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Raluca Rusovici, Chirag J Patel, Kakarla V ChalamUniversity of Florida, Department of Ophthalmology, Jacksonville, FL, USABackground: The purpose of this study was to evaluate cell cycle changes in choroidal endothelial cells treated with varying doses of bevacizumab in the presence of a range of concentrations of vascular endothelial growth factor (VEGF). Bevacizumab, a drug widely used in the treatment of neovascular age-related macular degeneration, choroidal neovascularization, and proliferative diabetic retinopathy, neutralizes all isoforms of VEGF. However, the effect of intravitreal administration of bevacizumab on the choroidal endothelial cell cycle has not been established.Methods: Monkey choroidal endothelial (RF/6A) cells were treated with VEGF 50 ng/mL and escalating doses of bevacizumab 0.1–2 mg/mL for 72 hours. Cell cycle changes in response to bevacizumab were analyzed by flow cytometry and propidium iodide staining. Cell proliferation was measured using the WST-1 assay. Morphological changes were recorded by bright field cell microscopy.Results: Bevacizumab inhibited proliferation of choroidal endothelial cells by stabilization of the cell cycle in G0/G1 phase. Cell cycle analysis of VEGF-enriched choroidal endothelial cells revealed a predominant increase in the G2/M population (21.84%, P < 0.01) and a decrease in the G0/G1 phase population (55.08%, P < 0.01). Addition of escalating doses of bevacizumab stabilized VEGF-enriched cells in the G0/G1 phase (55.08%, 54.49%, 56.3%, and 64% [P < 0.01]) and arrested proliferation by inhibiting the G2/M phase (21.84%, 21.46%, 20.59%, 20.94%, and 16.1% [P < 0.01]). The increase in GO/G1 subpopulation in VEGF-enriched and bevacizumab-treated cells compared with VEGF-enriched cells alone was dose-dependent.Conclusion: Bevacizumab arrests proliferation of VEGF-enriched choroidal endothelial cells by stabilizing the cell cycle in the G0/G1 phase and inhibiting the G2/M phase in a dose-dependent fashion.Keywords: bevacizumab, age-related macular degeneration, vascular endothelial growth factor.

KV Chalam, Vijay Khetpal, Chirag J PatelDepartment of Ophthalmology, University of Florida Jacksonville, FL, USAIntroduction: Pseudophakic cystoid macular edema is a common cause of poor vision after cataract surgery, and topical corticosteroids and nonsteroidal anti-inflammatory drugs are used for its treatment. We investigated the effectiveness of difluprednate (Durezol®, recently approved by the US Food and Drug Administration) in the treatment of cystoid macular edema, assisted with spectral domain optical coherence tomography (SD-OCT).Case report: A 63-year-old African-American woman presented 6 weeks after uneventful cataract surgery in her left eye with decreased vision and associated distortion of the central visual field. Fluorescein angiogram and SD-OCT confirmed pseudophakic cystoid macular edema. Difluprednate was topically administered twice daily and monitored with serial imaging. Resolution was noted after 1 month of topical therapy, with improvement in visual acuity and resolution of distortion.Conclusion: Difluprednate is an effective treatment for patients with severe pseudophakic cystoid macular edema. SD-OCT allows the physician to monitor resolution of the macular edema easily.Keywords: cystoid macular edema, difluprednate, spectral domain optical coherence tomography, pseudophakic

Background: Whole genome sequencing is poised to revolutionize personalized medicine, providing the capacity to classify individuals into risk categories for a wide range of diseases. Here we begin to explore how whole genome sequencing (WGS) might be inco

OBJECTIVE - Human blood glucose levels have likely evolved toward their current point of stability over hundreds of thousands of years. The robust population stability of this trait is called canalization. It has been represented by a hyperbolic function o

Genetic diversity across different human populations can enhance understanding of the genetic basis of disease. We calculated the genetic risk of 102 diseases in 1,043 unrelated individuals across 51 populations of the Human Genome Diversity Panel. We foun

Diseases such as type 2 diabetes (T2D) result from environmental and genetic factors, and risk varies considerably in the population. T2D-related genetic loci discovered to date explain only a small portion of the T2D heritability. Some heritability may be

Motivation: Complex diseases, such as Type 2 Diabetes Mellitus (T2D), result from the interplay of both environmental and genetic factors. However, most studies investigate either the genetics or the environment and there are a few that study their possibl

Background Both genetic and environmental factors contribute to triglyceride, low-density lipoprotein-cholesterol (LDL-C), and high-density lipoprotein-cholesterol (HDL-C) levels. Although genome-wide association studies are currently testing the genetic f

Supervised field trial studies were conducted to understand dissipation kinetics and harvest time residues of a combination product of fluxapyroxad and pyraclostrobin in cumin plant/leaves and seeds at different locations in India. The results showed initial accumulation of fluxapyroxad at the levels of 15.4 and 20.2 mg kg -1 and pyraclostrobin at the level of 21.2 and 33.4 mg kg -1 in cumin leaves/plant in Anand, Gujarat. Fluxapyroxad and pyraclostrobin followed zero-order and first-order dissipation kinetics in cumin plant/leaves samples respectively. The residues translocated to cumin seeds. As the hazard quotient (HQ) was <1 in all cases consumer health risk may be negligible.

Importance: Since 2013, the American College of Cardiology (ACC) and American Heart Association (AHA) have recommended the pooled cohort equations (PCEs) for estimating the 10-year risk of atherosclerotic cardiovascular disease (ASCVD). An AHA scientific advisory group recently developed the Predicting Risk of cardiovascular disease EVENTs (PREVENT) equations, which incorporated kidney measures, removed race as an input, and improved calibration in contemporary populations. PREVENT is known to produce ASCVD risk predictions that are lower than those produced by the PCEs, but the potential clinical implications have not been quantified., Objective: To estimate the number of US adults who would experience changes in risk categorization, treatment eligibility, or clinical outcomes when applying PREVENT equations to existing ACC and AHA guidelines., Design, Setting, and Participants: Nationally representative cross-sectional sample of 7765 US adults aged 30 to 79 years who participated in the National Health and Nutrition Examination Surveys of 2011 to March 2020, which had response rates ranging from 47% to 70%., Main Outcomes and Measures: Differences in predicted 10-year ASCVD risk, ACC and AHA risk categorization, eligibility for statin or antihypertensive therapy, and projected occurrences of myocardial infarction or stroke., Results: In a nationally representative sample of 7765 US adults aged 30 to 79 years (median age, 53 years; 51.3% women), it was estimated that using PREVENT equations would reclassify approximately half of US adults to lower ACC and AHA risk categories (53.0% [95% CI, 51.2%-54.8%]) and very few US adults to higher risk categories (0.41% [95% CI, 0.25%-0.62%]). The number of US adults receiving or recommended for preventive treatment would decrease by an estimated 14.3 million (95% CI, 12.6 million-15.9 million) for statin therapy and 2.62 million (95% CI, 2.02 million-3.21 million) for antihypertensive therapy. The study estimated that, over 10 years, these decreases in treatment eligibility could result in 107 000 additional occurrences of myocardial infarction or stroke. Eligibility changes would affect twice as many men as women and a greater proportion of Black adults than White adults., Conclusion and Relevance: By assigning lower ASCVD risk predictions, application of the PREVENT equations to existing treatment thresholds could reduce eligibility for statin and antihypertensive therapy among 15.8 million US adults.

Discovery and translation of gene-environment interactions (GxEs) influencing clinical outcomes is limited by low statistical power and poor mechanistic understanding. Molecular omics data may help address these limitations, but their incorporation into GxE testing requires principled analytic approaches. We focused on genetic modification of the established mechanistic link between dietary long-chain omega-3 fatty acid (dN3FA) intake, plasma N3FA (pN3FA), and chronic inflammation as measured by high sensitivity CRP (hsCRP). We considered an approach that decomposes the overall genetic effect modification into components upstream and downstream of a molecular mediator to increase the potential to discover gene-N3FA interactions. Simulations demonstrated improved power of the upstream and downstream tests compared to the standard approach when the molecular mediator for many biologically plausible scenarios. The approach was applied in the UK Biobank (N = 188,700) with regression models that used measures of dN3FA (based on fish and fish oil intake), pN3FA (% of total fatty acids measured by nuclear magnetic resonance), and hsCRP. Mediation analysis showed that pN3FA fully mediated the dN3FA-hsCRP main effect relationship. Next, we separately tested modification of the dN3FA-hsCRP ("standard"), dN3FA-pN3FA ("upstream"), and pN3FA-hsCRP ("downstream") associations. The known FADS1-3 locus variant rs174535 reached p = 1.6×10 -12 in the upstream discovery analysis, with no signal in the downstream analysis ( p = 0.94). It would not have been prioritized based on a naïve analysis with dN3FA exposure and hsCRP outcome ( p = 0.097), indicating the value of the decomposition approach. Gene-level enrichment testing of the genome-wide results further prioritized two genes from the downstream analysis, CBLL1 and MICA , with links to immune cell counts and function. In summary, a molecular mediator-focused interaction testing approach enhanced statistical power to identify GxEs while homing in on relevant sub-components of the dN3FA-hsCRP pathway.

While respiratory diseases such as COPD and asthma share many risk factors, most studies investigate them in insolation and in predominantly European ancestry populations. Here, we conducted the most powerful multi-trait and -ancestry genetic analysis of respiratory diseases and auxiliary traits to date. Our approach improves the power of genetic discovery across traits and ancestries, identifying 44 novel loci associated with lung function in individuals of East Asian ancestry. Using these results, we developed PRSxtra (cross TRait and Ancestry), a multi-trait and -ancestry polygenic risk score approach that leverages shared components of heritable risk via pleiotropic effects. PRSxtra significantly improved the prediction of asthma, COPD, and lung cancer compared to trait- and ancestry-matched PRS in a multi-ancestry cohort from the All of Us Research Program, especially in diverse populations. PRSxtra identified individuals in the top decile with over four-fold odds of asthma and COPD compared to the first decile. Our results present a new framework for multi-trait and -ancestry studies of respiratory diseases to improve genetic discovery and polygenic prediction., Competing Interests: MHC has received grant support from GSK, consulting fees from Apogee and BMS, and speaking fees from Illumina. MM has received consulting fees from TheaHealth, 2ndMD, Axon Advisors, Verona Pharma, and Sanofi. ARM has received speaker fees from Novartis. All other authors declare no competing interests.

Background: Body dissatisfaction can drive individuals to use personal care products, exposing themselves to Benzophenone-3 (BP3). Yet, no study has examined the link between body dissatisfaction and elevated chemical exposures., Objectives: Our study examines how body dissatisfaction impacts the racial differences in BP3 exposures., Methods: Using NHANES 2003-2016 data for 3,072 women, we ascertained body dissatisfaction with a questionnaire on weight perception. We ran two generalized linear models with log10-transformed urinary concentrations of BP3 as the outcome variable and the following main predictors: one with race/ethnicity and another combining race/ethnicity and body dissatisfaction. We also conducted stratified analyses by race/ethnicity. We adjusted for poverty income ratio, BMI, urinary creatinine, and sunscreen usage., Results: BP3 levels in Mexican American, Other Hispanic, Other Race, non-Hispanic White, and non-Hispanic Asian women were on average 59%, 56%, 33%, 16%, and 9% higher, respectively, compared to non-Hispanic Black women. Racial differences in BP3 levels are accentuated with body dissatisfaction. For example, Other Hispanic women perceiving themselves as overweight had 69% higher BP3 levels than non-Hispanic Black women (p-value = 0.01), while those perceiving themselves as at the right weight had 32% higher levels (p-value = 0.31). Moreover, minority women perceiving themselves as overweight tended to have higher BP3 levels than those who do not. For example, BP3 levels in Other Hispanic women perceiving themselves as overweight are significantly higher compared to those who do not (73%, p-value = 0.03). In contrast, such differences in the non-Hispanic White women are minimal (-0.5%, p-value = 0. 98)., Discussion: Minority women with body dissatisfaction show elevated BP3 exposure independent of sunscreen usage, implying that their elevated exposures may stem from using other personal care and consumer products. Further research is needed to determine if increases of exposure to potential toxicants occur among minority women with body dissatisfaction., Competing Interests: Conflict of interest statement The authors declare that they have no conflicts of interest.

Cognitive impairment among older adults is a growing public health challenge and environmental chemicals may be modifiable risk factors. A wide array of chemicals has not yet been tested for association with cognition in an environment-wide association framework. In the US National Health and Nutrition Examination Survey (NHANES) 1999-2000 and 2011-2014 cross-sectional cycles, cognition was assessed using the Digit Symbol Substitution Test (DSST, scores 0-117) among participants aged 60 years and older. Concentrations of environmental chemicals measured in blood or urine were log 2 transformed and standardized. Chemicals with at least 50% of measures above the lower limit of detection were included (n chemicals =147, n classes =14). We tested for associations between chemical concentrations and cognition using parallel survey-weighted multivariable linear regression models adjusted for age, sex, race/ethnicity, education, smoking status, fish consumption, cycle year, urinary creatinine, and cotinine. Participants with at least one chemical measurement (n=4,982) were mean age 69.8 years, 55.0% female, 78.2% non-Hispanic White, and 77.0% at least high school educated. The mean DSST score was 50.4 (standard deviation (SD)=17.4). In adjusted analyses, 5 of 147 exposures were associated with DSST at p-value<0.01. Notably, a SD increase in log 2 -scaled cotinine concentration was associated with 2.71 points lower DSST score (95% CI -3.69, -1.73). A SD increase in log 2 -scaled urinary tungsten concentration was associated with 1.34 points lower DSST score (95% CI -2.11, -0.56). Exposure to environmental chemicals, particularly heavy metals and tobacco smoke, may be modifiable factors for cognition among older adults.

Environmental chemical exposures influence immune system functions, and humans are exposed to a wide range of chemicals, termed the chemical "exposome". A comprehensive, discovery analysis of the associations of multiple chemical families with immune biomarkers is needed. In this study, we tested the associations between environmental chemical concentrations and immune biomarkers. We analyzed the United States cross-sectional National Health and Nutrition Examination Survey (NHANES, 1999-2018). Chemical biomarker concentrations were measured in blood or urine (196 chemicals, 17 chemical families). Immune biomarkers included counts of lymphocytes, neutrophils, monocytes, basophils, eosinophils, red blood cells, white blood cells, and mean corpuscular volume. We conducted separate survey-weighted, multivariable linear regressions of each log 2 -transformed chemical and immune measure, adjusted for relevant covariates. We accounted for multiple comparisons using a false discovery rate (FDR). Among 45,528 adult participants, the mean age was 45.7 years, 51.4% were female, and 69.3% were Non-Hispanic White. 71 (36.2%) chemicals were associated with at least one of the eight immune biomarkers. The most chemical associations (FDR<0.05) were observed with mean corpuscular volume (36 chemicals) and red blood cell counts (35 chemicals). For example, a doubling in the concentration of cotinine was associated with 0.16 fL (95% CI: 0.15, 0.17; FDR<0.001) increased mean corpuscular volume, and a doubling in the concentration of blood lead was associated with 61,736 increased red blood cells per μL (95% CI: 54,335, 69,138; FDR<0.001). A wide variety of chemicals, such as metals and smoking-related compounds, were highly associated with immune system biomarkers. This environmental chemical-wide association study identified chemicals from multiple families for further toxicological, immunologic, and epidemiological investigation., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Author(s). Published by Elsevier Inc. All rights reserved.)

Consensus statements can be very influential in medicine and public health. Some of these statements use systematic evidence synthesis but others fail on this front. Many consensus statements use panels of experts to deduce perceived consensus through Delphi processes. We argue that stacking of panel members towards one particular position or narrative is a major threat, especially in absence of systematic evidence review. Stacking may involve financial conflicts of interest, but non-financial conflicts of strong advocacy can also cause major bias. Given their emerging importance, we describe here how such consensus statements may be misleading, by analysing in depth a recent high-impact Delphi consensus statement on COVID-19 recommendations as a case example. We demonstrate that many of the selected panel members and at least 35% of the core panel members had advocated towards COVID-19 elimination (zero-COVID) during the pandemic and were leading members of aggressive advocacy groups. These advocacy conflicts were not declared in the Delphi consensus publication, with rare exceptions. Therefore, we propose that consensus statements should always require rigorous evidence synthesis and maximal transparency on potential biases towards advocacy or lobbyist groups to be valid. While advocacy can have many important functions, its biased impact on consensus panels should be carefully avoided., (Copyright © 2024 Elsevier Inc. All rights reserved.)

Background: Adjustment for race is discouraged in lung-function testing, but the implications of adopting race-neutral equations have not been comprehensively quantified., Methods: We obtained longitudinal data from 369,077 participants in the National Health and Nutrition Examination Survey, U.K. Biobank, the Multi-Ethnic Study of Atherosclerosis, and the Organ Procurement and Transplantation Network. Using these data, we compared the race-based 2012 Global Lung Function Initiative (GLI-2012) equations with race-neutral equations introduced in 2022 (GLI-Global). Evaluated outcomes included national projections of clinical, occupational, and financial reclassifications; individual lung-allocation scores for transplantation priority; and concordance statistics (C statistics) for clinical prediction tasks., Results: Among the 249 million persons in the United States between 6 and 79 years of age who are able to produce high-quality spirometric results, the use of GLI-Global equations may reclassify ventilatory impairment for 12.5 million persons, medical impairment ratings for 8.16 million, occupational eligibility for 2.28 million, grading of chronic obstructive pulmonary disease for 2.05 million, and military disability compensation for 413,000. These potential changes differed according to race; for example, classifications of nonobstructive ventilatory impairment may change dramatically, increasing 141% (95% confidence interval [CI], 113 to 169) among Black persons and decreasing 69% (95% CI, 63 to 74) among White persons. Annual disability payments may increase by more than $1 billion among Black veterans and decrease by $0.5 billion among White veterans. GLI-2012 and GLI-Global equations had similar discriminative accuracy with regard to respiratory symptoms, health care utilization, new-onset disease, death from any cause, death related to respiratory disease, and death among persons on a transplant waiting list, with differences in C statistics ranging from -0.008 to 0.011., Conclusions: The use of race-based and race-neutral equations generated similarly accurate predictions of respiratory outcomes but assigned different disease classifications, occupational eligibility, and disability compensation for millions of persons, with effects diverging according to race. (Funded by the National Heart Lung and Blood Institute and the National Institute of Environmental Health Sciences.)., (Copyright © 2024 Massachusetts Medical Society.)

Government responses to COVID-19 are among the most globally impactful events of the 21st century. The extent to which responses-such as school closures-were associated with changes in COVID-19 outcomes remains unsettled. Multiverse analyses offer a systematic approach to testing a large range of models. We used daily data on 16 government responses in 181 countries in 2020-2021, and 4 outcomes-cases, infections, COVID-19 deaths, and all-cause excess deaths-to construct 99,736 analytic models. Among those, 42% suggest outcomes improved following more stringent responses ("helpful"). No subanalysis (e.g. limited to cases as outcome) demonstrated a preponderance of helpful or unhelpful associations. Among the 14 associations with P values < 1 × 10 -30 , 5 were helpful and 9 unhelpful. In summary, we find no patterns in the overall set of models that suggests a clear relationship between COVID-19 government responses and outcomes. Strong claims about government responses' impacts on COVID-19 may lack empirical support.

Background: In 2020, the Lancet Commission identified 12 risk factors as priorities for prevention of dementia, and other studies identified APOE e4/e4 genotype and family history of Alzheimer's disease strongly associated with dementia outcomes; however, it is unclear how robust these relationships are across dementia subtypes and analytic scenarios. Specification curve analysis (SCA) is a new tool to probe how plausible analytical scenarios influence outcomes., Methods: We evaluated the heterogeneity of odds ratios for 12 risk factors reported from the Lancet 2020 report and two additional strong associated non-modifiable factors (APOE e4/e4 genotype and family history of Alzheimer's disease) with dementia outcomes across 450,707 UK Biobank participants using SCA with 5357 specifications across dementia subtypes (outcomes) and analytic models (e.g., standard demographic covariates such as age or sex and/or 14 correlated risk factors)., Results: SCA revealed variable dementia risks by subtype and age, with associations for TBI and APOE e4/e4 robust to model specification; in contrast, diabetes showed fluctuating links with dementia subtypes. We found that unattributed dementia participants had similar risk factor profiles to participants with defined subtypes., Conclusions: We observed heterogeneity in the risk of dementia, and estimates of risk were influenced by the inclusion of a combination of other modifiable risk factors; non-modifiable demographic factors had a minimal role in analytic heterogeneity. Future studies should report multiple plausible analytic scenarios to test the robustness of their association. Considering these combinations of risk factors could be advantageous for the clinical development and evaluation of novel screening models for different types of dementia., (© 2024. The Author(s).)

Phenome-wide association studies (PheWASs) serve as a way of documenting the relationship between genotypes and multiple phenotypes, helping to uncover unexplored genotype-phenotype associations (known as pleiotropy). Secondly, Mendelian randomization (MR) can be harnessed to make causal statements about a pair of phenotypes by comparing their genetic architecture. Thus, approaches that automate both PheWASs and MR can enhance biobank-scale analyses, circumventing the need for multiple tools by providing a comprehensive, end-to-end tool to drive scientific discovery. To this end, we present PYPE, a Python pipeline for running, visualizing, and interpreting PheWASs. PYPE utilizes input genotype or phenotype files to automatically estimate associations between the chosen independent variables and phenotypes. PYPE can also produce a variety of visualizations and can be used to identify nearby genes and functional consequences of significant associations. Finally, PYPE can identify possible causal relationships between phenotypes using MR under a variety of causal effect modeling scenarios., Competing Interests: The authors declare no competing interests., (© 2024 The Authors.)

Objectives: To present an application of specification curve analysis-a novel analytic method that involves defining and implementing all plausible and valid analytic approaches for addressing a research question-to nutritional epidemiology., Study Design and Setting: We reviewed all observational studies addressing the effect of red meat on all-cause mortality, sourced from a published systematic review, and documented variations in analytic methods (eg, choice of model, covariates, etc.). We enumerated all defensible combinations of analytic choices to produce a comprehensive list of all the ways in which the data may reasonably be analyzed. We applied specification curve analysis to data from National Health and Nutrition Examination Survey 2007 to 2014 to investigate the effect of unprocessed red meat on all-cause mortality. The specification curve analysis used a random sample of all reasonable analytic specifications we sourced from primary studies., Results: Among 15 publications reporting on 24 cohorts included in the systematic review on red meat and all-cause mortality, we identified 70 unique analytic methods, each including different analytic models, covariates, and operationalizations of red meat (eg, continuous vs quantiles). We applied specification curve analysis to National Health and Nutrition Examination Survey, including 10,661 participants. Our specification curve analysis included 1208 unique analytic specifications, of which 435 (36.0%) yielded a hazard ratio equal to or more than 1 for the effect of red meat on all-cause mortality and 773 (64.0%) less than 1. The specification curve analysis yielded a median hazard ratio of 0.94 (interquartile range: 0.83-1.05). Forty-eight specifications (3.97%) were statistically significant, 40 of which indicated unprocessed red meat to reduce all-cause mortality and eight of which indicated red meat to increase mortality., Conclusion: We show that the application of specification curve analysis to nutritional epidemiology is feasible and presents an innovative solution to analytic flexibility., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Author(s). Published by Elsevier Inc. All rights reserved.)

This paper explores the exposome concept and its role in elucidating the interplay between environmental exposures and human health. We introduce two key concepts critical for exposomics research. Firstly, we discuss the joint impact of genetics and environment on phenotypes, emphasizing the variance attributable to shared and nonshared environmental factors, underscoring the complexity of quantifying the exposome's influence on health outcomes. Secondly, we introduce the importance of advanced data-driven methods in large cohort studies for exposomic measurements. Here, we introduce the exposome-wide association study (ExWAS), an approach designed for systematic discovery of relationships between phenotypes and various exposures, identifying significant associations while controlling for multiple comparisons. We advocate for the standardized use of the term "exposome-wide association study, ExWAS," to facilitate clear communication and literature retrieval in this field. The paper aims to guide future health researchers in understanding and evaluating exposomic studies. Our discussion extends to emerging topics, such as FAIR Data Principles, biobanked healthcare datasets, and the functional exposome, outlining the future directions in exposomic research. This abstract provides a succinct overview of our comprehensive approach to understanding the complex dynamics of the exposome and its significant implications for human health., Competing Interests: The authors declare that they have no conflicts of interest., (© The Author(s) 2024. Published by Oxford University Press.)

Competing Interests: Competing interests: We have read and understood BMJ policy on declaration of interests and have the following interests to declare: FN received funding from the French National Research Agency (ANR-17-CE36-0010), the French Ministry of Health and the French Ministry of Research. He is a work package leader in the OSIRIS project (Open Science to Increase Reproducibility in Science), which has received funding from the European Union’s Horizon Europe research and innovation programme under the grant agreement No. 101094725. He is a work package leader for the doctoral network MSCA-DN SHARE-CTD (HORIZON-MSCA-2022-DN-01 101120360), funded by the EU. CJP received funding from National Institutes for Health (NIEHS R01ES0324702 and NIA RF1AG074372). NJD has received funding from the Horizon Europe programme, the OSIRIS project, the Naji Foundation, the German Federal Ministry of Education and Research, and the Fetzer Franklin Memorial Fund and has been employed on grants from the Mohn-Westlake Foundation, Laura and John Arnold Foundation, Elsevier, and the Good Thinking Society in the past five years. SH has received funding from Horizon Europe, the German Federal Ministry for the Environment, Nature Conservation, Nuclear Safety and Consumer Protection, and from LMUExcellent.

Background: The correlates responsible for the temporal changes of intrahousehold SARS-CoV-2 transmission in the United States have been understudied mainly due to a lack of available surveillance data. Specifically, early analyses of SARS-CoV-2 household secondary attack rates (SARs) were small in sample size and conducted cross-sectionally at single time points. From these limited data, it has been difficult to assess the role that different risk factors have had on intrahousehold disease transmission in different stages of the ongoing COVID-19 pandemic, particularly in children and youth., Objective: This study aimed to estimate the transmission dynamic and infectivity of SARS-CoV-2 among pediatric and young adult index cases (age 0 to 25 years) in the United States through the initial waves of the pandemic., Methods: Using administrative claims, we analyzed 19 million SARS-CoV-2 test records between January 2020 and February 2021. We identified 36,241 households with pediatric index cases and calculated household SARs utilizing complete case information. Using a retrospective cohort design, we estimated the household SARS-CoV-2 transmission between 4 index age groups (0 to 4 years, 5 to 11 years, 12 to 17 years, and 18 to 25 years) while adjusting for sex, family size, quarter of first SARS-CoV-2 positive record, and residential regions of the index cases., Results: After filtering all household records for greater than one member in a household and missing information, only 36,241 (0.85%) of 4,270,130 households with a pediatric case remained in the analysis. Index cases aged between 0 and 17 years were a minority of the total index cases (n=11,484, 11%). The overall SAR of SARS-CoV-2 was 23.04% (95% CI 21.88-24.19). As a comparison, the SAR for all ages (0 to 65+ years) was 32.4% (95% CI 32.1-32.8), higher than the SAR for the population between 0 and 25 years of age. The highest SAR of 38.3% was observed in April 2020 (95% CI 31.6-45), while the lowest SAR of 15.6% was observed in September 2020 (95% CI 13.9-17.3). It consistently decreased from 32% to 21.1% as the age of index groups increased. In a multiple logistic regression analysis, we found that the youngest pediatric age group (0 to 4 years) had 1.69 times (95% CI 1.42-2.00) the odds of SARS-CoV-2 transmission to any family members when compared with the oldest group (18 to 25 years). Family size was significantly associated with household viral transmission (odds ratio 2.66, 95% CI 2.58-2.74)., Conclusions: Using retrospective claims data, the pediatric index transmission of SARS-CoV-2 during the initial waves of the COVID-19 pandemic in the United States was associated with location and family characteristics. Pediatric SAR (0 to 25 years) was less than the SAR for all age other groups. Less than 1% (n=36,241) of all household data were retained in the retrospective study for complete case analysis, perhaps biasing our findings. We have provided measures of baseline household pediatric transmission for tracking and comparing the infectivity of later SARS-CoV-2 variants., (©Ming Kei Chung, Brian Hart, Mauricio Santillana, Chirag J Patel. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 04.01.2024.)

Smoking is the leading risk factor for chronic obstructive pulmonary disease (COPD) worldwide, yet many people who never smoke develop COPD. We perform a longitudinal analysis of COPD in the UK Biobank to derive and validate the Socioeconomic and Environmental Risk Score which captures additive and cumulative environmental, behavioral, and socioeconomic exposure risks beyond tobacco smoking. The Socioeconomic and Environmental Risk Score is more predictive of COPD than smoking status and pack-years. Individuals in the highest decile of the risk score have a greater risk for incident COPD compared to the remaining population. Never smokers in the highest decile of exposure risk are more likely to develop COPD than previous and current smokers in the lowest decile. In general, the prediction accuracy of the Social and Environmental Risk Score is lower in non-European populations. While smoking status is often considered in screening COPD, our finding highlights the importance of other non-smoking environmental and socioeconomic variables., (© 2023. The Author(s).)

Aims/hypothesis: We sought to quantify the relationship between morning, afternoon or evening physical activity and consistency (e.g. routine) and risk of type 2 diabetes., Methods: A cohort of 93,095 UK Biobank participants (mean age 62 years) without a history of type 2 diabetes wore a wrist-worn accelerometer for 1 week. We converted accelerometer information to estimate metabolic equivalent of task (MET), summing MET h of total physical activity completed within three intra-day time segments (morning, afternoon and evening). We quantified physical activity consistency as the SD of participants' daily total physical activity. We ultimately associated each of the following with incident type 2 diabetes: (1) morning, afternoon or evening 'time-segmented' MET h per week; and (2) consistency. We also considered moderate-to-vigorous physical activity (MVPA) and vigorous physical activity (VPA) in association with type 2 diabetes incidence., Results: When considering MET as the physical activity measure, we observed protective associations of morning (HR 0.90 [95% CI 0.86, 0.93], p=7×10 -8 ) and afternoon (HR 0.91 [95% CI 0.87, 0.95], p=1×10 -5 ) but did not have evidence for evening physical activity (HR 0.95 [95% CI 0.90, 1.00], p=0.07) with type 2 diabetes. There was no difference between MET-measured morning and afternoon physical activity. Our substitution model highlighted the importance of adjusting for lifestyle factors (e.g. sleep time and diet); the effect of a substitution between afternoon and evening physical activity was attenuated after adjustment for lifestyle variables. Consistency of MET-measured physical activity was not associated with type 2 diabetes (p=0.07). MVPA and VPA were associated with decreased risk for type 2 diabetes at all times of the day., Conclusions/interpretation: Total metabolic equivalents of physical activity in the morning and afternoon had a protective effect on diabetes risk and evening activity was not associated with diabetes. Consistency of physical activity did not play a role in decreasing risk for diabetes. Vigorous activity is associated with lower risk no matter the time of day of activity., (© 2023. The Author(s).)

Robust spatio-temporal delineation of extreme climate events and accurate identification of areas that are impacted by an event is a prerequisite for identifying population-level and health-related risks. In prior research, attributes such as temperature and humidity have often been linearly assigned to the population of the study unit from the closest weather station. This could result in inaccurate event delineation and biased assessment of extreme heat exposure. We have developed a spatio-temporal model to dynamically delineate boundaries for Extreme Heat Events (EHE) across space and over time, using a relative measure of Apparent Temperature (AT). Our surface interpolation approach offers a higher spatio-temporal resolution compared to the standard nearest-station (NS) assignment method. We show that the proposed approach can provide at least 80.8 percent improvement in identification of areas and populations impacted by EHEs. This improvement in average adjusts the misclassification of about one million Californians per day of an extreme event, who would be either unidentified or misidentified under EHEs between 2017 and 2021., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 The Authors. Published by Elsevier Inc. All rights reserved.)

Objectives: To assess how the results of published national registry-based pharmacoepidemiology studies (where select associations are of interest) compare with an agnostic medication-wide approach (where all possible drug associations are tested)., Study Design and Setting: We systematically searched for publications that reported drug associations with any, breast, colon/colorectal, or prostate cancer in the Swedish Prescribed Drug Registry. Results were compared against a previously performed agnostic medication-wide study on the same registry., Protocol: https://osf.io/kqj8n., Results: Most published studies (25/32) investigated previously reported associations. 421/913 (46%) associations had statistically significant results. 134 of the 162 unique drug-cancer associations could be paired with 70 associations in the agnostic study (corresponding drug categories and cancer types). Published studies reported smaller effect sizes and absolute effect sizes than the agnostic study, and generally used more adjustments. Agnostic analyses were less likely to report statistically significant protective associations (based on a multiplicity-corrected threshold) than their paired associations in published studies (McNemar odds ratio 0.13, P = 0.0022). Among 162 published associations, 36 (22%) showed increased risk signal and 25 (15%) protective signal at P < 0.05, while for agnostic associations, 237 (11%) showed increased risk signal and 108 (5%) protective signal at a multiplicity-corrected threshold. Associations belonging to drug categories targeted by individual published studies vs. nontargeted had smaller average effect sizes; smaller P values; and more frequent risk signals., Conclusion: Published pharmacoepidemiology studies using a national registry addressed mostly previously proposed associations, were mostly "negative", and showed only modest concordance with their respective agnostic analyses in the same registry., Competing Interests: Declaration of competing interest There are no additional relationships, patents, or other activities to disclose., (Copyright © 2023 The Authors. Published by Elsevier Inc. All rights reserved.)

The past decade of population research for diabetes has seen a dramatic proliferation of the use of real-world data (RWD) and real-world evidence (RWE) generation from non-research settings, including both health and non-health sources, to influence decisions related to optimal diabetes care. A common attribute of these new data is that they were not collected for research purposes yet have the potential to enrich the information around the characteristics of individuals, risk factors, interventions, and health effects. This has expanded the role of subdisciplines like comparative effectiveness research and precision medicine, new quasi-experimental study designs, new research platforms like distributed data networks, and new analytic approaches for clinical prediction of prognosis or treatment response. The result of these developments is a greater potential to progress diabetes treatment and prevention through the increasing range of populations, interventions, outcomes, and settings that can be efficiently examined. However, this proliferation also carries an increased threat of bias and misleading findings. The level of evidence that may be derived from RWD is ultimately a function of the data quality and the rigorous application of study design and analysis. This report reviews the current landscape and applications of RWD in clinical effectiveness and population health research for diabetes and summarizes opportunities and best practices in the conduct, reporting, and dissemination of RWD to optimize its value and limit its drawbacks., (© 2023 by the American Diabetes Association.)

Background: Alzheimer's Disease (AD) is a complex clinical phenotype with unprecedented social and economic tolls on an ageing global population. Real-world data (RWD) from electronic health records (EHRs) offer opportunities to accelerate precision drug development and scale epidemiological research on AD. A precise characterization of AD cohorts is needed to address the noise abundant in RWD., Methods: We conducted a retrospective cohort study to develop and test computational models for AD cohort identification using clinical data from 8 Massachusetts healthcare systems. We mined temporal representations from EHR data using the transitive sequential pattern mining algorithm (tSPM) to train and validate our models. We then tested our models against a held-out test set from a review of medical records to adjudicate the presence of AD. We trained two classes of Machine Learning models, using Gradient Boosting Machine (GBM), to compare the utility of AD diagnosis records versus the tSPM temporal representations (comprising sequences of diagnosis and medication observations) from electronic medical records for characterizing AD cohorts., Findings: In a group of 4985 patients, we identified 219 tSPM temporal representations (i.e., transitive sequences) of medical records for constructing the best classification models. The models with sequential features improved AD classification by a magnitude of 3-16 percent over the use of AD diagnosis codes alone. The computed cohort included 663 patients, 35 of whom had no record of AD. Six groups of tSPM sequences were identified for characterizing the AD cohorts., Interpretation: We present sequential patterns of diagnosis and medication codes from electronic medical records, as digital markers of Alzheimer's Disease. Classification algorithms developed on sequential patterns can replace standard features from EHRs to enrich phenotype modelling., Funding: National Institutes of Health: the National Institute on Aging (RF1AG074372) and the National Institute of Allergy and Infectious Diseases (R01AI165535)., Competing Interests: Declaration of interests C Ritchie report grants from NIH and Retirement Research Foundation for other projects and being part of the steering committee of IMPACT Collaboratory as well as a board member of the International Neuropalliative Care Society. The other authors declare no competing interests., (Copyright © 2023 The Author(s). Published by Elsevier B.V. All rights reserved.)

Objective: Quantify the impact of genetic and socioeconomic factors on risk of type 2 diabetes (T2D) and obesity., Research Design and Methods: Among participants in the Mass General Brigham Biobank (MGBB) and UK Biobank (UKB), we used logistic regression models to calculate cross-sectional odds of T2D and obesity using 1) polygenic risk scores for T2D and BMI and 2) area-level socioeconomic risk (educational attainment) measures. The primary analysis included 26,737 participants of European genetic ancestry in MGBB with replication in UKB (N = 223,843), as well as in participants of non-European ancestry (MGBB N = 3,468; UKB N = 7,459)., Results: The area-level socioeconomic measure most strongly associated with both T2D and obesity was percent without a college degree, and associations with disease prevalence were independent of genetic risk (P < 0.001 for each). Moving from lowest to highest quintiles of combined genetic and socioeconomic burden more than tripled T2D (3.1% to 22.2%) and obesity (20.9% to 69.0%) prevalence. Favorable socioeconomic risk was associated with lower disease prevalence, even in those with highest genetic risk (T2D 13.0% vs. 22.2%, obesity 53.6% vs. 69.0% in lowest vs. highest socioeconomic risk quintiles). Additive effects of genetic and socioeconomic factors accounted for 13.2% and 16.7% of T2D and obesity prevalence, respectively, explained by these models. Findings were replicated in independent European and non-European ancestral populations., Conclusions: Genetic and socioeconomic factors significantly interact to increase risk of T2D and obesity. Favorable area-level socioeconomic status was associated with an almost 50% lower T2D prevalence in those with high genetic risk., (© 2023 by the American Diabetes Association.)

Purpose: Past research has shown diabetic patients, including those of geriatric age, to be at an increased risk of postoperative complications following various surgeries, including revision total hip arthroplasty (rTHA). However, whether these risks are disproportionately greater in octogenarian patients has not been well investigated. This study aimed to determine whether diabetic octogenarians are at an increased risk of postoperative complications following rTHA., Methods: The national surgical quality improvement program database was used to identify all diabetic patients who underwent rTHA from 2007 to 2018. Patients were divided into two groups: an aged 65 to 79 cohort and an aged 80 to 89 cohort. Patient demographics, comorbidities, and postoperative complications were assessed and compared between the two aged cohorts, with the utilization of bivariate and multivariate analyses., Results: Of the 1184 diabetic patients who underwent rTHA, 906 (76.5%) patients were in the aged 65 to 79 cohort and 278 (23.5%) patients were in the aged 80 to 89 cohort. After adjusting for patient demographics and medical comorbidities, compared to patients in the aged 65 to 79 group, diabetic patients who were 80 to 89 years old were found to have an increased risk of extended length of hospital stay (OR 1.67; p = 0.017)., Conclusion: Diabetic octogenarian patients have an increased risk for a prolonged hospital stay following rTHA relative to their younger diabetic geriatric counterparts. Orthopedic surgeons should be aware of these increased risks to properly educate diabetic octogenarians and assist in surgical management decision making in these patients considering rTHA., (© 2022. The Author(s), under exclusive licence to Springer-Verlag France SAS, part of Springer Nature.)

In this paper we investigate which airborne pollutants have a short-term causal effect on cardiovascular and respiratory disease using the Ancestral Probabilities (AP) procedure, a novel Bayesian approach for deriving the probabilities of causal relationships from observational data. The results are largely consistent with EPA assessments of causality, however, in a few cases AP suggests that some pollutants thought to cause cardiovascular or respiratory disease are associated due purely to confounding. The AP procedure utilizes maximal ancestral graph (MAG) models to represent and assign probabilities to causal relationships while accounting for latent confounding. The algorithm does so locally by marginalizing over models with and without causal features of interest. Before applying AP to real data, we evaluate it in a simulation study and investigate the benefits of providing background knowledge. Overall, the results suggest that AP is an effective tool for causal discovery., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier B.V. All rights reserved.)

Measuring microbial diversity is traditionally based on microbe taxonomy. Here, in contrast, we aimed to quantify heterogeneity in microbial gene content across 14,183 metagenomic samples spanning 17 ecologies, including 6 human associated, 7 nonhuman host associated, and 4 in other nonhuman host environments. In total, we identified 117,629,181 nonredundant genes. The vast majority of genes (66%) occurred in only one sample (i.e., "singletons"). In contrast, we found 1,864 sequences present in every metagenome, but not necessarily every bacterial genome. Additionally, we report data sets of other ecology-associated genes (e.g., abundant in only gut ecosystems) and simultaneously demonstrated that prior microbiome gene catalogs are both incomplete and inaccurately cluster microbial genetic life (e.g., at gene sequence identities that are too restrictive). We provide our results and the sets of environmentally differentiating genes described above at http://www.microbial-genes.bio. IMPORTANCE The amount of shared genetic elements has not been quantified between the human microbiome and other host- and non-host-associated microbiomes. Here, we made a gene catalog of 17 different microbial ecosystems and compared them. We show that most species shared between environment and human gut microbiomes are pathogens and that prior gene catalogs described as "nearly complete" are far from it. Additionally, over two-thirds of all genes only appear in a single sample, and only 1,864 genes (0.001%) are found in all types of metagenomes. These results highlight the large diversity between metagenomes and reveal a new, rare class of genes, those found in every type of metagenome, but not every microbial genome.

Background: Post-treatment Lyme disease syndrome (PTLDS) is used to describe Lyme disease patients who have the infection cleared by antibiotic but then experienced persisting symptoms of pain, fatigue, or cognitive impairment. Currently, little is known about the cause or epidemiology of PTLDS., Methods: We conducted a data-driven study with a large nationwide administrative dataset, which consists of more than 98 billion billing and 1.4 billion prescription records between 2008 and 2016, to identify unique aspects of PTLDS that could have diagnostic and etiologic values. We defined PTLDS based on its symptomatology and compared the demographic, longitudinal changes of comorbidity, and antibiotic prescriptions between patients who have Lyme with absence of prolonged symptoms (APS) and PTLDS., Findings: The age and temporal distributions were similar between Lyme APS and PTLDS. The PTLDS-to-Lyme APS case ratio was 3.42%. The co-occurrence of 3 out of 19 chronic conditions were significantly higher in PTLDS versus Lyme APS-odds ratio and 95% CI for anemia, hyperlipidemia, and osteoarthrosis were 1.46 (1.11-1.92), 1.39 (1.15-1.68), and 1.62 (1.23-2.12) respectively. We did not find significant differences between PTLDS and Lyme APS for the number of types of antibiotics prescribed (incidence rate ratio = 1.009, p = 0.90) and for the prescription of each of the five antibiotics (FDR adjusted p values 0.72-0.95)., Interpretation: PTLDS cases have more codes corresponding to anemia, hyperlipidemia, and osteoarthrosis compared to Lyme APS. Our finding of hyperlipidemia is consistent with a dysregulation of fat metabolism reported by other researchers, and further investigation should be conducted to understand the potential biological relationship between the two., Funding: Steven & Alexandra Cohen Foundation, Global Lyme Alliance, and the Pazala Foundation; National Institutes of Health R01ES032470., Competing Interests: Declaration of interests The authors declare no competing financial interests., (Copyright © 2023 The Author(s). Published by Elsevier B.V. All rights reserved.)