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Wang Z, Yuen AS, Wong KH, Chan AY, Coghill D, Simonoff E, Lau WC, Wong IC, Park S, Man KK. Association between prenatal antipsychotic exposure and the risk of attention-deficit/hyperactivity disorder and autism spectrum disorder: a systematic review and meta-analysis. Neurosci Biobehav Rev 2024; 160:105635. [PMID: 38499117 DOI: 10.1016/j.neubiorev.2024.105635] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2023] [Revised: 03/14/2024] [Accepted: 03/15/2024] [Indexed: 03/20/2024]
Abstract
The paucity of evidence regarding the safety of gestational antipsychotic exposure has led to treatment discontinuation in pregnant women with severe mental health conditions. This systematic review and meta-analysis aimed to summarise the current evidence on the association between gestational antipsychotic exposure and attention-deficit/hyperactivity disorder (ADHD) and autism spectrum disorder (ASD) in children (Study protocol registered in PROSPERO:CRD42022311354). Five studies included in our meta-analysis with around 8.6 million pregnancy episodes in nine different countries/regions. Results from our meta-analysis indicate that the heightened risks of ASD and ADHD in children gestationally exposed to antipsychotics appear to be attributable to maternal characteristics, rather than having a causal relationship with the antipsychotic exposure during pregnancy. The results underscore the importance of meticulously monitoring the neurodevelopment of children born to mothers with mental illnesses, which can facilitate early interventions and provide requisite support.
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Affiliation(s)
- Zixuan Wang
- Department of Non-Communicable Disease Epidemiology, Faculty of Epidemiology and Population Health, London School of Hygiene & Tropical Medicine, United Kingdom; Laboratory of Data Discovery for Health (D24H), Hong Kong Science Park, Hong Kong Special Administrative Region China
| | - Andrew Sc Yuen
- Research Department of Practice and Policy, UCL School of Pharmacy, London, United Kingdom
| | - Kirstie Htw Wong
- Research Department of Practice and Policy, UCL School of Pharmacy, London, United Kingdom; Department of Paediatrics and Adolescent Medicine, Li Ka Shing Faculty of Medicine, University of Hong Kong, Hong Kong Special Administrative Region China
| | - Adrienne Yl Chan
- Laboratory of Data Discovery for Health (D24H), Hong Kong Science Park, Hong Kong Special Administrative Region China; Research Department of Practice and Policy, UCL School of Pharmacy, London, United Kingdom; Department of Pharmacology and Pharmacy, Li Ka Shing Faculty of Medicine, University of Hong Kong, Hong Kong Special Administrative Region China; Groningen Research Institute of Pharmacy, Unit of PharmacoTherapy, -Epidemiology and -Economics, University of Groningen, Groningen, the Netherlands
| | - David Coghill
- Department of Paediatrics and Psychiatry, Faculty of Medicine, Dentistry and Health Sciences, University of Melbourne, Melbourne, Australia; Murdoch Children's Research Institute, Melbourne, Australia
| | - Emily Simonoff
- Department of Child and Adolescent Psychiatry, King's College London, Institute of Psychiatry, Psychology and Neuroscience, London, United Kingdom
| | - Wallis Cy Lau
- Laboratory of Data Discovery for Health (D24H), Hong Kong Science Park, Hong Kong Special Administrative Region China; Research Department of Practice and Policy, UCL School of Pharmacy, London, United Kingdom; Department of Pharmacology and Pharmacy, Li Ka Shing Faculty of Medicine, University of Hong Kong, Hong Kong Special Administrative Region China; Centre for Medicines Optimisation Research and Education, University College London Hospitals NHS Foundation Trust, London, United Kingdom
| | - Ian Ck Wong
- Laboratory of Data Discovery for Health (D24H), Hong Kong Science Park, Hong Kong Special Administrative Region China; Research Department of Practice and Policy, UCL School of Pharmacy, London, United Kingdom; Department of Pharmacology and Pharmacy, Li Ka Shing Faculty of Medicine, University of Hong Kong, Hong Kong Special Administrative Region China; Aston Pharmacy School, Aston University, Birmingham, United Kingdom; Centre for Medicines Optimisation Research and Education, University College London Hospitals NHS Foundation Trust, London, United Kingdom
| | - Sohee Park
- Laboratory of Data Discovery for Health (D24H), Hong Kong Science Park, Hong Kong Special Administrative Region China; Research Department of Practice and Policy, UCL School of Pharmacy, London, United Kingdom.
| | - Kenneth Kc Man
- Laboratory of Data Discovery for Health (D24H), Hong Kong Science Park, Hong Kong Special Administrative Region China; Research Department of Practice and Policy, UCL School of Pharmacy, London, United Kingdom; Department of Pharmacology and Pharmacy, Li Ka Shing Faculty of Medicine, University of Hong Kong, Hong Kong Special Administrative Region China; Centre for Medicines Optimisation Research and Education, University College London Hospitals NHS Foundation Trust, London, United Kingdom.
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Khera R, Aminorroaya A, Dhingra LS, Thangaraj PM, Camargos AP, Bu F, Ding X, Nishimura A, Anand TV, Arshad F, Blacketer C, Chai Y, Chattopadhyay S, Cook M, Dorr DA, Duarte-Salles T, DuVall SL, Falconer T, French TE, Hanchrow EE, Kaur G, Lau WC, Li J, Li K, Liu Y, Lu Y, Man KK, Matheny ME, Mathioudakis N, McLeggon JA, McLemore MF, Minty E, Morales DR, Nagy P, Ostropolets A, Pistillo A, Phan TP, Pratt N, Reyes C, Richter L, Ross J, Ruan E, Seager SL, Simon KR, Viernes B, Yang J, Yin C, You SC, Zhou JJ, Ryan PB, Schuemie MJ, Krumholz HM, Hripcsak G, Suchard MA. Comparative Effectiveness of Second-line Antihyperglycemic Agents for Cardiovascular Outcomes: A Large-scale, Multinational, Federated Analysis of the LEGEND-T2DM Study. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.02.05.24302354. [PMID: 38370787 PMCID: PMC10871374 DOI: 10.1101/2024.02.05.24302354] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/20/2024]
Abstract
Background SGLT2 inhibitors (SGLT2is) and GLP-1 receptor agonists (GLP1-RAs) reduce major adverse cardiovascular events (MACE) in patients with type 2 diabetes mellitus (T2DM). However, their effectiveness relative to each other and other second-line antihyperglycemic agents is unknown, without any major ongoing head-to-head trials. Methods Across the LEGEND-T2DM network, we included ten federated international data sources, spanning 1992-2021. We identified 1,492,855 patients with T2DM and established cardiovascular disease (CVD) on metformin monotherapy who initiated one of four second-line agents (SGLT2is, GLP1-RAs, dipeptidyl peptidase 4 inhibitor [DPP4is], sulfonylureas [SUs]). We used large-scale propensity score models to conduct an active comparator, target trial emulation for pairwise comparisons. After evaluating empirical equipoise and population generalizability, we fit on-treatment Cox proportional hazard models for 3-point MACE (myocardial infarction, stroke, death) and 4-point MACE (3-point MACE + heart failure hospitalization) risk, and combined hazard ratio (HR) estimates in a random-effects meta-analysis. Findings Across cohorts, 16·4%, 8·3%, 27·7%, and 47·6% of individuals with T2DM initiated SGLT2is, GLP1-RAs, DPP4is, and SUs, respectively. Over 5·2 million patient-years of follow-up and 489 million patient-days of time at-risk, there were 25,982 3-point MACE and 41,447 4-point MACE events. SGLT2is and GLP1-RAs were associated with a lower risk for 3-point MACE compared with DPP4is (HR 0·89 [95% CI, 0·79-1·00] and 0·83 [0·70-0·98]), and SUs (HR 0·76 [0·65-0·89] and 0·71 [0·59-0·86]). DPP4is were associated with a lower 3-point MACE risk versus SUs (HR 0·87 [0·79-0·95]). The pattern was consistent for 4-point MACE for the comparisons above. There were no significant differences between SGLT2is and GLP1-RAs for 3-point or 4-point MACE (HR 1·06 [0·96-1·17] and 1·05 [0·97-1·13]). Interpretation In patients with T2DM and established CVD, we found comparable cardiovascular risk reduction with SGLT2is and GLP1-RAs, with both agents more effective than DPP4is, which in turn were more effective than SUs. These findings suggest that the use of GLP1-RAs and SGLT2is should be prioritized as second-line agents in those with established CVD. Funding National Institutes of Health, United States Department of Veterans Affairs.
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Affiliation(s)
- Rohan Khera
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale University, New Haven, CT, 06510, USA
- Center for Outcomes Research and Evaluation (CORE), Yale New Haven Hospital, New Haven, CT, 06510, USA
- Section of Health Informatics, Department of Biostatistics, Yale School of Public Health, New Haven, CT, 06520, USA
| | - Arya Aminorroaya
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale University, New Haven, CT, 06510, USA
| | - Lovedeep Singh Dhingra
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale University, New Haven, CT, 06510, USA
| | - Phyllis M Thangaraj
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale University, New Haven, CT, 06510, USA
| | - Aline Pedroso Camargos
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale University, New Haven, CT, 06510, USA
| | - Fan Bu
- Department of Biostatistics, University of Michigan - Ann Arbor, Ann Arbor, MI, 48105, USA
| | - Xiyu Ding
- Department of Biostatistics, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, 21205, USA
| | - Akihiko Nishimura
- Department of Biostatistics, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, 21205, USA
| | - Tara V Anand
- Department of Biomedical Informatics, Columbia University Irving Medical Center, New York, NY, 10027, USA
| | - Faaizah Arshad
- Department of Biostatistics, Fielding School of Public Health, University of California, Los Angeles, Los Angeles, CA, 90095, USA
| | - Clair Blacketer
- Observational Health Data Analytics, Janssen Research and Development, LLC, Titusville, NJ, 8560, USA
| | - Yi Chai
- Department of Pharmacology and Pharmacy, LKS Faculty of Medicine, The University of Hong Kong
| | - Shounak Chattopadhyay
- Department of Biostatistics, Fielding School of Public Health, University of California, Los Angeles, Los Angeles, CA, 90095, USA
| | - Michael Cook
- Department of Biostatistics, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, 21205, USA
| | - David A Dorr
- Department of Medical Informatics and Clinical Epidemiology, Oregon Health & Science University, Portland, OR, USA
| | - Talita Duarte-Salles
- Fundació Institut Universitari per a la recerca a l'Atenció Primària de Salut Jordi Gol i Gurina (IDIAPJGol), Barcelona, 8007, Spain
- Department of Medical Informatics, Erasmus University Medical Center, Rotterdam, The Netherlands
| | - Scott L DuVall
- Veterans Affairs Informatics and Computing Infrastructure, United States Department of Veterans Affairs, Salt Lake City, UT, USA
- Department of Internal Medicine, University of Utah School of Medicine, Salt Lake City, UT, USA
| | - Thomas Falconer
- Department of Biomedical Informatics, Columbia University Irving Medical Center, New York, NY, 10027, USA
| | - Tina E French
- Tennessee Valley Healthcare System, Veterans Affairs Medical Center, Nashville, TN, USA
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Elizabeth E Hanchrow
- Tennessee Valley Healthcare System, Veterans Affairs Medical Center, Nashville, TN, USA
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Guneet Kaur
- Division of Population Health and Genomics, School of Medicine, University of Dundee, Dundee, DD1 9SY, United Kingdom
| | - Wallis Cy Lau
- Research Department of Practice and Policy, School of Pharmacy, University College London, London, WC1H 9JP, United Kingdom
- Centre for Medicines Optimisation Research and Education, University College London Hospitals NHS Foundation Trust, London, United Kingdom
- Centre for Safe Medication Practice and Research, Department of Pharmacology and Pharmacy, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, Hong Kong
- Laboratory of Data Discovery for Health (D24H), Hong Kong Science Park, Hong Kong, Hong Kong
| | - Jing Li
- Data Transformation, Analytics, and Artificial Intelligence, Real World Solutions, IQVIA, Durham, NC, USA
| | - Kelly Li
- Department of Biostatistics, Fielding School of Public Health, University of California, Los Angeles, Los Angeles, CA, 90095, USA
| | - Yuntian Liu
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale University, New Haven, CT, 06510, USA
- Center for Outcomes Research and Evaluation (CORE), Yale New Haven Hospital, New Haven, CT, 06510, USA
| | - Yuan Lu
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale University, New Haven, CT, 06510, USA
| | - Kenneth Kc Man
- Research Department of Practice and Policy, School of Pharmacy, University College London, London, WC1H 9JP, United Kingdom
- Centre for Medicines Optimisation Research and Education, University College London Hospitals NHS Foundation Trust, London, United Kingdom
- Centre for Safe Medication Practice and Research, Department of Pharmacology and Pharmacy, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, Hong Kong
- Laboratory of Data Discovery for Health (D24H), Hong Kong Science Park, Hong Kong, Hong Kong
| | - Michael E Matheny
- Tennessee Valley Healthcare System, Veterans Affairs Medical Center, Nashville, TN, USA
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Nestoras Mathioudakis
- Division of Endocrinology, Diabetes, and Metabolism, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Jody-Ann McLeggon
- Department of Biomedical Informatics, Columbia University Irving Medical Center, New York, NY, 10027, USA
| | - Michael F McLemore
- Tennessee Valley Healthcare System, Veterans Affairs Medical Center, Nashville, TN, USA
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Evan Minty
- Faculty of Medicine, O'Brien Institute for Public Health, University of Calgary, Calgary, AB, T2N4N1, Canada
| | - Daniel R Morales
- Division of Population Health and Genomics, School of Medicine, University of Dundee, Dundee, DD1 9SY, United Kingdom
| | - Paul Nagy
- Division of Endocrinology, Diabetes, and Metabolism, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Anna Ostropolets
- Observational Health Data Analytics, Janssen Research and Development, LLC, Titusville, NJ, 8560, USA
| | - Andrea Pistillo
- Fundació Institut Universitari per a la recerca a l'Atenció Primària de Salut Jordi Gol i Gurina (IDIAPJGol), Barcelona, 8007, Spain
| | | | - Nicole Pratt
- Quality Use of Medicines and Pharmacy Research Centre, UniSA Clinical and Health Sciences, University of South Australia, Adelaide, Australia
| | - Carlen Reyes
- Fundació Institut Universitari per a la recerca a l'Atenció Primària de Salut Jordi Gol i Gurina (IDIAPJGol), Barcelona, 8007, Spain
| | - Lauren Richter
- Department of Biomedical Informatics, Columbia University Irving Medical Center, New York, NY, 10027, USA
| | - Joseph Ross
- Section of General Medicine and National Clinician Scholars Program, Department of Internal Medicine, Yale School of Medicine, New Haven, CT, USA
- Center for Outcomes Research and Evaluation (CORE), Yale New Haven Hospital, New Haven, CT, 06510, USA
| | - Elise Ruan
- Department of Biomedical Informatics, Columbia University Irving Medical Center, New York, NY, 10027, USA
| | - Sarah L Seager
- Data Transformation, Analytics, and Artificial Intelligence, Real World Solutions, IQVIA, London, UK
| | - Katherine R Simon
- Tennessee Valley Healthcare System, Veterans Affairs Medical Center, Nashville, TN, USA
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Benjamin Viernes
- Veterans Affairs Informatics and Computing Infrastructure, United States Department of Veterans Affairs, Salt Lake City, UT, USA
- Department of Internal Medicine, University of Utah School of Medicine, Salt Lake City, UT, USA
| | - Jianxiao Yang
- Department of Computational Medicine, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, 90095, USA
| | - Can Yin
- Data Transformation, Analytics, and Artificial Intelligence, Real World Solutions, IQVIA, Shanghai, China
| | - Seng Chan You
- Department of Biomedical Systems Informatics, Yonsei University College of Medicine, Seoul, South Korea
- Institute for Innovation in Digital Healthcare, Yonsei University College of Medicine, Seoul, South Korea
| | - Jin J Zhou
- Department of Biostatistics, Fielding School of Public Health, University of California, Los Angeles, Los Angeles, CA, 90095, USA
- Department of Medicine, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, 90024, USA
| | - Patrick B Ryan
- Department of Biomedical Informatics, Columbia University Irving Medical Center, New York, NY, 10027, USA
| | - Martijn J Schuemie
- Epidemiology, Office of the Chief Medical Officer, Johnson & Johnson, Titusville, NJ, 8560, USA
| | - Harlan M Krumholz
- Center for Outcomes Research and Evaluation (CORE), Yale New Haven Hospital, New Haven, CT, 06510, USA
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale University, New Haven, CT, 06510, USA
- Section of Cardiovascular Medicine, Department of Health Policy and Management, Yale School of Public Health, New Haven, CT, 06510, USA
| | - George Hripcsak
- Department of Biomedical Informatics, Columbia University Irving Medical Center, New York, NY, 10027, USA
| | - Marc A Suchard
- Department of Biostatistics, Fielding School of Public Health, University of California, Los Angeles, Los Angeles, CA, 90095, USA
- Department of Biomathematics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
- Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
- Veterans Affairs Informatics and Computing Infrastructure, United States Department of Veterans Affairs, Salt Lake City, UT, USA
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Sing CW, Kiel DP, Hubbard RB, Lau WC, Li GH, Kung AW, Wong IC, Cheung CL. Nitrogen-Containing Bisphosphonates Are Associated With Reduced Risk of Pneumonia in Patients With Hip Fracture. J Bone Miner Res 2020; 35:1676-1684. [PMID: 32488902 DOI: 10.1002/jbmr.4030] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/14/2019] [Revised: 03/10/2020] [Accepted: 03/31/2020] [Indexed: 01/12/2023]
Abstract
The objective of this work was to study the risk of pneumonia and pneumonia mortality among patients receiving nitrogen-containing bisphosphonates (N-BPs), non-N-BP anti-osteoporosis medications, and no anti-osteoporosis medications after hip fracture. We studied a historical cohort using a population-wide database. Patients with first hip fracture during 2005-2015 were identified and matched by time-dependent propensity score. The cohort was followed until December 31, 2016, to capture any pneumonia and pneumonia mortality. Hazard ratios (HRs) and 95% confidence intervals (CIs) were estimated using Cox-proportional hazards regression. Absolute risk difference (ARD) and number needed to treat (NNT) were calculated. We identified 54,047 patients with hip fracture. Of these, 4041 patients who received N-BPs and 11,802 without anti-osteoporosis medication were propensity score-matched. N-BPs were associated with a significantly lower risk of pneumonia compared with no treatment (6.9 versus 9.0 per 100 person-years; HR 0.76; 95% CI, 0.70 to 0.83), resulting in an ARD of 0.02 and NNT of 46. A similar association was observed with pneumonia mortality (HR 0.65; 95% CI, 0.56 to 0.75). When N-BPs were compared with non-N-BP anti-osteoporosis medications, the association remained significant. N-BPs were associated with lower risks of pneumonia and pneumonia mortality. Randomized controlled trials are now required to determine whether N-BPs, non-vaccine-based medications, can reduce pneumonia incidence in high risk groups. © 2020 American Society for Bone and Mineral Research.
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Affiliation(s)
- Chor-Wing Sing
- Department of Pharmacology and Pharmacy, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Pokfulam, Hong Kong
| | - Douglas P Kiel
- Hinda and Arthur Marcus Institute for Aging Research Hebrew SeniorLife, Boston, MA, USA.,Department of Medicine, Beth Israel Deaconess Medical Center, Boston, MA, USA.,Harvard Medical School, Harvard University, Boston, MA, USA
| | - Richard B Hubbard
- Division of Epidemiology and Public Health, School of Medicine, University of Nottingham, Nottingham, UK
| | - Wallis Cy Lau
- Department of Pharmacology and Pharmacy, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Pokfulam, Hong Kong.,Research Department of Practice and Policy, University College London (UCL) School of Pharmacy, London, UK
| | - Gloria Hy Li
- Department of Pharmacology and Pharmacy, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Pokfulam, Hong Kong
| | - Annie Wc Kung
- Department of Medicine, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Pokfulam, Hong Kong
| | - Ian Ck Wong
- Department of Pharmacology and Pharmacy, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Pokfulam, Hong Kong.,Research Department of Practice and Policy, University College London (UCL) School of Pharmacy, London, UK
| | - Ching-Lung Cheung
- Department of Pharmacology and Pharmacy, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Pokfulam, Hong Kong.,Department of Medicine, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Pokfulam, Hong Kong.,Centre for Genomic Sciences, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Pokfulam, Hong Kong
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