| Literature DB >> 35695553 |
Jiahuan Rao1, Peiyu Ye1, Jie Lu2,3, Bi Chen4, Nan Li5, Huiying Zhang6, Hui Bo7, Xinchun Chen8, Huiting Liu9, Chunhong Zhang10, Hua Wei11, Qin Wu11, Yinkun Yan1, Changgui Li2,3, Jie Mi1.
Abstract
BACKGROUND AND AIMS: Hyperuricaemia can lead to gout and is associated with an increased risk of cardiometabolic disease. We aimed to investigate the prevalence of hyperuricaemia and its related factors in Chinese children and adolescents.Entities:
Keywords: Children; adolescents; hyperuricaemia; prevalence; uric acid
Mesh:
Substances:
Year: 2022 PMID: 35695553 PMCID: PMC9225777 DOI: 10.1080/07853890.2022.2083670
Source DB: PubMed Journal: Ann Med ISSN: 0785-3890 Impact factor: 5.348
Characteristics of included studies.
| Author | Survey year | Province | Region | Age, year | Sample size | Boy, % | Population source | Specimen | Laboratory method |
|---|---|---|---|---|---|---|---|---|---|
| CHNS [ | 2009 | 9 Provincesa | South + North | 3–19 | 907 | 54.7 | Community | Serum | Enzymatic colorimetric |
| Bo et al. [ | 2010 | Tianjin | North | 7–17 | 1269 | 50.3 | Community | Serum | Enzymatic colorimetric |
| Chen et al. [ | 2012 | Hebei | North | 4–15 | 999 | 56.0 | Health examination centre | Serum | Enzymatic colorimetric |
| Liu et al. [ | 2013 | Shanxi | North | 8–14 | 809 | 52.8 | School | Serum | Enzymatic colorimetric |
| Zhuang et al. [ | 2014 | Heilongjiang | North | 10–18 | 1640 | 49.8 | Community | Serum | Enzymatic colorimetric |
| Li et al. [ | 2015 | Tianjin | North | 3–6 | 4073 | 52.5 | Community | Serum | Enzymatic colorimetric |
| Zhang et al. [ | 2015 | Nationwide | South + North | 17–19 | 600 | 100 | School | Serum | Enzymatic colorimetric |
| Lu et al. [ | 2017 | Shandong | North | 13–19 | 21,602 | 49.1 | Community | Serum | Enzymatic colorimetric |
| Chen et al. [ | 2017 | Zhejiang | South | 3–19 | 10,764 | 61.6 | Health examination centre | Serum | Enzymatic colorimetric |
| Wu et al. [ | 2018 | Jiangsu | South | 13–16 | 509 | 49.1 | Community | Serum | Enzymatic colorimetric |
| Ye et al. [ | 2019 | Beijing | North | 6–16 | 11,408 | 49.9 | School | Serum | Enzymatic colorimetric |
aIncluding Heilongjiang, Liaoning, Shandong, Jiangsu, Guangxi, Guizhou, Henan, Hubei and Hunan.
Characteristics of study population.
| Overall | Boys | Girls | |
|---|---|---|---|
| Age (years) | |||
| 5896 (10.8) | 3410 (11.8) | 2486 (9.7) | |
| 9025 (16.5) | 4887 (16.9) | 4138 (16.1) | |
| 7010 (12.8) | 3842 (13.3) | 3168 (12.3) | |
| 11,980 (21.9) | 6258 (21.7) | 5722 (22.2) | |
| 20,669 (37.9) | 10,452 (36.2) | 10,217 (39.7) | |
| Weight statusa | |||
| 36,433 (77.9) | 17,720 (73.1) | 18,713 (83.0) | |
| 7161 (15.3) | 4276 (17.7) | 2885 (12.8) | |
| 2639 (5.6) | 1831 (7.6) | 808 (3.6) | |
| 539 (1.2) | 399 (1.6) | 140 (0.6) | |
| Region | |||
| South | 12,326 (22.6) | 7667 (26.6) | 4659 (18.1) |
| North | 42,254 (77.4) | 21,182 (73.4) | 21,072 (81.9) |
| Survey year | |||
| 10,297 (18.9) | 5676 (19.7) | 4621 (18.0) | |
| 44,283 (81.1) | 23,173 (80.3) | 21,110 (82.0) |
aThe numbers of subjects with missing values were 7808 for height or weight.
Figure 1.Change in mean serum uric acid with age by sex.
Figure 2.Prevalence of hyperuricaemia among Chinese children and adolescents. non-ow: non-overweight; ow: overweight; ob: obesity; ex-ob: extreme obesity.
Odds ratios (95% confidence intervals) for hyperuricaemia associated with related factors.
| Overall | Boys | Girls | ||||
|---|---|---|---|---|---|---|
| Characteristics | Model 1 | Model 2 | Model 1 | Model 2 | Model 1 | Model 2 |
| Sex | ||||||
| Girl | Ref | Ref | – | – | – | – |
| Boy | 1.44 (1.38–1.50) | 1.54 (1.47–1.61) | – | – | – | – |
| Age | ||||||
| 3–11 | Ref | Ref | Ref | Ref | Ref | Ref |
| 12–15 | 5.16 (4.87–5.47) | 4.05 (3.77–4.35) | 11.06 (10.13–12.08) | 9.19 (8.25–10.23) | 2.39 (2.20–2.59) | 1.67 (1.51–1.85) |
| 16–19 | 4.16 (3.95–4.39) | 3.65 (3.41–3.91) | 9.87 (9.10–10.72) | 9.10 (8.21–10.09) | 1.70 (1.58–1.83) | 1.33 (1.21–1.46) |
| Weight status | ||||||
| Non-overweight | Ref | Ref | Ref | Ref | Ref | Ref |
| Overweight | 2.85 (2.70–3.01) | 2.99 (2.82–3.16) | 2.68 (2.50–2.87) | 2.93 (2.71–3.17) | 2.88 (2.65–3.13) | 2.98 (2.72–3.26) |
| Obesity | 4.38 (4.05–4.75) | 5.40 (4.93–5.91) | 3.56 (3.22–3.92) | 5.67 (5.03–6.39) | 5.62 (4.87–6.49) | 6.38 (5.48–7.42) |
| Extreme obesity | 7.05 (5.91–8.42) | 9.76 (8.03–11.87) | 5.70 (4.64–7.01) | 11.53 (9.03–14.74) | 9.22 (6.50–13.08) | 11.77 (8.13–17.03) |
| Region | ||||||
| South | Ref | Ref | Ref | Ref | Ref | Ref |
| North | 2.69 (2.54–2.85) | 1.55 (1.39–1.72) | 3.96 (3.67–4.28) | 1.52 (1.32–1.74) | 1.66 (1.52–1.81) | 1.52 (1.28–1.80) |
| Survey year | ||||||
| 2009–2015 | Ref | Ref | Ref | Ref | Ref | Ref |
| 2016–2019 | 2.90 (2.72–3.09) | 2.35 (2.19–2.53) | 3.02 (2.78–3.28) | 2.13 (1.94–2.35) | 2.83 (2.55–3.14) | 3.03 (2.70–3.41) |
Model 1: Univariate logistic regression analysis.
Model 2: Multivariate logistic regression analysis adjusted for sex, age period, weight status, region and survey year.