Nutritional risk status in CKD patients
In this study, 1277 CKD patients were investigated using two objective nutritional assessment methods, PNI and CONUT. It was found that the risk of malnutrition among these patients ranged from 87.8 to 89%. This is significantly higher than the malnutrition risk rates reported by Li Xueqin [12] and Müller M [13], who used the NRS2002 screening tool, with rates ranging from 19.5 to 35.6%. The disparity may be due to the fact that PNI and CONUT evaluation methods reduce the impact of subjective indicators, focusing instead on different aspects of nutritional status. Specifically, PNI emphasizes protein and inflammation indicators, while CONUT integrates indicators related to protein, immunity, and energy consumption. In this study, 381 patients (29.8% of the total) were identified as having malnutrition risk based on both evaluation criteria. The moderate agreement between the two methods (kappa = 0.368) suggests that while they measure overlapping aspects of nutritional status, they also capture distinct dimensions. Moreover, these two tools hold certain predictive value for disease prognosis and exhibit high credibility and operational feasibility [14, 15]. Further studies should explore how these tools can be used complementarily to improve nutritional assessment in CKD patients.
CKD patients with nutritional risk had longer hospital stay
Our findings demonstrated that CKD patients with higher risk of malnutrition, as assessed by both PNI and CONUT, had significantly longer hospital stays. This is consistent with previous studies, which suggest that malnutrition exacerbates underlying conditions like anemia, hypoalbuminemia, and inflammation, leading to prolonged recovery times and increased healthcare costs [16,17,18]. These results underscore the importance of early nutritional assessment and intervention to mitigate the adverse effects of malnutrition on both short-term and long-term outcomes.
Analysis of factors influencing nutritional risk in CKD patients
In this study, multivariate logistic regression identified several independent predictors of malnutrition in CKD patients, including CKD stage, hemoglobin level, BUN, BMI, and age. These variables were selected based on univariate analysis and clinical relevance, and were retained in the final stepwise model due to their significant association with nutritional risk. The findings emphasize that both declining kidney function and systemic metabolic factors contribute to poor nutritional status.
CKD staging
The results of this study indicated that CKD staging was a significant risk factor for malnutrition, especially in patients at stage 4 or 5 based on the PNI assessment. As CKD progresses, the accumulation of toxins and insufficient dialysis lead to inadequate intake, increased consumption, and decreased body synthesis function, all contributing to a higher probability of malnutrition [19]. Studies have shown that early nutritional risk assessment in CKD patients can provide important evidence for nutritional interventions [20]. In our regression model, CKD stage ≥ 4 was independently associated with increased malnutrition risk, highlighting the clinical importance of nutritional screening in advanced CKD.
Hemoglobin level
Hemoglobin level was identified an independent risk factor for malnutrition in CKD patients under the CONUT assessment. Lower hemoglobin level are associated with more severe the renal anemia and a higher risk of malnutrition. It is estimated that over 50% of CKD patients in China suffer from renal anemia [21]. The development of renal anemia is closely related to various factors, including decreased endogenous erythropoietin (EPO), iron deficiency, metabolic abnormalities, micro-inflammation, oxidative stress, and toxin accumulation [22]. Renal anemia is harmful, and long-term anemia can cause patients to feel fatigue, mental weakness, vitamin deficiency, and inflammatory reactions. In severe cases, it can also lead to malnutrition and growth retardation [23]. In our logistic regression model, patients with Hb ≤ 110 g/L had significantly higher odds of malnutrition, confirming its role as a strong predictor of nutritional risk.
Urea nitrogen
Urea nitrogen was identified as a risk factor for malnutrition in CKD patients under the PNI assessment. Higher levels of urea nitrogen in CKD patients correspond to a higher risk of malnutrition. Urea nitrogen and serum creatinine are small molecules that do not bind to proteins and are almost entirely excreted through urine after filtration by the glomeruli, with minimal reabsorption in the renal tubules. In CKD patients, the levels of these substances gradually increase as renal failure progresses [24]. Some studies have shown that uremic toxins accumulate in muscles, leading to mitochondrial respiratory and enzymatic damage, which is a fundamental cause of muscle atrophy and decreased physical endurance in CKD patients [25]. Therefore, future research may focus on reducing the level of urea nitrogen in CKD patients to mitigate the risk of muscle atrophy or malnutrition.
BMI
In this study, a decrease in BMI is identified as a reversible factor associated with malnutrition risk according to the CONUT assessment method. Lower BMI correlates with a higher risk of malnutrition. BMI is widely used in screening for the nutritional status in various diseases and among the elderly, but there is still a lack of clear diagnostic specificity. A prospective cohort study showed that maintaining a normal BMI can slow CKD progression, while an abnormal BMI can accelerate it [26]. In dialysis patients, a decrease in BMI is considered an important predictor closely related to mortality [27]. For CKD patients with lower BMI, muscle loss and related issues may occur. Therefore, efforts should be made to increase their skeletal muscle content to maintain an appropriate BMI.
Age
Both assessment methods identified age as a risk factor for malnutrition in CKD patients. Older age is associated with higher nutritional risk assessment scores. Zhu Jincheng et al. [28] pointed out that the possibility of malnutrition in patients aged 65 and above is 3.6 times higher than in those below 65. Another research showed that due to limited daily activities, decreased chewing ability, reduced protein intake, and decreased gastrointestinal absorption function, elderly patients experience a decline in nutritional status [29]. This situation makes the elderly more prone to inadequate dialysis, further aggravating malnutrition. Qiu Siao et al. [30] found that as age increases, organ function gradually declines, leading to decreased nutritional absorption ability. In addition, reduced physical activity in elderly patients makes them more susceptible to nutritional absorption disorders, further triggering malnutrition. Therefore, it is essential to monitor the nutritional status of elderly CKD patients early and regularly assess nutritional risks, implementing personalized nutritional management plans.
Study limitations
This study has several limitations. Firstly, the single-center, retrospective, and cross-sectional design limits the ability to establish a causal relationships between risk factors and malnutrition. Future research should consider a multi-center, prospective approach to validate these findings. Secondly, socio-economic factors were not adequately addressed, despite their potential influence on nutritional status. Incorporating these factors into future studies could offer a more nuanced understanding of the determinants of malnutrition in CKD patients. Additionally, this study did not evaluate prognosis-related outcomes such as rehospitalization rates, cardiovascular complications, and mortality, which could provide a more comprehensive understanding of the impact of nutritional risk. Future research should also explore the potential benefits of combining PNI and CONUT tools to enhance the accuracy and reliability of nutritional assessments. Investigating the joint application of these tools could reveal whether their combined use offers superior predictive power for clinical outcomes compared to their individual application. Furthermore, there is potential to introduce new assessment metrics, such as novel biomarkers or functional indicators, which could complement existing tools and provide a more holistic evaluation of nutritional status in CKD patients.