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Alarifi SN, Alyamani EJ, Alarawi M, Alquait AA, Alolayan MA, Aldossary AM, El-Rahman RAA, Mir R. Integrative Metagenomic Analyses Reveal Gut Microbiota-Derived Multiple Hits Connected to Development of Diabetes Mellitus. Metabolites 2024; 14:720. [PMID: 39728500 DOI: 10.3390/metabo14120720] [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: 11/27/2024] [Revised: 12/11/2024] [Accepted: 12/12/2024] [Indexed: 12/28/2024] Open
Abstract
BACKGROUND/OBJECTIVES Type 2 diabetes mellitus (T2DM) is a chronic metabolic disorder associated with gut dysbiosis. To investigate the association between gut microbiota and T2DM in a Saudi Arabian population. METHODS We conducted a comparative analysis of fecal microbiota from 35 individuals, including both T2DM patients and healthy controls. 16S rRNA gene sequencing was employed to characterize the microbial community structure. RESULTS Our findings revealed significant differences in microbial composition between the two groups. The T2DM group exhibited a higher abundance of Firmicutes and lower levels of Bacteroidetes compared to the healthy control group. At the genus level, T2DM patients showed a decrease in butyrate-producing bacteria such as Bacteroides and Akkermansia, while an increase in Ruminococcus and Prevotella was observed. Additionally, the T2DM group had a higher abundance of Faecalibacterium, Agathobacter, and Lachnospiraceae, along with a lower abundance of Bacteroides. CONCLUSIONS These results suggest that alterations in gut microbiota composition may contribute to the development of T2DM in the Saudi Arabian population. Further large-scale studies are needed to validate these findings and explore potential therapeutic interventions targeting the gut microbiome.
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Affiliation(s)
- Sehad N Alarifi
- Departments of Food and Nutrition Science, Al-Quwayiyah College of Sciences and Humanities, Shaqra University, Al-Quwayiyah 11971, Saudi Arabia
| | - Essam Jamil Alyamani
- Wellness & Preventive Medicine Institute Health Sector King Abdulaziz City for Science and Technology (KACST), Riyadh 11442, Saudi Arabia
| | - Mohammed Alarawi
- King Abdullah University of Science and Technology (KAUST), Jeddah 23955, Saudi Arabia
| | - Azzam A Alquait
- Wellness & Preventive Medicine Institute Health Sector King Abdulaziz City for Science and Technology (KACST), Riyadh 11442, Saudi Arabia
| | - Mohammed A Alolayan
- Wellness & Preventive Medicine Institute Health Sector King Abdulaziz City for Science and Technology (KACST), Riyadh 11442, Saudi Arabia
| | - Ahmad M Aldossary
- Wellness & Preventive Medicine Institute Health Sector King Abdulaziz City for Science and Technology (KACST), Riyadh 11442, Saudi Arabia
| | - Randa A Abd El-Rahman
- Department of Biology, Al-Quwayiyah College of Sciences and Humanities, Shaqra University, Riyadh 11971, Saudi Arabia
| | - Rashid Mir
- Department of Medical Lab Technology, Faculty of Applied Medical Sciences, Prince Fahad Bin Sultan Chair for Biomedical Research, University of Tabuk, Tabuk 71491, Saudi Arabia
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Zhou X, Shen X, Johnson JS, Spakowicz DJ, Agnello M, Zhou W, Avina M, Honkala A, Chleilat F, Chen SJ, Cha K, Leopold S, Zhu C, Chen L, Lyu L, Hornburg D, Wu S, Zhang X, Jiang C, Jiang L, Jiang L, Jian R, Brooks AW, Wang M, Contrepois K, Gao P, Rose SMSF, Tran TDB, Nguyen H, Celli A, Hong BY, Bautista EJ, Dorsett Y, Kavathas PB, Zhou Y, Sodergren E, Weinstock GM, Snyder MP. Longitudinal profiling of the microbiome at four body sites reveals core stability and individualized dynamics during health and disease. Cell Host Microbe 2024; 32:506-526.e9. [PMID: 38479397 PMCID: PMC11022754 DOI: 10.1016/j.chom.2024.02.012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2023] [Revised: 01/23/2024] [Accepted: 02/20/2024] [Indexed: 03/26/2024]
Abstract
To understand the dynamic interplay between the human microbiome and host during health and disease, we analyzed the microbial composition, temporal dynamics, and associations with host multi-omics, immune, and clinical markers of microbiomes from four body sites in 86 participants over 6 years. We found that microbiome stability and individuality are body-site specific and heavily influenced by the host. The stool and oral microbiome are more stable than the skin and nasal microbiomes, possibly due to their interaction with the host and environment. We identify individual-specific and commonly shared bacterial taxa, with individualized taxa showing greater stability. Interestingly, microbiome dynamics correlate across body sites, suggesting systemic dynamics influenced by host-microbial-environment interactions. Notably, insulin-resistant individuals show altered microbial stability and associations among microbiome, molecular markers, and clinical features, suggesting their disrupted interaction in metabolic disease. Our study offers comprehensive views of multi-site microbial dynamics and their relationship with host health and disease.
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Affiliation(s)
- Xin Zhou
- Department of Genetics, Stanford University School of Medicine, Stanford, CA 94305, USA; Stanford Center for Genomics and Personalized Medicine, Stanford, CA 94305, USA; Stanford Diabetes Research Center, Stanford, CA 94305, USA; The Jackson Laboratory for Genomic Medicine, Farmington, CT 06032, USA
| | - Xiaotao Shen
- Department of Genetics, Stanford University School of Medicine, Stanford, CA 94305, USA; Stanford Center for Genomics and Personalized Medicine, Stanford, CA 94305, USA
| | - Jethro S Johnson
- The Jackson Laboratory for Genomic Medicine, Farmington, CT 06032, USA; Oxford Centre for Microbiome Studies, Kennedy Institute of Rheumatology, University of Oxford, Roosevelt Drive, Headington, Oxford OX3 7FY, UK
| | - Daniel J Spakowicz
- The Jackson Laboratory for Genomic Medicine, Farmington, CT 06032, USA; Division of Medical Oncology, Ohio State University Wexner Medical Center, James Cancer Hospital and Solove Research Institute, Columbus, OH 43210, USA
| | | | - Wenyu Zhou
- Department of Genetics, Stanford University School of Medicine, Stanford, CA 94305, USA; Stanford Center for Genomics and Personalized Medicine, Stanford, CA 94305, USA
| | - Monica Avina
- Department of Genetics, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Alexander Honkala
- Department of Genetics, Stanford University School of Medicine, Stanford, CA 94305, USA; Stanford Healthcare Innovation Labs, Stanford University School of Medicine, Stanford, CA 94305, USA; Department of Biomedical Engineering, Oregon Health & Science University, Portland, OR 97239, USA
| | - Faye Chleilat
- Department of Genetics, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Shirley Jingyi Chen
- Stanford Healthcare Innovation Labs, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Kexin Cha
- Stanford Healthcare Innovation Labs, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Shana Leopold
- The Jackson Laboratory for Genomic Medicine, Farmington, CT 06032, USA; Department of Immunobiology, Yale University School of Medicine, New Haven, CT 06520, USA
| | - Chenchen Zhu
- Department of Genetics, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Lei Chen
- The Jackson Laboratory for Genomic Medicine, Farmington, CT 06032, USA; Shanghai Institute of Immunology, Shanghai Jiao Tong University, Shanghai 200240, PRC
| | - Lin Lyu
- Shanghai Institute of Immunology, Shanghai Jiao Tong University, Shanghai 200240, PRC
| | - Daniel Hornburg
- Department of Genetics, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Si Wu
- Department of Genetics, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Xinyue Zhang
- Department of Genetics, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Chao Jiang
- Department of Genetics, Stanford University School of Medicine, Stanford, CA 94305, USA; Life Sciences Institute, Zhejiang University, Hangzhou, Zhejiang 310058, PRC
| | - Liuyiqi Jiang
- Life Sciences Institute, Zhejiang University, Hangzhou, Zhejiang 310058, PRC
| | - Lihua Jiang
- Department of Genetics, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Ruiqi Jian
- Department of Genetics, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Andrew W Brooks
- Department of Genetics, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Meng Wang
- Department of Genetics, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Kévin Contrepois
- Department of Genetics, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Peng Gao
- Department of Genetics, Stanford University School of Medicine, Stanford, CA 94305, USA
| | | | | | - Hoan Nguyen
- The Jackson Laboratory for Genomic Medicine, Farmington, CT 06032, USA
| | - Alessandra Celli
- Department of Genetics, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Bo-Young Hong
- The Jackson Laboratory for Genomic Medicine, Farmington, CT 06032, USA; Woody L Hunt School of Dental Medicine, Texas Tech University Health Science Center, El Paso, TX 79905, USA
| | - Eddy J Bautista
- The Jackson Laboratory for Genomic Medicine, Farmington, CT 06032, USA; Corporación Colombiana de Investigación Agropecuaria (Agrosavia), Headquarters-Mosquera, Cundinamarca 250047, Colombia
| | - Yair Dorsett
- The Jackson Laboratory for Genomic Medicine, Farmington, CT 06032, USA; Department of Medicine, University of Connecticut Health Center, Farmington, CT 06032, USA
| | - Paula B Kavathas
- Department of Immunobiology, Yale University School of Medicine, New Haven, CT 06520, USA; Department of Laboratory Medicine, Yale University School of Medicine, New Haven, CT 06520, USA
| | - Yanjiao Zhou
- The Jackson Laboratory for Genomic Medicine, Farmington, CT 06032, USA; Department of Medicine, University of Connecticut Health Center, Farmington, CT 06032, USA
| | - Erica Sodergren
- The Jackson Laboratory for Genomic Medicine, Farmington, CT 06032, USA
| | | | - Michael P Snyder
- Department of Genetics, Stanford University School of Medicine, Stanford, CA 94305, USA; Stanford Center for Genomics and Personalized Medicine, Stanford, CA 94305, USA; Stanford Diabetes Research Center, Stanford, CA 94305, USA; Stanford Healthcare Innovation Labs, Stanford University School of Medicine, Stanford, CA 94305, USA.
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3
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Zhou X, Shen X, Johnson JS, Spakowicz DJ, Agnello M, Zhou W, Avina M, Honkala A, Chleilat F, Chen SJ, Cha K, Leopold S, Zhu C, Chen L, Lyu L, Hornburg D, Wu S, Zhang X, Jiang C, Jiang L, Jiang L, Jian R, Brooks AW, Wang M, Contrepois K, Gao P, Schüssler-Fiorenza Rose SM, Binh Tran TD, Nguyen H, Celli A, Hong BY, Bautista EJ, Dorsett Y, Kavathas P, Zhou Y, Sodergren E, Weinstock GM, Snyder MP. Longitudinal profiling of the microbiome at four body sites reveals core stability and individualized dynamics during health and disease. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.02.01.577565. [PMID: 38352363 PMCID: PMC10862915 DOI: 10.1101/2024.02.01.577565] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/26/2024]
Abstract
To understand dynamic interplay between the human microbiome and host during health and disease, we analyzed the microbial composition, temporal dynamics, and associations with host multi-omics, immune and clinical markers of microbiomes from four body sites in 86 participants over six years. We found that microbiome stability and individuality are body-site-specific and heavily influenced by the host. The stool and oral microbiome were more stable than the skin and nasal microbiomes, possibly due to their interaction with the host and environment. Also, we identified individual-specific and commonly shared bacterial taxa, with individualized taxa showing greater stability. Interestingly, microbiome dynamics correlated across body sites, suggesting systemic coordination influenced by host-microbial-environment interactions. Notably, insulin-resistant individuals showed altered microbial stability and associations between microbiome, molecular markers, and clinical features, suggesting their disrupted interaction in metabolic disease. Our study offers comprehensive views of multi-site microbial dynamics and their relationship with host health and disease. Study Highlights The stability of the human microbiome varies among individuals and body sites.Highly individualized microbial genera are more stable over time.At each of the four body sites, systematic interactions between the environment, the host and bacteria can be detected.Individuals with insulin resistance have lower microbiome stability, a more diversified skin microbiome, and significantly altered host-microbiome interactions.
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4
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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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5
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Ahmad MA, Karavetian M, Moubareck CA, Wazz G, Mahdy T, Venema K. Association of the gut microbiota with clinical variables in obese and lean Emirati subjects. Front Microbiol 2023; 14:1182460. [PMID: 37680528 PMCID: PMC10481963 DOI: 10.3389/fmicb.2023.1182460] [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: 03/10/2023] [Accepted: 08/01/2023] [Indexed: 09/09/2023] Open
Abstract
Background Growing evidence supports the role of gut microbiota in obesity, yet exact associations remain largely unknown. Specifically, very little is known about this association in the Emirati population. Methods We explored differences in gut microbiota composition, particularly the Firmicutes/Bacteroidetes (F/B) ratio, between 43 obese and 31 lean adult Emirate counterparts, and its association with obesity markers, by using V3-V4 regions of 16 S ribosomal RNA gene sequencing data. Furthermore, we collected anthropometric and biochemical data. Results The two major phyla in obese and lean groups were Firmicutes and Bacteroidetes. We observed a significantly lower alpha diversity (Shannon index) in obese subjects and a significant difference in beta diversity and phylum and genus levels between the two groups. The obese group had higher abundances of Verrucomicrobia and Saccharibacteira and lower abundances of Lentisphaerae. Acidaminococcus and Lachnospira were more abundant in obese subjects and positively correlated with adiposity markers. No correlations were found between the gut microbiota and biochemical variables, such as fasting blood sugar, total cholesterol, HDL cholesterol, LDL cholesterol, and triglycerides. Conclusion We reveal significant differences in the gut microbiota between obese and lean adult Emiratis and an association between certain microbial genera of the gut microbiota and obesity. A better understanding of the interactions between gut microbes, diet, lifestyle, and health is warranted.
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Affiliation(s)
- Manal Ali Ahmad
- School of Nutrition and Translational Research in Metabolism (NUTRIM), Faculty of Health, Medicine and Life Sciences, Maastricht University, Maastricht, Netherlands
| | - Mirey Karavetian
- Faculty of Kinesiology and Physical Education, University of Toronto, Toronto, ON, Canada
| | | | - Gabi Wazz
- Center of Excellence in Bariatric and Metabolic Surgery, Dr. Sulaiman Al Habib Hospital, Dubai, United Arab Emirates
| | - Tarek Mahdy
- Department of Bariatric Surgery, Sharjah University, Sharjah, United Arab Emirates
| | - Koen Venema
- Centre for Healthy Eating and Food Innovation (HEFI), Maastricht University-Campus Venlo, Venlo, Netherlands
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Aljuraiban GS, Alfhili MA, Aldhwayan MM, Aljazairy EA, Al-Musharaf S. Metagenomic Shotgun Sequencing Reveals Specific Human Gut Microbiota Associated with Insulin Resistance and Body Fat Distribution in Saudi Women. Biomolecules 2023; 13:biom13040640. [PMID: 37189387 DOI: 10.3390/biom13040640] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2023] [Revised: 03/22/2023] [Accepted: 03/29/2023] [Indexed: 04/05/2023] Open
Abstract
(1) Background: Gut microbiota dysbiosis may lead to diseases such as insulin resistance and obesity. We aimed to investigate the relationship between insulin resistance, body fat distribution, and gut microbiota composition. (2) Methods: The present study included 92 Saudi women (18–25 years) with obesity (body mass index (BMI) ≥ 30 kg/m2, n = 44) and with normal weight (BMI 18.50–24.99 kg/m2, n = 48). Body composition indices, biochemical data, and stool samples were collected. The whole-genome shotgun sequencing technique was used to analyze the gut microbiota. Participants were divided into subgroups stratified by the homeostatic model assessment for insulin resistance (HOMA-IR) and other adiposity indices. (3) Results: HOMA-IR was inversely correlated with Actinobacteria (r = −0.31, p = 0.003), fasting blood glucose was inversely correlated with Bifidobacterium kashiwanohense (r = −0.22, p = 0.03), and insulin was inversely correlated with Bifidobacterium adolescentis (r = −0.22, p = 0.04). There were significant differences in α- and β-diversities in those with high HOMA-IR and waist–hip ratio (WHR) compared to low HOMA-IR and WHR (p = 0.02, 0.03, respectively). (4) Conclusions: Our findings highlight the relationship between specific gut microbiota at different taxonomic levels and measures of glycemic control in Saudi Arabian women. Future studies are required to determine the role of the identified strains in the development of insulin resistance.
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Affiliation(s)
- Ghadeer S. Aljuraiban
- Department of Community Health Sciences, College of Applied Medical Sciences, King Saud University, Riyadh 11451, Saudi Arabia
| | - Mohammad A. Alfhili
- Department of Clinical Laboratory Sciences, College of Applied Medical Sciences, King Saud University, Riyadh 11451, Saudi Arabia
| | - Madhawi M. Aldhwayan
- Department of Community Health Sciences, College of Applied Medical Sciences, King Saud University, Riyadh 11451, Saudi Arabia
| | - Esra’a A. Aljazairy
- Department of Community Health Sciences, College of Applied Medical Sciences, King Saud University, Riyadh 11451, Saudi Arabia
| | - Sara Al-Musharaf
- Department of Community Health Sciences, College of Applied Medical Sciences, King Saud University, Riyadh 11451, Saudi Arabia
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