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Goeminne LJE, Vladimirova A, Eames A, Tyshkovskiy A, Argentieri MA, Ying K, Moqri M, Gladyshev VN. Plasma protein-based organ-specific aging and mortality models unveil diseases as accelerated aging of organismal systems. Cell Metab 2025; 37:205-222.e6. [PMID: 39488213 DOI: 10.1016/j.cmet.2024.10.005] [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: 04/30/2024] [Revised: 08/04/2024] [Accepted: 10/04/2024] [Indexed: 11/04/2024]
Abstract
Aging is a complex process manifesting at molecular, cellular, organ, and organismal levels. It leads to functional decline, disease, and ultimately death, but the relationship between these fundamental biomedical features remains elusive. By applying elastic net regularization to plasma proteome data of over 50,000 human subjects in the UK Biobank and other cohorts, we report interpretable organ-specific and conventional aging models trained on chronological age, mortality, and longitudinal proteome data. These models predict organ/system-specific disease and indicate that men age faster than women in most organs. Accelerated organ aging leads to diseases in these organs, and specific diets, lifestyles, professions, and medications influence organ aging rates. We then identify proteins driving these associations with organ-specific aging. Our analyses reveal that age-related chronic diseases epitomize accelerated organ- and system-specific aging, modifiable through environmental factors, advocating for both universal whole-organism and personalized organ/system-specific anti-aging interventions.
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Affiliation(s)
- Ludger J E Goeminne
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA; Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Anastasiya Vladimirova
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA; Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Alec Eames
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA; Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Alexander Tyshkovskiy
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA; Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - M Austin Argentieri
- Department of Medicine, Massachusetts General Hospital, Boston, MA, USA; Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA; Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Kejun Ying
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA; Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Mahdi Moqri
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA; Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Vadim N Gladyshev
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA; Broad Institute of MIT and Harvard, Cambridge, MA, USA.
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2
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Chen K, Ying H. Response. Gastrointest Endosc 2025; 101:229. [PMID: 39701626 DOI: 10.1016/j.gie.2024.09.016] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/04/2024] [Revised: 09/07/2024] [Accepted: 09/09/2024] [Indexed: 12/21/2024]
Affiliation(s)
- Kangjie Chen
- Department of Hepatobiliary Pancreatic Surgery, First Affiliated Hospital of Medical School, Zhejiang University, Hangzhou, China
| | - Huajie Ying
- Department of Hepatobiliary Pancreatic Surgery, First Affiliated Hospital of Medical School, Zhejiang University, Hangzhou, China
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Gibson K, Forrest IS, Petrazzini BO, Duffy Á, Park JK, Malick W, Rosenson RS, Rocheleau G, Jordan DM, Do R. Evaluation of a machine learning-based metabolic marker for coronary artery disease in the UK Biobank. Atherosclerosis 2024; 401:119103. [PMID: 39799755 DOI: 10.1016/j.atherosclerosis.2024.119103] [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: 08/26/2024] [Revised: 12/02/2024] [Accepted: 12/18/2024] [Indexed: 01/15/2025]
Abstract
BACKGROUND AND AIMS An in silico quantitative score of coronary artery disease (ISCAD), built using machine learning and clinical data from electronic health records, has been shown to result in gradations of risk of subclinical atherosclerosis, coronary artery disease (CAD) sequelae, and mortality. Large-scale metabolite biomarker profiling provides increased portability and objectivity in machine learning for disease prediction and gradation. However, these models have not been fully leveraged. We evaluated a quantitative score of CAD derived from probabilities of a machine learning model trained on metabolomic data. METHODS We developed a CAD-predictive learning model using metabolic data from 93,642 individuals from the UK Biobank (median [IQR] age, 57 [14] years; 39,796 [42 %] male; 5640 [6 %] with diagnosed CAD), and assessed its probabilities as a quantitative metabolic risk score for CAD (M-CAD; range 0 [lowest probability] to 1 [highest probability]) in participants of the UK Biobank. The relationship of M-CAD with arterial stiffness index, ejection fraction, CAD sequelae, and mortality was assessed. RESULTS The model predicted CAD with an area under the receiver-operating-characteristic curve of 0.712. Arterial Stiffness Index increased by 0.19 and ejection fraction decreased by 0.2 % per 0.1 increase in M-CAD. Both incident and recurrent myocardial infarction increased stepwise over M-CAD quartiles (odds ratio (OR) 15.3 [4.2 %] and 12.5 [0.2 %]) in top quartiles as compared to the first quartile of incident and recurrent MI respectively). Likewise, the hazard ratio and prevalence of all-cause mortality, CVD-associated mortality, and CAD-associated mortality increased stepwise over M-CAD deciles (2.98 [14 %], 9.34 [4.3 %], 26.7 [2.7 %] in the top deciles as compared to the first decile of all-cause, CVD, and CAD mortality respectively). CONCLUSIONS Metabolic-based machine learning can be used to build a quantitative risk score for CAD that is associated with atherosclerotic burden, CAD sequelae and mortality.
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Affiliation(s)
- Kyle Gibson
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA; Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Iain S Forrest
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA; Medical Scientist Training Program, Icahn School of Medicine at Mount Sinai, New York, NY, USA; Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Ben O Petrazzini
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA; Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA; Center for Genomic Data Analytics, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Áine Duffy
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA; Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Joshua K Park
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA; Medical Scientist Training Program, Icahn School of Medicine at Mount Sinai, New York, NY, USA; Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Waqas Malick
- The Zena and Michael A. Wiener Cardiovascular Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Robert S Rosenson
- The Zena and Michael A. Wiener Cardiovascular Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Ghislain Rocheleau
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA; Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA; Center for Genomic Data Analytics, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Daniel M Jordan
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA; Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA; Center for Genomic Data Analytics, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Ron Do
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA; Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA; Center for Genomic Data Analytics, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
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4
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Ma H, Wang X, Heianza Y, Manson JE, Qi L. Proteomic Signature of BMI and Risk of Cardiovascular Disease. Clin Chem 2024; 70:1474-1484. [PMID: 39392242 DOI: 10.1093/clinchem/hvae149] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2024] [Accepted: 08/21/2024] [Indexed: 10/12/2024]
Abstract
BACKGROUND Obesity, defined by body mass index (BMI) alone, is a metabolically heterogeneous disorder with distinct cardiovascular manifestations across individuals. This study aimed to investigate the associations of a proteomic signature of BMI with risk of major subtypes of cardiovascular disease (CVD). METHODS A total of 40 089 participants from UK Biobank, free of CVD at baseline, had complete data on proteomic data measured by the Olink assay. A BMI-proteomic score (pro-BMI score) was calculated from 67 pre-identified plasma proteins associated with BMI. RESULTS A higher pro-BMI score was significantly associated with higher risks of ischemic heart disease (IHD) and heart failure (HF), but not with risk of stroke. Comparing the highest with the lowest quartiles, the adjusted hazard ratio (HR) for IHD was 1.49 (95% CI, 1.32-1.67) (P-trend < 0.001), and the adjusted HR for HF was 1.52 (95% CI, 1.25-1.85) (P-trend < 0.001). Further analyses showed that the association of pro-BMI score with HF risk was largely driven by the actual BMI, whereas the association of the pro-BMI score with IHD risk was independent of actual BMI and waist-to-hip ratio (WHR). The association between pro-BMI score and IHD risk appeared to be stronger in the normal BMI group than other BMI groups (P-interaction = 0.004) and stronger in the normal WHR group than the high WHR group (P-interaction = 0.049). CONCLUSIONS Higher pro-BMI score is significantly associated with higher IHD risk, independent of actual BMI levels. Our findings suggest that plasma proteins hold promise as complementary markers for diagnosing obesity and may facilitate personalized interventions.
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Affiliation(s)
- Hao Ma
- Department of Epidemiology, School of Public Health and Tropical Medicine, Tulane University, New Orleans, LA, United States
| | - Xuan Wang
- Department of Epidemiology, School of Public Health and Tropical Medicine, Tulane University, New Orleans, LA, United States
| | - Yoriko Heianza
- Department of Epidemiology, School of Public Health and Tropical Medicine, Tulane University, New Orleans, LA, United States
| | - JoAnn E Manson
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, United States
- Division of Preventive Medicine, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, United States
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, United States
| | - Lu Qi
- Department of Epidemiology, School of Public Health and Tropical Medicine, Tulane University, New Orleans, LA, United States
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, United States
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5
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Khalafiyan A, Fadaie M, Khara F, Zarrabi A, Moghadam F, Khanahmad H, Cordani M, Boshtam M. Highlighting roles of autophagy in human diseases: a perspective from single-cell RNA sequencing analyses. Drug Discov Today 2024; 29:104224. [PMID: 39521332 DOI: 10.1016/j.drudis.2024.104224] [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: 07/14/2024] [Revised: 09/24/2024] [Accepted: 11/05/2024] [Indexed: 11/16/2024]
Abstract
Autophagy, the lysosome-driven breakdown of intracellular components, is pivotal in regulating eukaryotic cellular processes and maintaining homeostasis, making it physiologically important even under normal conditions. Cellular mechanisms involving autophagy include the response to nutrient deprivation, intracellular quality control, early development, and cell differentiation. Despite its established health significance, the role of autophagy in cancer and other diseases remains complex and not fully understood. A comprehensive understanding of autophagy is crucial to facilitate the development of novel therapies and drugs that can protect and improve human health. High-throughput technologies, such as single-cell RNA sequencing (scRNA-seq), have enabled researchers to study transcriptional landscapes at single-cell resolution, significantly advancing our knowledge of autophagy pathways across diverse physiological and pathological contexts. This review discusses the latest advances in scRNA-seq for autophagy research and highlights its potential in the molecular characterization of various diseases.
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Affiliation(s)
- Anis Khalafiyan
- Department of Genetics and Molecular Biology, Faculty of Medicine, Isfahan University of Medical Sciences, Isfahan, Iran
| | - Mahmood Fadaie
- Department of Genetics and Molecular Biology, Faculty of Medicine, Isfahan University of Medical Sciences, Isfahan, Iran
| | - Fatemeh Khara
- Department of Biology, Faculty of Sciences, Shahid Chamran University of Ahvaz, Ahvaz, Iran
| | - Ali Zarrabi
- Department of Biomedical Engineering, Faculty of Engineering and Natural Sciences, Istinye University, Istanbul 34396, Turkey; Graduate School of Biotechnology and Bioengineering, Yuan Ze University, Taoyuan 320315, Taiwan; Department of Research Analytics, Saveetha Dental College and Hospitals, Saveetha Institute of Medical and Technical Sciences, Saveetha University, Chennai 600 077, India
| | - Fariborz Moghadam
- Department of Genetics and Molecular Biology, Faculty of Medicine, Isfahan University of Medical Sciences, Isfahan, Iran
| | - Hossein Khanahmad
- Department of Genetics and Molecular Biology, Faculty of Medicine, Isfahan University of Medical Sciences, Isfahan, Iran.
| | - Marco Cordani
- Department of Biochemistry and Molecular Biology, Faculty of Biological Sciences, Complutense University of Madrid, 28040 Madrid, Spain; Instituto de Investigaciones Sanitarias San Carlos (IdISSC), 28040 Madrid, Spain.
| | - Maryam Boshtam
- Isfahan Cardiovascular Research Center, Cardiovascular Research Institute, Isfahan University of Medical Sciences, Isfahan, Iran.
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Sebastiani P, Monti S, Lustgarten MS, Song Z, Ellis D, Tian Q, Schwaiger-Haber M, Stancliffe E, Leshchyk A, Short MI, Ardisson Korat AV, Gurinovich A, Karagiannis T, Li M, Lords HJ, Xiang Q, Marron MM, Bae H, Feitosa MF, Wojczynski MK, O'Connell JR, Montasser ME, Schupf N, Arbeev K, Yashin A, Schork N, Christensen K, Andersen SL, Ferrucci L, Rappaport N, Perls TT, Patti GJ. Metabolite signatures of chronological age, aging, survival, and longevity. Cell Rep 2024; 43:114913. [PMID: 39504246 DOI: 10.1016/j.celrep.2024.114913] [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: 09/22/2023] [Revised: 07/05/2024] [Accepted: 10/10/2024] [Indexed: 11/08/2024] Open
Abstract
Metabolites that mark aging are not fully known. We analyze 408 plasma metabolites in Long Life Family Study participants to characterize markers of age, aging, extreme longevity, and mortality. We identify 308 metabolites associated with age, 258 metabolites that change over time, 230 metabolites associated with extreme longevity, and 152 metabolites associated with mortality risk. We replicate many associations in independent studies. By summarizing the results into 19 signatures, we differentiate between metabolites that may mark aging-associated compensatory mechanisms from metabolites that mark cumulative damage of aging and from metabolites that characterize extreme longevity. We generate and validate a metabolomic clock that predicts biological age. Network analysis of the age-associated metabolites reveals a critical role of essential fatty acids to connect lipids with other metabolic processes. These results characterize many metabolites involved in aging and point to nutrition as a source of intervention for healthy aging therapeutics.
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Affiliation(s)
- Paola Sebastiani
- Institute for Clinical Research and Health Policy Studies, Tufts Medical Center, Boston, MA 02111, USA; Department of Medicine, School of Medicine, Tufts University, Boston, MA 02111, USA.
| | - Stefano Monti
- Department of Medicine, Chobanian & Avedisian School of Medicine, Boston University, Boston, MA 02118, USA; Bioinformatics Program, Boston University, Boston, MA 02215, USA
| | - Michael S Lustgarten
- Jean Mayer USDA Human Nutrition Research Center on Aging, Tufts University, Boston, MA 02111, USA
| | - Zeyuan Song
- Institute for Clinical Research and Health Policy Studies, Tufts Medical Center, Boston, MA 02111, USA
| | - Dylan Ellis
- Institute for Systems Biology, Seattle, WA 98109, USA
| | - Qu Tian
- Longitudinal Studies Section, Translational Gerontology Branch, National Institute on Aging, Baltimore, MD 21224, USA
| | | | - Ethan Stancliffe
- Department of Chemistry, Washington University in St. Louis, St. Louis, MO 63130, USA
| | | | - Meghan I Short
- Institute for Clinical Research and Health Policy Studies, Tufts Medical Center, Boston, MA 02111, USA; Department of Medicine, School of Medicine, Tufts University, Boston, MA 02111, USA
| | - Andres V Ardisson Korat
- Jean Mayer USDA Human Nutrition Research Center on Aging, Tufts University, Boston, MA 02111, USA
| | - Anastasia Gurinovich
- Institute for Clinical Research and Health Policy Studies, Tufts Medical Center, Boston, MA 02111, USA; Department of Medicine, School of Medicine, Tufts University, Boston, MA 02111, USA
| | - Tanya Karagiannis
- Institute for Clinical Research and Health Policy Studies, Tufts Medical Center, Boston, MA 02111, USA; Department of Medicine, School of Medicine, Tufts University, Boston, MA 02111, USA
| | - Mengze Li
- Bioinformatics Program, Boston University, Boston, MA 02215, USA
| | - Hannah J Lords
- Bioinformatics Program, Boston University, Boston, MA 02215, USA
| | - Qingyan Xiang
- Institute for Clinical Research and Health Policy Studies, Tufts Medical Center, Boston, MA 02111, USA
| | - Megan M Marron
- Department of Epidemiology, University of Pittsburgh, Pittsburgh, PA 15213, USA
| | - Harold Bae
- Biostatistics Program, College of Health, Oregon State University, Corvallis, OR 97331, USA
| | - Mary F Feitosa
- Department of Genetics, Washington University School of Medicine, St. Louis, MO 63130, USA
| | - Mary K Wojczynski
- Department of Genetics, Washington University School of Medicine, St. Louis, MO 63130, USA
| | - Jeffrey R O'Connell
- Department of Medicine, University of Maryland School of Medicine, Baltimore, MD 21201, USA
| | - May E Montasser
- Department of Medicine, University of Maryland School of Medicine, Baltimore, MD 21201, USA
| | - Nicole Schupf
- Department of Epidemiology, Columbia University Medical Center, New York, NY 10032, USA
| | - Konstantin Arbeev
- Social Science Research Institute, Duke University, Durham, NC 27708, USA
| | - Anatoliy Yashin
- Social Science Research Institute, Duke University, Durham, NC 27708, USA
| | - Nicholas Schork
- The Translational Genomics Research Institute, Phoenix, AZ 85004, USA
| | - Kaare Christensen
- Danish Aging Research Center, University of Southern Denmark, 5000 Odense, Denmark
| | - Stacy L Andersen
- Department of Medicine, Chobanian & Avedisian School of Medicine, Boston University, Boston, MA 02118, USA
| | - Luigi Ferrucci
- Longitudinal Studies Section, Translational Gerontology Branch, National Institute on Aging, Baltimore, MD 21224, USA
| | - Noa Rappaport
- Institute for Systems Biology, Seattle, WA 98109, USA
| | - Thomas T Perls
- Department of Medicine, Chobanian & Avedisian School of Medicine, Boston University, Boston, MA 02118, USA
| | - Gary J Patti
- Department of Chemistry, Washington University in St. Louis, St. Louis, MO 63130, USA
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Liu R, Yang S, Zhong X, Zhu Z, Huang W, Wang W. Metabolomic signature of retinal ageing, polygenetic susceptibility, and major health outcomes. Br J Ophthalmol 2024:bjo-2024-325846. [PMID: 39581638 DOI: 10.1136/bjo-2024-325846] [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: 05/17/2024] [Accepted: 10/28/2024] [Indexed: 11/26/2024]
Abstract
BACKGROUND/AIMS To identify the metabolic underpinnings of retinal aging and examine how it is related to mortality and morbidity of common diseases. METHODS The retinal age gap has been established as essential aging indicator for mortality and systemic health. We applied neural network to train the retinal age gap among the participants in UK Biobank and used nuclear magnetic resonance (NMR) to profile plasma metabolites. The metabolomic signature of retinal ageing (MSRA) was identified using an elastic network model. Multivariable Cox regressions were used to assess associations between the signature with 12 serious health conditions. The participants in Guangzhou Diabetic Eye Study (GDES) cohort were analyzed for validation. RESULTS This study included 110 722 participants (mean age 56.5±8.1 years at baseline, 53.8% female), and 28 plasma metabolites associated with retinal ageing were identified. The MSRA revealed significant correlations with each 12 serious health conditions beyond traditional risk factors and genetic predispositions. Each SD increase in MSRA was linked to a 24%-76% higher risk of mortality, cardiovascular diseases, dementia and diabetes mellitus. MSRA showed dose-response relationships with risks of these diseases, with seven showing non-linear and five showing linear increases. Validation in the GDES further established the relation between retinal ageing-related metabolites and increased risks of cardiovascular and chronic kidney diseases (all p<0.05). CONCLUSIONS The metabolic connections between ocular and systemic health offer a novel tool for identifying individuals at high risk of premature ageing, promoting a more holistic view of human health.
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Affiliation(s)
- Riqian Liu
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangdong Provincial Clinical Study Center for Ocular Diseases, Sun Yat-Sen University, Guangzhou, Guangdong, China
| | - Shaopeng Yang
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangdong Provincial Clinical Study Center for Ocular Diseases, Sun Yat-Sen University, Guangzhou, Guangdong, China
| | - Xiaoying Zhong
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangdong Provincial Clinical Study Center for Ocular Diseases, Sun Yat-Sen University, Guangzhou, Guangdong, China
- Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China
| | - Ziyu Zhu
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangdong Provincial Clinical Study Center for Ocular Diseases, Sun Yat-Sen University, Guangzhou, Guangdong, China
| | - Wenyong Huang
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangdong Provincial Clinical Study Center for Ocular Diseases, Sun Yat-Sen University, Guangzhou, Guangdong, China
- Hainan Eye Hospital and Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Haikou, China
| | - Wei Wang
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangdong Provincial Clinical Study Center for Ocular Diseases, Sun Yat-Sen University, Guangzhou, Guangdong, China
- Hainan Eye Hospital and Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Haikou, China
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8
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Rein M, Elkan M, Godneva A, Dolev NC, Segal E. Sex-specific dietary habits and their association with weight change in healthy adults. BMC Med 2024; 22:512. [PMID: 39501340 PMCID: PMC11539530 DOI: 10.1186/s12916-024-03730-3] [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: 11/22/2023] [Accepted: 10/26/2024] [Indexed: 11/08/2024] Open
Abstract
BACKGROUND Dietary intake plays a pivotal role in the prevalence and management of obesity. While women and men exhibit differences in dietary habits and food-related behaviors, sex-based weight loss recommendations are lacking. This study aims to examine the impact of specific foods and food categories on weight reduction in men and women over a two-year period. METHODS A total of 8,548 participants from the 10K cohort, from 2019 to 2023, were included in the analysis (53.1% women, mean age 51.7 years). Anthropometric measurements and laboratory results were collected at baseline and at the two-year follow-up visit. Dietary assessment was based on daily food intake digitally logged through an application for at least 3 consecutive days at both timepoints. We compared intake of macronutrients, micronutrients, food groups and daily energy consumption between sex and body mass index (BMI) categories at baseline and weight change categories at follow-up. Using linear regression, we assessed the associations between food categories or specific foods and BMI at baseline as well as weight change percentage at follow-up. RESULTS Dietary habits varied by BMI and sex. Women and men living with obesity (BMI > 30 kg/m2) reported a greater intake of animal-based protein and lower intake of plant-based proteins and fats at baseline, as compared to participants with normal weight. In linear regression models predicting two-year weight change, including age, income, and baseline weight, the explained variance was 5.6% for men and 5.8% for women. Adding food categories and specific foods increased the explained variance to 20.6% for men and 17.5% for women. Weight reduction in men was linked to daily consumption of an egg (1.2% decrease) and beef (1.5% decrease), while in women, the most pronounced reductions were associated with an apple (1.2% decrease) and cashew nuts (3.4% decrease). Notably, total energy intake changes significantly impacted weight outcomes only in women. CONCLUSIONS Sex-specific dietary habits significantly influence weight change over time. In men, weight loss was primarily associated with the addition of animal-based protein, while in women, it was linked to caloric deficit and plant-based fat, suggesting that sex-based nutritional interventions may demonstrate greater efficacy. TRIAL REGISTRATION NCT05817734 (retrospectively registered January 31, 2023).
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Affiliation(s)
- Michal Rein
- Department of Computer Science and Applied Mathematics, The Weizmann Institute of Science, Rehovot, Israel
- Department of Molecular Cell Biology, The Weizmann Institute of Science, Rehovot, Israel
| | - Matan Elkan
- Department of Internal Medicine A, Shamir Medical Center (Assaf Harofeh), Zerifin, Israel
| | - Anastasia Godneva
- Department of Computer Science and Applied Mathematics, The Weizmann Institute of Science, Rehovot, Israel
- Department of Molecular Cell Biology, The Weizmann Institute of Science, Rehovot, Israel
| | - Noa Cohen Dolev
- Department of Computer Science and Applied Mathematics, The Weizmann Institute of Science, Rehovot, Israel
- Department of Molecular Cell Biology, The Weizmann Institute of Science, Rehovot, Israel
| | - Eran Segal
- Department of Computer Science and Applied Mathematics, The Weizmann Institute of Science, Rehovot, Israel.
- Department of Molecular Cell Biology, The Weizmann Institute of Science, Rehovot, Israel.
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9
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Wood C, Khalsa AS. Overview of BMI and Other Ways of Measuring and Screening for Obesity in Pediatric Patients. Pediatr Clin North Am 2024; 71:781-796. [PMID: 39343492 DOI: 10.1016/j.pcl.2024.07.002] [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] [Indexed: 10/01/2024]
Abstract
Despite a long history of advances in measuring body size and composition, body mass index (BMI) has remained the most commonly used clinical measure. We explore the advantages and disadvantages of using BMI and other measures to estimate adipose tissue, recognizing that no measure of body size or adiposity has fulfilled the goal of differentiating health from disease. BMI and waist circumference remain widely-used clinical screening measures for appropriate risk stratification as it relates to obesity.
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Affiliation(s)
- Charles Wood
- Department of Pediatrics, Division of General Pediatrics and Adolescent Health, Duke Center for Childhood Obesity Research, Duke University School of Medicine, 3116 N. Duke Street, Durham, NC 27704, USA.
| | - Amrik Singh Khalsa
- Division of Primary Care Pediatrics, Center for Child Health Equity and Outcomes Research, The Abigail Wexner Research Institute, Nationwide Children's Hospital, The Ohio State University College of Medicine, 700 Children's Drive, Columbus, OH 43205, USA
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Sevilla-González M, Garibay-Gutiérrez MF, Vargas-Vázquez A, Medina-García AC, Ordoñez-Sánchez ML, Clish CB, Almeda-Valdes P, Tusie-Luna T. Metabolomic Profile Alterations Associated with the SLC16A11 Risk Haplotype Following a Lifestyle Intervention in People With Prediabetes. Curr Dev Nutr 2024; 8:104444. [PMID: 39310668 PMCID: PMC11416210 DOI: 10.1016/j.cdnut.2024.104444] [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: 05/27/2024] [Revised: 08/11/2024] [Accepted: 08/15/2024] [Indexed: 09/25/2024] Open
Abstract
Background A risk haplotype in SLC16A11 characterized by alterations in fatty acid metabolism emerged as a genetic risk factor associated with increased susceptibility to type 2 diabetes (T2D) in Mexican population. Its role on treatment responses is not well understood. Objectives We aimed to determine the impact of the risk haplotype on the metabolomic profile during a lifestyle intervention (LSI). Methods We recruited Mexican-mestizo individuals with ≥1 prediabetes criteria according to the American Diabetes Association with a body mass index between 25 and 45 kg/m2. We conducted a 24-wk quasiexperimental LSI study for diabetes prevention. Here, we compared longitudinal plasma liquid chromatography/mass spectrometry metabolomic changes between carriers and noncarriers. We analyzed the association of risk haplotype with metabolites leveraging repeated assessments using multivariable-adjusted linear mixed models. Results Before the intervention, carriers (N = 21) showed higher concentrations of hippurate, C16 carnitine, glycine, and cinnamoylglycine. After 24 wk of LSI, carriers exhibited a deleterious metabolomic profile. This profile was characterized by increased concentrations of hippurate, cinnamoglycine, xanthosine, N-acetylputrescine, L-acetylcarnitine, ceramide (d18:1/24:1), and decreased concentrations of citrulline and phosphatidylethanolamine. These metabolites were associated with higher concentrations of total cholesterol, triglycerides, and low density lipoprotein cholesterol. The effect of LSI on the risk haplotype was notably more pronounced in its impact on 2 metabolites: methylmalonylcarnitine (β: -0.56; P-interaction = 0.014) and betaine (β: -0.64; P-interaction = 0.017). Interestingly, lower consumption across visits of polyunsaturated (β: -0.038; P = 0.017) fatty acids were associated with higher concentrations of methylmalonylcarnitine. Covariates for adjustment across models included age, sex, genetic ancestry principal components, and body mass index. Conclusions Our study highlights the persistence of deleterious metabolomic patterns associated with the risk haplotype before and during a 24-wk LSI. We also emphasize the potential regulatory role of polyunsaturated fatty acids on methylmalonylcarnitine concentrations suggesting a route for improving interventions for individuals with high-genetic risk.
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Affiliation(s)
- Magdalena Sevilla-González
- Clinical and Translational Epidemiology Unit, Mongan Institute, Massachusetts General Hospital, Boston, MA, United States
- Department of Medicine, Harvard Medical School, Boston, MA, United States
- Programs in Metabolism and Medical & Population Genetics, The Broad Institute of MIT and Harvard, Cambridge, MA, United States
| | - Maria Fernanda Garibay-Gutiérrez
- Unidad de Investigacion en Enfermedades Metabolicas, Instituto Nacional de Ciencias Medicas y Nutricion “Salvador Zubiran,” Mexico City, Mexico
- Departamento de Fisiología. Escuela Nacional de Ciencias Biológicas, Instituto Politecnico Nacional, Mexico City, Mexico
| | - Arsenio Vargas-Vázquez
- Unidad de Investigacion en Enfermedades Metabolicas, Instituto Nacional de Ciencias Medicas y Nutricion “Salvador Zubiran,” Mexico City, Mexico
| | - Andrea Celeste Medina-García
- Unidad de Biologia Molecular y Medicina Genomica, Insituto Nacional de Ciencias Medicas y Nutricion Salvador Zubiran, Mexico City, Mexico
- Instituto de Investigaciones Biomedicas, Universidad Nacional Autonoma de Mexico, Mexico City, Mexico
| | - Maria Luisa Ordoñez-Sánchez
- Unidad de Biologia Molecular y Medicina Genomica, Insituto Nacional de Ciencias Medicas y Nutricion Salvador Zubiran, Mexico City, Mexico
| | - Clary B Clish
- Metabolomics Platform, The Broad Institute of MIT and Harvard, Cambridge, MA, United States
| | - Paloma Almeda-Valdes
- Unidad de Investigacion en Enfermedades Metabolicas, Instituto Nacional de Ciencias Medicas y Nutricion “Salvador Zubiran,” Mexico City, Mexico
- Departamento de Endocrinologia y Metabolismo, Instituto Nacional de Ciencias Medicas y Nutricion Salvador Zubiran, Mexico City, Mexico
| | - Teresa Tusie-Luna
- Unidad de Biologia Molecular y Medicina Genomica, Insituto Nacional de Ciencias Medicas y Nutricion Salvador Zubiran, Mexico City, Mexico
- Instituto de Investigaciones Biomedicas, Universidad Nacional Autonoma de Mexico, Mexico City, Mexico
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11
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Pu Y, Sun Z, Zhang H, Huang Q, Wang Z, Mei Z, Wang P, Kong M, Yang W, Lin C, Zhou X, Lin S, Huang Q, Huang L, Sun L, Yuan C, Xu Q, Tang H, Wang X, Zheng Y. Gut microbial features and circulating metabolomic signatures of frailty in older adults. NATURE AGING 2024; 4:1249-1262. [PMID: 39054372 DOI: 10.1038/s43587-024-00678-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/28/2023] [Accepted: 07/03/2024] [Indexed: 07/27/2024]
Abstract
Frailty, a multidimensional indicator of suboptimal aging, reflects cumulative declines across multiple physiological systems. Although age-related changes have been reported in gut microbiota, their role in healthy aging remains unclear. In this study, we calculated frailty index (FI) from 33 health-related items to reflect the overall health status of 1,821 older adults (62-96 years, 55% female) and conducted multi-omics analysis using gut metagenomic sequencing data and plasma metabolomic data. We identified 18 microbial species and 17 metabolites shifted along with frailty severity, with stronger links observed in females. The associations of nine species, including various Clostridium species and Faecalibacterium prausnitzii, with FI were reproducible in two external populations. Plasma glycerol levels, white blood cell count and kidney function partially mediated these associations. A composite microbial score derived from FI significantly predicted 2-year mortality (adjusted hazard ratio across extreme quartiles, 2.86; 95% confidence interval, 1.38-5.93), highlighting the potential of microbiota-based strategies for risk stratification in older adults.
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Affiliation(s)
- Yanni Pu
- State Key Laboratory of Genetic Engineering, Human Phenome Institute, School of Life Sciences, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Zhonghan Sun
- State Key Laboratory of Genetic Engineering, Human Phenome Institute, School of Life Sciences, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Hui Zhang
- State Key Laboratory of Genetic Engineering, Human Phenome Institute, School of Life Sciences, Fudan University, Shanghai, China
- School of Global Health, Chinese Center for Tropical Diseases Research, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Qingxia Huang
- State Key Laboratory of Genetic Engineering, Human Phenome Institute, School of Life Sciences, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Zhengdong Wang
- Department of Gastroenterology, Rugao People's Hospital, Rugao, China
| | - Zhendong Mei
- State Key Laboratory of Genetic Engineering, Human Phenome Institute, School of Life Sciences, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Peilu Wang
- Ministry of Education Key Laboratory of Public Health Safety, School of Public Health, Institute of Nutrition, Fudan University, Shanghai, China
| | - Mengmeng Kong
- State Key Laboratory of Genetic Engineering, Human Phenome Institute, School of Life Sciences, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Wenjun Yang
- State Key Laboratory of Genetic Engineering, Human Phenome Institute, School of Life Sciences, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Chenhao Lin
- State Key Laboratory of Genetic Engineering, Human Phenome Institute, School of Life Sciences, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Xiaofeng Zhou
- State Key Laboratory of Genetic Engineering, Human Phenome Institute, School of Life Sciences, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Shuchun Lin
- State Key Laboratory of Genetic Engineering, Human Phenome Institute, School of Life Sciences, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Qiumin Huang
- State Key Laboratory of Genetic Engineering, Human Phenome Institute, School of Life Sciences, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Lili Huang
- State Key Laboratory of Genetic Engineering, Human Phenome Institute, School of Life Sciences, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Liang Sun
- Ministry of Education Key Laboratory of Public Health Safety, School of Public Health, Institute of Nutrition, Fudan University, Shanghai, China
| | - Changzheng Yuan
- School of Public Health, Zhejiang University School of Medicine, Hangzhou, China
| | - Qian Xu
- Institute of Gut Microbiota Research and Engineering Development, Shanghai Tenth People's Hospital, Tongji University School of Medicine, Shanghai, China
| | - Huiru Tang
- State Key Laboratory of Genetic Engineering, Human Phenome Institute, School of Life Sciences, Zhongshan Hospital, Fudan University, Shanghai, China.
| | - Xiaofeng Wang
- State Key Laboratory of Genetic Engineering, Human Phenome Institute, School of Life Sciences, Fudan University, Shanghai, China.
- National Clinical Research Center for Aging and Medicine, Huashan Hospital, Fudan University, Shanghai, China.
| | - Yan Zheng
- State Key Laboratory of Genetic Engineering, Human Phenome Institute, School of Life Sciences, Zhongshan Hospital, Fudan University, Shanghai, China.
- Ministry of Education Key Laboratory of Public Health Safety, School of Public Health, Institute of Nutrition, Fudan University, Shanghai, China.
- National Clinical Research Center for Aging and Medicine, Huashan Hospital, Fudan University, Shanghai, China.
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12
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Oh MY, Jung YM, Kim WP, Lee HC, Kim TK, Ko SB, Lim J, Lee SM. Prediction for Perioperative Stroke Using Intraoperative Parameters. J Am Heart Assoc 2024; 13:e032216. [PMID: 39119968 DOI: 10.1161/jaha.123.032216] [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: 08/19/2023] [Accepted: 06/20/2024] [Indexed: 08/10/2024]
Abstract
BACKGROUND Perioperative stroke is a severe complication following surgery. To identify patients at risk for perioperative stroke, several prediction models based on the preoperative factors were suggested. Prediction models often focus on preoperative patient characteristics to assess stroke risk. However, most existing models primarily base their predictions on the patient's baseline characteristics before surgery. We aimed to develop a machine-learning model incorporating both pre- and intraoperative variables to predict perioperative stroke. METHODS AND RESULTS This study included patients who underwent noncardiac surgery at 2 hospitals with the data of 15 752 patients from Seoul National University Hospital used for development and temporal internal validation, and the data of 449 patients from Boramae Medical Center used for external validation. Perioperative stroke was defined as a newly developed ischemic lesion on diffusion-weighted imaging within 30 days of surgery. We developed a prediction model composed of pre- and intraoperative factors (integrated model) and compared it with a model consisting of preoperative features alone (preoperative model). Perioperative stroke developed in 109 (0.69%) patients in the Seoul National University Hospital group and 11 patients (2.45%) in the Boramae Medical Center group. The integrated model demonstrated superior predictive performance with area under the curve values of 0.824 (95% CI, 0.762-0.880) versus 0.584 (95% CI, 0.499-0.667; P<0.001) in the internal validation; and 0.716 (95% CI, 0.560-0.859) versus 0.505 (95% CI, 0.343-0.654; P=0.018) in the external validation, compared to the preoperative model. CONCLUSIONS We suggest that incorporating intraoperative factors into perioperative stroke prediction models can improve their accuracy.
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Affiliation(s)
- Mi-Young Oh
- Department of Neurology Bucheon Sejong Hospital Bucheon-si Gyeonggi-do South Korea
| | - Young Mi Jung
- Department of Obstetrics and Gynecology Seoul National University College of Medicine Seoul South Korea
- Department of Obstetrics and Gynecology, Guro Hospital Korea University College of Medicine Seoul South Korea
| | | | - Hyung-Chul Lee
- Department of Anesthesiology and Pain Medicine Seoul National University College of Medicine Seoul South Korea
- Department of Anesthesiology and Pain Medicine Seoul National University Hospital Seoul South Korea
| | - Tae Kyong Kim
- Department of Anesthesiology and Pain Medicine Seoul National University College of Medicine Seoul South Korea
- Department of Anesthesiology and Pain Medicine Metropolitan Government Seoul National University Boramae Medical Center Seoul South Korea
| | - Sang-Bae Ko
- Department of Neurology Seoul National University Hospital Seoul South Korea
| | | | - Seung Mi Lee
- Department of Obstetrics and Gynecology Seoul National University College of Medicine Seoul South Korea
- Department of Obstetrics and Gynecology Seoul National University Hospital Seoul South Korea
- Innovative Medical Technology Research Institute Seoul National University Hospital Seoul South Korea
- Institute of Reproductive Medicine and Population & Medical Big Data Research Center, Medical Research Center Seoul National University Seoul South Korea
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13
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Anazco D, Acosta A. Precision medicine for obesity: current evidence and insights for personalization of obesity pharmacotherapy. Int J Obes (Lond) 2024:10.1038/s41366-024-01599-z. [PMID: 39127792 DOI: 10.1038/s41366-024-01599-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/18/2023] [Revised: 06/17/2024] [Accepted: 07/29/2024] [Indexed: 08/12/2024]
Abstract
Obesity is a chronic and complex disease associated with increased morbidity, mortality, and financial burden. It is expected that by 2030 one of two people in the United States will have obesity. The backbone for obesity management continues to be lifestyle interventions, consisting of calorie deficit diets and increased physical activity levels, however, these interventions are often insufficient to achieve sufficient and maintained weight loss. As a result, multiple patients require additional interventions such as antiobesity medications or bariatric interventions in order to achieve clinically significant weight loss and improvement or resolution of obesity-associated comorbidities. Despite the recent advances in the field of obesity pharmacotherapy that have resulted in never-before-seen weight loss outcomes, comorbidity improvement, and even reduction in cardiovascular mortality, there is still a significant interindividual variability in terms of response to antiobesity medications, with a subset of patients not achieving a clinically significant weight loss. Currently, the trial-and-error paradigm for the selection of antiobesity medications results in increased costs and risks for developing side effects, while also reduces engagement in weight management programs for patients with obesity. The implementation of a precision medicine framework to the selection of antiobesity medications might help reduce heterogeneity and optimize weight loss outcomes by identifying unique subsets of patients, or phenotypes, that have a better response to a specific intervention. The detailed study of energy balance regulation holds promise, as actionable behavioral and physiologic traits could help guide antiobesity medication selection based on previous mechanistic studies. Moreover, the rapid advances in genotyping, multi-omics, and big data analysis might hold the key to discover additional signatures or phenotypes that might respond better to a certain intervention and might permit the widespread adoption of a precision medicine approach for obesity management.
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Affiliation(s)
- Diego Anazco
- Precision Medicine for Obesity Program, Division of Gastroenterology and Hepatology, Department of Medicine, Mayo Clinic, Rochester, MN, USA
| | - Andres Acosta
- Precision Medicine for Obesity Program, Division of Gastroenterology and Hepatology, Department of Medicine, Mayo Clinic, Rochester, MN, USA.
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14
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Nam Y, Kim J, Jung SH, Woerner J, Suh EH, Lee DG, Shivakumar M, Lee ME, Kim D. Harnessing Artificial Intelligence in Multimodal Omics Data Integration: Paving the Path for the Next Frontier in Precision Medicine. Annu Rev Biomed Data Sci 2024; 7:225-250. [PMID: 38768397 DOI: 10.1146/annurev-biodatasci-102523-103801] [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: 05/22/2024]
Abstract
The integration of multiomics data with detailed phenotypic insights from electronic health records marks a paradigm shift in biomedical research, offering unparalleled holistic views into health and disease pathways. This review delineates the current landscape of multimodal omics data integration, emphasizing its transformative potential in generating a comprehensive understanding of complex biological systems. We explore robust methodologies for data integration, ranging from concatenation-based to transformation-based and network-based strategies, designed to harness the intricate nuances of diverse data types. Our discussion extends from incorporating large-scale population biobanks to dissecting high-dimensional omics layers at the single-cell level. The review underscores the emerging role of large language models in artificial intelligence, anticipating their influence as a near-future pivot in data integration approaches. Highlighting both achievements and hurdles, we advocate for a concerted effort toward sophisticated integration models, fortifying the foundation for groundbreaking discoveries in precision medicine.
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Affiliation(s)
- Yonghyun Nam
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA;
| | - Jaesik Kim
- Institute for Biomedical Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
- Department of Bioengineering, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Sang-Hyuk Jung
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA;
| | - Jakob Woerner
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA;
| | - Erica H Suh
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA;
| | - Dong-Gi Lee
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA;
| | - Manu Shivakumar
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA;
| | - Matthew E Lee
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA;
| | - Dokyoon Kim
- Institute for Biomedical Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA;
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15
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Kolic J, Sun WG, Cen HH, Ewald JD, Rogalski JC, Sasaki S, Sun H, Rajesh V, Xia YH, Moravcova R, Skovsø S, Spigelman AF, Manning Fox JE, Lyon J, Beet L, Xia J, Lynn FC, Gloyn AL, Foster LJ, MacDonald PE, Johnson JD. Proteomic predictors of individualized nutrient-specific insulin secretion in health and disease. Cell Metab 2024; 36:1619-1633.e5. [PMID: 38959864 PMCID: PMC11250105 DOI: 10.1016/j.cmet.2024.06.001] [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: 09/13/2023] [Revised: 02/26/2024] [Accepted: 06/03/2024] [Indexed: 07/05/2024]
Abstract
Population-level variation and mechanisms behind insulin secretion in response to carbohydrate, protein, and fat remain uncharacterized. We defined prototypical insulin secretion responses to three macronutrients in islets from 140 cadaveric donors, including those with type 2 diabetes. The majority of donors' islets exhibited the highest insulin response to glucose, moderate response to amino acid, and minimal response to fatty acid. However, 9% of donors' islets had amino acid responses, and 8% had fatty acid responses that were larger than their glucose-stimulated insulin responses. We leveraged this heterogeneity and used multi-omics to identify molecular correlates of nutrient responsiveness, as well as proteins and mRNAs altered in type 2 diabetes. We also examined nutrient-stimulated insulin release from stem cell-derived islets and observed responsiveness to fat but not carbohydrate or protein-potentially a hallmark of immaturity. Understanding the diversity of insulin responses to carbohydrate, protein, and fat lays the groundwork for personalized nutrition.
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Affiliation(s)
- Jelena Kolic
- Department of Cellular and Physiological Sciences, Life Sciences Institute, University of British Columbia, Vancouver, BC, Canada.
| | - WenQing Grace Sun
- Department of Cellular and Physiological Sciences, Life Sciences Institute, University of British Columbia, Vancouver, BC, Canada
| | - Haoning Howard Cen
- Department of Cellular and Physiological Sciences, Life Sciences Institute, University of British Columbia, Vancouver, BC, Canada
| | - Jessica D Ewald
- Institute of Parasitology, McGill University, Montreal, QC, Canada
| | - Jason C Rogalski
- Department of Biochemistry and Molecular Biology, University of British Columbia, Vancouver, BC, Canada
| | - Shugo Sasaki
- Diabetes Research Group, BC Children's Hospital Research Institute, Vancouver, BC, Canada; Department of Surgery, School of Biomedical Engineering, University of British Columbia, Vancouver, BC, Canada
| | - Han Sun
- Department of Pediatrics, Division of Endocrinology, Stanford School of Medicine, Stanford, CA, USA
| | - Varsha Rajesh
- Department of Pediatrics, Division of Endocrinology, Stanford School of Medicine, Stanford, CA, USA
| | - Yi Han Xia
- Department of Cellular and Physiological Sciences, Life Sciences Institute, University of British Columbia, Vancouver, BC, Canada
| | - Renata Moravcova
- Department of Biochemistry and Molecular Biology, University of British Columbia, Vancouver, BC, Canada
| | - Søs Skovsø
- Valkyrie Life Sciences, Vancouver, BC, Canada
| | - Aliya F Spigelman
- Department of Pharmacology, University of Alberta, Edmonton, AB, Canada; Alberta Diabetes Institute, University of Alberta, Edmonton, AB, Canada
| | - Jocelyn E Manning Fox
- Department of Pharmacology, University of Alberta, Edmonton, AB, Canada; Alberta Diabetes Institute, University of Alberta, Edmonton, AB, Canada
| | - James Lyon
- Department of Pharmacology, University of Alberta, Edmonton, AB, Canada; Alberta Diabetes Institute, University of Alberta, Edmonton, AB, Canada
| | - Leanne Beet
- Department of Cellular and Physiological Sciences, Life Sciences Institute, University of British Columbia, Vancouver, BC, Canada
| | - Jianguo Xia
- Institute of Parasitology, McGill University, Montreal, QC, Canada
| | - Francis C Lynn
- Diabetes Research Group, BC Children's Hospital Research Institute, Vancouver, BC, Canada; Department of Surgery, School of Biomedical Engineering, University of British Columbia, Vancouver, BC, Canada
| | - Anna L Gloyn
- Department of Pediatrics, Division of Endocrinology, Stanford School of Medicine, Stanford, CA, USA; Stanford Diabetes Research Center, Stanford School of Medicine, Stanford, CA, USA; Wellcome Center for Human Genetics, University of Oxford, Oxford, UK
| | - Leonard J Foster
- Department of Biochemistry and Molecular Biology, University of British Columbia, Vancouver, BC, Canada
| | - Patrick E MacDonald
- Department of Pharmacology, University of Alberta, Edmonton, AB, Canada; Alberta Diabetes Institute, University of Alberta, Edmonton, AB, Canada
| | - James D Johnson
- Department of Cellular and Physiological Sciences, Life Sciences Institute, University of British Columbia, Vancouver, BC, Canada; Vancouver Coastal Health Research Institute, Vancouver, BC, Canada.
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16
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Alazzam MF, Rasheed IB, Aljundi SH, Shamiyah DA, Khader YS, Abdelhafez RS, Alrashdan MS. Oral processing behavior and dental caries; an insight into a new relationship. PLoS One 2024; 19:e0306143. [PMID: 38954716 PMCID: PMC11218957 DOI: 10.1371/journal.pone.0306143] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2023] [Accepted: 06/11/2024] [Indexed: 07/04/2024] Open
Abstract
INTRODUCTION Previous evidence suggests an individual variation in the preferred oral processing behavior. Individuals can be classified as firm processing(FPL) or soft processing likers(SPL). FPL(crunchers and chewers) prefer using their teeth while SPL(smooshers and suckers) prefer using the tongue and the palate when processing different food items. Variation in the preferred oral processing behavior has been associated with differences in food texture preference and eating time. Time is one of the factors directly related to the development of dental caries(tooth decay). Oral retention and eating times are associated with greater caries experience. This study aims to explore if a relationship exists between the preferred oral processing behavior and the individual's caries experience. MATERIALS AND METHODS This was a cross-sectional, dental center-based study conducted at Jordan University of Science and Technology. Five hundred participants consented to fill out the preferred oral processing behavior(POPB) questionnaire. Anthropometric measurements (including weight, height, and waist circumference) were recorded. A single trained and calibrated dentist registered each participant's caries experience and plaque levels using the DMFS index and plaque index of Silness and Loe. RESULTS A total of 351(70.2%) and 149(29.8%) participants were typed as FPL and SPL, respectively. SPL demonstrated higher levels of dental caries experience compared to FPL. The mean DMFS score for SPL was 28.8(±25.43) while for FPL was 18.71(± 18.34). This difference remained significant after adjustment for confounders(P<0.001). SPL exhibited a significantly higher mean score for the "M" component(P <0.001) while no significant difference in the mean score of the "D"(P = 0.076) and "F"(P = 0.272) components was observed when compared to FPL. CONCLUSION The current findings provide new insight into a possible relationship between the preferred oral processing behavior and an individual's caries experience. A relationship in which the preferred oral processing behavior can potentially affect and/or be affected by the dental caries experience.
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Affiliation(s)
- Melanie F. Alazzam
- Department of Oral Medicine and Oral Surgery, Jordan University of Science and Technology, Irbid, Jordan
| | - Issam B. Rasheed
- Department of Oral Medicine and Oral Surgery, Jordan University of Science and Technology, Irbid, Jordan
| | - Suhad H. Aljundi
- Department of Preventive Dentistry, Jordan University of Science and Technology, Irbid, Jordan
| | - Dalal A. Shamiyah
- Undergraduate Bachelor of Dental Surgery Program, Jordan University of Science and Technology, Irbid, Jordan
| | - Yousef S. Khader
- Department of Public Health, Jordan University of Science and Technology, Irbid, Jordan
| | - Reem S. Abdelhafez
- Department of Preventive Dentistry, Jordan University of Science and Technology, Irbid, Jordan
| | - Mohammad S. Alrashdan
- Department of Oral Medicine and Oral Surgery, Jordan University of Science and Technology, Irbid, Jordan
- Department of Oral and Craniofacial Health Sciences, University of Sharjah, Sharjah, United Arab Emirates
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17
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Bizerea-Moga TO, Pitulice L, Bizerea-Spiridon O, Moga TV. Exploring the Link between Oxidative Stress, Selenium Levels, and Obesity in Youth. Int J Mol Sci 2024; 25:7276. [PMID: 39000383 PMCID: PMC11242909 DOI: 10.3390/ijms25137276] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2024] [Revised: 06/26/2024] [Accepted: 06/28/2024] [Indexed: 07/16/2024] Open
Abstract
Obesity is a worldwide increasing concern. Although in adults this is easily estimated with the body mass index, in children, who are constantly growing and whose bodies are changing, the reference points to assess weight status are age and gender, and need corroboration with complementary data, making their quantification highly difficult. The present review explores the interaction spectrum of oxidative stress, selenium status, and obesity in children and adolescents. Any factor related to oxidative stress that triggers obesity and, conversely, obesity that induces oxidative stress are part of a vicious circle, a complex chain of mechanisms that derive from each other and reinforce each other with serious health consequences. Selenium and its compounds exhibit key antioxidant activity and also have a significant role in the nutritional evaluation of obese children. The balance of selenium intake, retention, and metabolism emerges as a vital aspect of health, reflecting the complex interactions between diet, oxidative stress, and obesity. Understanding whether selenium status is a contributor to or a consequence of obesity could inform nutritional interventions and public health strategies aimed at preventing and managing obesity from an early age.
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Affiliation(s)
- Teofana Otilia Bizerea-Moga
- Department XI of Pediatrics-1st Pediatric Discipline, Center for Research on Growth and Developmental Disorders in Children, ‘Victor Babeș’ University of Medicine and Pharmacy Timișoara, Eftimie Murgu Sq No 2, 300041 Timișoara, Romania;
- 1st Pediatric Clinic, ‘Louis Țurcanu’ Children’s Clinical and Emergency Hospital, Iosif Nemoianu 2, 300011 Timișoara, Romania
| | - Laura Pitulice
- Department of Biology-Chemistry, West University of Timişoara, Pestallozi 16, 300115 Timişoara, Romania;
- The Institute for Advanced Environmental Research (ICAM), Popa Şapcă 4C, 300054 Timişoara, Romania
| | - Otilia Bizerea-Spiridon
- Department of Biology-Chemistry, West University of Timişoara, Pestallozi 16, 300115 Timişoara, Romania;
- The Institute for Advanced Environmental Research (ICAM), Popa Şapcă 4C, 300054 Timişoara, Romania
| | - Tudor Voicu Moga
- Department VII of Internal Medicine-Gastroenterology Discipline, Advanced Regional Research Center in Gastroenterology and Hepatology, ‘Victor Babeș’ University of Medicine and Pharmacy Timișoara, Eftimie Murgu Sq No 2, 300041 Timișoara, Romania;
- Gastroenterology and Hepatology Clinic, ‘Pius Brînzeu’ County Emergency Clinical Hospital, Liviu Rebreanu 156, 300723 Timișoara, Romania
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18
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Quinn-Bohmann N, Wilmanski T, Sarmiento KR, Levy L, Lampe JW, Gurry T, Rappaport N, Ostrem EM, Venturelli OS, Diener C, Gibbons SM. Microbial community-scale metabolic modelling predicts personalized short-chain fatty acid production profiles in the human gut. Nat Microbiol 2024; 9:1700-1712. [PMID: 38914826 DOI: 10.1038/s41564-024-01728-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2023] [Accepted: 05/09/2024] [Indexed: 06/26/2024]
Abstract
Microbially derived short-chain fatty acids (SCFAs) in the human gut are tightly coupled to host metabolism, immune regulation and integrity of the intestinal epithelium. However, the production of SCFAs can vary widely between individuals consuming the same diet, with lower levels often associated with disease. A systems-scale mechanistic understanding of this heterogeneity is lacking. Here we use a microbial community-scale metabolic modelling (MCMM) approach to predict individual-specific SCFA production profiles to assess the impact of different dietary, prebiotic and probiotic inputs. We evaluate the quantitative accuracy of our MCMMs using in vitro and ex vivo data, plus published human cohort data. We find that MCMM SCFA predictions are significantly associated with blood-derived clinical chemistries, including cardiometabolic and immunological health markers, across a large human cohort. Finally, we demonstrate how MCMMs can be leveraged to design personalized dietary, prebiotic and probiotic interventions aimed at optimizing SCFA production in the gut. Our model represents an approach to direct gut microbiome engineering for precision health and nutrition.
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Affiliation(s)
- Nick Quinn-Bohmann
- Institute for Systems Biology, Seattle, WA, USA
- Molecular Engineering Graduate Program, University of Washington, Seattle, WA, USA
| | | | | | - Lisa Levy
- Fred Hutchinson Cancer Center, Seattle, WA, USA
| | | | - Thomas Gurry
- Pharmaceutical Biochemistry Group, School of Pharmaceutical Sciences, University of Geneva, Geneva, Switzerland
- Myota GmbH, Berlin, Germany
| | - Noa Rappaport
- Center for Phenomic Health, Buck Institute for Research on Aging, Novato, CA, USA
| | - Erin M Ostrem
- Department of Biochemistry, University of Wisconsin-Madison, Madison, WI, USA
| | - Ophelia S Venturelli
- Department of Biochemistry, University of Wisconsin-Madison, Madison, WI, USA
- Department of Biomedical Engineering, University of Wisconsin-Madison, Madison, WI, USA
- Department of Chemical & Biological Engineering, University of Wisconsin-Madison, Madison, WI, USA
- Department of Bacteriology, University of Wisconsin-Madison, Madison, WI, USA
| | - Christian Diener
- Institute for Systems Biology, Seattle, WA, USA.
- Diagnostic and Research Institute of Hygiene, Microbiology and Environmental Medicine, Medical University of Graz, Graz, Austria.
| | - Sean M Gibbons
- Institute for Systems Biology, Seattle, WA, USA.
- Molecular Engineering Graduate Program, University of Washington, Seattle, WA, USA.
- Department of Bioengineering, University of Washington, Seattle, WA, USA.
- Department of Genome Sciences, University of Washington, Seattle, WA, USA.
- eScience Institute, University of Washington, Seattle, WA, USA.
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19
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Sun BB, Suhre K, Gibson BW. Promises and Challenges of populational Proteomics in Health and Disease. Mol Cell Proteomics 2024; 23:100786. [PMID: 38761890 PMCID: PMC11193116 DOI: 10.1016/j.mcpro.2024.100786] [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: 02/06/2024] [Revised: 05/13/2024] [Accepted: 05/15/2024] [Indexed: 05/20/2024] Open
Abstract
Advances in proteomic assay technologies have significantly increased coverage and throughput, enabling recent increases in the number of large-scale population-based proteomic studies of human plasma and serum. Improvements in multiplexed protein assays have facilitated the quantification of thousands of proteins over a large dynamic range, a key requirement for detecting the lowest-ranging, and potentially the most disease-relevant, blood-circulating proteins. In this perspective, we examine how populational proteomic datasets in conjunction with other concurrent omic measures can be leveraged to better understand the genomic and non-genomic correlates of the soluble proteome, constructing biomarker panels for disease prediction, among others. Mass spectrometry workflows are discussed as they are becoming increasingly competitive with affinity-based array platforms in terms of speed, cost, and proteome coverage due to advances in both instrumentation and workflows. Despite much success, there remain considerable challenges such as orthogonal validation and absolute quantification. We also highlight emergent challenges associated with study design, analytical considerations, and data integration as population-scale studies are run in batches and may involve longitudinal samples collated over many years. Lastly, we take a look at the future of what the nascent next-generation proteomic technologies might provide to the analysis of large sets of blood samples, as well as the difficulties in designing large-scale studies that will likely require participation from multiple and complex funding sources and where data sharing, study designs, and financing must be solved.
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Affiliation(s)
- Benjamin B Sun
- Human Genetics, Informatics and Predictive Sciences, Bristol-Myers Squibb, Cambridge, Massachusetts, USA.
| | - Karsten Suhre
- Bioinformatics Core, Weill Cornell Medicine-Qatar, Education City, Doha, Qatar; Englander Institute for Precision Medicine, Weill Cornell Medicine, New York, New York, USA
| | - Bradford W Gibson
- Pharmaceutical Chemistry, University of California, San Francisco, California, USA
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20
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Bever AM, Hang D, Lee DH, Tabung FK, Ugai T, Ogino S, Meyerhardt JA, Chan AT, Eliassen AH, Liang L, Stampfer MJ, Song M. Metabolomic signatures of inflammation and metabolic dysregulation in relation to colorectal cancer risk. J Natl Cancer Inst 2024; 116:1126-1136. [PMID: 38430005 PMCID: PMC11223797 DOI: 10.1093/jnci/djae047] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2023] [Revised: 01/18/2024] [Accepted: 02/22/2024] [Indexed: 03/03/2024] Open
Abstract
BACKGROUND Inflammation and metabolic dysregulation are associated with increased risk of colorectal cancer (CRC); the underlying mechanisms are not fully understood. We characterized metabolomic signatures of inflammation and metabolic dysregulation and evaluated the association of the signatures and individual metabolites with CRC risk. METHODS Among 684 incident CRC cases and 684 age-matched controls in the Nurses' Health Study (n = 818 women) and Health Professionals Follow-up Study (n = 550 men), we applied reduced rank and elastic net regression to 277 metabolites for markers of inflammation (C-reactive protein, interleukin 6, tumor necrosis factor receptor superfamily member 1B, and growth differentiation factor 15) or metabolic dysregulation (body mass index, waist circumference, C-peptide, and adiponectin) to derive metabolomic signatures. We evaluated the association of the signatures and individual metabolites with CRC using multivariable conditional logistic regression. All statistical tests were 2-sided. RESULTS We derived a signature of 100 metabolites that explained 24% of variation in markers of inflammation and a signature of 73 metabolites that explained 27% of variation in markers of metabolic dysregulation. Among men, both signatures were associated with CRC (odds ratio [OR] = 1.34, 95% confidence interval [CI] = 1.07 to 1.68 per 1-standard deviation increase, inflammation; OR = 1.25, 95% CI = 1.00 to 1.55 metabolic dysregulation); neither signature was associated with CRC in women. A total of 11 metabolites were individually associated with CRC and biomarkers of inflammation or metabolic dysregulation among either men or women. CONCLUSION We derived metabolomic signatures and identified individual metabolites associated with inflammation, metabolic dysregulation, and CRC, highlighting several metabolites as promising candidates involved in the inflammatory and metabolic dysregulation pathways for CRC incidence.
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Affiliation(s)
- Alaina M Bever
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Harvard-MIT Division of Health Sciences and Technology, Harvard Medical School, Boston, MA, USA
| | - Dong Hang
- Department of Epidemiology and Biostatistics, Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized Medicine, School of Public Health, Nanjing Medical University, Nanjing, China
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Dong Hoon Lee
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Department of Sport Industry Studies, Yonsei University, Seoul, Republic of Korea
| | - Fred K Tabung
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- The Ohio State University College of Medicine and Comprehensive Cancer Center, Columbus, OH, USA
| | - Tomotaka Ugai
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Program in Molecular Pathological Epidemiology, Department of Pathology, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
| | - Shuji Ogino
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Program in Molecular Pathological Epidemiology, Department of Pathology, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Jeffrey A Meyerhardt
- Department of Medical Oncology, Dana-Farber Cancer Institute and Harvard Medical School, Boston, MA, USA
| | - Andrew T Chan
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Clinical and Translational Epidemiology Unit, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - A Heather Eliassen
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA
| | - Liming Liang
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Meir J Stampfer
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA
| | - Mingyang Song
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Clinical and Translational Epidemiology Unit, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
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21
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Pathmasiri W, Rushing BR, McRitchie S, Choudhari M, Du X, Smirnov A, Pelleigrini M, Thompson MJ, Sakaguchi CA, Nieman DC, Sumner SJ. Untargeted metabolomics reveal signatures of a healthy lifestyle. Sci Rep 2024; 14:13630. [PMID: 38871777 PMCID: PMC11176323 DOI: 10.1038/s41598-024-64561-z] [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: 02/17/2024] [Accepted: 06/11/2024] [Indexed: 06/15/2024] Open
Abstract
This cross-sectional study investigated differences in the plasma metabolome in two groups of adults that were of similar age but varied markedly in body composition and dietary and physical activity patterns. Study participants included 52 adults in the lifestyle group (LIFE) (28 males, 24 females) and 52 in the control group (CON) (27 males, 25 females). The results using an extensive untargeted ultra high-performance liquid chromatography-high resolution mass spectrometry (UHPLC-HRMS) metabolomics analysis with 10,535 metabolite peaks identified 486 important metabolites (variable influence on projections scores of VIP ≥ 1) and 16 significantly enriched metabolic pathways that differentiated LIFE and CON groups. A novel metabolite signature of positive lifestyle habits emerged from this analysis highlighted by lower plasma levels of numerous bile acids, an amino acid profile characterized by higher histidine and lower glutamic acid, glutamine, β-alanine, phenylalanine, tyrosine, and proline, an elevated vitamin D status, higher levels of beneficial fatty acids and gut microbiome catabolism metabolites from plant substrates, and reduced levels of N-glycan degradation metabolites and environmental contaminants. This study established that the plasma metabolome is strongly associated with body composition and lifestyle habits. The robust lifestyle metabolite signature identified in this study is consistent with an improved life expectancy and a reduced risk for chronic disease.
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Affiliation(s)
- Wimal Pathmasiri
- Department of Nutrition, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA
- Nutrition Research Institute, University of North Carolina at Chapel Hill, Kannapolis, NC, 28081, USA
| | - Blake R Rushing
- Department of Nutrition, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA
- Nutrition Research Institute, University of North Carolina at Chapel Hill, Kannapolis, NC, 28081, USA
| | - Susan McRitchie
- Nutrition Research Institute, University of North Carolina at Chapel Hill, Kannapolis, NC, 28081, USA
| | - Mansi Choudhari
- Nutrition Research Institute, University of North Carolina at Chapel Hill, Kannapolis, NC, 28081, USA
| | - Xiuxia Du
- College of Computing and Informatics, University of North Carolina at Charlotte, Kannapolis, NC, 28081, USA
| | - Alexsandr Smirnov
- College of Computing and Informatics, University of North Carolina at Charlotte, Kannapolis, NC, 28081, USA
| | - Matteo Pelleigrini
- Department of Molecular, Cell, and Developmental Biology, University of California Los Angeles, Los Angeles, CA, USA
| | - Michael J Thompson
- Department of Molecular, Cell, and Developmental Biology, University of California Los Angeles, Los Angeles, CA, USA
| | - Camila A Sakaguchi
- Human Performance Laboratory, Department of Biology, Appalachian State University, North Carolina Research Campus, Kannapolis, NC, 28081, USA
| | - David C Nieman
- Human Performance Laboratory, Department of Biology, Appalachian State University, North Carolina Research Campus, Kannapolis, NC, 28081, USA.
| | - Susan J Sumner
- Department of Nutrition, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA.
- Nutrition Research Institute, University of North Carolina at Chapel Hill, Kannapolis, NC, 28081, USA.
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22
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Fayad ZA, Robson PM, Fuster V. Rethinking Heart Attack Prevention: The Myth of the "Vulnerable Plaque" and Reality of Patient Risk. J Am Coll Cardiol 2024; 83:2145-2147. [PMID: 38811092 DOI: 10.1016/j.jacc.2024.04.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/04/2024] [Accepted: 04/10/2024] [Indexed: 05/31/2024]
Affiliation(s)
- Zahi A Fayad
- BioMedical Engineering and Imaging Institute, Icahn School of Medicine at Mount Sinai, New York, New York, USA; Zena and Michael A. Wiener Cardiovascular Institute, Icahn School of Medicine at Mount Sinai, New York, New York, USA.
| | - Philip M Robson
- BioMedical Engineering and Imaging Institute, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Valentin Fuster
- Zena and Michael A. Wiener Cardiovascular Institute, Icahn School of Medicine at Mount Sinai, New York, New York, USA; Centro Nacional de Investigaciones Cardiovasculares, Madrid, Spain
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23
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Ayub H, Khan MA, Shehryar Ali Naqvi S, Faseeh M, Kim J, Mehmood A, Kim YJ. Unraveling the Potential of Attentive Bi-LSTM for Accurate Obesity Prognosis: Advancing Public Health towards Sustainable Cities. Bioengineering (Basel) 2024; 11:533. [PMID: 38927769 PMCID: PMC11200407 DOI: 10.3390/bioengineering11060533] [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: 04/09/2024] [Revised: 05/13/2024] [Accepted: 05/19/2024] [Indexed: 06/28/2024] Open
Abstract
The global prevalence of obesity presents a pressing challenge to public health and healthcare systems, necessitating accurate prediction and understanding for effective prevention and management strategies. This article addresses the need for improved obesity prediction models by conducting a comprehensive analysis of existing machine learning (ML) and deep learning (DL) approaches. This study introduces a novel hybrid model, Attention-based Bi-LSTM (ABi-LSTM), which integrates attention mechanisms with bidirectional Long Short-Term Memory (Bi-LSTM) networks to enhance interpretability and performance in obesity prediction. Our study fills a crucial gap by bridging healthcare and urban planning domains, offering insights into data-driven approaches to promote healthier living within urban environments. The proposed ABi-LSTM model demonstrates exceptional performance, achieving a remarkable accuracy of 96.5% in predicting obesity levels. Comparative analysis showcases its superiority over conventional approaches, with superior precision, recall, and overall classification balance. This study highlights significant advancements in predictive accuracy and positions the ABi-LSTM model as a pioneering solution for accurate obesity prognosis. The implications extend beyond healthcare, offering a precise tool to address the global obesity epidemic and foster sustainable development in smart cities.
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Affiliation(s)
- Hina Ayub
- Interdisciplinary Graduate Program in Advance Convergence Technology and Science, Jeju National University, Jeju 63243, Republic of Korea;
| | - Murad-Ali Khan
- Department of Computer Engineering, Jeju National University, Jeju 63243, Republic of Korea;
| | - Syed Shehryar Ali Naqvi
- Department of Electronics Engineering, Jeju National University, Jeju 63243, Republic of Korea; (S.S.A.N.)
| | - Muhammad Faseeh
- Department of Electronics Engineering, Jeju National University, Jeju 63243, Republic of Korea; (S.S.A.N.)
| | - Jungsuk Kim
- Department of Biomedical Engineering, College of IT Convergence, Gachon University, 1342 Seongnamdaero, Sujeong-gu, Seongnam-si 13120, Republic of Korea;
| | - Asif Mehmood
- Department of Biomedical Engineering, College of IT Convergence, Gachon University, 1342 Seongnamdaero, Sujeong-gu, Seongnam-si 13120, Republic of Korea;
| | - Young-Jin Kim
- Medical Device Development Center, Osong Medical Innovation Foundation, Cheongju 28160, Republic of Korea
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24
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Mishra M, Wu J, Kane AE, Howlett SE. The intersection of frailty and metabolism. Cell Metab 2024; 36:893-911. [PMID: 38614092 PMCID: PMC11123589 DOI: 10.1016/j.cmet.2024.03.012] [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: 11/07/2023] [Revised: 03/11/2024] [Accepted: 03/21/2024] [Indexed: 04/15/2024]
Abstract
On average, aging is associated with unfavorable changes in cellular metabolism, which are the processes involved in the storage and expenditure of energy. However, metabolic dysregulation may not occur to the same extent in all older individuals as people age at different rates. Those who are aging rapidly are at increased risk of adverse health outcomes and are said to be "frail." Here, we explore the links between frailty and metabolism, including metabolic contributors and consequences of frailty. We examine how metabolic diseases may modify the degree of frailty in old age and suggest that frailty may predispose toward metabolic disease. Metabolic interventions that can mitigate the degree of frailty in people are reviewed. New treatment strategies developed in animal models that are poised for translation to humans are also considered. We suggest that maintaining a youthful metabolism into older age may be protective against frailty.
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Affiliation(s)
- Manish Mishra
- Department of Pharmacology, Dalhousie University, Halifax, NS, Canada
| | - Judy Wu
- Institute for Systems Biology, Seattle, WA, USA
| | - Alice E Kane
- Institute for Systems Biology, Seattle, WA, USA; Department of Laboratory Medicine and Pathology, University of Washington, Seattle, WA, USA
| | - Susan E Howlett
- Department of Pharmacology, Dalhousie University, Halifax, NS, Canada; Department of Medicine (Geriatric Medicine), Dalhousie University, Halifax, NS, Canada.
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25
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Sathyan S, Milman S, Ayers E, Gao T, Verghese J, Barzilai N. Plasma proteomic profile of abdominal obesity in older adults. Obesity (Silver Spring) 2024; 32:938-948. [PMID: 38439214 PMCID: PMC11039368 DOI: 10.1002/oby.24000] [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: 08/17/2023] [Revised: 12/30/2023] [Accepted: 01/07/2024] [Indexed: 03/06/2024]
Abstract
OBJECTIVE This study examines the plasma proteomic profile of abdominal obesity in older adults. METHODS The association of abdominal obesity (waist circumference [WC]) with 4265 plasma proteins identified using the SomaScan Assay was examined in 969 Ashkenazi Jewish participants (LonGenity cohort), aged 65 years and older (mean [SD] age 75.7 [6.7] years, 55.4% women), using regression models. Pathway analysis, as well as weighted correlation network analysis, was performed. WC was determined from the proteome using elastic net regression. RESULTS A total of 480 out of 4265 proteins were associated with WC in the linear regression model. Leptin (β [SE] = 12.363 [0.490]), inhibin β C chain (INHBC; β [SE] = 24.324 [1.448]), insulin-like growth factor-binding protein 2 (IGFBP-2; β [SE] = -12.782 [0.841]), heparan-sulfate 6-O-sulfotransferase 3 (H6ST3; β [SE] = -39.995 [2.729]), and matrix-remodeling-associated protein 8 (MXRA8; β [SE] = -27.101 [1.850]) were the top proteins associated with WC. Cell adhesion, extracellular matrix remodeling, and IGF transport pathways were the top enriched pathways associated with WC. WC signature determined from plasma proteins was highly correlated with measured WC (r = 0.80) and was associated with various metabolic and physical traits. CONCLUSIONS The study unveiled a multifaceted plasma proteomic profile of abdominal obesity in older adults, offering insights into its wide-ranging impact on the proteome. It also elucidated novel proteins, clusters of correlated proteins, and pathways that are intricately associated with abdominal obesity.
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Affiliation(s)
- Sanish Sathyan
- Department of Neurology, Albert Einstein College of Medicine, Bronx, NY, USA
| | - Sofiya Milman
- Institute for Aging Research, Department of Medicine, Albert Einstein College of Medicine, Bronx, NY, USA
- Department of Genetics, Albert Einstein College of Medicine, Bronx, NY, USA
| | - Emmeline Ayers
- Department of Neurology, Albert Einstein College of Medicine, Bronx, NY, USA
| | - Tina Gao
- Institute for Aging Research, Department of Medicine, Albert Einstein College of Medicine, Bronx, NY, USA
| | - Joe Verghese
- Department of Neurology, Albert Einstein College of Medicine, Bronx, NY, USA
- Institute for Aging Research, Department of Medicine, Albert Einstein College of Medicine, Bronx, NY, USA
| | - Nir Barzilai
- Institute for Aging Research, Department of Medicine, Albert Einstein College of Medicine, Bronx, NY, USA
- Department of Genetics, Albert Einstein College of Medicine, Bronx, NY, USA
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26
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Kiseleva OI, Pyatnitskiy MA, Arzumanian VA, Kurbatov IY, Ilinsky VV, Ilgisonis EV, Plotnikova OA, Sharafetdinov KK, Tutelyan VA, Nikityuk DB, Ponomarenko EA, Poverennaya EV. Multiomics Picture of Obesity in Young Adults. BIOLOGY 2024; 13:272. [PMID: 38666884 PMCID: PMC11048234 DOI: 10.3390/biology13040272] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/29/2024] [Revised: 04/02/2024] [Accepted: 04/12/2024] [Indexed: 04/28/2024]
Abstract
Obesity is a socially significant disease that is characterized by a disproportionate accumulation of fat. It is also associated with chronic inflammation, cancer, diabetes, and other comorbidities. Investigating biomarkers and pathological processes linked to obesity is especially vital for young individuals, given their increased potential for lifestyle modifications. By comparing the genetic, proteomic, and metabolomic profiles of individuals categorized as underweight, normal, overweight, and obese, we aimed to determine which omics layer most accurately reflects the phenotypic changes in an organism that result from obesity. We profiled blood plasma samples by employing three omics methodologies. The untargeted GC×GC-MS metabolomics approach identified 313 metabolites. To augment the metabolomic dataset, we integrated a label-free HPLC-MS/MS proteomics method, leading to the identification of 708 proteins. The genomic layer encompassed the genotyping of 647,250 SNPs. Utilizing omics data, we trained sparse Partial Least Squares models to predict body mass index. Molecular features exhibiting frequently non-zero coefficients were selected as potential biomarkers, and we further explored enriched biological pathways. Proteomics was the most effective in single-omics analyses, with a median absolute error (MAE) of 5.44 ± 0.31 kg/m2, incorporating an average of 24 proteins per model. Metabolomics showed slightly lower performance (MAE = 6.06 ± 0.33 kg/m2), followed by genomics (MAE = 6.20 ± 0.34 kg/m2). As expected, multiomic models demonstrated better accuracy, particularly the combination of proteomics and metabolomics (MAE = 4.77 ± 0.33 kg/m2), while including genomics data did not enhance the results. This manuscript is the first multiomics study of obesity in a gender-balanced cohort of young adults profiled by genomic, proteomic, and metabolomic methods. The comprehensive approach provides novel insights into the molecular mechanisms of obesity, opening avenues for more targeted interventions.
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Affiliation(s)
- Olga I. Kiseleva
- Institute of Biomedical Chemistry, Moscow 119121, Russia; (O.I.K.)
| | - Mikhail A. Pyatnitskiy
- Institute of Biomedical Chemistry, Moscow 119121, Russia; (O.I.K.)
- Faculty of Computer Science, National Research University Higher School of Economics, Moscow 101000, Russia
| | | | - Ilya Y. Kurbatov
- Institute of Biomedical Chemistry, Moscow 119121, Russia; (O.I.K.)
| | | | | | - Oksana A. Plotnikova
- Federal Research Centre of Nutrition, Biotechnology and Food Safety, Russian Academy of Sciences, Moscow 109240, Russia
| | - Khaider K. Sharafetdinov
- Federal Research Centre of Nutrition, Biotechnology and Food Safety, Russian Academy of Sciences, Moscow 109240, Russia
- Russian Medical Academy of Continuing Professional Education, Ministry of Health of the Russian Federation, Moscow 125993, Russia
- I.M. Sechenov First Moscow State Medical University (Sechenov University), Ministry of Health of the Russian Federation, Moscow 119991, Russia
| | - Victor A. Tutelyan
- Federal Research Centre of Nutrition, Biotechnology and Food Safety, Russian Academy of Sciences, Moscow 109240, Russia
- I.M. Sechenov First Moscow State Medical University (Sechenov University), Ministry of Health of the Russian Federation, Moscow 119991, Russia
| | - Dmitry B. Nikityuk
- Federal Research Centre of Nutrition, Biotechnology and Food Safety, Russian Academy of Sciences, Moscow 109240, Russia
- I.M. Sechenov First Moscow State Medical University (Sechenov University), Ministry of Health of the Russian Federation, Moscow 119991, Russia
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27
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Muse ED, Topol EJ. Transforming the cardiometabolic disease landscape: Multimodal AI-powered approaches in prevention and management. Cell Metab 2024; 36:670-683. [PMID: 38428435 PMCID: PMC10990799 DOI: 10.1016/j.cmet.2024.02.002] [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: 10/26/2023] [Revised: 01/25/2024] [Accepted: 02/06/2024] [Indexed: 03/03/2024]
Abstract
The rise of artificial intelligence (AI) has revolutionized various scientific fields, particularly in medicine, where it has enabled the modeling of complex relationships from massive datasets. Initially, AI algorithms focused on improved interpretation of diagnostic studies such as chest X-rays and electrocardiograms in addition to predicting patient outcomes and future disease onset. However, AI has evolved with the introduction of transformer models, allowing analysis of the diverse, multimodal data sources existing in medicine today. Multimodal AI holds great promise in more accurate disease risk assessment and stratification as well as optimizing the key driving factors in cardiometabolic disease: blood pressure, sleep, stress, glucose control, weight, nutrition, and physical activity. In this article we outline the current state of medical AI in cardiometabolic disease, highlighting the potential of multimodal AI to augment personalized prevention and treatment strategies in cardiometabolic disease.
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Affiliation(s)
- Evan D Muse
- Scripps Research Translational Institute, Scripps Research, La Jolla, CA 92037, USA; Division of Cardiovascular Diseases, Scripps Clinic, La Jolla, CA 92037, USA
| | - Eric J Topol
- Scripps Research Translational Institute, Scripps Research, La Jolla, CA 92037, USA; Division of Cardiovascular Diseases, Scripps Clinic, La Jolla, CA 92037, USA.
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28
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Yurkovich JT, Evans SJ, Rappaport N, Boore JL, Lovejoy JC, Price ND, Hood LE. The transition from genomics to phenomics in personalized population health. Nat Rev Genet 2024; 25:286-302. [PMID: 38093095 DOI: 10.1038/s41576-023-00674-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 11/03/2023] [Indexed: 03/21/2024]
Abstract
Modern health care faces several serious challenges, including an ageing population and its inherent burden of chronic diseases, rising costs and marginal quality metrics. By assessing and optimizing the health trajectory of each individual using a data-driven personalized approach that reflects their genetics, behaviour and environment, we can start to address these challenges. This assessment includes longitudinal phenome measures, such as the blood proteome and metabolome, gut microbiome composition and function, and lifestyle and behaviour through wearables and questionnaires. Here, we review ongoing large-scale genomics and longitudinal phenomics efforts and the powerful insights they provide into wellness. We describe our vision for the transformation of the current health care from disease-oriented to data-driven, wellness-oriented and personalized population health.
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Affiliation(s)
- James T Yurkovich
- Phenome Health, Seattle, WA, USA
- Center for Phenomic Health, The Buck Institute for Research on Aging, Novato, CA, USA
- Department of Bioengineering, University of Texas at Dallas, Richardson, TX, USA
| | - Simon J Evans
- Phenome Health, Seattle, WA, USA
- Center for Phenomic Health, The Buck Institute for Research on Aging, Novato, CA, USA
| | - Noa Rappaport
- Center for Phenomic Health, The Buck Institute for Research on Aging, Novato, CA, USA
- Institute for Systems Biology, Seattle, WA, USA
| | - Jeffrey L Boore
- Phenome Health, Seattle, WA, USA
- Center for Phenomic Health, The Buck Institute for Research on Aging, Novato, CA, USA
| | - Jennifer C Lovejoy
- Phenome Health, Seattle, WA, USA
- Center for Phenomic Health, The Buck Institute for Research on Aging, Novato, CA, USA
- Institute for Systems Biology, Seattle, WA, USA
| | - Nathan D Price
- Institute for Systems Biology, Seattle, WA, USA
- Thorne HealthTech, New York, NY, USA
- Department of Bioengineering, University of Washington, Seattle, WA, USA
- Paul G. Allen School of Computer Science & Engineering, University of Washington, Seattle, WA, USA
| | - Leroy E Hood
- Phenome Health, Seattle, WA, USA.
- Center for Phenomic Health, The Buck Institute for Research on Aging, Novato, CA, USA.
- Institute for Systems Biology, Seattle, WA, USA.
- Department of Bioengineering, University of Washington, Seattle, WA, USA.
- Paul G. Allen School of Computer Science & Engineering, University of Washington, Seattle, WA, USA.
- Department of Immunology, University of Washington, Seattle, WA, USA.
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Lopez-Moreno A, Cerk K, Rodrigo L, Suarez A, Aguilera M, Ruiz-Rodriguez A. Bisphenol A exposure affects specific gut taxa and drives microbiota dynamics in childhood obesity. mSystems 2024; 9:e0095723. [PMID: 38426791 PMCID: PMC10949422 DOI: 10.1128/msystems.00957-23] [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: 09/06/2023] [Accepted: 01/15/2024] [Indexed: 03/02/2024] Open
Abstract
Cumulative xenobiotic exposure has an environmental and human health impact which is currently assessed under the One Health approach. Bisphenol A (BPA) exposure and its potential link with childhood obesity that has parallelly increased during the last decades deserve special attention. It stands during prenatal or early life and could trigger comorbidities and non-communicable diseases along life. Accumulation in the nature of synthetic chemicals supports the "environmental obesogen" hypothesis, such as BPA. This estrogen-mimicking xenobiotic has shown endocrine disruptive and obesogenic effects accompanied by gut microbiota misbalance that is not yet well elucidated. This study aimed to investigate specific microbiota taxa isolated and selected by direct BPA exposure and reveal its role on the overall children microbiota community and dynamics, driving toward specific obesity dysbiosis. A total of 333 BPA-resistant isolated species obtained through culturing after several exposure conditions were evaluated for their role and interplay with the global microbial community. The selected BPA-cultured taxa biomarkers showed a significant impact on alpha diversity. Specifically, Clostridium and Romboutsia were positively associated promoting the richness of microbiota communities, while Intestinibacter, Escherichia-Shigella, Bifidobacterium, and Lactobacillus were negatively associated. Microbial community dynamics and networks analyses showed differences according to the study groups. The normal-weight children group exhibited a more enriched, structured, and connected taxa network compared to overweight and obese groups, which could represent a more resilient community to xenobiotic substances. In this sense, subnetwork analysis generated with the BPA-cultured genera showed a correlation between taxa connectivity and more diverse potential enzymatic BPA degradation capacities.IMPORTANCEOur findings indicate how gut microbiota taxa with the capacity to grow in BPA were differentially represented within differential body mass index children study groups and how these taxa affected the overall dynamics toward patterns of diversity generally recognized in dysbiosis. Community network and subnetwork analyses corroborated the better connectedness and stability profiles for normal-weight group compared to the overweight and obese groups.
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Affiliation(s)
- Ana Lopez-Moreno
- Department of Microbiology, Faculty of Pharmacy, University of Granada, Campus of Cartuja, Granada, Spain
- Institute of Nutrition and Food Technology "José Mataix" (INYTA), Centre of Biomedical Research, University of Granada, Granada, Spain
- />Instituto de Investigación Biosanitaria ibs, Granada, Spain
| | - Klara Cerk
- Quadram Institute Bioscience, Rosalind Franklin Road, Norwich Research Park, Norwich, United Kingdom
| | - Lourdes Rodrigo
- Institute of Nutrition and Food Technology "José Mataix" (INYTA), Centre of Biomedical Research, University of Granada, Granada, Spain
| | - Antonio Suarez
- Institute of Nutrition and Food Technology "José Mataix" (INYTA), Centre of Biomedical Research, University of Granada, Granada, Spain
- Department of Biochemistry and Molecular Biology II, Faculty of Pharmacy, Campus of Cartuja, University of Granada, Granada, Spain
| | - Margarita Aguilera
- Department of Microbiology, Faculty of Pharmacy, University of Granada, Campus of Cartuja, Granada, Spain
- Institute of Nutrition and Food Technology "José Mataix" (INYTA), Centre of Biomedical Research, University of Granada, Granada, Spain
- />Instituto de Investigación Biosanitaria ibs, Granada, Spain
| | - Alicia Ruiz-Rodriguez
- Department of Microbiology, Faculty of Pharmacy, University of Granada, Campus of Cartuja, Granada, Spain
- Institute of Nutrition and Food Technology "José Mataix" (INYTA), Centre of Biomedical Research, University of Granada, Granada, Spain
- Department of Biochemistry and Molecular Biology II, Faculty of Pharmacy, Campus of Cartuja, University of Granada, Granada, Spain
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Kolic J, Sun WG, Cen HH, Ewald J, Rogalski JC, Sasaki S, Sun H, Rajesh V, Xia YH, Moravcova R, Skovsø S, Spigelman AF, Manning Fox JE, Lyon J, Beet L, Xia J, Lynn FC, Gloyn AL, Foster LJ, MacDonald PE, Johnson JD. Proteomic predictors of individualized nutrient-specific insulin secretion in health and disease. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2023.05.24.23290298. [PMID: 38496562 PMCID: PMC10942505 DOI: 10.1101/2023.05.24.23290298] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/19/2024]
Abstract
Population level variation and molecular mechanisms behind insulin secretion in response to carbohydrate, protein, and fat remain uncharacterized despite ramifications for personalized nutrition. Here, we define prototypical insulin secretion dynamics in response to the three macronutrients in islets from 140 cadaveric donors, including those diagnosed with type 2 diabetes. While islets from the majority of donors exhibited the expected relative response magnitudes, with glucose being highest, amino acid moderate, and fatty acid small, 9% of islets stimulated with amino acid and 8% of islets stimulated with fatty acids had larger responses compared with high glucose. We leveraged this insulin response heterogeneity and used transcriptomics and proteomics to identify molecular correlates of specific nutrient responsiveness, as well as those proteins and mRNAs altered in type 2 diabetes. We also examine nutrient-responsiveness in stem cell-derived islet clusters and observe that they have dysregulated fuel sensitivity, which is a hallmark of functionally immature cells. Our study now represents the first comparison of dynamic responses to nutrients and multi-omics analysis in human insulin secreting cells. Responses of different people's islets to carbohydrate, protein, and fat lay the groundwork for personalized nutrition. ONE-SENTENCE SUMMARY Deep phenotyping and multi-omics reveal individualized nutrient-specific insulin secretion propensity.
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31
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Yuan S, Xu F, Zhang H, Chen J, Ruan X, Li Y, Burgess S, Åkesson A, Li X, Gill D, Larsson SC. Proteomic insights into modifiable risk of venous thromboembolism and cardiovascular comorbidities. J Thromb Haemost 2024; 22:738-748. [PMID: 38029854 PMCID: PMC7615672 DOI: 10.1016/j.jtha.2023.11.013] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2023] [Revised: 10/30/2023] [Accepted: 11/03/2023] [Indexed: 12/01/2023]
Abstract
BACKGROUND Venous thromboembolism (VTE) has been associated with several modifiable factors (MFs) and cardiovascular comorbidities. However, the mechanisms are largely unknown. OBJECTIVES We aimed to decipher proteomic pathways underlying the associations of VTE with MFs and cardiovascular comorbidities. METHODS A 2-stage network Mendelian randomization analysis was conducted to explore the associations between 15 MFs, 1151 blood proteins, and VTE using data from a genome-wide meta-analysis including 81 190 cases of VTE. We used protein data from 35 559 individuals as the discovery analysis, and from 2 independent studies including 10 708 and 54 219 participants as the replication analyses. Based on the identified proteins, we assessed the druggability and examined the cardiovascular pleiotropy. RESULTS The network Mendelian randomization analyses identified 10 MF-VTE, 86 MF-protein, and 34 protein-VTE associations. These associations were overall consistent in the replication analyses. Thirty-eight pathways with directionally consistent direct and indirect effects in the MF-protein-VTE pathway were identified. Low-density lipoprotein receptor-related protein 12 (LRP12: 34.3%-58.1%) and coagulation factor (F)XI (20.6%-39.6%) mediated most of the associations between 3 obesity indicators and VTE. Likewise, coagulation FXI mediated most of the smoking-VTE association (40%; 95% CI, 20%-60%) and insomnia-VTE association (27%; 95% CI, 5%-49%). Many VTE-associated proteins were highly druggable for thrombotic conditions. Five proteins (interleukin-6 receptor subunit alpha, LRP12, prothrombin, angiopoietin-1, and low-density lipoprotein receptor-related protein 4) were associated with VTE and its cardiovascular comorbidities. CONCLUSION This study suggests that coagulation FXI, a druggable target, is an important mediator of the associations of obesity, smoking, and insomnia with VTE risk.
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Affiliation(s)
- Shuai Yuan
- Unit of Cardiovascular and Nutritional Epidemiology, Institute of Environmental Medicine, Karolinska Institute, Stockholm, Sweden
| | - Fengzhe Xu
- Key Laboratory of Growth Regulation and Translational Research of Zhejiang Province, School of Life Sciences, Westlake University, Hangzhou, China
| | - Han Zhang
- Department of Big Data in Health Science School of Public Health, Center of Clinical Big Data and Analytics of the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Jie Chen
- Department of Big Data in Health Science School of Public Health, Center of Clinical Big Data and Analytics of the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Xixian Ruan
- Department of Gastroenterology, the Third Xiangya Hospital, Central South University, Changsha, China
| | - Yuying Li
- Department of Medical Epidemiology and Biostatistics, Karolinska Institute, Stockholm, Sweden
| | - Stephen Burgess
- Medical Research Council Biostatistics Unit, University of Cambridge, Cambridge, UK
- Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
| | - Agneta Åkesson
- Unit of Cardiovascular and Nutritional Epidemiology, Institute of Environmental Medicine, Karolinska Institute, Stockholm, Sweden
| | - Xue Li
- Department of Big Data in Health Science School of Public Health, Center of Clinical Big Data and Analytics of the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Dipender Gill
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, UK
| | - Susanna C. Larsson
- Unit of Cardiovascular and Nutritional Epidemiology, Institute of Environmental Medicine, Karolinska Institute, Stockholm, Sweden
- Unit of Medical Epidemiology, Department of Surgical Sciences, Uppsala University, Uppsala, Sweden
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Halade GV, Upadhyay G, Marimuthu M, Wanling X, Kain V. Exercise reduces pro-inflammatory lipids and preserves resolution mediators that calibrate macrophage-centric immune metabolism in spleen and heart following obesogenic diet in aging mice. J Mol Cell Cardiol 2024; 188:79-89. [PMID: 38364731 DOI: 10.1016/j.yjmcc.2024.02.004] [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: 11/15/2023] [Revised: 02/07/2024] [Accepted: 02/08/2024] [Indexed: 02/18/2024]
Abstract
The study investigated the role of volunteer exercise and an obesogenic diet (OBD) in mice, focusing on the splenocardiac axis and inflammation-resolution signaling. Male C57BL/6J mice (2 months old) were assigned to control (CON) or OBD groups for ten months, then randomized into sedentary (Sed) or exercise (Exe) groups for two weeks. Leukocytes, heart function, structure, and spleen tissue examined for inflammation-resolution mediators and macrophage-centric gene transcripts. After two weeks of volunteer exercise, cardiac function shows limited changes, but structural changes were notable in the heart and spleen. Exercise induced cardiac nuclear hyperplasia observed in both CON and OBD groups. OBD-Sed mice showed splenic changes and increased neutrophils, whereas increased neutrophils were noted in the CON post exercise. OBD-Sed increased pro-inflammatory lipid mediators in the heart, reduced by exercise in OBD-Exe, while CON-Exe preserved resolution mediators. Chronic OBD-Sed depletes long chain fatty acids (DHA/EPA) in the heart and spleen, while exercise independently regulates lipid metabolism genes in both organs, affecting macrophage-centric lipid and lipoprotein pathways. Chronic obesity amplified cardiac inflammation, countered by exercise that lowered pro-inflammatory bioactive lipid mediators in the heart. OBD sustained inflammation in the heart and spleen, while exercise conserved resolution mediators in CON mice. In summary, these findings emphasize the interplay of diet with exercise and highlight the intricate connection of diet, exercise, inflammation-resolution signaling in splenocardiac axis and immune health.
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Affiliation(s)
- Ganesh V Halade
- Heart Institute, Division of Cardiovascular Sciences, Department of Internal Medicine, University of South Florida, Tampa, FL, USA.
| | - Gunjan Upadhyay
- Heart Institute, Division of Cardiovascular Sciences, Department of Internal Medicine, University of South Florida, Tampa, FL, USA
| | - MathanKumar Marimuthu
- Heart Institute, Division of Cardiovascular Sciences, Department of Internal Medicine, University of South Florida, Tampa, FL, USA
| | - Xuan Wanling
- Taneja College of Pharmacy, University of South Florida, Tampa, FL, USA
| | - Vasundhara Kain
- Heart Institute, Division of Cardiovascular Sciences, Department of Internal Medicine, University of South Florida, Tampa, FL, USA
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Shilo S, Segal E. Endocrinology in the multi-omics era. Nat Rev Endocrinol 2024; 20:73-74. [PMID: 38052868 DOI: 10.1038/s41574-023-00931-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/07/2023]
Affiliation(s)
- Smadar Shilo
- Department of Computer Science and Applied Mathematics, Weizmann Institute of Science, Rehovot, Israel
- Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot, Israel
- The Jesse Z and Sara Lea Shafer Institute for Endocrinology and Diabetes, National Center for Childhood Diabetes, Schneider Children's Medical Center of Israel, Petah Tikva, Israel
| | - Eran Segal
- Department of Computer Science and Applied Mathematics, Weizmann Institute of Science, Rehovot, Israel.
- Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot, Israel.
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Evans M, Lewis ED, Antony JM, Crowley DC, Charrette A, Guthrie N, Blumberg JB, Reid G. Revisiting the Definition of 'Healthy' Participants in Substantiation of Structure/Function Claims for Dietary Supplements. J Diet Suppl 2024; 22:41-57. [PMID: 38298107 DOI: 10.1080/19390211.2023.2301383] [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: 02/02/2024]
Abstract
Concepts and definitions of 'healthy' have been evolving within clinical treatment algorithms as well as reference standards such as Body Mass Index and Dietary Reference Intakes. Consumers' perception of the word 'healthy' is also changing to reflect longer life span, need to stay active and in a good state of mental well-being while managing multiple diseases. Guidelines from the US Food and Drug Administration indicate that substantiating evidence for support of Structure/Function (S/F) claims for dietary supplements is best derived from clinical research conducted in a 'healthy' population. S/F claims cannot be represented to diagnose, treat, cure or prevent any disease. However, in this context, the term 'healthy' is non-descriptive and largely interpreted as an absence of disease. Guidelines for treatment of disease have been broadened to include biomarkers of disease risk such that the pool of 'healthy' volunteers eligible to be enrolled in clinical trials for S/F claim substantiation is greatly diminished. This perspective presents the challenges faced by the food and dietary supplement industry and by researcher efforts designed to substantiate S/F claims and suggest the phrase 'physiologically stable' or 'apparently healthy' as descriptions better suited to replace the term 'healthy.'
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Affiliation(s)
- Malkanthi Evans
- Clinical Trials Division, KGK Science Inc, London, Ontario, Canada
| | - Erin D Lewis
- Clinical Trials Division, KGK Science Inc, London, Ontario, Canada
| | - Joseph M Antony
- Clinical Trials Division, KGK Science Inc, London, Ontario, Canada
| | - David C Crowley
- Clinical Trials Division, KGK Science Inc, London, Ontario, Canada
| | | | | | - Jeffrey B Blumberg
- Friedman School of Nutrition Science and Policy, Tufts University, Boston, MA, USA
| | - Gregor Reid
- Departments of Microbiology & Immunology and Surgery, Western University, London, Ontario, Canada
- Lawson Health Research Institute, London, Ontario, Canada
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35
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Zhang Y, Qin G, Aguilar B, Rappaport N, Yurkovich JT, Pflieger L, Huang S, Hood L, Shmulevich I. A framework towards digital twins for type 2 diabetes. Front Digit Health 2024; 6:1336050. [PMID: 38343907 PMCID: PMC10853398 DOI: 10.3389/fdgth.2024.1336050] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2023] [Accepted: 01/15/2024] [Indexed: 11/16/2024] Open
Abstract
Introduction A digital twin is a virtual representation of a patient's disease, facilitating real-time monitoring, analysis, and simulation. This enables the prediction of disease progression, optimization of care delivery, and improvement of outcomes. Methods Here, we introduce a digital twin framework for type 2 diabetes (T2D) that integrates machine learning with multiomic data, knowledge graphs, and mechanistic models. By analyzing a substantial multiomic and clinical dataset, we constructed predictive machine learning models to forecast disease progression. Furthermore, knowledge graphs were employed to elucidate and contextualize multiomic-disease relationships. Results and discussion Our findings not only reaffirm known targetable disease components but also spotlight novel ones, unveiled through this integrated approach. The versatile components presented in this study can be incorporated into a digital twin system, enhancing our grasp of diseases and propelling the advancement of precision medicine.
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Affiliation(s)
- Yue Zhang
- Institute for Systems Biology, Seattle, WA, United States
| | - Guangrong Qin
- Institute for Systems Biology, Seattle, WA, United States
| | - Boris Aguilar
- Institute for Systems Biology, Seattle, WA, United States
| | - Noa Rappaport
- Institute for Systems Biology, Seattle, WA, United States
- Center for Phenomic Health, Buck Institute for Research on Aging, Novato, CA, United States
| | - James T. Yurkovich
- Center for Phenomic Health, Buck Institute for Research on Aging, Novato, CA, United States
- Phenome Health, Seattle, WA, United States
| | - Lance Pflieger
- Center for Phenomic Health, Buck Institute for Research on Aging, Novato, CA, United States
- Phenome Health, Seattle, WA, United States
| | - Sui Huang
- Institute for Systems Biology, Seattle, WA, United States
| | - Leroy Hood
- Institute for Systems Biology, Seattle, WA, United States
- Center for Phenomic Health, Buck Institute for Research on Aging, Novato, CA, United States
- Phenome Health, Seattle, WA, United States
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Drouard G, Hagenbeek FA, Whipp AM, Pool R, Hottenga JJ, Jansen R, Hubers N, Afonin A, Willemsen G, de Geus EJC, Ripatti S, Pirinen M, Kanninen KM, Boomsma DI, van Dongen J, Kaprio J. Longitudinal multi-omics study reveals common etiology underlying association between plasma proteome and BMI trajectories in adolescent and young adult twins. BMC Med 2023; 21:508. [PMID: 38129841 PMCID: PMC10740308 DOI: 10.1186/s12916-023-03198-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/03/2023] [Accepted: 11/27/2023] [Indexed: 12/23/2023] Open
Abstract
BACKGROUND The influence of genetics and environment on the association of the plasma proteome with body mass index (BMI) and changes in BMI remains underexplored, and the links to other omics in these associations remain to be investigated. We characterized protein-BMI trajectory associations in adolescents and adults and how these connect to other omics layers. METHODS Our study included two cohorts of longitudinally followed twins: FinnTwin12 (N = 651) and the Netherlands Twin Register (NTR) (N = 665). Follow-up comprised 4 BMI measurements over approximately 6 (NTR: 23-27 years old) to 10 years (FinnTwin12: 12-22 years old), with omics data collected at the last BMI measurement. BMI changes were calculated in latent growth curve models. Mixed-effects models were used to quantify the associations between the abundance of 439 plasma proteins with BMI at blood sampling and changes in BMI. In FinnTwin12, the sources of genetic and environmental variation underlying the protein abundances were quantified by twin models, as were the associations of proteins with BMI and BMI changes. In NTR, we investigated the association of gene expression of genes encoding proteins identified in FinnTwin12 with BMI and changes in BMI. We linked identified proteins and their coding genes to plasma metabolites and polygenic risk scores (PRS) applying mixed-effects models and correlation networks. RESULTS We identified 66 and 14 proteins associated with BMI at blood sampling and changes in BMI, respectively. The average heritability of these proteins was 35%. Of the 66 BMI-protein associations, 43 and 12 showed genetic and environmental correlations, respectively, including 8 proteins showing both. Similarly, we observed 7 and 3 genetic and environmental correlations between changes in BMI and protein abundance, respectively. S100A8 gene expression was associated with BMI at blood sampling, and the PRG4 and CFI genes were associated with BMI changes. Proteins showed strong connections with metabolites and PRSs, but we observed no multi-omics connections among gene expression and other omics layers. CONCLUSIONS Associations between the proteome and BMI trajectories are characterized by shared genetic, environmental, and metabolic etiologies. We observed few gene-protein pairs associated with BMI or changes in BMI at the proteome and transcriptome levels.
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Affiliation(s)
- Gabin Drouard
- Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, Helsinki, Finland.
| | - Fiona A Hagenbeek
- Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, Helsinki, Finland
- Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Amsterdam Public Health Research Institute, Amsterdam, The Netherlands
| | - Alyce M Whipp
- Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, Helsinki, Finland
| | - René Pool
- Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Amsterdam Public Health Research Institute, Amsterdam, The Netherlands
| | - Jouke Jan Hottenga
- Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Amsterdam Public Health Research Institute, Amsterdam, The Netherlands
| | - Rick Jansen
- Department of Psychiatry, Amsterdam UMC Location Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
- Amsterdam Neuroscience, Mood, Anxiety, Psychosis, Sleep & Stress Program, Amsterdam, The Netherlands
| | - Nikki Hubers
- Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Amsterdam Public Health Research Institute, Amsterdam, The Netherlands
- Amsterdam Reproduction & Development (AR&D) Research Institute, Amsterdam, The Netherlands
| | - Aleksei Afonin
- A.I. Virtanen Institute for Molecular Sciences, University of Eastern Finland, Kuopio, Finland
| | - Gonneke Willemsen
- Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Amsterdam Public Health Research Institute, Amsterdam, The Netherlands
| | - Eco J C de Geus
- Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Amsterdam Public Health Research Institute, Amsterdam, The Netherlands
| | - Samuli Ripatti
- Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, Helsinki, Finland
- Department of Public Health, Faculty of Medicine, University of Helsinki, Helsinki, Finland
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Matti Pirinen
- Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, Helsinki, Finland
- Department of Public Health, Faculty of Medicine, University of Helsinki, Helsinki, Finland
- Department of Mathematics and Statistics, University of Helsinki, Helsinki, Finland
| | - Katja M Kanninen
- A.I. Virtanen Institute for Molecular Sciences, University of Eastern Finland, Kuopio, Finland
| | - Dorret I Boomsma
- Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Amsterdam Public Health Research Institute, Amsterdam, The Netherlands
- Amsterdam Reproduction & Development (AR&D) Research Institute, Amsterdam, The Netherlands
| | - Jenny van Dongen
- Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Amsterdam Public Health Research Institute, Amsterdam, The Netherlands
- Amsterdam Reproduction & Development (AR&D) Research Institute, Amsterdam, The Netherlands
| | - Jaakko Kaprio
- Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, Helsinki, Finland.
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Abraham A, Yaghootkar H. Identifying obesity subtypes: A review of studies utilising clinical biomarkers and genetic data. Diabet Med 2023; 40:e15226. [PMID: 37704218 DOI: 10.1111/dme.15226] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/20/2023] [Revised: 09/08/2023] [Accepted: 09/12/2023] [Indexed: 09/15/2023]
Abstract
Obesity is a complex and multifactorial condition that poses significant health risks. Recent advancements in our understanding of obesity have highlighted the heterogeneity within this disorder. Identifying distinct subtypes of obesity is crucial for personalised treatment and intervention strategies. This review paper aims to examine studies that have utilised clinical biomarkers and genetic data to identify clusters or subtypes of obesity. The findings of these studies may provide valuable insights into the underlying mechanisms and potential targeted approaches for managing obesity-related health issues such as type 2 diabetes.
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Affiliation(s)
- Angela Abraham
- Joseph Banks Laboratories, College of Health and Science, University of Lincoln, Lincoln, Lincolnshire, UK
| | - Hanieh Yaghootkar
- Joseph Banks Laboratories, College of Health and Science, University of Lincoln, Lincoln, Lincolnshire, UK
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Swaby A, Atallah A, Varol O, Cristea A, Quail DF. Lifestyle and host determinants of antitumor immunity and cancer health disparities. Trends Cancer 2023; 9:1019-1040. [PMID: 37718223 DOI: 10.1016/j.trecan.2023.08.007] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2023] [Revised: 08/14/2023] [Accepted: 08/15/2023] [Indexed: 09/19/2023]
Abstract
Lifestyle factors exert profound effects on host physiology and immunology. Disparities in cancer outcomes persist as a complex and multifaceted challenge, necessitating a comprehensive understanding of the interplay between host environment and antitumor immune responses. Determinants of health - such as obesity, diet, exercise, stress, or sleep disruption - have the potential for modification, yet some exert long-lasting effects and may challenge the notion of complete reversibility. Herein we review intersectional considerations of lifestyle immunity and the impact on tumor immunology and disparities in cancer outcomes, with a focus on obesity.
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Affiliation(s)
- Anikka Swaby
- Goodman Cancer Research Institute, Montreal, QC, Canada; Department of Experimental Medicine, McGill University, Montreal, QC, Canada
| | - Aline Atallah
- Goodman Cancer Research Institute, Montreal, QC, Canada; Department of Experimental Medicine, McGill University, Montreal, QC, Canada
| | - Ozgun Varol
- Goodman Cancer Research Institute, Montreal, QC, Canada; Department of Experimental Medicine, McGill University, Montreal, QC, Canada
| | - Alyssa Cristea
- Goodman Cancer Research Institute, Montreal, QC, Canada; Department of Experimental Medicine, McGill University, Montreal, QC, Canada
| | - Daniela F Quail
- Goodman Cancer Research Institute, Montreal, QC, Canada; Department of Experimental Medicine, McGill University, Montreal, QC, Canada; Department of Physiology, McGill University, Montreal, QC, Canada.
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Prone-Olazabal D, Davies I, González-Galarza FF. Metabolic Syndrome: An Overview on Its Genetic Associations and Gene-Diet Interactions. Metab Syndr Relat Disord 2023; 21:545-560. [PMID: 37816229 DOI: 10.1089/met.2023.0125] [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: 10/12/2023] Open
Abstract
Metabolic syndrome (MetS) is a cluster of cardiometabolic risk factors that includes central obesity, hyperglycemia, hypertension, and dyslipidemias and whose inter-related occurrence may increase the odds of developing type 2 diabetes and cardiovascular diseases. MetS has become one of the most studied conditions, nevertheless, due to its complex etiology, this has not been fully elucidated. Recent evidence describes that both genetic and environmental factors play an important role on its development. With the advent of genomic-wide association studies, single nucleotide polymorphisms (SNPs) have gained special importance. In this review, we present an update of the genetics surrounding MetS as a single entity as well as its corresponding risk factors, considering SNPs and gene-diet interactions related to cardiometabolic markers. In this study, we focus on the conceptual aspects, diagnostic criteria, as well as the role of genetics, particularly on SNPs and polygenic risk scores (PRS) for interindividual analysis. In addition, this review highlights future perspectives of personalized nutrition with regard to the approach of MetS and how individualized multiomics approaches could improve the current outlook.
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Affiliation(s)
- Denisse Prone-Olazabal
- Postgraduate Department, Faculty of Medicine, Autonomous University of Coahuila, Torreon, Mexico
| | - Ian Davies
- Research Institute of Sport and Exercise Science, The Institute for Health Research, Liverpool John Moores University, Liverpool, United Kingdom
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López‐Moreno A, Langella P, Martín R, Aguilera M. Microbiota analysis for risk assessment of xenobiotic exposure and the impact on dysbiosis: identifying potential next-generation probiotics. EFSA J 2023; 21:e211010. [PMID: 38047127 PMCID: PMC10687753 DOI: 10.2903/j.efsa.2023.e211010] [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] [Indexed: 12/05/2023] Open
Abstract
On-going projects of the team are currently dealing with microbiota, xenobiotics, endocrine-disrupting chemicals (EDCs), obesity, inflammation and probiotics. The combination of diet, lifestyle and the exposure to dietary xenobiotics categorised into microbiota-disrupting chemicals (MDCs) could determine obesogenic-related dysbiosis. This modification of the microbiota diversity impacts on individual health-disease balance, inducing altered phenotypes. Specific, complementary, and combined prevention and treatments are needed to face these altered microbial patterns and the specific misbalances triggered. In this sense, searching for next-generation probiotics (NGP) by microbiota culturing, and focusing on their demonstrated, extensive scope and well-defined functions could contribute to counteracting and repairing the effects of obesogens. Therefore, EU-FORA project contributes to present a perspective through compiling information and key strategies for directed taxa searching and culturing of NGP that could be administered for preventing obesity and endocrine-related dysbiosis by (i) observing the differential abundance of specific microbiota taxa in obesity-related patients and analysing their functional roles, (ii) developing microbiota-directed strategies for culturing these taxa groups, and (iii) design and applying the successful compiled criteria from recent NGP clinical studies. New isolated or cultivable microorganisms from healthy gut microbiota specifically related to xenobiotic obesogens' neutralisation effects might be used as an NGP single strain or in consortia, both presenting functions and the ability to palliate metabolic-related disorders. Identification of holistic approaches for searching and using potential NGP, key aspects, the bias, gaps and proposals of solutions were also considered in this workplan.
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Affiliation(s)
- Ana López‐Moreno
- Microbiology Department, Faculty of PharmacyUniversity of GranadaSpain
- "José Mataix Verdú" Institute of Nutrition and Food Technology, University of Granada (INYTA‐UGR)GranadaSpain
| | - Philippe Langella
- Commensal and Probiotics‐Host Interactions Laboratory, INRAE, AgroParisTech, Micalis Institute, Université Paris‐Saclay78350Jouy‐en‐JosasFrance
| | - Rebeca Martín
- Commensal and Probiotics‐Host Interactions Laboratory, INRAE, AgroParisTech, Micalis Institute, Université Paris‐Saclay78350Jouy‐en‐JosasFrance
| | - Margarita Aguilera
- Microbiology Department, Faculty of PharmacyUniversity of GranadaSpain
- "José Mataix Verdú" Institute of Nutrition and Food Technology, University of Granada (INYTA‐UGR)GranadaSpain
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Szczerbinski L, Florez JC. Precision medicine of obesity as an integral part of type 2 diabetes management - past, present, and future. Lancet Diabetes Endocrinol 2023; 11:861-878. [PMID: 37804854 DOI: 10.1016/s2213-8587(23)00232-2] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/25/2023] [Revised: 07/29/2023] [Accepted: 08/01/2023] [Indexed: 10/09/2023]
Abstract
Obesity is a complex and heterogeneous condition that leads to various metabolic complications, including type 2 diabetes. Unfortunately, for some, treatment options to date for obesity are insufficient, with many people not reaching sustained weight loss or having improvements in metabolic health. In this Review, we discuss advances in the genetics of obesity from the past decade-with emphasis on developments from the past 5 years-with a focus on metabolic consequences, and their potential implications for precision management of the disease. We also provide an overview of the potential role of genetics in guiding weight loss strategies. Finally, we propose a vision for the future of precision obesity management that includes developing an obesity-centred multidisease management algorithm that targets both obesity and its comorbidities. However, further collaborative efforts and research are necessary to fully realise its potential and improve metabolic health outcomes.
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Affiliation(s)
- Lukasz Szczerbinski
- Diabetes Unit, Endocrine Division, Department of Medicine, Massachusetts General Hospital, Boston, MA, USA; Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA; Programs in Metabolism and Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA; Clinical Research Centre, Medical University of Bialystok, Bialystok, Poland; Department of Endocrinology, Diabetology and Internal Medicine, Medical University of Bialystok, Bialystok, Poland
| | - Jose C Florez
- Department of Medicine, Massachusetts General Hospital, Boston, MA, USA; Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA; Programs in Metabolism and Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA; Department of Medicine, Harvard Medical School, Boston, MA, USA.
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Beyene HB, Giles C, Huynh K, Wang T, Cinel M, Mellett NA, Olshansky G, Meikle TG, Watts GF, Hung J, Hui J, Cadby G, Beilby J, Blangero J, Moses EK, Shaw JE, Magliano DJ, Meikle PJ. Metabolic phenotyping of BMI to characterize cardiometabolic risk: evidence from large population-based cohorts. Nat Commun 2023; 14:6280. [PMID: 37805498 PMCID: PMC10560260 DOI: 10.1038/s41467-023-41963-7] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2023] [Accepted: 09/26/2023] [Indexed: 10/09/2023] Open
Abstract
Obesity is a risk factor for type 2 diabetes and cardiovascular disease. However, a substantial proportion of patients with these conditions have a seemingly normal body mass index (BMI). Conversely, not all obese individuals present with metabolic disorders giving rise to the concept of "metabolically healthy obese". We use lipidomic-based models for BMI to calculate a metabolic BMI score (mBMI) as a measure of metabolic dysregulation associated with obesity. Using the difference between mBMI and BMI (mBMIΔ), we identify individuals with a similar BMI but differing in their metabolic health and disease risk profiles. Exercise and diet associate with mBMIΔ suggesting the ability to modify mBMI with lifestyle intervention. Our findings show that, the mBMI score captures information on metabolic dysregulation that is independent of the measured BMI and so provides an opportunity to assess metabolic health to identify "at risk" individuals for targeted intervention and monitoring.
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Affiliation(s)
- Habtamu B Beyene
- Baker Heart and Diabetes Institute, Melbourne, VIC, Australia
- Faculty of Medicine, Nursing and Health Sciences, Monash University, Melbourne, VIC, Australia
- Baker Department of Cardiovascular Research Translation and Implementation, La Trobe University, Melbourne, VIC, Australia
- Baker Department of Cardiometabolic Health, Melbourne University, Melbourne, VIC, Australia
| | - Corey Giles
- Baker Heart and Diabetes Institute, Melbourne, VIC, Australia
- Baker Department of Cardiovascular Research Translation and Implementation, La Trobe University, Melbourne, VIC, Australia
- Baker Department of Cardiometabolic Health, Melbourne University, Melbourne, VIC, Australia
| | - Kevin Huynh
- Baker Heart and Diabetes Institute, Melbourne, VIC, Australia
- Baker Department of Cardiovascular Research Translation and Implementation, La Trobe University, Melbourne, VIC, Australia
- Baker Department of Cardiometabolic Health, Melbourne University, Melbourne, VIC, Australia
| | - Tingting Wang
- Baker Heart and Diabetes Institute, Melbourne, VIC, Australia
- Baker Department of Cardiovascular Research Translation and Implementation, La Trobe University, Melbourne, VIC, Australia
- Baker Department of Cardiometabolic Health, Melbourne University, Melbourne, VIC, Australia
| | - Michelle Cinel
- Baker Heart and Diabetes Institute, Melbourne, VIC, Australia
| | | | | | - Thomas G Meikle
- Baker Heart and Diabetes Institute, Melbourne, VIC, Australia
- Baker Department of Cardiovascular Research Translation and Implementation, La Trobe University, Melbourne, VIC, Australia
| | - Gerald F Watts
- School of Medicine, University of Western Australia, Perth, WA, Australia
- Lipid Disorders Clinic, Department of Cardiology, Royal Perth Hospital, Perth, WA, Australia
| | - Joseph Hung
- School of Medicine, University of Western Australia, Perth, WA, Australia
| | - Jennie Hui
- PathWest Laboratory Medicine of Western Australia, Nedlands, WA, Australia
- School of Biomedical Sciences, University of Western Australia, Crawley, WA, Australia
- School of Population and Global Health, University of Western Australia, Crawley, WA, Australia
| | - Gemma Cadby
- School of Population and Global Health, University of Western Australia, Crawley, WA, Australia
| | - John Beilby
- School of Biomedical Sciences, University of Western Australia, Crawley, WA, Australia
| | - John Blangero
- South Texas Diabetes and Obesity Institute, The University of Texas Rio Grande Valley, Brownsville, TX, USA
| | - Eric K Moses
- School of Biomedical Sciences, University of Western Australia, Crawley, WA, Australia
- Menzies Institute for Medical Research, University of Tasmania, Hobart, TAS, Australia
| | - Jonathan E Shaw
- Baker Heart and Diabetes Institute, Melbourne, VIC, Australia
- Faculty of Medicine, Nursing and Health Sciences, Monash University, Melbourne, VIC, Australia
| | - Dianna J Magliano
- Baker Heart and Diabetes Institute, Melbourne, VIC, Australia.
- Faculty of Medicine, Nursing and Health Sciences, Monash University, Melbourne, VIC, Australia.
| | - Peter J Meikle
- Baker Heart and Diabetes Institute, Melbourne, VIC, Australia.
- Faculty of Medicine, Nursing and Health Sciences, Monash University, Melbourne, VIC, Australia.
- Baker Department of Cardiovascular Research Translation and Implementation, La Trobe University, Melbourne, VIC, Australia.
- Baker Department of Cardiometabolic Health, Melbourne University, Melbourne, VIC, Australia.
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Drouard G, Hagenbeek FA, Whipp A, Pool R, Hottenga JJ, Jansen R, Hubers N, Afonin A, Willemsen G, de Geus EJC, Ripatti S, Pirinen M, Kanninen KM, Boomsma DI, van Dongen J, Kaprio J. Longitudinal multi-omics study reveals common etiology underlying association between plasma proteome and BMI trajectories in adolescent and young adult twins. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.06.28.23291995. [PMID: 37425750 PMCID: PMC10327285 DOI: 10.1101/2023.06.28.23291995] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/11/2023]
Abstract
Background The influence of genetics and environment on the association of the plasma proteome with body mass index (BMI) and changes in BMI remain underexplored, and the links to other omics in these associations remain to be investigated. We characterized protein-BMI trajectory associations in adolescents and adults and how these connect to other omics layers. Methods Our study included two cohorts of longitudinally followed twins: FinnTwin12 (N=651) and the Netherlands Twin Register (NTR) (N=665). Follow-up comprised four BMI measurements over approximately 6 (NTR: 23-27 years old) to 10 years (FinnTwin12: 12-22 years old), with omics data collected at the last BMI measurement. BMI changes were calculated using latent growth curve models. Mixed-effects models were used to quantify the associations between the abundance of 439 plasma proteins with BMI at blood sampling and changes in BMI. The sources of genetic and environmental variation underlying the protein abundances were quantified using twin models, as were the associations of proteins with BMI and BMI changes. In NTR, we investigated the association of gene expression of genes encoding proteins identified in FinnTwin12 with BMI and changes in BMI. We linked identified proteins and their coding genes to plasma metabolites and polygenic risk scores (PRS) using mixed-effect models and correlation networks. Results We identified 66 and 14 proteins associated with BMI at blood sampling and changes in BMI, respectively. The average heritability of these proteins was 35%. Of the 66 BMI-protein associations, 43 and 12 showed genetic and environmental correlations, respectively, including 8 proteins showing both. Similarly, we observed 6 and 4 genetic and environmental correlations between changes in BMI and protein abundance, respectively. S100A8 gene expression was associated with BMI at blood sampling, and the PRG4 and CFI genes were associated with BMI changes. Proteins showed strong connections with many metabolites and PRSs, but we observed no multi-omics connections among gene expression and other omics layers. Conclusions Associations between the proteome and BMI trajectories are characterized by shared genetic, environmental, and metabolic etiologies. We observed few gene-protein pairs associated with BMI or changes in BMI at the proteome and transcriptome levels.
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Affiliation(s)
- Gabin Drouard
- Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, Helsinki, Finland
| | - Fiona A. Hagenbeek
- Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, Helsinki, Finland
- Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Amsterdam Public Health Research Institute, Amsterdam, The Netherlands
| | - Alyce Whipp
- Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, Helsinki, Finland
| | - René Pool
- Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Amsterdam Public Health Research Institute, Amsterdam, The Netherlands
| | - Jouke Jan Hottenga
- Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Amsterdam Public Health Research Institute, Amsterdam, The Netherlands
| | - Rick Jansen
- Amsterdam UMC location Vrije Universiteit Amsterdam, Department of Psychiatry, Amsterdam, the Netherlands
- Amsterdam Neuroscience, Mood, Anxiety, Psychosis, Sleep & Stress Program, Amsterdam, The Netherlands
| | - Nikki Hubers
- Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Amsterdam Reproduction & Development (AR&D) Research Institute, Amsterdam, The Netherlands
| | - Aleksei Afonin
- A.I. Virtanen Institute for Molecular Sciences, University of Eastern Finland, Kuopio, Finland
| | - BIOS Consortium
- Biobank-based Integrative Omics Study Consortium. Lists of authors and their affiliations appear in the supplementary material (see Additional file 1)
| | | | - Gonneke Willemsen
- Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Amsterdam Public Health Research Institute, Amsterdam, The Netherlands
| | - Eco J. C. de Geus
- Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Amsterdam Public Health Research Institute, Amsterdam, The Netherlands
| | - Samuli Ripatti
- Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, Helsinki, Finland
- Department of Public Health, Faculty of Medicine, University of Helsinki, Helsinki, Finland
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Matti Pirinen
- Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, Helsinki, Finland
- Department of Public Health, Faculty of Medicine, University of Helsinki, Helsinki, Finland
- Department of Mathematics and Statistics, University of Helsinki, Helsinki, Finland
| | - Katja M. Kanninen
- A.I. Virtanen Institute for Molecular Sciences, University of Eastern Finland, Kuopio, Finland
| | - Dorret I. Boomsma
- Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Amsterdam Public Health Research Institute, Amsterdam, The Netherlands
- Amsterdam Reproduction & Development (AR&D) Research Institute, Amsterdam, The Netherlands
| | - Jenny van Dongen
- Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Amsterdam Public Health Research Institute, Amsterdam, The Netherlands
- Amsterdam Reproduction & Development (AR&D) Research Institute, Amsterdam, The Netherlands
| | - Jaakko Kaprio
- Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, Helsinki, Finland
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Zhong P, Tan S, Zhu Z, Bulloch G, Long E, Huang W, He M, Wang W. Metabolomic phenotyping of obesity for profiling cardiovascular and ocular diseases. J Transl Med 2023; 21:384. [PMID: 37308902 DOI: 10.1186/s12967-023-04244-x] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2023] [Accepted: 06/02/2023] [Indexed: 06/14/2023] Open
Abstract
BACKGROUND We aimed to evaluate the impacts of metabolomic body mass index (metBMI) phenotypes on the risks of cardiovascular and ocular diseases outcomes. METHODS This study included cohorts in UK and Guangzhou, China. By leveraging the serum metabolome and BMI data from UK Biobank, this study developed and validated a metBMI prediction model using a ridge regression model among 89,830 participants based on 249 metabolites. Five obesity phenotypes were obtained by metBMI and actual BMI (actBMI): normal weight (NW, metBMI of 18.5-24.9 kg/m2), overweight (OW, metBMI of 25-29.9 kg/m2), obesity (OB, metBMI ≥ 30 kg/m2), overestimated (OE, metBMI-actBMI > 5 kg/m2), and underestimated (UE, metBMI-actBMI < - 5 kg/m2). Additional participants from the Guangzhou Diabetes Eye Study (GDES) were included for validating the hypothesis. Outcomes included all-cause and cardiovascular (CVD)-cause mortality, as well as incident CVD (coronary heart disease, heart failure, myocardial infarction [MI], and stroke) and age-related eye diseases (age-related macular degeneration [AMD], cataracts, glaucoma, and diabetic retinopathy [DR]). RESULTS In the UKB, although OE group had lower actBMI than NW group, the OE group had a significantly higher risk of all-cause mortality than those in NW prediction group (HR, 1.68; 95% CI 1.16-2.43). Similarly, the OE group had a 1.7-3.6-fold higher risk than their NW counterparts for cardiovascular mortality, heart failure, myocardial infarction, and coronary heart disease (all P < 0.05). In addition, risk of age-related macular denegation (HR, 1.96; 95% CI 1.02-3.77) was significantly higher in OE group. In the contrast, UE and OB groups showed similar risks of mortality and of cardiovascular and age-related eye diseases (all P > 0.05), though the UE group had significantly higher actBMI than OB group. In the GDES cohort, we further confirmed the potential of metabolic BMI (metBMI) fingerprints for risk stratification of cardiovascular diseases using a different metabolomic approach. CONCLUSIONS Gaps of metBMI and actBMI identified novel metabolic subtypes, which exhibit distinctive cardiovascular and ocular risk profiles. The groups carrying obesity-related metabolites were at higher risk of mortality and morbidity than those with normal health metabolites. Metabolomics allowed for leveraging the future of diagnosis and management of 'healthily obese' and 'unhealthily lean' individuals.
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Affiliation(s)
- Pingting Zhong
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-Sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangdong Provincial Clinical Research Center for Ocular Diseases, Guangzhou, China
| | - Shaoying Tan
- School of Optometry, The Hong Kong Polytechnic University, Hong Kong, China
- Research Centre for SHARP Vision, The Hong Kong Polytechnic University, Hong Kong, China
- Centre for Eye and Vision Research (CEVR), 17W Hong Kong Science Park, Hong Kong, China
| | - Zhuoting Zhu
- Centre for Eye Research Australia, Royal Victorian Eye and Ear Hospital, University of Melbourne, Level 7, 32 Gisborne Street, Melbourne, VIC, 3002, Australia
| | - Gabriella Bulloch
- Centre for Eye Research Australia, Royal Victorian Eye and Ear Hospital, University of Melbourne, Level 7, 32 Gisborne Street, Melbourne, VIC, 3002, Australia
| | - Erping Long
- Department of Ecology and Evolutionary Biology, University of Michigan, Ann Arbor, MI, 48109, USA
| | - Wenyong Huang
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-Sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangdong Provincial Clinical Research Center for Ocular Diseases, Guangzhou, China
| | - Mingguang He
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-Sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangdong Provincial Clinical Research Center for Ocular Diseases, Guangzhou, China.
- School of Optometry, The Hong Kong Polytechnic University, Hong Kong, China.
- Research Centre for SHARP Vision, The Hong Kong Polytechnic University, Hong Kong, China.
- Centre for Eye and Vision Research (CEVR), 17W Hong Kong Science Park, Hong Kong, China.
- Centre for Eye Research Australia, Royal Victorian Eye and Ear Hospital, University of Melbourne, Level 7, 32 Gisborne Street, Melbourne, VIC, 3002, Australia.
| | - Wei Wang
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-Sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangdong Provincial Clinical Research Center for Ocular Diseases, Guangzhou, China.
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Bray GA. Beyond BMI. Nutrients 2023; 15:nu15102254. [PMID: 37242136 DOI: 10.3390/nu15102254] [Citation(s) in RCA: 56] [Impact Index Per Article: 28.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2023] [Revised: 05/04/2023] [Accepted: 05/06/2023] [Indexed: 05/28/2023] Open
Abstract
This review examined the origins of the concept of the BMI in the work of Quetelet in the 19th century and its subsequent adoption and use in tracking the course of the pandemic of obesity during the 20th century. In this respect, it has provided a valuable international epidemiological tool that should be retained. However, as noted in this review, the BMI is deficient in at least three ways. First, it does not measure body fat distribution, which is probably a more important guide to the risk of excess adiposity than the BMI itself. Second, it is not a very good measure of body fat, and thus its application to the diagnosis of obesity or excess adiposity in the individual patient is limited. Finally, the BMI does not provide any insights into the heterogeneity of obesity or its genetic, metabolic, physiological or psychological origins. Some of these mechanisms are traced in this review.
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Affiliation(s)
- George A Bray
- Pennington Biomedical Research Center, Louisiana State University, Baton Rouge, LA 70808, USA
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