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Wang B, Xiong Y, Li R, Zhang S. Depression increases the risk of gallstone: A cross-sectional study and Mendelian randomization analysis. J Affect Disord 2024; 362:606-614. [PMID: 39029662 DOI: 10.1016/j.jad.2024.07.119] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/01/2024] [Revised: 06/28/2024] [Accepted: 07/16/2024] [Indexed: 07/21/2024]
Abstract
BACKGROUND Gallstone, a common digestive disorder, poses a significant public health burden. Concurrently, depression is acknowledged as a health risk. However, limited information exists on depression's impact on gallstone formation. This study investigates depression's causal effect on gallstone risk. METHODS Using National Health and Nutrition Examination Survey (NHANES) data, we conducted an observational study. The severity of depression was assessed using the Patient Health Questionnaire-9 (PHQ-9). Multivariable logistic regression and subgroup analyses explored the correlation between depression and gallstone risk. Mendelian Randomization (MR) analysis, leveraging Genome-Wide Association Studies (GWAS) data, reduced observational bias and elucidated causality. Inverse Variance Weighting (IVW) was the primary method, with sensitivity analyses validating results. RESULTS In the observational study (7707 participants), gallstone risk was elevated in mild (OR: 1.58, 95 % CI 1.31-1.90, P < 0.001), moderate (OR: 2.07, 95 % CI 1.59-2.67, P < 0.001), and severe (OR: 2.41, 95 % CI 1.70-3.34, P < 0.001) depression groups (P for trend <0.001). Subgroup analyses revealed a stronger association in those under 65, females, non-Hispanic Black, individuals with obesity, smokers, and those with college education or higher. Mendelian Randomization indicated a causal link between genetically predicted depression and higher cholelithiasis risk (OR: 2.06, 95 % CI 1.34-3.17, P = 0.001), validated through sensitivity analyses and multi-cohort verification. CONCLUSION Depression independently increases gallstone risk, particularly in those under 65, females, non-Hispanic Black, individuals with obesity, smokers, and those with college education or higher. Further validation is needed through multi-center, prospective cohort studies.
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Affiliation(s)
- Bo Wang
- Department of Geriatric Digestive Surgery, the Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China
| | - Yongqiang Xiong
- Department of Geriatric Digestive Surgery, the Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China
| | - Ren Li
- Department of Geriatric Digestive Surgery, the Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China
| | - Shu Zhang
- Department of Geriatric Digestive Surgery, the Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China; Experimental Teaching Center for Clinical Skills, the Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China.
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2
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Qiu D, He J, Zhang C, Li Y, Ling Z, Shen M, Xiao S. Associations between frailty, depression and risk of hospitalisation for infection: A large prospective cohort study. J Affect Disord 2024; 361:104-112. [PMID: 38857629 DOI: 10.1016/j.jad.2024.06.021] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/14/2024] [Revised: 06/01/2024] [Accepted: 06/05/2024] [Indexed: 06/12/2024]
Abstract
BACKGROUND There is a considerable lack of epidemiological evidence on whether frailty, and frailty comorbid depression could increase the risk of infections in older adults. This study aimed to examine the prospective association between frailty, depression, and risk of infections. METHODS A total of 308,892 eligible participants were included. Linked hospital admission records (HES) were used to identify a primary or secondary diagnosis of depression, and infection. Frailty was assessed by Fried frailty phenotype indicators. Cox proportional hazard model was conducted to examine the associated risk between frailty, depression, comorbid frailty and depression and risk of incident infections. Results were stratified by age and gender. RESULTS During the follow-up, 74,749 (24.19 %) incident any infection cases were identified, the incidence density of any infection was 17.29/1000 person years. Frailty alone (HR = 1.38, 95 % CI: 1.33-1.43), depression alone (HR = 1.90, 95 % CI: 1.86-1.94), and comorbid frailty and depression (HR = 1.91, 95 % CI: 1.82-1.99) were associated with greater risks of any infections relative to participants with neither frailty nor depression. The associations between frailty alone, depression alone, comorbid frailty and depression, and any infections/most infection subtypes were significant for all age strata in both male and female. LIMITATIONS Frailty phenotype was assessed through the adapted Fried criteria, based on a mix of self-reported and objective measurements. CONCLUSION Frailty, depression, and comorbid frailty and depression were significantly associated with increased risk of incident infections.
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Affiliation(s)
- Dan Qiu
- Department of Social Medicine and Health Management, Xiangya School of Public Health, Central South University, Changsha, Hunan, China.
| | - Jun He
- Department of Psychiatry, Yale School of Medicine, New Haven, CT 06510, USA.
| | - ChengCheng Zhang
- Department of Social Medicine and Health Management, Xiangya School of Public Health, Central South University, Changsha, Hunan, China.
| | - Yilu Li
- Department of Social Medicine and Health Management, Xiangya School of Public Health, Central South University, Changsha, Hunan, China.
| | - Zhen Ling
- Department of Social Medicine and Health Management, Xiangya School of Public Health, Central South University, Changsha, Hunan, China.
| | - Minxue Shen
- Department of Social Medicine and Health Management, Xiangya School of Public Health, Central South University, Changsha, Hunan, China.
| | - Shuiyuan Xiao
- Department of Social Medicine and Health Management, Xiangya School of Public Health, Central South University, Changsha, Hunan, China; Department of Psychiatry, The Second Xiangya Hospital, Central South University, Changsha, Hunan, China.
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3
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Gao M, Liu M, Zhang Y, Tang L, Chen H, Zhu Z. The impact of anxiety on the risk of kidney stone disease: Insights into eGFR-mediated effects. J Affect Disord 2024; 364:125-131. [PMID: 39147144 DOI: 10.1016/j.jad.2024.08.061] [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: 02/14/2024] [Revised: 06/14/2024] [Accepted: 08/12/2024] [Indexed: 08/17/2024]
Abstract
BACKGROUND Previous studies have linked kidney stone disease (KSD) with depression, but there are no reports on the relationship between anxiety and KSD, and the mechanism underlying the potential relationship remains unclear. METHODS Associations of anxiety and incident KSD were assessed in the National Health and Nutrition Examination Survey (NHENES) using multivariate logistic regression. Two-sample bidirectional Mendelian randomization studies and a two-step two-sample MR was used to estimate the mediating factors that influence KSD risk. RESULTS Examinations of NHANES data revealed that a rise in the frequency and intensity of anxiety were independently associated with incident KSD. In MR analysis, anxiety (uk-a-51 and uk-b-6519) were from the UK Biobank, with sample sizes of 328,717 and 450,765 respectively. KSD data were from the FinnGen, including 8597 cases and 333,128 controls. In the IVW analysis, genetically predicted anxieties (ukb-a-51 and ukb-b-6519) were found to be causally associated with a higher risk of KSD, with odds ratios of 6.18 (95 % CI 2.54-15.04) and 3.44 (95 % CI 1.67-7.08), respectively. There were no reverse causal effects. Further mediation analysis indicated that anxiety increases the risk of KSD by raising eGFR, through which 11.8 % of the effect of anxiety on KSD risk was mediated. LIMITATIONS The research was confined to individuals of European heritage, and there could be specific genetic variances among diverse ethnicities. CONCLUSION The current study suggests anxiety as an independent causal risk factor for KSD and unveils a new pathogenic mechanism, showing that anxiety raises eGFR, thereby increasing the risk of KSD.
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Affiliation(s)
- Meng Gao
- Department of Urology, Xiangya Hospital, Central South University, Changsha 410008, China; National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, China
| | - Minghui Liu
- Department of Urology, Xiangya Hospital, Central South University, Changsha 410008, China; National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, China
| | - Youjie Zhang
- Department of Urology, Xiangya Hospital, Central South University, Changsha 410008, China; National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, China
| | - Liang Tang
- Department of Urology, Xiangya Hospital, Central South University, Changsha 410008, China; National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, China
| | - Hequn Chen
- Department of Urology, Xiangya Hospital, Central South University, Changsha 410008, China
| | - Zewu Zhu
- Department of Urology, Xiangya Hospital, Central South University, Changsha 410008, China; National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, China.
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4
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Kerexeta-Sarriegi J, García-Navarro T, Rollan-Martinez-Herrera M, Larburu N, Espejo-Mambié MD, Beristain Iraola A, Graña M. Analysing disease trajectories in a cohort of 71,849 Patients: A visual analytics and statistical approach. Int J Med Inform 2024; 188:105466. [PMID: 38761458 DOI: 10.1016/j.ijmedinf.2024.105466] [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: 02/16/2024] [Revised: 04/08/2024] [Accepted: 04/22/2024] [Indexed: 05/20/2024]
Abstract
BACKGROUND Disease trajectories have become increasingly relevant within the context of an aging population and the rising prevalence of chronic illnesses. Understanding the temporal progression of diseases is crucial for enhancing patient care, preventive measures, and effective management. OBJECTIVE The objective of this study is to propose and validate a novel methodology for trajectory impact analysis and interactive visualization of disease trajectories over a cohort of 71,849 patients. METHODS This article introduces an innovative comprehensive approach for analysis and interactive visualization of disease trajectories. First, Risk Increase (RI) index is defined that assesses the impact of the initial disease diagnosis on the development of subsequent illnesses. Secondly, visual graphics methods are used to represent cohort trajectories, ensuring a clear and semantically rich presentation that facilitates easy data interpretation. RESULTS The proposed approach is demonstrated over the disease trajectories of a cohort comprising 71,849 patients from Tolosaldea, Spain. The study finds several clinically relevant trajectories in this cohort, such as that after suffering a cerebral ischemic stroke, the probability of suffering dementia increases 10.77 times. The clinical relevance of the study outcomes have been assessed by an in-depth analysis conducted by expert clinicians. The identified disease trajectories are in agreement with the latest advancements in the field. CONCLUSION The proposed approach for trajectory impact analysis and interactive visualization offers valuable graphs for the comprehensive study of disease trajectories for improved clinical decision-making. The simplicity and interpretability of our methods make them valuable approach for healthcare professionals.
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Affiliation(s)
- Jon Kerexeta-Sarriegi
- Vicomtech Foundation, Basque Research and Technology Alliance (BRTA), Mikeletegi 57, 20009 Donostia-San Sebastián, Spain; Biogipuzkoa Health Research Institute, Bioengineering Area, Group of E-Health, 20014 San Sebastián, Spain; Computational Intelligence Group, Computer Science Faculty, University of the Basque Country, UPV/EHU, 20018 San Sebastián, Spain.
| | - Teresa García-Navarro
- Vicomtech Foundation, Basque Research and Technology Alliance (BRTA), Mikeletegi 57, 20009 Donostia-San Sebastián, Spain
| | - María Rollan-Martinez-Herrera
- Vicomtech Foundation, Basque Research and Technology Alliance (BRTA), Mikeletegi 57, 20009 Donostia-San Sebastián, Spain; Servicio de Pediatría, Hospital Universitario Marqués de Valdecilla, 39011 Santander, Cantabria, Spain; Instituto de Investigación Sanitaria IDIVAL. Grupo de Epidemiología y Salud Pública, 39008 Santander, Cantabria, Spain
| | - Nekane Larburu
- Vicomtech Foundation, Basque Research and Technology Alliance (BRTA), Mikeletegi 57, 20009 Donostia-San Sebastián, Spain; Biogipuzkoa Health Research Institute, Bioengineering Area, Group of E-Health, 20014 San Sebastián, Spain
| | - Moisés D Espejo-Mambié
- Universidad de Alcalá, Facultad de Medicina y Ciencias de la Salud, Departamento de Biología de Sistemas, Alcalá de Henares, Spain; Asunción Klinika, 20400 Tolosa, Gipuzkoa, Spain
| | - Andoni Beristain Iraola
- Vicomtech Foundation, Basque Research and Technology Alliance (BRTA), Mikeletegi 57, 20009 Donostia-San Sebastián, Spain; Biogipuzkoa Health Research Institute, Bioengineering Area, Group of E-Health, 20014 San Sebastián, Spain; Computational Intelligence Group, Computer Science Faculty, University of the Basque Country, UPV/EHU, 20018 San Sebastián, Spain
| | - Manuel Graña
- Computational Intelligence Group, Computer Science Faculty, University of the Basque Country, UPV/EHU, 20018 San Sebastián, Spain
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5
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Jørgensen IF, Haue AD, Placido D, Hjaltelin JX, Brunak S. Disease Trajectories from Healthcare Data: Methodologies, Key Results, and Future Perspectives. Annu Rev Biomed Data Sci 2024; 7:251-276. [PMID: 39178424 DOI: 10.1146/annurev-biodatasci-110123-041001] [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] [Indexed: 08/25/2024]
Abstract
Disease trajectories, defined as sequential, directional disease associations, have become an intense research field driven by the availability of electronic population-wide healthcare data and sufficient computational power. Here, we provide an overview of disease trajectory studies with a focus on European work, including ontologies used as well as computational methodologies for the construction of disease trajectories. We also discuss different applications of disease trajectories from descriptive risk identification to disease progression, patient stratification, and personalized predictions using machine learning. We describe challenges and opportunities in the area that eventually will benefit from initiatives such as the European Health Data Space, which, with time, will make it possible to analyze data from cohorts comprising hundreds of millions of patients.
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Affiliation(s)
- Isabella Friis Jørgensen
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark;
| | - Amalie Dahl Haue
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark;
| | - Davide Placido
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark;
| | - Jessica Xin Hjaltelin
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark;
| | - Søren Brunak
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark;
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6
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Bergstedt J, Pasman JA, Ma Z, Harder A, Yao S, Parker N, Treur JL, Smit DJA, Frei O, Shadrin AA, Meijsen JJ, Shen Q, Hägg S, Tornvall P, Buil A, Werge T, Hjerling-Leffler J, Als TD, Børglum AD, Lewis CM, McIntosh AM, Valdimarsdóttir UA, Andreassen OA, Sullivan PF, Lu Y, Fang F. Distinct biological signature and modifiable risk factors underlie the comorbidity between major depressive disorder and cardiovascular disease. NATURE CARDIOVASCULAR RESEARCH 2024; 3:754-769. [PMID: 38898929 PMCID: PMC11182748 DOI: 10.1038/s44161-024-00488-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/31/2023] [Accepted: 05/08/2024] [Indexed: 06/21/2024]
Abstract
Major depressive disorder (MDD) and cardiovascular disease (CVD) are often comorbid, resulting in excess morbidity and mortality. Here we show that CVDs share most of their genetic risk factors with MDD. Multivariate genome-wide association analysis of shared genetic liability between MDD and atherosclerotic CVD revealed seven loci and distinct patterns of tissue and brain cell-type enrichments, suggesting the involvement of the thalamus. Part of the genetic overlap was explained by shared inflammatory, metabolic and psychosocial or lifestyle risk factors. Our data indicated causal effects of genetic liability to MDD on CVD risk, but not from most CVDs to MDD, and showed that the causal effects were partly explained by metabolic and psychosocial or lifestyle factors. The distinct signature of MDD-atherosclerotic CVD comorbidity suggests an immunometabolic subtype of MDD that is more strongly associated with CVD than overall MDD. In summary, we identified biological mechanisms underlying MDD-CVD comorbidity and modifiable risk factors for prevention of CVD in individuals with MDD.
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Affiliation(s)
- Jacob Bergstedt
- Unit of Integrative Epidemiology, Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden
| | - Joëlle A. Pasman
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Ziyan Ma
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Arvid Harder
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Shuyang Yao
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
- Department Medical Biochemistry and Biophysics, Karolinska Institutet, Stockholm, Sweden
| | - Nadine Parker
- Centre for Precision Psychiatry, Division of Mental Health and Addiction, University of Oslo and Oslo University Hospital, Oslo, Norway
| | - Jorien L. Treur
- Genetic Epidemiology, Department of Psychiatry, Amsterdam UMC, University of Amsterdam, Amsterdam, the Netherlands
| | - Dirk J. A. Smit
- Genetic Epidemiology, Department of Psychiatry, Amsterdam UMC, University of Amsterdam, Amsterdam, the Netherlands
| | - Oleksandr Frei
- Centre for Precision Psychiatry, Division of Mental Health and Addiction, University of Oslo and Oslo University Hospital, Oslo, Norway
- Centre for Bioinformatics, Department of Informatics, University of Oslo, Oslo, Norway
| | - Alexey A. Shadrin
- Centre for Precision Psychiatry, Division of Mental Health and Addiction, University of Oslo and Oslo University Hospital, Oslo, Norway
- K.G. Jebsen Centre for Neurodevelopmental Disorders, University of Oslo and Oslo University Hospital, Oslo, Norway
| | - Joeri J. Meijsen
- Institute of Biological Psychiatry, Mental Health Center Sct. Hans, Mental Health Services Copenhagen, Roskilde, Denmark
| | - Qing Shen
- Unit of Integrative Epidemiology, Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden
- Clinical Research Center for Mental Disorders, Shanghai Pudong New Area Mental Health Center, Tongji University School of Medicine, Shanghai, China
- Institute for Advanced Study, Tongji University, Shanghai, China
| | - Sara Hägg
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Per Tornvall
- Department of Clinical Science and Education Södersjukhuset, Karolinska Institutet, Stockholm, Sweden
| | - Alfonso Buil
- Institute of Biological Psychiatry, Mental Health Center Sct. Hans, Mental Health Services Copenhagen, Roskilde, Denmark
| | - Thomas Werge
- Institute of Biological Psychiatry, Mental Health Center Sct. Hans, Mental Health Services Copenhagen, Roskilde, Denmark
- Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Jens Hjerling-Leffler
- Department Medical Biochemistry and Biophysics, Karolinska Institutet, Stockholm, Sweden
| | - Thomas D. Als
- Department of Molecular Medicine (MOMA), Molecular Diagnostic Laboratory, Aarhus University Hospital, Aarhus, Denmark
- Department of Biomedicine, Aarhus University, Aarhus, Denmark
- Center for Genomics and Personalized Medicine, Aarhus, Denmark
| | - Anders D. Børglum
- Department of Biomedicine, Aarhus University, Aarhus, Denmark
- Center for Genomics and Personalized Medicine, Aarhus, Denmark
| | - Cathryn M. Lewis
- Social, Genetic and Developmental Psychiatry Centre, King’s College London, London, UK
- Department of Medical and Molecular Genetics, King’s College London, London, UK
| | - Andrew M. McIntosh
- Centre for Clinical Brain Sciences, University of Edinburgh, Royal Edinburgh Hospital, Edinburgh, UK
- Centre for Genomics and Experimental Medicine, University of Edinburgh, Edinburgh, UK
| | - Unnur A. Valdimarsdóttir
- Unit of Integrative Epidemiology, Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden
- Centre of Public Health Sciences, Faculty of Medicine, School of Health Sciences, University of Iceland, Reykjavik, Iceland
- Department of Epidemiology, Harvard TH Chan School of Public Health, Harvard University, Boston, MA USA
| | - Ole A. Andreassen
- Centre for Precision Psychiatry, Division of Mental Health and Addiction, University of Oslo and Oslo University Hospital, Oslo, Norway
- K.G. Jebsen Centre for Neurodevelopmental Disorders, University of Oslo and Oslo University Hospital, Oslo, Norway
| | - Patrick F. Sullivan
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
- Department of Psychiatry, University of North Carolina at Chapel Hill, Chapel Hill, NC USA
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC USA
| | - Yi Lu
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Fang Fang
- Unit of Integrative Epidemiology, Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden
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7
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Li Y, Yang AY, Marelli A, Li Y. MixEHR-SurG: A joint proportional hazard and guided topic model for inferring mortality-associated topics from electronic health records. J Biomed Inform 2024; 153:104638. [PMID: 38631461 DOI: 10.1016/j.jbi.2024.104638] [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: 12/20/2023] [Revised: 03/07/2024] [Accepted: 04/03/2024] [Indexed: 04/19/2024]
Abstract
Survival models can help medical practitioners to evaluate the prognostic importance of clinical variables to patient outcomes such as mortality or hospital readmission and subsequently design personalized treatment regimes. Electronic Health Records (EHRs) hold the promise for large-scale survival analysis based on systematically recorded clinical features for each patient. However, existing survival models either do not scale to high dimensional and multi-modal EHR data or are difficult to interpret. In this study, we present a supervised topic model called MixEHR-SurG to simultaneously integrate heterogeneous EHR data and model survival hazard. Our contributions are three-folds: (1) integrating EHR topic inference with Cox proportional hazards likelihood; (2) integrating patient-specific topic hyperparameters using the PheCode concepts such that each topic can be identified with exactly one PheCode-associated phenotype; (3) multi-modal survival topic inference. This leads to a highly interpretable survival topic model that can infer PheCode-specific phenotype topics associated with patient mortality. We evaluated MixEHR-SurG using a simulated dataset and two real-world EHR datasets: the Quebec Congenital Heart Disease (CHD) data consisting of 8211 subjects with 75,187 outpatient claim records of 1767 unique ICD codes; the MIMIC-III consisting of 1458 subjects with multi-modal EHR records. Compared to the baselines, MixEHR-SurG achieved a superior dynamic AUROC for mortality prediction, with a mean AUROC score of 0.89 in the simulation dataset and a mean AUROC of 0.645 on the CHD dataset. Qualitatively, MixEHR-SurG associates severe cardiac conditions with high mortality risk among the CHD patients after the first heart failure hospitalization and critical brain injuries with increased mortality among the MIMIC-III patients after their ICU discharge. Together, the integration of the Cox proportional hazards model and EHR topic inference in MixEHR-SurG not only leads to competitive mortality prediction but also meaningful phenotype topics for in-depth survival analysis. The software is available at GitHub: https://github.com/li-lab-mcgill/MixEHR-SurG.
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Affiliation(s)
- Yixuan Li
- Department of Mathematics and Statistics, McGill University, Montreal, Canada; Mila - Quebec AI institute, Montreal, Canada
| | - Archer Y Yang
- Department of Mathematics and Statistics, McGill University, Montreal, Canada; Mila - Quebec AI institute, Montreal, Canada; School of Computer Science, McGill University, Montreal, Canada.
| | - Ariane Marelli
- McGill Adult Unit for Congenital Heart Disease (MAUDE Unit), McGill University of Health Centre, Montreal, Canada.
| | - Yue Li
- Mila - Quebec AI institute, Montreal, Canada; School of Computer Science, McGill University, Montreal, Canada.
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8
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Dervić E, Sorger J, Yang L, Leutner M, Kautzky A, Thurner S, Kautzky-Willer A, Klimek P. Unraveling cradle-to-grave disease trajectories from multilayer comorbidity networks. NPJ Digit Med 2024; 7:56. [PMID: 38454004 PMCID: PMC10920888 DOI: 10.1038/s41746-024-01015-w] [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: 06/20/2023] [Accepted: 01/18/2024] [Indexed: 03/09/2024] Open
Abstract
We aim to comprehensively identify typical life-spanning trajectories and critical events that impact patients' hospital utilization and mortality. We use a unique dataset containing 44 million records of almost all inpatient stays from 2003 to 2014 in Austria to investigate disease trajectories. We develop a new, multilayer disease network approach to quantitatively analyze how cooccurrences of two or more diagnoses form and evolve over the life course of patients. Nodes represent diagnoses in age groups of ten years; each age group makes up a layer of the comorbidity multilayer network. Inter-layer links encode a significant correlation between diagnoses (p < 0.001, relative risk > 1.5), while intra-layers links encode correlations between diagnoses across different age groups. We use an unsupervised clustering algorithm for detecting typical disease trajectories as overlapping clusters in the multilayer comorbidity network. We identify critical events in a patient's career as points where initially overlapping trajectories start to diverge towards different states. We identified 1260 distinct disease trajectories (618 for females, 642 for males) that on average contain 9 (IQR 2-6) different diagnoses that cover over up to 70 years (mean 23 years). We found 70 pairs of diverging trajectories that share some diagnoses at younger ages but develop into markedly different groups of diagnoses at older ages. The disease trajectory framework can help us to identify critical events as specific combinations of risk factors that put patients at high risk for different diagnoses decades later. Our findings enable a data-driven integration of personalized life-course perspectives into clinical decision-making.
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Affiliation(s)
- Elma Dervić
- Complexity Science Hub Vienna, Vienna, Austria
- Supply Chain Intelligence Institute Austria (ASCII), Vienna, Austria
- Medical University of Vienna, Section for Science of Complex Systems, CeMSIIS, Vienna, Austria
| | | | | | - Michael Leutner
- Medical University of Vienna, Department of Internal Medicine III, Clinical Division of Endocrinology and Metabolism, Vienna, Austria
| | - Alexander Kautzky
- Medical University of Vienna, Department of Psychiatry and Psychotherapy, Vienna, Austria
| | - Stefan Thurner
- Complexity Science Hub Vienna, Vienna, Austria
- Medical University of Vienna, Section for Science of Complex Systems, CeMSIIS, Vienna, Austria
- Santa Fe Institute, Santa Fe, NM, USA
| | - Alexandra Kautzky-Willer
- Medical University of Vienna, Department of Internal Medicine III, Clinical Division of Endocrinology and Metabolism, Vienna, Austria
- Gender Institute, Gars am Kamp, Austria
| | - Peter Klimek
- Complexity Science Hub Vienna, Vienna, Austria.
- Supply Chain Intelligence Institute Austria (ASCII), Vienna, Austria.
- Medical University of Vienna, Section for Science of Complex Systems, CeMSIIS, Vienna, Austria.
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9
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Xu Z, Wu X, Xiao C, Zhang W, Yan P, Yang C, Zhang L, Cui H, Tang M, Wang Y, Chen L, Liu Y, Zou Y, Qu Y, Xiao C, Zhang L, Yang C, Li J, Liu Z, Liao J, Yao Y, Zhang B, Jiang X. Observational and genetic analyses of the bidirectional relationship between depression and hypertension. J Affect Disord 2024; 348:62-69. [PMID: 38123074 DOI: 10.1016/j.jad.2023.12.028] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/26/2023] [Revised: 11/20/2023] [Accepted: 12/09/2023] [Indexed: 12/23/2023]
Abstract
BACKGROUND While the association between depression and hypertension has been extensively investigated, the pattern and nature of such association remain inconclusive. We sought to investigate the bidirectional relationship between depression and hypertension and its causal. METHODS We first performed observational analyses using longitudinal data from the UK Biobank. We then performed genetic analyses leveraging summary statistics from large-scale genome-wide association studies (GWASs) conducted in European ancestry for depression and hypertension. RESULTS Observational analysis suggested a significant bidirectional phenotypic association between depression and hypertension (Depression → Hypertension: HR = 1.27, 95 % CI: 1.19, 1.36; Hypertension → Depression: HR = 1.65, 95 % CI: 1.58, 1.72). Linkage disequilibrium score regression demonstrated a positive genetic correlation between the two conditions (rg=0.15, P = 5.75 × 10-10). Bidirectional two-sample Mendelian randomization (MR) suggested that genetic liability to depression was significantly associated with an increased risk of hypertension (OR = 1.27, 95 % CI: 1.12, 1.43), while the genetic liability to hypertension was not associated with the risk of depression (OR = 1.01, 95 % CI: 0.99, 1.03). Multivariate MR, after adjusting for smoking, drinking, and body mass index, further supported an independent causal effect of genetic liability to depression on hypertension risk (OR = 1.10, 95 % CI: 1.02, 1.18). LIMITATIONS (1) interference of confounders, (2) absence of adequate statistical power, and (3) limitation to European populations. CONCLUSION Our study indicates depression is a causal risk factor for hypertension, whereas the reverse maybe not. Findings support that prevention of depression might help in decreasing hypertension incidence.
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Affiliation(s)
- Zhengxing Xu
- Department of Epidemiology and Biostatistics, Institute of Systems Epidemiology, and West China-PUMC C. C. Chen Institute of Health, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, Sichuan, China; School of Public Health, Southwest Medical University, Luzhou, Sichuan, China
| | - Xueyao Wu
- Department of Epidemiology and Biostatistics, Institute of Systems Epidemiology, and West China-PUMC C. C. Chen Institute of Health, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Changfeng Xiao
- Department of Epidemiology and Biostatistics, Institute of Systems Epidemiology, and West China-PUMC C. C. Chen Institute of Health, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Wenqiang Zhang
- Department of Epidemiology and Biostatistics, Institute of Systems Epidemiology, and West China-PUMC C. C. Chen Institute of Health, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Peijing Yan
- Department of Epidemiology and Biostatistics, Institute of Systems Epidemiology, and West China-PUMC C. C. Chen Institute of Health, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Chao Yang
- Department of Epidemiology and Biostatistics, Institute of Systems Epidemiology, and West China-PUMC C. C. Chen Institute of Health, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Li Zhang
- Department of Epidemiology and Biostatistics, Institute of Systems Epidemiology, and West China-PUMC C. C. Chen Institute of Health, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Huijie Cui
- Department of Epidemiology and Biostatistics, Institute of Systems Epidemiology, and West China-PUMC C. C. Chen Institute of Health, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Mingshuang Tang
- Department of Epidemiology and Biostatistics, Institute of Systems Epidemiology, and West China-PUMC C. C. Chen Institute of Health, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Yutong Wang
- Department of Epidemiology and Biostatistics, Institute of Systems Epidemiology, and West China-PUMC C. C. Chen Institute of Health, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Lin Chen
- Department of Epidemiology and Biostatistics, Institute of Systems Epidemiology, and West China-PUMC C. C. Chen Institute of Health, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Yunjie Liu
- Department of Epidemiology and Biostatistics, Institute of Systems Epidemiology, and West China-PUMC C. C. Chen Institute of Health, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Yanqiu Zou
- Department of Epidemiology and Biostatistics, Institute of Systems Epidemiology, and West China-PUMC C. C. Chen Institute of Health, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Yang Qu
- Department of Epidemiology and Biostatistics, Institute of Systems Epidemiology, and West China-PUMC C. C. Chen Institute of Health, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Chenghan Xiao
- Department of Epidemiology and Biostatistics, Institute of Systems Epidemiology, and West China-PUMC C. C. Chen Institute of Health, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, Sichuan, China; Department of Maternal, Child and Adolescent Health, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China
| | - Ling Zhang
- Department of Epidemiology and Biostatistics, Institute of Systems Epidemiology, and West China-PUMC C. C. Chen Institute of Health, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, Sichuan, China; Department of Iatrical Polymer Material and Artificial Apparatus, School of Polymer Science and Engineering, Sichuan University, Chengdu, China
| | - Chunxia Yang
- Department of Epidemiology and Biostatistics, Institute of Systems Epidemiology, and West China-PUMC C. C. Chen Institute of Health, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Jiayuan Li
- Department of Epidemiology and Biostatistics, Institute of Systems Epidemiology, and West China-PUMC C. C. Chen Institute of Health, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Zhenmi Liu
- Department of Epidemiology and Biostatistics, Institute of Systems Epidemiology, and West China-PUMC C. C. Chen Institute of Health, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, Sichuan, China; Department of Maternal, Child and Adolescent Health, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China
| | - Jiaqiang Liao
- Department of Epidemiology and Biostatistics, Institute of Systems Epidemiology, and West China-PUMC C. C. Chen Institute of Health, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, Sichuan, China.
| | - Yuqin Yao
- Department of Epidemiology and Biostatistics, Institute of Systems Epidemiology, and West China-PUMC C. C. Chen Institute of Health, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, Sichuan, China; Department of Occupational and Environmental Health, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China.
| | - Ben Zhang
- Department of Epidemiology and Biostatistics, Institute of Systems Epidemiology, and West China-PUMC C. C. Chen Institute of Health, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, Sichuan, China; Department of Occupational and Environmental Health, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China.
| | - Xia Jiang
- Department of Epidemiology and Biostatistics, Institute of Systems Epidemiology, and West China-PUMC C. C. Chen Institute of Health, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, Sichuan, China; Department of Nutrition and Food Hygiene, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China; Department of Clinical Neuroscience, Karolinska Institute, Stockholm, Sweden.
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10
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Chen J, Zhang F, Zhang Y, Lin Z, Deng K, Hou Q, Li L, Gao Y. Trajectories network analysis of chronic diseases among middle-aged and older adults: evidence from the China Health and Retirement Longitudinal Study (CHARLS). BMC Public Health 2024; 24:559. [PMID: 38389048 PMCID: PMC10882875 DOI: 10.1186/s12889-024-17890-7] [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: 08/09/2023] [Accepted: 01/25/2024] [Indexed: 02/24/2024] Open
Abstract
BACKGROUND Given the increased risk of chronic diseases and comorbidity among middle-aged and older adults in China, it is pivotal to identify the disease trajectory of developing chronic multimorbidity and address the temporal correlation among chronic diseases. METHOD The data of 15895 participants from the China Health and Retirement Longitudinal Study (CHARLS 2011 - 2018) were analyzed in the current study. Binomial tests and the conditional logistic regression model were conducted to estimate the associations among 14 chronic diseases, and the disease trajectory network analysis was adopted to visualize the relationships. RESULTS The analysis showed that hypertension is the most prevalent disease among the 14 chronic conditions, with the highest cumulative incidence among all chronic diseases. In the disease trajectory network, arthritis was found to be the starting point, and digestive diseases, hypertension, heart diseases, and dyslipidemia were at the center, while memory-related disease (MRD), stroke, and diabetes were at the periphery of the network. CONCLUSIONS With the chronic disease trajectory network analysis, we found that arthritis was prone to the occurrence and development of various other diseases. In addition, patients of heart diseases/hypertension/digestive disease/dyslipidemia were under higher risk of developing other chronic conditions. For patients with multimorbidity, early prevention can preclude them from developing into poorer conditions, such as stroke, MRD, and diabetes. By identifying the trajectory network of chronic disease, the results provided critical insights for developing early prevention and individualized support services to reduce disease burden and improve patients' quality of life.
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Affiliation(s)
- Jiade Chen
- Department of Public Health and Preventive Medicine, School of Medicine, Jinan University, Guangzhou, Guangdong, China
| | - Fan Zhang
- Department of Public Health and Preventive Medicine, School of Medicine, Jinan University, Guangzhou, Guangdong, China
| | - Yuan Zhang
- Guangdong Provincial Institute of Sports Science, Guangzhou, Guangdong, China
| | - Ziqiang Lin
- Department of Public Health and Preventive Medicine, School of Medicine, Jinan University, Guangzhou, Guangdong, China
| | - Kaisheng Deng
- School of Public Health, Guangdong Pharmaceutical University, Guangzhou, Guangdong, China
| | - Qingqin Hou
- School of Public Health, Guangdong Pharmaceutical University, Guangzhou, Guangdong, China
| | - Lixia Li
- School of Public Health, Guangdong Pharmaceutical University, Guangzhou, Guangdong, China.
| | - Yanhui Gao
- Department of Public Health and Preventive Medicine, School of Medicine, Jinan University, Guangzhou, Guangdong, China.
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11
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Wen S, Zhu J, Han X, Li Y, Liu H, Yang H, Hou C, Xu S, Wang J, Hu Y, Qu Y, Liu D, Aspelund T, Fang F, Valdimarsdóttir UA, Song H. Childhood maltreatment and risk of endocrine diseases: an exploration of mediating pathways using sequential mediation analysis. BMC Med 2024; 22:59. [PMID: 38331807 PMCID: PMC10854183 DOI: 10.1186/s12916-024-03271-9] [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: 04/18/2023] [Accepted: 01/22/2024] [Indexed: 02/10/2024] Open
Abstract
BACKGROUND Adverse childhood experiences (ACEs), including childhood maltreatment, have been linked with increased risk of diabetes and obesity during adulthood. A comprehensive assessment on the associations between childhood maltreatment and all major endocrine diseases, as well as the relative importance of different proposed mechanistic pathways on these associations, is currently lacking. METHODS Based on the UK Biobank, we constructed a cohort including 151,659 participants with self-reported data on childhood maltreatment who were 30 years of age or older on/after January 1, 1985. All participants were followed from the index date (i.e., January 1, 1985, or their 30th birthday, whichever came later) until the first diagnosis of any or specific (12 individual diagnoses and 9 subtypes) endocrine diseases, death, or the end of follow-up (December 31, 2019), whichever occurred first. We used Cox models to examine the association of childhood maltreatment, treated as continuous (i.e., the cumulative number of experienced childhood maltreatment), ordinal (i.e., 0, 1 and ≥ 2), or binary (< 2 and ≥ 2) variable, with any and specific endocrine diseases, adjusted for multiple covariates. We further examined the risk of having multiple endocrine diseases using Linear or Logistic Regression models. Then, sequential mediation analyses were performed to assess the contribution of four possible mechanisms (i.e., suboptimal socioeconomic status (SES), psychological adversities, unfavorable lifestyle, and biological alterations) on the observed associations. RESULTS During an average follow-up of 30.8 years, 20,885 participants received a diagnosis of endocrine diseases. We observed an association between the cumulative number of experienced childhood maltreatment and increased risk of being diagnosed with any endocrine disease (adjusted hazard ratio (HR) = 1.10, 95% confidence interval 1.09-1.12). The HR was 1.26 (1.22-1.30) when comparing individuals ≥ 2 with those with < 2 experienced childhood maltreatment. We further noted the most pronounced associations for type 2 diabetes (1.40 (1.33-1.48)) and hypothalamic-pituitary-adrenal (HPA)-axis-related endocrine diseases (1.38 (1.17-1.62)), and the association was stronger for having multiple endocrine diseases, compared to having one (odds ratio (95% CI) = 1.24 (1.19-1.30), 1.35 (1.27-1.44), and 1.52 (1.52-1.53) for 1, 2, and ≥ 3, respectively). Sequential mediation analyses showed that the association between childhood maltreatment and endocrine diseases was consistently and most distinctly mediated by psychological adversities (15.38 ~ 44.97%), while unfavorable lifestyle (10.86 ~ 25.32%) was additionally noted for type 2 diabetes whereas suboptimal SES (14.42 ~ 39.33%) for HPA-axis-related endocrine diseases. CONCLUSIONS Our study demonstrates that adverse psychological sequel of childhood maltreatment constitutes the main pathway to multiple endocrine diseases, particularly type 2 diabetes and HPA-axis-related endocrine diseases. Therefore, increased access to evidence-based mental health services may also be pivotal in reducing the risk of endocrine diseases among childhood maltreatment-exposed individuals.
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Affiliation(s)
- Shu Wen
- Mental Health Center and West China Biomedical Big Data Center West China Hospital, Sichuan University, Guo Xue Lane 37, Chengdu, China
- Med-X Center for Informatics, Sichuan University, Chengdu, China
- Department of Critical Care Medicine, West China Hospital, Sichuan University, Chengdu, China
| | - Jianwei Zhu
- Department of Orthopedic Surgery, West China Hospital, Sichuan University, Chengdu, China
| | - Xin Han
- Mental Health Center and West China Biomedical Big Data Center West China Hospital, Sichuan University, Guo Xue Lane 37, Chengdu, China
- Med-X Center for Informatics, Sichuan University, Chengdu, China
| | - Yuchen Li
- Mental Health Center and West China Biomedical Big Data Center West China Hospital, Sichuan University, Guo Xue Lane 37, Chengdu, China
- Mental Health Center, West China Hospital, Sichuan University, Chengdu, China
| | - Haowen Liu
- Mental Health Center and West China Biomedical Big Data Center West China Hospital, Sichuan University, Guo Xue Lane 37, Chengdu, China
- Med-X Center for Informatics, Sichuan University, Chengdu, China
| | - Huazhen Yang
- Mental Health Center and West China Biomedical Big Data Center West China Hospital, Sichuan University, Guo Xue Lane 37, Chengdu, China
- Med-X Center for Informatics, Sichuan University, Chengdu, China
| | - Can Hou
- Mental Health Center and West China Biomedical Big Data Center West China Hospital, Sichuan University, Guo Xue Lane 37, Chengdu, China
- Med-X Center for Informatics, Sichuan University, Chengdu, China
| | - Shishi Xu
- Mental Health Center and West China Biomedical Big Data Center West China Hospital, Sichuan University, Guo Xue Lane 37, Chengdu, China
- Division of Endocrinology & Metabolism, West China Hospital, Sichuan University, Chengdu, China
| | - Junren Wang
- Mental Health Center and West China Biomedical Big Data Center West China Hospital, Sichuan University, Guo Xue Lane 37, Chengdu, China
- Med-X Center for Informatics, Sichuan University, Chengdu, China
| | - Yao Hu
- Mental Health Center and West China Biomedical Big Data Center West China Hospital, Sichuan University, Guo Xue Lane 37, Chengdu, China
- Med-X Center for Informatics, Sichuan University, Chengdu, China
| | - Yuanyuan Qu
- Mental Health Center and West China Biomedical Big Data Center West China Hospital, Sichuan University, Guo Xue Lane 37, Chengdu, China
- Med-X Center for Informatics, Sichuan University, Chengdu, China
| | - Di Liu
- Mental Health Center and West China Biomedical Big Data Center West China Hospital, Sichuan University, Guo Xue Lane 37, Chengdu, China
- Med-X Center for Informatics, Sichuan University, Chengdu, China
- Sichuan University - Pittsburgh Institute, Sichuan University, Chengdu, China
| | - Thor Aspelund
- Center of Public Health Sciences, Faculty of Medicine, University of Iceland, Reykjavík, Iceland
| | - Fang Fang
- Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden
| | - Unnur A Valdimarsdóttir
- Center of Public Health Sciences, Faculty of Medicine, University of Iceland, Reykjavík, Iceland
- Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden
- Department of Epidemiology, Harvard T H Chan School of Public Health, Boston, MA, USA
| | - Huan Song
- Mental Health Center and West China Biomedical Big Data Center West China Hospital, Sichuan University, Guo Xue Lane 37, Chengdu, China.
- Med-X Center for Informatics, Sichuan University, Chengdu, China.
- Center of Public Health Sciences, Faculty of Medicine, University of Iceland, Reykjavík, Iceland.
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12
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Han X, Shen Q, Hou C, Yang H, Chen W, Zeng Y, Qu Y, Suo C, Ye W, Fang F, Valdimarsdóttir UA, Song H. Disease clusters subsequent to anxiety and stress-related disorders and their genetic determinants. Nat Commun 2024; 15:1209. [PMID: 38332132 PMCID: PMC10853285 DOI: 10.1038/s41467-024-45445-2] [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: 05/01/2023] [Accepted: 01/23/2024] [Indexed: 02/10/2024] Open
Abstract
Anxiety/stress-related disorders have been associated with multiple diseases, whereas a comprehensive assessment of the structure and interplay of subsequent associated diseases and their genetic underpinnings is lacking. Here, we first identify 136, out of 454 tested, medical conditions associated with incident anxiety/stress-related disorders attended in specialized care using a population-based cohort from the nationwide Swedish Patient Register, comprising 70,026 patients with anxiety/stress-related disorders and 1:10 birth year- and sex-matched unaffected individuals. By combining findings from the comorbidity network and disease trajectory analyses, we identify five robust disease clusters to be associated with a prior diagnosis of anxiety/stress-related disorders, featured by predominance of psychiatric disorders, eye diseases, ear diseases, cardiovascular diseases, and skin and genitourinary diseases. These five clusters and their featured diseases are largely validated in the UK Biobank. GWAS analyses based on the UK Biobank identify 3, 33, 40, 4, and 16 significantly independent single nucleotide polymorphisms for the link to the five disease clusters, respectively, which are mapped to several distinct risk genes and biological pathways. These findings motivate further mechanistic explorations and aid early risk assessment for cluster-based disease prevention among patients with newly diagnosed anxiety/stress-related disorders in specialized care.
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Affiliation(s)
- Xin Han
- Mental Health Center, West China Hospital, Sichuan University, Chengdu, China
- West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, China
- Med-X Center for Informatics, Sichuan University, Chengdu, China
| | - Qing Shen
- Clinical Research Center for Mental Disorders, Shanghai Pudong New Area Mental Health Center, Tongji University School of Medicine, Shanghai, China
- Institute for Advanced Study, Tongji University, Shanghai, China
- Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden
| | - Can Hou
- West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, China
- Med-X Center for Informatics, Sichuan University, Chengdu, China
| | - Huazhen Yang
- West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, China
- Med-X Center for Informatics, Sichuan University, Chengdu, China
| | - Wenwen Chen
- West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, China
- Med-X Center for Informatics, Sichuan University, Chengdu, China
| | - Yu Zeng
- West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, China
- Med-X Center for Informatics, Sichuan University, Chengdu, China
| | - Yuanyuan Qu
- West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, China
- Med-X Center for Informatics, Sichuan University, Chengdu, China
| | - Chen Suo
- Department of Epidemiology & Ministry of Education Key Laboratory of Public Health Safety, School of Public Health, Fudan University, Shanghai, China
- Taizhou Institute of Health Sciences, Fudan University, Taizhou, China
| | - Weimin Ye
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Fang Fang
- Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden
| | - Unnur A Valdimarsdóttir
- Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden
- Center of Public Health Sciences, Faculty of Medicine, University of Iceland, Reykjavík, Iceland
- Department of Epidemiology, Harvard T H Chan School of Public Health, Boston, MA, USA
| | - Huan Song
- West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, China.
- Med-X Center for Informatics, Sichuan University, Chengdu, China.
- Center of Public Health Sciences, Faculty of Medicine, University of Iceland, Reykjavík, Iceland.
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Bergstedt J, Pasman JA, Ma Z, Harder A, Yao S, Parker N, Treur JL, Smit DJA, Frei O, Shadrin A, Meijsen JJ, Shen Q, Hägg S, Tornvall P, Buil A, Werge T, Hjerling-Leffler J, Als TD, Børglum AD, Lewis CM, McIntosh AM, Valdimarsdóttir UA, Andreassen OA, Sullivan PF, Lu Y, Fang F. Distinct genomic signatures and modifiable risk factors underly the comorbidity between major depressive disorder and cardiovascular disease. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2023.09.01.23294931. [PMID: 37693619 PMCID: PMC10491387 DOI: 10.1101/2023.09.01.23294931] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/12/2023]
Abstract
Major depressive disorder (MDD) and cardiovascular disease (CVD) are often comorbid, resulting in excess morbidity and mortality. Using genomic data, this study elucidates biological mechanisms, key risk factors, and causal pathways underlying their comorbidity. We show that CVDs share a large proportion of their genetic risk factors with MDD. Multivariate genome-wide association analysis of the shared genetic liability between MDD and atherosclerotic CVD (ASCVD) revealed seven novel loci and distinct patterns of tissue and brain cell-type enrichments, suggesting a role for the thalamus. Part of the genetic overlap was explained by shared inflammatory, metabolic, and psychosocial/lifestyle risk factors. Finally, we found support for causal effects of genetic liability to MDD on CVD risk, but not from most CVDs to MDD, and demonstrated that the causal effects were partly explained by metabolic and psychosocial/lifestyle factors. The distinct signature of MDD-ASCVD comorbidity aligns with the idea of an immunometabolic sub-type of MDD more strongly associated with CVD than overall MDD. In summary, we identify plausible biological mechanisms underlying MDD-CVD comorbidity, as well as key modifiable risk factors for prevention of CVD in individuals with MDD.
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Affiliation(s)
- Jacob Bergstedt
- Unit of Integrative Epidemiology, Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden
| | - Joëlle A Pasman
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Ziyan Ma
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Arvid Harder
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Shuyang Yao
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
- Department Medical Biochemistry and Biophysics, Karolinska Institutet, 171 77 Stockholm, Sweden
| | - Nadine Parker
- NORMENT Centre, Institute of Clinical Medicine, University of Oslo and Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
| | - Jorien L Treur
- Genetic Epidemiology, Department of Psychiatry, Amsterdam UMC, University of Amsterdam, Amsterdam, the Netherlands
| | - Dirk J A Smit
- Genetic Epidemiology, Department of Psychiatry, Amsterdam UMC, University of Amsterdam, Amsterdam, the Netherlands
| | - Oleksandr Frei
- NORMENT Centre, Institute of Clinical Medicine, University of Oslo and Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
| | - Alexey Shadrin
- NORMENT Centre, Institute of Clinical Medicine, University of Oslo and Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
| | - Joeri J Meijsen
- Institute of Biological Psychiatry, Mental Health Center Sct. Hans, Mental Health Services Copenhagen, Roskilde, Denmark
| | - Qing Shen
- Unit of Integrative Epidemiology, Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden
- Clinical Research Center for Mental Disorders, Shanghai Pudong New Area Mental Health Center, Tongji University School of Medicine, Shanghai, China
- Institute for Advanced Study, Tongji University, Shanghai, China
| | - Sara Hägg
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Per Tornvall
- Department of Clinical Science and Education Södersjukhuset, Karolinska Institutet, Stockholm, Sweden
| | - Alfonso Buil
- Institute of Biological Psychiatry, Mental Health Center Sct. Hans, Mental Health Services Copenhagen, Roskilde, Denmark
| | - Thomas Werge
- Institute of Biological Psychiatry, Mental Health Center Sct. Hans, Mental Health Services Copenhagen, Roskilde, Denmark
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research (iPSYCH), Copenhagen, Denmark
- Lundbeck Foundation GeoGenetics Centre, GLOBE Institute, University of Copenhagen, Copenhagen, Denmark
- Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Jens Hjerling-Leffler
- Department Medical Biochemistry and Biophysics, Karolinska Institutet, 171 77 Stockholm, Sweden
| | - Thomas D Als
- Department of Molecular Medicine (MOMA), Molecular Diagnostic Laboratory, Aarhus University Hospital, Aarhus, Denmark
| | - Anders D Børglum
- Department of Biomedicine, Aarhus University, Aarhus, Denmark
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCH, Aarhus, Denmark
- Center for Genomics and Personalized Medicine, Aarhus, Denmark
| | - Cathryn M Lewis
- Social, Genetic and Developmental Psychiatry Centre, King's College London, London, UK
- Department of Medical and Molecular Genetics, King's College London, London, UK
| | - Andrew M McIntosh
- Centre for Clinical Brain Sciences, University of Edinburgh, Royal Edinburgh Hospital, Edinburgh, UK
- Centre for Genomics and Experimental Medicine, University of Edinburgh, Edinburgh, UK
| | - Unnur A Valdimarsdóttir
- Centre of Public Health Sciences, Faculty of Medicine, School of Health Sciences, University of Iceland, Reykjavik, Iceland
- Unit of Integrative Epidemiology, Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden
- Department of Epidemiology, Harvard TH Chan School of Public Health, Harvard University, Boston, Massachusetts, USA
| | - Ole A Andreassen
- NORMENT Centre, Institute of Clinical Medicine, University of Oslo and Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
- K.G. Jebsen Centre for Neurodevelopmental disorders, University of Oslo and Oslo University Hospital, Oslo, Norway
| | - Patrick F Sullivan
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
- Departments of Genetics and Psychiatry, University of North Carolina at Chapel Hill, NC, USA
| | - Yi Lu
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Fang Fang
- Unit of Integrative Epidemiology, Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden
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Chen H, Wang B, Lv R, Zhou T, Shen J, Song H, Xu X, Ma Y, Yuan C. Progression and trajectory network of age-related functional impairments and their combined associations with mortality. iScience 2023; 26:108368. [PMID: 38058300 PMCID: PMC10696261 DOI: 10.1016/j.isci.2023.108368] [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: 03/27/2023] [Revised: 07/06/2023] [Accepted: 10/26/2023] [Indexed: 12/08/2023] Open
Abstract
Age-related functional impairments (ARFIs) contribute to the loss of independence in older adults, but their progressions, interrelations, and combined relations with mortality are largely unknown. We conducted a prospective study among 17,914 participants in the Health and Retirement Study (2000-2020). The incidence rates of visual impairment, hearing impairment, physical frailty, and cognitive impairment increased exponentially with age, while those of restless sleep and depression increased relatively slowly. These ARFIs were associated with each other in temporal sequence and constituted a hazard network. We observed a dose-response relationship between the number of ARFIs and mortality risk, and the dyads involving physical frailty demonstrated the strongest associations with mortality. Our findings may assist in the identification of individuals at higher mortality risk and highlight the potential for future investigations to explore the impact of multiple ARFIs in aging.
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Affiliation(s)
- Hui Chen
- School of Public Health, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Binghan Wang
- School of Public Health, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Rongxia Lv
- School of Public Health, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Tianjing Zhou
- School of Public Health, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Jie Shen
- School of Public Health, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Huan Song
- West China Biomedical Big Data Center and National Clinical Research Center for Geriatrics, West China Hospital, Sichuan University, Chengdu, Sichuan, China
- Center of Public Health Sciences, Faculty of Medicine, University of Iceland, Reykjavík, Iceland
| | - Xiaolin Xu
- School of Public Health, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
- School of Public Health, Faculty of Medicine, The University of Queensland, Brisbane, QLD, Australia
| | - Yuan Ma
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Changzheng Yuan
- School of Public Health, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA
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15
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Zhang Y, Li J, Feng L, Luo Y, Pang W, Qiu K, Mao M, Song Y, Cheng D, Rao Y, Wang X, Hu Y, Ying Z, Pu X, Lin S, Huang S, Liu G, Zhang W, Xu W, Zhao Y, Ren J. A Population-Based Outcome-Wide Association Study of the Comorbidities and Sequelae Following COVID-19 Infection. J Epidemiol Glob Health 2023; 13:870-885. [PMID: 37889436 PMCID: PMC10686900 DOI: 10.1007/s44197-023-00161-w] [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: 05/03/2023] [Accepted: 10/06/2023] [Indexed: 10/28/2023] Open
Abstract
BACKGROUND Immense attention has been given to the outcome of COVID-19 infection. However, comprehensive studies based on large populational cohort with long-term follow-up are still lacking. This study aimed to investigate the risk of various short-term comorbidities (within one month) and long-term sequelae (above one month) after COVID-19 infection. METHODS In this large prospective cohort study with 14 months follow-up information based on UK biobank, we included 16,776 COVID-19-positive participants and 58,281 COVID-19-negative participants matched for comparison. The risk of each comorbidity and sequela was evaluated by multivariable logistic regression analysis and presented as hazard ratio (HR) and 95% confidence interval (95% CI). RESULTS COVID-19-positive individuals had a higher risk of 47 types of comorbidities within one month following COVID-19 infection, especially those who were older, male, overweight/obese, ever-smoked, with more pre-existing comorbidities and hospitalized. About 70.37% of COVID-19 patients with comorbidities had more than one co-occurring comorbidities. Additionally, only 6 high-risk sequelae were observed after one month of COVID-19 infection, and the incidence was relatively low (< 1%). CONCLUSION In addition to long-term sequelae following COVID-19 infection, plenty of comorbidities were observed, especially in patients with older age, male gender, overweight/obese, more pre-existing comorbidities and severe COVID-19, indicating that more attention should be given to these susceptible persons within this period.
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Affiliation(s)
- Yuyang Zhang
- Department of Otolaryngology-Head & Neck Surgery, West China Hospital, Sichuan University, Chengdu, China
| | - Junhong Li
- Department of Otolaryngology-Head & Neck Surgery, West China Hospital, Sichuan University, Chengdu, China
| | - Lan Feng
- Department of Otolaryngology-Head & Neck Surgery, West China Hospital, Sichuan University, Chengdu, China
| | - Yaxin Luo
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Wendu Pang
- Department of Otolaryngology-Head & Neck Surgery, West China Hospital, Sichuan University, Chengdu, China
| | - Ke Qiu
- Department of Otolaryngology-Head & Neck Surgery, West China Hospital, Sichuan University, Chengdu, China
| | - Minzi Mao
- Department of Otolaryngology-Head & Neck Surgery, West China Hospital, Sichuan University, Chengdu, China
| | - Yao Song
- Department of Otolaryngology-Head & Neck Surgery, West China Hospital, Sichuan University, Chengdu, China
| | - Danni Cheng
- Department of Otolaryngology-Head & Neck Surgery, West China Hospital, Sichuan University, Chengdu, China
| | - Yufang Rao
- Department of Otolaryngology-Head & Neck Surgery, West China Hospital, Sichuan University, Chengdu, China
| | - Xinyi Wang
- Department of Otolaryngology-Head & Neck Surgery, West China Hospital, Sichuan University, Chengdu, China
| | - Yao Hu
- West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, China
| | - Zhiye Ying
- West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, China
| | - Xiaobin Pu
- Department of Oto-Rhino-Laryngology, Langzhong People's Hospital, Langzhong, China
| | - Shuyan Lin
- Department of Otolaryngology-Head & Neck Surgery, West China Hospital, Sichuan University, Chengdu, China
| | - Shaohui Huang
- Department of Radiation Oncology, Princess Margaret Cancer Centre and University of Toronto, Toronto, Canada
| | - Geoffrey Liu
- Division of Medical Oncology and Hematology, Department of Medicine, Princess Margaret Cancer Center, University Health Network, University of Toronto, Toronto, Canada
| | - Wei Zhang
- West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, China
| | - Wei Xu
- Department of Biostatistics, Princess Margaret Cancer Centre and Dalla Lana School of Public Health, 10-511, 610 University Avenue Toronto, Toronto, ON, Canada.
| | - Yu Zhao
- Department of Otolaryngology-Head & Neck Surgery, West China Hospital, Sichuan University, Chengdu, China.
- West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, China.
| | - Jianjun Ren
- Department of Otolaryngology-Head & Neck Surgery, West China Hospital, Sichuan University, Chengdu, China.
- West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, China.
- Department of Oto-Rhino-Laryngology, Langzhong People's Hospital, Langzhong, China.
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16
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Jia Y, Li D, You Y, Yu J, Jiang W, Liu Y, Zeng R, Wan Z, Lei Y, Liao X. Multi-system diseases and death trajectory of metabolic dysfunction-associated fatty liver disease: findings from the UK Biobank. BMC Med 2023; 21:398. [PMID: 37864216 PMCID: PMC10590000 DOI: 10.1186/s12916-023-03080-6] [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: 06/20/2023] [Accepted: 09/13/2023] [Indexed: 10/22/2023] Open
Abstract
BACKGROUND Metabolic dysfunction-associated fatty liver disease (MAFLD) is a newly defined condition encompassing hepatic steatosis and metabolic dysfunction. However, the relationship between MAFLD and multi-system diseases remains unclear, and the time-dependent sequence of these diseases requires further clarification. METHODS After propensity score matching, 163,303 MAFLD subjects and 163,303 matched subjects were included in the community-based UK Biobank study. The International Classification of Diseases, Tenth Revision (ICD-10), was used to reclassify medical conditions into 490 and 16 specific causes of death. We conducted a disease trajectory analysis to map the key pathways linking MAFLD to various health conditions, providing an overview of their interconnections. RESULTS Participants aged 59 (51-64) years, predominantly males (62.5%), were included in the study. During the 12.9-year follow-up period, MAFLD participants were found to have a higher risk of 113 medical conditions and eight causes of death, determined through phenome-wide association analysis using Cox regression models. Temporal disease trajectories of MAFLD were established using disease pairing, revealing intermediary diseases such as asthma, diabetes, hypertension, hypothyroid conditions, tobacco abuse, diverticulosis, chronic ischemic heart disease, obesity, benign tumors, and inflammatory arthritis. These trajectories primarily resulted in acute myocardial infarction, disorders of fluid, electrolyte, and acid-base balance, infectious gastroenteritis and colitis, and functional intestinal disorders. Regarding death trajectories of MAFLD, malignant neoplasms, cardiovascular diseases, and respiratory system deaths were the main causes, and organ failure, infective disease, and internal environment disorder were the primary end-stage conditions. Disease trajectory analysis based on the level of genetic susceptibility to MAFLD yielded consistent results. CONCLUSIONS Individuals with MAFLD have a risk of a number of different medical conditions and causes of death. Notably, these diseases and potential causes of death constitute many pathways that may be promising targets for preventing general health decline in patients with MAFLD.
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Affiliation(s)
- Yu Jia
- General Practice Ward/International Medical Center Ward, General Practice Medical Center, West China Hospital, Sichuan University, 37 Guoxue Road, Chengdu, 610041, China
| | - Dongze Li
- Department of Emergency Medicine, West China Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Yi You
- School of Computer Science, Sichuan University, Chengdu, Sichuan, China
| | - Jing Yu
- Department of Emergency Medicine, West China Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Wenli Jiang
- General Practice Ward/International Medical Center Ward, General Practice Medical Center, West China Hospital, Sichuan University, 37 Guoxue Road, Chengdu, 610041, China
| | - Yi Liu
- Department of Emergency Medicine, West China Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Rui Zeng
- Department of Cardiology, West China Hospital, West China School of Medicine, Sichuan University, Chengdu, Sichuan, China
| | - Zhi Wan
- Department of Emergency Medicine, West China Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Yi Lei
- General Practice Ward/International Medical Center Ward, General Practice Medical Center, West China Hospital, Sichuan University, 37 Guoxue Road, Chengdu, 610041, China.
| | - Xiaoyang Liao
- General Practice Ward/International Medical Center Ward, General Practice Medical Center, West China Hospital, Sichuan University, 37 Guoxue Road, Chengdu, 610041, China.
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17
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Yuan M, Yang B, Rothschild G, Mann JJ, Sanford LD, Tang X, Huang C, Wang C, Zhang W. Epigenetic regulation in major depression and other stress-related disorders: molecular mechanisms, clinical relevance and therapeutic potential. Signal Transduct Target Ther 2023; 8:309. [PMID: 37644009 PMCID: PMC10465587 DOI: 10.1038/s41392-023-01519-z] [Citation(s) in RCA: 14] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2022] [Revised: 05/14/2023] [Accepted: 05/31/2023] [Indexed: 08/31/2023] Open
Abstract
Major depressive disorder (MDD) is a chronic, generally episodic and debilitating disease that affects an estimated 300 million people worldwide, but its pathogenesis is poorly understood. The heritability estimate of MDD is 30-40%, suggesting that genetics alone do not account for most of the risk of major depression. Another factor known to associate with MDD involves environmental stressors such as childhood adversity and recent life stress. Recent studies have emerged to show that the biological impact of environmental factors in MDD and other stress-related disorders is mediated by a variety of epigenetic modifications. These epigenetic modification alterations contribute to abnormal neuroendocrine responses, neuroplasticity impairment, neurotransmission and neuroglia dysfunction, which are involved in the pathophysiology of MDD. Furthermore, epigenetic marks have been associated with the diagnosis and treatment of MDD. The evaluation of epigenetic modifications holds promise for further understanding of the heterogeneous etiology and complex phenotypes of MDD, and may identify new therapeutic targets. Here, we review preclinical and clinical epigenetic findings, including DNA methylation, histone modification, noncoding RNA, RNA modification, and chromatin remodeling factor in MDD. In addition, we elaborate on the contribution of these epigenetic mechanisms to the pathological trait variability in depression and discuss how such mechanisms can be exploited for therapeutic purposes.
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Affiliation(s)
- Minlan Yuan
- Mental Health Center and Psychiatric Laboratory, the State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu, 610041, China
| | - Biao Yang
- Department of Abdominal Oncology, West China Hospital, Sichuan University, Chengdu, 610041, China
| | - Gerson Rothschild
- Department of Microbiology and Immunology, Vagelos College of Physicians and Surgeons, Columbia University, New York, NY, 10032, USA
| | - J John Mann
- Department of Psychiatry, Columbia University, New York, NY, 10032, USA
- Molecular Imaging and Neuropathology Division, New York State Psychiatric Institute, New York, NY, 10032, USA
- Department of Radiology, Columbia University, New York, NY, 10032, USA
| | - Larry D Sanford
- Sleep Research Laboratory, Center for Integrative Neuroscience and Inflammatory Diseases, Pathology and Anatomy, Eastern Virginia Medical School, Norfolk, VA, USA
| | - Xiangdong Tang
- Sleep Medicine Center, Department of Respiratory and Critical Care Medicine, Mental Health Center, Translational Neuroscience Center, and State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu, 610041, China
| | - Canhua Huang
- Department of Biotherapy, Cancer Center and State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu, 610041, China
| | - Chuang Wang
- Department of Pharmacology, and Provincial Key Laboratory of Pathophysiology in School of Medicine, Ningbo University, Ningbo, Zhejiang, 315211, China.
| | - Wei Zhang
- Mental Health Center and Psychiatric Laboratory, the State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu, 610041, China.
- West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, 610041, China.
- Medical Big Data Center, Sichuan University, Chengdu, 610041, China.
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18
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Wu YS, Taniar D, Adhinugraha K, Wang CH, Pai TW. Progression to myocardial infarction short-term death based on interval sequential pattern mining. BMC Cardiovasc Disord 2023; 23:394. [PMID: 37563547 PMCID: PMC10416354 DOI: 10.1186/s12872-023-03393-7] [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: 11/09/2022] [Accepted: 07/12/2023] [Indexed: 08/12/2023] Open
Abstract
BACKGROUND Myocardial infarction (MI) is one of the significant cardiovascular diseases (CVDs). According to Taiwanese health record analysis, the hazard rate reaches a peak in the initial year after diagnosis of MI, drops to a relatively low value, and maintains stable for the following years. Therefore, identifying suspicious comorbidity patterns of short-term death before the diagnosis may help achieve prolonged survival for MI patients. METHODS Interval sequential pattern mining was applied with odds ratio to the hospitalization records from the Taiwan National Health Insurance Research Database to evaluate the disease progression and identify potential subjects at the earliest possible stage. RESULTS Our analysis resulted in five disease pathways, including "diabetes mellitus," "other disorders of the urethra and urinary tract," "essential hypertension," "hypertensive heart disease," and "other forms of chronic ischemic heart disease" that led to short-term death after MI diagnosis, and these pathways covered half of the cohort. CONCLUSION We explored the possibility of establishing trajectory patterns to identify the high-risk population of early mortality after MI.
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Affiliation(s)
- Yang-Sheng Wu
- Computer Science and Information Engineering, National Taipei University of Technology, Taipei, 106344 Taiwan
| | - David Taniar
- Clayton Faculty of Information Technology, Monash University, Melbourne, VIC 3800 Australia
| | - Kiki Adhinugraha
- Computer Science & Information Technology, La Trobe University, Melbourne, VIC 3086 Australia
| | - Chao-Hung Wang
- Heart Failure Research Center, Division of Cardiology, Department of Internal Medicine, Chang Gung Memorial Hospital, Keelung, 204201 Taiwan
- Chang Gung University College of Medicine, Taoyuan, 333010 Taiwan
| | - Tun-Wen Pai
- Computer Science and Information Engineering, National Taipei University of Technology, Taipei, 106344 Taiwan
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19
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Li Y, Li X, Wang W, Guo R, Huang X. Spatiotemporal evolution and characteristics of worldwide life expectancy. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2023; 30:87145-87157. [PMID: 37418193 DOI: 10.1007/s11356-023-28330-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/07/2022] [Accepted: 06/14/2023] [Indexed: 07/08/2023]
Abstract
Exploring global differences in life expectancy can facilitate the development of strategies to narrow regional disparities. However, few researchers have systematically examined patterns in the evolution of worldwide life expectancy over a long time period. Spatial differences among 181 countries in 4 types of worldwide life expectancy patterns from 1990 to 2019 were investigated via geographic information system (GIS) analysis. The aggregation characteristics of the spatiotemporal evolution of life expectancy were revealed by local indicators of spatial association. The analysis employed spatiotemporal sequence-based kernel density estimation and explored the differences in life expectancy among regions with the Theil index. We found that the global life expectancy progress rate shows upward then downward patterns over the last 30 years. Female have higher rates of spatiotemporal progression in life expectancy than male, with less internal variation and a wider spatial aggregation. The global spatial and temporal autocorrelation of life expectancy shows a weakening trend. The difference in life expectancy between male and female is reflected in both intrinsic causes of biological differences and extrinsic causes such as environment and lifestyle habits. Investment in education pulls apart differences in life expectancy over long time series. These results provide scientific guidelines for obtaining the highest possible level of health in countries around the world.
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Affiliation(s)
- Yaxing Li
- Research Institute for Smart Cities, School of Architecture and Urban Planning, Shenzhen University, Shenzhen, 518060, China
- College of Design and Engineering, National University of Singapore, Singapore, 119077, Singapore
| | - Xiaoming Li
- Research Institute for Smart Cities, School of Architecture and Urban Planning, Shenzhen University, Shenzhen, 518060, China
- Shenzhen Key Laboratory of Spatial Smart Sensing and Services & MNR Technology Innovation Center of Territorial & Spatial Big Data & Guangdong-Hong Kong-Macau Joint Laboratory for Smart Cities, Shenzhen, 518060, China
| | - Weixi Wang
- Research Institute for Smart Cities, School of Architecture and Urban Planning, Shenzhen University, Shenzhen, 518060, China
- Shenzhen Key Laboratory of Spatial Smart Sensing and Services & MNR Technology Innovation Center of Territorial & Spatial Big Data & Guangdong-Hong Kong-Macau Joint Laboratory for Smart Cities, Shenzhen, 518060, China
| | - Renzhong Guo
- Research Institute for Smart Cities, School of Architecture and Urban Planning, Shenzhen University, Shenzhen, 518060, China.
- Shenzhen Key Laboratory of Spatial Smart Sensing and Services & MNR Technology Innovation Center of Territorial & Spatial Big Data & Guangdong-Hong Kong-Macau Joint Laboratory for Smart Cities, Shenzhen, 518060, China.
| | - Xiaojin Huang
- Research Institute for Smart Cities, School of Architecture and Urban Planning, Shenzhen University, Shenzhen, 518060, China
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Shen Q, Mikkelsen DH, Luitva LB, Song H, Kasela S, Aspelund T, Bergstedt J, Lu Y, Sullivan PF, Ye W, Fall K, Tornvall P, Pawitan Y, Andreassen OA, Buil A, Milani L, Fang F, Valdimarsdóttir U. Psychiatric disorders and subsequent risk of cardiovascular disease: a longitudinal matched cohort study across three countries. EClinicalMedicine 2023; 61:102063. [PMID: 37425374 PMCID: PMC10329128 DOI: 10.1016/j.eclinm.2023.102063] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/16/2023] [Revised: 06/07/2023] [Accepted: 06/09/2023] [Indexed: 07/11/2023] Open
Abstract
Background Several psychiatric disorders have been associated with increased risk of cardiovascular disease (CVD), however, the role of familial factors and the main disease trajectories remain unknown. Methods In this longitudinal cohort study, we identified a cohort of 900,240 patients newly diagnosed with psychiatric disorders during January 1, 1987 and December 31, 2016, their 1,002,888 unaffected full siblings, and 1:10 age- and sex-matched reference population from nationwide medical records in Sweden, who had no prior diagnosis of CVD at enrolment. We used flexible parametric models to determine the time-varying association between first-onset psychiatric disorders and incident CVD and CVD death, comparing rates of CVD among patients with psychiatric disorders to the rates of unaffected siblings and matched reference population. We also used disease trajectory analysis to identify main disease trajectories linking psychiatric disorders to CVD. Identified associations and disease trajectories of the Swedish cohort were validated in a similar cohort from nationwide medical records in Denmark (N = 875,634 patients, same criteria during January 1, 1969 and December 31, 2016) and in Estonian cohorts from the Estonian Biobank (N = 30,656 patients, same criteria during January 1, 2006 and December 31, 2020), respectively. Findings During up to 30 years of follow-up of the Swedish cohort, the crude incidence rate of CVD was 9.7, 7.4 and 7.0 per 1000 person-years among patients with psychiatric disorders, their unaffected siblings, and the matched reference population. Compared with their siblings, patients with psychiatric disorders experienced higher rates of CVD during the first year after diagnosis (hazard ratio [HR], 1.88; 95% confidence interval [CI], 1.79-1.98) and thereafter (1.37; 95% CI, 1.34-1.39). Similar rate increases were noted when comparing with the matched reference population. These results were replicated in the Danish cohort. We identified several disease trajectories linking psychiatric disorders to CVD in the Swedish cohort, with or without mediating medical conditions, including a direct link between psychiatric disorders and hypertensive disorder, ischemic heart disease, venous thromboembolism, angina pectoris, and stroke. These trajectories were validated in the Estonian Biobank cohort. Interpretation Independent of familial factors, patients with psychiatric disorders are at an elevated risk of subsequent CVD, particularly during first year after diagnosis. Increased surveillance and treatment of CVDs and CVD risk factors should be considered as an integral part of clinical management, in order to reduce risk of CVD among patients with psychiatric disorders. Funding This research was supported by EU Horizon 2020 Research and Innovation Action Grant, European Research Council Consolidator grant, Icelandic Research fund, Swedish Research Council, US NIMH, the Outstanding Clinical Discipline Project of Shanghai Pudong, the Fundamental Research Funds for the Central Universities, and the European Union through the European Regional Development Fund; the Research Council of Norway; the South-East Regional Health Authority, the Stiftelsen Kristian Gerhard Jebsen, and the EEA-RO-NO-2018-0535.
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Affiliation(s)
- Qing Shen
- Clinical Research Center for Mental Disorders, Shanghai Pudong New Area Mental Health Center, Tongji University School of Medicine, Shanghai, China
- Institute for Advanced Study, Tongji University, Shanghai, China
- Unit of Integrative Epidemiology, Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden
| | - Dorte Helenius Mikkelsen
- Institute of Biological Psychiatry, Mental Health Services Copenhagen, Copenhagen University Hospital, Copenhagen, Denmark
| | - Laura Birgit Luitva
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Huan Song
- Unit of Integrative Epidemiology, Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden
- Centre of Public Health Sciences, Faculty of Medicine, University of Iceland, Reykjavík, Iceland
- West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, China
- Medical Big Data Center, Sichuan University, Chengdu, China
| | - Silva Kasela
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Thor Aspelund
- Centre of Public Health Sciences, Faculty of Medicine, University of Iceland, Reykjavík, Iceland
| | - Jacob Bergstedt
- Unit of Integrative Epidemiology, Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Yi Lu
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Patrick F. Sullivan
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
- Departments of Genetics and Psychiatry, University of North Carolina, Chapel Hill, United States
| | - Weimin Ye
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Katja Fall
- Unit of Integrative Epidemiology, Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden
- Clinical Epidemiology and Biostatistics, School of Medical Sciences, Örebro University, Örebro, Sweden
| | - Per Tornvall
- Department of Clinical Science and Education Södersjukhuset, Karolinska Institutet, Stockholm, Sweden
| | - Yudi Pawitan
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Ole A. Andreassen
- NORMENT Centre, Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
- Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Alfonso Buil
- iPSYCH, The Lundbeck Foundation Initiative for Integrative Psychiatric Research, Aarhus, Denmark
- Mental Health Center Sct. Hans, Mental Health Services, Capital Region Denmark, Roskilde, Denmark
| | - Lili Milani
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Fang Fang
- Unit of Integrative Epidemiology, Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden
| | - Unnur Valdimarsdóttir
- Unit of Integrative Epidemiology, Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden
- Centre of Public Health Sciences, Faculty of Medicine, University of Iceland, Reykjavík, Iceland
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, United States
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Frank P, Batty GD, Pentti J, Jokela M, Poole L, Ervasti J, Vahtera J, Lewis G, Steptoe A, Kivimäki M. Association Between Depression and Physical Conditions Requiring Hospitalization. JAMA Psychiatry 2023; 80:690-699. [PMID: 37133850 PMCID: PMC10157511 DOI: 10.1001/jamapsychiatry.2023.0777] [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: 10/29/2022] [Accepted: 02/27/2023] [Indexed: 05/04/2023]
Abstract
Importance Depression is associated with an increased risk of physical illness, but the most common causes of hospitalization among people with depression are unclear. Objective To examine the association of depression with an array of physical conditions requiring hospital treatment. Design, Setting, and Participants In this outcomewide prospective multicohort study, primary analysis was based on data from the UK Biobank, a population-based study in the United Kingdom. Analyses were repeated in an independent data set of 2 cohorts in Finland, a population-based study and an occupational cohort. Data analysis was conducted between April and September 2022. Exposures Self-reported depression, recurrent severe major depression, recurrent moderate major depression, and a single major depressive episode. Main Outcomes and Measures A total of 77 common health conditions ascertained from linkage data to national hospital and mortality registries. Results The analytical sample of UK Biobank participants consisted of 130 652 individuals (71 565 women [54.8%]; 59 087 men [45.2%]; mean [SD] age at baseline, 63.3 [7.8] years). The pooled data from the Finnish replication cohorts included 109 781 participants (82 921 women [78.6%]; 26 860 men [21.4%]; mean [SD] age, 42 [10.8] years). In the main analysis, severe/moderately severe depression was associated with the incidence of 29 nonoverlapping conditions requiring hospital treatment during a 5-year follow-up. Twenty-five of these associations remained after adjustment for confounders and multiple testing (adjusted hazard ratio [HR] range, 1.52-23.03) and were confirmed in the analysis of the Finnish cohorts. These included sleep disorders (HR, 5.97; 95% CI, 3.27-10.89), diabetes (HR, 5.15; 95% CI, 2.52-10.50), ischemic heart disease (HR, 1.76; 95% CI, 1.36-2.29), chronic obstructive bronchitis (HR, 4.11; 95% CI, 2.56-6.60), bacterial infections (HR, 2.52; 95% CI, 1.99-3.19), back pain (HR, 3.99; 95% CI, 2.96-5.38), and osteoarthritis (HR, 1.80; 95% CI, 1.46-2.20). The highest cumulative incidence was observed for endocrine and related internal organ diseases (245 per 1000 persons with depression; risk difference relative to unaffected individuals: 9.8%), musculoskeletal diseases (91 per 1000 persons; risk difference, 3.7%), and diseases of the circulatory system and blood (86 per 1000 persons; risk difference, 3.9%). The cumulative incidence was lower for hospital-treated mental, behavioral, and neurological disorders (20 in 1000 persons; risk difference, 1.7%). Depression was also associated with disease progression in people with prevalent heart disease or diabetes, and for 12 conditions, there was evidence of a bidirectional relationship. Conclusions and Relevance In this study, the most common causes of hospitalization in people with depression were endocrine, musculoskeletal, and vascular diseases, not psychiatric disorders. These findings suggest that depression should be considered as a target for the prevention of physical and mental disease.
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Affiliation(s)
- Philipp Frank
- Research Department of Epidemiology and Public Health, University College London, London, United Kingdom
- UCL Brain Sciences, University College London, London, United Kingdom
| | - G. David Batty
- Research Department of Epidemiology and Public Health, University College London, London, United Kingdom
| | - Jaana Pentti
- Clinicum, Faculty of Medicine, University of Helsinki, Helsinki, Finland
- Finnish Institute of Occupational Health, Helsinki, Finland
| | - Markus Jokela
- Department of Psychology and Logopedics, Faculty of Medicine, University of Helsinki, Helsinki, Finland
| | - Lydia Poole
- School of Psychology, University of Surrey, Guildford, Surrey, United Kingdom
| | - Jenni Ervasti
- Finnish Institute of Occupational Health, Helsinki, Finland
| | - Jussi Vahtera
- Department of Public Health, University of Turku, Turku, Finland
| | - Glyn Lewis
- UCL Brain Sciences, University College London, London, United Kingdom
| | - Andrew Steptoe
- Research Department of Behavioural Science and Health, University College London, London, United Kingdom
| | - Mika Kivimäki
- Clinicum, Faculty of Medicine, University of Helsinki, Helsinki, Finland
- Finnish Institute of Occupational Health, Helsinki, Finland
- UCL Brain Sciences, University College London, London, United Kingdom
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22
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Lin Z, Chan YH, Cheung BMY. Dissecting Relations between Depression Severity, Antidepressant Use, and Metabolic Syndrome Components in the NHANES 2005-2020. J Clin Med 2023; 12:3891. [PMID: 37373586 PMCID: PMC10299566 DOI: 10.3390/jcm12123891] [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: 03/20/2023] [Revised: 05/26/2023] [Accepted: 06/05/2023] [Indexed: 06/29/2023] Open
Abstract
We aimed to dissect the complex relations between depressive symptoms, antidepressant use, and constituent metabolic syndrome (MetS) components in a representative U.S. population sample. A total of 15,315 eligible participants were included from 2005 to March 2020. MetS components were defined as hypertension, elevated triglycerides, reduced high-density lipoprotein cholesterol, central obesity, and elevated blood glucose. Depressive symptoms were classified as mild, moderate, or severe. Logistic regression was used to evaluate the relationship between depression severity, antidepressant use, individual MetS components and their degree of clustering. Severe depression was associated with the number of MetS components in a graded fashion. ORs for severe depression ranged from 2.08 [95%CI, 1.29-3.37] to 3.35 [95%CI, 1.57-7.14] for one to five clustered components. Moderate depression was associated with hypertension, central obesity, raised triglyceride, and elevated blood glucose (OR = 1.37 [95%CI, 1.09-1.72], 1.82 [95%CI, 1.21-2.74], 1.63 [95%CI, 1.25-2.14], and 1.37 [95%CI, 1.05-1.79], respectively). Antidepressant use was associated with hypertension (OR = 1.40, 95%CI [1.14-1.72]), raised triglyceride (OR = 1.43, 95%CI [1.17-1.74]), and the presence of five MetS components (OR = 1.74, 95%CI [1.13-2.68]) after adjusting for depressive symptoms. The depression severity and antidepressant use were associated with individual MetS components and their graded clustering. Metabolic abnormalities in patients with depression need to be recognized and treated.
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Affiliation(s)
- Ziying Lin
- Department of Medicine, School of Clinical Medicine, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Queen Mary Hospital, 102 Pokfulam Road, Pokfulam, Hong Kong, China
| | - Yap-Hang Chan
- Department of Medicine, School of Clinical Medicine, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Queen Mary Hospital, 102 Pokfulam Road, Pokfulam, Hong Kong, China
| | - Bernard Man Yung Cheung
- Department of Medicine, School of Clinical Medicine, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Queen Mary Hospital, 102 Pokfulam Road, Pokfulam, Hong Kong, China
- State Key Laboratory of Pharmaceutical Biotechnology, The University of Hong Kong, Pokfulam, Hong Kong, China
- Institute of Cardiovascular Science and Medicine, The University of Hong Kong, Pokfulam, Hong Kong, China
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23
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de Andrade TB, de Andrade FB, Viana MC. Prevalence of depressive symptoms and its association with social support among older adults: The Brazilian National Health Survey. J Affect Disord 2023; 333:468-473. [PMID: 37080499 DOI: 10.1016/j.jad.2023.04.051] [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: 11/02/2022] [Revised: 04/01/2023] [Accepted: 04/14/2023] [Indexed: 04/22/2023]
Abstract
BACKGROUND The study aimed to assess the association between depressive symptoms and social support measures among non-institutionalized Brazilian older adults. METHODS A cross-sectional study was carried out with data from the last National Health Survey, conducted in 2019. A descriptive analysis of the study variables was performed, followed by a multiple regression analysis to teste the association between the dependent variable (depressive symptoms, assessed by the Patient Health Questionnaire (PHQ-9) and independent variables (household arrangement, marital relationship, leisure activities, voluntary activity, religious activity, activities in associations/leaderships, work and receiving social support (friends and family). RESULTS The sample comprised 22,723 older adults and 13.1 % had depressive symptoms. There was a positive relationship between the number of people in the household and the prevalence of depressive symptoms. Individuals with support from family members only (PR 0.69; 95 % CI 0.51; 0.92) and those with support from family and friends (PR 0.53 95 % CI 0.40; 0.70) had a lower prevalence of depressive symptoms than those without support. Participation in leisure and religious activities was inversely associated with depressive symptoms. A negative association was found between work and the presence of depressive symptoms (PR 0.83; 95 % CI 0.70; 0.99). LIMITATIONS The cross-sectional design limits causal inferences and the survey did not include institutionalized individuals. CONCLUSIONS The lower prevalence of the outcome among older adults with social support highlights the importance of evaluating these measures and practicing activities that allow personal and social interactions among this age group.
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24
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Fond G, Trouve M, Andrieu-Haller C, Sunhary de Verville PL, Boyer L. Barriers in psychiatrists' mind to active smoking cessation promotion in severe psychiatric disorders. L'ENCEPHALE 2023; 49:21-26. [PMID: 34862009 DOI: 10.1016/j.encep.2021.09.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/13/2021] [Revised: 09/26/2021] [Accepted: 09/30/2021] [Indexed: 01/21/2023]
Abstract
BACKGROUND Promoting the cessation of smoking in mental healthcare is a priority of international health organizations as it is the most cost-effective intervention in psychiatry. AIM To explore the representations of psychiatrists on their role in active smoking cessation prevention in severe psychiatric disorders. METHODS Psychiatrists and residents in psychiatry were recruited at a national level by professional mailings. RESULTS One thousand four hundred and sixty participants were included in the study, and only 46% reported actively promoting smoking cessation. In multivariate analyses, participants aged<35years were more likely to promote cessation of tobacco smoking, as well as the two thirds who believe that psychiatry is a systemic discipline with complex interactions between brain, body and mind. Almost two thirds of those promoting tobacco cessation reported lacking time to combine psychiatric and physical examination during one session. The psychiatrists who reported not promoting tobacco smoking cessation also reported never dealing with physical health in case of the absence of a general practitioner and thinking that physical examination may have a negative impact on the therapeutic relationship. Almost all (96%) reported promoting the need for a general practitioner for their patients. We found no significant difference between the public and private sectors (P>0.05). INTERPRETATION Young psychiatrists are more prone than their elders to promote smoking cessation but report lacking time to include it in their daily practice. Promotion of tobacco smoking cessation should be included in the components for quality evaluation for mental health services and specific sessions dedicated to this intervention.
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Affiliation(s)
- G Fond
- Pole psychiatrie, centre expert depression resistante et schizophrénie FondaMental, CHU La Conception, Marseille, France; Aix-Marseille university, CEReSS-health service research and quality of life center, 27, boulevard Jean-Moulin, 13005 Marseille, France.
| | - M Trouve
- Centre hospitalier Montperrin, Aix en Provence, France
| | - C Andrieu-Haller
- Pole psychiatrie, centre expert depression resistante et schizophrénie FondaMental, CHU La Conception, Marseille, France
| | - P-L Sunhary de Verville
- Pole psychiatrie, centre expert depression resistante et schizophrénie FondaMental, CHU La Conception, Marseille, France; Aix-Marseille university, CEReSS-health service research and quality of life center, 27, boulevard Jean-Moulin, 13005 Marseille, France
| | - L Boyer
- Aix-Marseille university, CEReSS-health service research and quality of life center, 27, boulevard Jean-Moulin, 13005 Marseille, France
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25
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Fu Z, Liu Q, Liang J, Weng Z, Li W, Xu J, Zhang X, Xu C, Huang T, Gu A. Air pollution, genetic factors and the risk of depression. THE SCIENCE OF THE TOTAL ENVIRONMENT 2022; 850:158001. [PMID: 35973541 DOI: 10.1016/j.scitotenv.2022.158001] [Citation(s) in RCA: 19] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/16/2022] [Revised: 08/04/2022] [Accepted: 08/09/2022] [Indexed: 06/15/2023]
Abstract
Both genetics and ambient air pollutants contribute to depression, but the degree to which genetic susceptibility modifies the effect of air pollution on depression remains unknown. We aimed to investigate the effect of the modification of genetic susceptibility on depression. Notably, 490,780 participants who were free of depression at baseline in the UK Biobank study were recruited from 2006 to 2010. A land use regression (LUR) model was performed to estimate the concentrations of particulate matter with diameters ranging from ≤2.5-≤10 μm (PM2.5, PM2.5-10 and PM10), nitrogen dioxide (NO2), and nitrogen oxides (NOx). The International Classification of Diseases 10th Revision (ICD-10) code was used to identify depression cases. Cox proportional hazard models adjusted for covariates were used to investigate the association between ambient air pollutants and depression. Moreover, the polygenic risk score (PRS) was calculated to evaluate cumulative genetic effects, and additive interaction models were established to explore whether genetic susceptibility modified the effects of air pollutants on depression. PM2.5, PM10, NO2 and NOx exposure were significantly positively associated with the risk of depression, and the hazard ratios and 95 % confidence intervals for a 10-μg/m3 increase in PM2.5, PM10, NO2 and NOx concentrations were 2.12 (1.82, 2.47), 1.12 (1.03, 1.23), 1.07 (1.05, 1.10) and 1.04 (1.03, 1.05), respectively. Air pollutants and genetic variants exerted significant additive effects on the risk of depression (relative excess risk due to the interaction [RERI]: 0.15 for PM2.5, 0.12 for PM10, 0.10 for NO2, and 0.12 for NOx; attributable proportion due to the interaction [AP]: 0.12 for PM2.5, 0.10 for PM10, 0.08 for NO2, and 0.09 for NOx). Air pollution exposure was significantly associated with the risk of depression, and participants with a higher genetic risk were more likely to develop depression when exposed to high levels of air pollution.
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Affiliation(s)
- Zuqiang Fu
- State Key Laboratory of Reproductive Medicine, School of Public Health, Nanjing Medical University, Nanjing, China; Key Laboratory of Modern Toxicology of Ministry of Education, Center for Global Health, Nanjing Medical University, Nanjing, China; School of Public Health, Southeast University, Nanjing, China
| | - Qian Liu
- State Key Laboratory of Reproductive Medicine, School of Public Health, Nanjing Medical University, Nanjing, China; Key Laboratory of Modern Toxicology of Ministry of Education, Center for Global Health, Nanjing Medical University, Nanjing, China
| | - Jingjia Liang
- State Key Laboratory of Reproductive Medicine, School of Public Health, Nanjing Medical University, Nanjing, China; Key Laboratory of Modern Toxicology of Ministry of Education, Center for Global Health, Nanjing Medical University, Nanjing, China
| | - Zhenkun Weng
- State Key Laboratory of Reproductive Medicine, School of Public Health, Nanjing Medical University, Nanjing, China; Key Laboratory of Modern Toxicology of Ministry of Education, Center for Global Health, Nanjing Medical University, Nanjing, China
| | - Wenxiang Li
- State Key Laboratory of Reproductive Medicine, School of Public Health, Nanjing Medical University, Nanjing, China; Key Laboratory of Modern Toxicology of Ministry of Education, Center for Global Health, Nanjing Medical University, Nanjing, China
| | - Jin Xu
- State Key Laboratory of Reproductive Medicine, School of Public Health, Nanjing Medical University, Nanjing, China; Key Laboratory of Modern Toxicology of Ministry of Education, Center for Global Health, Nanjing Medical University, Nanjing, China; Department of Maternal, Child, and Adolescent Health, School of Public Health, Nanjing Medical University, Nanjing, China
| | - Xin Zhang
- State Key Laboratory of Reproductive Medicine, School of Public Health, Nanjing Medical University, Nanjing, China; Key Laboratory of Modern Toxicology of Ministry of Education, Center for Global Health, Nanjing Medical University, Nanjing, China
| | - Cheng Xu
- State Key Laboratory of Reproductive Medicine, School of Public Health, Nanjing Medical University, Nanjing, China; Key Laboratory of Modern Toxicology of Ministry of Education, Center for Global Health, Nanjing Medical University, Nanjing, China.
| | - Tao Huang
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China.
| | - Aihua Gu
- State Key Laboratory of Reproductive Medicine, School of Public Health, Nanjing Medical University, Nanjing, China; Key Laboratory of Modern Toxicology of Ministry of Education, Center for Global Health, Nanjing Medical University, Nanjing, China; School of Public Health, Southeast University, Nanjing, China.
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26
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Hou C, Zeng Y, Chen W, Han X, Yang H, Ying Z, Hu Y, Sun Y, Qu Y, Fang F, Song H. Medical conditions associated with coffee consumption: Disease-trajectory and comorbidity network analyses of a prospective cohort study in UK Biobank. Am J Clin Nutr 2022; 116:730-740. [PMID: 35849013 PMCID: PMC9437992 DOI: 10.1093/ajcn/nqac148] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2022] [Accepted: 05/14/2022] [Indexed: 02/05/2023] Open
Abstract
BACKGROUND Habitual coffee consumption has been associated with multiple health benefits. A comprehensive analysis of disease trajectory and comorbidity networks in relation to coffee consumption is, however, currently lacking. OBJECTIVES We aimed to comprehensively examine the health outcomes associated with habitual coffee consumption, through clarifying its disease trajectory and comorbidity networks. METHODS Based on the UK Biobank cohort, we included 395,539 individuals with available information on coffee intake collected at recruitment between 2006 and 2010. These individuals were categorized as having low (<1 cup per day), moderate (1-3 cups), and high (≥4 cups) levels of coffee intake, and were followed through 2020 to ascertain 496 medical conditions. Cox regression was used to assess the associations between high-level coffee intake and the risk of medical conditions with a prevalence ≥0.5% in the study population, after adjusting for multiple confounders, using low-level coffee intake as the reference. Disease-trajectory and comorbidity network analyses were then applied to visualize the temporal and nontemporal relationships between the medical conditions that had an inverse association with high-level coffee intake. RESULTS During a median follow-up of 11.8 years, 31 medical conditions were found to be associated with high-level coffee intake, among which 30 showed an inverse association (HRs ranged from 0.61 to 0.94). The inverse associations were more pronounced for women, compared with men. Disease-trajectory and comorbidity network analyses of these 30 conditions identified 4 major clusters of medical conditions, mainly in the cardiometabolic and gastrointestinal systems, among both men and women; 1 cluster of medical conditions following alcohol-related disorders, primarily among men; as well as a cluster of estrogen-related conditions among women. CONCLUSIONS Habitual coffee consumption was associated with lower risks of many medical conditions, especially those in the cardiometabolic and gastrointestinal systems and those related to alcohol use and estrogen regulation.
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Affiliation(s)
- Can Hou
- West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, China.,Med-X Center for Informatics, Sichuan University, Chengdu, China
| | - Yu Zeng
- West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, China.,Med-X Center for Informatics, Sichuan University, Chengdu, China
| | - Wenwen Chen
- Division of Nephrology, Kidney Research Institute, State Key Laboratory of Biotherapy and Cancer Center, West China Hospital, Sichuan University, Chengdu, China
| | - Xin Han
- West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, China.,Med-X Center for Informatics, Sichuan University, Chengdu, China
| | - Huazhen Yang
- West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, China.,Med-X Center for Informatics, Sichuan University, Chengdu, China
| | - Zhiye Ying
- West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, China.,Med-X Center for Informatics, Sichuan University, Chengdu, China
| | - Yao Hu
- West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, China.,Med-X Center for Informatics, Sichuan University, Chengdu, China
| | - Yajing Sun
- West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, China.,Med-X Center for Informatics, Sichuan University, Chengdu, China
| | - Yuanyuan Qu
- West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, China.,Med-X Center for Informatics, Sichuan University, Chengdu, China
| | - Fang Fang
- Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden
| | - Huan Song
- West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, China.,Med-X Center for Informatics, Sichuan University, Chengdu, China.,Center of Public Health Sciences, Faculty of Medicine, University of Iceland, Reykjavík, Iceland
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27
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Wang M, Jian Z, Ma Y, Jin X, Li H, Wang K. Depression increases the risk of kidney stone: Results from the National Health and Nutrition Examination Survey 2007-2018 and Mendelian randomization analysis. J Affect Disord 2022; 312:17-21. [PMID: 35691420 DOI: 10.1016/j.jad.2022.06.008] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/06/2022] [Revised: 05/24/2022] [Accepted: 06/06/2022] [Indexed: 02/05/2023]
Abstract
BACKGROUND To investigate whether depression increases the kidney stone risk. METHODS First, we performed an observational study in the National Health and Nutrition Examination Survey (NHANES) 2007-2018. Depression severity was evaluated using the Patient Health Questionnaire-9 (PHQ-9) and classified into no, mild, moderate, and severe depression groups. Multivariable-adjusted logistic regression was used to assess the correlation between depression severity and kidney stone risk. Second, Mendelian randomization (MR) was applied to decrease the bias and avoid the reverse causality in the observational study. Genetic instruments were obtained from a large genome-wide association study (GWAS) meta-analysis of depression involved 246,363 cases and 561,190 controls. We obtained summary data for kidney stone from another large GWAS, which integrates data from 6536 stone formers and 388,508 controls. Inverse variance weighted (IVW) was the primary analytical method. RESULTS In the observational study, a total of 24,892 individuals were enrolled. Individuals with moderate (OR 1.38, 95 % CI 1.05-1.83, P = 0.022) and severe (OR 1.56, 95 % CI 1.02-2.40, P = 0.040) depression had a higher risk of kidney stone (P for trend = 0.006) compared with the control. For the MR, results also showed that genetically predicted depression was causally associated with a higher risk of kidney stone disease (OR 1.26, 95 % CI 1.04-1.53, P = 0.017) in IVW. CONCLUSIONS Depression might be associated with kidney stone risk. This finding is needed to be verified in further prospective cohort studies with a large sample size and enough follow-up time.
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Affiliation(s)
- Menghua Wang
- Department of Urology, Institute of Urology (Laboratory of Reconstructive Urology), West China Hospital, Sichuan University, Chengdu, Sichuan, PR China
| | - Zhongyu Jian
- Department of Urology, Institute of Urology (Laboratory of Reconstructive Urology), West China Hospital, Sichuan University, Chengdu, Sichuan, PR China; West China Biomedical Big Data Center, Sichuan University, Chengdu, Sichuan, PR China
| | - Yucheng Ma
- Department of Urology, Institute of Urology (Laboratory of Reconstructive Urology), West China Hospital, Sichuan University, Chengdu, Sichuan, PR China
| | - Xi Jin
- Department of Urology, Institute of Urology (Laboratory of Reconstructive Urology), West China Hospital, Sichuan University, Chengdu, Sichuan, PR China
| | - Hong Li
- Department of Urology, Institute of Urology (Laboratory of Reconstructive Urology), West China Hospital, Sichuan University, Chengdu, Sichuan, PR China
| | - Kunjie Wang
- Department of Urology, Institute of Urology (Laboratory of Reconstructive Urology), West China Hospital, Sichuan University, Chengdu, Sichuan, PR China.
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28
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Shang X, Zhang X, Huang Y, Zhu Z, Zhang X, Liu S, Liu J, Tang S, Wang W, Yu H, Ge Z, He M. Temporal trajectories of important diseases in the life course and premature mortality in the UK Biobank. BMC Med 2022; 20:185. [PMID: 35619136 PMCID: PMC9137080 DOI: 10.1186/s12916-022-02384-3] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/02/2021] [Accepted: 04/25/2022] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Little is known regarding life-course trajectories of important diseases. We aimed to identify diseases that were strongly associated with mortality and test temporal trajectories of these diseases before mortality. METHODS Our analysis was based on UK Biobank. Diseases were identified using questionnaires, nurses' interviews, or inpatient data. Mortality register data were used to identify mortality up to January 2021. The association between 60 individual diseases at baseline and in the life course and incident mortality was examined using Cox proportional regression models. Those diseases with great contribution to mortality were identified and disease trajectories in life course were then derived. RESULTS During a median follow-up of 11.8 years, 31,373 individuals (median age at death (interquartile range): 70.7 (65.3-74.8) years, 59.4% male) died of all-cause mortality (with complete data on diagnosis date of disease), with 16,237 dying with cancer and 6702 with cardiovascular disease (CVD). We identified 37 diseases including cancers and heart diseases that were associated with an increased risk of mortality independent of other diseases (hazard ratio ranged from 1.09 to 7.77). Among those who died during follow-up, 2.2% did not have a diagnosis of any disease of interest and 90.1% were diagnosed with two or more diseases in their life course. Individuals who were diagnosed with more diseases in their life course were more likely to have longer longevity. Cancer was more likely to be diagnosed following hypertension, hypercholesterolemia, CVD, or digestive disorders and more likely to be diagnosed ahead of CVD, chronic kidney disease (CKD), or digestive disorders. CVD was more likely to be diagnosed following hypertension, hypercholesterolemia, or digestive disorders and more likely to be diagnosed ahead of cancer or CKD. Hypertension was more likely to precede other diseases, and CKD was more likely to be diagnosed as the last disease before more mortality. CONCLUSIONS There are significant interplays between cancer and CVD for mortality. Cancer and CVD were frequently clustered with hypertension, CKD, and digestive disorders with CKD highly being diagnosed as the last disease in the life course. Our findings underline the importance of health checks among middle-aged adults for the prevention of premature mortality.
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Affiliation(s)
- Xianwen Shang
- Department of Ophthalmology, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangdong Eye Institute, Guangzhou, 510080, China. .,Guangdong Cardiovascular Institute, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, 510080, China. .,Centre for Eye Research Australia, Melbourne, VIC, 3002, Australia.
| | - Xueli Zhang
- Department of Ophthalmology, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangdong Eye Institute, Guangzhou, 510080, China
| | - Yu Huang
- Department of Ophthalmology, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangdong Eye Institute, Guangzhou, 510080, China.,Guangdong Cardiovascular Institute, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, 510080, China
| | - Zhuoting Zhu
- Department of Ophthalmology, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangdong Eye Institute, Guangzhou, 510080, China.,Guangdong Cardiovascular Institute, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, 510080, China.,Centre for Eye Research Australia, Melbourne, VIC, 3002, Australia
| | - Xiayin Zhang
- Department of Ophthalmology, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangdong Eye Institute, Guangzhou, 510080, China.,Guangdong Cardiovascular Institute, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, 510080, China
| | - Shunming Liu
- Department of Ophthalmology, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangdong Eye Institute, Guangzhou, 510080, China
| | - Jiahao Liu
- Melbourne School of Population and Global Health, University of Melbourne, Melbourne, VIC, 3010, Australia
| | - Shulin Tang
- Department of Ophthalmology, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangdong Eye Institute, Guangzhou, 510080, China
| | - Wei Wang
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, 510060, China
| | - Honghua Yu
- Department of Ophthalmology, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangdong Eye Institute, Guangzhou, 510080, China
| | - Zongyuan Ge
- Monash e-Research Center, Faculty of Engineering, Airdoc Research, Nvidia AI Technology Research Center, Monash University, Melbourne, VIC, 3800, Australia
| | - Mingguang He
- Department of Ophthalmology, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangdong Eye Institute, Guangzhou, 510080, China. .,Centre for Eye Research Australia, Melbourne, VIC, 3002, Australia. .,State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, 510060, China.
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Yang H, Pawitan Y, Fang F, Czene K, Ye W. Biomarkers and Disease Trajectories Influencing Women's Health: Results from the UK Biobank Cohort. PHENOMICS (CHAM, SWITZERLAND) 2022; 2:184-193. [PMID: 35578620 PMCID: PMC9096057 DOI: 10.1007/s43657-022-00054-1] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/03/2021] [Revised: 04/13/2022] [Accepted: 04/15/2022] [Indexed: 02/02/2023]
Abstract
Women's health is important for society. Despite the known biological and sex-related factors influencing the risk of diseases among women, the network of the full spectrum of diseases in women is underexplored. This study aimed to systematically examine the women-specific temporal pattern (trajectory) of the disease network, including the role of baseline physical examination indexes, and blood and urine biomarkers. In the UK Biobank study, 502,650 participants entered the cohort from 2006 to 2010, and were followed up until 2019 to identify disease incidence via linkage to the patient registers. For those diseases with increased risk among women, conditional logistic regression models were used to estimate odds ratios (ORs), and the binomial test of direction was further used to build disease trajectories. Among 301 diseases, 82 diseases in women had ORs > 1.2 and p < 0.00017 when compared to men, involving mainly diseases in the endocrine, skeletal and digestive systems. Diseases with the highest ORs included breast diseases, osteoporosis, hyperthyroidism, and deformity of the toes. The biomarker and disease trajectories suggested estradiol as a risk predictor for breast cancer, while a high percentage of reticulocyte, body mass index and waist circumference were associated with an increased risk of upper-limb neuropathy. In addition, the risk of cholelithiasis was increased in women diagnosed with dyspepsia and diaphragmatic hernia. In conclusion, women are at an increased risk of endocrine, skeletal and digestive diseases. The biomarker and disease trajectories in women suggested key pathways to a range of adverse outcomes downstream, which may shed light on promising targets for early detection and prevention of these diseases. Supplementary Information The online version contains supplementary material available at 10.1007/s43657-022-00054-1.
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Affiliation(s)
- Haomin Yang
- Department of Epidemiology and Health Statistics, School of Public Health and Key Laboratory of Ministry of Education for Gastrointestinal Cancer, Fujian Medical University, Xue Yuan Road 1, University Town, Fuzhou, 350122 China ,Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, 17177 Stockholm, Sweden
| | - Yudi Pawitan
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, 17177 Stockholm, Sweden
| | - Fang Fang
- Institute of Environmental Medicine, Karolinska Institutet, 17177 Stockholm, Sweden
| | - Kamila Czene
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, 17177 Stockholm, Sweden
| | - Weimin Ye
- Department of Epidemiology and Health Statistics, School of Public Health and Key Laboratory of Ministry of Education for Gastrointestinal Cancer, Fujian Medical University, Xue Yuan Road 1, University Town, Fuzhou, 350122 China ,Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, 17177 Stockholm, Sweden
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Künnapuu K, Ioannou S, Ligi K, Kolde R, Laur S, Vilo J, Rijnbeek PR, Reisberg S. Trajectories: a framework for detecting temporal clinical event sequences from health data standardized to the Observational Medical Outcomes Partnership (OMOP) Common Data Model. JAMIA Open 2022; 5:ooac021. [PMID: 35571357 PMCID: PMC9097714 DOI: 10.1093/jamiaopen/ooac021] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2021] [Revised: 02/16/2022] [Accepted: 03/05/2022] [Indexed: 11/14/2022] Open
Abstract
Objective To develop a framework for identifying temporal clinical event trajectories from Observational Medical Outcomes Partnership-formatted observational healthcare data. Materials and Methods A 4-step framework based on significant temporal event pair detection is described and implemented as an open-source R package. It is used on a population-based Estonian dataset to first replicate a large Danish population-based study and second, to conduct a disease trajectory detection study for type 2 diabetes patients in the Estonian and Dutch databases as an example. Results As a proof of concept, we apply the methods in the Estonian database and provide a detailed breakdown of our findings. All Estonian population-based event pairs are shown. We compare the event pairs identified from Estonia to Danish and Dutch data and discuss the causes of the differences. The overlap in the results was only 2.4%, which highlights the need for running similar studies in different populations. Conclusions For the first time, there is a complete software package for detecting disease trajectories in health data.
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Affiliation(s)
| | - Solomon Ioannou
- Department of Medical Informatics, Erasmus University Medical
Center, Rotterdam, the Netherlands
| | - Kadri Ligi
- STACC, Tartu, Estonia
- Institute of Computer Science, University of Tartu, Tartu,
Estonia
| | - Raivo Kolde
- Institute of Computer Science, University of Tartu, Tartu,
Estonia
| | - Sven Laur
- STACC, Tartu, Estonia
- Institute of Computer Science, University of Tartu, Tartu,
Estonia
| | - Jaak Vilo
- STACC, Tartu, Estonia
- Institute of Computer Science, University of Tartu, Tartu,
Estonia
- Quretec, Tartu, Estonia
| | - Peter R Rijnbeek
- Department of Medical Informatics, Erasmus University Medical
Center, Rotterdam, the Netherlands
| | - Sulev Reisberg
- STACC, Tartu, Estonia
- Institute of Computer Science, University of Tartu, Tartu,
Estonia
- Quretec, Tartu, Estonia
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31
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Recommendations of the treatment-resistant depression expert center network for promoting tobacco smoking cessation based on the results from the real-world FACE-TRD national cohort. Prog Neuropsychopharmacol Biol Psychiatry 2022; 114:110479. [PMID: 34826559 DOI: 10.1016/j.pnpbp.2021.110479] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/12/2021] [Revised: 10/29/2021] [Accepted: 11/21/2021] [Indexed: 12/14/2022]
Abstract
BACKGROUND Tobacco smoking has been associated with suicide, impulsivity and depression in non-clinical populations with differences across sexes. OBJECTIVE To determine the role of tobacco smoking in Treatment-Resistant Depression (TRD) according to sex in a precision-medicine approach. METHOD The FACE-TRD cohort is a national cohort of TRD patients recruited in 13 resistant depression expert centers between 2014 and 2021 and followed-up at 6 months. A standardized one-day long comprehensive battery was carried out, including trained-clinician and patient-reported outcomes, and patients were reevaluated at 6 months on their smoking and psychiatric hospitalization outcomes. RESULTS 355 TRD participants were included (222 women). The smoking rate was much higher in TRD women compared to the French general population (34% vs 24%) while it was comparable for men (approximately 29%). In multivariate analyses, compared to non-smoking women, female smokers had significantly increased number of lifetime psychiatric hospitalizations (standardized beta B = 0.232, p = 0.014) and electro-convulsive therapy (adjusted odds ratio (aOR) = 2.748, p = 0.005), increased suicidal ideations (aOR = 4.047, p = 0.031), history of suicide attempt (aOR = 1.994, p = 0.033), and increased impulsivity (B = 0.210, p = 0.006) and were more frequently treated by benzodiazepines (aOR = 1.848, p = 0.035) and third- or fourth-line TRD treatments (antipsychotics aOR = 2.270, p = 0.006, mood stabilizers aOR = 2.067 p = 0.044). Tobacco smoking at baseline was predictive of psychiatric hospitalization within 6 months in persistent smoking women (aOR = 2.636, p = 0.031). These results were not replicated in men, for whom tobacco smoking was only associated with increased clinician-rated and self-reported depressive symptoms (respectively B = 0.207, p = 0.022 and B = 0.184, p = 0.048). The smoking cessation rate at 6 months was higher in women than in men (12% vs. 7%). No patient was administered nicotine substitute or varenicline at the two timepoints. INTERPRETATION Combining these results and those of the literature, we recommend that active tobacco cessation should be promoted in TRD to improve depression, suicide and impulsivity especially in women. Female smokers appear as a specific population with heavier mental health outcomes that should be specifically addressed.
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Wang L, Qiu H, Luo L, Zhou L. Age- and Sex-Specific Differences in Multimorbidity Patterns and Temporal Trends on Assessing Hospital Discharge Records in Southwest China: Network-Based Study. J Med Internet Res 2022; 24:e27146. [PMID: 35212632 PMCID: PMC8917436 DOI: 10.2196/27146] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2021] [Revised: 05/06/2021] [Accepted: 01/12/2022] [Indexed: 02/06/2023] Open
Abstract
Background Multimorbidity represents a global health challenge, which requires a more global understanding of multimorbidity patterns and trends. However, the majority of studies completed to date have often relied on self-reported conditions, and a simultaneous assessment of the entire spectrum of chronic disease co-occurrence, especially in developing regions, has not yet been performed. Objective We attempted to provide a multidimensional approach to understand the full spectrum of chronic disease co-occurrence among general inpatients in southwest China, in order to investigate multimorbidity patterns and temporal trends, and assess their age and sex differences. Methods We conducted a retrospective cohort analysis based on 8.8 million hospital discharge records of about 5.0 million individuals of all ages from 2015 to 2019 in a megacity in southwest China. We examined all chronic diagnoses using the ICD-10 (International Classification of Diseases, 10th revision) codes at 3 digits and focused on chronic diseases with ≥1% prevalence for each of the age and sex strata, which resulted in a total of 149 and 145 chronic diseases in males and females, respectively. We constructed multimorbidity networks in the general population based on sex and age, and used the cosine index to measure the co-occurrence of chronic diseases. Then, we divided the networks into communities and assessed their temporal trends. Results The results showed complex interactions among chronic diseases, with more intensive connections among males and inpatients ≥40 years old. A total of 9 chronic diseases were simultaneously classified as central diseases, hubs, and bursts in the multimorbidity networks. Among them, 5 diseases were common to both males and females, including hypertension, chronic ischemic heart disease, cerebral infarction, other cerebrovascular diseases, and atherosclerosis. The earliest leaps (degree leaps ≥6) appeared at a disorder of glycoprotein metabolism that happened at 25-29 years in males, about 15 years earlier than in females. The number of chronic diseases in the community increased over time, but the new entrants did not replace the root of the community. Conclusions Our multimorbidity network analysis identified specific differences in the co-occurrence of chronic diagnoses by sex and age, which could help in the design of clinical interventions for inpatient multimorbidity.
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Affiliation(s)
- Liya Wang
- Big Data Research Center, University of Electronic Science and Technology of China, Chengdu, China
| | - Hang Qiu
- Big Data Research Center, University of Electronic Science and Technology of China, Chengdu, China.,School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, China
| | - Li Luo
- Business School, Sichuan University, Chengdu, China
| | - Li Zhou
- Health Information Center of Sichuan Province, Chengdu, China
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