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\begin{document}


\def\RunningAuthor{Santhini Gopalakrishnan S et al.}
\firstPage{7229}
\articleType{Original Article}
\receivedDate{21 Aug 2020}
\acceptedDate{28 Sep 2020}
\revisedDate{21 Sep 2020}
\journalVolume{2020, 11}
\journalIssue{4}
\journalDoi{ijrps.v11i4.3854}
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\title{\textbf{A Study of Relationship Between Serum Uric Acid, and Fasting Plasma Glucose in Type 2 Diabetes Mellitus Patients}}
\author{Anuradha~G,  
            Santhini~Gopalakrishnan~S\textsuperscript{*}, 
            Vinodakumar~H~R~\\[5pt]{Department of Biochemistry, Chettinad hospital and research institute, Kelambakkam, Chengalpattu district-603103, Tamil Nadu, India}}

\begin{abstract}
The current study was conducted to observe the relationship between serum uric acid, lipid profile and fasting plasma glucose in type 2 DM patients. It was a cross-sectional study. A total of 618 participants were included in the study (203-healthy, 206-prediabetic and 209-T2DM). One way analysis of variance was used to compare the mean between these three groups. A linear regression model was used to find the relationship between SUA and FPG in T2DM. The mean values of serum uric acid in pre-diabetes and T2DM (4.929\ensuremath{\pm}1.33 and 4.69\ensuremath{\pm}1.41 mg/dl, respectively) were lower compared to healthy (5.40\ensuremath{\pm}1.08 mg/dl). SUA showed a significant positive correlation with serum triglycerides in T2DM (p{\textless}0.05). The linear regression model showed that SUA was inversely associated with FPG in T2DM after adjustment for age and gender. The biological interrelationship observed in the current study raises the possibility of potential pathogenic overlap between SUA and FPG. SUA might be involved in a metabolic imbalance which in turn leads to T2DM. 
\end{abstract}\def\keywordstitle{Keywords}

\begin{keywords}Lipid profile,\newline cardiovascular disease,\newline Antioxidant,\newline Obesity
\end{keywords}
\twocolumn[ \maketitle {\printKwdAbsBox}]
\makeatletter{\raggedright\textsuperscript{*}Corresponding Author\par Name:\ Santhini~Gopalakrishnan~S~\\ Phone:\ 7299069636~\\  Email:\ santhini.research@gmail.com
        \par}\vspace*{-11pt}\hrulefill\par{\fontsize{12}{14}\selectfont ISSN: 0975-7538}\par% 
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\section{Introduction}
Diabetes mellitus is a potential epidemic in India. The prevalence of Type 2 Diabetes mellitus (T2DM) may increase drastically from 171 million in the year 2000 to 366 million by the year 2030 \unskip~\citep{888213:20351574,888213:20351591}. The aetiology for the development of T2DM is multifactorial. It is a genetic disease. Various environmental factors such as obesity, migration from rural to an urban area, and lifestyle modifications may also be the causative factors. Among the various risk factors, obesity plays a significant role [3]. Indians with lower body mass index (BMI) has a high propionate of diabetes \unskip~\citep{888213:20351584,888213:20351588}. Hence they are at equal risk as those who are in western countries with obesity \unskip~\citep{888213:20351585}. Moreover, Indians are genetically predisposed to coronary artery disease because of dyslipidemia \unskip~\citep{888213:20351583}. To reduce the cardiovascular disease burden that diabetes creates, diabetes patients must be screened at an early stage itself. 

Many previous studies have found the relationship between serum uric acid (SUA) and various risk factors that cause cardiovascular disease. But there is a lack of clear causal mechanism. 

Uric acid (UA) is obtained as the final product of purine metabolism. UA reacts to oxidative stress based on its localization. Intracellular Uric acid has anti-oxidant role while extracellular have pro-oxidant property \unskip~\citep{888213:20351589}. Being an anti-oxidant, uric acid protects the cardiovascular system. It prevents the peroxidation of proteins and lipids, scavenges free radicals, and chelates transitional metal ions \unskip~\citep{888213:20351580}. Its intrinsic anti-oxidant activity has been confirmed by the administration of UA in healthy volunteers and athletes, which, in turn, reduced reactive oxygen species production (ROS) \unskip~\citep{888213:20351576}. UA also acts as a pro-oxidant. The Xanthine-oxidase, which is an isoform of Xanthine-oxidoreductase enzyme, generates ROS, which in turn induce endothelial dysfunction. This has been observed in an animal study on a rat. In rats, oxonic acid, which is an inhibitor of the enzyme, uricase has been administered. This has induced hyperuricemia which in turn raised blood pressure \unskip~\citep{888213:20351592}.

Some studies have found a positive relationship between elevated SUA and diabetes \unskip~\citep{888213:20351590,888213:20351579},  few reported no correlation \unskip~\citep{888213:20351575,888213:20351593},  and some have reported inverse relationship \unskip~\citep{888213:20351582,888213:20351581}. The current study was undertaken to know the actual trend of SUA in healthy, prediabetic, and T2DM and the relationship between SUA and Fasting plasma glucose (FPG) in T2DM.

\textbf{Study design}

It was a cross-sectional study. The study was done after getting approval from the Institutional Human Ethics Committee, Chettinad hospital and research institute, Kelambakkam.

\textbf{Inclusion criteria and exclusion criteria}

Those who underwent a volunteer health check-up at Chettinad hospital and research institute from 1\ensuremath{^{st}} August 2019 to 30\ensuremath{^{th}} August 2020 were included in the study. The study participants included both genders from the age of 35 to 65 years.

Based on the American diabetes association criteria, study participants were classified as normoglycemic (n=203), prediabetic (n=206), and T2DM (n=209).

A standard questionnaire was used to obtain personal history. Smokers and chronic alcoholics were excluded from the study. Those who suffer from systemic disorders like hypertension, renal disease, cardiovascular disease, and pulmonary disease were excluded from the study.

\textbf{Anthropometric measurement and blood collection}

Height, weight, and blood pressure were measured using a standard protocol. The formula used for calculating Body mass index was weight in Kg divided by height in meter squared. After ensuring that participants were under 12hours fasting, a venous blood sample was drawn under aseptic precautions. Baseline variables like glycated haemoglobin, FPG, Blood urea nitrogen, creatinine, uric acid, total cholesterol, Triglycerides, and High- density cholesterol were measured using Siemens RXL automated chemistry multi dimension system. The Calculated parameters were LDL-C and VLDL-C. The Friedewald formula was used to calculate LDL-C. VLDL-C was calculated by dividing TGL by 5.

\textbf{Statistical analysis}

SPSS software was used for doing statistical analysis. One way analysis of variance (ANOVA) was used to find the p-Value between the mean of the variables between healthy, pre-diabetes, and T2DM. The correlation between serum uric acid and TC, TGL, HDL-C, LDL-C, and VLDL-C was found using Pearson's correlation coefficient test. The relationship between SUA and FPG was found using a simple linear regression model. 
    
\section{Results}

\begin{table*}[!htbp]
\caption{\boldmath {Baseline variables of Healthy, Pre-diabetes and T2DM} }
\label{tw-39c367751af7}
\centering 
\begin{threeparttable}

\def\arraystretch{1.1}
\ignorespaces 
\centering 
\begin{tabulary}{\linewidth}{p{\dimexpr.2332\linewidth-2\tabcolsep}p{\dimexpr.1967\linewidth-2\tabcolsep}p{\dimexpr.18839999999999996\linewidth-2\tabcolsep}p{\dimexpr.205\linewidth-2\tabcolsep}p{\dimexpr.1767\linewidth-2\tabcolsep}}
\tbltoprule \rowcolor{kwdboxcolor}{\multirow{3}{*}{}} & Healthy & Pre-diabetes & Type 2 DM & {\multirow{3}{*}{P-Value}}\\
\rowcolor{kwdboxcolor} & Mean \ensuremath{\pm}SD & Mean \ensuremath{\pm}SD & Mean \ensuremath{\pm}SD & \\
\rowcolor{kwdboxcolor} & (N=203) & (N= 206 ) & (N= 209 ) & \\
\tblmidrule 
Age (years) &
  43.18\ensuremath{\pm}7.53 &
  45.2\ensuremath{\pm}9.04 &
  51.48\ensuremath{\pm}9.47 &
  {\textless}.0001*\\
BMI (kg/m2) &
  24.9\ensuremath{\pm}0.42 &
  26.1\ensuremath{\pm}0.51 &
  28.51\ensuremath{\pm}0.98 &
  {\textless}.0001*\\
WC(cm) &
  77.43\ensuremath{\pm}1.97 &
  91.83\ensuremath{\pm}2.44 &
  96.17\ensuremath{\pm}5.39 &
  {\textless}.0001*\\
SBP(mmHg) &
  108.6\ensuremath{\pm}8.2 &
  114.1\ensuremath{\pm}10.1 &
  116.2\ensuremath{\pm}11.6 &
  {\textless}.0001*\\
DBP(mmHg) &
  72.9\ensuremath{\pm}8.2 &
  74.4\ensuremath{\pm}8.2 &
  74.9\ensuremath{\pm}9.8 &
  0.057\\
HbA1C(\%) &
  5.18\ensuremath{\pm}0.22 &
  6.2\ensuremath{\pm}0.12 &
  9.02\ensuremath{\pm}1.74 &
  {\textless}.0001*\\
FPG(mg/dl) &
  93.53\ensuremath{\pm}4.72 &
  107.29\ensuremath{\pm}3.08 &
  177.45\ensuremath{\pm}52.16 &
  {\textless}.0001*\\
BUN(mg/dl) &
  10.48\ensuremath{\pm}3.09 &
  10.29\ensuremath{\pm}3.06 &
  10.5\ensuremath{\pm}3.26 &
  0.755\\
Uric acid(mg/dl) &
  5.4\ensuremath{\pm}1.08 &
  4.929\ensuremath{\pm}1.33 &
  4.69\ensuremath{\pm}1.41 &
  {\textless}.0001*\\
Creatinine(mg/dl) &
  0.95\ensuremath{\pm}0.17 &
  0.93\ensuremath{\pm}0.18 &
  0.98\ensuremath{\pm}0.2 &
  0.021*\\
TC(mg/dl) &
  169.97 \ensuremath{\pm} 32.51 &
  182.03\ensuremath{\pm}39.53 &
  188.48\ensuremath{\pm}45.65 &
  {\textless}.0001*\\
TGL(mg/dl) &
  109.07 \ensuremath{\pm}68.71 &
  122.59\ensuremath{\pm}61.78 &
  151.72\ensuremath{\pm}74.99 &
  {\textless}.0001*\\
HDL-C(mg/dl) &
  38.16\ensuremath{\pm}8.55 &
  37.36\ensuremath{\pm}8.43 &
  37.02\ensuremath{\pm}8.19 &
  0.369\\
LDL-C(mg/dl) &
  110.04\ensuremath{\pm}29.53 &
  119.71\ensuremath{\pm}33.43 &
  121.27\ensuremath{\pm}40.07 &
  0.002*\\
VLDL-C(mg/dl) &
  21.77\ensuremath{\pm}13.78 &
  24.16\ensuremath{\pm}11.39 &
  30.19\ensuremath{\pm}15.06 &
  {\textless}.0001*\\
\tblbottomrule 
\end{tabulary}\par 
\begin{tablenotes}\footnotesize 
    
\item{*p-value less than 0.05 was considered statistically significant}
    
\item{BMI: Body mass index }
    
\item{WC: Waist circumference }
    
\item{SBP: Systolic blood pressure }
    
\item{DBP: Diastolic blood pressure}
\end{tablenotes}
\end{threeparttable}

\end{table*}

\begin{table*}[!htbp]
\caption{\boldmath {Correlation analysis between Serum Uric acid (SUA) and Lipid profile among T2DM} }
\label{tw-04778990247d}
\centering 
\begin{threeparttable}

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\tbltoprule \rowcolor{kwdboxcolor} &  & SUA\\
\tblmidrule 
\multicolumn{1}{p{\dimexpr(.3569\linewidth-2\tabcolsep)}}{\multirow{2}{\linewidth}{TC(mg/dl)}} &
  \cAlignHack r &
  0.1022\\
 &
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  .14089\\
\multicolumn{1}{p{\dimexpr(.3569\linewidth-2\tabcolsep)}}{\multirow{2}{\linewidth}{TGL(mg/dl)}} &
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 &
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\multicolumn{1}{p{\dimexpr(.3569\linewidth-2\tabcolsep)}}{\multirow{2}{\linewidth}{HDL-C(mg/dl)}} &
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  -0.0966\\
 &
  \cAlignHack p &
  0.195\\
\multicolumn{1}{p{\dimexpr(.3569\linewidth-2\tabcolsep)}}{\multirow{2}{\linewidth}{LDL-C(mg/dl)}} &
  \cAlignHack r &
  0.068\\
 &
  \cAlignHack p &
  .327925\\
\multicolumn{1}{p{\dimexpr(.3569\linewidth-2\tabcolsep)}}{\multirow{2}{\linewidth}{VLDL-C(mg/dl)}} &
  \cAlignHack r &
  0.1824\\
 &
  \cAlignHack p &
  0.00821*\\
\tblbottomrule 
\end{tabulary}\par 
\begin{tablenotes}\footnotesize 
    
\item{*p-value less than 0.05 was considered statistically significant}
\end{tablenotes}
\end{threeparttable}

\end{table*}

\begin{table*}[!htbp]
\caption{\boldmath {Simple Linear Regression for FPG/SUA} }
\label{tw-318aacc24bff}
\centering 
\begin{threeparttable}

\def\arraystretch{1.1}
\ignorespaces 
\centering 
\begin{tabulary}{\linewidth}{p{\dimexpr.11989999999999998\linewidth-2\tabcolsep}p{\dimexpr.1186\linewidth-2\tabcolsep}p{\dimexpr.08419999999999998\linewidth-2\tabcolsep}p{\dimexpr.2195\linewidth-2\tabcolsep}p{\dimexpr.18979999999999997\linewidth-2\tabcolsep}p{\dimexpr.1305\linewidth-2\tabcolsep}p{\dimexpr.1375\linewidth-2\tabcolsep}}
\tbltoprule \rowcolor{kwdboxcolor}\multicolumn{7}{p{\dimexpr(.9999999999999999\linewidth-2\tabcolsep)}}{\cAlignHack Number of T2DM Patients-209 Parameter Estimates}\\
\rowcolor{kwdboxcolor}Variable & Label & DF & Parameter Estimate & Standard Error  & t Value & Pr {\textgreater} |t|\\
\tblmidrule 
Intercept &
  Intercept &
  1  &
  223.91366 &
  13.61729 &
  16.44 &
  {\textless}.0001\\
Uric acid &
  Uric acid &
  1  &
  -9.35542 &
  2.66768 &
  -3.51 &
  0.0006\\
\tblbottomrule 
\end{tabulary}\par 
\begin{tablenotes}\footnotesize 
    
\item{*p-value less than 0.05 was considered statistically significant}
\end{tablenotes}
\end{threeparttable}

\end{table*}

\bgroup
\fixFloatSize{images/e1a3afa6-79e6-41e5-9e52-ddcbab8d6313-upicture1.png}
\begin{figure}[!htbp]
\centering \makeatletter\IfFileExists{images/e1a3afa6-79e6-41e5-9e52-ddcbab8d6313-upicture1.png}{\includegraphics{images/e1a3afa6-79e6-41e5-9e52-ddcbab8d6313-upicture1.png}}{}
\makeatother 
\caption{\boldmath {Shows a scatter plot with regression data between SUA and FPG. The Dependent variable was FPG. The figure shows a significant negative correlation between SUA and FPG in T2DM patients.}}
\label{f-89f127dfd6ea}
\end{figure}
\egroup
Table~\ref{tw-39c367751af7} shows the mean values of healthy, pre-diabetes, and T2DM patients. A Statistically significant p-Value was obtained for the biochemical parameters HbA1c, FPG, UC, Creatinine, TC, TGL, LDL-C and VLDL-C. Variables such as age, BMI, WC and SBP also showed statistically significant p values.

Table~\ref{tw-04778990247d} shows Pearson's correlation analysis between SUA and Lipid profile among T2DM. A Significant and positive relation existed between SUA and TGL. Also, there was a negative but not significant relation between SUA and HDL-C.VLDL-C also shows a significant positive correlation with SUA. 

Table~\ref{tw-318aacc24bff} shows that serum uric acid was statistically significant and negatively related to FPG.

Figure~\ref{f-89f127dfd6ea} shows a scatter plot with regression data between SUA and FPG. The Dependent variable was FPG. The figure shows a significant negative correlation between SUA and FPG in T2DM patients.
    
\section{Discussion}
Previous few studies have shown a positive relationship between SUA and FPG \unskip~\citep{888213:20351590,888213:20351579},  and a recent study has shown no correlation between SUA and FPG \unskip~\citep{888213:20351593}. The inconsistent findings seen in previous studies might be due to the small sample size, more of aged participants, selection of participants from a particular group rather than the general population, influence other factors like food habits, lifestyle, genetic and environmental factors, etc.

In the current study, as shown in Figure~\ref{f-89f127dfd6ea}, a simple linear regression model adjusted for age and sex showed a negative and significant association between SUA and FPG in T2DM. These findings were supported by the previous study \unskip~\citep{888213:20351582}.

A possible mechanism for the negative correlation between SUA and FPG may be due to inhibition of uric acid reabsorption in the proximal convoluted tubule by the high level of glucose concentration in the kidney \unskip~\citep{888213:20351586,888213:20351587}. The kidney mainly regulates glucose homeostasis. In normal circumstances, plasma glucose is freely filtered, and the majority of them are reabsorbed by sodium-dependent glucose transporter. Both Uric acid and glucose are reabsorbed at the same site in the proximal tubule. Hence, Glucose levels influence the uric acid excretion by regulating the reabsorption of uric acid in the proximal tubule of the kidney.

Uric acid, which has intrinsic anti-oxidant activity, is reduced in pre-diabetes and T2DM. It may be one of the potential causes of oxidative stress in T2DM.
    
\section{Conclusion}
Our study has confirmed that there exists an inverse relationship between SUA and FPG in T2DM patients. Further studies with data on the levels of uric acid in the urine of T2DM patients would better reveal the underlying mechanisms for the association between SUA and FPG.

\textbf{Conflict of interest}

The authors declare that they have no conflict of interest for this study.

\textbf{Funding Support}

The authors declare that they have no funding support for this study.
    

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