ArticlePDF Available

Comparison of a Carbohydrate-Free Diet vs. Fasting on Plasma Glucose, Insulin and Glucagon in Type 2 Diabetes

Comparison of a carbohydrate-free diet vs. fasting
on plasma glucose, insulin and glucagon in
type 2 diabetes
Frank Q. Nuttall
, Rami M. Almokayyad
, Mary C. Gannon
Section of Endocrinology, Metabolism & Nutrition, and the Metabolic Research Laboratory, Minneapolis VA Health Care System,
Minneapolis/St Paul, MN, USA
Department of Medicine, University of Minnesota, Minneapolis/St Paul, MN, USA
Department of Food Science & Nutrition, University of Minnesota, Minneapolis/St Paul, MN, USA
Article history:
Received 4 June 2014
Accepted 5 October 2014
Objective. Hyperglycemia improves when patients with type 2 diabetes are placed on a
weight-loss diet. Improvement typically occurs soon after diet implementation. This rapid
response could result from low fuel supply (calories), lower carbohydrate content of the
weight-loss diet, and/or weight loss per se. To differentiate these effects, glucose, insulin,
C-peptide and glucagon were determined during the last 24 h of a 3-day period without
food (severe calorie restriction) and a calorie-sufficient, carbohydrate-free diet.
Research design. Seven subjects with untreated type 2 diabetes were studied. A
randomized-crossover design with a 4-week washout period between arms was used.
Methods. Results from both the calorie-sufficient, carbohydrate-free diet and the 3-day
fast were compared with the initial standard diet consisting of 55% carbohydrate, 15%
protein and 30% fat.
Results. The overnight fasting glucose concentration decreased from 196 (standard diet)
to 160 (carbohydrate-free diet) to 127 mg/dl (fasting). The 24 h glucose and insulin area
responses decreased by 35% and 48% on day 3 of the carbohydrate-free diet, and by 49% and
69% after fasting. Overnight basal insulin and glucagon remained unchanged.
Conclusions. Short-term fasting dramatically lowered overnight fasting and 24 h
integrated glucose concentrations. Carbohydrate restriction per se could account for 71%
of the reduction. Insulin could not entirely explain the glucose responses. In the absence of
carbohydrate, the net insulin response was 28% of the standard diet. Glucagon did not
contribute to the metabolic adaptations observed.
Published by Elsevier Inc.
Circadian rhythm
Abbreviations: T2DM, type 2 diabetes mellitus; CHO, carbohydrate; IRB, Institutional Review Board; h, hour; kcal, kilocalorie; kg,
kilogram; DPC, Diagnostic Product Corporation; DSL, Diagnostic Systems Laboratories, Incorporated; SDTU, Special Diagnostic &
Treatment Unit; AM, ante meridiem; STD, standard; SEM, Standard Error of the Mean; M.T., Medical Technologist.
Supported in partby Merit Review Funds fromthe Departmentof Veterans Affairs. TheDepartment ofVeterans Affairs had no involvement
in study design, collection, analysis and interpretation of data, writing of the report or decision to submit the article for publication.
☆☆ Identifier: NCT01469104.
Corresponding author at: Section of Endocrinology, Metabolism and Nutrition, Minneapolis VA Health Care System (111G), One Veterans
Drive, Minneapolis, MN 55417 USA. Tel.: +1 612 467 4424; fax: +1 612 725 2273.
E-mail address: (F.Q. Nuttall).
Dr. Almokayyad was a Fellow in Endocrinology when these studies were done. His current address is Medical Arts Building, 1015 Duff
Avenue, Ames, IA 50010.
0026-0495/Published by Elsevier Inc.
Available online at
1. Introduction
In people with type 2 diabetes it has been demonstrated that a
low food energy diet [1], short-term fasting [2], a low CHO-diet
[3] as well as weight loss result in a rapid decrease in glucose
concentration. Thus, various iterations of these have been
advocated as a means of improving the hyperglycemia
present in these patients. However, to our knowledge a direct
comparison of the results of each of these approaches has not
been reported. In order to address these issues, we have
determined the metabolic effects of fasting for 3 days, a time
duration in which loss of fat mass should be minimal, but the
glucose and insulin concentrations have reached a new
quasi-steady-state [4]. This is compared with the effects of
an energy-sufficient, carbohydrate-free diet of the same
duration. Both were compared with data obtained when the
subjects ingested a standard, mixed diet. Twenty-four hour
glucose, insulin, C-peptide and glucagon data were obtained
while on each diet or when the subjects were fasting. Part of
these data was published previously in abstract form [5].
2. Materials and methods
2.1. Subjects
Seven male subjects with type 2 diabetes were studied. One
subject was untreated; 3 subjects had been receiving metformin.
Three had been receiving a sulfonylurea. These medications
were discontinued for 24 or more days before the study. Patient
characteristics are listed in Table 1.
Inclusion criteria were: 4575 years of age, with type 2
diabetes, currently not on any oral diabetic medications,
never having been on insulin, and a C-peptide > 1.5 ng/ml
(reference range 0.83.1 ng/ml). Patients may have been on
metformin and or a sulfonylurea, but would be taken off these
oral agents for the time required to stabilize their HbA1c, with
prior approval of their primary provider. Subjects remained
off these medications until the end of the study. This was
done with the patient consent, and with the approval of and
coordination with the primary care physician. As a result of
the dietary intervention of this study, no adverse health
effects were noted during the study, nor were there adverse
health effects due to the study after its completion.
Exclusion Criteria were: hematologic abnormalities, liver
disease, kidney disease, macroalbuminuria (300 mg albumin/
24 h), untreated thyroid disease, congestive heart failure,
angina, myocardial infarction within 6 months, life threatening
malignancies, proliferative retinopathy, severe diabetic neu-
ropathy, peripheral vascular disease, serious psychiatric disor-
ders (preventing the patient from competently signing the
informed consent), fasting triglyceride > 400 mg/dl, fasting
blood glucose > 250 mg/dl or HbA1c > 11%.
Written informed consent was obtained from all subjects,
and the study was approved by the Department of Veterans
Affairs Medical CenterInstitutional Review Board (IRB). All work
was conducted in compliance with the principles of the
Declaration of Helsinki. The study is registered at clinicaltrials.
gov: NCT01469104.
Table 1 Subject characteristics.
Subject Age Years of
Days off
1 55 2 Metformin 54 93 183 28 160 hypogonadism,
testosterone, lisinopril,
2 60 4 Metformin 43 93 183 29 228 CVD, HTN atenolol, gemfibrozil,
lisinopril, simvastatin
3 60 2 Glyburide 47 81 155 34 171 dyslipidemia simvastatin,
4 62 18 Metformin 32 130 170 38 219 dyslipidemia,
losartan, omeprazole,
5 64 10 Glipizide 45 108 174 36 168 dyslipidemia,
lisinopril, simvastatin
6 72 5 None 88 188 25 118 COPD, CVD,
lisinopril, amlodipine,
atenolol, simvastatin,
7 49 3 Glipizide 24 88 170 27 225 HTN hydrochlorothiazide,
Average ± SEM 60 ± 3 6 ± 2 41 ± 4 97 ± 6 175 ± 4 31 ± 2 184 ± 16
Range 4972 218 2454 81130 155188 2838 118228
Med = medication
BMI = body mass index
FPG = overnight fasting plasma glucose
CVD = cardiovascular disease
HTN = hypertension
COPD = chronic obstructive pulmonary disease
2.2. Study design
This is a randomized crossover study design with a four-week
washout period between study arms. On one occasion
subjects received a calorie-sufficient, carbohydrate-free diet
for 72 h. This diet contained the same amount of protein as
the standard diet. Dietary fat replaced the carbohydrate
content. On another occasion, the subjects fasted for 72 h.
All subjects participated in both study arms.
The day before admission, each was provided with a
standardized dinner (composed of 55% carbohydrate, 15%
protein, and 30% fat), to be ingested at 1800 h at home. This
was based on 25 kcal/kg body weight. The subjects were
admitted to a clinical research unit (Special Diagnostic and
Treatment Unit, SDTU) for 4 days.
On the day of admission, subjects reported to the SDTU at
0700 h. An indwelling intravenous catheter was inserted, and
blood samples were obtained at 0730 h, 0745 h and 0800 h for
baseline determinations. Subjects received a standard break-
fast, lunch and dinner at 0800 h, 1200 h and 1800 h, respec-
tively. Standard meals were composed of 55% carbohydrate,
15% protein, and 30% fat. Caloric distribution was: breakfast
32%, lunch 40%, dinner 28%. Blood samples were obtained
every 15 min after each meal for the first hour, every 30 min
for the second and third hour and hourly thereafter until the
next meal or until 0800 h the next morning. The urine was
collected on that day for 24 h as well.
On the second day, subjects were provided with the
carbohydrate-free meals at 0800 h, 1200 h and 1800 h daily
for 3 days (72 h), or the 72-h fast was begun. The carbohy-
drate-free meals were composed of <3% carbohydrate, 15%
protein, 82% fat. The amount of food energy provided in the
meals was based on 2530 kcal/kg body weight. Ingestion of
water was encouraged. Black coffee, tea without sugar or
cream, and calorie-free beverages were allowed when receiv-
ing the carbohydrate-free meals or while fasting. Activity was
limited to quiet diversions such as reading or watching TV.
The subjects were under nearly constant observation in the
SDTU including during meals. Blood samples were obtained
during last 24 h of the 72-h intervention, with the same
time intervals as the first day (i.e. pre-study, Day 1). Urine
was collected during the last 24 h of the 72-h intervention
as well.
Plasma or serum was analyzed for glucose, insulin, C-
peptide and glucagon, during two 24-h periods (days 12
and 45).
Urine volume and glucose were quantified on days 12
and 45.
2.3. Assays
Plasma glucose was determined by an automated method on
an Abbott Architect ci 8200 analyzer; serum immunoreactive
insulin using an automated chemiluminescent assay on
DPCs IMMULITE platform (Diagnostic Products Corp, Los
Angeles CA); serum C-peptide by a radioimmunoassay
method using a kit manufactured by DSL, Webster, TX;
purchased from Linco Research (purchased by Millipore,
Billerica, MA).
2.4. Area determination
The net integrated 24-h area responses were calculated using
the overnight fasting concentration as baseline. For net
glucose area response, we also used 85 mg/dl as baseline.
This glucose concentration is typical for a person without
diabetes in the AM after an overnight fast, i.e. it could
represent an ideal goal. Total integrated 24-h area responses
were calculated using zero as baseline.
2.5. Statistical analysis
Statistics were determined using Prism 5 software for the iMac
by Graphpad (LaJolla, CA). First, the three diets were compared
using Friedmans non-parametric test. If the Friedmans test
indicated that there were significant differences among the 3
diets, planned pairwise comparisons were made using
Wilcoxons signed ranks test with a Bonferroni adjustment for
3 comparisons. Students t test was used to analyze the
difference in weight loss between the two arms of the study. A
p-value less than 0.05 was the criterion for significance. Data
are presented as the mean ± SEM.
3. Results
Data for the first day of each arm of the study on which
standard meals were ingested, i.e. pre-CHO-free diet and pre-
72 h fast, were similar and have been combined for analysis
and presentation of all of the data and for comparison with
the CHO-free diet and fasting data.
3.1. Glucose
3.1.1. Standard diet
The mean AM plasma glucose concentration before ingesting
the standard diet was 196 ± 18 mg/dl (Fig. 1). Following
ingestion of the standard diet, it increased after meals, as
expected, reaching a maximum at 1.5 h after breakfast, at 1 h
after lunch, and at 2 h after dinner (Fig. 1). The nadir of 158 ±
19 mg/dl was reached at ~10 h after the start of breakfast. At
the end of the 24 h the concentration was 188 ± 15 mg/dl.
3.1.2. CHO-free diet
On day 4 of the study following ingestion of the CHO-free diet
for 2 days the mean glucose concentration was 160 ± 15 mg/dl
at 0800 h. Small but distinct increases in glucose concentration
were detected after the meals at essentially the same times
noted for the standard meals. A nadir of 118 ± 9 mg/dl was
observed 10 h after the start of breakfast (at 1800 h). The final
mean glucose concentration at the end of the 72 h of CHO-free
meals was 149 ± 15 mg/dl.
3.1.3. Fasting (zero food energy)
The mean glucose concentration was 127 ± 10 mg/dl at 0800 h
on day 4 of the fasting arm of the study. During the last 24 h of
the 72-h fast, a nadir of 103 ± 8 mg/dl was observedat 10 h after
the start of breakfast (at 1800 h) and again during the night at
0200, 0300, and 0400 h. The mean glucose concentration at the
end of the 72-h fast was 114 ±11 mg/dl.
3.1.4. Net area
(Fig. 1, left insert) The net glucose area, using the fasting
glucose concentration as baseline for each intervention was
548 ± 163, 494 ± 106 and 389 ± 66 mg h/dl for the standard
diet, the CHO-free diet, and with fasting, respectively.
When 85 mg/dl was used as a baseline, the net areas were
3031 ± 665 mg h/dl, 1368 ± 291 and 621 ± 221 for the standard
diet, the CHO-free diet, and fasting, respectively.
3.1.5. Total area
(Fig. 1, right insert) The mean total 24 h integrated glucose area
response, using zero as baseline was 5248 ± 601 mg h/dl, 3408 ±
291, and 2661 ± 221 mg h/dl, for the standard diet, the CHO-free
diet, and with fasting, respectively. Thus, in the absence of
carbohydrate, the total glucose area response decreased by 35%,
compared to a 49% decrease with fasting. Of the decrease in total
integrated glucose area observed with fasting, 71% could be
attributed to the removal of carbohydrate from the diet.
3.2. Insulin
3.2.1. Standard diet
The mean AM serum concentration before ingesting the
standard diet was 18 ± 1.5 μU/ml (Fig. 2). Following ingestion
of the standard diet, the insulin concentration reached a
maximum 1 h after breakfast, 1 h after lunch, and 2 h after
dinner (Fig. 2). The concentration returned to the fasting
baseline during the night at ~ 0400 h.
3.2.2. CHO-free diet
After ingestion of the CHO-free diet, the AM insulin
concentration also was 18 ± 3 μU/ml. The insulin con-
centration modestly increased after meals, but the
increase was markedly attenuated compared to that
observed with the standard meals. At the end of the
72 h on a carbohydrate-free diet the concentration was
21 ± 4 μU/ml.
Fig. 1 24-h glucose response.The open circlesolid line represents the mean glucose concentration at several time points
during the first 24 h of both days during which the standard diet was ingested (i.e. day 1 of each arm of the study). The
triangledotted line represents the mean glucose concentration during the last 24 h on a carbohydrate-free diet. The closed
circlesolid line represents the mean glucose concentration during the last 24 h of the fast (energy-free) diet. B, L, D, indicate
the times at which breakfast, lunch, and dinner were ingested.The net area response (Left Insert) indicates the area under the
curve using the fasting concentration as baseline. Different letters on bars indicate statistically significant differences
(Friedman P = < 0.0012).The total area response (Right Insert) indicates the area under the curve, using zero as baseline.
Different letters on bars indicate statistically significant differences (Friedman: P = <0.0001).
3.2.3. Fasting (zero food energy)
At 0800 h on day 4 of the study, the mean insulin concentra-
tion was 14 ± 3 μU/ml, and it changed very little during the
subsequent 24 h. At the end of the 72-h fast the insulin
concentration was 14 ± 2 μU/ml. It is noteworthy that there
was little change in the AM insulin concentration or final
concentrations the next morning, regardless of diet.
3.2.4. Net area
(Fig. 2, left insert) Using the respective individual mean overnight
AM values as baseline, the mean net 24 h insulin area response
was 573 ± 93, 159 ± 34, and 29 ± 30 μUh/ml following the
standard diet, the CHO-free diet, and fasting, respectively.
3.2.5. Total area
(Fig. 2 right insert) The mean total 24 h insulin area responses
were 1005 ± 133, 533 ± 81, and 309 ± 51 μU h/ml, following
the standard diet, the CHO-free diet, and fasting, respectively.
3.3. C-peptide
The respective C-peptide dynamic responses are very
similar to the insulin responses (Fig. 3). The statistical
significance was modestly different between the insulin
area results and the C-peptide area results (Figs. 2 and 3).
Neither the C-peptide net or total area responses were
statistically significantly different between fasting, the
CHO-free or the standard diet. Although some significance
was noted with the Friedman analysis a post hoc analysis
of pairwise comparisons did not reach the Bonferroni
p value.
3.4. Glucagon
The 0800 h plasma glucagon concentration and the 24-h
glucagon concentration profiles were similar following the
standard diet, the CHO-free diet, or fasting (Fig. 4). During the
last 12 h of the study the average glucagon concentration
was ~25% lower than during the first 12 h for the standard
diet and the carbohydrate-free diet. It was only 10% lower
with fasting.
3.4.1. Net area
(Fig. 4, left insert) The 24 h integrated glucagon net area responses
were 89 ± 125, 268 ± 124 and 9 ± 112 pg h/ml following the
standard diet, CHO-free diet, and fasting, respectively.
Fig. 2 24-h insulin response.The open circlesolid line represents the mean insulin concentration at several time points
during the first 24 h of both days during which the standard diet was ingested (i.e. day 1 of each arm of the study). The
triangledotted line represents the mean insulin concentration during the last 24 h on a carbohydrate-free diet. The closed
circlesolid line represents the mean insulin concentration during the last 24 h of the fast (energy-free) diet. B, L, D, indicate the
times at which breakfast, lunch, and dinner were ingested.The net area response (Left Insert) indicates the area under the
curve using the fasting concentration as baseline. Different letters on bars indicate statistically significant differences
(Friedman: P = < 0.0001).The total area response (Right Insert) indicates the area under the curve, using zero as baseline.
Different letters on bars indicate statistically significant differences. (Friedman: P = <0.0001).
3.4.2. Total area
(Fig. 4, right insert) the 24 h integrated total glucagon area
responses were not different. The total area responses were
1787 ± 175, 1748 ± 73 and 2034 ± 331 pg h/ml following the
standard diet, CHO-free diet, and fasting, respectively.
3.5. Body weight
The body weight decreased from 213 ± 9 lb to 210 ± 6 lb
following ingestion of the calorie-sufficient, carbohydrate-
free diet and from 212 ± 6 lb to 205 ± 6 lb after fasting
(Students t test, P = 0.0006).
3.6. Urine
The urine volume decreased from 3247 ± 425 ml on the
standard diet to 3012 ± 311 ml and 2399 ± 297 ml on the
carbohydrate-free and energy-free diets, respectively. The
urinary glucose excretion decreased from 26.5 ± 12 g on the
standard diet to 0.26 ± 0.1 g and to 0.07 ± 0.01 g on the
carbohydrate-free and energy-free diets, respectively.
4. Discussion
4.1. Glucose
The effect of short-term fasting on the circulating glucose
concentration has been reported previously by us [2],by
others reviewed therein and by others [6,7]. However, as
indicated in the introduction, a direct comparison of fasting
(zero food energy intake), with a carbohydrate-free, food
energy-adequate diet over the same time and in the same
individuals, has not been reported to our knowledge either in
ng hr/ml
nmol hr/L
Net Areas
Sta ndard
ng hr/ml
nmol hr/L
Tot al Ar ea s
Fast ing
CHO Free
Standard Diet
Time (Hours)
0 2 4 6 8 10 12 14 16 18 20 22 24
C-Peptide Response
Fig. 3 24-h C-peptide response.The open circlesolid line represents the mean C-peptide concentration at several time points
during the first 24 h of both days during which the standard diet was ingested (i.e. day 1 of each arm of the study). The
triangledotted line represents the mean C-peptide concentration during the last 24 h on a carbohydrate-free diet. The closed
circlesolid line represents the mean C-peptide concentration during the last 24 h of the fast (energy-free) diet. B, L, D, indicate
the times at which breakfast, lunch, and dinner were ingested.The net area response (Left Insert) indicates the area under
the curve using the fasting concentration as baseline. Although some significance was noted with the Friedman analysis
(P = < 0.0017), a post hoc analysis of pairwise comparisons did not reach the Bonferroni p value. Identical letter on bars
indicates that results were not statistically different. The total area response (Right Insert) indicates the area under the
curve, using zero as baseline Although some significance was noted with the Friedman analysis (P = < 0.0120), a post hoc
analysis of pairwise comparisons did not reach the Bonferroni p value. Identical letter on bars indicates that results were
not statistically different.
people with or without diabetes. A correlation with the
insulin and glucagon concentrations also has not been
reported. In addition, 24-h data relating metabolic responses
to food intake rarely are reported.
4.1.1. Overnight fasting glucose
In a review of the literature we noted, and our data confirmed,
that short-term fasting lowers the morning glucose by ~ 20%
40% when compared to when subjects were ingesting a
regular mixed diet [2]. This was regardless of the initial
morning (overnight fasting) glucose concentration, i.e. the
higher the preceding morning glucose concentration the
greater the absolute glucose decrease with fasting [2]. The
current data also are compatible with this concept. In the
present study, a 3-day fast resulted in a mean 36% decrease in
the morning glucose. After three days on a carbohydrate-free,
energy-sufficient diet it decreased 18% when compared to the
standard diet. Thus, eliminating carbohydrate from the diet
could account for 50% of the reduction in AM glucose
concentration observed in the absence of food intake. If the
overnight fasting glucose baseline is set at 85 mg/dl, a typical
glucose concentration for an individual without type 2
diabetes, who is eating a regular, food-energy sufficient diet,
then the decrease with no food intake is 80%. With eliminat-
ing carbohydrate from the diet it is 55%.
Our previous long-term data also suggest that in people
with type 2 diabetes the overnight fasting glucose is largely
dependent on the carbohydrate content of the diet. In
addition, in those studies, there was a critical threshold for
maintenance of the typical high AM fasting values. This was
between 30% and 40% of total food energy [8]. The glucose
concentration also was independent of the fasting insulin and
glucagon concentrations.
4.1.2. 24 h integrated glucose area
The 24 h integrated total glucose area response after fasting
for 3 days was decreased 49%, after the CHO-free diet it was
35%, when compared with the standard diet. Thus ~ 70% of
the reduction in integrated glucose area response can be
attributed to elimination of carbohydrate.
Using the respective 0800 h values as baseline, the 24 h
integrated net glucose areas resulting from elimination of all
food energy intake, or eliminating carbohydrate from the diet,
were negative and similar as a percentage when compared to
their respective 0800 h fasting values even though those
fasting values were quite different (Fig. 1).
0 2 4 6 8 1012141618202224
Time (Hours)
Fast ing
CHO Free
Standard Diet
ng hr/L
pg hr/ml
Tot al A re a s
Sta ndard
ng hr/L
pg hr/ml
Net Areas
Sta ndard
Glucagon Response
Fig. 4 24-h glucagon response.The open circlesolid line represents the mean glucagon concentration at several time points
during the first 24 h of both days during which the standard diet was ingested (i.e. day 1 of each arm of the study). The
triangledotted line represents the mean glucagon concentration during the last 24 h on a carbohydrate-free diet. The closed
circlesolid line represents the mean glucagon concentration during the last 24 h of the fast (energy-free) diet. B, L., D. indicate
the times at which breakfast, lunch, and dinner were ingested.The net area response (Left Insert) indicates the area under the
curve using the fasting concentration as baseline (Friedman: P = 0.0845). Identical letter on bars indicates that results were not
statistically different.The total area response (Right Insert) indicates the area under the curve, using zero as baseline
(Freidman, P = 0.4861).
If the baseline is set at 85 mg/dl, the 24-h integrated net
area decreased by 80% with elimination of food energy and
55% by the CHO-free diet. Compared to the standard diet
response, the decrease was 171% and 190%, respectively.
Typical of people with type 2 diabetes, the lowest glucose
concentrations occurred late in the afternoon whether on the
standard or carbohydrate-free diet or with no food energy
intake (Fig. 1). That is, they have a prominent glucose
circadian rhythm [2,9]. This circadian rhythm can explain at
least in part, the net 24-h negative area responses described
above. It occurs without a significant change in insulin or
glucagon concentration when food is not being ingested
(Figs. 2 and 4), but is modified by food ingestion (Fig. 1). The
mechanism remains unexplained but may be mediated by a
local circadian entrainment in the liver and/or to neural input
to the liver through a hypothalamic control system [1014].
In summary, a lack of carbohydrate played a dominant
role in reducing the 24 h integrated glucose concentration
just as it did with the 0800 h fasting concentration. Presum-
ably this is mediated by a hormone-independent reduction
in glycogenolysis [8,15].
4.2. Insulin
4.2.1. Overnight fasting insulin
The current dietary manipulations did not result in a
significant change in the AM fasting insulin concentration
whether on the standard or carbohydrate-free diet, or with no
fuel consumption (18, 18 and 14 μU/ml, respectively, Fig. 2).
The stability in insulin concentration occurred even though
there was a considerable change in the fasting glucose
concentration (196, 160, and 127 mg/dl, respectively). Thus,
the glucose concentrations per se could not explain the
insulin concentrations obtained. Also, contrariwise, the
difference in glucose concentration cannot be attributed to a
change in insulin concentration. Clearly the insulin concen-
tration is dissociated from the glucose concentration.
These dissociations have been noted previously after short-
term fasting in people with type 2 diabetes [16] and without
fasting [17], although not commented on. It also was observed
in people with or without type 2 diabetes by others [18].
These data suggest an abnormality, or more likely an
adjustment, in βcell glucose sensing such that the phase 2
insulin secretion rate maintains the glucose concentration at
a particular level, i.e. there is a new fasting glucose concen-
tration being maintained to which the insulin concentration
adapts, as suggested by Goodner and associates [18]. The 24-h
basal glucose and insulin concentrations also remained
essentially unchanged by the following morning.
Clearly as pointed out by Goodner et al. [18], and as
demonstrated here, the post-meal insulin secretory response
is vigorous and responsive to increases in the usually ingested
fuel secretagogues. Thus, the total insulin secretory capacity
is large (Fig. 2), and not an issue.
Alternatively, the glucose concentration is being regulated
independent of the insulin concentration, but the insulin
change accommodates to these independent regulators such
that the metabolic disposition of the ingested macronutrients
occurs completely within 24 h, regardless of type and amount
ingested. If so, this regulation occurs in people with type 2
diabetes at an elevated glucose concentration and is depen-
dent on the type of fuel ingested, but also to just fasting.
The glucose concentration in the absence of concurrent
carbohydrate intake correlates very well with the glucose
production rate both in people with [1921] or without [2123]
diabetes. Thus, the observed decreases in AM fasting glucose
in the present study likely represent a decreased glucose
production rate. If so, the differences in production rate again
suggest an insulin-independent dietary effect on liver func-
tion associated with an adjustable beta cell insulin secretory
response that maintains that concentration of glucose [18] as
indicated above.
The insulin concentration did not change throughout 24 h
when the subjects were not ingesting fuel. The net increased
response in insulin when the subjects ingested the carbohy-
drate-free diet was 28% as great as with the standard diet.
This is due to the increased fat content as well as the
unchanged protein content. Protein is known to stimulate
insulin secretion [24]. In subjects with type 2 diabetes it is
just as potent as glucose on a weight basis but has little effect
on the glucose concentration [25]. Dietary fats stimulate a
rise in insulin concentration [2629] but this has not been
extensively studied.
In the present study, in spite of the clear increase in insulin
stimulated by the protein and fat ingested in the carbohy-
drate-free diet, the net glucose area response was similar
to that observed without food energy intake (Fig. 1). In both
cases it was actually negative. Thus, again there was
dissociation between the insulin response and the glucose
response to these dietary manipulations.
4.3. C-peptide
The C-peptide response was similar to the insulin response
indicating that a major change in insulin turnover rate was
not present with any of the dietary manipulations (Fig. 3).
Although statistical significance was not obtained with
comparisons of the C-peptide area responses, unlike the
statistical significance for all comparisons with the insulin
area responses, both insulin and C-peptide area response
results to the dietary interventions were nearly identical.
4.4. Glucagon
The 0800 h fasting glucagon concentrations were similar
regardless of the diet composition or without food energy
intake (standard, 74 ± 5, CHO-free, 71 ± 11, fasting, 83 ±
14 pg/ml) (Fig. 4).
The glucagon concentrations also were rather stable
throughout the 24-h period of the study regardless of the
diet composition or with just fasting (Fig. 4). A clear meal-
related change was not observed. Overall, these data suggest
that changes in overnight fasting glucagon or absolute 24-h
integrated glucagon concentrations do not contribute mech-
anistically to the differences in glucose concentrations
observed in each different nutritional state.
In people without diabetes, an increase in glucagon during
a 72 h fast has been reported [7,3033]. This could be
attributed to a decrease in insulin concentration [34] and an
increase in non-esterified fatty acids (NEFAs) [35]. Also in all
nutritional states including during states of dietary carbohy-
drate deprivation, a reciprocal relationship between insulin
and glucagon is considered to have major effects on glucose
production and thus the glucose concentration in people
without diabetes [34]. Insulin secretion is considered to play
the major role both by a direct inhibition of glucose
production but also by an indirect effect due to its inhibitory
effect on glucagon secretion [36]. Insulin and perhaps zinc
secreted by the β-cell can inhibit glucagon secretion directly
through a proposed paracrine mechanism [37].
The current data are not compatible with this concept.
There was a considerable change in 24 h integrated insulin
area response but essentially no change in glucagon area
response. Thus, the current data and other data in the
literature [38] suggest that this inhibition can be regulated.
4.5. Strengths and limitations
The strength of this report is that it represents complete 24-h
data obtained in people with type 2 diabetes ingesting real
world foods of carefully designed macronutrient composition.
The limitations are 1) the relatively small number of subjects
studied, and 2) all were males. Our primary outcome was
glucose area response. Based on previous data in subjects
with type 2 diabetes from our laboratory, which showed a 29%
decrease in 0800 h glucose concentration after 48 h of
starvation, and a 27% decrease in glucose concentration
after 5 weeks on a 20% carbohydrate diet, the power analysis
indicated that we would have to study 2 subjects to test our
current hypothesis. We studied 7 subjects to provide clinical
significance to the data. Thus, although the number of
subjects is small, it should be noted that such studies require
very dedicated subjects and are very labor intensive. It is not
practical to do such studies in a large population of people
with type 2 diabetes. It also would be difficult to obtain an
equal male:female ratio when recruiting subjects.
It should be noted that our results may only be valid in
the early stages of type 2 diabetes. It is not known whether
similar results would be obtained in subjects with advanced
stages of diabetes, i.e. when beta-cell function is expected to
be diminished.
Nevertheless, these data demonstrate a dramatic and
rapid improvement in blood glucose without weight loss and
without a change in insulin. This suggests that the rapid
improvement in glycemic control following low food energy
diets as well as surgeries for weight loss at least in part, can be
attributes to the diet per se rather than just a weight loss.
That is, it is due to an intrinsic metabolic adjustment to a
change in fuel consumption. Whether these data have
translational potential in other areas of human physiology
and therapeutics remains to be determined.
5. Conclusion
Short-term fasting dramatically lowers the plasma glucose
concentration. A lack of carbohydrate could explain approx-
imately 50% of the decrease in the overnight fasting glucose
and 70% of the 24 h integrated glucose concentration ob-
served with short-term fasting.
Thus, changes in the amount and macronutrient compo-
sition of the diet not only strongly affect the postprandial
glucose but also the basal glucose concentration in people
with type 2 diabetes. The latter occurs without a significant
change in insulin and glucagon concentrations. The mecha-
nism or mechanisms which maintain the different but
abnormally elevated glucose concentrations remain to be
determined. Most likely, it is multifactorial. Nevertheless, it is
clear that a limitation in insulin secretory capacity and/or a
static state of insulin resistance cannot explain the hypergly-
cemia in these subjects.
Author Contributions
Dr. R.M. Almokayyad was an Endocrine Fellow in training at
the time this study was done. Dr. Almokayyad applied for and
obtained IRB approval for the study, recruited the subjects,
obtained the blood specimens, and contributed to the data
analysis and preliminary draft of the manuscript. Drs. F.Q.
Nuttall and M.C. Gannon obtained the funding, formulated
and designed the study, performed the final analysis of the
data and wrote the final manuscript. Dr. Nuttall is responsible
for the contents.
Supported in part by funds from the Department of Veterans
Affairs. The Department of Veterans Affairs had no involve-
ment in the study design, the collection, analysis and
interpretation of data, in the writing of the manuscript or in
the decision to submit the manuscript for publication.
Disclosure statement
The authors have no conflicts of interest and have nothing
to disclose.
The authors thank the volunteers for participating in the
study, the Staff of the Special Diagnostic and Treatment Unit,
Heidi Hoover, R.D., M.S., Research Dietitian, the Staff of the
Clinical Chemistry Laboratory, Linda Hartich, M.T. for labora-
tory assistance, David Prentiss for preparing the figures for
publication and Rachel Anderson for preparing the manu-
script for electronic submission.
[1] Kelley DE, Wing R, Buonocore C, et al. Relative effects of
calorie restriction and weight loss in non-insulin dependent
diabetes mellitus. J Clin Endocrinol Metab 1993;77(5):128793.
[2] Gannon MC, Nuttall FQ, Lane JT, et al. Effect of 24 hours of
starvation on plasma glucose and insulin concentrations in
subjects with untreated non-insulin dependent diabetes
mellitus. Metabolism 1996;45(4):4927.
[3] Wall JR, Pyke DA, Oakley WG. Effect of carbohydrate
restriction in obese diabetics: relationship of control to
weight loss. Br Med J 1973;1(5853):5778.
[4] Cahill GF, Herrera MG, Morigan AP, et al. Hormone fuel
interrelationships during fasting. J Clin Invest 1966;45(11):
[5] Nuttall FQ, Almokayyad RM, Gannon MC. Effect of a
carbohydrate-free diet vs fasting on plasma glucose in type 2
diabetes. Diabetes 2013;62:A191.
[6] Nair KS, Ford GC, Haliday D. Effect of intravenous insulin
treatment on in-vivo whole body leucine kinetics and oxygen
consumption in insulin-deprived type I diabetic patients.
Metabolism 1987;36(5):4915.
[7] Hojlund K, Wildner-Christensen M, Eshoj O, et al. Reference
intervals for glucose, beta-cell polypeptides, and
counterregulatory factors during prolonged fasting. Am J
Physiol Endocrinol Metab 2001;280(1):E508.
[8] Nuttall FQ, Schweim K, Hoover H, Gannon MC. Effect of the
LoBAG30 diet on blood glucose control in people with type 2
diabetes. Br J Nutr 2008;99(3):5119.
[9] Faiman C, Moorhouse JA. Diurnal variation in the levels of
glucose and related substances in healthy and diabetic
subjects during starvation. Clin Sci 1967;32(1):11126.
[10] Boden G, Chen X, Urbain JL. Evidence for a circadian rhythm
of insulin sensitivity in patients with NIDDM caused by cyclic
changes in hepatic glucose production. Diabetes 1996;45(8):
[11] Cailotto C, La Fleur SE, Van Heijningen C, et al. The
suprachiasmatic nucleus controls the daily variation of
plasma glucose via the autonomic output to the liver: are the
clock genes involved? Eur J Neurosci 2005;22(10):253140.
[12] Lamia KA, Evans RM. Metabolism: tick, tock, a beta-cell clock.
Nature 2010;466(7306):5712.
[13] Bass J. Circadian topology of metabolism. Nature 2012;491
[14] Radziuk JM. The suprachiasmatic nucleus, circadian clocks,
and the liver. Diabetes 2013;62(4):10179.
[15] Glauber H, Wallace P, Brechtel G. Effects of fasting on plasma
glucose and prolonged tracer measurement of hepatic
glucose output in NIDDM. Diabetes 1987;36(10):118794.
[16] Jackson RA, Moloney M, Lowy C, et al. Differences between
metabolic responses to fasting in obese diabetic and obese
non-diabetic subjects. Diabetes 1971;20(4):21427.
[17] Reaven GM. Role of insulin resistance in human disease.
Diabetes 1988;37(12):1595607.
[18] Goodner CJ, Conway MJ, Werbach JH. Control of insulin
secretion during fasting hyperglycemia in adult diabetics and
in nondiabetic subjects during infusion of glucose. J Clin
Invest 1969;48(10):187887.
[19] Baron AD, Schaeffer L, Shragg P, Kolterman OG. Role of
hyperglucagonemia in maintenance of increased rates of
hepatic glucose output in type II diabetics. Diabetes 1987;36
[20] Khan M, Gannon MC, Nuttall FQ. Glucose appearance rate
following protein ingestion in normal subjects. J Am Coll Nutr
[21] Féry F. Role of hepatic glucose production and glucose uptake
in the pathogenesis of fasting hyperglycemia in Type 2
diabetes: normalization of glucose kinetics by short-term
fasting. J Clin Endocrinol Metab 1994;78(3):53642.
[22] Nuttall FQ, Khan M, Gannon MC. Peripheral glucose
appearnace rate following fructose ingestion in normal
subjects. Metabolism 2000;49(12):156571.
[23] Gannon MC, Khan MA, Nuttall FQ. Glucose appearance rate
after the ingestion of galactose. Metabolism 2001;50(1):938.
[24] Nuttall FQ, Gannon MC. Dietary protein and the blood
glucose concentration. Diabetes 2013;62(5):13712.
[25] Nuttall FQ, Mooradian AD, Gannon MC, et al. Effect of protein
ingestion on the glucose and insulin response to a
standardized oral glucose load. Diabetes Care 1984;7(5):46570.
[26] Gannon MC, Ercan N, Westphal SA, Nuttall FQ. Effect of
added fat on the plasma glucose and insulin response to
ingested potato in individuals with NIDDM. Diabetes Care
[27] Gannon MC, Nuttall FQ, Westphal SA, Seaquist ER. The effect
of fat with carbohydrate on plasma glucose, insulin, C-
peptide and triglycerides in normal male subjects. J Am Coll
Nutr 1993;12(1):3641.
[28] Radulescu A, Hassan Y, Gannon MC, Nuttall FQ. The degree of
saturation of fatty acids in dietary fats does not affect the
metabolic response to ingested carbohydrate. J Am Coll Nutr
[29] Collier G, O'Dea K. The effect of co-ingestion of fat on the
glucose, insulin, and gastric inhibitory polypeptide responses
to carbohydrate and protein. Am J Clin Nutr 1983;37(6):9414.
[30] Merimee TJ, Fineberg SE. Homeostasis during fasting. II.
Hormone substrate differences between men and women.
J Clin Endocrinol Metab 1973;37(5):698702.
[31] Nair KS, Woolf PD, Welle SL, Matthews DE. Leucine, glucose,
and energy metabolism after 3 days of fasting in healthy
human subjects. Am J Clin Nutr 1987;46(4):55762.
[32] Boyle PJ, Shah SD, Cryer PE. Insulin, glucagon, and
catecholamines in prevention of hypoglycemia during
fasting. Am J Physiol 1989;256(5 Pt 1):E65161.
[33] Beer SF, Bircham PM, Bloom SR, et al. The effect of a 72-h fast
on plasma levels of pituitary, adrenal, thyroid, pancreatic
and gastrointestinal hormones in healthy men and women.
J Endocrinol 1989;120(2):33750.
[34] Unger RH, Cherrington AD. Glucagonocentric restructuring of
diabetes: a pathophysiologic and therapeutic makeover.
J Clin Invest 2012;122(1):412.
[35] Gerich JE, Langlois M, Schneider V, et al. Effects of
alternations of plasma free fatty acid levels on pancreatic
glucagon secretion in man. J Clin Invest 1974;53(5):12849.
[36] Samols E, Tyler J, Marks V, Glucagon insulin inter-
relationship. In: Lefebvre PJ, Unger RH, editors. Glucagon:
molecular physiology, clinical and therapeutic implications.
Oxford, United Kingdom: Pergamon Press; 1972. p. 15774.
[37] Robertson RP, Zhou H, Slucca M. A role for zinc in pancreatic
islet beta-cell cross-talk with the alpha-cell during
hypoglycaemia. Diabetes Obes Metab 2011;13(Suppl. 1):10611.
[38] Walker JN, Ramracheya R, Zhang Q, et al. Regulation of
glucagon secretion by glucose: paracrine, intrinsic or both?
Diabetes Obes Metab 2011;13(Suppl. 1):95105.
... The proposed model attempts to emulate dynamic or kinetic bloodstream signals such as glucose, insulin, ghrelin, and leptin, which modulate energy homeostasis and have side effects on producing hunger. For this, it analyzes different works of neuroscience and from the health field (Boden, Sargrad, Homko, Mozzoli, & Stein, 2005;Cummings et al., 2001Cummings et al., , 2002Jamshed et al., 2019;Nuttall, Almokayyad, & Gannon, 2015;Shiiya et al., 2002), whose objective is to analyze the behavior of particular chemical signals (or BBs) and their relationship in humans. Primary data about these works are condensed in Table 3. ...
... Jamshed et al. (2019) Fasting effects on glucose level and circadian rhythm *Glucose There is a bidirectional relationship between mealtime and circadian rhythm. Nuttall et al. (2015) Diet effects under insulin, glucose, and glucagon level *Glucose *Insulin Low intake of CHO induces a decrease in the glucose level at night. Steinert et al. (2012) Analyzing the interaction between gastric signals as negative feedback to regulate satiation *Ghrelin *Glucose *Insulin *Hunger *DG ...
... example, picomol-pM, nanomol-nM, and milimol-mM over a liter (y-axis). Concerning curves that describe BBs, the red line represents our model's results, while the green and blue lines illustrate other works' outcomes (Boden et al., 2005;Cummings et al., 2001Cummings et al., , 2002Jamshed et al., 2019;Nuttall et al., 2015;Shiiya et al., 2002); meals are described with vertical dash lines. In addition, the behavior of hunger and satiation signals over time is presented in Fig. 9(i) to Fig. 9(l); for this case, the -axis is given in minutes, and the -axis represents a subjective self-assessment; the Visual Analog Scale (VAS) is a personal way of assessing hunger and satiety states through a questionnaire. ...
Appetitive motivation is a process inherent to human beings, which stimulates behaviors directed at searching, pursuing, and achieving specific objectives based on a set of impulses. Consequently, the motivational process has been considered part of the control system in some cognitive architectures because it can influence the execution of goal-directed behaviors of different kinds. Different motivational computational systems integrate physiological needs and environmental conditions to produce human-like behavior in artificial agents. However, these motivational computational systems limit abstract properties defining a motivational state; consequently, the adaptability characterizing motivated human behavior does not arise. In this research line, this article proposes a bioinspired model for generating the motivational state from the physiological condition, capable of triggering processes involved in developing appetitive behavior. The proposed system incorporates bloodstream and viscerosensory biomarkers to represent a motivational state of hunger regulated by energy homeostasis or balance control. The proposed model is based on neuroscientific studies and implemented under a distributed paradigm emulating the way it happens in living beings. The validation of the proposed model’s functioning uses study cases comparing the proposal’s results with neuroscientific evidence.
... Nevertheless, these improvements in HbA 1c may be Data at baseline and changes from baseline are presented as means ± SD or medians (25th, 75th percentiles); between-diet differences are estimated marginal means (95% CIs), presented as absolute or relative differences (CRHP vs CD) for normally distributed or log-transformed data, respectively, and derived from constrained linear mixed models with inherent baseline adjustment using all available data a Missing data were observed for measurements of waist circumference, fat distribution and body composition due to technical failures and unwillingness to undergo scanning procedure b Relative difference (%) ‡p < 0.001 vs baseline Diabetologia Table 3 Measures of glucose and lipid metabolism and CGM before and after matched weight loss by a CD or a CRHP diet in individuals with overweight or obesity and type 2 diabetes Data at baseline and changes from baseline are presented as means ± SD or medians (25th, 75th percentiles); between-diet differences are estimated marginal means (95% CIs), presented as absolute or relative differences (CRHP vs CD) for normally distributed or log-transformed data, respectively, and derived from constrained linear mixed models with inherent baseline adjustment using all available data a Relative difference (%) b Missing data were observed for CGM due to technical failures and misplacement of equipment *p < 0.05 and ‡p < 0.001 vs baseline TAR, time-above-range; TBR, time-below-range; TIR, time-in-range Diabetologia modest during energy restriction compared with iso-energetic feeding [10], underlying the primary importance of weight reduction over macronutrient composition. Iso-energetic meals with lower carbohydrate content acutely attenuate postprandial glucose and insulin excursions in type 2 diabetes, thus decreasing daily mean glucose [23,24]. We made a similar observation during 6 weeks of a weight-maintaining CRHP diet that led to reduced HbA 1c compared with an isoenergetic CD diet [10]. ...
... Diurnal mean glucose decreased significantly more with the CRHP diet, thus the greater reduction in HbA 1c has likely resulted from the persistent reductions in postprandial glucose excursions following the CRHP meals. Less carbohydrate being ingested with the CRHP meals compared with the CD meals may primarily drive the reductions in postprandial hyperglycaemia [23]. Diurnal glucose profiles are considered as supplementary to HbA 1c in evaluating the quality of glucose control; this includes time-in-range and glucose excursions as independent therapy targets [19,21]. ...
Full-text available
Aims/hypothesis Lifestyle modification and weight loss are cornerstones of type 2 diabetes management. However, carbohydrate restriction may have weight-independent beneficial effects on glycaemic control. This has been difficult to demonstrate because low-carbohydrate diets readily decrease body weight. We hypothesised that carbohydrate restriction enhances the beneficial metabolic effects of weight loss in type 2 diabetes. Methods This open-label, parallel RCT included adults with type 2 diabetes, HbA1c 48–97 mmol/mol (6.5–11%), BMI >25 kg/m², eGFR >30 ml min⁻¹ [1.73 m]⁻² and glucose-lowering therapy restricted to metformin or dipeptidyl peptidase-4 inhibitors. Participants were randomised by a third party and assigned to 6 weeks of energy restriction (all foods were provided) aiming at ~6% weight loss with either a carbohydrate-reduced high-protein diet (CRHP, percentage of total energy intake [E%]: CH30/P30/F40) or a conventional diabetes diet (CD, E%: CH50/P17/F33). Fasting blood samples, continuous glucose monitoring and magnetic resonance spectroscopy were used to assess glycaemic control, lipid metabolism and intrahepatic fat. Change in HbA1c was the primary outcome; changes in circulating and intrahepatic triacylglycerol were secondary outcomes. Data were collected at Copenhagen University Hospital (Bispebjerg and Herlev). Results Seventy-two adults (CD 36, CRHP 36, all white, 38 male sex) with type 2 diabetes (mean duration 8 years, mean HbA1c 57 mmol/mol [7.4%]) and mean BMI of 33 kg/m² were enrolled, of which 67 (CD 33, CRHP 34) completed the study. Body weight decreased by 5.8 kg (5.9%) in both groups after 6 weeks. Compared with the CD diet, the CRHP diet further reduced HbA1c (mean [95% CI] −1.9 [−3.5, −0.3] mmol/mol [−0.18 (−0.32, −0.03)%], p = 0.018) and diurnal mean glucose (mean [95% CI] −0.8 [−1.2, −0.4] mmol/l, p < 0.001), stabilised glucose excursions by reducing glucose CV (mean [95% CI] −4.1 [−5.9, −2.2]%, p < 0.001), and augmented the reductions in fasting triacylglycerol concentration (by mean [95% CI] −18 [−29, −6]%, p < 0.01) and liver fat content (by mean [95% CI] −26 [−45, 0]%, p = 0.051). However, pancreatic fat content was decreased to a lesser extent by the CRHP than the CD diet (mean [95% CI] 33 [7, 65]%, p = 0.010). Fasting glucose, insulin, HOMA2-IR and cholesterol concentrations (total, LDL and HDL) were reduced significantly and similarly by both diets. Conclusions/interpretation Moderate carbohydrate restriction for 6 weeks modestly improved glycaemic control, and decreased circulating and intrahepatic triacylglycerol levels beyond the effects of weight loss itself compared with a CD diet in individuals with type 2 diabetes. Concurrent differences in protein and fat intakes, and the quality of dietary macronutrients, may have contributed to these results and should be explored in future studies. Trial registration NCT03814694. Funding The study was funded by Arla Foods amba, The Danish Dairy Research Foundation, and Copenhagen University Hospital Bispebjerg Frederiksberg. Graphical abstract
... On the other hand, ignoring the current scientific evidence and potential benefits of such a diet and sticking to the old paradigm may be surprising. Moreover, when using that nutrition model, it is easier to control the glycemia level since the postprandial glucose and insulin levels are usually similar to the fasting ones, thus undergoing almost no changes [95]. Comparing studies on the ketogenic diet with the recommended standard diabetes diet model, it is clear that the ketogenic diet can produce even better effects, as has been described in the earlier parts of the current paper. ...
Full-text available
The exponentially growing frequency of diagnosing diabetes mellitus means that a verification of the previous dietetic approach to treating the disease seems justified. The simultaneous growth of interest in the ketogenic diet and the development of knowledge in this field have contributed to the increasingly frequent application of the ketogenic diet in diabetes treatment. This paper also deals with that issue; its aim includes an extensive analysis of the influence of the ketogenic diet on the prophylaxis and treatment of diabetes. The paper has been prepared based on a wide, meticulous analysis of the available literature on the subject. Among other findings, a favorable effect of that nutrition model has been demonstrated on the values of glycated hemoglobin, glucose, insulin, or other metabolic parameters in diabetes patients. The effect of the ketogenic diet on the pharmacotherapy of type 1 and type 2 diabetes has been presented and compared with the standard nutritional management plan recommended for that disease. Further research is needed in this field, especially studies with a long follow-up period. The discussed articles report interesting therapeutic advantages to the ketogenic diet in comparison with standard diets.
... Some studies satisfying the low-carbohydrate diet specification were excluded because of the addition of a medication as part of the intervention. [105][106][107][108] Several otherwise relevant low-carbohydrate diet trials examining just males [109] or females [110][111][112][113] could not be included. Ironically, these researchers may have decided to include just males or females as they anticipated that the two groups would behave differently. ...
Full-text available
It has been widely demonstrated that there are a broad range of individual responses to all weight management regimens, often masked by reports of the mean. Identifying features of responders and non-responders to weight loss regimens enables a more tailored approach to the provision of weight management advice. Low-carbohydrate diets are currently popular, and anecdote suggests that males are more successful at losing weight using this approach. This is feasible given the physiological and socio-psychological differences between the genders. We analysed the extent and variation in weight change for males and females separately through a systematic search for all low-carbohydrate diet trials published since 1985. Very few studies compared weight loss outcomes by gender and, of those that did, most lacked supporting data. The majority of studies reported no gender difference but when a gender difference was found, males were more frequently reported as losing more weight than females on a low-carbohydrate diet. The lack of gender stratification in weight loss trials is concerning, as there are a range of gender-based factors that affect weight loss outcomes. This study highlights the importance of examining weight change for males and females separately, since as failure to do so may mask any potential differences, which, if detected, could assist with better weight loss outcomes.
... All these physiological responses are tightly regulated by hormonal and molecular mechanisms. At the hormonal level, fasting decreases blood insulin and leptin, and increases blood ghrelin and glucagon 3,4 , while blood adiponectin remains unchanged 5 . Several signal transduction pathways are affected by fasting; one of them is the peroxisome proliferator-activated receptor α (PPARα), a nuclear receptor of fatty acids. ...
Full-text available
Fasting exerts beneficial effects in mice and humans, including protection from chemotherapy toxicity. To explore the involved mechanisms, we collect blood from humans and mice before and after 36 or 24 hours of fasting, respectively, and measure lipid composition of erythrocyte membranes, circulating micro RNAs (miRNAs), and RNA expression at peripheral blood mononuclear cells (PBMCs). Fasting coordinately affects the proportion of polyunsaturated versus saturated and monounsaturated fatty acids at the erythrocyte membrane; and reduces the expression of insulin signaling-related genes in PBMCs. When fasted for 24 hours before and 24 hours after administration of oxaliplatin or doxorubicin, mice show a strong protection from toxicity in several tissues. Erythrocyte membrane lipids and PBMC gene expression define two separate groups of individuals that accurately predict a differential protection from chemotherapy toxicity, with important clinical implications. Our results reveal a mechanism of fasting associated with lipid homeostasis, and provide biomarkers of fasting to predict fasting-mediated protection from chemotherapy toxicity.
... This diet has been previously demonstrated to decrease glucose levels, and it lowers insulin quickly-particularly when insulin resistance is present. 26,[43][44][45] In the fasting state, hepatic glycogen is the main source of glucose, and prolonged fasting or the ketogenic diet depletes glycogen stores ( Figure 4). 25,26,46 In a preclinical study, mice that fasted for 16 to 20 hours showed >90% depletion of liver glycogen. ...
Phosphatidylinositol-3-kinase (PI3K) pathway hyperactivation has been associated with the development of cancer and treatment resistance. PI3K inhibitors are now used to treat hormone receptor-positive (HR+), human epidermal growth factor receptor-2-negative (HER2-), PIK3CA-mutated advanced breast cancer. Hyperglycemia, a frequently observed adverse event with PI3K inhibitors (PI3Ki), is regarded as an on-target effect because inhibition of the PI3K pathway has been shown to decrease glucose transport and increase glycogenolysis and gluconeogenesis. PI3Ki-induced hyperglycemia results in a compensatory increase in insulin release, which has been shown to reduce the efficacy of treatment by reactivating the PI3K pathway in preclinical models. Patients with an absolute or relative deficiency in insulin, and those with insulin resistance or pancreatic dysfunction, may experience exacerbated or prolonged hyperglycemia. Therefore, the effective management of PI3Ki-associated hyperglycemia depends on early identification of patients at risk, frequent monitoring to allow prompt recognition of hyperglycemia and its sequelae, and initiating appropriate management strategies. Risk factors for the development of hyperglycemia include older age (≥75 years), overweight/obese at baseline, and family history of diabetes. Consultation with an endocrinologist is recommended for patients considered high risk. The management of PI3Ki-induced hyperglycemia requires an integrative approach that combines diets low in carbohydrates and glucose-lowering medications. Medications that do not affect the PI3K pathway are preferred as the primary and secondary agents for the management of hyperglycemia. These include metformin, sodium-glucose co-transporter 2 inhibitors, thiazolidinediones, and α-glucosidase inhibitors. Insulin should only be considered as a last-line agent for PI3Ki-associated hyperglycemia due to its stimulatory effect of PI3K signaling. Clinical studies show that alpelisib-associated hyperglycemia is reversible and manageable, rarely leading to treatment discontinuation. Management of PI3Ki-associated hyperglycemia in patients with breast cancer should focus on the prevention of acute and subacute complications of hyperglycemia, allowing patients to remain on anticancer treatment longer.
... Intermittent fasting, time-restricted feeding, caloric restriction, and carbohydrate restriction positively modify risk factors in diabetes, including reducing hyperinsulinemia, increasing insulin sensitivity, improving βcell responsiveness, and lowering the levels of circulating glucose. [75][76][77] Several human trials suggest that fasting regimes can be more effective for reducing insulin and increasing insulin sensitivity than they are for reducing glucose. 78,79 By mimicking the low-insulin state, the serum starvation phase of our studies revealed some possible molecular mechanisms of the beneficial effects of fasting on muscle cells, including the restoration of protein phosphorylation in insulin signaling pathways and partial recovery of Insr transcription, INSR protein and overall transcriptomic changes. ...
Full-text available
Hyperinsulinemia is commonly viewed as a compensatory response to insulin resistance, yet studies have demonstrated that chronically elevated insulin may also drive insulin resistance. The molecular mechanisms underpinning this potentially cyclic process remain poorly defined, especially on a transcriptome-wide level. Transcriptomic meta-analysis in >450 human samples demonstrated that fasting insulin reliably and negatively correlated with INSR mRNA in skeletal muscle. To establish causality and study the direct effects of prolonged exposure to excess insulin in muscle cells, we incubated C2C12 myotubes with elevated insulin for 16 h, followed by 6 h of serum starvation, and established that acute AKT and ERK signaling were attenuated in this model of in vitro hyperinsulinemia. Global RNA-sequencing of cells both before and after nutrient withdrawal highlighted genes in the insulin receptor (INSR) signaling, FOXO signaling, and glucose metabolism pathways indicative of 'hyperinsulinemia' and 'starvation' programs. Consistently, we observed that hyperinsulinemia led to a substantial reduction in Insr gene expression, and subsequently a reduced surface INSR and total INSR protein, both in vitro and in vivo. Bioinformatic modeling combined with RNAi identified SIN3A as a negative regulator of Insr mRNA (and JUND, MAX, and MXI as positive regulators of Irs2 mRNA). Together, our analysis identifies mechanisms which may explain the cyclic processes underlying hyperinsulinemia-induced insulin resistance in muscle, a process directly relevant to the etiology and disease progression of type 2 diabetes.
... Nevertheless, even though studies report that a KD has beneficial effects, for example in weight loss in obese patients [70], the possible long-term effects of this diet remains unknown, probably due to the difficulty of adherence to a strict KD over time. Some studies have already reported that there can be adverse effects such as lipid abnormalities [71,72], hypoglycemia and dehydration [73,74], dysregulation of glucose levels [75,76] or nephrolithiasis [77]. Although some of these consequences have been seen in patients on the KD [77], the effects of a ketotic state in patients with an alcohol-use disorder may be pronounced, as they already present a poor nutritional status [78]. ...
Full-text available
The classic ketogenic diet is a diet high in fat, low in carbohydrates, and well-adjusted proteins. The reduction in glucose levels induces changes in the body’s metabolism, since the main energy source happens to be ketone bodies. Recent studies have suggested that nutritional interventions may modulate drug addiction. The present work aimed to study the potential effects of a classic ketogenic diet in modulating alcohol consumption and its rewarding effects. Two groups of adult male mice were employed in this study, one exposed to a standard diet (SD, n = 15) and the other to a ketogenic diet (KD, n = 16). When a ketotic state was stable for 7 days, animals were exposed to the oral self-administration paradigm to evaluate the reinforcing and motivating effects of ethanol. Rt-PCR analyses were performed evaluating dopamine, adenosine, CB1, and Oprm gene expression. Our results showed that animals in a ketotic state displayed an overall decrease in ethanol consumption without changes in their motivation to drink. Gene expression analyses point to several alterations in the dopamine, adenosine, and cannabinoid systems. Our results suggest that nutritional interventions may be a useful complementary tool in treating alcohol-use disorders.
Over the last several decades, there has been an increase in chronic diseases such as neurodegenerative, inflammatory, cardiovascular diseases, and cancers. Two eating patterns, a low-carbohydrate diet, and fasting have been researched independently over this period and found to be beneficial in reducing many of these chronic diseases’ detrimental effects. However, there have been limited studies about the synergy of these eating patterns. This current scoping review aims to explore the evidence of the health outcomes of using a low-carbohydrate diet in conjunction with fasting. Four databases were searched, and fifteen articles were found that fit the inclusion criteria. The articles reported positive effects of combining the two eating patterns for type 2 diabetes, cardiovascular disease, inflammatory conditions, and weight reduction and maintenance. Low-carbohydrate diet and fasting together provide synergy in decreasing metabolic syndrome (as the key causes of chronic illnesses), such as insulin levels, fasting glucose, blood pressure, triglycerides, and regulating lipid profile. Due to the paucity of research, further high-quality studies are needed to substantiate this evidence.
Obesity and type 2 diabetes mellitus (T2DM) have reached epidemic proportions in the modern world. Because the excessive fat mass of obesity leads to insulin resistance, and insulin resistance contributes to T2DM, obesity is an underlying contributing cause of T2DM. So, treating obesity and T2DM simultaneously would be of particular interest in the treatment of T2DM. Low-carbohydrate diets, also known as carbohydrate-restricted diets, have been used since the late 1800s to treat obesity and type 2 diabetes, but only recently have they been included in clinical research to assess their mechanisms and long-term safety. Clinical studies have shown that carbohydrate restriction leads to appetite reduction, weight loss, and improvements in glycemic and insulin control. Over a 2-year period, carbohydrate restriction can lead to remission and cure of T2DM. The recent clinical research supporting the use of a carbohydrate-restricted diet in individuals with obesity and T2DM is reviewed.
Full-text available
Body proteins are being synthesized and degraded continuously (1). The estimated turnover is ∼210 g/day (2). Amino acids resulting from protein degradation can be recycled (reused for synthesis), but this is incomplete. Therefore, dietary protein is necessary for maintenance of lean body mass. Also, dietary protein is required to replace protein lost from the shedding of skin, hair, nails, cells in the gastrointestinal tract, and protein-containing secretions. However, the actual losses are estimated to be only 6–8 g/day (3). Overall, approximately ∼32–46 g of high-quality dietary protein/day is reported to be required to maintain protein balance (2). This is considerably less than amounts of protein reportedly consumed by American adults (∼65–100+ g/day) (4). The excess food-derived amino acids then are oxidized as fuel directly or indirectly after conversion to glucose. In 1915, using a phlorhizinized dog preparation, Janney (5) demonstrated clearly that the deaminated amino acids (carbon skeletons) present in dietary proteins could be used to produce glucose endogenously. For most common proteins, 50–80 g of glucose can be derived from 100 g of ingested protein. Nevertheless, as early as 1913, Jacobson (6) reported that ingestion of proteins did not raise the blood glucose. Later, in 1924, MacLean (7) fed 50 g of meat protein to two subjects, one with and one without mild diabetes. The …
Full-text available
The circadian clock system adapts phasic physiological activities, such as sleeping and eating, to environmental cycles. The “master clock” is in the suprachiasmatic nucleus (SCN) and the principal cue (Zeitgeber) is the light–dark cycle, around which most mammalian (and those of all living organisms) functions have evolved. In our society, with activity 24 h per day, the clock is frequently overridden or does not match the activity schedule, with increased susceptibility to disease (obesity, type 2 diabetes, and their cardiovascular sequelae) resulting. In the SCN and in other cells, the core molecular clock mechanism consists of specific genes (“clock” genes). These include Clock (and Npas2) , Bmal , the period homologs, Per1 and Per2 , and cryptochrome 1 ( Cry1 ) and Cry2 . The circadian clock mechanism revolves around transcription–translation feedback loops, in which repression and activation of transcriptional activity are dependent on dimerization, posttranslational modification, and degradation rate constants that define the reactions. Kinetically (in conjunction with a secondary feedback loop comprising nuclear receptor genes, Reverbs and Rors ), this system defines an oscillator with a period that is near 24 h (1,2). Photic cues channeled via the SCN fine-tune the period to correspond to that of the environment. Information on this period is then transmitted to the periphery. The same molecular clocks are also found in peripheral cells (e.g., kidney, liver, pancreas). They can function autonomously. However, the master clock in the SCN generally coordinates these peripheral clocks by way of the autonomic or humoral (e.g., corticosteroids, melatonin) routes to generate a synchronized signal that aligns metabolic and other activities with environmental conditions (1,3). Sleep–waking cycles are clearly set by the light–dark cycle. Disrupted sleep patterns impose alterations in this cycle. As, for example, with jet lag, the circadian clock adapts to these changes (4 …
Full-text available
Biological clocks are genetically encoded oscillators that allow organisms to anticipate changes in the light-dark environment that are tied to the rotation of Earth. Clocks enhance fitness and growth in prokaryotes, and they are expressed throughout the central nervous system and peripheral tissues of multicelled organisms in which they influence sleep, arousal, feeding and metabolism. Biological clocks capture the imagination because of their tie to geophysical time, and tools are now in hand to analyse their function in health and disease at the cellular and molecular level.
Full-text available
The hormone glucagon has long been dismissed as a minor contributor to metabolic disease. Here we propose that glucagon excess, rather than insulin deficiency, is the sine qua non of diabetes. We base this on the following evidence: (a) glucagon increases hepatic glucose and ketone production, catabolic features present in insulin deficiency; (b) hyperglucagonemia is present in every form of poorly controlled diabetes; (c) the glucagon suppressors leptin and somatostatin suppress all catabolic manifestations of diabetes during total insulin deficiency; (d) total β cell destruction in glucagon receptor-null mice does not cause diabetes; and (e) perfusion of normal pancreas with anti-insulin serum causes marked hyperglucagonemia. From this and other evidence, we conclude that glucose-responsive β cells normally regulate juxtaposed α cells and that without intraislet insulin, unregulated α cells hypersecrete glucagon, which directly causes the symptoms of diabetes. This indicates that glucagon suppression or inactivation may provide therapeutic advantages over insulin monotherapy.
Full-text available
The daily light-dark cycle affects many aspects of normal physiology through the activity of circadian clocks. It emerges that the pancreas has a clock of its own, which responds to energy fluctuations.
Resistance to insulin-stimulated glucose uptake is present in the majority of patients with impaired glucose tolerance (IGT) or non-insulin-dependent diabetes mellitus (NIDDM) and in ∼25% of nonobese individuals with normal oral glucose tolerance. In these conditions, deterioration of glucose tolerance can only be prevented if the β-cell is able to increase its insulin secretory response and maintain a state of chronic hyperinsulinemia. When this goal cannot be achieved, gross decompensation of glucose homeostasis occurs. The relationship between insulin resistance, plasma insulin level, and glucose intolerance is mediated to a significant degree by changes in ambient plasma free-fatty acid (FFA) concentration. Patients with NIDDM are also resistant to insulin suppression of plasma FFA concentration, but plasma FFA concentrations can be reduced by relatively small increments in insulin concentration.Consequently, elevations of circulating plasma FFA concentration can be prevented if large amounts of insulin can be secreted. If hyperinsulinemia cannot be maintained, plasma FFA concentration will not be suppressed normally, and the resulting increase in plasma FFA concentration will lead to increased hepatic glucose production. Because these events take place in individuals who are quite resistant to insulinstimulated glucose uptake, it is apparent that even small increases in hepatic glucose production are likely to lead to significant fasting hyperglycemia under these conditions. Although hyperinsulinemia may prevent frank decompensation of glucose homeostasis in insulin-resistant individuals, this compensatory response of the endocrine pancreas is not without its price. Patients with hypertension, treated or untreated, are insulin resistant, hyperglycemic, and hyperinsulinemic. In addition, a direct relationship between plasma insulin concentration and blood pressure has been noted. Hypertension can also be produced in normal rats when they are fed a fructose-enriched diet, an intervention that also leads to the development of insulin resistance and hyperinsulinemia. The development of hypertension in normal rats by an experimental manipulation known to induce insulin resistance and hyperinsulinemia provides further support for the view that the relationship between the three variables may be a causal one. However, even if insulin resistance and hyperinsulinemia are not involved in the etiology of hypertension, it is likely that the increased risk of coronary artery disease (CAD) in patients with hypertension and the fact that this risk if not reduced with antihypertensive treatment are due to the clustering of risk factors for CAD, in addition to high blood pressure, associated with insulin resistance. These include hyperinsulinemia, IGT, increased plasma triglyceride concentration, and decreased high-density lipoprotein cholesterol concentration, all of which are associated with increased risk for CAD. It is likely that the same risk factors play a significant role in the genesis of CAD in the population as a whole. Based on these considerations the possibility is raised that resistance to insulin-stimulated glucose uptake and hyperinsulinemia are involved in the etiology and clinical course of three major related diseases— NIDDM, hypertension, and CAD.
Signalling by intraislet β-cells to neighbouring α-cells was recognized almost 40 years ago, leading to the hypothesis that this is an essential mechanism to regulate the glucagon counterregulatory response to hypoglycaemia. The thesis was that during normoglycaemia or hyperglycaemia insulin secretion from β-cells would enter the islet periportal circulation and travel downstream to α-cells to dampen glucagon secretion. As a corollary, during hypoglycaemia β-cells would stop secreting insulin, which would permit α-cells to release glucagon into the hepatic portal circulation so it could travel to the liver to increase glucose production and thereby correct hypoglycaemia. This mini-review briefly mentions the early work that established this hypothesis and more extensively examines more recent work that has provided direct evidence supporting the hypothesis. A new twist has been introduced based on the fact that zinc is bound to insulin within β-cells and co-secreted with insulin. Zinc is released from insulin when it reaches the higher pH of blood, and zinc has recently been shown to negatively regulate α-cell secretion. It is now suggested that a switch-off signal provided by a sudden cessation of zinc secretion from β-cells during hypoglycaemia may play a critical role in stimulating glucagon secretion that is independent of the effect of insulin.
Glucagon secretion is regulated by glucose but the mechanisms involved remain hotly debated. Both intrinsic (within the α-cell itself) and paracrine (mediated by factors released β- and/or δ-cells) have been postulated. Glucagon secretion is maximally suppressed by glucose concentrations that do not affect insulin and somatostatin secretion, a finding that highlights the significance of intrinsic regulation of glucagon secretion. Experiments on islets from mice lacking functional ATP-sensitive potassium channels (K(ATP)-channels) indicate that these channels are critical to the α-cell's capacity to sense changes in extracellular glucose. Here, we review recent data on the intrinsic and paracrine regulation of glucagon secretion in human pancreatic islets. We propose that glucose-induced closure of the K(ATP)-channels, via membrane depolarization, culminates in reduced electrical activity and glucagon secretion by voltage-dependent inactivation of the ion channels involved in action potential firing. We further demonstrate that glucagon secretion measured in islets isolated from donors with type-2 diabetes is reduced at low glucose and that glucose stimulates rather than inhibits secretion in these islets. We finally discuss the relative significance of paracrine and intrinsic regulation in the fed and fasted states and propose a unifying model for the regulation of glucagon secretion that incorporates both modes of control.