P = 1.5 × 10-4); cluster 3 with multiple decreased blood markers, including mean platelet volume (P = 3.1 ×10-5); and cluster 4 with increased alkaline phosphatase (P = .007). PCOS genetic clusters GWAS-pPSs were also associated with disease outcomes: cluster 1 pPS with increased T2D (odds ratio [OR] 1.07; P = 7.3 × 10-50), with replication in MGBB all participants (OR 1.09, P = 2.7 × 10-7) and females only (OR 1.11, 4.8 × 10-5). CONCLUSION: Distinct genetic backgrounds in individuals with PCOS may underlie clinical heterogeneity and disease outcomes.

Biomass smoke exposure and somatic growth among children: The RESPIRE and CRECER prospective cohort studies in rural Guatemala.

BACKGROUND: Cooking-related biomass smoke is a major source of household air pollution (HAP) and an important health hazard. Prior studies identified associations between HAP exposure and childhood stunting; less is known for underweight and wasting. Few studies had personal HAP measurements. METHODS: 557 households in rural Guatemala were enrolled in the CRECER study, the follow-up study of the RESPIRE randomized intervention trial. They were assigned to three groups that received chimney stoves at different ages of the study children. Multiple personal carbon monoxide (CO) exposure measurements were used as proxies for HAP exposures. Children's heights and weights were measured from 24 to 60 months of age. Height-for-age z-score (HAZ), weight-for-age z-score (WAZ), and weight-for-height z-score (WHZ) were calculated based on the World Health Organization's Multicentre Growth Reference Study. HAZ, WAZ, and WHZ below -2 were classified as stunting, underweight, and wasting, respectively. Generalized linear models and mixed effects models were applied. RESULTS: 541 children had valid anthropometric data, among whom 488 (90.2 %) were stunted, 192 (35.5 %) were underweight, and 2 (0.3 %) were wasted. A 1 ppm higher average CO exposure was associated with a 0.21 lower HAZ (95 % CI: 0.17-0.25), a 0.13 lower WAZ (95 % CI: 0.10-0.17) and a 0.06 lower WHZ (95 % CI: 0.02-0.10).The associations for HAZ were stronger among boys (coefficient = -0.29, 95 % CI: -0.35 - -0.22) than among girls (coefficient = -0.15, 95 % CI: -0.20 - -0.10). A 1 ppm-year higher cumulative CO exposure was associated with a higher risk of moderate stunting among boys (OR = 1.27, 95 % CI: 1.05-1.59), but not among girls. DISCUSSION: In this rural Guatemalan population, higher HAP exposure was associated with lower HAZ and WAZ. The associations between HAP and HAZ/stunting were stronger among boys. Reducing HAP might benefit childhood somatic growth in rural populations of low-income countries.

Heterogeneous effects on type 2 diabetes and cardiovascular outcomes of genetic variants and traits associated with fasting insulin.

Hyperinsulinemia is a complex and heterogeneous phenotype that characterizes molecular alterations that precede the development of type 2 diabetes (T2D). It results from a complex combination of molecular processes, including insulin secretion and insulin sensitivity, that differ between individuals. To better understand the physiology of hyperinsulinemia and ultimately T2D, we implemented a genetic approach grouping fasting insulin (FI)-associated genetic variants based on their molecular and phenotypic similarities. We identified seven distinctive genetic clusters representing different physiologic mechanisms leading to rising FI levels, ranging from clusters of variants with effects on increased FI, but without increased risk of T2D (non-diabetogenic hyperinsulinemia), to clusters of variants that increase FI and T2D risk with demonstrated strong effects on body fat distribution, liver, lipid, and inflammatory processes (diabetogenic hyperinsulinemia). We generated cluster-specific polygenic scores in 1,104,258 individuals from five multi-ancestry cohorts to show that the clusters differed in associations with cardiometabolic traits. Among clusters characterized by non-diabetogenic hyperinsulinemia, there was both increased and decreased risk of coronary artery disease despite the non-increased risk of T2D. Similarly, the clusters characterized by diabetogenic hyperinsulinemia were associated with an increased risk of T2D, yet had differing risks of cardiovascular conditions, including coronary artery disease, myocardial infarction, and stroke. The strongest cluster-T2D associations were observed with the same direction of effect in non-Hispanic Black, Hispanic, non-Hispanic White, and non-Hispanic East Asian populations. These genetic clusters provide important insights into granular metabolic processes underlying the physiology of hyperinsulinemia, notably highlighting specific processes that decouple increasing FI levels from T2D and cardiovascular risk. Our findings suggest that increasing FI levels are not invariably associated with adverse cardiometabolic outcomes.

Metabolic stratification of human breast tumors reveal subtypes of clinical and therapeutic relevance.

Extensive metabolic heterogeneity in breast cancers has limited the deployment of metabolic therapies. To enable patient stratification, we studied the metabolic landscape in breast cancers (∼3000 patients combined) and identified three subtypes with increasing degrees of metabolic deregulation. Subtype M1 was found to be dependent on bile-acid biosynthesis, whereas M2 showed reliance on methionine pathway, and M3 engaged fatty-acid, nucleotide, and glucose metabolism. The extent of metabolic alterations correlated strongly with tumor aggressiveness and patient outcome. This pattern was reproducible in independent datasets and using in vivo tumor metabolite data. Using machine-learning, we identified robust and generalizable signatures of metabolic subtypes in tumors and cell lines. Experimental inhibition of metabolic pathways in cell lines representing metabolic subtypes revealed subtype-specific sensitivity, therapeutically relevant drugs, and promising combination therapies. Taken together, metabolic stratification of breast cancers can thus aid in predicting patient outcome and designing precision therapies.

Aedes aegypti abundance in urban neighborhoods of Maricopa County, Arizona, is linked to increasing socioeconomic status and tree cover.

BACKGROUND: Understanding coupled human-environment factors which promote Aedes aegypti abundance is critical to preventing the spread of Zika, chikungunya, yellow fever and dengue viruses. High temperatures and aridity theoretically make arid lands inhospitable for Ae. aegypti mosquitoes, yet their populations are well established in many desert cities. METHODS: We investigated associations between socioeconomic and built environment factors and Ae. aegypti abundance in Maricopa County, Arizona, home to Phoenix metropolitan area. Maricopa County Environmental Services conducts weekly mosquito surveillance with CO2-baited Encephalitis Vector Survey or BG-Sentinel traps at > 850 locations throughout the county. Counts of adult female Ae. aegypti from 2014 to 2017 were joined with US Census data, precipitation and temperature data, and 2015 land cover from high-resolution (1 m) aerial images from the National Agricultural Imagery Program. RESULTS: From 139,729 trap-nights, 107,116 Ae. aegypti females were captured. Counts were significantly positively associated with higher socioeconomic status. This association was partially explained by higher densities of non-native landscaping in wealthier neighborhoods; a 1% increase in the density of tree cover around the trap was associated with a ~ 7% higher count of Ae. aegypti (95% CI: 6-9%). CONCLUSIONS: Many models predict that climate change will drive aridification in some heavily populated regions, including those where Ae. aegypti are widespread. City climate change adaptation plans often include green spaces and vegetation cover to increase resilience to extreme heat, but these may unintentionally create hospitable microclimates for Ae. aegypti. This possible outcome should be addressed to reduce the potential for outbreaks of Aedes-borne diseases in desert cities.

Laboratory Criteria for Exclusion and Readmission of Potentially Infectious Persons in Sensitive Settings in the Age of Culture-Independent Diagnostic Tests: Report of a Multidisciplinary Workgroup.

Culture-independent diagnostic tests (CIDTs) are increasingly used for clinical diagnosis of gastrointestinal diseases such as salmonellosis, Shiga toxin-producing E. coli disease, and shigellosis because of their speed, convenience, and generally high-performance characteristics. These tests are also used to screen potentially infectious asymptomatic persons during outbreak investigations in sensitive settings such as childcare, food service, and healthcare. However, only limited performance data are available for CIDTs used on specimens from asymptomatic persons. The Association of Public Health Laboratories (APHL) and Council of State and Territorial Epidemiologists (CSTE) convened a workgroup to examine the available scientific data to inform interim decision-making related to exclusion and readmission criteria for potentially infectious persons in sensitive settings, the risks and benefits of different testing strategies, and to identify knowledge gaps for further research. This is the report on the Workgroup findings.

Multi-ancestry Polygenic Mechanisms of Type 2 Diabetes Elucidate Disease Processes and Clinical Heterogeneity.

We identified genetic subtypes of type 2 diabetes (T2D) by analyzing genetic data from diverse groups, including non-European populations. We implemented soft clustering with 650 T2D-associated genetic variants, capturing known and novel T2D subtypes with distinct cardiometabolic trait associations. The twelve genetic clusters were distinctively enriched for single-cell regulatory regions. Polygenic scores derived from the clusters differed in distribution between ancestry groups, including a significantly higher proportion of lipodystrophy-related polygenic risk in East Asian ancestry. T2D risk was equivalent at a BMI of 30 kg/m2 in the European subpopulation and 24.2 (22.9-25.5) kg/m2 in the East Asian subpopulation; after adjusting for cluster-specific genetic risk, the equivalent BMI threshold increased to 28.5 (27.1-30.0) kg/m2 in the East Asian group, explaining about 75% of the difference in BMI thresholds. Thus, these multi-ancestry T2D genetic subtypes encompass a broader range of biological mechanisms and help explain ancestry-associated differences in T2D risk profiles.

Multi-ancestry Polygenic Mechanisms of Type 2 Diabetes Elucidate Disease Processes and Clinical Heterogeneity.

We identified genetic subtypes of type 2 diabetes (T2D) by analyzing genetic data from diverse groups, including non-European populations. We implemented soft clustering with 650 T2D-associated genetic variants, capturing known and novel T2D subtypes with distinct cardiometabolic trait associations. The twelve genetic clusters were distinctively enriched for single-cell regulatory regions. Polygenic scores derived from the clusters differed in distribution between ancestry groups, including a significantly higher proportion of lipodystrophy-related polygenic risk in East Asian ancestry. T2D risk was equivalent at a BMI of 30 kg/m2 in the European subpopulation and 24.2 (22.9-25.5) kg/m2 in the East Asian subpopulation; after adjusting for cluster-specific genetic risk, the equivalent BMI threshold increased to 28.5 (27.1-30.0) kg/m2 in the East Asian group, explaining about 75% of the difference in BMI thresholds. Thus, these multi-ancestry T2D genetic subtypes encompass a broader range of biological mechanisms and help explain ancestry-associated differences in T2D risk profiles.