Pub Date : 2024-08-08DOI: 10.1101/2024.08.07.24311628
Yiwen Zhu, Katherine H Shutta, Tianyi Huang, Raji Balasubramanian, Oana A Zeleznik, Clary B Clish, Julián Ávila-Pacheco, Susan E Hankinson, Laura D Kubzansky
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.
背景：创伤后应激障碍（PTSD创伤后应激障碍（PTSD）的特征是严重的痛苦，并与心脏代谢疾病相关。对军队和临床人群的研究表明，代谢组过程失调可能是一个关键机制。之前的研究发现并验证了基于代谢组的痛苦评分（MDS）与抑郁和焦虑以及随后的心脏代谢疾病有关。在此，我们评估了创伤后应激障碍是否与抑郁和焦虑有共同的代谢改变，以及是否有其他代谢物与创伤后应激障碍有关。研究方法我们利用了嵌套在 "护士健康研究 II"（Nurses' Health Study II）中的三个子样本的血浆代谢组学数据，其中包括 2835 名女性在三个时间点（1996-2014 年）采集的 2950 份血液样本，以及通过质谱技术持续测定的 339 种已知代谢物。2008年对创伤和创伤后应激障碍暴露进行了评估，其特征如下：终生遭受创伤但未患创伤后应激障碍、终生创伤后应激障碍缓解以及创伤后应激障碍症状持续存在。在对潜在混杂因素进行调整后，在每个子样本中估算了暴露与 MDS 或单个代谢物之间的关联，并在随机效应元分析中进行了合并。结果持续的创伤后应激障碍症状与之前开发的抑郁和焦虑MDS水平较高有关。在 339 种代谢物中，我们发现有 9 种代谢物（主要是甘油磷脂的升高）与持续性症状有关（假发现率为 0.05）。没有发现代谢物与其他创伤后应激障碍相关暴露有关。结论作为首个大规模、基于人群的创伤后应激障碍代谢组学分析，我们的研究强调了创伤后应激障碍与抑郁或焦虑相关的共同和独特的代谢差异。我们发现了与创伤后应激障碍症状持续相关的新型代谢物标记物，这表明代谢失调与创伤后应激障碍有进一步的联系，可能会对健康产生下游影响。
{"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":"6 1","pages":""},"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}
Pub Date : 2024-08-06DOI: 10.1101/2024.08.02.24311367
Lise Hobeika, Matt Fillingim, Christophe Tanguay-Sabourin, Mathieu Roy, Alain Londero, Séverine Samson, Etienne Vachon-Presseau
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 Using 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 This 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.
{"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":"22 1","pages":""},"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}
Pub Date : 2024-08-06DOI: 10.1101/2024.08.06.24311533
Jure Mur, Lucy E. Stirland, Graciela Muniz-Terrera, Anja K. Leist
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.
{"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":"304 1","pages":""},"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}
Pub Date : 2024-08-05DOI: 10.1101/2024.08.05.24311488
Mhairi Maskew, Shantelle Smith, Lucien De Voux, Kieran Sharpey-Schafer, Thomas Crompton, Ashley Govender, Pedro Pisa, Sydney Rosen
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.
背景：为了实现南非的艾滋病治疗和病毒抑制目标，必须提高抗逆转录病毒疗法（ART）的保留率。我们投入了大量的精力和资源来追踪那些已经脱离治疗的患者，并将他们送回治疗项目，但成效不一。在此，我们的目标是在脱离治疗前成功识别出有脱离治疗风险的抗逆转录病毒疗法患者。方法和结果：我们将之前开发的机器学习和预测建模算法（PREDICT）应用于从 SLATE I 和 SLATE II 试验中例行收集的 ART 客户数据，这两项试验在 2017-18 年评估了当日 ART 启动情况。我们使用治疗中断（IIT）这一主要结果（IIT定义为错过下一次预定门诊时间达>28天），调查了PREDICT在SLATE数据集中的可重复性。我们还测试了两种风险分级方法：1）阈值法，将个体分为低、中、高 IIT 风险；2）原型法，识别具有 ITT 风险相关特征的亚组。我们使用粗风险差异和带 95% 置信区间的相对风险来报告风险类别组与下次预定就诊时的后续 IIT 之间的关联。SLATE 数据集包括 1,193 名客户在 14 个月随访期间的 7,199 次客户访问。该算法的准确率为 63%，阴性预测值为 89%，下次预定就诊就诊率的曲线下面积为 0.61，与之前仅使用医疗记录数据得出的结果相似。阈值法能够持续、准确地为护理级联的多个阶段分配 IIT 风险等级。原型法确定了几类 IIT 风险较高的亚群，包括之前就诊迟到者、脱离一段时间后再次就诊者、独居者或没有治疗支持者者。与人口统计学相比，原型中的行为要素往往更能一致地驱动治疗中断风险；例如，按时就诊的少男/青年男子的治疗中断率最低（10%，PREDICT 数据集和 7% SLATE 数据集），而之前脱离治疗后重返的少男/青年男子的后续治疗中断率最高（31%，PREDICT 数据集和 40% SLATE 数据集）。结论常规收集的医疗记录数据可与基本人口和社会经济数据相结合，利用机器学习和预测建模技术评估个人未来脱离治疗的风险。这种方法提供了一个机会，可以在脱离艾滋病治疗之前进行干预，并有可能预防脱离治疗，而不是在脱离治疗之后才采取应对措施。
{"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":"370 1","pages":""},"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}
Pub Date : 2024-08-05DOI: 10.1101/2024.08.05.24311486
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
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? Methodology/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. Conclusions/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.
{"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":"14 1","pages":""},"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}
Pub Date : 2024-08-05DOI: 10.1101/2024.08.05.24311461
Asad Hashmi, Sophie Scott, Mijin Jung, Qing-Jun Meng, Jonathan H Tobias, Rhona A Beynon, Benjamin G Faber
Objectives Shift 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. Design UK 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. Results This 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. Conclusions Shift 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.
{"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":"33 1","pages":""},"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}
Pub Date : 2024-08-05DOI: 10.1101/2024.08.04.24311480
Shenbo Xu, Stan N. Finkelstein, Roy E. Welsch, Kenney Ng, Ioanna Tzoulaki, Lefkos Middleton
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.
{"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":"104 1","pages":""},"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}
Pub Date : 2024-08-05DOI: 10.1101/2024.08.04.24311457
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
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. Result: 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.
{"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":"41 1","pages":""},"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}
Pub Date : 2024-08-03DOI: 10.1101/2024.08.02.24311429
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
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.
导言 宫颈癌（CC）是女性生殖器长期持续感染各种致癌人类乳头瘤病毒（HPV）的结果。在巴西等发展中国家，这种疾病的发病率仍然很高，而且通常在晚期才能确诊。HPV 16 是全球最常见的 CC 类型。有关不同 HPV 16 株系与 CC 总存活率和无病存活率关系的研究有助于进一步了解不同 HPV 16 株系对 CC 病例预后的影响。目的 评估在巴西一家机构接受治疗的 CC 患者的 HPV16 株系预后。方法 从 2011 年 7 月至 2014 年 3 月间招募的 334 名 CC 患者的前瞻性队列中获取数据，这些患者在巴西里约热内卢的巴西国家癌症研究所（INCA）接受了治疗。在肿瘤组织样本中确定了 HPV 16 株系。除了HPV 16系外，还对CC预后的年龄、组织病理学类型、分期和治疗完成情况进行了评估。结果 患者年龄中位数为 48 岁。最常见的组织病理学类型是鳞状细胞癌（82.3%），其次是腺癌。最常发现的疾病分期是局部晚期，II期和III期的比例相似（36.2%和37.7%），其次是初始I期（19.2%）和出现远处疾病的IV期（6.9%）。只有 187 名患者完成了 CC 治疗。年龄、组织学类型、分期和完成治疗与较高的死亡风险有关，而在HPV 16系变量中没有观察到这一点。在年龄方面，每增加一岁，死亡风险就会增加约1%。与腺癌相比，其他组织病理学类型（分化不良癌、腺鳞癌、神经内分泌癌和肉瘤）的死亡风险更高。与腺癌相比，鳞状细胞癌的死亡风险也更高，尽管没有统计学意义。晚期患者的死亡风险更高，未完成治疗的患者的死亡风险增加了 2 倍多。结论 本研究未发现 HPV 16 A、B、C 和 D 族系与 CC 预后之间存在关联。
{"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":"77 1","pages":""},"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}
Pub Date : 2024-08-03DOI: 10.1101/2024.08.01.24311375
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
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.
{"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":"11 1","pages":""},"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}
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