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• Professor
  C Mary Schooling
  Published on: 1 April 2025

• Published on: 1 April 2025

  Professor

  • C Mary Schooling, Professor CUNY School of Public Health and Health Policy

  To the editor: Zhang et al. provide a fascinating observational study showing that weight loss among obese UK Biobank participants with cardiovascular disease may not be beneficial [1]. The authors specifically highlight the following point “Maintaining a stable weight in obese people with cardiovascular disease may reduce the risk of mortality” [1]. Evidence from a large trial has shown that the weight loss drug semaglutide reduces mortality amongst the overweight/obese with pre-existing cardiovascular disease [2]. It is possible that some of the benefits of semaglutide could be driven by factors other than weight loss, given some benefits are evident before substantial weight has occurred [2]. However, some of the benefits of semaglutide are undoubtedly due to weight loss making Zhang et al.’s observations puzzling.

  Zhang at al.’s study is an observational study of weight change in obese people with cardiovascular disease [1], so it is essentially a case series. Inference from case series is seen as the least reliable type of observational evidence [3]. Correspondingly, Zhang et al.’s study of the role of weight change in obese people with cardiovascular disease [1] is open to both the major sources of bias in causal inference, i.e., to confounding and selection bias [4].

  Specifically, Zhang et al.’s study is open to confounding by obesity because obesity is likely a common cause of weight change and death. The study is also open to selection bias because...

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  To the editor: Zhang et al. provide a fascinating observational study showing that weight loss among obese UK Biobank participants with cardiovascular disease may not be beneficial [1]. The authors specifically highlight the following point “Maintaining a stable weight in obese people with cardiovascular disease may reduce the risk of mortality” [1]. Evidence from a large trial has shown that the weight loss drug semaglutide reduces mortality amongst the overweight/obese with pre-existing cardiovascular disease [2]. It is possible that some of the benefits of semaglutide could be driven by factors other than weight loss, given some benefits are evident before substantial weight has occurred [2]. However, some of the benefits of semaglutide are undoubtedly due to weight loss making Zhang et al.’s observations puzzling.

  Zhang at al.’s study is an observational study of weight change in obese people with cardiovascular disease [1], so it is essentially a case series. Inference from case series is seen as the least reliable type of observational evidence [3]. Correspondingly, Zhang et al.’s study of the role of weight change in obese people with cardiovascular disease [1] is open to both the major sources of bias in causal inference, i.e., to confounding and selection bias [4].

  Specifically, Zhang et al.’s study is open to confounding by obesity because obesity is likely a common cause of weight change and death. The study is also open to selection bias because the mean age at recruitment was 56.6 years [1], so the study only includes those who survived obesity and cardiovascular disease to recruitment. Typically, such selection on survival to recruitment biases towards the null or even gives estimates in the opposite direction to the true association. Given these possible biases and the lack of consistency with trial evidence [2], Zhang et al.’s study should not be interpreted as providing evidence that maintaining a stable weight in obese people with cardiovascular disease may reduce the risk of mortality.

  1. Zhang J, Schutte R, Pierscionek B. Association of weight change with cardiovascular events and all-cause mortality in obese participants with cardiovascular disease: a prospective cohort study. . Heart. 2025;10.1136/heartjnl-2024-324383.
  2. Lincoff AM, Brown-Frandsen K, Colhoun HM, et al. Semaglutide and Cardiovascular Outcomes in Obesity without Diabetes. N Engl J Med. 2023 Dec 14;389(24):2221-2232.
  3. Greenhalgh T. How to read a paper. Getting your bearings (deciding what the paper is about). BMJ. 1997;315(7102):243-6.
  4. Bareinboim E, Pearl J. Causal inference and the data-fusion problem. Proc Natl Acad Sci U S A. 2016 Jul 5;113(27):7345-52.

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  Conflict of Interest:
  None declared.

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