Nursing Research, and Primary Care (Due 24 hours)

1) Minimum 7 full pages (Follow the 3 x 3 rule: minimum three paragraphs per part)

Part 1: Minimum 1 page

Part 2: minimum 1 page

Part 3: minimum 1 page

Part 4: minimum 1 page

Part 5: minimum 3 pages

Submit 1 document per part

2)¨******APA norms

All paragraphs must be narrative and cited in the text- each paragraphs

Bulleted responses are not accepted

Dont write in the first person

Dont copy and pase the questions.

Answer the question objectively, do not make introductions to your answers, answer it when you start the paragraph

Submit 1 document per part

3)****************************** It will be verified by Turnitin (Identify the percentage of exact match of writing with any other resource on the internet and academic sources, including universities and data banks)

********************************It will be verified by SafeAssign (Identify the percentage of similarity of writing with any other resource on the internet and academic sources, including universities and data banks)

4) Minimum 3 references per part not older than 5 years

5) Identify your answer with the numbers, according to the question. Start your answer on the same line, not the next

Example:

Q 1. Nursing is XXXXX

Q 2. Health is XXXX

6) You must name the files according to the part you are answering:

Example:
Part 1
Part 2

__________________________________________________________________________________

Part 1: Nursing Research

Research topic: Childhood obesity

1. What type of research topic would be of interest to you?

2. Elaborate on factors that motivated you and what are you seeking?

Part 2: Nursing Research 2

Qualitative data has been described as voluminous and sometimes overwhelming to the researcher.

1. Discuss two strategies that would help a researcher manage and organize the data.

Part 3: Nursing Research 3

The three types of qualitative research are phenomenological, grounded theory, and ethnographic research.

1. Compare the differences and similarities between two of the three types of qualitative studies

2. give an example of each one.

Part 4: Nursing Research 4

Topic: Middle-aged, type 2 diabetes, and medication

Picot Question:

In adults with diabetes mellitus, how does physical exercise compare with healthy dietary patterns in establishing glycemic control?

Review the two attached articles (Article 1 and 2) based on these articles, in the form of an Essay develop each of the following points. Use headings.

1. Background of Study

a. Summary of studies. Include problem, significance to nursing, purpose, objective, and research question.

2. How do these two articles support the nurse practice issue you chose?

a. Discuss how these two articles will be used to answer your PICOT question.

b. Describe how the interventions and comparison groups in the articles compare to those identified in your PICOT question.

3. Method of Study:

a. State the methods of the two articles you are comparing and describe how they are different.

b. Consider the methods you identified in your chosen articles and state one benefit and one limitation of each method.

4. Results of Study

a. Summarize the key findings of each study in one or two comprehensive paragraphs.

b. What are the implications of the two studies in nursing practice?

5. Ethical Considerations

a. Discuss two ethical consideration in conducting research.

b. Describe how the researchers in the two articles you choose took these ethical considerations into account while performing their research.

Part 5: Primary Care

Topic: Anxiety and Depression

Common mental health problems such as depression, generalized anxiety disorder, panic disorder, obsessive-compulsive disorder (OCD), post-traumatic stress disorder, and social phobia may affect up to 15% of the population at any one time. The severity of symptoms experienced will vary considerably, but all of these conditions can be associated with significant long-term disability. Good communication skills including active listening are key components for building a trusting relationship with patients, for example through demonstrating empathy, by making eye contact and explaining and talking through diagnoses, symptom profiles, and possible treatment options. The evidence base shows that adopting a collaborative approach with patients can help facilitate a greater engagement from them in any resulting treatments.

Jerome is a 35-year-old welder who lives with his partner and two children aged 3 and 5 years. Jerome has come to see you at your primary care clinic as he is feeling tired all the time. Medical history Jerome has a history of anxiety and depression. He joined your clinic as a patient 5 years ago, at which time he was taking sertraline for moderately severe depression and associated panic attacks. This was prescribed by his previous provider. The sertraline was effective and Jerome stopped taking the medication after 6 months of treatment. He has not returned to the clinic since that time. Jerome is otherwise physically fit and well and is not prescribed any medication. On examination, Jerome describes a lack of drive and energy for the past six weeks. He feels stressed at having to face his job, but is still going to work. Jerome admits trying to cope with disrupted sleep patterns by drinking more alcohol than usual. He is now drinking 3 bottles of beer every night instead of only twice per week as he used to. His physical examination is normal but he appears to be sad and apathetic.

1. What will be your approach to addressing Jerome’s anxiety and depression?

2. What assessment and screening tools will you use to support your diagnosis?

3. What might be the physiological causes of Jerome’s anxiety and depression?

4. Does Jerome fit into a DSM-5 category/classification?

5. What is your plan of care for Jerome? Please support with up-to-date evidence-based standard of care guidelines.

Benefits of a Paleolithic diet with and without
supervised exercise on fat mass, insulin sensitivity,
and glycemic control: a randomized controlled trial
in individuals with type 2 diabetes

Julia Otten1*†

Andreas Stomby1†

Maria Waling2

Andreas Isaksson3

Anna Tellström1

Lillemor Lundin-Olsson4

Søren Brage5

Mats Ryberg1

Michael Svensson3

Tommy Olsson1

1Department of Public Health and
Clinical Medicine, Medicine, Umeå
University, Umeå, Sweden
2Department of Food and Nutrition,
Umeå University, Umeå, Sweden
3Department of Community Medicine
and Rehabilitation, Sports Medicine
Unit, Umeå University, Umeå, Sweden
4Department of Community Medicine
and Rehabilitation, Physiotherapy,
Umeå University, Umeå, Sweden
5MRC Epidemiology Unit, University of
Cambridge, Cambridge, UK

*Correspondence to: Julia Otten,
Department of Public Health and
Clinical Medicine, Umeå University,
SE-90185 Umeå, Sweden.
E-mail: julia.otten@umu.se

†The authors Julia Otten and Andreas
Stomby have contributed equally to
this paper.

Abstract

Background Means to reduce future risk for cardiovascular disease in subjects
with type 2 diabetes are urgently needed.

Methods Thirty-two patients with type 2 diabetes (age 59±8 years) followed
a Paleolithic diet for 12 weeks. Participants were randomized to either standard
care exercise recommendations (PD) or 1-h supervised exercise sessions (aerobic
exercise and resistance training) three times per week (PD-EX).

Results For thewithin group analyses, fatmass decreased by 5.7 kg (IQR:�6.6,
�4.1; p<0.001) in the PD group and by 6.7 kg (�8.2,�5.3; p<0.001) in the PD- EX group. Insulin sensitivity (HOMA-IR) improved by 45% in the PD (p<0.001) and PD-EX (p<0.001) groups. HbA1c decreased by 0.9% (�1.2,�0.6; p<0.001) in the PD group and 1.1% (�1.7, �0.7; p<0.01) in the PD-EX group. Leptin decreased by 62% (p<0.001) in the PD group and 42% (p<0.001) in the PD-EX group. Maximum oxygen uptake increased by 0.2 L/min (0.0, 0.3) in the PD-EX group, and remained unchanged in the PD group (p<0.01 for the differ- ence between intervention groups). Male participants decreased lean mass by 2.6 kg (�3.6,�1.3) in the PD group and by 1.2 kg (�1.3, 1.0) in the PD-EX group (p<0.05 for the difference between intervention groups).

Conclusions A Paleolithic diet improves fat mass and metabolic balance in-
cluding insulin sensitivity, glycemic control, and leptin in subjects with type
2 diabetes. Supervised exercise training may not enhance the effects on these
outcomes, but preserves lean mass in men and increases cardiovascular fitness.
Copyright © 2016 John Wiley & Sons, Ltd.

Keywords type 2 diabetes; Paleolithic diet; diet intervention; exercise; glycosyl-
ated haemoglobin A; insulin sensitivity; leptin

Abbreviations LDL, low density lipoprotein; HDL, high density lipoprotein;
HOMA-IR, homeostatic model assessment of insulin resistance; NEFAs, non-
esterified fatty acids; PD, Paleolithic diet and general exercise recommendations;
PD-EX, Paleolithic diet with 3-h supervised exercise training per week; QUICKI,
quantitative insulin sensitivity check index; VO2max, maximal oxygen uptake

Introduction

Among patients with diabetes, cardiovascular disease is the primary cause of
death [1]. Thus, in this population, it is imperative to counteract cardiovascular

RESEARCH ARTICLE

Received: 27 November 2015
Revised: 6 May 2016
Accepted: 10 May 2016

DIABETES/METABOLISM RESEARCH AND REVIEWS
Diabetes Metab Res Rev 2017; 33: e2828.
Published online 30 June 2016 in Wiley Online Library (wileyonlinelibrary.com). DOI: 10.1002/dmrr.2828

risk factors, such as hyperglycemia, high blood pressure,
and dyslipidemia through diet, exercise, and drug treatment
[2]. Earlier studies suggested a Paleolithic diet had powerful
beneficial metabolic effects on obesity, as well as in type 2
diabetes [3,4]. This diet emphasizes a high intake of vegeta-
bles, fruit, nuts, eggs, fish, and lean meat, while excluding
refined sugar, salt, legumes, dairy products, and grains.

Additional metabolic effects beyond diet may be
achieved with structured exercise interventions [5,6].
The combination of diet interventions with energy restric-
tions and resistance training or aerobic exercise is benefi-
cial for body composition in non-diabetic subjects [7]. In
subjects with type 2 diabetes, the combination of aerobic
exercise with resistance training lowers HbA1c levels more
than either exercise modality separately [8]. To the best of
our knowledge, studies on Paleolithic diet combined with
resistance training and aerobic exercise have not been per-
formed in subjects with type 2 diabetes.

In the present study, subjects with type 2 diabetes con-
sumed a Paleolithic diet for 12 weeks, with or without super-
vised aerobic exercise and resistance training. Our hypothesis
was that exercise training would improve the beneficial ef-
fects of a Paleolithic diet on fat mass and metabolic balance
including insulin sensitivity, glycemic control, and leptin.

Materials and methods

Study design

We conducted a randomized controlled trial with two
arms: Paleolithic diet and standard care exercise recom-
mendations (PD) and Paleolithic diet with 1-h supervised
exercise sessions three times per week (PD-EX). In a sec-
ondary analysis, we included a non-randomized observa-
tional group as a reference.

Participants of the randomized
controlled trial

Subjects were recruited from the greater Umeå area of
Northern Sweden through advertisements in local newspa-
pers and posters at Umeå University Hospital. Recruitment
began in 2012, and the study was completed in June
2014.We included individuals diagnosed with type 2 diabe-
tes within the past 10 years, who had a BMI of 25–40 kg/m2

and were weight stable (i.e.<5%weight loss) for 6 months before study inclusion. Eligible males were 30–70 years old, while women were included after menopause and up to 70 years of age. All participants had HbA1c values between 6.5% and 10.8% (47–94 mmol/mol), and were using life- style modification and/or metformin for diabetes

treatment. Exclusion criteria were treatment with anti-
diabetic drugs other than metformin, use of beta-blockers,
blood pressure>160/100 mmHg, macroalbuminuria,
heart disease, and being a smoker. Because we aimed to
study the effect of exercise on sedentary individuals, we ex-
cluded those who reported more than 30 min of moderate
physical activity 5 days per week or resistance trainingmore
than once every other week during the past 6 months. Of
261 volunteers who were interested in participating, 32
met the inclusion criteria and were randomized into the
PD or PD-EX groups (Figure 1). All participants provided
written informed consent. The study protocol was in accord
with the Helsinki declaration, and was approved by the Re-
gional Ethical Review Board, Umeå, Sweden.

Randomization and blinding

Participants were assigned to the PD and PD-EX groups
using biassed coin minimization with an allocation ratio
of 1:1 [9]. To minimize marginal imbalance based on
the prognostic factors (sex and BMI above/below 30), par-
ticipants were sequentially allocated using a base proba-
bility of 0.9. The computer program MinimPy was used
for treatment allocation [10]. Randomization was con-
ducted after the baseline examinations. The study was
single-blinded, such that group allocation was unknown
to all staff that performed examinations and dietary
counselling. Additionally, the statistician who randomized
the participants and the research assistant who informed
the participants of the randomization outcome were not
involved in data collection or data analysis. The study
was unblinded after the analysis of results.

Diet intervention

Both randomized groups (PD and PD-EX) were intro-
duced to the Paleolithic diet after baseline examinations,
and were instructed to follow the diet until all study mea-
surements were completed. The diet was based on con-
suming lean meat, fish, seafood, eggs, vegetables, fruits,
berries, and nuts. Cereals, dairy products, legumes, re-
fined fats, refined sugars, and salt were excluded with
the exception of canned fish and cold cuts like ham. The
diet was consumed ad libitum, with restrictions of the fol-
lowing: eggs (1–2/day but a maximum of 5/week), pota-
toes (1 medium sized/day), dried fruit (130 g/day), and
nuts (60 g/day). Rapeseed or olive oil (maximum
15 g/day) and small amounts of honey and vinegar were
allowed as flavouring in cooking. Participants were
instructed to drink mainly still water. Coffee and tea were
restricted to a maximum of 300 g/day, and red wine to a
maximum of one glass/week.

2 of 11 J. Otten et al.

Each group participated separately in five group ses-
sions held by a trained dietician at the Department of
Food and Nutrition, Umeå University, Sweden. The first
two meetings were held during the first 2 weeks, and
the following meetings took place once a month. The par-
ticipants received information about the diet and cooked
food and were given recipes. Between the meetings, the
participants could contact the dietician, who held the
meetings by e-mail or phone.

Exercise intervention

Prior to randomization, both intervention groups received
exercise recommendations based on the current guidelines
for patients with type 2 diabetes. Thus, all study partici-
pants were advised to performmoderate exercise (e.g. brisk
walking) for at least 30 min every day. The PD-EX group
underwent a program comprising a combination of aerobic
exercise and resistance training in 1-h sessions three times
weekly at the Sports Medicine unit at Umeå University.
The exercise sessions were performed on weekdays, with
at least 1 day of rest between sessions. They were super-
vised by experienced personal trainers with bachelor’s de-
grees in Sports Medicine. The training protocol had a
progressive design in accord with the guidelines of the
American College of Sports Medicine [11].

All exercise sessions started with aerobic exercise. The
first session of each week consisted of low-intensity aerobic

training at 70% of the maximum heart rate on a cross-
trainer (Monark Prime, XT 50, Vansbro, Sweden). The
second session of the week consisted of ten high-intensity
sprint intervals at 100% of the maximal workload on a
cycle-ergometer (Monark, Ergomedic 839E, Vansbro, Swe-
den), with low-intensity cycling between the sprints. The
third session of each week comprised six moderate-intensity
5-min intervals between 45 and 60% of maximal workload
on a cycle-ergometer. The duration/workload of the inter-
vals increased every other week.When necessary, the inten-
sity of the aerobic exercise sessions was adjusted in accord
with the participant’s performance.

After the aerobic exercise, the sessions progressed to re-
sistance training with both upper and lower body exer-
cises, including leg presses, seated leg extensions, leg
curls, hip raises, flat and incline bench presses, seated
rows, dumbbell rows, lat pull-downs, shoulder raises,
back extensions, burpees, sit-ups, step-ups, and wall ball
shots. At each training session, the participant performed
three to five of the aforementioned resistance exercises,
with 10–15 repetitions and two to four sets. Once partici-
pants could complete all repetitions, the workload was in-
creased for the following session.

Measurements

At baseline and at 12 weeks, dietary intake was assessed
using a 4-day self-reported weighed food record. Each

Figure 1. CONSORT flow diagram

Paleolithic Diet in Type 2 Diabetes 3 of 11

4-day food record period included one or two weekend
days. Participants were instructed to weigh all food,
beverages, and leftovers. Any uncertainties regarding the
food records were clarified during meetings, via e-mail,
or by phone. A trained dietician converted the reported
food intake into estimated energy and nutrient intake
using the nutritional analysis software Dietist XP version
3.2 (Kost och Näringsdata AB, Bromma, Sweden), based
on the Swedish National Food Administration’s food
database.

Participants were examined at baseline and after
12 weeks of the intervention by experienced physicians
and nurses at the Clinical Research Center at Umeå Univer-
sity Hospital, Umeå, Sweden. Resting energy expenditure
was measured using indirect calorimetry (Datex-Ohmeda
Deltatrac II; Datex-Ohmeda Inc., Madison, WI, USA) and
adjusted by subtracting 5% during 8 h of sleep. Daily physi-
cal activity energy expenditure over a 7-day period was
estimated using data from a combined accelerometer and
heart rate monitor (Actiheart®; CamNtech Ltd.,
Cambridge, UK) [12], modelled as described previously
[13–15]. Diet-induced thermogenesis was fixed at 10% of
the total energy expenditure. Total energy expenditure
was calculated as the sum of the resting energy expenditure
and physical activity expenditure plus 10%.

Fat mass (i.e. the primary outcome) and lean mass
were analysed by dual-energy X-ray absorptiometry
(Lunar Prodigy X-ray Tube Housing Assembly, Brand
BX-1 L, Model 8743; GE Medical Systems, Madison, WI,
USA). The participants were weighed on a digital cali-
brated scale, wearing light clothing. Height was measured
with a calibrated height-measuring gauge. Waist circum-
ference was assessed with a measuring tape placed
midway between the lowest rib and iliac crest during
gentle exhalation. The abdomen height was measured at
the umbilicus level with the participant lying down with
straight legs.

An automated blood pressure meter (Boso Medicus,
Bosch, Germany) was used to measure systolic and diastolic
blood pressure from the right arm with the participant in a
sitting position. Measurements were made twice at 2-min
intervals, after 5 min of rest. Fasting venous blood samples
were collected from patients in the intervention groups for
analysis of HbA1c, serum insulin, serum cholesterol, high
density lipoprotein (HDL), serum triglycerides, and plasma
high-sensitivity C-reactive protein at the Department for
Clinical Chemistry, Umeå University Hospital. We analysed
fasting glucose from a capillary sample (HemoCue 201 RT;
Radiometer Medical Aps, Brønshøj, Denmark). Aliquots of
plasma were immediately stored at �80 °C for analysis of
non-esterified fatty acids (NEFAs), adiponectin, and leptin
after study completion. The NEFAs were analysed with
NEFA-HR2 (Wako Chemicals, Neuss, Germany),
adiponectin with the Human Adiponectin ELISA Kit, and

leptin with the Human Leptin ELISA Kit, both from Merck
Millipore (Darmstadt, Germany). Insulin sensitivity was
calculated as follows: homeostatic model assessment of
insulin resistance (HOMA-IR)=(fasting glucose× fasting
insulin) / 22.5 and the revised quantitative insulin sensitiv-
ity check index (Revised QUICKI)=1/ (log fasting glucose
+ log fasting insulin+ log NEFA) [16,17]. The low density
lipoprotein (LDL) was calculated as follows: (serum
cholesterol� serum HDL� serum triglycerides) / 2.2. The
maximal oxygen uptake (VO2max) and maximal workload
were measured via cardiopulmonary exercise test at the De-
partment of Clinical Physiology, Umeå University Hospital,
Umeå, Sweden.

Observational group

For a secondary analysis, we recruited an observational
group by advertisement in local newspapers and among
those who were excluded from the intervention because
of a lack of time, beta-blocker use, and cardiovascular
disease. Nine individuals were included in the observa-
tional group, one of whom could not attend the assess-
ments at the end of the intervention period because of
illness. Fasting glucose, fasting insulin, HbA1c, leptin,
adiponectin, and blood lipids were analysed from venous
blood samples. Body composition, weight, blood pressure,
dietary intake, and physical activity energy expenditure
were examined as described above.

Sample size and statistical analysis

The primary outcome in this study was the change in fat
mass. Based on previous results from a similar study
[18], we calculated that 13 individuals in each interven-
tion group would be sufficient to detect a significant
difference (p<0.05) with 80% power. Because several variables had a skewed distribution, the Wilcoxon rank- sum test was used to compare groups. All data were reported as medians with the interquartile range. The pri- mary analysis compared treatment effects (change from 0 to 12 weeks) between the PD and PD-EX groups. The change over time within each intervention group was determined using the Wilcoxon signed-rank test. The sec- ondary analysis compared the treatment effect (change from 0 to 12 weeks) in each intervention group with the observational group. A two-sided p value of <0.05 was considered statistically significant. All statistical analyses were performed using R version 3.1.1, a language and environment for statistical computing (R Foundation for Statistical Computing, Vienna, Austria).

4 of 11 J. Otten et al.

Results

Subject characteristics

The participants’ baseline characteristics are presented in
Tables 1 and 2. The randomized groups did not differ in
age, sex, BMI, or diabetes duration. The PD-EX group
had higher fasting glucose and HDL levels than the PD
group. During the course of the study, one participant in
the PD group stopped his metformin treatment, two par-
ticipants in the PD group stopped their blood pressure
medication, and one participant in the PD group started
antihypertensive treatment.

Compliance with the diet and
supervised exercise program

Dietary intake did not differ between the groups at base-
line and 12 weeks, except for a higher fibre intake in the
PD-group at 12 weeks (Table 3). Both groups increased
their relative intake of protein and their intake of mono-
unsaturated and polyunsaturated fatty acids. Both groups
lowered their intake of carbohydrates and saturated fatty
acids. The reduction of sodium intake was only significant
in the PD-EX group. Nine of the 14 participants in the PD-
EX group completed the 36 exercise sessions according to
the study protocol. The remaining five participants com-
pleted between 27 and 35 workouts during the study pe-
riod. The participants in the PD-EX group increased the
cumulative weight load (weight× repetitions× sets) with
the leg press during one exercise session from 1350 kg
(900–1800) to 3000 kg (2700–4000) after 12 weeks.

Energy balance

At baseline, energy intake (kcal/day) and total energy ex-
penditure (kcal/day) did not differ between groups
(Table 2). Baseline energy intake in the PD group was
1112 kcal/day (�1434, �609) less than the total energy
expenditure. In the PD-EX group, baseline energy intake
was 1340 kcal/day (�1909, �778) less than the total en-
ergy expenditure. Energy intake decreased in both groups
during the intervention (Table 2). Furthermore, total en-
ergy expenditure decreased in the PD group (p<0.05), but remained stable in the PD-EX-group (p=0.17, Table 2). This was caused by a decrease in the resting en- ergy expenditure, while the physical activity energy ex- penditure was unchanged. At the end of the study, the PD group reported an energy intake that was 1245 kcal/day (�1480, �905) less than the total energy expenditure; while, the energy intake of the PD-EX group was 1657 kcal/day (�2533, �881) less than total energy expenditure.

Body composition

Fat mass decreased during the study in both the PD and
PD-EX groups (Figure 2). Both groups also showed de-
creases in body weight, abdominal height, and waist cir-
cumference, without differences between intervention
groups (Table 2). Male participants decreased their waist
circumference more in the PD-group compared to the PD-
EX group (p<0.05, Supplementary Table S2). Males in the PD-EX group retained more lean mass than males in the PD-group (p<0.05, Supplementary Table S2).

Glucose metabolism

Insulin sensitivity and glycemic control improved in both
groups, without a difference between groups. The
HOMA-IR and revised QUICKI improved in both interven-
tion groups (Figure 2, Table 2), and the HbA1c decreased
during the study in both the PD group (19%) and the PD-
EX group (20%, Table 2).

Cardiovascular fitness

Resting heart rate decreased more in the PD-EX group
than the PD group (Table 2). The VO2max and the ergom-
eter cycling workload increased during the study in the
PD-EX group, but not in the PD group (Figure 2).

Table 1. Participants’ baseline characteristics

Paleolithic diet
(n=15)

Paleolithic diet
+ exercise (n=14)

Age (years) 60 (53–64) 61 (58–66)
Men/women (n) 10/5 9/5
Diabetes duration (years) 3 (1–5) 5.5 (1–8)
BMI (kg/m2) 31.4 (29.4–33.1) 31.7 (29.2–35.4)
Diabetes treatment (n)
Diet only 5 4
Metformin 10 10
Other treatment (n)
ACEI/ARB 9 10
Diuretic 6 5
Calcium-channel blocker 4 5
Statin 6 8
Antiplatelet drug 2 3
Other 8 2

Data are reported as the median (interquartile range).
Abbreviations: ACEI, ACE inhibitor; ARB, angiotensin receptor
blocker.

Paleolithic Diet in Type 2 Diabetes 5 of 11

Table 2. Energy balance, body composition, and cardiovascular risk factors during 12 weeks of intervention

Paleolithic diet (n=15) Paleolithic diet + exercise (n=14)

Energy balance
Energy intake (kcal/day)
Baseline 2022 (1583–2268) 1595 (1428–2257)
Change 0–12 weeks �291 (�587, �66)## �530 (�863, �157)###

Physical activity energy expenditure (kcal/day)
Baseline 1022 (904–1319) 997 (806–1568)
Change 0–12 weeks �28 (�208, 30) �18 (�368, 340)

Physical activity energy expenditure (kcal/kg/day)
Baseline 11.6 (10.6–13.0) 10.1 (9.2–16.6)
Change 0–12 weeks 0.1 (�1.5, 2.1) 0.6 (�2.9, 4.8)

Resting energy expenditure (kcal/day)
Baseline 1620 (1463–1719) 1709 (1319–1883)
Change 0–12 weeks �120 (�157, �71)### �89 (�164, �44)##

Resting energy expenditure (kcal/kg/day)
Baseline 17.0 (16.4–17.6) 16.7 (15.7–18.6)
Change 0–12 weeks 0.0 (�0.4, 0.8) 0.5 (�0.3, 1.4)

Total energy expenditure (kcal/day)
Baseline 2995 (2754–3356) 2960 (2433–3855)
Change 0–12 weeks �227 (�307, �91)# �312 (�562, 122)

Weight (kg)
Baseline 90.0 (83.3–100.8) 97.3 (83.9–110.3)
Change 0–12 weeks �7.1 (�9.7, �6.3)### �7.1 (�8.7, �6.2)###

Body composition
Body fat (%)
Baseline 37.8 (33.1–40.8) 37.7 (34.7–43.1)
Change 0–12 weeks �3.5 (�4.4, �2.6)### �4.1 (�5.8, �3.4)###

Lean mass (kg)
Baseline 56.5 (49.0–63.8) 61.0 (44.1–66.8)
Change 0–12 weeks �1.4 (�3.3, �1.2)### �1.2 (�1.4, �0.2)

Waist circumference (cm)
Baseline 111 (105–116) 108 (104–115)
Change 0–12 weeks �9 (�12, �7)### �8 (�10, �7)###

Abdominal height (cm)
Baseline 27.2 (25.0–29.0) 26.5 (23.0–29.8)
Change 0–12 weeks �3.6 (�4.7, �2.5)### �3.0 (�4.3, �1.1)###

Glucose metabolism
HbA1c (%)
Baseline 7.1 (6.5–7.3) 7.3 (6.8–7.6)
Change 0–12 weeks �0.9 (�1.2, �0.6)### �1.1 (�1.7, �0.7)##

HbA1c (mmol/mol)
Baseline 54 (48–57) 57 (51–60)
Change 0–12 weeks �10 (�13, �6)### �12 (�19, �8)##

Fasting glucose (mmol/L)
Baseline 8.0 (7.2–8.4) 8.9 (7.9–10.5)*
Change 0–12 weeks �0.9 (�1.7, �0.2)# �2.0 (�3.2, �1.1)##

Fasting insulin (mIU/L)
Baseline 23 (15–30) 16 (11–20)
Change 0–12 weeks �8 (�16, �3)## �4 (�8, �2)###

Revised QUICKI
Baseline 0.223 (0.192–0.227) 0.207 (0.196–0.224)
Change 0–12 weeks 0.027 (0.004, 0.054)## 0.041 (0.031, 0.054)###

Cardiovascular fitness
VO2max (mL/kg/min)
Baseline 23.4 (21.5–27.0) 22.5 (21.0–25.2)
Change 0–12 weeks 1.9 (0.6, 2.9)### 3.3 (2.7, 6.2)###*

Resting heart rate (bpm)
Baseline 70 (65–78) 72 (66–77)
Change 0–12 weeks �3 (�8, 1) �11 (�12, �7)#*

Blood pressure
Systolic (mmHg)
Baseline 135 (127–148) 132 (122–143)
Change 0–12 weeks �17 (�24, 0)## �11 (�14, �7)###

Diastolic (mmHg)
Baseline 86 (82–94) 82 (74–91)
Change 0–12 weeks �9 (�15, �6)### �10 (�13, �7)###

(Continues)

6 of 11 J. Otten et al.

Blood pressure and blood lipids

Blood pressure decreased during the study in both interven-
tion groups without any group difference: systolic, 13% in
PD and 8% in PD-EX; diastolic, 10% in PD and 12% in PD-
EX (Table 2). Triglycerides decreased in both study groups
between baseline and 12 weeks; while, the HDL, LDL, and
NEFA levels remained unchanged throughout the interven-
tion (Table 2).

Adipokines

Leptin decreased in both the PD group (62%) and the PD-
EX group (42%) (Table 2). Adiponectin increased in the
PD group (8%) compared with the PD-EX group (Table 2).

Intervention groups (PD and PD-EX)
versus the observational group

There were no significant differences in baseline characteris-
tics between the observational group and the PD and PD-EX
intervention groups (Supplementary Table S1). Compared
with the observational group, the PD and PD-EX groups de-
creased their total energy intakes and intakes of carbohy-
drates and saturated fatty acids, but increased their relative

intake of protein and monounsaturated fatty acids during
the 12 weeks of intervention (Supplementary Tables S2
and S3). The intervention groups improved fat mass
(p<0.001), HOMA-IR, fasting insulin, HbA1c, systolic and diastolic blood pressure, and leptin comparedwith the obser- vational group (Supplementary Table S2). Triglycerides did not decrease in the intervention group compared with the observational group (Supplementary Table S2).

Discussion

Twelve weeks on a Paleolithic diet improved fat mass and
metabolic balance including insulin sensitivity, glycemic
control and leptin among individuals with type 2 diabetes.
The addition of resistance training and aerobic exercise un-
der observation increased cardiovascular fitness, without
further improvements in fat mass or glycemic control. The
observed effects of the Paleolithic diet were substantial.
The lowering of HbA1c by 0.9% units was an effect size sim-
ilar to that reported withmetformin in type 2 diabetes [19].
A previous study demonstrated that the Paleolithic diet re-
duced HbA1c by 0.4%units more than a conventional diabe-
tes diet [4]. The UK prospective diabetes study stated that a
1% unit improvement of HbA1c reduces microvascular com-
plications by 37% and reduces diabetes-related death by
21% [20]. Thus, if sustained over time, the improvement

Table 2. (continued)

Paleolithic diet (n=15) Paleolithic diet + exercise (n=14)

Blood lipids
Total cholesterol (mmol/L)
Baseline 4.2 (3.4–4.7) 4.3 (4.1–4.9)
Change 0–12 weeks �0.3 (�0.6, 0.1) �0.6 (�0.6, �0.4)##

Triglycerides (mmol/L)
Baseline 2.1 (1.4–2.9) 1.7 (1.1–2.4)
Change 0–12 weeks �0.6 (�1.5, �0.2)## �0.5 (�1.0, �0.2)###

HDL (mmol/L)
Baseline 0.85 (0.81–0.99) 1.09 (0.98–1.21)**
Change 0–12 weeks �0.01 (�0.08, 0.05) 0.01 (�0.03, 0.07)

LDL (mmol/L)
Baseline 2.1 (1.8–2.7) 2.4 (2.0–3.0)
Change 0–12 weeks �0.1 (�0.4, 0.2) �0.1 (�0.5, 0.1)

NEFA (μmol/L)
Baseline 599 (560–756) 826 (670–922)
Change 0–12 weeks 26 (�12, 173) �56 (�128, 117)

Adipokines
Leptin (ng/mL)
Baseline 13.8 (6.4–26.5) 13.3 (7.2–16.7)
Change 0–12 weeks �8.5 (�12.2, �2.6)### �5.6 (�9.4, �3.5)###

Adiponectin (ng/mL)
Baseline 4685 (2942–5939) 4864 (4004–6268)
Change 0–12 weeks 379 (213, 717)## 5 (�256, 282)*

High-sensitivity CRP (mg/L)
Baseline 1.2 (0.7–1.9) 1.5 (0.7–2.5)
Change 0–12 weeks �0.4 (�1.1, 0.0) �0.4 (�0.9, 0.0)##

Data are reported as the median (interquartile range); *p< 0.05, **p< 0.01 between the Paleolithic diet group and the Paleolithic diet+ exercise group; #p< 0.05, ##p< 0.01, ###p< 0.001 for the change over time from baseline to 12 weeks within the group. Abbreviation: CRP, C-reactive protein.

Paleolithic Diet in Type 2 Diabetes 7 of 11

in glycemic control will provide large benefits in terms of
morbidity and mortality.

These powerful observed effects of the Paleolithic diet
may be explained by altered dietary patterns. The partici-
pants reported reduced intakes of carbohydrates and satu-
rated fatty acids, with relatively higher intakes of protein,
as well as monounsaturated and polyunsaturated fatty

acids. The reduction of carbohydrates with a high glycemic
index may be an important part of the beneficial effects of
this diet [21]. Furthermore, increased intake of monounsat-
urated fat may reduce postprandial hyperglycemia [22].

Supervised training with aerobic exercise in combina-
tion with resistance training did not improve glycemic
control and insulin sensitivity beyond the improvements
observed with the Paleolithic diet alone. This was unex-
pected, as exercise training has previously been shown
to improve glycemic control substantially, particularly
when combining resistance training with aerobic exercise
for more than 150 min per week [6,23,24]. Among indi-
viduals with type 2 diabetes, structured exercise interven-
tions reduce HbA1c levels by about 0.6% units without
weight changes [6]. In contrast, the addition of resistance
or aerobic exercise to short-term (16 weeks) dietary inter-
ventions with a calorie-restricted high-protein diet or a
very low-calorie diet had limited additive effects on insu-
lin sensitivity and glycemic control in patients with type
2 diabetes [18,25]. Notably, our diet recommendations
were given ad libitum without any restrictions of caloric
intake. However, it is possible that the catabolic state
caused by decreased energy intake may have masked
any potential effects of exercise on glycemic control and
insulin sensitivity. Moreover, several study participants
were using metformin or statins on a daily basis. These
drugs may blunt the positive effects of exercise [26–28].

Importantly, cardiovascular fitness improved signifi-
cantly in the PD-EX group compared with the PD group.
Low cardiorespiratory fitness is a strong risk factor for
all-cause mortality, independent of glycemic status and
other cardiovascular risk factors [29,30]. A large cohort
study showed that an increase of 1.44 mL/kg/min in
VO2max (equivalent to a 1-min increase in the Balke pro-
tocol treadmill time) corresponded to a 7.9% reduction in
overall mortality [31]. Using these data, the presently ob-
served increase of 3.3 mL/kg/min in VO2max would lead
to an 18% reduction in all-cause mortality if changes can
be sustained over time. Furthermore, it is of major interest
to study if tissue-specific insulin sensitivity is influenced
differently between groups. We demonstrated earlier that
weight reduction by a Paleolithic diet had a profound
effect on liver insulin sensitivity, while peripheral insulin
sensitivity was unaltered [32]. The exercise intervention
would be expected to add an increased muscular
(peripheral) sensitivity. This is of interest because insulin
resistance in skeletal muscle can play a key role in the de-
velopment of metabolic complications in obesity-related
disorders, including type 2 diabetes [33].

The combination of aerobic and resistance training is
known to preserve or even increase lean mass during diet
intervention [34]. In our study male participants in the
PD-EX group lost less lean mass compared to males in
the PD group. This difference was not significant if men

Table 3. Dietary intake

Paleolithic
diet (n=14)

Paleolithic diet +
exercise (n=13)

Protein (g/day)
Baseline 83 (72–99) 77 (67–106)
12 weeks 96 (80–111) 79 (58–100)

Carbohydrate (g/day)
Baseline 200 (160–262) 169 (152–197)
12 weeks 127 (93–158)### 77 (71–102)###

Total fat (g/day)
Baseline 88 (66–102) 67 (48–94)
12 weeks 71 (56–97) 61 (47–79)

Protein (E%)
Baseline 17 (15–19) 18 (17–20)
12 weeks 24 (19–27)### 26 (22–29)###

Carbohydrate (E%)
Baseline 41 (37–45) 42 (33–48)
12 weeks 31 (24–39)## 27 (24–29)###

Total fat (E%)
Baseline 39 (37–40) 34 (31–41)
12 weeks 42 (37–48) 45 (37–47)#

Saturated fatty acids (E%)
Baseline 15.3 (13.4–16.8) 13.3 (12.1–17.1)
12 weeks 9.6 (7.9–11.6)### 8.8 (8.7–10.7)###

Monounsaturated fatty acids (E%)
Baseline 14 (14–17) 12 (11–15)
12 weeks 20 (16–24)# 23 (20–24)##

Polyunsaturated fatty acids (E%)
Baseline 5.0 (4.8–6.4) 5.4 (4.0–6.3)
12 weeks 7.9 (6.6–8.7)# 8.4 (6.8–9.7)##

Saturated fatty acids (g/day)
Baseline 34 (24–44) 27 (20–35)
12 weeks 15 (12–23)### 14 (10–17)###

Monounsaturated fatty acids (g/day)
Baseline 31 (26–38) 27 (17–36)
12 weeks 36 (25–52) 28 (23–40)

Polyunsaturated fatty acids (g/day)
Baseline 12 (10–13) 10 (7–14)
12 weeks 15 (10–19) 13 (7–16)

Omega-3 fatty acids (g/day)
Baseline 2.3 (2.0–3.1) 2.2 (1.4–2.9)
12 weeks 2.4 (1.3–4.6) 2.7 (1.3–2.9)

Omega-6 fatty acids (g/day)
Baseline 10.9 (8.6–12.0) 8.0 (6.7–10.8)
12 weeks 11.8 (7.5–16.1) 10.6 (6.1–13.8)

Dietary cholesterol (mg/day)
Baseline 315 (213–364) 324 (206–526)
12 weeks 531 (390–686)## 510 (399–632)

Dietary fibre (g/day)
Baseline 21 (18–26) 20 (18–22)
12 weeks 23 (15–30) 14 (13–17)##*

Sodium (mg/day)
Baseline 3051 (2610–3863) 3003 (2449–4097)
12 weeks 2119 (1745–2843) 1789 (1223–2786)#

Data are reported as the median (interquartile range); *p< 0.05 between the Paleolithic diet group and the Paleolithic diet+ exer- rcise group; #p< 0.05, ##p< 0.01, ###p< 0.001 for the change over time from baseline to 12 weeks within the group.

8 of 11 J. Otten et al.

and women were analysed together. Notably, a recent
study showed that 12 weeks of exercise increased lean
mass in obese males, but not in women [35].

In line with earlier studies, another beneficial effect of
the Paleolithic diet is a major reduction in blood pressure
[3,4,32]. The combination of weight reduction and re-
duced sodium intake may be important for this effect.
The reduced triglyceride levels, in both study groups, are
also consistent with earlier studies. Previous studies
showed that a Paleolithic diet decreased triglycerides even
more than a consensus diet [3,4], but exercise under su-
pervision did not significantly improved blood lipids [6].

Leptin levels decreased 62% in the PD group and 42% in
the PD-EX group. Compared to other diet interventions this
is a powerful reduction relative to the weight loss of 7.1 kg
[36,37]. This is in line with a study of individuals with is-
chemic heart disease where a Paleolithic diet for 12 weeks
reduced leptin more relative to the amount of weight loss
than a Mediterranean-like diet [38]. These beneficial ef-
fects on leptin levels are of major importance because
hyperleptinemia increases inflammation [39,40] and is an
independent risk factor for cardiovascular events [41–43].

The increased adiponectin levels in the PD group may
relate to weight loss, with increased protein intake as a
contributing factor [44,45]. The unaltered hormone

levels in the PD-EX group may indicate the increased
plasma volume because of physical activity [46].

In a secondary analysis, we compared the non-
randomized observational group with the intervention
groups. The intervention groups improved their anthropo-
metric status and metabolic balance versus the observa-
tional group, except for triglycerides, which improved in
the observational group. Notably, the observational group
was not randomized and therefore, we cannot guarantee
equal distribution of confounding factors between the inter-
vention groups and the observational group. Furthermore,
some participants in the observational group suffered from
cardiovascular disease, used beta-blockers, and did not
have the time to participate in the interventions.

A strength of the present study is that energy intake was
validated with objectively measured total energy expendi-
ture. Differences between reported energy intake and mea-
sured total energy expenditure at baselinemight be because
of undereating, underreporting, overestimation of physical
activity energy expenditure, and/or increased physical ac-
tivity during the measurement period. Participants may
have started changing their dietary intake and physical ac-
tivity during the baseline-measuring period, even though
they were not introduced to the intervention part of the
study until baseline measurements were finished. Despite

Figure 2. Fat mass (a), insulin sensitivity (b), and cardiovascular fitness (c and d) during 12 weeks following either a Paleolithic diet
with a supervised exercise program (PD-EX) or a Paleolithic diet combined with general exercise recommendations (PD). Boxes rep-
resent medians and IQRs, whiskers represent the most extreme values besides outliers, and filled circles represent outliers (>1.5
IQR); **p< 0.01, ***p< 0.001

Paleolithic Diet in Type 2 Diabetes 9 of 11

the validation of energy intake, it is a weakness of the study
that it is not known to what degree the participants actually
followed the Paleolithic diet.

Based on our results, we conclude that the Paleolithic diet
is a powerful tool to improve fatmass andmetabolic balance
including insulin sensitivity, glycemic control, and leptin in
individuals with type 2 diabetes. Supervised exercise train-
ing did not provide additional effects on these outcomes,
but preserved lean mass in men and increased cardiovascu-
lar fitness. Detailed analyses of tissue-specific effects of
these interventions, including putative effects on hepatic
versusmuscle insulin sensitivity, are of further interest.

Acknowledgements

The authors gratefully acknowledge the study participants. We
thank the research nurses Inger Arnesjö, Katarina Iselid, Lena
Uddståhl, Camilla Ring, and Liv-Helene Bergman for their skillful
technical assistance. We thank Marie Eriksson of the Department
of Statistics, Umeå University, Sweden, for the randomization
and Magnus Hedström of the Heart Centre, Umeå University
Hospital, Sweden for planning and interpretation of the cardio-
pulmonary exercise test. We thank laboratory technician Kristina
Eriksson for adiponectin and leptin analysis and Kate Westgate
(MRC Epidemiology Unit, University of Cambridge, UK) for assis-
tance with physical activity data processing.

This study was supported by grants from the Swedish Heart
and Lung Foundation (20120450), King Gustav V and Queen Vic-
toria’s Foundation, The Swedish Diabetes Research Foundation
(2014-096), the County Council of Västerbotten (VLL-460481),
and Umeå University, Sweden.

Author contributions

JO and AS designed the study, recruited participants,
collected the data, performed the statistical analysis, and
wrote the manuscript. MW designed the study, imple-
mented the dietary intervention, and analysed the data.
AI conducted the exercise intervention and analysed the
data. AT conducted the dietary intervention and analysed
the data. LLO and MS designed the study and interpreted
the data. SB analysed and interpreted the Actiheart data.
MR designed the study, recruited participants, collected
the data, and edited the manuscript. TO designed the
study, interpreted the data, and wrote the manuscript.
All authors actively participated in revising the article
and approved the final version. JO is the guarantor of this
work and takes responsibility for the integrity of the data
and the accuracy of the data analysis.

Conflicts of interest

The authors declare that there is no duality of interest as-
sociated with this manuscript.

Clinical trial registration number

ClinicalTrials.gov NCT01513798

References

1. Gregg EW, Cheng YJ, Saydah S, et al.
Trends in death rates among U.S. adults
with and without diabetes between
1997 and 2006: findings from the Na-
tional Health Interview Survey. Diabetes
Care 2012; 35(6): 1252–7.

2. Authors/Task Force M, Ryden L, Grant
PJ, et al. ESC Guidelines on diabetes,
pre-diabetes, and cardiovascular dis-
eases developed in collaboration with
the EASD: the Task Force on diabetes,
pre-diabetes, and cardiovascular dis-
eases of the European Society of Car-
diology (ESC) and developed in
collaboration with the European Associ-
ation for the Study of Diabetes (EASD).
Eur Heart J 2013; 34(39): 3035–87.

3. Mellberg C, Sandberg S, Ryberg M, et al.
Long-term effects of a Palaeolithic-type
diet in obese postmenopausal women:
a 2-year randomized trial. Eur J Clin
Nutr 2014; 68: 350–7.

4. Jonsson T, Granfeldt Y, Ahren B, et al.
Beneficial effects of a Paleolithic diet on
cardiovascular risk factors in type 2

diabetes: a randomized cross-over pilot
study. Cardiovasc Diabetol 2009; 8: 35.

5. Snowling NJ, Hopkins WG. Effects of dif-
ferent modes of exercise training on glu-
cose control and risk factors for
complications in type 2 diabetic pa-
tients: a meta-analysis. Diabetes Care
2006; 29(11): 2518–27.

6. Thomas DE, Elliott EJ, Naughton GA. Exer-
cise for type 2 diabetes mellitus. Cochrane
Database Syst Rev 2006; 3: CD002968.

7. Layman DK, Evans E, Baum JI, Seyler J,
Erickson DJ, Boileau RA. Dietary protein
and exercise have additive effects on body
composition during weight loss in adult
women. J Nutr 2005; 135(8): 1903–10.

8. Church TS, Blair SN, Cocreham S, et al.
Effects of aerobic and resistance training
on hemoglobin A1c levels in patients with
type 2 diabetes: a randomized controlled
trial. JAMA 2010; 304(20): 2253–62.

9. Pocock SJ, Simon R. Sequential treatment
assignment with balancing for prognostic
factors in the controlled clinical trial. Bio-
metrics 1975; 31(1): 103–15.

10. Saghaei M, Saghaei S. Implementation
of an open-source customizable minimi-
zation program for allocation of patients
to parallel groups in clinical trials. J
Biomed Sci Eng 2011; 4: 734–9.

11. Garber CE, Blissmer B, Deschenes MR,
et al. American College of Sports Medicine
position stand. Quantity and quality of
exercise for developing and maintaining
cardiorespiratory, musculoskeletal, and
neuromotor fitness in apparently healthy
adults: guidance for prescribing exercise.
Med Sci Sports Exerc2011;43(7): 1334–59.

12. Brage S, Brage N, Franks PW, Ekelund
U, Wareham NJ. Reliability and validity
of the combined heart rate and move-
ment sensor Actiheart. Eur J Clin Nutr
2005; 59(4): 561–70.

13. Brage S, Ekelund U, Brage N, et al. Hier-
archy of individual calibration levels for
heart rate and accelerometry to measure
physical activity. J Appl Physiol (1985)
2007; 103(2): 682–92.

14. Stegle O, Fallert SV, MacKay DJ, Brage
S. Gaussian process robust regression

10 of 11 J. Otten et al.

http://ClinicalTrials.gov

for noisy heart rate data. IEEE Trans
Biomed Eng 2008; 55(9): 2143–51.

15. Brage S,Westgate K,Wijndaele K, Godinho
J, Griffin S, Wareham N (2013) Evaluation
of a method for minimizing diurnal infor-
mation bias in objective sensor data. In:
3rd International Conference on Ambula-
tory Monitoring of Physical Activity and
Movement, Amherst, Massachusetts, USA

16. Matthews DR, Hosker JP, Rudenski AS,
Naylor BA, Treacher DF, Turner RC. Ho-
meostasis model assessment: insulin re-
sistance and beta-cell function from
fasting plasma glucose and insulin con-
centrations in man. Diabetologia 1985;
28(7): 412–9.

17. Perseghin G, Caumo A, Caloni M,
Testolin G, Luzi L. Incorporation of the
fasting plasma FFA concentration into
QUICKI improves its association with in-
sulin sensitivity in nonobese individuals.
J Clin Endocrinol Metab 2001; 86(10):
4776–81.

18. Wycherley TP, Noakes M, Clifton PM,
Cleanthous X, Keogh JB, Brinkworth
GD. A high-protein diet with resistance
exercise training improves weight loss
and body composition in overweight
and obese patients with type 2 diabetes.
Diabetes Care 2010; 33(5): 969–76.

19. Campbell IW, Howlett HC. Worldwide
experience of metformin as an effective
glucose-lowering agent: a meta-
analysis. Diabetes Metab Rev 1995; 11
(Suppl 1): S57–62.

20. Stratton IM, Adler AI, Neil HA, et al. Asso-
ciation of glycaemia with macrovascular
and microvascular complications of type
2 diabetes (UKPDS 35): prospective ob-
servational study. BMJ 2000; 321
(7258): 405–12.

21. Manheimer EW, van Zuuren EJ,
Fedorowicz Z, Pijl H. Paleolithic nutri-
tion for metabolic syndrome: systematic
review and meta-analysis. Am J Clin
Nutr 2015; 102(4): 922–32.

22. O’Keefe JH, Gheewala NM, O’Keefe JO.
Dietary strategies for improving post-
prandial glucose, lipids, inflammation,
and cardiovascular health. J Am Coll
Cardiol 2008; 51(3): 249–55.

23. Sigal RJ, Kenny GP, Wasserman DH,
Castaneda-Sceppa C, Physical activity/
exercise and type 2 diabetes. Diabetes
Care 2004; 27(10): 2518–39.

24. Sigal RJ, Kenny GP, Boule NG, et al. Ef-
fects of aerobic training, resistance train-
ing, or both on glycemic control in type

2 diabetes: a randomized trial. Ann In-
tern Med 2007; 147(6): 357–69.

25. Snel M, Gastaldelli A, Ouwens DM, et al.
Effects of adding exercise to a 16-week
very low-calorie diet in obese, insulin-
dependent type 2 diabetes mellitus pa-
tients. J Clin Endocrinol Metab 2012; 97
(7): 2512–20.

26. Malin SK, Gerber R, Chipkin SR, Braun
B. Independent and combined effects of
exercise training and metformin on insu-
lin sensitivity in individuals with predia-
betes. Diabetes Care 2012; 35(1): 131–6.

27. Sharoff CG, Hagobian TA, Malin SK, et al.
Combining short-term metformin treat-
ment and one bout of exercise does not in-
crease insulin action in insulin-resistant
individuals.Am J Physiol Endocrinol Metab
2010; 298(4): E815–23.

28. Mikus CR, Boyle LJ, Borengasser SJ,
et al. Simvastatin impairs exercise train-
ing adaptations. J Am Coll Cardiol
2013; 62(8): 709–14.

29. Kohl HW, Gordon NF, Villegas JA, Blair
SN. Cardiorespiratory fitness, glycemic
status, and mortality risk in men. Diabe-
tes Care 1992; 15(2): 184–92.

30. Wei M, Gibbons LW, Kampert JB,
Nichaman MZ, Blair SN. Low cardiorespi-
ratory fitness and physical inactivity as
predictors of mortality in men with type
2 diabetes. Ann Intern Med 2000; 132
(8): 605–11.

31. Blair SN, Kohl HW 3rd, Barlow CE,
Paffenbarger RS Jr, Gibbons LW, Macera
CA. Changes in physical fitness and all-
cause mortality. A prospective study of
healthy and unhealthy men. JAMA
1995; 273(14): 1093–8.

32. RybergM, Sandberg S,Mellberg C, et al.A
Palaeolithic-type diet causes strong tissue-
specific effects on ectopic fat deposition in
obese postmenopausal women. J Intern
Med 2013; 274(1): 67–76.

33. Shulman GI. Ectopic fat in insulin resis-
tance, dyslipidemia, and cardiometabolic
disease. N Engl J Med 2014; 371(12):
1131–41.

34. Ghroubi S, Elleuch H, Chikh T, Kaffel N,
Abid M, Elleuch MH. Physical training
combined with dietary measures in the
treatment of adult obesity. A comparison
of two protocols. Ann Phys Rehabil Med
2009; 52(5): 394–413.

35. Sanal E, Ardic F, Kirac S. Effects of aer-
obic or combined aerobic resistance ex-
ercise on body composition in
overweight and obese adults: gender

differences. A randomized intervention
study. Eur J Phys Rehabil Med 2013; 49
(1): 1–11.

36. Cummings DE, Weigle DS, Frayo RS,
et al. Plasma ghrelin levels after diet-
induced weight loss or gastric bypass
surgery. N Engl J Med 2002; 346(21):
1623–30.

37. Sumithran P, Prendergast LA, Delbridge
E, et al. Long-term persistence of hor-
monal adaptations to weight loss. N Engl
J Med 2011; 365(17): 1597–604.

38. Jonsson T, Granfeldt Y, Erlanson-
Albertsson C, Ahren B, Lindeberg S. A
paleolithic diet is more satiating per cal-
orie than a mediterranean-like diet in in-
dividuals with ischemic heart disease.
Nutr Metab (Lond) 2010; 7: 85.

39. Loffreda S, Yang SQ, Lin HZ, et al. Leptin
regulates proinflammatory immune re-
sponses. FASEB J 1998; 12(1): 57–65.

40. Yamagishi SI, Edelstein D, Du XL, Kaneda
Y, Guzman M, Brownlee M. Leptin in-
duces mitochondrial superoxide produc-
tion and monocyte chemoattractant
protein-1 expression in aortic endothelial
cells by increasing fatty acid oxidation via
protein kinase A. J Biol Chem 2001; 276
(27): 25096–100.

41. Soderberg S, Ahren B, Stegmayr B, et al.
Leptin is a risk marker for first-ever hem-
orrhagic stroke in a population-based co-
hort. Stroke 1999; 30(2): 328–37.

42. Soderberg S, Stegmayr B, Stenlund H,
et al. Leptin, but not adiponectin, pre-
dicts stroke in males. J Intern Med
2004; 256(2): 128–36.

43. Soderberg S, Ahren B, Jansson JH, et al.
Leptin is associated with increased risk
of myocardial infarction. J Intern Med
1999; 246(4): 409–18.

44. Belalcazar LM, Lang W, Haffner SM,
et al. Adiponectin and the mediation of
HDL-cholesterol change with improved
lifestyle: the Look AHEAD Study. J Lipid
Res 2012; 53(12): 2726–33.

45. Kitabchi AE, McDaniel KA, Wan JY, et al.
Effects of high-protein versus high-
carbohydrate diets on markers of beta-
cell function, oxidative stress, lipid per-
oxidation, proinflammatory cytokines,
and adipokines in obese, premenopausal
women without diabetes: a randomized
controlled trial. Diabetes Care 2013; 36
(7): 1919–25.

46. Convertino VA. Blood volume response
to physical activity and inactivity. Am J
Med Sci 2007; 334(1): 72–9.

Supporting information

Supporting information may be found in the online version of this article.

Paleolithic Diet in Type 2 Diabetes 11 of 11

RESEARCH Open Access

Dietary fiber intake and glycemic control:
coronary artery calcification in type 1
diabetes (CACTI) study
Arpita Basu1,2*, Amy C. Alman1 and Janet K. Snell-Bergeon3

Abstract

Background

: Dietary fiber has been recommended for glucose control, and typically low intakes are observed in
the general population. The role of fiber in glycemic control in reported literature is inconsistent and few reports
are available in populations with type 1 diabetes (T1D).

Methods

: Using data from the Coronary Artery Calcification in Type 1 Diabetes (CACTI) study [n = 1257; T1D: n =
568; non-diabetic controls: n = 689] collected between March 2000 and April 2002, we examined cross-sectional
(baseline) and longitudinal (six-year follow-up in 2006–2008) associations of dietary fiber and HbA1c. Participants
completed a validated food frequency questionnaire, and a physical examination and fasting biochemical analyses
(12 h fast) at baseline visit and at the year 6 visit. We used a linear regression model stratified by diabetes status,
and adjusted for age, sex and total calories, and diabetes duration in the T1D group. We also examined correlations
of dietary fiber with HbA1c.

Results

: Baseline dietary fiber intake and serum HbA1c in the T1D group were 16 g [median (IQ): 11–22 g) and 7.9
± 1.3% mean (SD), respectively, and in the non-diabetic controls were 15 g [median (IQ): 11–21 g) and 5.4 ± 0.4%,
respectively. Pearson partial correlation coefficients revealed a significant but weak inverse association of total
dietary fiber with HbA1c when adjusted for age, sex, diabetes status and total calories (r = − 0.07, p = 0.01). In the
adjusted linear regression model at baseline, total dietary fiber revealed a significant inverse association with HbA1c
in the T1D group [β ± SE = − 0.32 ± 0.15, p = 0.034], as well as in the non-diabetic controls [− 0.10 ± 0.04, p = 0.009].
However, these results were attenuated after adjustment for dietary carbohydrates, fats and proteins, or for
cholesterol and triglycerides. No such significance was observed at the year 6 follow-up, and with the HbA1c
changes over 6 years.

Conclusion: Thus, at observed levels of intake, total dietary fiber reveals modest inverse associations with poor
glycemic control. Future studies must further investigate the role of overall dietary quality adjusting for fiber-rich
foods in T1D management.

Keywords: Dietary fiber, Hemoglobin A1c, Type 1 diabetes, Glycemia

© The Author(s). 2019 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0
International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and
reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to
the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver
(http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

* Correspondence: Arpita.basu@unlv.edu
1Epidemiology and Biostatistics, University of South Florida, Tampa, USA
2Department of Kinesiology and Nutrition Sciences, University of Nevada Las
Vegas, Las Vegas, USA
Full list of author information is available at the end of the article

Basu et al. Nutrition Journal (2019) 18:23
https://doi.org/10.1186/s12937-019-0449-z

http://crossmark.crossref.org/dialog/?doi=10.1186/s12937-019-0449-z&domain=pdf

http://creativecommons.org/licenses/by/4.0/

http://creativecommons.org/publicdomain/zero/1.0/

mailto:Arpita.basu@unlv.edu

Background
The incidence of type 1 diabetes (T1D), an autoimmune
disorder, as well as cardiovascular disease (CVD), the major
vascular complication of diabetes have been increasing
worldwide [1, 2]. Based on the statistics reported by the
American Heart Association, only 1.5% of US adults meet
the guidelines for healthy diet pattern [2]. While the recom-
mendation of healthy dietary pattern may have poor com-
pliance in populations, the role of modifying individual
nutrients and bioactive compounds has gained much atten-
tion in the management of chronic diseases such as T1D.
Among the dietary components, fiber has been shown to
play an important role in glycemic control in diabetes [3].
In a meta-analysis of randomized clinical trials reported by
Silva et al. (2013), higher fiber diets (up to 42.5 g/day) or
supplements containing soluble fiber (15 g/day) were found
to significantly decrease HbA1c and fasting plasma glucose
in adults with type 2 diabetes [4]. In another systematic re-
view, foods rich in soluble fiber, such as beta-glucans, were
shown to improve glycemia in diabetes patients [5]. These
effects of fiber have been explained by biological mecha-
nisms in delaying gastric emptying and decreasing glucose
absorption that subsequently lead to decreases in postpran-
dial rise of blood glucose [6, 7]. In addition, dietary fiber, es-
pecially increased soluble fiber intake, has also been
associated with anti-inflammatory properties and in modu-
lating the immune system with potential implications in the
prevention of T1D in children [8].
Epidemiological evidence on the associations of dietary

fiber and glycemic control in adults with type 1 diabetes is
limited and conflicting. In a longitudinal study of youths
with type 1 diabetes (n = 136), following behavioral nutri-
tion intervention, fiber intake was associated with im-
proved glycemic control [9]. Data from the European
Diabetes Centers (EURODIAB) Prospective Complications
Study have reported lower fiber intake in adults with type
1 diabetes (n = 1102) [10], as well as an inverse association
of fiber with HbA1c in these adults (n = 1659) [11]. In an-
other study, fiber intake was not associated with HbA1c
control in youths (n = 908) in a longitudinal dietary study
[12]. While several factors may be involved in the differ-
ences in these study findings, participant characteristics,
such as levels of dietary fiber intake and cardio-metabolic
profiles, and duration of diabetes may play an important
role, and thus a need to further investigate these associa-
tions in well-defined cohorts of T1D.
We have previously reported low prevalence of ideal

cardiovascular health (1.1%), especially based on the
American Heart Association definition of health matrices,
in adults with type 1 diabetes in the coronary artery calci-
fication in type 1 diabetes (CACTI) study [13]. We now
aim to identify the associations of dietary fiber with
glycemic control in the same cohort at cross-sectional and
longitudinal time points.

Methods
Study participants
The data presented in this report were collected as part of
the baseline examination of the CACTI study. The study
enrolled 1416 individuals between 19 and 56 years of age,
with no known history of CHD: 652 subjects with type 1
diabetes and 764 nondiabetic control subjects. Participants
with type 1 diabetes had long-standing disease (mean dur-
ation 23 years, range 4–52 years), were insulin dependent
within a year of diagnosis, and were diagnosed prior to
age 30 or had positive antibodies or a clinical course con-
sistent with type 1 diabetes. Non-diabetic control subjects
had fasting blood glucose < 110mg/dL and were generally spouses, friends and neighbors of cases. The inclusion and exclusion criteria have been described previously [14]. All study participants provided informed consent and the study protocol was approved by the Colorado Multiple Institutional Review Board.

Dietary intake
Study participants who completed the baseline screening
visit were asked to fill out a validated self-administered
semi quantitative food-frequency questionnaire of 126
food items (Harvard FFQ, 1988) [15]. The procedures
have been previously published in detail [14]. The nutri-
ent values and dietary fiber content of foods for the Har-
vard FFQ were estimated primarily on the basis of the
USDA food composition database, and the correlation
coefficient for dietary fiber was 0.68 based on the valid-
ation studies [15, 16]. The total dietary fiber content was
mainly derived from the following food groups included
in the FFQ: breakfast cereals, breads, other cereal foods,
potatoes, legumes, lentils, vegetables, fruits, and nuts
and seeds. One thousand three hundred six study partic-
ipants completed the FFQ. Calorie intake was calculated
per the guidelines suggested by Willett [17] for exclud-
ing individuals with implausible reported energy intake,
40 participants were excluded due to reported caloric in-
take that was very low (< 500 cal per day if female or < 800 cal per day if male), or very high (3500 or higher if female, 4000 or higher if male), leaving 571 participants with type 1 diabetes and 696 controls in this analysis [14]. Among these, for the current analysis, complete biochemical and dietary information was available in 568 participants with type 1 diabetes and 689 controls and were included in the final cross-sectional analysis.

Cardiovascular risk factors
Participants completed a baseline examination between
March 2000 and April 2002. Anthropometric measure-
ments were obtained and included height, weight and waist
circumference. Body mass index was calculated in kg/m2.
Resting systolic blood pressure (SBP) and fifth-phase dia-
stolic blood pressure (DBP) were measured three times

Basu et al. Nutrition Journal (2019) 18:23 Page 2 of 8

while the patients were seated, following a 5min rest, and
the second and third measurements were averaged (Omron
HEM-705CP). In addition, participants completed stan-
dardized questionnaires that enquired about medical his-
tory, current medication, insulin doses, physical activity,
alcohol and tobacco use and family medical history. Follow-
ing a 12 h fast, participants came to the clinic in the morn-
ing for blood collection and analyses of biochemical
variables. Lipids (total cholesterol, HDL-cholesterol, LDL-
cholesterol and triglycerides), fasting glucose and HbA1c
were measured. After an overnight fast, blood was col-
lected, centrifuged and separated. Plasma was stored at 4 °C
until assayed. Total plasma cholesterol and triglyceride
levels were measured using standard enzymatic methods;
HDL cholesterol was separated using dextran sulphate, and
LDL cholesterol was calculated using the Friedewald for-
mula. High-performance liquid chromatography was used
to measure HbA1c (HPLC, BioRad variant).

Statistical analysis
Variables were examined for normality (normal plots),
and non-normally distributed variables (dietary fiber,
plasma triglycerides) were log transformed. Differences
in risk factors between men and women with type 1
diabetes and without diabetes were examined using a
Student’s t test. A χ2 test for goodness of fit was used to
determine if categorical risk factors differed between pa-
tients with type 1 diabetes and non-diabetic participants.
Wilcoxon rank sum test was used to compare differ-
ences of continuous variables with skewed distributions.
Correlations of dietary fiber with HbA1c, and dietary
macronutrients (carbohydrates, total fats and proteins as
percentage of daily caloric intake) and cardiovascular
risk factors (systolic and diastolic blood pressure, BMI,
waist circumference, plasma total-, HDL- and LDL-chol-
esterol and triglycerides, and glucose) were examined
using Pearson correlation coefficients after adjusting for
age, sex, diabetes status and total calories in the entire
cohort, as well as by T1D status. Linear regression ana-
lysis was used to examine associations of dietary fiber in-
take at baseline with contemporaneous HbA1c, as well
as with HbA1c at year 6 follow-up and the change
between year 6 and baseline. Models were adjusted for
relevant covariates as follows: model 1 (age, sex, and
total calories, and diabetes duration for T1D), model 2
(model 1 + dietary carbohydrates, fats and proteins) and
model 3 (model 1 + plasma lipids). In addition, longitu-
dinal analyses were adjusted for baseline HbA1c and the
duration of follow-up. Logistic regression analysis was
used to examine associations of quintiles of total dietary
fiber with the probability of poor control (> 7%) vs. opti-
mal control (< 7%) of HbA1c at baseline and the 6-year follow-up.

Results
A total of 1257 participants were included in the
cross-sectional analysis, and a total of 990 participants
who had HbA1c values at both baseline and Year 3 were
included in the longitudinal analysis in this report. Table 1
shows the baseline characteristics of the study participants
stratified by sex and diabetes status. Women were younger
than men in both groups, significantly so in the non-dia-
betic control group. Among the anthropometric and bio-
chemical measures in the T1D group, BMI, waist
circumference, systolic and diastolic blood pressure were
significantly lower in women, while HDL-cholesterol was
higher when compared to men. We observed similar dif-
ferences in the control group, in addition to significantly
lower fasting glucose, total cholesterol and triglycerides in
women than in men. Among the dietary nutrient intakes,
total energy intake was significantly lower, while carbohy-
drate and protein intake were modestly higher in women
than men in both groups. No significant differences in
total fiber intake were observed between men and women
in the T1D or control group (Table 1).
Table 2 shows adjusted correlation coefficients of diet-

ary fiber intake with anthropometrics, biochemical and
dietary variables in the entire cohort, as well as by
diabetes status. Dietary fiber exhibited a significant and
inverse correlation with HbA1c, BMI, waist circumfer-
ence, systolic and diastolic blood pressure, serum choles-
terol and triglyceride. Among the dietary nutrients, fiber
intake revealed a significant positive association with
total carbohydrates, and an inverse association with total
fat intake in the entire cohort (all p < 0.05). These signifi- cant correlations persisted in non-diabetic controls, but were somewhat attenuated in T1D cases for BMI, waist circumference, plasma HDL-C, triglycerides and glucose. Overall, total dietary fiber intake remained inversely cor- related with HbA1c in T1D cases as well as non-diabetic controls (Table 2). Table 3 shows the associations of baseline dietary

total fiber intake as a continuous variable with HbA1c
at year 6 and the change (year 6 – baseline) stratified
by diabetes status. Cross-sectional analysis at baseline
revealed a significant inverse association of dietary fiber
intake with HbA1c in the model adjusted for age, sex,
total calories and diabetes duration in the T1D group,
as well as in non-diabetic controls (Model 1). The sig-
nificance did not persist in models further adjusted for
dietary nutrients (Model 2) and conventional lipids
(Model 3). Longitudinal analyses revealed no significant
association in any of the models examined. The
six-year change in HbA1c was observed to be 0.05 (−
0.69–0.63) %, median (IQR) in the T1D group, and 0.01
(− 0.2–0.30) %, in the non-diabetic controls.
Log-transformed values of fiber were used for analyses
presented in Tables 2 and 3.

Basu et al. Nutrition Journal (2019) 18:23 Page 3 of 8

Table 1 Baseline characteristics of the CACTI cohort

Type 1 diabetes Non-diabetic control

Men (n = 251) Women (n = 320) p-value Men (n = 347) Women (n = 349) p-value

Age (years) 38.0 ± 9.0 36.0 ± 9.0 0.07 40.0 ± 9.0 38.0 ± 9.0 0.01

HbA1c (%) 7.9 ± 1.2 7.9 ± 1.3 0.75 5.6 ± 0.4 5.4 ± 0.4 < 0.0001

Met < 7% HbA1c goal (%) 18.7 22.0 0.67 N/A N/A N/A

Duration of diabetes (years) 24.0 ± 9.0 23.0 ± 9.0 0.18 N/A N/A N/A

Duration of follow-up (years) 6.2 ± 0.6 6.2 ± 0.5 0.23 6.1 ± 0.5 6.2 ± 0.6 0.25

BMI (kg/m2) 26.6 ± 3.8 25.8 ± 4.7 0.04 27.2 ± 4.1 25.1 ± 5.6 < 0.0001

Waist circumference (cm) 90.8 ± 11.0 81.0 ± 12.0 < 0.0001 93.0 ± 12.0 79.0 ± 13.0 < 0.0001

Systolic blood pressure (mm Hg) 122.0 ± 13.0 114.0 ± 14.0 < 0.0001 118.0 ± 11.0 111.0 ± 13.0 < 0.0001

Diastolic blood pressure (mm Hg) 80.0 ± 9.0 75.0 ± 8.0 < 0.0001 82.0 ± 8.0 76.0 ± 8.0 < 0.0001

Plasma glucose (mg/dL) 199.0 ± 102.0 187.0 ± 93.0 0.14 93.0 ± 10.0 87.0 ± 9.0 < 0.0001

Plasma cholesterol (mg/dL) 175.0 ± 35.0 176.0 ± 33.0 0.81 198.0 ± 43.0 185.0 ± 34.0 < 0.0001

Plasma HDL-cholesterol (mg/dL) 50.0 ± 13.0 60.0 ± 17.0 < 0.0001 43.0 ± 11.0 58.0 ± 14.0 < 0.0001

Plasma triglycerides (mg/dL) 83.0 (62.0–114.0) 77.0 (62.0–103.0) 0.08 123.0 (89.0–181.0) 89.0 (66.0–124.0) < 0.0001

Energy intake (kcal/day) 1954.0 ± 625.0 1633.0 ± 561.0 < 0.0001 1992.0 ± 655.0 1655.0 ± 528.0 < 0.0001

Carbohydrate intake (% kcal/day) 44.0 (38.0–51.0) 46.0 (40.0–52.0) 0.04 47.0 (42.0–52.0) 48.0 (42.0–54.0) 0.04

Fat intake (% kcal/day) 36.0 (31.0–41.0) 35.0 (30.0–39.0) 0.08 34.0 (29.0–37.0) 33.0 (28.0–36.0) 0.06

Protein intake (% kcal/day) 18.0 (16.0–21.0) 19.0 (17.0–21.0) 0.004 18.0 (15.0–20.0) 19.0 (16.0–21.0) 0.0004

Total dietary fiber (g) 16.0 (12.0–22.0) 15.0 (11.0–21.0) 0.06 15.0 (11.0–21.0) 16.0 (11.0–21.0) 0.88

Physical activity (kJ/week) 7517 (3165–14,118) 5020 (1938–10,862) 0.32 6986 (3048–12,861) 6232 (2859–11,255) 0.21

Data are presented as means ± SD and median (IQ range)
P < 0.05 in bold; Comparison between men and women: t test for difference in means, χ2 test for difference in proportions, and Wilcoxon rank sum test for difference of continuous variables with skewed distributions

Table 2 Pearson partial correlation coefficients of total dietary fiber with clinical parameters and dietary variables in the CACTI
cohort

Variable Total dietary fiber (all subjects)
(n = 1257)

Total dietary Fiber T1D cases
(n = 568)

Total dietary fiber non-diabetic controls
(n = 689)

R p-value R p-value R p-value

HbA1c −0.07 0.01 −0.08 0.03 − 0.10 0.009

Systolic blood pressure −0.11 0.0001 −0.08 0.05 −0.12 0.002

Diastolic blood pressure −0.13 < 0.0001 −0.09 0.03 −0.13 0.001

BMI −0.14 < 0.0001 −0.02 0.56 −0.16 < 0.0001

Waist circumference −0.12 < 0.0001 −0.01 0.73 −0.14 0.0003

Plasma cholesterol −0.09 0.0007 −0.12 0.005 −0.06 0.11

Plasma HDL- cholesterol 0.04 0.16 −0.02 0.56 0.11 0.004

Plasma triglyceride −0.07 0.009 −0.008 0.85 −0.10 0.007

Plasma glucose −0.03 0.33 −0.02 0.59 −0.08 0.022

Carbohydrate (% daily intake) 0.43 < 0.0001 0.45 < 0.0001 0.43 < 0.0001

Protein intake (% daily intake) −0.05 0.089 −0.08 0.05 −0.04 0.26

Fat intake (% daily intake) −0.41 < 0.0001 −0.40 < 0.0001 − 0.43 < 0.0001

Based on log transformed values of dietary fiber
Adjusted for age, sex, total calories and diabetes status, and duration (T1D)
T1D type 1 diabetes
P < 0.05 in bold

Basu et al. Nutrition Journal (2019) 18:23 Page 4 of 8

Table 4 shows data from logistic regression analysis to
examine the associations of quintiles of dietary fiber in-
take with the probability of poor vs. optimal glycemic
control (HbA1c > 7% vs. < 7%). At baseline, no signifi- cant associations were noted, but those in the highest category of fiber intake [29 (23-69 g), mean (range)] compared to the lowest (reference), trended towards an inverse association in adjusted analysis (p = 0.08, Table 4). No significant associations were noted at year 6 follow-up. No significant interaction effects of dietary fiber and sex were noted in our analyses (p = 0.22).

Discussion

To our knowledge few reports have been published on
the association between dietary fiber intake and glycemic
control in T1D. Thus, our study found a significant in-
verse association between total dietary fiber intake and
HbA1c levels at the baseline visit of the CACTI study in
those with diabetes, as well as in non-diabetic controls
in a model adjusted for standard covariates including
total calories. Our significant cross-sectional associations
of fiber with glycemic control did not persist in models
further adjusted for dietary macronutrients and blood
lipids, largely due to their known independent associa-
tions with glycemic control [18, 19]. We did not observe

any significant predictive association of baseline fiber in-
take with HbA1c levels at year 6, and the six-year
changes of HbA1c in adjusted models. These discrepan-
cies in observations between cross-sectional and pro-
spective associations may be explained by the smaller
sample size in our prospective analysis and the habitual
low baseline fiber intake that was not predictive of gly-
cemic control 6 years later. This argument may further
explain our observation of an inverse trend between
fiber intake and poorly controlled HbA1c (> 7%) but
only in the highest quintile of fiber intake in the T1D
group. Poor glycemic control in T1D has been associ-
ated with excess mortality and cardiovascular disease
(CVD) when compared to non-diabetic matched con-
trols [20, 21], and few adults with T1D meet current gly-
cemic control targets [20], thus necessitating additional
preventive strategies such as dietary fiber intake in this
high-risk population. Dietary fiber has been identified as
a nutrient of public health concern and most of the US
adults do not meet the recommendations of 38 g/day for
adults [22]. We observed an intake of dietary fiber of less
than half the recommendations in our cohort of individ-
uals with and without T1D. These findings in adults
may also be reflective of a continuum of poor dietary
fiber intake observed in children with T1D [23], thus

Table 3 Linear associations of total dietary fiber with HbA1c at baseline, prospective (year 6) and change data in the CACTI cohort

Dietary
fiber
(Baseline)

HbA1c (Baseline) HbA1c (year 6) HbA1c change (year 6-Baseline)

T1D
(n = 568)

Non-diabetic control
(n = 689)

T1D (n = 452) Non-diabetic control
(n = 538)

beta±SE P-value beta±SE P-value beta±SE P-value beta±SE P-value beta±SE P-value beta±SE P-value

Model 1a − 0.32 ± 0.15 0.034 −0.10 ± 0.04 0.009 0.017±0.138 0.90 0.033±0.057 0.56 0.017 ± 0.138 0.90 0.033 ± 0.057 0.56

Model 2b − 0.14 ± 0.18 0.43 −0.06 ± 0.05 0.27 0.112±0.158 0.48 0.044±0.065 0.50 0.112±0.158 0.48 0.044±0.065 0.50

Model 3c − 0.08 ± 0.11 0.46 −0.05 ± 0.03 0.19 0.119±0.158 0.45 0.058±0.065 0.37 0.119±0.158 0.45 0.058±0.065 0.37

T1D type 1 diabetes
P < 0.05 are in bold font aModel adjusted for age, sex, diabetes duration and total calories for T1D; age, sex and total calories for non-diabetic control; year 6 and change model also adjusted for baseline HbA1c and duration of follow-up bModel 1+ dietary carbohydrates, fats and proteins cModel 1 + plasma total cholesterol and triglycerides

Table 4 Logistic regression associations of quintiles of total dietary fiber with the probability of poor control (> 7%) vs. optimal
control (< 7%) HbA1c at baseline and at year 6 of CACTI study

Dietary fiber
quintiles (g)

HbA1c goals (baseline)
(> 7% N = 453 vs. < 7% N = 115)

HbA1c goals (year 6)
(> 7% N = 361 vs. < 7% N = 91)

OR (95% CI) P-value OR (95% CI) P-value

Quintile 1 Reference Reference

Quintile 2 0.487 (0.147, 1.617) 0.24 1.197 (0.546, 2.625) 0.65

Quintile 3 0.420 (0.126, 1.407) 0.16 1.059 (0.475, 2.359) 0.88

Quintile 4 0.370 (0.096, 1.423) 0.15 1.987 (0.787, 5.021) 0.15

Quintile 5 0.283 (0.07, 1.153) 0.08 1.573 (0.559, 4.432) 0.39

Data presented as OR (95% CI)
Dietary fiber quintiles: Quintile 1[N: 253; mean: 7.8 g (range: 2.8–10.3 g)]; Quintile 2 [N: 254; mean: 12.0 g (range: 10.3–13.7 g)]; Quintile 3 [N: 253; mean: 15.5 g
(range: 13.7–17.4 g)]; Quintile 4 [N: 254; mean: 19.8 g (range: 17.4–22.7 g)]; Quintile 5 [N: 253; mean: 28.8 g (range: 22.7–68.5 g)]
Model adjusted for age, sex, diabetes duration and total calories; year 6 also adjusted for baseline HbA1c and duration of follow up

Basu et al. Nutrition Journal (2019) 18:23 Page 5 of 8

identifying a strong need to improve dietary fiber intake
in early life in T1D populations.
Very few studies have been reported on the association of

dietary fiber with glycemic control, especially in T1D. Our
study findings agree with a few previously reported studies
showing no prospective associations of dietary fiber with
HbA1c in youths with T1D [9, 12], while, we observe simi-
lar findings of inverse cross-sectional association between
dietary fiber and HbA1c in adults with T1D in the EURO-
DIAB study [24], as well as in youths with T1D [25]. Our
longitudinal findings differ from another report from the
EURODIAB study in which baseline fiber intake revealed
significant protective association against elevated HbA1c
levels in a 6.8 year follow-up period [11]. In comparing our
study with this previously reported study, we observe some
differences, especially higher HbA1c at baseline and
follow-up years, 8.25 ± 1.85% and 8.27 ± 1.44% (mean ±
SD), respectively, in their study [11], vs. 7.9 ± 1.2% and 7.8
± 1.12%, respectively, in our study. Also, our smaller sample
size at the six-year follow-up (n = 990), when compared to
this previous study (n = 1659) could explain the null find-
ings in our longitudinal analysis. Further, there are numer-
ous factors that impact changes in glycemic control over
time, including adoption of new diabetes technology, such
as the use of diabetes apps and remote glucose monitoring
system [26] during the study period. Overall, the import-
ance of increasing consumption of dietary fiber must be
emphasized in the T1D population, based on the numerous
health benefits of dietary fiber in delaying gastric transit
time and improving postprandial glucose load, decreasing
inflammation and cholesterol levels [27–29].
Our findings of the inverse association of dietary fiber

with HbA1c in the non-diabetic controls agree with pre-
vious reports in such populations, and have implications
for reducing risks associated with obesity, the metabolic
syndrome and type 2 diabetes in the general population
[30–33]. In addition to our main outcome of HbA1c, we
also observed significant inverse correlations of dietary
fiber with BMI, waist circumference, systolic and dia-
stolic blood pressure, as well as serum cholesterol and
triglycerides. These findings conform to reported studies
on the protective associations of dietary fiber against
obesity, the metabolic syndrome and elevated blood
lipids [31, 32]. We also observed a moderately strong
and significant inverse association between dietary fiber
and total fat intake in the entire cohort, and this pro-
vides some evidence of unhealthy dietary patterns in
T1D populations [34]. We have previously reported
higher intake of total and saturated fats in the CACTI
cohort that were associated with poor glycemic control
[14]. Together with our current findings on fiber intake,
selected nutrients, such as dietary fats, and dietary
bioactive compounds may play an important role in
glycemic control in T1D.

Our analyses have some limitations that must be consid-
ered during the interpretation of results. In the first place,
nutritional exposure data at baseline dietary intakes from
the FFQ relied on a retrospective self-report and, therefore,
may have been prone to recall bias. Secondly, we did not
have information on the distribution of soluble vs. insol-
uble fiber intake, food groups contributing to total fiber
intake, as well as food bioactive compounds, such as resist-
ant starch shown to be associated with improved glycemic
mangement [9, 35, 36]. Thirdly, our study considered
HbA1c as a marker of long-term glycemic control, and we
did not have data on daily glucose measures to capture
day-to-day glycemic fluctuations, which have recently been
suggested as a strong predictor of overall glycemic control
[37]. Future prospective cohort studies should thus exam-
ine associations of dietary fiber intake from different food
groups, as well as different types of fiber using multiple
biomarkers of glycemic control.

Conclusions

In conclusion, our significant inverse association ob-
served between dietary fiber intake and HbA1c in the
model adjusted for age, sex and total calories, and dia-
betes duration for T1D at baseline visit (cross-sectional
analysis) provides some evidence on the role of fiber in-
take in glycemic control, which is of importance in the
management of T1D patients at a high risk of mortality
from CVD. This association did not persist in models
further adjusted for dietary macronutrients and plasma
total cholesterol and triglycerides, and this may indicate
that higher levels of fiber intake than the observed low
habitual intakes are needed to counteract the positive as-
sociations of these variables with HbA1c. Overall, our
adjusted correlation coefficients also revealed a signifi-
cant inverse association of fiber intake with HbA1c in
the entire cohort, as well as in T1D cases and
non-diabetic controls. We used data from a
well-characterized cohort of T1D patients as well as
matched non-diabetic controls, thereby permitting
generalizability of our data to these populations. Further
research may explore whether overall dietary quality
when adjusted for fiber intake is associated with gly-
cemic control in participants with T1D or non-diabetic
individuals with habitual low fiber intakes.

Acknowledgements

We would like to thank all participants for their contributions to this study.

Funding

Support for this study was provided by the National Institutes of Health
National Heart, Lung and Blood Institute grants R01 HL61753, R01 HL079611
and R01 HL113029, American Diabetes Association grant 7-06-CVD-28, Ameri-
can Diabetes Association Grant 7-13-CD-10 (Snell-Bergeon) and Diabetes
Endocrinology Research Center Clinical Investigation Core P30 DK57516. The
study was performed at the Adult General Clinical Research Center at the
University of Colorado Denver Anschutz Medical Center supported by the
NIH M01 RR000051 and NIH/NCATS Colorado CTSA Grant Number UL1

Basu et al. Nutrition Journal (2019) 18:23 Page 6 of 8

TR002535, the Barbara Davis Center for Childhood Diabetes in Denver and at
Colorado Heart Imaging Center in Denver, CO, USA.

Availability of data and materials

No applicable

Authors’ contributions

AB, ACA and JKS-B contributed to the conception and design of the study,
analysis, and interpretation of the data and drafted the manuscript. All au-
thors critically revised, read, and approved the final manuscript and agreed
to be fully accountable for ensuring the integrity and accuracy of the work.

Ethics approval and consent to participate

All study participants provided informed consent and the study protocol was
approved by the Colorado Multiple Institutional Review Board. This
observational study was performed in consistent with the approved
guidelines.

Consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in
published maps and institutional affiliations.

Author details

1Epidemiology and Biostatistics, University of South Florida, Tampa, USA.
2Department of Kinesiology and Nutrition Sciences, University of Nevada Las
Vegas, Las Vegas, USA. 3Barbara Davis Center for Childhood Diabetes,
University of Colorado, Anschutz Medical Campus, Aurora, USA.

Received: 1 November 2018 Accepted: 26 March 2019

References

1. Forlenza GP, Rewers M. The epidemic of type 1 diabetes: what is it telling

us? Curr Opin Endocrinol Diabetes Obes. 2011;18(4):248–51. https://doi.org/
10.1097/MED.0b013e32834872ce [published Online First: 2011/08/17].

2. Mozaffarian D, Benjamin EJ, Go AS, et al. Executive summary: heart disease
and stroke statistics–2016 update: a report from the American Heart
Association. Circulation. 2016;133(4):447–54. https://doi.org/10.1161/cir.
0000000000000366 [published Online First: 2016/01/27].

3. Weickert MO, Pfeiffer AFH. Impact of dietary fiber consumption on insulin
resistance and the prevention of type 2 diabetes. J Nutr. 2018;148(1):7–12.
https://doi.org/10.1093/jn/nxx008 [published Online First: 2018/01/30].

4. Silva FM, Kramer CK, de Almeida JC, et al. Fiber intake and glycemic control
in patients with type 2 diabetes mellitus: a systematic review with meta-
analysis of randomized controlled trials. Nutr Rev. 2013;71(12):790–801.
https://doi.org/10.1111/nure.12076 [published Online First: 2013/11/05].

5. Francelino Andrade E, Vieira Lobato R, Vasques Araujo T, et al. Effect of
beta-glucans in the control of blood glucose levels of diabetic patients: a
systematic review. Nutr Hosp. 2014;31(1):170–7. https://doi.org/10.3305/nh.
2015.31.1.7597 [published Online First: 2015/01/07].

6. Gemen R, de Vries JF, Slavin JL. Relationship between molecular structure of
cereal dietary fiber and health effects: focus on glucose/insulin response
and gut health. Nutr Rev. 2011;69(1):22–33. https://doi.org/10.1111/j.1753-
4887.2010.00357.x [published Online First: 2011/01/05].

7. Papathanasopoulos A, Camilleri M. Dietary fiber supplements: effects in
obesity and metabolic syndrome and relationship to gastrointestinal
functions. Gastroenterology. 2010;138(1):65–72.e1-2. https://doi.org/10.1053/
j.gastro.2009.11.045 [published Online First: 2009/11/26].

8. Thomson P, Medina DA, Ortuzar V, et al. Anti-inflammatory effect of
microbial consortia during the utilization of dietary polysaccharides.
Foodserv Res Int (Ottawa, Ont). 2018;109:14–23. https://doi.org/10.1016/j.
foodres.2018.04.008 [published Online First: 2018/05/29].

9. Nansel TR, Lipsky LM, Liu A. Greater diet quality is associated with more
optimal glycemic control in a longitudinal study of youth with type 1

diabetes. Am J Clin Nutr. 2016;104(1):81–7. https://doi.org/10.3945/ajcn.115.
126136 [published Online First: 2016/05/20].

10. Soedamah-Muthu SS, Chaturvedi N, Fuller JH, et al. Do European people
with type 1 diabetes consume a high atherogenic diet? 7-year follow-up of
the EURODIAB prospective complications study. Eur J Nutr. 2013;52(7):1701–
10. https://doi.org/10.1007/s00394-012-0473-7 [published Online First: 2012/
12/12].

11. Balk SN, Schoenaker DA, Mishra GD, et al. Association of diet and lifestyle
with glycated haemoglobin in type 1 diabetes participants in the
EURODIAB prospective complications study. Eur J Clin Nutr. 2016;70(2):229–
36. https://doi.org/10.1038/ejcn.2015.110 [published Online First: 2015/07/
16].

12. Lamichhane AP, Crandell JL, Jaacks LM, et al. Longitudinal associations of
nutritional factors with glycated hemoglobin in youth with type 1 diabetes:
the SEARCH nutrition ancillary study. Am J Clin Nutr. 2015;101(6):1278–85.
https://doi.org/10.3945/ajcn.114.103747 [published Online First: 2015/05/08].

13. Alman AC, Maahs DM, Rewers MJ, et al. Ideal cardiovascular health and the
prevalence and progression of coronary artery calcification in adults with
and without type 1 diabetes. Diabetes Care. 2014;37(2):521–8. https://doi.
org/10.2337/dc13-0997 [published Online First: 2013/10/17].

14. Snell-Bergeon JK, Chartier-Logan C, Maahs DM, et al. Adults with type 1
diabetes eat a high-fat atherogenic diet that is associated with coronary
artery calcium. Diabetologia. 2009;52(5):801–9. https://doi.org/10.1007/
s00125-009-1280-4 [published Online First: 2009/02/17].

15. Rimm EB, Giovannucci EL, Stampfer MJ, et al. Reproducibility and validity of
an expanded self-administered semiquantitative food frequency
questionnaire among male health professionals. Am J Epidemiol. 1992;
135(10):1114–26 discussion 27-36. [published Online First: 1992/05/15].

16. Willett WC, Sampson L, Stampfer MJ, et al. Reproducibility and validity of a
semiquantitative food frequency questionnaire. Am J Epidemiol. 1985;
122(1):51–65 [published Online First: 1985/07/01].

17. Willett WC, Sampson L, Browne ML, et al. The use of a self-administered
questionnaire to assess diet four years in the past. Am J Epidemiol. 1988;
127(1):188–99 [published Online First: 1988/01/01].

18. Davis PJ, Liu M, Sherman S, et al. HbA1c, lipid profiles and risk of incident
type 2 diabetes in United States Veterans. PLoS One. 2018;13(9):e0203484.
https://doi.org/10.1371/journal.pone.0203484 [published Online First: 2018/
09/14].

19. Wang X, Jung M, Mossavar-Rahmani Y, et al. Macronutrient intake, diagnosis
status, and glycemic control among US Hispanics/Latinos with diabetes. J
Clin Endocrinol Metab. 2016;101(4):1856–64. https://doi.org/10.1210/jc.2015-
3237 [published Online First: 2016/03/08].

20. Lind M, Svensson AM, Kosiborod M, et al. Glycemic control and excess
mortality in type 1 diabetes. N Engl J Med. 2014;371(21):1972–82. https://
doi.org/10.1056/NEJMoa1408214 [published Online First: 2014/11/20].

21. Nystrom T, Holzmann MJ, Eliasson B, et al. Glycemic control in type 1
diabetes and long-term risk of cardiovascular events or death after coronary
artery bypass grafting. J Am Coll Cardiol. 2015;66(5):535–43. https://doi.org/
10.1016/j.jacc.2015.05.054 [published Online First: 2015/08/01].

22. McGill CR, Fulgoni VL 3rd, Devareddy L. Ten-year trends in fiber and whole
grain intakes and food sources for the United States population: national
health and nutrition examination survey 2001-2010. Nutrients. 2015;7(2):
1119–30. https://doi.org/10.3390/nu7021119 [published Online First: 2015/
02/12].

23. Mehta SN, Volkening LK, Quinn N, et al. Intensively managed young
children with type 1 diabetes consume high-fat, low-fiber diets similar to
age-matched controls. Nutr Res (New York, NY). 2014;34(5):428–35. https://
doi.org/10.1016/j.nutres.2014.04.008 [published Online First: 2014/06/12].

24. Buyken AE, Toeller M, Heitkamp G, et al. Relation of fibre intake to HbA1c
and the prevalence of severe ketoacidosis and severe hypoglycaemia.
EURODIAB IDDM complications study group. Diabetologia. 1998;41(8):882–
90 [published Online First: 1998/09/03].

25. Katz ML, Mehta S, Nansel T, et al. Associations of nutrient intake with
glycemic control in youth with type 1 diabetes: differences by insulin
regimen. Diabetes Technol Ther. 2014;16(8):512–8. https://doi.org/10.1089/
dia.2013.0389 [published Online First: 2014/04/29].

26. Akturk HK, Garg S. Technological advances shaping diabetes care. Curr Opin
Endocrinol Diabetes Obes. 2019. https://doi.org/10.1097/med.
0000000000000467 [published Online First: 2019/02/06].

27. Muller M, Canfora EE, Blaak EE. Gastrointestinal transit time, glucose
homeostasis and metabolic health: modulation by dietary fibers. Nutrients.

Basu et al. Nutrition Journal (2019) 18:23 Page 7 of 8

https://doi.org/10.1097/MED.0b013e32834872ce

https://doi.org/10.1161/cir.0000000000000366

https://doi.org/10.1093/jn/nxx008

https://doi.org/10.1111/nure.12076

https://doi.org/10.3305/nh.2015.31.1.7597

https://doi.org/10.1111/j.1753-4887.2010.00357.x

https://doi.org/10.1053/j.gastro.2009.11.045

https://doi.org/10.1016/j.foodres.2018.04.008

https://doi.org/10.3945/ajcn.115.126136

https://doi.org/10.1007/s00394-012-0473-7

https://doi.org/10.1038/ejcn.2015.110

https://doi.org/10.3945/ajcn.114.103747

https://doi.org/10.2337/dc13-0997

https://doi.org/10.1007/s00125-009-1280-4

https://doi.org/10.1371/journal.pone.0203484

https://doi.org/10.1210/jc.2015-3237

https://doi.org/10.1056/NEJMoa1408214

https://doi.org/10.1016/j.jacc.2015.05.054

https://doi.org/10.3390/nu7021119

https://doi.org/10.1016/j.nutres.2014.04.008

https://doi.org/10.1089/dia.2013.0389

https://doi.org/10.1097/med.0000000000000467

2018;10(3). https://doi.org/10.3390/nu10030275 [published Online First:
2018/03/03].

28. Korcz E, Kerenyi Z, Varga L. Dietary fibers, prebiotics, and exopolysaccharides
produced by lactic acid bacteria: potential health benefits with special
regard to cholesterol-lowering effects. Food Funct. 2018;9(6):3057–68.
https://doi.org/10.1039/c8fo00118a [published Online First: 2018/05/24].

29. Wannamethee SG, Whincup PH, Thomas MC, et al. Associations between
dietary fiber and inflammation, hepatic function, and risk of type 2 diabetes
in older men: potential mechanisms for the benefits of fiber on diabetes
risk. Diabetes Care. 2009;32(10):1823–5. https://doi.org/10.2337/dc09-0477
[published Online First: 2009/07/25].

30. Wei B, Liu Y, Lin X, et al. Dietary fiber intake and risk of metabolic syndrome:
a meta-analysis of observational studies. Clin Nutr (Edinburgh, Scotland).
2017. https://doi.org/10.1016/j.clnu.2017.10.019 [published Online First:
2017/11/16].

31. Lie L, Brown L, Forrester TE, et al. The association of dietary fiber intake with
cardiometabolic risk in four countries across the epidemiologic transition.
Nutrients. 2018;10(5). https://doi.org/10.3390/nu10050628 [published Online
First: 2018/05/19].

32. Grooms KN, Ommerborn MJ, Pham DQ, et al. Dietary fiber intake and
cardiometabolic risks among US adults, NHANES 1999-2010. Am J Med.
2013;126(12):1059–67.e1-4. https://doi.org/10.1016/j.amjmed.2013.07.023
[published Online First: 2013/10/19].

33. Ning H, Van Horn L, Shay CM, et al. Associations of dietary fiber intake with
long-term predicted cardiovascular disease risk and C-reactive protein levels
(from the National Health and nutrition examination survey data [2005-
2010]). Am J Cardiol. 2014;113(2):287–91. https://doi.org/10.1016/j.amjcard.
2013.09.020 [published Online First: 2013/11/02].

34. van Bussel BC, Soedamah-Muthu SS, Henry RM, et al. Unhealthy dietary
patterns associated with inflammation and endothelial dysfunction in type
1 diabetes: the EURODIAB study. Nutr Metab Cardiovasc Dis. 2013;23(8):758–
64. https://doi.org/10.1016/j.numecd.2012.04.005 [published Online First:
2012/07/17].

35. Thompson SV, Hannon BA, An R, et al. Effects of isolated soluble fiber
supplementation on body weight, glycemia, and insulinemia in adults with
overweight and obesity: a systematic review and meta-analysis of
randomized controlled trials. Am J Clin Nutr. 2017;106(6):1514–28. https://
doi.org/10.3945/ajcn.117.163246 [published Online First: 2017/11/03].

36. Stewart ML, Zimmer JP. Postprandial glucose and insulin response to a
high-fiber muffin top containing resistant starch type 4 in healthy adults: a
double-blind, randomized, controlled trial. Nutrition (Burbank, Los Angeles
County, Calif). 2018;53:59–63. https://doi.org/10.1016/j.nut.2018.01.002
[published Online First: 2018/04/16].

37. Beck RW, Riddlesworth T, Ruedy K, et al. Effect of continuous glucose
monitoring on glycemic control in adults with type 1 diabetes using insulin
injections: the DIAMOND randomized clinical trial. JAMA. 2017;317(4):371–8.
https://doi.org/10.1001/jama.2016.19975 [published Online First: 2017/01/25].

Basu et al. Nutrition Journal (2019) 18:23 Page 8 of 8

https://doi.org/10.3390/nu10030275

https://doi.org/10.1039/c8fo00118a

https://doi.org/10.2337/dc09-0477

https://doi.org/10.1016/j.clnu.2017.10.019

https://doi.org/10.3390/nu10050628

https://doi.org/10.1016/j.amjmed.2013.07.023

https://doi.org/10.1016/j.amjcard.2013.09.020

https://doi.org/10.1016/j.numecd.2012.04.005

https://doi.org/10.3945/ajcn.117.163246

https://doi.org/10.1016/j.nut.2018.01.002

https://doi.org/10.1001/jama.2016.19975