Content uploaded by Andrea Natali
Author content
All content in this area was uploaded by Andrea Natali on Aug 29, 2016
Content may be subject to copyright.
Content uploaded by Andrea Natali
Author content
All content in this area was uploaded by Andrea Natali on Aug 29, 2016
Content may be subject to copyright.
OPEN
SHORT COMMUNICATION
Manipulating the sequence of food ingestion improves
glycemic control in type 2 diabetic patients under free-living
conditions
D Tricò, E Filice, S Trifirò and A Natali
Lipid and protein ingested before carbohydrate reduce postprandial hyperglycemia. We tested feasibility, safety and clinical efficacy
of manipulating the sequence of nutrient ingestion in patients with type 2 diabetes (T2D). After a 4-week run-in, 17 T2D patients
were randomized to either a control diet (CD) or to an experimental diet (ED) allowing the consumption of high-carbohydrate foods
only after high-protein and high-fat foods at each main meal (lunch+dinner). Both diets were accurately followed and neutral on
arterial blood pressure, plasma lipids and indices of hepatic and kidney function. After 8 weeks, in spite of a similar reduction of
body weight (ED −1.9 95% confidence interval (−3.4/ −0.4)kg, Po0.03; CD −2.0 (−3.6/ −0.5)kg, Po0.02) and waist circumference
(ED −2.9 (−4.3/ −1.5)cm, Po0.002; CD −3.3 (−5.9/ −0.7)cm, Po0.02), the ED only was associated with significant reductions of
HbA1c (−0.3 (−0.50/ −0.02)%, Po0.04), fasting plasma glucose (−1.0 (−1.8/ −0.3)mmol l
−1
,Po0.01), postprandial glucose
excursions (lunch −1.8 (−3.2/ −0.4)mmol l
−1
,Po0.01; dinner: −1.0 (−1.9/ −0.1)mmol l
−1
,Po0.04) and other indices of glucose
variability (s.d.: −0.5 (−0.7/ −0.2)mmol l
−1
,Po0.02; Coefficient of variation: −6.6 (−10.4/ −2.7)%, Po0.02). When compared with
the CD, the ED was associated with lower post-lunch glucose excursions (Po0.02) and lower glucose coefficients of variation
(Po0.05). Manipulating the sequence of nutrient ingestion might reveal a rapid, feasible, economic and safe strategy for optimizing
glucose control in T2D.
Nutrition & Diabetes (2016) 6, e226; doi:10.1038/nutd.2016.33; published online 22 August 2016
INTRODUCTION
Lipid and protein ingested before carbohydrate, as a ‘preload’,
have been shown to acutely improve glucose tolerance, mainly by
delaying gastric emptying and by enhancing insulin secretion.
1–6
Indeed, we recently reported that a small mixed non-glucidic
preload markedly improved glucose tolerance by delaying glucose
absorption, enhancing beta cell function and reducing insulin
clearance in patients with type 2 diabetes.
1,2
Whether these acute
effects persist over time is unclear.
7,8
Furthermore, adding a
nutrient preload to each meal to improve postprandial glucose
control could be unfeasible and/or increase the total daily caloric
intake. As recently suggested by an acute pilot study,
9
we tested
the hypothesis that manipulating the sequence of food consump-
tion during each main meal (i.e., high-protein and high-lipid foods
before carbohydrate) would exploit the same marked hypogly-
cemic effects of non-glucidic nutrient preloads, revealing a simple,
safe and effective strategy to improve glucose control in type
2 diabetic patients.
SUBJECTS AND METHODS
Study population
Twenty well-controlled type 2 diabetic patients were enrolled. The
inclusion criteria were age 50–75 years, body mass index (BMI)
26–35 kg m
−2
, stable weight for at least 6 months, glycated
hemoglobin 48–58 mmol mol
−1
, disease duration ⩽5 years. None
had diseases other than diabetes or was taking medications other
than metformin and/or sitagliptin that could potentially interfere
with carbohydrate absorption and/or metabolism. The institutional
Ethics Committee approved the study and all participants provided
written informed consent before inclusion in the study.
Study design
This was a parallel, randomized, open clinical trial. Participants
were evaluated on four consecutive visits separated by 28 ± 2 days
at 08:00 am after an overnight fast. On each occasion, body
weight, fat mass (FM) and basal metabolic rate (BMR) were
assessed by bioelectrical impedance (TBF-300 Body Composition
Analyzer, Tanita Corporation, Arlington Heights, IL, USA); waist and
hip circumferences, and systolic and diastolic blood pressure were
measured according to standard procedures. Blood samples were
collected at study entry (visit 1), after 28 days of run-in (visit 2) and
after 56 days of diet (visit 4) for the measurement of blood
glucose, glycated hemoglobin, total cholesterol, LDL cholesterol,
HDL cholesterol, triglycerides, and standard indices of renal,
hepatic, pancreatic and thyroid function. Volunteers were also
asked to measure their blood glucose concentrations by
glucometer (Contour XT, Bayer HealthCare LLC, Whippany, NJ,
USA) once a week six times in a single day (before and two hours
after breakfast, lunch, and dinner) for the full length of the study.
The total daily caloric need was estimated in each volunteer by
adding the BMR to the individual caloric expenditure during
Department of Clinical and Experimental Medicine, University of Pisa, Pisa, Italy. Correspondence: Dr D Tricò, Department of Clinical and Experimental Medicine, University of Pisa,
Via Roma 67, Pisa 56126 Italy.
E-mail: domenico.trico@for.unipi.it
Received 3 April 2016; revised 1 June 2016; accepted 12 June 2016
Citation: Nutrition & Diabetes (2016) 6, e226; doi:10.1038/nutd.2016.33
www.nature.com/nutd
working and leisure time physical activity. On visit 2, volunteers
were randomized into two different groups. Subjects from control
group were asked to follow an 8-week standard balanced mild-
hypocaloric diet (control diet (CD)).
10
Each subject received a
dietary plan with the food composition of three typical meals
(breakfast, lunch and dinner) and a table of possible substitutions
with variable equicaloric amounts of different foods. Meals and
variants were pondered to yield a caloric deficit of ~ 200 kcal
per day with respect to the total daily caloric need, to produce an
expected weight loss of ~ 1 kilogram a month. Patients from the
experimental group received the same diet plan in terms of food
quality and quantity (experimental diet, ED). In addition, they
received indications on macronutrient composition of foods and
were strongly recommended to fix the sequence of macronutrient
ingestion at each main meal (lunch and dinner), so as to eat high-
carbohydrate-containing foods (e.g., bread, pasta, potatoes)
preferably after the ingestion of high-protein and high-fat foods
(e.g., meat, cheese, fish). A typical main meal in the ED was so
composed of meat as the first course, then vegetables, bread
and/or pasta and fruit. All volunteers were asked to report their
overall compliance to the caloric content and to the sequence of
nutrients of the prescribed diet by checking on an ad hoc
designed form at each meal.
Statistical analysis
Data are shown as mean ± s.e.m. The changes induced by our
experimental maneuver were evaluated by using either Wilcoxon
signed rank or Mixed-model multivariate analysis of variance
(MANOVA) for repeated measures with time as within-subject
factor, diet as between subjects factor, and the interaction time*diet
as main outcome variable. Data from self-monitoring of blood
glucose were analyzed by calculating mean 2-hours glucose
increments over pre-meal values for each meal (breakfast, lunch,
dinner) and by calculating mean concentrations, s.d. and
percentage coefficients of variation (CV; s.d./mean ratio) of lunch
+dinner glucose values over 4 days (on week apart) during the
run-in, the first and the second 4 weeks of diet.
11
Statistical analyses
were performed using JMP 9.0 (SAS Institute Inc., Cary, NC, USA).
A value of P⩽0.05 was considered statistically significant.
RESULTS
Characteristics of the study population
Three volunteers were excluded due to their poor compliance
to the study protocol, nine were included in the control group
(age 64 ± 8 years, 6 males and 3 females, 4 on metformin and 2 on
metformin+sitagliptin therapy) and eight in the experimental
group (age 65 ±7 years, 6 males and 2 females, 4 on metformin
and 1 on metformin+sitagliptin therapy). Clinical and metabolic
characteristics were similar between the two study groups
(Table 1). Although a proper subgroup analysis was not performed
due to the small number of subjects, neither metformin nor
sitagliptin use has reasonably affected study results, since subjects
taking medications showed no large difference in glycemic
responses compared with other group members. Self-reported
dietary compliance at each main meal was 495% in terms of
caloric intake and 490% in terms of food sequence. None of the
patients randomized to the ED complained of any distress
associated with the fixed sequence of nutrient consumption
during their two main meals (lunch and dinner).
Clinical variables
The two diet regimens produced similar (diet and time*diet
effects = ns by MANOVA) and close-to-the-expected reductions in
body weight (ED −1.9 kg, 95% confidence interval (CI) (−3.4/ −0.4);
CD −2.0 kg, 95% CI (−3.6/ −0.5); time effect Po0.003), BMI
(ED −0.7 kg m
−2
, 95% CI (−1.2/ −0.2); CD −0.7 kg m
−2
, 95% CI
(−1.2/ −0.2); time effect Po0.002), FM (ED −1.5%, 95% CI
(−2.8/ −0.3); CD –0.8%, 95% CI (−1.8/0.2); time effect Po0.02)
and waist circumference (ED −2.9 cm, 95% CI (−4.3/ −1.5); CD
−3.3 cm, 95% CI (−5.9/ −0.7); time effect Po0.002) (Table 1). No
differences were found in serum lipids or systolic and diastolic
blood pressure values in any of the four visits in the two study
groups (Table 1). Neither diet affected renal, hepatic, pancreatic
and thyroid function indices (data not shown).
Glucose control variables
After 8 weeks, the ED produced an improvement of the overall
glucose control, as assessed by the reduction of glycated
Table 1. Clinical and metabolic variables
Experimental diet Control diet
−4 weeks 0 4 weeks 8 weeks −4 weeks 0 4 weeks 8 weeks
Weight (kg) 85.6 ±2.4 84.9 ±2.3 83.4 ±2.5
a
83.0 ±2.5
b
85.3 ±5.1 84.8 ±5.1 83.2 ±4.7
a
82.7 ±4.7
b
BMI (kg m
−2
) 31.1 ±1.3 30.9 ±1.3 30.2 ±1.2
a
30.2 ±1.2
b
30.5 ±1.2 30.3 ±1.2 29.8 ±1.1
a
29.6 ±1.2
b
Fat mass (%) 32.2 ±3.3 31.3 ±2.8 29.9 ±2.8 29.8 ±3.0
b
31.0 ±2.8 30.9 ±2.7 30.2 ±2.6 30.1 ±2.6
Fat-free mass (%) 60.9 ±1.3 61.0 ±1.1 61.2 ±1.0 61.4 ±1.2 58.9 ±4.2 58.5 ±4.3 58.0 ±3.9 57.8 ±3.9
Waist (cm) 104 ±2 103 ±2 102 ±2 100 ±2
b,c
105 ±4 104 ±4 104 ±4 101 ±4
b,c
Waist/hip ratio 0.99 ±0.02 0.99 ±0.01 0.98 ±0.02 0.97 ±0.02 1.00 ±0.02 0.99 ±0.02 1.00 ±0.03 0.99 ±0.02
Systolic blood pressure (mm Hg) 134 ±4 136 ±9 125 ±6 131 ±7 129 ±5 127 ±4 133 ±4 128 ±3
Diastolic blood pressure (mm Hg) 86 ±376±781±382±484±483±479±280±1
HbA
1c
(%) 6.7 ±0.2 6.7 ±0.2 —6.4 ±0.2
b
6.8 ±0.1 6.8 ±0.1 —6.6 ±0.1
HbA
1c
(mmol mol
−1
) 49.3 ±1.7 49.4 ±2.0 —46.7 ±1.7
b
51.3 ±1.6 51.2 ±1.6 —48.4 ±1.4
Fasting plasma glucose (mmol l
−1
) 6.9 ±0.4 7.1 ±0.5 —6.1 ±0.3
b
6.8 ±0.3 6.4 ±0.3 —5.6 ±0.4
PGE breakfast (mmol l
−1
)—1.3 ±0.4 0.8 ±0.3 0.5 ±0.3 —1.2 ±0.4 0.7 ±0.3 0.9 ±0.5
PGE lunch (mmol l
−1
)—2.3 ±0.6 0.8 ±0.3
a
0.5 ±0.3
b
—1.3 ±0.4 1.5 ±0.5 1.5 ±0.5
PGE dinner (mmol l
−1
)—1.5 ±0.5 0.5 ±0.3
a
0.6 ±0.4
b
—1.7 ±0.6 1.6 ±0.4 1.8 ±0.6
Mean glucose (mmol l
−1
)—7.5 ±0.4 6.7 ±0.4
a
6.7 ±0.3
b
—7.4 ±0.3 6.9 ±0.3 6.9 ±0.3
s.d. (mmol l
−1
)—1.5 ±0.2 1.1 ±0.2 0.9 ±0.1
b
—1.7 ±0.2 1.4 ±0.2 1.5 ±0.2
Coefficient of variation (%) —19.6 ±2.2 16.6 ±1.9 13.0 ±1.2
b
—22.9 ±2.1 19.3 ±2.2 21.3 ±2.2
Abbreviations: PGE, postprandial glucose excursions.
a
4 weeks vs 0, Po0.05;
b
8 weeks vs 0, Po0.05;
c
8 weeks vs 4 weeks, Po0.05. PGE are the mean 2-hours
glucose increments over pre-meal values following each meal during the run-in (−4 to 0 weeks), the first (0–4 weeks) and the second (4–8 weeks) 4 weeks of
diet. Data are mean ±s.e.m.
Nutrient sequence and glucose control
D Tricò et al
2
Nutrition & Diabetes (2016), 1 –4
hemoglobin (−0.3%, 95% CI (−0.50/ −0.02), Po0.04 by Wilcoxon)
(Table 1). This was associated to a decline of 1.0 mmol l
−1
(95% CI
(−1.8/ −0.3), Po0.01) in fasting plasma glucose and of 0.8
mmol l
−1
(95% CI (−1.4/ −0.2), Po0.04) in mean lunch+dinner
glucose (Table 1), and to a marked reduction of postprandial
glucose excursions (lunch: −1.8 mmol l
−1
, 95% CI (−3.2/ −0.4),
Po0.01; dinner: −1.0 mmol l
−1
, 95% CI (−1.9/ −0.1), Po0.04) and
other indices of glucose variability (SD −0.5 mmol l
−1
, 95%CI
(−0.7/ −0.2), Po0.02; CV −6.6%, 95% CI (−10.4/ −2.7), Po0.02)
(Figure 1). The CD produced a non-significant reduction of
glycated hemoglobin (−0.3%, 95% CI (−0.6/0.1), P= 0.09) and
fasting plasma glucose (−0.7 mmol l
−1
, 95% CI (−1.6/0.2), P= 0.06),
and it failed to improve postprandial glucose excursions and other
glucose variability indices (Table 1). Among these variables, the
time*diet effect by MANOVA was statistically significant for post-
lunch glucose excursions (Po0.04) and for the CV of glucose
concentrations (Po0.05).
DISCUSSION
This study demonstrates that by only manipulating the sequence
of nutrient ingestion it is possible to improve glycemic excursions
in type 2 diabetic patients in free-living conditions, and that this
intervention is safe and well accepted. More in general, it proves
the concept that it is effective and feasible to rely upon the
physiologic responses acutely activated by nutrient ingestion (i.e.,
nutrient sensing
12
) to improve glucose homeostasis. Participants
were instructed to consume high-carbohydrate-containing foods
only after non-glucidic nutrients, to exploit and combine the
well-known positive effects of lipid and protein on glucose
tolerance
1,3–6
without increasing the total amount of foods and
without requiring supplements (artificial formula) that might be
expensive and poorly accepted. Despite the high variability
inherent to the real-life setting and the small populations, the
time course of blood glucose self-monitoring revealed that an
overall reduction in glycemic variability, particularly at the
manipulated meals (lunch and dinner), was already evident at
the first month of diet and sustained through the following
4 weeks (Figure 1). Accordingly, the effects of the ED on glucose
variability indices were not related to the extent of individual
weight loss. If applied also to the breakfast (scarcely feasible for
Italian habits), the overall effect of this dietary intervention on
glucose control, namely on glycated hemoglobin, would have
probably been greater. Although conceived on the bases of the
same experimental evidences, our approach may have several
advantages with respect to the already proposed protein
supplement preloads.
7
First, the physiological combination of
lipid and protein is likely to be more effective, by acting on
multiple targets;
1,13
indeed, the effect on glucose tolerance of
protein alone, though persistent, was quantitatively small.
7
Second, with our approach the daily caloric intake and the
proportions of macronutrients are not altered; as expected, the ED
has no impact on body weight nor it alters body mass
composition, lipid profile or indices of renal function.
CONCLUSIONS
In conclusion, this pilot study supports the concept that
manipulating the sequence of nutrient ingestion might reveal a
useful, feasible and inexpensive strategy for long-term manage-
ment of type 2 diabetes and provides encouragement for further
longer-term and larger clinical trial.
CONFLICT OF INTEREST
The authors declare no conflict of interest.
ACKNOWLEDGEMENTS
We would like to acknowledge Alberto Tulipani and Angelica Lucchesi from the
Department of Clinical and Experimental Medicine at the University of Pisa for their
assistance with the collection of the data. We would also like to thank all the
volunteers enrolled in this trial. This work was supported by institutional grants from
the University of Pisa (Fondi di Ateneo).
AUTHOR CONTRIBUTIONS
DT conceived, designed and conducted the clinical studies, provided a
substantial contribution to the acquisition, analysis and interpretation of the
data and drafted the manuscript. EF conducted the clinical studies and
provided a substantial contribution to the acquisition of the data. ST conducted
the clinical studies and provided a substantial contribution to the acquisition of
the data. AN conceived and designed the study, provided a substantial
Figure 1. Mean capillary blood glucose concentrations and postprandial glucose excursions (PGE) (top right corner) before and after breakfast
(B), lunch (L) and dinner (D) during the run-in (light gray), the first 4 weeks (dark gray) and the second 4 weeks (black) of experimental diet (ED,
continuous line) and control diet (CD, dashed line). *Po0.05 by Wilcoxon in comparison with the run-in PGE value.
Nutrient sequence and glucose control
D Tricò et al
3
Nutrition & Diabetes (2016), 1 –4
contribution to the analysis and interpretation of the data. DT and AN are the
guarantors of this work and, as such, had full access to all of the data in the
study and take responsibility for the integrity of the data and the accuracy of
the data analysis. All authors revised the manuscript critically and approved the
final version of the article.
REFERENCES
1 Trico D, Baldi S, Tulipani A, Frascerra S, Macedo MP, Mari A et al. Mechanisms
through which a small protein and lipid preload improves glucose tolerance.
Diabetologia 2015; 58: 2503–2512.
2 Trico D, Filice E, Baldi S, Frascerra S, Mari A, Natali A. Sustained effects of a protein
and lipid preload on glucose tolerance in type 2 diabetes patients. Diabetes Metab
2016; 16: 30388–30393.
3 Welch IM, Bruce C, Hill SE, Read NW. Duodenal and ileal lipid suppresses post-
prandial blood glucose and insulin responses in man: possible implications for the
dietary management of diabetes mellitus. Clin Sci (Lond) 1987; 72:209–216.
4 Gentilcore D, Chaikomin R, Jones KL, Russo A, Feinle-Bisset C, Wishart JM et al.
Effects of fat on gastric emptying of and the glycemic, insulin, and incretin
responses to a carbohydrate meal in type 2 diabetes. J Clin Endocrinol Metab 2006;
91: 2062–2067.
5 Jakubowicz D, Froy O, Ahren B, Boaz M, Landau Z, Bar-Dayan Y et al. Incretin,
insulinotropic and glucose-lowering effects of whey protein pre-load in type 2
diabetes: a randomised clinical trial. Diabetologia 2014; 57: 1807–1811.
6 Ma J, Stevens JE, Cukier K, Maddox AF, Wishart JM, Jones KL et al. Effects of a
protein preload on gastric emptying, glycemia, and gut hormones after a car-
bohydrate meal in diet-controlled type 2 diabetes. Diabetes Care 2009;
32: 1600–1602.
7 Ma J, Jesudason DR, Stevens JE, Keogh JB, Jones KL, Clifton PM et al. Sustained
effects of a protein 'preload' on glycaemia and gastric emptying over 4 weeks in
patients with type 2 diabetes: A randomized clinical trial. Diabetes Res Clin Pract
2015; 108: e31–e34.
8 Cunningham KM, Daly J, Horowitz M, Read NW. Gastrointestinal adaptation
to diets of differing fat composition in human volunteers. Gut 1991;
32:483–486.
9 Shukla AP, Iliescu RG, Thomas CE, Aronne LJ. Food order has a significant
impact on postprandial glucose and insulin levels. Diabetes Care 2015;
38: e98–e99.
10 American Diabetes Association. (4) Foundations of care: education, nutrition,
physical activity, smoking cessation, psychosocial care, and immunization.
Diabetes Care 2015; 38: S20–S30.
11 DeVries JH. Glucose variability: where it is import ant and how to measure it.
Diabetes 2013; 62: 1405–1408.
12 Efeyan A, Comb WC, Sabatini DM. Nutrient-sensing mechanisms and pathways.
Nature 2015; 517:302–310.
13 Alsalim W, Tura A, Pacini G, Omar B, Bizzotto R, Mari A et al.. Mixed meal ingestion
diminishes glucose excursion in comparison with glucose ingestion via several
adaptive mechanisms in people with and without type 2 diabetes. Diabetes Obes
Metab 2016; 18:24–33.
This work is licensed under a Creative Commons Attribution 4.0
International License. The images or other third party material in this
article are included in the article’s Creative Commons license, unless indicated
otherwise in the credit line; if the material is not included under the Creative Commons
license, users will need to obtain permission from the license holder to reproduce the
material. To view a copy of this license, visit http://creativecommons.org/licenses/
by/4.0/
© The Author(s) 2016
Nutrient sequence and glucose control
D Tricò et al
4
Nutrition & Diabetes (2016), 1 –4
