Pub Date : 2024-10-03DOI: 10.1038/s43856-024-00604-w
Sumanth Reddy Nakkireddy, Inyeop Jang, Minji Kim, Linda X. Yin, Michael Rivera, Joaquin J. Garcia, Kathleen R. Bartemes, David M. Routman, Eric. J. Moore, Chadi N. Abdel-Halim, Daniel J. Ma, Kathryn M. Van Abel, Tae Hyun Hwang
Deep learning techniques excel at identifying tumor-infiltrating lymphocytes (TILs) and immune phenotypes in hematoxylin and eosin (H&E)-stained slides. However, their ability to elucidate detailed functional characteristics of diverse cellular phenotypes within tumor immune microenvironment (TME) is limited. We aimed to enhance our understanding of cellular composition and functional characteristics across TME regions and improve patient stratification by integrating H&E with adjacent immunohistochemistry (IHC) images. A retrospective study was conducted on patients with Human Papillomavirus-positive oropharyngeal squamous cell carcinoma (OPSCC). Using paired H&E and IHC slides for 11 proteins, a deep learning pipeline was used to quantify tumor, stroma, and TILs in the TME. Patients were classified into immune inflamed (IN), immune excluded (IE), or immune desert (ID) phenotypes. By registering the IHC and H&E slides, we integrated IHC data to capture protein expression in the corresponding tumor regions. We further stratified patients into specific immune subtypes, such as IN, with increased or reduced CD8+ cells, based on the abundance of these proteins. This characterization provided functional insight into the H&E-based subtypes. Analysis of 88 primary tumors and 70 involved lymph node tissue images reveals an improved prognosis in patients classified as IN in primary tumors with high CD8 and low CD163 expression (p = 0.007). Multivariate Cox regression analysis confirms a significantly better prognosis for these subtypes. Integrating H&E and IHC data enhances the functional characterization of immune phenotypes of the TME with biological interpretability, and improves patient stratification in HPV( + ) OPSCC. In this study, we investigated whether differences in the immune cell population surrounding head and neck cancers impact disease progression. We used advanced computer programs to analyze tissue samples from tumors and nearby lymph nodes, a part of the immune system. These tumor and lymph node samples were stained to show the structure of the tissue and to identify the different types of immune cells present. We grouped patients into different categories based on differences in their immune cells. We found that patients with certain patterns of immune cells tended to have better outcomes. This method could help doctors predict how well patients will respond to treatments. Nakkireddy, Jang, Kim, et al. explore tumor immune microenvironment (TME) types in HPV-positive oropharyngeal squamous cell carcinoma. Deep learning analysis of tumor and lymph node tissues identifies immune cell patterns that correlate with improved prognosis.
{"title":"Integrative analysis of H&E and IHC identifies prognostic immune subtypes in HPV related oropharyngeal cancer","authors":"Sumanth Reddy Nakkireddy, Inyeop Jang, Minji Kim, Linda X. Yin, Michael Rivera, Joaquin J. Garcia, Kathleen R. Bartemes, David M. Routman, Eric. J. Moore, Chadi N. Abdel-Halim, Daniel J. Ma, Kathryn M. Van Abel, Tae Hyun Hwang","doi":"10.1038/s43856-024-00604-w","DOIUrl":"10.1038/s43856-024-00604-w","url":null,"abstract":"Deep learning techniques excel at identifying tumor-infiltrating lymphocytes (TILs) and immune phenotypes in hematoxylin and eosin (H&E)-stained slides. However, their ability to elucidate detailed functional characteristics of diverse cellular phenotypes within tumor immune microenvironment (TME) is limited. We aimed to enhance our understanding of cellular composition and functional characteristics across TME regions and improve patient stratification by integrating H&E with adjacent immunohistochemistry (IHC) images. A retrospective study was conducted on patients with Human Papillomavirus-positive oropharyngeal squamous cell carcinoma (OPSCC). Using paired H&E and IHC slides for 11 proteins, a deep learning pipeline was used to quantify tumor, stroma, and TILs in the TME. Patients were classified into immune inflamed (IN), immune excluded (IE), or immune desert (ID) phenotypes. By registering the IHC and H&E slides, we integrated IHC data to capture protein expression in the corresponding tumor regions. We further stratified patients into specific immune subtypes, such as IN, with increased or reduced CD8+ cells, based on the abundance of these proteins. This characterization provided functional insight into the H&E-based subtypes. Analysis of 88 primary tumors and 70 involved lymph node tissue images reveals an improved prognosis in patients classified as IN in primary tumors with high CD8 and low CD163 expression (p = 0.007). Multivariate Cox regression analysis confirms a significantly better prognosis for these subtypes. Integrating H&E and IHC data enhances the functional characterization of immune phenotypes of the TME with biological interpretability, and improves patient stratification in HPV( + ) OPSCC. In this study, we investigated whether differences in the immune cell population surrounding head and neck cancers impact disease progression. We used advanced computer programs to analyze tissue samples from tumors and nearby lymph nodes, a part of the immune system. These tumor and lymph node samples were stained to show the structure of the tissue and to identify the different types of immune cells present. We grouped patients into different categories based on differences in their immune cells. We found that patients with certain patterns of immune cells tended to have better outcomes. This method could help doctors predict how well patients will respond to treatments. Nakkireddy, Jang, Kim, et al. explore tumor immune microenvironment (TME) types in HPV-positive oropharyngeal squamous cell carcinoma. Deep learning analysis of tumor and lymph node tissues identifies immune cell patterns that correlate with improved prognosis.","PeriodicalId":72646,"journal":{"name":"Communications medicine","volume":" ","pages":"1-10"},"PeriodicalIF":5.4,"publicationDate":"2024-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11450009/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142373677","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 : 2024-10-03DOI: 10.1038/s43856-024-00620-w
Vincent Damotte, Chao Zhao, Chris Lin, Eric Williams, Yoram Louzoun, Abeer Madbouly, Rochelle Kotlarz, Marissa McDaniel, Paul J. Norman, Yong Wang, Martin Maiers, Jill A. Hollenbach
Questions persist around whether and how to use race or geographic ancestry in biomedical research and medicine, but these forms of self-identification serve as a critical tool to inform matching algorithms for human leukocyte antigen (HLA) of varying levels of resolution for unrelated hematopoietic stem cell transplant in large donor registries. Here, we examined multiple self-reported measures of race and ancestry from a survey of a cohort of over 100,000 U.S. volunteer bone marrow donors alongside their high-resolution HLA genotype data. We find that these self-report measures are often non-overlapping, and that no single self-reported measure alone provides a better fit to HLA genetic ancestry than a combination including both race and geographic ancestry. We also found that patterns of reporting for race and ancestry appear to be influenced by participation in direct-to-consumer genetic ancestry testing. While these data are not used directly in matching for transplant, our results demonstrate that there is a place for the language of both race and geographic ancestry in the critical process of facilitating accurate prediction of HLA in the donor registry context. Self-identification with respect to race and ancestry is an important component in the process of finding a matching unrelated bone marrow donor for a patient in large donor registries. Here, we considered whether terms specific to either race or the geographic ancestry of donors would be more useful in the matching process. We found that rather than using either of these terms alone, collecting responses for both race and geographic ancestry from potential donors is most likely to provide the information necessary to find a genetic match among millions of donors for a patient in need of a transplant. Damotte et al. examine the utility of multiple measures of race and ancestry self-identification in the context of matching HLA for potential unrelated bone marrow donors with patients. They show that combining both race and geographic ancestry provides a better fit to HLA than either measure alone.
{"title":"Multiple measures for self-identification improve matching donors with patients in unrelated hematopoietic stem cell transplant","authors":"Vincent Damotte, Chao Zhao, Chris Lin, Eric Williams, Yoram Louzoun, Abeer Madbouly, Rochelle Kotlarz, Marissa McDaniel, Paul J. Norman, Yong Wang, Martin Maiers, Jill A. Hollenbach","doi":"10.1038/s43856-024-00620-w","DOIUrl":"10.1038/s43856-024-00620-w","url":null,"abstract":"Questions persist around whether and how to use race or geographic ancestry in biomedical research and medicine, but these forms of self-identification serve as a critical tool to inform matching algorithms for human leukocyte antigen (HLA) of varying levels of resolution for unrelated hematopoietic stem cell transplant in large donor registries. Here, we examined multiple self-reported measures of race and ancestry from a survey of a cohort of over 100,000 U.S. volunteer bone marrow donors alongside their high-resolution HLA genotype data. We find that these self-report measures are often non-overlapping, and that no single self-reported measure alone provides a better fit to HLA genetic ancestry than a combination including both race and geographic ancestry. We also found that patterns of reporting for race and ancestry appear to be influenced by participation in direct-to-consumer genetic ancestry testing. While these data are not used directly in matching for transplant, our results demonstrate that there is a place for the language of both race and geographic ancestry in the critical process of facilitating accurate prediction of HLA in the donor registry context. Self-identification with respect to race and ancestry is an important component in the process of finding a matching unrelated bone marrow donor for a patient in large donor registries. Here, we considered whether terms specific to either race or the geographic ancestry of donors would be more useful in the matching process. We found that rather than using either of these terms alone, collecting responses for both race and geographic ancestry from potential donors is most likely to provide the information necessary to find a genetic match among millions of donors for a patient in need of a transplant. Damotte et al. examine the utility of multiple measures of race and ancestry self-identification in the context of matching HLA for potential unrelated bone marrow donors with patients. They show that combining both race and geographic ancestry provides a better fit to HLA than either measure alone.","PeriodicalId":72646,"journal":{"name":"Communications medicine","volume":" ","pages":"1-8"},"PeriodicalIF":5.4,"publicationDate":"2024-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s43856-024-00620-w.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142368750","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 : 2024-09-30DOI: 10.1038/s43856-024-00610-y
Daniel R. Kollath, Francisca J. Grill, Ashley N. Itogawa, Ana Fabio-Braga, Matthew M. Morales, Kelly M. Shepardson, Mitchell L. Bryant, Jinhee Yi, Marieke L. Ramsey, Emily T. Luberto, Kimberly R. Celona, Paul S. Keim, Erik W. Settles, Douglas Lake, Bridget M. Barker
Early reports showed that patients with COVID-19 had recrudescence of previously resolved coccidioidomycosis (Valley fever, VF), and there were indications that coinfection had more severe outcomes. We therefore investigated serial infection of Coccidioides posadasii and SARS-CoV-2 in a K18-hACE2 mouse model to assess disease outcomes. In our model, we challenged K18-hACE2 mice sequentially with a sub-lethal dose of SARS-CoV-2 and 24 hours later with low virulence strain of Coccidioides posadasii, and vice versa, compared to mice that only received a single infection challenge. We performed survival and pathogenesis mouse studies as well as looked at the systemic immune response differences between treatment groups. Here we show that co-infected groups have a more severe disease progression as well as a decrease in survival. Importantly, results differ depending on the SARS-CoV-2 variant (WA-1, Delta, or Omicron) and infection timing (SARS-CoV-2 first, C. posadasii second or vice versa). We find that groups that are infected with the virus first had a decrease in survival, increased morbidity and weight loss, increased fungal and viral burdens, differences in immune responses, and the amount and size of fungal spherules. We also find that groups coinfected with C. posadasii first have a decrease fungal burden and inflammatory responses. This is the first in vivo model investigation of a coinfection of SARS-CoV-2 and Coccidioides. Because of the potential for increased severity of disease in a coinfection, we suggest populations that live in areas of high coccidioidomycosis endemicity may experience higher incidence of complicated disease progression with COVID-19. The Covid-19 pandemic presented significant challenges to healthcare systems. One of these was the increase in secondary infections, where a patient had both SARS-Cov2 and another infectious disease. Fungal infections co-occurring with or after a Covid-19 infection are of interest due to treatment challenges and more severe illness in patients. Valley fever is a fungal infection prevalent in the southwestern United States and arid regions of Central and South America. Reports from these regions showed an increase in Valley fever cases coinciding with the rise of Covid-19. We therefore investigated how these two pathogens interacted with each other and the host in laboratory-controlled mouse experiments. We observed increased mortality when mice were exposed to the virus first followed by a fungal infection. Although more investigations are needed, our results should be taken into consideration in a clinical setting. Kollath et al. develop a murine model for testing the pathogenesis of a SARS-CoV-2 and Coccidioides posadasii co-infection and find that when mice are infected first with the virus, greater disease severity occurs. This has implications for people living in the endemic area for coccidioidomycosis as well as other emerging viruses.
{"title":"Developing a Coccidioides posadasii and SARS-CoV-2 Co-infection Model in the K18-hACE2 Transgenic Mouse","authors":"Daniel R. Kollath, Francisca J. Grill, Ashley N. Itogawa, Ana Fabio-Braga, Matthew M. Morales, Kelly M. Shepardson, Mitchell L. Bryant, Jinhee Yi, Marieke L. Ramsey, Emily T. Luberto, Kimberly R. Celona, Paul S. Keim, Erik W. Settles, Douglas Lake, Bridget M. Barker","doi":"10.1038/s43856-024-00610-y","DOIUrl":"10.1038/s43856-024-00610-y","url":null,"abstract":"Early reports showed that patients with COVID-19 had recrudescence of previously resolved coccidioidomycosis (Valley fever, VF), and there were indications that coinfection had more severe outcomes. We therefore investigated serial infection of Coccidioides posadasii and SARS-CoV-2 in a K18-hACE2 mouse model to assess disease outcomes. In our model, we challenged K18-hACE2 mice sequentially with a sub-lethal dose of SARS-CoV-2 and 24 hours later with low virulence strain of Coccidioides posadasii, and vice versa, compared to mice that only received a single infection challenge. We performed survival and pathogenesis mouse studies as well as looked at the systemic immune response differences between treatment groups. Here we show that co-infected groups have a more severe disease progression as well as a decrease in survival. Importantly, results differ depending on the SARS-CoV-2 variant (WA-1, Delta, or Omicron) and infection timing (SARS-CoV-2 first, C. posadasii second or vice versa). We find that groups that are infected with the virus first had a decrease in survival, increased morbidity and weight loss, increased fungal and viral burdens, differences in immune responses, and the amount and size of fungal spherules. We also find that groups coinfected with C. posadasii first have a decrease fungal burden and inflammatory responses. This is the first in vivo model investigation of a coinfection of SARS-CoV-2 and Coccidioides. Because of the potential for increased severity of disease in a coinfection, we suggest populations that live in areas of high coccidioidomycosis endemicity may experience higher incidence of complicated disease progression with COVID-19. The Covid-19 pandemic presented significant challenges to healthcare systems. One of these was the increase in secondary infections, where a patient had both SARS-Cov2 and another infectious disease. Fungal infections co-occurring with or after a Covid-19 infection are of interest due to treatment challenges and more severe illness in patients. Valley fever is a fungal infection prevalent in the southwestern United States and arid regions of Central and South America. Reports from these regions showed an increase in Valley fever cases coinciding with the rise of Covid-19. We therefore investigated how these two pathogens interacted with each other and the host in laboratory-controlled mouse experiments. We observed increased mortality when mice were exposed to the virus first followed by a fungal infection. Although more investigations are needed, our results should be taken into consideration in a clinical setting. Kollath et al. develop a murine model for testing the pathogenesis of a SARS-CoV-2 and Coccidioides posadasii co-infection and find that when mice are infected first with the virus, greater disease severity occurs. This has implications for people living in the endemic area for coccidioidomycosis as well as other emerging viruses.","PeriodicalId":72646,"journal":{"name":"Communications medicine","volume":" ","pages":"1-11"},"PeriodicalIF":5.4,"publicationDate":"2024-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11442577/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142333614","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 : 2024-09-30DOI: 10.1038/s43856-024-00612-w
Arne Claeys, Jimmy Van den Eynden
Immune checkpoint blockade is a highly successful anti-cancer immunotherapy. Both CTLA4 and PD1 checkpoint blockers are clinically available for melanoma treatment, with anti-PD1 therapy reaching response rates of 35-40%. These responses, which are mediated via neoantigen presentation by the polymorphic MHC complex, are hard to predict and the tumor mutation burden is currently one of the few available biomarkers. While MHC genotypes are expected to determine therapy responses, association studies have remained largely elusive. We developed an overall MHC genotype binding score (MGBS), indicative of a patient’s MHC class I (MHC-I) and class II (MHC-II) neoantigen binding capacity and solely based on the germline MHC-I (MGBS-I) and MHC-II (MGBS-II) genotypes. These scores were then correlated to survival and clinical responses following anti-PD1 immunotherapy in a previously published dataset of 144 melanoma patients. We demonstrate that MGBS scores are TMB-independent predictors of anti-PD1 immunotherapy responses in melanoma. Opposite outcomes were found for both MHC classes, with high MGBS-I and MGBS-II predicting good and bad outcomes, respectively. Interestingly, high MGBS-II is mainly associated with treatment response failure in a subgroup of anti-CTLA4 pretreated patients. Our results suggest that MGBS, calculated solely from the MHC genotype, has clinical potential as a non-invasive and tumor-independent biomarker to guide anti-cancer immunotherapy in melanoma. Many cancer patients are successfully treated with immunotherapy, which boosts the immune system to eliminate cancer cells. While this therapy is successful in around half of skin cancer melanoma patients, it is currently hard to determine in advance which patients respond well. Immune cells react to tumor proteins that are presented at the cancer cell surface by molecules called MHC. These are unique for every patient. We aimed to determine whether the ability of MHC to bind to tumor proteins determines how well therapy works and developed a new way to quantify this interaction. Surprisingly, less ability for tumor proteins to bind to the unconventional class II MHC resulted in better clinical outcome in patients with melanoma. Our results provide new understanding of tumor-immune interaction and the new method may help determine which patients with melanoma will respond to therapy. Claeys and Van den Eynden demonstrate that the genotype-specific binding properties of the Major Histocompatibility Complex (MHC) can predict outcome in melanoma patients treated with immunotherapy. Their results suggest an immunomodulatory role of non-canonical MHC-II presentable neoantigens.
{"title":"MHC class II genotypes are independent predictors of anti-PD1 immunotherapy response in melanoma","authors":"Arne Claeys, Jimmy Van den Eynden","doi":"10.1038/s43856-024-00612-w","DOIUrl":"10.1038/s43856-024-00612-w","url":null,"abstract":"Immune checkpoint blockade is a highly successful anti-cancer immunotherapy. Both CTLA4 and PD1 checkpoint blockers are clinically available for melanoma treatment, with anti-PD1 therapy reaching response rates of 35-40%. These responses, which are mediated via neoantigen presentation by the polymorphic MHC complex, are hard to predict and the tumor mutation burden is currently one of the few available biomarkers. While MHC genotypes are expected to determine therapy responses, association studies have remained largely elusive. We developed an overall MHC genotype binding score (MGBS), indicative of a patient’s MHC class I (MHC-I) and class II (MHC-II) neoantigen binding capacity and solely based on the germline MHC-I (MGBS-I) and MHC-II (MGBS-II) genotypes. These scores were then correlated to survival and clinical responses following anti-PD1 immunotherapy in a previously published dataset of 144 melanoma patients. We demonstrate that MGBS scores are TMB-independent predictors of anti-PD1 immunotherapy responses in melanoma. Opposite outcomes were found for both MHC classes, with high MGBS-I and MGBS-II predicting good and bad outcomes, respectively. Interestingly, high MGBS-II is mainly associated with treatment response failure in a subgroup of anti-CTLA4 pretreated patients. Our results suggest that MGBS, calculated solely from the MHC genotype, has clinical potential as a non-invasive and tumor-independent biomarker to guide anti-cancer immunotherapy in melanoma. Many cancer patients are successfully treated with immunotherapy, which boosts the immune system to eliminate cancer cells. While this therapy is successful in around half of skin cancer melanoma patients, it is currently hard to determine in advance which patients respond well. Immune cells react to tumor proteins that are presented at the cancer cell surface by molecules called MHC. These are unique for every patient. We aimed to determine whether the ability of MHC to bind to tumor proteins determines how well therapy works and developed a new way to quantify this interaction. Surprisingly, less ability for tumor proteins to bind to the unconventional class II MHC resulted in better clinical outcome in patients with melanoma. Our results provide new understanding of tumor-immune interaction and the new method may help determine which patients with melanoma will respond to therapy. Claeys and Van den Eynden demonstrate that the genotype-specific binding properties of the Major Histocompatibility Complex (MHC) can predict outcome in melanoma patients treated with immunotherapy. Their results suggest an immunomodulatory role of non-canonical MHC-II presentable neoantigens.","PeriodicalId":72646,"journal":{"name":"Communications medicine","volume":" ","pages":"1-8"},"PeriodicalIF":5.4,"publicationDate":"2024-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11443121/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142333616","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 : 2024-09-30DOI: 10.1038/s43856-024-00595-8
Prakasini Satapathy, Muhammad Aaqib Shamim, Bijaya K. Padhi, Aravind P. Gandhi, Mokanpally Sandeep, Tarun Kumar Suvvari, Jogendra Kumar, Gunjeet Kaur, Joshuan J. Barboza, Patricia Schlagenhauf, Ranjit Sah
Although the recent literature indicates that mpox (monkeypox) primarily affects men, there are also multiple reports in women. Estimates of the sex distribution of mpox patients and patterns will enable a better understanding of the ongoing mpox outbreak. In this systematic review and meta-analysis, seven databases were searched for studies published in English up to January 4th, 2023. The proportion of women with mpox was the primary outcome. A random-effects model was fitted for the primary outcome, and a sensitivity analysis was performed to check possible outliers in the studies. Here we screened 470 articles and included 60 studies for qualitative synthesis. 42 studies with 3125 women out of 47,407 confirmed cases were found suitable for meta-analysis. The pooled proportion of female patients is 17.22% (95% CI: 10.49-25.11; I2 = 98.86%). Subgroup analyses reveal higher proportion before 2022 [44.09% (42.93–46.86] than 2022 onwards [2.40% (1.17–3.98)], and in endemic countries [43.13% (37.63–48.72)] than in nonendemic countries [6.15% (2.20–11.65)]. There is considerable caseload (17.22%) amongst women, which must be seen in the context of a much higher proportion (44.09%) in studies prior to 2022 compared to 2.40% in the 2022 outbreak indicating an epidemiological shift. Data on disease characteristics among women with mpox disease are scarce. Further studies should focus on these aspects to better understand the disease in women and empower epidemiologists and clinicians to make evidence-based decisions for this vulnerable group. Mpox (formerly known as monkeypox) is an infection caused by the monkeypox virus. While it is known to affect men more commonly than women, there are also reports of this infection in women. We have searched the literature to find out how frequently mpox affected women. We found that 17% of mpox patients were female. However, this number was 44% before 2022, and has reduced to 2% from 2022 onwards. This indicates changes in mpox disease characteristics and in the ability to infect different sexes. Further studies are needed to better understand the disease in women and empower epidemiologists and clinicians to make evidence-based decisions for this group. Satapathy, Shamim et al. perform a systematic review and meta-analysis to characterize the epidemiology of mpox in women across regions and outbreaks. They uncover different trends in the 2022/2023 outbreak compared to previous outbreaks, and in endemic countries versus non-endemic countries.
{"title":"Mpox virus infection in women and outbreak sex disparities: A Systematic Review and Meta-analysis","authors":"Prakasini Satapathy, Muhammad Aaqib Shamim, Bijaya K. Padhi, Aravind P. Gandhi, Mokanpally Sandeep, Tarun Kumar Suvvari, Jogendra Kumar, Gunjeet Kaur, Joshuan J. Barboza, Patricia Schlagenhauf, Ranjit Sah","doi":"10.1038/s43856-024-00595-8","DOIUrl":"10.1038/s43856-024-00595-8","url":null,"abstract":"Although the recent literature indicates that mpox (monkeypox) primarily affects men, there are also multiple reports in women. Estimates of the sex distribution of mpox patients and patterns will enable a better understanding of the ongoing mpox outbreak. In this systematic review and meta-analysis, seven databases were searched for studies published in English up to January 4th, 2023. The proportion of women with mpox was the primary outcome. A random-effects model was fitted for the primary outcome, and a sensitivity analysis was performed to check possible outliers in the studies. Here we screened 470 articles and included 60 studies for qualitative synthesis. 42 studies with 3125 women out of 47,407 confirmed cases were found suitable for meta-analysis. The pooled proportion of female patients is 17.22% (95% CI: 10.49-25.11; I2 = 98.86%). Subgroup analyses reveal higher proportion before 2022 [44.09% (42.93–46.86] than 2022 onwards [2.40% (1.17–3.98)], and in endemic countries [43.13% (37.63–48.72)] than in nonendemic countries [6.15% (2.20–11.65)]. There is considerable caseload (17.22%) amongst women, which must be seen in the context of a much higher proportion (44.09%) in studies prior to 2022 compared to 2.40% in the 2022 outbreak indicating an epidemiological shift. Data on disease characteristics among women with mpox disease are scarce. Further studies should focus on these aspects to better understand the disease in women and empower epidemiologists and clinicians to make evidence-based decisions for this vulnerable group. Mpox (formerly known as monkeypox) is an infection caused by the monkeypox virus. While it is known to affect men more commonly than women, there are also reports of this infection in women. We have searched the literature to find out how frequently mpox affected women. We found that 17% of mpox patients were female. However, this number was 44% before 2022, and has reduced to 2% from 2022 onwards. This indicates changes in mpox disease characteristics and in the ability to infect different sexes. Further studies are needed to better understand the disease in women and empower epidemiologists and clinicians to make evidence-based decisions for this group. Satapathy, Shamim et al. perform a systematic review and meta-analysis to characterize the epidemiology of mpox in women across regions and outbreaks. They uncover different trends in the 2022/2023 outbreak compared to previous outbreaks, and in endemic countries versus non-endemic countries.","PeriodicalId":72646,"journal":{"name":"Communications medicine","volume":" ","pages":"1-10"},"PeriodicalIF":5.4,"publicationDate":"2024-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11443027/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142333617","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 : 2024-09-30DOI: 10.1038/s43856-024-00608-6
Suzanne Schrock-Kelley, Vivienne Souter, Michael J. Hall, Youbao Sha, Urmi Sengupta, Adam C. ElNaggar, Minetta C. Liu, Jeffrey N. Weitzel
Approximately 15% of colorectal cancers (CRCs) are associated with germline mutations. There is increasing adoption of DNA-based assays for molecular residual disease (MRD) and growing evidence supporting its clinical utility, particularly for CRC by oncologists in the U.S. We assessed the uptake of germline multi-gene panel testing (MGPT) for hereditary cancer in CRC patients receiving MRD analyses in community oncology settings. This retrospective study included 80 patients receiving care for CRC through community oncology practices who were referred for MRD testing at a commercial laboratory (January–March 2022). Clinical data, including test requisition forms, pathology reports, and clinical notes were reviewed. Documentation of tumor microsatellite instability and/or immunohistochemical (IHC) testing for mismatch repair (MMR) deficiency, age of CRC diagnosis, family history of cancer, and any order or recommendation for MGPT were assessed. Overall, 5/80 (6.3%) patients in the study have documented germline MGPT; 65/80 (81.3%) patients have documented MMR testing of their colorectal tumor. Among the 5 cases with abnormal MMR IHC, 2 have MGPT. Of the 33 patients meeting the 2021 National Comprehensive Cancer Network (NCCN) criteria for genetic/familial high-risk assessment, only 2 have MGPT. Our real-world data suggest that many CRC patients receiving MRD testing and meeting NCCN (v. 2021) criteria for germline MGPT may not be receiving evaluation beyond routine MMR status. Process and educational improvements are needed in community health settings to increase access and uptake of germline testing among CRC patients regardless of age at diagnosis or MMR status. Colorectal cancer is a major health concern worldwide. Identifying patients with hereditary cancer syndromes is important to patient care as well as their family members. We reviewed health records of 80 colorectal cancer patients undergoing different laboratory testing. Only 6.3% had specific genetic testing for inherited cancer risks, even though many patients met national guidelines for this testing. This points to a gap in clinical care. Enhancing access to genetic testing in community clinics could help more people and their families understand and manage their cancer risks. Schrock-Kelley et al. investigate the compliance with germline testing recommendations among colorectal cancer (CRC) patients who received molecular residual disease testing. Despite NCCN guidelines recommending consideration of multi-gene panel testing (MGPT) for all CRC patients, only 6.3% of this cohort received this, highlighting a gap in precision medicine.
{"title":"Poor compliance with germline testing recommendations in colorectal cancer patients undergoing molecular residual disease testing","authors":"Suzanne Schrock-Kelley, Vivienne Souter, Michael J. Hall, Youbao Sha, Urmi Sengupta, Adam C. ElNaggar, Minetta C. Liu, Jeffrey N. Weitzel","doi":"10.1038/s43856-024-00608-6","DOIUrl":"10.1038/s43856-024-00608-6","url":null,"abstract":"Approximately 15% of colorectal cancers (CRCs) are associated with germline mutations. There is increasing adoption of DNA-based assays for molecular residual disease (MRD) and growing evidence supporting its clinical utility, particularly for CRC by oncologists in the U.S. We assessed the uptake of germline multi-gene panel testing (MGPT) for hereditary cancer in CRC patients receiving MRD analyses in community oncology settings. This retrospective study included 80 patients receiving care for CRC through community oncology practices who were referred for MRD testing at a commercial laboratory (January–March 2022). Clinical data, including test requisition forms, pathology reports, and clinical notes were reviewed. Documentation of tumor microsatellite instability and/or immunohistochemical (IHC) testing for mismatch repair (MMR) deficiency, age of CRC diagnosis, family history of cancer, and any order or recommendation for MGPT were assessed. Overall, 5/80 (6.3%) patients in the study have documented germline MGPT; 65/80 (81.3%) patients have documented MMR testing of their colorectal tumor. Among the 5 cases with abnormal MMR IHC, 2 have MGPT. Of the 33 patients meeting the 2021 National Comprehensive Cancer Network (NCCN) criteria for genetic/familial high-risk assessment, only 2 have MGPT. Our real-world data suggest that many CRC patients receiving MRD testing and meeting NCCN (v. 2021) criteria for germline MGPT may not be receiving evaluation beyond routine MMR status. Process and educational improvements are needed in community health settings to increase access and uptake of germline testing among CRC patients regardless of age at diagnosis or MMR status. Colorectal cancer is a major health concern worldwide. Identifying patients with hereditary cancer syndromes is important to patient care as well as their family members. We reviewed health records of 80 colorectal cancer patients undergoing different laboratory testing. Only 6.3% had specific genetic testing for inherited cancer risks, even though many patients met national guidelines for this testing. This points to a gap in clinical care. Enhancing access to genetic testing in community clinics could help more people and their families understand and manage their cancer risks. Schrock-Kelley et al. investigate the compliance with germline testing recommendations among colorectal cancer (CRC) patients who received molecular residual disease testing. Despite NCCN guidelines recommending consideration of multi-gene panel testing (MGPT) for all CRC patients, only 6.3% of this cohort received this, highlighting a gap in precision medicine.","PeriodicalId":72646,"journal":{"name":"Communications medicine","volume":" ","pages":"1-5"},"PeriodicalIF":5.4,"publicationDate":"2024-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11442811/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142333676","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 : 2024-09-30DOI: 10.1038/s43856-024-00603-x
Reetam Ganguli, Jordan Franklin, Xiaotian Yu, Alice Lin, Aditi Vichare, Stephen Wagner
Transgender patients face a higher burden of cardiovascular morbidity due to structural and biological stressors, particularly in low-resource settings. No studies exist comparing machine learning model development strategies for this unique patient cohort and limited literature exists comparing data/outcomes between transgender and cisgender populations. We compare machine learning models trained solely on transgender patients against models developed on a size-matched and ratio-matched cohort of cisgender patients and a 300-fold larger, ratio-matched cohort of cisgender patients undergoing obstetric/gynecologic procedures in the National Surgical Quality Improvement Program from January 1, 2005 through December 31, 2019. All models were developed to predict the outcome of hypertension. Statistical significance between models was calculated using 5-by-2 fold cross validation hypothesis testing. Among 626,102 patients having an obstetric/gynecologic surgery, there are 1959 transgender patients of which 85,405 (13.7%) have hypertension requiring medication. Saliently, the logistic regression machine learning models trained selectively on the transgender cohort have an AUC of 0.865 (95% CI: 0.83–0.90), with an accuracy of 85% (95% CI: 0.80–0.87) compared to (p < 0.05) the logistic regression model trained on the 300-fold larger combined cohort which has an AUC of 0.861 (95% CI: 0.82–0.90), with an accuracy of 83% (95% CI: 0.80–0.87). Machine learning models can be trained on smaller, selectively transgender populations and may perform similarly or better to predict cardiovascular outcomes in transgender patients, than models developed on predominantly cisgender patients; this can be useful in lower-resource settings with smaller-volume transgender patients. Transgender patients face a higher burden of cardiovascular disease. Statistical models that predict cardiovascular disease-related outcomes, such as high blood pressure (hypertension), may be useful to clinicians to guide treatment, but existing models are mainly developed in cisgender populations. Here, we developed models to predict hypertension in patients undergoing surgery, and compared models developed using data from cisgender patients, transgender patients, or mixed populations to see if this affected how well these models could predict hypertension in the transgender population. We ultimately found that one of our models trained on a much smaller cohort of solely transgender patients outperformed the same model trained on a 300-times larger population of mixed cisgender and transgender patients. These findings might help to guide future efforts to develop statistical approaches to accurately predict health outcomes in transgender patients. Ganguli et al. compare the performance of machine learning models to predict hypertension in transgender patients undergoing gynecologic surgery. Logistic regression models trained on data from a cohort of transgender patients perform better than
{"title":"Comparison of machine learning models for the prediction of hypertension in transgender patients undergoing gynecologic surgery","authors":"Reetam Ganguli, Jordan Franklin, Xiaotian Yu, Alice Lin, Aditi Vichare, Stephen Wagner","doi":"10.1038/s43856-024-00603-x","DOIUrl":"10.1038/s43856-024-00603-x","url":null,"abstract":"Transgender patients face a higher burden of cardiovascular morbidity due to structural and biological stressors, particularly in low-resource settings. No studies exist comparing machine learning model development strategies for this unique patient cohort and limited literature exists comparing data/outcomes between transgender and cisgender populations. We compare machine learning models trained solely on transgender patients against models developed on a size-matched and ratio-matched cohort of cisgender patients and a 300-fold larger, ratio-matched cohort of cisgender patients undergoing obstetric/gynecologic procedures in the National Surgical Quality Improvement Program from January 1, 2005 through December 31, 2019. All models were developed to predict the outcome of hypertension. Statistical significance between models was calculated using 5-by-2 fold cross validation hypothesis testing. Among 626,102 patients having an obstetric/gynecologic surgery, there are 1959 transgender patients of which 85,405 (13.7%) have hypertension requiring medication. Saliently, the logistic regression machine learning models trained selectively on the transgender cohort have an AUC of 0.865 (95% CI: 0.83–0.90), with an accuracy of 85% (95% CI: 0.80–0.87) compared to (p < 0.05) the logistic regression model trained on the 300-fold larger combined cohort which has an AUC of 0.861 (95% CI: 0.82–0.90), with an accuracy of 83% (95% CI: 0.80–0.87). Machine learning models can be trained on smaller, selectively transgender populations and may perform similarly or better to predict cardiovascular outcomes in transgender patients, than models developed on predominantly cisgender patients; this can be useful in lower-resource settings with smaller-volume transgender patients. Transgender patients face a higher burden of cardiovascular disease. Statistical models that predict cardiovascular disease-related outcomes, such as high blood pressure (hypertension), may be useful to clinicians to guide treatment, but existing models are mainly developed in cisgender populations. Here, we developed models to predict hypertension in patients undergoing surgery, and compared models developed using data from cisgender patients, transgender patients, or mixed populations to see if this affected how well these models could predict hypertension in the transgender population. We ultimately found that one of our models trained on a much smaller cohort of solely transgender patients outperformed the same model trained on a 300-times larger population of mixed cisgender and transgender patients. These findings might help to guide future efforts to develop statistical approaches to accurately predict health outcomes in transgender patients. Ganguli et al. compare the performance of machine learning models to predict hypertension in transgender patients undergoing gynecologic surgery. Logistic regression models trained on data from a cohort of transgender patients perform better than ","PeriodicalId":72646,"journal":{"name":"Communications medicine","volume":" ","pages":"1-8"},"PeriodicalIF":5.4,"publicationDate":"2024-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11442826/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142333613","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}
Gene editing of immunomodulating molecules is a potential transplantation strategy to control immune rejection. As we noticed the successful transplantation of retinal pigment epithelium (RPE) derived from embryonic stem cells of a cynomolgus monkey that accidentally lacked MHC class II (MHC-II) molecules, we hypothesized immune rejection could be evaded by suppressing MHC-II. Gene editing by the Crispr/Cas9 system was performed in induced pluripotent stem cells derived from a cynomolgus monkey (miPSCs) for targeted deletion of the gene coding class II MHC trans-activator (CIITA). Then the CIITA-knocked out miPSCs were differentiated into RPE cells to generate miPSC-derived MHC-II knockout RPE. The MHC-II knockout or wild-type RPEs were transplanted into the eyes of healthy cynomolgus monkeys. All monkeys used in this study were male. Here we show when MHC-II knockout RPE are transplanted into monkey eyes, they show suppressed immunogenicity with no infiltration of inflammatory cells, leading to successful engraftment. Our results reasonably evidence the efficacy of MHC-II knockout iPSC-RPE transplants for clinical application. Transplantation of healthy cells can be used to treat irreversibly damaged organs. However, a concern is that the transplanted cells will be rejected by the immune system. Generally, the immune system protects our body when unknown materials invade. But this is undesirable during cell transplantation as the transplanted cells are often eliminated by the host’s immune cells. We demonstrated in monkeys that deletion of part of the immune system in cells prior to transplantation reduced the amount of immune system activity following transplantation. Using similar strategies in the future could enable cell transplants to be used more successfully in humans, making cell transplantation therapy safer and applicable to a wider number of patients. Ishida et al. transplant Crispr/Cas9 gene edited MHC-II knockout or wild-type retinal pigment epithelium into cynomolgus monkey eyes. MHC-II knockout RPE engraft successfully with no infiltration of inflammatory cells.
{"title":"Graft survival of major histocompatibility complex deficient stem cell-derived retinal cells","authors":"Masaaki Ishida, Tomohiro Masuda, Noriko Sakai, Yoko Nakai-Futatsugi, Hiroyuki Kamao, Takashi Shiina, Masayo Takahashi, Sunao Sugita","doi":"10.1038/s43856-024-00617-5","DOIUrl":"10.1038/s43856-024-00617-5","url":null,"abstract":"Gene editing of immunomodulating molecules is a potential transplantation strategy to control immune rejection. As we noticed the successful transplantation of retinal pigment epithelium (RPE) derived from embryonic stem cells of a cynomolgus monkey that accidentally lacked MHC class II (MHC-II) molecules, we hypothesized immune rejection could be evaded by suppressing MHC-II. Gene editing by the Crispr/Cas9 system was performed in induced pluripotent stem cells derived from a cynomolgus monkey (miPSCs) for targeted deletion of the gene coding class II MHC trans-activator (CIITA). Then the CIITA-knocked out miPSCs were differentiated into RPE cells to generate miPSC-derived MHC-II knockout RPE. The MHC-II knockout or wild-type RPEs were transplanted into the eyes of healthy cynomolgus monkeys. All monkeys used in this study were male. Here we show when MHC-II knockout RPE are transplanted into monkey eyes, they show suppressed immunogenicity with no infiltration of inflammatory cells, leading to successful engraftment. Our results reasonably evidence the efficacy of MHC-II knockout iPSC-RPE transplants for clinical application. Transplantation of healthy cells can be used to treat irreversibly damaged organs. However, a concern is that the transplanted cells will be rejected by the immune system. Generally, the immune system protects our body when unknown materials invade. But this is undesirable during cell transplantation as the transplanted cells are often eliminated by the host’s immune cells. We demonstrated in monkeys that deletion of part of the immune system in cells prior to transplantation reduced the amount of immune system activity following transplantation. Using similar strategies in the future could enable cell transplants to be used more successfully in humans, making cell transplantation therapy safer and applicable to a wider number of patients. Ishida et al. transplant Crispr/Cas9 gene edited MHC-II knockout or wild-type retinal pigment epithelium into cynomolgus monkey eyes. MHC-II knockout RPE engraft successfully with no infiltration of inflammatory cells.","PeriodicalId":72646,"journal":{"name":"Communications medicine","volume":" ","pages":"1-14"},"PeriodicalIF":5.4,"publicationDate":"2024-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11442691/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142333615","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 : 2024-09-25DOI: 10.1038/s43856-024-00609-5
Soroosh Tayebi Arasteh, Tomás Arias-Vergara, Paula Andrea Pérez-Toro, Tobias Weise, Kai Packhäuser, Maria Schuster, Elmar Noeth, Andreas Maier, Seung Hee Yang
Integration of speech into healthcare has intensified privacy concerns due to its potential as a non-invasive biomarker containing individual biometric information. In response, speaker anonymization aims to conceal personally identifiable information while retaining crucial linguistic content. However, the application of anonymization techniques to pathological speech, a critical area where privacy is especially vital, has not been extensively examined. This study investigates anonymization’s impact on pathological speech across over 2700 speakers from multiple German institutions, focusing on privacy, pathological utility, and demographic fairness. We explore both deep-learning-based and signal processing-based anonymization methods. We document substantial privacy improvements across disorders—evidenced by equal error rate increases up to 1933%, with minimal overall impact on utility. Specific disorders such as Dysarthria, Dysphonia, and Cleft Lip and Palate experience minimal utility changes, while Dysglossia shows slight improvements. Our findings underscore that the impact of anonymization varies substantially across different disorders. This necessitates disorder-specific anonymization strategies to optimally balance privacy with diagnostic utility. Additionally, our fairness analysis reveals consistent anonymization effects across most of the demographics. This study demonstrates the effectiveness of anonymization in pathological speech for enhancing privacy, while also highlighting the importance of customized and disorder-specific approaches to account for inversion attacks. When someone’s way of speaking is disrupted due to health issues, making it hard for them to communicate clearly, it is described as pathological speech. Our study explores whether this type of speech can be modified to protect patient privacy without losing its ability to help diagnose health conditions. We evaluated automatic anonymization for over 2,700 speakers. The results show that these methods can substantially enhance privacy while still maintaining the usefulness of speech in medical diagnostics. This means we can keep speech data private whilst still being able to use it to identify health issues. However, our results show the effectiveness of these methods can vary depending on the specific condition being diagnosed. Our study provides a method that can help maintain patient privacy, whilst highlighting that further customized approaches will be required to ensure optimal privacy. Tayebi Arasteh et al. investigate the impact of speaker anonymization on pathological speech, focusing on preserving pathological utility while safeguarding patient privacy. Their study reveals privacy improvements with minimal utility loss across most disorders, highlighting the need for disorder-specific anonymization strategies.
{"title":"Addressing challenges in speaker anonymization to maintain utility while ensuring privacy of pathological speech","authors":"Soroosh Tayebi Arasteh, Tomás Arias-Vergara, Paula Andrea Pérez-Toro, Tobias Weise, Kai Packhäuser, Maria Schuster, Elmar Noeth, Andreas Maier, Seung Hee Yang","doi":"10.1038/s43856-024-00609-5","DOIUrl":"10.1038/s43856-024-00609-5","url":null,"abstract":"Integration of speech into healthcare has intensified privacy concerns due to its potential as a non-invasive biomarker containing individual biometric information. In response, speaker anonymization aims to conceal personally identifiable information while retaining crucial linguistic content. However, the application of anonymization techniques to pathological speech, a critical area where privacy is especially vital, has not been extensively examined. This study investigates anonymization’s impact on pathological speech across over 2700 speakers from multiple German institutions, focusing on privacy, pathological utility, and demographic fairness. We explore both deep-learning-based and signal processing-based anonymization methods. We document substantial privacy improvements across disorders—evidenced by equal error rate increases up to 1933%, with minimal overall impact on utility. Specific disorders such as Dysarthria, Dysphonia, and Cleft Lip and Palate experience minimal utility changes, while Dysglossia shows slight improvements. Our findings underscore that the impact of anonymization varies substantially across different disorders. This necessitates disorder-specific anonymization strategies to optimally balance privacy with diagnostic utility. Additionally, our fairness analysis reveals consistent anonymization effects across most of the demographics. This study demonstrates the effectiveness of anonymization in pathological speech for enhancing privacy, while also highlighting the importance of customized and disorder-specific approaches to account for inversion attacks. When someone’s way of speaking is disrupted due to health issues, making it hard for them to communicate clearly, it is described as pathological speech. Our study explores whether this type of speech can be modified to protect patient privacy without losing its ability to help diagnose health conditions. We evaluated automatic anonymization for over 2,700 speakers. The results show that these methods can substantially enhance privacy while still maintaining the usefulness of speech in medical diagnostics. This means we can keep speech data private whilst still being able to use it to identify health issues. However, our results show the effectiveness of these methods can vary depending on the specific condition being diagnosed. Our study provides a method that can help maintain patient privacy, whilst highlighting that further customized approaches will be required to ensure optimal privacy. Tayebi Arasteh et al. investigate the impact of speaker anonymization on pathological speech, focusing on preserving pathological utility while safeguarding patient privacy. Their study reveals privacy improvements with minimal utility loss across most disorders, highlighting the need for disorder-specific anonymization strategies.","PeriodicalId":72646,"journal":{"name":"Communications medicine","volume":" ","pages":"1-16"},"PeriodicalIF":5.4,"publicationDate":"2024-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s43856-024-00609-5.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142328559","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}
Although polygenic risk scores (PRSs) are expected to be helpful in precision medicine, it remains unclear whether high-PRS groups are more likely to benefit from preventive interventions for diseases. Recent methodological advancements enable us to predict treatment effects at the individual level. We employed causal forest to explore the relationship between PRSs and individual risk of diseases associated with certain environmental factors. Following simulations illustrating its performance, we applied our approach to investigate the individual risk of cardiometabolic diseases, including coronary artery diseases (CAD) and type 2 diabetes (T2D), associated with obesity and smoking among individuals from UK Biobank (UKB; n = 369,942) and BioBank Japan (BBJ; n = 149,421). Here we find the heterogeneous association of obesity and smoking with diseases across PRS values, complicated by the multi-dimensional combination of individual characteristics such as age and sex. The highest positive correlations of PRSs and the exposure-related disease risks are observed between obesity and T2D in UKB and between smoking and CAD in BBJ (Spearman’s ρ = 0.61 and 0.32, respectively). However, most relationships are weak or negative, suggesting that high-PRS groups will not necessarily benefit most from environmental factor prevention. Our study highlights the importance of individual-level prediction of disease risks associated with target exposure in precision medicine. This study aimed to understand if people with a high genetic risk for certain diseases benefit more from preventive strategies. Using a machine-learning-based method, we analyzed data from large groups of people in the UK and Japan. We examined the risk of heart and metabolic diseases in relation to obesity and smoking. The results showed that the link between genetic risk and disease is complex and varies widely among individuals. Our results suggested that those with a high genetic risk for disease may not always benefit more from the prevention of obesity and smoking. This finding suggests that we need to consider more than risk in decisions on how to prevent diseases in individuals. Naito and Inoue, et al. apply machine learning to reveal heterogeneous associations between environmental factors and diseases across polygenic risk scores. Focusing on cardiometabolic diseases shows that those with high genetic disease susceptibility may not necessarily benefit the most from the reduction of corresponding disease risk factors.
{"title":"Machine learning reveals heterogeneous associations between environmental factors and cardiometabolic diseases across polygenic risk scores","authors":"Tatsuhiko Naito, Kosuke Inoue, Shinichi Namba, Kyuto Sonehara, Ken Suzuki, BioBank Japan, Koichi Matsuda, Naoki Kondo, Tatsushi Toda, Toshimasa Yamauchi, Takashi Kadowaki, Yukinori Okada","doi":"10.1038/s43856-024-00596-7","DOIUrl":"10.1038/s43856-024-00596-7","url":null,"abstract":"Although polygenic risk scores (PRSs) are expected to be helpful in precision medicine, it remains unclear whether high-PRS groups are more likely to benefit from preventive interventions for diseases. Recent methodological advancements enable us to predict treatment effects at the individual level. We employed causal forest to explore the relationship between PRSs and individual risk of diseases associated with certain environmental factors. Following simulations illustrating its performance, we applied our approach to investigate the individual risk of cardiometabolic diseases, including coronary artery diseases (CAD) and type 2 diabetes (T2D), associated with obesity and smoking among individuals from UK Biobank (UKB; n = 369,942) and BioBank Japan (BBJ; n = 149,421). Here we find the heterogeneous association of obesity and smoking with diseases across PRS values, complicated by the multi-dimensional combination of individual characteristics such as age and sex. The highest positive correlations of PRSs and the exposure-related disease risks are observed between obesity and T2D in UKB and between smoking and CAD in BBJ (Spearman’s ρ = 0.61 and 0.32, respectively). However, most relationships are weak or negative, suggesting that high-PRS groups will not necessarily benefit most from environmental factor prevention. Our study highlights the importance of individual-level prediction of disease risks associated with target exposure in precision medicine. This study aimed to understand if people with a high genetic risk for certain diseases benefit more from preventive strategies. Using a machine-learning-based method, we analyzed data from large groups of people in the UK and Japan. We examined the risk of heart and metabolic diseases in relation to obesity and smoking. The results showed that the link between genetic risk and disease is complex and varies widely among individuals. Our results suggested that those with a high genetic risk for disease may not always benefit more from the prevention of obesity and smoking. This finding suggests that we need to consider more than risk in decisions on how to prevent diseases in individuals. Naito and Inoue, et al. apply machine learning to reveal heterogeneous associations between environmental factors and diseases across polygenic risk scores. Focusing on cardiometabolic diseases shows that those with high genetic disease susceptibility may not necessarily benefit the most from the reduction of corresponding disease risk factors.","PeriodicalId":72646,"journal":{"name":"Communications medicine","volume":" ","pages":"1-12"},"PeriodicalIF":5.4,"publicationDate":"2024-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s43856-024-00596-7.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142273342","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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