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Luo K, Taryn A, Moon EH, Peters BA, Solomon SD, Daviglus ML, Kansal MM, Thyagarajan B, Gellman MD, Cai J, Burk RD, Knight R, Kaplan RC, Cheng S, Rodriguez CJ, Qi Q, Yu B. Gut microbiota, blood metabolites, and left ventricular diastolic dysfunction in US Hispanics/Latinos. Microbiome 2024; 12:85. [PMID: 38725043 PMCID: PMC11084054 DOI: 10.1186/s40168-024-01797-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/02/2023] [Accepted: 03/21/2024] [Indexed: 05/12/2024]
Abstract
BACKGROUND Left ventricular diastolic dysfunction (LVDD) is an important precursor of heart failure (HF), but little is known about its relationship with gut dysbiosis and microbial-related metabolites. By leveraging the multi-omics data from the Hispanic Community Health Study/Study of Latinos (HCHS/SOL), a study with population at high burden of LVDD, we aimed to characterize gut microbiota associated with LVDD and identify metabolite signatures of gut dysbiosis and incident LVDD. RESULTS We included up to 1996 Hispanic/Latino adults (mean age: 59.4 years; 67.1% female) with comprehensive echocardiography assessments, gut microbiome, and blood metabolome data. LVDD was defined through a composite criterion involving tissue Doppler assessment and left atrial volume index measurements. Among 1996 participants, 916 (45.9%) had prevalent LVDD, and 212 out of 594 participants without LVDD at baseline developed incident LVDD over a median 4.3 years of follow-up. Using multivariable-adjusted analysis of compositions of microbiomes (ANCOM-II) method, we identified 7 out of 512 dominant gut bacterial species (prevalence > 20%) associated with prevalent LVDD (FDR-q < 0.1), with inverse associations being found for Intestinimonas_massiliensis, Clostridium_phoceensis, and Bacteroide_coprocola and positive associations for Gardnerella_vaginali, Acidaminococcus_fermentans, Pseudomonas_aeruginosa, and Necropsobacter_massiliensis. Using multivariable adjusted linear regression, 220 out of 669 circulating metabolites with detection rate > 75% were associated with the identified LVDD-related bacterial species (FDR-q < 0.1), with the majority being linked to Intestinimonas_massiliensis, Clostridium_phoceensis, and Acidaminococcus_fermentans. Furthermore, 46 of these bacteria-associated metabolites, mostly glycerophospholipids, secondary bile acids, and amino acids, were associated with prevalent LVDD (FDR-q < 0.1), 21 of which were associated with incident LVDD (relative risk ranging from 0.81 [p = 0.001, for guanidinoacetate] to 1.25 [p = 9 × 10-5, for 1-stearoyl-2-arachidonoyl-GPE (18:0/20:4)]). The inclusion of these 21 bacterial-related metabolites significantly improved the prediction of incident LVDD compared with a traditional risk factor model (the area under the receiver operating characteristic curve [AUC] = 0.73 vs 0.70, p = 0.001). Metabolite-based proxy association analyses revealed the inverse associations of Intestinimonas_massilliensis and Clostridium_phoceensis and the positive association of Acidaminococcus_fermentans with incident LVDD. CONCLUSION In this study of US Hispanics/Latinos, we identified multiple gut bacteria and related metabolites linked to LVDD, suggesting their potential roles in this preclinical HF entity. Video Abstract.
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Affiliation(s)
- Kai Luo
- Department of Epidemiology and Population Health, Albert Einstein College of Medicine, Bronx, NY, 10461, USA
| | - Alkis Taryn
- Department of Epidemiology, Human Genetics and Environmental Sciences, School of Public Health, University of Texas Health Science Center at Houston, Houston, TX, 77030, USA
| | - Eun-Hye Moon
- Department of Epidemiology, Human Genetics and Environmental Sciences, School of Public Health, University of Texas Health Science Center at Houston, Houston, TX, 77030, USA
| | - Brandilyn A Peters
- Department of Epidemiology and Population Health, Albert Einstein College of Medicine, Bronx, NY, 10461, USA
| | - Scott D Solomon
- Cardiovascular Medicine, Brigham and Women's Hospital, Boston, MA, 02115, USA
| | - Martha L Daviglus
- Institute for Minority Health Research, University of Illinois Chicago College of Medicine, Chicago, IL, 60612, USA
| | - Mayank M Kansal
- Clinical Medicine, University of Illinois College of Medicine, Chicago, IL, 60612, USA
| | - Bharat Thyagarajan
- Department of Laboratory Medicine & Pathology, University of Minnesota Medical School, Minneapolis, MN, 55455, USA
| | - Marc D Gellman
- Department of Psychology, Clinical Research Building, Miller School of Medicine, University of Miami, Miami, FL, 33136, USA
| | - Jianwen Cai
- Department of Biostatistics, The University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA
| | - Robert D Burk
- Department of Epidemiology and Population Health, Albert Einstein College of Medicine, Bronx, NY, 10461, USA
- Department of Microbiology and Immunology, Albert Einstein College of Medicine, Bronx, NY, 10461, USA
- Department of Obstetrics and Gynecology and Women's Health, Albert Einstein College of Medicine, Bronx, NY, 10461, USA
- Department of Pediatrics, Albert Einstein College of Medicine, NY10461, Bronx, USA
| | - Rob Knight
- Center for Microbiome Innovation, University of California, La Jolla, San Diego, CA, 92093, USA
- Department of Bioengineering, University of California, La Jolla, San Diego, CA, 92093, USA
- Department of Pediatrics, University of California, La Jolla, San Diego, CA, 92093, USA
- Department of Computer Science and Engineering, University of California, La Jolla, San Diego, CA, 92093, 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
| | - Susan Cheng
- Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA, 90048, USA
| | - Carlos J Rodriguez
- Department of Epidemiology and Population Health, Albert Einstein College of Medicine, Bronx, NY, 10461, USA
- Department of Medicine, Albert Einstein College of Medicine, Bronx, NY, 10461, USA
| | - Qibin Qi
- Department of Epidemiology and Population Health, Albert Einstein College of Medicine, Bronx, NY, 10461, USA.
- Department of Nutrition, 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, University of Texas Health Science Center at Houston, Houston, TX, 77030, USA.
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Luo K, Peters BA, Moon JY, Xue X, Wang Z, Usyk M, Hanna DB, Landay AL, Schneider MF, Gustafson D, Weber KM, French A, Sharma A, Anastos K, Wang T, Brown T, Clish CB, Kaplan RC, Knight R, Burk RD, Qi Q. Metabolic and inflammatory perturbation of diabetes associated gut dysbiosis in people living with and without HIV infection. Genome Med 2024; 16:59. [PMID: 38643166 PMCID: PMC11032597 DOI: 10.1186/s13073-024-01336-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2023] [Accepted: 04/16/2024] [Indexed: 04/22/2024] Open
Abstract
BACKGROUND Gut dysbiosis has been linked with both HIV infection and diabetes, but its interplay with metabolic and inflammatory responses in diabetes, particularly in the context of HIV infection, remains unclear. METHODS We first conducted a cross-sectional association analysis to characterize the gut microbial, circulating metabolite, and immune/inflammatory protein features associated with diabetes in up to 493 women (~ 146 with prevalent diabetes with 69.9% HIV +) of the Women's Interagency HIV Study. Prospective analyses were then conducted to determine associations of identified metabolites with incident diabetes over 12 years of follow-up in 694 participants (391 women from WIHS and 303 men from the Multicenter AIDS Cohort Study; 166 incident cases were recorded) with and without HIV infection. Mediation analyses were conducted to explore whether gut bacteria-diabetes associations are explained by altered metabolites and proteins. RESULTS Seven gut bacterial genera were identified to be associated with diabetes (FDR-q < 0.1), with positive associations for Shigella, Escherichia, Megasphaera, and Lactobacillus, and inverse associations for Adlercreutzia, Ruminococcus, and Intestinibacter. Importantly, the associations of most species, especially Adlercreutzia and Ruminococcus, were largely independent of antidiabetic medications use. Meanwhile, 18 proteins and 76 metabolites, including 3 microbially derived metabolites (trimethylamine N-oxide, phenylacetylglutamine (PAGln), imidazolepropionic acid (IMP)), 50 lipids (e.g., diradylglycerols (DGs) and triradylglycerols (TGs)) and 23 non-lipid metabolites, were associated with diabetes (FDR-q < 0.1), with the majority showing positive associations and more than half of them (59/76) associated with incident diabetes. In mediation analyses, several proteins, especially interleukin-18 receptor 1 and osteoprotegerin, IMP and PAGln partially mediate the observed bacterial genera-diabetes associations, particularly for those of Adlercreutzia and Escherichia. Many diabetes-associated metabolites and proteins were altered in HIV, but no effect modification on their associations with diabetes was observed by HIV. CONCLUSION Among individuals with and without HIV, multiple gut bacterial genera, blood metabolites, and proinflammatory proteins were associated with diabetes. The observed mediated effects by metabolites and proteins in genera-diabetes associations highlighted the potential involvement of inflammatory and metabolic perturbations in the link between gut dysbiosis and diabetes in the context of HIV infection.
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Affiliation(s)
- Kai Luo
- Department of Epidemiology and Population Health, Albert Einstein College of Medicine, Bronx, NY, USA
| | - Brandilyn A Peters
- Department of Epidemiology and Population Health, Albert Einstein College of Medicine, Bronx, NY, USA
| | - Jee-Young Moon
- Department of Epidemiology and Population Health, Albert Einstein College of Medicine, Bronx, NY, USA
| | - Xiaonan Xue
- Department of Epidemiology and Population Health, Albert Einstein College of Medicine, Bronx, NY, USA
| | - Zheng Wang
- Department of Epidemiology and Population Health, Albert Einstein College of Medicine, Bronx, NY, USA
| | - Mykhaylo Usyk
- Department of Microbiology and Immunology, Albert Einstein College of Medicine, Bronx, NY, USA
| | - David B Hanna
- Department of Epidemiology and Population Health, Albert Einstein College of Medicine, Bronx, NY, USA
| | - Alan L Landay
- Department of Internal Medicine, Rush University Medical Center, Chicago, IL, USA
| | - Michael F Schneider
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Deborah Gustafson
- Department of Neurology, State University of New York-Downstate Medical Center, Brooklyn, NY, USA
| | | | - Audrey French
- Department of Internal Medicine, Rush University Medical Center, Chicago, IL, USA
| | - Anjali Sharma
- Department of Medicine, Albert Einstein College of Medicine, Bronx, NY, USA
| | - Kathryn Anastos
- Department of Epidemiology and Population Health, Albert Einstein College of Medicine, Bronx, NY, USA
- Department of Medicine, Albert Einstein College of Medicine, Bronx, NY, USA
- Department of Obstetrics and Gynecology and Women's Health, Albert Einstein College of Medicine, Bronx, NY, USA
| | - Tao Wang
- Department of Epidemiology and Population Health, Albert Einstein College of Medicine, Bronx, NY, USA
| | - Todd Brown
- Division of Endocrinology, Diabetes, and Metabolism, Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Clary B Clish
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Robert C Kaplan
- Department of Epidemiology and Population Health, Albert Einstein College of Medicine, Bronx, NY, USA
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
| | - Rob Knight
- Center for Microbiome Innovation, University of California, San Diego, La Jolla, CA, USA
- Department of Bioengineering, University of California, San Diego, La Jolla, CA, USA
- Department of Pediatrics, University of California, San Diego, La Jolla, CA, USA
- Department of Computer Science and Engineering, University of California, San Diego, La Jolla, CA, USA
| | - Robert D Burk
- Department of Epidemiology and Population Health, Albert Einstein College of Medicine, Bronx, NY, USA
- Department of Microbiology and Immunology, Albert Einstein College of Medicine, Bronx, NY, USA
- Department of Obstetrics and Gynecology and Women's Health, Albert Einstein College of Medicine, Bronx, NY, USA
- Department of Pediatrics, Albert Einstein College of Medicine, Bronx, NY, USA
| | - Qibin Qi
- Department of Epidemiology and Population Health, Albert Einstein College of Medicine, Bronx, NY, USA.
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
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Lanng SK, Oxfeldt M, Johansen FT, Risikesan J, Hansen M, Bertram HC. Acute changes in the metabolome following resistance exercise combined with intake of different protein sources (cricket, pea, whey). Metabolomics 2023; 19:98. [PMID: 37999866 DOI: 10.1007/s11306-023-02064-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/17/2023] [Accepted: 11/08/2023] [Indexed: 11/25/2023]
Abstract
INTRODUCTION Separately, both exercise and protein ingestion have been shown to alter the blood and urine metabolome. This study goes a step further and examines changes in the metabolome derived from blood, urine and muscle tissue extracts in response to resistance exercise combined with ingestion of three different protein sources. METHODS In an acute parallel study, 52 young males performed one-legged resistance exercise (leg extension, 4 × 10 repetitions at 10 repetition maximum) followed by ingestion of either cricket (insect), pea or whey protein (0.25 g protein/kg fat free mass). Blood and muscle tissue were collected at baseline and three hours after protein ingestion. Urine was collected at baseline and four hours after protein ingestion. Mixed-effects analyses were applied to examine the effect of the time (baseline vs. post), protein (cricket, pea, whey), and time x protein interaction. RESULTS Nuclear magnetic resonance (NMR)-based metabolomics resulted in the annotation and quantification of 25 metabolites in blood, 35 in urine and 21 in muscle tissue. Changes in the muscle metabolome after combined exercise and protein intake indicated effects related to the protein source ingested. Muscle concentrations of leucine, methionine, glutamate and myo-inositol were higher after intake of whey protein compared to both cricket and pea protein. The blood metabolome revealed changes in a more ketogenic direction three hours after exercise reflecting that the trial was conducted after overnight fasting. Urinary concentration of trimethylamine N-oxide was significantly higher after ingestion of cricket than pea and whey protein. CONCLUSION The blood, urine and muscle metabolome showed different and supplementary responses to exercise and ingestion of the different protein sources, and in synergy the summarized results provided a more complete picture of the metabolic state of the body.
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Affiliation(s)
- Sofie Kaas Lanng
- Department of Food Science, Aarhus University, Aarhus N, 8200, Denmark
- CiFOOD, Centre for Innovative Food Research, Aarhus University, Aarhus N, 8200, Denmark
| | - Mikkel Oxfeldt
- Department of Public Health, Aarhus University, Aarhus C, 8000, Denmark
| | | | - Jeyanthini Risikesan
- Department of Child and Adolescent Medicine, Regional Hospital Gødstrup, Aarhus C, Denmark
| | - Mette Hansen
- Department of Public Health, Aarhus University, Aarhus C, 8000, Denmark
| | - Hanne Christine Bertram
- Department of Food Science, Aarhus University, Aarhus N, 8200, Denmark.
- CiFOOD, Centre for Innovative Food Research, Aarhus University, Aarhus N, 8200, Denmark.
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Xu Q, Wang C, Wang L, Feng R, Guo Y, Feng S, Zhang L, Zheng Z, Su G, Fan L, Bian C, Zhang L, Su X. Correlation analysis of serum reproductive hormones and metabolites during multiple ovulation in sheep. BMC Vet Res 2022; 18:290. [PMID: 35883090 PMCID: PMC9317590 DOI: 10.1186/s12917-022-03387-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2022] [Accepted: 07/14/2022] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND The establishment of non-invasive diagnostic method for multiple ovulation prediction is helpful to improve the efficiency of multiple ovulation. The blood hormones and metabolites would be suitable indexes for this subject. METHODS In this study, 86 estrus ewes (65 of induced estrus (IE) and 21 of spontaneous estrus (SE)) were selected and the blood samples were collected at the day before follicle-stimulating hormone (FSH) injection (1st) and before artificial insemination (2nd). The serum reproductive hormones ofFSH, luteinizing hormone (LH), 17β-Estradiol (E2), progesterone (P4) and anti-Mullerian hormone (AMH) were measured through enzyme linked immunosorbent assay (ELISA) and the untargeted metabolomics analysis was processed through LC-MS/MS. The embryos were collected after 6.5 days of artificial insemination. RESULTS In total, 975 and 406 embryos were collected in IE and SE group, respectively. The analysis of reproductive hormones showed that concentrations of FSH, E2 and AMH were positive correlated with the embryo yield while concentrations of LH and P4 were negative correlated in both group at 1st detection. At 2nd detection, the trends of reproductive hormones were similar with 1st except P4, which was positive correlated with embryo yield. The metabolomics analysis showed that 1158 metabolites (721 in positive iron mode and 437 in negative iron mode) were detected and 617 were annotated. In 1st comparation of high and low embryonic yield populations, 56 and 53 differential metabolites were identified in IE and SE group, respectively. The phosphatidyl choline (PC) (19:0/20:5) and PC (18:2/18:3) were shared in two groups. In 2nd comparation, 48 and 49 differential metabolites were identified in IE and SE group, respectively. The PC (18:1/18:2) and pentadecanoic acid were shared. Most differential metabolites were significantly enriched in amino acid, fatty acid metabolism, digestive system secretion and ovarian steroidogenesis pathways. CONCLUSIONS This study showed that FSH, P4, AMH, the PC relevant metabolites and some anomic acids could be potential biomarkers for embryonic yield prediction in ovine multiple ovulation. The results would help to explain the relation between blood material and ovarian function and provide a theoretical basis for the multiple ovulation prediction.
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Affiliation(s)
- Quanzhong Xu
- State Key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock, Inner Mongolia University, No.49, Xilinguolenan Road, Hohhot, Inner Mongolia Autonomous Region, 010017, People's Republic of China.,School of Life Sciences, Inner Mongolia University, No.49, Xilinguolenan Road, Hohhot, Inner Mongolia Autonomous Region, 010017, People's Republic of China
| | - Chunwei Wang
- State Key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock, Inner Mongolia University, No.49, Xilinguolenan Road, Hohhot, Inner Mongolia Autonomous Region, 010017, People's Republic of China.,School of Life Sciences, Inner Mongolia University, No.49, Xilinguolenan Road, Hohhot, Inner Mongolia Autonomous Region, 010017, People's Republic of China
| | - Lequn Wang
- State Key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock, Inner Mongolia University, No.49, Xilinguolenan Road, Hohhot, Inner Mongolia Autonomous Region, 010017, People's Republic of China.,School of Life Sciences, Inner Mongolia University, No.49, Xilinguolenan Road, Hohhot, Inner Mongolia Autonomous Region, 010017, People's Republic of China
| | - Rui Feng
- State Key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock, Inner Mongolia University, No.49, Xilinguolenan Road, Hohhot, Inner Mongolia Autonomous Region, 010017, People's Republic of China.,School of Life Sciences, Inner Mongolia University, No.49, Xilinguolenan Road, Hohhot, Inner Mongolia Autonomous Region, 010017, People's Republic of China
| | - Yulin Guo
- State Key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock, Inner Mongolia University, No.49, Xilinguolenan Road, Hohhot, Inner Mongolia Autonomous Region, 010017, People's Republic of China.,School of Life Sciences, Inner Mongolia University, No.49, Xilinguolenan Road, Hohhot, Inner Mongolia Autonomous Region, 010017, People's Republic of China
| | - Shuang Feng
- State Key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock, Inner Mongolia University, No.49, Xilinguolenan Road, Hohhot, Inner Mongolia Autonomous Region, 010017, People's Republic of China.,School of Life Sciences, Inner Mongolia University, No.49, Xilinguolenan Road, Hohhot, Inner Mongolia Autonomous Region, 010017, People's Republic of China
| | - Liguo Zhang
- Ulanqab Agriculture and Animal Husbandry Bureau, Ulanqab Animal Husbandry Workstation, Ulanqab, Inner Mongolia Autonomous Region, 012000, People's Republic of China
| | - Zhong Zheng
- State Key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock, Inner Mongolia University, No.49, Xilinguolenan Road, Hohhot, Inner Mongolia Autonomous Region, 010017, People's Republic of China.,School of Life Sciences, Inner Mongolia University, No.49, Xilinguolenan Road, Hohhot, Inner Mongolia Autonomous Region, 010017, People's Republic of China
| | - Guanghua Su
- State Key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock, Inner Mongolia University, No.49, Xilinguolenan Road, Hohhot, Inner Mongolia Autonomous Region, 010017, People's Republic of China.,School of Life Sciences, Inner Mongolia University, No.49, Xilinguolenan Road, Hohhot, Inner Mongolia Autonomous Region, 010017, People's Republic of China
| | - Lifen Fan
- Department of Orthopedics, Ordos Central Hospital, Ordos, Inner Mongolia Autonomous Region, 017000, People's Republic of China
| | - Chao Bian
- Tumor Radiotherapy Department, Inner Mongolia People's Hospital, Hohhot, Inner Mongolia Autonomous Region, 010017, People's Republic of China
| | - Li Zhang
- State Key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock, Inner Mongolia University, No.49, Xilinguolenan Road, Hohhot, Inner Mongolia Autonomous Region, 010017, People's Republic of China.,School of Life Sciences, Inner Mongolia University, No.49, Xilinguolenan Road, Hohhot, Inner Mongolia Autonomous Region, 010017, People's Republic of China
| | - Xiaohu Su
- State Key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock, Inner Mongolia University, No.49, Xilinguolenan Road, Hohhot, Inner Mongolia Autonomous Region, 010017, People's Republic of China. .,School of Life Sciences, Inner Mongolia University, No.49, Xilinguolenan Road, Hohhot, Inner Mongolia Autonomous Region, 010017, People's Republic of China.
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Brachem C, Oluwagbemigun K, Langenau J, Weinhold L, Alexy U, Schmid M, Nöthlings U. Exploring the association between habitual food intake and the urine and blood metabolome in adolescents and young adults: a cohort study. Mol Nutr Food Res 2022; 66:e2200023. [PMID: 35785518 DOI: 10.1002/mnfr.202200023] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2022] [Revised: 05/08/2022] [Indexed: 11/07/2022]
Abstract
SCOPE Habitual diet may be reflected in metabolite profiles that can improve accurate assessment of dietary exposure and further enhance our understanding of their link to health conditions. We aimed to explore the relationship of habitual food intake with blood and urine metabolites in adolescents and young adults. METHODS The study population comprised 228 participants (94 male and 134 female) of the DONALD study. Dietary intake was assessed by yearly repeated 3d-food records. Habitual diet was estimated as the average consumption of 23 food groups in adolescence. Using an untargeted metabolomics approach, we quantified 2638 metabolites in plasma and 1407 metabolites in urine. In each sex, we determined unique diet-metabolite associations using orthogonal projection to latent structures (oPLS) and random forests (RF). RESULTS We observed 6 metabolites in agreement between oPLS and RF in urine, 1 in females (vanillylmandelate to processed/ other meat) and 5 in males (indole-3-acetamide, and N6-methyladenosine to eggs; hippurate, citraconate/glutaconate, and X - 12111 to vegetables). We observed no association in blood in agreement. CONCLUSION We observed a limited reflection of habitual food group intake by single metabolites in urine and not in blood. The explored biomarkers should be confirmed in additional studies. This article is protected by copyright. All rights reserved.
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Affiliation(s)
- Christian Brachem
- Nutritional Epidemiology, Department of Nutrition and Food Sciences, Rheinische Friedrich-Wilhelms-University Bonn, 53115, Bonn, Germany
| | - Kolade Oluwagbemigun
- Nutritional Epidemiology, Department of Nutrition and Food Sciences, Rheinische Friedrich-Wilhelms-University Bonn, 53115, Bonn, Germany
| | - Julia Langenau
- Nutritional Epidemiology, Department of Nutrition and Food Sciences, Rheinische Friedrich-Wilhelms-University Bonn, 53115, Bonn, Germany
| | - Leonie Weinhold
- Institute for Medical Biometry, Informatics and Epidemiology (IMBIE), University Hospital Bonn, 53127, Bonn, Germany
| | - Ute Alexy
- Nutritional Epidemiology, Department of Nutrition and Food Sciences, Rheinische Friedrich-Wilhelms-University Bonn, DONALD Study, Heinstück 11, 44225, Dortmund, Germany
| | - Matthias Schmid
- Institute for Medical Biometry, Informatics and Epidemiology (IMBIE), University Hospital Bonn, 53127, Bonn, Germany
| | - Ute Nöthlings
- Nutritional Epidemiology, Department of Nutrition and Food Sciences, Rheinische Friedrich-Wilhelms-University Bonn, 53115, Bonn, Germany.,Nutritional Epidemiology, Department of Nutrition and Food Sciences, Rheinische Friedrich-Wilhelms-University Bonn, DONALD Study, Heinstück 11, 44225, Dortmund, Germany
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