The Association Between Serum Uric Acid and the Cardiometabolic Phenotype Among Healthcare Workers in Azar Cohort Study

Background: As of now, it is unknown whether hyperuricemia can be considered as an independent risk factor or just as a marker to represent the correlation between uric acid and other metabolic syndrome (MetS) risk factors. To the best of the authors’ knowledge, no other work has been reported to study this relationship between serum uric acid (SUA) and cardiometabolic phenotypes. In this work, we intend to study the correlation between SUA and the cardiometabolic phenotype among healthcare workers in Azar cohort study. Method: In this cross-sectional study, anthropometric measurements, serum fasting blood sugar (FBS), triglyceride (TG), cholesterol, high lipoprotein density (HDL), liver enzymes, blood urea nitrogen (BUN), SUA, creatinine (Cr), and blood pressures of 1458 healthcare workers were evaluated. MetS was diagnosed based on ATP III. We classied the participants into four cardiometabolic phenotypes, i.e., metabolically-healthy lean (MHL), metabolically-unhealthy lean (MUHL), metabolically-healthy obese (MHO), and metabolically-unhealthy obese (MUHO). Results: MHL (32.6%) and MHO (66%) have the highest prevalence rate in the rst and second SUA tertiles, respectively, which are statistically signicant (P-value ≤ 0.001). We observed an ascending trend in the mean values of WC, TG, cholesterol, low HDL, FBS, BUN, Cr, SBP, DBP, BMI, and liver enzymes from the rst SUA tertile to the third SUA tertile (P-value <0.05). Compared to the lowest SUA tertile, the odds of MHO and MUHO increased by 2.29 (95% CI 1.46-3.59) and 5.38 (95%CI 3.45-9.39), respectively. In contrast, no similar trend was observed regarding the association between MUHL and the SUA tertile. Conclusion: We proposed the use of the easily-measured SUA level as a marker for early diagnosis of at-risk MUHL and MHO individuals to administer proper interventions. Further prospective works are needed to identify the effects of SUA on the progression of MetS in various body size subgroups.


Introduction
One of the growing and escalating health challenges of the twenty-rst century is the metabolic syndrome (MetS) with an increasing prevalence in both developed and developing countries [1]. In African and Asian countries, MetS prevalence ranges from 16.3 to 33.4% [2], while in Tehran, 33.7% of the adult Several studies have revealed the relationship between urate, MetS, and its components [7,8]. Moreover, a correlation between body mass index (BMI) and circulating urate concentrations has been reported.
Individuals classi ed in the high BMI category show high uric acid levels, which are highly associated with the metabolic syndrome [9,10]. It has been shown that the genetic predisposition of people with high uric acid levels is associated with blood pressure elevation and dyslipidemia, but not with obesity/diabetes, all of which are components of MetS. This may suggest that high serum uric acid (SUA) may involve a separate pathway for the development of MetS, independent of obesity [1].
In this regard, despite the fact that obesity has been documented as a major risk factor for MetS, some individuals identi ed as obese may not display any signs of typical metabolic disorders, and they may have a lower risk of obesity-related complications. According to the obtained data, 10%-25% of obese people can be classi ed as metabolically-healthy obese (MHO). According to the results of the study carried out by Velho et al., the prevalence of MHO varies from 3.3 to 32.1% in men and from 11.4-43.3% in women [11]. The ndings of various studies suggest that MHO subjects have more dangerous conditions than metabolically-healthy subjects because of the higher risk for developing hypertension, type 2 diabetes, and the metabolic syndrome. In addition, a study on normal-weight adults (body mass index [BMI] < 25.0 kg/m 2 ) living in the United States shows that 24% of the adults are metabolically abnormal. Abnormal metabolic conditions predispose this group to chronic diseases compared to metabolically-healthy normal-weight individuals [12].
However, the exact metabolic biomarkers that cause healthy individuals to become metabolically unhealthy during their lifetime are not fully understood. We are hypothesizing that the elevated SUA may play a role in the pathogenesis of MetS in metabolically-unhealthy lean (MUHL) or metabolicallyunhealthy obese (MUHO) individuals. As of now, it is unknown whether hyperuricemia can be considered as an independent risk factor or just as a marker to represent the correlation between uric acid and other MetS risk factors. To the best of the authors' knowledge, no other work has been reported to study this relationship between SUA and cardiometabolic phenotypes. In this work, we intend to study the correlation between SUA and the cardiometabolic phenotype among healthcare workers in Azar cohort study.

Materials And Methods
We studied a cohort that is a part of a large prospective epidemiological researches in Iran (the Persian cohort study) [13]. The cohort study on the healthcare workers was carried out in 2020 as a part of the Azar cohort study, which was conducted by the liver and Gastrointestinal diseases Research center of Tabriz University of medical sciences [14]. The purpose of this cohort study was to evaluate 6000 participants who were related to Tabriz University of Medical Sciences (TUMS), including healthcare employees in hospitals, schools, and district health networks. This study intended to characterize the risk factors of non-communicable diseases (NCD) among healthcare providers, o cial staff, and professors of Tabriz University of Medical Sciences.
Our baseline assessment consisted of a face-to-face health interview or a health examination in terms of a broad range of established and novel risk factors of NCDs.
Data from a total of 1458 participants were used for this cohort study. All involved participants provided written informed consent, and the study was approved by the Ethics Committee of Tabriz University of Medical Sciences (IR.TBZMED.REC.1396.1263).
Participants of this study include fulltime and long-term contract employees aged between 18 and 75 years who are not pregnant or lactating, and who are not planning to retire within the next ve years. Patients who reported having been diagnosed with debilitating psychiatric disorders or physical illnesses by a health professional were excluded from this study

Demographic Characteristics of the Participants
We used a questionnaire for evaluating demographic characteristics, such as age, gender, marital status, and educational level. Moreover, lifestyle patterns, i.e., smoking, drug use, hookah use, alcohol consumption, and being a passive smoker were also assessed by the questionnaire.

Anthropometric and Blood Pressure Measurements
Bodyweight, height, and waist circumference of all the subjects were measured, and their body mass index was determined using the standard formula, i.e., weight (kg)/height 2 (m). The anthropometric measurements are described in detail elsewhere [14]. Blood pressure was measured by a trained nurse twice with a two-minute interval, and twice in each arm in a sitting position after 10 minutes of rest by using a mercury sphygmomanometer (Rudolf Richter, DE-72417, Germany). The average values were calculated and used in the analysis as the systolic and diastolic blood pressure.

De nition of MetS
The Adult Treatment Panel III (ATP III) of the National Cholesterol Education Program de nes individuals with MetS as subjects who meet three or more of the following conditions: hypertension, de ned as a systolic blood pressure ≥ 130 and/or a diastolic blood pressure ≥ 85 mmHg, or subjects using antihypertensive medication; waist circumference ≥ 102 cm in men and ≥ 88 cm in women; hypertriglyceridemia, de ned as TG ≥ 150 mg/dl, or individuals treated for elevated triglycerides; low HDL-C values < 40 mg/dl in men and < 50 mg/dl in women; and high fasting glucose ≥ 100 mg/dl, or the use of glucose-lowering medication [15].

Statistical Analysis
Statistical analysis was performed using IBM SPSS Statistics version 11.5 (IBM, Chicago, IL). Continuous variables were expressed as mean ± standard deviation, and their differences were assessed using a chisquare analysis across the four study groups. Categorical variables were presented as numbers (percentages), and their differences among the four groups were measured using one-way analysis of variance (ANOVA).
Multinomial logistic regression analysis was performed to determine the relationship between cardiometabolic phenotype and serum SUA tertile. Moreover, crude and adjusted odds ratios (OR) and their corresponding 95% con dence intervals (95%CI) were calculated. We perform the analysis after adjusting for confounding factors, including age, gender, educational level, marital status, and current smoking status, while MHL was used as a reference group.
We considered BMI as the basis for classi cation, and seven underweight interviewees were excluded.
Eventually, statistical analysis was carried out on 1451 subjects. P values < 0.05 were considered statistically signi cant. Table 1 presents the baseline characteristics of the participants for individual SUA tertiles. The third tertile includes a higher percentage of male and married participants than the rst two tertiles (P-value < 0.001). Moreover, MHL (32.6%) and MHO (66%) have the greatest prevalence in the rst and second SUA tertiles, which are statistically signi cant (P-value ≤ 0.001). We observed an ascending trend in the mean values of WC, TG, cholesterol, low HDL, FBS, BUN, Cr, SBP, DBP, BMI, and liver enzymes from the rst to the third SUA tertile (P-value < 0.05).  Table 2 shows that in all cardiometabolic phenotype classes, the lowest (P-value < 0.001) proportion of females as dose-dependent of SUA was in the third tertile.  Additionally, similar to the trends for increasing SUA tertiles, the mean values for SBP and DBP have also increased markedly (P-value < 0.05).

Results
In contrast to the other factors, WC, FBS, and cholesterol did not increase with increasing SUA. In addition, even WC in the MUHL group decreased noticeably based on SUA tertiles (P = 0.04). We present the relationship between SUA and cardiometabolic phenotype in Table 3. Our applied multinomial regression analysis indicates that compared to the lowest SUA tertile, the odds of MHO and MUHO increased by 2.29 (95% CI 1.46-3.59) and 5.38 (95%CI 3.45-9.39), respectively. After adjustment for different confounding factors (i.e., age, gender, marital status, education level, smoking, and alcohol consumption), the correlation was still signi cant.

Discussion
We assessed the relationship between SUA and the cardiometabolic phenotype in the current study, and our ndings show increasing mean values of metabolic syndrome factors, including LDL and cholesterol, in a dose-response manner corresponding to the SUA tertiles. These results are similar to the ndings of the studies carried out previously in various countries [16][17][18]. According to our ndings, the prevalence rates of MHO and MUHL were higher in the second and third tertiles compared to the rst tertile, which had the lowest SUA. As far as we know, no studies have been published focusing on the relationship between SUA and the cardiometabolic phenotype. Therefore, we contrasted our results with previous studies that evaluated the connection between SUA and MetS. An increase in MetS due to the elevation of serum uric acid level has been reported [19]. Hemostasis and the relationship between SUA homeostasis and MetS are highly complex [21]. It's still debatable whether an elevated SUA level is a risk factor or just a biomarker in the progress and improvement of MetS [22]. Some researchers have stated that hyperuricemia can be an exclusive component of MetS [7,23], while other studies have proposed to consider hyperuricemia as a supplementary component of MetS [24,25]. Elevated SUA levels will cause outcomes such as hypertension [26], hypertriglyceridemia, and hypercholesterolemia [27]. The suggested procedures for the connection between SUA and MetS include the following: rstly, hyperuricemia has been proved to lead to endothelial dysfunction in human and animal bodies [28,29]. Secondly, SUA has been shown to prevent NO production [30], which is a signi cant factor in the functioning of insulin [31]. The defect of endothelial-formed NO is supposed to decrease blood ow to the cells, which stops the normal functioning of insulin and causes hyperinsulinemia [21]. Therefore, hyperuricemia may play a potential role in causing and increasing insulin resistance. Similarly, insulin resistance is recognized to play an essential role in the pathogenesis of MetS [32]. Thirdly, another role of uric acid involves inducing oxidative stress, which causes in ammation in adipocytes [33,34] and hepatocytes [35]. However, the complicated correlation between uric acid and oxidative stress is noteworthy because it can be paradoxical [36]. Although uric acid is an antioxidant that disables superoxide anion, peroxynitrite, and hydroxyl radicals [37,38], there is some evidence showing that under ischemic stress or high SUA, uric acid functions as a pro-oxidant.
Furthermore, we noticed that the prevalence of males in the third tertile (i.e., the highest SUA level) was signi cantly higher than the other tertiles. In line with our results, this phenomenon has been mentioned in previous studies [39,40]. It seems that the lower tubular urate post-secretory reabsorption and the higher renal clearance of urate in women can be related to this observation.
Moreover, according to our ndings, the average serum levels of liver enzymes were elevated in MHL, MHO, and MUHO individuals in a dose-response manner corresponding to SUA tertiles. Interestingly, the mean serum liver enzymes elevated with increasing SUA levels in MHL individuals. This may suggest an association between liver enzymes and SUA that is independent of BMI. At the same time, it may be an indicator that the MHL subjects in the third tertile are at risk of shifting to MUHL. Nevertheless, we did not observe the same trend in the MUHL group, which we thought might be because of the limited sample size of this group. These ndings are in line with the ndings of prior studies [41,42]. For instance, Shih et al. state that individuals with hyperuricemia are more likely to have heightened liver enzymes (AST or ALT), even after adjustment [41]. As shown in several studies, NAFLD is closely linked with obesity, dyslipidemia, diabetes mellitus, MetS, and cardiovascular disorders [43,44]. Therefore, NAFLD is believed to be a hepatic outcome of metabolic diseases [45,46]. It turns out that the SUA level increases in most NAFLD patients [47], indicating that it can be an independent predisposing factor for NAFLD [48,49].
Additionally, hyperuricemia, even in the reference range, was a component of MetS [50].
The main limitation of the current study involves the fact that due to its cross-sectional design, causal inferences in the relationship between serum SUA and cardiometabolic phenotype could not be evaluated. However, the main strength of this study is that it is the rst to evaluate the relationship between SUA and cardiometabolic phenotype in healthcare workers. Serum SUA is easily accessible in regular clinical practice, and it is measured using standardized techniques. It would be useful to distinguish the transition from MHO to MUHO. Therefore, it may lead to earlier and more precise identi cation of MHO subjects at risk of transition to MUHL, which can facilitate the administration of better preventive strategies. Another strong point of this work lies in using data from a cohort study and a large sample size.

Conclusions
Our ndings indicate that increases in the prevalence of MHO and MUHO are related to elevated SUA levels. Furthermore, the average MetS components and lipid pro le increased with the elevation in SUA levels. Additionally, there is a positive dose-response manner associated with the mean serum liver enzymes in MHL, MHO, and MUHO groups. Accordingly, we proposed the use of the easily-measured SUA level as a marker for the early diagnosis of at-risk MUHL and MHO individuals in order to provide proper intervention. However, the detailed mechanisms that cause SUA to lead to this disorder are still at an early stage of investigation, and they need further explanation. Consequently, further prospective works are needed to identify the effects of SUA on the progression of MetS in various body-size subgroups.

Declarations
Ethics approval and consent to participate This study was approved by the ethic committee of Tabriz University of medical sciences (IR.TBZMED.REC.1396.1263).