Open Access

Is fruit and vegetable intake associated with asthma or chronic rhino-sinusitis in European adults? Results from the Global Allergy and Asthma Network of Excellence (GA2LEN) Survey

  • Vanessa Garcia-Larsen1, 19Email authorView ORCID ID profile,
  • Rhonda Arthur2,
  • James F. Potts1,
  • Peter H. Howarth3,
  • Matti Ahlström4,
  • Tari Haahtela4,
  • Carlos Loureiro5,
  • Ana Todo Bom5,
  • Grzegorz Brożek6,
  • Joanna Makowska7,
  • Marek L. Kowalski7,
  • Trine Thilsing8,
  • Thomas Keil9, 10,
  • Paolo M. Matricardi11,
  • Kjell Torén12,
  • Thibaut van Zele13,
  • Claus Bachert14,
  • Barbara Rymarczyk15,
  • Christer Janson16,
  • Bertil Forsberg17,
  • Ewa Niżankowska-Mogilnicka18 and
  • Peter G. J. Burney1
Clinical and Translational Allergy20177:3

DOI: 10.1186/s13601-016-0140-9

Received: 22 August 2016

Accepted: 28 December 2016

Published: 27 January 2017

Abstract

Background

Fruits and vegetables are rich in compounds with proposed antioxidant, anti-allergic and anti-inflammatory properties, which could contribute to reduce the prevalence of asthma and allergic diseases.

Objective

We investigated the association between asthma, and chronic rhino-sinusitis (CRS) with intake of fruits and vegetables in European adults.

Methods

A stratified random sample was drawn from the Global Allergy and Asthma Network of Excellence (GA2LEN) screening survey, in which 55,000 adults aged 15–75 answered a questionnaire on respiratory symptoms. Asthma score (derived from self-reported asthma symptoms) and CRS were the outcomes of interest. Dietary intake of 22 subgroups of fruits and vegetables was ascertained using the internationally validated GA2LEN Food Frequency Questionnaire. Adjusted associations were examined with negative binomial and multiple regressions. Simes procedure was used to control for multiple testing.

Results

A total of 3206 individuals had valid data on asthma and dietary exposures of interest. 22.8% reported having at least 1 asthma symptom (asthma score ≥1), whilst 19.5% had CRS. After adjustment for potential confounders, asthma score was negatively associated with intake of dried fruits (β-coefficient −2.34; 95% confidence interval [CI] −4.09, −0.59), whilst CRS was statistically negatively associated with total intake of fruits (OR 0.73; 95% CI 0.55, 0.97). Conversely, a positive association was observed between asthma score and alliums vegetables (adjusted β-coefficient 0.23; 95% CI 0.06, 0.40). None of these associations remained statistically significant after controlling for multiple testing.

Conclusion and clinical relevance

There was no consistent evidence for an association of asthma or CRS with fruit and vegetable intake in this representative sample of European adults.

Keywords

Fruits Vegetables Asthma Chronic rhino-sinusitis Adults Europe Meta-analysis GA2LEN

Background

Fruits and vegetables are rich sources of nutrients and compounds with antioxidant, anti-allergic and anti-inflammatory properties, which could modulate the expression of asthma and allergic diseases [1]. A recent systematic review suggested an overall reduced risk of wheeze or self-reported Dr diagnosed asthma in adults and children with higher intakes of fruits and vegetables [2]. Several observational studies in adults have shown a negative association between various asthma prevalence outcomes, and intake of apples [3], citrus fruits [4], tomatoes or leafy vegetables [4]. Smaller studies in asthmatic adults with a dietary pattern mainly comprised of fruits and vegetables have also been shown to have a lower risk of severe asthma [2]. The current evidence on a possible protective effect of fruits and vegetables on allergic diseases is mixed, with some studies showing a negative association between intake of vegetables [5] or food groups that contain them [6] and a lower asthma prevalence, whilst several population-based studies have reported no association between allergic symptoms and fruits or vegetables when measured individually [7, 8] or as part of a dietary pattern [9, 10].

Epidemiological studies use different operational definitions to assess asthma, as well as different instruments to ascertain usual dietary intake. These issues may make it more difficult to ascribe a consistent interpretation on their relationship. The current observational evidence in European adults is inconclusive, with very few multi-national studies examining in some standardised fashion, the association between asthma and diet [10]. Within the Global Allergy and Asthma Network of Excellence (GA2LEN), we designed and piloted a single, common, food frequency questionnaire (FFQ) [11], which was used to estimate usual dietary intake of over 3500 adults from 10 European countries participating in the GA2LEN Follow-up survey. In this analysis, we investigate the cross-sectional association between asthma and chronic rhino-sinusitis (CRS), with dietary intake of fruits and vegetables in these adults.

Methods

The GA2LEN study—screening and clinical surveys

The core protocol for the GA2LEN survey required 18 European participating centres to identify a random sample of at least 3000 adults aged 15–74 years from an available population-based sampling frame. A stratified random sample was drawn, in which 55,000 adults aged 15–75 answered a questionnaire on respiratory symptoms. The following countries (and cities) were included in this cross-sectional analysis: Belgium (Ghent), Denmark (Odense), Finland (Helsinki), Germany (Duisberg, Brandenburg), The Netherlands (Amsterdam), Poland (Krakow, Lodz, Katowice), Portugal (Coimbra), Sweden (Gothenburg, Stockholm, Umea, Uppsala), and the UK (Southampton, London). In 2008–2009, potential participants were sent a short questionnaire by mail, and at least three attempts were made to elicit a response [12]. The questionnaire collected information on age, gender, smoking and the presence of symptoms of asthma (including age of onset), and CRS. Four sub-samples were selected to define cases and controls: (1) those with self-reported asthma and at least one respiratory symptom reported in the last 12 months (‘asthma’), (2) those having chronic sinusitis (defined following the EP3OS criteria, that is, the presence of at least two of the following symptoms for at least 12 weeks in the past year: (i) nasal blockage, (ii) nasal discharge, (iii) facial pain or pressure or (iv) reduction in sense of smell with at least one of the symptoms being nasal blockage or nasal discharge), (3) those who had both ‘asthma’ and ‘chronic sinusitis’, and those who had none of these conditions. [13] Five questions on symptoms in the last 12 months (breathless when wheezing, woken with tightness in chest, shortness of breath while at rest, shortness of breath after exercise, woken by shortness of breath) were used to construct an asthma symptom score on a five-point scale [14].

Dietary intake

The GA2LEN food frequency questionnaire (FFQ) was designed to assess usual dietary intake across countries, using a single, common, and standardised instrument. The FFQ was validated in a random sample of adults from 5 participant centres in GA2LEN, namely Finland, Portugal, Germany, Greece, and Poland, each representing a different European Region [11]. All centres adhered to the same standard operational procedure (SOP) to translate the questionnaires and the same procedure was used to translate and standardise all other questionnaires in the GA2LEN survey. The GA2LEN FFQ has been translated into more than 25 languages for use in several single and multi-national epidemiological studies [15]. To facilitate international food comparisons, the FFQ was organised into 32 sections of food groups [16]. The FFQ collected data on a wide range of foods, including 43 vegetables and 25 fruits (Table 1). Total energy intake (TEI) was calculated using the latest available food composition estimates from the British Food Composition Table [17].
Table 1

Fruit and vegetable subgroup classification in the GA2LEN Follow-up study

Food group

Food items included

Vegetables

 Leafy vegetables

Lettuce, spinach, chard, fenugreek, wild greens

 Fruit vegetables

Capers, tomatoes, aubergine, courgette, sweet peppers, pumpkin, artichoke, okra, mushroom

 Cucurbitacea

Cucumber, melon, watermelon, bitter melon

 Apiaceae

Celery, carrot, herbs (coriander, parsley, chervil, dill), parsnip

 Other root vegetables

Turnip or swede, radish, beetroot, ginger, taro

 Maiz/Corn

Sweet corn

 Alliums

Onion, garlic, leek

 Brassicaceae

Brussels sprouts, broccoli, cabbage, cauliflower, coleslaw

 Potatoes

Mashed potatoes, baked/roasted/casserole potatoes, chips/french fries, potatoes in salad, potato dumping/bread dumpling/gnocchi, potato tortilla

 Pickled vegetables

Cucumber, radish, cabbage

 All vegetables

Average intake of all above

Fruits

 Hard fruits

Apple, pear

 Citrus fruits

Lemon, orange, mandarin/tangerine, grape-fruit, kiwi

 Oily fruits

Olives, avocado

 Fruit juice

Freshly squeezed fruits

 Berries

Blueberries, strawberries, raspberries (‘forest berries’)

 Nectarines

Nectarine, apricot, peach

 Dried fruits

Raisin, prune

 Tropical fruits

Mango, pineapple (banana assessed individually)

 Canned fruits

Any canned fruits

 Dark pigmented fruit

Cherries, rhubarb, grape, fig, plum

 All fruits

Average intake of all above

Statistical analyses

Sampling probability weights were used to standardise prevalences by gender and age to a European Standard Population.

Multivariable logistic regression was used to assess the relationship between food consumption and CRS within each country, controlling for education, employment, smoking status (never, ex-smoker, current smoker), BMI, age, gender, supplement use and TEI. The country level logistic analyses were weighted to take into account the case–control sampling selection. Negative binomial regression was used to assess the relationship between food consumption and asthma score within each country. This analysis controlled for the same variables and used the same sampling weights as in the logistic regression described above. There was only weak collinearity between the variables when we tested this in each of the multivariable models. The regression coefficients from the country level analyses were meta-analysed to give an overall coefficient. The I2 statistic was used to assess heterogeneity between countries. Simes procedure was used to correct statistical estimates derived from multiple testing [18].

All analyses were run using Stata 13.1 (StataCorp, 4905 Lakeway Drive, College Station, Texas 77845 USA).

Results

The main characteristics of the 3202 participants with valid data on diet and asthma score are summarised in Table 2. Of these, 22.8% reported having at least 1 symptom of asthma (asthma score = 1) whereas 9.3% had 3 or more symptoms. CRS was reported by 23.4% of individuals. Over half of all participants reported eating fruits or vegetables 5 times a week, with Portugal and Poland having the highest intake of these food groups.
Table 2

General characteristics of the study population (based on individuals with complete data on dietary exposures and asthma score)

Variables

Countries

Denmark

Finland

Sweden

United Kingdom

Germany

The Netherlands

Odense (359)

Helsinki (160)

Total (1261)

Total (173)

Total (376)

Amsterdam (215)

Age, years; mean (SD)

48.1 (14.5)

46.8 (15.1)

45.7 (15.1)

51.6 (13.2)

48.8 (15.6)

52.6 (13.9)

Males, n (%)

162 (45.1)

62 (38.8)

556 (44.1)

70 (40.5)

152 (403)

111 (51.6)

BMI (kg/m2)

27.4 (14.8)

26.5 (4.6)

25.9 (7.2)

27.1 (5.6)

26.3 (4.8)

25.7 (3.7)

Age at completing full-time education; years (SD)

23.4 (5.5)

23.5 (5.5)

24.5 (7.7)

18.1 (3.6)

20.6 (5.2)

20.2 (4.6)

Employment status

      

 Employed

188 (52.7)

94 (58.9)

737 (58.5)

85 (49.7)

196 (52.0)

103 (47.9)

 Retired

82 (23.0)

32 (20.0)

199 (15.8)

39 (22.8)

88 (23.3)

56 (21.1)

 Unemployed

11 (3.1)

3 (1.9)

38 (3.0)

4 (2.3)

12 (3.2)

5 (2.3)

 Other

76 (21.5)

31 (19.4)

286 (22.7)

43 (25.1)

81 (21.5)

51 (23.7)

Smoking

      

 Never smokers

155 (43.4)

83 (51.9)

672 (53.3)

77 (44.5)

183 (48.4)

84 (39.1)

 Ex-smokers

102 (28.6)

37 (23.1)

428 (33.9)

70 (40.5)

131 (34.7)

88 (40.9)

 Current smokers

100 (28.0)

40 (25.0)

162 (12.8)

26 (15.0)

64 (16.9)

43 (20.0)

Asthma score; N (%)

      

 0

145 (40.4)

96 (59.6)

583 (46.2)

66 (38.2)

161 (42.6)

100 (41.0)

 1

85 (23.7)

31 (19.3)

276 (21.9)

37 (21.4)

107 (28.3)

40 (18.6)

 2

50 (13.9)

15 (9.3)

195 (15.5)

22 (12.7)

47 (12.4)

37 (17.2)

 3

47 (13.1)

10 (6.2)

114 (9.0)

17 (11.5)

35 (9.3)

23 (10.7)

 4

24 (6.7)

7 (4.4)

61 (4.8)

26 (15.0)

16 (4.2)

12 (5.6)

 5

8 (2.2)

2 (1.2)

33 (2.6)

5 (2.9)

12 (3.2)

3 (1.4)

Chronic rhino-sinusitis; n (%)

63 (17.6)

29 (17.8)

234 (18.3)

22 (12.6)

62 (16.2)

52 (23.9)

Asthma ever (n; %)

115 (32.0)

44 (27.0)

510 (39.8)

80 (45.7)

83 (21.7)

44 (20.2)

CRS only (n; %)

42 (11.7)

17 (10.4)

102 (8.0)

10 (5.7)

38 (9.9)

40 (18.4)

Both asthma ever and CRS (n; %)

21 (5.9)

12 (7.4)

132 (10.3)

12 (6.9)

23 (6.0)

12 (5.5)

Total Energy Intake (TEI)

2577 (761)

3197 (1140)

3110 (978)

2833 (889.6)

2821 (1049)

2817 (827)

Use of nutritional supplements, n (%)

143 (40.4)

70 (43.5)

325 (26.0)

58 (33.7)

102 (27.1)

88 (41.0)

% people eating fruits (all types) ≥5 times/week

202 (56.4)

93 (57.1)

717 (56.0)

101 (57.7)

213 (55.8)

114 (52.3)

% people eating total vegetables (all types) ≥5 times/week

224 (62.4)

128 (78.5)

906 (70.7)

92 (52.6)

194 (50.7)

78 (35.8)

Variables

Countries

Portugal

Belgium

Poland

Total

Coimbra (266)

Ghent (148)

Total (244)

3202

Age, years; mean (SD)

47.1 (15.0)

45.7 (15.1)

49.7 (15.7)

47.6 (15.1)

Males, n (%)

93 (35.0)

71 (48.0)

104 (42.6)

1381 (43.1)

BMI, kg/m2 (SD)

25.9 (5.1)

24.9 (4.4)

27.4 (5.2)

26.3 (5.2)

Age at completing full-time education; years (SD)

20.1 (4.6)

20.6 (6.6)

20.4 (3.4)

22.4 (6.6)

Employment status

    

 Employed

140 (52.6)

75 (51.0)

89 (38.0)

1707 (53.6)

 Retired

56 (26.5)

26 (17.7)

86 (36.8)

664 (20.8)

 Unemployed

11 (4.1)

3 (2.0)

12 (5.1)

99 (3.1)

 Other

59 (22.2)

30 (22.4)

47 (20.0)

717 (22.5)

Smoking

    

 Never smokers

172 (64.7)

75 (50.7)

111 (45.7)

1612 (50.4)

 Ex-smokers

56 (21.1)

45 (30.4)

78 (32.1)

1035 (32.2)

 Current smokers

38 (14.3)

28 (18.9)

54 (22.2)

555 (17.3)

Asthma score

    

 0

109 (41.0)

57 (38.5)

78 (32.0)

1395 (43.5)

 1

49 (18.4)

34 (23.0)

73 (29.9)

732 (22.8)

 2

41 (15.4)

22 (14.9)

34 (13.9)

463 (14.4)

 3

27 (910.2)

17 (11.5)

28 (11.5)

318 (9.9)

 4

23 (8.7)

12 (8.1)

17 (7.0)

198 (6.2)

 5

17 (6.4)

6 (4.1)

14 (5.7)

100 (3.1)

Chronic rhino-sinusitis; n (%)

78 (29.2)

43 (29.1)

50 (20.2)

633 (19.5)

Asthma ever (n; %)

59 (22.1)

23 (15.5)

37 (15.0)

995 (30.7)

CRS only (n; %)

44 (16.5)

28 (18.9)

39 (15.8)

360 (11.1)

Both asthma ever and CRS (n; %)

34 (12.7)

15 (10.1)

11 (4.5)

272 (8.4)

Total Energy Intake (TEI); mean (SD)

3195 (1296)

2937 (885)

3211 (1661)

2993 (1072)

Use of nutritional supplements, n (%)

16 (6.0)

50 (33.8)

53 (22.0)

905 (28.4)

% people eating fruits (all types) ≥5 times/week

189 (70.8)

80 (54.1)

158 (64.0)

1867 (57.6)

% people eating total vegetables (all types) ≥5 times/week

206 (77.2)

77 (52.0)

182 (73.7)

2087 (64.4)

The association between asthma score and fruit and vegetable intake is illustrated in Table 3. After controlling for potential confounders, a statistically significant negative association was observed between having an increasing asthma score and eating dried fruits (β-coefficient −2.34; 95% CI −4.09, −0.59; P value = 0.009). No other fruit groups were associated with asthma. Intake of fruity vegetables (which included capers, tomatoes, aubergine, courgette, sweet peppers, pumpkin, artichoke, okra, and mushroom) was positively associated with asthma score (β-coefficient 0.17; 95% CI 0.04, 0.30). Similarly, a higher asthma score was related to intake of alliums vegetables (onion, garlic, leek) (β-coefficient 0.23; 95% CI 0.06, 0.40). Figure 1 illustrates the per-country associations between asthma score and total fruit intake and fruity vegetables. There was no heterogeneity across countries (I2 = 0%).
Table 3

Association between severity of asthma (asthma score) and fruit and vegetable intake in adults from GA2LEN

Fruit and vegetable groups

Asthma score

Effect size (β-coefficient (95% confidence intervals)

Unadjusted (n = 3206)

Adjusted (n = 2945)

Fruits

  

 Hard fruits

0.01 (−0.11, 0.14) n = 3196

−0.02 (0.15, 0.11) n = 2940

 Bananas

0.03 (−0.14, 0.21) n = 3187

0.04 (−0.19, 0.27) n = 2934

 Citrus fruits

−0.05 (−0.19, 0.09) n = 3196

−0.03 (−0.18, 0.12) n = 2938

 Oily fruits

0.25 (0.02, 0.48) n = 3196

0.24 (0.01, 0.46) n = 2942

 Freshly squeezed fruit

0.16 (−0.03, 0.36) n = 3184

0.18 (−0.01, 0.38) n = 2930

 Berries

−0.07 (−0.32, 0.19) n = 3159

−0.12 (−0.37, 0.13) n = 2907

 Nectarines

0.26 (−0.10, 0.62) n = 3197

0.16 (−0.33, 0.65) n = 2942

 Dried fruits

1.89 (3.36,0.42) n = 3190

2.34 (4.09,0.59) n = 2937

 Tropical fruits

0.13 (−0.31, 0.56) n = 3194

0.21 (−0.15, 0.55) n = 2940

 Canned fruits

−4.62 (−6.50, −2.74) n = 3181

−5.66 (−11.4, 0.07) n = 2930

 Dark pigmented fruits

−0.11 (−0.41, 0.19) n = 3201

−0.09 (−0.37, 0.19) n = 2944

 All fruits

−0.03 (−0.16, 0.10) n = 3203

0.04 (−0.09, 0.17) n = 2944

 Nuts

0.21 (−0.12, 0.54) n = 3192

0.20 (−0.21, 0.61) n = 2935

Vegetables

 Leafy vegetables

0.11 (−0.04, 0.26) n = 3195

0.03 (−0.15, 0.22) n = 2937

 Fruity vegetables

0.16 (0.04, 0.28) n = 3202

0.17 (0.04, 0.30) n = 2942

 Cucurbitacea

0.07 (−0.10, 0.24) n = 3202

−0.02 (−0.22, 0.18) n = 2943

 Apiaceae

0.05 (−0.12, 0.21) n = 3204

0.05 (−0.09, 0.19) n = 2943

 Other root vegetables

0.13 (−0.08, 0.33) n = 3200

0.12 (−0.13, 0.37) n = 2942

 Maize/corn

0.41 (−0.12, 0.93) n = 3189

0.47 (−0.04, 0.98) n = 2936

 Alliums

0.27 (0.15, 0.39) n = 3203

0.23 (0.06, 0.40) n = 2944

 Brassicaceae

0.30 (0.01. 0.59) n = 3202

0.20 (−0.02, 0.41) n = 2943

 Potatoes

0.09 (−0.21, 0.38) n = 3194

0.002 (−0.24, 0.24) n = 2937

 Pickled vegetables

−2.32 (−4.17, −0.47) n = 3175

−1.90 (−3.94, 0.14) n = 2924

 Legumes

2.10 (3.65,0.45) n = 3196

−1.98 (−4.13, 0.18) n = 2939

 All vegetables

0.12 (−0.001, 0.25) n = 3206

0.11 (−0.03, 0.25) n = 2945

Italics indicate a statistically significant effect size

https://static-content.springer.com/image/art%3A10.1186%2Fs13601-016-0140-9/MediaObjects/13601_2016_140_Fig1_HTML.gif
Fig. 1

Weighted adjusted negative binomial regressions of asthma score association with total intake of fruits (top) and fruity vegetables (below) (per centre, and meta-analysis of pooled results)

Table 4 shows the associations found between CRS and fruit and vegetable intake. A 27% lower risk of disease was observed in those with a total intake of fruit ≥5 versus those who ate fruit below this cut-off point (OR 0.23; 95% CI 0.55, 0.97). As illustrated in Fig. 2, there was no evidence of heterogeneity between the estimates across countries (I2 = 0.0%; P value = 0.62).
Table 4

Association between CRS and fruit and vegetable intake in adults from GA2LEN

Fruit and vegetable groups

Effect size (odds ratio (95% confidence intervals)

Unadjusted (n = 3242)

Adjusted (2970)

Fruit group

  

 Hard fruit

0.83 (0.64–1.06) n = 3232

0.82 (0.62–1.09) n = 2965

 Bananas

1.04 (0.78–1.40) n = 3223

0.99 (0.68–1.44) n = 2959

 Citrus fruit

0.78 (0.48–1.26) n = 3232

0.87 (0.52–1.46) n = 2963

 Oily fruits

1.40 (0.91–2.16) n = 3232

1.67 (0.91–3.06) n = 2967

 Freshly squeezed fruit

0.73 (0.44–1.20) n = 3219

0.74 (0.44–1.24) n = 2954

 Berries

1.08 (0.61–1.94) n = n = 3195

1.23 (0.55–2.76) n = 2932

 Nectarines

1.42 (0.84–2.41) n = 3233

1.57 (0.79–3.11) n = 2967

 Dried fruits

0.95 (0.42–2.14) n = 3226

0.98 (0.42–2.32) n = 2962

 Tropical fruits

2.14 (1.10–4.16) n = 3230

2.50 (0.91–6.92) n = 2965

 Canned fruitsa

 Dark pigmented fruits

1.01 (0.71–1.45) n = 3237

1.11 (0.75–1.64) n = 2969

 All fruits

0.75 (0.580.96) n = 3239

0.73 (0.550.97) n = 2969

 Nuts

0.47 (0.21–1.06) n = 3227

0.64 (0.23–1.80) n = 2960

Vegetables

 Leafy vegetables

1.15 (0.86–1.53) n = 3229

1.22 (0.86–1.71) n = 2961

 Fruity vegetables

1.16 (0.87–1.53) n = 3237

1.22 (0.81–1.85) n = 2967

 Cucurbitacea

1.15 (0.85–1.56) n = 3238

1.03 (0.73–1.44) n = 2968

 Apiaceae

1.22 (0.93–1.62) n = 3239

1.22 (0.90–1.64) n = 2968

 Other root vegetables

1.63 (0.98–2.70) n = 3235

1.77 (0.89–3.53) n = 2967

 Maize/corn

1.64 (0.55–4.87) n = 3224

1.74 (0.42–7.22) n = 2961

 Alliums

1.19 (0.91–1.55) n = 3238

0.99 (0.68–1.42) n = 2969

 Brassicaceae

1.09 (0.73–1.62) n = 3237

1.05 (0.67–1.65) n = 2968

 Potatoes

2.27 (1.47–3.52) n = 3229

1.82 (1.03–3.23) n = 2962

 Pickled vegetables

1.73 (0.88–3.4) n = 3210

1.61 (0.72–3.59) n = 2949

 All vegetables

1.11 (0.80–1.54) n = 3242

1.09 (0.67–1.77) n = 2970

 Legumes

1.54 (0.51–4.64) n = 3231

1.24 (0.30–5.10) n = 2964

Italics indicate a statistically significant effect size

aNot enough people with data on this exposure to carry out analyses

https://static-content.springer.com/image/art%3A10.1186%2Fs13601-016-0140-9/MediaObjects/13601_2016_140_Fig2_HTML.gif
Fig. 2

Weighted multivariable analyses of association between CRS with total intake of fruits (per centre, and meta-analysis of pooled results)

After applying Simes procedure, the statistical significance of the association between asthma score and dried fruits was attenuated (P value = 0.05), and all the other associations were no longer statistically significant (>0.15).

Discussion

In this multi-national study of adults participating in the GA2LEN Follow-up survey, asthma symptom score and CRS were negatively associated with dietary intake of dried fruits and total fruit intake, respectively. Asthma symptom score was also positively associated with a higher intake of fruity vegetables and alliums. These associations were observed after adjusting for several potential confounders, which included socio-economic, smoking, and lifestyle-related variables (including BMI, TEI, and nutritional supplement use). After controlling for multiple comparisons, the statistical significance of these associations was lost.

To our knowledge, this is the first multi-national population-based study to examine the association between asthma, CRS and allergic rhinitis, with fruit and vegetable intake, using a standardised method to ascertain both respiratory outcomes and dietary exposures. The results of this study were weighted to make results generalizable to the European adult population. We used an asthma score to ascertain individuals with a variety of symptoms, for its good predictability to ascertain outcomes related to asthma [14, 19]. Asthma is characterised for its clinical phenotypic heterogeneity and temporal phenotypic variability. Being a multi-categorical measure, the score provides more power to detect risk factors for asthma [19].

The GA2LEN FFQ was translated into each of the participant countries’ languages following international guidelines, and was previously piloted and validated in a subsample of 5 participating countries [11]. The FFQ uses a semi-quantitative approach to enquiring about the frequency of intake of 250 food items, which includes staple foods representative of each nation, but also foods that are commonly consumed in all these countries. The GA2LEN FFQ is being used in several other multi-national countries and appears to be a functional and accurate tool to ascertain usual dietary intake [15]. Given the large number of dietary exposure studied, we used Simes procedure to adjust the P values for multiple testing. This method has more power to identify true associations and its use is helpful when there are several highly correlated variables, as it is the case of dietary exposures [18].

The absence of robust evidence suggesting an association between dietary intake of fruits and vegetables with respiratory outcomes in this study has been confirmed in other population-based observational studies. Several authors have reported no association between asthma risk and intake of citrus fruits. As reported in other studies, we did not observe an association between the outcomes studies and citrus fruits [3, 2022] nor with vitamin C, for which observational studies show mixed evidence of a beneficial effect [23].

We did find a negative association between dried fruit intake and asthma score, which remained statistically significant after controlling for multiple comparisons. Recent experimental evidence has demonstrated in an asthma-induced model in rats, that administering V. vinifera dried fruits inhibited the recruitment of inflammatory cytokines (IL)-4, IL-5, IL-1β, tumour necrosis factor, as well as IgE levels, and circulating levels of eosinophils in blood/serum and broncho-alveolar fluid [24]. Treatment with raisin extract also normalised lung function and histamine levels compared to control animals. Although no experimental evidence has demonstrated that prunes might exert similar effects, it has been proposed that the potential beneficial role of prunes on asthma might be mediated through their role in maintaining the gut microbiota balance [25]. Our findings of a negative association between dried fruits (raisins and prunes) might be explained at least partly by these biological mechanisms.

Several other studies have used a more integrative approach to elucidate the association between asthma and dietary exposures using dietary patterns, derived from Principal Component or Factor analysis, or through other indexes. However, dietary patterns that include fruits and vegetables as main food contributors have so far been unrelated to prevalence [9] or risk of adult asthma [26]. The uniformity of the associations observed per country in our study, and the absence of heterogeneity observed in most analyses, would lend further support to the notion that in general intakes of fruits and vegetables are not strongly associated with adult asthma.

Fruits and vegetables are also rich in various subclasses of flavonoids, for which strong anti-oxidant, anti-inflammatory and anti-allergic properties have been demonstrated in experimental studies of induced asthma [27]. These results have been echoed in some observational studies in adults showing a reduced risk of BHR [7] or asthma incidence [28], though others have reported no association with current asthma or allergic symptoms [29]. This is partly explained by the differences in the subclasses studied. In our study, we found some evidence that a lower risk of CRS was associated with a higher intake of fruits, which could partly be explained by the high content of vitamin C and flavonoids in them. We err on the cautious side though as this association was no longer statistically significant after controlling for multiple testing.

Due to the cross-sectional nature of our analysis, we cannot ascribe causality (or lack of) in the association between asthma, CRS, and allergic rhinitis with dietary intake of fruits and vegetables. Although we adjusted for several important potential confounders, there are likely to be other unmeasured confounders involved in the complex association between asthma and diet.

In conclusion, we found no consistent evidence for an association of asthma and allergic rhino-sinusitis with fruit and vegetable intake. The overall effect size observed for CRS and total fruit intake is suggestive of a protective effect, but this needs to be taken with caution given the multiple comparisons carried out in the study.

Abbreviations

GA2LEN: 

The Global Asthma and Allergy Network of Excellence

FFQ: 

food frequency questionnaire

CRS: 

chronic rhino-sinusitis

TEI: 

total energy intake

BMI: 

body mass index

Declarations

Authors’ contributions

VGL and PGB conceived the hypothesis for this analysis. VGL wrote the first draft of manuscript. VGL designed the GA2LEN FFQ which was used to collect data on dietary intake in the GA2LEN participants. PGB led the research efforts to make possible the international validation of the GA2LEN FFQ. RA and JFP contributed with statistical analyses. RA helped to interpret and classify the nutritional variables used in the study. All co-authors listed in the manuscript contributed to and approved the final version of the manuscript and led the research efforts to assess dietary intake in their centres. All authors read and approved the final manuscript.

Acknowledgements

We are indebted to the participants of the GA2LEN Follow-up survey across Europe.

Competing interests

The authors declare that they have no competing interests.

Availability of data and materials

Data requests can be submitted to the GA2LEN Network of Excellence for the consideration and approval of the Scientific Steering Committee. Informal enquiries about data access and material availability can be sent to Dr Vanessa Garcia Larsen (v.garcialarsen@imperial.ac.uk). The Ga2len FFQ is free to use for academic and research purposes.

Consent for publication

All co-authors have read and approved the final version of the manuscript and gave their consent to publish it.

Ethics approval and consent to participate

All participant centres were granted ethical approval to take part in the GA2LEN Follow-up survey from which this analysis was done. Belgium: Committee for Medical Ethics, University of Ghent. Denmark: Den Videnskabsetiske Komite for Region Syddanmark. Finland: Helsingin Ja Uudenmaan Saira Anhoitopiirin Kuntayhtyma, Eetiset toimukunnat. Germany (both centres): Commission of Ethic, Faculty of Medicine, Heirich Heine Universitat Dusseldorf. Poland: Katowice: Biotethics Commision University of Katowice; Krakow (Commision of Bioethics University of Jagiellonskiego; Lodz (Committee of Bioethics University of Lodz. Portugal: Commission of Ethics for Health, Hospital of the University of Coimbra. Sweden (Joint ethical approval for all four participant centres) Karolinska Institute Ethics Committee. The Netherlands: Medical Ethics Committee, Academic Medical Centre, University of Amsterdam. United Kingdom: National Health Ethics Committee (NRES).

Funding

The GA2LEN study was supported by EU Framework programme for research; contract no. FOOD-CT-2004-506378.

Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), 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 (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

Authors’ Affiliations

(1)
Population Health and Occupational Medicine Group, National Heart and Lung Institute, Imperial College London
(2)
Department of Nutrition, King’s College London
(3)
Faculty of Medicine, University of Southampton
(4)
Skin and Allergy Hospital, Helsinki University Hospital
(5)
Immuno-allergology Department, Coimbra University Hospital
(6)
Department of Epidemiology, College of Medicine, Medical University of Silesia
(7)
Department of Immunology, Rheumatology and Allergy, Medical University of Lodz
(8)
Research Unit for Occupational and Environmental Medicine, Institute of Clinical Research, University of Southern Denmark
(9)
Institute of Social Medicine, Epidemiology and Health Economics, Charité - Universitätsmedizin Berlin
(10)
Institute of Clinical Epidemiology and Biometry, Würzburg University
(11)
Deptartment of Pediatrics, Charité – Universitätsmedizin Berlin
(12)
Section of Occupational and Environmental Medicine, University of Gothenburg
(13)
Upper Airway Research Laboratory, Ghent University
(14)
Division of ENT Diseases, Karolinska Institute
(15)
Clinical Department of Internal Diseases, Allergology and Clinical Immunology, Medical University of Silesia
(16)
Department of Medical Sciences, Respiratory, Allergy and Sleep Research, Uppsala University
(17)
Division of Occupational and Environmental Medicine, Department of Public Health and Clinical Medicine, Umeå University
(18)
Jagiellonian University School of Medicine
(19)
Respiratory Epidemiology, Occupational Medicine and Public Health Group, National Heart and Lung Institute, Imperial College London

References

  1. Julia V, Macia L, Dombrowicz D. The impact of diet on asthma and allergic diseases. Nat Rev Immunol. 2015;15:308–22.View ArticlePubMedGoogle Scholar
  2. Seyedrezazadeh E, Moghaddam MP, Ansarin K, Vafa MR, Sharma S, Kolahdooz F. Fruit and vegetable intake and risk of wheezing and asthma: a systematic review and meta-analysis. Nutr Rev. 2014;72:411–28.View ArticlePubMedGoogle Scholar
  3. Shaheen SO, Sterne JA, Thompson RL, Songhurst CE, Margetts BM, Burney PG. Dietary antioxidants and asthma in adults: population-based case–control study. Am J Respir Crit Care Med. 2001;164:1823–8.View ArticlePubMedGoogle Scholar
  4. Patel BD, Welch AA, Bingham SA, Luben RN, Day NE, Khaw KT, Lomas DA, Wareham NJ. Dietary antioxidants and asthma in adults. Thorax. 2006;61:388–93.View ArticlePubMedPubMed CentralGoogle Scholar
  5. Romieu I, Varraso R, Avenel V, Leynaert B, Kauffmann F, Clavel-Chapelon F. Fruit and vegetable intakes and asthma in the E3N study. Thorax. 2006;61:209–15.View ArticlePubMedPubMed CentralGoogle Scholar
  6. Barros R, Moreira A, Padrão P, Teixeira VH, Carvalho P, Delgado L, Lopes C, Severo M, Moreira P. Dietary patterns and asthma prevalence, incidence and control. Clin Exp Allergy. 2015;45:1673–80.View ArticlePubMedGoogle Scholar
  7. Garcia-Larsen V, Chinn S, Arts IC, Amigo H, Rona RJ. Atopy, wheeze and bronchial responsiveness in young Chilean adults. Do dietary antioxidants matter? Allergy. 2007;62:714–5.View ArticlePubMedGoogle Scholar
  8. Liang W, Chikritzhs T, Lee AH. Lifestyle of young Australian adults with asthma. Asia Pac J Public Health. 2015;27:NP248–54.View ArticlePubMedGoogle Scholar
  9. Lv N, Xiao L, Ma J. Dietary pattern and asthma: a systematic review and meta-analysis. J Asthma Allergy. 2014;7:105–21.PubMedPubMed CentralGoogle Scholar
  10. Hooper R, Heinrich J, Omenaas E, Sausenthaler S, Garcia-Larsen V, Bakolis I, Burney P. Dietary patterns and risk of asthma: results from three countries in European Community Respiratory Health Survey-II. Br J Nutr. 2010;103:1354–65.View ArticlePubMedGoogle Scholar
  11. Garcia-Larsen V, Luczynska M, Kowalski ML, et al. Use of a common food frequency questionnaire (FFQ) to assess dietary patterns and their relation to allergy and asthma in Europe: pilot study of the GA2LEN FFQ. Eur J Clin Nutr. 2011;65:750–6.View ArticlePubMedGoogle Scholar
  12. Bousquet J, et al. GA2LEN (Global Allergy and Asthma European Network) addresses the allergy and asthma ‘epidemic’. Allergy. 2009;64:969–77.View ArticlePubMedGoogle Scholar
  13. Tomassen P, Newson RB, Hoffmans R, et al. Reliability of EP3OS symptom criteria and nasal endoscopy in the assessment of chronic rhinosinusitis: a GA2LEN study. Allergy. 2011;66:556–61.View ArticlePubMedGoogle Scholar
  14. Sunyer J, Pekkanen J, Garcia-Esteban R, Svanes C, Künzli N, Janson C, de Marco R, Antó JM, Burney P. Asthma score: predictive ability and risk factors. Allergy. 2007;62:142–8.View ArticlePubMedGoogle Scholar
  15. Palmer SC, Ruospo M, Campbell KL, et al. DIET-HD Study investigators. Nutrition and dietary intake and their association with mortality and hospitalisation in adults with chronic kidney disease treated with haemodialysis: protocol for DIET-HD, a prospective multinational cohort study. BMJ Open. 2015;5:e006897.View ArticlePubMedPubMed CentralGoogle Scholar
  16. Ireland J, van Erp-Baart AM, Charrondière UR, Møller A, Smithers G. Trichopoulou A; EFCOSUM Group. Selection of a food classification system and a food composition database for future food consumption surveys. Eur J Clin Nutr. 2002;56:S33–45.View ArticlePubMedGoogle Scholar
  17. FSA (Food Standards Agency). McCance and widdowson’s the composition of foods. Seventh Summary edn. Royal Society of Chemistry: Cambridge; 2002.Google Scholar
  18. Simes RJ. An improved Bonferroni procedure for multiple tests of significance. Biometrika. 1986;73:751–4.View ArticleGoogle Scholar
  19. Pekkanen J, Sunyer J, Anto JM, Burney P, European Community Respiratory Survey. Operational definitions of asthma in studies on its aetiology. Eur Respir J. 2005;26:28–35.View ArticlePubMedGoogle Scholar
  20. Kelly Y, Sacker A, Marmot M. Nutrition and respiratory health in adults: findings from the health survey for Scotland. Eur Respir J. 2003;21:664–71.View ArticlePubMedGoogle Scholar
  21. Troisi RJ, Willett WC, Weiss ST, et al. A prospective study of diet and adult-onset asthma. Am J Respir Crit Care Med. 1995;151:1401–8.View ArticlePubMedGoogle Scholar
  22. Soutar A, Seaton A, Brown K. Bronchial reactivity and dietary antioxidants. Thorax. 1997;52:166–70.View ArticlePubMedPubMed CentralGoogle Scholar
  23. Moreno-Macias H, Romieu I. Effects of antioxidant supplements and nutrients on patients with asthma and allergies. J Allergy Clin Immunol. 2014;133:1237–44.View ArticlePubMedGoogle Scholar
  24. Arora P, Ansari SH, Najmi AK, et al. Investigation of anti-asthmatic potential of dried fruits of Vitis vinifera L. in animal model of bronchial asthma. Allergy Asthma Clin Immunol. 2016;12:42.View ArticlePubMedPubMed CentralGoogle Scholar
  25. Anhê FF, Varin TV, Le Barz M, et al. Gut microbiota dysbiosis in obesity-linked metabolic diseases and prebiotic potential of polyphenol-rich extracts. Curr Obes Rep. 2015;4:389–400.View ArticlePubMedGoogle Scholar
  26. Bédard A, Garcia-Aymerich J, Sanchez M, et al. Confirmatory factor analysis compared with principal component analysis to derive dietary patterns: a longitudinal study in adult women. J Nutr. 2015;145:1559–68.View ArticlePubMedGoogle Scholar
  27. Tanaka T, Takahashi R. Flavonoids and asthma. Nutrients. 2013;5:2128–43.View ArticlePubMedPubMed CentralGoogle Scholar
  28. Garcia V, Arts IC, Sterne JA, et al. Dietary intake of flavonoids and asthma in adults. Eur Respir J. 2005;26:449–52.View ArticlePubMedGoogle Scholar
  29. Knekt P, Kumpulainen J, Jarvinen R, et al. Flavonoid intake and risk of chronic diseases. Am J Clin Nutr. 2002;76:560–8.PubMedGoogle Scholar

Copyright

© The Author(s) 2017

Advertisement