1
|
Bodnar LM, Kirkpatrick SI, Parisi SM, Jin Q, Naimi AI. Periconceptional Dietary Patterns and Adverse Pregnancy and Birth Outcomes. J Nutr 2024; 154:680-690. [PMID: 38122847 PMCID: PMC10900249 DOI: 10.1016/j.tjnut.2023.12.013] [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] [Revised: 12/06/2023] [Accepted: 12/12/2023] [Indexed: 12/23/2023] Open
Abstract
BACKGROUND The periconceptional period is a critical window for the origins of adverse pregnancy and birth outcomes, yet little is known about the dietary patterns that promote perinatal health. OBJECTIVE We used machine learning methods to determine the effect of periconceptional dietary patterns on risk of preeclampsia, gestational diabetes, preterm birth, small-for-gestational-age (SGA) birth, and a composite of these outcomes. METHODS We used data from 8259 participants in the Nulliparous Pregnancy Outcomes Study: Monitoring Mothers-to-Be (8 US medical centers, 2010‒2013). Usual daily periconceptional intake of 82 food groups was estimated from a food frequency questionnaire. We used k-means clustering with a Euclidean distance metric to identify dietary patterns. We estimated the effect of dietary patterns on each perinatal outcome using targeted maximum likelihood estimation and an ensemble of machine learning algorithms, adjusting for confounders including health behaviors and psychological, neighborhood, and sociodemographic factors. RESULTS The 4 dietary patterns that emerged from our data were identified as "Sandwiches and snacks" (34% of the sample); "High fat, sugar, and sodium" (29%); "Beverages, refined grains, and mixed dishes" (21%); and "High fruits, vegetables, whole grains, and plant proteins" (16%). One-quarter of pregnancies had preeclampsia (8% incidence), gestational diabetes (5%), preterm birth (8%), or SGA birth (8%). Compared with the "High fat, sugar, and sodium" pattern, there were 3.3 to 4.3 fewer cases of the composite adverse outcome per 100 pregnancies among participants following the "Beverages, refined grains and mixed dishes" pattern (risk difference -0.043; 95% confidence interval -0.078, -0.009), "High fruits, vegetables, whole grains and plant proteins" pattern (-0.041; 95% confidence interval -0.078, -0.004), and "Sandwiches and snacks" pattern (-0.033; 95% confidence interval -0.065, -0.002). CONCLUSIONS Our results highlight that there are a variety of periconceptional dietary patterns that are associated with perinatal health and reinforce the negative health implications of diets high in fat, sugars, and sodium.
Collapse
Affiliation(s)
- Lisa M Bodnar
- Department of Epidemiology, School of Public Health, University of Pittsburgh, Pittsburgh, Pennsylvania, United States; Department of Obstetrics, Gynecology, and Reproductive Sciences, School of Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania, United States.
| | - Sharon I Kirkpatrick
- School of Public Health Sciences, University of Waterloo, Waterloo, Ontario, Canada
| | - Sara M Parisi
- Department of Epidemiology, School of Public Health, University of Pittsburgh, Pittsburgh, Pennsylvania, United States
| | - Qianhui Jin
- Department of Epidemiology, School of Public Health, University of Pittsburgh, Pittsburgh, Pennsylvania, United States
| | - Ashley I Naimi
- Department of Epidemiology, Rollins School of Public Health, Emory University, Atlanta, Georgia, United States
| |
Collapse
|
2
|
Newby D, Orgeta V, Marshall CR, Lourida I, Albertyn CP, Tamburin S, Raymont V, Veldsman M, Koychev I, Bauermeister S, Weisman D, Foote IF, Bucholc M, Leist AK, Tang EYH, Tai XY, Llewellyn DJ, Ranson JM. Artificial intelligence for dementia prevention. Alzheimers Dement 2023; 19:5952-5969. [PMID: 37837420 PMCID: PMC10843720 DOI: 10.1002/alz.13463] [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: 04/12/2023] [Revised: 08/01/2023] [Accepted: 08/07/2023] [Indexed: 10/16/2023]
Abstract
INTRODUCTION A wide range of modifiable risk factors for dementia have been identified. Considerable debate remains about these risk factors, possible interactions between them or with genetic risk, and causality, and how they can help in clinical trial recruitment and drug development. Artificial intelligence (AI) and machine learning (ML) may refine understanding. METHODS ML approaches are being developed in dementia prevention. We discuss exemplar uses and evaluate the current applications and limitations in the dementia prevention field. RESULTS Risk-profiling tools may help identify high-risk populations for clinical trials; however, their performance needs improvement. New risk-profiling and trial-recruitment tools underpinned by ML models may be effective in reducing costs and improving future trials. ML can inform drug-repurposing efforts and prioritization of disease-modifying therapeutics. DISCUSSION ML is not yet widely used but has considerable potential to enhance precision in dementia prevention. HIGHLIGHTS Artificial intelligence (AI) is not widely used in the dementia prevention field. Risk-profiling tools are not used in clinical practice. Causal insights are needed to understand risk factors over the lifespan. AI will help personalize risk-management tools for dementia prevention. AI could target specific patient groups that will benefit most for clinical trials.
Collapse
Affiliation(s)
- Danielle Newby
- University of Oxford, Department of Psychiatry, Warneford Hospital, Oxford, OX3 7JX, UK
| | - Vasiliki Orgeta
- Division of Psychiatry, University College London, London, W1T 7BN, UK
| | - Charles R Marshall
- Preventive Neurology Unit, Wolfson Institute of Population Health, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, E1 4NS, UK
- Department of Neurology, Royal London Hospital, London, E1 1BB, UK
| | - Ilianna Lourida
- Population Health Sciences Institute, Newcastle University, Newcastle, NE2 4AX, UK
- University of Exeter Medical School, Exeter, EX1 2HZ, UK
| | - Christopher P Albertyn
- Department of Old Age Psychiatry, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, SE5 8AF, UK
| | - Stefano Tamburin
- Department of Neurosciences, Biomedicine and Movement Sciences, University of Verona, Verona, 37129, Italy
| | - Vanessa Raymont
- University of Oxford, Department of Psychiatry, Warneford Hospital, Oxford, OX3 7JX, UK
| | - Michele Veldsman
- Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, OX3 9DU, UK
- Department of Experimental Psychology, University of Oxford, Oxford, OX2 6GG, UK
| | - Ivan Koychev
- University of Oxford, Department of Psychiatry, Warneford Hospital, Oxford, OX3 7JX, UK
| | - Sarah Bauermeister
- University of Oxford, Department of Psychiatry, Warneford Hospital, Oxford, OX3 7JX, UK
| | - David Weisman
- Abington Neurological Associates, Abington, PA 19001, USA
| | - Isabelle F Foote
- Preventive Neurology Unit, Wolfson Institute of Population Health, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, E1 4NS, UK
- Institute for Behavioral Genetics, University of Colorado Boulder, Boulder, CO 80309, USA
| | - Magda Bucholc
- Cognitive Analytics Research Lab, School of Computing, Engineering & Intelligent Systems, Ulster University, Derry, BT48 7JL, UK
| | - Anja K Leist
- Institute for Research on Socio-Economic Inequality (IRSEI), Department of Social Sciences, University of Luxembourg, L-4365, Luxembourg
| | - Eugene Y H Tang
- Population Health Sciences Institute, Newcastle University, Newcastle, NE2 4AX, UK
| | - Xin You Tai
- Nuffield Department of Clinical Neuroscience, University of Oxford, Oxford, OX3 9DU, UK
- Division of Clinical Neurology, John Radcliffe Hospital, Oxford University Hospitals Trust, Oxford, OX3 9DU, UK
| | | | - David J. Llewellyn
- University of Exeter Medical School, Exeter, EX1 2HZ, UK
- The Alan Turing Institute, London, NW1 2DB, UK
| | | |
Collapse
|
3
|
Namkhah Z, Fatemi SF, Mansoori A, Nosratabadi S, Ghayour-Mobarhan M, Sobhani SR. Advancing sustainability in the food and nutrition system: a review of artificial intelligence applications. Front Nutr 2023; 10:1295241. [PMID: 38035357 PMCID: PMC10687214 DOI: 10.3389/fnut.2023.1295241] [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: 09/19/2023] [Accepted: 11/02/2023] [Indexed: 12/02/2023] Open
Abstract
Promoting sustainability in food and nutrition systems is essential to address the various challenges and trade-offs within the current food system. This imperative is guided by key principles and actionable steps, including enhancing productivity and efficiency, reducing waste, adopting sustainable agricultural practices, improving economic growth and livelihoods, and enhancing resilience at various levels. However, in order to change the current food consumption patterns of the world and move toward sustainable diets, as well as increase productivity in the food production chain, it is necessary to employ the findings and achievements of other sciences. These include the use of artificial intelligence-based technologies. Presented here is a narrative review of possible applications of artificial intelligence in the food production chain that could increase productivity and sustainability. In this study, the most significant roles that artificial intelligence can play in enhancing the productivity and sustainability of the food and nutrition system have been examined in terms of production, processing, distribution, and food consumption. The research revealed that artificial intelligence, a branch of computer science that uses intelligent machines to perform tasks that require human intelligence, can significantly contribute to sustainable food security. Patterns of production, transportation, supply chain, marketing, and food-related applications can all benefit from artificial intelligence. As this review of successful experiences indicates, artificial intelligence, machine learning, and big data are a boon to the goal of sustainable food security as they enable us to achieve our goals more efficiently.
Collapse
Affiliation(s)
- Zahra Namkhah
- Department of Nutrition, Faculty of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Seyedeh Fatemeh Fatemi
- Department of Nutrition, Faculty of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Amin Mansoori
- Department of Biostatistics, School of Health, Mashhad University of Medical Sciences, Mashhad, Iran
- International UNESCO Center for Health Related Basic Sciences and Human Nutrition, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Saeid Nosratabadi
- Department of Nutrition, Electronic Health and Statistics Surveillance Research Center, Science and Research Branch, Islamic Azad University, Tehran, Iran
| | - Majid Ghayour-Mobarhan
- International UNESCO Center for Health Related Basic Sciences and Human Nutrition, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Seyyed Reza Sobhani
- Department of Nutrition, Faculty of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran
| |
Collapse
|
4
|
Smith MJ, Phillips RV, Luque-Fernandez MA, Maringe C. Application of targeted maximum likelihood estimation in public health and epidemiological studies: a systematic review. Ann Epidemiol 2023; 86:34-48.e28. [PMID: 37343734 DOI: 10.1016/j.annepidem.2023.06.004] [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: 03/03/2023] [Revised: 05/24/2023] [Accepted: 06/06/2023] [Indexed: 06/23/2023]
Abstract
PURPOSE The targeted maximum likelihood estimation (TMLE) statistical data analysis framework integrates machine learning, statistical theory, and statistical inference to provide a least biased, efficient, and robust strategy for estimation and inference of a variety of statistical and causal parameters. We describe and evaluate the epidemiological applications that have benefited from recent methodological developments. METHODS We conducted a systematic literature review in PubMed for articles that applied any form of TMLE in observational studies. We summarized the epidemiological discipline, geographical location, expertize of the authors, and TMLE methods over time. We used the Roadmap of Targeted Learning and Causal Inference to extract key methodological aspects of the publications. We showcase the contributions to the literature of these TMLE results. RESULTS Of the 89 publications included, 33% originated from the University of California at Berkeley, where the framework was first developed by Professor Mark van der Laan. By 2022, 59% of the publications originated from outside the United States and explored up to seven different epidemiological disciplines in 2021-2022. Double-robustness, bias reduction, and model misspecification were the main motivations that drew researchers toward the TMLE framework. Through time, a wide variety of methodological, tutorial, and software-specific articles were cited, owing to the constant growth of methodological developments around TMLE. CONCLUSIONS There is a clear dissemination trend of the TMLE framework to various epidemiological disciplines and to increasing numbers of geographical areas. The availability of R packages, publication of tutorial papers, and involvement of methodological experts in applied publications have contributed to an exponential increase in the number of studies that understood the benefits and adoption of TMLE.
Collapse
Affiliation(s)
- Matthew J Smith
- Inequalities in Cancer Outcomes Network, London School of Hygiene and Tropical Medicine, London, UK.
| | - Rachael V Phillips
- Division of Biostatistics, School of Public Health, University of California at Berkeley, Berkeley, CA
| | - Miguel Angel Luque-Fernandez
- Inequalities in Cancer Outcomes Network, London School of Hygiene and Tropical Medicine, London, UK; Department of Statistics and Operations Research, University of Granada, Granada, Spain
| | - Camille Maringe
- Inequalities in Cancer Outcomes Network, London School of Hygiene and Tropical Medicine, London, UK
| |
Collapse
|
5
|
Petersen JM, Naimi AI, Bodnar LM. Does heterogeneity underlie differences in treatment effects estimated from SuperLearner versus logistic regression? An application in nutritional epidemiology. Ann Epidemiol 2023; 83:30-34. [PMID: 37121376 PMCID: PMC10330341 DOI: 10.1016/j.annepidem.2023.04.017] [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: 01/17/2023] [Revised: 04/02/2023] [Accepted: 04/24/2023] [Indexed: 05/02/2023]
Abstract
PURPOSE A strength of SuperLearner is that it may accommodate key interactions between model variables without a priori specification. In prior research, protective associations between fruit intake and preeclampsia were stronger when estimated using SuperLearner with targeted maximum likelihood estimation (TMLE) compared with multivariable logistic regression without any interaction terms. We explored whether heterogeneity (i.e., differences in the effect estimate due to interactions between fruit intake and covariates) may partly explain differences in estimates from these two models. METHODS Using a U.S. prospective pregnancy cohort (2010-2013, n = 7781), we estimated preeclampsia risk differences (RDs) for higher versus lower fruit density using multivariable logistic regression and included two-way statistical interactions between fruit density and each of the 25 model covariates. We compared the RDs with those from SuperLearner with TMLE (gold standard) and logistic regression with no interaction. RESULTS From the logistic regression models with two-way statistical interactions, 48% of the preeclampsia RDs were ≤-0.02 (closer to SuperLearner with TMLE estimate); 40% equaled -0.01 (same as logistic regression with no interaction estimate); the minority of RDs were at or crossed the null. CONCLUSIONS Our exploratory analysis provided preliminary evidence that heterogeneity may partly explain differences in estimates from logistic regression versus SuperLearner with TMLE.
Collapse
Affiliation(s)
- Julie M Petersen
- Department of Epidemiology, University of Pittsburgh School of Public Health, Pittsburgh, PA
| | - Ashley I Naimi
- Epidemiology Department, Emory University, Rollins School of Public Health, Atlanta, GA
| | - Lisa M Bodnar
- Department of Epidemiology, University of Pittsburgh School of Public Health, Pittsburgh, PA; Department of Obstetrics, Gynecology, and Reproductive Sciences, School of Medicine, University of Pittsburgh, Pittsburgh, PA.
| |
Collapse
|
6
|
Mennickent D, Rodríguez A, Opazo MC, Riedel CA, Castro E, Eriz-Salinas A, Appel-Rubio J, Aguayo C, Damiano AE, Guzmán-Gutiérrez E, Araya J. Machine learning applied in maternal and fetal health: a narrative review focused on pregnancy diseases and complications. Front Endocrinol (Lausanne) 2023; 14:1130139. [PMID: 37274341 PMCID: PMC10235786 DOI: 10.3389/fendo.2023.1130139] [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] [Received: 12/22/2022] [Accepted: 05/04/2023] [Indexed: 06/06/2023] Open
Abstract
Introduction Machine learning (ML) corresponds to a wide variety of methods that use mathematics, statistics and computational science to learn from multiple variables simultaneously. By means of pattern recognition, ML methods are able to find hidden correlations and accomplish accurate predictions regarding different conditions. ML has been successfully used to solve varied problems in different areas of science, such as psychology, economics, biology and chemistry. Therefore, we wondered how far it has penetrated into the field of obstetrics and gynecology. Aim To describe the state of art regarding the use of ML in the context of pregnancy diseases and complications. Methodology Publications were searched in PubMed, Web of Science and Google Scholar. Seven subjects of interest were considered: gestational diabetes mellitus, preeclampsia, perinatal death, spontaneous abortion, preterm birth, cesarean section, and fetal malformations. Current state ML has been widely applied in all the included subjects. Its uses are varied, the most common being the prediction of perinatal disorders. Other ML applications include (but are not restricted to) biomarker discovery, risk estimation, correlation assessment, pharmacological treatment prediction, drug screening, data acquisition and data extraction. Most of the reviewed articles were published in the last five years. The most employed ML methods in the field are non-linear. Except for logistic regression, linear methods are rarely used. Future challenges To improve data recording, storage and update in medical and research settings from different realities. To develop more accurate and understandable ML models using data from cutting-edge instruments. To carry out validation and impact analysis studies of currently existing high-accuracy ML models. Conclusion The use of ML in pregnancy diseases and complications is quite recent, and has increased over the last few years. The applications are varied and point not only to the diagnosis, but also to the management, treatment, and pathophysiological understanding of perinatal alterations. Facing the challenges that come with working with different types of data, the handling of increasingly large amounts of information, the development of emerging technologies, and the need of translational studies, it is expected that the use of ML continue growing in the field of obstetrics and gynecology.
Collapse
Affiliation(s)
- Daniela Mennickent
- Departamento de Bioquímica Clínica e Inmunología, Facultad de Farmacia, Universidad de Concepción, Concepción, Chile
- Departamento de Análisis Instrumental, Facultad de Farmacia, Universidad de Concepción, Concepción, Chile
- Machine Learning Applied in Biomedicine (MLAB), Concepción, Chile
| | - Andrés Rodríguez
- Machine Learning Applied in Biomedicine (MLAB), Concepción, Chile
- Departamento de Ciencias Básicas, Facultad de Ciencias, Universidad del Bío-Bío, Chillán, Chile
| | - Ma. Cecilia Opazo
- Instituto de Ciencias Naturales, Facultad de Medicina Veterinaria y Agronomía, Universidad de Las Américas, Santiago, Chile
- Millennium Institute on Immunology and Immunotherapy, Santiago, Chile
| | - Claudia A. Riedel
- Millennium Institute on Immunology and Immunotherapy, Santiago, Chile
- Departamento de Ciencias Biológicas, Facultad de Ciencias de la Vida, Universidad Andrés Bello, Santiago, Chile
| | - Erica Castro
- Departamento de Obstetricia y Puericultura, Facultad de Ciencias de la Salud, Universidad de Atacama, Copiapó, Chile
| | - Alma Eriz-Salinas
- Departamento de Obstetricia y Puericultura, Facultad de Medicina, Universidad de Concepción, Concepción, Chile
| | - Javiera Appel-Rubio
- Departamento de Bioquímica Clínica e Inmunología, Facultad de Farmacia, Universidad de Concepción, Concepción, Chile
| | - Claudio Aguayo
- Departamento de Bioquímica Clínica e Inmunología, Facultad de Farmacia, Universidad de Concepción, Concepción, Chile
| | - Alicia E. Damiano
- Cátedra de Biología Celular y Molecular, Departamento de Ciencias Biológicas, Facultad de Farmacia y Bioquímica, Universidad de Buenos Aires, Buenos Aires, Argentina
- Laboratorio de Biología de la Reproducción, Instituto de Fisiología y Biofísica Bernardo Houssay (IFIBIO-Houssay)- CONICET, Universidad de Buenos Aires, Buenos Aires, Argentina
| | - Enrique Guzmán-Gutiérrez
- Departamento de Bioquímica Clínica e Inmunología, Facultad de Farmacia, Universidad de Concepción, Concepción, Chile
- Machine Learning Applied in Biomedicine (MLAB), Concepción, Chile
| | - Juan Araya
- Departamento de Análisis Instrumental, Facultad de Farmacia, Universidad de Concepción, Concepción, Chile
- Machine Learning Applied in Biomedicine (MLAB), Concepción, Chile
| |
Collapse
|
7
|
Wang YX, Li XL, Zhang LH, Li HN, Liu XM, Song W, Pang XF. Machine learning algorithms assist early evaluation of enteral nutrition in ICU patients. Front Nutr 2023; 10:1060398. [PMID: 37125050 PMCID: PMC10140307 DOI: 10.3389/fnut.2023.1060398] [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: 10/03/2022] [Accepted: 03/27/2023] [Indexed: 05/02/2023] Open
Abstract
Background This study applied machine learning (ML) algorithms to construct a model for predicting EN initiation for patients in the intensive care unit (ICU) and identifying populations in need of EN at an early stage. Methods This study collected patient information from the Medical Information Mart for Intensive Care IV database. All patients enrolled were split randomly into a training set and a validation set. Six ML models were established to evaluate the initiation of EN, and the best model was determined according to the area under curve (AUC) and accuracy. The best model was interpreted using the Local Interpretable Model-Agnostic Explanations (LIME) algorithm and SHapley Additive exPlanation (SHAP) values. Results A total of 53,150 patients participated in the study. They were divided into a training set (42,520, 80%) and a validation set (10,630, 20%). In the validation set, XGBoost had the optimal prediction performance with an AUC of 0.895. The SHAP values revealed that sepsis, sequential organ failure assessment score, and acute kidney injury were the three most important factors affecting EN initiation. The individualized forecasts were displayed using the LIME algorithm. Conclusion The XGBoost model was established and validated for early prediction of EN initiation in ICU patients.
Collapse
Affiliation(s)
- Ya-Xi Wang
- Department of Hospital-acquired Infection Control, The Affiliated Hospital of Qingdao University, Qingdao, Shandong, China
| | - Xun-Liang Li
- Department of Nephrology, The Second Hospital of Anhui Medical University, Hefei, Anhui, China
| | - Ling-Hui Zhang
- School of Nursing, Qingdao University, Qingdao, Shandong, China
| | - Hai-Na Li
- Department of Nephrology, The Affiliated Hospital of Qingdao University, Qingdao, Shandong, China
| | - Xiao-Min Liu
- Department of Critical Care Medicine, The Affiliated Hospital of Qingdao University, Qingdao, Shandong, China
| | - Wen Song
- Department of Endoscopy, The Affiliated Hospital of Qingdao University, Qingdao, Shandong, China
| | - Xu-Feng Pang
- Department of Hospital-acquired Infection Control, The Affiliated Hospital of Qingdao University, Qingdao, Shandong, China
- *Correspondence: Xu-Feng Pang,
| |
Collapse
|
8
|
Kirk D, Kok E, Tufano M, Tekinerdogan B, Feskens EJM, Camps G. Machine Learning in Nutrition Research. Adv Nutr 2022; 13:2573-2589. [PMID: 36166846 PMCID: PMC9776646 DOI: 10.1093/advances/nmac103] [Citation(s) in RCA: 19] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2022] [Revised: 08/02/2022] [Accepted: 09/22/2022] [Indexed: 01/29/2023] Open
Abstract
Data currently generated in the field of nutrition are becoming increasingly complex and high-dimensional, bringing with them new methods of data analysis. The characteristics of machine learning (ML) make it suitable for such analysis and thus lend itself as an alternative tool to deal with data of this nature. ML has already been applied in important problem areas in nutrition, such as obesity, metabolic health, and malnutrition. Despite this, experts in nutrition are often without an understanding of ML, which limits its application and therefore potential to solve currently open questions. The current article aims to bridge this knowledge gap by supplying nutrition researchers with a resource to facilitate the use of ML in their research. ML is first explained and distinguished from existing solutions, with key examples of applications in the nutrition literature provided. Two case studies of domains in which ML is particularly applicable, precision nutrition and metabolomics, are then presented. Finally, a framework is outlined to guide interested researchers in integrating ML into their work. By acting as a resource to which researchers can refer, we hope to support the integration of ML in the field of nutrition to facilitate modern research.
Collapse
Affiliation(s)
- Daniel Kirk
- Division of Human Nutrition and Health, Wageningen University and Research, Wageningen, The Netherlands
| | - Esther Kok
- Division of Human Nutrition and Health, Wageningen University and Research, Wageningen, The Netherlands
| | - Michele Tufano
- Division of Human Nutrition and Health, Wageningen University and Research, Wageningen, The Netherlands
| | - Bedir Tekinerdogan
- Information Technology Group, Wageningen University and Research, Wageningen, The Netherlands
| | - Edith J M Feskens
- Division of Human Nutrition and Health, Wageningen University and Research, Wageningen, The Netherlands
| | - Guido Camps
- Division of Human Nutrition and Health, Wageningen University and Research, Wageningen, The Netherlands.,OnePlanet Research Center, Wageningen, The Netherlands
| |
Collapse
|
9
|
Leist AK, Klee M, Kim JH, Rehkopf DH, Bordas SPA, Muniz-Terrera G, Wade S. Mapping of machine learning approaches for description, prediction, and causal inference in the social and health sciences. SCIENCE ADVANCES 2022; 8:eabk1942. [PMID: 36260666 PMCID: PMC9581488 DOI: 10.1126/sciadv.abk1942] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/28/2021] [Accepted: 09/01/2022] [Indexed: 05/20/2023]
Abstract
Machine learning (ML) methodology used in the social and health sciences needs to fit the intended research purposes of description, prediction, or causal inference. This paper provides a comprehensive, systematic meta-mapping of research questions in the social and health sciences to appropriate ML approaches by incorporating the necessary requirements to statistical analysis in these disciplines. We map the established classification into description, prediction, counterfactual prediction, and causal structural learning to common research goals, such as estimating prevalence of adverse social or health outcomes, predicting the risk of an event, and identifying risk factors or causes of adverse outcomes, and explain common ML performance metrics. Such mapping may help to fully exploit the benefits of ML while considering domain-specific aspects relevant to the social and health sciences and hopefully contribute to the acceleration of the uptake of ML applications to advance both basic and applied social and health sciences research.
Collapse
Affiliation(s)
- Anja K. Leist
- Department of Social Sciences, Institute for Research on Socio-Economic Inequality (IRSEI), University of Luxembourg, Esch-sur-Alzette, Luxembourg
- Corresponding author.
| | - Matthias Klee
- Department of Social Sciences, Institute for Research on Socio-Economic Inequality (IRSEI), University of Luxembourg, Esch-sur-Alzette, Luxembourg
| | - Jung Hyun Kim
- Department of Social Sciences, Institute for Research on Socio-Economic Inequality (IRSEI), University of Luxembourg, Esch-sur-Alzette, Luxembourg
| | - David H. Rehkopf
- Department of Epidemiology and Population Health, Stanford University, Palo Alto, CA, USA
| | | | - Graciela Muniz-Terrera
- Centre for Dementia Prevention, University of Edinburgh, Edinburgh, UK
- Ohio University, Athens, OH, USA
| | - Sara Wade
- School of Mathematics, University of Edinburgh, Edinburgh, UK
| |
Collapse
|
10
|
Joint Associations of Food Groups with All-Cause and Cause-Specific Mortality in the Mr. OS and Ms. OS Study: A Prospective Cohort. Nutrients 2022; 14:nu14193915. [PMID: 36235568 PMCID: PMC9573629 DOI: 10.3390/nu14193915] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2022] [Revised: 09/16/2022] [Accepted: 09/17/2022] [Indexed: 11/17/2022] Open
Abstract
Despite continuous growth in dietary pattern research, the relative importance of each dietary component in the overall pattern and their joint effects on mortality risk have not been examined adequately. We explored the individual and joint associations of multiple food groups with all-cause and cause-specific mortality (cardiovascular disease (CVD) or cancer), by analyzing data from a cohort of 3995 Hong Kong Chinese older adults in the Mr. Osteoporosis (OS) and Ms. OS Study. Cox proportional hazards models were used to examine the associations of food groups with mortality risk. The individual and joint contribution of food groups to mortality risk has been quantified by a machine learning approach, i.e., the Quantile G-Computation. When comparing the highest with the lowest quartile of intake, dark green and leafy vegetables (hazard ratio (HR) = 0.82, 95% confidence interval (CI) = 0.70 to 0.96, Ptrend = 0.049), fruit (HR = 0.79, 95% CI = 0.68 to 0.93, Ptrend = 0.006), legumes (HR = 0.75, 95% CI = 0.63 to 0.87, Ptrend = 0.052), mushroom and fungi (HR = 0.76, 95% CI = 0.65 to 0.88, Ptrend = 0.023), soy and soy products (HR = 0.77, 95% CI = 0.66 to 0.90, Ptrend = 0.143), and whole grains (HR = 0.76, 95% CI = 0.65 to 0.89, Ptrend = 0.008) were inversely associated with all-cause mortality. Legume intake was associated with a lower risk of CVD mortality, while fruit, nuts, soy and soy products were associated with a lower risk of cancer mortality. From the Quantile G-Computation, whole grains, legumes, fruits, mushroom and fungi, soy and soy products had a higher relative weighting on mortality risk, and the joint effect of food groups was inversely associated with the mortality risk due to all-causes (HR = 0.39, 95% CI = 0.27 to 0.55), CVD (HR = 0.78, 95% CI = 0.67 to 0.91), and cancer (HR = 0.31, 95% CI = 0.15 to 0.65). From a sex-stratified analysis, most associations between food groups (whole grains, legumes, fruits, mushroom and fungi, soy and soy products) and mortality risk remained significant among men. In conclusion, whole grains, legumes, fruits, mushroom and fungi, soy and soy products were the main contributors to a reduction in mortality risk, and their joint effects were stronger than individual food groups. Moreover, the sex-specific association of sweets and desserts with cancer mortality may be worth further investigation.
Collapse
|
11
|
Machine learning can improve the development of evidence-based dietary guidelines. Public Health Nutr 2022; 25:2566-2569. [PMID: 35757839 DOI: 10.1017/s1368980022001392] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
|
12
|
Vision for Improving Pregnancy Health: Innovation and the Future of Pregnancy Research. Reprod Sci 2022; 29:2908-2920. [PMID: 35534766 PMCID: PMC9537127 DOI: 10.1007/s43032-022-00951-w] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2022] [Accepted: 04/15/2022] [Indexed: 10/25/2022]
Abstract
Understanding, predicting, and preventing pregnancy disorders have been a major research target. Nonetheless, the lack of progress is illustrated by research results related to preeclampsia and other hypertensive pregnancy disorders. These remain a major cause of maternal and infant mortality worldwide. There is a general consensus that the rate of progress toward understanding pregnancy disorders lags behind progress in other aspects of human health. In this presentation, we advance an explanation for this failure and suggest solutions. We propose that progress has been impeded by narrowly focused research training and limited imagination and innovation, resulting in the failure to think beyond conventional research approaches and analytical strategies. Investigations have been largely limited to hypothesis-generating approaches constrained by attempts to force poorly defined complex disorders into a single "unifying" hypothesis. Future progress could be accelerated by rethinking this approach. We advise taking advantage of innovative approaches that will generate new research strategies for investigating pregnancy abnormalities. Studies should begin before conception, assessing pregnancy longitudinally, before, during, and after pregnancy. Pregnancy disorders should be defined by pathophysiology rather than phenotype, and state of the art agnostic assessment of data should be adopted to generate new ideas. Taking advantage of new approaches mandates emphasizing innovation, inclusion of large datasets, and use of state of the art experimental and analytical techniques. A revolution in understanding pregnancy-associated disorders will depend on networks of scientists who are driven by an intense biological curiosity, a team spirit, and the tools to make new discoveries.
Collapse
|
13
|
Morgenstern JD, Rosella LC, Costa AP, Anderson LN. Development of machine learning prediction models to explore nutrients predictive of cardiovascular disease using Canadian linked population-based data. Appl Physiol Nutr Metab 2022; 47:529-546. [PMID: 35113677 DOI: 10.1139/apnm-2021-0502] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
Machine learning may improve use of observational data to understand the nutritional epidemiology of cardiovascular disease (CVD) through better modelling of non-linearity, non-additivity, and dietary complexity. Our objective was to develop machine learning prediction models for exploring how nutrients are related to CVD risk and to evaluate their predictive performance. We established a population-based cohort from the Canadian Community Health Survey and measured CVD incidence and mortality from 2004 to 2018 using administrative databases of national hospital discharges and deaths. Predictors included 61 nutrition variables and fourteen socioeconomic, demographic, psychological, and behavioural variables. Conditional inference forest models were interpreted and evaluated by permutation feature importance, accumulated local effects, and predictive discrimination and calibration. A total of 12 130 individuals were included in the study. Use of supplements, caffeine, and alcohol were the most important nutrition variables for prediction of CVD. Supplement use was associated with decreased risk, caffeine was associated with increasing risk, and alcohol had a u-shaped association with risk. The model had an out-of-sample c-statistic of 0.821 (95% confidence interval = 0.801-0.842). Exploratory findings included both known and novel associations and predictive performance was competitive, suggesting that further application of machine learning to nutritional epidemiology may help elucidate risks and improve predictive models. Novelty: Machine learning prediction models were developed for CVD using dietary data. Models were interpreted with interpretable machine learning techniques, revealing diverse associations between diet and CVD. Models achieved comparable or superior predictive performance to existing CVD risk prediction models.
Collapse
Affiliation(s)
- Jason D Morgenstern
- Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, Ontario, Canada
| | - Laura C Rosella
- Dalla Lana School of Public Health, University of Toronto, Toronto, Ontario, Canada.,Institute for Clinical Evaluative Sciences (ICES), Toronto, Ontario, Canada.,Vector Institute, Toronto, Ontario, Canada
| | - Andrew P Costa
- Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, Ontario, Canada.,Institute for Clinical Evaluative Sciences (ICES), Toronto, Ontario, Canada.,Department of Medicine, McMaster University, Hamilton, Ontario, Canada
| | - Laura N Anderson
- Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, Ontario, Canada.,Population Health Research Institute, Hamilton Health Sciences, Hamilton, ON, Canada
| |
Collapse
|
14
|
Russo S, Bonassi S. Prospects and Pitfalls of Machine Learning in Nutritional Epidemiology. Nutrients 2022; 14:nu14091705. [PMID: 35565673 PMCID: PMC9105182 DOI: 10.3390/nu14091705] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2022] [Revised: 04/13/2022] [Accepted: 04/14/2022] [Indexed: 02/06/2023] Open
Abstract
Nutritional epidemiology employs observational data to discover associations between diet and disease risk. However, existing analytic methods of dietary data are often sub-optimal, with limited incorporation and analysis of the correlations between the studied variables and nonlinear behaviours in the data. Machine learning (ML) is an area of artificial intelligence that has the potential to improve modelling of nonlinear associations and confounding which are found in nutritional data. These opportunities notwithstanding, the applications of ML in nutritional epidemiology must be approached cautiously to safeguard the scientific quality of the results and provide accurate interpretations. Given the complex scenario around ML, judicious application of such tools is necessary to offer nutritional epidemiology a novel analytical resource for dietary measurement and assessment and a tool to model the complexity of dietary intake and its relation to health. This work describes the applications of ML in nutritional epidemiology and provides guidelines to avoid common pitfalls encountered in applying predictive statistical models to nutritional data. Furthermore, it helps unfamiliar readers better assess the significance of their results and provides new possible future directions in the field of ML in nutritional epidemiology.
Collapse
Affiliation(s)
- Stefania Russo
- EcoVision Lab, Photogrammetry and Remote Sensing Group, ETH Zürich, 8092 Zurich, Switzerland
- Correspondence:
| | - Stefano Bonassi
- Department of Human Sciences and Quality of Life Promotion, San Raffaele University, 00166 Rome, Italy;
- Unit of Clinical and Molecular Epidemiology, IRCCS San Raffaele Roma, 00163 Rome, Italy
| |
Collapse
|
15
|
Vilne B, Ķibilds J, Siksna I, Lazda I, Valciņa O, Krūmiņa A. Could Artificial Intelligence/Machine Learning and Inclusion of Diet-Gut Microbiome Interactions Improve Disease Risk Prediction? Case Study: Coronary Artery Disease. Front Microbiol 2022; 13:627892. [PMID: 35479632 PMCID: PMC9036178 DOI: 10.3389/fmicb.2022.627892] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2020] [Accepted: 02/24/2022] [Indexed: 12/14/2022] Open
Abstract
Coronary artery disease (CAD) is the most common cardiovascular disease (CVD) and the main leading cause of morbidity and mortality worldwide, posing a huge socio-economic burden to the society and health systems. Therefore, timely and precise identification of people at high risk of CAD is urgently required. Most current CAD risk prediction approaches are based on a small number of traditional risk factors (age, sex, diabetes, LDL and HDL cholesterol, smoking, systolic blood pressure) and are incompletely predictive across all patient groups, as CAD is a multi-factorial disease with complex etiology, considered to be driven by both genetic, as well as numerous environmental/lifestyle factors. Diet is one of the modifiable factors for improving lifestyle and disease prevention. However, the current rise in obesity, type 2 diabetes (T2D) and CVD/CAD indicates that the “one-size-fits-all” approach may not be efficient, due to significant variation in inter-individual responses. Recently, the gut microbiome has emerged as a potential and previously under-explored contributor to these variations. Hence, efficient integration of dietary and gut microbiome information alongside with genetic variations and clinical data holds a great promise to improve CAD risk prediction. Nevertheless, the highly complex nature of meals combined with the huge inter-individual variability of the gut microbiome poses several Big Data analytics challenges in modeling diet-gut microbiota interactions and integrating these within CAD risk prediction approaches for the development of personalized decision support systems (DSS). In this regard, the recent re-emergence of Artificial Intelligence (AI) / Machine Learning (ML) is opening intriguing perspectives, as these approaches are able to capture large and complex matrices of data, incorporating their interactions and identifying both linear and non-linear relationships. In this Mini-Review, we consider (1) the most used AI/ML approaches and their different use cases for CAD risk prediction (2) modeling of the content, choice and impact of dietary factors on CAD risk; (3) classification of individuals by their gut microbiome composition into CAD cases vs. controls and (4) modeling of the diet-gut microbiome interactions and their impact on CAD risk. Finally, we provide an outlook for putting it all together for improved CAD risk predictions.
Collapse
Affiliation(s)
- Baiba Vilne
- Bioinformatics Lab, Riga Stradins University, Riga, Latvia
- COST Action CA18131 - Statistical and Machine Learning Techniques in Human Microbiome Studies, Brussels, Belgium
- *Correspondence: Baiba Vilne
| | - Juris Ķibilds
- Institute of Food Safety, Animal Health and Environment BIOR, Riga, Latvia
| | - Inese Siksna
- Institute of Food Safety, Animal Health and Environment BIOR, Riga, Latvia
| | - Ilva Lazda
- Institute of Food Safety, Animal Health and Environment BIOR, Riga, Latvia
| | - Olga Valciņa
- Institute of Food Safety, Animal Health and Environment BIOR, Riga, Latvia
| | - Angelika Krūmiņa
- Institute of Food Safety, Animal Health and Environment BIOR, Riga, Latvia
- Department of Infectology and Dermatology, Riga Stradins University, Riga, Latvia
| |
Collapse
|
16
|
Bodnar LM, Cartus AR, Kennedy EH, Kirkpatrick SI, Parisi SM, Himes KP, Parker CB, Grobman WA, Simhan HN, Silver RM, Wing DA, Perry S, Naimi AI. Use of a Doubly Robust Machine-Learning-Based Approach to Evaluate Body Mass Index as a Modifier of the Association Between Fruit and Vegetable Intake and Preeclampsia. Am J Epidemiol 2022; 191:1396-1406. [PMID: 35355047 PMCID: PMC9614933 DOI: 10.1093/aje/kwac062] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2021] [Revised: 03/07/2022] [Accepted: 03/25/2022] [Indexed: 01/28/2023] Open
Abstract
The Dietary Guidelines for Americans rely on summaries of the effect of dietary pattern on disease risk, independent of other population characteristics. We explored the modifying effect of prepregnancy body mass index (BMI; weight (kg)/height (m)2) on the relationship between fruit and vegetable density (cup-equivalents/1,000 kcal) and preeclampsia using data from a pregnancy cohort study conducted at 8 US medical centers (n = 9,412; 2010-2013). Usual daily periconceptional intake of total fruits and total vegetables was estimated from a food frequency questionnaire. We quantified the effects of diets with a high density of fruits (≥1.2 cups/1,000 kcal/day vs. <1.2 cups/1,000 kcal/day) and vegetables (≥1.3 cups/1,000 kcal/day vs. <1.3 cups/1,000 kcal/day) on preeclampsia risk, conditional on BMI, using a doubly robust estimator implemented in 2 stages. We found that the protective association of higher fruit density declined approximately linearly from a BMI of 20 to a BMI of 32, by 0.25 cases per 100 women per each BMI unit, and then flattened. The protective association of higher vegetable density strengthened in a linear fashion, by 0.3 cases per 100 women for every unit increase in BMI, up to a BMI of 30, where it plateaued. Dietary patterns with a high periconceptional density of fruits and vegetables appear more protective against preeclampsia for women with higher BMI than for leaner women.
Collapse
Affiliation(s)
- Lisa M Bodnar
- Correspondence to Dr. Lisa M. Bodnar, 5129 Public Health, Department of Epidemiology, School of Public Health, University of Pittsburgh, 130 DeSoto Street, Pittsburgh, PA 15261 (e-mail: )
| | | | | | | | | | | | | | | | | | | | | | | | | |
Collapse
|
17
|
Cullen MR, Baiocchi M, Chamberlain L, Chu I, Horwitz RI, Mello M, O'Hara A, Roosz S. Population health science as a unifying foundation for translational clinical and public health research. SSM Popul Health 2022; 18:101047. [PMID: 35252530 PMCID: PMC8885441 DOI: 10.1016/j.ssmph.2022.101047] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2021] [Revised: 01/31/2022] [Accepted: 02/13/2022] [Indexed: 12/04/2022] Open
Abstract
Separated both in academics and practice since the Rockefeller Foundation effort to “liberate” public health from perceived subservience to clinical medicine a century ago, research in public health and clinical medicine have evolved separately. Today, translational research in population health science offers a means of fostering their convergence, with potentially great benefit to both domains. Although evidence that the two fields need not and should not be entirely distinct in their methods and goals has been accumulating for over a decade, the prodigious efforts of biomedical and social sciences over the past year to address the COVID-19 pandemic has placed this unifying approach to translational research in both fields in a new light. Specifically, the coalescence of clinical and population-level strategies to control disease and novel uses of population-level data and tools in research relating to the pandemic have illuminated a promising future for translational research. We exploit this unique window to re-examine how translational research is conducted and where it may be going. We first discuss the transformation that has transpired in the research firmament over the past two decades and the opportunities these changes afford. Next, we present some of the challenges—technical, cultural, legal, and ethical— that need attention if these opportunities are to be successfully exploited. Finally, we present some recommendations for addressing these challenges. Population datasets with extensive biologic, medical and social information augur a new approach for translational research. Important questions about health throughout the life course could be answered were relevant person-level data accessible. Technical, cultural, and legal constraints presently limit the ability to actualize this vision. Analysis of these limitations in the context of the recent pandemic reveals each could be overcome with proper attention.
Collapse
Affiliation(s)
- Mark R. Cullen
- Center for Population Health Sciences, Stanford School of Medicine, Stanford, CA, USA
- Retired, USA
- Corresponding author. 2776 Stirrup Way, Los Altos Hills, CA, 94022, USA.
| | - Michael Baiocchi
- Department of Epidemiology and Population Health, Stanford School of Medicine, Stanford, CA, USA
| | - Lisa Chamberlain
- Department of Pediatrics, Stanford School of Medicine, Stanford, CA, USA
| | - Isabella Chu
- Center for Population Health Sciences, Stanford School of Medicine, Stanford, CA, USA
| | - Ralph I. Horwitz
- Department of Medicine, Lewis Katz School of Medicine, Temple University, USA
| | - Michelle Mello
- Stanford Health Policy and the Department of Medicine, Stanford University School of Medicine, and Stanford Law School, and the Freeman Spogli Institute for International Studies, all in Stanford, CA, USA
| | - Amy O'Hara
- McCourt School of Public Policy, Georgetown University Washington, DC, USA
| | | |
Collapse
|
18
|
C1431T Variant of PPARγ Is Associated with Preeclampsia in Pregnant Women. Life (Basel) 2021; 11:life11101052. [PMID: 34685423 PMCID: PMC8540421 DOI: 10.3390/life11101052] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2021] [Revised: 10/01/2021] [Accepted: 10/02/2021] [Indexed: 12/16/2022] Open
Abstract
Peroxisome proliferator-activated receptor γ (PPARγ) is essential for placental development, whose SNPs have shown increased susceptibility to pregnancy-related diseases, such as preeclampsia. Our aim was to investigate the association between preeclampsia and three PPARγ SNPs (Pro12Ala, C1431T, and C681G), which together with nine clinical factors were used to build a pragmatic model for preeclampsia prediction. Data were collected from 1648 women from the EDEN cohort, of which 35 women had preeclamptic pregnancies, and the remaining 1613 women had normal pregnancies. Univariate analysis comparing preeclamptic patients to the control resulted in the SNP C1431T being the only factor significantly associated with preeclampsia (p < 0.05), with a confidence interval of 95% and odds ratio ranging from 4.90 to 8.75. On the other hand, three methods of multivariate feature selection highlighted seven features that could be potential predictors of preeclampsia: maternal C1431T and C681G variants, obesity, body mass index, number of pregnancies, primiparity, cigarette use, and education. These seven features were further used as input into eight different machine-learning algorithms to create predictive models, whose performances were evaluated based on metrics of accuracy and the area under the receiver operating characteristic curve (AUC). The boost tree-based model performed the best, with respective accuracy and AUC values of 0.971 ± 0.002 and 0.991 ± 0.001 in the training set and 0.951 and 0.701 in the testing set. A flowchart based on the boost tree model was constructed to depict the procedure for preeclampsia prediction. This final decision tree showed that the C1431T variant of PPARγ is significantly associated with susceptibility to preeclampsia. We believe that this final decision tree could be applied in the clinical prediction of preeclampsia in the very early stages of pregnancy.
Collapse
|
19
|
Côté M, Lamarche B. Artificial intelligence in nutrition research: perspectives on current and future applications. Appl Physiol Nutr Metab 2021; 47:1-8. [PMID: 34525321 DOI: 10.1139/apnm-2021-0448] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
Abstract
Artificial intelligence (AI) is a rapidly evolving area that offers unparalleled opportunities of progress and applications in many healthcare fields. In this review, we provide an overview of the main and latest applications of AI in nutrition research and identify gaps to address to potentialize this emerging field. AI algorithms may help better understand and predict the complex and non-linear interactions between nutrition-related data and health outcomes, particularly when large amounts of data need to be structured and integrated, such as in metabolomics. AI-based approaches, including image recognition, may also improve dietary assessment by maximizing efficiency and addressing systematic and random errors associated with self-reported measurements of dietary intakes. Finally, AI applications can extract, structure and analyze large amounts of data from social media platforms to better understand dietary behaviours and perceptions among the population. In summary, AI-based approaches will likely improve and advance nutrition research as well as help explore new applications. However, further research is needed to identify areas where AI does deliver added value compared with traditional approaches, and other areas where AI is simply not likely to advance the field. Novelty: Artificial intelligence offers unparalleled opportunities of progress and applications in nutrition. There remain gaps to address to potentialize this emerging field.
Collapse
Affiliation(s)
- Mélina Côté
- Centre de recherche Nutrition, santé et société (NUTRISS), INAF, Université Laval, Québec, QC, Canada
- School of Nutrition, Université Laval, Québec, QC, Canada
| | - Benoît Lamarche
- Centre de recherche Nutrition, santé et société (NUTRISS), INAF, Université Laval, Québec, QC, Canada
- School of Nutrition, Université Laval, Québec, QC, Canada
| |
Collapse
|
20
|
Oliveira Chaves L, Gomes Domingos AL, Louzada Fernandes D, Ribeiro Cerqueira F, Siqueira-Batista R, Bressan J. Applicability of machine learning techniques in food intake assessment: A systematic review. Crit Rev Food Sci Nutr 2021; 63:902-919. [PMID: 34323627 DOI: 10.1080/10408398.2021.1956425] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
Abstract
The evaluation of food intake is important in scientific research and clinical practice to understand the relationship between diet and health conditions of an individual or a population. Large volumes of data are generated daily in the health sector. In this sense, Artificial Intelligence (AI) tools have been increasingly used, for example, the application of Machine Learning (ML) algorithms to extract useful information, find patterns, and predict diseases. This systematic review aimed to identify studies that used ML algorithms to assess food intake in different populations. A literature search was conducted using five electronic databases, and 36 studies met all criteria and were included. According to the results, there has been a growing interest in the use of ML algorithms in the area of nutrition in recent years. Also, supervised learning algorithms were the most used, and the most widely used method of nutritional assessment was the food frequency questionnaire. We observed a trend in using the data analysis programs, such as R and WEKA. The use of ML in nutrition is recent and challenging. Therefore, it is encouraged that more studies are carried out relating these themes for the development of food reeducation programs and public policies.
Collapse
Affiliation(s)
| | | | | | | | - Rodrigo Siqueira-Batista
- Department of Medicine and Nursing, Universidade Federal de Viçosa, Viçosa, Brazil.,School of Medicine of the Faculdade Dinâmica do Vale do Piranga, Ponte Nova, Brazil
| | - Josefina Bressan
- Department of Nutrition and Health, Universidade Federal de Viçosa, Viçosa, Brazil
| |
Collapse
|
21
|
Kino S, Hsu YT, Shiba K, Chien YS, Mita C, Kawachi I, Daoud A. A scoping review on the use of machine learning in research on social determinants of health: Trends and research prospects. SSM Popul Health 2021; 15:100836. [PMID: 34169138 PMCID: PMC8207228 DOI: 10.1016/j.ssmph.2021.100836] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2021] [Revised: 05/15/2021] [Accepted: 06/01/2021] [Indexed: 02/08/2023] Open
Abstract
Background Machine learning (ML) has spread rapidly from computer science to several disciplines. Given the predictive capacity of ML, it offers new opportunities for health, behavioral, and social scientists. However, it remains unclear how and to what extent ML is being used in studies of social determinants of health (SDH). Methods Using four search engines, we conducted a scoping review of studies that used ML to study SDH (published before May 1, 2020). Two independent reviewers analyzed the relevant studies. For each study, we identified the research questions, Results, data, and algorithms. We synthesized our findings in a narrative report. Results Of the initial 8097 hits, we identified 82 relevant studies. The number of publications has risen during the past decade. More than half of the studies (n = 46) used US data. About 80% (n = 66) utilized surveys, and 70% (n = 57) employed ML for common prediction tasks. Although the number of studies in ML and SDH is growing rapidly, only a few studies used ML to improve causal inference, curate data, or identify social bias in predictions (i.e., algorithmic fairness). Conclusions While ML equips researchers with new ways to measure health outcomes and their determinants from non-conventional sources such as text, audio, and image data, most studies still rely on traditional surveys. Although there are no guarantees that ML will lead to better social epidemiological research, the potential for innovation in SDH research is evident as a result of harnessing the predictive power of ML for causality, data curation, or algorithmic fairness.
Collapse
Affiliation(s)
- Shiho Kino
- Department of Social and Behavioral Sciences, Harvard T.H. Chan School of Public Health, Boston, MA, USA.,Department of Social Epidemiology, Kyoto University, Kyoto, Japan
| | - Yu-Tien Hsu
- Department of Social and Behavioral Sciences, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Koichiro Shiba
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Yung-Shin Chien
- Department of Social and Behavioral Sciences, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Carol Mita
- Countway Library of Medicine, Harvard University, Boston, MA, USA
| | - Ichiro Kawachi
- Department of Social and Behavioral Sciences, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Adel Daoud
- Center for Population and Development Studies, Harvard T.H. Chan School of Public Health, Harvard University, Boston, MA, USA.,Department of Sociology and Work Science, University of Gothenburg, Sweden.,The Division of Data Science and Artificial Intelligence of the Department of Computer Science and Engineering, Chalmers University of Technology, Sweden.,Institute for Analytical Sociology, Linköping University, Sweden
| |
Collapse
|
22
|
Abstract
When causal inference is of primary interest, a range of target parameters can be chosen to define the causal effect, such as average treatment effects (ATEs). However, ATEs may not always align with the research question at hand. Furthermore, the assumptions needed to interpret estimates as ATEs, such as exchangeability, consistency, and positivity, are often not met. Here, we present the incremental propensity score (PS) approach to quantify the effect of shifting each person's exposure propensity by some predetermined amount. Compared with the ATE, incremental PS may better reflect the impact of certain policy interventions and do not require that positivity hold. Using the Nulliparous Pregnancy Outcomes Study: monitoring mothers-to-be (nuMoM2b), we quantified the relationship between total vegetable intake and the risk of preeclampsia and compared it to average treatment effect estimates. The ATE estimates suggested a reduction of between two and three preeclampsia cases per 100 pregnancies for consuming at least half a cup of vegetables per 1,000 kcal. However, positivity violations obfuscate the interpretation of these results. In contrast, shifting each woman's exposure propensity by odds ratios ranging from 0.20 to 5.0 yielded no difference in the risk of preeclampsia. Our analyses show the utility of the incremental PS effects in addressing public health questions with fewer assumptions.
Collapse
|
23
|
Lo Siou G, Akawung AK, Solbak NM, McDonald KL, Al Rajabi A, Whelan HK, Kirkpatrick SI. The effect of different methods to identify, and scenarios used to address energy intake misestimation on dietary patterns derived by cluster analysis. Nutr J 2021; 20:42. [PMID: 33964947 PMCID: PMC8106845 DOI: 10.1186/s12937-021-00696-3] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2020] [Accepted: 04/07/2021] [Indexed: 11/10/2022] Open
Abstract
Background All self-reported dietary intake data are characterized by measurement error, and validation studies indicate that the estimation of energy intake (EI) is particularly affected. Methods Using self-reported food frequency and physical activity data from Alberta’s Tomorrow Project participants (n = 9847 men 16,241 women), we compared the revised-Goldberg and the predicted total energy expenditure methods in their ability to identify misreporters of EI. We also compared dietary patterns derived by k-means clustering under different scenarios where misreporters are included in the cluster analysis (Inclusion); excluded prior to completing the cluster analysis (ExBefore); excluded after completing the cluster analysis (ExAfter); and finally, excluded before the cluster analysis but added to the ExBefore cluster solution using the nearest neighbor method (InclusionNN). Results The predicted total energy expenditure method identified a significantly higher proportion of participants as EI misreporters compared to the revised-Goldberg method (50% vs. 47%, p < 0.0001). k-means cluster analysis identified 3 dietary patterns: Healthy, Meats/Pizza and Sweets/Dairy. Among both men and women, participants assigned to dietary patterns changed substantially between ExBefore and ExAfter and also between the Inclusion and InclusionNN scenarios (Hubert and Arabie’s adjusted Rand Index, Kappa and Cramer’s V statistics < 0.8). Conclusions Different scenarios used to account for EI misreporters influenced cluster analysis and hence the composition of the dietary patterns. Continued efforts are needed to explore and validate methods and their ability to identify and mitigate the impact of EI misestimation in nutritional epidemiology. Supplementary Information The online version contains supplementary material available at 10.1186/s12937-021-00696-3.
Collapse
Affiliation(s)
- Geraldine Lo Siou
- Cancer Research & Analytics, Alberta Health Services, Richmond Road Diagnostic & Treatment Centre, 1820 Richmond Rd SW, Calgary, Alberta, T2T 5C7, Canada
| | - Alianu K Akawung
- Cancer Research & Analytics, Alberta Health Services, Richmond Road Diagnostic & Treatment Centre, 1820 Richmond Rd SW, Calgary, Alberta, T2T 5C7, Canada
| | - Nathan M Solbak
- Cancer Research & Analytics, Alberta Health Services, Richmond Road Diagnostic & Treatment Centre, 1820 Richmond Rd SW, Calgary, Alberta, T2T 5C7, Canada
| | - Kathryn L McDonald
- Cancer Research & Analytics, Alberta Health Services, Richmond Road Diagnostic & Treatment Centre, 1820 Richmond Rd SW, Calgary, Alberta, T2T 5C7, Canada.
| | - Ala Al Rajabi
- Cancer Research & Analytics, Alberta Health Services, Richmond Road Diagnostic & Treatment Centre, 1820 Richmond Rd SW, Calgary, Alberta, T2T 5C7, Canada.,Health Sciences Department, College of Natural and Health Sciences, Zayed University, Abu Dhabi, UAE
| | - Heather K Whelan
- Department of Health and Physical Education, Faculty of Health, Community and Education, Mount Royal University, Calgary, AB, Canada
| | - Sharon I Kirkpatrick
- School of Public Health and Health Systems, University of Waterloo, Waterloo, ON, Canada
| |
Collapse
|
24
|
Morgenstern JD, Rosella LC, Costa AP, de Souza RJ, Anderson LN. Perspective: Big Data and Machine Learning Could Help Advance Nutritional Epidemiology. Adv Nutr 2021; 12:621-631. [PMID: 33606879 PMCID: PMC8166570 DOI: 10.1093/advances/nmaa183] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2020] [Revised: 11/04/2020] [Accepted: 12/29/2020] [Indexed: 01/09/2023] Open
Abstract
The field of nutritional epidemiology faces challenges posed by measurement error, diet as a complex exposure, and residual confounding. The objective of this perspective article is to highlight how developments in big data and machine learning can help address these challenges. New methods of collecting 24-h dietary recalls and recording diet could enable larger samples and more repeated measures to increase statistical power and measurement precision. In addition, use of machine learning to automatically classify pictures of food could become a useful complimentary method to help improve precision and validity of dietary measurements. Diet is complex due to thousands of different foods that are consumed in varying proportions, fluctuating quantities over time, and differing combinations. Current dietary pattern methods may not integrate sufficient dietary variation, and most traditional modeling approaches have limited incorporation of interactions and nonlinearity. Machine learning could help better model diet as a complex exposure with nonadditive and nonlinear associations. Last, novel big data sources could help avoid unmeasured confounding by offering more covariates, including both omics and features derived from unstructured data with machine learning methods. These opportunities notwithstanding, application of big data and machine learning must be approached cautiously to ensure quality of dietary measurements, avoid overfitting, and confirm accurate interpretations. Greater use of machine learning and big data would also require substantial investments in training, collaborations, and computing infrastructure. Overall, we propose that judicious application of big data and machine learning in nutrition science could offer new means of dietary measurement, more tools to model the complexity of diet and its relations with diseases, and additional potential ways of addressing confounding.
Collapse
Affiliation(s)
| | - Laura C Rosella
- Dalla Lana School of Public Health, University of Toronto, Toronto, Ontario, Canada,Vector Institute, Toronto, Ontario, Canada
| | - Andrew P Costa
- Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, Ontario, Canada
| | - Russell J de Souza
- Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, Ontario, Canada,Population Health Research Institute, Hamilton Health Sciences, Hamilton, Ontario, Canada
| | - Laura N Anderson
- Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, Ontario, Canada
| |
Collapse
|
25
|
Limketkai BN, Mauldin K, Manitius N, Jalilian L, Salonen BR. The Age of Artificial Intelligence: Use of Digital Technology in Clinical Nutrition. CURRENT SURGERY REPORTS 2021; 9:20. [PMID: 34123579 PMCID: PMC8186363 DOI: 10.1007/s40137-021-00297-3] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 04/28/2021] [Indexed: 02/05/2023]
Abstract
PURPOSE OF REVIEW Computing advances over the decades have catalyzed the pervasive integration of digital technology in the medical industry, now followed by similar applications for clinical nutrition. This review discusses the implementation of such technologies for nutrition, ranging from the use of mobile apps and wearable technologies to the development of decision support tools for parenteral nutrition and use of telehealth for remote assessment of nutrition. RECENT FINDINGS Mobile applications and wearable technologies have provided opportunities for real-time collection of granular nutrition-related data. Machine learning has allowed for more complex analyses of the increasing volume of data collected. The combination of these tools has also translated into practical clinical applications, such as decision support tools, risk prediction, and diet optimization. SUMMARY The state of digital technology for clinical nutrition is still young, although there is much promise for growth and disruption in the future.
Collapse
Affiliation(s)
- Berkeley N. Limketkai
- Vatche & Tamar Manoukian Division of Digestive Diseases, UCLA School of Medicine, 100 UCLA Medical Plaza, Suite 345, Los Angeles, CA 90095 USA
| | - Kasuen Mauldin
- Department of Nutrition, Food Science, and Packaging, San José State University, San José, CA USA
| | - Natalie Manitius
- Vatche & Tamar Manoukian Division of Digestive Diseases, UCLA School of Medicine, 100 UCLA Medical Plaza, Suite 345, Los Angeles, CA 90095 USA
| | - Laleh Jalilian
- Department of Anesthesiology, UCLA School of Medicine, Los Angeles, CA USA
| | | |
Collapse
|
26
|
Krishnan S, Ramyaa R. When two heads are better than one: nutritional epidemiology meets machine learning. Am J Clin Nutr 2020; 111:1124-1126. [PMID: 32433722 DOI: 10.1093/ajcn/nqaa113] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Affiliation(s)
- Sridevi Krishnan
- Department of Nutrition, University of California Davis, Davis, CA, USA
| | - Ramyaa Ramyaa
- Computer Science and Engineering, New Mexico Tech, Socorro, NM, USA
| |
Collapse
|