Development of a Predictive Model and Nomogram for Neuropathy Risk in T2DM Patients

Development of a Predictive Model and Nomogram for Neuropathy Risk in T2DM Patients | 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 Article Development of a Predictive Model and Nomogram for Neuropathy Risk in T2DM Patients Biao Wang, Siyu Feng, Yuan Fang, Runyu Jing, Jiesi Luo, Yonglin Zhang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6556009/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 The global prevalence of type 2 diabetes mellitus (T2DM) continues to rise, with diabetic neuropathy significantly impacting patient outcomes. This study developed a predictive nomogram for neuropathy risk in T2DM patients using retrospectively analyzed electronic medical records (2013–2023) from the Affiliated Hospital of North Sichuan Medical College. After rigorous data cleaning, univariate logistic regression and XGBoost screening identified ten predictors including age, creatine kinase (CK), total urinary protein, free thyroxine (FT4), α-hydroxybutyrate dehydrogenase (α-HBDH), cystatin C (CysC), urinary creatinine (Ucr), serum calcium (Ca), α1-microglobulin (X1-MG), and urinary albumin (UALB), integrated via multivariate logistic regression into a visual nomogram. These variables were integrated into a visual nomogram via multivariate logistic regression. Model evaluation demonstrated robust discriminative ability, with area under the receiver operating characteristic curve (AUC) values of 0.743 (95% CI: 0.733–0.754) in the training dataset and 0.751 (95% CI: 0.735–0.768) in the testing dataset. Calibration curves confirmed prediction consistency, while DCA highlighted significant clinical net benefit. This nomogram provides a practical, visual tool for clinicians to estimate individual neuropathy risk, enabling early identification of high-risk patients and targeted interventions to delay or prevent neuropathy onset. The model demonstrates reliable predictive accuracy and substantial clinical utility in managing T2DM complications. Health sciences/Medical research/Biomarkers/Predictive markers Health sciences/Diseases/Endocrine system and metabolic diseases/Diabetes/Diabetes complications Health sciences/Diseases/Endocrine system and metabolic diseases/Diabetes/Type 2 diabetes mellitus Health sciences/Risk factors type 2 diabetes mellitus diabetic neuropathy logistic regression nomogram decision curve analysis Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 1. INTRODUCTION Diabetes mellitus is a metabolic disorder defined by persistent hyperglycemia arising from insufficient insulin secretion, impaired insulin bioactivity, or both [ 1 , 2 ]. As classified by the World Health Organization and the International Diabetes Federation, diabetes mellitus encompasses four primary types, including type 1 diabetes mellitus (T1DM), type 2 diabetes mellitus (T2DM), gestational diabetes mellitus, and other special forms [ 3 ]. The international diabetes burden is severe: IDF data report 463 million global patients in 2019, projecting increases to 592 million by 2035 and surpassing 700 million by 2045 [ 4 , 5 ]. T2DM constitutes over 90% of all diabetic cases, making it the most prevalent subtype globally [ 6 ]. As the nation with the largest diabetic population, China’s recent epidemiological survey reveals a 12.8% prevalence of diabetes mellitus among adults aged 18 and older [ 7 , 8 ]. Notably, awareness (36.7%), treatment (32.9%), and control (49.2%) rates remain significantly lower than in developed countries [ 9 ]. Clinical evidence confirms that early lifestyle interventions, standardized pharmacotherapy, and multidisciplinary care effectively slow disease progression and reduce complication risks. Strengthening primary healthcare systems, enhancing public awareness, and optimizing screening and tiered care mechanisms are imperative to mitigate diabetes-related socioeconomic burdens. Diabetic complications, including acute metabolic emergencies (e.g., diabetic ketoacidosis, hyperosmolar hyperglycemic syndrome) and chronic multiorgan damage (including cardiovascular, renal, retinal, and neurologic damage), are the leading causes of disability and death in patients [ 10 ]. Among them, diabetic neuropathy is common chronic complication, which can be categorized into peripheral neuropathy and autonomic neuropathy, both of which can severely affect patients' quality of life and clinical prognosis [ 11 , 12 ]. Epidemiologic data suggested that more than 50% of diabetic patients developed diabetic neuropathy during the course of their disease [ 13 ]. Hyperglycemia contributes to neurological damage through mechanisms such as polyol pathway activation, protein kinase C signaling, and accumulation of advanced glycosylation end products, leading to neuronal metabolic dysfunction, oxidative stress, and vascular endothelial damage [ 14 ]. Other risk factors, including disease duration, aging, poor glycemic control, dyslipidemia, and hypertension, act synergistically with each other to exacerbate the risk of neuropathy [ 15 – 17 ]. Early and accurate prediction of diabetic neuropathy is essential for prevention and management. In clinical practice, neuropathy risk is assessed by vibration perception threshold, a measure of peripheral nerve dysfunction, and glycosylated hemoglobin (HbA1c), an indicator of long-term glycemic control [ 18 , 19 ]. Recent studies have driven the development of multimetric predictive models, such as tools that integrate HbA1c, total red blood cell count, C-reactive protein (CRP), and total symptom score, to improve predictive accuracy by combining biochemical and symptomatic data to inform early intervention and personalized care [ 20 ]. Early risk stratification and comprehensive strategies-including intensive glycemic control, symptom-targeted therapies, and lifestyle modifications-delay neuropathic progression and reduce disability risks. This study introduces a diabetic neuropathy risk prediction model integrating demographic, clinical, and laboratory data into a visualized nomogram. By quantifying individual risk factors, this tool aims to provide clinicians with a standardized, visual assessment framework for early diabetic neuropathy identification, personalized intervention, and improved long-term management of T2DM patients. 2. MATERIALS AND METHODS 2.1 Subjects of the study and data collection This was a cross-sectional study, and the diagnosis of diabetes mellitus strictly followed the diagnostic criteria for diabetes mellitus published by the American Diabetes Association [ 21 ]. The study retrospectively collected electronic medical records of diabetic patients from the department of endocrinology, affiliated hospital of north sichuan medical college, between 2013 and 2023, and the data were screened by inclusion and exclusion criteria: patients with other serious underlying diseases such as combined severe cardiovascular and cerebrovascular diseases, malignant tumors, and end-stage renal disease were excluded in order to minimize the interference of confounding factors. Diabetic neuropathy was diagnosed using a combination of neurologic symptom score and neurologic disability score [ 22 ]. At the same time, cases of neurological symptoms caused by other clear etiologies, such as adverse drug reactions, infectious diseases, autoimmune diseases, etc., were strictly excluded to ensure the homogeneity of the study population and the reliability of the results. A total of 13589 diabetic patients were collected and after exclusion, 12285 patients with type 2 diabetes were included in the study. 1304 patients were excluded out of which 386 patients were type 1 diabetes, 573 patients were gestational diabetes, 75 patients were other specific diabetes, and 270 patients were suffering from other serious diseases. Moreover, the sample size was estimated based on Kendall's criteria. Demographic, clinical, and laboratory data at the time of diabetes diagnosis were collected for the study, including information on age, gender, admission time, erythrocyte sedimentation rate (ESR), white blood cell count (WBC), platelet count (PLT), and HbA1c, as well as adverse lifestyle habits. Informed consent was obtained from all participants and the study was approved by the medical ethics committee of the affiliated hospital of north sichuan medical college (2023ER336-1, August 28, 2023). 2.2 Data processing In this study, a systematic approach to data processing and modeling was employed. Initially, the “mice” package in R was utilized for data cleaning; variables with missing rates exceeding 5% were eliminated, while those with missing rates below 5% were imputed using the multiple imputation method. The dataset was then split into a training dataset and a testing dataset in a 7:3 ratio for model construction and validation, respectively. The research comprised several specific steps: initially, a univariate logistic regression analysis was conducted on the training set to identify variables significantly associated with the outcome. Subsequently, a SHapley Additive exPlanations (SHAP) plot was generated using the XGBoost algorithm to quantify and rank the contributions of these variables to the model predictions. The top 10 variables by importance were selected. These key variables were then used to construct a multivariate logistic regression model for predicting diabetic neuropathy. Finally, the model was visualized with a nomogram that intuitively displayed the variable weights affecting risk prediction. Model performance was evaluated using multiple metrics: discrimination was assessed by plotting the ROC curve, while calibration was examined using a calibration curve to compare predicted probabilities with observed outcomes. The decision curve analysis quantified the model’s net benefit in clinical decision-making, thereby evaluating its practical application value. The comprehensive research process is illustrated in the technological roadmap in Fig. 1 . 2.3 Statistical analysis In this study, Excel was employed for data cleaning and variable assignment. Qualitative data were presented as percentages, whereas quantitative data were described using the median with interquartile ranges (P 25 , P 75 ). For statistical analysis, group comparisons of qualitative data utilized the chi-square test or Fisher’s exact test, depending on data characteristics. Differences in quantitative data between groups were evaluated via the Wilcoxon rank-sum test. RStudio 3.1.1 was used to analyze characteristic disparities between the training and test datasets. Variable screening involved univariate analysis and the XGBoost model, followed by the construction of multivariate logistic regression models. Furthermore, RStudio was used to generate DCA curves, ROC curves, and calibration curves to evaluate model performance. In this study, P < 0.05 was established as the criterion for statistical significance. 3. RESULTS 3.1 Characteristics of patients with type 2 diabetes. Data from 12,285 patients with T2DM were included in this study, which randomly divided the patients into a training set (n = 8,600) and a test set (n = 3,685) in a 7:3 ratio. Statistical testing showed no significant differences between the two groups regarding demographics, clinical characteristics, and laboratory parameters (see Table S1 for details). The study cohort consisted of 6286 female patients (51.1%) and the mean age of the patients was 61.00 (53.00, 72.00) years. Among them, a total of 7526 patients (61.2%) developed neuropathy, and the mean age of onset of neuropathy in the neuropathy group was 65.00 (55.00, 73.00) years, suggesting that neuropathy is highly prevalent in the middle-aged and elderly stages. 3.2 Variables associated with type 2 diabetic neuropathy were identified in the training dataset by univariate logistic analysis. In this study, univariate logistic regression analysis was carried out on the training dataset (n = 8600) with the aim of screening for potential influencing factors associated with the development of neuropathy in patients with T2DM. The results of the analysis are systematically presented in Table S2 to provide the basis of key variables for subsequent model construction. The results of univariate logistic regression analysis showed that in terms of adverse lifestyle habits and individual characteristics, smoking (OR = 0.835, P < 0.001) and drinking (OR = 0.861, P = 0.004) were significantly negatively correlated with the risk of neuropathy in patients with T2DM, which implied that the habits of smoking and drinking might have a potential protective effect on neuropathy, and the risk of neuropathy increased by 4.5% for every 1-unit increase in age (OR = 1.045, P < 0.001) increased by 4.5% for every 1-unit increase in age, which emphasized that ageing contributed to the progression of the disease. Regarding urinary indicators, increased ketone bodies (KET) levels elevated the risk of neuropathy by 1.908-fold ( P < 0.001). Conversely, urinary proteins (PRO) (OR = 0.736, P < 0.001), nitrites (NIT) (OR = 0.636, P < 0.001), and N-acetyl-β-D-amino glucosidase (NAG) (OR = 0.990, P < 0.001) were negatively linked with neuropathy risk. White blood cell (WBC-urine) (OR = 1.000, P = 0.002), urinary albumin (UALB) (OR = 1.000, P < 0.001), total urinary protein (OR = 1.000, P < 0.001), leukocyte esterase (LEU) (OR = 1.000, P = 0.027), epithelial cells (EC) (OR = 1.007, P < 0.001), and α1-Microglobulin (X1-MG) (OR = 1.007, P < 0.001) demonstrated a minor but significant uptick in neuropathy risk with each 1-unit index increase. Notably, urine specific gravity (SG) (OR = 0.000, P < 0.001) was strongly negatively correlated with neuropathy risk, suggesting a robust inverse predictive capacity, warranting further investigation into its underlying mechanisms. Blood-related factors significantly influenced neuropathy risk in T2DM patients. Seventeen parameters, including hemoglobin (HGB) (OR = 1.011, P < 0.001), WBC (OR = 1.029, P < 0.001), and red blood cell count (RBC) (OR = 1.351, P < 0.001), were positively correlated with neuropathy risk, implying an increased risk per unit increment. Hematocrit (HCT) showed a notably high OR of 32.347 ( P < 0.001), indicating a major impact on neuropathy risk, with procalcitonin (PCT) (OR = 4.930, P < 0.001) also playing a significant predictive role. Conversely, five indicators, including eosinophil count (EOS) (OR = 0.513, P < 0.001) and basophil percentage (BASO-R) (OR = 0.710, P < 0.001), were negatively correlated with neuropathy risk, suggesting a protective effect from elevated values. While hypersensitive C-reactive protein (hsCRP) (OR = 0.998, P < 0.001) and ESR (OR = 0.994, P < 0.001) had weak negative associations, their OR values near 1 indicate limited impact. These findings provide a foundation for further exploring the link between hematological indicators and diabetic neuropathy. Among the liver function and related biochemical indexes, a number of parameters were significantly associated with the risk of neuropathy in patients with T2DM. 18 indexes, including very low-density lipoprotein (VLDL) (OR = 1.226, P < 0.001) and A/G (OR = 1.565, P < 0.001), were positively correlated, suggesting that the risk of neuropathy increased for every 1-unit increase in the index level. The OR of apolipoprotein B (APOB) was 1.743, ( P < 0.001), suggesting that it has a particularly prominent effect on the risk. In contrast, 11 indicators, including globulin (GLO) (OR = 0.989, P = 0.001) and Ca (OR = 0.315, P < 0.001), were negatively correlated, suggesting that elevated levels of these indicators may have a potential protective effect. Notably, the OR for Ca was as low as 0.315, suggesting that elevated Ca levels may significantly reduce the risk of neuropathy. While cholinesterase (CHE) (OR = 1.000, P < 0.001), lipoprotein a (LP-a) (OR = 1.000, P = 0.003) and urinary creatinine (Ucr) (OR = 1.000, P < 0.001) showed statistical significance, but the OR values were close to 1, suggesting that their effects on the risk of neuropathy are relatively subtle and need to be further explored in the context of clinical practice. These results provide important clues for in-depth analysis of the association between metabolic indicators and diabetic neuropathy. In the analysis of coagulation-related indices, plasminogen activity (PT-%) (OR = 1.007, P < 0.001), fibrin degradation products (FDP) (OR = 1.013, P < 0.001), and D-dimer (D-Di) (OR = 1.028, P < 0.001) were independently associated with an increased risk of neuropathy in patients with T2DM. Specifically, each 1-unit increase in these indices was linked to a corresponding rise in neuropathy risk. In contrast, the international normalized ratio of prothrombin time (PT-INR) (OR = 0.498, P < 0.001), prothrombin time (PT) (OR = 0.951, P < 0.001), and activated partial thromboplastin time (APTT) (OR = 0.976, P < 0.001) exhibited negative associations with neuropathy risk. These findings suggest that elevated levels of PT-INR, PT, and APTT may confer a potential protective effect against the development of neuropathy in this population, as their inverse relationship with risk indicates that higher values are associated with lower odds of neuropathy. In terms of thyroid function indicators, free triiodothyronine (FT 3 ) (OR = 0.821, P < 0.001) and free thyroxine (FT 4 ) (OR = 0.939, P < 0.001) were inversely associated with the risk of neuropathy, suggesting that higher thyroid hormone levels may reduce the likelihood of neuropathy development. Additionally, complement 1q (Clq) demonstrated a statistically significant association (OR = 1.000, P = 0.030), despite an odds ratio close to 1, indicating a minimal yet detectable effect. Further research is warranted to clarify the precise role of Clq in neuropathy risk, particularly given the subtle magnitude of its association. 3.3 Filtering key variables for model construction based on the importance ranking of feature variables in the training set and plotting a nomogram. In this study, SHAP plots generated via the XGBoost model were used to identify the top 10 key feature variables associated with diabetic neuropathy development in patients with T2DM: age, creatine kinase (CK), total urinary protein, FT 4 , α-hydroxybutyrate dehydrogenase (α-HBDH), cystatin C (CysC), Ucr, Ca, X1-MG, and UALB (Fig. 2). These feature variables were integrated into a multivariate logistic regression model to construct a risk prediction system, with a nomogram developed for visualizing individual and combined effects (Fig. 3 ). As detailed in Table 1 , the model assigns scores to each risk factor, which are visualized in the nomogram. Summing these ten individual scores yields a total score corresponding to a specific risk ratio, enabling precise assessment of the probability of diabetic neuropathy in T2DM patients. Table 1 A multivariate logistic regression predictive model for the occurrence of neuropathy in type 2 diabetes mellitus Variables Coef S.E. Wald Z P Intercept 0.666 0.339 1.970 0.049 Age 0.042 0.002 25.660 < 0.0001 CK -0.001 0.000 -7.920 < 0.0001 Total urine protein 0.000 0.000 6.310 < 0.0001 FT 4 0.062 0.004 15.670 < 0.0001 α-HBDH -0.001 0.000 -4.510 < 0.0001 CysC 0.043 0.027 1.590 0.112 Ucr 0.000 0.000 -9.310 < 0.0001 Ca -1.159 0.140 -8.300 < 0.0001 X1-MG 0.005 0.001 7.220 < 0.0001 UALB 0.000 0.000 6.020 < 0.0001 CK, creatine kinase; FT 4 , free thyroxine; CysC, Cystatin C; Ucr, urinary creatinine; a_HBDH, α-Hydroxybutyrate dehydrogenase; X1-MG, α1-Microglobulin; UALB, urinary albumin. 3.4 Evaluation of the predictive model. Discriminative ability for predicting diabetic neuropathy risk in patients with T2DM was evaluated using ROC curves (Fig. 4 ). The AUC values for the training and testing datasets were 0.743 (95% CI: 0.733–0.754) and 0.751 (95% CI: 0.735–0.768), respectively, indicating substantial discriminative efficacy. An optimal probability threshold of 0.579 was identified in the training dataset (sensitivity = 0.604, specificity = 0.748), meaning patients with risk estimates exceeding this threshold had significantly elevated neuropathy risk. Calibration curves (Fig. 5 ) demonstrated close alignment between predicted probabilities and observed outcomes in both datasets, while DCA curves (Fig. 6 ) showed that clinical decision-making using the model yielded greater net benefit when the predicted neuropathy probability ranged from 0.4 to 0.8. 4. DISCUSSION In this study, we developed a risk prediction model for diabetic neuropathy in patients with T2DM, visualizing and enabling precise risk assessment via a nomogram. The model incorporated 10 key feature variables, including age, CK, total urine protein, FT 4 , α-HBDH, CysC, Ucr, Ca, X1-MG and UALB, and was validated multidimensionally and showed good performance in terms of discriminative ability, predictive accuracy, and clinical application value. Our findings confirmed that CK, FT 4 , α-HBDH, Ucr, and Ca exhibit negative associations with diabetic neuropathy risk in T2DM, suggesting potential protective roles. As a critical enzyme in energy metabolism, CK maintains nerve cell homeostasis by regulating the phosphocreatine-creatine system, which stabilizes energy supply and participates in intracellular signaling to facilitate nerve repair [ 23 , 24 ]. FT 4 , a bioactive thyroid hormone, is essential for neuronal development and adult neurological function, modulating central nervous system excitability and sympathetic activity [ 25 ]. Prior research linked low serum FT 3 levels to impaired nerve conduction in diabetic patients [ 26 ], while FT 4 levels inversely correlated with diabetic peripheral neuropathy (DPN) prevalence in euthyroid T2DM individuals [ 27 ], aligning with our results. Reduced α-HBDH expression in DPN patients [ 27 ] implies that low α-HBDH may increase neuropathy susceptibility, though its protective mechanisms require further investigation. Although serum creatinine is established as a DPN risk factor [ 28 ], our study uniquely identifies Ucr as protective, likely reflecting its role in renal filtration: higher Ucr indicates efficient metabolic waste clearance, preserving metabolic homeostasis and mitigating hyperglycemic neurotoxicity [ 29 ]. Serum calcium, inversely associated with T2DM risk [ 30 ], is critical for nerve cell membrane potential stability and neurotransmitter release [ 31 , 32 ], reinforcing its role in reducing diabetic neuropathy risk through these mechanisms. Collectively, these feature variables (CK, FT 4 , α-HBDH, Ucr, and Ca) mediate protection against T2DM neuropathy via energy metabolism, neurological maintenance, renal clearance, and calcium homeostasis, highlighting the clinical importance of targeting these pathways for prevention. Conversely, age, total urinary protein, CysC, X1-MG, and UALB emerged as independent risk factors. Age-related neuropathy risk aligns with prior evidence: a 3.6% annual risk increase and elevated risk in patients ≥ 71 years underscore aging as a key driver [ 33 , 34 ], with molecular mechanisms like exosome miR-21-mediated neurite growth decline supporting prioritized screening for elderly T2DM patients [ 35 ]. Renal injury biomarkers, such as total urinary protein, CysC, X1-MG, and UALB, are pathologically linked to neuropathy: proteinuria accelerates disease via cytokine-mediated neurotoxicity [ 38 ], while CysC predicts peripheral neuropathy and microvascular ischemia, a hallmark of diabetic neurovascular damage [ 36 – 38 ]. X1-MG, a proximal tubule injury marker, suggests renal tubular dysfunction may impair reabsorption of neuroessential nutrients (e.g., amino acids, vitamins), indirectly contributing to neuropathy [ 39 ]. These findings emphasize the need for integrated renal-neurological monitoring in diabetes management to detect early multi-organ injury. Notably, HbA1c, an established neuropathy risk factor in prior studies [ 40 ], was excluded from our final model due to feature importance-based variable selection, indicating context-dependent roles that warrant validation in expanded datasets. This study has several limitations. First, its single-center design may introduce selection bias, thereby limiting the generalizability of the findings to broader populations. Second, potential confounding variables were not fully accounted for in the analysis, which could affect the robustness of the associations identified. Third, the cross-sectional nature of the data precludes the establishment of causal relationships or the examination of dynamic disease pathways over time. To address these limitations, future research should involve multi-center, prospective cohort studies to validate the model, identify additional biomarkers, and elucidate the longitudinal mechanisms underlying the development of neuropathy in this population. In conclusion, our prediction model and identified predictors offer novel insights for early risk stratification and intervention in T2DM neuropathy. Prioritizing research to validate the model, decode protective mechanisms, and translate findings into clinical practice could significantly reduce neuropathy burden in T2DM populations. Declarations Authorship contribution statement B.W. contributed to software development, formal analysis, validation, visualization, and investigation. S.Y. F. was responsible for formal analysis, data curation, and writing the original draft. Y.F. contributed to data curation and formal analysis. R.Y. J. took charge of conceptualization, methodology, data curation, formal analysis, and funding acquisition. J.S. L. contributed to methodology, software development, formal analysis, validation, visualization, and funding acquisition. Y.L. Z. contributed to conceptualization, methodology, formal analysis, investigation, funding acquisition, writing the original draft, and writing the review & editing. All authors reviewed the manuscript. Competing interests The authors declare that they have no known competing financial interests or personal relationships that could have influenced the research presented in this paper. Funding This work was supported by the National Natural Science Foundation of China (No. 22203057), Science and Technology Strategic Cooperation Programs of Luzhou Municipal People’s Government and Southwest Medical University (2024LZXNYDT001, China), Research and Development Fund Project of Sichuan North Medical College (CBY22-QNA38). Data availability The datasets generated during and/or analysed during the current study are not publicly available due to patient privacy but are available from the corresponding author on reasonable request. References Raguraman, R., Srivastava, A., Munshi, A. & Ramesh, R. Therapeutic approaches targeting molecular signaling pathways common to diabetes, lung diseases and cancer. Adv. Drug Deliv Rev. 178 , 113918. 10.1016/j.addr.2021.113918 (2021). Song, K. et al. Targeting APLN/APJ restores blood-testis barrier and improves spermatogenesis in murine and human diabetic models. Nat. Commun. 13 (1), 7335. 10.1038/s41467-022-34990-3 (2022). Lontchi-Yimagou, E. et al. Non-autoimmune diabetes mellitus and the risk of virus infections: a systematic review and meta-analysis of case-control and cohort studies. Sci. Rep. 11 (1), 8968. 10.1038/s41598-021-88598-6 (2021). Raimundo, A. F., Ferreira, S., Farrim, M. I., Santos, C. N. & Menezes, R. Heterologous Expression of Immature Forms of Human Islet Amyloid Polypeptide in Yeast Triggers Intracellular Aggregation and Cytotoxicity. Front Microbiol . 11: (2035). 10.3389/fmicb.2020.02035 (2020). Luo, Q. et al. Renal Protective Effects of Melatonin in Animal Models of Diabetes Mellitus-Related Kidney Damage: A Systematic Review and Meta-Analysis. J Diabetes Res . :3770417. (2022). 10.1155/2022/3770417 (2022). Chen, L. et al. Label-free optical metabolic imaging of adipose tissues for prediabetes diagnosis. Theranostics 13 (11), 3550–3567. 10.7150/thno.82697 (2023). Zhao, Y. et al. The SNPs of mitochondrial DNA displacement loop region and mitochondrial DNA copy number associated with risk of polymyositis and dermatomyositis. Sci. Rep. 12 (1), 5903. 10.1038/s41598-022-09943-x (2022). Pan, L. et al. Dose-response relationship between Chinese visceral adiposity index and type 2 diabetes mellitus among middle-aged and elderly Chinese. Front. Endocrinol. (Lausanne) . 13 , 959860. 10.3389/fendo.2022.959860 (2022). Li, W. et al. Association of Serum Magnesium with Insulin Resistance and Type 2 Diabetes among Adults in China. Nutrients 14 (9), 1799. 10.3390/nu14091799 (2022). Wu, Y., Sun, L., Zhuang, Z., Hu, X. & Dong, D. Mitochondrial-Derived Peptides in Diabetes and Its Complications. Front. Endocrinol. (Lausanne) . 12 , 808120. 10.3389/fendo.2021.808120 (2022). Zhou, X. et al. Schisandrol A protects AGEs-induced neuronal cells death by allosterically targeting ATP6V0d1 subunit of V-ATPase. Acta Pharm. Sin B . 12 (10), 3843–3860. 10.1016/j.apsb.2022.06.013 (2022). Dufour, A. B., Kiel, D. P., Williams, S. A., Weiss, R. J. & Samelson, E. J. Risk Factors for Incident Fracture in Older Adults With Type 2 Diabetes: The Framingham Heart Study. Diabetes Care . 44 (7), 1547–1555. 10.2337/dc20-3150 (2021). Feldman, E. L. et al. Diabetic neuropathy. Nat. Rev. Dis. Primers . 5 (1), 42. 10.1038/s41572-019-0097-9 (2019). Oates, P. J. Aldose reductase, still a compelling target for diabetic neuropathy. Curr. Drug Targets . 9 (1), 14–36. 10.2174/138945008783431781 (2008). Han, L. et al. Peripheral neuropathy is associated with insulin resistance independent of metabolic syndrome. Diabetol. Metab. Syndr. 7 , 14. 10.1186/s13098-015-0010-y (2015). Jeannin, A. C. et al. Inactive matrix gla protein plasma levels are associated with peripheral neuropathy in Type 2 diabetes. PLoS One . 15 (2), e0229145. 10.1371/journal.pone.0229145 (2020). Gregory, J. A., Jolivalt, C. G., Goor, J., Mizisin, A. P. & Calcutt, N. A. Hypertension-induced peripheral neuropathy and the combined effects of hypertension and diabetes on nerve structure and function in rats. Acta Neuropathol. 124 (4), 561–573. 10.1007/s00401-012-1012-6 (2012). Srinivasan, S., Singh, P., Kulothungan, V., Sharma, T. & Raman, R. Relationship between triglyceride glucose index, retinopathy and nephropathy in Type 2 diabetes. Endocrinol. Diabetes Metab. 4 (1), e00151. 10.1002/edm2.151 (2020). Putz, Z. et al. Is there an association between diabetic neuropathy and low vitamin D levels? Curr. Diab Rep. 14 (10), 537. 10.1007/s11892-014-0537-6 (2014). Laxmi, M. S. & Prabhakar, O. Development of risk prediction scores for diabetic peripheral neuropathy patients. J. Neurosci. Rural Pract. 14 (4), 667–670. 10.25259/JNRP-151-2023 (2023). Gou, W. et al. Interpretable Machine Learning Framework Reveals Robust Gut Microbiome Features Associated With Type 2 Diabetes. Diabetes Care . 44 (2), 358–366. 10.2337/dc20-1536 (2021). Panagoulias, G. S. et al. Dryness of Foot Skin Assessed by the Visual Indicator Test and Risk of Diabetic Foot Ulceration: A Prospective Observational Study. Front. Endocrinol. (Lausanne) . 11 , 625. 10.3389/fendo.2020.00625 (2020). Hamilton, P. D., Bozeman, S. L. & Andley, U. P. Creatine kinase/α-crystallin interaction functions in cataract development. Biochem. Biophys. Rep. 22 , 100748. 10.1016/j.bbrep.2020.100748 (2020). Farid, M. M., Yang, X., Kuboyama, T. & Tohda, C. Trigonelline recovers memory function in Alzheimer's disease model mice: evidence of brain penetration and target molecule. Sci. Rep. 10 (1), 16424. 10.1038/s41598-020-73514-1 (2020). Raymond, L. J. & Ralston, N. V. C. Mercury: selenium interactions and health implications. Neurotoxicology 81 , 294–299. 10.1016/j.neuro.2020.09.020 (2020). Zhu, F. F. & Yang, L. Z. The Association Between the Levels of Thyroid Hormones and Peripheral Nerve Conduction in Patients with Type 2 Diabetes Mellitus. Exp. Clin. Endocrinol. Diabetes . 126 (8), 493–504. 10.1055/a-0635-0826 (2018). He, Q., Zeng, Z., Zhao, M., Ruan, B. & Chen, P. Association between thyroid function and diabetes peripheral neuropathy in euthyroid type 2 diabetes mellitus patients. Sci. Rep. 13 (1), 13499. 10.1038/s41598-023-40752-y (2023). Gao, L. et al. Risk Factors for Subclinical Diabetic Peripheral Neuropathy in Type 2 Diabetes Mellitus. Diabetes Metab. Syndr. Obes. 17 , 417–426. 10.2147/DMSO.S433024 (2024). Huang, Y., Jiang, K., Zhang, X. & Chung, E. J. The effect of size, charge, and peptide ligand length on kidney targeting by small, organic nanoparticles. Bioeng. Transl Med. 5 (3), e10173. 10.1002/btm2.10173 (2020). Zhu, J. et al. Circulating calcium levels and the risk of type 2 diabetes: a systematic review and meta-analysis. Br. J. Nutr. 122 (4), 376–387. 10.1017/S0007114519001430 (2019). Lai, J. et al. Displacement and hybridization reactions in aptamer-functionalized hydrogels for biomimetic protein release and signal transduction. Chem. Sci. 8 (11), 7306–7311. 10.1039/c7sc03023a (2017). Peng, Z., Hou, H., Zhang, K. & Li, B. Effect of calcium-binding peptide from Pacific cod (Gadus macrocephalus) bone on calcium bioavailability in rats. Food Chem. 221 , 373–378. 10.1016/j.foodchem.2016.10.078 (2017). Mao, F. et al. Age as an Independent Risk Factor for Diabetic Peripheral Neuropathy in Chinese Patients with Type 2 Diabetes. Aging Dis. 10 (3), 592–600. 10.14336/AD.2018.0618 (2019). Amelia, R., Wahyuni, A. S. & Yunanda, Y. Diabetic Neuropathy among Type 2 Diabetes Mellitus Patients at Amplas Primary Health Care in Medan City. Open. Access. Maced J. Med. Sci. 7 (20), 3400–3403. 10.3889/oamjms.2019.433 (2019). Liu, Y. P. et al. Schwann cells-derived exosomal miR-21 participates in high glucose regulation of neurite outgrowth. iScience 25 (10), 105141. 10.1016/j.isci.2022.105141 (2022). Yang, X. et al. Cystatin C Is an Important Biomarker for Cardiovascular Autonomic Dysfunction in Chinese Type 2 Diabetic Patients. J. Diabetes Res. 2019 , 1706964. 10.1155/2019/1706964 (2019). Hu, Y. et al. Association between serum cystatin C and diabetic peripheral neuropathy: a cross-sectional study of a Chinese type 2 diabetic population. Eur. J. Endocrinol. 171 (5), 641–648. 10.1530/EJE-14-0381 (2014). Zhang, H. et al. Assessing the diagnostic utility of urinary albumin-to-creatinine ratio as a potential biomarker for diabetic peripheral neuropathy in type 2 diabetes mellitus patients. Sci. Rep. 14 (1), 27198. 10.1038/s41598-024-78828-y (2024). Ohta, K. et al. Normal values for pediatric urinary biochemistry in early infancy. Ir. J. Med. Sci. 192 (5), 2507–2511. 10.1007/s11845-023-03296-8 (2023). Zhong, M., Yang, Y. R., Zhang, Y. Z. & Yan, S. J. Change in Urine Albumin-to-Creatinine Ratio and Risk of Diabetic Peripheral Neuropathy in Type 2 Diabetes: A Retrospective Cohort Study. Diabetes Metab. Syndr. Obes. 14 , 1763–1772. 10.2147/DMSO.S303096 (2021). Additional Declarations No competing interests reported. Supplementary Files TableS1.docx TableS2.docx 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-6556009","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":505841939,"identity":"bd580132-06de-41f4-9f81-91f946aa6fa8","order_by":0,"name":"Biao Wang","email":"","orcid":"","institution":"Affiliated Hospital of North Sichuan Medical College","correspondingAuthor":false,"prefix":"","firstName":"Biao","middleName":"","lastName":"Wang","suffix":""},{"id":505841940,"identity":"04588f81-b07f-45a5-9a7f-a6ad61acf1b7","order_by":1,"name":"Siyu Feng","email":"","orcid":"","institution":"Nanchong Mental Health Center Of Sichuan Province","correspondingAuthor":false,"prefix":"","firstName":"Siyu","middleName":"","lastName":"Feng","suffix":""},{"id":505841941,"identity":"6d8dc04a-8c6e-4d8a-a0dc-533e857194fd","order_by":2,"name":"Yuan Fang","email":"","orcid":"","institution":"Nanchong Hospital of Capital Medical University Affiliated Beijing Anzhen Hospital","correspondingAuthor":false,"prefix":"","firstName":"Yuan","middleName":"","lastName":"Fang","suffix":""},{"id":505841942,"identity":"8acda0a5-0652-42be-9400-2e099f031056","order_by":3,"name":"Runyu Jing","email":"","orcid":"","institution":"Guizhou Education University","correspondingAuthor":false,"prefix":"","firstName":"Runyu","middleName":"","lastName":"Jing","suffix":""},{"id":505841943,"identity":"548eb4cb-4754-425f-be13-d2dcd4bd1196","order_by":4,"name":"Jiesi Luo","email":"","orcid":"","institution":"Southwest Medical University","correspondingAuthor":false,"prefix":"","firstName":"Jiesi","middleName":"","lastName":"Luo","suffix":""},{"id":505841944,"identity":"5b592501-1e00-4ec9-a3a3-043ed211722a","order_by":5,"name":"Yonglin Zhang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA7klEQVRIie3QMWrDMBTG8ScE8SLqjM+kkCs8CIQMJmeRMKhLKIEunYqh4Cw5gI7RG0Tpo55ygAxdumQ2tIOHDlWHDl1kj4Xqj8bvNzwBpFJ/ti1CLqX3HZXrkYQQil1jjm5rq7EkvNNpwap7FvXQeO6q9u2DVrdw1sQleQkZvzzFiHD2ZnFNeCec1ryh1ytQ1p5jROJmOUNC84jaB3KRgGoZJZMf0qCpeUUs6iGiAim6QPaKgWEMQXWxs/DJxmUNHPdkq8nQLfNd1Rb9/YM5cP7e9Z/lOs+4jRKYapDq13XR+Xe5B9EPrlKpVOpf9wVmg0okhWw31wAAAABJRU5ErkJggg==","orcid":"","institution":"Affiliated Hospital of North Sichuan Medical College","correspondingAuthor":true,"prefix":"","firstName":"Yonglin","middleName":"","lastName":"Zhang","suffix":""}],"badges":[],"createdAt":"2025-04-29 11:38:05","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6556009/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6556009/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":90313463,"identity":"0a66666b-29e1-4978-ae8f-ac844036e669","added_by":"auto","created_at":"2025-09-01 10:07:47","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":113183,"visible":true,"origin":"","legend":"\u003cp\u003eFlowchart of the study.\u003c/p\u003e","description":"","filename":"Onlinefigure1.png","url":"https://assets-eu.researchsquare.com/files/rs-6556009/v1/55b6c83ba2df7935d517ba2a.png"},{"id":90315762,"identity":"697e2e9d-b564-4a01-b833-c7085323a670","added_by":"auto","created_at":"2025-09-01 10:15:47","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":84413,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe top 15 feature importances of target outcome classes determined by the SHAP algorithm.\u003c/strong\u003e CK, creatine kinase; FT\u003csub\u003e4\u003c/sub\u003e, free thyroxine; CysC, Cystatin C; Ucr, urinary creatinine; α-HBDH, α-Hydroxybutyrate dehydrogenase; X1-MG, α1-Microglobulin; UALB, urinary albumin, hsCRP, hypersensitive C-reactive protein; ESR, erythrocyte sedimentation rate; D-Di, D-dimer; PT-INR, international normalized ratio of prothrombin time.\u003c/p\u003e","description":"","filename":"Onlinefigure2.png","url":"https://assets-eu.researchsquare.com/files/rs-6556009/v1/6db41cabeb3bf8664d5c0971.png"},{"id":90315763,"identity":"974d9a3f-c78b-420d-a40e-fb4b9f6b1b50","added_by":"auto","created_at":"2025-09-01 10:15:47","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":61600,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eNomogram for predicting the risk of neuropathy in patients with type 2 diabetic.\u003c/strong\u003e CK, creatine kinase; FT\u003csub\u003e4\u003c/sub\u003e, free thyroxine; CysC, Cystatin C; Ucr, urinary creatinine; α-HBDH, α-Hydroxybutyrate dehydrogenase; X1-MG, α1-Microglobulin; UALB, urinary albumin.\u003c/p\u003e","description":"","filename":"Onlinefigure3.png","url":"https://assets-eu.researchsquare.com/files/rs-6556009/v1/95640f96c8c501d033a52e42.png"},{"id":90315766,"identity":"3e4705d9-16c8-40b0-b98c-a6da6fb7ea15","added_by":"auto","created_at":"2025-09-01 10:15:47","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":114575,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe ROC curve analysis of the predictive model.\u003c/strong\u003e (A) ROC curve for the training set, AUC=0.743 (0.733-0.754). (B) ROC curve for the validation set, AUC=0.751 (0.735-0.768).\u003c/p\u003e","description":"","filename":"Onlinefigure4.png","url":"https://assets-eu.researchsquare.com/files/rs-6556009/v1/f4ac418b67e5711420f3ad21.png"},{"id":90315774,"identity":"42022142-4c10-4349-9738-c36eb181a77f","added_by":"auto","created_at":"2025-09-01 10:15:48","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":115060,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAssessment of performance of predictive model.\u003c/strong\u003e (A) DCA curve for the training dataset. (B) DCA curve for the validation dataset. DCA, decision curve analysis.\u003c/p\u003e","description":"","filename":"Onlinefigure5.png","url":"https://assets-eu.researchsquare.com/files/rs-6556009/v1/19f158bcc09a5bb35fec8547.png"},{"id":90315771,"identity":"a782910a-6d93-4c02-9bdd-929306691694","added_by":"auto","created_at":"2025-09-01 10:15:48","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":110093,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eClinical applicability of predictive model.\u003c/strong\u003e (A) Calibration curves for the training dataset. (B) Calibration curves for the validation dataset.\u003c/p\u003e","description":"","filename":"Onlinefigure6.png","url":"https://assets-eu.researchsquare.com/files/rs-6556009/v1/97953c2576372c09f9003322.png"},{"id":94640607,"identity":"a1681b97-d5e7-4bde-be20-df0326c9347f","added_by":"auto","created_at":"2025-10-29 07:49:55","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1767782,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6556009/v1/ba8c16c0-092a-47cb-8f7f-1b4f39642b2f.pdf"},{"id":90316966,"identity":"63261c6e-b097-4341-a1ef-6f9c31edb588","added_by":"auto","created_at":"2025-09-01 10:23:47","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":37545,"visible":true,"origin":"","legend":"","description":"","filename":"TableS1.docx","url":"https://assets-eu.researchsquare.com/files/rs-6556009/v1/962117ec0ebaa5d572dfbe1d.docx"},{"id":90315764,"identity":"6b933a03-b4e9-4e1f-b017-f64969f88f9a","added_by":"auto","created_at":"2025-09-01 10:15:47","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":30912,"visible":true,"origin":"","legend":"","description":"","filename":"TableS2.docx","url":"https://assets-eu.researchsquare.com/files/rs-6556009/v1/32582588c95f25cfe105088e.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Development of a Predictive Model and Nomogram for Neuropathy Risk in T2DM Patients","fulltext":[{"header":"1. INTRODUCTION","content":"\u003cp\u003eDiabetes mellitus is a metabolic disorder defined by persistent hyperglycemia arising from insufficient insulin secretion, impaired insulin bioactivity, or both [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. As classified by the World Health Organization and the International Diabetes Federation, diabetes mellitus encompasses four primary types, including type 1 diabetes mellitus (T1DM), type 2 diabetes mellitus (T2DM), gestational diabetes mellitus, and other special forms [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. The international diabetes burden is severe: IDF data report 463\u0026nbsp;million global patients in 2019, projecting increases to 592\u0026nbsp;million by 2035 and surpassing 700\u0026nbsp;million by 2045 [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. T2DM constitutes over 90% of all diabetic cases, making it the most prevalent subtype globally [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. As the nation with the largest diabetic population, China\u0026rsquo;s recent epidemiological survey reveals a 12.8% prevalence of diabetes mellitus among adults aged 18 and older [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Notably, awareness (36.7%), treatment (32.9%), and control (49.2%) rates remain significantly lower than in developed countries [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Clinical evidence confirms that early lifestyle interventions, standardized pharmacotherapy, and multidisciplinary care effectively slow disease progression and reduce complication risks. Strengthening primary healthcare systems, enhancing public awareness, and optimizing screening and tiered care mechanisms are imperative to mitigate diabetes-related socioeconomic burdens.\u003c/p\u003e\u003cp\u003eDiabetic complications, including acute metabolic emergencies (e.g., diabetic ketoacidosis, hyperosmolar hyperglycemic syndrome) and chronic multiorgan damage (including cardiovascular, renal, retinal, and neurologic damage), are the leading causes of disability and death in patients [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Among them, diabetic neuropathy is common chronic complication, which can be categorized into peripheral neuropathy and autonomic neuropathy, both of which can severely affect patients' quality of life and clinical prognosis [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Epidemiologic data suggested that more than 50% of diabetic patients developed diabetic neuropathy during the course of their disease [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Hyperglycemia contributes to neurological damage through mechanisms such as polyol pathway activation, protein kinase C signaling, and accumulation of advanced glycosylation end products, leading to neuronal metabolic dysfunction, oxidative stress, and vascular endothelial damage [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Other risk factors, including disease duration, aging, poor glycemic control, dyslipidemia, and hypertension, act synergistically with each other to exacerbate the risk of neuropathy [\u003cspan additionalcitationids=\"CR16\" citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Early and accurate prediction of diabetic neuropathy is essential for prevention and management. In clinical practice, neuropathy risk is assessed by vibration perception threshold, a measure of peripheral nerve dysfunction, and glycosylated hemoglobin (HbA1c), an indicator of long-term glycemic control [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Recent studies have driven the development of multimetric predictive models, such as tools that integrate HbA1c, total red blood cell count, C-reactive protein (CRP), and total symptom score, to improve predictive accuracy by combining biochemical and symptomatic data to inform early intervention and personalized care [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eEarly risk stratification and comprehensive strategies-including intensive glycemic control, symptom-targeted therapies, and lifestyle modifications-delay neuropathic progression and reduce disability risks. This study introduces a diabetic neuropathy risk prediction model integrating demographic, clinical, and laboratory data into a visualized nomogram. By quantifying individual risk factors, this tool aims to provide clinicians with a standardized, visual assessment framework for early diabetic neuropathy identification, personalized intervention, and improved long-term management of T2DM patients.\u003c/p\u003e"},{"header":"2. MATERIALS AND METHODS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1 Subjects of the study and data collection\u003c/h2\u003e\u003cp\u003eThis was a cross-sectional study, and the diagnosis of diabetes mellitus strictly followed the diagnostic criteria for diabetes mellitus published by the American Diabetes Association [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. The study retrospectively collected electronic medical records of diabetic patients from the department of endocrinology, affiliated hospital of north sichuan medical college, between 2013 and 2023, and the data were screened by inclusion and exclusion criteria: patients with other serious underlying diseases such as combined severe cardiovascular and cerebrovascular diseases, malignant tumors, and end-stage renal disease were excluded in order to minimize the interference of confounding factors. Diabetic neuropathy was diagnosed using a combination of neurologic symptom score and neurologic disability score [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. At the same time, cases of neurological symptoms caused by other clear etiologies, such as adverse drug reactions, infectious diseases, autoimmune diseases, etc., were strictly excluded to ensure the homogeneity of the study population and the reliability of the results.\u003c/p\u003e\u003cp\u003eA total of 13589 diabetic patients were collected and after exclusion, 12285 patients with type 2 diabetes were included in the study. 1304 patients were excluded out of which 386 patients were type 1 diabetes, 573 patients were gestational diabetes, 75 patients were other specific diabetes, and 270 patients were suffering from other serious diseases. Moreover, the sample size was estimated based on Kendall's criteria.\u003c/p\u003e\u003cp\u003eDemographic, clinical, and laboratory data at the time of diabetes diagnosis were collected for the study, including information on age, gender, admission time, erythrocyte sedimentation rate (ESR), white blood cell count (WBC), platelet count (PLT), and HbA1c, as well as adverse lifestyle habits. Informed consent was obtained from all participants and the study was approved by the medical ethics committee of the affiliated hospital of north sichuan medical college (2023ER336-1, August 28, 2023).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2 Data processing\u003c/h2\u003e\u003cp\u003eIn this study, a systematic approach to data processing and modeling was employed. Initially, the \u0026ldquo;mice\u0026rdquo; package in R was utilized for data cleaning; variables with missing rates exceeding 5% were eliminated, while those with missing rates below 5% were imputed using the multiple imputation method. The dataset was then split into a training dataset and a testing dataset in a 7:3 ratio for model construction and validation, respectively.\u003c/p\u003e\u003cp\u003eThe research comprised several specific steps: initially, a univariate logistic regression analysis was conducted on the training set to identify variables significantly associated with the outcome. Subsequently, a SHapley Additive exPlanations (SHAP) plot was generated using the XGBoost algorithm to quantify and rank the contributions of these variables to the model predictions. The top 10 variables by importance were selected. These key variables were then used to construct a multivariate logistic regression model for predicting diabetic neuropathy. Finally, the model was visualized with a nomogram that intuitively displayed the variable weights affecting risk prediction.\u003c/p\u003e\u003cp\u003eModel performance was evaluated using multiple metrics: discrimination was assessed by plotting the ROC curve, while calibration was examined using a calibration curve to compare predicted probabilities with observed outcomes. The decision curve analysis quantified the model\u0026rsquo;s net benefit in clinical decision-making, thereby evaluating its practical application value. The comprehensive research process is illustrated in the technological roadmap in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e2.3 Statistical analysis\u003c/h2\u003e\u003cp\u003eIn this study, Excel was employed for data cleaning and variable assignment. Qualitative data were presented as percentages, whereas quantitative data were described using the median with interquartile ranges (P\u003csub\u003e25\u003c/sub\u003e, P\u003csub\u003e75\u003c/sub\u003e). For statistical analysis, group comparisons of qualitative data utilized the chi-square test or Fisher\u0026rsquo;s exact test, depending on data characteristics. Differences in quantitative data between groups were evaluated via the Wilcoxon rank-sum test. RStudio 3.1.1 was used to analyze characteristic disparities between the training and test datasets. Variable screening involved univariate analysis and the XGBoost model, followed by the construction of multivariate logistic regression models. Furthermore, RStudio was used to generate DCA curves, ROC curves, and calibration curves to evaluate model performance. In this study, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was established as the criterion for statistical significance.\u003c/p\u003e\u003c/div\u003e"},{"header":"3. RESULTS","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003e3.1 Characteristics of patients with type 2 diabetes.\u003c/h2\u003e\u003cp\u003eData from 12,285 patients with T2DM were included in this study, which randomly divided the patients into a training set (n\u0026thinsp;=\u0026thinsp;8,600) and a test set (n\u0026thinsp;=\u0026thinsp;3,685) in a 7:3 ratio. Statistical testing showed no significant differences between the two groups regarding demographics, clinical characteristics, and laboratory parameters (see Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e for details). The study cohort consisted of 6286 female patients (51.1%) and the mean age of the patients was 61.00 (53.00, 72.00) years. Among them, a total of 7526 patients (61.2%) developed neuropathy, and the mean age of onset of neuropathy in the neuropathy group was 65.00 (55.00, 73.00) years, suggesting that neuropathy is highly prevalent in the middle-aged and elderly stages.\u003c/p\u003e\u003cp\u003e\u003cb\u003e3.2 Variables associated with type 2 diabetic neuropathy were identified in the training dataset by univariate logistic analysis.\u003c/b\u003e\u003c/p\u003e\u003cp\u003eIn this study, univariate logistic regression analysis was carried out on the training dataset (n\u0026thinsp;=\u0026thinsp;8600) with the aim of screening for potential influencing factors associated with the development of neuropathy in patients with T2DM. The results of the analysis are systematically presented in Table \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e to provide the basis of key variables for subsequent model construction. The results of univariate logistic regression analysis showed that in terms of adverse lifestyle habits and individual characteristics, smoking (OR\u0026thinsp;=\u0026thinsp;0.835, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and drinking (OR\u0026thinsp;=\u0026thinsp;0.861, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.004) were significantly negatively correlated with the risk of neuropathy in patients with T2DM, which implied that the habits of smoking and drinking might have a potential protective effect on neuropathy, and the risk of neuropathy increased by 4.5% for every 1-unit increase in age (OR\u0026thinsp;=\u0026thinsp;1.045, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) increased by 4.5% for every 1-unit increase in age, which emphasized that ageing contributed to the progression of the disease.\u003c/p\u003e\u003cp\u003eRegarding urinary indicators, increased ketone bodies (KET) levels elevated the risk of neuropathy by 1.908-fold (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Conversely, urinary proteins (PRO) (OR\u0026thinsp;=\u0026thinsp;0.736, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), nitrites (NIT) (OR\u0026thinsp;=\u0026thinsp;0.636, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and N-acetyl-β-D-amino glucosidase (NAG) (OR\u0026thinsp;=\u0026thinsp;0.990, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) were negatively linked with neuropathy risk. White blood cell (WBC-urine) (OR\u0026thinsp;=\u0026thinsp;1.000, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.002), urinary albumin (UALB) (OR\u0026thinsp;=\u0026thinsp;1.000, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), total urinary protein (OR\u0026thinsp;=\u0026thinsp;1.000, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), leukocyte esterase (LEU) (OR\u0026thinsp;=\u0026thinsp;1.000, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.027), epithelial cells (EC) (OR\u0026thinsp;=\u0026thinsp;1.007, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and α1-Microglobulin (X1-MG) (OR\u0026thinsp;=\u0026thinsp;1.007, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) demonstrated a minor but significant uptick in neuropathy risk with each 1-unit index increase. Notably, urine specific gravity (SG) (OR\u0026thinsp;=\u0026thinsp;0.000, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) was strongly negatively correlated with neuropathy risk, suggesting a robust inverse predictive capacity, warranting further investigation into its underlying mechanisms.\u003c/p\u003e\u003cp\u003eBlood-related factors significantly influenced neuropathy risk in T2DM patients. Seventeen parameters, including hemoglobin (HGB) (OR\u0026thinsp;=\u0026thinsp;1.011, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), WBC (OR\u0026thinsp;=\u0026thinsp;1.029, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and red blood cell count (RBC) (OR\u0026thinsp;=\u0026thinsp;1.351, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), were positively correlated with neuropathy risk, implying an increased risk per unit increment. Hematocrit (HCT) showed a notably high OR of 32.347 (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), indicating a major impact on neuropathy risk, with procalcitonin (PCT) (OR\u0026thinsp;=\u0026thinsp;4.930, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) also playing a significant predictive role. Conversely, five indicators, including eosinophil count (EOS) (OR\u0026thinsp;=\u0026thinsp;0.513, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and basophil percentage (BASO-R) (OR\u0026thinsp;=\u0026thinsp;0.710, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), were negatively correlated with neuropathy risk, suggesting a protective effect from elevated values. While hypersensitive C-reactive protein (hsCRP) (OR\u0026thinsp;=\u0026thinsp;0.998, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and ESR (OR\u0026thinsp;=\u0026thinsp;0.994, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) had weak negative associations, their OR values near 1 indicate limited impact. These findings provide a foundation for further exploring the link between hematological indicators and diabetic neuropathy.\u003c/p\u003e\u003cp\u003eAmong the liver function and related biochemical indexes, a number of parameters were significantly associated with the risk of neuropathy in patients with T2DM. 18 indexes, including very low-density lipoprotein (VLDL) (OR\u0026thinsp;=\u0026thinsp;1.226, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and A/G (OR\u0026thinsp;=\u0026thinsp;1.565, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), were positively correlated, suggesting that the risk of neuropathy increased for every 1-unit increase in the index level. The OR of apolipoprotein B (APOB) was 1.743, (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), suggesting that it has a particularly prominent effect on the risk. In contrast, 11 indicators, including globulin (GLO) (OR\u0026thinsp;=\u0026thinsp;0.989, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.001) and Ca (OR\u0026thinsp;=\u0026thinsp;0.315, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), were negatively correlated, suggesting that elevated levels of these indicators may have a potential protective effect. Notably, the OR for Ca was as low as 0.315, suggesting that elevated Ca levels may significantly reduce the risk of neuropathy. While cholinesterase (CHE) (OR\u0026thinsp;=\u0026thinsp;1.000, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), lipoprotein a (LP-a) (OR\u0026thinsp;=\u0026thinsp;1.000, P\u0026thinsp;=\u0026thinsp;0.003) and urinary creatinine (Ucr) (OR\u0026thinsp;=\u0026thinsp;1.000, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) showed statistical significance, but the OR values were close to 1, suggesting that their effects on the risk of neuropathy are relatively subtle and need to be further explored in the context of clinical practice. These results provide important clues for in-depth analysis of the association between metabolic indicators and diabetic neuropathy.\u003c/p\u003e\u003cp\u003eIn the analysis of coagulation-related indices, plasminogen activity (PT-%) (OR\u0026thinsp;=\u0026thinsp;1.007, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), fibrin degradation products (FDP) (OR\u0026thinsp;=\u0026thinsp;1.013, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and D-dimer (D-Di) (OR\u0026thinsp;=\u0026thinsp;1.028, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) were independently associated with an increased risk of neuropathy in patients with T2DM. Specifically, each 1-unit increase in these indices was linked to a corresponding rise in neuropathy risk. In contrast, the international normalized ratio of prothrombin time (PT-INR) (OR\u0026thinsp;=\u0026thinsp;0.498, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), prothrombin time (PT) (OR\u0026thinsp;=\u0026thinsp;0.951, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and activated partial thromboplastin time (APTT) (OR\u0026thinsp;=\u0026thinsp;0.976, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) exhibited negative associations with neuropathy risk. These findings suggest that elevated levels of PT-INR, PT, and APTT may confer a potential protective effect against the development of neuropathy in this population, as their inverse relationship with risk indicates that higher values are associated with lower odds of neuropathy.\u003c/p\u003e\u003cp\u003eIn terms of thyroid function indicators, free triiodothyronine (FT\u003csub\u003e3\u003c/sub\u003e) (OR\u0026thinsp;=\u0026thinsp;0.821, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and free thyroxine (FT\u003csub\u003e4\u003c/sub\u003e) (OR\u0026thinsp;=\u0026thinsp;0.939, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) were inversely associated with the risk of neuropathy, suggesting that higher thyroid hormone levels may reduce the likelihood of neuropathy development. Additionally, complement 1q (Clq) demonstrated a statistically significant association (OR\u0026thinsp;=\u0026thinsp;1.000, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.030), despite an odds ratio close to 1, indicating a minimal yet detectable effect. Further research is warranted to clarify the precise role of Clq in neuropathy risk, particularly given the subtle magnitude of its association.\u003c/p\u003e\u003cp\u003e\u003cb\u003e3.3 Filtering key variables for model construction based on the importance ranking of feature variables in the training set and plotting a nomogram.\u003c/b\u003e\u003c/p\u003e\u003cp\u003eIn this study, SHAP plots generated via the XGBoost model were used to identify the top 10 key feature variables associated with diabetic neuropathy development in patients with T2DM: age, creatine kinase (CK), total urinary protein, FT\u003csub\u003e4\u003c/sub\u003e, α-hydroxybutyrate dehydrogenase (α-HBDH), cystatin C (CysC), Ucr, Ca, X1-MG, and UALB (Fig.\u0026nbsp;2). These feature variables were integrated into a multivariate logistic regression model to construct a risk prediction system, with a nomogram developed for visualizing individual and combined effects (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e3\u003c/span\u003e). As detailed in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, the model assigns scores to each risk factor, which are visualized in the nomogram. Summing these ten individual scores yields a total score corresponding to a specific risk ratio, enabling precise assessment of the probability of diabetic neuropathy in T2DM patients.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eA multivariate logistic regression predictive model for the occurrence of neuropathy in type 2 diabetes mellitus\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariables\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCoef\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eS.E.\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eWald \u003cem\u003eZ\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIntercept\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.666\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.339\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.970\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.049\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.042\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.002\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e25.660\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCK\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-7.920\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTotal urine protein\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e6.310\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFT\u003csub\u003e4\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.062\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.004\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e15.670\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eα-HBDH\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-4.510\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCysC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.043\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.027\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.590\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.112\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUcr\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-9.310\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCa\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-1.159\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.140\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-8.300\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eX1-MG\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.005\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e7.220\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUALB\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e6.020\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"5\"\u003eCK, creatine kinase; FT\u003csub\u003e4\u003c/sub\u003e, free thyroxine; CysC, Cystatin C; Ucr, urinary creatinine; a_HBDH, α-Hydroxybutyrate dehydrogenase; X1-MG, α1-Microglobulin; UALB, urinary albumin.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003e3.4 Evaluation of the predictive model.\u003c/h2\u003e\u003cp\u003eDiscriminative ability for predicting diabetic neuropathy risk in patients with T2DM was evaluated using ROC curves (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e4\u003c/span\u003e). The AUC values for the training and testing datasets were 0.743 (95% CI: 0.733\u0026ndash;0.754) and 0.751 (95% CI: 0.735\u0026ndash;0.768), respectively, indicating substantial discriminative efficacy. An optimal probability threshold of 0.579 was identified in the training dataset (sensitivity\u0026thinsp;=\u0026thinsp;0.604, specificity\u0026thinsp;=\u0026thinsp;0.748), meaning patients with risk estimates exceeding this threshold had significantly elevated neuropathy risk. Calibration curves (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e5\u003c/span\u003e) demonstrated close alignment between predicted probabilities and observed outcomes in both datasets, while DCA curves (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e6\u003c/span\u003e) showed that clinical decision-making using the model yielded greater net benefit when the predicted neuropathy probability ranged from 0.4 to 0.8.\u003c/p\u003e\u003c/div\u003e"},{"header":"4. DISCUSSION","content":"\u003cp\u003eIn this study, we developed a risk prediction model for diabetic neuropathy in patients with T2DM, visualizing and enabling precise risk assessment via a nomogram. The model incorporated 10 key feature variables, including age, CK, total urine protein, FT\u003csub\u003e4\u003c/sub\u003e, α-HBDH, CysC, Ucr, Ca, X1-MG and UALB, and was validated multidimensionally and showed good performance in terms of discriminative ability, predictive accuracy, and clinical application value.\u003c/p\u003e\u003cp\u003eOur findings confirmed that CK, FT\u003csub\u003e4\u003c/sub\u003e, α-HBDH, Ucr, and Ca exhibit negative associations with diabetic neuropathy risk in T2DM, suggesting potential protective roles. As a critical enzyme in energy metabolism, CK maintains nerve cell homeostasis by regulating the phosphocreatine-creatine system, which stabilizes energy supply and participates in intracellular signaling to facilitate nerve repair [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. FT\u003csub\u003e4\u003c/sub\u003e, a bioactive thyroid hormone, is essential for neuronal development and adult neurological function, modulating central nervous system excitability and sympathetic activity [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Prior research linked low serum FT\u003csub\u003e3\u003c/sub\u003e levels to impaired nerve conduction in diabetic patients [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e], while FT\u003csub\u003e4\u003c/sub\u003e levels inversely correlated with diabetic peripheral neuropathy (DPN) prevalence in euthyroid T2DM individuals [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e], aligning with our results. Reduced α-HBDH expression in DPN patients [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e] implies that low α-HBDH may increase neuropathy susceptibility, though its protective mechanisms require further investigation. Although serum creatinine is established as a DPN risk factor [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e], our study uniquely identifies Ucr as protective, likely reflecting its role in renal filtration: higher Ucr indicates efficient metabolic waste clearance, preserving metabolic homeostasis and mitigating hyperglycemic neurotoxicity [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Serum calcium, inversely associated with T2DM risk [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e], is critical for nerve cell membrane potential stability and neurotransmitter release [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e], reinforcing its role in reducing diabetic neuropathy risk through these mechanisms. Collectively, these feature variables (CK, FT\u003csub\u003e4\u003c/sub\u003e, α-HBDH, Ucr, and Ca) mediate protection against T2DM neuropathy via energy metabolism, neurological maintenance, renal clearance, and calcium homeostasis, highlighting the clinical importance of targeting these pathways for prevention.\u003c/p\u003e\u003cp\u003eConversely, age, total urinary protein, CysC, X1-MG, and UALB emerged as independent risk factors. Age-related neuropathy risk aligns with prior evidence: a 3.6% annual risk increase and elevated risk in patients\u0026thinsp;\u0026ge;\u0026thinsp;71 years underscore aging as a key driver [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e], with molecular mechanisms like exosome miR-21-mediated neurite growth decline supporting prioritized screening for elderly T2DM patients [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. Renal injury biomarkers, such as total urinary protein, CysC, X1-MG, and UALB, are pathologically linked to neuropathy: proteinuria accelerates disease via cytokine-mediated neurotoxicity [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e], while CysC predicts peripheral neuropathy and microvascular ischemia, a hallmark of diabetic neurovascular damage [\u003cspan additionalcitationids=\"CR37\" citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. X1-MG, a proximal tubule injury marker, suggests renal tubular dysfunction may impair reabsorption of neuroessential nutrients (e.g., amino acids, vitamins), indirectly contributing to neuropathy [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. These findings emphasize the need for integrated renal-neurological monitoring in diabetes management to detect early multi-organ injury. Notably, HbA1c, an established neuropathy risk factor in prior studies [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e], was excluded from our final model due to feature importance-based variable selection, indicating context-dependent roles that warrant validation in expanded datasets.\u003c/p\u003e\u003cp\u003eThis study has several limitations. First, its single-center design may introduce selection bias, thereby limiting the generalizability of the findings to broader populations. Second, potential confounding variables were not fully accounted for in the analysis, which could affect the robustness of the associations identified. Third, the cross-sectional nature of the data precludes the establishment of causal relationships or the examination of dynamic disease pathways over time. To address these limitations, future research should involve multi-center, prospective cohort studies to validate the model, identify additional biomarkers, and elucidate the longitudinal mechanisms underlying the development of neuropathy in this population.\u003c/p\u003e\u003cp\u003eIn conclusion, our prediction model and identified predictors offer novel insights for early risk stratification and intervention in T2DM neuropathy. Prioritizing research to validate the model, decode protective mechanisms, and translate findings into clinical practice could significantly reduce neuropathy burden in T2DM populations.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthorship contribution statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eB.W. contributed to software development, formal analysis, validation, visualization, and investigation. S.Y. F. was responsible for formal analysis, data curation, and writing the original draft. Y.F. contributed to data curation and formal analysis. R.Y. J. took charge of conceptualization, methodology, data curation, formal analysis, and funding acquisition. J.S. L. contributed to methodology, software development, formal analysis, validation, visualization, and funding acquisition. Y.L. Z. contributed to conceptualization, methodology, formal analysis, investigation, funding acquisition, writing the original draft, and writing the review \u0026amp; editing. All authors reviewed the manuscript.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no known competing financial interests or personal relationships that could have influenced the research presented in this paper.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the National Natural Science Foundation of China (No. 22203057), Science and Technology Strategic Cooperation Programs of Luzhou Municipal People\u0026rsquo;s Government and Southwest Medical University (2024LZXNYDT001, China), Research and Development Fund Project of Sichuan North Medical College (CBY22-QNA38).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated during and/or analysed during the current study are not publicly available due to patient privacy but are available from the corresponding author on reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eRaguraman, R., Srivastava, A., Munshi, A. \u0026amp; Ramesh, R. Therapeutic approaches targeting molecular signaling pathways common to diabetes, lung diseases and cancer. \u003cem\u003eAdv. Drug Deliv Rev.\u003c/em\u003e \u003cb\u003e178\u003c/b\u003e, 113918. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.addr.2021.113918\u003c/span\u003e\u003cspan address=\"10.1016/j.addr.2021.113918\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2021).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSong, K. et al. Targeting APLN/APJ restores blood-testis barrier and improves spermatogenesis in murine and human diabetic models. \u003cem\u003eNat. Commun.\u003c/em\u003e \u003cb\u003e13\u003c/b\u003e (1), 7335. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/s41467-022-34990-3\u003c/span\u003e\u003cspan address=\"10.1038/s41467-022-34990-3\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2022).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLontchi-Yimagou, E. et al. Non-autoimmune diabetes mellitus and the risk of virus infections: a systematic review and meta-analysis of case-control and cohort studies. \u003cem\u003eSci. Rep.\u003c/em\u003e \u003cb\u003e11\u003c/b\u003e (1), 8968. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/s41598-021-88598-6\u003c/span\u003e\u003cspan address=\"10.1038/s41598-021-88598-6\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2021).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRaimundo, A. F., Ferreira, S., Farrim, M. I., Santos, C. N. \u0026amp; Menezes, R. Heterologous Expression of Immature Forms of Human Islet Amyloid Polypeptide in Yeast Triggers Intracellular Aggregation and Cytotoxicity. \u003cem\u003eFront Microbiol\u003c/em\u003e. 11: (2035). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3389/fmicb.2020.02035\u003c/span\u003e\u003cspan address=\"10.3389/fmicb.2020.02035\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2020).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLuo, Q. et al. Renal Protective Effects of Melatonin in Animal Models of Diabetes Mellitus-Related Kidney Damage: A Systematic Review and Meta-Analysis. \u003cem\u003eJ Diabetes Res\u003c/em\u003e. :3770417. (2022). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1155/2022/3770417\u003c/span\u003e\u003cspan address=\"10.1155/2022/3770417\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2022).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eChen, L. et al. Label-free optical metabolic imaging of adipose tissues for prediabetes diagnosis. \u003cem\u003eTheranostics\u003c/em\u003e \u003cb\u003e13\u003c/b\u003e (11), 3550\u0026ndash;3567. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.7150/thno.82697\u003c/span\u003e\u003cspan address=\"10.7150/thno.82697\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2023).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZhao, Y. et al. The SNPs of mitochondrial DNA displacement loop region and mitochondrial DNA copy number associated with risk of polymyositis and dermatomyositis. \u003cem\u003eSci. Rep.\u003c/em\u003e \u003cb\u003e12\u003c/b\u003e (1), 5903. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/s41598-022-09943-x\u003c/span\u003e\u003cspan address=\"10.1038/s41598-022-09943-x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2022).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePan, L. et al. Dose-response relationship between Chinese visceral adiposity index and type 2 diabetes mellitus among middle-aged and elderly Chinese. \u003cem\u003eFront. Endocrinol. (Lausanne)\u003c/em\u003e. \u003cb\u003e13\u003c/b\u003e, 959860. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3389/fendo.2022.959860\u003c/span\u003e\u003cspan address=\"10.3389/fendo.2022.959860\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2022).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLi, W. et al. Association of Serum Magnesium with Insulin Resistance and Type 2 Diabetes among Adults in China. \u003cem\u003eNutrients\u003c/em\u003e \u003cb\u003e14\u003c/b\u003e (9), 1799. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3390/nu14091799\u003c/span\u003e\u003cspan address=\"10.3390/nu14091799\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2022).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWu, Y., Sun, L., Zhuang, Z., Hu, X. \u0026amp; Dong, D. Mitochondrial-Derived Peptides in Diabetes and Its Complications. \u003cem\u003eFront. Endocrinol. (Lausanne)\u003c/em\u003e. \u003cb\u003e12\u003c/b\u003e, 808120. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3389/fendo.2021.808120\u003c/span\u003e\u003cspan address=\"10.3389/fendo.2021.808120\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2022).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZhou, X. et al. Schisandrol A protects AGEs-induced neuronal cells death by allosterically targeting ATP6V0d1 subunit of V-ATPase. \u003cem\u003eActa Pharm. Sin B\u003c/em\u003e. \u003cb\u003e12\u003c/b\u003e (10), 3843\u0026ndash;3860. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.apsb.2022.06.013\u003c/span\u003e\u003cspan address=\"10.1016/j.apsb.2022.06.013\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2022).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDufour, A. B., Kiel, D. P., Williams, S. A., Weiss, R. J. \u0026amp; Samelson, E. J. Risk Factors for Incident Fracture in Older Adults With Type 2 Diabetes: The Framingham Heart Study. \u003cem\u003eDiabetes Care\u003c/em\u003e. \u003cb\u003e44\u003c/b\u003e (7), 1547\u0026ndash;1555. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.2337/dc20-3150\u003c/span\u003e\u003cspan address=\"10.2337/dc20-3150\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2021).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFeldman, E. L. et al. Diabetic neuropathy. \u003cem\u003eNat. Rev. Dis. Primers\u003c/em\u003e. \u003cb\u003e5\u003c/b\u003e (1), 42. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/s41572-019-0097-9\u003c/span\u003e\u003cspan address=\"10.1038/s41572-019-0097-9\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2019).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eOates, P. J. Aldose reductase, still a compelling target for diabetic neuropathy. \u003cem\u003eCurr. Drug Targets\u003c/em\u003e. \u003cb\u003e9\u003c/b\u003e (1), 14\u0026ndash;36. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.2174/138945008783431781\u003c/span\u003e\u003cspan address=\"10.2174/138945008783431781\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2008).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHan, L. et al. Peripheral neuropathy is associated with insulin resistance independent of metabolic syndrome. \u003cem\u003eDiabetol. Metab. Syndr.\u003c/em\u003e \u003cb\u003e7\u003c/b\u003e, 14. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1186/s13098-015-0010-y\u003c/span\u003e\u003cspan address=\"10.1186/s13098-015-0010-y\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2015).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eJeannin, A. C. et al. Inactive matrix gla protein plasma levels are associated with peripheral neuropathy in Type 2 diabetes. \u003cem\u003ePLoS One\u003c/em\u003e. \u003cb\u003e15\u003c/b\u003e (2), e0229145. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1371/journal.pone.0229145\u003c/span\u003e\u003cspan address=\"10.1371/journal.pone.0229145\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2020).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGregory, J. A., Jolivalt, C. G., Goor, J., Mizisin, A. P. \u0026amp; Calcutt, N. A. Hypertension-induced peripheral neuropathy and the combined effects of hypertension and diabetes on nerve structure and function in rats. \u003cem\u003eActa Neuropathol.\u003c/em\u003e \u003cb\u003e124\u003c/b\u003e (4), 561\u0026ndash;573. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/s00401-012-1012-6\u003c/span\u003e\u003cspan address=\"10.1007/s00401-012-1012-6\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2012).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSrinivasan, S., Singh, P., Kulothungan, V., Sharma, T. \u0026amp; Raman, R. Relationship between triglyceride glucose index, retinopathy and nephropathy in Type 2 diabetes. \u003cem\u003eEndocrinol. Diabetes Metab.\u003c/em\u003e \u003cb\u003e4\u003c/b\u003e (1), e00151. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1002/edm2.151\u003c/span\u003e\u003cspan address=\"10.1002/edm2.151\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2020).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePutz, Z. et al. Is there an association between diabetic neuropathy and low vitamin D levels? \u003cem\u003eCurr. Diab Rep.\u003c/em\u003e \u003cb\u003e14\u003c/b\u003e (10), 537. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/s11892-014-0537-6\u003c/span\u003e\u003cspan address=\"10.1007/s11892-014-0537-6\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2014).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLaxmi, M. S. \u0026amp; Prabhakar, O. Development of risk prediction scores for diabetic peripheral neuropathy patients. \u003cem\u003eJ. Neurosci. Rural Pract.\u003c/em\u003e \u003cb\u003e14\u003c/b\u003e (4), 667\u0026ndash;670. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.25259/JNRP-151-2023\u003c/span\u003e\u003cspan address=\"10.25259/JNRP-151-2023\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2023).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGou, W. et al. Interpretable Machine Learning Framework Reveals Robust Gut Microbiome Features Associated With Type 2 Diabetes. \u003cem\u003eDiabetes Care\u003c/em\u003e. \u003cb\u003e44\u003c/b\u003e (2), 358\u0026ndash;366. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.2337/dc20-1536\u003c/span\u003e\u003cspan address=\"10.2337/dc20-1536\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2021).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePanagoulias, G. S. et al. Dryness of Foot Skin Assessed by the Visual Indicator Test and Risk of Diabetic Foot Ulceration: A Prospective Observational Study. \u003cem\u003eFront. Endocrinol. (Lausanne)\u003c/em\u003e. \u003cb\u003e11\u003c/b\u003e, 625. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3389/fendo.2020.00625\u003c/span\u003e\u003cspan address=\"10.3389/fendo.2020.00625\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2020).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHamilton, P. D., Bozeman, S. L. \u0026amp; Andley, U. P. Creatine kinase/α-crystallin interaction functions in cataract development. \u003cem\u003eBiochem. Biophys. Rep.\u003c/em\u003e \u003cb\u003e22\u003c/b\u003e, 100748. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.bbrep.2020.100748\u003c/span\u003e\u003cspan address=\"10.1016/j.bbrep.2020.100748\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2020).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFarid, M. M., Yang, X., Kuboyama, T. \u0026amp; Tohda, C. Trigonelline recovers memory function in Alzheimer's disease model mice: evidence of brain penetration and target molecule. \u003cem\u003eSci. Rep.\u003c/em\u003e \u003cb\u003e10\u003c/b\u003e (1), 16424. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/s41598-020-73514-1\u003c/span\u003e\u003cspan address=\"10.1038/s41598-020-73514-1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2020).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRaymond, L. J. \u0026amp; Ralston, N. V. C. Mercury: selenium interactions and health implications. \u003cem\u003eNeurotoxicology\u003c/em\u003e \u003cb\u003e81\u003c/b\u003e, 294\u0026ndash;299. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.neuro.2020.09.020\u003c/span\u003e\u003cspan address=\"10.1016/j.neuro.2020.09.020\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2020).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZhu, F. F. \u0026amp; Yang, L. Z. The Association Between the Levels of Thyroid Hormones and Peripheral Nerve Conduction in Patients with Type 2 Diabetes Mellitus. \u003cem\u003eExp. Clin. Endocrinol. Diabetes\u003c/em\u003e. \u003cb\u003e126\u003c/b\u003e (8), 493\u0026ndash;504. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1055/a-0635-0826\u003c/span\u003e\u003cspan address=\"10.1055/a-0635-0826\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2018).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHe, Q., Zeng, Z., Zhao, M., Ruan, B. \u0026amp; Chen, P. Association between thyroid function and diabetes peripheral neuropathy in euthyroid type 2 diabetes mellitus patients. \u003cem\u003eSci. Rep.\u003c/em\u003e \u003cb\u003e13\u003c/b\u003e (1), 13499. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/s41598-023-40752-y\u003c/span\u003e\u003cspan address=\"10.1038/s41598-023-40752-y\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2023).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGao, L. et al. Risk Factors for Subclinical Diabetic Peripheral Neuropathy in Type 2 Diabetes Mellitus. \u003cem\u003eDiabetes Metab. Syndr. Obes.\u003c/em\u003e \u003cb\u003e17\u003c/b\u003e, 417\u0026ndash;426. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.2147/DMSO.S433024\u003c/span\u003e\u003cspan address=\"10.2147/DMSO.S433024\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2024).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHuang, Y., Jiang, K., Zhang, X. \u0026amp; Chung, E. J. The effect of size, charge, and peptide ligand length on kidney targeting by small, organic nanoparticles. \u003cem\u003eBioeng. Transl Med.\u003c/em\u003e \u003cb\u003e5\u003c/b\u003e (3), e10173. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1002/btm2.10173\u003c/span\u003e\u003cspan address=\"10.1002/btm2.10173\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2020).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZhu, J. et al. Circulating calcium levels and the risk of type 2 diabetes: a systematic review and meta-analysis. \u003cem\u003eBr. J. Nutr.\u003c/em\u003e \u003cb\u003e122\u003c/b\u003e (4), 376\u0026ndash;387. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1017/S0007114519001430\u003c/span\u003e\u003cspan address=\"10.1017/S0007114519001430\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2019).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLai, J. et al. Displacement and hybridization reactions in aptamer-functionalized hydrogels for biomimetic protein release and signal transduction. \u003cem\u003eChem. Sci.\u003c/em\u003e \u003cb\u003e8\u003c/b\u003e (11), 7306\u0026ndash;7311. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1039/c7sc03023a\u003c/span\u003e\u003cspan address=\"10.1039/c7sc03023a\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2017).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePeng, Z., Hou, H., Zhang, K. \u0026amp; Li, B. Effect of calcium-binding peptide from Pacific cod (Gadus macrocephalus) bone on calcium bioavailability in rats. \u003cem\u003eFood Chem.\u003c/em\u003e \u003cb\u003e221\u003c/b\u003e, 373\u0026ndash;378. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.foodchem.2016.10.078\u003c/span\u003e\u003cspan address=\"10.1016/j.foodchem.2016.10.078\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2017).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMao, F. et al. Age as an Independent Risk Factor for Diabetic Peripheral Neuropathy in Chinese Patients with Type 2 Diabetes. \u003cem\u003eAging Dis.\u003c/em\u003e \u003cb\u003e10\u003c/b\u003e (3), 592\u0026ndash;600. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.14336/AD.2018.0618\u003c/span\u003e\u003cspan address=\"10.14336/AD.2018.0618\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2019).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAmelia, R., Wahyuni, A. S. \u0026amp; Yunanda, Y. Diabetic Neuropathy among Type 2 Diabetes Mellitus Patients at Amplas Primary Health Care in Medan City. \u003cem\u003eOpen. Access. Maced J. Med. Sci.\u003c/em\u003e \u003cb\u003e7\u003c/b\u003e (20), 3400\u0026ndash;3403. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3889/oamjms.2019.433\u003c/span\u003e\u003cspan address=\"10.3889/oamjms.2019.433\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2019).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLiu, Y. P. et al. Schwann cells-derived exosomal miR-21 participates in high glucose regulation of neurite outgrowth. \u003cem\u003eiScience\u003c/em\u003e \u003cb\u003e25\u003c/b\u003e (10), 105141. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.isci.2022.105141\u003c/span\u003e\u003cspan address=\"10.1016/j.isci.2022.105141\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2022).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eYang, X. et al. Cystatin C Is an Important Biomarker for Cardiovascular Autonomic Dysfunction in Chinese Type 2 Diabetic Patients. \u003cem\u003eJ. Diabetes Res.\u003c/em\u003e \u003cb\u003e2019\u003c/b\u003e, 1706964. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1155/2019/1706964\u003c/span\u003e\u003cspan address=\"10.1155/2019/1706964\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2019).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHu, Y. et al. Association between serum cystatin C and diabetic peripheral neuropathy: a cross-sectional study of a Chinese type 2 diabetic population. \u003cem\u003eEur. J. Endocrinol.\u003c/em\u003e \u003cb\u003e171\u003c/b\u003e (5), 641\u0026ndash;648. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1530/EJE-14-0381\u003c/span\u003e\u003cspan address=\"10.1530/EJE-14-0381\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2014).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZhang, H. et al. Assessing the diagnostic utility of urinary albumin-to-creatinine ratio as a potential biomarker for diabetic peripheral neuropathy in type 2 diabetes mellitus patients. \u003cem\u003eSci. Rep.\u003c/em\u003e \u003cb\u003e14\u003c/b\u003e (1), 27198. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/s41598-024-78828-y\u003c/span\u003e\u003cspan address=\"10.1038/s41598-024-78828-y\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2024).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eOhta, K. et al. Normal values for pediatric urinary biochemistry in early infancy. \u003cem\u003eIr. J. Med. Sci.\u003c/em\u003e \u003cb\u003e192\u003c/b\u003e (5), 2507\u0026ndash;2511. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/s11845-023-03296-8\u003c/span\u003e\u003cspan address=\"10.1007/s11845-023-03296-8\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2023).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZhong, M., Yang, Y. R., Zhang, Y. Z. \u0026amp; Yan, S. J. Change in Urine Albumin-to-Creatinine Ratio and Risk of Diabetic Peripheral Neuropathy in Type 2 Diabetes: A Retrospective Cohort Study. \u003cem\u003eDiabetes Metab. Syndr. Obes.\u003c/em\u003e \u003cb\u003e14\u003c/b\u003e, 1763\u0026ndash;1772. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.2147/DMSO.S303096\u003c/span\u003e\u003cspan address=\"10.2147/DMSO.S303096\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2021).\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 mellitus, diabetic neuropathy, logistic regression, nomogram, decision curve analysis","lastPublishedDoi":"10.21203/rs.3.rs-6556009/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6556009/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe global prevalence of type 2 diabetes mellitus (T2DM) continues to rise, with diabetic neuropathy significantly impacting patient outcomes. This study developed a predictive nomogram for neuropathy risk in T2DM patients using retrospectively analyzed electronic medical records (2013\u0026ndash;2023) from the Affiliated Hospital of North Sichuan Medical College. After rigorous data cleaning, univariate logistic regression and XGBoost screening identified ten predictors including age, creatine kinase (CK), total urinary protein, free thyroxine (FT4), α-hydroxybutyrate dehydrogenase (α-HBDH), cystatin C (CysC), urinary creatinine (Ucr), serum calcium (Ca), α1-microglobulin (X1-MG), and urinary albumin (UALB), integrated via multivariate logistic regression into a visual nomogram. These variables were integrated into a visual nomogram via multivariate logistic regression. Model evaluation demonstrated robust discriminative ability, with area under the receiver operating characteristic curve (AUC) values of 0.743 (95% CI: 0.733\u0026ndash;0.754) in the training dataset and 0.751 (95% CI: 0.735\u0026ndash;0.768) in the testing dataset. Calibration curves confirmed prediction consistency, while DCA highlighted significant clinical net benefit. This nomogram provides a practical, visual tool for clinicians to estimate individual neuropathy risk, enabling early identification of high-risk patients and targeted interventions to delay or prevent neuropathy onset. The model demonstrates reliable predictive accuracy and substantial clinical utility in managing T2DM complications.\u003c/p\u003e","manuscriptTitle":"Development of a Predictive Model and Nomogram for Neuropathy Risk in T2DM Patients","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-01 10:07:42","doi":"10.21203/rs.3.rs-6556009/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":"d9efbc2a-3bb5-46e2-abcb-b2757dedc1f7","owner":[],"postedDate":"September 1st, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":53729626,"name":"Health sciences/Medical research/Biomarkers/Predictive markers"},{"id":53729627,"name":"Health sciences/Diseases/Endocrine system and metabolic diseases/Diabetes/Diabetes complications"},{"id":53729628,"name":"Health sciences/Diseases/Endocrine system and metabolic diseases/Diabetes/Type 2 diabetes mellitus"},{"id":53729629,"name":"Health sciences/Risk factors"}],"tags":[],"updatedAt":"2025-10-29T06:24:02+00:00","versionOfRecord":[],"versionCreatedAt":"2025-09-01 10:07:42","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6556009","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6556009","identity":"rs-6556009","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}