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Singh MK, Han S, Ju S, Ranbhise JS, Akter S, Kim SS, Kang I. Fruit Carbohydrates and Their Impact on the Glycemic Index: A Study of Key Determinants. Foods 2025; 14:646. [PMID: 40002091 PMCID: PMC11854304 DOI: 10.3390/foods14040646] [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: 01/02/2025] [Revised: 02/11/2025] [Accepted: 02/13/2025] [Indexed: 02/27/2025] Open
Abstract
Background: Fruits are a convenient and natural source of carbohydrates that can rapidly affect blood sugar levels and the glycemic index (GI). The GI plays a crucial role in the management of chronic diseases, including diabetes, obesity, hyperglycemia, and diet-related illnesses. Despite there being several health benefits linked with consuming fruits, it remains unclear which specific components of fruits are the key determinants that significantly influence the GI. Methods: This study retrospectively examined the relationship between different types of carbohydrates and the GI of various fruits to determine their correlation. The fruits' sugar and fiber contents were identified from available public databases, the U.S. Department of Agriculture (USDA), FooDB, PubMed, and published sources. Results: Previously, the GI was determined by the available carbohydrates, which include different types of sugar. In this study, individual hexose sugars, along with the total carbohydrates and dietary fiber, were examined. The results indicated a strong correlation between fructose and the GI, whereas glucose and total glucose did not exhibit such a correlation. The total carbohydrate-to-fiber ratio displayed a stronger correlation (R = 0.57 and p > 0.0001) with the GI compared to glucose alone (R = 0.37; p = 0.01) or the total glucose (R = 0.45; p = 0.0009) with the consideration of fiber, while the scattering of data points around the regression line suggested that factors beyond the total carbohydrate and fiber also contribute to determining the GI. Conclusions: This study demonstrated that individual hexose sugars, especially fructose, significantly influence the GI. These findings suggest that the carbohydrate-to-fiber ratio may offer a more accurate and reliable metric for determining the GI than traditional methods. Further research is warranted to investigate the specific contribution of dietary fiber components, fruit texture, micronutrients, vitamins, genetic predispositions, gut microbiota, and the body's physiological status to gain a deeper understanding of GI regulation.
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Affiliation(s)
- Manish Kumar Singh
- Department of Biochemistry and Molecular Biology, School of Medicine, Kyung Hee University, Seoul 02447, Republic of Korea; (M.K.S.); (S.H.); (S.J.); (J.S.R.); (S.A.)
- Biomedical Science Institute, Kyung Hee University, Seoul 02447, Republic of Korea
| | - Sunhee Han
- Department of Biochemistry and Molecular Biology, School of Medicine, Kyung Hee University, Seoul 02447, Republic of Korea; (M.K.S.); (S.H.); (S.J.); (J.S.R.); (S.A.)
- Biomedical Science Institute, Kyung Hee University, Seoul 02447, Republic of Korea
- Department of Biomedical Science, Graduate School, Kyung Hee University, Seoul 02447, Republic of Korea
| | - Songhyun Ju
- Department of Biochemistry and Molecular Biology, School of Medicine, Kyung Hee University, Seoul 02447, Republic of Korea; (M.K.S.); (S.H.); (S.J.); (J.S.R.); (S.A.)
- Biomedical Science Institute, Kyung Hee University, Seoul 02447, Republic of Korea
- Department of Biomedical Science, Graduate School, Kyung Hee University, Seoul 02447, Republic of Korea
| | - Jyotsna Suresh Ranbhise
- Department of Biochemistry and Molecular Biology, School of Medicine, Kyung Hee University, Seoul 02447, Republic of Korea; (M.K.S.); (S.H.); (S.J.); (J.S.R.); (S.A.)
- Biomedical Science Institute, Kyung Hee University, Seoul 02447, Republic of Korea
- Department of Biomedical Science, Graduate School, Kyung Hee University, Seoul 02447, Republic of Korea
| | - Salima Akter
- Department of Biochemistry and Molecular Biology, School of Medicine, Kyung Hee University, Seoul 02447, Republic of Korea; (M.K.S.); (S.H.); (S.J.); (J.S.R.); (S.A.)
- Biomedical Science Institute, Kyung Hee University, Seoul 02447, Republic of Korea
| | - Sung Soo Kim
- Department of Biochemistry and Molecular Biology, School of Medicine, Kyung Hee University, Seoul 02447, Republic of Korea; (M.K.S.); (S.H.); (S.J.); (J.S.R.); (S.A.)
- Biomedical Science Institute, Kyung Hee University, Seoul 02447, Republic of Korea
- Department of Biomedical Science, Graduate School, Kyung Hee University, Seoul 02447, Republic of Korea
| | - Insug Kang
- Department of Biochemistry and Molecular Biology, School of Medicine, Kyung Hee University, Seoul 02447, Republic of Korea; (M.K.S.); (S.H.); (S.J.); (J.S.R.); (S.A.)
- Biomedical Science Institute, Kyung Hee University, Seoul 02447, Republic of Korea
- Department of Biomedical Science, Graduate School, Kyung Hee University, Seoul 02447, Republic of Korea
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Rojo-López MI, Barranco-Altirriba M, Rossell J, Antentas M, Castelblanco E, Yanes O, Weber RJM, Lloyd GR, Winder C, Dunn WB, Julve J, Granado-Casas M, Mauricio D. The Lipidomic Profile Is Associated with the Dietary Pattern in Subjects with and without Diabetes Mellitus from a Mediterranean Area. Nutrients 2024; 16:1805. [PMID: 38931159 PMCID: PMC11206394 DOI: 10.3390/nu16121805] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2024] [Revised: 05/31/2024] [Accepted: 06/05/2024] [Indexed: 06/28/2024] Open
Abstract
Lipid functions can be influenced by genetics, age, disease states, and lifestyle factors, particularly dietary patterns, which are crucial in diabetes management. Lipidomics is an expanding field involving the comprehensive exploration of lipids from biological samples. In this cross-sectional study, 396 participants from a Mediterranean region, including individuals with type 1 diabetes (T1D), type 2 diabetes (T2D), and non-diabetic individuals, underwent lipidomic profiling and dietary assessment. Participants completed validated food frequency questionnaires, and lipid analysis was conducted using ultra-high-performance liquid chromatography coupled with mass spectrometry (UHPLC/MS). Multiple linear regression models were used to determine the association between lipid features and dietary patterns. Across all subjects, acylcarnitines (AcCa) and triglycerides (TG) displayed negative associations with the alternate Healthy Eating Index (aHEI), indicating a link between lipidomic profiles and dietary habits. Various lipid species (LS) showed positive and negative associations with dietary carbohydrates, fats, and proteins. Notably, in the interaction analysis between diabetes and the aHEI, we found some lysophosphatidylcholines (LPC) that showed a similar direction with respect to aHEI in non-diabetic individuals and T2D subjects, while an opposite direction was observed in T1D subjects. The study highlights the significant association between lipidomic profiles and dietary habits in people with and without diabetes, particularly emphasizing the role of healthy dietary choices, as reflected by the aHEI, in modulating lipid concentrations. These findings underscore the importance of dietary interventions to improve metabolic health outcomes, especially in the context of diabetes management.
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Affiliation(s)
- Marina Idalia Rojo-López
- Institut de Recerca Sant Pau (IR SANT PAU), Sant Quintí 77-79, 08041 Barcelona, Spain; (M.I.R.-L.); (M.B.-A.); (J.R.); (M.A.); (J.J.)
| | - Maria Barranco-Altirriba
- Institut de Recerca Sant Pau (IR SANT PAU), Sant Quintí 77-79, 08041 Barcelona, Spain; (M.I.R.-L.); (M.B.-A.); (J.R.); (M.A.); (J.J.)
- B2SLab, Departament d’Enginyeria de Sistemes, Automàtica i Informàtica Industrial, Universitat Politècnica de Catalunya, 08028 Barcelona, Spain
- Networking Biomedical Research Centre in the Subject Area of Bioengineering, Biomaterials and Nanomedicine (CIBER-BBN), 28029 Barcelona, Spain
| | - Joana Rossell
- Institut de Recerca Sant Pau (IR SANT PAU), Sant Quintí 77-79, 08041 Barcelona, Spain; (M.I.R.-L.); (M.B.-A.); (J.R.); (M.A.); (J.J.)
- Centro de Investigación Biomédica en Red de Diabetes y Enfermedades Metabólicas Asociadas, Instituto de Salud Carlos III, 28029 Madrid, Spain;
| | - Maria Antentas
- Institut de Recerca Sant Pau (IR SANT PAU), Sant Quintí 77-79, 08041 Barcelona, Spain; (M.I.R.-L.); (M.B.-A.); (J.R.); (M.A.); (J.J.)
| | - Esmeralda Castelblanco
- Department of Internal Medicine, Endocrinology, Metabolism and Lipid Research Division, Washington University School of Medicine, St. Louis, MO 63110, USA;
| | - Oscar Yanes
- Centro de Investigación Biomédica en Red de Diabetes y Enfermedades Metabólicas Asociadas, Instituto de Salud Carlos III, 28029 Madrid, Spain;
- Department of Electronic Engineering, Institut d’Investigació Sanitària Pere Virgili (IISPV), Universitat Rovira i Virgili, 43007 Tarragona, Spain
| | - Ralf J. M. Weber
- School of Biosciences, University of Birmingham, Edgbaston, Birmingham B15 2TT, UK; (R.J.M.W.); (G.R.L.); (C.W.); (W.B.D.)
- Phenome Centre Birmingham, University of Birmingham, Edgbaston, Birmingham B15 2TT, UK
- Institute of Metabolism and Systems Research, University of Birmingham, Edgbaston, Birmingham B15 2TT, UK
| | - Gavin R. Lloyd
- School of Biosciences, University of Birmingham, Edgbaston, Birmingham B15 2TT, UK; (R.J.M.W.); (G.R.L.); (C.W.); (W.B.D.)
- Phenome Centre Birmingham, University of Birmingham, Edgbaston, Birmingham B15 2TT, UK
- Institute of Metabolism and Systems Research, University of Birmingham, Edgbaston, Birmingham B15 2TT, UK
| | - Catherine Winder
- School of Biosciences, University of Birmingham, Edgbaston, Birmingham B15 2TT, UK; (R.J.M.W.); (G.R.L.); (C.W.); (W.B.D.)
- Phenome Centre Birmingham, University of Birmingham, Edgbaston, Birmingham B15 2TT, UK
- Centre for Metabolomics Research, Department of Biochemistry, Cell and Systems Biology, Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Liverpool L69 7ZB, UK
| | - Warwick B. Dunn
- School of Biosciences, University of Birmingham, Edgbaston, Birmingham B15 2TT, UK; (R.J.M.W.); (G.R.L.); (C.W.); (W.B.D.)
- Phenome Centre Birmingham, University of Birmingham, Edgbaston, Birmingham B15 2TT, UK
- Centre for Metabolomics Research, Department of Biochemistry, Cell and Systems Biology, Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Liverpool L69 7ZB, UK
| | - Josep Julve
- Institut de Recerca Sant Pau (IR SANT PAU), Sant Quintí 77-79, 08041 Barcelona, Spain; (M.I.R.-L.); (M.B.-A.); (J.R.); (M.A.); (J.J.)
- Centro de Investigación Biomédica en Red de Diabetes y Enfermedades Metabólicas Asociadas, Instituto de Salud Carlos III, 28029 Madrid, Spain;
| | - Minerva Granado-Casas
- Centro de Investigación Biomédica en Red de Diabetes y Enfermedades Metabólicas Asociadas, Instituto de Salud Carlos III, 28029 Madrid, Spain;
- Department of Nursing and Physiotherapy, University of Lleida, 25198 Lleida, Spain
- Research Group of Health Care (GreCS), IRBLleida, 25198 Lleida, Spain
| | - Dídac Mauricio
- Institut de Recerca Sant Pau (IR SANT PAU), Sant Quintí 77-79, 08041 Barcelona, Spain; (M.I.R.-L.); (M.B.-A.); (J.R.); (M.A.); (J.J.)
- Centro de Investigación Biomédica en Red de Diabetes y Enfermedades Metabólicas Asociadas, Instituto de Salud Carlos III, 28029 Madrid, Spain;
- Department of Endocrinology and Nutrition, Hospital de la Santa Creu i Sant Pau, 08041 Barcelona, Spain
- Faculty of Medicine, University of Vic (UVIC/UCC), 08500 Vic, Spain
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Zhang T, Naudin S, Hong HG, Albanes D, Männistö S, Weinstein SJ, Moore SC, Stolzenberg-Solomon RZ. Dietary Quality and Circulating Lipidomic Profiles in 2 Cohorts of Middle-Aged and Older Male Finnish Smokers and American Populations. J Nutr 2023; 153:2389-2400. [PMID: 37328109 PMCID: PMC10493471 DOI: 10.1016/j.tjnut.2023.06.010] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2023] [Revised: 06/02/2023] [Accepted: 06/07/2023] [Indexed: 06/18/2023] Open
Abstract
BACKGROUND Higher dietary quality is associated with lower disease risks and has not been examined extensively with lipidomic profiles. OBJECTIVES Our goal was to examine associations of the Healthy Eating Index (HEI)-2015, Alternate HEI-2010 (AHEI-2010), and alternate Mediterranean Diet Index (aMED) diet quality indices with serum lipidomic profiles. METHODS We conducted a cross-sectional analysis of HEI-2015, AHEI-2010, and aMED with lipidomic profiles from 2 nested case-control studies within the Prostate, Lung, Colorectal and Ovarian Cancer Screening Trial (n = 627) and the Alpha-Tocopherol, Beta-Carotene Cancer Prevention Study (n = 711). We used multivariable linear regression to determine associations of the indices, derived from baseline food-frequency questionnaires (Prostate, Lung, Colorectal and Ovarian Cancer Screening Trial: 1993-2001, Alpha-Tocopherol, Beta-Carotene Cancer Prevention Study: 1985-1988) with serum concentrations of 904 lipid species and 252 fatty acids (FAs) across 15 lipid classes and 28 total FAs, within each cohort and meta-analyzed results using fixed-effect models for lipids significant at Bonferroni-corrected threshold in common in both cohorts. RESULTS Adherence to HEI-2015, AHEI-2010, or aMED was associated positively with 31, 41, and 54 lipid species and 8, 6, and 10 class-specific FAs and inversely with 2, 8, and 34 lipid species and 1, 3, and 5 class-specific FAs, respectively. Twenty-five lipid species and 5 class-specific FAs were common to all indices, predominantly triacylglycerols, FA22:6 [docosahexaenoic acid (DHA)]-containing species, and DHA. All indices were positively associated with total FA22:6. AHEI-2010 and aMED were inversely associated with total FA18:1 (oleic acid) and total FA17:0 (margaric acid), respectively. The identified lipids were most associated with components of seafood and plant proteins and unsaturated:saturated fat ratio in HEI-2015; eicosapentaenoic acid plus DHA in AHEI-2010; and fish and monounsaturated:saturated fat ratio in aMED. CONCLUSIONS Adherence to HEI-2015, AHEI-2010, and aMED is associated with serum lipidomic profiles, mostly triacylglycerols or FA22:6-containing species, which are related to seafood and plant proteins, eicosapentaenoic acid-DHA, fish, or fat ratio index components.
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Affiliation(s)
- Ting Zhang
- Metabolic Epidemiology Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institute of Health, Rockville, MD, United States
| | - Sabine Naudin
- Metabolic Epidemiology Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institute of Health, Rockville, MD, United States; Nutrition and Metabolism Branch, International Agency for Research on Cancer, World Health Organization, Lyon, France
| | - Hyokyoung G Hong
- Biostatistics Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institute of Health, Rockville, MD, United States
| | - Demetrius Albanes
- Metabolic Epidemiology Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institute of Health, Rockville, MD, United States
| | - Satu Männistö
- Department of Public Health and Welfare, Finnish Institute for Health and Welfare, Helsinki, Finland
| | - Stephanie J Weinstein
- Metabolic Epidemiology Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institute of Health, Rockville, MD, United States
| | - Steven C Moore
- Metabolic Epidemiology Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institute of Health, Rockville, MD, United States
| | - Rachael Z Stolzenberg-Solomon
- Metabolic Epidemiology Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institute of Health, Rockville, MD, United States.
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Navarro SL, Nagana Gowda GA, Bettcher LF, Pepin R, Nguyen N, Ellenberger M, Zheng C, Tinker LF, Prentice RL, Huang Y, Yang T, Tabung FK, Chan Q, Loo RL, Liu S, Wactawski-Wende J, Lampe JW, Neuhouser ML, Raftery D. Demographic, Health and Lifestyle Factors Associated with the Metabolome in Older Women. Metabolites 2023; 13:metabo13040514. [PMID: 37110172 PMCID: PMC10143141 DOI: 10.3390/metabo13040514] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2023] [Revised: 03/17/2023] [Accepted: 03/23/2023] [Indexed: 04/07/2023] Open
Abstract
Demographic and clinical factors influence the metabolome. The discovery and validation of disease biomarkers are often challenged by potential confounding effects from such factors. To address this challenge, we investigated the magnitude of the correlation between serum and urine metabolites and demographic and clinical parameters in a well-characterized observational cohort of 444 post-menopausal women participating in the Women’s Health Initiative (WHI). Using LC-MS and lipidomics, we measured 157 aqueous metabolites and 756 lipid species across 13 lipid classes in serum, along with 195 metabolites detected by GC-MS and NMR in urine and evaluated their correlations with 29 potential disease risk factors, including demographic, dietary and lifestyle factors, and medication use. After controlling for multiple testing (FDR < 0.01), we found that log-transformed metabolites were mainly associated with age, BMI, alcohol intake, race, sample storage time (urine only), and dietary supplement use. Statistically significant correlations were in the absolute range of 0.2–0.6, with the majority falling below 0.4. Incorporation of important potential confounding factors in metabolite and disease association analyses may lead to improved statistical power as well as reduced false discovery rates in a variety of data analysis settings.
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Affiliation(s)
- Sandi L. Navarro
- Cancer Prevention Program, Division of Public Health Sciences, Fred Hutchinson Cancer Center, Seattle, WA 98109, USA
| | - G. A. Nagana Gowda
- Department of Anesthesiology and Pain Medicine, University of Washington, Seattle, WA 98109, USA
| | - Lisa F. Bettcher
- Department of Anesthesiology and Pain Medicine, University of Washington, Seattle, WA 98109, USA
| | - Robert Pepin
- Department of Anesthesiology and Pain Medicine, University of Washington, Seattle, WA 98109, USA
| | - Natalie Nguyen
- Department of Anesthesiology and Pain Medicine, University of Washington, Seattle, WA 98109, USA
| | - Mathew Ellenberger
- Department of Anesthesiology and Pain Medicine, University of Washington, Seattle, WA 98109, USA
| | - Cheng Zheng
- Department of Biostatistics, University of Nebraska Medical Center, Omaha, NE 68198, USA
| | - Lesley F. Tinker
- Cancer Prevention Program, Division of Public Health Sciences, Fred Hutchinson Cancer Center, Seattle, WA 98109, USA
| | - Ross L. Prentice
- Cancer Prevention Program, Division of Public Health Sciences, Fred Hutchinson Cancer Center, Seattle, WA 98109, USA
| | - Ying Huang
- Biostatistics Program, Division of Public Health Sciences, Fred Hutchinson Cancer Center, Seattle, WA 98109, USA
| | - Tao Yang
- School of Public Health, Xinjiang Medical University, Urumqi 830011, China
| | - Fred K. Tabung
- Department of Internal Medicine, Division of Medical Oncology, College of Medicine and Comprehensive Cancer Center, The Ohio State University, Columbus, OH 43210, USA
| | - Queenie Chan
- School of Public Health, Imperial College of London, London SW7 2AZ, UK
| | - Ruey Leng Loo
- Australian National Phenome Centre, Health Futures Institute, Murdoch University, Murdoch, WA 6150, Australia
| | - Simin Liu
- Center for Global Cardiometabolic Health, Department of Epidemiology, School of Public Health, Providence, RI 02912, USA
- Department of Medicine and Surgery, Alpert School of Medicine, Brown University, Providence, RI 02903, USA
| | - Jean Wactawski-Wende
- Department of Epidemiology and Environmental Health, University at Buffalo, Buffalo, NY 14214, USA
| | - Johanna W. Lampe
- Cancer Prevention Program, Division of Public Health Sciences, Fred Hutchinson Cancer Center, Seattle, WA 98109, USA
| | - Marian L. Neuhouser
- Cancer Prevention Program, Division of Public Health Sciences, Fred Hutchinson Cancer Center, Seattle, WA 98109, USA
| | - Daniel Raftery
- Cancer Prevention Program, Division of Public Health Sciences, Fred Hutchinson Cancer Center, Seattle, WA 98109, USA
- Department of Anesthesiology and Pain Medicine, University of Washington, Seattle, WA 98109, USA
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5
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Castro-Alves V, Orešič M, Hyötyläinen T. Lipidomics in nutrition research. Curr Opin Clin Nutr Metab Care 2022; 25:311-318. [PMID: 35788540 DOI: 10.1097/mco.0000000000000852] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
Abstract
PURPOSE OF REVIEW This review focuses on the recent findings from lipidomics studies as related to nutrition and health research. RECENT FINDINGS Several lipidomics studies have investigated malnutrition, including both under- and overnutrition. Focus has been both on the early-life nutrition as well as on the impact of overfeeding later in life. Multiple studies have investigated the impact of different macronutrients in lipidome on human health, demonstrating that overfeeding with saturated fat is metabolically more harmful than overfeeding with polyunsaturated fat or carbohydrate-rich food. Diet rich in saturated fat increases the lipotoxic lipids, such as ceramides and saturated fatty-acyl-containing triacylglycerols, increasing also the low-density lipoprotein aggregation rate. In contrast, diet rich in polyunsaturated fatty acids, such as n-3 fatty acids, decreases the triacylglycerol levels, although some individuals are poor responders to n-3 supplementation. SUMMARY The results highlight the benefits of lipidomics in clinical nutrition research, also providing an opportunity for personalized nutrition. An area of increasing interest is the interplay of diet, gut microbiome, and metabolome, and how they together impact individuals' responses to nutritional challenges.
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Affiliation(s)
| | - Matej Orešič
- School of Medical Sciences, Örebro University, Örebro, Sweden
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, Turku, Finland
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Su B, Bettcher LF, Hsieh WY, Hornburg D, Pearson MJ, Blomberg N, Giera M, Snyder MP, Raftery D, Bensinger SJ, Williams KJ. A DMS Shotgun Lipidomics Workflow Application to Facilitate High-Throughput, Comprehensive Lipidomics. JOURNAL OF THE AMERICAN SOCIETY FOR MASS SPECTROMETRY 2021; 32:2655-2663. [PMID: 34637296 PMCID: PMC8985811 DOI: 10.1021/jasms.1c00203] [Citation(s) in RCA: 67] [Impact Index Per Article: 16.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/27/2023]
Abstract
Differential mobility spectrometry (DMS) is highly useful for shotgun lipidomic analysis because it overcomes difficulties in measuring isobaric species within a complex lipid sample and allows for acyl tail characterization of phospholipid species. Despite these advantages, the resulting workflow presents technical challenges, including the need to tune the DMS before every batch to update compensative voltages settings within the method. The Sciex Lipidyzer platform uses a Sciex 5500 QTRAP with a DMS (SelexION), an LC system configured for direction infusion experiments, an extensive set of standards designed for quantitative lipidomics, and a software package (Lipidyzer Workflow Manager) that facilitates the workflow and rapidly analyzes the data. Although the Lipidyzer platform remains very useful for DMS-based shotgun lipidomics, the software is no longer updated for current versions of Analyst and Windows. Furthermore, the software is fixed to a single workflow and cannot take advantage of new lipidomics standards or analyze additional lipid species. To address this multitude of issues, we developed Shotgun Lipidomics Assistant (SLA), a Python-based application that facilitates DMS-based lipidomics workflows. SLA provides the user with flexibility in adding and subtracting lipid and standard MRMs. It can report quantitative lipidomics results from raw data in minutes, comparable to the Lipidyzer software. We show that SLA facilitates an expanded lipidomics analysis that measures over 1450 lipid species across 17 (sub)classes. Lastly, we demonstrate that the SLA performs isotope correction, a feature that was absent from the original software.
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Affiliation(s)
- Baolong Su
- Department of Biological Chemistry, University of California, Los Angeles, CA 90095, USA
- UCLA Lipidomics Lab, University of California, Los Angeles, CA, USA
| | - Lisa F. Bettcher
- Department of Anesthesiology and Pain Medicine, Northwest Metabolomics Research Center, University of Washington, Seattle, WA, USA
| | - Wei-Yuan Hsieh
- Department of Microbiology, Immunology and Molecular Genetics, University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - Daniel Hornburg
- Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA
| | | | - Niek Blomberg
- Center for Proteomics and Metabolomics, Leiden University Medical Center, 2333ZA Leiden, Netherlands
| | - Martin Giera
- Center for Proteomics and Metabolomics, Leiden University Medical Center, 2333ZA Leiden, Netherlands
| | - Michael P Snyder
- Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA
| | - Daniel Raftery
- Department of Anesthesiology and Pain Medicine, Northwest Metabolomics Research Center, University of Washington, Seattle, WA, USA
| | - Steven J. Bensinger
- UCLA Lipidomics Lab, University of California, Los Angeles, CA, USA
- Department of Microbiology, Immunology and Molecular Genetics, University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - Kevin J. Williams
- Department of Biological Chemistry, University of California, Los Angeles, CA 90095, USA
- UCLA Lipidomics Lab, University of California, Los Angeles, CA, USA
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7
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Evaluation of potential metabolomic-based biomarkers of protein, carbohydrate and fat intakes using a controlled feeding study. Eur J Nutr 2021; 60:4207-4218. [PMID: 33991228 DOI: 10.1007/s00394-021-02577-1] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2020] [Accepted: 04/27/2021] [Indexed: 10/21/2022]
Abstract
PURPOSE Objective biomarkers of dietary exposure are needed to establish reliable diet-disease associations. Unfortunately, robust biomarkers of macronutrient intakes are scarce. We aimed to assess the utility of serum, 24-h urine and spot urine high-dimensional metabolites for the development of biomarkers of daily intake of total energy, protein, carbohydrate and fat, and the percent of energy from these macronutrients (%E). METHODS A 2-week controlled feeding study mimicking the participants' habitual diets was conducted among 153 postmenopausal women from the Women's Health Initiative (WHI). Fasting serum metabolomic profiles were analyzed using a targeted liquid chromatography-tandem mass spectrometry (LC-MS/MS) assay for aqueous metabolites and a direct-injection-based quantitative lipidomics platform. Urinary metabolites were analyzed using 1H nuclear magnetic resonance (NMR) spectroscopy at 800 MHz and by untargeted gas chromatography-mass spectrometry (GC-MS). Variable selection was performed to build prediction models for each dietary variable. RESULTS The highest cross-validated multiple correlation coefficients (CV-R2) for protein intake (%E) and carbohydrate intake (%E) using metabolites only were 36.3 and 37.1%, respectively. With the addition of established dietary biomarkers (doubly labeled water for energy and urinary nitrogen for protein), the CV-R2 reached 55.5% for energy (kcal/d), 52.0 and 45.0% for protein (g/d, %E), 55.9 and 37.0% for carbohydrate (g/d, %E). CONCLUSION Selected panels of serum and urine metabolites, without the inclusion of doubly labeled water and urinary nitrogen biomarkers, give a reliable and robust prediction of daily intake of energy from protein and carbohydrate.
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