Analysis of the association between hemoglobin and hyperuricemia by NHANES database and Mendel randomization

doi:10.21203/rs.3.rs-5022392/v1

Background The relationship between hemoglobin and hyperuricemia is still unclear.

Objective To explore the association between hemoglobin and hyperuricemia.

Methods The data for our cross-sectional study were obtained from the National Health and Nutrition Examination Survey (NHANES) 2011–2020. Weighted multivariate logistic regression was performed to examine the association between hemoglobin and hyperuricemia. Inverse variance weighted (IVW), weighted-median, weighted-mode and MR-Egger were used in MR analyses to assess the causal relationship between hemoglobin and hyperuricemia. Heterogeneity and horizontal pleiotropy were evaluated using Cochrane Q-test and MR Egger intercept test, respectively. Sensitivity analysis was conducted using Leave one out to evaluate the bias of individual SNPs in MR analysis. All analyses were carried out using IBM SPSS 24.0 and R version 4.3.1.

Results A total of 21,855 NHANES participants were included in this study. After adjusting for all covariates, the association between hemoglobin and hyperuricemia was signifcant (P < 0.05). IVW estimates revealed that hemoglobin had signifcant efect on the risk of hyperuricemia (OR = 1.0329487, 95% CI = 1.005145–1.061521, P < 0.05). Findings of MR Egger regression, weighted median method and weighted mode were consistent with those from IVW (weighted median: OR = 1.056927, 95% CI = 1.029481–1.085105; P < 0.05; MR-Egger: OR = 1.0776376, 95% CI = 1.022953–1.135246, P < 0.05; Weighted mode: OR = 1.047532, 95% CI = 1.02109–1.074639, P < 0.05). The results of three MR Analyses methods also showed causal association between hemoglobin and hyperuricemia.

Conclusions Our findings support a causal association between hemoglobin and hyperuricemia. This suggests that monitoring hemoglobin levels is equally important in preventing the occurrence of hyperuricemia.

Hemoglobin

Hyperuricemia

NHANES

MR analysis

Uric acid is the end product of purine metabolism, Hyperuricemia (HUA) is caused by decreased uric acid excretion or abnormal purine metabolism[^1]. The incidence of HUA is constantly increasing[^2][3], and genetic and lifestyle factors such as consuming a purine rich diet, alcohol and/or fructose intake, and physical activity are considered to be the causes of HUA[^4].

Hemoglobin (Hb), an iron-containing metalloprotein in red blood cells. Research has found that Hb levels are closely related to metabolic levels, and when Hb levels are abnormal, blood uric acid levels often also show abnormal states[^5][6][7]. Through previous research in high-altitude areas, the research team found a phenomenon where hemoglobin levels and blood uric acid levels increase simultaneously. The hemoglobin level can reflect the hypoxia state to some extent[^8]. However, large-scale studies are still needed to confirm the relationship between Hb levels and HUA. Mendelian randomization (MR) is a data analysis technique used in epidemiological studies to evaluate causal inference. It uses genetic variations that are strongly correlated with exposure factors as instrumental variables to evaluate the causal relationship between exposure factors and outcomes[^9].

Therefore, we used data from the NHANES database for cross-sectional analysis, adjusting for demographic, behavioral, and chronic disease covariates, to analyze the relationship between Hb levels and HUA. In addition, for the first time, Mendelian randomization method was used to evaluate the causal relationship between Hb and HUA.

Cross‑sectional study

Database and study subjects

NHANES is a large-scale, multi-stage, ongoing, and nationally representative study conducted by the National Center for Health Statistics, a subsidiary of the Centers for Disease Control and Prevention in the United States. Details of sampling methods and data collection have been announced, and the research protocol has been approved by the National Center for Health Statistics Ethics Review Committee. All study participants have signed informed consent forms during the survey[^10]. This study used adult NHANES data from 2011 to 2020 (age ≥ 20 years old) to collect information on Hb, blood uric acid, age, gender, education level, marital status, chronic disease, smoking status, body mass index, etc. Participants lacking the above information were excluded.

Definition of diseases

The HUA is defined as more than 6 mg per deciliter [360μmol per liter] in women and more than 7 mg per deciliter [420μmol per liter] in men[^11].

Other covariates

According to the data definition in the report, gender is divided into male and female. Race is divided into Mexican Americans, other Hispanic, non-Hispanic White, non Hispanic black, non Hispanic Asian, and other races. The educational level is divided into less than 9th grade, 9-11th grade (includes 12th grade with no diploma), high school graduate/GED or equivalent, some college or AA degree, college graduate or above. Marital status includes Married/Living with Partner, Widowed/Divorced/ Separated, never married. The calculation method for body mass index (BMI) is to divide the measured weight (kg) by the measured height (m2). Smoking more than 100 cigarettes in one's life is defined as a smoker[^12]. Participants who reported hypertension, diabetes and hyperlipidemia were defined as having related chronic diseases.

Statistical analyses

Mean values and proportions of baseline characteristics were compared using linear regression for continuous variables and logistic regression for categorical variables. Covariates with statistical signifcance were included in the multivariate logistic analysis to examine the association between Hb and HUA. Odds ratios (OR) with 95% CIs and p values were calculated. A two-tailed test with p＜0.05 was considered significant.

Mendelian randomization study

Data sources

This study is based on aggregated genome-wide association study (GWAS) data from participants of the European Large Genome Consortium (EUR) to assess the association between plasma Hb and HUA. The GWAS ID for the HUA dataset is ebi-a-GCST90018977, which involves 343,836 samples containing a total of 19,041,286 snps. And the Hb dataset with GWAS ID ebi-a-GCST90025969, which involves 445,373 samples containing a total of 4,234,826 snps. Detailed data can be found at https://gwas.mrcieu.ac.uk/. As this study used a publicly available GWAS dataset for MR analysis, ethics committee approval and signing of informed consent by participants can be found in the original study from each data source[^13][14].

Selection of SNPs

SNPs related to Hb concentration were used as exposure data, screen SNPs of instrumental variables based on MR’s three core assumptions (correlation, independence, exclusivity), with a significance level of P<5×10-8, with r2<0.001 and a locus width of N=1 000kb, indicating that the selected SNPs are in a non linkage imbalanced state to ensure their independence from each other. Export to Excel to calculate the F-statistic. The calculation method for the F-statistic is F=R2/(1-R2) * (N-K-1)/K, R2=2 * MAF * (1-MAF)* β 2/sd2; MAF represents minor allele frequency. EAF represents the effect allele frequency. If EAF>0.5, then MAF=1-EAF; If EAF<0.5, then MAF=EAF. All weak instrumental variables with F<10 were removed. Then remove SNPs related to confounding factors such as metabolic disorders, obesity, gout, smoking and alcohol consumption, such as "rs477992", "rs760077", "rs1209384", "rs3791020", "rs1264081", "rs2275426", etc. (see supplementary materials for details). And the significance of SNPs with outcome factors is P>5×10-8.

Statistical analyses

We use the four MR methods in the TwoSampleMR software package to calculate the ratio between the impact of SNPs on exposure and outcomes, and then combine the results of each SNP calculation to evaluate the causal relationship between exposure and outcomes. These MR methods include inverse variance weighting (IVW), MR Egger regression, weighted median method and weighted mode. Among them, controlling for the level pleiotropy of IV to obtain unbiased estimates of the results has the greatest statistical power and is considered the most effective method by MR analysis. Therefore, this study used the results obtained from IVW analysis as the main reference result, and P<0.05 indicates statistical significance. The weighted median method is suitable for situations with a large number of invalid IVs, and even if the number reaches 50%, it can generate reliable causal estimates. The MR Egger method has a high tolerance for SNPs with horizontal pleiotropy.

To evaluate whether there is heterogeneity among the selected IVs, the Cochrane's Q test is first conducted. If P>0.05 shows no significant heterogeneity, the IVW fixed effects model is used to analyze the estimated Wald values for each SNP. Otherwise, a random effects model is used. MR Egger regression refers to evaluating the level of validity of an analysis by the size of the intercept. In a scatter plot, the correlation between the intercept and zero is tested. If P>0.05, it means that there is no level of validity; otherwise, there is. In addition, MR-PRESO analysis was conducted on exposure factors with a number of SNPs≥4, with a setting of K=10000. Abnormal SNPs were excluded, and the corrected results were estimated. Finally, sensitivity analysis was conducted using Leave one out to evaluate the bias of individual SNPs in MR analysis. SNPs with potential impact on the results were removed one by one, and MR analysis was performed again to ensure the stability of the results.

Cross‑sectional study

A total of 21855 NHANSE participants were included in this study. Table 1 presents the basic characteristics of patients with and without HUA. Compared with patients without HUA, people with HUA are older [56.0 (39.0, 68.0) vs 48.0 (33.0, 62.0)], have higher BMI [30.2 (26.4, 35.5) vs 26.8 (23.5, 31.2)], and have higher Hb levels [14.1 (13.0, 15.1) vs 13.9 (13.0, 15.0)]. A larger proportion of male (54.6%) had hypertension compared with female(45.4%). The prevalence of smoking status, hypertension, hyperlipoidemia and diabetes was higher among HUA participants than non-HUAparticipants (all P < 0.001). All variables included in this study are associated with the presence or absence of HUA (all p < 0.05).

Table 2 shows the results of logistic regression in four models. In unadjusted model 1, there was a significant correlation between Hb levels and HUA (OR=1.039, 95%CI:1.017-1.061). After adjusting for gender, age, and race, this association remains significant (OR=1.041, 95%CI:1.014-1.069). The subsequent model continued to adjust for covariates such as education level, marital status, and chronic disease, and the results showed that Hb levels were still significantly associated with HUA.

Table 1 The basic characteristics of patients

	HUA	Non-HUA	P
Age (years)	56.0 (39.0, 68.0)	48.0 (33.0, 62.0)	＜0.001
Gender (%)			＜0.001
Male (%)	54.6	47.1
Female (%)	45.4	52.9
Race (%)			＜0.001
Mexican Americans	8.9	13.2
other Hispanic	8.1	10.9
non-Hispanic White	39.8	37.6
non Hispanic black	27.6	22.1
non Hispanic Asian	12.0	12.3
other races	3.7	3.8
Education level (%)			＜0.001
less than 9th grade	7.9	8.0
9-11th grade (includes 12th grade with no diploma)	11.7	12.1
high school graduate/GED or equivalent	24.0	22.3
some college or AA degree	33.4	31.5
college graduate or above	23.0	26.1
Marital status (%)			＜0.001
married	51.3	53.7
widowed	16.1	11.6
divorced	14.8	13.5
separated	2.2	2.1
never married	11.6	13.4
living with Partner	4.1	5.7
BMI(kg/m²)	30.2 (26.4, 35.5)	26.8 (23.5, 31.2)	0.028
Somke (%)	45.9	42	＜0.001
Diabetes (%)	20.2	12.8	＜0.001
hypertension (%)	54.7	32.1	＜0.001
Hyperlipoidemia (%)	43.1	32.3	＜0.001
Hb (g/dL)	14.1 (13.0, 15.1)	13.9 (13.0, 15.0)	0.001

Table 2 Results of weighted multivariate logistic regression analysis

	OR	95%CI	P
Mode1	1.039	1.017-1.061	0.001
Mode2	1.041	1.014-1.069	0.003
Mode3	1.039	1.012-1.067	0.004
Mode4	1.051	1.023-1.080	0.000

Model 1: unadjusted;

Model 2: gender, age, and race were adjusted;

Model 3: Model 2+education, marital, BMI, and smoking

were adjusted;

Model 4: Model 3+chronic disease were adjusted;

OR Odds Ratio, CI Confdence Interval.

Mendelian randomization study

A total of 341 SNPs were associated with Hb levels. After removing chain imbalance and excluding SNPs associated with self reported gout history, urate content, metabolism, and other confounding factors, such as rs477992 and rs760077 etc, a total of 237 Hb related SNPs were included in the analysis. The F-statistic value of a single SNP ranges from 23.11 to 2143.94, indicating that the strength of the genetic tool remains strong even after conditional cleaning of SNPs. The MR Egger suggests heterogeneity between Hb levels and HUA, therefore a random effects model was used for analysis.

Figure 1 show the MR results of Hb in patients with HUA. The IVW results showed that Hb levels had a significant impact on HUA (OR=1.0329487, 95% CI=1.005145-1.061521, P<0.05). And the fndings of Weighted median, MR-Egger and Weighted mode were consistent with those from IVW(weighted median: OR=1.056927, 95% CI=1.029481–1.085105; P <0.05; MR-Egger: OR = 1.0776376, 95% CI = 1.022953–1.135246, P <0.05; Weighted mode: OR=1.047532, 95% CI=1.02109–1.074639). All methods have shown a significant correlation between Hb levels and HUA. Moreover, the most important IVW test results suggest a positive correlation between Hb levels and HUA (beta=0.032). The intercept of MR Egger regression in the scatter plot (Figure 2) is -0.001139469, P=0.06323077, indicating the absence of significant pleiotropy; MR PRESSO test global P=<0.001. The sensitivity analysis of the leave one out method did not yield different conclusions.

In this study, we conducted NHANES and MR analyses to confirm the relationship between Hb levels and HUA, marking the first study on the causal relationship between Hb levels and HUA. The results indicate that there is a significant correlation between Hb levels and the occurrence of HUA, and as Hb levels increase, HUA is more likely to occur, with Hb levels being the determining factor for HUA.

Related reports have found a certain correlation between Hb levels and blood uric acid levels. Pu et al.[^15]included data from 777 subjects and divided them into a HUA group and a healthy group. The hematological indicators between the two groups were compared, and the results showed that subjects with higher blood uric acid levels were more likely to exhibit higher levels of Hb. A retrospective study was conducted on 607 patients with HUA to explore the relationship between hematological indicators and HUA. It was found that blood uric acid levels were significantly positively correlated with Hb levels[^16]. Similar to previous results, the cross-sectional analysis using the NHANES database showed a significant correlation between Hb levels and elevated blood uric acid levels, and even after adjusting for other confounding factors, Hb levels still affect blood uric acid levels.

HUA is a metabolic disorder caused by an abnormal increase in blood uric acid levels due to purine metabolism disorders in the body[^17], the main mechanisms include excessive production or reduced excretion of uric acid, which is the final product of purine catabolism, including catabolism, de novo synthesis, and salvage pathways[^18], about 70% is excreted through the kidneys, while the remaining 20% -30% is excreted through the intestines[^19]. When the human body ingests excessive amounts of high purine foods, under the action of key enzymes in uric acid synthesis in the body, purine can be catalyzed to produce a large amount of uric acid, exceeding the renal excretion function, leading to a significant increase in blood uric acid levels. When renal dysfunction leads to renal excretion disorders, HUA can also occur.

Under hypoxic conditions, the production and secretion of uric acid can be increased. Studies have shown that hypoxia reduces the utilization of adenosine triphosphate (ATP) by downregulating the activity of Na-K-ATPase in proximal renal tubular epithelial cells, thereby limiting renal filtration and excretion function[^20][21]. High altitude polycythemia is diagnosed based on Hb levels and is caused by long-term exposure to high-altitude, low-pressure, and hypoxic environments. It is one of the main adaptation mechanisms of the human body to high-altitude and low oxygen. Multiple studies have shown that elevated hemoglobin levels and hyperuricemia often coexist among people in high-altitude areas[^22][23][24].

Hb is a transport protein within red blood cells. High Hb increases blood viscosity, slows down blood circulation and renal blood flow, and ultimately slows down the body's process of clearing uric acid. This may be the mechanism by which Hb affects the pathogenesis of HUA. This is consistent with the results of Mendelian analysis.

Similarly, when studying the clinical characteristics of metabolic syndrome, Seo et al.[^25] adjusted for relevant variables and found that the Hb levels in the HUA subgroup were higher than those in the normal subgroup. A prospective study showed that the Hb value in the gout patient group was significantly higher than that in the control group (14.8±1.4g/dl vs 13.7±1.6g/dl, P＜0.05)[^26].

This study has certain limitations. Firstly, NHANES is a cross-sectional survey and does not provide longitudinal follow-up data; Secondly, our exposure and outcome data sources are based on studies of European ancestry populations, so it is unclear whether our research findings are applicable to other races or regions.

Through cross-sectional analysis using the NHANES database and Mendelian randomization analysis, we found a correlation between Hb levels and HUA, that is, an increase in Hb levels is associated with the risk of HUA. When this study requires large-scale real-world data, we hope to further validate this conclusion.

Funding

This work was supported by Special funds for the construction of the Clinical Medical Research Center of the Chinese Academy of Traditional Chinese Medicine (2022LYJSZX05) and Scientific and technological innovation project of China Academy of Chinese Medical Sciences (CI2021A01603).

Conflicts of Interest

The author of this article does not have any conflicts of interest.

Declarations of interest

None.

Data availability

The data of this study can be found in NHANES database (https://www.cdc.gov/nchs/nhanes/index.htm) and GWAS database (https://gwas.mrcieu.ac.uk/).

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No competing interests reported.

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Analysis of the association between hemoglobin and hyperuricemia by NHANES database and Mendel randomization

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Abstract

Figures

Introduction

Methods

Results

Discussion

Conclusions

Declarations

References

Additional Declarations

Supplementary Files

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