Correlation between serum uric acid and diabetic peripheral neuropathy in T2DM patients

Accepted Manuscript Correlation between serum uric acid and diabetic peripheral neuropathy in T2DM patients

Xiaopu Lin, Lingling Xu, Deqiang Zhao, Zhiyin Luo, Suyue Pan PII: DOI: Reference:

S0022-510X(17)34475-1 doi:10.1016/j.jns.2017.11.034 JNS 15680

To appear in:

Journal of the Neurological Sciences

Received date: Revised date: Accepted date:

16 August 2017 5 November 2017 26 November 2017

Please cite this article as: Xiaopu Lin, Lingling Xu, Deqiang Zhao, Zhiyin Luo, Suyue Pan , Correlation between serum uric acid and diabetic peripheral neuropathy in T2DM patients. The address for the corresponding author was captured as affiliation for all authors. Please check if appropriate. Jns(2017), doi:10.1016/j.jns.2017.11.034

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ACCEPTED MANUSCRIPT Correlation between serum uric acid and diabetic peripheral neuropathy in T2DM patients Xiaopu Lin 1 †, Lingling Xu2 †, Deqiang Zhao1 , Zhiyin Luo1 , Suyue Pan3* Department of Huiqiao Building, Nanfang Hospital, Southern Medical University,

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2

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Guangzhou, Guangdong, 510515, China

Department of Endocrinology, Nanfang Hospital, Southern Medical University,

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Guangzhou, Guangdong, 510515, China

Department of Neurology, Nanfang Hospital, Southern Medical University,

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Guangzhou, Guangdong, 510515, China

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†Those authors contributed equally to this work.

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Suyue Pan

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*Corresponding author

Department of Neurology, Nanfang Hospital, Southern Medical University, Guangzhou, Guangdong, 510515, China

E-mail: [email protected]

ACCEPTED MANUSCRIPT

Abstract Aim: To investigate the correlation between serum uric acid (SUA) and diabetic

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peripheral neuropathy (DPN) in type 2 diabetes mellitus (T2DM) patients.

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Methods: Two hundred T2DM patients were divided into four groups at the cut-off points of 5, 7, and 9 mg/dL of SUA levels. Nerve conduction studies (NCS), Semmes-Weinstein monofilament testing (SWMT), and vibration perception

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threshold (VPT) tests were performed on these patients.

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Results: Significant differences in motor/sensory nerve amplitude and conduction velocity (CV) parameters among different SUA level groups were observed (all

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P<0.05). SUA levels were negatively correlated with the means of motor/sensory nerve amplitude and CV (all P<0.05). Duration of T2DM >10 years, SUA >9 mg/dL

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and total cholesterol (TC) >5.2 mmol/L were found to be significantly associated with

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DPN (all P<0.05). Receiver-operating characteristic (ROC) analysis revealed that the

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cut-off points of T2DM duration combined with SUA and TC were 9 years, 7.8 mg/dL, and 4.97 mmol/L, respectively (AUC=0.65; 95% CI: 0.53–0.77; sensitivity, 70.6%; specificity, 65.2%, P=0.009).

Conclusion: There is a significant association between elevated SUA levels and DPN, and T2DM duration, SUA, and TC may be valuable indicators to predict the occurrence of DPN in T2DM patients.

ACCEPTED MANUSCRIPT Highlights 

Serum uric acid (SUA) might be closely associated with the occurrence of diabetic peripheral neuropathy (DPN). Type 2 diabetes mellitus (T2DM) duration, SUA, and total

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cholesterol (TC) might be useful indicators to predict DPN.

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Keywords

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Type 2 diabetes mellitus; Diabetic peripheral neuropathy; Serum uric acid

ACCEPTED MANUSCRIPT 1. Introduction

Diabetic peripheral neuropathy (DPN) is one of the most common long-term complications of type 2 diabetes mellitus (T2DM) and plays a major role in the development of foot ulcers, which seriously affects the quality of life leading to

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significant disability and mortality among patients[1,2]. Since there is no known treatment that can arrest, prevent, or slow the development of DPN[3,4], it is necessary to define, identify, and control the modifiable risk factors that contribute to

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DPN. Early screening of risk factors can provide us with avenues for the developme nt of new therapies for DPN.

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The factors involved in the pathogenesis of DPN have not been understood

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completely, and many hypotheses have been proposed but it is commonly accepted to be a multifactorial process[5,6]. According to previous studies, the occurrence of

smoking,

excessive

drinking,

obesity,

and

genetic

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hypertension,

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DPN depends on various factors, such as duration of diabetes, dyslipidemia,

polymorphisms[7-11]. So far, research specifically aimed at the relationship between

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serum uric acid (SUA) and DPN is very limited. Vascular complications and microalbuminuria were reported in T2DM patients with elevated SUA levels[12], following which, another case control study was able to identify an association between SUA and DPN by noting higher SUA levels in patients with DPN than those without DPN[13].

The present study was designed to further explore the potential association between

ACCEPTED MANUSCRIPT SUA and DPN, and to elucidate whether SUA and other factors may predict DPN progression.

2. Methods

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2.1. Study design

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The present study was an open-label, controlled, cross-sectional study in T2DM patients to explore the correlation between SUA and DPN. The study was approved by the Ethics Committee of Nanfang Hospital, Southern Medical University. All the

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patients signed the informed consent. The study was registered at the Chinese Clinical

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Trials Register (ChiCTR-ROC-17010380).

2.2. Subjects

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This study enrolled 200 T2DM inpatients aged 55.89±12.62 years old (123 males and

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77 females) presenting at the Department of Endocrinology of Nanfang Hospital,

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Southern Medical University during January 2016 to November 2016. T2DM was diagnosed based on the 1999 World Health Organization criteria[14]. All participants were screened for DPN. Patients were stratified into four groups according to SUA levels (≤5 mg/dl, 5.1-7 mg/dl, 7.1-9 mg/dl, and >9 mg/dl).

Exclusion criteria were as follows: age <17 or >75 years, other causes of peripheral nerve lesions, alcohol abuse, vitamin B12 deficiency, peripheral arterial disease, malignant tumor, limb trauma, chronic rheumatic disease, acute or chronic infections

ACCEPTED MANUSCRIPT or inflammation, history of hepatic dysfunction (transaminase >2-fold higher), history of renal dysfunction (serum creatinine >2.0 mg/dl or eGFR <30 ml.min-1 /1.73 m-2 ), acute attack of gouty arthritis, and the use of any medication that might influence SUA (benzbromarone, allopurinol, febuxostat, diuretics, losartan, fenofibrate,

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cyclosporin, salic ylates, nicotinic acid, estrogens, ethambutol or pyrazinamide) within one month.

2.3. Measurements

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Blood samples were collected after an overnight fast for at least 8 h.

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Serum uric acid (SUA), total cholesterol (TC), triglycerides (TG), high-density lipoprotein (HDL) and low-density lipoprotein (LDL) were measured on a

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BECKMAN AU5431 biochemistry autoanalyzer (Beckman Coulter, CA, USA). Glycated hemoglobin (HbA1c) was estimated using automated high-performance

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liquid chromatography analyzer (Tosoh Corporation, Japan).

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Electrophysiological assessments were performed using Viking Quest (Nicolet

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VIASYS Healthcare, USA). The sensory and motor nerves of the right upper and both lower limbs were tested. The compound muscle action potential (CMAP) amplitude and conduction velocity (CV) of motor nerve for the median, ulnar, posterior tibial, and peroneal nerves were measured and recorded. The measurements of sensory nerve action potential (SNAP) amplitude and CV were performed using the median, ulnar, and sural nerves. The mean of motor nerve amplitude was calculated using the formula: Amplitude

motor nerve=

(Amplitude

median nerve M +

Amplitude

ulnar nerve M+

ACCEPTED MANUSCRIPT Amplitude

posterior tibial nerve M +

Amplitude

peroneal nerve M )/4.

The mean of motor nerve CV,

sensory nerve amplitude, and sensory nerve CV were calculated respectively using the same method. All data obtained were compared with reference values from our laboratory.

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The peripheral neuropathy was also assessed using Semmes-Weinstein monofilament testing (SWMT) and vibration perception threshold (VPT) test. SWMT was conducted by pressing the monofilaments at five sites (great toe, fourth toe, and three

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sites in the metatarsal area) on the plantar surface of both feet, randomly avoiding heavily callused areas. The tests were repeated thrice, and the results were recorded as

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an insensate at a particular site if the patient answered incorrectly two or more times. The SWMT abnormality was defined as at least two of five test sites being insensate

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on both feet[15].

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Vibration perception threshold was tested using a biothesiometer (Bio-Medical

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Instrument Co, OH, USA) in a standardized manner[16]. The test was repeated thrice, and the mean voltage was calculated. The higher mean value of VPT in either limb

[17].

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was used for analysis. The VPT value higher than 25 mV was defined as abnormal

In the present study, DPN was defined as the presence of one or more abnormal nerve conduction results (amplitude or CV) in at least two different peripheral nerves combined with an abnormality in SWMT or VPT test[18].

2.4. Statistical analysis

ACCEPTED MANUSCRIPT The IBM SPSS Statistics 2010 (V.19.0, IBM Corp., USA) was used for data analysis. Results were expressed as the mean with standard deviation (SD) for normally distributed data and median with interquartile range (IQR) for non- normally distributed data. Differences among the groups were analyzed by ANOVA test for

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normally distributed values and by the Kruskal–Wallis test for nonparametric values.

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Pearson's χ2 test was employed to analyze the categorical data. Spearman’s correlation analysis was performed to investigate the relationship between the SUA and nerve conduction (the mean of motor/sensory nerve amplitude/CV). Multivariate logistic

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regression was performed to determine the correlation between the clinical parameter s

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and DPN. Furthermore, receiver-operating characteristic (ROC) analysis was conducted to identify the optimal cut-off point of duration combined with SUA and

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TC for indicating DPN. All P values reported were two-tailed, and P<0.05 was

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3. Results

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considered statistically significant while P<0.001 was highly significant.

3.1. Baseline characteristics

Seventy seven (38.5%) patients were clinically diagnosed with DPN. Patients were grouped according to whether DPN was present (n=77) or absent (n=123) (Table1). There were significant differences in age (P<0.001), duration (P<0.001), SBP (P<0.05), SUA (P<0.05), TC (P<0.05), and HbA1c (P<0.05) between the DPN and the Non-DPN groups.

ACCEPTED MANUSCRIPT Also, the patients were grouped based on SUA levels. The clinical characteristics of each group are summarized in Table 2. Duration (P<0.05), BMI (P<0.05), TG (P<0.001), and HbA1c (P<0.05) increased with the rise in SUA levels.

3.2. Nerve conduction studies (NCS) parameters in T2DM patients in different

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SUA level groups

Comparisons of NCS parameters in different SUA level groups are given in Table 3. Patients with higher SUA levels showed significant impairment in the NCS

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parameters including the mean of motor nerve amplitude (P<0.05), motor nerve CV

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(P<0.05), sensory nerve amplitude (P<0.001) and sensory nerve CV (P<0.05).

3.3. Correlation analysis between SUA and NCS parameters in T2DM patients

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We performed Spearman’s correlation analysis between the SUA level and the mean

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amplitude/CV levels of motor/sensory nerves resp ectively, and the results showed that the SUA levels were negatively correlated to the means of motor nerve amplitude

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(r=-0.142, P<0.05), motor nerve CV (r=-0.191, P<0.05), sensory nerve amplitude

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(r=-0.194, P<0.05) and sensory nerve CV (r=-0.191, P<0.05) (Table 4).

3.4. Risk factors of DPN

For logistic regression, we transformed the continuous variables into grade variables according to the intervals as below: age (≤60 years, >60 years), duration of diabetes (≤10 years, >10 years), SBP (≤140 mmHg, 140-160 mmHg, >160 mmHg), DBP (≤90 mmHg, 90-100 mmHg, >100 mmHg), BMI (≤24, 24-28, >28), HbA1c (≤6%, >6%),

ACCEPTED MANUSCRIPT SUA (≤5 mg/dl, 5.1-7 mg/dl, 7.1-9 mg/dl, >9 mg/dl), TG (≤1.7 mmol/L, >1.7 mmol/L), TC (≤5.2 mmol/L, >5.2 mmol/L), LDL (≤3.3 mmol/L, >3.3 mmol/L) and HDL (≤1.03 mmol/L, >1.03 mmol/L).

To investigate the risk factors of DPN, we performed multivariate logistic regression

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analysis with the clinical parameters including age, sex, smok ing history, duration of diabetes, SBP, DBP, BMI, HbA1c, SUA, TG, TC, LDL, and HDL which had been transformed into qualitative variables as independent variables.

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As shown in Table 4, the duration of >10 years (OR 3.26, 95% CI 1.41-7.56, P<0.05) was found to be significantly associated with DPN. Furthermore, SUA levels higher

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than 9 mg/dl showed a 7- fold (OR 7.98, 95% CI 1.47-43.40, P<0.05) increased risk

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for DPN than the lower levels (≤5 mg/dl). A higher TC also showed a positive association with DPN (OR 2.14, 95% CI 1.11-4.11, P<0.05).

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ROC analysis was performed to identify the optimal cut-off point of T2DM duration,

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SUA, and TC for predicting DPN. The result revealed that the area under the curve

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(AUC) of the SUA or TC value was not statistically significant (data not shown), which indicated that SUA or TC could not be considered as sole independent factors. Therefore, we performed ROC analysis to investigate the optimal cut-off points of T2DM duration combined with SUA and TC value to indicate DPN.

Furthermore, we repeated the multivariate logistic regression analysis with DPN as a dependent variable, whereas the T2DM duration, SUA, and TC value were independent variables. Using this approach, we obtained the predicted probability of

ACCEPTED MANUSCRIPT the combined parameter of T2DM duration, the SUA and TC values, which was used for the multivariable ROC analysis as an independent value.

The cut-off points of T2DM duration combined SUA and TC values were revealed as :

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sensitivity, 70.6%; specificity, 65.2%, P<0.05) (Figure.1).

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9 years, 7.8 mg/dl, and 4.97 mmol/L, respectively (AUC=0.65; 95% CI: 0.53–0.77;

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4. Discussion

Early detection of DPN is very important because it increases opportunities for both

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physicians and patients to benefit from better glycemic control and to initiate strategies to prevent its complications[6,19-21]. Therefore, identifying and controlling

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the risk factors to prevent and slow the process of DPN also becomes significant.

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Our present study showed that there were obvious higher SUA levels in T2DM

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patients with peripheral neuropathy. In fact, this phenomenon has been reported in several studies. A recent study showed that elevated SUA was significantly correlated

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with the prevalence of DPN[13]. A meta-analysis, which included 12 studies involving a total of 1388 T2DM patients with DPN and 4746 patients without DPN, showed that hyperuricemia was significantly associated with increased r isk of DPN, and patients with DPN had obvious increased SUA levels [22]. However, our study specially aimed at exploring the relationship between SUA and DPN, where, DPN was defined as the abnormality of nerve conduction combined with an abnormality in the SWMT or the VPT test. We found that uric acid might be involved in the development

ACCEPTED MANUSCRIPT of DPN in T2DM patients.

Nerve conduction studies are considered as the gold standard of diabetic neuropathy[23]. In the present study, the results showed both the amplitude and CV of sensory/motor nerves were decreased with the increasing SUA levels. Furthermore,

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there were negative correlations between SUA and NCSs (the amplitude and CV of sensory/motor nerves). Di lorio et al. found that diabetes, interleukin-6 (IL-6), plasma level of α-tocopherol, and uric acid were significant independent predictors of nerve

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conduction velocity (NCV) in the peripheral nervous system during the aging process[24]. In addition, the effect of SUA on central nervous system diseases has

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been widely discussed. Some previous studies showed hyperuricemia was positively associated with cerebral white matter lesions [25] and stroke[26,27]. To some extent,

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these studies demonstrated that high levels of SUA might play an important role in

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nerve impairment.

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Although the mechanism of the development of DPN has not yet been fully elucidated, previous studies

have shown

that

inflammatory reaction[28-30],

oxidative

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stress[31-33], and endothelial dysfunction[34,35] may contribute to the development of DPN. Uric acid is widely recognized as a pro-oxidative and an inflammatory agent [36]. In addition, uric acid may contribute to endothelial dysfunction. Khosla et al. reported that soluble uric acid impaired nitric oxide generation in cultured endothelial cells[37]. These factors further indicate that SUA appears to play an important role in the development of DPN also.

ACCEPTED MANUSCRIPT In the present study, the duration of T2DM and levels of TC were higher in the DPN group than in the Non-DPN group. Except for SUA, the risk factors of DPN also included the duration of T2DM and TC according to the results of logistic regression. The duration of diabetes is a well-established risk factor for neuropathy[38-40], which

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was consistent with the result of our study. In addition, several large-scale trials[41-43]

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have suggested that dyslipidemia is a major independent risk factor for the development of diabetic neuropathy. In the European Diabetes Prospective Complications (EURODIAB) study[8], TC, LDL cholesterol, and TG levels were

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significantly associated with incident DPN over a 7.3 year follow-up, even after

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adjustment for baseline HbA1c and diabetes duration. Smith AG et al. showed that both low HDL and high LDL cholesterol levels were associated with the development

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of neuropathy[42]. Many previous studies have investigated the mechanisms linking lipids to diabetic neuropathy. A recent study[34] showed both elevated cholesterol

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and obesity may be particularly instrumental in inciting perip heral nervous system

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damage, particularly in myelinated large fibers, which are consistent with the findings

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of the present study that there exists a close association between dyslipidemia and the occurrence of DPN.

Conclusion Based on the results in the present study, we speculate that T2DM duration, SUA, and TC might be useful indicators to predict the prevalence of DPN. Further, T2DM duration of >9 years with an SUA of >7.8 mg/dl, and a TC of > 4.97 mmol/L carry a higher risk of DPN. Therefore, controlling the levels of SUA and TC might be

ACCEPTED MANUSCRIPT beneficial for delaying the development of DPN.

Acknowledgement

This study was funded by the National Natural Science Foundation of China (no.

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81500656), Natural Science Fund of Guangdong Province (no.2016A030313521),

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President Foundation of Nanfang Hospital, Southern Medical University (no. 2014C017)

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Conflict of Interest

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The authors declared that they have no conflict of interest.

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[43]

ACCEPTED MANUSCRIPT Figure legends

Figure 1. Receiver-operating characteristic (ROC) analysis of the duration of diabetes combined SUA and TC to predict DPN in T2DM patients (AUC=0.65; 95% CI: 0.53–

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0.77; sensitivity, 70.6%; specificity, 65.2%, P<0.05)

ACCEPTED MANUSCRIPT Table 1. Clinical characteristic of type 2 diabetes mellitus (T2DM) patients with diabetic peripheral neuropathy (DPN) versus without DPN (Non-DPN) Non-DPN (n=123)

Age (years)

60.79±13.40

52.83±11.10

<0.001**

Duration (years)

11.23±7.92

6.90±5.64

<0.001**

Smoking (%)

32.47%

39.02%

BMI (kg/m2 )

24.77±5.17

25.01±4.04

>0.05

SBP (mmHg)

143.12±22.72

135.52±17.84

<0.05*

DBP (mmHg)

79.33±10.90

81.12±11.02

>0.05

SUA (mg/dL)

6.80±1.78

TG (mmol/L)

2.32±2.13

TC (mmol/L)

5.30±1.52

HDL (mmol/L)

1.03±0.30

LDL (mmol/L)

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>0.05

<0.05*

1.97±1.33

>0.05

4.90±1.09

<0.05*

1.05±0.28

>0.05

3.39±1.28

3.19±0.94

>0.05

8.42±2.00

<0.05*

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6.06±1.39

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HbA1C (%)

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DPN (n=77)

9.23±2.14

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Values are expressed as mean ± SD for normally distributed data and median with

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interquartile range for non- normally distributed data, or n (%). Differences among the groups were analyzed by ANOVA for normally distributed values and by the Kruskal–Wallis test for nonparametric values. Pearson's χ2 test was employed to analyze categorical data. *P<0.05, **P<0.001. BMI, body mass index; SBP, systolic blood pressure; DBP, diastolic blood pressure; SUA, serum uric acid; TG, triglycerides; TC, total cholesterol; HDL, high-density lipoprotein; LDL, low-density lipoprotein; HbA1c, glycated hemoglobin.

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Table 2. Clinical characteristics of type 2 diabetes mellitus (T2DM) patients stratified by baseline serum uric acid (SUA) categories Serum uric acid (SUA) levels Characteristic

≤5.0mg/dl

5.1-7.0mg/dl

7.1-9.0mg/dl

(n=38)

(n=98)

(n=52)

57.24±13.05

54.47±11.53

7.48±6.88

7.87±6.59

Smoking (%)

31.6%

37.1%

BMI (kg/m2 )

22.81±3.10

SBP (mmHg)

137.37±22.51

DBP (mmHg)

78.03±10.49

Age (years) Duration (years)

TC (mmol/L) HDL (mmol/L) LDL (mmol/L)

P

(n=12)

60.75±13.59

>0.05

9.33±6.88

14.50±7.45

<0.05*

40.4%

33.3%

>0.05

24.97±4.80

25.98±4.37

26.98±3.89

<0.05*

137.46±19.83

140.67±19.38

140.42±19.78

>0.05

81.63±11.13

80.59±11.25

77.83±9.72

>0.05

1.87±1.43

1.73±0.97

2.55±1.78

3.81±1.94

<0.001**

5.11±1.00

5.01±1.27

5.16±1.42

4.74±1.56

>0.05

1.05±0.19

1.08±0.26

1.00±0.37

0.94±0.30

>0.05

3.25±0.91

3.29±1.05

3.35±1.28

2.78±0.87

>0.05

C C

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C S U

>9.0mg/dl

56.48±13.92

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TG (mmol/L)

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HbA1C (%)

9.87±2.58

8.56±1.99

8.30±1.63

8.22±1.59

<0.05*

Values are expressed as mean ± SD for normally distributed data and median with interquartile range for non-normally distributed data, or n (%).

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Differences among the groups were analyzed by ANOVA for normally distributed values and by the Kruskal–Wallis test for nonparametric values. Pearson's χ2 test was employed to analyze categorical data. *P<0.05, **P<0.001. BMI, body mass index; SBP, systolic blood pressure;

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DBP, diastolic blood pressure; TG, triglycerides; TC, total cholesterol; HDL, high-density lipoprotein; LDL, low-density lipoprotein; HbA1c,

N A

glycated hemoglobin.

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Table 3. Nerve conduction studies of type 2 diabetes mellitus (T2DM) patients with different serum uric acid (SUA) levels

D E

Motor nerve

Sensory nerve

Serum uric acid (SUA) levels

≤5.0mg/dl

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5.1-7.0mg/dl

7.1-9.0mg/dl

>9.0mg/dl

(n=38)

(n=98)

(n=52)

(n=12)

Amp (mV)

9.63±1.98

10.57±3.05

9.60±2.73

8.29±2.53

<0.05*

CV (m/s)

48.93±3.45

50.07±5.27

47.22±5.92

45.83±6.20

<0.05*

Amp (mV)

26.34±12.09

29.80±14.91

22.26±11.72

16.97±8.69

<0.001**

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CV (m/s)

48.93±3.45

50.07±5.27

47.22±5.92

45.83±6.20

<0.05*

Data were present as mean ± SD or median with interquartile range. Differences among the groups were analyzed by ANOVA or Kruskal-Wallis

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I R

test. *P<0.05, **P<0.001.Amp, amplitude; CV, conduction velocity

C S U

N A

D E

T P E

A

C C

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R

P

Amp (mV)

-0.142

<0.05*

CV (m/s)

-0.191

<0.05*

Amp (mV)

-0.194

CV (m/s)

-0.191

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Amp, amplitude; CV, conduction velocity; *P<0.05.

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<0.05*

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Motor nerve

<0.05*

ACCEPTED MANUSCRIPT Table 5. Risk factors of diabetic peripheral neuropathy (DPN) in multivariate logistic regression Variables

Odd ratio (95% Cl)

P

Duration

>10 years

3.26(1.41-7.56)

≤5 mg/dl

1(Ref.)

SUA

0.49(0.21-1.12)

7.1-9 mg/dl

2.06(0.85-5.01)

>9 mg/dl

7.98(1.47-43.40)

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5.1-7 mg/dl

<0.05*

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1(Ref.)

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≤10 years

0.11 <0.05*

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TC

0.09

1(Ref.)

>5.2 mmol/L

2.14(1.11-4.11)

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≤5.2 mmol/L

<0.05*

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SUA，serum uric acid; TC, total cholesterol; 95% CI, 95% confidential interval.

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*P<0.05.

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Figure 1