Journal of Cachexia Sarcopenia and Muscle最新文献

I read with interest the paper entitled ‘Weight-adjusted Waist as an Integrated Index for Fat, Muscle and Bone Health in Adults’ by Kim et al.,¹ which utilizes the KNHANES (Korean National Health and Nutrition Examination Survey) to innovatively investigate the association between a novel index for assessing obesity, the weight-adjusted waist circumference index (WWI), and unhealthy body composition in the Korean population. WWI was found to be negatively correlated with bone and muscle mass but positively correlated with fat mass, and significantly higher outcomes of unhealthy body composition (high-fat mass, low muscle mass and low bone mass) were found in higher quartiles of WWI than in lower quartiles (18.08 [95% confidence interval, CI, 4.32–75.61] for men and 6.36 [95% CI, 3.65–11.07] for women). Tissue dysfunction in muscle, bone and fat is closely related to human health and may be a risk factor for disease and death. Compared with traditional anthropometric measures for assessing obesity, WWI can better differentiate between fat and muscle mass and may help us better identify people at risk for unhealthy body composition. All in all, I think this is a very interesting study. But I also have some questions about the study.

Firstly, in the selection of covariates, the authors adjusted for important covariates such as age, smoking status and hypertension status in this study, which is excellent, but I noticed that the authors also adjusted for dyslipidaemia (including total cholesterol, triglycerides, LDL cholesterol and HDL cholesterol) during the course of the study, so may I ask the authors if they took into account the multiple covariances?

Secondly, can the authors complement the limitations of the remaining obesity indices in their study and thus explore whether WWI has a stronger association with unhealthy body composition than when using body mass index (BMI) or waist circumference (WC)? This is because the authors also mentioned in their study that WWI has unique advantages over the traditional assessment of obesity indices. And also, the authors plotted the receiver operating characteristic curve to analyse the predictive ability of WWI, so is it possible to consider comparing WWI with other obesity indices (e.g., WC, BMI, waist-to-height ratio, a body shape index [ABSI], etc.) to observe whether it is a better predictor of unhealthy body composition?

In conclusion, my suggestion is to make an already excellent study even better, and I also hope that the authors can focus on my questions so that readers will get more accurate conclusions from the study.

The author declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

The author declares that no financial support was received for the research, authorship and/or publication of this article.

Regulatory agencies require evidence that endpoints correlate with clinical benefit before they can be used to approve drugs. Biomarkers are often considered surrogate endpoints. In cancer cachexia trials, the measurement of biomarkers features frequently. The aim of this systematic review was to assess the frequency and diversity of biomarker endpoints in cancer cachexia trials. A comprehensive electronic literature search of MEDLINE, Embase and Cochrane (1990–2023) was completed. Eligible trials met the following criteria: adults (≥18 years), prospective design, more than 40 participants, use of a cachexia intervention for more than 14 days and use of a biomarker(s) as an endpoint. Biomarkers were defined as any objective measure that was assayed from a body fluid, including scoring systems based on these assays. Routine haematology and biochemistry to monitor intervention toxicity were not considered. Data extraction was performed using Covidence, and reporting followed PRISMA guidance (PROSPERO: CRD42022276710). A total of 5975 studies were assessed, of which 52 trials (total participants = 6522) included biomarkers as endpoints. Most studies (n = 29, 55.7%) included a variety of cancer types. Pharmacological interventions (n = 27, 51.9%) were most evaluated, followed by nutritional interventions (n = 20, 38.4%). Ninety-nine different biomarkers were used across the trials, and of these, 96 were assayed from blood. Albumin (n = 29, 55.8%) was assessed most often, followed by C-reactive protein (n = 22, 42.3%), interleukin-6 (n = 16, 30.8%) and tumour necrosis factor-α (n = 14, 26.9%), the latter being the only biomarker that was used to guide sample size calculations. Biomarkers were explicitly listed as a primary outcome in six trials. In total, 12 biomarkers (12.1% of 99) were used in six trials or more. Insulin-like growth factor binding protein 3 (IGFBP-3) and insulin-like growth factor 1 (IGF-1) levels both increased significantly in all three trials in which they were both used. This corresponded with a primary outcome, lean body mass, and was related to the pharmacological mechanism. Biomarkers were predominately used as exploratory rather than primary endpoints. The most commonly used biomarker, albumin, was limited by its lack of responsiveness to nutritional intervention. For a biomarker to be responsive to change, it must be related to the mechanism of action of the intervention and/or the underlying cachexia process that is modified by the intervention, as seen with IGFBP-3, IGF-1 and anamorelin. To reach regulatory approval as an endpoint, the relationship between the biomarker and clinical benefit must be clarified.