Airflow Obstruction and Cardio-metabolic Comorbidities

Abstract

Chronic obstructive pulmonary disease (COPD) is characterized by airflow obstruction and often co-exists with cardiovascular disease (CVD), hypertension and diabetes. This international study assessed the association between airflow obstruction and these comorbidities. 23,623 participants (47.5% males, 19.0% current smokers, age: 55.1 ± 10.8 years) in 33 centers in the Burden of Obstructive Lung Disease (BOLD) initiative were included. 10.4% of subjects had airflow obstruction. Self-reports of physician-diagnosed CVD (heart disease or stroke), hypertension and diabetes were regressed against airflow obstruction (post-bronchodilator FEV₁/FVC < 5th percentile of reference values), adjusting for age, sex, smoking (including pack-years), body mass index and education. Analyses were undertaken within center and meta-analyzed across centers checking heterogeneity using the I²-statistic. Crude odds ratios for the association with airflow obstruction were 1.42 (95% CI: 1.20-1.69) for CVD, 1.24 (1.02-1.51) for hypertension, and 0.93 (0.76-1.15) for diabetes. After adjustment these were 1.00 (0.86-1.16) (I²:6%) for CVD, 1.14 (0.99-1.31) (I²:53%) for hypertension, and 0.76 (0.64-0.89) (I²:1%) for diabetes with similar results for men and women, smokers and nonsmokers, in richer and poorer centers. Alternatively defining airflow obstruction by FEV₁/FVC < 2.5th percentile or 0.70, did not yield significant other results. In conclusion, the associations of CVD and hypertension with airflow obstruction in the general population are largely explained by age and smoking habits. The adjusted risk for diabetes is lower in subjects with airflow obstruction. These findings emphasize the role of common risk factors in explaining the coexistence of cardio-metabolic comorbidities and COPD.

Keywords: Airflow obstruction; COPD; cardiovascular; comorbidity; diabetes; hypertension.

Figure 1.

Flow chart of data extraction.

Figure 2.

Meta-analysis of the adjusted odds ratios for CVD in subjects with airflow obstruction. Forest plot showing the meta-analysis of odds ratios for CVD, adjusting for age, smoking (pack-years and current smoking status), BMI, education and sex in subjects with airflow obstruction compared to those without airflow obstruction. Heterogeneity chi-squared = 27.68, d.f. = 26 (P = 0.374). I-squared (variation in ES attributable to heterogeneity) = 6.1%. Estimate of between-study variance Tau-squared = 0.0095. Test for overall effect: Z = 0.00 (P = 0.999). The following sites could not be included in the analysis due to a low number of subjects reporting CVD or singularity in the data: Blantyre (Malawi), Ife (Nigeria), Mumbai (India), Penang (Malaysia), Pune (India), Srinagar (India).

Figure 3.

Meta-analysis of the adjusted odds ratios for hypertension in subjects with airflow obstruction. Forest plot showing the meta-analysis of odds ratios for hypertension, adjusting for age, smoking (pack-years and current smoking status), BMI, education and sex in subjects with airflow obstruction compared to those without airflow obstruction. Heterogeneity chi-squared = 65.31, d.f. = 31 (P = 0.000). I-squared (variation in ES attributable to heterogeneity) = 52.5%. Estimate of between-study variance Tau-squared = 0.0694. Test for overall effect: Z = 1.79 (P = 0.074). The following sites could not be included in the analysis due to a low number of subjects reporting hypertension: Ife (Nigeria).

Figure 4.

Meta-analysis of the adjusted odds ratios for diabetes in subjects with airflow obstruction. Forest plot showing the meta-analysis of odds ratios for diabetes, adjusting for age, smoking (pack-years and current smoking status), BMI, education and sex in subjects with airflow obstruction compared to those without airflow obstruction. Heterogeneity chi-squared = 24.24, d.f. = 24 (P = 0.448). I-squared (variation in ES attributable to heterogeneity) = 1.0%. Estimate of between-study variance Tau-squared = 0.0017. Test for overall effect: Z = 3.35 (P = 0.001). The following sites could not be included in the analysis due to a low number of subjects reporting diabetes or singularity in the data: Cotonou (Benin), Guangzhou (China), Ife (Nigeria), NampicuanTalugtug (Philippines), Naryn (Kyrgyztan), Pune(India), Srinagar (India), Uppsala (Sweden).

Figure 5.

Meta-analyses of the odds ratios for comorbidities in subjects with airflow obstruction, showing the influence of the threshold to define airflow obstruction and adjusting for covariates. Summary forest plots showing the unadjusted, partly adjusted, and completely adjusted odds ratios (meta-OR) and the 95% CI for A. diabetes, B. hypertension, and C. CVD in subjects with airflow obstruction compared to those without airflow obstruction (left column: FEV₁/FVC < 2.5th percentile, middle column: FEV₁/FVC < 5th percentile, right column: FEV₁/FVC < 0.70). Adjusting for all variables at the same time (as shown bottom of each panel), leaving out one variable for each model (middle of each panel), and running one model without any adjusting variables (as shown in the upper part of each panel). The variables age, smoking (pack-years and current smoking status), BMI, education and sex were analyzed. Details of these meta-analyses are reported in Supplementary Table 4.