Pub Date : 2024-09-19DOI: 10.1038/s43856-024-00607-7
Yi-Jiun Pan, Mei-Chen Lin, Jyh-Ming Liou, Chun-Chieh Fan, Mei-Hsin Su, Cheng-Yun Chen, Chi-Shin Wu, Pei-Chun Chen, Yen-Tsung Huang, Shi-Heng Wang
It has been proposed that having a psychiatric disorder could increase the risk of developing a gastrointestinal disorder, and vice versa. The role of familial coaggregation and shared genetic loading between psychiatric and gastrointestinal disorders remains unclear. This study used the Taiwan National Health Insurance Research Database; 4,504,612 individuals born 1970–1999 with parental information, 51,664 same-sex twins, and 3,322,959 persons with full-sibling(s) were enrolled. Genotyping was available for 106,796 unrelated participants from the Taiwan Biobank. A logistic regression model was used to examine the associations of individual history, affected relatives, and polygenic risk scores (PRS) for schizophrenia (SCZ), bipolar disorder (BPD), major depressive disorder (MDD), and obsessive-compulsive disorder (OCD), with the risk of peptic ulcer disease (PUD), gastroesophageal reflux disease (GERD), irritable bowel syndrome (IBS), and inflammatory bowel disease (IBD), and vice versa. Here we show that parental psychiatric disorders are associated with gastrointestinal disorders. Full-siblings of psychiatric cases have an increased risk of gastrointestinal disorders except for SCZ/BPD and IBD; the magnitude of coaggregation is higher in same-sex twins than in full-siblings. The results of bidirectional analyses mostly remain unchanged. PRS for SCZ, MDD, and OCD are associated with IBS, PUD/GERD/IBS/IBD, and PUD/GERD/IBS, respectively. PRS for PUD, GERD, IBS, and IBD are associated with MDD, BPD/MDD, SCZ/BPD/MDD, and BPD, respectively. There is familial coaggregation and shared genetic etiology between psychiatric and gastrointestinal comorbidity. Individuals with psychiatric disorder-affected relatives or with higher genetic risk for psychiatric disorders should be monitored for gastrointestinal disorders, and vice versa. It has been proposed that people with psychiatric disorders such as depression could have an increased chance of developing gastrointestinal disorders such as irritable bowel syndrome. We looked at whether this was the case in a large number of people from Taiwan. We found that people with a psychiatric disorder, or with relatives having a psychiatric disorder, were more likely to have gastrointestinal disorders, and vice versa. These findings suggest that people who have psychiatric disorders or have psychiatric disorder-affected relatives should be monitored for gastrointestinal disorders, and vice versa, to enable them to benefit from all the treatments they might need to improve their health. Pan et al. examine whether there is a shared pathophysiological mechanism underlying brain-gut comorbidity. This population-based cohort and biobank study demonstrates that there is familial coaggregation and shared genetic etiology between psychiatric and gastrointestinal comorbidity.
{"title":"A population-based study of familial coaggregation and shared genetic etiology of psychiatric and gastrointestinal disorders","authors":"Yi-Jiun Pan, Mei-Chen Lin, Jyh-Ming Liou, Chun-Chieh Fan, Mei-Hsin Su, Cheng-Yun Chen, Chi-Shin Wu, Pei-Chun Chen, Yen-Tsung Huang, Shi-Heng Wang","doi":"10.1038/s43856-024-00607-7","DOIUrl":"10.1038/s43856-024-00607-7","url":null,"abstract":"It has been proposed that having a psychiatric disorder could increase the risk of developing a gastrointestinal disorder, and vice versa. The role of familial coaggregation and shared genetic loading between psychiatric and gastrointestinal disorders remains unclear. This study used the Taiwan National Health Insurance Research Database; 4,504,612 individuals born 1970–1999 with parental information, 51,664 same-sex twins, and 3,322,959 persons with full-sibling(s) were enrolled. Genotyping was available for 106,796 unrelated participants from the Taiwan Biobank. A logistic regression model was used to examine the associations of individual history, affected relatives, and polygenic risk scores (PRS) for schizophrenia (SCZ), bipolar disorder (BPD), major depressive disorder (MDD), and obsessive-compulsive disorder (OCD), with the risk of peptic ulcer disease (PUD), gastroesophageal reflux disease (GERD), irritable bowel syndrome (IBS), and inflammatory bowel disease (IBD), and vice versa. Here we show that parental psychiatric disorders are associated with gastrointestinal disorders. Full-siblings of psychiatric cases have an increased risk of gastrointestinal disorders except for SCZ/BPD and IBD; the magnitude of coaggregation is higher in same-sex twins than in full-siblings. The results of bidirectional analyses mostly remain unchanged. PRS for SCZ, MDD, and OCD are associated with IBS, PUD/GERD/IBS/IBD, and PUD/GERD/IBS, respectively. PRS for PUD, GERD, IBS, and IBD are associated with MDD, BPD/MDD, SCZ/BPD/MDD, and BPD, respectively. There is familial coaggregation and shared genetic etiology between psychiatric and gastrointestinal comorbidity. Individuals with psychiatric disorder-affected relatives or with higher genetic risk for psychiatric disorders should be monitored for gastrointestinal disorders, and vice versa. It has been proposed that people with psychiatric disorders such as depression could have an increased chance of developing gastrointestinal disorders such as irritable bowel syndrome. We looked at whether this was the case in a large number of people from Taiwan. We found that people with a psychiatric disorder, or with relatives having a psychiatric disorder, were more likely to have gastrointestinal disorders, and vice versa. These findings suggest that people who have psychiatric disorders or have psychiatric disorder-affected relatives should be monitored for gastrointestinal disorders, and vice versa, to enable them to benefit from all the treatments they might need to improve their health. Pan et al. examine whether there is a shared pathophysiological mechanism underlying brain-gut comorbidity. This population-based cohort and biobank study demonstrates that there is familial coaggregation and shared genetic etiology between psychiatric and gastrointestinal comorbidity.","PeriodicalId":72646,"journal":{"name":"Communications medicine","volume":" ","pages":"1-11"},"PeriodicalIF":5.4,"publicationDate":"2024-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s43856-024-00607-7.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142273317","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 : 2024-09-11DOI: 10.1038/s43856-024-00600-0
Maxime Fajgenblat, Geert Molenberghs, Johan Verbeeck, Lander Willem, Jonas Crèvecoeur, Christel Faes, Niel Hens, Patrick Deboosere, Geert Verbeke, Thomas Neyens
Across Europe, countries have responded to the COVID-19 pandemic with a combination of non-pharmaceutical interventions and vaccination. Evaluating the effectiveness of such interventions is of particular relevance to policy-makers. We leverage almost three years of available data across 38 European countries to evaluate the effectiveness of governmental responses in controlling the pandemic. We developed a Bayesian hierarchical model that flexibly relates daily COVID-19 incidence to past levels of vaccination and non-pharmaceutical interventions as summarised in the Stringency Index. Specifically, we use a distributed lag approach to temporally weight past intervention values, a tensor-product smooth to capture non-linearities and interactions between both types of interventions, and a hierarchical approach to parsimoniously address heterogeneity across countries. We identify a pronounced negative association between daily incidence and the strength of non-pharmaceutical interventions, along with substantial heterogeneity in effectiveness among European countries. Similarly, we observe a strong but more consistent negative association with vaccination levels. Our results show that non-linear interactions shape the effectiveness of interventions, with non-pharmaceutical interventions becoming less effective under high vaccination levels. Finally, our results indicate that the effects of interventions on daily incidence are most pronounced at a lag of 14 days after being in place. Our Bayesian hierarchical modelling approach reveals clear negative and lagged effects of non-pharmaceutical interventions and vaccination on confirmed COVID-19 cases across European countries. As soon as COVID-19 hit Europe in early 2020, non-pharmaceutical interventions such as movement restrictions and social distancing were employed to contain the pandemic. Towards the end of 2020, vaccination was available and promoted as an additional defence. We analysed almost three years of public COVID-19 data to determine how effective both types of strategies were in containing the pandemic across 38 European countries. We developed a statistical model to relate confirmed cases to how strict non-pharmaceutical interventions were and to vaccination levels. Both non-pharmaceutical interventions and vaccination resulted in decreased confirmed cases, although variation exists among countries. When an intervention is applied, the effect on number of confirmed cases could be seen most about fourteen days after implementation. Fajgenblat et. al utilize almost three years of COVID-19 data to model consequences of interventions across Europe. They find that both non-pharmaceutical interventions and vaccination impact daily case rates, with the strongest effect at a lag of 14 days post-implementation.
{"title":"Evaluating the direct effect of vaccination and non-pharmaceutical interventions during the COVID-19 pandemic in Europe","authors":"Maxime Fajgenblat, Geert Molenberghs, Johan Verbeeck, Lander Willem, Jonas Crèvecoeur, Christel Faes, Niel Hens, Patrick Deboosere, Geert Verbeke, Thomas Neyens","doi":"10.1038/s43856-024-00600-0","DOIUrl":"10.1038/s43856-024-00600-0","url":null,"abstract":"Across Europe, countries have responded to the COVID-19 pandemic with a combination of non-pharmaceutical interventions and vaccination. Evaluating the effectiveness of such interventions is of particular relevance to policy-makers. We leverage almost three years of available data across 38 European countries to evaluate the effectiveness of governmental responses in controlling the pandemic. We developed a Bayesian hierarchical model that flexibly relates daily COVID-19 incidence to past levels of vaccination and non-pharmaceutical interventions as summarised in the Stringency Index. Specifically, we use a distributed lag approach to temporally weight past intervention values, a tensor-product smooth to capture non-linearities and interactions between both types of interventions, and a hierarchical approach to parsimoniously address heterogeneity across countries. We identify a pronounced negative association between daily incidence and the strength of non-pharmaceutical interventions, along with substantial heterogeneity in effectiveness among European countries. Similarly, we observe a strong but more consistent negative association with vaccination levels. Our results show that non-linear interactions shape the effectiveness of interventions, with non-pharmaceutical interventions becoming less effective under high vaccination levels. Finally, our results indicate that the effects of interventions on daily incidence are most pronounced at a lag of 14 days after being in place. Our Bayesian hierarchical modelling approach reveals clear negative and lagged effects of non-pharmaceutical interventions and vaccination on confirmed COVID-19 cases across European countries. As soon as COVID-19 hit Europe in early 2020, non-pharmaceutical interventions such as movement restrictions and social distancing were employed to contain the pandemic. Towards the end of 2020, vaccination was available and promoted as an additional defence. We analysed almost three years of public COVID-19 data to determine how effective both types of strategies were in containing the pandemic across 38 European countries. We developed a statistical model to relate confirmed cases to how strict non-pharmaceutical interventions were and to vaccination levels. Both non-pharmaceutical interventions and vaccination resulted in decreased confirmed cases, although variation exists among countries. When an intervention is applied, the effect on number of confirmed cases could be seen most about fourteen days after implementation. Fajgenblat et. al utilize almost three years of COVID-19 data to model consequences of interventions across Europe. They find that both non-pharmaceutical interventions and vaccination impact daily case rates, with the strongest effect at a lag of 14 days post-implementation.","PeriodicalId":72646,"journal":{"name":"Communications medicine","volume":" ","pages":"1-10"},"PeriodicalIF":5.4,"publicationDate":"2024-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s43856-024-00600-0.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142170351","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 : 2024-09-11DOI: 10.1038/s43856-024-00598-5
Lukas Heinlein, Roman C. Maron, Achim Hekler, Sarah Haggenmüller, Christoph Wies, Jochen S. Utikal, Friedegund Meier, Sarah Hobelsberger, Frank F. Gellrich, Mildred Sergon, Axel Hauschild, Lars E. French, Lucie Heinzerling, Justin G. Schlager, Kamran Ghoreschi, Max Schlaak, Franz J. Hilke, Gabriela Poch, Sören Korsing, Carola Berking, Markus V. Heppt, Michael Erdmann, Sebastian Haferkamp, Konstantin Drexler, Dirk Schadendorf, Wiebke Sondermann, Matthias Goebeler, Bastian Schilling, Eva Krieghoff-Henning, Titus J. Brinker
Early detection of melanoma, a potentially lethal type of skin cancer with high prevalence worldwide, improves patient prognosis. In retrospective studies, artificial intelligence (AI) has proven to be helpful for enhancing melanoma detection. However, there are few prospective studies confirming these promising results. Existing studies are limited by low sample sizes, too homogenous datasets, or lack of inclusion of rare melanoma subtypes, preventing a fair and thorough evaluation of AI and its generalizability, a crucial aspect for its application in the clinical setting. Therefore, we assessed “All Data are Ext” (ADAE), an established open-source ensemble algorithm for detecting melanomas, by comparing its diagnostic accuracy to that of dermatologists on a prospectively collected, external, heterogeneous test set comprising eight distinct hospitals, four different camera setups, rare melanoma subtypes, and special anatomical sites. We advanced the algorithm with real test-time augmentation (R-TTA, i.e., providing real photographs of lesions taken from multiple angles and averaging the predictions), and evaluated its generalization capabilities. Overall, the AI shows higher balanced accuracy than dermatologists (0.798, 95% confidence interval (CI) 0.779–0.814 vs. 0.781, 95% CI 0.760–0.802; p = 4.0e−145), obtaining a higher sensitivity (0.921, 95% CI 0.900–0.942 vs. 0.734, 95% CI 0.701–0.770; p = 3.3e−165) at the cost of a lower specificity (0.673, 95% CI 0.641–0.702 vs. 0.828, 95% CI 0.804–0.852; p = 3.3e−165). As the algorithm exhibits a significant performance advantage on our heterogeneous dataset exclusively comprising melanoma-suspicious lesions, AI may offer the potential to support dermatologists, particularly in diagnosing challenging cases. Melanoma is a type of skin cancer that can spread to other parts of the body, often resulting in death. Early detection improves survival rates. Computational tools that use artificial intelligence (AI) can be used to detect melanoma. However, few studies have checked how well the AI works on real-world data obtained from patients. We tested a previously developed AI tool on data obtained from eight different hospitals that used different types of cameras, which also included images taken of rare melanoma types and from a range of different parts of the body. The AI tool was more likely to correctly identify melanoma than dermatologists. This AI tool could be used to help dermatologists diagnose melanoma, particularly those that are difficult for dermatologists to diagnose. Heinlein, Maron, Hekler et al. evaluate an AI algorithm for detecting melanoma and compare its performance to that of dermatologist on a prospectively collected, external, heterogeneous dataset. The AI exhibits a significant performance advantage, especially in diagnosing challenging cases.
黑色素瘤是一种可能致命的皮肤癌，在全球发病率很高，早期发现黑色素瘤可改善患者的预后。在回顾性研究中，人工智能（AI）已被证明有助于提高黑色素瘤的检测率。然而，很少有前瞻性研究能证实这些令人鼓舞的结果。现有研究受到样本量少、数据集过于单一或未纳入罕见黑色素瘤亚型等因素的限制，无法对人工智能及其普适性进行公平、全面的评估，而这正是人工智能应用于临床的关键所在。因此，我们评估了 "All Data are Ext"（ADAE）--一种用于检测黑色素瘤的成熟开源集合算法，将其诊断准确性与皮肤科医生在前瞻性收集的外部异构测试集上的诊断准确性进行了比较，该测试集包括八家不同的医院、四种不同的相机设置、罕见黑色素瘤亚型和特殊解剖部位。我们利用真实测试时间增强（R-TTA，即提供从多个角度拍摄的病变真实照片并对预测结果求平均值）推进了该算法，并对其泛化能力进行了评估。总体而言，人工智能显示出比皮肤科医生更高的平衡准确度（0.798，95% 置信区间 (CI) 0.779-0.814 vs. 0.781，95% CI 0.760-0.802; p = 4.0e-145），获得更高的灵敏度（0.921，95% CI 0.900-0.942 vs. 0.734，95% CI 0.701-0.770; p = 3.3e-165），但特异性较低（0.673，95% CI 0.641-0.702 vs. 0.828，95% CI 0.804-0.852; p = 3.3e-165）。由于该算法在由黑色素瘤可疑病变组成的异构数据集上表现出显著的性能优势，因此人工智能有可能为皮肤科医生提供支持，尤其是在诊断具有挑战性的病例时。黑色素瘤是一种皮肤癌，可扩散到身体其他部位，往往导致死亡。早期发现可提高存活率。使用人工智能（AI）的计算工具可用于检测黑色素瘤。然而，很少有研究检验过人工智能在从患者那里获得的真实世界数据上的工作效果。我们对以前开发的人工智能工具进行了测试，测试的数据来自八家使用不同类型相机的医院，其中还包括罕见黑色素瘤类型和身体不同部位的图像。与皮肤科医生相比，人工智能工具更有可能正确识别黑色素瘤。这种人工智能工具可用于帮助皮肤科医生诊断黑色素瘤，尤其是那些皮肤科医生难以诊断的黑色素瘤。Heinlein、Maron、Hekler 等人评估了一种用于检测黑色素瘤的人工智能算法，并将其与皮肤科医生在前瞻性收集的外部异构数据集上的表现进行了比较。人工智能在性能上表现出明显的优势，尤其是在诊断具有挑战性的病例时。
{"title":"Prospective multicenter study using artificial intelligence to improve dermoscopic melanoma diagnosis in patient care","authors":"Lukas Heinlein, Roman C. Maron, Achim Hekler, Sarah Haggenmüller, Christoph Wies, Jochen S. Utikal, Friedegund Meier, Sarah Hobelsberger, Frank F. Gellrich, Mildred Sergon, Axel Hauschild, Lars E. French, Lucie Heinzerling, Justin G. Schlager, Kamran Ghoreschi, Max Schlaak, Franz J. Hilke, Gabriela Poch, Sören Korsing, Carola Berking, Markus V. Heppt, Michael Erdmann, Sebastian Haferkamp, Konstantin Drexler, Dirk Schadendorf, Wiebke Sondermann, Matthias Goebeler, Bastian Schilling, Eva Krieghoff-Henning, Titus J. Brinker","doi":"10.1038/s43856-024-00598-5","DOIUrl":"10.1038/s43856-024-00598-5","url":null,"abstract":"Early detection of melanoma, a potentially lethal type of skin cancer with high prevalence worldwide, improves patient prognosis. In retrospective studies, artificial intelligence (AI) has proven to be helpful for enhancing melanoma detection. However, there are few prospective studies confirming these promising results. Existing studies are limited by low sample sizes, too homogenous datasets, or lack of inclusion of rare melanoma subtypes, preventing a fair and thorough evaluation of AI and its generalizability, a crucial aspect for its application in the clinical setting. Therefore, we assessed “All Data are Ext” (ADAE), an established open-source ensemble algorithm for detecting melanomas, by comparing its diagnostic accuracy to that of dermatologists on a prospectively collected, external, heterogeneous test set comprising eight distinct hospitals, four different camera setups, rare melanoma subtypes, and special anatomical sites. We advanced the algorithm with real test-time augmentation (R-TTA, i.e., providing real photographs of lesions taken from multiple angles and averaging the predictions), and evaluated its generalization capabilities. Overall, the AI shows higher balanced accuracy than dermatologists (0.798, 95% confidence interval (CI) 0.779–0.814 vs. 0.781, 95% CI 0.760–0.802; p = 4.0e−145), obtaining a higher sensitivity (0.921, 95% CI 0.900–0.942 vs. 0.734, 95% CI 0.701–0.770; p = 3.3e−165) at the cost of a lower specificity (0.673, 95% CI 0.641–0.702 vs. 0.828, 95% CI 0.804–0.852; p = 3.3e−165). As the algorithm exhibits a significant performance advantage on our heterogeneous dataset exclusively comprising melanoma-suspicious lesions, AI may offer the potential to support dermatologists, particularly in diagnosing challenging cases. Melanoma is a type of skin cancer that can spread to other parts of the body, often resulting in death. Early detection improves survival rates. Computational tools that use artificial intelligence (AI) can be used to detect melanoma. However, few studies have checked how well the AI works on real-world data obtained from patients. We tested a previously developed AI tool on data obtained from eight different hospitals that used different types of cameras, which also included images taken of rare melanoma types and from a range of different parts of the body. The AI tool was more likely to correctly identify melanoma than dermatologists. This AI tool could be used to help dermatologists diagnose melanoma, particularly those that are difficult for dermatologists to diagnose. Heinlein, Maron, Hekler et al. evaluate an AI algorithm for detecting melanoma and compare its performance to that of dermatologist on a prospectively collected, external, heterogeneous dataset. The AI exhibits a significant performance advantage, especially in diagnosing challenging cases.","PeriodicalId":72646,"journal":{"name":"Communications medicine","volume":" ","pages":"1-9"},"PeriodicalIF":5.4,"publicationDate":"2024-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s43856-024-00598-5.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142170394","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 : 2024-09-11DOI: 10.1038/s43856-024-00599-4
Taeyoung Choi, Yan Xie, Ziyad Al-Aly
Whether use of SGLT2 inhibitors reduces the risk of cardiovascular and kidney events in people who contracted SARS-CoV-2 infection is not clear. We used the healthcare databases of the United States Department of Veterans Affairs to build a cohort of 107,776 participants on antihyperglycemic therapy and had SARS-CoV-2 infection between March 01, 2020 and June 10, 2023. Within them, 11,588 used SGLT2 inhibitors and 96,188 used other antihyperglycemics. We examined the risks of major adverse cardiovascular events (MACE)—a composite of death, myocardial infarction and stroke, and major adverse kidney events (MAKE)—a composite of death, eGFR decline > 50%, and end stage kidney disease after balancing baseline characteristics between groups through inverse probability weighting. Survival analyses were conducted to generate hazard ratio (HR) and absolute risk reduction per 100 person-years (ARR). Over a median follow up of 1.57 (IQR: 1.05–2.49) years, compared to the control group, SGLT2 inhibitors use is associated with reduced risk of MACE (HR 0.82 (0.77, 0.88), ARR 1.73 (1.21, 2.25)) and reduced risk of MAKE (HR 0.75 (0.71, 0.80), ARR 2.62 (2.13, 3.11)). Compared to the control group, SGLT2 inhibitors use is associated with reduced risk of the secondary outcomes of hospitalization (HR 0.94 (0.90, 0.98), ARR 1.06 (1.36, 1.76)), anemia (HR 0.71 (0.65, 0.76), ARR 2.43 (1.95, 2.90)), and acute kidney injury (HR 0.84 (0.79, 0.89), ARR 1.86 (1.29, 2.42)). Among people with SARS-CoV-2 infection on antihyperglycemic therapy, compared to those on other antihyperglycemics, those on SGLT2 inhibitors have less risk of adverse cardiovascular and kidney outcomes. SARS-CoV-2 infection leads to significant increase in risk of heart and kidney problems both shortly after infection and in the long-term. In this study, we evaluated whether SGLT2 inhibitors could reduce the risk of major adverse heart and kidney events in people with SARS-CoV-2 infection. SGLT2 inhibitors are a type of medication used to treat diabetes by lowering the amount of sugar in the blood. We compared a large group of people during and after SARS-CoV-2 infection and found that those who were using SGLT2 inhibitors had less major adverse heart and kidney problems than those who were using other types of sugar-lowering medications. Our findings could be useful for optimizing approaches to reduce risk of heart and kidney problems among people with diabetes and SARS-CoV-2 infection. Choi et al. report on the effectiveness of SGLT2 inhibitors in reducing risk of major adverse cardiovascular events (MACE) and major adverse kidney events (MAKE) in people with SARS-CoV-2 infection. They show that compared to other antihyperglycemics, SGLT2 inhibitors reduce the risk of both MACE and MAKE after SARS-CoV-2 infection.
{"title":"Adverse cardiovascular and kidney outcomes in people with SARS-CoV-2 treated with SGLT2 inhibitors","authors":"Taeyoung Choi, Yan Xie, Ziyad Al-Aly","doi":"10.1038/s43856-024-00599-4","DOIUrl":"10.1038/s43856-024-00599-4","url":null,"abstract":"Whether use of SGLT2 inhibitors reduces the risk of cardiovascular and kidney events in people who contracted SARS-CoV-2 infection is not clear. We used the healthcare databases of the United States Department of Veterans Affairs to build a cohort of 107,776 participants on antihyperglycemic therapy and had SARS-CoV-2 infection between March 01, 2020 and June 10, 2023. Within them, 11,588 used SGLT2 inhibitors and 96,188 used other antihyperglycemics. We examined the risks of major adverse cardiovascular events (MACE)—a composite of death, myocardial infarction and stroke, and major adverse kidney events (MAKE)—a composite of death, eGFR decline > 50%, and end stage kidney disease after balancing baseline characteristics between groups through inverse probability weighting. Survival analyses were conducted to generate hazard ratio (HR) and absolute risk reduction per 100 person-years (ARR). Over a median follow up of 1.57 (IQR: 1.05–2.49) years, compared to the control group, SGLT2 inhibitors use is associated with reduced risk of MACE (HR 0.82 (0.77, 0.88), ARR 1.73 (1.21, 2.25)) and reduced risk of MAKE (HR 0.75 (0.71, 0.80), ARR 2.62 (2.13, 3.11)). Compared to the control group, SGLT2 inhibitors use is associated with reduced risk of the secondary outcomes of hospitalization (HR 0.94 (0.90, 0.98), ARR 1.06 (1.36, 1.76)), anemia (HR 0.71 (0.65, 0.76), ARR 2.43 (1.95, 2.90)), and acute kidney injury (HR 0.84 (0.79, 0.89), ARR 1.86 (1.29, 2.42)). Among people with SARS-CoV-2 infection on antihyperglycemic therapy, compared to those on other antihyperglycemics, those on SGLT2 inhibitors have less risk of adverse cardiovascular and kidney outcomes. SARS-CoV-2 infection leads to significant increase in risk of heart and kidney problems both shortly after infection and in the long-term. In this study, we evaluated whether SGLT2 inhibitors could reduce the risk of major adverse heart and kidney events in people with SARS-CoV-2 infection. SGLT2 inhibitors are a type of medication used to treat diabetes by lowering the amount of sugar in the blood. We compared a large group of people during and after SARS-CoV-2 infection and found that those who were using SGLT2 inhibitors had less major adverse heart and kidney problems than those who were using other types of sugar-lowering medications. Our findings could be useful for optimizing approaches to reduce risk of heart and kidney problems among people with diabetes and SARS-CoV-2 infection. Choi et al. report on the effectiveness of SGLT2 inhibitors in reducing risk of major adverse cardiovascular events (MACE) and major adverse kidney events (MAKE) in people with SARS-CoV-2 infection. They show that compared to other antihyperglycemics, SGLT2 inhibitors reduce the risk of both MACE and MAKE after SARS-CoV-2 infection.","PeriodicalId":72646,"journal":{"name":"Communications medicine","volume":" ","pages":"1-8"},"PeriodicalIF":5.4,"publicationDate":"2024-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s43856-024-00599-4.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142170380","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 : 2024-09-10DOI: 10.1038/s43856-024-00605-9
Yorrick R. J. Jaspers, Hemmo A. F. Yska, Caroline G. Bergner, Inge M. E. Dijkstra, Irene C. Huffnagel, Marije M. C. Voermans, Eric Wever, Gajja S. Salomons, Frédéric M. Vaz, Aldo Jongejan, Jill Hermans, Rebecca K. Tryon, Troy C. Lund, Wolfgang Köhler, Marc Engelen, Stephan Kemp
X-linked adrenoleukodystrophy (ALD) is a neurometabolic disorder caused by pathogenic variants in ABCD1 resulting very long-chain fatty acids (VLCFA) accumulation in plasma and tissues. Males can present with various clinical manifestations, including adrenal insufficiency, spinal cord disease, and leukodystrophy. Female patients typically develop spinal cord disease and peripheral neuropathy. Predicting the clinical outcome of an individual patient remains impossible due to the lack of genotype-phenotype correlation and predictive biomarkers. The availability of a large prospective cohort of well-characterized patients and associated biobank samples allowed us to investigate the relationship between lipidome and disease severity in ALD. We performed a lipidomic analysis of plasma samples from 24 healthy controls, 92 male and 65 female ALD patients. Here we show that VLCFA are incorporated into different lipid classes, including lysophosphatidylcholines, phosphatidylcholines, triglycerides, and sphingomyelins. Our results show a strong association between higher levels of VLCFA-containing lipids and the presence of leukodystrophy, adrenal insufficiency, and severe spinal cord disease in male ALD patients. In female ALD patients, VLCFA-lipid levels correlate with X-inactivation patterns in blood mononuclear cells, and higher levels are associated with more severe disease manifestations. Finally, hematopoietic stem cell transplantation significantly reduces, but does not normalize, plasma C26:0-lysophosphatidylcholine levels in male ALD patients. Our findings are supported by the concordance of C26:0-lysophosphatidylcholine and total VLCFA analysis with the lipidomics results. This study reveals the profound impact of ALD on the lipidome and provides potential biomarkers for predicting clinical outcomes in ALD patients. X-linked adrenoleukodystrophy (ALD) affects the brain, spinal cord, and adrenal glands. ALD is caused by too many very long-chain fatty acids (VLCFAs) in the body. We don’t know how ALD progresses in individual patients. We have analyzed blood samples from male and female ALD patients. We found that certain changes in fatty acid (or lipid) composition are associated with more severe symptoms. Our findings may lead to new ways to predict which symptoms are likely to change over time and to monitor the effectiveness of treatment. This research increases our understanding of ALD and may improve patient care in the future. Jaspers et al. investigate whether plasma VLCFA-lipid levels are associated with disease severity in both male and female patients with X-linked adrenoleukodystrophy (ALD). Lipidomic analyses reveal a profound impact of ALD on the lipidome and provide potential biomarkers for predicting clinical outcomes in ALD patients.
{"title":"Lipidomic biomarkers in plasma correlate with disease severity in adrenoleukodystrophy","authors":"Yorrick R. J. Jaspers, Hemmo A. F. Yska, Caroline G. Bergner, Inge M. E. Dijkstra, Irene C. Huffnagel, Marije M. C. Voermans, Eric Wever, Gajja S. Salomons, Frédéric M. Vaz, Aldo Jongejan, Jill Hermans, Rebecca K. Tryon, Troy C. Lund, Wolfgang Köhler, Marc Engelen, Stephan Kemp","doi":"10.1038/s43856-024-00605-9","DOIUrl":"10.1038/s43856-024-00605-9","url":null,"abstract":"X-linked adrenoleukodystrophy (ALD) is a neurometabolic disorder caused by pathogenic variants in ABCD1 resulting very long-chain fatty acids (VLCFA) accumulation in plasma and tissues. Males can present with various clinical manifestations, including adrenal insufficiency, spinal cord disease, and leukodystrophy. Female patients typically develop spinal cord disease and peripheral neuropathy. Predicting the clinical outcome of an individual patient remains impossible due to the lack of genotype-phenotype correlation and predictive biomarkers. The availability of a large prospective cohort of well-characterized patients and associated biobank samples allowed us to investigate the relationship between lipidome and disease severity in ALD. We performed a lipidomic analysis of plasma samples from 24 healthy controls, 92 male and 65 female ALD patients. Here we show that VLCFA are incorporated into different lipid classes, including lysophosphatidylcholines, phosphatidylcholines, triglycerides, and sphingomyelins. Our results show a strong association between higher levels of VLCFA-containing lipids and the presence of leukodystrophy, adrenal insufficiency, and severe spinal cord disease in male ALD patients. In female ALD patients, VLCFA-lipid levels correlate with X-inactivation patterns in blood mononuclear cells, and higher levels are associated with more severe disease manifestations. Finally, hematopoietic stem cell transplantation significantly reduces, but does not normalize, plasma C26:0-lysophosphatidylcholine levels in male ALD patients. Our findings are supported by the concordance of C26:0-lysophosphatidylcholine and total VLCFA analysis with the lipidomics results. This study reveals the profound impact of ALD on the lipidome and provides potential biomarkers for predicting clinical outcomes in ALD patients. X-linked adrenoleukodystrophy (ALD) affects the brain, spinal cord, and adrenal glands. ALD is caused by too many very long-chain fatty acids (VLCFAs) in the body. We don’t know how ALD progresses in individual patients. We have analyzed blood samples from male and female ALD patients. We found that certain changes in fatty acid (or lipid) composition are associated with more severe symptoms. Our findings may lead to new ways to predict which symptoms are likely to change over time and to monitor the effectiveness of treatment. This research increases our understanding of ALD and may improve patient care in the future. Jaspers et al. investigate whether plasma VLCFA-lipid levels are associated with disease severity in both male and female patients with X-linked adrenoleukodystrophy (ALD). Lipidomic analyses reveal a profound impact of ALD on the lipidome and provide potential biomarkers for predicting clinical outcomes in ALD patients.","PeriodicalId":72646,"journal":{"name":"Communications medicine","volume":" ","pages":"1-16"},"PeriodicalIF":5.4,"publicationDate":"2024-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s43856-024-00605-9.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142170349","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 : 2024-09-10DOI: 10.1038/s43856-024-00601-z
Yifan Yang, Xiaoyu Liu, Qiao Jin, Furong Huang, Zhiyong Lu
Large language models like GPT-3.5-turbo and GPT-4 hold promise for healthcare professionals, but they may inadvertently inherit biases during their training, potentially affecting their utility in medical applications. Despite few attempts in the past, the precise impact and extent of these biases remain uncertain. We use LLMs to generate responses that predict hospitalization, cost and mortality based on real patient cases. We manually examine the generated responses to identify biases. We find that these models tend to project higher costs and longer hospitalizations for white populations and exhibit optimistic views in challenging medical scenarios with much higher survival rates. These biases, which mirror real-world healthcare disparities, are evident in the generation of patient backgrounds, the association of specific diseases with certain racial and ethnic groups, and disparities in treatment recommendations, etc. Our findings underscore the critical need for future research to address and mitigate biases in language models, especially in critical healthcare applications, to ensure fair and accurate outcomes for all patients. Large language models (LLMs) such as GPT-3.5-turbo and GPT-4 are advanced computer programs that can understand and generate text. They have the potential to help doctors and other healthcare professionals to improve patient care. We looked at how well these models predicted the cost of healthcare for patients, and the chances of them being hospitalized or dying. We found that these models often projected higher costs and longer hospital stays for white people than people from other racial or ethnicity groups. These biases mirror the disparities in real-world healthcare. Our findings show the need for more research to ensure that inappropriate biases are removed from LLMs to ensure fair and accurate healthcare predictions of possible outcomes for all patients. This will help ensure that these tools can be used effectively to improve healthcare for everyone. Yang et al. investigate racial biases in GPT-3.5-turbo and GPT-4 generated predictions for hospitalization, cost, and mortality obtained from real patient cases. They find tendencies to project differing costs and hospitalizations depending on race, highlighting the need for further research to mitigate racial biases and enable fair and accurate healthcare outcomes.
{"title":"Unmasking and quantifying racial bias of large language models in medical report generation","authors":"Yifan Yang, Xiaoyu Liu, Qiao Jin, Furong Huang, Zhiyong Lu","doi":"10.1038/s43856-024-00601-z","DOIUrl":"10.1038/s43856-024-00601-z","url":null,"abstract":"Large language models like GPT-3.5-turbo and GPT-4 hold promise for healthcare professionals, but they may inadvertently inherit biases during their training, potentially affecting their utility in medical applications. Despite few attempts in the past, the precise impact and extent of these biases remain uncertain. We use LLMs to generate responses that predict hospitalization, cost and mortality based on real patient cases. We manually examine the generated responses to identify biases. We find that these models tend to project higher costs and longer hospitalizations for white populations and exhibit optimistic views in challenging medical scenarios with much higher survival rates. These biases, which mirror real-world healthcare disparities, are evident in the generation of patient backgrounds, the association of specific diseases with certain racial and ethnic groups, and disparities in treatment recommendations, etc. Our findings underscore the critical need for future research to address and mitigate biases in language models, especially in critical healthcare applications, to ensure fair and accurate outcomes for all patients. Large language models (LLMs) such as GPT-3.5-turbo and GPT-4 are advanced computer programs that can understand and generate text. They have the potential to help doctors and other healthcare professionals to improve patient care. We looked at how well these models predicted the cost of healthcare for patients, and the chances of them being hospitalized or dying. We found that these models often projected higher costs and longer hospital stays for white people than people from other racial or ethnicity groups. These biases mirror the disparities in real-world healthcare. Our findings show the need for more research to ensure that inappropriate biases are removed from LLMs to ensure fair and accurate healthcare predictions of possible outcomes for all patients. This will help ensure that these tools can be used effectively to improve healthcare for everyone. Yang et al. investigate racial biases in GPT-3.5-turbo and GPT-4 generated predictions for hospitalization, cost, and mortality obtained from real patient cases. They find tendencies to project differing costs and hospitalizations depending on race, highlighting the need for further research to mitigate racial biases and enable fair and accurate healthcare outcomes.","PeriodicalId":72646,"journal":{"name":"Communications medicine","volume":" ","pages":"1-6"},"PeriodicalIF":5.4,"publicationDate":"2024-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s43856-024-00601-z.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142170400","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 : 2024-09-09DOI: 10.1038/s43856-024-00597-6
Makoto Takahashi, Yoshihiro Kushida, Yasumasa Kuroda, Shohei Wakao, Yasuhiro Horibata, Hiroyuki Sugimoto, Mari Dezawa, Yoshikatsu Saiki
Stanford type B-acute aortic dissection (type B-AAD) is often life-threatening without invasive surgery. Multilineage-differentiating stress enduring cell (Muse cells), which comprise several percent of mesenchymal stem cells (MSCs), are endogenous pluripotent-like stem cells that selectively home to damaged tissue and replace damaged/apoptotic cells by in-vivo differentiation. Mortality, aortic diameter expansion, cell localization, cell differentiation, and inflammation of the dissected aorta were evaluated in type B-AAD model mice intravenously injected with human-Muse cells, -elastin-knockdown (KD)-Muse cells, -human leukocyte antigen-G (HLA-G)-KD-Muse cells, or MSCs, all without immunosuppressant. Here, we show the Muse (50,000 cells) group has a lower incidence of aortic rupture and mortality of AAD compared with the MSC-50K (50,000 human-MSCs) and vehicle groups. Spectrum computed tomography in-vivo dynamics and 3-dimensional histologic analyses demonstrate that Muse cells more effectively home to the AAD tissue and survive for 8 weeks in the Muse group than in the MSC-750K (750,000 human-MSCs containing 50,000 Muse cells) group. Homing of Muse cells is impeded in the HLA-G-KD-Muse (50,000 cells) group. Differentiation of homed Muse cells into CD31(+) and alpha-smooth muscle actin (+) cells, production and reorganization of elastic fibers in the AAD tissue, and suppression of diameter expansion are greater in the Muse group than in the MSC-750K and elastin-KD-Muse (50,000 cells) groups. Intravenously administered Muse cells reconstruct the dissected aorta and improve mortality and diameter enlargement rates. Moreover, small doses of purified Muse cells are more effective than large doses of MSCs. HLA-G is suggested to contribute to the successful survival and homing of Muse cells. Acute aortic dissection (AAD) is a serious disease in which the largest artery in the body, called the aorta, enlarges and ruptures. Surgery is often required to prevent death. Cells called Muse cells have been injected into people during clinical trials to treat other diseases. In this study, we injected Muse cells into mice with dissected aorta. The cells accumulated in damaged parts of the aorta and strengthened the structure of the aorta, reducing the number of mice that died. If further research shows this treatment works in humans, this could enable AAD to be treated without surgery and potentially improve the treatment and survival of people with AAD. Takahashi et al. intravenously inject human Muse cells into model mice with Stanford type B acute aortic dissection. This effectively reduces diameter expansion rates, mortality and has a greater therapeutic effect than intravenous injection of large amounts of human mesenchymal stem cells.
{"title":"Structural reconstruction of mouse acute aortic dissection by intravenously administered human Muse cells without immunosuppression","authors":"Makoto Takahashi, Yoshihiro Kushida, Yasumasa Kuroda, Shohei Wakao, Yasuhiro Horibata, Hiroyuki Sugimoto, Mari Dezawa, Yoshikatsu Saiki","doi":"10.1038/s43856-024-00597-6","DOIUrl":"10.1038/s43856-024-00597-6","url":null,"abstract":"Stanford type B-acute aortic dissection (type B-AAD) is often life-threatening without invasive surgery. Multilineage-differentiating stress enduring cell (Muse cells), which comprise several percent of mesenchymal stem cells (MSCs), are endogenous pluripotent-like stem cells that selectively home to damaged tissue and replace damaged/apoptotic cells by in-vivo differentiation. Mortality, aortic diameter expansion, cell localization, cell differentiation, and inflammation of the dissected aorta were evaluated in type B-AAD model mice intravenously injected with human-Muse cells, -elastin-knockdown (KD)-Muse cells, -human leukocyte antigen-G (HLA-G)-KD-Muse cells, or MSCs, all without immunosuppressant. Here, we show the Muse (50,000 cells) group has a lower incidence of aortic rupture and mortality of AAD compared with the MSC-50K (50,000 human-MSCs) and vehicle groups. Spectrum computed tomography in-vivo dynamics and 3-dimensional histologic analyses demonstrate that Muse cells more effectively home to the AAD tissue and survive for 8 weeks in the Muse group than in the MSC-750K (750,000 human-MSCs containing 50,000 Muse cells) group. Homing of Muse cells is impeded in the HLA-G-KD-Muse (50,000 cells) group. Differentiation of homed Muse cells into CD31(+) and alpha-smooth muscle actin (+) cells, production and reorganization of elastic fibers in the AAD tissue, and suppression of diameter expansion are greater in the Muse group than in the MSC-750K and elastin-KD-Muse (50,000 cells) groups. Intravenously administered Muse cells reconstruct the dissected aorta and improve mortality and diameter enlargement rates. Moreover, small doses of purified Muse cells are more effective than large doses of MSCs. HLA-G is suggested to contribute to the successful survival and homing of Muse cells. Acute aortic dissection (AAD) is a serious disease in which the largest artery in the body, called the aorta, enlarges and ruptures. Surgery is often required to prevent death. Cells called Muse cells have been injected into people during clinical trials to treat other diseases. In this study, we injected Muse cells into mice with dissected aorta. The cells accumulated in damaged parts of the aorta and strengthened the structure of the aorta, reducing the number of mice that died. If further research shows this treatment works in humans, this could enable AAD to be treated without surgery and potentially improve the treatment and survival of people with AAD. Takahashi et al. intravenously inject human Muse cells into model mice with Stanford type B acute aortic dissection. This effectively reduces diameter expansion rates, mortality and has a greater therapeutic effect than intravenous injection of large amounts of human mesenchymal stem cells.","PeriodicalId":72646,"journal":{"name":"Communications medicine","volume":" ","pages":"1-16"},"PeriodicalIF":5.4,"publicationDate":"2024-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s43856-024-00597-6.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142170352","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 : 2024-09-06DOI: 10.1038/s43856-024-00602-y
Mie Møller, Lennart Friis-Hansen, Nikolai Kirkby, Christine Dilling-Hansen, Mikael Andersson, Peter Vedsted, Kåre Mølbak, Anders Koch
In Greenland, the COVID-19 pandemic was characterised by a late onset of community transmission and a low impact on the healthcare system, hypothesised as being partly due to a high uptake of vaccinations. To underpin this description, we aimed to assess the SARS-CoV-2 immune response post-vaccination in a Greenlandic population. In this observational cohort study, we included 430 adults in Greenland who had received a complete two-dose SARS-CoV-2 vaccination at enrolment. The total plasma SARS-CoV-2 spike glycoprotein Ig antibodies (S-Ab) induced by either the BNT162b2 or mRNA-1273 vaccine, was measured up to 11 months after the second vaccine dose. In addition, total salivary S-Abs were examined in 107 participants, and the T-cell response to the spike glycoprotein was assessed in 78 participants out of the entire study cohort. Here we demonstrate that two months after the second vaccine dose, 96% of participants have protective plasma S-Ab levels. By 11 months, 98% have protective levels, with prior SARS-CoV-2 infection particularly enhancing S-Ab levels by 37% (95% CI 25–51%). Among individuals aged 60 years and older, we observe a 21% (95% CI 7–33%) reduction in antibody response. Total salivary S-Ab levels are detectable in all participants and significantly correlate with plasma levels. Moreover, all participants exhibit a robust SARS-CoV-2-specific T-cell response 11 months post-primary vaccination. Our findings show that Greenlanders exhibit a robust and lasting immune response, both humoral and cellular, comparable to other population groups up to at least 11 months after the second vaccine dose. These results corroborate the hypothesis that vaccines contributed to the mild impact of the COVID-19 pandemic in the Greenlandic population. Effective public health measures were crucial to protect Greenland’s vulnerable population against the COVID-19 pandemic. Vaccines were particularly important, although their effectiveness in Greenland’s unique and isolated population had not been explored. Our aim was to determine the COVID-19 vaccines’ immunological response as a measure of protection among Greenlanders. By measuring antibody levels and immune cell activity, we demonstrate that over nine out of ten Greenlanders remained well protected by COVID-19 vaccines up to 11 months after their second vaccine dose, although older adults were less well protected. Prior COVID-19 infection or a booster dose enhanced protection against severe disease. These findings provide valuable insights for Greenland and similar ancestral and geographical populations, aiding in their ongoing vaccination strategies and future pandemic preparedness. Møller et al. examine reasoning behind the mild impact of COVID-19 on the population of Greenland by measuring immune factors in this highly vaccinated population. Both humoral and cellular immune responses remained high at least 11 months after vaccination in a vast majority of study participants.
{"title":"Robust immune response to COVID-19 vaccination in the island population of Greenland","authors":"Mie Møller, Lennart Friis-Hansen, Nikolai Kirkby, Christine Dilling-Hansen, Mikael Andersson, Peter Vedsted, Kåre Mølbak, Anders Koch","doi":"10.1038/s43856-024-00602-y","DOIUrl":"10.1038/s43856-024-00602-y","url":null,"abstract":"In Greenland, the COVID-19 pandemic was characterised by a late onset of community transmission and a low impact on the healthcare system, hypothesised as being partly due to a high uptake of vaccinations. To underpin this description, we aimed to assess the SARS-CoV-2 immune response post-vaccination in a Greenlandic population. In this observational cohort study, we included 430 adults in Greenland who had received a complete two-dose SARS-CoV-2 vaccination at enrolment. The total plasma SARS-CoV-2 spike glycoprotein Ig antibodies (S-Ab) induced by either the BNT162b2 or mRNA-1273 vaccine, was measured up to 11 months after the second vaccine dose. In addition, total salivary S-Abs were examined in 107 participants, and the T-cell response to the spike glycoprotein was assessed in 78 participants out of the entire study cohort. Here we demonstrate that two months after the second vaccine dose, 96% of participants have protective plasma S-Ab levels. By 11 months, 98% have protective levels, with prior SARS-CoV-2 infection particularly enhancing S-Ab levels by 37% (95% CI 25–51%). Among individuals aged 60 years and older, we observe a 21% (95% CI 7–33%) reduction in antibody response. Total salivary S-Ab levels are detectable in all participants and significantly correlate with plasma levels. Moreover, all participants exhibit a robust SARS-CoV-2-specific T-cell response 11 months post-primary vaccination. Our findings show that Greenlanders exhibit a robust and lasting immune response, both humoral and cellular, comparable to other population groups up to at least 11 months after the second vaccine dose. These results corroborate the hypothesis that vaccines contributed to the mild impact of the COVID-19 pandemic in the Greenlandic population. Effective public health measures were crucial to protect Greenland’s vulnerable population against the COVID-19 pandemic. Vaccines were particularly important, although their effectiveness in Greenland’s unique and isolated population had not been explored. Our aim was to determine the COVID-19 vaccines’ immunological response as a measure of protection among Greenlanders. By measuring antibody levels and immune cell activity, we demonstrate that over nine out of ten Greenlanders remained well protected by COVID-19 vaccines up to 11 months after their second vaccine dose, although older adults were less well protected. Prior COVID-19 infection or a booster dose enhanced protection against severe disease. These findings provide valuable insights for Greenland and similar ancestral and geographical populations, aiding in their ongoing vaccination strategies and future pandemic preparedness. Møller et al. examine reasoning behind the mild impact of COVID-19 on the population of Greenland by measuring immune factors in this highly vaccinated population. Both humoral and cellular immune responses remained high at least 11 months after vaccination in a vast majority of study participants.","PeriodicalId":72646,"journal":{"name":"Communications medicine","volume":" ","pages":"1-10"},"PeriodicalIF":5.4,"publicationDate":"2024-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11379896/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142147032","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 : 2024-09-05DOI: 10.1038/s43856-024-00586-9
Jennifer Y. Kim, Maria Florez, Emily Botto, Xoli Belgrave, Clare Grace, Ken Getz
Characterizing perceptions of clinical trials among the socioeconomically disadvantaged is necessary for understanding how social determinants of health such as socioeconomic disparities in education and income can affect people’s awareness of and exposure to clinical trials A survey was distributed in spring 2023 among a survey taking sample stratified by demographic variables to reflect the U.S. population. The survey assessed the socioeconomic status of the respondent and related covariates, as well as outcome measures including interest in joining a clinical trial, concerns relating to participation, and whether the respondent had previously been asked to participate. Multiple and logistic regression were used to assess the relationship between predictor and outcome variables Here we show the results of outcome measures regressed on main predictors related to socioeconomic status and related demographic predictors. Education, employment status, insurance coverage, and English proficiency were significant predictors of interest in clinical trial participation. Education and the presence of a healthcare professional or former clinical trial participant in the respondent’s personal network were significant predictors of whether the respondent had previously been asked to participate in a clinical trial The results of the analysis reveal how socioeconomically vulnerable groups, including those from low income and low education groups, are being excluded in clinical research. Analyses also uncovered the impact of clinical trial social influence—the presence of having a family or friend in one’s social network who participated in a clinical trial—on willingness to participate and exposure to clinical trials. Participation in clinical trials has remained largely inaccessible to historically underrepresented communities, which includes groups that are low income and low education. Here, we examine socioeconomic and demographic factors that can influence individuals’ willingness to participate in clinical trials and their experience being asked to participate in clinical trials. Using several types of analysis, we show that those who are low income and less educated are less willing to participate in clinical trials and are less likely to be asked to participate in clinical trials when compared to those with higher income and more education. This highlights the need for improved outreach among healthcare providers and clinical research staff to include these communities and provide individuals with the knowledge, awareness, and opportunity to participate in clinical trials. Kim et. al explore the impact of socioeconomic vulnerability on clinical trial participation. Findings highlight barriers to trial entry including participant concerns and implications of exclusion of specific groups.
{"title":"The influence of socioeconomic status on individual attitudes and experience with clinical trials","authors":"Jennifer Y. Kim, Maria Florez, Emily Botto, Xoli Belgrave, Clare Grace, Ken Getz","doi":"10.1038/s43856-024-00586-9","DOIUrl":"10.1038/s43856-024-00586-9","url":null,"abstract":"Characterizing perceptions of clinical trials among the socioeconomically disadvantaged is necessary for understanding how social determinants of health such as socioeconomic disparities in education and income can affect people’s awareness of and exposure to clinical trials A survey was distributed in spring 2023 among a survey taking sample stratified by demographic variables to reflect the U.S. population. The survey assessed the socioeconomic status of the respondent and related covariates, as well as outcome measures including interest in joining a clinical trial, concerns relating to participation, and whether the respondent had previously been asked to participate. Multiple and logistic regression were used to assess the relationship between predictor and outcome variables Here we show the results of outcome measures regressed on main predictors related to socioeconomic status and related demographic predictors. Education, employment status, insurance coverage, and English proficiency were significant predictors of interest in clinical trial participation. Education and the presence of a healthcare professional or former clinical trial participant in the respondent’s personal network were significant predictors of whether the respondent had previously been asked to participate in a clinical trial The results of the analysis reveal how socioeconomically vulnerable groups, including those from low income and low education groups, are being excluded in clinical research. Analyses also uncovered the impact of clinical trial social influence—the presence of having a family or friend in one’s social network who participated in a clinical trial—on willingness to participate and exposure to clinical trials. Participation in clinical trials has remained largely inaccessible to historically underrepresented communities, which includes groups that are low income and low education. Here, we examine socioeconomic and demographic factors that can influence individuals’ willingness to participate in clinical trials and their experience being asked to participate in clinical trials. Using several types of analysis, we show that those who are low income and less educated are less willing to participate in clinical trials and are less likely to be asked to participate in clinical trials when compared to those with higher income and more education. This highlights the need for improved outreach among healthcare providers and clinical research staff to include these communities and provide individuals with the knowledge, awareness, and opportunity to participate in clinical trials. Kim et. al explore the impact of socioeconomic vulnerability on clinical trial participation. Findings highlight barriers to trial entry including participant concerns and implications of exclusion of specific groups.","PeriodicalId":72646,"journal":{"name":"Communications medicine","volume":" ","pages":"1-7"},"PeriodicalIF":5.4,"publicationDate":"2024-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s43856-024-00586-9.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142137900","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 : 2024-08-30DOI: 10.1038/s43856-024-00593-w
Lauren K. Dillard, Lois J. Matthews, Lizmarie Maldonado, Annie N. Simpson, Judy R. Dubno
Little is known about the natural history of hearing loss in adults, despite it being an important public health problem. The purpose of this study is to describe the rate of hearing change per year over the adult lifespan. The 1436 participants are from the MUSC Longitudinal Cohort Study of Age-related Hearing Loss (1988-present). Outcomes are audiometric thresholds at 250, 500, 1000, 2000, 3000, 4000, 6000, and 8000 Hz, averaged across right and left ears, and pure-tone average (PTA). Demographic factors are sex (female/male), race, which is categorized as white or racial Minority, and baseline age group (18-39, 40–59, 60–69, 70+ years). Linear mixed regression models are used to estimate the effect of age (per year) on the rate of threshold and PTA change. Participants’ mean age is 63.1 (SD 14.9) years, 57.7% are female, and 17.8% are racial Minority (17.1% were Black or African American). In sex-race-adjusted models, rates of threshold change are 0.42 to 1.44 dB across thresholds. Rates of change differ by sex at most individual thresholds, but not PTA. Females (versus males) showed higher rates of threshold change in higher frequencies but less decline per year in lower frequencies. Black/African American (versus white) participants have lower rates of threshold and PTA change per year. Hearing thresholds decline across the adult lifespan, with older (versus younger) baseline age groups showing higher rates of decline per year. Declines to hearing occur across the adult lifespan, and the rate of decline varies by sex, race, and baseline age. Hearing loss is a common health condition, yet little is known about how hearing changes over time. In this study of 1436 individuals from across the adult lifespan, declines in hearing occurred throughout adulthood. The rate of decline per year varied by sex, in that females experienced more decline in higher pitches but less decline in lower pitches. The rate of decline per year varied by race, in that Black/African American (versus white) participants showed lower rates of hearing decline per year. The rate of decline per year also varied by age, in that older (versus younger) baseline age groups had higher rates of hearing decline per year. This study contributes to understanding of the natural history of hearing loss and could be used to better understand how to focus efforts to prevent and/or manage hearing loss across populations. Dillard et al. evaluate changes in hearing over the adult lifespan. Females show higher rates of threshold change in higher frequencies but less decline per year in lower frequencies compared to males.
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