Glycemic status, adiposity indices and cardiovascular risk in chronic kidney disease: Core findings from a nationwide cohort study

Table 2 Summary of limitations and optimization suggestions

Limitations	Core issues	Optimization suggestions
Obesity assessment index is too single	Unable to distinguish between body fat rate and muscle mass, ignoring the impact of muscle loss in patients with CKD	DXA or BIA could be used to evaluate body composition, including body fat rate and muscle mass index. Indicators such as BRI and VAI can enhance the accuracy of CVD assessment
Not stratified by eGFR	There are differences in metabolic status in different stages of CKD, and their risk models may also vary accordingly	When analyzing the risk association between blood glucose and obesity, CKD should be classified by stages according to eGFR
Lack of long-term glycemic control assessment	The regulatory effect of long-term blood glucose control on obesity-related CVD risk is not clear	TIR derived from CGM and HbA1c index (for long-term monitoring) could be included to analyze the correlation between the quality of blood glucose control and risk
Not studied targeted intervention	Clinical intervention programs corresponding to different risk stratification have not been studied	Intervention research should be carried out to verify the effectiveness of personalized nutrition, exercise, and drug intervention
Inadequate ethnic representation	The study was based on the data of the Korean population only, so universality was limited	A multi-ethnic, multi-center cohort study should be conducted to verify the cross-ethnic applicability of the risk model

CKD: Chronic kidney disease; DXA: Dual-energy X-ray absorptiometry; BIA: Bioelectrical impedance analysis; BRI: Body roundness index; VAI: Visceral adiposity index; CVD: Cardiovascular disease; TIR: Time-in-range; CGM: Continuous glucose monitoring; HbA1c: Hemoglobin A1c.