As is shown in Table 1, four dietary patterns were generated from the food groups. The four dietary pattens includes pattern 1 (potato, bread, and fruit pattern), pattern 2 (soups and vegetables pattern), pattern 3 (fast-food pattern) and pattern 4 (meat and vegetables pattern). Table 2 shows the analysis of variance (ANOVA) of the participants characteristics such as age, weight, body mass index (BMI), waist circumference (WC), activity energy expenditure (AEE), energy intake and lipid profile by tertiles of dietary pattern.
Table 1: Factor loading matrix for dietary patterns
Food groups
|
Potato, bread, and fruit pattern (Pattern 1)
|
Soups and vegetables pattern (Pattern 2)
|
Fast-food pattern (Pattern 3)
|
Meat and vegetables pattern (Pattern 4)
|
Potato
|
0.711
|
-
|
-
|
-0.231
|
Bread
|
0.696
|
-0.217
|
-0.285
|
-
|
Fruit
|
0.661
|
-
|
0.153
|
-
|
Tin/dry fruit
|
0.528
|
-
|
-
|
0.180
|
Spreads
|
0.164
|
0.725
|
-
|
-
|
Dairy
|
0.267
|
-0.523
|
0.163
|
-0.104
|
Water, tap
|
-0.166
|
0.514
|
-
|
-0.174
|
Soup
|
0.111
|
0.510
|
-0.123
|
0.228
|
Nuts, mixed
|
0.397
|
0.264
|
-0.659
|
-
|
Pizzas/burgers
|
-
|
-
|
0.600
|
-
|
Rice/pasta
|
-
|
0.354
|
0.598
|
-
|
Oily fish
|
-0.136
|
0.225
|
-0.566
|
-
|
White meat
|
-0.305
|
-
|
-
|
0.636
|
Vegetables
|
0.112
|
0.446
|
-0.177
|
0.634
|
Sauces/dressings
|
0.115
|
-
|
0.515
|
0.584
|
Red meat
|
-
|
-0.458
|
-
|
0.560
|
Variance explained (%)
|
14.526
|
13.536
|
12.006
|
8.691
|
Eigenvalue
|
2.324
|
2.166
|
1.921
|
1.391
|
Loadings |< 0.10| are excluded for ease of interpretation; Positive loadings indicate positive association, and negative loadings indicate negative association with the dietary pattern
Table 2 shows the mean concentration of the inflammatory cytokines as well as the standard deviations.
Table 2: Levels of cytokines (inflammatory markers)
Inflammatory markers
|
Mean ± SD
|
IL-1β (pg/mL)
|
2.27 ± 8.01
|
IFN-α2 (pg/mL)
|
30.45 ± 131.32
|
IFN-λ (pg/mL)
|
68.35 ± 112.62
|
TNF-α (pg/mL)
|
2.35 ± 4.89
|
MCP-1 (pg/mL)
|
193.92 ± 62.31
|
IL-6 (pg/mL)
|
2.31 ± 7.76
|
IL-8 (pg/mL)
|
12.56 ± 21.30
|
IL-10 (pg/mL)
|
1.29 ± 6.49
|
IL-12p70 (pg/mL)
|
2.00 ± 7.94
|
IL-17A (pg/mL)
|
45.92 ± 110.20
|
IL-18 (pg/mL)
|
140.62 ± 60.52
|
IL-23 (pg/mL)
|
62.96 ± 142.77
|
IL-33 (pg/mL)
|
4.00 ± 17.25
|
Ferritin (µg/L)
|
145.06 ± 103.76
|
CRP (mg/L)
|
1.81 ± 1.61
|
IL = interleukin; IFN = interferon; TNF = tumour necrosis factor; CRP = C-reactive protein; SD = standard deviation
Table 3: Participants’ characteristics and lipid profile by dietary pattern
|
Pattern 1
|
Pattern 2
|
T1 (low)
|
T2
|
T3 (high)
|
P for trend
|
T1 (low)
|
T2
|
T3 (high)
|
P for trend
|
Age (years)
|
63.50
|
61.73
|
65.00
|
0.034
|
63.27
|
63.27
|
63.74
|
0.913
|
Weight (kg)
|
68.92
|
65.67
|
65.82
|
0.448
|
67.60
|
69.48
|
63.43
|
0.094
|
BMI (kg/m2)
|
26.58
|
25.25
|
25.14
|
0.337
|
26.61
|
26.65
|
23.76
|
0.007
|
WC (cm)
|
80.72
|
78.27
|
79.22
|
0.736
|
80.32
|
82.20
|
75.82
|
0.102
|
AEE (kJ/min)
|
975.27
|
1063.51
|
1849.68
|
0.265
|
554.56
|
1498.53
|
1835.90
|
0.079
|
Energy Intake (kJ)
|
6397.32
|
8402.06
|
9881.90
|
<0.001
|
8607.27
|
8136.98
|
8009.05
|
0.704
|
Total cholesterol
|
5.64
|
5.79
|
5.78
|
0.813
|
5.59
|
5.69
|
5.94
|
0.362
|
TG
|
1.32
|
1.47
|
1.50
|
0.679
|
1.40
|
1.42
|
1.47
|
0.958
|
HDL
|
1.84
|
1.78
|
1.80
|
0.935
|
1.68
|
1.78
|
1.96
|
0.111
|
LDL
|
3.20
|
3.33
|
3.29
|
0.871
|
3.27
|
3.25
|
3.31
|
0.974
|
BMI = body mass index; WC = waist circumference; AEE = activity energy expenditure; TG = triglycerides;
HDL = high density lipoprotein; LDL = low density lipoprotein
Table 3: Participants’ characteristics and lipid profile by dietary pattern continued
|
Pattern 3
|
Pattern 4
|
T1 (low)
|
T2
|
T3 (high)
|
P for trend
|
T1 (low)
|
T2
|
T3 (high)
|
P for trend
|
Age (years)
|
63.35
|
62.92
|
64.00
|
0.698
|
62.96
|
63.92
|
63.41
|
0.759
|
Weight (kg)
|
64.60
|
66.23
|
69.44
|
0.227
|
64.67
|
66.88
|
68.75
|
0.365
|
BMI (kg/m2)
|
24.51
|
25.76
|
26.64
|
0.140
|
25.14
|
25.65
|
26.14
|
0.652
|
WC (cm)
|
76.59
|
78.03
|
83.43
|
0.062
|
77.74
|
78.98
|
81.40
|
0.486
|
AEE (kJ/min)
|
1879.96
|
737.96
|
1292.31
|
0.159
|
816.65
|
1798.88
|
1294.29
|
0.259
|
Energy Intake (kJ)
|
8366.46
|
7591.91
|
8765.82
|
0.279
|
7050.09
|
8632.63
|
9031.26
|
0.017
|
Total cholesterol
|
5.66
|
5.80
|
5.74
|
0.858
|
5.88
|
5.96
|
5.39
|
0.049
|
TG
|
1.25
|
1.47
|
1.56
|
0.367
|
1.38
|
1.60
|
1.31
|
0.402
|
HDL
|
1.90
|
1.76
|
1.77
|
0.552
|
1.83
|
1.72
|
1.87
|
0.538
|
LDL
|
3.20
|
3.37
|
3.25
|
0.793
|
3.42
|
3.51
|
2.92
|
0.032
|
BMI = body mass index; WC = waist circumference; AEE = activity energy expenditure; TG = triglycerides;
HDL = high density lipoprotein; LDL = low density lipoprotein
The results of the correlational analysis are shown in Table 3 and 4. It is apparent from Table 3 that there were significant negative association between the selected nutrients and some of the inflammation markers. The table shows negative correlations between the intake of dietary fibre, soluble and insoluble non-starch polysaccharides (NSP), vitamin C and niacin and with almost all the inflammatory markers. Vitamin D2 was negatively associated with IL-6 and CRP levels while Vitamin C was inversely correlated to IL-1β, IFN-α2, IFN-λ and IL-23. Niacin and folic acid were also negatively correlated with some of the inflammatory biomarkers as shown in Table 3 below. Sodium was however positively associated with MCP-1 while potassium had an inverse association. However, interleukin (IL)-10, IL-12p70, IL-17A and IL-18 were not significantly correlated with these nutrients.
Table 4: Correlations between intake of selected nutrients and inflammation markers
|
Fibre
|
Soluble NSP
|
Insoluble NSP
|
Vitamin D2
|
Vitamin C
|
Niacin
|
Folic acid
|
Sodium
|
Potassium
|
IL-1β
|
-0.252*
|
-0.211
|
-0.265*
|
-0.017
|
-0.270*
|
-0.155
|
0.068
|
-0.003
|
-0.252*
|
IFN-α2
|
-0.328**
|
-0.249*
|
-0.360**
|
0.073
|
-0.220*
|
-0.272*
|
0.052
|
-0.115
|
-0.246*
|
IFN-λ
|
-0.358**
|
-0.271*
|
-0.395**
|
-0.010
|
-0.228*
|
-0.244*
|
0.001
|
-0.008
|
-0.215
|
TNF-α
|
-0.281*
|
-0.199
|
-0.321**
|
0.024
|
-0.096
|
-0.227*
|
0.039
|
0.095
|
-0.048
|
MCP-1 (pg/ml)
|
-0.096
|
-0.020
|
-0.142
|
-0.067
|
-0.047
|
-0.095
|
0.032
|
0.275*
|
-0.026
|
IL-6
|
-0.331**
|
-0.247*
|
-0.375**
|
-0.276*
|
-0.199
|
-0.256*
|
-0.246*
|
-0.067
|
-0.213
|
IL-8
|
-0.202
|
-0.110
|
-0.257*
|
0.023
|
-0.183
|
-0.129
|
0.050
|
0.011
|
-0.082
|
IL-10
|
-0.067
|
-0.043
|
-0.100
|
0.103
|
-0.086
|
-0.043
|
0.127
|
-0.001
|
-0.018
|
IL-12p70
|
0.024
|
0.047
|
-0.006
|
0.078
|
-0.058
|
-0.042
|
0.133
|
0.011
|
-0.008
|
IL-17A
|
-0.030
|
-0.007
|
-0.088
|
0.061
|
-0.180
|
-0.068
|
0.101
|
-0.020
|
-0.031
|
IL-18 (pg/ml)
|
-0.083
|
-0.016
|
-0.083
|
0.013
|
-0.049
|
-0.036
|
0.018
|
0.080
|
-0.103
|
IL-23
|
-0.246*
|
-0.159
|
-0.303**
|
0.121
|
-0.355**
|
-0.058
|
0.164
|
-0.007
|
-0.150
|
IL-33
|
0.022
|
0.049
|
0.007
|
0.110
|
-0.101
|
0.058
|
0.155
|
0.186
|
0.049
|
CRP (µg/ml)
|
-0.015
|
-0.028
|
-0.012
|
-0.299**
|
-0.089
|
-0.226
|
-0.282*
|
-0.195
|
-0.156
|
Ferritin (µg/L)
|
-0.273*
|
-0.302**
|
-0.259*
|
0.024
|
-0.041
|
-0.113
|
-0.062
|
-0.169
|
-0.050
|
NSP = non-starch polysaccharides; IL = interleukin; IFN = interferon; TNF = tumour necrosis factor; CRP = C-reactive protein; * = P < 0.05; ** = P < 0.01
As shown in Table 4, intake of vegetables and especially fruits are negatively correlated with the inflammatory markers. Intake of yoghurt was also negatively associated IL-8, IL-12p70 and IL-17A. Surprisingly, tea/coffee intake also showed a negative correlation with the cytokines. However, intake of confectionery was positively associated with the inflammatory markers.
Table 5: Correlations between intake of selected foods and inflammation markers
|
Bread
|
Fruit
|
Vegetable
|
White meat
|
Red meat
|
Sauces/dressings
|
Yoghurt
|
Confectionery
|
Tea/coffee
|
IL-1β
|
0.046
|
-0.158
|
-0.201
|
0.194
|
-0.020
|
-0.060
|
-0.072
|
0.048
|
-0.128
|
IFN-α2
|
-0.054
|
-0.287*
|
-0.118
|
0.140
|
-0.079
|
0.016
|
-0.140
|
-0.014
|
-0.258*
|
IFN-λ
|
-0.041
|
-0.313**
|
-0.187
|
0.185
|
-0.151
|
0.021
|
-0.212
|
0.032
|
-0.267*
|
TNF-α
|
0.058
|
-0.187
|
-0.237*
|
0.080
|
-0.008
|
0.156
|
-0.089
|
0.175
|
-0.271*
|
MCP-1 (pg/ml)
|
-0.019
|
-0.163
|
0.066
|
0.090
|
0.037
|
0.034
|
-0.018
|
0.089
|
-0.151
|
IL-6
|
-0.064
|
-0.242*
|
-0.142
|
0.263*
|
-0.034
|
-0.037
|
-0.133
|
0.026
|
-0.229*
|
IL-8
|
0.125
|
-0.296**
|
0.088
|
0.214
|
-0.065
|
-0.080
|
-0.258*
|
0.146
|
-0.250*
|
IL-10
|
0.134
|
-0.045
|
-0.085
|
0.095
|
0.163
|
-0.005
|
-0.137
|
0.045
|
-0.104
|
IL-12p70
|
0.257*
|
0.073
|
0.049
|
-0.018
|
0.034
|
-0.280*
|
-0.288**
|
0.234*
|
-0.091
|
IL-17A
|
0.100
|
-0.126
|
-0.014
|
-0.078
|
-0.069
|
-0.249*
|
-0.269*
|
0.177
|
-0.219
|
IL-18 (pg/ml)
|
-0.056
|
-0.168
|
0.046
|
-0.001
|
0.033
|
0.067
|
0.018
|
0.239*
|
0.005
|
IL-23
|
0.082
|
-0.358**
|
0.006
|
0.070
|
-0.013
|
-0.070
|
-0.187
|
0.068
|
-0.155
|
IL-33
|
0.182
|
0.044
|
-0.082
|
-0.078
|
0.112
|
-0.088
|
-0.176
|
0.280*
|
0.023
|
Ln CRP (µg/ml)
|
-0.189
|
-0.130
|
-0.344**
|
0.035
|
0.014
|
0.024
|
0.035
|
0.168
|
-0.024
|
Ln Ferritin (µg/L)
|
-0.023
|
-0.061
|
-0.053
|
0.306**
|
0.161
|
0.016
|
0.031
|
-0.066
|
-0.074
|
NSP = non-starch polysaccharides; IL = interleukin; IFN = interferon; TNF = tumour necrosis factor; CRP = C-reactive protein; * = P < 0.05; ** = P < 0.01
Furthermore, a binary logistic regression of the dietary patterns by the inflammatory markers was conducted to predict the likelihood and risk for high inflammation status among the participants. Table 5 shows a high intake of Pattern 1 (potato, bread, and fruit pattern) was associated with a low risk of high IFN-α2, IFN-λ, IL-6 and IL-8 levels while a high intake of Pattern 3 (fast-food pattern) was associated high risk of IFN-α2 levels.
Table 6: Binary logistic regression estimates predicting likelihood and risk of high inflammatory marker
|
B
|
SE
|
P-value
|
OR
|
95%CI for OR
|
IL-1 β
|
-2.007
|
0.406
|
<0.001
|
0.134
|
|
|
Pattern 1
|
-1.096
|
0.582
|
0.060
|
0.334
|
0.107
|
1.046
|
Pattern 2
|
0.111
|
0.376
|
0.768
|
1.118
|
0.534
|
2.337
|
Pattern 3
|
0.165
|
0.368
|
0.653
|
1.180
|
0.573
|
2.428
|
Pattern 4
|
-0.180
|
0.352
|
0.608
|
0.835
|
0.419
|
1.664
|
IFN- α2
|
0.698
|
0.273
|
0.010
|
2.011
|
|
|
Pattern 1
|
-1.292
|
0.417
|
0.002
|
0.275
|
0.121
|
0.623
|
Pattern 2
|
0.606
|
0.316
|
0.055
|
1.834
|
0.988
|
3.404
|
Pattern 3
|
0.635
|
0.324
|
0.050
|
1.886
|
1.000
|
3.557
|
Pattern 4
|
-0.322
|
0.257
|
0.211
|
0.725
|
0.437
|
1.200
|
IFN-λ
|
-0.204
|
0.246
|
0.406
|
0.816
|
|
|
Pattern 1
|
-0.850
|
0.359
|
0.018
|
0.427
|
0.212
|
0.863
|
Pattern 2
|
0.183
|
0.251
|
0.466
|
1.201
|
0.734
|
1.964
|
Pattern 3
|
0.320
|
0.273
|
0.241
|
1.377
|
0.807
|
2.350
|
Pattern 4
|
-0.305
|
0.255
|
0.232
|
0.737
|
0.448
|
1.215
|
TNF-α
|
-0.876
|
0.265
|
0.001
|
0.416
|
|
|
Pattern 1
|
-0.575
|
0.355
|
0.105
|
0.563
|
0.281
|
1.128
|
Pattern 2
|
-0.032
|
0.271
|
0.907
|
0.969
|
0.570
|
1.648
|
Pattern 3
|
0.544
|
0.304
|
0.074
|
1.723
|
0.949
|
3.125
|
Pattern 4
|
-0.164
|
0.269
|
0.544
|
0.849
|
0.501
|
1.440
|
IL-6
|
-1.506
|
0.352
|
<0.001
|
0.222
|
|
|
Pattern 1
|
-1.410
|
0.533
|
0.008
|
0.244
|
0.086
|
0.695
|
Pattern 2
|
0.032
|
0.334
|
0.923
|
1.033
|
0.537
|
1.988
|
Pattern 3
|
0.320
|
0.324
|
0.323
|
1.377
|
0.730
|
2.600
|
Pattern 4
|
-0.100
|
0.292
|
0.732
|
0.905
|
0.511
|
1.603
|
IL-8
|
0.215
|
0.240
|
0.372
|
1.239
|
|
|
Pattern 1
|
-0.649
|
0.324
|
0.045
|
0.523
|
0.277
|
0.987
|
Pattern 2
|
0.439
|
0.262
|
0.094
|
1.551
|
0.928
|
2.593
|
Pattern 3
|
-0.024
|
0.266
|
0.928
|
0.976
|
0.579
|
1.645
|
Pattern 4
|
-0.023
|
0.243
|
0.924
|
0.977
|
0.607
|
1.574
|
IL-10
|
-1.877
|
0.342
|
<0.001
|
0.902
|
|
|
Pattern 1
|
-0.171
|
0.387
|
0.658
|
0.843
|
0.394
|
1.800
|
Pattern 2
|
-0.171
|
0.363
|
0.637
|
0.843
|
0.413
|
1.718
|
Pattern 3
|
0.316
|
0.376
|
0.401
|
1.372
|
0.656
|
2.869
|
Pattern 4
|
-0.103
|
0.355
|
0.772
|
0.902
|
0.450
|
1.810
|
IL-12p70
|
-1.795
|
0.335
|
<0.001
|
0.166
|
|
|
Pattern 1
|
0.000
|
0.293
|
1.000
|
1.000
|
0.563
|
1.776
|
Pattern 2
|
0.066
|
0.306
|
0.829
|
1.068
|
0.586
|
1.948
|
Pattern 3
|
-0.369
|
0.313
|
0.239
|
0.692
|
0.374
|
1.278
|
Pattern 4
|
-0.300
|
0.375
|
0.424
|
0.741
|
0.355
|
1.545
|
IL-17A
|
-0.773
|
0.253
|
<0.001
|
0.462
|
|
|
Pattern 1
|
-0.038
|
0.246
|
0.877
|
0.963
|
0.595
|
1.559
|
Pattern 2
|
0.070
|
0.244
|
0.773
|
1.073
|
0.665
|
1.730
|
Pattern 3
|
-0.138
|
0.254
|
0.588
|
0.871
|
0.530
|
1.434
|
Pattern 4
|
-0.566
|
0.312
|
0.070
|
0.568
|
0.308
|
1.047
|
IL-23
|
-0.044
|
0.232
|
0.848
|
0.957
|
|
|
Pattern 1
|
-0.429
|
0.292
|
0.141
|
0.651
|
0.367
|
1.153
|
Pattern 2
|
0.181
|
0.238
|
0.446
|
1.199
|
0.752
|
1.910
|
Pattern 3
|
0.168
|
0.251
|
0.503
|
1.183
|
0.723
|
1.936
|
Pattern 4
|
-0.133
|
0.232
|
0.567
|
0.875
|
0.555
|
1.380
|
IL-33
|
-2.213
|
0.381
|
<0.001
|
0.109
|
|
|
Pattern 1
|
0.220
|
0.314
|
0.483
|
1.246
|
0.674
|
2.307
|
Pattern 2
|
-0.108
|
0.385
|
0.779
|
0.897
|
0.422
|
1.910
|
Pattern 3
|
-0.081
|
0.361
|
0.821
|
0.922
|
0.455
|
1.869
|
Pattern 4
|
-0.032
|
0.393
|
0.935
|
0.968
|
0.448
|
2.091
|
B = unstandardized coefficient; SE = standard error; OR = odds ratio; IL = interleukin; IFN = interferon; TNF = tumour necrosis factor
Multiple linear regression analysis was conducted between the inflammatory biomarkers and dietary patterns generated as shown in Table 6. The results showed a negative correlation between Pattern 2 (soups and vegetables pattern) and levels of CRP as well as ferritin. A positive association was also observed between Pattern 3 (fast-food pattern) and CRP levels.
Table 7: Multiple linear regression of the association between inflammatory markers and dietary patterns
Dependent variable
|
Independent variable
|
R2
|
β
|
Standard error
|
P-value
|
Ln CRP
|
|
0.143*
|
|
|
|
|
Pattern 1
|
|
-0.165
|
0.085
|
0.134
|
|
Pattern 2
|
|
-0.224
|
0.084
|
0.044
|
|
Pattern 3
|
|
0.229
|
0.084
|
0.039
|
|
Pattern 4
|
|
-0.124
|
0.085
|
0.259
|
Ln ferritin
|
|
0.096
|
|
|
|
|
Pattern 1
|
|
-0.115
|
0.078
|
0.306
|
|
Pattern 2
|
|
-0.246
|
0.078
|
0.030
|
|
Pattern 3
|
|
0.033
|
0.078
|
0.768
|
|
Pattern 4
|
|
0.149
|
0.078
|
0.184
|
MCP-1
|
|
0.021
|
|
|
|
|
Pattern 1
|
|
-0.079
|
6.811
|
0.492
|
|
Pattern 2
|
|
0.093
|
6.811
|
0.423
|
|
Pattern 3
|
|
-0.017
|
6.811
|
0.882
|
|
Pattern 4
|
|
0.074
|
6.811
|
0.522
|
IL-18
|
|
0.037
|
|
|
|
|
Pattern 1
|
|
-0.137
|
6.776
|
0.235
|
|
Pattern 2
|
|
-0.029
|
6.776
|
0.801
|
|
Pattern 3
|
|
0.126
|
6.776
|
0.272
|
|
Pattern 4
|
|
0.037
|
6.776
|
0.748
|
β =standardized coefficient; CRP = C-reactive protein; MCP-1 = monocyte chemoattractant protein-1; IL = interleukin.
Further multiple linear regression indicated a positive correlation which was observed between Pattern 2 and total cholesterol (TC) and HDL levels. Meanwhile, pattern 4 (meat and vegetables pattern) was however negatively correlated with TC, LDL and TC/HDL ratio.
Table 8: Multiple linear regression of the association between lipid profile and dietary patterns
Dependent variable
|
Independent variable
|
R2
|
β
|
Standard error
|
P-value
|
Total cholesterol
|
|
0.145*
|
|
|
|
|
Pattern 1
|
|
-0.067
|
0.098
|
0.538
|
|
Pattern 2
|
|
0.250
|
0.099
|
0.024
|
|
Pattern 3
|
|
0.173
|
0.099
|
0.115
|
|
Pattern 4
|
|
-0.218
|
0.099
|
0.047
|
Triglycerides
|
|
0.023
|
|
|
|
|
Pattern 1
|
|
0.007
|
0.092
|
0.953
|
|
Pattern 2
|
|
-0.071
|
0.092
|
0.539
|
|
Pattern 3
|
|
0.133
|
0.092
|
0.254
|
|
Pattern 4
|
|
-0.007
|
0.092
|
0.955
|
HDL-cholesterol
|
|
0.071
|
|
|
|
|
Pattern 1
|
|
0.034
|
0.055
|
0.763
|
|
Pattern 2
|
|
0.226
|
0.055
|
0.049
|
|
Pattern 3
|
|
-0.072
|
0.055
|
0.524
|
|
Pattern 4
|
|
0.115
|
0.055
|
0.312
|
LDL-cholesterol
|
|
0.142*
|
|
|
|
|
Pattern 1
|
|
-0.090
|
0.095
|
0.411
|
|
Pattern 2
|
|
0.162
|
0.095
|
0.140
|
|
Pattern 3
|
|
0.162
|
0.096
|
0.139
|
|
Pattern 4
|
|
-0.285
|
0.096
|
0.010
|
Total/HDL Cholesterol ratio
|
|
0.089
|
|
|
|
|
Pattern 1
|
|
-0.093
|
0.105
|
0.409
|
|
Pattern 2
|
|
-0.090
|
0.105
|
0.423
|
|
Pattern 3
|
|
0.134
|
0.105
|
0.235
|
|
Pattern 4
|
|
-0.231
|
0.105
|
0.043
|
β =standardized coefficient; HDL = high density lipoprotein cholesterol; LDL = low density lipoprotein cholesterol.