Artikel
Development of an innovative methodology to utilise prospective observational studies in network meta-analysis
Suche in Medline nach
Autoren
-
Sabina Wallerer
- Institute for Evidence in Medicine, Faculty of Medicine and Medical Center, University of Freiburg, Deutschland
-
Adriani Nikolakopoulou
- Institute of Medical Biometry and Statistics, Faculty of Medicine and Medical Center, University of Freiburg, Deutschland; Laboratory of Hygiene, Social and Preventive Medicine and Medical Statistics, School of Medicine, Aristotle University of Thessaloniki, Griechenland
-
Theodoros Papakonstantinou
- Institute of Medical Biometry and Statistics, Faculty of Medicine and Medical Center, University of Freiburg, Deutschland; Laboratory of Hygiene, Social and Preventive Medicine and Medical Statistics, School of Medicine, Aristotle University of Thessaloniki, Griechenland
-
Theodoros Evrenoglou
- Institute of Medical Biometry and Statistics, Faculty of Medicine and Medical Center, University of Freiburg, Deutschland
-
Eva Kiesswetter
- Institute for Evidence in Medicine, Faculty of Medicine and Medical Center, University of Freiburg, Deutschland
-
Sabrina Schlesinger
- Institute for Biometrics and Epidemiology, German Diabetes Center, Leibniz Center for Diabetes Research at the Heinrich Heine University Düsseldorf, Deutschland; German Center for Diabetes Research (DZD), Partner Düsseldorf, Deutschland
-
Jörg J. Meerpohl
- Institute for Evidence in Medicine, Faculty of Medicine and Medical Center, University of Freiburg, Deutschland; Cochrane Germany, Cochrane Germany Foundation, Deutschland
-
Lukas Schwingshackl
- Institute for Evidence in Medicine, Faculty of Medicine and Medical Center, University of Freiburg, Deutschland
| Veröffentlicht: | 27. März 2025 |
|---|
Gliederung
Text
Background/research question: Network meta-analysis (NMA) is an emerging technique for evidence synthesis. It allows for the simultaneous comparison of multiple interventions, often from randomized controlled trials. Respective methodological approaches for prospective observational studies are scarce, despite serving as important source of evidence to examine exposure-disease associations. In nutrition research, for instance, well-designed prospective observational studies are the main evidence source to assess the health impact of diet over an extended period of time.
Our aim is to develop a new framework for integrating information on exposure-disease associations in NMA. This is demonstrated by means of substitution analyses, evaluating diverse dietary shifts in an isocaloric setting [1].
Methods: As a first step, several NMAs on macronutrient substitution (e.g. ↑ fat vs. ↓ carbohydrates and ↑ fat vs. ↓ protein) and all-cause mortality or chronic disease risk (e.g. cancer, type-2 diabetes) assuming linear associations are established. We will analyse seven networks with different dietary macronutrients as network-nodes per each outcome. In a second step, we will develop a concept to model non-linear relationships of macronutrient substitutions and the risk of all-cause mortality or chronic diseases. For this, two NMA extensions – dose-effect and component NMA – will be considered. Finally, previously analysed outcomes will be synthesized using multivariate NMA [2].
Results: We recently confirmed the feasibility of using NMA to analyse the linear association between macronutrient substitution and chronic disease risk. First results indicate a favourable association of replacing carbohydrates, saturated fatty acids, and trans-fatty acids with polyunsaturated fatty acids and plant-monounsaturated fatty acids, and replacing animal protein with plant protein on the risk of all-cause mortality [3]. Further results regarding other outcomes and non-linear relationships are pending.
Conclusion: We were able to show that associations between multiple exposures and chronic disease risk, particularly in nutrition research encompassing substitution analysis of prospective observational studies, can be modelled within the framework of an NMA. Our results can inform upcoming dietary guidelines and provide the extent to analyse other relevant exposures within an NMA in the future.
Competing interests: The authors have no relevant financial or non-financial interest to disclose.
References
- 1.
- Song M, Giovannucci E. Substitution analysis in nutritional epidemiology: proceed with caution. Eur J Epidemiol. 2018;33(2):137-40.
- 2.
- Riley RD, Jackson D, Salanti G, Burke DL, Price M, Kirkham J, White IR. Multivariate and network meta-analysis of multiple outcomes and multiple treatments: rationale, concepts, and examples. BMJ. 2017;358:j3932.
- 3.
- Wallerer S, Papakonstantinou T, Morze J, Stadelmaier J, Kiesswetter E, Gorenflo L, Barbaresko J, Szczerba E, Neuenschwander M, Bell W, Kühn T, Lohner S, Guasch-Ferré M, Hoffmann G, Meerpohl JJ, Schlesinger S, Nikolakopoulou A, Schwingshackl L. Association between substituting macronutrients and all-cause mortality: a network meta-analysis of prospective observational studies. EClinicalMedicine. 2024 Sep 5;75:102807. DOI: 10.1016/j.eclinm.2024.102807
