“Spirometric” lung age reference equations: A narrative review

Accepted Manuscript Title: “Spirometric” lung age reference equations: a narrative review Authors: Mouna Ben Khelifa, Halima Ben Salem, Raoudha Sfaxi, Souheil Chatti, Sonia Rouatbi, Helmi Ben Saad PII: DOI: Reference:

S1569-9048(17)30184-2 http://dx.doi.org/10.1016/j.resp.2017.08.018 RESPNB 2857

To appear in:

Respiratory Physiology & Neurobiology

Received date: Revised date: Accepted date:

14-6-2017 29-8-2017 31-8-2017

Please cite this article as: Khelifa, Mouna Ben, Salem, Halima Ben, Sfaxi, Raoudha, Chatti, Souheil, Rouatbi, Sonia, Saad, Helmi Ben, “Spirometric” lung age reference equations: a narrative review.Respiratory Physiology and Neurobiology http://dx.doi.org/10.1016/j.resp.2017.08.018 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

Type. Narrative Review English Title. “Spirometric” lung age reference equations: a narrative review Short title. How to estimate lung age? Authors’ names. Mouna BEN KHELIFA (MD)1, Halima BEN SALEM (MD)1, Raoudha SFAXI (MD)2, Souheil CHATTI (MD)3, Sonia ROUATBI (MD, PhD)4,5, Helmi BEN SAAD (MD, PhD)4-6 Authors’ affiliations 1

Department of Pulmonology. Farhat HACHED Hospital. Sousse. Tunisia.

2

Outpatient pulmonology. Basic health group. Sousse. Tunisia

3

Department of Medicine and occupational diseases. Farhat HACHED Hospital. Sousse,

Tunisia. 4

Department of Physiology and Functional Exploration. Farhat HACHED University Hospital

of Sousse. Tunisia. 5

Laboratory of Physiology, Faculty of Medicine of Sousse. University of Sousse. Tunisia.

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Research Laboratory N° LR14ES05: interactions of the cardiopulmonary system. Faculty

of Medicine of Sousse. University of Sousse. Tunisia. Authors’ emails. Mouna BEN KHELIFA: [email protected] Halima BEN SALEM: [email protected] Raoudha SFAXI: [email protected] Souheil CHATTI: [email protected] Sonia ROUATBI: [email protected]

1

Helmi BEN SAAD: [email protected]

Authors' contributions

MBK Halima BS RS SC SR Helmi BS

Literature search x

Data collection x

x

x

Study design x x x x x x

Analysis of data x

x

Manuscript preparation x x x x x x

Review of manuscript x x x x x x

Name and location of the institution where the study was performed: Faculty of Medicine of Sousse. University of Sousse. Tunisia Sources of financial support: none. Conflicts of interest: HBS reports personal fees from AstraZeneca, Boehringer Ingelheim, GSK and Chiesi. The remaining authors declare that they have no conflicts of interest concerning this article. Corresponding author. Helmi BEN SAAD (MD, PhD). Laboratory of Physiology, Faculty of Medicine of Sousse, Rue Mohamed KAROUI, Sousse 4000, Tunisia. Tel.: 0021698697024. Fax.: 0021673224899. Email: [email protected]. Number of words in the manuscript: 5261 Number of words in the abstract: 170 Number of references: 60 Number of tables: 5 and 1 box 2

Appendixes: 2 (A and B)

Highlights Current knowledge •The interpretation basis of lung-age data relies upon comparison of the chronological-age values with the “spirometric” lung-age (SLA) predicted from available norms. What this review contributes to our knowledge •A literature search, from 1970 through 12th of June 2017 was conducted on studies reporting SLA norms. •Only six studies published norms predicting SLA for adults aged 18-90 years [USA (n=2), Japan (n=2); Australia (n=1) and Tunisia (n=1)]. •The following points were discussed: studies designs and population source, applied inclusion and non-inclusion criteria, characteristics of the included participants, spirometric measurements, applied statistical methods, developed reference equations, interpretation algorithms and validation groups.

NARRATIVE ABSTRACT The aim of the present paper was to conduct a narrative review of the published norms of the “spirometric” lung-age (SLA). A literature search which covered the period 1970 to June 2017, was conducted using the Pubmed. The search strategy had used the following MeSH words: "Spirometry"[Majr]) AND "Aging"[Majr]. Six original studies have reported equations predicting SLA for adults aged 18-90 years [USA (n=2), Japan (n=2); Australia (n=1) and Tunisia (n=1)]. Their sample sizes varied from 125 to 15238, with a total of 32334 volunteers (11788 men). Several models of norms were developed. They 3

included one (often, FEV1) or more spirometric data in addition to one (often, height) or more anthropometric data. All studies have validated their norms in additional one or more groups, with satisfactory results. Only three authors have proposed algorithms to interpret SLA. All studies presented several limitations concerning the sample size and/or representation, the age distribution, the use of old spirometric data and/or equipment, the application of old spirometric methods, and especially mathematical and statistical flaws.

Keywords: Ageing ; Lung function test ; Norms; Reference equation ; Normal limit, 1 st s forced expiratory volume.

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ABBREVIATION LIST ATS BMI BSA CA COPD ERS FEF200-1200 FEFx FEV1 FEV6 FVC LLN MMEF PEF SLA ULN

: American thoracic society : Body mass index : Body surface area : Chronological-age : Chronic obstructive pulmonary disease : European respiratory society : Forced expiratory flow between 200 and 1200 ml of the FVC : Forced expiratory flow when x% of FVC has been exhaled : 1st s forced expiratory volume : 6th s forced expiratory volume : Forced vital capacity : Lower limit of normal : Maximal mid expiratory flow : Peak expiratory flow : “Spirometric” lung-age : Upper limit of normal

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1. Introduction Smoking, recognized as one of the major health problems all over the world (Ng et al., 2014; Vogelmeier et al., 2017), has proved to be detrimental to health for many years (Doll et al., 2004; Fletcher and Peto, 1977; James et al., 2005; Kohansal et al., 2009). It has an adverse effect on the 1st s forced expiratory volume (FEV1) throughout a lifetime. In fact, it reduces the maximal FEV1 achieved, brings forward the age of onset of decline in FEV1, and hastens its rate of decline (Kerstjens et al., 1997). The single most useful intervention to reduce lung damage and prevent premature death for smokers, with or without underlying chronic diseases, such as chronic obstructive pulmonary disease (COPD), is to stop smoking (Jimenez-Ruiz and Fagerstrom, 2013a, b). Health professionals play a crucial role in motivating patients to change their smoking behavior (Cummings, 1982). In order to convince smokers of the harmful effects of smoking and to encourage them to quit tobacco, physicians refer frequently to spirometry results. Yet, although spirometry has usually been the best assessment of smoking lung impairment, such raw measurements cannot be easily understood (Yamaguchi et al., 2012). This leads to frequent failure in the smoking cessation process especially if spirometric values are within the predicted normal range (Jimenez-Ruiz and Fagerstrom, 2013a; Yamaguchi et al., 2012). To overcome the difficulties existing in the raw results of spirometric measurements, the concept of “spirometric” lung-age (SLA) was suggested by Morris and Temple (1985). It is presented as an easy instrument to evaluate the degree of ventilatory deficit induced by tobacco-use. As part of an educational program used by health professional, SLA can offer supplementary motivation to prevent additional loss of respiratory function and lung-age reduction (Morris and Temple, 1985). SLA norms were developed not only as a way of making spirometric data easier to understand for patients, but also as a psychological tool to show smokers the apparent premature ageing of their 6

lungs (Ishida et al., 2015; Morris and Temple, 1985; Newbury et al., 2010). That’s why, the mere announcement of SLA can, on its own, lead to the success of smoking cessation (Ben Mdalla et al., 2013). Yet, the real merit of SLA was largely controversial and it was even described as an “assassin of quality diagnostic spirometry” (Cooper, 2012). SLA is an approximation that uses the measured spirometric parameters of a smoker to estimate lung-age of a healthy non-smoker with similar spirometric parameters based on theoretical values (Morris and Temple, 1985; Petty, 2001). The concept of SLA was firstly proposed about 32 years ago (Morris and Temple, 1985). It has been later explored in several publications, not only as an aid for smoking cessation counseling, but also as a clinical indicator of several situations (eg; asthma severity and obesity effects on lung function) (see Appendix A for more details). The interpretation basis of lung-age data relies upon comparison of the chronological-age (CA, age at check-up, verified from an identity card for example) values with SLA predicted from accessible norms. Since nowadays, there is a call to update SLA equations suited to the population of interest (Ivey et al., 2014) and as the American thoracic and the European respiratory societies (ATS/ERS) (Pellegrino et al., 2005; Quanjer et al., 2012b) encourage investigators to publish norms for healthy persons, and as the success in medical decision-making depends as much on selecting and properly using norms and their limits, the purpose of the present narrative review was to expose the available SLA norms and to highlight their similarities and differences. The authors expect that the results reached in this review will support the development of ethnically-specific norms which can enable physicians to predict SLA in different races.

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2. Methods 2.1. Selection criteria Only original articles written in English were included. Publications that were not in compliance with the present review purpose as well as publications that did not represent original research including editorials and letters to editors were excluded. 2.2. Search strategy A literature search was carried-out about studies reporting SLA norms from 1970 to June 12th 2017. Pubmed was applied as search engines. The search was carried-out using a strategy employing the following two MeSH words: "Spirometry"[Majr]) AND "Aging"[Majr]. Reference lists of retrieved English articles were searched. 2.3. Selection process The studies were selected based on the eligibility criteria described previously. Titles and abstracts resulting from the Pubmed engine search were screened. Then, the full texts of citations considered as potentially eligible were obtained. Finally, the full texts were screened for eligibility. Two authors (MBK and HBS in the authors’ list) agreed on the publications to be included in this review. 2.4. Data abstraction and analysis Each included study was reviewed thoroughly and the selected studies were organized and summarized into tables prior to analysis. Strengths as well as flaws associated with the methodology of studies were discussed. Studies results were presented in the context of all other available evidence.

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3. Results 3.1. Retained studies Six studies (Ben Saad et al., 2014; Hansen et al., 2010; Ishida et al., 2015; Morris and Temple, 1985; Newbury et al., 2010; Yamaguchi et al., 2012) published norms predicting SLA for adults aged 18-90 years. They are largely described in Tables 15. These norms were first developed in 1985 for the USA population (Morris and Temple, 1985). In 2010, two norms were developed for South Australian (Newbury et al., 2010) and USA (Hansen et al., 2010) populations. In 2012 and 2014, SLA norms were developed, respectively, for Japanese (Yamaguchi et al., 2012) and North-African (Ben Saad et al., 2014) populations. In 2015, additional norms for the Japanese population were developed (Ishida et al., 2015) (Table 1). 3.2. Studies designs and population source Among the six studies, only two were prospective (Ben Saad et al., 2014; Yamaguchi et al., 2012) (Table 2). In these two studies, participants were undergoing a general health screening examination or were recruited from the local Faculty of Medicine and/or Hospital staff. The remaining four teams (Hankinson et al., 1999; JRS, 2001; Morris et al., 1971; Newbury et al., 2008) opted for the use of retrospective cohorts. None of the retained studies applied a random population sample. The sample sizes varied from 125 (Newbury et al., 2010) to 15238 (Ishida et al., 2015), with a total of 32334 volunteers (11788 men) (Table 1). Only the study of Ben Saad et al. (2014) carried-out a sample size calculation using a predictive equation. 3.3. Applied inclusion and non-inclusion criteria The applied inclusion (not reported in the studies of Hansen et al. (2010) and Morris and Temple (1985)) and non-inclusion (not reported in the Morris and Temple study 9

(1985)) criteria were different from one study to another (Table 3). In the North-African study, international guidelines (ATS, 1991; Quanjer et al., 1993) were applied to define a “healthy subject”. 3.4. Characteristics of the included participants Table 1 reports the characteristics of the included participants. Two, two and one studies were performed, respectively, in Caucasian (Morris and Temple, 1985; Newbury et al., 2010), Asian (Ishida et al., 2015; Yamaguchi et al., 2012) and Arab (Ben Saad et al., 2014) populations. The study of Hansen et al. (2010) included black, white and Latin subjects (Table 1). The participants’ age, height, weight and BMI means varied respectively, from 45 to 54 years, from 156 to 178 cm, from 53 to 87 kg and from 21 to 27 kg/m 2. Information about age distribution was noted only in three studies (Ishida et al., 2015; Morris and Temple, 1985; Newbury et al., 2010). It was strongly skewed toward young ages in one study (Morris and Temple, 1985) and was stratified in two others (Ishida et al., 2015; Newbury et al., 2010) (Table 1). Only the North-African study reported data concerning the body surface area (BSA). In this study, a quarter and 48% of the participants were, respectively, obese or in overweight and the parity mean’ of included women was 5±3 (Table 1). 3.5. Spirometric measurements Five types of spirometers were used: old ones (stead-Wells and dry rolling-seal spirometers) and new ones (pneumo-tachograph, electric spirometer and digital volume transducer) (Table 2). Spirometry guidelines were not reported in two studies (Hansen et al., 2010; Morris and Temple, 1985). One study (Newbury et al., 2010) applied the ATS-1979

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(1979) guidelines and three studies (Ben Saad et al., 2014; Ishida et al., 2015; Yamaguchi et al., 2012) applied the ATS/ERS-2005 (Miller et al., 2005b) ones (Table 2). The spirometric data were not reported in three studies (Hansen et al., 2010; Morris and Temple, 1985; Newbury et al., 2010) (Table 1). The following spirometric data were determined: FEV1, 6th s forced expiratory volume (FEV6), forced vital capacity (FVC), FEV1/FVC, FEV6/FVC, peak expiratory flow (PEF), maximal mid expiratory flow (MMEF), forced expiratory flow (FEF) when x% of FVC has been exhaled (FEFx%), mean FEF between 200 and 1200 ml of the FVC (FEF200-1200). The participants FEV1 and FVC means varied, respectively, from 82% to 117% and from 95% to 114% (Table 1). 3.6. Applied statistical methods Two statistical methods were applied (Table 2). The first one, applied in four studies (Hansen et al., 2010; Ishida et al., 2015; Morris and Temple, 1985; Newbury et al., 2010), consists of a rearrangement of some previously published norms to solve for lungage. In the second method, applied in two studies (Ben Saad et al., 2014; Yamaguchi et al., 2012), SLA (dependent variable) was predicted from a function including some physical data (ie; height, BMI, BSA, parity) and various spirometric data as explanatory variables. 3.7. Developed reference equations Several models of SLA norms were developed (Table 4). Morris and Temple (1985) developed four models of norms for each sex. All models included height in addition to FVC (model 1), FEV1 (model 2), MMEF (model 3) and FEF200-1200 (model 4). The correlation coefficient («r») of these models varied from 0.44 to 0.73. The most pertinent model to calculate SLA values was the one using FEV1 (Box 1). Hansen et al. (2010) proposed, for the total sample, two simplified norms. The two models included CA in addition to the FEV1/FVC (model 1) or the FEV6/FVC (model 2) ratios. For 11

normal never-smoker adults, FEV1/FVC and FEV1/FEV6 were independent of ethnicity and sex (Hansen et al., 2006a, b). The norms determination coefficients («r2») were not reported. The retained model was that using FEV1/FVC ratio (Box 1). Newbury et al. (2010) proposed, for each sex, a simple model including height and FEV1 (Box 1). The norms «r2» were not reported. Yamaguchi et al. (2012) presented, for each sex, an equation including various spirometric parameters such as FVC, FEV1, FEV1/FVC ratio, PEF, FEFx% and MMEF (Box 1). The norms «r2» were 0.50 in men and 0.42 in women. Ben Saad et al. (2014) presented, for each sex and for the total sample, two models of norms including some anthropometric data and several spirometric data (model 1) or only FEV1 (model 2). Models 1 and 2 were recommended, respectively, for research proposals and for daily practice. The norms «r2» varied from 0.38 to 0.62. The retained models are presented in Box 1. Ishida et al. (2015) developed, for each sex, three models of norms. Model 1 included height and FEV1, model 2 included in addition to height, FEV1 and FVC and model 3 included FVC and FEV1. The norms «r2» varied from 0.43 to 0.84. Model 3 was recommended by the authors (Box 1). 3.8. Interpretation algorithms Only three authors (Ben Saad et al., 2014; Morris and Temple, 1985; Yamaguchi et al., 2012) proposed algorithms to interpret SLA (Table 4). Two interpretation methods were proposed. The first one, developed by Morris and Temple (1985), consisted in a nomogram where physician should place a straight edge connecting the individual’s height and test value and read the intersecting value for age. The second method is a three-step procedure with the use of the upper and lower limits of normal (ULN, LLN, respectively) (Ben Saad et al., 2014; Yamaguchi et al., 2012). The physician should examine whether the lung-age deficit (the difference between CA and SLA) exists within ULN and LLN (±13.4 years in men and ± 15.0 years in women for Japanese population (Yamaguchi et 12

al., 2012), and ±16.90 years in men and ±14.77 years in women for the North-African population (Ben Saad et al., 2014)). Hence, three situations are possible: (1) LLN < lungage deficit < ULN: SLA is consistent with CA; (2) Lung-age deficit > ULN: SLA older than CA and (3) Lung-age deficit < LLN: SLA younger than CA. In order to easily calculate the SLA from the published norms and to interpret it correctly; Excel software was created (Appendix B). 3.9. Validation groups All the retained studies validated their norms in additional one or more groups, with satisfactory results (Table 5). Morris and Temple (1985) used data from a retrospective study (Brugman et al., 1986). They classified participants into “normal” and “abnormal” groups based on answers to a respiratory health questionnaire and spirometry results. The CA and SLA of the “normal” group were similar. However, the SLA of the “abnormal” group was significantly higher than their CA. Hansen et al. (2010) included two groups of non-smokers and current-smokers. The SLA of the non-smokers was significantly lower than the CA; nevertheless, the SLA of the current-smokers was significantly higher than the CA. Moreover, the current-smokers’ SLA differed from the one of non-smokers by 7-28 years. Newbury et al. (2010) included two male groups of non-smokers and current-smokers. They concluded that Morris and Temple (1985) equations significantly underestimate lungage in both Australian non-smokers and current-smokers. In Australian males nonsmokers and current-smokers, the equations developed by Newbury et al. (2010) produced a SLA that is approximately 20 years greater than does the Morris and Temple (1985) ones. Yamaguchi et al. (2012) included two groups of healthy participants and of smokers with a FEV1/FVC ratio < LLN. They defined three categories of patients depending on FEV1 (%). Among the healthy participants, acceptable agreement between 13

SLA and CA in either sex was found. In addition, frequencies of participants in whose lungage deficit exceeded the ULN or LLN were 12.2% (men) and 11.4% (women). For the smokers group, the lung-age deficit averaged +6.7 years in those with a FEV1 ≤ 80%, +10.7 years in those with a FEV1 between 50% and 80%, and +22.4 years in those with a FEV1 < 50%. The lung-age deficit in smokers with FEV1 < 50%, but not in those with FEV1 ≥ 50%., exceeded the ULN, indicating that allowance was made for judging that the lungage deficit was significantly older than the CA, only in subjects with severe airflow limitation (FEV1 < 50%). Ben Saad et al. (2014) included three groups of healthy nonsmokers with normal spirometric data, COPD and obstructive sleep apnea patients. The SLA of the healthy group was similar to their CA and it exceeded the ULN or LLN only in 12% of subjects. The SLA of the COPD group exceeded the CA by 16 years and it exceeded the ULN or LLN in 35% of patients. The SLA of the obstructive sleep apnea group exceeded the CA by 10 years and it exceeded the ULN or LLN in 23% of patients. Ishida et al. (2015) included three groups of non-, ex- and current- smokers. In both women and men, model 3 revealed higher values of “r2”, nearer to one as the slopes in the validation cohort. 4. Discussion Six studies published norms predicting SLA for adults aged 18-90 years. They are only applicable to the correspondent populations. At the best of the authors’ knowledge, this is the first narrative review reporting published SLA norms. Using the published norms presented in Box 1 and Appendix B, any person can simply calculate and notify subjects of their SLAs from any spirometric result. This should elicit a response and open discussion regarding the dangers of carrying on smoking. Referral to support groups, educational and counseling sessions, and the use of newer

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pharmaceuticals all offer avenues for success (Parkes et al., 2008; Tashkin and Murray, 2009). 4.1. Studies limitations The six studies presented several limitations discussed hereafter. 4.1.1. Studies designs Among the six studies, some (Hansen et al., 2010; Ishida et al., 2015; Morris and Temple, 1985; Newbury et al., 2010) were retrospective and none used a randomized population sample. Thus, the hypothesis that significant differences in the SLA and CA produced by the published SLA norms can be observed because a possible selection bias and cohort effects, can be admitted (Ganguli et al., 1998). Consequently, the effects of selection factors associated with the subject’s recruitment may limit the interpretation and the generalization of results (Hansen et al., 2010). Newbury et al. (2012) have recently demonstrated this. They concluded that SLA using six norms spanning 50 years showed differences attributable to cohort and period effects. They suggested that a well-designed randomized controlled trial is needed to determine any impact on the observed rates. Guidelines for spirometry (Miller et al., 2005b) recommend that norms should be derived from a ‘relevant’ population and should be updated at least every 10 years. These recommendations should equally apply to SLA norms. Predictive equations (Morris et al., 1971) which were the basis of the Morris and Temple (1985) SLA norms are 46 years old. The NHANES-3 (Hankinson et al., 1999) data used by Hansen et al. (2010) are now approximately 20 years old. The cohort effect suggests that a 40 years-old today will not be the same as someone of the same age 40 years ago due to demographic and environmental differences (ATS, 1991). Some caution should be warranted when interpreting the results of cross-sectional studies in volunteers (Ben Saad et al., 2014; Yamaguchi et al., 2012), because of a possible selection bias and cohort effects (Ganguli 15

et al., 1998). Thus, longitudinal studies analyzed by appropriate statistical models are necessary to correctly describe the functional changes associated with age (ATS, 1979). 4.1.2. Sample sizes A previous study (Quanjer et al., 2011) aiming at establishing the number of local subjects required to validate published norms, found that at least 300 subjects (150 men) would be necessary to ”authorize” norms and then to avoid spurious differences due to sampling error. This criterion was lacking in the South Australian study (Newbury et al., 2010) (Table 1). In future studies, a relatively large number of subjects (n=300) is necessary to make sure that there is no significant difference between the published norms and the values collected from the local community (Pellegrino et al., 2005). 4.1.3. Subjects characteristics Five out of six samples were dominated by women (Table 1). The percentages of women were, respectively, 48%, 53%, 59%, 64%, 67% and 69% in Morris and Temple (1985), in Newbury et al. (2010), in Hansen et al. (2010), in Ishida et al. (2015), in Ben Saad et al. (2014) and in Yamaguchi et al. (2012) studies. The inclusion of disproportionate samples can influence results since women and men lungs seem to be different (Amaral et al., 2017). Two studies (Ishida et al., 2015; Newbury et al., 2010) were evenly age-stratified (Table 1). Morris and Temple (1985) sample was robustly distorted to the right with over 30% of subjects in the youngest 10-year age group. One of the strengths of the South Australian (Newbury et al., 2010) and Japanese (Ishida et al., 2015) samples were that their ages were evenly stratified (Table 1), resulting in norms that are equally relevant across the whole age range. There have been guidelines to carry-out spirometric tests on a representative sample of healthy subjects (Pellegrino et al., 2005). A great percentage of subjects (79%) included in the Morris and Temple (1985) study was from two churches in rural USA. The principles of these churches prohibit tobacco 16

smoking, alcohol or caffeine drink and promote a vegetarian diet. The population sample of the South Australian study (Newbury et al., 2010) was drawn from the broad rural community, targeting non-smokers with no history of lung disease (Newbury et al., 2012). Nevertheless, retained subjects maintain a high level of fitness and contrarily have occasional occupational exposures to smoke when attending fires (Newbury et al., 2010). The Japanese study participants’ socioeconomic status does not seem to be representative of the general population of local patients (Ishida et al., 2015). Participants might consist of higher-income upper-class individuals, with white-collar occupations. In the North-African study (Ben Saad et al., 2014), 25% of participants have a moderate obesity. This is a serious limitation, since obesity is responsible for early damage and functional accelerated pulmonary aging (Melo et al., 2016; Melo et al., 2010). These samples (Ben Saad et al., 2014; Ishida et al., 2015; Morris and Temple, 1985; Newbury et al., 2010; Yamaguchi et al., 2012) cannot be described as representative of a ‘normal’ every day population and may not be representative of a normal population. In Morris and Temple (1985) study, the height was measured in inch. Yet, inch should not be used as conversion to the centimeter because it may generate error (Quanjer et al., 2012a). Hansen et al. (2010) included different ethnic groups (Table 1). It is nowadays well known that many differences in lung measurements exist between ethnic groups (Braun et al., 2013). Therefore, their norm can be widely criticized. 4.1.4. Use of old equipment and application of old spirometric methods Respiratory testing equipment and procedures have been progressively refined over the last 12 years in line with recommendations that have been regularly updated by the ATS/ERS. Spirometric norms are preferably obtained by means of the same type of tool and testing method (Miller et al., 2005a; Miller et al., 2005b; Pellegrino et al., 2005). The Morris and Temple (1985) and Hansen et al. (2010) outcomes were obtained using 17

tools and methods that give records which are different from those advocated by the ATS1979 (Miller and Thornton, 1980) (Table 2). The study of Newbury et al. (2010), although performed in 2010, applied the first ATS-1979 guidelines (ATS, 1979). Only three studies (Ben Saad et al., 2014; Ishida et al., 2015; Yamaguchi et al., 2012) applied the ATS/ERS-2005 spirometry guidelines (Miller et al., 2005b). 4.1.5. Statistical reasons How to evaluate the SLA and what method is approvable? This query was tackled in order to support the creation of ethnically-specific norms in different races (Yamaguchi et al., 2011). The different models of published SLA norms present some mathematical and statistical flaws, especially concerning the hypothesized linear relationship between ageing and lung function data. Although the original method proposed by Morris and Temple (1985) has undoubtedly contributed to the smoking-cessation program (Ben Mdalla et al., 2013; Parkes et al., 2008), it has a couple of mathematical and statistical flaws. Since predicting lung-age from spirometric measurements is absolutely important when considering the enlightenment concerning COPD and smoking cessation, it is necessary to establish the reliable method allowing the estimation of SLA (Yamaguchi, 2011; Yamaguchi et al., 2011). In the South Australian study (Newbury et al., 2010), lung-age was an estimated value based on the population mean. This has potential difficulties when predicting values for individuals. This was demonstrated by the large standard-deviation of SLA for never-smokers (18.66 years) or current-smokers (22.52 years) derived from the South Australian SLA norms (Newbury et al., 2010). Hansen et al. (2010) applied a circular argument: equation predicts the actual mean age of the subjects from whom they were derived. Morris and Temple (1985) and Newbury et al. (2010) included only FEV1 in their retained norms and have presented different models for each sex (Box 1). Hansen et al. (2010) included only the FEV1/FVC ratio that is independent of 18

ethnicity and sex (Hansen et al., 2006a, b) (Box 1). They justified their choice by the fact that in normal American populations, the FEV1/FVC ratio has much less variability than absolute measures of other spirometric data (Hansen, 2010). Ben Saad et al. (2014) recommended using, in daily practice, the simple model including FEV 1 (Box 1). However, it is indistinct whether the SLA can be reliably predicted simply from one spirometric parameter (Yamaguchi, 2011; Yamaguchi et al., 2011). For that reason, some authors (Ben Saad et al., 2014; Ishida et al., 2015; Yamaguchi et al., 2012) included more than one parameter as explanatory variables (n=6 (Ben Saad et al., 2014), n=5 (Yamaguchi et al., 2012) and n=2 (Ishida et al., 2015)) (Box 1 and table 4). There are no consistent bases for sustaining the idea that the association between lung-ageing and different spirometric data can be estimated by the linear function (Ben Saad et al., 2014; Yamaguchi et al., 2012). Kohansal et al. (2009) confirmed that, in the male population, peaks of FEV1 or FVC would be reached at 20-25 years of age and after that declined with age. However, in the female population, complete lung growth would be reached earlier than in the male one (Kohansal et al., 2009). In practice, norms should be derived by valid and biologically meaningful statistical models taking into account the dependence of lung function with age and height (Pellegrino et al., 2005). Furthermore, the backward calculation of age from the reference value of FEV 1 may not be allowed in a statistical sense (Yamaguchi, 2011; Yamaguchi et al., 2012; Yamaguchi et al., 2011). For example, when the original method of Morris and Temple (1985) was applied for the North-African population, the SLA of a person with measured FEV1 above ULN resulted in being remarkably young (sometimes, below zero year old), while that of a person with FEV1 below LLN was estimated as being very old (sometimes, over 100 years) (Ben Saad et al., 2013).

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The changeability of spirometry records of normal healthy subjects is itself relatively large, being about 80-120% predicted, resulting in the existence of an extensive difference in SLA. Publications on norms should include explicit definitions of the ULN and LLN, or provide information to allow the reader to calculate a lower range (Pellegrino et al., 2005). Only three authors (Ben Saad et al., 2014; Morris and Temple, 1985; Yamaguchi et al., 2012) proposed algorithms for judging the SLA abnormality. The suggested sequence (Ben Saad et al., 2014; Morris and Temple, 1985; Yamaguchi et al., 2012), was to recognize a smoker, carry-out spirometry, and, if the FEV1, is lower than the LLN, calculate the SLA. Morris and Temple (1985) presented a nomogram to interpret SLA and two other authors (Ben Saad et al., 2014; Yamaguchi et al., 2012) recommended the use of ULN and LLN (Table 4). In order to assess SLA, Hansen et al. (2010) selected four years for each percent reduction in FEV1/FEV6 and three years for each percent reduction of FEV1/FVC. This is probably imperfect and possibly five or two years would be better multipliers. There is a continual debate concerning the use of LLN or percent predicted, with the definitions of stages of the disease easily described by percent predicted (Yamaguchi et al., 2012). As an alternative to a single SLA value, it may be promising to announce SLA as being “LLN and ULN” (Yamaguchi et al., 2012; Yamaguchi et al., 2011). SLA association to smoking may be divisive as there is also a decline in lung function with growing as well as with chronic diseases. However, there is continued support in the literature for the more rapid decline in FEV1 in smokers than in non-smokers (Bernhard et al., 2007; MacNee, 2009). 4.1.6. Some factors known to influence lung function Other factors that also pressure lung function during a person’s life were not assessed (Holt et al., 2012; Lum et al., 2001; Rouatbi et al., 1999; Simpson et al., 2012). In addition, the association between SLA and parity, a special factor in developing 20

nations, has not been evaluated. Published studies present modest information on the impact of parity on SLA. However, this may be a promising axis for research, mainly for emergent countries. In the North-African female sample (Ben Saad et al., 2014), parity was positively associated with CA and was a positive independent parameter included in the SLA norm (Table 4). This outcome may be clinically pertinent when analyzing SLA in females from North-Africa. An easy method to resolve this problem would be to deduct, from their SLA value a number of years equal to the number of parity multiplied by 1.16 (Ben Saad et al., 2014). This phenomenon may reflect the general findings about aging and parity effects on health (Hart and Reno, 1999). In fact, repeated gestations have been found to have potentially noxious effects on health (Ben Saad et al., 2003; Ben Saad et al., 2006). 4.2. Published reference equations: between studies comparisons and validity The above methodological shortcomings explain some discrepancies in the findings. For example, in the North-African population, it was powerfully suggested that accessible SLA norms were in need of review (Ben Saad et al., 2013). The published norms did not consistently predict SLA data in a group of healthy adults (Ben Saad et al., 2013): SLA mean ± standard-deviation was significantly underestimated by 17±19 years, by 12±23 years and by 2±13 years, respectively by Hansen et al. (2010), by Morris and Temple (1985) and by Yamaguchi et al. (2012) norms and was significantly overestimated by 4±19 years by Newbury et al. (2010) norm. Indeed, Newbury et al. (2010) showed that Morris and Temple (1985) norms significantly underestimate SLA in both never-smokers and smokers. For South Australian male never-smokers and current-smokers, Newbury et al. (2010) also showed that SLA predicted by their norms produced lung-ages that are approximately 20 years greater than does the Morris and Temple (1985) norms. One great concern was that, in the smoker subgroup of Newbury et al. (2010), the SLA mean by the 21

Morris and Temple (1985) norms was 12 years lower than the CA mean, indicating a ‘protective’ effect of tobacco smoking. A couple of authors, however, questioned whether the SLA was truly useful as a tool for motivating the cessation of smoking (Lin, 2008; Quanjer and Enright, 2010). They asserted that the SLA from the method of Morris and Temple (1985) entirely disregarded the variability of FEV1 in normal subjects, thus causing a physiologically serious flaw. For instance, the SLA of a normal person whose FEV1 is below the reference value but above the LLN is forcibly estimated to be older than his/her CA, though the SLA of this person should be equal to the CA. This happens because the SLA is calculated by counting back the regression equation predicting the reference value, but not the LLN, of FEV1. The developed norms could predict the incremental difference between SLA and CA in smokers, ex-smokers, COPD and severe obstructive sleep apnea patients (Table 5). Thus, published SLA norms can be helpful in a clinical setting in the corresponding population. In the COPD groups of the Japanese and North-African studies (Ben Saad et al., 2014; Yamaguchi et al., 2012), the norms generated a SLA greater than CA. This signifies that smoking altered lungs more quickly than the expected age-related decline, as predicted by Fletcher and Peto (Fletcher and Peto, 1977). 5. Conclusion Six published norms to interpret the SLA were presented and discussed. The SLA can be estimated from some anthropometric (sex, height, BMI, BSA) and spirometric data. However, their applicability should be tested with regards to other adult population, in order to avoid invalid clinical interpretation of SLA data in these populations. The published SLA norms can help physicians and researchers in their choice according to their patients local and ethnic background. For a general practice, the authors recommend the use of

22

the Excel software (Appendix B) to simply and correctly calculate SLAs as well as notify subjects about them. ACKNOWLEDGMENTS Authors wish to thank Mrs Imen BEN KHELIFA (English language program manager) for her invaluable contribution in the improvement of the quality of the writing in the present review.

23

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Tsushima, H., (2009). Practice in multiple regression analysis. , in: Tsushima, H. (Eds.), Multivariate Data Analysis in Medical Field - Learning from SPSS. Tokyo-Tosho Co, Tokyo, pp. 57-95. Vogelmeier, C.F., Criner, G.J., Martinez, F.J., Anzueto, A., Barnes, P.J., Bourbeau, J., Celli, B.R., Chen, R., Decramer, M., Fabbri, L.M., Frith, P., Halpin, D.M., Lopez Varela, M.V., Nishimura, M., Roche, N., Rodriguez-Roisin, R., Sin, D.D., Singh, D., Stockley, R., Vestbo, J., Wedzicha, J.A., Agusti, A., 2017. Global Strategy for the Diagnosis, Management, and Prevention of Chronic Obstructive Lung Disease 2017 Report. GOLD Executive Summary. American journal of respiratory and critical care medicine 195, 557582. Yamaguchi, K., 2011. [A new method for evaluating lung age]. Nihon Kokyuki Gakkai zasshi 49, 713-716. Yamaguchi, K., Omori, H., Onoue, A., Katoh, T., Ogata, Y., Kawashima, H., Onizawa, S., Tsuji, T., Aoshiba, K., Nagai, A., 2012. Novel regression equations predicting lung age from varied spirometric parameters. Respiratory physiology & neurobiology 183, 108-114. Yamaguchi, K., Onizawa, S., Tsuji, T., Aoshiba, K., Nagai, A., 2011. How to evaluate "spirometric" lung age--what method is approvable? Respiratory physiology & neurobiology 178, 349-351.

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Table 1. Characteristics of the participants included in the retained six studies. Morris and Temple Hansen (2010) 1st Author(s)

Newbury (2010)

(1985)

Country Race

USA Caucasian

Number (M/W) White Black

988 (517/471) 988 (517/471) 0 (0/0)

USA African-American (Black), EuropeanAmerican (White), Mexican-American (Latin) 7428 (3041/4387) 898/1383 1027/1481

Latin TS

0 (0/0) 20-84a

1116/1523 20-80a

M W Distribution

NR NR Strongly skewed to the young ages

NR NR NR

TS M W TS M W TS M W TS M W

NR 178b 163b NR NR NR NR NR NR NR NR NR

NR NR NR NR NR NR NR NR NR NR NR NR

Color (M/W)

Age (Y)

Height (cm)

Weight (kg)

BMI (kg/m2)

BSA (m2)

Table 1. Continued. 32

Yamaguchi (2012)

Ben Saad (2014)

Ishida (2015)

Australia Caucasian

Japan Asian

Tunisia Arab

Japan Asian

125 (59/66) NA

8015 (2495/5519) NA

540 (176/364) NA

15238 (5499/9739) NA

25-74a

25-87a

18-80a

49b 49b Sample agestratified. 25-34Y: 20%d 35-44Y: 20%d 45-54Y: 20%d 55-64Y: 24%d 65-74Y: 16%d NR 176±9c 164±6c NR 87±11c 73±12c NR NR NR NR NR NR

54±11c 53±10c NR

19-90a 49±13c 45±15c 50±11c NR

NR 168±6c 156±6c NR 68±10c 54±8c NR 24±3c 22±3c NR NR NR

164±10c 166±8c 163±11b 73±12c 74±12c 73±12c 27±4c 27±4c 27±3c 1.79±0.18c 1.81±0.17c 1.79±0.19c

NR 172±6c 159±1c NR 66±6c 53±5c NR 22±2c 21±2c NR NR NR

40±9b 42±9b < 30Y:6%d 30-40Y: 41%d 40-50Y: 35%d 50-60Y: 13%d 60-70Y: 5%d 70-80Y: 1%d 80Y:0%d

1st Author(s)

Morris and Temple (1985)

Hansen (2010)

FEV1 (%)

Newbury (2010)

TS NR NR NR M NR NR NR W NR NR NR FVC (% or L) TS NR NR NR M NR NR NR W NR NR NR FEV1/FVC TS NR NR NR (absolute value) M NR NR NR W NR NR NR BMI: body mass index. BSA: body surface area. FEV1: 1st s forced expiratory volume. FVC: forced vital capacity. total sample. Y: year. W: women. Data are: aRange (min-max), bMean, cMean ± standard-deviation, dPercentage.

33

Yamaguchi (2012)

Ben Saad (2014)

Ishida (2015)

109±20c NR NR c 99±13 97±11c 82±7c c c 117±26 114±21 83±6c c 108±21 NR NR 4.1±0.6c 95±11c 113±13c c c 3.2±0.7 114±22 112±13c c 0.85±0.06 NR NR 0.81±0.05c 0.85±0.06c NR 0.81±0.05c 0.85±0.06c NR M: men. NA: not-applied. NR: not-reported. TS:

Table 2. Study designs and applied statistical analysis in the retained six studies Morris and Temple Hansen (2010) Newbury (2010) 1st (1985) Author(s) Study Retrospective Retrospective Retrospective design Data USA normal values 3rd national health and nutrition Predictive source (Morris et al., evaluation survey (NHANES-3) equations 1971). (Hankinson et al., 1999). (Newbury et Selection (according ATS (1995) al., 2008). recommendation) of 9353 selfidentified adults with satisfactory spirometry data.

Yamaguchi (2012)

Ben Saad (2014)

Ishida (2015)

Prospective

Prospective

Retrospective

Participants undergoing a general health screening examination

85%: participants undergoing a general health screening 25%: participants from the staff of the local Faculty of Medicine and Hospital 2011-2012

Retrospective cohort (JRS, 2001)

Y of data collection Type of spirometer

1971

1988-1994

2007

2008-2010

Stead-Wells

Dry rolling-seal

Electric

Digital volume transducer

Discom-21

Spirometry guidelines

NR

NR

Pneumotachograph (Jaeger) ATS-1979 (1979)

ATS/ERS-2005 (Miller et al., 2005b)

Statistical analysis

Rearrangement of the prediction equations to solve for lung-age.

They calculated and graphed for each sex, mean values of all White NS of decade 3 and CS of decade 8. This allowed ascertaining the effect of aging and smoking on FEV1, FEV6, and FVC and thus the changes in %FEV1/FEV6 and %FEV1/FVC over 5 decades

ATS/ERS-2005 (Miller et al., 2005b) Same method described by Yamaguchi et al. (2012) with equations using other data (ie;, BMI, BSA, parity).

ATS/ERS-2005 (Miller et al., 2005b) Method described by Morris and Temple (1985).

Method described by Morris and Temple (1985).

2004-2012

SLA (Y)= a0 + a1 x Height + a2 x FVC + a3 x FEV1 + a4 x FEV1/FVC + a5 x PEF + a6 x MMEF + a7 x FEF50% + a8 x·FEF25%. ai: partial regression coefficient. a0: invariable constant. ULN and LLN: Z-score (Tsushima, 2009) ATS: American thoracic society. BMI: body mass index. BSA: body surface area. CS: current smokers. SLA: “spirometric” lung-age. ERS: European respiratory society. FEFx%: forced expiratory flow when x% of FVC has been exhaled. FEVx: 1st or 6th s forced expiratory volume. FVC: forced vital capacity. LLN: lower limit of normal. MMEF: maximal mid expiratory-flow. NR: not-reported. NS: non-smokers. PEF: peak expiratory flow. ULN: upper limit of normal. Y: years.

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Table 3. Applied inclusion and non-inclusion criteria in the retained six studies Morris and Hansen (2010) Newbury (2010) Yamaguchi (2012) Ben Saad (2014) Ishida (2015) 1st Temple(1985) Author(s) Inclusion NR NR Previously Healthy NS Healthy NS Healthy criteria described Normal spirometric data Normal spirometric data Never-smoker (Newbury et al., North African Normal spirometric 2008) data Age  19 Y Caucasian Normal BMI Age  25 Y NonNR  Unusable spirometry  Current or  Occupational history exposed to  Cigarette or narghile  Cardiovascular inclusion previous either biomass fuels or dusts smokers diseases  Age > 80 Y criteria history of  Conspicuous respiratory  Accidental smokers  Cancer  Cigarette, cigar or asthma symptom (dyspnea on exertion,  Acute or past chronic  Hypertension pipe smokers nocturnal dyspnea, cough,  Chronic lung respiratory disease.  Smoked cigarettes  Dyslipidaemia disease sputum, or wheezing) cigars, and/or pipes  Major respiratory  Diabetes mellitus  Current acute  Cardiovascular disease during the 5 days diseases  Gout respiratory prior to exam  Respiratory diseases (lung  Systemic disease which  Lung disease infection cancer, bronchial asthma, may influence directly  Medication-use  Asthma  CS COPD, interstitial lung disease, or indirectly the  Abnormal spirometric  Chronic bronchitis infiltrative lung disease, respiratory system.  Past smokers  Emphysema data (FEV1/FVC < bronchiectasis) (> 10 Cig/day  Upper respiratory tract  Lung cancer 0.70 or FVC < 80%) for more than infection during the 3  Abnormal BMI (< 18.5  Whistling and/or 5 Y). last weeks wheezing in chest in or > 25 kg/m2)  Underweight or last 12 months moderate to severe  Whistling and/or obesity wheezing in chest apart from colds  Persistent cough  Persistent phlegm production  Moderate shortness of breath. BMI: body mass index. Cig: cigarettes. COPD: chronic obstructive pulmonary disease. CS: current-smokers. FEV1: 1st s forced expiratory volume. FVC: forced vital capacity. NR: not-reported. NS: non-smokers. Y: year. 35

Table 4. Norms for “spirometric” lung-age (SLA, years) in the retained six studies. Morris and Temple (1985) Hansen (2010) Newbury (2010) 1st Author(s) Model 1

= 2.331xH - 40.000xFVC 169.640. ESE=0.74. r=0.65

Model 2

= 1.130xH - 31.250xFEV1 39.375. ESE=0.55. r=0.73

Model 3

= 0.411xH - 22.222xMMEF + 55.844. ESE=1.12. r=0.53 =1.382xH - 21.277xFEF2001200+ 42.766. ESE=1.66. r=0.44 = 1.887xH - 41.667xFVC 118.833. ESE=0.52. r=0.71

= 1.56xH 33.69xFEV185.62. r=NR

Yamaguchi (2012)

= 209.195 - 0.455xH 11.521xFEV1 0.602xFEV1/FVC (%) + 1.956xFEF50%. r2:0.50. ULN and LLN: ±13.4 Y

M

Model 4

Model 1

= 1.33xH 31.98xFEV1 74.65. r=NR

W Model 2

= 1.401xH - 40.000xFEV1 77.280. ESE=0.47. r=0.73

Model 3

= 0.787xH - 33.333xMMEF + 18.367. ESE=0.89. r=0.56 =1.678xH - 27.778xFEF2001200 - 70.333. ESE=1.19. r=0.53

Model 4

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= 234.441 - 0.792xH 7.295xFEV1 0.610xFEV1/FVC (%) + 0.301xPEF+ 2.647xFEF50%. r2: 0.42. ULN and LLN: ±15.0 Y

Ben Saad (2014)

Ishida (2015)

=53.37 – 4.12xFEF25%– 6.26xFVC + 36.54xBSA – 8.19xFEV1 – 0.45x BMI + 3.66 x FEF50%- 5.17xMMEF. r2=0.62. ULN and LLN: ±15.1 Y = 42.85 - 20.74xFEV1+ 47.41xBSA - 0.62xBMI. r2=0.56. ULN and LLN: ±16.90 Y

= 2.22×H 45.5×FEV1 – 132.3. r2:0.423

=52.83 – 3.64xFEV1+ 1.16xParity + 1.08xPEF1.54xMMEF - 3.30xFVC + 0.11xH - 0.15xBMI 0.47xFEF25%. r2=0.45. ULN: 14.04 Y = 64.64 - 8xFEV1 - 0.17xBMI + 0.09xH. r2=0.38. ULN and LLN: ±14.77 Y

= 1.67×H 55.6×FEV1 79.1. r2:0.46

= 0.74×H 37.0×FEV1 + 1.0xFVC – 63.7. r2:0.813 = 1.00×FVC33.3×FEV1 + 50.7. r2:0.77

= 0.65×H 43.5×FEV1 +0.83xFVC– 47.3. r2:0.839 = 0.84×FVC + 50.2- 40×FEV1. r2:0.80

Table 4. Continued. 1st Author(s)

Morris and Temple (1985)

Hansen (2010)

Model 1

NR

= CA + 3x(predicted observed) FEV1/FVC (%). r=NR

Model 2

NR

= CA + 4x(predicted observed) FEV1/FEV6 (%). r=NR NR

TS

Methods of interpretation

Nomogram: place a straight edge connecting the individual’s height and test value and read the intersecting value for age

Newbury (2010)

Yamaguchi (2012)

Ben Saad (2014)

=210.67 - 5.48xFEV1+ 2.17xPEF – 2.65xH + 3.84xSex (0. M; 1. W) 2.97xMMEF - 3.99xFVC 0.857xFEF25% + 289.51xBSA - 1.94xBMI. r2=0.47. ULN and LLN: ±15.70 Y

Ishida (2015)

NA

NA

NR

3-step procedure: examine 3-step procedures: examine NR whether LAD exists within whether LAD exists within ULN and LLN (i.e., ±13.4 Y ULN and LLN (i.e., ±16.90 Y in men and ±15.0 Y in in men and ±14.77 Y in women). women: (1) LLN < LAD < ULN: SLA (1) LLN < LAD < ULN: SLA is consistent with CA is consistent with CA (2) LAD > ULN: SLA older (2) LAD > ULN: SLA older than CA than CA (3) LAD < LLN: SLA (3) LAD < LLN: SLA younger younger than CA. than CA. BMI: body mass index (kg/m 2). BSA: body surface area (m 2). CA: chronological-age. ESE: standard error of estimated. FEF200-1200: mean forced expiratory flow between 200 and 1200 ml of the FVC (L/s). FEFx%: forced expiratory flow when x% of FVC has been exhaled (L/s). FEVx: 1st or 6th s forced expiratory volume (L). FVC: forced vital capacity. H: height (cm). LAD: Lung-age deficit (= CA minus SLA) in years. LLN: lower limit of normal. M: men. MMEF: maximal mid-expiratory flow (L/s). NR: not-reported. PEF: peak expiratory flow (L/s). r: coefficient of correlation. r2: coefficient of determination. TS: total sample. ULN: upper limit of normal. W: women. Y: years. Note: FVC was expressed in L, except in Ishida et al. study, where it was expressed in %.

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Table 5. Validity groups of the six retained studies 1st Morris and Temple Hansen Newbury (2010) Author(s) (1985) (2010) Groups Group 1: Different population Two male groups of unpublished dataset Group 1: 340 NS (hospital employees 5835 NS Group 2: 50 CS and patients with a diversity of pulmonary Group 2: function). 3518 CS. Data from a retrospective study(Brugman et al., 1986). Population was classified into a normal and abnormal groups based on answers to a respiratory health questionnaire and spirometry results.

Ben Saad Ishida (2015) (2014) Group 1: 6398 participants (2074 men, Group 1: 41 2079 age: 22-89a Y). healthy NS participants with normal (1294 men) Group 2: 446 NS (197 men, age: 20-85a Y, spirometric Group 1: NS FEV1/FVC < LLN). Three categories data (176 (51%) depending on FEV1 (%). men, age: 19- Group 2: ex90a Y) smoker Group 2: 91 (35%) Group 3: CS COPD patients (14%) COPD (65 men, age: 1980a Y) Group 3: 60 severe OSA patients (42 men; age: 2670a Y). Comparison Normal group: Group 1: Group 1: participants in whom LAD Group 1: CA Model 3 was SLA = CA. SLA < exceeded the ULN or LLN: 12.2% (men); = SLA in considered Australian equation Morris and Abnormal group: CA. 11.4% (women). Acceptable agreement either sex the best (Newbury et al., Temple Group 2: SLA > CA. between SLA and CA in either sex. 12%: SLA model in both 2008) (1985) model Group 2: SLA > exceeded the sexes. using FEV1 CA. ULN or LLN Grade: Average Group 1 Group 2: SLA FEV1 in % (n) LAD LAD + 1.6 -18.1* SLA of > CA by 16 Y. I. 80% ≤ (n=248) +6.7 Group 2 group 2 35%: SLA II. 50%-80% (n=170) +10.7 LAD +7.5 -12.4* differed exceeded the III. 30%-50% (n=28) +22.4 from SLA ULN or LLN Group 3: SLA of group 1 by 7-28 Y > CA by 10 Y. 23%: SLA exceeded the ULN or LLN CA: chronological-age. COPD: chronic obstructive pulmonary disease. CS: current-smokers. SLA: “spirometric” lung-age. FEV1: 1st s forced expiratory volume. FVC: forced vital capacity. LAD: Lung-age deficit (CA minus SLA) in years. LLN: lower limit of normal. OSA: obstructive sleep apnea. NS: non-smokers. ULN: upper limit of normal. Y: year. aData are range (min-max). * Probability < 0.05.

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Yamaguchi (2012)

Box 1. Recommended “spirometric” lung-age (SLA) norms.

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Morris and Temple (1985) Men: SLA (Yrs) = 1.130 x Height (cm) - 31.250 x FEV1 (L) - 39.375. r=0.73 Women: SLA (Yrs) = 1.401 x Height (cm) - 40.000 x FEV1 (L) - 77.280. r=0.73 Hansen et al. (2010) Men/women: SLA (Yrs)= CA (Yrs)+ 3 x (predicted-observed) FEV1/FVC ratio (%). r2=notreported Newbury et al. (2010) Men: SLA (Yrs) = 1.56 x Height (cm) - 33.69 x FEV1 (L) - 85.62. r2=not-reported Women: SLA (Yrs) = 1.33 x Height (cm) - 31.98 x FEV1 (L) - 74.65. r2=not-reported Yamaguchi et al. (2012) Men: SLA (Yrs) = 209.195 - 0.455 x Height (cm) - 11.521 x FEV1 (L) - 0.602 x FEV1/FVC (%) + 1.956 x FEF50 (L/s). r2=0.50 Women: SLA (Yrs) = 234.441 - 0.792 x Height (cm) - 7.295 x FEV1 (L) - 0.610 x· FEV1/FVC (%) + 0.301 x PEF (L/s) + 2.647 x FEF50 (L/s). r2=0.42 Ben Saad et al. (2014) Men: SLA (Yrs) = 42.85 - 20.74 x FEV1 (L) + 47.41 x BSA (m2) - 0.62 x BMI (kg/m2). r2=0.56 Women: SLA (Yrs) = 64.64 - 8 x FEV1 (L) - 0.17 x BMI (kg/m2) + 0.09 x Height (cm). r2=0.38 40

Ishida et al. (2015) Men: SLA (Yrs) = 1.00 × FVC (%)- 33.3 × FEV1 (L) + 50.7. r2:0.77 Women: SLA (Yrs) = 0.84 × FVC (%)- 40 × FEV1 (L) + 50.2. r2:0.80