1
|
Ulusoy-Gezer HG, Rakıcıoğlu N. The Future of Obesity Management through Precision Nutrition: Putting the Individual at the Center. Curr Nutr Rep 2024; 13:455-477. [PMID: 38806863 PMCID: PMC11327204 DOI: 10.1007/s13668-024-00550-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 05/18/2024] [Indexed: 05/30/2024]
Abstract
PURPOSE OF REVIEW: The prevalence of obesity continues to rise steadily. While obesity management typically relies on dietary and lifestyle modifications, individual responses to these interventions vary widely. Clinical guidelines for overweight and obesity stress the importance of personalized approaches to care. This review aims to underscore the role of precision nutrition in delivering tailored interventions for obesity management. RECENT FINDINGS: Recent technological strides have expanded our ability to detect obesity-related genetic polymorphisms, with machine learning algorithms proving pivotal in analyzing intricate genomic data. Machine learning algorithms can also predict postprandial glucose, triglyceride, and insulin levels, facilitating customized dietary interventions and ultimately leading to successful weight loss. Additionally, given that adherence to dietary recommendations is one of the key predictors of weight loss success, employing more objective methods for dietary assessment and monitoring can enhance sustained long-term compliance. Biomarkers of food intake hold promise for a more objective dietary assessment. Acknowledging the multifaceted nature of obesity, precision nutrition stands poised to transform obesity management by tailoring dietary interventions to individuals' genetic backgrounds, gut microbiota, metabolic profiles, and behavioral patterns. However, there is insufficient evidence demonstrating the superiority of precision nutrition over traditional dietary recommendations. The integration of precision nutrition into routine clinical practice requires further validation through randomized controlled trials and the accumulation of a larger body of evidence to strengthen its foundation.
Collapse
Affiliation(s)
- Hande Gül Ulusoy-Gezer
- Department of Nutrition and Dietetics, Faculty of Health Sciences, Hacettepe University, 06100, Sıhhiye, Ankara, Türkiye
| | - Neslişah Rakıcıoğlu
- Department of Nutrition and Dietetics, Faculty of Health Sciences, Hacettepe University, 06100, Sıhhiye, Ankara, Türkiye.
| |
Collapse
|
2
|
Guess N. Big data and personalized nutrition: the key evidence gaps. Nat Metab 2024; 6:1420-1422. [PMID: 38278944 DOI: 10.1038/s42255-023-00960-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/28/2024]
Affiliation(s)
- Nicola Guess
- Department of Primary Care Health Sciences, University of Oxford, Oxford, UK.
| |
Collapse
|
3
|
Berube LT, Popp CJ, Curran M, Hu L, Pompeii ML, Barua S, Bernstein E, Salcedo V, Li H, St-Jules DE, Segal E, Bergman M, Williams NJ, Sevick MA. Diabetes Telemedicine Mediterranean Diet (DiaTeleMed) Study: study protocol for a fully remote randomized clinical trial evaluating personalized dietary management in individuals with type 2 diabetes. Trials 2024; 25:506. [PMID: 39049121 PMCID: PMC11271038 DOI: 10.1186/s13063-024-08337-w] [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: 05/28/2024] [Accepted: 07/11/2024] [Indexed: 07/27/2024] Open
Abstract
BACKGROUND The Diabetes Telemedicine Mediterranean Diet (DiaTeleMed) Study is a fully remote randomized clinical trial evaluating personalized dietary management in individuals with type 2 diabetes (T2D). The study aims to test the efficacy of a personalized behavioral approach for dietary management of moderately controlled T2D, versus a standardized behavioral intervention that uses one-size-fits-all dietary recommendations, versus a usual care control (UCC). The primary outcome will compare the impact of each intervention on the mean amplitude of glycemic excursions (MAGE). METHODS Eligible participants are between 21 and 80 years of age diagnosed with moderately controlled T2D (HbA1c: 6.0 to 8.0%) and managed on lifestyle alone or lifestyle plus metformin. Participants must be willing and able to attend virtual counseling sessions and log meals into a dietary tracking smartphone application (DayTwo), and wear a continuous glucose monitor (CGM) for up to 12 days. Participants are randomized with equal allocation (n = 255, n = 85 per arm) to one of three arms: (1) Personalized, (2) Standardized, or (3) UCC. Measurements occur at 0 (baseline), 3, and 6 months. All participants receive isocaloric energy and macronutrient targets to meet Mediterranean diet guidelines, in addition to 14 intervention contacts over 6 months (4 weekly then 10 biweekly) to cover diabetes self-management education. The first 4 UCC intervention contacts are delivered via synchronous videoconferences followed by educational video links. Participants in Standardized receive the same educational content as those in the UCC arm, following the same schedule. However, all intervention contacts are conducted via synchronous videoconferences, paired with Social Cognitive Theory (SCT)-based behavioral counseling, plus dietary self-monitoring of planned meals using a mobile app that provides real-time feedback on calories and macronutrients. Participants in the Personalized arm receive all elements of the Standardized intervention, in addition to real-time feedback on predicted post-prandial glycemic response (PPGR) to meals and snacks logged into the mobile app. DISCUSSION The DiaTeleMed Study aims to address an important gap in the current landscape of precision nutrition by determining the contributions of behavioral counseling and personalized nutrition recommendations on glycemic control in individuals with T2D. The fully remote methodology of the study allows for scalability and innovative delivery of personalized dietary recommendations at a population level. TRIAL REGISTRATION ClinicalTrials.gov NCT05046886. Registered on September 16, 2021.
Collapse
Affiliation(s)
- Lauren T Berube
- Center for Healthful Behavior Change, Institute for Excellence in Health Equity, New York University Langone Health, 180 Madison Ave, New York, NY, 10016, USA.
- Department of Population Health, New York University Langone Health, 180 Madison Ave, New York, NY, 10016, USA.
| | - Collin J Popp
- Center for Healthful Behavior Change, Institute for Excellence in Health Equity, New York University Langone Health, 180 Madison Ave, New York, NY, 10016, USA
- Department of Population Health, New York University Langone Health, 180 Madison Ave, New York, NY, 10016, USA
| | - Margaret Curran
- Center for Healthful Behavior Change, Institute for Excellence in Health Equity, New York University Langone Health, 180 Madison Ave, New York, NY, 10016, USA
- Department of Population Health, New York University Langone Health, 180 Madison Ave, New York, NY, 10016, USA
| | - Lu Hu
- Center for Healthful Behavior Change, Institute for Excellence in Health Equity, New York University Langone Health, 180 Madison Ave, New York, NY, 10016, USA
- Department of Population Health, New York University Langone Health, 180 Madison Ave, New York, NY, 10016, USA
| | - Mary Lou Pompeii
- Center for Healthful Behavior Change, Institute for Excellence in Health Equity, New York University Langone Health, 180 Madison Ave, New York, NY, 10016, USA
- Department of Population Health, New York University Langone Health, 180 Madison Ave, New York, NY, 10016, USA
| | - Souptik Barua
- Division of Precision Medicine, Department of Medicine, New York University Langone Health, New York, NY, USA
| | - Emma Bernstein
- Center for Healthful Behavior Change, Institute for Excellence in Health Equity, New York University Langone Health, 180 Madison Ave, New York, NY, 10016, USA
- Department of Population Health, New York University Langone Health, 180 Madison Ave, New York, NY, 10016, USA
| | - Vanessa Salcedo
- Department of Population Health, New York University Langone Health, 180 Madison Ave, New York, NY, 10016, USA
| | - Huilin Li
- Division of Biostatistics, Department of Population Health, New York University Langone Health, 180 Madison Ave, New York, NY, 10016, USA
| | - David E St-Jules
- Department of Nutrition, University of Nevada, Reno, 1664 N. Virginia Street, Reno, NV, 89557, USA
| | - Eran Segal
- Department of Computer Science and Applied Mathematics, Weizmann Institute of Science, Rehovot, Israel
| | - Michael Bergman
- Department of Population Health, New York University Langone Health, 180 Madison Ave, New York, NY, 10016, USA
- Department of Medicine, New York University Langone Health, New York, NY, USA
- Holman Division of Endocrinology, Diabetes and Metabolism, Manhattan VA Medical Center, 423 East 23rd Street, New York, NY, 10010, USA
| | - Natasha J Williams
- Center for Healthful Behavior Change, Institute for Excellence in Health Equity, New York University Langone Health, 180 Madison Ave, New York, NY, 10016, USA
- Department of Population Health, New York University Langone Health, 180 Madison Ave, New York, NY, 10016, USA
| | - Mary Ann Sevick
- Center for Healthful Behavior Change, Institute for Excellence in Health Equity, New York University Langone Health, 180 Madison Ave, New York, NY, 10016, USA
- Department of Population Health, New York University Langone Health, 180 Madison Ave, New York, NY, 10016, USA
- Department of Medicine, New York University Langone Health, New York, NY, USA
| |
Collapse
|
4
|
Kolic J, Sun WG, Cen HH, Ewald JD, Rogalski JC, Sasaki S, Sun H, Rajesh V, Xia YH, Moravcova R, Skovsø S, Spigelman AF, Manning Fox JE, Lyon J, Beet L, Xia J, Lynn FC, Gloyn AL, Foster LJ, MacDonald PE, Johnson JD. Proteomic predictors of individualized nutrient-specific insulin secretion in health and disease. Cell Metab 2024; 36:1619-1633.e5. [PMID: 38959864 PMCID: PMC11250105 DOI: 10.1016/j.cmet.2024.06.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/13/2023] [Revised: 02/26/2024] [Accepted: 06/03/2024] [Indexed: 07/05/2024]
Abstract
Population-level variation and mechanisms behind insulin secretion in response to carbohydrate, protein, and fat remain uncharacterized. We defined prototypical insulin secretion responses to three macronutrients in islets from 140 cadaveric donors, including those with type 2 diabetes. The majority of donors' islets exhibited the highest insulin response to glucose, moderate response to amino acid, and minimal response to fatty acid. However, 9% of donors' islets had amino acid responses, and 8% had fatty acid responses that were larger than their glucose-stimulated insulin responses. We leveraged this heterogeneity and used multi-omics to identify molecular correlates of nutrient responsiveness, as well as proteins and mRNAs altered in type 2 diabetes. We also examined nutrient-stimulated insulin release from stem cell-derived islets and observed responsiveness to fat but not carbohydrate or protein-potentially a hallmark of immaturity. Understanding the diversity of insulin responses to carbohydrate, protein, and fat lays the groundwork for personalized nutrition.
Collapse
Affiliation(s)
- Jelena Kolic
- Department of Cellular and Physiological Sciences, Life Sciences Institute, University of British Columbia, Vancouver, BC, Canada.
| | - WenQing Grace Sun
- Department of Cellular and Physiological Sciences, Life Sciences Institute, University of British Columbia, Vancouver, BC, Canada
| | - Haoning Howard Cen
- Department of Cellular and Physiological Sciences, Life Sciences Institute, University of British Columbia, Vancouver, BC, Canada
| | - Jessica D Ewald
- Institute of Parasitology, McGill University, Montreal, QC, Canada
| | - Jason C Rogalski
- Department of Biochemistry and Molecular Biology, University of British Columbia, Vancouver, BC, Canada
| | - Shugo Sasaki
- Diabetes Research Group, BC Children's Hospital Research Institute, Vancouver, BC, Canada; Department of Surgery, School of Biomedical Engineering, University of British Columbia, Vancouver, BC, Canada
| | - Han Sun
- Department of Pediatrics, Division of Endocrinology, Stanford School of Medicine, Stanford, CA, USA
| | - Varsha Rajesh
- Department of Pediatrics, Division of Endocrinology, Stanford School of Medicine, Stanford, CA, USA
| | - Yi Han Xia
- Department of Cellular and Physiological Sciences, Life Sciences Institute, University of British Columbia, Vancouver, BC, Canada
| | - Renata Moravcova
- Department of Biochemistry and Molecular Biology, University of British Columbia, Vancouver, BC, Canada
| | - Søs Skovsø
- Valkyrie Life Sciences, Vancouver, BC, Canada
| | - Aliya F Spigelman
- Department of Pharmacology, University of Alberta, Edmonton, AB, Canada; Alberta Diabetes Institute, University of Alberta, Edmonton, AB, Canada
| | - Jocelyn E Manning Fox
- Department of Pharmacology, University of Alberta, Edmonton, AB, Canada; Alberta Diabetes Institute, University of Alberta, Edmonton, AB, Canada
| | - James Lyon
- Department of Pharmacology, University of Alberta, Edmonton, AB, Canada; Alberta Diabetes Institute, University of Alberta, Edmonton, AB, Canada
| | - Leanne Beet
- Department of Cellular and Physiological Sciences, Life Sciences Institute, University of British Columbia, Vancouver, BC, Canada
| | - Jianguo Xia
- Institute of Parasitology, McGill University, Montreal, QC, Canada
| | - Francis C Lynn
- Diabetes Research Group, BC Children's Hospital Research Institute, Vancouver, BC, Canada; Department of Surgery, School of Biomedical Engineering, University of British Columbia, Vancouver, BC, Canada
| | - Anna L Gloyn
- Department of Pediatrics, Division of Endocrinology, Stanford School of Medicine, Stanford, CA, USA; Stanford Diabetes Research Center, Stanford School of Medicine, Stanford, CA, USA; Wellcome Center for Human Genetics, University of Oxford, Oxford, UK
| | - Leonard J Foster
- Department of Biochemistry and Molecular Biology, University of British Columbia, Vancouver, BC, Canada
| | - Patrick E MacDonald
- Department of Pharmacology, University of Alberta, Edmonton, AB, Canada; Alberta Diabetes Institute, University of Alberta, Edmonton, AB, Canada
| | - James D Johnson
- Department of Cellular and Physiological Sciences, Life Sciences Institute, University of British Columbia, Vancouver, BC, Canada; Vancouver Coastal Health Research Institute, Vancouver, BC, Canada.
| |
Collapse
|
5
|
de Roos B. How good are we at predicting the individual response to personalized diets? Am J Clin Nutr 2024; 120:3-4. [PMID: 38960577 DOI: 10.1016/j.ajcnut.2024.04.031] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2024] [Accepted: 04/26/2024] [Indexed: 07/05/2024] Open
Affiliation(s)
- Baukje de Roos
- The Rowett Institute, University of Aberdeen, Foresterhill, Aberdeen, United Kingdom.
| |
Collapse
|
6
|
Bermingham KM, Linenberg I, Polidori L, Asnicar F, Arrè A, Wolf J, Badri F, Bernard H, Capdevila J, Bulsiewicz WJ, Gardner CD, Ordovas JM, Davies R, Hadjigeorgiou G, Hall WL, Delahanty LM, Valdes AM, Segata N, Spector TD, Berry SE. Effects of a personalized nutrition program on cardiometabolic health: a randomized controlled trial. Nat Med 2024; 30:1888-1897. [PMID: 38714898 PMCID: PMC11271409 DOI: 10.1038/s41591-024-02951-6] [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: 10/18/2023] [Accepted: 03/26/2024] [Indexed: 05/15/2024]
Abstract
Large variability exists in people's responses to foods. However, the efficacy of personalized dietary advice for health remains understudied. We compared a personalized dietary program (PDP) versus general advice (control) on cardiometabolic health using a randomized clinical trial. The PDP used food characteristics, individual postprandial glucose and triglyceride (TG) responses to foods, microbiomes and health history, to produce personalized food scores in an 18-week app-based program. The control group received standard care dietary advice (US Department of Agriculture Guidelines for Americans, 2020-2025) using online resources, check-ins, video lessons and a leaflet. Primary outcomes were serum low-density lipoprotein cholesterol and TG concentrations at baseline and at 18 weeks. Participants (n = 347), aged 41-70 years and generally representative of the average US population, were randomized to the PDP (n = 177) or control (n = 170). Intention-to-treat analysis (n = 347) between groups showed significant reduction in TGs (mean difference = -0.13 mmol l-1; log-transformed 95% confidence interval = -0.07 to -0.01, P = 0.016). Changes in low-density lipoprotein cholesterol were not significant. There were improvements in secondary outcomes, including body weight, waist circumference, HbA1c, diet quality and microbiome (beta-diversity) (P < 0.05), particularly in highly adherent PDP participants. However, blood pressure, insulin, glucose, C-peptide, apolipoprotein A1 and B, and postprandial TGs did not differ between groups. No serious intervention-related adverse events were reported. Following a personalized diet led to some improvements in cardiometabolic health compared to standard dietary advice. ClinicalTrials.gov registration: NCT05273268 .
Collapse
Affiliation(s)
- Kate M Bermingham
- Department of Nutritional Sciences, King's College London, London, UK
- Zoe Ltd, London, UK
| | - Inbar Linenberg
- Department of Nutritional Sciences, King's College London, London, UK
- Zoe Ltd, London, UK
| | | | - Francesco Asnicar
- Department of Cellular, Computational and Integrative Biology, University of Trento, Trento, Italy
| | | | | | | | | | | | | | | | - Jose M Ordovas
- Jean Mayer USDA Human Nutrition Research Center on Aging, Tufts University, Boston, MA, USA
- IMDEA Food Institute, Campus of International Excellence, Universidad Autónoma de Madrid, Consejo Superior de Investigaciones Científicas, Madrid, Spain
- Universidad Camilo José Cela, Madrid, Spain
| | | | | | - Wendy L Hall
- Department of Nutritional Sciences, King's College London, London, UK
| | - Linda M Delahanty
- Diabetes Center, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Ana M Valdes
- School of Medicine, University of Nottingham, Nottingham, UK
- Nottingham National Institute for Health and Care Research Biomedical Research Centre, Nottingham, UK
| | - Nicola Segata
- Department of Cellular, Computational and Integrative Biology, University of Trento, Trento, Italy
| | - Tim D Spector
- Department of Nutritional Sciences, King's College London, London, UK
- Department of Twin Research and Genetic Epidemiology, King's College London, London, UK
| | - Sarah E Berry
- Department of Nutritional Sciences, King's College London, London, UK.
| |
Collapse
|
7
|
Berube LT, Popp CJ, Curran M, Hu L, Pompeii ML, Barua S, Bernstein E, Salcedo V, Li H, St-Jules DE, Segal E, Bergman M, Williams NJ, Sevick MA. Diabetes Telemedicine Mediterranean Diet (DiaTeleMed) Study: study protocol for a fully remote randomized clinical trial evaluating personalized dietary management in individuals with type 2 diabetes. RESEARCH SQUARE 2024:rs.3.rs-4492352. [PMID: 38978573 PMCID: PMC11230484 DOI: 10.21203/rs.3.rs-4492352/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/10/2024]
Abstract
Background The Diabetes Telemedicine Mediterranean Diet (DiaTeleMed) Study is a fully remote randomized clinical trial evaluating personalized dietary management in individuals with type 2 diabetes (T2D). The study aims to test the efficacy of a personalized behavioral approach for dietary management of moderately-controlled T2D, versus a standardized behavioral intervention that uses one-size-fits-all dietary recommendations, versus a usual care control (UCC). The primary outcome will compare the impact of each intervention on the mean amplitude of glycemic excursions (MAGE). Methods Eligible participants are between 21 to 80 years of age diagnosed with moderately-controlled T2D (HbA1c: 6.0-8.0%), and managed on lifestyle alone or lifestyle plus metformin. Participants must be willing and able to attend virtual counseling sessions and log meals into a dietary tracking smartphone application (DayTwo), and wear a continuous glucose monitor (CGM) for up to 12 days. Participants are randomized with equal allocation (n = 255, n = 85 per arm) to one of three arms: 1) Personalized, 2) Standardized, or 3) UCC. Measurements occur at 0 (baseline), 3, and 6 months. All participants receive isocaloric energy and macronutrients targets to meet Mediterranean diet guidelines plus 14 intervention contacts over 6 months (4 weekly then 10 biweekly) to cover diabetes self-management education. The first 4 UCC intervention contacts are delivered via synchronous videoconferences followed by educational video links. Participants in Standardized receive the same education content as UCC on the same schedule. However, all intervention contacts are conducted via synchronous videoconferences, paired with Social Cognitive Theory (SCT)-based behavioral counseling, plus dietary self-monitoring of planned meals using a mobile app that provides real-time feedback on calories and macronutrients. Participants in the Personalized arm receive all elements of the Standardized intervention, plus real-time feedback on predicted post-prandial glycemic response (PPGR) to meals and snacks logged into the mobile app. Discussion The DiaTeleMed study will address an important gap in the current landscape of precision nutrition by determining the contributions of behavioral counseling and personalized nutrition recommendations on glycemic control in individuals with T2D. The fully remote methodology of the study allows for scalability and innovative delivery of personalized dietary recommendations at a population level. Trial registration The DiaTeleMed Study is registered with ClinicalTrials.gov (Identifier: NCT05046886).
Collapse
Affiliation(s)
| | | | | | - Lu Hu
- New York University Grossman School of Medicine
| | | | | | | | | | - Huilin Li
- New York University Grossman School of Medicine
| | | | | | | | | | | |
Collapse
|
8
|
Reik A, Schauberger G, Wiechert M, Hauner H, Holzapfel C. Association Between the Postprandial Response to an Oral Glucose Tolerance Test and Anthropometric Changes After an 8-Week Low-Calorie Formula Diet - Results From the Lifestyle Intervention (LION) Study. Mol Nutr Food Res 2024; 68:e2400106. [PMID: 38850172 DOI: 10.1002/mnfr.202400106] [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: 02/09/2024] [Revised: 05/07/2024] [Indexed: 06/10/2024]
Abstract
SCOPE Interindividual variations in postprandial metabolism and weight loss outcomes have been reported. The literature suggests links between postprandial metabolism and weight regulation. Therefore, the study aims to evaluate if postprandial glucose metabolism after a glucose load predicts anthropometric outcomes of a weight loss intervention. METHODS AND RESULTS Anthropometric data from adults with obesity (18-65 years, body mass index [BMI] 30.0-39.9 kg m-2) are collected pre- and post an 8-week formula-based weight loss intervention. An oral glucose tolerance test (OGTT) is performed at baseline, from which postprandial parameters are derived from glucose and insulin concentrations. Linear regression models explored associations between these parameters and anthropometric changes (∆) postintervention. A random forest model is applied to identify predictive parameters for anthropometric outcomes after intervention. Postprandial parameters after an OGTT of 158 participants (63.3% women, age 45 ± 12, BMI 34.9 ± 2.9 kg m-2) reveal nonsignificant associations with changes in anthropometric parameters after weight loss (p > 0.05). Baseline fat-free mass (FFM) and sex are primary predictors for ∆ FFM [kg]. CONCLUSION Postprandial glucose metabolism after a glucose load does not predict anthropometric outcomes after short-term weight loss via a formula-based low-calorie diet in adults with obesity.
Collapse
Affiliation(s)
- Anna Reik
- Institute for Nutritional Medicine, School of Medicine and Health, Technical University of Munich, 80992, Munich, Germany
| | - Gunther Schauberger
- Chair of Epidemiology, School of Medicine and Health, Technical University of Munich, 80992, Munich, Germany
| | - Meike Wiechert
- Institute for Nutritional Medicine, School of Medicine and Health, Technical University of Munich, 80992, Munich, Germany
| | - Hans Hauner
- Institute for Nutritional Medicine, School of Medicine and Health, Technical University of Munich, 80992, Munich, Germany
- Else Kroener-Fresenius-Center for Nutritional Medicine, School of Life Sciences, Technical University of Munich, 85354, Freising, Germany
| | - Christina Holzapfel
- Institute for Nutritional Medicine, School of Medicine and Health, Technical University of Munich, 80992, Munich, Germany
- Department of Nutritional, Food and Consumer Sciences, Fulda University of Applied Sciences, 36037, Fulda, Germany
| |
Collapse
|
9
|
Ratiner K, Ciocan D, Abdeen SK, Elinav E. Utilization of the microbiome in personalized medicine. Nat Rev Microbiol 2024; 22:291-308. [PMID: 38110694 DOI: 10.1038/s41579-023-00998-9] [Citation(s) in RCA: 12] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 11/17/2023] [Indexed: 12/20/2023]
Abstract
Inter-individual human variability, driven by various genetic and environmental factors, complicates the ability to develop effective population-based early disease detection, treatment and prognostic assessment. The microbiome, consisting of diverse microorganism communities including viruses, bacteria, fungi and eukaryotes colonizing human body surfaces, has recently been identified as a contributor to inter-individual variation, through its person-specific signatures. As such, the microbiome may modulate disease manifestations, even among individuals with similar genetic disease susceptibility risks. Information stored within microbiomes may therefore enable early detection and prognostic assessment of disease in at-risk populations, whereas microbiome modulation may constitute an effective and safe treatment tailored to the individual. In this Review, we explore recent advances in the application of microbiome data in precision medicine across a growing number of human diseases. We also discuss the challenges, limitations and prospects of analysing microbiome data for personalized patient care.
Collapse
Affiliation(s)
- Karina Ratiner
- Systems Immunology Department, Weizmann Institute of Science, Rehovot, Israel
| | - Dragos Ciocan
- Systems Immunology Department, Weizmann Institute of Science, Rehovot, Israel
| | - Suhaib K Abdeen
- Systems Immunology Department, Weizmann Institute of Science, Rehovot, Israel.
| | - Eran Elinav
- Systems Immunology Department, Weizmann Institute of Science, Rehovot, Israel.
- Division of Cancer-Microbiome Research, DKFZ, Heidelberg, Germany.
| |
Collapse
|
10
|
Muse ED, Topol EJ. Transforming the cardiometabolic disease landscape: Multimodal AI-powered approaches in prevention and management. Cell Metab 2024; 36:670-683. [PMID: 38428435 PMCID: PMC10990799 DOI: 10.1016/j.cmet.2024.02.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/26/2023] [Revised: 01/25/2024] [Accepted: 02/06/2024] [Indexed: 03/03/2024]
Abstract
The rise of artificial intelligence (AI) has revolutionized various scientific fields, particularly in medicine, where it has enabled the modeling of complex relationships from massive datasets. Initially, AI algorithms focused on improved interpretation of diagnostic studies such as chest X-rays and electrocardiograms in addition to predicting patient outcomes and future disease onset. However, AI has evolved with the introduction of transformer models, allowing analysis of the diverse, multimodal data sources existing in medicine today. Multimodal AI holds great promise in more accurate disease risk assessment and stratification as well as optimizing the key driving factors in cardiometabolic disease: blood pressure, sleep, stress, glucose control, weight, nutrition, and physical activity. In this article we outline the current state of medical AI in cardiometabolic disease, highlighting the potential of multimodal AI to augment personalized prevention and treatment strategies in cardiometabolic disease.
Collapse
Affiliation(s)
- Evan D Muse
- Scripps Research Translational Institute, Scripps Research, La Jolla, CA 92037, USA; Division of Cardiovascular Diseases, Scripps Clinic, La Jolla, CA 92037, USA
| | - Eric J Topol
- Scripps Research Translational Institute, Scripps Research, La Jolla, CA 92037, USA; Division of Cardiovascular Diseases, Scripps Clinic, La Jolla, CA 92037, USA.
| |
Collapse
|
11
|
Ziolkovska A, Sina C. Personalized nutrition as the catalyst for building food-resilient cities. NATURE FOOD 2024; 5:267-269. [PMID: 38561460 DOI: 10.1038/s43016-024-00959-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/04/2024]
Affiliation(s)
- Anna Ziolkovska
- Topian, NEOM, Gayal, Tabuk province, Kingdom of Saudi Arabia
| | - Christian Sina
- Institute of Nutritional Medicine, University of Lübeck and University Medical Center Schleswig-Holstein, Lübeck, Germany.
- Fraunhofer Research Institution for Individualized and Cell-Based Medical Engineering (IMTE), Lübeck, Germany.
| |
Collapse
|
12
|
Kolic J, Sun WG, Cen HH, Ewald J, Rogalski JC, Sasaki S, Sun H, Rajesh V, Xia YH, Moravcova R, Skovsø S, Spigelman AF, Manning Fox JE, Lyon J, Beet L, Xia J, Lynn FC, Gloyn AL, Foster LJ, MacDonald PE, Johnson JD. Proteomic predictors of individualized nutrient-specific insulin secretion in health and disease. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2023.05.24.23290298. [PMID: 38496562 PMCID: PMC10942505 DOI: 10.1101/2023.05.24.23290298] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/19/2024]
Abstract
Population level variation and molecular mechanisms behind insulin secretion in response to carbohydrate, protein, and fat remain uncharacterized despite ramifications for personalized nutrition. Here, we define prototypical insulin secretion dynamics in response to the three macronutrients in islets from 140 cadaveric donors, including those diagnosed with type 2 diabetes. While islets from the majority of donors exhibited the expected relative response magnitudes, with glucose being highest, amino acid moderate, and fatty acid small, 9% of islets stimulated with amino acid and 8% of islets stimulated with fatty acids had larger responses compared with high glucose. We leveraged this insulin response heterogeneity and used transcriptomics and proteomics to identify molecular correlates of specific nutrient responsiveness, as well as those proteins and mRNAs altered in type 2 diabetes. We also examine nutrient-responsiveness in stem cell-derived islet clusters and observe that they have dysregulated fuel sensitivity, which is a hallmark of functionally immature cells. Our study now represents the first comparison of dynamic responses to nutrients and multi-omics analysis in human insulin secreting cells. Responses of different people's islets to carbohydrate, protein, and fat lay the groundwork for personalized nutrition. ONE-SENTENCE SUMMARY Deep phenotyping and multi-omics reveal individualized nutrient-specific insulin secretion propensity.
Collapse
|
13
|
Popp CJ, Wang C, Hoover A, Gomez LA, Curran M, St-Jules DE, Barua S, Sevick MA, Kleinberg S. Objective Determination of Eating Occasion Timing: Combining Self-Report, Wrist Motion, and Continuous Glucose Monitoring to Detect Eating Occasions in Adults With Prediabetes and Obesity. J Diabetes Sci Technol 2024; 18:266-272. [PMID: 37747075 PMCID: PMC10973869 DOI: 10.1177/19322968231197205] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 09/26/2023]
Abstract
BACKGROUND Accurately identifying eating patterns, specifically the timing, frequency, and distribution of eating occasions (EOs), is important for assessing eating behaviors, especially for preventing and managing obesity and type 2 diabetes (T2D). However, existing methods to study EOs rely on self-report, which may be prone to misreporting and bias and has a high user burden. Therefore, objective methods are needed. METHODS We aim to compare EO timing using objective and subjective methods. Participants self-reported EO with a smartphone app (self-report [SR]), wore the ActiGraph GT9X on their dominant wrist, and wore a continuous glucose monitor (CGM, Abbott Libre Pro) for 10 days. EOs were detected from wrist motion (WM) using a motion-based classifier and from CGM using a simulation-based system. We described EO timing and explored how timing identified with WM and CGM compares with SR. RESULTS Participants (n = 39) were 59 ± 11 years old, mostly female (62%) and White (51%) with a body mass index (BMI) of 34.2 ± 4.7 kg/m2. All had prediabetes or moderately controlled T2D. The median time-of-day first EO (and interquartile range) for SR, WM, and CGM were 08:24 (07:00-09:59), 9:42 (07:46-12:26), and 06:55 (04:23-10:03), respectively. The median last EO for SR, WM, and CGM were 20:20 (16:50-21:42), 20:12 (18:30-21:41), and 21:43 (20:35-22:16), respectively. The overlap between SR and CGM was 55% to 80% of EO detected with tolerance periods of ±30, 60, and 120 minutes. The overlap between SR and WM was 52% to 65% EO detected with tolerance periods of ±30, 60, and 120 minutes. CONCLUSION The continuous glucose monitor and WM detected overlapping but not identical meals and may provide complementary information to self-reported EO.
Collapse
Affiliation(s)
- Collin J. Popp
- Department of Population Health,
Institute for Excellence in Health Equity, NYU Langone Health, New York, NY,
USA
| | - Chan Wang
- Division of Biostatistics, Department
of Population Health, NYU Langone Health, New York, NY, USA
| | - Adam Hoover
- Holcombe Department of Electrical and
Computer Engineering, Clemson University, Clemson, SC, USA
| | - Louis A. Gomez
- Department of Computer Science, Stevens
Institute of Technology, Hoboken, NJ, USA
| | - Margaret Curran
- Department of Population Health,
Institute for Excellence in Health Equity, NYU Langone Health, New York, NY,
USA
| | | | - Souptik Barua
- Department of Medicine, NYU Langone
Health, New York, NY, USA
| | - Mary Ann Sevick
- Division of Precision Medicine,
Department of Medicine, NYU Langone Health, New York, NY, USA
- Department of Medicine, NYU Langone
Health, New York, NY, USA
| | - Samantha Kleinberg
- Department of Computer Science, Stevens
Institute of Technology, Hoboken, NJ, USA
| |
Collapse
|
14
|
Mehta NH, Huey SL, Kuriyan R, Peña-Rosas JP, Finkelstein JL, Kashyap S, Mehta S. Potential Mechanisms of Precision Nutrition-Based Interventions for Managing Obesity. Adv Nutr 2024; 15:100186. [PMID: 38316343 PMCID: PMC10914563 DOI: 10.1016/j.advnut.2024.100186] [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: 09/20/2023] [Revised: 01/17/2024] [Accepted: 02/01/2024] [Indexed: 02/07/2024] Open
Abstract
Precision nutrition (PN) considers multiple individual-level and environmental characteristics or variables to better inform dietary strategies and interventions for optimizing health, including managing obesity and metabolic disorders. Here, we review the evidence on potential mechanisms-including ones to identify individuals most likely to respond-that can be leveraged in the development of PN interventions addressing obesity. We conducted a review of the literature and included laboratory, animal, and human studies evaluating biochemical and genetic data, completed and ongoing clinical trials, and public programs in this review. Our analysis describes the potential mechanisms related to 6 domains including genetic predisposition, circadian rhythms, physical activity and sedentary behavior, metabolomics, the gut microbiome, and behavioral and socioeconomic characteristics, i.e., the factors that can be leveraged to design PN-based interventions to prevent and treat obesity-related outcomes such as weight loss or metabolic health as laid out by the NIH 2030 Strategic Plan for Nutrition Research. For example, single nucleotide polymorphisms can modify responses to certain dietary interventions, and epigenetic modulation of obesity risk via physical activity patterns and macronutrient intake have also been demonstrated. Additionally, we identified limitations including questions of equitable implementation across a limited number of clinical trials. These include the limited ability of current PN interventions to address systemic influences such as supply chains and food distribution, healthcare systems, racial or cultural inequities, and economic disparities, particularly when designing and implementing PN interventions in low- and middle-income communities. PN has the potential to help manage obesity by addressing intra- and inter-individual variation as well as context, as opposed to "one-size fits all" approaches though there is limited clinical trial evidence to date.
Collapse
Affiliation(s)
- Neel H Mehta
- Division of Nutritional Sciences, Cornell University, Ithaca, NY, United States
| | - Samantha L Huey
- Division of Nutritional Sciences, Cornell University, Ithaca, NY, United States; Center for Precision Nutrition and Health, Cornell University, Ithaca, NY, United States
| | - Rebecca Kuriyan
- Division of Nutrition, St. John's Research Institute, Bengaluru, Karnataka, India
| | - Juan Pablo Peña-Rosas
- Global Initiatives, The Department of Nutrition and Food Safety, World Health Organization, Geneva, Switzerland
| | - Julia L Finkelstein
- Division of Nutritional Sciences, Cornell University, Ithaca, NY, United States; Center for Precision Nutrition and Health, Cornell University, Ithaca, NY, United States; Division of Nutrition, St. John's Research Institute, Bengaluru, Karnataka, India
| | - Sangeeta Kashyap
- Division of Endocrinology, Diabetes and Metabolism, Weill Cornell Medicine New York Presbyterian, New York, NY, United States
| | - Saurabh Mehta
- Division of Nutritional Sciences, Cornell University, Ithaca, NY, United States; Center for Precision Nutrition and Health, Cornell University, Ithaca, NY, United States; Division of Medical Informatics, St. John's Research Institute, Bengaluru, Karnataka, India.
| |
Collapse
|
15
|
Mansour S, Alkhaaldi SMI, Sammanasunathan AF, Ibrahim S, Farhat J, Al-Omari B. Precision Nutrition Unveiled: Gene-Nutrient Interactions, Microbiota Dynamics, and Lifestyle Factors in Obesity Management. Nutrients 2024; 16:581. [PMID: 38474710 DOI: 10.3390/nu16050581] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2023] [Revised: 02/05/2024] [Accepted: 02/18/2024] [Indexed: 03/14/2024] Open
Abstract
BACKGROUND Obesity is a complex metabolic disorder that is associated with several diseases. Recently, precision nutrition (PN) has emerged as a tailored approach to provide individualised dietary recommendations. AIM This review discusses the major intrinsic and extrinsic components considered when applying PN during the management of obesity and common associated chronic conditions. RESULTS The review identified three main PN components: gene-nutrient interactions, intestinal microbiota, and lifestyle factors. Genetic makeup significantly contributes to inter-individual variations in dietary behaviours, with advanced genome sequencing and population genetics aiding in detecting gene variants associated with obesity. Additionally, PN-based host-microbiota evaluation emerges as an advanced therapeutic tool, impacting disease control and prevention. The gut microbiome's composition regulates diverse responses to nutritional recommendations. Several studies highlight PN's effectiveness in improving diet quality and enhancing adherence to physical activity among obese patients. PN is a key strategy for addressing obesity-related risk factors, encompassing dietary patterns, body weight, fat, blood lipids, glucose levels, and insulin resistance. CONCLUSION PN stands out as a feasible tool for effectively managing obesity, considering its ability to integrate genetic and lifestyle factors. The application of PN-based approaches not only improves current obesity conditions but also holds promise for preventing obesity and its associated complications in the long term.
Collapse
Affiliation(s)
- Samy Mansour
- College of Medicine and Health Sciences, Khalifa University of Science and Technology, Abu Dhabi P.O. Box 127788, United Arab Emirates
| | - Saif M I Alkhaaldi
- College of Medicine and Health Sciences, Khalifa University of Science and Technology, Abu Dhabi P.O. Box 127788, United Arab Emirates
| | - Ashwin F Sammanasunathan
- College of Medicine and Health Sciences, Khalifa University of Science and Technology, Abu Dhabi P.O. Box 127788, United Arab Emirates
| | - Saleh Ibrahim
- College of Medicine and Health Sciences, Khalifa University of Science and Technology, Abu Dhabi P.O. Box 127788, United Arab Emirates
- Institute of Experimental Dermatology, University of Lübeck, Ratzeburger Allee 160, 23538 Lübeck, Germany
| | - Joviana Farhat
- Department of Public Health and Epidemiology, College of Medicine and Health Sciences, Khalifa University of Science and Technology, Abu Dhabi P.O. Box 127788, United Arab Emirates
| | - Basem Al-Omari
- Department of Public Health and Epidemiology, College of Medicine and Health Sciences, Khalifa University of Science and Technology, Abu Dhabi P.O. Box 127788, United Arab Emirates
| |
Collapse
|
16
|
Liu H, Feng J, Shi Z, Su J, Sun J, Wu F, Zhu Z. Effects of a Novel Applet-Based Personalized Dietary Intervention on Dietary Intakes: A Randomized Controlled Trial in a Real-World Scenario. Nutrients 2024; 16:565. [PMID: 38398889 PMCID: PMC10892066 DOI: 10.3390/nu16040565] [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: 01/24/2024] [Revised: 02/12/2024] [Accepted: 02/16/2024] [Indexed: 02/25/2024] Open
Abstract
The objective of this study was to assess the feasibility and effectiveness of a novel WeChat applet-based personalized dietary intervention aimed at promoting healthier dietary intakes. A two-arm parallel, randomized, controlled trial was conducted in a real-world scenario and involved a total of 153 participants (the intervention group, n = 76; the control group, n = 77), lasting for 4 months in Shanghai, China. The intervention group had access to visualized nutrition evaluations through the applet during workday lunch time, while the control group received no interventions. A total of 3413 lunch dietary intake records were captured through the applet. Linear mixed models were utilized to assess the intervention effects over time. At baseline, the participants' lunchtime dietary intakes were characterized by insufficient consumption of plant foods (86.9% of the participants) and excessive intake of animal foods (79.7% of the participants). Following the commencement of the intervention, the intervention group showed a significant decrease in the animal/plant food ratio (β = -0.03/week, p = 0.024) and the consumption of livestock and poultry meat (β = -1.80 g/week, p = 0.035), as well as a borderline significant increase in the consumption of vegetables and fruits (β = 3.22 g/week, p = 0.055) and plant foods (β = 3.26 g/week, p = 0.057) over time at lunch compared to the control group. The applet-based personalized dietary intervention was feasible and effective in improving dietary intakes and, consequently, possibly may manage body weight issues in real-world scenarios.
Collapse
Affiliation(s)
- Hongwei Liu
- School of Public Health, Fudan University, Shanghai 200032, China; (H.L.); (J.F.)
| | - Jingyuan Feng
- School of Public Health, Fudan University, Shanghai 200032, China; (H.L.); (J.F.)
| | - Zehuan Shi
- Division of Health Risk Factors Monitoring and Control, Shanghai Municipal Center for Disease Control and Prevention, Shanghai 200336, China; (Z.S.); (J.S.)
| | - Jin Su
- Division of Health Risk Factors Monitoring and Control, Shanghai Municipal Center for Disease Control and Prevention, Shanghai 200336, China; (Z.S.); (J.S.)
| | - Jing Sun
- National Institute for Nutrition and Health, Chinese Center for Disease Control and Prevention, Beijing 100050, China;
| | - Fan Wu
- School of Public Health, Fudan University, Shanghai 200032, China; (H.L.); (J.F.)
| | - Zhenni Zhu
- Division of Health Risk Factors Monitoring and Control, Shanghai Municipal Center for Disease Control and Prevention, Shanghai 200336, China; (Z.S.); (J.S.)
| |
Collapse
|
17
|
Alhusseini N, Alsinan N, Almutahhar S, Khader M, Tamimi R, Elsarrag MI, Warar R, Alnasser S, Ramadan M, Omair A, Aouabdi S, Saleem R, Alabadi-Bierman A. Dietary trends and obesity in Saudi Arabia. Front Public Health 2024; 11:1326418. [PMID: 38274536 PMCID: PMC10808649 DOI: 10.3389/fpubh.2023.1326418] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2023] [Accepted: 12/19/2023] [Indexed: 01/27/2024] Open
Abstract
Introduction Dietary habits in Saudi Arabia have been shifting toward the Western diet, which is high in fat, salt, and sugar, leading to a high obesity rate. Different dietary strategies such as the Ketogenic Diet (KD), Intermittent Fasting (IF), Gluten Free Diet (GFD), and Calorie Restriction Diet (CRD) have shown an influential role in weight loss. This study aimed to compare trending diets and correlate different types of diet with obesity and lifestyle among adults in Saudi Arabia. Methods A cross-sectional study was performed on Saudis and non-Saudis over 18 years old. We used convenience sampling, an online questionnaire distributed via social media channels, including WhatsApp, LinkedIn, and Twitter. SPSS 28 software was applied for data analysis. The chi-square test was used to determine associations between different variables. Statistical significance was considered at a value of p less than 0.05. Results Most participants were females residing in the Eastern and Central regions of Saudi Arabia. Although most do not follow any dietary plan, they exhibited acceptable exercise and lifestyle. The minority of the study population followed different types of diet plans, such as KD, IF, and GFD. The purpose of most of the participants who have used these strategies was for weight loss but failed to sustain the dietary plan for more than 1 month. Conclusion Obesity remains a challenging issue in Saudi Arabia. Adherence to dietary regimes could help in controlling obesity. Increasing the awareness of the benefits of each dietary plan for health, choosing the appropriate one, and sustaining a balanced nutrition pattern.
Collapse
Affiliation(s)
| | - Nawra Alsinan
- College of Medicine, Alfaisal University, Riyadh, Saudi Arabia
| | | | - Majd Khader
- College of Medicine, Alfaisal University, Riyadh, Saudi Arabia
| | - Rawand Tamimi
- College of Medicine, Alfaisal University, Riyadh, Saudi Arabia
| | | | - Rabah Warar
- College of Medicine, Alfaisal University, Riyadh, Saudi Arabia
| | - Sara Alnasser
- College of Medicine, Alfaisal University, Riyadh, Saudi Arabia
- Prince Sultan Military Medical City, Riyadh, Saudi Arabia
| | - Majed Ramadan
- King Abdullah International Medical Research Center, Jeddah, Saudi Arabia
- King Saud Bin Abdulaziz University for Health Sciences, Jeddah, Saudi Arabia
| | - Aamir Omair
- King Saud bin Abdulaziz University for Health Sciences, Riyadh, Saudi Arabia
| | - Sihem Aouabdi
- Ministry of National Guard, King Abdulaziz Medical City, Jeddah, Saudi Arabia
| | - Rimah Saleem
- College of Medicine, Alfaisal University, Riyadh, Saudi Arabia
| | - Alaa Alabadi-Bierman
- School of Public Health, Loma Linda University, Loma Linda, CA, United States
- University of California, Riverside, Riverside, CA, United States
| |
Collapse
|
18
|
Zhang K, Feng Y, Chai Y, Wang C, Yu S. Association between dinner timing and glucose metabolism in rural China: A large-scale cross-sectional study. Nutrition 2023; 115:112158. [PMID: 37544210 DOI: 10.1016/j.nut.2023.112158] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2023] [Revised: 07/02/2023] [Accepted: 07/03/2023] [Indexed: 08/08/2023]
Abstract
OBJECTIVES Meal timing is a major risk factor for metabolic disease. The aim of this study was to assess the relationship between dinner timing and glucose metabolism in the rural Chinese population. METHODS This cross-sectional study included 7701 participants from a Henan rural cohort study. Basic information was collected by in-person questionnaires. Multiple linear regression analysis was used to evaluate the relationship between dinner timing and fasting insulin (FINS), fasting plasma glucose (FPG), and homeostatic model assessment for insulin resistance (HOMA-IR). Restricted cubic spline was employed to investigate the dose-response relationship between dinner timing and FINS, FPG, and HOMA-IR. A generalized linear model was used to explore the interaction effect of age and dinner timing on FINS, FPG, and HOMA-IR. RESULTS After adjusting for confounding factors, FINS concentration was reduced by 0.482 mmol/L (P < 0.001) for each hour delay in dinner timing. Furthermore, the HOMA-IR index decreased by 0.122 mmol/L for each hour delay. The results indicated a noticeable trend of decreasing values associated with later dinner timing (FINS: Poverall association < 0.001, Pnonlinear association = 0.144; HOMA-IR: Poverall association = 0.001, Pnonlinear association = 0.186). The interaction between age and dinner time significantly correlated with FINS and HOMA-IR (P < 0.05). This relationship was statistically significant before 69 y (P < 0.05). CONCLUSION A significant association between dinner timing and glucose metabolism was observed in the rural Chinese population. Delayed dinner timing may be associated with lower fasting insulin. The negative effect of dinner timing on FINS and HOMA-IR was diminished with age.
Collapse
Affiliation(s)
- Kaiyang Zhang
- College of Public Health, Zhengzhou University, Zhengzhou, China
| | - Yinhua Feng
- College of Public Health, Zhengzhou University, Zhengzhou, China
| | - Yuanyuan Chai
- College of Public Health, Zhengzhou University, Zhengzhou, China
| | - Chongjian Wang
- College of Public Health, Zhengzhou University, Zhengzhou, China
| | - Songcheng Yu
- College of Public Health, Zhengzhou University, Zhengzhou, China; School of Nursing and Health, Zhengzhou University, Zhengzhou, China.
| |
Collapse
|
19
|
Feng J, Liu H, Mai S, Su J, Sun J, Zhou J, Zhang Y, Wang Y, Wu F, Zheng G, Zhu Z. Protocol of a parallel, randomized controlled trial on the effects of a novel personalized nutrition approach by artificial intelligence in real world scenario. BMC Public Health 2023; 23:1700. [PMID: 37660022 PMCID: PMC10474697 DOI: 10.1186/s12889-023-16434-9] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2023] [Accepted: 08/01/2023] [Indexed: 09/04/2023] Open
Abstract
BACKGROUND Nutrition service needs are huge in China. Previous studies indicated that personalized nutrition (PN) interventions were effective. The aim of the present study is to identify the effectiveness and feasibility of a novel PN approach supported by artificial intelligence (AI). METHODS This study is a two-arm parallel, randomized, controlled trial in real world scenario. The participants will be enrolled among who consume lunch at a staff canteen. In Phase I, a total of 170 eligible participants will be assigned to either intervention or control group on 1:1 ratio. The intervention group will be instructed to use the smartphone applet to record their lunches and reach the real-time AI-based information of dish nutrition evaluation and PN evaluation after meal consumption for 3 months. The control group will receive no nutrition information but be asked to record their lunches though the applet. Dietary pattern, body weight or blood pressure optimizing is expected after the intervention. In phase II, the applet will be free to all the diners (about 800) at the study canteen for another one year. Who use the applet at least 2 days per week will be regarded as the intervention group while the others will be the control group. Body metabolism normalization is expected after this period. Generalized linear mixed models will be used to identify the dietary, anthropometric and metabolic changes. DISCUSSION This novel approach will provide real-time AI-based dish nutrition evaluation and PN evaluation after meal consumption in order to assist users with nutrition information to make wise food choice. This study is designed under a real-life scenario which facilitates translating the trial intervention into real-world practice. TRIAL REGISTRATION This trial has been registered with the Chinese Clinical Trial Registry (ChiCTR2100051771; date registered: 03/10/2021).
Collapse
Affiliation(s)
- Jingyuan Feng
- School of Public Health, Fudan University, Shanghai, China
| | - Hongwei Liu
- School of Public Health, Fudan University, Shanghai, China
| | - Shupeng Mai
- Division of Health Risk Factors Monitoring and Control, Shanghai Municipal Center for Disease Control and Prevention, Shanghai, China
| | - Jin Su
- Division of Health Risk Factors Monitoring and Control, Shanghai Municipal Center for Disease Control and Prevention, Shanghai, China
| | - Jing Sun
- Chinese Center for Disease Control and Prevention, National Institute for Nutrition and Health, Beijing, China
| | - Jianjie Zhou
- Basebit (Shanghai) Information Technology Co., Ltd, Shanghai, China
| | - Yingyao Zhang
- Basebit (Shanghai) Information Technology Co., Ltd, Shanghai, China
| | - Yinyi Wang
- Department of Nutrition and Food Science, Education, and Human Development, Steinhardt School of Culture, New York University, New York, USA
| | - Fan Wu
- School of Public Health, Fudan University, Shanghai, China.
| | - Guangyong Zheng
- Institute of Interdisciplinary Integrative Medicine Research, Shanghai University of Traditional Chinese Medicine, Shanghai, China.
| | - Zhenni Zhu
- Division of Health Risk Factors Monitoring and Control, Shanghai Municipal Center for Disease Control and Prevention, Shanghai, China.
| |
Collapse
|
20
|
Brennan L, de Roos B. Role of metabolomics in the delivery of precision nutrition. Redox Biol 2023; 65:102808. [PMID: 37423161 PMCID: PMC10461186 DOI: 10.1016/j.redox.2023.102808] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2023] [Revised: 06/14/2023] [Accepted: 07/04/2023] [Indexed: 07/11/2023] Open
Abstract
Precision nutrition aims to deliver personalised dietary advice to individuals based on their personal genetics, metabolism and dietary/environmental exposures. Recent advances have demonstrated promise for the use of omic technologies for furthering the field of precision nutrition. Metabolomics in particular is highly attractive as measurement of metabolites can capture information on food intake, levels of bioactive compounds and the impact of diets on endogenous metabolism. These aspects contain useful information for precision nutrition. Furthermore using metabolomic profiles to identify subgroups or metabotypes is attractive for the delivery of personalised dietary advice. Combining metabolomic derived metabolites with other parameters in prediction models is also an exciting avenue for understanding and predicting response to dietary interventions. Examples include but not limited to role of one carbon metabolism and associated co-factors in blood pressure response. Overall, while evidence exists for potential in this field there are also many unanswered questions. Addressing these and clearly demonstrating that precision nutrition approaches enable adherence to healthier diets and improvements in health will be key in the near future.
Collapse
Affiliation(s)
- Lorraine Brennan
- Institute of Food and Health and Conway Institute, UCD School of Agriculture and Food Science, UCD, Belfield, Dublin 4, Ireland.
| | - Baukje de Roos
- The Rowett Institute, University of Aberdeen, Foresterhill, Aberdeen, AB25 2ZD, United Kingdom
| |
Collapse
|
21
|
Kharmats AY, Popp C, Hu L, Berube L, Curran M, Wang C, Pompeii ML, Li H, Bergman M, St-Jules DE, Segal E, Schoenthaler A, Williams N, Schmidt AM, Barua S, Sevick MA. A randomized clinical trial comparing low-fat with precision nutrition-based diets for weight loss: impact on glycemic variability and HbA1c. Am J Clin Nutr 2023; 118:443-451. [PMID: 37236549 PMCID: PMC10447469 DOI: 10.1016/j.ajcnut.2023.05.026] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2022] [Revised: 05/16/2023] [Accepted: 05/23/2023] [Indexed: 05/28/2023] Open
Abstract
BACKGROUND Recent studies have demonstrated considerable interindividual variability in postprandial glucose response (PPGR) to the same foods, suggesting the need for more precise methods for predicting and controlling PPGR. In the Personal Nutrition Project, the investigators tested a precision nutrition algorithm for predicting an individual's PPGR. OBJECTIVE This study aimed to compare changes in glycemic variability (GV) and HbA1c in 2 calorie-restricted weight loss diets in adults with prediabetes or moderately controlled type 2 diabetes (T2D), which were tertiary outcomes of the Personal Diet Study. METHODS The Personal Diet Study was a randomized clinical trial to compare a 1-size-fits-all low-fat diet (hereafter, standardized) with a personalized diet (hereafter, personalized). Both groups received behavioral weight loss counseling and were instructed to self-monitor diets using a smartphone application. The personalized arm received personalized feedback through the application to reduce their PPGR. Continuous glucose monitoring (CGM) data were collected at baseline, 3 mo and 6 mo. Changes in mean amplitude of glycemic excursions (MAGEs) and HbA1c at 6 mo were assessed. We performed an intention-to-treat analysis using linear mixed regressions. RESULTS We included 156 participants [66.5% women, 55.7% White, 24.1% Black, mean age 59.1 y (standard deviation (SD) = 10.7 y)] in these analyses (standardized = 75, personalized = 81). MAGE decreased by 0.83 mg/dL per month for standardized (95% CI: 0.21, 1.46 mg/dL; P = 0.009) and 0.79 mg/dL per month for personalized (95% CI: 0.19, 1.39 mg/dL; P = 0.010) diet, with no between-group differences (P = 0.92). Trends were similar for HbA1c values. CONCLUSIONS Personalized diet did not result in an increased reduction in GV or HbA1c in patients with prediabetes and moderately controlled T2D, compared with a standardized diet. Additional subgroup analyses may help to identify patients who are more likely to benefit from this personalized intervention. This trial was registered at clinicaltrials.gov as NCT03336411.
Collapse
Affiliation(s)
- Anna Y Kharmats
- Center for Healthful Behavior Change, Institute for Excellence in Health Equity, New York University Langone Health, New York, NY, United States; Department of Population Health, New York University Grossman School of Medicine, New York, NY, United States
| | - Collin Popp
- Center for Healthful Behavior Change, Institute for Excellence in Health Equity, New York University Langone Health, New York, NY, United States; Department of Population Health, New York University Grossman School of Medicine, New York, NY, United States
| | - Lu Hu
- Center for Healthful Behavior Change, Institute for Excellence in Health Equity, New York University Langone Health, New York, NY, United States; Department of Population Health, New York University Grossman School of Medicine, New York, NY, United States
| | - Lauren Berube
- Center for Healthful Behavior Change, Institute for Excellence in Health Equity, New York University Langone Health, New York, NY, United States; Department of Population Health, New York University Grossman School of Medicine, New York, NY, United States.
| | - Margaret Curran
- Center for Healthful Behavior Change, Institute for Excellence in Health Equity, New York University Langone Health, New York, NY, United States; Department of Population Health, New York University Grossman School of Medicine, New York, NY, United States
| | - Chan Wang
- Department of Population Health, New York University Grossman School of Medicine, New York, NY, United States
| | - Mary Lou Pompeii
- Center for Healthful Behavior Change, Institute for Excellence in Health Equity, New York University Langone Health, New York, NY, United States; Department of Population Health, New York University Grossman School of Medicine, New York, NY, United States
| | - Huilin Li
- Department of Population Health, New York University Grossman School of Medicine, New York, NY, United States
| | - Michael Bergman
- Department of Population Health, New York University Grossman School of Medicine, New York, NY, United States; Division of Endocrinology, Diabetes and Metabolism, New York University Grossman School of Medicine, New York, NY, United States
| | - David E St-Jules
- Department of Nutrition, University of Nevada, Reno, Reno, NV, United States
| | - Eran Segal
- Department of Computer Science and Applied Math, Weizmann Institute of Science, Rehovot, Israel
| | - Antoinette Schoenthaler
- Center for Healthful Behavior Change, Institute for Excellence in Health Equity, New York University Langone Health, New York, NY, United States; Department of Population Health, New York University Grossman School of Medicine, New York, NY, United States
| | - Natasha Williams
- Center for Healthful Behavior Change, Institute for Excellence in Health Equity, New York University Langone Health, New York, NY, United States; Department of Population Health, New York University Grossman School of Medicine, New York, NY, United States
| | - Ann Marie Schmidt
- Diabetes Research Program, Department of Medicine, New York University Langone Health, New York, NY, United States
| | - Souptik Barua
- Division of Precision Medicine, Department of Medicine, New York University Langone Health, New York, NY, United States
| | - Mary Ann Sevick
- Center for Healthful Behavior Change, Institute for Excellence in Health Equity, New York University Langone Health, New York, NY, United States; Department of Population Health, New York University Grossman School of Medicine, New York, NY, United States; Division of Endocrinology, Diabetes and Metabolism, New York University Grossman School of Medicine, New York, NY, United States
| |
Collapse
|
22
|
Nakadate K, Kawakami K, Yamazaki N. Combined Ingestion of Tea Catechin and Citrus β-Cryptoxanthin Improves Liver Function via Adipokines in Chronic Obesity. Nutrients 2023; 15:3345. [PMID: 37571282 PMCID: PMC10421220 DOI: 10.3390/nu15153345] [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: 06/09/2023] [Revised: 07/12/2023] [Accepted: 07/25/2023] [Indexed: 08/13/2023] Open
Abstract
Recently, there has been an increase in the number of obese individuals, which has elevated the risk of related diseases. Although several studies have been performed to develop a definitive treatment for obesity, no solution has yet been achieved. Recent evidence suggests that tea catechins possess antiobesity effects; however, an impractical amount of catechin may be required to achieve antiobesity effects in humans. Moreover, studies are yet to elucidate the effects of the combined treatment of tea catechins with other substances. Here, we investigated the synergistic effects of catechins and β-cryptoxanthin in high-calorie diet-induced mice. Combined treatment with catechins and β-cryptoxanthin significantly suppressed obesity-induced weight gain and adipocyte size and area, restoring serum parameters to normal. Additionally, combined treatment with catechins and β-cryptoxanthin suppressed inflammatory responses in adipocytes, restored adiponectin levels to normal, protected the liver against obesity-induced damage, and restored normal liver function. Moreover, activin E level was restored to normal, possibly affecting the energy metabolism of brown adipocytes. Overall, these results suggest that the combined ingestion of tea catechins and β-cryptoxanthin was not only effective against obesity but may also help to prevent obesity-related diseases, such as diabetes and cardiovascular diseases.
Collapse
Affiliation(s)
- Kazuhiko Nakadate
- Department of Basic Science, Educational and Research Center for Pharmacy, Meiji Pharmaceutical University, 2-522-1, Noshio, Kiyose 204-8588, Tokyo, Japan;
| | - Kiyoharu Kawakami
- Department of Basic Science, Educational and Research Center for Pharmacy, Meiji Pharmaceutical University, 2-522-1, Noshio, Kiyose 204-8588, Tokyo, Japan;
| | - Noriko Yamazaki
- Department of Community Health Care and Sciences, Meiji Pharmaceutical University, 2-522-1, Noshio, Kiyose 204-8588, Tokyo, Japan;
| |
Collapse
|
23
|
Clark JM, Garvey WT, Niswender KD, Schmidt AM, Ahima RS, Aleman JO, Battarbee AN, Beckman J, Bennett WL, Brown NJ, Chandler‐Laney P, Cox N, Goldberg IJ, Habegger KM, Harper LM, Hasty AH, Hidalgo BA, Kim SF, Locher JL, Luther JM, Maruthur NM, Miller ER, Sevick MA, Wells Q. Obesity and Overweight: Probing Causes, Consequences, and Novel Therapeutic Approaches Through the American Heart Association's Strategically Focused Research Network. J Am Heart Assoc 2023; 12:e027693. [PMID: 36752232 PMCID: PMC10111504 DOI: 10.1161/jaha.122.027693] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/08/2022] [Accepted: 01/03/2023] [Indexed: 02/09/2023]
Abstract
As the worldwide prevalence of overweight and obesity continues to rise, so too does the urgency to fully understand mediating mechanisms, to discover new targets for safe and effective therapeutic intervention, and to identify biomarkers to track obesity and the success of weight loss interventions. In 2016, the American Heart Association sought applications for a Strategically Focused Research Network (SFRN) on Obesity. In 2017, 4 centers were named, including Johns Hopkins University School of Medicine, New York University Grossman School of Medicine, University of Alabama at Birmingham, and Vanderbilt University Medical Center. These 4 centers were convened to study mechanisms and therapeutic targets in obesity, to train a talented cadre of American Heart Association SFRN-designated fellows, and to initiate and sustain effective and enduring collaborations within the individual centers and throughout the SFRN networks. This review summarizes the central themes, major findings, successful training of highly motivated and productive fellows, and the innovative collaborations and studies forged through this SFRN on Obesity. Leveraging expertise in in vitro and cellular model assays, animal models, and humans, the work of these 4 centers has made a significant impact in the field of obesity, opening doors to important discoveries, and the identification of a future generation of obesity-focused investigators and next-step clinical trials. The creation of the SFRN on Obesity for these 4 centers is but the beginning of innovative science and, importantly, the birth of new collaborations and research partnerships to propel the field forward.
Collapse
Affiliation(s)
- Jeanne M. Clark
- Division of General Internal Medicine, Department of MedicineThe Johns Hopkins University School of MedicineBaltimoreMD
- Department of EpidemiologyThe Johns Hopkins Bloomberg School of Public HealthBaltimoreMD
- Welch Center for Prevention, Epidemiology and Clinical ResearchThe Johns Hopkins UniversityBaltimoreMD
| | - W. Timothy Garvey
- Department of Nutrition SciencesUniversity of Alabama at BirminghamBirminghamAL
| | - Kevin D. Niswender
- Tennessee Valley Healthcare SystemVanderbilt University Medical CenterNashvilleTN
- Division of Diabetes, Department of Medicine, Endocrinology and MetabolismVanderbilt University Medical CenterNashvilleTN
| | - Ann Marie Schmidt
- Department of Medicine, Diabetes Research Program, Division of Endocrinology, Diabetes and MetabolismNew York University Grossman School of MedicineNew YorkNY
| | - Rexford S. Ahima
- Department of Medicine, Division of Endocrinology, Diabetes and MetabolismThe Johns Hopkins University School of MedicineBaltimoreMD
| | - Jose O. Aleman
- Division of Endocrinology, Department of Medicine, Diabetes and MetabolismNew York University Grossman School of MedicineNew YorkNY
| | - Ashley N. Battarbee
- Division of Maternal Fetal Medicine, Department of Obstetrics and GynecologyUniversity of Alabama at BirminghamBirminghamAL
| | - Joshua Beckman
- Division of Cardiovascular Medicine, Department of MedicineVanderbilt University Medical CenterNashvilleTN
| | - Wendy L. Bennett
- Division of General Internal Medicine, Department of MedicineThe Johns Hopkins University School of MedicineBaltimoreMD
- Department of EpidemiologyThe Johns Hopkins Bloomberg School of Public HealthBaltimoreMD
- Welch Center for Prevention, Epidemiology and Clinical ResearchThe Johns Hopkins UniversityBaltimoreMD
- Department of Population, Family and Reproductive HealthThe Johns Hopkins Bloomberg School of Public HealthBaltimoreMD
| | | | | | - Nancy Cox
- Vanderbilt Genetics Institute and Division of Genetic Medicine, Department of MedicineVanderbilt University Medical CenterNashvilleTNUSA
| | - Ira J. Goldberg
- Division of Endocrinology, Department of Medicine, Diabetes and MetabolismNew York University Grossman School of MedicineNew YorkNY
| | - Kirk M. Habegger
- Division of Endocrinology, Department of Medicine, Diabetes, and MetabolismUniversity of Alabama at BirminghamBirminghamAL
| | - Lorie M. Harper
- Division of Maternal Fetal Medicine, Department of Obstetrics and GynecologyUniversity of Alabama at BirminghamBirminghamAL
- Division of Maternal‐Fetal Medicine, Department of Women’s Health, Dell Medical SchoolUniversity of Texas at AustinAustinTXUSA
| | - Alyssa H. Hasty
- Department of Molecular Physiology and BiophysicsVanderbilt University School of MedicineNashvilleTN
- VA Tennessee Valley Healthcare SystemNashvilleTN
| | - Bertha A. Hidalgo
- Department of EpidemiologyUniversity of Alabama at BirminghamBirminghamAL
| | - Sangwon F. Kim
- Department of Medicine, Division of Endocrinology, Diabetes and MetabolismThe Johns Hopkins University School of MedicineBaltimoreMD
- Department of NeuroscienceThe Johns Hopkins University School of MedicineBaltimoreMD
| | - Julie L. Locher
- Division of Gerontology, Department of Medicine, Geriatrics, and Palliative CareUniversity of Alabama at BirminghamBirminghamAL
| | - James M. Luther
- Division of Clinical Pharmacology, Department of MedicineVanderbilt University Medical Center TennesseeNashvilleTN
| | - Nisa M. Maruthur
- Division of General Internal Medicine, Department of MedicineThe Johns Hopkins University School of MedicineBaltimoreMD
- Department of EpidemiologyThe Johns Hopkins Bloomberg School of Public HealthBaltimoreMD
- Welch Center for Prevention, Epidemiology and Clinical ResearchThe Johns Hopkins UniversityBaltimoreMD
| | - Edgar R. Miller
- Division of General Internal Medicine, Department of MedicineThe Johns Hopkins University School of MedicineBaltimoreMD
- Department of EpidemiologyThe Johns Hopkins Bloomberg School of Public HealthBaltimoreMD
- Welch Center for Prevention, Epidemiology and Clinical ResearchThe Johns Hopkins UniversityBaltimoreMD
| | - Mary Ann Sevick
- Division of Endocrinology, Department of Medicine, Diabetes and MetabolismNew York University Grossman School of MedicineNew YorkNY
- Department of Population Health, Center for Healthful Behavior ChangeNew York University Langone HealthNew YorkNY
| | - Quinn Wells
- Department of PharmacologyVanderbilt UniversityNashvilleTN
- Department of MedicineVanderbilt University Medical CenterNashvilleTN
| |
Collapse
|
24
|
Donghia R, Guerra V, Pesole PL, Liso M. Contribution of macro- and micronutrients intake to gastrointestinal cancer mortality in the ONCONUT cohort: Classical vs. modern approaches. Front Nutr 2023; 10:1066749. [PMID: 36755992 PMCID: PMC9899894 DOI: 10.3389/fnut.2023.1066749] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2022] [Accepted: 01/09/2023] [Indexed: 01/24/2023] Open
Abstract
The aim of this study was to evaluate the contribution of macro- and micronutrients intake to mortality in patients with gastrointestinal cancer, comparing the classical statistical approaches with a new generation algorithm. In 1992, the ONCONUT project was started with the aim of evaluating the relationship between diet and cancer development in a Southern Italian elderly population. Patients who died of specific death causes (ICD-10 from 150.0 to 159.9) were included in the study (n = 3,505) and survival analysis was applied. This cohort was used to test the performance of different techniques, namely Cox proportional-hazards model, random survival forest (RSF), Survival Support Vector Machine (SSVM), and C-index, applied to quantify the performance. Lastly, the new prediction mode, denominated Shapley Additive Explanation (SHAP), was adopted. RSF had the best performance (0.7653711 and 0.7725246, for macro- and micronutrients, respectively), while SSVM had the worst C-index (0.5667753 and 0.545222). SHAP was helpful to understand the role of single patient features on mortality. Using SHAP together with RSF and classical CPH was most helpful, and shows promise for future clinical applications.
Collapse
|