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. 2016 Feb 4;15:15. doi: 10.1186/s12937-016-0132-6

Table 6.

Cardio-renal-metabolic parameters according to quintile categories in each dietary pattern

Variable Meat, fats and oils, seasonings and eggs Fruit, dairy products and sweets Rice and miso soups
Quintile 1 Quintile 5 Model 1 Model 2 Quintile 1 Quintile 5 Model 1 Model 2 Quintile 1 Quintile 5 Model 1 Model 2
AST (U/L) 22 [19, 27] 21 [17, 26] −1.14 −1.15 22 [18, 28] 21 [18, 27] 0.49 0.09 21 [17, 27] 22[18, 27] 1.05 1.45
ALT (U/L) 21 [16, 31] 24 [17, 35] −0.40 −0.49 22 [16, 33] 23 [17, 34] 2.58* 2.10* 22 [15, 35] 24 [17, 34] 0.92 1.48
γ-GTP (U/L) 25 [16, 36] 27 [17, 46] −1.19 −1.33 32 [20, 56] 24 [17, 36] −3.51*** −4.20*** 26 [17, 38] 26 [19, 42] 0.11 0.69
Uric Acid (mg/dl) 5.4 ± 1.2 5.5 ± 1.2 −2.27* −2.31* 5.8 ± 1.3 5.3 ± 1.2 −0.58 −1.16 5.4 ± 1.3 5.6 ± 1.1 0.23 0.46
eGFR (ml/min/ 1.73 m2) 75 ± 15 80 ± 19 - 1.24 78 ± 17 79 ± 19 - −0.56 79 ± 19 77 ± 17 - −0.87
Total cholesterol (mg/dl) 185 ± 26 188 ± 30 0.79 0.83 185 ± 27 185 ± 29 −1.04 −1.11 188 ± 29 183 ± 29 −0.26 −0.09
HDL-C (mg/dl) 59 ± 14 59 ± 16 1.25 1.35 61 ± 14 60 ± 14 −2.87** −2.65** 59 ± 15 58 ± 13 −0.50 −1.05
Triglycerides (mg/dl) 98 [68,144] 112 [72, 157] −0.14 −0.27 112 [70, 158] 98 [71, 143] −0.45 −0.99 99 [71, 148] 100 [70, 158] −0.07 0.70
Fasting blood glucose (mg/dl) 130 ± 30 137 ± 33 0.93 0.64 136 ± 30 130 ± 31 −1.22 −1.74 135 ± 33 136 ± 31 0.28 0.21
HbA1c 6.8 ± 0.9 7.0 ± 1.0 1.55 1.25 6.8 ± 1.0 6.9 ± 0.9 0.56 0.12 7.0 ± 1.1 7.0 ± 0.9 0.65 0.79
Systolic BP(mmHg) 126 ± 15 127 ± 13 0.37 0.22 128 ± 13 125 ± 14 −2.65** −3.01** 128 ± 15 127 ± 13 −0.47 0.10
Diastolic BP (mmHg) 76 ± 10 79 ± 13 −0.37 −0.44 80 ± 11 76 ± 12 −1.89 −2.31* 77 ± 11 78 ± 13 1.19 1.61
UAE (mg/g creatinine) 10 [6, 24] 10 [5, 24] −0.17 −0.28 12[7, 36] 9 [6, 18] −3.20** −3.44*** 10 [6, 23] 11 [6, 23] −1.23 −0.77
baPWV (cm/s) 1577 ± 282 1508 ± 265 −0.07 −0.33 1574 ± 314 1530 ± 259 −3.04** −3.14** 1536 ± 256 1556 ± 277 0.18 0.15

Data are mean ± SD, median [range: 25 % to 75 %] or number of subjects (proportion) before adjustment. *P < 0.05, **P < 0.01, ***P < 0.001

Model 1: Trend estimation for linear trends across quintiles is based on linear regression analysis for continuous variables or logistic regression analysis for categorical variables adjusted for age and gender. Model 2: Trend estimation for linear trends across quintiles is based on linear regression analysis for continuous variables or logistic regression analysis for categorical variables adjusted for age, gender, BMI, morningness-eveningness questionnaire, Pittsburg Sleep Quality Index, Beck Depression inventory, current smoking, and physical activity. Standardized regression coefficients are shown. See Table 5 for abbreviations