Abstract
Background
Body fat distribution and diet quality influence clinical outcomes in general populations but are understudied in patients with cystic fibrosis (CF). The aim of this pilot study was to assess body fat distribution and diet quality in relation to fasting glucose and lung function in adults with CF.
Methods
Subjects were 24 adults (ages 18–50) with CF and 25 age-matched controls. The Healthy Eating Index 2015 (HEI-2015) was calculated from 3-day food records and data were adjusted/1000 kcal intake. Whole and regional body composition, including visceral adipose tissue (VAT), was assessed by dual energy X-ray absorptiometry.
Results
Subjects with CF reported more added sugar intake [26.1 (IQR 18.1) vs. 12.9 (12.5) g/1000kcal, p<0.001] and had lower HEI-2015 scores [48.3 (IQR 9.9) vs. 63.9 (27.3), p<0.001] compared to controls. There were no differences in BMI, total body fat, or lean body mass (LBM) between subjects with CF and controls (p>0.05 for all), although subjects with CF had higher VAT than control subjects [0.3 (IQR 0.3) vs 0.1 (0.3) kg, p=0.02]. Among subjects with CF, VAT was positively associated with added sugar intake (p<0.001) and fasting blood glucose (p=0.04). Lung function was positively associated with BMI (p=0.005) and LBM (p=0.03) but not with adiposity indicators.
Conclusions
These novel data link body fat distribution with diet quality and fasting glucose levels in adults with CF, whereas LBM was associated with lung function. This study highlights the importance of increasing diet quality and assessing body composition and fat distribution in the CF population.
Keywords: Cystic Fibrosis, Diet Quality, Body Composition, Fat Distribution, Nutrition, Healthy Eating Index
Introduction
Poor nutritional status plays a key role in the progression of cystic fibrosis (CF)[1]. The high risk of malnutrition in patients with CF has prompted the long-standing clinical practice of prescribing unrestricted high-calorie, high-fat diets to meet sex-specific body mass index (BMI) goals[2]. In the general population, an emphasis on dietary quality above individual macronutrients is the basis for dietary recommendations to reduce the risk of chronic disease[3]. However, evidence-based research is lacking to guide CF-specific nutritional recommendations regarding diet quality. Recent advances in the management of CF have led to prolonged life expectancy, yet the long-term clinical and metabolic effects of an unrestricted diet in adults with CF are unknown. A recent study in children with CF revealed a disproportionately high intake of energy-dense, yet nutrient-poor, foods[4]. With a growing prevalence of glucose intolerance and CF-related diabetes (CFRD) in an aging CF population, studies are needed to determine if diet quality plays a role in the metabolic health of these patients.
Body mass index (BMI) positively correlates with lung function and survival in the CF population [5, 6]; however, clinical studies have identified a discordance between BMI and body composition measures[7, 8]. BMI lacks the sensitivity needed to assess optimal body composition for health, particularly in clinical populations such as patients with CF[9]. The sole use of BMI as a nutritional index in patients with CF can result in the misidentification of at-risk patients, including those with a “healthy” BMI but concomitant depletion of lean body mass (LBM)[8] or patients with normal weight obesity (NWO, defined as a normal weight BMI but elevated percent body fat)[7]. Further, BMI measurements alone provide no indication of body fat distribution. Excess accumulation of adipose tissue in the abdominal area is linked to adverse cardiometabolic health outcomes in the general population[10] and is correlated with adverse pulmonary outcomes in patients with chronic obstructive pulmonary disease and other non-CF lung diseases[11, 12]. Two previous studies found elevated visceral adipose tissue (VAT) in patients with CF compared to healthy controls[13, 14]. However, the clinical implications of elevated VAT in CF are unknown. It is also unknown if dietary factors, such as poor diet quality, contribute to VAT deposition in individuals with CF.
The aims of this study were to: 1) describe and compare diet quality and body fat distribution between adults with CF and healthy controls, and 2) investigate the inter-relationships between dietary intake, fat distribution, and clinical outcomes (lung function and fasting blood glucose concentrations). We hypothesized that increased VAT would be associated with poor diet quality and negative clinical outcomes.
Materials and Methods
Subjects and Study Design
This pilot, cross-sectional study included twenty-four adults with clinically-stable CF and twenty-five age-matched healthy controls within the Atlanta, Georgia, United States region. The Emory Institutional Review Board approved the study, and all participants provided written informed consent prior to participation. Testing was conducted in the Emory University Hospital Clinical Research Network unit of the Georgia Clinical and Translational Science Alliance. Inclusion criteria for participants with CF were having a confirmed diagnosis of CF by a chloride sweat test and/or CFTR genetic test, confirmation that at least one of the CFTR mutations was Class I, II, or III, and a stable medical regimen for at least three weeks, including no recent pulmonary exacerbation involving administration of oral or intravenous antibiotics or glucocorticoids. Exclusion criteria were current pregnancy, inability or unwillingness to discontinue enteral tube feeds for one night prior to the study visit, the most recent forced expiratory volume in 1 second expressed as a percentage of the predicted value (FEV1%) <40%, or recreational or prescription drug or alcohol abuse. Healthy control volunteers were recruited by flyers and word-of-mouth and were aged-matched within 18 months to a subject with CF. Inclusion criteria for healthy controls were 18–50 years of age and absence of any hospitalization in the previous year aside from accidents. Exclusion criteria were presence of a chronic infection, respiratory disease, cardiometabolic disease, acute illness within the previous two weeks, history of malignancy in the past 5 years, weight instability (+/− 10% of body weight within six months), drug or alcohol abuse, or BMI >30 kg/m2.
Following a 10-hour overnight fast, participants without previously diagnosed CFRD (n=13) and all control subjects completed a 2-hour oral glucose tolerance test (OGTT), which included a fasting baseline blood glucose draw and a two-hour blood draw following ingestion of 75 g glucose. Patients with a CFRD diagnosis (n=11) only had a fasting blood glucose draw. Glucose concentrations were determined in the Emory University Hospital Clinical Laboratory. Glucose tolerance was determined based on the results of the OGTT[15] or from a previous clinical diagnosis of CFRD. CF glucose tolerance subtypes based on previously published literature were also determined [16]. For subjects with CF, available HbA1c values assessed within the last three months were obtained from the electronic medical record (EMR). Due to the limited number of CF subjects with 2-hour glucose (n=13) or HbA1c% (n=20) values, analyses examining clinical outcomes with diet and body composition utilized fasting glucose concentrations. For subjects with CF, spirometry was performed at the Emory University Hospital Adult CF Clinic following the American Thoracic Society/European Respiratory Society guidelines for pulmonary function testing [17]. Absolute values from spirometric testing was compared to population reference values for determination of FEV1 percent predicted (FEV1% predicted). Clinical spirometry data was extracted from the EMR for baseline FEV1% predicted and to calculate average rate of FEV1% decline in the last three years. Baseline FEV1% was calculated by averaging the best FEV1% value within each quarter of the calendar year. Mean rate of lung function decline was calculated by finding the difference in FEV1% between each year and averaging those values. Best FEV1% in the previous year was also assessed. Lung function was not assessed in healthy controls.
Body Composition and Fat Distribution
Whole and regional body composition was assessed by dual energy x-ray absorptiometry (DXA) using a Lunar iDXA densitometer. Fat mass index and LBM index were calculated to correct for height as kg/m2. VAT was determined via DXA using an automated software program (CoreScan® GE Healthcare, Madison, WI, USA) that segments images into VAT and subcutaneous adipose tissue compartments in the abdominal region and has been validated against computed tomography and magnetic resonance imaging in non-CF populations[18]. Subjects with a normal weight BMI (<25 kg/m2) and body fat percent above sex-specific cut points (>23% and >30% body fat for males and females, respectively) were classified as NWO, while those with BMI values ≥25 kg/m2 [7]. Waist circumference was measured using a measuring tape by a registered dietitian trained in anthropometry.
Dietary Intake
Prior to their study visit, all participants completed a three-day food record of two consecutive weekdays and one weekend day. A registered dietitian reviewed each record and asked probing questions for details that may have been missed. All records were analyzed using the Nutrition Data System for Research software (NDSR, Nutrition Coordinating Center, University of Minnesota, MN, USA; database version 2016). Food record data were not available for one control subject. The Healthy Eating Index-2015 (HEI) score was calculated for each participant to assess diet quality, based upon the current Dietary Guidelines for Americans 2015–2020 [3, 19]. It considers adequate amounts of high-quality foods such as vegetables, fruits, and whole grains as well as limited amounts of poor quality diet components such as added sugars, refined grains, and saturated fats (Supplementary Table 1). HEI-2015 scores range from 0–100, with a score of 100 indicating the highest diet quality. Components of the HEI-2015 are important for the health of all people and in line with recommendations from the World Health Organization [20]. To account for differences of total caloric intake, individual dietary and macronutrient data were adjusted per 1000 kilocalorie (kcal) intake. Dietary intake in subjects with CF was also compared to current CF nutrition guidelines[2]. Total energy requirements were calculated according to the Institute of Medicine’s Dietary Reference Intakes (DRI) equations, accounting for sex, age, height, weight, and a physical activity coefficient of 1.0[21].
Statistical Analyses
Descriptive statistics were performed on all variables. Wilcoxon sum rank tests were used to compare dietary intake and body composition between subjects with CF and controls. Multiple linear regression analyses were used to test for associations between body composition, dietary intake, and clinical outcomes, adjusting for age and sex. Variables that were not normally distributed were log-transformed for use in regression analyses. Analyses were performed among all subjects [also adjusting for health status (CF or control)] and within subjects with CF only. Additional exploratory analyses were performed to assess for interactions between health status and outcomes, and to assess potential confounding effects of added sugars on group differences. All analyses were conducted in JMP® Pro software version 13.0.0 (SAS Institute, Cary, NC), using two-sided tests with an alpha significance value of 0.05.
Results
Demographic and clinical characteristics for all participants are presented in Table 1. The distribution of sex and race and the mean age were similar between groups. The majority of subjects with CF were homozygous (50%) or heterozygous (46%) for ΔF508. All subjects with CF had pancreatic insufficiency. The median FEV1% of subjects with CF indicated mild to moderate lung disease, and average rate of decline was 1.2% each year. Median fasting glucose concentrations were higher in subjects with CF compared to controls (p=0.04). Among CF participants, 29% had normal glucose tolerance, 25% had impaired glucose tolerance, and 46% had CFRD (median CFRD HbA1c= 7.1%). One healthy control had impaired fasting glucose (IFG). Among subjects with CF, four had IFG and two had fasting hyperglycemia (Supplementary Table 2). Five subjects with CF were taking CFTR modulators.
Table 1.
Demographic and Clinical Characteristics
| Variable | CF group | Control group | p-value |
|---|---|---|---|
| n | 24 | 25 | |
| Age (y) | 26.4 (13.7) | 25.5 (8.4) | 0.52 |
| Males n (%) | 12 (50.0%) | 9 (36.0%) | 0.39 |
| Caucasian n (%) | 21 (87.5%) | 20 (80.0%) | 0.55 |
| Genotype | |||
| ΔF508 homozygous n(%) | 12 (50%) | - | |
| ΔF508 heterozygous n(%) | 11 (46%) | - | |
| Other | 1 (5%) | - | |
| Pancreatic Insufficiency n (%) | 24 (100%) | - | |
| FEV1 (% predicted) | 74.9 (28) | - | |
| FEV1 Average Rate of Decline (%) | −1.0 (2.3) | - | |
| Fasting Glucose (mg/dL) | 91.5 (26.5) | 79.5 (14.8) | 0.04 |
| Glucose Tolerance n (%) | |||
| Normal | 7 (29%) | 25 (100%) | |
| Impaired | 6 (25%) | - | |
| Diabetes (CFRD) | 11 (46%) | - | |
| HbA1c %1 | 6.0 (1.7) | - |
Values are presented as median (IQR) or n (%).
n=20 in CF subjects; no data for healthy controls
Abbreviations: FEV1, forced expiratory volume; CFRD, cystic fibrosis related diabetes; HbA1c, hemoglobin A1c
Dietary Intake in Subjects with CF versus Control Subjects
A shown in Table 2, participants with CF reported a higher median total energy intake compared to controls (2,673 vs 1,914 kcal/day, p<0.001), although intake of total dietary fat, carbohydrate, and protein did not differ between groups (p>0.05). On average, subjects with CF reported consuming 136% of the estimated DRI for daily calorie requirements, 35.2% of total daily caloric intake from fat, and protein intake at 1.9 gm/kg body weight/day. All but one subject with CF (96%) met total energy requirement recommendations for CF. Despite similarities in total macronutrient intake between subjects with CF versus controls, there were significant differences specific macronutrient substrates. Subjects with CF consumed more trans-fatty acids (p=0.002) and added sugars (p<0.001), and consumed less dietary fiber (p<0.001) compared to healthy controls. Subjects with CF had significantly lower HEI-2015 scores compared to healthy controls (p<0.001), indicating worse overall diet quality.
Table 2.
Dietary Intake in Participants with CF versus Control Subjects
| CF group (n=24) | Control group (n=24) | p-value | |
|---|---|---|---|
| Total Calories (kcal) | 2,673 (1,131) | 1,914 (881) | <0.001 |
| % Daily Recommended Intake | 133.7 (36.5) | 91.1 (37.4) | <0.001 |
| Total Fat (g) | 39.7 (9.2) | 39.7 (10.1) | 0.95 |
| % Calories from Fat | 35.2 (7.9) | 34.5 (7.7) | 0.98 |
| Total Saturated Fat (g) | 13.5 (5.8) | 10.8 (5.8) | 0.07 |
| Trans Fatty Acids (g) | 1.0 (0.9) | 0.6 (0.4) | 0.002 |
| Total Carbohydrate (g) | 114.8 (19.9) | 113.4 (28.8) | 0.49 |
| % Calories from Carbohydrates | 46.2 (7.8) | 45.0 (10.4) | 0.34 |
| Added Sugar (g) | 26.1 (18.1) | 12.9 (12.5) | <0.001 |
| Total Dietary Fiber (g) | 7.2 (3.2) | 13.9 (9.1) | <0.001 |
| Whole Grains (%) | 18 (16) | 23 (49) | 0.16 |
| Refined Grains (%) | 82 (16) | 77 (49) | 0.16 |
| Total Protein (g) | 43.6 (9.1) | 40.0 (19.2) | 0.72 |
| % Calories from protein | 17.5 (4.8) | 15.5 (7.7) | 0.43 |
| Healthy Eating Index-2015 Score | 48.3 (9.9) | 63.9 (27.3) | <0.001 |
Values are presented as median (IQR). All dietary data reported in grams were adjusted per 1000 kcal/day.
Body composition and fat distribution in subjects with CF versus Control Subjects
Table 3 compares body composition and fat distribution between participants with CF and control subjects. Measures of total body composition, including BMI, lean body mass (LBM), body fat percent, and total fat mass, were similar between groups. Participants with CF had significantly more VAT than controls (p=0.02). Among subjects with CF, VAT was greater in males compared to females [median (IQR): 0.35 (0.34) kg vs 0.12 (0.025) kg, p=0.02]. Waist circumference was similar between subjects with CF and control subjects. Among subjects with CF, 29% exhibited NWO, and 8% were considered overweight-obese, a similar distribution as the controls (32% NWO and 12% overweight-obese).
Table 3.
Body Composition of Participants with CF versus Control Subjects
| CF group (n=24) | Control group (n=24) | p-value | |
|---|---|---|---|
| Height (cm) | 167.6 (12.7) | 170.1 (18.2) | 0.39 |
| Weight (cm) | 62.7 (18.6) | 63.6 (14.2) | 0.67 |
| Body Mass Index (kg/m2) | 21.6 (3.1) | 21.8 (2.9) | 0.66 |
| Lean Body Mass (kg) | 43.8 (13.0) | 43.2 (18.8) | 0.64 |
| Lean Body Mass Index (kg/m2) | 15.7 (2.7) | 15.2 (2.3) | 0.99 |
| Total Fat Mass (kg) | 12.1 (9.2) | 16.1 (7.1) | 0.13 |
| Fat Mass Index (kg/m2) | 4.5 (4.0) | 5.9 (2.1) | 0.28 |
| Body Fat (%) | 23.7 (14.4) | 26.9 (8.6) | 0.30 |
| Waist Circumference (cm)1 | 81.5 (10.8) | 78.8 (15) | 0.26 |
| Visceral Adipose Tissue (kg) | 0.3 (0.3) | 0.1 (0.3) | 0.02 |
Values are presented as median (IQR).
n=14 for both groups
Relationships between diet, body composition, and clinical variables
Among subjects with CF, there was a significant, positive association between fasting blood glucose concentrations and VAT, independent of age and sex (p=0.04; Figure 1A, Table 4). FEV1% predicted was positively, independently associated with BMI (p=0.005) and LBM (p=0.03) but was not associated with any indicators of adiposity (all p>0.05, Table 4). Results were similar when using best FEV1% in the previous year. There were no significant associations between average rate of FEV1% decline and body composition variables (all p>0.05, Table 3). Added dietary sugar intake was significantly, positively associated with VAT among CF subjects (β=0.61 ± 0.30, p=0.047, Figure 1B). The interaction term between health status and added sugar was not significant (p=0.88). The difference in VAT between participants with CF and controls became non-significant after controlling for added sugar intake (p=0.37). Additional dietary variables were not significantly associated with body composition or clinical outcomes (Supplementary Table 3).
Figure 1.
A) Positive association between VAT and fasting blood glucose (mg/dL) in CF participants (β=33.4 ± 14.9, p=0.04), controlling for age and sex. B) Positive association between added sugar intake and visceral adipose tissue (VAT) among CF subjects (β=0.61 ± 0.30, p=0.047), independent of age and sex. *VAT and added sugars are log10-transformed.
Table 4.
Multiple linear regression analyses of body composition (independent variables) and clinical outcomes (dependent variables) in participants with CF
| BMI (kg/m2) |
LBM (kg) |
LBM Index (kg/m2) |
Total Fat Mass (kg) |
Fat Mass Index (kg/m2) |
Body Fat (%) | Waist Circumference (cm) |
VAT (kg)1 |
|
|---|---|---|---|---|---|---|---|---|
| FEV1% Average Rate of Decline | 0.2 ± 0.2 (0.3) | 0.002 ± 0.08 (0.98) | 0.37 ± 0.32 (0.26) | 0.11 ± 0.09 (0.3) | 0.38 ± 0.27 (0.17) | 0.11 ± 0.09 (0.2) | 0.09 ± 0.09 (0.4) | 2.4 ± 1.7 (0.2) |
| FEV1% predicted | 4.0 ± 1.3 (0.005) | 1.3 ± 0.6 (0.03) | 8.0 ± 2.0 (<0.001) | 1.04 ± 0.8 (0.2) | 3.3 ± 2.2 (0.14) | 0.64 ± 0.7 (0.4) | 1.0 ± 1.4 (0.5) | 12.6 ± 14.5 (0.4) |
| Fasting glucose (mg/dL) | −0.81 ± 1.7 (0.6) | −0.4 7 ± 0.7 (0.5) | −1.3 8 ± 1.89 (0.47) | −0.1 5 ± 0.9 (0.9) | 0.31 ± 1.5 (0.84) | 0.09 ± 0.8 (0.9) | 0.31 ± 0.5 (0.6) | 33.4 ± 14.9 (0.04) |
Analyses were conducted in CF subjects only, adjusting for age and sex, and reported as β ± SE (p - value). Abbreviations: FEV, forced expiratory volume; BMI, body mass index; LBM, lean body mass; VAT, visceral adipose tissue.
Data were log transformed for use in regression analyses
Supplemental Tables 4 and 5 provide the relationships between diet, body composition, and fasting glucose among all subjects. Among all subjects, added sugar and saturated fat were positively associated with VAT (p=0.005 and 0.03, respectively), while dietary fiber and protein were inversely associated with VAT (p=0.006 for each). Whole grain intake and HEI-2015 scores were inversely associated with total body fat (p=0.04 and 0.03, respectively). Neither dietary intake nor body composition variables were significantly associated with fasting glucose among all subjects (all p>0.05).
Discussion
To our knowledge, this is the first study to examine associations of dietary intake and diet quality with body composition and clinical outcomes in adults with CF. Our data showed that patients with CF reported poorer diet quality compared to healthy controls. Further, subjects with CF had significantly greater amounts of VAT, which was positively associated with added sugar intake and fasting glucose levels. Together, these data indicate a need for increased surveillance of diet quality and body fat distribution in patients with CF.
Although most patients with CF achieved the recommended energy intake, mean reported dietary fat intake was below the recommended intake, consistent with a recent large European study of pediatric patients with CF [22]. Additionally, our novel findings demonstrate poor diet quality in adults with CF compared to healthy controls, attributed to higher intakes of added sugar, trans-fatty acids, and refined grains, and lower intakes of total fiber and whole grains. Similarly reflective of low diet quality, Sutherland et al. recently reported that children with CF in Australia consumed a disproportionately high intake of energy-dense, yet nutrient-poor, foods[4]. In addition to meeting recommended macronutrient guidelines, registered dietitians and other healthcare providers should emphasize the importance of choosing foods that enhance diet quality in their patients with CF. Future studies should identify causes for lower quality food choices by patients with CF to better design effective interventions.
We found a significantly greater amount of VAT in adult subjects with CF compared to healthy controls, despite comparable measures of BMI, total fat mass, and LBM. To date, only two published studies, using DXA[13] or a computed tomography scan[14], have investigated body fat distribution in patients with CF. Both studies reported greater VAT in adolescents and adults with CF compared to healthy controls, even in CF subjects considered to be malnourished [14]. In our study, waist circumference did not differ between CF and controls, indicating the need for more sensitive measures of visceral adiposity that distinguish between subcutaneous and visceral adipose tissue. In agreement with known effects of VAT on glucose intolerance in non-CF populations[23], our novel data suggest that VAT in CF is positively associated with fasting glucose concentrations. Elevated fasting glucose is reflective of hepatic insulin resistance and/or dysregulation of hepatic glucose production, both of which are exhibited in subjects with CF [16]. However, fasted glucose values are typically in the normal range, presumably due to compensatory mechanisms in CF[16]. Our data suggest that VAT may alter this adaptive response. Although findings require confirmation in larger, longitudinal studies, the clinical implications of altered endocrine function are important.
Dietary factors such as added sugars have been hypothesized to promote VAT accumulation [24]. Our findings demonstrated that the high intake of added sugars in adults with CF was associated with increased VAT. In addition, adjustment for differences in added sugar intake mitigated significant differences in VAT between groups. Thus, current clinical dogmas favoring unrestricted diets in CF may promote VAT accumulation in this population. Future studies to decrease added sugars while maintaining a high-calorie intake may be important to consider based on our data in adult patients with CF.
Experimental data also provide a mechanistic basis for a CF-specific dysregulation of body fat deposition. The transcription factor PPARγ is critical for functional lipid storage capabilities of adipocytes[25], yet is decreased with CFTR deficiency[26]. Elevated pro-inflammatory cytokines, characteristic of CF, may also impair adipocyte lipid storage capabilities[27]. In addition, Bederman et al.[28] reported impaired de novo lipogenesis in CF mouse models resulting in low subcutaneous adipose tissue stores, and increased hepatic triglycerides following a high-fat diet. These studies suggest an inability of CF adipose tissue to store excess lipids, which, in turn, may be deposited as ectopic fat. Although our study did not include a sufficient number of participants on CFTR modulators to make comparisons, an investigation of the role of CFTR modulators on lipid handling and fat distribution is warranted.
Currently, the primary clinical determinant of adequate nutritional status in patients with CF is the maintenance of a goal BMI or associated growth chart percentile[2]. While BMI is a widely utilized and validated tool to assess nutritional status in patients with CF, it has severe limitations because it only reflects body size. BMI does not differentiate between metabolically active components of body weight (LBM vs. fat mass) or provide an indication of body fat topography such as VAT. In this study, LBM but not body fat, was positively associated with lung function, as shown previously[7]. Body composition and fat distribution assessment are more sensitive determinants of nutritional status and may facilitate more targeted interventions compared to BMI.
In this study, we used DXA to assess total body composition and fat distribution. Although magnetic resonance imaging (MRI) and computed tomography (CT) are considered gold-standard for assessing fat distribution, their costs and radiation exposure (with CT) limit their clinical utility. Other available clinical tools to assess body composition such as bioelectrical impedance analysis do not provide information about VAT. As it is recommended that patients with CF begin regular assessments of bone mineral density assessment using DXA scanning at age eight[2], the addition of total and regional body composition with VAT assessment should not be difficult to implement. However, DXA-derived VAT assessment will require further validation in populations with CF before its recommendation for routine clinical application.
A major strength of this study was the careful matching of subjects with CF versus healthy controls, which allowed for group comparisons of body fat distribution without the added bias of differences in total body composition. Furthermore, we describe novel associations of body fat distribution against dietary intake and clinical outcomes in adults with CF. It is possible that there are limitations in the generalizability to non-United States populations; however, high intakes of added sugar have been reported in nationally representative European populations [29], and implications of poor diet quality are a world-wide concern [20]. As this was a pilot study, any null findings may have resulted from a lack of power; although these data will be useful for informing larger, adequately-powered studies. Further, as this was a cross-sectional study, we cannot infer causality in the reported outcomes. Finally, there are inherent limitations in dietary intake assessment, such as recall bias and social desirability bias, which may influence diet data.
Conclusions
In this pilot study, adults with CF consumed poor-quality diets and had increased amounts of VAT compared to age-matched healthy controls. The low diet quality scores in participants with CF were primarily driven by increased intakes of added sugars, refined grains, trans-fatty acids, and low intakes of whole grains and dietary fiber. These preliminary data further suggest that VAT was associated with poor diet quality and elevated fasting glucose concentrations. Thus, adults with CF may have an increased propensity to store metabolically-detrimental ectopic fat, which may be exacerbated by an unrestricted high-calorie diet. These findings highlight the importance of assessing nutritional status using body composition and fat distribution. Larger, prospective studies are needed to determine if body fat distribution and dietary intake are causally related to clinical outcomes, and if they better identify at-risk patients with CF and provide targets for future interventions.
Supplementary Material
Highlights.
Adults with CF reported lower diet quality compared to healthy controls
Visceral adipose tissue (VAT) was higher in adults with CF compared to controls
Added sugar intake was positively associated with VAT in adults with CF
VAT was positively associated with fasting glucose levels in adults with CF
Lung function was positively associated with lean body mass but not body fat mass
Acknowledgements
We appreciate all study participants for donating their time and information and the clinical research staff of the Emory University Hospital Clinical Research Unit of the Georgia CTSA.
Funding
This work was supported by the National Institutes of Health grants K01 DK102851 (JAA), K24 DK096574 (TRZ), and UL1 TR002378 (Georgia Clinical and Translational Science Alliance).
Abbreviations
- BMI
body mass index
- HEI
Health Eating Index
- LBM
lean body mass
- VAT
visceral adipose tissue
- LBMI
lean body mass index
- FMI
fat mass index
Footnotes
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