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Leiherer A, Muendlein A, Mink S, Mader A, Saely CH, Festa A, Fraunberger P, Drexel H. Machine Learning Approach to Metabolomic Data Predicts Type 2 Diabetes Mellitus Incidence. Int J Mol Sci 2024; 25:5331. [PMID: 38791370 PMCID: PMC11120685 DOI: 10.3390/ijms25105331] [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: 03/20/2024] [Revised: 04/30/2024] [Accepted: 05/09/2024] [Indexed: 05/26/2024] Open
Abstract
Metabolomics, with its wealth of data, offers a valuable avenue for enhancing predictions and decision-making in diabetes. This observational study aimed to leverage machine learning (ML) algorithms to predict the 4-year risk of developing type 2 diabetes mellitus (T2DM) using targeted quantitative metabolomics data. A cohort of 279 cardiovascular risk patients who underwent coronary angiography and who were initially free of T2DM according to American Diabetes Association (ADA) criteria was analyzed at baseline, including anthropometric data and targeted metabolomics, using liquid chromatography (LC)-mass spectroscopy (MS) and flow injection analysis (FIA)-MS, respectively. All patients were followed for four years. During this time, 11.5% of the patients developed T2DM. After data preprocessing, 362 variables were used for ML, employing the Caret package in R. The dataset was divided into training and test sets (75:25 ratio) and we used an oversampling approach to address the classifier imbalance of T2DM incidence. After an additional recursive feature elimination step, identifying a set of 77 variables that were the most valuable for model generation, a Support Vector Machine (SVM) model with a linear kernel demonstrated the most promising predictive capabilities, exhibiting an F1 score of 50%, a specificity of 93%, and balanced and unbalanced accuracies of 72% and 88%, respectively. The top-ranked features were bile acids, ceramides, amino acids, and hexoses, whereas anthropometric features such as age, sex, waist circumference, or body mass index had no contribution. In conclusion, ML analysis of metabolomics data is a promising tool for identifying individuals at risk of developing T2DM and opens avenues for personalized and early intervention strategies.
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Affiliation(s)
- Andreas Leiherer
- Vorarlberg Institute for Vascular Investigation and Treatment (VIVIT), A-6800 Feldkirch, Austria; (A.M.); (A.M.); (C.H.S.); (A.F.); (H.D.)
- Central Medical Laboratories, A-6800 Feldkirch, Austria; (S.M.); (P.F.)
- Faculty of Medical Sciences, Private University of the Principality of Liechtenstein, FL-9495 Triesen, Liechtenstein
| | - Axel Muendlein
- Vorarlberg Institute for Vascular Investigation and Treatment (VIVIT), A-6800 Feldkirch, Austria; (A.M.); (A.M.); (C.H.S.); (A.F.); (H.D.)
| | - Sylvia Mink
- Central Medical Laboratories, A-6800 Feldkirch, Austria; (S.M.); (P.F.)
- Faculty of Medical Sciences, Private University of the Principality of Liechtenstein, FL-9495 Triesen, Liechtenstein
| | - Arthur Mader
- Vorarlberg Institute for Vascular Investigation and Treatment (VIVIT), A-6800 Feldkirch, Austria; (A.M.); (A.M.); (C.H.S.); (A.F.); (H.D.)
- Department of Internal Medicine III, Academic Teaching Hospital Feldkirch, A-6800 Feldkirch, Austria
| | - Christoph H. Saely
- Vorarlberg Institute for Vascular Investigation and Treatment (VIVIT), A-6800 Feldkirch, Austria; (A.M.); (A.M.); (C.H.S.); (A.F.); (H.D.)
- Faculty of Medical Sciences, Private University of the Principality of Liechtenstein, FL-9495 Triesen, Liechtenstein
- Department of Internal Medicine III, Academic Teaching Hospital Feldkirch, A-6800 Feldkirch, Austria
| | - Andreas Festa
- Vorarlberg Institute for Vascular Investigation and Treatment (VIVIT), A-6800 Feldkirch, Austria; (A.M.); (A.M.); (C.H.S.); (A.F.); (H.D.)
| | - Peter Fraunberger
- Central Medical Laboratories, A-6800 Feldkirch, Austria; (S.M.); (P.F.)
- Faculty of Medical Sciences, Private University of the Principality of Liechtenstein, FL-9495 Triesen, Liechtenstein
| | - Heinz Drexel
- Vorarlberg Institute for Vascular Investigation and Treatment (VIVIT), A-6800 Feldkirch, Austria; (A.M.); (A.M.); (C.H.S.); (A.F.); (H.D.)
- Faculty of Medical Sciences, Private University of the Principality of Liechtenstein, FL-9495 Triesen, Liechtenstein
- Vorarlberger Landeskrankenhausbetriebsgesellschaft, Academic Teaching Hospital Feldkirch, A-6800 Feldkirch, Austria
- Drexel University College of Medicine, Philadelphia, PA 19129, USA
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Lanuza F, Meroño T, Zamora-Ros R, Bondonno NP, Rostgaard-Hansen AL, Sánchez-Pla A, Miro B, Carmona-Pontaque F, Riccardi G, Tjønneland A, Landberg R, Halkjær J, Andres-Lacueva C. Plasma metabolomic profiles of plant-based dietary indices reveal potential pathways for metabolic syndrome associations. Atherosclerosis 2023; 382:117285. [PMID: 37778133 DOI: 10.1016/j.atherosclerosis.2023.117285] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/01/2023] [Revised: 08/24/2023] [Accepted: 09/06/2023] [Indexed: 10/03/2023]
Abstract
BACKGROUND AND AIMS Plant-based dietary patterns have been associated with improved health outcomes. This study aims to describe the metabolomic fingerprints of plant-based diet indices (PDI) and examine their association with metabolic syndrome (MetS) and its components in a Danish population. METHODS The MAX study comprised 676 participants (55% women, aged 18-67 y) from Copenhagen. Sociodemographic and dietary data were collected using questionnaires and three 24-h dietary recalls over one year (at baseline, and at 6 and 12 months). Mean dietary intakes were computed, as well as overall PDI, healthful (hPDI) and unhealthful (uPDI) scores, according to food groups for each plant-based index. Clinical variables were also collected at the same time points in a health examination that included complete blood tests. MetS was defined according to the International Diabetes Federation criteria. Plasma metabolites were measured using a targeted metabolomics approach. Metabolites associated with PDI were selected using random forest models and their relationships with PDIs and MetS were analyzed using generalized linear mixed models. RESULTS The mean prevalence of MetS was 10.8%. High, compared to low, hPDI and uPDI scores were associated with a lower and higher odd of MetS, respectively [odds ratio (95%CI); hPDI: 0.56 (0.43-0.74); uPDI: 1.61 (1.26-2.05)]. Out of 411 quantified plasma metabolites, machine-learning metabolomics fingerprinting revealed 13 metabolites, including food and food-related microbial metabolites, like hypaphorine, indolepropionic acid and lignan-derived enterolactones. These metabolites were associated with all PDIs and were inversely correlated with MetS components (p < 0.05). Furthermore, they had an explainable contribution of 12% and 14% for the association between hPDI or uPDI, respectively, and MetS only among participants with overweight/obesity. CONCLUSIONS Metabolites associated with PDIs were inversely associated with MetS and its components, and may partially explain the effects of plant-based diets on cardiometabolic risk factors.
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Affiliation(s)
- Fabian Lanuza
- Biomarkers and Nutrimetabolomics Laboratory, Department de Nutrició, Ciències de L'Alimentació i Gastronomia, Institut de Recerca en Nutrició i Seguretat Alimentària (INSA-UB), Facultat de Farmàcia i Ciències de L'Alimentació, Universitat de Barcelona (UB), 08028, Barcelona, Spain; Centro de Investigación Biomédica en Red de Fragilidad y Envejecimiento Saludable (CIBERFES), Instituto de Salud Carlos III, Madrid, 28029, Spain
| | - Tomas Meroño
- Biomarkers and Nutrimetabolomics Laboratory, Department de Nutrició, Ciències de L'Alimentació i Gastronomia, Institut de Recerca en Nutrició i Seguretat Alimentària (INSA-UB), Facultat de Farmàcia i Ciències de L'Alimentació, Universitat de Barcelona (UB), 08028, Barcelona, Spain; Centro de Investigación Biomédica en Red de Fragilidad y Envejecimiento Saludable (CIBERFES), Instituto de Salud Carlos III, Madrid, 28029, Spain.
| | - Raul Zamora-Ros
- Biomarkers and Nutrimetabolomics Laboratory, Department de Nutrició, Ciències de L'Alimentació i Gastronomia, Institut de Recerca en Nutrició i Seguretat Alimentària (INSA-UB), Facultat de Farmàcia i Ciències de L'Alimentació, Universitat de Barcelona (UB), 08028, Barcelona, Spain; Unit of Nutrition and Cancer, Cancer Epidemiology Research Program, Catalan Institute of Oncology (ICO), Bellvitge Biomedical Research Institute (IDIBELL), Barcelona, Spain.
| | - Nicola P Bondonno
- Danish Cancer Society Research Center, Strandboulevarden 49, DK 2100, Copenhagen, Denmark; Nutrition and Health Innovation Research Institute, School of Medical and Health Sciences, Edith Cowan University, Perth, Australia
| | | | - Alex Sánchez-Pla
- Statistics and Bioinformatics Research Group, Department of Genetics, Microbiology and Statistics, University of Barcelona, Barcelona, Spain
| | - Berta Miro
- Biomarkers and Nutrimetabolomics Laboratory, Department de Nutrició, Ciències de L'Alimentació i Gastronomia, Institut de Recerca en Nutrició i Seguretat Alimentària (INSA-UB), Facultat de Farmàcia i Ciències de L'Alimentació, Universitat de Barcelona (UB), 08028, Barcelona, Spain; Statistics and Bioinformatics Research Group, Department of Genetics, Microbiology and Statistics, University of Barcelona, Barcelona, Spain
| | - Francesc Carmona-Pontaque
- Statistics and Bioinformatics Research Group, Department of Genetics, Microbiology and Statistics, University of Barcelona, Barcelona, Spain
| | - Gabriele Riccardi
- Department of Clinical Medicine and Surgery, University of Naples Federico II, Naples, Italy
| | - Anne Tjønneland
- Danish Cancer Society Research Center, Strandboulevarden 49, DK 2100, Copenhagen, Denmark
| | - Rikard Landberg
- Department of Biology and Biological Engineering, Division of Food and Nutrition Science, Chalmers University of Technology, Gothenburg, Sweden
| | - Jytte Halkjær
- Danish Cancer Society Research Center, Strandboulevarden 49, DK 2100, Copenhagen, Denmark
| | - Cristina Andres-Lacueva
- Biomarkers and Nutrimetabolomics Laboratory, Department de Nutrició, Ciències de L'Alimentació i Gastronomia, Institut de Recerca en Nutrició i Seguretat Alimentària (INSA-UB), Facultat de Farmàcia i Ciències de L'Alimentació, Universitat de Barcelona (UB), 08028, Barcelona, Spain; Centro de Investigación Biomédica en Red de Fragilidad y Envejecimiento Saludable (CIBERFES), Instituto de Salud Carlos III, Madrid, 28029, Spain
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Lanuza F, Zamora-Ros R, Bondonno NP, Meroño T, Rostgaard-Hansen AL, Riccardi G, Tjønneland A, Landberg R, Halkjær J, Andres-Lacueva C. Dietary polyphenols, metabolic syndrome and cardiometabolic risk factors: An observational study based on the DCH-NG subcohort. Nutr Metab Cardiovasc Dis 2023; 33:1167-1178. [PMID: 36948936 DOI: 10.1016/j.numecd.2023.02.022] [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: 11/14/2022] [Revised: 02/17/2023] [Accepted: 02/27/2023] [Indexed: 03/06/2023]
Abstract
BACKGROUND AND AIMS Polyphenol-rich foods have beneficial properties that may lower cardiometabolic risk. We aimed to prospectively investigate the relationship between intakes of dietary polyphenols, and metabolic syndrome (MetS) and its components, in 676 Danish residents from the MAX study, a subcohort of the Danish Diet, Cancer and Health-Next Generations (DCH-NG) cohort. METHODS AND RESULTS Dietary data were collected using web-based 24-h dietary recalls over one year (at baseline, and at 6 and 12 months). The Phenol-Explorer database was used to estimate dietary polyphenol intake. Clinical variables were also collected at the same time point. Generalized linear mixed models were used to investigate relationships between polyphenol intake and MetS. Participants had a mean age of 43.9y, a mean total polyphenol intake of 1368 mg/day, and 75 (11.6%) had MetS at baseline. Compared to individuals with MetS in Q1 and after adjusting for age, sex, lifestyle and dietary confounders, those in Q4 - for total polyphenols, flavonoids and phenolic acids-had a 50% [OR (95% CI): 0.50 (0.27, 0.91)], 51% [0.49 (0.26, 0.91)] and 45% [0.55 (0.30, 1.00)] lower odds of MetS, respectively. Higher total polyphenols, flavonoids and phenolic acids intakes as continuous variable were associated with lower risk for elevated systolic blood pressure (SBP) and low high-density lipoprotein cholesterol (HDL-c) (p < 0.05). CONCLUSIONS Total polyphenol, flavonoid and phenolic acid intakes were associated with lower odds of MetS. These intakes were also consistently and significantly associated with a lower risk for higher SBP and lower HDL-c concentrations.
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Affiliation(s)
- Fabian Lanuza
- Biomarkers and Nutrimetabolomics Laboratory, Department of Nutrition, Food Sciences and Gastronomy, Food Innovation Network (XIA), Nutrition and Food Safety Research Institute (INSA), Faculty of Pharmacy and Food Sciences, University of Barcelona (UB), 08028 Barcelona, Spain; Centro de Investigación Biomédica en Red Fragilidad y Envejecimiento Saludable (CIBERFES), Instituto de Salud Carlos III, Madrid, 28029, Spain
| | - Raul Zamora-Ros
- Biomarkers and Nutrimetabolomics Laboratory, Department of Nutrition, Food Sciences and Gastronomy, Food Innovation Network (XIA), Nutrition and Food Safety Research Institute (INSA), Faculty of Pharmacy and Food Sciences, University of Barcelona (UB), 08028 Barcelona, Spain; Unit of Nutrition and Cancer, Cancer Epidemiology Research Program, Catalan Institute of Oncology (ICO), Bellvitge Biomedical Research Institute (IDIBELL), Barcelona, Spain.
| | - Nicola P Bondonno
- Danish Cancer Society Research Center, Strandboulevarden 49, DK 2100 Copenhagen, Denmark; Nutrition and Health Innovation Research Institute, School of Medical and Health Sciences, Edith Cowan University, Perth, Australia
| | - Tomas Meroño
- Biomarkers and Nutrimetabolomics Laboratory, Department of Nutrition, Food Sciences and Gastronomy, Food Innovation Network (XIA), Nutrition and Food Safety Research Institute (INSA), Faculty of Pharmacy and Food Sciences, University of Barcelona (UB), 08028 Barcelona, Spain; Centro de Investigación Biomédica en Red Fragilidad y Envejecimiento Saludable (CIBERFES), Instituto de Salud Carlos III, Madrid, 28029, Spain
| | | | - Gabriele Riccardi
- Department of Clinical Medicine and Surgery, University of Naples Federico II, Naples, Italy
| | - Anne Tjønneland
- Danish Cancer Society Research Center, Strandboulevarden 49, DK 2100 Copenhagen, Denmark
| | - Rikard Landberg
- Department of Biology and Biological Engineering, Division of Food and Nutrition Science, Chalmers University of Technology, Gothenburg, Sweden
| | - Jytte Halkjær
- Danish Cancer Society Research Center, Strandboulevarden 49, DK 2100 Copenhagen, Denmark
| | - Cristina Andres-Lacueva
- Biomarkers and Nutrimetabolomics Laboratory, Department of Nutrition, Food Sciences and Gastronomy, Food Innovation Network (XIA), Nutrition and Food Safety Research Institute (INSA), Faculty of Pharmacy and Food Sciences, University of Barcelona (UB), 08028 Barcelona, Spain; Centro de Investigación Biomédica en Red Fragilidad y Envejecimiento Saludable (CIBERFES), Instituto de Salud Carlos III, Madrid, 28029, Spain
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Li Z, Zhang Y, Hoene M, Fritsche L, Zheng S, Birkenfeld A, Fritsche A, Peter A, Liu X, Zhao X, Zhou L, Luo P, Weigert C, Lin X, Xu G, Lehmann R. Diagnostic Performance of Sex-Specific Modified Metabolite Patterns in Urine for Screening of Prediabetes. Front Endocrinol (Lausanne) 2022; 13:935016. [PMID: 35909528 PMCID: PMC9333093 DOI: 10.3389/fendo.2022.935016] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/03/2022] [Accepted: 06/13/2022] [Indexed: 12/03/2022] Open
Abstract
AIMS/HYPOTHESIS Large-scale prediabetes screening is still a challenge since fasting blood glucose and HbA1c as the long-standing, recommended analytes have only moderate diagnostic sensitivity, and the practicability of the oral glucose tolerance test for population-based strategies is limited. To tackle this issue and to identify reliable diagnostic patterns, we developed an innovative metabolomics-based strategy deviating from common concepts by employing urine instead of blood samples, searching for sex-specific biomarkers, and focusing on modified metabolites. METHODS Non-targeted, modification group-assisted metabolomics by liquid chromatography-mass spectrometry (LC-MS) was applied to second morning urine samples of 340 individuals from a prediabetes cohort. Normal (n = 208) and impaired glucose-tolerant (IGT; n = 132) individuals, matched for age and BMI, were randomly divided in discovery and validation cohorts. ReliefF, a feature selection algorithm, was used to extract sex-specific diagnostic patterns of modified metabolites for the detection of IGT. The diagnostic performance was compared with conventional screening parameters fasting plasma glucose (FPG), HbA1c, and fasting insulin. RESULTS Female- and male-specific diagnostic patterns were identified in urine. Only three biomarkers were identical in both. The patterns showed better AUC and diagnostic sensitivity for prediabetes screening of IGT than FPG, HbA1c, insulin, or a combination of FPG and HbA1c. The AUC of the male-specific pattern in the validation cohort was 0.889 with a diagnostic sensitivity of 92.6% and increased to an AUC of 0.977 in combination with HbA1c. In comparison, the AUCs of FPG, HbA1c, and insulin alone reached 0.573, 0.668, and 0.571, respectively. Validation of the diagnostic pattern of female subjects showed an AUC of 0.722, which still exceeded the AUCs of FPG, HbA1c, and insulin (0.595, 0.604, and 0.634, respectively). Modified metabolites in the urinary patterns include advanced glycation end products (pentosidine-glucuronide and glutamyl-lysine-sulfate) and microbiota-associated compounds (indoxyl sulfate and dihydroxyphenyl-gamma-valerolactone-glucuronide). CONCLUSIONS/INTERPRETATION Our results demonstrate that the sex-specific search for diagnostic metabolite biomarkers can be superior to common metabolomics strategies. The diagnostic performance for IGT detection was significantly better than routinely applied blood parameters. Together with recently developed fully automatic LC-MS systems, this opens up future perspectives for the application of sex-specific diagnostic patterns for prediabetes screening in urine.
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Affiliation(s)
- Zaifang Li
- CAS Key Laboratory of Separation Science for Analytical Chemistry, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian, China
- University of Chinese Academy of Sciences, Beijing, China
- Liaoning Province Key Laboratory of Metabolomics, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian, China
| | - Yanhui Zhang
- School of Computer Science & Technology, Dalian University of Technology, Dalian, China
| | - Miriam Hoene
- Department for Diagnostic Laboratory Medicine, Institute for Clinical Chemistry and Pathobiochemistry, University Hospital Tübingen, Tübingen, Germany
| | - Louise Fritsche
- Institute for Diabetes Research and Metabolic Diseases (IDM) of the Helmholtz Zentrum München at the University of Tuebingen, Tuebingen, Germany
- German Center for Diabetes Research (DZD), Tübingen, Germany
| | - Sijia Zheng
- CAS Key Laboratory of Separation Science for Analytical Chemistry, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian, China
- University of Chinese Academy of Sciences, Beijing, China
- Liaoning Province Key Laboratory of Metabolomics, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian, China
| | - Andreas Birkenfeld
- Institute for Diabetes Research and Metabolic Diseases (IDM) of the Helmholtz Zentrum München at the University of Tuebingen, Tuebingen, Germany
- German Center for Diabetes Research (DZD), Tübingen, Germany
- Internal Medicine 4, University Hospital Tuebingen, Tuebingen, Germany
| | - Andreas Fritsche
- Institute for Diabetes Research and Metabolic Diseases (IDM) of the Helmholtz Zentrum München at the University of Tuebingen, Tuebingen, Germany
- German Center for Diabetes Research (DZD), Tübingen, Germany
- Internal Medicine 4, University Hospital Tuebingen, Tuebingen, Germany
| | - Andreas Peter
- Department for Diagnostic Laboratory Medicine, Institute for Clinical Chemistry and Pathobiochemistry, University Hospital Tübingen, Tübingen, Germany
- Institute for Diabetes Research and Metabolic Diseases (IDM) of the Helmholtz Zentrum München at the University of Tuebingen, Tuebingen, Germany
- German Center for Diabetes Research (DZD), Tübingen, Germany
| | - Xinyu Liu
- CAS Key Laboratory of Separation Science for Analytical Chemistry, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian, China
- University of Chinese Academy of Sciences, Beijing, China
- Liaoning Province Key Laboratory of Metabolomics, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian, China
| | - Xinjie Zhao
- CAS Key Laboratory of Separation Science for Analytical Chemistry, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian, China
- University of Chinese Academy of Sciences, Beijing, China
- Liaoning Province Key Laboratory of Metabolomics, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian, China
| | - Lina Zhou
- CAS Key Laboratory of Separation Science for Analytical Chemistry, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian, China
- University of Chinese Academy of Sciences, Beijing, China
- Liaoning Province Key Laboratory of Metabolomics, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian, China
| | - Ping Luo
- CAS Key Laboratory of Separation Science for Analytical Chemistry, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian, China
- University of Chinese Academy of Sciences, Beijing, China
- Liaoning Province Key Laboratory of Metabolomics, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian, China
| | - Cora Weigert
- Department for Diagnostic Laboratory Medicine, Institute for Clinical Chemistry and Pathobiochemistry, University Hospital Tübingen, Tübingen, Germany
- Institute for Diabetes Research and Metabolic Diseases (IDM) of the Helmholtz Zentrum München at the University of Tuebingen, Tuebingen, Germany
- German Center for Diabetes Research (DZD), Tübingen, Germany
| | - Xiaohui Lin
- School of Computer Science & Technology, Dalian University of Technology, Dalian, China
- *Correspondence: Guowang Xu, ; Rainer Lehmann,
| | - Guowang Xu
- CAS Key Laboratory of Separation Science for Analytical Chemistry, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian, China
- University of Chinese Academy of Sciences, Beijing, China
- Liaoning Province Key Laboratory of Metabolomics, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian, China
- *Correspondence: Guowang Xu, ; Rainer Lehmann,
| | - Rainer Lehmann
- Department for Diagnostic Laboratory Medicine, Institute for Clinical Chemistry and Pathobiochemistry, University Hospital Tübingen, Tübingen, Germany
- Institute for Diabetes Research and Metabolic Diseases (IDM) of the Helmholtz Zentrum München at the University of Tuebingen, Tuebingen, Germany
- German Center for Diabetes Research (DZD), Tübingen, Germany
- *Correspondence: Guowang Xu, ; Rainer Lehmann,
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