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Novel regression equations predicting lung age from varied spirometric parameters
Respiratory Physiology & Neurobiology 183 (2012) 108–114
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Novel regression equations predicting lung age from varied spirometric parameters Kazuhiro Yamaguchi a,∗ , Hisamitsu Omori b , Ayumi Onoue b , Takahiko Katoh b , Yasuhiro Ogata c , Hidetoshi Kawashima c , Shigemitsu Onizawa d , Takao Tsuji d , Kazutetsu Aoshiba d , Atsushi Nagai d a
Comprehensive and Internal Medicine, Tokyo Women’s Medical University Medical Center East, Station Port Tower 4F, Nishi-Nippori, Arakawa-ku, Tokyo 116-0013, Japan Department of Public Health Faculty of Life Sciences, Kumamoto University, 1-1-1 Honjou, Kumamoto 860-8556, Japan c Health Care Center, Japanese Red Cross Kumamoto, 2-1-1 Nagamineminami, Kumamoto 861-8528, Japan d The First Department of Medicine, Tokyo Women’s Medical University, 8-1 Kawata-cho, Shinjuku-ku, Tokyo 162-8666, Japan b
a r t i c l e
i n f o
Article history: Accepted 20 June 2012 Keywords: Lung ageing Spirometry Reference value Normal limits Airflow limitation
a b s t r a c t Although lung age calculated backward from regression formulas constructed for FEV1 estimation is widely used, it possesses a couple of faults. We developed novel equations predicting lung age from varied spirometric parameters (spirometry-derived lung age (SDL-age)). Applying multiple regression analysis, equations predicting SDL-age were invented using data from 8015 never-smokers with normal spirometry (group I). Validation was made based on data from 6398 never-smokers with normal spirometry (group II). Equations were further applied for 446 subjects with airflow limitation. FEV1 , FEV1 /FVC, FEF50 , and PEF were selected as explanatory variables for reference value of SDL-age. Normal limits of difference between SDL-age and chronological-age were ±13.4 years in the male and ±15.0 years in the female. Established equations predicted SDL-age of group II. SDL-age was older than chronological-age only in subjects with severe airflow limitation. Novel regression equations allowing prediction of reference value of SDL-age and normal limits of difference between SDL-age and chronological-age were elaborated in both genders. © 2012 Elsevier B.V. All rights reserved.
1. Introduction Cigarette smoking is the leading cause of morbidity and mortality in a variety of diseases. Although smoking-related harmful effects are generally assessed in terms of spirometric measurements, it is not easy to let smokers understand spirometric data
Abbreviations: Absolute  value, absolute value of standardized partial regression coefficient; Adjusted-R2 , coefficient of determination adjusted for degrees of freedom; LLN, lower limit of normal; ULN, upper limit of normal; RSD, residual standard deviation; VIF, variance inflation factor; Z-score, standardized residual; FEV1 , forced expiratory volume in one second; FEV1 %pred, (measured FEV1 )/(predicted FEEV1 ) (%); FVC, forced vital capacity in expiration; FEF50 , forced expiratory flow rate at 50% of FVC; FEF25 , forced expiratory flow rate at 25% of FVC; FEF25–75 , forced expiratory flow rate between 75% and 25% of FVC; PEF, peak expiratory flow rate; AC, abdominal circumference (cm); BMI, body mass index (kg/m2 ); %FAT, fat percentage of body mass (%); H, height (cm); SDL-age, spirometry-derived lung age (years-old); SDL-age, (SDL-age) – (chronological age) (years). ∗ Corresponding author. Tel.: +81 3 3805 7790; fax: +81 3 3805 7775. E-mail addresses: [email protected] (K. Yamaguchi), [email protected] (H. Omori), [email protected] (A. Onoue), [email protected] (T. Katoh), [email protected] (Y. Ogata), [email protected] (H. Kawashima), [email protected] (S. Onizawa), [email protected] (T. Tsuji), [email protected] (K. Aoshiba), [email protected] (A. Nagai). 1569-9048/$ – see front matter © 2012 Elsevier B.V. All rights reserved. http://dx.doi.org/10.1016/j.resp.2012.06.025
properly, leading to difficulty in persuading them to quit smoking when they simply received the raw results of spirometric measurements (Tashkin and Murray, 2009). One approach to increase smoking-cessation rate is to communicate spirometric results in a manner that is easily understood by smokers (Newbury et al., 2010). To conquer the difficulty existing in the raw results of spirometric measurements, Morris and Temple (1985) proposed the concept of lung age about 25 years ago. The original formula developed by Morris and Temple had a form in which the regression equation constructed for predicting reference value of FEV1 from age and height was rearranged for “age”. The original method of Morris and Temple has greatly contributed toward providing incentive to abstain from smoking (Lipkus and Prokhorov, 2007; Parker et al., 2008; Parkers et al., 2008; Tashkin and Murray, 2009). Furthermore, it allowed several groups of investigators to estimate the lung age of patients suffering from varied pathological conditions such as COPD (Parker et al., 2008; Tashkin and Murray, 2009; Toda et al., 2009) and morbid obesity (D’Avila Melo et al., 2010; Mitsumune et al., 2009). To extend the clinical application of lung age, Hansen et al. (2010) proposed a simplified equation allowing its estimation from FEV1 /FVC, the method qualitatively differing from that of Morris and Temple. Parkers et al. (2008) assessed the effect on smokingquit rate of telling subjects about their lung ages, leading to the conclusion that the smoking-quit rate was higher in the subjects
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who received the spirometric results in terms of lung age than those only received the raw results of spirometric measurements. A couple of authors, however, questioned whether the lung age was truly useful as a tool for motivating the cessation of smoking (Lin, 2008; Quanjer and Enright, 2010). They asserted that the lung age calculated from the method of Morris and Temple entirely disregarded the variability of FEV1 in normal subjects, thus causing a physiologically serious flaw, i.e., the lung age of a normal person whose FEV1 is below the reference value but above the lower limit of normal (LLN) is forcibly estimated to be older than his (her) chronological age, though the lung age of this person should be equal to the chronological age. This happens because the lung age is calculated by counting back the regression equation predicting the reference value, but not the LLN, of FEV1 . In addition, it is indistinct whether the lung age can be reliably predicted simply from one spirometric parameter of FEV1 (Yamaguchi et al., 2011). Furthermore, the backward calculation of age from the regression equation for the reference value of FEV1 may not be allowed in a statistical sense (Yamaguchi et al., 2008, 2011; Yamaguchi, 2011). Based on these backgrounds, the aim of the present study is to establish the novel regression equations allowing prediction of the reference value of lung age and its normal limits using the data harvested from a large number of healthy never-smokers with normal spirometric measurements. The equations thus developed were validated using the data obtained from the other group of healthy never-smokers with normal spirometry and the group of subjects with deteriorating pulmonary function.
2. Methods and materials 2.1. Study population Eligible participants were sorted from those undergoing the general health screening examination at the Japanese Red Cross Kumamoto Health Care Center during the two years from April 2008 to March 2010. Firstly, the subjects who declared, in the questionnaire, that they never smoked before the health screening examination were selected and defined as “never-smokers”. Among them, the subjects who reported to have no occupational history exposed to either biomass fuels or dusts and no conspicuous respiratory symptom including dyspnea on exertion, nocturnal dyspnea, cough, sputum, or wheezing were selected as the candidates of “healthy never-smokers”. Medical histories, results of various blood examinations, chest X-ray, and ECG of these candidates were carefully inspected by the staff physicians at the Health Care Center. The candidates who were confirmed to have neither cardiovascular disease nor respiratory disease such as lung cancer, bronchial asthma, COPD, interstitial lung disease, infiltrative lung disease, or bronchiectasis were defined as “healthy never-smokers”. Finally, “healthy never-smokers” in whom spirometric measurements were within normal ranges were defined as “healthy never-smokers with normal spirometric measurements”. The spirometric normality was judged by consulting the following criteria; FEV1 , FVC, and FEV1 /FVC > LLN of each parameter. The LLN of a given parameter was calculated from the formula reported by the Japanese Respiratory Society (Report from the Special Committee of Pulmonary Physiology of the Japanese Respiratory Society, 2001). Thus, the total of 8015 participants (males: 2496, females: 5519) with age distribution ranging between 25 and 87 years-old, who underwent the general health screening from April 2009 to March 2010, met the criteria of “healthy never-smokers with normal spirometric measurements”. These participants were categorized as group I (Table 1). Similarly, 6398 participants (males: 2074, females: 4324) with age distribution in a range from 22 to 89 years-old, who received
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Table 1 Demographic, anthropometric, and spirometric characteristics of group I. Male (2496) Age (years) Height (cm) Body weight (kg) BMI (kg/m2 ) %FAT (%) AC (cm) FVC (L) FEV1 (L) FEV1 %pred (%) FEV1 /FVC (%) PEF (L/s) FEF25–75 (L/s) FEF50 (L/s) FEF25 (L/s)
54.2 168.1 67.2 23.7 21.9 84.8 4.1 3.3 99.0 80.7 8.4 3.8 4.2 1.3
± ± ± ± ± ± ± ± ± ± ± ± ± ±
11.5 6.4 10.2 3.0 5.0 8.2 0.6 0.5 12.6 5.1 1.7 1.1 1.2 0.5
Female (5519) 53.3 155.9 53.6 22.1 27.7 80.5 3.2 2.6 117.2 81.1 6.4 3.1 3.4 1.0
± ± ± ± ± ± ± ± ± ± ± ± ± ±
10.4 5.8 8.4 3.2 6.1 9.0 0.7 0.6 26.5 5.5 1.8 1.0 1.1 0.5
BMI: body mass index; %FAT: fat percentage of body mass; AC: abdominal circumference; FEV1 %pred: percentage of FEV1 against the predicted value. Values are presented as means ± SD.
the health screening examination from April 2008 to March 2009, satisfied the criteria of “healthy never-smokers with normal spirometric measurements”, and were assigned to group II (Table 2). Since obese subjects with BMI over 30 kg/m2 were only 3.1% in the male and 2.5% in the female of groups I and II, we did not exclude them from the analysis. The members of group II did not overlap those of group I. The diverse parameters of group I were used for constructing the novel prediction equations of lung age in both genders. Group II was devoted to validation of the prediction equations. The 446 never-smoking subjects (males: 197, females: 249) with age ranging from 20 to 85 years-old, undergoing the health screening examination from April 2008 to March 2009, showed the FEV1 /FVC being less than the LLN, seemingly satisfying the criteria of airflow limitation. They had no interstitial or infiltrative shadow on their chest X-ray images as well as no abnormality in their ECG and blood examinations. Since the pulmonary function test was conducted under a condition with no inhalation of a short-acting bronchodilator, it was difficult to diagnose them as having COPD pathology only. We considered that they might contract either non-smoking COPD (Kohansal et al., 2009) or latent asthma. These participants were allocated to group III (Table 3) and used for certifying whether the newly developed equations would reliably detect the abnormal lung age in subjects with airflow limitation.
Table 2 Demographic, anthropometric, and spirometric characteristics of group II. Male (2074) Age (years) Height (cm) Body weight (kg) BMI (kg/m2 ) %FAT (%) AC (cm) FVC (L) FEV1 (L) FEV1 %pred (%) FEV1 /FVC (%) PEF (L/s) FEF25–75 (L/s) FEF50 (L/s) FEF25 (L/s)
49.4 169.1 68.0 23.8 22.0 84.5 4.2 3.4 96.2 81.1 8.7 4.0 4.3 1.3
± ± ± ± ± ± ± ± ± ± ± ± ± ±
11.2 6.3 10.1 3.0 4.9 8.0 0.6 0.5 9.4 5.1 1.5 1.1 1.3 0.3
Female (4324) 50.3 156.2 53.7 22.0 27.6 80.2 2.9 2.4 102.8 82.2 5.5 2.9 3.2 1.0
± ± ± ± ± ± ± ± ± ± ± ± ± ±
10.6 5.7 8.4 3.3 6.1 8.8 0.4 0.4 11.3 5.4 1.1 0.8 0.9 0.5
BMI: body mass index; %FAT: fat percentage of body mass; AC: abdominal circumference; FEV1 %pred: percentage of FEV1 against the predicted value. Values are presented as means ± SD.
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Table 3 Demographic, anthropometric, and spirometric characteristics of group III. Male (197) Age (years) Height (cm) Body weight (kg) BMI (kg/m2 ) %FAT (%) AC (cm) FVC (L) FEV1 (L) FEV1 %pred (%) FEV1 /FVC (%) PEF (L/s) FEF25–75 (L/s) FEF50 (L/s) FEF25 (L/s)
57.1 167.2 65.9 23.5 21.4 84.4 3.7 2.4 82.2 64.0 6.6 1.6 1.6 0.5
± ± ± ± ± ± ± ± ± ± ± ± ± ±
13.4* 7.0 10.0 3.0 5.4 8.1 0.8 0.6** 19.8** 5.8** 1.7** 0.6** 0.6** 0.2**
Female (249) 56.1 155.6 53.6 22.1 27.6 80.5 3.3 2.1 84.6 64.8 5.9 1.4 1.5 0.4
± ± ± ± ± ± ± ± ± ± ± ± ± ±
12.4* 5.8 8.0 3.0 5.7 9.1 0.9 0.6** 22.6** 4.9** 1.9** 0.6** 0.5** 0.2**
BMI: body mass index; %FAT: fat percentage of body mass; AC: abdominal circumference; FEV1 %pred: percentage of FEV1 against the predicted value. Values are presented as means ± SD. * p < 0.03 vs. group I. ** p < 0.0001 vs. group I.
All participants were given the informed consent telling that their data would be used for the clinical research aiming at establishment of the equations predicting lung age and were asked whether they agreed to the registration of their details in the database. Our research protocol was approved by the Human Ethics Committee of the Japanese Red Cross Kumamoto Health Care Center (registration number: 137). 2.2. Spirometric measurements Dynamic pulmonary function tests were performed using an electric spirometer (DISCOM-21 FX, CHEST Co., Tokyo, Japan), and the data of FVC (L), FEV1 (L), FEV1 /FVC (%), PEF (L/s), FEF25–75 (L/s), FEF50 (L/s) and FEF25 (L/s) were collected for each participant. Maneuvers were performed according to the standardization of lung function testing recommended by ATS/ERS Task Force (Miller et al., 2005).
larger than 10, this variable was removed from the Eq. (1). The overall agreement between SDL-age and chronological age was judged by the coefficient of determination adjusted for degrees of freedom (adjusted-R2 ). The normal limits, i.e., upper and lower limits of normal (ULN and LLN) for the disparity between SDL-age and chronological age, defined as SDL-age, was evaluated with the standardized residual called Z-score (Sall et al., 2004; Tsushima, 2009). The ULN and LLN were assumed to be equal to 95th and 5th percentiles of Z distribution, corresponding to Z-scores of ±1.64, respectively. The issue of whether SDL-age followed the normal distribution was examined by means of the Kolmogorov–Smirnov and Shapiro–Wilk tests. 2.4. Validation of prediction equations Since the concept of lung age is lacking in the physiological basis, the Eq. (1) should be taken as the empirical one and needs the validation concerning its applicability to prediction of SDL-age. We, therefore, estimated the SDL-age of group-II subjects with normal spirometric measurements and that of group-III subjects with airflow limitation by applying the regression equations constructed on the ground of Eq. (1). The overall agreement between SDL-age and chronological age in group II was evaluated from the best fitting line between the two drawn up by the least-squares minimization and from the number of subjects in whom the SDL-age exceeded its ULN and LLN determined for group I. 2.5. Other statistical analysis The difference between the three categories was judged in terms of the one-way ANOVA followed by the multiple comparison of the Turkey test. All calculations were performed using the IBM SPSS Statistics (Version 20.0, SPSS Inc., IBM Co., NY, USA). Otherwise specified, values were expressed as means and 95% confidence intervals. A p-value lower than 0.05 was deemed statistically significant. 3. Results
2.3. Prediction equations for spirometry-derived lung age
3.1. Prediction equations for SDL-age
The implicit assumption in the original method of Morris and Temple is that lung age is expressed by a linear function of FEV1 and height, the latter of which works as the factor supplementing the influence of anthropometric difference on FEV1 . However, other spirometric parameters may provide the useful information on lung age estimation, as well. We, therefore, hypothesized that lung age (objective variable) would be predicted from a function including height (H, cm) and various spirometric parameters as explanatory variables and defined it as spirometry-derived lung age (SDL-age); SDL-age = a0 + a1 · H + a2 · FVC + a3 · FEV1 + a4 · + a6 · FEF25–75 + a7 · FEF50 + a8 · FEF25
FEV1 + a5 · PEF FVC (1)
In the Eq. (1), ai (i = 1–8) is the partial regression coefficient for a particular explanatory variable, while a0 is the invariable constant. Since SDL-age should be equal to chronological age in a healthy never-smoker with normal spirometric measurements, we decided the coefficients of ai by applying the multiple regression analysis with the least-squares minimization to the data obtained from the group I, in which SDL-age was replaced by chronological age. In the midst of minimization, multicolinearity between variables was examined in cooperation with the variance-inflation-factor (VIF) analysis (Tsushima, 2009). When VIF for a particular variable was
In the male of group I, three spirometric parameters including FEV1 , FEV1 /FVC, and FEF50 were statistically selected as explanatory variables predicting SDL-age, but the others were removed because of either multicolinearity or fading statistical significance (Table 4). In the female of group I, four spirometric parameters of FEV1 , FEV1 /FVC, PEF, and FEF50 were picked up as explanatory parameters (Table 5). Thus, the regression line predicting the reference value of SDL-age for the male (M) and that for the female (F) were given by: SDL-age(M) = 209.195 − 0.455 · H − 11.521 · FEV1 − 0.602 · + 1.956 · FEF50
SDL-age(F) = 234.441 − 0.792 · H − 7.295 · FEV1 − 0.610 · + 0.301 · PEF + 2.647 · FEF50
FEV1 FVC (2)
FEV1 FVC (3)
The adjusted-R2 was 0.50 for the male equation or 0.42 for the female equation. The SDL-age in either gender followed the normal distribution with no dependence on the SDL-age and its ULN and LLN were ±13.4 years in the male or ±15.0 years in the female (Fig. 1).
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Table 4 Partial regression coefficients predicting the reference value of SDL-age for the male in group I. Explanatory variable
Partial regression coefficient
Constant Height (cm) FVC (L)* FEV1 (L) FEV1 /FVC (%) PEF (L/s)+ FEF25–75 (L/s)+ FEF50 (L/s) FEF25 (L/s)+
209.195 (197.48, 220.91) −0.455 (−0.52, −0.39) −5.562 (−13.36, 2.24) −11.521 (−12.43, −10.62) −0.602 (−0.70, −0.51) −0.189 (−0.44, 0.06) 0.104 (−0.58, 0.79) 1.956 (1.46, 2.45) −2.129 (−3.89, 0.37)
Absolute ˇ value
p value
VIF
0.26 0.32 0.55 0.27 0.03 0.01 0.21 0.02
<0.0001 <0.0001 0.162 <0.0001 <0.0001 0.132 0.766 <0.0001 0.180
1.50 247.87 2.35 2.33 1.62 5.47 3.40 8.62
Parenthesis: 95% confidence interval; absolute  value: absolute value of standardized partial regression coefficient; VIF: variance inflation factor; adjusted-R2 : 0.50; RSD: 8.2 years; * : FVC was excluded from the equation for SDL-age because of high VIF value causing high multicolinearity as well as insignificant p value; + : PEF; FEF25–75 , and FEF25 were excluded from the equation due to insignificant p values.
Fig. 1. Scattering of Z-score for the disparity between SDL-age and chronological age (SDL-age) against SDL-age in group I. Solid line: Z-score being equal to zero. Dotted lines: normal limits of Z-score corresponding to ±1.64 (standardized ULN and LLN). (A) male (n = 2496), non-standardized ULN and LLN are ±13.4 years. (B) Female (n = 5519), non-standardized ULN and LLN correspond to ±15.0 years.
3.2. SDL-age in subjects with normal spirometry (validation-1) The overall relationship between SDL-age (X) predicted from respective regression line and chronological age (Y) was Y = −0.25 + 0.97·X for the group-II male and Y = −3.59 + 1.06·X for
the group-II female (Fig. 2). The relative frequency of participants in whom SDL-age exceeded the ULN or LLN was 12.2% in the male and 11.4% in the female (Fig. 3), indicating an acceptable agreement between SDL-age and chronological age in either gender of group II.
Table 5 Partial regression coefficients predicting the reference value of SDL-age for the female in group I. Explanatory variable
Partial regression coefficient
Absolute ˇ value
p value
VIF
Constant Height (cm) FVC (L)* FEV1 (L) FEV1 /FVC (%) PEF (L/s) FEF25–75 (L/s)+ FEF50 (L/s) FEF25 (L/s)+
234.441 (227.20, 241.68) −0.792 (−0.83, −0.75) −14.610 (−19.20, −10.02) −7.295 (−7.99, −6.60) −0.610 (−0.67, −0.55) 0.301 (0.12, 0.49) 0.046 (−0.51, 0.60) 2.647 (2.24, 3.06) −0.450 (−1.66, 0.76)
0.44 0.03 0.42 0.32 0.05 0.004 0.28 0.02
<0.0001 <0.0001 <0.0001 <0.0001 <0.0001 0.001 0.872 <0.0001 0.465
1.11 249.32 3.74 2.32 2.26 6.85 4.26 7.74
Parenthesis: 95% confidence interval; absolute ˇ value: absolute value of standardized partial regression coefficient; VIF: variance inflation factor; adjusted-R2 : 0.42; RSD: 9.1 years; * : FVC was excluded from the equation because of high VIF; + : FEF25–75 and FEF25 were excluded from the equation due to insignificant p values.
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Fig. 2. Comparison of SDL-age with chronological age in group II. (A) Male (n = 2074), the best fitted line of chronological age (Y) against SDL-age (X) is expressed by Y = −0.25 + 0.97·X (solid line). Dotted line: line on identity. (B) Female (n = 4324), the best fitted line of chronological age (Y) against SDL-age (X) is given by Y = −3.59 + 1.06·X (solid line). Dotted line: line on identity.
3.3. SDL-age in subjects with airflow limitation (validation-2) The 446 participants in group III were divided into three categories depending on FEV1 %pred (measured FEV1 /predicted FEV1 ) for the sake of convenience, i.e., grade I: 80% ≤ FEV1 %pred (n = 248), grade II: 50% ≤ FEV1 %pred < 80% (n = 170), and grade III: 30% ≤ FEV1 %pred < 50% (n = 28). The SDL-ages of these participants were evaluated from the regression Eqs. (2) and (3). The SDL-age averaged +6.7 years in the subjects with grade I, +10.7 years in those with grade II, or +22.4 years in those with grade III (Fig. 4). The SDL-age in subjects with grade III, but not in those with grades I and II, certainly exceeded the ULN, indicating that allowance was made for judging that only the SDL-age in subjects with grade-III
airflow limitation was significantly older than the chronological age. 4. Discussion 4.1. Limitations of the study One of the crucial issues acknowledged is that we have no reliable grounds for supporting the idea that the relationship between lung ageing and various spirometric parameters can be approximated by the linear function. Concerning this issue, however, Kohansal et al. (2009) demonstrated that, in the male, peak of FEV1 or FVC would be attained at an age between 20 and 25 years-old
Fig. 3. Scattering of SDL-age defined as the difference between SDL-age and chronological age against SDL-age in group II. Solid line: SDL-age being equal to zero. Dotted lines: non-standardized ULN and LLN for SDL-age, determined from the analysis of group-I data. (A) Male, non-standardized ULN and LLN are ±13.4 years. (B) Female, non-standardized ULN and LLN are ±15.0 years.
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Fig. 4. Difference between SDL-age and chronological age (SDL-age) in group III with airflow limitation. Grade I: n = 248, grade II: n = 170, and grade III: n = 28. † : larger than grade I (p < 0.003 between grades I and II, p < 0.0001 between grades I and III). # : Larger than grade II (p < 0.0001). Values are means and their 95% confidence intervals. Grade I: +6.7 years (5.4–7.4), grade II: +10.7 years (8.5–12.8), and grade III: +22.4 years (17.2–27.7). Dotted line: average of male ULN and female ULN for SDL-age (+14.2 years).
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and then declined with age, but, in the female, full lung growth would be achieved earlier than the male. Almost the same results were reported by Hankinson et al. (1999). These findings suggest that the relation between lung age (≈chronological age) and most of the spirometric parameters is approximated by a linear function as far as the subjects studied are over 20 years-old and their spirometry is normal. However, it may be difficult to say that these findings sufficiently provide the physiologically relevant grounds for the linear assumption between SDL-age and various spirometric parameters. Therefore, we validated their applicability by calculating the SDL-ages of subjects with normal spirometry (group II) and those with deteriorating pulmonary function (group III). We found that the newly developed equations could predict not only the equality between SDL-age and chronological age in group II within an allowable margin of error but also the incremental disparity between SDL-age and chronological age in group III with airflow limitation. Thus, we concluded that the Eqs. (2) and (3) would be practically useful in a clinical setting. It should be noted that the Eqs. (2) and (3) are only applicable to the Japanese population. However, we anticipate that the findings obtained in the present study will promote the development of ethnic-specific regression equations allowing prediction of SDL-age in various races.
Fig. 5. Comparison of lung age of Morris and Temple with chronological age in group II. (A) Male, the best fitted line of chronological age (Y) against lung age of Morris and Temple (X) is described by Y = 23.8 + 0.47·X (solid line). Dotted line: line on identity. (B) Female, the best fitted line of chronological age (Y) against lung age of Morris and Temple (X) is given by Y = 29.4 + 0.44·X (solid line). Dotted line: line on identity.
Fig. 6. Three-step procedure for judging the abnormality in SDL-age. See text for further explanation (Section 4.3).
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Since the prediction equations include FEV1 /FVC and FEF50 as explanatory variables, they are expected to be sensitive to the functional abnormality caused by the interstitial lung pathology, as well. However, a further study is absolutely needed to elucidate the applicability of these equations to the patients with a variety of interstitial lung diseases. 4.2. Lung age estimated from the original method of Morris and Temple According to the method of Morris and Temple (1985), the lung age in participants of group II was calculated by counting back the regression equations predicting reference values of FEV1 reported by the Japanese Respiratory Society (Report from the Special Committee of Pulmonary Physiology of the Japanese Respiratory Society, 2001). The overall relation between lung age of Morris and Temple (X) and chronological age (Y) was expressed as Y = 23.8 + 0.47·X in the male and Y = 29.4 + 0.44·X in the female (Fig. 5). The fitted lines revealed that the discrepancy between chronological age and lung age became remarkable as the age was shifted from a center of the data set, i.e., around 50 years-old, suggesting that the original method of Morris and Temple inevitably overestimated the lung age in older persons but underestimated in younger persons. This leads to a serious problem when persuading young persons to quit smoking, because their lung ages estimated from the method of Morris and Temple are frequently younger than their chronological ages, thus losing the persuasiveness against smoking cessation. 4.3. Algorithm for judging the abnormality from spirometry-derived lung age We would recommend the three-step procedure when judging the abnormality in SDL-age (Fig. 6). The first thing to do is to examine whether the disparity between SDL-age and chronological age of a person in question (SDL-age) exists within normal limits formed by ULN and LLN, i.e., ±13.4 years in the male and ±15.0 years in the female. (1) If SDL-age exists within ULN and LLN, the SDL-age of a person should be interpreted to be consistent with his (her) chronological age, even when the SDL-age is above or below the chronological age. (2) If SDL-age exceeds ULN, the SDL-age is judged to be older than the chronological age. (3) If SDL-age is below LLN, the SDL-age is judged to be younger than the chronological age. Conflict of interest All authors declared no support from any organization for the submitted work, no financial relationships with any organizations
that might have an interest in the submitted work, and no other relationships or activities that could appear to have influenced the submitted work. References D’Avila Melo, S.M., Aragao de Melo, V., Vieira de Melo, E., Sotero de Menezes, R., Leite de Castro, V., Barreto, M.S.P., 2010. Accelerated lung aging in patients with morbid obesity. Journal Brasileiro de Pneumologia 36, 746–752. Hankinson, J.L., Odencrantz, J.R., Fedan, K.B., 1999. Spirometric reference values from a sample of the general U.S. population. American Journal of Respiratory and Critical Care Medicine 159, 179–187. Hansen, J.E., Sun, X.-G., Wasserman, K., 2010. Calculating gambling odds and lung ages for smokers. European Respiratory Journal 35, 776–780. Kohansal, R., Martinez-Camblor, P., Agusti, A., Buist, A.S., Mannino, D.M., Soriano, J.B., 2009. The natural history of chronic airflow obstruction revisited. American Journal of Respiratory and Critical Care Medicine 180, 3–10. Lin, K.W., 2008. Study’s conclusion about screening is unwarranted. British Medical Journal 306, 1034. Lipkus, I.M., Prokhorov, A.V., 2007. The effects of providing lung age and respiratory symptoms feedback on community college smokers’ perceived smoking-related health risks, worries and desire to quit. Addictive Behaviors 32, 516–532. Miller, M.R., Hankinson, J., Brusasco, V., Burgos, F., Casaburi, R., Coates, A., Crapo, R., Enright, P., van der Grinten, C.P.M., Gustafsson, P., Jensen, R., Johnson, D.C., MacIntyre, N., McKay, R., Navajas, D., Pedersen, O.F., Pellegrino, R., Viegi, G., Wanger, J., 2005. Standardisation of spirometry (series ATS/ERS task force: standardisation of lung function testing). European Respiratory Journal 26, 319–338. Mitsumune, T., Senoh, E., Nishikawa, H., Adach, I.M., Kajii, A., 2009. The effect of obesity and smoking status on lung age in Japanese men. Respirology 14, 757–760. Morris, J.F., Temple, W., 1985. Spirometric lung age estimation for motivating smoking cessation. Preventive Medicine 14, 655–662. Newbury, W., Newbury, J., Briggs, N., Crockett, A., 2010. Exploring the need to update lung age equations. Primary Care Respiratory Journal 19, 242–247. Parker, D., Goldman, R.E., Eaton, C.B., 2008. A qualitative study of individuals at risk for or who have chronic obstructive pulmonary disease: what do they understand about their disease? Lung 186, 313–316. Parkers, G., Greenhalgh, T., Griffin, M., Dent, R., 2008. Effect on smoking quit rate of telling patients their lung age: the step2quit randomized controlled trial. British Medical Journal 336, 598–600. Quanjer, P.H., Enright, P.L., 2010. Should we use lung age? Primary Care Respiratory Journal 19, 197–199. Report from the Special Committee of Pulmonary Physiology of the Japanese Respiratory Society, 2001. Reference values for spirogram and blood gas analysis in Japanese non-smoking healthy adults. Journal of Japanese Respiratory Society, April; index: 1–17. Sall, J., Creighton, L., Lehman, A., 2004. Regression and correlation analysis. In: Sall, J., Creighton, L., Lehman, A. (Eds.), JMP Start Statistics. , 3rd ed. SAS Institute Inc., Cary, NC, pp. 219–246. Tashkin, D.P., Murray, R.P., 2009. Smoking cessation in chronic obstructive pulmonary disease. Respiratory Medicine 103, 963–974. Toda, R., Hoshino, T., Kawayama, T., Imaoka, H., Sakazaki, Y., Tsuda, T., Takada, S., Kinoshita, M., Iwanaga, T., Aizawa, H., 2009. Validation of lung age measured by spirometry and handy electronic FEV1 /FEV6 meter in pulmonary diseases. Internal Medicine 48, 513–521. Tsushima, H., 2009. Practice in multiple regression analysis. In: Tsushima, H. (Ed.), Multivariate Data Analysis in Medical Field – Learning from SPSS. , 2nd ed. TokyoTosho Co, Tokyo, pp. 57–95. Yamaguchi, K., Onizawa, S., Tsuji, T., Aoshiba, K., Nagai, A., 2008. A novel method for evaluating spirometric lung age. British Medical Journal, http://dx.doi.org/10.1136/bmj.39503.582396.25 (Rapid Responses to the paper of Parkers et al.). Yamaguchi, K., 2011. A new method for evaluating lung age. Journal of Japanese Respiratory Society 49, 713–716. Yamaguchi, K., Onizawa, S., Tsuji, T., Aoshiba, K., Nagai, A., 2011. How to evaluate spirometric lung age – what method is approvable? Respiratory Physiology and Neurobiology 178, 349–351.
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Respiratory Physiology & Neurobiology journal homepage: www.elsevier.com/locate/resphysiol
Novel regression equations predicting lung age from varied spirometric parameters Kazuhiro Yamaguchi a,∗ , Hisamitsu Omori b , Ayumi Onoue b , Takahiko Katoh b , Yasuhiro Ogata c , Hidetoshi Kawashima c , Shigemitsu Onizawa d , Takao Tsuji d , Kazutetsu Aoshiba d , Atsushi Nagai d a
Comprehensive and Internal Medicine, Tokyo Women’s Medical University Medical Center East, Station Port Tower 4F, Nishi-Nippori, Arakawa-ku, Tokyo 116-0013, Japan Department of Public Health Faculty of Life Sciences, Kumamoto University, 1-1-1 Honjou, Kumamoto 860-8556, Japan c Health Care Center, Japanese Red Cross Kumamoto, 2-1-1 Nagamineminami, Kumamoto 861-8528, Japan d The First Department of Medicine, Tokyo Women’s Medical University, 8-1 Kawata-cho, Shinjuku-ku, Tokyo 162-8666, Japan b
a r t i c l e
i n f o
Article history: Accepted 20 June 2012 Keywords: Lung ageing Spirometry Reference value Normal limits Airflow limitation
a b s t r a c t Although lung age calculated backward from regression formulas constructed for FEV1 estimation is widely used, it possesses a couple of faults. We developed novel equations predicting lung age from varied spirometric parameters (spirometry-derived lung age (SDL-age)). Applying multiple regression analysis, equations predicting SDL-age were invented using data from 8015 never-smokers with normal spirometry (group I). Validation was made based on data from 6398 never-smokers with normal spirometry (group II). Equations were further applied for 446 subjects with airflow limitation. FEV1 , FEV1 /FVC, FEF50 , and PEF were selected as explanatory variables for reference value of SDL-age. Normal limits of difference between SDL-age and chronological-age were ±13.4 years in the male and ±15.0 years in the female. Established equations predicted SDL-age of group II. SDL-age was older than chronological-age only in subjects with severe airflow limitation. Novel regression equations allowing prediction of reference value of SDL-age and normal limits of difference between SDL-age and chronological-age were elaborated in both genders. © 2012 Elsevier B.V. All rights reserved.
1. Introduction Cigarette smoking is the leading cause of morbidity and mortality in a variety of diseases. Although smoking-related harmful effects are generally assessed in terms of spirometric measurements, it is not easy to let smokers understand spirometric data
Abbreviations: Absolute  value, absolute value of standardized partial regression coefficient; Adjusted-R2 , coefficient of determination adjusted for degrees of freedom; LLN, lower limit of normal; ULN, upper limit of normal; RSD, residual standard deviation; VIF, variance inflation factor; Z-score, standardized residual; FEV1 , forced expiratory volume in one second; FEV1 %pred, (measured FEV1 )/(predicted FEEV1 ) (%); FVC, forced vital capacity in expiration; FEF50 , forced expiratory flow rate at 50% of FVC; FEF25 , forced expiratory flow rate at 25% of FVC; FEF25–75 , forced expiratory flow rate between 75% and 25% of FVC; PEF, peak expiratory flow rate; AC, abdominal circumference (cm); BMI, body mass index (kg/m2 ); %FAT, fat percentage of body mass (%); H, height (cm); SDL-age, spirometry-derived lung age (years-old); SDL-age, (SDL-age) – (chronological age) (years). ∗ Corresponding author. Tel.: +81 3 3805 7790; fax: +81 3 3805 7775. E-mail addresses: [email protected] (K. Yamaguchi), [email protected] (H. Omori), [email protected] (A. Onoue), [email protected] (T. Katoh), [email protected] (Y. Ogata), [email protected] (H. Kawashima), [email protected] (S. Onizawa), [email protected] (T. Tsuji), [email protected] (K. Aoshiba), [email protected] (A. Nagai). 1569-9048/$ – see front matter © 2012 Elsevier B.V. All rights reserved. http://dx.doi.org/10.1016/j.resp.2012.06.025
properly, leading to difficulty in persuading them to quit smoking when they simply received the raw results of spirometric measurements (Tashkin and Murray, 2009). One approach to increase smoking-cessation rate is to communicate spirometric results in a manner that is easily understood by smokers (Newbury et al., 2010). To conquer the difficulty existing in the raw results of spirometric measurements, Morris and Temple (1985) proposed the concept of lung age about 25 years ago. The original formula developed by Morris and Temple had a form in which the regression equation constructed for predicting reference value of FEV1 from age and height was rearranged for “age”. The original method of Morris and Temple has greatly contributed toward providing incentive to abstain from smoking (Lipkus and Prokhorov, 2007; Parker et al., 2008; Parkers et al., 2008; Tashkin and Murray, 2009). Furthermore, it allowed several groups of investigators to estimate the lung age of patients suffering from varied pathological conditions such as COPD (Parker et al., 2008; Tashkin and Murray, 2009; Toda et al., 2009) and morbid obesity (D’Avila Melo et al., 2010; Mitsumune et al., 2009). To extend the clinical application of lung age, Hansen et al. (2010) proposed a simplified equation allowing its estimation from FEV1 /FVC, the method qualitatively differing from that of Morris and Temple. Parkers et al. (2008) assessed the effect on smokingquit rate of telling subjects about their lung ages, leading to the conclusion that the smoking-quit rate was higher in the subjects
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who received the spirometric results in terms of lung age than those only received the raw results of spirometric measurements. A couple of authors, however, questioned whether the lung age was truly useful as a tool for motivating the cessation of smoking (Lin, 2008; Quanjer and Enright, 2010). They asserted that the lung age calculated from the method of Morris and Temple entirely disregarded the variability of FEV1 in normal subjects, thus causing a physiologically serious flaw, i.e., the lung age of a normal person whose FEV1 is below the reference value but above the lower limit of normal (LLN) is forcibly estimated to be older than his (her) chronological age, though the lung age of this person should be equal to the chronological age. This happens because the lung age is calculated by counting back the regression equation predicting the reference value, but not the LLN, of FEV1 . In addition, it is indistinct whether the lung age can be reliably predicted simply from one spirometric parameter of FEV1 (Yamaguchi et al., 2011). Furthermore, the backward calculation of age from the regression equation for the reference value of FEV1 may not be allowed in a statistical sense (Yamaguchi et al., 2008, 2011; Yamaguchi, 2011). Based on these backgrounds, the aim of the present study is to establish the novel regression equations allowing prediction of the reference value of lung age and its normal limits using the data harvested from a large number of healthy never-smokers with normal spirometric measurements. The equations thus developed were validated using the data obtained from the other group of healthy never-smokers with normal spirometry and the group of subjects with deteriorating pulmonary function.
2. Methods and materials 2.1. Study population Eligible participants were sorted from those undergoing the general health screening examination at the Japanese Red Cross Kumamoto Health Care Center during the two years from April 2008 to March 2010. Firstly, the subjects who declared, in the questionnaire, that they never smoked before the health screening examination were selected and defined as “never-smokers”. Among them, the subjects who reported to have no occupational history exposed to either biomass fuels or dusts and no conspicuous respiratory symptom including dyspnea on exertion, nocturnal dyspnea, cough, sputum, or wheezing were selected as the candidates of “healthy never-smokers”. Medical histories, results of various blood examinations, chest X-ray, and ECG of these candidates were carefully inspected by the staff physicians at the Health Care Center. The candidates who were confirmed to have neither cardiovascular disease nor respiratory disease such as lung cancer, bronchial asthma, COPD, interstitial lung disease, infiltrative lung disease, or bronchiectasis were defined as “healthy never-smokers”. Finally, “healthy never-smokers” in whom spirometric measurements were within normal ranges were defined as “healthy never-smokers with normal spirometric measurements”. The spirometric normality was judged by consulting the following criteria; FEV1 , FVC, and FEV1 /FVC > LLN of each parameter. The LLN of a given parameter was calculated from the formula reported by the Japanese Respiratory Society (Report from the Special Committee of Pulmonary Physiology of the Japanese Respiratory Society, 2001). Thus, the total of 8015 participants (males: 2496, females: 5519) with age distribution ranging between 25 and 87 years-old, who underwent the general health screening from April 2009 to March 2010, met the criteria of “healthy never-smokers with normal spirometric measurements”. These participants were categorized as group I (Table 1). Similarly, 6398 participants (males: 2074, females: 4324) with age distribution in a range from 22 to 89 years-old, who received
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Table 1 Demographic, anthropometric, and spirometric characteristics of group I. Male (2496) Age (years) Height (cm) Body weight (kg) BMI (kg/m2 ) %FAT (%) AC (cm) FVC (L) FEV1 (L) FEV1 %pred (%) FEV1 /FVC (%) PEF (L/s) FEF25–75 (L/s) FEF50 (L/s) FEF25 (L/s)
54.2 168.1 67.2 23.7 21.9 84.8 4.1 3.3 99.0 80.7 8.4 3.8 4.2 1.3
± ± ± ± ± ± ± ± ± ± ± ± ± ±
11.5 6.4 10.2 3.0 5.0 8.2 0.6 0.5 12.6 5.1 1.7 1.1 1.2 0.5
Female (5519) 53.3 155.9 53.6 22.1 27.7 80.5 3.2 2.6 117.2 81.1 6.4 3.1 3.4 1.0
± ± ± ± ± ± ± ± ± ± ± ± ± ±
10.4 5.8 8.4 3.2 6.1 9.0 0.7 0.6 26.5 5.5 1.8 1.0 1.1 0.5
BMI: body mass index; %FAT: fat percentage of body mass; AC: abdominal circumference; FEV1 %pred: percentage of FEV1 against the predicted value. Values are presented as means ± SD.
the health screening examination from April 2008 to March 2009, satisfied the criteria of “healthy never-smokers with normal spirometric measurements”, and were assigned to group II (Table 2). Since obese subjects with BMI over 30 kg/m2 were only 3.1% in the male and 2.5% in the female of groups I and II, we did not exclude them from the analysis. The members of group II did not overlap those of group I. The diverse parameters of group I were used for constructing the novel prediction equations of lung age in both genders. Group II was devoted to validation of the prediction equations. The 446 never-smoking subjects (males: 197, females: 249) with age ranging from 20 to 85 years-old, undergoing the health screening examination from April 2008 to March 2009, showed the FEV1 /FVC being less than the LLN, seemingly satisfying the criteria of airflow limitation. They had no interstitial or infiltrative shadow on their chest X-ray images as well as no abnormality in their ECG and blood examinations. Since the pulmonary function test was conducted under a condition with no inhalation of a short-acting bronchodilator, it was difficult to diagnose them as having COPD pathology only. We considered that they might contract either non-smoking COPD (Kohansal et al., 2009) or latent asthma. These participants were allocated to group III (Table 3) and used for certifying whether the newly developed equations would reliably detect the abnormal lung age in subjects with airflow limitation.
Table 2 Demographic, anthropometric, and spirometric characteristics of group II. Male (2074) Age (years) Height (cm) Body weight (kg) BMI (kg/m2 ) %FAT (%) AC (cm) FVC (L) FEV1 (L) FEV1 %pred (%) FEV1 /FVC (%) PEF (L/s) FEF25–75 (L/s) FEF50 (L/s) FEF25 (L/s)
49.4 169.1 68.0 23.8 22.0 84.5 4.2 3.4 96.2 81.1 8.7 4.0 4.3 1.3
± ± ± ± ± ± ± ± ± ± ± ± ± ±
11.2 6.3 10.1 3.0 4.9 8.0 0.6 0.5 9.4 5.1 1.5 1.1 1.3 0.3
Female (4324) 50.3 156.2 53.7 22.0 27.6 80.2 2.9 2.4 102.8 82.2 5.5 2.9 3.2 1.0
± ± ± ± ± ± ± ± ± ± ± ± ± ±
10.6 5.7 8.4 3.3 6.1 8.8 0.4 0.4 11.3 5.4 1.1 0.8 0.9 0.5
BMI: body mass index; %FAT: fat percentage of body mass; AC: abdominal circumference; FEV1 %pred: percentage of FEV1 against the predicted value. Values are presented as means ± SD.
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Table 3 Demographic, anthropometric, and spirometric characteristics of group III. Male (197) Age (years) Height (cm) Body weight (kg) BMI (kg/m2 ) %FAT (%) AC (cm) FVC (L) FEV1 (L) FEV1 %pred (%) FEV1 /FVC (%) PEF (L/s) FEF25–75 (L/s) FEF50 (L/s) FEF25 (L/s)
57.1 167.2 65.9 23.5 21.4 84.4 3.7 2.4 82.2 64.0 6.6 1.6 1.6 0.5
± ± ± ± ± ± ± ± ± ± ± ± ± ±
13.4* 7.0 10.0 3.0 5.4 8.1 0.8 0.6** 19.8** 5.8** 1.7** 0.6** 0.6** 0.2**
Female (249) 56.1 155.6 53.6 22.1 27.6 80.5 3.3 2.1 84.6 64.8 5.9 1.4 1.5 0.4
± ± ± ± ± ± ± ± ± ± ± ± ± ±
12.4* 5.8 8.0 3.0 5.7 9.1 0.9 0.6** 22.6** 4.9** 1.9** 0.6** 0.5** 0.2**
BMI: body mass index; %FAT: fat percentage of body mass; AC: abdominal circumference; FEV1 %pred: percentage of FEV1 against the predicted value. Values are presented as means ± SD. * p < 0.03 vs. group I. ** p < 0.0001 vs. group I.
All participants were given the informed consent telling that their data would be used for the clinical research aiming at establishment of the equations predicting lung age and were asked whether they agreed to the registration of their details in the database. Our research protocol was approved by the Human Ethics Committee of the Japanese Red Cross Kumamoto Health Care Center (registration number: 137). 2.2. Spirometric measurements Dynamic pulmonary function tests were performed using an electric spirometer (DISCOM-21 FX, CHEST Co., Tokyo, Japan), and the data of FVC (L), FEV1 (L), FEV1 /FVC (%), PEF (L/s), FEF25–75 (L/s), FEF50 (L/s) and FEF25 (L/s) were collected for each participant. Maneuvers were performed according to the standardization of lung function testing recommended by ATS/ERS Task Force (Miller et al., 2005).
larger than 10, this variable was removed from the Eq. (1). The overall agreement between SDL-age and chronological age was judged by the coefficient of determination adjusted for degrees of freedom (adjusted-R2 ). The normal limits, i.e., upper and lower limits of normal (ULN and LLN) for the disparity between SDL-age and chronological age, defined as SDL-age, was evaluated with the standardized residual called Z-score (Sall et al., 2004; Tsushima, 2009). The ULN and LLN were assumed to be equal to 95th and 5th percentiles of Z distribution, corresponding to Z-scores of ±1.64, respectively. The issue of whether SDL-age followed the normal distribution was examined by means of the Kolmogorov–Smirnov and Shapiro–Wilk tests. 2.4. Validation of prediction equations Since the concept of lung age is lacking in the physiological basis, the Eq. (1) should be taken as the empirical one and needs the validation concerning its applicability to prediction of SDL-age. We, therefore, estimated the SDL-age of group-II subjects with normal spirometric measurements and that of group-III subjects with airflow limitation by applying the regression equations constructed on the ground of Eq. (1). The overall agreement between SDL-age and chronological age in group II was evaluated from the best fitting line between the two drawn up by the least-squares minimization and from the number of subjects in whom the SDL-age exceeded its ULN and LLN determined for group I. 2.5. Other statistical analysis The difference between the three categories was judged in terms of the one-way ANOVA followed by the multiple comparison of the Turkey test. All calculations were performed using the IBM SPSS Statistics (Version 20.0, SPSS Inc., IBM Co., NY, USA). Otherwise specified, values were expressed as means and 95% confidence intervals. A p-value lower than 0.05 was deemed statistically significant. 3. Results
2.3. Prediction equations for spirometry-derived lung age
3.1. Prediction equations for SDL-age
The implicit assumption in the original method of Morris and Temple is that lung age is expressed by a linear function of FEV1 and height, the latter of which works as the factor supplementing the influence of anthropometric difference on FEV1 . However, other spirometric parameters may provide the useful information on lung age estimation, as well. We, therefore, hypothesized that lung age (objective variable) would be predicted from a function including height (H, cm) and various spirometric parameters as explanatory variables and defined it as spirometry-derived lung age (SDL-age); SDL-age = a0 + a1 · H + a2 · FVC + a3 · FEV1 + a4 · + a6 · FEF25–75 + a7 · FEF50 + a8 · FEF25
FEV1 + a5 · PEF FVC (1)
In the Eq. (1), ai (i = 1–8) is the partial regression coefficient for a particular explanatory variable, while a0 is the invariable constant. Since SDL-age should be equal to chronological age in a healthy never-smoker with normal spirometric measurements, we decided the coefficients of ai by applying the multiple regression analysis with the least-squares minimization to the data obtained from the group I, in which SDL-age was replaced by chronological age. In the midst of minimization, multicolinearity between variables was examined in cooperation with the variance-inflation-factor (VIF) analysis (Tsushima, 2009). When VIF for a particular variable was
In the male of group I, three spirometric parameters including FEV1 , FEV1 /FVC, and FEF50 were statistically selected as explanatory variables predicting SDL-age, but the others were removed because of either multicolinearity or fading statistical significance (Table 4). In the female of group I, four spirometric parameters of FEV1 , FEV1 /FVC, PEF, and FEF50 were picked up as explanatory parameters (Table 5). Thus, the regression line predicting the reference value of SDL-age for the male (M) and that for the female (F) were given by: SDL-age(M) = 209.195 − 0.455 · H − 11.521 · FEV1 − 0.602 · + 1.956 · FEF50
SDL-age(F) = 234.441 − 0.792 · H − 7.295 · FEV1 − 0.610 · + 0.301 · PEF + 2.647 · FEF50
FEV1 FVC (2)
FEV1 FVC (3)
The adjusted-R2 was 0.50 for the male equation or 0.42 for the female equation. The SDL-age in either gender followed the normal distribution with no dependence on the SDL-age and its ULN and LLN were ±13.4 years in the male or ±15.0 years in the female (Fig. 1).
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Table 4 Partial regression coefficients predicting the reference value of SDL-age for the male in group I. Explanatory variable
Partial regression coefficient
Constant Height (cm) FVC (L)* FEV1 (L) FEV1 /FVC (%) PEF (L/s)+ FEF25–75 (L/s)+ FEF50 (L/s) FEF25 (L/s)+
209.195 (197.48, 220.91) −0.455 (−0.52, −0.39) −5.562 (−13.36, 2.24) −11.521 (−12.43, −10.62) −0.602 (−0.70, −0.51) −0.189 (−0.44, 0.06) 0.104 (−0.58, 0.79) 1.956 (1.46, 2.45) −2.129 (−3.89, 0.37)
Absolute ˇ value
p value
VIF
0.26 0.32 0.55 0.27 0.03 0.01 0.21 0.02
<0.0001 <0.0001 0.162 <0.0001 <0.0001 0.132 0.766 <0.0001 0.180
1.50 247.87 2.35 2.33 1.62 5.47 3.40 8.62
Parenthesis: 95% confidence interval; absolute  value: absolute value of standardized partial regression coefficient; VIF: variance inflation factor; adjusted-R2 : 0.50; RSD: 8.2 years; * : FVC was excluded from the equation for SDL-age because of high VIF value causing high multicolinearity as well as insignificant p value; + : PEF; FEF25–75 , and FEF25 were excluded from the equation due to insignificant p values.
Fig. 1. Scattering of Z-score for the disparity between SDL-age and chronological age (SDL-age) against SDL-age in group I. Solid line: Z-score being equal to zero. Dotted lines: normal limits of Z-score corresponding to ±1.64 (standardized ULN and LLN). (A) male (n = 2496), non-standardized ULN and LLN are ±13.4 years. (B) Female (n = 5519), non-standardized ULN and LLN correspond to ±15.0 years.
3.2. SDL-age in subjects with normal spirometry (validation-1) The overall relationship between SDL-age (X) predicted from respective regression line and chronological age (Y) was Y = −0.25 + 0.97·X for the group-II male and Y = −3.59 + 1.06·X for
the group-II female (Fig. 2). The relative frequency of participants in whom SDL-age exceeded the ULN or LLN was 12.2% in the male and 11.4% in the female (Fig. 3), indicating an acceptable agreement between SDL-age and chronological age in either gender of group II.
Table 5 Partial regression coefficients predicting the reference value of SDL-age for the female in group I. Explanatory variable
Partial regression coefficient
Absolute ˇ value
p value
VIF
Constant Height (cm) FVC (L)* FEV1 (L) FEV1 /FVC (%) PEF (L/s) FEF25–75 (L/s)+ FEF50 (L/s) FEF25 (L/s)+
234.441 (227.20, 241.68) −0.792 (−0.83, −0.75) −14.610 (−19.20, −10.02) −7.295 (−7.99, −6.60) −0.610 (−0.67, −0.55) 0.301 (0.12, 0.49) 0.046 (−0.51, 0.60) 2.647 (2.24, 3.06) −0.450 (−1.66, 0.76)
0.44 0.03 0.42 0.32 0.05 0.004 0.28 0.02
<0.0001 <0.0001 <0.0001 <0.0001 <0.0001 0.001 0.872 <0.0001 0.465
1.11 249.32 3.74 2.32 2.26 6.85 4.26 7.74
Parenthesis: 95% confidence interval; absolute ˇ value: absolute value of standardized partial regression coefficient; VIF: variance inflation factor; adjusted-R2 : 0.42; RSD: 9.1 years; * : FVC was excluded from the equation because of high VIF; + : FEF25–75 and FEF25 were excluded from the equation due to insignificant p values.
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Fig. 2. Comparison of SDL-age with chronological age in group II. (A) Male (n = 2074), the best fitted line of chronological age (Y) against SDL-age (X) is expressed by Y = −0.25 + 0.97·X (solid line). Dotted line: line on identity. (B) Female (n = 4324), the best fitted line of chronological age (Y) against SDL-age (X) is given by Y = −3.59 + 1.06·X (solid line). Dotted line: line on identity.
3.3. SDL-age in subjects with airflow limitation (validation-2) The 446 participants in group III were divided into three categories depending on FEV1 %pred (measured FEV1 /predicted FEV1 ) for the sake of convenience, i.e., grade I: 80% ≤ FEV1 %pred (n = 248), grade II: 50% ≤ FEV1 %pred < 80% (n = 170), and grade III: 30% ≤ FEV1 %pred < 50% (n = 28). The SDL-ages of these participants were evaluated from the regression Eqs. (2) and (3). The SDL-age averaged +6.7 years in the subjects with grade I, +10.7 years in those with grade II, or +22.4 years in those with grade III (Fig. 4). The SDL-age in subjects with grade III, but not in those with grades I and II, certainly exceeded the ULN, indicating that allowance was made for judging that only the SDL-age in subjects with grade-III
airflow limitation was significantly older than the chronological age. 4. Discussion 4.1. Limitations of the study One of the crucial issues acknowledged is that we have no reliable grounds for supporting the idea that the relationship between lung ageing and various spirometric parameters can be approximated by the linear function. Concerning this issue, however, Kohansal et al. (2009) demonstrated that, in the male, peak of FEV1 or FVC would be attained at an age between 20 and 25 years-old
Fig. 3. Scattering of SDL-age defined as the difference between SDL-age and chronological age against SDL-age in group II. Solid line: SDL-age being equal to zero. Dotted lines: non-standardized ULN and LLN for SDL-age, determined from the analysis of group-I data. (A) Male, non-standardized ULN and LLN are ±13.4 years. (B) Female, non-standardized ULN and LLN are ±15.0 years.
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Fig. 4. Difference between SDL-age and chronological age (SDL-age) in group III with airflow limitation. Grade I: n = 248, grade II: n = 170, and grade III: n = 28. † : larger than grade I (p < 0.003 between grades I and II, p < 0.0001 between grades I and III). # : Larger than grade II (p < 0.0001). Values are means and their 95% confidence intervals. Grade I: +6.7 years (5.4–7.4), grade II: +10.7 years (8.5–12.8), and grade III: +22.4 years (17.2–27.7). Dotted line: average of male ULN and female ULN for SDL-age (+14.2 years).
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and then declined with age, but, in the female, full lung growth would be achieved earlier than the male. Almost the same results were reported by Hankinson et al. (1999). These findings suggest that the relation between lung age (≈chronological age) and most of the spirometric parameters is approximated by a linear function as far as the subjects studied are over 20 years-old and their spirometry is normal. However, it may be difficult to say that these findings sufficiently provide the physiologically relevant grounds for the linear assumption between SDL-age and various spirometric parameters. Therefore, we validated their applicability by calculating the SDL-ages of subjects with normal spirometry (group II) and those with deteriorating pulmonary function (group III). We found that the newly developed equations could predict not only the equality between SDL-age and chronological age in group II within an allowable margin of error but also the incremental disparity between SDL-age and chronological age in group III with airflow limitation. Thus, we concluded that the Eqs. (2) and (3) would be practically useful in a clinical setting. It should be noted that the Eqs. (2) and (3) are only applicable to the Japanese population. However, we anticipate that the findings obtained in the present study will promote the development of ethnic-specific regression equations allowing prediction of SDL-age in various races.
Fig. 5. Comparison of lung age of Morris and Temple with chronological age in group II. (A) Male, the best fitted line of chronological age (Y) against lung age of Morris and Temple (X) is described by Y = 23.8 + 0.47·X (solid line). Dotted line: line on identity. (B) Female, the best fitted line of chronological age (Y) against lung age of Morris and Temple (X) is given by Y = 29.4 + 0.44·X (solid line). Dotted line: line on identity.
Fig. 6. Three-step procedure for judging the abnormality in SDL-age. See text for further explanation (Section 4.3).
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Since the prediction equations include FEV1 /FVC and FEF50 as explanatory variables, they are expected to be sensitive to the functional abnormality caused by the interstitial lung pathology, as well. However, a further study is absolutely needed to elucidate the applicability of these equations to the patients with a variety of interstitial lung diseases. 4.2. Lung age estimated from the original method of Morris and Temple According to the method of Morris and Temple (1985), the lung age in participants of group II was calculated by counting back the regression equations predicting reference values of FEV1 reported by the Japanese Respiratory Society (Report from the Special Committee of Pulmonary Physiology of the Japanese Respiratory Society, 2001). The overall relation between lung age of Morris and Temple (X) and chronological age (Y) was expressed as Y = 23.8 + 0.47·X in the male and Y = 29.4 + 0.44·X in the female (Fig. 5). The fitted lines revealed that the discrepancy between chronological age and lung age became remarkable as the age was shifted from a center of the data set, i.e., around 50 years-old, suggesting that the original method of Morris and Temple inevitably overestimated the lung age in older persons but underestimated in younger persons. This leads to a serious problem when persuading young persons to quit smoking, because their lung ages estimated from the method of Morris and Temple are frequently younger than their chronological ages, thus losing the persuasiveness against smoking cessation. 4.3. Algorithm for judging the abnormality from spirometry-derived lung age We would recommend the three-step procedure when judging the abnormality in SDL-age (Fig. 6). The first thing to do is to examine whether the disparity between SDL-age and chronological age of a person in question (SDL-age) exists within normal limits formed by ULN and LLN, i.e., ±13.4 years in the male and ±15.0 years in the female. (1) If SDL-age exists within ULN and LLN, the SDL-age of a person should be interpreted to be consistent with his (her) chronological age, even when the SDL-age is above or below the chronological age. (2) If SDL-age exceeds ULN, the SDL-age is judged to be older than the chronological age. (3) If SDL-age is below LLN, the SDL-age is judged to be younger than the chronological age. Conflict of interest All authors declared no support from any organization for the submitted work, no financial relationships with any organizations
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