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Kancheva AK, Lyall DM, Millard L, Wardlaw JM, Quinn TJ. Clinical Phenotypes Associated With Cerebral Small Vessel Disease: A Study of 45,013 UK Biobank Participants. Neurology 2024; 103:e209919. [PMID: 39321409 DOI: 10.1212/wnl.0000000000209919] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/27/2024] Open
Abstract
BACKGROUND AND OBJECTIVES Cerebral small vessel disease (cSVD) is the most common pathology underlying vascular cognitive impairment. Although other clinical features of cSVD are increasingly recognized, it is likely that certain symptoms are being overlooked. A comprehensive description of cSVD associations with clinical phenotypes at scale is lacking. The objective of this study was to conduct a large-scale, hypothesis-free study of associations between cSVD and clinical phenotypes in UK Biobank (UKB). METHODS We included participants from the UKB imaging study who had available information on total volume of white matter hyperintensities (WMHs), the most common cSVD neuroimaging feature. We included various UKB variables describing clinical phenotypes, defined as observable signs and symptoms of individuals with concurrent neuroimaging evidence of cSVD. We conducted a phenome scan using the open-source PHESANT software package. Total volume of WMHs was introduced as the independent variable and clinical phenotypes as the dependent variables in the regression model. The association of each phenotype with total volume of WMHs was tested using one of several regression analyses (all age at recruitment and sex-adjusted). All associations were corrected for multiple comparisons using the false discovery rate (FDR) correction method. RESULTS We included 45,013 participants in the analysis (mean age = 54.97 years, SD = 7.55). We confirm previously reported associations with depression (odds ratio [OR] = 1.07 [95% CI 1.05-1.10]), apathy (OR = 1.11 [95% CI 1.08-1.14]), falls (OR = 1.11 [95% CI 1.09-1.13]), respiratory problems (OR = 1.14 [95% CI 1.04-1.25]), and sleep disturbance (OR = 1.07 [95% CI 1.04-1.09], all FDR-adjusted p < 0.001). We further identified associations with all-cause dental issues (OR = 0.94 [95% CI 0.96-0.92]), hearing problems (OR = 1.06 [95% CI 1.03-1.08]), and eye problems (OR = 0.93 [95% CI 0.91-0.95], all FDR-adjusted p < 0.001). DISCUSSION Our findings suggest that presence of cSVD associates with concurrent clinical phenotypes across several body systems. We have corroborated established associations of cSVD and present novel ones. While our results do not provide causality or direction of association because of the cross-sectional nature of our study, they support the need for a more holistic view of cSVD in research, practice, and policy.
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Affiliation(s)
- Angelina K Kancheva
- From the School of Cardiovascular and Metabolic Health (A.K.K., T.J.Q.) School of Health and Wellbeing (D.M.L.), University of Glasgow; MRC Integrative Epidemiology Unit (L.M.), University of Bristol; and Centre for Clinical Brain Sciences (J.M.W.), University of Edinburgh, United Kingdom
| | - Donald M Lyall
- From the School of Cardiovascular and Metabolic Health (A.K.K., T.J.Q.) School of Health and Wellbeing (D.M.L.), University of Glasgow; MRC Integrative Epidemiology Unit (L.M.), University of Bristol; and Centre for Clinical Brain Sciences (J.M.W.), University of Edinburgh, United Kingdom
| | - Louise Millard
- From the School of Cardiovascular and Metabolic Health (A.K.K., T.J.Q.) School of Health and Wellbeing (D.M.L.), University of Glasgow; MRC Integrative Epidemiology Unit (L.M.), University of Bristol; and Centre for Clinical Brain Sciences (J.M.W.), University of Edinburgh, United Kingdom
| | - Joanna M Wardlaw
- From the School of Cardiovascular and Metabolic Health (A.K.K., T.J.Q.) School of Health and Wellbeing (D.M.L.), University of Glasgow; MRC Integrative Epidemiology Unit (L.M.), University of Bristol; and Centre for Clinical Brain Sciences (J.M.W.), University of Edinburgh, United Kingdom
| | - Terence J Quinn
- From the School of Cardiovascular and Metabolic Health (A.K.K., T.J.Q.) School of Health and Wellbeing (D.M.L.), University of Glasgow; MRC Integrative Epidemiology Unit (L.M.), University of Bristol; and Centre for Clinical Brain Sciences (J.M.W.), University of Edinburgh, United Kingdom
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2
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Coombes BJ, Sanchez-Ruiz JA, Fennessy B, Pazdernik VK, Adekkanattu P, Nuñez NA, Lepow L, Melhuish Beaupre LM, Ryu E, Talati A, Mann JJ, Weissman MM, Olfson M, Pathak J, Charney AW, Biernacka JM. Clinical associations with treatment resistance in depression: An electronic health record study. Psychiatry Res 2024; 342:116203. [PMID: 39321638 DOI: 10.1016/j.psychres.2024.116203] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/25/2024] [Revised: 09/03/2024] [Accepted: 09/15/2024] [Indexed: 09/27/2024]
Abstract
Treatment resistance is common in major depressive disorder (MDD), yet clinical risk factors are not well understood. Using a discovery-replication design, we conducted phenome-wide association studies (PheWASs) of MDD treatment resistance in two electronic health record (EHR)-linked biobanks. The PheWAS included participants with an MDD diagnosis in the EHR and at least one antidepressant (AD) prescription. Participant lifetime diagnoses were mapped to phecodes. PheWASs were conducted for three treatment resistance outcomes based on AD prescription data: number of unique ADs prescribed, ≥1 and ≥2 CE switches. Of the 180 phecodes significantly associated with these outcomes in the discovery cohort (n = 12,558), 71 replicated (n = 8,206). In addition to identifying known clinical factors for treatment resistance in MDD, the total unique AD prescriptions was associated with additional clinical variables including irritable bowel syndrome, gastroesophageal reflux disease, symptomatic menopause, and spondylosis. We calculated polygenic risk of specific-associated conditions and tested their association with AD outcomes revealing that genetic risk for many of these conditions is also associated with the total unique AD prescriptions. The number of unique ADs prescribed, which is easily assessed in EHRs, provides a more nuanced measure of treatment resistance, and may facilitate future research and clinical application in this area.
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Affiliation(s)
- Brandon J Coombes
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN, USA.
| | | | - Brian Fennessy
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | | | - Prakash Adekkanattu
- Department of Population Health Sciences, Weill Cornell Medicine, New York, NY, USA; Clinical and Translational Science Center, Weill Cornell Medicine, New York, NY, USA
| | - Nicolas A Nuñez
- Department of Psychiatry & Psychology, Mayo Clinic, Rochester, MN, USA
| | - Lauren Lepow
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | | | - Euijung Ryu
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN, USA
| | - Ardesheer Talati
- Department of Psychiatry, Vagelos College of Physicians and Surgeons Columbia University & NY State Psychiatric Institute, New York, NY, USA
| | - J John Mann
- Department of Psychiatry, Vagelos College of Physicians and Surgeons Columbia University & NY State Psychiatric Institute, New York, NY, USA
| | - Myrna M Weissman
- Department of Psychiatry, Vagelos College of Physicians and Surgeons Columbia University & NY State Psychiatric Institute, New York, NY, USA
| | - Mark Olfson
- Department of Psychiatry, Vagelos College of Physicians and Surgeons Columbia University & NY State Psychiatric Institute, New York, NY, USA
| | - Jyotishman Pathak
- Department of Population Health Sciences, Weill Cornell Medicine, New York, NY, USA; Department of Psychiatry, Weill Cornell Medicine, New York, NY, USA
| | - Alexander W Charney
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA; Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA; Mount Sinai Clinical Intelligence Center, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Joanna M Biernacka
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN, USA; Department of Psychiatry & Psychology, Mayo Clinic, Rochester, MN, USA.
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Yang L, Sadler MC, Altman RB. Genetic association studies using disease liabilities from deep neural networks. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2023.01.18.23284383. [PMID: 36712099 PMCID: PMC9882423 DOI: 10.1101/2023.01.18.23284383] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/21/2023]
Abstract
The case-control study is a widely used method for investigating the genetic underpinnings of binary traits. However, long-term, prospective cohort studies often grapple with absent or evolving health-related outcomes. Here, we propose two methods, liability and meta , for conducting genome-wide association study (GWAS) that leverage disease liabilities calculated from deep patient phenotyping. Analyzing 38 common traits in ∼300,000 UK Biobank participants, we identified an increased number of loci compared to the conventional case-control approach, with high replication rates in larger external GWAS. Further analyses confirmed the disease-specificity of the genetic architecture with the meta method demonstrating higher robustness when phenotypes were imputed with low accuracy. Additionally, polygenic risk scores based on disease liabilities more effectively predicted newly diagnosed cases in the 2022 dataset, which were controls in the earlier 2019 dataset. Our findings demonstrate that integrating high-dimensional phenotypic data into deep neural networks enhances genetic association studies while capturing disease-relevant genetic architecture.
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Affiliation(s)
- Lu Yang
- Deparment of Bioengineering, Stanford University, Stanford, CA, 94305, USA
- Department of Computer Science, Stanford University, Stanford, CA, 94305, USA
| | - Marie C. Sadler
- Deparment of Bioengineering, Stanford University, Stanford, CA, 94305, USA
- University Center for Primary Care and Public Health, Lausanne, 1010, Switzerland
- Swiss Institute of Bioinformatics, Lausanne, 1015, Switzerland
| | - Russ B. Altman
- Deparment of Bioengineering, Stanford University, Stanford, CA, 94305, USA
- Department of Genetics, Stanford University, Stanford, CA, 94305, USA
- Department of Medicine, Stanford University, Stanford, CA, 94305, USA
- Department of Computer Science, Stanford University, Stanford, CA, 94305, USA
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Shi C, Ma D, Li M, Wang Z, Hao C, Liang Y, Feng Y, Hu Z, Hao X, Guo M, Li S, Zuo C, Sun Y, Tang M, Mao C, Zhang C, Xu Y, Sun S. Identifying potential causal effects of Parkinson's disease: A polygenic risk score-based phenome-wide association and mendelian randomization study in UK Biobank. NPJ Parkinsons Dis 2024; 10:166. [PMID: 39242620 PMCID: PMC11379879 DOI: 10.1038/s41531-024-00780-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2024] [Accepted: 08/20/2024] [Indexed: 09/09/2024] Open
Abstract
There is considerable uncertainty regarding the associations between various risk factors and Parkinson's Disease (PD). This study systematically screened and validated a wide range of potential PD risk factors from 502,364 participants in the UK Biobank. Baseline data for 1851 factors across 11 categories were analyzed through a phenome-wide association study (PheWAS). Polygenic risk scores (PRS) for PD were used to diagnose Parkinson's Disease and identify factors associated with PD diagnosis through PheWAS. Two-sample Mendelian randomization (MR) analysis was employed to assess causal relationships. PheWAS results revealed 267 risk factors significantly associated with PD-PRS among the 1851 factors, and of these, 27 factors showed causal evidence from MR analysis. Compelling evidence suggests that fluid intelligence score, age at first sexual intercourse, cereal intake, dried fruit intake, and average total household income before tax have emerged as newly identified risk factors for PD. Conversely, maternal smoking around birth, playing computer games, salt added to food, and time spent watching television have been identified as novel protective factors against PD. The integration of phenotypic and genomic data may help to identify risk factors and prevention targets for PD.
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Affiliation(s)
- Changhe Shi
- Department of Neurology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou University, Zhengzhou, Henan, China.
- Henan Key Laboratory of Cerebrovascular Diseases, The First Affiliated Hospital of Zhengzhou University, Zhengzhou University, Zhengzhou, Henan, China.
- NHC Key Laboratory of Prevention and Treatment of Cerebrovascular Diseases, The First Affiliated Hospital of Zhengzhou University, Zhengzhou University, Zhengzhou, Henan, China.
- Institute of Neuroscience, Zhengzhou University, Zhengzhou, Henan, China.
| | - Dongrui Ma
- Department of Neurology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou University, Zhengzhou, Henan, China
| | - Mengjie Li
- Department of Neurology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou University, Zhengzhou, Henan, China
| | - Zhiyun Wang
- Department of Neurology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou University, Zhengzhou, Henan, China
| | - Chenwei Hao
- Department of Neurology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou University, Zhengzhou, Henan, China
| | - Yuanyuan Liang
- Department of Neurology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou University, Zhengzhou, Henan, China
| | - Yanmei Feng
- Department of Neurology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou University, Zhengzhou, Henan, China
| | - Zhengwei Hu
- Department of Neurology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou University, Zhengzhou, Henan, China
| | - Xiaoyan Hao
- Department of Neurology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou University, Zhengzhou, Henan, China
| | - Mengnan Guo
- Department of Neurology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou University, Zhengzhou, Henan, China
| | - Shuangjie Li
- Department of Neurology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou University, Zhengzhou, Henan, China
| | - Chunyan Zuo
- Department of Neurology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou University, Zhengzhou, Henan, China
| | - Yuemeng Sun
- Department of Neurology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou University, Zhengzhou, Henan, China
| | - Mibo Tang
- Department of Neurology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou University, Zhengzhou, Henan, China
- Henan Key Laboratory of Cerebrovascular Diseases, The First Affiliated Hospital of Zhengzhou University, Zhengzhou University, Zhengzhou, Henan, China
| | - Chengyuan Mao
- Department of Neurology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou University, Zhengzhou, Henan, China
- Henan Key Laboratory of Cerebrovascular Diseases, The First Affiliated Hospital of Zhengzhou University, Zhengzhou University, Zhengzhou, Henan, China
| | - Chan Zhang
- Department of Neurology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou University, Zhengzhou, Henan, China
- Henan Key Laboratory of Cerebrovascular Diseases, The First Affiliated Hospital of Zhengzhou University, Zhengzhou University, Zhengzhou, Henan, China
| | - Yuming Xu
- Department of Neurology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou University, Zhengzhou, Henan, China
- Henan Key Laboratory of Cerebrovascular Diseases, The First Affiliated Hospital of Zhengzhou University, Zhengzhou University, Zhengzhou, Henan, China
- NHC Key Laboratory of Prevention and Treatment of Cerebrovascular Diseases, The First Affiliated Hospital of Zhengzhou University, Zhengzhou University, Zhengzhou, Henan, China
- Institute of Neuroscience, Zhengzhou University, Zhengzhou, Henan, China
| | - Shilei Sun
- Department of Neurology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou University, Zhengzhou, Henan, China
- Henan Key Laboratory of Cerebrovascular Diseases, The First Affiliated Hospital of Zhengzhou University, Zhengzhou University, Zhengzhou, Henan, China
- NHC Key Laboratory of Prevention and Treatment of Cerebrovascular Diseases, The First Affiliated Hospital of Zhengzhou University, Zhengzhou University, Zhengzhou, Henan, China
- Institute of Neuroscience, Zhengzhou University, Zhengzhou, Henan, China
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5
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Sanchez-Ruiz JA, Coombes BJ, Pazdernik VM, Melhuish Beaupre LM, Jenkins GD, Pendegraft RS, Batzler A, Ozerdem A, McElroy SL, Gardea-Resendez MA, Cuellar-Barboza AB, Prieto ML, Frye MA, Biernacka JM. Clinical and genetic contributions to medical comorbidity in bipolar disorder: a study using electronic health records-linked biobank data. Mol Psychiatry 2024; 29:2701-2713. [PMID: 38548982 DOI: 10.1038/s41380-024-02530-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/12/2023] [Revised: 02/21/2024] [Accepted: 03/13/2024] [Indexed: 06/14/2024]
Abstract
Bipolar disorder is a chronic and complex polygenic disease with high rates of comorbidity. However, the independent contribution of either diagnosis or genetic risk of bipolar disorder to the medical comorbidity profile of individuals with the disease remains unresolved. Here, we conducted a multi-step phenome-wide association study (PheWAS) of bipolar disorder using phenomes derived from the electronic health records of participants enrolled in the Mayo Clinic Biobank and the Mayo Clinic Bipolar Disorder Biobank. First, we explored the conditions associated with a diagnosis of bipolar disorder by conducting a phenotype-based PheWAS followed by LASSO-penalized regression to account for correlations within the phenome. Then, we explored the conditions associated with bipolar disorder polygenic risk score (BD-PRS) using a PRS-based PheWAS with a sequential exclusion approach to account for the possibility that diagnosis, instead of genetic risk, may drive such associations. 53,386 participants (58.7% women) with a mean age at analysis of 67.8 years (SD = 15.6) were included. A bipolar disorder diagnosis (n = 1479) was associated with higher rates of psychiatric conditions, injuries and poisonings, endocrine/metabolic and neurological conditions, viral hepatitis C, and asthma. BD-PRS was associated with psychiatric comorbidities but, in contrast, had no positive associations with general medical conditions. While our findings warrant confirmation with longitudinal-prospective studies, the limited associations between bipolar disorder genetics and medical conditions suggest that shared environmental effects or environmental consequences of diagnosis may have a greater impact on the general medical comorbidity profile of individuals with bipolar disorder than its genetic risk.
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Affiliation(s)
| | - Brandon J Coombes
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN, USA
| | | | | | - Greg D Jenkins
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN, USA
| | | | - Anthony Batzler
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN, USA
| | - Aysegul Ozerdem
- Department of Psychiatry & Psychology, Mayo Clinic, Rochester, MN, USA
| | - Susan L McElroy
- Lindner Center of HOPE/University of Cincinnati, Cincinnati, OH, USA
| | - Manuel A Gardea-Resendez
- Department of Psychiatry & Psychology, Mayo Clinic, Rochester, MN, USA
- Department of Psychiatry, Universidad Autónoma de Nuevo León, Monterrey, Mexico
| | - Alfredo B Cuellar-Barboza
- Department of Psychiatry & Psychology, Mayo Clinic, Rochester, MN, USA
- Department of Psychiatry, Universidad Autónoma de Nuevo León, Monterrey, Mexico
| | - Miguel L Prieto
- Department of Psychiatry & Psychology, Mayo Clinic, Rochester, MN, USA
- Department of Psychiatry, Faculty of Medicine, Universidad de Los Andes, Santiago, Chile
- Mental Health Service, Clínica Universidad de los Andes, Santiago, Chile
| | - Mark A Frye
- Department of Psychiatry & Psychology, Mayo Clinic, Rochester, MN, USA
| | - Joanna M Biernacka
- Department of Psychiatry & Psychology, Mayo Clinic, Rochester, MN, USA.
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN, USA.
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6
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Zhang B, You J, Rolls ET, Wang X, Kang J, Li Y, Zhang R, Zhang W, Wang H, Xiang S, Shen C, Jiang Y, Xie C, Yu J, Cheng W, Feng J. Identifying behaviour-related and physiological risk factors for suicide attempts in the UK Biobank. Nat Hum Behav 2024; 8:1784-1797. [PMID: 38956227 DOI: 10.1038/s41562-024-01903-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2023] [Accepted: 04/29/2024] [Indexed: 07/04/2024]
Abstract
Suicide is a global public health challenge, yet considerable uncertainty remains regarding the associations of both behaviour-related and physiological factors with suicide attempts (SA). Here we first estimated polygenic risk scores (PRS) for SA in 334,706 UK Biobank participants and conducted phenome-wide association analyses considering 2,291 factors. We identified 246 (63.07%) behaviour-related and 200 (10.41%, encompassing neuroimaging, blood and metabolic biomarkers, and proteins) physiological factors significantly associated with SA-PRS, with robust associations observed in lifestyle factors and mental health. Further case-control analyses involving 3,558 SA cases and 149,976 controls mirrored behaviour-related associations observed with SA-PRS. Moreover, Mendelian randomization analyses supported a potential causal effect of liability to 58 factors on SA, such as age at first intercourse, neuroticism, smoking, overall health rating and depression. Notably, machine-learning classification models based on behaviour-related factors exhibited high discriminative accuracy in distinguishing those with and without SA (area under the receiver operating characteristic curve 0.909 ± 0.006). This study provides comprehensive insights into diverse risk factors for SA, shedding light on potential avenues for targeted prevention and intervention strategies.
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Affiliation(s)
- Bei Zhang
- Institute of Science and Technology for Brain-Inspired Intelligence and Department of Neurology, Huashan Hospital, Fudan University, Shanghai, China
- Key Laboratory of Computational Neuroscience and Brain Inspired Intelligence (Fudan University), Ministry of Education, Shanghai, China
| | - Jia You
- Institute of Science and Technology for Brain-Inspired Intelligence and Department of Neurology, Huashan Hospital, Fudan University, Shanghai, China
- Key Laboratory of Computational Neuroscience and Brain Inspired Intelligence (Fudan University), Ministry of Education, Shanghai, China
| | - Edmund T Rolls
- Institute of Science and Technology for Brain-Inspired Intelligence and Department of Neurology, Huashan Hospital, Fudan University, Shanghai, China
- Oxford Centre for Computational Neuroscience, Oxford, UK
- Department of Computer Science, University of Warwick, Coventry, UK
| | - Xiang Wang
- Medical Psychological Centre, The Second Xiangya Hospital, Central South University, Changsha, China
- Medical Psychological Institute, Central South University, Changsha, China
- China National Clinical Research Centre on Mental Disorders (Xiangya), Changsha, China
| | - Jujiao Kang
- Institute of Science and Technology for Brain-Inspired Intelligence and Department of Neurology, Huashan Hospital, Fudan University, Shanghai, China
- Key Laboratory of Computational Neuroscience and Brain Inspired Intelligence (Fudan University), Ministry of Education, Shanghai, China
| | - Yuzhu Li
- Institute of Science and Technology for Brain-Inspired Intelligence and Department of Neurology, Huashan Hospital, Fudan University, Shanghai, China
- Key Laboratory of Computational Neuroscience and Brain Inspired Intelligence (Fudan University), Ministry of Education, Shanghai, China
| | - Ruohan Zhang
- Department of Computer Science, University of Warwick, Coventry, UK
| | - Wei Zhang
- Institute of Science and Technology for Brain-Inspired Intelligence and Department of Neurology, Huashan Hospital, Fudan University, Shanghai, China
- Key Laboratory of Computational Neuroscience and Brain Inspired Intelligence (Fudan University), Ministry of Education, Shanghai, China
| | - Huifu Wang
- Department of Neurology, Qingdao Municipal Hospital, Qingdao University, Qingdao, China
| | - Shitong Xiang
- Institute of Science and Technology for Brain-Inspired Intelligence and Department of Neurology, Huashan Hospital, Fudan University, Shanghai, China
- Key Laboratory of Computational Neuroscience and Brain Inspired Intelligence (Fudan University), Ministry of Education, Shanghai, China
| | - Chun Shen
- Institute of Science and Technology for Brain-Inspired Intelligence and Department of Neurology, Huashan Hospital, Fudan University, Shanghai, China
- Key Laboratory of Computational Neuroscience and Brain Inspired Intelligence (Fudan University), Ministry of Education, Shanghai, China
| | - Yuchao Jiang
- Institute of Science and Technology for Brain-Inspired Intelligence and Department of Neurology, Huashan Hospital, Fudan University, Shanghai, China
- Key Laboratory of Computational Neuroscience and Brain Inspired Intelligence (Fudan University), Ministry of Education, Shanghai, China
| | - Chao Xie
- Institute of Science and Technology for Brain-Inspired Intelligence and Department of Neurology, Huashan Hospital, Fudan University, Shanghai, China
- Key Laboratory of Computational Neuroscience and Brain Inspired Intelligence (Fudan University), Ministry of Education, Shanghai, China
| | - Jintai Yu
- Institute of Science and Technology for Brain-Inspired Intelligence and Department of Neurology, Huashan Hospital, Fudan University, Shanghai, China
| | - Wei Cheng
- Institute of Science and Technology for Brain-Inspired Intelligence and Department of Neurology, Huashan Hospital, Fudan University, Shanghai, China.
- Key Laboratory of Computational Neuroscience and Brain Inspired Intelligence (Fudan University), Ministry of Education, Shanghai, China.
- MOE Frontiers Centre for Brain Science, Fudan University, Shanghai, China.
- Fudan ISTBI-ZJNU Algorithm Centre for Brain-inspired Intelligence, Zhejiang Normal University, Jinhua, China.
| | - Jianfeng Feng
- Institute of Science and Technology for Brain-Inspired Intelligence and Department of Neurology, Huashan Hospital, Fudan University, Shanghai, China.
- Key Laboratory of Computational Neuroscience and Brain Inspired Intelligence (Fudan University), Ministry of Education, Shanghai, China.
- Department of Computer Science, University of Warwick, Coventry, UK.
- MOE Frontiers Centre for Brain Science, Fudan University, Shanghai, China.
- Fudan ISTBI-ZJNU Algorithm Centre for Brain-inspired Intelligence, Zhejiang Normal University, Jinhua, China.
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7
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Meng W, Xu J, Huang Y, Wang C, Song Q, Ma A, Song L, Bian J, Ma Q, Yin R. Autoencoder to Identify Sex-Specific Sub-phenotypes in Alzheimer's Disease Progression Using Longitudinal Electronic Health Records. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.07.07.24310055. [PMID: 39040206 PMCID: PMC11261930 DOI: 10.1101/2024.07.07.24310055] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/24/2024]
Abstract
Alzheimer's Disease (AD) is a complex neurodegenerative disorder significantly influenced by sex differences, with approximately two-thirds of AD patients being women. Characterizing the sex-specific AD progression and identifying its progression trajectory is a crucial step to developing effective risk stratification and prevention strategies. In this study, we developed an autoencoder to uncover sex-specific sub-phenotypes in AD progression leveraging longitudinal electronic health record (EHR) data from OneFlorida+ Clinical Research Consortium. Specifically, we first constructed temporal patient representation using longitudinal EHRs from a sex-stratified AD cohort. We used a long short-term memory (LSTM)-based autoencoder to extract and generate latent representation embeddings from sequential clinical records of patients. We then applied hierarchical agglomerative clustering to the learned representations, grouping patients based on their progression sub-phenotypes. The experimental results show we successfully identified five primary sex-based AD sub-phenotypes with corresponding progression pathways with high confidence. These sex-specific sub-phenotypes not only illustrated distinct AD progression patterns but also revealed differences in clinical characteristics and comorbidities between females and males in AD development. These findings could provide valuable insights for advancing personalized AD intervention and treatment strategies.
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Affiliation(s)
- Weimin Meng
- Department of Health Outcomes &Biomedical Informatics, University of Florida, Gainesville, FL, 32610, USA
| | - Jie Xu
- Department of Health Outcomes &Biomedical Informatics, University of Florida, Gainesville, FL, 32610, USA
| | - Yu Huang
- Department of Health Outcomes &Biomedical Informatics, University of Florida, Gainesville, FL, 32610, USA
| | - Cankun Wang
- Department of Biomedical Informatics, Ohio State University, Columbus, OH, 43210, USA
| | - Qianqian Song
- Department of Health Outcomes &Biomedical Informatics, University of Florida, Gainesville, FL, 32610, USA
| | - Anjun Ma
- Department of Biomedical Informatics, Ohio State University, Columbus, OH, 43210, USA
| | - Lixin Song
- School of Nursing, University of Texas Health Science Center at San Antonio, San Antonio, TX, 78229, USA
| | - Jiang Bian
- Department of Health Outcomes &Biomedical Informatics, University of Florida, Gainesville, FL, 32610, USA
| | - Qin Ma
- Department of Biomedical Informatics, Ohio State University, Columbus, OH, 43210, USA
| | - Rui Yin
- Department of Health Outcomes &Biomedical Informatics, University of Florida, Gainesville, FL, 32610, USA
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Jafari E, Blackman MH, Karnes JH, Van Driest SL, Crawford DC, Choi L, McDonough CW. Using electronic health records for clinical pharmacology research: Challenges and considerations. Clin Transl Sci 2024; 17:e13871. [PMID: 38943244 PMCID: PMC11213823 DOI: 10.1111/cts.13871] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2024] [Revised: 05/21/2024] [Accepted: 05/24/2024] [Indexed: 07/01/2024] Open
Abstract
Electronic health records (EHRs) contain a vast array of phenotypic data on large numbers of individuals, often collected over decades. Due to the wealth of information, EHR data have emerged as a powerful resource to make first discoveries and identify disparities in our healthcare system. While the number of EHR-based studies has exploded in recent years, most of these studies are directed at associations with disease rather than pharmacotherapeutic outcomes, such as drug response or adverse drug reactions. This is largely due to challenges specific to deriving drug-related phenotypes from the EHR. There is great potential for EHR-based discovery in clinical pharmacology research, and there is a critical need to address specific challenges related to accurate and reproducible derivation of drug-related phenotypes from the EHR. This review provides a detailed evaluation of challenges and considerations for deriving drug-related data from EHRs. We provide an examination of EHR-based computable phenotypes and discuss cutting-edge approaches to map medication information for clinical pharmacology research, including medication-based computable phenotypes and natural language processing. We also discuss additional considerations such as data structure, heterogeneity and missing data, rare phenotypes, and diversity within the EHR. By further understanding the complexities associated with conducting clinical pharmacology research using EHR-based data, investigators will be better equipped to design thoughtful studies with more reproducible results. Progress in utilizing EHRs for clinical pharmacology research should lead to significant advances in our ability to understand differential drug response and predict adverse drug reactions.
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Affiliation(s)
- Eissa Jafari
- Department of Pharmacotherapy and Translational Research, Center for Pharmacogenomics and Precision Medicine, College of PharmacyUniversity of FloridaGainesvilleFloridaUSA
- Department of Pharmacy Practice, College of PharmacyJazan UniversityJazanSaudi Arabia
| | - Marisa H. Blackman
- Department of BiostatisticsVanderbilt University Medical CenterNashvilleTennesseeUSA
| | - Jason H. Karnes
- Department of Pharmacy Practice and ScienceUniversity of Arizona R. Ken Coit College of PharmacyTucsonArizonaUSA
| | - Sara L. Van Driest
- Department of PediatricsVanderbilt University Medical Center (VUMC)NashvilleTennesseeUSA
- Present address:
All of US Research Program, National Institutes of HealthBethesdaMarylandUSA
| | - Dana C. Crawford
- Department of Population and Quantitative Health Sciences, Cleveland Institute for Computational BiologyCase Western Reserve UniversityClevelandOhioUSA
- Department of Genetics and Genome Sciences, Cleveland Institute for Computational BiologyCase Western Reserve UniversityClevelandOhioUSA
| | - Leena Choi
- Department of Biostatistics and Biomedical InformaticsVanderbilt University Medical CenterNashvilleTennesseeUSA
| | - Caitrin W. McDonough
- Department of Pharmacotherapy and Translational Research, Center for Pharmacogenomics and Precision Medicine, College of PharmacyUniversity of FloridaGainesvilleFloridaUSA
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9
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Toikumo S, Jennings MV, Pham BK, Lee H, Mallard TT, Bianchi SB, Meredith JJ, Vilar-Ribó L, Xu H, Hatoum AS, Johnson EC, Pazdernik VK, Jinwala Z, Pakala SR, Leger BS, Niarchou M, Ehinmowo M, Jenkins GD, Batzler A, Pendegraft R, Palmer AA, Zhou H, Biernacka JM, Coombes BJ, Gelernter J, Xu K, Hancock DB, Cox NJ, Smoller JW, Davis LK, Justice AC, Kranzler HR, Kember RL, Sanchez-Roige S. Multi-ancestry meta-analysis of tobacco use disorder identifies 461 potential risk genes and reveals associations with multiple health outcomes. Nat Hum Behav 2024; 8:1177-1193. [PMID: 38632388 PMCID: PMC11199106 DOI: 10.1038/s41562-024-01851-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2023] [Accepted: 02/21/2024] [Indexed: 04/19/2024]
Abstract
Tobacco use disorder (TUD) is the most prevalent substance use disorder in the world. Genetic factors influence smoking behaviours and although strides have been made using genome-wide association studies to identify risk variants, most variants identified have been for nicotine consumption, rather than TUD. Here we leveraged four US biobanks to perform a multi-ancestral meta-analysis of TUD (derived via electronic health records) in 653,790 individuals (495,005 European, 114,420 African American and 44,365 Latin American) and data from UK Biobank (ncombined = 898,680). We identified 88 independent risk loci; integration with functional genomic tools uncovered 461 potential risk genes, primarily expressed in the brain. TUD was genetically correlated with smoking and psychiatric traits from traditionally ascertained cohorts, externalizing behaviours in children and hundreds of medical outcomes, including HIV infection, heart disease and pain. This work furthers our biological understanding of TUD and establishes electronic health records as a source of phenotypic information for studying the genetics of TUD.
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Affiliation(s)
- Sylvanus Toikumo
- Mental Illness Research, Education and Clinical Center, Crescenz VAMC, Philadelphia, PA, USA
- Department of Psychiatry, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Mariela V Jennings
- Department of Psychiatry, University of California San Diego, La Jolla, CA, USA
| | - Benjamin K Pham
- Department of Psychiatry, University of California San Diego, La Jolla, CA, USA
| | - Hyunjoon Lee
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Travis T Mallard
- Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
- Department of Psychiatry, Harvard Medical School, Boston, MA, USA
- Center for Precision Psychiatry, Department of Psychiatry, Massachusetts General Hospital, Boston, MA, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Boston, MA, USA
| | - Sevim B Bianchi
- Department of Psychiatry, University of California San Diego, La Jolla, CA, USA
| | - John J Meredith
- Department of Psychiatry, University of California San Diego, La Jolla, CA, USA
| | - Laura Vilar-Ribó
- Psychiatric Genetics Unit, Group of Psychiatry, Mental Health and Addiction, Vall d'Hebron Research Institute (VHIR), Universitat Autònoma de Barcelona, Barcelona, Spain
| | - Heng Xu
- Department of Psychiatry, University of California San Diego, La Jolla, CA, USA
| | - Alexander S Hatoum
- Psychological & Brain Sciences, Washington University in St. Louis, St. Louis, MO, USA
| | - Emma C Johnson
- Psychological & Brain Sciences, Washington University in St. Louis, St. Louis, MO, USA
| | | | - Zeal Jinwala
- Department of Psychiatry, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Shreya R Pakala
- Department of Psychiatry, University of California San Diego, La Jolla, CA, USA
| | - Brittany S Leger
- Department of Psychiatry, University of California San Diego, La Jolla, CA, USA
- Program in Biomedical Sciences, University of California San Diego, La Jolla, CA, USA
| | - Maria Niarchou
- Department of Medicine, Division of Genetic Medicine, Vanderbilt University, Nashville, TN, USA
| | | | - Greg D Jenkins
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN, USA
| | - Anthony Batzler
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN, USA
| | - Richard Pendegraft
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN, USA
| | - Abraham A Palmer
- Department of Psychiatry, University of California San Diego, La Jolla, CA, USA
- Institute for Genomic Medicine, University of California San Diego, La Jolla, CA, USA
| | - Hang Zhou
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA
- Veterans Affairs Connecticut Healthcare System, West Haven, CT, USA
| | - Joanna M Biernacka
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN, USA
- Department of Psychiatry and Psychology, Mayo Clinic, Rochester, MN, USA
| | - Brandon J Coombes
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN, USA
| | - Joel Gelernter
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA
- Veterans Affairs Connecticut Healthcare System, West Haven, CT, USA
| | - Ke Xu
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA
- Veterans Affairs Connecticut Healthcare System, West Haven, CT, USA
| | | | - Nancy J Cox
- Department of Psychiatry and Behavioral Sciences, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Jordan W Smoller
- Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
- Department of Psychiatry, Harvard Medical School, Boston, MA, USA
- Center for Precision Psychiatry, Department of Psychiatry, Massachusetts General Hospital, Boston, MA, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Boston, MA, USA
| | - Lea K Davis
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, USA
- Department of Medicine, Division of Genetic Medicine, Vanderbilt University, Nashville, TN, USA
- Department of Psychiatry and Behavioral Sciences, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Amy C Justice
- Veterans Affairs Connecticut Healthcare System, West Haven, CT, USA
- Yale University School of Public Health, New Haven, CT, USA
- Yale University School of Medicine, New Haven, CT, USA
| | - Henry R Kranzler
- Mental Illness Research, Education and Clinical Center, Crescenz VAMC, Philadelphia, PA, USA
- Department of Psychiatry, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Rachel L Kember
- Mental Illness Research, Education and Clinical Center, Crescenz VAMC, Philadelphia, PA, USA
- Department of Psychiatry, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Sandra Sanchez-Roige
- Department of Psychiatry, University of California San Diego, La Jolla, CA, USA.
- Department of Medicine, Division of Genetic Medicine, Vanderbilt University, Nashville, TN, USA.
- Institute for Genomic Medicine, University of California San Diego, La Jolla, CA, USA.
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10
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Kolla BP, Mansukhani MP, Chakravorty S, Frank JA, Coombes BJ. Prevalence and associations of multiple hypnotic prescriptions in a clinical sample. J Clin Sleep Med 2024; 20:793-800. [PMID: 38189358 PMCID: PMC11063698 DOI: 10.5664/jcsm.10988] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2023] [Revised: 12/28/2023] [Accepted: 12/29/2023] [Indexed: 01/09/2024]
Abstract
STUDY OBJECTIVES We examined the prevalence of multiple hypnotic prescriptions and its association with clinical and demographic characteristics from the electronic health record (EHR) in the Mayo Clinic Biobank. METHODS Adult participants enrolled in the Mayo Clinic Biobank with an EHR number of ≥ 1 year were included (n = 52,940). Clinical and demographic characteristics were compared between participants who were and were not prescribed any hypnotic approved for insomnia by the US Food and Drug Administration and/or trazodone and in those prescribed a single vs multiple (≥ 2) hypnotics. A phenotype-based, phenome-wide association study (PheWAS) examining associations between hypnotic prescriptions and diagnoses across the EHR was performed adjusting for demographic and other confounders. RESULTS A total of 17,662 (33%) participants were prescribed at least 1 hypnotic and 5,331 (10%) received ≥ 2 hypnotics. Participants who were prescribed a hypnotic were more likely to be older, female, White, with a longer EHR, and a greater number of diagnostic codes (all P < .001). Those with multiple hypnotic prescriptions were more likely to be younger, female, with a longer EHR, and a greater number of diagnostic codes (all P < .001) compared with those prescribed a single hypnotic. The PheWAS revealed that participants with multiple hypnotic prescriptions had higher rates of mood disorders, anxiety disorders, suicidal ideation, restless legs syndrome, and chronic pain (all P < 1 e-10). CONCLUSIONS Receiving multiple hypnotic prescriptions is common and associated with a greater prevalence of psychiatric, chronic pain, and sleep-related movement disorders. Future studies should examine potential genetic associations with multiple hypnotic prescriptions to personalize treatments for chronic insomnia. CITATION Kolla BP, Mansukhani MP, Chakravorty S, Frank JA, Coombes BJ. Prevalence and associations of multiple hypnotic prescriptions in a clinical sample. J Clin Sleep Med. 2024;20(5):793-800.
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Affiliation(s)
- Bhanu Prakash Kolla
- Department of Psychiatry and Psychology, Mayo Clinic, Rochester, Minnesota
- Center for Sleep Medicine, Mayo Clinic, Rochester, Minnesota
| | | | | | - Jacob A. Frank
- Department of Quantitative Health Sciences, Division of Computational Biology, Mayo Clinic, Rochester, Minnesota
| | - Brandon J. Coombes
- Department of Quantitative Health Sciences, Division of Computational Biology, Mayo Clinic, Rochester, Minnesota
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11
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Nguyen TQ, Kerley CI, Key AP, Maxwell-Horn AC, Wells QS, Neul JL, Cutting LE, Landman BA. Phenotyping Down syndrome: discovery and predictive modelling with electronic medical records. JOURNAL OF INTELLECTUAL DISABILITY RESEARCH : JIDR 2024; 68:491-511. [PMID: 38303157 PMCID: PMC11023778 DOI: 10.1111/jir.13124] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/01/2021] [Revised: 11/20/2023] [Accepted: 12/27/2023] [Indexed: 02/03/2024]
Abstract
BACKGROUND Individuals with Down syndrome (DS) have a heightened risk for various co-occurring health conditions, including congenital heart disease (CHD). In this two-part study, electronic medical records (EMRs) were leveraged to examine co-occurring health conditions among individuals with DS (Study 1) and to investigate health conditions linked to surgical intervention among DS cases with CHD (Study 2). METHODS De-identified EMRs were acquired from Vanderbilt University Medical Center and facilitated creating a cohort of N = 2282 DS cases (55% females), along with comparison groups for each study. In Study 1, DS cases were one-by-two sex and age matched with samples of case-controls and of individuals with other intellectual and developmental difficulties (IDDs). The phenome-disease association study (PheDAS) strategy was employed to reveal co-occurring health conditions in DS versus comparison groups, which were then ranked for how often they are discussed in relation to DS using the PubMed database and Novelty Finding Index. In Study 2, a subset of DS individuals with CHD [N = 1098 (48%)] were identified to create longitudinal data for N = 204 cases with surgical intervention (19%) versus 204 case-controls. Data were included in predictive models and assessed which model-based health conditions, when more prevalent, would increase the likelihood of surgical intervention. RESULTS In Study 1, relative to case-controls and those with other IDDs, co-occurring health conditions among individuals with DS were confirmed to include heart failure, pulmonary heart disease, atrioventricular block, heart transplant/surgery and primary pulmonary hypertension (circulatory); hypothyroidism (endocrine/metabolic); and speech and language disorder and Alzheimer's disease (neurological/mental). Findings also revealed more versus less prevalent co-occurring health conditions in individuals with DS when comparing with those with other IDDs. Findings with high Novelty Finding Index were abnormal electrocardiogram, non-rheumatic aortic valve disorders and heart failure (circulatory); acid-base balance disorder (endocrine/metabolism); and abnormal blood chemistry (symptoms). In Study 2, the predictive models revealed that among individuals with DS and CHD, presence of health conditions such as congestive heart failure (circulatory), valvular heart disease and cardiac shunt (congenital), and pleural effusion and pulmonary collapse (respiratory) were associated with increased likelihood of surgical intervention. CONCLUSIONS Research efforts using EMRs and rigorous statistical methods could shed light on the complexity in health profile among individuals with DS and other IDDs and motivate precision-care development.
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Affiliation(s)
- T Q Nguyen
- Vanderbilt Brain Institute, Vanderbilt University, Nashville, TN, USA
- Peabody College of Education and Human Development, Vanderbilt University, Nashville, TN, USA
| | - C I Kerley
- School of Engineering, Vanderbilt University, Nashville, TN, USA
| | - A P Key
- Vanderbilt Brain Institute, Vanderbilt University, Nashville, TN, USA
- Vanderbilt Kennedy Center, Vanderbilt University Medical Center, Nashville, TN, USA
- Department of Speech and Hearing Sciences, Vanderbilt University Medical Center, Nashville, TN, USA
| | - A C Maxwell-Horn
- Department of Pediatrics, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Q S Wells
- Division of Cardiovascular Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | - J L Neul
- Vanderbilt Brain Institute, Vanderbilt University, Nashville, TN, USA
- Vanderbilt Kennedy Center, Vanderbilt University Medical Center, Nashville, TN, USA
- Department of Pediatrics, Vanderbilt University Medical Center, Nashville, TN, USA
| | - L E Cutting
- Vanderbilt Brain Institute, Vanderbilt University, Nashville, TN, USA
- Peabody College of Education and Human Development, Vanderbilt University, Nashville, TN, USA
- Vanderbilt Kennedy Center, Vanderbilt University Medical Center, Nashville, TN, USA
- Department of Pediatrics, Vanderbilt University Medical Center, Nashville, TN, USA
| | - B A Landman
- Vanderbilt Brain Institute, Vanderbilt University, Nashville, TN, USA
- School of Engineering, Vanderbilt University, Nashville, TN, USA
- Vanderbilt Kennedy Center, Vanderbilt University Medical Center, Nashville, TN, USA
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12
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Cao X, Zhang S, Sha Q. A novel method for multiple phenotype association studies based on genotype and phenotype network. PLoS Genet 2024; 20:e1011245. [PMID: 38728360 PMCID: PMC11111089 DOI: 10.1371/journal.pgen.1011245] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2023] [Revised: 05/22/2024] [Accepted: 03/29/2024] [Indexed: 05/12/2024] Open
Abstract
Joint analysis of multiple correlated phenotypes for genome-wide association studies (GWAS) can identify and interpret pleiotropic loci which are essential to understand pleiotropy in diseases and complex traits. Meanwhile, constructing a network based on associations between phenotypes and genotypes provides a new insight to analyze multiple phenotypes, which can explore whether phenotypes and genotypes might be related to each other at a higher level of cellular and organismal organization. In this paper, we first develop a bipartite signed network by linking phenotypes and genotypes into a Genotype and Phenotype Network (GPN). The GPN can be constructed by a mixture of quantitative and qualitative phenotypes and is applicable to binary phenotypes with extremely unbalanced case-control ratios in large-scale biobank datasets. We then apply a powerful community detection method to partition phenotypes into disjoint network modules based on GPN. Finally, we jointly test the association between multiple phenotypes in a network module and a single nucleotide polymorphism (SNP). Simulations and analyses of 72 complex traits in the UK Biobank show that multiple phenotype association tests based on network modules detected by GPN are much more powerful than those without considering network modules. The newly proposed GPN provides a new insight to investigate the genetic architecture among different types of phenotypes. Multiple phenotypes association studies based on GPN are improved by incorporating the genetic information into the phenotype clustering. Notably, it might broaden the understanding of genetic architecture that exists between diagnoses, genes, and pleiotropy.
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Affiliation(s)
- Xuewei Cao
- Department of Mathematical Sciences, Michigan Technological University, Houghton, Michigan, United States of America
| | - Shuanglin Zhang
- Department of Mathematical Sciences, Michigan Technological University, Houghton, Michigan, United States of America
| | - Qiuying Sha
- Department of Mathematical Sciences, Michigan Technological University, Houghton, Michigan, United States of America
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13
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Lin YC, Zhang S, Vessels T, Bastarache L, Bejan CA, Hsie RS, Philips EJ, Ruderfer DM, Pulley JM, Edwards TL, Wells QS, Warner JL, Denny JC, Roden DM, Kang H, Xu Y. Overcome the Limitation of Phenome-Wide Association Studies (PheWAS): Extension of PheWAS to Efficient and Robust Large-Scale ICD Codes Analysis. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.04.15.24305098. [PMID: 38699370 PMCID: PMC11065011 DOI: 10.1101/2024.04.15.24305098] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/05/2024]
Abstract
The Phenome-wide association studies (PheWAS) have become widely used for efficient, high-throughput evaluation of relationship between a genetic factor and a large number of disease phenotypes, typically extracted from a DNA biobank linked with electronic medical records (EMR). Phecodes, billing code-derived disease case-control status, are usually used as outcome variables in PheWAS and logistic regression has been the standard choice of analysis method. Since the clinical diagnoses in EMR are often inaccurate with errors which can lead to biases in the odds ratio estimates, much effort has been put to accurately define the cases and controls to ensure an accurate analysis. Specifically in order to correctly classify controls in the population, an exclusion criteria list for each Phecode was manually compiled to obtain unbiased odds ratios. However, the accuracy of the list cannot be guaranteed without extensive data curation process. The costly curation process limits the efficiency of large-scale analyses that take full advantage of all structured phenotypic information available in EMR. Here, we proposed to estimate relative risks (RR) instead. We first demonstrated the desired nature of R R that overcomes the inaccuracy in the controls via theoretical formula. With simulation and real data application, we further confirmed that R R is unbiased without compiling exclusion criteria lists. With R R as estimates, we are able to efficiently extend PheWAS to a larger-scale, phenome construction agnostic analysis of phenotypes, using ICD 9/10 codes, which preserve much more disease-related clinical information than Phecodes.
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Affiliation(s)
- Ya-Chen Lin
- Department of Biostatistics, Vanderbilt University Medical Center, Nashville, TN
| | - Siwei Zhang
- Department of Biostatistics, Vanderbilt University Medical Center, Nashville, TN
| | - Tess Vessels
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Lisa Bastarache
- Department of Biomedical informatics, Vanderbilt University Medical Center, Nashville, TN
| | - Cosmin Adrian Bejan
- Department of Biomedical informatics, Vanderbilt University Medical Center, Nashville, TN
| | - Ryan S Hsie
- Department of Urology, Vanderbilt University Medical Center, Nashville, TN
| | - Elizabeth J Philips
- Center for Drug Safety and Immunology, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Doug M Ruderfer
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA
- Department of Biomedical informatics, Vanderbilt University Medical Center, Nashville, TN
| | - Jill M Pulley
- Department of Allergy, Pulmonary and Critical Care Medicine, Vanderbilt University School of Medicine, Nashville, TN
| | - Todd L Edwards
- Division of Epidemiology, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Quinn S Wells
- Department of Cardiovascular Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Jeremy L Warner
- Division of Hematology and Oncology, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN 37232, USA
| | - Joshua C Denny
- Department of Biomedical informatics, Vanderbilt University Medical Center, Nashville, TN
| | - Dan M Roden
- Department of Pharmacology, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Hakmook Kang
- Department of Biostatistics, Vanderbilt University Medical Center, Nashville, TN
| | - Yaomin Xu
- Department of Biostatistics, Vanderbilt University Medical Center, Nashville, TN
- Department of Biomedical informatics, Vanderbilt University Medical Center, Nashville, TN
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14
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Takundwa MM, Thimiri Govinda Raj DB. Novel strategies for drug repurposing. PROGRESS IN MOLECULAR BIOLOGY AND TRANSLATIONAL SCIENCE 2024; 205:9-21. [PMID: 38789188 DOI: 10.1016/bs.pmbts.2024.03.021] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/26/2024]
Abstract
Synthetic biology, precision medicine, and nanobiotechnology are the three main emerging areas that drive translational innovation toward commercialization. There are several strategies used in precision medicine and drug repurposing is one of the key approaches as it addresses the challenges in drug discovery (high cost and time). Here, we provide a perspective on various new approaches to drug repurposing for cancer precision medicine. We report here our optimized wound healing methodology that can be used to validate drug sensitivity and drug repurposing. Using HeLa as our benchmark, we demonstrated that the assay can be applied to identify drugs that limit cell proliferation. From a future perspective, this assay can be expanded to ex vivo culturing of solid tumors in 2D culture and leukemia in 3D culture.
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Affiliation(s)
- Mutsa Monica Takundwa
- Synthetic Nanobiotechnology and Biomachines, Synthetic Biology and Precision Medicine Centre, Future Production Chemicals Cluster, Council for Scientific and Industrial Research, Pretoria, South Africa
| | - Deepak B Thimiri Govinda Raj
- Synthetic Nanobiotechnology and Biomachines, Synthetic Biology and Precision Medicine Centre, Future Production Chemicals Cluster, Council for Scientific and Industrial Research, Pretoria, South Africa.
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15
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Davogustto G, Zhao S, Li Y, Farber-Eger E, Lowery BD, Shaffer LL, Mosley JD, Shoemaker MB, Xu Y, Roden DM, Wells QS. Unbiased characterization of atrial fibrillation phenotypic architecture provides insight to genetic liability and clinically relevant outcomes. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.02.13.24302788. [PMID: 38405916 PMCID: PMC10888988 DOI: 10.1101/2024.02.13.24302788] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/27/2024]
Abstract
Background Atrial Fibrillation (AF) is a common and clinically heterogeneous arrythmia. Machine learning (ML) algorithms can define data-driven disease subtypes in an unbiased fashion, but whether the AF subgroups defined in this way align with underlying mechanisms, such as high polygenic liability to AF or inflammation, and associate with clinical outcomes is unclear. Methods We identified individuals with AF in a large biobank linked to electronic health records (EHR) and genome-wide genotyping. The phenotypic architecture in the AF cohort was defined using principal component analysis of 35 expertly curated and uncorrelated clinical features. We applied an unsupervised co-clustering machine learning algorithm to the 35 features to identify distinct phenotypic AF clusters. The clinical inflammatory status of the clusters was defined using measured biomarkers (CRP, ESR, WBC, Neutrophil %, Platelet count, RDW) within 6 months of first AF mention in the EHR. Polygenic risk scores (PRS) for AF and cytokine levels were used to assess genetic liability of clusters to AF and inflammation, respectively. Clinical outcomes were collected from EHR up to the last medical contact. Results The analysis included 23,271 subjects with AF, of which 6,023 had available genome-wide genotyping. The machine learning algorithm identified 3 phenotypic clusters that were distinguished by increasing prevalence of comorbidities, particularly renal dysfunction, and coronary artery disease. Polygenic liability to AF across clusters was highest in the low comorbidity cluster. Clinically measured inflammatory biomarkers were highest in the high comorbid cluster, while there was no difference between groups in genetically predicted levels of inflammatory biomarkers. Subgroup assignment was associated with multiple clinical outcomes including mortality, stroke, bleeding, and use of cardiac implantable electronic devices after AF diagnosis. Conclusion Patient subgroups identified by unsupervised clustering were distinguished by comorbidity burden and associated with risk of clinically important outcomes. Polygenic liability to AF across clusters was greatest in the low comorbidity subgroup. Clinical inflammation, as reflected by measured biomarkers, was lowest in the subgroup with lowest comorbidities. However, there were no differences in genetically predicted levels of inflammatory biomarkers, suggesting associations between AF and inflammation is driven by acquired comorbidities rather than genetic predisposition.
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16
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Paz V, Wilcox H, Goodman M, Wang H, Garfield V, Saxena R, Dashti HS. Associations of a multidimensional polygenic sleep health score and a sleep lifestyle index on health outcomes and their interaction in a clinical biobank. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.02.06.24302416. [PMID: 38370718 PMCID: PMC10871384 DOI: 10.1101/2024.02.06.24302416] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/20/2024]
Abstract
Sleep is a complex behavior regulated by genetic and environmental factors, and is known to influence health outcomes. However, the effect of multidimensional sleep encompassing several sleep dimensions on diseases has yet to be fully elucidated. Using the Mass General Brigham Biobank, we aimed to examine the association of multidimensional sleep with health outcomes and investigate whether sleep behaviors modulate genetic predisposition to unfavorable sleep on mental health outcomes. First, we generated a Polygenic Sleep Health Score using previously identified single nucleotide polymorphisms for sleep health and constructed a Sleep Lifestyle Index using data from self-reported sleep questions and electronic health records; second, we performed phenome-wide association analyses between these indexes and clinical phenotypes; and third, we analyzed the interaction between the indexes on prevalent mental health outcomes. Fifteen thousand eight hundred and eighty-four participants were included in the analysis (mean age 54.4; 58.6% female). The Polygenic Sleep Health Score was associated with the Sleep Lifestyle Index (β=0.050, 95%CI=0.032, 0.068) and with 114 disease outcomes spanning 12 disease groups, including obesity, sleep, and substance use disease outcomes (p<3.3×10-5). The Sleep Lifestyle Index was associated with 458 disease outcomes spanning 17 groups, including sleep, mood, and anxiety disease outcomes (p<5.1×10-5). No interactions were found between the indexes on prevalent mental health outcomes. These findings suggest that favorable sleep behaviors and genetic predisposition to healthy sleep may independently be protective of disease outcomes. This work provides novel insights into the role of multidimensional sleep on population health and highlights the need to develop prevention strategies focused on healthy sleep habits.
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Affiliation(s)
- Valentina Paz
- Instituto de Psicología Clínica, Facultad de Psicología, Universidad de la República, Montevideo, Uruguay
- MRC Unit for Lifelong Health & Ageing, Institute of Cardiovascular Science, University College London, London, United Kingdom
- Center for Genomic Medicine, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, United States of America
| | - Hannah Wilcox
- Center for Genomic Medicine, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, United States of America
- Department of Anesthesia, Critical Care and Pain Medicine, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, United States of America
| | - Matthew Goodman
- Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts, United States of America
| | - Heming Wang
- Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts, United States of America
| | - Victoria Garfield
- MRC Unit for Lifelong Health & Ageing, Institute of Cardiovascular Science, University College London, London, United Kingdom
| | - Richa Saxena
- Center for Genomic Medicine, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, United States of America
- Department of Anesthesia, Critical Care and Pain Medicine, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, United States of America
- Broad Institute, Cambridge, Massachusetts, United States of America
- Division of Sleep Medicine, Harvard Medical School, Boston, Massachusetts, United States of America
| | - Hassan S. Dashti
- Center for Genomic Medicine, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, United States of America
- Department of Anesthesia, Critical Care and Pain Medicine, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, United States of America
- Broad Institute, Cambridge, Massachusetts, United States of America
- Division of Sleep Medicine, Harvard Medical School, Boston, Massachusetts, United States of America
- Division of Nutrition, Harvard Medical School, Boston, Massachusetts, United States of America
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17
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Goodman MO, Faquih T, Paz V, Nagarajan P, Lane JM, Spitzer B, Maher M, Chung J, Cade BE, Purcell SM, Zhu X, Noordam R, Phillips AJK, Kyle SD, Spiegelhalder K, Weedon MN, Lawlor DA, Rotter JI, Taylor KD, Isasi CR, Sofer T, Dashti HS, Rutter MK, Redline S, Saxena R, Wang H. Genome-wide association analysis of composite sleep health scores in 413,904 individuals. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.02.02.24302211. [PMID: 38352337 PMCID: PMC10863010 DOI: 10.1101/2024.02.02.24302211] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 02/19/2024]
Abstract
Recent genome-wide association studies (GWASs) of several individual sleep traits have identified hundreds of genetic loci, suggesting diverse mechanisms. Moreover, sleep traits are moderately correlated, and together may provide a more complete picture of sleep health, while also illuminating distinct domains. Here we construct novel sleep health scores (SHSs) incorporating five core self-report measures: sleep duration, insomnia symptoms, chronotype, snoring, and daytime sleepiness, using additive (SHS-ADD) and five principal components-based (SHS-PCs) approaches. GWASs of these six SHSs identify 28 significant novel loci adjusting for multiple testing on six traits (p<8.3e-9), along with 341 previously reported loci (p<5e-08). The heritability of the first three SHS-PCs equals or exceeds that of SHS-ADD (SNP-h2=0.094), while revealing sleep-domain-specific genetic discoveries. Significant loci enrich in multiple brain tissues and in metabolic and neuronal pathways. Post GWAS analyses uncover novel genetic mechanisms underlying sleep health and reveal connections to behavioral, psychological, and cardiometabolic traits.
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Affiliation(s)
- Matthew O Goodman
- Division of Sleep and Circadian Disorders, Brigham and Women’s Hospital, Boston, MA, USA
- Department of Neurology and Medicine, Harvard Medical School, Brigham and Women’s Hospital, Boston, MA, USA
- Broad Institute, Cambridge, MA, USA
| | - Tariq Faquih
- Division of Sleep and Circadian Disorders, Brigham and Women’s Hospital, Boston, MA, USA
- Department of Neurology and Medicine, Harvard Medical School, Brigham and Women’s Hospital, Boston, MA, USA
- Broad Institute, Cambridge, MA, USA
| | - Valentina Paz
- Instituto de Psicología Clínica, Facultad de Psicología, Universidad de la República, Montevideo, Uruguay
- MRC Unit for Lifelong Health & Ageing, Institute of Cardiovascular Science, University College London, London, United Kingdom
- Center for Genomic Medicine, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Pavithra Nagarajan
- Division of Sleep and Circadian Disorders, Brigham and Women’s Hospital, Boston, MA, USA
| | - Jacqueline M Lane
- Division of Sleep and Circadian Disorders, Brigham and Women’s Hospital, Boston, MA, USA
- Department of Neurology and Medicine, Harvard Medical School, Brigham and Women’s Hospital, Boston, MA, USA
- Broad Institute, Cambridge, MA, USA
- Center for Genomic Medicine, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Brian Spitzer
- Division of Cardiology, Department of Medicine, Beth Israel Deaconess Medical Center, Boston, MA, USA
| | - Matthew Maher
- Center for Genomic Medicine, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Joon Chung
- Division of Sleep and Circadian Disorders, Brigham and Women’s Hospital, Boston, MA, USA
- Department of Neurology and Medicine, Harvard Medical School, Brigham and Women’s Hospital, Boston, MA, USA
| | - Brian E Cade
- Division of Sleep and Circadian Disorders, Brigham and Women’s Hospital, Boston, MA, USA
- Department of Neurology and Medicine, Harvard Medical School, Brigham and Women’s Hospital, Boston, MA, USA
- Broad Institute, Cambridge, MA, USA
| | - Shaun M Purcell
- Division of Sleep and Circadian Disorders, Brigham and Women’s Hospital, Boston, MA, USA
- Department of Neurology and Medicine, Harvard Medical School, Brigham and Women’s Hospital, Boston, MA, USA
- Broad Institute, Cambridge, MA, USA
- Department of Psychiatry, Brigham and Women’s Hospital, Boston, MA, USA
| | - Xiaofeng Zhu
- Department of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, OH, USA
| | - Raymond Noordam
- Department of Internal Medicine, Section of Gerontology and Geriatrics, Leiden University Medical Center, Leiden, The Netherlands
| | - Andrew J. K. Phillips
- School of Psychological Sciences, Turner Institute for Brain and Mental Health, Monash University, Melbourne, VIC, Australia
| | - Simon D. Kyle
- Sir Jules Thorn Sleep and Circadian Neuroscience Institute, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - Kai Spiegelhalder
- Department of Psychiatry and Psychotherapy, Medical Centre - University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Michael N Weedon
- Genetics of Complex Traits, University of Exeter Medical School, Exeter, UK
| | - Deborah A. Lawlor
- MRC Integrative Epidemiology Unit at the University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Jerome I Rotter
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Kent D Taylor
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Carmen R Isasi
- Department of Epidemiology and Population Health, Albert Einstein College of Medicine, Bronx, NY, USA
| | - Tamar Sofer
- Division of Sleep and Circadian Disorders, Brigham and Women’s Hospital, Boston, MA, USA
- Department of Neurology and Medicine, Harvard Medical School, Brigham and Women’s Hospital, Boston, MA, USA
- Division of Cardiology, Department of Medicine, Beth Israel Deaconess Medical Center, Boston, MA, USA
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Hassan S Dashti
- Broad Institute, Cambridge, MA, USA
- Center for Genomic Medicine, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
- Department of Anesthesia, Critical Care and Pain Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - Martin K Rutter
- Division of Endocrinology, Diabetes & Gastroenterology, School of Medical Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester, UK
- Diabetes, Endocrinology and Metabolism Centre, Manchester University NHS Foundation Trust, NIHR Manchester Biomedical Research Centre, Manchester Academic Health Science Centre, Manchester, UK
| | - Susan Redline
- Division of Sleep and Circadian Disorders, Brigham and Women’s Hospital, Boston, MA, USA
- Department of Neurology and Medicine, Harvard Medical School, Brigham and Women’s Hospital, Boston, MA, USA
| | - Richa Saxena
- Broad Institute, Cambridge, MA, USA
- Center for Genomic Medicine, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
- Department of Anesthesia, Critical Care and Pain Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - Heming Wang
- Division of Sleep and Circadian Disorders, Brigham and Women’s Hospital, Boston, MA, USA
- Department of Neurology and Medicine, Harvard Medical School, Brigham and Women’s Hospital, Boston, MA, USA
- Broad Institute, Cambridge, MA, USA
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18
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Ji X, Peng X, Tang H, Pan H, Wang W, Wu J, Chen J, Wei N. Alzheimer's disease phenotype based upon the carrier status of the apolipoprotein E ɛ4 allele. Brain Pathol 2024; 34:e13208. [PMID: 37646624 PMCID: PMC10711266 DOI: 10.1111/bpa.13208] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2022] [Accepted: 08/05/2023] [Indexed: 09/01/2023] Open
Abstract
The apolipoprotein E ɛ4 allele (APOE4) is universally acknowledged as the most potent genetic risk factor for Alzheimer's disease (AD). APOE4 promotes the initiation and progression of AD. Although the underlying mechanisms are unclearly understood, differences in lipid-bound affinity among the three APOE isoforms may constitute the basis. The protein APOE4 isoform has a high affinity with triglycerides and cholesterol. A distinction in lipid metabolism extensively impacts neurons, microglia, and astrocytes. APOE4 carriers exhibit phenotypic differences from non-carriers in clinical examinations and respond differently to multiple treatments. Therefore, we hypothesized that phenotypic classification of AD patients according to the status of APOE4 carrier will help specify research and promote its use in diagnosing and treating AD. Recent reviews have mainly evaluated the differences between APOE4 allele carriers and non-carriers from gene to protein structures, clinical features, neuroimaging, pathology, the neural network, and the response to various treatments, and have provided the feasibility of phenotypic group classification based on APOE4 carrier status. This review will facilitate the application of APOE phenomics concept in clinical practice and promote further medical research on AD.
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Affiliation(s)
- Xiao‐Yu Ji
- Department of NeurosurgeryThe First Affiliated Hospital of Shantou University Medical CollegeGuangdongChina
- Brain Function and Disease LaboratoryShantou University Medical CollegeGuangdongChina
| | - Xin‐Yuan Peng
- Department of NeurosurgeryThe First Affiliated Hospital of Shantou University Medical CollegeGuangdongChina
| | - Hai‐Liang Tang
- Fudan University Huashan Hospital, Department of Neurosurgery, State Key Laboratory for Medical NeurobiologyInstitutes of Brain Science, Shanghai Medical College‐Fudan UniversityShanghaiChina
| | - Hui Pan
- Shantou Longhu People's HospitalShantouGuangdongChina
| | - Wei‐Tang Wang
- Department of NeurosurgeryThe First Affiliated Hospital of Shantou University Medical CollegeGuangdongChina
| | - Jie Wu
- Department of NeurosurgeryThe First Affiliated Hospital of Shantou University Medical CollegeGuangdongChina
- Brain Function and Disease LaboratoryShantou University Medical CollegeGuangdongChina
| | - Jian Chen
- Department of NeurosurgeryThe First Affiliated Hospital of Shantou University Medical CollegeGuangdongChina
| | - Nai‐Li Wei
- Department of NeurosurgeryThe First Affiliated Hospital of Shantou University Medical CollegeGuangdongChina
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19
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Cao Q, Du X, Jiang XY, Tian Y, Gao CH, Liu ZY, Xu T, Tao XX, Lei M, Wang XQ, Ye LL, Duan DD. Phenome-wide association study and precision medicine of cardiovascular diseases in the post-COVID-19 era. Acta Pharmacol Sin 2023; 44:2347-2357. [PMID: 37532784 PMCID: PMC10692238 DOI: 10.1038/s41401-023-01119-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2023] [Accepted: 05/29/2023] [Indexed: 08/04/2023] Open
Abstract
SARS-CoV-2 infection causes injuries of not only the lungs but also the heart and endothelial cells in vasculature of multiple organs, and induces systemic inflammation and immune over-reactions, which makes COVID-19 a disease phenome that simultaneously affects multiple systems. Cardiovascular diseases (CVD) are intrinsic risk and causative factors for severe COVID-19 comorbidities and death. The wide-spread infection and reinfection of SARS-CoV-2 variants and the long-COVID may become a new common threat to human health and propose unprecedented impact on the risk factors, pathophysiology, and pharmacology of many diseases including CVD for a long time. COVID-19 has highlighted the urgent demand for precision medicine which needs new knowledge network to innovate disease taxonomy for more precise diagnosis, therapy, and prevention of disease. A deeper understanding of CVD in the setting of COVID-19 phenome requires a paradigm shift from the current phenotypic study that focuses on the virus or individual symptoms to phenomics of COVID-19 that addresses the inter-connectedness of clinical phenotypes, i.e., clinical phenome. Here, we summarize the CVD manifestations in the full clinical spectrum of COVID-19, and the phenome-wide association study of CVD interrelated to COVID-19. We discuss the underlying biology for CVD in the COVID-19 phenome and the concept of precision medicine with new phenomic taxonomy that addresses the overall pathophysiological responses of the body to the SARS-CoV-2 infection. We also briefly discuss the unique taxonomy of disease as Zheng-hou patterns in traditional Chinese medicine, and their potential implications in precision medicine of CVD in the post-COVID-19 era.
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Affiliation(s)
- Qian Cao
- Center for Phenomics of Traditional Chinese Medicine, the Affiliated Traditional Chinese Medicine Hospital, Southwest Medical University, Luzhou, 646000, China
| | - Xin Du
- Center for Phenomics of Traditional Chinese Medicine, the Affiliated Traditional Chinese Medicine Hospital, Southwest Medical University, Luzhou, 646000, China
| | - Xiao-Yan Jiang
- Center for Phenomics of Traditional Chinese Medicine, the Affiliated Traditional Chinese Medicine Hospital, Southwest Medical University, Luzhou, 646000, China
| | - Yuan Tian
- Center for Phenomics of Traditional Chinese Medicine, the Affiliated Traditional Chinese Medicine Hospital, Southwest Medical University, Luzhou, 646000, China
| | - Chen-Hao Gao
- Center for Phenomics of Traditional Chinese Medicine, the Affiliated Traditional Chinese Medicine Hospital, Southwest Medical University, Luzhou, 646000, China
| | - Zi-Yu Liu
- Center for Phenomics of Traditional Chinese Medicine, the Affiliated Traditional Chinese Medicine Hospital, Southwest Medical University, Luzhou, 646000, China
| | - Ting Xu
- Center for Phenomics of Traditional Chinese Medicine, the Affiliated Traditional Chinese Medicine Hospital, Southwest Medical University, Luzhou, 646000, China
| | - Xing-Xing Tao
- Center for Phenomics of Traditional Chinese Medicine, the Affiliated Traditional Chinese Medicine Hospital, Southwest Medical University, Luzhou, 646000, China
| | - Ming Lei
- Center for Phenomics of Traditional Chinese Medicine, the Affiliated Traditional Chinese Medicine Hospital, Southwest Medical University, Luzhou, 646000, China
| | - Xiao-Qiang Wang
- Center for Phenomics of Traditional Chinese Medicine, the Affiliated Traditional Chinese Medicine Hospital, Southwest Medical University, Luzhou, 646000, China
| | - Lingyu Linda Ye
- Center for Phenomics of Traditional Chinese Medicine, the Affiliated Traditional Chinese Medicine Hospital, Southwest Medical University, Luzhou, 646000, China.
- Institute of Integrated Chinese and Western Medicine, Southwest Medical University, Luzhou, 646000, China.
- Key Laboratory of Autoimmune Diseases and Precision Medicie, People's Hospital of Ningxia Hui Autonomous Region, Yinchuan, 750001, China.
| | - Dayue Darrel Duan
- Center for Phenomics of Traditional Chinese Medicine, the Affiliated Traditional Chinese Medicine Hospital, Southwest Medical University, Luzhou, 646000, China.
- Institute of Integrated Chinese and Western Medicine, Southwest Medical University, Luzhou, 646000, China.
- Key Laboratory of Autoimmune Diseases and Precision Medicie, People's Hospital of Ningxia Hui Autonomous Region, Yinchuan, 750001, China.
- The Department of Pharmacology, University of Nevada Reno School of Medicine, Reno, NV, 89557, USA.
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20
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Toikumo S, Jennings MV, Pham BK, Lee H, Mallard TT, Bianchi SB, Meredith JJ, Vilar-Ribó L, Xu H, Hatoum AS, Johnson EC, Pazdernik V, Jinwala Z, Pakala SR, Leger BS, Niarchou M, Ehinmowo M, Jenkins GD, Batzler A, Pendegraft R, Palmer AA, Zhou H, Biernacka JM, Coombes BJ, Gelernter J, Xu K, Hancock DB, Cox NJ, Smoller JW, Davis LK, Justice AC, Kranzler HR, Kember RL, Sanchez-Roige S. Multi-ancestry meta-analysis of tobacco use disorder prioritizes novel candidate risk genes and reveals associations with numerous health outcomes. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.03.27.23287713. [PMID: 37034728 PMCID: PMC10081388 DOI: 10.1101/2023.03.27.23287713] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 04/29/2023]
Abstract
Tobacco use disorder (TUD) is the most prevalent substance use disorder in the world. Genetic factors influence smoking behaviors, and although strides have been made using genome-wide association studies (GWAS) to identify risk variants, the majority of variants identified have been for nicotine consumption, rather than TUD. We leveraged five biobanks to perform a multi-ancestral meta-analysis of TUD (derived via electronic health records, EHR) in 898,680 individuals (739,895 European, 114,420 African American, 44,365 Latin American). We identified 88 independent risk loci; integration with functional genomic tools uncovered 461 potential risk genes, primarily expressed in the brain. TUD was genetically correlated with smoking and psychiatric traits from traditionally ascertained cohorts, externalizing behaviors in children, and hundreds of medical outcomes, including HIV infection, heart disease, and pain. This work furthers our biological understanding of TUD and establishes EHR as a source of phenotypic information for studying the genetics of TUD.
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Affiliation(s)
- Sylvanus Toikumo
- Mental Illness Research, Education and Clinical Center, Crescenz VAMC, Philadelphia, PA, USA
- Department of Psychiatry, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Mariela V Jennings
- Department of Psychiatry, University of California San Diego, San Diego, CA, USA
| | - Benjamin K Pham
- Department of Psychiatry, University of California San Diego, San Diego, CA, USA
| | - Hyunjoon Lee
- Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - Travis T Mallard
- Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
- Department of Psychiatry, Harvard Medical School, Boston, MA, USA
| | - Sevim B Bianchi
- Department of Psychiatry, University of California San Diego, San Diego, CA, USA
| | - John J Meredith
- Department of Psychiatry, University of California San Diego, San Diego, CA, USA
| | - Laura Vilar-Ribó
- Psychiatric Genetics Unit, Group of Psychiatry, Mental Health and Addiction, Vall d’Hebron Research Institute (VHIR), Universitat Autònoma de Barcelona, Barcelona, Spain
| | - Heng Xu
- Department of Psychiatry, University of California San Diego, San Diego, CA, USA
| | - Alexander S Hatoum
- Department of Psychiatry, Washington University School of Medicine, Saint Louis, Missouri, USA
| | - Emma C Johnson
- Department of Psychiatry, Washington University School of Medicine, Saint Louis, Missouri, USA
| | - Vanessa Pazdernik
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN, USA
| | - Zeal Jinwala
- Department of Psychiatry, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Shreya R Pakala
- Department of Psychiatry, University of California San Diego, San Diego, CA, USA
| | - Brittany S Leger
- Department of Psychiatry, University of California San Diego, San Diego, CA, USA
- Program in Biomedical Sciences, University of California San Diego, La Jolla, CA, USA
| | - Maria Niarchou
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA
| | | | | | - Greg D Jenkins
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN, USA
| | - Anthony Batzler
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN, USA
| | - Richard Pendegraft
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN, USA
| | - Abraham A Palmer
- Department of Psychiatry, University of California San Diego, San Diego, CA, USA
- Institute for Genomic Medicine, University of California San Diego, La Jolla, CA, USA
| | - Hang Zhou
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA
- Veterans Affairs Connecticut Healthcare System, West Haven, CT, USA
| | - Joanna M Biernacka
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN, USA
- Department of Psychiatry & Psychology, Mayo Clinic, Rochester, MN, USA
| | - Brandon J Coombes
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN, USA
| | - Joel Gelernter
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA
- Veterans Affairs Connecticut Healthcare System, West Haven, CT, USA
| | - Ke Xu
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA
- Veterans Affairs Connecticut Healthcare System, West Haven, CT, USA
| | - Dana B Hancock
- Behavioral and Urban Health Program, Behavioral Health and Criminal Justice Division, RTI International, Research Triangle Park, NC, USA
| | - Nancy J Cox
- Department of Medicine, Division of Genetic Medicine, Vanderbilt University, Nashville, TN, USA
| | - Jordan W Smoller
- Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
- Department of Psychiatry, Harvard Medical School, Boston, MA, USA
| | - Lea K Davis
- Department of Medicine, Division of Genetic Medicine, Vanderbilt University, Nashville, TN, USA
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, USA
- Department of Psychiatry and Behavioral Sciences, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Amy C Justice
- Yale University School of Public Health, New Haven, CT, USA
- Veterans Affairs Connecticut Healthcare System, West Haven, CT, USA
- Yale University School of Medicine, New Haven, CT, USA
| | - Henry R Kranzler
- Mental Illness Research, Education and Clinical Center, Crescenz VAMC, Philadelphia, PA, USA
- Department of Psychiatry, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Rachel L Kember
- Mental Illness Research, Education and Clinical Center, Crescenz VAMC, Philadelphia, PA, USA
- Department of Psychiatry, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Sandra Sanchez-Roige
- Department of Psychiatry, University of California San Diego, San Diego, CA, USA
- Department of Medicine, Division of Genetic Medicine, Vanderbilt University, Nashville, TN, USA
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21
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Nealon CL, Halladay CW, Gorman BR, Simpson P, Roncone DP, Canania RL, Anthony SA, Rogers LRS, Leber JN, Dougherty JM, Bailey JNC, Crawford DC, Sullivan JM, Galor A, Wu WC, Greenberg PB, Lass JH, Iyengar SK, Peachey NS. Association Between Fuchs Endothelial Corneal Dystrophy, Diabetes Mellitus, and Multimorbidity. Cornea 2023; 42:1140-1149. [PMID: 37170406 PMCID: PMC10523841 DOI: 10.1097/ico.0000000000003311] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2022] [Accepted: 04/11/2023] [Indexed: 05/13/2023]
Abstract
PURPOSE The aim of this study was to assess risk for demographic variables and other health conditions that are associated with Fuchs endothelial corneal dystrophy (FECD). METHODS We developed a FECD case-control algorithm based on structured electronic health record data and confirmed accuracy by individual review of charts at 3 Veterans Affairs (VA) Medical Centers. This algorithm was applied to the Department of VA Million Veteran Program cohort from whom sex, genetic ancestry, comorbidities, diagnostic phecodes, and laboratory values were extracted. Single-variable and multiple variable logistic regression models were used to determine the association of these risk factors with FECD diagnosis. RESULTS Being a FECD case was associated with female sex, European genetic ancestry, and a greater number of comorbidities. Of 1417 diagnostic phecodes evaluated, 213 had a significant association with FECD, falling in both ocular and nonocular conditions, including diabetes mellitus (DM). Five of 69 laboratory values were associated with FECD, with the direction of change for 4 being consistent with DM. Insulin dependency and type 1 DM raised risk to a greater degree than type 2 DM, like other microvascular diabetic complications. CONCLUSIONS Female sex, European ancestry, and multimorbidity increased FECD risk. Endocrine/metabolic clinic encounter codes and altered patterns of laboratory values support DM increasing FECD risk. Our results evoke a threshold model in which the FECD phenotype is intensified by DM and potentially other health conditions that alter corneal physiology. Further studies to better understand the relationship between FECD and DM are indicated and may help identify opportunities for slowing FECD progression.
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Affiliation(s)
- Cari L. Nealon
- Eye Clinic, VA Northeast Ohio Healthcare System, Cleveland, Ohio, USA
| | - Christopher W. Halladay
- Center of Innovation in Long Term Services and Supports, Providence VA Medical Center, Providence, Rhode Island, USA
| | - Bryan R. Gorman
- VA Cooperative Studies Program, VA Boston Healthcare System, Boston, Massachusetts
- Booz Allen Hamilton, McLean, Virginia, USA
| | - Piana Simpson
- Eye Clinic, VA Northeast Ohio Healthcare System, Cleveland, Ohio, USA
| | - David P. Roncone
- Eye Clinic, VA Northeast Ohio Healthcare System, Cleveland, Ohio, USA
| | | | - Scott A. Anthony
- Eye Clinic, VA Northeast Ohio Healthcare System, Cleveland, Ohio, USA
| | | | - Jenna N. Leber
- Ophthalmology Section, VA Western NY Health Care System, Buffalo, New York, USA
| | | | - Jessica N. Cooke Bailey
- Cleveland Institute for Computational Biology, Case Western Reserve University School of Medicine, Cleveland, Ohio, USA
- Department of Population & Quantitative Health Sciences, Case Western Reserve University School of Medicine, Cleveland, Ohio, USA
- Research Service, VA Northeast Ohio Healthcare System, Cleveland, Ohio, USA
| | - Dana C. Crawford
- Cleveland Institute for Computational Biology, Case Western Reserve University School of Medicine, Cleveland, Ohio, USA
- Department of Population & Quantitative Health Sciences, Case Western Reserve University School of Medicine, Cleveland, Ohio, USA
- Research Service, VA Northeast Ohio Healthcare System, Cleveland, Ohio, USA
| | - Jack M. Sullivan
- Ophthalmology Section, VA Western NY Health Care System, Buffalo, New York, USA
- Research Service, VA Western NY Health Care System, Buffalo, New York, USA
- Department of Ophthalmology (Ross Eye Institute), University at Buffalo-SUNY, Buffalo, New York, USA
| | - Anat Galor
- Miami Veterans Affairs Medical Center, Miami, Florida, USA
- Bascom Palmer Eye Institute, University of Miami, Miami, Florida, USA
| | - Wen-Chih Wu
- Cardiology Section, Medical Service, Providence VA Medical Center, Providence, Rhode Island, USA
| | - Paul B. Greenberg
- Ophthalmology Section, Providence VA Medical Center, Providence, Rhode Island, USA
- Division of Ophthalmology, Alpert Medical School, Brown University, Providence, Rhode Island, USA
| | | | - Jonathan H. Lass
- Department of Ophthalmology & Visual Sciences, Case Western Reserve University, Cleveland, Ohio, USA
- University Hospitals Eye Institute, Cleveland, Ohio, USA
| | - Sudha K. Iyengar
- Cleveland Institute for Computational Biology, Case Western Reserve University School of Medicine, Cleveland, Ohio, USA
- Department of Population & Quantitative Health Sciences, Case Western Reserve University School of Medicine, Cleveland, Ohio, USA
- Research Service, VA Northeast Ohio Healthcare System, Cleveland, Ohio, USA
| | - Neal S. Peachey
- Research Service, VA Northeast Ohio Healthcare System, Cleveland, Ohio, USA
- Cole Eye Institute, Cleveland Clinic Foundation, Cleveland, Ohio, USA
- Department of Ophthalmology, Cleveland Clinic Lerner College of Medicine of Case Western Reserve University, Cleveland, Ohio, USA
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22
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Lu Z, Zhang Y, Sun Y, Liao Y, Kang Z, Feng X, Yan H, Wang L, Lu T, Zhang D, Yue W. Therapeutic outcomes wide association scan of different antipsychotics in patients with schizophrenia: Randomized clinical trials and multi-ancestry validation. Psychiatry Clin Neurosci 2023; 77:486-496. [PMID: 37210704 DOI: 10.1111/pcn.13567] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/25/2023] [Revised: 05/05/2023] [Accepted: 05/15/2023] [Indexed: 05/22/2023]
Abstract
AIM This study identified discrepant therapeutic outcomes of antipsychotics. METHODS A total of 5191 patients with schizophrenia were enrolled, 3030 as discovery cohort, 1395 as validation cohort, and 766 as multi-ancestry validation cohort. Therapeutic Outcomes Wide Association Scan was conducted. Types of antipsychotics (one antipsychotic vs other antipsychotics) were dependent variables, therapeutic outcomes including efficacy and safety were independent variables. RESULTS In discovery cohort, olanzapine related to higher risk of weight gain (AIWG, OR: 2.21-2.86), liver dysfunction (OR: 1.75-2.33), sedation (OR: 1.76-2.86), increased lipid level (OR: 2.04-2.12), and lower risk of extrapyramidal syndrome (EPS, OR: 0.14-0.46); risperidone related to higher risk of hyperprolactinemia (OR: 12.45-20.53); quetiapine related to higher risk of sedation (OR = 1.73), palpitation (OR = 2.87), increased lipid level (OR = 1.69), lower risk of hyperprolactinemia (OR: 0.09-0.11), and EPS (OR: 0.15-0.44); aripiprazole related to lower risk of hyperprolactinemia (OR: 0.09-0.14), AIWG (OR = 0.44), sedation (OR: 0.33-0.47), and QTc prolongation (β = -2.17); ziprasidone related to higher risk of increased QT interval (β range: 3.11-3.22), nausea (OR: 3.22-3.91), lower risk of AIWG (OR: 0.27-0.46), liver dysfunction (OR: 0.41-0.38), and increased lipid level (OR: 0.41-0.55); haloperidol related to higher risk of EPS (OR: 2.64-6.29), hyperprolactinemia (OR: 5.45-9.44), and increased salivation (OR: 3.50-3.68). Perphenazine related to higher risk of EPS (OR: 1.89-2.54). Higher risk of liver dysfunction in olanzapine and lower risk of hyperprolactinemia in aripiprazole were confirmed in validation cohort, and higher risk of AIWG in olanzapine and hyperprolactinemia in risperidone were confirmed in multi-ancestry validation cohort. CONCLUSION Future precision medicine should focus on personalized side-effects.
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Affiliation(s)
- Zhe Lu
- Peking University Sixth Hospital, Peking University Institute of Mental Health, Beijing, China
- National Clinical Research Center for Mental Disorders, Peking University Sixth Hospital, Beijing, China
- NHC Key Laboratory of Mental Health, Peking University, Beijing, China
| | - Yuyanan Zhang
- Peking University Sixth Hospital, Peking University Institute of Mental Health, Beijing, China
- National Clinical Research Center for Mental Disorders, Peking University Sixth Hospital, Beijing, China
- NHC Key Laboratory of Mental Health, Peking University, Beijing, China
| | - Yaoyao Sun
- Peking University Sixth Hospital, Peking University Institute of Mental Health, Beijing, China
- National Clinical Research Center for Mental Disorders, Peking University Sixth Hospital, Beijing, China
- NHC Key Laboratory of Mental Health, Peking University, Beijing, China
| | - Yundan Liao
- Peking University Sixth Hospital, Peking University Institute of Mental Health, Beijing, China
- National Clinical Research Center for Mental Disorders, Peking University Sixth Hospital, Beijing, China
- NHC Key Laboratory of Mental Health, Peking University, Beijing, China
| | - Zhewei Kang
- Peking University Sixth Hospital, Peking University Institute of Mental Health, Beijing, China
- National Clinical Research Center for Mental Disorders, Peking University Sixth Hospital, Beijing, China
- NHC Key Laboratory of Mental Health, Peking University, Beijing, China
| | - Xiaoyang Feng
- Peking University Sixth Hospital, Peking University Institute of Mental Health, Beijing, China
- National Clinical Research Center for Mental Disorders, Peking University Sixth Hospital, Beijing, China
- NHC Key Laboratory of Mental Health, Peking University, Beijing, China
| | - Hao Yan
- Peking University Sixth Hospital, Peking University Institute of Mental Health, Beijing, China
- National Clinical Research Center for Mental Disorders, Peking University Sixth Hospital, Beijing, China
- NHC Key Laboratory of Mental Health, Peking University, Beijing, China
| | - Lifang Wang
- Peking University Sixth Hospital, Peking University Institute of Mental Health, Beijing, China
- National Clinical Research Center for Mental Disorders, Peking University Sixth Hospital, Beijing, China
- NHC Key Laboratory of Mental Health, Peking University, Beijing, China
| | - Tianlan Lu
- Peking University Sixth Hospital, Peking University Institute of Mental Health, Beijing, China
- National Clinical Research Center for Mental Disorders, Peking University Sixth Hospital, Beijing, China
- NHC Key Laboratory of Mental Health, Peking University, Beijing, China
| | - Dai Zhang
- Peking University Sixth Hospital, Peking University Institute of Mental Health, Beijing, China
- National Clinical Research Center for Mental Disorders, Peking University Sixth Hospital, Beijing, China
- NHC Key Laboratory of Mental Health, Peking University, Beijing, China
- PKU-IDG/McGovern Institute for Brain Research, Peking University, Beijing, China
- Chinese Institute for Brain Research, Beijing, China
| | - Weihua Yue
- Peking University Sixth Hospital, Peking University Institute of Mental Health, Beijing, China
- National Clinical Research Center for Mental Disorders, Peking University Sixth Hospital, Beijing, China
- NHC Key Laboratory of Mental Health, Peking University, Beijing, China
- PKU-IDG/McGovern Institute for Brain Research, Peking University, Beijing, China
- Chinese Institute for Brain Research, Beijing, China
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23
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Ou H, Kawaguchi S, Sonomura K, Kawaguchi T, Kitada S, Yoshiji S, Brial F, Gauguier D, Xia J, Matsuda F. A phenome-wide association study (PheWAS) to identify the health impacts of 4-cresol sulfate in the Nagahama Study. Sci Rep 2023; 13:13926. [PMID: 37626071 PMCID: PMC10457396 DOI: 10.1038/s41598-023-40697-2] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2023] [Accepted: 08/16/2023] [Indexed: 08/27/2023] Open
Abstract
Gut-microbiota derived metabolites are important regulators of host biology and metabolism. To understand the impacts of the microbial metabolite 4-cresol sulfate (4-CS) on four chronic diseases [type 2 diabetes mellitus, metabolic syndrome (MetS), non-alcoholic fatty liver disease, and chronic kidney disease (CKD)], we conducted association analyses of plasma 4-CS quantified by liquid chromatography coupled to mass spectrometry (LC-MS) in 3641 participants of the Nagahama study. Our results validated the elevation of 4-CS in CKD and identified a reducing trend in MetS. To delineate the holistic effects of 4-CS, we performed a phenome-wide association analysis (PheWAS) with 937 intermediate biological and behavioral traits. We detected associations between 4-CS and 39 phenotypes related to blood pressure regulation, hepatic and renal functions, hematology, sleep quality, intraocular pressure, ion regulation, ketone and fatty acid metabolisms, disease history and dietary habits. Among them, 19 PheWAS significant traits, including fatty acids and 14 blood pressure indices, were correlated with MetS, suggesting that 4-CS is a potential biomarker for MetS. Consistent associations of this gut microbial-derived metabolite on multiple endophenotypes underlying distinct etiopathogenesis support its role in the overall host health, with prospects of probiotic-based therapeutic solutions in chronic diseases.
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Affiliation(s)
- Huiting Ou
- Center for Genomic Medicine, Graduate School of Medicine, Kyoto University, Kyoto, 606-8507, Japan
- Department of Human Genetics, McGill University, Montreal, QC, H3A 0C7, Canada
| | - Shuji Kawaguchi
- Center for Genomic Medicine, Graduate School of Medicine, Kyoto University, Kyoto, 606-8507, Japan
| | - Kazuhiro Sonomura
- Center for Genomic Medicine, Graduate School of Medicine, Kyoto University, Kyoto, 606-8507, Japan
- Life Science Research Center, Technology Research Laboratory, Shimadzu Corporation, Kyoto, 604-8511, Japan
| | - Takahisa Kawaguchi
- Center for Genomic Medicine, Graduate School of Medicine, Kyoto University, Kyoto, 606-8507, Japan
| | - Seri Kitada
- Center for Genomic Medicine, Graduate School of Medicine, Kyoto University, Kyoto, 606-8507, Japan
| | - Satoshi Yoshiji
- Center for Genomic Medicine, Graduate School of Medicine, Kyoto University, Kyoto, 606-8507, Japan
- Department of Human Genetics, McGill University, Montreal, QC, H3A 0C7, Canada
| | - François Brial
- Center for Genomic Medicine, Graduate School of Medicine, Kyoto University, Kyoto, 606-8507, Japan
| | - Dominique Gauguier
- Center for Genomic Medicine, Graduate School of Medicine, Kyoto University, Kyoto, 606-8507, Japan
- Department of Human Genetics, McGill University, Montreal, QC, H3A 0C7, Canada
- University Paris Cité, INSERM UMR1124, 45 rue des Saints Peres, 75006, Paris, France
| | - Jianguo Xia
- Department of Human Genetics, McGill University, Montreal, QC, H3A 0C7, Canada
- Institute of Parasitology, McGill University, Montreal, QC, H9X 3V9, Canada
| | - Fumihiko Matsuda
- Center for Genomic Medicine, Graduate School of Medicine, Kyoto University, Kyoto, 606-8507, Japan.
- Department of Human Genetics, McGill University, Montreal, QC, H3A 0C7, Canada.
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24
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Eyoh EE, Failla MD, Williams ZJ, Schwartz KL, Cutting LE, Landman BA, Cascio CJ. Brief Report: The Characterization of Medical Comorbidity Prior to Autism Diagnosis in Children Before Age Two. J Autism Dev Disord 2023; 53:2540-2547. [PMID: 34853956 PMCID: PMC9156724 DOI: 10.1007/s10803-021-05380-3] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 11/16/2021] [Indexed: 01/07/2023]
Abstract
In autism spectrum disorder (ASD), medical conditions in infancy could be predictive markers for later ASD diagnosis. In this study, electronic medical records of 579 autistic individuals and 1897 matched controls prior to age 2 were analyzed for potential predictive conditions. Using a novel tool, the relative association of each condition in the autistic group was compared to the control group using logistic regressions across medical records. Generalized convulsive epilepsy, nystagmus, lack of normal physiological development, delayed milestones, and strabismus were more likely in those later diagnosed with ASD while perinatal jaundice was less likely to be associated. Lesser-known conditions, such as strabismus and nystagmus, may point to novel predictive co-occurring condition profiles which could improve screening practices for ASD.
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Affiliation(s)
- Ekomobong E Eyoh
- Department of Psychiatry and Behavioral Science, Vanderbilt University Medical Center, Nashville, TN, USA.
- Institute of Child Development, University of Minnesota, 51 East River Rd, Minneapolis, MN, 55455, USA.
| | | | - Zachary J Williams
- Department of Hearing and Speech Sciences, Vanderbilt University Medical Center, Nashville, TN, USA
- Vanderbilt Brain Institute, Vanderbilt University, Nashville, TN, USA
- Frist Center for Autism and Innovation, Vanderbilt University, Nashville, TN, USA
- Vanderbilt University School of Medicine, Nashville, TN, USA
| | - Kyle L Schwartz
- Vanderbilt University School of Medicine, Nashville, TN, USA
| | - Laurie E Cutting
- Department of Special Education, Vanderbilt University, Nashville, TN, USA
- Vanderbilt Kennedy Center, Vanderbilt University, Nashville, TN, USA
| | - Bennett A Landman
- Department of Psychiatry and Behavioral Science, Vanderbilt University Medical Center, Nashville, TN, USA
- Vanderbilt Kennedy Center, Vanderbilt University, Nashville, TN, USA
- Departments of Electrical Engineering and Computer Science, Vanderbilt University, Nashville, TN, USA
| | - Carissa J Cascio
- Department of Psychiatry and Behavioral Science, Vanderbilt University Medical Center, Nashville, TN, USA
- Vanderbilt Brain Institute, Vanderbilt University, Nashville, TN, USA
- Frist Center for Autism and Innovation, Vanderbilt University, Nashville, TN, USA
- Vanderbilt Kennedy Center, Vanderbilt University, Nashville, TN, USA
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25
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Mulugeta A, Suppiah V, Hyppönen E. Schizophrenia and co-morbidity risk: Evidence from a data driven phenomewide association study. J Psychiatr Res 2023; 162:1-10. [PMID: 37060872 DOI: 10.1016/j.jpsychires.2023.04.010] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/09/2022] [Revised: 03/06/2023] [Accepted: 04/05/2023] [Indexed: 04/17/2023]
Abstract
Schizophrenia is a chronic debilitating psychiatric disorder with significant morbidity and mortality. In this study, we used information from 337,484 UK Biobank participants and performed PheWAS using schizophrenia genetic risk score on 1135 disease outcomes. Signals that passed the false discovery rate threshold were further analyzed for evidence on the causality of the association. We extended the analysis to 30 serum, four urine, and six neuroimaging biomarkers to identify biomarkers that could be affected by schizophrenia. Schizophrenia GRS was associated with 54 (39 distinct) disease outcomes including schizophrenia in the PheWAS analysis. Of these, a causal association were found with 10 distinct diseases in the MR analysis. Schizophrenia causally linked with higher odds of anxiety (OR = 1.41, 95%CI 1.12 to 1.21), bipolar disorder (OR = 1.52, 95%CI 1.36 to 1.70), major depressive disorder (OR = 1.12, 95%CI 1.08 to 1.16) and suicidal ideation (OR = 1.30, 95%CI 1.19 to 1.42). Lower odds were found for several diseases including type 1 diabetes, coronary atherosclerosis and some musculoskeletal disorders. In analyses using biomarkers, schizophrenia was associated with lower serum 25(OH)D, gamma glutamyltransferase, cystatin C, serum creatinine. However, we did not find association with any of the brain imaging markers. Our analyses confirmed the co-existence of schizophrenia with other mental health disorders but did not otherwise suggest strong effects on disease risk. Biomarker analyses reflected associations which could be explained by unhealthy lifestyles, suggesting patients with schizophrenia may benefit from screening for and managing broader health aspects.
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Affiliation(s)
- Anwar Mulugeta
- Australian Centre for Precision Health, University of South Australia, Adelaide, Australia; Department of Pharmacology, College of Health Sciences, Addis Ababa University, Addis Ababa, Ethiopia
| | - Vijayaprakash Suppiah
- Australian Centre for Precision Health, University of South Australia, Adelaide, Australia; Clinical and Health Sciences, University of South Australia, Adelaide, Australia.
| | - Elina Hyppönen
- Australian Centre for Precision Health, University of South Australia, Adelaide, Australia; Population, Policy and Practice, UCL Great Ormond Street Institute of Child Health, London, UK; South Australian Health and Medical Research Institute, Adelaide, Australia
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26
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Callahan TJ, Stefanski AL, Wyrwa JM, Zeng C, Ostropolets A, Banda JM, Baumgartner WA, Boyce RD, Casiraghi E, Coleman BD, Collins JH, Deakyne Davies SJ, Feinstein JA, Lin AY, Martin B, Matentzoglu NA, Meeker D, Reese J, Sinclair J, Taneja SB, Trinkley KE, Vasilevsky NA, Williams AE, Zhang XA, Denny JC, Ryan PB, Hripcsak G, Bennett TD, Haendel MA, Robinson PN, Hunter LE, Kahn MG. Ontologizing health systems data at scale: making translational discovery a reality. NPJ Digit Med 2023; 6:89. [PMID: 37208468 PMCID: PMC10196319 DOI: 10.1038/s41746-023-00830-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2022] [Accepted: 04/28/2023] [Indexed: 05/21/2023] Open
Abstract
Common data models solve many challenges of standardizing electronic health record (EHR) data but are unable to semantically integrate all of the resources needed for deep phenotyping. Open Biological and Biomedical Ontology (OBO) Foundry ontologies provide computable representations of biological knowledge and enable the integration of heterogeneous data. However, mapping EHR data to OBO ontologies requires significant manual curation and domain expertise. We introduce OMOP2OBO, an algorithm for mapping Observational Medical Outcomes Partnership (OMOP) vocabularies to OBO ontologies. Using OMOP2OBO, we produced mappings for 92,367 conditions, 8611 drug ingredients, and 10,673 measurement results, which covered 68-99% of concepts used in clinical practice when examined across 24 hospitals. When used to phenotype rare disease patients, the mappings helped systematically identify undiagnosed patients who might benefit from genetic testing. By aligning OMOP vocabularies to OBO ontologies our algorithm presents new opportunities to advance EHR-based deep phenotyping.
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Affiliation(s)
- Tiffany J Callahan
- Computational Bioscience Program, University of Colorado Anschutz Medical Campus, Aurora, CO, 80045, USA.
- Department of Biomedical Informatics, Columbia University Irving Medical Center, New York, NY, 10032, USA.
| | - Adrianne L Stefanski
- Computational Bioscience Program, University of Colorado Anschutz Medical Campus, Aurora, CO, 80045, USA
| | - Jordan M Wyrwa
- Department of Physical Medicine and Rehabilitation, School of Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO, 80045, USA
| | - Chenjie Zeng
- National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, 20892, USA
| | - Anna Ostropolets
- Department of Biomedical Informatics, Columbia University Irving Medical Center, New York, NY, 10032, USA
| | - Juan M Banda
- Department of Computer Science, Georgia State University, Atlanta, GA, 30303, USA
| | - William A Baumgartner
- Computational Bioscience Program, University of Colorado Anschutz Medical Campus, Aurora, CO, 80045, USA
| | - Richard D Boyce
- Department of Biomedical Informatics, University of Pittsburgh School of Medicine, Pittsburgh, PA, 15260, USA
| | - Elena Casiraghi
- Computer Science, Università degli Studi di Milano, Milan, Italy
- The Jackson Laboratory for Genomic Medicine, Farmington, CT, 06032, USA
| | - Ben D Coleman
- The Jackson Laboratory for Genomic Medicine, Farmington, CT, 06032, USA
| | - Janine H Collins
- Department of Haematology, University of Cambridge, Cambridge, UK
| | - Sara J Deakyne Davies
- Department of Research Informatics & Data Science, Analytics Resource Center, Children's Hospital Colorado, Aurora, CO, 80045, USA
| | - James A Feinstein
- Adult and Child Center for Health Outcomes Research and Delivery Science (ACCORDS), University of Colorado Anschutz School of Medicine, Aurora, CO, 80045, USA
| | - Asiyah Y Lin
- National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, 20892, USA
| | - Blake Martin
- Departments of Biomedical Informatics and Pediatrics, University of Colorado School of Medicine, Aurora, CO, 80045, USA
| | | | | | - Justin Reese
- Division of Environmental Genomics and Systems Biology, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA
| | | | - Sanya B Taneja
- Intelligent Systems Program, University of Pittsburgh, Pittsburgh, PA, 15260, USA
| | - Katy E Trinkley
- Department of Family Medicine, University of Colorado Anschutz School of Medicine, Aurora, CO, 80045, USA
| | - Nicole A Vasilevsky
- Translational and Integrative Sciences Lab, University of Colorado Anschutz Medical Campus, Aurora, CO, 80045, USA
| | - Andrew E Williams
- Tufts Institute for Clinical Research and Health Policy Studies, Tufts University, Boston, MA, 02155, USA
| | - Xingmin A Zhang
- The Jackson Laboratory for Genomic Medicine, Farmington, CT, 06032, USA
| | - Joshua C Denny
- National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, 20892, USA
| | - Patrick B Ryan
- Janssen Research and Development, Raritan, NJ, 08869, USA
| | - George Hripcsak
- Department of Biomedical Informatics, Columbia University Irving Medical Center, New York, NY, 10032, USA
| | - Tellen D Bennett
- Departments of Biomedical Informatics and Pediatrics, University of Colorado School of Medicine, Aurora, CO, 80045, USA
| | - Melissa A Haendel
- Departments of Biomedical Informatics and Pediatrics, University of Colorado School of Medicine, Aurora, CO, 80045, USA
| | - Peter N Robinson
- The Jackson Laboratory for Genomic Medicine, Farmington, CT, 06032, USA
| | - Lawrence E Hunter
- Computational Bioscience Program, University of Colorado Anschutz Medical Campus, Aurora, CO, 80045, USA
- Department of Biomedical Informatics, University of Colorado School of Medicine, Aurora, CO, 80045, USA
| | - Michael G Kahn
- Department of Biomedical Informatics, University of Colorado School of Medicine, Aurora, CO, 80045, USA
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27
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Statzer C, Luthria K, Sharma A, Kann MG, Ewald CY. The Human Extracellular Matrix Diseasome Reveals Genotype-Phenotype Associations with Clinical Implications for Age-Related Diseases. Biomedicines 2023; 11:biomedicines11041212. [PMID: 37189830 DOI: 10.3390/biomedicines11041212] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2023] [Revised: 04/07/2023] [Accepted: 04/14/2023] [Indexed: 05/17/2023] Open
Abstract
The extracellular matrix (ECM) is earning an increasingly relevant role in many disease states and aging. The analysis of these disease states is possible with the GWAS and PheWAS methodologies, and through our analysis, we aimed to explore the relationships between polymorphisms in the compendium of ECM genes (i.e., matrisome genes) in various disease states. A significant contribution on the part of ECM polymorphisms is evident in various types of disease, particularly those in the core-matrisome genes. Our results confirm previous links to connective-tissue disorders but also unearth new and underexplored relationships with neurological, psychiatric, and age-related disease states. Through our analysis of the drug indications for gene-disease relationships, we identify numerous targets that may be repurposed for age-related pathologies. The identification of ECM polymorphisms and their contributions to disease will play an integral role in future therapeutic developments, drug repurposing, precision medicine, and personalized care.
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Affiliation(s)
- Cyril Statzer
- Department of Health Sciences and Technology, Institute of Translational Medicine, Eidgenössische Technische Hochschule Zürich, Schwerzenbach, CH-8603 Zurich, Switzerland
| | - Karan Luthria
- Department of Biological Sciences, University of Maryland, Baltimore County, 1000 Hilltop Circle, Baltimore, MD 21250, USA
| | - Arastu Sharma
- Department of Health Sciences and Technology, Institute of Translational Medicine, Eidgenössische Technische Hochschule Zürich, Schwerzenbach, CH-8603 Zurich, Switzerland
| | - Maricel G Kann
- Department of Biological Sciences, University of Maryland, Baltimore County, 1000 Hilltop Circle, Baltimore, MD 21250, USA
| | - Collin Y Ewald
- Department of Health Sciences and Technology, Institute of Translational Medicine, Eidgenössische Technische Hochschule Zürich, Schwerzenbach, CH-8603 Zurich, Switzerland
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28
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Xie H, Cao X, Zhang S, Sha Q. Joint analysis of multiple phenotypes for extremely unbalanced case-control association studies. Genet Epidemiol 2023; 47:185-197. [PMID: 36691904 DOI: 10.1002/gepi.22513] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2022] [Revised: 11/16/2022] [Accepted: 01/11/2023] [Indexed: 01/25/2023]
Abstract
In genome-wide association studies (GWAS) for thousands of phenotypes in biobanks, most binary phenotypes have substantially fewer cases than controls. Many widely used approaches for joint analysis of multiple phenotypes produce inflated type I error rates for such extremely unbalanced case-control phenotypes. In this research, we develop a method to jointly analyze multiple unbalanced case-control phenotypes to circumvent this issue. We first group multiple phenotypes into different clusters based on a hierarchical clustering method, then we merge phenotypes in each cluster into a single phenotype. In each cluster, we use the saddlepoint approximation to estimate the p value of an association test between the merged phenotype and a single nucleotide polymorphism (SNP) which eliminates the issue of inflated type I error rate of the test for extremely unbalanced case-control phenotypes. Finally, we use the Cauchy combination method to obtain an integrated p value for all clusters to test the association between multiple phenotypes and a SNP. We use extensive simulation studies to evaluate the performance of the proposed approach. The results show that the proposed approach can control type I error rate very well and is more powerful than other available methods. We also apply the proposed approach to phenotypes in category IX (diseases of the circulatory system) in the UK Biobank. We find that the proposed approach can identify more significant SNPs than the other viable methods we compared with.
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Affiliation(s)
- Hongjing Xie
- Department of Mathematical Sciences, Michigan Technological University, Houghton, Michigan, USA
| | - Xuewei Cao
- Department of Mathematical Sciences, Michigan Technological University, Houghton, Michigan, USA
| | - Shuanglin Zhang
- Department of Mathematical Sciences, Michigan Technological University, Houghton, Michigan, USA
| | - Qiuying Sha
- Department of Mathematical Sciences, Michigan Technological University, Houghton, Michigan, USA
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29
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Carss KJ, Deaton AM, Del Rio-Espinola A, Diogo D, Fielden M, Kulkarni DA, Moggs J, Newham P, Nelson MR, Sistare FD, Ward LD, Yuan J. Using human genetics to improve safety assessment of therapeutics. Nat Rev Drug Discov 2023; 22:145-162. [PMID: 36261593 DOI: 10.1038/s41573-022-00561-w] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 09/02/2022] [Indexed: 02/07/2023]
Abstract
Human genetics research has discovered thousands of proteins associated with complex and rare diseases. Genome-wide association studies (GWAS) and studies of Mendelian disease have resulted in an increased understanding of the role of gene function and regulation in human conditions. Although the application of human genetics has been explored primarily as a method to identify potential drug targets and support their relevance to disease in humans, there is increasing interest in using genetic data to identify potential safety liabilities of modulating a given target. Human genetic variants can be used as a model to anticipate the effect of lifelong modulation of therapeutic targets and identify the potential risk for on-target adverse events. This approach is particularly useful for non-clinical safety evaluation of novel therapeutics that lack pharmacologically relevant animal models and can contribute to the intrinsic safety profile of a drug target. This Review illustrates applications of human genetics to safety studies during drug discovery and development, including assessing the potential for on- and off-target associated adverse events, carcinogenicity risk assessment, and guiding translational safety study designs and monitoring strategies. A summary of available human genetic resources and recommended best practices is provided. The challenges and future perspectives of translating human genetic information to identify risks for potential drug effects in preclinical and clinical development are discussed.
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Affiliation(s)
| | - Aimee M Deaton
- Amgen, Cambridge, MA, USA.,Alnylam Pharmaceuticals, Cambridge, MA, USA
| | - Alberto Del Rio-Espinola
- Novartis Institutes for BioMedical Research, Basel, Switzerland.,GentiBio Inc., Cambridge, MA, USA
| | | | - Mark Fielden
- Amgen, Thousand Oaks, MA, USA.,Kate Therapeutics, San Diego, CA, USA
| | | | - Jonathan Moggs
- Novartis Institutes for BioMedical Research, Basel, Switzerland
| | | | | | - Frank D Sistare
- Merck & Co., West Point, PA, USA.,315 Meadowmont Ln, Chapel Hill, NC, USA
| | - Lucas D Ward
- Amgen, Cambridge, MA, USA. .,Alnylam Pharmaceuticals, Cambridge, MA, USA.
| | - Jing Yuan
- Amgen, Cambridge, MA, USA.,Pfizer, Cambridge, MA, USA
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30
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Andreassen OA, Hindley GFL, Frei O, Smeland OB. New insights from the last decade of research in psychiatric genetics: discoveries, challenges and clinical implications. World Psychiatry 2023; 22:4-24. [PMID: 36640404 PMCID: PMC9840515 DOI: 10.1002/wps.21034] [Citation(s) in RCA: 48] [Impact Index Per Article: 48.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 10/07/2022] [Indexed: 01/15/2023] Open
Abstract
Psychiatric genetics has made substantial progress in the last decade, providing new insights into the genetic etiology of psychiatric disorders, and paving the way for precision psychiatry, in which individual genetic profiles may be used to personalize risk assessment and inform clinical decision-making. Long recognized to be heritable, recent evidence shows that psychiatric disorders are influenced by thousands of genetic variants acting together. Most of these variants are commonly occurring, meaning that every individual has a genetic risk to each psychiatric disorder, from low to high. A series of large-scale genetic studies have discovered an increasing number of common and rare genetic variants robustly associated with major psychiatric disorders. The most convincing biological interpretation of the genetic findings implicates altered synaptic function in autism spectrum disorder and schizophrenia. However, the mechanistic understanding is still incomplete. In line with their extensive clinical and epidemiological overlap, psychiatric disorders appear to exist on genetic continua and share a large degree of genetic risk with one another. This provides further support to the notion that current psychiatric diagnoses do not represent distinct pathogenic entities, which may inform ongoing attempts to reconceptualize psychiatric nosology. Psychiatric disorders also share genetic influences with a range of behavioral and somatic traits and diseases, including brain structures, cognitive function, immunological phenotypes and cardiovascular disease, suggesting shared genetic etiology of potential clinical importance. Current polygenic risk score tools, which predict individual genetic susceptibility to illness, do not yet provide clinically actionable information. However, their precision is likely to improve in the coming years, and they may eventually become part of clinical practice, stressing the need to educate clinicians and patients about their potential use and misuse. This review discusses key recent insights from psychiatric genetics and their possible clinical applications, and suggests future directions.
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Affiliation(s)
- Ole A Andreassen
- NORMENT Centre, Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
| | - Guy F L Hindley
- NORMENT Centre, Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Oleksandr Frei
- NORMENT Centre, Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- Centre for Bioinformatics, Department of Informatics, University of Oslo, Oslo, Norway
| | - Olav B Smeland
- NORMENT Centre, Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
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Kerchberger VE, Peterson JF, Wei WQ. Scanning the medical phenome to identify new diagnoses after recovery from COVID-19 in a US cohort. J Am Med Inform Assoc 2023; 30:233-244. [PMID: 36005898 PMCID: PMC9452157 DOI: 10.1093/jamia/ocac159] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2022] [Revised: 06/29/2022] [Accepted: 08/23/2022] [Indexed: 01/20/2023] Open
Abstract
OBJECTIVE COVID-19 survivors are at risk for long-term health effects, but assessing the sequelae of COVID-19 at large scales is challenging. High-throughput methods to efficiently identify new medical problems arising after acute medical events using the electronic health record (EHR) could improve surveillance for long-term consequences of acute medical problems like COVID-19. MATERIALS AND METHODS We augmented an existing high-throughput phenotyping method (PheWAS) to identify new diagnoses occurring after an acute temporal event in the EHR. We then used the temporal-informed phenotypes to assess development of new medical problems among COVID-19 survivors enrolled in an EHR cohort of adults tested for COVID-19 at Vanderbilt University Medical Center. RESULTS The study cohort included 186 105 adults tested for COVID-19 from March 5, 2020 to November 1, 2021; of which 30 088 (16.2%) tested positive. Median follow-up after testing was 412 days (IQR 274-528). Our temporal-informed phenotyping was able to distinguish phenotype chapters based on chronicity of their constituent diagnoses. PheWAS with temporal-informed phenotypes identified increased risk for 43 diagnoses among COVID-19 survivors during outpatient follow-up, including multiple new respiratory, cardiovascular, neurological, and pregnancy-related conditions. Findings were robust to sensitivity analyses, and several phenotypic associations were supported by changes in outpatient vital signs or laboratory tests from the pretesting to postrecovery period. CONCLUSION Temporal-informed PheWAS identified new diagnoses affecting multiple organ systems among COVID-19 survivors. These findings can inform future efforts to enable longitudinal health surveillance for survivors of COVID-19 and other acute medical conditions using the EHR.
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Affiliation(s)
- Vern Eric Kerchberger
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee, USA
- Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Josh F Peterson
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee, USA
- Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Wei-Qi Wei
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee, USA
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Yang L, Wang S, Altman RB. POPDx: an automated framework for patient phenotyping across 392 246 individuals in the UK Biobank study. J Am Med Inform Assoc 2023; 30:245-255. [PMID: 36469791 PMCID: PMC9846671 DOI: 10.1093/jamia/ocac226] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2022] [Revised: 10/19/2022] [Accepted: 11/18/2022] [Indexed: 12/12/2022] Open
Abstract
OBJECTIVE For the UK Biobank, standardized phenotype codes are associated with patients who have been hospitalized but are missing for many patients who have been treated exclusively in an outpatient setting. We describe a method for phenotype recognition that imputes phenotype codes for all UK Biobank participants. MATERIALS AND METHODS POPDx (Population-based Objective Phenotyping by Deep Extrapolation) is a bilinear machine learning framework for simultaneously estimating the probabilities of 1538 phenotype codes. We extracted phenotypic and health-related information of 392 246 individuals from the UK Biobank for POPDx development and evaluation. A total of 12 803 ICD-10 diagnosis codes of the patients were converted to 1538 phecodes as gold standard labels. The POPDx framework was evaluated and compared to other available methods on automated multiphenotype recognition. RESULTS POPDx can predict phenotypes that are rare or even unobserved in training. We demonstrate substantial improvement of automated multiphenotype recognition across 22 disease categories, and its application in identifying key epidemiological features associated with each phenotype. CONCLUSIONS POPDx helps provide well-defined cohorts for downstream studies. It is a general-purpose method that can be applied to other biobanks with diverse but incomplete data.
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Affiliation(s)
- Lu Yang
- Department of Bioengineering, Stanford University, Stanford, CA, USA
| | - Sheng Wang
- Paul G. Allen School of Computer Science and Engineering, University of Washington, Seattle, WA, USA
| | - Russ B Altman
- Department of Bioengineering, Stanford University, Stanford, CA, USA.,Department of Genetics, Stanford University, Stanford, CA, USA.,Department of Medicine, Stanford University, Stanford, CA, USA
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Harlow CE, Patel VV, Waterworth DM, Wood AR, Beaumont RN, Ruth KS, Tyrrell J, Oguro-Ando A, Chu AY, Frayling TM. Genetically proxied therapeutic prolyl-hydroxylase inhibition and cardiovascular risk. Hum Mol Genet 2023; 32:496-505. [PMID: 36048866 PMCID: PMC9851745 DOI: 10.1093/hmg/ddac215] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2022] [Revised: 08/05/2022] [Accepted: 08/22/2022] [Indexed: 01/24/2023] Open
Abstract
Prolyl hydroxylase (PHD) inhibitors are in clinical development for anaemia in chronic kidney disease. Epidemiological studies have reported conflicting results regarding safety of long-term therapeutic haemoglobin (Hgb) rises through PHD inhibition on risk of cardiovascular disease. Genetic variation in genes encoding PHDs can be used as partial proxies to investigate the potential effects of long-term Hgb rises. We used Mendelian randomization to investigate the effect of long-term Hgb level rises through genetically proxied PHD inhibition on coronary artery disease (CAD: 60 801 cases; 123 504 controls), myocardial infarction (MI: 42 561 cases; 123 504 controls) or stroke (40 585 cases; 406 111 controls). To further characterize long-term effects of Hgb level rises, we performed a phenome-wide association study (PheWAS) in up to 451 099 UK Biobank individuals. Genetically proxied therapeutic PHD inhibition, equivalent to a 1.00 g/dl increase in Hgb levels, was not associated (at P < 0.05) with increased odds of CAD; odd ratio (OR) [95% confidence intervals (CI)] = 1.06 (0.84, 1.35), MI [OR (95% CI) = 1.02 (0.79, 1.33)] or stroke [OR (95% CI) = 0.91 (0.66, 1.24)]. PheWAS revealed associations with blood related phenotypes consistent with EGLN's role, relevant kidney- and liver-related biomarkers like estimated glomerular filtration rate and microalbuminuria, and non-alcoholic fatty liver disease (Bonferroni-adjusted P < 5.42E-05) but these were not clinically meaningful. These findings suggest that long-term alterations in Hgb through PHD inhibition are unlikely to substantially increase cardiovascular disease risk; using large disease genome-wide association study data, we could exclude ORs of 1.35 for cardiovascular risk with a 1.00 g/dl increase in Hgb.
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Affiliation(s)
- Charli E Harlow
- College of Medicine and Health, University of Exeter, Exeter, Devon EX2 5DW, UK
| | - Vickas V Patel
- GlaxoSmithKline, Collegeville, PA 19426, USA
- Spark Therapeutics, Inc., Philadelphia, PA 19104, USA
| | - Dawn M Waterworth
- GlaxoSmithKline, Collegeville, PA 19426, USA
- Immunology Translational Sciences, Janssen, Spring House, PA 19044, USA
| | - Andrew R Wood
- College of Medicine and Health, University of Exeter, Exeter, Devon EX2 5DW, UK
| | - Robin N Beaumont
- College of Medicine and Health, University of Exeter, Exeter, Devon EX2 5DW, UK
| | - Katherine S Ruth
- College of Medicine and Health, University of Exeter, Exeter, Devon EX2 5DW, UK
| | - Jessica Tyrrell
- College of Medicine and Health, University of Exeter, Exeter, Devon EX2 5DW, UK
| | - Asami Oguro-Ando
- College of Medicine and Health, University of Exeter, Exeter, Devon EX2 5DW, UK
| | | | - Timothy M Frayling
- College of Medicine and Health, University of Exeter, Exeter, Devon EX2 5DW, UK
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Choi KW. Depression Genetics as a Window Into Physical and Mental Health. Biol Psychiatry 2022; 92:918-919. [PMID: 36396244 DOI: 10.1016/j.biopsych.2022.09.027] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/27/2022] [Accepted: 09/28/2022] [Indexed: 11/16/2022]
Affiliation(s)
- Karmel W Choi
- Center for Precision Psychiatry, Department of Psychiatry, and the Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, and the Department of Psychiatry, Harvard Medical School, Boston, Massachusetts.
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35
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Liang X, Cao X, Sha Q, Zhang S. HCLC-FC: A novel statistical method for phenome-wide association studies. PLoS One 2022; 17:e0276646. [PMID: 36350801 PMCID: PMC9645610 DOI: 10.1371/journal.pone.0276646] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2022] [Accepted: 10/11/2022] [Indexed: 11/11/2022] Open
Abstract
The emergence of genetic data coupled to longitudinal electronic medical records (EMRs) offers the possibility of phenome-wide association studies (PheWAS). In PheWAS, the whole phenome can be divided into numerous phenotypic categories according to the genetic architecture across phenotypes. Currently, statistical analyses for PheWAS are mainly univariate analyses, which test the association between one genetic variant and one phenotype at a time. In this article, we derived a novel and powerful multivariate method for PheWAS. The proposed method involves three steps. In the first step, we apply the bottom-up hierarchical clustering method to partition a large number of phenotypes into disjoint clusters within each phenotypic category. In the second step, the clustering linear combination method is used to combine test statistics within each category based on the phenotypic clusters and obtain p-values from each phenotypic category. In the third step, we propose a new false discovery rate (FDR) control approach. We perform extensive simulation studies to compare the performance of our method with that of other existing methods. The results show that our proposed method controls FDR very well and outperforms other methods we compared with. We also apply the proposed approach to a set of EMR-based phenotypes across more than 300,000 samples from the UK Biobank. We find that the proposed approach not only can well-control FDR at a nominal level but also successfully identify 1,244 significant SNPs that are reported to be associated with some phenotypes in the GWAS catalog. Our open-access tools and instructions on how to implement HCLC-FC are available at https://github.com/XiaoyuLiang/HCLCFC.
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Affiliation(s)
- Xiaoyu Liang
- Department of Preventive Medicine, Division of Biostatistics, University of Tennessee Health Science Center, Memphis, Tennessee, United States of America
| | - Xuewei Cao
- Department of Mathematical Sciences, Michigan Technological University, Houghton, Michigan, United States of America
| | - Qiuying Sha
- Department of Mathematical Sciences, Michigan Technological University, Houghton, Michigan, United States of America
| | - Shuanglin Zhang
- Department of Mathematical Sciences, Michigan Technological University, Houghton, Michigan, United States of America
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36
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The Role of Systems Biology in Deciphering Asthma Heterogeneity. LIFE (BASEL, SWITZERLAND) 2022; 12:life12101562. [PMID: 36294997 PMCID: PMC9605413 DOI: 10.3390/life12101562] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/29/2022] [Revised: 09/28/2022] [Accepted: 10/04/2022] [Indexed: 11/17/2022]
Abstract
Asthma is one of the most common and lifelong and chronic inflammatory diseases characterized by inflammation, bronchial hyperresponsiveness, and airway obstruction episodes. It is a heterogeneous disease of varying and overlapping phenotypes with many confounding factors playing a role in disease susceptibility and management. Such multifactorial disorders will benefit from using systems biology as a strategy to elucidate molecular insights from complex, quantitative, massive clinical, and biological data that will help to understand the underlying disease mechanism, early detection, and treatment planning. Systems biology is an approach that uses the comprehensive understanding of living systems through bioinformatics, mathematical, and computational techniques to model diverse high-throughput molecular, cellular, and the physiologic profiling of healthy and diseased populations to define biological processes. The use of systems biology has helped understand and enrich our knowledge of asthma heterogeneity and molecular basis; however, such methods have their limitations. The translational benefits of these studies are few, and it is recommended to reanalyze the different studies and omics in conjugation with one another which may help understand the reasons for this variation and help overcome the limitations of understanding the heterogeneity in asthma pathology. In this review, we aim to show the different factors that play a role in asthma heterogeneity and how systems biology may aid in understanding and deciphering the molecular basis of asthma.
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Khaire AS, Wimberly CE, Semmes EC, Hurst JH, Walsh KM. An integrated genome and phenome-wide association study approach to understanding Alzheimer's disease predisposition. Neurobiol Aging 2022; 118:117-123. [PMID: 35715361 PMCID: PMC9787699 DOI: 10.1016/j.neurobiolaging.2022.05.011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2022] [Revised: 05/13/2022] [Accepted: 05/23/2022] [Indexed: 12/25/2022]
Abstract
Genome-wide association studies (GWAS) have identified common single nucleotide polymorphisms (SNPs) that increase late-onset Alzheimer's disease (LOAD) risk. To identify additional LOAD-associated variants and provide insight into underlying disease biology, we performed a phenome-wide association study on 23 known LOAD-associated SNPs and 4:1 matched control SNPs using UK Biobank data. LOAD-associated SNPs were significantly enriched for associations with 8/778 queried traits, including 3 platelet traits. The strongest enrichment was for platelet distribution width (PDW) (p = 1.2 × 10-5), but increased PDW was not associated with LOAD susceptibility in Mendelian randomization analysis. Of 384 PDW-associated SNPs identified by prior GWAS, 36 were nominally associated with LOAD risk (17,008 cases; 37,154 controls) and 5 survived false-discovery rate correction. Associations confirmed known LOAD risk loci near PICALM, CD2AP, SPI1, and NDUFAF6, and identified a novel risk locus in epidermal growth factor receptor. Integrating GWAS and phenome-wide association study data reveals substantial pleiotropy between genetic determinants of LOAD and of platelet morphology, and for the first time implicates epidermal growth factor receptor - a mediator of β-amyloid toxicity - in Alzheimer's disease susceptibility.
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Affiliation(s)
- Archita S Khaire
- Division of Neuro-epidemiology, Department of Neurosurgery, Duke University, Durham, NC, USA
| | - Courtney E Wimberly
- Division of Neuro-epidemiology, Department of Neurosurgery, Duke University, Durham, NC, USA
| | - Eleanor C Semmes
- Medical Scientist Training Program, Duke University, Durham, NC, USA; Children's Health and Discovery Initiative, Department of Pediatrics, Duke University, Durham, NC, USA
| | - Jillian H Hurst
- Children's Health and Discovery Initiative, Department of Pediatrics, Duke University, Durham, NC, USA
| | - Kyle M Walsh
- Division of Neuro-epidemiology, Department of Neurosurgery, Duke University, Durham, NC, USA; Children's Health and Discovery Initiative, Department of Pediatrics, Duke University, Durham, NC, USA; Center for the Study of Aging and Human Development, Duke University, Durham, NC, USA.
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38
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Hung TY, Liu KL, Wen SH. Using the Phecode System to Identify the Preoperative Clinical Phenotypes Associated with Surgical Site Infection in Patients Undergoing Primary Total Knee Arthroplasty: The Sex Differences. J Clin Med 2022; 11:5784. [PMID: 36233652 PMCID: PMC9573756 DOI: 10.3390/jcm11195784] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2022] [Revised: 09/27/2022] [Accepted: 09/27/2022] [Indexed: 11/17/2022] Open
Abstract
Sex-related differences among comorbid conditions associated with surgical site infection (SSI) after total knee arthroplasty (TKA) are unclear. This population-based cohort study used a novel approach with a Phecode system to evaluate preoperative clinical phenotypes (i.e., comorbid conditions) associated with SSI after TKA and delineate sex-related differences in phenotypes. Using the Taiwan National Health Insurance Research Database (2014-2018), 83,870 patients who underwent TKA were identified. Demographic and SSI data during the 90-day postoperative follow-up were obtained. Comorbidities identified by the International Classification of Diseases within 1 year before TKA were recorded and mapped into Phecodes representing phenotypes. The overall rate of 90-day SSI was 1.3%. In total, 1663 phenotypes were identified among 83,870 patients-1585 and 1458 phenotypes for female (n = 62,018) and male (n = 21,852) patients, respectively. According to multivariate logistic regression analysis, the SSI odds ratio significantly increased with the presence of each of the 16 phenotypes. Subgroup analysis revealed that the presence of 10 and 4 phenotypes significantly increased SSI risk in both sexes; only one phenotype was common to both sexes. Therefore, comorbid conditions and sex should be considered in preoperative SSI risk evaluation in patients undergoing primary TKA. These findings provide new perspectives on susceptibility, prevention, and treatment in these patients.
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Affiliation(s)
- Ting-Yu Hung
- Sports Medicine Center, Hualien Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation, Hualien 970473, Taiwan
- Department of Orthopedics, Hualien Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation, Hualien 970473, Taiwan
| | - Kuan-Lin Liu
- Sports Medicine Center, Hualien Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation, Hualien 970473, Taiwan
- Department of Orthopedics, Hualien Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation, Hualien 970473, Taiwan
- School of Medicine, Tzu Chi University, Hualien 97004, Taiwan
| | - Shu-Hui Wen
- Department of Public Health, College of Medicine, Tzu Chi University, Hualien 97004, Taiwan
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Lee EY, Akhtari F, House JS, Simpson RJ, Schmitt CP, Fargo DC, Schurman SH, Hall JE, Motsinger-Reif AA. Questionnaire-based exposome-wide association studies (ExWAS) reveal expected and novel risk factors associated with cardiovascular outcomes in the Personalized Environment and Genes Study. ENVIRONMENTAL RESEARCH 2022; 212:113463. [PMID: 35605674 DOI: 10.1016/j.envres.2022.113463] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/26/2021] [Revised: 04/01/2022] [Accepted: 05/07/2022] [Indexed: 06/15/2023]
Abstract
While multiple factors are associated with cardiovascular disease (CVD), many environmental exposures that may contribute to CVD have not been examined. To understand environmental effects on cardiovascular health, we performed an exposome-wide association study (ExWAS), a hypothesis-free approach, using survey data on endogenous and exogenous exposures at home and work and data from health and medical histories from the North Carolina-based Personalized Environment and Genes Study (PEGS) (n = 5015). We performed ExWAS analyses separately on six cardiovascular outcomes (cardiac arrhythmia, congestive heart failure, coronary artery disease, heart attack, stroke, and a combined atherogenic-related outcome comprising angina, angioplasty, atherosclerosis, coronary artery disease, heart attack, and stroke) using logistic regression and a false discovery rate of 5%. For each CVD outcome, we tested 502 single exposures and built multi-exposure models using the deletion-substitution-addition (DSA) algorithm. To evaluate complex nonlinear relationships, we employed the knockoff boosted tree (KOBT) algorithm. We adjusted all analyses for age, sex, race, BMI, and annual household income. ExWAS analyses revealed novel associations that include blood type A (Rh-) with heart attack (OR[95%CI] = 8.2[2.2:29.7]); paint exposures with stroke (paint related chemicals: 6.1[2.2:16.0], acrylic paint: 8.1[2.6:22.9], primer: 6.7[2.2:18.6]); biohazardous materials exposure with arrhythmia (1.8[1.5:2.3]); and higher paternal education level with reduced risk of multiple CVD outcomes (stroke, heart attack, coronary artery disease, and combined atherogenic outcome). In multi-exposure models, trouble sleeping and smoking remained important risk factors. KOBT identified significant nonlinear effects of sleep disorder, regular intake of grapefruit, and a family history of blood clotting problems for multiple CVD outcomes (combined atherogenic outcome, congestive heart failure, and coronary artery disease). In conclusion, using statistics and machine learning, these findings identify novel potential risk factors for CVD, enable hypothesis generation, provide insights into the complex relationships between risk factors and CVD, and highlight the importance of considering multiple exposures when examining CVD outcomes.
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Affiliation(s)
- Eunice Y Lee
- Biostatistics & Computational Biology Branch, National Institute of Environmental Health Sciences, Durham, NC, USA
| | - Farida Akhtari
- Biostatistics & Computational Biology Branch, National Institute of Environmental Health Sciences, Durham, NC, USA; Clinical Research Branch, National Institute of Environmental Health Sciences, Durham, NC, USA
| | - John S House
- Biostatistics & Computational Biology Branch, National Institute of Environmental Health Sciences, Durham, NC, USA
| | - Ross J Simpson
- Department of Epidemiology, Gillings School of Public Health and Department of Medicine, University of North Carolina, Chapel Hill, NC, USA
| | - Charles P Schmitt
- National Toxicology Program, National Institute of Health, Durham, NC, USA
| | - David C Fargo
- Office of the Director, National Institute of Environmental Health Sciences, Durham, NC, USA
| | - Shepherd H Schurman
- Clinical Research Branch, National Institute of Environmental Health Sciences, Durham, NC, USA
| | - Janet E Hall
- Clinical Research Branch, National Institute of Environmental Health Sciences, Durham, NC, USA
| | - Alison A Motsinger-Reif
- Biostatistics & Computational Biology Branch, National Institute of Environmental Health Sciences, Durham, NC, USA.
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Zhou Y, Tremmel R, Schaeffeler E, Schwab M, Lauschke VM. Challenges and opportunities associated with rare-variant pharmacogenomics. Trends Pharmacol Sci 2022; 43:852-865. [PMID: 36008164 DOI: 10.1016/j.tips.2022.07.002] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2022] [Revised: 06/15/2022] [Accepted: 07/29/2022] [Indexed: 12/26/2022]
Abstract
Recent advances in next-generation sequencing (NGS) have resulted in the identification of tens of thousands of rare pharmacogenetic variations with unknown functional effects. However, although such pharmacogenetic variations have been estimated to account for a considerable amount of the heritable variability in drug response and toxicity, accurate interpretation at the level of the individual patient remains challenging. We discuss emerging strategies and concepts to close this translational gap. We illustrate how massively parallel experimental assays, artificial intelligence (AI), and machine learning can synergize with population-scale biobank projects to facilitate the interpretation of NGS data to individualize clinical decision-making and personalized medicine.
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Affiliation(s)
- Yitian Zhou
- Department of Physiology and Pharmacology, Karolinska Institutet, 171 77 Stockholm, Sweden
| | - Roman Tremmel
- Dr Margarete Fischer-Bosch Institute of Clinical Pharmacology, Stuttgart, Germany; University of Tübingen, Tübingen, Germany
| | - Elke Schaeffeler
- Dr Margarete Fischer-Bosch Institute of Clinical Pharmacology, Stuttgart, Germany; University of Tübingen, Tübingen, Germany; Cluster of Excellence iFIT (EXC2180) Image-Guided and Functionally Instructed Tumor Therapies, University of Tübingen, Tübingen, Germany
| | - Matthias Schwab
- Dr Margarete Fischer-Bosch Institute of Clinical Pharmacology, Stuttgart, Germany; Cluster of Excellence iFIT (EXC2180) Image-Guided and Functionally Instructed Tumor Therapies, University of Tübingen, Tübingen, Germany; Department of Clinical Pharmacology, and Department of Biochemistry and Pharmacy, University of Tübingen, Tübingen, Germany
| | - Volker M Lauschke
- Department of Physiology and Pharmacology, Karolinska Institutet, 171 77 Stockholm, Sweden; Dr Margarete Fischer-Bosch Institute of Clinical Pharmacology, Stuttgart, Germany; University of Tübingen, Tübingen, Germany.
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Predictive Modelling in Clinical Bioinformatics: Key Concepts for Startups. BIOTECH 2022; 11:biotech11030035. [PMID: 35997343 PMCID: PMC9397027 DOI: 10.3390/biotech11030035] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2022] [Revised: 07/30/2022] [Accepted: 08/03/2022] [Indexed: 11/17/2022] Open
Abstract
Clinical bioinformatics is a newly emerging field that applies bioinformatics techniques for facilitating the identification of diseases, discovery of biomarkers, and therapy decision. Mathematical modelling is part of bioinformatics analysis pipelines and a fundamental step to extract clinical insights from genomes, transcriptomes and proteomes of patients. Often, the chosen modelling techniques relies on either statistical, machine learning or deterministic approaches. Research that combines bioinformatics with modelling techniques have been generating innovative biomedical technology, algorithms and models with biotech applications, attracting private investment to develop new business; however, startups that emerge from these technologies have been facing difficulties to implement clinical bioinformatics pipelines, protect their technology and generate profit. In this commentary, we discuss the main concepts that startups should know for enabling a successful application of predictive modelling in clinical bioinformatics. Here we will focus on key modelling concepts, provide some successful examples and briefly discuss the modelling framework choice. We also highlight some aspects to be taken into account for a successful implementation of cost-effective bioinformatics from a business perspective.
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Korologou-Linden R, Bhatta L, Brumpton BM, Howe LD, Millard LAC, Kolaric K, Ben-Shlomo Y, Williams DM, Smith GD, Anderson EL, Stergiakouli E, Davies NM. The causes and consequences of Alzheimer's disease: phenome-wide evidence from Mendelian randomization. Nat Commun 2022; 13:4726. [PMID: 35953482 PMCID: PMC9372151 DOI: 10.1038/s41467-022-32183-6] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2020] [Accepted: 07/20/2022] [Indexed: 12/12/2022] Open
Abstract
Alzheimer's disease (AD) has no proven causal and modifiable risk factors, or effective interventions. We report a phenome-wide association study (PheWAS) of genetic liability for AD in 334,968 participants of the UK Biobank study, stratified by age. We also examined the effects of AD genetic liability on previously implicated risk factors. We replicated these analyses in the HUNT study. PheWAS hits and previously implicated risk factors were followed up in a Mendelian randomization (MR) framework to identify the causal effect of each risk factor on AD risk. A higher genetic liability for AD was associated with medical history and cognitive, lifestyle, physical and blood-based measures as early as 39 years of age. These effects were largely driven by the APOE gene. The follow-up MR analyses were primarily null, implying that most of these associations are likely to be a consequence of prodromal disease or selection bias, rather than the risk factor causing the disease.
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Affiliation(s)
- Roxanna Korologou-Linden
- Medical Research Council Integrative Epidemiology Unit, Bristol Medical School, University of Bristol, Bristol, BS8 2BN, UK.
- Population Health Sciences, Bristol Medical School, University of Bristol, Barley House, Oakfield Grove, Bristol, BS8 2BN, UK.
| | - Laxmi Bhatta
- K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, NTNU, Norwegian University of Science and Technology, Trondheim, Norway
| | - Ben M Brumpton
- K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, NTNU, Norwegian University of Science and Technology, Trondheim, Norway
- Clinic of Medicine, St. Olavs Hospital, Trondheim University Hospital, Trondheim, Norway
- HUNT Research Center, Department of Public Health and Nursing, NTNU, Norwegian University of Science and Technology, Levanger, Norway
| | - Laura D Howe
- Medical Research Council Integrative Epidemiology Unit, Bristol Medical School, University of Bristol, Bristol, BS8 2BN, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Barley House, Oakfield Grove, Bristol, BS8 2BN, UK
| | - Louise A C Millard
- Medical Research Council Integrative Epidemiology Unit, Bristol Medical School, University of Bristol, Bristol, BS8 2BN, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Barley House, Oakfield Grove, Bristol, BS8 2BN, UK
- Intelligent Systems Laboratory, Department of Computer Science, University of Bristol, Bristol, UK
| | - Katarina Kolaric
- Medical Research Council Integrative Epidemiology Unit, Bristol Medical School, University of Bristol, Bristol, BS8 2BN, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Barley House, Oakfield Grove, Bristol, BS8 2BN, UK
| | - Yoav Ben-Shlomo
- Population Health Sciences, Bristol Medical School, University of Bristol, Barley House, Oakfield Grove, Bristol, BS8 2BN, UK
| | - Dylan M Williams
- MRC Unit for Lifelong Health and Ageing at UCL, University College London, London, UK
- Department of Medical Epidemiology & Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - George Davey Smith
- Medical Research Council Integrative Epidemiology Unit, Bristol Medical School, University of Bristol, Bristol, BS8 2BN, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Barley House, Oakfield Grove, Bristol, BS8 2BN, UK
| | - Emma L Anderson
- Medical Research Council Integrative Epidemiology Unit, Bristol Medical School, University of Bristol, Bristol, BS8 2BN, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Barley House, Oakfield Grove, Bristol, BS8 2BN, UK
| | - Evie Stergiakouli
- Medical Research Council Integrative Epidemiology Unit, Bristol Medical School, University of Bristol, Bristol, BS8 2BN, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Barley House, Oakfield Grove, Bristol, BS8 2BN, UK
| | - Neil M Davies
- Medical Research Council Integrative Epidemiology Unit, Bristol Medical School, University of Bristol, Bristol, BS8 2BN, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Barley House, Oakfield Grove, Bristol, BS8 2BN, UK
- K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, NTNU, Norwegian University of Science and Technology, Trondheim, Norway
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Zhang HG, Dagliati A, Shakeri Hossein Abad Z, Xiong X, Bonzel CL, Xia Z, Tan BWQ, Avillach P, Brat GA, Hong C, Morris M, Visweswaran S, Patel LP, Gutiérrez-Sacristán A, Hanauer DA, Holmes JH, Samayamuthu MJ, Bourgeois FT, L'Yi S, Maidlow SE, Moal B, Murphy SN, Strasser ZH, Neuraz A, Ngiam KY, Loh NHW, Omenn GS, Prunotto A, Dalvin LA, Klann JG, Schubert P, Vidorreta FJS, Benoit V, Verdy G, Kavuluru R, Estiri H, Luo Y, Malovini A, Tibollo V, Bellazzi R, Cho K, Ho YL, Tan ALM, Tan BWL, Gehlenborg N, Lozano-Zahonero S, Jouhet V, Chiovato L, Aronow BJ, Toh EMS, Wong WGS, Pizzimenti S, Wagholikar KB, Bucalo M, Cai T, South AM, Kohane IS, Weber GM. International electronic health record-derived post-acute sequelae profiles of COVID-19 patients. NPJ Digit Med 2022; 5:81. [PMID: 35768548 PMCID: PMC9242995 DOI: 10.1038/s41746-022-00623-8] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2022] [Accepted: 05/19/2022] [Indexed: 11/10/2022] Open
Abstract
The risk profiles of post-acute sequelae of COVID-19 (PASC) have not been well characterized in multi-national settings with appropriate controls. We leveraged electronic health record (EHR) data from 277 international hospitals representing 414,602 patients with COVID-19, 2.3 million control patients without COVID-19 in the inpatient and outpatient settings, and over 221 million diagnosis codes to systematically identify new-onset conditions enriched among patients with COVID-19 during the post-acute period. Compared to inpatient controls, inpatient COVID-19 cases were at significant risk for angina pectoris (RR 1.30, 95% CI 1.09–1.55), heart failure (RR 1.22, 95% CI 1.10–1.35), cognitive dysfunctions (RR 1.18, 95% CI 1.07–1.31), and fatigue (RR 1.18, 95% CI 1.07–1.30). Relative to outpatient controls, outpatient COVID-19 cases were at risk for pulmonary embolism (RR 2.10, 95% CI 1.58–2.76), venous embolism (RR 1.34, 95% CI 1.17–1.54), atrial fibrillation (RR 1.30, 95% CI 1.13–1.50), type 2 diabetes (RR 1.26, 95% CI 1.16–1.36) and vitamin D deficiency (RR 1.19, 95% CI 1.09–1.30). Outpatient COVID-19 cases were also at risk for loss of smell and taste (RR 2.42, 95% CI 1.90–3.06), inflammatory neuropathy (RR 1.66, 95% CI 1.21–2.27), and cognitive dysfunction (RR 1.18, 95% CI 1.04–1.33). The incidence of post-acute cardiovascular and pulmonary conditions decreased across time among inpatient cases while the incidence of cardiovascular, digestive, and metabolic conditions increased among outpatient cases. Our study, based on a federated international network, systematically identified robust conditions associated with PASC compared to control groups, underscoring the multifaceted cardiovascular and neurological phenotype profiles of PASC.
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Affiliation(s)
- Harrison G Zhang
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Arianna Dagliati
- Department of Electrical Computer and Biomedical Engineering, University of Pavia, Pavia, Italy
| | | | - Xin Xiong
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Clara-Lea Bonzel
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Zongqi Xia
- Department of Neurology, University of Pittsburgh, Pittsburgh, PA, USA
| | - Bryce W Q Tan
- Department of Medicine, National University Hospital, Singapore, Singapore
| | - Paul Avillach
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Gabriel A Brat
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Chuan Hong
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA.,Department of Biostatistics and Bioinformatics, Duke University, Durham, NC, USA
| | - Michele Morris
- Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, PA, USA
| | - Shyam Visweswaran
- Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, PA, USA
| | - Lav P Patel
- Department of Internal Medicine, Division of Medical Informatics, University Of Kansas Medical Center, Kansas City, MO, USA
| | | | - David A Hanauer
- Department of Learning Health Sciences, University of Michigan Medical School, Ann Arbor, MI, USA
| | - John H Holmes
- Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA.,Institute for Biomedical Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | | | | | - Sehi L'Yi
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Sarah E Maidlow
- Michigan Institute for Clinical and Health Research (MICHR) Informatics, University of Michigan, Ann Arbor, MI, USA
| | - Bertrand Moal
- IAM unit, Bordeaux University Hospital, Bordeaux, France
| | - Shawn N Murphy
- Department of Neurology, Massachusetts General Hospital, Boston, MA, USA
| | | | - Antoine Neuraz
- Department of biomedical informatics, Hôpital Necker-Enfants Malade, Assistance Publique Hôpitaux de Paris (APHP), University of Paris, Paris, France
| | - Kee Yuan Ngiam
- Department of Biomedical informatics, WiSDM, National University Health Systems Singapore, Singapore, Singapore
| | - Ne Hooi Will Loh
- Department of Anaesthesia, National University Health Systems Singapore, Singapore, Singapore
| | - Gilbert S Omenn
- Department of Computational Medicine & Bioinformatics, Internal Medicine, Human Genetics, and School of Public Health, University of Michigan, Ann Arbor, MI, USA
| | - Andrea Prunotto
- Institute of Medical Biometry and Statistics, Faculty of Medicine and Medical Center, University of Freiburg, Freiburg, Germany
| | - Lauren A Dalvin
- Department of Ophthalmology, Mayo Clinic, Rochester, NY, USA
| | - Jeffrey G Klann
- Department of Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - Petra Schubert
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, Boston, MA, USA
| | | | - Vincent Benoit
- IT Department, Innovation & Data, APHP Greater Paris University Hospital, Paris, France
| | | | - Ramakanth Kavuluru
- Division of Biomedical Informatics (Department of Internal Medicine), University of Kentucky, Lexington, KY, USA
| | - Hossein Estiri
- Department of Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - Yuan Luo
- Department of Preventive Medicine, Northwestern University, Chicago, IL, USA
| | - Alberto Malovini
- Laboratory of Informatics and Systems Engineering for Clinical Research, Istituti Clinici Scientifici Maugeri SpA SB IRCCS, Pavia, Italy
| | - Valentina Tibollo
- Laboratory of Informatics and Systems Engineering for Clinical Research, Istituti Clinici Scientifici Maugeri SpA SB IRCCS, Pavia, Italy
| | - Riccardo Bellazzi
- Department of Electrical, Computer and Biomedical Engineering, University of Pavia, Pavia, Italy
| | - Kelly Cho
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, Boston, MA, USA.,Population Health and Data Science, VA Boston Healthcare System, Boston, MA, USA
| | - Yuk-Lam Ho
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, Boston, MA, USA
| | - Amelia L M Tan
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Byorn W L Tan
- Department of Medicine, National University Hospital, Singapore, Singapore
| | - Nils Gehlenborg
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Sara Lozano-Zahonero
- Institute of Medical Biometry and Statistics, Faculty of Medicine and Medical Center, University of Freiburg, Freiburg, Germany
| | - Vianney Jouhet
- IAM unit, INSERM Bordeaux Population Health ERIAS TEAM, Bordeaux University Hospital / ERIAS - Inserm, U1219 BPH, Bordeaux, France
| | - Luca Chiovato
- Unit of Internal Medicine and Endocrinology, Istituti Clinici Scientifici Maugeri SpA SB IRCCS, Pavia, Italy
| | - Bruce J Aronow
- Departments of Biomedical Informatics, Pediatrics, Cincinnati Children's Hospital Medical Center, University of Cincinnati, Cincinnati, OH, USA
| | - Emma M S Toh
- Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - Wei Gen Scott Wong
- Department of Medicine, National University Health Systems Singapore, Singapore, Singapore
| | - Sara Pizzimenti
- Scientific Direction, IRCCS Ca' Granda Ospedale Maggiore Policlinico di Milano, Milan, Italy
| | | | - Mauro Bucalo
- BIOMERIS (BIOMedical Research Informatics Solutions), Pavia, Italy
| | | | - Tianxi Cai
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Andrew M South
- Department of Pediatrics-Section of Nephrology, Brenner Children's, Wake Forest School of Medicine, Winston Salem, NC, USA
| | - Isaac S Kohane
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Griffin M Weber
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA.
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44
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Dumancas GG, Harrison D, Rico JA, Zamora PRFC, Liwag AG, Villaruz JF, Guanzon MLVV, Ferraris HFD, Jalandoni PJB, Padernal WF, Villareal BNL, Maculada RA, Fernandez RMA, Villa FR, de Castro R. Are phenome-wide association studies feasible in a developing country? Trends Genet 2022; 38:885-888. [PMID: 35660028 DOI: 10.1016/j.tig.2022.05.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2022] [Revised: 05/02/2022] [Accepted: 05/03/2022] [Indexed: 11/18/2022]
Abstract
Phenome-wide association studies (PheWASs), a powerful approach that examines phenotypes associated with a genetic marker, have been used extensively in highly developed countries. Although there may be a clear need for PheWAS in a developing country such as the Philippines, limitations related to resources and practicality would make conducting them a challenge.
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Affiliation(s)
- Gerard G Dumancas
- Department of Chemistry, Loyola Science Center, The University of Scranton, Scranton, PA 18510, USA; Balik Scientist Program, Department of Science and Technology-Philippine Council for Health Research and Development, Bicutan, Taguig City, 1631, Philippines.
| | - Destiny Harrison
- Department of Mathematics and Physical Sciences, Louisiana State University - Alexandria, Alexandria, LA 71302, USA
| | - Jonathan Adam Rico
- Center for Informatics, University of San Agustin, Gen. Luna St, Iloilo City, 5000, Philippines
| | | | - Aretha G Liwag
- West Visayas State University, Luna St, Lapaz, Iloilo City, 5000, Philippines
| | - Joselito F Villaruz
- West Visayas State University, Luna St, Lapaz, Iloilo City, 5000, Philippines
| | - Ma Luz Vicenta V Guanzon
- Corazon Locsin Montelibano Memorial Regional Hospital, Lacson St, Bacolod, Negros Occidental, 6100, Philippines
| | - Hans Francis D Ferraris
- Corazon Locsin Montelibano Memorial Regional Hospital, Lacson St, Bacolod, Negros Occidental, 6100, Philippines
| | | | | | | | | | | | | | - Romulo de Castro
- Center for Informatics, University of San Agustin, Gen. Luna St, Iloilo City, 5000, Philippines; Balik Scientist Program, Department of Science and Technology-Philippine Council for Health Research and Development, Bicutan, Taguig City, 1631, Philippines
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45
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Dagliati A, Gatta R, Malovini A, Tibollo V, Sacchi L, Cascini F, Chiovato L, Bellazzi R. A Process Mining Pipeline to Characterize COVID-19 Patients' Trajectories and Identify Relevant Temporal Phenotypes From EHR Data. Front Public Health 2022; 10:815674. [PMID: 35677768 PMCID: PMC9168006 DOI: 10.3389/fpubh.2022.815674] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2021] [Accepted: 04/21/2022] [Indexed: 11/24/2022] Open
Abstract
The impact of the COVID-19 pandemic involved the disruption of the processes of care and the need for immediately effective re-organizational procedures. In the context of digital health, it is of paramount importance to determine how a specific patients' population reflects into the healthcare dynamics of the hospital, to investigate how patients' sub-group/strata respond to the different care processes, in order to generate novel hypotheses regarding the most effective healthcare strategies. We present an analysis pipeline based on the heterogeneous collected data aimed at identifying the most frequent healthcare processes patterns, jointly analyzing them with demographic and physiological disease trajectories, and stratify the observed cohort on the basis of the mined patterns. This is a process-oriented pipeline which integrates process mining algorithms, and trajectory mining by topological data analyses and pseudo time approaches. Data was collected for 1,179 COVID-19 positive patients, hospitalized at the Italian Hospital "Istituti Clinici Salvatore Maugeri" in Lombardy, integrating different sources including text admission letters, EHR and hospital infrastructure data. We identified five temporal phenotypes, from laboratory values trajectories, which are characterized by statistically significant different death risk estimates. The process mining algorithms allowed splitting the data in sub-cohorts as function of the pandemic waves and of the temporal trajectories showing statistically significant differences in terms of events characteristics.
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Affiliation(s)
- Arianna Dagliati
- Department of Electrical, Computer and Biomedical Engineering, University of Pavia, Pavia, Italy
| | - Roberto Gatta
- Dipartimento di Scienze Cliniche e Sperimentali dell'Università degli Studi di Brescia, Brescia, Italy
- Department of Oncology, Lausanne University Hospital, Lausanne, Switzerland
| | - Alberto Malovini
- Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS) Istituti Clinici Scientifici Maugeri, Pavia, Italy
| | - Valentina Tibollo
- Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS) Istituti Clinici Scientifici Maugeri, Pavia, Italy
| | - Lucia Sacchi
- Department of Electrical, Computer and Biomedical Engineering, University of Pavia, Pavia, Italy
| | - Fidelia Cascini
- Dipartimento di Scienze della Vita e Sanità Pubblica, Università Cattolica del Sacro Cuore, Roma, Italy
| | - Luca Chiovato
- Department of Electrical, Computer and Biomedical Engineering, University of Pavia, Pavia, Italy
- Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS) Istituti Clinici Scientifici Maugeri, Pavia, Italy
| | - Riccardo Bellazzi
- Department of Electrical, Computer and Biomedical Engineering, University of Pavia, Pavia, Italy
- Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS) Istituti Clinici Scientifici Maugeri, Pavia, Italy
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46
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Irlmeier R, Hughey JJ, Bastarache L, Denny JC, Chen Q. Cox regression is robust to inaccurate EHR-extracted event time: an application to EHR-based GWAS. Bioinformatics 2022; 38:2297-2306. [PMID: 35157022 PMCID: PMC10060718 DOI: 10.1093/bioinformatics/btac086] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2021] [Revised: 12/14/2021] [Accepted: 02/09/2022] [Indexed: 02/03/2023] Open
Abstract
MOTIVATION Logistic regression models are used in genomic studies to analyze the genetic data linked to electronic health records (EHRs), and do not take full usage of the time-to-event information available in EHRs. Previous work has shown that Cox regression, which can account for left truncation and right censoring in EHRs, increased the power to detect genotype-phenotype associations compared to logistic regression. We extend this to evaluate the relative performance of Cox regression and various logistic regression models in the presence of positive errors in event time (delayed event time), relating to recorded event time accuracy. RESULTS One Cox model and three logistic regression models were considered under different scenarios of delayed event time. Extensive simulations and a genomic study application were used to evaluate the impact of delayed event time. While logistic regression does not model the time-to-event directly, various logistic regression models used in the literature were more sensitive to delayed event time than Cox regression. Results highlighted the importance to identify and exclude the patients diagnosed before entry time. Cox regression had similar or modest improvement in statistical power over various logistic regression models at controlled type I error. This was supported by the empirical data, where the Cox models steadily had the highest sensitivity to detect known genotype-phenotype associations under all scenarios of delayed event time. AVAILABILITY AND IMPLEMENTATION Access to individual-level EHR and genotype data is restricted by the IRB. Simulation code and R script for data process are at: https://github.com/QingxiaCindyChen/CoxRobustEHR.git. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Rebecca Irlmeier
- Department of Biostatistics, Vanderbilt University Medical Center, Nashville, TN 37203, USA
| | - Jacob J Hughey
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN 37203, USA.,Department of Biomedical Sciences, Vanderbilt University, Nashville, TN 37203, USA
| | - Lisa Bastarache
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN 37203, USA
| | - Joshua C Denny
- All of Us Research Program, National Institutes of Health, Bethesda, MD 20892, USA
| | - Qingxia Chen
- Department of Biostatistics, Vanderbilt University Medical Center, Nashville, TN 37203, USA.,Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN 37203, USA
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47
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Lu C, Jin D, Palmer N, Fox K, Kohane IS, Smoller JW, Yu KH. Large-scale real-world data analysis identifies comorbidity patterns in schizophrenia. Transl Psychiatry 2022; 12:154. [PMID: 35410453 PMCID: PMC9001711 DOI: 10.1038/s41398-022-01916-y] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/27/2021] [Revised: 03/22/2022] [Accepted: 03/23/2022] [Indexed: 11/23/2022] Open
Abstract
Schizophrenia affects >3.2 million people in the USA. However, its comorbidity patterns have not been systematically characterized in real-world populations. To address this gap, we conducted an observational study using a cohort of 86 million patients in a nationwide health insurance dataset. We identified participants with schizophrenia and those without schizophrenia matched by age, sex, and the first three digits of zip code. For each phenotype encoded in phecodes, we compared their prevalence in schizophrenia patients and the matched non-schizophrenic participants, and we performed subgroup analyses stratified by age and sex. Results show that anxiety, posttraumatic stress disorder, and substance abuse commonly occur in adolescents and young adults prior to schizophrenia diagnoses. Patients aged 60 and above are at higher risks of developing delirium, alcoholism, dementia, pelvic fracture, and osteomyelitis than their matched controls. Type 2 diabetes, sleep apnea, and eating disorders were more prevalent in women prior to schizophrenia diagnosis, whereas acute renal failure, rhabdomyolysis, and developmental delays were found at higher rates in men. Anxiety and obesity are more commonly seen in patients with schizoaffective disorders compared to patients with other types of schizophrenia. Leveraging a large-scale insurance claims dataset, this study identified less-known comorbidity patterns of schizophrenia and confirmed known ones. These comorbidity profiles can guide clinicians and researchers to take heed of early signs of co-occurring diseases.
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Affiliation(s)
- Chenyue Lu
- grid.38142.3c000000041936754XDepartment of Biomedical Informatics, Harvard Medical School, Boston, MA USA
| | - Di Jin
- grid.116068.80000 0001 2341 2786Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA USA
| | - Nathan Palmer
- grid.38142.3c000000041936754XDepartment of Biomedical Informatics, Harvard Medical School, Boston, MA USA
| | - Kathe Fox
- grid.38142.3c000000041936754XDepartment of Biomedical Informatics, Harvard Medical School, Boston, MA USA
| | - Isaac S. Kohane
- grid.38142.3c000000041936754XDepartment of Biomedical Informatics, Harvard Medical School, Boston, MA USA
| | - Jordan W. Smoller
- grid.32224.350000 0004 0386 9924Department of Psychiatry, Massachusetts General Hospital, Boston, MA USA ,grid.38142.3c000000041936754XDepartment of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA USA
| | - Kun-Hsing Yu
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA.
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48
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Pharmacogenomics: A Step forward Precision Medicine in Childhood Asthma. Genes (Basel) 2022; 13:genes13040599. [PMID: 35456405 PMCID: PMC9031013 DOI: 10.3390/genes13040599] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2022] [Revised: 03/23/2022] [Accepted: 03/27/2022] [Indexed: 02/05/2023] Open
Abstract
Personalized medicine, an approach to care in which individual characteristics are used for targeting interventions and maximizing health outcomes, is rapidly becoming a reality for many diseases. Childhood asthma is a heterogeneous disease and many children have uncontrolled symptoms. Therefore, an individualized approach is needed for improving asthma outcomes in children. The rapidly evolving fields of genomics and pharmacogenomics may provide a way to achieve asthma control and reduce future risks in children with asthma. In particular, pharmacogenomics can provide tools for identifying novel molecular mechanisms and biomarkers to guide treatment. Emergent high-throughput technologies, along with patient pheno-endotypization, will increase our knowledge of several molecular mechanisms involved in asthma pathophysiology and contribute to selecting and stratifying appropriate treatment for each patient.
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49
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Yengo-Kahn AM, Hibshman N, Bonfield CM, Torstenson ES, Gifford KA, Belikau D, Davis LK, Zuckerman SL, Dennis JK. Association of Preinjury Medical Diagnoses With Pediatric Persistent Postconcussion Symptoms in Electronic Health Records. J Head Trauma Rehabil 2022; 37:E80-E89. [PMID: 33935230 DOI: 10.1097/htr.0000000000000686] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
Abstract
OBJECTIVE To identify risk factors and generate hypotheses for pediatric persistent postconcussion symptoms (PPCS). SETTING A regional healthcare system in the Southeastern United States. PARTICIPANTS An electronic health record-based algorithm was developed and validated to identify PPCS cases and controls from an institutional database of more than 2.8 million patients. PPCS cases (n = 274) were patients aged 5 to 18 years with PPCS-related diagnostic codes or with PPCS key words identified by natural language processing of clinical notes. Age, sex, and year of index event-matched controls (n = 1096) were patients with mild traumatic brain injury codes only. Patients with moderate or severe traumatic brain injury were excluded. All patients used our healthcare system at least 3 times 180 days before their injury. DESIGN Case-control study. MAIN MEASURES The outcome was algorithmic classification of PPCS. Exposures were all preinjury medical diagnoses assigned at least 180 days before the injury. RESULTS Cases and controls both had a mean of more than 9 years of healthcare system use preinjury. Of 221 preinjury medical diagnoses, headache disorder was associated with PPCS after accounting for multiple testing (odds ratio [OR] = 2.9; 95% confidence interval [CI]: 1.6-5.0; P = 2.1e-4). Six diagnoses were associated with PPCS at a suggestive threshold for statistical significance (false discovery rate P < .10): gastritis/duodenitis (OR = 2.8; 95% CI: 1.6-5.1; P = 5.0e-4), sleep disorders (OR = 2.3; 95% CI: 1.4-3.7; P = 7.4e-4), abdominal pain (OR = 1.6; 95% CI: 1.2-2.2; P = 9.2e-4), chronic sinusitis (OR = 2.8; 95% CI: 1.5-5.2; P = 1.3e-3), congenital anomalies of the skin (OR = 2.9; 95% CI: 1.5-5.5; P = 1.9e-3), and chronic pharyngitis/nasopharyngitis (OR = 2.4; 95% CI: 1.4-4.3; P = 2.5e-3). CONCLUSIONS These results support the strong association of preinjury headache disorders with PPCS. An association of PPCS with prior gastritis/duodenitis, sinusitis, and pharyngitis/nasopharyngitis suggests a role for chronic inflammation in PPCS pathophysiology and risk, although results could equally be attributable to a higher likelihood of somatization among PPCS cases. Identified risk factors should be investigated further and potentially considered during the management of pediatric mild traumatic brain injury cases.
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Affiliation(s)
- Aaron M Yengo-Kahn
- Department of Neurological Surgery (Drs Yengo-Kahn, Bonfield, and Zuckerman), Vanderbilt Sport Concussion Center (Drs Yengo-Kahn, Bonfield, Gifford, Zuckerman, and Dennis and Ms Hibshman), Division of Epidemiology, Department of Medicine (Mr Torstenson), Vanderbilt Epidemiology Center, Institute for Medicine and Public Health (Mr Torstenson), Vanderbilt Genetics Institute (Mr Torstenson and Drs Davis and Dennis), Department of Neurology (Dr Gifford), and Division of Genetic Medicine, Department of Medicine (Drs Davis and Dennis), Vanderbilt University Medical Center, Nashville, Tennessee; Vanderbilt University School of Medicine, Nashville, Tennessee (Ms Hibshman); British Columbia Children's Hospital Research Institute, Vancouver, British Columbia, Canada (Ms Belikau and Dr Dennis); and Department of Medical Genetics, The University of British Columbia, Vancouver, British Columbia, Canada (Dr Dennis)
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Choe EK, Shivakumar M, Verma A, Verma SS, Choi SH, Kim JS, Kim D. Leveraging deep phenotyping from health check-up cohort with 10,000 Korean individuals for phenome-wide association study of 136 traits. Sci Rep 2022; 12:1930. [PMID: 35121771 PMCID: PMC8817039 DOI: 10.1038/s41598-021-04580-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2021] [Accepted: 12/17/2021] [Indexed: 11/09/2022] Open
Abstract
The expanding use of the phenome-wide association study (PheWAS) faces challenges in the context of using International Classification of Diseases billing codes for phenotype definition, imbalanced study population ethnicity, and constrained application of the results in research. We performed a PheWAS utilizing 136 deep phenotypes corroborated by comprehensive health check-ups in a Korean population, along with trans-ethnic comparisons through using the UK Biobank and Biobank Japan Project. Meta-analysis with Korean and Japanese population was done. The PheWAS associated 65 phenotypes with 14,101 significant variants (P < 4.92 × 10-10). Network analysis, visualization of cross-phenotype mapping, and causal inference mapping with Mendelian randomization were conducted. Among phenotype pairs from the genotype-driven cross-phenotype associations, we evaluated penetrance in correlation analysis using a clinical database. We focused on the application of PheWAS in order to make it robust and to aid the derivation of biological meaning post-PheWAS. This comprehensive analysis of PheWAS results based on a health check-up database will provide researchers and clinicians with a panoramic overview of the networks among multiple phenotypes and genetic variants, laying groundwork for the practical application of precision medicine.
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Affiliation(s)
- Eun Kyung Choe
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, B304 Richards Building, 3700 Hamilton Walk, Philadelphia, PA, 19104-6116, USA.,Department of Surgery, Seoul National University Hospital Healthcare System Gangnam Center, Seoul, 06236, South Korea
| | - Manu Shivakumar
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, B304 Richards Building, 3700 Hamilton Walk, Philadelphia, PA, 19104-6116, USA
| | - Anurag Verma
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Shefali Setia Verma
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Seung Ho Choi
- Department of Internal Medicine, Seoul National University Hospital Healthcare System Gangnam Center, Seoul, 06236, South Korea
| | - Joo Sung Kim
- Department of Internal Medicine, Seoul National University Hospital Healthcare System Gangnam Center, Seoul, 06236, South Korea. .,Department of Internal Medicine and Liver Research Institute, Seoul National University College of Medicine, Seoul, 03080, South Korea.
| | - Dokyoon Kim
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, B304 Richards Building, 3700 Hamilton Walk, Philadelphia, PA, 19104-6116, USA. .,Institute for Biomedical Informatics, University of Pennsylvania, Philadelphia, PA, 19104, USA.
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