The role of NMR-based circulating metabolic biomarkers in development and risk prediction of new onset type 2 diabetes
Abstract Associations of circulating metabolic biomarkers with type 2 diabetes (T2D) and their added value for risk prediction are uncertain among Chinese adults. A case-cohort study included 882 T2D cases diagnosed during 8-years’ follow-up and a subcohort of 789 participants. NMR-metabolomic profi...
| Main Authors: | , , , , , , , , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2022-09-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-022-19159-8 |
| _version_ | 1798002942749442048 |
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| author | Fiona Bragg Christiana Kartsonaki Yu Guo Michael Holmes Huaidong Du Canqing Yu Pei Pei Ling Yang Donghui Jin Yiping Chen Dan Schmidt Daniel Avery Jun Lv Junshi Chen Robert Clarke Michael R. Hill Liming Li Iona Y. Millwood Zhengming Chen |
| author_facet | Fiona Bragg Christiana Kartsonaki Yu Guo Michael Holmes Huaidong Du Canqing Yu Pei Pei Ling Yang Donghui Jin Yiping Chen Dan Schmidt Daniel Avery Jun Lv Junshi Chen Robert Clarke Michael R. Hill Liming Li Iona Y. Millwood Zhengming Chen |
| author_sort | Fiona Bragg |
| collection | DOAJ |
| description | Abstract Associations of circulating metabolic biomarkers with type 2 diabetes (T2D) and their added value for risk prediction are uncertain among Chinese adults. A case-cohort study included 882 T2D cases diagnosed during 8-years’ follow-up and a subcohort of 789 participants. NMR-metabolomic profiling quantified 225 plasma biomarkers in stored samples taken at recruitment into the study. Cox regression yielded adjusted hazard ratios (HRs) for T2D associated with individual biomarkers, with a set of biomarkers incorporated into an established T2D risk prediction model to assess improvement in discriminatory ability. Mean baseline BMI (SD) was higher in T2D cases than in the subcohort (25.7 [3.6] vs. 23.9 [3.6] kg/m2). Overall, 163 biomarkers were significantly and independently associated with T2D at false discovery rate (FDR) controlled p < 0.05, and 138 at FDR-controlled p < 0.01. Branched chain amino acids (BCAA), apolipoprotein B/apolipoprotein A1, triglycerides in VLDL and medium and small HDL particles, and VLDL particle size were strongly positively associated with T2D (HRs 1.74–2.36 per 1 SD, p < 0.001). HDL particle size, cholesterol concentration in larger HDL particles and docosahexaenoic acid levels were strongly inversely associated with T2D (HRs 0.43–0.48, p < 0.001). With additional adjustment for plasma glucose, most associations (n = 147 and n = 129 at p < 0.05 and p < 0.01, respectively) remained significant. HRs appeared more extreme among more centrally adipose participants for apolipoprotein B/apolipoprotein A1, BCAA, HDL particle size and docosahexaenoic acid (p for heterogeneity ≤ 0.05). Addition of 31 selected biomarkers to an established T2D risk prediction model modestly, but significantly, improved risk discrimination (c-statistic 0.86 to 0.91, p < 0.001). In relatively lean Chinese adults, diverse metabolic biomarkers are associated with future risk of T2D and can help improve established risk prediction models. |
| first_indexed | 2024-04-11T12:01:24Z |
| format | Article |
| id | doaj.art-fd5db2a5053440278eda1ba4c676b10b |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-04-11T12:01:24Z |
| publishDate | 2022-09-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-fd5db2a5053440278eda1ba4c676b10b2022-12-22T04:24:52ZengNature PortfolioScientific Reports2045-23222022-09-0112111010.1038/s41598-022-19159-8The role of NMR-based circulating metabolic biomarkers in development and risk prediction of new onset type 2 diabetesFiona Bragg0Christiana Kartsonaki1Yu Guo2Michael Holmes3Huaidong Du4Canqing Yu5Pei Pei6Ling Yang7Donghui Jin8Yiping Chen9Dan Schmidt10Daniel Avery11Jun Lv12Junshi Chen13Robert Clarke14Michael R. Hill15Liming Li16Iona Y. Millwood17Zhengming Chen18Clinical Trial Service Unit and Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of OxfordClinical Trial Service Unit and Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of OxfordFuwai Hospital Chinese Academy of Medical Sciences, National Center for Cardiovascular DiseasesClinical Trial Service Unit and Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of OxfordClinical Trial Service Unit and Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of OxfordDepartment of Epidemiology and Biostatistics, School of Public Health, Peking University Health Science CenterChinese Academy of Medical SciencesClinical Trial Service Unit and Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of OxfordHunan Centre for Disease Control and PreventionClinical Trial Service Unit and Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of OxfordClinical Trial Service Unit and Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of OxfordClinical Trial Service Unit and Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of OxfordDepartment of Epidemiology and Biostatistics, School of Public Health, Peking University Health Science CenterChina National Center for Food Safety Risk AssessmentClinical Trial Service Unit and Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of OxfordClinical Trial Service Unit and Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of OxfordDepartment of Epidemiology and Biostatistics, School of Public Health, Peking University Health Science CenterClinical Trial Service Unit and Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of OxfordClinical Trial Service Unit and Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of OxfordAbstract Associations of circulating metabolic biomarkers with type 2 diabetes (T2D) and their added value for risk prediction are uncertain among Chinese adults. A case-cohort study included 882 T2D cases diagnosed during 8-years’ follow-up and a subcohort of 789 participants. NMR-metabolomic profiling quantified 225 plasma biomarkers in stored samples taken at recruitment into the study. Cox regression yielded adjusted hazard ratios (HRs) for T2D associated with individual biomarkers, with a set of biomarkers incorporated into an established T2D risk prediction model to assess improvement in discriminatory ability. Mean baseline BMI (SD) was higher in T2D cases than in the subcohort (25.7 [3.6] vs. 23.9 [3.6] kg/m2). Overall, 163 biomarkers were significantly and independently associated with T2D at false discovery rate (FDR) controlled p < 0.05, and 138 at FDR-controlled p < 0.01. Branched chain amino acids (BCAA), apolipoprotein B/apolipoprotein A1, triglycerides in VLDL and medium and small HDL particles, and VLDL particle size were strongly positively associated with T2D (HRs 1.74–2.36 per 1 SD, p < 0.001). HDL particle size, cholesterol concentration in larger HDL particles and docosahexaenoic acid levels were strongly inversely associated with T2D (HRs 0.43–0.48, p < 0.001). With additional adjustment for plasma glucose, most associations (n = 147 and n = 129 at p < 0.05 and p < 0.01, respectively) remained significant. HRs appeared more extreme among more centrally adipose participants for apolipoprotein B/apolipoprotein A1, BCAA, HDL particle size and docosahexaenoic acid (p for heterogeneity ≤ 0.05). Addition of 31 selected biomarkers to an established T2D risk prediction model modestly, but significantly, improved risk discrimination (c-statistic 0.86 to 0.91, p < 0.001). In relatively lean Chinese adults, diverse metabolic biomarkers are associated with future risk of T2D and can help improve established risk prediction models.https://doi.org/10.1038/s41598-022-19159-8 |
| spellingShingle | Fiona Bragg Christiana Kartsonaki Yu Guo Michael Holmes Huaidong Du Canqing Yu Pei Pei Ling Yang Donghui Jin Yiping Chen Dan Schmidt Daniel Avery Jun Lv Junshi Chen Robert Clarke Michael R. Hill Liming Li Iona Y. Millwood Zhengming Chen The role of NMR-based circulating metabolic biomarkers in development and risk prediction of new onset type 2 diabetes Scientific Reports |
| title | The role of NMR-based circulating metabolic biomarkers in development and risk prediction of new onset type 2 diabetes |
| title_full | The role of NMR-based circulating metabolic biomarkers in development and risk prediction of new onset type 2 diabetes |
| title_fullStr | The role of NMR-based circulating metabolic biomarkers in development and risk prediction of new onset type 2 diabetes |
| title_full_unstemmed | The role of NMR-based circulating metabolic biomarkers in development and risk prediction of new onset type 2 diabetes |
| title_short | The role of NMR-based circulating metabolic biomarkers in development and risk prediction of new onset type 2 diabetes |
| title_sort | role of nmr based circulating metabolic biomarkers in development and risk prediction of new onset type 2 diabetes |
| url | https://doi.org/10.1038/s41598-022-19159-8 |
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