Monocyte to High-Density Lipoprotein Cholesterol Ratio (MHR) and Serum Uric Acid in Chinese Adults: A Cross-Sectional Study

DOI: https://doi.org/10.21203/rs.3.rs-1089629/v1

Abstract

Background: Previous studies have shown the monocyte to high-density lipoprotein cholesterol (HDL-C) ratio (MHR) is a predictor of various diseases such as coronary heart disease, diabetic microangiopathy, and metabolic syndrome. However, there are few scientific reports on the correlation between MHR and serum uric acid. The objective of this report is to explore the interrelation between MHR and serum uric acid in Chinese.

Methods: This study is a cross-sectional study. A total of 646 participants in the southwest of China received a health examination at the Health Management Center of Deyang People's Hospital. The examination included blood pressure, blood routine examination, lipids, fasting glucose, serum transaminases, serum uric acid, and various standardized questionnaires. Generalized additive model (GAM) and smoothed curve fitting were used to explore the relationship between MHR and serum uric acid. Then, subgroup analyses were performed to investigate the robustness of this relationship.

Results: After adjusted confounders (age, sex, BMI, SBP, DBP, AST, ALT, fasting glucose, TC, LDL, smoking, drinking, and exercise status), MHR was positively correlated with serum uric acid(p<0.001). As shown in the smoothing curve, there was an approximately linear correlation between MHR and serum uric acid, and the linear correlation coefficient was 146.74 (95% CI: 96.16 ~ 197.33, P <0.0001). Subgroup analyses showed the effect of MHR on serum uric acid was smaller in occasional smokers and smokers compared to nonsmokers (P = 0.0194).

Conclusion: MHR was significantly and positively correlated with serum uric acid. Additionally, the effect of MHR on serum uric acid was rather less in people who smoked more.

Introduction

Hyperuricemia is a dysmetabolic caused by disruption of purine metabolism. When serum uric acid exceeds its saturation in the blood and tissue fluids, uric acid crystals can be deposited locally in the joints. This deposition can lead to a local inflammation and tissue damage, which is called gout. Hyperuricemia and gout are associated with the morbidity and poor prognosis in chronic kidney disease(1-3), cardiovascular disease(2, 4), diabetes(2), atrial fibrillation(5), stroke(6), and dyslipidemia(2). MHR, a new predictive marker of inflammation, indicates the ratio of inflammatory markers (monocyte) to anti-inflammatory markers (HDL-C)(7). MHR is an independent risk factor for metabolic syndrome(8), coronary artery disease(9) and diabetic microangiopathy(10). Perhaps MHR is also associated with the prevalence of hyperuricemia and gout. The main objective of this study is to explore the correlation between MHR and serum uric acid in Chinese adults.

Methods And Materials

Study population

We conducted a cross-sectional study in August 2021 at the Health Management Center of Deyang People's Hospital, Sichuan Province, China. A total of 646 participants aged 24-84 in southwest of China enrolled in the study according to the exclusion criteria.

Exclusion criteria: (1) participants with acute gout attack, (2) participants receiving uric acid-reducing treatment, (3) participants with acute or chronic infection, (4) Participants with abnormal liver function, abnormal renal function, anemia, bleeding, and hemolytic diseases, (5) uremic patients, (6) Participants taking medications that might affect hematopoiesis, (7) Participants taking medications that might affect kidney function, (8) Participants taking lipid-modulating medications, (9) participants with tumor history, (10) Participants unwilling to accept the questionnaire.

Clinical And Biochemical Measurements

After more than eight hours of overnight fasting, we took participants' elbow venous blood to test total cholesterol (TC), high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), glutathione aminotransferase (AST), alanine aminotransferase (ALT), fasting glucose, serum uric acid, and blood routine examination. The height, weight, systolic blood pressure (SBP), and diastolic blood pressure (DBP) were also recorded. The body mass index (BMI)was calculated based on height and weight [BMI = weight (kg)/ height (m)2]. MHR was calculated from HDL-C and monocyte count [MHR = monocyte/HDL-C].

Based on the standardized methodological recommendations of the World Health Organization (WHO) for smoking surveys(11), we defined smokers as those who smoked more than 1 cigarette a day for at least 6 months continually or accumulatively. Those who smoked more than 4 cigarettes a week but fewer than 1 cigarette a day were defined as occasional smokers. People who had never or rarely smoked in the past were categorized as nonsmokers, as well as those who smoked daily for at least six months but did not smoke at the survey time. To classify drinking status, we considered participants who drank at least once a month as drinkers, those who drank less than once a month but more than once a year as occasional drinkers, others nondrinkers(12). The total intensity of the participants' various physical activities in the past 7 days was estimated based on the metabolic equivalent (MET) from the International Physical Activity Questionnaire (IPAQ) short form survey(13). Walking refers to walks work or at home, including walking for transport or exercise, which costs about 3.3 METS. Moderate physical activities cost about 4.0 METS, refer to those that require moderate effort to complete, with respiratory slightly heavier than usual (including cycling, table tennis, badminton, and ballroom dancing). Vigorous physical activities cost about 8.0 METS, refer to those that require significant effort to complete, with respiratory significantly heavier than usual (including weight lifting, running, swimming, and prolonged healthy exercise). Total METS (MET-min/week) = 3.3 * walking time (minutes) * days + 4.0 * moderate physical activities time (minutes) * days + 8.0 * high intensity physical activities time (minutes) * days. Individuals with total METS ≥3000 were divided into the high-intensity group, whose total METS between 600- 3000 were divided into the medium-intensity group, the rest were the low-intensity group(13).

Statistical analysis

Continuous variables were expressed as mean ± standard deviation (SD) (normal distribution) or median (maximum, minimum) (skewed distribution), and categorical variables were expressed as percentages. One-way ANOVA and Kruscal Whallis H test were applied to explore the statistical differences of continuous variables. Chi-square test was used to explore the statistical differences of categorical variables. The univariate linear regression model was used to analyze the association between MHR and serum uric acid. We showed the results of unadjusted, minimally adjusted, and fully adjusted model according to the STROBE statement on an observational study reporting specification. Covariates were adjusted or not based on the principle that: The matching dominance ratio would be changed by at least 10% after being added to the model(14). Generalized additive model (GAM) and smoothed curve fitting were used to explore the relationship between MHR and serum uric acid. If there was a nonlinear relationship, the inflection point of the maximum likelihood model would automatically be calculated with a recursive method(15). Otherwise, the linear correlation coefficient between MHR and serum uric acid would be calculated. Subgroup analyses were performed with a stratified linear regression model. The likelihood ratio test was used to analyze the modifiability and interactions among subgroups. All the analyses were performed with the statistical software packages R (http://www.R-project.org,The R Foundation) and EmpowerStats (http://www.empowerstats.com,X&Y Solutions, Inc., Boston, MA). A p-value less than 0.05 (bilateral) was considered statistically significant.

Results

Baseline characteristics of participants

The average age of participants was 49.31±11.16 years old, and about 57.14% of them were male and 42.88% female. The baseline characteristics were listed in Table 1. Depending on the MHR, participants were divided into MHR low-level, medium-level, and high-level groups. There was no statistically significant difference in age and LDL-C among the different MHR groups. Compared with the MHR low-level group, ALT, AST, BMI, SBP, DBP, fasting glucose, and serum uric acid were significantly higher and TC was significantly lower in the medium-level and high-level groups.

Table 1

Baseline characteristics of participants

MHR

Low-level group

Middle-level group

High-level group

P-value

Number

214

216

216

 

AGE (years, mean±SD)

49.40 ± 11.11

49.81 ± 10.90

48.70 ± 11.47

0.579

LDL-C (mmol/L, mean±SD)

2.72 ± 0.70

2.86 ± 0.71

2.80 ± 0.68

0.115

TC (mmol/L, mean±SD)

4.92 ± 0.90

4.88 ± 0.90

4.71 ± 0.86

0.044

ALT (U/L, median, min-max)

15.00 (4.00-67.00)

19.00 (2.00-130.00)

26.00 (5.00-112.00)

<0.001

AST (U/L, mean±SD)

22.26 ± 6.55

23.63 ± 9.57

25.22 ± 8.77

0.001

BMI (kg/m2, mean±SD)

22.19 ± 2.84

23.77 ± 3.14

25.60 ± 3.03

<0.001

SBP (mmHg, mean±SD)

119.64 ± 16.25

122.72 ± 15.51

127.55 ± 16.39

<0.001

DBP (mmHg, mean±SD)

71.01 ± 10.87

74.47 ± 10.67

78.00 ± 11.43

<0.001

Fasting glucose (mmol/L, mean±SD)

4.87 ± 0.58

5.09 ± 0.80

5.25 ± 1.22

<0.001

Uric acid (umol/L, mean±SD)

310.88 ± 72.25

360.71 ± 88.72

404.25 ± 94.84

<0.001

SEX (n,%)

     

<0.001

Male

64 (29.91%)

127 (58.80%)

178 (82.41%)

 

Female

150 (70.09%)

89 (41.20%)

38 (17.59%)

 

Smoking status (n,%)

     

<0.001

Nonsmokers

194 (90.65%)

154 (71.63%)

114 (52.78%)

 

Occasional smoking

7 (3.27%)

16 (7.44%)

14 (6.48%)

 

Smokers

13 (6.01%)

45 (20.93%)

88 (40.74%)

 

Drinking state (n,%)

     

<0.001

Nondrinkers

151 (70.56%)

113 (525.6%)

84 (38.89%)

 

Occasional drinking

44 (20.56%)

62 (28.84%)

79 (36.57%)

 

Drinkers

19 (8.88%)

40 (18.60%)

53 (24.54%)

 

Exercise status (n,%)

     

0.229

Low-intensity group

171 (79.91%)

172 (79.63%)

157 (72.69%)

 

Medium-intensity group

29 (13.55%)

25 (11.57%)

33 (15.28%)

 

High-intensity group

14 (6.54%)

19 (8.80%)

26 (12.04%)

 
LDL-C low-density lipoprotein cholesterol, TC total cholesterol, ALT alanine aminotransferase, AST aspartate transaminase, BMI body mass index, SBP systolic blood pressure, DBP diastolic blood pressure
P < 0.05

Univariate Analysis

The results of univariate analyses were shown in Table 2. Univariate analyses showed MHR, LDL, ALT, AST, BMI, SBP, DBP, fasting glucose, smoking, and drinking were positively associated with serum uric acid. We also found that age, TC, and exercise were not associated with serum uric acid while being female was a protective factor for elevated serum uric acid. Compared to males, serum uric acid decreased by 103.08 umol/L in females on average (P<0.0001).

Table 2

The results of univariate analysis

 

Statistics

Effect size (β)

P-value

MHR (umol/L, mean±SD)

0.31 ± 0.14

285.34 (239.17, 331.51)

<0.0001

AGE (years, mean ±SD)

49.31 ± 11.16

-0.57 (-1.22, 0.08)

0.0850

LDL-C (mmol/L, mean ±SD)

2.79 ± 0.70

16.92 (6.58, 27.26)

0.0014

TC (mmol/L, mean ±SD)

4.84 ± 0.89

3.68 (-4.46, 11.81)

0.3761

ALT (U/L, median, min-max)

24.26 ± 16.12

1.91 (1.48, 2.33)

<0.0001

AST (U/L, mean ±SD)

23.71 ± 8.47

2.51 (1.68, 3.35)

<0.0001

BMI (kg/m2, mean ±SD)

23.86 ± 3.31

10.44 (8.41, 12.48)

<0.0001

SBP (mmHg, mean ±SD)

123.31 ± 16.36

1.07 (0.64, 1.51)

<0.0001

DBP (mmHg, mean ±SD)

74.50 ± 11.34

2.16 (1.54, 2.78)

<0.0001

Fasting glucose (mmol/L, mean ±SD)

5.07 ± 0.92

8.93 (1.09, 16.78)

0.0259

SEX (n, %)

     

Male

369 (57.12%)

Reference

 

Female

277 (42.88%)

-103.08 (-115.35, -90.81)

<0.0001

Smoking status (n, %)

     

Nonsmokers

462 (71.63%)

Reference

 

Occasional smoking

37 (5.74%)

55.18 (24.69, 85.66)

0.0004

Smokers

146 (22.64%)

50.93 (33.99, 67.87)

<0.0001

Drinking state (n, %)

     

Nondrinkers

348 (53.95%)

Reference

 

Occasional drinking

185 (28.68%)

55.63 (39.88, 71.38)

<0.0001

Drinkers

112 (17.36%)

75.63 (56.83, 94.43)

<0.0001

Exercise status (n, %)

     

Low-intensity group

500 (77.40%)

0

Medium-intensity group

87 (13.47%)

15.54 (-5.80, 36.88)

0.1540

High-intensity group

59 (9.13%)

15.61 (-9.68, 40.89)

0.2269

LDL-C low-density lipoprotein cholesterol, TC total cholesterol, ALT alanine aminotransferase, AST aspartate transaminase, BMI body mass index, SBP systolic blood pressure, DBP diastolic blood pressure
P < 0.05

The Relationship Between Mhr And Serum Uric Acid

To further demonstrate that MHR was an independent predictor of serum uric acid elevation, we performed the unadjusted and adjusted models with logistic regression analyses. As shown in Table 3, there was a positive correlation between MHR and serum uric acid in the unadjusted model (β = 285.340, 95% confidence interval (CI): 239.175~ 331.506, P<0.0001). A positive correlation also existed in the minimally adjusted model (adjusted for age and sex, β = 155.848, 95%CI: 109.641~ 202.055, P<0.0001) and fully adjusted model (adjusted for sex, age, LDL, TC, ALT, AST, BMI, SBP, DBP, GLU, smoking, drinking, exercise status, β = 128.77, 95%CI: 79.96~177.59, P<0.0001). We found significantly higher serum uric acid in MHR middle-level and high-level groups compared with the MHR low-level group among all the unadjusted and adjusted models (P for trend <0.001). 

Table 3

Relationship between MHR and serum uric acid in different models

 

Non-adjusted (β, 95%CI, P)

minimally adjusted (β, 95%CI, P)

fully adjusted (β, 95%CI, P)

MHR (umol/L)

285.340 (239.175,331.506) <0.0001

155.848 (109.641,202.055) <0.0001

128.77 (79.96, 177.59) <0.0001

MHR grouping

     

Low-level

Reference

Reference

Reference

Middle-level

49.830 (33.603, 66.057) <0.0001

25.243 (10.240, 40.246) 0.0010

20.51 (5.51, 35.50) 0.0075

High-level

93.371 (77.145, 109.598) <0.0001

48.003 (31.911, 64.096) <0.0001

36.33 (19.42, 53.24) <0.0001

P for trend

<0.001

<0.001

<0.0001

Non-adjusted: we did not adjust other covariates
Minimally adjusted model adjust for: SEX and age
Fully adjusted model adjust for: sex, age, LDL, TC, ALT, AST, BMI, SBP, DBP, Fasting glucose, smoking and drinking status, exercise status
P < 0.05

The Analyses Of Linear Relationship

After adjusted covariates, smooth fitting showed there was an approximately linear relationship between MHR and serum uric acid. As shown in Figure 1, after adjusted sex, age, LDL, TC, ALT, AST, BMI, SBP, DBP, fasting glucose, smoking, drinking, exercise status, the linear correlation coefficient of MHR on serum uric acid was 146.74 (95% CI: 96.16 ~ 197.33, p value<0.0001) (Table 4). 

Table 4

analysis of the concentration-effect relationship

Independent variable

Effect size (β)

95% CI

P-value

MHR

146.74

96.16 to 197.33

<0.0001

Effect: uric acid Cause: MHR
Adjusted: sex, age, LDL, TC, ALT, AST, BMI, SBP, DBP, fasting glucose, smoking status, drinking status, exercise status
P < 0.05

The Results Of Subgroup Analyses

As shown in Table 5, the interactive test for smoking status was statistically significant (P = 0.0194). Compared with the nonsmokers, for per SD increase in MHR, serum uric acid rose by an average of 110.9 umol/L less in the occasional smokers and 134.1 umol/L less in smokers. There were no statistically significant in the interactive tests for sex, age, BMI, SBP, DBP, drinking, and exercise status (P were 0.5992, 0.6114, 0.6586, 0.5773, 0.0782, and 0.4333, 0.2961, respectively)

 
Table 5

Effect size of MHR on serum uric acid in established and exploratory subgroups

Characteristic

No of participants

Effect size (95%CI)

P (interaction)

SEX

   

0.5992

male

369

139.6 (82.2, 197.0)

 

female

277

167.0 (75.0, 259.1)

 

Age(year)

   

0.6114

\(\le\)50

349

156.96 (93.43, 220.49)

 

>50

297

135.62 (67.40, 203.83)

 

BMI (kg/m2)

   

0.6586

\(\le\)18.5

27

54.5 (-379.3, 488.3)

 

18.5<BMI\(\le\)24

312

174.8 (104.0, 245.7)

 

>24

307

139.5 (75.4, 203.7)

 

SBP (mmHg)

   

0.5773

\(\le\)120

321

160.7 (89.4, 232.0)

 

>129

325

137.0 (75.9, 198.1)

 

DBP (mmHg)

   

0.0782

\(\le\)80

452

174.7 (115.1, 234.2)

 

>80

194

96.1 (19.9, 172.3)

 

Smoking status

   

0.0194

Nonsmokers

462

202.6 (138.5, 266.6)

 

Occasional smoking

37

91.7 (-87.3, 270.7)

 

Smokers

146

68.5 (-10.7, 147.8)

 

Drinking status

   

0.4333

Nondrinkers

348

152.5 (80.4, 224.6)

 

Occasional drinking

185

175.6 (97.4, 253.7)

 

Drinkers

112

98.7 (4.5, 192.9

 

Exercise status

   

0.2961

Low-intensity group

500

149.7 (92.9, 206.5)

 

Medium-intensity group

87

197.2 (87.6, 306.9)

 

High-intensity group

59

171.5 (-50.8, 193.9)

 
Adjusted for sex, age, LDL, TC, ALT, AST, BMI, SBP, DBP, fasting glucose, smoking status, drinking status, and exercise status, but not the stratification variable
P < 0.05

Discussion

The present study was to explore the relationship between MHR and serum uric acid. Among all the unadjusted and adjusted models, MHR was positively correlated with serum uric acid. After adjusted covariates, there was an approximately linear relationship between MHR and serum uric acid in the smoothed curve, with a correlation coefficient of 146.74 (95%CI: 96.16~197.33, p-value < 0.0001). It means that serum uric acid increased by an average of 146.74 umol/L for each SD increase of MHR. Interestingly, the MHR had a smaller effect on serum uric acid in occasional smokers and smokers compared to nonsmokers.

We performed a PubMed search with the keywords "serum uric acid" and “monocyte to high-density lipoprotein cholesterol (HDL-C) ratio" simultaneously. By the end of October 2021, only a scientific paper was searched in the database. The finding that a linear correlation existed between MHR and serum uric acid in this study is consistent with the result of Chen and his team based on the NCRCHS cross-sectional epidemiological survey(16). After adjusted confounders, they utilized multivariate logistic regression to demonstrate the independent relationship between MHR and the prevalence of hyperuricemia. Then a smoothed curve fitting and a generalized additive model were also used to further describe the linear relationship. Since stratification analyses were extremely important in scientific research, Chen and his team also made stratification analyses to detect the robustness of this association. In their study, the stratification variables included age, sex, BMI, SBP, fasting glucose, eGFR. Nevertheless, this adjustment is still controversial. For example, HDL-C has been used to calculate MHR already, did it still need to be adjusted as a confounder? Then, LDL-C, ALT, AST, and DBP also affect serum uric acid (16-18), those also need to be matched. More importantly, stratification variables as smoking, drinking, and exercise status should also be included in the subgroup analyses, which was neglected by Chen neglected. Therefore, their conclusions are limited. In our study, we used sex, age, BMI, SBP, DBP, smoking, drinking, and exercise status as stratified variables. It is interesting to note that the effect size of MHR on serum uric acid significantly differed across the different smoking statuses. For every SD increase in MHR, serum uric acid increased by 110.9 mmol/l less in occasional smokers and 134.1 mmol/l less in smokers, compared to the nonsmokers. Clearly, the risks for the prevalence of hyperuricemia and gout include male(19), age(20), BMI(2, 21, 22), blood pressure(22, 23), alcohol consumption(24, 25) and physical activity(26). There is no consensus on the relationship between smoking and the prevalence of hyperuricemia and gout yet(27). Although numerous studies demonstrated either a positive or non-significant correlation between smoking and serum uric acid(28-30), it has also been suggested that smoking can lower serum uric acid(31-35). Masahiko Tsuchiya(36) measured plasma antioxidant concentrations such as uric acid, ascorbic acid, nitrate, cysteine, and methionine of smokers. They found that plasma antioxidant concentrations were significantly lower in the smokers one hour after smoking, suggesting that the free radical component of cigarettes may deplete serum antioxidants in serum (including serum uric acid) (37). This may partly explain why MHR has a smaller effect on serum uric acid in higher smoking subgroups in our study. Another possible mechanism is that cyanide in cigarette smoke reduces serum urate by inhibiting xanthine oxidase, a key enzyme of uric acid(38).

Our research has many advantages. First, this is an observational study, and the potential for confounding is inevitable. To avoid residual confusion, we not only verified the linear relationship between MHR and serum uric acid but also made rigorous statistical adjustments for sex, age, LDL, TC, ALT, AST, BMI, SBP, DBP, fasting glucose, smoking, drinking, and exercise status. Second, effect modification factor analysis allows for fuller use of the data. The subgroup analyses showed that the effect of MHR on serum uric acid was smaller in occasional smokers and smokers compared to nonsmokers.

There are some limits to our study. First, this study is an analytical cross-sectional study, which provides weak evidence between exposure and result. It is difficult to distinguish causality. Second, the findings may not be generalizable for other ethnic due to the fact the study population included only Chinese in the southwest of China.

Conclusion

MHR was significantly and positively correlated with serum uric acid. In addition, the effect of MHR on serum uric acid was rather less in people who smoked more.

Abbreviations

HDL-C: high-density lipoprotein cholesterol; MHR: monocyte to high-density lipoprotein cholesterol ratio; TC: total cholesterol; LDL-C: low-density lipoprotein cholesterol; AST: glutathione aminotransferase; ALT: alanine aminotransferase; SBP: systolic blood pressure; DBP: diastolic blood pressure; BMI: body mass index; WHO: World Health Organization; MET: metabolic equivalent; IPAQ: International Physical Activity Questionnaire; SD: standard deviation; GAM: Generalized additive model; CI: confidence interval.

Declarations

Availability of data and materials

The datasets generated and analyzed during the current study are available from the corresponding author on reasonable request.

Acknowledgements

The authors are grateful to all participants for their time and effort.

Funding

This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.

Author information

Yuexi Li, Xiaoqin Liu, and Yuehan Luo contributed equally to this work.

Ethic declarations

This study was approved by the ethics committee of Deyang people's hospital. All patients signed informed consent before health examination. We guarantee that all data identifying participants are anonymous. 

Competing interest

The authors declare that they have no competing interests.

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