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Dai Z, Wang H, Shen Q, Hu Y, Xue Y. Raw and heat-treated quinoa protein protects against glucose metabolism disorders in high-fat diet (HFD)-induced mice by reshaping gut microbiota and fecal metabolic profiles. Food Funct 2024. [PMID: 39189421 DOI: 10.1039/d4fo02904f] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/28/2024]
Abstract
Plant-based proteins have received considerable global attention due to their nutritional value and potential health effects. As a high-quality plant protein, the hypoglycemic effect of quinoa protein and its potential mechanism have not been fully elucidated. In the present study, we compared the hypoglycemic effects of raw quinoa protein (RP-quinoa) and heat-treated quinoa protein (HP-quinoa) and further explored their potential mechanisms using multi-omics analysis based on gut microbiota and fecal metabolic profiles in HFD-fed mice. Our results showed that both RP-quinoa and HP-quinoa effectively improved glucose metabolism and protected against alterations in gut microbiota induced by a chronic HFD. In addition, quinoa protein increased the relative abundance of beneficial bacteria such as the g__Lachnospiraceae_NK4A136_group, g__Eubacterium_xylanophilum_group, and g__Negativibacillus, followed by an increase in short-chain fatty acids and potentially beneficial metabolites such as L-phenylalanine and L-cysteine. Together, these findings provided the basis for linking gut microbiota and their metabolites to the hypoglycemic effect of quinoa protein.
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Affiliation(s)
- Zijian Dai
- National Engineering and Technology Research Center for Fruits and Vegetables, College of Food Science and Nutritional Engineering, China Agricultural University, Beijing 100083, P.R. China.
- National Center of Technology Innovation (Deep Processing of Highland Barley) in Food Industry, China Agricultural University, No. 17 Qinghua East Road, Haidian District, Beijing 100083, P.R. China
| | - Han Wang
- National Engineering and Technology Research Center for Fruits and Vegetables, College of Food Science and Nutritional Engineering, China Agricultural University, Beijing 100083, P.R. China.
| | - Qun Shen
- National Engineering and Technology Research Center for Fruits and Vegetables, College of Food Science and Nutritional Engineering, China Agricultural University, Beijing 100083, P.R. China.
- National Center of Technology Innovation (Deep Processing of Highland Barley) in Food Industry, China Agricultural University, No. 17 Qinghua East Road, Haidian District, Beijing 100083, P.R. China
| | - Yichen Hu
- Key Laboratory of Coarse Cereal Processing, Ministry of Agriculture and Rural Affairs, Sichuan Engineering & Technology Research Center of Coarse Cereal Industralization, School of Food and Biological Engineering, Chengdu University, Chengdu 610106, P.R. China
| | - Yong Xue
- National Engineering and Technology Research Center for Fruits and Vegetables, College of Food Science and Nutritional Engineering, China Agricultural University, Beijing 100083, P.R. China.
- National Center of Technology Innovation (Deep Processing of Highland Barley) in Food Industry, China Agricultural University, No. 17 Qinghua East Road, Haidian District, Beijing 100083, P.R. China
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Safari-Alighiarloo N, Bostanghadiri N, Sedighi M, Mohebbi A, Vafaei E, Mirshekar M, Razavi S, Alaei-Shahmiri F. Quantification and Correlation Analysis of Bacteroides Species with Diabetes-Related Amino Acids in Individuals with Prediabetes and Type 2 Diabetes Mellitus. Metab Syndr Relat Disord 2024. [PMID: 39133120 DOI: 10.1089/met.2024.0071] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/13/2024] Open
Abstract
Background: The relationship between gut microbiota and diabetes-related amino acids significantly impacts insulin resistance and obesity. We aimed to quantify two Bacteroidetes species and their correlation with branched-chain amino acids, aromatic amino acids, and glutamate in prediabetes (preDM) and type 2 diabetes mellitus (T2DM). Methods: Fecal samples were collected from 68 participants, including 21 with T2DM, 23 with preDM, and 24 with normal glycemic tolerance (NGT). The abundance of Bacteroides vulgatus and Bacteroides thetaiotaomicron was determined by quantitative real-time polymerase chain reaction. Plasma amino acid measurements were performed using liquid chromatography coupled with tandem mass spectrometry. Results: The quantities of B. vulgatus and B. thetaiotaomicron were reduced in preDM and T2DM than in NGT subjects, but it was not statistically significant. The concentrations of leucine, valine, and tyrosine were significantly higher in preDM and T2DM than in NGT subjects (P < 0.05). A negative correlation was observed between B. thetaiotaomicron abundance and two aromatic amino acids (tyrosine, r = -0.28, P = 0.04; phenylalanine, r = -0.26, P = 0.05). Conclusions: These findings imply that, since gut microbiota varies throughout ethnic groups, further research with many participants will be required to determine the abundance of B. vulgatus and B. thetaiotaomicron in preDM and T2DM and their association with diabetes-related amino acids.
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Affiliation(s)
- Nahid Safari-Alighiarloo
- Institute of Endocrinology and Metabolism, Endocrine Research Center, Iran University of Medical Sciences, Tehran, Iran
| | - Narjess Bostanghadiri
- Department of Microbiology, School of Medicine, Iran University of Medical Sciences, Tehran, Iran
| | - Mansour Sedighi
- Department of Microbiology, School of Medicine, Kurdistan University of Medical Sciences, Tehran, Iran
- Research Institute for Health Development, Zoonoses Research Center, Kurdistan University of Medical Sciences, Sanandaj, Iran
| | - Ali Mohebbi
- Stem Cell and Regenerative Medicine Innovation Center, Tehran University of Medical Sciences, Tehran, Iran
| | - Elahe Vafaei
- Fetal and Pediatric Cardiovascular Research Center, Children's Medical Center, Tehran University of Medical Sciences, Tehran, Iran
| | - Maryam Mirshekar
- Department of Microbiology, School of Medicine, Iran University of Medical Sciences, Tehran, Iran
| | - Shabnam Razavi
- Department of Microbiology, School of Medicine, Iran University of Medical Sciences, Tehran, Iran
| | - Fariba Alaei-Shahmiri
- Institute of Endocrinology and Metabolism, Endocrine Research Center, Iran University of Medical Sciences, Tehran, Iran
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Zhong Y, Yang C, Zhang B, Chen Y, Cai W, Wang G, Zhao C, Zhao W. Causal impact of human blood metabolites and metabolic pathways on serum uric acid and gout: a mendelian randomization study. Front Endocrinol (Lausanne) 2024; 15:1378645. [PMID: 39027467 PMCID: PMC11256090 DOI: 10.3389/fendo.2024.1378645] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/01/2024] [Accepted: 06/20/2024] [Indexed: 07/20/2024] Open
Abstract
Objective Hyperuricaemia and gout are common metabolic disorders. However, the causal relationships between blood metabolites and serum urate levels, as well as gout, remain unclear. A systematic evaluation of the causal connections between blood metabolites, hyperuricemia, and gout could enhance early screening and prevention of hyperuricemia and gout in clinical settings, providing novel insights and approaches for clinical treatment. Methods In this study, we employed a bidirectional two-sample Mendelian randomization analysis utilizing data from a genome-wide association study involving 7,286 participants, encompassing 486 blood metabolites. Serum urate and gout data were sourced from the Chronic Kidney Disease Genetics consortium, including 288,649 participants for serum urate and 9,819 African American and 753,994 European individuals for gout. Initially, LDSC methodology was applied to identify blood metabolites with a genetic relationship to serum urate and gout. Subsequently, inverse-variance weighting was employed as the primary analysis method, with a series of sensitivity and pleiotropy analyses conducted to assess the robustness of the results. Results Following LDSC, 133 blood metabolites exhibited a potential genetic relationship with serum urate and gout. In the primary Mendelian randomization analysis using inverse-variance weighting, 19 blood metabolites were recognized as potentially influencing serum urate levels and gout. Subsequently, the IVW p-values of potential metabolites were corrected using the false discovery rate method. We find leucine (IVW P FDR = 0.00004), N-acetylornithine (IVW P FDR = 0.0295), N1-methyl-3-pyridone-4-carboxamide (IVW P FDR = 0.0295), and succinyl carnitine (IVW P FDR = 0.00004) were identified as significant risk factors for elevated serum urate levels. Additionally, 1-oleoylglycerol (IVW P FDR = 0.0007) may lead to a substantial increase in the risk of gout. Succinyl carnitine exhibited acceptable weak heterogeneity, and the results for other blood metabolites remained robust after sensitivity, heterogeneity, and pleiotropy testing. We conducted an enrichment analysis on potential blood metabolites, followed by a metabolic pathway analysis revealing four pathways associated with serum urate levels. Conclusion The identified causal relationships between these metabolites and serum urate and gout offer a novel perspective, providing new mechanistic insights into serum urate levels and gout.
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Affiliation(s)
- Yan Zhong
- College of Traditional Chinese Medicine, Changchun University of Chinese Medicine, Changchun, China
| | - ChengAn Yang
- College of Traditional Chinese Medicine, Changchun University of Chinese Medicine, Changchun, China
| | - BingHua Zhang
- College of Traditional Chinese Medicine, Changchun University of Chinese Medicine, Changchun, China
| | - YingWen Chen
- College of Integrated Chinese and Western Medicine, Tianjin University of Chinese Medicine, Tianjin, China
| | - WenJun Cai
- Department of Orthopaedic Center, The Third Clinical Hospital of Changchun University of Chinese Medicine, Changchun, China
| | - GuoChen Wang
- College of Traditional Chinese Medicine, Changchun University of Chinese Medicine, Changchun, China
| | - ChangWei Zhao
- Department of Orthopedics Center, Affiliated Hospital of Changchun University of Chinese Medicine, Changchun, China
| | - WenHai Zhao
- Department of Orthopedics Center, Affiliated Hospital of Changchun University of Chinese Medicine, Changchun, China
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Holt RIG, Cockram CS, Ma RCW, Luk AOY. Diabetes and infection: review of the epidemiology, mechanisms and principles of treatment. Diabetologia 2024; 67:1168-1180. [PMID: 38374451 PMCID: PMC11153295 DOI: 10.1007/s00125-024-06102-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/31/2023] [Accepted: 12/04/2023] [Indexed: 02/21/2024]
Abstract
An association between diabetes and infection has been recognised for many years, with infection being an important cause of death and morbidity in people with diabetes. The COVID-19 pandemic has re-kindled an interest in the complex relationship between diabetes and infection. Some infections occur almost exclusively in people with diabetes, often with high mortality rates without early diagnosis and treatment. However, more commonly, diabetes is a complicating factor in many infections. A reciprocal relationship occurs whereby certain infections and their treatments may also increase the risk of diabetes. People with diabetes have a 1.5- to 4-fold increased risk of infection. The risks are the most pronounced for kidney infection, osteomyelitis and foot infection, but are also increased for pneumonia, influenza, tuberculosis, skin infection and general sepsis. Outcomes from infection are worse in people with diabetes, with the most notable example being a twofold higher rate of death from COVID-19. Hyperglycaemia has deleterious effects on the immune response. Vascular insufficiency and neuropathy, together with altered skin, mucosal and gut microbial colonisation, contribute to the increased risk of infection. Vaccination is important in people with diabetes although the efficacy of certain immunisations may be compromised, particularly in the presence of hyperglycaemia. The principles of treatment largely follow those of the general population with certain notable exceptions.
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Affiliation(s)
- Richard I G Holt
- Human Development and Health, Faculty of Medicine, University of Southampton, Southampton, UK.
- Southampton National Institute for Health Research Biomedical Research Centre, University Hospital Southampton NHS Foundation Trust, Southampton, UK.
| | - Clive S Cockram
- Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Hong Kong Special Administrative Region, People's Republic of China
| | - Ronald C W Ma
- Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Hong Kong Special Administrative Region, People's Republic of China
- Laboratory for Molecular Epidemiology in Diabetes, Li Ka Shing Institute of Health Sciences, The Chinese University of Hong Kong, Hong Kong Special Administrative Region, People's Republic of China
- Hong Kong Institute of Diabetes and Obesity, The Chinese University of Hong Kong, Hong Kong Special Administrative Region, People's Republic of China
| | - Andrea O Y Luk
- Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Hong Kong Special Administrative Region, People's Republic of China
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Xu R, Liu S, Li LY, Bu Y, Bai PM, Luo GC, Wang XJ. Exploring the causal association between serum metabolites and erectile dysfunction: a bidirectional Mendelian randomisation study. Int J Impot Res 2024:10.1038/s41443-024-00926-2. [PMID: 38858529 DOI: 10.1038/s41443-024-00926-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2024] [Revised: 05/23/2024] [Accepted: 05/31/2024] [Indexed: 06/12/2024]
Abstract
Erectile dysfunction is a common sexual disorder in men. Some studies have found a strong association between some serum metabolites and erectile dysfunction. To investigate this association further, we used bidirectional Mendelian randomisation to investigate causality and possible biological mechanisms.Firstly, this study screened the statistics of genome-wide association studies of serum metabolites and erectile dysfunction to obtain instrumental variables. Inverse variance weighting was used as the primary method for causal effect analysis of instrumental variables in forward or reverse Mendelian randomisation, and the results obtained by MR-Egger regression and the weighted median method were used as references. Subsequently, the metabolites causally associated with erectile dysfunction were subjected to replication analyses and meta-analyses, and the results of the meta-analyses were analysed by pathway analyses to find influential pathways. In this process, Mendelian randomisation results need to be assessed for stability and reliability using sensitivity analysis.It was found that a total of six serum metabolites were causally associated with erectile dysfunction in a forward Mendelian randomisation study. 1,3,7-trimethyluraten (0.85 (0.73-0.99), P = 0.0368), ergothioneine (0.65 (0.45-0.94), P = 0.0226) and gamma-glutamylglutamate (0.63 (0.46-0.88), P = 0.0059) were protective against the development of erectile dysfunction, whereas 2-hydroxyhippurate (1.10 (1.02-1.19), P = 0.0152), N2,N2-dimethylguanosine (1.57 (1.02-2.40), P = 0.0395) and octanoylcarnitine (1.38 (1.06-1.82), P = 0.0183) were able to induce the development of erectile dysfunction. In addition, metabolic pathway analysis showed that 1,3,7-trimethylurate was able to influence the development of erectile dysfunction via the caffeine metabolism pathway (P = 0.0454). On the other hand, reverse Mendelian randomisation analysis showed that erectile dysfunction reduced serum homocitrulline levels (0.99 (0.97-1.00), P = 0.0360). Sensitivity analyses, including heterogeneity tests and pleiotropy tests, confirmed the reliability of the results.In conclusion, this study demonstrated a bidirectional causal relationship between serum metabolites and erectile dysfunction using bidirectional Mendelian randomisation analysis and replication meta-analysis. On this basis, this study provides a new direction of thinking and strong evidence for the therapeutic application and adjunctive diagnosis of serum metabolites in erectile dysfunction, and provides a certain reference value for subsequent related studies.
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Affiliation(s)
- Ran Xu
- Department of Urology, Zhongshan Hospital Xiamen University, School of Medicine, Xiamen University, Xiamen, China
| | - Shuo Liu
- Department of Urology, Zhongshan Hospital Xiamen University, School of Medicine, Xiamen University, Xiamen, China
| | - Lu-Yi Li
- Department of Urology, Zhongshan Hospital Xiamen University, School of Medicine, Xiamen University, Xiamen, China
| | - Yue Bu
- Department of Urology, Zhongshan Hospital Xiamen University, School of Medicine, Xiamen University, Xiamen, China
| | - Pei-Ming Bai
- Department of Urology, Zhongshan Hospital Xiamen University, School of Medicine, Xiamen University, Xiamen, China
| | - Guang-Cheng Luo
- Department of Urology, Zhongshan Hospital Xiamen University, School of Medicine, Xiamen University, Xiamen, China
- Department of Urology, Zhongshan Hospital Xiamen University, The School of Clinical Medicine, Fujian Medical University, Xiamen, China
| | - Xin-Jun Wang
- Department of Urology, Zhongshan Hospital Xiamen University, School of Medicine, Xiamen University, Xiamen, China.
- Department of Urology, Zhongshan Hospital Xiamen University, The School of Clinical Medicine, Fujian Medical University, Xiamen, China.
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6
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Haslam DE, Liang L, Guo K, Martínez-Lozano M, Pérez CM, Lee CH, Morou-Bermudez E, Clish C, Wong DTW, Manson JE, Hu FB, Stampfer MJ, Joshipura K, Bhupathiraju SN. Discovery and validation of plasma, saliva and multi-fluid plasma-saliva metabolomic scores predicting insulin resistance and diabetes progression or regression among Puerto Rican adults. Diabetologia 2024:10.1007/s00125-024-06169-6. [PMID: 38772919 DOI: 10.1007/s00125-024-06169-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/15/2023] [Accepted: 03/21/2024] [Indexed: 05/23/2024]
Abstract
AIMS/HYPOTHESIS Many studies have examined the relationship between plasma metabolites and type 2 diabetes progression, but few have explored saliva and multi-fluid metabolites. METHODS We used LC/MS to measure plasma (n=1051) and saliva (n=635) metabolites among Puerto Rican adults from the San Juan Overweight Adults Longitudinal Study. We used elastic net regression to identify plasma, saliva and multi-fluid plasma-saliva metabolomic scores predicting baseline HOMA-IR in a training set (n=509) and validated these scores in a testing set (n=340). We used multivariable Cox proportional hazards models to estimate HRs for the association of baseline metabolomic scores predicting insulin resistance with incident type 2 diabetes (n=54) and prediabetes (characterised by impaired glucose tolerance, impaired fasting glucose and/or high HbA1c) (n=130) at 3 years, along with regression from prediabetes to normoglycaemia (n=122), adjusting for traditional diabetes-related risk factors. RESULTS Plasma, saliva and multi-fluid plasma-saliva metabolomic scores predicting insulin resistance included highly weighted metabolites from fructose, tyrosine, lipid and amino acid metabolism. Each SD increase in the plasma (HR 1.99 [95% CI 1.18, 3.38]; p=0.01) and multi-fluid (1.80 [1.06, 3.07]; p=0.03) metabolomic scores was associated with higher risk of type 2 diabetes. The saliva metabolomic score was associated with incident prediabetes (1.48 [1.17, 1.86]; p=0.001). All three metabolomic scores were significantly associated with lower likelihood of regressing from prediabetes to normoglycaemia in models adjusting for adiposity (HRs 0.72 for plasma, 0.78 for saliva and 0.72 for multi-fluid), but associations were attenuated when adjusting for lipid and glycaemic measures. CONCLUSIONS/INTERPRETATION The plasma metabolomic score predicting insulin resistance was more strongly associated with incident type 2 diabetes than the saliva metabolomic score. Only the saliva metabolomic score was associated with incident prediabetes.
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Affiliation(s)
- Danielle E Haslam
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Liming Liang
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Kai Guo
- Center for Clinical Research and Health Promotion, Graduate School of Public Health, University of Puerto Rico Medical Sciences Campus, San Juan, Puerto Rico
| | - Marijulie Martínez-Lozano
- Center for Clinical Research and Health Promotion, Graduate School of Public Health, University of Puerto Rico Medical Sciences Campus, San Juan, Puerto Rico
| | - Cynthia M Pérez
- Department of Biostatistics and Epidemiology, Graduate School of Public Health, University of Puerto Rico Medical Sciences Campus, San Juan, Puerto Rico
| | - Chih-Hao Lee
- Department of Molecular Metabolism, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Evangelia Morou-Bermudez
- School of Dental Medicine, University of Puerto Rico Medical Sciences Campus, San Juan, Puerto Rico
| | - Clary Clish
- Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA, USA
| | - David T W Wong
- Center for Oral/Head and Neck Oncology Research, School of Dentistry, University of California Los Angeles, Los Angeles, CA, USA
| | - JoAnn E Manson
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Division of Preventive Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Frank B Hu
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Meir J Stampfer
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Kaumudi Joshipura
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Center for Clinical Research and Health Promotion, Graduate School of Public Health, University of Puerto Rico Medical Sciences Campus, San Juan, Puerto Rico
| | - Shilpa N Bhupathiraju
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA.
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
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7
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Zhang J, Qi H, Li M, Wang Z, Jia X, Sun T, Du S, Su C, Zhi M, Du W, Ouyang Y, Wang P, Huang F, Jiang H, Li L, Bai J, Wei Y, Zhang X, Wang H, Zhang B, Feng Q. Diet Mediate the Impact of Host Habitat on Gut Microbiome and Influence Clinical Indexes by Modulating Gut Microbes and Serum Metabolites. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2024; 11:e2310068. [PMID: 38477427 PMCID: PMC11109649 DOI: 10.1002/advs.202310068] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/27/2023] [Revised: 02/04/2024] [Indexed: 03/14/2024]
Abstract
The impact of external factors on the human gut microbiota and how gut microbes contribute to human health is an intriguing question. Here, the gut microbiome of 3,224 individuals (496 with serum metabolome) with 109 variables is studied. Multiple analyses reveal that geographic factors explain the greatest variance of the gut microbiome and the similarity of individuals' gut microbiome is negatively correlated with their geographic distance. Main food components are the most important factors that mediate the impact of host habitats on the gut microbiome. Diet and gut microbes collaboratively contribute to the variation of serum metabolites, and correlate to the increase or decrease of certain clinical indexes. Specifically, systolic blood pressure is lowered by vegetable oil through increasing the abundance of Blautia and reducing the serum level of 1-palmitoyl-2-palmitoleoyl-GPC (16:0/16:1), but it is reduced by fruit intake through increasing the serum level of Blautia improved threonate. Besides, aging-related clinical indexes are also closely correlated with the variation of gut microbes and serum metabolites. In this study, the linkages of geographic locations, diet, the gut microbiome, serum metabolites, and physiological indexes in a Chinese population are characterized. It is proved again that gut microbes and their metabolites are important media for external factors to affect human health.
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Affiliation(s)
- Jiguo Zhang
- National Institute for Nutrition and HealthChinese Center for Disease Control and PreventionBeijing100050China
- Key Laboratory of Trace Element NutritionNational Health CommissionBeijing100050China
| | - Houbao Qi
- Department of Human MicrobiomeSchool and Hospital of StomatologyCheeloo College of MedicineSD University & SD Key Laboratory of Oral Tissue Regeneration & SD Engineering Laboratory for Dental Materials and Oral Tissue RegenerationJinan250012China
| | - Meihui Li
- Department of Human MicrobiomeSchool and Hospital of StomatologyCheeloo College of MedicineSD University & SD Key Laboratory of Oral Tissue Regeneration & SD Engineering Laboratory for Dental Materials and Oral Tissue RegenerationJinan250012China
| | - Zhihong Wang
- National Institute for Nutrition and HealthChinese Center for Disease Control and PreventionBeijing100050China
- Key Laboratory of Trace Element NutritionNational Health CommissionBeijing100050China
| | - Xiaofang Jia
- National Institute for Nutrition and HealthChinese Center for Disease Control and PreventionBeijing100050China
- Key Laboratory of Trace Element NutritionNational Health CommissionBeijing100050China
| | - Tianyong Sun
- Department of Human MicrobiomeSchool and Hospital of StomatologyCheeloo College of MedicineSD University & SD Key Laboratory of Oral Tissue Regeneration & SD Engineering Laboratory for Dental Materials and Oral Tissue RegenerationJinan250012China
| | - Shufa Du
- Department of NutritionGillings School of Global Public HealthUniversity of North Carolina at Chapel HillChapel HillNC27599USA
| | - Chang Su
- National Institute for Nutrition and HealthChinese Center for Disease Control and PreventionBeijing100050China
- Key Laboratory of Trace Element NutritionNational Health CommissionBeijing100050China
| | - Mengfan Zhi
- Department of Human MicrobiomeSchool and Hospital of StomatologyCheeloo College of MedicineSD University & SD Key Laboratory of Oral Tissue Regeneration & SD Engineering Laboratory for Dental Materials and Oral Tissue RegenerationJinan250012China
| | - Wenwen Du
- National Institute for Nutrition and HealthChinese Center for Disease Control and PreventionBeijing100050China
- Key Laboratory of Trace Element NutritionNational Health CommissionBeijing100050China
| | - Yifei Ouyang
- National Institute for Nutrition and HealthChinese Center for Disease Control and PreventionBeijing100050China
- Key Laboratory of Trace Element NutritionNational Health CommissionBeijing100050China
| | - Pingping Wang
- Department of Human MicrobiomeSchool and Hospital of StomatologyCheeloo College of MedicineSD University & SD Key Laboratory of Oral Tissue Regeneration & SD Engineering Laboratory for Dental Materials and Oral Tissue RegenerationJinan250012China
| | - Feifei Huang
- National Institute for Nutrition and HealthChinese Center for Disease Control and PreventionBeijing100050China
- Key Laboratory of Trace Element NutritionNational Health CommissionBeijing100050China
| | - Hongru Jiang
- National Institute for Nutrition and HealthChinese Center for Disease Control and PreventionBeijing100050China
- Key Laboratory of Trace Element NutritionNational Health CommissionBeijing100050China
| | - Li Li
- National Institute for Nutrition and HealthChinese Center for Disease Control and PreventionBeijing100050China
- Key Laboratory of Trace Element NutritionNational Health CommissionBeijing100050China
| | - Jing Bai
- National Institute for Nutrition and HealthChinese Center for Disease Control and PreventionBeijing100050China
- Key Laboratory of Trace Element NutritionNational Health CommissionBeijing100050China
| | - Yanli Wei
- National Institute for Nutrition and HealthChinese Center for Disease Control and PreventionBeijing100050China
- Key Laboratory of Trace Element NutritionNational Health CommissionBeijing100050China
| | - Xiaofan Zhang
- National Institute for Nutrition and HealthChinese Center for Disease Control and PreventionBeijing100050China
- Key Laboratory of Trace Element NutritionNational Health CommissionBeijing100050China
| | - Huijun Wang
- National Institute for Nutrition and HealthChinese Center for Disease Control and PreventionBeijing100050China
- Key Laboratory of Trace Element NutritionNational Health CommissionBeijing100050China
| | - Bing Zhang
- National Institute for Nutrition and HealthChinese Center for Disease Control and PreventionBeijing100050China
- Key Laboratory of Trace Element NutritionNational Health CommissionBeijing100050China
| | - Qiang Feng
- Department of Human MicrobiomeSchool and Hospital of StomatologyCheeloo College of MedicineSD University & SD Key Laboratory of Oral Tissue Regeneration & SD Engineering Laboratory for Dental Materials and Oral Tissue RegenerationJinan250012China
- State key laboratory of microbial technologySD UniversityQingdao266237China
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8
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Zhang J, Wang H, Liu Y, Shi M, Zhang M, Zhang H, Chen J. Advances in fecal microbiota transplantation for the treatment of diabetes mellitus. Front Cell Infect Microbiol 2024; 14:1370999. [PMID: 38660489 PMCID: PMC11039806 DOI: 10.3389/fcimb.2024.1370999] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2024] [Accepted: 03/27/2024] [Indexed: 04/26/2024] Open
Abstract
Diabetes mellitus (DM) refers to a group of chronic diseases with global prevalence, characterized by persistent hyperglycemia resulting from various etiologies. DM can harm various organ systems and lead to acute or chronic complications, which severely endanger human well-being. Traditional treatment mainly involves controlling blood sugar levels through replacement therapy with drugs and insulin; however, some patients still find a satisfactory curative effect difficult to achieve. Extensive research has demonstrated a close correlation between enteric dysbacteriosis and the pathogenesis of various types of DM, paving the way for novel therapeutic approaches targeting the gut microbiota to manage DM. Fecal microbiota transplantation (FMT), a method for re-establishing the intestinal microbiome balance, offers new possibilities for treating diabetes. This article provides a comprehensive review of the correlation between DM and the gut microbiota, as well as the current advancements in FMT treatment for DM, using FMT as an illustrative example. This study aims to offer novel perspectives and establish a theoretical foundation for the clinical diagnosis and management of DM.
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Affiliation(s)
- Juan Zhang
- Department of Endocrinology, the Affiliated Huai’an No.1 People’s Hospital of Nanjing Medical University, Huai’an, Jiangsu, China
| | - Honggang Wang
- Department of Gastroenterology, the Affiliated Huai’an No.1 People’s Hospital of Nanjing Medical University, Huai’an, Jiangsu, China
| | - Ying Liu
- Department of Endocrinology, the Affiliated Huai’an No.1 People’s Hospital of Nanjing Medical University, Huai’an, Jiangsu, China
| | - Min Shi
- Department of Endocrinology, the Affiliated Huai’an No.1 People’s Hospital of Nanjing Medical University, Huai’an, Jiangsu, China
| | - Minna Zhang
- Department of Gastroenterology, the Affiliated Huai’an No.1 People’s Hospital of Nanjing Medical University, Huai’an, Jiangsu, China
| | - Hong Zhang
- Department of Endocrinology, the Affiliated Huai’an No.1 People’s Hospital of Nanjing Medical University, Huai’an, Jiangsu, China
| | - Juan Chen
- Department of Endocrinology, the Affiliated Huai’an No.1 People’s Hospital of Nanjing Medical University, Huai’an, Jiangsu, China
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9
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Mirzaei S, DeVon HA, Cantor RM, Cupido AJ, Pan C, Ha SM, Silva LF, Hilser JR, Hartiala J, Allayee H, Rey FE, Laakso M, Lusis AJ. Relationships and Mendelian Randomization of Gut Microbe-Derived Metabolites with Metabolic Syndrome Traits in the METSIM Cohort. Metabolites 2024; 14:174. [PMID: 38535334 PMCID: PMC10972019 DOI: 10.3390/metabo14030174] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2024] [Revised: 03/13/2024] [Accepted: 03/15/2024] [Indexed: 07/17/2024] Open
Abstract
The role of gut microbe-derived metabolites in the development of metabolic syndrome (MetS) remains unclear. This study aimed to evaluate the associations of gut microbe-derived metabolites and MetS traits in the cross-sectional Metabolic Syndrome In Men (METSIM) study. The sample included 10,194 randomly related men (age 57.65 ± 7.12 years) from Eastern Finland. Levels of 35 metabolites were tested for associations with 13 MetS traits using lasso and stepwise regression. Significant associations were observed between multiple MetS traits and 32 metabolites, three of which exhibited particularly robust associations. N-acetyltryptophan was positively associated with Homeostatic Model Assessment for Insulin Resistant (HOMA-IR) (β = 0.02, p = 0.033), body mass index (BMI) (β = 0.025, p = 1.3 × 10-16), low-density lipoprotein cholesterol (LDL-C) (β = 0.034, p = 5.8 × 10-10), triglyceride (0.087, p = 1.3 × 10-16), systolic (β = 0.012, p = 2.5 × 10-6) and diastolic blood pressure (β = 0.011, p = 3.4 × 10-6). In addition, 3-(4-hydroxyphenyl) lactate yielded the strongest positive associations among all metabolites, for example, with HOMA-IR (β = 0.23, p = 4.4 × 10-33), and BMI (β = 0.097, p = 5.1 × 10-52). By comparison, 3-aminoisobutyrate was inversely associated with HOMA-IR (β = -0.19, p = 3.8 × 10-51) and triglycerides (β = -0.12, p = 5.9 × 10-36). Mendelian randomization analyses did not provide evidence that the observed associations with these three metabolites represented causal relationships. We identified significant associations between several gut microbiota-derived metabolites and MetS traits, consistent with the notion that gut microbes influence metabolic homeostasis, beyond traditional risk factors.
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Affiliation(s)
- Sahereh Mirzaei
- Department of Medicine, Division of Cardiology, David Geffen School of Medicine, University of California, Los Angeles, CA 90055, USA
- School of Nursing, University of California, Los Angeles, CA 90095, USA
| | - Holli A. DeVon
- School of Nursing, University of California, Los Angeles, CA 90095, USA
| | - Rita M. Cantor
- Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, CA 90095, USA
| | - Arjen J. Cupido
- Department of Vascular Medicine, Amsterdam University Medical Centers, University of Amsterdam, Amsterdam Cardiovascular Sciences, 1007 AZ Amsterdam, The Netherlands
| | - Calvin Pan
- Department of Medicine, Division of Cardiology, David Geffen School of Medicine, University of California, Los Angeles, CA 90055, USA
| | - Sung Min Ha
- Department of Integrative Biology and Physiology, University of California, Los Angeles, CA 90095, USA
| | - Lilian Fernandes Silva
- Department of Medicine, Division of Cardiology, David Geffen School of Medicine, University of California, Los Angeles, CA 90055, USA
- Department of Clinical Medicine, Internal Medicine, University of Eastern Finland, 70210 Kuopio, Finland
| | - James R. Hilser
- Department of Population & Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA 90032, USA
- Department of Biochemistry & Molecular Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA 90033, USA
| | - Jaana Hartiala
- Department of Population & Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA 90032, USA
| | - Hooman Allayee
- Department of Population & Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA 90032, USA
- Department of Biochemistry & Molecular Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA 90033, USA
| | - Federico E. Rey
- Department of Bacteriology, University of Wisconsin-Madison, Madison, WI 53706, USA
| | - Markku Laakso
- Department of Clinical Medicine, Internal Medicine, University of Eastern Finland, 70210 Kuopio, Finland
| | - Aldons J. Lusis
- Department of Medicine, Division of Cardiology, David Geffen School of Medicine, University of California, Los Angeles, CA 90055, USA
- Department of Human Genetics and Microbiology, Immunology & Molecular Genetics, David Geffen School of Medicine, University of California, Los Angeles, CA 90095, USA
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10
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Zhou Z, Yao Y, Sun Y, Wang X, Huang S, Hou J, Wang L, Wei F. Serum betaine and dimethylglycine in mid-pregnancy and the risk of gestational diabetes mellitus: a case-control study. Endocrine 2024:10.1007/s12020-024-03732-4. [PMID: 38448678 DOI: 10.1007/s12020-024-03732-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/02/2023] [Accepted: 02/04/2024] [Indexed: 03/08/2024]
Abstract
PURPOSE To investigate the associations of choline, betaine, dimethylglycine (DMG), L-carnitine, and Trimethylamine-N-oxide (TMAO) with the risk of Gestational diabetes mellitus (GDM) as well as the markers of glucose homeostasis. METHODS We performed a case-control study including 200 diagnosed GDM cases and 200 controls matched by maternal age (±2 years) and gestational age (±2 weeks). Concentrations of serum metabolites were measured by the high-performance liquid chromatography - tandem mass spectrometry (HPLC-MS/MS). RESULTS Compared to the control group, GDM group had significantly lower serum betaine concentration and betaine/choline ratio, and higher DMG concentration. Furthermore, decreased betaine concentration and betaine/choline ratio, increased DMG concentration showed significant association with the risk of GDM. In addition, serum betaine concentrations were negatively associated with blood glucose levels at 1-h post-glucose load (OGTT-1h), and both betaine and L-carnitine concentrations were positively associated with 1,5-anhydroglucitol levels. Betaine/choline ratio was negatively associated with OGTT-1h and blood glucose levels at 2-h post-glucose load (OGTT-2h) and serum choline concentrations were negatively associated with fasting blood glucose and positively associated with OGTT-2h. CONCLUSION Decreased serum betaine concentrations and betaine/choline ratio, and elevated DMG concentrations could be significant risk factors for GDM. Furthermore, betaine may be associated with blood glucose regulation and short-term glycemic fluctuations.
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Affiliation(s)
- Ziqing Zhou
- Department of Maternal, Child and Adolescent Health, School of Public Health, Anhui Medical University, Hefei, Anhui Province, China
- The Genetics Laboratory, Longgang District Maternity & Child Healthcare Hospital of Shenzhen City (Longgang Maternity and Child Institute of Shantou University Medical College), Shenzhen, Guangdong Province, China
| | - Yao Yao
- The Genetics Laboratory, Longgang District Maternity & Child Healthcare Hospital of Shenzhen City (Longgang Maternity and Child Institute of Shantou University Medical College), Shenzhen, Guangdong Province, China
| | - Yanan Sun
- The Genetics Laboratory, Longgang District Maternity & Child Healthcare Hospital of Shenzhen City (Longgang Maternity and Child Institute of Shantou University Medical College), Shenzhen, Guangdong Province, China
- Medical Insurance Office of Shenzhen Longgang Central Hospital, Shenzhen, Guangdong Province, China
| | - Xin Wang
- The Genetics Laboratory, Longgang District Maternity & Child Healthcare Hospital of Shenzhen City (Longgang Maternity and Child Institute of Shantou University Medical College), Shenzhen, Guangdong Province, China
- Jiamusi University, Jiamusi, Heilongjiang Province, China
| | - Shang Huang
- The Genetics Laboratory, Longgang District Maternity & Child Healthcare Hospital of Shenzhen City (Longgang Maternity and Child Institute of Shantou University Medical College), Shenzhen, Guangdong Province, China
- Shenzhen Children's Hospital of China Medical University, Shenzhen, Guangdong Province, China
| | - Jianli Hou
- Department of Gynecology and Obstetrics, Longgang District Maternity & Child Healthcare Hospital of Shenzhen City (Longgang Maternity and Child Institute of Shantou University Medical College), Shenzhen, Guangdong Province, China
| | - Lijun Wang
- Department of Nutrition, School of Medicine, Jinan University, Guangzhou, Guangdong Province, China.
| | - Fengxiang Wei
- Department of Maternal, Child and Adolescent Health, School of Public Health, Anhui Medical University, Hefei, Anhui Province, China.
- The Genetics Laboratory, Longgang District Maternity & Child Healthcare Hospital of Shenzhen City (Longgang Maternity and Child Institute of Shantou University Medical College), Shenzhen, Guangdong Province, China.
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11
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Das S, Devi Rajeswari V, Venkatraman G, Elumalai R, Dhanasekaran S, Ramanathan G. Current updates on metabolites and its interlinked pathways as biomarkers for diabetic kidney disease: A systematic review. Transl Res 2024; 265:71-87. [PMID: 37952771 DOI: 10.1016/j.trsl.2023.11.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/04/2023] [Revised: 11/09/2023] [Accepted: 11/09/2023] [Indexed: 11/14/2023]
Abstract
Diabetic kidney disease (DKD) is a major microvascular complication of diabetes mellitus (DM) that poses a serious risk as it can lead to end-stage renal disease (ESRD). DKD is linked to changes in the diversity, composition, and functionality of the microbiota present in the gastrointestinal tract. The interplay between the gut microbiota and the host organism is primarily facilitated by metabolites generated by microbial metabolic processes from both dietary substrates and endogenous host compounds. The production of numerous metabolites by the gut microbiota is a crucial factor in the pathogenesis of DKD. However, a comprehensive understanding of the precise mechanisms by which gut microbiota and its metabolites contribute to the onset and progression of DKD remains incomplete. This review will provide a summary of the current scenario of metabolites in DKD and the impact of these metabolites on DKD progression. We will discuss in detail the primary and gut-derived metabolites in DKD, and the mechanisms of the metabolites involved in DKD progression. Further, we will address the importance of metabolomics in helping identify potential DKD markers. Furthermore, the possible therapeutic interventions and research gaps will be highlighted.
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Affiliation(s)
- Soumik Das
- School of Biosciences and Technology, Vellore Institute of Technology (VIT), Vellore, Tamil Nadu 632014, India
| | - V Devi Rajeswari
- School of Biosciences and Technology, Vellore Institute of Technology (VIT), Vellore, Tamil Nadu 632014, India
| | - Ganesh Venkatraman
- School of Biosciences and Technology, Vellore Institute of Technology (VIT), Vellore, Tamil Nadu 632014, India
| | - Ramprasad Elumalai
- Department of Nephrology, Sri Ramachandra Institute of Higher Education and Research, Porur, Chennai, Tamil Nadu 600116, India
| | - Sivaraman Dhanasekaran
- School of Energy Technology, Pandit Deendayal Energy University, Knowledge Corridor, Raisan Village, PDPU Road, Gandhinagar, Gujarat 382426, India
| | - Gnanasambandan Ramanathan
- School of Biosciences and Technology, Vellore Institute of Technology (VIT), Vellore, Tamil Nadu 632014, India.
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12
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Ren S, Wu D, Li P. Evaluation of insulin secretion and insulin sensitivity in pregnant women: Application value of simple indices. Clin Chim Acta 2024; 554:117753. [PMID: 38185282 DOI: 10.1016/j.cca.2023.117753] [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: 06/15/2023] [Revised: 12/29/2023] [Accepted: 12/29/2023] [Indexed: 01/09/2024]
Abstract
The prevalence of gestational diabetes mellitus (GDM) is increasing annually, which poses substantial harm to the health of both mothers and children. Therefore, selection of clinically applicable and easily detectable indicators in the assessment of maternal insulin secretory function and insulin sensitivity in pregnant women undoubtedly holds great importance in evaluating the risk of GDM, guiding the choice of GDM therapy modalities, and improving the ability to provide early warning of adverse pregnancy outcomes. Compared with the classic clamp technique, many simple indices are more suited for use among pregnant women due to the low frequency of blood sampling and simple administration involved. While indices derived from fasting blood glucose and fasting insulin levels are most readily available, they are unable to provide information on the ability of insulin to manage the glucose load during pregnancy. Although the indices derived from the insulin and glucose values at each time point of the oral glucose tolerance test can provide a more comprehensive picture of the insulin sensitivity and insulin secretory function of the body, their application is constrained by the complexity of the procedure and associated high costs. Concomitantly, the findings from different studies are influenced by a variety of confounding factors, such as the gestational age during testing, race, and detection method. Furthermore, insulin secretory function and insulin sensitivity in pregnant women differ from those in non-pregnant women in that they change significantly with prolonged pregnancy; hence, there is an urgent need to develop a pregnancy-specific reference range. This article reviews the progress in the application of simple indices to help clinicians better understand their potential application in detecting GDM.
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Affiliation(s)
- Shuying Ren
- Department of Endocrinology, Shengjing Hospital of China Medical University, Shenyang, Liaoning Province, People's Republic of China
| | - Dan Wu
- Department of Endocrinology, 242 Hospital Affilliated to Shenyang Medical College, Shenyang, Liaoning Province, People's Republic of China
| | - Ping Li
- Department of Endocrinology, Shengjing Hospital of China Medical University, Shenyang, Liaoning Province, People's Republic of China.
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13
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Zhang B, Zhang X, Luo Z, Ren J, Yu X, Zhao H, Wang Y, Zhang W, Tian W, Wei X, Ding Q, Yang H, Jin Z, Tong X, Wang J, Zhao L. Microbiome and metabolome dysbiosis analysis in impaired glucose tolerance for the prediction of progression to diabetes mellitus. J Genet Genomics 2024; 51:75-86. [PMID: 37652264 DOI: 10.1016/j.jgg.2023.08.005] [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: 04/29/2023] [Revised: 08/20/2023] [Accepted: 08/21/2023] [Indexed: 09/02/2023]
Abstract
Gut microbiota and circulating metabolite dysbiosis predate important pathological changes in glucose metabolic disorders; however, comprehensive studies on impaired glucose tolerance (IGT), a diabetes mellitus (DM) precursor, are lacking. Here, we perform metagenomic sequencing and metabolomics on 47 pairs of individuals with IGT and newly diagnosed DM and 46 controls with normal glucose tolerance (NGT); patients with IGT are followed up after 4 years for progression to DM. Analysis of baseline data reveals significant differences in gut microbiota and serum metabolites among the IGT, DM, and NGT groups. In addition, 13 types of gut microbiota and 17 types of circulating metabolites showed significant differences at baseline before IGT progressed to DM, including higher levels of Eggerthella unclassified, Coprobacillus unclassified, Clostridium ramosum, L-valine, L-norleucine, and L-isoleucine, and lower levels of Eubacterium eligens, Bacteroides faecis, Lachnospiraceae bacterium 3_1_46FAA, Alistipes senegalensis, Megaspaera elsdenii, Clostridium perfringens, α-linolenic acid, 10E,12Z-octadecadienoic acid, and dodecanoic acid. A random forest model based on differential intestinal microbiota and circulating metabolites can predict the progression from IGT to DM (AUC = 0.87). These results suggest that microbiome and metabolome dysbiosis occur in individuals with IGT and have important predictive values and potential for intervention in preventing IGT from progressing to DM.
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Affiliation(s)
- Boxun Zhang
- Institute of Metabolic Diseases, Guang'anmen Hospital, China Academy of Chinese Medical Sciences, Beijing 100053, China
| | - Xuan Zhang
- Faculty of Biological Science and Technology, Baotou Teacher's College, Baotou, Inner Mongolia 014030, China; CAS Key Laboratory of Pathogenic Microbiology and Immunology, Institute of Microbiology, Chinese Academy of Sciences, Beijing 100101, China
| | - Zhen Luo
- Infinitus (China) Company Ltd, Guangzhou, Guangdong 510405, China
| | - Jixiang Ren
- Affiliated Hospital of Changchun University of Chinese Medicine, Changchun, Jilin 130021, China
| | - Xiaotong Yu
- Department of Endocrinology, Guang'anmen Hospital, China Academy of Chinese Medical Sciences, Beijing 100053, China
| | - Haiyan Zhao
- Xinjiekou Community Health Service Center in Xicheng District, Beijing 100035, China
| | - Yitian Wang
- Department of Spleen and Stomach, Shenzhen Traditional Chinese Medicine Hospital, Shenzhen, Guangdong 518033, China
| | - Wenhui Zhang
- CAS Key Laboratory of Pathogenic Microbiology and Immunology, Institute of Microbiology, Chinese Academy of Sciences, Beijing 100101, China; University of Chinese Academy of Sciences, Beijing 100049, China
| | - Weiwei Tian
- Xinjiekou Community Health Service Center in Xicheng District, Beijing 100035, China
| | - Xiuxiu Wei
- Beijing University of Chinese Medicine, Beijing 100105, China
| | - Qiyou Ding
- Institute of Metabolic Diseases, Guang'anmen Hospital, China Academy of Chinese Medical Sciences, Beijing 100053, China
| | - Haoyu Yang
- Institute of Metabolic Diseases, Guang'anmen Hospital, China Academy of Chinese Medical Sciences, Beijing 100053, China
| | - Zishan Jin
- Institute of Metabolic Diseases, Guang'anmen Hospital, China Academy of Chinese Medical Sciences, Beijing 100053, China; Beijing University of Chinese Medicine, Beijing 100105, China
| | - Xiaolin Tong
- Institute of Metabolic Diseases, Guang'anmen Hospital, China Academy of Chinese Medical Sciences, Beijing 100053, China; Northeast Asia Institute of Traditional Chinese Medicine, Changchun University of Chinese Medicine, Changchun, Jilin 130117, China.
| | - Jun Wang
- CAS Key Laboratory of Pathogenic Microbiology and Immunology, Institute of Microbiology, Chinese Academy of Sciences, Beijing 100101, China; University of Chinese Academy of Sciences, Beijing 100049, China.
| | - Linhua Zhao
- Institute of Metabolic Diseases, Guang'anmen Hospital, China Academy of Chinese Medical Sciences, Beijing 100053, China.
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14
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Agarwal J, Pandey P, Saxena SK, Kumar S. Comparative analysis of salivary microbiota in diabetic and non-diabetic individuals of North India using metagenomics. J Oral Biol Craniofac Res 2024; 14:22-26. [PMID: 38130425 PMCID: PMC10733697 DOI: 10.1016/j.jobcr.2023.12.003] [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: 05/01/2023] [Revised: 10/28/2023] [Accepted: 12/05/2023] [Indexed: 12/23/2023] Open
Abstract
Background Saliva, an oral secretion is considered an essential biological modulator involved in maintaining oral homeostasis. Increased glucose levels in diabetic patients' saliva may have an impact on diversity of microbes. Comparing the salivary microflora of diabetic and non-diabetic cohorts will help in diagnosis and risk assessment of oral health complications. This will provide greater knowledge about the contribution of oral microbes to the development of oral illnesses. The association between salivary microbiota and diabetic state is less explored in the North Indian population, hence current observational study was performed to analyze the salivary microflora of diabetic and non-diabetic individuals using metagenomic analysis. Materials and methods This single-center non-randomized observational trial was conducted in Uttar Pradesh, India. Participants were enrolled into either diabetic (n = 68) or non-diabetic groups (n = 68) based on their diabetes status. Following saliva collection, DNA was extracted and metagenomic sequencing was performed. Results Phylum Bacteroidetes and Fusobacteria were significantly abundant in diabetic individuals (p < 0.0001), while Proteobacteria was significantly higher among non-diabetic individuals (p < 0.0001). No statistical difference in phylum Actinobacteria and Firmicutes among diabetics and non-diabetics. Veillonella, Prevotella, Porphyromonas, Leptotrichia, Lactobacillus, and Streptococcus were greater in diabetics whereas the abundance of Capnocytophaga and Neisseria was more among non-diabetics (p < 0.05). Conclusions The genera Veillonella, Prevotella, Porphyromonas, Leptotrichia, Lactobacillus, and Streptococcus were comparatively over the odds with the diabetics in India. The association between microbiota in diabetic population and risk related to increase in occurrence of caries, gingivitis, and periodontitis in diabetic population prevalence should be investigated.
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Affiliation(s)
- Jyotsana Agarwal
- Department of Conservative Dentistry & Endodontics, King George's Medical University, Lucknow, India
| | - Pragya Pandey
- Department of Conservative Dentistry & Endodontics, King George's Medical University, Lucknow, India
| | | | - Swatantra Kumar
- Centre for Advanced Research, King George's Medical University, Lucknow, India
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15
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Li J, Zhu N, Wang Y, Bao Y, Xu F, Liu F, Zhou X. Application of Metabolomics and Traditional Chinese Medicine for Type 2 Diabetes Mellitus Treatment. Diabetes Metab Syndr Obes 2023; 16:4269-4282. [PMID: 38164418 PMCID: PMC10758184 DOI: 10.2147/dmso.s441399] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/21/2023] [Accepted: 11/21/2023] [Indexed: 01/03/2024] Open
Abstract
Diabetes is a major global public health problem with high incidence and case fatality rates. Traditional Chinese medicine (TCM) is used to help manage Type 2 Diabetes Mellitus (T2DM) and has steadily gained international acceptance. Despite being generally accepted in daily practice, the TCM methods and hypotheses for understanding diseases lack applicability in the current scientific characterization systems. To date, there is no systematic evaluation system for TCM in preventing and treating T2DM. Metabonomics is a powerful tool to predict the level of metabolites in vivo, reveal the potential mechanism, and diagnose the physiological state of patients in time to guide the follow-up intervention of T2DM. Notably, metabolomics is also effective in promoting TCM modernization and advancement in personalized medicine. This review provides updated knowledge on applying metabolomics to TCM syndrome differentiation, diagnosis, biomarker discovery, and treatment of T2DM by TCM. Its application in diabetic complications is discussed. The combination of multi-omics and microbiome to fully elucidate the use of TCM to treat T2DM is further envisioned.
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Affiliation(s)
- Jing Li
- Jilin Ginseng Academy, Changchun University of Chinese Medicine, Changchun, People’s Republic of China
| | - Na Zhu
- Clinical Trial Research Center, Affiliated Qingdao Central Hospital of Qingdao University, Qingdao Central Hospital, Qingdao, People’s Republic of China
| | - Yaqiong Wang
- Clinical Trial Research Center, Affiliated Qingdao Central Hospital of Qingdao University, Qingdao Central Hospital, Qingdao, People’s Republic of China
| | - Yanlei Bao
- Department of Pharmacy, Liaoyuan People’s Hospital, Liaoyuan, People’s Republic of China
| | - Feng Xu
- Clinical Trial Research Center, Affiliated Qingdao Central Hospital of Qingdao University, Qingdao Central Hospital, Qingdao, People’s Republic of China
| | - Fengjuan Liu
- Clinical Trial Research Center, Affiliated Qingdao Central Hospital of Qingdao University, Qingdao Central Hospital, Qingdao, People’s Republic of China
| | - Xuefeng Zhou
- Clinical Trial Research Center, Affiliated Qingdao Central Hospital of Qingdao University, Qingdao Central Hospital, Qingdao, People’s Republic of China
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16
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Su X, Cheung CYY, Zhong J, Ru Y, Fong CHY, Lee CH, Liu Y, Cheung CKY, Lam KSL, Xu A, Cai Z. Ten metabolites-based algorithm predicts the future development of type 2 diabetes in Chinese. J Adv Res 2023:S2090-1232(23)00365-X. [PMID: 38030128 DOI: 10.1016/j.jare.2023.11.026] [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: 09/07/2023] [Revised: 11/10/2023] [Accepted: 11/21/2023] [Indexed: 12/01/2023] Open
Abstract
INTRODUCTION Type 2 diabetes (T2D) is a heterogeneous metabolic disease with large variations in the relative contributions of insulin resistance and β-cell dysfunction across different glucose tolerance subgroups and ethnicities. A more precise yet feasible approach to categorize risk preceding T2D onset is urgently needed. This study aimed to identify potential metabolic biomarkers that could contribute to the development of T2D and investigate whether their impact on T2D is mediated through insulin resistance and β-cell dysfunction. METHODS A non-targeted metabolomic analysis was performed in plasma samples of 196 incident T2D cases and 196 age- and sex-matched non-T2D controls recruited from a long-term prospective Chinese community-based cohort with a follow-up period of ∼ 16 years. RESULTS Metabolic profiles revealed profound perturbation of metabolomes before T2D onset. Overall metabolic shifts were strongly associated with insulin resistance rather than β-cell dysfunction. In addition, 188 out of the 578 annotated metabolites were associated with insulin resistance. Bi-directional mediation analysis revealed putative causal relationships among the metabolites, insulin resistance and T2D risk. We built a machine-learning based prediction model, integrating the conventional clinical risk factors (age, BMI, TyG index and 2hG) and 10 metabolites (acetyl-tryptophan, kynurenine, γ-glutamyl-phenylalanine, DG(18:2/22:6), DG(38:7), LPI(18:2), LPC(P-16:0), LPC(P-18:1), LPC(P-20:0) and LPE(P-20:0)) (AUROC = 0.894, 5.6% improvement comparing to the conventional clinical risk model), that successfully predicts the development of T2D. CONCLUSIONS Our findings support the notion that the metabolic changes resulting from insulin resistance, rather than β-cell dysfunction, are the primary drivers of T2D in Chinese adults. Metabolomes as a valuable phenotype hold potential clinical utility in the prediction of T2D.
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Affiliation(s)
- Xiuli Su
- State Key Laboratory of Environmental and Biological Analysis, Hong Kong Baptist University, Hong Kong, China
| | - Chloe Y Y Cheung
- Department of Medicine, The University of Hong Kong, Hong Kong, China; State Key Laboratory of Pharmaceutical Biotechnology, The University of Hong Kong, Hong Kong, China
| | - Junda Zhong
- Department of Medicine, The University of Hong Kong, Hong Kong, China; State Key Laboratory of Pharmaceutical Biotechnology, The University of Hong Kong, Hong Kong, China
| | - Yi Ru
- State Key Laboratory of Environmental and Biological Analysis, Hong Kong Baptist University, Hong Kong, China
| | - Carol H Y Fong
- Department of Medicine, The University of Hong Kong, Hong Kong, China; State Key Laboratory of Pharmaceutical Biotechnology, The University of Hong Kong, Hong Kong, China
| | - Chi-Ho Lee
- Department of Medicine, The University of Hong Kong, Hong Kong, China; State Key Laboratory of Pharmaceutical Biotechnology, The University of Hong Kong, Hong Kong, China
| | - Yan Liu
- Department of Medicine, The University of Hong Kong, Hong Kong, China; State Key Laboratory of Pharmaceutical Biotechnology, The University of Hong Kong, Hong Kong, China
| | - Cynthia K Y Cheung
- Department of Medicine, The University of Hong Kong, Hong Kong, China; State Key Laboratory of Pharmaceutical Biotechnology, The University of Hong Kong, Hong Kong, China
| | - Karen S L Lam
- Department of Medicine, The University of Hong Kong, Hong Kong, China; State Key Laboratory of Pharmaceutical Biotechnology, The University of Hong Kong, Hong Kong, China.
| | - Aimin Xu
- Department of Medicine, The University of Hong Kong, Hong Kong, China; State Key Laboratory of Pharmaceutical Biotechnology, The University of Hong Kong, Hong Kong, China; Department of Pharmacology & Pharmacy, The University of Hong Kong, Hong Kong, China.
| | - Zongwei Cai
- State Key Laboratory of Environmental and Biological Analysis, Hong Kong Baptist University, Hong Kong, China.
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17
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Zhang T, Cao Y, Zhao J, Yao J, Liu G. Assessing the causal effect of genetically predicted metabolites and metabolic pathways on stroke. J Transl Med 2023; 21:822. [PMID: 37978512 PMCID: PMC10655369 DOI: 10.1186/s12967-023-04677-4] [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: 09/13/2023] [Accepted: 10/29/2023] [Indexed: 11/19/2023] Open
Abstract
BACKGROUND Stroke is a common neurological disorder that disproportionately affects middle-aged and elderly individuals, leading to significant disability and mortality. Recently, human blood metabolites have been discovered to be useful in unraveling the underlying biological mechanisms of neurological disorders. Therefore, we aimed to evaluate the causal relationship between human blood metabolites and susceptibility to stroke. METHODS Summary data from genome-wide association studies (GWASs) of serum metabolites and stroke and its subtypes were obtained separately. A total of 486 serum metabolites were used as the exposure. Simultaneously, 11 different stroke phenotypes were set as the outcomes, including any stroke (AS), any ischemic stroke (AIS), large artery stroke (LAS), cardioembolic stroke (CES), small vessel stroke (SVS), lacunar stroke (LS), white matter hyperintensities (WMH), intracerebral hemorrhage (ICH), subarachnoid hemorrhage (SAH), transient ischemic attack (TIA), and brain microbleeds (BMB). A two-sample Mendelian randomization (MR) study was conducted to investigate the causal effects of serum metabolites on stroke and its subtypes. The inverse variance-weighted MR analyses were conducted as causal estimates, accompanied by a series of sensitivity analyses to evaluate the robustness of the results. Furthermore, a reverse MR analysis was conducted to assess the potential for reverse causation. Additionally, metabolic pathway analysis was performed using the web-based MetOrigin. RESULTS After correcting for the false discovery rate (FDR), MR analysis results revealed remarkable causative associations with 25 metabolites. Further sensitivity analyses confirmed that only four causative associations involving three specific metabolites passed all sensitivity tests, namely ADpSGEGDFXAEGGGVR* for AS (OR: 1.599, 95% CI 1.283-1.993, p = 2.92 × 10-5) and AIS (OR: 1.776, 95% CI 1.380-2.285, p = 8.05 × 10-6), 1-linoleoylglycerophosph-oethanolamine* for LAS (OR: 0.198, 95% CI 0.091-0.428, p = 3.92 × 10-5), and gamma-glutamylmethionine* for SAH (OR: 3.251, 95% CI 1.876-5.635, p = 2.66 × 10-5), thereby demonstrating a high degree of stability. Moreover, eight causative associations involving seven other metabolites passed both sensitivity tests and were considered robust. The association result of one metabolite (glutamate for LAS) was considered non-robust. As for the remaining metabolites, we speculate that they may potentially possess underlying causal relationships. Notably, no common metabolites emerged from the reverse MR analysis. Moreover, after FDR correction, metabolic pathway analysis identified 40 significant pathways across 11 stroke phenotypes. CONCLUSIONS The identified metabolites and their associated metabolic pathways are promising circulating metabolic biomarkers, holding potential for their application in stroke screening and preventive strategies within clinical settings.
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Affiliation(s)
- Tianlong Zhang
- Department of Critical Medicine, The Fourth Affiliated Hospital of Zhejiang University School of Medicine, Yiwu, Zhejiang, China
| | - Yina Cao
- Department of Neurology, The Fourth Affiliated Hospital of Zhejiang University School of Medicine, Yiwu, Zhejiang, China
| | - Jianqiang Zhao
- Department of Cardiology, The Fourth Affiliated Hospital of Zhejiang University School of Medicine, Yiwu, Zhejiang, China
| | - Jiali Yao
- Department of Critical Care Medicine, Jinhua Hospital Affiliated to Zhejiang University, Jinhua, Zhejiang, China.
| | - Gang Liu
- Department of Infection Control, The Fourth Affiliated Hospital of Zhejiang University School of Medicine, Yiwu, Zhejiang, China.
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Slouha E, Rezazadah A, Farahbod K, Gerts A, Clunes LA, Kollias TF. Type-2 Diabetes Mellitus and the Gut Microbiota: Systematic Review. Cureus 2023; 15:e49740. [PMID: 38161953 PMCID: PMC10757596 DOI: 10.7759/cureus.49740] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 11/30/2023] [Indexed: 01/03/2024] Open
Abstract
The gut microbiota is a community situated in the gastrointestinal tract that consists of bacteria thriving and contributing to the functions of our body. It is heavily influenced by what individuals eat, as the quality, amount, and frequency of food consumed can favor and inhibit specific bacteria. Type-2 diabetes mellitus (T2DM) is a common but detrimental condition that arises from excessive hyperglycemia, leading to either insulin resistance or damage to the B-cells that produce insulin in the pancreas. A poor diet high in sugar and fats leads to hyperglycemia, and as this persists, it can lead to the development of T2DM. Both insulin resistance and damage to B-cells are greatly affected by the diet an individual consumes, but is there a more involved relationship between the gut microbiota and T2DM? This paper aimed to evaluate the changes in the gut microbiota in patients with T2DM and the impacts of the changes in gut microbiota. Bacteroides, Proteobacteria, Firmicutes, and Actinobacteria prevailed in patients with T2DM and healthy control, but their abundance varied greatly. There was also a significant decrease in bacteria like Lactobacilli spp.and F. prausnitizii associated with insulin resistance. High levels of BMI in patients with T2DM have also been associated with increased levels of A. muciniphilia, which has been associated with decreased fat metabolism and increased BMI. Metabolites such as butyrates and melatonin have also been identified as influencing the development and progression of T2DM. Testosterone levels have also been greatly influenced by the gut microbiota changes in T2DM, such that males with lower testosterone have a greater abundance of bacteria like Gemella, Lachnospiraceae, and Massiia. Identifying these changes and how they impact the body may lead to a treatment addressing insulin dysfunction and the changes that the altered gut microbiota leads to. Future research should address how treatment methods such as healthy diets, exercise, and anti-diabetics affect the gut microbiota and see if they influence sustained changes and reduced hyperglycemia.
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Affiliation(s)
- Ethan Slouha
- Pharmacology, St. George's University School of Medicine, St. George's, GRD
| | - Atbeen Rezazadah
- Pharmacology, St. George's University School of Medicine, St. George's, GRD
| | - Kiana Farahbod
- Pharmacology, St. George's University School of Medicine, St. George's, GRD
| | - Andrew Gerts
- Pharmacology, St. George's University School of Medicine, St. George, GRD
| | - Lucy A Clunes
- Pharmacology, St. George's University, St. George's, GRD
| | - Theofanis F Kollias
- Microbiology, Immunology and Pharmacology, St. George's University School of Medicine, St. George's, GRD
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19
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Patra D, Banerjee D, Ramprasad P, Roy S, Pal D, Dasgupta S. Recent insights of obesity-induced gut and adipose tissue dysbiosis in type 2 diabetes. Front Mol Biosci 2023; 10:1224982. [PMID: 37842639 PMCID: PMC10575740 DOI: 10.3389/fmolb.2023.1224982] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2023] [Accepted: 09/14/2023] [Indexed: 10/17/2023] Open
Abstract
An imbalance in microbial homeostasis, referred to as dysbiosis, is critically associated with the progression of obesity-induced metabolic disorders including type 2 diabetes (T2D). Alteration in gut microbial diversity and the abundance of pathogenic bacteria disrupt metabolic homeostasis and potentiate chronic inflammation, due to intestinal leakage or release of a diverse range of microbial metabolites. The obesity-associated shifts in gut microbial diversity worsen the triglyceride and cholesterol level that regulates adipogenesis, lipolysis, and fatty acid oxidation. Moreover, an intricate interaction of the gut-brain axis coupled with the altered microbiome profile and microbiome-derived metabolites disrupt bidirectional communication for instigating insulin resistance. Furthermore, a distinct microbial community within visceral adipose tissue is associated with its dysfunction in obese T2D individuals. The specific bacterial signature was found in the mesenteric adipose tissue of T2D patients. Recently, it has been shown that in Crohn's disease, the gut-derived bacterium Clostridium innocuum translocated to the mesenteric adipose tissue and modulates its function by inducing M2 macrophage polarization, increasing adipogenesis, and promoting microbial surveillance. Considering these facts, modulation of microbiota in the gut and adipose tissue could serve as one of the contemporary approaches to manage T2D by using prebiotics, probiotics, or faecal microbial transplantation. Altogether, this review consolidates the current knowledge on gut and adipose tissue dysbiosis and its role in the development and progression of obesity-induced T2D. It emphasizes the significance of the gut microbiota and its metabolites as well as the alteration of adipose tissue microbiome profile for promoting adipose tissue dysfunction, and identifying novel therapeutic strategies, providing valuable insights and directions for future research and potential clinical interventions.
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Affiliation(s)
- Debarun Patra
- Department of Biomedical Engineering, Indian Institute of Technology Ropar, Punjab, Punjab, India
| | - Dipanjan Banerjee
- Department of Molecular Biology and Biotechnology, Tezpur University, Napaam, Assam, India
| | - Palla Ramprasad
- Department of Biomedical Engineering, Indian Institute of Technology Ropar, Punjab, Punjab, India
| | - Soumyajit Roy
- Department of Biomedical Engineering, Indian Institute of Technology Ropar, Punjab, Punjab, India
| | - Durba Pal
- Department of Biomedical Engineering, Indian Institute of Technology Ropar, Punjab, Punjab, India
| | - Suman Dasgupta
- Department of Molecular Biology and Biotechnology, Tezpur University, Napaam, Assam, India
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20
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Ding Y, Wang S, Lu J. Unlocking the Potential: Amino Acids' Role in Predicting and Exploring Therapeutic Avenues for Type 2 Diabetes Mellitus. Metabolites 2023; 13:1017. [PMID: 37755297 PMCID: PMC10535527 DOI: 10.3390/metabo13091017] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2023] [Revised: 09/08/2023] [Accepted: 09/13/2023] [Indexed: 09/28/2023] Open
Abstract
Diabetes mellitus, particularly type 2 diabetes mellitus (T2DM), imposes a significant global burden with adverse clinical outcomes and escalating healthcare expenditures. Early identification of biomarkers can facilitate better screening, earlier diagnosis, and the prevention of diabetes. However, current clinical predictors often fail to detect abnormalities during the prediabetic state. Emerging studies have identified specific amino acids as potential biomarkers for predicting the onset and progression of diabetes. Understanding the underlying pathophysiological mechanisms can offer valuable insights into disease prevention and therapeutic interventions. This review provides a comprehensive summary of evidence supporting the use of amino acids and metabolites as clinical biomarkers for insulin resistance and diabetes. We discuss promising combinations of amino acids, including branched-chain amino acids, aromatic amino acids, glycine, asparagine and aspartate, in the prediction of T2DM. Furthermore, we delve into the mechanisms involving various signaling pathways and the metabolism underlying the role of amino acids in disease development. Finally, we highlight the potential of targeting predictive amino acids for preventive and therapeutic interventions, aiming to inspire further clinical investigations and mitigate the progression of T2DM, particularly in the prediabetic stage.
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Affiliation(s)
- Yilan Ding
- Department of Endocrine and Metabolic Diseases, Shanghai Institute of Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China; (Y.D.); (S.W.)
- Shanghai National Clinical Research Center for Endocrine and Metabolic Diseases, Key Laboratory for Endocrine and Metabolic Diseases of the National Health Commission of the PR China, Shanghai National Center for Translational Medicine, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
| | - Shuangyuan Wang
- Department of Endocrine and Metabolic Diseases, Shanghai Institute of Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China; (Y.D.); (S.W.)
- Shanghai National Clinical Research Center for Endocrine and Metabolic Diseases, Key Laboratory for Endocrine and Metabolic Diseases of the National Health Commission of the PR China, Shanghai National Center for Translational Medicine, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
| | - Jieli Lu
- Department of Endocrine and Metabolic Diseases, Shanghai Institute of Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China; (Y.D.); (S.W.)
- Shanghai National Clinical Research Center for Endocrine and Metabolic Diseases, Key Laboratory for Endocrine and Metabolic Diseases of the National Health Commission of the PR China, Shanghai National Center for Translational Medicine, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
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21
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Liu J, Liu J, Zhang J, Liu C, Qu C, Na L. Vitamin D deficiency in early life regulates gut microbiome composition and leads to impaired glucose tolerance in adult and offspring rats. Food Funct 2023. [PMID: 37285306 DOI: 10.1039/d3fo00503h] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/09/2023]
Abstract
Vitamin D has been found to be involved in glucose metabolism in recent years. Its deficiency is very common, especially in children. Whether vitamin D deficiency in early life affects adult diabetes risk is unknown. In this study, a rat model of early life vitamin D deficiency (F1 Early-VDD) was established by depriving it of vitamin D from the 0 to the 8th week. Further, some rats were switched to normal feeding conditions and sacrificed at the 18th week. Other rats were mated randomly to generate offspring rats (F2 Early-VDD), and F2 rats were fed under normal conditions and sacrificed at the 8th week. Serum 25(OH)D3 level decreased in F1 Early-VDD at the 8th week and returned to normal at the 18th week. Serum 25(OH)D3 level in F2 Early-VDD at the 8th week was also lower than that in control rats. Impaired glucose tolerance was observed in F1 Early-VDD at the 8th week and 18th week and also in F2 Early-VDD at the 8th week. The gut microbiota composition in F1 Early-VDD at the 8th week significantly changed. Among the top ten genera with a rich difference, Desulfovibrio, Roseburia, Ruminiclostridium, Lachnoclostridium, A2, GCA-900066575, Peptococcus, Lachnospiraceae_FCS020_ group, and Bilophila increased owing to vitamin D deficiency, whereas Blautia decreased. There were 108 significantly changed metabolites in F1 Early-VDD at the 8th week, of which 63 were enriched in known metabolic pathways. Correlations between gut microbiota and metabolites were analyzed. Blautia was positively related to 2-picolinic acid, whereas Bilophila was negatively related to indoleacetic acid. Moreover, some of the changes in microbiota, metabolites, and enriched metabolic pathways still existed in F1 Early-VDD rats at the 18th week and F2 Early-VDD rats at the 8th week. In conclusion, vitamin D deficiency in early life leads to impaired glucose tolerance in adult and offspring rats. This effect may be partly achieved by regulating gut microbiota and their co-metabolites.
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Affiliation(s)
- Jing Liu
- The College of Medical Technology, Shanghai University of Medicine and Health Sciences, Shanghai, China
- Department of Research, Shanghai University of Medicine and Health Sciences Affiliated Zhoupu Hospital, Shanghai, China
| | - Junyi Liu
- Department of Clinical Nutrition, Shanghai University of Medicine and Health Sciences Affiliated Zhoupu Hospital, Shanghai, China
| | - Jingyi Zhang
- College of Public Health, Shanghai University of Medicine and Health Sciences, Shanghai, China.
| | - Chunyan Liu
- College of Public Health, Shanghai University of Medicine and Health Sciences, Shanghai, China.
| | - Chunbo Qu
- College of Public Health, Shanghai University of Medicine and Health Sciences, Shanghai, China.
| | - Lixin Na
- College of Public Health, Shanghai University of Medicine and Health Sciences, Shanghai, China.
- Collaborative Innovation Center of Shanghai University of Medicine and Health Sciences, Shanghai, China
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22
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Rahat ST, Mäkelä M, Nasserinejad M, Ikäheimo TM, Hyrkäs-Palmu H, Valtonen RIP, Röning J, Sebert S, Nieminen AI, Ali N, Vainio S. Clinical-Grade Patches as a Medium for Enrichment of Sweat-Extracellular Vesicles and Facilitating Their Metabolic Analysis. Int J Mol Sci 2023; 24:ijms24087507. [PMID: 37108669 PMCID: PMC10139190 DOI: 10.3390/ijms24087507] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2023] [Revised: 04/03/2023] [Accepted: 04/11/2023] [Indexed: 04/29/2023] Open
Abstract
Cell-secreted extracellular vesicles (EVs), carrying components such as RNA, DNA, proteins, and metabolites, serve as candidates for developing non-invasive solutions for monitoring health and disease, owing to their capacity to cross various biological barriers and to become integrated into human sweat. However, the evidence for sweat-associated EVs providing clinically relevant information to use in disease diagnostics has not been reported. Developing cost-effective, easy, and reliable methodologies to investigate EVs' molecular load and composition in the sweat may help to validate their relevance in clinical diagnosis. We used clinical-grade dressing patches, with the aim being to accumulate, purify and characterize sweat EVs from healthy participants exposed to transient heat. The skin patch-based protocol described in this paper enables the enrichment of sweat EVs that express EV markers, such as CD63. A targeted metabolomics study of the sweat EVs identified 24 components. These are associated with amino acids, glutamate, glutathione, fatty acids, TCA, and glycolysis pathways. Furthermore, as a proof-of-concept, when comparing the metabolites' levels in sweat EVs isolated from healthy individuals with those of participants with Type 2 diabetes following heat exposure, our findings revealed that the metabolic patterns of sweat EVs may be linked with metabolic changes. Moreover, the concentration of these metabolites may reflect correlations with blood glucose and BMI. Together our data revealed that sweat EVs can be purified using routinely used clinical patches, setting the foundations for larger-scale clinical cohort work. Furthermore, the metabolites identified in sweat EVs also offer a realistic means to identify relevant disease biomarkers. This study thus provides a proof-of-concept towards a novel methodology that will focus on the use of the sweat EVs and their metabolites as a non-invasive approach, in order to monitor wellbeing and changes in diseases.
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Affiliation(s)
- Syeda Tayyiba Rahat
- Laboratory of Developmental Biology, Faculty of Biochemistry and Molecular Medicine, University of Oulu, 90220 Oulu, Finland
| | - Mira Mäkelä
- Laboratory of Developmental Biology, Faculty of Biochemistry and Molecular Medicine, University of Oulu, 90220 Oulu, Finland
| | - Maryam Nasserinejad
- Research Unit of Population Health Research, Faculty of Medicine, University of Oulu, 90570 Oulu, Finland
- Infotech Oulu, University of Oulu, 90014 Oulu, Finland
| | - Tiina M Ikäheimo
- Department of Community Medicine, University of Tromsø, N-9037 Tromsø, Norway
- Research Unit of Population Health, University of Oulu, 90220 Oulu, Finland
| | - Henna Hyrkäs-Palmu
- Research Unit of Population Health, University of Oulu, 90220 Oulu, Finland
| | - Rasmus I P Valtonen
- Research Unit of Biomedicine, Medical Research Center, Faculty of Medicine, University of Oulu, Oulu University Hospital, 90220 Oulu, Finland
| | - Juha Röning
- Infotech Oulu, University of Oulu, 90014 Oulu, Finland
- Biomimetics and Intelligent Systems Group, Faculty of Information Technology and Electrical Engineering, University of Oulu, 90570 Oulu, Finland
| | - Sylvain Sebert
- Research Unit of Population Health Research, Faculty of Medicine, University of Oulu, 90570 Oulu, Finland
- Infotech Oulu, University of Oulu, 90014 Oulu, Finland
| | - Anni I Nieminen
- FIMM Metabolomics Unit, Institute for Molecular Medicine Finland, University of Helsinki, 00014 Helsinki, Finland
| | - Nsrein Ali
- Laboratory of Developmental Biology, Faculty of Biochemistry and Molecular Medicine, University of Oulu, 90220 Oulu, Finland
- Infotech Oulu, University of Oulu, 90014 Oulu, Finland
- Flagship GeneCellNano, University of Oulu, 90220 Oulu, Finland
| | - Seppo Vainio
- Laboratory of Developmental Biology, Faculty of Biochemistry and Molecular Medicine, University of Oulu, 90220 Oulu, Finland
- Infotech Oulu, University of Oulu, 90014 Oulu, Finland
- Flagship GeneCellNano, University of Oulu, 90220 Oulu, Finland
- Kvantum Institute, University of Oulu, 90014 Oulu, Finland
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23
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Zhao JD, Sun M, Li Y, Yu CJ, Cheng RD, Wang SH, Du X, Fang ZH. Characterization of gut microbial and metabolite alterations in faeces of Goto Kakizaki rats using metagenomic and untargeted metabolomic approach. World J Diabetes 2023; 14:255-270. [PMID: 37035219 PMCID: PMC10075032 DOI: 10.4239/wjd.v14.i3.255] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/02/2022] [Revised: 12/31/2022] [Accepted: 02/07/2023] [Indexed: 03/15/2023] Open
Abstract
BACKGROUND In recent years, the incidence of type 2 diabetes (T2DM) has shown a rapid growth trend. Goto Kakizaki (GK) rats are a valuable model for the study of T2DM and share common glucose metabolism features with human T2DM patients. A series of studies have indicated that T2DM is associated with the gut microbiota composition and gut metabolites. We aimed to systematically characterize the faecal gut microbes and metabolites of GK rats and analyse the relationship between glucose and insulin resistance.
AIM To evaluate the gut microbial and metabolite alterations in GK rat faeces based on metagenomics and untargeted metabolomics.
METHODS Ten GK rats (model group) and Wistar rats (control group) were observed for 10 wk, and various glucose-related indexes, mainly including weight, fasting blood glucose (FBG) and insulin levels, homeostasis model assessment of insulin resistance (HOMA-IR) and homeostasis model assessment of β cell (HOMA-β) were assessed. The faecal gut microbiota was sequenced by metagenomics, and faecal metabolites were analysed by untargeted metabolomics. Multiple metabolic pathways were evaluated based on the differential metabolites identified, and the correlations between blood glucose and the gut microbiota and metabolites were analysed.
RESULTS The model group displayed significant differences in weight, FBG and insulin levels, HOMA-IR and HOMA-β indexes (P < 0.05, P < 0.01) and a shift in the gut microbiota structure compared with the control group. The results demonstrated significantly decreased abundances of Prevotella sp. CAG:604 and Lactobacillus murinus (P < 0.05) and a significantly increased abundance of Allobaculum stercoricanis (P < 0.01) in the model group. A correlation analysis indicated that FBG and HOMA-IR were positively correlated with Allobaculum stercoricanis and negatively correlated with Lactobacillus murinus. An orthogonal partial least squares discriminant analysis suggested that the faecal metabolic profiles differed between the model and control groups. Fourteen potential metabolic biomarkers, including glycochenodeoxycholic acid, uric acid, 13(S)-hydroxyoctadecadienoic acid (HODE), N-acetylaspartate, β-sitostenone, sphinganine, 4-pyridoxic acid, and linoleic acid, were identified. Moreover, FBG and HOMA-IR were found to be positively correlated with glutathione, 13(S)-HODE, uric acid, 4-pyridoxic acid and allantoic acid and ne-gatively correlated with 3-α, 7-α, chenodeoxycholic acid glycine conjugate and 26-trihydroxy-5-β-cholestane (P < 0.05, P < 0.01). Allobaculum stercoricanis was positively correlated with linoleic acid and sphinganine (P < 0.01), and 2-methyl-3-hydroxy-5-formylpyridine-4-carboxylate was negatively associated with Prevotella sp. CAG:604 (P < 0.01). The metabolic pathways showing the largest differences were arginine biosynthesis; primary bile acid biosynthesis; purine metabolism; linoleic acid metabolism; alanine, aspartate and glutamate metabolism; and nitrogen metabolism.
CONCLUSION Metagenomics and untargeted metabolomics indicated that disordered compositions of gut microbes and metabolites may be common defects in GK rats.
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Affiliation(s)
- Jin-Dong Zhao
- Department of Endocrinology, The First Affiliated Hospital of Anhui University of Chinese Medicine, Hefei 230031, Anhui Province, China
- Graduate School, Anhui University of Chinese Medicine, Hefei 230012, Anhui Province, China
| | - Min Sun
- School of Life Sciences, Anhui University, Hefei 230039, Anhui Province, China
| | - Yan Li
- Department of Infectious Disease, The First Affiliated Hospital of Anhui University of Chinese Medicine, Hefei 230031, Anhui Province, China
| | - Chan-Juan Yu
- Department of Endocrinology, The First Affiliated Hospital of Anhui University of Chinese Medicine, Hefei 230031, Anhui Province, China
| | - Ruo-Dong Cheng
- Department of Endocrinology, The First Affiliated Hospital of Anhui University of Chinese Medicine, Hefei 230031, Anhui Province, China
| | - Si-Hai Wang
- Department of Endocrinology, The First Affiliated Hospital of Anhui University of Chinese Medicine, Hefei 230031, Anhui Province, China
| | - Xue Du
- Department of Endocrinology, The First Affiliated Hospital of Anhui University of Chinese Medicine, Hefei 230031, Anhui Province, China
| | - Zhao-Hui Fang
- Department of Endocrinology, The First Affiliated Hospital of Anhui University of Chinese Medicine, Hefei 230031, Anhui Province, China
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24
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Kim JE, Nam H, Park JI, Cho H, Lee J, Kim HE, Kim DK, Joo KW, Kim YS, Kim BS, Park S, Lee H. Gut Microbial Genes and Metabolism for Methionine and Branched-Chain Amino Acids in Diabetic Nephropathy. Microbiol Spectr 2023; 11:e0234422. [PMID: 36877076 PMCID: PMC10100834 DOI: 10.1128/spectrum.02344-22] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2022] [Accepted: 01/23/2023] [Indexed: 03/07/2023] Open
Abstract
Diabetic mellitus nephropathy (DMN) is a serious complication of diabetes and a major health concern. Although the pathophysiology of diabetes mellitus (DM) leading to DMN is uncertain, recent evidence suggests the involvement of the gut microbiome. This study aimed to determine the relationships among gut microbial species, genes, and metabolites in DMN through an integrated clinical, taxonomic, genomic, and metabolomic analysis. Whole-metagenome shotgun sequencing and nuclear magnetic resonance metabolomic analyses were performed on stool samples from 15 patients with DMN and 22 healthy controls. Six bacterial species were identified to be significantly elevated in the DMN patients after adjusting for age, sex, body mass index, and estimated glomerular filtration rate (eGFR). Multivariate analysis found 216 microbial genes and 6 metabolites (higher valine, isoleucine, methionine, valerate, and phenylacetate levels in the DMN group and higher acetate levels in the control group) that were differentially present between the DMN and control groups. Integrated analysis of all of these parameters and clinical data using the random-forest model showed that methionine and branched-chain amino acids (BCAAs) were among the most significant features, next to the eGFR and proteinuria, in differentiating the DMN group from the control group. Metabolic pathway gene analysis of BCAAs and methionine also revealed that many genes involved in the biosynthesis of these metabolites were elevated in the six species that were more abundant in the DMN group. The suggested correlation among taxonomic, genetic, and metabolic features of the gut microbiome would expand our understanding of gut microbial involvement in the pathogenesis of DMN and may provide potential therapeutic targets for DMN. IMPORTANCE Whole metagenomic sequencing uncovered specific members of the gut microbiota associated with DMN. The gene families derived from the discovered species are involved in the metabolic pathways of methionine and branched-chain amino acids. Metabolomic analysis using stool samples showed increased methionine and branched-chain amino acids in DMN. These integrative omics results provide evidence of the gut microbiota-associated pathophysiology of DMN, which can be further studied for disease-modulating effects via prebiotics or probiotics.
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Affiliation(s)
- Ji Eun Kim
- Department of Internal Medicine, Korea University Guro Hospital, Seoul, South Korea
| | - Hoonsik Nam
- College of Pharmacy, Natural Products Research Institute, Seoul National University, Seoul, South Korea
| | - Ji In Park
- Department of Internal Medicine, Kangwon National University Hospital, Chuncheon, South Korea
| | - Hyunjeong Cho
- Department of Internal Medicine, Chungbuk National University Hospital, Cheongju, South Korea
| | - Jangwook Lee
- Department of Internal Medicine, Dongguk University Ilsan Hospital, Ilsan, South Korea
| | - Hyo-Eun Kim
- Seoul National University Hospital Biomedical Research Institute, Seoul, South Korea
| | - Dong Ki Kim
- Kidney Research Institute, Seoul National University, Seoul, South Korea
- Department of Internal Medicine, Seoul National University Hospital, Seoul, South Korea
- Department of Internal Medicine, Seoul National University College of Medicine, Seoul, South Korea
| | - Kwon Wook Joo
- Kidney Research Institute, Seoul National University, Seoul, South Korea
- Department of Internal Medicine, Seoul National University Hospital, Seoul, South Korea
- Department of Internal Medicine, Seoul National University College of Medicine, Seoul, South Korea
| | - Yon Su Kim
- Kidney Research Institute, Seoul National University, Seoul, South Korea
- Department of Internal Medicine, Seoul National University Hospital, Seoul, South Korea
- Department of Internal Medicine, Seoul National University College of Medicine, Seoul, South Korea
| | - Bong-Soo Kim
- Department of Life Science, Multidisciplinary Genome Institute, Hallym University, Chuncheon, South Korea
| | - Sunghyouk Park
- College of Pharmacy, Natural Products Research Institute, Seoul National University, Seoul, South Korea
| | - Hajeong Lee
- Department of Internal Medicine, Seoul National University Hospital, Seoul, South Korea
- Department of Internal Medicine, Seoul National University College of Medicine, Seoul, South Korea
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Yan T, Liu T, Shi L, Yan L, Li Z, Zhang X, Dai X, Sun X, Yang X. Integration of microbial metabolomics and microbiomics uncovers a novel mechanism underlying the antidiabetic property of stachyose. J Funct Foods 2023. [DOI: 10.1016/j.jff.2023.105457] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/12/2023] Open
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Sun M, Li D, Hua M, Miao X, Su Y, Chi Y, Li Y, Sun R, Niu H, Wang J. Analysis of the alleviating effect of black bean peel anthocyanins on type 2 diabetes based on gut microbiota and serum metabolome. J Funct Foods 2023. [DOI: 10.1016/j.jff.2023.105456] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/17/2023] Open
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Comparison of fecal and blood metabolome reveals inconsistent associations of the gut microbiota with cardiometabolic diseases. Nat Commun 2023; 14:571. [PMID: 36732517 PMCID: PMC9894915 DOI: 10.1038/s41467-023-36256-y] [Citation(s) in RCA: 23] [Impact Index Per Article: 23.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2022] [Accepted: 01/20/2023] [Indexed: 02/04/2023] Open
Abstract
Blood metabolome is commonly used in human studies to explore the associations of gut microbiota-derived metabolites with cardiometabolic diseases. Here, in a cohort of 1007 middle-aged and elderly adults with matched fecal metagenomic (149 species and 214 pathways) and paired fecal and blood targeted metabolomics data (132 metabolites), we find disparate associations with taxonomic composition and microbial pathways when using fecal or blood metabolites. For example, we observe that fecal, but not blood butyric acid significantly associates with both gut microbiota and prevalent type 2 diabetes. These findings are replicated in an independent validation cohort involving 103 adults. Our results suggest that caution should be taken when inferring microbiome-cardiometabolic disease associations from either blood or fecal metabolome data.
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Kynurenine Pathway in Diabetes Mellitus-Novel Pharmacological Target? Cells 2023; 12:cells12030460. [PMID: 36766803 PMCID: PMC9913876 DOI: 10.3390/cells12030460] [Citation(s) in RCA: 24] [Impact Index Per Article: 24.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2022] [Revised: 01/24/2023] [Accepted: 01/27/2023] [Indexed: 02/04/2023] Open
Abstract
The tryptophan-kynurenine pathway (Trp-KYN) is the major route for tryptophan conversion in the brain and in the periphery. Kynurenines display a wide range of biological actions (which are often contrasting) such as cytotoxic/cytoprotective, oxidant/antioxidant or pro-/anti-inflammatory. The net effect depends on their local concentration, cellular environment, as well as a complex positive and negative feedback loops. The imbalance between beneficial and harmful kynurenines was implicated in the pathogenesis of various neurodegenerative disorders, psychiatric illnesses and metabolic disorders, including diabetes mellitus (DM). Despite available therapies, DM may lead to serious macro- and microvascular complications including cardio- and cerebrovascular disease, peripheral vascular disease, chronic renal disease, diabetic retinopathy, autonomic neuropathy or cognitive impairment. It is well established that low-grade inflammation, which often coincides with DM, can affect the function of KP and, conversely, that kynurenines may modulate the immune response. This review provides a detailed summary of findings concerning the status of the Trp-KYN pathway in DM based on available animal, human and microbiome studies. We highlight the importance of the molecular interplay between the deranged (functionally and qualitatively) conversion of Trp to kynurenines in the development of DM and insulin resistance. The Trp-KYN pathway emerges as a novel target in the search for preventive and therapeutic interventions in DM.
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A Single Strain of Lactobacillus (CGMCC 21661) Exhibits Stable Glucose- and Lipid-Lowering Effects by Regulating Gut Microbiota. Nutrients 2023; 15:nu15030670. [PMID: 36771383 PMCID: PMC9920280 DOI: 10.3390/nu15030670] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2022] [Revised: 01/23/2023] [Accepted: 01/26/2023] [Indexed: 02/01/2023] Open
Abstract
Type 2 diabetes (T2D) is usually accompanied by obesity and nonalcoholic fatty-liver-related insulin resistance. The link between T2D and dysbiosis has been receiving increasing attention. Probiotics can improve insulin sensitivity by regulating imbalances in microbiota, but efficacy varies based on the probiotic used. This study screened the main strain in the feces of healthy adult mice and found it to be a new Lactobacillus (abbreviated as Lb., named as CGMCC No. 21661) after genetic testing. We designed the most common Bifidobacterium longum subsp. longum (CGMCC1.2186, abbreviated as B. longum. subsp.), fecal microbiota transplantation (FMT), and Lb. CGMCC No. 21661 protocols to explore the best way for modulating dysbiosis to improve T2D. After 6 weeks of gavage in T2D mice, it was found that all three protocols had a therapeutic alleviating effect. Among them, compared with the B. longum. subsp. and FMT, the Lb. CGMCC No. 21661 showed a 1- to 2-fold decrease in blood glucose (11.84 ± 1.29 mmol/L, p < 0.05), the lowest HOMA-IR (p < 0.05), a 1 fold increase in serum glucagon-like peptide-1 (5.84 ± 1.1 pmol/L, p < 0.05), and lowest blood lipids (total cholesterol, 2.21 ± 0.68 mmol/L, p < 0.01; triglycerides, 0.4 ± 0.15 mmol/L, p < 0.01; Low-density lipoprotein cholesterol, 0.53 ± 0.16 mmol/L, p < 0.01). In addition, tissue staining in the Lb. CGMCC No. 21661 showed a 2- to 3-fold reduction in T2D-induced fatty liver (p < 0.0001), a 1- to 2-fold decrease in pancreatic apoptotic cells (p < 0.05), and a significant increase in colonic mucus layer thickness (p < 0.05) compared with the B. longum. subsp. and FMT. The glucose and lipid lowering effects of this Lb. CGMCC No. 21661 indicate that it may provide new ideas for the treatment of diabetes.
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Yan T, Shi L, Liu T, Zhang X, Yang M, Peng W, Sun X, Yan L, Dai X, Yang X. Diet-rich in wheat bran modulates tryptophan metabolism and AhR/IL-22 signalling mediated metabolic health and gut dysbacteriosis: A novel prebiotic-like activity of wheat bran. Food Res Int 2023; 163:112179. [PMID: 36596122 DOI: 10.1016/j.foodres.2022.112179] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2022] [Revised: 11/01/2022] [Accepted: 11/15/2022] [Indexed: 11/21/2022]
Abstract
Tryptophan metabolism has shown to involve in pathogenesis of various metabolic diseases. Gut microbiota-orientated diets hold great potentials to improve metabolic health via regulating tryptophan metabolism. The present study showed that the 6-week high fat diet (HFD) disturbed tryptophan metabolism accompanied with gut dysbacteriosis, also influenced the dietary tryptophan induced changes in cecum microbiome and serum metabolome in mice. The colonic expressions of aryl hydrocarbon receptor (AhR) and interleukin-22 (IL-22) were significantly reduced in mice fed on HFD. Notably, a diet- rich in wheat bran effectively inhibited transformation of tryptophan to kynurenine-pathway metabolites, while increased melatonin and microbial catabolites, i.e. indole-3-propionic acid, indole-3-acetaldehyde and 5-hydroxy-indole-3-acetic acid. Such regulatory effects were accompanied with reduced fasting glucose and total triglycerides, and promoted AhR and IL-22 levels in HFD mice. Wheat bran increased the abundance of health promoting bacteria (e.g., Akkermansia and Lactobacillus), which were significantly correlated with tryptophan derived indolic metabolites. Additionally, beneficial modulatory effects of wheat bran on indolic metabolites in associations with gut dysbacteriosis from type 2 diabetes patients were confirmed in vitro fecal fermentation experiment. Our study proves the detrimental effects of HFD induced gut dysbacteriosis on tryptophan metabolism that may influence immune modulation, and provides novel insights in the mechanisms by which wheat bran could induce health benefits.
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Affiliation(s)
- Tao Yan
- School of Food Engineering and Nutritional Science, Shaanxi Normal University, Xi'an, Shaanxi 710119, China
| | - Lin Shi
- School of Food Engineering and Nutritional Science, Shaanxi Normal University, Xi'an, Shaanxi 710119, China; Division of Food and Nutrition Science, Department of Biology and Biological Engineering, Chalmers University of Technology, Gothenburg SE-412 96, Sweden.
| | - Tianqi Liu
- School of Food Engineering and Nutritional Science, Shaanxi Normal University, Xi'an, Shaanxi 710119, China
| | - Xiangnan Zhang
- School of Food Engineering and Nutritional Science, Shaanxi Normal University, Xi'an, Shaanxi 710119, China
| | - Minmin Yang
- College of Life Sciences, Shaanxi Normal University, Xi'an, Shaanxi 710119, China
| | - Wen Peng
- Nutrition and Health Promotion Center, Department of Public Health, Medical College, Qinghai University, Xining, Qinghai 810016, China
| | - Xiaomin Sun
- Global Health Institute, Department of Nutrition and Food Safety, School of Public Health, Xi'an Jiaotong University Health Science Center, Xi'an, Shaanxi 710061, China
| | - Lijing Yan
- The Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shaanxi 710004, China
| | - Xiaoshuang Dai
- BGI Institute of Applied Agriculture, BGI-Agro, Shenzhen, Guangdong 518083, China.
| | - Xinbing Yang
- School of Food Engineering and Nutritional Science, Shaanxi Normal University, Xi'an, Shaanxi 710119, China.
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Chen Z, Wang Z, Li D, Zhu B, Xia Y, Wang G, Ai L, Zhang C, Wang C. The gut microbiota as a target to improve health conditions in a confined environment. Front Microbiol 2022; 13:1067756. [PMID: 36601399 PMCID: PMC9806127 DOI: 10.3389/fmicb.2022.1067756] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2022] [Accepted: 11/22/2022] [Indexed: 12/23/2022] Open
Abstract
Confined environments increase psychological stress and lead to health problems such as abnormal mood and rhythm disruption. However, the mechanism by which confined environments impact health has remained unclear. Significant correlations have been reported between psychological stress and changes in gut microbiota. Therefore, we investigated the effect of a confined environment on the composition of the gut microbiota by 16s rDNA high-throughput sequencing, and analyzed the correlation between gut microbiota and health indicators such as uric acid (UA), sleep, and mood. We found that the gut microbiota of the subjects clustered into two enterotypes (Bi and Bla), and that the groups differed significantly. There were notable differences in the abundances of genera such as Bifidobacterium, Dorea, Ruminococcus_torques_group, Ruminococcus_gnavus_group, Klebsiella, and UCG-002 (p < 0.05). A confined environment significantly impacted the subjects' health indicators. We also observed differences in how the subjects of the two enterotypes adapted to the confined environment. The Bi group showed no significant differences in health indicators before and after confinement; however, the Bla group experienced several health problems after confinement, such as increased UA, anxiety, and constipation, and lack of sleep. Redundancy analysis (RDA) showed that UA, RBC, mood, and other health problems were significantly correlated with the structure of the gut microbiota. We concluded that genera such as UCG-002, Ruminococcus, CAG352, and Ruminococcus_torques_group increased vulnerability to confined environments, resulting in abnormal health conditions. We found that the differences in the adaptability of individuals to confined environments were closely related to the composition of their gut microbiota.
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Affiliation(s)
- Zheng Chen
- School of Food Science and Technology, Dalian Polytechnic University, Dalian, China
| | - ZiYing Wang
- Navy Special Medical Center, Naval Medical University, Shanghai, China
| | - Dan Li
- Navy Special Medical Center, Naval Medical University, Shanghai, China
| | - Beiwei Zhu
- School of Food Science and Technology, Dalian Polytechnic University, Dalian, China
| | - Yongjun Xia
- School of Health Science and Engineering, Shanghai Engineering Research Center of Food Microbiology, University of Shanghai for Science and Technology, Shanghai, China
| | - Guangqiang Wang
- School of Health Science and Engineering, Shanghai Engineering Research Center of Food Microbiology, University of Shanghai for Science and Technology, Shanghai, China
| | - Lianzhong Ai
- School of Health Science and Engineering, Shanghai Engineering Research Center of Food Microbiology, University of Shanghai for Science and Technology, Shanghai, China
| | - Chunhong Zhang
- Navy Special Medical Center, Naval Medical University, Shanghai, China,*Correspondence: Chunhong Zhang,
| | - Chuan Wang
- Navy Special Medical Center, Naval Medical University, Shanghai, China,Chuan Wang,
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Liu S, Li C, Chu M, Zhang W, Wang W, Wang Y, Guo X, Deng F. Associations of forest negative air ions exposure with cardiac autonomic nervous function and the related metabolic linkages: A repeated-measure panel study. THE SCIENCE OF THE TOTAL ENVIRONMENT 2022; 850:158019. [PMID: 35973547 DOI: 10.1016/j.scitotenv.2022.158019] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/21/2022] [Revised: 07/29/2022] [Accepted: 08/09/2022] [Indexed: 06/15/2023]
Abstract
Forest environment has many health benefits, and negative air ions (NAI) is one of the major forest environmental factors. Many studies have explored the effect of forest environment on cardiac autonomic nervous function, while forest NAI in the among function and the underlying mechanism still remain unclear. To explore the associations and molecular linkages between short-term exposure to forest NAI and heart rate variability (HRV), a repeated-measure panel study was conducted among 31 healthy adults. Participants were randomly selected to stay in a forest park for 3 days and 2 nights. Individual exposures including NAI were monitored simultaneously and HRV indices were measured repeatedly at the follow-up period. Urine samples were collected for non-targeted metabolomics analysis. Mixed-effect models were adopted to evaluate associations among NAI, HRV indices and metabolites. The median of NAI concentration was 68.11 (138.20) cm-3 during the study period. Short-term exposure to forest NAI was associated with the ameliorative HRV indices, especially the excitatory parasympathetic nerve. For instance, per interquartile range increase of 5-min moving average of NAI was associated with 9.99 % (95%CI: 8.95 %, 11.03 %) increase of power in high frequency. Eight metabolites were associated with NAI exposure. The down-regulated tyrosine metabolism was firstly observed, followed by other amino acid metabolic alterations. The NAI-related metabolic changes reflect the reduction of inflammation and oxidative stress. HRV indices were associated with 25 metabolites, mainly including arginine, proline and histidine metabolism. Short-term exposure to forest NAI is beneficial to HRV, especially to the parasympathetic nerve activity, by successively disturbing different metabolic pathways which mainly reflect the increased anti-inflammation and the reduced inflammation. The results will provide epidemiological evidences for developing forest therapy and improving cardiac autonomic nervous function.
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Affiliation(s)
- Shan Liu
- Department of Occupational and Environmental Health Sciences, School of Public Health, Peking University, Beijing 100191, China
| | - Chen Li
- Department of Occupational and Environmental Health Sciences, School of Public Health, Peking University, Beijing 100191, China
| | - Mengtian Chu
- Department of Occupational and Environmental Health Sciences, School of Public Health, Peking University, Beijing 100191, China
| | - Wenlou Zhang
- Department of Occupational and Environmental Health Sciences, School of Public Health, Peking University, Beijing 100191, China
| | - Wanzhou Wang
- Department of Occupational and Environmental Health Sciences, School of Public Health, Peking University, Beijing 100191, China
| | - Yazheng Wang
- Department of Occupational and Environmental Health Sciences, School of Public Health, Peking University, Beijing 100191, China
| | - Xinbiao Guo
- Department of Occupational and Environmental Health Sciences, School of Public Health, Peking University, Beijing 100191, China
| | - Furong Deng
- Department of Occupational and Environmental Health Sciences, School of Public Health, Peking University, Beijing 100191, China.
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Liu Y, Chen L, Liu L, Zhao SS, You JQ, Zhao XJ, Liu HX, Xu GW, Wen DL. Interplay between dietary intake, gut microbiota, and metabolic profile in obese adolescents: Sex-dependent differential patterns. Clin Nutr 2022; 41:2706-2719. [PMID: 36351362 DOI: 10.1016/j.clnu.2022.10.009] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2022] [Revised: 09/22/2022] [Accepted: 10/13/2022] [Indexed: 01/27/2023]
Abstract
BACKGROUND & AIMS The interplay among dietary intake, gut microbiota, gut metabolites and circulating metabolites in adolescents is barely known, not to mention sex-dependent pattern. We aimed to explore unique profiles of gut bacterial, gut metabolites and circulating metabolites from both genders of adolescents due to BMI and eating pattern. METHODS Clinical indices, fecal gut microbiota, fecal and plasma metabolites, and diet intake information were collected in case-control sample matched for normal and obesity in girls (normal = 12, obesity = 12) and boys (normal = 20, obesity = 20), respectively. 16S rRNA gene sequencing and untargeted metabolomics was performed to analysis the signature of gut microbiota and metabolites. Unique profiles of girls associated with BMI and eating pattern was revealed by Spearman's correlations analysis, co-occurrence network analysis, Kruskal-Wallis test, and Wilcoxon rank-sum test. RESULTS Gender difference was found between normal and obese adolescents in gut microbiota, fecal metabolites, and plasma metabolites. The Parabacteroides were only decreased in obese girls. And the characteristic of obese girls' and boys' cases in fecal and plasma was xanthine and glutamine, ornithine and LCA, respectively. Soy products intake was negatively associated with Parabacteroides. The predicted model has a higher accuracy based on the combined markers in obesity boys (AUC = 0.97) and girls (AUC = 0.97), respectively. CONCLUSIONS Reduced abundance of Phascolarctobacterium and Parabacteroides, as well as the increased fecal xanthine and ornithine, may provide a novel biomarker signature in obesity girls and boys. Soy products intake was positively and negatively associated with Romboutsia and Parabacteroides abundance, respectively. And the combined markers facilitate the accuracy of predicting obesity in girls and boys in advance.
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Affiliation(s)
- Yang Liu
- Health Sciences Institute, China Medical University, Shenyang 110122, Liaoning Province, PR China; Liaoning Key Laboratory of Obesity and Glucose/Lipid Associated Metabolic Diseases, China Medical University, Shenyang 110122, Liaoning Province, PR China
| | - Lei Chen
- Health Sciences Institute, China Medical University, Shenyang 110122, Liaoning Province, PR China; Liaoning Key Laboratory of Obesity and Glucose/Lipid Associated Metabolic Diseases, China Medical University, Shenyang 110122, Liaoning Province, PR China; Institute of Life Sciences, China Medical University, Shenyang 110122, Liaoning Province, PR China
| | - Lei Liu
- Health Sciences Institute, China Medical University, Shenyang 110122, Liaoning Province, PR China; Liaoning Key Laboratory of Obesity and Glucose/Lipid Associated Metabolic Diseases, China Medical University, Shenyang 110122, Liaoning Province, PR China
| | - Shan-Shan Zhao
- Health Sciences Institute, China Medical University, Shenyang 110122, Liaoning Province, PR China; Liaoning Key Laboratory of Obesity and Glucose/Lipid Associated Metabolic Diseases, China Medical University, Shenyang 110122, Liaoning Province, PR China; Institute of Life Sciences, China Medical University, Shenyang 110122, Liaoning Province, PR China
| | - Jun-Qiao You
- Health Sciences Institute, China Medical University, Shenyang 110122, Liaoning Province, PR China; Liaoning Key Laboratory of Obesity and Glucose/Lipid Associated Metabolic Diseases, China Medical University, Shenyang 110122, Liaoning Province, PR China
| | - Xin-Jie Zhao
- CAS Key Laboratory of Separation Science for Analytical Chemistry, Dalian Institute of Chemical Physics, The Chinese Academy of Sciences, Dalian 116023, Liaoning Province, PR China.
| | - Hui-Xin Liu
- Health Sciences Institute, China Medical University, Shenyang 110122, Liaoning Province, PR China; Liaoning Key Laboratory of Obesity and Glucose/Lipid Associated Metabolic Diseases, China Medical University, Shenyang 110122, Liaoning Province, PR China; Institute of Life Sciences, China Medical University, Shenyang 110122, Liaoning Province, PR China.
| | - Guo-Wang Xu
- CAS Key Laboratory of Separation Science for Analytical Chemistry, Dalian Institute of Chemical Physics, The Chinese Academy of Sciences, Dalian 116023, Liaoning Province, PR China
| | - De-Liang Wen
- Health Sciences Institute, China Medical University, Shenyang 110122, Liaoning Province, PR China; Liaoning Key Laboratory of Obesity and Glucose/Lipid Associated Metabolic Diseases, China Medical University, Shenyang 110122, Liaoning Province, PR China.
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Hahn SJ, Kim S, Choi YS, Lee J, Kang J. Prediction of type 2 diabetes using genome-wide polygenic risk score and metabolic profiles: A machine learning analysis of population-based 10-year prospective cohort study. EBioMedicine 2022; 86:104383. [PMID: 36462406 PMCID: PMC9713286 DOI: 10.1016/j.ebiom.2022.104383] [Citation(s) in RCA: 20] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2022] [Revised: 11/09/2022] [Accepted: 11/09/2022] [Indexed: 12/05/2022] Open
Abstract
BACKGROUND Previous work on predicting type 2 diabetes by integrating clinical and genetic factors has mostly focused on the Western population. In this study, we use genome-wide polygenic risk score (gPRS) and serum metabolite data for type 2 diabetes risk prediction in the Asian population. METHODS Data of 1425 participants from the Korean Genome and Epidemiology Study (KoGES) Ansan-Ansung cohort were used in this study. For gPRS analysis, genotypic and clinical information from KoGES health examinee (n = 58,701) and KoGES cardiovascular disease association (n = 8105) sub-cohorts were included. Linkage disequilibrium analysis identified 239,062 genetic variants that were used to determine the gPRS, while the metabolites were selected using the Boruta algorithm. We used bootstrapped cross-validation to evaluate logistic regression and random forest (RF)-based machine learning models. Finally, associations of gPRS and selected metabolites with the values of homeostatic model assessment of beta-cell function (HOMA-B) and insulin resistance (HOMA-IR) were further estimated. FINDINGS During the follow-up period (8.3 ± 2.8 years), 331 participants (23.2%) were diagnosed with type 2 diabetes. The areas under the curves of the RF-based models were 0.844, 0.876, and 0.883 for the model using only demographic and clinical factors, model including the gPRS, and model with both gPRS and metabolites, respectively. Incorporation of additional parameters in the latter two models improved the classification by 11.7% and 4.2% respectively. While gPRS was significantly associated with HOMA-B value, most metabolites had a significant association with HOMA-IR value. INTERPRETATION Incorporating both gPRS and metabolite data led to enhanced type 2 diabetes risk prediction by capturing distinct etiologies of type 2 diabetes development. An RF-based model using clinical factors, gPRS, and metabolites predicted type 2 diabetes risk more accurately than the logistic regression-based model. FUNDING This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korean government (MEST) (No. 2019M3E5D1A02070863 and 2022R1C1C1005458). This work was also supported by the 2020 Research Fund (1.200098.01) of UNIST (Ulsan National Institute of Science & Technology).
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Affiliation(s)
- Seok-Ju Hahn
- Department of Industrial Engineering, Ulsan National Institute of Science and Technology (UNIST), Ulsan 44919, Republic of Korea
| | - Suhyeon Kim
- Department of Industrial Engineering, Ulsan National Institute of Science and Technology (UNIST), Ulsan 44919, Republic of Korea
| | - Young Sik Choi
- Division of Endocrinology, Department of Internal Medicine, Kosin University College of Medicine, Kosin University Gospel Hospital, Busan 49267, Republic of Korea
| | - Junghye Lee
- Department of Industrial Engineering, Ulsan National Institute of Science and Technology (UNIST), Ulsan 44919, Republic of Korea,Graduate School of Artificial Intelligence, Ulsan National Institute of Science and Technology (UNIST), Ulsan 44919, Republic of Korea,Corresponding author. Department of Industrial Engineering & Graduate School of Artificial Intelligence, Ulsan National Institute of Science and Technology (UNIST), 50 UNIST-gil, Ulsan, 44919, Republic of Korea.
| | - Jihun Kang
- Department of Family Medicine, Kosin University College of Medicine, Kosin University Gospel Hospital, Busan 49267, Republic of Korea,Corresponding author. Department of Family Medicine, Kosin University College of Medicine, Kosin University Gospel Hospital, 262 Gamcheon-ro, Busan 49267, Republic of Korea.
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Indolepropionic Acid, a Gut Bacteria-Produced Tryptophan Metabolite and the Risk of Type 2 Diabetes and Non-Alcoholic Fatty Liver Disease. Nutrients 2022; 14:nu14214695. [PMID: 36364957 PMCID: PMC9653718 DOI: 10.3390/nu14214695] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2022] [Revised: 10/30/2022] [Accepted: 11/04/2022] [Indexed: 11/09/2022] Open
Abstract
An intricate relationship between gut microbiota, diet, and the human body has recently been extensively investigated. Gut microbiota and gut-derived metabolites, especially, tryptophan derivatives, modulate metabolic and immune functions in health and disease. One of the tryptophan derivatives, indolepropionic acid (IPA), is increasingly being studied as a marker for the onset and development of metabolic disorders, including type 2 diabetes (T2D) and non-alcoholic fatty liver disease (NAFLD). The IPA levels heavily depend on the diet, particularly dietary fiber, and show huge variations among individuals. We suggest that these variations could partially be explained using genetic variants known to be associated with specific diseases such as T2D. In this narrative review, we elaborate on the beneficial effects of IPA in the mitigation of T2D and NAFLD, and further study the putative interactions between IPA and well-known genetic variants (TCF7L2, FTO, and PPARG), known to be associated with the risk of T2D. We have investigated the long-term preventive value of IPA in the development of T2D in the Finnish prediabetic population and the correlation of IPA with phytosterols in obese individuals from an ongoing Kuopio obesity surgery study. The diversity in IPA-linked mechanisms affecting glucose metabolism and liver fibrosis makes it a unique small metabolite and a promising candidate for the reversal or management of metabolic disorders, mainly T2D and NAFLD.
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Alka Ahuja, Saraswathy Mp, Nandakumar S, Prakash F A, Kn G, Um D. Role of the Gut Microbiome in Diabetes and Cardiovascular Diseases Including Restoration and Targeting Approaches- A Review. DRUG METABOLISM AND BIOANALYSIS LETTERS 2022; 15:133-149. [PMID: 36508273 DOI: 10.2174/2949681015666220615120300] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/11/2021] [Revised: 03/15/2022] [Accepted: 03/18/2022] [Indexed: 12/15/2022]
Abstract
Metabolic diseases, including cardiovascular diseases (CVD) and diabetes, have become the leading cause of morbidity and mortality worldwide. Gut microbiota appears to play a vital role in human disease and health, according to recent scientific reports. The gut microbiota plays an important role in sustaining host physiology and homeostasis by creating a cross-talk between the host and microbiome via metabolites obtained from the host's diet. Drug developers and clinicians rely heavily on therapies that target the microbiota in the management of metabolic diseases, and the gut microbiota is considered the biggest immune organ in the human body. They are highly associated with intestinal immunity and systemic metabolic disorders like CVD and diabetes and are reflected as potential therapeutic targets for the management of metabolic diseases. This review discusses the mechanism and interrelation between the gut microbiome and metabolic disorders. It also highlights the role of the gut microbiome and microbially derived metabolites in the pathophysiological effects related to CVD and diabetes. It also spotlights the reasons that lead to alterations of microbiota composition and the prominence of gut microbiota restoration and targeting approaches as effective treatment strategies in diabetes and CVD. Future research should focus onunderstanding the functional level of some specific microbial pathways that help maintain physiological homeostasis, multi-omics, and develop novel therapeutic strategies that intervene with the gut microbiome for the prevention of CVD and diabetes that contribute to a patient's well-being.
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Affiliation(s)
- Alka Ahuja
- College of Pharmacy, National University of Science and Technology, PC130, Muscat, Sultanate of Oman
| | - Saraswathy Mp
- Department of Microbiology, ESIC Medical College and PGIMSR, Chennai-600078, India
| | - Nandakumar S
- Department of Biotechnology, Pondicherry University, Kalapet, Puducherry-605014, India
| | - Arul Prakash F
- Centre of Molecular Medicine and Diagnostics (COMMAND), Saveetha Dental College and Hospital, Saveetha Institute of Medical & Technical Sciences, Chennai- 600077, India
| | - Gurpreet Kn
- College of Pharmacy, National University of Science and Technology, PC130, Muscat, Sultanate of Oman
| | - Dhanalekshmi Um
- College of Pharmacy, National University of Science and Technology, PC130, Muscat, Sultanate of Oman
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Hernández-Flores TDJ, Pedraza-Brindis EJ, Cárdenas-Bedoya J, Ruíz-Carrillo JD, Méndez-Clemente AS, Martínez-Guzmán MA, Iñiguez-Gutiérrez L. Role of Micronutrients and Gut Microbiota-Derived Metabolites in COVID-19 Recovery. Int J Mol Sci 2022; 23:12324. [PMID: 36293182 PMCID: PMC9604189 DOI: 10.3390/ijms232012324] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2022] [Revised: 10/11/2022] [Accepted: 10/11/2022] [Indexed: 01/08/2023] Open
Abstract
A balanced and varied diet provides diverse beneficial effects on health, such as adequate micronutrient availability and a gut microbiome in homeostasis. Besides their participation in biochemical processes as cofactors and coenzymes, vitamins and minerals have an immunoregulatory function; meanwhile, gut microbiota and its metabolites coordinate directly and indirectly the cell response through the interaction with the host receptors. Malnourishment is a crucial risk factor for several pathologies, and its involvement during the Coronavirus Disease 2019 pandemic has been reported. This pandemic has caused a significant decline in the worldwide population, especially those with chronic diseases, reduced physical activity, and elder age. Diet and gut microbiota composition are probable causes for this susceptibility, and its supplementation can play a role in reestablishing microbial homeostasis and improving immunity response against Coronavirus Disease 2019 infection and recovery. This study reviews the role of micronutrients and microbiomes in the risk of infection, the severity of disease, and the Coronavirus Disease 2019 sequelae.
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Affiliation(s)
- Teresita de Jesús Hernández-Flores
- Departamento de Disciplinas Filosófico, Metodológicas e Instrumentales, Centro Universitario de Ciencias de la Salud, Universidad de Guadalajara, Guadalajara 44340, Jalisco, Mexico
- Instituto de Investigación de Inmunodeficiencias y VIH, Hospital Civil de Guadalajara “Fray Antonio Alcalde”, Guadalajara 44280, Jalisco, Mexico
| | - Eliza Julia Pedraza-Brindis
- Departamento de Aparatos y Sistemas I, Facultad de Medicina, Universidad Autónoma de Guadalajara, Guadalajara 44670, Jalisco, Mexico
| | - Jhonathan Cárdenas-Bedoya
- Departamento de Disciplinas Filosófico, Metodológicas e Instrumentales, Centro Universitario de Ciencias de la Salud, Universidad de Guadalajara, Guadalajara 44340, Jalisco, Mexico
- Laboratorio de Inmunodeficiencias y Retrovirus Humanos, Centro de Investigación Biomédica de Occidente, Centro Médico Nacional de Occidente, Instituto Mexicano del Seguro Social, Guadalajara 44340, Jalisco, Mexico
| | - José Daniel Ruíz-Carrillo
- Clínica Medicina Familiar 1 del ISSSTE “Dr. Arturo González Guzmán”, Guadalajara 44340, Jalisco, Mexico
| | - Anibal Samael Méndez-Clemente
- Instituto de Investigación de Inmunodeficiencias y VIH, Hospital Civil de Guadalajara “Fray Antonio Alcalde”, Guadalajara 44280, Jalisco, Mexico
| | - Marco Alonso Martínez-Guzmán
- Departamento de Aparatos y Sistemas I, Facultad de Medicina, Universidad Autónoma de Guadalajara, Guadalajara 44670, Jalisco, Mexico
| | - Liliana Iñiguez-Gutiérrez
- Instituto de Investigación de Inmunodeficiencias y VIH, Hospital Civil de Guadalajara “Fray Antonio Alcalde”, Guadalajara 44280, Jalisco, Mexico
- Departamento de Aparatos y Sistemas I, Facultad de Medicina, Universidad Autónoma de Guadalajara, Guadalajara 44670, Jalisco, Mexico
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Zhu Q, Qin M, Wang Z, Wu Y, Chen X, Liu C, Ma Q, Liu Y, Lai W, Chen H, Cai J, Liu Y, Lei F, Zhang B, Zhang S, He G, Li H, Zhang M, Zheng H, Chen J, Huang M, Zhong S. Plasma metabolomics provides new insights into the relationship between metabolites and outcomes and left ventricular remodeling of coronary artery disease. Cell Biosci 2022; 12:173. [PMID: 36242008 PMCID: PMC9569076 DOI: 10.1186/s13578-022-00863-x] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2022] [Accepted: 07/28/2022] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Coronary artery disease (CAD) is a metabolically perturbed pathological condition. However, the knowledge of metabolic signatures on outcomes of CAD and their potential causal effects and impacts on left ventricular remodeling remains limited. We aim to assess the contribution of plasma metabolites to the risk of death and major adverse cardiovascular events (MACE) as well as left ventricular remodeling. RESULTS In a prospective study with 1606 Chinese patients with CAD, we have identified and validated several independent metabolic signatures through widely-targeted metabolomics. The predictive model respectively integrating four metabolic signatures (dulcitol, β-pseudouridine, 3,3',5-Triiodo-L-thyronine, and kynurenine) for death (AUC of 83.7% vs. 76.6%, positive IDI of 0.096) and metabolic signatures (kynurenine, lysoPC 20:2, 5-methyluridine, and L-tryptophan) for MACE (AUC of 67.4% vs. 59.8%, IDI of 0.068) yielded better predictive value than trimethylamine N-oxide plus clinical model, which were successfully applied to predict patients with high risks of death (P = 0.0014) and MACE (P = 0.0008) in the multicenter validation cohort. Mendelian randomisation analysis showed that 11 genetically inferred metabolic signatures were significantly associated with risks of death or MACE, such as 4-acetamidobutyric acid, phenylacetyl-L-glutamine, tryptophan metabolites (kynurenine, kynurenic acid), and modified nucleosides (β-pseudouridine, 2-(dimethylamino) guanosine). Mediation analyses show that the association of these metabolites with the outcomes could be partly explained by their roles in promoting left ventricular dysfunction. CONCLUSIONS This study provided new insights into the relationship between plasma metabolites and clinical outcomes and its intermediate pathological process left ventricular dysfunction in CAD. The predictive model integrating metabolites can help to improve the risk stratification for death and MACE in CAD. The metabolic signatures appear to increase death or MACE risks partly by promoting adverse left ventricular dysfunction, supporting potential therapeutic targets of CAD for further investigation.
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Affiliation(s)
- Qian Zhu
- grid.413405.70000 0004 1808 0686Department of Pharmacy, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, Guangzhou, 510080 Guangdong China ,grid.413405.70000 0004 1808 0686Guangdong Provincial Key Laboratory of Coronary Heart Disease Prevention, Guangdong Cardiovascular Institute, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, Guangzhou, 510080 Guangdong China ,grid.79703.3a0000 0004 1764 3838School of Medicine, South China University of Technology, Guangzhou, 510080 Guangdong China
| | - Min Qin
- grid.413405.70000 0004 1808 0686Department of Pharmacy, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, Guangzhou, 510080 Guangdong China ,grid.413405.70000 0004 1808 0686Guangdong Provincial Key Laboratory of Coronary Heart Disease Prevention, Guangdong Cardiovascular Institute, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, Guangzhou, 510080 Guangdong China ,grid.79703.3a0000 0004 1764 3838School of Medicine, South China University of Technology, Guangzhou, 510080 Guangdong China
| | - Zixian Wang
- grid.413405.70000 0004 1808 0686Department of Pharmacy, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, Guangzhou, 510080 Guangdong China ,grid.413405.70000 0004 1808 0686Guangdong Provincial Key Laboratory of Coronary Heart Disease Prevention, Guangdong Cardiovascular Institute, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, Guangzhou, 510080 Guangdong China
| | - Yonglin Wu
- grid.413405.70000 0004 1808 0686Department of Pharmacy, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, Guangzhou, 510080 Guangdong China ,grid.413405.70000 0004 1808 0686Guangdong Provincial Key Laboratory of Coronary Heart Disease Prevention, Guangdong Cardiovascular Institute, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, Guangzhou, 510080 Guangdong China
| | - Xiaoping Chen
- grid.452223.00000 0004 1757 7615Department of Clinical Pharmacology, Xiangya Hospital, Central South University, Changsha, 410008 Hunan China
| | - Chen Liu
- grid.412615.50000 0004 1803 6239Department of Cardiology, The First Affiliated Hospital of Sun Yat-Sen University, Guangzhou, 510080 Guangdong China
| | - Qilin Ma
- grid.452223.00000 0004 1757 7615Department of Cardiology, Xiangya Hospital, Central South University, Changsha, 410008 Hunan China
| | - Yibin Liu
- grid.413405.70000 0004 1808 0686Department of Pharmacy, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, Guangzhou, 510080 Guangdong China ,grid.413405.70000 0004 1808 0686Guangdong Provincial Key Laboratory of Coronary Heart Disease Prevention, Guangdong Cardiovascular Institute, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, Guangzhou, 510080 Guangdong China ,grid.79703.3a0000 0004 1764 3838School of Medicine, South China University of Technology, Guangzhou, 510080 Guangdong China
| | - Weihua Lai
- grid.413405.70000 0004 1808 0686Department of Pharmacy, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, Guangzhou, 510080 Guangdong China
| | - Hui Chen
- grid.413405.70000 0004 1808 0686Department of Pharmacy, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, Guangzhou, 510080 Guangdong China ,grid.79703.3a0000 0004 1764 3838School of Medicine, South China University of Technology, Guangzhou, 510080 Guangdong China
| | - Jingjing Cai
- grid.49470.3e0000 0001 2331 6153Institute of Model Animal, Wuhan University, Wuhan, 430072 Hubei China
| | - Yemao Liu
- grid.49470.3e0000 0001 2331 6153Institute of Model Animal, Wuhan University, Wuhan, 430072 Hubei China
| | - Fang Lei
- grid.49470.3e0000 0001 2331 6153Institute of Model Animal, Wuhan University, Wuhan, 430072 Hubei China
| | - Bin Zhang
- grid.413405.70000 0004 1808 0686Guangdong Provincial Key Laboratory of Coronary Heart Disease Prevention, Guangdong Cardiovascular Institute, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, Guangzhou, 510080 Guangdong China ,grid.79703.3a0000 0004 1764 3838School of Medicine, South China University of Technology, Guangzhou, 510080 Guangdong China
| | - Shuyao Zhang
- grid.258164.c0000 0004 1790 3548Department of Pharmacy, Guangzhou Red Cross Hospital, Jinan University, Guangzhou, 510220 Guangdong China
| | - Guodong He
- grid.413405.70000 0004 1808 0686Guangdong Provincial Key Laboratory of Coronary Heart Disease Prevention, Guangdong Cardiovascular Institute, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, Guangzhou, 510080 Guangdong China ,grid.79703.3a0000 0004 1764 3838School of Medicine, South China University of Technology, Guangzhou, 510080 Guangdong China
| | - Hanping Li
- grid.413405.70000 0004 1808 0686Guangdong Provincial Key Laboratory of Coronary Heart Disease Prevention, Guangdong Cardiovascular Institute, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, Guangzhou, 510080 Guangdong China
| | - Mingliang Zhang
- Wuhan Metware Biotechnology Co., Ltd., Wuhan, 430000 Hubei China
| | - Hui Zheng
- Wuhan Metware Biotechnology Co., Ltd., Wuhan, 430000 Hubei China
| | - Jiyan Chen
- grid.413405.70000 0004 1808 0686Guangdong Provincial Key Laboratory of Coronary Heart Disease Prevention, Guangdong Cardiovascular Institute, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, Guangzhou, 510080 Guangdong China
| | - Min Huang
- grid.12981.330000 0001 2360 039XInstitute of Clinical Pharmacology, School of Pharmaceutical Sciences, Sun Yat-Sen University, Guangzhou, 510006 Guangdong China
| | - Shilong Zhong
- grid.413405.70000 0004 1808 0686Department of Pharmacy, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, Guangzhou, 510080 Guangdong China ,grid.413405.70000 0004 1808 0686Guangdong Provincial Key Laboratory of Coronary Heart Disease Prevention, Guangdong Cardiovascular Institute, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, Guangzhou, 510080 Guangdong China ,grid.79703.3a0000 0004 1764 3838School of Medicine, South China University of Technology, Guangzhou, 510080 Guangdong China
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Cao Y, Aquino-Martinez R, Hutchison E, Allayee H, Lusis AJ, Rey FE. Role of gut microbe-derived metabolites in cardiometabolic diseases: Systems based approach. Mol Metab 2022; 64:101557. [PMID: 35870705 PMCID: PMC9399267 DOI: 10.1016/j.molmet.2022.101557] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/30/2022] [Revised: 06/30/2022] [Accepted: 07/18/2022] [Indexed: 02/08/2023] Open
Abstract
BACKGROUND The gut microbiome influences host physiology and cardiometabolic diseases by interacting directly with intestinal cells or by producing molecules that enter the host circulation. Given the large number of microbial species present in the gut and the numerous factors that influence gut bacterial composition, it has been challenging to understand the underlying biological mechanisms that modulate risk of cardiometabolic disease. SCOPE OF THE REVIEW Here we discuss a systems-based approach that involves simultaneously examining individuals in populations for gut microbiome composition, molecular traits using "omics" technologies, such as circulating metabolites quantified by mass spectrometry, and clinical traits. We summarize findings from landmark studies using this approach and discuss future applications. MAJOR CONCLUSIONS Population-based integrative approaches have identified a large number of microbe-derived or microbe-modified metabolites that are associated with cardiometabolic traits. The knowledge gained from these studies provide new opportunities for understanding the mechanisms involved in gut microbiome-host interactions and may have potentially important implications for developing novel therapeutic approaches.
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Affiliation(s)
- Yang Cao
- Departments of Medicine, Human Genetics, and Microbiology, Immunology, & Molecular Genetics, David Geffen School of Medicine of UCLA, Los Angeles, CA 90095, USA
| | - Ruben Aquino-Martinez
- Department of Bacteriology, University of Wisconsin, Madison, Madison, WI 53706, USA
| | - Evan Hutchison
- Department of Bacteriology, University of Wisconsin, Madison, Madison, WI 53706, USA
| | - Hooman Allayee
- Departments of Population & Public Health Sciences and Biochemistry & Molecular Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA 90033, USA
| | - Aldons J Lusis
- Departments of Medicine, Human Genetics, and Microbiology, Immunology, & Molecular Genetics, David Geffen School of Medicine of UCLA, Los Angeles, CA 90095, USA.
| | - Federico E Rey
- Department of Bacteriology, University of Wisconsin, Madison, Madison, WI 53706, USA
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40
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Lu Y, Li X, Zhao K, Qiu P, Deng Z, Yao W, Wang J. Global landscape of 2-hydroxyisobutyrylation in human pancreatic cancer. Front Oncol 2022; 12:1001807. [PMID: 36249039 PMCID: PMC9563853 DOI: 10.3389/fonc.2022.1001807] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2022] [Accepted: 09/12/2022] [Indexed: 11/26/2022] Open
Abstract
As a new type of post-translational modification (PTM), lysine 2-hydroxyisobutyrylation (Khib) was firstly identified in histones and functioned as a regulator of transactivation in mammals. However, the role of Khib proteins remains to be investigated. Here, we firstly identified 10,367 Khib sites on 2,325 modified proteins in seven patients with pancreatic cancer by applying liquid chromatography with tandem mass spectrometry (LC-MS/MS) qualitative proteomics techniques. Among them, 27 Khib-modified sites were identified in histones. Bioinformatics analysis revealed that the Khib-modified proteins were mainly distributed in the cytoplasm and enhanced in metabolic pathways, including glycolysis/gluconeogenesis, the tricarboxylic acid cycle (TCA cycle), and fatty acid degradation. In an overlapping comparison of lysine 2-hydroxyisobutyrylation, succinylation, and acetylation in humans, 105 proteins with 80 sites were modified by all three PTMs, suggesting there may be a complex network among the different modified proteins and sites. Furthermore, MG149, which was identified as a Tip60 inhibitor, significantly decreased the total Khib modification level in pancreatic cancer (PC) and strongly suppressed PC’s proliferation, migration, and invasion ability. Overall, our study is the first profiling of lysine 2-hydroxyisobutyrylome and provides a new database for better investigating Khib in PC.
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Affiliation(s)
- Yun Lu
- Department of Biliary and Pancreatic Surgery, Cancer Research Center Affiliated Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Xiangyu Li
- Department of Biliary and Pancreatic Surgery, Cancer Research Center Affiliated Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Kai Zhao
- Department of Biliary and Pancreatic Surgery, Cancer Research Center Affiliated Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Peng Qiu
- Department of Biliary and Pancreatic Surgery, Cancer Research Center Affiliated Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Zhengdong Deng
- Department of Biliary and Pancreatic Surgery, Cancer Research Center Affiliated Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Wei Yao
- Department of Oncology Affiliated Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- *Correspondence: Wei Yao, ; Jianming Wang,
| | - Jianming Wang
- Department of Biliary and Pancreatic Surgery, Cancer Research Center Affiliated Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Affiliated Tianyou Hospital, Wuhan University of Science & Technology, Wuhan, China
- *Correspondence: Wei Yao, ; Jianming Wang,
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41
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Wang S, Li M, Lin H, Wang G, Xu Y, Zhao X, Hu C, Zhang Y, Zheng R, Hu R, Shi L, Du R, Su Q, Wang J, Chen Y, Yu X, Yan L, Wang T, Zhao Z, Liu R, Wang X, Li Q, Qin G, Wan Q, Chen G, Xu M, Dai M, Zhang D, Tang X, Gao Z, Shen F, Luo Z, Qin Y, Chen L, Huo Y, Li Q, Ye Z, Zhang Y, Liu C, Wang Y, Wu S, Yang T, Deng H, Zhao J, Lai S, Mu Y, Chen L, Li D, Xu G, Ning G, Wang W, Bi Y, Lu J. Amino acids, microbiota-related metabolites, and the risk of incident diabetes among normoglycemic Chinese adults: Findings from the 4C study. Cell Rep Med 2022; 3:100727. [PMID: 35998626 PMCID: PMC9512668 DOI: 10.1016/j.xcrm.2022.100727] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2022] [Revised: 06/16/2022] [Accepted: 07/22/2022] [Indexed: 11/26/2022]
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42
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Lu W, Hu C. Molecular biomarkers for gestational diabetes mellitus and postpartum diabetes. Chin Med J (Engl) 2022; 135:1940-1951. [PMID: 36148588 PMCID: PMC9746787 DOI: 10.1097/cm9.0000000000002160] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2021] [Indexed: 11/25/2022] Open
Abstract
ABSTRACT Gestational diabetes mellitus (GDM) is a growing public health problem worldwide that threatens both maternal and fetal health. Identifying individuals at high risk for GDM and diabetes after GDM is particularly useful for early intervention and prevention of disease progression. In the last decades, a number of studies have used metabolomics, genomics, and proteomic approaches to investigate associations between biomolecules and GDM progression. These studies clearly demonstrate that various biomarkers reflect pathological changes in GDM. The established markers have potential use as screening and diagnostic tools in GDM and in postpartum diabetes research. In the present review, we summarize recent studies of metabolites, single-nucleotide polymorphisms, microRNAs, and proteins associated with GDM and its transition to postpartum diabetes, with a focus on their predictive value in screening and diagnosis.
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Affiliation(s)
- Wenqian Lu
- Shanghai Diabetes Institute, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, Shanghai 200233, China
- The Third School of Clinical Medicine, Southern Medical University, Guangzhou, Guangdong 510630, China
- Department of Endocrinology and Metabolism, Fengxian Central Hospital Affiliated to the Southern Medical University, Shanghai 201400, China
| | - Cheng Hu
- Shanghai Diabetes Institute, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, Shanghai 200233, China
- The Third School of Clinical Medicine, Southern Medical University, Guangzhou, Guangdong 510630, China
- Department of Endocrinology and Metabolism, Fengxian Central Hospital Affiliated to the Southern Medical University, Shanghai 201400, China
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43
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Ma Y, Liu X, Wang J. Small molecules in the big picture of gut microbiome-host cross-talk. EBioMedicine 2022; 81:104085. [PMID: 35636316 PMCID: PMC9156878 DOI: 10.1016/j.ebiom.2022.104085] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2022] [Revised: 05/06/2022] [Accepted: 05/13/2022] [Indexed: 12/12/2022] Open
Abstract
Research on the gut microbiome and related diseases is rapidly growing with the development of sequencing technologies. An increasing number of studies offer new perspectives on disease development or treatment. Among these, the mechanisms of gut microbial metabolite-mediated effects merit better understanding. In this review, we first summarize the shifts in gut microbial metabolites within complex diseases, in which metabolites have correlational and occasionally causal effects on diseases and discuss the reported mechanisms. We further investigate the interactions between gut microbes and drugs, providing insights for precision medication as well as limitations of current research. Finally, we provide new research directions and research strategies for the development of drugs from gut microbial metabolites. FUNDING STATEMENT: None.
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Affiliation(s)
- Yue Ma
- CAS Key Laboratory of Pathogenic Microbiology and Immunology, Institute of Microbiology, Chinese Academy of Sciences, Beijing 100101, China; University of Chinese Academy of Sciences, Beijing 100049, China
| | - Xiaolin Liu
- CAS Key Laboratory of Pathogenic Microbiology and Immunology, Institute of Microbiology, Chinese Academy of Sciences, Beijing 100101, China; University of Chinese Academy of Sciences, Beijing 100049, China
| | - Jun Wang
- CAS Key Laboratory of Pathogenic Microbiology and Immunology, Institute of Microbiology, Chinese Academy of Sciences, Beijing 100101, China; University of Chinese Academy of Sciences, Beijing 100049, China.
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44
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Barupal DK, Mahajan P, Fakouri-Baygi S, Wright RO, Arora M, Teitelbaum SL. CCDB: A database for exploring inter-chemical correlations in metabolomics and exposomics datasets. ENVIRONMENT INTERNATIONAL 2022; 164:107240. [PMID: 35461097 PMCID: PMC9195052 DOI: 10.1016/j.envint.2022.107240] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/01/2022] [Revised: 04/01/2022] [Accepted: 04/08/2022] [Indexed: 05/18/2023]
Abstract
Inter-chemical correlations in metabolomics and exposomics datasets provide valuable information for studying relationships among chemicals reported for human specimens. With an increase in the number of compounds for these datasets, a network graph analysis and visualization of the correlation structure is difficult to interpret. We have developed the Chemical Correlation Database (CCDB), as a systematic catalogue of inter-chemical correlation in publicly available metabolomics and exposomics studies. The database has been provided via an online interface to create single compound-centric views. We have demonstrated various applications of the database to explore: 1) the chemicals from a chemical class such as Per- and Polyfluoroalkyl Substances (PFAS), polycyclic aromatic hydrocarbons (PAHs), polychlorinated biphenyls (PCBs), phthalates and tobacco smoke related metabolites; 2) xenobiotic metabolites such as caffeine and acetaminophen; 3) endogenous metabolites (acyl-carnitines); and 4) unannotated peaks for PFAS. The database has a rich collection of 35 human studies, including the National Health and Nutrition Examination Survey (NHANES) and high-quality untargeted metabolomics datasets. CCDB is supported by a simple, interactive and user-friendly web-interface to retrieve and visualize the inter-chemical correlation data. The CCDB has the potential to be a key computational resource in metabolomics and exposomics facilitating the expansion of our understanding about biological and chemical relationships among metabolites and chemical exposures in the human body. The database is available at www.ccdb.idsl.me site.
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Affiliation(s)
- Dinesh Kumar Barupal
- Department of Environmental Medicine and Public Health, Institute for Exposomic Research, Icahn School of Medicine at Mount Sinai, 17 E 102nd St, CAM Building, New York 10029, USA.
| | - Priyanka Mahajan
- Department of Environmental Medicine and Public Health, Institute for Exposomic Research, Icahn School of Medicine at Mount Sinai, 17 E 102nd St, CAM Building, New York 10029, USA
| | - Sadjad Fakouri-Baygi
- Department of Environmental Medicine and Public Health, Institute for Exposomic Research, Icahn School of Medicine at Mount Sinai, 17 E 102nd St, CAM Building, New York 10029, USA
| | - Robert O Wright
- Department of Environmental Medicine and Public Health, Institute for Exposomic Research, Icahn School of Medicine at Mount Sinai, 17 E 102nd St, CAM Building, New York 10029, USA
| | - Manish Arora
- Department of Environmental Medicine and Public Health, Institute for Exposomic Research, Icahn School of Medicine at Mount Sinai, 17 E 102nd St, CAM Building, New York 10029, USA
| | - Susan L Teitelbaum
- Department of Environmental Medicine and Public Health, Institute for Exposomic Research, Icahn School of Medicine at Mount Sinai, 17 E 102nd St, CAM Building, New York 10029, USA
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Qi Q, Li J, Yu B, Moon JY, Chai JC, Merino J, Hu J, Ruiz-Canela M, Rebholz C, Wang Z, Usyk M, Chen GC, Porneala BC, Wang W, Nguyen Q, Feofanova EV, Grove ML, Wang TJ, Gerszten RE, Dupuis J, Salas-Salvadó J, Bao W, Perkins DL, Daviglus ML, Thyagarajan B, Cai J, Wang T, Manson JE, Martínez-González MA, Selvin E, Rexrode KM, Clish CB, Hu FB, Meigs JB, Knight R, Burk RD, Boerwinkle E, Kaplan RC. Host and gut microbial tryptophan metabolism and type 2 diabetes: an integrative analysis of host genetics, diet, gut microbiome and circulating metabolites in cohort studies. Gut 2022; 71:1095-1105. [PMID: 34127525 PMCID: PMC8697256 DOI: 10.1136/gutjnl-2021-324053] [Citation(s) in RCA: 106] [Impact Index Per Article: 53.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/07/2021] [Accepted: 06/07/2021] [Indexed: 01/05/2023]
Abstract
OBJECTIVE Tryptophan can be catabolised to various metabolites through host kynurenine and microbial indole pathways. We aimed to examine relationships of host and microbial tryptophan metabolites with incident type 2 diabetes (T2D), host genetics, diet and gut microbiota. METHOD We analysed associations between circulating levels of 11 tryptophan metabolites and incident T2D in 9180 participants of diverse racial/ethnic backgrounds from five cohorts. We examined host genome-wide variants, dietary intake and gut microbiome associated with these metabolites. RESULTS Tryptophan, four kynurenine-pathway metabolites (kynurenine, kynurenate, xanthurenate and quinolinate) and indolelactate were positively associated with T2D risk, while indolepropionate was inversely associated with T2D risk. We identified multiple host genetic variants, dietary factors, gut bacteria and their potential interplay associated with these T2D-relaetd metabolites. Intakes of fibre-rich foods, but not protein/tryptophan-rich foods, were the dietary factors most strongly associated with tryptophan metabolites. The fibre-indolepropionate association was partially explained by indolepropionate-associated gut bacteria, mostly fibre-using Firmicutes. We identified a novel association between a host functional LCT variant (determining lactase persistence) and serum indolepropionate, which might be related to a host gene-diet interaction on gut Bifidobacterium, a probiotic bacterium significantly associated with indolepropionate independent of other fibre-related bacteria. Higher milk intake was associated with higher levels of gut Bifidobacterium and serum indolepropionate only among genetically lactase non-persistent individuals. CONCLUSION Higher milk intake among lactase non-persistent individuals, and higher fibre intake were associated with a favourable profile of circulating tryptophan metabolites for T2D, potentially through the host-microbial cross-talk shifting tryptophan metabolism toward gut microbial indolepropionate production.
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Affiliation(s)
- Qibin Qi
- Department of Epidemiology and Population Health, Yeshiva University Albert Einstein College of Medicine, Bronx, New York, USA .,Department of Nutrtion, Harvard University T H Chan School of Public Health, Boston, Massachusetts, USA
| | - Jun Li
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA,Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA
| | - Bing Yu
- Department of Epidemiology, Human Genetics, and Environmental Sciences, School of Public Health, the University of Texas Health Science Center at Houston, Houston, TX 77030, USA
| | - Jee-Young Moon
- Department of Epidemiology and Population Health, Albert Einstein College of Medicine, Bronx, NY 10461, USA
| | - Jin Choul Chai
- Department of Epidemiology and Population Health, Albert Einstein College of Medicine, Bronx, NY 10461, USA
| | - Jordi Merino
- Diabetes Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA 02114, USA,Programs in Metabolism and Medical & Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA,Department of Medicine, Harvard Medical School, Boston, MA 02115, USA,Vascular Medicine and Metabolism Unit, Research Unit on Lipids and Atherosclerosis, Institut d’Investigacio Sanitaria Pere Virgili, Universitat Rovira i Virgili, Reus 43201, Spain
| | - Jie Hu
- Division of Women’s Health, Department of Medicine at Brigham and Women’s Hospital, Harvard Medical School, Boston, MA 02115, USA
| | - Miguel Ruiz-Canela
- Department of Preventive Medicine and Public Health, University of Navarra, Pamplona 31008, Spain,CIBER Fisiopatologıa de la Obesidad y Nutricion, Instituto de Salud Carlos III, Madrid 28029, Spain,Instituto de Investigacion Sanitaria de Navarra, Edificio LUNA-Navarrabiomed, Pamplona 31008, Spain
| | - Casey Rebholz
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland 21287, USA
| | - Zheng Wang
- Department of Epidemiology and Population Health, Albert Einstein College of Medicine, Bronx, NY 10461, USA
| | - Mykhaylo Usyk
- Departments of Pediatrics, Microbiology and Immunology, and Gynecology and Women’s Health, Albert Einstein College of Medicine, Bronx, NY 10461, USA
| | - Guo-Chong Chen
- Department of Epidemiology and Population Health, Albert Einstein College of Medicine, Bronx, NY 10461, USA
| | - Bianca C. Porneala
- Division of General Internal Medicine, Massachusetts General Hospital, Boston, MA 02114, USA
| | - Wenshuang Wang
- Department of Epidemiology, Human Genetics, and Environmental Sciences, School of Public Health, the University of Texas Health Science Center at Houston, Houston, TX 77030, USA,Department of Mathematics, University of Houston, Houston, TX 77204, USA
| | - Quynh Nguyen
- Department of Epidemiology, Human Genetics, and Environmental Sciences, School of Public Health, the University of Texas Health Science Center at Houston, Houston, TX 77030, USA
| | - Elena V. Feofanova
- Department of Epidemiology, Human Genetics, and Environmental Sciences, School of Public Health, the University of Texas Health Science Center at Houston, Houston, TX 77030, USA
| | - Megan L. Grove
- Department of Epidemiology, Human Genetics, and Environmental Sciences, School of Public Health, the University of Texas Health Science Center at Houston, Houston, TX 77030, USA
| | - Thomas J. Wang
- Department of Internal Medicine, University of Texas Southwestern Medical Center, Dallas, USA
| | - Robert E. Gerszten
- Programs in Metabolism and Medical & Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA,Division of Cardiovascular Medicine, Beth Israel Deaconess Medical Center, Boston, MA 02215, USA
| | - Josée Dupuis
- Department of Biostatistics, Boston University School of Public Health, Boston, MA 02118, USA
| | - Jordi Salas-Salvadó
- CIBER Fisiopatologıa de la Obesidad y Nutricion, Instituto de Salud Carlos III, Madrid 28029, Spain,Human Nutrition Unit, Faculty of Medicine and Health Sciences, Institut d’Investigacio Sanitaria Pere Virgili, Universitat Rovira i Virgili, Reus 43201, Spain
| | - Wei Bao
- Department of Epidemiology, the University of Iowa College of Public Health, Iowa City, IA 52242, USA
| | - David L. Perkins
- Department of Medicine, University of Illinois College of Medicine, Chicago, IL 60612, USA
| | - Martha L. Daviglus
- Institute of Minority Health Research, University of Illinois College of Medicine, Chicago, IL 60612, USA
| | - Bharat Thyagarajan
- Department of Laboratory Medicine and Pathology, University of Minnesota, Minneapolis, MN 55455, USA
| | - Jianwen Cai
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27516 USA
| | - Tao Wang
- Department of Epidemiology and Population Health, Albert Einstein College of Medicine, Bronx, NY 10461, USA
| | - JoAnn E. Manson
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA,Division of Preventive Medicine, Department of Medicine at Brigham and Women’s Hospital, Harvard Medical School, Boston, MA 02215, USA
| | - Miguel Angel Martínez-González
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA,Department of Preventive Medicine and Public Health, University of Navarra, Pamplona 31008, Spain,CIBER Fisiopatologıa de la Obesidad y Nutricion, Instituto de Salud Carlos III, Madrid 28029, Spain,Instituto de Investigacion Sanitaria de Navarra, Edificio LUNA-Navarrabiomed, Pamplona 31008, Spain
| | - Elizabeth Selvin
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland 21287, USA
| | - Kathryn M. Rexrode
- Division of Women’s Health, Department of Medicine at Brigham and Women’s Hospital, Harvard Medical School, Boston, MA 02115, USA
| | - Clary B. Clish
- Metabolomics Platform, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Frank B. Hu
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA,Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA,Channing Division of Network Medicine, Department of Medicine at Brigham and Women’s Hospital, Harvard Medical School, Boston, MA 02115, USA
| | - James B. Meigs
- Programs in Metabolism and Medical & Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA,Department of Medicine, Harvard Medical School, Boston, MA 02115, USA,Division of General Internal Medicine, Massachusetts General Hospital, Boston, MA 02114, USA
| | - Rob Knight
- Departments of Pediatrics, School of Medicine; Center for Microbiome Innovation, Department of Computer Science and Engineering, Jacobs School of Engineering, University of California San Diego, La Jolla, CA 92093, USA
| | - Robert D. Burk
- Department of Epidemiology and Population Health, Albert Einstein College of Medicine, Bronx, NY 10461, USA,Departments of Pediatrics, Microbiology and Immunology, and Gynecology and Women’s Health, Albert Einstein College of Medicine, Bronx, NY 10461, USA
| | - Eric Boerwinkle
- Department of Epidemiology, Human Genetics, and Environmental Sciences, School of Public Health, the University of Texas Health Science Center at Houston, Houston, TX 77030, USA
| | - Robert C. Kaplan
- Department of Epidemiology and Population Health, Albert Einstein College of Medicine, Bronx, NY 10461, USA,Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA 98109, USA
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46
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The contribution of intact structure and food processing to functionality of plant cell wall-derived dietary fiber. Food Hydrocoll 2022. [DOI: 10.1016/j.foodhyd.2022.107511] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
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47
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Chai JC, Chen GC, Yu B, Xing J, Li J, Khambaty T, Perreira KM, Perera MJ, Vidot DC, Castaneda SF, Selvin E, Rebholz CM, Daviglus ML, Cai J, Van Horn L, Isasi CR, Sun Q, Hawkins M, Xue X, Boerwinkle E, Kaplan RC, Qi Q. Serum Metabolomics of Incident Diabetes and Glycemic Changes in a Population With High Diabetes Burden: The Hispanic Community Health Study/Study of Latinos. Diabetes 2022; 71:1338-1349. [PMID: 35293992 PMCID: PMC9163555 DOI: 10.2337/db21-1056] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/24/2021] [Accepted: 03/02/2022] [Indexed: 01/22/2023]
Abstract
Metabolomic signatures of incident diabetes remain largely unclear for the U.S. Hispanic/Latino population, a group with high diabetes burden. We evaluated the associations of 624 known serum metabolites (measured by a global, untargeted approach) with incident diabetes in a subsample (n = 2,010) of the Hispanic Community Health Study/Study of Latinos without diabetes and cardiovascular disease at baseline (2008-2011). Based on the significant metabolites associated with incident diabetes, metabolite modules were detected using topological network analysis, and their associations with incident diabetes and longitudinal changes in cardiometabolic traits were further examined. There were 224 incident cases of diabetes after an average 6 years of follow-up. After adjustment for sociodemographic, behavioral, and clinical factors, 134 metabolites were associated with incident diabetes (false discovery rate-adjusted P < 0.05). We identified 10 metabolite modules, including modules comprising previously reported diabetes-related metabolites (e.g., sphingolipids, phospholipids, branched-chain and aromatic amino acids, glycine), and 2 reflecting potentially novel metabolite groups (e.g., threonate, N-methylproline, oxalate, and tartarate in a plant food metabolite module and androstenediol sulfates in an androgenic steroid metabolite module). The plant food metabolite module and its components were associated with higher diet quality (especially higher intakes of healthy plant-based foods), lower risk of diabetes, and favorable longitudinal changes in HOMA for insulin resistance. The androgenic steroid module and its component metabolites decreased with increasing age and were associated with a higher risk of diabetes and greater increases in 2-h glucose over time. We replicated the associations of both modules with incident diabetes in a U.S. cohort of non-Hispanic Black and White adults (n = 1,754). Among U.S. Hispanic/Latino adults, we identified metabolites across various biological pathways, including those reflecting androgenic steroids and plant-derived foods, associated with incident diabetes and changes in glycemic traits, highlighting the importance of hormones and dietary intake in the pathogenesis of diabetes.
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Affiliation(s)
- Jin Choul Chai
- Department of Epidemiology and Population Health, Albert Einstein College of Medicine, Bronx, NY
| | - Guo-Chong Chen
- Department of Epidemiology and Population Health, Albert Einstein College of Medicine, Bronx, NY
- Department of Nutrition and Food Hygiene, School of Public Health, Soochow University, Suzhou, China
| | - Bing Yu
- Department of Epidemiology and Human Genetics Center, The University of Texas Health Science Center at Houston, Houston, TX
| | - Jiaqian Xing
- Department of Epidemiology and Population Health, Albert Einstein College of Medicine, Bronx, NY
| | - Jun Li
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA
| | | | - Krista M. Perreira
- Department of Social Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC
| | | | - Denise C. Vidot
- School of Nursing and Health Studies, University of Miami, Coral Gables, FL
| | | | - Elizabeth Selvin
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD
| | - Casey M. Rebholz
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD
| | - Martha L. Daviglus
- Institute for Minority Health Research, University of Illinois at Chicago, Chicago, IL
| | - Jianwen Cai
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC
| | - Linda Van Horn
- Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL
| | - Carmen R. Isasi
- Department of Epidemiology and Population Health, Albert Einstein College of Medicine, Bronx, NY
| | - Qi Sun
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women’s Hospital, Boston, MA
- Joslin Diabetes Center, Boston, MA
| | - Meredith Hawkins
- Diabetes Research Center, Albert Einstein College of Medicine, Bronx, NY
- Department of Medicine, Albert Einstein College of Medicine, Bronx, NY
| | - Xiaonan Xue
- Department of Epidemiology and Population Health, Albert Einstein College of Medicine, Bronx, NY
| | - Eric Boerwinkle
- Department of Epidemiology and Human Genetics Center, The University of Texas Health Science Center at Houston, Houston, TX
| | - Robert C. Kaplan
- Department of Epidemiology and Population Health, Albert Einstein College of Medicine, Bronx, NY
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA
| | - Qibin Qi
- Department of Epidemiology and Population Health, Albert Einstein College of Medicine, Bronx, NY
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA
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48
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Du L, Li Q, Yi H, Kuang T, Tang Y, Fan G. Gut microbiota-derived metabolites as key actors in type 2 diabetes mellitus. Biomed Pharmacother 2022; 149:112839. [PMID: 35325852 DOI: 10.1016/j.biopha.2022.112839] [Citation(s) in RCA: 39] [Impact Index Per Article: 19.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2022] [Revised: 03/14/2022] [Accepted: 03/16/2022] [Indexed: 12/01/2022] Open
Abstract
Type 2 diabetes mellitus (T2DM) is one of the most risk factors threatening human health. Although genetic and environmental factors contribute to the development of T2DM, gut microbiota has also been found to be involved. Gut microbiota-derived metabolites are a key factor in host-microbe crosstalk, and have been revealed to play a central role in the physiology and physiopathology of T2DM. In this review, we provide a timely and comprehensive summary of the microbial metabolites that are protective or causative for T2DM, including some amino acids-derived metabolites, short-chain fatty acids, trimethylamine N-oxide, and bile acids. The mechanisms by which metabolites affect T2DM have been elaborated. Knowing more about these processes will increase our understanding of the causal relationship between gut microbiota and T2DM. Moreover, some frontier therapies that target gut microbes and their metabolites to improve T2DM, including dietary intervention, fecal microbiota transplantation, probiotics, prebiotics or synbiotics intervention, and drugging microbial metabolism, have been critically discussed. This review may provide novel insights for the development of targeted and personalized treatments for T2DM based on gut microbial metabolites. More high-quality clinical trials are needed to accelerate the clinical translation of gut-targeted therapies for T2DM.
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Affiliation(s)
- Leilei Du
- State Key Laboratory of Southwestern Chinese Medicine Resources, School of Pharmacy, Chengdu University of Traditional Chinese Medicine, Chengdu 611137, China; School of Ethnic Medicine, Chengdu University of Traditional Chinese Medicine, Chengdu 611137, China
| | - Qi Li
- State Key Laboratory of Southwestern Chinese Medicine Resources, School of Pharmacy, Chengdu University of Traditional Chinese Medicine, Chengdu 611137, China
| | - Huan Yi
- State Key Laboratory of Southwestern Chinese Medicine Resources, School of Pharmacy, Chengdu University of Traditional Chinese Medicine, Chengdu 611137, China
| | - Tingting Kuang
- School of Ethnic Medicine, Chengdu University of Traditional Chinese Medicine, Chengdu 611137, China
| | - Yi Tang
- Department of Endocrinology, Chengdu Fifth People's Hospital, Chengdu 611130, China.
| | - Gang Fan
- State Key Laboratory of Southwestern Chinese Medicine Resources, School of Pharmacy, Chengdu University of Traditional Chinese Medicine, Chengdu 611137, China; School of Ethnic Medicine, Chengdu University of Traditional Chinese Medicine, Chengdu 611137, China.
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49
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Morze J, Wittenbecher C, Schwingshackl L, Danielewicz A, Rynkiewicz A, Hu FB, Guasch-Ferré M. Metabolomics and Type 2 Diabetes Risk: An Updated Systematic Review and Meta-analysis of Prospective Cohort Studies. Diabetes Care 2022; 45:1013-1024. [PMID: 35349649 PMCID: PMC9016744 DOI: 10.2337/dc21-1705] [Citation(s) in RCA: 92] [Impact Index Per Article: 46.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/13/2021] [Accepted: 01/20/2022] [Indexed: 02/03/2023]
Abstract
BACKGROUND Due to the rapidly increasing availability of metabolomics data in prospective studies, an update of the meta evidence on metabolomics and type 2 diabetes risk is warranted. PURPOSE To conduct an updated systematic review and meta-analysis of plasma, serum, and urine metabolite markers and incident type 2 diabetes. DATA SOURCES We searched PubMed and Embase until 6 March 2021. STUDY SELECTION We selected prospective observational studies where investigators used high-throughput techniques to investigate the relationship between plasma, serum, or urine metabolites and incident type 2 diabetes. DATA EXTRACTION Baseline metabolites per-SD risk estimates and 95% CIs for incident type 2 diabetes were extracted from all eligible studies. DATA SYNTHESIS A total of 61 reports with 71,196 participants and 11,771 type 2 diabetes cases/events were included in the updated review. Meta-analysis was performed for 412 metabolites, of which 123 were statistically significantly associated (false discovery rate-corrected P < 0.05) with type 2 diabetes risk. Higher plasma and serum levels of certain amino acids (branched-chain, aromatic, alanine, glutamate, lysine, and methionine), carbohydrates and energy-related metabolites (mannose, trehalose, and pyruvate), acylcarnitines (C4-DC, C4-OH, C5, C5-OH, and C8:1), the majority of glycerolipids (di- and triacylglycerols), (lyso)phosphatidylethanolamines, and ceramides included in meta-analysis were associated with higher risk of type 2 diabetes (hazard ratio 1.07-2.58). Higher levels of glycine, glutamine, betaine, indolepropionate, and (lyso)phosphatidylcholines were associated with lower type 2 diabetes risk (hazard ratio 0.69-0.90). LIMITATIONS Substantial heterogeneity (I2 > 50%, τ2 > 0.1) was observed for some of the metabolites. CONCLUSIONS Several plasma and serum metabolites, including amino acids, lipids, and carbohydrates, are associated with type 2 diabetes risk.
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Affiliation(s)
- Jakub Morze
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA
- Department of Cardiology and Internal Medicine, School of Medicine, University of Warmia and Mazury in Olsztyn, Olsztyn, Poland
- Department of Human Nutrition, Faculty of Food Sciences, University of Warmia and Mazury in Olsztyn, Olsztyn, Poland
| | - Clemens Wittenbecher
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA
- Department of Molecular Epidemiology, German Institute of Human Nutrition Potsdam-Rehbruecke, Nuthetal, Germany
| | - Lukas Schwingshackl
- Institute for Evidence in Medicine, Medical Centre—University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Anna Danielewicz
- Department of Human Nutrition, Faculty of Food Sciences, University of Warmia and Mazury in Olsztyn, Olsztyn, Poland
| | - Andrzej Rynkiewicz
- Department of Cardiology and Internal Medicine, School of Medicine, University of Warmia and Mazury in Olsztyn, Olsztyn, Poland
| | - Frank B. Hu
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA
- Channing Division for Network Medicine, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA
| | - Marta Guasch-Ferré
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA
- Channing Division for Network Medicine, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA
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50
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Wang Y, Gao X, Lv J, Zeng Y, Li Q, Wang L, Zhang Y, Gao W, Wang J. Gut Microbiome Signature Are Correlated With Bone Mineral Density Alterations in the Chinese Elders. Front Cell Infect Microbiol 2022; 12:827575. [PMID: 35433497 PMCID: PMC9008261 DOI: 10.3389/fcimb.2022.827575] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2021] [Accepted: 03/10/2022] [Indexed: 01/03/2023] Open
Abstract
Objective Osteoporosis (OP), clinically featured with a low bone mineral density (BMD) and high risk of bone fracture, has become a major risk factor of disability and death in the elders, especially in postmenopausal women. The gut microbiome (GM) is thought to be implicated in bone metabolism. Herein, we clarified the composition signature and gene functional profile of GM in older people with normal and low BMD. Design and Methods A total of 455 participants underwent the BMD measurement and biochemical detection. GM analysis was further performed on 113 cases of postmenopausal women and men aged over 50, including both 16S rRNA and metagenomic sequencing. Results Generally, the BMD value was significantly lower in the older age groups, especially in the postmenopausal women. Consistently, we observed obvious vitamin D deficiency or insufficiency in females (compared to the male, P < 0.0001). The results from 16S rRNA sequencing revealed higher numbers of OTUs and diversity indexes in females than in males. The abundance in composition of Firmicutes and Clostridiales were correlated with the BMD values in females. LEfSe analysis discovered several enriched bacteria taxons in OP and normal control (NC) subgroups. A positive correlation between the number of genes and BMD values was observed in females based on metagenomic sequencing analysis. Furthermore, we identified the connecting modules among the GM composition – gene functional signature – BMD value/T score in both females and males. Conclusions This study provides evidences upon which to understand the mechanisms of the effects of GM on bone health, consequently revealing the physiology status and potential diagnostic/therapeutic targets based on GM for OP and postmenopausal osteoporosis (PMOP). Besides, the status of vitamin D deficiency or insufficiency need to be concerned and improved in the Chinese people.
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Affiliation(s)
- Yangyang Wang
- School of Electronics and Information, Northwestern Polytechnical University, Xi’an, China
| | - Xiaoguang Gao
- School of Electronics and Information, Northwestern Polytechnical University, Xi’an, China
| | - Jing Lv
- Clinical Laboratory of Honghui Hospital, Xi’an Jiaotong University, Xi’an, China
| | - Yuhong Zeng
- Department of Osteoporosis, Honghui Hospital, Xi’an Jiaotong University, Xi’an, China
| | - Qingmei Li
- Department of Osteoporosis, Honghui Hospital, Xi’an Jiaotong University, Xi’an, China
| | - Liping Wang
- Department of Cardiology, Honghui Hospital, Xi’an Jiaotong University, Xi’an, China
| | - Yuanyuan Zhang
- Department of Cardiology, Honghui Hospital, Xi’an Jiaotong University, Xi’an, China
| | - Wenjie Gao
- Department of Spine Surgery, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, China
- *Correspondence: Wenjie Gao, ; Jihan Wang,
| | - Jihan Wang
- Xi’an Key Laboratory of Stem Cell and Regenerative Medicine, Institute of Medical Research, Northwestern Polytechnical University, Xi’an, China
- *Correspondence: Wenjie Gao, ; Jihan Wang,
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