Controlling confounding in nutrition research: Methodological strategies, software implementation, and reporting guidelines

Mujie Shen; Jiawei Zhou; Dongfang You; Fang Shao; Guoshuang Feng; Yang Zhao

doi:10.59717/j.xinn-nutri.2026.100010

The Innovation Nutrition > 2026 Vol. 1 > No. 1 > 100010

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Controlling confounding in nutrition research: Methodological strategies, software implementation, and reporting guidelines

1.
Department of Biostatistics, School of Public Health, Nanjing Medical University, Nanjing 211166, China
2.
Department of Health Statistics, College of Public Health, Chongqing Medical University, Chongqing 401331, China
3.
Big Data Center, Beijing Children’s Hospital, Capital Medical University, National Center for Children's Health, Beijing 100045, China
4.
Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, CIC for Cancer Personalized Medicine, Nanjing Medical University, Nanjing 211166, China
5.
National Vaccine Innovation Platform, School of Public Health, Nanjing Medical University, Nanjing 211166, China
6.
These authors contributed equally

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Corresponding authors: yzhao@njmu.edu.cn (Y.Z.); glxfgsh@163.com (G.F.); shaofang@njmu.edu.cn (F.S.)
Received Date: March 04, 2026

Accepted Date: April 09, 2026

Published Online: April 16, 2026

The Innovation Nutrition 1(1), https://doi.org/10.59717/j.xinn-nutri.2026.100010 | Cite this article

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PUBLIC SUMMARY

PUBLIC SUMMARY
1. Confounding is an important challenge for causal inference in nutrition research.
  
  Confounder management involves pre-study identification, design-stage control, and analysis-stage control.
  
  This review summarizes methods to identify and address confounders across these stages.
  
  Clear reporting guidelines are essential for enabling the correct interpretation of research findings.

ABSTRACT

ABSTRACT

In nutrition research, confounding is an important challenge. Owing to the complexities involved in dietary interventions, such research is often subject to three types of confounders: measured confounders, unmeasured confounders, and time-varying confounders. Inadequate control of these confounders can lead to distorted research conclusions and even undermine the credibility of dietary guidelines. This review systematically summarizes strategies for confounder control in nutrition research, including confounder identification at the pre-study stage, confounder control at the design and analysis stages, and relevant R packages for practical implementation. In addition, the review collates and compares the specific requirements for confounding control in general reporting guidelines and their nutrition-specific extensions, thereby providing reporting guidance for the identification and control of confounders in nutrition research.
- Nutrition Research /
- Confounder Control /
- Software Implementation /
- Reporting Guideline

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Shen M., Zhou J., You D., et al. (2026). Controlling confounding in nutrition research: Methodological strategies, software implementation, and reporting guidelines. The Innovation Nutrition 1:100010. https://doi.org/10.59717/j.xinn-nutri.2026.100010

Shen M., Zhou J., You D., et al. (2026). Controlling confounding in nutrition research: Methodological strategies, software implementation, and reporting guidelines. The Innovation Nutrition 1:100010. https://doi.org/10.59717/j.xinn-nutri.2026.100010

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