Next Article in Journal
Reconfiguration of Gut Microbiota and Reprogramming of Liver Metabolism with Phycobiliproteins Bioactive Peptides to Rehabilitate Obese Rats
Next Article in Special Issue
Immunonutrition in Orthopedic and Traumatic Patients
Previous Article in Journal
Evaluation of Activity of Sesquiterpene Lactones and Chicory Extracts as Acetylcholinesterase Inhibitors Assayed in Calorimetric and Docking Simulation Studies
Previous Article in Special Issue
Healthy Immunity on Preventive Medicine for Combating COVID-19
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Nutrients
Volume 14
Issue 17
10.3390/nu14173634
nutrients-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Prognostic Nutritional Index as a Predictor of Diabetic Nephropathy Progression

by
Junlin Zhang
1,2,3,†,
Xiang Xiao
1,3,4,†,
Yucheng Wu
1,3,
Jia Yang
1,3,
Yutong Zou
1,3,
Yuancheng Zhao
1,3,
Qing Yang
1,3 and
Fang Liu
1,3,*
1
Division of Nephrology, West China Hospital of Sichuan University, Chengdu 610041, China
2
Department of Nephrology, The Third People’s Hospital of Chengdu, Chengdu 610014, China
3
Laboratory of Diabetic Kidney Disease, Centre of Diabetes and Metabolism Research, West China Hospital of Sichuan University, Chengdu 610041, China
4
Department of Nephrology, The First Affiliated Hospital of Chengdu Medical College, Chengdu 610500, China
*
Author to whom correspondence should be addressed.
†
These authors contributed equally to this work.
Nutrients 2022, 14(17), 3634; https://doi.org/10.3390/nu14173634
Submission received: 15 July 2022 / Revised: 24 August 2022 / Accepted: 28 August 2022 / Published: 2 September 2022
(This article belongs to the Special Issue The Role of Immunonutrition: Immune Development and Disease Prevention)
Browse Figures
Versions Notes

Abstract

:

Abstract

Malnutrition and immunologic derangement were not uncommon in patients with chronic kidney disease (CKD). However, the long-term effects of prognostic nutritional index (PNI), an immunonutrition indictor, on renal outcomes in patients with diabetic nephropathy (DN) and type 2 diabetes mellitus (T2DM) are unknown. In this retrospective cohort study, 475 patients with T2DM and biopsy-confirmed DN from West China Hospital between January 2010 and September 2019 were evaluated. PNI was evaluated as serum albumin (g/L) + 5 × lymphocyte count (109/L). The study endpoint was defined as progression to end-stage renal disease (ESRD). The Cox regression analysis was performed to investigate the risk factors of renal failure in DN patients. A total of 321 eligible individuals were finally included in this study. The patients with higher PNI had a higher eGFR and lower proteinuria at baseline. Correlation analysis indicated PNI was positively related eGFR (r = 0.325, p < 0.001), and negatively correlated with proteinuria (r = −0.68, p < 0.001), glomerular lesion (r = −0.412, p < 0.001) and interstitial fibrosis and tubular atrophy (r = −0.282, p < 0.001). During a median follow-up of 30 months (16–50 months), the outcome event occurred in 164(51.09%) of all the patients. After multivariable adjustment, each SD (per-SD) increment of PNI at baseline was associated with a lower incidence of ESRD (hazard ratio, 0.705, 95% CI, 0.523–0.952, p = 0.023), while the hypoalbuminemia and anemia were not. For the prediction of ESRD, the area under curves (AUC) evaluated with time-dependent receiver operating characteristics were 0.79 at 1 year, 0.78 at 2 years, and 0.74 at 3 years, respectively, and the addition of PNI could significantly improve the predictive ability of the model incorporating traditional risk factors. In summary, PNI correlated with eGFR and glomerular injury and was an independent predictor for DN progression in patients with T2DM. Thus, it may facilitate the risk stratification of DN patients and contribute to targeted management.

1. Introduction

Diabetic nephropathy (DN), as one of the serious microvascular complications of diabetes, casts shadows on 30~40% of individuals with diabetes and has become the major cause of chronic kidney disease (CKD) worldwide [1,2]. The past few years have witnessed an alarming increase in DN and the consequent worrying health and socioeconomic burdens, especially in China [3,4,5]. Although multifactorial strategic interventions [6,7], such as dietary and lifestyle adjustments, and intense control of blood glucose and pressure, have been implemented, quite a few DN patients develop renal failure. The identification of new markers associated with DN progression is imminent, so as to better detect high-risk patients for early intervention.
Emerging evidence has demonstrated that DN is a metabolic disorder, where protein malnutrition, low-grade chronic inflammation, and immune modulation are intimately linked to the development and outcomes of the disease [8,9,10,11]. Prognostic nutritional index (PNI), evaluated by the lymphocyte count of the peripheral blood and serum albumin, is a biomarker that integrates nutritional status, immune state, and inflammation condition. It was initially established by the Japanese scholars Onodera et al. [12] in 1984 to assess the nutritional and immunological status of cancer patients undergoing gastrointestinal surgery. In recent years, PNI has gradually become a new predictor for the prognosis of many diseases, such as heart failure [13] and COVID-19 [14]. PNI had also been reported to be a predictor of mortality in elderly individuals with CKD [15]. However, the relationship between PNI and the prognosis of DN in T2DM patients is unclear.
In this retrospective cohort study, we aimed to determine whether PNI, as a noninvasive and immunonutrition status biomarker, predicted the incident ESRD in T2DM patients with biopsy-confirmed DN.

2. Methods

2.1. Patients

This study was performed with 475 patients (≥18 years of age) with biopsy-confirmed pure DN from West China Hospital of Sichuan University between January 2010 and September 2019. The diagnosis of T2DM was according to the 2018 American Diabetes Association (ADA) diagnostic criteria [16] or self-reported. The DN was diagnosed based on the criteria established by the Renal Pathology Society (RPS) in 2010 [17]. The general principles for renal biopsy in this study were T2DM patients with renal injury (defined as eGFR decline or proteinuria) who lacked absolute contraindications, especially T2DM patients without diabetic retinopathy, or T2DM patients with short diabetic duration (<5 y). The study outcome was progression to end-stage renal disease (ESRD) or renal failure, which was defined as an eGFR < 15 mL/min/1.73 m2 or initiation of long-term dialysis or kidney transplantation.
Patients with follow-up time > 1 year were eligible. Additionally, the patients who progressed to renal failure within 1 year were also included. The exclusion criteria were as follows (Figure 1): (1) patients with follow-up time < 1 year; (2) deaths that occurred during the follow-up period; (3) patients with ESRD at baseline; (4) patients with type 1 diabetes; (5) patients with incomplete pathological information or blood routine examination; (6) patients with malignant tumor, hepatic cirrhosis or active infection.
The protocol of this research was approved by the ethics committee of West China Hospital and written informed consent were obtained from all the patient at baseline.

2.2. Data Collection

Clinical information of the patients was collected at baseline from the hospital information system, including (1) demographic data, such as age, gender, and ethics, etc., (2) medical history, such as hypertension, diabetes, and coronary heart disease, etc., (3) examination data including height, weight, and systolic/diastolic blood pressure, etc., (4) laboratory data including serum creatinine, blood urea nitrogen, serum lipid, uric acid, HbA1c, fasting blood sugar, blood routine, proteinuria, and pathological information, etc. The eGFR was calculated with the formula of Chronic Kidney Disease Epidemiology Collaboration [18]. Pathological lesions were routinely evaluated using light and electron microscopy by two renal pathologists based on the 2010 RPS criteria.
PNI value was evaluated as serum albumin (g/L) + 5 × lymphocyte count (109/L). The anemia was defined as hemoglobin levels < 130 g/L for males and <120 g/L for females. Hypoalbuminemia was defined as serum albumin < 35 g/L. The early onset of T2DM was defined as the age of T2DM diagnosis being less than 40 years old.

2.3. Statistical Analysis

All data were analyzed using SPSS 26.0 statistical software (SPSS, Chicago, IL, USA) and R version 4.1.3 (http://www.rproject.org, accessed on 20 April 2022). Variables were presented as the mean ± standard, the medians with interquartile ranges (25th and 75th percentiles) or counts and percentages. The two-tailed one-way ANOVA or Kruskal–Wall was used to analyze the differences among groups. Chi-squared test was used to compare the categorical variables. Correlations between variables were assessed by the Spearman rank correlation coefficient. The PNI was Z-Score normalized for Cox regression analysis. Comparison of renal survival among different groups was evaluated by the Kaplan–Meier method. Cox regression analysis was performed to discriminate the risk factors for ESRD. The prediction of PNI at different times for ESRD was evaluated with time-dependent receiver operating characteristic (td-ROC) curve. Area under the receiver operating curve (AUC) was also used to establish the discrimination ability of different models. Integrated discrimination improvement (IDI) and net reclassification improvement (NRI) were calculated using logistic regression method to analyze the added prognostic value of PNI [19]. A two-way p < 0.05 was considered significant.

3. Results

3.1. Baseline Features of the Patients

A total of 321 individuals were included in this study (Figure 1). The demographic and clinical data of all the patients are shown in Table 1. The median PNI was 43.4 (36.53–49.78) in all the patients. The mean age was 51.64 ± 8.83 years old and 70.7% were male. The median DM duration was 96 (36–132) months. A total of 36.4% of the patients were early onset of T2DM, 52.5% were smokers, and 86.6% of patients had hypertension. The median of eGFR and proteinuria were 57.72 (41–86.02) mL/min/1.73 m2 and 3.81 (1.79–7.09) g/day, respectively. Among them, 69.47% of the subjects used RAS inhibitors.

3.2. Associations of PNI with Baseline Clinical Characteristics

We divided all the patients into the following three groups based on PNI: tertile 1 (<38.78), tertile 2 (38.78–47.12), and tertile 3 (≥47.12). In comparison with individuals with lower PNI, patients with higher PNI were more likely to have a lower incidence of hypertension, anemia, hypoalbuminemia, and RASI use; to have lower levels of proteinuria, serum creatinine, blood urea nitrogen, total cholesterol, LDL-C, HDL-C; to have higher levels of eGFR and uric acid, serum albumin, lymphocyte count. There was no difference in age, gender, DM duration, early onset of T2DM, smoking, body mass index, and HbA1c among individuals in different PNI tertiles.

3.3. Correlation of PNI with Clinical and Pathological Characteristics

The correlation analysis demonstrated that the PNI level was positively related with eGFR (r = 0.325, p < 0.001), and negatively correlated with proteinuria (r = −0.68, p < 0.001) and blood urea nitrogen (r = −0.214, p < 0.001) in terms of clinical characteristics (Figure 2). Furthermore, there was a significant negative correlation between PNI and glomerular lesions (r = −0.412, p < 0.001), interstitial fibrosis and tubular atrophy (IFTA, r = −0.282, p < 0.001), interstitial inflammation (r = −0.271, p < 0.001) and arteriolar hyalinosis (r = −0.156, p = 0.005) in terms of renal pathological changes (Table 2).

3.4. PNI and ESRD in Patients with DN

During the median follow-up of 30 months (16–50 months), 164 of 321 (51.09%) patients progressed to ESRD. Comparing patients with lower PNI, those with higher PNI were likely to have a lower incidence of ESRD, as shown in Table 1. Kaplan–Meier analysis indicated that patients with higher PNI at baseline had a significantly lower risk for progression to ESRD (p < 0.001, Figure 3A) than patients with DN. Univariable Cox regression analysis suggested that per-SD increment in PNI was associated with decreased risk of ESRD (HR 0.445, 95% CI 0.38–0.522, p < 0.001), and higher tertiles were also predicted to lower the risk of ESRD (HR 0.368, 95% CI 0.296–0.456, p < 0.001), as shown in Table 3. In addition, early onset of T2DM, eGFR, proteinuria, anemia, hypoalbuminemia, glomerular lesion, IFTA, and RASI use were also risk factors for progression to renal failure (Supplement Table S1). After adjustment for baseline age, gender, hypertension, smoking, DM duration, early onset of T2DM, e-GFR, proteinuria, hypoalbuminemia, anemia, glomerular lesion, IFTA, and RASI use, per-SD increment in PNI was still associated with a lower incidence of ESRD (HR 0.705, 95% CI 0.523–0.952, p = 0.023). While hypoalbuminemia and anemia were not risk factors for ESRD after multivariate adjustment (Supplement Table S1).
We next investigated the association of PNI with ESRD in subgroups of patients with different clinical characteristics. The results demonstrated that higher PNI was still significantly associated with a reduced risk of ESRD in males and females (Figure 4A,B), in patients with or without the early onset of T2DM (Figure 4C,D), in patients with eGFR < 60 mL/min/1.73 m2 or not (Figure 4E,F), in patients with or without hypoalbuminemia (Figure 4G,H), in patients with proteinuria > 3.5 g/day or not (Figure 4I,J) and in patients with or without anemia (Figure 4K,L).

3.5. PNI and the Prediction of Incident ESRD

For the prediction of ESRD, the area under curves (td-AUC) evaluated with time-dependent receiver operating characteristics were 0.79 at 1 year, 0.78 at 2 years, and 0.74 at 3 years, respectively (Figure 3B,C). The AUC for prediction model 1 (including baseline age, gender, hypertension, smoking, DM duration, early onset of T2DM, e-GFR, proteinuria, hypoalbuminemia, and anemia) and prediction model 1 + PNI tertiles for progression to ESRD were 0.843(95% CI, 0.797–0.890) and 0.855 (95% CI, 0.811–0.900), respectively. Compared with those in model 1, the IDI (p = 0.011) and NRI (p < 0.001) in model 1 + PNI were significantly higher (Table 4 and Figure 3D).

4. Discussion

As far as we know, this was the first cohort research to investigate whether the PNI was associated with DN progression. In this study, PNI was evidenced to be an independent protective factor for kidney disease progression to ESRD in individuals with biopsy-confirmed DN and T2DM, even after adjustment for serum albumin and anemia. We observed that baseline PNI was positively related to eGFR, and negatively associated with proteinuria, glomerular injury, and IFTA. Our previous study suggested that baseline anemia and serum albumin were independent risk factors for incident ESRD in DN patients [20,21]. Additionally, several studies have shown that protein malnutrition is associated with CKD and ESRD [22]. These findings suggested that nutrition status may be a latent predictor of kidney disease progression.
To more accurately examine the prediction of PNI to ESRD, we performed different subgroup analyses. Grouped by gender, individuals with higher PNI tended to have higher survival rates compared with those with lower PNI in both males and females. In the subgroups of patients with different ages of T2DM diagnosis, patients with higher PNI had a lower risk of progression to ESRD in both ages < 40 y and age ≥ 40 y of T2DM diagnosis. Similarly, grouped based on the clinical features, PNI still was related to renal outcomes in patients with or without eGFR decline, even in the patients with mass proteinuria. We also found that the addition of PNI tertiles significantly enhanced the prediction power of DN developing to ESRD. The inclusion of PNI into model 1 based on traditional predictors improved the discrimination of model 1 for adverse renal outcomes with higher AUC, IDI, and NRI. These findings demonstrated that PNI might be an effective predictor for DN progression in T2DM patients. Undoubtedly, this conclusion awaits further large sample studies to confirm.
Interestingly, anemia and hypoalbuminemia have been evidenced to be associated with poor outcomes in diabetes with CKD [20,23]. However, in this study, PNI was still related to disease progression, while anemia and hypoalbuminemia were not. Furthermore, in the subgroups of patients without anemia or hypoalbuminemia, the PNI still predicted the risk of ESRD in DN. In line with these findings, we presumed that the PNI may be a better predictor than serum albumin, inflammation index, or lymphocyte count to predict the ESRD in DN patients, most likely because the PNI is a more comprehensive marker that reflects nutrition, immune and inflammation [24], all of which are closely related with DN. In addition, DN is a chronic inflammatory condition that may contribute to immune disorder and protein malnutrition, which in turn could exacerbate inflammation and create a vicious cycle to accelerate disease progression [10,22,25]. However, the hypothesis remains to be confirmed.
In particular, several studies have shown that the PNI was associated with kidney diseases. Hu et al. [24] and Dong et al. [26] revealed that PNI could predict the incident acute kidney injury (AKI) or mortality in patients with severe cardiac diseases. Zhang et al. [27] found that patients with a lower PNI had a more significant eGFR decline than those with higher PNI in children with CKD during follow-up. Additionally, as mentioned above, the PNI was also significantly related with mortality in elderly CKD patients [15]. These studies indicate that PNI may be very important for incident AKI and CKD prognosis.
The dietary protein intake may affect the PNI level and whether the DN patients should have a relatively low protein diet (LPD) is still controversial. In the ADA guidelines for the nutrition management of non-dialysis CKD in diabetic patients, a restricted dietary protein intake (DPI) of 0.8 g/kg/day, which is less than the average consumption level of Americans (1.2–1.4 g/kg/day) [28], is recommended to slow the renal function decline, but less than the level is not allowed [29]. Moreover, in the KDOQI guideline for nutrition recommendation in non-dialysis CKD with diabetes, it is appropriate to have an LPD of 0.6–0.8 g/kg/day to preserve a balanced nutritional state [30]. However, a recent study based on the DIALECT cohort suggested that high-normal dietary protein intake (>163 g/day) did not deteriorate renal function decline in T2DM patients [31], but a DPI < 92 g/day contributed to the renal function deterioration. Therefore, considering the influence of dietary protein intake on the PNI, whether the T2DM patients with kidney disease should have a low protein diet awaits more studies to clarify, and the management of individual precision nutrition will be the direction of future efforts.
Leukocytes in the peripheral blood have been evidenced to be involved in vascular endothelial cell injury in diabetes, and contribute to the development of atherosclerosis, cardiovascular disease, and kidney damage [32,33,34]. Our previous study demonstrated that the neutrophil-to-lymphocyte (NLR) was significantly associated with eGFR decline and histologic lesions in DN patients [35]. Furthermore, in this study, the lymphocyte count was also related to the risk of ESRD in univariate Cox analysis (HR 0.543, 95% CI 0.393–0.749, p < 0.001), but not emerging as an independent predictor in multivariate analysis. Relatively little research has been conducted on the relationship between absolute lymphocyte count and prognosis. The limited evidence [34,36,37,38] suggested that it is related to chronic inflammation markers, such as C-reactive protein, and predicted cardiovascular events and all-cause mortality in patients with or without diabetes. Cardoso et al. [34] also found the lymphocyte count did not involve in the development of microvascular complications, such as retinopathy outcome and renal function decline, in T2DM individuals, which was similar to ours.
Of course, there are some limitations to our study. First, this research was conducted in a single center in Chinese patients with biopsy-proven DN, and it may therefore not be appropriate to generalize to other races and diabetic kidney disease diagnosed on the basis of clinical presentations. Second, our sample size is limited, and the patients included had a relatively short follow-up time. Third, some disrupting factors, such as dietary factors and the use of α ketoacid, were not considered.
In summary, our findings afforded new evidence that PNI was a better predictor of the risks of DN progression than anemia and hypoalbuminemia in patients with T2DM. Furthermore, its addition to traditional risk factors, such as hypertension, smoking, proteinuria, eGFR, anemia, and hypoalbuminemia could improve the predictive ability for DN progression to renal failure in T2DM patients. Therefore, the utilization of PNI in estimating immunonutrition status condition might be very important in the comprehensive management of DN patients.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/nu14173634/s1, Table S1: Cox regression analysis of risk factors for renal outcomes in DN patients.

Author Contributions

F.L. and J.Z. designed and conducted the study. X.X., Y.W. and Y.Z. (Yutong Zou) participated in the data collection and analysis. J.Y., Y.Z. (Yuancheng Zhao) and Q.Y. participated in data collection. The manuscript was prepared by J.Z. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by Health Commission of Sichuan Province Program (No.21ZD001).

Institutional Review Board Statement

The study was conducted in accordance with the Declaration of Helsinki, and approved by the ethics committee of West China Hospital (protocol code 1210 and approval on 24 May 2022).

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study.

Data Availability Statement

The data presented in this study are available on request from the corresponding author. The data are not publicly available due to the protection of patient’s rights of privacy.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Alicic, R.Z.; Rooney, M.T.; Tuttle, K.R. Diabetic Kidney Disease: Challenges, Progress, and Possibilities. Clin. J. Am. Soc. Nephrol. 2017, 12, 2032–2045. [Google Scholar] [CrossRef] [PubMed]
  2. Ruiz-Ortega, M.; Rodrigues-Diez, R.R.; Lavoz, C.; Rayego-Mateos, S. Special Issue “Diabetic Nephropathy: Diagnosis, Prevention and Treatment”. J. Clin. Med. 2020, 9, 813. [Google Scholar] [CrossRef] [PubMed]
  3. Diabetes around the World in 2021. The IDF Diabetes Atlas 10th Edition [Internet] ed: International Diabetes Federation. Available online: https://diabetesatlas.org/ (accessed on 13 November 2021).
  4. Li, Y.; Teng, D.; Shi, X.; Qin, G.; Qin, Y.; Quan, H.; Shi, B.; Sun, H.; Ba, J.; Chen, B.; et al. Prevalence of diabetes recorded in mainland China using 2018 diagnostic criteria from the American Diabetes Association: National cross sectional study. BMJ 2020, 369, m997. [Google Scholar] [CrossRef] [PubMed]
  5. Zhang, L.; Long, J.; Jiang, W.; Shi, Y.; He, X.; Zhou, Z.; Li, Y.; Yeung, R.O.; Wang, J.; Matsushita, K.; et al. Trends in Chronic Kidney Disease in China. N. Engl. J. Med. 2016, 375, 905–906. [Google Scholar] [CrossRef]
  6. Gaede, P.; Lund-Andersen, H.; Parving, H.H.; Pedersen, O. Effect of a multifactorial intervention on mortality in type 2 diabetes. N. Engl. J. Med. 2008, 358, 580–591. [Google Scholar] [CrossRef]
  7. Block, T.J.; Batu, D.; Cooper, M.E. Recent advances in the pharmacotherapeutic management of diabetic kidney disease. Expert Opin. Pharmacother. 2022, 23, 791–803. [Google Scholar] [CrossRef]
  8. Kitada, M.; Ogura, Y.; Monno, I.; Koya, D. A low-protein diet for diabetic kidney disease: Its effect and molecular mechanism, an approach from animal studies. Nutrients 2018, 10, 544. [Google Scholar] [CrossRef]
  9. Tauchi, E.; Hanai, K.; Babazono, T. Effects of dietary protein intake on renal outcome and mortality in patients with advanced diabetic nephropathy. Clin. Exp. Nephrol. 2020, 24, 119–125. [Google Scholar] [CrossRef]
  10. Hickey, F.B.; Martin, F. Diabetic kidney disease and immune modulation. Curr. Opin. Pharmacol. 2013, 13, 602–612. [Google Scholar] [CrossRef]
  11. Flyvbjerg, A. The role of the complement system in diabetic nephropathy. Nat. Rev. Nephrol. 2017, 13, 311–318. [Google Scholar] [CrossRef]
  12. Onodera, T.; Goseki, N.; Kosaki, G. Prognostic nutritional index in gastrointestinal surgery of malnourished cancer patients. Nihon Geka Gakkai Zasshi 1984, 85, 1001–1005. [Google Scholar]
  13. Zencirkiran Agus, H.; Kahraman, S. Prognostic nutritional index predicts one-year outcome in heart failure with preserved ejection fraction. Acta Cardiol. 2020, 75, 450–455. [Google Scholar] [CrossRef]
  14. Wei, W.; Wu, X.; Jin, C.; Mu, T.; Gu, G.; Min, M.; Mu, S.; Han, Y. Predictive Significance of the Prognostic Nutritional Index (PNI) in Patients with Severe COVID-19. J. Immunol. Res. 2021, 2021, 9917302. [Google Scholar] [CrossRef]
  15. Barutcu Atas, D.; Tugcu, M.; Asicioglu, E.; Velioglu, A.; Arikan, H.; Koc, M.; Tuglular, S. Prognostic nutritional index is a predictor of mortality in elderly patients with chronic kidney disease. Int. Urol. Nephrol. 2022, 54, 1155–1162. [Google Scholar] [CrossRef]
  16. American Diabetes Association. 2. Classification and Diagnosis of Diabetes: Standards of Medical Care in Diabetes-2018. Diabetes Care 2018, 41, S13–S27. [Google Scholar] [CrossRef]
  17. Tervaert, T.W.; Mooyaart, A.L.; Amann, K.; Cohen, A.H.; Cook, H.T.; Drachenberg, C.B.; Ferrario, F.; Fogo, A.B.; Haas, M.; de Heer, E.; et al. Pathologic classification of diabetic nephropathy. J. Am. Soc. Nephrol. 2010, 21, 556–563. [Google Scholar] [CrossRef]
  18. Levey, A.S.; Stevens, L.A.; Schmid, C.H.; Zhang, Y.L.; Castro, A.F., 3rd; Feldman, H.I.; Kusek, J.W.; Eggers, P.; Van Lente, F.; Greene, T.; et al. A new equation to estimate glomerular filtration rate. Ann. Intern. Med. 2009, 150, 604–612. [Google Scholar] [CrossRef] [PubMed]
  19. Pencina, M.J.; D’Agostino, R.B., Sr.; D’Agostino, R.B., Jr.; Vasan, R.S. Evaluating the added predictive ability of a new marker: From area under the ROC curve to reclassification and beyond. Stat. Med. 2008, 27, 157–172; discussion 207–112. [Google Scholar] [CrossRef]
  20. Zhang, J.; Zhang, R.; Wang, Y.; Li, H.; Han, Q.; Wu, Y.; Wang, T.; Liu, F. The Level of Serum Albumin Is Associated with Renal Prognosis in Patients with Diabetic Nephropathy. J. Diabetes Res. 2019, 2019, 7825804. [Google Scholar] [CrossRef]
  21. Zhao, L.; Han, Q.; Zhou, L.; Bai, L.; Wang, Y.; Wu, Y.; Ren, H.; Zou, Y.; Li, S.; Su, Q.; et al. Addition of glomerular lesion severity improves the value of anemia status for the prediction of renal outcomes in Chinese patients with type 2 diabetes. Ren. Fail. 2022, 44, 346–357. [Google Scholar] [CrossRef]
  22. Zha, Y.; Qian, Q. Protein Nutrition and Malnutrition in CKD and ESRD. Nutrients 2017, 9, 208. [Google Scholar] [CrossRef] [PubMed]
  23. Vlagopoulos, P.T.; Tighiouart, H.; Weiner, D.E.; Griffith, J.; Pettitt, D.; Salem, D.N.; Levey, A.S.; Sarnak, M.J. Anemia as a Risk Factor for Cardiovascular Disease and All-Cause Mortality in Diabetes: The Impact of Chronic Kidney Disease. J. Am. Soc. Nephrol. 2005, 16, 3403. [Google Scholar] [CrossRef]
  24. Hu, Y.; Cao, Q.; Wang, H.; Yang, Y.; Xiong, Y.; Li, X.; Zhou, Q. Prognostic nutritional index predicts acute kidney injury and mortality of patients in the coronary care unit. Exp. Ther. Med. 2021, 21, 123. [Google Scholar] [CrossRef] [PubMed]
  25. Shang, S.; Huang, Y.; Zhan, X.; Peng, F.; Wang, X.; Wen, Y.; Feng, X.; Zhou, Q.; Tang, L.W.; Long, H.; et al. The relationship between the prognostic nutritional index and new-onset pneumonia in peritoneal dialysis patients. Int. Urol. Nephrol. 2022, 1–8. [Google Scholar] [CrossRef]
  26. Dong, X.; Wang, B.; Chen, S.; Liu, J.; Xia, Y.; Wang, S.; Li, B.; Wang, S.; Ying, M.; Li, H.; et al. Association between Prognostic Nutritional Index and Contrast-Associated Acute Kidney Injury in Patients Complicated with Chronic Kidney Disease and Coronary Artery Disease. J. Interv. Cardiol. 2021, 2021, 2274430. [Google Scholar] [CrossRef]
  27. Zhang, H.; Tao, Y.; Wang, Z.; Lu, J. Evaluation of nutritional status and prognostic impact assessed by the prognostic nutritional index in children with chronic kidney disease. Medicine 2019, 98, e16713. [Google Scholar] [CrossRef]
  28. Moore, L.W.; Byham-Gray, L.D.; Scott Parrott, J.; Rigassio-Radler, D.; Mandayam, S.; Jones, S.L.; Mitch, W.E.; Osama Gaber, A. The mean dietary protein intake at different stages of chronic kidney disease is higher than current guidelines. Kidney Int. 2013, 83, 724–732. [Google Scholar] [CrossRef]
  29. Draznin, B.; Aroda, V.R.; Bakris, G.; Benson, G.; Brown, F.M.; Freeman, R.; Green, J.; Huang, E.; Isaacs, D.; Kahan, S.; et al. 11. Chronic Kidney Disease and Risk Management: Standards of Medical Care in Diabetes-2022. Diabetes Care 2022, 45, S175–S184. [Google Scholar] [CrossRef]
  30. Ikizler, T.A.; Burrowes, J.D.; Byham-Gray, L.D.; Campbell, K.L.; Carrero, J.J.; Chan, W.; Fouque, D.; Friedman, A.N.; Ghaddar, S.; Goldstein-Fuchs, D.J.; et al. KDOQI Clinical Practice Guideline for Nutrition in CKD: 2020 Update. Am. J. Kidney Dis. Off. J. Natl. Kidney Found. 2020, 76, S1–S107. [Google Scholar] [CrossRef]
  31. Oosterwijk, M.M.; Groothof, D.; Navis, G.; Bakker, S.J.L.; Laverman, G.D. High-Normal Protein Intake Is Not Associated with Faster Renal Function Deterioration in Patients with Type 2 Diabetes: A Prospective Analysis in the DIALECT Cohort. Diabetes Care 2022, 45, 35–41. [Google Scholar] [CrossRef]
  32. Drechsler, M.; Döring, Y.; Megens, R.T.; Soehnlein, O. Neutrophilic granulocytes—Promiscuous accelerators of atherosclerosis. Thromb. Haemost. 2011, 106, 839–848. [Google Scholar] [CrossRef]
  33. Zernecke, A.; Bot, I.; Djalali-Talab, Y.; Shagdarsuren, E.; Bidzhekov, K.; Meiler, S.; Krohn, R.; Schober, A.; Sperandio, M.; Soehnlein, O.; et al. Protective role of CXC receptor 4/CXC ligand 12 unveils the importance of neutrophils in atherosclerosis. Circ. Res. 2008, 102, 209–217. [Google Scholar] [CrossRef]
  34. Cardoso, C.R.L.; Leite, N.C.; Salles, G.F. Importance of hematological parameters for micro- and macrovascular outcomes in patients with type 2 diabetes: The Rio de Janeiro type 2 diabetes cohort study. Cardiovasc. Diabetol. 2021, 20, 133. [Google Scholar] [CrossRef]
  35. Zhang, J.; Zhang, R.; Wang, Y.; Wu, Y.; Li, H.; Han, Q.; Guo, R.; Wang, T.; Wang, J.; Grung, P.; et al. Effects of neutrophil-lymphocyte ratio on renal function and histologic lesions in patients with diabetic nephropathy. Nephrology 2019, 24, 1115–1121. [Google Scholar] [CrossRef]
  36. Liu, J.; Liu, X.; Li, Y.; Quan, J.; Wei, S.; An, S.; Yang, R.; Liu, J. The association of neutrophil to lymphocyte ratio, mean platelet volume, and platelet distribution width with diabetic retinopathy and nephropathy: A meta-analysis. Biosci. Rep. 2018, 38, BSR20180172. [Google Scholar] [CrossRef]
  37. Vaduganathan, M.; Ambrosy, A.P.; Greene, S.J.; Mentz, R.J.; Subacius, H.P.; Maggioni, A.P.; Swedberg, K.; Nodari, S.; Zannad, F.; Konstam, M.A.; et al. Predictive value of low relative lymphocyte count in patients hospitalized for heart failure with reduced ejection fraction: Insights from the EVEREST trial. Circ. Heart Fail. 2012, 5, 750–758. [Google Scholar] [CrossRef]
  38. Berry, C.; Norrie, J.; Hogg, K.; Brett, M.; Stevenson, K.; McMurray, J.J. The prevalence, nature, and importance of hematologic abnormalities in heart failure. Am. Heart J. 2006, 151, 1313–1321. [Google Scholar] [CrossRef] [PubMed]
Nutrients 14 03634 g001 550
Figure 1. Flowchart of included patients in this study. ESRD: end-stage renal disease.
Figure 1. Flowchart of included patients in this study. ESRD: end-stage renal disease.
Nutrients 14 03634 g001
Nutrients 14 03634 g002 550
Figure 2. Correlations of prognostic nutritional index (PNI) with (A) eGFR, (B) urine protein, and (C) blood urea nitrogen among patients with diabetic nephropathy.
Figure 2. Correlations of prognostic nutritional index (PNI) with (A) eGFR, (B) urine protein, and (C) blood urea nitrogen among patients with diabetic nephropathy.
Nutrients 14 03634 g002
Nutrients 14 03634 g003 550
Figure 3. The prediction of PNI for ESRD in DN patients. (A) Kaplan–Meier curves of renal survival rate in patients with different PNI levels. (B,C) The prediction of PNI at different times for ESRD evaluated with time-dependent receiver operating characteristic (td-ROC) curve. (D) The prediction of models for ESRD evaluated with ROC curve.
Figure 3. The prediction of PNI for ESRD in DN patients. (A) Kaplan–Meier curves of renal survival rate in patients with different PNI levels. (B,C) The prediction of PNI at different times for ESRD evaluated with time-dependent receiver operating characteristic (td-ROC) curve. (D) The prediction of models for ESRD evaluated with ROC curve.
Nutrients 14 03634 g003
Nutrients 14 03634 g004 550
Figure 4. Kaplan–Meier curves of subgroup analysis in DN patients with different types of clinical manifestations. (A) The renal survival rate of male patients with different PNI tertiles. (B) The renal survival rate of female patients with different PNI tertiles. (C) The renal survival rate of DN patients with later-onset T2DM in different PNI tertiles. (D) The renal survival rate of DN patients with early onset T2DM in different PNI tertiles. (E,F) The renal survival rate of DN patients with eGFR ≥ 60 mL/min/1.73 m2 or <60 mL/min/1.73 m2 in different PNI tertiles. (G,H) The renal survival rate of DN patients with serum albumin ≥ 35 g/L or <35 g/L in different PNI tertiles. (I,J) The renal survival rate of DN patients with proteinuria > 3.5 g/day or ≤3.5 g/day in different PNI tertiles. (K,L) The renal survival rate of DN patients with or without anemia in different PNI tertiles.
Figure 4. Kaplan–Meier curves of subgroup analysis in DN patients with different types of clinical manifestations. (A) The renal survival rate of male patients with different PNI tertiles. (B) The renal survival rate of female patients with different PNI tertiles. (C) The renal survival rate of DN patients with later-onset T2DM in different PNI tertiles. (D) The renal survival rate of DN patients with early onset T2DM in different PNI tertiles. (E,F) The renal survival rate of DN patients with eGFR ≥ 60 mL/min/1.73 m2 or <60 mL/min/1.73 m2 in different PNI tertiles. (G,H) The renal survival rate of DN patients with serum albumin ≥ 35 g/L or <35 g/L in different PNI tertiles. (I,J) The renal survival rate of DN patients with proteinuria > 3.5 g/day or ≤3.5 g/day in different PNI tertiles. (K,L) The renal survival rate of DN patients with or without anemia in different PNI tertiles.
Nutrients 14 03634 g004
Table
Table 1. Clinical features of the patients stratified across the tertiles of PNI.
Table 1. Clinical features of the patients stratified across the tertiles of PNI.
ValuableAll (n = 321)Tertile 1 (n = 107)
<38.78		Tertile 2 (n = 107)
38.78–47.12		Tertile 3 (n = 107)
≥47.12		p Value
PNI43.4 (36.53–49.78)34.05 (30.55–36.55)43.4 (41.3–45.35)52.7 (49.75–55.5)<0.001
Age (years)51.64 ± 8.8351.83 ± 9.0851.92 ± 7.9251.18 ± 9.480.8
Gender (male, %)227 (70.7)74 (69.2)74 (69.2)79 (73.8)0.687
DM duration (months)96 (36–132)96 (55–156)120 (36–168)96 (45–132)0.474
Early onset of T2DM (%)117 (36.4)44 (41.1)39 (36.4)34 (31.8)0.365
Smoking (%)168 (52.5)57 (53.3)50 (47.2)61 (57)0.349
Body mass index (kg/m2)25.65 (23.13–27.61)24.61 (22.3–28.34)25.781 (23.14–28.04)25.93 (24.24–27.68)0.372
MAP (mmHg)105.59 ± 15.07108.18 ± 15.4105.00 ± 15.29103.57 ± 14.260.072
Hypertension (%)278 (86.6)99 (92.5)95 (88.8)84 (78.5)0.008
Initial proteinuria (g/day)3.81 (1.79–7.09)8.64 (4.35–10.5)3.68 (2.58–6.82)1.20 (0.54–2.58)<0.001
e-GFR (ml/min/1.73 m2)57.72 (41–86.02)45.63 (35.80–67.3)59.79 (43.8–78.67)73.44 (46.11–99.55)<0.001
Serum creatinine (μmol/L)115 (80–154.5)145 (106–167.5)113 (87–137.6)89 (73–140)<0.001
BUN (mmol/L) 7.6 (5.9–10.13)7.9 (6.83–13.29)7.6 (5.9–9.9)6.8 (5.18–8.98)<0.001
Uric acid (μmol/L)388.14 ± 79.98372.23 ± 74.24383.08 ± 78.70409.1 ± 82.950.002
FBS (mmol/L)7.15 (5.58–9.64)8.13 (6.52–11.34)7.37 (5.15–9.43)6.98 (6.01–8.82)0.18
HbA1c (%)7.65 ± 1.877.7 ± 2.157.62 ± 1.827.65 ± 1.640.959
Triglyceride (mmol/L)1.73 (1.27–2.33)1.53 (1.22–2.18)1.81 (1.31–2.31)1.81 (1.25–2.66)0.474
Total cholesterol (mmol/L)4.95 (4.19–5.89)5.26 (4.62–6.56)5.13 (4.49–6.16)4.16 (3.47–5.15)<0.001
LDL-C (mmol/L)2.83 (2.17–3.64)2.95 (2.58–3.76)3.03 (2.47–3.59)2.21 (1.64–3.03)<0.001
HDL-C (mmol/L)1.21 (1.01–1.54)1.35 (1.11–1.72)1.15 (0.93–1.44)1.14 (0.96–1.32)<0.001
Anemia (%)200 (62.3)93 (86.9)74 (69.2)33 (30.8)<0.001
Albumin (g/L)35.2 (28.5–40.75)28.2 (26.1–29.9)35.4 (33.9–37.4)42.9 (40.55–45.7)<0.001
Hypoalbuminemia (%)156 (48.6)107 (100%)47 (43.9)2 (1.9)<0.001
White blood cell (109/L)6.56 ± 1.676.35 ± 1.786.32 ± 1.627.01 ± 1.510.003
Lymphocyte (109/L)1.61 (1.3–2.03)1.39 (1.17–1.74)1.57 (1.29–1.94)1.88 (1.54–2.28)<0.001
RASI use (%)223 (69.47)83 (77.57)82 (76.64)58 (54.21)<0.001
Insulin use (%)256 (79.75)84 (78.5)87 (81.31)85 (79.44)0.874
Progressed to ESRD (%)164 (51.09)84 (78.5)58 (54.21)22 (20.56)<0.001
Note: MAP, mean arterial pressure; BUN, blood urea nitrogen; e-GFR, estimated glomerular filtration rate; FBS, fasting blood sugar; LDL-C, low-density lipoprotein cholesterol; HDL-C, high-density lipoprotein cholesterol. RASI, renin–angiotensin system inhibitor. ESRD, end-stage renal disease. Data are presented as the mean ± standard, the median with (interquartile range) or counts and percentages.
Table
Table 2. Pathologic features of patients with different PNI tertiles.
Table 2. Pathologic features of patients with different PNI tertiles.
Pathological LesionsAll Tertile 1Tertile 2Tertile 3p #r *p *
Glomerular class <0.001−0.412<0.001
I16 (5)0 (0)1 (0.9)15 (14)
II a68 (21.2)8 (7.5)20 (18.7)40 (37.4)
II b42 (13.1)12 (11.2)13 (12.1)17 (15.9)
III155 (48.3)74 (69.2)55 (51.4)26 (24.3)
IV40 (12.5)13 (12.1)18 (16.8)9 (8.4)
IFTA <0.001−0.282<0.001
09 (2.8)1 (0.9)1 (0.9)7 (6.5)
1148 (46.1)38 (35.5)44 (41.1)66 (61.7)
2134 (41.7)53 (49.5)51 (47.7)30 (28)
330 (9.3)15 (14)11 (10.3)4 (3.7)
Interstitial inflammation <0.001−0.271<0.001
019 (5.9)1 (0.9)3 (2.8)15 (14)
1234 (72.9)74 (69.2)80 (74.8)80 (74.8)
268 (21.2)32 (29.9)24 (22.4)12 (11.2)
Arteriolar hyalinosis 0.03−0.1560.005
032 (10)7 (6.5)7 (6.5)18 (16.8)
1176 (54.8)57 (53.3)59 (55.1)60 (56.1)
2113 (35.2)43 (40.2)41 (38.3)29 (27.1)
IFTA, interstitial fibrosis and tubular atrophy. # p value for the chi-squared test. * r: correlation coefficient analyzed using the Spearman test. * p value for the Spearman test.
Table
Table 3. Associations between PNI level and renal outcomes.
Table 3. Associations between PNI level and renal outcomes.
Per-SD Increment of PNIPNI Tertiles
HR (95% CI)p ValueHR (95% CI)p ValueHR (95% CI)
Univariate0.445 (0.38–0.522)<0.0010.368 (0.296–0.456)<0.001 Nutrients 14 03634 i001
Model 10.694 (0.516–0.934)0.0160.642 (0.430–0.959)0.030
Model 20.702 (0.520–0.946)0.0200.645 (0.432–0.964)0.033
Model 30.705 (0.523–0.952)0.0230.649 (0.434–0.971)0.036
Note: PNI was analyzed as a continuous variable with hazard ratios (HRs) calculated per SD increment of PNI. SD, standard deviation; CI, confidence interval. Model 1 adjusted for baseline age, gender, hypertension, smoking, DM duration, early onset of T2DM, e-GFR, proteinuria, hypoalbuminemia, and anemia. Model 2 adjusted for covariates in model 1 plus renal pathological findings (the glomerular class and IFTA). Model 3 adjusted for covariates in model 2 plus RASI use.
Table
Table 4. Prediction of ESRD among DN patients.
Table 4. Prediction of ESRD among DN patients.
VariablesModel 1Model 1 + PNI Tertilesp Value a
AUC0.843 (95% CI, 0.797–0.890)0.855 (95% CI, 0.811–0.900)0.10 b
IDI-0.621 (95% CI, 0.390–0.844)0.011
NRI-0.023 (95% CI, 0.005–0.040)<0.001
Model 1 included baseline age, gender, hypertension, smoking, DM duration, early onset of T2DM, e-GFR, proteinuria, hypoalbuminemia and anemia; AUC, area under the curve; IDI, integrated discrimination improvement; 95% CI, confidence interval; NRI, net reclassification improvement. a p value (model 1 + PNI tertiles versus model 1. b DeLong’s test.
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Share and Cite

MDPI and ACS Style

Zhang, J.; Xiao, X.; Wu, Y.; Yang, J.; Zou, Y.; Zhao, Y.; Yang, Q.; Liu, F. Prognostic Nutritional Index as a Predictor of Diabetic Nephropathy Progression. Nutrients 2022, 14, 3634. https://doi.org/10.3390/nu14173634

AMA Style

Zhang J, Xiao X, Wu Y, Yang J, Zou Y, Zhao Y, Yang Q, Liu F. Prognostic Nutritional Index as a Predictor of Diabetic Nephropathy Progression. Nutrients. 2022; 14(17):3634. https://doi.org/10.3390/nu14173634

Chicago/Turabian Style

Zhang, Junlin, Xiang Xiao, Yucheng Wu, Jia Yang, Yutong Zou, Yuancheng Zhao, Qing Yang, and Fang Liu. 2022. "Prognostic Nutritional Index as a Predictor of Diabetic Nephropathy Progression" Nutrients 14, no. 17: 3634. https://doi.org/10.3390/nu14173634

APA Style

Zhang, J., Xiao, X., Wu, Y., Yang, J., Zou, Y., Zhao, Y., Yang, Q., & Liu, F. (2022). Prognostic Nutritional Index as a Predictor of Diabetic Nephropathy Progression. Nutrients, 14(17), 3634. https://doi.org/10.3390/nu14173634

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop