New biomarker in nomogram model for predicting diabetic neuropathy : a cross-sectional study

New biomarker in nomogram model for predicting diabetic neuropathy : a cross-sectional study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article New biomarker in nomogram model for predicting diabetic neuropathy : a cross-sectional study Yanyan Ji, Shujun Ren, Zilu Liu, Xiang Wang, Zhixin Zhu, Haitong Liu, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7936390/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 Background The number of patients with diabetic peripheral neuropathy (DPN) is increasing steadily. Currently, no reliable method exists to detect or prevent DPN at an early stage. This study aimed to develop a practical clinical model to help clinicians identify T2DM patients who are at risk of developing DPN and to facilitate early intervention. Methods We conducted a cross‑sectional study of 122 T2DM patients, with and without DPN, between 1 June and 31 December 2022. Indices of glucose and lipid metabolism, UDP‑glucose ceramide glucosyltransferase (Ugcg), fasting C‑peptide and demographic variables were compared and screened with logistic regression analysis. A nomogram was then constructed. Model discrimination, calibration and clinical utility were assessed with the area under the receiver operating characteristic curve (AUC), calibration plots and decision curve analysis (DCA). Main results: Multivariate analysis yielded a nomogram comprising age (OR 1.076, 95% CI 1.028–1.126; P = 0.002), alcohol consumption (OR 4.073, 95% CI 1.349–12.297; P = 0.013), HDL‑C (OR 6.024, 95% CI 1.072–33.847; P = 0.041), Ugcg (OR 0.979, 95% CI 0.963–0.994; P = 0.007) and fasting C‑peptide (OR 0.591, 95% CI 0.365–0.956; P = 0.032). The nomogram achieved an AUC of 0.85 (95% CI 0.78–0.91; P < 0.001). Calibration curves demonstrated good agreement between predicted and observed values (mean absolute error = 0.03). Decision curve analysis indicated maximal clinical benefit at a threshold probability of approximately 50%. Conclusion A Ugcg‑inclusive nomogram can identify diabetic patients who are at high risk of developing DPN. It also provides clinicians with a theoretical basis for early intervention. Diabetic neuropathy Nomogram model New biomarker Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction According to the International Diabetes Federation (International Diabetes Federation (IDF) in 2021, the global prevalence of diabetes mellitus (DM) among people aged 20–79 years reached 10.5% (about 536 million people) and this number is still rapidly growing [ 1 ] . Diabetic Neuropathy (DN) is a clinical syndrome caused by peripheral or autonomic nerves damage, with a prevalence of up to 40%-50% in diabetes patients [ 2 ] . The most common type of DN is diabetic peripheral neuropathy (DPN), which is also known as “stocking-glove neuropathy”. Patients with DPN usually have pain, numbness or sensations that gradually move from the distal to the proximal body [ 3 ] . Previous studies on DPN were mainly based on direct or indirect damage to nerve by the metabolites deriving from hyperglycaemic and hypermetabolic states, including increased levels of polyols, formation of advanced glycation end products (AGEs) and increased production of reactive oxygen species (ROS). Currently, some scholars have focused on the prevention of DPN by targeting risk factors. One retrospective study identified older age, elevated glycosylated haemoglobin, and proteinuria as risk factors for DPN [ 4 ] . Moreover, some studies confirmed some indicatiors of lipid metabolism, such as high ApoC III [ 5 ] , triglycerides, cholesterol and LDL [ 6 ] were also risk factors for DPN. However, some studies found strict blood glucose control measures did not reduce the incidence of DPN [ 7 – 8 ] , and triglyceride levels also had no impact on neuronal axon function [ 9 ] . In view of these, effective methods for preventing DPN are still limited In clinical setting [ 10 ] . Ugcg is a synthetic enzyme found widely in the Golgi apparatus of neuronal cells (mainly Schwann cells and oligodendrocytes) and has been proven to produce sphingolipids for myelin structure synthesis, neurodevelopmental growth, and modulation of some transmembrane receptor activities. Literatures reported that the total myelin content [ 11 ] and the levels of the sphingolipids GD1a, GD1b and GM1 [ 12 ] decreased significantly with age. In some neurodegenerative diseases, such as Alzheimer's disease [ 12 – 14 ] and Parkinson's disease [ 15 ] , the sphingolipids are also significantly reduced. Our previous bioinformation analysis studies found that the content of Ugcg in the sciatic nerve tissue of diabetic neuropathic mice was significantly lower compared with normal mice. DPN is a specific degenerative disease of the peripheral nerves, with demyelinating morphological features. Therefore, we inferred low levels of Ugcg might correlate with diabetic neuropathy. In this study, we aimed to construct a clinical predictive model including Ugcg. Furthermore,we assessed the ability of this model to predict the development of DPN. Materials and methods Study design and patients This retrospective study recruited all Type 2 diabetes mellitus (T2DM) patients admitted to the endocrinology department of Affiliated People's Hospital of Jiangsu University from June 1, 2022 to December 31, 2022. The diagnosis of T2DM based on the guidelines of the American Diabetes Association (ADA). According to the diagnosis and treatment of diabetic neuropathy [ 16 ] , T2DM patients were divided into a group with diabetic peripheral neuropathy group (DN group) and a group without neuropathy (DM group). This study was approved by the Ethics Committee of Affiliated People’s Hospital of Jiangsu University (Y2022005-Z), and all patients provided their written informed consent prior to enrollment. Inclusion and exclusion criteria We recruited T2DM patients aged 18–75 years. Exclusion criteria included spinal pathology (e.g. fracture, disc disease, etc.); central nerve diseases (e.g. cerebral infarction, Parkinson's disease, Alzheimer's disease, etc.); peripheral nerve diseases (e.g. carpal tunnel syndrome, herpes zoster residual neuropathy, inflammatory demyelinating neuropathy, etc.); nerve damage caused by a history of radiotherapy and chemotherapy or poor liver/renal function. Patients previously diagnosed with diabetic neuropathy and those who refused to sign the informed consent form(or could not cooperate to complete of the relevant checks) were also excluded. All patients were evaluated and screened for study eligibility by the research investigators. Data collection Clinical information including gender, age, height, weight, history of smoking, history of alcohol drinking and hypertension was obtained through the electronic medical record system of Affiliated People's Hospital of Jiangsu University. It included Body mass index (BMI) was calculated by divided the weight (kg) by height squared (kg/m 2 ). Blood samples were collected from the upper limb vein after an overnight fast. Biochemical data, including glycated hemoglobin (HbA1c), fasting C-peptide, fasting blood glucose (FBG), oral glucose tolerance test 1-hour glucose (OGTT 1h), oral glucose tolerance test 2-hour glucose (OGTT 2h), triglyceride (TG), total cholesterol (TC), high density lipoprotein (HDL), low density lipoprotein (LDL), apolipoprotein-a1 (APO-a1) and apolipoprotein-b (APO-b) were collected. An ELISA kit (Jiangsu Jingmei Biotechnology Co.) was used to measure serum Ugcg. The electrophysiological examinations were perfomered by Electromyographyer of Affiliated People's Hospital of Jiangsu University. The sensory nerve conduction velocity (SNCV) and sensory nerve conduction amplitudes (SNCA) of the right median nerve, right ulnar nerve, bilateral superficial peroneal nerves, bilateral peroneal nerves were recorded. Statistical analyses In this study, the sample size was calculated based on the incidence of DPN and the number of expected risk factors. According to the 10-EPV (10-events per variable) principle [ 17 ] and previous literature, no more than 5 risk factors for diabetic neuropathy were expected, and the incidence of DPN was about 40% to 50%. Therefore, at least 50 DPN patients and 50–75 T2DM patients without DPN were needed. Missing data were filled by using multiple imputation. All study data were processed using SPSS statistical software(version 24.0, Chicago, IL,USA). Measurement data were tested for normality using Kolmogorov–Smirnov tests. Measurement data were presented as means (standard deviation, SD) or medians (interquartile ranges, IQR), as appropriate. Measurement data were compared between groups using the student’s t -test(or t ’-test if variances were not homogeneous) and Mann-Whitney U test based on normality. Categorical data were compared using the chi-square test or Fisher's exact test. In univariate analysis, variables with “ P < 0.1” were considered significant and used for further multivariate logistic regression. Multivariate analysis used "conditional backward stepwise regression". The "rms" package of the R software (version 4.3.0) was used to construct the nomogram model for predicting DPN, including variables with " P < 0.05" in the multivariate logisitic regression. The receiver operating characteristic (ROC) curve was constructed and the area under the curve (AUC) was calculated to analyze the discrimination of the nomogram model. The specificity and sensitivity of the nomogram model were determined according to the rule of maximizing Youden's J index. The internal validation for prediction reliability of the nomogram model was evaluated with the bootstrap method (number of resampling: n = 1000), and the calibration curve was prepared to show the consistency results. The “rmda” package of the R software was used to construct a decision curve analysis (DCA) to evaluate the clinical utility of model. P < 0.05 indicated statistical significance. Results Patients characteristics From June 1, 2022 to December 31, 2022, 808 T2DM patients were admitted to the Department of Endocrinology of Affiliated People's Hospital of Jiangsu University. According to inclusion and exclusion criteria, 122 patients were enrolled. Among which, 62 patients had a neuropathy based on the guidelines of diagnosis and treatment of diabetic neuropathy [ 16 ] . Comparison of clinical information data between the DN and DM groups were shown in Table 1 . Demographic data showed only age, weight, BMI and history of alcohol drinking were statistically different between the two groups ( P < 0.05). In terms of glucose metabolism, FBG and OGTT 1h in the DN group were lower than those in the DM group ( P 0.05); As for, the DN group had lower triglyceride levels ( P 0.05). In addition, fasting C-peptide and ugcg in the DN group were lower than those in the DM group ( P < 0.01). Table 1 The factors of the logistic regression analysis. Factors Univariate analysis Multivariate analysis P -value Odds ratio (95%CI) Odds ratio (95%CI) Male 1.846 (0.832–4.098) - - Age 1.086 (1.045–1.128) 1.076 (1.028–1.126) 0.002 Helight 0.984 (0.937–1.033) - - BMI 0.839 (0.734–0.960) Droped - Smoke 1.953 (0.949–4.020) - - Drinking History 2.750 (1.169–6.469) 4.073 (1.349–12.297) 0.013 Hypertension 0.772 (0.377–1.582) - - HbA1c 0.915 (0.780–1.073) - - FBG 0.902 (0.816–0.996) - - OGTT 1h 0.903 (0.829–0.984) - - OGTT 2h 0.933 (0.866–1.005) - - TG 0.608 (0.437–0.847) Droped - TC 0.677 (0.463–0.990) Droped - HDL 9.935 (2.378–41.515) 6.024 (1.072–33.847) 0.041 LDL 0.551 (0.321–0.947) Droped - Apo-a1 0.492 (0.120–2.020) - - Apo-b 1.371 (0.414–4.539) - - Ugcg 0.980 (0.968–0.991) 0.979 (0.963–0.994) 0.007 Fasting C-peptide 0.451 (0.286–0.712) 0.591 (0.365–0.956) 0.032 BMI, body mass index; HbA1c, glycated hemoglobin; FBG, fasting blood glucose; OGTT 1h, blood glucose at one hour of oral glucose tolerance test; OGTT 2h, blood glucose at two hours of oral glucose tolerance test; TG, triglycerides; TC, total cholesterol; HDL, high density lipoprotein; LDL, low density lipoprotein; Apo-a1, apolipoprotein-a1; Apo-b, apolipoprotein-b. Ugcg, udp-Glucosylceramide glucosyltransferase. "Droped" means that the variable was dropped from the multivariate analysis. P-value adjusted for multiple variables. Constructing a nomogram model using Logistic regression In univariate analysis, the factors associated with DPN included age, BMI, history of alcohol drinking, TG, TC, HDL, LDL, serum Ugcg levels and fasting C-peptide. Especially, we did not include FBG and OGTT 1h in the modelling considering that there are too many factors interfering with blood glucose. A backward sstepwise regression multivariate analysis showed five variables were independently associated with DPN, including age (OR: 1.076, 95% CI:1.028–1.126, P = 0.002), history of alcohol drinking (OR:4.073, 95%CI: 1.349–12.297, P = 0.013), HDL (OR:6.024, 95%CI: 1.072–33.847, P = 0.041), Ugcg (OR:0.979, 95%CI: 0.963–0.994, P = 0.007), fasting C-peptide (OR: 0.591, 95%CI: 0.365–0.956, P = 0.032). Therefore, a nomogram model was established using the five variables. The score for each variable was based on its respective regression coefficients. Figure.1 showed the risk of developing DPN corresponding to the total score of each variable. Evaluation and validation of the nomogram models The discrimination of the nomogram model was evaluated using ROC curve. The AUC of the nomogram was 0.85 (95%CI: 0.78–0.91, P < 0.001). According to the rule of maximizing Youden’s J index, the specificity and sensitivity of the nomogram model were 74% and 85% respectively (Figure.2). The calibration curve showed a good accuracy between observed and predicted probabilities in Figure.3(the average absolute error was 0.03). That is, this nomogram model performed well in predicting DPN. In addition, we also used decision curve analysis to measure the clinical applicability of the model. The figure showed that the maximum clinical benefits was obtained when the threshold probability was around 50% (Figure.4). Discussion In this retrospective observational study, we found Ugcg levels are downregulated in expression in patients with DPN based on bioinformatic analysis of gene expression profiles in the sciatic nerve of diabetic mice. On this basis, we constructed a DPN predicitive nomogram model that includes Ugcg. Our study shows that this prediction model can accurately and effectively predict DPN with great clinical benefit in terms of earlier intervention and treatment. It is well known that in the early stages of DPN for small nerve fibre damage [ 18 – 19 ] . Compared to invasive skin biopsy techniques [ 20 ] , corneal confocal microscopy (CCM) is an objective and non-invasive imaging technique that has been proved to quantify small corneal nerve fibres [ 21 ] . However, it is not widely available because of its costly examination. Predictive models constructed in previous studies based on clinical features and relevant metabolic indicators were relatively lacking in predictive specificity due to the lack of indicators directly relevant to neuropathy. In this study, we found that the nerve conduction velocity and nerve conduction amplitude of patients in the DN group were significantly lower than those in the DM group. Using Spearman's rank correlation, we concluded that Ugcg was generally positively correlated with nerve conduction velocity and amplitude, and the logistic regression results suggested that Ugcg was an independent influencing factor of DPN. Therefore, we consider that Ugcg may better reflect the degree of peripheral neuropathy. To our knowledge, this study is the first to apply the serum Ugcg for the prediction of DPN. Our prediction model compares favourably with other methods of detection for DPN in that it is non-invasive, simple and economical to measure, and has shown good accuracy. Our model showed that, with the exception of HDL, other indicators of glucose and lipid metabolism had no significant effect on DPN. In view of the differences in patients' dietary habits, medication regimens, and baseline control of blood glucose, which made it difficult for the results of a one-time fasting blood glucose tolerance test to reflect the effect of overall blood glucose levels in the T2DM patients on DPN. Therefore, we only performed regression analysis on HbA1c. The results suggest that HbA1c is not an independent risk factor for DPN, which is consistent with the findings of Calcutt et al [ 7 ] and Callaghan et al [ 8 ] . In a retrospective study [ 4 ] , patients in the DN group had a higher insulin use rate and lower glycaemic indicesthan than those in the DM group. A possible explanation for this is that after the peripheral nerves have gradually built up tolerance to the high-glucose environment, the patients' short-term use of insulin or hypoglycaemic drugs led to too large a drop in blood glucose, or long-term over-tight glycaemic control led to microvascular hyperplasia in the periphery of the nerves, similar to the "arteriovenous shunt", resulting in ischaemia and nerve cell necrosis and axon degeneration [ 22 – 24 ] . Given this, more research is needed on how to control blood glucose in the appropriate range. After multivariate regression analysis of factors related to lipid metabolism, only elevated HDL was an independent risk factor for the development of DPN. This is different from the view of many previous studies, but the findings of Pai et al [ 4 ] were in concordance with ours. Excluding the effect of lipid-lowering drugs taken for the treatment of coronary heart disease, we considered that DPN might be associated with the downregulation of the expression of proteins related to glycolipid metabolism (e.g. Lipin1 protein) [ 25 ] . Currently, the relationship between the HDL and DPN is not clear and more in-depth studies are needed to elucidate the mechanism of its role.In addition, our data show that age and history of alcohol consumption are risk factors for DPN and that fasting C-peptide is a protective factor against the development of DPN, which is in line with most studies. With age, human nerves undergo spontaneous degeneration and become less able to repair themselves, and alcohol itself is a nerve-damaging agent. This may make peripheral nerves more susceptible to diabetes. Previous studies [ 26 ] reported that a reduction in fasting C-peptide may be associated with microvascular disease. This may lead to inadequate perfusion of the peripheral nerve endothelium, resulting in nerve damage [ 27 ] . There are limitations to our study. First, this study used a cross-sectional study to collect data, and it was not possible to elaborate on the causal relationship between the variables and DPN from a chronological perspective. However, due to the variables' own characteristic and the fact that patients did not significantly change their lifestyle habits, we deduced that they had a causal relationship with DPN. This also needs to be further verified by prospective or interventional studies. Second, according to the previous study, the measurement of ugcg levels in patients should be performed by collecting peripheral nerve samples as a way to obtain more accurate conclusions, but this may be against the requirements of medical ethics. Although the present study used serum samples for the measurement of ugcg, its effect on DPN was equally reliable after correction for confounding factors by multiple logistic regression analysis. The correlation between neural tissue and serum expression of Ugcg can be further investigated by animal experiments in the future. Third, in previous studies, the duration of diabetes was considered an independent risk factor for DPN. However, due to the recall deficit in some patients, we were unable to count this part of the data, so this was not considered for inclusion in the model in this study. In conclusion, our study developed a new clinical prediction model to predict DPN, which is made more accurate by its inclusion of Ugcg, an indicator of nerve damage. Validation datasets can be developed in the future to externally validate the model. Our nomogram model could be useful for clinicians in the early identification and intervention of DPN. Declarations Acknowledgments We are grateful to the team members and participants for their cooperation with the study. Disclosure The author declares that there is no conflict of interest. Approval of the research protocol: Y/A Informed Consent: Y/A Approval date of Registry and the Registration No. of the study/trial: N/A Animal Studies: N/A Conflict of interest:N/A Funding The author disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by the Chinese Medical Development Plan Projects of Jiangsu Province (Grant No. MS2021083) ; and the Science and Technology Planning Social Development Project of Zhenjiang City (Grant No. SH2022071) ; and the Research Project at the Hospital Level of Zhenjiang First People's Hospital (Grant No. Y2022025-Z). Data availability: The data that support the findings of this study are available from the corresponding author upon reasonable request. References Sun H, Saeedi P, Karuranga S et al (2022) IDF Diabetes Atlas: Global, regional and country-level diabetes prevalence estimates for 2021 and projections for 2045. Diabetes Res Clin Pract 183:109119. 10.1016/j.diabres.2021.109119 Feldman EL, Callaghan BC, Pop-Busui R et al (2019) Diabetic neuropathy. Nat Rev Dis Primers 5(1):42 Published 2019 Jun 13. 10.1038/s41572-019-0097-9 Feldman EL, Nave KA, Jensen TS, Bennett DLH (2017) New Horizons in Diabetic Neuropathy: Mechanisms, Bioenergetics, and Pain. Neuron 93(6):1296–1313. 10.1016/j.neuron.2017.02.005 Pai YW, Lin CH, Lee IT, Chang MH (2018) Prevalence and biochemical risk factors of diabetic peripheral neuropathy with or without neuropathic pain in Taiwanese adults with type 2 diabetes mellitus. Diabetes Metab Syndr 12(2):111–116 Pek SLT, Sum CF, Yeoh LY et al (2017) Association of apolipoprotein-CIII (apoC-III), endothelium-dependent vasodilation and peripheral neuropathy in a multi-ethnic population with type 2 diabetes. Metabolism 72:75–82. 10.1016/j.metabol.2017.03.016 Jaiswal M, Divers J, Dabelea D et al (2017) Prevalence of and Risk Factors for Diabetic Peripheral Neuropathy in Youth With Type 1 and Type 2 Diabetes: SEARCH for Diabetes in Youth Study. Diabetes Care 40(9):1226–1232. 10.2337/dc17-0179 Calcutt NA (2020) Diabetic neuropathy and neuropathic pain: a (con)fusion of pathogenic mechanisms? Pain 161(Suppl 1):S65–S86. 10.1097/j.pain.0000000000001922 Callaghan BC, Little AA, Feldman EL, Hughes RA (2012) Enhanced glucose control for preventing and treating diabetic neuropathy. Cochrane Database Syst Rev 6(6):CD007543 Published 2012 Jun 13. 10.1002/14651858.CD007543.pub2 Kwai NC, Nigole W, Poynten AM, Brown C, Krishnan AV (2016) The Relationship between Dyslipidemia and Acute Axonal Function in Type 2 Diabetes Mellitus In Vivo. PLoS One. ;11(4):e0153389. Published 2016 Apr 14. 10.1371/journal.pone.0153389 American Diabetes Association Professional Practice Committee. 12. Retinopathy, Neuropathy, and Foot Care: Standards of Medical Care in Diabetes-2022. Diabetes Care (2022) ;45(Suppl 1):S185-S194. 10.2337/dc22-S012 Naudí A, Cabré R, Jové M et al (2015) Lipidomics of human brain aging and Alzheimer's disease pathology. Int Rev Neurobiol 122:133–189. 10.1016/bs.irn.2015.05.008 Kracun I, Rosner H, Drnovsek V, Heffer-Lauc M, Cosović C, Lauc G (1991) Human brain gangliosides in development, aging and disease. Int J Dev Biol 35(3):289–295 Wu G, Lu ZH, Kulkarni N, Amin R, Ledeen RW (2011) Mice lacking major brain gangliosides develop parkinsonism. Neurochem Res 36(9):1706–1714. 10.1007/s11064-011-0437-y Zha Q, Ruan Y, Hartmann T, Beyreuther K, Zhang D (2004) GM1 ganglioside regulates the proteolysis of amyloid precursor protein. Mol Psychiatry 9(10):946–952. 10.1038/sj.mp.4001509 Davidson WS, Jonas A, Clayton DF, George JM (1998) Stabilization of alpha-synuclein secondary structure upon binding to synthetic membranes. J Biol Chem 273(16):9443–9449. 10.1074/jbc.273.16.9443 Pop-Busui R, Boulton AJ, Feldman EL et al (2017) Diabetic Neuropathy: A Position Statement by the American Diabetes Association. Diabetes Care 40(1):136–154. 10.2337/dc16-2042 Concato J, Peduzzi P, Holford TR, Feinstein AR (1995) Importance of events per independent variable in proportional hazards analysis. I. Background, goals, and general strategy. J Clin Epidemiol 48(12):1495–1501. 10.1016/0895-4356(95)00510-2 Baron R (2010) Binder Andreas,Wasner Gunnar,Neuropathic pain: diagnosis, pathophysiological mechanisms, and treatment.[J].Lancet Neurol, 9: 807 – 19. 10.1016/S1474-4422(10)70143-5 Breiner Ari,Lovblom Leif Erik, Perkins Bruce A et al (2014) Does the prevailing hypothesis that small-fiber dysfunction precedes large-fiber dysfunction apply to type 1 diabetic patients?[J]. Diabetes Care 37:1418–1424. 10.2337/dc13-2005 Quattrini Cristian,Tavakoli Mitra,Jeziorska Maria (2007) Surrogate markers of small fiber damage in human diabetic neuropathy.[J].Diabetes. 56:2148–2154. 10.2337/db07-0285 Petropoulos Ioannis N, Adnan et al (2020) Corneal confocal microscopy: ready for prime time[. J] Clin Exp Optom 103:265–277. 10.1111/cxo.12887 Dabby R, Sadeh M, Lampl Y, Gilad R, Watemberg N (2009) Acute painful neuropathy induced by rapid correction of serum glucose levels in diabetic patients. Biomed Pharmacother 63(10):707–709. 10.1016/j.biopha.2008.08.011 Nicodemus JM, Enriquez C, Marquez A, Anaya CJ, Jolivalt CG (2017) Murine model and mechanisms of treatment-induced painful diabetic neuropathy. Neuroscience 354:136–145. 10.1016/j.neuroscience.2017.04.036 Gibbons CH (2020) Treatment induced neuropathy of diabetes. Auton Neurosci 226:102668. 10.1016/j.autneu.2020.102668 Wang M, Xie M, Yu S et al (2021) Lipin1 Alleviates Autophagy Disorder in Sciatic Nerve and Improves Diabetic Peripheral Neuropathy. Mol Neurobiol 58(11):6049–6061. 10.1007/s12035-021-02540-5 Huang Y, Wang Y, Liu C et al (2022) C-peptide, glycaemic control, and diabetic complications in type 2 diabetes mellitus: A real-world study. Diabetes Metab Res Rev 38(4):e3514. 10.1002/dmrr.3514 Cotter Mary A et al (2003) Ekberg Karin,Wahren John. Effects of proinsulin C-peptide in experimental diabetic neuropathy: vascular actions and modulation by nitric oxide synthase inhibition.[J].Diabetes, 52: 1812-7. 10.2337/diabetes.52.7.1812 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7936390","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":546341244,"identity":"77fb8183-4ae1-4c4f-94bd-64c9a7e61013","order_by":0,"name":"Yanyan Ji","email":"","orcid":"","institution":"Affiliated People’s Hospital of Jiangsu University","correspondingAuthor":false,"prefix":"","firstName":"Yanyan","middleName":"","lastName":"Ji","suffix":""},{"id":546341245,"identity":"cd1ce8fe-5fab-4152-9f34-3a47b514c29e","order_by":1,"name":"Shujun Ren","email":"","orcid":"","institution":"Affiliated People’s Hospital of Jiangsu University","correspondingAuthor":false,"prefix":"","firstName":"Shujun","middleName":"","lastName":"Ren","suffix":""},{"id":546341246,"identity":"2e99519c-8add-42c4-81f9-7017f434e292","order_by":2,"name":"Zilu Liu","email":"","orcid":"","institution":"Affiliated People’s Hospital of Jiangsu University","correspondingAuthor":false,"prefix":"","firstName":"Zilu","middleName":"","lastName":"Liu","suffix":""},{"id":546341247,"identity":"33789fbd-9731-411b-b86c-c69d266b83c1","order_by":3,"name":"Xiang Wang","email":"","orcid":"","institution":"Affiliated People’s Hospital of Jiangsu University","correspondingAuthor":false,"prefix":"","firstName":"Xiang","middleName":"","lastName":"Wang","suffix":""},{"id":546341248,"identity":"f0093153-6073-4343-8fd4-946e97dedfc1","order_by":4,"name":"Zhixin Zhu","email":"","orcid":"","institution":"Affiliated People’s Hospital of Jiangsu University","correspondingAuthor":false,"prefix":"","firstName":"Zhixin","middleName":"","lastName":"Zhu","suffix":""},{"id":546341249,"identity":"db3fd6b0-550c-4bae-a82b-cb520418c43b","order_by":5,"name":"Haitong Liu","email":"","orcid":"","institution":"Affiliated People’s Hospital of Jiangsu University","correspondingAuthor":false,"prefix":"","firstName":"Haitong","middleName":"","lastName":"Liu","suffix":""},{"id":546341250,"identity":"df556723-2f58-481c-a856-fcc3664edad3","order_by":6,"name":"Qinyuan Lu","email":"","orcid":"","institution":"Affiliated People’s Hospital of Jiangsu University","correspondingAuthor":false,"prefix":"","firstName":"Qinyuan","middleName":"","lastName":"Lu","suffix":""},{"id":546341251,"identity":"452e55bf-f60d-42fd-8f35-3f457dc25219","order_by":7,"name":"Yingzhao Liu","email":"","orcid":"","institution":"Affiliated People’s Hospital of Jiangsu University","correspondingAuthor":false,"prefix":"","firstName":"Yingzhao","middleName":"","lastName":"Liu","suffix":""},{"id":546341252,"identity":"cec04089-b589-4ccd-a26c-115ee75e2aa5","order_by":8,"name":"Caixia Sun","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA40lEQVRIiWNgGAWjYBACNmbGhgMJBhI8/OzNBxgkQEIHCGjhY28++OBDgYWMZM+xBOK0yPEcSzac8aHCxuCGjwFEiJAWNokcM2keoMMMbvB8k7D4wyDHdyOB8XMBMVokb/duk5BsYzCWvJHALD2DGC18d84CtTQwJG64kcDGzEOMFoYbOc8kJP4w1BPWAvY+UIvAjRw2CQk2hgQDglrAgQzyS88xYwvJNgnDmWceNkvj0yLfDIrKP3X2wKh8eFvij4083/Hkg5/xaUEBzBLgyGRsIFYDUO0H4tWOglEwCkbBCAIACYJHiXXQkPgAAAAASUVORK5CYII=","orcid":"","institution":"Affiliated People’s Hospital of Jiangsu University","correspondingAuthor":true,"prefix":"","firstName":"Caixia","middleName":"","lastName":"Sun","suffix":""}],"badges":[],"createdAt":"2025-10-24 03:38:21","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7936390/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7936390/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":96556421,"identity":"53e46138-4773-4e85-ab75-c25c39dce01a","added_by":"auto","created_at":"2025-11-23 11:48:11","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":584003,"visible":true,"origin":"","legend":"","description":"","filename":"Manuscript1.docx","url":"https://assets-eu.researchsquare.com/files/rs-7936390/v1/a2e4fb14fafdb5e88553c2d7.docx"},{"id":96556419,"identity":"e4645fd5-0b63-4d98-bc00-9f268e89751c","added_by":"auto","created_at":"2025-11-23 11:48:11","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":18002,"visible":true,"origin":"","legend":"","description":"","filename":"Table1.docx","url":"https://assets-eu.researchsquare.com/files/rs-7936390/v1/fb27661e3e1e9f6806f3cd67.docx"},{"id":96556422,"identity":"96ac0dbc-c75a-406a-914f-105ff8070a61","added_by":"auto","created_at":"2025-11-23 11:48:11","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":95080,"visible":true,"origin":"","legend":"","description":"","filename":"Figure.1.docx","url":"https://assets-eu.researchsquare.com/files/rs-7936390/v1/8f4b09b5600dedac712aa263.docx"},{"id":96556420,"identity":"02824203-7c98-494c-b95f-c580ef0ba6b2","added_by":"auto","created_at":"2025-11-23 11:48:11","extension":"docx","order_by":3,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":245890,"visible":true,"origin":"","legend":"","description":"","filename":"Figure.2.docx","url":"https://assets-eu.researchsquare.com/files/rs-7936390/v1/f762b487a0c4765ae0ede0e0.docx"},{"id":96604914,"identity":"8e720fc3-23dd-4b2b-b013-60320178889a","added_by":"auto","created_at":"2025-11-24 09:15:58","extension":"docx","order_by":4,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":117714,"visible":true,"origin":"","legend":"","description":"","filename":"Figure.3.docx","url":"https://assets-eu.researchsquare.com/files/rs-7936390/v1/1f36651e0012cbd557c80428.docx"},{"id":96605429,"identity":"7628917b-0bad-48b4-ba49-8f7964b09195","added_by":"auto","created_at":"2025-11-24 09:22:51","extension":"docx","order_by":5,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":114926,"visible":true,"origin":"","legend":"","description":"","filename":"Figure.4.docx","url":"https://assets-eu.researchsquare.com/files/rs-7936390/v1/d871bbe446ec9cfab9deea59.docx"},{"id":96604480,"identity":"9498fd57-ab86-4cee-ac6f-a3d077626c61","added_by":"auto","created_at":"2025-11-24 09:14:02","extension":"json","order_by":6,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":9989,"visible":true,"origin":"","legend":"","description":"","filename":"8143e43e9acc4159a3c4c9ec40afa071.json","url":"https://assets-eu.researchsquare.com/files/rs-7936390/v1/5120bb6bf321a58a50e725b7.json"},{"id":96604654,"identity":"efc02ab2-b4d3-46d8-872b-70abf32fb838","added_by":"auto","created_at":"2025-11-24 09:14:29","extension":"xml","order_by":7,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":92896,"visible":true,"origin":"","legend":"","description":"","filename":"8143e43e9acc4159a3c4c9ec40afa0711enriched.xml","url":"https://assets-eu.researchsquare.com/files/rs-7936390/v1/eab16b681847cdf7d9b9c436.xml"},{"id":96556431,"identity":"e277c3f8-6805-44c6-aaf8-81952bfbd597","added_by":"auto","created_at":"2025-11-23 11:48:12","extension":"jpeg","order_by":8,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":3043860,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7936390/v1/045696e2e39147a28e5dcdf2.jpeg"},{"id":96604872,"identity":"29af8487-61d9-4ac3-84fa-4b78f6d3d0b4","added_by":"auto","created_at":"2025-11-24 09:15:32","extension":"jpeg","order_by":9,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":1005510,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7936390/v1/eef94ec676cf0bfc4de82498.jpeg"},{"id":96556437,"identity":"4dacb5dc-d0f1-470c-9aa1-b06dfae41d22","added_by":"auto","created_at":"2025-11-23 11:48:12","extension":"jpeg","order_by":10,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":3046884,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7936390/v1/770fd224609f5dff8f82279f.jpeg"},{"id":96556435,"identity":"f8f86920-f404-4271-a58f-bcbd87f82167","added_by":"auto","created_at":"2025-11-23 11:48:12","extension":"jpeg","order_by":11,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":2793814,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage4.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7936390/v1/7822a226f93d6f08414eab97.jpeg"},{"id":96556438,"identity":"b6280cbc-dc9a-4053-a322-78bd425fc930","added_by":"auto","created_at":"2025-11-23 11:48:12","extension":"jpeg","order_by":12,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":3043860,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7936390/v1/dd16bc94a4de869964e523a6.jpeg"},{"id":96605058,"identity":"b644a8a6-cf69-4730-bb65-1862691b5136","added_by":"auto","created_at":"2025-11-24 09:17:54","extension":"jpeg","order_by":13,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":1005510,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7936390/v1/2c889d2e202c815a4f80e1a4.jpeg"},{"id":96556436,"identity":"af1d2bc6-d6ef-4a43-bcc1-1b0ab152eea8","added_by":"auto","created_at":"2025-11-23 11:48:12","extension":"jpeg","order_by":14,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":3642258,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage7.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7936390/v1/18391639ad88433c9e6e1726.jpeg"},{"id":96556426,"identity":"ac8faef6-a78a-42fb-a209-72ea723d7594","added_by":"auto","created_at":"2025-11-23 11:48:12","extension":"jpeg","order_by":15,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":4112382,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage8.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7936390/v1/bcede6e85a069bb9314deade.jpeg"},{"id":96556439,"identity":"7d27417a-1ca2-44f6-abd1-83d4352b01a4","added_by":"auto","created_at":"2025-11-23 11:48:12","extension":"png","order_by":16,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":38749,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7936390/v1/244b559720aa751174edad98.png"},{"id":96556430,"identity":"34a4a060-7574-40b6-a075-a82829c30e87","added_by":"auto","created_at":"2025-11-23 11:48:12","extension":"png","order_by":17,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":122969,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-7936390/v1/cab6d7b3918054f54e1ecd33.png"},{"id":96556442,"identity":"4470b118-793b-42ac-b483-1f312892055e","added_by":"auto","created_at":"2025-11-23 11:48:12","extension":"png","order_by":18,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":32450,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-7936390/v1/f10ee8387f64e70154d1c6ca.png"},{"id":96556445,"identity":"22bbedb0-7b5c-4799-81ba-cfdd94272570","added_by":"auto","created_at":"2025-11-23 11:48:12","extension":"png","order_by":19,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":26067,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-7936390/v1/c23821dd14044abce4972da9.png"},{"id":96605712,"identity":"615fca5c-7844-44ba-b6c9-fd605272c6b8","added_by":"auto","created_at":"2025-11-24 09:23:54","extension":"png","order_by":20,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":38749,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7936390/v1/e0605520c8a530c147688576.png"},{"id":96556432,"identity":"e966f46e-e079-463a-aa11-af47c98996e4","added_by":"auto","created_at":"2025-11-23 11:48:12","extension":"png","order_by":21,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":122969,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-7936390/v1/86871a8bc56828e5abca4f2b.png"},{"id":96604600,"identity":"5e6e049a-03e5-44bc-98cd-88e0c1c92a5f","added_by":"auto","created_at":"2025-11-24 09:14:19","extension":"png","order_by":22,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":29490,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage7.png","url":"https://assets-eu.researchsquare.com/files/rs-7936390/v1/481a631f6e3975d534ffa572.png"},{"id":96556428,"identity":"6096489f-9c5d-4fa8-b982-bd74f5e8fd17","added_by":"auto","created_at":"2025-11-23 11:48:12","extension":"png","order_by":23,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":28676,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage8.png","url":"https://assets-eu.researchsquare.com/files/rs-7936390/v1/ba0ecba53e08c905bb546657.png"},{"id":96556441,"identity":"69d89046-dd3e-4abd-b932-40309e91ea73","added_by":"auto","created_at":"2025-11-23 11:48:12","extension":"xml","order_by":24,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":89559,"visible":true,"origin":"","legend":"","description":"","filename":"8143e43e9acc4159a3c4c9ec40afa0711structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-7936390/v1/1717efac0964838eddd2254c.xml"},{"id":96556433,"identity":"6fd659f6-938d-4a12-80d8-877bb0c65767","added_by":"auto","created_at":"2025-11-23 11:48:12","extension":"html","order_by":25,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":100144,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7936390/v1/e754e8305c1208cbd0ad481b.html"},{"id":96556416,"identity":"55eed817-d39b-480e-88d5-4578819c252b","added_by":"auto","created_at":"2025-11-23 11:48:11","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":327592,"visible":true,"origin":"","legend":"\u003cp\u003eThe prognostic nomogram model of diabetic peripheral neuropathy. HDL, high density lipoprotein; Ugcg, Udp-Glucosylceramide glucosyltransferase; C-peptide, fasting C-peptide; Drinking, history of alcohol drinking.\u003c/p\u003e","description":"","filename":"image1.png","url":"https://assets-eu.researchsquare.com/files/rs-7936390/v1/188dedf7bd39ea69c3e98bb3.png"},{"id":96556417,"identity":"05289a6e-e764-4e6e-ab61-35c64ade426c","added_by":"auto","created_at":"2025-11-23 11:48:11","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":105685,"visible":true,"origin":"","legend":"\u003cp\u003eA receiver operating characteristic (ROC) curve was used to evaluate the discriminatory ability of the nomogram model. AUC, area under the ROC curve; CI, confidence interval. According to the rule of maximizing Youden’s J index, the specificity and sensitivity of the nomogram model were 74% and 85%, respectively.\u003c/p\u003e","description":"","filename":"image2.png","url":"https://assets-eu.researchsquare.com/files/rs-7936390/v1/3b592e78fdf98cbf4330176b.png"},{"id":96604956,"identity":"94e74a27-936d-42bc-9230-bfb6994b4643","added_by":"auto","created_at":"2025-11-24 09:16:46","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":465202,"visible":true,"origin":"","legend":"\u003cp\u003eThe nomogram calibration curve was used to assess the consistency between the observed and predicted values. The bias-corrected line indicates the internal validation of the model using the bootstrap method (n=1000). The mean absolute error between the observed and predicted values was 0.03.\u003c/p\u003e","description":"","filename":"image3.png","url":"https://assets-eu.researchsquare.com/files/rs-7936390/v1/afd2b67994f75c9371351935.png"},{"id":96556424,"identity":"e839a124-2cf0-4f24-bd57-2e750e9e97da","added_by":"auto","created_at":"2025-11-23 11:48:11","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":353501,"visible":true,"origin":"","legend":"\u003cp\u003eDecision curve analysis measures the clinical applicability of nomogram models for intervention in diabetic peripheral neuropathy patients.\u003c/p\u003e","description":"","filename":"image4.png","url":"https://assets-eu.researchsquare.com/files/rs-7936390/v1/901ab259d3b41101a6d34cc7.png"},{"id":98441070,"identity":"b0f4991b-96a5-4c10-ba54-d883611edfd5","added_by":"auto","created_at":"2025-12-17 17:04:49","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1855069,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7936390/v1/f8202661-11f0-41ea-9769-79457f69a67f.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"New biomarker in nomogram model for predicting diabetic neuropathy : a cross-sectional study","fulltext":[{"header":"Introduction","content":"\u003cp\u003eAccording to the International Diabetes Federation (International Diabetes Federation (IDF) in 2021, the global prevalence of diabetes mellitus (DM) among people aged 20\u0026ndash;79 years reached 10.5% (about 536\u0026nbsp;million people) and this number is still rapidly growing\u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]\u003c/sup\u003e. Diabetic Neuropathy (DN) is a clinical syndrome caused by peripheral or autonomic nerves damage, with a prevalence of up to 40%-50% in diabetes patients\u003csup\u003e[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]\u003c/sup\u003e. The most common type of DN is diabetic peripheral neuropathy (DPN), which is also known as \u0026ldquo;stocking-glove neuropathy\u0026rdquo;. Patients with DPN usually have pain, numbness or sensations that gradually move from the distal to the proximal body\u003csup\u003e[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003ePrevious studies on DPN were mainly based on direct or indirect damage to nerve by the metabolites deriving from hyperglycaemic and hypermetabolic states, including increased levels of polyols, formation of advanced glycation end products (AGEs) and increased production of reactive oxygen species (ROS). Currently, some scholars have focused on the prevention of DPN by targeting risk factors. One retrospective study identified older age, elevated glycosylated haemoglobin, and proteinuria as risk factors for DPN\u003csup\u003e[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]\u003c/sup\u003e. Moreover, some studies confirmed some indicatiors of lipid metabolism, such as high ApoC III\u003csup\u003e[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]\u003c/sup\u003e, triglycerides, cholesterol and LDL\u003csup\u003e[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]\u003c/sup\u003e were also risk factors for DPN. However, some studies found strict blood glucose control measures did not reduce the incidence of DPN\u003csup\u003e[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]\u003c/sup\u003e, and triglyceride levels also had no impact on neuronal axon function\u003csup\u003e[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]\u003c/sup\u003e. In view of these, effective methods for preventing DPN are still limited In clinical setting\u003csup\u003e[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eUgcg is a synthetic enzyme found widely in the Golgi apparatus of neuronal cells (mainly Schwann cells and oligodendrocytes) and has been proven to produce sphingolipids for myelin structure synthesis, neurodevelopmental growth, and modulation of some transmembrane receptor activities. Literatures reported that the total myelin content\u003csup\u003e[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]\u003c/sup\u003e and the levels of the sphingolipids GD1a, GD1b and GM1\u003csup\u003e[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]\u003c/sup\u003e decreased significantly with age. In some neurodegenerative diseases, such as Alzheimer's disease\u003csup\u003e[\u003cspan additionalcitationids=\"CR13\" citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]\u003c/sup\u003e and Parkinson's disease\u003csup\u003e[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]\u003c/sup\u003e, the sphingolipids are also significantly reduced. Our previous bioinformation analysis studies found that the content of Ugcg in the sciatic nerve tissue of diabetic neuropathic mice was significantly lower compared with normal mice. DPN is a specific degenerative disease of the peripheral nerves, with demyelinating morphological features. Therefore, we inferred low levels of Ugcg might correlate with diabetic neuropathy.\u003c/p\u003e\u003cp\u003eIn this study, we aimed to construct a clinical predictive model including Ugcg. Furthermore,we assessed the ability of this model to predict the development of DPN.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eStudy design and patients\u003c/h2\u003e\u003cp\u003eThis retrospective study recruited all Type 2 diabetes mellitus (T2DM) patients admitted to the endocrinology department of Affiliated People's Hospital of Jiangsu University from June 1, 2022 to December 31, 2022. The diagnosis of T2DM based on the guidelines of the American Diabetes Association (ADA). According to the diagnosis and treatment of diabetic neuropathy\u003csup\u003e[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]\u003c/sup\u003e, T2DM patients were divided into a group with diabetic peripheral neuropathy group (DN group) and a group without neuropathy (DM group). This study was approved by the Ethics Committee of Affiliated People\u0026rsquo;s Hospital of Jiangsu University (Y2022005-Z), and all patients provided their written informed consent prior to enrollment.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eInclusion and exclusion criteria\u003c/h3\u003e\n\u003cp\u003eWe recruited T2DM patients aged 18\u0026ndash;75 years. Exclusion criteria included spinal pathology (e.g. fracture, disc disease, etc.); central nerve diseases (e.g. cerebral infarction, Parkinson's disease, Alzheimer's disease, etc.); peripheral nerve diseases (e.g. carpal tunnel syndrome, herpes zoster residual neuropathy, inflammatory demyelinating neuropathy, etc.); nerve damage caused by a history of radiotherapy and chemotherapy or poor liver/renal function. Patients previously diagnosed with diabetic neuropathy and those who refused to sign the informed consent form(or could not cooperate to complete of the relevant checks) were also excluded. All patients were evaluated and screened for study eligibility by the research investigators.\u003c/p\u003e\n\u003ch3\u003eData collection\u003c/h3\u003e\n\u003cp\u003e Clinical information including gender, age, height, weight, history of smoking, history of alcohol drinking and hypertension was obtained through the electronic medical record system of Affiliated People's Hospital of Jiangsu University. It included Body mass index (BMI) was calculated by divided the weight (kg) by height squared (kg/m\u003csup\u003e2\u003c/sup\u003e).\u003c/p\u003e\u003cp\u003eBlood samples were collected from the upper limb vein after an overnight fast. Biochemical data, including glycated hemoglobin (HbA1c), fasting C-peptide, fasting blood glucose (FBG), oral glucose tolerance test 1-hour glucose (OGTT 1h), oral glucose tolerance test 2-hour glucose (OGTT 2h), triglyceride (TG), total cholesterol (TC), high density lipoprotein (HDL), low density lipoprotein (LDL), apolipoprotein-a1 (APO-a1) and apolipoprotein-b (APO-b) were collected. An ELISA kit (Jiangsu Jingmei Biotechnology Co.) was used to measure serum Ugcg.\u003c/p\u003e\u003cp\u003eThe electrophysiological examinations were perfomered by Electromyographyer of Affiliated People's Hospital of Jiangsu University. The sensory nerve conduction velocity (SNCV) and sensory nerve conduction amplitudes (SNCA) of the right median nerve, right ulnar nerve, bilateral superficial peroneal nerves, bilateral peroneal nerves were recorded.\u003c/p\u003e\n\u003ch3\u003eStatistical analyses\u003c/h3\u003e\n\u003cp\u003eIn this study, the sample size was calculated based on the incidence of DPN and the number of expected risk factors. According to the 10-EPV (10-events per variable) principle\u003csup\u003e[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]\u003c/sup\u003e and previous literature, no more than 5 risk factors for diabetic neuropathy were expected, and the incidence of DPN was about 40% to 50%. Therefore, at least 50 DPN patients and 50\u0026ndash;75 T2DM patients without DPN were needed. Missing data were filled by using multiple imputation.\u003c/p\u003e\u003cp\u003eAll study data were processed using SPSS statistical software(version 24.0, Chicago, IL,USA). Measurement data were tested for normality using Kolmogorov\u0026ndash;Smirnov tests. Measurement data were presented as means (standard deviation, SD) or medians (interquartile ranges, IQR), as appropriate. Measurement data were compared between groups using the student\u0026rsquo;s \u003cem\u003et\u003c/em\u003e-test(or \u003cem\u003et\u003c/em\u003e\u0026rsquo;-test if variances were not homogeneous) and Mann-Whitney U test based on normality. Categorical data were compared using the chi-square test or Fisher's exact test.\u003c/p\u003e\u003cp\u003eIn univariate analysis, variables with \u0026ldquo;\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.1\u0026rdquo; were considered significant and used for further multivariate logistic regression. Multivariate analysis used \"conditional backward stepwise regression\". The \"rms\" package of the R software (version 4.3.0) was used to construct the nomogram model for predicting DPN, including variables with \"\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05\" in the multivariate logisitic regression. The receiver operating characteristic (ROC) curve was constructed and the area under the curve (AUC) was calculated to analyze the discrimination of the nomogram model. The specificity and sensitivity of the nomogram model were determined according to the rule of maximizing Youden's J index. The internal validation for prediction reliability of the nomogram model was evaluated with the bootstrap method (number of resampling: n\u0026thinsp;=\u0026thinsp;1000), and the calibration curve was prepared to show the consistency results. The \u0026ldquo;rmda\u0026rdquo; package of the R software was used to construct a decision curve analysis (DCA) to evaluate the clinical utility of model. \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 indicated statistical significance.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003ePatients characteristics\u003c/h2\u003e\u003cp\u003eFrom June 1, 2022 to December 31, 2022, 808 T2DM patients were admitted to the Department of Endocrinology of Affiliated People's Hospital of Jiangsu University. According to inclusion and exclusion criteria, 122 patients were enrolled. Among which, 62 patients had a neuropathy based on the guidelines of diagnosis and treatment of diabetic neuropathy\u003csup\u003e[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eComparison of clinical information data between the DN and DM groups were shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Demographic data showed only age, weight, BMI and history of alcohol drinking were statistically different between the two groups (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). In terms of glucose metabolism, FBG and OGTT 1h in the DN group were lower than those in the DM group (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05); No statistical differences existed in HbA1c and OGTT 2h between the two groups(\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05); As for, the DN group had lower triglyceride levels (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and higher HDL levels (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.001) compared with the DM group. There were no significant differences in the other lipid metabolism indicators (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05). In addition, fasting C-peptide and ugcg in the DN group were lower than those in the DM group (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eThe factors of the logistic regression analysis.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\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=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eFactors\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eUnivariate analysis\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMultivariate analysis\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOdds ratio (95%CI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eOdds ratio (95%CI)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.846 (0.832\u0026ndash;4.098)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\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\u003e1.086 (1.045\u0026ndash;1.128)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.076 (1.028\u0026ndash;1.126)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.002\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHelight\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.984 (0.937\u0026ndash;1.033)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBMI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.839 (0.734\u0026ndash;0.960)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eDroped\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSmoke\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.953 (0.949\u0026ndash;4.020)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDrinking History\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2.750 (1.169\u0026ndash;6.469)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4.073 (1.349\u0026ndash;12.297)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.013\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHypertension\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.772 (0.377\u0026ndash;1.582)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHbA1c\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.915 (0.780\u0026ndash;1.073)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFBG\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.902 (0.816\u0026ndash;0.996)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOGTT 1h\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.903 (0.829\u0026ndash;0.984)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOGTT 2h\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.933 (0.866\u0026ndash;1.005)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTG\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.608 (0.437\u0026ndash;0.847)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eDroped\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.677 (0.463\u0026ndash;0.990)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eDroped\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHDL\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e9.935 (2.378\u0026ndash;41.515)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6.024 (1.072\u0026ndash;33.847)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.041\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLDL\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.551 (0.321\u0026ndash;0.947)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eDroped\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eApo-a1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.492 (0.120\u0026ndash;2.020)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eApo-b\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.371 (0.414\u0026ndash;4.539)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUgcg\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.980 (0.968\u0026ndash;0.991)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.979 (0.963\u0026ndash;0.994)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.007\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFasting C-peptide\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.451 (0.286\u0026ndash;0.712)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.591 (0.365\u0026ndash;0.956)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.032\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cp\u003eBMI, body mass index; HbA1c, glycated hemoglobin; FBG, fasting blood glucose; OGTT 1h, blood glucose at one hour of oral glucose tolerance test; OGTT 2h, blood glucose at two hours of oral glucose tolerance test; TG, triglycerides; TC, total cholesterol; HDL, high density lipoprotein; LDL, low density lipoprotein; Apo-a1, apolipoprotein-a1; Apo-b, apolipoprotein-b. Ugcg, udp-Glucosylceramide glucosyltransferase. \u0026quot;Droped\u0026quot; means that the variable was dropped from the multivariate analysis. P-value adjusted for multiple variables.\u003c/p\u003e\n\u003ch3\u003eConstructing a nomogram model using Logistic regression\u003c/h3\u003e\n\u003cp\u003eIn univariate analysis, the factors associated with DPN included age, BMI, history of alcohol drinking, TG, TC, HDL, LDL, serum Ugcg levels and fasting C-peptide. Especially, we did not include FBG and OGTT 1h in the modelling considering that there are too many factors interfering with blood glucose. A backward sstepwise regression multivariate analysis showed five variables were independently associated with DPN, including age (OR: 1.076, 95% CI:1.028\u0026ndash;1.126, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.002), history of alcohol drinking (OR:4.073, 95%CI: 1.349\u0026ndash;12.297, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.013), HDL (OR:6.024, 95%CI: 1.072\u0026ndash;33.847, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.041), Ugcg (OR:0.979, 95%CI: 0.963\u0026ndash;0.994, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.007), fasting C-peptide (OR: 0.591, 95%CI: 0.365\u0026ndash;0.956, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.032). Therefore, a nomogram model was established using the five variables. The score for each variable was based on its respective regression coefficients. Figure.1 showed the risk of developing DPN corresponding to the total score of each variable.\u003c/p\u003e\n\u003ch3\u003eEvaluation and validation of the nomogram models\u003c/h3\u003e\n\u003cp\u003eThe discrimination of the nomogram model was evaluated using ROC curve. The AUC of the nomogram was 0.85 (95%CI: 0.78\u0026ndash;0.91, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). According to the rule of maximizing Youden\u0026rsquo;s J index, the specificity and sensitivity of the nomogram model were 74% and 85% respectively (Figure.2). The calibration curve showed a good accuracy between observed and predicted probabilities in Figure.3(the average absolute error was 0.03). That is, this nomogram model performed well in predicting DPN. In addition, we also used decision curve analysis to measure the clinical applicability of the model. The figure showed that the maximum clinical benefits was obtained when the threshold probability was around 50% (Figure.4).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this retrospective observational study, we found Ugcg levels are downregulated in expression in patients with DPN based on bioinformatic analysis of gene expression profiles in the sciatic nerve of diabetic mice. On this basis, we constructed a DPN predicitive nomogram model that includes Ugcg. Our study shows that this prediction model can accurately and effectively predict DPN with great clinical benefit in terms of earlier intervention and treatment.\u003c/p\u003e\u003cp\u003eIt is well known that in the early stages of DPN for small nerve fibre damage\u003csup\u003e[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]\u003c/sup\u003e. Compared to invasive skin biopsy techniques\u003csup\u003e[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]\u003c/sup\u003e, corneal confocal microscopy (CCM) is an objective and non-invasive imaging technique that has been proved to quantify small corneal nerve fibres\u003csup\u003e[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]\u003c/sup\u003e. However, it is not widely available because of its costly examination. Predictive models constructed in previous studies based on clinical features and relevant metabolic indicators were relatively lacking in predictive specificity due to the lack of indicators directly relevant to neuropathy. In this study, we found that the nerve conduction velocity and nerve conduction amplitude of patients in the DN group were significantly lower than those in the DM group. Using Spearman's rank correlation, we concluded that Ugcg was generally positively correlated with nerve conduction velocity and amplitude, and the logistic regression results suggested that Ugcg was an independent influencing factor of DPN. Therefore, we consider that Ugcg may better reflect the degree of peripheral neuropathy. To our knowledge, this study is the first to apply the serum Ugcg for the prediction of DPN. Our prediction model compares favourably with other methods of detection for DPN in that it is non-invasive, simple and economical to measure, and has shown good accuracy.\u003c/p\u003e\u003cp\u003eOur model showed that, with the exception of HDL, other indicators of glucose and lipid metabolism had no significant effect on DPN. In view of the differences in patients' dietary habits, medication regimens, and baseline control of blood glucose, which made it difficult for the results of a one-time fasting blood glucose tolerance test to reflect the effect of overall blood glucose levels in the T2DM patients on DPN. Therefore, we only performed regression analysis on HbA1c. The results suggest that HbA1c is not an independent risk factor for DPN, which is consistent with the findings of Calcutt et al\u003csup\u003e[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]\u003c/sup\u003e and Callaghan et al\u003csup\u003e[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]\u003c/sup\u003e. In a retrospective study\u003csup\u003e[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]\u003c/sup\u003e, patients in the DN group had a higher insulin use rate and lower glycaemic indicesthan than those in the DM group. A possible explanation for this is that after the peripheral nerves have gradually built up tolerance to the high-glucose environment, the patients' short-term use of insulin or hypoglycaemic drugs led to too large a drop in blood glucose, or long-term over-tight glycaemic control led to microvascular hyperplasia in the periphery of the nerves, similar to the \"arteriovenous shunt\", resulting in ischaemia and nerve cell necrosis and axon degeneration\u003csup\u003e[\u003cspan additionalcitationids=\"CR23\" citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]\u003c/sup\u003e. Given this, more research is needed on how to control blood glucose in the appropriate range. After multivariate regression analysis of factors related to lipid metabolism, only elevated HDL was an independent risk factor for the development of DPN. This is different from the view of many previous studies, but the findings of Pai et al\u003csup\u003e[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]\u003c/sup\u003e were in concordance with ours. Excluding the effect of lipid-lowering drugs taken for the treatment of coronary heart disease, we considered that DPN might be associated with the downregulation of the expression of proteins related to glycolipid metabolism (e.g. Lipin1 protein)\u003csup\u003e[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]\u003c/sup\u003e. Currently, the relationship between the HDL and DPN is not clear and more in-depth studies are needed to elucidate the mechanism of its role.In addition, our data show that age and history of alcohol consumption are risk factors for DPN and that fasting C-peptide is a protective factor against the development of DPN, which is in line with most studies. With age, human nerves undergo spontaneous degeneration and become less able to repair themselves, and alcohol itself is a nerve-damaging agent. This may make peripheral nerves more susceptible to diabetes. Previous studies\u003csup\u003e[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]\u003c/sup\u003e reported that a reduction in fasting C-peptide may be associated with microvascular disease. This may lead to inadequate perfusion of the peripheral nerve endothelium, resulting in nerve damage\u003csup\u003e[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eThere are limitations to our study. First, this study used a cross-sectional study to collect data, and it was not possible to elaborate on the causal relationship between the variables and DPN from a chronological perspective. However, due to the variables' own characteristic and the fact that patients did not significantly change their lifestyle habits, we deduced that they had a causal relationship with DPN. This also needs to be further verified by prospective or interventional studies. Second, according to the previous study, the measurement of ugcg levels in patients should be performed by collecting peripheral nerve samples as a way to obtain more accurate conclusions, but this may be against the requirements of medical ethics. Although the present study used serum samples for the measurement of ugcg, its effect on DPN was equally reliable after correction for confounding factors by multiple logistic regression analysis. The correlation between neural tissue and serum expression of Ugcg can be further investigated by animal experiments in the future. Third, in previous studies, the duration of diabetes was considered an independent risk factor for DPN. However, due to the recall deficit in some patients, we were unable to count this part of the data, so this was not considered for inclusion in the model in this study.\u003c/p\u003e\u003cp\u003eIn conclusion, our study developed a new clinical prediction model to predict DPN, which is made more accurate by its inclusion of Ugcg, an indicator of nerve damage. Validation datasets can be developed in the future to externally validate the model. Our nomogram model could be useful for clinicians in the early identification and intervention of DPN.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe are grateful to the team members and participants for their cooperation with the study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDisclosure\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe author declares that there is no conflict of interest.\u003c/p\u003e\n\u003cp\u003eApproval of the research protocol: Y/A\u003c/p\u003e\n\u003cp\u003eInformed Consent: Y/A\u003c/p\u003e\n\u003cp\u003eApproval date of Registry and the Registration No. of the study/trial: N/A\u003c/p\u003e\n\u003cp\u003eAnimal Studies: N/A\u003c/p\u003e\n\u003cp\u003eConflict of interest:N/A\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe author disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by the Chinese Medical Development Plan Projects of Jiangsu Province (Grant No. MS2021083) ; and the Science and Technology Planning Social Development Project of Zhenjiang City (Grant No. SH2022071) ; and the Research Project at the Hospital Level of Zhenjiang First People\u0026apos;s Hospital (Grant No. Y2022025-Z).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability: \u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data that support the findings of this study are available from the corresponding author upon reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eSun H, Saeedi P, Karuranga S et al (2022) IDF Diabetes Atlas: Global, regional and country-level diabetes prevalence estimates for 2021 and projections for 2045. Diabetes Res Clin Pract 183:109119. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.diabres.2021.109119\u003c/span\u003e\u003cspan address=\"10.1016/j.diabres.2021.109119\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFeldman EL, Callaghan BC, Pop-Busui R et al (2019) Diabetic neuropathy. Nat Rev Dis Primers 5(1):42 Published 2019 Jun 13. \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\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFeldman EL, Nave KA, Jensen TS, Bennett DLH (2017) New Horizons in Diabetic Neuropathy: Mechanisms, Bioenergetics, and Pain. Neuron 93(6):1296\u0026ndash;1313. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.neuron.2017.02.005\u003c/span\u003e\u003cspan address=\"10.1016/j.neuron.2017.02.005\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePai YW, Lin CH, Lee IT, Chang MH (2018) Prevalence and biochemical risk factors of diabetic peripheral neuropathy with or without neuropathic pain in Taiwanese adults with type 2 diabetes mellitus. Diabetes Metab Syndr 12(2):111\u0026ndash;116\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePek SLT, Sum CF, Yeoh LY et al (2017) Association of apolipoprotein-CIII (apoC-III), endothelium-dependent vasodilation and peripheral neuropathy in a multi-ethnic population with type 2 diabetes. Metabolism 72:75\u0026ndash;82. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.metabol.2017.03.016\u003c/span\u003e\u003cspan address=\"10.1016/j.metabol.2017.03.016\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eJaiswal M, Divers J, Dabelea D et al (2017) Prevalence of and Risk Factors for Diabetic Peripheral Neuropathy in Youth With Type 1 and Type 2 Diabetes: SEARCH for Diabetes in Youth Study. Diabetes Care 40(9):1226\u0026ndash;1232. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.2337/dc17-0179\u003c/span\u003e\u003cspan address=\"10.2337/dc17-0179\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCalcutt NA (2020) Diabetic neuropathy and neuropathic pain: a (con)fusion of pathogenic mechanisms? Pain 161(Suppl 1):S65\u0026ndash;S86. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1097/j.pain.0000000000001922\u003c/span\u003e\u003cspan address=\"10.1097/j.pain.0000000000001922\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCallaghan BC, Little AA, Feldman EL, Hughes RA (2012) Enhanced glucose control for preventing and treating diabetic neuropathy. Cochrane Database Syst Rev 6(6):CD007543 Published 2012 Jun 13. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1002/14651858.CD007543.pub2\u003c/span\u003e\u003cspan address=\"10.1002/14651858.CD007543.pub2\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKwai NC, Nigole W, Poynten AM, Brown C, Krishnan AV (2016) The Relationship between Dyslipidemia and Acute Axonal Function in Type 2 Diabetes Mellitus In Vivo. PLoS One. ;11(4):e0153389. Published 2016 Apr 14. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1371/journal.pone.0153389\u003c/span\u003e\u003cspan address=\"10.1371/journal.pone.0153389\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAmerican Diabetes Association Professional Practice Committee. 12. Retinopathy, Neuropathy, and Foot Care: Standards of Medical Care in Diabetes-2022. Diabetes Care (2022) ;45(Suppl 1):S185-S194. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.2337/dc22-S012\u003c/span\u003e\u003cspan address=\"10.2337/dc22-S012\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eNaud\u0026iacute; A, Cabr\u0026eacute; R, Jov\u0026eacute; M et al (2015) Lipidomics of human brain aging and Alzheimer's disease pathology. Int Rev Neurobiol 122:133\u0026ndash;189. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/bs.irn.2015.05.008\u003c/span\u003e\u003cspan address=\"10.1016/bs.irn.2015.05.008\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKracun I, Rosner H, Drnovsek V, Heffer-Lauc M, Cosović C, Lauc G (1991) Human brain gangliosides in development, aging and disease. Int J Dev Biol 35(3):289\u0026ndash;295\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWu G, Lu ZH, Kulkarni N, Amin R, Ledeen RW (2011) Mice lacking major brain gangliosides develop parkinsonism. Neurochem Res 36(9):1706\u0026ndash;1714. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/s11064-011-0437-y\u003c/span\u003e\u003cspan address=\"10.1007/s11064-011-0437-y\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZha Q, Ruan Y, Hartmann T, Beyreuther K, Zhang D (2004) GM1 ganglioside regulates the proteolysis of amyloid precursor protein. Mol Psychiatry 9(10):946\u0026ndash;952. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/sj.mp.4001509\u003c/span\u003e\u003cspan address=\"10.1038/sj.mp.4001509\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDavidson WS, Jonas A, Clayton DF, George JM (1998) Stabilization of alpha-synuclein secondary structure upon binding to synthetic membranes. J Biol Chem 273(16):9443\u0026ndash;9449. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1074/jbc.273.16.9443\u003c/span\u003e\u003cspan address=\"10.1074/jbc.273.16.9443\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePop-Busui R, Boulton AJ, Feldman EL et al (2017) Diabetic Neuropathy: A Position Statement by the American Diabetes Association. Diabetes Care 40(1):136\u0026ndash;154. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.2337/dc16-2042\u003c/span\u003e\u003cspan address=\"10.2337/dc16-2042\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eConcato J, Peduzzi P, Holford TR, Feinstein AR (1995) Importance of events per independent variable in proportional hazards analysis. I. Background, goals, and general strategy. J Clin Epidemiol 48(12):1495\u0026ndash;1501. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/0895-4356(95)00510-2\u003c/span\u003e\u003cspan address=\"10.1016/0895-4356(95)00510-2\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBaron R (2010) Binder Andreas,Wasner Gunnar,Neuropathic pain: diagnosis, pathophysiological mechanisms, and treatment.[J].Lancet Neurol, 9: 807\u0026thinsp;\u0026ndash;\u0026thinsp;19. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/S1474-4422(10)70143-5\u003c/span\u003e\u003cspan address=\"10.1016/S1474-4422(10)70143-5\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBreiner Ari,Lovblom Leif Erik, Perkins Bruce A et al (2014) Does the prevailing hypothesis that small-fiber dysfunction precedes large-fiber dysfunction apply to type 1 diabetic patients?[J]. Diabetes Care 37:1418\u0026ndash;1424. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.2337/dc13-2005\u003c/span\u003e\u003cspan address=\"10.2337/dc13-2005\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eQuattrini Cristian,Tavakoli Mitra,Jeziorska Maria (2007) Surrogate markers of small fiber damage in human diabetic neuropathy.[J].Diabetes. 56:2148\u0026ndash;2154. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.2337/db07-0285\u003c/span\u003e\u003cspan address=\"10.2337/db07-0285\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePetropoulos Ioannis N, Adnan et al (2020) Corneal confocal microscopy: ready for prime time[. J] Clin Exp Optom 103:265\u0026ndash;277. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1111/cxo.12887\u003c/span\u003e\u003cspan address=\"10.1111/cxo.12887\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDabby R, Sadeh M, Lampl Y, Gilad R, Watemberg N (2009) Acute painful neuropathy induced by rapid correction of serum glucose levels in diabetic patients. Biomed Pharmacother 63(10):707\u0026ndash;709. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.biopha.2008.08.011\u003c/span\u003e\u003cspan address=\"10.1016/j.biopha.2008.08.011\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eNicodemus JM, Enriquez C, Marquez A, Anaya CJ, Jolivalt CG (2017) Murine model and mechanisms of treatment-induced painful diabetic neuropathy. Neuroscience 354:136\u0026ndash;145. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.neuroscience.2017.04.036\u003c/span\u003e\u003cspan address=\"10.1016/j.neuroscience.2017.04.036\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGibbons CH (2020) Treatment induced neuropathy of diabetes. Auton Neurosci 226:102668. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.autneu.2020.102668\u003c/span\u003e\u003cspan address=\"10.1016/j.autneu.2020.102668\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWang M, Xie M, Yu S et al (2021) Lipin1 Alleviates Autophagy Disorder in Sciatic Nerve and Improves Diabetic Peripheral Neuropathy. Mol Neurobiol 58(11):6049\u0026ndash;6061. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/s12035-021-02540-5\u003c/span\u003e\u003cspan address=\"10.1007/s12035-021-02540-5\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHuang Y, Wang Y, Liu C et al (2022) C-peptide, glycaemic control, and diabetic complications in type 2 diabetes mellitus: A real-world study. Diabetes Metab Res Rev 38(4):e3514. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1002/dmrr.3514\u003c/span\u003e\u003cspan address=\"10.1002/dmrr.3514\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCotter Mary A et al (2003) Ekberg Karin,Wahren John. Effects of proinsulin C-peptide in experimental diabetic neuropathy: vascular actions and modulation by nitric oxide synthase inhibition.[J].Diabetes, 52: 1812-7. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.2337/diabetes.52.7.1812\u003c/span\u003e\u003cspan address=\"10.2337/diabetes.52.7.1812\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\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":"Diabetic neuropathy, Nomogram model, New biomarker","lastPublishedDoi":"10.21203/rs.3.rs-7936390/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7936390/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eThe number of patients with diabetic peripheral neuropathy (DPN) is increasing steadily. Currently, no reliable method exists to detect or prevent DPN at an early stage. This study aimed to develop a practical clinical model to help clinicians identify T2DM patients who are at risk of developing DPN and to facilitate early intervention.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eWe conducted a cross‑sectional study of 122 T2DM patients, with and without DPN, between 1 June and 31 December 2022. Indices of glucose and lipid metabolism, UDP‑glucose ceramide glucosyltransferase (Ugcg), fasting C‑peptide and demographic variables were compared and screened with logistic regression analysis. A nomogram was then constructed. Model discrimination, calibration and clinical utility were assessed with the area under the receiver operating characteristic curve (AUC), calibration plots and decision curve analysis (DCA).\u003c/p\u003e\u003ch2\u003eMain results:\u003c/h2\u003e\u003cp\u003eMultivariate analysis yielded a nomogram comprising age (OR 1.076, 95% CI 1.028\u0026ndash;1.126; P\u0026thinsp;=\u0026thinsp;0.002), alcohol consumption (OR 4.073, 95% CI 1.349\u0026ndash;12.297; P\u0026thinsp;=\u0026thinsp;0.013), HDL‑C (OR 6.024, 95% CI 1.072\u0026ndash;33.847; P\u0026thinsp;=\u0026thinsp;0.041), Ugcg (OR 0.979, 95% CI 0.963\u0026ndash;0.994; P\u0026thinsp;=\u0026thinsp;0.007) and fasting C‑peptide (OR 0.591, 95% CI 0.365\u0026ndash;0.956; P\u0026thinsp;=\u0026thinsp;0.032). The nomogram achieved an AUC of 0.85 (95% CI 0.78\u0026ndash;0.91; P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Calibration curves demonstrated good agreement between predicted and observed values (mean absolute error\u0026thinsp;=\u0026thinsp;0.03). Decision curve analysis indicated maximal clinical benefit at a threshold probability of approximately 50%.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e\u003cp\u003eA Ugcg‑inclusive nomogram can identify diabetic patients who are at high risk of developing DPN. It also provides clinicians with a theoretical basis for early intervention.\u003c/p\u003e","manuscriptTitle":"New biomarker in nomogram model for predicting diabetic neuropathy : a cross-sectional study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-23 11:48:06","doi":"10.21203/rs.3.rs-7936390/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":"c9b04529-3099-4f07-919e-e846798dae3d","owner":[],"postedDate":"November 23rd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-12-17T10:54:18+00:00","versionOfRecord":[],"versionCreatedAt":"2025-11-23 11:48:06","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7936390","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7936390","identity":"rs-7936390","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}