Skip to main content

Thank you for visiting You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

A pilot study examining the effects of consuming a high-protein vs normal-protein breakfast on free-living glycemic control in overweight/obese ‘breakfast skipping’ adolescents


To examine whether the daily consumption of normal-protein (NP) vs higher-protein (HP) breakfasts improve free-living glycemic control in overweight/obese, ‘breakfast skipping’ adolescents. Twenty-eight healthy, but overweight, teens (age: 19±1 year; BMI: 29.9±0.8 kg m−2) completed a 12-week randomized parallel-arm study in which the adolescents consumed either a 350 kcal NP breakfast (13 g protein) or HP breakfast (35 g protein). Pre- and post-study 24-h blood glucose measures were assessed using continuous glucose monitoring. Although no main effects of time or group were detected, time by group interactions were observed. Post hoc pairwise comparisons assessing the post–pre changes revealed that the daily consumption of the HP breakfasts tended to reduce the 24-h glucose variability (s.d.) vs NP (−0.17±0.09 vs +0.09±0.10 s.d.; P=0.06) and tended to reduce the time spent above the high glucose limit (−292±118 vs −24±80 min; P=0.09). The consumption of the HP breakfasts also reduced the 24-h maximal (peak) glucose response (−0.94±0.36 vs +0.30±0.18 mmol l−1; P<0.01) and reduced postprandial glucose fluctuations (−0.88±0.44 vs +0.49±0.34 mmol l−1; P<0.03) vs NP. These data suggest that the daily addition of a HP breakfast, containing 35 g of high-quality protein, has better efficacy at improving free-living glycemic control compared with a NP breakfast in overweight/obese, but otherwise healthy, ‘breakfast skipping’ adolescents.


Breakfast behaviors in young people have drastically changed over the past 50 years.1, 2, 3 Approximately 30% of adolescents skip breakfast every day and as many as 60% skip breakfast 3–4 times per week.1, 4, 5 Although breakfast skipping is generally associated with weight gain and obesity,6 several studies also illustrate associations with cardio-metabolic risk factors. Specifically, those who skip breakfast experience poor glucose control and have an increased risk for developing type 2 diabetes (T2D), further exacerbating the health complications associated with weight gain.7, 8, 9 Thus, strategies to reduce postprandial glycemic excursions might be beneficial in preventing the manifestation of T2D and cardiovascular disease.

Substantial evidence exists supporting the consumption of increased dietary protein as a successful strategy to improve body weight management through reductions in body weight and fat mass during energy restriction, as well as through the prevention of weight re-gain following weight loss (Leidy et al.10). In addition to these outcomes, a few studies have also shown marked improvements in cardio-metabolic risk factors including reductions in triglycerides, blood pressure, waist circumference and fasting blood glucose with the long-term consumption of higher-protein (HP) diets.10

We recently completed an acute, randomized crossover design study examining the effects of consuming breakfast meals, varying in protein content, on 4-h postprandial glucose, and illustrated marked reductions in glucose fluctuations throughout the morning following the consumption of a HP breakfast compared with a normal-protein (NP) version in adults.11 Thus, we sought to extend the current findings to examine the longer-term effects of consuming HP vs NP breakfast on free-living, glycemic control measured by continuous glucose monitors across the entire day in overweight/obese breakfast-skipping adolescents.

Subjects and methods

This study was part of a larger study to examine whether the daily consumption of breakfast improves weight management in overweight, ‘breakfast skipping’ teens.12

Twenty-eight overweight/obese, habitual breakfast skipping adolescents were randomly assigned to a NP breakfast group or a HP breakfast group. For 12 weeks, the NP and HP groups were provided with specific breakfast meals to consume between 0600–0945 hours each day. Continuous glucose monitoring (CGM) was measured for 24 h at baseline and post study.

Adolescents were recruited from the Columbia, MO area through local advertisements. Eligibility was determined through the following inclusion criteria: (i) age 13–20 years; (ii) body mass index 25–39.9 kg m−2; (iii) no metabolic diseases/conditions; (iv) not currently/previously on a weight loss diet; and (v) consistently skips breakfast every weekday. The participants were aged 19±1 years with a body mass index of 29.9±0.8 kg m−2, had a fasting glucose of 5.0±0.2 mmol l−1 and skipped breakfast 6±1 times per week with their first eating occasion of the day at 1300±0030 hours. All participants and their parents were informed of the study purpose, procedures and risks, and signed the consent/assent forms. The study was approved by the University of Missouri Health Sciences Institutional Review Board (IRB), and all procedures followed ethical IRB standards.

The NP meals were 350 kcals, contained 15% (13 g) protein, 65% carbohydrates and 20% fat, and were ready-to-eat cereals with milk. The HP meals were isocaloric, contained 40% (35 g) protein, 40% carbohydrates and 20% fat, and were egg- and pork-based meals. To document adherence to the breakfasts, the participants returned any uneaten breakfast food and containers each week. Breakfast compliance was not different between groups and was >97%.

During day 1 of baseline and post study, a CGM sensor (CGMS; iPRO, Medtronic; Minneapolis, MN, USA) was subcutaneously inserted in the abdomen as previously described by our group.13, 14, 15 The system automatically records an interstitial glucose value every 5 min. The CGM was worn for 3 consecutive days; however, only the last day was used for analyses. Throughout each of the days, 4–5 finger-stick blood glucose readings from a standard glucose meter (Accu-Chek Compact Plus, Roche Diagnostics; Indianapolis, IN, USA) were completed for CGM calibration.

To document daily intake, participants were provided with a pack-out cooler during day 3 of baseline and post-study. Specifically during baseline, the cooler contained a standardized 500 kcal lunch and ad libitum dinner and snacks to consume throughout the day. A similar cooler was also provided during post-study except that the respective NP or HP breakfast was also included. All foods and beverages were weighed before consumption. The participants were asked to return any uneaten foods and beverages. Energy content and macronutrient composition were determined. This approach has been utilized in our previous breakfast study.16 Both groups consumed ~8996±837 kJ per day (2150±200 kcal per day) during baseline and 8368±1046 kJ per day (2000±250 kcal per day) during post-study with no differences over time (post- vs pre-study, P=0.42) or between groups (NP vs HP, P=0.80). In addition, macronutrient content of the diet during day 3 of baseline and post-study were not different (data not shown).

Data and statistical analyses

The CGM data from day 3 of baseline and post-study were divided into the following time intervals: 24 h (0000–2400 hours); morning (0630–1230 hours); overnight (2200–0500 hours). Within each period, glucose net incremental area under the curve, average, s.d., maximal response (peak), glucose fluctuation (max – min), and time spent above high limit (5.6 mmol l−1 (100 mg dl−1)) were calculated.

Two-way repeated measure analyses of variance were performed examining main effects of time (pre- and post-study), group (NP, HP) and time by group interactions for all outcomes. When main effects and/or interactions were detected, post hoc pairwise comparisons were performed using Fisher’s least significant differences. Data are presented as mean±s.e.m. Analyses were conducted using SPSS (version 21.0; SPSS, Chicago, IL, USA). P<0.05 was considered significant.


The post–pre study changes in free-living glucose responses across the day following the 12-week breakfast interventions are illustrated in Figure 1. Table 1 includes the statistical analyses within specific time segments.

Figure 1

Post–pre study changes in blood glucose responses measured every 5 min for 24 h in the NP breakfast group compared with the HP breakfast group. Breakfast (during post-study) occurred at ~0900 hours.

Table 1 Pre- and post-study glucose responses following the 12-week breakfast interventions

Although no main effects of group were observed for the 24-h glucose responses, a time effect was detected for the 24-h time spent above the high limit, such that the consumption of breakfast, regardless of breakfast type, reduced the time spent above the high limit (post vs pre, P<0.05). In addition, time × group interactions were observed for 24-h glucose variability (P=0.06), maximal response (P<0.01), postprandial fluctuations (P<0.03) and time spent above the high limit (P=0.09). When examining the within-group post vs pre responses, only the HP breakfast group displayed significant reductions in peak glucose (P<0.03) and time spent above the high limit (P<0.03) over the 12 weeks. Furthermore, non-statistically significant trends demonstrated that the HP breakfast group also displayed reductions in 24-h glucose net incremental area under the curve (P=0.10), average (P=0.09) and postprandial fluctuations (P=0.06), whereas the NP breakfast group did not.

When comparing the 24-h post–pre change scores between groups, post hoc pairwise comparisons revealed that the daily consumption of the HP breakfasts significantly reduced the glucose peak response (P<0.01) and postprandial glucose fluctuations (P<0.03) vs the NP breakfast. In addition, non-statistically significant trends demonstrated that the consumption of the HP breakfasts led to reduced glucose variability (P=0.06) and the time spent above the high limit (P=0.09) vs the consumption of the NP breakfasts.

As shown in Figure 1 and Table 1, the post–pre 24-h glucose changes between the HP vs NP breakfast groups appear to be driven by morning glucose levels.


Although there is a myriad of data from acute trials assessing the effects of breakfast on postprandial glucose responses throughout the morning period, only a few studies have examined the breakfast effects over a 24-h period using CGM.17, 18 In Kobayashi et al.18 eight healthy, habitual breakfast consuming young men participated in a 24-h indirect calorimetry chamber study. On separate days, the participants consumed eucaloric diets that included either a 2929 kJ (700 kcal) NP breakfast (18% of the meal as protein) or skipped breakfast. Skipping breakfast led to elevated blood glucose responses throughout the 24-h period compared with eating breakfast. The glucose elevations were primarily observed in the afternoon and sleep hours. Betts et al.17 completed a long-term study of 6 weeks in healthy, normal weight adults who were randomized to either a breakfast skipping group or a breakfast consuming group. In this study, the breakfast consuming group was instructed to eat 2929 kJ (700 kcal) before 1100 hours; however, breakfast content was not controlled. Although no differences in 24-h glucose responses were detected between those who ate breakfast vs those who skipped breakfast, afternoon and evening glucose variability was higher in those who skipped vs those who ate breakfast. Collectively, these data support the role of breakfast in improving glycemic control in healthy adults.

The current study extends the previous findings to examine the effect of breakfast type on 24-h glycemic control in overweight/obese individuals who habitually skip breakfast. The daily consumption of a HP breakfast over a 12-week period led to reductions in 24-h glucose variability, peak concentrations, postprandial fluctuations and time spent above 5.6 mmol l−1 (100 mg dl−1), whereas the consumption of a NP breakfast did not. Thus, although the previous studies suggest that eating breakfast, regardless of macronutrient content, improves glycemic control throughout the day, the current study challenges these findings by illustrating marked differences in glycemic responses following breakfast meals rich in protein vs those that are lower in protein. However, it is important to note that the current study included a modest breakfast meal containing half as much energy (that is, 1464 kJ; 350 kcal) as the previous studies, but contained a similar amount of absolute protein (~30–35 g). Thus, more research is required to examine the effects of breakfast size and type on glycemic control.

Lastly, the relevance of improved glycemic control with HP breakfasts tends to focus on preventing the manifestation of type 2 diabetes and cardiovascular disease. However, reduced glucose variability might also drive the satiety responses observed with HP breakfasts,12, 16 which have direct implications on the prevention and/or treatment of obesity through improvements in weight management.19


These data suggest that the daily addition of a HP breakfast containing 35 g of high-quality protein, improves glycemic control throughout the day compared with a NP breakfast, in overweight/obese, but otherwise healthy, ‘breakfast skipping’ adolescents.


  1. 1

    Deshmukh-Taskar PR, Nicklas TA, O'Neil CE, Keast DR, Radcliffe JD, Cho S . The relationship of breakfast skipping and type of breakfast consumption with nutrient intake and weight status in children and adolescents: the National Health and Nutrition Examination Survey 1999–2006. J Am Diet Assoc 2010; 110: 869–878.

    CAS  Article  Google Scholar 

  2. 2

    Siega-Riz AM, Popkin BM, Carson T . Trends in breakfast consumption for children in the United States from 1965–1991. Am J Clin Nutr 1998; 67: 748S–756S.

    CAS  Article  Google Scholar 

  3. 3

    Song WO, Chun OK, Kerver J, Cho S, Chung CE, Chung SJ . Ready-to-eat breakfast cereal consumption enhances milk and calcium intake in the US population. J Am Diet Assoc 2006; 106: 1783–1789.

    Article  Google Scholar 

  4. 4

    Moag-Stahlberg A . ADAF Family nutrition and physical activity survey background data 2010. Available from: accessed 1 October 2015.

  5. 5

    Brian W . Breakfast habits by the numbers (Hint: It isn't good for breakfast), 2013. Available from accessed 1 October 2015.

  6. 6

    Brown AW, Bohan Brown MM, Allison DB . Belief beyond the evidence: using the proposed effect of breakfast on obesity to show 2 practices that distort scientific evidence. Am J Clin Nutr 2013; 98: 1298–1308.

    CAS  Article  Google Scholar 

  7. 7

    Deshmukh-Taskar P, Nicklas TA, Radcliffe JD, O'Neil CE, Liu Y . The relationship of breakfast skipping and type of breakfast consumed with overweight/obesity, abdominal obesity, other cardiometabolic risk factors and the metabolic syndrome in young adults. The National Health and Nutrition Examination Survey (NHANES): 1999–2006. Public Health Nutr 2012; 16: 2073–2082.

    Article  Google Scholar 

  8. 8

    Mekary RA, Giovannucci E, Willett WC, van Dam RM, Hu FB . Eating patterns and type 2 diabetes risk in men: breakfast omission, eating frequency, and snacking. Am J Clin Nutr 2012; 95: 1182–1189.

    CAS  Article  Google Scholar 

  9. 9

    Jovanovic A, Gerrard J, Taylor R . The second-meal phenomenon in type 2 diabetes. Diabetes Care 2009; 32: 1199–1201.

    Article  Google Scholar 

  10. 10

    Leidy HJ, Clifton PM, Astrup A, Wycherley TP, Westerterp-Plantenga MS, Luscombe-Marsh ND et al. The role of protein in weight loss and maintenance. Am J Clin Nutr 2015. e-pub ahead of print 29 April 2015 doi:10.3945/ajcn.114.084038.

    CAS  Article  Google Scholar 

  11. 11

    Rains T, Leidy H, Sanoshy K, Lawless A, Maki K . A randomized, controlled, crossover trial to assess the acute appetitive and metabolic effects of sausage and egg-based convenience breakfast meals in overweight premenopausal women. Nutr J 2015; 14: 17.

    Article  Google Scholar 

  12. 12

    Heather JL, Heather AH, Steve MD, Rebecca SS . Daily Addition of a Protein-rich Breakfast for Long-term Improvements In Energy Intake Regulation and Body Weight Management in Overweight & Obese ‘Breakfast Skipping’ Young People. FASEB J 2013; 27: 249.7.

    Google Scholar 

  13. 13

    Oberlin DJ, Mikus CR, Kearney ML, Hinton PS, Manrique C, Leidy HJ et al. One bout of exercise alters free-living postprandial glycemia in type 2 diabetes. Med Sci Sports Exerc 2014; 46: 232–238.

    CAS  Article  Google Scholar 

  14. 14

    Mikus CR, Oberlin DJ, Libla J, Boyle LJ, Thyfault JP . Glycaemic control is improved by 7 days of aerobic exercise training in patients with type 2 diabetes. Diabetologia 2012; 55: 1417–1423.

    CAS  Article  Google Scholar 

  15. 15

    Mikus CR, Oberlin DJ, Libla JL, Taylor AM, Booth FW, Thyfault JP . Lowering physical activity impairs glycemic control in healthy volunteers. Med Sci Sports Exerc 2012; 44: 225–231.

    CAS  Article  Google Scholar 

  16. 16

    Leidy HJ, Ortinau LC, Douglas SM, Hoertel HA . Beneficial effects of a higher-protein breakfast on the appetitive, hormonal, and neural signals controlling energy intake regulation in overweight/obese, ‘breakfast-skipping,’ late-adolescent girls. Am J Clin Nutr 2013; 97: 677–688.

    CAS  Article  Google Scholar 

  17. 17

    Betts JA, Richardson JD, Chowdhury EA, Holman GD, Tsintzas K, Thompson D . The causal role of breakfast in energy balance and health: a randomized controlled trial in lean adults. Am J Clin Nutr 2014; 100: 539–547.

    CAS  Article  Google Scholar 

  18. 18

    Kobayashi F, Ogata H, Omi N, Nagasaka S, Yamaguchi S, Hibi M et al. Effect of breakfast skipping on diurnal variation of energy metabolism and blood glucose. Obes Res Clin Pract 2014; 8: e201–e298.

    Article  Google Scholar 

  19. 19

    Chaput JP, Tremblay A . The glucostatic theory of appetite control and the risk of obesity and diabetes. Int J Obes 2009; 33: 46–53.

    Article  Google Scholar 

Download references


We thank Lana Merrick, research chief, for the development and preparation of the breakfast meals. The study was funded by the Pork Checkoff.

Author information



Corresponding author

Correspondence to H J Leidy.

Ethics declarations

Competing interests

The authors declare no conflict of interest.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Bauer, L., Reynolds, L., Douglas, S. et al. A pilot study examining the effects of consuming a high-protein vs normal-protein breakfast on free-living glycemic control in overweight/obese ‘breakfast skipping’ adolescents. Int J Obes 39, 1421–1424 (2015).

Download citation


Quick links