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Table 6 Log binomial models predicting cardio-metabolic risk by PCA-derived dietary pattern tertile for Guatemalan men. INCAP Nutrition Supplementation Trial Longitudinal Cohort, 2002–2004. (n = 681)

From: Dietary patterns and cardio-metabolic risk in a population of Guatemalan young adults

  Meat-based modern Starch-based modern Traditional
  T2
PR (95% CI)
T3
PR (95% CI)
P-trend T2
PR (95% CI)
T3
PR (95% CI)
P-trend T2
PR (95% CI)
T3
PR (95% CI)
P-trend
Overweighta
 Model 1 1.07
(0.84, 1.37)
1.15
(0.90, 1.47)
0.2 1.03
(0.80, 1.32)
1.24
(0.98, 1,57)
0.06 0.96
(0.76, 1.20)
0.78
(0.61, 0.99)
0.04
 Model 2 1,05
(0.82, 1.34)
1.07
(0.82, 1.41)
0.5 1.01
(0.78, 1.31)
1.18
(0.89, 1.56)
0.2 0.94
(0.75, 1.18)
0.68
(0.52, 0.88)
0.004
 Model 3 1.08
(0.87, 1.33)
1.15
(0.91, 1.45)
0.9 0.91
(0.72, 1.16)
0.98
(0.75, 1.27)
0.9 0.98
(0.80, 1.19)
0.84
(0.67, 1.05)
0.9
Abdominal obesityb
 Model 1 1.35
(0.56, 3.24)
1.53
(0.63, 3.71)
0.6 0.74
(0.30, 1.81)
1.15
(0.50, 2.62)
0.6 0.94
(0.41, 2.16)
0.64
(0.26, 1.54)
0.5
 Model 2 1.25
(0.55, 2.84)
1.26
(0.49, 3.24)
0.6 0.68
(0.29, 1.61)
0.92
(0.39, 2.17)
0.8 0.95
(0.76, 1.20)
1.13
(0.90, 1.42)
0.2
 Model 3 1.36
(0.53, 3.45)
1.75
(0.60, 5.15)
0.5 0.58
(0.22, 1.54)
0.56
(0.19, 1.61)
0.4 1.19
(0.49, 2.84)
0.61
(0.21, 1.73)
0.4
Elevated triglyceridesc
 Model 1 1.11
(0.91, 1.36)
1.03
(0.83, 1.29)
0.7 0.96
(0.77, 1.20)
1.16
(0.95, 1.42)
0.1 0.91
(0.74, 1.11)
0.92
(0.75, 1.,13)
0.4
 Model 2 1.10
(0.89, 1.34)
0.97
(0.76, 1.23)
0.8 0.95
(0.76, 1.20)
1.13
(0.90, 1.42)
0.1 0.89
(0.72, 1.09)
0.85
(0.68, 1.06)
0.1
 Model 3 1.12
(0.91, 1.37)
1.03
(0.81, 1.31)
0.7 0.92
(0.73, 1.16)
1.05
(0.83, 1.33)
0.6 0.88
(0.74, 1.11)
0.79
(0.62, 1.01)
0.06
Low HDL-cd
 Model 1 1.02
(0.89, 1.16)
0.96
(0.83, 1.11)
0.6 1.15
(1.00, 1.32)
1.11
(0.96, 1.28)
0.1 0.86
(0.76, 0.98)
0.85
(0.74, 0.97)
0.01
 Model 2 1.03
(0.90, 1.17)
0.99
(0.84, 1.17)
0.9 1.18
(1.03, 1.37)
1.21
(1.01, 1.44)
0.03 0.85
(0.74, 0.98)
0.83
(0.71, 0.97)
0.02
 Model 3 1.07
(0.94, 1.23)
1.02
(0.86, 1.20)
0.7 1.17
(1.01, 1.35)
1.20
(1.00, 1.44)
0.04 0.88
(0.76, 1.00)
0.85
(0.72, 1.00)
0.05
Dysglycemiae
 Model 1 0.93
(0.57, 1.49)
1.08
(0.68, 1.72)
0.7 0.95
(0.59, 1.54)
1.10
(0.69, 1.75)
0.6 0.84
(0.52, 1.34)
0.84
(0.53, 1.33)
0.4
 Model 2          
 Model 3 0.98
(0.60, 1.57)
1.19
(0.70, 2.02)
0.5 1.03
(0.62, 1.70)
1.13
(0.65, 1.98)
0.6 0.83
(0.52, 1.33)
0.79
(0.47, 1.33)
0.3
Metabolic syndromef
 Model 1 1.21
(0.78, 1.88)
1.17
(0.74, 1.84)
0.4 0.88
(0.56, 1.36)
0.94
(0.61, 1.46)
0.8 0.77
(0.49, 1.19)
0.76
(0.49, 1.17)
0.2
 Model 2 0.94
(0.58, 1.53)
1.14
(0.67, 1.93)
0.6 0.98
(0.60, 1.61)
1.17
(0.69, 1.98)
0.5 0.84
(0.52, 1.34)
0.83
(0.50, 1.38)
0.4
 Model 3 1.35
(0.88, 2.06)
1.48
(0.89, 2,.46)
0.1 0.89
(0.56, 1.41)
0.88
(0.53, 1.47)
0.6 0.79
(0.51, 1.21)
0.82
(0.51, 1.32)
0.4
  1. Abbreviations: BMI body mass index, CI confidence interval, HDL-c high density lipoprotein cholesterol, INCAP Institute of Nutrition for Central America and Panama, NCEP ATP III National Cholesterol Education Program Adult Treatment Panel III, PCA principal component analysis, PR prevalence ratio, SES socioeconomic status, T tertile
  2. Model 1 = dietary pattern tertile + age
  3. Model 2 = model 1 + kcal/day
  4. Model 3 = model 2 + SES + urban residence + multivitamin use + smoking status (current vs. other) + low physical activity physical activity
  5. aOverweight defined as BMI ≥25 kg/m2
  6. bAbdominal obesity defined as waist circumference ≥ 102 cm
  7. cElevated triglycerides defined as ≥150 mg/dL or medication
  8. dLow HDL-c defined as HDL-c < 40 mg/dL
  9. eDysglycemia defined as impaired fasting glucose (fasting plasma glucose 100–125 mg/dL) or diabetes (self-report, fasting plasma glucose ≥126 mg/dL, and/or use of diabetes medication)
  10. fMetabolic syndrome defined according to 2005 NCEP ATP III diagnostic criteria based on presence ≥3 of the following: abdominal obesity (waist circumference ≥ 88 cm); fasting plasma glucose ≥100 mg/dL or medication; triglycerides ≥150 mg/dL or medication; HDL-c < 50 mg/dL); blood pressure > 130 mmHg systolic, >85 mmHg diastolic and/or medication use