Pub Date : 2024-08-12DOI: 10.1101/2024.08.11.24311846
M. Mori, Y. Mori, Nakao Yuki, Shintaro Mandai, T. Fujiki, Hiroaki Kikuchi, Fumiaki Ando, K. Susa, Takayasu Mori, Y. Waseda, S. Yoshida, Y. Fujii, E. Sohara, Shinichi Uchida
Introduction: Organoids are miniature organs produced by newly emerging technologies. Kidney organoids originated from human inducible pluripotent stem cells (iPSCs) were developed to recapitulate renal diseases. However, producing iPSC kidney organoids from multiple individuals at the same time and in a uniform condition is still impossible. Here, we report adult renal tubular organoids, "tubuloids", established from primary renal epithelial cells from multiple human individuals in a uniform manner. Methods: Kidneys obtained from patients due to the surgery for malignancy were minced into small pieces, and primary renal epithelial tubule cells are cultured. 4 patients had normal kidney function and 4 had mild chronic kidney disease (CKD). Growth factors were added to the primary cultured cells at the same time and Matrigel was added to these 8 lines. Results: Primary cultured renal epithelial cells from normal kidneys showed a large number of fine, swollen epithelial appearance. On the other hand, primary cultured kidney epithelial cells from mild CKD kidneys were smaller and slightly elongated than those of normal kidneys. The growth speed was faster in normal kidney cells than in mild CKD cells. At the beginning of the three-dimensionalization (day 0), normal renal tubuloids grew faster than mild CKD tubuloids. The difference in size between normal tubuloids and mild CKD ones became less noticeable on day 5. Both types of tubuloids reached almost same size on day 10. All 8 strains are of different human origin, and uniform tubuloids could be produced at the same time and in a uniform protocol. Conclusion: In terms of pathological models, the differences between mouse models and humans cannot be ignored, and there is a great need for a more human-like model of human pathology from both medical and research perspectives. Our renal tubular organoids can be produced in a uniform manner at the same time. It is expected to be used as a new type of convenient human pathological model.
{"title":"Adult renal tubular organoids can be produced from different human individuals in a completely same protocol","authors":"M. Mori, Y. Mori, Nakao Yuki, Shintaro Mandai, T. Fujiki, Hiroaki Kikuchi, Fumiaki Ando, K. Susa, Takayasu Mori, Y. Waseda, S. Yoshida, Y. Fujii, E. Sohara, Shinichi Uchida","doi":"10.1101/2024.08.11.24311846","DOIUrl":"https://doi.org/10.1101/2024.08.11.24311846","url":null,"abstract":"Introduction: Organoids are miniature organs produced by newly emerging technologies. Kidney organoids originated from human inducible pluripotent stem cells (iPSCs) were developed to recapitulate renal diseases. However, producing iPSC kidney organoids from multiple individuals at the same time and in a uniform condition is still impossible. Here, we report adult renal tubular organoids, \"tubuloids\", established from primary renal epithelial cells from multiple human individuals in a uniform manner. Methods: Kidneys obtained from patients due to the surgery for malignancy were minced into small pieces, and primary renal epithelial tubule cells are cultured. 4 patients had normal kidney function and 4 had mild chronic kidney disease (CKD). Growth factors were added to the primary cultured cells at the same time and Matrigel was added to these 8 lines. Results: Primary cultured renal epithelial cells from normal kidneys showed a large number of fine, swollen epithelial appearance. On the other hand, primary cultured kidney epithelial cells from mild CKD kidneys were smaller and slightly elongated than those of normal kidneys. The growth speed was faster in normal kidney cells than in mild CKD cells. At the beginning of the three-dimensionalization (day 0), normal renal tubuloids grew faster than mild CKD tubuloids. The difference in size between normal tubuloids and mild CKD ones became less noticeable on day 5. Both types of tubuloids reached almost same size on day 10. All 8 strains are of different human origin, and uniform tubuloids could be produced at the same time and in a uniform protocol. Conclusion: In terms of pathological models, the differences between mouse models and humans cannot be ignored, and there is a great need for a more human-like model of human pathology from both medical and research perspectives. Our renal tubular organoids can be produced in a uniform manner at the same time. It is expected to be used as a new type of convenient human pathological model.","PeriodicalId":18505,"journal":{"name":"medRxiv","volume":"42 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141919280","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-08-12DOI: 10.1101/2024.08.11.24311845
Yaning Feng, Kenneth Chi-Yin, Wong, Wai Kai Tsui, Ruoyu Zhang, Yong XIANG, SO Hon-Cheong, Lo
Background: The COVID-19 pandemic has led to substantial health and financial burden worldwide, and vaccines provide hope to reduce the burden of this pandemic. However, vaccinated people remain at risk for SARS-CoV-2 infection. Genome-wide association studies (GWAS) may allow for the identification of potential genetic factors involved in the development of COVID-19 breakthrough infections (BI), however very few or no GWAS have been conducted for COVID-19 BI so far. Methods: We conducted a GWAS and detailed bioinformatics analysis on COVID-19 BI in a European population based on the UK-Biobank (UKBB). We conducted a series of analyses at different levels, including SNP-based, gene-based, pathway, and transcriptome-wide association analyses, to investigate genetic factors associated with COVID-19 BI and hospitalized infection. Polygenic risk score (PRS) and Hoeffding's test were performed to reveal genetic relationships between BI and other medical conditions. Results: Two independent loci (LD-clumped at r2=0.01) reached genome-wide significance (p<5e-08), including rs36170929 mapped to LOC102725191/VWDE, and rs28645263 mapped to RETREG1. Pathway enrichment analysis highlighted pathways such as viral myocarditis, Rho-selective guanine exchange factor AKAP13 signaling, and lipid metabolism. PRS analyses showed significant genetic overlap between COVID-19 BI and heart failure, HbA1c and type 1 diabetes. Genetic dependence was also observed between COVID-19 BI and asthma, lung abnormalities, schizophrenia, and type 1 diabetes, based on the Hoeffding's test. Conclusions: This GWAS study revealed two significant loci that may be associated with COVID-19 BI, and a number of genes and pathways that may be involved in BI. Genetic overlap with other diseases was identified. Further studies are warranted to replicate the findings and elucidate the mechanisms involved.
背景：COVID-19 大流行给全世界带来了巨大的健康和经济负担，而疫苗则为减轻这一流行病的负担带来了希望。然而，接种过疫苗的人仍有感染 SARS-CoV-2 的风险。全基因组关联研究（GWAS）可能有助于确定与 COVID-19 突发性感染（BI）发生有关的潜在遗传因素，但迄今为止，针对 COVID-19 BI 的 GWAS 研究很少或根本没有。方法：我们以英国生物库（UKBB）为基础，在欧洲人群中对 COVID-19 BI 进行了 GWAS 和详细的生物信息学分析。我们进行了一系列不同层次的分析，包括基于 SNP、基于基因、通路和转录组的关联分析，以研究与 COVID-19 BI 和住院感染相关的遗传因素。此外，还进行了多基因风险评分（PRS）和Hoeffding检验，以揭示BI与其他疾病之间的遗传关系。结果显示两个独立位点（LD-clumped at r2=0.01）达到了全基因组显著性（p<5e-08），包括映射到 LOC102725191/VWDE 的 rs36170929 和映射到 RETREG1 的 rs28645263。通路富集分析突出了病毒性心肌炎、Rho-选择性鸟嘌呤交换因子 AKAP13 信号转导和脂质代谢等通路。PRS 分析显示，COVID-19 BI 与心力衰竭、HbA1c 和 1 型糖尿病之间存在明显的遗传重叠。根据 Hoeffding 检验，还观察到 COVID-19 BI 与哮喘、肺部异常、精神分裂症和 1 型糖尿病之间存在遗传依赖性。结论这项基因组学分析研究发现了两个可能与 COVID-19 BI 相关的重要基因位点，以及一些可能与 BI 相关的基因和通路。研究还发现了与其他疾病的基因重叠。有必要开展进一步研究，以复制研究结果并阐明相关机制。
{"title":"Genome-wide association study of COVID-19 Breakthrough Infections and genetic overlap with other diseases: A study of the UK Biobank","authors":"Yaning Feng, Kenneth Chi-Yin, Wong, Wai Kai Tsui, Ruoyu Zhang, Yong XIANG, SO Hon-Cheong, Lo","doi":"10.1101/2024.08.11.24311845","DOIUrl":"https://doi.org/10.1101/2024.08.11.24311845","url":null,"abstract":"Background: The COVID-19 pandemic has led to substantial health and financial burden worldwide, and vaccines provide hope to reduce the burden of this pandemic. However, vaccinated people remain at risk for SARS-CoV-2 infection. Genome-wide association studies (GWAS) may allow for the identification of potential genetic factors involved in the development of COVID-19 breakthrough infections (BI), however very few or no GWAS have been conducted for COVID-19 BI so far. Methods: We conducted a GWAS and detailed bioinformatics analysis on COVID-19 BI in a European population based on the UK-Biobank (UKBB). We conducted a series of analyses at different levels, including SNP-based, gene-based, pathway, and transcriptome-wide association analyses, to investigate genetic factors associated with COVID-19 BI and hospitalized infection. Polygenic risk score (PRS) and Hoeffding's test were performed to reveal genetic relationships between BI and other medical conditions. Results: Two independent loci (LD-clumped at r2=0.01) reached genome-wide significance (p<5e-08), including rs36170929 mapped to LOC102725191/VWDE, and rs28645263 mapped to RETREG1. Pathway enrichment analysis highlighted pathways such as viral myocarditis, Rho-selective guanine exchange factor AKAP13 signaling, and lipid metabolism. PRS analyses showed significant genetic overlap between COVID-19 BI and heart failure, HbA1c and type 1 diabetes. Genetic dependence was also observed between COVID-19 BI and asthma, lung abnormalities, schizophrenia, and type 1 diabetes, based on the Hoeffding's test. Conclusions: This GWAS study revealed two significant loci that may be associated with COVID-19 BI, and a number of genes and pathways that may be involved in BI. Genetic overlap with other diseases was identified. Further studies are warranted to replicate the findings and elucidate the mechanisms involved.","PeriodicalId":18505,"journal":{"name":"medRxiv","volume":"19 9","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141919235","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-08-12DOI: 10.1101/2024.08.11.24311830
Joanne Igoli, Temidayo Osunronbi, O. Olukoya, Jeremiah Oluwatomi, Itodo Daniel, Hillary O. Alemenzohu, Alieu Kanu, Alex Mwangi Kihunyu, Ebuka Okeleke, Henry Oyoyo, Oluwatobi Shekoni, D. Jesuyajolu, Andrew F Alalade
Introduction: Accurate identification of study designs and risk of bias (RoB) assessment is crucial for evidence synthesis in research. However, mislabelling of case-control studies (CCS) is prevalent, leading to a downgraded quality of evidence. Large Language Models (LLMs), a form of artificial intelligence, have shown impressive performance in various medical tasks. Still, their utility and application in categorising study designs and assessing RoB needs to be further explored. This study will evaluate the performance of four publicly available LLMs (ChatGPT-3.5, ChatGPT-4, Claude 3 Sonnet, Claude 3 Opus) in accurately identifying CCS designs from the neurosurgical literature. Secondly, we will assess the human-LLM interrater agreement for RoB assessment of true CCS. Methods: We identified thirty-four top-ranking neurosurgical-focused journals and searched them on PubMed/MEDLINE for manuscripts reported as CCS in the title/abstract. Human reviewers will independently assess study designs and RoB using the Newcastle-Ottawa Scale. The methods sections/full-text articles will be provided to LLMs to determine study designs and assess RoB. Cohen's kappa will be used to evaluate human-human, human-LLM and LLM-LLM interrater agreement. Logistic regression will be used to assess study characteristics affecting performance. A p-value < 0.05 at a 95% confidence interval will be considered statistically significant. Conclusion If the human-LLM agreement is high, LLMs could become valuable teaching and quality assurance tools for critical appraisal in neurosurgery and other medical fields. This study will contribute to validating LLMs for specialised scientific tasks in evidence synthesis. This could lead to reduced review costs, faster completion, standardisation, and minimal errors in evidence synthesis.
{"title":"The accuracy of large language models in labelling neurosurgical 'case-control studies and risk of bias assessment: protocol for a study of interrater agreement with human reviewers.","authors":"Joanne Igoli, Temidayo Osunronbi, O. Olukoya, Jeremiah Oluwatomi, Itodo Daniel, Hillary O. Alemenzohu, Alieu Kanu, Alex Mwangi Kihunyu, Ebuka Okeleke, Henry Oyoyo, Oluwatobi Shekoni, D. Jesuyajolu, Andrew F Alalade","doi":"10.1101/2024.08.11.24311830","DOIUrl":"https://doi.org/10.1101/2024.08.11.24311830","url":null,"abstract":"Introduction: Accurate identification of study designs and risk of bias (RoB) assessment is crucial for evidence synthesis in research. However, mislabelling of case-control studies (CCS) is prevalent, leading to a downgraded quality of evidence. Large Language Models (LLMs), a form of artificial intelligence, have shown impressive performance in various medical tasks. Still, their utility and application in categorising study designs and assessing RoB needs to be further explored. This study will evaluate the performance of four publicly available LLMs (ChatGPT-3.5, ChatGPT-4, Claude 3 Sonnet, Claude 3 Opus) in accurately identifying CCS designs from the neurosurgical literature. Secondly, we will assess the human-LLM interrater agreement for RoB assessment of true CCS. Methods: We identified thirty-four top-ranking neurosurgical-focused journals and searched them on PubMed/MEDLINE for manuscripts reported as CCS in the title/abstract. Human reviewers will independently assess study designs and RoB using the Newcastle-Ottawa Scale. The methods sections/full-text articles will be provided to LLMs to determine study designs and assess RoB. Cohen's kappa will be used to evaluate human-human, human-LLM and LLM-LLM interrater agreement. Logistic regression will be used to assess study characteristics affecting performance. A p-value < 0.05 at a 95% confidence interval will be considered statistically significant. Conclusion If the human-LLM agreement is high, LLMs could become valuable teaching and quality assurance tools for critical appraisal in neurosurgery and other medical fields. This study will contribute to validating LLMs for specialised scientific tasks in evidence synthesis. This could lead to reduced review costs, faster completion, standardisation, and minimal errors in evidence synthesis.","PeriodicalId":18505,"journal":{"name":"medRxiv","volume":"40 9","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141919202","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-08-12DOI: 10.1101/2024.08.11.24311839
H. Thakkar, C. Reddy, V. R. Passi, A. Miyajiwala, S. Kale, A. Raj, S. Zadey
Background: While studies have investigated the availability of Medical College Hospitals (MCHs) in India, data on geographical accessibility is limited. Our study looks at the current geographical accessibility to these MCHs across 36 states and union territories (UTs) and 735 districts. Methods and Findings: We provided and validated the MCH data acquired from the National Health Profile Report 2022. We took motorized and walking travel-time friction surface rasters from the Malaria Atlas Project 2019 and high-resolution population estimates from WorldPop 2020. Using these, we examined the density of MCHs per million population and the median travel time to the nearest MCH. We assessed the Access Population Coverage (APC), defined as the proportion of the population within 30, 60, 90, and 120 minutes by motorized transport and within 30 and 60 minutes from the nearest MCH by walking. In 2022, India had an average density of 0.47 MCHs per million. The median travel time to the nearest MCH was 67.94 minutes by motorized transport and 589.82 minutes by walking. 71.76% of the population could access the nearest MCH by motorized transport within 60 minutes (range across districts: 0-100%). 4.22% of the population could access the nearest MCH by walking within 30 minutes (range across districts: 0-71.86%). The APC was 62.20% within 60 minutes by motorized transport in rural vs. 92.34% in urban areas. The APC within 60 minutes by motorized transport for public MCHs was 63.62%, while that for private was 45.95%. These estimates do not account for resource availability at the hospitals or vehicular ownership in the population. Conclusions: Median travel time and APC are useful for assessing geographical accessibility. Our study found a wide disparity in MCH access across Indian states and rural vs. urban areas. These analyses can guide the optimal placement of new MCHs.
{"title":"Assessing Population-level Accessibility to Medical College Hospitals in India: A Geospatial Modeling Study","authors":"H. Thakkar, C. Reddy, V. R. Passi, A. Miyajiwala, S. Kale, A. Raj, S. Zadey","doi":"10.1101/2024.08.11.24311839","DOIUrl":"https://doi.org/10.1101/2024.08.11.24311839","url":null,"abstract":"Background: While studies have investigated the availability of Medical College Hospitals (MCHs) in India, data on geographical accessibility is limited. Our study looks at the current geographical accessibility to these MCHs across 36 states and union territories (UTs) and 735 districts. Methods and Findings: We provided and validated the MCH data acquired from the National Health Profile Report 2022. We took motorized and walking travel-time friction surface rasters from the Malaria Atlas Project 2019 and high-resolution population estimates from WorldPop 2020. Using these, we examined the density of MCHs per million population and the median travel time to the nearest MCH. We assessed the Access Population Coverage (APC), defined as the proportion of the population within 30, 60, 90, and 120 minutes by motorized transport and within 30 and 60 minutes from the nearest MCH by walking. In 2022, India had an average density of 0.47 MCHs per million. The median travel time to the nearest MCH was 67.94 minutes by motorized transport and 589.82 minutes by walking. 71.76% of the population could access the nearest MCH by motorized transport within 60 minutes (range across districts: 0-100%). 4.22% of the population could access the nearest MCH by walking within 30 minutes (range across districts: 0-71.86%). The APC was 62.20% within 60 minutes by motorized transport in rural vs. 92.34% in urban areas. The APC within 60 minutes by motorized transport for public MCHs was 63.62%, while that for private was 45.95%. These estimates do not account for resource availability at the hospitals or vehicular ownership in the population. Conclusions: Median travel time and APC are useful for assessing geographical accessibility. Our study found a wide disparity in MCH access across Indian states and rural vs. urban areas. These analyses can guide the optimal placement of new MCHs.","PeriodicalId":18505,"journal":{"name":"medRxiv","volume":"40 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141919003","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-08-12DOI: 10.1101/2024.08.12.24311821
P. Saini, E. Yu, M. Estiar, L. Krohn, Kheireddin Mufti, Uladzislau Rudakou, J. Ruskey, F. Asayesh, S. Laurent, D. Spiegelman, J. Trempe, T. Quinnell, Nicholas Oscroft, Isabelle Arnulf, J. Montplaisir, J. Gagnon, A. Desautels, Y. Dauvilliers, Gian Luigi Gigli, M. Valente, Francesco Janes, A. Bernardini, K. Šonka, D. Kemlink, Wolfgang Oertel, Karri Kaivola, International Lbd Genomics Consortium, A. Janzen, G. Plazzi, E. Antelmi, F. Biscarini, M. Figorilli, M. Puligheddu, B. Mollenhauer, C. Trenkwalder, F. Sixel-Döring, V. C. Cock, C. Monaca, Donald G. Grosset, A. Heidbreder, Luigi Ferini-Strambi, F. Dijkstra, M. Viaene, B. Abril, B. Boeve, R. Postuma, Guy A. Rouleau, Victoria Anselmi, Abubaker Ibrahim, A. Stefani, Birgit Högl, Michele T.M. Hu, Sonja W Scholz, Z. Gan-Or, Montreal Neurological, Institute
Two recent studies suggested that the APOE {varepsilon}4 haplotype was associated with increased -synuclein pathology in cell and mouse models. Genetic variants in the SNCA region have strong association with Parkinson's disease (PD), Dementia with Lewy Bodies (DLB), and idiopathic REM Sleep Behavior Disorder (iRBD), while APOE is a genetic risk determinant for only DLB. To determine if genetic-level interactions between SNCA and APOE exists that can explain the protein-level association, we investigated the genotypic interaction of APOE and SNCA in cohorts of PD, DLB, and iRBD. We analyzed genome-wide association study (GWAS) data from 5,229 PD patients and 5,480 controls, 2,610 DLB patients and 1,920 controls, and 1,055 iRBD patients and 3,667 controls. We used logistic regression interaction models across all 3 cohorts independently between the 1) top GWAS signals of SNCA SNPs and APOE haplotypes, 2) SNP x SNP and 3-way SNP interaction across the entire coding region plus 200kb flanking each gene. No significant interactions were found to be associated with any of the synucleinopathies after correction for multiple testing. Our results do not support a role for genetic interactions between APOE and SNCA across PD, DLB, and iRBD. Since the tested genetic variants affect the expression and function of these proteins, it is likely that any interactions between them does not affect the risk of PD, DLB and iRBD.
{"title":"Lack of Epistatic Interaction of SNCA with APOE in Synucleinopathies","authors":"P. Saini, E. Yu, M. Estiar, L. Krohn, Kheireddin Mufti, Uladzislau Rudakou, J. Ruskey, F. Asayesh, S. Laurent, D. Spiegelman, J. Trempe, T. Quinnell, Nicholas Oscroft, Isabelle Arnulf, J. Montplaisir, J. Gagnon, A. Desautels, Y. Dauvilliers, Gian Luigi Gigli, M. Valente, Francesco Janes, A. Bernardini, K. Šonka, D. Kemlink, Wolfgang Oertel, Karri Kaivola, International Lbd Genomics Consortium, A. Janzen, G. Plazzi, E. Antelmi, F. Biscarini, M. Figorilli, M. Puligheddu, B. Mollenhauer, C. Trenkwalder, F. Sixel-Döring, V. C. Cock, C. Monaca, Donald G. Grosset, A. Heidbreder, Luigi Ferini-Strambi, F. Dijkstra, M. Viaene, B. Abril, B. Boeve, R. Postuma, Guy A. Rouleau, Victoria Anselmi, Abubaker Ibrahim, A. Stefani, Birgit Högl, Michele T.M. Hu, Sonja W Scholz, Z. Gan-Or, Montreal Neurological, Institute","doi":"10.1101/2024.08.12.24311821","DOIUrl":"https://doi.org/10.1101/2024.08.12.24311821","url":null,"abstract":"Two recent studies suggested that the APOE {varepsilon}4 haplotype was associated with increased -synuclein pathology in cell and mouse models. Genetic variants in the SNCA region have strong association with Parkinson's disease (PD), Dementia with Lewy Bodies (DLB), and idiopathic REM Sleep Behavior Disorder (iRBD), while APOE is a genetic risk determinant for only DLB. To determine if genetic-level interactions between SNCA and APOE exists that can explain the protein-level association, we investigated the genotypic interaction of APOE and SNCA in cohorts of PD, DLB, and iRBD. We analyzed genome-wide association study (GWAS) data from 5,229 PD patients and 5,480 controls, 2,610 DLB patients and 1,920 controls, and 1,055 iRBD patients and 3,667 controls. We used logistic regression interaction models across all 3 cohorts independently between the 1) top GWAS signals of SNCA SNPs and APOE haplotypes, 2) SNP x SNP and 3-way SNP interaction across the entire coding region plus 200kb flanking each gene. No significant interactions were found to be associated with any of the synucleinopathies after correction for multiple testing. Our results do not support a role for genetic interactions between APOE and SNCA across PD, DLB, and iRBD. Since the tested genetic variants affect the expression and function of these proteins, it is likely that any interactions between them does not affect the risk of PD, DLB and iRBD.","PeriodicalId":18505,"journal":{"name":"medRxiv","volume":"23 6","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141919132","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-08-12DOI: 10.1101/2024.08.12.24311859
Alina Pelikh, KenjiKamiyama PhD, Mikko Myrskylä PhD, Michelle P Debbink PhD, PhD Alice Goisis
Study question: How are Medically Assisted Reproduction (MAR) treatments (Fertility enhancing drugs (FED), artificial/intrauterine insemination (AI/IUI)), assisted reproductive technology (ART) with autologous/donor oocytes) associated with maternal morbidity (MM)? Summary answer: More invasive MAR treatments (ART and AI/IUI) are associated with higher risk of MM, whilst less invasive treatments are not; this relationship is partially explained by higher prevalence of multifetal gestation and obstetric comorbidities in women undergoing more invasive treatment, but the persistent association suggests subfertility itself may contribute to maternal morbidity risk. What is known already: Women conceiving through MAR are at higher risk of MM, however, reported risks vary depending on the measurement of MM and data available on confounding. Study design, size, duration: Birth certificates were used to study maternal morbidity among all women giving birth in Utah, U.S., between 2009 and 2017 (N=460,976 deliveries); 19,448 conceived through MAR (4.2%). The MM outcome measure included the presence of any of the following: blood transfusion; unplanned operating room procedure; admission to ICU; eclampsia; unplanned hysterectomy; ruptured uterus. Participants/materials, setting, methods: Logistic regressions were estimated for the binary outcome (presence of any of the MM conditions). We assessed MM among women conceiving through MAR (overall and by type of treatment) compared to those conceiving spontaneously in the overall sample before and after adjustment for maternal socio-demographic characteristics (maternal age, family structure, level of education, Hispanic origin, parity), pre-existing maternal comorbidities (i.e., chronic hypertension, heart disease, asthma), multifetal gestation, and obstetric comorbidities (i.e., placenta previa, placental abruption, preterm delivery, cesarean delivery). Main results and the role of chance: Women conceiving through MAR had higher risk of MM; however, the magnitude of the association differed depending on the type of treatment. In the unadjusted models, more invasive treatments were associated with higher odds of MM: OR 5.71 (95% CI 3.50-9.31) among women conceiving through ART with donor oocytes, OR 3.20 (95% CI 2.69-3.81) among women conceiving through ART with autologous oocytes, and OR 1.85 (95% CI 1.39-2.46) among women conceiving through AI/IUI, whereas women conceiving through FED had similar risks of MM to compared to women conceiving spontaneously (SC), OR 1.09 (95% CI 0.91-1.30). The associations between MAR and MM were largely attenuated once multifetal gestation was accounted for. After controlling for obstetric comorbidities, the associations were further attenuated, yet the coefficients remained higher among women conceiving through ART with either donor oocytes OR 1.70 (95% CI 0.95-3.04) or autologous oocytes OR 1.46 (95% CI 1.20-1.78) compared to women conceiving spontaneously. In analyses limite
MM的研究得到了战略研究委员会（SRC）、FLUX联盟（决定号：345130和345131）、美国国立老龄化研究所（R01AG075208）、马克斯-普朗克协会（决定号：5714240218）对马克斯-普朗克-赫尔辛基大学中心、赫尔辛基大学社会科学学院Jane和Aatos Erkko基金会、赫尔辛基市、万塔市和埃斯波市的资助，以及欧盟（ERC Synergy, BIOSFER, 101071773）的资助。然而，本文所表达的观点和意见仅代表作者本人，并不一定反映欧盟或欧洲研究理事会的观点和意见。欧盟和拨款机构均不对此负责。我们感谢犹他大学亨茨曼癌症研究所的血统和人口资源中心（由亨茨曼癌症基金会提供部分资助）在持续收集、维护和支持犹他人口数据库（UPDB）方面所发挥的作用。我们还感谢犹他大学国家癌症研究所和犹他大学个性化健康项目以及犹他临床和转化科学研究所通过 P30 CA2014 基金为犹他人口数据库提供的部分支持。作为生殖科学家发展计划的一部分，MPD 从 March of Dimes 和美国妇产科委员会以及 NICHD 1U54HD113169 和 NIMHD 1R21MD019175-01A1 获得工资支持。关键字：辅助生殖技术；人工授精；促排卵；孕产妇发病率；产科合并症
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Pub Date : 2024-08-12DOI: 10.1101/2024.08.12.24311854
A. Kwong, A. Edmondson-Stait, Eileen Xu, Ellen J. Thompson, Richard M. A. Parker, Ahmed Elhakeem, L. Romaniuk, Rebecca M. Pearson, Kate Tilling, Thalia C. Eley, McIntosh Andrew M, Heather C. Whalley
Motivation: Growth curve modelling is one method used to model trajectories of traits and behaviours over time. However, accessing, analysing and interpreting trajectories requires statistical expertise, thereby creating potential barriers for users to implement and understand longitudinal traits. TIDAL is a user-friendly research tool designed to facilitate trajectory modelling by improving access, analysis and interpretation of trajectory and longitudinal data. Implementation: TIDAL is available in two formats: an R package and an online Shiny application. The R package can be used offline, negating the need to upload potentially sensitive data. General features: TIDAL includes all the main steps of trajectory analysis including: 1) data preparation, (converting data from wide to long format); 2) data exploration, via basic plots and descriptive information; 3) analysis of trajectories using mixed effects modelling, interpretation of results, visualisation of trajectories, and extraction of key features (scores at different ages; area under the curve); and 4) interactions to derive population specific trajectories, combined with all the above. TIDAL is built with a simple graphical interface to guide users through each step. R syntax accompanies each step. Availability: Both versions of TIDAL can be found here: [https://tidal-modelling.github.io/].
{"title":"TIDAL: Tool to Implement Developmental Analysis of Longitudinal data","authors":"A. Kwong, A. Edmondson-Stait, Eileen Xu, Ellen J. Thompson, Richard M. A. Parker, Ahmed Elhakeem, L. Romaniuk, Rebecca M. Pearson, Kate Tilling, Thalia C. Eley, McIntosh Andrew M, Heather C. Whalley","doi":"10.1101/2024.08.12.24311854","DOIUrl":"https://doi.org/10.1101/2024.08.12.24311854","url":null,"abstract":"Motivation: Growth curve modelling is one method used to model trajectories of traits and behaviours over time. However, accessing, analysing and interpreting trajectories requires statistical expertise, thereby creating potential barriers for users to implement and understand longitudinal traits. TIDAL is a user-friendly research tool designed to facilitate trajectory modelling by improving access, analysis and interpretation of trajectory and longitudinal data. Implementation: TIDAL is available in two formats: an R package and an online Shiny application. The R package can be used offline, negating the need to upload potentially sensitive data. General features: TIDAL includes all the main steps of trajectory analysis including: 1) data preparation, (converting data from wide to long format); 2) data exploration, via basic plots and descriptive information; 3) analysis of trajectories using mixed effects modelling, interpretation of results, visualisation of trajectories, and extraction of key features (scores at different ages; area under the curve); and 4) interactions to derive population specific trajectories, combined with all the above. TIDAL is built with a simple graphical interface to guide users through each step. R syntax accompanies each step. Availability: Both versions of TIDAL can be found here: [https://tidal-modelling.github.io/].","PeriodicalId":18505,"journal":{"name":"medRxiv","volume":"10 31","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141919709","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-08-12DOI: 10.1101/2024.08.12.24311859
Alina Pelikh, KenjiKamiyama PhD, Mikko Myrskylä PhD, Michelle P Debbink PhD, PhD Alice Goisis
Study question: How are Medically Assisted Reproduction (MAR) treatments (Fertility enhancing drugs (FED), artificial/intrauterine insemination (AI/IUI)), assisted reproductive technology (ART) with autologous/donor oocytes) associated with maternal morbidity (MM)? Summary answer: More invasive MAR treatments (ART and AI/IUI) are associated with higher risk of MM, whilst less invasive treatments are not; this relationship is partially explained by higher prevalence of multifetal gestation and obstetric comorbidities in women undergoing more invasive treatment, but the persistent association suggests subfertility itself may contribute to maternal morbidity risk. What is known already: Women conceiving through MAR are at higher risk of MM, however, reported risks vary depending on the measurement of MM and data available on confounding. Study design, size, duration: Birth certificates were used to study maternal morbidity among all women giving birth in Utah, U.S., between 2009 and 2017 (N=460,976 deliveries); 19,448 conceived through MAR (4.2%). The MM outcome measure included the presence of any of the following: blood transfusion; unplanned operating room procedure; admission to ICU; eclampsia; unplanned hysterectomy; ruptured uterus. Participants/materials, setting, methods: Logistic regressions were estimated for the binary outcome (presence of any of the MM conditions). We assessed MM among women conceiving through MAR (overall and by type of treatment) compared to those conceiving spontaneously in the overall sample before and after adjustment for maternal socio-demographic characteristics (maternal age, family structure, level of education, Hispanic origin, parity), pre-existing maternal comorbidities (i.e., chronic hypertension, heart disease, asthma), multifetal gestation, and obstetric comorbidities (i.e., placenta previa, placental abruption, preterm delivery, cesarean delivery). Main results and the role of chance: Women conceiving through MAR had higher risk of MM; however, the magnitude of the association differed depending on the type of treatment. In the unadjusted models, more invasive treatments were associated with higher odds of MM: OR 5.71 (95% CI 3.50-9.31) among women conceiving through ART with donor oocytes, OR 3.20 (95% CI 2.69-3.81) among women conceiving through ART with autologous oocytes, and OR 1.85 (95% CI 1.39-2.46) among women conceiving through AI/IUI, whereas women conceiving through FED had similar risks of MM to compared to women conceiving spontaneously (SC), OR 1.09 (95% CI 0.91-1.30). The associations between MAR and MM were largely attenuated once multifetal gestation was accounted for. After controlling for obstetric comorbidities, the associations were further attenuated, yet the coefficients remained higher among women conceiving through ART with either donor oocytes OR 1.70 (95% CI 0.95-3.04) or autologous oocytes OR 1.46 (95% CI 1.20-1.78) compared to women conceiving spontaneously. In analyses limite
MM的研究得到了战略研究委员会（SRC）、FLUX联盟（决定号：345130和345131）、美国国立老龄化研究所（R01AG075208）、马克斯-普朗克协会（决定号：5714240218）对马克斯-普朗克-赫尔辛基大学中心、赫尔辛基大学社会科学学院Jane和Aatos Erkko基金会、赫尔辛基市、万塔市和埃斯波市的资助，以及欧盟（ERC Synergy, BIOSFER, 101071773）的资助。然而，本文所表达的观点和意见仅代表作者本人，并不一定反映欧盟或欧洲研究理事会的观点和意见。欧盟和拨款机构均不对此负责。我们感谢犹他大学亨茨曼癌症研究所的血统和人口资源中心（由亨茨曼癌症基金会提供部分资助）在持续收集、维护和支持犹他人口数据库（UPDB）方面所发挥的作用。我们还感谢犹他大学国家癌症研究所和犹他大学个性化健康项目以及犹他临床和转化科学研究所通过 P30 CA2014 基金为犹他人口数据库提供的部分支持。作为生殖科学家发展计划的一部分，MPD 从 March of Dimes 和美国妇产科委员会以及 NICHD 1U54HD113169 和 NIMHD 1R21MD019175-01A1 获得工资支持。关键字：辅助生殖技术；人工授精；促排卵；孕产妇发病率；产科合并症
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Pub Date : 2024-08-12DOI: 10.1101/2024.08.12.24311869
A. W. Jung, I. Louloudis, S. Brunak, L. Mortensen
Electronic health records can be used to track diagnoses and drug prescriptions in large heterogeneous populations over time. Coupled with recent advances in causal inference from observational data, these records offer new opportunities to emulate clinical trials and identify potential targets for drug repositioning. Here, we run a hypothesis generating cohort study of Danes aged 50 to 80 years from 2001 to 2015 (n = 2,512,380), covering a total of 23,371,354 years of observations. We examine prescription drugs at ATC level-4 and their effect on 9 major disease outcomes. Using Bayesian time-varying Cox regression and longitudinal minimum loss estimation, our analysis successfully reproduces known drug-disease associations from clinical trials, such as the reduction in the 3-year absolute risk of death associated with Statins (ATC:C10AA) -0.8% (95% CI =[-1.2%, -0.5%]) and -0.8% (95% CI =[-1.3%, -0.2%]) for females and males, respectively. Additionally, we discovered novel associations that suggest potential repositioning opportunities. For instance, Statins were associated with a reduction in the 3-year absolute risk of dementia by -0.3% (95% CI =[-0.5%, -0.1%]) for females and -0.2% (95% CI =[-0.4%, 0.1%]) for males. Furthermore, Biguanides (ATC:P01BB) stands out as a particularly interesting candidate with absolute risk reductions across various outcomes. In total, we identified 76 potential drug-disease pairs for further investigation. However, it should be stressed that the emulation of clinical trials here is solely of hypothesis generating nature and identified effects need to be corroborated with additional evidence, preferably from RTCs, as the risk of confounding by indication in this study is substantial. In summary, this study provides a large-scale screen of prescribed drugs and their effect on major debilitating disease in the Danish health registries. This provides an additional source of information that can be used in the search for possible repositioning candidates.
电子健康记录可用于长期跟踪大量异质人群的诊断和药物处方。这些记录与观察数据因果推断的最新进展相结合，为模拟临床试验和确定药物重新定位的潜在目标提供了新的机会。在此，我们对 2001 年至 2015 年期间年龄在 50 岁至 80 岁之间的丹麦人（n = 2,512,380 人）进行了假设生成队列研究，共覆盖 23,371,354 年的观察数据。我们研究了 ATC 4 级处方药及其对 9 种主要疾病结果的影响。利用贝叶斯时变 Cox 回归和纵向最小损失估计，我们的分析成功地再现了临床试验中已知的药物-疾病关联，如他汀类药物（ATC：C10AA）对女性和男性的 3 年绝对死亡风险分别降低了 -0.8%（95% CI =[-1.2%，-0.5%]）和 -0.8%（95% CI =[-1.3%，-0.2%]）。此外，我们还发现了一些新的关联，表明可能存在重新定位的机会。例如，他汀类药物可使女性和男性3年痴呆绝对风险分别降低-0.3% (95% CI =[-0.5%, -0.1%])和-0.2% (95% CI =[-0.4%, 0.1%])。此外，双胍类药物（ATC:P01BB）是一个特别有趣的候选药物，它能降低各种结果的绝对风险。我们总共确定了 76 种潜在的药物-疾病配对，以供进一步研究。不过，需要强调的是，本研究中对临床试验的模仿仅是假设性的，确定的效果还需要更多的证据来证实，最好是从临床试验中获得，因为本研究中适应症混淆的风险很大。总之，本研究对丹麦健康登记中的处方药及其对主要衰弱性疾病的影响进行了大规模筛查。这为寻找可能的重新定位候选药物提供了额外的信息来源。
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Pub Date : 2024-08-12DOI: 10.1101/2024.08.12.24311872
Masab A. Mansoor, Dba, MD Kashif Ansari
Background: Early detection of mental health crises is crucial for timely intervention and improved outcomes. This study explores the potential of artificial intelligence (AI) in analyzing social media data to identify early signs of mental health crises. Methods: We developed a multi-modal deep learning model integrating natural language processing and temporal analysis techniques. The model was trained on a diverse dataset of 996,452 social media posts in multiple languages (English, Spanish, Mandarin, and Arabic) collected from Twitter, Reddit, and Facebook over a 12-month period. Performance was evaluated using standard metrics and validated against expert psychiatric assessment. Results: The AI model demonstrated high accuracy (89.3%) in detecting early signs of mental health crises, with an average lead time of 7.2 days before human expert identification. Performance was consistent across languages (F1 scores: 0.827-0.872) and platforms (F1 scores: 0.839-0.863). Key digital markers included linguistic patterns, behavioral changes, and temporal trends. The model showed varying accuracy for different crisis types: depressive episodes (91.2%), manic episodes (88.7%), suicidal ideation (93.5%), and anxiety crises (87.3%). Conclusions: AI-powered analysis of social media data shows promise for early detection of mental health crises across diverse linguistic and cultural contexts. However, ethical challenges including privacy concerns, potential stigmatization, and cultural biases need careful consideration. Future research should focus on longitudinal outcome studies, ethical integration with existing mental health services, and development of personalized, culturally-sensitive models. Keywords: artificial intelligence, mental health, crisis detection, social media analysis, early intervention
{"title":"Early Detection of Mental Health Crises through AI-Powered Social Media Analysis: A Prospective Observational Study","authors":"Masab A. Mansoor, Dba, MD Kashif Ansari","doi":"10.1101/2024.08.12.24311872","DOIUrl":"https://doi.org/10.1101/2024.08.12.24311872","url":null,"abstract":"Background: Early detection of mental health crises is crucial for timely intervention and improved outcomes. This study explores the potential of artificial intelligence (AI) in analyzing social media data to identify early signs of mental health crises. Methods: We developed a multi-modal deep learning model integrating natural language processing and temporal analysis techniques. The model was trained on a diverse dataset of 996,452 social media posts in multiple languages (English, Spanish, Mandarin, and Arabic) collected from Twitter, Reddit, and Facebook over a 12-month period. Performance was evaluated using standard metrics and validated against expert psychiatric assessment. Results: The AI model demonstrated high accuracy (89.3%) in detecting early signs of mental health crises, with an average lead time of 7.2 days before human expert identification. Performance was consistent across languages (F1 scores: 0.827-0.872) and platforms (F1 scores: 0.839-0.863). Key digital markers included linguistic patterns, behavioral changes, and temporal trends. The model showed varying accuracy for different crisis types: depressive episodes (91.2%), manic episodes (88.7%), suicidal ideation (93.5%), and anxiety crises (87.3%). Conclusions: AI-powered analysis of social media data shows promise for early detection of mental health crises across diverse linguistic and cultural contexts. However, ethical challenges including privacy concerns, potential stigmatization, and cultural biases need careful consideration. Future research should focus on longitudinal outcome studies, ethical integration with existing mental health services, and development of personalized, culturally-sensitive models. Keywords: artificial intelligence, mental health, crisis detection, social media analysis, early intervention","PeriodicalId":18505,"journal":{"name":"medRxiv","volume":"8 13","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141919553","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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