ABSTRACT
Background
The metabolic impact of poor diet quality in cystic fibrosis (CF), coupled with a rise in obesity and modulator‐induced weight gain, is a growing concern. Our study aimed to understand knowledge and perspectives regarding dietary changes on modulators, and how measured nutrient intake changes with different dietary patterns in response to diet education.
Methods
A cross‐sectional survey was administered to 82 adult CF patients at the University of Alabama Birmingham. A subset of 10 participants received diet education and followed two diet patterns sequentially: a general healthful (GH) diet and a high fat (HF) diet. Three‐day diet records were analyzed following each diet pattern.
Results
A total of 82 adults responded to the survey and 42% of respondents reported making healthful dietary changes on modulators. Only 56% of respondents were able to correctly identify which foods contained fat. Diet record analyses showed a significant decrease in energy intake and fat intake on the GH diet compared to the HF diet. Baseline Healthy Eating Index (HEI) scores improved significantly (p = 0.0254) following education on a GH diet pattern, but were significantly lower following the HF diet (p = 0.0179).
Conclusions
While nearly half of survey respondents reported making healthy dietary changes on modulators, basic nutrition knowledge deficits persist. Measured diet quality was poor at baseline and significantly improved after receiving an educational session on GH eating. Findings highlight the need for targeted, basic education on GH eating patterns in the clinical practice and larger studies of nutrition interventions for improving diet quality in CF.
Keywords: CFTR modulators, cystic Fibrosis, diet education, diet quality
1. Introduction
Cystic fibrosis transmembrane conductance regulator (CFTR) modulators are novel drug treatments for people with cystic fibrosis (PwCF) that correct the underlying protein defect that leads to electrolyte imbalance in epithelial cells. CFTR modulators have a variable effect on nutrition status in PwCF depending on an individual's genetic mutations and which type of modulator therapy they take. [1] Highly effective modulator therapies (HEMT) such as ivacaftor and elexacaftor‐tezacaftor‐ivacaftor (ETI) have been associated with remarkable improvements in lung function, growth, and body mass index (BMI). [2, 3, 4] However, long‐term follow‐up of PwCF who take HEMT has also demonstrated an increase in the proportion of PwCF who are overweight and obese. [5, 6] Body composition changes have also been noted, with 65% of weight gained on ivacaftor as fat mass. [6] A recent study found an increase in the prevalence of sarcopenic obesity after 6 months on ETI, particularly in women. [7] Further, evidence suggests that overnutrition in CF is associated with increased fasting insulin, insulin resistance, and dyslipidemia, which may increase the risk of diabetes and cardiovascular disease [8, 9, 10, 11, 12].
Overnutrition in CF has increased over the past decade [13] and may accelerate with widespread ETI use, as 90% of PwCF are eligible for HEMT. The classic CF diet may no longer be appropriate with weight and body composition shifts in PwCF, particularly the diet's poor quality, emphasizing increased energy and fat intake with low nutrient density. [14] Recent nutrition guidance from the Cystic Fibrosis Foundation recommends consumption of a diet in line with the guidance for the general population. [15, 16] Knowledge deficits exist for patients and clinicians regarding dietary adjustment and over‐nutrition when initiating ETI [17], and no studies address dietary intervention for weight management or improving diet quality in PwCF on HEMT. The objectives of this study were to 1) evaluate dietary goals, knowledge, and reported changes in response to initiation of CFTR modulators in PwCF, 2) determine diet quality at baseline, and 3) assess changes in diet quality on different dietary patterns in response to diet education intervention.
2. Methods
2.1. Survey
The survey was developed with multidisciplinary clinician input and was pilot‐tested with members of the target audience for readability, clarity, length of completion, and ease of use. Feedback from patient partners was incorporated to create the final draft of the survey prior to distribution. Additonal details on survey methodology and questions are presented in supplementary material. The survey was built in REDCap and was fielded between November 2022–June 2023. Participants were recruited to take the survey in CF clinic using a flyer with a QR code and direct recruitment methods. Eligibility criteria for survey participants included diagnosis of CF, over the age of 21 years, and taking a CFTR modulator. Informed consent was obtained from all participants with ethics approval from the University of Alabama at Birmingham (UAB) Institutional Review Board (IRB‐300009359).
2.1.1. Survey Measures
2.1.1.1. Nutrition Goals
Respondents were asked if they had any of the following nutrition goals: gain weight, maintain weight, lose weight, weight neutral, no goals prior to modulator use and after they started using modulators. Respondents could choose more than one option.
2.1.1.2. Nutritional Challenges on Modulators
Respondents were asked what nutritional challenges they have encountered while on modulators. Choices included understanding what diet to eat, eating a healthier diet/healthier fats, managing weight, body image issues, increased appetite, gastrointestinal side effects, financial difficulties, changing lifelong eating behaviors, and no challenges. Respondents could choose more than one option.
2.1.1.3. Preferred Nutrition Resources
Respondents were asked which of the following resources would they find the most helpful: more dietitian visits, group nutrition classes, educational handouts, financial assistance, and grocery shopping assistance/meal planning Respondents could choose more than one option.
2.1.1.4. Self‐Reported Dietary Changes
Among those who reported making dietary changes on modulators, they were asked if they made any of the following changes: decreasing calories, decreasing fat, decreasing sugar, decreasing portion size, increasing fruits, or increasing vegetables. Respondents could choose more than one option. Additionally, they were asked if these changes were self‐initiated or based on team recommendation. Likert scales were used to assess both confidence and readiness levels to make changes recommended by the team.
2.1.1.5. Nutritional Knowledge Assessment
Respondents were asked to identify which of the following foods had fat: bread, beans, egg whites, rice, skim milk, whole milk, peanut butter, potato chips, oil, or they could state that they did not know which foods contained fat. Those who indicated that whole milk, peanut butter, potato chips, or oil as fat containing were correct identifications and indicating any of the others were considered incorrect. To identify knowledge about different types of fat, respondents were asked which fat was considered heart healthy and could choose saturated fat, unsaturated fat, and don't know. Those that indicated unsaturated fat were considered correct and don't know or saturated fat were considered incorrect. Additionally, respondents were asked if the following foods had saturated fat: butter, skim milk, cheese, avocado, steak, almonds, salmon, or they could state that they did not know which foods had saturated fats. Choosing butter, cheese and steak are considered correct and choosing any of the others is considered incorrect. Lastly, respondents were given that same list of foods for saturated fat and asked to identify foods high in unsaturated fat or if they did not know which foods contained unsaturated fats. Choosing avocado, salmon, and almonds was considered correct.
2.2. Diet Intake
2.2.1. Study Design
Recruitment for the dietary intake portion of the study was completed by directly approaching eligible patients in the adult CF clinic and using the nutrition survey. The survey contained a final question asking respondents if they would like to participate in a second in‐person portion of a pilot and feasibilty study assessing ETI concentrations [18] where participants were randomized to follow two dietary patterns for 7 days each, sequentially: a standard fat‐containing general healthful (GH) diet aligned with advice for the general population or the high‐fat (HF) classic CF diet and have modulator concentrations measured at certain time intervals. Inclusion criteria were diagnosis of CF, ≥ 21 years of age, and taking ETI. Exclusion criteria included history of solid organ transplant, waitlisted for solid organ transplant, pregnancy, and CF liver disease. Informed consent was obtained from all participants with ethics approval from UAB Institutional Review Board (IRB‐300009359).
2.2.2. Diet Education
Diet education was provided by a registered dietitian (RD) via telehealth consult that lasted 30–60 min. Diet education primarily focused on amount of fat to consume, with GH diet recommendations of 25%–30% of calories from fat and the HFdiet providing 40% of calories from fat, primarily focused on how to add additional fats to meals and snacks in line with the legacy CF diet recommendations. Participants were provided with supplemental funding to aid in the purchase of foods to adhere to the recommended diet. Diet recalls were obtained during the education session to determine estimated energy intake and typical percentage of calories from fat. Based on this, individualized fat gram goal ranges were provided for each diet, along with education on food label reading, fat sources, and different types of fat. Written material was provided electronically for each diet after each session that included sample meal patterns. No specific instruction was provided to adjust total energy or other macronutrient intake. Participants were able to contact the RD for questions during each diet pattern.
2.2.3. Diet Records
Participants were directed, with instructions and examples, to complete a 3‐day diet record, 2 weekdays and 1 weekend day. The first set of diet records was completed after enrollment and reflective of the participant's typical diet prior to diet education and assignment to a specific dietary pattern in order to assess diet composition at baseline prior to intervention. Participants then completed a 3‐day diet record during the 7 days following both the GH and HF diet patterns. Diet records were reviewed for completeness by study staff, and participants were contacted to clarify entries that required more detail.
2.2.4. Healthy Eating Index
The Healthy Eating Index (HEI) is a validated measure of American diet quality. [19] The HEI measures to what extent dietary intake follows the Dietary Guidelines for Americans (DGA) and is scored on a scale of 0–100, with 100 most closely matching the recommended eating pattern of the DGA. The HEI consists of 13 components that fall into two categories: moderation components, in which lower intake is reflected in a higher score, and adequacy components, in which higher intake reflects a higher score.
2.2.5. Anthropometrics
Height (cm) and weight (kg) were measured, and BMI calculated, at a baseline visit where randomization (1:1) to a diet pattern occurred. Body composition measurements were obtained at baseline using total body dual‐energy X‐ray absorptiometry (DEXA) scans using an iDXA scanner (GE‐Lunar Radiation Corp. Madison, WI).
2.3. Data Analysis
For the survey respondents, data were summarized with descriptive statistics. For the dietary record sample, dietary intake data were analyzed using Nutrition Data System for Research (NDSR) software version 2021, developed by the Nutrition Coordinating Center (NCC), University of Minnesota, Minneapolis, U.S. Relationships between dependent and independent (demographic) variables were determined with chi‐square (χ [2]) analyses. Paired t‐tests were performed to assess differences in dietary intake variables between baseline diet, GH diet, and HF diet. For comparisons with the HF diet, one participant was excluded due to missing data on that diet pattern. Pearson correlations were used to determine relationships between dietary intake and body composition parameters. Statistical significance was defined as p‐value < 0.05. HEI component scores were calculated using Statistical Analysis Software (SAS) code provided by the National Cancer Institute. [20] Statistical analyses were completed using GraphPad Prism Version 10 (San Diego, California) and STATA version 18.
3. Results
3.1. Participants
A total of 80 survey responses were collected. The majority of responses were from participants at UAB, and limited responses were collected from Augusta Georgia CF Center when recruitment was opened to the Southeastern CF Research Cooperative. A subset of 10 participants recruited to the pilot and feasibility pharmacokinetic (PK) study of ETI underwent dietary education and provided diet records at baseline and on each diet pattern. One participant did not provide a diet record for the HF diet. Survey respondents demographic information is presented in Table 1. Demographic questions were optional in our survey and some patients chose not to disclose their age or gender. Differences in sample size for those questions are noted in Table 1.
Table 1.
Demographic and clinical characteristics of participants who completed the survey and baseline diet records.
| Demographics | Value |
|---|---|
| Descriptive Statistics of Survey Respondants (n = 80) | |
| Insurance n (%) | |
| Private only | 45 (56.25) |
| Public only | 19 (23.75) |
| Public and Private | 10 (12.5) |
| Other/none | 6 (7.5) |
| Non‐Hispanic White n (%) | 73 (91) |
| College educated n (%) | 33 (41) |
| Employed (n = 79), n (%) | 42 (53) |
| Age (n = 72), mean (SD) | 37 (14) |
| Female (n = 80), n (%) | 48 (60) |
| Descriptive Statistics of Diet Record Sample (n = 10) | |
| Age, median (range) | 29.5 (22–53) |
| Race n (%) | |
| White | 8 (80) |
| Black | 2 (20) |
| Hispanic | 2 (20) |
| Female n (%) | 5 (50) |
| Weight (kg), mean (SD) | 64.7 (14.8) |
| CF Related Diabetes, n (%) | 6 (60) |
| BMI (kg/m²), mean (SD) | 24.5 (5.6) |
| Fat free mass index (kg/m²), mean, (SD) | 17.23 (2.84) |
| Fat mass index (kg/m²) mean, (SD) | 7.39 (4.08) |
| Visceral adipose tissue (kg), median (range) | 0.365 (0.057–1.944) |
| Bone mineral density (g/cm²), median (range) | 1.175 (0.945–1.946) |
3.2. Survey
3.2.1. Pre‐Modulator Diet
Prior to modulators, 40% of respondents reported consuming a high‐fat diet, 30% reported following a GH diet, and 30% reported not following a specific diet. Most respondents (86%, n = 70) reported taking their modulator with a fat‐containing food. Of patients taking their modulator with a fat‐containing food, 34% (n = 24) knew how many fat grams they consumed with their modulator. In patients who monitored fat grams consumed with their modulator, 54% (n = 13) consumed the medication with 10–20 g of fat, 29% (n = 7) with 21–30 g of fat, and 17% (n = 4) with less than 10 g of fat. Of respondents who did not take their modulator with fat‐containing food, 64% (n = 7) forgot, 18% (n = 2) did not know which foods contain fat, and 18% (n = 2) did not know modulators required fat for absorption.
3.2.2. Nutrition Goals
Before modulator therapy, 32% of respondents reported a goal of weight gain. This decreased to 12% on modulators. Additionally, 15% of respondents reported their goal was to eat a diet that promotes health without focusing on weight, known as a weight‐neutral approach. This increased to 26% of respondents on modulators. See Figure 1A for a complete comparison of reported nutrition goals before and during modulator use.
Figure 1.
Survey Responses: A. Comparison of nutrition goals before and during modulator use (n = 34), B. Comparison of self‐initiated versus CF team recommended dietary changes on modulators (n = 34), C. Responses to survey question “What are your main nutrition challenges since being on modulators?” (n = 80), D. Responses to survey question “What resources would be helpful in making dietary changes on modulators?” (n = 80).
3.2.3. Reported Dietary Changes on Modulators
Of the 42% (n = 34) of respondents who reported making healthful dietary changes since taking modulators, 50% reported decreasing total calorie intake, 55% reported decreasing fat intake, 64% reported decreasing sugar intake, while 44% and 50% reported increasing fruit and vegetable intake, respectively. Figure 1B compares self‐initiated dietary changes versus CF Care Team recommended dietary changes. The level of readiness to make dietary changes was used as an exploratory indicator of participants' willingness to engage with nutrition interventions. Respondents (n = 29) reported an average readiness level of 3.9 (±0.9) and an average confidence level of 4.1 (±1.0) in making dietary changes recommended by their care team.
3.2.4. Nutrition Barriers and Facilitators
The main barriers to achieving nutrition goals on modulators were not understanding what type of diet to eat (42%) and managing weight (40%). Reported challenges with nutrition goals are graphed in Figure 1C. Just under half (42%) of respondents expressed that educational handouts on nutrition and modulators would be a helpful resource in facilitating healthful dietary changes. A requested educational resource breakdown is presented in Figure 1D.
3.2.5. Nutrition Knowledge Levels
In response to knowledge‐based questions on fat‐containing foods, 30% of participants were able to accurately select which foods contained fat. Further, 75% of participants did not know which foods contained saturated fat, and 22% could not correctly identify which foods contained unsaturated fat. Additionally, 21% couldn't correctly identify any saturated fat foods, and 28% couldn't identify any unsaturated fat‐containing foods. See Figure S3 for breakdown of number of misidentified foods by fat type.
3.3. Dietary Intake
3.3.1. Anthropometric and Clinical Parameters
In 10 participants who completed diet education and diet records, BMI ranged from 18.9 to 37.1 kg/m². While 60% of participants had BMI within normal range for the general population (18.5–24.9 kg/m²), 50% of our sample had BMI below the CFF targets of 22 kg/m² for women and 23 kg/m² for men. Three participants had BMI classified as overweight (25–29.9 kg/m²), and one participant classified as obese (BMI > 30 kg/m²). Fat free mass index (FFMI) was assessed using cut‐points of 15 for women and 17 for men [21], and 50% of our sample (three women and two men) had low FFMI. There were no significant correlations between macronutrient content of baseline diet and weight or body composition parameters, see Figure S2. All participants were pancreatic insufficient, and 60% had CF‐related diabetes.
3.3.2. Diet Records
When switching from the GH to the HF diet, increases were observed in total energy intake by 521 kcal/day (p = 0.0277), total fat intake by 38 g/day (p = 0.0218), and saturated fat intake by 19 g/day (p = 0.0122). Carbohydrate, protein, unsaturated fat, cholesterol, and fat‐soluble vitamins A, E, D, and K intakes were not significantly different between baseline and either GH or HF diet patterns. Added sugar intake was 29 g/day higher on the HF diet than on the GH diet (p = 0.0539) and was reduced by 26 g/day from baseline to the GH diet. The average glycemic index (GI) improved significantly (difference of −1.8, SD: 2.5, p‐value = 0.0479) from baseline when participants followed the GH diet pattern. There were no statistically significant differences between the baseline and the HF diet. Full details of nutrient intake on different diet patterns are reported in Table 2 and Figure S1.
Table 2.
Dietary intake parameters at baseline and on each diet pattern and changes in nutrient intake between each diet.
| Changes in nutrient intake between different diet patterns (n = 10) | ||||||
|---|---|---|---|---|---|---|
| Variable, mean ± SD | Difference Scores | |||||
| Baseline | GH | HF | Baseline:GH (n = 10) | Baseline:HF (n = 9) | GH:HF (n = 9) | |
| Kcal/day | 2118 ± 712 | 1966 ± 381 | 2473 ± 786 | −152 | 333 | 521* |
| Fat, g/day | 89 ± 39 | 69 ± 30 | 107 ± 50 | −20 | 17 | 38* |
| Saturated fat, g/day | 30 ± 14 | 21 ± 9 | 39 ± 20 | −9 | 9 | 19* |
| Unsaturated fat, g/day | 51 ± 27 | 42 ± 20 | 58 ± 9 | −9 | 7 | 16 |
| Cholesterol, g/day | 251 ± 100 | 286 ± 105 | 376 ± 198 | 35.5 | 140.7 | 105 |
| Protein, g/day | 86 ± 26 | 105 ± 34 | 108 ± 38 | 19 | 25 | 4 |
| Carbohydate, g/day | 249 ± 101 | 238 ± 32 | 275 ± 80 | −11 | 20 | 38 |
| Added sugars, g/day | 76 ± 55 | 50 ± 20 | 77 ± 40 | −26 | −0.5 | 29 |
| Glycemic index | 63 ± 4.2 | 61 ± 3.3 | 62 ± 3.8 | −1.8* | −0.9 | 0.3 |
| Vitamin A, units per day | 4165 ± 3479 | 8933 ± 8246 | 5980 ± 5200 | 4768 | 2128 | 3383 |
| Vitamin E, units per day | 16.4 ± 19.3 | 11.8 ± 10.1 | 15.4 ± 14.6 | −4.589 | −2.238 | 3.315 |
| Vitamin D, mcg per day | 5.2 ± 5.5 | 11.8 ± 12.3 | 8.9 ± 7.7 | 6.645 | 3.475 | −2.022 |
| Vitamin K, mcg per day | 84.7 ± 66.1 | 98.7 ± 64.4 | 88.5 ± 73.5 | 14.02 | 0.9644 | −11.13 |
| Healthy eating index | 43 ± 14.7 | 57 ± 11 | 44 ± 10 | 14.41* | 0.76 | −14.32* |
Note: *p < 0.05 ***p < 0.001.
3.3.3. Healthy Eating Index
The HEI significantly improved by a mean difference of 14.41 points when participants switched from their baseline diet to the GH diet (p = 0.0245). A significant reduction of 14.32 points in HEI total score was observed between the HF and GH diet patterns. Total HEI score changes are presented in Figure 2A.
Figure 2.
A. Change in global Healthy Eating Index Score between diet patterns. B. Radar plot of Healthy Eating Index component changes on each diet pattern. The outer edge of the circle represents a 100% score for each component, and the center of the circle represents a score of 0%. [Color figure can be viewed at wileyonlinelibrary.com]
Intake of total fruits (p = 0.022), whole fruits (p = 0.036), whole grains (p = 0.0392), seafood/plant proteins (p = 0.0311), and beans and greens (p = 0.0333) were significantly increased, and HEI score added sugars were significantly decreased (p = 0.0024) from baseline to the GH diet. Component scores of the HEI that significantly increased when switching from the HF to the GH diet pattern include whole grains (p = 0.0296), seafood and plant proteins (p = 0.0182), and unsaturated fatty acids (p = 0.0379). Differences in HEI component scores compared to the U.S. national average for adults are reported in Table 3. A radar graph depicting HEI scores for baseline, GH, and HF diets with different patterns of quality according to HEI components is presented in Figure 2B.
Table 3.
Healthy Eating Index Component scores for baseline diet and each diet pattern compared to U.S. HEI national averages.
| HEI Components between different dietary patterns compared with U.S. National Average for Adults | |||||||
|---|---|---|---|---|---|---|---|
| Baseline (n = 10) | GH (n = 10) | HF (n = 9) | U.S. average | ||||
| Mean (SD) | 95% CI | Mean (SD) | 95% CI | Mean (SD) | 95% CI | Mean | |
| Moderation components | |||||||
| Refined grains | 4.4 (2.7) | 2.4–6.3 | 6.2 (2.3) | 4.6–7.9 | 4.9 (2.7) | 2.8–6.9 | 6.2 |
| Sodium | 2.9 (2.3) | 1.3–4.6 | 2.4 (2.2) | 0.8–3.9 | 4.0 (2.9) | 1.8–6.3 | 3.9 |
| Added sugars | 6.4 (2.7) | 4.4–8.3 | 7.8 (2.4) | 6.1–9.5 | 6.3 (3.3) | 3.8–8.8 | 6.7 |
| Saturated fats | 5.1 (2.6) | 3.2–6.9 | 8.1 (1.8) | 6.9–9.4 | 4.2 (3.3) | 1.6–6.7 | 5.2 |
| Adequacy components | |||||||
| Total fruits | 0.8 (1.3) | −0.15–1.8 | 2.3 (1.9) | 0.9–3.6 | 0.6 (0.8) | −0.02–1.2 | 2.4 |
| Whole fruits | 1.0 (1.6) | −0.08–2.1 | 2.6 (2.1) | 1.1–4.1 | 0.9 (1.3) | −0.1–1.9 | 3.6 |
| Total vegetables | 2.3 (1.1) | 1.5–3.1 | 2.7 (1.6) | 1.5–3.9 | 2.0 (1.2) | 1.1–2.9 | 3.4 |
| Greens and beans | 0.9 (1.3) | −0.04–1.8 | 2.3 (1.9) | 0.9–3.6 | 0.6 (0.8) | −0.02–1.2 | 3.4 |
| Whole grains | 3.1 (3.6) | 0.5–5.7 | 5.8 (3.0) | 3.7–7.9 | 3.7 (2.7) | 1.6–5.8 | 2.3 |
| Dairy | 5.2 (3.0) | 3.1–7.3 | 4.7 (3.0) | 2.6–6.8 | 6.5 (2.0) | 4.9–8.0 | 5.2 |
| Total protein foods | 4.2 (1.0) | 3.5–4.9 | 4.5 (0.7) | 4.0–5.0 | 4.1 (1.1) | 3.3–4.9 | 5.0 |
| Seafood and plant proteins | 1.5 (1.6) | 0.4–2.7 | 2.5 (1.7) | 1.2–3.7 | 1.2 (1.3) | 0.3‐2.2 | 5.0 |
| Fatty acids | 4.4 (3.0) | 2.3–6.6 | 6.2 (2.3) | 4.6–8.0 | 3.7 (2.7) | 1.6–5.9 | 4.4 |
Note: HEI moderation components receive higher scores for lower intake, and adequacy components of the HEI receive higher scores for higher consumption.
4. Discussion
To our knowledge, this is the first study assessing the impact of diet education on diet quality in PwCF taking ETI. Baseline diet quality in our sample was poor, which is consistent with other studies that assessed diet quality in CF using the HEI. [22] Participants consumed a diet higher in sodium, refined grains, and saturated fat than the general U.S. population. [23] At baseline, a total HEI score of 43 was lower than the U.S. national average for adults of 55.3. [24] Tailored diet education and record keeping enabled participants to successfully implement dietary recommendations short‐term. Although diet education in this study focused on altering the amount of fat participants consumed, we observed a significant improvement in overall diet quality while participants followed a diet pattern containing the amount of fat recommended for the general population. In contrast, when patients received education on the classic CF eating pattern, diet quality decreased and was not significantly different from baseline. Notably, even with a significant increase in diet quality on the GH diet, the total HEI score was similar to the U.S. national average [24].
Body composition abnormalities are documented in PwCF [25], and lower fat‐free mass is associated with poorer lung function and more severe disease. [26] Evidence indicates PwCF have higher levels of visceral adipose tissue (VAT) than healthy controls [22, 27] and that VAT is associated with increased added sugar intake, highlighting the importance of developing dietary interventions aimed at reducing added sugars in PwCF. We observed that half of our sample had low FFMI despite having normal or overweight BMI classification, and VAT measurements in our cohort are similar to other studies in PwCF. [22] Our study was also able to demonstrate a short‐term clinically significant reduction in added sugar intake in response to diet education. Future work with a larger sample size should focus on long‐term diet quality improvements and their impact on body composition in PwCF taking CFTR modulators, particularly those who develop excessive adiposity or undesired rapid weight gain on the drug.
Survey data revealed that while most respondents reported taking modulators with fat‐containing foods, their knowledge about the fat content of foods was low. This was an interesting finding, given that PwCF receive routine visits from dietitians as part of their care, and extra education is provided around fat intake when CFTR modulators are prescribed. [15, 28] This education gap could exist in a wider cohort of PwCF, particularly young adults recently transitioned to adult care and previously more reliant on a caregiver's knowledge of disease management. While GH diet education focused on decreasing fat intake, clinical care nutritional messaging potentially impacted diet quality improvements, particularly related to sugar intake. Our survey also revealed nutrition goals on CFTR modulators shifting from weight gain to weight maintenance, with more patients seeking weight loss. The survey cohort's desire for a weight‐neutral approach nearly doubled on modulators, with almost one‐third reporting body image issues as a challenge. This indicates an opportunity for health behavior interventions that support PwCF in eating a diet that would promote overall health without solely focusing on diets for weight management.
Our study had several limitations. First, our survey was primarily conducted in a single adult CF program and, therefore, may not be representative of the U.S. or global CF population. Future studies should focus on understanding national patterns and assessing nutritional knowledge gaps related to GH eating in CF. Large national surveys could highlight regional differences in knowledge levels, diet improvement barriers, and intake perceptions. The dietary education pilot and feasibility is limited by the small sample size, and should be replicated in a larger, multi‐site study. However, using participants as their own controls in trialing different diet patterns revealed significant differences in dietary intake parameters following diet education. Given that the survey occurred before randomization to a dietary pattern and diet education, it is possible that the knowledge‐based questions on fat intake primed participants for learning about this topic in the education session and contributed to the positive impact of education on diet quality. Finally, participants followed the diets for a short duration. Improving diet quality long term requires a complex set of health behavior changes. Additional challenges include historical instruction to PwCF, beginning in childhood, to consume a HF diet with little regard for nutrient content [29], and childhood eating experiences have been shown to impact food behaviors and preferences into adulthood. [30] Additional nutrition education and counselling, as well as more research, are needed to elucidate how to produce sustainable changes to dietary intake while taking CFTR modulators.
5. Conclusions
Our study delivered pilot data to describe dietary changes in response to the initiation of CFTR modulators and demonstrated short‐term effectiveness of diet education on improving diet quality. This study provides some support for dietary education interventions to modify food choices to improve nutritional balance. Larger longitudinal prospective studies of diet education and behavioral counseling with increased intervention dosage and frequency are needed to optimize diet quality and nutrition outcomes in the setting of CFTR modulators.
Author Contributions
Julianna Bailey: conceptualization, methodology, data curation, visualization, investigation, funding acquisition, project administration, writing – original draft, review and editing. Natalie R. Rose: investigation, data curation, visualization, writing – review and editing. Ashritha R. Chalamalla: data curation, formal analysis, writing – review and editing. Justin D. Anderson: investigation, data curation, writing – review and editing. Elizabeth Baker: formal analysis, writing – review and editing. Jennifer S. Guimbellot: conceptualization, methodology, data curation, investigation, funding acquisition, project administration, supervision, writing – review and editing.
Conflicts of Interest
Julianna Bailey reports consulting fees from Anagram Therapeutics, has received Honoria from the Cystic Fibrosis Foundation, and is an unpaid consultant for Vertex Pharmaceuticals Incorporated, outside the submitted work. Jennifer S. Guimbellot reports consulting fees from Vertex Pharmaceuticals Incorporated, outside the submitted work. All other authors declare no conflicts of interest.
Supporting information
Supplemental Figures Peds Pulm NORC R1_Tracked.
Acknowledgments
We would like to thank the participants who contributed to this study. The authors gratefully acknowledge support for the authors from the Cystic Fibrosis Foundation (CFF) (005227Y5123 and GUIMBE20A0‐KB to J.S.G.; GUIMBE23Y5 to J.S.G. and E.B.), the NHLBI/NIH (1K23HL143167 to J.S.G.). the Gregory Fleming James Cystic Fibrosis Research Center (Director Brian Davis), supported by the NIH (DK072482) and CFF (R35HL135816), and the UAB Center for Clinical and Translational Science (UL1TR003096). This study was directly supported by pilot and feasibility funding to the UAB Nutrition Obesity Research Center through NIDDK (P30DK056336). The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIDDK or the NIH.
Data Availability Statement
The data that support the findings of this study are not publicly available but are available from the corresponding author upon reasonable request.
References
- 1. Bailey J., Rozga M., McDonald C. M., et al., “Effect of CFTR Modulators on Anthropometric Parameters in Individuals With Cystic Fibrosis: An Evidence Analysis Center Systematic Review,” Journal of the Academy of Nutrition and Dietetics 121, no. 7 (2021): 1364–1378.e2, 10.1016/j.jand.2020.03.014. [DOI] [PubMed] [Google Scholar]
- 2. Middleton P. G., Mall M. A., Dřevínek P., et al., “Elexacaftor‐Tezacaftor‐Ivacaftor for Cystic Fibrosis With a Single Phe508del Allele,” New England Journal of Medicine 381, no. 19 (2019): 1809–1819, 10.1056/NEJMoa1908639. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 3. Heijerman H. G. M., McKone E. F., Downey D. G., et al., “Efficacy and Safety of the Elexacaftor Plus Tezacaftor Plus Ivacaftor Combination Regimen in People With Cystic Fibrosis Homozygous for the F508del Mutation: A Double‐Blind, Randomised, Phase 3 Trial,” Lancet 394, no. 10212 (2019): 1940–1948, 10.1016/S0140-6736(19)32597-8. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 4. Borowitz D., Lubarsky B., Wilschanski M., et al., “Nutritional Status Improved in Cystic Fibrosis Patients With the G551D Mutation After Treatment With Ivacaftor,” Digestive Diseases and Sciences 61, no. 1 (2016): 198–207, 10.1007/s10620-015-3834-2. [DOI] [PubMed] [Google Scholar]
- 5. Guimbellot J. S., Baines A., Paynter A., et al., “Long Term Clinical Effectiveness of Ivacaftor in People With the G551D CFTR Mutation,” Journal of Cystic Fibrosis 20, no. 2 (2021): 213–219, 10.1016/j.jcf.2020.11.008. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 6. Petersen M. C., Begnel L., Wallendorf M., and Litvin M., “Effect of Elexacaftor‐Tezacaftor‐Ivacaftor on Body Weight and Metabolic Parameters in Adults With Cystic Fibrosis,” Journal of Cystic Fibrosis 21, no. 2 (2022): 265–271, 10.1016/j.jcf.2021.11.012. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 7. Proud D. and Duckers J., “Weight a Minute: Exploring the Effect on Weight and Body Composition After the Initiation of Elexacaftor/Tezacaftor/Ivacaftor in Adults With CF,” Journal of Cystic Fibrosis 22, no. 5 (2023): 847–850, 10.1016/j.jcf.2023.06.002. [DOI] [PubMed] [Google Scholar]
- 8. Coderre L., Fadainia C., Belson L., et al., “LDL‐Cholesterol and Insulin Are Independently Associated With Body Mass Index in Adult Cystic Fibrosis Patients,” Journal of Cystic Fibrosis 11, no. 5 (2012): 393–397, 10.1016/j.jcf.2012.03.006. [DOI] [PubMed] [Google Scholar]
- 9. Rhodes B., Nash E. F., Tullis E., et al., “Prevalence of Dyslipidemia in Adults With Cystic Fibrosis,” Journal of Cystic Fibrosis 9, no. 1 (2010): 24–28, 10.1016/j.jcf.2009.09.002. [DOI] [PubMed] [Google Scholar]
- 10. Georgiopoulou V. V., Denker A., Bishop K. L., et al., “Metabolic Abnormalities in Adults With Cystic Fibrosis,” Respirology 15, no. 5 (2010): 823–829, 10.1111/j.1440-1843.2010.01771.x. [DOI] [PubMed] [Google Scholar]
- 11. Nowak J. K., Szczepanik M., Wojsyk‐Banaszak I., et al., “Cystic Fibrosis Dyslipidaemia: A Cross‐Sectional Study,” Journal of Cystic Fibrosis 18, no. 4 (2019): 566–571, 10.1016/j.jcf.2019.04.001. [DOI] [PubMed] [Google Scholar]
- 12. González Jiménez D., Bousoño García C., Rivas Crespo M. F., et al., “Insulin Resistance in Overweight Cystic Fibrosis Paediatric Patients,” Anales de Pediatria (Barcelona, Spain: 2003) 76, no. 5 (2012): 279–284, 10.1016/j.anpedi.2011.11.016. [DOI] [PubMed] [Google Scholar]
- 13.“Cystic Fibrosis Foundation ,” Cystic Fibrosis Foundation Patient Registry 2022 Data Report, accessed January 20, 2024, https://www.cff.org/medical-professionals/patient-registry.
- 14. Greaney C., Doyle A., Drummond N., et al., “What Do People With Cystic Fibrosis Eat? Diet Quality, Macronutrient and Micronutrient Intakes (Compared to Recommended Guidelines) in Adults With Cystic Fibrosis—A Systematic Review,” Journal of Cystic Fibrosis 22, no. 6 (2023): 1036–1047, 10.1016/j.jcf.2023.08.004. [DOI] [PubMed] [Google Scholar]
- 15. McDonald C. M., Alvarez J. A., Bailey J., et al., “Academy of Nutrition and Dietetics: 2020 Cystic Fibrosis Evidence Analysis Center Evidence‐Based Nutrition Practice Guideline,” Journal of the Academy of Nutrition and Dietetics 121, no. 8 (2021): 1591–1636.e3, 10.1016/j.jand.2020.03.015. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 16. Leonard A., Bailey J., Bruce A., et al., “Nutritional Considerations for a New Era: A CF Foundation Position Paper,” Journal of Cystic Fibrosis 22, no. 5 (2023): 788–795, 10.1016/j.jcf.2023.05.010. [DOI] [PubMed] [Google Scholar]
- 17. Snowball J. E., Flight W. G., Heath L., and Koutoukidis D. A., “A Paradigm Shift in Cystic Fibrosis Nutritional Care: Clinicians' Views on the Management of Patients With Overweight and Obesity,” Journal of Cystic Fibrosis 22, no. 5 (2023): 836–842, 10.1016/j.jcf.2023.03.011. [DOI] [PubMed] [Google Scholar]
- 18. Rose N. R., Bailey J., Anderson J. D., et al., “Pilot and Feasibility Study of Dietary Composition With Elexacaftor‐Tezacaftor‐Ivacaftor Concentrations in People With Cystic Fibrosis,” Pharmacotherapy 44, no. 12 (2024): 920–926, 10.1002/phar.4630. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 19. Krebs‐Smith S. M., Pannucci T. E., Subar A. F., et al., “Update of the Healthy Eating Index: HEI‐2015,” Journal of the Academy of Nutrition and Dietetics 118, no. 9 (2018): 1591–1602, 10.1016/j.jand.2018.05.021. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 20.National Cancer Institute, The Healthy Eating Index—Population Ratio Method. Published online December 14, 2021, National Cancer Institute Website. accessed January 20, 2024. https://epi.grants.cancer.gov/hei/population-ratio-method.html.
- 21. Cederholm T., Bosaeus I., Barazzoni R., et al., “Diagnostic Criteria for Malnutrition—An ESPEN Consensus Statement,” Clinical Nutrition 34, no. 3 (2015): 335–340, 10.1016/j.clnu.2015.03.001. [DOI] [PubMed] [Google Scholar]
- 22. Bellissimo M. P., Zhang I., Ivie E. A., et al., “Visceral Adipose Tissue Is Associated With Poor Diet Quality and Higher Fasting Glucose in Adults With Cystic Fibrosis,” Journal of Cystic Fibrosis 18, no. 3 (2019): 430–435, 10.1016/j.jcf.2019.01.002. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 23.“U.S. Department of Agriculture,” HEI Scores for Americans. USDA Food and Nutrition Service website, accessed June 20, 2024, https://www.fns.usda.gov/cnpp/hei-scores-americans.
- 24. Shams‐White M. M., Pannucci T. E., Lerman J. L., et al., “Healthy Eating Index‐2020: Review and Update Process to Reflect the Dietary Guidelines for Americans, 2020–2025,” Journal of the Academy of Nutrition and Dietetics 123, no. 9 (2023): 1280–1288, 10.1016/j.jand.2023.05.015. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 25. Calella P., Valerio G., Brodlie M., Donini L. M., and Siervo M., “Cystic Fibrosis, Body Composition, and Health Outcomes: A Systematic Review,” Nutrition 55–56 (2018): 131–139, 10.1016/j.nut.2018.03.052. [DOI] [PubMed] [Google Scholar]
- 26. Sheikh S., Zemel B. S., Stallings V. A., Rubenstein R. C., and Kelly A., “Body Composition and Pulmonary Function in Cystic Fibrosis,” Frontiers in Pediatrics 2 (2014): 33, 10.3389/fped.2014.00033. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 27. Panagopoulou P., Fotoulaki M., Manolitsas A., Pavlitou‐Tsiontsi E., Tsitouridis I., and Nousia‐Arvanitakis S., “Adiponectin and Body Composition in Cystic Fibrosis,” Journal of Cystic Fibrosis 7, no. 3 (2008): 244–251, 10.1016/j.jcf.2007.10.003. [DOI] [PubMed] [Google Scholar]
- 28. Anderson H. L., Lynch V., Moore J. E., and Millar B. C., “What Is the Perceived Role of the Dietitian Amongst People With Cystic Fibrosis? Results of an International Survey,” Canadian Journal of Dietetic Practice and Research: A Publication of Dietitians of Canada = Revue Canadienne de la Pratique et de la Recherche en Dietetique: Une Publication des Dietetistes du Canada 84, no. 3 (2023): 149–153, 10.3148/cjdpr-2022-044. [DOI] [PubMed] [Google Scholar]
- 29. Altman K., McDonald C. M., Michel S. H., and Maguiness K., “Nutrition in Cystic Fibrosis: From the Past to the Present and into the Future,” Pediatric Pulmonology 54, no. Suppl 3 (2019): S56–S73, 10.1002/ppul.24521. [DOI] [PubMed] [Google Scholar]
- 30. Barrett J., Slatter G., Whitehouse J. L., and Nash E. F., “Perception, Experience and Relationship With Food and Eating in Adults With Cystic Fibrosis,” Journal of Human Nutrition and Dietetics 35, no. 5 (2022): 757–764, 10.1111/jhn.12967. [DOI] [PubMed] [Google Scholar]
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Supplemental Figures Peds Pulm NORC R1_Tracked.
Data Availability Statement
The data that support the findings of this study are not publicly available but are available from the corresponding author upon reasonable request.