Abstract

Introduction: Diet quality is an important, but often underappreciated part of heart failure (HF) care. Tailoring of dietary interventions must take into consideration how patients with HF eat. Purpose: To identify distinct dietary profiles representing eating patterns in patients with HF. Methods: This was a secondary analysis of data from a randomized controlled double-blind clinical trial of a nutrition intervention with patients with HF. Baseline dietary data were collected using 24-hour dietary recall that included a weekend and two weekdays. Nutritional data were generated from the Nutrition Data Systems for Research. Nine unique diet components were quantified (along with a total Healthy Eating Index) and included in modeling based on theoretical and empirical knowledge: sodium, added sugars, trans-fat, vitamin C, vitamin D, vitamin E, folate, beta-cryptoxanthin, and lutein + zeaxanthin. Latent class mixture modeling was used to identify distinct dietary profiles among these 9 components representing different eating patterns that could serve as the bases of future interventions. Results: There were 112 participants with an average age 63 ± 12, 59.8% male, and 62.5% with class III or IV HF. Two dietary profiles were identified and were labeled by the prominent dimensional characteristic. The “higher intake” profile (n=79 (69.9% of the sample)) had significantly higher intake levels of sodium, vitamin D, vitamin E, vitamin C, beta-cryptoxanthin, lutein + zeaxanthin, and folate, and a “lower intake” profile (n=34 (30.1% of the sample) had significantly lower intake of these same nutrients. Patients with HF in the lower intake profile also had significant higher quality diets (Healthy Eating Index = 63.7± 13.2) compared with those in the higher intake profile (Healthy Eating Index = 53.9 ± 14.2, p<0.001). Conclusion: Two different nutrient patterns were identified that would be amenable to different interventions; one focused on total calorie reduction and the other focused on optimizing nutrient intake.

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