Nursing Research and Primary Care

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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.

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


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 (, 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 ( applies to the data made available in this article, unless otherwise stated.

* Correspondence: 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



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.

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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

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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)

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

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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

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  • Abstract
    • Background
    • Methods
    • Results
    • Conclusion
  • Background
  • Methods
    • Study participants
    • Dietary intake
    • Cardiovascular risk factors
    • Statistical analysis
  • Results
  • Discussion
  • Conclusions
  • Acknowledgements
  • Funding
  • Availability of data and materials
  • Authors’ contributions
  • Ethics approval and consent to participate
  • Consent for publication
  • Competing interests
  • Publisher’s Note
  • Author details
  • References

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