Skip to main content
×
×
Home

Generating the evidence for risk reduction: a contribution to the future of food-based dietary guidelines

  • Lukas Schwingshackl (a1) (a2), Sabrina Schlesinger (a3), Brecht Devleesschauwer (a4), Georg Hoffmann (a5), Angela Bechthold (a6), Carolina Schwedhelm (a1) (a2), Khalid Iqbal (a1), Sven Knüppel (a1) and Heiner Boeing (a1) (a2)...
Abstract

A major advantage of analyses on the food group level is that the results are better interpretable compared with nutrients or complex dietary patterns. Such results are also easier to transfer into recommendations on primary prevention of non-communicable diseases. As a consequence, food-based dietary guidelines (FBDG) are now the preferred approach to guide the population regarding their dietary habits. However, such guidelines should be based on a high grade of evidence as requested in many other areas of public health practice. The most straightforward approach to generate evidence is meta-analysing published data based on a careful definition of the research question. Explicit definitions of study questions should include participants, interventions/exposure, comparisons, outcomes and study design. Such type of meta-analyses should not only focus on categorical comparisons, but also on linear and non-linear dose–response associations. Risk of bias of the individual studies of the meta-analysis should be assessed, rated and the overall credibility of the results scored (e.g. using NutriGrade). Tools such as a measurement tool to assess systematic reviews or ROBIS are available to evaluate the methodological quality/risk of bias of meta-analyses. To further evaluate the complete picture of evidence, we propose conducting network meta-analyses (NMA) of intervention trials, mostly on intermediate disease markers. To rank food groups according to their impact, disability-adjusted life years can be used for the various clinical outcomes and the overall results can be compared across the food groups. For future FBDG, we recommend to implement evidence from pairwise and NMA and to quantify the health impact of diet–disease relationships.

  • View HTML
    • Send article to Kindle

      To send this article to your Kindle, first ensure no-reply@cambridge.org is added to your Approved Personal Document E-mail List under your Personal Document Settings on the Manage Your Content and Devices page of your Amazon account. Then enter the ‘name’ part of your Kindle email address below. Find out more about sending to your Kindle. Find out more about sending to your Kindle.

      Note you can select to send to either the @free.kindle.com or @kindle.com variations. ‘@free.kindle.com’ emails are free but can only be sent to your device when it is connected to wi-fi. ‘@kindle.com’ emails can be delivered even when you are not connected to wi-fi, but note that service fees apply.

      Find out more about the Kindle Personal Document Service.

      Generating the evidence for risk reduction: a contribution to the future of food-based dietary guidelines
      Available formats
      ×
      Send article to Dropbox

      To send this article to your Dropbox account, please select one or more formats and confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your <service> account. Find out more about sending content to Dropbox.

      Generating the evidence for risk reduction: a contribution to the future of food-based dietary guidelines
      Available formats
      ×
      Send article to Google Drive

      To send this article to your Google Drive account, please select one or more formats and confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your <service> account. Find out more about sending content to Google Drive.

      Generating the evidence for risk reduction: a contribution to the future of food-based dietary guidelines
      Available formats
      ×
Copyright
This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted re-use, distribution, and reproduction in any medium, provided the original work is properly cited.
Corresponding author
*Corresponding author: Lukas Schwingshackl, email lukas.schwingshackl@dife.de
References
Hide All
1.Chiuve, SE, McCullough, ML, Sacks, FM et al. (2006) Healthy lifestyle factors in the primary prevention of coronary heart disease among men: benefits among users and nonusers of lipid-lowering and antihypertensive medications. Circulation 114, 160167.
2.Ford, ES, Bergmann, MM, Kroger, J et al. (2009) Healthy living is the best revenge: findings from the European prospective investigation into cancer and nutrition-potsdam study. Arch Intern Med 169, 13551362.
3.GBD 2016 Risk Factors Collaborators (2017) Global, regional, and national comparative risk assessment of 84 behavioural, environmental and occupational, and metabolic risks or clusters of risks, 1990–2016: a systematic analysis for the global burden of disease study 2016. Lancet 390, 13451422.
4.European Food Safety Authority (2010) Scientific opinion on establishing food-based dietary guidelines. EFSA J 8, 1460.
5.Food and Agriculture Organization, WHO (World Health Organization), eds. World Declaration and Plan of Action for Nutrition. Rome: FAO/WHO International Conference on Nutrition.
6.Lichtenstein, AH, Yetley, EA & Lau, J. (2009) In Application of Systematic Review Methodology to the Field of Nutrition: Nutritional Research Series, vol. 1. pp. 136. Rockville, MD: AHRQ Technical Reviews.
7.Ioannidis, JP (2016) The mass production of redundant, misleading, and conflicted systematic reviews and meta-analyses. Milbank Q 94, 485514.
8.Page, MJ & Moher, D (2016) Mass production of systematic reviews and meta-analyses: an exercise in mega-silliness? Milbank Q 94, 515519.
9.Cook, DJ, Mulrow, CD & Haynes, RB (1997) Systematic reviews: synthesis of best evidence for clinical decisions. Ann Intern Med 126, 376380.
10.Larsen, PO & von Ins, M (2010) The rate of growth in scientific publication and the decline in coverage provided by science citation index. Scientometrics 84, 575603.
11.Higgins, J & Green, S (2011) Cochrane Handbook for Systematic Reviews of Interventions Version 5·1·0 [updated March 2011]. The Cochrane Collaboration, 2011.
12.Bastian, H, Glasziou, P & Chalmers, I (2010) Seventy-five trials and eleven systematic reviews a day: how will we ever keep up? PLoS Med 7, e1000326.
13.Moher, D, Liberati, A, Tetzlaff, J et al. (2009) Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement. PLoS Med 6, e1000097.
14.Stroup, DF, Berlin, JA & Morton, SC (2000) Meta-analysis of observational studies in epidemiology: a proposal for reporting. Meta-analysis Of Observational Studies in Epidemiology (MOOSE) group. JAMA 283, 20082012.
15.Schwingshackl, L, Chaimani, A, Hoffmann, G et al. (2018) A network meta-analysis on the comparative efficacy of different dietary approaches on glycaemic control in patients with type 2 diabetes mellitus. Eur J Epidemiol 33, 157170.
16.Schwingshackl, L, Chaimani, A, Hoffmann, G et al. (2017) Impact of different dietary approaches on glycemic control and cardiovascular risk factors in patients with type 2 diabetes: a protocol for a systematic review and network meta-analysis. Syst Rev 6, 57.
17.Ioannidis, JP, Patsopoulos, NA & Evangelou, E (2007) Uncertainty in heterogeneity estimates in meta-analyses. BMJ 335, 914916.
18.Egger, M, Davey, SG, Schneider, M et al. (1997) Bias in meta-analysis detected by a simple, graphical test. BMJ 315, 629634.
19.Begg, CB & Mazumdar, M (1994) Operating characteristics of a rank correlation test for publication bias. Biometrics 50, 10881101.
20.Gleser, LJ & Olkin, I (1994) Stochastically dependent effect sizes. In The handbook of research synthesis, pp. 339355 [Cooper, H and Hedges, LV, editors]. New York: Russell Sage Foundation.
21.Greenland, S & Longnecker, MP (1992) Methods for trend estimation from summarized dose-response data, with applications to meta-analysis. Am J Epidemiol 135, 13011309.
22.Hamling, J, Lee, P, Weitkunat, R et al. (2008) Facilitating meta-analyses by deriving relative effect and precision estimates for alternative comparisons from a set of estimates presented by exposure level or disease category. Stat Med 27, 954970.
23.Riley, RD, Higgins, JP & Deeks, JJ (2011) Interpretation of random effects meta-analyses. BMJ 342, d549.
24.Lewis, S & Clarke, M (2001) Forest plots: trying to see the wood and the trees. BMJ 322, 14791480.
25.Bjelakovic, G, Nikolova, D, Gluud, LL et al. (2012) Antioxidant supplements for prevention of mortality in healthy participants and patients with various diseases. Cochrane Database Syst Rev 3, CD007176.
26.Savovic, J, Jones, HE, Altman, DG et al. (2012) Influence of reported study design characteristics on intervention effect estimates from randomized, controlled trials. Ann Intern Med 157, 429438.
27.Higgins, JP, Altman, DG, Gotzsche, PC et al. (2011) The Cochrane collaboration's tool for assessing risk of bias in randomised trials. BMJ 343, d5928.
28.Schwingshackl, L, Knuppel, S, Schwedhelm, C et al. (2016) Perspective: Nutrigrade: a scoring system to assess and judge the meta-evidence of randomized controlled trials and cohort studies in nutrition research. Adv Nutr 7, 9941004.
29.Jadad, AR, Moore, RA, Carroll, D et al. (1996) Assessing the quality of reports of randomized clinical trials: is blinding necessary? Control Clin Trials 17, 112.
30.Khalesi, S, Irwin, C & Schubert, M (2015) Flaxseed consumption may reduce blood pressure: a systematic review and meta-analysis of controlled trials. J Nutr 145, 758765.
31.Schwingshackl, L, Dias, S & Hoffmann, G (2014) Impact of long-term lifestyle programmes on weight loss and cardiovascular risk factors in overweight/obese participants: a systematic review and network meta-analysis. Syst Rev 3, 130.
32.Schwingshackl, L, Dias, S, Strasser, B et al. (2013) Impact of different training modalities on anthropometric and metabolic characteristics in overweight/obese subjects: a systematic review and network meta-analysis. PLoS ONE 8, e82853.
33.Schwingshackl, L, Missbach, B, Dias, S et al. (2014) Impact of different training modalities on glycaemic control and blood lipids in patients with type 2 diabetes: a systematic review and network meta-analysis. Diabetologia 57, 17891797.
34.Salanti, G, Ades, AE & Ioannidis, JP (2011) Graphical methods and numerical summaries for presenting results from multiple-treatment meta-analysis: an overview and tutorial. J Clin Epidemiol 64, 163171.
35.Salanti, G (2012) Indirect and mixed-treatment comparison, network, or multiple-treatments meta-analysis: many names, many benefits, many concerns for the next generation evidence synthesis tool. Res Synth Methods 3, 8097.
36.Leucht, S, Chaimani, A, Cipriani, AS et al. (2016) Network meta-analyses should be the highest level of evidence in treatment guidelines. Eur Arch Psychiatry and Clin Neurosci 266, 477480.
37.Mavridis, D, Giannatsi, M, Cipriani, A et al. (2015) A primer on network meta-analysis with emphasis on mental health. Evid Based Ment Health 18, 4046.
38.Bucher, HC, Guyatt, GH, Griffith, LE et al. (1997) The results of direct and indirect treatment comparisons in meta-analysis of randomized controlled trials. J Clin Epidemiol 50, 683691.
39.Dias, S, Welton, NJ, Caldwell, DM et al. (2010) Checking consistency in mixed treatment comparison meta-analysis. Stat Med 29, 932944.
40.DerSimonian, R & Laird, N (1986) Meta-analysis in clinical trials. Control Clin Trials 7, 177188.
41.Orsini, N, Li, R, Wolk, A et al. (2012) Meta-analysis for linear and nonlinear dose-response relations: examples, an evaluation of approximations, and software. Am J Epidemiol 175, 6673.
42.Chene, G & Thompson, SG (1996) Methods for summarizing the risk associations of quantitative variables in epidemiologic studies in a consistent form. Am J Epidemiol 144, 610621.
43.Aune, D, Greenwood, DC, Chan, DS et al. (2012) Body mass index, abdominal fatness and pancreatic cancer risk: a systematic review and non-linear dose-response meta-analysis of prospective studies. Ann Oncol 23, 843852.
44.Durrleman, S & Simon, R (1989) Flexible regression models with cubic splines. Stat Med 8, 551561.
45.Bagnardi, V, Zambon, A, Quatto, P et al. (2004) Flexible meta-regression functions for modeling aggregate dose-response data, with an application to alcohol and mortality. Am J Epidemiol 159, 10771086.
46.Wells, GASB, O'Connell, D, Peterson, J et al. The Newcastle-Ottawa Scale (NOS) for assessing the quality of nonrandomised studies in meta-analyses. http://www.ohri.ca/programs/clinical_epidemiology/nosgen.pdf (accessed November 2015).
47.Freedman, LS, Schatzkin, A, Midthune, D et al. (2011) Dealing with dietary measurement error in nutritional cohort studies. J Natl Cancer Inst 103, 10861092.
48.National cancer Institute (2017) Dietary Assessment Primer, Section Name. National Institutes of Health, National Cancer Institute. https://dietassessmentprimer.cancer.gov/ (accessed December 2017).
49.Salanti, G, Del Giovane, C, Chaimani, A et al. (2014) Evaluating the quality of evidence from a network meta-analysis. PLoS ONE 9, e99682.
50.Shea, BJ, Grimshaw, JM, Wells, GA et al. (2007) Development of AMSTAR: a measurement tool to assess the methodological quality of systematic reviews. BMC Med Res Methodol 7, 10.
51.Schwingshackl, L, Hoffmann, G, Missbach, B et al. (2017) An umbrella review of nuts intake and risk of cardiovascular disease. Curr Pharm Des 23, 10161027.
52.Huedo-Medina, TB, Garcia, M, Bihuniak, JD et al. (2016) Methodologic quality of meta-analyses and systematic reviews on the Mediterranean diet and cardiovascular disease outcomes: a review. Am J Clin Nutr 103, 841850.
53.Dinu, M, Pagliai, G, Casini, A et al. (2017) Mediterranean diet and multiple health outcomes: an umbrella review of meta-analyses of observational studies and randomised trials. Eur J Clin Nutr 72, 3043.
54.Shea, BJ, Reeves, BC, Wells, G et al. (2017) AMSTAR 2: a critical appraisal tool for systematic reviews that include randomised or non-randomised studies of healthcare interventions, or both. BMJ 358, j4008.
55.Whiting, P, Savovic, J, Higgins, JP et al. (2016) ROBIS: a new tool to assess risk of bias in systematic reviews was developed. J Clin Epidemiol 69, 225234.
56.Devleesschauwer, B, Maertens de Noordhout, C, Smit, GS et al. (2014) Quantifying burden of disease to support public health policy in Belgium: opportunities and constraints. BMC Public Health 14, 1196.
57.Devleesschauwer, B, Havelaar, AH, Maertens de Noordhout, C et al. (2014) Calculating disability-adjusted life years to quantify burden of disease. Int J Public Health 59, 565569.
58.Murray, CJ (1994) Quantifying the burden of disease: the technical basis for disability-adjusted life years. Bull World Health Organ 72, 429445.
59.Murray, CJ, Ezzati, M, Flaxman, AD et al. (2012) GBD 2010: design, definitions, and metrics. Lancet 380(9859), 20632066.
60.Devleesschauwer, B, Haagsma, JA, Angulo, FJ et al. (2015) Methodological framework for world health organization estimates of the global burden of foodborne disease. PLoS ONE 10, e0142498.
61.Institute of Medicine Food F. (2007) The National Academies Collection: Reports funded by National Institutes of Health. Nutritional Risk Assessment: Perspectives, Methods, and Data Challenges, Workshop Summary. Washington, DC: National Academies Press (US) National Academy of Sciences.
62.Micha, R, Kalantarian, S, Wirojratana, P et al. (2012) Estimating the global and regional burden of suboptimal nutrition on chronic disease: methods and inputs to the analysis. Eur J Clin Nutr 66, 119129.
63.GBD 2016 DALYs and HALE Collaborators (2017) Global, regional, and national disability-adjusted life-years (DALYs) for 333 diseases and injuries and healthy life expectancy (HALE) for 195 countries and territories, 1990–2016: a systematic analysis for the Global Burden of Disease Study 2016. Lancet 390, 12601344.
64.Viechtbauer, W (2010) Conducting meta-analyses in R with the metafor package. J Stat Softw 36, 148.
65.Thompson, CG & Becker, BJ (2014) The impact of multiple endpoint dependency on Q and I(2) in meta-analysis. Res Synth Methods 5, 235253.
66.Riley, RD (2009) Multivariate meta-analysis: the effect of ignoring within-study correlation. J R Stat Soc: Series A (Stat Soc) 172, 789811.
67.Becker, BJ. (2000) Multivariate meta-analysis. In Handbook of Applied Multivariate Statistics and Mathematical Modeling, pp. 499525 [Tinsley, HEA and Brown, ED, editors]. Orlando: Academic Press.
68.Raudenbush, SW, Becker, BJ & Kalaian, H (1988) Modeling multivariate effect sizes. Psychol Bull 103, 111.
69.Rosenthal, R & Rubin, DB (1986) Meta-analytic procedures for combining studies with multiple effect sizes. Psychol Bull. 99, 400406.
70.Borenstein, M, Hedges, LV, Higgins, J et al. (2009) Multiple outcomes or time-points within a study. Intro Meta-Anal 225238.
71.Konstantopoulos, S (2011) Fixed effects and variance components estimation in three-level meta-analysis. Res Synth Methods 2, 6176.
72.Cheung, MW-L (2014) Modeling dependent effect sizes with three-level meta-analyses: a structural equation modeling approach. Psychol Methods 19, 211229.
73.Hedges, LV, Tipton, E & Johnson, MC (2010) Robust variance estimation in meta-regression with dependent effect size estimates. Res Synth Methods 1, 3965.
74.Jackson, D, White, IR & Riley, RD (2013) A matrix-based method of moments for fitting the multivariate random effects model for meta-analysis and meta-regression. Bio J 55, 231245.
75.Chen, Y, Cai, Y, Hong, C et al. (2016) Inference for correlated effect sizes using multiple univariate meta-analyses. Stat Med 35, 14051422.
76.Gasparrini, A, Armstrong, B & Kenward, M (2012) Multivariate meta-analysis for non-linear and other multi-parameter associations. Stat Med 31, 38213839.
77.Cheung, MW-L (2015) MetaSEM: an R package for meta-analysis using structural equation modeling. Front Psychol 5, 1521.
78.Estruch, R, Ros, E, Salas-Salvado, J et al. (2013) Primary prevention of cardiovascular disease with a Mediterranean diet. N Engl J Med 368, 12791290.
79.Howard, BV, Manson, JE, Stefanick, ML et al. (2006) Low-fat dietary pattern and weight change over 7 years: the Women's Health Initiative Dietary Modification Trial. JAMA 295, 3949.
80.Hollaender, PL, Ross, AB & Kristensen, M (2015) Whole-grain and blood lipid changes in apparently healthy adults: a systematic review and meta-analysis of randomized controlled studies. Am J Clin Nutr 102, 556572.
81.Ye, EQ, Chacko, SA, Chou, EL et al. (2012) Greater whole-grain intake is associated with lower risk of type 2 diabetes, cardiovascular disease, and weight gain. J Nutr 142, 13041313.
82.Pol, K, Christensen, R, Bartels, EM et al. (2013) Whole grain and body weight changes in apparently healthy adults: a systematic review and meta-analysis of randomized controlled studies. Am J Clin Nutr 98, 872884.
83.Hartley, L, Igbinedion, E, Holmes, J et al. (2013) Increased consumption of fruit and vegetables for the primary prevention of cardiovascular diseases. Cochrane Database Syst Rev Cd009874.
84.Shin, JY, Kim, JY, Kang, HT et al. (2015) Effect of fruits and vegetables on metabolic syndrome: a systematic review and meta-analysis of randomized controlled trials. Int J Food Sci Nutr 66, 416425.
85.Kaiser, KA, Brown, AW, Bohan Brown, MM et al. (2014) Increased fruit and vegetable intake has no discernible effect on weight loss: a systematic review and meta-analysis. Am J Clin Nutr 100, 567576.
86.Musa-Veloso, K, Paulionis, L, Poon, T et al. (2016) The effects of almond consumption on fasting blood lipid levels: a systematic review and meta-analysis of randomised controlled trials. J Nutr Sci 5, e34.
87.Mohammadifard, N, Salehi-Abargouei, A, Salas-Salvado, J et al. (2015) The effect of tree nut, peanut, and soy nut consumption on blood pressure: a systematic review and meta-analysis of randomized controlled clinical trials. Am J Clin Nutr 101, 966982.
88.Viguiliouk, E, Kendall, CW, Blanco Mejia, S et al. (2014) Effect of tree nuts on glycemic control in diabetes: a systematic review and meta-analysis of randomized controlled dietary trials. PLoS ONE 9, e103376.
89.Flores-Mateo, G, Rojas-Rueda, D, Basora, J et al. (2013) Nut intake and adiposity: meta-analysis of clinical trials. Am J Clin Nutr 97, 13461355.
90.Neale, EP, Tapsell, LC, Guan, V et al. (2017) The effect of nut consumption on markers of inflammation and endothelial function: a systematic review and meta-analysis of randomised controlled trials. BMJ Open 7, e016863.
91.Bazzano, LA, Thompson, AM, Tees, MT et al. (2011) Non-soy legume consumption lowers cholesterol levels: a meta-analysis of randomized controlled trials. Nutr Metab Cardiovasc Dis 21, 94103.
92.Viguiliouk, E, Blanco Mejia, S, Kendall, CW et al. (2017) Can pulses play a role in improving cardiometabolic health? Evidence from systematic reviews and meta-analyses. Ann NY Acad Sci 1392, 4357.
93.Salehi-Abargouei, A, Saraf-Bank, S, Bellissimo, N et al. (2015) Effects of non-soy legume consumption on C-reactive protein: a systematic review and meta-analysis. Nutrition 31, 631639.
94.Malik, VS, Pan, A, Willett, WC et al. (2013) Sugar-sweetened beverages and weight gain in children and adults: a systematic review and meta-analysis. Am J Clin Nutr 98, 10841102.
95.Te Morenga, L, Mallard, S & Mann, J (2012) Dietary sugars and body weight: systematic review and meta-analyses of randomised controlled trials and cohort studies. BMJ 346, e7492.
96.Rouhani, MH, Rashidi-Pourfard, N, Salehi-Abargouei, A et al. (2018) Effects of egg consumption on blood lipids: a systematic review and meta-analysis of randomized clinical trials. J Am Coll Nutr 37, 99110.
97.Ding, M, Huang, T, Bergholdt, HK et al. (2017) Dairy consumption, systolic blood pressure, and risk of hypertension: Mendelian randomization study. BMJ 356, j1000.
98.Benatar, JR, Sidhu, K & Stewart, RA (2013) Effects of high and low fat dairy food on cardio-metabolic risk factors: a meta-analysis of randomized studies. PLoS ONE 8, e76480.
99.Chen, M, Pan, A, Malik, VS et al. (2012) Effects of dairy intake on body weight and fat: a meta-analysis of randomized controlled trials. Am J Clin Nutr 96, 735747.
100.Alhassan, A, Young, J, Lean, MEJ et al. (2017) Consumption of fish and vascular risk factors: a systematic review and meta-analysis of intervention studies. Atherosclerosis 266, 8794.
101.O'Connor, LE, Kim, JE & Campbell, WW (2017) Total red meat intake of ≥0·5 servings/d does not negatively influence cardiovascular disease risk factors: a systemically searched meta-analysis of randomized controlled trials. Am J Clin Nutr 105, 5769.
102.Schwingshackl, L, Chaimani, A, Bechthold, A et al. (2016) Food groups and risk of chronic disease: a protocol for a systematic review and network meta-analysis of cohort studies. Syst Rev 5, 125.
103.Schwingshackl, L, Schwedhelm, C, Hoffmann, G et al. (2017) Food groups and risk of all-cause mortality: a systematic review and meta-analysis of prospective studies. Am J Clin Nutr 105, 14621473.
104.Schwingshackl, L, Hoffmann, G, Lampousi, AM et al. (2017) Food groups and risk of type 2 diabetes mellitus: a systematic review and meta-analysis of prospective studies. Eur J Epidemiol 32, 363375.
105.Bechthold, A, Boeing, H, Schwedhelm, C et al. (2017) Food groups and risk of coronary heart disease, stroke and heart failure: a systematic review and dose-response meta-analysis of prospective studies. Crit Rev Food Sci Nutr [Epublication ahead of print version].
106.Schwingshackl, L, Schwedhelm, C, Hoffmann, G et al. (2017) Food groups and risk of hypertension: a systematic review and dose-response meta-analysis of prospective studies. Adv Nutr 8, 793803.
107.Schwingshackl, L, Schwedhelm, C, Hoffmann, G et al. (2017) Food groups and risk of colorectal cancer. Int J Cancer 142, 17481758.
108.Schwingshackl, L, Hoffmann, G, Kalle-Uhlmann, T et al. (2015) Fruit and vegetable consumption and changes in anthropometric variables in adult populations: a systematic review and meta-analysis of prospective cohort studies. PLoS ONE 10, e0140846.
109.Schwingshackl, L, Hoffmann, G, Schwedhelm, C et al. (2016) Consumption of dairy products in relation to changes in anthropometric variables in adult populations: a systematic review and meta-analysis of cohort studies. PLoS ONE 11, e0157461.
110.Rouhani, MH, Salehi-Abargouei, A, Surkan, PJ et al. (2014) Is there a relationship between red or processed meat intake and obesity? A systematic review and meta-analysis of observational studies. Obes Rev 15, 740748.
Recommend this journal

Email your librarian or administrator to recommend adding this journal to your organisation's collection.

Proceedings of the Nutrition Society
  • ISSN: 0029-6651
  • EISSN: 1475-2719
  • URL: /core/journals/proceedings-of-the-nutrition-society
Please enter your name
Please enter a valid email address
Who would you like to send this to? *
×

Keywords

Metrics

Altmetric attention score

Full text views

Total number of HTML views: 42
Total number of PDF views: 227 *
Loading metrics...

Abstract views

Total abstract views: 384 *
Loading metrics...

* Views captured on Cambridge Core between 30th April 2018 - 18th September 2018. This data will be updated every 24 hours.