Characteristics of the study subjects
Table 1 shows the general characteristics of the participants. Approximately half of the participants were girls (50.7%, n=1,001), and most of them were Han nationality (88.4%, n=1,794). Nearly 60.0% of participants (n=1,228) came from countryside.
Table 1. Descriptive statistics of the participants (n=2029) in this study.
|
Number
|
Percentage (%)
|
Gender
|
|
|
Girl
|
1001
|
50.7
|
Boy
|
1028
|
49.3
|
Age
|
|
|
10
|
342
|
16.9
|
11
|
348
|
17.2
|
12
|
325
|
16.0
|
13
|
320
|
15.8
|
14
|
334
|
16.5
|
15
|
360
|
17.7
|
Nationality
|
|
|
Minority nationality
|
235
|
11.6
|
Han nationality
|
1794
|
88.4
|
Household registration
|
|
|
Countryside
|
1228
|
60.5
|
Urban
|
801
|
39.5
|
School
|
|
|
Common school
|
1940
|
95.6
|
Key school
|
89
|
4.4
|
Father education level a
Low
Medium
High
|
935
973
121
|
46.1
48.0
6.0
|
Mother education level a
Low
Medium
High
|
1245
693
91
|
61.4
34.2
4.5
|
Family learning environment
Bad
Neutrality
Good
|
195
877
957
|
9.61
43.22
47.17
|
Annual household income (per person)
<3500RMB
3500~7000RMB
>7000RMB
|
680
696
653
|
33.5
34.3
32.2
|
Family size
3~6
7~14
|
1822
207
|
89.8
10.2
|
Mathematics test (score)b
0-8
9-11
12-14
14-24
|
536
476
536
481
|
26.4
23.5
26.4
23.7
|
Vocabulary test (score)c
0-17
18-22
23-26
27-34
|
486
504
462
577
|
24.0
24.8
22.8
28.4
|
a Education level: low (completed primary school or less); medium (completed junior middle school but did not undergo the tertiary entrance exam); and high (had taken the tertiary entrance exam or higher).
b The scores of mathematics test were ranged from 0 to 24.
c The scores of vocabulary test were ranged from 0 to 34.
Dietary patterns of the study participants
Table 2 presents the factor loadings of different dietary patterns. Three dietary patterns were identified and labeled as ‘High protein’, ‘High fat’ and ‘High salt-oil’ dietary pattern. Foods that loaded highly on the ‘High protein’ dietary pattern were milk, dairy products, eggs, beans and bean products. Meat and aquatic products were loaded highly on the ‘High fat’ dietary pattern. Pickled food, as well as puffed and fried food were loaded highly on the ‘High salt-oil’ dietary pattern.
Table2. Factor loadings of the three dietary patterns a.
Food group
|
‘High protein’ b
|
‘High fat’ c
|
‘High salt-oil’ d
|
Meat
|
0.0261
|
0.6531a
|
0.0189
|
Aquatic products
|
-0.0321
|
0.6523a
|
-0.1367
|
Vegetables and fruits
|
0.0031
|
0.3687
|
0.3287
|
Milk and dairy products
|
0.5713a
|
0.0485
|
-0.0633
|
Bean and bean products
|
0.5605a
|
-0.0514
|
0.0791
|
Eggs
|
0.5851a
|
-0.0076
|
-0.0528
|
Pickled food
|
-0.0547
|
-0.0751
|
0.7632a
|
Puffed and fried food
|
0.1111
|
0.0375
|
0.5266a
|
a Dietary pattern factor loadings≥0.4.
b ‘High protein’ dietary pattern (e.g., milk and dairy products, bean and bean products and eggs).
c ‘High fat’ dietary pattern (e.g., meat and aquatic products).
d ‘High salt-oil’ dietary pattern (e.g., pickled food, puffed and fried food).
Influencing factors of cognitive ability: results from Chi-square test
Table 3 displays the chi-square test results for the association between children’s cognitive ability and its influencing factors. In the cognitive ability test, there were statistically significant relationships between mathematics scores and all variables (P<0.05), except for gender(P>0.05). Similarly, there were statistically significant relationships between vocabulary scores and all variables (P<0.05).
Table 3.Influencing factors for the cognitive ability of 10 to 15 year-old children.
Variables
|
Mathematics test scores a
|
c2
|
P value
|
Vocabulary test scores a
|
c2
|
P value
|
Q1[0-8] n(%)
|
Q2[9-11] n (%)
|
Q3[12-14]
n (%)
|
Q4[15-24]
n (%)
|
Q1[0-17] n (%)
|
Q2[18-22]
n (%)
|
Q3[23-26]
n(%)
|
Q4[27-34]
n (%)
|
n
|
536
|
476
|
536
|
481
|
|
|
486
|
504
|
462
|
577
|
|
|
Gender
|
|
|
|
|
|
|
|
|
|
|
|
|
Girl
|
271(50.6)
|
226(47.5)
|
27(50.4)
|
234(48.6)
|
1.299
|
0.729
|
208(42.8)
|
237(47.0)
|
233(50.4)
|
323(56.0)
|
19.798
|
<0.001
|
Boy
|
265(49.4)
|
250(52.5)
|
26(49.6)
|
247(51.4)
|
|
|
278(57.2)
|
267(53.0)
|
229(49.6)
|
254(44.0)
|
|
|
Age
|
|
|
|
|
|
|
|
|
|
|
|
|
10
|
224(41.8)
|
106(22.3)
|
9(1.7)
|
3(0.6)
|
1.4e+03
|
<0.001
|
180(37.0)
|
97(19.3)
|
43(9.3)
|
22(3.8)
|
485.985
|
<0.001
|
11
|
147(27.4)
|
161(33.8)
|
36(6.7)
|
4(0.8)
|
|
|
125(25.7)
|
107(21.2)
|
76(16.5)
|
40(6.9)
|
|
|
12
|
87(16.2)
|
115(24.2)
|
100(18.7)
|
23(4.8)
|
|
|
80(16.5)
|
98(19.4)
|
78(16.9)
|
69(12.0)
|
|
|
13
|
46(8.6)
|
60(12.6)
|
161(30.0)
|
53(11.0)
|
|
|
49(10.1)
|
77(15.3)
|
87(18.8)
|
107(18.5)
|
|
|
14
|
18(3.4)
|
22(4.6)
|
144(26.9)
|
150(31.2)
|
|
|
22(4.5)
|
66(13.1)
|
99(21.4)
|
147(25.5)
|
|
|
15
|
14(2.6)
|
12(2.5)
|
86(16.0)
|
248(51.6)
|
|
|
30(6.2)
|
59(11.7)
|
79(17.1)
|
192(33.3)
|
|
|
Nationality
|
|
|
|
|
|
|
|
|
|
|
|
|
Minority nationality
|
439(81.9)
|
412(86.6)
|
495(92.4)
|
448(93.1)
|
42.395
|
<0.001
|
104(21.4)
|
65(12.9)
|
36(7.8)
|
30(5.2)
|
76.023
|
<0.001
|
Han nationality
|
97(18.1)
|
64(13.4)
|
41(7.6)
|
33(6.9)
|
|
|
382(78.6)
|
439(87.1)
|
426(92.2)
|
547(94.8)
|
|
|
Household registration
|
|
|
|
|
|
|
|
|
|
|
|
|
Countryside
|
386(72.0)
|
277(58.2)
|
313(58.4)
|
252(52.4)
|
45.037
|
<0.001
|
340(70.0)
|
327(64.9)
|
272(58.9)
|
289(50.1)
|
48.945
|
<0.001
|
Urban
|
150(28.0)
|
199(41.8)
|
223(41.6)
|
229(47.6)
|
|
|
146(30.0)
|
177(35.1)
|
190(41.1)
|
288(49.9)
|
|
|
School
|
|
|
|
|
|
|
|
|
|
|
|
|
Common school
|
525(98.0)
|
466(97.9)
|
505(94.2)
|
444(92.3)
|
27.921
|
<0.001
|
476(97.9)
|
495(98.2)
|
447(96.8)
|
522(90.5)
|
52.271
|
<0.001
|
Key school
|
11(2.0)
|
10(2.1)
|
31(5.8)
|
37(7.7)
|
|
|
10(2.1)
|
9(1.8)
|
15(3.2)
|
55(9.5)
|
|
|
Family learning environment
|
|
|
|
|
|
|
|
|
|
|
|
|
Bad
|
79(40.5)
|
36(18.5)
|
58(29.7)
|
22(11.3)
|
56.343
|
<0.001
|
68(34.9)
|
50(25.6)
|
49(25.1)
|
28(14.4)
|
61.321
|
<0.001
|
Neutrality
|
261(29.8)
|
203(23.2)
|
221(25.2)
|
192(21.9)
|
|
|
240(27.4)
|
238(27.1)
|
176(20.1)
|
223(25.4)
|
|
|
Good
|
196(20.5)
|
237(24.8)
|
257(26.9)
|
267(27.9)
|
|
|
178(18.6)
|
216(22.6)
|
237(24.8)
|
326(34.1)
|
|
|
Father education level
|
|
|
|
|
|
|
|
|
|
|
|
|
Low
|
309(57.7)
|
211(44.3)
|
244(45.5)
|
171(35.5)
|
90.999
|
<0.001
|
286(58.9)
|
246(48.8)
|
207(44.8)
|
196(34.0)
|
116.108
|
<0.001
|
Medium
|
211(44.3)
|
237(49.8)
|
260(48.5)
|
265(55.1)
|
|
|
186(38.3)
|
240(47.6)
|
227(49.1)
|
320(55.5)
|
|
|
High
|
16(3.0)
|
28(5.9)
|
32(6.0)
|
45(9.4)
|
|
|
14(2.9)
|
18(3.6)
|
28(6.1)
|
61(10.6)
|
|
|
Mother education level
|
|
|
|
|
|
|
|
|
|
|
|
|
Low
|
405(75.6)
|
276(58.0)
|
305(56.9)
|
259(53.8)
|
119.648
|
<0.001
|
361(74.3)
|
337(66.9)
|
271(58.7)
|
276(47.8)
|
156.641
|
<0.001
|
Medium
|
116(21.6)
|
181(38.0)
|
209(39.0)
|
187(38.9)
|
|
|
116(23.9)
|
155(30.8)
|
170(36.8)
|
252(43.7)
|
|
|
High
|
15(2.8)
|
19(4.0)
|
22(4.1)
|
35(7.3)
|
|
|
9(1.9)
|
12(2.4)
|
21(4.6)
|
49(8.5)
|
|
|
Annual household income (per person)
|
|
|
|
|
|
|
|
|
|
|
|
|
<3500RMB
|
244(45.5)
|
143(30.0)
|
155(32.6)
|
122(25.4)
|
67.300
|
<0.001
|
208(42.8)
|
182(36.1)
|
145(31.4)
|
145(25.1)
|
55.669
|
<0.001
|
3500~7000 RMB
|
167(31.2)
|
178(37.4)
|
190(35.5)
|
153(31.8)
|
|
|
163(33.5)
|
176(34.9)
|
158(34.2)
|
191(33.1)
|
|
|
>7000 RMB
|
125(23.3)
|
155(32.6)
|
175(32.7)
|
206(42.8)
|
|
|
115(23.7)
|
146(29.0)
|
159(34.4)
|
241(41.8)
|
|
|
Family size
|
|
|
|
|
|
|
|
|
|
|
|
|
3~6
|
455(84.9)
|
425(89.3)
|
487(90.9)
|
455(94.6)
|
26.978
|
<0.001
|
412(84.8)
|
445(88.3)
|
423(91.6)
|
542(93.9)
|
26.975
|
<0.001
|
7~14
|
81(15.1)
|
51(10.7)
|
49(9.1)
|
26(5.4)
|
|
|
74(15.2)
|
59(11.7)
|
39(8.4)
|
35(6.1)
|
|
|
a Q=Quartile; Q1 represented 0% to 25%, Q2 represented 25% to 50%, Q3 represented 50% to 75%, and Q4 represented 75% to 100%.
Influencing factors of cognitive ability: results from Ordinal-Logistic regression
As shown in Table 4, there was a significant positive relationship between ‘High protein’ dietary pattern and mathematics /vocabulary test scores. The results revealed that a 1-unit increase in ‘High protein’ dietary pattern scores was associated with a 1.28-fold (CI: 1.21~1.35) increase in children’s mathematics test scores or a 1.25-fold (CI: 1.18~1.32) increase in children’s vocabulary test scores. In contract, there were significant inverse relationships between ‘High fat’ (OR=0.89, CI: 0.83~0.96; P=0.002) and ‘High salt-oil’ (OR=0.91, CI:0.84~0.98; P=0.012) dietary patterns and vocabulary test scores. The findings of Ordinal-Logistic regression for the influencing factors associated with children’s cognitive ability were similar to those of chi-square test.
Table 4. Ordinal-Logistic regression between cognitive ability test scores and covariates of children aged 10-15 years in the 2010 CFPS.
Variables
|
|
Mathematics
|
|
|
Vocabulary
|
|
OR
|
P value
|
95%CI
|
OR
|
P value
|
95%CI
|
‘High protein’ dietary patterns
|
1.28
|
<0.001
|
(1.21;1.35)
|
1.25
|
<0.001
|
(1.18;1.32)
|
‘High fat’ dietary pattern
|
0.96
|
0.287
|
(0.90;1.03)
|
0.89
|
0.002
|
(0.83;0.96)
|
‘ High salt-oil ’ dietary pattern
|
0.96
|
0.503
|
(0.90;1.05)
|
0.91
|
0.012
|
(0.84;0.98)
|
Gender
|
|
|
|
|
|
|
Girl
|
|
|
0
|
|
|
0
|
Boy
|
1.02
|
0.763
|
(0.88;1.20)
|
0.70
|
<0.001
|
(0.60;0.82)
|
Age
|
|
|
|
|
|
|
10~15
|
2.93
|
<0.001
|
(2.73; 3.13)
|
1.73
|
<0.001
|
(1.64;1.82)
|
Nationality
|
|
|
|
|
|
|
Minority nationality
|
|
|
0
|
|
|
0
|
Han nationality
|
2.23
|
<0.001
|
(1.74;2.86)
|
2.97
|
<0.001
|
(2.31;3.83)
|
Household registration
|
|
|
|
|
|
|
Countryside
|
|
|
0
|
|
|
0
|
Urban
|
1.65
|
<0.001
|
(1.40;1.93)
|
1.77
|
<0.001
|
(1.51;2.08)
|
School
|
|
|
|
|
|
|
Common school
|
|
|
0
|
|
|
0
|
Key school
|
2.68
|
<0.001
|
(1.82; 3.94)
|
4.08
|
<0.001
|
(2.66;6.27)
|
Family learning environment
|
|
|
|
|
|
|
Bad
|
|
|
0
|
|
|
0
|
Neutrality
|
1.56
|
0.002
|
(1.18; 2.07)
|
1.46
|
0.007
|
(1.11; 1.93)
|
Good
|
2.26
|
<0.001
|
(1.71; 2.99)
|
2.38
|
<0.001
|
(1.81; 3.14)
|
Father education level
|
|
|
|
|
|
|
Low
|
|
|
0
|
|
|
0
|
Medium
|
1.62
|
<0.001
|
(1.38;1.91)
|
1.79
|
<0.001
|
(1.52;2.10)
|
High
|
2.56
|
<0.001
|
(1.81;3.60)
|
3.72
|
<0.001
|
(2.60;5.31)
|
Mother education level
|
|
|
|
|
|
|
Low
|
|
|
0
|
|
|
0
|
Medium
|
1.71
|
<0.001
|
(1.45;2.02)
|
2.00
|
<0.001
|
(1.69;2.37)
|
High
|
2.35
|
<0.001
|
(1.59;3.47)
|
4.11
|
<0.001
|
(2.73;6.16)
|
Annual household income(per person)
|
|
|
|
|
|
|
<3500RMB
|
|
|
0
|
|
|
0
|
3500~7000 RMB
|
1.46
|
<0.001
|
(1.20;1.76)
|
1.41
|
<0.001
|
(1.16;1.70)
|
>7000 RMB
|
1.46
|
<0.001
|
(1.71;2.53)
|
2.09
|
<0.001
|
(1.72;2.54)
|
Family size
|
|
|
|
|
|
|
7~14
|
|
|
0
|
|
|
0
|
3~6
|
1.97
|
<0.001
|
(1.52;2.55)
|
1.98
|
<0.001
|
(1.53;2.56)
|
Association between dietary patterns and cognitive ability
Table 5 demonstrates the relationship between cognitive ability and dietary patterns (both as continuous variables and quartiles) in three models. An increase in ‘High protein’ dietary pattern score (continuous variable) was associated with higher mathematics test scores, and the results were similar for crude model (OR=1.29, CI: 1.21~1.37; P<0.001) and fully adjusted model (OR=1.15, CI: 1.07~1.23; P<0.001). The association between ‘High protein’ dietary pattern and vocabulary test scores was statistically significant in the first two models, but not in model Ⅲ(OR=1.06, CI: 0.99~1.13; P=0.076). While, an increase in ‘High fat’ dietary pattern score (continuous variable) was significantly associated with lower scores of mathematics test in crude model (OR=0.89, CI: 0.82~0.96; P =0.004) and vocabulary test in the three models (OR=0.88, CI: 0.82~0.96; P=0.002 in model Ⅲ). However, children with higher scores of ‘High salt-oil’ dietary pattern (continuous variable) tended to have lower scores of vocabulary test in modelⅠ(OR=0.90, CI: 0.83~0.98; P=0.012).
When dividing the ‘High protein’, ‘High fat’ and ‘High salt-oil’ dietary patterns into four quartiles, the results obtained were similar to the above-described associations between continuous dietary pattern scores and cognitive ability outcomes.
Table 5. Ordinal-Logistic regression models of the association between 10 to 15 year-old children’ cognitive ability test scores and dietary patterns (both as continuous variables and quartiles).
Test
|
Dietary pattern
|
Quartile
|
Model Ⅰa
|
Model Ⅱb
|
Model Ⅲc
|
OR
|
P value
|
95%CI
|
OR
|
P value
|
95%CI
|
OR
|
P value
|
95%CI
|
Mathematics
|
‘High protein’
|
|
1.29
|
<0.001
|
(1.21;1.37)
|
1.19
|
<0.001
|
(1.11;1.27)
|
1.15
|
<0.001
|
(1.07;1.23)
|
|
‘High fat’
|
|
0.89
|
0.004
|
(0.82;0.96)
|
0.93
|
0.090
|
(0.86;1.01)
|
0.94
|
0.136
|
(0.86;1.02)
|
|
‘High salt-oil’
|
|
0.94
|
0.182
|
(0.87;1.03)
|
1.00
|
0.955
|
(0.92;1.09)
|
1.03
|
0.568
|
(0.94;1.12)
|
Vocabulary
|
‘High protein’
|
|
1.19
|
<0.001
|
(1.12;1.26)
|
1.10
|
0.005
|
(1.03;1.17)
|
1.06
|
0.076
|
(0.99;1.13)
|
|
‘High fat’
|
|
0.85
|
<0.001
|
(0.78;0.91)
|
0.88
|
0.002
|
(0.82;0.95)
|
0.88
|
0.002
|
(0.82;0.96)
|
|
‘High salt-oil’
|
|
0.90
|
0.012
|
(0.83;0.98)
|
0.95
|
0.238
|
(0.88;1.03)
|
0.96
|
0.372
|
(0.89;1.05)
|
Mathematics
|
‘High protein’
|
Q1
|
|
|
0
|
|
|
0
|
|
|
0
|
|
|
Q2
|
1.20
|
0.151
|
(0.94;1.54)
|
1.10
|
0.449
|
(0.86;1.42)
|
1.06
|
0.664
|
(0.82;1.36)
|
|
|
Q3
|
2.09
|
<0.001
|
(1.63;2.68)
|
1.80
|
<0.001
|
(1.40;2.33)
|
1.67
|
<0.001
|
(1.29;2.16)
|
|
|
Q4
|
2.47
|
<0.001
|
(1.90;3.20)
|
1.84
|
<0.001
|
(1.41;2.41)
|
1.62
|
0.001
|
(1.23;2.15)
|
|
‘High fat’
|
Q1
|
|
|
0
|
|
|
0
|
|
|
0
|
|
|
Q2
|
0.74
|
0.014
|
(0.58;0.94)
|
0.84
|
0.160
|
(0.66;1.07)
|
0.86
|
0.234
|
(0.67;1.10)
|
|
|
Q3
|
0.85
|
0.182
|
(0.67;1.08)
|
0.95
|
0.700
|
(0.75;1.22)
|
1.00
|
0.995
|
(0.78;1.28)
|
|
|
Q4
|
0.69
|
0.002
|
(0.54;0.87)
|
0.77
|
0.042
|
(0.61;0.99)
|
0.76
|
0.031
|
(0.59;0.98)
|
|
‘High salt-oil’
|
Q1
|
|
|
0
|
|
|
0
|
|
|
0
|
|
|
Q2
|
0.58
|
<0.001
|
(0.45;0.74)
|
0.66
|
0.001
|
(0.52;0.85)
|
0.73
|
0.013
|
(0.56;0.93)
|
|
|
Q3
|
0.55
|
<0.001
|
(0.43;0.71)
|
0.68
|
0.003
|
(0.53;0.87)
|
0.75
|
0.028
|
(0.59;0.97)
|
|
|
Q4
|
0.76
|
0.028
|
(0.60;0.97)
|
0.91
|
0.431
|
(0.71;1.16)
|
0.99
|
0.915
|
(0.77;1.27)
|
Vocabulary
|
‘High protein’
|
Q1
|
|
|
0
|
|
|
0
|
|
|
0
|
|
|
Q2
|
1.18
|
0.170
|
(0.93;1.48)
|
1.07
|
0.580
|
(0.85;1.35)
|
1.03
|
0.776
|
(0.82;1.31)
|
|
|
Q3
|
1.29
|
0.036
|
(1.02;1.63)
|
1.10
|
0.438
|
(0.87;1.40)
|
1.03
|
0.809
|
(0.81;1.31)
|
|
|
Q4
|
1.82
|
<0.001
|
(1.43;2.33)
|
1.36
|
0.017
|
(1.06;1.75)
|
1.21
|
0.149
|
(0.93;1.58)
|
|
‘High fat’
|
Q1
|
|
|
0
|
|
|
0
|
|
|
0
|
|
|
Q2
|
0.94
|
0.622
|
(0.75;1.19)
|
1.03
|
0.778
|
(0.82;1.31)
|
1.06
|
0.624
|
(0.84;1.34)
|
|
|
Q3
|
0.85
|
0.165
|
(0.68;1.07)
|
0.94
|
0.627
|
(0.75;1.19)
|
0.97
|
0.780
|
(0.76;1.22)
|
|
|
Q4
|
0.69
|
0.002
|
(0.55;0.87)
|
0.78
|
0.036
|
(0.62;0.98)
|
0.77
|
0.029
|
(0.61;0.97)
|
|
‘High salt-oil’
|
Q1
|
|
|
0
|
|
|
0
|
|
|
0
|
|
|
Q2
|
0.65
|
<0.001
|
(0.51;0.81)
|
0.75
|
0.016
|
(0.59;0.95)
|
0.80
|
0.064
|
(0.63;1.01)
|
|
|
Q3
|
0.67
|
0.001
|
(0.53;0.85)
|
0.82
|
0.101
|
(0.65;1.04)
|
0.88
|
0.305
|
(0.69;1.12)
|
|
|
Q4
|
0.75
|
0.014
|
(0.59;0.94)
|
0.87
|
0.269
|
(0.69;1.11)
|
0.93
|
0.544
|
(0.73;1.18)
|
aModelⅠ includes: gender, age, nationality, household registration.
bModel Ⅱ includes: variables in model 1+ school type,, mother education, father education.
cModel Ⅲ includes: variables in model 2 + family education environment, family income, family size.