| Literature DB >> 34308318 |
Wenhua Liu1, Jing Hu2, Yuanyuan Fang3, Peng Wang4, Yanjun Lu5, Na Shen5.
Abstract
BACKGROUND: Low vitamin D (VitD) status is becoming a global health issue. Previous heterogenous results are urging a meta-analysis to delineate a panorama of VitD conditions in the general population in Mainland of China.Entities:
Keywords: Deficiency; Mainland of China; Meta-analysis; Systematic review; Vitamin D
Year: 2021 PMID: 34308318 PMCID: PMC8283334 DOI: 10.1016/j.eclinm.2021.101017
Source DB: PubMed Journal: EClinicalMedicine ISSN: 2589-5370
Fig. 1Study selection. The search date was updated to June 4, 2021. A total of 105 eligible studies were finally included. Of these eligible studies, 54 included only adults, 48 included only children and adolescents, and 3 included both children/adolescents and adults. Abbreviations: VitD = Vitamin D.
Fig. 2Forest plot of the pooled prevalence of VitD deficiency (< 30 nmol/L) and VitD inadequacy (< 50 nmol/L) of adults in Mainland of China. N is the total number of subjects of the studies, and case is the number of subjects with low VitD status. The forest plot was drawn based on the observational prevalence with 95%CrI of each study, and the pooled prevalence with 95%CrI was estimated using the Hierarchical Bayesian models. For VitD deficiency, the between-study variance () and 95%CrI was 0.8 (0.2–2.9). For VitD inadequacy, the between-study variance () and 95%CrI was 1.8 (1.2–2.8). Abbreviations: VitD = Vitamin D; CrI = Credible Interval.
Fig. 3Forest plot of the pooled prevalence of VitD deficiency (< 30 nmol/L) and VitD inadequacy (< 50 nmol/L) of children and adolescents in Mainland of China. N is the total number of subjects of the studies, and case is the number of subjects with low VitD status. The forest plot was drawn based on the observational prevalence with 95%CrI of each study, and the pooled prevalence with 95%CrI was estimated using the Hierarchical Bayesian models. For VitD deficiency, the between-study variance () and 95%CrI was 2.4 (0.7–7.5). For VitD inadequacy, the between-study variance () and 95%CrI was 1.6 (1.0–2.5). Abbreviations: VitD = Vitamin D; CrI = Credible Interval.
Hierarchical Bayesian meta-regression analyses on the VitD status in Mainland of China.
| (Province or City) vs. National | 0.4(0.1 to 1.1) | 1.6(0.4 to 4.3) |
| County vs. National | 0.4(0.1 to 1.1) | 2.1(0.4 to 7.0) |
| North vs. South | 4.1(1.8 to 7.9) | 2.7(1.0 to 5.9) |
| Rural vs. Urban | 1.1(0.4 to 2.2) | 1.1(0.2 to 3.5) |
| (Summer/Autumn) vs.(Winter/Spring) | 0.9(0.3 to 2.0) | 1.1(0.4 to 2.6) |
| ELISA vs. chemical assays | 1.5(0.4 to 3.8) | 1.4(0.2 to 4.8) |
| ECLIA vs. chemical assays | 1.2(0.2 to 3.5) | 1.1(0.2 to 4.0) |
| CLIA vs. chemical assays | 6.3(1.3 to 19.7) | 1.8(0.2 to 7.0) |
| RIA vs. chemical assays | 2.6(0.7 to 6.9) | 0.9(0.1 to 3.0) |
| Female vs. Male | 1.6(1.4 to 1.9) | 1.7(1.5 to 2.0) |
| OR per one year | 1.13(1.07 to 1.19) | 1.02(1.01 to 1.04) * |
| Province or City vs. National | 10.9(−7.8 to 29.1) | −2.8(−17.3 to 11.0) |
| County vs. National | 11.6(−6.7 to 29.7) | −3.1(−19.5 to 13.3) |
| North vs. South | −2.8(−15.3 to 9.3) | −4.3(−14.4 to 5.3) |
| Rural vs. Urban | −1.3(−13.6 to 10.8) | NA† |
| Summer/Autumn vs. Winter/Spring | 5.6(−6.2 to 17.7) | 7.5(−4.1 to 19.0) |
| ELISA vs. chemical assays | 13.4(−2.6 to 29.4) | 1.0(−16.6 to 19.6) |
| ECLIA vs. chemical assays | −4.4(−19.1 to 10.8) | −4.9(−24.7 to 16.4) |
| CLIA vs. chemical assays | −14.9(−33.4 to 3.2) | −1.8(−21.4 to 17.9) |
| RIA vs. chemical assays | 6.6(−9.9 to 23.5) | 5.4(−13.8 to 25.2) |
| Female vs. Male | −4.1(−5.4 to −2.8) | −4.8(−6.4 to −3.2) |
| Diff. per one year | −1.04(−2.14 to 0.09) | −0.18(−0.36 to −0.01) * |
Abbreviations: VitD = Vitamin D; 25(OH)D = 25-hydroxyvitamin D; OR = Odds Ratio; CrI = Credible Interval; Diff. = Difference; vs. = versus; NA = Not available; ELISA = Enzyme-linked immunosorbent assay; ECLIA = Electrochemiluminescence immunoassay; CLIA = Chemiluminescent assay; RIA = Radioimmunoassay.
Note: We used a Hierarchical Bayesian meta-regression model to estimate the pooled prevalence of VitD inadequacy and mean 25(OH) D concentration based on each covariate (sampling frame, latitude, urbanization, season, assays, sex, and age) separately. Diff., OR, and the corresponding 95%CrI were estimated. If the 95%CrI of the effect sizes included the null effect (OR = 1 or difference = 0), the covariate was not considered as a significant factor contributing to the between-study heterogeneity. Chemical assays included high-performance liquid chromatography (HPLC) and liquid chromatography coupled with mass spectrometry (LC-MS/MS).
† For the univariate Hierarchical Bayesian meta-regression analysis, the number of the studies for analysis in each level of a covariate should be 3 or more. The model was performed without rural category because of only two studies sampling from the rural areas.
* The results showed the data of the elderly people with age ≥ 60 years old. For all the adults, the OR and 95%CrI between age and VitD inadequacy was 1.00 (0.98 to 1.01); the diff. between age and mean 25(OH)D concentration was 0.01 (−0.13 to 0.15).
Fig. 4The relationship of age with the estimated 25(OH)D concentration (A) and with VitD inadequacy (B) of elderly people in Mainland of China. A Hierarchical Bayesian meta-regression model was used to estimate the trend of age with the effect sizes. A random effect of age between studies was specified in the model, and both the between- and within-study variances were taken account in the model. Abbreviations: VitD = Vitamin D; 25(OH)D = 25-hydroxyvitamin D.
Fig. 5Potential effects of sex, latitude and age on VitD inadequacy of children and adolescents in Mainland of China. A shows the estimated prevalence of VitD inadequacy based on sex and latitude. The between-study variance () and 95%CrI was 1.2 (0.7–2.2). B shows the relationship between age and VitD inadequacy based on latitude. The between-study variance () and 95%CrI was 1.0 (0.4–2.2). The detailed model and parameter estimation were described in the Supplementary file 2.2 and 2.3.