| Literature DB >> 32366316 |
Auyon J Ghosh1,2, Matthew Moll1,2, Lystra P Hayden1,3, Jessica Bon4, Elizabeth Regan5, Craig P Hersh6,7.
Abstract
BACKGROUND: Previous studies have established a higher prevalence of vitamin D deficiency in patients with COPD, but the relationship between vitamin D levels and COPD exacerbations remains controversial. In addition, the effect of vitamin D levels on imaging characteristics remains mostly unexplored. Using cross-sectional and longitudinal follow up data from the COPDGene Study, we assessed the association between vitamin D levels on respiratory symptoms, exacerbations, and imaging characteristics. We hypothesized that vitamin D deficiency will be associated with worse respiratory-related outcomes.Entities:
Keywords: COPD; Quantitative imaging; Respiratory symptoms; Vitamin D
Mesh:
Substances:
Year: 2020 PMID: 32366316 PMCID: PMC7199369 DOI: 10.1186/s12890-020-1148-4
Source DB: PubMed Journal: BMC Pulm Med ISSN: 1471-2466 Impact factor: 3.317
Subject Characteristics
| Vitamin D Deficient | Vitamin D Sufficient | ||
|---|---|---|---|
| n | 563 | 981 | |
| Age in years | 57.07 (7.92) | 62.09 (8.67) | < 0.001 |
| Female | 302 (53.6) | 520 (53.0) | 0.851 |
| Non-Hispanic White | 236 (41.9) | 789 (80.4) | < 0.001 |
| African American | 327 (58.1) | 192 (19.6) | < 0.001 |
| Current Smoker | 352 (62.5) | 385 (39.2) | < 0.001 |
| Pack-years smoking | 42.26 (25.99) | 44.83 (24.56) | 0.053 |
| Post-bronchodilator FEV1 percent predicted | 75.96 (25.01) | 73.24 (24.86) | 0.039 |
| COPD | 291 (51.8) | 572 (58.3) | 0.015 |
Mean (SD) or N(%) are shown
Fig. 1Vitamin D concentration by GOLD stage: Boxplots demonstrating the median (bold black line), middle quartiles (box), and 1st and 4th quartiles (whiskers) for vitamin D concentration, stratified by GOLD stage. There was no significant difference in median vitamin D concentration by GOLD stage
Respiratory symptoms, functional status, and exacerbations
| Vitamin D Deficient | Vitamin D Sufficient | Vitamin D Deficiency Effect Estimateb | |||
|---|---|---|---|---|---|
| na | 534 | 957 | |||
| SGRQ total score | 33.23 (23.33) | 26.89 (21.19) | < 0.001 | 3.14 (1.12) | 0.005 |
| Chronic Bronchitis | 113 (21.2) | 204 (21.3) | 1.0 | 0.91 [0.68, 1.22] | 0.54 |
| BODE score | 1.68 (1.75) | 1.50 (1.72) | 0.068 | 0.061 (0.07) | 0.38 |
| MMRC dyspnea score | 1.67 (1.46) | 1.38 (1.42) | < 0.001 | 0.11 (0.074) | 0.16 |
| 6 min walk distance, feet | 1312.76 (366.38) | 1400.16 (361.25) | < 0.001 | −31.0 (18.9) | 0.1 |
| Exacerbations per year | 0.43 (0.76) | 0.43 (0.72) | 0.94 | 0.032 (0.041) | 0.44 |
| Severe exacerbations per year | 0.19 (0.52) | 0.14 (0.32) | 0.038 | 0.024 (0.023) | 0.3 |
Mean (SD) or N(%) are shown
aLongitudinal follow up cohort had 1491 subjects, due to missing data
bCovariates in linear and logistic regression include age, race, gender, current smoking status, smoking pack years, and FEV1 percent predicted. Beta (SE) or Odds Ratio (95% CI) are shown
Regression analyses showing effect of vitamin D levels on respiratory symptoms, functional status, and exacerbationsa
| Vitamin D Effect Estimateb | SE or 95% CI | ||
|---|---|---|---|
| COPD Diagnosis | 1.02 | [0.94, 1.10] | 0.66 |
| FEV1 percent predictedc | 0.024 | 0.056 | 0.66 |
| SGRQ total score | − 0.19 | 0.045 | < 0.001 |
| BODE score | −0.003 | 0.0028 | 0.29 |
| MMRC score | −0.0093 | 0.0030 | 0.002 |
| 6 min walk distance, feet | 2.25 | 0.76 | 0.0032 |
| Exacerbations per year | −0.0027 | 0.0017 | 0.11 |
| Severe Exacerbations per year | −0.0019 | 0.00095 | 0.046 |
aEach row represents the outcome of a separate regression model
bCovariates include age, race, gender, current smoking status, smoking pack years, and FEV1 percent predicted. Beta (SE) or Odds Ratio (95% CI) are shown
cCovariates include age, race, gender, height, current smoking status, and smoking pack years
Fig. 2Time to exacerbation; Kaplan-Meier curves demonstrating the time to exacerbation in the vitamin D sufficient group (blue line) and the vitamin D deficient group (orange line). There was no significant difference between to two groups (p = 0.095)
Imaging characteristicsa
| Vitamin D Deficient | Vitamin D Sufficient | Vitamin D Deficiency Effect Estimate (SE)d | |||
|---|---|---|---|---|---|
| nb | 549 | 970 | |||
| % emphysema | 5.80 (8.33) | 7.33 (9.48) | 0.002 | 0.76 (0.49) | 0.12 |
| 15th percentile of lung density histogram (+ 1000 HU) | 85.70 (32.25) | 76.81 (28.45) | < 0.001 | −2.1 (1.42) | 0.14 |
| % gas trapping | 21.47 (18.76) | 24.99 (19.20) | 0.002 | 1.81 (1.05) | 0.085 |
| Segmental airway wall thickness | 1.08 (0.23) | 1.05 (0.22) | 0.019 | 0.027 (0.012) | 0.021 |
| Pi10c | 2.44 (0.63) | 2.37 (0.59) | 0.053 | 0.058 (0.036) | 0.11 |
| Segmental wall area % | 52.03 (8.65) | 51.07 (8.21) | 0.040 | 0.81 (0.48) | 0.092 |
| Change in % emphysema | 0.88 (3.87) | 0.56 (4.21) | 0.17 | 0.36 (0.25) | 0.16 |
| Change in % gas trapping | 3.44 (9.89) | 2.01 (9.09) | 0.024 | 0.73 (0.64) | 0.26 |
| Change in segmental airway wall thickness | −0.00 (0.13) | 0.01 (0.11) | 0.12 | −0.0052 (0.0077) | 0.50 |
Mean (SD) or N(%) are shown
aEach row represents the outcome of a separate regression model
bCT scan data was available for 1519 subjects
csquare root of wall area of hypothetical airway with 10 mm internal perimeter
dCovariates in linear regression models include age, race, gender, BMI, current smoking status, and scanner model
Vitamin D concentration effects on imaging characteristicsa
| Vitamin D Effect Estimatec | Standard Error | ||
|---|---|---|---|
| % emphysema | −0.018 | 0.020 | 0.36 |
| 15th percentile of lung density histogram (+ 1000 HU) | 0.11 | 0.059 | 0.074 |
| % gas trapping | −0.012 | 0.043 | 0.78 |
| Segmental airway wall thickness | −0.0013 | 0.00049 | 0.0085 |
| Pi10b | −0.0017 | 0.0015 | 0.26 |
| Segmental wall area % | −0.033 | 0.020 | 0.098 |
| Change in % emphysema | −0.020 | 0.011 | 0.056 |
| Change in % gas trapping | −0.065 | 0.0263 | 0.014 |
| Change in segmental airway wall thickness | −0.00014 | 0.00032 | 0.65 |
aEach row represents the result of a separate regression model
bsquare root of wall area of hypothetical airway with 10 mm internal perimeter
cCovariates in linear regression models include age, race, gender, BMI, current smoking status, and scanner model