Pub Date : 2024-12-18DOI: 10.1038/s43856-024-00711-8
Kia Hee Schultz Dungu, Christian Munch Hagen, Marie Bækvad-Hansen, Victor Yakimov, Alfonso Buil Demur, Emma Malchau Carlsen, Nadja Hawwa Vissing, Tine Brink Henriksen, Trine Hyrup Mogensen, David Michael Hougaard, Ulrikka Nygaard, Jonas Bybjerg-Grauholm
Neonatal herpes simplex virus (HSV) infection is life-threatening, with a mortality of up to 70–80% when disseminated, often due to vague symptoms and delayed treatment. Neonatal screening using dried blood spot (DBS) samples is among the most impactful preventative health measures ever implemented, but screening for HSV has not been investigated. We investigated high throughput multiplexed proteomics on DBS samples collected on days 2–3 of life from a nationwide cohort of neonates with HSV infection (n = 53) and matched controls. We measured 2941 proteins using the Olink Explore 3072 panels and proximity extension assays, followed by differential protein expression by Analysis of Variance with post-hoc correction and functional annotation. Here, we show distinct protein profiles in neonates with disseminated HSV disease, with differences in 20 proteins compared to controls. These proteins are associated with innate and adaptive immune responses and cytokine activation. Our findings indicate the potential of neonatal screening for disseminated HSV disease to ensure early treatment and reduce the high mortality. Herpes simplex virus (HSV) infection in newborns has a 70% risk of death if infection becomes widespread in the body. Initial symptoms are often vague, leading to delayed treatment. Early dried blood spot (DBS) screening of newborns is very effective for identifying disorders present at birth, but its use to identify HSV infection has not been investigated. Here, we analysed DBS samples taken on days 2–3 of life from newborns developing HSV infection in the neonatal period. We identified 20 proteins that differed between those with widespread HSV infection compared to healthy babies. These findings suggest that HSV screening on DBS samples have the potential to detect severe infections early, enabling prompt treatment and reducing the risk of death. Dungu et al. use high throughput multiplexed proteomics on dried blood spot samples from neonates with herpes simplex virus infection. Distinct protein profiles were seen in proteins associated with innate and adaptive immune responses neonates with disseminated HSV disease compared to controls.
{"title":"Proteomic profiling of neonatal herpes simplex virus infection on dried blood spots","authors":"Kia Hee Schultz Dungu, Christian Munch Hagen, Marie Bækvad-Hansen, Victor Yakimov, Alfonso Buil Demur, Emma Malchau Carlsen, Nadja Hawwa Vissing, Tine Brink Henriksen, Trine Hyrup Mogensen, David Michael Hougaard, Ulrikka Nygaard, Jonas Bybjerg-Grauholm","doi":"10.1038/s43856-024-00711-8","DOIUrl":"10.1038/s43856-024-00711-8","url":null,"abstract":"Neonatal herpes simplex virus (HSV) infection is life-threatening, with a mortality of up to 70–80% when disseminated, often due to vague symptoms and delayed treatment. Neonatal screening using dried blood spot (DBS) samples is among the most impactful preventative health measures ever implemented, but screening for HSV has not been investigated. We investigated high throughput multiplexed proteomics on DBS samples collected on days 2–3 of life from a nationwide cohort of neonates with HSV infection (n = 53) and matched controls. We measured 2941 proteins using the Olink Explore 3072 panels and proximity extension assays, followed by differential protein expression by Analysis of Variance with post-hoc correction and functional annotation. Here, we show distinct protein profiles in neonates with disseminated HSV disease, with differences in 20 proteins compared to controls. These proteins are associated with innate and adaptive immune responses and cytokine activation. Our findings indicate the potential of neonatal screening for disseminated HSV disease to ensure early treatment and reduce the high mortality. Herpes simplex virus (HSV) infection in newborns has a 70% risk of death if infection becomes widespread in the body. Initial symptoms are often vague, leading to delayed treatment. Early dried blood spot (DBS) screening of newborns is very effective for identifying disorders present at birth, but its use to identify HSV infection has not been investigated. Here, we analysed DBS samples taken on days 2–3 of life from newborns developing HSV infection in the neonatal period. We identified 20 proteins that differed between those with widespread HSV infection compared to healthy babies. These findings suggest that HSV screening on DBS samples have the potential to detect severe infections early, enabling prompt treatment and reducing the risk of death. Dungu et al. use high throughput multiplexed proteomics on dried blood spot samples from neonates with herpes simplex virus infection. Distinct protein profiles were seen in proteins associated with innate and adaptive immune responses neonates with disseminated HSV disease compared to controls.","PeriodicalId":72646,"journal":{"name":"Communications medicine","volume":" ","pages":"1-8"},"PeriodicalIF":5.4,"publicationDate":"2024-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s43856-024-00711-8.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142856914","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-12-18DOI: 10.1038/s43856-024-00706-5
Wenming Wei, Xin Qi, Bolun Cheng, Na Zhang, Yijing Zhao, Xiaoyue Qin, Dan He, Xiaoge Chu, Sirong Shi, Qingqing Cai, Xuena Yang, Shiqiang Cheng, Peilin Meng, Jingni Hui, Chuyu Pan, Li Liu, Yan Wen, Huan Liu, Yumeng Jia, Feng Zhang
Musculoskeletal disorders pose major public health challenges, and accelerated biological aging may increase their risk. This study investigates the association between biological aging and musculoskeletal disorders, with a focus on sex-related differences. We analyzed data from 172,332 UK Biobank participants (mean age of 56.03 ± 8.10 years). Biological age was calculated using the KDM-BA and PhenoAge algorithms based on blood biomarkers. Musculoskeletal disorders were diagnosed using the ICD-10 criteria, with sample sizes ranging from 1,182 to 23,668. Logistic regression assessed cross-sectional associations between age acceleration (AA) metrics and musculoskeletal disorders. Accelerated Failure Time (AFT) model was used for survival analysis to evaluate the relationships between AAs and musculoskeletal disorders onset. Models were adjusted for demographic, lifestyle, and socio-economic covariates. The threshold of P-values were set by the Holm-Bonferroni correction. Cross-sectional analyses reveal significant associations between AAs and fourteen musculoskeletal disorders. Survival analyses indicate that AAs significantly accelerate the onset of nine musculoskeletal disorders, including inflammatory polyarthropathies (RTKDM-BA = 0.993; RTPhenoAge = 0.983), systemic connective tissue disorders (RTKDM-BA = 0.987; RTPhenoAge = 0.980), spondylopathies (RTPhenoAge= 0.994), disorders of bone density and structure (RTPhenoAge= 0.991), gout (RTPhenoAge= 0.968), arthritis (RTPhenoAge= 0.991), pain in joint (RTPhenoAge= 0.989), low back pain (RTPhenoAge= 0.986), and osteoporosis (RTPhenoAge= 0.994). Sensitivity analyses are consistent with the primary findings. Sex-specific variations are observed, with AAs accelerating spondylopathies, arthritis, and low back pain in females, while osteoporosis is accelerated in males. Accelerated biological aging is significantly associated with the incidence of several musculoskeletal disorders. These insights highlight the importance of biological age assessments in gauging musculoskeletal disorder risk, aiding early detection, prevention, and management. As we age, our bodies experience changes that can lead to health problems, including musculoskeletal disorders such as arthritis and back pain. This study explores how biological aging, a measure of how old our bodies seem based on biomarkers, affects the risk of developing these disorders. Using data from over 170,000 people, we found that faster biological aging is linked to an increased risk of several musculoskeletal disorders, and that these risks can vary between men and women. These findings could help identify people at risk earlier, leading to better prevention and treatment strategies. Wei et al. investigate the link between accelerated biological aging and the risk of musculoskeletal disorders, highlighting sex-related disparities. Age acceleration significantly increases the risk and onset of nine musculoskeletal disorders, with notable differences betw
{"title":"A prospective study of associations between accelerated biological aging and twenty musculoskeletal disorders","authors":"Wenming Wei, Xin Qi, Bolun Cheng, Na Zhang, Yijing Zhao, Xiaoyue Qin, Dan He, Xiaoge Chu, Sirong Shi, Qingqing Cai, Xuena Yang, Shiqiang Cheng, Peilin Meng, Jingni Hui, Chuyu Pan, Li Liu, Yan Wen, Huan Liu, Yumeng Jia, Feng Zhang","doi":"10.1038/s43856-024-00706-5","DOIUrl":"10.1038/s43856-024-00706-5","url":null,"abstract":"Musculoskeletal disorders pose major public health challenges, and accelerated biological aging may increase their risk. This study investigates the association between biological aging and musculoskeletal disorders, with a focus on sex-related differences. We analyzed data from 172,332 UK Biobank participants (mean age of 56.03 ± 8.10 years). Biological age was calculated using the KDM-BA and PhenoAge algorithms based on blood biomarkers. Musculoskeletal disorders were diagnosed using the ICD-10 criteria, with sample sizes ranging from 1,182 to 23,668. Logistic regression assessed cross-sectional associations between age acceleration (AA) metrics and musculoskeletal disorders. Accelerated Failure Time (AFT) model was used for survival analysis to evaluate the relationships between AAs and musculoskeletal disorders onset. Models were adjusted for demographic, lifestyle, and socio-economic covariates. The threshold of P-values were set by the Holm-Bonferroni correction. Cross-sectional analyses reveal significant associations between AAs and fourteen musculoskeletal disorders. Survival analyses indicate that AAs significantly accelerate the onset of nine musculoskeletal disorders, including inflammatory polyarthropathies (RTKDM-BA = 0.993; RTPhenoAge = 0.983), systemic connective tissue disorders (RTKDM-BA = 0.987; RTPhenoAge = 0.980), spondylopathies (RTPhenoAge= 0.994), disorders of bone density and structure (RTPhenoAge= 0.991), gout (RTPhenoAge= 0.968), arthritis (RTPhenoAge= 0.991), pain in joint (RTPhenoAge= 0.989), low back pain (RTPhenoAge= 0.986), and osteoporosis (RTPhenoAge= 0.994). Sensitivity analyses are consistent with the primary findings. Sex-specific variations are observed, with AAs accelerating spondylopathies, arthritis, and low back pain in females, while osteoporosis is accelerated in males. Accelerated biological aging is significantly associated with the incidence of several musculoskeletal disorders. These insights highlight the importance of biological age assessments in gauging musculoskeletal disorder risk, aiding early detection, prevention, and management. As we age, our bodies experience changes that can lead to health problems, including musculoskeletal disorders such as arthritis and back pain. This study explores how biological aging, a measure of how old our bodies seem based on biomarkers, affects the risk of developing these disorders. Using data from over 170,000 people, we found that faster biological aging is linked to an increased risk of several musculoskeletal disorders, and that these risks can vary between men and women. These findings could help identify people at risk earlier, leading to better prevention and treatment strategies. Wei et al. investigate the link between accelerated biological aging and the risk of musculoskeletal disorders, highlighting sex-related disparities. Age acceleration significantly increases the risk and onset of nine musculoskeletal disorders, with notable differences betw","PeriodicalId":72646,"journal":{"name":"Communications medicine","volume":" ","pages":"1-8"},"PeriodicalIF":5.4,"publicationDate":"2024-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s43856-024-00706-5.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142856885","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-12-16DOI: 10.1038/s43856-024-00637-1
Phong B. H. Nguyen, Daniel Garger, Diyuan Lu, Haifa Maalmi, Holger Prokisch, Barbara Thorand, Jerzy Adamski, Gabi Kastenmüller, Melanie Waldenberger, Christian Gieger, Annette Peters, Karsten Suhre, Gidon J. Bönhof, Wolfgang Rathmann, Michael Roden, Harald Grallert, Dan Ziegler, Christian Herder, Michael P. Menden
Distal sensorimotor polyneuropathy (DSPN) is a common neurological disorder in elderly adults and people with obesity, prediabetes and diabetes and is associated with high morbidity and premature mortality. DSPN is a multifactorial disease and not fully understood yet. Here, we developed the Interpretable Multimodal Machine Learning (IMML) framework for predicting DSPN prevalence and incidence based on sparse multimodal data. Exploiting IMMLs interpretability further empowered biomarker identification. We leveraged the population-based KORA F4/FF4 cohort including 1091 participants and their deep multimodal characterisation, i.e. clinical data, genomics, methylomics, transcriptomics, proteomics, inflammatory proteins and metabolomics. Clinical data alone is sufficient to stratify individuals with and without DSPN (AUROC = 0.752), whilst predicting DSPN incidence 6.5 ± 0.2 years later strongly benefits from clinical data complemented with two or more molecular modalities (improved ΔAUROC > 0.1, achieved AUROC of 0.714). Important and interpretable features of incident DSPN prediction include up-regulation of proinflammatory cytokines, down-regulation of SUMOylation pathway and essential fatty acids, thus yielding novel insights in the disease pathophysiology. These may become biomarkers for incident DSPN, guide prevention strategies and serve as proof of concept for the utility of IMML in studying complex diseases. Distal sensorimotor polyneuropathy (DSPN) is a common neurological disorder in elderly adults and people with obesity, prediabetes, and diabetes in which there is tingling or numbness with or without pain. It is not fully understood why it develops. We developed a computational method that uses various sources of information to enable people with DSPN to be identified and also to predict which people might develop DSPN in the future. Further development of our method might provide additional information that can be used to prevent development of DSPN in people with obesity, prediabetes, and diabetes. Also, our method could potentially be adapted to enable other complex diseases to be better understood. Nguyen et al. present IMML, an interpretable multimodal machine learning framework that utilizes prior biological knowledge, integrating multiomic and clinical data. IMML successfully predicts and identifies putative modifiable biomarkers for incident distal sensorimotor polyneuropathy.
{"title":"Interpretable multimodal machine learning (IMML) framework reveals pathological signatures of distal sensorimotor polyneuropathy","authors":"Phong B. H. Nguyen, Daniel Garger, Diyuan Lu, Haifa Maalmi, Holger Prokisch, Barbara Thorand, Jerzy Adamski, Gabi Kastenmüller, Melanie Waldenberger, Christian Gieger, Annette Peters, Karsten Suhre, Gidon J. Bönhof, Wolfgang Rathmann, Michael Roden, Harald Grallert, Dan Ziegler, Christian Herder, Michael P. Menden","doi":"10.1038/s43856-024-00637-1","DOIUrl":"10.1038/s43856-024-00637-1","url":null,"abstract":"Distal sensorimotor polyneuropathy (DSPN) is a common neurological disorder in elderly adults and people with obesity, prediabetes and diabetes and is associated with high morbidity and premature mortality. DSPN is a multifactorial disease and not fully understood yet. Here, we developed the Interpretable Multimodal Machine Learning (IMML) framework for predicting DSPN prevalence and incidence based on sparse multimodal data. Exploiting IMMLs interpretability further empowered biomarker identification. We leveraged the population-based KORA F4/FF4 cohort including 1091 participants and their deep multimodal characterisation, i.e. clinical data, genomics, methylomics, transcriptomics, proteomics, inflammatory proteins and metabolomics. Clinical data alone is sufficient to stratify individuals with and without DSPN (AUROC = 0.752), whilst predicting DSPN incidence 6.5 ± 0.2 years later strongly benefits from clinical data complemented with two or more molecular modalities (improved ΔAUROC > 0.1, achieved AUROC of 0.714). Important and interpretable features of incident DSPN prediction include up-regulation of proinflammatory cytokines, down-regulation of SUMOylation pathway and essential fatty acids, thus yielding novel insights in the disease pathophysiology. These may become biomarkers for incident DSPN, guide prevention strategies and serve as proof of concept for the utility of IMML in studying complex diseases. Distal sensorimotor polyneuropathy (DSPN) is a common neurological disorder in elderly adults and people with obesity, prediabetes, and diabetes in which there is tingling or numbness with or without pain. It is not fully understood why it develops. We developed a computational method that uses various sources of information to enable people with DSPN to be identified and also to predict which people might develop DSPN in the future. Further development of our method might provide additional information that can be used to prevent development of DSPN in people with obesity, prediabetes, and diabetes. Also, our method could potentially be adapted to enable other complex diseases to be better understood. Nguyen et al. present IMML, an interpretable multimodal machine learning framework that utilizes prior biological knowledge, integrating multiomic and clinical data. IMML successfully predicts and identifies putative modifiable biomarkers for incident distal sensorimotor polyneuropathy.","PeriodicalId":72646,"journal":{"name":"Communications medicine","volume":" ","pages":"1-12"},"PeriodicalIF":5.4,"publicationDate":"2024-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s43856-024-00637-1.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142826477","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-12-11DOI: 10.1038/s43856-024-00696-4
John Karlsson Valik, Christian G. Giske, Badrul Hasan, Mónica Gozalo-Margüello, Luis Martínez-Martínez, Manica Mueller Premru, Žiga Martinčič, Bojana Beović, Sofia Maraki, Maria Zacharioudaki, Diamantis Kofteridis, Kate McCarthy, David Paterson, Marina de Cueto, Isabel Morales, Leonard Leibovici, Tanya Babich, Fredrik Granath, Jesús Rodríguez-Baño, Antonio Oliver, Dafna Yahav, Pontus Nauclér
Pseudomonas aeruginosa (PA) bloodstream infection (BSI) is a common healthcare-associated complication linked to antimicrobial resistance and high mortality. Ongoing clinical trials are exploring novel anti-virulence agents, yet studies on how bacterial virulence affects PA infection outcomes is conflicting and data from real-world clinical populations is limited. We studied a multicentre cohort of 773 adult patients with PA BSI consecutively collected during 7-years from sites in Europe and Australia. Comprehensive clinical data and whole-genome sequencing of all bacterial strains were obtained. Based on the virulence genotype, we identify several virulence clusters, each showing varying proportions of multidrug-resistant phenotypes. Genes tied to biofilm synthesis and epidemic clones ST175 and ST235 are associated with mortality, while the type III secretion system is associated with septic shock. Adding genomic biomarkers to machine learning models based on clinical data indicates improved prediction of severe outcomes in PA BSI patients. These findings suggest that virulence markers provide prognostic information with potential applications in guiding adjuvant sepsis treatments. Pseudomonas aeruginosa bacteria are often found in the hospital environment, primarily infecting vulnerable patients with underlying health conditions. Due to antibiotic resistance, which occurs when bacteria are not killed by antibiotic treatment, these infections are often difficult to treat, and death rates are high. In this study, we analyzed data from patients in Europe and Australia with bloodstream infections to understand how bacterial traits affect patient outcomes. Using genetic information from the bacteria, we identified characteristics associated with antibiotic resistance. In addition, we found certain bacterial traits, such as the ability to synthesize toxins and biofilms, were linked to disease severity and mortality risk. These findings indicate that specific characteristics of P. aeruginosa may influence the severity of infection and could be targeted in newly developed treatments. Valik et al. incorporate genomic biomarkers from Pseudomonas aeruginosa isolates and clinical data into machine-learning models to predict the severity of bloodstream infection. Bacterial virulence markers identified through whole genome sequencing offer prognostic insights that could inform treatment strategies in sepsis management.
{"title":"Genomic virulence markers are associated with severe outcomes in patients with Pseudomonas aeruginosa bloodstream infection","authors":"John Karlsson Valik, Christian G. Giske, Badrul Hasan, Mónica Gozalo-Margüello, Luis Martínez-Martínez, Manica Mueller Premru, Žiga Martinčič, Bojana Beović, Sofia Maraki, Maria Zacharioudaki, Diamantis Kofteridis, Kate McCarthy, David Paterson, Marina de Cueto, Isabel Morales, Leonard Leibovici, Tanya Babich, Fredrik Granath, Jesús Rodríguez-Baño, Antonio Oliver, Dafna Yahav, Pontus Nauclér","doi":"10.1038/s43856-024-00696-4","DOIUrl":"10.1038/s43856-024-00696-4","url":null,"abstract":"Pseudomonas aeruginosa (PA) bloodstream infection (BSI) is a common healthcare-associated complication linked to antimicrobial resistance and high mortality. Ongoing clinical trials are exploring novel anti-virulence agents, yet studies on how bacterial virulence affects PA infection outcomes is conflicting and data from real-world clinical populations is limited. We studied a multicentre cohort of 773 adult patients with PA BSI consecutively collected during 7-years from sites in Europe and Australia. Comprehensive clinical data and whole-genome sequencing of all bacterial strains were obtained. Based on the virulence genotype, we identify several virulence clusters, each showing varying proportions of multidrug-resistant phenotypes. Genes tied to biofilm synthesis and epidemic clones ST175 and ST235 are associated with mortality, while the type III secretion system is associated with septic shock. Adding genomic biomarkers to machine learning models based on clinical data indicates improved prediction of severe outcomes in PA BSI patients. These findings suggest that virulence markers provide prognostic information with potential applications in guiding adjuvant sepsis treatments. Pseudomonas aeruginosa bacteria are often found in the hospital environment, primarily infecting vulnerable patients with underlying health conditions. Due to antibiotic resistance, which occurs when bacteria are not killed by antibiotic treatment, these infections are often difficult to treat, and death rates are high. In this study, we analyzed data from patients in Europe and Australia with bloodstream infections to understand how bacterial traits affect patient outcomes. Using genetic information from the bacteria, we identified characteristics associated with antibiotic resistance. In addition, we found certain bacterial traits, such as the ability to synthesize toxins and biofilms, were linked to disease severity and mortality risk. These findings indicate that specific characteristics of P. aeruginosa may influence the severity of infection and could be targeted in newly developed treatments. Valik et al. incorporate genomic biomarkers from Pseudomonas aeruginosa isolates and clinical data into machine-learning models to predict the severity of bloodstream infection. Bacterial virulence markers identified through whole genome sequencing offer prognostic insights that could inform treatment strategies in sepsis management.","PeriodicalId":72646,"journal":{"name":"Communications medicine","volume":" ","pages":"1-14"},"PeriodicalIF":5.4,"publicationDate":"2024-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s43856-024-00696-4.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142798573","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-12-10DOI: 10.1038/s43856-024-00691-9
Madhav Chaturvedi, Denise Köster, Patrick M. Bossuyt, Oke Gerke, Annette Jurke, Mirjam E. Kretzschmar, Marc Lütgehetmann, Rafael Mikolajczyk, Johannes B. Reitsma, Nicole Schneiderhan-Marra, Uwe Siebert, Carina Stekly, Christoph Ehret, Nicole Rübsamen, André Karch, Antonia Zapf
Evaluating diagnostic test accuracy during epidemics is difficult due to an urgent need for test availability, changing disease prevalence and pathogen characteristics, and constantly evolving testing aims and applications. Based on lessons learned during the SARS-CoV-2 pandemic, we introduce a framework for rapid diagnostic test development, evaluation, and validation during outbreaks of emerging infections. The framework is based on the feedback loop between test accuracy evaluation, modelling studies for public health decision-making, and impact of public health interventions. We suggest that building on this feedback loop can help future diagnostic test evaluation platforms better address the requirements of both patient care and public health. Chaturvedi, Köster et al. discuss challenges faced by studies evaluating tests for emerging infectious agents. They propose a unified framework for test development, evaluation, and validation based on the feedback loop between test accuracy evaluation, use of accuracy estimates in modelling studies, and interventions based on modelling results.
{"title":"A unified framework for diagnostic test development and evaluation during outbreaks of emerging infections","authors":"Madhav Chaturvedi, Denise Köster, Patrick M. Bossuyt, Oke Gerke, Annette Jurke, Mirjam E. Kretzschmar, Marc Lütgehetmann, Rafael Mikolajczyk, Johannes B. Reitsma, Nicole Schneiderhan-Marra, Uwe Siebert, Carina Stekly, Christoph Ehret, Nicole Rübsamen, André Karch, Antonia Zapf","doi":"10.1038/s43856-024-00691-9","DOIUrl":"10.1038/s43856-024-00691-9","url":null,"abstract":"Evaluating diagnostic test accuracy during epidemics is difficult due to an urgent need for test availability, changing disease prevalence and pathogen characteristics, and constantly evolving testing aims and applications. Based on lessons learned during the SARS-CoV-2 pandemic, we introduce a framework for rapid diagnostic test development, evaluation, and validation during outbreaks of emerging infections. The framework is based on the feedback loop between test accuracy evaluation, modelling studies for public health decision-making, and impact of public health interventions. We suggest that building on this feedback loop can help future diagnostic test evaluation platforms better address the requirements of both patient care and public health. Chaturvedi, Köster et al. discuss challenges faced by studies evaluating tests for emerging infectious agents. They propose a unified framework for test development, evaluation, and validation based on the feedback loop between test accuracy evaluation, use of accuracy estimates in modelling studies, and interventions based on modelling results.","PeriodicalId":72646,"journal":{"name":"Communications medicine","volume":" ","pages":"1-11"},"PeriodicalIF":5.4,"publicationDate":"2024-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s43856-024-00691-9.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142798581","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-12-10DOI: 10.1038/s43856-024-00690-w
Lara Blömeke, Fabian Rehn, Marlene Pils, Victoria Kraemer-Schulien, Anneliese Cousin, Janine Kutzsche, Tuyen Bujnicki, Silka D. Freiesleben, Luisa-Sophie Schneider, Lukas Preis, Josef Priller, Eike J. Spruth, Slawek Altenstein, Anja Schneider, Klaus Fliessbach, Jens Wiltfang, Niels Hansen, Ayda Rostamzadeh, Emrah Düzel, Wenzel Glanz, Enise I. Incesoy, Katharina Buerger, Daniel Janowitz, Michael Ewers, Robert Perneczky, Boris-Stephan Rauchmann, Stefan Teipel, Ingo Kilimann, Christoph Laske, Matthias H. Munk, Annika Spottke, Nina Roy, Michael T. Heneka, Frederic Brosseron, Michael Wagner, Sandra Roeske, Alfredo Ramirez, Matthias Schmid, Frank Jessen, Oliver Bannach, Oliver Peters, Dieter Willbold
Quantification of Amyloid beta (Aβ) oligomers in plasma enables early diagnosis of Alzheimer’s Disease (AD) and improves our understanding of underlying pathologies. However, quantification necessitates an extremely sensitive and selective technology because of very low Aβ oligomer concentrations and possible interference from matrix components. In this report, we developed and validated a surface-based fluorescence distribution analysis (sFIDA) assay for quantification of Aβ oligomers in plasma. The blood-based sFIDA assay delivers a sensitivity of 1.8 fM, an inter- and intra-assay variation below 20% for oligomer calibration standards and no interference with matrix components. Quantification of Aβ oligomers in 359 plasma samples from the DELCODE cohort reveals lower oligomer concentrations in subjective cognitive decline and AD patients than healthy Control participants. Correlation analysis between CSF and plasma oligomer concentrations indicates an impaired clearance of Aβ oligomers that is dependent on the ApoE ε4 status. People with Alzheimer’s disease have difficulties with reasoning and communication. In Alzheimer’s disease, small proteins called amyloid beta (Aβ) stick together, forming tiny clusters in the brain that eventually grow larger. In this study, we aimed to measure these clusters in the blood. When we tested our method on blood samples from 359 people, we surprisingly found that people with Alzheimer’s disease and memory problems had fewer clusters of Aβ compared to healthy individuals. Our finding suggests that genetic factors may influence the body’s ability to clear these clusters from the brain. Bloemeke et al. develop a method for measuring Aβ oligomers in plasma to diagnose Alzheimer’s Disease. Correlation analysis between cerebral spinal fluid and plasma oligomer concentrations indicates an impaired clearance of Aβ oligomers that is dependent on ApoE ε4 status.
{"title":"Blood-based quantification of Aβ oligomers indicates impaired clearance from brain in ApoE ε4 positive subjects","authors":"Lara Blömeke, Fabian Rehn, Marlene Pils, Victoria Kraemer-Schulien, Anneliese Cousin, Janine Kutzsche, Tuyen Bujnicki, Silka D. Freiesleben, Luisa-Sophie Schneider, Lukas Preis, Josef Priller, Eike J. Spruth, Slawek Altenstein, Anja Schneider, Klaus Fliessbach, Jens Wiltfang, Niels Hansen, Ayda Rostamzadeh, Emrah Düzel, Wenzel Glanz, Enise I. Incesoy, Katharina Buerger, Daniel Janowitz, Michael Ewers, Robert Perneczky, Boris-Stephan Rauchmann, Stefan Teipel, Ingo Kilimann, Christoph Laske, Matthias H. Munk, Annika Spottke, Nina Roy, Michael T. Heneka, Frederic Brosseron, Michael Wagner, Sandra Roeske, Alfredo Ramirez, Matthias Schmid, Frank Jessen, Oliver Bannach, Oliver Peters, Dieter Willbold","doi":"10.1038/s43856-024-00690-w","DOIUrl":"10.1038/s43856-024-00690-w","url":null,"abstract":"Quantification of Amyloid beta (Aβ) oligomers in plasma enables early diagnosis of Alzheimer’s Disease (AD) and improves our understanding of underlying pathologies. However, quantification necessitates an extremely sensitive and selective technology because of very low Aβ oligomer concentrations and possible interference from matrix components. In this report, we developed and validated a surface-based fluorescence distribution analysis (sFIDA) assay for quantification of Aβ oligomers in plasma. The blood-based sFIDA assay delivers a sensitivity of 1.8 fM, an inter- and intra-assay variation below 20% for oligomer calibration standards and no interference with matrix components. Quantification of Aβ oligomers in 359 plasma samples from the DELCODE cohort reveals lower oligomer concentrations in subjective cognitive decline and AD patients than healthy Control participants. Correlation analysis between CSF and plasma oligomer concentrations indicates an impaired clearance of Aβ oligomers that is dependent on the ApoE ε4 status. People with Alzheimer’s disease have difficulties with reasoning and communication. In Alzheimer’s disease, small proteins called amyloid beta (Aβ) stick together, forming tiny clusters in the brain that eventually grow larger. In this study, we aimed to measure these clusters in the blood. When we tested our method on blood samples from 359 people, we surprisingly found that people with Alzheimer’s disease and memory problems had fewer clusters of Aβ compared to healthy individuals. Our finding suggests that genetic factors may influence the body’s ability to clear these clusters from the brain. Bloemeke et al. develop a method for measuring Aβ oligomers in plasma to diagnose Alzheimer’s Disease. Correlation analysis between cerebral spinal fluid and plasma oligomer concentrations indicates an impaired clearance of Aβ oligomers that is dependent on ApoE ε4 status.","PeriodicalId":72646,"journal":{"name":"Communications medicine","volume":" ","pages":"1-13"},"PeriodicalIF":5.4,"publicationDate":"2024-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s43856-024-00690-w.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142798582","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-12-09DOI: 10.1038/s43856-024-00683-9
Elza Rechtman, Michelle A. Rodriguez, Elena Colicino, Christopher J. Hahn, Esmeralda Navarro, Azzurra Invernizzi, Christopher R. Dasaro, Susan L. Teitelbaum, Andrew C. Todd, Megan K. Horton
In the aftermath of the World Trade Center (WTC) attack on 11 September 2001, rescue and recovery workers faced hazardous conditions and toxic agents. Prior research linked these exposures to adverse health effects, but mainly examined individual factors, overlooking complex mixture effects. This study applies an exposomic approach encompassing the totality of responders’ experience, defined as the WTC exposome. We analyzed data from 34,096 members of the WTC Health Program General Responder, including mental and physical health, occupational history, traumatic and environmental exposures using generalized weighted quantile sum regression. We find a statistically significant association between the exposure mixture index and all investigated health outcomes. Factors identified as risk factors include working in an enclosed heavily contaminated area, construction occupation, and exposure to blood and body fluids. Conversely, full-time employment emerged as a protective factor. This exposomics study emphasizes the importance of considering combined exposures for the identification of harmful and protective factors within the WTC exposome, providing valuable insights for targeted interventions and preventive measures. In an era marked by more frequent and severe natural disasters due to the evolving climate crisis, the exposomic framework is a promising tool for disaster preparedness. After the 9/11 World Trade Center attacks, rescue and recovery workers were exposed to many harmful substances and hazardous conditions. We assessed all aspects of these exposures and compared these with responders’ health and work history. We found that exposure to harmful substances and hazardous conditions was associated with all investigated health problems. Working in contaminated areas, in construction, or with blood and body fluids increased health risks. However, being employed full-time seemed to protect against some health issues. This research highlights the need to consider all things that rescue and recovery workers are exposed to during disasters to better understand and prevent health problems during and after future events. Rechtman et al. analyse the exposome of rescue and recovery workers who attended the World Trade Center on and after September 11, 2001. Mental and physical health outcomes associate with all aspects of occupational history, as well as traumatic and environmental exposures.
{"title":"The World Trade Center exposome and health effects in 9/11 rescue and recovery workers","authors":"Elza Rechtman, Michelle A. Rodriguez, Elena Colicino, Christopher J. Hahn, Esmeralda Navarro, Azzurra Invernizzi, Christopher R. Dasaro, Susan L. Teitelbaum, Andrew C. Todd, Megan K. Horton","doi":"10.1038/s43856-024-00683-9","DOIUrl":"10.1038/s43856-024-00683-9","url":null,"abstract":"In the aftermath of the World Trade Center (WTC) attack on 11 September 2001, rescue and recovery workers faced hazardous conditions and toxic agents. Prior research linked these exposures to adverse health effects, but mainly examined individual factors, overlooking complex mixture effects. This study applies an exposomic approach encompassing the totality of responders’ experience, defined as the WTC exposome. We analyzed data from 34,096 members of the WTC Health Program General Responder, including mental and physical health, occupational history, traumatic and environmental exposures using generalized weighted quantile sum regression. We find a statistically significant association between the exposure mixture index and all investigated health outcomes. Factors identified as risk factors include working in an enclosed heavily contaminated area, construction occupation, and exposure to blood and body fluids. Conversely, full-time employment emerged as a protective factor. This exposomics study emphasizes the importance of considering combined exposures for the identification of harmful and protective factors within the WTC exposome, providing valuable insights for targeted interventions and preventive measures. In an era marked by more frequent and severe natural disasters due to the evolving climate crisis, the exposomic framework is a promising tool for disaster preparedness. After the 9/11 World Trade Center attacks, rescue and recovery workers were exposed to many harmful substances and hazardous conditions. We assessed all aspects of these exposures and compared these with responders’ health and work history. We found that exposure to harmful substances and hazardous conditions was associated with all investigated health problems. Working in contaminated areas, in construction, or with blood and body fluids increased health risks. However, being employed full-time seemed to protect against some health issues. This research highlights the need to consider all things that rescue and recovery workers are exposed to during disasters to better understand and prevent health problems during and after future events. Rechtman et al. analyse the exposome of rescue and recovery workers who attended the World Trade Center on and after September 11, 2001. Mental and physical health outcomes associate with all aspects of occupational history, as well as traumatic and environmental exposures.","PeriodicalId":72646,"journal":{"name":"Communications medicine","volume":" ","pages":"1-7"},"PeriodicalIF":5.4,"publicationDate":"2024-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s43856-024-00683-9.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142798609","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-12-06DOI: 10.1038/s43856-024-00689-3
Mridula Shankar, A. Metin Gülmezoglu, Joshua P. Vogel, Shivaprasad S. Goudar, Annie McDougall, Manjunath S. Somannavar, Sara Rushwan, Yeshita V. Pujar, Umesh Charantimath, Anne Ammerdorffer, Meghan A. Bohren
{"title":"Author Correction: Eliminating gender bias in biomedical research requires fair inclusion of pregnant women and gender diverse people","authors":"Mridula Shankar, A. Metin Gülmezoglu, Joshua P. Vogel, Shivaprasad S. Goudar, Annie McDougall, Manjunath S. Somannavar, Sara Rushwan, Yeshita V. Pujar, Umesh Charantimath, Anne Ammerdorffer, Meghan A. Bohren","doi":"10.1038/s43856-024-00689-3","DOIUrl":"10.1038/s43856-024-00689-3","url":null,"abstract":"","PeriodicalId":72646,"journal":{"name":"Communications medicine","volume":" ","pages":"1-1"},"PeriodicalIF":5.4,"publicationDate":"2024-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11624188/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142792847","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-12-05DOI: 10.1038/s43856-024-00676-8
David Y. Lo, Boyukkhanim Ahmadzada, MacKenna A. Stachel, Melia Schaefer, Usman Ashraf, John I. Wagner, Ethan J. Vanderslice, Madie Tornquist, Kendra Mariakis, Peggy Halsten, Christopher D. Lindsay, Emily C. Beck, Scott L. Nyberg, Jeffrey J. Ross
End-stage renal disease is a growing global health issue, disproportionately impacting low- and middle-income countries. While kidney transplantation remains the best treatment for end-stage renal disease, access to this treatment modality is limited by chronic donor organ shortages. To address this critical need, we are developing transplantable bioengineered kidney grafts. Podocyte differentiation was achieved in adherent monoculture through Wnt and TGF-β inhibition with IWR-1 and SB431542, respectively. Podocytes along with endothelial cells were then used to recapitulate glomeruli within decellularized porcine kidney scaffolds to generate bioengineered kidneys grafts. These bioengineered kidney grafts were functionally assessed via normothermic perfusion which compared kidney grafts recellularized with only endothelial cells as a control to bi-culture kidney grafts comprised of endothelial cells and podocytes. Heterotopic implantation further tested bi-culture kidney graft function over 3 successive implant sessions with 1–2 grafts per session. We demonstrate the ability to source primary human podocytes at scale. Decellularized porcine kidney grafts repopulated with podocytes and endothelial cells exhibit native glomerular structure and display blood filtration capabilities during normothermic perfusion testing. Extending these findings to a clinically relevant model, bioengineered kidneys produce urine with indices of filtration when heterotopically implanted in pigs. Our results showcase a human-scale, transplantable bioengineered kidney capable of performing requisite filtration function. This study reinforces the possibility for the bioengineering of transplantable human kidneys, which could someday provide increased and more equitable access to kidney grafts for the treatment of end-stage renal disease. End-stage renal disease is a growing global health issue and while kidney transplantation remains the best treatment option, access to kidney grafts is limited by chronic donor organ shortages. To address this critical need, we are developing transplantable bioengineered kidney grafts. Our bioengineered kidneys are generated by first removing all cellular material from pig kidneys followed by delivery of human cells to appropriate sites within the pig kidneys. We show that our bioengineered kidneys carry out essential kidney functions in being able to filter blood and produce urine. This is a promising step toward the development of a bioengineered kidney, which has future potential to provide widespread access to kidney grafts for the treatment of end-stage renal disease. Lo et al. implanted decellularized porcine kidney grafts repopulated with podocytes and endothelial cells into pigs. The bioengineered kidneys have glomerular structure and filter blood during normothermic perfusion testing.
{"title":"Transplantation of decellularized porcine kidney grafts repopulated with primary human cells demonstrates filtration function in pigs","authors":"David Y. Lo, Boyukkhanim Ahmadzada, MacKenna A. Stachel, Melia Schaefer, Usman Ashraf, John I. Wagner, Ethan J. Vanderslice, Madie Tornquist, Kendra Mariakis, Peggy Halsten, Christopher D. Lindsay, Emily C. Beck, Scott L. Nyberg, Jeffrey J. Ross","doi":"10.1038/s43856-024-00676-8","DOIUrl":"10.1038/s43856-024-00676-8","url":null,"abstract":"End-stage renal disease is a growing global health issue, disproportionately impacting low- and middle-income countries. While kidney transplantation remains the best treatment for end-stage renal disease, access to this treatment modality is limited by chronic donor organ shortages. To address this critical need, we are developing transplantable bioengineered kidney grafts. Podocyte differentiation was achieved in adherent monoculture through Wnt and TGF-β inhibition with IWR-1 and SB431542, respectively. Podocytes along with endothelial cells were then used to recapitulate glomeruli within decellularized porcine kidney scaffolds to generate bioengineered kidneys grafts. These bioengineered kidney grafts were functionally assessed via normothermic perfusion which compared kidney grafts recellularized with only endothelial cells as a control to bi-culture kidney grafts comprised of endothelial cells and podocytes. Heterotopic implantation further tested bi-culture kidney graft function over 3 successive implant sessions with 1–2 grafts per session. We demonstrate the ability to source primary human podocytes at scale. Decellularized porcine kidney grafts repopulated with podocytes and endothelial cells exhibit native glomerular structure and display blood filtration capabilities during normothermic perfusion testing. Extending these findings to a clinically relevant model, bioengineered kidneys produce urine with indices of filtration when heterotopically implanted in pigs. Our results showcase a human-scale, transplantable bioengineered kidney capable of performing requisite filtration function. This study reinforces the possibility for the bioengineering of transplantable human kidneys, which could someday provide increased and more equitable access to kidney grafts for the treatment of end-stage renal disease. End-stage renal disease is a growing global health issue and while kidney transplantation remains the best treatment option, access to kidney grafts is limited by chronic donor organ shortages. To address this critical need, we are developing transplantable bioengineered kidney grafts. Our bioengineered kidneys are generated by first removing all cellular material from pig kidneys followed by delivery of human cells to appropriate sites within the pig kidneys. We show that our bioengineered kidneys carry out essential kidney functions in being able to filter blood and produce urine. This is a promising step toward the development of a bioengineered kidney, which has future potential to provide widespread access to kidney grafts for the treatment of end-stage renal disease. Lo et al. implanted decellularized porcine kidney grafts repopulated with podocytes and endothelial cells into pigs. The bioengineered kidneys have glomerular structure and filter blood during normothermic perfusion testing.","PeriodicalId":72646,"journal":{"name":"Communications medicine","volume":" ","pages":"1-11"},"PeriodicalIF":5.4,"publicationDate":"2024-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11621697/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142787960","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-12-04DOI: 10.1038/s43856-024-00657-x
Terence Stephenson, Snehal M. Pinto Pereira, Manjula D. Nugawela, Emma Dalrymple, Anthony Harnden, Elizabeth Whittaker, Isobel Heyman, Tamsin Ford, Terry Segal, Trudie Chalder, Shamez N. Ladhani, Kelsey McOwat, Ruth Simmons, Laila Xu, Lana Fox-Smith, CLoCk Consortium, Roz Shafran
Some children and young people (CYP) infected with SARS-COV-2 experience impairing symptoms post-infection, known as post-COVID-19 condition (PCC). Using data from the National Long COVID in Children and Young People (CloCk) study, we report symptoms and their impact up to 24-months post-infection. CloCk is a cohort of CYP in England aged 11-to-17-years when they had a SARS-CoV-2 PCR-test (between September 2020 and March 2021). Of 31,012 eligible CYP 24-months post-PCR test, 12,632 participated (response = 40.7%). CYP were grouped by infection status: ‘initial test-negatives; no subsequent positive-test’ (NN); ‘initial test-negatives; subsequent positive-test’ (NP); ‘initial test-positives; no reported re-infection’ (PN); and ‘initial test-positives; reported re-infection’ (PP). The Delphi research definition of PCC in CYP was operationalised; symptom severity/impact and validated scales (e.g., Chalder Fatigue Scale) were recorded. We examine symptom profiles 24-month post-index-test by infection status. 7.2% of CYP consistently fulfil the PCC definition at 3-, 6-, 12- and 24-months. These CYPs have a median of 5-to-6 symptoms at each time-point. Between 20% and 25% of all infection status groups report 3+ symptoms 24-months post-testing; 10–25% experience 5+ symptoms. The reinfected group has more symptoms than the other positive groups; the NN group has the lowest symptom burden (p < 0.001). PCC is more common in older CYPs and in the most deprived. Symptom severity/impact is higher in those fulfilling the PCC definition. The discrepancy in the proportion of CYP fulfilling the Delphi PCC definition at 24-months and those consistently fulfilling the definition across time, highlights the importance of longitudinal studies and the need to consider clinical impairment and range of symptoms. Some children and young people infected with SARS-COV-2 experience impairing symptoms long after infection; this is known as ‘Long COVID’. We used data from the Long COVID in Children and Young People (CloCk) study to describe symptoms and how much they impact children and young people’s lives 24-months post-infection. We found that 7.2% of children and young people consistently meet the ‘Long COVID’ research definition at 3-, 6-, 12- and 24-months post-infection. These children and young people reported around 5-to-6 symptoms at each time-point. Reinfected children and young people had more symptoms than children and young people who report one infection; those who report no infection had the lowest symptom burden. When researching Long COVID, we need to consider clinical impairment and the range of symptoms reported. Stephenson, Pinto Pereira et al. investigate the proportion of children and young people with Post Covid-19 condition 24-months post-infection. Only 7.2% meet the definition consistently at 3-, 6-, 12- and 24-months post-infection, highlighting the importance of longitudinal studies.
{"title":"A 24-month National Cohort Study examining long-term effects of COVID-19 in children and young people","authors":"Terence Stephenson, Snehal M. Pinto Pereira, Manjula D. Nugawela, Emma Dalrymple, Anthony Harnden, Elizabeth Whittaker, Isobel Heyman, Tamsin Ford, Terry Segal, Trudie Chalder, Shamez N. Ladhani, Kelsey McOwat, Ruth Simmons, Laila Xu, Lana Fox-Smith, CLoCk Consortium, Roz Shafran","doi":"10.1038/s43856-024-00657-x","DOIUrl":"10.1038/s43856-024-00657-x","url":null,"abstract":"Some children and young people (CYP) infected with SARS-COV-2 experience impairing symptoms post-infection, known as post-COVID-19 condition (PCC). Using data from the National Long COVID in Children and Young People (CloCk) study, we report symptoms and their impact up to 24-months post-infection. CloCk is a cohort of CYP in England aged 11-to-17-years when they had a SARS-CoV-2 PCR-test (between September 2020 and March 2021). Of 31,012 eligible CYP 24-months post-PCR test, 12,632 participated (response = 40.7%). CYP were grouped by infection status: ‘initial test-negatives; no subsequent positive-test’ (NN); ‘initial test-negatives; subsequent positive-test’ (NP); ‘initial test-positives; no reported re-infection’ (PN); and ‘initial test-positives; reported re-infection’ (PP). The Delphi research definition of PCC in CYP was operationalised; symptom severity/impact and validated scales (e.g., Chalder Fatigue Scale) were recorded. We examine symptom profiles 24-month post-index-test by infection status. 7.2% of CYP consistently fulfil the PCC definition at 3-, 6-, 12- and 24-months. These CYPs have a median of 5-to-6 symptoms at each time-point. Between 20% and 25% of all infection status groups report 3+ symptoms 24-months post-testing; 10–25% experience 5+ symptoms. The reinfected group has more symptoms than the other positive groups; the NN group has the lowest symptom burden (p < 0.001). PCC is more common in older CYPs and in the most deprived. Symptom severity/impact is higher in those fulfilling the PCC definition. The discrepancy in the proportion of CYP fulfilling the Delphi PCC definition at 24-months and those consistently fulfilling the definition across time, highlights the importance of longitudinal studies and the need to consider clinical impairment and range of symptoms. Some children and young people infected with SARS-COV-2 experience impairing symptoms long after infection; this is known as ‘Long COVID’. We used data from the Long COVID in Children and Young People (CloCk) study to describe symptoms and how much they impact children and young people’s lives 24-months post-infection. We found that 7.2% of children and young people consistently meet the ‘Long COVID’ research definition at 3-, 6-, 12- and 24-months post-infection. These children and young people reported around 5-to-6 symptoms at each time-point. Reinfected children and young people had more symptoms than children and young people who report one infection; those who report no infection had the lowest symptom burden. When researching Long COVID, we need to consider clinical impairment and the range of symptoms reported. Stephenson, Pinto Pereira et al. investigate the proportion of children and young people with Post Covid-19 condition 24-months post-infection. Only 7.2% meet the definition consistently at 3-, 6-, 12- and 24-months post-infection, highlighting the importance of longitudinal studies.","PeriodicalId":72646,"journal":{"name":"Communications medicine","volume":" ","pages":"1-12"},"PeriodicalIF":5.4,"publicationDate":"2024-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s43856-024-00657-x.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142762883","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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