Dietary patterns and asthma among Peruvian children and adolescents

BMC Pulm Med. 2020 Mar 14;20(1):63. doi: 10.1186/s12890-020-1087-0.

Abstract

Background: Asthma is one of the conditions that contributes to the global burden of respiratory diseases and has been previously associated with diet intake. The goal of this study was to determine the relationship between diet, assessed by a developed score, and asthma in Peruvian children.

Methods: This study was a cross sectional analysis nested within an unmatched case-control study of children in two peri-urban communities of Lima, Peru. We evaluated 767 children and adolescents (573 with asthma, 194 controls) between 9 and 19 years. Diet was assessed using a food frequency questionnaire (FFQ), with food groups classified as "healthy" or "unhealthy". Asthma control, Lung function and atopy were assessed by Asthma Control Test, Spirometry and InmunoCAP 250 test, respectively.

Results: Mean age of participants was 13.8 years (SD 2.6). Mean diet score was 5 (SD 1.23; range 2-8). Healthy Diet Score was associated with asthma status [OR 0.83, 95% CI (0.72, 0.95), p = 0.009] in adjusted analysis. Thus, participants with higher HDS, had lower odds of asthma. In sensitivity analyses, when adjusting for atopy, results did not change significantly. [OR 0.85, 95% CI (0.72, 0.99); p = 0.04]. No association between the HDS and asthma control, FEV1, nor FeNO were observed. Atopy did not modify the association between diet and asthma outcomes.

Conclusions: In our study cohort, better diet quality was associated with lower odds of asthma, but was not associated with asthma control. Diet modification may be a potential intervention to impact the increasing prevalence of this disease.

Keywords: Adolescents; Asthma; Children; Diet; Peruvian.

MeSH terms

  • Adolescent
  • Asthma / epidemiology*
  • Asthma / physiopathology
  • Case-Control Studies
  • Child
  • Cross-Sectional Studies
  • Diet, Healthy / statistics & numerical data*
  • Female
  • Forced Expiratory Volume
  • Humans
  • Hypersensitivity, Immediate / epidemiology*
  • Linear Models
  • Logistic Models
  • Male
  • Multivariate Analysis
  • Peru / epidemiology
  • Prevalence
  • Risk Factors
  • Spirometry