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Lee AM, Xu Y, Hu J, Xiao R, Hooper SR, Hartung EA, Coresh J, Rhee EP, Vasan RS, Kimmel PL, Warady BA, Furth SL, Denburg MR. Longitudinal Plasma Metabolome Patterns and Relation to Kidney Function and Proteinuria in Pediatric CKD. Clin J Am Soc Nephrol 2024; 19:837-850. [PMID: 38709558 PMCID: PMC11254025 DOI: 10.2215/cjn.0000000000000463] [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: 12/20/2023] [Accepted: 04/29/2024] [Indexed: 05/08/2024]
Abstract
Key Points Longitudinal untargeted metabolomics. Children with CKD have a circulating metabolome that changes over time. Background Understanding plasma metabolome patterns in relation to changing kidney function in pediatric CKD is important for continued research for identifying novel biomarkers, characterizing biochemical pathophysiology, and developing targeted interventions. There are a limited number of studies of longitudinal metabolomics and virtually none in pediatric CKD. Methods The CKD in Children study is a multi-institutional, prospective cohort that enrolled children aged 6 months to 16 years with eGFR 30–90 ml/min per 1.73 m2. Untargeted metabolomics profiling was performed on plasma samples from the baseline, 2-, and 4-year study visits. There were technologic updates in the metabolomic profiling platform used between the baseline and follow-up assays. Statistical approaches were adopted to avoid direct comparison of baseline and follow-up measurements. To identify metabolite associations with eGFR or urine protein-creatinine ratio (UPCR) among all three time points, we applied linear mixed-effects (LME) models. To identify metabolites associated with time, we applied LME models to the 2- and 4-year follow-up data. We applied linear regression analysis to examine associations between change in metabolite level over time (∆level) and change in eGFR (∆eGFR) and UPCR (∆UPCR). We reported significance on the basis of both the false discovery rate (FDR) <0.05 and P < 0.05. Results There were 1156 person-visits (N : baseline=626, 2-year=254, 4-year=276) included. There were 622 metabolites with standardized measurements at all three time points. In LME modeling, 406 and 343 metabolites associated with eGFR and UPCR at FDR <0.05, respectively. Among 530 follow-up person-visits, 158 metabolites showed differences over time at FDR <0.05. For participants with complete data at both follow-up visits (n =123), we report 35 metabolites with ∆level–∆eGFR associations significant at FDR <0.05. There were no metabolites with significant ∆level–∆UPCR associations at FDR <0.05. We report 16 metabolites with ∆level–∆UPCR associations at P < 0.05 and associations with UPCR in LME modeling at FDR <0.05. Conclusions We characterized longitudinal plasma metabolomic patterns associated with eGFR and UPCR in a large pediatric CKD population. Many of these metabolite signals have been associated with CKD progression, etiology, and proteinuria in previous CKD Biomarkers Consortium studies. There were also novel metabolite associations with eGFR and proteinuria detected.
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Affiliation(s)
- Arthur M. Lee
- Division of Nephrology, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania
| | - Yunwen Xu
- Department of Epidemiology, Johns Hopkins University Bloomberg School of Public Health, Baltimore, Maryland
| | - Jian Hu
- Department of Human Genetics, Emory University School of Medicine, Atlanta, Georgia
| | - Rui Xiao
- Children's Hospital of Philadelphia, Philadelphia, Pennsylvania
- Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, Pennsylvania
| | - Stephen R. Hooper
- Department of Health Sciences, School of Medicine, University of North Carolina-Chapel Hill, Chapel Hill, North Carolina
| | - Erum A. Hartung
- Division of Nephrology, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania
| | - Josef Coresh
- Department of Epidemiology, Johns Hopkins University Bloomberg School of Public Health, Baltimore, Maryland
- Department of Pediatrics, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, Pennsylvania
- NYU Grossman School of Medicine, New York, New York
| | - Eugene P. Rhee
- Division of Nephrology, Massachusetts General Hospital, Boston, Massachusetts
- Harvard Medical School, Boston, Massachusetts
| | - Ramachandran S. Vasan
- Boston University School of Medicine, Boston, Massachusetts
- Boston University School of Public Health, Boston, Massachusetts
| | - Paul L. Kimmel
- Division of Kidney, Urologic, and Hematologic Diseases, National Institutes of Health, National Institute of Diabetes and Digestive and Kidney Diseases, Bethesda, Maryland
| | - Bradley A. Warady
- Division of Nephrology, Children’s Mercy Kansas City, Kansas City, Missouri
- University of Missouri-Kansas City School of Medicine, Kansas City, Missouri
| | - Susan L. Furth
- Division of Nephrology, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania
- Children’s Hospital of Philadelphia Research Institute, Philadelphia, Pennsylvania
- Department of Pediatrics and Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, Pennsylvania
| | - Michelle R. Denburg
- Division of Nephrology, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania
- Department of Pediatrics and Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, Pennsylvania
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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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3
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Lee AM, Xu Y, Hooper SR, Abraham AG, Hu J, Xiao R, Matheson MB, Brunson C, Rhee EP, Coresh J, Vasan RS, Schrauben S, Kimmel PL, Warady BA, Furth SL, Hartung EA, Denburg MR. Circulating Metabolomic Associations with Neurocognitive Outcomes in Pediatric CKD. Clin J Am Soc Nephrol 2024; 19:13-25. [PMID: 37871960 PMCID: PMC10843217 DOI: 10.2215/cjn.0000000000000318] [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: 06/06/2023] [Accepted: 10/16/2023] [Indexed: 10/25/2023]
Abstract
BACKGROUND Children with CKD are at risk for impaired neurocognitive functioning. We investigated metabolomic associations with neurocognition in children with CKD. METHODS We leveraged data from the Chronic Kidney Disease in Children (CKiD) study and the Neurocognitive Assessment and Magnetic Resonance Imaging Analysis of Children and Young Adults with Chronic Kidney Disease (NiCK) study. CKiD is a multi-institutional cohort that enrolled children aged 6 months to 16 years with eGFR 30-90 ml/min per 1.73 m 2 ( n =569). NiCK is a single-center cross-sectional study of participants aged 8-25 years with eGFR<90 ml/min per 1.73 m 2 ( n =60) and matched healthy controls ( n =67). Untargeted metabolomic quantification was performed on plasma (CKiD, 622 metabolites) and serum (NiCK, 825 metabolites) samples. Four neurocognitive domains were assessed: intelligence, attention regulation, working memory, and parent ratings of executive function. Repeat assessments were performed in CKiD at 2-year intervals. Linear regression and linear mixed-effects regression analyses adjusting for age, sex, delivery history, hypertension, proteinuria, CKD duration, and glomerular versus nonglomerular diagnosis were used to identify metabolites associated with neurocognitive z-scores. Analyses were performed with and without adjustment for eGFR. RESULTS There were multiple metabolite associations with neurocognition observed in at least two of the analytic samples (CKiD baseline, CKiD follow-up, and NiCK CKD). Most of these metabolites were significantly elevated in children with CKD compared with healthy controls in NiCK. Notable signals included associations with parental ratings of executive function: phenylacetylglutamine, indoleacetylglutamine, and trimethylamine N-oxide-and with intelligence: γ -glutamyl amino acids and aconitate. CONCLUSIONS Several metabolites were associated with neurocognitive dysfunction in pediatric CKD, implicating gut microbiome-derived substances, mitochondrial dysfunction, and altered energy metabolism, circulating toxins, and redox homeostasis. PODCAST This article contains a podcast at https://dts.podtrac.com/redirect.mp3/www.asn-online.org/media/podcast/CJASN/2023_11_17_CJN0000000000000318.mp3.
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Affiliation(s)
- Arthur M. Lee
- Division of Nephrology, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania
| | - Yunwen Xu
- Department of Epidemiology, Johns Hopkins University Bloomberg School of Public Health, Baltimore, Maryland
| | - Stephen R. Hooper
- Department of Health Sciences, School of Medicine, University of North Carolina-Chapel Hill, Chapel Hill, North Carolina
| | - Alison G. Abraham
- Department of Epidemiology, Colorado University School of Public Health, Aurora, Colorado
| | - Jian Hu
- Department of Human Genetics, Emory University School of Medicine, Atlanta, Georgia
| | - Rui Xiao
- Children's Hospital of Philadelphia, Philadelphia, Pennsylvania
- Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, Pennsylvania
| | - Matthew B. Matheson
- Department of Epidemiology, Johns Hopkins University Bloomberg School of Public Health, Baltimore, Maryland
| | - Celina Brunson
- Division of Nephrology, Children's National Hospital, Washington, DC
| | - Eugene P. Rhee
- Division of Nephrology, Massachusetts General Hospital, Boston, Massachusetts
- Harvard School of Medicine, Boston, Massachusetts
| | - Josef Coresh
- Department of Epidemiology, Johns Hopkins University Bloomberg School of Public Health, Baltimore, Maryland
- Johns Hopkins University School of Medicine, Baltimore, Maryland
| | - Ramachandran S. Vasan
- Boston University School of Medicine, Boston, Massachusetts
- Boston University School of Public Health, Boston, Massachusetts
| | - Sarah Schrauben
- Perelman School of Medicine at the University of Pennsylvania, Department of Medicine and Department of Biostatistics, Epidemiology, and Informatics, Philadelphia, Pennsylvania
- Perelman School of Medicine at the University of Pennsylvania, Philadelphia, Pennsylvania
| | - Paul L. Kimmel
- Division of Kidney, Urologic, and Hematologic Diseases, National Institutes of Health, National Institute of Diabetes and Digestive and Kidney Diseases, Bethesda, Maryland
| | - Bradley A. Warady
- Division of Nephrology, Children's Mercy Kansas City, Kansas City, Missouri
- University of Missouri-Kansas City School of Medicine, Kansas City, Missouri
| | - Susan L. Furth
- Division of Nephrology, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania
- Perelman School of Medicine at the University of Pennsylvania, Philadelphia, Pennsylvania
- Children's Hospital of Philadelphia Research Institute, Philadelphia, Pennsylvania
- Perelman School of Medicine at the University of Pennsylvania, Department of Pediatrics and Department of Biostatistics, Epidemiology, and Informatics, Philadelphia, Pennsylvania
| | - Erum A. Hartung
- Division of Nephrology, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania
- Perelman School of Medicine at the University of Pennsylvania, Department of Pediatrics, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, Pennsylvania
| | - Michelle R. Denburg
- Division of Nephrology, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania
- Perelman School of Medicine at the University of Pennsylvania, Philadelphia, Pennsylvania
- Perelman School of Medicine at the University of Pennsylvania, Department of Pediatrics and Department of Biostatistics, Epidemiology, and Informatics, Philadelphia, Pennsylvania
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Baldeon AD, McDonald D, Gonzalez A, Knight R, Holscher HD. Diet Quality and the Fecal Microbiota in Adults in the American Gut Project. J Nutr 2023; 153:2004-2015. [PMID: 36828255 DOI: 10.1016/j.tjnut.2023.02.018] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2022] [Revised: 01/18/2023] [Accepted: 02/13/2023] [Indexed: 02/24/2023] Open
Abstract
BACKGROUND The Dietary Guidelines for Americans advises on dietary intake to meet nutritional needs, promote health, and prevent diseases. Diet affects the intestinal microbiota and is increasingly linked to health. It is vital to investigate the relationships between diet quality and the microbiota to better understand the impact of nutrition on human health. OBJECTIVES This study aimed to investigate the differences in fecal microbiota composition in adults from the American Gut Project based on their adherence to the Dietary Guidelines for Americans. METHODS This study was a cross-sectional analysis of the 16S sequencing and food frequency data of a subset of adults (n = 432; age = 18-60 y; 65% female, 89% white) participating in the crowdsourced American Gut Project. The Healthy Eating Index-2015 assessed the compliance with Dietary Guideline recommendations. The cohort was divided into tertiles based on Healthy Eating Index-2015 scores, and differences in taxonomic abundances and diversity were compared between high and low scorers. RESULTS The mean Total Score for low-scoring adults (58.1 ± 5.4) was comparable with the reported score of the average American adult (56.7). High scorers for the Total Score and components related to vegetables, grains, and dairy had greater alpha diversity than low scorers. High scorers in the fatty acid component had a lower alpha diversity than low scorers (95% CI: 0.35, 1.85). A positive log-fold difference in abundance of plant carbohydrate-metabolizing taxa in the families Lachnospiraceae and Ruminococcaceae was observed in high-scoring tertiles for Total Score, vegetable, fruit, and grain components (Benjamini-Hochberg; q < 0.05). CONCLUSIONS Adults with greater compliance to the Dietary Guidelines demonstrated higher diversity in their fecal microbiota and greater abundance of bacteria capable of metabolizing complex carbohydrates, providing evidence on how Dietary Guidelines support the gut microbiota.
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Affiliation(s)
- Alexis D Baldeon
- Division of Nutritional Sciences, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA
| | - Daniel McDonald
- Department of Pediatrics, University of California San Diego, La Jolla, California, USA
| | - Antonio Gonzalez
- Department of Pediatrics, University of California San Diego, La Jolla, California, USA
| | - Rob Knight
- Department of Pediatrics, University of California San Diego, La Jolla, California, USA; Center for Microbiome Innovation, University of California San Diego, La Jolla, California, USA; Department of Bioengineering, University of California San Diego, La Jolla, California, USA; Department of Computer Science and Engineering, University of California San Diego, La Jolla, California, USA
| | - Hannah D Holscher
- Division of Nutritional Sciences, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA; Department of Food Science and Human Nutrition, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA.
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Avelino DC, Duffy VB, Puglisi M, Ray S, Lituma-Solis B, Nosal BM, Madore M, Chun OK. Can Ordering Groceries Online Support Diet Quality in Adults Who Live in Low Food Access and Low-Income Environments? Nutrients 2023; 15:nu15040862. [PMID: 36839221 PMCID: PMC9964317 DOI: 10.3390/nu15040862] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2023] [Revised: 01/29/2023] [Accepted: 02/07/2023] [Indexed: 02/10/2023] Open
Abstract
During the COVID-19 pandemic, U.S. food assistance programs allowed the use of program benefits to order groceries online. We examined relationships between the food environment, food assistance, online grocery ordering, and diet quality among adults from one low-income, low food access community in Northeastern Connecticut during the pandemic. Via online survey, adults (n = 276) reported their perceived home and store food environments, food assistance participation, whether they ordered groceries online, and consumption frequency and liking of foods/beverages to calculate diet quality indices. Those who ordered groceries online (44.6%) were more likely to participate in food assistance programs and report greater diet quality. Perceived healthiness of store and home food environments was variable, with the ease of obtaining and selecting unhealthy foods in the neighborhood significantly greater than healthy foods. Healthier perceived home food environments were associated with significantly higher diet qualities, especially among individuals who participated in multiple food assistance programs. Ordering groceries online interacted with multiple measures of the food environment to influence diet quality. Generally, the poorest diet quality was observed among individuals who perceived their store and home food environments as least healthy and who did not order groceries online. Thus, ordering groceries online may support higher diet quality among adults who can use their food assistance for purchasing groceries online and who live in low-income, low-access food environments.
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Affiliation(s)
- Daniela C. Avelino
- Department of Allied Health Sciences, University of Connecticut, Storrs, CT 06269, USA
| | - Valerie B. Duffy
- Department of Allied Health Sciences, University of Connecticut, Storrs, CT 06269, USA
- Correspondence: (V.B.D.); (O.K.C.); Tel.: +860-486-1997 (V.B.D.); +860-486-6275 (O.K.C.)
| | - Michael Puglisi
- Department of Nutritional Sciences, University of Connecticut, Storrs, CT 06269, USA
| | - Snehaa Ray
- Department of Nutritional Sciences, University of Connecticut, Storrs, CT 06269, USA
| | - Brenda Lituma-Solis
- Department of Allied Health Sciences, University of Connecticut, Storrs, CT 06269, USA
| | - Briana M. Nosal
- Department of Nutritional Sciences, University of Connecticut, Storrs, CT 06269, USA
| | - Matthew Madore
- Department of Nutritional Sciences, University of Connecticut, Storrs, CT 06269, USA
| | - Ock K. Chun
- Department of Nutritional Sciences, University of Connecticut, Storrs, CT 06269, USA
- Correspondence: (V.B.D.); (O.K.C.); Tel.: +860-486-1997 (V.B.D.); +860-486-6275 (O.K.C.)
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Ramos-Lopez O, Martinez JA, Milagro FI. Holistic Integration of Omics Tools for Precision Nutrition in Health and Disease. Nutrients 2022; 14:nu14194074. [PMID: 36235725 PMCID: PMC9572439 DOI: 10.3390/nu14194074] [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: 09/13/2022] [Revised: 09/23/2022] [Accepted: 09/29/2022] [Indexed: 11/16/2022] Open
Abstract
The combination of multiple omics approaches has emerged as an innovative holistic scope to provide a more comprehensive view of the molecular and physiological events underlying human diseases (including obesity, dyslipidemias, fatty liver, insulin resistance, and inflammation), as well as for elucidating unique and specific metabolic phenotypes. These omics technologies include genomics (polymorphisms and other structural genetic variants), epigenomics (DNA methylation, histone modifications, long non-coding RNA, telomere length), metagenomics (gut microbiota composition, enterotypes), transcriptomics (RNA expression patterns), proteomics (protein quantities), and metabolomics (metabolite profiles), as well as interactions with dietary/nutritional factors. Although more evidence is still necessary, it is expected that the incorporation of integrative omics could be useful not only for risk prediction and early diagnosis but also for guiding tailored dietary treatments and prognosis schemes. Some challenges include ethical and regulatory issues, the lack of robust and reproducible results due to methodological aspects, the high cost of omics methodologies, and high-dimensional data analyses and interpretation. In this review, we provide examples of system biology studies using multi-omics methodologies to unravel novel insights into the mechanisms and pathways connecting the genotype to clinically relevant traits and therapy outcomes for precision nutrition applications in health and disease.
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Affiliation(s)
- Omar Ramos-Lopez
- Medicine and Psychology School, Autonomous University of Baja California, Tijuana 22390, Mexico
- Correspondence:
| | - J. Alfredo Martinez
- Precision Nutrition and Cardiometabolic Health, IMDEA Food Institute, CEI UAM+CSIC, 28049 Madrid, Spain
| | - Fermin I. Milagro
- Department of Nutrition, Food Sciences and Physiology, University of Navarra, 31008 Pamplona, Spain
- Center for Nutrition Research, University of Navarra, 31008 Pamplona, Spain
- Centro de Investigación Biomédica en Red de la Fisiopatología de la Obesidad y Nutrición (CIBERobn), Institute of Health Carlos III, 28029 Madrid, Spain
- Navarra Institute for Health Research (IdiSNA), 31008 Pamplona, Spain
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Xu R, Wang T, Ding FF, Zhou NN, Qiao F, Chen LQ, Du ZY, Zhang ML. Lactobacillus plantarum Ameliorates High-Carbohydrate Diet-Induced Hepatic Lipid Accumulation and Oxidative Stress by Upregulating Uridine Synthesis. Antioxidants (Basel) 2022; 11:antiox11071238. [PMID: 35883730 PMCID: PMC9312134 DOI: 10.3390/antiox11071238] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2022] [Revised: 06/12/2022] [Accepted: 06/21/2022] [Indexed: 02/06/2023] Open
Abstract
The overconsumption of carbohydrates induces oxidative stress and lipid accumulation in the liver, which can be alleviated by modulation of intestinal microbiota; however, the underlying mechanism remains unclear. Here, we demonstrated that a strain affiliated with Lactobacillus plantarum (designed as MR1) efficiently attenuated lipid deposition, oxidative stress, as well as inflammatory response, which are caused by high-carbohydrate diet (HC) in fish with poor utilization ability of carbohydrates. Serum untargeted metabolome analysis indicated that pyrimidine metabolism was the significantly changed pathway among the groups. In addition, the content of serum uridine was significantly decreased in the HC group compared with the control group, while it increased by supplementation with L. plantarum MR1. Further analysis showed that addition of L. plantarum MR1 reshaped the composition of gut microbiota and increased the content of intestinal acetate. In vitro experiment showed that sodium acetate could induce the synthesis of uridine in hepatocytes. Furthermore, we proved that uridine could directly ameliorate oxidative stress and decrease liver lipid accumulation in the hepatocytes. In conclusion, this study indicated that probiotic L. plantarum MR1 ameliorated high-carbohydrate diet-induced hepatic lipid accumulation and oxidative stress by increasing the circulating uridine, suggesting that intestinal microbiota can regulate the metabolism of nucleotides to maintain host physiological homeostasis.
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Bombin A, Yan S, Bombin S, Mosley JD, Ferguson JF. Obesity influences composition of salivary and fecal microbiota and impacts the interactions between bacterial taxa. Physiol Rep 2022; 10:e15254. [PMID: 35384379 PMCID: PMC8980904 DOI: 10.14814/phy2.15254] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2021] [Revised: 03/04/2022] [Accepted: 03/17/2022] [Indexed: 04/23/2023] Open
Abstract
Obesity is an increasing global health concern and is associated with a broad range of morbidities. The gut microbiota are increasingly recognized as important contributors to obesity and cardiometabolic health. This study aimed to characterize oral and gut microbial communities, and evaluate host: microbiota interactions between clinical obesity classifications. We performed 16S rRNA sequencing on fecal and salivary samples, global metabolomics profiling on plasma and stool samples, and dietary profiling in 135 healthy individuals. We grouped individuals by obesity status, based on body mass index (BMI), including lean (BMI 18-124.9), overweight (BMI 25-29.9), or obese (BMI ≥30). We analyzed differences in microbiome composition, community inter-relationships, and predicted microbial function by obesity status. We found that salivary bacterial communities of lean and obese individuals were compositionally and phylogenetically distinct. An increase in obesity status was positively associated with strong correlations between bacterial taxa, particularly with bacterial groups implicated in metabolic disorders including Fretibacterium, and Tannerella. Consumption of sweeteners, especially xylitol, significantly influenced compositional and phylogenetic diversities of salivary and fecal bacterial communities. In addition, obesity groups exhibited differences in predicted bacterial metabolic activity, which was correlated with host's metabolite concentrations. Overall, obesity was associated with distinct changes in bacterial community dynamics, particularly in saliva. Consideration of microbiome community structure and inclusion of salivary samples may improve our ability to understand pathways linking microbiota to obesity and cardiometabolic disease.
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Affiliation(s)
- Andrei Bombin
- Division of Clinical PharmacologyDepartment of MedicineVanderbilt University Medical CenterNashvilleTennesseeUSA
| | - Shun Yan
- Department of GeneticsThe University of AlabamaBirminghamAlabamaUSA
| | - Sergei Bombin
- Department of Biological SciencesThe University of AlabamaTuscaloosaAlabamaUSA
| | - Jonathan D. Mosley
- Division of Clinical PharmacologyDepartment of MedicineVanderbilt University Medical CenterNashvilleTennesseeUSA
- Department of Biomedical InformaticsVanderbilt University Medical CenterNashvilleTennesseeUSA
| | - Jane F. Ferguson
- Division of Cardiovascular MedicineDepartment of MedicineVanderbilt University Medical CenterNashvilleTennesseeUSA
- Vanderbilt Microbiome Innovation Center (VMIC)NashvilleTennesseeUSA
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Lee AM, Hu J, Xu Y, Abraham AG, Xiao R, Coresh J, Rebholz C, Chen J, Rhee EP, Feldman HI, Ramachandran VS, Kimmel PL, Warady BA, Furth SL, Denburg MR. Using Machine Learning to Identify Metabolomic Signatures of Pediatric Chronic Kidney Disease Etiology. J Am Soc Nephrol 2022; 33:375-386. [PMID: 35017168 PMCID: PMC8819986 DOI: 10.1681/asn.2021040538] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2021] [Accepted: 11/13/2021] [Indexed: 02/03/2023] Open
Abstract
BACKGROUND Untargeted plasma metabolomic profiling combined with machine learning (ML) may lead to discovery of metabolic profiles that inform our understanding of pediatric CKD causes. We sought to identify metabolomic signatures in pediatric CKD based on diagnosis: FSGS, obstructive uropathy (OU), aplasia/dysplasia/hypoplasia (A/D/H), and reflux nephropathy (RN). METHODS Untargeted metabolomic quantification (GC-MS/LC-MS, Metabolon) was performed on plasma from 702 Chronic Kidney Disease in Children study participants (n: FSGS=63, OU=122, A/D/H=109, and RN=86). Lasso regression was used for feature selection, adjusting for clinical covariates. Four methods were then applied to stratify significance: logistic regression, support vector machine, random forest, and extreme gradient boosting. ML training was performed on 80% total cohort subsets and validated on 20% holdout subsets. Important features were selected based on being significant in at least two of the four modeling approaches. We additionally performed pathway enrichment analysis to identify metabolic subpathways associated with CKD cause. RESULTS ML models were evaluated on holdout subsets with receiver-operator and precision-recall area-under-the-curve, F1 score, and Matthews correlation coefficient. ML models outperformed no-skill prediction. Metabolomic profiles were identified based on cause. FSGS was associated with the sphingomyelin-ceramide axis. FSGS was also associated with individual plasmalogen metabolites and the subpathway. OU was associated with gut microbiome-derived histidine metabolites. CONCLUSION ML models identified metabolomic signatures based on CKD cause. Using ML techniques in conjunction with traditional biostatistics, we demonstrated that sphingomyelin-ceramide and plasmalogen dysmetabolism are associated with FSGS and that gut microbiome-derived histidine metabolites are associated with OU.
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Affiliation(s)
- Arthur M. Lee
- Division of Nephrology, The Children’s Hospital of Philadelphia, Philadelphia, Pennsylvania
| | - Jian Hu
- Center for Clinical Epidemiology and Biostatistics, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania
| | - Yunwen Xu
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore Maryland
| | - Alison G. Abraham
- School of Public Health, University of Colorado Denver, Denver, Colorado
| | - Rui Xiao
- Center for Clinical Epidemiology and Biostatistics, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania
| | - Josef Coresh
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore Maryland
| | - Casey Rebholz
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore Maryland
| | - Jingsha Chen
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore Maryland
| | - Eugene P. Rhee
- Department of Medicine, Massachusetts General Hospital, Harvard University, Boston, Massachusetts
| | - Harold I. Feldman
- Center for Clinical Epidemiology and Biostatistics, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania
| | - Vasan S. Ramachandran
- Department of Medicine, Boston University School of Medicine, Boston University School of Public Health, Boston University Center for Computing and Data Science, Boston, Massachusetts
| | - Paul L. Kimmel
- National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Bethesda, Maryland
| | - Bradley A. Warady
- Department of Pediatrics, Children’s Mercy Hospital, University of Missouri-Kansas City School of Medicine, Kansas City, Missouri
| | - Susan L. Furth
- Division of Nephrology, The Children’s Hospital of Philadelphia, Philadelphia, Pennsylvania
- Department of Pediatrics, University of Pennsylvania School of Medicine, Philadelphia, Pennsylvania
| | - Michelle R. Denburg
- Division of Nephrology, The Children’s Hospital of Philadelphia, Philadelphia, Pennsylvania
- Department of Pediatrics, University of Pennsylvania School of Medicine, Philadelphia, Pennsylvania
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