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Chen BD, Lee C, Tapia AL, Reiner AP, Tang H, Kooperberg C, Manson JE, Li Y, Raffield LM. Proteome-wide association study using cis and trans variants and applied to blood cell and lipid-related traits in the Women's Health Initiative study. Genet Epidemiol 2024. [PMID: 38940271 DOI: 10.1002/gepi.22578] [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: 09/11/2023] [Revised: 05/26/2024] [Accepted: 06/13/2024] [Indexed: 06/29/2024]
Abstract
In most Proteome-Wide Association Studies (PWAS), variants near the protein-coding gene (±1 Mb), also known as cis single nucleotide polymorphisms (SNPs), are used to predict protein levels, which are then tested for association with phenotypes. However, proteins can be regulated through variants outside of the cis region. An intermediate GWAS step to identify protein quantitative trait loci (pQTL) allows for the inclusion of trans SNPs outside the cis region in protein-level prediction models. Here, we assess the prediction of 540 proteins in 1002 individuals from the Women's Health Initiative (WHI), split equally into a GWAS set, an elastic net training set, and a testing set. We compared the testing r2 between measured and predicted protein levels using this proposed approach, to the testing r2 using only cis SNPs. The two methods usually resulted in similar testing r2, but some proteins showed a significant increase in testing r2 with our method. For example, for cartilage acidic protein 1, the testing r2 increased from 0.101 to 0.351. We also demonstrate reproducible findings for predicted protein association with lipid and blood cell traits in WHI participants without proteomics data and in UK Biobank utilizing our PWAS weights.
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Affiliation(s)
- Brian D Chen
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| | - Chanhwa Lee
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| | - Amanda L Tapia
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
| | - Alexander P Reiner
- Department of Epidemiology, University of Washington, Seattle, Washington, USA
| | - Hua Tang
- Department of Genetics, Stanford University School of Medicine, Stanford, California, USA
| | - Charles Kooperberg
- Division of Public Health Sciences, Fred Hutchinson Cancer Center, Seattle, Washington, USA
| | - JoAnn E Manson
- Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Yun Li
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| | - Laura M Raffield
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
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2
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Yao P, Iona A, Pozarickij A, Said S, Wright N, Lin K, Millwood I, Fry H, Kartsonaki C, Mazidi M, Chen Y, Bragg F, Liu B, Yang L, Liu J, Avery D, Schmidt D, Sun D, Pei P, Lv J, Yu C, Hill M, Bennett D, Walters R, Li L, Clarke R, Du H, Chen Z. Proteomic Analyses in Diverse Populations Improved Risk Prediction and Identified New Drug Targets for Type 2 Diabetes. Diabetes Care 2024; 47:1012-1019. [PMID: 38623619 PMCID: PMC7615965 DOI: 10.2337/dc23-2145] [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: 11/13/2023] [Accepted: 03/09/2024] [Indexed: 04/17/2024]
Abstract
OBJECTIVE Integrated analyses of plasma proteomics and genetic data in prospective studies can help assess the causal relevance of proteins, improve risk prediction, and discover novel protein drug targets for type 2 diabetes (T2D). RESEARCH DESIGN AND METHODS We measured plasma levels of 2,923 proteins using Olink Explore among ∼2,000 randomly selected participants from China Kadoorie Biobank (CKB) without prior diabetes at baseline. Cox regression assessed associations of individual protein with incident T2D (n = 92 cases). Proteomic-based risk models were developed with discrimination, calibration, reclassification assessed using area under the curve (AUC), calibration plots, and net reclassification index (NRI), respectively. Two-sample Mendelian randomization (MR) analyses using cis-protein quantitative trait loci identified in a genome-wide association study of CKB and UK Biobank for specific proteins were conducted to assess their causal relevance for T2D, along with colocalization analyses to examine shared causal variants between proteins and T2D. RESULTS Overall, 33 proteins were significantly associated (false discovery rate <0.05) with risk of incident T2D, including IGFBP1, GHR, and amylase. The addition of these 33 proteins to a conventional risk prediction model improved AUC from 0.77 (0.73-0.82) to 0.88 (0.85-0.91) and NRI by 38%, with predicted risks well calibrated with observed risks. MR analyses provided support for the causal relevance for T2D of ENTR1, LPL, and PON3, with replication of ENTR1 and LPL in Europeans using different genetic instruments. Moreover, colocalization analyses showed strong evidence (pH4 > 0.6) of shared genetic variants of LPL and PON3 with T2D. CONCLUSIONS Proteomic analyses in Chinese adults identified novel associations of multiple proteins with T2D with strong genetic evidence supporting their causal relevance and potential as novel drug targets for prevention and treatment of T2D.
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Affiliation(s)
- Pang Yao
- Clinical Trial Service Unit & Epidemiological Studies Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Andri Iona
- Clinical Trial Service Unit & Epidemiological Studies Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Alfred Pozarickij
- Clinical Trial Service Unit & Epidemiological Studies Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Saredo Said
- Clinical Trial Service Unit & Epidemiological Studies Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Neil Wright
- Clinical Trial Service Unit & Epidemiological Studies Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Kuang Lin
- Clinical Trial Service Unit & Epidemiological Studies Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Iona Millwood
- Clinical Trial Service Unit & Epidemiological Studies Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK
- Medical Research Council Health Research Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Hannah Fry
- Clinical Trial Service Unit & Epidemiological Studies Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK
- Medical Research Council Health Research Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Christiana Kartsonaki
- Clinical Trial Service Unit & Epidemiological Studies Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK
- Medical Research Council Health Research Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Mohsen Mazidi
- Clinical Trial Service Unit & Epidemiological Studies Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Yiping Chen
- Clinical Trial Service Unit & Epidemiological Studies Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK
- Medical Research Council Health Research Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Fiona Bragg
- Clinical Trial Service Unit & Epidemiological Studies Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Bowen Liu
- Clinical Trial Service Unit & Epidemiological Studies Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Ling Yang
- Clinical Trial Service Unit & Epidemiological Studies Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK
- Medical Research Council Health Research Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Junxi Liu
- Clinical Trial Service Unit & Epidemiological Studies Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Daniel Avery
- Clinical Trial Service Unit & Epidemiological Studies Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK
- Medical Research Council Health Research Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Dan Schmidt
- Clinical Trial Service Unit & Epidemiological Studies Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK
- Medical Research Council Health Research Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Dianjianyi Sun
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University Health Science Center, Beijing, China
- Peking University Center for Public Health and Epidemic Preparedness & Response, Beijing, China
- Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education, Beijing, China
| | - Pei Pei
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University Health Science Center, Beijing, China
| | - Jun Lv
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University Health Science Center, Beijing, China
- Peking University Center for Public Health and Epidemic Preparedness & Response, Beijing, China
- Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education, Beijing, China
| | - Canqing Yu
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University Health Science Center, Beijing, China
- Peking University Center for Public Health and Epidemic Preparedness & Response, Beijing, China
- Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education, Beijing, China
| | - Michael Hill
- Clinical Trial Service Unit & Epidemiological Studies Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Derrick Bennett
- Clinical Trial Service Unit & Epidemiological Studies Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK
- Medical Research Council Health Research Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Robin Walters
- Clinical Trial Service Unit & Epidemiological Studies Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK
- Medical Research Council Health Research Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Liming Li
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University Health Science Center, Beijing, China
- Peking University Center for Public Health and Epidemic Preparedness & Response, Beijing, China
- Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education, Beijing, China
| | - Robert Clarke
- Clinical Trial Service Unit & Epidemiological Studies Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Huaidong Du
- Clinical Trial Service Unit & Epidemiological Studies Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK
- Medical Research Council Health Research Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Zhengming Chen
- Clinical Trial Service Unit & Epidemiological Studies Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK
- Medical Research Council Health Research Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK
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Steffen BT, McDonough DJ, Pankow JS, Tang W, Rooney MR, Demmer RT, Lutsey PL, Guan W, Gabriel KP, Palta P, Moser ED, Pereira MA. Plasma Neuronal Growth Regulator 1 May Link Physical Activity to Reduced Risk of Type 2 Diabetes: A Proteome-Wide Study of ARIC Participants. Diabetes 2024; 73:318-324. [PMID: 37935012 PMCID: PMC10796298 DOI: 10.2337/db23-0383] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/16/2023] [Accepted: 10/26/2023] [Indexed: 11/09/2023]
Abstract
Habitual physical activity (PA) impacts the plasma proteome and reduces the risk of developing type 2 diabetes (T2D). Using a large-scale proteome-wide approach in Atherosclerosis Risk in Communities study participants, we aimed to identify plasma proteins associated with PA and determine which of these may be causally related to lower T2D risk. PA was associated with 92 plasma proteins in discovery (P < 1.01 × 10-5), and 40 remained significant in replication (P < 5.43 × 10-4). Eighteen of these proteins were independently associated with incident T2D (P < 1.25 × 10-3), including neuronal growth regulator 1 (NeGR1; hazard ratio per SD 0.85; P = 7.5 × 10-11). Two-sample Mendelian randomization (MR) inverse variance weighted analysis indicated that higher NeGR1 reduces T2D risk (odds ratio [OR] per SD 0.92; P = 0.03) and was consistent with MR-Egger, weighted median, and weighted mode sensitivity analyses. A stronger association was observed for the single cis-acting NeGR1 genetic variant (OR per SD 0.80; P = 6.3 × 10-5). Coupled with previous evidence that low circulating NeGR1 levels promote adiposity, its association with PA and potential causal role in T2D shown here suggest that NeGR1 may link PA exposure with metabolic outcomes. Further research is warranted to confirm our findings and examine the interplay of PA, NeGR1, adiposity, and metabolic health. ARTICLE HIGHLIGHTS
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Affiliation(s)
- Brian T. Steffen
- Division of Computational Health Sciences, Department of Surgery, University of Minnesota, Minneapolis, MN
| | - Daniel J. McDonough
- Division of Epidemiology and Community Health, School of Public Health, University of Minnesota, Minneapolis, MN
| | - James S. Pankow
- Division of Epidemiology and Community Health, School of Public Health, University of Minnesota, Minneapolis, MN
| | - Weihong Tang
- Division of Epidemiology and Community Health, School of Public Health, University of Minnesota, Minneapolis, MN
| | - Mary R. Rooney
- Department of Epidemiology, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD
- Welch Center for Prevention, Epidemiology and Clinical Research, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD
| | - Ryan T. Demmer
- Division of Epidemiology and Community Health, School of Public Health, University of Minnesota, Minneapolis, MN
- Department of Epidemiology, Mailman School of Public Health, Columbia University, New York, NY
| | - Pamela L. Lutsey
- Division of Epidemiology and Community Health, School of Public Health, University of Minnesota, Minneapolis, MN
| | - Weihua Guan
- Division of Biostatistics, School of Public Health, University of Minnesota, Minneapolis, MN
| | - Kelley Pettee Gabriel
- Department of Epidemiology, School of Public Health, University of Alabama at Birmingham, Birmingham, AL
| | - Priya Palta
- Department of Epidemiology, Mailman School of Public Health, Columbia University, New York, NY
| | - Ethan D. Moser
- Division of Epidemiology and Community Health, School of Public Health, University of Minnesota, Minneapolis, MN
| | - Mark A. Pereira
- Division of Epidemiology and Community Health, School of Public Health, University of Minnesota, Minneapolis, MN
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Costello E, Goodrich JA, Patterson WB, Walker DI, Chen J(C, Baumert BO, Rock S, Gilliland FD, Goran MI, Chen Z, Alderete TL, Conti DV, Chatzi L. Proteomic and Metabolomic Signatures of Diet Quality in Young Adults. Nutrients 2024; 16:429. [PMID: 38337712 PMCID: PMC10857402 DOI: 10.3390/nu16030429] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2023] [Revised: 01/27/2024] [Accepted: 01/30/2024] [Indexed: 02/12/2024] Open
Abstract
The assessment of "omics" signatures may contribute to personalized medicine and precision nutrition. However, the existing literature is still limited in the homogeneity of participants' characteristics and in limited assessments of integrated omics layers. Our objective was to use post-prandial metabolomics and fasting proteomics to identify biological pathways and functions associated with diet quality in a population of primarily Hispanic young adults. We conducted protein and metabolite-wide association studies and functional pathway analyses to assess the relationships between a priori diet indices, Healthy Eating Index-2015 (HEI) and Dietary Approaches to Stop Hypertension (DASH) diets, and proteins (n = 346) and untargeted metabolites (n = 23,173), using data from the MetaAIR study (n = 154, 61% Hispanic). Analyses were performed for each diet quality index separately, adjusting for demographics and BMI. Five proteins (ACY1, ADH4, AGXT, GSTA1, F7) and six metabolites (undecylenic acid, betaine, hyodeoxycholic acid, stearidonic acid, iprovalicarb, pyracarbolid) were associated with both diets (p < 0.05), though none were significant after adjustment for multiple comparisons. Overlapping proteins are involved in lipid and amino acid metabolism and in hemostasis, while overlapping metabolites include amino acid derivatives, bile acids, fatty acids, and pesticides. Enriched biological pathways were involved in macronutrient metabolism, immune function, and oxidative stress. These findings in young Hispanic adults contribute to efforts to develop precision nutrition and medicine for diverse populations.
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Affiliation(s)
- Elizabeth Costello
- Department of Population and Public Health Sciences, University of Southern California, Los Angeles, CA 90032, USA; (J.A.G.); (J.C.); (B.O.B.); (S.R.); (F.D.G.); (M.I.G.); (Z.C.); (D.V.C.); (L.C.)
| | - Jesse A. Goodrich
- Department of Population and Public Health Sciences, University of Southern California, Los Angeles, CA 90032, USA; (J.A.G.); (J.C.); (B.O.B.); (S.R.); (F.D.G.); (M.I.G.); (Z.C.); (D.V.C.); (L.C.)
| | - William B. Patterson
- Department of Integrative Physiology, University of Colorado Boulder, Boulder, CO 80309, USA; (W.B.P.); (T.L.A.)
| | - Douglas I. Walker
- Gangarosa Department of Environmental Health, Emory University, Atlanta, GA 30329, USA;
| | - Jiawen (Carmen) Chen
- Department of Population and Public Health Sciences, University of Southern California, Los Angeles, CA 90032, USA; (J.A.G.); (J.C.); (B.O.B.); (S.R.); (F.D.G.); (M.I.G.); (Z.C.); (D.V.C.); (L.C.)
| | - Brittney O. Baumert
- Department of Population and Public Health Sciences, University of Southern California, Los Angeles, CA 90032, USA; (J.A.G.); (J.C.); (B.O.B.); (S.R.); (F.D.G.); (M.I.G.); (Z.C.); (D.V.C.); (L.C.)
| | - Sarah Rock
- Department of Population and Public Health Sciences, University of Southern California, Los Angeles, CA 90032, USA; (J.A.G.); (J.C.); (B.O.B.); (S.R.); (F.D.G.); (M.I.G.); (Z.C.); (D.V.C.); (L.C.)
| | - Frank D. Gilliland
- Department of Population and Public Health Sciences, University of Southern California, Los Angeles, CA 90032, USA; (J.A.G.); (J.C.); (B.O.B.); (S.R.); (F.D.G.); (M.I.G.); (Z.C.); (D.V.C.); (L.C.)
| | - Michael I. Goran
- Department of Population and Public Health Sciences, University of Southern California, Los Angeles, CA 90032, USA; (J.A.G.); (J.C.); (B.O.B.); (S.R.); (F.D.G.); (M.I.G.); (Z.C.); (D.V.C.); (L.C.)
- Department of Pediatrics, Children’s Hospital Los Angeles, The Saban Research Institute, Los Angeles, CA 90027, USA
| | - Zhanghua Chen
- Department of Population and Public Health Sciences, University of Southern California, Los Angeles, CA 90032, USA; (J.A.G.); (J.C.); (B.O.B.); (S.R.); (F.D.G.); (M.I.G.); (Z.C.); (D.V.C.); (L.C.)
| | - Tanya L. Alderete
- Department of Integrative Physiology, University of Colorado Boulder, Boulder, CO 80309, USA; (W.B.P.); (T.L.A.)
| | - David V. Conti
- Department of Population and Public Health Sciences, University of Southern California, Los Angeles, CA 90032, USA; (J.A.G.); (J.C.); (B.O.B.); (S.R.); (F.D.G.); (M.I.G.); (Z.C.); (D.V.C.); (L.C.)
| | - Lida Chatzi
- Department of Population and Public Health Sciences, University of Southern California, Los Angeles, CA 90032, USA; (J.A.G.); (J.C.); (B.O.B.); (S.R.); (F.D.G.); (M.I.G.); (Z.C.); (D.V.C.); (L.C.)
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5
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Yuan S, Xu F, Li X, Chen J, Zheng J, Mantzoros CS, Larsson SC. Plasma proteins and onset of type 2 diabetes and diabetic complications: Proteome-wide Mendelian randomization and colocalization analyses. Cell Rep Med 2023; 4:101174. [PMID: 37652020 PMCID: PMC10518626 DOI: 10.1016/j.xcrm.2023.101174] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2023] [Revised: 06/16/2023] [Accepted: 08/06/2023] [Indexed: 09/02/2023]
Abstract
We conduct proteome-wide Mendelian randomization and colocalization analyses to decipher the associations of blood proteins with the risk of type 2 diabetes and diabetic complications. Genetic data on plasma proteome are obtained from 54,306 UK Biobank participants and 35,559 Icelanders. Summary-level data on type 2 diabetes are obtained from the DIAGRAM (DIAbetes Genetics Replication And Meta-analysis consortium) consortium (74,124 cases) and FinnGen study (33,043 cases). Data on 10 diabetic complications are obtained from FinnGen and corresponding studies. Among 1,886 proteins, genetically predicted levels of 47 plasma proteins are associated with type 2 diabetes. Eleven of these proteins have strong support of colocalization. Seventeen proteins are associated with at least one diabetic complication, although a few have colocalization support. HLA-DRA, AGER, HSPA1A, and HSPA1B are associated with most microvascular complications. This study reveals causal proteins for the onset of type 2 diabetes and diabetic complications, which enhances the understanding of molecular etiology and development of therapeutics.
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Affiliation(s)
- Shuai Yuan
- Unit of Cardiovascular and Nutritional Epidemiology, Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden.
| | - Fengzhe Xu
- Key Laboratory of Growth Regulation and Translational Research of Zhejiang Province, School of Life Sciences, Westlake University, Hangzhou, China
| | - 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
| | - 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
| | - Jie Zheng
- Department of Endocrine and Metabolic Diseases, Shanghai Institute of Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China; Shanghai National Clinical Research Center for Metabolic Diseases, Key Laboratory for Endocrine and Metabolic Diseases of the National Health Commission of the PR China, Shanghai Key Laboratory for Endocrine Tumor, Shanghai Digital Medicine Innovation Center, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Christos S Mantzoros
- Department of Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA; Section of Endocrinology, VA Boston Healthcare System, Harvard Medical School, Boston, MA, USA
| | - Susanna C Larsson
- Unit of Cardiovascular and Nutritional Epidemiology, Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden; Unit of Medical Epidemiology, Department of Surgical Sciences, Uppsala University, Uppsala, Sweden.
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