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The relationship between yogurt consumption, body weight, and metabolic profiles in youth with a familial predisposition to obesity



This study examined the relationship between yogurt consumption, family history of obesity (FHO), and health determinants.


Youth (n = 198; mean age: 20 ± 0.5 years) from the Québec Family Study were first classified based on their FHO, defined as the presence or absence of at least one obese (BMI ≥30 kg/m2) parent [with FHO (FHO+; n = 112) or without FHO (FHO−; n = 86)] and then on their yogurt consumption [yogurt consumers (YC+) n = 61 or non-consumers (YC−) n = 137]. A two-factor mixed ANOVA was performed to evaluate the association between FHO, YC, and their interaction with health determinant such as weight and body composition, metabolic and behavioral profiles.


There was a main effect of FHO, but not YC, for weight and body composition, but no interaction between YC and FHO for these measures. However, a significant interaction between YC and FHO was observed for fasting insulin (P = 0.02), insulin area under the curve (AUC) (P = 0.02), and homeostatic model assessment of insulin resistance (HOMA-IR; P = 0.03) after adjustment for studied covariates. Specifically, lower fasting plasma insulin, insulin AUC, and HOMA-IR were observed in FHO+ and YC+ youth compared to YC− youth of the same group while no differences were found between the FHO− sub-groups.


Consuming yogurt may protect against insulin resistance more specifically among youth at risk of obesity, and this relationship appears to be independent of body composition and lifestyle factors measured in this study.


The prevalence of childhood obesity has risen substantially around the world [1]. Overweight children have a 6-fold increased risk of becoming an overweight or obese adult [2], which is associated with co-morbidities including type 2 diabetes, poor overall health, and premature death [3,4,5]. Furthermore, children with an obese parent are more likely to have a weight problem in adulthood [6] and the risk is even greater if both parents are obese (10-fold increase) compared to if only one parent is obese (4-fold increase) [7]. This familial risk may be due to genetic, behavioral, and family environmental factors (e.g., diet, socioeconomic status) [8, 9]. Some studies have examined the interaction between familial susceptibility to obesity, environmental factors, and the development of obesity and/or metabolic diseases [10, 11]; however, to our knowledge, none have examined the role of specific dietary components.

Unhealthy eating habits have been suggested to contribute to the obesity epidemic in children and youth. The decline in consumption of dairy products such as milk and yogurt [12], particularly among youth (<1 serving/day for ages 14–18 years) [13], and concurrent rise in obesity in youth has prompted researchers to examine whether there is a causal relationship between the two. In a recent review, 34 of 35 observational and intervention studies reported null or inverse associations between dairy intakes and body mass index (BMI), body fat, or energy balance [14]. In a systematic review and meta-analysis examining the longitudinal associations between dairy consumption and obesity in children, those with the higest intakes were 38% less likely to have childhood overweight/obesity [15]. Although this study considered yogurt consumption, it did not include it as a criteria in the data analysis, perhaps due to the lack of studies considering yogurt separately. Observational studies conducted in adults have shown an inverse association between dairy consumption, particularly milk intake, and body weight [16,17,18,19,20,21,22,23,24]; however, few studies in youth and adults have considered yogurt alone. Adult yogurt consumers have been found to have lower body weights and a more favorable metabolic health profile, in addition to reporting higher diet quality and greater physical activity levels, markers of healthier lifestyles, compared with non-consumers [25,26,27]. Although milk and yogurt are similar in their nutritional composition, yogurt possesses unique properties such as its food matrix, high bioavailability of micronutrients, and lactic acid bacteria that may contribute to its beneficial health effects [28].

Overweight/obese adults have been suggested to obtain the most weight-related benefits from increased dairy intakes [29, 30]; however, the reason for this is unclear. Genetics may be implicated as the development of overweight/obesity is known to be under polygenetic influence [31]. Various susceptibility genes associated with obesity in both youth [32] and adults [33] may possibly interact with environmental factors (i.e., food and its components) and potentially modulate the relationship between diet and obesity [34, 35]. To our knowledge, no studies have considered the role of familial predisposition to obesity in the relationship between yogurt intake, body weight, and metabolic health in youth.

It was hypothesized that yogurt consumption in youth is associated with lower body weights and healthier metabolic profiles and that these associations are stronger in those with a family history of obesity (FHO). The aim of this study was to examine the relationship between physiological and lifestyle indicators of health and yogurt consumption based on FHO.



Participants were drawn from the Québec Family Study (QFS), a tri-phase (phase 1: 1978; phase 2: 1989–1994; and phase 3: 1995–2001) prospective study of French Canadian families from the greater Québec City area to examine the role of genetics in the etiology of obesity, physical fitness, cardiovascular and diabetes risk factors. Additional details about the QFS have been previously published [36]. Families in phase 1 (n = 375 families) include children with and without obese parents (BMI ≥30 kg/m2), whereas the new families introduced in phases 2 (n = 105 families + 74 new families) and 3 (n = 49 families) included children with at least one obese parent. Even though the QFS is a longitudinal study, the number of participants who engaged in at least two phases was too small to permit longitudinal analysis. To maximize the number of participants included in this study all youth participating for the first time in each of the QFS phases were selected to permit cross-sectional analyses. All participants provided written informed consent to participate in the study. The project was approved by the Medical Ethics Committee of Université Laval.

The subsample for the current analyses included participants who were between 8–26 years of age from phases 2 and 3 because no oral glucose tolerance tests (OGTT) were performed in phase 1. From the 223 Caucasian nuclear families available from phases 2 and 3, 96 young men and 102 young women were eligible for cross-sectional analyses.

Genetic susceptibility to obesity

A FHO, defined as the presence (FHO+) or absence (FHO−) of at least one obese parent (BMI ≥30 kg/m2) was used as a genetic marker of obesity susceptibility.

Anthropometric and body composition measurements

Anthropometric measurements including height, body weight, and waist circumference were performed according to standardized procedures recommended at the Airlie Conference [37]. BMI was calculated as body weight divided by height squared (kg/m2). Body density was measured by underwater weighing as previously described [38, 39]. The Siri equation was used to obtain the percentage of total body fat [40]. Fat mass was estimated from body weight and % body fat.

Metabolic variables

A fasting blood sample for the measurement of glycemic variables and serum lipids was collected. An OGTT was performed in the morning after a 12-h overnight fast. Participants consumed 75 g glucose (Glucodex; Ratiopharm Inc., Mississauga, Canada) and blood samples were collected at −15, 0, 15, 30, 45, 60, 90, 120, 150, and 180 min via an indwelling venous catheter in tubes containing EDTA and Trasylol (Miles Pharmaceutics, Rexdale, Canada). Plasma glucose and insulin concentrations were measured enzymatically [41] and by radioimmunoassay [42], respectively. The average of the −15 and 0 min concentrations were used to calculate fasting glucose, insulin, and C-peptide. Incremental areas under the curve (AUC) were calculated for OGTT glycemic variables [43]. The homeostasis model assessment of insulin resistance (HOMA-IR) was calculated using the following formula: (fasting glucose × fasting insulin)/22.5 [44]. Total cholesterol, low-density lipoprotein (LDL), high-density lipoprotein (HDL), and triglyceride concentrations were determined enzymatically by use of commercial kits, as previously described [45, 46]. Blood pressure was measured with a mercury sphygmomanometer after a 5 min rest period [47].

Diet composition and quality

Self-reported, 3-day dietary records (2 weekdays and 1 weekend day) were collected to measure usual energy, macronutrient, and micronutrient intakes [48] and analyzed using a computerized version of the Canadian Nutrient File.

The Nutrient-Rich Foods (NRF9.3) index was used to assess diet quality. This nutrient-based index is calculated based on nine nutrients to encourage and three nutrients to limit, as previously described [49], provides a validated tool to assess the nutrient density of the total diet, individual foods or meals [50], and has been shown to best predict the Healthy Eating Index [49].

Assessment of yogurt consumption

Yogurt consumption was evaluated from the 3-day dietary records where an average consumption was estimated. All yogurt types were included with the exception of frozen yogurt. Youth in the FHO− and FHO+ groups were classified further into yogurt consumers (YC+; ≥1 serving/day of yogurt) and non-consumers (YC−; <1 servings/day of yogurt).

Physical activity and sleep habits

Dominant physical activity was recorded in a 3-day activity diary that included 1 weekend day, every 15 min during the day, as previously described [51]. Sleep duration (number of hours of sleep) was evaluated by a question added in the physical activity questionnaire [52].

Statistical analyses

Statistical analyses were performed using JMP 12.0 Statistical Software (SAS Institute, Cary, NC, USA). Participants were first classified based on their FHO (FHO+, n = 112 or FHO−, n = 86) and then on yogurt consumption (YC+, n = 61 or YC−, n = 137). Cross-sectional analyses were conducted on a maximum of 198 participants from either phase 2 or 3 for which data on yogurt consumption were available. The sample size was provided as a range due to missing values for some variables. First, physical, behavioral, and metabolic variables were compared between groups. Second, a two-factor mixed ANOVA was conducted to assess the impact of FHO, YC, and their interaction on the various outcome measures, while adjusting for age and sex. Following these analyses, the Benjamini–Hochberg procedure [53] was conducted to control for false positive results and establish the persistence of statistical significance after taking into account the number of comparisons. Third, because metabolic responses may also be influenced by lifestyle factors, these analyses were then statistically adjusted for the NRF9.3, physical activity participation score, and % body fat (except for body composition variables).

All results are presented as mean ± standard error of the mean (SEM). Statistical significance was established at P-value < 0.05. To determine clinical relevance, the effect size was also calculated for statistically significant P-values using Cohen’s d term (0.2 = small; 0.5 = medium; 0.8 = large; 1.3 = very large), which measures the magnitude of the standardized differences between the group means.


A total of 198 youth (86 FHO− and 112 FHO+; mean age: 20 ± 0.5 years) were included in the analyses (Table 1). In the FHO− group, 27 were YC+ and 59 were YC−, and in the FHO+ group 34 were YC+ and 78 were YC− (Table 1).

Table 1 Body weight and composition and metabolic profiles in a cross-sectional sample of adolescents stratified based on familial predisposition to obesity and yogurt consumption

Body weight and composition and metabolic health variables

After adjustment for age and sex (Model 1), a main “effect” of FHO was observed for body weight (P < 0.0001), BMI (P < 0.0001), % body fat (P < 0.0001), fat mass (P < 0.0001), fat-free mass (P < 0.0001), fasting insulin (P < 0.0001), fasting C-peptide (P < 0.0001), insulin AUC (P = 0.01), C-peptide AUC (P = 0.002), HDL-cholesterol (P = 0.001), triglycerides (P = 0.02), systolic blood pressure (P = 0.002), and HOMA-IR (P = 0.0001), while a main “effect” of YC was observed for total and HDL cholesterol. An interaction was observed for fasting insulin (P = 0.03) and HOMA-IR (P = 0.05).

Model 2 shows that these significant main “effects” and interactions generally persisted after adjusting for age, sex, NRF9.3, physical activity participation scores, and % body fat. An additional significant interaction “effect” was observed for insulin AUC (P = 0.02). The interaction “effects” are illustrated in Fig. 1 and show that FHO+/YC+ had similar fasting insulin, insulin AUC, and HOMA-IR compared to FHO−/YC− and FHO−/YC+.

Fig. 1

Yogurt consumers with a family history of obesity had similar fasting insulin, insulin AUC, and HOMA-IR compared to those with no family history of obesity. FHO family history of obesity, FHO− absence of an obese parent, FHO+ presence of at least one obese parent, YC yogurt consumption, YC− yogurt non-consumer, YC+ yogurt consumer. Means with different superscripts are significantly different

The main “effects”, but not the interactions remained statistically significant after taking into account the number of comparisons, beyond the adjustment for age, using the Benjamini–Hochberg procedure. Despite these observations, when the magnitude of the differences observed between FHO+/YC− and FHO+/YC+ were calculated to determine clinical relevance, fasting insulin (d = 0.47), insulin AUC (d = 0.66), and HOMA-IR (d = 0.46) had relatively medium effect sizes.

Diet, physical activity, and sleep habits

After adjustment for age and sex, a main “effect” of FHO was observed for NRF9.3 (P = 0.005) and a main effect of YC was observed for glycemic index (P < 0.0001). No significant main effects were observed for energy and macronutrient intakes, glycemic index, glycemic load, physical activity participation, or sleep habits (Table 2). Furthermore, there was no interaction between FHO and YC for any of the dietary and lifestyle factors.

Table 2 Diet and lifestyle factors in a cross-sectional sample of adolescents stratified based on familial predisposition to obesity and yogurt consumption


The present study demonstrates that youth with a FHO are associated with less favorable body composition and metabolic profiles, independent of studied covariates. In contrast with our hypothesis, there was no significant effect of yogurt consumption on most of these variables; however, an interaction between FHO and yogurt consumption was observed for fasting insulin, insulin AUC, and HOMA-IR. These results suggest a potential beneficial effect of yogurt consumption on insulin-related variables, but only in youth with a familial predisposition to obesity.

The effect of FHO on body weight and composition, glycemic, lipid and blood pressure profiles suggests a role for genetics in these relationships. This is important as parental obesity implies at least a partly genetic predisposition in the child that may be influenced by biologically mediated mechanisms in addition to parenting and environmental factors [54]. Numerous well-established obesity susceptibility genes associated with childhood obesity have been identified using genome-wide association studies [34]; however, because gene differences were not examined, mechanistic explanations that underlie the associations between parental obesity and these variables are difficult to discern.

Unlike having a FHO, yogurt consumption was not independently associated with any of the outcome variables. These results are in contrast with previous studies in children and adult yogurt consumers demonstrating an improvement in body weight, metabolic profiles (e.g., lower fasting glucose/insulin, HOMA-IR, systolic blood pressure, trigclycerides), and better diet quality [26, 55, 56]. Although most studies support a benefit of milk and/or yogurt, the lack of associations between yogurt consumption and body weight and metabolic variables in the present study may be due to the limited sample size and characteristics, being a young population who is already metabolically healthy and where the consumption of yogurt may not necessarily provide additional metabolic changes. However, this does not exclude that yogurt consumption can influence the metabolic profile, particularly insulin-related variables, and body weight gain in later adulthood.

Nevertheless, youth who consumed yogurt with a FHO had similar insulin concentrations and HOMA-IR values, a measure of insulin resistance and β-cell function, compared with those who had no family history regardless of their yogurt consumption. These results suggest that although genetics is implicated in the risk for obesity and insulin resistance, yogurt consumption may help to lessen the effects of genetic susceptibility on glycemic variables. It is important to consider that the interactions did not remain statistically significant after taking into account the number of comparisons, suggesting that the significant interactions observed may be a type I error (false positive) which cannot be excluded. However, it is also important to note that there is a mechanistic hypothesis which supports the link between yogurt consumption and glycemic/insulinemic control. It has been well documented that the protein content and composition and milk-derived bioactive peptides are likely implicated in yogurt’s effects on insulin variables [57,58,59]. These bioactive peptides, which are naturally present in milk and also produced through the action of bacteria in fermented dairy products such as yogurt, contribute to glycemic control through the stimulation of gastrointestinal hormones such as gastric inhibitory peptide, glucagon-like peptide-1, and peptide tyrosine tyrosine [58, 59]. In a study conducted in children, frequent consumers of yogurt (≥1 serving/week) were found to have lower glucose, insulin, and HOMA-IR compared with infrequent consumers (<1/week) [60]. Finally, it is noteworthy to highlight the medium effect sizes obtained from these analyses which indicate clinical relevance. Together, these observations provide insight into a potential beneficial role of yogurt on insulin control; however, further research is needed to elucidate the precise mechanisms in youth at risk of obesity.

In this study, the exact mechanisms as to why the consumption of yogurt is associated with more favorable insulin-related parameters only in those with a genetic susceptibility to obesity are unclear. Interactions between genetic and environmental factors such as diet and lifestyle promote the progression and pathogenesis of polygenic diet-related diseases. Perhaps yogurt may modulate the expression of genes involved in various metabolic pathways and genetic variants may modulate the health effects of various nutrients [31]. It is also possible that the genetic susceptibility to obesity may be partly mediated by alterations in gut microbiota and that children of parents with obesity have an obesogenic microbiome which is modified by yogurt consumption itself or by related components of a healthier diet associated with regular yogurt consumption [61]. However, this has not yet been elucidated.

To our knowledge, this is the first study to consider familial predisposition to obesity in the relationship between yogurt, body weight, and metabolic health; however, there are some limitations. The cross-sectional nature of the study does not allow for the detection of a cause and effect relationship and follow-up data to support the latter was not included due to the small sample size. However, the data are hypothesis generating and mechanisms to explain this association have been suggested. Furthermore, although the information regarding the types of yogurt (i.e., low vs. full-fat and plain vs. sweetened) was available in this study, the small sample size did not allow us to further stratify on the basis of these variables.

In conclusion, consuming yogurt may protect against insulin resistance among young youth with a familial susceptibility to obesity, and this relationship appears to be independent of body composition and lifestyle factors considered in this study.


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We thank participants in the QFS and staff of the Physical Activity Sciences Laboratory at Université Laval for their contribution to this study. SP is the recipient of a postdoctoral fellowship from Mitacs Accelerate in partnership with Alliance Santé Québec.


Financial support from the Medical Research Council of Canada (presently Canadian Institutes of Health Research) for the QFS and other agencies from the governments of Québec and Canada. The current study was funded by the 2015 YINI grant.

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VD, SP, and AG are the recipients of the 2015 Yogurt in Nutrition Initiative (YINI) grant. This initiative is an international collaborative project between the Danone Institute International, the American Society for Nutrition, and International Osteoporosis Foundation. VD has received grants from Danone and Dairy Farmers of Canada (DFC). AT holds a Canada Research Chair in Environment and Energy Balance sponsored and serves on the Yogurt in Nutrition for Health Advisory Board for the Danone Institute International. His research has been partially funded by the DFC and Dairy Research Institute of the United States. The remaining authors declare that they have no conflict of interest.

Correspondence to Vicky Drapeau.

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