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Nattero-Chávez L, Insenser M, Amigó N, Samino S, Martínez-Micaelo N, Dorado Avendaño B, Quintero Tobar A, Escobar-Morreale HF, Luque-Ramírez M. Quantification of lipoproteins by proton nuclear magnetic resonance spectroscopy ( 1H-NMRS) improves the prediction of cardiac autonomic dysfunction in patients with type 1 diabetes. J Endocrinol Invest 2024; 47:2075-2085. [PMID: 38182920 DOI: 10.1007/s40618-023-02289-9] [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: 10/06/2023] [Accepted: 12/17/2023] [Indexed: 01/07/2024]
Abstract
AIMS To assess if advanced characterization of serum glycoprotein and lipoprotein profile, measured by proton nuclear magnetic resonance spectroscopy (1H-NMRS) improves a predictive clinical model of cardioautonomic neuropathy (CAN) in subjects with type 1 diabetes (T1D). METHODS Cross-sectional study (ClinicalTrials.gov Identifier: NCT04950634). CAN was diagnosed using Ewing's score. Advanced characterization of macromolecular complexes including glycoprotein and lipoprotein profiles in serum samples were measured by 1H-NMRS. We addressed the relationships between these biomarkers and CAN using correlation and regression analyses. Diagnostic performance was assessed by analyzing their areas under the receiver operating characteristic curves (AUCROC). RESULTS Three hundred and twenty-three patients were included (46% female, mean age and duration of diabetes of 41 ± 13 years and 19 ± 11 years, respectively). The overall prevalence of CAN was 28% [95% confidence interval (95%CI): 23; 33]. Glycoproteins such as N-acetylglucosamine/galactosamine and sialic acid showed strong correlations with inflammatory markers such as high-sensitive C-reactive protein, fibrinogen, IL-10, IL-6, and TNF-α. On the contrary, we did not find any association between the former and CAN. A stepwise binary logistic regression model (R2 = 0.078; P = 0.003) retained intermediate-density lipoprotein-triglycerides (IDL-TG) [β:0.082 (95%CI: 0.005; 0.160); P = 0.039], high-density lipoprotein-triglycerides (HDL-TGL)/HDL-Cholesterol [β:3.633 (95%CI: 0.873; 6.394); P = 0.010], and large-HDL particle number [β: 3.710 (95%CI: 0.677; 6.744); P = 0.001] as statistically significant determinants of CAN. Adding these lipoprotein particles to a clinical prediction model of CAN that included age, duration of diabetes, and A1c enhanced its diagnostic performance, improving AUCROC from 0.546 (95%CI: 0.404; 0.688) to 0.728 (95%CI: 0.616; 0.840). CONCLUSIONS When added to clinical variables, 1H-NMRS-lipoprotein particle profiles may be helpful to identify those patients with T1D at risk of CAN.
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Affiliation(s)
- L Nattero-Chávez
- Department of Endocrinology and Nutrition, Hospital Universitario Ramón y Cajal, Madrid, Spain.
- Diabetes, Obesity and Human Reproduction Research Group, Universidad de Alcalá, Instituto Ramón y Cajal de Investigación Sanitaria (IRYCIS) and Centro de Investigación Biomédica en Red Diabetes y Enfermedades Metabólicas Asociadas (CIBERDEM), Madrid, Spain.
| | - M Insenser
- Diabetes, Obesity and Human Reproduction Research Group, Universidad de Alcalá, Instituto Ramón y Cajal de Investigación Sanitaria (IRYCIS) and Centro de Investigación Biomédica en Red Diabetes y Enfermedades Metabólicas Asociadas (CIBERDEM), Madrid, Spain.
| | - N Amigó
- Biosfer Teslab, CIBERDEM, Madrid, Spain
- Department of Basic Medical Sciences, Universitat Rovira i Virgili (URV), Institut d'Investigació Sanitària Pere Virgili (IISPV), Reus, Spain
| | - S Samino
- Biosfer Teslab, CIBERDEM, Madrid, Spain
| | | | - B Dorado Avendaño
- Department of Endocrinology and Nutrition, Hospital Universitario Ramón y Cajal, Madrid, Spain
| | - A Quintero Tobar
- Diabetes, Obesity and Human Reproduction Research Group, Universidad de Alcalá, Instituto Ramón y Cajal de Investigación Sanitaria (IRYCIS) and Centro de Investigación Biomédica en Red Diabetes y Enfermedades Metabólicas Asociadas (CIBERDEM), Madrid, Spain
| | - H F Escobar-Morreale
- Department of Endocrinology and Nutrition, Hospital Universitario Ramón y Cajal, Madrid, Spain
- Diabetes, Obesity and Human Reproduction Research Group, Universidad de Alcalá, Instituto Ramón y Cajal de Investigación Sanitaria (IRYCIS) and Centro de Investigación Biomédica en Red Diabetes y Enfermedades Metabólicas Asociadas (CIBERDEM), Madrid, Spain
| | - M Luque-Ramírez
- Department of Endocrinology and Nutrition, Hospital Universitario Ramón y Cajal, Madrid, Spain
- Diabetes, Obesity and Human Reproduction Research Group, Universidad de Alcalá, Instituto Ramón y Cajal de Investigación Sanitaria (IRYCIS) and Centro de Investigación Biomédica en Red Diabetes y Enfermedades Metabólicas Asociadas (CIBERDEM), Madrid, Spain
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Yang S, Liu R, Xin Z, Zhu Z, Chu J, Zhong P, Zhu LZ, Shang X, Huang W, Zhang L, He M, Wang W. Plasma metabolomics identifies key metabolites and improves prediction of diabetic retinopathy: development and validation across multi-national cohorts. Ophthalmology 2024:S0161-6420(24)00415-9. [PMID: 38972358 DOI: 10.1016/j.ophtha.2024.07.004] [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: 02/22/2024] [Revised: 05/13/2024] [Accepted: 07/01/2024] [Indexed: 07/09/2024] Open
Abstract
PURPOSE To identify longitudinal metabolomic fingerprints of diabetic retinopathy (DR) and evaluate their utility in predicting DR development and progression. DESIGN Multicenter, multi-ethnic cohort study. PARTICIPANTS This study included 17,675 participants with baseline pre-diabetes/diabetes, in accordance with the 2021 American Diabetes Association guideline, and free of baseline DR from the UK Biobank (UKB); and an additional 638 diabetic participants from the Guangzhou Diabetic Eye Study (GDES) for external validation. METHODS Longitudinal DR metabolomic fingerprints were identified through nuclear magnetic resonance assay in UKB participants. The predictive value of these fingerprints for predicting DR development were assessed in a fully withheld test set. External validation and extrapolation analyses of DR progression and microvascular damage were conducted in the GDES cohort. Model assessments included the C-statistic, net classification improvement (NRI), integrated discrimination improvement (IDI), calibration, and clinical utility in both cohorts. MAIN OUTCOME MEASURES DR development, progression, and retinal microvascular damage. RESULTS Of 168 metabolites, 118 were identified as candidate metabolomic fingerprints for future DR development. These fingerprints significantly improved the predictability for DR development beyond traditional indicators (C-statistic: 0.802, 95% CI, 0.760-0.843 vs. 0.751, 95% CI, 0.706-0.796; P = 5.56×10-4). Glucose, lactate, and citrate were among the fingerprints validated in the GDES cohort. Using these parsimonious and replicable fingerprints yielded similar improvements for predicting DR development (C-statistic: 0.807, 95% CI, 0.711-0.903 vs. 0.617, 95% CI, 0.494, 0.740; P = 1.68×10-4) and progression (C-statistic: 0.797, 95% CI, 0.712-0.882 vs. 0.665, 95% CI, 0.545-0.784; P = 0.003) in the external cohort. Improvements in NRIs, IDIs, and clinical utility were also evident in both cohorts (all P <0.05). In addition, lactate and citrate were associated to microvascular damage across macular and optic disc regions (all P <0.05). CONCLUSIONS Metabolomic profiling has proven effective in identifying robust fingerprints for predicting future DR development and progression, providing novel insights into the early and advanced stages of DR pathophysiology.
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Affiliation(s)
- Shaopeng Yang
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, China
| | - Riqian Liu
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, China
| | - Zhuoyao Xin
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland, USA; F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, Maryland, USA; Department of Biomedical Engineering, Columbia University, New York, New York, USA
| | - Ziyu Zhu
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, China
| | - Jiaqing Chu
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, China
| | - Pingting Zhong
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, China
| | - Lisa Zhuoting Zhu
- Centre for Eye Research Australia, Royal Victorian Eye and Ear Hospital, Melbourne, Australia
| | - Xianwen Shang
- Centre for Eye Research Australia, Royal Victorian Eye and Ear Hospital, Melbourne, Australia
| | - Wenyong Huang
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, China
| | - Lei Zhang
- Centre for Eye Research Australia, Royal Victorian Eye and Ear Hospital, Melbourne, Australia; Central Clinical School, Faculty of Medicine, Monash University, Melbourne, Australia
| | - Mingguang He
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, China; Experimental Ophthalmology, The Hong Kong Polytechnic University, Hong Kong, China
| | - Wei Wang
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, China; Hainan Eye Hospital and Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Haikou, Hainan Province, China.
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Association of Advanced Lipoprotein Subpopulation Profiles with Insulin Resistance and Inflammation in Patients with Type 2 Diabetes Mellitus. J Clin Med 2023; 12:jcm12020487. [PMID: 36675414 PMCID: PMC9864672 DOI: 10.3390/jcm12020487] [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: 10/20/2022] [Revised: 11/10/2022] [Accepted: 11/20/2022] [Indexed: 01/11/2023] Open
Abstract
Plasma lipoproteins exist as several subpopulations with distinct particle number and size that are not fully reflected in the conventional lipid panel. In this study, we sought to quantify lipoprotein subpopulations in patients with type 2 diabetes mellitus (T2DM) to determine whether specific lipoprotein subpopulations are associated with insulin resistance and inflammation markers. The study included 57 patients with T2DM (age, 61.14 ± 9.99 years; HbA1c, 8.66 ± 1.60%; mean body mass index, 35.15 ± 6.65 kg/m2). Plasma lipoprotein particles number and size were determined by nuclear magnetic resonance spectroscopy. Associations of different lipoprotein subpopulations with lipoprotein insulin resistance (LPIR) score and glycoprotein acetylation (GlycA) were assessed using multi-regression analysis. In stepwise regression analysis, VLDL and HDL large particle number and size showed the strongest associations with LPIR (R2 = 0.960; p = 0.0001), whereas the concentrations of the small VLDL and HDL particles were associated with GlycA (R2 = 0.190; p = 0.008 and p = 0.049, respectively). In adjusted multi-regression analysis, small and large VLDL particles and all sizes of lipoproteins independently predicted LPIR, whereas only the number of small LDL particles predicted GlycA. Conventional markers HbA1c and Hs-CRP did not exhibit any significant association with lipoprotein subpopulations. Our data suggest that monitoring insulin resistance-induced changes in lipoprotein subpopulations in T2DM might help to identify novel biomarkers that can be useful for effective clinical intervention.
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