(2022) 22:185
Sugawara et al. BMC Pulmonary Medicine
https://doi.org/10.1186/s12890-022-01980-6

Open Access

RESEARCH

Association between annual change in ­FEV1
and comorbidities or impulse oscillometry
in chronic obstructive pulmonary disease
Hiroyuki Sugawara1,2, Atsushi Saito2*, Saori Yokoyama2, Kazunori Tsunematsu1 and Hirofumi Chiba2

Abstract
Background: Chronic obstructive pulmonary disease (COPD) is characterized by persistent respiratory symptoms
and airflow limitation. The decline in forced expiratory volume in one second ­(FEV1) is considered to be one of themost important outcome measures for evaluating disease progression. However, the only intervention proven to
improve COPD prognosis is smoking cessation. This study therefore investigated the factors associated with annual­FEV1 decline in COPD.
Methods: This retrospective study followed up 65 patients treated for COPD for 5 years: 13 current smokers and 52
former smokers, 25 with pneumonia, 24 with asthma, 18 with cancer, and 17 with cardiovascular disease. The patientswere divided into groups based on clinical cutoff parameters of the impulse oscillometry system (IOS): 11 high and
54 low R5, 8 high and 57 low R20, 21 high and 44 low R5–R20, 26 high and 39 low X5, 38 high and 27 low Fres, and36 high and 29 low AX. We investigated whether the decline in ­FEV1 was associated with comorbidities and IOS
parameters.
Results: The annual change in F­ EV1 over 5 years was significantly affected by smoking status (current − 66.2 mL/year vs. former − 5.7 mL/year, p < 0.01), pneumonia (with − 31.5 mL/year vs. without − 8.9 mL/year, p < 0.05), asthma(with − 30.2 mL/year vs. − 10.8 mL/year, p < 0.01), but not by cancer and cardiovascular disease. In the groups definedby IOS results, only the high AX group had significantly more annual decline in F­ EV1 and %FEV1 than the low AX group(− 22.1 vs. − 12.8, p < 0.05 and − 0.20 vs. 0.40, p < 0.05, respectively).
Conclusions: Continuing smoking as well as complications in pneumonia and asthma would be risk factors for the
progression of COPD. AX might be a suitable parameter to predict the prognosis of patients with COPD.
Keywords: Chronic obstructive pulmonary disease, Impulse oscillometry system, Pulmonary function test, COPD
Assessment Test, St. George’s Respiratory Questionnaire
Background
Chronic obstructive pulmonary disease (COPD), which
is characterized by persistent respiratory symptoms andairflow limitation caused by a mixture of small airway*Correspondence: a-saito@sapmed.ac.jp
2
Department of Respiratory Medicine and Allergology, Sapporo Medical
University School of Medicine, S1W16 Chuoku Sapporo, Hokkaido 060‑8543,
Japan
Full list of author information is available at the end of the article

disease and parenchymal destruction, has become one of
the top three causes of death worldwide and is a major
cause of chronic morbidity and mortality [1]. To evaluate disease progression, one of the most important outcome biomarkers is the decline in forced expiratory
volume in one second (­ FEV1) [2–5]. Cigarette smoking iswell known as the most common risk factor for COPD;
smokers have a higher prevalence of respiratory symptoms and abnormalities of respiratory function, a greater© The Author(s) 2022. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which
permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to theoriginal author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images orother third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line
to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory
regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of thislicence, visit http://creativecommons.org/licenses/by/4.0/. 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 in a credit line to the data.

Sugawara et al. BMC Pulmonary Medicine(2022) 22:185

annual rate of decline in ­FEV1, and higher mortality thannon-smokers [6]. Smoking cessation is the only intervention that has conclusively been shown to alter the rate of
decline in F
­ EV1 [7]. Long acting muscarinic antagonist(LAMA) and long acting β2 agonist (LABA) are now
used for the treatment of COPD, but there are few dataassessing the effects of smoking cessation in patients taking these drugs.
COPD often coexists with other diseases (comorbidities), such as pneumonia, asthma, cancer, and cardiovascular disease, that may have a significant impact on
prognosis [8], but the impact of these comorbidities on­FEV1 decline requires further investigation. Exacerbations have negative impacts on respiratory function andlead to worsening of the chronic progressive course ofthis disease [9], and pneumonia is a known cause of exacerbation, but few studies have investigated the relationship between episodes of pneumonia and annual declineof ­FEV1 in COPD. The prognosis of ACO (asthma-COPD
overlap) patients is controversial [10–12]. Cancer has agreat impact on the quality of life (QOL) and prognosis
of COPD patients [13], and reduction in F
­ EV1 is strongly
associated with a higher incidence of atrial fibrillationand heart failure [14], but there have been few studies on
how much cancer and cardiovascular disease affect respiratory function.
The impulse oscillometry system (IOS) can be used
to assess the function of large and small airways. It is anoninvasive device which assesses respiratory function
by the forced oscillation technique [15–17]. Our previous
work revealed that phenotypic difference in IOS parameters could be associated with the efficacy of inhaled corticosteroids (ICS) in asthma and cough variant asthma[18, 19]. We therefore hypothesized that in COPD, IOS
may have the potential to detect airway obstruction earlier than spirometry.
The aim of this study is to clarify how the rate of ­FEV1
decline is affected by smoking and comorbidities such as
pneumonia and asthma, and to investigate whether baseline IOS parameters could predict the future decline ofrespiratory function in patients with COPD.

Methods
Participants and treatment

This was a single-center retrospective observational
study. Patients with COPD treated in the clinic between
January 2012 and December 2015 were included in
the study. All patients presented with chronic dyspnea, chronic cough or sputum production, and a history of smoking, and were diagnosed by confirming thepresence of persistent airflow limitation based on postbronchodilator ­FEV1/forced vital capacity (FVC) < 0.70
[20]. Patients were assessed by spirometry and IOS andPage 2 of 11

followed up for 5 years. Patients were excluded if treatment was interrupted during the follow-up period or ifthey were transferred to another hospital.
At the first visit, demographic information, including
gender, age, height, weight, smoking history, medical history and any medications, and information on pulmonary
symptoms (modified Medical Research Council [mMRC]
Dyspnea Scale and COPD Assessment Test [CAT]), andcomorbidities were collected.
Patients diagnosed with COPD were treated with
LAMA, LABA, and added ICS if they had an asthma
phenotype, according to the Global Initiative for Chronic
Obstructive Lung Disease (GOLD) guidelines [21]. This
study was conducted in accordance with the declarationof Helsinki and was approved by the institutional ethicalcommittee of Sapporo Medical Association. The experimental protocols and the purpose of the research wereexplained to all participants and informed consent was
obtained in the form of opt-out on the website.
Measurements of IOS and respiratory function

IOS was measured using a commercially available
impulse oscillometry device (MasterScreen IOS, Jaeger,
Germany) according to the manufacturer’s recommendations [17]. The resistance at 5 Hz (R5: indicating total
airway resistance), resistance at 20 Hz (R20: representing
central airway resistance), difference between R5 and R20(R5 − R20: index of the small airways), reactance at 5 Hz
(X5: relating to compliance), resonant frequency (Fres),and integrated area of low frequency X (AX) were evaluated [22–24]. The use of Fres and AX has been proposed
to detect the degree of obstruction in the peripheral airways [15, 22, 25].
After measuring IOS, a pulmonary function test was
performed using spirometry (MasterScreen IOS, Jaeger, Germany). The tests were performed in this orderto prevent any negative effects of forced expiration on
the airway. The percentage predicted forced vital capacity (%FVC), percentage predicted forced expiratory volume in one second (%FEV1), ­FEV1/FVC ratio, percentage
predicted maximal mid-expiratory flow (%MMEF), andpercentage predicted peak expiratory flow (%PEF) wereassessed.
For parameters of IOS, only a few predicted values areavailable for Caucasians according to Vogel & Smidt [26]and there are no defined reference values for COPD. We
adopted cutoff IOS values for COPD based on previous
reports [25, 27–31], as follows: R5 = 0.39, R20 = 0.27,
R5–R20 = 0.10, X5 =  − 0.13, Fres = 17.7, and AX = 0.55.
If the measured value was higher or lower than the cutoff value, it was recorded as a high or low IOS parameter,
respectively.

Sugawara et al. BMC Pulmonary Medicine(2022) 22:185

Page 3 of 11

Assessment of dyspnea and QOL

Statistical analysis

We assessed the two most widely used measures ofsymptoms according to GOLD [8]. Dyspnea was evaluated according to the modified British Medical ResearchCouncil (mMRC) scale. A questionnaire scale was considered adequate to assess symptoms. The mMRC is a
grading scale from 0–4 which relates well to other measurements of health status [32].
The COPD Assessment Test (CAT™) is an eight-item
unidimensional measure of health status impairment
in COPD [33]. This score ranges from 0–40 and correlates closely with St. George’s Respiratory Questionnaire(SGRQ) [34].

Numeric variables are expressed as means ± standard
error of mean. Differences between two groups wereassessed using unpaired two tailed t-tests. Categorical variables were compared using chi-square tests. A
p value < 0.05 was considered statistically significant.
Microsoft Excel 2016 (Microsoft Corporation, the USA),
Excel Statistical Program File (ystat2008.xls, Igakutoshoshuppan Ltd., Tokyo, Japan), GraphPad Prism v8 (GraphPad, Inc., San Diego, CA, the USA), and an open-source
R statistical software package were used for data analysis
and graph generation.

Results
Selection of participants

Retrospective observation of annual change in ­FEV1

For patients who were current smokers, we explained theneed for smoking cessation to patients diagnosed with
COPD, instructed them to quit smoking, and provided
smoking cessation treatment if necessary. The patients’
respiratory function was retrospectively observed for
5 years. Baseline respiratory function and smoking status
were recorded at least 3 months after the start of COPD
treatment. Spirometry was performed every 6 months inpatients who inhaled LAMA or/and LABA the morningof the test, and the annual change in values and % predicted was calculated.

We screened 97 patients with COPD during the inclusion period. Of these, 23 were excluded due to treatment
interruption [in < 1 year (n = 13), 1 to < 2 years (n = 3),
2 to < 3 years (n = 5), or in 3 to < 4 years (n = 2)] and 9
patients were excluded due to hospital transfer [for respiratory failure (n = 5), for cancer (n = 2), or for pneumonia (n = 2)]. After exclusions, 65 patients with COPD
were assessed by spirometry and IOS and followed up for
5 years (Fig. 1).
Smoking status and comorbidities are described inFig. 1. Patients were current smokers (n = 13, comprising 3 continuous smokers and 10 intermittent smokers)

Fig. 1 Selection of participants. Of 97 patients diagnosed with COPD who were treated in the clinic between January 2012 and December 2015, 32
patients were excluded and 65 participated in this study

Sugawara et al. BMC Pulmonary Medicine(2022) 22:185

or former smokers (n = 52, including patients who quit
smoking on being diagnosed with COPD). Recorded
comorbidities were pneumonia (n = 25: hospital admission n = 9, outpatient n = 16), bronchial asthma (n = 24:high dose ICS n = 12, intermediate ICS n = 12, low dose
ICS n = 0), cancer (n = 18: lung cancer n = 7, colon cancer
n = 4, stomach cancer n = 2, prostate cancer n = 2, laryngeal cancer n = 1, esophageal cancer n = 1, bladder cancern = 1), cardiovascular disease (n = 17: arrhythmia n = 9,aortic aneurysm n = 5, arterial sclerosis obliterans n = 4,
cerebral infarction n = 3, ischemic heart disease n = 1).
Some patients had more than one disease.
The baseline characteristics of the 65 participants
and their spirometry and IOS 5 years after treatment
are shown in Table 1. ­FEV1 and ­FEV1/FVC significantly
decreased between baseline and 5 years but there wereno significant changes in % ­FEV1, % FVC, % MMEF, or%PEF. Except R20, all IOS parameters were significantly
reduced at 5 years after treatment.
Annual change in FEV1 and IOS in smoking status

The annual change in ­FEV1 (mL/year) across 5 years varied widely, with a mean ± SEM of − 17.8 ± 4.0 and a rangeof − 142.0 to 34.0 (Fig. 2). Comparing changes over timein current and former smokers, ­FEV1 or %FEV1 wereboth significantly lower at 4 and 5 years after treatment
in current smokers (Fig. 3). Comparisons between thesecohorts are presented in Table 2. At baseline there wasno significant difference in F
­ EV1 between current andformer smokers (p = 0.44) but 5 years after treatment
it was lower in current smokers (p < 0.05). The annualchanges in ­FEV1 and %FEV1 were significantly different
between current and former smokers (− 66.2 mL/yearvs. − 5.7 mL/year; p < 0.01 and − 2.1 mL/year vs. 0.6 mL/
year; p < 0.01, respectively). No IOS parameter differed
between current and former smokers (Table 2).
Characteristics, the annual change in F
­ EV1, and IOS
in patients with and without comorbidities

Table 2 describes the characteristics of patients withand without each COPD comorbidity. There were nodifferences in age, sex, BMI, smoking history, total IgE,
and CAT in the groups defined by presence or absenceof any of the comorbidities. Patients with pneumonia,
asthma, or cancer had higher neutrophil counts thanthose without each disease respectively. The asthmaticgroup included higher numbers of current smokers andhad higher eosinophil counts than the non-asthmaticgroup (10:14 vs. 3:38; p < 0.01 and 248.2 vs. 179.4; p < 0.05,
respectively). In the patients with pneumonia, the mMRC
was higher than in those without pneumonia (1.5 vs. 1.1,
p < 0.05). CAT scores did not differ between any group.

Page 4 of 11

Table 1 Baseline characteristics and comparison of respiratory
function between baseline and 5 years after observation inpatients with COPD
Baseline

5 years after
65

Number of participants

65

Age

69.7 (1.0)

Sex; male: female

53:12

Body mass index

22.5 (0.4)

Smoking history (pack-years)

51.7 (1.9)

Current: former

13: 5221.8 (0.4)
8: 57

mMRC

1.3 (0.1)

1.6 (0.1)**CAT​

6.0 (0.6)

6.9 (0.6)*

GOLD stage
I, n (%)

11 (16.9)

13 (20.0)

II, n (%)

39 (60.0)35 (53.8)

III, n (%)

14 (21.5)

15 (23.1)

IV, n (%)

1 (1.5)2 (3.1)

Spirometry
FEV1 (L)1.66 (0.07)

1.57 (0.08)**

FEV1 (% predicted)

62.8 (2.0)

63.1 (2.2)

FVC (% predicted)

88.7 (1.7)

90.3 (2.0)

FEV1/FVC (%)

56.4 (1.1)

54.8 (1.2)*

MMEF (% predicted)

19.9 (1.2)

19.3 (12)PEF (% predicted)

68.7 (2.5)

68.1 (3.3)

IOS
R5 (kPa/L/s)

0.29 (0.01)

0.33 (0.02)**

R20 (kPa/L/s)

0.21 (0.01)

0.21 (0.01)

R5-R20 (kPa/L/s)

0.08 (0.01)

0.11 (0.01)**

X5 (kPa/L/s)− 0.13 (0.01)− 0.17 (0.02)*

Fres (Hz)18.7 (0.7)

20.4 (0.8)**

AX (kPa/L)

0.92 (0.11)

1.42 (0.17)**

Data are presented as mean (standard error of mean) or number (percentage).
The differences between baseline and 5 years after treatment were evaluatedusing paired t-tests*

p < 0.05**

p < 0.01

At almost every annual timepoint, ­FEV1 and %FEV1
were significantly lower in patients with pneumonia orasthma than those without, but there were no differencesin patients with or without cancer or cardiovascular disease (Fig. 4). There was significantly more annual decline
in ­FEV1 in the pneumonia group than in the non-pneumonia group (− 31.5 vs. − 8.9, p < 0.01) and in the asthma
group than in the non-asthma group (− 30.2 vs. − 10.8,
p < 0.05). The annual decline in %FEV1 was notable inpatients with pneumonia (− 0.49 vs. 0.44 in patients
without pneumonia, p < 0.01) and asthma (− 0.53 vs. 0.42
in patients without asthma, p < 0.01). However, there
was no change in annual decline in FEV1 and %FEV1 inpatients with cancer and cardiovascular disease (Table 2).

Sugawara et al. BMC Pulmonary Medicine(2022) 22:185

Page 5 of 11

Characteristics, the annual change in F
­ EV1, and the rateof comorbidity in groups classified by IOS parameters

Fig. 2 Distribution of annual change in F­ EV1 in patients with C­ OPD.
According to observation over 5 years, the annual change in ­FEV1(mL) varied widely. The mean (SEM) was − 17.8 (4.0) mL/year (n = 65)

Fig. 3 Comparison of ­FEV1 and %FEV1 over time between current
and former smokers with COPD. There were significant differencesin ­FEV1 (A) or %FEV1 (B) between current and former smokers both 4
and 5 years after baseline. The bars represent mean ± standard errorof the mean. The differences between current and former smokers
were analyzed using unpaired t-tests. NS: not significant, *: p < 0.05,** p < 0.01

Similar trends according to comorbidity were seen
in IOS. Patients with pneumonia or asthma had higher
R5, R5–R20, Fres, and AX values than those without,while the presence or absence of cancer or cardiovascular disease did not have a significant effect on the IOS
results.

COPD patients were divided into high or low groups
for each baseline IOS parameter and the differences inbaseline characteristics, annual decline in ­FEV1 (%FEV1),
and comorbidity rates compared (Table 3). The results oflogistic regression analysis are shown in Additional file 1:
Table S1. Notably, similar results were obtained.
There was no significant difference in any baselinecharacteristic based on IOS parameters, except that thehigh R5 group had a higher age (74.6 vs. 68.6, p < 0.05)
and higher proportion of males (6:5 vs. 47:7, p < 0.05).
The mMRC of the high R5–R20 group, the low X5 group,
the high Fres group, and the high AX group was significantly higher than the corresponding other group in eachcase (1.5 vs. 1.2, p < 0.05; 1.5 vs. 1.2, p < 0.05; 1.4 vs. 1.1,
p < 0.01; 1.4 vs. 1.1, p < 0.05, respectively). The CAT score
was significantly higher only in the low X5 group and thehigh Fres group (7.5 vs. 4.9, p < 0.05, 7.3 vs. 4.3 p < 0.01,
respectively).
For the annual decline in ­FEV1 and %FEV1, only thehigh AX group was significantly lower than the low AXgroup (− 22.1 vs. − 12.8, p < 0.05 and − 0.20 vs. 0.40,
p < 0.05, respectively).
The ratios of each comorbidity were also compared
between the groups classified by IOS values. The highR5–R20 group, the low X5 group, the high Fres group,and the high AX group had significantly higher numbers of patients with pneumonia than the corresponding other group (13:8 vs. 13:31, p < 0.05; 15:11 vs. 11:28,
p < 0.05; 20:18 vs. 6:21, p < 0.05; 19:17 vs. 7:22, p < 0.05,
respectively). For asthma, significant differences wereobserved between the groups based on R20 (6:2 vs.
18:39, p < 0.05), X5 (14:12 vs. 10:29, p < 0.05), Fres (19:19
vs. 5:22, p < 0.05), and AX (20:16 vs. 4:25, p < 0.01). There
were no differences in the ratios of patients with cancer
or cardiovascular disease between the groups defined by
any IOS parameter.

Discussion
In this observational study, we demonstrated that smoking cessation with proper treatment could prevent
decline in ­FEV1, that pneumonia and asthma as comorbidities might result in decline in lung function, and that
the IOS parameter AX at baseline can predict future
decline in ­FEV1 in patients with COPD.
Recent studies have shown that the rate of decline in­FEV1 in properly treated patients with COPD varies
widely from rapid to relatively modest decline in lungfunction over a 3–5 year period [5, 35]. Because COPD
patients who continue to smoke are at increased risk ofmarked progression compared with former smokers,

49.4 (5.2)

Smoking history (pack-years)52

5.6 (0.6)

7.3 (1.9)− 5.7 (2.8)**
62.9 (2.3)− 66.2 (7.6)

Annual change (ml/year)− 2.1 (0.20)

Annual change (%/year)

0.09 (0.01)− 0.12 (0.01)

20.3 (1.4)

0.99 (0.17)

R5-R20 ( kPa/L/s)

X5 (kPa/L/s)

Fres (Hz)AX (kPa/L)

0.90 (0.13)

18.3 (0.8)− 0.13 (0.01)

0.08 (0.01)

0.20 (0.01)

0.28 (0.01)

0.6 (0.12)**65.9 (2.4)**25

1.27 (0.20)

21.4 (1.0)− 0.15 (0.02)

0.11 (0.01)

0.22 (0.01)

0.33 (0.03)− 0.49 (0.35)

55.8 (3.6)

58.3 (3.5)− 31.5 (7.8)

1.25 (0.08)

1.41 (0.09)

7.3 (1.1)

1.5 (0.1)

468.4 (224.6)

227.3 (25.1)

4166 (220)8:17

50.5 (3.0)

22.7 (0.6)

19:6

71.1 (1.7)

0.64 (0.09)**16.5 (0.8)**− 0.11 (0.01)*

0.06 (0.01)**

0.30 (0.01)

0.26 (0.01)**

0.44 (0.17)**68.0 (2.4)**

65.8 (2.3)− 8.9 (3.7)**

1.79 (0.10)**

1.83 (0.10)**5.1 (0.6)

1.1 (0.1)**

374.9 (177.6)

189.7 (20.3)

3594 (159)*

4:36

52.5 (2.4)

22.3 (0.5)

34:6

68.7 (1.2)

40−

1.21 (0.18)

20.7 (1.0)− 0.15 (0.02)

0.11 (0.01)

0.23 (0.01)

0.33 (0.02)− 0.53 (0.37)

55.0 (2.9)

57.7 (2.6)− 30.2 (8.0)

1.31 (0.10)

1.46 (0.09)

6.9 (1.3)

1.4 (0.1)

686.6 (252.4)

248.2 (29.4)

4238 (208)10:14

46.4 (1.4)

22.9 (0.7)

18:6

70.0 (2.0)

24+Asthma0.75 (0.13)*

17.5 (1.0)*− 0.12 (0.01)

0.07 (0.01)*

0.20 (0.01)*

0.26 (0.01)**

0.42 (0.16)**67.9 (2.7)**

65.8 (2.7)*− 10.8 (4.0)*

1.72 (0.10)**

1.78 (0.10)*

5.4 (0.6)

1.2 (0.1)

179.4 (78.4)

179.4 (17.4)*

3579 (163)*

3:38**

54.9 (2.8)

22.2 (0.4)

35:6

69.0 (1.1)

41−

0.95 (0.22)

19.1 (1.4)− 0.13 (0.02)

0.09 (0.02)

0.20 (0.01)

0.29 (0.02)− 0.05 (0.40)

57.8 (33.3)

58.0 (2.9)− 19.9 (8.8)

1.36 (0.10)

1.46 (0.09)

7.7 (1.5)

1.4 (0.1)

680.1 (297.9)

220.5 (40.4)

4367 (237)

3:15

49.8 (2.1)

23.4 (0.8)

16:2

72.8 (1.6)

18+Cancerp < 0.01

p < 0.05

0.92 (0.12)

18.5 (0.9)− 0.13 (0.01)

0.08 (0.01)

0.21 (0.01)

0.29 (0.01)

0.11 (0.19)

65.2 (2.7)

64.6 (2.5)− 17.2 (4.5)

1.65 (0.10)

1.74 (0.09)

5.3 (0.6)

1.2 (0.1)

309.7 (184.4)

198.7 (15.8)3614 (151)*

10:37

52.5 (2.5)

22.1 (0.4)

37:10

68.5 (1.2)

47−

0.97 (0.21)

17.8 1.6)− 0.13 (0.02)

0.09 (0.02)

0.19 (0.01)

0.28 (0.02)

0.26 (0.24)

60.1 (4.6)

58.8 (4.5)− 14.6 (0.4)

1.45 (0.12)

1.53 (0.13)

5.4 (0.7)

1.3 (0.1)

451.6 (171.0)

169.2 (27.0)

3688 (283)

3:14

55.9 (4.7)

21.9 (0.8)

15:2

72.4 (1.4)

17+0.90 (0.12)− 0.13 (0.01)
19.0 (1.8)

0.08 (0.01)

0.21 (0.01)

0.29 (0.02)

0.00 (0.23)

64.2 (2.4)

64.2 (2.2)− 19.1 (5.0)

1.61 (0.09)

1.71(0.09)

6.2 (0.8)

1.3 (0.1)

398.4 (133.3)

217.4 (19.0)

3870 (151)

10:38

50.3 (1.9)

22.6 (0.4)

40:8

68.7 (1.2)

48−

Cardio-vascular disease(2022) 22:185***

The data are shown as mean (standard error of the mean) or number ratio. The differences between each pair were analyzed by unpaired t-tests or the chi-squared test

0.32 (0.01)

0.24 (0.02)

R5 (kPa/L/s)

R20 ( kPa/L/s)

IOS

62.4 (3.8)

52.0 (3.9)

Baseline (%)

After 5 years (%)%FEV1

1.66 (0.09)*

1.54 (0.12)

1.21 (0.12)

Baseline (L)1.69 (0.09)

417.2 (120.0)

After 5 years (L)

FEV1

CAT​

1.3 (0.1)

392.7 (251.7)

1.2 (0.1)

Total IgE in serum (IU/ml)

mMRC

200.8 (18.0)4152 (285)220.9 (34.0)

Neutrophil in blood (count/µl)

3740 (150)

52.3 (2.0)

22.5 (0.4)

44:8

70.4 (1.0)

Eosinophil in blood (count/µl)

Laboratory values

Current: Former

9:4

22.1 (0.4)

Sex; Male:Female

Body mass index

13

66.5 (2.9)

Number of participants

Age+

Current

FormerPneumonia

Smoking status

Table 2 Comparison of annual change in ­FEV1 between patients with COPD classified based on smoking status at baseline and comorbidity during the 5-year follow-up period

Sugawara et al. BMC Pulmonary Medicine
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Sugawara et al. BMC Pulmonary Medicine(2022) 22:185

Page 7 of 11

Fig. 4 Comparison of %FEV1 in COPD patients with and without comorbidities. There were significant differences in %FEV1 in patients with andwithout pneumonia or asthma but not cancer or cardiovascular disease. The bars represent mean ± standard error of the mean. The differences
between patients with and without each comorbidity were analyzed using unpaired t-tests. NS: not significant, *: p < 0.05, ** p < 0.01

smoking cessation is considered to be the most important
tool in secondary prevention of F
­ EV1 decline. Many studies have compared ­FEV1 decline between smokers andformer smokers [2, 5, 36, 37]. In the present study, F­ EV1
in current smokers on LAMA and LABA treatment was
reduced by 66.2 mL (2.1%) per year, far more than for former smokers, in which it was reduced by 5.7 ml (+ 0.6%)
per year. Since 80% of patients were former smokers, the overall F­ EV1 reduction rate was relatively low
at − 17.8 mL (+ 0.06%) per year. Change in respiratory
function is considered to be suppressed by smoking cessation and appropriate treatment, which could improve
the prognosis of patients with COPD. On the other hand,when IOS parameters were compared between current
and former smokers, there were no differences, in agreement with a previous study [27]. Overall, in the present
study smoking cessation and COPD therapy improved
the prognosis of respiratory function as in previous studies, but could not be simply detected by IOS.
COPD often has various comorbidities related tosmoking and aging, such as pneumonia, asthma, cancer,
and cardiovascular diseases, and these negatively affect
QOL and prognosis. Patients with a high frequencyof exacerbations have a greater decline in ­FEV1 thanthose with a low frequency of exacerbations [9], andone cause of exacerbation is pneumonia. With regard to
exacerbation, similar results were obtained (Additional
file 1: Table S1). However, few studies have directly
shown that pneumonia is associated with annual
decline in ­FEV1. Our data showed that ­FEV1 decline inpatients with pneumonia was higher than those without
pneumonia (− 31.5 mL/year vs. − 8.9 mL/year). This
indicates that the prognosis for COPD patients withepisodes of pneumonia may be poor. The link between
COPD and pneumonia may be explained by airway
inflammation caused by a viral or bacterial infection.
Regarding ACO, the prognosis is controversial [10,11, 38], so we examined changes in respiratory function over 5 years in COPD patients with or without
asthma. We found that the %FEV1 changed more inpatients with ACO than in those with COPD alone; theannual decline in F
­ EV1 (%FEV1) in ACO was − 30 mL(− 0.53%) per year, compared with − 10.8 mL (0.42%)/ year in COPD. This suggested that the prognosis for
ACO could be poorer than that for COPD. Asthmarelated airway inflammation in addition to COPD

51.4 (2.4)

2:9

Smoking history (pack-years)

Current: Former

54

5.4 (0.6)

184 (91)

1.6 (0.2)

8.5 (1,6)

Total IgE in serum (IU/ml)

mMRC

5:3

5.9 (2.1)

1.4 (0.2)

528 (410)

231 (48)4445 (344)

4:4

49.4 (2.3)

22.2 (1.4)

57

53.4 (3.9)

Pneumonia n (+ : −)

14:30

15:39

3:8

3:8

19:35
17:37

7:4

64.1 (2.1)
65.2 (2.3)**

1:7

1:7

6:2

5:3

16:41

17:40

18:3*

19:38

− 1.05 (0.54) 0.22 (0.18)*

48.1 (2.7)

50.0 (2.7)

8:13

7:14

11:10

13:8

0.03 (0.29)

68.9 (2.1)**

9:35

11:33

13:31

13:31*

0.08 (0.23)

69.3 (23)**

39

15:11

7:19

8:18

14:12

38

7.3 (0.9)

1.4 (0.1)

448 (166)

215 (20)4036 (177)

10:28

51.7 (2.0)

22.2 (0.5)

29:9

70.6 (1.4)

70.4 (2.5)**

68.7 (2.3)**− 13.8 (4.3)

11:28*

10:29

10:29

10:29*

20:18

8:30

11:27

19:19

p < 0.01

p < 0.05***

6.6 (0.9)

1.4 (0.1)

448 (175)

221 (22)4038 (178)

10:26

52.3 (2.6)

22.3 (0.6)

27:9

71.1 (1.5)

36≥ 0.55

AX

76.6 (5.4)**

74.7 (2.0)**− 14.8 (4.9)

6:21*

9:18

7:20

5:22*

75.0 (2.8)**

73.0 (2.6)**− 12.8 (4.8)*

2.00 (0.11)**2.07 (0.11)**5.1 (0.8)

1.1 (0.1)*367 (105)

184 (22)3555 (192)

3:26

51.1 (2.7)

22.7 (0.5)

26:3

67.9 (1.2)

29< 0.55

9:27

11:25

20:16

19:17

8:21

7:22

4:25**

7:22*− 0.20 (0.28) 0.40 (0.19)*

53.5 (2.1)

54.6 (2.0)− 22.1 (6.1)

2.04 (0.10)** 1.22 (0.06)

2.11 (0.10)** 1.34 (0.06)

4.1 (0.6)**

1.1 (0.1)**

361 (113)

189 (25)3522 (191)

3:24

51.8 (3.5)

22.8 (0.5)

24:3

68.3 (1.3)

27< 17.7− 0.16 (0.32) 0.38 (0.19)

53.6 (2.2)

54.3 (2.0)− 20.2 (5.9)

1.85 (0.09)** 1.24 (0.07)

1.91 (0.09)** 1.34 (0.07)

4.9 (0.6)*

1.2 (0.1)*458 (148)

200 (21)3676 (166)

7:32

51.1 (2.9)

22.7 (0.5)

33:6

67.6 (1.3)− 0.35 (0.35) 0.34 (0.18)

52.2 (2.8)

54.0 (2.8)− 24.2 (7.7)

1.80 (0.09)** 1.16 (0.07)− 17.1 (6.7) − 18.3 (5.0)

50.1 (2.9)

7.5 (1.1)

1.5 (0.1)

342 (153)

212 (23)4043 (219)

6:20

52.7 (1.6)

22.0 (0.6)

20:6

72.7 (1.4)

26≥ 17.7>  − 0.13≦ − 0.13

1.89 (0.09)** 1.28 (0.07)

5.3 (0.7)

1.2 (0.1)*503 (149)

218 (21)3746 (155)

8:36

51.3 (2.3)

22.9 (0.5)

38:6

68.5 (1.1)

44< 0.10

Fres

X5

The data are shown as mean (standard error of the mean) or number ratio. The differences between each pair were analyzed by unpaired t-tests or the chi-squared test

Cardio-vascular dis. n (+ : −)Cancer n (+ : −)Asthma n (+ : −)7:4

0.001 (0.50) 0.08

Annual change (%/year)

Comorbidity

65.4 (2.2)**
65.8 (2.4)**

50.1 (2.5)

50.1 (2.7)

Baseline (%)

After 5 years (%)%FEV1− 36.8 (11.0) − 15.3 (4.2)− 12.7 (8.8) − 19.0 (4.5)

1.17 (0.06)

7.4 (1.1)

1.5 (0.1)

221 (106)176 (20)3984 (255)

5:16

52.6 (3.2)

21.4 (0.7)

15:6

72.1 (2.0)

21≥ 0.10

R5-R201.65 (0.08)** 1.09 (0.05)

Annual change (ml/year)

1.69 (0.08)** 1.04 (0.13)

1.04 (0.07)

0.98 (0.08)

1.72 (0.08)*

6.0 (0.6)

1.3 (0.1)

396 (109)

201 (18)3735 (129)9:48

52.1 (2.0)

22.5 (0.4)

48:9

69.5 (1.0)

Baseline (L)1.79 (0.08)** 1.22 (0.10)458 (127)8
70.8 (3.4)< 0.27After 5 years (L)

FEV1

CAT​

1.2 (0.1)

185 (36)

Eosinophil in blood (count/µl)

208 (17)4253 (373)3735 (140)

11:43

51.8 (2.2)

22.7 (0.4)

47:7*

68.6 (1.1)*

Neutrophil in blood (count/µl)

Laboratory values

6:5

21.4 (1.0)

Sex; Male:Female

Body mass index

1174.6 (1.8)

Number of participantsAge≥ 0.27≥ 0.39< 0.39R20

R5Table 3 Comparison of annual change in FEV1 and comorbidity between patients with COPD classified by baseline impulse oscillometry system (IOS) parameter values

Sugawara et al. BMC Pulmonary Medicine(2022) 22:185
Page 8 of 11

Sugawara et al. BMC Pulmonary Medicine(2022) 22:185

airway obstruction could have contributed to thedecline in respiratory function.
Cancers other than lung cancer are often associatedwith COPD in the real world. However, while many studies report a strong relationship between reduction in­FEV1 and the risk of lung cancer [39], there are few studies on the relationship between other cancers and FEV1
decline in COPD. In the present study, we found no differences in %FEV1 over 5 years or annual change in FEV1
in patients with and without cancer. The subset of caseswith lung cancer was too small to analyze separately, andit remains a possibility that there would be a significant
difference in FEV1 in this cohort. This is a topic for future
study.
Regarding cardiovascular disease, low FEV1 and airflow obstruction are associated with atrial fibrillation[14], coronary heart disease [40], heart failure [41], andstroke [42]. However, there have been few studies onwhether cardiovascular diseases are associated with theannual decline in FEV1 in COPD patients. In this study,
we found no significant difference in the decline of FEV1
between patients with or without cardiovascular disease.
In terms of the combined effect of cardiovascular diseaseand cancer on COPD, the annual decline in respiratory
function should be more fully investigated in future.
IOS can determine the mechanical properties of thelung and differentiate between large and small airway obstruction. It seems to be more sensitive anddetect changes in lung function earlier than spirometry in COPD patients [43]. The IOS parameters R5,
R5–R20, and X5 (and not R20) were shown to correlate with ­FEV1 in a cohort of COPD patients, and over
1 year the changes in X5 correlated with the changes
in ­FEV1 [25]. Peripheral measurement by IOS (R5–R20
and X5) correlates with the SGRQ and mMRC scores[29]. Although there are no defined reference values
for COPD, pathologically abnormal IOS cutoffs havebeen proposed [27, 28]. Franz et. al. described subjects
reporting respiratory symptoms with differing lungmechanics as measured by IOS, and in whom IOS had
the potential to detect COPD pathology earlier thanspirometry, and found that cutoff values in those withsymptoms and meeting GOLD criteria were as follows:
R20 = 0.39, R20 = 0.27, R5–R20 = 0.10, X5 =  − 0.13,
and AX = 0.55 [30]. Separately, the cutoff value forFres optimal to diagnose airflow obstruction in COPD
patients was shown to be 17.7 [31]. As described in themethods, we classified the 65 COPD patients into twogroups based on the above IOS cutoff values in the present study. To investigate the difference between inhaler
treatments at baseline, the participants were classified
into two groups according to the IOS value. Patients

Page 9 of 11

who had values more than the cutoff values had used
more drugs, which indicates that patients with severe
COPD tended to need stronger treatment (Additional
file 1: Table S2). The annual change of ­FEV1 was compared in each pair. In these groups classified according to IOS, COPD patients with high AX at baseline
had significantly greater annual decline in ­FEV1 thanpatients with low AX (p < 0.05), indicating a correlation between AX value and annual change in ­FEV1 over
5 years. The group with decreased %FEV1 (n = 25) had
significantly higher AX than the group with increased%FEV1 (n = 40), with an odds ratio of 4.19 and 95%
confidence period of 1.26–15.71 (Additional file 1:
Table S1). Similar results were also obtained when compared between the amount of change in AX and that in­FEV1 decline (r = 0.28, p = 0.013, Spearman’s correlation analysis). R5–R20, X5, and Fres did not correlatewith deterioration of respiratory function. It is known
that R5–R20 reflects obstruction in the distal airways[44] and X5 reflects the elastic components of the lung.
On the other hand, AX reflects changes in peripheral
airway obstruction and reduction of lung compliance,
which are typically observed in COPD [22, 45]. Thismay be why only AX was significantly associated with
­FEV1 decline.
The current study had some limitations. First, thestudy population was small, and the study design was
retrospective. To consider potential confounding factors
and covariates such as smoking status, exacerbation,
pneumonia, and high AX, logistic regression analysis
was performed (Additional file 1: Table S1). Notably,
very similar results were obtained. However, because
this was a small cohort study, it was difficult to judge
whether the results were statistically significant andmultivariate analysis could not be performed. We hope
to clarify this in future research. Second, 32 of the 97
patients were excluded due to treatment interruption
or hospital transfer. This is unlikely to have introduced
bias, as there was no difference in the background characteristics of these patients compared to the 65 patients
included in the study (data not shown). COPD involves
various factors and is difficult to analyze simply.
Although COPD treatment changes over time depending on the stage, risk of exacerbation, and symptoms,
we did not consider whether the treatment method isrelated to the decrease in ­FEV1. Third, since IOS is an
emerging technique, the exact meaning and interpretation of its parameters are limited. Reference values
in different populations have not yet been established.
However, pathologically abnormal values were proposed
for COPD patients in previous studies [30, 31] and weused these to investigate the decline in ­FEV1.

Sugawara et al. BMC Pulmonary Medicine(2022) 22:185

Conclusion
This study showed that smoking cessation in addition to proper treatment was able to slow ­FEV1 decline
in patients with COPD. Coexistence of pneumonia orasthma was associated with F­EV1 decline, but cancer
and cardiovascular disease were not. Thus, continuingsmoking, complications in pneumonia and asthma would
be risk factors for the progression of COPD. Our datasuggest that baseline AX, which may detect and evaluate small airways and emphysema simultaneously, wasthe IOS parameter most related to the annual decline in­FEV1 for COPD and may accurately predict prognosis inpatients with COPD.
Abbreviations
ACO: Asthma–COPD overlap; mMRC: Modified British Research Council scale;CAT​: COPD Assessment Test; LAMA: Long acting muscarinic antagonist; LABA:Long acting β2 agonist; ICS: Inhaled corticosteroid; FVC: Forced vital capacity;
FEV1: Forced expiratory volume in one second; MMEF: Maximum mid-expiratory flow; PEF: Peak expiratory flow; IOS: Impulse oscillometry system; R5:Resistance at 5 Hz; R20: Resistance at 20 Hz; R5 − R20: The difference between
R5 and R20; X5: Reactance at 5 Hz; Fres: Resonance frequency; AX: Area of
low-frequency reactance.

Supplementary Information
The online version contains supplementary material available at https://​doi.​
org/​10.​1186/​s12890-​022-​01980-6.
Additional file1: Table S1. Odds ratio for COPD patients with and without decline in FEV1 using the logistic regression analysis. *: p < 0.05, **:p < 0.01. Table S2. Comparison of single- or multiple-inhaler therapy for
COPD (single, double or triple therapy) at baseline between groups basedon IOS values. *: p < 0.05, **: p < 0.01 (Pearson’s chi-squared test).
Acknowledgements
Not applicable.
Author contributions
HS developed the study design, performed the experiments, analyzed andinterpreted the data, and wrote the manuscript. AS contributed to data
analysis and interpretation and manuscript revision. SY, KT, and HC assistedwith data analysis and interpretation and supervised the statistical analysis. All
authors read and approved the final manuscript.
Funding
This work was supported by Novartis Pharma Research Grants 2019 (to A. S.)
and JSPS KAKENHI Grant Number 19K23610 (to S. Y.).
Availability of data and materials
The data that support the findings of this study are available on request fromthe corresponding author on reasonable request.

Declarations
Ethics approval and consent to participate
This study was conducted in accordance with the declaration of Helsinki andwas approved by the institutional ethical committee of Sapporo Medical
Association. The experimental protocols and the purpose of the research wereexplained to all participants and informed consent was obtained in the formof opt-out on the website.

Page 10 of 11

Consent for publication
Not applicable.
Competing interests
None of the authors have a conflict of interest to declare.
Author details
1
Sugawara Internal Medicine and Respiratory Clinic, Tomakomai 053‑0821,
Japan. 2 Department of Respiratory Medicine and Allergology, Sapporo
Medical University School of Medicine, S1W16 Chuoku Sapporo, Hokkaido 060‑8543, Japan.
Received: 15 November 2021 Accepted: 18 April 2022

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