medRxiv - Epidemiology最新文献

{"title":"Persistent PTSD symptoms are associated with plasma metabolic alterations relevant to long-term health: A metabolome-wide investigation in women","authors":"Yiwen Zhu, Katherine H Shutta, Tianyi Huang, Raji Balasubramanian, Oana A Zeleznik, Clary B Clish, Julián Ávila-Pacheco, Susan E Hankinson, Laura D Kubzansky","doi":"10.1101/2024.08.07.24311628","DOIUrl":"https://doi.org/10.1101/2024.08.07.24311628","url":null,"abstract":"Background: Posttraumatic stress disorder (PTSD) is characterized by severe distress and associated with cardiometabolic diseases. Studies in military and clinical populations suggest dysregulated metabolomic processes may be a key mechanism. Prior work identified and validated a metabolite-based distress score (MDS) linked with depression and anxiety and subsequent cardiometabolic diseases. Here, we assessed whether PTSD shares metabolic alterations with depression and anxiety and also if additional metabolites are related to PTSD. Methods: We leveraged plasma metabolomics data from three subsamples nested within the Nurses' Health Study II, including 2835 women with 2950 blood samples collected across three timepoints (1996-2014) and 339 known metabolites consistently assayed by mass spectrometry-based techniques. Trauma and PTSD exposures were assessed in 2008 and characterized as follows: lifetime trauma without PTSD, lifetime PTSD in remission, and persistent PTSD symptoms. Associations between the exposures and the MDS or individual metabolites were estimated within each subsample adjusting for potential confounders and combined in random-effects meta-analyses. Results: Persistent PTSD symptoms were associated with higher levels of the previously developed MDS for depression and anxiety. Out of 339 metabolites, we identified nine metabolites (primarily elevated glycerophospholipids) associated with persistent symptoms (false discovery rate<0.05). No metabolite associations were found with the other PTSD-related exposures. Conclusions: As the first large-scale, population-based metabolomics analysis of PTSD, our study highlighted shared and distinct metabolic differences linked to PTSD versus depression or anxiety. We identified novel metabolite markers associated with PTSD symptom persistence, suggesting further connections with metabolic dysregulation that may have downstream consequences for health.","PeriodicalId":501071,"journal":{"name":"medRxiv - Epidemiology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141933605","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Tinnitus risk factors and its evolution over time: a cohort study","authors":"Lise Hobeika, Matt Fillingim, Christophe Tanguay-Sabourin, Mathieu Roy, Alain Londero, Séverine Samson, Etienne Vachon-Presseau","doi":"10.1101/2024.08.02.24311367","DOIUrl":"https://doi.org/10.1101/2024.08.02.24311367","url":null,"abstract":"Background. Subjective tinnitus is an auditory percept unrelated to an external sound source. The lack of curative treatments and limited understanding of its risk factors complicate the prevention and management of this distressing symptom. This study seeks to identify socio-demographic, psychological, and health-related risk factors predicting tinnitus presence (how often individuals perceive tinnitus) and severity separately, and their evolution over time. Methods\u0000Using the UK Biobank dataset which encompasses data on the socio-demographic, physical, mental and hearing health from more than 170,000 participants, we trained two distinct machine learning models to identify risk scores predicting tinnitus presence and severity separately. These models were used to predict tinnitus over time and were replicated in 463 individuals from the Tinnitus Research Initiative database. Finding Machine learning based approach identified hearing health as a primary risk factor for the presence and severity of tinnitus, while mood, neuroticism, hearing health, and sleep only predicted tinnitus severity. Only the severity model accurately predicted the evolution over nine years, with a large effect size for individuals developing severe tinnitus (Cohen's d = 1.10, AUC-ROC = 0.70). To facilitate its clinical applications, we simplified the severity model and validated a five-item questionnaire to detect individuals at risk of developing severe tinnitus. Interpretation\u0000This study is the first to clearly identify risk factors predicting tinnitus presence and severity separately. Hearing health emerges as a major predictor of tinnitus presence, while mental health plays a crucial role in its severity. The successful prediction of the evolution of tinnitus severity over nine years based on socio-emotional, hearing and sleep factors suggests that modifying these factors could mitigate the impact of tinnitus. The newly developed questionnaire represents a significant advancement in identifying individuals at risk of severe tinnitus, for which early supportive care would be crucial.","PeriodicalId":501071,"journal":{"name":"medRxiv - Epidemiology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141933454","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Is there an anticholinergic effect of drugs beyond polypharmacy? A simulation study on death, dementia, and delirium in UK Biobank","authors":"Jure Mur, Lucy E. Stirland, Graciela Muniz-Terrera, Anja K. Leist","doi":"10.1101/2024.08.06.24311533","DOIUrl":"https://doi.org/10.1101/2024.08.06.24311533","url":null,"abstract":"The use of anticholinergic drugs has been associated with adverse health outcomes. However, their effects cannot be completely separated from the effects of general polypharmacy using standard methods. The objective of this study was to explore the extent to which the detrimental health effects attributed to anticholinergic burden measured by anticholinergic burden scales (ABS) were distinct from those of polypharmacy. We compared observed effects of ABS against simulated effects of generated pseudoscales intended to measure polypharmacy using UK Biobank primary care data. We randomly sampled from 525 anticholinergic and non-anticholinergic drugs prescribed in the year 2015 to ~200,000 participants with an average age of 65 years. We then created 1,000 pseudoscales, the score of which was designed to represent the strength of the background effect of polypharmacy, differentiating pseudoscales constructed to capture either general polypharmacy or putative anticholinergic polypharmacy, and exhibiting similar distributional properties to 23 real-world ABS (statistical equivalence). We performed individual logistic regressions for each scale to estimate associations between ABS scales and pseudoscales, respectively, and risk of death, dementia, or delirium. Across outcomes, odds ratios for anticholinergic-polypharmacy pseudoscales were on average 0.03-0.05 greater than those of general-polypharmacy pseudoscales. The number of drugs composing the scales was correlated with the size of adverse effects for both pseudoscales (r=~0.5, p<0.001) and ABS (r=~0.7, p<0.001). In total, 50-90% of ABS showed stronger effects than the majority of pseudoscales. ABS exhibited stronger associations with the studied adverse health outcomes than would be expected from polypharmacy alone (range of differences in odds ratios: -0.05 to 0.20). Most existing ABS capture more variance in the association with death, dementia, and delirium than polypharmacy alone, but with varying degrees of strength.","PeriodicalId":501071,"journal":{"name":"medRxiv - Epidemiology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141933456","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Triaging clients at risk of disengagement from HIV care: Application of a predictive model to clinical trial data in South Africa","authors":"Mhairi Maskew, Shantelle Smith, Lucien De Voux, Kieran Sharpey-Schafer, Thomas Crompton, Ashley Govender, Pedro Pisa, Sydney Rosen","doi":"10.1101/2024.08.05.24311488","DOIUrl":"https://doi.org/10.1101/2024.08.05.24311488","url":null,"abstract":"Background: To reach South Africa's targets for HIV treatment and viral suppression, retention on antiretroviral therapy (ART) must increase. Much effort and resources have been invested in tracing those already disengaged and returning them to care programs with mixed success. Here we aim to successfully identify ART clients at risk of loss from care prior to disengagement. Methods and Findings: We applied a previously developed machine learning and predictive modelling algorithm (PREDICT) to routinely collected ART client data from the SLATE I and SLATE II trials, which evaluated same-day ART initiation in 2017-18. Using a primary outcome of an interruption in treatment (IIT), defined as missing the next scheduled clinic visit by >28 days, we investigated the reproducibility of PREDICT in SLATE datasets. We also tested two risk triaging approaches: 1) threshold approach classifying individuals into low, moderate, or high risk of IIT; and 2) archetype approach identifying subgroups with characteristics associated with risk of ITT. We report associations between risk category groups and subsequent IIT at the next scheduled visit using crude risk differences and relative risks with 95% confidence intervals. SLATE datasets included 7,199 client visits for 1,193 clients over 14 months of follow-up. The algorithm achieved 63% accuracy, 89% negative predictive value, and an area under the curve of 0.61 for attendance at next scheduled visit, similar to previous results using only medical record data. The threshold approach consistently and accurately assigned levels of IIT risk for multiple stages of the care cascade. The archetype approach identified several subgroups at increased risk of IIT, including those late to previous appointments, those returning after a period of disengagement, those living alone or without a treatment supporter. Behavioural elements of the archetypes tended to drive risk of treatment interruption more consistently than demographics; e.g. adolescent boys/young men who attended visits on time experienced lowest rates of treatment interruption (10%, PREDICT datasets and 7% SLATE datasets), while adolescent boys/young men returning after previously disengaging from care had highest rates of subsequent treatment interruption (31%, PREDICT datasets and 40% SLATE datasets). Conclusion: Routinely collected medical record data can be combined with basic demographic and socioeconomic data to assess individual risk of future treatment disengagement using machine learning and predictive modelling. This approach offers an opportunity to intervene prior to and potentially prevent disengagement from HIV care, rather than responding only after it has occurred.","PeriodicalId":501071,"journal":{"name":"medRxiv - Epidemiology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141933467","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Navigating the Cholera Elimination Roadmap in Zambia - a Scoping Review (2013-2023)","authors":"Nyuma Mbewe, John Tembo, Mpanga Kasonde, Kelvin Mwangilwa, Paul Msanzya Zulu, Joseph Adive Seriki, William Ngosa, Kennedy Lishimpi, Lloyd Mulenga, Roma Chilengi, Nathan Kapata, Martin Peter Grobusch","doi":"10.1101/2024.08.05.24311486","DOIUrl":"https://doi.org/10.1101/2024.08.05.24311486","url":null,"abstract":"Background: Cholera outbreaks are increasing in frequency and severity, particularly in Sub-Saharan Africa. Zambia, committed to ending cholera by 2025, is coming off its most significant outbreak in 2024. This review examines the perceived regression in elimination efforts by addressing two questions: (1) what is known about cholera in Zambia; and (2) what are the main suggested mechanisms and strategies to further elimination efforts in the region?\u0000Methodology/Principal Findings: A scoping literature search was conducted in PUBMED to identify relevant studies published between January 2013 and June 2024 using the search terms ‘cholera’ and ‘Zambia’. We identified 45 relevant publications. With the increasing influence of climate change, population growth, and rural-urban migration, further increases in outbreak frequency and magnitude are expected. Major risk factors for recurrent outbreaks include poor access to water, sanitation, and hygiene services in urban unplanned settlements and rural fishing villages. Interventions are best planned at a decentralized, community-centric approach to prevent elimination and reintroduction at the district level. Pre-emptive vaccination campaigns before the rainy season and climate-resilient WASH infrastructure are also recommended.\u0000Conclusions/Significance: The goal to eliminate cholera by 2025 was unrealistic as evidence points to the disease becoming endemic. Our findings confirm the need to align health and WASH investments with the Global Roadmap to Cholera Elimination by 2030 through a climate-focused lens. Recommendations for cholera elimination, including improved access to safe drinking water and sanitation, remain elusive in many low-income settings like Zambia. Patient-level information on survival and transmissibility is lacking. New research tailored to country-level solutions is urgently required. Insights from this review will be integrated into the next iteration of the National Cholera Control Plan and could be applicable to other countries with similar settings.","PeriodicalId":501071,"journal":{"name":"medRxiv - Epidemiology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141933458","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Associations between work characteristics and large joint osteoarthritis: a cross-sectional study of 285,947 UK Biobank participants","authors":"Asad Hashmi, Sophie Scott, Mijin Jung, Qing-Jun Meng, Jonathan H Tobias, Rhona A Beynon, Benjamin G Faber","doi":"10.1101/2024.08.05.24311461","DOIUrl":"https://doi.org/10.1101/2024.08.05.24311461","url":null,"abstract":"Objectives\u0000Shift work-induced circadian rhythm disruption has been identified as a risk factor for specific diseases. Additionally, physically demanding work has been linked to osteoarthritis. This study investigated the independent associations of shift work and physical work with risk of large joint osteoarthritis.\u0000Design\u0000UK Biobank participants completed questionnaires detailing their employment status, including shift work, night shifts, heavy manual work and prolonged non-sedentary work. Responses were categorised into binary and categorical variables. Knee and hip osteoarthritis diagnoses were extracted from hospital records and osteoarthritis (any site) was self-reported. Logistic regression models, adjusted for age, sex, BMI, Townsend Deprivation Index and other work factors, were used to investigate the relationships between work characteristics and osteoarthritis outcomes.\u0000Results\u0000This study included 285,947 participants (mean age 52.7 years; males 48.0%). Shift work and night shifts were associated with knee osteoarthritis (fully adjusted OR: 1.12 [95% CI:1.07-1.17] and 1.12 [1.04-1.20], respectively), and self-reported osteoarthritis but there was little evidence of an association with hip osteoarthritis (1.01 [0.95-1.08] and 1.03 [0.93-1.14]). Heavy manual work and prolonged non-sedentary work were associated with increased risk of all osteoarthritis outcomes.\u0000Conclusions\u0000Shift work showed independent associations with knee osteoarthritis and self-reported osteoarthritis but not hip osteoarthritis, suggesting circadian rhythm dysfunction may play a role in knee osteoarthritis pathogenesis. Heavy manual work and prolonged non-sedentary work were associated with all outcomes, with stronger associations in knee osteoarthritis, possibly reflecting the knee's higher susceptibility to biomechanical stress. Further research is needed to explore workplace interventions for reducing these risks.","PeriodicalId":501071,"journal":{"name":"medRxiv - Epidemiology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141933459","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Large language model aided automatic high-throughput drug screening using self-controlled cohort study","authors":"Shenbo Xu, Stan N. Finkelstein, Roy E. Welsch, Kenney Ng, Ioanna Tzoulaki, Lefkos Middleton","doi":"10.1101/2024.08.04.24311480","DOIUrl":"https://doi.org/10.1101/2024.08.04.24311480","url":null,"abstract":"Background: Developing medicine from scratch to governmental authorization and detecting adverse drug reactions (ADR) have barely been economical, expeditious, and risk-averse investments. The availability of large-scale observational healthcare databases and the popularity of large language models offer an unparalleled opportunity to enable automatic high-throughput drug screening for both repurposing and pharmacovigilance. Objectives: To demonstrate a general workflow for automatic high-throughput drug screening with the following advantages: (i) the association of various exposure on diseases can be estimated; (ii) both repurposing and pharmacovigilance are integrated; (iii) accurate exposure length for each prescription is parsed from clinical texts; (iv) intrinsic relationship between drugs and diseases are removed jointly by bioinformatic mapping and large language model - ChatGPT; (v) causal-wise interpretations for incidence rate contrasts are provided. Methods: Using a self-controlled cohort study design where subjects serve as their own control group, we tested the intention-to-treat association between medications on the incidence of diseases. Exposure length for each prescription is determined by parsing common dosages in English free text into a structured format. Exposure period starts from initial prescription to treatment discontinuation. A same exposure length preceding initial treatment is the control period. Clinical outcomes and categories are identified using existing phenotyping algorithms. Incident rate ratios (IRR) are tested using uniformly most powerful (UMP) unbiased tests. Results: We assessed 3,444 medications on 276 diseases on 6,613,198 patients from the Clinical Practice Research Datalink (CPRD), an UK primary care electronic health records (EHR) spanning from 1987 to 2018. Due to the built-in selection bias of self-controlled cohort studies, ingredients-disease pairs confounded by deterministic medical relationships are removed by existing map from RxNorm and nonexistent maps by calling ChatGPT. A total of 16,901 drug-disease pairs reveals significant risk reduction, which can be considered as candidates for repurposing, while a total of 11,089 pairs showed significant risk increase, where drug safety might be of a concern instead. Conclusions: This work developed a data-driven, nonparametric, hypothesis generating, and automatic high-throughput workflow, which reveals the potential of natural language processing in pharmacoepidemiology. We demonstrate the paradigm to a large observational health dataset to help discover potential novel therapies and adverse drug effects. The framework of this study can be extended to other observational medical databases.","PeriodicalId":501071,"journal":{"name":"medRxiv - Epidemiology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141933455","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Prevalence of Mild and Severe Cognitive Impairment in World Trade Center Exposed Fire Department of the City of New York (FDNY) and General Emergency Responders","authors":"Frank D Mann, Alexandra K. Mueller, Rachel Zeig-Owens, Jaeun Choi, David J. Prezant, Melissa M. Carr, Alicia M. Fels, Christina M. Hennington, Megan P. Armstrong, Alissa Barber, Ashley E. Fontana, Cassandra H. Kroll, Kevin Chow, Onix A Melendez, Abigail J. Smith, Benjamin J Luft, Charles B. Hall, Sean Clouston","doi":"10.1101/2024.08.04.24311457","DOIUrl":"https://doi.org/10.1101/2024.08.04.24311457","url":null,"abstract":"Background: The emergency personnel who responded to the World Trade Center (WTC) attacks endured severe occupational exposures, yet the prevalence of cognitive impairment remains unknown among WTC-exposed-FDNY-responders. The present study screened for mild and severe cognitive impairment in WTC-exposed FDNY responders using objective tests, compared prevalence rates to a cohort of non-FDNY WTC-exposed responders, and descriptively to meta-analytic estimates of MCI from global, community, and clinical populations. Methods: A sample of WTC-exposed-FDNY responders (n = 343) was recruited to complete an extensive battery of cognitive, psychological, and physical tests. The prevalences of domain-specific impairments were estimated based on the results of norm-referenced tests, and the Montreal Cognitive Assessment (MoCA), Jak/Bondi criteria, Petersen criteria, and the National Institute on Aging and Alzheimer′s Association (NIA-AA) criteria were used to diagnose MCI. NIA-AA criteria were also used to diagnose severe cognitive impairment. Generalized linear models were used to compare prevalence estimates of cognitive impairment to a large sample of WTC-exposed-non-FDNY responders from the General Responder Cohort (GRC; n = 7102) who completed the MoCA during a similar time frame.\u0000Result: Among FDNY responders under 65 years, the unadjusted prevalence of MCI varied from 52.57% to 71.37% depending on the operational definition of MCI, apart from using a conservative cut-off applied to MoCA total scores (18 < MoCA < 23), which yielded a markedly lower crude prevalence (24.31%) compared to alternative criteria. The prevalence of MCI was higher among WTC-exposed-FDNY-responders, compared to WTC-exposed-non-FDNY-GRC-responders (adjusted RR = 1.53, 95% C.I. = [1.24, 1.88], p < .001) and meta-analytic estimates from different global, community, and clinical populations. Following NIA-AA diagnostic guidelines, 4.96% of WTC-exposed-FDNY-responders met the criteria for severe impairments (95% CI = [2.91% to 7.82%]), a prevalence that remained largely unchanged after excluding responders over the age of 65 years. Discussion: There is a high prevalence of mild and severe cognitive impairment among WTC-responders highlighting the putative role of occupational/environmental and disaster-related exposures in the etiology of accelerated cognitive decline.","PeriodicalId":501071,"journal":{"name":"medRxiv - Epidemiology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141933460","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Associations between genetic HPV 16 diversity and cervical cancer prognosis","authors":"Patrícia Patury, Fabio B. Russomano, Luiz F. L. Martins, Miguel Angelo Martins Moreira, Raquel B. M. Carvalho, Nadia Roberta Chaves Kappaun, Liz Maria de Almeida","doi":"10.1101/2024.08.02.24311429","DOIUrl":"https://doi.org/10.1101/2024.08.02.24311429","url":null,"abstract":"Introduction Cervical cancer (CC) arises as a result of chronic and persistent female genitalia infection by different oncogenic human papillomaviruses (HPV). The incidence of this disease is still high in developing countries such as Brazil, where the diagnosis is often made in advanced stages. HPV 16 is the most common type of CC worldwide. Studies concerning the association of different HPV 16 lineages with overall and disease-free CC survival rates can contribute to further understanding the behavior of different HPV 16 lineages concerning the prognosis of CC cases. Objective Assess the CC prognosis of patients treated in a Brazilian institution with regard to HPV16 strains. Methods Data were obtained from a prospective cohort of 334 CC patients recruited between July 2011 and March 2014 and treated at the Brazilian National Cancer Institute (INCA), in Rio de Janeiro, Brazil. HPV 16 lineages were identified in tumor tissue samples. Genetic HPV 16 diversity comprised 218 cases of lineage A, 10 of lineage B, 10 of lineage C and 96 of lineage D. In addition to HPV 16 lineages, age, histopathological type, staging, and treatment completion were evaluated regarding CC prognosis. Results Median patient age was 48 years old. The most common histopathological type was squamous cell carcinoma (82.3%), followed by adenocarcinoma. Locally advanced disease staging was the most frequently detected, represented by similar stage II and III percentages (36.2% and 37.7%), followed by initial stage I (19.2%) and stage IV presenting distant disease (6.9%). Only 187 patients completed CC treatment. Age, histological type, staging, and treatment completion were associated with a higher risk of death, which was not observed for the HPV 16 lineage variable. With regard to age, each one year of life increase led to about a 1% increase in risk of death. Other histopathological types (poorly differentiated carcinoma, adenosquamous, neuroendocrine and sarcoma) were associated with a higher risk of death compared to adenocarcinoma. Squamous cell carcinoma also represented a higher risk of death compared to adenocarcinoma, albeit non-statistically significant. Patients diagnosed in advanced stages exhibited a higher risk of death, and those who did not complete treatment exhibited an over 2-fold increased risk of death. Conclusion This study found no associations between HPV 16 lineages A, B, C and D and CC prognosis.","PeriodicalId":501071,"journal":{"name":"medRxiv - Epidemiology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-08-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141933466","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Durability of SARS-CoV-2 IgG Antibodies: Insights from a Longitudinal Study, Puerto Rico","authors":"Zachary J. Madewell, Nathan Graff, Velma K Lopez, Dania M. Rodriguez, Joshua M. Wong, Panagiotis Maniatis, Freddy A. Medina, Jorge Munoz Jordan, Melissa Briggs-Hagen, Laura E. Adams, Vanessa Rivera-Amill, Gabriela Paz-Bailey, Chelsea G. Major","doi":"10.1101/2024.08.01.24311375","DOIUrl":"https://doi.org/10.1101/2024.08.01.24311375","url":null,"abstract":"Understanding the dynamics of antibody responses following vaccination and SARS-CoV-2 infection is important for informing effective vaccination strategies and other public health interventions. This study investigates SARS-CoV-2 antibody dynamics in a Puerto Rican cohort, analyzing how IgG levels vary by vaccination status and previous infection. We assess waning immunity and the distribution of hybrid immunity with the aim to inform public health strategies and vaccination programs in Puerto Rico and similar settings. We conducted a prospective, longitudinal cohort study to identify SARS-CoV-2 infections and related outcomes in Ponce, Puerto Rico, from June 2020-August 2022. Participants provided self-collected nasal swabs every week and serum every six months for RT-PCR and IgG testing, respectively. IgG reactivity against nucleocapsid (N) antigens, which generally indicate previous infection, and spike (S1) and receptor-binding domain (RBD) antigens, which indicate history of either infection or vaccination, was assessed using the Luminex Corporation xMAP SARS-CoV-2 Multi-Antigen IgG Assay. Prior infection was defined by positive RT-PCRs, categorized by the predominant circulating SARS-CoV-2 variant at the event time. Demographic information, medical history, and COVID-19 vaccination history were collected through standardized questionnaires. Of 882 participants included in our analysis, 34.0% experienced at least one SARS-CoV-2 infection, with most (78.7%) occurring during the Omicron wave (December 2021 onwards). SARS-CoV-2 antibody prevalence increased over time, reaching 98.4% by the final serum collection, 67.0% attributable to vaccination alone, 1.6% from infection alone, and 31.4% from both. Regardless of prior infection status, RBD and S1 IgG levels gradually declined following two vaccine doses. A third dose boosted these antibody levels and showed a slower decline over time. N-antibody levels peaked during the Omicron surge and waned over time. Vaccination in individuals with prior SARS-CoV-2 infection elicited the highest and most durable antibody responses. N or S1 seropositivity was associated with lower odds of a subsequent positive PCR test during the Omicron period, with N antibodies showing a stronger association. By elucidating the differential decay of RBD and S1 antibodies following vaccination and the complexities of N-antibody response following infection, this study in a Puerto Rican cohort strengthens the foundation for developing targeted interventions and public health strategies.","PeriodicalId":501071,"journal":{"name":"medRxiv - Epidemiology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-08-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141933464","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}