Nonlinear association of serum uric acid and C-peptide with arterial stiffness in patients with type 2 diabetes: a real-world study

Background: Diabetes has become one of the most serious and prevalent chronic diseases, and its cardiovascular complications are responsible for over 50% of diabetes-related deaths. However, the relationships between uric acid (UA) and C-peptide on arterial stiffness (AS) in patients with type 2 diabetes mellitus (T2DM) are still poorly understood. This study aimed to evaluate the associations between UA and C-peptide with AS in T2DM patients.

Methods: In this cross-sectional study of 1,715 participants with T2DM, we recorded levels of fasting UA, C-peptide, and other characteristics. Elevated AS was defined as a brachial-ankle pulse wave velocity (baPWV) of ≥1,400 cm/s. Logistic regression and a restricted cubic spline (RCS) model were employed to assess the associations of UA and C-peptide with AS.

Results: Fasting UA and C-peptide levels were independently and significantly associated with elevated AS in patients with T2DM, as determined by multivariate analyses (P < 0.05). Notably, RCS analyses revealed nonlinear relationships with threshold effects between fasting UA, C-peptide, and elevated AS (P for nonlinearity < 0.05). Compared to patients with C-peptide levels < 0.580 μg/L, those with levels ≥ 0.580 μg/L had an approximately 87% relatively higher odds of elevated AS (OR = 1.87, 95% CI: 1.32, 2.65).

Conclusion: The elevated AS odds in patients with T2DM were nonlinearly associated with the levels of serum fasting UA and C-peptide.

Introduction

Diabetes has emerged as one of the most serious and prevalent chronic diseases in recent years (1). Global epidemiological data from the 10th edition of the International Diabetes Federation (IDF) Diabetes Atlas revealed a prevalence of 537 million adults with diabetes in 2021, with China shouldering the largest burden (140 million cases), a figure anticipated to increase by 24% to 174 million within 25 years (2). Notably, diabetes mellitus significantly increases cardiovascular disease (CVD) risk (3, 4), and cardiovascular complications constitute the leading cause of death, responsible for more than 50% of diabetes-associated mortality (5).

Current evidence demonstrates that diabetes significantly elevates CVD risk, primarily through the progression of arterial stiffness (AS)—an independent predictor of cardiovascular events (6, 7). The pathophysiology of AS involves vascular endothelial dysfunction, arterial wall thickening, and reduced vascular elasticity (8). These structural changes decrease arterial compliance and impair diastolic function (9). Extensive research has established significant associations between conventional CVD risk factors and AS development in diabetes (3, 4). Notably, early intervention targeting low-density lipoprotein cholesterol (LDL-C), triglycerides (TG), and waist circumference (WC) can mitigate AS progression in type 2 diabetes mellitus (T2DM) (10), highlighting the particular vulnerability of diabetic patients with coexisting cardiovascular risk factors.

Emerging evidence implicates serum uric acid (UA) as a novel metabolic risk factor for CVD (11, 12). As the end product of purine metabolism in humans, elevated UA levels (hyperuricemia) result from disorders in purine nucleotide metabolism (13–15). Hyperuricemia demonstrates strong associations with elevated plasma triglyceride levels and atherogenic indices (16, 17). While multiple studies have established UA’s role in vascular dysfunction and atherosclerotic progression (18), its relationship with AS remains understudied.

As a 31-amino acid peptide derived from pancreatic proinsulin cleavage, C-peptide is secreted in equimolar amounts with insulin (19). Its circulating concentration serves as a reliable biomarker for assessing endogenous insulin secretion, β-cell function, and insulin resistance (20–23). Elevated levels of C-peptide are observed in individuals with insulin resistance and early T2DM (24). The influence of C-peptide concentration on diabetes patients is still controversial. While some studies associate low C-peptide levels with increased CVD risk in T2DM patients (25), others report that elevated baseline C-peptide predicts higher all-cause and cardiovascular mortality in newly diagnosed T2DM (26).

Existing research has indicated correlations between hyperuricemia and insulin resistance (27–29). Elevated UA levels correlate with increased C-peptide concentrations, and prediabetic individuals with high C-peptide levels exhibit greater susceptibility to gout development (30). The relationship between elevated AS risk and varying strata of serum C-peptide levels in T2DM patients has not been clearly established. Based on this, we postulated that UA and C-peptide exhibit nonlinear associations with the odds of elevated AS. Accordingly, a real-world observational study was carried out to investigate the associations between different levels of UA and C-peptide and elevated AS in a T2DM population.

Methods

Study design and ethics

We conducted a real-world cross-sectional study at The First People’s Hospital of Shunde (Foshan, China) from June 2020 to September 2022, enrolling 4,469 patients with T2DM. All participants underwent standardized physical examinations, clinical assessments, and questionnaire surveys. Exclusion criteria comprised patients receiving UA-lowering therapy (n = 87), those on medications affecting C-peptide levels or CVD (n = 2,369), and cases with missing critical data (UA, C-peptide, or brachial-ankle pulse wave velocity [baPWV]) (n = 298). The final analysis included 1,715 eligible participants. A detailed study flowchart is provided in Figure 1.

Figure 1

Figure 1. Flowchart of participants selection.

The study protocol was approved by the Medical Ethics Committee of The First People’s Hospital of Shunde and adhered to the principles of the Declaration of Helsinki. As a retrospective analysis using anonymized hospital system data (without personal identifiers), written informed consent was waived.

Basic information collection, anthropometric assessment, and laboratory measurements

All participants completed structured questionnaires to collect demographic and lifestyle data, including age, sex, education level, smoking status, alcohol consumption, medication use, and physical activity patterns. Smoking status was categorized as current smokers (regular smoking at the time of survey) or non-smokers (including never-smokers and former smokers who had quit for at least 6 months). Alcohol consumption was similarly classified as current drinkers (consumption at least once per week) or never drinkers (no regular alcohol intake). Trained nurses conducted standardized anthropometric measurements using calibrated instruments, with height measured to the nearest 0.1 cm and weight to the nearest 0.1 kg while participants wore light clothing without shoes. Body mass index (BMI) was calculated as weight in kilograms divided by height in meters squared. Trained nurses measured waist circumference (at the midpoint between the lower rib margin and the iliac crest) and hip circumference (at the level of the greater trochanter) to the nearest 0.1 cm using calibrated measuring tapes at the end of normal expiration. Waist-to-hip ratio (WHR) was calculated as waist circumference divided by hip circumference. After at least 15 min of seated rest, systolic (SBP) and diastolic (DBP) blood pressure were measured in triplicate with 1-min intervals using validated automated devices.

All blood samples were collected under standardized conditions: venipuncture was performed after a 12h overnight fast, and participants were instructed to take their regular medications as prescribed. Samples were transported under refrigerated conditions to a College of American Pathologists (CAP)–certified central laboratory within 2h–4h for analysis, and all biochemical assessments were conducted in the same laboratory using standardized methods.

Study variables and outcome measures

The exposure variables were serum UA and C-peptide levels. The primary outcome was elevated AS, assessed through baPWV measurements using an Omron BP-203RPE III device (Kyoto, Japan). Following a 5-min supine rest, this validated method automatically recorded bilateral pressures and pulse waves, with baPWV calculated as the pulse wave travel distance divided by transit time. An elevated AS was defined as baPWV ≥1,400 cm/s, with final values representing the mean of left and right measurements. Elevated baPWV values indicate both increased AS and elevated CVD risk (31).

Statistical analysis

A total of 1,715 subjects were stratified by AS status (normal/elevated) and categorized into quartiles based on UA and C-peptide levels, with continuous variables expressed as mean ± standard deviation (SD) for normal distributions or median (interquartile range, IQR) for non-normal distributions, and categorical variables presented as frequencies (percentages). Statistical comparisons employed one-way analysis of covariance (ANCOVA) for normally distributed continuous variables, the Kruskal–Wallis test for non-normally distributed continuous variables, and Pearson’s chi-square or Fisher’s exact test for categorical variables, with Bonferroni correction for multiple testing.

Adjusted logistic regression analyses were performed to evaluate the associations between UA/C-peptide levels and elevated AS odds. Three hierarchical models were constructed: Model 1 adjusted for age and sex; Model 2 included Model 1 covariates plus SBP, WHR, aspartate aminotransferase (AST), gamma-glutamyl transferase (GGT), and high-density lipoprotein cholesterol (HDL-C); and Model 3 further incorporated Model 2 covariates with additional adjustments for current alcohol consumption, smoking status, education level, and family history of diabetes. Quartiles were modeled as ordinal variables for trend testing, with odds ratios (ORs) and 95% confidence intervals (CIs) calculated per 1-unit increase in exposure variables. Nonlinear relationships were examined using restricted cubic splines (RCS) with knots at the 10th, 50th, and 90th percentiles. All statistical analyses were conducted using R software (version 4.2.2; https://www.R-project.org), with two-tailed P-values < 0.05 considered statistically significant.

Result

Characteristics of participants

The final analysis included 1,715 participants (1,156 [67.4%] with elevated AS), with a mean age of 51.07 ± 12.42 years. Median (interquartile range) serum C-peptide levels were 1.55 (1.00–2.44) μg/L in normal AS versus 1.81 (1.14–2.58) μg/L in elevated AS, while UA levels were 309.00 (241.55–400.50) μmol/L in normal AS versus 330.50 (258.75–400.00) μmol/L in elevated AS. Baseline characteristics stratified by AS status are summarized in Table 1. Supplementary Tables S1, S2 display baseline characteristics of participants by UA and C-peptide quartiles, showing that both third quartiles of UA and C-peptide were associated with higher baPWV levels.

Table 1

Table 1. Baseline characteristics according to AS status.

The association of UA and C-peptide with AS

After multivariable adjustment, both elevated UA and C-peptide levels showed significant associations with increased AS risk in T2DM patients (P for trend < 0.01 for both biomarkers, Table 2). Each unit increase in UA was marginally associated with higher AS risk (OR = 1.00, 95% CI: 1.00–1.00, P = 0.05), while each unit increase in C-peptide demonstrated a stronger association (OR = 1.04, 95% CI:1.02–1.07, P < 0.001). In fully adjusted models, the third and fourth UA quartiles showed significantly elevated AS risk versus Q1 (Q3: OR = 1.14, 95% CI: 1.07–1.23, P < 0.001; Q4: OR = 1.07, 95% CI: 1.01–1.16, P = 0.02), with similar patterns observed for C-peptide (Q3: OR = 1.13, 95% CI: 1.06–1.21, P < 0.001; Q4: OR = 1.08, 95% CI: 1.01–1.15, P = 0.03).

Table 2

Table 2. The risk for elevated AS according to serum UA and C-peptide concentrations.

RCS analysis based on logistic regression

RCS analyses revealed nonlinear relationships for both biomarkers (P for nonlinearity: UA, P = 0.034; C-peptide, P < 0.001), with UA showing risk escalation below 350 μmol/L (OR = 1 cutoff: 207 μmol/L) and C-peptide demonstrating analogous trends (OR = 1 cutoff: 0.580 μg/L, Figure 2).

Figure 2

Figure 2. Nonlinearity association between UA, C-peptide level and elevated AS. RCS model was used to assess “the overall” and the “nonlinearity” association between UA, C-peptide level and elevated AS odds. Adjustments were made for age, gender, SBP, WHR, AST, GGT, HDL-C, current drinking, current smoking, education and family history of diabetes. (A) nonlinear relationship between UA and elevated AS odds; (B) nonlinear relationship between C-peptide and elevated AS odds.

Threshold effects of UA and C-peptide for AS

The interval effects of fasting UA and C-peptide for elevated AS odds were evaluated using logistic regression analysis (Table 3). After adjusting for covariates, the increase of elevated AS odds in the subjects with UA ≥207 μmol/L had no statistical significance, compared with patients with UA <207 μmol/L. After adjusting for age, gender, SBP, WHR, AST, GGT, HDL-C, current drinking, current smoking, education, and family history of diabetes, the odds of elevated AS in the patients with C-peptide ≥0.580 μg/L relatively increased by about 87%, compared with that in the patients with C-peptide <0.580 μg/L (OR = 1.87, 95% CIs = 1.32, 2.65).

Table 3

Table 3. Threshold effects of UA and C-peptide for AS.

Discussion

In the present study, we found that although UA and C-peptide were independently associated with the odds of elevated AS in patients with T2DM, the probability of elevated AS did not increase linearly with rising serum UA and C-peptide levels. Based on RCS analysis, the risk of elevated AS was nonlinearly associated with UA and C-peptide levels after adjusting for potential confounders. Specifically, when C-peptide levels were ≥ 0.580 μg/L, patients with T2DM appeared to have a relatively higher probability of elevated AS.

Although diabetes has more than 100 complications, more than half of diabetes deaths are caused by CVD (5). In this study, we observed a nonlinear association between UA and AS. While the RCS model suggested a potential inflection point around 207 µmol/L, this threshold did not achieve statistical significance in binary analysis. This may reflect that the vascular effect of UA follows a continuous and gradual pattern rather than an abrupt change at a precise cutoff, indicating that clinical evaluation of UA should focus on its continuous trend rather than relying on a single threshold. In alignment with our findings, the positive association between UA and AS was reported in previous cross-sectional and cohort studies (18, 32). In the relationship between UA and AS, UA inhibits the production of nitric oxide, leading to endothelial dysfunction (33). In addition, hyperuricemia stimulates the renin-angiotensin system, leading to excessive production of reactive oxygen species (ROS) (34). ROS causes vascular oxidative stress and endothelial dysfunction and inhibits the bioavailability of nitric oxide, which is associated with the risk of AS (35). On the other hand, UA also acts as an important free radical scavenger with antioxidant properties (36), which may explain the nonlinear association observed in this study, specifically, the plateau in the relationship between UA and AS beyond the inflection point. This antioxidant mechanism is consistent with reports of UA exhibiting protective effects in certain populations, such as cancer patients (37). Therefore, while UA may contribute to the pathophysiology of AS, its predictive value for macrovascular complications in patients with T2DM should be interpreted cautiously in light of this level-dependent, nonlinear relationship.

Several investigations have explored the link between fasting C-peptide concentrations and cardiovascular outcomes, revealing that elevated C-peptide levels are associated with an increased risk of cardiovascular complications in individuals with T2DM (38–41). The present study is the first to investigate the association between C-peptide and AS in patients with T2DM. This focus holds significant clinical relevance, as previous research on C-peptide and diabetic complications has primarily centered on microvascular pathologies (42), with limited attention to macrovascular complications—particularly atherosclerosis, which remains the leading cause of mortality in diabetic populations. Our findings provide evidence into the relationship between C-peptide and macrovascular complications in T2DM. The results demonstrated a positive association between fasting C-peptide levels and elevated AS risk, with further analysis revealing a nonlinear relationship. In nondiabetic individuals, several studies reported a positive linear association between C-peptide and AS markers (43, 44). Although previous studies have indicated that C-peptide levels in T2DM patients exhibit nonlinear relationships with glycemic control rates and renal dysfunction (45, 46), no prior research has specifically elucidated the association between C-peptide levels and AS in patients with T2DM.

The observed nonlinear relationship suggests a complex biological mechanism underlying the role of C-peptide in AS. At lower concentrations, increased C-peptide may reflect residual β-cell function and exert protective anti-inflammatory and vasoprotective effects via activation of the AMP-activated protein kinase α (AMPKα) pathway, which could potentially slow AS progression (47, 48). However, beyond a certain threshold, elevated C-peptide may promote AS by enhancing inflammatory responses (49), stimulating vascular smooth muscle cell proliferation (50, 51), or exacerbating insulin resistance. This dual role aligns with the previously described “double-edged sword” effect of C-peptide in diabetic complications (52, 53). Therefore, normal concentrations of C-peptide may exert a protective effect, whereas excessive C-peptide delivery may not confer additional benefits and could even be harmful. In the present study, the mean fasting C-peptide level was 1.55 ng/ml, and the turning point was observed at 0.58 ng/ml among participants with T2DM. In comparison, previous studies have reported different turning points regarding the association between C-peptide and cardiovascular risk in nondiabetic adults and patients with newly diagnosed T2DM (40). This indicates that both the normal physiological range of fasting C-peptide and its turning point require further estimation in future research.

The highlights of the present study include the conduct of a cross-sectional analysis that clarified the nonlinear associations between fasting C-peptide, UA, and the prevalence of elevated AS in patients with T2DM. However, several limitations should be considered. First, the study enrolled participants from a single hospital, which may limit the generalizability of the findings. Second, due to the cross-sectional design, it was not possible to establish a temporal sequence between UA or C-peptide levels and elevated AS; thus, the evidence provided is not conclusive. Therefore, larger-scale cohort studies are warranted. Third, although antidiabetic medications were accounted for, the impact of specific medication types on UA and C-peptide levels and subsequent AS risk was not examined. Importantly, our analysis did not include several clinically relevant variables, such as the duration of diabetes and more detailed measures of long-term glycemic control. These unmeasured or residual factors may confound the observed associations and should be considered when interpreting the results. Overall, this exploratory study underscores the need for and lays the groundwork for a prospective confirmatory study using an appropriately sized random sample to further validate the nonlinear relationship between new cases of elevated AS and UA or C-peptide levels.

Conclusions

In summary, our findings indicate a nonlinear association of serum UA and C-peptide with elevated AS odds in patients with T2DM. When C-peptide ≥ 0.580 μg/L, patients with T2DM might have the relatively higher elevated AS odds. These findings highlight the potential relevance of fasting C-peptide and UA in assessing AS risk in this population. Further longitudinal studies are warranted to clarify their clinical utility and any possible implications for cardiovascular risk stratification.

Data availability statement

The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.

Ethics statement

The studies involving humans were approved by Medical Ethics Committee of The First People’s Hospital of Shunde. The studies were conducted in accordance with the local legislation and institutional requirements. The human samples used in this study were acquired from primarily isolated as part of your previous study for which ethical approval was obtained. Written informed consent for participation was not required from the participants or the participants’ legal guardians/next of kin in accordance with the national legislation and institutional requirements.

Author contributions

YH: Conceptualization, Investigation, Methodology, Writing – original draft, Writing – review & editing, Formal analysis, Project administration. YJ: Conceptualization, Data curation, Formal analysis, Methodology, Software, Visualization, Writing – original draft. MG: Methodology, Supervision, Validation, Writing – original draft. JJ: Investigation, Methodology, Supervision, Writing – original draft. KC: Data curation, Investigation, Resources, Writing – original draft. LZ: Data curation, Investigation, Resources, Writing – original draft. JS: Data curation, Investigation, Resources, Writing – original draft. BL: Data curation, Investigation, Resources, Writing – original draft. YL: Conceptualization, Data curation, Funding acquisition, Project administration, Resources, Supervision, Writing – review & editing.

Funding

The author(s) declared that financial support was received for this work and/or its publication. This work was supported by the Scientific Research Start Plan of Shunde Hospital, Southern Medical University (Grant code: CRSP2022010).

Conflict of interest

The author(s) declared that this work was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

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Supplementary material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fendo.2026.1700359/full#supplementary-material

Abbreviations

ALT, alanine aminotransferase; AS, arterial stiffness; AST, aspartate aminotransferase; baPWV, brachial-ankle pulse wave velocity; BMI, body mass index; CVD, cardiovascular disease; DBP, diastolic blood pressure; FPG, fasting plasma glucose; GGT, γ-glutamyl transpeptidase; HDL-C, high-density lipoprotein cholesterol; IDF, International Diabetes Federation; IQR, interquartile range; LDL-C, low-density lipoprotein cholesterol; UA, uric acid; TC, total cholesterol; TG, triglyceride; T2DM, type 2 diabetes mellitus; WHR, waist–hip ratio; SD, standard deviation; SBP, systolic blood pressure.

References

1. Sun H, Saeedi P, Karuranga S, Pinkepank M, Ogurtsova K, Duncan BB, et al. IDF Diabetes Atlas: Global, regional and country-level diabetes prevalence estimates for 2021 and projections for 2045. Diabetes Res Clin Pract. (2022) 183:109119. doi: 10.1016/j.diabres.2021.109119

PubMed Abstract | Crossref Full Text | Google Scholar

2. International Diabetes Federation. IDF Diabetes Atlas. Available online at: https://diabetesatlas.org/ (Accessed July 8, 2023).

Google Scholar

3. Fox CS, Coady SC, Sorlie PD, D'Agostino RBSR, Pencina MJ, Vasan RS, et al. Increasing cardiovascular disease burden due to diabetes mellitus: the Framingham Heart Study. Circulation. (2007) 115:1544–50. doi: 10.1161/circulationaha.106.658948

PubMed Abstract | Crossref Full Text | Google Scholar

4. Zhao D, Liu J, Wang M, Zhang X, and Zhou M. Epidemiology of cardiovascular disease in China: current features and implications. Nat Rev Cardiol. (2019) 16:203–12. doi: 10.1038/s41569-018-0119-4

PubMed Abstract | Crossref Full Text | Google Scholar

5. Rhee SY and Kim YS. Peripheral arterial disease in patients with type 2 diabetes mellitus. Diabetes Metab J. (2015) 39:283–90. doi: 10.4093/dmj.2015.39.4.283

PubMed Abstract | Crossref Full Text | Google Scholar

6. Tian X, Zuo Y, Chen S, Zhang Y, Zhang X, Xu Q, et al. Hypertension, arterial stiffness, and diabetes: a prospective cohort study. Hypertension. (2022) 79:1487–96. doi: 10.1161/hypertensionaha.122.19256

PubMed Abstract | Crossref Full Text | Google Scholar

7. Smulyan H, Lieber A, and Safar ME. Hypertension, diabetes type II, and their association: role of arterial stiffness. Am J Hypertens. (2016) 29:5–13. doi: 10.1093/ajh/hpv107

PubMed Abstract | Crossref Full Text | Google Scholar

8. Urbina EM, Isom S, Dabelea D, D'Agostino R, Daniels SR, Dolan LM, et al. Association of elevated arterial stiffness with cardiac target organ damage and cardiac autonomic neuropathy in young adults with diabetes: the SEARCH for diabetes in youth study. Diabetes Care. (2023) 46:786–93. doi: 10.2337/dc22-1703

PubMed Abstract | Crossref Full Text | Google Scholar

9. Shah AS, Gidding SS, El Ghormli L, Tryggestad JB, Nadeau KJ, Bacha F, et al. Relationship between arterial stiffness and subsequent cardiac structure and function in young adults with youth-onset type 2 diabetes: results from the TODAY study. J Am Soc Echocardiogr. (2022) 35:620–628.e4. doi: 10.1016/j.echo.2022.02.001

PubMed Abstract | Crossref Full Text | Google Scholar

10. Staef M, Ott C, Kannenkeril D, Striepe K, Schiffer M, Schmieder RE, et al. Determinants of arterial stiffness in patients with type 2 diabetes mellitus: a cross sectional analysis. Sci Rep. (2023) 13:8944. doi: 10.1038/s41598-023-35589-4

PubMed Abstract | Crossref Full Text | Google Scholar

11. Yang S, Ding Y, Yu C, Guo Y, Pang Y, Sun D, et al. WHO cardiovascular disease risk prediction model performance in 10 regions, China. Bull World Health Organ. (2023) 101:238–47. doi: 10.2471/blt.22.288645

PubMed Abstract | Crossref Full Text | Google Scholar

12. Lim DH, Lee Y, Park GM, Choi SW, Kim YG, Lee SW, et al. Serum uric acid level and subclinical coronary atherosclerosis in asymptomatic individuals: An observational cohort study. Atherosclerosis. (2019) 288:112–7. doi: 10.1016/j.atherosclerosis.2019.07.017

PubMed Abstract | Crossref Full Text | Google Scholar

13. Yu W, Xie D, Yamamoto T, Koyama H, and Cheng J. Mechanistic insights of soluble uric acid-induced insulin resistance: Insulin signaling and beyond. Rev Endocr Metab Disord. (2023) 24:327–43. doi: 10.1007/s11154-023-09787-4

PubMed Abstract | Crossref Full Text | Google Scholar

14. Rehman K and Akash MSH. Mechanism of generation of oxidative stress and pathophysiology of type 2 diabetes mellitus: how are they interlinked? J Cell Biochem. (2017) 118:3577–85. doi: 10.1002/jcb.26097

PubMed Abstract | Crossref Full Text | Google Scholar

15. Rehman K and Akash MS. Mechanisms of inflammatory responses and development of insulin resistance: how are they interlinked? J BioMed Sci. (2016) 23:87. doi: 10.1186/s12929-016-0303-y

PubMed Abstract | Crossref Full Text | Google Scholar

16. Lippi G, Montagnana M, Luca Salvagno G, Targher G, and Cesare Guidi G. Epidemiological association between uric acid concentration in plasma, lipoprotein(a), and the traditional lipid profile. Clin Cardiol. (2010) 33:E76–80. doi: 10.1002/clc.20511

PubMed Abstract | Crossref Full Text | Google Scholar

17. Rehman K, Khan II, Akash MSH, Jabeen K, and Haider K. Naringenin downregulates inflammation-mediated nitric oxide overproduction and potentiates endogenous antioxidant status during hyperglycemia. J Food Biochem. (2020) 44:e13422. doi: 10.1111/jfbc.13422

PubMed Abstract | Crossref Full Text | Google Scholar

18. Bae JS, Shin DH, Park PS, Choi BY, Kim MK, Shin MH, et al. The impact of serum uric acid level on arterial stiffness and carotid atherosclerosis: the Korean Multi-Rural Communities Cohort study. Atherosclerosis. (2013) 231:145–51. doi: 10.1016/j.atherosclerosis.2013.08.017

PubMed Abstract | Crossref Full Text | Google Scholar

19. Hills CE and Brunskill NJ. Cellular and physiological effects of C-peptide. Clin Sci (Lond). (2009) 116:565–74. doi: 10.1042/cs20080441

PubMed Abstract | Crossref Full Text | Google Scholar

20. Effects of metabolic factors, race-ethnicity, and sex on the development of nephropathy in adolescents and young adults with type 2 diabetes: results from the TODAY study. Diabetes Care. (2021) 45:1056–64. doi: 10.2337/dc21-1085

PubMed Abstract | Crossref Full Text | Google Scholar

21. Miya A, Nakamura A, Suzuki Y, Nomoto H, Kameda H, Cho KY, et al. Inverse association between glucose variability and body fat in type 2 diabetes with impaired endogenous insulin secretion assessed using continuous glucose monitoring: A prospective observational study. Diabetes Obes Metab. (2023) 25:1883–9. doi: 10.1111/dom.15049

PubMed Abstract | Crossref Full Text | Google Scholar

22. Hills CE, Brunskill NJ, and Squires PE. C-peptide as a therapeutic tool in diabetic nephropathy. Am J Nephrol. (2010) 31:389–97. doi: 10.1159/000289864

PubMed Abstract | Crossref Full Text | Google Scholar

23. Rehman K, Munawar SM, Akash MSH, Buabeid MA, Chohan TA, Tariq M, et al. Hesperidin improves insulin resistance via down-regulation of inflammatory responses: Biochemical analysis and in silico validation. PloS One. (2020) 15:e0227637. doi: 10.1371/journal.pone.0227637

PubMed Abstract | Crossref Full Text | Google Scholar

24. Wahren J, Shafqat J, Johansson J, Chibalin A, Ekberg K, and Jörnvall H. Molecular and cellular effects of C-peptide–new perspectives on an old peptide. Exp Diabesity Res. (2004) 5:15–23. doi: 10.1080/15438600490424479

PubMed Abstract | Crossref Full Text | Google Scholar

25. Koska J, Nuyujukian DS, Bahn GD, Zhou JJ, and Reaven PD. Association of low fasting C-peptide levels with cardiovascular risk, visit-to-visit glucose variation and severe hypoglycemia in the Veterans Affairs Diabetes Trial (VADT). Cardiovasc Diabetol. (2021) 20:232. doi: 10.1186/s12933-021-01418-z

PubMed Abstract | Crossref Full Text | Google Scholar

26. Pikkemaat M, Andersson T, Melander O, Chalmers J, Rådholm K, and Bengtsson Boström K. C-peptide predicts all-cause and cardiovascular death in a cohort of individuals with newly diagnosed type 2 diabetes. The Skaraborg diabetes register. Diabetes Res Clin Pract. (2019) 150:174–83. doi: 10.1016/j.diabres.2019.03.014

PubMed Abstract | Crossref Full Text | Google Scholar

27. Elizalde-Barrera CI, Estrada-García T, Lozano-Nuevo JJ, Garro-Almendaro AK, López-Saucedo C, and Rubio-Guerra AF. Serum uric acid levels are associated with homeostasis model assessment in obese nondiabetic patients: HOMA and uric acid. Ther Adv Endocrinol Metab. (2017) 8:141–6. doi: 10.1177/2042018817732731

PubMed Abstract | Crossref Full Text | Google Scholar

28. Wang S, Wei Y, Hidru TH, Li D, Wang N, Yang Y, et al. Combined effect of homocysteine and uric acid to identify patients with high risk for subclinical atrial fibrillation. J Am Heart Assoc. (2022) 11:e021997. doi: 10.1161/jaha.121.021997

PubMed Abstract | Crossref Full Text | Google Scholar

29. Rehman K, Saeed K, Munawar SM, and Akash MSH. Resveratrol regulates hyperglycemia-induced modulations in experimental diabetic animal model. BioMed Pharmacother. (2018) 102:140–6. doi: 10.1016/j.biopha.2018.03.050

PubMed Abstract | Crossref Full Text | Google Scholar

30. Choi HK and Ford ES. Haemoglobin A1c, fasting glucose, serum C-peptide and insulin resistance in relation to serum uric acid levels–the Third National Health and Nutrition Examination Survey. Rheumatol (Oxford). (2008) 47:713–7. doi: 10.1093/rheumatology/ken066

PubMed Abstract | Crossref Full Text | Google Scholar

31. Ohkuma T, Ninomiya T, Tomiyama H, Kario K, Hoshide S, Kita Y, et al. Brachial-ankle pulse wave velocity and the risk prediction of cardiovascular disease: an individual participant data meta-analysis. Hypertension. (2017) 69:1045–52. doi: 10.1161/hypertensionaha.117.09097

PubMed Abstract | Crossref Full Text | Google Scholar

32. Hwang J, Hwang JH, Chung SM, Kwon MJ, and Ahn JK. Association between serum uric acid and arterial stiffness in a low-risk, middle-aged, large Korean population: A cross-sectional study. Med (Baltimore). (2018) 97:e12086. doi: 10.1097/md.0000000000012086

PubMed Abstract | Crossref Full Text | Google Scholar

33. Jayachandran M and Qu S. Harnessing hyperuricemia to atherosclerosis and understanding its mechanistic dependence. Med Res Rev. (2021) 41:616–29. doi: 10.1002/med.21742

PubMed Abstract | Crossref Full Text | Google Scholar

34. Ponticelli C, Podestà MA, and Moroni G. Hyperuricemia as a trigger of immune response in hypertension and chronic kidney disease. Kidney Int. (2020) 98:1149–59. doi: 10.1016/j.kint.2020.05.056

PubMed Abstract | Crossref Full Text | Google Scholar

35. Puddu P, Puddu GM, Cravero E, Vizioli L, and Muscari A. Relationships among hyperuricemia, endothelial dysfunction and cardiovascular disease: molecular mechanisms and clinical implications. J Cardiol. (2012) 59:235–42. doi: 10.1016/j.jjcc.2012.01.013

PubMed Abstract | Crossref Full Text | Google Scholar

36. Beberashvili I, Sinuani I, Azar A, Shapiro G, Feldman L, Stav K, et al. Serum uric acid as a clinically useful nutritional marker and predictor of outcome in maintenance hemodialysis patients. Nutrition. (2015) 31:138–47. doi: 10.1016/j.nut.2014.06.012

PubMed Abstract | Crossref Full Text | Google Scholar

37. Antelo-Pais P, Prieto-Díaz M, Micó-Pérez RM, Pallarés-Carratalá V, Velilla-Zancada S, Polo-García J, et al. Prevalence of hyperuricemia and its association with cardiovascular risk factors and subclinical target organ damage. J Clin Med. (2022) 12:50. doi: 10.3390/jcm12010050

PubMed Abstract | Crossref Full Text | Google Scholar

38. Kim ST, Kim BJ, Lim DM, Song IG, Jung JH, Lee KW, et al. Basal C-peptide level as a surrogate marker of subclinical atherosclerosis in type 2 diabetic patients. Diabetes Metab J. (2011) 35:41–9. doi: 10.4093/dmj.2011.35.1.41

PubMed Abstract | Crossref Full Text | Google Scholar

39. Wang L, Lin P, Ma A, Zheng H, Wang K, Li W, et al. C-peptide is independently associated with an increased risk of coronary artery disease in T2DM subjects: A cross-sectional study. PloS One. (2015) 10:e0127112. doi: 10.1371/journal.pone.0127112

PubMed Abstract | Crossref Full Text | Google Scholar

40. Yan ST, Sun J, Gu ZY, Miao XY, Ma LC, Sun BR, et al. The bidirectional association of C-peptide with cardiovascular risk in nondiabetic adults and patients with newly diagnosed type 2 diabetes mellitus: a retrospective cohort study. Cardiovasc Diabetol. (2022) 21:201. doi: 10.1186/s12933-022-01636-z

PubMed Abstract | Crossref Full Text | Google Scholar

41. Wahab MA, Alhabibi AM, Sakr AK, Zakaria MY, Saleh OI, Ahmad IH, et al. The correlation between C-peptide and severity of peripheral atherosclerosis in type 2 diabetes mellitus. Diabetes Metab Syndr Obes. (2023) 16:2617–25. doi: 10.2147/dmso.S426956

PubMed Abstract | Crossref Full Text | Google Scholar

42. Kim BY, Jung CH, Mok JO, Kang SK, and Kim CH. Association between serum C-peptide levels and chronic microvascular complications in Korean type 2 diabetic patients. Acta Diabetol. (2012) 49:9–15. doi: 10.1007/s00592-010-0249-6

PubMed Abstract | Crossref Full Text | Google Scholar

43. Sun Y, Zhu Y, Zhang L, Lu Y, Liu Y, Zhang Y, et al. Relationship between insulin secretion and arterial stiffness in essential hypertension. Int J Hypertens. (2021) 2021:5015797. doi: 10.1155/2021/5015797

PubMed Abstract | Crossref Full Text | Google Scholar

44. Tam WH, Ma RC, Yip GW, Yang X, Li AM, Ko GT, et al. The association between in utero hyperinsulinemia and adolescent arterial stiffness. Diabetes Res Clin Pract. (2012) 95:169–75. doi: 10.1016/j.diabres.2011.10.017

PubMed Abstract | Crossref Full Text | Google Scholar

45. Huang Y, Wang Y, Liu C, Zhou Y, Wang X, Cheng B, et al. C-peptide, glycaemic control, and diabetic complications in type 2 diabetes mellitus: A real-world study. Diabetes Metab Res Rev. (2022) 38:e3514. doi: 10.1002/dmrr.3514

PubMed Abstract | Crossref Full Text | Google Scholar

46. Chen L, Hu Y, Ma Y, and Wang H. Non-linear association of fasting C-peptide and uric acid levels with renal dysfunction based on restricted cubic spline in patients with type 2 diabetes: A real-world study. Front Endocrinol (Lausanne). (2023) 14:1157123. doi: 10.3389/fendo.2023.1157123

PubMed Abstract | Crossref Full Text | Google Scholar

47. Haidet J, Cifarelli V, Trucco M, and Luppi P. Anti-inflammatory properties of C-peptide. Rev Diabetes Stud. (2009) 6:168–79. doi: 10.1900/rds.2009.6.168

PubMed Abstract | Crossref Full Text | Google Scholar

48. Bhatt MP, Lim YC, Kim YM, and Ha KS. C-peptide activates AMPKα and prevents ROS-mediated mitochondrial fission and endothelial apoptosis in diabetes. Diabetes. (2013) 62:3851–62. doi: 10.2337/db13-0039

PubMed Abstract | Crossref Full Text | Google Scholar

49. Marx N. C-peptide as a mediator of lesion development in early diabetes–a novel hypothesis. Trends Cardiovasc Med. (2008) 18:67–71. doi: 10.1016/j.tcm.2007.12.005

PubMed Abstract | Crossref Full Text | Google Scholar

50. Walcher D, Babiak C, Poletek P, Rosenkranz S, Bach H, Betz S, et al. C-Peptide induces vascular smooth muscle cell proliferation: involvement of SRC-kinase, phosphatidylinositol 3-kinase, and extracellular signal-regulated kinase 1/2. Circ Res. (2006) 99:1181–7. doi: 10.1161/01.Res.0000251231.16993.88

PubMed Abstract | Crossref Full Text | Google Scholar

51. Walcher D and Marx N. Advanced glycation end products and C-peptide-modulators in diabetic vasculopathy and atherogenesis. Semin Immunopathol. (2009) 31:103–11. doi: 10.1007/s00281-009-0144-9

PubMed Abstract | Crossref Full Text | Google Scholar

52. Chen J, Huang Y, Liu C, Chi J, Wang Y, and Xu L. The role of C-peptide in diabetes and its complications: an updated review. Front Endocrinol (Lausanne). (2023) 14:1256093. doi: 10.3389/fendo.2023.1256093

PubMed Abstract | Crossref Full Text | Google Scholar

53. Alves MT, Ortiz MMO, Dos Reis G, Dusse LMS, Carvalho MDG, Fernandes AP, et al. The dual effect of C-peptide on cellular activation and atherosclerosis: Protective or not? Diabetes Metab Res Rev. (2019) 35:e3071. doi: 10.1002/dmrr.3071

PubMed Abstract | Crossref Full Text | Google Scholar