1
|
Kilanowski A, Thiering E, Wang G, Kumar A, Kress S, Flexeder C, Bauer CP, Berdel D, von Berg A, Bergström A, Gappa M, Heinrich J, Herberth G, Koletzko S, Kull I, Melén E, Schikowski T, Peters A, Standl M. Allergic disease trajectories up to adolescence: Characteristics, early-life, and genetic determinants. Allergy 2023; 78:836-850. [PMID: 36069615 DOI: 10.1111/all.15511] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2022] [Revised: 07/27/2022] [Accepted: 08/13/2022] [Indexed: 11/27/2022]
Abstract
BACKGROUND Allergic diseases often develop jointly during early childhood but differ in timing of onset, remission, and progression. Their disease course over time is often difficult to predict and determinants are not well understood. OBJECTIVES We aimed to identify trajectories of allergic diseases up to adolescence and to investigate their association with early-life and genetic determinants and clinical characteristics. METHODS Longitudinal k-means clustering was used to derive trajectories of allergic diseases (asthma, atopic dermatitis, and rhinitis) in two German birth cohorts (GINIplus/LISA). Associations with early-life determinants, polygenic risk scores, food and aeroallergen sensitization, and lung function were estimated by multinomial models. The results were replicated in the independent Swedish BAMSE cohort. RESULTS Seven allergic disease trajectories were identified: "Intermittently allergic," "rhinitis," "early-resolving dermatitis," "mid-persisting dermatitis," "multimorbid," "persisting dermatitis plus rhinitis," and "early-transient asthma." Family history of allergies was more prevalent in all allergic disease trajectories compared the non-allergic controls with stronger effect sizes for clusters comprising more than one allergic disease (e.g., RRR = 5.0, 95% CI = [3.1-8.0] in the multimorbid versus 1.8 [1.4-2.4] in the mild intermittently allergic cluster). Specific polygenic risk scores for single allergic diseases were significantly associated with their relevant trajectories. The derived trajectories and their association with genetic effects and clinical characteristics showed similar results in BAMSE. CONCLUSION Seven robust allergic clusters were identified and showed associations with early life and genetic factors as well as clinical characteristics.
Collapse
Affiliation(s)
- Anna Kilanowski
- Institute of Epidemiology, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, Germany.,Institute for Medical Information Processing, Biometry, and Epidemiology, Pettenkofer School of Public Health, LMU Munich, Munich, Germany.,Division of Metabolic and Nutritional Medicine, Dr. von Hauner Children's Hospital, University of Munich Medical Center, Munich, Germany
| | - Elisabeth Thiering
- Institute of Epidemiology, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, Germany.,Division of Metabolic and Nutritional Medicine, Dr. von Hauner Children's Hospital, University of Munich Medical Center, Munich, Germany
| | - Gang Wang
- Department of Integrated Traditional Chinese and Western Medicine, West China Hospital, Sichuan University, Chengdu, Sichuan, China.,Department of Clinical Science and Education, Södersjukhuset, Karolinska Institutet, Stockholm, Sweden
| | - Ashish Kumar
- Department of Clinical Science and Education, Södersjukhuset, Karolinska Institutet, Stockholm, Sweden
| | - Sara Kress
- Leibniz Research Institute for Environmental Medicine, Düsseldorf, Germany.,Medical Research School Düsseldorf, Heinrich Heine University, Düsseldorf, Germany
| | - Claudia Flexeder
- Institute of Epidemiology, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, Germany.,German Center for Lung Research (DZL), Munich, Germany.,Institute and Clinic for Occupational, Social and Environmental Medicine, University Hospital, LMU Munich, Munich, Germany
| | - Carl-Peter Bauer
- Department of Pediatrics, Technical University of Munich, Munich, Germany
| | - Dietrich Berdel
- Research Institute, Department of Pediatrics, Marien-Hospital Wesel, Wesel, Germany
| | - Andrea von Berg
- Research Institute, Department of Pediatrics, Marien-Hospital Wesel, Wesel, Germany
| | - Anna Bergström
- Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden.,Centre for Occupational and Environmental Medicine, Stockholm, Sweden
| | - Monika Gappa
- Evangelisches Krankenhaus Düsseldorf, Children's Hospital, Düsseldorf, Germany
| | - Joachim Heinrich
- Institute and Clinic for Occupational, Social and Environmental Medicine, University Hospital, LMU Munich, Munich, Germany.,Allergy and Lung Health Unit, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, Australia
| | - Gunda Herberth
- Department of Environmental Immunology, Helmholtz Centre for Environmental Research-UFZ, Leipzig, Germany
| | - Sibylle Koletzko
- Department of Pediatrics, Dr. von Hauner Children's Hospital, University Hospital, LMU Munich, Munich, Germany.,Department of Pediatrics, Gastroenterology and Nutrition, School of Medicine Collegium Medicum, University of Warmia and Mazury, Olsztyn, Poland
| | - Inger Kull
- Department of Clinical Science and Education, Södersjukhuset, Karolinska Institutet, Stockholm, Sweden.,Södersjukhuset, Karolinska Institutet, Stockholm, Sweden.,Sachs Children's and Youth Hospital, Stockholm, Sweden
| | - Erik Melén
- Department of Clinical Science and Education, Södersjukhuset, Karolinska Institutet, Stockholm, Sweden
| | - Tamara Schikowski
- Leibniz Research Institute for Environmental Medicine, Düsseldorf, Germany
| | - Annette Peters
- Institute of Epidemiology, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, Germany.,Chair of Epidemiology, Ludwig-Maximilians University, Munich, Germany
| | - Marie Standl
- Institute of Epidemiology, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, Germany.,German Center for Lung Research (DZL), Munich, Germany
| |
Collapse
|
2
|
Salmanpour MR, Shamsaei M, Hajianfar G, Soltanian-Zadeh H, Rahmim A. Longitudinal clustering analysis and prediction of Parkinson's disease progression using radiomics and hybrid machine learning. Quant Imaging Med Surg 2022; 12:906-919. [PMID: 35111593 DOI: 10.21037/qims-21-425] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2021] [Accepted: 08/13/2021] [Indexed: 11/06/2022]
Abstract
BACKGROUND We employed machine learning approaches to (I) determine distinct progression trajectories in Parkinson's disease (PD) (unsupervised clustering task), and (II) predict progression trajectories (supervised prediction task), from early (years 0 and 1) data, making use of clinical and imaging features. METHODS We studied PD-subjects derived from longitudinal datasets (years 0, 1, 2 & 4; Parkinson's Progressive Marker Initiative). We extracted and analyzed 981 features, including motor, non-motor, and radiomics features extracted for each region-of-interest (ROIs: left/right caudate and putamen) using our standardized standardized environment for radiomics analysis (SERA) radiomics software. Segmentation of ROIs on dopamine transposer - single photon emission computed tomography (DAT SPECT) images were performed via magnetic resonance images (MRI). After performing cross-sectional clustering on 885 subjects (original dataset) to identify disease subtypes, we identified optimal longitudinal trajectories using hybrid machine learning systems (HMLS), including principal component analysis (PCA) + K-Means algorithms (KMA) followed by Bayesian information criterion (BIC), Calinski-Harabatz criterion (CHC), and elbow criterion (EC). Subsequently, prediction of the identified trajectories from early year data was performed using multiple HMLSs including 16 Dimension Reduction Algorithms (DRA) and 10 classification algorithms. RESULTS We identified 3 distinct progression trajectories. Hotelling's t squared test (HTST) showed that the identified trajectories were distinct. The trajectories included those with (I, II) disease escalation (2 trajectories, 27% and 38% of patients) and (III) stable disease (1 trajectory, 35% of patients). For trajectory prediction from early year data, HMLSs including the stochastic neighbor embedding algorithm (SNEA, as a DRA) as well as locally linear embedding algorithm (LLEA, as a DRA), linked with the new probabilistic neural network classifier (NPNNC, as a classifier), resulted in accuracies of 78.4% and 79.2% respectively, while other HMLSs such as SNEA + Lib_SVM (library for support vector machines) and t_SNE (t-distributed stochastic neighbor embedding) + NPNNC resulted in 76.5% and 76.1% respectively. CONCLUSIONS This study moves beyond cross-sectional PD subtyping to clustering of longitudinal disease trajectories. We conclude that combining medical information with SPECT-based radiomics features, and optimal utilization of HMLSs, can identify distinct disease trajectories in PD patients, and enable effective prediction of disease trajectories from early year data.
Collapse
Affiliation(s)
- Mohammad R Salmanpour
- Department of Energy Engineering and Physics, Amirkabir University of Technology, Tehran, Iran.,Department of Physics & Astronomy, University of British Columbia, Vancouver BC, Canada
| | - Mojtaba Shamsaei
- Department of Energy Engineering and Physics, Amirkabir University of Technology, Tehran, Iran
| | - Ghasem Hajianfar
- Rajaie Cardiovascular Medical and Research Center, Iran University of Medical Science, Tehran, Iran
| | - Hamid Soltanian-Zadeh
- CIPCE, School of Electrical & Computer Engineering, University of Tehran, Tehran, Iran.,Departments of Radiology and Research Administration, Henry Ford Health System, Detroit, USA
| | - Arman Rahmim
- Department of Physics & Astronomy, University of British Columbia, Vancouver BC, Canada.,Department of Radiology, University of British Columbia, Vancouver BC, Canada
| |
Collapse
|
3
|
Zou C, Li F, Choi J, Haghighi B, Choi S, Rajaraman PK, Comellas AP, Newell JD, Lee CH, Barr RG, Bleecker E, Cooper CB, Couper D, Han M, Hansel NN, Kanner RE, Kazerooni EA, Kleerup EC, Martinez FJ, O’Neal W, Paine R, Rennard SI, Smith BM, Woodruff PG, Hoffman EA, Lin CL. Longitudinal Imaging-Based Clusters in Former Smokers of the COPD Cohort Associate with Clinical Characteristics: The SubPopulations and Intermediate Outcome Measures in COPD Study (SPIROMICS). Int J Chron Obstruct Pulmon Dis 2021; 16:1477-1496. [PMID: 34103907 PMCID: PMC8178702 DOI: 10.2147/copd.s301466] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2021] [Accepted: 04/19/2021] [Indexed: 11/23/2022] Open
Abstract
PURPOSE Quantitative computed tomography (qCT) imaging-based cluster analysis identified clinically meaningful COPD former-smoker subgroups (clusters) based on cross-sectional data. We aimed to identify progression clusters for former smokers using longitudinal data. PATIENTS AND METHODS We selected 472 former smokers from SPIROMICS with a baseline visit and a one-year follow-up visit. A total of 150 qCT imaging-based variables, comprising 75 variables at baseline and their corresponding progression rates, were derived from the respective inspiration and expiration scans of the two visits. The COPD progression clusters identified were then associated with subject demography, clinical variables and biomarkers. RESULTS COPD severities at baseline increased with increasing cluster number. Cluster 1 patients were an obese subgroup with rapid progression of functional small airway disease percentage (fSAD%) and emphysema percentage (Emph%). Cluster 2 exhibited a decrease of fSAD% and Emph%, an increase of tissue fraction at total lung capacity and airway narrowing over one year. Cluster 3 showed rapid expansion of Emph% and an attenuation of fSAD%. Cluster 4 demonstrated severe emphysema and fSAD and significant structural alterations at baseline with rapid progression of fSAD% over one year. Subjects with different progression patterns in the same cross-sectional cluster were identified by longitudinal clustering. CONCLUSION qCT imaging-based metrics at two visits for former smokers allow for the derivation of four statistically stable clusters associated with unique progression patterns and clinical characteristics. Use of baseline variables and their progression rates enables identification of longitudinal clusters, resulting in a refinement of cross-sectional clusters.
Collapse
Affiliation(s)
- Chunrui Zou
- Department of Mechanical Engineering, University of Iowa, Iowa City, IA, USA
- IIHR-Hydroscience & Engineering, University of Iowa, Iowa City, IA, USA
| | - Frank Li
- IIHR-Hydroscience & Engineering, University of Iowa, Iowa City, IA, USA
- Department of Biomedical Engineering, University of Iowa, Iowa City, IA, USA
| | - Jiwoong Choi
- Department of Mechanical Engineering, University of Iowa, Iowa City, IA, USA
- Department of Internal Medicine, School of Medicine, University of Kansas, Kansas City, KS, USA
| | - Babak Haghighi
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Sanghun Choi
- School of Mechanical Engineering, Kyungpook National University, Daegu, Republic of Korea
| | - Prathish K Rajaraman
- Department of Mechanical Engineering, University of Iowa, Iowa City, IA, USA
- IIHR-Hydroscience & Engineering, University of Iowa, Iowa City, IA, USA
| | | | - John D Newell
- Department of Radiology, University of Iowa, Iowa City, IA, USA
| | - Chang Hyun Lee
- Department of Radiology, University of Iowa, Iowa City, IA, USA
- Department of Radiology, College of Medicine, Seoul National University, Seoul, Republic of Korea
| | - R Graham Barr
- Mailman School of Public Health, Columbia University, New York, NY, USA
| | - Eugene Bleecker
- Department of Medicine, The University of Arizona, Tucson, AZ, USA
| | | | - David Couper
- Department of Biostatistics, University of North Carolina, Chapel Hill, NC, USA
| | - Meilan Han
- Department of Internal Medicine, University of Michigan, Ann Arbor, MI, USA
| | | | | | - Ella A Kazerooni
- Department of Radiology, University of Michigan, Ann Arbor, MI, USA
| | | | | | - Wanda O’Neal
- School of Medicine, University of North Carolina, Chapel Hill, NC, USA
| | - Robert Paine
- School of Medicine, University of Utah, Salt Lake City, UT, USA
| | - Stephen I Rennard
- Department of Internal Medicine, University of Nebraska College of Medicine, Omaha, NE, USA
| | - Benjamin M Smith
- Department of Medicine, College of Physicians and Surgeons, Columbia University, New York, NY, USA
- Department of Medicine, McGill University Health Centre Research Institute, Montreal, Canada
| | - Prescott G Woodruff
- Department of Medicine, University of California at San Francisco, San Francisco, CA, USA
| | - Eirc A Hoffman
- Department of Biomedical Engineering, University of Iowa, Iowa City, IA, USA
- Department of Internal Medicine, University of Iowa, Iowa City, IA, USA
- Department of Radiology, University of Iowa, Iowa City, IA, USA
| | - Ching-Long Lin
- Department of Mechanical Engineering, University of Iowa, Iowa City, IA, USA
- IIHR-Hydroscience & Engineering, University of Iowa, Iowa City, IA, USA
- Department of Biomedical Engineering, University of Iowa, Iowa City, IA, USA
- Department of Radiology, University of Iowa, Iowa City, IA, USA
| |
Collapse
|
4
|
Poulakis K, Reid RI, Przybelski SA, Knopman DS, Graff-Radford J, Lowe VJ, Mielke MM, Machulda MM, Jack CR, Petersen RC, Westman E, Vemuri P. Longitudinal deterioration of white-matter integrity: heterogeneity in the ageing population. Brain Commun 2021; 3:fcaa238. [PMID: 33615218 PMCID: PMC7884606 DOI: 10.1093/braincomms/fcaa238] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2020] [Revised: 11/18/2020] [Accepted: 11/19/2020] [Indexed: 02/07/2023] Open
Abstract
Deterioration in white-matter health plays a role in cognitive ageing. Our goal was to discern heterogeneity of white-matter tract vulnerability in ageing using longitudinal imaging data (two to five imaging and cognitive assessments per participant) from a population-based sample of 553 elderly participants (age ≥60 years). We found that different clusters (healthy white matter, fast white-matter decliners and intermediate white-matter group) were heterogeneous in the spatial distribution of white-matter integrity, systemic health and cognitive trajectories. White-matter health of specific tracts (genu of corpus callosum, posterior corona radiata and anterior internal capsule) informed about cluster assignments. Not surprisingly, brain amyloidosis was not significantly different between clusters. Clusters had differential white-matter tract vulnerability to ageing (commissural fibres > association/brainstem fibres). Identification of vulnerable white-matter tracts is a valuable approach to assessing risk for cognitive decline.
Collapse
Affiliation(s)
- Konstantinos Poulakis
- Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm 141 52, Sweden
| | - Robert I Reid
- Department of Radiology, Mayo Clinic, Rochester, MN 559 05, USA
| | | | - David S Knopman
- Department of Radiology, Mayo Clinic, Rochester, MN 559 05, USA
| | | | - Val J Lowe
- Department of Radiology, Mayo Clinic, Rochester, MN 559 05, USA
| | | | - Mary M Machulda
- Department of Radiology, Mayo Clinic, Rochester, MN 559 05, USA
| | - Clifford R Jack
- Department of Radiology, Mayo Clinic, Rochester, MN 559 05, USA
| | | | - Eric Westman
- Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm 141 52, Sweden
| | | |
Collapse
|