A risk prediction model for kidney disease in type 2 diabetes mellitus based on the ratio of fibrinogen to prealbumin

A risk prediction model for kidney disease in type 2 diabetes mellitus based on the ratio of fibrinogen to prealbumin | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article A risk prediction model for kidney disease in type 2 diabetes mellitus based on the ratio of fibrinogen to prealbumin Beiyi Li, Kexin Gan, Peng Qiu, Pei Zhao, Xiuqin Lv, Dongmei Zhang, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6703304/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Objective The relationship between the fibrinogen to prealbumin ratio (FPR) and diabetic kidney disease (DKD) in patients with type 2 diabetes mellitus (T2DM), to explore the related risk factors of DKD and construct a nomogram model for the risk of DKD, so as to provide a simpler observation index for clinical prediction of DKD and a basis for early intervention and delaying the progress of DKD. Methods This cross-sectional study enrolled 500 patients with T2DM who visited the Endocrinology Department of Hebei Provincial People’s Hospital from January 2023 to May 2024. Clinical data, including demographic characteristics (gender, age), disease duration, smoking/alcohol history, and laboratory serum indicators (including the fibrinogen-to-prealbumin ratio, FPR), were collected. Participants were categorized into a DKD group and a NDKD group based on the presence or absence of DKD. Intergroup differences in clinical characteristics were analyzed using t-tests or Mann-Whitney U tests. A nomogram model for predicting DKD risk was developed using SPSS 25.0 and R software, with emphasis on evaluating the predictive value of FPR. A significance threshold of P < 0.05 was applied for all statistical analyses. Results This study found that DKD patients had significantly lower ALB and eGFR but higher hypertension prevalence, SBP, TG, Scr, BUN, FIB, and FPR compared to NDKD patients (P 16.49 significantly increased DKD risk (P < 0.001). A predictive model incorporating these factors achieved an AUC of 0.738 (95%CI:0.694–0.782) and a C-index of 0.738 after Bootstrap validation, with optimal net benefit (0.122) at a 0.6 threshold. Conclusions FPR, duration of DM, SBP, TG, and BUN are risk factors for the development of DKD in patients with T2DM. Additionally, the combination of FPR, duration of DM, SBP, TG, and BUN can accurately predict the risk of DKD development, offering significant clinical value in the diagnosis of DKD. Type 2 diabetes Fibrinogen to prealbumin ratio Diabetic kidney disease Risk factors Nomogram Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction The global burden of diabetes continues to escalate, leading to severe complications and premature mortality. According to the latest research reports, in 2021, there were an estimated 536.6 million diabetic patients aged 20–79 years across 215 countries and territories. By 2045, this number is projected to rise to 783.2 million. China has the world's largest diabetic population, with over 140 million cases estimated in 2021, expected to surpass 174 million by 2045 [ 1 ] . Diabetes can cause damage to multiple organ systems, including the eyes, nervous system, feet, vascular system, heart, and kidneys. DKD is one of the most common complications of T2DM and a leading cause of End-Stage Renal Disease (ESRD) [ 2 ] . In its early stages, DKD is characterized by persistent elevation of albuminuria and gradual decline in glomerular filtration function. If untreated at an early stage, the condition progresses rapidly, potentially leading to ESRD [ 3 ] . Therefore, enhancing early screening and clinical interventions for DKD holds significant practical importance.Currently, practiceRenal biopsy is often considered the gold standard for diagnosing DKD clinically, but it is typically performed only when other renal pathologies are suspected. A clinical diagnosis of DKD is usually made when there is persistent albuminuria or a gradual decrease in the estimated Glomerular Filtration Rate (eGFR) below 60ml/min/1.73m² [ 4 ] . DKD can manifest without persistent albuminuria. Consequently, when eGFR is still within the normal range and before episodes of elevated urinary albumin, these markers are clearly inadequate for assessing early-stage DKD [ 5 ] . This study seeks to identify additional biomarkers that can predict the progression of DKD. Research by Liu et al. indicates that chronic inflammation is a crucial factor in DKD progression and mediates key mechanisms of tubular injury [ 6 ] . Fibrinogen (FIB), primarily produced by the liver, acts as an acute-phase reactant and promotes platelet aggregation. Elevated blood glucose levels in diabetic patients lead to vascular damage, increasing FIB levels, which results in higher blood viscosity and hypercoagulability. This predisposes individuals to thrombosis, obstructing renal vasculature, inducing ischaemia-hypoxia, and damaging endothelial cells, thereby accelerating kidney injury [ 7 – 9 ] . Prealbumin (PALB), a negative acute-phase reactant, enhances catabolism to counteract inflammatory responses and reflects short-term inflammatory changes. The FPR, emerging as a novel biomarker for assessing systemic inflammation, has been increasingly used in cancer diagnostics and prognosis [ 10 ] .This study is the first to develop a DKD risk prediction model based on the FPR, offering a simpler and more effective tool for assessing DKD risk in T2DM patients. It is designed to improve the accuracy of DKD risk assessment. Methods 1.1 Subjects A cohort of 500 patients with T2DM who were admitted to the Endocrinology Department at Hebei Provincial People's Hospital for treatment between January 2023 and May 2024 was selected. Inclusion criteria: The diagnosis of T2DM adhered to the criteria outlined in the 2024 American Diabetes Association (ADA). Exclusion criteria: (1)Presence of concurrent infections; (2)Type 1 diabetes and other special types of diabetes; (3)Presence of acute diabetic complications (diabetic ketoacidosis, hyperosmolar hyperglycaemic state, etc. (4)Acute cardiovascular or cerebrovascular diseases, severe liver dysfunction (alanine aminotransferase > 2.5 times the upper limit of normal); (5)Presence of autoimmune diseases, malignancies, or haematological disorders; (6)History of renal diseases other than DKD; (7)Presence of other significant diseases; (8)Incomplete clinical data or records. 1.2 Data Collection and Laboratory Analysis Data were collected for each participant, including gender, age, duration of diabetes, smoking history, alcohol consumption history, history of hypertension, systolic blood pressure (SBP), and diastolic blood pressure (DBP). Height (in cm) and weight (in kg) were measured to calculate body mass index (BMI). All inpatients maintained a fasting state of at least 8 hours, and venous blood and urine samples were collected the following morning. The following parameters were measured: Albumin (ALB), Prealbumin (PALB), Alanine aminotransferase (ALT), Aspartate aminotransferase (AST), Total cholesterol (TC), Triglycerides (TG), High-density lipoprotein cholesterol (HDL-C), Low-density lipoprotein cholesterol (LDL-C), Estimated glomerular filtration rate (eGFR), Serum creatinine (Scr), Blood urea nitrogen (BUN), Fibrinogen (FIB), Glycated haemoglobin (HbA1c). Complete blood counts were analysed using a flow cytometer, biochemical indicators were measured using an automated biochemical analyser, HbA1c was determined by high-performance liquid chromatography, and FIB was measured using an automated coagulation analyser. All laboratory tests were performed by our clinical laboratory technicians. 1.3 Data Calculation and Grouping The Fibrinogen to Prealbumin Ratio (FPR) was calculated using the following formula: FPR=[FIB(g/L)/PALB(mg/dL)]×100 Based on the aforementioned diagnostic criteria for diabetic kidney disease, patients with T2DM were grouped according to the presence or absence of DKD. The participants were divided into two groups: the DKD group (n = 218) and the NDKD group (n = 282). Results 2.1 Comparison of Basic Clinical Characteristics and Laboratory Parameters Between DKD and NDKD Groups Comparative analysis revealed that the DKD group had significantly lower levels of albumin (ALB) and estimated glomerular filtration rate (eGFR) compared to the NDKD group (P<0.05). In contrast, the DKD group exhibited significantly higher levels of hypertension history, systolic blood pressure (SBP), triglycerides (TG), serum creatinine (Scr), blood urea nitrogen (BUN), fibrinogen (FIB), and fibrinogen to prealbumin ratio (FPR) (P<0.05). There were no significant differences between the two groups regarding the following parameters: gender, smoking history, alcohol consumption history, age body mass index (BMI), diastolic blood pressure (DBP), glycated haemoglobin (HbA1c), alanine aminotransferase (ALT), aspartate aminotransferase (AST), prealbumin (PALB), total cholesterol (TC), low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C). (Table 1) 2.2 Correlation Analysis of FPR with Other Variables in T2DM Patients Spearman correlation analysis indicated that, in T2DM patients, the FPR was positively correlated with the presence of DKD, age, duration of diabetes mellitus (DM), HbA1c, and FIB levels (P<0.05). Conversely, FPR was negatively correlated with BMI, DBP, ALT, AST, ALB, PALB, TC, TG, HDL-C, and eGFR levels (P<0.05). There was no significant correlation between FPR, SBP, LDL-C, Scr, and BUN levels. (Table 2) 2.3 Binary Logistic Regression Analysis of DKD Risk Factors A logistic regression analysis was conducted to develop a model predicting the occurrence of DKD that includes FPR. Dependent variable was the presence or absence of DKD, while independent variables included statistically significant indicators identified previously. Univariate and multivariate logistic regression analyses were performed. After removing collinear variables such as ALB, PALB, and FIB, multivariate logistic regression analysis revealed that FPR, duration of DM, SBP, TG, and BUN are risk factors for DKD (P<0.05). Even after adjusting for multiple confounding factors, FPR remained an independent risk factor for DKD. Therefore, T2DM patients with higher FPR, longer DM duration, higher SBP, higher TG, and higher BUN have a greater risk of developing DKD (P<0.05). (Table 3) 2.4 Development of a Nomogram to Predict DKD Risk in T2DM Patients 2.4.1 Construction of a Nomogram for Predicting DKD Risk in T2DM Patients Use R software to construct a nomogram model predicting the risk of DKD based on DM duration, SBP, TG, BUN, and FPR (Figure 1). Each factor is assigned different values according to its impact on DKD. On the point scale in the graph, determine the score corresponding to each predictive indicator, calculate the total score, and the value on the risk scale corresponding to the total point scale represents the probability of DKD occurrence in T2DM patients. 2.4.2 Evaluation of the Nomogram Model for Predicting DKD Risk The Receiver Operating Characteristic (ROC) curve was plotted for the nomogram model designed to predict the risk of DKD (Figure 2). The Area Under the Curve (AUC) for this model is 0.738 (95%CI: 0.694-0.782). For internal validation, the Bootstrap resampling method was employed with 1000 iterations. The C-index, a measure of the model’s discriminatory power, was determined to be 0.738. This high C-index value confirms that the model's predictions are accurate and reliable. Additionally, the calibration curves—both the predicted calibration curve and the bias calibration curve—are closely aligned with the ideal curve, indicating that the model has excellent calibration. (Figure 3) Additionally, a decision curve of the model was plotted to directly visualize whether patients would benefit and to evaluate the clinical utility of the model. The All line represents the scenario where all patients are assumed to have DKD, while the None line corresponds to the assumption that no patients have DKD. The model demonstrates clinical value when its curve lies above both the All and None lines. By adjusting the threshold, we assessed the model’s net benefit and determined the optimal threshold to be 0.6, at which the net benefit reached 0.122. (Figure 4) Table 1 Comparison of baseline clinical features and laboratory parameters in DKD group and NDKD group NDKD(n=282) DKD(n=218) P Male(%) 182(64.5%) 137(62.8%) 0.696 Smoking(%) 49(17.4%) 43(19.7%) 0.501 Drinking(%) 41(14.5%) 31(14.2%) 0.920 Age(years) 58.00(50.75,65.00) 60.00(49.75,68.00) 0.079 DM duration(years) 10.00(5.00,15.00) 10.00(5.00,20.00) 0.002 Hypertension(%) 153(54.3%) 143(65.6%) 0.011 BMI(kg/m 2 ) 26.36(24.00,28.59) 26.55(23.91,29.18) 0.639 SBP(mmHg) 131.00±17.60 135.00(121.00,152.00) 0.007 DBP(mmHg) 83.57±11.58 85.00(78.00,93.25) 0.197 HbA1c 8.60(7.43,10.08) 8.65(7.38,10.10) 0.684 ALT 21.30(14.75,29.63) 19.20(12.73,28.83) 0.051 AST 20.10(16.50,26.43) 19.40(15.40,26.40) 0.390 ALB 42.40(39.48,45.43) 40.35(36.18,43.75) <0.001 PALB 23.75(19.58,28.63) 23.88±8.07 1 TC 4.70(3.82,5.57) 4.77(3.75,5.85) 0.437 TG 1.44(0.99,2.10) 1.60(1.08,2.77) 0.029 LDL-C 2.97(2.31,3.66) 2.90(2.27,3.67) 0.863 HDL-C 1.13(1.00,1.36) 1.12(0.94,1.35) 0.140 eGFR 99.53(91.51,109.62) 92.41(65.87,105.64) <0.001 Scr 62.80(54.10,73.00) 73.40(57.80,90.85) <0.001 BUN 5.40(4.60,6.50) 6.10(4.65,8.15) <0.001 FIB 2.87(2.42,3.33) 3.19(2.70,4.26) <0.001 FPR 11.92(9.62,15.67) 12.91(9.94,20.50) 0.006 Note: a indicates χ2 value. Significance at a P value of <0.05. Abbreviations: DM, diabetes mellitus; BMI, body mass index; SBP, systolic blood pressure; DBP, diastolic blood pressure; HbA1c, hemoglobin A1c; ALT, alanine aminotransferase; AST, aspartate aminotransferase; ALB, albumin; PALB, prealbumin; TC, total cholesterol; TG, triglyceride; LDL-C, low-density lipoprotein cholesterol; HDL-C, high-density lipoprotein cholesterol; eGFR, estimated glomerular filtration rate; Scr, serum creatinine; BUN, blood urea nitrogen; FIB, fibrinogen; FPR, fibrinogen to prealbumin ratio. Table 2 Correlation analysis between FPR and clinical indicators in DKD patients r P DKD 0.123 0.006 Age(years) 0.277 ＜0.001 DM duration(years) 0.117 0.009 BMI(kg/m 2 ) -0.091 0.042 SBP(mmHg) 0.012 0.783 DBP(mmHg) -0.179 ＜0.001 HbA1c 0.108 0.019 ALT -0.235 ＜0.001 AST -0.130 0.004 ALB -0.543 ＜0.001 PALB -0.666 ＜0.001 TC -0.090 0.045 TG -0.276 ＜0.001 LDL-C -0.048 0.295 HDL-C -0.118 0.009 eGFR -0.215 ＜0.001 Scr -0.003 0.953 BUN -0.021 0.646 FIB 0.745 ＜0.001 Note: a indicates χ2 value. Significance at a P value of <0.05. Abbreviations: DKD, diabetic kidney disease; DM, diabetes mellitus; BMI, body mass index; SBP, systolic blood pressure; DBP, diastolic blood pressure; HbA1c, hemoglobin A1c; ALT, alanine aminotransferase; AST, aspartate aminotransferase; ALB, albumin; PALB, prealbumin; TC, total cholesterol; TG, triglyceride; LDL-C, low-density lipoprotein cholesterol; HDL-C, high-density lipoprotein cholesterol; eGFR, estimated glomerular filtration rate; Scr, serum creatinine; BUN, blood urea nitrogen; FIB, fibrinogen. Table 3 Binary logistic regression analysis of factors influencing DKD Univariate Logistic Regression Analysis Multivariate Logistic Regression Analysis OR 95%CI P OR 95%CI P DM duration(years) 1.041 1.017-1.065 0.001 1.036 1.007-1.065 0.014 Hypertension(%) 1.608 1.116-2.315 0.011 0.990 0.616-1.589 0.965 SBP(mmHg) 1.015 1.005-1.024 0.002 1.018 1.006-1.030 0.002 TG 1.186 1.071-1.314 0.001 1.327 1.174-1.501 ＜0.001 eGFR 0.973 0.965-0.982 ＜0.001 0.992 0.973-1.011 0.384 Scr 1.023 1.014-1.031 ＜0.001 1.009 0.994-1.025 0.246 BUN 1.255 1.150-1.371 ＜0.001 1.150 1.011-1.309 0.033 FPR 1.033 1.016-1.050 ＜0.001 1.041 1.022-1.061 ＜0.001 Note: a indicates χ2 value. Significance at a P value of <0.05. Abbreviations: DM, diabetes mellitus; SBP, systolic blood pressure; TG, triglyceride; eGFR, estimated glomerular filtration rate; Scr, serum creatinine; BUN, blood urea nitrogen; FPR, fibrinogen to prealbumin Discussion Diabetic kidney disease (DKD) is one of the microvascular complications of diabetes, ultimately leading to end-stage renal disease, accounting for approximately 40% of patients requiring renal replacement therapy. DKD is a progressive chronic condition characterised by glomerular hypertrophy, proteinuria, declining glomerular filtration, and renal fibrosis. Hyperglycaemic conditions can result in loss of renal function. As DKD does not present with noticeable clinical symptoms in its early stages, patients may rapidly progress to end-stage renal disease without receiving appropriate treatment. Although microalbuminuria serves as an early biomarker for DKD, its sensitivity and specificity in predicting DKD risk are relatively low. Furthermore, the gold standard for diagnosing DKD remains largely dependent on pathological changes observed through renal biopsy. However, renal biopsy is an invasive procedure that carries risks such as infection and bleeding during the puncture process. Therefore, it is particularly important to identify non-invasive biomarkers with high sensitivity to predict the progression of DKD [11] . The findings of this study indicate that the occurrence of DKD is positively correlated with levels of FPR, with DKD patients exhibiting significantly elevated FPR levels. Even after adjusting for multiple confounding factors, FPR remains an independent risk factor for DKD. The higher the FPR levels, the greater the risk of DKD in patients with Type 2 Diabetes Mellitus (T2DM). Additionally, the research demonstrates that FPR correlates with various inflammatory markers, suggesting that FPR may contribute to the pathogenesis of DKD through its involvement in inflammatory responses. Fibrinogen (FIB), primarily synthesised by hepatocytes as an acute-phase protein, serves as a precursor in the formation of blood clots. Numerous studies have confirmed that FIB plays a significant role in patients with T2DM, including those with DKD [12] . Elevated FIB levels leading to a hypercoagulable state can result in glomerular microvascular lesions [7] . Potential factors contributing to this hypercoagulable state include glucose and lipid metabolism abnormalities, a micro-inflammatory environment, oxidative stress, hypoalbuminaemia, haemodynamic changes, and platelet activation in DKD patients. These factors interweave with the coagulation system to collectively exacerbate the condition. Additionally, these factors can induce vascular endothelial cell damage, creating a hypercoagulable environment. This can lead to microthrombus formation, reduced renal blood flow, and glomerulosclerosis, thereby perpetuating a self-sustaining cycle that aggravates the progression of DKD [13] . Similarly, our study found that FIB levels are significantly higher in DKD patients compared to non-DKD patients. Prealbumin (PALB) serves as an important serum marker for nutrition and inflammation. It is a negative acute-phase protein synthesised by the liver, primarily comprising Retinol-Binding Protein and Thyroxine-Binding Globulin (TTR). Compared to Albumin (ALB), PALB has a shorter half-life and higher sensitivity, and it is less affected by parenteral nutrition, making it more responsive to changes in nutritional status. Consequently, PALB is considered a valuable biomarker for assessing patient prognosis [14] . PALB functions as a non-specific host defence substance in the body, clearing toxic metabolic waste produced during infections, which leads to its gradual depletion. As a result, during the acute-phase inflammatory response, PALB levels tend to decrease with the progression of inflammation [15] . However, in our study, we did not observe a decrease in PALB levels among DKD patients. Currently, there is no consensus on whether PALB levels decline in DKD patients, indicating a need for further research to clarify this aspect. FPR, through its interactions with FIB and PALB, is increasingly recognised as a novel biomarker for defining inflammatory responses and immunonutrition. Recent studies have identified FPR as a prognostic marker for various cancers [16,17] . Concerning its relevance to diabetes, a retrospective study by Zhao et al. found that FPR levels are significantly higher in patients with Diabetic Cardioautonomic Neuropathy (DCAN). Even after adjusting for confounding variables, FPR remains independently associated with DCAN [18] . Additionally, a cross-sectional study from China demonstrated that FPR is positively correlated with tubular injury in early-stage DKD [10] . These findings underscore the potential significance of FPR as a biomarker in understanding and managing diabetic complications. Given the high prevalence of DKD, it is essential to identify risk factors for DKD early and implement timely interventions to slow its progression. Several known risk factors are linked to an increased risk of diabetic nephropathy, including the duration of diabetes, smoking, age, systolic blood pressure (SBP), glycated haemoglobin (HbA1c), body mass index (BMI), serum creatinine (Scr), blood urea nitrogen (BUN), urinary protein, lipid levels, and hyperglycaemia [19] . Our study analysed the risk factors contributing to the development of DKD and identified that the duration of diabetes, SBP, triglycerides (TG), and BUN are significant risk factors. Consistent with previous research, these findings suggest that better management of blood glucose, lipid levels, and normalisation of blood pressure can reduce the incidence and progression of DKD. Therefore, integrating the aforementioned indicators into a nomogram provides a reliable and practical predictive tool, effectively illustrating the relationships among various factors. By incorporating these five indicators into a risk prediction model, we found that the model has an area under the curve (AUC) of 0.738 (95% CI: 0.694-0.782) and a C-index of 0.738, indicating strong predictive accuracy and the ability to accurately assess DKD risk in patients. The model is simple to use, requiring no complex calculations and relying on routine clinical indicators, thus reducing data collection difficulties and enhancing its flexibility and adaptability. Moreover, the decision curve analysis reveals that at a threshold probability of 0.6, the net benefit reaches 0.122, confirming the model’s positive net benefits across a broad range of clinical decision thresholds, which minimises the risks of over-treating low-risk patients and under-diagnosing high-risk ones, ultimately benefiting patients more effectively. However, this study has several limitations. First, as it is a cross-sectional study, we cannot determine a causal relationship between FPR and DKD. Second, the study population was from a single medical centre and exclusively comprised individuals of Chinese ethnicity, limiting its applicability to other ethnic groups. Finally, the relatively small sample size may introduce selection bias, compromising the robustness of the findings. Future research should aim to refine clinical decision models by incorporating additional clinical data to enhance their practical utility. Prospective cohort studies are also necessary to confirm the relationship between FPR and DKD development. Conclusion FPR, duration of DM, SBP, TG, and BUN are risk factors for the development of DKD in patients with T2DM. Additionally, the combination of FPR, duration of DM, SBP, TG, and BUN can accurately predict the risk of DKD development, offering significant clinical value in the diagnosis of DKD. Declarations Ethics Statement The study followed the principles in the Declaration of Helsinki and was approved by the Ethical Committees of Hebei General Hospital (No.2025-LW-0049). In addition, this study was a retrospective non-interventional study, and the patient’s information was anonymous and confidential, so the signed informed consent was exempted. Disclosure The authors report no conflicts of interest in this work. Funding This study is supported by the 2024 Hebei Provincial government will subsidize the training project of clinical medical talents (ZF2024024), the 2023 Government Funded Clinical Medicine Outstanding Talent Cultivation Program Quantitative Detection of Early Renal Damage Biomarkers in Urine Based on Smartphone Artificial Intelligence Algorithms (ZF202312). And the 2025 Hebei Province medical applicable technology tracking project (ZG20250088). Author Contribution Li Beiyi wrote the main manuscript text and Li Beiyi prepared figures 1-4 and Table 1-3. All authors reviewed the manuscript. References Sun H, Saeedi P, Karuranga S, et al. IDF Diabetes Atlas: Global, regional and country-level diabetes prevalence estimates for 2021 and projections for 2045[J]. Diabetes Res Clin Pract. 2022;183:109119. Thipsawat S. Early detection of diabetic nephropathy in patient with type 2 diabetes mellitus: A review of the literature[J]. Diab Vasc Dis Res. 2021;18(6):1476901544. Ling J, Yang Y. 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A Nomogram Model that Predicts the Risk of Diabetic Nephropathy in Type 2 Diabetes Mellitus Patients: A Retrospective Study[J]. Int J Endocrinol. 2021;2021:6672444. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6703304","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":478053667,"identity":"25128f2f-0925-4395-ab5a-4de8dfe8c918","order_by":0,"name":"Beiyi Li","email":"","orcid":"","institution":"Graduate School of Hebei Medical University","correspondingAuthor":false,"prefix":"","firstName":"Beiyi","middleName":"","lastName":"Li","suffix":""},{"id":478053668,"identity":"c8af8978-84c7-495e-ab67-41a40e5c544b","order_by":1,"name":"Kexin Gan","email":"","orcid":"","institution":"Hebei General Hospital","correspondingAuthor":false,"prefix":"","firstName":"Kexin","middleName":"","lastName":"Gan","suffix":""},{"id":478053669,"identity":"fda85a38-1896-4428-a0e6-d969bbbc7c84","order_by":2,"name":"Peng Qiu","email":"","orcid":"","institution":"Hebei General Hospital","correspondingAuthor":false,"prefix":"","firstName":"Peng","middleName":"","lastName":"Qiu","suffix":""},{"id":478053670,"identity":"a3e2e1f9-d3a8-4690-8d2b-e7530bf2edbd","order_by":3,"name":"Pei Zhao","email":"","orcid":"","institution":"Hebei General Hospital","correspondingAuthor":false,"prefix":"","firstName":"Pei","middleName":"","lastName":"Zhao","suffix":""},{"id":478053671,"identity":"b982c0b6-d2cb-4827-a97d-2b06892ae52b","order_by":4,"name":"Xiuqin Lv","email":"","orcid":"","institution":"Hebei General Hospital","correspondingAuthor":false,"prefix":"","firstName":"Xiuqin","middleName":"","lastName":"Lv","suffix":""},{"id":478053672,"identity":"3d9d2393-f2fe-4556-98f0-1a18029773f3","order_by":5,"name":"Dongmei Zhang","email":"","orcid":"","institution":"Hebei General Hospital","correspondingAuthor":false,"prefix":"","firstName":"Dongmei","middleName":"","lastName":"Zhang","suffix":""},{"id":478053673,"identity":"3dd7745b-43cd-49a4-8a1f-461aaa919686","order_by":6,"name":"Boqing Ma","email":"","orcid":"","institution":"Hebei General Hospital","correspondingAuthor":false,"prefix":"","firstName":"Boqing","middleName":"","lastName":"Ma","suffix":""},{"id":478053676,"identity":"77f69ee1-1cb6-4762-814e-b934a6ee801c","order_by":7,"name":"Jing Liu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA7UlEQVRIie3PMWrDMBSA4WcE8qLaq0yJQ2/wjJYeR1oylRLIkiE1AoE7dg6UnsGT6WgQJEsga0aruUS7xQkZMlkeA9W/SIL3IQkgFLrDEgKAEiBPSXrs5JLnUx+hVyKyd0ax2z2LQvvIdVX1nkHmqqWC1kdihnP3/abW5qFF9cVlpIn7OQw+jCGq3VZ8kER2quGvMVAhXryk2kzWBvpNwxeRZvRxDIlqC8jVJ1e6HUdWT7VlPdGjCJ33pBWZoTOUGy4K4/lLmtqm+KvKvN9Y97sq82ls3HGInC9CAHtzJsPjl5EOoPSPhUKh0P/tBLiWRywvefN7AAAAAElFTkSuQmCC","orcid":"","institution":"Graduate School of Hebei Medical University","correspondingAuthor":true,"prefix":"","firstName":"Jing","middleName":"","lastName":"Liu","suffix":""}],"badges":[],"createdAt":"2025-05-20 03:38:17","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6703304/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6703304/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":85824953,"identity":"d0d968aa-85dd-4ca8-b3cf-c266270d8127","added_by":"auto","created_at":"2025-07-02 07:05:52","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":8893,"visible":true,"origin":"","legend":"\u003cp\u003eNomogram for DKD risk\u003c/p\u003e\n\u003cp\u003eNote: The scale range on the line segment corresponding to each risk factor in the figure represents the value range of the factor, and the length of the line segment reflects the contribution value of each predictor to clinical events.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-6703304/v1/6ad48869ae49cca32bbc7a54.png"},{"id":85828418,"identity":"d2f38a13-fd63-4053-b865-a6999fac1745","added_by":"auto","created_at":"2025-07-02 07:29:52","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":150634,"visible":true,"origin":"","legend":"\u003cp\u003eROC curve of the combined indicator model\u003c/p\u003e","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6703304/v1/06aa978b95ea20cf9b3224a4.jpeg"},{"id":85824966,"identity":"3d01e187-20f5-49d2-a4fc-a7239999d4c9","added_by":"auto","created_at":"2025-07-02 07:05:53","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":87421,"visible":true,"origin":"","legend":"\u003cp\u003eCalibration curve of the combined indicator model\u003c/p\u003e\n\u003cp\u003eNote: The x-axis represents the predicted probability of the model, while the y-axis represents the actual probability of DKD occurrence. The Ideal line indicates the ideal curve, where the actual probability aligns with the predicted probability. The Apparent line represents the predicted calibration curve, and the Bias-corrected line represents the bias-corrected calibration curve.\u003c/p\u003e","description":"","filename":"floatimage4.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6703304/v1/2e8a60d769104f5edfca3fb5.jpeg"},{"id":85824959,"identity":"38e122f1-5182-4b42-83e7-456107624781","added_by":"auto","created_at":"2025-07-02 07:05:53","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":8195,"visible":true,"origin":"","legend":"\u003cp\u003eThe clinical decision curve of the combined index model\u003c/p\u003e\n\u003cp\u003eNote: The x-axis represents the high-risk threshold, while the y-axis represents the net benefit, calculated as the benefit minus the harm. The Combined Indicator line represents the decision curve of the model, the All line indicates the scenario where all patients have DKD, and the None line represents the scenario where no patients have DKD.\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-6703304/v1/325d54c481216ef64cbae4cd.png"},{"id":86753066,"identity":"6e3b8e87-479d-40cf-8d2d-47d07228d5a3","added_by":"auto","created_at":"2025-07-15 08:54:25","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":997250,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6703304/v1/c405bf16-dacb-46c3-8557-cdacb9f7bdbe.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"A risk prediction model for kidney disease in type 2 diabetes mellitus based on the ratio of fibrinogen to prealbumin","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe global burden of diabetes continues to escalate, leading to severe complications and premature mortality. According to the latest research reports, in 2021, there were an estimated 536.6\u0026nbsp;million diabetic patients aged 20\u0026ndash;79 years across 215 countries and territories. By 2045, this number is projected to rise to 783.2\u0026nbsp;million. China has the world's largest diabetic population, with over 140\u0026nbsp;million cases estimated in 2021, expected to surpass 174\u0026nbsp;million by 2045\u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]\u003c/sup\u003e. Diabetes can cause damage to multiple organ systems, including the eyes, nervous system, feet, vascular system, heart, and kidneys. DKD is one of the most common complications of T2DM and a leading cause of End-Stage Renal Disease (ESRD)\u003csup\u003e[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eIn its early stages, DKD is characterized by persistent elevation of albuminuria and gradual decline in glomerular filtration function. If untreated at an early stage, the condition progresses rapidly, potentially leading to ESRD\u003csup\u003e[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]\u003c/sup\u003e. Therefore, enhancing early screening and clinical interventions for DKD holds significant practical importance.Currently, practiceRenal biopsy is often considered the gold standard for diagnosing DKD clinically, but it is typically performed only when other renal pathologies are suspected. A clinical diagnosis of DKD is usually made when there is persistent albuminuria or a gradual decrease in the estimated Glomerular Filtration Rate (eGFR) below 60ml/min/1.73m\u0026sup2;\u003csup\u003e[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]\u003c/sup\u003e. DKD can manifest without persistent albuminuria. Consequently, when eGFR is still within the normal range and before episodes of elevated urinary albumin, these markers are clearly inadequate for assessing early-stage DKD\u003csup\u003e[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]\u003c/sup\u003e. This study seeks to identify additional biomarkers that can predict the progression of DKD.\u003c/p\u003e \u003cp\u003eResearch by Liu et al. indicates that chronic inflammation is a crucial factor in DKD progression and mediates key mechanisms of tubular injury\u003csup\u003e[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]\u003c/sup\u003e. Fibrinogen (FIB), primarily produced by the liver, acts as an acute-phase reactant and promotes platelet aggregation. Elevated blood glucose levels in diabetic patients lead to vascular damage, increasing FIB levels, which results in higher blood viscosity and hypercoagulability. This predisposes individuals to thrombosis, obstructing renal vasculature, inducing ischaemia-hypoxia, and damaging endothelial cells, thereby accelerating kidney injury\u003csup\u003e[\u003cspan additionalcitationids=\"CR8\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]\u003c/sup\u003e. Prealbumin (PALB), a negative acute-phase reactant, enhances catabolism to counteract inflammatory responses and reflects short-term inflammatory changes. The FPR, emerging as a novel biomarker for assessing systemic inflammation, has been increasingly used in cancer diagnostics and prognosis\u003csup\u003e[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]\u003c/sup\u003e.This study is the first to develop a DKD risk prediction model based on the FPR, offering a simpler and more effective tool for assessing DKD risk in T2DM patients. It is designed to improve the accuracy of DKD risk assessment.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e1.1 Subjects\u003c/h2\u003e \u003cp\u003eA cohort of 500 patients with T2DM who were admitted to the Endocrinology Department at Hebei Provincial People's Hospital for treatment between January 2023 and May 2024 was selected. Inclusion criteria: The diagnosis of T2DM adhered to the criteria outlined in the 2024 American Diabetes Association (ADA). Exclusion criteria: (1)Presence of concurrent infections; (2)Type 1 diabetes and other special types of diabetes; (3)Presence of acute diabetic complications (diabetic ketoacidosis, hyperosmolar hyperglycaemic state, etc. (4)Acute cardiovascular or cerebrovascular diseases, severe liver dysfunction (alanine aminotransferase\u0026thinsp;\u0026gt;\u0026thinsp;2.5 times the upper limit of normal); (5)Presence of autoimmune diseases, malignancies, or haematological disorders; (6)History of renal diseases other than DKD; (7)Presence of other significant diseases; (8)Incomplete clinical data or records.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003e1.2 Data Collection and Laboratory Analysis\u003c/h3\u003e\n\u003cp\u003eData were collected for each participant, including gender, age, duration of diabetes, smoking history, alcohol consumption history, history of hypertension, systolic blood pressure (SBP), and diastolic blood pressure (DBP). Height (in cm) and weight (in kg) were measured to calculate body mass index (BMI). All inpatients maintained a fasting state of at least 8 hours, and venous blood and urine samples were collected the following morning. The following parameters were measured: Albumin (ALB), Prealbumin (PALB), Alanine aminotransferase (ALT), Aspartate aminotransferase (AST), Total cholesterol (TC), Triglycerides (TG), High-density lipoprotein cholesterol (HDL-C), Low-density lipoprotein cholesterol (LDL-C), Estimated glomerular filtration rate (eGFR), Serum creatinine (Scr), Blood urea nitrogen (BUN), Fibrinogen (FIB), Glycated haemoglobin (HbA1c). Complete blood counts were analysed using a flow cytometer, biochemical indicators were measured using an automated biochemical analyser, HbA1c was determined by high-performance liquid chromatography, and FIB was measured using an automated coagulation analyser. All laboratory tests were performed by our clinical laboratory technicians.\u003c/p\u003e\n\u003ch3\u003e1.3 Data Calculation and Grouping\u003c/h3\u003e\n\u003cp\u003eThe Fibrinogen to Prealbumin Ratio (FPR) was calculated using the following formula: FPR=[FIB(g/L)/PALB(mg/dL)]\u0026times;100\u003c/p\u003e \u003cp\u003eBased on the aforementioned diagnostic criteria for diabetic kidney disease, patients with T2DM were grouped according to the presence or absence of DKD. The participants were divided into two groups: the DKD group (n\u0026thinsp;=\u0026thinsp;218) and the NDKD group (n\u0026thinsp;=\u0026thinsp;282).\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003e2.1 Comparison of Basic Clinical Characteristics and Laboratory Parameters Between DKD and NDKD Groups\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eComparative analysis revealed that the DKD group had significantly lower levels of albumin (ALB) and estimated glomerular filtration rate (eGFR) compared to the NDKD group (P\u0026lt;0.05). In contrast, the DKD group exhibited significantly higher levels of hypertension history, systolic blood pressure (SBP), triglycerides (TG), serum creatinine (Scr), blood urea nitrogen (BUN), fibrinogen (FIB), and fibrinogen to prealbumin ratio (FPR) (P\u0026lt;0.05). There were no significant differences between the two groups regarding the following parameters: gender, smoking history, alcohol consumption history, age body mass index (BMI), diastolic blood pressure (DBP), glycated haemoglobin (HbA1c), alanine aminotransferase (ALT), aspartate aminotransferase (AST), prealbumin (PALB), total cholesterol (TC), low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C). (Table 1)\u003c/p\u003e\n\u003ch3\u003e2.2 Correlation Analysis of FPR with Other Variables in T2DM Patients\u003c/h3\u003e\n\u003cp\u003eSpearman correlation analysis indicated that, in T2DM patients, the FPR was positively correlated with the presence of DKD, age, duration of diabetes mellitus (DM), HbA1c, and FIB levels (P\u0026lt;0.05). Conversely, FPR was negatively correlated with BMI, DBP, ALT, AST, ALB, PALB, TC, TG, HDL-C, and eGFR levels (P\u0026lt;0.05). There was no significant correlation between FPR, SBP, LDL-C, Scr, and BUN levels. (Table 2)\u003c/p\u003e\n\u003ch3\u003e2.3 Binary Logistic Regression Analysis of DKD Risk Factors\u003c/h3\u003e\n\u003cp\u003eA logistic regression analysis was conducted to develop a model predicting the occurrence of DKD that includes FPR. Dependent variable was the presence or absence of DKD, while independent variables included statistically significant indicators identified previously. Univariate and multivariate logistic regression analyses were performed. After removing collinear variables such as ALB, PALB, and FIB, multivariate logistic regression analysis revealed that FPR, duration of DM, SBP, TG, and BUN are risk factors for DKD (P\u0026lt;0.05). Even after adjusting for multiple confounding factors, FPR remained an independent risk factor for DKD. Therefore, T2DM patients with higher FPR, longer DM duration, higher SBP, higher TG, and higher BUN have a greater risk of developing DKD (P\u0026lt;0.05). (Table 3)\u003c/p\u003e\n\u003ch3\u003e2.4 Development of a Nomogram to Predict DKD Risk in T2DM Patients\u003c/h3\u003e\n\u003ch3\u003e2.4.1 Construction of a Nomogram for Predicting DKD Risk in T2DM Patients\u003c/h3\u003e\n\u003cp\u003eUse R software to construct a nomogram model predicting the risk of DKD based on DM duration, SBP, TG, BUN, and FPR (Figure 1). Each factor is assigned different values according to its impact on DKD. On the point scale in the graph, determine the score corresponding to each predictive indicator, calculate the total score, and the value on the risk scale corresponding to the total point scale represents the probability of DKD occurrence in T2DM patients.\u003c/p\u003e\n\u003ch3\u003e2.4.2\u0026nbsp;Evaluation of the Nomogram Model for Predicting DKD Risk\u003c/h3\u003e\n\u003cp\u003eThe Receiver Operating Characteristic (ROC) curve was plotted for the nomogram model designed to predict the risk of DKD (Figure 2). The Area Under the Curve (AUC) for this model is 0.738 (95%CI: 0.694-0.782).\u003c/p\u003e\n\u003cp\u003eFor internal validation, the Bootstrap resampling method was employed with 1000 iterations. The C-index, a measure of the model\u0026rsquo;s discriminatory power, was determined to be 0.738. This high C-index value confirms that the model\u0026apos;s predictions are accurate and reliable. Additionally, the calibration curves\u0026mdash;both the predicted calibration curve and the bias calibration curve\u0026mdash;are closely aligned with the ideal curve, indicating that the model has excellent calibration. (Figure 3)\u003c/p\u003e\n\u003cp\u003eAdditionally, a decision curve of the model was plotted to directly visualize whether patients would benefit and to evaluate the clinical utility of the model. The All line represents the scenario where all patients are assumed to have DKD, while the None line corresponds to the assumption that no patients have DKD. The model demonstrates clinical value when its curve lies above both the All and None lines. By adjusting the threshold, we assessed the model\u0026rsquo;s net benefit and determined the optimal threshold to be 0.6, at which the net benefit reached 0.122. (Figure 4)\u003c/p\u003e\n\u003ch3\u003eTable 1 Comparison of baseline clinical features and laboratory parameters in DKD group and NDKD group\u003c/h3\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 168px;\"\u003e\n \u003cp\u003eNDKD(n=282)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 191px;\"\u003e\n \u003cp\u003eDKD(n=218)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 82px;\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003eMale(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 168px;\"\u003e\n \u003cp\u003e182(64.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 191px;\"\u003e\n \u003cp\u003e137(62.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 82px;\"\u003e\n \u003cp\u003e0.696\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003eSmoking(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 168px;\"\u003e\n \u003cp\u003e49(17.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 191px;\"\u003e\n \u003cp\u003e43(19.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 82px;\"\u003e\n \u003cp\u003e0.501\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003eDrinking(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 168px;\"\u003e\n \u003cp\u003e41(14.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 191px;\"\u003e\n \u003cp\u003e31(14.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 82px;\"\u003e\n \u003cp\u003e0.920\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003eAge(years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 168px;\"\u003e\n \u003cp\u003e58.00(50.75,65.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 191px;\"\u003e\n \u003cp\u003e60.00(49.75,68.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 82px;\"\u003e\n \u003cp\u003e0.079\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003eDM duration(years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 168px;\"\u003e\n \u003cp\u003e10.00(5.00,15.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 191px;\"\u003e\n \u003cp\u003e10.00(5.00,20.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 82px;\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003eHypertension(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 168px;\"\u003e\n \u003cp\u003e153(54.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 191px;\"\u003e\n \u003cp\u003e143(65.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 82px;\"\u003e\n \u003cp\u003e0.011\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003eBMI(kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 168px;\"\u003e\n \u003cp\u003e26.36(24.00,28.59)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 191px;\"\u003e\n \u003cp\u003e26.55(23.91,29.18)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 82px;\"\u003e\n \u003cp\u003e0.639\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003eSBP(mmHg)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 168px;\"\u003e\n \u003cp\u003e131.00\u0026plusmn;17.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 191px;\"\u003e\n \u003cp\u003e135.00(121.00,152.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 82px;\"\u003e\n \u003cp\u003e0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003eDBP(mmHg)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 168px;\"\u003e\n \u003cp\u003e83.57\u0026plusmn;11.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 191px;\"\u003e\n \u003cp\u003e85.00(78.00,93.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 82px;\"\u003e\n \u003cp\u003e0.197\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003eHbA1c\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 168px;\"\u003e\n \u003cp\u003e8.60(7.43,10.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 191px;\"\u003e\n \u003cp\u003e8.65(7.38,10.10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 82px;\"\u003e\n \u003cp\u003e0.684\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003eALT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 168px;\"\u003e\n \u003cp\u003e21.30(14.75,29.63)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 191px;\"\u003e\n \u003cp\u003e19.20(12.73,28.83)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 82px;\"\u003e\n \u003cp\u003e0.051\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003eAST\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 168px;\"\u003e\n \u003cp\u003e20.10(16.50,26.43)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 191px;\"\u003e\n \u003cp\u003e19.40(15.40,26.40)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 82px;\"\u003e\n \u003cp\u003e0.390\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003eALB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 168px;\"\u003e\n \u003cp\u003e42.40(39.48,45.43)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 191px;\"\u003e\n \u003cp\u003e40.35(36.18,43.75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 82px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003ePALB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 168px;\"\u003e\n \u003cp\u003e23.75(19.58,28.63)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 191px;\"\u003e\n \u003cp\u003e23.88\u0026plusmn;8.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 82px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003eTC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 168px;\"\u003e\n \u003cp\u003e4.70(3.82,5.57)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 191px;\"\u003e\n \u003cp\u003e4.77(3.75,5.85)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 82px;\"\u003e\n \u003cp\u003e0.437\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003eTG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 168px;\"\u003e\n \u003cp\u003e1.44(0.99,2.10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 191px;\"\u003e\n \u003cp\u003e1.60(1.08,2.77)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 82px;\"\u003e\n \u003cp\u003e0.029\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003eLDL-C\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 168px;\"\u003e\n \u003cp\u003e2.97(2.31,3.66)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 191px;\"\u003e\n \u003cp\u003e2.90(2.27,3.67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 82px;\"\u003e\n \u003cp\u003e0.863\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003eHDL-C\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 168px;\"\u003e\n \u003cp\u003e1.13(1.00,1.36)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 191px;\"\u003e\n \u003cp\u003e1.12(0.94,1.35)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 82px;\"\u003e\n \u003cp\u003e0.140\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003eeGFR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 168px;\"\u003e\n \u003cp\u003e99.53(91.51,109.62)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 191px;\"\u003e\n \u003cp\u003e92.41(65.87,105.64)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 82px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003eScr\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 168px;\"\u003e\n \u003cp\u003e62.80(54.10,73.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 191px;\"\u003e\n \u003cp\u003e73.40(57.80,90.85)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 82px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003eBUN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 168px;\"\u003e\n \u003cp\u003e5.40(4.60,6.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 191px;\"\u003e\n \u003cp\u003e6.10(4.65,8.15)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 82px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003eFIB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 168px;\"\u003e\n \u003cp\u003e2.87(2.42,3.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 191px;\"\u003e\n \u003cp\u003e3.19(2.70,4.26)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 82px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003eFPR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 168px;\"\u003e\n \u003cp\u003e11.92(9.62,15.67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 191px;\"\u003e\n \u003cp\u003e12.91(9.94,20.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 82px;\"\u003e\n \u003cp\u003e0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eNote: a indicates \u0026chi;2 value. Significance at a P value of \u0026lt;0.05.\u003c/p\u003e\n\u003cp\u003eAbbreviations: DM, diabetes mellitus; BMI, body mass index; SBP, systolic blood pressure; DBP, diastolic blood pressure; HbA1c, hemoglobin A1c; ALT, alanine aminotransferase; AST, aspartate aminotransferase; ALB, albumin; PALB, prealbumin; TC, total cholesterol; TG, triglyceride; LDL-C, low-density lipoprotein cholesterol; HDL-C, high-density lipoprotein cholesterol; eGFR, estimated glomerular filtration rate; Scr, serum creatinine; BUN, blood urea nitrogen; FIB, fibrinogen; FPR, fibrinogen to prealbumin ratio.\u003c/p\u003e\n\u003cp\u003eTable 2 Correlation analysis between FPR and clinical indicators in DKD patients\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 184px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 184px;\"\u003e\n \u003cp\u003er\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 184px;\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 184px;\"\u003e\n \u003cp\u003eDKD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 184px;\"\u003e\n \u003cp\u003e0.123\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 184px;\"\u003e\n \u003cp\u003e0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 184px;\"\u003e\n \u003cp\u003eAge(years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 184px;\"\u003e\n \u003cp\u003e0.277\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 184px;\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 184px;\"\u003e\n \u003cp\u003eDM duration(years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 184px;\"\u003e\n \u003cp\u003e0.117\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 184px;\"\u003e\n \u003cp\u003e0.009\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 184px;\"\u003e\n \u003cp\u003eBMI(kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 184px;\"\u003e\n \u003cp\u003e-0.091\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 184px;\"\u003e\n \u003cp\u003e0.042\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 184px;\"\u003e\n \u003cp\u003eSBP(mmHg)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 184px;\"\u003e\n \u003cp\u003e0.012\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 184px;\"\u003e\n \u003cp\u003e0.783\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 184px;\"\u003e\n \u003cp\u003eDBP(mmHg)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 184px;\"\u003e\n \u003cp\u003e-0.179\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 184px;\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 184px;\"\u003e\n \u003cp\u003eHbA1c\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 184px;\"\u003e\n \u003cp\u003e0.108\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 184px;\"\u003e\n \u003cp\u003e0.019\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 184px;\"\u003e\n \u003cp\u003eALT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 184px;\"\u003e\n \u003cp\u003e-0.235\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 184px;\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 184px;\"\u003e\n \u003cp\u003eAST\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 184px;\"\u003e\n \u003cp\u003e-0.130\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 184px;\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 184px;\"\u003e\n \u003cp\u003eALB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 184px;\"\u003e\n \u003cp\u003e-0.543\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 184px;\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 184px;\"\u003e\n \u003cp\u003ePALB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 184px;\"\u003e\n \u003cp\u003e-0.666\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 184px;\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 184px;\"\u003e\n \u003cp\u003eTC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 184px;\"\u003e\n \u003cp\u003e-0.090\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 184px;\"\u003e\n \u003cp\u003e0.045\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 184px;\"\u003e\n \u003cp\u003eTG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 184px;\"\u003e\n \u003cp\u003e-0.276\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 184px;\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 184px;\"\u003e\n \u003cp\u003eLDL-C\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 184px;\"\u003e\n \u003cp\u003e-0.048\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 184px;\"\u003e\n \u003cp\u003e0.295\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 184px;\"\u003e\n \u003cp\u003eHDL-C\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 184px;\"\u003e\n \u003cp\u003e-0.118\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 184px;\"\u003e\n \u003cp\u003e0.009\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 184px;\"\u003e\n \u003cp\u003eeGFR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 184px;\"\u003e\n \u003cp\u003e-0.215\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 184px;\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 184px;\"\u003e\n \u003cp\u003eScr\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 184px;\"\u003e\n \u003cp\u003e-0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 184px;\"\u003e\n \u003cp\u003e0.953\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 184px;\"\u003e\n \u003cp\u003eBUN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 184px;\"\u003e\n \u003cp\u003e-0.021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 184px;\"\u003e\n \u003cp\u003e0.646\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 184px;\"\u003e\n \u003cp\u003eFIB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 184px;\"\u003e\n \u003cp\u003e0.745\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 184px;\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eNote: a indicates \u0026chi;2 value. Significance at a P value of \u0026lt;0.05.\u003c/p\u003e\n\u003cp\u003eAbbreviations: DKD, diabetic kidney disease; DM, diabetes mellitus; BMI, body mass index; SBP, systolic blood pressure; DBP, diastolic blood pressure; HbA1c, hemoglobin A1c; ALT, alanine aminotransferase; AST, aspartate aminotransferase; ALB, albumin; PALB, prealbumin; TC, total cholesterol; TG, triglyceride; LDL-C, low-density lipoprotein cholesterol; HDL-C, high-density lipoprotein cholesterol; eGFR, estimated glomerular filtration rate; Scr, serum creatinine; BUN, blood urea nitrogen; FIB, fibrinogen.\u003c/p\u003e\n\u003cp\u003eTable 3 Binary logistic regression analysis of factors influencing DKD\u003c/p\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"604\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 143px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 230px;\"\u003e\n \u003cp\u003eUnivariate Logistic Regression Analysis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 231px;\"\u003e\n \u003cp\u003eMultivariate Logistic Regression Analysis\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003eOR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e95%CI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003eOR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e95%CI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 143px;\"\u003e\n \u003cp\u003eDM duration(years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003e1.041\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e1.017-1.065\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003e1.036\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e1.007-1.065\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003e0.014\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 143px;\"\u003e\n \u003cp\u003eHypertension(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003e1.608\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e1.116-2.315\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003e0.011\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003e0.990\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e0.616-1.589\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003e0.965\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 143px;\"\u003e\n \u003cp\u003eSBP(mmHg)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003e1.015\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e1.005-1.024\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003e1.018\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e1.006-1.030\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 143px;\"\u003e\n \u003cp\u003eTG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003e1.186\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e1.071-1.314\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003e1.327\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e1.174-1.501\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 143px;\"\u003e\n \u003cp\u003eeGFR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003e0.973\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e0.965-0.982\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003e0.992\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e0.973-1.011\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003e0.384\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 143px;\"\u003e\n \u003cp\u003eScr\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003e1.023\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e1.014-1.031\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003e1.009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e0.994-1.025\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003e0.246\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 143px;\"\u003e\n \u003cp\u003eBUN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003e1.255\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e1.150-1.371\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003e1.150\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e1.011-1.309\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003e0.033\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 143px;\"\u003e\n \u003cp\u003eFPR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003e1.033\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e1.016-1.050\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003e1.041\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e1.022-1.061\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eNote: a indicates \u0026chi;2 value. Significance at a P value of \u0026lt;0.05.\u003c/p\u003e\n\u003cp\u003eAbbreviations: DM, diabetes mellitus; SBP, systolic blood pressure; TG, triglyceride; eGFR, estimated glomerular filtration rate; Scr, serum creatinine; BUN, blood urea nitrogen; FPR, fibrinogen to prealbumin\u0026nbsp;\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eDiabetic kidney disease (DKD) is one of the microvascular complications of diabetes, ultimately leading to end-stage renal disease, accounting for approximately 40% of patients requiring renal replacement therapy. DKD is a progressive chronic condition characterised by glomerular hypertrophy, proteinuria, declining glomerular filtration, and renal fibrosis. Hyperglycaemic conditions can result in loss of renal function. As DKD does not present with noticeable clinical symptoms in its early stages, patients may rapidly progress to end-stage renal disease without receiving appropriate treatment. Although microalbuminuria serves as an early biomarker for DKD, its sensitivity and specificity in predicting DKD risk are relatively low. Furthermore, the gold standard for diagnosing DKD remains largely dependent on pathological changes observed through renal biopsy. However, renal biopsy is an invasive procedure that carries risks such as infection and bleeding during the puncture process. Therefore, it is particularly important to identify non-invasive biomarkers with high sensitivity to predict the progression of DKD\u003csup\u003e[11]\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eThe findings of this study indicate that the occurrence of DKD is positively correlated with levels of FPR, with DKD patients exhibiting significantly elevated FPR levels. Even after adjusting for multiple confounding factors, FPR remains an independent risk factor for DKD. The higher the FPR levels, the greater the risk of DKD in patients with Type 2 Diabetes Mellitus (T2DM). Additionally, the research demonstrates that FPR correlates with various inflammatory markers, suggesting that FPR may contribute to the pathogenesis of DKD through its involvement in inflammatory responses.\u003c/p\u003e\n\u003cp\u003eFibrinogen (FIB), primarily synthesised by hepatocytes as an acute-phase protein, serves as a precursor in the formation of blood clots. Numerous studies have confirmed that FIB plays a significant role in patients with T2DM, including those with DKD\u003csup\u003e[12]\u003c/sup\u003e. Elevated FIB levels leading to a hypercoagulable state can result in glomerular microvascular lesions\u003csup\u003e[7]\u003c/sup\u003e. Potential factors contributing to this hypercoagulable state include glucose and lipid metabolism abnormalities, a micro-inflammatory environment, oxidative stress, hypoalbuminaemia, haemodynamic changes, and platelet activation in DKD patients. These factors interweave with the coagulation system to collectively exacerbate the condition. Additionally, these factors can induce vascular endothelial cell damage, creating a hypercoagulable environment. This can lead to microthrombus formation, reduced renal blood flow, and glomerulosclerosis, thereby perpetuating a self-sustaining cycle that aggravates the progression of DKD\u003csup\u003e[13]\u003c/sup\u003e. Similarly, our study found that FIB levels are significantly higher in DKD patients compared to non-DKD patients.\u003c/p\u003e\n\u003cp\u003ePrealbumin (PALB) serves as an important serum marker for nutrition and inflammation. It is a negative acute-phase protein synthesised by the liver, primarily comprising Retinol-Binding Protein and Thyroxine-Binding Globulin (TTR). Compared to Albumin (ALB), PALB has a shorter half-life and higher sensitivity, and it is less affected by parenteral nutrition, making it more responsive to changes in nutritional status. Consequently, PALB is considered a valuable biomarker for assessing patient prognosis\u003csup\u003e[14]\u003c/sup\u003e. PALB functions as a non-specific host defence substance in the body, clearing toxic metabolic waste produced during infections, which leads to its gradual depletion. As a result, during the acute-phase inflammatory response, PALB levels tend to decrease with the progression of inflammation\u003csup\u003e[15]\u003c/sup\u003e. However, in our study, we did not observe a decrease in PALB levels among DKD patients. Currently, there is no consensus on whether PALB levels decline in DKD patients, indicating a need for further research to clarify this aspect.\u003c/p\u003e\n\u003cp\u003eFPR, through its interactions with FIB and PALB, is increasingly recognised as a novel biomarker for defining inflammatory responses and immunonutrition. Recent studies have identified FPR as a prognostic marker for various cancers\u003csup\u003e[16,17]\u003c/sup\u003e. Concerning its relevance to diabetes, a retrospective study by Zhao et al. found that FPR levels are significantly higher in patients with Diabetic Cardioautonomic Neuropathy (DCAN). Even after adjusting for confounding variables, FPR remains independently associated with DCAN\u003csup\u003e[18]\u003c/sup\u003e. Additionally, a cross-sectional study from China demonstrated that FPR is positively correlated with tubular injury in early-stage DKD\u003csup\u003e[10]\u003c/sup\u003e. These findings underscore the potential significance of FPR as a biomarker in understanding and managing diabetic complications.\u003c/p\u003e\n\u003cp\u003eGiven the high prevalence of DKD, it is essential to identify risk factors for DKD early and implement timely interventions to slow its progression. Several known risk factors are linked to an increased risk of diabetic nephropathy, including the duration of diabetes, smoking, age, systolic blood pressure (SBP), glycated haemoglobin (HbA1c), body mass index (BMI), serum creatinine (Scr), blood urea nitrogen (BUN), urinary protein, lipid levels, and hyperglycaemia\u003csup\u003e[19]\u003c/sup\u003e. Our study analysed the risk factors contributing to the development of DKD and identified that the duration of diabetes, SBP, triglycerides (TG), and BUN are significant risk factors. Consistent with previous research, these findings suggest that better management of blood glucose, lipid levels, and normalisation of blood pressure can reduce the incidence and progression of DKD.\u003c/p\u003e\n\u003cp\u003eTherefore, integrating the aforementioned indicators into a nomogram provides a reliable and practical predictive tool, effectively illustrating the relationships among various factors. By incorporating these five indicators into a risk prediction model, we found that the model has an area under the curve (AUC) of 0.738 (95% CI: 0.694-0.782) and a C-index of 0.738, indicating strong predictive accuracy and the ability to accurately assess DKD risk in patients. The model is simple to use, requiring no complex calculations and relying on routine clinical indicators, thus reducing data collection difficulties and enhancing its flexibility and adaptability. Moreover, the decision curve analysis reveals that at a threshold probability of 0.6, the net benefit reaches 0.122, confirming the model\u0026rsquo;s positive net benefits across a broad range of clinical decision thresholds, which minimises the risks of over-treating low-risk patients and under-diagnosing high-risk ones, ultimately benefiting patients more effectively.\u003c/p\u003e\n\u003cp\u003eHowever, this study has several limitations. First, as it is a cross-sectional study, we cannot determine a causal relationship between FPR and DKD. Second, the study population was from a single medical centre and exclusively comprised individuals of Chinese ethnicity, limiting its applicability to other ethnic groups. Finally, the relatively small sample size may introduce selection bias, compromising the robustness of the findings. Future research should aim to refine clinical decision models by incorporating additional clinical data to enhance their practical utility. Prospective cohort studies are also necessary to confirm the relationship between FPR and DKD development.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eFPR, duration of DM, SBP, TG, and BUN are risk factors for the development of DKD in patients with T2DM. Additionally, the combination of FPR, duration of DM, SBP, TG, and BUN can accurately predict the risk of DKD development, offering significant clinical value in the diagnosis of DKD.\u003c/p\u003e"},{"header":"Declarations","content":" \u003cp\u003e \u003cstrong\u003eEthics Statement\u003c/strong\u003e \u003cp\u003e The study followed the principles in the Declaration of Helsinki and was approved by the Ethical Committees of Hebei General Hospital (No.2025-LW-0049). In addition, this study was a retrospective non-interventional study, and the patient\u0026rsquo;s information was anonymous and confidential, so the signed informed consent was exempted.\u003c/p\u003e\u003ch2\u003eDisclosure\u003c/h2\u003e \u003cp\u003eThe authors report no conflicts of interest in this work.\u003c/p\u003e \u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThis study is supported by the 2024 Hebei Provincial government will subsidize the training project of clinical medical talents (ZF2024024), the 2023 Government Funded Clinical Medicine Outstanding Talent Cultivation Program Quantitative Detection of Early Renal Damage Biomarkers in Urine Based on Smartphone Artificial Intelligence Algorithms (ZF202312). And the 2025 Hebei Province medical applicable technology tracking project (ZG20250088).\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eLi Beiyi wrote the main manuscript text and Li Beiyi prepared figures 1-4 and Table 1-3. All authors reviewed the manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eSun H, Saeedi P, Karuranga S, et al. IDF Diabetes Atlas: Global, regional and country-level diabetes prevalence estimates for 2021 and projections for 2045[J]. Diabetes Res Clin Pract. 2022;183:109119.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eThipsawat S. Early detection of diabetic nephropathy in patient with type 2 diabetes mellitus: A review of the literature[J]. Diab Vasc Dis Res. 2021;18(6):1476901544.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLing J, Yang Y. Effect of sitagliptin combined with Yiqi yangyin huoxue decoction on clinical efficacy and hemorheology in early diabetic nephropathy[J]. World J Diabetes. 2023;14(9):1412\u0026ndash;21.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSelby NM, Taal MW. An updated overview of diabetic nephropathy: Diagnosis, prognosis, treatment goals and latest guidelines[J]. Diabetes Obes Metab. 2020;22(Suppl 1):3\u0026ndash;15.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLooker HC, Mauer M, Nelson RG. Role of KiDKDey Biopsies for Biomarker Discovery in Diabetic KiDKDey Disease[J]. Adv Chronic KiDKDey Dis. 2018;25(2):192\u0026ndash;201.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiu B, Tang T, Lv L, et al. Renal tubule injury: a driving force toward chronic kiDKDey disease[J]. KiDKDey Int. 2018;93(3):568\u0026ndash;79.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTang J, Chang FF, HuangPu B, et al. Diagnostic Value of Combined Detection of Peripheral Blood C-Reactive Protein, Platelet-to-Lymphocyte Ratio, and Fibrinogen in Early Diabetic Kidney Disease [J]. J Xinxiang Med Univ. 2023;40(08):736\u0026ndash;9. (in Chinese).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang XY, Tian FS. Research Progress on the Correlation Between Fibrinogen and Diabetic Kidney Disease [J]. China Med Herald. 2023;20(18):61\u0026ndash;4. (in Chinese).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhao Y, Luo W, Xu D, et al. Correlation Analysis of Mean Platelet Volume, Fibrinogen, and Urinary Microalbumin-to-Creatinine Ratio in Type 2 Diabetes Mellitus Patients [J]. Diabetes New World. 2022;25(10):19\u0026ndash;22. (in Chinese).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWo MH, Lu YC, Jin J, et al. Correlation Study of Fibrinogen/Prealbumin Ratio and Fibrinogen/Albumin Ratio with Tubular Injury in Early Diabetic Kidney Disease Patients [J]. Chin J Diabetes. 2021;29(12):896\u0026ndash;901. (in Chinese).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLu Y, Liu D, Feng Q, et al. Diabetic Nephropathy: Perspective on Extracellular Vesicles[J]. Front Immunol. 2020;11:943.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYang ZW. Diagnostic Value of the Fibrinogen/Albumin Ratio for Abnormal Carotid Intima-Media Thickness in Diabetic Kidney Disease Patients [J]. Med Theory Pract. 2024;37(13):2282\u0026ndash;5. (in Chinese).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiu M, Niu W, Wang Y, et al. Coagulation Function and Type 2 Diabetic KiDKDey Disease: A Real-World Observational Study[J]. J Diabetes Res. 2023;2023:8848096.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi C, Fan Z, Guo W, et al. Fibrinogen-to-prealbumin ratio: A new prognostic marker of resectable pancreatic cancer[J]. Front Oncol. 2023;13:1149942.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang DS, Xie X, Liu Q. The Significance of TC, PA, and Renal Pathology in the Evaluation of Nephrotic Syndrome Treatment Efficacy [J]. China Practical Med. 2019;14(10):51\u0026ndash;2. (in Chinese).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYing H, Sun F, Liao Y, et al. The value of circulating fibrinogen-to-pre-albumin ratio in predicting survival and benefit from chemotherapy in colorectal cancer[J]. Ther Adv Med Oncol. 2021;13:17532378.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang HP, Huang YJ, Wen PP, et al. The Value of the Fibrinogen/Prealbumin Ratio in the Prognostic Assessment of Elderly Sepsis Patients [J]. J Pharm Forum. 2024;45(24):2584\u0026ndash;8. (in Chinese).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhao S, Yang Z, Yu M, et al. Influence of Fibrinogen/Albumin Ratio and Fibrinogen/Pre-Albumin Ratio on Cardiac Autonomic Neuropathy in Type 2 Diabetes[J]. Diabetes Metab Syndr Obes. 2023;16:3249\u0026ndash;59.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eXi C, Wang C, Rong G, et al. A Nomogram Model that Predicts the Risk of Diabetic Nephropathy in Type 2 Diabetes Mellitus Patients: A Retrospective Study[J]. Int J Endocrinol. 2021;2021:6672444.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Type 2 diabetes, Fibrinogen to prealbumin ratio, Diabetic kidney disease, Risk factors, Nomogram","lastPublishedDoi":"10.21203/rs.3.rs-6703304/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6703304/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eObjective\u003c/h2\u003e \u003cp\u003eThe relationship between the fibrinogen to prealbumin ratio (FPR) and diabetic kidney disease (DKD) in patients with type 2 diabetes mellitus (T2DM), to explore the related risk factors of DKD and construct a nomogram model for the risk of DKD, so as to provide a simpler observation index for clinical prediction of DKD and a basis for early intervention and delaying the progress of DKD.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eThis cross-sectional study enrolled 500 patients with T2DM who visited the Endocrinology Department of Hebei Provincial People\u0026rsquo;s Hospital from January 2023 to May 2024. Clinical data, including demographic characteristics (gender, age), disease duration, smoking/alcohol history, and laboratory serum indicators (including the fibrinogen-to-prealbumin ratio, FPR), were collected. Participants were categorized into a DKD group and a NDKD group based on the presence or absence of DKD. Intergroup differences in clinical characteristics were analyzed using t-tests or Mann-Whitney U tests. A nomogram model for predicting DKD risk was developed using SPSS 25.0 and R software, with emphasis on evaluating the predictive value of FPR. A significance threshold of \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was applied for all statistical analyses.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThis study found that DKD patients had significantly lower ALB and eGFR but higher hypertension prevalence, SBP, TG, Scr, BUN, FIB, and FPR compared to NDKD patients (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Multivariate analysis identified DM duration, SBP, TG, BUN, and FPR as risk factors for the occurrence of DKD. FPR\u0026thinsp;\u0026gt;\u0026thinsp;16.49 significantly increased DKD risk (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). A predictive model incorporating these factors achieved an AUC of 0.738 (95%CI:0.694\u0026ndash;0.782) and a C-index of 0.738 after Bootstrap validation, with optimal net benefit (0.122) at a 0.6 threshold.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eFPR, duration of DM, SBP, TG, and BUN are risk factors for the development of DKD in patients with T2DM. Additionally, the combination of FPR, duration of DM, SBP, TG, and BUN can accurately predict the risk of DKD development, offering significant clinical value in the diagnosis of DKD.\u003c/p\u003e","manuscriptTitle":"A risk prediction model for kidney disease in type 2 diabetes mellitus based on the ratio of fibrinogen to prealbumin","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-02 07:05:48","doi":"10.21203/rs.3.rs-6703304/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"8e6d2a61-a5f9-4e42-9000-27732138721b","owner":[],"postedDate":"July 2nd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-07-15T08:54:09+00:00","versionOfRecord":[],"versionCreatedAt":"2025-07-02 07:05:48","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6703304","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6703304","identity":"rs-6703304","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}