DAILY YOGURT CONSUMPTION HELPS PREVENT HEART DISEASE - Luis Moreno (University of Zaragoza, Spain) - #YINI2014

Yogurt consumption, dietary patterns and decreased metabolic diseases in children and adolescents
Luis A. Moreno
GENUD research group
University of Zaragoza, Spain

Luis A. Moreno Aznar lmoreno@unizar.es
GENUD Research Group Universidad de Zaragoza
Yogurt consumption and metabolic diseases in children and adolescents

“...obesity is an excess of body fat”
●How is body fat measured?
●What cut-off is used to define “excess”?
(Cole TJ, Rolland-Cachera MF. In: Child and adolescent obesity. Cambridge University Press, 2002)
Definition of childhood obesity

Complications of obesity in children
(Ebbeling CB et al. Lancet 2002; 360: 473-482)

Luis A. Moreno Aznar lmoreno@unizar.es GENUD Research Group Universidad de Zaragoza
The most frequent metabolic syndrome components
Obesity
Insulin resistance – hyperinsulinemia
Dyslipidaemia (high triglyceride and low HDL-C concentrations)
Glucose intolerance and/or type 2 diabetes
Hypertension
(Hansen BC. Ann N Y Acad Sci 1999; 892: 1-24)

Luis A. Moreno Aznar lmoreno@unizar.es GENUD Research Group Universidad de Zaragoza
“Available evidence does not allow
recommendations on the role of calcium or dairy products in the development of obesity”
(Agostoni C et al. J Pediatr Gastroenterol Nutr 2011; 52: 662-669)

“Despite concerns that energy provided by dairy may contribute to childhood obesity, evidence presented in this review overwhelmingly supports a null or inverse association between milk or dairy product intake and indicators of adiposity”
(Dror DK, Allen LH. Nutr Rev 2014; 72: 68-81)

Objective
To investigate the relationship between dairy consumption and CVD risk factors in a sample of adolescents (12.5–17.5 years) from eight European cities participating in the cross-sectional (2006–2007) HELENA (Healthy Lifestyle in Europe by Nutrition in Adolescence) study

Healthy Lifestyle in EUROPE by Nutrition in Adolescence - FP6 EU funded study
 Heathy Lifestye in EUROPE
 Heathy Lifestye in EUROPE
Zaragoza
Lille
Ghent
Dortmund
Stockholm
Vienna
Pécs
Athens
Rome
Heraklion
HELENA-CSS
N = 300 x 10
13.0 - 16.9 y

Austria
Belgium
France
Germany
Greece
Hungary
Italy
Spain
Sweden
Vienna
Ghent
Lille
Athens
Heraklion
Pecs
Rome
Zaragoza
Stockholm
Dortmund
Countries
Cities
10 schools
15-20 classess
300-350 adolescents
10 schools
15-20 classess
300-350 adolescents
10 schools
15-20 classess
300-350 adolescents
10 schools
15-20 classess
300-350 adolescents
10 schools
15-20 classess
300-350 adolescents
10 schools 15-20 classess 300-350 adolescents
10 schools
15-20 classess
300-350 adolescents
10 schools
15-20 classess
300-350 adolescents
Schools
3000 – 3500 adolescents
1000 – 1200 adolescents
Adolescents
Blood samples
100-120 adolescents
100-120
adolescents
100-120
adolescents
100-120
adolescents
100-120
adolescents
100-120
adolescents
100-120 adolescents
100-120 adolescents
100-120 adolescents
100-120
adolescents
(1)
(2)
(3)

HELENA-CSS total sample
(Béghin L et al. Arch Publ Health 2012; 70: 14)
1845 1683
3528

Research tools. Diet
EWI-C questionnaire. Eating attitudes.
NKT-C questionnaire. Nutrition knowledge.
Food choices and preferences.
Diet determinants questionnaire.
HELENA-DIAT software for repeated 24-h recalls.
WP leader: Dr. Mathilde Kersting (kersting@fke-do.de)
HELENA-DIAT: Dr. Carine Vereecken (carine.vereecken@ugent.be)
HELENA-CSS

Uniqueness of the Study
Innovative methodology for computer dietary assessment and intervention.
HELENA-CSS

Innovative methodology for dietary assessment
HELENA-CSS
YANA-C software for repeated 24-h recalls

HELENA-CSS
(Vereecken CA et al. Int J Obes 2008; 32: S26-S34)
31 food groups

HELENA-CSS
(Vandevijvere S et al. Br J Nutr 2013; 109: 736-747)

Dairy products
‘Milk’: Milk and buttermilk
‘Yogurt, milk- & yogurt-based beverages’: Yogurt, yogurt and milk beverages like chocolate milk and probiotic beverages, and ‘fromage blanc’
Cheese
Milk-based desserts
No distinction on the fat content in any of the food groups was made
(Bel-Serrat S et al. Pediatr Obes 2014; 9: 401-410)

Body composition
Anthropometry.
Bioelectrical impedance.
Bod Pod in 3 cities.
WP leader: Prof. Dénes Molnár (denes.molnar@aok.pte.hu)
HELENA-CSS

Intra-observer reliability
0
10
20
30
40
50
60
70
80
90
100
Triceps Subscapular
Skinfold thickness
Reliability (%)
Athens
Dortmund
Heraklion
Gent
Lille
Pécs
Rome
Stockholm
Vienna
Zaragoza
(Nagy E et al. Int J Obes (Lond) 2008; 32 Suppl 5: S58-65)

Inter-observer reliability
(Nagy E et al. Int J Obes (Lond) 2008; 32 Suppl 5: S58-65)
0
10
20
30
40
50
60
70
80
90
100
Biceps Triceps Subscapular Suprailiac
Skinfold thickness
Reliability (%)

(Ortega FB et al. Br J Sports Med 2011; 45: 20-29)
Cardio-respiratory fitness

Measurements BONN
University Hospital
•Albumin
•Creatinine
•Glucose
•Uric acid
•Lp(a)
•ApoA, ApoB
•Chol
•HDL
•Triacylglycerols
•Ceruloplasmin
•C3, C4
•IGA, M, G, IGE
Ernährungsphysiologie
•Insulin
•Vitamin A, E and ß-Carotene (RBP)
•Vitamin C
•Vitamin D
•TEAC
•Fatty acids
•Vitamin B12
•tHcy
•Serum and RBC Folate
•Holo-TC
•Vitamin B6
•Adiponectin, Leptin, Cortisol

(Andersen LB et al. Lancet 2006; 368: 299-304)
Continuous score of clustered CVD risk factors
●Systolic blood pressure
●Sum of four skin-folds (biceps, triceps, subscapular, supra-iliac)
●Triglycerides
●Ratio total cholesterol/HDL-cholesterol
●HOMA index
●Cardio-respiratory fitness Sex-specific z-scores were calculated for each risk factor variable

Statistical analysis
●Discriminant analysis was used to statistically distinguish participants at high and low CVD risk according to the distribution of food consumption
●The stepwise method was used, with all food groups (31 food groups) entered simultaneously
●In comparison to the rest of food groups, milk and yogurt, milk- & yogurt-based beverages showed higher standardized canonical discriminant coefficients (0.30) and accounted for greater variability for most CVD risk factors in both genders

Statistical analysis
●Multiple linear regression was used to examine the association of individual CVD risk factors and CVD risk score (dependent variables) with dairy consumption (independent variables)
●One-way analysis of covariance was applied to assess differences in CVD risk factors across sex-specific tertiles of overall dairy consumption. Bonferroni correction was used for post hoc multiple comparison test
●Pubertal maturity, SES, MVPA, sedentary behaviours and daily energy intake were used as covariates

Studied sample
●Adolescents aged 12.5–17.5 years with complete measurements on waist circumference , skinfolds thickness, systolic blood pressure, triglycerides, total cholesterol, HDL-cholesterol, glucose, insulin, cardio-respiratory fitness and two 24-hour dietary recalls
●511, 49.9 % boys
●No significant differences (P < 0.05) were found between included and excluded participants in mean age, weight, height and BMI

Standardized regression coefficients examining the association of milk and yogurt, milk- & yogurt-based beverages consumption with CVD risk factors
Milk (g/day)
Yogurt, milk- & yogurt-based beverages (g/day)
Overall dairy consumption
(g/day)
β
p value
β
p value
β
p value
Boys
z-BMI
-0.096
0.192
-0.065
0.368
-0.107
0.148
Waist circumference
-0.132
0.084
-0.158*
0.035
-0.176*
0.020
Sum 4 skinfolds
-0.144*
0.034
-0.086
0.199
-0.154*
0.023
Systolic blood pressure
-0.009
0.895
-0.001
0.983
-0.008
0.908
Triglycerides
0.049
0.505
0.034
0.465
0.055
0.458
Ratio TC/HDL-c
0.017
0.823
0.090
0.226
0.054
0.478
HOMA index
0.056
0.400
0.078
0.231
0.080
0.228
Cardiorespiratory fitness
0.133
0.062
0.148*
0.034
0.173*
0.015
CVD risk score
-0.059
0.385
-0.008
0.903
-0.052
0.450
Girls
z- BMI
-0.060
0.407
-0.156*
0.030
-0.156*
0.034
Waist circumference
-0.093
0.211
-0.212*
0.004
-0.221*
0.003
Sum 4 skinfolds
0.017
0.805
-0.235*
0.001
-0.142*
0.046
Systolic BP
0.012
0.865
0.025
0.726
0.027
0.709
Triglycerides
-0.165*
0.024
-0.024
0.743
-0.162*
0.032
Ratio TC/HDL-c
-0.138
0.052
-0.054
0.455
-0.157*
0.031
HOMA index
-0.103
0.141
-0.005
0.939
-0.095
0.190
Cardiorespiratory fitness
0.099
0.166
0.181*
0.010
0.206*
0.004
CVD risk score
-0.140*
0.041
-0.165*
0.015
-0.230*
0.001
Confounders:
Tanner
Centre
SES
PA
Sedentary
behaviours
Energy
Intake
BMI

*p<0.05 after Bonferroni correction for post-hoc multiple comparisons.
Covariates: pubertal maturity, study centre, SES, sedentary behaviours,
physical activity, and daily energy intake
Waist circumference by tertiles of dairy products
consumption
Milk consumption (g/day)
Tertile 1 Tertile 2 Tertile 3
Waist circumference (cm)
0
20
40
60
80 Boys
Girls
Yogurt, milk- and yogurt-based beverages consumption (g/day)
0
20
40
60
80
Boys
Girls
*
*

*p<0.05 after Bonferroni correction for post-hoc multiple comparisons. Covariates: pubertal maturity, study centre, SES, sedentary behaviours, physical activity, and daily energy intake
Body composition by tertiles of overall dairy consumption

CVD risk score by tertiles of dairy products consumption
Milk consumption (g/day)
CVD risk score (sum of z-scores)
-2,0
-1,5
-1,0
-0,5
0,0
0,5
1,0
1,5 Boys
Girls
*
*
Yogurt, milk- and yogurt-based beverages consumption (g/day)
-2,0
-1,5
-1,0
-0,5
0,0
0,5
1,0
1,5 Boys
Girls

Overall dairy consumption (g/day)
-2
-1
0
1
2
Boys
* Girls
*
Mean (SE) CVD risk score by tertiles of overall dairy consumption

Studied sample
●Adolescents aged 12.5–17.5 years with complete measurements on waist circumference , skinfolds thickness, and two 24-hour dietary recalls
●1422, 44.9 % boys
●No significant differences (P < 0.05) were found between included and excluded participants in mean age, weight, height and BMI

*p<0.05 after Bonferroni correction for post-hoc multiple comparisons. Covariates: pubertal maturity, study centre, SES, sedentary behaviours, physical activity, and daily energy intake
BMI z-score by tertiles of overall dairy consumption
Overall dairy consumption (g/day) Tertile 1Tertile 2Tertile 3 z-BMI -0,3-0,2-0,10,00,10,20,3BoysGirls* *

Body composition by tertiles of overall dairy consumption
0
20
40
60
80 Boys
Girls
*
* * *
Sum of skinfolds (mm)
0
10
20
30
40
50
60
70 Boys
Girls
*
*

Discussion
●Of 35 observational and intervention studies included in the review, 34 reported null and/or inverse associations between dairy intake and BMI, body fat, or energy balance
●Of five randomized controlled trials, four found no association between dairy intake and measures of adiposity, while one found an inverse association
●Twenty-three of the 35 studies included in the review analyzed data collected in the United States

Mechanisms to explain the results
●When milk and milk products are removed from the diet they are replaced by other energy-dense foods and beverages (i.e.: sweetened beverages)
●Dietary calcium suppresses calcitriol and calcitriol-stimulated influx of calcium into adipocytes, inhibiting lipogenesis and promoting lipolysis. It is also often associated with increased energy expenditure, thermogenesis, and faecal fat loss
●Angiotensin-converting enzyme inhibitory peptides in whey protein stimulate adipocyte lipogenesis

Mechanisms to explain the results
●Dairy proteins, better support muscle protein synthesis than plant foods and the enhanced anabolism could potentially increase energy expenditure
●Leucine, abundant in dairy foods, may play a role in the repartitioning of dietary energy from adipose tissue to skeletal muscle, promoting fat loss
●Conjugated linoleic acid, present in dairy products, reduce adipose tissue mass in humans

Conclusions
●In this cross-sectional study in European adolescents, we observed an inverse association between yogurt, milk & yogurt-based beverages and some CVD risk factors, specially excess total and abdominal body fat
●The association was stronger when we added-up milk intake
●There is a need of having observational studies considering yogurt as an individual food category
●RCTS are needed to provide evidence supporting these findings and to try understand the specific mechanisms

HELENA Study
Thank you very much for your attention!

DAILY YOGURT CONSUMPTION HELPS PREVENT HEART DISEASE - Luis Moreno (University of Zaragoza, Spain) - #YINI2014

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