Abstract
Background
The role of nutritional status, particularly the risk of malnutrition as quantified by the Geriatric Nutritional Risk Index (GNRI), in the development of diabetic nephropathy (DN) among patients with type 2 diabetes mellitus (T2DM) remains to be fully elucidated. Thus, this study aimed to investigate the cross-sectional association between GNRI-assessed nutritional risk and DN and to explore the potential mediating role of inflammatory markers.
Methods
A total of 675 hospitalized T2DM patients were enrolled. Multivariate logistic regression was used to determine whether GNRI is independently associated with DN. A restricted cubic spline (RCS) curve was used to visualize the association between GNRI and DN, and exploratory mediation analysis was conducted to assess the roles of systemic immune-inflammation index (SII) and neutrophil-to-lymphocyte ratio (NLR).
Results
Of the 675 T2DM patients, 190 (28.1%) were diagnosed with DN. Compared with individuals without DN, the GNRI score was significantly lower in the DN group (P < 0.001). Multivariate logistic regression analysis revealed that compared with the lowest tertile (T1) of GNRI, the highest tertile (T3) was significantly associated with a lower prevalence of DN (OR 0.53, 95%CI 0.31–0.89, P = 0.017). The RCS curve demonstrated a significant linear inverse association between higher GNRI scores and DN (P = 0.006). Mediation analysis suggested that SII and NLR statistically mediated 13.60% and 7.51% of the association between GNRI and DN, respectively.
Conclusion
In this cross-sectional study, lower GNRI scores were independently associated with DN in T2DM patients, and systemic inflammation partially explained this association. GNRI may serve as a potential and readily available clinical marker for nutritional-inflammation risk screening in this population.
Clinical trial number
Not applicable.
Supplementary Information
The online version contains supplementary material available at 10.1186/s12902-026-02198-x.
Keywords: Malnutrition risk, Diabetic nephropathy, Mediation analysis, Type 2 diabetes mellitus, Inflammation
Introduction
Diabetes mellitus (DM) is one of the significant global public health challenges, with its prevalence showing a continuous upward trend. It is projected to affect 1.31 billion people worldwide by 2050, predominantly in the form of type 2 diabetes mellitus (T2DM) [1]. As one of the countries with the highest diabetes burden, China currently has 140 million diabetic patients, accounting for approximately one-fifth of the global total [2]. Diabetic nephropathy (DN), one of the most severe microvascular complications of T2DM, is characterized by persistent proteinuria and progressive decline in glomerular filtration rate. Its pathogenesis involves multiple interacting pathways, including oxidative stress and chronic inflammation induced by disordered glucose and lipid metabolism, as well as epigenetic abnormalities [3]. Clinical data indicate that DN accounts for 30%-50% of end-stage renal disease cases and is a significant risk factor for cardiovascular disease and premature mortality in diabetic patients [4, 5]. However, current strategies for preventing and managing renal dysfunction in patients with DN remain limited, making the identification of modifiable risk factors clinically urgent.
Malnutrition is a common comorbidity in patients with chronic kidney disease (CKD) and is strongly linked to a range of adverse clinical outcomes, such as infection, cardiovascular events, and overall mortality [6]. Notably, emerging evidence suggests that malnutrition may not only be a consequence of diabetes and its complications but also a contributing factor to their development and progression [7]. This highlights the importance of accurate nutritional assessment and targeted interventions in DN patients to improve nutritional deficiencies, slow the progression of renal failure, and enhance prognosis. Among various nutritional assessment tools, the Geriatric Nutritional Risk Index (GNRI) is outstanding for its practicality. It uses routinely available parameters such as serum albumin, height, and body weight to provide an objective assessment [8]. GNRI is closely related to protein-energy wasting and has been validated as an effective predictor of clinical outcomes in patients with end-stage renal disease [9]. While the GNRI has been established as a predictor of mortality in dialysis patients and nutritional risk in various chronic diseases [10–12], its specific association with DN in T2DM patients and the underlying mechanisms remain unclear.
Systemic inflammation is recognized as a common underlying mechanism for the development of various chronic diseases [13]. It plays a key role in the progression of T2DM and the formation of DN [14, 15]. Research evidence also indicates that malnutrition is closely associated with systemic inflammatory status [16]. Therefore, systemic inflammation may be a key mechanistic link in the relationship between malnutrition and DN among patients with T2DM. Moreover, recent studies have highlighted the interplay between malnutrition, sarcopenia, and immuno-inflammatory states in aging and chronic diseases [17–19], underscoring the need to evaluate nutritional risk within a broader pathophysiological framework.
Thus, this cross-sectional study aimed to investigate the association between nutritional risk (quantified by GNRI) and DN in T2DM patients, and to explore the potential statistical mediation of systemic inflammation in this relationship.
Materials and methods
Study population
In this cross-sectional study, patients with T2DM who were hospitalized in the Department of Endocrinology at Hebei General Hospital from January 2022 to January 2025 were included.
Patients meeting any of the following criteria were excluded: age < 18 years; presence of serious acute diabetic complications (e.g., diabetic ketoacidosis or hyperosmolar hyperglycemic syndrome); pregnancy; primary kidney disease or other secondary kidney diseases; severe acute or chronic infections; history of malignancy; major organ failure (e.g., heart, lung); or other metabolic diseases (e.g., hyperthyroidism, hypothyroidism, hyperparathyroidism). A total of 675 patients with concomitant T2DM (based on the 2022 American Diabetes Association guideline [20]) were included in this study (Fig S1). They were then divided into two groups: patients with and without DN. In this study, DN was defined as T2DM patients with a urinary albumin-to-creatinine ratio (UACR) of ≥ 30 mg/g (measured at least twice during hospitalization), or an estimated glomerular filtration rate (eGFR) < 60 mL/min/1.73 m² [21].
Data collection and definition
Data were extracted retrospectively from medical records, laboratory reports, and imaging data using standardized electronic forms. Information on age, sex, comorbidities, alcohol consumption, smoking history, and medication history was recorded. Body mass index (BMI) was calculated as weight (in kilograms) divided by the square of height (in meters). Laboratory data included albumin, triglycerides, total cholesterol (TC), high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), uric acid (UA), creatinine, eGFR, fasting blood glucose (FBG), glycated hemoglobin (HbA1c), white blood cell count (WBC), neutrophil count (NC), lymphocyte count (LC), hemoglobin, platelet count (PLT), UACR, and 24-hour urinary protein excretion were measured. Urinary albumin concentrations were evaluated by an immunoturbidimetric assay. Proteinuria is defined as a 24-hour urine protein quantification > 0.15 g/24 h. Creatinine was measured using an enzymatic method on an automated analyzer. Estimated GFR was calculated by the Modification of Diet in Renal Disease equation. All laboratory assays were performed in the clinical laboratory of Hebei General Hospital using standardized clinical protocols and internal quality controls. Moreover, all data were anonymized at the source and managed on secure, password-protected hospital servers with restricted access limited to the research team.
The GNRI assessment formula: GNRI = 1.489×albumin (g/L) + 41.7×(actual body weight/ideal body weight). For males, the ideal body weight (kg) =height (cm)-100-(height [cm]-150)/4; for females, the ideal body weight (kg) =height (cm) -100-(height [cm]-150)/2.5 [22]. If the current body weight was less than the ideal body weight, the actual ratio (current/ideal) was used; otherwise, the ratio was set to 1.
The systemic immune-inflammation index (SII) is a novel, reliable, and effective inflammatory biomarker that reflects both local immune responses and systemic inflammatory reactions [23], defined as (PLT×NC)/LC. Additionally, the neutrophil-to-lymphocyte ratio (NLR) was calculated as NC/LC. The platelet-to-lymphocyte ratio (PLR) was also computed as PLT/LC for exploratory analysis.
Statistical analysis
Statistical analyses were performed using SPSS software (version 25.0; IBM Corp.) and R software (version 4.4.2; R Foundation for Statistical Computing). Continuous variables with non-normal distributions were expressed as medians (interquartile ranges, IQR). Categorical variables were presented as frequencies (percentages), and group differences were assessed using the chi-square test. Comparisons of various indicators between the DN group and the non-DN group were conducted using the Mann-Whitney U test. In contrast, comparisons among different GNRI groups were performed using the Kruskal-Wallis test. The Spearman rank correlation coefficient was used to evaluate the association between GNRI and DN. Univariate and multivariate binary logistic regression analyses were conducted to explore the relationship between GNRI and DN in patients with T2DM. Based on the multivariable-adjusted logistic regression model, a restricted cubic spline (RCS) curve with four knots was constructed to visualize the association between GNRI and DN further. To address potential overadjustment, a sensitivity analysis was performed by excluding proteinuria and eGFR from the multivariable model. Multicollinearity among covariates was assessed using variance inflation factors (VIF); all VIF values were < 3, indicating no substantial multicollinearity. Furthermore, the robustness of the results was validated through subgroup analysis stratified by age, gender, smoking status, alcohol consumption, BMI, hypertension, hyperlipidemia, and proteinuria, based on the results of multivariate adjusted logistic regression. Finally, mediation effect analysis was performed using the “mediation” package based on the multivariate adjusted logistic regression model to investigate the mediating roles of SII and NLR in the association between GNRI and DN. Given the cross-sectional design, this analysis evaluates statistical mediation and does not imply causality. The indirect effect, total effect, and proportion of the mediated effect were calculated through 1,000 simulations. Futhermore, data for the key variables used in this analysis (GNRI components, UACR, eGFR, inflammatory indices) were complete for all included participants. No imputation was performed as there were no missing values. A P-value < 0.05 was considered statistically significant.
Results
Study population
Among the 675 patients with T2DM, there were 402 males and 273 females, with a mean age of 61. Of these patients, 190 (28.1%) had DN.
A comparison of clinical parameters between the DN and non-DN groups is summarized in Table 1. Compared to patients without DN, those with DN were significantly older, had a longer diabetes duration, and exhibited lower albumin, eGFR, LDL-C, LC, hemoglobin, and GNRI levels, but higher UA, HbA1c, UACR, 24-hour urinary protein excretion, white WBC, NC, SII, NLR, and PLR (all P < 0.05). Furthermore, the prevalence of hypertension and proteinuria was significantly higher in the DN group (all P < 0.05). There was no significant difference in BMI, gender, smoking, drinking, hyperlipidemia, TC, TG, HDL-C, FBG, or PLT.
Table 1.
Comparison of characteristics between T2DM patients with DN and those without DN
| Variables | Non-DN (n = 485) | DN (n = 190) | Z/χ² | P* |
|---|---|---|---|---|
| Age (years) | 60.00 (53.00, 68.00) | 63.00 (56.00, 73.00) | -3.64 | < 0.001 |
| Body mass index (kg/m²) | 25.77 (23.53, 28.38) | 25.58 (23.63, 27.71) | -1.18 | 0.238 |
| Duration of diabetes (years) | 10.00 (5.00, 19.00) | 14.00 (10.00, 20.00) | -3.99 | < 0.001 |
| Male (%) | 193 (39.79) | 80 (42.11) | 0.30 | 0.582 |
| Smoking history (%) | 79 (16.53) | 29 (15.43) | 0.12 | 0.728 |
| Drinking history (%) | 71 (14.85) | 24 (12.77) | 0.48 | 0.488 |
| Hypertension (%) | 251 (51.75) | 122 (64.21) | 8.57 | 0.003 |
| Hyperlipidemia (%) | 168 (34.64) | 63 (33.16) | 0.13 | 0.715 |
| Proteinuria (%) | 125 (25.77) | 129 (67.89) | 103.20 | < 0.001 |
| Albumin (g/L) | 42.30 (39.60, 45.00) | 39.50 (36.60, 42.68) | -7.23 | < 0.001 |
| Uric acid (µmol/L) | 295.20 (241.60, 353.70) | 323.70 (246.18, 403.85) | -3.12 | 0.002 |
| Estimated glomerular filtration rate (mL/min/1.73 m2) | 97.64 (88.57, 105.66) | 82.17 (54.47, 96.45) | -9.41 | < 0.001 |
| Total cholesterol (mmol/L) | 4.54 (3.84, 5.54) | 4.45 (3.65, 5.36) | -0.72 | 0.470 |
| Triglyceride (mmol/L) | 1.27 (0.87, 1.95) | 1.33 (0.94, 2.12) | -1.23 | 0.219 |
| High-density lipoprotein cholesterol (mmol/L) | 1.17 (1.00, 1.36) | 1.15 (0.96, 1.36) | -1.23 | 0.218 |
| Low-density lipoprotein cholesterol (mmol/L) | 2.74 (2.08, 3.40) | 2.46 (1.86, 3.19) | -2.43 | 0.015 |
| Fasting blood glucose (mmol/L) | 8.18 (6.10, 10.99) | 8.50 (6.28, 11.84) | -1.24 | 0.217 |
| Glycated hemoglobin A1c (%) | 8.30 (7.10, 10.00) | 8.80 (7.35, 10.47) | -2.21 | 0.027 |
| Urinary albumin-to-creatinine ratio (mg/g) | 6.42 (1.51, 13.99) | 68.95 (37.31, 282.27) | -17.77 | < 0.001 |
| 24-hour urine protein quantification/g | 0.10 (0.06, 0.17) | 0.40 (0.18, 1.02) | -12.28 | < 0.001 |
| White blood cell count (×109 /L) | 6.01 (5.02, 7.04) | 6.34 (5.24, 7.84) | -3.18 | 0.001 |
| Neutrophil count (×109 /L) | 3.72 (2.93, 4.58) | 4.17 (3.30, 5.43) | -4.52 | < 0.001 |
| Lymphocyte count (×109 /L) | 1.76 (1.40, 2.13) | 1.58 (1.27, 1.94) | -3.44 | < 0.001 |
| Hemoglobin (g/L) | 141.00 (131.00, 153.00) | 133.00 (117.25, 144.00) | -6.20 | < 0.001 |
| Platelet (×109 /L) | 222.00 (190.00, 261.00) | 219.00 (183.00, 269.00) | -0.04 | 0.971 |
| Systemic immune-inflammation index | 465.06 (327.13, 655.39) | 598.29 (402.32, 812.80) | -4.93 | < 0.001 |
| Neutrophil-to-lymphocyte ratio | 2.09 (1.58, 2.72) | 2.57 (1.99, 3.57) | -5.67 | < 0.001 |
| Geriatric Nutritional Risk Index | 104.39 (100.07, 108.26) | 99.77 (95.19, 104.76) | -7.32 | < 0.001 |
Note: Data are presented as number (%) or median (25th–75th percentiles)
*P < 0.05 was considered statistically significant
Next, Patients were categorized into GNRI tertiles based on sample distribution: T1 (< 100.22), T2 (100.22-105.88), T3 (> 105.88) (Table 2). These cut-offs were data-driven for exploratory analysis and are not intended as clinical thresholds. Patients in the lowest GNRI tertile (T1) were significantly older, more likely to be female, and had lower BMI, albumin, eGFR, HDL-C, FBG, and hemoglobin levels compared to those in T2 and T3. Conversely, UACR, 24-hour urinary protein excretion, and NLR were higher in T1. Additionally, T1 patients had a longer diabetes duration and higher prevalence of proteinuria and DN, but a lower prevalence of hyperlipidemia (all P < 0.05). There were no statistically significant differences among the three groups in terms of smoking and drinking history, history of hypertension, UA, TC, TG, LDL-C, HbA1c, blood cell counts, SII, or PLR.
Table 2.
Comparison of characteristics of patients with T2DM based on the tertiles of GNRI scores
| Variables | T1 (n = 222) | T2 (n = 226) | T3 (n = 227) | Z/χ² | P* |
|---|---|---|---|---|---|
| Age (years) | 67.00 (58.25,73.00) | 61.00 (55.00,69.00) | 58.00 (52.00,65.50) | 51.35 | < 0.001 |
| Body mass index (kg/m²) | 24.86 (22.51,27.63) | 25.80 (23.63,28.41) | 26.15 (24.22,28.73) | 19.27 | < 0.001 |
| Duration of diabetes (years) | 15.00 (8.00,20.00) | 13.00 (6.25,20.00) | 9.50 (4.00,15.00) | 33.97 | < 0.001 |
| Male (%) | 105 (47.30) | 144 (63.72) | 153 (67.40) | 21.27 | < 0.001 |
| Smoking history (%) | 32 (14.48) | 33 (14.93) | 43 (19.20) | 2.22 | 0.329 |
| Drinking history (%) | 25 (11.31) | 32 (14.48) | 38 (16.96) | 2.92 | 0.232 |
| Hypertension (%) | 126 (56.76) | 132 (58.41) | 115 (50.66) | 3.05 | 0.218 |
| Hyperlipidemia (%) | 67 (30.18) | 69 (30.53) | 95 (41.85) | 8.85 | 0.012 |
| Proteinuria (%) | 106 (47.75) | 87 (38.50) | 61 (26.87) | 20.95 | < 0.001 |
| Diabetic Nephropathy (%) | 99 (44.59) | 53 (23.45) | 38 (16.74) | 46.76 | < 0.001 |
| Albumin (g/L) | 37.20 (35.52,38.50) | 41.60 (40.50,42.40) | 45.40 (44.20,46.75) | 553.68 | < 0.001 |
| Uric acid (µmol/L) | 286.70 (236.95,382.00) | 304.65 (243.08,361.32) | 301.90 (247.90,359.20) | 0.48 | 0.785 |
| Estimated glomerular filtration rate (mL/min/1.73 m2) | 87.43 (69.31,98.50) | 96.88 (83.23,103.09) | 99.25 (91.06,107.39) | 66.38 | < 0.001 |
| Total cholesterol (mmol/L) | 4.51 (3.65,5.35) | 4.48 (3.87,5.46) | 4.63 (3.79,5.56) | 1.70 | 0.427 |
| Triglyceride (mmol/L) | 1.27 (0.88,1.80) | 1.32 (0.89,1.89) | 1.30 (0.90,2.37) | 2.07 | 0.355 |
| High-density lipoprotein cholesterol (mmol/L) | 1.12 (0.92,1.35) | 1.15 (0.99,1.37) | 1.20 (1.06,1.37) | 11.77 | 0.003 |
| Low-density lipoprotein cholesterol (mmol/L) | 2.65 (2.05,3.25) | 2.68 (2.01,3.41) | 2.70 (1.96,3.38) | 0.72 | 0.698 |
| Fasting blood glucose (mmol/L) | 7.67 (5.53,10.78) | 8.02 (6.09,10.86) | 9.14 (6.89,12.00) | 19.26 | < 0.001 |
| Glycated hemoglobin A1c (%) | 8.60 (7.50,10.50) | 8.50 (7.20,10.10) | 8.10 (7.00,9.80) | 5.36 | 0.068 |
| Urinary albumin-to-creatinine ratio (mg/g) | 16.64 (2.44,89.22) | 9.48 (2.23,22.78) | 9.75 (3.13,21.29) | 13.82 | < 0.001 |
| 24-hour urine protein quantification/g | 0.18 (0.08,0.50) | 0.12 (0.08,0.25) | 0.10 (0.06,0.17) | 29.84 | < 0.001 |
| White blood cell count (×109 /L) | 6.12 (5.07,7.63) | 6.14 (5.13,7.22) | 5.99 (5.08,6.97) | 2.30 | 0.316 |
| Neutrophil count (×109 /L) | 3.92 (3.05,5.01) | 3.73 (2.90,4.75) | 3.77 (3.08,4.60) | 3.97 | 0.137 |
| Lymphocyte count (×109 /L) | 1.66 (1.25,2.01) | 1.74 (1.42,2.19) | 1.71 (1.37,2.04) | 4.34 | 0.114 |
| Hemoglobin (g/L) | 127.00 (116.00,138.00) | 139.00 (129.25,150.00) | 148.00 (139.00,159.00) | 165.17 | < 0.001 |
| Platelet (×109 /L) | 219.50 (181.25,267.75) | 223.00 (183.00,257.00) | 221.00 (194.00,263.00) | 1.12 | 0.571 |
| Systemic immune-inflammation index | 524.86 (347.54,770.94) | 458.43 (322.29,680.96) | 485.86 (371.26,700.07) | 4.34 | 0.114 |
| Neutrophil-to-lymphocyte ratio | 2.46 (1.71,3.52) | 2.09 (1.56,2.72) | 2.18 (1.74,2.84) | 9.61 | 0.008 |
| Geriatric Nutritional Risk Index | 96.64 (93.55,98.43) | 103.15 (101.60,104.54) | 109.15 (107.43,111.24) | 599.12 | < 0.001 |
Note: Data are presented as number (%) or median (25th–75th percentiles)
*P < 0.05 was considered statistically significant
Association between GNRI and DN in patients with T2DM
Spearman correlation analysis revealed a weak but significant inverse association between GNRI and DN (r =-0.282, P < 0.001; Fig. 1). This suggests that while lower GNRI is associated with higher DN prevalence, other factors likely contribute substantially. Univariate logistic regression identified age, diabetes duration, hypertension, proteinuria, UA, eGFR, LDL-C, and GNRI as significant factors associated with DN. Multivariable logistic regression, adjusted for age, diabetes duration, hypertension, proteinuria, UA, eGFR, and LDL-C, demonstrated a significant inverse dose-response relationship between GNRI and DN. Specifically, compared to the lowest GNRI tertile (T1), the highest tertile (T3) was associated with a significantly reduced risk of DN (OR 0.53, 95%CI 0.31–0.89, P = 0.017) (Table 3). Furthermore, proteinuria (OR 2.13, 95%CI 1.73–2.62, P < 0.001) and reduced eGFR (OR 0.96, 95%CI 0.94–0.97, P < 0.001) were also independent risk factors for DN. Finally, RCS was plotted based on the fully adjusted logistic regression model to visually describe the relationship between GNRI scores and DN in patients with T2DM (Fig. 2). The RCS curve demonstrated a significant linear inverse association between higher GNRI scores and the risk of DN (P = 0.006).
Fig. 1.
Scatter plot of GNRI scores versus DN status (1 = Non-DN, 2 = DN)
Table 3.
Univariate and multivariate logistic regression analysis of risk factors for DN in patients with T2DM
| Variables | Univariate analysis | Multivariate analysis | ||
|---|---|---|---|---|
| Odds ratio (95% CI) | P | Odds ratio (95% CI) | P | |
| Age | 1.03 (1.02 ~ 1.05) | < 0.001 | 0.99 (0.97 ~ 1.01) | 0.338 |
| Body mass index | 0.96 (0.92 ~ 1.01) | 0.090 | ||
| Duration of diabetes | 1.04 (1.02 ~ 1.06) | < 0.001 | 1.01 (0.99 ~ 1.04) | 0.354 |
| Gender | 1.10 (0.78 ~ 1.55) | 0.582 | ||
| Smoking history | 0.92 (0.58 ~ 1.46) | 0.729 | ||
| Drinking history | 0.84 (0.51 ~ 1.38) | 0.488 | ||
| Hypertension | 1.67 (1.18 ~ 2.36) | 0.004 | 0.99 (0.65 ~ 1.52) | 0.962 |
| Hyperlipidemia | 0.94 (0.66 ~ 1.34) | 0.715 | ||
| Proteinuria | 6.09 (4.22 ~ 8.78) | < 0.001 | 2.13 (1.73 ~ 2.62) | < 0.001 |
| Uric acid | 1.01 (1.01 ~ 1.01) | < 0.001 | 1.00 (1.00 ~ 1.00) | 0.408 |
| Estimated glomerular filtration rate | 0.95 (0.94 ~ 0.96) | < 0.001 | 0.96 (0.94 ~ 0.97) | < 0.001 |
| Total cholesterol | 0.97 (0.85 ~ 1.11) | 0.686 | ||
| Triglyceride | 1.03 (0.93 ~ 1.15) | 0.562 | ||
| High-density lipoprotein cholesterol | 0.70 (0.39 ~ 1.25) | 0.222 | ||
| Low-density lipoprotein cholesterol | 0.83 (0.70 ~ 0.99) | 0.043 | 0.91 (0.74 ~ 1.13) | 0.383 |
| Fasting blood glucose | 1.03 (0.99 ~ 1.08) | 0.185 | ||
| Glycated hemoglobin A1c | 1.08 (1.00 ~ 1.17) | 0.057 | ||
| Geriatric Nutritional Risk Index | ||||
| T1 (< 100.22) | 1 | |||
| T2 (≥ 100.22& < 105.88) | 0.38 (0.25 ~ 0.57) | < 0.001 | 0.51 (0.31 ~ 0.84) | 0.007 |
| T3 (≥ 105.88) | 0.25 (0.16 ~ 0.39) | < 0.001 | 0.53 (0.31 ~ 0.89) | 0.017 |
Fig. 2.
The association between GNRI scores and DN in patients with T2DM visualized by restricted cubic spline
Sensitivity analysis (Table S1), excluding proteinuria and eGFR from the multivariable model, yielded consistent results for the association between GNRI tertiles and DN (T3 vs. T1: OR 0.29, 95% CI 0.18–0.47, P < 0.001), suggesting the main finding is robust to potential overadjustment concerns.
Subgroup analysis
This study examined the association between GNRI and DN in patients with T2DM across subgroups defined by age, gender, BMI, smoking status, alcohol consumption, hypertension, hyperlipidemia, and proteinuria, using multivariate logistic regression analysis after adjusting for potential confounding factors (Table 4). In most predefined subgroups, a consistent inverse association between GNRI and DN was observed. A significant interaction (P for interaction < 0.05) was found only for smoking status, indicating a potential regulatory effect.
Table 4.
Stratified analysis of the relationship between GNRI and DN in patients with T2DM
| Variables | OR (95%CI) | P | P for interaction |
|---|---|---|---|
| All patients | 0.95 (0.92 ~ 0.98) | < 0.001 | |
| Age | 0.081 | ||
| < 60 | 0.91 (0.86 ~ 0.97) | 0.002 | |
| ≥ 60 | 0.96 (0.92 ~ 1.00) | 0.053 | |
| Gender | 0.885 | ||
| Female | 0.95 (0.90 ~ 1.00) | 0.045 | |
| Male | 0.94 (0.90 ~ 0.99) | 0.010 | |
| Smoking | 0.008 | ||
| Yes | 0.85 (0.77 ~ 0.94) | 0.002 | |
| No | 0.96 (0.93 ~ 1.00) | 0.045 | |
| Drinking | 0.846 | ||
| Yes | 1.00 (0.89 ~ 1.12) | 0.964 | |
| No | 0.95 (0.91 ~ 0.98) | 0.002 | |
| Body mass index | 0.219 | ||
| < 24 | 0.96 (0.90 ~ 1.01) | 0.113 | |
| 24–28 | 0.96 (0.91 ~ 1.02) | 0.172 | |
| ≥ 28 | 0.90 (0.84 ~ 0.97) | 0.003 | |
| Hypertension | 0.438 | ||
| Yes | 0.96 (0.92 ~ 1.01) | 0.087 | |
| No | 0.93 (0.88 ~ 0.97) | 0.003 | |
| Hyperlipidemia | 0.809 | ||
| Yes | 0.95 (0.90 ~ 1.01) | 0.133 | |
| No | 0.94 (0.91 ~ 0.98) | 0.003 | |
| Proteinuria | 0.827 | ||
| Yes | 0.93 (0.89 ~ 0.98) | 0.004 | |
| No | 0.95 (0.91 ~ 1.00) | 0.049 |
Stratified analysis was constructed based on multivariate logistic regression model
Mediation analyses
As shown in Fig. 3, the mediating roles of SII and NLR in the association between nutritional risk, as assessed by GNRI, and DN were evaluated. The results showed that SII mediated 13.60% of the association between nutritional risk assessed by GNRI and DN (P < 0.05). NLR mediated 7.51% of the association between nutritional risk assessed by GNRI and DN (P < 0.05). We also explored the potential mediating role of the PLR. While PLR levels were higher in the DN group (Table 1), its indirect effect on the GNRI-DN association was not statistically significant (proportion mediated: 3.77%, P = 0.11). Therefore, PLR was not considered a significant mediator in the final model.
Fig. 3.
Estimated proportion of the association between GNRI and DN mediated by SII (A) and NLR (B)
Discussion
This study mainly investigated the association between nutritional risk (assessed by GNRI) and DN in T2DM patients. Although previous studies have linked nutritional status to CKD [10, 11], the predictive value of GNRI for DN and the potential mediating role of inflammation are not well established. To our knowledge, this study is the first to demonstrate in a T2DM population that nutritional risk assessed by GNRI is independently associated with DN, and that systemic inflammation partially mediates this association. Moreover, in subgroup analysis, no interactive factors affecting the relationship between GNRI and DN were identified except for smoking.
Malnutrition is a critical risk factor for various chronic disease complications and mortality. Previous studies have shown that chronic malnutrition may promote the occurrence of DM in individuals by gradually impairing β-cell function and reducing islet cell volume [24]. In addition, malnutrition may exacerbate the progression of DN through multiple mechanisms: hypoalbuminemia leads to a decrease in plasma colloid osmotic pressure, thereby worsening edema and glomerular hyperfiltration; increased muscle wasting enhances protein catabolism, promoting the accumulation of uremic toxins; meanwhile, nutritional deficiencies may weaken the antioxidant defense system, thereby exacerbating oxidative stress-induced kidney damage [25]. The GNRI is an index designed to assess the risk of complications related to nutritional status [8]. Therefore, actively exploring the relationship between nutritional risk and diabetic complications is of great clinical significance.
Our analysis revealed a distinct clinical characteristics associated with lower GNRI values. These patients were typically older, more often female, and presented with a lower BMI, an extended history of diabetes, as well as reduced circulating levels of albumin and hemoglobin. They also exhibited more advanced renal impairment and a greater burden of hypertension and proteinuria. Consequently, the prevalence of DN was markedly higher in this group with poorer nutritional status. These findings align with those of Kim et al. [11], who similarly reported that a lower GNRI was correlated with decreased eGFR, hemoglobin, BMI, and albumin levels in a T2DM cohort. Additionally, the observed correlation between GNRI and DN, though statistically significant, was weak (r =-0.282). This indicates that while nutritional risk is associated with DN, it accounts for only a modest proportion of variance, underscoring the multifactorial nature of DN pathogenesis.
It is important to note that the GNRI incorporates both serum albumin and body weight. In our study, its lower value in the DN group appeared to be driven primarily by hypoalbuminemia rather than low BMI. The fact that GNRI was associated with DN independent of BMI underscores that it captures dimensions of risk beyond adiposity, notably inflammation-driven hypoalbuminemia and potential sarcopenia, which remain relevant even in patients with normal or elevated BMI. This suggests that GNRI likely reflects not only nutritional deficiency but also inflammation-related protein catabolism and renal protein loss. Albumin is not only a nutritional marker but also a negative acute-phase reactant; its synthesis is suppressed during systemic inflammation, linking low GNRI to inflammatory states [26]. Moreover, research in dialysis patients has shown that GNRI is a more robust predictor of mortality than albumin or BMI alone, supporting its value as a composite index that may encapsulate both nutritional and inflammatory risk dimensions [27]. The prognostic significance of albumin and albumin-based indices like GNRI extends beyond renal outcomes. For instance, they have been associated with atrial fibrillation recurrence after ablation [28], risk stratification in acute coronary syndrome [29], and long-term mortality in patients with cardiac implantable electronic devices [30]. These findings underscore that the pathophysiological state captured by GNRI (a blend of malnutrition and inflammation) is a common denominator for increased vulnerability to diverse diabetic complications, including both microvascular (e.g., DN) and macrovascular events.
Our multivariable logistic regression analysis, adjusting for key confounders including age, diabetes duration, hypertension, proteinuria, and eGFR, revealed a significant inverse dose-dependent relationship between GNRI scores and DN. Additionally, proteinuria and lower eGFR were confirmed as strong, independent risk factors for DN. Consistent with our findings, a cohort study by Fujiwara et al. found that lower GNRI was associated with an increased risk of renal events and all-cause mortality in patients with T2DM [12]. Moreover, another cohort study in the T2DM population reported that a low GNRI score is an important prognostic indicator for the progression of CKD [11]. However, Kiuchi et al. showed that while a lower GNRI in CKD patients was significantly associated with mortality and cardiovascular events, it had no impact on renal outcomes despite the presence of heavy proteinuria [31]. The discrepancies between these findings may stem from differences in study populations (e.g., inclusion criteria), diagnostic criteria for renal outcomes, and ethnic backgrounds. Our study confirms an independent association between lower GNRI and DN in T2DM patients, utilizing a sufficiently large sample size and appropriate statistical methods. Consequently, early nutritional assessment and intervention are considered optimal strategies for patients with DN. Notably, our subgroup analysis revealed that smoking status may potentially moderate the association between GNRI and DN. This may be attributed to smoking exacerbating renal damage through oxidative stress, while also potentially suppressing appetite and impairing nutrient intake. In smokers, the combined insults of tobacco-induced oxidative stress, endothelial damage, and exacerbated nutritional deficits may create a multiple-hit scenario, intensifying the link between nutritional risk and renal pathology. This finding suggests that smokers with DN may require particular attention to nutritional status.
In addition, our mediation analysis revealed that systemic inflammation, as assessed by the SII and NLR, significantly mediated the inverse association between GNRI and DN. However, due to the cross-sectional design and simultaneous measurement of variables, we cannot infer temporal or causal mediation. Previous evidence suggests that inflammation plays a significant role in the pathogenesis and progression of renal function decline. A large database study revealed that systemic inflammation, as assessed by the SII, was positively correlated with increased urinary albumin excretion in American adults [13]. Animal studies have also suggested that inflammation may contribute to the decline in renal function and the development of CKD [32–34]. This involves a complex interplay among factors such as chronic inflammation, oxidative stress, hypoxia, and mitochondrial dysfunction, all of which play key roles in the etiology, pathophysiology, and progression of CKD. Furthermore, systemic inflammation can activate intersecting pathways, including the coagulation system, which may exacerbate endothelial dysfunction and microvascular injury in the kidney - a process relevant to the progression of DN [35]. Previous research has found that malnutrition may exacerbate kidney damage through an “inflammation-catabolic cycle” [16]. Specifically, inadequate energy intake triggers muscle catabolism, releasing pro-inflammatory cytokines (e.g., interleukin-6), which in turn suppress appetite and further promote protein breakdown, establishing a vicious cycle. Moreover, low albumin levels reduce antioxidant capacity, making the kidneys more susceptible to oxidative stress-induced damage and accelerating interstitial fibrosis [36]. Additionally, a study by Nakagawa et al. [37] found that higher levels of tumor necrosis factor-alpha and C-reactive protein were associated with increased all-cause and cardiovascular mortality in malnourished patients, indicating that inflammatory markers may serve as predictors of all-cause and cardiovascular mortality in individuals with malnutrition. Collectively, these findings, along with our mediation results, suggest that systemic inflammation may be one of the mechanisms linking malnutrition to the development of DN. Thus, routine screening of T2DM patients for nutritional risk using GNRI, coupled with assessment of inflammatory markers, is warranted. Early nutritional support and anti-inflammatory dietary interventions should be considered for patients identified as having a low GNRI.
This study has limitations. First, its single-center, retrospective, cross-sectional design precludes the establishment of causal relationships or temporal sequences between GNRI and DN. Second, residual confounding from unmeasured factors such as detailed dietary intake, physical activity, socioeconomic status, and specific medication use (e.g., sodium-dependent glucose transporters 2 inhibitors, glucagon-like peptide-1 receptor agonists) cannot be excluded. The assessment of inflammation was also limited, as classic markers like interleukin-6 and C-reactive protein were not included. Third, the operational definition of DN, based on albuminuria and/or reduced eGFR, aligns with clinical standards but may not fully capture all diabetic kidney disease phenotypes, particularly normoalbuminuric DN-an inherent challenge in large-scale clinical studies lacking renal histopathology. Fourth, the mediation analysis indicates statistical mediation, not necessarily biological causation, due to the cross-sectional design. Fifth, the GNRI tertile cut-offs were data-driven for this cohort and may not represent generalizable clinical thresholds. Finally, the GNRI itself is a composite index reflecting both nutritional status and inflammation-related protein metabolism, which are inherently intertwined. Future prospective cohort studies incorporating multi-omics approaches are needed to validate the association between GNRI and DN and to elucidate the underlying mechanisms.
In conclusion, this cross-sectional study found that lower GNRI scores are associated with a higher prevalence of DN in older T2DM patients. Systemic inflammation partially accounted for this association in a statistical mediation model. GNRI may serve as a potential and readily available clinical marker for nutritional-inflammation risk screening in this population. Future longitudinal studies are needed to confirm these findings and explore the utility of GNRI in risk stratification and nutritional intervention.
Supplementary Information
Below is the link to the electronic supplementary material.
Acknowledgements
The authors thank all the institutions and individuals who supported this research.
Abbreviations
- T2DM
Type 2 diabetes mellitus
- DN
Diabetic nephropathy
- GNRI
Geriatric nutritional risk index
- SII
Systemic immune-inflammation index
- NLR
Neutrophil-to-lymphocyte ratio
- PLR
Platelet-to-lymphocyte ratio
- VIF
Variance inflation factors
- CKD
Chronic kidney disease
- eGFR
Estimated glomerular filtration rate
- UACR
Urinary albumin-to-creatinine ratio
- BMI
Body mass index
- HDL-C
High-density lipoprotein cholesterol
- LDL-C
Low-density lipoprotein cholesterol
- UA
Uric acid
- FBG
Fasting blood glucose
- HbA1c
Glycated hemoglobin A1c
- WBC
White blood cell count
- NC
Neutrophil count
- LC
Lymphocyte count
- PLT
Platelet count
- OR
Odds ratio
- CI
Confidence interval
- RCS
Restricted cubic spline
Author contributions
YL T and LM W conceptualized and supervised the study. YL T and LM W organized the data and conducted the analyses. YQ X contributed to the interpretation of the results, revision. YL T and YQ X prepared the manuscript. LM W revised the manuscript. All authors have read and approved the manuscript.
Funding
This study was supported by the Clinical Medical Talent Project of Hebei Provincial Government (Jicai She [2021] No. 73, 2022.1–2024.12). Project title: Effects and mechanisms of thyroid hormone receptor β1 regulation on glucose and lipid metabolism.
Data availability
The partial datasets used and/or analyzed during the current study are available from the corresponding author on reasonable requests.
Declarations
Ethics approval and consent to participate
This study has been approved by the Medical Ethics Committee of Hebei General Hospital ([2025] Scientific Research Ethics Review No. 442). The study was conducted in accordance with the Declaration of Helsinki. Due to the retrospective nature of the study, the need for informed consent was waived by the ethics committee; however, all patient data were anonymized and de-identified prior to analysis. This observational study was reviewed and approved by the Medical Ethics Committee of Hebei General Hospital (Approval No. [2025] Scientific Research Ethics Review No. 442). The study was conducted in accordance with the ethical standards of the Declaration of Helsinki. Due to its retrospective design, the requirement for informed consent was waived by the aforementioned ethics committee. All patient data were anonymized and de-identified prior to analysis to protect participant privacy.
Consent for publication
Not applicable.
Competing interests
The authors declare no competing interests.
Footnotes
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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Supplementary Materials
Data Availability Statement
The partial datasets used and/or analyzed during the current study are available from the corresponding author on reasonable requests.