https://orcid.org/0000-0002-2118-4254
Figures
Abstract
Introduction
Diet-related inflammation is associated with adiposity. Obesity and inflammation in early life may have adverse health outcomes in later life; however, the socio-ecological predictors of a pro-inflammatory diet in childhood and adolescence are not well understood. This rapid scoping review aims to summarise the current state of research from observational studies investigating socio-ecological predictors (childhood, parental, familial, demographic and chronobiological risk factors) and their association with diet-associated inflammation and adiposity in children and adolescents.
Methods
This scoping review will be conducted using the frameworks based on the Joanna Briggs Institute and Arksey and O’Malley and the Population, Concept and Context (PCC) mnemonic. Searches were conducted in OVID Medline, Cinahl and Embase, with adaptations as required. The piloted study selection process will utilise two reviewers for study selection, with reference lists checked for included studies. A third reviewer will moderate disagreements. Data will be extracted by one reviewer and calibrated by a second reviewer.
Results
The results will be reported using the Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) checklist and PRISMA-ScR flow diagram. The main findings will be synthesised into themes and concepts narratively. Tables and graphs will present frequencies, study details and categorical descriptions.
Discussion
This scoping review will provide an overview of the research conducted to date regarding predictors of diet-related inflammation in childhood and their associations with adiposity. Better understanding of the factors associated with a more inflammatory diet in childhood may be useful for clinicians and policy makers when designing and implementing health interventions.
Citation: Vingrys K, McCarthy H, Segurado R, Hébert JR, Phillips CM (2023) Socio-ecological predictors of dietary inflammatory scores and associations with childhood and adolescent adiposity: A protocol for a rapid scoping review of observational studies. PLoS ONE 18(5): e0286200. https://doi.org/10.1371/journal.pone.0286200
Editor: Paolo Magni, Università degli Studi di Milano, ITALY
Received: July 22, 2022; Accepted: April 30, 2023; Published: May 25, 2023
Copyright: © 2023 Vingrys et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Data Availability: No datasets were generated or analysed during the current study. All relevant data from this study will be made available upon study completion.
Funding: This scoping review protocol is supported by funding from the Biostime Institute for Nutrition and Care – Geneva Funding programs (BINC-Geneva, available at https://binc-geneva.org/en/binc-geneva-funded-projects/). The funders did not and will not have a role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Competing interests: KV, HMc, RS, CMP - no conflict of interest to declare. JH: Dr. James R. Hébert owns controlling interest in Connecting Health Innovations LLC (CHI), a company that has licensed the right to his invention of the dietary inflammatory index (DII®) from the University of South Carolina in order to develop computer and smart phone applications for patient counselling and dietary intervention in clinical settings. CHI owns exclusive right to the E-DIITM and C-DIITM. The subject matter of this paper has no direct bearing on that work, nor has any CHI-related activity exerted any influence on this project. This did not and will not alter our adherence to PLoS ONE policies on sharing data and materials. This does not alter our adherence to PLOS ONE policies on sharing data and materials.
Introduction
Childhood obesity has reached epidemic proportions worldwide [1–3] and has major consequences for producing short- and long-term adverse health outcomes [4–6], detracting from quality of life and creating a huge societal burden in terms of lost productivity and medical costs [7–9]. The risk factors that increase the risk of a child developing overweight, known as predictors of child overweight, are important in understanding the multifactorial aetiology of childhood overweight [10]. The interplay between multiple risk factors are recognised in adiposity research, that highlights the importance of foetal and early childhood exposures [11], foetal programming [12] and ecological predictors, including parental, familial, societal and demographic characteristics [10]. However, the relationships among predictors of diet-associated inflammation and adiposity in children, remains unclear [13].
Critical phases of childhood development represent distinct periods during which physical alterations occur that may increase the prevalence of subsequent obesity [14]. Importantly, predictors may have different effects on childhood dietary inflammation and associations with childhood adiposity at different times in childhood. For example, early adiposity rebound (AR) in childhood is a risk factor for overweight [15], and early AR has been correlated with diet and lifestyle factors including sugar-sweetened beverage consumption [16].
Chronic inflammation, with persistently elevated levels of circulating pro-inflammatory biomarkers, is characteristic of obesity [17, 18] and can be systemic or tissue-specific simmering inflammation [19]. Dietary intake is a key environmental factor that contributes to body weight outcomes, chronic inflammation and immune response [18, 20, 21].
Associations between dietary components as ecological predictors of diet-related inflammation are relatively well understood. For example, it is known that specific food components (ultra-processed foods, added sugars, saturated fatty acids) and dietary patterns (Western-style diet) are associated with elevated biological inflammatory markers in children and adolescents [22]. However, the wider socio-ecological predictors of more pro-inflammatory diets, relating to the child and familial demographic characteristics, are under-researched. Likewise, the associations between dietary inflammation and childhood adiposity are unclear (Fig 1), with overweight and obesity associated in some studies [23–26], but not others [27, 28].
When assessing diet as an environmental factor associated with inflammation in children, several indices have been used to score the inflammatory potential of the diet, including the Dietary Inflammatory Index (DII®) [29, 30] and variations: the energy-adjusted DII (E-DII™) [21]; the Children’s DII (C-DII™) [21, 28] and the child Dietary Inflammatory Score (cDIS) [31]. The DII and variations such as the E-DII evaluate the inflammatory potential of dietary intake by estimating the degree of inflammatory potential of specific nutrients and foods using a score on a scale ranging from around -9 to +8 [29]. Higher scores, reflecting more pro-inflammatory diets, have been associated with increased risk of obesity and non-communicable diseases in adults [21, 28, 32] a higher risk of obesity in children at ages 5 and 9 years in the Lifeways cohort [33]. A recent systematic review also found an increased risk of cardiometabolic, inflammatory and adiposity related measures associated with higher E-DII and C-DII in children and adolescents from 6 studies [34], suggesting that cohort studies are a useful model to explore socio-ecological predictors of dietary inflammation in children and their associations with adiposity, where familial, parental and personal factors may be at play.
Aims and objectives
Prediction research is useful to help forecast future disease occurrence. This includes predictor finding studies (of risk factors) and predictor modelling studies [35, 36]. The rationale that underpins the aims of this scoping review is that studies designed to find predictors have largely focussed on associations between specific dietary components and inflammatory markers, relating to cardiometabolic factors and adiposity [31, 34]; however, the wider universe of socio-ecological predictors of childhood dietary inflammation and their associations with childhood adiposity are not well studied. Moreover, observational studies can estimate differences in risk attributed to predictors (risk factors) [37] with the concomitant potential to identify candidate predictors for prediction modelling studies. Therefore, as an exploratory analysis, establishing the extent, and types, of prediction modelling studies available will assist in understanding whether new models are needed or whether existing models can be feasibly adapted to undergo validation or updating.
Therefore, the aims of this scoping review are to:
- Summarise the current state of research and gaps in the literature investigating the association between dietary inflammation scores and adiposity in children and adolescents in observational studies;
- Summarise the current research reporting associations between socio-ecological predictors (risk factors), or prediction models, of dietary inflammation in childhood and adolescence, measured by dietary inflammation scores and associations with adiposity in observational studies; and
- Describe whether there is any variation of risk at different childhood ages (early, middle, and late childhood) of a more pro-inflammatory diet associated with adiposity.
The objective of this review is to subsequently inform future research investigating predictors and prediction models of dietary inflammation and obesity in children in observational studies. The feasibility of prediction modelling from potential candidate predictors will also be explored [38].
Methods
The scoping review methods were developed using the Joanna Briggs Institute’s (JBI) updated guidance on scoping reviews [39], incorporating extensions based on the Arksey and O’Malley framework [40–42]. The reporting of this protocol followed the PRISMA for systematic review protocols (PRISMA-P) checklist [43–45] (S1 Table). The reporting of the subsequent scoping review will follow the Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) checklist [45]. Within the scoping review framework, a rapid evidence synthesis approach has been adopted to accommodate completion timelines whilst maintaining methodological rigour. This includes selected methodological efficiencies identified from other rapid reviews [46, 47], including applying search limits (restriction to humans, date range, publication types and English language), and maintaining independent double reviewers for study selection; however, utilising a single reviewer for extraction with calibration by a second reviewer. Any deviations from the protocol will be agreed by the reviewers and documented [39]. The database searches were performed in July 2022.
Stage 1. Research question identification
To formulate the key factors and criteria being investigated in this review, the Population, Concept and Context (PCC) mnemonic was used as recommended by JBI [48].
Population.
The target population consists of children and adolescents ≥2 years and <19 years of age based on World Health Organization definitions [49, 50]. The children included in our study are restricted to ≥ 2 years of age to limit to children with dietary intake coming mostly from foods rather than breast milk. Given the different stages of growth within childhood and adolescence, the population will also be considered categorically as early, mid and late childhood and adolescence. The target population includes generally healthy children and adolescents that may have overweight or obesity. Those diagnosed with chronic diseases (including diabetes and cancer) will be excluded.
Concept.
The breadth of inquiry will include studies with outcomes in the phenomena of interest; predictors of diet-related inflammation and adiposity. ‘Predictors’ in this study refers to risk factor variables in relation to a more pro-inflammatory diet and their associations with adiposity, including socio-ecological predictors: biological, familial or environmental. Socio-ecological predictors will be adapted from ecological factors in childhood obesity described by Davison and Birch (2001) and matched to potential cohort data and include:
Child characteristics include sex, age, adiposity measures and risk factors: sedentary behaviour, physical activity;
Parental styles and familial characteristics include parental dietary intake, weight status, activity patterns, smoking and alcohol intake;
Parental demographic characteristics include ethnicity, work hours, leisure time, socio-economic status and education level;
Chronobiological characteristics include meal timing and sleep.
Specific dietary factors (food groups and nutrients) will not be the focus as these associations with the C-DII are comparatively well explored in contrast to wider socio-ecological risk factors [32].
As an exploratory analysis, studies reporting prediction modelling of dietary-related inflammation also is of interest. These include studies developing new prediction models or validating or updating existing prediction models [51]. Concepts for prediction modelling were identified from the transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): The TRIPOD statement [51], using the TRIPOD Checklist for Prediction Model Development and Validation. Diet-related inflammation in this study was defined as biological inflammation related to dietary components that are measured by indices or scores including the DII and variations including the E-DII, C-DII and cDIS. Adiposity will be defined as the amount of body fat [52] and measured by proxy outcomes including body weight, body mass index (BMI), abdominal circumference, skinfold thickness and weight-for-height. Studies of human subjects with overweight or obesity as indicators of adiposity will be included provided that subjects were otherwise generally healthy.
Context.
The context of this scoping review is in observational studies, defined as studies with either cohort, case-control or cross-sectional designs [53] that will have been reported in peer-reviewed journal publications, articles in press that are published online and reviews or conference papers, from 2009 to the time of running the searches. The earliest date limitation reflects 2009, the year when the concept of the DII was first published [30]. Peer-reviewed literature is prioritised in this context to align with the rapid methodology and recognising that peer-review is as an important process aimed at ensuring study quality [54]. The research questions will be reviewed as literature is obtained. The primary and secondary research questions identified were:
Primary questions
- 1. What is known from the current published literature in observational studies about the association between a more inflammatory diet, measured by higher dietary inflammation scores, and association with adiposity in childhood and adolescence?
- 2. What is known from the current published literature in observational studies about socio-ecological predictors of a more inflammatory diet, measured by higher dietary inflammation scores, and their association with childhood and adolescent adiposity?
Secondary questions
- 3. What is known from the current published literature about the variation in risk of a more inflammatory diet at different stages of childhood and adolescence (early, middle and late) identified from the primary research questions?
- 4. What is known from the current published literature about the variation in risk of a more inflammatory diet and association with chronobiological factors (such as meal timing and sleep)?
- 5. What is known from the current published literature about prediction modelling developed in observational studies, investigating candidate predictors in determining the risk of dietary inflammation in childhood and adolescence?
Stage 2. Identifying relevant studies—Search strategy
The search strategy was developed primarily by one reviewer with input from the research team and a research librarian. Variations were discussed and agreed by the reviewers and were documented. As the search process is recommended to be iterative, there is provision to allow modification of keywords, search terms and information sources as the reviewers become increasingly familiar with the literature [39, 40].
The databases to be searched were OVID Medline, Cinahl and Embase. Initial scoping searches were piloted in OVID Medline and adapted as required for other databases. The search results were documented and the full search strategy of one major database (OVID Medline) is provided.
Where databases allowed, limits were used to restrict studies to ‘human’, ‘title and abstract’ though this was trialled in each database to determine impact on the output specificity and sensitivity. The search strategy was piloted and refined before final run and modified minimally as required for each database. The search strategy was finalised after trialling using a combination of key words and phrases, synonyms and controlled vocabulary (e.g., MeSH) (Table 1).
Stage 3. Study selection
The inclusion and exclusion criteria for the searches are matched to the PCC (Table 2). These criteria may be changed post-hoc as familiarity with the literature increases [40] and on discussion with the research team. The study selection process will be piloted by the reviewers before the formal run [39]. Title and Abstracts of the identified studies will be screened by two reviewers based on the inclusion and exclusion criteria. Reasons for exclusion will be recorded. Following abstract review, the first round of selected studies will proceed to full text screening for final selection by two reviewers. The reference lists of included studies will be hand-searched for further studies. Disagreements will be moderated by a third reviewer. Experts in the field will be consulted to identify other studies.
The search outputs will be combined within Endnote (version 20.3, Clarivate Analytics 2020). The citations will be uploaded to Covidence systematic review software [55] for screening and data extraction and duplications removed. Online training videos are available to support the review team. The process and results of the searches will be documented in a shared file including search dates, key terms used and number of results and translated to results in a flow diagram. If sources are excluded at the full-text review stage, details will be attached to the review as an appendix [39]. Study records will be maintained on University drives with password protection for online files.
Critical appraisal of the selected studies will not be conducted as it is not required to meet the specific aims of this scoping review [39].
Stage 4. Data extraction (charting the data)
Extracted information will be synthesised using a piloted electronic data extraction table in Microsoft Excel, using the first 4 studies retrieved [56] and revising the extraction form as needed [57]. One reviewer will extract the data and a second experienced reviewer will verify the extracted data for calibration [57, 58].
Data underlying the findings will be made available at the time of publication as part of the manuscript or included as supplemental material. Data will be presented in tables/charts, supported by a narrative summary discussing the findings in relation to the objectives of the review.
The final studies selected will undergo syntheses into themes to extract and record the relevant findings for thematic arrangement into outcome domains and comparative overview. Categorisation into the outcome domains and themes will utilise an iterative approach to guide conceptualisation of the thematic structure, undertaken by one reviewer and then referred to the review team for consensus [56]. These may include various sub-headings identified under modifiable and non-modifiable predictors as previously by Chi and colleagues [59].
If authors need to be contacted for clarification or to request additional data, it will be documented and reported in the review.
The following data will be extracted:
Study details: Author(s), publication year, study location.
Study methodology: Aims, study type, methodology (eligibility criteria for participants), comparator, duration, study population/setting (e.g., general population, primary care, number and location of centres), outcome measures. Additionally, for prediction modelling studies this information will be recorded: key study dates (start and end of accrual), candidate predictors and how/when they were measured, model development methodology, model performance, discrimination, calibration, validation, sensitivity, specificity, predictive capacity.
Results:
Number of participants/sample size, participant characteristics (demographics, age, sex, clinical), missing data, mean follow up/length of investigation, number of outcome events, loss to follow up, predictors/risk factors identified.
Additionally, for predictor finding studies hazard ratios (HR), risk ratios (RR) or odds ratios (OR) from the most fully adjusted model for the highest versus the lowest exposure categories or levels and corresponding 95% Confidence Intervals (CIs) for exposure categories, or P-values, and authors’ conclusions will be reported.
Additionally, for prediction modelling studies: type of study (development or validation or updating), number of participants/sample size, final model predictors, goodness of fit test for models, probabilities (ORs, AUROC), sensitivity, specificity, accuracy, RMSE, prediction intervals, R2 values, unadjusted associations between candidate predictors and outcome, prediction model performance measures (with CIs), and proposed use of the prediction model will be reported.
Results
Stage 5. Collating, summarizing and reporting results
To ensure accurate and transparent reporting, each stage of the review will be reported using the Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) checklist [45]. A narrative synthesis will be conducted for the main findings.
First, the search results will be presented narratively and in a PRISMA-ScR flow diagram to present an overview of the identification, screening, assessment of eligibility and selection processes [39]. The main results will be summarised to include an overview of the themes and concepts categorised in outcome domains and types of evidence retrieved. Descriptive results will be narratively reported and visually presented in tables and graphs to report frequencies such as counts of concepts, study locations, categorical description of outcome measures [39]. The results will be contextualised with the review aims and objectives as well as consider relevance to population groups, limitations, including publication bias, and future directions. The protocol and ensuing review will be submitted for publication in a peer-reviewed journal.
Discussion
Discussion of the results of the ensuing rapid scoping review will be guided by the PRISMA ScR checklist and will include a summary of evidence as described in Stage 5, a discussion of the limitations and strengths of the review, and concluding remarks to interpret the results, implications and future directions.
Understanding the predictors of childhood dietary-related inflammation and its association with childhood obesity is important in the development of public health prevention strategies to reduce the burden that obesity places on individuals and society, including health trajectories and outcomes into adolescence and adulthood [60]. This review will add to our understanding of the complex factors that contribute to the development of diet-related inflammation in childhood that is emerging as a serious co-morbidity associated with childhood obesity but is not yet well understood.
Supporting information
S1 Table. PRISMA for systematic review protocols (PRISMA-P) checklist.
https://doi.org/10.1371/journal.pone.0286200.s001
(PDF)
Acknowledgments
Diarmuid Stokes, College Liaison Librarian, College of Health and Agricultural Sciences, College of Science, Health Sciences Library, University College Dublin is acknowledged for expertise provided in the development of the search strategy.
References
- 1. Spinelli A, Buoncristiano M, Kovacs VA, Yngve A, Spiroski I, Obreja G, et al. Prevalence of severe obesity among primary school children in 21 European countries. Obesity Facts. 2019;12(2):244–58. Epub 2019/04/26. pmid:31030201.
- 2. Buoncristiano M, Spinelli A, Williams J, Nardone P, Rito AI, García-Solano M, et al. Childhood overweight and obesity in Europe: Changes from 2007 to 2017. Obesity Reviews. 2021;22(S6):e13226. pmid:34378305
- 3. Garrido-Miguel M, Cavero-Redondo I, Álvarez-Bueno C, Rodríguez-Artalejo F, Moreno LA, Ruiz JR, et al. Prevalence and trends of overweight and obesity in European children from 1999 to 2016: A systematic review and meta-analysis. JAMA Pediatrics. 2019;173(10):e192430–e. Epub 2019/10/07. pmid:31381031.
- 4. Faienza MF, Chiarito M, Molina-Molina E, Shanmugam H, Lammert F, Krawczyk M, et al. Childhood obesity, cardiovascular and liver health: a growing epidemic with age. World Journal of Pediatrics. 2020;16(5):438–45. pmid:32020441
- 5. Weihe P, Spielmann J, Kielstein H, Henning-Klusmann J, Weihrauch-Blüher S. Childhood obesity and cancer risk in adulthood. Current Obesity Reports. 2020;9(3):204–12. Epub 2020/06/11. pmid:32519271.
- 6. Weihrauch-Blüher S, Schwarz P, Klusmann JH. Childhood obesity: increased risk for cardiometabolic disease and cancer in adulthood. Metabolism. 2019;92:147–52. Epub 2018/12/12. pmid:30529454.
- 7. Hasan T, Ainscough TS, West J, Fraser LK. Healthcare utilisation in overweight and obese children: A systematic review and meta-analysis. BMJ open. 2020;10(10). pmid:33033082
- 8. Hamilton D, Dee A, Perry IJ. The lifetime costs of overweight and obesity in childhood and adolescence: a systematic review. Obesity Reviews. 2018;19(4):452–63. pmid:29271111
- 9.
JANPA–Joint Action on Nutrition and Physical Activity. Lifetime impact and cost of childhood obesity in Europe (Part 1) 2018 Ref. Ares(2018)1856494–06/04/2018.
- 10. Davison KK, Birch LL. Childhood overweight: a contextual model and recommendations for future research. Obesity Reviews: an official journal of the International Association for the Study of Obesity. 2001;2(3):159–71. pmid:12120101.
- 11. Monthé-Drèze C, Rifas-Shiman SL, Aris IM, Shivappa N, Hebert JR, Sen S, et al. Maternal diet in pregnancy is associated with differences in child body mass index trajectories from birth to adolescence. Am J Clin Nutr. 2021;113(4):895–904. pmid:33721014.
- 12. Sutton EF, Gilmore LA, Dunger DB, Heijmans BT, Hivert M-F, Ling C, et al. Developmental programming: State-of-the-science and future directions-Summary from a Pennington Biomedical symposium. Obesity (Silver Spring). 2016;24(5):1018–26. Epub 2016/04/01. pmid:27037645.
- 13.
Phillips CM, Andrews C, Brigham E, Chen L-W, Han Y-Y, Hanson C, et al. Chapter 12—Inflammatory potential of diet and health outcomes in pregnancy, infancy, and childhood. In: Hébert JR, Hofseth LJ, editors. Diet, Inflammation, and Health: Academic Press; 2022. p. 609–63.
- 14. Cole TJ. Children grow and horses race: is the adiposity rebound a critical period for later obesity? BMC Pediatrics. 2004;4(1):6. pmid:15113440
- 15. Rolland-Cachera MF, Deheeger M, Maillot M, Bellisle F. Early adiposity rebound: causes and consequences for obesity in children and adults. International Journal of Obesity. 2006;30(4):S11–S7. pmid:17133230
- 16. Hwang IT, Ju Y-S, Lee HJ, Shim YS, Jeong HR, Kang MJ. Body mass index trajectories and adiposity rebound during the first 6 years in Korean children: Based on the National Health Information Database, 2008–2015. PloS one. 2020;15(10):e0232810. pmid:33125366
- 17. Libby P. Inflammatory mechanisms: the molecular basis of inflammation and disease. Nutrition Reviews 2007;65(12 Pt 2):S140–6. Epub 2008/02/05. pmid:18240538.
- 18. Monteiro R, Azevedo I. Chronic inflammation in obesity and the metabolic syndrome. Mediators of Inflammation. 2010;2010:289645. pmid:20706689
- 19.
Diet, inflammation and health. 1 ed. Amsterdam: Elsevier; 2022.
- 20. Marx W, Veronese N, Kelly JT, Smith L, Hockey M, Collins S, et al. The Dietary Inflammatory Index and human health: An umbrella review of meta-analyses of observational studies. Adv Nutr. 2021;12(5):1681–90. pmid:33873204
- 21. Hébert JR, Shivappa N, Wirth MD, Hussey JR, Hurley TG. Perspective: The Dietary Inflammatory Index (DII)-Lessons learned, improvements made, and future directions. Advances in Nutrition (Bethesda, Md). 2019;10(2):185–95. pmid:30615051.
- 22. Bujtor M, Turner AI, Torres SJ, Esteban-Gonzalo L, Pariante CM, Borsini A. Associations of dietary intake on biological markers of inflammation in children and adolescents: A systematic review. Nutrients. 2021;13(2):356. pmid:33503979.
- 23. Mărginean CO, Meliţ LE, Ghiga DV, Mărginean MO. Early inflammatory status related to pediatric obesity. Frontiers in Pediatrics. 2019;7:241-. pmid:31275906.
- 24. Christaki EV, Pervanidou P, Papassotiriou I, Bastaki D, Valavani E, Mantzou A, et al. Stress, inflammation and metabolic biomarkers are associated with body composition measures in lean, overweight, and obese children and adolescents. Children (Basel). 2022;9(2):291. pmid:35205011.
- 25. Visser M, Bouter LM, McQuillan GM, Wener MH, Harris TB. Low-grade systemic inflammation in overweight children. Pediatrics. 2001;107(1):e13–e. pmid:11134477
- 26. Carolan E, Hogan AE, Corrigan M, Gaotswe G, O’Connell J, Foley N, et al. The impact of childhood obesity on inflammation, innate immune cell frequency, and metabolic MicroRNA expression. The Journal of Clinical Endocrinology & Metabolism. 2014;99(3):E474–E8. pmid:24423308
- 27. Chavira-Suárez E, Rosel-Pech C, Polo-Oteyza E, Ancira-Moreno M, Ibarra-González I, Vela-Amieva M, et al. Simultaneous evaluation of metabolomic and inflammatory biomarkers in children with different body mass index (BMI) and waist-to-height ratio (WHtR). PloS one. 2020;15(8):e0237917–e. pmid:32834003.
- 28. Khan S, Wirth MD, Ortaglia A, Alvarado CR, Shivappa N, Hurley TG, et al. Design, Development and Construct Validation of the Children’s Dietary Inflammatory Index. Nutrients. 2018;10(8). pmid:30061487
- 29. Shivappa N, Steck SE, Hurley TG, Hussey JR, Hebert JR. Designing and developing a literature-derived, population-based dietary inflammatory index. Public Health Nutr. 2014;17(8):1689–96. Epub 2013/08/15. pmid:23941862.
- 30. Cavicchia PP, Steck SE, Hurley TG, Hussey JR, Ma Y, Ockene IS, et al. A new dietary inflammatory index predicts interval changes in serum high-sensitivity C-reactive protein. J Nutr. 2009;139(12):2365–72. Epub 2009/10/28. pmid:19864399.
- 31. Buckland G, Northstone K, Emmett PM, Taylor CM. The inflammatory potential of the diet in childhood is associated with cardiometabolic risk in adolescence/young adulthood in the ALSPAC birth cohort. Eur J Nutr. 2022. pmid:35596006
- 32. Phillips CM, Chen L-W, Heude B, Bernard JY, Harvey NC, Duijts L, et al. Dietary Inflammatory Index and non-communicable disease risk: A narrative review. Nutrients. 2019;11(8):1873. pmid:31408965.
- 33. Navarro P, Shivappa N, Hebert JR, Mehegan J, Murrin CM, Kelleher CC, et al. Predictors of the dietary inflammatory index in children and associations with childhood weight status: A longitudinal analysis in the Lifeways Cross-Generation Cohort Study. Clinical Nutrition. 2020;39(7):2169–79. Epub 2019/10/14. pmid:31606243.
- 34. Suhett LG, Hermsdorff HHM, Cota BC, Ribeiro SAV, Shivappa N, Hébert JR, et al. Dietary inflammatory potential, cardiometabolic risk and inflammation in children and adolescents: a systematic review. Crit Rev Food Sci Nutr. 2021;61(3):407–16. Epub 2020/03/12. pmid:32156146.
- 35. Chen L. Overview of clinical prediction models. Annals of Translational Medicine. 2020;8(4):71-. pmid:32175364.
- 36. Hemingway H, Croft P, Perel P, Hayden JA, Abrams K, Timmis A, et al. Prognosis research strategy (PROGRESS) 1: A framework for researching clinical outcomes. BMJ: British Medical Journal. 2013;346:e5595. pmid:23386360
- 37. Mann CJ. Observational research methods. Research design II: cohort, cross sectional, and case-control studies. Emergency Medicine Journal. 2003;20(1):54. pmid:12533370
- 38. Leisman DE, Harhay MO, Lederer DJ, Abramson M, Adjei AA, Bakker J, et al. Development and reporting of prediction models: Guidance for authors from editors of respiratory, sleep, and critical care journals. Critical Care Medicine. 2020;48(5). pmid:32141923
- 39. Peters MDJ, Marnie C, Tricco AC, Pollock D, Munn Z, Alexander L, et al. Updated methodological guidance for the conduct of scoping reviews. JBI Evid Synth. 2020;18(10):2119–26. pmid:33038124.
- 40. Arksey H, O’Malley L. Scoping studies: towards a methodological framework. International Journal of Social Research Methodology. 2005;8(1):19–32.
- 41. Colquhoun HL, Levac D, O’Brien KK, Straus S, Tricco AC, Perrier L, et al. Scoping reviews: time for clarity in definition, methods, and reporting. J Clin Epidemiol. 2014;67(12):1291–4. Epub 20140714. pmid:25034198.
- 42. Levac D, Colquhoun H, O’Brien KK. Scoping studies: advancing the methodology. Implementation Science. 2010;5(1):69. pmid:20854677
- 43. Moher D, Shamseer L, Clarke M, Ghersi D, Liberati A, Petticrew M, et al. Preferred reporting items for systematic review and meta-analysis protocols (PRISMA-P) 2015 statement. Syst Rev. 2015;4(1):1. Epub 2015/01/03. pmid:25554246.
- 44. Shamseer L, Moher D, Clarke M, Ghersi D, Liberati A, Petticrew M, et al. Preferred reporting items for systematic review and meta-analysis protocols (PRISMA-P) 2015: elaboration and explanation. BMJ. 2015;349:g7647. Epub 2015/01/04. pmid:25555855.
- 45. Tricco AC, Lillie E, Zarin W, O’Brien KK, Colquhoun H, Levac D, et al. PRISMA Extension for Scoping Reviews (PRISMA-ScR): Checklist and Explanation. Ann Intern Med. 2018;169(7):467–73. Epub 20180904. pmid:30178033.
- 46. Abou-Setta AM, Jeyaraman M, Attia A, Al-Inany HG, Ferri M, Ansari MT, et al. Methods for Developing Evidence Reviews in Short Periods of Time: A Scoping Review. PloS one. 2016;11(12):e0165903. pmid:27930662
- 47. Abou-Setta AM, Jeyaraman MM, Attia A, Al-Inany HG, Ferri M, Ansari MT, et al. Correction: Methods for Developing Evidence Reviews in Short Periods of Time: A Scoping Review. PloS one. 2017;12(2):e0172372. pmid:28192493
- 48.
The Joanna Briggs Institute. Joanna Briggs Institute Reviewers’ Manual: 2015 edition/supplement. South Australia: The University of Adelaide, 2015.
- 49.
Convention on the Rights of the Child, (1989).
- 50.
World Health Organization. Health for the World’s Adolescents: A second chance in the second decade: Summary. Geneva: World Health Organization, 2014 Contract No.: WHO/FWC/MCA/14.05.
- 51. Collins GS, Reitsma JB, Altman DG, Moons KG. Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): the TRIPOD statement. Annals of Internal Medicine 2015;162(1):55–63. Epub 2015/01/07. pmid:25560714.
- 52. Tompuri TT, Jääskeläinen J, Lindi V, Laaksonen DE, Eloranta A-M, Viitasalo A, et al. Adiposity Criteria in Assessing Increased Cardiometabolic Risk in Prepubertal Children. Frontiers in Endocrinology. 2019;10. pmid:31293520
- 53. Concato J, Shah N, Horwitz RI. Randomized, controlled trials, observational studies, and the hierarchy of research designs. The New England journal of medicine. 2000;342(25):1887–92. pmid:10861325.
- 54.
Council of Science Editors. 2.3 Reviewer roles and responsibilities online: Council of Science Editors; 2020 [updated March 13, 2020; cited 2022 13 June]. https://www.councilscienceeditors.org/resource-library/editorial-policies/publication-ethics/2-3-reviewer-roles-and-responsibilities/.
- 55.
Veritas Health Innovation Ltd. Covidence. v2883 655a4086 ed. Melbourne, Australia 2022.
- 56. Brown V, Moodie M, Sultana M, Hunter KE, Byrne R, Zarnowiecki D, et al. A scoping review of outcomes commonly reported in obesity prevention interventions aiming to improve obesity-related health behaviors in children to age 5 years. Obesity Reviews. 2022;23(5):e13427. pmid:35122457
- 57. Krishnaratne S, Pfadenhauer LM, Coenen M, Geffert K, Jung-Sievers C, Klinger C, et al. Measures implemented in the school setting to contain the COVID-19 pandemic: a rapid scoping review. Cochrane Database of Systematic Reviews. 2020;(12). CD013812.
- 58. Curran JA, Gallant AJ, Wong H, Shin HD, Urquhart R, Kontak J, et al. Knowledge translation strategies for policy and action focused on sexual, reproductive, maternal, newborn, child and adolescent health and well-being: a rapid scoping review. BMJ open. 2022;12(1):e053919. pmid:35039297
- 59. Chi DL, Luu M, Chu F. A scoping review of epidemiologic risk factors for pediatric obesity: Implications for future childhood obesity and dental caries prevention research. J Public Health Dent. 2017;77 Suppl 1:S8–s31. Epub 20170610. pmid:28600842.
- 60. Simmonds M, Llewellyn A, Owen CG, Woolacott N. Predicting adult obesity from childhood obesity: a systematic review and meta-analysis. Obesity Reviews. 2016;17(2):95–107. pmid:26696565