Dietary diversity and determinants of young adults in central China: A cross-sectional study from 2015 to 2020

Abstract

BackgroundEarly adulthood is a vulnerable period for improved nutrition at all phases of the life cycle. However, there is limited research on diversity information in young adults from middle-income countries undergoing an apparent nutritional transition. The purpose of this study was to explore dietary diversity and determinants among young adults aged 18–35 years in central China.MethodsFrom January 2015 to December 2020, a cross-sectional survey of 49,021 young adults in a health management center of central China was conducted through report and phone-assisted self-report for information. The outcome variable was the Dietary Diversity Score. Independent variables included age, sex, race, material status, education, BMI, taste preference, regular meals, midnight snacks, sugared beverage/coffee consumption, and smoking/drinking status. Multivariate logistic regression was performed.ResultsOf 49,021 young adults, 38,374 (78.3%) reported insufficient dietary diversity, and 422 (0.9%) reported sufficient dietary diversity. Light taste preference [adjusted odds ratio (aOR) = 2.325; 95% CI: 1.779, 3.039] and those who had meals regularly (aOR = 1.241; 95% CI: 1.018, 1.513) and consumed coffee (aOR = 2.765; 95% CI: 2.257, 3.387) were more likely to be associated with sufficient dietary diversity. Midnight snacks (aOR = 0.728; 95% CI: 0.588, 0.901) and sugary beverages (aOR = 0.666; 95% CI: 0.535, 0.829) were less likely to be associated with sufficient dietary diversity. Higher BMI (aOR = 1.092; 95% CI: 1.061, 1.125) was associated with higher odds of sufficient dietary diversity. Additionally, participants who were 18–30 years old, with master or above degree and away from cigarette/alcohol were more likely to report better dietary diversity.ConclusionOur results painted a less than ideal nutritional condition affecting young adults. High-fat/sugar/salt dietary practices can lead to low dietary diversity, while high dietary diversity might have adverse BMI outcomes in youth. This study highlighted the importance of increasing the diversity of healthy and selective food items before wide recommendation for dietary diversity.

A scoping review of approaches used to develop plant-based diet quality indices

“…Of the 137 included articles, 31 articles described the original development of 35 plant-based diet quality indices ( Table 1 [ [13] , [14] , [15] , 18 , [40] , [41] , [42] , [43] , [44] , [45] , [46] , [47] , [48] , [49] , [50] , [51] , [52] , [53] , [54] , [55] , [56] , [57] , [58] , [59] , [60] , [61] , [62] , [63] , [64] , [65] , [66] , [67] , [68] ] and Table 2 [ 15 , 18 , [40] , [41] , [42] , [43] , [45] , [46] , [47] , [48] , [49] , [51] , [52] , [53] , 55 ]), and the remaining 106 articles applied the 35 plant-based diet quality indices in an independent sample to the original development population ( Supplemental Table 2 ). The 137 included articles were published from 2007 to 2022, with most published in 2022 ( n = 39), 2021 ( n = 41), 2020 ( n = 20), and 2019 ( n = 11).…”

“… 0–55 Y, on the basis of epidemiological evidence indicating a positive or inverse association between the plant food and health outcomes Plant-based food variety score (PFVS) Zhou et al. 2020 China [ 65 ] Based on a general food variety score FFQ 5 Whole grain cereals, legumes, vegetables, fruits, and nuts One point was awarded for each food item consumed at least twice a week. The food items and their corresponding points for the total FVS are: whole grain cereals (1), legumes (4), vegetables (22), fruits (9), and nuts (4).…”

“…Across the 137 articles, 35 unique plant-based diet quality indices were reported. The indices were initially published in 31 articles between 2007 ( n = 2) [ 44 , 50 ] to 2022 ( n = 6) [ 43 , 44 , 48 , 53 , 57 , 67 ], but most were first published after 2017 ( n = 20) [ 13 , 41 , 42 , 46 , 48 , 49 , [52] , [53] , [54] , [55] , 57 , [60] , [61] , [62] , [63] , [65] , [66] , [67] , [68] , [69] ] ( Figure 2 and Table 1 ). The indices were mostly developed using populations from the US ( n = 13) [ 15 , [41] , [42] , [43] , 45 , 47 , 54 , 55 , 68 ], followed by the Netherlands ( n = 6) [ 13 , 18 , 48 , 53 , 61 , 69 ] and China ( n = 2) [ 54 , 63 , 65 , 66 ].…”

“…The indices were initially published in 31 articles between 2007 ( n = 2) [ 44 , 50 ] to 2022 ( n = 6) [ 43 , 44 , 48 , 53 , 57 , 67 ], but most were first published after 2017 ( n = 20) [ 13 , 41 , 42 , 46 , 48 , 49 , [52] , [53] , [54] , [55] , 57 , [60] , [61] , [62] , [63] , [65] , [66] , [67] , [68] , [69] ] ( Figure 2 and Table 1 ). The indices were mostly developed using populations from the US ( n = 13) [ 15 , [41] , [42] , [43] , 45 , 47 , 54 , 55 , 68 ], followed by the Netherlands ( n = 6) [ 13 , 18 , 48 , 53 , 61 , 69 ] and China ( n = 2) [ 54 , 63 , 65 , 66 ]. The sample sizes of the development studies ranged from 54 [ 47 ] to 212,142 [ 55 ], with almost all conducted in samples of men and women ( n = 32) [13,14,15,18, 41–50,52,53,55–63,65,68,69) ( Table 2 ).…”

“…To develop the plant-based diet quality indices, dietary intake data was collected using a variety of dietary assessment tools. Most indices used a food frequency questionnaire (FFQ) as the dietary assessment tool ( n = 19) [ 18 , 41 , 44 , 45 , 48 , 50 , [52] , [53] , [54] , [55] , 57 , 58 , 60 , 63 , [65] , [66] , [67] , [68] , [69] ], followed by a semiquantitative FFQ ( n = 9) [ [13] , [14] , [15] , 46 , 59 , 61 , 62 ] or 24-h dietary recall ( n = 5) [ 42 , 49 , 54 ] ( Table 1 ).…”