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Li T, Geng L, Yang Y, Liu G, Li H, Long C, Chen Q. Protective effect of phospholipids in lipoproteins against diabetic kidney disease: A Mendelian randomization analysis. PLoS One 2024; 19:e0302485. [PMID: 38691537 PMCID: PMC11062548 DOI: 10.1371/journal.pone.0302485] [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: 10/04/2023] [Accepted: 04/05/2024] [Indexed: 05/03/2024] Open
Abstract
BACKGROUND The etiology of diabetic kidney disease is complex, and the role of lipoproteins and their lipid components in the development of the disease cannot be ignored. However, phospholipids are an essential component, and no Mendelian randomization studies have yet been conducted to examine potential causal associations between phospholipids and diabetic kidney disease. METHODS Relevant exposure and outcome datasets were obtained through the GWAS public database. The exposure datasets included various phospholipids, including those in LDL, IDL, VLDL, and HDL. IVW methods were the primary analytical approach. The accuracy of the results was validated by conducting heterogeneity, MR pleiotropy, and F-statistic tests. MR-PRESSO analysis was utilized to identify and exclude outliers. RESULTS Phospholipids in intermediate-density lipoprotein (OR: 0.8439; 95% CI: 0.7268-0.9798), phospholipids in large low- density lipoprotein (OR: 0.7913; 95% CI: 0.6703-0.9341), phospholipids in low- density lipoprotein (after removing outliers, OR: 0.788; 95% CI: 0.6698-0.9271), phospholipids in medium low- density lipoprotein (OR: 0.7682; 95% CI: 0.634-0.931), and phospholipids in small low-density lipoprotein (after removing outliers, OR: 0.8044; 95% CI: 0.6952-0.9309) were found to be protective factors. CONCLUSIONS This study found that a higher proportion of phospholipids in intermediate-density lipoprotein and the various subfractions of low-density lipoprotein, including large LDL, medium LDL, and small LDL, is associated with a lower risk of developing diabetic kidney disease.
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Affiliation(s)
- Tongyi Li
- Hospital of Chengdu University of Traditional Chinese Medicine, Chengdu, Sichuan, China
| | - Liangliang Geng
- Hospital of Chengdu University of Traditional Chinese Medicine, Chengdu, Sichuan, China
| | - Yunjiao Yang
- Hospital of Chengdu University of Traditional Chinese Medicine, Chengdu, Sichuan, China
| | - Guannan Liu
- Hospital of Chengdu University of Traditional Chinese Medicine, Chengdu, Sichuan, China
| | - Haichen Li
- Hospital of Chengdu University of Traditional Chinese Medicine, Chengdu, Sichuan, China
| | - Cong Long
- Hospital of Chengdu University of Traditional Chinese Medicine, Chengdu, Sichuan, China
| | - Qiu Chen
- Hospital of Chengdu University of Traditional Chinese Medicine, Chengdu, Sichuan, China
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Yu G, Tam HCH, Huang C, Shi M, Lim CKP, Chan JCN, Ma RCW. Lessons and Applications of Omics Research in Diabetes Epidemiology. Curr Diab Rep 2024; 24:27-44. [PMID: 38294727 PMCID: PMC10874344 DOI: 10.1007/s11892-024-01533-7] [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] [Accepted: 01/04/2024] [Indexed: 02/01/2024]
Abstract
PURPOSE OF REVIEW Recent advances in genomic technology and molecular techniques have greatly facilitated the identification of disease biomarkers, advanced understanding of pathogenesis of different common diseases, and heralded the dawn of precision medicine. Much of these advances in the area of diabetes have been made possible through deep phenotyping of epidemiological cohorts, and analysis of the different omics data in relation to detailed clinical information. In this review, we aim to provide an overview on how omics research could be incorporated into the design of current and future epidemiological studies. RECENT FINDINGS We provide an up-to-date review of the current understanding in the area of genetic, epigenetic, proteomic and metabolomic markers for diabetes and related outcomes, including polygenic risk scores. We have drawn on key examples from the literature, as well as our own experience of conducting omics research using the Hong Kong Diabetes Register and Hong Kong Diabetes Biobank, as well as other cohorts, to illustrate the potential of omics research in diabetes. Recent studies highlight the opportunity, as well as potential benefit, to incorporate molecular profiling in the design and set-up of diabetes epidemiology studies, which can also advance understanding on the heterogeneity of diabetes. Learnings from these examples should facilitate other researchers to consider incorporating research on omics technologies into their work to advance the field and our understanding of diabetes and its related co-morbidities. Insights from these studies would be important for future development of precision medicine in diabetes.
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Affiliation(s)
- Gechang Yu
- Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Hong Kong, HKSAR, China
- Chinese University of Hong Kong- Shanghai Jiao Tong University Joint Research Centre in Diabetes Genomics and Precision Medicine, Hong Kong Institute of Diabetes and Obesity, The Chinese University of Hong Kong, Hong Kong, HKSAR, China
- Laboratory for Molecular Epidemiology in Diabetes, Li Ka Shing Institute of Health Sciences, The Chinese University of Hong Kong, Hong Kong, HKSAR, China
| | - Henry C H Tam
- Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Hong Kong, HKSAR, China
- Chinese University of Hong Kong- Shanghai Jiao Tong University Joint Research Centre in Diabetes Genomics and Precision Medicine, Hong Kong Institute of Diabetes and Obesity, The Chinese University of Hong Kong, Hong Kong, HKSAR, China
- Laboratory for Molecular Epidemiology in Diabetes, Li Ka Shing Institute of Health Sciences, The Chinese University of Hong Kong, Hong Kong, HKSAR, China
| | - Chuiguo Huang
- Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Hong Kong, HKSAR, China
- Chinese University of Hong Kong- Shanghai Jiao Tong University Joint Research Centre in Diabetes Genomics and Precision Medicine, Hong Kong Institute of Diabetes and Obesity, The Chinese University of Hong Kong, Hong Kong, HKSAR, China
- Laboratory for Molecular Epidemiology in Diabetes, Li Ka Shing Institute of Health Sciences, The Chinese University of Hong Kong, Hong Kong, HKSAR, China
| | - Mai Shi
- Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Hong Kong, HKSAR, China
- Chinese University of Hong Kong- Shanghai Jiao Tong University Joint Research Centre in Diabetes Genomics and Precision Medicine, Hong Kong Institute of Diabetes and Obesity, The Chinese University of Hong Kong, Hong Kong, HKSAR, China
- Laboratory for Molecular Epidemiology in Diabetes, Li Ka Shing Institute of Health Sciences, The Chinese University of Hong Kong, Hong Kong, HKSAR, China
| | - Cadmon K P Lim
- Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Hong Kong, HKSAR, China
- Chinese University of Hong Kong- Shanghai Jiao Tong University Joint Research Centre in Diabetes Genomics and Precision Medicine, Hong Kong Institute of Diabetes and Obesity, The Chinese University of Hong Kong, Hong Kong, HKSAR, China
- Laboratory for Molecular Epidemiology in Diabetes, Li Ka Shing Institute of Health Sciences, The Chinese University of Hong Kong, Hong Kong, HKSAR, China
| | - Juliana C N Chan
- Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Hong Kong, HKSAR, China
- Chinese University of Hong Kong- Shanghai Jiao Tong University Joint Research Centre in Diabetes Genomics and Precision Medicine, Hong Kong Institute of Diabetes and Obesity, The Chinese University of Hong Kong, Hong Kong, HKSAR, China
- Laboratory for Molecular Epidemiology in Diabetes, Li Ka Shing Institute of Health Sciences, The Chinese University of Hong Kong, Hong Kong, HKSAR, China
| | - Ronald C W Ma
- Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Hong Kong, HKSAR, China.
- Chinese University of Hong Kong- Shanghai Jiao Tong University Joint Research Centre in Diabetes Genomics and Precision Medicine, Hong Kong Institute of Diabetes and Obesity, The Chinese University of Hong Kong, Hong Kong, HKSAR, China.
- Laboratory for Molecular Epidemiology in Diabetes, Li Ka Shing Institute of Health Sciences, The Chinese University of Hong Kong, Hong Kong, HKSAR, China.
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Geng TT, Chen JX, Lu Q, Wang PL, Xia PF, Zhu K, Li Y, Guo KQ, Yang K, Liao YF, Zhou YF, Liu G, Pan A. Nuclear Magnetic Resonance-Based Metabolomics and Risk of CKD. Am J Kidney Dis 2024; 83:9-17. [PMID: 37678743 DOI: 10.1053/j.ajkd.2023.05.014] [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: 11/09/2022] [Revised: 05/07/2023] [Accepted: 05/18/2023] [Indexed: 09/09/2023]
Abstract
RATIONALE & OBJECTIVE Chronic kidney disease (CKD) leads to lipid and metabolic abnormalities, but a comprehensive investigation of lipids, lipoprotein particles, and circulating metabolites associated with the risk of CKD has been lacking. We examined the associations of nuclear magnetic resonance (NMR)-based metabolomics data with CKD risk in the UK Biobank study. STUDY DESIGN Observational cohort study. SETTING & PARTICIPANTS A total of 91,532 participants in the UK Biobank Study without CKD and not receiving lipid-lowering therapy. EXPOSURE Levels of metabolites including lipid concentration and composition within 14 lipoprotein subclasses, as well as other metabolic biomarkers were quantified via NMR spectroscopy. OUTCOME Incident CKD identified using ICD codes in any primary care data, hospital admission records, or death register records. ANALYTICAL APPROACH Cox proportional hazards regression models were used to estimate hazard ratios and 95% confidence intervals. RESULTS We identified 2,269 CKD cases over a median follow-up period of 13.1 years via linkage with the electronic health records. After adjusting for covariates and correcting for multiple testing, 90 of 142 biomarkers were significantly associated with incident CKD. In general, higher concentrations of very-low-density lipoprotein (VLDL) particles were associated with a higher risk of CKD whereas higher concentrations of high-density lipoprotein (HDL) particles were associated with a lower risk of CKD. Higher concentrations of cholesterol, phospholipids, and total lipids within VLDL were associated with a higher risk of CKD, whereas within HDL they were associated with a lower risk of CKD. Further, higher triglyceride levels within all lipoprotein subclasses, including all HDL particles, were associated with greater risk of CKD. We also identified that several amino acids, fatty acids, and inflammatory biomarkers were associated with risk of CKD. LIMITATIONS Potential underreporting of CKD cases because of case identification via electronic health records. CONCLUSIONS Our findings highlight multiple known and novel pathways linking circulating metabolites to the risk of CKD. PLAIN-LANGUAGE SUMMARY The relationship between individual lipoprotein particle subclasses and lipid-related traits and risk of chronic kidney disease (CKD) in general population is unclear. Using data from 91,532 participants in the UK Biobank, we evaluated the associations of metabolites measured using nuclear magnetic resonance testing with the risk of CKD. We identified that 90 out of 142 lipid biomarkers were significantly associated with incident CKD. We found that very-low-density lipoproteins, high-density lipoproteins, the lipid concentration and composition within these lipoproteins, triglycerides within all the lipoprotein subclasses, fatty acids, amino acids, and inflammation biomarkers were associated with CKD risk. These findings advance our knowledge about mechanistic pathways that may contribute to the development of CKD.
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Affiliation(s)
- Ting-Ting Geng
- Department of Epidemiology and Biostatistics, Ministry of Education Key Laboratory of Environment and Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan
| | - Jun-Xiang Chen
- Department of Epidemiology and Biostatistics, Ministry of Education Key Laboratory of Environment and Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan
| | - Qi Lu
- Department of Nutrition and Food Hygiene, Hubei Key Laboratory of Food Nutrition and Safety, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan
| | - Pei-Lu Wang
- Department of Epidemiology, T.H. Chan School of Public Health, Harvard University, Boston, Massachusetts
| | - Peng-Fei Xia
- Department of Epidemiology and Biostatistics, Ministry of Education Key Laboratory of Environment and Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan
| | - Kai Zhu
- Department of Nutrition and Food Hygiene, Hubei Key Laboratory of Food Nutrition and Safety, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan
| | - Yue Li
- Department of Epidemiology and Biostatistics, Ministry of Education Key Laboratory of Environment and Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan
| | - Kun-Quan Guo
- Department of Endocrinology, Sinopharm Dongfeng General Hospital, Hubei University of Medicine, Shiyan
| | - Kun Yang
- Department of Endocrinology, Sinopharm Dongfeng General Hospital, Hubei University of Medicine, Shiyan
| | - Yun-Fei Liao
- Department of Endocrinology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Yan-Feng Zhou
- Department of Epidemiology and Biostatistics, Ministry of Education Key Laboratory of Environment and Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan
| | - Gang Liu
- Department of Nutrition and Food Hygiene, Hubei Key Laboratory of Food Nutrition and Safety, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan.
| | - An Pan
- Department of Epidemiology and Biostatistics, Ministry of Education Key Laboratory of Environment and Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan.
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Li Y, Wang H, Xiao Y, Yang H, Wang S, Liu L, Cai H, Zhang X, Tang H, Wu T, Qiu G. Lipidomics identified novel cholesterol-independent predictors for risk of incident coronary heart disease: Mediation of risk from diabetes and aggravation of risk by ambient air pollution. J Adv Res 2023:S2090-1232(23)00396-X. [PMID: 38104795 DOI: 10.1016/j.jare.2023.12.009] [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: 04/04/2023] [Revised: 09/16/2023] [Accepted: 12/10/2023] [Indexed: 12/19/2023] Open
Abstract
INTRODUCTION Previous lipidomics studies have identified various lipid predictors for cardiovascular risk, however, with limited predictive increment, sometimes using too many predictor variables at the expense of practical efficiency. OBJECTIVES To search for lipid predictors of future coronary heart disease (CHD) with stronger predictive power and efficiency to guide primary intervention. METHODS We conducted a prospective nested case-control study involving 1,621 incident CHD cases and 1:1 matched controls. Lipid profiling of 161 lipid species for baseline fasting plasma was performed by liquid chromatography-mass spectrometry. RESULTS In search of CHD predictors, seven lipids were selected by elastic-net regression during over 90% of 1000 cross-validation repetitions, and the derived composite lipid score showed an adjusted odds ratio of 3.75 (95% confidence interval: 3.15, 4.46) per standard deviation increase. Addition of the lipid score into traditional risk model increased c-statistic to 0.736 by an increment of 0.077 (0.063, 0.092). From the seven lipids, we found mediation of CHD risk from baseline diabetes through sphingomyelin (SM) 41:1b with a considerable mediation proportion of 36.97% (P < 0.05). We further found that the positive associations of phosphatidylcholine (PC) 36:0a, SM 41:1b, lysophosphatidylcholine (LPC) 18:0 and LPC 20:3 were more pronounced among participants with higher exposure to fine particulate matter or its certain components, also to ozone for LPC 18:0 and LPC 20:3, while the negative association of cholesteryl ester (CE) 18:2 was attenuated with higher black carbon exposure (P < 0.05). CONCLUSION We identified seven lipid species with greatest predictive increment so-far achieved for incident CHD, and also found novel biomarkers for CHD risk stratification among individuals with diabetes or heavy air pollution exposure.
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Affiliation(s)
- Yingmei Li
- Ministry of Education and State Key Laboratory of Environmental Health (Incubating), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei 430030, China
| | - Hao Wang
- Ministry of Education and State Key Laboratory of Environmental Health (Incubating), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei 430030, China
| | - Yang Xiao
- Ministry of Education and State Key Laboratory of Environmental Health (Incubating), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei 430030, China
| | - Handong Yang
- Department of Cardiovascular Disease, Sinopharm Dongfeng General Hospital, Hubei University of Medicine, Shiyan, China
| | - Sihan Wang
- Ministry of Education and State Key Laboratory of Environmental Health (Incubating), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei 430030, China
| | - Ling Liu
- Ministry of Education and State Key Laboratory of Environmental Health (Incubating), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei 430030, China
| | - Hao Cai
- Ministry of Education and State Key Laboratory of Environmental Health (Incubating), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei 430030, China
| | - Xiaomin Zhang
- Ministry of Education and State Key Laboratory of Environmental Health (Incubating), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei 430030, China
| | - Huiru Tang
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Metabonomics and Systems Biology Laboratory at Shanghai International Centre for Molecular Phenomics, Human Phenome Institute, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Tangchun Wu
- Ministry of Education and State Key Laboratory of Environmental Health (Incubating), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei 430030, China.
| | - Gaokun Qiu
- Ministry of Education and State Key Laboratory of Environmental Health (Incubating), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei 430030, China.
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Krasauskaite J, Conway B, Weir C, Huang Z, Price J. Exploration of Metabolomic Markers Associated With Declining Kidney Function in People With Type 2 Diabetes Mellitus. J Endocr Soc 2023; 8:bvad166. [PMID: 38174155 PMCID: PMC10763986 DOI: 10.1210/jendso/bvad166] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/10/2023] [Indexed: 01/05/2024] Open
Abstract
Background Metabolomics, the study of small molecules in biological systems, can provide valuable insights into kidney dysfunction in people with type 2 diabetes mellitus (T2DM), but prospective studies are scarce. We investigated the association between metabolites and kidney function decline in people with T2DM. Methods The Edinburgh Type 2 Diabetes Study, a population-based cohort of 1066 men and women aged 60 to 75 years with T2DM. We measured 149 serum metabolites at baseline and investigated individual associations with baseline estimated glomerular filtration rate (eGFR), incident chronic kidney disease [CKD; eGFR <60 mL/min/(1.73 m)2], and decliner status (5% eGFR decline per year). Results At baseline, mean eGFR was 77.5 mL/min/(1.73 m)2 (n = 1058), and 216 individuals had evidence of CKD. Of those without CKD, 155 developed CKD over a median 7-year follow-up. Eighty-eight metabolites were significantly associated with baseline eGFR (β range -4.08 to 3.92; PFDR < 0.001). Very low density lipoproteins, triglycerides, amino acids (AAs), glycoprotein acetyls, and fatty acids showed inverse associations, while cholesterol and phospholipids in high-density lipoproteins exhibited positive associations. AA isoleucine, apolipoprotein A1, and total cholines were not only associated with baseline kidney measures (PFDR < 0.05) but also showed stable, nominally significant association with incident CKD and decline. Conclusion Our study revealed widespread changes within the metabolomic profile of CKD, particularly in lipoproteins and their lipid compounds. We identified a smaller number of individual metabolites that are specifically associated with kidney function decline. Replication studies are needed to confirm the longitudinal findings and explore if metabolic signals at baseline can predict kidney decline.
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Affiliation(s)
| | - Bryan Conway
- Centre for Cardiovascular Science, The Queen's Medical Research Institute, Edinburgh BioQuarter, University of Edinburgh, EH16 4TJ, Edinburgh, UK
| | - Christopher Weir
- Usher Institute, University of Edinburgh, EH8 9AG, Edinburgh, UK
| | - Zhe Huang
- Usher Institute, University of Edinburgh, EH8 9AG, Edinburgh, UK
| | - Jackie Price
- Usher Institute, University of Edinburgh, EH8 9AG, Edinburgh, UK
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Guo C, Lin Y, Wu S, Li H, Wu M, Wang F. Association of the dietary inflammation index (DII) with the prevalence of chronic kidney disease in patients with type-2 diabetes mellitus. Ren Fail 2023; 45:2277828. [PMID: 37994461 PMCID: PMC11011236 DOI: 10.1080/0886022x.2023.2277828] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2023] [Accepted: 10/26/2023] [Indexed: 11/24/2023] Open
Abstract
Chronic kidney disease (CKD) is a major complication of diabetes mellitus (DM). Inflammation is an essential component in the process of CKD progression in patients with DM. Diet is a significant determinant of systemic inflammation levels. However, the association between the dietary inflammatory index (DII) and CKD in individuals with DM remains largely unknown; therefore, the aim of this study was to explore whether the DII is linked to the prevalence of CKD in patients with DM. The research method was as follows: first, data from the National Health and Nutrition Examination Survey (NHANES) between 1999 and 2018 were obtained. There were 7,974 participants in our study. These individuals were then classified into three groups according to DII tertiles (T1-T3), with each group consisting of 2,658 participants. Logistic regression analysis was employed to examine whether there was a connection between the DII and CKD. We observed a significant association between the DII and the prevalence of CKD in individuals with DM. After full adjustment for age, sex, ethnicity, smoking, drinking, body mass index (BMI), triglyceride (TG), total cholesterol (TC), metabolic equivalents (METs), energy intake, hypoglycemic medications, hypertension, and cardiovascular disease (CVD), the group with a higher DII had a greater frequency of CKD (T2 group: OR: 1.40; 95% CI: 1.10-1.76; p = 0.006; T3 group: OR: 1.67; 95% CI: 1.29-2.17; p < 0.001). The implementation of an anti-inflammatory diet could serve as an intervention strategy for patients with DM to prevent the onset of CKD.
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Affiliation(s)
- Chunhua Guo
- Department of Cardiology, Longyan First Affiliated Hospital of Fujian Medical University, Longyan, China
| | - Yong Lin
- Department of Cardiology, Longyan First Affiliated Hospital of Fujian Medical University, Longyan, China
| | - Senchao Wu
- Department of Cardiology, Longyan First Affiliated Hospital of Fujian Medical University, Longyan, China
| | - Huaqing Li
- Department of Cardiology, Longyan First Affiliated Hospital of Fujian Medical University, Longyan, China
| | - Meng Wu
- Department of Cardiology, Longyan First Affiliated Hospital of Fujian Medical University, Longyan, China
| | - Fuzhen Wang
- Department of Cardiology, Longyan First Affiliated Hospital of Fujian Medical University, Longyan, China
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Liu C, Du J, Wang Y, Qian X, Ji B, Wang M, Xia Z. Protein Recognition Based on Temperature-Stimulated Multiparameter Response Virtual Array Sensing Strategy. Anal Chem 2023; 95:16996-17002. [PMID: 37943990 DOI: 10.1021/acs.analchem.3c03336] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2023]
Abstract
In the field of array sensing, researchers are committed to miniaturizing array sensing systems while ensuring the acquisition of multiple sensing information. Here, a new strategy called "stimulus responsive array sensing" was presented to obtain virtual multiple sensing without constructing multiple physical sensing units. Based on bioluminescence resonance energy transfer, where luciferase acts as the donor and temperature stimulus response polymers act as the receptors, by using only one sensing unit to output multiple stimulus responsive sensing signals in temperature dimension, an equivalent array sensing could be achieved. This strategy can distinguish and quantify a variety of proteins. More importantly, glucose responsive monomers were doped in polymers; thus, more virtual sensing units can be further increased to obtain more sensing signals, greatly increasing the accuracy of protein recognition, and it can also be used to differentiate several compositions of protein under different glucose concentrations in urine caused by different renal diseases. The results show the potential of the "stimulus responsive array sensing" for analyzing molecular compositions in complex biological systems and show a new tack in array sensing.
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Affiliation(s)
- Chunlan Liu
- Chongqing Key Laboratory of Natural Product Synthesis and Drug Research, Innovative Drug Research Center, School of Pharmaceutical Sciences, Chongqing University, Chongqing 401331, China
| | - Jiayin Du
- Chongqing Key Laboratory of Natural Product Synthesis and Drug Research, Innovative Drug Research Center, School of Pharmaceutical Sciences, Chongqing University, Chongqing 401331, China
| | - Yue Wang
- Chongqing Key Laboratory of Natural Product Synthesis and Drug Research, Innovative Drug Research Center, School of Pharmaceutical Sciences, Chongqing University, Chongqing 401331, China
| | - Xin Qian
- Chongqing Key Laboratory of Natural Product Synthesis and Drug Research, Innovative Drug Research Center, School of Pharmaceutical Sciences, Chongqing University, Chongqing 401331, China
| | - Baian Ji
- Chongqing Key Laboratory of Natural Product Synthesis and Drug Research, Innovative Drug Research Center, School of Pharmaceutical Sciences, Chongqing University, Chongqing 401331, China
| | - Min Wang
- Chongqing Key Laboratory of Natural Product Synthesis and Drug Research, Innovative Drug Research Center, School of Pharmaceutical Sciences, Chongqing University, Chongqing 401331, China
| | - Zhining Xia
- Chongqing Key Laboratory of Natural Product Synthesis and Drug Research, Innovative Drug Research Center, School of Pharmaceutical Sciences, Chongqing University, Chongqing 401331, China
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Lithovius R, Mutter S, Parente EB, Mäkinen VP, Valo E, Harjutsalo V, Groop PH. Medication profiling in women with type 1 diabetes highlights the importance of adequate, guideline-based treatment in low-risk groups. Sci Rep 2023; 13:17893. [PMID: 37857707 PMCID: PMC10587128 DOI: 10.1038/s41598-023-44695-2] [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: 05/23/2023] [Accepted: 10/11/2023] [Indexed: 10/21/2023] Open
Abstract
Effective treatment may prevent kidney complications, but women might be underprescribed. Novel, data-driven insights into prescriptions and their relationship with kidney health in women with type 1 diabetes may help to optimize treatment. We identified six medication profiles in 1164 women from the FinnDiane Study with normal albumin excretion rate based on clusters of their baseline prescription data using a self-organizing map. Future rapid kidney function decline was defined as an annual estimated glomerular filtration rate (eGFR) loss > 3 ml/min/1.73 m2 after baseline. Two profiles were associated with future decline: Profile ARB with the highest proportion of angiotensin receptor blockers (odds ratio [OR] 2.75, P = 0.02) and highly medicated women in profile HighMed (OR 2.55, P = 0.03). Compared with profile LowMed (low purchases of all), profile HighMed had worse clinical characteristics, whereas in profile ARB only systolic blood pressure was elevated. Importantly, the younger women in profile ARB with fewer kidney protective treatments developed a rapid decline despite otherwise similar baseline characteristics to profile ACE & Lipids (the highest proportions of ACE inhibitors and lipid-modifying agents) without a future rapid decline. In conclusion, medication profiles identified different future eGFR trajectories in women with type 1 diabetes revealing potential treatment gaps for younger women.
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Affiliation(s)
- Raija Lithovius
- Folkhälsan Institute of Genetics, Folkhälsan Research Center, Helsinki, Finland
- Department of Nephrology, University of Helsinki and Helsinki University Hospital, Helsinki, Finland
- Research Program for Clinical and Molecular Metabolism, Faculty of Medicine, University of Helsinki, Helsinki, Finland
| | - Stefan Mutter
- Folkhälsan Institute of Genetics, Folkhälsan Research Center, Helsinki, Finland
- Department of Nephrology, University of Helsinki and Helsinki University Hospital, Helsinki, Finland
- Research Program for Clinical and Molecular Metabolism, Faculty of Medicine, University of Helsinki, Helsinki, Finland
| | - Erika B Parente
- Folkhälsan Institute of Genetics, Folkhälsan Research Center, Helsinki, Finland
- Department of Nephrology, University of Helsinki and Helsinki University Hospital, Helsinki, Finland
- Research Program for Clinical and Molecular Metabolism, Faculty of Medicine, University of Helsinki, Helsinki, Finland
| | - Ville-Petteri Mäkinen
- Systems Epidemiology, Faculty of Medicine, University of Oulu, Oulu, Finland
- Research Unit of Population Health, Faculty of Medicine, University of Oulu, Oulu, Finland
| | - Erkka Valo
- Folkhälsan Institute of Genetics, Folkhälsan Research Center, Helsinki, Finland
- Department of Nephrology, University of Helsinki and Helsinki University Hospital, Helsinki, Finland
- Research Program for Clinical and Molecular Metabolism, Faculty of Medicine, University of Helsinki, Helsinki, Finland
| | - Valma Harjutsalo
- Folkhälsan Institute of Genetics, Folkhälsan Research Center, Helsinki, Finland
- Department of Nephrology, University of Helsinki and Helsinki University Hospital, Helsinki, Finland
- Research Program for Clinical and Molecular Metabolism, Faculty of Medicine, University of Helsinki, Helsinki, Finland
- Chronic Disease Prevention Unit, National Institute for Health and Welfare, Helsinki, Finland
| | - Per-Henrik Groop
- Folkhälsan Institute of Genetics, Folkhälsan Research Center, Helsinki, Finland.
- Department of Nephrology, University of Helsinki and Helsinki University Hospital, Helsinki, Finland.
- Research Program for Clinical and Molecular Metabolism, Faculty of Medicine, University of Helsinki, Helsinki, Finland.
- Department of Diabetes, Central Clinical School, Monash University, Melbourne, VIC, Australia.
- Folkhälsan Institute of Genetics, Folkhälsan Research Center, Biomedicum Helsinki, Helsinki University, Haartmaninkatu 8 [C318b], PO Box 63, 00014, Helsinki, Finland.
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Aleidi SM, Al Fahmawi H, Masoud A, Rahman AA. Metabolomics in diabetes mellitus: clinical insight. Expert Rev Proteomics 2023; 20:451-467. [PMID: 38108261 DOI: 10.1080/14789450.2023.2295866] [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: 08/02/2023] [Accepted: 12/13/2023] [Indexed: 12/19/2023]
Abstract
INTRODUCTION Diabetes Mellitus (DM) is a chronic heterogeneous metabolic disorder characterized by hyperglycemia due to the destruction of insulin-producing pancreatic β cells and/or insulin resistance. It is now considered a global epidemic disease associated with serious threats to a patient's life. Understanding the metabolic pathways involved in disease pathogenesis and progression is important and would improve prevention and management strategies. Metabolomics is an emerging field of research that offers valuable insights into the metabolic perturbation associated with metabolic diseases, including DM. AREA COVERED Herein, we discussed the metabolomics in type 1 and 2 DM research, including its contribution to understanding disease pathogenesis and identifying potential novel biomarkers clinically useful for disease screening, monitoring, and prognosis. In addition, we highlighted the metabolic changes associated with treatment effects, including insulin and different anti-diabetic medications. EXPERT OPINION By analyzing the metabolome, the metabolic disturbances involved in T1DM and T2DM can be explored, enhancing our understanding of the disease progression and potentially leading to novel clinical diagnostic and effective new therapeutic approaches. In addition, identifying specific metabolites would be potential clinical biomarkers for predicting the disease and thus preventing and managing hyperglycemia and its complications.
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Affiliation(s)
- Shereen M Aleidi
- Department of Biopharmaceutics and Clinical Pharmacy, School of Pharmacy, The University of Jordan, Amman, Jordan
| | - Hiba Al Fahmawi
- Department of Biopharmaceutics and Clinical Pharmacy, School of Pharmacy, The University of Jordan, Amman, Jordan
| | - Afshan Masoud
- Proteomics Resource Unit, Obesity Research Center, College of Medicine, King Saud University, Riyadh, Saudi Arabia
| | - Anas Abdel Rahman
- Department of Biochemistry and Molecular Medicine, College of Medicine, Al Faisal University, Riyadh, Saudi Arabia
- Metabolomics Section, Department of Clinical Genomics, Center for Genomics Medicine, King Faisal Specialist Hospital and Research Centre, Riyadh, Saudi Arabia
- Department of Chemistry, Memorial University of Newfoundland, St. John's, Newfoundland and Labrador, Canada
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10
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Das S, Gnanasambandan R. Intestinal microbiome diversity of diabetic and non-diabetic kidney disease: Current status and future perspective. Life Sci 2023; 316:121414. [PMID: 36682521 DOI: 10.1016/j.lfs.2023.121414] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2022] [Revised: 01/09/2023] [Accepted: 01/17/2023] [Indexed: 01/21/2023]
Abstract
A significant portion of the health burden of diabetic kidney disease (DKD) is caused by both type 1 and type 2 diabetes which leads to morbidity and mortality globally. It is one of the most common diabetic complications characterized by loss of renal function with high prevalence, often leading to acute kidney disease (AKD). Inflammation triggered by gut microbiota is commonly associated with the development of DKD. Interactions between the gut microbiota and the host are correlated in maintaining metabolic and inflammatory homeostasis. However, the fundamental processes through which the gut microbiota affects the onset and progression of DKD are mainly unknown. In this narrative review, we summarised the potential role of the gut microbiome, their pathogenicity between diabetic and non-diabetic kidney disease (NDKD), and their impact on host immunity. A well-established association has already been seen between gut microbiota, diabetes and kidney disease. The gut-kidney interrelationship is confirmed by mounting evidence linking gut dysbiosis to DKD, however, it is still unclear what is the real cause of gut dysbiosis, the development of DKD, and its progression. In addition, we also try to distinguish novel biomarkers for early detection of DKD and the possible therapies that can be used to regulate the gut microbiota and improve the host immune response. This early detection and new therapies will help clinicians for better management of the disease and help improve patient outcomes.
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Affiliation(s)
- Soumik Das
- School of Biosciences and Technology, Vellore Institute of Technology (VIT), Vellore, Tamil Nadu 632014, India
| | - Ramanathan Gnanasambandan
- School of Biosciences and Technology, Vellore Institute of Technology (VIT), Vellore, Tamil Nadu 632014, India.
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11
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Mäkinen VP, Kettunen J, Lehtimäki T, Kähönen M, Viikari J, Perola M, Salomaa V, Järvelin MR, Raitakari OT, Ala-Korpela M. Longitudinal metabolomics of increasing body-mass index and waist-hip ratio reveals two dynamic patterns of obesity pandemic. Int J Obes (Lond) 2023; 47:453-462. [PMID: 36823293 DOI: 10.1038/s41366-023-01281-w] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/26/2022] [Revised: 02/07/2023] [Accepted: 02/13/2023] [Indexed: 02/25/2023]
Abstract
BACKGROUND/OBJECTIVE This observational study dissects the complex temporal associations between body-mass index (BMI), waist-hip ratio (WHR) and circulating metabolomics using a combination of longitudinal and cross-sectional population-based datasets and new systems epidemiology tools. SUBJECTS/METHODS Firstly, a data-driven subgrouping algorithm was employed to simplify high-dimensional metabolic profiling data into a single categorical variable: a self-organizing map (SOM) was created from 174 metabolic measures from cross-sectional surveys (FINRISK, n = 9708, ages 25-74) and a birth cohort (NFBC1966, n = 3117, age 31 at baseline, age 46 at follow-up) and an expert committee defined four subgroups of individuals based on visual inspection of the SOM. Secondly, the subgroups were compared regarding BMI and WHR trajectories in an independent longitudinal dataset: participants of the Young Finns Study (YFS, n = 1286, ages 24-39 at baseline, 10 years follow-up, three visits) were categorized into the four subgroups and subgroup-specific age-dependent trajectories of BMI, WHR and metabolic measures were modelled by linear regression. RESULTS The four subgroups were characterised at age 39 by high BMI, WHR and dyslipidemia (designated TG-rich); low BMI, WHR and favourable lipids (TG-poor); low lipids in general (Low lipid) and high low-density-lipoprotein cholesterol (High LDL-C). Trajectory modelling of the YFS dataset revealed a dynamic BMI divergence pattern: despite overlapping starting points at age 24, the subgroups diverged in BMI, fasting insulin (three-fold difference at age 49 between TG-rich and TG-poor) and insulin-associated measures such as triglyceride-cholesterol ratio. Trajectories also revealed a WHR progression pattern: despite different starting points at the age of 24 in WHR, LDL-C and cholesterol-associated measures, all subgroups exhibited similar rates of change in these measures, i.e. WHR progression was uniform regardless of the cross-sectional metabolic profile. CONCLUSIONS Age-associated weight variation in adults between 24 and 49 manifests as temporal divergence in BMI and uniform progression of WHR across metabolic health strata.
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Affiliation(s)
- Ville-Petteri Mäkinen
- Systems Epidemiology, Faculty of Medicine, University of Oulu, Oulu, Finland. .,Research Unit of Population Health, Faculty of Medicine, University of Oulu, Oulu, Finland. .,Computational and Systems Biology Program, Precision Medicine Theme, South Australian Health and Medical Research Institute, Adelaide, SA, Australia. .,Australian Centre for Precision Health, University of South Australia, Adelaide, SA, Australia.
| | - Johannes Kettunen
- Systems Epidemiology, Faculty of Medicine, University of Oulu, Oulu, Finland.,Research Unit of Population Health, Faculty of Medicine, University of Oulu, Oulu, Finland.,Biocenter Oulu, Oulu, Finland.,Department of Health and Welfare, Finnish Institute for Health and Welfare, Helsinki, Finland
| | - Terho Lehtimäki
- Department of Clinical Chemistry, Fimlab Laboratories, and Finnish Cardiovascular Research Center Tampere, Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland
| | - Mika Kähönen
- Department of Clinical Physiology, Tampere University Hospital, and Finnish Cardiovascular Research Center Tampere, Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland
| | - Jorma Viikari
- Department of Medicine, University of Turku, Turku, Finland.,Division of Medicine, Turku University Hospital, Turku, Finland
| | - Markus Perola
- Department of Health and Welfare, Finnish Institute for Health and Welfare, Helsinki, Finland.,Institute for Molecular Medicine (FIMM), University of Helsinki, Helsinki, Finland.,Estonian Genome Center, University of Tartu, Tartu, Estonia
| | - Veikko Salomaa
- Department of Health and Welfare, Finnish Institute for Health and Welfare, Helsinki, Finland
| | - Marjo-Riitta Järvelin
- Research Unit of Population Health, Faculty of Medicine, University of Oulu, Oulu, Finland.,Unit of Primary Health Care, Oulu University Hospital, OYS, Oulu, Finland.,Department of Epidemiology and Biostatistics, MRC-PHE Centre for Environment and Health, School of Public Health, Imperial College London, London, UK.,Department of Life Sciences, College of Health and Life Sciences, Brunel University London, London, UK
| | - Olli T Raitakari
- Research Centre of Applied and Preventive Cardiovascular Medicine, University of Turku, Turku, Finland.,Department of Clinical Physiology and Nuclear Medicine, Turku University Hospital, Turku, Finland.,Centre for Population Health Research, University of Turku and Turku University Hospital, Turku, Finland
| | - Mika Ala-Korpela
- Systems Epidemiology, Faculty of Medicine, University of Oulu, Oulu, Finland. .,Research Unit of Population Health, Faculty of Medicine, University of Oulu, Oulu, Finland. .,Biocenter Oulu, Oulu, Finland. .,NMR Metabolomics Laboratory, School of Pharmacy, University of Eastern Finland, Kuopio, Finland.
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12
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Reynolds KM, Lin BM, Armstrong ND, Ottosson F, Zhang Y, Williams AS, Yu B, Boerwinkle E, Thygarajan B, Daviglus ML, Muoio D, Qi Q, Kaplan R, Melander O, Lash JP, Cai J, Irvin MR, Newgard CB, Sofer T, Franceschini N. Circulating Metabolites Associated with Albuminuria in a Hispanic/Latino Population. Clin J Am Soc Nephrol 2023; 18:204-212. [PMID: 36517247 PMCID: PMC10103280 DOI: 10.2215/cjn.09070822] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2022] [Revised: 11/22/2022] [Accepted: 12/02/2022] [Indexed: 12/15/2022]
Abstract
BACKGROUND Albuminuria is associated with metabolic abnormalities, but these relationships are not well understood. We studied the association of metabolites with albuminuria in Hispanic/Latino people, a population with high risk for metabolic disease. METHODS We used data from 3736 participants from the Hispanic Community Health Study/Study of Latinos, of which 16% had diabetes and 9% had an increased urine albumin-to-creatinine ratio (UACR). Metabolites were quantified in fasting serum through nontargeted mass spectrometry (MS) analysis using ultra-performance liquid chromatography-MS/MS. Spot UACR was inverse normally transformed and tested for the association with each metabolite or combined, correlated metabolites, in covariate-adjusted models that accounted for the study design. In total, 132 metabolites were available for replication in the Hypertension Genetic Epidemiology Network study ( n =300), and 29 metabolites were available for replication in the Malmö Offspring Study ( n =999). RESULTS Among 640 named metabolites, we identified 148 metabolites significantly associated with UACR, including 18 novel associations that replicated in independent samples. These metabolites showed enrichment for D-glutamine and D-glutamate metabolism and arginine biosynthesis, pathways previously reported for diabetes and insulin resistance. In correlated metabolite analyses, we identified two modules significantly associated with UACR, including a module composed of lipid metabolites related to the biosynthesis of unsaturated fatty acids and alpha linolenic acid and linoleic acid metabolism. CONCLUSIONS Our study identified associations of albuminuria with metabolites involved in glucose dysregulation, and essential fatty acids and precursors of arachidonic acid in Hispanic/Latino population. PODCAST This article contains a podcast at https://dts.podtrac.com/redirect.mp3/www.asn-online.org/media/podcast/CJASN/2023_02_08_CJN09070822.mp3.
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Affiliation(s)
- Kaylia M. Reynolds
- Department of Biostatistics, University of North Carolina, Chapel Hill, North Carolina
| | - Bridget M. Lin
- Department of Biostatistics, University of North Carolina, Chapel Hill, North Carolina
| | - Nicole D. Armstrong
- Department of Epidemiology, University of Alabama at Birmingham, Birmingham, Alabama
| | - Filip Ottosson
- Department of Clinical Sciences, Lund University, Malmö, Sweden
- Section for Clinical Mass Spectrometry, Danish Center for Neonatal Screening, Department of Congenital Disorders, Statens Serum Institut, Copenhagen, Denmark
| | - Ying Zhang
- Division of Sleep and Circadian Disorders, Brigham and Women's Hospital, Boston, Massachusetts
| | | | - Bing Yu
- Human Genetics Center, University of Texas Health Science Center at Houston, Houston, Texas
| | - Eric Boerwinkle
- Human Genetics Center, University of Texas Health Science Center at Houston, Houston, Texas
| | - Bharat Thygarajan
- Division of Molecular Pathology and Genomics, University of Minnesota, Minneapolis, Minnesota
| | - Martha L. Daviglus
- Institute for Minority Health Research, University of Illinois at Chicago College of Medicine, Chicago, Illinois
| | - Deborah Muoio
- Duke University Medical Center, Durham, North Carolina
| | - Qibin Qi
- Department of Epidemiology and Population Health, Albert Einstein College of Medicine, Bronx, New York
| | - Robert Kaplan
- Department of Epidemiology and Population Health, Albert Einstein College of Medicine, Bronx, New York
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, Washington
| | - Olle Melander
- Department of Clinical Sciences, Lund University, Malmö, Sweden
| | - James P. Lash
- Division of Nephrology, Department of Medicine, University of Illinois, Chicago, Illinois
| | - Jianwen Cai
- Department of Biostatistics, University of North Carolina, Chapel Hill, North Carolina
| | - Marguerite R. Irvin
- Department of Epidemiology, University of Alabama at Birmingham, Birmingham, Alabama
| | | | - Tamar Sofer
- Division of Sleep and Circadian Disorders, Brigham and Women's Hospital, Boston, Massachusetts
- Departments of Medicine and Biostatistics, Harvard University, Boston, Massachusetts
| | - Nora Franceschini
- Department of Epidemiology, University of North Carolina, Chapel Hill, North Carolina
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13
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Sandholm N, Hotakainen R, Haukka JK, Jansson Sigfrids F, Dahlström EH, Antikainen AA, Valo E, Syreeni A, Kilpeläinen E, Kytölä A, Palotie A, Harjutsalo V, Forsblom C, Groop PH. Whole-exome sequencing identifies novel protein-altering variants associated with serum apolipoprotein and lipid concentrations. Genome Med 2022; 14:132. [PMID: 36419110 PMCID: PMC9685920 DOI: 10.1186/s13073-022-01135-6] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2022] [Accepted: 11/04/2022] [Indexed: 11/25/2022] Open
Abstract
BACKGROUND Dyslipidemia is a major risk factor for cardiovascular disease, and diabetes impacts the lipid metabolism through multiple pathways. In addition to the standard lipid measurements, apolipoprotein concentrations provide added awareness of the burden of circulating lipoproteins. While common genetic variants modestly affect the serum lipid concentrations, rare genetic mutations can cause monogenic forms of hypercholesterolemia and other genetic disorders of lipid metabolism. We aimed to identify low-frequency protein-altering variants (PAVs) affecting lipoprotein and lipid traits. METHODS We analyzed whole-exome (WES) and whole-genome sequencing (WGS) data of 481 and 474 individuals with type 1 diabetes, respectively. The phenotypic data consisted of 79 serum lipid and apolipoprotein phenotypes obtained with clinical laboratory measurements and nuclear magnetic resonance spectroscopy. RESULTS The single-variant analysis identified an association between the LIPC p.Thr405Met (rs113298164) and serum apolipoprotein A1 concentrations (p=7.8×10-8). The burden of PAVs was significantly associated with lipid phenotypes in LIPC, RBM47, TRMT5, GTF3C5, MARCHF10, and RYR3 (p<2.9×10-6). The RBM47 gene is required for apolipoprotein B post-translational modifications, and in our data, the association between RBM47 and apolipoprotein C-III concentrations was due to a rare 21 base pair p.Ala496-Ala502 deletion; in replication, the burden of rare deleterious variants in RBM47 was associated with lower triglyceride concentrations in WES of >170,000 individuals from multiple ancestries (p=0.0013). Two PAVs in GTF3C5 were highly enriched in the Finnish population and associated with cardiovascular phenotypes in the general population. In the previously known APOB gene, we identified novel associations at two protein-truncating variants resulting in lower serum non-HDL cholesterol (p=4.8×10-4), apolipoprotein B (p=5.6×10-4), and LDL cholesterol (p=9.5×10-4) concentrations. CONCLUSIONS We identified lipid and apolipoprotein-associated variants in the previously known LIPC and APOB genes, as well as PAVs in GTF3C5 associated with LDLC, and in RBM47 associated with apolipoprotein C-III concentrations, implicated as an independent CVD risk factor. Identification of rare loss-of-function variants has previously revealed genes that can be targeted to prevent CVD, such as the LDL cholesterol-lowering loss-of-function variants in the PCSK9 gene. Thus, this study suggests novel putative therapeutic targets for the prevention of CVD.
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Affiliation(s)
- Niina Sandholm
- Folkhälsan Research Center, Biomedicum Helsinki, Haartmaninkatu 8, Helsinki, 00290, Finland.
- Department of Nephrology, University of Helsinki and Helsinki University Hospital, Helsinki, Finland.
- Research Program for Clinical and Molecular Metabolism, Faculty of Medicine, University of Helsinki, Helsinki, Finland.
| | - Ronja Hotakainen
- Folkhälsan Research Center, Biomedicum Helsinki, Haartmaninkatu 8, Helsinki, 00290, Finland
- Department of Nephrology, University of Helsinki and Helsinki University Hospital, Helsinki, Finland
- Research Program for Clinical and Molecular Metabolism, Faculty of Medicine, University of Helsinki, Helsinki, Finland
| | - Jani K Haukka
- Folkhälsan Research Center, Biomedicum Helsinki, Haartmaninkatu 8, Helsinki, 00290, Finland
- Department of Nephrology, University of Helsinki and Helsinki University Hospital, Helsinki, Finland
- Research Program for Clinical and Molecular Metabolism, Faculty of Medicine, University of Helsinki, Helsinki, Finland
| | - Fanny Jansson Sigfrids
- Folkhälsan Research Center, Biomedicum Helsinki, Haartmaninkatu 8, Helsinki, 00290, Finland
- Department of Nephrology, University of Helsinki and Helsinki University Hospital, Helsinki, Finland
- Research Program for Clinical and Molecular Metabolism, Faculty of Medicine, University of Helsinki, Helsinki, Finland
| | - Emma H Dahlström
- Folkhälsan Research Center, Biomedicum Helsinki, Haartmaninkatu 8, Helsinki, 00290, Finland
- Department of Nephrology, University of Helsinki and Helsinki University Hospital, Helsinki, Finland
- Research Program for Clinical and Molecular Metabolism, Faculty of Medicine, University of Helsinki, Helsinki, Finland
| | - Anni A Antikainen
- Folkhälsan Research Center, Biomedicum Helsinki, Haartmaninkatu 8, Helsinki, 00290, Finland
- Department of Nephrology, University of Helsinki and Helsinki University Hospital, Helsinki, Finland
- Research Program for Clinical and Molecular Metabolism, Faculty of Medicine, University of Helsinki, Helsinki, Finland
| | - Erkka Valo
- Folkhälsan Research Center, Biomedicum Helsinki, Haartmaninkatu 8, Helsinki, 00290, Finland
- Department of Nephrology, University of Helsinki and Helsinki University Hospital, Helsinki, Finland
- Research Program for Clinical and Molecular Metabolism, Faculty of Medicine, University of Helsinki, Helsinki, Finland
| | - Anna Syreeni
- Folkhälsan Research Center, Biomedicum Helsinki, Haartmaninkatu 8, Helsinki, 00290, Finland
- Department of Nephrology, University of Helsinki and Helsinki University Hospital, Helsinki, Finland
- Research Program for Clinical and Molecular Metabolism, Faculty of Medicine, University of Helsinki, Helsinki, Finland
| | - Elina Kilpeläinen
- Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, Helsinki, Finland
| | - Anastasia Kytölä
- Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, Helsinki, Finland
| | - Aarno Palotie
- Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, Helsinki, Finland
- Analytic and Translational Genetics Unit, Department of Medicine, Department of Neurology and Department of Psychiatry, Massachusetts General Hospital, Boston, MA, USA
- The Stanley Center for Psychiatric Research and Program in Medical and Population Genetics, The Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Valma Harjutsalo
- Folkhälsan Research Center, Biomedicum Helsinki, Haartmaninkatu 8, Helsinki, 00290, Finland
- Department of Nephrology, University of Helsinki and Helsinki University Hospital, Helsinki, Finland
- Research Program for Clinical and Molecular Metabolism, Faculty of Medicine, University of Helsinki, Helsinki, Finland
| | - Carol Forsblom
- Folkhälsan Research Center, Biomedicum Helsinki, Haartmaninkatu 8, Helsinki, 00290, Finland
- Department of Nephrology, University of Helsinki and Helsinki University Hospital, Helsinki, Finland
- Research Program for Clinical and Molecular Metabolism, Faculty of Medicine, University of Helsinki, Helsinki, Finland
| | - Per-Henrik Groop
- Folkhälsan Research Center, Biomedicum Helsinki, Haartmaninkatu 8, Helsinki, 00290, Finland.
- Department of Nephrology, University of Helsinki and Helsinki University Hospital, Helsinki, Finland.
- Research Program for Clinical and Molecular Metabolism, Faculty of Medicine, University of Helsinki, Helsinki, Finland.
- Department of Diabetes, Central Clinical School, Monash University, Melbourne, Victoria, Australia.
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14
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Ala-Korpela M, Zhao S, Järvelin MR, Mäkinen VP, Ohukainen P. Apt interpretation of comprehensive lipoprotein data in large-scale epidemiology: disclosure of fundamental structural and metabolic relationships. Int J Epidemiol 2022; 51:996-1011. [PMID: 34405869 PMCID: PMC9189959 DOI: 10.1093/ije/dyab156] [Citation(s) in RCA: 23] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2021] [Accepted: 07/09/2021] [Indexed: 12/27/2022] Open
Abstract
BACKGROUND Quantitative lipoprotein analytics using nuclear magnetic resonance (NMR) spectroscopy is currently commonplace in large-scale studies. One methodology has become widespread and is currently being utilized also in large biobanks. It allows the comprehensive characterization of 14 lipoprotein subclasses, clinical lipids, apolipoprotein A-I and B. The details of these data are conceptualized here in relation to lipoprotein metabolism with particular attention on the fundamental characteristics of subclass particle numbers, lipid concentrations and compositional measures. METHODS AND RESULTS The NMR methodology was applied to fasting serum samples from Northern Finland Birth Cohorts 1966 and 1986 with 5651 and 5605 participants, respectively. All results were highly consistent between the cohorts. Circulating lipid concentrations in a particular lipoprotein subclass arise predominantly as the result of the circulating number of those subclass particles. The spherical lipoprotein particle shape, with a radially oriented surface monolayer, imposes size-dependent biophysical constraints for the lipid composition of individual subclass particles and inherently restricts the accommodation of metabolic changes via compositional modifications. The new finding that the relationship between lipoprotein subclass particle concentrations and the particle size is log-linear reveals that circulating lipoprotein particles are also under rather strict metabolic constraints for both their absolute and relative concentrations. CONCLUSIONS The fundamental structural and metabolic relationships between lipoprotein subclasses elucidated in this study empower detailed interpretation of lipoprotein metabolism. Understanding the intricate details of these extensive data is important for the precise interpretation of novel therapeutic opportunities and for fully utilizing the potential of forthcoming analyses of genetic and metabolic data in large biobanks.
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Affiliation(s)
- Mika Ala-Korpela
- Corresponding author. Computational Medicine, Center for Life Course Health Research, Faculty of Medicine, University of Oulu, Oulu, Finland. E-mail:
| | - Siyu Zhao
- Computational Medicine, Faculty of Medicine, University of Oulu, Oulu, Finland
- Center for Life Course Health Research, Faculty of Medicine, University of Oulu, Oulu, Finland
- Biocenter Oulu, University of Oulu, Oulu, Finland
| | - Marjo-Riitta Järvelin
- Center for Life Course Health Research, Faculty of Medicine, University of Oulu, Oulu, Finland
- Biocenter Oulu, University of Oulu, Oulu, Finland
- Unit of Primary Health Care, Oulu University Hospital, OYS, Oulu, Finland
- Department of Epidemiology and Biostatistics, MRC-PHE Centre for Environment and Health, School of Public Health, Imperial College London, London, UK
- Department of Life Sciences, College of Health and Life Sciences, Brunel University London, UK
| | - Ville-Petteri Mäkinen
- Australian Centre for Precision Health, University of South Australia, Adelaide, Australia
- Computational and Systems Biology Program, Precision Medicine Theme, South Australian Health and Medical Research Institute, Adelaide, Australia
| | - Pauli Ohukainen
- Computational Medicine, Faculty of Medicine, University of Oulu, Oulu, Finland
- Center for Life Course Health Research, Faculty of Medicine, University of Oulu, Oulu, Finland
- Biocenter Oulu, University of Oulu, Oulu, Finland
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15
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Cross-sectional metabolic subgroups and 10-year follow-up of cardiometabolic multimorbidity in the UK Biobank. Sci Rep 2022; 12:8590. [PMID: 35597771 PMCID: PMC9124207 DOI: 10.1038/s41598-022-12198-1] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2022] [Accepted: 04/29/2022] [Indexed: 12/26/2022] Open
Abstract
We assigned 329,908 UK Biobank participants into six subgroups based on a self-organizing map of 51 biochemical measures (blinded for clinical outcomes). The subgroup with the most favorable metabolic traits was chosen as the reference. Hazard ratios (HR) for incident disease were modeled by Cox regression. Enrichment ratios (ER) of incident multi-morbidity versus randomly expected co-occurrence were evaluated by permutation tests; ER is like HR but captures co-occurrence rather than event frequency. The subgroup with high urinary excretion without kidney stress (HR = 1.24) and the subgroup with the highest apolipoprotein B and blood pressure (HR = 1.52) were associated with ischemic heart disease (IHD). The subgroup with kidney stress, high adiposity and inflammation was associated with IHD (HR = 2.11), cancer (HR = 1.29), dementia (HR = 1.70) and mortality (HR = 2.12). The subgroup with high liver enzymes and triglycerides was at risk of diabetes (HR = 15.6). Multimorbidity was enriched in metabolically favorable subgroups (3.4 ≤ ER ≤ 4.0) despite lower disease burden overall; the relative risk of co-occurring disease was higher in the absence of obvious metabolic dysfunction. These results provide synergistic insight into metabolic health and its associations with cardiovascular disease in a large population sample.
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16
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Wu Y, Bai J, Zhang M, Shao F, Yi H, You D, Zhao Y. Heterogeneity of Treatment Effects for Intensive Blood Pressure Therapy by Individual Components of FRS: An Unsupervised Data-Driven Subgroup Analysis in SPRINT and ACCORD. Front Cardiovasc Med 2022; 9:778756. [PMID: 35187120 PMCID: PMC8850629 DOI: 10.3389/fcvm.2022.778756] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2021] [Accepted: 01/04/2022] [Indexed: 11/21/2022] Open
Abstract
Background Few studies have answered the guiding significance of individual components of the Framingham risk score (FRS) to the risk of cardiovascular disease (CVD) after antihypertensive treatment. This study on the systolic blood pressure intervention trial (SPRINT) and the Action to Control Cardiovascular Risk in Diabetes blood pressure trial (ACCORD-BP) aimed to reveal previously undetected association patterns between individual components of the FRS and heterogeneity of treatment effects (HTEs) of intensive blood pressure control. Methods A self-organizing map (SOM) methodology was applied to identify CVD-risk-specific subgroups in the SPRINT (n = 8,773), and the trained SOM was utilized directly in 4,495 patients from the ACCORD. The primary endpoints were myocardial infarction (MI), non-myocardial infarction acute coronary syndrome (non-MI ACS), stroke, heart failure (HF), death from CVD causes, and a primary composite cardiovascular outcome. Cox proportional hazards models were then used to explore the potential heterogeneous response to intensive SBP control. Results We identified four SOM-based subgroups with distinct individual components of FRS profiles and the CVD risk. For individuals with type 2 diabetes mellitus (T2DM) in the ACCORD or without diabetes in the SPRINT, subgroup I characterized by male with the lowest concentrations for total cholesterol (TC) and high-density lipoprotein (HDL) cholesterol measures, experienced the highest risk for major CVD. Conversely, subgroup III characterized by a female with the highest values for these measures represented as the lowest CVD risk. Furthermore, subgroup II, with the highest systolic blood pressure (SBP) and no antihypertensive agent use at baseline, had a significantly greater frequency of non-MI ACS under intensive BP control, the number needed to harm (NNH) was 84.24 to cause 1 non-MI ACS [absolute risk reduction (ARR) = −1.19%; 95% CI: −2.08, −0.29%] in the SPRINT [hazard ratio (HR) = 3.62; 95% CI: 1.33, 9.81; P = 0.012], and the NNH of was 43.19 to cause 1 non-MI ACS (ARR = −2.32%; 95% CI: −4.63, 0.00%) in the ACCORD (HR = 1.81; 95% CI: 1.01–3.25; P = 0.046). Finally, subgroup IV characterized by mostly younger patients with antihypertensive medication use and smoking history represented the lowest risk for stroke, HF, and relatively low risk for death from CVD causes and primary composite CVD outcome in SPRINT, however, except stroke, a low risk for others were not observed in ACCORD. Conclusion Similar findings in patients with hypertensive with T2DM or without diabetes by multivariate subgrouping suggested that the individual components of the FRS could enrich or improve CVD risk assessment. Further research was required to clarify the potential mechanism.
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Affiliation(s)
- Yaqian Wu
- Department of Biostatistics, Nanjing Medical University, Nanjing, China
| | - Jianling Bai
- Department of Biostatistics, Nanjing Medical University, Nanjing, China
- Key Laboratory of Medical Big Data Research and Application, Nanjing Medical University, Nanjing, China
| | - Mingzhi Zhang
- Department of Biostatistics, Nanjing Medical University, Nanjing, China
| | - Fang Shao
- Department of Biostatistics, Nanjing Medical University, Nanjing, China
| | - Honggang Yi
- Department of Biostatistics, Nanjing Medical University, Nanjing, China
| | - Dongfang You
- Department of Biostatistics, Nanjing Medical University, Nanjing, China
- Dongfang You
| | - Yang Zhao
- Department of Biostatistics, Nanjing Medical University, Nanjing, China
- Key Laboratory of Medical Big Data Research and Application, Nanjing Medical University, Nanjing, China
- Jiangsu Provincial Key Laboratory of Biomarkers of Cancer Prevention and Control, Nanjing Medical University, Nanjing, China
- Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing Medical University, Nanjing, China
- Key Laboratory of Modern Toxicology, Nanjing Medical University, Nanjing, China
- *Correspondence: Yang Zhao
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17
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Clos-Garcia M, Ahluwalia TS, Winther SA, Henriksen P, Ali M, Fan Y, Stankevic E, Lyu L, Vogt JK, Hansen T, Legido-Quigley C, Rossing P, Pedersen O. Multiomics signatures of type 1 diabetes with and without albuminuria. Front Endocrinol (Lausanne) 2022; 13:1015557. [PMID: 36531462 PMCID: PMC9755599 DOI: 10.3389/fendo.2022.1015557] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/09/2022] [Accepted: 11/11/2022] [Indexed: 12/05/2022] Open
Abstract
AIMS/HYPOTHESIS To identify novel pathophysiological signatures of longstanding type 1 diabetes (T1D) with and without albuminuria we investigated the gut microbiome and blood metabolome in individuals with T1D and healthy controls (HC). We also mapped the functional underpinnings of the microbiome in relation to its metabolic role. METHODS One hundred and sixty-one individuals with T1D and 50 HC were recruited at the Steno Diabetes Center Copenhagen, Denmark. T1D cases were stratified based on levels of albuminuria into normoalbuminuria, moderate and severely increased albuminuria. Shotgun sequencing of bacterial and viral microbiome in stool samples and circulating metabolites and lipids profiling using mass spectroscopy in plasma of all participants were performed. Functional mapping of microbiome into Gut Metabolic Modules (GMMs) was done using EggNog and KEGG databases. Multiomics integration was performed using MOFA tool. RESULTS Measures of the gut bacterial beta diversity differed significantly between T1D and HC, either with moderately or severely increased albuminuria. Taxonomic analyses of the bacterial microbiota identified 51 species that differed in absolute abundance between T1D and HC (17 higher, 34 lower). Stratified on levels of albuminuria, 10 species were differentially abundant for the moderately increased albuminuria group, 63 for the severely increased albuminuria group while 25 were common and differentially abundant both for moderately and severely increased albuminuria groups, when compared to HC. Functional characterization of the bacteriome identified 23 differentially enriched GMMs between T1D and HC, mostly involved in sugar and amino acid metabolism. No differences in relation to albuminuria stratification was observed. Twenty-five phages were differentially abundant between T1D and HC groups. Six of these varied with albuminuria status. Plasma metabolomics indicated differences in the steroidogenesis and sugar metabolism and circulating sphingolipids in T1D individuals. We identified association between sphingolipid levels and Bacteroides sp. abundances. MOFA revealed reduced interactions between gut microbiome and plasma metabolome profiles albeit polar metabolite, lipids and bacteriome compositions contributed to the variance in albuminuria levels among T1D individuals. CONCLUSIONS Individuals with T1D and progressive kidney disease stratified on levels of albuminuria show distinct signatures in their gut microbiome and blood metabolome.
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Affiliation(s)
- Marc Clos-Garcia
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
- LEITAT Technological Center, Terrassa, Spain
| | - Tarunveer S. Ahluwalia
- Complications Research, Steno Diabetes Center Copenhagen, Herlev, Denmark
- The Bioinformatics Center, Department of Biology, University of Copenhagen, Copenhagen, Denmark
| | - Signe A. Winther
- Complications Research, Steno Diabetes Center Copenhagen, Herlev, Denmark
| | - Peter Henriksen
- Complications Research, Steno Diabetes Center Copenhagen, Herlev, Denmark
| | - Mina Ali
- Complications Research, Steno Diabetes Center Copenhagen, Herlev, Denmark
| | - Yong Fan
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Evelina Stankevic
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Liwei Lyu
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Josef K. Vogt
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
- Clinical Microbiomics, Copenhagen, Denmark
| | - Torben Hansen
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | | | - Peter Rossing
- Complications Research, Steno Diabetes Center Copenhagen, Herlev, Denmark
- Department of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark
| | - Oluf Pedersen
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
- Center for Clinical Metabolic Research, Gentofte University Hospital, Copenhagen, Denmark
- *Correspondence: Oluf Pedersen,
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18
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Hammad SM, Hunt KJ, Baker NL, Klein RL, Lopes-Virella MF. Diabetes and kidney dysfunction markedly alter the content of sphingolipids carried by circulating lipoproteins. J Clin Lipidol 2022; 16:173-183. [DOI: 10.1016/j.jacl.2021.12.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2021] [Revised: 11/18/2021] [Accepted: 12/28/2021] [Indexed: 10/19/2022]
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19
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Mutter S, Valo E, Aittomäki V, Nybo K, Raivonen L, Thorn LM, Forsblom C, Sandholm N, Würtz P, Groop PH. Urinary metabolite profiling and risk of progression of diabetic nephropathy in 2670 individuals with type 1 diabetes. Diabetologia 2022; 65:140-149. [PMID: 34686904 PMCID: PMC8660744 DOI: 10.1007/s00125-021-05584-3] [Citation(s) in RCA: 22] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/22/2021] [Accepted: 08/11/2021] [Indexed: 12/25/2022]
Abstract
AIMS/HYPOTHESIS This prospective, observational study examines associations between 51 urinary metabolites and risk of progression of diabetic nephropathy in individuals with type 1 diabetes by employing an automated NMR metabolomics technique suitable for large-scale urine sample collections. METHODS We collected 24-h urine samples for 2670 individuals with type 1 diabetes from the Finnish Diabetic Nephropathy study and measured metabolite concentrations by NMR. Individuals were followed up for 9.0 ± 5.0 years until their first sign of progression of diabetic nephropathy, end-stage kidney disease or study end. Cox regressions were performed on the entire study population (overall progression), on 1999 individuals with normoalbuminuria and 347 individuals with macroalbuminuria at baseline. RESULTS Seven urinary metabolites were associated with overall progression after adjustment for baseline albuminuria and chronic kidney disease stage (p < 8 × 10-4): leucine (HR 1.47 [95% CI 1.30, 1.66] per 1-SD creatinine-scaled metabolite concentration), valine (1.38 [1.22, 1.56]), isoleucine (1.33 [1.18, 1.50]), pseudouridine (1.25 [1.11, 1.42]), threonine (1.27 [1.11, 1.46]) and citrate (0.84 [0.75, 0.93]). 2-Hydroxyisobutyrate was associated with overall progression (1.30 [1.16, 1.45]) and also progression from normoalbuminuria (1.56 [1.25, 1.95]). Six amino acids and pyroglutamate were associated with progression from macroalbuminuria. CONCLUSIONS/INTERPRETATION Branched-chain amino acids and other urinary metabolites were associated with the progression of diabetic nephropathy on top of baseline albuminuria and chronic kidney disease. We found differences in associations for overall progression and progression from normo- and macroalbuminuria. These novel discoveries illustrate the utility of analysing urinary metabolites in entire population cohorts.
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Affiliation(s)
- Stefan Mutter
- Folkhälsan Institute of Genetics, Folkhälsan Research Center, Helsinki, Finland
- Department of Nephrology, University of Helsinki and Helsinki University Hospital, Helsinki, Finland
- Research Program for Clinical and Molecular Metabolism, Faculty of Medicine, University of Helsinki, Helsinki, Finland
| | - Erkka Valo
- Folkhälsan Institute of Genetics, Folkhälsan Research Center, Helsinki, Finland
- Department of Nephrology, University of Helsinki and Helsinki University Hospital, Helsinki, Finland
- Research Program for Clinical and Molecular Metabolism, Faculty of Medicine, University of Helsinki, Helsinki, Finland
| | | | | | | | - Lena M Thorn
- Folkhälsan Institute of Genetics, Folkhälsan Research Center, Helsinki, Finland
- Department of Nephrology, University of Helsinki and Helsinki University Hospital, Helsinki, Finland
- Research Program for Clinical and Molecular Metabolism, Faculty of Medicine, University of Helsinki, Helsinki, Finland
- Department of General Practice and Primary Health Care, University of Helsinki and Helsinki University Hospital, Helsinki, Finland
| | - Carol Forsblom
- Folkhälsan Institute of Genetics, Folkhälsan Research Center, Helsinki, Finland
- Department of Nephrology, University of Helsinki and Helsinki University Hospital, Helsinki, Finland
- Research Program for Clinical and Molecular Metabolism, Faculty of Medicine, University of Helsinki, Helsinki, Finland
| | - Niina Sandholm
- Folkhälsan Institute of Genetics, Folkhälsan Research Center, Helsinki, Finland
- Department of Nephrology, University of Helsinki and Helsinki University Hospital, Helsinki, Finland
- Research Program for Clinical and Molecular Metabolism, Faculty of Medicine, University of Helsinki, Helsinki, Finland
| | | | - Per-Henrik Groop
- Folkhälsan Institute of Genetics, Folkhälsan Research Center, Helsinki, Finland.
- Department of Nephrology, University of Helsinki and Helsinki University Hospital, Helsinki, Finland.
- Research Program for Clinical and Molecular Metabolism, Faculty of Medicine, University of Helsinki, Helsinki, Finland.
- Department of Diabetes, Central Clinical School, Monash University, Melbourne, VIC, Australia.
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20
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Berdún R, Jové M, Sol J, Cai W, He JC, Rodriguez-Mortera R, Martin-Garí M, Pamplona R, Uribarri J, Portero-Otin M. Restriction of Dietary Advanced Glycation End Products Induces a Differential Plasma Metabolome and Lipidome Profile. Mol Nutr Food Res 2021; 65:e2000499. [PMID: 34599622 DOI: 10.1002/mnfr.202000499] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2020] [Revised: 02/10/2021] [Indexed: 12/30/2022]
Abstract
SCOPE Diets with low content in advanced glycation end products (AGEs) lead to beneficial properties in highly prevalent age-related diseases. To shed light on the mechanisms behind, the changes induced by a low AGE dietary intervention in the circulating metabolome are analyzed. METHODS AND RESULTS To this end, 20 non-diabetic patients undergoing peritoneal dialysis are randomized to continue their usual diet or to one with a low content of AGEs for 1 month. Then, plasmatic metabolome and lipidomes are analyzed by liquid-chromatography coupled to mass spectrometry. The levels of defined AGE structures are also quantified by ELISA and by mass-spectrometry. The results show that the low AGE diet impinged significant changes in circulating metabolomes (166 molecules) and lipidomes (91 lipids). Metabolic targets of low-AGE intake include sphingolipid, ether-lipids, and glycerophospholipid metabolism. Further, it reproduces some of the plasma characteristics of healthy aging. CONCLUSION The finding of common pathways induced by low-AGE diets with previous metabolic traits implicated in aging, insulin resistance, and obesity suggest the usefulness of the chosen approach and supports the potential extension of this study to other populations.
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Affiliation(s)
- Rebeca Berdún
- Department of Experimental Medicine, University of Lleida-Lleida Biomedical Research Institute (UdL-IRBLleida), Lleida, Spain
| | - Mariona Jové
- Department of Experimental Medicine, University of Lleida-Lleida Biomedical Research Institute (UdL-IRBLleida), Lleida, Spain
| | - Joaquim Sol
- Department of Experimental Medicine, University of Lleida-Lleida Biomedical Research Institute (UdL-IRBLleida), Lleida, Spain.,Primary Care, Catalan Health Institute (ICS), Lleida, Spain.,Research Support Unit Lleida, Fundació Institut Universitari per a la recerca a l'Atenció Primària de Salut Jordi Gol i Gurina (IDIAPJGol), Lleida, Spain
| | - Weijing Cai
- Department of Internal Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - John C He
- Department of Internal Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Reyna Rodriguez-Mortera
- Department of Experimental Medicine, University of Lleida-Lleida Biomedical Research Institute (UdL-IRBLleida), Lleida, Spain
| | - Meritxell Martin-Garí
- Department of Experimental Medicine, University of Lleida-Lleida Biomedical Research Institute (UdL-IRBLleida), Lleida, Spain
| | - Reinald Pamplona
- Department of Experimental Medicine, University of Lleida-Lleida Biomedical Research Institute (UdL-IRBLleida), Lleida, Spain
| | - Jaime Uribarri
- Department of Internal Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Manuel Portero-Otin
- Department of Experimental Medicine, University of Lleida-Lleida Biomedical Research Institute (UdL-IRBLleida), Lleida, Spain
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21
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Ning Z, Song Z, Wang C, Peng S, Wan X, Liu Z, Lu A. How Perturbated Metabolites in Diabetes Mellitus Affect the Pathogenesis of Hypertension? Front Physiol 2021; 12:705588. [PMID: 34483960 PMCID: PMC8416465 DOI: 10.3389/fphys.2021.705588] [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: 05/05/2021] [Accepted: 07/26/2021] [Indexed: 11/17/2022] Open
Abstract
The presence of hypertension (HTN) in type 2 diabetes mellitus (DM) is a common phenomenon in more than half of the diabetic patients. Since HTN constitutes a predictor of vascular complications and cardiovascular disease in type 2 DM patients, it is of significance to understand the molecular and cellular mechanisms of type 2 DM binding to HTN. This review attempts to understand the mechanism via the perspective of the metabolites. It reviewed the metabolic perturbations, the biological function of perturbated metabolites in two diseases, and the mechanism underlying metabolic perturbation that contributed to the connection of type 2 DM and HTN. DM-associated metabolic perturbations may be involved in the pathogenesis of HTN potentially in insulin, angiotensin II, sympathetic nervous system, and the energy reprogramming to address how perturbated metabolites in type 2 DM affect the pathogenesis of HTN. The recent integration of the metabolism field with microbiology and immunology may provide a wider perspective. Metabolism affects immune function and supports immune cell differentiation by the switch of energy. The diverse metabolites produced by bacteria modified the biological process in the inflammatory response of chronic metabolic diseases either. The rapidly evolving metabolomics has enabled to have a better understanding of the process of diseases, which is an important tool for providing some insight into the investigation of diseases mechanism. Metabolites served as direct modulators of biological processes were believed to assess the pathological mechanisms involved in diseases.
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Affiliation(s)
- Zhangchi Ning
- Institute of Basic Theory for Chinese Medicine, China Academy of Chinese Medical Sciences, Beijing, China
| | - Zhiqian Song
- Institute of Basic Theory for Chinese Medicine, China Academy of Chinese Medical Sciences, Beijing, China
| | - Chun Wang
- Institute of Basic Theory for Chinese Medicine, China Academy of Chinese Medical Sciences, Beijing, China
| | - Shitao Peng
- Institute of Basic Theory for Chinese Medicine, China Academy of Chinese Medical Sciences, Beijing, China
| | - Xiaoying Wan
- Institute of Basic Theory for Chinese Medicine, China Academy of Chinese Medical Sciences, Beijing, China
| | - Zhenli Liu
- Institute of Basic Theory for Chinese Medicine, China Academy of Chinese Medical Sciences, Beijing, China
| | - Aiping Lu
- School of Chinese Medicine, Hong Kong Baptist University, Hong Kong, China
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22
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Schultheiss UT, Kosch R, Kotsis F, Altenbuchinger M, Zacharias HU. Chronic Kidney Disease Cohort Studies: A Guide to Metabolome Analyses. Metabolites 2021; 11:460. [PMID: 34357354 PMCID: PMC8304377 DOI: 10.3390/metabo11070460] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2021] [Revised: 07/08/2021] [Accepted: 07/12/2021] [Indexed: 12/14/2022] Open
Abstract
Kidney diseases still pose one of the biggest challenges for global health, and their heterogeneity and often high comorbidity load seriously hinders the unraveling of their underlying pathomechanisms and the delivery of optimal patient care. Metabolomics, the quantitative study of small organic compounds, called metabolites, in a biological specimen, is gaining more and more importance in nephrology research. Conducting a metabolomics study in human kidney disease cohorts, however, requires thorough knowledge about the key workflow steps: study planning, sample collection, metabolomics data acquisition and preprocessing, statistical/bioinformatics data analysis, and results interpretation within a biomedical context. This review provides a guide for future metabolomics studies in human kidney disease cohorts. We will offer an overview of important a priori considerations for metabolomics cohort studies, available analytical as well as statistical/bioinformatics data analysis techniques, and subsequent interpretation of metabolic findings. We will further point out potential research questions for metabolomics studies in the context of kidney diseases and summarize the main results and data availability of important studies already conducted in this field.
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Affiliation(s)
- Ulla T. Schultheiss
- Institute of Genetic Epidemiology, Faculty of Medicine and Medical Center, University of Freiburg, 79106 Freiburg, Germany; (U.T.S.); (F.K.)
- Department of Medicine IV—Nephrology and Primary Care, Faculty of Medicine and Medical Center, University of Freiburg, 79106 Freiburg, Germany
| | - Robin Kosch
- Computational Biology, University of Hohenheim, 70599 Stuttgart, Germany;
| | - Fruzsina Kotsis
- Institute of Genetic Epidemiology, Faculty of Medicine and Medical Center, University of Freiburg, 79106 Freiburg, Germany; (U.T.S.); (F.K.)
- Department of Medicine IV—Nephrology and Primary Care, Faculty of Medicine and Medical Center, University of Freiburg, 79106 Freiburg, Germany
| | - Michael Altenbuchinger
- Institute of Medical Bioinformatics, University Medical Center Göttingen, 37077 Göttingen, Germany;
| | - Helena U. Zacharias
- Department of Internal Medicine I, University Medical Center Schleswig-Holstein, Campus Kiel, 24105 Kiel, Germany
- Institute of Clinical Molecular Biology, Kiel University and University Medical Center Schleswig-Holstein, Campus Kiel, 24105 Kiel, Germany
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23
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Moon S, Tsay JJ, Lampert H, Md Dom ZI, Kostic AD, Smiles A, Niewczas MA. Circulating short and medium chain fatty acids are associated with normoalbuminuria in type 1 diabetes of long duration. Sci Rep 2021; 11:8592. [PMID: 33883567 PMCID: PMC8060327 DOI: 10.1038/s41598-021-87585-1] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2020] [Accepted: 03/30/2021] [Indexed: 11/08/2022] Open
Abstract
A substantial number of subjects with Type 1 Diabetes (T1D) of long duration never develop albuminuria or renal function impairment, yet the underlying protective mechanisms remain unknown. Therefore, our study included 308 Joslin Kidney Study subjects who had T1D of long duration (median: 24 years), maintained normal renal function and had either normoalbuminuria or a broad range of albuminuria within the 2 years preceding the metabolomic determinations. Serum samples were subjected to global metabolomic profiling. 352 metabolites were detected in at least 80% of the study population. In the logistic analyses adjusted for multiple testing (Bonferroni corrected α = 0.000028), we identified 38 metabolites associated with persistent normoalbuminuria independently from clinical covariates. Protective metabolites were enriched in Medium Chain Fatty Acids (MCFAs) and in Short Chain Fatty Acids (SCFAs) and particularly involved odd-numbered and dicarboxylate Fatty Acids. One quartile change of nonanoate, the top protective MCFA, was associated with high odds of having persistent normoalbuminuria (OR (95% CI) 0.14 (0.09, 0.23); p < 10-12). Multivariable Random Forest analysis concordantly indicated to MCFAs as effective classifiers. Associations of the relevant Fatty Acids with albuminuria seemed to parallel associations with tubular biomarkers. Our findings suggest that MCFAs and SCFAs contribute to the metabolic processes underlying protection against albuminuria development in T1D that are independent from mechanisms associated with changes in renal function.
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Affiliation(s)
- Salina Moon
- Research Division, Joslin Diabetes Center, One Joslin Place, Boston, MA, 02215, USA
| | - John J Tsay
- Research Division, Joslin Diabetes Center, One Joslin Place, Boston, MA, 02215, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
- Medicine, Veterans Affairs Boston Healthcare System, Boston, MA, USA
| | - Heather Lampert
- Research Division, Joslin Diabetes Center, One Joslin Place, Boston, MA, 02215, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
- Department of Family Medicine, Brown University, Providence, RI, USA
| | - Zaipul I Md Dom
- Research Division, Joslin Diabetes Center, One Joslin Place, Boston, MA, 02215, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
| | - Aleksandar D Kostic
- Research Division, Joslin Diabetes Center, One Joslin Place, Boston, MA, 02215, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
- Department of Microbiology, Harvard Medical School, Boston, MA, USA
| | - Adam Smiles
- Research Division, Joslin Diabetes Center, One Joslin Place, Boston, MA, 02215, USA
| | - Monika A Niewczas
- Research Division, Joslin Diabetes Center, One Joslin Place, Boston, MA, 02215, USA.
- Department of Medicine, Harvard Medical School, Boston, MA, USA.
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24
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Fu J, Luo Y, Mou M, Zhang H, Tang J, Wang Y, Zhu F. Advances in Current Diabetes Proteomics: From the Perspectives of Label- free Quantification and Biomarker Selection. Curr Drug Targets 2021; 21:34-54. [PMID: 31433754 DOI: 10.2174/1389450120666190821160207] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2019] [Revised: 07/17/2019] [Accepted: 07/24/2019] [Indexed: 12/13/2022]
Abstract
BACKGROUND Due to its prevalence and negative impacts on both the economy and society, the diabetes mellitus (DM) has emerged as a worldwide concern. In light of this, the label-free quantification (LFQ) proteomics and diabetic marker selection methods have been applied to elucidate the underlying mechanisms associated with insulin resistance, explore novel protein biomarkers, and discover innovative therapeutic protein targets. OBJECTIVE The purpose of this manuscript is to review and analyze the recent computational advances and development of label-free quantification and diabetic marker selection in diabetes proteomics. METHODS Web of Science database, PubMed database and Google Scholar were utilized for searching label-free quantification, computational advances, feature selection and diabetes proteomics. RESULTS In this study, we systematically review the computational advances of label-free quantification and diabetic marker selection methods which were applied to get the understanding of DM pathological mechanisms. Firstly, different popular quantification measurements and proteomic quantification software tools which have been applied to the diabetes studies are comprehensively discussed. Secondly, a number of popular manipulation methods including transformation, pretreatment (centering, scaling, and normalization), missing value imputation methods and a variety of popular feature selection techniques applied to diabetes proteomic data are overviewed with objective evaluation on their advantages and disadvantages. Finally, the guidelines for the efficient use of the computationbased LFQ technology and feature selection methods in diabetes proteomics are proposed. CONCLUSION In summary, this review provides guidelines for researchers who will engage in proteomics biomarker discovery and by properly applying these proteomic computational advances, more reliable therapeutic targets will be found in the field of diabetes mellitus.
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Affiliation(s)
- Jianbo Fu
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
| | - Yongchao Luo
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
| | - Minjie Mou
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
| | - Hongning Zhang
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
| | - Jing Tang
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China.,School of Pharmaceutical Sciences and Innovative Drug Research Centre, Chongqing University, Chongqing 401331, China
| | - Yunxia Wang
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
| | - Feng Zhu
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China.,School of Pharmaceutical Sciences and Innovative Drug Research Centre, Chongqing University, Chongqing 401331, China
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25
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Pongrac Barlovic D, Harjutsalo V, Sandholm N, Forsblom C, Groop PH. Sphingomyelin and progression of renal and coronary heart disease in individuals with type 1 diabetes. Diabetologia 2020; 63:1847-1856. [PMID: 32564139 PMCID: PMC7406485 DOI: 10.1007/s00125-020-05201-9] [Citation(s) in RCA: 36] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/11/2019] [Accepted: 05/11/2020] [Indexed: 02/08/2023]
Abstract
AIMS/HYPOTHESIS Lipid abnormalities are associated with diabetic kidney disease and CHD, although their exact role has not yet been fully explained. Sphingomyelin, the predominant sphingolipid in humans, is crucial for intact glomerular and endothelial function. Therefore, the objective of our study was to investigate whether sphingomyelin impacts kidney disease and CHD progression in individuals with type 1 diabetes. METHODS Individuals (n = 1087) from the Finnish Diabetic Nephropathy (FinnDiane) prospective cohort study with serum sphingomyelin measured using a proton NMR metabolomics platform were included. Kidney disease progression was defined as change in eGFR or albuminuria stratum. Data on incident end-stage renal disease (ESRD) and CHD were retrieved from national registries. HRs from Cox regression models and regression coefficients from the logistic or linear regression analyses were reported per 1 SD increase in sphingomyelin level. In addition, receiver operating curves were used to assess whether sphingomyelin improves eGFR decline prediction compared with albuminuria. RESULTS During a median (IQR) 10.7 (6.4, 13.5) years of follow-up, sphingomyelin was independently associated with the fastest eGFR decline (lowest 25%; median [IQR] for eGFR change: <-4.4 [-6.8, -3.1] ml min-1 [1.73 m-2] year-1), even after adjustment for classical lipid variables such as HDL-cholesterol and triacylglycerols (OR [95% CI]: 1.36 [1.15, 1.61], p < 0.001). Similarly, sphingomyelin increased the risk of progression to ESRD (HR [95% CI]: 1.53 [1.19, 1.97], p = 0.001). Moreover, sphingomyelin increased the risk of CHD (HR [95% CI]: 1.24 [1.01, 1.52], p = 0.038). However, sphingomyelin did not perform better than albuminuria in the prediction of eGFR decline. CONCLUSIONS/INTERPRETATION This study demonstrates for the first time in a prospective setting that sphingomyelin is associated with the fastest eGFR decline and progression to ESRD in type 1 diabetes. In addition, sphingomyelin is a risk factor for CHD. These data suggest that high sphingomyelin level, independently of classical lipid risk factors, may contribute not only to the initiation and progression of kidney disease but also to CHD. Graphical abstract.
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Affiliation(s)
- Drazenka Pongrac Barlovic
- University Medical Center Ljubljana, Ljubljana, Slovenia
- Faculty of Medicine, University of Ljubljana, Ljubljana, Slovenia
- Folkhälsan Institute of Genetics, Folkhälsan Research Center Biomedicum Helsinki, University of Helsinki, Haartmaninkatu 8, PO Box 63, FIN-00014, Helsinki, Finland
| | - Valma Harjutsalo
- Folkhälsan Institute of Genetics, Folkhälsan Research Center Biomedicum Helsinki, University of Helsinki, Haartmaninkatu 8, PO Box 63, FIN-00014, Helsinki, Finland
- Abdominal Center, Nephrology, University of Helsinki and Helsinki University Hospital, Helsinki, Finland
- Research Program for Clinical and Molecular Metabolism, Faculty of Medicine, University of Helsinki, Helsinki, Finland
- National Institute for Health and Welfare, Helsinki, Finland
| | - Niina Sandholm
- Folkhälsan Institute of Genetics, Folkhälsan Research Center Biomedicum Helsinki, University of Helsinki, Haartmaninkatu 8, PO Box 63, FIN-00014, Helsinki, Finland
- Abdominal Center, Nephrology, University of Helsinki and Helsinki University Hospital, Helsinki, Finland
- Research Program for Clinical and Molecular Metabolism, Faculty of Medicine, University of Helsinki, Helsinki, Finland
| | - Carol Forsblom
- Folkhälsan Institute of Genetics, Folkhälsan Research Center Biomedicum Helsinki, University of Helsinki, Haartmaninkatu 8, PO Box 63, FIN-00014, Helsinki, Finland
- Abdominal Center, Nephrology, University of Helsinki and Helsinki University Hospital, Helsinki, Finland
- Research Program for Clinical and Molecular Metabolism, Faculty of Medicine, University of Helsinki, Helsinki, Finland
| | - Per-Henrik Groop
- Folkhälsan Institute of Genetics, Folkhälsan Research Center Biomedicum Helsinki, University of Helsinki, Haartmaninkatu 8, PO Box 63, FIN-00014, Helsinki, Finland.
- Abdominal Center, Nephrology, University of Helsinki and Helsinki University Hospital, Helsinki, Finland.
- Research Program for Clinical and Molecular Metabolism, Faculty of Medicine, University of Helsinki, Helsinki, Finland.
- Department of Diabetes, Monash University, Melbourne, Victoria, Australia.
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Ekholm J, Ohukainen P, Kangas AJ, Kettunen J, Wang Q, Karsikas M, Khan AA, Kingwell BA, Kähönen M, Lehtimäki T, Raitakari OT, Järvelin MR, Meikle PJ, Ala-Korpela M. EpiMetal: an open-source graphical web browser tool for easy statistical analyses in epidemiology and metabolomics. Int J Epidemiol 2020; 49:1075-1081. [PMID: 31943015 PMCID: PMC7660139 DOI: 10.1093/ije/dyz244] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2019] [Accepted: 11/11/2019] [Indexed: 01/22/2023] Open
Abstract
MOTIVATION An intuitive graphical interface that allows statistical analyses and visualizations of extensive data without any knowledge of dedicated statistical software or programming. IMPLEMENTATION EpiMetal is a single-page web application written in JavaScript, to be used via a modern desktop web browser. GENERAL FEATURES Standard epidemiological analyses and self-organizing maps for data-driven metabolic profiling are included. Multiple extensive datasets with an arbitrary number of continuous and category variables can be integrated with the software. Any snapshot of the analyses can be saved and shared with others via a www-link. We demonstrate the usage of EpiMetal using pilot data with over 500 quantitative molecular measures for each sample as well as in two large-scale epidemiological cohorts (N >10 000). AVAILABILITY The software usage exemplar and the pilot data are open access online at [http://EpiMetal.computationalmedicine.fi]. MIT licensed source code is available at the Github repository at [https://github.com/amergin/epimetal].
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Affiliation(s)
- Jussi Ekholm
- Computational Medicine, Faculty of Medicine, University of Oulu, Oulu, Finland
- Biocenter Oulu, Oulu, Finland
- Center for Life Course Health Research, Faculty of Medicine, University of Oulu, Oulu, Finland
| | - Pauli Ohukainen
- Computational Medicine, Faculty of Medicine, University of Oulu, Oulu, Finland
- Biocenter Oulu, Oulu, Finland
- Center for Life Course Health Research, Faculty of Medicine, University of Oulu, Oulu, Finland
| | | | - Johannes Kettunen
- Computational Medicine, Faculty of Medicine, University of Oulu, Oulu, Finland
- Biocenter Oulu, Oulu, Finland
- Center for Life Course Health Research, Faculty of Medicine, University of Oulu, Oulu, Finland
- THL: National Institute for Health and Welfare, Helsinki, Finland
| | - Qin Wang
- Computational Medicine, Faculty of Medicine, University of Oulu, Oulu, Finland
- Biocenter Oulu, Oulu, Finland
- Center for Life Course Health Research, Faculty of Medicine, University of Oulu, Oulu, Finland
- Systems Epidemiology, Baker Heart and Diabetes Institute, Melbourne, VIC, Australia
| | - Mari Karsikas
- Computational Medicine, Faculty of Medicine, University of Oulu, Oulu, Finland
- Biocenter Oulu, Oulu, Finland
- Center for Life Course Health Research, Faculty of Medicine, University of Oulu, Oulu, Finland
- Solita Ltd, Tampere, Finland
| | - Anmar A Khan
- Metabolomics, Baker Heart and Diabetes Institute, Melbourne, VIC, Australia
- Laboratory Medicine Department, Faculty of Applied Medical Sciences, Umm Al-Qura University, Makkah, Kingdom of Saudi Arabia
| | - Bronwyn A Kingwell
- Metabolic and Vascular Physiology, Baker Heart and Diabetes Institute, Melbourne, VIC, Australia
| | - Mika Kähönen
- Department of Clinical Physiology, Tampere University Hospital and Finnish Cardiovascular Research Center - Tampere, Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland
| | - Terho Lehtimäki
- Department of Clinical Chemistry, Fimlab Laboratories and Finnish Cardiovascular Research Center - Tampere, Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland
| | - Olli T Raitakari
- Research Centre of Applied and Preventive Cardiovascular Medicine, University of Turku, Turku, Finland
- Department of Clinical Physiology and Nuclear Medicine, Turku University Hospital, Turku, Finland
| | - Marjo-Riitta Järvelin
- Biocenter Oulu, Oulu, Finland
- Center for Life Course Health Research, Faculty of Medicine, University of Oulu, Oulu, Finland
- Unit of Primary Health Care, Oulu University Hospital, OYS, Oulu, Finland
- Department of Epidemiology and Biostatistics, MRC-PHE Centre for Environment and Health, Imperial College London, London, UK
- Department of Life Sciences, College of Health and Life Sciences, Brunel University London, London, UK
| | - Peter J Meikle
- Metabolomics, Baker Heart and Diabetes Institute, Melbourne, VIC, Australia
| | - Mika Ala-Korpela
- Computational Medicine, Faculty of Medicine, University of Oulu, Oulu, Finland
- Biocenter Oulu, Oulu, Finland
- Center for Life Course Health Research, Faculty of Medicine, University of Oulu, Oulu, Finland
- Systems Epidemiology, Baker Heart and Diabetes Institute, Melbourne, VIC, Australia
- NMR Metabolomics Laboratory, School of Pharmacy, University of Eastern Finland, Kuopio, Finland
- Medical Research Council Integrative Epidemiology Unit at the University of Bristol, Bristol, UK
- Population Health Science, Bristol Medical School, University of Bristol, Bristol, UK
- Department of Epidemiology and Preventive Medicine, School of Public Health and Preventive Medicine, Alfred Hospital, Monash University, Melbourne, VIC, Australia
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27
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Tofte N, Vogelzangs N, Mook-Kanamori D, Brahimaj A, Nano J, Ahmadizar F, van Dijk KW, Frimodt-Møller M, Arts I, Beulens JWJ, Rutters F, van der Heijden AA, Kavousi M, Stehouwer CDA, Nijpels G, van Greevenbroek MMJ, van der Kallen CJH, Rossing P, Ahluwalia TS, 't Hart LM. Plasma Metabolomics Identifies Markers of Impaired Renal Function: A Meta-analysis of 3089 Persons with Type 2 Diabetes. J Clin Endocrinol Metab 2020; 105:5818360. [PMID: 32271379 DOI: 10.1210/clinem/dgaa173] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/27/2019] [Accepted: 04/08/2020] [Indexed: 01/10/2023]
Abstract
CONTEXT There is a need for novel biomarkers and better understanding of the pathophysiology of diabetic kidney disease. OBJECTIVE To investigate associations between plasma metabolites and kidney function in people with type 2 diabetes (T2D). DESIGN 3089 samples from individuals with T2D, collected between 1999 and 2015, from 5 independent Dutch cohort studies were included. Up to 7 years follow-up was available in 1100 individuals from 2 of the cohorts. MAIN OUTCOME MEASURES Plasma metabolites (n = 149) were measured by nuclear magnetic resonance spectroscopy. Associations between metabolites and estimated glomerular filtration rate (eGFR), urinary albumin-to-creatinine ratio (UACR), and eGFR slopes were investigated in each study followed by random effect meta-analysis. Adjustments included traditional cardiovascular risk factors and correction for multiple testing. RESULTS In total, 125 metabolites were significantly associated (PFDR = 1.5×10-32 - 0.046; β = -11.98-2.17) with eGFR. Inverse associations with eGFR were demonstrated for branched-chain and aromatic amino acids (AAAs), glycoprotein acetyls, triglycerides (TGs), lipids in very low-density lipoproteins (VLDL) subclasses, and fatty acids (PFDR < 0.03). We observed positive associations with cholesterol and phospholipids in high-density lipoproteins (HDL) and apolipoprotein A1 (PFDR < 0.05). Albeit some metabolites were associated with UACR levels (P < 0.05), significance was lost after correction for multiple testing. Tyrosine and HDL-related metabolites were positively associated with eGFR slopes before adjustment for multiple testing (PTyr = 0.003; PHDLrelated < 0.05), but not after. CONCLUSIONS This study identified metabolites associated with impaired kidney function in T2D, implying involvement of lipid and amino acid metabolism in the pathogenesis. Whether these processes precede or are consequences of renal impairment needs further investigation.
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Affiliation(s)
- Nete Tofte
- Steno Diabetes Center, Copenhagen, Denmark
| | - Nicole Vogelzangs
- Department of Epidemiology & Maastricht Centre for Systems Biology (MaCSBio), Maastricht University, Maastricht, the Netherlands
- Cardiovascular Research Institute Maastricht (CARIM), Maastricht University, Maastricht, the Netherlands
| | - Dennis Mook-Kanamori
- Departments of Clinical Epidemiology and Public Health and Primary Care, Leiden University Medical Center, Leiden, the Netherlands
| | - Adela Brahimaj
- Department of Epidemiology, Erasmus University Medical Center, Rotterdam, the Netherlands
- Department of General Practice, Erasmus Medical Center, Rotterdam, the Netherlands
| | - Jana Nano
- Department of Epidemiology, Erasmus University Medical Center, Rotterdam, the Netherlands
- Institute of Epidemiology, Helmholtz Zentrum, Munich, Germany
- German Center for Diabetes Research, Munich, Germany
| | - Fariba Ahmadizar
- Department of Epidemiology, Erasmus University Medical Center, Rotterdam, the Netherlands
| | - Ko Willems van Dijk
- Departments of Human Genetics and Internal Medicine/Endocrinology, Leiden University Medical Center, Leiden, the Netherlands
| | | | - Ilja Arts
- Department of Epidemiology & Maastricht Centre for Systems Biology (MaCSBio), Maastricht University, Maastricht, the Netherlands
- Cardiovascular Research Institute Maastricht (CARIM), Maastricht University, Maastricht, the Netherlands
| | - Joline W J Beulens
- Department of Epidemiology and Biostatistics, Amsterdam University Medical Center, Amsterdam, the Netherlands
| | - Femke Rutters
- Department of Epidemiology and Biostatistics, Amsterdam University Medical Center, Amsterdam, the Netherlands
| | - Amber A van der Heijden
- Department of General Practice and Elderly Care Medicine, Amsterdam University Medical Center, Amsterdam, the Netherlands
| | - Maryam Kavousi
- Department of Epidemiology, Erasmus University Medical Center, Rotterdam, the Netherlands
| | - Coen D A Stehouwer
- Cardiovascular Research Institute Maastricht (CARIM), Maastricht University, Maastricht, the Netherlands
- Department of Internal Medicine, Maastricht University Medical Center, Maastricht, the Netherlands
| | - Giel Nijpels
- Department of General Practice and Elderly Care Medicine, Amsterdam University Medical Center, Amsterdam, the Netherlands
| | - Marleen M J van Greevenbroek
- Cardiovascular Research Institute Maastricht (CARIM), Maastricht University, Maastricht, the Netherlands
- Department of Internal Medicine, Maastricht University Medical Center, Maastricht, the Netherlands
| | - Carla J H van der Kallen
- Cardiovascular Research Institute Maastricht (CARIM), Maastricht University, Maastricht, the Netherlands
- Department of Internal Medicine, Maastricht University Medical Center, Maastricht, the Netherlands
| | - Peter Rossing
- Steno Diabetes Center, Copenhagen, Denmark
- University of Copenhagen, Copenhagen, Denmark
| | | | - Leen M 't Hart
- Department of Epidemiology and Biostatistics, Amsterdam University Medical Center, Amsterdam, the Netherlands
- Department of Cell and Chemical Biology & Department of Biomedical Data Sciences, Section Molecular Epidemiology, Leiden University Medical Center, Leiden, the Netherlands
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28
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Omics research in diabetic kidney disease: new biomarker dimensions and new understandings? J Nephrol 2020; 33:931-948. [DOI: 10.1007/s40620-020-00759-4] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2020] [Accepted: 05/23/2020] [Indexed: 12/14/2022]
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Lipidomic analysis reveals sphingomyelin and phosphatidylcholine species associated with renal impairment and all-cause mortality in type 1 diabetes. Sci Rep 2019; 9:16398. [PMID: 31705008 PMCID: PMC6841673 DOI: 10.1038/s41598-019-52916-w] [Citation(s) in RCA: 50] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2019] [Accepted: 10/24/2019] [Indexed: 12/11/2022] Open
Abstract
There is an urgent need for a better molecular understanding of the pathophysiology underlying development and progression of diabetic nephropathy. The aim of the current study was to identify novel associations between serum lipidomics and diabetic nephropathy. Non-targeted serum lipidomic analyses were performed with mass spectrometry in 669 individuals with type 1 diabetes. Cross-sectional associations of lipid species with estimated glomerular filtration rate (eGFR) and urinary albumin excretion were assessed. Moreover, associations with register-based longitudinal follow-up for progression to a combined renal endpoint including ≥30% decline in eGFR, ESRD and all-cause mortality were evaluated. Median follow-up time was 5.0–6.4 years. Adjustments included traditional risk factors and multiple testing correction. In total, 106 lipid species were identified. Primarily, alkyl-acyl phosphatidylcholines, triglycerides and sphingomyelins demonstrated cross-sectional associations with eGFR and macroalbuminuria. In longitudinal analyses, thirteen lipid species were associated with the slope of eGFR or albuminuria. Of these lipids, phosphatidylcholine and sphingomyelin species, PC(O-34:2), PC(O-34:3), SM(d18:1/24:0), SM(d40:1) and SM(d41:1), were associated with lower risk of the combined renal endpoint. PC(O-34:3), SM(d40:1) and SM(d41:1) were associated with lower risk of all-cause mortality while an SM(d18:1/24:0) was associated with lower risk of albuminuria group progression. We report distinct associations between lipid species and risk of renal outcomes in type 1 diabetes, independent of traditional markers of kidney function.
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Cañadas-Garre M, Anderson K, McGoldrick J, Maxwell AP, McKnight AJ. Proteomic and metabolomic approaches in the search for biomarkers in chronic kidney disease. J Proteomics 2019; 193:93-122. [PMID: 30292816 DOI: 10.1016/j.jprot.2018.09.020] [Citation(s) in RCA: 38] [Impact Index Per Article: 7.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2018] [Revised: 09/20/2018] [Accepted: 09/30/2018] [Indexed: 12/15/2022]
Abstract
Chronic kidney disease (CKD) is an aging-related disorder that represents a major global public health burden. Current biochemical biomarkers, such as serum creatinine and urinary albumin, have important limitations when used to identify the earliest indication of CKD or in tracking the progression to more advanced CKD. These issues underline the importance of finding and testing new molecular biomarkers that are capable of successfully meeting this clinical need. The measurement of changes in nature and/or levels of proteins and metabolites in biological samples from patients provide insights into pathophysiological processes. Proteomic and metabolomic techniques provide opportunities to record dynamic chemical signatures in patients over time. This review article presents an overview of the recent developments in the fields of metabolomics and proteomics in relation to CKD. Among the many different proteomic biomarkers proposed, there is particular interest in the CKD273 classifier, a urinary proteome biomarker reported to predict CKD progression and with implementation potential. Other individual non-invasive peptidomic biomarkers that are potentially relevant for CKD detection include type 1 collagen, uromodulin and mucin-1. Despite the limited sample sizes and variability of the metabolomics studies, some metabolites such as trimethylamine N-oxide, kynurenine and citrulline stand out as potential biomarkers in CKD.
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Affiliation(s)
- M Cañadas-Garre
- Epidemiology and Public Health Research Group, Centre for Public Health, Queen's University of Belfast, Regional Genetics Centre, Level A, Tower Block, Belfast City Hospital, Lisburn Road, Belfast BT9 7AB, United Kingdom; Regional Nephrology Unit, Belfast City Hospital, Belfast, United Kingdom.
| | - K Anderson
- Epidemiology and Public Health Research Group, Centre for Public Health, Queen's University of Belfast, Regional Genetics Centre, Level A, Tower Block, Belfast City Hospital, Lisburn Road, Belfast BT9 7AB, United Kingdom; Regional Nephrology Unit, Belfast City Hospital, Belfast, United Kingdom.
| | - J McGoldrick
- Epidemiology and Public Health Research Group, Centre for Public Health, Queen's University of Belfast, Regional Genetics Centre, Level A, Tower Block, Belfast City Hospital, Lisburn Road, Belfast BT9 7AB, United Kingdom; Regional Nephrology Unit, Belfast City Hospital, Belfast, United Kingdom.
| | - A P Maxwell
- Epidemiology and Public Health Research Group, Centre for Public Health, Queen's University of Belfast, Regional Genetics Centre, Level A, Tower Block, Belfast City Hospital, Lisburn Road, Belfast BT9 7AB, United Kingdom; Regional Nephrology Unit, Belfast City Hospital, Belfast, United Kingdom.
| | - A J McKnight
- Epidemiology and Public Health Research Group, Centre for Public Health, Queen's University of Belfast, Regional Genetics Centre, Level A, Tower Block, Belfast City Hospital, Lisburn Road, Belfast BT9 7AB, United Kingdom; Regional Nephrology Unit, Belfast City Hospital, Belfast, United Kingdom.
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Vignoli A, Ghini V, Meoni G, Licari C, Takis PG, Tenori L, Turano P, Luchinat C. High-Throughput Metabolomics by 1D NMR. Angew Chem Int Ed Engl 2019; 58:968-994. [PMID: 29999221 PMCID: PMC6391965 DOI: 10.1002/anie.201804736] [Citation(s) in RCA: 218] [Impact Index Per Article: 43.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2018] [Indexed: 12/12/2022]
Abstract
Metabolomics deals with the whole ensemble of metabolites (the metabolome). As one of the -omic sciences, it relates to biology, physiology, pathology and medicine; but metabolites are chemical entities, small organic molecules or inorganic ions. Therefore, their proper identification and quantitation in complex biological matrices requires a solid chemical ground. With respect to for example, DNA, metabolites are much more prone to oxidation or enzymatic degradation: we can reconstruct large parts of a mammoth's genome from a small specimen, but we are unable to do the same with its metabolome, which was probably largely degraded a few hours after the animal's death. Thus, we need standard operating procedures, good chemical skills in sample preparation for storage and subsequent analysis, accurate analytical procedures, a broad knowledge of chemometrics and advanced statistical tools, and a good knowledge of at least one of the two metabolomic techniques, MS or NMR. All these skills are traditionally cultivated by chemists. Here we focus on metabolomics from the chemical standpoint and restrict ourselves to NMR. From the analytical point of view, NMR has pros and cons but does provide a peculiar holistic perspective that may speak for its future adoption as a population-wide health screening technique.
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Affiliation(s)
- Alessia Vignoli
- C.I.R.M.M.P.Via Luigi Sacconi 650019 Sesto FiorentinoFlorenceItaly
| | - Veronica Ghini
- CERMUniversity of FlorenceVia Luigi Sacconi 650019 Sesto FiorentinoFlorenceItaly
| | - Gaia Meoni
- CERMUniversity of FlorenceVia Luigi Sacconi 650019 Sesto FiorentinoFlorenceItaly
| | - Cristina Licari
- CERMUniversity of FlorenceVia Luigi Sacconi 650019 Sesto FiorentinoFlorenceItaly
| | | | - Leonardo Tenori
- Department of Experimental and Clinical MedicineUniversity of FlorenceLargo Brambilla 3FlorenceItaly
| | - Paola Turano
- CERMUniversity of FlorenceVia Luigi Sacconi 650019 Sesto FiorentinoFlorenceItaly
- Department of Chemistry “Ugo Schiff”University of FlorenceVia della Lastruccia 3–1350019 Sesto FiorentinoFlorenceItaly
| | - Claudio Luchinat
- CERMUniversity of FlorenceVia Luigi Sacconi 650019 Sesto FiorentinoFlorenceItaly
- Department of Chemistry “Ugo Schiff”University of FlorenceVia della Lastruccia 3–1350019 Sesto FiorentinoFlorenceItaly
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Matanes F, Twal WO, Hammad SM. Sphingolipids as Biomarkers of Disease. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2019; 1159:109-138. [DOI: 10.1007/978-3-030-21162-2_7] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
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Vignoli A, Ghini V, Meoni G, Licari C, Takis PG, Tenori L, Turano P, Luchinat C. Hochdurchsatz‐Metabolomik mit 1D‐NMR. Angew Chem Int Ed Engl 2018. [DOI: 10.1002/ange.201804736] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Affiliation(s)
- Alessia Vignoli
- C.I.R.M.M.P. Via Luigi Sacconi 6 50019 Sesto Fiorentino Florence Italien
| | - Veronica Ghini
- CERMUniversity of Florence Via Luigi Sacconi 6 50019 Sesto Fiorentino Florence Italien
| | - Gaia Meoni
- CERMUniversity of Florence Via Luigi Sacconi 6 50019 Sesto Fiorentino Florence Italien
| | - Cristina Licari
- CERMUniversity of Florence Via Luigi Sacconi 6 50019 Sesto Fiorentino Florence Italien
| | | | - Leonardo Tenori
- Department of Experimental and Clinical MedicineUniversity of Florence Largo Brambilla 3 Florence Italien
| | - Paola Turano
- CERMUniversity of Florence Via Luigi Sacconi 6 50019 Sesto Fiorentino Florence Italien
- Department of Chemistry “Ugo Schiff”University of Florence Via della Lastruccia 3–13 50019 Sesto Fiorentino Florence Italien
| | - Claudio Luchinat
- CERMUniversity of Florence Via Luigi Sacconi 6 50019 Sesto Fiorentino Florence Italien
- Department of Chemistry “Ugo Schiff”University of Florence Via della Lastruccia 3–13 50019 Sesto Fiorentino Florence Italien
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Barrios C, Zierer J, Würtz P, Haller T, Metspalu A, Gieger C, Thorand B, Meisinger C, Waldenberger M, Raitakari O, Lehtimäki T, Otero S, Rodríguez E, Pedro-Botet J, Kähönen M, Ala-Korpela M, Kastenmüller G, Spector TD, Pascual J, Menni C. Circulating metabolic biomarkers of renal function in diabetic and non-diabetic populations. Sci Rep 2018; 8:15249. [PMID: 30323304 PMCID: PMC6189123 DOI: 10.1038/s41598-018-33507-7] [Citation(s) in RCA: 38] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2018] [Accepted: 09/26/2018] [Indexed: 01/18/2023] Open
Abstract
Using targeted NMR spectroscopy of 227 fasting serum metabolic traits, we searched for novel metabolic signatures of renal function in 926 type 2 diabetics (T2D) and 4838 non-diabetic individuals from four independent cohorts. We furthermore investigated longitudinal changes of metabolic measures and renal function and associations with other T2D microvascular complications. 142 traits correlated with glomerular filtration rate (eGFR) after adjusting for confounders and multiple testing: 59 in diabetics, 109 in non-diabetics with 26 overlapping. The amino acids glycine and phenylalanine and the energy metabolites citrate and glycerol were negatively associated with eGFR in all the cohorts, while alanine, valine and pyruvate depicted opposite association in diabetics (positive) and non-diabetics (negative). Moreover, in all cohorts, the triglyceride content of different lipoprotein subclasses showed a negative association with eGFR, while cholesterol, cholesterol esters (CE), and phospholipids in HDL were associated with better renal function. In contrast, phospholipids and CEs in LDL showed positive associations with eGFR only in T2D, while phospholipid content in HDL was positively associated with eGFR both cross-sectionally and longitudinally only in non-diabetics. In conclusion, we provide a wide list of kidney function-associated metabolic traits and identified novel metabolic differences between diabetic and non-diabetic kidney disease.
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Affiliation(s)
- Clara Barrios
- Department for Twin Research, King's College London, London, UK
- Department of Nephrology, Hospital del Mar, Institut Mar d'Investigacions Mediques, Barcelona, Spain
| | - Jonas Zierer
- Department for Twin Research, King's College London, London, UK.
- Institute of Bioinformatics and Systems Biology, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, Germany.
- Weill Cornell Medical College, New York City, USA.
| | - Peter Würtz
- Research Programs Unit, Diabetes and Obesity, University of Helsinki, Helsinki, Finland
- Nightingale Health Ltd, Helsinki, Finland
| | - Toomas Haller
- Estonian Genome Center, University of Tartu, Tartu, Estonia
| | | | - Christian Gieger
- Institute of Epidemiology II, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, Germany
- Research Unit Molecular Epidemiology, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, Germany
| | - Barbara Thorand
- Institute of Epidemiology II, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, Germany
| | - Christa Meisinger
- Institute of Epidemiology II, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, Germany
- Chair of Epidemiology, Ludwig-Maximilians-Universität München, UNIKA-T, Augsburg, Germany
| | - Melanie Waldenberger
- Institute of Epidemiology II, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, Germany
- Research Unit Molecular Epidemiology, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, Germany
| | - Olli Raitakari
- Research Centre of Applied and Preventive Cardiovascular Medicine, University of Turku, Turku, Finland
- Department of Clinical Physiology and Nuclear Medicine, Turku University Hospital, Turku, Finland
| | - Terho Lehtimäki
- Department of Clinical Chemistry, Fimlab Laboratories and Finnish Cardiovascular Research Center-Tampere, Faculty of Medicine and Life Sciences, University of Tampere, Tampere, Finland
| | - Sol Otero
- Department of Nephrology, Hospital del Mar, Institut Mar d'Investigacions Mediques, Barcelona, Spain
- Department of Nephrology, Consorci Sanitari del Garraf, Barcelona, Spain
| | - Eva Rodríguez
- Department of Nephrology, Hospital del Mar, Institut Mar d'Investigacions Mediques, Barcelona, Spain
| | - Juan Pedro-Botet
- Department of Endocrinology and Nutrition, Hospital del Mar, Institut Mar d'Investigacions Mediques, Barcelona, Spain
| | - Mika Kähönen
- Department of Clinical Physiology, University of Tampere and Tampere University Hospital, Tampere, Finland
| | - Mika Ala-Korpela
- Systems Epidemiology, Baker Heart and Diabetes Institute, Melbourne, Victoria, Australia
- Population Health Science, Bristol Medical School, University of Bristol, Bristol, UK
- Medical Research Council Integrative Epidemiology Unit at the University of Bristol, Bristol, UK
- Computational Medicine, Faculty of Medicine, University of Oulu and Biocenter Oulu, Oulu, Finland
- NMR Metabolomics Laboratory, School of Pharmacy, University of Eastern Finland, Kuopio, Finland
- Department of Epidemiology and Preventive Medicine, School of Public Health and Preventive Medicine, Faculty of Medicine, Nursing and Health Sciences, The Alfred Hospital, Monash University, Melbourne, Victoria, Australia
| | - Gabi Kastenmüller
- Institute of Bioinformatics and Systems Biology, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, Germany
| | - Tim D Spector
- Department for Twin Research, King's College London, London, UK
| | - Julio Pascual
- Department of Nephrology, Hospital del Mar, Institut Mar d'Investigacions Mediques, Barcelona, Spain
| | - Cristina Menni
- Department for Twin Research, King's College London, London, UK.
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35
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Haukka JK, Sandholm N, Forsblom C, Cobb JE, Groop PH, Ferrannini E. Metabolomic Profile Predicts Development of Microalbuminuria in Individuals with Type 1 Diabetes. Sci Rep 2018; 8:13853. [PMID: 30217994 PMCID: PMC6138633 DOI: 10.1038/s41598-018-32085-y] [Citation(s) in RCA: 36] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2018] [Accepted: 08/13/2018] [Indexed: 12/28/2022] Open
Abstract
Elevated urinary albumin excretion (microalbuminuria) is an early marker of diabetic nephropathy, but there is an unmet need for better biomarkers that capture the individuals at risk with higher accuracy and earlier than the current markers do. We performed an untargeted metabolomic study to assess baseline differences between individuals with type 1 diabetes who either developed microalbuminuria or remained normoalbuminuric. A total of 102 individuals progressed to microalbuminuria during a median follow-up of 3.2 years, whereas 98 sex-, age- and body mass index (BMI) matched non-progressors remained normoalbuminuric during a median follow-up of 7.1 years. Metabolomic screening identified 1,242 metabolites, out of which 111 differed significantly between progressors and non-progressors after adjustment for age of diabetes onset, baseline glycosylated hemoglobin A1c (HbA1c), and albumin excretion rate (AER). The metabolites that predicted development of microalbumiuria included several uremic toxins and carnitine metabolism related molecules. Iterative variable selection indicated erythritol, 3-phenylpropionate, and N-trimethyl-5-aminovalerate as the best set of variables to predict development of microalbuminuria. A metabolomic index based on these metabolites improved the prediction of incident microalbuminuria on top of the clinical variables age of diabetes onset, baseline HbA1c and AER (ROCAUC = 0.842 vs 0.797), highlighting their ability to predict early-phase diabetic nephropathy.
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Affiliation(s)
- Jani K Haukka
- Folkhälsan Institute of Genetics, Folkhälsan Research Center, Helsinki, Finland
- Abdominal Center Nephrology, University of Helsinki and Helsinki University Hospital, Helsinki, Finland
- Diabetes & Obesity Research Program, Research Program's Unit, University of Helsinki, Helsinki, Finland
| | - Niina Sandholm
- Folkhälsan Institute of Genetics, Folkhälsan Research Center, Helsinki, Finland
- Abdominal Center Nephrology, University of Helsinki and Helsinki University Hospital, Helsinki, Finland
- Diabetes & Obesity Research Program, Research Program's Unit, University of Helsinki, Helsinki, Finland
| | - Carol Forsblom
- Folkhälsan Institute of Genetics, Folkhälsan Research Center, Helsinki, Finland
- Abdominal Center Nephrology, University of Helsinki and Helsinki University Hospital, Helsinki, Finland
- Diabetes & Obesity Research Program, Research Program's Unit, University of Helsinki, Helsinki, Finland
| | | | - Per-Henrik Groop
- Folkhälsan Institute of Genetics, Folkhälsan Research Center, Helsinki, Finland.
- Abdominal Center Nephrology, University of Helsinki and Helsinki University Hospital, Helsinki, Finland.
- Diabetes & Obesity Research Program, Research Program's Unit, University of Helsinki, Helsinki, Finland.
- Department of Diabetes, Central Clinical School, Monash University, Melbourne, Victoria, Australia.
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Gao S, Mutter S, Casey A, Mäkinen VP. Numero: a statistical framework to define multivariable subgroups in complex population-based datasets. Int J Epidemiol 2018; 48:369-374. [DOI: 10.1093/ije/dyy113] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2018] [Accepted: 05/28/2018] [Indexed: 12/11/2022] Open
Affiliation(s)
- Song Gao
- Heart Health Theme, South Australian Health and Medical Research Institute, Adelaide, SA, Australia
| | - Stefan Mutter
- Heart Health Theme, South Australian Health and Medical Research Institute, Adelaide, SA, Australia
| | - Aaron Casey
- Heart Health Theme, South Australian Health and Medical Research Institute, Adelaide, SA, Australia
| | - Ville-Petteri Mäkinen
- Heart Health Theme, South Australian Health and Medical Research Institute, Adelaide, SA, Australia
- School of Biological Sciences, University of Adelaide, Adelaide, SA, Australia
- Computational Medicine, University of Oulu and Biocenter Oulu, Oulu, Finland
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37
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Translational science in albuminuria: a new view of de novo albuminuria under chronic RAS suppression. Clin Sci (Lond) 2018; 132:739-758. [DOI: 10.1042/cs20180097] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2018] [Revised: 03/09/2018] [Accepted: 03/22/2018] [Indexed: 12/29/2022]
Abstract
The development of de novo albuminuria during chronic renin–angiotensin system (RAS) suppression is a clinical entity that remains poorly recognized in the biomedical literature. It represents a clear increment in global cardiovascular (CV) and renal risk that cannot be counteracted by RAS suppression. Although not specifically considered, it is clear that this entity is present in most published and ongoing trials dealing with the different forms of CV and renal disease. In this review, we focus on the mechanisms promoting albuminuria, and the predictors and new markers of de novo albuminuria, as well as the potential treatment options to counteract the excretion of albumin. The increase in risk that accompanies de novo albuminuria supports the search for early markers and predictors that will allow practising physicians to assess and prevent the development of de novo albuminuria in their patients.
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Mutter S, Casey AE, Zhen S, Shi Z, Mäkinen VP. Multivariable Analysis of Nutritional and Socio-Economic Profiles Shows Differences in Incident Anemia for Northern and Southern Jiangsu in China. Nutrients 2017; 9:nu9101153. [PMID: 29065474 PMCID: PMC5691769 DOI: 10.3390/nu9101153] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2017] [Revised: 09/06/2017] [Accepted: 10/19/2017] [Indexed: 12/20/2022] Open
Abstract
Anemia is a prevalent public health problem associated with nutritional and socio-economic factors that contribute to iron deficiency. To understand the complex interplay of risk factors, we investigated a prospective population sample from the Jiangsu province in China. At baseline, three-day food intake was measured for 2849 individuals (20 to 87 years of age, mean age 47 ± 14, range 20-87 years, 64% women). At a five-year follow-up, anemia status was re-assessed for 1262 individuals. The dataset was split and age-matched to accommodate cross-sectional (n = 2526), prospective (n = 837), and subgroup designs (n = 1844). We applied a machine learning framework (self-organizing map) to define four subgroups. The first two subgroups were primarily from the less affluent North: the High Fibre subgroup had a higher iron intake (35 vs. 21 mg/day) and lower anemia incidence (10% vs. 25%) compared to the Low Vegetable subgroup. However, the predominantly Southern subgroups were surprising: the Low Fibre subgroup showed a lower anemia incidence (10% vs. 27%), yet also a lower iron intake (20 vs. 28 mg/day) compared to the High Rice subgroup. These results suggest that interventions and iron intake guidelines should be tailored to regional, nutritional, and socio-economic subgroups.
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Affiliation(s)
- Stefan Mutter
- South Australian Health and Medical Research Institute (SAHMRI), Adelaide SA 5000, Australia.
- School of Biological Sciences, University of Adelaide, Adelaide SA 5005, Australia.
| | - Aaron E Casey
- South Australian Health and Medical Research Institute (SAHMRI), Adelaide SA 5000, Australia.
- School of Biological Sciences, University of Adelaide, Adelaide SA 5005, Australia.
| | - Shiqi Zhen
- Department of Nutrition and Foodborne Disease Prevention, Jiangsu Provincial Centre for Disease Control and Prevention, Nanjing 210009, China.
| | - Zumin Shi
- School of Medicine, University of Adelaide, Adelaide SA 5005, Australia.
| | - Ville-Petteri Mäkinen
- South Australian Health and Medical Research Institute (SAHMRI), Adelaide SA 5000, Australia.
- School of Biological Sciences, University of Adelaide, Adelaide SA 5005, Australia.
- Computational Medicine, Faculty of Medicine, University of Oulu and Biocenter Oulu, 90014 Oulu, Finland.
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39
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Ala-Korpela M, Davey Smith G. Metabolic profiling-multitude of technologies with great research potential, but (when) will translation emerge? Int J Epidemiol 2016; 45:1311-1318. [PMID: 27789667 PMCID: PMC5100630 DOI: 10.1093/ije/dyw305] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022] Open
Affiliation(s)
- Mika Ala-Korpela
- Computational Medicine, Faculty of Medicine, University of Oulu and Biocenter Oulu, Oulu, Finland .,Medical Research Council Integrative Epidemiology Unit and School of Social and Community Medicine, University of Bristol, Bristol, UK
| | - George Davey Smith
- Medical Research Council Integrative Epidemiology Unit and School of Social and Community Medicine, University of Bristol, Bristol, UK
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40
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Lee J, Choi JY, Kwon YK, Lee D, Jung HY, Ryu HM, Cho JH, Ryu DH, Kim YL, Hwang GS. Changes in serum metabolites with the stage of chronic kidney disease: Comparison of diabetes and non-diabetes. Clin Chim Acta 2016; 459:123-131. [DOI: 10.1016/j.cca.2016.05.018] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2016] [Revised: 05/02/2016] [Accepted: 05/20/2016] [Indexed: 10/21/2022]
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Abstract
The longstanding focus in chronic kidney disease (CKD) research has been on the glomerulus, which is sensible because this is where glomerular filtration occurs, and a large proportion of progressive CKD is associated with significant glomerular pathology. However, it has been known for decades that tubular atrophy is also a hallmark of CKD and that it is superior to glomerular pathology as a predictor of glomerular filtration rate decline in CKD. Nevertheless, there are vastly fewer studies that investigate the causes of tubular atrophy, and fewer still that identify potential therapeutic targets. The purpose of this review is to discuss plausible mechanisms of tubular atrophy, including tubular epithelial cell apoptosis, cell senescence, peritubular capillary rarefaction and downstream tubule ischemia, oxidative stress, atubular glomeruli, epithelial-to-mesenchymal transition, interstitial inflammation, lipotoxicity and Na(+)/H(+) exchanger-1 inactivation. Once a a better understanding of tubular atrophy (and interstitial fibrosis) pathophysiology has been obtained, it might then be possible to consider tandem glomerular and tubular therapeutic strategies, in a manner similar to cancer chemotherapy regimens, which employ multiple drugs to simultaneously target different mechanistic pathways.
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42
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Chen L, Cheng CY, Choi H, Ikram MK, Sabanayagam C, Tan GSW, Tian D, Zhang L, Venkatesan G, Tai ES, Wang JJ, Mitchell P, Cheung CMG, Beuerman RW, Zhou L, Chan ECY, Wong TY. Plasma Metabonomic Profiling of Diabetic Retinopathy. Diabetes 2016; 65:1099-108. [PMID: 26822086 DOI: 10.2337/db15-0661] [Citation(s) in RCA: 87] [Impact Index Per Article: 10.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/20/2015] [Accepted: 01/06/2016] [Indexed: 12/12/2022]
Abstract
Diabetic retinopathy (DR) is the most common microvascular complication of diabetes and the leading cause of visual impairment in working-age adults. Patients with diabetes often develop DR despite appropriate control of systemic risk factors, suggesting the involvement of other pathogenic factors. We hypothesize that the plasma metabolic signature of DR is distinct and resolvable from that of diabetes alone. A nested population-based case-control metabonomic study was first performed on 40 DR cases and 40 control subjects with diabetes using gas chromatography-mass spectrometry. Eleven metabolites were found to be correlated with DR, and the majority were robust when adjusted for metabolic risk factors and confounding kidney disease. The metabolite markers 2-deoxyribonic acid; 3,4-dihydroxybutyric acid; erythritol; gluconic acid; and ribose were validated in an independent sample set with 40 DR cases, 40 control subjects with diabetes, and 40 individuals without diabetes. DR cases and control subjects with diabetes were matched by HbA1c in the validation set. Activation of the pentose phosphate pathway was identified from the list of DR metabolite markers. The identification of novel metabolite markers for DR provides insights into potential new pathogenic pathways for this microvascular complication and holds translational value in DR risk stratification and the development of new therapeutic measures.
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Affiliation(s)
- Liyan Chen
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore Department of Pharmacy, Faculty of Science, National University of Singapore, Singapore
| | - Ching-Yu Cheng
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore Department of Ophthalmology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore Ophthalmology & Visual Sciences Academic Clinical Program, Duke-National University of Singapore Graduate Medical School, Singapore
| | - Hyungwon Choi
- Saw Swee Hock School of Public Health, National University of Singapore, Singapore Institute of Molecular and Cell Biology, Agency for Science, Technology and Research, Singapore
| | - Mohammad Kamran Ikram
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore Ophthalmology & Visual Sciences Academic Clinical Program, Duke-National University of Singapore Graduate Medical School, Singapore
| | - Charumathi Sabanayagam
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore Department of Ophthalmology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore Ophthalmology & Visual Sciences Academic Clinical Program, Duke-National University of Singapore Graduate Medical School, Singapore
| | - Gavin S W Tan
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore
| | - Dechao Tian
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore Department of Statistics and Applied Probability, Faculty of Science, National University of Singapore, Singapore
| | - Liang Zhang
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore Department of Statistics and Applied Probability, Faculty of Science, National University of Singapore, Singapore
| | | | - E Shyong Tai
- Saw Swee Hock School of Public Health, National University of Singapore, Singapore Department of Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore Cardiovascular & Metabolic Disorders Program, Duke-National University of Singapore Graduate Medical School, Singapore
| | - Jie Jin Wang
- Department of Ophthalmology, Centre for Vision Research, Westmead Millennium Institute, The University of Sydney, Sydney, New South Wales, Australia
| | - Paul Mitchell
- Department of Ophthalmology, Centre for Vision Research, Westmead Millennium Institute, The University of Sydney, Sydney, New South Wales, Australia
| | | | - Roger Wilmer Beuerman
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore Department of Ophthalmology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore Signature Research Program in Neuroscience & Behavioral Disorders, Duke-National University of Singapore Graduate Medical School, Singapore Department of Biochemistry, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
| | - Lei Zhou
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore Department of Ophthalmology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore Signature Research Program in Neuroscience & Behavioral Disorders, Duke-National University of Singapore Graduate Medical School, Singapore
| | - Eric Chun Yong Chan
- Department of Pharmacy, Faculty of Science, National University of Singapore, Singapore
| | - Tien Yin Wong
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore Department of Ophthalmology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore Ophthalmology & Visual Sciences Academic Clinical Program, Duke-National University of Singapore Graduate Medical School, Singapore Saw Swee Hock School of Public Health, National University of Singapore, Singapore
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43
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Okazaki M, Yamashita S. Recent Advances in Analytical Methods on Lipoprotein Subclasses: Calculation of Particle Numbers from Lipid Levels by Gel Permeation HPLC Using “Spherical Particle Model”. J Oleo Sci 2016; 65:265-82. [DOI: 10.5650/jos.ess16020] [Citation(s) in RCA: 47] [Impact Index Per Article: 5.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Affiliation(s)
| | - Shizuya Yamashita
- Rinku General Medical Center
- Department of Community Medicine & Department of Cardiovascular Medicine, Osaka University Graduate School of Medicine
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44
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Zhang Y, Zhang S, Wang G. Metabolomic biomarkers in diabetic kidney diseases--A systematic review. J Diabetes Complications 2015; 29:1345-51. [PMID: 26253264 DOI: 10.1016/j.jdiacomp.2015.06.016] [Citation(s) in RCA: 29] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/05/2015] [Revised: 06/18/2015] [Accepted: 06/29/2015] [Indexed: 01/26/2023]
Abstract
Diabetic kidney disease (DKD) is generally characterized by increasing albuminuria in diabetic patients; however, few biomarkers are available to facilitate early diagnosis of this disease. The application of metabolomics has shown promises addressing this need. In this review, we conducted a search about metabolomic biomarkers in DKD patients through MEDLINE, EMBASE, and Cochrane Database up to the end of March, 2015. 12 eligible studies were selected and evaluated subsequently through the use of QUADOMICS, a quality assessment tool. 7 of the 12 included studies were classified as 'high quality'. We also recorded specific study characteristics including participants' characteristics, metabolomic techniques, sample types, and significantly altered metabolites between DKD and control groups. Products of lipid metabolisms including esterified and non-esterified fatty acids, carnitines, phospholipids and metabolites involved in branch-chained amino acids and aromatic amino acids metabolisms were frequently affected biomarkers of DKD. Other differential metabolites were also found, while some of their associations with DKD were unclear. Further more studies are required to test these findings in larger, diverse ethnic populations with elaborate study designs, and finally we could translate them into the benefits of DKD patients.
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Affiliation(s)
- Yumin Zhang
- Department of Endocrinology and Metabolism, the First Hospital of Jilin University, Changchun, 130021, China
| | - Siwen Zhang
- Department of Endocrinology and Metabolism, the First Hospital of Jilin University, Changchun, 130021, China
| | - Guixia Wang
- Department of Endocrinology and Metabolism, the First Hospital of Jilin University, Changchun, 130021, China.
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45
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Barrios C, Spector TD, Menni C. Blood, urine and faecal metabolite profiles in the study of adult renal disease. Arch Biochem Biophys 2015; 589:81-92. [PMID: 26476344 DOI: 10.1016/j.abb.2015.10.006] [Citation(s) in RCA: 30] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2015] [Revised: 10/08/2015] [Accepted: 10/09/2015] [Indexed: 01/04/2023]
Abstract
Chronic kidney disease (CKD) is a major public health burden and to date traditional biomarkers of renal function (such as serum creatinine and cystatin C) are unable to identify at-risk individuals before the disease process is well under way. To help preventive strategies and maximize the potential for effective interventions, it is important to characterise the molecular changes that take place in the development of renal damage. Metabolomics is a promising tool to identify markers of renal disease since the kidneys are involved in the handling of major biochemical classes of metabolites. These metabolite levels capture a snap-shot of the metabolic profile of the individual, allowing for the potential identification of early biomarkers, and the monitoring of real-time kidney function. In this review, we describe the current status of the identification of blood/urine/faecal metabolic biomarkers in different entities of kidney diseases including: acute kidney injury, chronic kidney disease, renal transplant, diabetic nephropathy and other disorders.
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Affiliation(s)
- Clara Barrios
- Department of Twin Research and Genetic Epidemiology, King's College London, London, UK; Department of Nephrology, Hospital del Mar. Institut Mar d'Investigacions Mediques, Barcelona, Spain
| | - Tim D Spector
- Department of Twin Research and Genetic Epidemiology, King's College London, London, UK
| | - Cristina Menni
- Department of Twin Research and Genetic Epidemiology, King's College London, London, UK.
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46
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Abstract
This paper reviews the use of NMR metabolomics for the metabolic characterization of renal cancer. The existing challenges in the clinical management of this disease are first presented, followed by a brief introduction to the metabolomics approach, in the context of cancer research. A subsequent review of the literature on NMR metabolic studies of renal cancer reveals that the subject has been clearly underdeveloped, compared with other types of cancer, particularly regarding cultured cells and tissue analysis. NMR analysis of biofluids has focused on blood (plasma or serum) metabolomics, comprising no account of studies on human urine, in spite of its noninvasiveness and physiological proximity to the affected organs. Finally, some areas of potential future development are identified.
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47
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Napolitano JG, Simmler C, McAlpine JB, Lankin DC, Chen SN, Pauli GF. Digital NMR profiles as building blocks: assembling ¹H fingerprints of steviol glycosides. JOURNAL OF NATURAL PRODUCTS 2015; 78:658-65. [PMID: 25714117 PMCID: PMC4696868 DOI: 10.1021/np5008203] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/31/2023]
Abstract
This report describes a fragment-based approach to the examination of congeneric organic compounds by NMR spectroscopy. The method combines the classic interpretation of 1D- and 2D-NMR data sets with contemporary computer-assisted NMR analysis. Characteristic NMR profiles of key structural motifs were generated by (1)H iterative full spin analysis and then joined together as building blocks to recreate the (1)H NMR spectra of increasingly complex molecules. To illustrate the methodology described, a comprehensive analysis of steviol (1), seven steviol glycosides (2-8) and two structurally related isosteviol compounds (9, 10) was carried out. The study also assessed the potential impact of this method on relevant aspects of natural product research including structural verification, chemical dereplication, and mixture analysis.
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Affiliation(s)
- José G. Napolitano
- Department of Medicinal Chemistry and Pharmacognosy, Institute for Tuberculosis Research, and UIC/NIH Center for Botanical Dietary Supplements Research, College of Pharmacy, University of Illinois at Chicago, 833 South Wood Street, Chicago, Illinois 60612, United States
| | - Charlotte Simmler
- Department of Medicinal Chemistry and Pharmacognosy, Institute for Tuberculosis Research, and UIC/NIH Center for Botanical Dietary Supplements Research, College of Pharmacy, University of Illinois at Chicago, 833 South Wood Street, Chicago, Illinois 60612, United States
| | - James B. McAlpine
- Department of Medicinal Chemistry and Pharmacognosy, Institute for Tuberculosis Research, and UIC/NIH Center for Botanical Dietary Supplements Research, College of Pharmacy, University of Illinois at Chicago, 833 South Wood Street, Chicago, Illinois 60612, United States
| | - David C. Lankin
- Department of Medicinal Chemistry and Pharmacognosy, Institute for Tuberculosis Research, and UIC/NIH Center for Botanical Dietary Supplements Research, College of Pharmacy, University of Illinois at Chicago, 833 South Wood Street, Chicago, Illinois 60612, United States
| | - Shao-Nong Chen
- Department of Medicinal Chemistry and Pharmacognosy, Institute for Tuberculosis Research, and UIC/NIH Center for Botanical Dietary Supplements Research, College of Pharmacy, University of Illinois at Chicago, 833 South Wood Street, Chicago, Illinois 60612, United States
| | - Guido F. Pauli
- Department of Medicinal Chemistry and Pharmacognosy, Institute for Tuberculosis Research, and UIC/NIH Center for Botanical Dietary Supplements Research, College of Pharmacy, University of Illinois at Chicago, 833 South Wood Street, Chicago, Illinois 60612, United States
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48
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Abstract
Diabetes is characterized by altered metabolism of key molecules and regulatory pathways. The phenotypic expression of diabetes and associated complications encompasses complex interactions between genetic, environmental, and tissue-specific factors that require an integrated understanding of perturbations in the network of genes, proteins, and metabolites. Metabolomics attempts to systematically identify and quantitate small molecule metabolites from biological systems. The recent rapid development of a variety of analytical platforms based on mass spectrometry and nuclear magnetic resonance have enabled identification of complex metabolic phenotypes. Continued development of bioinformatics and analytical strategies has facilitated the discovery of causal links in understanding the pathophysiology of diabetes and its complications. Here, we summarize the metabolomics workflow, including analytical, statistical, and computational tools, highlight recent applications of metabolomics in diabetes research, and discuss the challenges in the field.
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Affiliation(s)
- Kelli M Sas
- Division of Nephrology, Department of Internal Medicine, University of Michigan, Ann Arbor, MI
| | - Alla Karnovsky
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI
| | | | - Subramaniam Pennathur
- Division of Nephrology, Department of Internal Medicine, University of Michigan, Ann Arbor, MI
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49
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Duarte IF, Diaz SO, Gil AM. NMR metabolomics of human blood and urine in disease research. J Pharm Biomed Anal 2014; 93:17-26. [DOI: 10.1016/j.jpba.2013.09.025] [Citation(s) in RCA: 82] [Impact Index Per Article: 8.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2013] [Revised: 09/16/2013] [Accepted: 09/24/2013] [Indexed: 02/06/2023]
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50
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Treatment of albuminuria due to diabetic nephropathy: recent trial results. ACTA ACUST UNITED AC 2014. [DOI: 10.4155/cli.14.19] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
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