Pub Date : 2024-08-03DOI: 10.1101/2024.08.01.24311368
Bradley Olinger, Reema Banarjee, Amit Dey, Dimitrios Tsitsipatis, Toshiko Tanaka, Anjana Ram, Thedoe Nyunt, Gulzar Daya, Zhongsheng Peng, Linna Cui, Julian Candia, Eleanor M Simonsick, Myriam Gorospe, Keenan A Walker, Luigi Ferrucci, Nathan Basisty
Cellular senescence increases with age and contributes to age-related declines and pathologies. We identified circulating biomarkers of senescence associated with diverse clinical traits in humans to facilitate future non-invasive assessment of individual senescence burden and efficacy testing of novel senotherapeutics. Using a novel nanoparticle-based proteomic workflow, we profiled the senescence-associated secretory phenotype (SASP) in monocytes and examined these proteins in plasma samples (N = 1060) from the Baltimore Longitudinal Study of Aging (BLSA). Machine learning models trained on monocyte SASP associated with several age-related phenotypes in a test cohort, including body fat composition, blood lipids, inflammation, and mobility-related traits, among others. Notably, a subset of SASP-based predictions, including a high impact SASP panel that predicts age- and obesity-related clinical traits, were validated in InCHIANTI, an independent aging cohort. These results demonstrate the clinical relevance of the circulating SASP and identify relevant biomarkers of senescence that could inform future clinical studies.
细胞衰老会随着年龄的增长而加剧，并导致与年龄相关的衰退和病变。我们确定了与人类不同临床特征相关的衰老循环生物标志物，以促进未来对个体衰老负担的无创评估和新型衰老治疗药物的疗效测试。利用基于纳米粒子的新型蛋白质组学工作流程，我们分析了单核细胞中的衰老相关分泌表型（SASP），并检测了巴尔的摩老龄化纵向研究（Baltimore Longitudinal Study of Aging，BLSA）血浆样本（N = 1060）中的这些蛋白质。根据单核细胞SASP训练的机器学习模型与测试队列中的几种年龄相关表型有关，包括体脂组成、血脂、炎症和运动相关特征等。值得注意的是，基于 SASP 的预测结果子集（包括预测年龄和肥胖相关临床特征的高影响 SASP 面板）在独立的老龄化队列 InCHIANTI 中得到了验证。这些结果证明了循环 SASP 的临床相关性，并确定了衰老的相关生物标志物，可为未来的临床研究提供参考。
{"title":"A plasma proteomic signature links secretome of senescent monocytes to aging- and obesity-related clinical outcomes in humans","authors":"Bradley Olinger, Reema Banarjee, Amit Dey, Dimitrios Tsitsipatis, Toshiko Tanaka, Anjana Ram, Thedoe Nyunt, Gulzar Daya, Zhongsheng Peng, Linna Cui, Julian Candia, Eleanor M Simonsick, Myriam Gorospe, Keenan A Walker, Luigi Ferrucci, Nathan Basisty","doi":"10.1101/2024.08.01.24311368","DOIUrl":"https://doi.org/10.1101/2024.08.01.24311368","url":null,"abstract":"Cellular senescence increases with age and contributes to age-related declines and pathologies. We identified circulating biomarkers of senescence associated with diverse clinical traits in humans to facilitate future non-invasive assessment of individual senescence burden and efficacy testing of novel senotherapeutics. Using a novel nanoparticle-based proteomic workflow, we profiled the senescence-associated secretory phenotype (SASP) in monocytes and examined these proteins in plasma samples (N = 1060) from the Baltimore Longitudinal Study of Aging (BLSA). Machine learning models trained on monocyte SASP associated with several age-related phenotypes in a test cohort, including body fat composition, blood lipids, inflammation, and mobility-related traits, among others. Notably, a subset of SASP-based predictions, including a high impact SASP panel that predicts age- and obesity-related clinical traits, were validated in InCHIANTI, an independent aging cohort. These results demonstrate the clinical relevance of the circulating SASP and identify relevant biomarkers of senescence that could inform future clinical studies.","PeriodicalId":501071,"journal":{"name":"medRxiv - Epidemiology","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141933461","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: 10.1101/2024.08.02.24311410
Wan Yang, Enoma Omoregie, Aaron Olsen, Elizabeth A. Watts, Hilary Parton, Ellen Lee
Background: Wastewater-based surveillance is an important tool for monitoring the COVID-19 pandemic. However, it remains challenging to translate wastewater SARS-CoV-2 viral load to infection number, due to unclear shedding patterns in wastewater and potential differences between variants. Objectives: We utilized comprehensive wastewater surveillance data and estimates of infection prevalence (i.e., the source of the viral shedding) available for New York City (NYC) to characterize SARS-CoV-2 fecal shedding pattern over multiple COVID-19 waves. Methods: We collected SARS-CoV-2 viral wastewater measurements in NYC during August 31, 2020 - August 29, 2023 (N = 3794 samples). Combining with estimates of infection prevalence (number of infectious individuals including those not detected as cases), we estimated the time-lag, duration, and per-infection fecal shedding rate for the ancestral/Iota, Delta, and Omicron variants, separately. We also developed a procedure to identify occasions with intensified transmission. Results: Models suggested fecal viral shedding likely starts around the same time as and lasts slightly longer than respiratory tract shedding. Estimated fecal viral shedding rate was highest during the ancestral/Iota variant wave, at 1.44 (95% CI: 1.35 - 1.53) billion RNA copies in wastewater per day per infection (measured by RT-qPCR), and decreased by ~20% and 50-60% during the Delta wave and Omicron period, respectively. We identified around 200 occasions during which the wastewater SARS-CoV-2 viral load exceeded the expected level in any of 14 sewersheds. These anomalies disproportionally occurred during late January, late April - early May, early August, and from late-November to late-December, with frequencies exceeding the expectation assuming random occurrence (P < 0.05; bootstrapping test). Discussion: These estimates may be useful in understanding changes in underlying infection rate and help quantify changes in COVID-19 transmission and severity over time. We have also demonstrated that wastewater surveillance data can support the identification of time periods with potentially intensified transmission.
{"title":"The Use of Wastewater Surveillance to Estimate SARS-CoV-2 Fecal Viral Shedding Pattern and Identify Time Periods with Intensified Transmission","authors":"Wan Yang, Enoma Omoregie, Aaron Olsen, Elizabeth A. Watts, Hilary Parton, Ellen Lee","doi":"10.1101/2024.08.02.24311410","DOIUrl":"https://doi.org/10.1101/2024.08.02.24311410","url":null,"abstract":"Background: Wastewater-based surveillance is an important tool for monitoring the COVID-19 pandemic. However, it remains challenging to translate wastewater SARS-CoV-2 viral load to infection number, due to unclear shedding patterns in wastewater and potential differences between variants. Objectives: We utilized comprehensive wastewater surveillance data and estimates of infection prevalence (i.e., the source of the viral shedding) available for New York City (NYC) to characterize SARS-CoV-2 fecal shedding pattern over multiple COVID-19 waves. Methods: We collected SARS-CoV-2 viral wastewater measurements in NYC during August 31, 2020 - August 29, 2023 (N = 3794 samples). Combining with estimates of infection prevalence (number of infectious individuals including those not detected as cases), we estimated the time-lag, duration, and per-infection fecal shedding rate for the ancestral/Iota, Delta, and Omicron variants, separately. We also developed a procedure to identify occasions with intensified transmission. Results: Models suggested fecal viral shedding likely starts around the same time as and lasts slightly longer than respiratory tract shedding. Estimated fecal viral shedding rate was highest during the ancestral/Iota variant wave, at 1.44 (95% CI: 1.35 - 1.53) billion RNA copies in wastewater per day per infection (measured by RT-qPCR), and decreased by ~20% and 50-60% during the Delta wave and Omicron period, respectively. We identified around 200 occasions during which the wastewater SARS-CoV-2 viral load exceeded the expected level in any of 14 sewersheds. These anomalies disproportionally occurred during late January, late April - early May, early August, and from late-November to late-December, with frequencies exceeding the expectation assuming random occurrence (P < 0.05; bootstrapping test). Discussion: These estimates may be useful in understanding changes in underlying infection rate and help quantify changes in COVID-19 transmission and severity over time. We have also demonstrated that wastewater surveillance data can support the identification of time periods with potentially intensified transmission.","PeriodicalId":501071,"journal":{"name":"medRxiv - Epidemiology","volume":"22 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141933462","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 : 2024-08-02DOI: 10.1101/2024.08.01.24311280
Huan Jiang, Jurgen Rehm, Alexander Tran, Shannon Lange
Interrupted time series design is a quasi-experimental study design commonly used to evaluate the impact of a particular intervention (e.g., a health policy implementation) on a specific outcome. Two of the most often recommended analytical approaches to interrupted time series analysis are autoregressive integrated moving average (ARIMA) and Generalized Additive Models (GAM). We conducted simulation tests to determine the performance differences between ARIMA and GAM methodology across different policy effect sizes, with or without seasonality, and with or without misspecification of policy variables. We found that ARIMA exhibited more consistent results under certain conditions, such as with different policy effect sizes, with or without seasonality, while GAM were more robust when the model was misspecified. Given these findings, the variation between the models underscores the need for careful model selection and validation in health policy studies.
{"title":"Interrupted Time Series Design and Analyses in Health Policy Assessment","authors":"Huan Jiang, Jurgen Rehm, Alexander Tran, Shannon Lange","doi":"10.1101/2024.08.01.24311280","DOIUrl":"https://doi.org/10.1101/2024.08.01.24311280","url":null,"abstract":"Interrupted time series design is a quasi-experimental study design commonly used to evaluate the impact of a particular intervention (e.g., a health policy implementation) on a specific outcome. Two of the most often recommended analytical approaches to interrupted time series analysis are autoregressive integrated moving average (ARIMA) and Generalized Additive Models (GAM). We conducted simulation tests to determine the performance differences between ARIMA and GAM methodology across different policy effect sizes, with or without seasonality, and with or without misspecification of policy variables. We found that ARIMA exhibited more consistent results under certain conditions, such as with different policy effect sizes, with or without seasonality, while GAM were more robust when the model was misspecified. Given these findings, the variation between the models underscores the need for careful model selection and validation in health policy studies.","PeriodicalId":501071,"journal":{"name":"medRxiv - Epidemiology","volume":"75 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141884117","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 : 2024-08-02DOI: 10.1101/2024.07.31.24311274
Fanny Kilpi, Ana Luiza Goncalves Soares, Laura D Howe
Background The comorbidity of depression and overweight is a manifestation of mental-physical multimorbidity, a marker of complex healthcare needs. We sought to examine how adverse childhood experiences (ACEs) are associated with depression-overweight comorbidity in the period of adolescence and early adulthood, and the extent to which associations are sensitive to age, sex and socioeconomic background. Methods Using data from 4734 adolescents from the Avon Longitudinal Study of Parents and Children (ALSPAC) birth cohort we estimated relative risk ratios (RRR) for the associations of multiple ACEs (physical, emotional, and sexual abuse, emotional neglect, being bullied, parental substance abuse, violence between parents, parental criminal conviction, parental separation, parental mental illness or suicide) with depression only, overweight only or their comorbidity at ages 17 and 24. We tested whether associations differed by sex and socioeconomic background, indicated by parental education. Results Most ACEs were associated with depression-overweight comorbidity, and there was a dose-response relationship whereby a greater number of ACEs was associated with greater risk and this continued from adolescence to young adulthood. Some ACEs associations with comorbidity appeared to be influenced by sex: at age 17, females had stronger associations for parental separation and mental health problems, and at age 24, sexual abuse had a stronger association in males. We did not find evidence that the sensitivity to ACEs varied by parental education. Conclusions ACEs across childhood are associated with depression-overweight comorbidity in late adolescence, which demonstrates their potential impact on the early manifestation of complex healthcare needs.
{"title":"Adverse childhood experiences as a risk factor for depression-overweight comorbidity in adolescence and young adulthood","authors":"Fanny Kilpi, Ana Luiza Goncalves Soares, Laura D Howe","doi":"10.1101/2024.07.31.24311274","DOIUrl":"https://doi.org/10.1101/2024.07.31.24311274","url":null,"abstract":"Background\u0000The comorbidity of depression and overweight is a manifestation of mental-physical multimorbidity, a marker of complex healthcare needs. We sought to examine how adverse childhood experiences (ACEs) are associated with depression-overweight comorbidity in the period of adolescence and early adulthood, and the extent to which associations are sensitive to age, sex and socioeconomic background.\u0000Methods Using data from 4734 adolescents from the Avon Longitudinal Study of Parents and Children (ALSPAC) birth cohort we estimated relative risk ratios (RRR) for the associations of multiple ACEs (physical, emotional, and sexual abuse, emotional neglect, being bullied, parental substance abuse, violence between parents, parental criminal conviction, parental separation, parental mental illness or suicide) with depression only, overweight only or their comorbidity at ages 17 and 24. We tested whether associations differed by sex and socioeconomic background, indicated by parental education.\u0000Results\u0000Most ACEs were associated with depression-overweight comorbidity, and there was a dose-response relationship whereby a greater number of ACEs was associated with greater risk and this continued from adolescence to young adulthood. Some ACEs associations with comorbidity appeared to be influenced by sex: at age 17, females had stronger associations for parental separation and mental health problems, and at age 24, sexual abuse had a stronger association in males. We did not find evidence that the sensitivity to ACEs varied by parental education.\u0000Conclusions\u0000ACEs across childhood are associated with depression-overweight comorbidity in late adolescence, which demonstrates their potential impact on the early manifestation of complex healthcare needs.","PeriodicalId":501071,"journal":{"name":"medRxiv - Epidemiology","volume":"298 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141884119","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 : 2024-08-02DOI: 10.1101/2024.07.31.24311312
Jonathan Judd, Jeffrey P Spence, Jonathan K Pritchard, Linda Kachuri, John S Witte
Background: Genetic factors play an important role in prostate cancer (PCa) development with polygenic risk scores (PRS) predicting disease risk across genetic ancestries. However, there are few convincing modifiable factors for PCa and little is known about their potential interaction with genetic risk. We analyzed incident PCa cases (n=6,155) and controls (n=98,257) of European and African ancestry from the UK Biobank (UKB) cohort to evaluate the role of neighborhood socioeconomic status (nSES)-and how it may interact with PRS-on PCa risk. Methods: We evaluated a multi-ancestry PCa PRS containing 269 genetic variants to understand the association of germline genetics with PCa in UKB. Using the English Indices of Deprivation, a set of validated metrics that quantify lack of resources within geographical areas, we performed logistic regression to investigate the main effects and interactions between nSES deprivation, PCa PRS, and PCa. Results: The PCa PRS was strongly associated with PCa (OR=2.04; 95%CI=2.00-2.09; P<0.001). Additionally, nSES deprivation indices were inversely associated with PCa: employment (OR=0.91; 95%CI=0.86-0.96; P<0.001), education (OR=0.94; 95%CI=0.83-0.98; P<0.001), health (OR=0.91; 95%CI=0.86-0.96; P<0.001), and income (OR=0.91; 95%CI=0.86-0.96; P<0.001). The PRS effects showed little heterogeneity across nSES deprivation indices, except for the Townsend Index (P=0.03) Conclusions: We reaffirmed genetics as a risk factor for PCa and identified nSES deprivation domains that influence PCa detection and are potentially correlated with environmental exposures that are a risk factor for PCa. These findings also suggest that nSES and genetic risk factors for PCa act independently.
{"title":"Investigating the Role of Neighborhood Socioeconomic Status and Germline Genetics on Prostate Cancer Risk","authors":"Jonathan Judd, Jeffrey P Spence, Jonathan K Pritchard, Linda Kachuri, John S Witte","doi":"10.1101/2024.07.31.24311312","DOIUrl":"https://doi.org/10.1101/2024.07.31.24311312","url":null,"abstract":"Background: Genetic factors play an important role in prostate cancer (PCa)\u0000development with polygenic risk scores (PRS) predicting disease risk across genetic\u0000ancestries. However, there are few convincing modifiable factors for PCa and little is\u0000known about their potential interaction with genetic risk. We analyzed incident PCa\u0000cases (n=6,155) and controls (n=98,257) of European and African ancestry from the\u0000UK Biobank (UKB) cohort to evaluate the role of neighborhood socioeconomic status\u0000(nSES)-and how it may interact with PRS-on PCa risk. Methods: We evaluated a multi-ancestry PCa PRS containing 269 genetic variants to\u0000understand the association of germline genetics with PCa in UKB. Using the English\u0000Indices of Deprivation, a set of validated metrics that quantify lack of resources within\u0000geographical areas, we performed logistic regression to investigate the main effects\u0000and interactions between nSES deprivation, PCa PRS, and PCa. Results: The PCa PRS was strongly associated with PCa (OR=2.04;\u000095%CI=2.00-2.09; P<0.001). Additionally, nSES deprivation indices were inversely\u0000associated with PCa: employment (OR=0.91; 95%CI=0.86-0.96; P<0.001), education\u0000(OR=0.94; 95%CI=0.83-0.98; P<0.001), health (OR=0.91; 95%CI=0.86-0.96;\u0000P<0.001), and income (OR=0.91; 95%CI=0.86-0.96; P<0.001). The PRS effects\u0000showed little heterogeneity across nSES deprivation indices, except for the Townsend\u0000Index (P=0.03) Conclusions: We reaffirmed genetics as a risk factor for PCa and identified nSES\u0000deprivation domains that influence PCa detection and are potentially correlated with\u0000environmental exposures that are a risk factor for PCa. These findings also suggest\u0000that nSES and genetic risk factors for PCa act independently.","PeriodicalId":501071,"journal":{"name":"medRxiv - Epidemiology","volume":"57 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141884115","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 : 2024-08-02DOI: 10.1101/2024.07.31.24311269
Jing Chen, Nick Shrine, Abril Izquierdo, Anna Louise Guyatt, Henry Völzke, Stephanie J London, Ian Hall, Frank Dudbridge, Louise Wain, Martin Tobin, Catherine John
Background and aim Epidemiological studies of lung function may discard one-third to one-half of participants due to spirometry measures deemed "low quality" using criteria adapted from clinical practice. We aimed to define new spirometry quality control (QC) criteria that optimise the signal-to-noise ratio in epidemiological studies of lung function. Material and methods We proposed a genetic risk score (GRS) informed strategy to categorize spirometer blows according to quality criteria. We constructed three GRSs comprised of SNPs associated with forced expiratory volume in 1 second (FEV1), forced vital capacity (FVC) and the ratio of FEV1 to FVC (FEV1/FVC) in individuals from non-UK Biobank cohorts included in prior genome-wide association studies (GWAS). In the UK Biobank, we applied a step-wise testing of the GRS association across groups of spirometry blows stratified by acceptability flags to rank the blow quality. To reassess the QC criteria, we compared the genetic association results between analyses including different acceptability flags and applying different repeatability thresholds for spirometry measurements to determine the trade-off between sample size and measurement error. Results We found that including blows previously excluded for cough, hesitation, excessive time to peak flow, or inadequate terminal plateau, and applying a repeatability threshold of 250ml, would maximise the statistical power for GWAS and retain acceptable precision in the UK Biobank. This approach allowed the inclusion of 29% more participants compared to the strictest ATS/ERS guidelines. Conclusion Our findings demonstrate the utility of GRS-informed QC to maximise the power of epidemiological studies for lung function traits.
{"title":"Genetic risk score-informed re-evaluation of spirometry quality control to maximise power in epidemiological studies of lung function","authors":"Jing Chen, Nick Shrine, Abril Izquierdo, Anna Louise Guyatt, Henry Völzke, Stephanie J London, Ian Hall, Frank Dudbridge, Louise Wain, Martin Tobin, Catherine John","doi":"10.1101/2024.07.31.24311269","DOIUrl":"https://doi.org/10.1101/2024.07.31.24311269","url":null,"abstract":"Background and aim\u0000Epidemiological studies of lung function may discard one-third to one-half of participants due to spirometry measures deemed \"low quality\" using criteria adapted from clinical practice. We aimed to define new spirometry quality control (QC) criteria that optimise the signal-to-noise ratio in epidemiological studies of lung function. Material and methods\u0000We proposed a genetic risk score (GRS) informed strategy to categorize spirometer blows according to quality criteria. We constructed three GRSs comprised of SNPs associated with forced expiratory volume in 1 second (FEV1), forced vital capacity (FVC) and the ratio of FEV1 to FVC (FEV1/FVC) in individuals from non-UK Biobank cohorts included in prior genome-wide association studies (GWAS). In the UK Biobank, we applied a step-wise testing of the GRS association across groups of spirometry blows stratified by acceptability flags to rank the blow quality. To reassess the QC criteria, we compared the genetic association results between analyses including different acceptability flags and applying different repeatability thresholds for spirometry measurements to determine the trade-off between sample size and measurement error. Results\u0000We found that including blows previously excluded for cough, hesitation, excessive time to peak flow, or inadequate terminal plateau, and applying a repeatability threshold of 250ml, would maximise the statistical power for GWAS and retain acceptable precision in the UK Biobank. This approach allowed the inclusion of 29% more participants compared to the strictest ATS/ERS guidelines. Conclusion\u0000Our findings demonstrate the utility of GRS-informed QC to maximise the power of epidemiological studies for lung function traits.","PeriodicalId":501071,"journal":{"name":"medRxiv - Epidemiology","volume":"186 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141884118","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 : 2024-08-02DOI: 10.1101/2024.08.01.24311349
Stephanie Page, Mark J Gibson, Marcus Munafo, Jasmine N Khouja, Rebecca Richmond
Research has shown bidirectional relationships between smoking and adverse sleep behaviours, including late chronotype and insomnia, but the underlying mechanisms are not understood. One potential driver is nicotine, but its role in sleep is unclear. For this study, we estimated the direct effect of nicotine on six sleep behaviours measured in UK Biobank (chronotype, ease of getting up in the morning, insomnia symptoms, napping, daytime sleepiness and sleep duration). We conducted a Mendelian randomisation (MR) study to explore whether nicotine metabolism has a causal effect on these sleep behaviours. We explored whether the effects could be explained by regular nicotine exposure using genetic proxies of the nicotine metabolite ratio (NMR) and cigarettes per day (CPD) in a multivariable MR design. We found a higher NMR (indicating lower levels of circulating nicotine per cigarette smoked) decreased the likelihood of being an evening person when accounting for CPD in current (β = -0.04, 95%CI -0.06 to -0.02, p < 0.001) and ever smokers (β = -0.03 95%CI -0.04 to -0.01, p = 0.003). A higher NMR also increased the ease of getting up (β = 0.02, 95%CI 0.01 to 0.04, p = 0.015) and likelihood of napping (β = 0.02, 95%CI CI 0.002 to 0.03, p = 0.029) in current smokers. Increased nicotine exposure may directly affect sleep and could underlie relationships between smoking and sleep behaviours identified previously. Sleep could also be impacted in individuals using nicotine delivery systems or using nicotine replacement therapies. Further research is warranted to strengthen this conclusion.
{"title":"Exploring the role of nicotine and smoking in sleep behaviours: A multivariable Mendelian Randomisation study","authors":"Stephanie Page, Mark J Gibson, Marcus Munafo, Jasmine N Khouja, Rebecca Richmond","doi":"10.1101/2024.08.01.24311349","DOIUrl":"https://doi.org/10.1101/2024.08.01.24311349","url":null,"abstract":"Research has shown bidirectional relationships between smoking and adverse sleep behaviours, including late chronotype and insomnia, but the underlying mechanisms are not understood. One potential driver is nicotine, but its role in sleep is unclear. For this study, we estimated the direct effect of nicotine on six sleep behaviours measured in UK Biobank (chronotype, ease of getting up in the morning, insomnia symptoms, napping, daytime sleepiness and sleep duration). We conducted a Mendelian randomisation (MR) study to explore whether nicotine metabolism has a causal effect on these sleep behaviours. We explored whether the effects could be explained by regular nicotine exposure using genetic proxies of the nicotine metabolite ratio (NMR) and cigarettes per day (CPD) in a multivariable MR design. We found a higher NMR (indicating lower levels of circulating nicotine per cigarette smoked) decreased the likelihood of being an evening person when accounting for CPD in current (β = -0.04, 95%CI -0.06 to -0.02, p < 0.001) and ever smokers (β = -0.03 95%CI -0.04 to -0.01, p = 0.003). A higher NMR also increased the ease of getting up (β = 0.02, 95%CI 0.01 to 0.04, p = 0.015) and likelihood of napping (β = 0.02, 95%CI CI 0.002 to 0.03, p = 0.029) in current smokers. Increased nicotine exposure may directly affect sleep and could underlie relationships between smoking and sleep behaviours identified previously. Sleep could also be impacted in individuals using nicotine delivery systems or using nicotine replacement therapies. Further research is warranted to strengthen this conclusion.","PeriodicalId":501071,"journal":{"name":"medRxiv - Epidemiology","volume":"10 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141884113","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 : 2024-08-02DOI: 10.1101/2024.08.01.24311336
Francesco Bonacina, Pierre-Yves Boëlle, Vittoria Colizza, Olivier Lopez, Maud Thomas, Chiara Poletto
The (sub)type composition of seasonal influenza waves varies in space and time. (Sub)types tend to have different impacts on population groups, therefore understanding the drivers of their co-circulation and anticipating their composition is important for epidemic preparedness and response. FluNet provides data on influenza specimens by (sub)type for more than one hundred fifty countries. However, due to surveillance variations across countries, global analyses usually focus on (sub)type compositions, a kind of data which is difficult to treat with advanced statistical methods. We used Compositional Data Analysis to circumvent the problem and study trajectories of annual (sub)type compositions of countries. First, we examined global trends from 2000 to 2022. We identified a few seasons which stood out for the strong within-country (sub)type dominance due to either a new virus/clade taking over (2003/2004 season, A/H1N1pdm pandemic) or (sub)types' spatial segregation (COVID-19 pandemic). Second, we showed that the composition trajectories of countries between 2010 and 2019 clustered in two macroregions characterized by (sub)type alternation vs. persistent mixing. Finally, we defined five algorithms for forecasting the next-year composition and we found that taking into account the global history of (sub)type composition in a Bayesian Hierarchical Vector AutoRegressive model improved predictions compared with naive methods. The joint analysis of spatiotemporal dynamics of influenza (sub)types worldwide revealed a hidden structure in (sub)type circulation that can be used to improve predictions of the (sub)type composition of next year's epidemic according to place.
{"title":"Characterization and forecast of global influenza (sub)type dynamics","authors":"Francesco Bonacina, Pierre-Yves Boëlle, Vittoria Colizza, Olivier Lopez, Maud Thomas, Chiara Poletto","doi":"10.1101/2024.08.01.24311336","DOIUrl":"https://doi.org/10.1101/2024.08.01.24311336","url":null,"abstract":"The (sub)type composition of seasonal influenza waves varies in space and time. (Sub)types tend to have different impacts on population groups, therefore understanding the drivers of their co-circulation and anticipating their composition is important for epidemic preparedness and response. FluNet provides data on influenza specimens by (sub)type for more than one hundred fifty countries. However, due to surveillance variations across countries, global analyses usually focus on (sub)type compositions, a kind of data which is difficult to treat with advanced statistical methods. We used Compositional Data Analysis to circumvent the problem and study trajectories of annual (sub)type compositions of countries. First, we\u0000examined global trends from 2000 to 2022. We identified a few seasons which stood out for\u0000the strong within-country (sub)type dominance due to either a new virus/clade taking over\u0000(2003/2004 season, A/H1N1pdm pandemic) or (sub)types' spatial segregation (COVID-19\u0000pandemic). Second, we showed that the composition trajectories of countries between 2010 and 2019 clustered in two macroregions characterized by (sub)type alternation vs. persistent mixing. Finally, we defined five algorithms for forecasting the next-year composition and we found that taking into account the global history of (sub)type composition in a Bayesian Hierarchical Vector AutoRegressive model improved predictions compared with naive methods. The joint analysis of spatiotemporal dynamics of influenza (sub)types worldwide revealed a hidden structure in (sub)type circulation that can be used to improve predictions of the (sub)type composition of next year's epidemic according to place.","PeriodicalId":501071,"journal":{"name":"medRxiv - Epidemiology","volume":"20 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141884116","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 : 2024-08-02DOI: 10.1101/2024.08.01.24311365
Vaibhava Srivastava, Drik Sarkar, Claus Kadelka
Infectious diseases thrive in war-torn societies. The recent sharp increase in human conflict and war thus requires the development of disease mitigation tools that account for the specifics of war, such as scarcity of important public health resources. Differential equation-based compartmental models constitute the standard tool for forecasting disease dynamics and evaluating intervention strategies. We developed a compartmental disease model that considers key social, war, and disease mechanisms, such as gender homophily and the replacement of soldiers. This model enables the identification of optimal allocation strategies that, given limited resources required for treating infected individuals, minimize disease burden, assessed by total mortality and final epidemic size. A comprehensive model analysis reveals that the level of resource scarcity fundamentally affects the optimal allocation. Desynchronization of the epidemic peaks among several population subgroups emerges as a desirable principle since it reduces disease spread between different subgroups. Further, the level of preferential mixing among people of the same gender, gender homophily, proves to strongly affect disease dynamics and optimal treatment allocation strategies, highlighting the importance of accurately accounting for heterogeneous mixing patterns. Altogether, the findings help answer a timely question: how can infectious diseases be best controlled in societies at war? The developed model can be easily extended to specific diseases, countries, and interventions.
{"title":"Model-informed optimal allocation of limited resources to mitigate infectious disease outbreaks in societies at war","authors":"Vaibhava Srivastava, Drik Sarkar, Claus Kadelka","doi":"10.1101/2024.08.01.24311365","DOIUrl":"https://doi.org/10.1101/2024.08.01.24311365","url":null,"abstract":"Infectious diseases thrive in war-torn societies. The recent sharp increase in human conflict and war thus requires the development of disease mitigation tools that account for the specifics of war, such as scarcity of important public health resources. Differential equation-based compartmental models constitute the standard tool for forecasting disease dynamics and evaluating intervention strategies. We developed a compartmental disease model that considers key social, war, and disease mechanisms, such as gender homophily and the replacement of soldiers. This model enables the identification of optimal allocation strategies that, given limited resources required for treating infected individuals, minimize disease burden, assessed by total mortality and final epidemic size. A comprehensive model analysis reveals that the level of resource scarcity fundamentally affects the optimal allocation. Desynchronization of the epidemic peaks among several population subgroups emerges as a desirable principle since it reduces disease spread between different subgroups. Further, the level of preferential mixing among people of the same gender, gender homophily, proves to strongly affect disease dynamics and optimal treatment allocation strategies, highlighting the importance of accurately accounting for heterogeneous mixing patterns. Altogether, the findings help answer a timely question: how can infectious diseases be best controlled in societies at war? The developed model can be easily extended to specific diseases, countries, and interventions.","PeriodicalId":501071,"journal":{"name":"medRxiv - Epidemiology","volume":"80 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141884120","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 : 2024-08-01DOI: 10.1101/2024.07.30.24311220
Alexander Richard Kaye, Giorgio Guzzetta, Michael Tildesley, Robin Thompson
For many infectious diseases, the risk of outbreaks varies seasonally. If a pathogen is usually absent from a host population, a key public health policy question is whether the pathogen's arrival will initiate local transmission, which depends on the season in which arrival occurs. This question can be addressed by estimating the probability of a major outbreak (the probability that introduced cases will initiate sustained local transmission). A standard approach for inferring this probability exists for seasonal pathogens (involving calculating the Case Epidemic Risk; CER) based on the mathematical theory of branching processes. Under that theory, the probability of pathogen extinction is estimated, neglecting depletion of susceptible individuals. The CER is then one minus the extinction probability. However, as we show, if transmission cannot occur for long periods of the year (e.g., over winter or over summer), the pathogen will inevitably go extinct, leading to a CER of zero even if seasonal outbreaks can occur. This renders the CER uninformative in those scenarios. We therefore devise an alternative approach for inferring outbreak risks for seasonal pathogens (involving calculating the Threshold Epidemic Risk; TER). Estimation of the TER involves calculating the probability that introduced cases will initiate a local outbreak in which a threshold number of infections is exceeded before outbreak extinction. For simple seasonal epidemic models, such as the stochastic Susceptible-Infectious-Removed model, the TER can be calculated numerically (without model simulations). For more complex models, such as stochastic host-vector models, the TER can be estimated using model simulations. We demonstrate the application of our approach by considering Chikungunya virus in northern Italy as a case study. In that context, transmission is most likely in summer, when environmental conditions promote vector abundance. We show that the TER provides more useful assessments of outbreak risks than the CER, enabling practically relevant risk quantification for seasonal pathogens.
{"title":"Quantifying infectious disease epidemic risks: A practical approach for seasonal pathogens","authors":"Alexander Richard Kaye, Giorgio Guzzetta, Michael Tildesley, Robin Thompson","doi":"10.1101/2024.07.30.24311220","DOIUrl":"https://doi.org/10.1101/2024.07.30.24311220","url":null,"abstract":"For many infectious diseases, the risk of outbreaks varies seasonally. If a pathogen is usually absent from a host population, a key public health policy question is whether the pathogen's arrival will initiate local transmission, which depends on the season in which arrival occurs. This question can be addressed by estimating the probability of a major outbreak (the probability that introduced cases will initiate sustained local transmission). A standard approach for inferring this probability exists for seasonal pathogens (involving calculating the Case Epidemic Risk; CER) based on the mathematical theory of branching processes. Under that theory, the probability of pathogen extinction is estimated, neglecting depletion of susceptible individuals. The CER is then one minus the extinction probability. However, as we show, if transmission cannot occur for long periods of the year (e.g., over winter or over summer), the pathogen will inevitably go extinct, leading to a CER of zero even if seasonal outbreaks can occur. This renders the CER uninformative in those scenarios. We therefore devise an alternative approach for inferring outbreak risks for seasonal pathogens (involving calculating the Threshold Epidemic Risk; TER). Estimation of the TER involves calculating the probability that introduced cases will initiate a local outbreak in which a threshold number of infections is exceeded before outbreak extinction. For simple seasonal epidemic models, such as the stochastic Susceptible-Infectious-Removed model, the TER can be calculated numerically (without model simulations). For more complex models, such as stochastic host-vector models, the TER can be estimated using model simulations. We demonstrate the application of our approach by considering Chikungunya virus in northern Italy as a case study. In that context, transmission is most likely in summer, when environmental conditions promote vector abundance. We show that the TER provides more useful assessments of outbreak risks than the CER, enabling practically relevant risk quantification for seasonal pathogens.","PeriodicalId":501071,"journal":{"name":"medRxiv - Epidemiology","volume":"48 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141866366","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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