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School of Public Health, Wannan Medical College, Wuhu, Anhui, ChinaNutritional Epidemiology Institute and School of Public Health, Tianjin Medical University, Tianjin, China
School of Public Health, Wannan Medical College, Wuhu, Anhui, ChinaNutritional Epidemiology Institute and School of Public Health, Tianjin Medical University, Tianjin, China
Requests for reprints should be addressed to Kaijun Niu, MD, School of Public Health of Tianjin University of Traditional Chinese Medicine, 10 Poyanghu Road, West Area, Tuanbo New Town, Jinghai District, Tianjin 301617, China.
School of Public Health of Tianjin University of Traditional Chinese Medicine, Tianjin, ChinaNutritional Epidemiology Institute and School of Public Health, Tianjin Medical University, Tianjin, ChinaDepartment of Toxicology and Sanitary Chemistry, School of Public Health, Tianjin Medical University, Tianjin, ChinaTianjin Key Laboratory of Environment, Nutrition and Public Health, Tianjin, ChinaCenter for International Collaborative Research on Environment, Nutrition and Public Health, Tianjin, China
Genetic factors have been associated with hyperuricemia in large studies, but the extent to which this can be offset by a healthy lifestyle is unknown. This study aimed to examine whether healthy lifestyle could reduce hyperuricemia risk among individuals with different genetic profiles.
Methods
We defined a lifestyle score using body mass index, smoking, alcohol consumption, physical activities, and diets in 2796 unrelated individuals from the Tianjin Chronic Low-grade Systemic Inflammation and Health (TCLSIH) cohort study. Polygenic risk scores (PRS) were constructed based on uric acid loci. Associations of combined lifestyle factors and genetic risk and incident hyperuricemia were estimated using Cox proportional hazard regression.
Results
Of 2796 individuals, 747 participants (26.7%) developed hyperuricemia. Genetic risk and lifestyle were predictors of incident events, and they showed an interaction for the outcome. Compared with high PRS, low PRS reduced risk of incident hyperuricemia by 40%, and compared with unhealthy lifestyle, healthy lifestyle reduced risk of incident hyperuricemia by 41%. Compared with unhealthy lifestyle and high genetic risk, adherence to healthy lifestyle was associated with a 68% (95% confidence interval, 44%-81%) lower risk of hyperuricemia among those at a low genetic risk.
Conclusions
In this prospective cohort study, we observed an interaction between genetics and lifestyle and the risk of hyperuricemia. The public health implication is that a healthy lifestyle is important for hyperuricemia prevention, especially for individuals with high genetic risk scores.
With improvements in living standards and changes in dietary habits, the prevalence of hyperuricemia has increased annually and constitutes a burden of health-related quality of life and direct health care costs.
According to the National Health and Nutrition Examination Survey (NHANES), the prevalence of hyperuricemia in the United States more than doubled between 1960 and 2008, and remained stable over the past decade.
Contemporary prevalence of gout and hyperuricemia in the United States and decadal trends: the National Health and Nutrition Examination Survey, 2007-2016.
The important drivers of this complex disease can be attributed to both genetic and lifestyle factors. During the past decade, genome-wide association studies have identified a number of genetic variants, mostly single nucleotide polymorphisms (SNPs), which were associated with hyperuricemia.
ABCG2 rs2231142 variant in hyperuricemia is modified by SLC2A9 and SLC22A12 polymorphisms and cardiovascular risk factors in an elderly community-dwelling population.
These SNPs can provide a continuous and quantitative measure of genetic susceptibility and are useful for evaluating the risk of the development of hyperuricemia in individuals.
A fast method that uses polygenic scores to estimate the variance explained by genome-wide marker panels and the proportion of variants affecting a trait.
have markedly reduced risk of hyperuricemia. This strategy underlies an effective way to improve the prevention of hyperuricemia in the general population.
However, whether genetic susceptibility to hyperuricemia is attenuated by a favorable lifestyle remains unknown. The purpose of this study is to test the hypothesis that both genetic factors and baseline adherence to a healthy lifestyle contribute independently to the risk of hyperuricemia; as well as to determine whether healthy lifestyle factors are associated with a lower risk of hyperuricemia among participants with different genetic profiles. The secondary aim was to investigate possible interactions between health behaviors and genetic risk.
Materials and Methods
Study Populations
The Tianjin Chronic Low-grade Systemic Inflammation and Health (TCLSIH) Cohort Study is a dynamic prospective cohort that enrolled Chinese patients between the ages of 18 and 90 years. The design, methods, and other details of the TCLSIH have been previously described elsewhere.
A dietary pattern rich in animal organ, seafood and processed meat products is associated with newly diagnosed hyperuricaemia in Chinese adults: a propensity score-matched case-control study.
Briefly, all participants completed baseline questionnaires, medical examinations, and provided baseline blood samples since May 2013. Informed consent was provided in writing by all participants, written, and the study was approved by the Institutional Review Board of Tianjin Medical University. The study has been performed in accordance with the ethical standards of the 1964 Declaration of Helsinki and its later amendments. In this study, from the 3778 unrelated individuals with available genotypes, participants with hyperuricemia at baseline or missing data on lifestyle factors and covariates were excluded (details in Figure 1). Finally, 2796 participants (747 cases and 2049 controls) remained to investigate the associations of lifestyle and genetic risk with incident hyperuricemia.
Figure 1Flow diagram showing the selection of the study population. SNP = single nucleotide polymorphism.
Five lifestyle factors were collected and considered to define lifestyle score: smoking, alcohol consumption, body mass index (BMI), physical activity, and diet, which was consistent with previous studies.
Smoking questions included smoking status and the amounts of tobacco smoked per day for ever-smokers. Drinking frequency and types of alcoholic beverages were acquired to define alcohol information. Height and body weight were recorded using a standard protocol, and the BMI was calculated by dividing the weight in kilograms by the square of the height in meters. The short form of the International Physical Activity Questionnaire was used to evaluate physical activity.
We asked about whether subjects had performed any type of activities during the previous week and calculated metabolic equivalent of task (MET) coefficients according to the following formula: MET coefficient of activity × duration (hours) × frequency (days). Dietary intake was assessed using a food frequency questionnaire including 100 food items with specified serving sizes; details have been described in previous studies.
We defined a healthy lifestyle as one associated with normal BMI (18.5-23.9 kg/m2), no smoking, no alcohol consumption, a moderate level of physical activity (2-3 quartiles of MET), and healthy diets (lowest quartile score in meat dietary pattern). The lifestyle index scores ranged from 0 to 5, with lower scores indicating higher adherence to healthy lifestyle. Alternatively, a weighted lifestyle score was then derived by using the percentage of the β coefficient to the sum of the β coefficients of each lifestyle factor in the Cox proportional hazards regression model, with all 5 lifestyle factors and adjustment for age, sex, education, household income, and total energy intake, and the first 10 principal components of ancestry and hyperuricemia as an outcome. The weighted lifestyle score ranged from 0 to 100 and was categorized as healthy (quartile 1) and unhealthy (quartile 2-4).
Illumina Asian Screening Array was used to genotype genomic DNA samples isolated from peripheral blood leukocytes; eventually, we identified the genotypes of 743,722 SNPs from 3778 participants. After stringent quality control filtering, we excluded: 1) individual call rate <98%; 2) SNP genotype call rates with <98% completeness; 3) minor allele frequency <1%; 4) Hardy–Weinberg equilibrium tests with value of P <1 × 10−6; 5) a mismatch between genetic and reported sex; 6) duplication of genetic data; 7) extremely low or high heterozygosity (>3 standard deviations from the mean); 8) SNPs in sex chromosomes or mitochondrial DNA.
A total of 468,600 SNPs in 3675 participants with an overall call rate of 99.8% were included.
The software IMPUTE with the 1000GP_Phase3 data (v2.3.2) (1,000 Genomes haplotypes – Phase 3, October 2014) as reference panel was used for imputing the genotype data following pre-phasing using SHAPEIT v2.12.
Polygenic risk scores (PRS) were created following an additive model for hyperuricemia. For each individual, PRS was calculated across selected SNPs by summing the number of risk alleles after multiplication with the effect sizes of the SNPs on disease. Effect sizes of SNP–disease associations were based on previously published genome-wide association studies,
SNPs were excluded if they were missing in TCLSIH data. Independent SNPs were selected by using the linkage disequilibrium clumping procedure (at r2 <0.2, 500 Kb apart) implemented in PLINK version 1.9, and the best-fit P-value threshold was identified by using PRSice version 2.3.5
(Supplementary Figure 1). Finally, 4432 SNPs were included (details of the SNPs were presented in Supplementary Table 2). PRS categorized into low (quartile 1) and high (quartile 2-4).
Supplementary Figure 1Best-fit P value threshold selection.
Demographic data (age, sex, education level, and monthly household income) were collected using self-administered questionnaires. Dietary intake was assessed using a food frequency questionnaire with specified serving sizes that were described by natural portions or standard weight and volume measures of the servings commonly consumed in this study population.
The nutrient database was from the valid and reliable Chinese Food Composition Tables and we analyzed the questionnaire using an ad hoc computer program. By combining the information obtained from the food frequency questionnaire with the Chinese Food Composition Tables, total energy intake for each participant was computed.
Ascertainment of Hyperuricemia
Blood samples were collected from the antecubital vein, with fasting time longer than 8 hours, while subjects were in the sitting position. Hemodynamic measurements were performed on the same day as blood sampling. Serum uric acid levels were measured by enzymatic colorimetric test using the Roche 912 analyzer (Roche Diagnostics, Indianapolis, Ind); the lower limit of detection was 0.2 mg/dL. Hyperuricemia was defined as uric acid levels ≥7.0 mg/ dL in men and ≥6.0mg/dL in women.
All participants underwent blood test during annual medical examinations.
Statistical Analysis
Normality of continuous variables was assessed using the Kolmogorov–Smirnov test. We log-transformed all continuous variables, owing to their skewed distributions. Continuous variables were presented as the geometric mean (95% confidence interval [CI]) and categorical variables were presented as percentages in baseline characteristics. Follow-up lasted from the completion of the initial lifestyle survey to the first diagnosis of hyperuricemia, death, loss to follow-up, or the end of follow-up (December 31, 2019), whichever came first. The weighted lifestyle score and PRS are divided into 2 categories. Cox proportional regression model was used to evaluate the associations of lifestyle score and PRS with incident hyperuricemia risk. Multivariable models were adjusted for sex (men or women), age (continuous: years), education level (college graduate or not), monthly household income (≤ or >10,000 Yuan), total energy intake (continuous: kcal/day), and first 10 principal components of ancestry. We stratified our analyses by PRS to assess whether the association between healthy lifestyle and hyperuricemia persisted across different genetic profiles and stratified by lifestyle to assess whether the association between PRS and hyperuricemia persisted across different lifestyle profiles.
We performed several sensitivity analyses to assess the robustness of our results. First, to rule out the effect of diuretic use on the results, we excluded participants on antihypertensive drugs or with a history of cardiovascular or chronic kidney disease. Second, to rule out the effect of baseline disease states on the outcomes, we repeated the main analysis after excluding participants with a history of cardiovascular or chronic kidney disease or cancer at baseline.
We used the likelihood ratio test to compare models with and without cross-product terms to test the interaction between genetic risk and lifestyle. P values were 2-sided, with P < .05 defined as statistically significant. All analyses were performed using the Statistical Analysis System 9.3 edition for Windows (SAS Institute Inc., Cary, NC).
Results
In this prospective cohort study, we included 2796 participants for the analysis of the combination of genetic and lifestyle. The mean (SD) age was 37.7 (11.3) years, and 1364 (48.7%) were female. In total, 701 individuals (25.0%) had healthy lifestyle and 699 (25.0%) had low genetic risk. During a median follow-up of 4.2 years for new-onset hyperuricemia, 747 participants (26.7%) developed it. The geometric mean (95% CI) age of non-hyperuricemia and incident hyperuricemia was 38.5 (38.0-38.9) and 38.5 (37.7-39.3) years, respectively. In total, 100 incident hyperuricemia individuals (3.5%) had healthy lifestyle and 133 individuals (4.7%) had low genetic risk. Baseline characteristics are provided in Table 1 and per lifestyle category and genetic risk group of hyperuricemia is presented in Supplementary Table 1.
Table 1Baseline Characteristics of Participants According to Hyperuricemia Status
PRS obeyed a normal distribution within the current study (Supplementary Figure 2). We found that a lower PRS was associated with a lower risk of hyperuricemia (Table 2). We observed that participants in the bottom category of PRS (a low genetic risk), as compared with participants in the other category of PRS (a high genetic), were at significantly lower risk of hyperuricemia, with adjusted hazard ratio (HR) of 0.60 (95% CI, 0.49-0.72; P < .0001).
Supplementary Figure 2Distribution of PRS of hyperuricemia. PRS = polygenetic risk score.
Model 3: Cox proportional hazards regression adjusted for age, sex, education levels, household income, total energy intake, first 10 principal components of ancestry and weighted lifestyle score.
1 (reference)
0.60 (0.49–0.72)
< .0001
Model 1: Crude model.
† Model 2: Cox proportional hazards regression adjusted for age and sex.
‡ Model 3: Cox proportional hazards regression adjusted for age, sex, education levels, household income, total energy intake, first 10 principal components of ancestry and weighted lifestyle score.
Lifestyle score obeyed a normal distribution (Supplementary Figure 3). Our study showed that a lower lifestyle score was significantly associated with a lower risk of hyperuricemia. Compared with an unhealthy lifestyle, the adjusted HR of a healthy lifestyle was 0.59 (95% CI, 0.47-0.74; P < .0001). Individuals with healthy lifestyle generally experienced lower incidence of hyperuricemia (Table 3). The associations between each lifestyle factor and hyperuricemia are supplied in Supplementary Table 3.
Supplementary Figure 3Distribution of lifestyle score of hyperuricemia.
Model 3: Cox proportional hazards regression adjusted for age, sex, education levels, household income, total energy intake, first 10 principal components of ancestry and genetic risk score.
1 (reference)
0.59 (0.47–0.74)
< .0001
Model 1: Crude model.
† Model 2: Cox proportional hazards regression adjusted for age and sex.
‡ Model 3: Cox proportional hazards regression adjusted for age, sex, education levels, household income, total energy intake, first 10 principal components of ancestry and genetic risk score.
Model 3: Cox proportional hazards regression adjusted for age, sex, education levels, household income, total energy intake, first 10 principal components of ancestry and genetic risk score.
Hazard ratios (95% confidence interval)
P Value
Hazard ratios (95% confidence interval)
P Value
Hazard ratios (95% confidence interval)
P Value
BMI (Normal vs Abnormal)
0.50 (0.43-0.58)
< .001
0.61 (0.52-0.71)
< .001
0.60 (0.51-0.71)
< .001
Smoking (No vs Yes)
0.56 (0.48-0.65)
< .001
0.87 (0.73-1.04)
.13
0.87 (0.73-1.04)
.14
No alcohol consumption (Yes vs No)
0.57 (0.48-0.68)
< .001
0.84 (0.70-1.02)
.08
0.87 (0.72-1.05)
.17
Moderate physical activity (Yes vs No)
0.84 (0.73-0.97)
.02
0.86 (0.75-1.00)
.05
0.84 (0.72-0.97)
.02
Low animal dietary pattern score (Yes vs No)
0.63 (0.52-0.76)
< .001
0.82 (0.68-1.00)
.05
0.83 (0.68-1.01)
.06
BMI = body mass index; HR = hazard ratio; MET = metabolic equivalent of task; SUA = serum uric acid.
Model 1: Crude model.
† Model 2: Cox proportional hazards regression adjusted for age and sex.
‡ Model 3: Cox proportional hazards regression adjusted for age, sex, education levels, household income, total energy intake, first 10 principal components of ancestry and genetic risk score.
A healthy lifestyle was associated with a lower risk of hyperuricemia within each category of genetic risk, meanwhile, a low genetic risk was associated with a lower risk of hyperuricemia within each category of lifestyle (Figure 2). As compared with a high genetic risk and an unhealthy lifestyle, a healthy lifestyle was associated with a 39% (95% CI, 22%-53%) lower risk of hyperuricemia among those at a high genetic risk. As compared with a low genetic risk and an unhealthy lifestyle, a healthy lifestyle was associated with a 47% (95% CI, 13%-71%) lower risk of hyperuricemia among those at a low genetic risk (Supplementary Table 4). Among participants with an unhealthy lifestyle, those with lower genetic risk showed significantly lower adjusted HR of 0.60 (95% CI, 0.49-0.74). Similarly, among participants with a healthy lifestyle, those with lower genetic risk showed an adjusted HR of 0.54 (95% CI, 0.30-0.96) (Supplementary Table 5).
Figure 2Hyperuricemia survival curve, stratified by lifestyle and genetic risk, of participants in the Tianjin Chronic Low-grade Systemic Inflammation and Health (TCLSIH) cohort.
Model 3: Cox proportional hazards regression adjusted for age, sex, education levels, household income, total energy intake, first 10 principal components of ancestry and genetic risk score.
1 (reference)
0.61 (0.47–0.78)
<.0001
1 (reference)
0.53 (0.29–0.97)
.04
Model 1: Crude model.
† Model 2: Cox proportional hazards regression adjusted for age and sex.
‡ Model 3: Cox proportional hazards regression adjusted for age, sex, education levels, household income, total energy intake, first 10 principal components of ancestry and genetic risk score.
Model 3: Cox proportional hazards regression adjusted for age, sex, education levels, household income, total energy intake, first 10 principal components of ancestry and genetic risk score.
1 (reference)
0.60 (0.49–0.74)
< .0001
1 (reference)
0.54 (0.30–0.96)
.03
Model 1: Crude model.
† Model 2: Cox proportional hazards regression adjusted for age and sex.
‡ Model 3: Cox proportional hazards regression adjusted for age, sex, education levels, household income, total energy intake, first 10 principal components of ancestry and genetic risk score.
The lowest risks were observed among individuals with low genetic risk and healthy lifestyle. Compared with unhealthy lifestyle and high genetic risk, adherence to healthy lifestyle was associated with a 40% (95% CI, 27%-51%) lower risk of hyperuricemia among those at a high genetic risk, and 68% (95% CI, 44%-81%) lower risk of hyperuricemia among those at a low genetic risk. Moreover, there was a significant multiplication interaction between genetic risk and lifestyle score of hyperuricemia (P-interaction < .0001) (Figure 3).
Figure 3Adjusted HRs for hyperuricemia rates, according to lifestyle and genetic risk. In these comparisons, participants with an unhealthy lifestyle and a high genetic risk as the reference group. The model was adjusted for age, sex, education levels, household income, total energy intake and first 10 principal components of ancestry. There was evidence of significant interaction between lifestyle and genetic risk (P-interaction < .0001).
Among participants with a high genetic risk score, those with normal BMI, no smoking, no drinking, moderate physical activity, and low animal dietary pattern score have the lowest risk for hyperuricemia compared with other individual lifestyle factors (Supplementary Table 6).
Supplementary Table 6Associations of Individual Lifestyle Factors with Incident Hyperuricemia Stratified by Genetic Risk Status
Cox proportional hazards regression adjusted for age, sex, education levels, household income, total energy intake, first 10 principal components of ancestry and genetic risk score.
High Genetic Risk
Low Genetic Risk
No. of Cases/Total No. of Participants
Estimate
P Value
No. of Cases/Total No. of Participants
Estimate
P Value
BMI
Abnormal
391/1055
1 (reference)
90/365
0.61 (0.48–0.76)
< .0001
Normal
223/1042
0.61 (0.51–0.72)
< .0001
43/334
0.35 (0.25–0.48)
< .0001
Smoking
Yes
200/483
1 (reference)
47/169
0.62 (0.45–0.86)
.004
No
414/1614
0.88 (0.73–1.06)
.20
86/530
0.52 (0.4–0.68)
< .0001
Alcohol
Yes
477/1428
1 (reference)
103/473
0.61 (0.49–0.75)
< .0001
No
137/669
0.86 (0.70–1.06)
.17
30/226
0.50 (0.34–0.73)
.0004
Moderate physical activity
No
153/522
1 (reference)
38/198
0.60 (0.42–0.86)
.005
Yes
461/1575
0.92 (0.77–1.11)
.42
95/501
0.55 (0.43–0.72)
< .0001
Animal dietary pattern score
High
490/1570
1 (reference)
119/530
0.68 (0.56–0.83)
.0002
Low
124/527
0.94 (0.76–1.15)
.55
14/169
0.28 (0.16–0.47)
< .0001
BMI = body mass index.
Cox proportional hazards regression adjusted for age, sex, education levels, household income, total energy intake, first 10 principal components of ancestry and genetic risk score.
We obtained similar results when we 1) performed stratified analysis by age, excluding participants who had a history of cardiovascular disease or chronic kidney disease (Supplementary Table 7); and 2) excluded participants with a history of cardiovascular disease or chronic kidney disease or cancer (Supplementary Table 8).
Supplementary Table 7Risk of Incident Hyperuricemia According to Genetic and Lifestyle Risk After Excluding a History of Cardiovascular Disease or Chronic Kidney Disease
Cox proportional hazards regression adjusted for age, sex, education levels, household income, total energy intake, and first 10 principal components of ancestry.
No. of Cases/Total No. of Participants
Person-Years Follow-Up
Hazard ratios (95% confidence interval)
P Value
High genetic risk
Unhealthy lifestyle
507/1494
6003
1 (reference)
Healthy lifestyle
110/517
2212
0.58 (0.47-0.71)
< .0001
Low genetic risk
Unhealthy lifestyle
79/522
2366
0.58 (0.45-0.75)
< .0001
Healthy lifestyle
13/155
706
0.31 (0.17-0.54)
< .0001
CI = confidence interval; HR = hazard ratio.
Cox proportional hazards regression adjusted for age, sex, education levels, household income, total energy intake, and first 10 principal components of ancestry.
Supplementary Table 8Risk of Incident Hyperuricemia According to Genetic and Lifestyle Risk After Excluding a History of Cardiovascular Disease or Chronic Kidney Disease or Cancer
Cox proportional hazards regression adjusted for age, sex, education levels, household income, total energy intake, and first 10 principal components of ancestry.
No. of Cases/Total No. of Participants
Person-Years Follow-Up
Hazard ratios (95% confidence interval)
P Value
High genetic risk
Unhealthy lifestyle
501/1483
5955
1 (reference)
Healthy lifestyle
110/515
2201
0.58 (0.47-0.72)
< .0001
Low genetic risk
Unhealthy lifestyle
79/520
2357
0.59 (0.45-0.76)
< .0001
Healthy lifestyle
12/153
697
0.29 (0.16-0.52)
< .0001
CI = confidence interval; HR = hazard ratio.
Cox proportional hazards regression adjusted for age, sex, education levels, household income, total energy intake, and first 10 principal components of ancestry.
In this population-based cohort study, we observed that genetic risk and a healthy lifestyle were significantly associated with the risk of incident hyperuricemia. The favorable association of a healthy lifestyle with hyperuricemia was found across all genetic risk categories, suggesting that the genetic risk rise in hyperuricemia can be offset, at least to some extent, by a healthy lifestyle. The results suggested that preventive policies should promote stricter adherence to healthy lifestyle factors (eg, eliminating smoking, no alcohol consumption, eating a healthy diet, maintaining a healthy weight, and engaging in moderate physical activity) for all.
To our knowledge, no previous study has investigated the association between a combination of genetic risk and lifestyle factors and incidence of hyperuricemia. Study based on the NHANES III data revealed that 44%, 9%, and 8% of hyperuricemia risk in the general population of adults could be attributed to obesity, unhealthy diet, and alcohol consumption, respectively.
A review recommended a better lifestyle in hyperuricemia management, including physical activity, normal BMI, a healthy diet, and limiting alcohol consumption.
A cross-sectional study conducted in Mexicans showed that both genetic and non-genetic factors (such as diet, obesity, or smoking) contributed to the elevated prevalence of hyperuricemia.
Compared with previous studies, the current study provided consistent evidence for the association of a healthy lifestyle with reduced hyperuricemia incidence, and incorporates a comprehensive indicator of genetic risk. Genetic risk can be known from birth and is nonmodifiable, whereas lifestyle-related risk factors usually appear in midlife. Because of this, our observations raise the possibility of targeting individuals at high genetic risk early in life for lifestyle modification.
The mechanism behind the results has not been proposed; however, these findings were biologically plausible. On one hand, evidence from Mendelian randomization analyses implied that components of lifestyle, such as obesity,
were causally associated with hyperuricemia. On the other hand, hyperuricemia SNP-based heritability was most-highly enriched in kidney regulatory regions and driven by kidney regulatory variation.
Healthy lifestyle may contribute to reducing hyperuricemia risk through renal mechanisms, including inhibiting insulin resistance, reduced oxidative damage, anti-inflammatory effects, and slowing the accumulation of cellular and organ damage.
Regular, intense exercise training as a healthy aging lifestyle strategy: preventing DNA damage, telomere shortening and adverse DNA methylation changes over a lifetime.
Healthy lifestyle and risk of kidney disease progression, atherosclerotic events, and death in CKD: findings from the Chronic Renal Insufficiency Cohort (CRIC) Study.
Our study has several strengths. First, to the best of our knowledge, this study is the first to provide quantitative data about genetic risk, lifestyle risk, and their interactions on the risk of hyperuricemia. And an updated genetic risk score in the latest published results for urate and hyperuricemia was used. Second, we used a multiplicative scale to quantify the interaction, and consistently found a favorable association of lifestyle with hyperuricemia across all genetic risk categories. The results were relevant to the potential clinical utility of genetic information for personalized lifestyle recommendations.
Our study also has several limitations. First, the present study was an observational study, and although we adjusted for multiple confounders, it might still be confounded by other factors. Second, lifestyle factors were measured by questionnaire, which might be prone to measurement error. Third, lifestyle changes prior to or after the baseline examinations might have had an effect on the estimates. Fourth, as diuretic use was not measured, we could not rule out its possible effect on the results.
Conclusion
Adherence to a healthy lifestyle—including no smoking, no alcohol consumption, maintaining a normal weight, being physically active, and adhering to a healthy diet—is important for hyperuricemia prevention, especially for individuals within a high genetic risk category.
Contemporary prevalence of gout and hyperuricemia in the United States and decadal trends: the National Health and Nutrition Examination Survey, 2007-2016.
ABCG2 rs2231142 variant in hyperuricemia is modified by SLC2A9 and SLC22A12 polymorphisms and cardiovascular risk factors in an elderly community-dwelling population.
A fast method that uses polygenic scores to estimate the variance explained by genome-wide marker panels and the proportion of variants affecting a trait.
A dietary pattern rich in animal organ, seafood and processed meat products is associated with newly diagnosed hyperuricaemia in Chinese adults: a propensity score-matched case-control study.
Regular, intense exercise training as a healthy aging lifestyle strategy: preventing DNA damage, telomere shortening and adverse DNA methylation changes over a lifetime.
Healthy lifestyle and risk of kidney disease progression, atherosclerotic events, and death in CKD: findings from the Chronic Renal Insufficiency Cohort (CRIC) Study.
Funding: This study was supported by grants from the National Natural Science Foundation of China (No. 81941024, 81872611, 82103837 and 81903315), Tianjin Major Public Health Science and Technology Project (No. 21ZXGWSY00090), National Health Commission of China (No. SPSYYC 2020015), Food Science and Technology Foundation of Chinese Institute of Food Science and Technology (No. 2019-12), 2014 and 2016 Chinese Nutrition Society (CNS) Nutrition Research Foundation—DSM Research Fund (Nos. 2016-046, 2014-071 and 2016-023), Natural Science Major Project of the Anhui Provincial Department of Education (No. 2022AH051233), and the Youth Key Talents Program of Wannan Medical College (No. WK202211), China.
Conflicts of Interest: The authors declare that they have no potential conflicts of interest.
Authorship: All authors had access to the data and a role in writing the manuscript.
Continuous variables are expressed as geometric mean (95% confidence interval) and categorical variables as percentages.
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