INTRODUCTION:

Uric acid, the final metabolic product resulting from purine degradation, contributes significant quantity of the antioxidant capacity that exists in blood and saliva. But the enzyme that produces uric acid also produces free radicals and so the uric acid also can be considered as pro–inflammatory and pro–oxidant agent¹. Metabolic syndrome includes a group of biochemical, pathophysiological, clinical and metabolic factors which increases the cardiovascular disease risk and diabetes². Recently, Lee et al (2012) in their published paper reported that uric acid plays vital role in the genesis of MS³.

Further, one other study associates increased serum uric acid levels with various individual components of MS like obesity, hypertension, hypertriglyceridemia, and hyperglycemia; the resultant clustering of these factors that increases the risk of CVD in an individual⁴. Another report associates higher value of serum uric acid level within the normal range with MS and its components⁵. When metabolic disorders are not treated, then it becomes the cause for type 2 diabetes and coronary artery disease⁶.

Since insulin favours urea excretion, hyperuricemia is seen in subjects with insulin resistance as renal uric acid excretion is decreased⁷.

Thus, it is clearly established that the serum uric acid level increases in conditions lead to insulin resistance, then metabolic syndrome and cardiac autonomic dysfunctions (CAD). Hence, by using uric acid level as biomarker for identifying these conditions may be of use in detection of these diseases at the earlier stages. Relationship between serum and salivary uric acid levels are found to be linear. So, estimating salivary uric acid level (SUA) instead of serum uric acid level itself is enough to serve the purpose¹.

The present study is aimed to compare salivary uric acid level with the severity of metabolic syndrome and to correlate it with the cardiac autonomic modulation.

MATERIALS AND METHODS:

Ethics:

The data collected from the participants after giving the detailed explanation about the procedure of the study and their cooperation and willingness was obtained with written informed consent. The present study started after getting approval from the Institutional Human Ethical Committee, Saveetha University-IHEC No. 005/12/2014/IEC/SU Dated 18-12-2014, Chennai, Tamilnadu.

Study groups:

This cross–sectional study consisting of 195 participants between the age of 30 to 60 years and were divided into 3 groups based on the presence of metabolic abnormalities as group I or control (with less than 3 components of MS), group II/ metabolic syndrome group (with any 3 components of MS) and group III/ severe metabolic syndrome (with more than 3 components of MS).

Saliva sample collection:

Saliva samples were collected in a sterile container from all the subjects in fasting conditions during morning. Measurement of salivary uric acid was done with semi-automated biochemical analyzer by enzymatic colorimetric method.

Recording of ECG:

ECG recordings of all the subjects were performed for 5 minutes after 10 minutes of seated rest, under standardized conditions to minimize artifacts. The ECG was recorded by using limb lead II and the signal obtained was analyzed by using HRV analysis software version 1.1. (Biomedical Signal Analysis Group, Department of Applied Physics, University of Kuopio, Finland). The recording was made in the morning hours between 9 AM and 11 AM. The subjects were instructed to avoid coffee or alcohol 24 hours and food two hours prior to testing. While recording, the subjects were instructed to close the eyes, and to avoid talking, coughing, moving of hands, shaking the legs and body and sleeping during the test⁸. The HRV parameters used in this study includes, time–domain parameters which consist of mean HR (/min), mean RR (s), RMSSD (ms), NN50 (count), pNN50 (%) and frequency – domain parameters which consist of total power (ms²), LF (ms²), HF (ms²) and LF/HF ratio.

Statistical analysis:

SigmaPlot 13.0 (Systat software, USA) was used for statistical analysis. Comparison of salivary uric acid level among the study groups was done with ANOVA. Correlations were investigated by calculating Pearson’s correlation coefficient for all the participants. It was used to investigate correlations between salivary uric acid with the autonomic function parameters. p – value less than 0.05 is considered statistically significant.

RESULT:

Table 1: Comparison of salivary uric acid (mg/dl) in control, metabolic syndrome (MS) and severe metabolic syndrome (SMS). n = 65 each.

Variable	Control	Metabolic syndrome (MS)	Severe metabolic syndrome (SMS)	P – value
Salivary uric acid (mg/dl)	3 ±0.70	3.6 ±0.86	5 ± 1.00	<0.001*

In the present study (Table 1), the value of mean salivary uric acid in control, MS and severe MS group was 3.0mg/dL, 3.6mg/dl and 5.0mg/dL respectively and the ANOVA statistical analysis showed that these values were significantly different between the groups.

Table 2: Correlation of salivary uric acid with the time – domain parameters of HRV

Variables	Pearson’s correlation coefficient (r)	p- value
Mean HR (/min)	0.141	0.048*
Mean RR (s)	-0.281	<0.001*
RMSSD (ms)	-0.107	0.136
NN50 (count)	-0.157	0.027*
pNN50 (%)	-0.166	0.02*

In the present study, Pearson’s correlation of salivary uric acid with the time – domain parameters of HRV (table 2) were also carried out and the following result was obtained. Here only mean HR had positive correlation (p = 0.048) with salivary uric acid, whereas the other time-domain parameters like mean RR interval (p<0.001), NN50 (p = 0.027) and pNN50 (p = 0.02) showed significant negative correlation with salivary uric acid.

Table 3: Correlation of salivary uric acid with the frequency – domain parameters of HRV

Variables	Pearson’s correlation coefficient (r)	p- value
TP (ms²)	0.060	0.399
LF (ms²)	0.282	<0.001*
HF (ms²)	-0.258	<0.001*
LF/HF	0.475	<0.001*

With the frequency-domain HRV parameters, the Pearson’s correlation analysis of salivary uric acid (table 3) showed a significant positive correlation with LF (r = 0.282, p<0.001) and LF/HF (r = 0.475, p<0.001) and on the other hand, showed a significant negative correlation (r = -0.258, p<0.001) with HF.

DISCUSSION:

Metabolic syndrome is an increasingly health problem worldwide as it is the major risk factor for CVD. Since, the metabolic syndrome contains complex interrelated components and diagnosing the same also becomes complex, tiresome, costly and time-consuming process. So, it is necessary to identify a biomarker for metabolic syndrome in order to understand and treat the disease at the earliest and more effectively. Also, if the putative biomarker can be obtained by a non–invasive method, then it can be used for screening the population whenever community camp is conducted as well as for monitoring the disease status which helps in preventing further complications. The current study was carried out to throw some light in this direction.

In the present study, the salivary uric acid (SUA) is identified as the possible biomarker for MS. The uric acid was chosen as literature review showed the association between high serum uric acid with the metabolic syndrome³. Further, Lin et al (2006) had shown that serum uric acid level increases with the increase in components of metabolic syndrome in an individual⁴. Recently, Lee et al (2012) in their published work reported that the gradual increase in the number components of metabolic syndrome was observed with the increase in serum uric acid level³. The conclusion of the above observations that indicate an increase in uric acid concentration in metabolic syndrome people supports the selection of uric acid as biomarker in the present study. There exists a linear relationship between serum and salivary uric acid (SUA) concentration (Soukup et al, 2012) and so in the present study, salivary uric acid was chosen as a possible biomarker for MS instead of serum uric acid, as saliva can be obtained non-invasively. It is important that individuals should be advised about treatment approach which will reduce the complications as well as risk of developing metabolic syndrome².

In this study, the salivary uric acid concentration was compared among the study groups (table 1) and the result showed increase in the salivary uric acid level proportionate to increase in the number of metabolic syndrome components which was in agreement with the report published by Lee et al (2012) in which serum uric acid level was measured³. Thus, the present study proved that level of not only serum uric acid but also salivary uric acid could correlate with the severity of metabolic syndrome.

These components are also the risk factors for CVD and so in order to find out whether CVD could also be correlated with salivary uric acid in the present study, the different parameters of HRV, the diagnosing tool of CVD were correlated. A very good correlation of HRV and salivary uric level was evident in correlation analysis (table 2 and table 3). In this study, the positive correlation parameters were mean HR of time-domain and LF and LF/HF ratio of frequency-domain HRV parameters and the negative correlation was evident with mean RR interval, NN50 and pNN50 of time–domain and HF of frequency domain. It was reported that the elevated uric acid stands as an independent risk factor for any of the CVD including cerebrovascular disease, coronary vascular disease and congestive heart failure in subjects with hypertension, diabetes and hyperlipidemia and further increased serum uric acid level has been significantly correlated with higher mortality rate in subjects with congestive heart failure⁹.

Since salivary uric acid measurement is a non-invasive, less time-consuming and also cheap, it can be used to screen metabolic syndrome and its components thereby reducing the cardiovascular disease burden too¹⁰. The HRV parameters which showed correlation with SUA also indicated increased sympathetic activity and thus may be used along with SUA to assess the severity of both MS and CVD.

CONFLICT OF INTEREST:

The authors have no conflicts of interest.

ACKNOWLEDGMENT:

Authors would like to deliver sincere thanks to the Department of Physiology, A C S Medical College and Hospital and Saveetha Medical College and Hospital, Chennai for providing the facilities to carry out research work.

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Received on 18.09.2021 Modified on 28.03.2022

Research J. Pharm. and Tech 2023; 16(3):1347-1350.

DOI: 10.52711/0974-360X.2023.00221