Abstract
The neutrophil-to-albumin ratio (NPAR) has been identified as an emerging biomarker for systemic inflammation. However, its association with diabetic kidney disease (DKD) remains unclear. This study aims to evaluate the relationship between NPAR and DKD using data from the National Health and Nutrition Examination Survey 2017 to 2020. A total of 8031 participants were included after excluding subjects under 18 years and those with missing data on DKD or NPAR. Participants were categorized based on DKD status. Weighted logistic regression and restricted cubic spline analyses were performed to investigate the association between NPAR and DKD. Higher NPAR levels were significantly associated with increased DKD risk (odds ratio = 2.11, 95% confidence interval: 1.60–2.80, P < .001 in Model 3, adjusted for demographic and clinical covariates). Subgroup analysis indicated that the association was consistent across age, gender, and body mass index categories. Restricted cubic spline analysis suggested a linear relationship between NPAR and DKD risk (P-overall < .001, P-nonlinear = .174). The area under the curve for NPAR predicting DKD was 0.652. Elevated NPAR is associated with an increased prevalence of DKD. However, due to the cross-sectional nature of the study, causality cannot be inferred. These findings highlight the potential of NPAR as an inflammatory marker for DKD risk stratification.
Keywords: diabetic kidney disease, inflammation, logistic regression, neutrophil percentage to albumin ratio, NHANES
1. Introduction
Diabetic kidney disease (DKD), a microvascular complication of diabetes mellitus, remains one of the leading causes of end-stage renal disease globally. According to recent epidemiological data, approximately 30% to 40% of individuals with type 2 diabetes will eventually develop DKD, making it a major contributor to both renal morbidity and mortality.[1–3] In the United States, DKD accounts for nearly 50% of dialysis initiation cases, disproportionately affecting aging and minority populations.[4] Despite advances in glycemic and blood pressure control, the prevalence of DKD continues to rise in parallel with global diabetes burden.[5–8] The pathophysiology of DKD is multifactorial, involving a complex interplay of metabolic, hemodynamic, and inflammatory processes. Traditionally, hyperglycemia was viewed as the principal driver of glomerular injury, mediated through pathways such as advanced glycation end products, oxidative stress, and intraglomerular hypertension.[9–13] However, emerging evidence has shifted focus toward systemic inflammation as a crucial accelerator of kidney injury in diabetes. Persistent low-grade inflammation, characterized by elevated levels of cytokines (e.g., IL-6, TNF-α) and immune cell activation, promotes endothelial dysfunction, mesangial expansion, and tubulointerstitial fibrosis (all of which are hallmarks of DKD progression).[14–16]
In this context, the identification of inflammatory biomarkers that are both practical and predictive is of clinical value. Among these, the neutrophil-to-albumin ratio (NPAR) has emerged as a novel composite index that integrates 2 important aspects of disease biology: innate immune activation and nutritional status.[17,18] Neutrophils are pro-inflammatory effector cells that infiltrate renal tissue in early DKD, while serum albumin reflects both nutritional reserve and anti-inflammatory capacity.[19,20] A higher NPAR may therefore indicate a pro-inflammatory, catabolic state associated with adverse renal outcomes. From a prevention and treatment standpoint, early identification of at-risk individuals is critical, given that DKD is largely asymptomatic in early stages.[21] Current therapeutic strategies (such as renin–angiotensin system blockers, SGLT2 inhibitors, and GLP-1 receptor agonists) have shown renal protective effects.[22,23] Nonetheless, these treatments are most effective when initiated before irreversible damage occurs. The integration of reliable inflammatory biomarkers like NPAR could potentially enhance risk stratification and prompt timely intervention.[24,25] This study aims to bridge this knowledge gap by analyzing data from the National Health and Nutrition Examination Survey (NHANES) to examine the relationship between NPAR and diabetic kidney disease among U.S. adults. Specifically, the objectives of this study are to examine the association between NPAR and the presence of diabetic kidney disease, and assess whether this relationship persists after adjusting for traditional metabolic and demographic risk factors. We hypothesize that elevated NPAR levels are independently associated with increased odds of DKD, reflecting a synergy of inflammatory burden and compromised protein status. Addressing these questions will provide a deeper understanding of NPAR as a potential predictor of DKD risk and its relevance in clinical practice.
2. Materials and methods
2.1. NHANES study population
The data for this study were sourced from the NHANES database (https://www.cdc.gov/nchs/nhanes/index.htm). NHANES represents a large cross-sectional survey of the United States population, conducted using a complex, stratified, multistage sampling approach. Investigators collected a comprehensive array of information from participants through home interviews, laboratory testing, and other methods (detailed methods can be found in the NHANES survey manuals and analysis guidelines). This dataset provides a representative reflection of the health and nutritional status of the noninstitutionalized civilian population. NHANES has received approval from the Ethics Review Board of the National Center for Health Statistics, and all participants provided written informed consent.
Data were extracted from the NHANES 2017 to 2020 cycles, a nationwide, cross-sectional survey employing a complex, multistage, probability sampling design. A total of 15,560 individuals were initially considered. Participants under the age of 18 (n = 5868) were excluded. Among those with complete demographic and laboratory data (n = 9692), individuals with missing information on DKD status (n = 1042) and NPAR (n = 619) were excluded. Ultimately, 8031 participants were included in the final analysis (Fig. 1), participants with missing values for key variables (DKD status, NPAR, and covariates) were excluded using a complete-case analysis approach. No imputation was performed.
Figure 1.
Flowchart of study participant inclusion and exclusion criteria.
2.2. Definition of diabetic kidney disease
The diagnosis of diabetes mellitus[26]: the use of diabetes medication or insulin, a diagnosis by a doctor, glycated hemoglobin HbA1c ≥ 6.5%, fasting glucose ≥ 7 mmol/L (126 mg/dL), 2-hour oral glucose tolerance test with blood glucose ≥ 11.1 mmol/L (200 mg/dL). The DKD was defined as the diabetes combined with albuminuria (urinary albumin-to-creatinine ratio ≥ 30 mg/g) and/or low-estimated glomerular filtration rate (eGFR) (eGFR < 60 mL/min/1.73 m2) according to the KDIGO 2021 Guidelines.[27] For eGFR calculation, we applied the CKD-Epidemiology Collaboration equation as follows[28]: GFR = 141·min (Scr/κ,1)α × max (Scr/κ, 1) − 1.209 × 0.993 age × 1.018 (if women) × 1.159 (if black); κ was 0.7 (women) or 0.9 (men), a was −0.329 (women) or −0.411 (men), and min/max indicate the minimum/maximum of Scr/κ or 1.
2.3. Assessment of NPAR
The Coulter VCS system was used to determine the white blood cell (WBC) differential. NPAR was calculated using the following formula:The NPAR was calculated as: neutrophil percentage (% of total WBC count) × 100/albumin (g/dL).[29,30] The same blood sample was used for both the WBC differential and albumin measurement. NPAR values were used as the primary exposure variable in this study.
2.4. Covariates
Based on previous literature and biological plausibility, a wide range of covariates were considered to minimize confounding. Demographic variables included age (years), sex (male/female), and race/ethnicity (Mexican American, Non-Hispanic Black, Non-Hispanic White, Other). Socioeconomic factors were represented by education level (less than high school, high school or equivalent, above high school), marital status (living alone, married/living with partner), and the ratio of family income to poverty level (<1.0, 1.0–2.9, ≥3.0). Lifestyle-related factors included smoking status (yes/no) and alcohol consumption (yes/no), both self-reported. Clinical covariates consisted of body mass index (BMI, kg/m²), systolic and diastolic blood pressure, and comorbid conditions such as hypertension and hyperlipidemia, defined based on self-report and measured biomarkers.
Laboratory variables reflecting renal, hepatic, and metabolic function included uric acid (mg/dL), blood urea nitrogen (mg/dL), serum phosphorus (mg/dL), serum calcium (mg/dL), alanine aminotransferase (ALT, U/L), and aspartate aminotransferase (AST, U/L). All laboratory tests were performed using standardized procedures in the NHANES Mobile Examination Centers and are publicly available with complete documentation. Covariates were selected based on biological plausibility and prior literature linking them to DKD risk. Other inflammatory biomarkers such as C-reactive protein (CRP) or IL-6 were not available in NHANES 2017 to 2020, which limited our ability to include them in the models.
2.5. Statistical analysis
Analyses were conducted using R software (version 4.1.3, R Foundation for Statistical Computing, Vienna, Austria) incorporating NHANES-provided sample weights, stratification, and primary sampling units to account for the complex sampling design. Continuous variables were expressed as means ± standard deviations and compared using Student t tests. Categorical variables were reported as frequencies (percentages) and compared using Chi-square tests. All P-values were 2-sided, with <.05 considered statistically significant.
To assess the association between NPAR and DKD, weighted multivariable logistic regression models were constructed. Odds ratios and 95% confidence intervals (CIs) were calculated across NPAR quartiles, using the lowest quartile (Q1) as the reference.
adjusted for age, sex, and race/ethnicity.
further adjusted for education level, marital status, and poverty ratio.
additionally adjusted for BMI, smoking, alcohol consumption, calcium, ALT, AST, uric acid, blood urea nitrogen, phosphorus, hypertension, and hyperlipidemia (Table 2).
Table 2.
Odds ratios for associations between NPAR and DKD.
| Model | Quartiles of NPAR | P for trend | |||
|---|---|---|---|---|---|
| Q1 | Q2 | Q3 | Q4 | ||
| Model 1 | 1.00 | 1.27 (0.94–1.71) | 1.92 (1.47–2.54) | 2.89 (2.23–3.77) | <.001 |
| Model 2 | 1.00 | 1.26 (0.93–1.70) | 1.91 (1.45–2.52) | 2.83 (2.18–3.71) | <.001 |
| Model 3 | 1.00 | 1.09 (0.80–1.49) | 1.63 (1.23–2.18) | 2.11 (1.60-2.80) | <.001 |
Weighted logistic regression analysis on the association between NPAR and DKD. Model 1 was adjusted for age, sex, race. Model 2 was adjusted for age, sex, race, education level, marital status, ratio of family income to poverty level. Model 3 was adjusted for age, race, sex, education level, marital status, ratio of family income to poverty level, smoking, drinking, BMI, calcium, ALT, AST, uric acid, BUN, phosphorus, hypertension, hyperlipidemia.
ALT = alanine aminotransferase, AST = aspartate aminotransferase, BMI = body mass index, BUN = blood urea nitrogen, DKD = diabetic kidney disease, NPAR = neutrophil-to-albumin ratio.
Trend analysis was conducted by assigning the median value of each quartile as a continuous variable.
Restricted cubic spline regression was used to investigate potential nonlinear relationships between NPAR and DKD risk, with knots placed at the 10th, 50th, and 90th percentiles of the NPAR distribution. The reference value was set at the median of Q1. The overall and nonlinear P-values were calculated using likelihood ratio tests (Fig. 2). Subgroup analyses were performed stratified by age (<60 vs ≥60 years), sex, BMI (<25 vs ≥25 kg/m²), smoking, drinking status, hypertension, and hyperlipidemia. Interaction terms were included in the fully adjusted model to assess effect modification (Fig. 3). The diagnostic performance of NPAR in identifying DKD was evaluated using receiver operating characteristic (ROC) curve analysis. The area under the curve (AUC) was calculated to assess discriminatory ability (Fig. 4).
Figure 2.
Restricted cubic spline analysis. Survey-weighted logistic regression with knots at the 10th, 50th, and 90th percentiles. The solid line shows adjusted ORs, and the shaded area the 95% CI. Odds of DKD increased linearly with higher NPAR. Covariates included demographics, lifestyle, and clinical factors. CI = confidence interval, DKD = diabetic kidney disease, NPAR = neutrophil-to-albumin ratio, ORs = odds ratios.
Figure 3.
Forest plot subgroup analysis. Forest plot of adjusted ORs per 1-unit increase in NPAR across age, sex, BMI, smoking, drinking, hypertension, and hyperlipidemia. Associations were consistent, with stronger effects in participants < 60 years. BMI = body mass index, NPAR = neutrophil-to-albumin ratio, ORs = odds ratios.
Figure 4.
ROC curve analysis. ROC analysis showed modest discrimination (AUC = 0.652). The dot marks the Youden-optimal cutoff with sensitivity, specificity, and 95% CIs (bootstrap, 2000 resamples). A weighted analysis gave similar results. AUC = area under the curve, CIs = confidence intervals, ROC = receiver operating characteristic.
3. Results
3.1. Baseline characteristics
A total of 8031 participants from NHANES 2017 to 2020 were included in the analysis (Fig. 1), among whom 641 individuals (8.0%) met the criteria for DKD. As summarized in Table 1, participants with DKD were older (64.8 ± 12.4 vs 48.2 ± 18.1 years, P < .001), had a higher proportion of males (57.3% vs 48.3%, P < .001), and were more likely to have lower educational attainment (P < .001). The DKD group had higher body mass index (32.7 ± 7.6 vs 29.6 ± 7.4 kg/m², P < .001) and uric acid levels (6.09 ± 1.72 vs 5.35 ± 1.43 mg/dL, P < .001). There were no significant differences in calcium, ALT, or AST between the 2 groups.
Table 1.
Summary descriptives table by groups of ``DKD’’.
| [ALL] | No | Yes | P.overall | |
|---|---|---|---|---|
| N = 8031 | N = 7390 | N = 641 | ||
| Age | 49.5 (18.3) | 48.2 (18.1) | 64.8 (12.4) | <.001 |
| Gender | <.001 | |||
| Female | 4094 (51.0%) | 3820 (51.7%) | 274 (42.7%) | |
| Male | 3937 (49.0%) | 3570 (48.3%) | 367 (57.3%) | |
| Race | .683 | |||
| Black | 2007 (25.0%) | 1836 (24.8%) | 171 (26.7%) | |
| Mexican | 999 (12.4%) | 916 (12.4%) | 83 (12.9%) | |
| Other | 2202 (27.4%) | 2031 (27.5%) | 171 (26.7%) | |
| White | 2823 (35.2%) | 2607 (35.3%) | 216 (33.7%) | |
| Education | <.001 | |||
| Above high school | 4761 (59.3%) | 4461 (60.4%) | 300 (46.8%) | |
| High school or equivalent | 1839 (22.9%) | 1678 (22.7%) | 161 (25.1%) | |
| Under high school | 1431 (17.8%) | 1251 (16.9%) | 180 (28.1%) | |
| Marital status | .086 | |||
| Living alone | 3196 (39.8%) | 2920 (39.5%) | 276 (43.1%) | |
| Married/living with partner | 4835 (60.2%) | 4470 (60.5%) | 365 (56.9%) | |
| Ratio of family income to poverty level | <.001 | |||
| <1.0 | 1655 (20.6%) | 1523 (20.6%) | 132 (20.6%) | |
| ≥3.0 | 3093 (38.5%) | 2894 (39.2%) | 199 (31.0%) | |
| 1.0–2.9 | 3283 (40.9%) | 2973 (40.2%) | 310 (48.4%) | |
| BMI | 29.9 (7.44) | 29.6 (7.38) | 32.7 (7.59) | <.001 |
| Calcium | 9.28 (0.38) | 9.28 (0.37) | 9.28 (0.43) | .737 |
| ALT | 22.2 (19.1) | 22.2 (19.2) | 22.6 (17.9) | .592 |
| AST | 21.9 (14.6) | 21.9 (14.7) | 21.9 (13.6) | .940 |
| Uric acid | 5.41 (1.47) | 5.35 (1.43) | 6.09 (1.72) | <.001 |
| ALP | 4.07 (0.34) | 4.08 (0.33) | 3.90 (0.38) | <.001 |
| BUN | 14.9 (5.89) | 14.3 (5.02) | 21.1 (10.1) | <.001 |
| Phosphorus | 3.57 (0.53) | 3.57 (0.52) | 3.60 (0.64) | .261 |
| Smoking | <.001 | |||
| No | 4790 (59.6%) | 4468 (60.5%) | 322 (50.2%) | |
| Yes | 3241 (40.4%) | 2922 (39.5%) | 319 (49.8%) | |
| Drinking | .254 | |||
| No | 1186 (14.8%) | 1081 (14.6%) | 105 (16.4%) | |
| Yes | 6845 (85.2%) | 6309 (85.4%) | 536 (83.6%) | |
| Hypertension | <.001 | |||
| No | 5068 (63.1%) | 4900 (66.3%) | 168 (26.2%) | |
| Yes | 2963 (36.9%) | 2490 (33.7%) | 473 (73.8%) | |
| Hyperlipidemia | .576 | |||
| No | 7325 (91.2%) | 6736 (91.2%) | 589 (91.9%) | |
| Yes | 706 (8.79%) | 654 (8.85%) | 52 (8.11%) | |
| NPAR | 14.1 (2.75) | 14.0 (2.68) | 15.6 (3.11) | <.001 |
Mean + SD for continuous variables: the P value was calculated by the weighted linear regression model. (%) for categorical variables: the P value was calculated by the weighted Chi-square test.
BMI = body mass index, DKD = diabetic kidney disease, NPAR = neutrophil-to-albumin ratio.
3.2. Association between NPAR and DKD
Table 2 presents the association between NPAR quartiles and DKD status. In the fully adjusted model (Model 3), the odds of DKD were significantly higher in participants within the highest NPAR quartile (Q4), with an odds ratio (OR) of 2.11 (95% CI: 1.60–2.80) compared to the lowest quartile (Q1). This trend remained consistent across all models and was statistically significant (P for trend < .001), suggesting a graded increase in DKD risk with higher NPAR levels.
3.3. Dose–response relationship
Restricted cubic spline analysis was used to visualize the relationship between NPAR and DKD risk as a continuous variable (Fig. 2). The association appeared approximately linear (P-overall < .001; P-nonlinear = .174), with the odds of DKD gradually increasing as NPAR rose. The slope of the curve became steeper beyond an NPAR value of approximately 10.7.
3.4. Subgroup and interaction analyses
Subgroup analyses demonstrated that the positive association between NPAR and DKD was consistent across all strata (Fig. 3). A significant interaction was observed by age group (P for interaction = .007); the association was stronger among participants aged < 60 years (OR = 1.82, 95% CI: 1.57–2.12) than those aged ≥ 60 years (OR = 1.48, 95% CI: 1.34–1.64). No significant interactions were detected for sex, BMI, smoking, drinking, hypertension, or hyperlipidemia.
3.5. Discriminative ability of NPAR for DKD
ROC curve analysis was performed to assess the discriminative performance of NPAR in predicting DKD (Fig. 4). The AUC was 0.652, indicating modest ability of NPAR to distinguish between individuals with and without DKD.
4. Discussion
This study presents novel evidence that an elevated NPAR is independently associated with DKD in U.S. adults, based on a large, nationally representative NHANES cohort. The association remained robust after adjustment for a broad spectrum of confounding variables and was consistently observed across subgroups. Notably, a linear dose–response relationship was evident between NPAR and DKD, with a steep increase in risk beyond NPAR levels of approximately 10.7. These results support the hypothesis that NPAR (reflecting both immune activation and protein homeostasis) captures a pathophysiologic dimension highly relevant to renal impairment in diabetes. NPAR integrates these 2 dimensions (pro-inflammatory neutrophilia and hypoalbuminemia) into a single index. Compared to standalone markers such as CRP or neutrophil-to-lymphocyte ratio, NPAR may offer a more nuanced perspective, particularly in diabetic populations where inflammation and nutritional status are frequently intertwined.[31] Moreover, NPAR has demonstrated predictive value not only in renal outcomes but also in broader diabetic complications including cardiovascular mortality, sepsis progression, and hepatic fibrosis.[30,32] In our stratified analyses, we found that the association between NPAR and DKD was particularly pronounced in individuals aged < 60 years, which may be explained by the more dynamic inflammatory profiles in younger adults or by longer duration of uncontrolled diabetes. These findings align with recent observations by Ye et al, who reported that inflammatory biomarkers were more predictive of microvascular complications in early-onset type 2 diabetes.[33] It is plausible that in younger individuals, an elevated NPAR may serve as an early signal of immune-nutritional imbalance, prompting earlier intervention before the onset of irreversible nephron loss.Our ROC analysis indicated a modest discriminatory ability of NPAR for DKD (AUC = 0.652). Although this modest AUC (0.652) indicates limited discriminatory power for clinical screening, it is comparable to many single-point inflammatory indices. Future research should evaluate whether combining NPAR with other inflammatory or renal biomarkers could enhance predictive accuracy in risk models. Importantly, the clinical utility of NPAR may lie in its simplicity and accessibility. Both neutrophil percentage and serum albumin are commonly measured in routine practice, making NPAR an attractive adjunct to risk models in both hospital and community settings, especially where advanced biomarkers such as cystatin C, KIM-1, or NGAL are unavailable.[34] In resource-constrained environments or large-scale screening programs, NPAR may serve as a preliminary filter to identify high-risk individuals for further evaluation.
Several prior studies have examined the relationship between NPAR and chronic kidney disease. Li et al first reported a significant association between higher NPAR levels and CKD prevalence using pooled NHANES data from 2009 to 2018.[35] More recently, Li et al extended this analysis to diabetic kidney disease and identified a similar linear relationship using NHANES 1999 to 2018 data.[36] Our findings not only validate but strengthen this body of evidence by demonstrating that the NPAR–DKD association remains intact in more contemporary cohorts (2017–2020), and by further quantifying the inflection threshold and subgroup interactions.
It is worth noting that while NPAR is a stable, reproducible, and cost-effective metric, its biological interpretation may vary across clinical contexts. For instance, in patients with acute infection or malignancy, NPAR may reflect transient inflammation rather than chronic low-grade processes. In the setting of DKD, however, the persistent inflammatory state (often coexisting with protein-energy wasting) renders NPAR particularly relevant. Our study also suggests that NPAR may act as a surrogate for the complex interplay between microinflammation, renal endothelial dysfunction, and metabolic dysregulation, all hallmarks of progressive DKD.[37]
Nevertheless, our study has several limitations. First, the cross-sectional nature of NHANES data precludes causal inference. While we observed a strong association between NPAR and DKD, temporality cannot be established. Longitudinal studies are necessary to determine whether elevated NPAR precedes renal decline or merely reflects preexisting renal injury. Second, DKD status was assessed using single-point eGFR and urinary albumin-to-creatinine ratio values, which may be influenced by transient physiological fluctuations or medication use. Third, residual confounding is possible, particularly from factors such as dietary intake, infection status, or unrecorded medication history (e.g., corticosteroids, statins), which may influence both neutrophil counts and serum albumin levels.
Fourth, although we adjusted for multiple clinical covariates, certain key inflammatory cytokines (e.g., IL-1β, CRP, and adiponectin) and oxidative stress markers were not available in the NHANES dataset. Their inclusion in future studies could help clarify the precise inflammatory pathways linking NPAR to renal dysfunction. Fifth, the modest AUC value suggests that while NPAR is associated with DKD, it should not be used as a standalone diagnostic tool but rather integrated into composite prediction models. Finally, generalizability to non-U.S. populations, especially those with different inflammatory profiles or nutritional baselines, warrants further investigation.
Despite these limitations, the strengths of this study are substantial. We leveraged a large, ethnically diverse, and rigorously collected dataset representative of the U.S. adult population. Our analytic strategy included multiple adjustments, spline modeling, subgroup analyses, and ROC testing to ensure robustness of the findings. To our knowledge, this is the first NHANES-based analysis to comprehensively characterize the NPAR–DKD relationship in the most recent cohort cycles with a clear threshold estimation and clinical interpretability.
From a translational standpoint, our findings highlight the potential of NPAR as a pragmatic tool to identify patients at heightened risk of DKD. It offers a window into the combined burden of inflammation and nutritional imbalance (2 domains frequently under-assessed yet highly actionable). As the global burden of diabetes continues to rise, and with DKD being a major contributor to end-stage renal disease, early detection of at-risk individuals is critical. Whether NPAR could guide treatment stratification, such as timing of SGLT2 inhibitors or nutritional interventions, remains an important question for future research.
5. Conclusion
Our cross-sectional study shows that NPAR is independently associated with the prevalence of diabetic kidney disease in U.S. adults, though causal relationships cannot be established. As a simple and accessible marker, it reflects both inflammation and nutritional status. NPAR may help identify high-risk patients earlier and support better disease monitoring. Its integration into clinical practice could improve DKD prevention and management. Future studies should explore its role in risk prediction models and treatment response tracking. By bridging basic pathophysiology and practical screening, NPAR offers new insight into personalized care for diabetic complications.
Acknowledgments
We thank the NCHS for their efforts in creating the data for the NHANES.
Author contributions
Formal analysis: Zhengxin Liu.
Methodology: Zhengxin Liu, Yining He.
Project administration: Weiming He.
Resources: Zhengxin Liu.
Software: Qin Song, Weiming He.
Supervision: Xiaofan Yin.
Writing – original draft: Zhengxin Liu, Qin Song, Yining He, Xiaofan Yin.
Writing – review & editing: Weiming He, Han Zhang, Jiaqian Li.
Abbreviations:
- ALT
- alanine aminotransferase
- AST
- aspartate aminotransferase
- AUC
- area under the curve
- BMI
- body mass index
- CIs
- confidence intervals
- CRP
- C-reactive protein
- DKD
- diabetic kidney disease
- NHANES
- National Health and Nutrition Examination Survey
- NPAR
- neutrophil-to-albumin ratio
- ORs
- odds ratios
- WBC
- white blood cell
This project was funded by the the Jiangsu Provincial Medical Innovation Center (grant no. 202215) and National Natural Science Foundation of China (82174294).
The ethics review board of the National Center for Health Statistics approved all NHANES protocols, and written informed consents were obtained from all participants or their proxies. All the experiment protocol for involving humans was in accordance to guidelines of national/international/institutional or Declaration of Helsinki in the manuscript.
The authors have no conflicts of interest to disclose.
The datasets generated during and/or analyzed during the current study are publicly available.
How to cite this article: Liu Z, Song Q, He Y, Yin X, He W, Zhang H, Li J. Association between NPAR and diabetic kidney disease: Insights from NHANES 2017 to 2020. Medicine 2025;104:40(e44867).
Contributor Information
Zhengxin Liu, Email: lxinguangzx@163.com.
Qin Song, Email: 1402239734@qq.com.
Yining He, Email: yfy0019@njucm.edu.cn.
Xiaofan Yin, Email: 1579614770@qq.com.
Han Zhang, Email: hanzhang985@163.com.
Jiaqian Li, Email: 1910687268@qq.com.
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