| Literature DB >> 29518947 |
Xiaofang Jia1, Zhihong Wang2, Bing Zhang3, Chang Su4, Wenwen Du5, Jiguo Zhang6, Ji Zhang7, Hongru Jiang8, Feifei Huang9, Yifei Ouyang10, Yun Wang11, Li Li12, Huijun Wang13.
Abstract
Vitamin C is essential for human health. It is important to estimate the dietary vitamin C intake in the Chinese population to examine the effects of the nutritional transition occurred in recent decades. The present study aimed to estimate the dietary vitamin C intake in Chinese adults by using cross-sectional data from the 2015 China Nutritional Transition Cohort Study and selecting those aged 18-65 years with complete records of sociodemographic characteristics and dietary measurements (n = 11,357). Wilcoxon rank-sum test, Kruskal-Wallis analysis, Chi-squared test, and multiple logistic regression were employed to analyze the daily dietary vitamin C intake on the basis of three-day 24 h dietary recalls and food sources in relation to demographic factors, to evaluate vitamin C intake status using the estimated average requirement cut-off point, and to explore underlying influencing factors. The mean (SD (standard deviation)) and median (interquartile range) levels of the dietary vitamin C intake in adults were 78.1 (54.6) and 65.4 (61.4) mg/day, respectively. Light vegetables, dark vegetables, fruits, and tubers were the top four food sources, contributing a combined 97.3% of total daily dietary vitamin C intake in the study population. The prevalence of risk of insufficient dietary vitamin C intake was 65.1%. Both the distribution of vitamin C intake and the prevalence of risk of insufficient dietary vitamin C intake differed by several demographic factors. Educational level, residence area, geographic location, vegetable consumption, and total energy intake were independent determinants of the risk of insufficient dietary vitamin C intake. In conclusion, dietary vitamin C intake is inadequate in Chinese adult population, and an increase in vitamin C intake should be recommended especially to the population at risk for vitamin C insufficiency.Entities:
Keywords: adults; determinants; dietary intake; food sources; vitamin C
Mesh:
Substances:
Year: 2018 PMID: 29518947 PMCID: PMC5872738 DOI: 10.3390/nu10030320
Source DB: PubMed Journal: Nutrients ISSN: 2072-6643 Impact factor: 5.717
Sociodemographic characteristics and daily dietary vitamin C intake in adults.
| Parameters | Vitamin C Intake (mg/Day) | |||
|---|---|---|---|---|
| Mean ± SD | Median (Interquartile Range) | |||
| Total subjects | 11,357 (100) | 78.1 ± 54.6 | 65.4 (61.4) | |
| Age group (years) | 0.069 | |||
| 18–49 | 6290 (55.4) | 76.9 ± 53.1 | 64.7 (60.6) | |
| 50–65 | 5067 (44.6) | 79.5 ± 56.4 | 66.3 (62.2) | |
| Gender | 0.001 | |||
| male | 4944 (43.5) | 79.4 ± 53.7 | 67.6 (64.1) | |
| female | 6413 (56.5) | 77.1 ± 55.3 | 63.9 (59.3) | |
| Education level | 0.003 | |||
| primary school and below a | 3070 (27.0) | 75.5 ± 52.6 | 63.9 (58.1) | |
| middle school b,c | 3998 (35.2) | 80.4 ± 56.2 | 67.1 (66.1) | |
| high school and above a,c | 4289 (37.8) | 77.8 ± 54.8 | 65.3 (59.9) | |
| Annual household income level (thousand yuan/per capital) 1 | 0.463 | |||
| low | 0.9 ± 0.7 | 77.6 ± 54.8 | 64.7 (63.4) | |
| moderate | 6.1 ± 2.4 | 78.4 ± 54.0 | 66.1 (60.7) | |
| high | 27.6 ± 19.1 | 78.3 ± 55.1 | 65.3 (59.8) | |
| Residence area | <0.001 | |||
| city a | 2276 (20.0) | 81.2 ± 61.7 | 65.8 (65.4) | |
| suburban b | 1865 (16.4) | 87.9 ± 57.5 | 75.2 (69.8) | |
| town or county capital city c | 1979 (17.4) | 72.7 ± 49.3 | 60.5 (57.7) | |
| rural village a,c | 5237 (46.1) | 75.3 ± 51.6 | 64.2 (57.6) | |
| Geographic location | <0.001 | |||
| north | 4283 (37.7) | 73.8 ± 51.7 | 62.2 (56.7) | |
| south | 7074 (62.3) | 80.7 ± 56.2 | 67.5 (64.6) | |
| Smoking status | 0.015 | |||
| never smoker | 8482 (74.7) | 77.6 ± 55.1 | 64.6 (60.0) | |
| former smoker | 225 (2.0) | 81.9 ± 53.4 | 68.7 (62.1) | |
| current smoker | 2650 (23.3) | 79.5 ± 53.1 | 68.1 (66.2) | |
| Alcohol intake | <0.001 | |||
| no | 8130 (71.6) | 77.3 ± 55.2 | 64.2 (60.3) | |
| yes | 3227 (28.4) | 80.1 ± 53.1 | 68.8 (64.5) | |
| Fruit consumption (times/day) 1 | 0.073 | |||
| low | 0.2 ± 0.1 | 78.3 ± 52.7 | 66.0 (61.9) | |
| moderate | 0.5 ± 0.1 | 76.9 ± 54.7 | 64.5 (60.4) | |
| high | 1.2 ± 0.4 | 79.1 ± 56.4 | 66.2 (61.6) | |
| Vegetable consumption (times/day) 1 | <0.001 | |||
| low a | 1.2 ± 04 | 71.4 ± 50.2 | 60.5 (55.9) | |
| moderate b | 2.3 ± 0.3 | 77.9 ± 53.6 | 65.4 (59.7) | |
| high c | 4.3 ± 1.3 | 85.0 ± 58.8 | 71.6 (68.6) | |
| Body mass index (kg/m2) | 0.084 | |||
| <18.5 | 469 (4.1) | 76.2 ± 52.8 | 65.9 (58.2) | |
| 18.5–24 | 5388 (47.4) | 76.8 ± 52.7 | 64.6 (61.6) | |
| 24–28 | 3938 (34.7) | 79.1 ± 57.0 | 65.3 (62.1) | |
| ≥28 | 1562 (13.8) | 80.8 ± 55.3 | 68.3 (59.5) | |
1 Data are expressed as mean ± SD (standard deviation); Wilcoxon rank-sum test or Kruskal-Wallis analysis was performed to test the difference of the distribution of dietary vitamin C by sociodemographic factors; subgroups with different superscript letters were significantly different by multiple comparison of SNK (Student-Newman-Keuls method).
Proportion of subjects at risk of insufficient dietary vitamin C intake and proportion of subjects with a lower risk of insufficient dietary vitamin C by sociodemographic factors.
| Parameters | Subjects at Risk of Insufficient Dietary Vitamin C Intake (<85 mg/Day) | Subjects with a Lower Likelihood of Inadequate Dietary Vitamin C Intake (≥100 mg/Day) | ||
|---|---|---|---|---|
| Total subjects | 7396 (65.1) | 2938 (25.9) | ||
| Age group (years) | 0.367 | 0.193 | ||
| 18–49 | 4119 (65.5) | 1597 (25.4) | ||
| 50–65 | 3277 (64.7) | 1341 (26.5) | ||
| Gender | <0.001 | 0.001 | ||
| male | 3118 (63.1) | 1360 (27.5) | ||
| female | 4278 (66.7) | 1578 (24.6) | ||
| Education level | <0.001 | <0.001 | ||
| primary school and below | 2071 (67.5) | 733 (23.9) | ||
| middle school | 2512 (62.8) | 1123 (28.1) | ||
| high school and above | 2813 (65.6) | 1082 (25.2) | ||
| Household income level | 0.612 | 0.220 | ||
| low | 2460 (65.0) | 1003 (26.5) | ||
| moderate | 2453 (64.8) | 978 (25.8) | ||
| high | 2483 (65.6) | 957 (25.3) | ||
| Residence area | <0.001 | <0.001 | ||
| city | 1460 (64.2) | 615 (27.0) | ||
| suburban | 1059 (56.8) | 633 (33.9) | ||
| town or county capital city | 1359 (68.7) | 452 (22.8) | ||
| rural village | 3518 (67.2) | 1238 (23.6) | ||
| Geographic location | <0.001 | <0.001 | ||
| north | 2948 (68.8) | 953 (22.3) | ||
| south | 4448 (62.9) | 1985 (28.1) | ||
| Smoking status | 0.003 | 0.002 | ||
| never smoker | 5599 (66.0) | 2124 (25.0) | ||
| former smoker | 137 (60.9) | 67 (29.8) | ||
| current smoker | 1660 (62.6) | 747 (28.2) | ||
| Alcohol intake | <0.001 | 0.002 | ||
| no | 5375 (66.1) | 2039 (25.1) | ||
| yes | 2021 (62.6) | 899 (27.9) | ||
| Fruit consumption | 0.648 | 0.749 | ||
| low | 2432 (64.7) | 1002 (26.6) | ||
| moderate | 2558 (66.5) | 949 (24.7) | ||
| high | 2406 (64.2) | 987 (26.3) | ||
| Vegetable consumption | <0.001 | <0.001 | ||
| low | 2659 (70.3) | 798 (21.1) | ||
| moderate | 2506 (66.2) | 958 (25.3) | ||
| high | 2231 (58.9) | 1182 (31.2) | ||
| Body mass index (kg/m2) | 0.208 | 0.536 | ||
| <18.5 | 314 (67.0) | 117 (25.0) | ||
| 18.5–24 | 3522 (65.4) | 1386 (25.7) | ||
| 24–28 | 2562 (65.1) | 1025 (26.0) | ||
| ≥28 | 998 (63.9) | 410 (26.3) | ||
Chi-squared test was performed to generate the p-values.
Figure 1Contribution percentage of food sources to the total vitamin C intake by sociodemographic factors. (a) Total subjects; (b) subgroups by gender; (c) educational level; (d) residence area; (e) geographic location; (f) smoking status; (g) alcohol intake; (h) vegetable consumption. ** p < 0.01, and *** p < 0.001 indicate significant differences in the distribution of the contribution percentages of food sources to the total vitamin C intake by sociodemographic factors by Wilcoxon rank-sum test or Kruskal-Wallis analysis.
Association of the risk of insufficient dietary vitamin C intake (<85 mg/day) with sociodemographic factors using the multiple logistic regression model.
| Independent Variables | <85 vs. ≥85 mg/Day of Dietary Vitamin C Intake | |
|---|---|---|
| OR (95% CI) | ||
| Gender | ||
| male | 1.0 | |
| female | 0.93 (0.83, 1.04) | 0.205 |
| Education level | ||
| primary school and below | 1.0 | |
| middle school | 0.84 (0.76, 0.93) | 0.001 |
| high school and above | 0.95 (0.85, 1.07) | 0.406 |
| Residence area | ||
| city | 1.0 | |
| suburban | 0.72 (0.63, 0.82) | <0.001 |
| town or county capital city | 1.20 (1.05, 1.37) | 0.007 |
| rural village | 1.17 (1.04, 1.32) | 0.009 |
| Geographic location | ||
| north | 1.0 | |
| south | 0.85 (0.78, 0.92) | <0.001 |
| Smoking status | ||
| never smoker | 1.0 | |
| former smoker | 0.90 (0.67, 1.20) | 0.470 |
| current smoker | 0.97 (0.86, 1.09) | 0.582 |
| Alcohol intake | ||
| no | 1.0 | |
| yes | 1.00 (0.89, 1.11) | 0.924 |
| Vegetable consumption | ||
| low | 1.0 | |
| moderate | 0.91 (0.82, 1.01) | 0.064 |
| high | 0.68 (0.62, 0.75) | <0.001 |
| Total daily energy intake | ||
| low | 1.0 | |
| moderate | 0.60 (0.54, 0.66) | <0.001 |
| high | 0.33 (0.30, 0.37) | <0.001 |
The logistic regression model was employed, considering gender, educational level, residence area, geographic location, smoking status, alcohol intake, vegetable consumption, and total daily energy intake as independent variables. OR: odds ratio; CI: confidence interval.