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Feng Z, Chen Y, Guo Y, Lyu J. Deciphering the environmental chemical basis of muscle quality decline by interpretable machine learning models. Am J Clin Nutr 2024; 120:407-418. [PMID: 38825185 DOI: 10.1016/j.ajcnut.2024.05.022] [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: 11/22/2023] [Revised: 05/07/2024] [Accepted: 05/28/2024] [Indexed: 06/04/2024] Open
Abstract
BACKGROUND Sarcopenia is known as a decline in skeletal muscle quality and function that is associated with age. Sarcopenia is linked to diverse health problems, including endocrine-related diseases. Environmental chemicals (ECs), a broad class of chemicals released from industry, may influence muscle quality decline. OBJECTIVES In this work, we aimed to simultaneously elucidate the associations between muscle quality decline and diverse EC exposures based on the data from the 2011-2012 and 2013-2014 survey cycles in the National Health and Nutrition Examination Survey (NHANES) project using machine learning models. METHODS Six machine learning models were trained based on the EC and non-EC exposures from NHANES to distinguish low from normal muscle quality index status. Different machine learning metrics were evaluated for these models. The Shapley additive explanations (SHAP) approach was used to provide explainability for machine learning models. RESULTS Random forest (RF) performed best on the independent testing data set. Based on the testing data set, ECs can independently predict the binary muscle quality status with good performance by RF (area under the receiver operating characteristic curve = 0.793; area under the precision-recall curve = 0.808). The SHAP ranked the importance of ECs for the RF model. As a result, several metals and chemicals in urine, including 3-phenoxybenzoic acid and cobalt, were more associated with the muscle quality decline. CONCLUSIONS Altogether, our analyses suggest that ECs can independently predict muscle quality decline with a good performance by RF, and the SHAP-identified ECs can be closely related to muscle quality decline and sarcopenia. Our analyses may provide valuable insights into ECs that may be the important basis of sarcopenia and endocrine-related diseases in United States populations.
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Affiliation(s)
- Zhen Feng
- Joint Centre of Translational Medicine, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, People's Republic of China; Joint Centre of Translational Medicine, Wenzhou Institute, University of Chinese Academy of Sciences, Wenzhou, Zhejiang, People's Republic of China; College of Information and Engineering, Wenzhou Medical University, Wenzhou, Zhejiang, People's Republic of China
| | - Ying'ao Chen
- Postgraduate Training Base Alliance of Wenzhou Medical University, Wenzhou, Zhejiang, People's Republic of China; Wenzhou Institute, University of Chinese Academy of Sciences, Wenzhou, Zhejiang, People's Republic of China
| | - Yuxin Guo
- College of Information and Engineering, Wenzhou Medical University, Wenzhou, Zhejiang, People's Republic of China
| | - Jie Lyu
- Joint Centre of Translational Medicine, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, People's Republic of China; Joint Centre of Translational Medicine, Wenzhou Institute, University of Chinese Academy of Sciences, Wenzhou, Zhejiang, People's Republic of China; Oujiang Laboratory (Zhejiang Lab for Regenerative Medicine, Vision and Brain Health), Wenzhou, Zhejiang, People's Republic of China.
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Al Abed AS, Allen TV, Ahmed NY, Sellami A, Sontani Y, Rawlinson EC, Marighetto A, Desmedt A, Dehorter N. Parvalbumin interneuron activity in autism underlies susceptibility to PTSD-like memory formation. iScience 2024; 27:109747. [PMID: 38741709 PMCID: PMC11089364 DOI: 10.1016/j.isci.2024.109747] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2023] [Revised: 02/13/2024] [Accepted: 04/11/2024] [Indexed: 05/16/2024] Open
Abstract
A rising concern in autism spectrum disorder (ASD) is the heightened sensitivity to trauma, the potential consequences of which have been overlooked, particularly upon the severity of the ASD traits. We first demonstrate a reciprocal relationship between ASD and post-traumatic stress disorder (PTSD) and reveal that exposure to a mildly stressful event induces PTSD-like memory in four mouse models of ASD. We also establish an unanticipated consequence of stress, as the formation of PTSD-like memory leads to the aggravation of core autistic traits. Such a susceptibility to developing PTSD-like memory in ASD stems from hyperactivation of the prefrontal cortex and altered fine-tuning of parvalbumin interneuron firing. Traumatic memory can be treated by recontextualization, reducing the deleterious effects on the core symptoms of ASD in the Cntnap2 KO mouse model. This study provides a neurobiological and psychological framework for future examination of the impact of PTSD-like memory in autism.
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Affiliation(s)
- Alice Shaam Al Abed
- Eccles Institute of Neuroscience, John Curtin School of Medical Research, The Australian National University, Canberra, ACT, Australia
- The Queensland Brain Institute, The University of Queensland, Brisbane, QLD, Australia
| | - Tiarne Vickie Allen
- Eccles Institute of Neuroscience, John Curtin School of Medical Research, The Australian National University, Canberra, ACT, Australia
| | - Noorya Yasmin Ahmed
- Eccles Institute of Neuroscience, John Curtin School of Medical Research, The Australian National University, Canberra, ACT, Australia
- The Queensland Brain Institute, The University of Queensland, Brisbane, QLD, Australia
| | - Azza Sellami
- Neurocentre Magendie, Physiopathologie de la plasticité neuronale, U1215, INSERM, F-33000 Bordeaux, France
- Université de Bordeaux, F-33000 Bordeaux, France
| | - Yovina Sontani
- Eccles Institute of Neuroscience, John Curtin School of Medical Research, The Australian National University, Canberra, ACT, Australia
| | - Elise Caitlin Rawlinson
- Eccles Institute of Neuroscience, John Curtin School of Medical Research, The Australian National University, Canberra, ACT, Australia
| | - Aline Marighetto
- Neurocentre Magendie, Physiopathologie de la plasticité neuronale, U1215, INSERM, F-33000 Bordeaux, France
- Université de Bordeaux, F-33000 Bordeaux, France
| | - Aline Desmedt
- Neurocentre Magendie, Physiopathologie de la plasticité neuronale, U1215, INSERM, F-33000 Bordeaux, France
- Université de Bordeaux, F-33000 Bordeaux, France
| | - Nathalie Dehorter
- Eccles Institute of Neuroscience, John Curtin School of Medical Research, The Australian National University, Canberra, ACT, Australia
- The Queensland Brain Institute, The University of Queensland, Brisbane, QLD, Australia
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Wang Y, Wei W, Ouyang R, Chen R, Wang T, Yuan X, Wang F, Hou H, Wu S. Novel multiclass classification machine learning approach for the early-stage classification of systemic autoimmune rheumatic diseases. Lupus Sci Med 2024; 11:e001125. [PMID: 38302133 PMCID: PMC10831448 DOI: 10.1136/lupus-2023-001125] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2023] [Accepted: 01/19/2024] [Indexed: 02/03/2024]
Abstract
OBJECTIVE Systemic autoimmune rheumatic diseases (SARDs) encompass a diverse group of complex conditions with overlapping clinical features, making accurate diagnosis challenging. This study aims to develop a multiclass machine learning (ML) model for early-stage SARDs classification using accessible laboratory indicators. METHODS A total of 925 SARDs patients were included, categorised into SLE, Sjögren's syndrome (SS) and inflammatory myositis (IM). Clinical characteristics and laboratory markers were collected and nine key indicators, including anti-dsDNA, anti-SS-A60, anti-Sm/nRNP, antichromatin, anti-dsDNA (indirect immunofluorescence assay), haemoglobin (Hb), platelet, neutrophil percentage and cytoplasmic patterns (AC-19, AC-20), were selected for model building. Various ML algorithms were used to construct a tripartite classification ML model. RESULTS Patients were divided into two cohorts, cohort 1 was used to construct a tripartite classification model. Among models assessed, the random forest (RF) model demonstrated superior performance in distinguishing SLE, IM and SS (with area under curve=0.953, 0.903 and 0.836; accuracy= 0.892, 0.869 and 0.857; sensitivity= 0.890, 0.868 and 0.795; specificity= 0.910, 0.836 and 0.748; positive predictive value=0.922, 0.727 and 0.663; and negative predictive value= 0.854, 0.915 and 0.879). The RF model excelled in classifying SLE (precision=0.930, recall=0.985, F1 score=0.957). For IM and SS, RF model outcomes were (precision=0.793, 0.950; recall=0.920, 0.679; F1 score=0.852, 0.792). Cohort 2 served as an external validation set, achieving an overall accuracy of 87.3%. Individual classification performances for SLE, SS and IM were excellent, with precision, recall and F1 scores specified. SHAP analysis highlighted significant contributions from antibody profiles. CONCLUSION This pioneering multiclass ML model, using basic laboratory indicators, enhances clinical feasibility and demonstrates promising potential for SARDs classification. The collaboration of clinical expertise and ML offers a nuanced approach to SARDs classification, with potential for enhanced patient care.
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Affiliation(s)
- Yun Wang
- Department of Laboratory Medicine, Tongji Hospital of Tongji Medical College of Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Wei Wei
- Department of Laboratory Medicine, Tongji Hospital of Tongji Medical College of Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Renren Ouyang
- Department of Laboratory Medicine, Tongji Hospital of Tongji Medical College of Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Rujia Chen
- Department of Laboratory Medicine, Tongji Hospital of Tongji Medical College of Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Ting Wang
- Department of Laboratory Medicine, Tongji Hospital of Tongji Medical College of Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Xu Yuan
- Department of Laboratory Medicine, Tongji Hospital of Tongji Medical College of Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Feng Wang
- Department of Laboratory Medicine, Tongji Hospital of Tongji Medical College of Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Hongyan Hou
- Department of Laboratory Medicine, Tongji Hospital of Tongji Medical College of Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Shiji Wu
- Department of Laboratory Medicine, Tongji Hospital of Tongji Medical College of Huazhong University of Science and Technology, Wuhan, Hubei, China
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Papini S, Iturralde E, Lu Y, Greene JD, Barreda F, Sterling SA, Liu VX. Development and validation of a machine learning model using electronic health records to predict trauma- and stressor-related psychiatric disorders after hospitalization with sepsis. Transl Psychiatry 2023; 13:400. [PMID: 38114475 PMCID: PMC10730505 DOI: 10.1038/s41398-023-02699-6] [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: 04/25/2023] [Revised: 11/17/2023] [Accepted: 11/29/2023] [Indexed: 12/21/2023] Open
Abstract
A significant minority of individuals develop trauma- and stressor-related disorders (TSRD) after surviving sepsis, a life-threatening immune response to infections. Accurate prediction of risk for TSRD can facilitate targeted early intervention strategies, but many existing models rely on research measures that are impractical to incorporate to standard emergency department workflows. To increase the feasibility of implementation, we developed models that predict TSRD in the year after survival from sepsis using only electronic health records from the hospitalization (n = 217,122 hospitalizations from 2012-2015). The optimal model was evaluated in a temporally independent prospective test sample (n = 128,783 hospitalizations from 2016-2017), where patients in the highest-risk decile accounted for nearly one-third of TSRD cases. Our approach demonstrates that risk for TSRD after sepsis can be stratified without additional assessment burden on clinicians and patients, which increases the likelihood of model implementation in hospital settings.
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Affiliation(s)
- Santiago Papini
- Division of Research, Kaiser Permanente Northern California, Oakland, CA, USA.
- Department of Psychology, University of Hawai'i at Mānoa, Honolulu, HI, USA.
| | - Esti Iturralde
- Division of Research, Kaiser Permanente Northern California, Oakland, CA, USA
| | - Yun Lu
- Division of Research, Kaiser Permanente Northern California, Oakland, CA, USA
| | - John D Greene
- Division of Research, Kaiser Permanente Northern California, Oakland, CA, USA
| | - Fernando Barreda
- Division of Research, Kaiser Permanente Northern California, Oakland, CA, USA
| | - Stacy A Sterling
- Division of Research, Kaiser Permanente Northern California, Oakland, CA, USA
| | - Vincent X Liu
- Division of Research, Kaiser Permanente Northern California, Oakland, CA, USA
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