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Relations between obesity and asthma in young adult females

Alyssa L. Mueller

Department of Preventive Medicine, Faculty of Medicine, University of São Paulo, Brazil

Bianca Muñoz

Department of Preventive Medicine, Faculty of Medicine, University of São Paulo, Brazil

Rebecca L. Ranous

Department of Preventive Medicine, Faculty of Medicine, University of São Paulo, Brazil

Isabel Laiseca-Ruiz

Department of Preventive Medicine, Faculty of Medicine, University of São Paulo, Brazil

Jessica L. Hartos

Department of Preventive Medicine, Faculty of Medicine, University of São Paulo, Brazil

DOI: 10.15761/FWH.1000161

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Abstract

Purpose: Previous research has linked obesity and asthma, but results have shown conflicting findings overall and do not focus solely on young adult females. Therefore, the purpose of this study is to assess the relationship between obesity and asthma among females ages 18-34 in the general population.

Methods: This cross-sectional analysis used 2017 BRFSS data for females ages 18-34 in Kansas (N = 1557), Kentucky (N = 615), Maine (N = 502), and Michigan (N = 847). Multiple logistic regression analysis by state was performed to determine the relationship between obesity and asthma after controlling for health-related, socioeconomic, and demographic factors.

Results: Across states, up to one-quarter of the participants reported having asthma (16-24%) and up to one-half reported obesity (29%-52%). Results of adjusted analysis indicated that asthma did not differ by weight status in any state. However, asthma was related to having two or more health conditions in three out of four states.

Conclusion: Overall, asthma was not related to obesity in young adult females ages 18-34 in the general population; however, asthma was highly related to having two or more health conditions. The results of this study may be generalizable to young adult females in primary care practice. Practitioners should always screen patients for obesity and educate on the causes of obesity, including genetics, metabolism, and lifestyle, and possible treatment options. Practitioners should also screen young adult females for asthma and chronic health conditions if they present with symptoms of either; educate about the management of comorbid conditions; and assess the treatment options for comorbid conditions.

Key words

obesity, asthma, females, comorbid conditions, primary care

Introduction

Worldwide, over 300 million people of all ages, genders, and races suffer from asthma [1-3]. Of the 25 million people in the United States with asthma, 18.7 million, or around 7%, are adults, and the prevalence is increasing by about 0.5% every year [2,4-6]. Asthma is the chronic inflammation and constriction of airways accompanied by thick mucus secretion that can further impede air flow [5-8] with visible symptoms including coughing, wheezing, and shortness of breath. Unfortunately, a person’s inability to effectively manage their asthma symptoms can lead to excessive healthcare utilization and even mortality [10-11]  

Obesity may be a major risk factor for asthma and increased asthma symptom severity [2,4,11]. Obesity is most commonly measured via Body Mass Index (BMI), with a BMI of 18.5-24.9 considered normal, 25-29.9 considered overweight, and 30 or higher considered obese [4,12,13]. Over 20% of the U.S. adult population are considered obese, or about 44.3 million people – 21.4 million men and 22.9 million women, and these numbers are only predicted to increase [4,11]. Worldwide, at least 2.8 million people die annually due to complications of overweight or obese with other diseases including diabetes, high blood pressure, high cholesterol level, arthritis, stroke incidence, cardiovascular disease, and even cancer [2,4,11,13]. Furthermore, women are more likely to be obese than men, and the prevalence of obesity is higher in older populations than in younger [11]. Finally, socioeconomic status, such as unemployment, has shown to be related to BMI in the general population [4,12].  

Research reviews have found that about 10% of overweight and obese individuals also suffer from asthma, and that factors such as age, gender, activity level and diet influence the relationship between obesity and asthma [4,11]. However, many of these studies have included small sample sizes and inconsistent measurements for obesity [4,10]. Moreover, there are conflicting findings on asthma and gender with some evidence showing that being female increases your chance of having obesity and asthma concurrently, and others finding that gender plays no role in the relationship [4]. Furthermore, no studies focus solely on the obesity-asthma relationship for young adult females in the general population [4], and this may be important as asthma is the second leading health concern for use of health care services in young adults, and the average BMI for young adult females has increased over time at a much higher rate than BMI increases for young adult males [14]. Therefore, the purpose of this study is to explore whether obesity is related to asthma in young adult females in the general population. 

Methods

Design

This cross-sectional analysis used data from the 2017 Behavior Risk Factor Surveillance System (BRFSS) conducted by the Center for Disease Control and Prevention [15]. The purpose of BRFFS is to collect data on health risks behaviors, chronic diseases, health conditions, and health prevention practices. More than 400,000 adults 18 and older are interviewed annually using random digit dialing survey techniques across all 50 States and the District of Columbia. The CDC compiles all BRFFS data and makes de-identified data available to researchers for secondary data analysis. This study was given exempt status by the Institutional Review Board of the University of North Texas Health Science Center.  

Sample

The samples for this study included females ages 18-34 in Kansas (N = 1557), Kentucky (N = 615), Maine (N = 502), and Michigan (N = 847) who had data for obesity and asthma. These states were selected because of higher prevalence of (a) obesity, (b) asthma, and (c) young adult females based on the BRFFS 2016 prevalence survey data maps [16].

Data

The outcome, asthma, was measured as “yes” or “no” to having ever been diagnosed with asthma. The factor of interest, obesity, was measured in BRFSS by calculating the participants’ BMI based on their reported height and weight, and “obese” was categorized as a BMI of 30.00 or higher. Control variables included general health status, health conditions, healthcare access, vegetable consumption, physical activity, alcohol use, tobacco use, age category, ethnicity/race, education level, employment status, and income level. All variables and categories are shown in table 1. Health conditions was calculated as the number of “yes” responses to ever being diagnosed with any of the following: heart attack/myocardial infarction; angina or coronary heart disease; stroke; skin cancer; other types of cancer; chronic obstructive pulmonary disease, emphysema or chronic bronchitis; arthritis; depressive disorder; kidney disease; diabetes; high blood cholesterol; and high blood pressure. We then categorized values as “0 health conditions,” “1 health condition,” or “2 or more health conditions.”

Table 1. Participant characteristics by state

Variable

Kansas

n = 1557

Kentucky

n = 615

Maine

n = 502

Michigan

n = 847

N

%

N

%

N

%

N

%

Asthma

1557

100

615

100

502

100

847

100

   Yes

299

19

99

16

122

24

190

22

   No

1258

81

516

84

380

76

657

78

Weight Status

1557

100

615

100

502

100

847

100

   Obese

457

29

196

32

262

52

221

26

   Not obese

1100

71

419

68

240

48

626

74

General Health Status

1553

100

614

99

502

100

847

100

   Good or better

1369

88

539

88

445

89

749

88

   Fair or poor

184

12

75

12

57

11

98

12

Health Conditions

1206

77

505

82

375

75

588

69

   0

668

55

223

44

184

49

297

51

   1

325

27

168

33

133

35

188

32

   2 or more

213

18

114

23

58

15

103

17

Vegetable Consumption

1402

90

564

92

469

93

787

92

   Daily

1157

83

475

84

429

91

787

92

   Not daily

245

17

89

16

40

9

152

19

Physical Activity

1395

90

550

89

457

91

780

92

   Inactive or insufficient

689

49

294

53

226

49

404

52

   Active or highly active

706

51

256

47

231

51

376

48

Healthcare Access

1555

100

615

100

502

100

847

100

   Cost did not influence

1265

81

526

86

413

82

710

84

   Cost did influence

290

19

89

14

89

18

137

16

Alcohol Use

1466

94

584

95

467

93

794

94

   Use

171

25

277

47

316

68

506

64

   No use

111

18

307

53

151

32

288

36

Tobacco Use

1515

97

606

99

490

98

827

98

   Never smoker

1044

69

388

64

336

69

590

71

   Former smoker

184

12

64

11

60

12

95

11

   Current smoker

287

19

154

25

94

19

142

17

Age

1557

100

615

100

502

100

847

100

   18-24

552

35

222

36

149

30

332

39

   25-34

1002

65

393

64

353

70

515

61

Ethnicity/Race

1541

99

612

99

499

99

842

99

   White

1161

75

541

88

465

93

582

69

   Other

380

25

71

12

34

7

260

31

Education Level

1557

100

615

100

500

100

845

99

   No college

467

30

166

27

146

29

243

29

   Some college

588

38

264

43

151

30

313

37

   Graduated college

502

32

185

30

203

    41

289

34

Employment Status

1542

99

613

99

501

100

840

99

   Work

1059

69

374

61

337

67

520

62

   Student

221

14

116

19

68

14

168

20

   Other

262

17

123

20

96

19

152

18

Income Level

1294

83

437

71

468

93

702

83

   Less than $25,000

405

31

140

32

132

28

216

31

   $25,000 to $49,999

379

29

110

25

158

32

181

26

   $50,000 or more

510

39

187

43

178

38

305

43

Analysis

Frequency distributions by state were used to describe the samples and identify any issues among the distribution of variables. We analyzed data separately by state to determine any patterns in relationships across similar samples. Multiple logistic regression by state was conducted to assess the relationship between obesity and asthma after controlling for health-related, demographic, and socioeconomic factors. Similar results in three or four out of four states were considered reliable evidence for relations. Any observations with missing data for any variables were excluded from adjusted analysis. All analyses were conducted in STATA 15.1 (Copyright 1985-2017 StataCorp LLC). 

Results

Descriptive statistics

Table 1 lists participant characteristics for young adult females in Kansas, Kentucky, Maine, and Michigan. Up to one-quarter of the participants reported having asthma (16-24%) and up to one-half reported as obese (29-52%). For health-related factors, most participants reported good or better general health status (88-89%) and about half reported having one or more health conditions (45-56%). Most of participants reported consuming vegetables daily (83-92%), up to one-half reported being inactive or insufficiently active (26-53%), and most reported that cost did not influence their decision to see a doctor (81-86%). For substance use, up to two-thirds of the participants reported drinking in the last 30 days (25-68%) and never having smoked (64-71%). For socioeconomic factors, the participants were fairly divided amongst those who did not attend, attended, or graduated college; the majority of participants were employed (61-69%); and participants were fairly divided amongst annual income categories. Most of the participants were white (69-93%), and over two-thirds were ages 25-34 years (61-70%).

Adjusted statistics

As shown in table 2, the results of multiple logistic regression analysis for young adult females in Kansas, Kentucky, Maine, and Michigan indicated that after controlling for all other variables in the model, asthma did not differ by weight status in any state. However, across states, participants who reported two or more health conditions were about 3.2-4.2 times more likely to report asthma compared to those with zero health conditions.  

Table 2. Adjusted results across states

Predicting Asthma 

(yes vs. no) 

Kansas 

Kentucky 

Maine 

Michigan 

AOR 

95 % CI 

AOR 

95 % CI 

AOR 

95 % CI 

AOR 

95 % CI 

Low 

High 

Low 

High 

Low 

High 

Low 

High 

Weight Status (ref: not obese)

 

 

 

 

 

 

 

 

 

 

 

 

Obese  

1.13 

0.75 

1.71 

1.14 

0.51 

2.56 

1.06 

0.55 

2.04 

1.23 

0.71 

2.13 

Health Conditions (ref: 0)

 

 

 

 

 

 

 

 

 

 

 

 

   1  

1.13 

0.70 

1.80 

0.80 

0.34 

1.91 

2.41 

1.24 

4.68 

2.84 

1.59 

5.08 

   2 or more

3.77 

2.29 

6.33 

0.98 

0.33 

2.88 

3.15 

1.33 

7.49 

4.23 

1.99 

9.03 

AOR= adjusted odds ratio; 95 % CI=95 % confidence intervals; ref=referent group; boldface indicates significance (AORs with 95% CI that do not include 1.00 are significant); results shown are only for the factor of interest and any control variables that were significant in three or more states; model also included general health status, healthcare access, vegetable consumption, physical activity, alcohol use, tobacco use, age category, ethnicity/race, education level, employment status, and income level.

Discussion

The purpose of this study was to explore whether obesity was related to asthma in young adult females when controlling for health-related, socioeconomic, and demographic factors. Across states, up to one-quarter of the participants reported having asthma and up to one-half reported as obese. The results of adjusted analysis indicated that obesity was not related to asthma in young adult females. Our findings are similar to a previous study whose research showed that there was no significant association between asthma and obesity among young adult Brazilian male and females ages 23-25 who were randomly selected [17]. However, other studies have shown significant relations, especially among women of all ages [18,19].  It may be that health consequences of obesity and asthma become more interlinked as women age.

Although our study found that obesity may not relate to asthma in young adult females, having multiple health conditions may.  Our study indicated that participants with two or more health conditions were up to four times more likely to report asthma. These results are similar to prior research which suggests that asthma shares close relationships with a variety of obstructive diseases and depression [8,20]. Therefore, issues with comorbid health conditions may show earlier than issues complicated by obesity in this younger demographic.

Limitations

The use of 2017 BRFSS data allowed access to multiple large samples for determining the association between asthma and obesity in our target population, and the data was current.  However, cross-sectional data only indicates relations and not direction of relations and our samples were not representative of different races, both of which could limit the generalizability of the results. Furthermore, BRFSS measured weight status by asking participants for their height and weight to calculate BMI, which may be inaccurately reported as well as inaccurate in estimating weight status. Utilizing a more appropriate measure such as an abdominal circumference may be beneficial to assess health status in future research [4]. In addition, we lacked information on symptom severity, management strategies, and medications related to asthma or other health conditions, all of which may impact the relationship between asthma and obesity.

Conclusions

Because this was a population-based study, the results may be generalizable to young adult females in primary care practice. In the clinic, up to one-half of this target population may be obese but obesity may not be related to asthma. Practitioners should screen for asthma if symptoms present, educate on ways to manage asthma symptoms, and refer to allergy or asthma specialists as needed. Although obesity may not be related to asthma in this target population, obesity can lead to other complications over time. Therefore, practitioners should always screen young adult females for obesity and educate patients on the causes of obesity, implementing lifestyle changes, testing that can inform the patient on the role genetics and metabolism play in their obesity, and surgical options. Referrals should be made to weight reduction specialists as needed. Additionally, up to one-fourth of the young adult females seen in primary care may have multiple health conditions, and having comorbidities may be highly related to asthma in this target population. Thus, practitioners should screen for comorbidities if asthma symptoms present and educate on preventative measures for chronic conditions and the importance of managing comorbid conditions. Practitioners should also assess the compatibility of treatments for multiple chronic conditions and make referrals to specialists as needed.

References

  1. Hosseini B, Berthon BS, Wark P, Wood LG (2017) Effects of Fruit and Vegetable Consumption on Risk of Asthma, Wheezing and Immune Responses: A Systematic Review and Meta-Analysis. Nutrients 9: E341. [Crossref]
  2. Novosad S, Khan S, Wolfe B, Khan A (2013) Role of obesity in asthma control, the obesity-asthma phenotype. J Allergy (Cairo) 2013: 538642. [Crossref]
  3. Torgerson DG, Ampleford EJ, Chiu GY, Gauderman WJ, Gignoux CR, et al. (2011) Meta-analysis of genome wide association studies of asthma in ethnically diverse North American populations. Nat Genet 43: 887-892. [Crossref]
  4. Beuther AD, Sutherland ER (2007) Overweight, obesity and incident asthma: A meta-analysis of prospective epidemiologic studies. Am J Respir Crit Care Med 175: 661-666. [Crossref]
  5. Kim KH, Jahan SA, Kabir E (2013) A review on human health perspective of air pollution with respect to allergies and asthma. Environ Int 59: 41-52. [Crossref]
  6. National Medical Association (2018) Asthma fact sheet. Available at: http://asthma.nmanet.org/docs/factsheet-asthma-print.pdf
  7. Chest Foundation (2018) Asthma infographic. Available at: https://foundation.chestnet.org/wp-content/uploads/2017/01/asthma-infographic.pdf
  8. Kasasbeh A, Kasasbeh E, Krishnaswamy G (2007) Potential mechanisms connecting asthma, esophageal reflux, and obesity/sleep apnea complex--a hypothetical review. Sleep Med Rev 11: 47-58. [Crossref]
  9. Press VG, Pappalardo AA, Conwell WD, Pincavage AT, Prochaska MH, et al. (2012) Interventions to improve outcomes for minority adults with asthma: a systematic review. J Gen Intern Med 27: 1001-1015. [Crossref]
  10. Weiser EB (2007) The prevalence of anxiety disorders among adults with asthma: A meta-analytic review. J Clin Psychol Med Settings 14: 297-307.
  11. Mokdad AH, Ford ES, Bowman BA, Dietz WH, Vinicor F, et al. (2003) Prevalence of obesity, diabetes, and obesity-related health risk factors, 2001. JAMA 289: 76-79. [Crossref]
  12. Ali Z, Ulrik CS (2013) Obesity and asthma: a coincidence or a causal relationship? A systematic review. Respir Med 107: 1287-1300. [Crossref]
  13. World Health Organization (2018) Obesity and obesity in the western pacific region: An equity perspective. Available at: http://apps.who.int/mediacentre/factsheets/fs311/en/index.html
  14. Park MJ, Paul Mulye T, Adams SH, Brindis CD, Irwin CE Jr (2006) The health status of young adults in the United States. J Adolesc Health 39: 305-317. [Crossref]
  15. Centers for Disease Control and Prevention (2008) 2017 BRFSS survey data and documentation. CDC website. Available at: https://www.cdc.gov/brfss/annual_data/annual_2017.html
  16. Fisher MA, Ma ZQ (2014) Multiple chronic conditions: diabetes associated with comorbidity and shared risk factors using CDC WEAT and SAS analytic tools. J Prim Care Community Health 5: 112-121. [Crossref]
  17. Cetlin AA, Gutierrez MR, Bettiol H, Barbieri MA, Vianna EO (2012) Influence of asthma definition on the asthma-obesity relationship. BMC Public Health 12: 844. [Crossref]
  18. Barros R, Moreira P, Padrão P, Teixeira VH, Carvalho P, et al. (2017) Obesity increases the prevalence and the incidence of asthma and worsens asthma severity. Clin Nutr 36: 1068-1074. [Crossref]
  19. Wang L, Wang K, Gao X, Paul TK, Cai J, Wang Y (2015) Sex difference in the association between obesity and asthma in US adults: Findings from a national study. Respir Med 109: 955-962.
  20. Opolski M, Wilson I (2005) Asthma and depression: a pragmatic review of the literature and recommendations for future research. Clin Pract Epidemiol Ment Health 1: 18. [Crossref]

Editorial Information

Editor-in-Chief

Article Type

Research Article

Publication history

Received: March 07, 2019
Accepted: March 18, 2019
Published: March 21, 2019

Copyright

©2019 Mueller AL. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Citation

Mueller AL, Muñoz B, Ranous RL, Laiseca-Ruiz I, Hartos JL (2019) Relations between obesity and asthma in young adult females. Front Womens Health. 4: DOI: 10.15761/FWH.1000161

Corresponding author

Jessica Hartos

Department of Physician Assistant Studies, University of North Texas Health Science Center, 3500 Camp Bowie Blvd EAD 416, Fort Worth, Texas, 76107, USA

Table 1. Participant characteristics by state

Variable

Kansas

n = 1557

Kentucky

n = 615

Maine

n = 502

Michigan

n = 847

N

%

N

%

N

%

N

%

Asthma

1557

100

615

100

502

100

847

100

   Yes

299

19

99

16

122

24

190

22

   No

1258

81

516

84

380

76

657

78

Weight Status

1557

100

615

100

502

100

847

100

   Obese

457

29

196

32

262

52

221

26

   Not obese

1100

71

419

68

240

48

626

74

General Health Status

1553

100

614

99

502

100

847

100

   Good or better

1369

88

539

88

445

89

749

88

   Fair or poor

184

12

75

12

57

11

98

12

Health Conditions

1206

77

505

82

375

75

588

69

   0

668

55

223

44

184

49

297

51

   1

325

27

168

33

133

35

188

32

   2 or more

213

18

114

23

58

15

103

17

Vegetable Consumption

1402

90

564

92

469

93

787

92

   Daily

1157

83

475

84

429

91

787

92

   Not daily

245

17

89

16

40

9

152

19

Physical Activity

1395

90

550

89

457

91

780

92

   Inactive or insufficient

689

49

294

53

226

49

404

52

   Active or highly active

706

51

256

47

231

51

376

48

Healthcare Access

1555

100

615

100

502

100

847

100

   Cost did not influence

1265

81

526

86

413

82

710

84

   Cost did influence

290

19

89

14

89

18

137

16

Alcohol Use

1466

94

584

95

467

93

794

94

   Use

171

25

277

47

316

68

506

64

   No use

111

18

307

53

151

32

288

36

Tobacco Use

1515

97

606

99

490

98

827

98

   Never smoker

1044

69

388

64

336

69

590

71

   Former smoker

184

12

64

11

60

12

95

11

   Current smoker

287

19

154

25

94

19

142

17

Age

1557

100

615

100

502

100

847

100

   18-24

552

35

222

36

149

30

332

39

   25-34

1002

65

393

64

353

70

515

61

Ethnicity/Race

1541

99

612

99

499

99

842

99

   White

1161

75

541

88

465

93

582

69

   Other

380

25

71

12

34

7

260

31

Education Level

1557

100

615

100

500

100

845

99

   No college

467

30

166

27

146

29

243

29

   Some college

588

38

264

43

151

30

313

37

   Graduated college

502

32

185

30

203

    41

289

34

Employment Status

1542

99

613

99

501

100

840

99

   Work

1059

69

374

61

337

67

520

62

   Student

221

14

116

19

68

14

168

20

   Other

262

17

123

20

96

19

152

18

Income Level

1294

83

437

71

468

93

702

83

   Less than $25,000

405

31

140

32

132

28

216

31

   $25,000 to $49,999

379

29

110

25

158

32

181

26

   $50,000 or more

510

39

187

43

178

38

305

43

Table 2. Adjusted results across states

Predicting Asthma 

(yes vs. no) 

Kansas 

Kentucky 

Maine 

Michigan 

AOR 

95 % CI 

AOR 

95 % CI 

AOR 

95 % CI 

AOR 

95 % CI 

Low 

High 

Low 

High 

Low 

High 

Low 

High 

Weight Status (ref: not obese)

 

 

 

 

 

 

 

 

 

 

 

 

Obese  

1.13 

0.75 

1.71 

1.14 

0.51 

2.56 

1.06 

0.55 

2.04 

1.23 

0.71 

2.13 

Health Conditions (ref: 0)

 

 

 

 

 

 

 

 

 

 

 

 

   1  

1.13 

0.70 

1.80 

0.80 

0.34 

1.91 

2.41 

1.24 

4.68 

2.84 

1.59 

5.08 

   2 or more

3.77 

2.29 

6.33 

0.98 

0.33 

2.88 

3.15 

1.33 

7.49 

4.23 

1.99 

9.03 

AOR= adjusted odds ratio; 95 % CI=95 % confidence intervals; ref=referent group; boldface indicates significance (AORs with 95% CI that do not include 1.00 are significant); results shown are only for the factor of interest and any control variables that were significant in three or more states; model also included general health status, healthcare access, vegetable consumption, physical activity, alcohol use, tobacco use, age category, ethnicity/race, education level, employment status, and income level.