Longitudinal Renal Function Degradation Among Florida Agricultural Workers.

OBJECTIVE: This longitudinal study evaluated renal function and acute kidney injury (AKI) over time in U.S. agricultural workers. METHODS: We followed Florida agricultural workers from January 2020 to August 2022, collecting blood and urine pre- and post-workday during 5 visits. RESULTS: Pre-workday eGFR function in all participants was lower in summers but relatively consistent over time. In participants who worked almost exclusively in fernery operations (piece-rate compensation), we observed a high incidence of post-workday AKI in 2020 (21%) that increased to 43% by the end of the study. In comparison, 11% of nursery workers (hourly compensation) had AKI, and this rate was fairly stable. CONCLUSION: AKI risk over time differs according to the type of agricultural work. Piece rate workers who are incentivized to forgo rest breaks and hydration to earn higher wages demonstrate steadily increasing rates of AKI.

Belatacept with time-limited tacrolimus coimmunosuppression modifies the 3-year risk of eplet mismatch in kidney transplantation.

Solid organ transplant donor-recipient eplet mismatch has been correlated with donor-specific antibody (DSA) formation, antibody-mediated rejection, and overall rejection rates. However, studies have been predominantly in patients on tacrolimus-based immunosuppression regimens and have not fully explored differences in ethnically and racially diverse populations. Evidence indicates that patients on belatacept have lower rates of DSA formation, suggesting mediation of the immunogenicity of mismatched human leukocyte antigen polymorphisms. We performed a retrospective, single-center analysis of class II eplet disparity in a cohort of kidney transplant recipients treated using belatacept with tacrolimus induction (Bela/TacTL) or tacrolimus regimens between 2016 and 2019. Bela/TacTL (n = 294) and tacrolimus (n = 294) cohorts were propensity score-matched with standardized difference <0.15. Single-molecule eplet risk level was associated with immune event rates for both groups. In Cox regression analysis stratified by eplet risk level, Bela/TacTL immunosuppression was associated with a decreased rate of DSA (hazard ratio [HR] = 0.4), antibody-mediated rejection (HR = 0.2), and rejection (HR = 0.45). In the low-risk group, cumulative graft failure was lower for patients on Bela/TacTL (P < .02). Analysis of eplet mismatch burden may be a useful adjunct in identifying high-risk populations with increased immunosuppression requirements and should encourage the design of allocation rules to incentivize lower-risk pairings without negatively impacting equity in access.

An AdaBoost-based algorithm to detect hospital-acquired pressure injury in the presence of conflicting annotations.

Hospital-acquired pressure injury is one of the most harmful events in clinical settings. Patients who do not receive early prevention and treatment can experience a significant financial burden and physical trauma. Several hospital-acquired pressure injury prediction algorithms have been developed to tackle this problem, but these models assume a consensus, gold-standard label (i.e., presence of pressure injury or not) is present for all training data. Existing definitions for identifying hospital-acquired pressure injuries are inconsistent due to the lack of high-quality documentation surrounding pressure injuries. To address this issue, we propose in this paper an ensemble-based algorithm that leverages truth inference methods to resolve label inconsistencies between various case definitions and the level of disagreements in annotations. Application of our method to MIMIC-III, a publicly available intensive care unit dataset, gives empirical results that illustrate the promise of learning a prediction model using truth inference-based labels and observed conflict among annotators.

Evaluating Natural Language Processing Packages for Predicting Hospital-Acquired Pressure Injuries From Clinical Notes.

Incidence of hospital-acquired pressure injury, a key indicator of nursing quality, is directly proportional to adverse outcomes, increased hospital stays, and economic burdens on patients, caregivers, and society. Thus, predicting hospital-acquired pressure injury is important. Prediction models use structured data more often than unstructured notes, although the latter often contain useful patient information. We hypothesize that unstructured notes, such as nursing notes, can predict hospital-acquired pressure injury. We evaluate the impact of using various natural language processing packages to identify salient patient information from unstructured text. We use named entity recognition to identify keywords, which comprise the feature space of our classifier for hospital-acquired pressure injury prediction. We compare scispaCy and Stanza, two different named entity recognition models, using unstructured notes in Medical Information Mart for Intensive Care III, a publicly available ICU data set. To assess the impact of vocabulary size reduction, we compare the use of all clinical notes with only nursing notes. Our results suggest that named entity recognition extraction using nursing notes can yield accurate models. Moreover, the extracted keywords play a significant role in the prediction of hospital-acquired pressure injury.

The impact of heat exposures on biomarkers of AKI and plasma metabolome among agricultural and non-agricultural workers.

BACKGROUND: Agricultural workers are consistently exposed to elevated heat exposures and vulnerable to acute kidney injury. The underlying pathophysiology and detailed molecular mechanisms of AKI among agricultural workers, and the disproportionate burden of HRI and heat stress exposure are not well understood, especially at the level of cellular metabolism. OBJECTIVE: The aim of this study was to examine the impact of heat exposures on renal biomarkers and on the human metabolome via untargeted high-resolution metabolomics among agricultural and non-agricultural workers. METHODS: Blood and urine samples were collected pre- and post-work shift from 63 agricultural workers and 27 non- agricultural workers. We evaluated pre- and post-work shift renal biomarkers and completed untargeted metabolomics using high-resolution mass spectrometry with liquid chromatography. Metabolome-wide association studies (MWAS) models identified the metabolic features differentially expressed between agricultural workers and non-agricultural workers. RESULTS: Median values of pre-shift creatinine and osteopontin (p < 0.05) were higher for agricultural workers than non-agricultural workers. Metabolic pathway enrichment analyses revealed 27 diverse pathways differed between agricultural workers and non-agricultural workers (p < 0.05) including TCA cycle and urea cycle, carbohydrate metabolism, histidine metabolism and evidence for altered microbiome shikimate pathway. CONCLUSION: This is the first investigation on the metabolic pathways that are affected among agricultural workers who are exposed to heat compared to non-heat exposed workers. This study shows extensive responses of central metabolic systems to heat exposures that impact human health.

Evaluation of available risk scores to predict multiple cardiovascular complications for patients with type 2 diabetes mellitus using electronic health records.

Aims: Various cardiovascular risk prediction models have been developed for patients with type 2 diabetes mellitus. Yet few models have been validated externally. We perform a comprehensive validation of existing risk models on a heterogeneous population of patients with type 2 diabetes using secondary analysis of electronic health record data. Methods: Electronic health records of 47,988 patients with type 2 diabetes between 2013 and 2017 were used to validate 16 cardiovascular risk models, including 5 that had not been compared previously, to estimate the 1-year risk of various cardiovascular outcomes. Discrimination and calibration were assessed by the c-statistic and the Hosmer-Lemeshow goodness-of-fit statistic, respectively. Each model was also evaluated based on the missing measurement rate. Sub-analysis was performed to determine the impact of race on discrimination performance. Results: There was limited discrimination (c-statistics ranged from 0.51 to 0.67) across the cardiovascular risk models. Discrimination generally improved when the model was tailored towards the individual outcome. After recalibration of the models, the Hosmer-Lemeshow statistic yielded p-values above 0.05. However, several of the models with the best discrimination relied on measurements that were often imputed (up to 39% missing). Conclusion: No single prediction model achieved the best performance on a full range of cardiovascular endpoints. Moreover, several of the highest-scoring models relied on variables with high missingness frequencies such as HbA1c and cholesterol that necessitated data imputation and may not be as useful in practice. An open-source version of our developed Python package, cvdm, is available for comparisons using other data sources.

A Pilot Study Examining the Use of Ultrasound to Measure Intravascular Volume Status in Agricultural Workers in a Field-Based Research Setting.

OBJECTIVE: The aim of this pilot study was to explore if internal jugular vein (IJV) ultrasound studies on agricultural workers in a field-based research setting could assess volume status during a hydration intervention. METHODS: We performed pre- and post-work shift IJV ultrasound images on 30 agricultural workers. The IJV collapsibility index values were <39% (euvolemic) or ≥39% (hypovolemic). RESULTS: Of the water group, 13% (2/15) had an IJV collapsibility index ≥39%, and this increased to 19% (3/16) by the end of the work shifts. The electrolyte group did not have any workers start the work shift with an IJV collapsibility index ≥39%; however, at the postshift assessment, 15% (2/13) were hypovolemic. CONCLUSION: Internal jugular vein ultrasounds may have the potential to be a useful tool to determine volume status in field-based research settings. Further investigation is needed to confirm these findings.

Hepatitis C care cascade in a large academic healthcare system, 2012 to 2018.

To determine the hepatitis C virus (HCV) care cascade among persons who were born during 1945 to 1965 and received outpatient care on or after January 2014 at a large academic healthcare system. Deidentified electronic health record data in an existing research database were analyzed for this study. Laboratory test results for HCV antibody and HCV ribonucleic acid (RNA) indicated seropositivity and confirmatory testing. HCV genotyping was used as a proxy for linkage to care. A direct-acting antiviral (DAA) prescription indicated treatment initiation, an undetectable HCV RNA at least 20 weeks after initiation of antiviral treatment indicated a sustained virologic response. Of the 121,807 patients in the 1945 to 1965 birth cohort who received outpatient care between January 1, 2014 and June 30, 2017, 3399 (3%) patients were screened for HCV; 540 (16%) were seropositive. Among the seropositive, 442 (82%) had detectable HCV RNA, 68 (13%) had undetectable HCV RNA, and 30 (6%) lacked HCV RNA testing. Of the 442 viremic patients, 237 (54%) were linked to care, 65 (15%) initiated DAA treatment, and 32 (7%) achieved sustained virologic response. While only 3% were screened for HCV, the seroprevalence was high in the screened sample. Despite the established safety and efficacy of DAAs, only 15% initiated treatment during the study period. To achieve HCV elimination, improved HCV screening and linkage to HCV care and DAA treatment are needed.

A Comprehensive and Improved Definition for Hospital-Acquired Pressure Injury Classification Based on Electronic Health Records: Comparative Study.

BACKGROUND: Patients develop pressure injuries (PIs) in the hospital owing to low mobility, exposure to localized pressure, circulatory conditions, and other predisposing factors. Over 2.5 million Americans develop PIs annually. The Center for Medicare and Medicaid considers hospital-acquired PIs (HAPIs) as the most frequent preventable event, and they are the second most common claim in lawsuits. With the growing use of electronic health records (EHRs) in hospitals, an opportunity exists to build machine learning models to identify and predict HAPI rather than relying on occasional manual assessments by human experts. However, accurate computational models rely on high-quality HAPI data labels. Unfortunately, the different data sources within EHRs can provide conflicting information on HAPI occurrence in the same patient. Furthermore, the existing definitions of HAPI disagree with each other, even within the same patient population. The inconsistent criteria make it impossible to benchmark machine learning methods to predict HAPI. OBJECTIVE: The objective of this project was threefold. We aimed to identify discrepancies in HAPI sources within EHRs, to develop a comprehensive definition for HAPI classification using data from all EHR sources, and to illustrate the importance of an improved HAPI definition. METHODS: We assessed the congruence among HAPI occurrences documented in clinical notes, diagnosis codes, procedure codes, and chart events from the Medical Information Mart for Intensive Care III database. We analyzed the criteria used for the 3 existing HAPI definitions and their adherence to the regulatory guidelines. We proposed the Emory HAPI (EHAPI), which is an improved and more comprehensive HAPI definition. We then evaluated the importance of the labels in training a HAPI classification model using tree-based and sequential neural network classifiers. RESULTS: We illustrate the complexity of defining HAPI, with <13% of hospital stays having at least 3 PI indications documented across 4 data sources. Although chart events were the most common indicator, it was the only PI documentation for >49% of the stays. We demonstrate a lack of congruence across existing HAPI definitions and EHAPI, with only 219 stays having a consensus positive label. Our analysis highlights the importance of our improved HAPI definition, with classifiers trained using our labels outperforming others on a small manually labeled set from nurse annotators and a consensus set in which all definitions agreed on the label. CONCLUSIONS: Standardized HAPI definitions are important for accurately assessing HAPI nursing quality metric and determining HAPI incidence for preventive measures. We demonstrate the complexity of defining an occurrence of HAPI, given the conflicting and incomplete EHR data. Our EHAPI definition has favorable properties, making it a suitable candidate for HAPI classification tasks.

Inflammatory profiles, gut microbiome, and kidney function are impacted after high-fidelity firefighter training.

BACKGROUND: Firefighters are frequently exposed to high temperatures, environmental toxicants, and strenuous physical demands. The health impacts of these occupational exposures on processes including inflammation and kidney function as well as on the gut microbiota are poorly understood. A firefighter training course may provide a controlled environment to assess these health risks. METHODS: Basic health measures, stool, and blood samples were obtained from 24 firefighters participating in a one-week, heat-intensive training course. Indicators of inflammation, gut permeability, kidney health, and stool microbiota composition were measured before and after the training course in 18 participants. Urine specific gravity was measured before and after a heat-intensive training day to evaluate dehydration. RESULTS: The majority of firefighters in this cohort were categorized as hypertensive and experienced multiple heat-related illness symptoms during the training week and dehydration after the heat-intensive training day. While plasma IL-1ß, CXCL8, and NGAL decreased over the training week, other indicators of inflammation and acute kidney injury increased, and estimated kidney function declined. Microbiota composition shifted over the course of the training week, with changes in Peptostreptococcus anaerobius and Streptococcus. CONCLUSIONS: This pilot study conducted in a controlled field setting suggests that the occupational environment of firefighters may increase their risk for systemic inflammation and kidney disease.

A Pilot Study of the Gut Microbiota Associated With Depressive Symptoms and Sleep Disturbance Among Chinese and Korean Immigrants in the United States.

CONTEXT: Depression is prevalent among Asian Americans (AsA) during the COVID-19 pandemic, and depression often leads to sleep disturbance in this population. The gut microbiota (GM) plays a critical role in mental health and sleep quality, and the composition of the GM is largely unknown among AsA. OBJECTIVES: Examine associations of the GM with depressive symptoms and sleep disturbance among Chinese and Korean American immigrants. METHODS: Depressive symptoms (PROMIS Short Form-Depression) and sleep quality (Pittsburgh Sleep Quality Index [PSQI]) were collected via surveys. PROMIS measure T-score > 55 indicates positive depressive symptoms, and a total PSQI score > 5 indicates sleep disturbance. 16S rRNA V3-V4 gene regions were sequenced from fecal specimens to measure GM. Permutational multivariate analysis of variance and linear discriminant analysis effect size were applied to examine associations of the GM with symptoms. RESULTS: Among 20 participants, 55% (n = 11) reported depressive symptoms and 35% (n = 7) reported sleep disturbance. A higher α-diversity was marginally associated with lower depressive symptoms: Chao1 (r = -0.39, p = 0.09) and Shannon index (r = -0.41, p = 0.08); ß-diversity distinguished participants between categories of depressive symptoms (weighted UniFrac, p=0.04) or sleep disturbance (Jaccard, p=0.05). Those with depressive symptoms showed a higher abundance of Actinobacteria, while those without depressive symptoms had a higher abundance of Bacteroidetes. No significant taxa were identified for sleep disturbance. CONCLUSIONS: Gut microbial diversity showed promising associations with depressive symptoms and sleep disturbance among Chinese and Korean immigrants. Specific taxa were identified as associated with depressive symptoms. Future studies with a larger sample size are warranted to confirm our findings.

Enhancing Personalized Healthcare via Capturing Disease Severity, Interaction, and Progression.

Personalized diagnosis prediction based on electronic health records (EHR) of patients is a promising yet challenging task for AI in healthcare. Existing studies typically ignore the heterogeneity of diseases across different patients. For example, diabetes can have different complications across different patients (e.g., hyperlipidemia and circulatory disorder), which requires personalized diagnoses and treatments. Specifically, existing models fail to consider 1) varying severity of the same diseases for different patients, 2) complex interactions among syndromic diseases, and 3) dynamic progression of chronic diseases. In this work, we propose to perform personalized diagnosis prediction based on EHR data via capturing disease severity, interaction, and progression. In particular, we enable personalized disease representations via severity-driven embeddings at the disease level. Then, at the visit level, we propose to capture higher-order interactions among diseases that can collectively affect patients' health status via hypergraph-based aggregation; at the patient level, we devise a personalized generative model based on neural ordinary differential equations to capture the continuous-time disease progressions underlying discrete and incomplete visits. Extensive experiments on two real-world EHR datasets show significant performance gains brought by our approach, yielding average improvements of 10.70% for diagnosis prediction over state-of-the-art competitors.

Subgingival Microbiome in Pregnancy and a Potential Relationship to Early Term Birth.

Background: Periodontal disease in pregnancy is considered a risk factor for adverse birth outcomes. Periodontal disease has a microbial etiology, however, the current state of knowledge about the subgingival microbiome in pregnancy is not well understood. Objective: To characterize the structure and diversity of the subgingival microbiome in early and late pregnancy and explore relationships between the subgingival microbiome and preterm birth among pregnant Black women. Methods: This longitudinal descriptive study used 16S rRNA sequencing to profile the subgingival microbiome of 59 Black women and describe microbial ecology using alpha and beta diversity metrics. We also compared microbiome features across early (8-14 weeks) and late (24-30 weeks) gestation overall and according to gestational age at birth outcomes (spontaneous preterm, spontaneous early term, full term). Results: In this sample of Black pregnant women, the top twenty bacterial taxa represented in the subgingival microbiome included a spectrum representative of various stages of biofilm progression leading to periodontal disease, including known periopathogens Porphyromonas gingivalis and Tannerella forsythia. Other organisms associated with periodontal disease reflected in the subgingival microbiome included several Prevotella spp., and Campylobacter spp. Measures of alpha or beta diversity did not distinguish the subgingival microbiome of women according to early/late gestation or full term/spontaneous preterm birth; however, alpha diversity differences in late pregnancy between women who spontaneously delivered early term and women who delivered full term were identified. Several taxa were also identified as being differentially abundant according to early/late gestation, and full term/spontaneous early term births. Conclusions: Although the composition of the subgingival microbiome is shifted toward complexes associated with periodontal disease, the diversity of the microbiome remains stable throughout pregnancy. Several taxa were identified as being associated with spontaneous early term birth. Two, in particular, are promising targets of further investigation. Depletion of the oral commensal Lautropia mirabilis in early pregnancy and elevated levels of Prevotella melaninogenica in late pregnancy were both associated with spontaneous early term birth.

Sphinganine is associated with 24-h MAP in the non-sleepy with OSA.

INTRODUCTION: Excessive daytime sleepiness is a debilitating symptom of obstructive sleep apnea (OSA) linked to cardiovascular disease, and metabolomic mechanisms underlying this relationship remain unknown. We examine whether metabolites from inflammatory and oxidative stress-related pathways that were identified in our prior work could be involved in connecting the two phenomena. METHODS: This study included 57 sleepy (Epworth Sleepiness Scale (ESS) ≥ 10) and 37 non-sleepy (ESS < 10) participants newly diagnosed and untreated for OSA that completed an overnight in-lab or at home sleep study who were recruited from the Emory Mechanisms of Sleepiness Symptoms Study (EMOSS). Differences in fasting blood samples of metabolites were explored in participants with sleepiness versus those without and multiple linear regression models were utilized to examine the association between metabolites and mean arterial pressure (MAP). RESULTS: The 24-h MAP was higher in sleepy 92.8 mmHg (8.4) as compared to non-sleepy 88.8 mmHg (8.1) individuals (P = 0.03). Although targeted metabolites were not significantly associated with MAP, when we stratified by sleepiness group, we found that sphinganine is significantly associated with MAP (Estimate = 8.7, SE = 3.7, P = 0.045) in non-sleepy patients when controlling for age, BMI, smoking status, and apnea-hypopnea index (AHI). CONCLUSION: This is the first study to evaluate the relationship of inflammation and oxidative stress related metabolites in sleepy versus non-sleepy participants with newly diagnosed OSA and their association with 24-h MAP. Our study suggests that Sphinganine is associated with 24 hour MAP in the non-sleepy participants with OSA.

Hydration Interventions Among Agricultural Workers: A Pilot Study.

OBJECTIVE: To estimate the impact of hydration interventions on postworkday hydration status and incidence of acute kidney injury (AKI). METHODS: Thirty agricultural workers were first monitored on a workday without any interventions. On the intervention workday, the same workers were randomized to one of two groups: 169 ounces (oz) (5 L) of plain water (n = 16) or 169 oz (5L) of water with electrolytes (n = 14). RESULTS: No participants in the electrolyte group had an estimate glomerular filtration rate (eGFR) at the end of the workday of less than 90 mL/min/1.73 m2 or met the criteria for AKI in comparison to the water group (eGFR < 90: 15%; AKI: 23%) or the control group (eGFR < 90: 28%; AKI: 18%). CONCLUSION: The study showed that drinking water with electrolytes may lower the risk for development of AKI among agricultural workers.

Soft Wireless Bioelectronics Designed for Real-Time, Continuous Health Monitoring of Farmworkers.

Hotter summers caused by global warming and increased workload and duration are endangering the health of farmworkers, a high-risk population for heat-related illness (HRI), and deaths. Although prior studies using wearable sensors show the feasibility of employing field-collected data for HRI monitoring, existing devices still have limitations, such as data loss from motion artifacts, device discomfort from rigid electronics, difficulties with administering ingestible sensors, and low temporal resolution. Here, this paper introduces a wireless, wearable bioelectronic system with functionalities for continuous monitoring of skin temperature, electrocardiograms (ECG), heart rates (HR), and activities, configured in a single integrated package. Advanced nanomanufacturing based on laser machining allows rapid device fabrication and direct incorporation of sensors with a highly breathable substrate, allowing for managing excessive sweating and multimodal stresses. To validate the device's performance in agricultural settings, the device is applied to multiple farmworkers at various operations, including fernery, nursery, and crop. The accurate data recording, including high-fidelity ECG (signal-to-noise ratio: >20 dB), accurate HR (r = 0.89, r2 = 0.65 in linear correlation), and reliable temperature/activity, confirms the device's capability for multiparameter health monitoring of farmworkers.

Using Occupational Histories to Assess Heat Exposure in Undocumented Workers Receiving Emergent Renal Dialysis in Georgia.

BACKGROUND: Immigrants often work in jobs that are known as dirty, demanding, and dangerous. Globally, the agricultural occupations have been associated with the emergence of chronic kidney disease of unknown etiology (CKDu) primarily in outdoor worker populations. The disease has also been reported in immigrants in the United States who work in agricultural occupations, but little research has been done outside of agricultural workers to determine whether immigrants who work other occupations are at risk for developing CKDu. METHODS: This study assessed the self-reported occupational histories of undocumented immigrants receiving frequent, emergent-only dialysis in Atlanta, GA. We assessed demographics, employment status, and work history, using the Grady Dialysis Questionnaire and the Occupational/Environmental Health History Form. RESULTS: Fifty undocumented immigrants receiving frequent, emergent-only hemodialysis were recruited for this study. The average age was 49.5 years (SD ± 11.5), and the majority (52%) were female and originated from Mexico (66%). A majority (74%) reported having worked in the past 5 years and 28% reported currently working. A total of 68 unique jobs were reported. In decreasing order of frequency, our sample worked in occupations with documented renal toxicant exposures, such as applying pesticides in landscaping, heat exposure in agriculture, construction, landscaping, and dry cleaning, and lead paint fumes in construction. DISCUSSION: Occupational histories provide a greater understanding of the exposures and working conditions of those receiving frequent, emergent-only hemodialysis. This exploratory study suggests that further research is needed to investigate and assess whether renal toxicants are associated with occupations with high numbers of undocumented workers. APPLICATION TO PRACTICE: A detailed and thorough occupational history should be conducted from those receiving frequent, emergent-only hemodialysis. Developing continuing education for nursing and medical staff in dialysis centers on taking an occupational history, mandatory State reporting requirements, and hazard communication training for workers should be considered. Collaborations between occupational health professionals, public health authorities, employers, dialysis providers, and clinicians who see undocumented workers is required to understand and develop appropriate prevention measures for this population.

The Health Status of Hispanic Agricultural Workers in Georgia and Florida.

To examine the health status of Hispanic agricultural workers in Florida and Georgia. Health data from agricultural workers in the Farm Worker Family Health Program (June 2019) and research studies in Florida (May 2015 and May 2019) were examined. Data from 728 agricultural workers were collected through sociodemographic questionnaire and clinical data. In the Florida sample, 83% were overweight or obese, 70% elevated blood pressure, 60% met the definition of prediabetes. In Georgia, 64% were overweight or obese and 67% had elevated blood pressure. Weak correlations were observed between BMI and systolic blood pressure (unadjusted r = 0.20), diastolic blood pressure (unadjusted r = 0.19), and glucose (unadjusted r = 0.14). Adjusting for age and gender did not show statistically significant correlation between BMI and systolic and diastolic blood pressure or glucose. While BMI has been shown to be strongly associated with high blood pressure and impaired glucose, we found a weak correlation among agricultural workers. Given the common and high use of pesticides and elevated rates of hypertension, impaired glucose, and adiposity in agricultural workers, the public health impact of this relationship may require and lead to occupational reform that protects the health of agricultural workers. Future studies should assess occupational and environmental factors and lifestyle differences between agricultural workers and the general population to better understand these discrepancies in health status.

Heat Stress and Kidney Function in Farmworkers in the US: A Scoping Review.

Chronic kidney disease of unknown etiology (CKDu) has been well described in farmworkers in Latin America. Agricultural workers in the United States (US) are exposed to similar hot and humid working conditions, but CKDu in the US is under-described. This review aims to better understand the current literature describing the connection between heat stress and kidney function in farmworkers in the United States. Utilizing a scoping review methodology, we searched CINAHL, Embase, PubMed, and Web of Science databases to better understand the current state of the heat stress and kidney function research in farmworkers within the United States. In this review, 229 pieces of literature were screened. Ultimately, 4 articles were chosen to be included in the scoping review. Common themes within the articles were variations in study protocol lengths and type of heat stress measurement. Additionally, the majority of the work completed was quantitative to date, with only one study providing a critical social lens for analysis of CKDu in the United States. We found evidence that more work is needed within the US to understand the relationship between working in the heat and kidney function in agricultural and other workers who experience high heat conditions at work and are susceptible to the deleterious effects of working in said conditions.

Gut microbiome differences between amyotrophic lateral sclerosis patients and spouse controls.

Objective: Amyotrophic lateral sclerosis (ALS) is a progressive neurodegenerative disease that is incurable and ultimately fatal. Few therapeutic options are available to patients. In this study, we explored differences in microbiome composition associated with ALS. Methods: We compared the gut microbiome and inflammatory marker profiles of ALS patients (n = 10) to those of their spouses (n = 10). Gut microbiome profiles were determined by 16S rRNA gene sequencing. Results: The gut microbial communities of the ALS patients were more diverse and were deficient in Prevotella spp. compared with those of their spouses. In contrast, healthy couples (n = 10 couples of the opposite sex) recruited from the same geographic region as the patient population did not exhibit these differences. Stool and plasma inflammatory markers were similar between ALS patients and their spouses. Predictive analysis of microbial enzymes revealed that ALS patients had decreased activity in several metabolic pathways, including carbon metabolism, butyrate metabolism, and systems involving histidine kinase and response regulators. Conclusions: ALS patients exhibit differences in their gut microbial communities compared with spouse controls. Our findings suggest that modifying the gut microbiome, such as via amelioration of Prevotella spp. deficiency, and/or altering butyrate metabolism may have translational value for ALS treatment.