Lower prognostic nutritional index is associated with a greater decline in long-term kidney function in general population

Abstract

Background

The prognostic nutritional index (PNI) is an integrated index of serum albumin and peripheral lymphocyte count, where low values may reflect poor nutritional status or inflammation. The long-term effect of PNI on renal function is not well known in the general population. Therefore, we investigated whether the PNI is related to renal function changes in the general population.

Methods

Data from participants who underwent a health check-up between 2002 and 2018 were retrospectively examined. PNI was computed by 10×serum albumin (g/dL) + 0.005×total lymphocyte count (per mm3). The primary exposure was PNI, divided into quintiles. The primary outcome was a 25% decline in eGFR from baseline over a 5-year follow-up period.

Results

This study included 15,437 participants (mean [standard deviation, SD] age, 43.7 [7.9] years; 46% male). The median (interquartile ranges) 5-year change of eGFR was − 5.2 (− 18.8, − 3.3) mL/min/1.73m². A total of 2,272 participants (14.7%) experienced a 25% decline in eGFR at 5 years. Compared to the highest PNI group, lower PNI groups were at greater risk for a 25% decline in eGFR; odds ratios and 95% confidence intervals were 1.42 (1.20, 1.68), 1.23 (1.04, 1.45), 1.21 (1.03, 1.43), and 1.19 (1.01, 1.40) for the first to fourth quintiles of PNI, respectively. In linear regression analyses, lower PNI groups also showed a larger decline in eGFR over 5 years compared to the highest PNI group.

Conclusions

Lower PNI was associated with a larger decline in renal function in the general population.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12937-024-01047-8.

Introduction

Chronic kidney disease (CKD) is a prevalent and increasingly recognized global health problem, characterized by a gradual loss of renal function [1, 2]. The prevalence of CKD in the general population varies depending on the definition used and the population studied, with an estimated 10–13% of adults worldwide having CKD [3, 4]. The risk of decreased renal function increases with age, and it is more common in individuals with certain medical conditions like diabetes, hypertension, and obesity. However, the prevalence of CKD is still roughly 13.7% among those 30–40 years old, 4.9% in non-diabetics, and 10.2% in non-hypertensive individuals, respectively [3, 5]. CKD is associated with a multitude of adverse health outcomes, cardiovascular disease, end-stage renal disease, and premature mortality [6–9]. Given its substantial public health burden, identifying modifiable factors influencing the progression of CKD is of paramount importance to mitigate its consequences.

These days, the association between nutritional status or inflammation and changes in renal function has been suggested [10, 11]. The association between malnutrition and CKD is bidirectional. Malnutrition is not only a common complication of CKD, but also a risk factor for the occurrence and progression of CKD [12–14]. Several methods including biochemical markers such as serum albumin, anthropometric measurements such as weight, body mass index (BMI) and skin fold thickness, geriatric nutritional risk index, and subjective global assessment are used to evaluate nutritional status for this population [12, 15, 16]. In particular, hypoalbuminemia reflects both inflammation and poor nutritional status and is associated with decreased renal function [17, 18]. For BMI, both high and low BMI are known to be associated with worsening renal function [19]. Systemic inflammation is also related to the progression of CKD. Increased C-reactive protein (CRP) or decreased peripheral lymphocyte counts have been reported to be linked with renal function decline [20, 21]. The prognostic nutritional index (PNI) is a composite marker that integrates serum albumin level as an indicator of nutritional status and the peripheral lymphocyte counts as an indicator of systemic inflammation, and has emerged as a potential predictor for various health outcomes, including cancer prognosis, surgical outcomes, and cardiovascular events [22–27]. Moreover, PNI is noninvasive, convenient, and inexpensive to assess nutritional-inflammatory status in clinical settings. Several studies have shown an association between PNI and acute kidney injury in patients with coronary artery disease or between PNI and progression of diabetic kidney disease in patients with diabetes [14, 28]. Despite the growing body of evidence linking PNI to adverse health outcomes, prior studies have predominantly focused on its implications in cancer and surgical settings or on specific patient groups (e.g., patients with CKD or diabetes), with limited exploration of its relevance in the context of renal dysfunction, particularly in the general population [14, 22–28]. Therefore, we sought to investigate the potential role of PNI as a risk factor of renal function decline in the general population.

Methods

Study population and data source

We retrospectively extracted and examined de-identified data from a population who underwent comprehensive health check-ups at Samsung Changwon Hospital between 2002 and 2018. We included participants aged ≥ 18 years with baseline data on serum creatinine, lymphocyte count, and serum albumin. Participants who were undergoing dialysis at baseline or had no data on serum creatinine levels at 5 years of follow-up were excluded.

Demographic information on age, sex, comorbid conditions [diabetes mellitus, hypertension, coronary artery disease (CAD), and stroke], BMI, smoking status, systolic blood pressure (SBP), and diastolic blood pressure (DBP) as well as laboratory variables including serum creatinine, serum albumin, serum hemoglobin, total cholesterol, serum glucose, CRP, uric acid, hemoglobin A1c (HbA1c), and proteinuria were collected from the database. Proteinuria was defined as a urine dipstick test reading of ≥ 1+.

The study received approval from the Institutional Review Board of Samsung Changwon Hospital and was exempted from the requirement for written informed consent from the participants because researchers retrospectively accessed the anonymized dataset based on the health screening cohort of the epidemiological research center at Samsung Changwon Hospital for the purposes of analysis (IRB No. SCMC 2022-12-015).

Exposures and outcomes

The primary exposure of interest was baseline PNI quintile. PNI was computed using the formula: 10 × serum albumin (g/dL) + 0.005 × total lymphocyte count (per mm³). The primary outcome of interest was a decline in renal function—specifically, a 25% decline in eGFR from baseline during the 5-year follow-up period. The secondary outcomes of interest were the 5-year change in renal function, calculated by subtracting baseline eGFR from the eGFR at 5-year follow-up. A negative value of eGFR change indicates a decline in renal function. The Chronic Kidney Disease Epidemiology Collaboration equation was used to calculate the eGFR [29].

Statistical analyses

Baseline characteristics were reported as mean ± standard deviation (SD) or median [interquartile range (IQR)] for continuous variables and frequencies (%) for categorical variables. The significance of trends in parameters across PNI quintiles was determined by a linear regression analysis or a Wilcoxon-type non-parametric trend test, as appropriate. Logistic regression analyses were conducted to evaluate the association between PNI quintiles and a 25% decline in eGFR. Linear regression analyses were conducted to evaluate the association between PNI quintile and the 5-year change in renal function. Three hierarchical adjustments were used as follows: (1) un adjusted model (model 1); (2) a case mix–adjusted model (model 2) including sex, age, comorbid conditions (diabetes mellitus, hypertension, coronary artery disease, and stroke), smoking status, BMI, SBP, and DBP; and (3) a fully adjusted model (model 3) that included hemoglobin, total cholesterol, serum glucose, CRP, uric acid, HbA1c, eGFR, and proteinuria in addition to the variables included in model 2. To assess effect modification by age, sex, BMI, SBP, DBP, diabetes, hypertension, CAD, smoking status, hemoglobin, serum glucose, total cholesterol, CRP, uric acid, HbA1c, and eGFR on the association between PNI and 5-year change in renal function, a likelihood ratio test was performed by adding interaction terms between PNI and each of the above covariates to the logistic regression model; then, subgroup analyses were performed according to age (< 50 or ≥ 50 years), sex, BMI (< 23 or ≥ 23 kg/m²), SBP (< 120 or ≥ 120 mmHg), DBP (< 80 or ≥ 80 mmHg), diabetes, hypertension, hemoglobin (< 14.0 or ≥ 14.0 g/dL), total cholesterol (< 200 or ≥ 200 mg/dL), serum glucose (< 100 or ≥ 100 mg/dL), CRP (< 0.5 or ≥ 0.5 mg/dL), uric acid (< 7.0 or ≥ 7.0 mg/dL), HbA1c (< 5.7 or ≥ 5.7%) and eGFR (< 90 or ≥ 90 mL/min/1.73 m²). Restricted cubic spline models with four knots were used for evaluating the association between 5-year change in renal function and PNI as a continuous variable.

Baseline covariates that were missing included diabetes, hypertension, coronary artery disease, stroke, smoking status, BMI, SBP, DBP, total cholesterol, glucose, CRP, uric acid, HbA1c, and proteinuria. The frequency of missing data was consistently < 0.05% for BMI, SBP, DBP, and serum glucose, whereas those for total cholesterol, uric acid, proteinuria, HbA1c, diabetes, hypertension, coronary artery disease, stroke, and smoking status were 0.4%, 1.7%, 3.4%, 11.6%, 9.8%, 11.4%, 9.2%, 9.1%, and 17.9%, respectively. The frequency of CRP missing was 42.8%. We performed multiple imputation methods with a multivariate normal model. The imputation model incorporated all variables from the fully adjusted model, along with an outcome variable, using 20 imputed data sets. All statistical analyses were performed using STATA, version 14.2 (StataCorp LLC, College Station, TX, USA).

Results

Participant characteristics

Data from 403,677 participants who underwent health check-ups between January 2002 and December 2018 were extracted from an anonymized dataset, and 113,813 participants aged ≥ 18 years with baseline data on total lymphocyte count, serum albumin, and serum creatinine were included in the study cohort. After excluding subjects with missing data on serum creatinine at 5 years (n = 98,375) and those on dialysis at baseline (n = 1), data from 15,437 subjects were finally used for analysis.

The baseline characteristics for the participants across PNI categories are presented in Table 1. The participants’ age (mean ± SD) was 43.7 ± 7.9 years, and 45.9% were male. The mean ± SD PNI and baseline eGFR were 56.9 ± 4.2 and 95.4 ± 15.0 mL/min/1.73 m², respectively. Participants in the higher PNI groups were younger, and these groups included a larger proportion of male, smokers, diabetics, individuals with hypertension, and individuals with coronary artery disease. In the higher PNI groups, levels of BMI, SBP, DBP, hemoglobin, total cholesterol, CRP, uric acid, HbA1c, and serum glucose were also higher, while the baseline eGFR was lower.

Table 1.

Baseline characteristics of 15,437 participants across PNI categories

	Prognostic nutritional index						P value
	Total	Quintile 1	Quintile 2	Quintile 3	Quintile 4	Quintile 5
N (%)	15,437	3,086 (20)	3,083 (20)	3,092 (20)	3,088 (20)	3,088 (20)
PNI	56.9 ± 4.2	51.3 ± 1.9	54.6 ± 0.7	56.8 ± 0.6	59.0 ± 0.7	62.9 ± 2.7
Age, years	43.7 ± 7.9	44.3 ± 7.6	44.2 ± 7.7	43.7 ± 8.0	43.2 ± 7.9	43.1 ± 8.3	< 0.001
Male, %	45.9	21.7	35.9	46.1	55.3	70.4	< 0.001
Comorbidities, %
Diabetes	2.4	1.7	1.7	1.8	2.2	4.4	< 0.001
Hypertension	6.9	5.1	4.8	7.0	6.6	10.6	< 0.001
Coronary artery disease	0.6	0.4	0.4	0.7	0.6	1.1	0.001
Stroke	0.4	0.3	0.4	0.4	0.6	0.5	0.142
Current smoker, %	10.8	3.8	7.6	10.0	12.2	20.3	< 0.001
Body mass index, kg/m2	23.6 ± 3.1	22.8 ± 2.9	23.4 ± 3.0	23.5 ± 3.1	23.9 ± 3.2	24.4 ± 3.3	< 0.001
SBP, mmHg	121.6 ± 17.0	117.3 ± 16.3	120.3 ± 16.9	121.4 ± 16.6	123.2 ± 16.7	125.9 ± 17.0	< 0.001
DBP, mmHg	73.9 ± 11.7	70.8 ± 11.2	73.0 ± 11.9	73.9 ± 11.4	75.0 ± 11.6	76.8 ± 11.5	< 0.001
Laboratory parameters
Hemoglobin, g/dL	14.2 ± 1.7	13.1 ± 1.6	13.8 ± 1.6	14.3 ± 1.6	14.6 ± 1.5	15.1 ± 1.4	< 0.001
Total lymphocyte count, cells/m3	2,098 ± 574	1,585 ± 334	1,854 ± 336	2,054 ± 363	2,263 ± 401	2,732 ± 611	< 0.001
Serum albumin, g/dL	4.6 ± 0.3	4.3 ± 0.2	4.5 ± 0.2	4.6 ± 0.2	4.8 ± 0.2	4.9 ± 0.3	< 0.001
Total cholesterol, mg/dL	195.9 ± 35.9	185.4 ± 36.8	191.7 ± 33.0	195.1 ± 33.5	201.6 ± 35.7	205.6 ± 36.5	< 0.001
Serum glucose, mg/dL	91.8 ± 16.9	88.6 ± 14.3	91.0 ± 16.5	91.6 ± 15.9	92.6 ± 17.1	95.2 ± 19.9	< 0.001
C-reactive protein, mg/dL	0.4 (0.2–0.9)	0.4 (0.2–0.8)	0.4 (0.2–0.8)	0.4 (0.2–0.9)	0.4 (0.2–0.9)	1.2 (0.2–1.1)	< 0.001
Uric acid, mg/dL	4.8 (3.9–6.0)	4.2 (3.6–5.0)	4.5 (3.8–5.6)	4.8 (4.0–6.0)	5.2 (4.1–6.3)	5.6 (4.5–6.6)	< 0.001
Hemoglobin A1c, %	5.3 ± 0.6	5.2 ± 0.6	5.2 ± 0.5	5.3 ± 0.6	5.3 ± 0.6	5.4 ± 0.7	< 0.001
Serum creatinine, mg/dL	0.8 (0.7–1.0)	0.8 (0.7–0.9)	0.8 (0.7–1.0)	0.9 (0.7–1.0)	0.9 (0.7–1.0)	0.9 (0.8–1.1)	< 0.001
eGFR, mL/min/1.73 m2	95.4 ± 15.0	98.1 ± 15.1	96.2 ± 14.5	95.3 ± 14.9	94.4 ± 14.8	92.9 ± 15.2	< 0.001
Proteinuria	0.4	0.6	0.2	0.2	0.5	0.6	0.469

Note: Continuous variables are shown as mean ± standard deviation or median (interquartile range), while categorical variables are shown as percentages

Abbreviations: DBP, diastolic blood pressure; eGFR, estimated glomerular filtration rate; PNI, prognostic nutritional index; SBP, systolic blood pressure

Association between PNI quintile and 25% decline in eGFR after 5 years from baseline

A total of 2,272 (14.7%) participants experienced a 25% decline in eGFR after 5 years from baseline, including 18.7%, 15.7%, 14.5%, 13.5%, and 11.1% of the first, second, third, fourth, and fifth PNI groups, respectively. In the logistic regression analysis with full adjustment, lower PNI groups had a greater risk of a 25% decline in eGFR compared to the highest PNI group; odds ratios and 95% confidence intervals (CIs) were 1.42 (1.20, 1.68), 1.23 (1.04, 1.45), 1.21 (1.03, 1.43), and 1.19 (1.01, 1.40) for the first, second, third, and fourth quintiles of PNI, respectively (Table 2). When PNI was introduced as a continuous variable in the fully adjusted model, the odds ratio and 95% CIs for 25% decline in eGFR associated with a decrease of 1 unit in PNI level were 1.03 (1.01, 1.03). In subgroup analyses, the association between PNI quintiles and a 25% decline in eGFR after 5 years was not modified by age (P_interaction = 0.99), sex (P_interaction = 0.20), diabetes (P_interaction = 0.22), hypertension (P_interaction = 0.17), CAD (P_interaction = 0.55), smoking status (P_interaction = 0.49), SBP (P_interaction = 0.16), DBP (P_interaction = 0.28), BMI (P_interaction = 0.37), hemoglobin (P_interaction = 0.22), total cholesterol (P_interaction = 0.54), serum glucose (P_interaction = 0.57), CRP (P_interaction = 0.05), uric acid (P_interaction = 0.09), or HbA1c (P_interaction = 0.71) (Supplemental Fig. 1). In contrast, the association between PNI quintile and a 25% decline in eGFR after 5 years was modified by eGFR (P_interaction = 0.01); the association was more evident in participants with an eGFR ≥ 90 mL/min/1.73 m² compared to those with an eGFR < 90 mL/min/1.73 m², but showed similar trends between the two groups (Fig. 1).

Table 2.

Odds ratios for 25% decline in eGFR after 5 year according to PNI quintile

	Model 1		Model 2		Model 3
	OR	95% CI	OR	95% CI	OR	95% CI
Quintile 1	1.84	1.59, 2.12	1.66	1.42, 1.94	1.42	1.20, 1.68
Quintile 2	1.49	1.28, 1.73	1.39	1.19, 1.62	1.23	1.04, 1.45
Quintile 3	1.36	1.17, 1.57	1.30	1.11, 1.51	1.21	1.03, 1.43
Quintile 4	1.24	1.07, 1.45	1.22	1.04, 1.42	1.19	1.01, 1.40
Quintile 5	Reference		Reference		Reference

Model 1 is unadjusted. Model 2 is adjusted for age, sex, diabetes, hypertension, coronary artery disease, stroke, smoking status, body mass index, systolic blood pressure, and diastolic blood pressure. Model 3 is adjusted for all variables included in model 2 as well as hemoglobin, total cholesterol, serum glucose, hemoglobin A1c, C-reactive protein, uric acid, eGFR, and proteinuria

Abbreviations: CI, confidence interval; eGFR, estimated glomerular filtration rate; OR, odds ratio; PNI, prognostic nutritional index

Fig. 1.

Association between PNI quintile and risk of a 25% decline in eGFR after 5 years, stratified by baseline eGFR (≥ 90 and < 90 mL/min/1.73 m2) among 15,437 participants. Points and bars depict odds ratios and 95% confidence intervals, respectively. Abbreviations: eGFR, estimated glomerular filtration rate; PNI, prognostic nutritional index

Association between PNI quintile and 5-year change in renal function

The mean (SD) eGFR levels after 5 years were 85.8 (15.0), 85.6 (14.4), 85.2 (14.4), 84.8 (14.2), and 84.6 (14.3) mL/min/1.73 m² from the first to fifth quintiles of PNI, respectively. The median (IQR) 5-year change in eGFR was − 5.2 (− 18.8, − 3.3) mL/min/1.73 m². In the linear regression analysis with full adjustment, lower PNI groups showed a greater decline in the eGFR over 5 years compared to the highest PNI group (the reference group); beta (β) coefficients and 95% CIs were − 2.57 (− 3.18, − 1.97), − 1.20 (− 1.77, − 0.63), − 1.03 (− 1.59, − 0.48), and − 0.86 (− 1.40, − 0.31) for the first, second, third, and fourth quintiles of PNI, respectively (Fig. 2). When PNI was introduced as a continuous variable in the fully adjusted model, the β coefficient and 95% CIs for 5-year change in eGFR associated with a decrease of 1 unit in PNI level were − 0.19 (− 0.24, − 0.15). The restricted cubic splines showed an incremental association between PNI as a continuous variable and 5-year change in eGFR (Fig. 3).

Fig. 2.

Association between PNI quintile and 5-year change in eGFR among 15,437 participants. Lower PNI groups showed a larger decline in the eGFR over 5 years compared to the highest PNI group. Points and bars depict beta coefficients and 95% confidence intervals, respectively. Abbreviations: eGFR, estimated glomerular filtration rate; PNI, prognostic nutritional index

Fig. 3.

A linear regression model with restricted cubic splines showing an incremental association between PNI as a continuous variable and 5-year change in eGFR: unadjusted model (A); fully adjusted model (B). Abbreviations: eGFR, estimated glomerular filtration rate; PNI, prognostic nutritional index

Sensitivity analysis

We investigated the 5-year change in PNI assessed by PNI slope, considering the influence of PNI change on changes in renal function. PNI increased more in the lower baseline PNI groups over a 5-year period. The median (IQR) 5-year slope of PNI were 0.16 (0.10–0.22), 0.11 (0.06–0.17), 0.09 (0.03–0.13), 0.05 (0.01–0.10), and 0.01 (− 0.05–0.04) for the first to fifth quintiles of PNI, respectively (P for trend < 0.001). When we further adjusted the PNI slope in a fully adjusted logistic regression model to assess the association between PNI and renal function decline, the results were still robust; odds ratios and 95% CIs were 1.37 (1.14, 1.63), 1.20 (1.01, 1.42), 1.19 (1.01, 1.40), and 1.18 (1.00, 1.39) for the first, second, third, and fourth quintiles of PNI, respectively (reference: the highest PNI quintile) (Supplemental Fig. 2).

Considering that the definition of proteinuria in this study cannot rule out the possibility of temporary proteinuria or false positives, we also investigated the association between PNI quintile and 25% decline in eGFR among participants without proteinuria, the results remained similar; odds ratios and 95% CIs were 1.41 (1.19, 1.67), 1.22 (1.04, 1.45), 1.21 (1.02, 1.42), and 1.19 (1.01, 1.40) for the first, second, third, and fourth quintiles of PNI, respectively (Supplemental Table 1).

To address the potential for selection bias when excluding participants without data on serum creatinine at 5 years, we analyzed the association between the PNI quintile and the 5-year eGFR slope by including all participants with at least one follow-up eGFR data, and the results remained robust; β coefficients and 95% CIs were − 0.28 (− 0.32, − 0.24), − 0.15 (− 0.19, − 0.11), − 0.10 (− 0.13, − 0.06), and − 0.02 (− 0.06, 0.01) for the first, second, third, and fourth quintiles of PNI, respectively (Supplemental Table 2).

Discussion

We investigated whether PNI relates to changes in renal function in the general population. We found that a lower baseline PNI was associated with a larger decline in renal function after 5 years; lower PNI groups had a greater risk of a 25% decline in eGFR and a greater decline in the eGFR over 5 years compared to the highest PNI group.

PNI is a composite marker that integrates albumin and peripheral lymphocyte counts, which are affected by an individual’s nutritional status and inflammatory status. Previous studies have investigated the association between PNI and different outcomes such as mortality, cardiovascular disease, or cancer prognosis in various populations, which showed the association between low PNI and poor outcomes [22–24, 26]. Regarding renal outcomes, there are some studies that have reported the effect of PNI on the occurrence and progression of diabetic kidney disease in patients with type 2 diabetes or the development of acute kidney injury in patients with critical illness [14, 28, 30, 31]. They showed the negative relationship between PNI and AKI development or the occurrence and progression of diabetic kidney disease. Although the association between PNI and long-term decline in renal function observed in our study aligns with prior research suggesting a link between nutritional status, inflammation, and kidney health, our findings offer several distinctive features that contribute to its strength and relevance. Unlike in previous studies focusing on populations with established CKD, cancer, or cardiovascular disease or high-risk individuals with prevalent comorbidities such as diabetes or hypertension, our study cohort consisted mainly of healthy individuals with a low baseline prevalence of CKD and fewer traditional risk factors for renal function decline [14, 32, 33]. Thus, our findings shed light on modifiable risk factors associated with decreased renal function in a general population, which is often overlooked in CKD studies. Furthermore, the use of changes in eGFR as the outcome variable, rather than the development of CKD, provides a more sensitive measure of renal function decline over time in this relatively young and healthy cohort. This approach allows early identification of subtle changes in renal function before the onset of clinically significant renal impairment.

In this study, the association between a lower PNI and a greater risk of 25% decline in eGFR after 5 years was different between participants with an eGFR of 90 mL/min/1.73 m² or higher and those with an eGFR of less than 90 mL/min/1.73 m², even if the trend was similar between the two groups. Although the underlying mechanism is not clear, there are some possible explanations for the findings. First of all, participants with an eGFR ≥ 90 mL/min/1.73 m² are likely to have low serum creatinine levels. In some cases, low serum creatinine levels can be associated with poor nutritional status, which may contribute more to kidney damage [34, 35]. Therefore, some individuals with high eGFR might be experiencing early stages of kidney function decline, potentially driven by poor nutritional status, which can eventually exacerbate kidney damage. In addition, high eGFR in some individuals may indicate hyperfiltration and these patients might be at increased risk for subsequent kidney damage [36].

Although the precise mechanisms linking PNI and renal function decline remain to be fully elucidated, several have been proposed. A decrease in serum albumin, one of the PNI components, is closely related to the deterioration of renal function [18]. Serum albumin levels can decrease in response to inflammation and can also decrease in malnutrition [17]. Lymphocyte counts, another component of PNI, also appear to protect kidney function [21]. Low lymphocyte counts may be associated with subclinical infection or microinflammation [37]. A study found that a drug-induced increase in lymphocyte counts was related to a reduced progression of CKD [38]. These malnutrition and inflammation can independently contribute to endothelial dysfunction, oxidative stress, and impaired immune response, all of which are involved in the pathogenesis of CKD [39, 40]. Inadequate nutrient intake, particularly protein–energy malnutrition, may lead to muscle wasting and reduced renal perfusion, exacerbating kidney damage over time [41]. Additionally, systemic inflammation, as reflected by low PNI, can directly contribute to kidney injury through cytokine-mediated endothelial injury and fibrosis [42]. Moreover, micronutrient deficiencies associated with poor nutritional status may impair kidney antioxidant defenses, rendering the kidneys more susceptible to oxidative damage and CKD progression [43].

In the process of interpreting the results, it is essential to consider several limitations inherent in our study. First, the retrospective design of this study introduces intrinsic limitations, including potential selection bias and reliance on existing medical records, which may affect the completeness of data collection. In particular, selection bias when excluding participants without data on serum creatinine level at 5 years may be a limitation of our study. However, in the sensitivity analysis, the results remained robust when we examined the association between the PNI quintile and the 5-year eGFR slope, including all participants with at least one follow-up eGFR measurement. In addition, no substantial disparities in baseline characteristics were observed between included and excluded participants, except for smoking status and HbA1c, although the prevalence of current smokers and HbA1c levels were slightly higher in the excluded participants (Supplemental Table 3). Although various potential confounding factors have been adjusted, residual confounding arising from unmeasured variables including dietary habits or use of antihypertensive drugs such as angiotensin blockades may affect the observed association. For example, the dietary protein intake may affect both PNI and renal function. However, high protein intake may increase PNI but may also reduce long-term renal function [44], which would like bias our results toward the null. Moreover, the use of angiotensin blockades or salt intake could also affect renal function in the future [45, 46]. However, to mitigate the potential impact of these missing variables, we have adjusted the analysis for BP, a crucial intermediary in the pathway through which both salt intake and antihypertensive medications exert their effects. We also adjusted for CRP, an inflammatory marker, given that high salt intake and angiotensin blockades can affect inflammation levels. Further studies including data on these potential confounders are needed to confirm the results and reduce the risk of bias. Second, a single measurement of PNI at baseline may not fully capture changes in nutritional or inflammatory status over time, potentially influencing the robustness of our findings. However, in sensitivity analysis, we additionally adjusted for the 5-year PNI slope and the association between PNI and renal function decline remained unchanged. Third, our study cohort included relatively young individuals with few traditional CKD risk factors, which may restrict the applicability of our findings to older populations or those with higher baseline risk profiles.

In conclusion, our study showed that in the general population, lower PNI was associated with a greater risk of renal function decline after 5 years. These findings indicate that an integrated approach to nutritional status and inflammatory status using PNI may be considered in renal health management strategies in the general population. However, future prospective studies are needed to confirm the results by controlling confounding factors more rigorously.

Electronic supplementary material

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Author contributions

Research idea and study design: YJL; data analysis /interpretation: NGK, MJ, YJL; statistical analysis: NGK, MJ, YJL, IHP; supervision or mentorship: YJL; drafting the article: IHP, YJL; reviewing critical version of the article: YJL, IHP, NGK, MJ. All authors approved the final version of the manuscript.

Funding

This study was not supported by any sponsor or funder.

Data availability

No datasets were generated or analysed during the current study.

Declarations

Ethics approval and consent to participate

The study received approval from the Institutional Review Board of Samsung Changwon Hospital and was exempted from the requirement for written informed consent from the participants because researchers retrospectively accessed the anonymized dataset based on the health screening cohort of the epidemiological research center at Samsung Changwon Hospital for the purposes of analysis (IRB No. SCMC 2022-12-015).

Conflict of interest

The authors have no conflicts of interest to declare.