Abstract
Purpose:
Spirometry, the standard for diagnosing chronic obstructive pulmonary disease, has limited sensitivity for early identification of tobacco-exposed individuals with preserved spirometry (TEPS) at risk for respiratory decline. We aimed to determine whether abnormalities in residual volume to total lung capacity ratio (RV/TLC) and total lung capacity (TLC) identify TEPS at higher risk of subsequent spirometric decline.
Methods:
1986 tobacco-exposed individuals (≥ 25 years, ≥ 10 pack-years) with preserved spirometry (FEV1/FVC ≥ lower limit of normal, z-scores, ≥ − 1.645) over ≥ 4 years follow-up from the population-based Austrian LEAD study were analysed. Participants were stratified by into four groups according to RV/TLC and TLC abnormalities.
Results:
High RV/TLC (≥ upper limit of normal, ULN, z-scores > + 1.645) was present in 6.5% of TEPS, predominantly with normal TLC (87.6%) and associated with greater smoking intensity. Notably, elevated RV/TLC associated with disproportionate declines in FEV1/FVC driven by discordant reductions in FEV1 and increases in FVC. While FEV1 z-scores remained largely normal, RV/TLC variability was pronounced, suggesting its potential as an early marker of pre-COPD progression.
Conclusion:
These findings underscore the limitations of spirometry alone and advocate for routine lung volume assessment in at-risk smokers.
Supplementary Information
The online version contains supplementary material available at 10.1007/s00408-026-00878-y.
Keywords: Tobacco-exposed preserved spirometry, Residual volume, Total lung capacity, Lung volumes, Pre-COPD, FEV1/FVC
Introduction
Identification of smokers without airflow obstruction who are at highest risk for development of respiratory problems and chronic airflow obstruction, manifested by a decline in FEV1 /FVC ratio is of great interest for prognostication and intervention: FEV1 /FVC is still the most robust and widely available marker of airflow limitation. To provide greater awareness in the medical community the term pre-COPD was introduced for those individuals with respiratory symptoms and/or functional abnormalities and/or structural abnormalities but no evidence of airflow obstruction [1].
Less attention has been paid to changes in lung volume and in particular to the impact of air trapping, defined as an abnormal increase in volume of air remaining in the lungs at the end of exhalation. Large longitudinal studies like COPDGene and Spiromics have remedied this deficit [2, 3]. However, data of lung volume assessment among tobacco-exposed persons with preserved spirometry (TEPS) as part of population-based studies are scarce.
Methods
We present data from the LEAD (Lung, hEart, sociAl, boDy) Study (clinicaltrials.gov identifier NCT01727518), a single-center longitudinal, observational, population-based cohort study of 15,056 subjects aged 6–82 years from Vienna and lower Austria. A detailed study protocol has been published elsewhere [4]. This study was performed conducted within the LEAD study which was approved by the local Ethics Committee of Vienna (protocol number: EK-11-117-0711) and all procedures were performed in accordance with the Declaration of Helsinki. Written informed consent was obtained from all individuals participating in the LEAD study. All participants underwent spirometry and lung volume measurement by body plethysmography (BP). Spirometric parameters were defined according to the Global Lung Function Initiative (GLI) reference equations [5]; lung volumes are related to population specific reference equations [6]. The RV/TLC ratio was computed directly from these values and its z-scores were derived from the same population-specific reference set used for TLC and RV. In this study, 1986 participants aged ≥ 25 years (mean age ± standard deviation, 54 ± 11.8; 55.9% males) have complete and valid post-bronchodilator lung function tests at Visit 1 (V1) and after a minimum follow-up of 4 years (V2). Smokers were defined as individuals with a cumulative exposure of ≥10 pack years (PY). Preserved spirometry was defined as FEV1 /FVC ≥ lower limit of normal (LLN, z-score -1.645). TEPS were further stratified into categories of normal RV/TLC and normal TLC (≥ LLN and ≤ upper limit of normal, ULN, z-score + 1.645), normal RV/TLC and high TLC (TLC > ULN), high RV/TLC (RV/TLC > ULN) with normal TLC, and high RV/TLC and high TLC (RV/TLC > ULN, TLC > ULN). An additional flow parameter, maximal mid-expiratory flow75-25 (MMEF), was included. Baseline characteristics of the four subgroups are summarised in Table 1 and all reported values are unadjusted. RV/TLC and TLC groups were modelled in linear regression analyses to assess their associations with longitudinal changes in FEV1, FVC and FEV1/FVC. These analyses were adjusted for age, sex, height, weight, smoking status and pack-years. Additional sensitivity analyses were conducted to address potential limitations of using LLN and ULN-based thresholds by modelling RV/TLC and TLC quartiles and as continuous predictors.
Table 1.
Characteristics of participants
| High RV/TLC and high TLC | High RV/TLC and normal TLC | High TLC and normal RV/TLC | Normal TLC and normal RV/TLC | |
|---|---|---|---|---|
| Participants | 16 (0.8) | 129 (6.5) | 68 (3.4) | 1670 (84.1) |
| Males | 13 (81.2) | 70 (54.3) | 38 (55.9) | 925 (55.4) |
| Age, years | 56.7 ± 9.3 | 55.3 ± 10.0 | 54.2 ± 12.4 | 53.9 ± 11.6 |
| Height, cm | 173.6 ± 8.7 | 170.7 ± 8.9 | 170.5 ± 8.4 | 171.5 ± 9.1 |
| BMI, kg/m2 | 26.8 ± 5.2 | 26.7 ± 5.6 | 25.9 ± 3.6 | 27.1 ± 4.7 |
| Smoking exposures | ||||
| Pack-years | 33.6 ± 20.8 | 40.6 ± 31.4 | 27.1 ± 16.1 | 27.6 ± 18.4 |
| Former smokers | 6 (37.5) | 49 (38.0) | 49 (72.1) | 996 (59.6) |
| Current smokers | 10 (62.5) | 80 (62.0) | 19 (27.9) | 674 (40.4) |
| Pulmonary function measurements | ||||
| FEV1, L | 3.5 ± 0.9 | 2.8 ± 0.7 | 3.9 ± 0.8 | 3.3 ± 0.8 |
| FEV1, %predicted GLI | 99.8 ± 9.6 | 85.9 ± 10.7 | 119.2 ± 10.1 | 100.8 ± 11.4 |
| FVC, L | 4.7 ± 1.0 | 3.6 ± 0.9 | 5.1 ± 1.0 | 4.2 ± 0.9 |
| FVC, %predicted GLI | 106.1 ± 6.8 | 88.4 ± 9.1 | 123.5 ± 7.3 | 100.7 ± 10.4 |
| FEV1/FVC, % | 73.5 ± 5.1 | 76.5 ± 5.7 | 76.2 ± 5.0 | 79.0 ± 5.1 |
| MMEF, L/s | 2.7 ± 1.2 | 2.3 ± 0.9 | 3.3 ± 1.0 | 3.1 ± 1.0 |
| MMEF, %predicted GLI | 56.7 ± 18.7 | 79.4 ± 22.1 | 111.7 ± 29.0 | 103.8 ± 27.4 |
| MMEF < LLN | 0 (0.0) | 10 (7.8) | 0 (0.0) | 13 (0.8) |
| TLC, L | 8.5 ± 1.1 | 6.7 ± 1.1 | 8.0 ± 1.2 | 6.6 ± 1.1 |
| TLC, %predicted LEAD | 123.0 ± 3.7 | 102.4 ± 8.1 | 121.8 ± 4.4 | 100.0 ± 7.8 |
| RV, L | 3.7 ± 0.5 | 3.0 ± 0.5 | 2.8 ± 0.5 | 2.3 ± 0.5 |
| RV, %predicted LEAD | 156.9 ± 12.8 | 130.5 ± 12.0 | 122.8 ± 11.7 | 101.7 ± 12.4 |
| RV/TLC, % | 44.2 ± 6.3 | 45.0 ± 5.8 | 35.3 ± 5.8 | 35.5 ± 6.2 |
| Clinical conditions | ||||
| Any respiratory symptoms | 5 (31.2) | 49 (38.0) | 12 (17.9) | 446 (26.8) |
| Asthma, diagnosed | 4 (25.0) | 9 (7.0) | 5 (7.4) | 113 (6.8) |
| COPD, diagnosed | 2 (12.5) | 18 (14.0) | 4 (5.9) | 99 (5.9) |
| Visit 2 | ||||
| Follow-up, years | 4.2 ± 0.4 | 4.4 ± 0.9 | 4.3 ± 0.7 | 4.3 ± 0.7 |
| ∆ BMI, kg/m2 | 0.3 ± 2.1 [0.5 (− 0.9, 1.3)] | 0.5 ± 2.1 [0.4 (− 0.6, 1.5)] | 0.5 ± 1.7 [0.6 (− 0.2, 1.2)] | 0.4 ± 1.7 [0.4 (− 0.5, 1.3)] |
| Smoking cessation | 1 (6.2) | 13 (10.2) | 5 (7.4) | 140 (8.5) |
| ∆ FEV1, ml | − 184.4 ± 265.4 [− 110.0 (− 270.0, − 15.0)] | − 122.6 ± 278.2 [− 90.0 (− 250.0, 40.0)] | − 213.4 ± 216.4 [− 170.0 (− 302.5, − 120.0)] | − 109.4 ± 225.2 [− 110.0 (− 220.0, 10.0)] |
| ∆ FVC, ml | 58.7 ± 449.9 [− 5.0 (− 242.5, 337.5)] | 50.6 ± 362.6 [70.0 (− 150.0, 290.0)] | − 133.1 ± 400.5 [− 95.0 (− 305.0, 7.5)] | − 33.9 ± 305.9 [− 40.0 (− 210.0, 137.5)] |
| ∆ FEV1/FVC, % | − 4.5 ± 5.1 [− 4.5 (− 8.8, − 1.5)] | − 4.4 ± 4.7 [− 4.5 (− 7.2, − 1.7)] | − 2.1 ± 3.8 [− 1.6 (− 3.9, 0.0)] | − 2.0 ± 3.7 [− 1.8 (− 3.9, 0.2)] |
| ∆ TLC, ml | − 332.5 ± 451.8 [− 425.0 (− 545.0, 2.5)] | − 79.0 ± 471.8 [− 30.0 (− 350.0, 250.0)] | − 314.1 ± 549.5 [− 285.0 (− 542.5, 70.0)] | − 22.0 ± 382.3 [− 30.0 (− 240.0, 190.0)] |
| ∆, RV, ml | − 315.0 ± 564.1 [− 325.0 (− 835.0, 20.0)] | − 98.7 ± 439.7 [− 50.0 (− 330.0, 170.0)] | − 138.5 ± 431.5 [− 85.0 (− 315.0, 130.0)] | 41.7 ± 337.4 [50.0 (− 157.5, 230.0)] |
| Transitioned to AFL-V2 | 4 (25.0) | 28 (21.7) | 11 (16.2) | 70 (4.2) |
| New cases of respiratory symptoms | 2 (13.3) | 22 (17.5) | 8 (11.9) | 132 (8.1) |
| Incident asthma, diagnosed | 0 (0.0) | 9 (7.3) | 0 (0.0) | 39 (2.4) |
| Incident COPD, diagnosed | 2 (12.5) | 7 (5.4) | 2 (2.9) | 56 (3.4) |
Data are presented as mean ± standard deviations [median (interquartile range)] or [prevalence (frequency, %)]. RV, residual volume; TLC, total lung capacity; AFL, airflow limitation; BMI, body mass index; FEV1, forced expiratory volume in 1 s; GLI, Global Lung Initiative; FVC, forced vital capacity; MMEF, maximal forced expiratory flow between 25 and 75% of the FVC; LEAD, Lung hEart, sociAl, boDy; COPD, chronic obstructive pulmonary disease; AFL, airflow limitation (FEV1/FVC < lower limit of normal, z-score -1.645). ∆ is calculated by values in Visit 2 minus Visit 1. All subgroup comparisons are unadjusted.
Results
High RV/TLC ratio was found in 6.5% of smoking participants, with the majority presenting with normal TLC (87.6%, Table 1). The proportion of active smokers as well as smoking intensity (PY) was highest in those with increased RV/TLC. In TEPS participants, RV/TLC %predicted values ranged from 14.0% to 57.6% (median 35.5%), corresponding to z-scores of -5.13 to 4.36 (median 0.17) with ~ 8% exceeding the ULN. (Fig. 1A). FEV₁ z-scores remained largely within the normal range (median − 0.01, 75th percentile 0.63), yet RV/TLC varied widely across these values (Fig. 1B). Of note, MMEF was largely preserved in all four TEPS groups; only 8 participants (6.6%) in the high RV/TLC group had MMEF < LLN (Table 1). Approximately 10% of TEPS with elevated RV/TLC reported a medical diagnosis of COPD.
Fig. 1.
Distribution of RV/TLC in individuals with tobacco-exposed preserved spirometry (A) and the relationship between RV/TLC and spirometry in individuals with tobacco-exposed preserved spirometry (B). Legend: The dashed line indicates the median. Scatter plots are colored by the lower limit of normal (LLN, z-score -1.645). Boxplots summarize median RV/TLC within each RV/TLC tertile.
After a follow-up period of at least 4 years, both groups with baseline high TLC had a significant drop in TLC (high TLC and RV/TLC, − 332.5 ± 451.8 ml and isolated high TLC, − 314.1 ± 549.5 ml) and RV (high TLC and RV/TLC, − 315.0 ± 564.1 ml and isolated TLC, − 138.5 ± 431.5 ml). Declines in both FEV1 and FVC were observed in the isolated high TLC group whereas in participants with combined high RV/TLC and TLC, decline was limited to FEV1. Notably, both groups with elevated RV/TLC exhibited a discordant spirometric pattern: FEV1 decreased while FVC increases, leading to pronounced reductions in FEV1/FVC among those with increased lung volumes. In adjusted linear regression models, isolated high TLC was associated with declines in both FEV1 and FVC whereas combined elevations in RV/TLC and TLC were not. In contrast, elevated RV/TLC, both alone and in combination with high TLC, was consistently associated with greater declines in FEV1/FVC ratio, independent of lung size, with larger effect sizes and stronger statistical support than observed for changes in FEV1 or FVC (Table 2).
Table 2.
Changes in spirometry stratified by lung volumes in smokers with normal spirometry at baseline
| Unstandardised β (95% CI) | Standardised β (95% CI) | t-value | p-value | |
|---|---|---|---|---|
| Unadjusted model | ||||
| ∆ FEV1, ml | ||||
| High RV/TLC & high TLC | − 75.02 (− 186.18, 36.15) | − 0.33 (− 0.82, 0.16) | − 1.32 | 0.186 |
| High RV/TLC | − 12.40 (− 53.46, 28.66) | − 0.05 (− 0.23, 0.13) | − 0.59 | 0.554 |
| High TLC | − 104.02 (− 158.60, − 49.45) | − 0.46 (− 0.70, − 0.22) | − 3.74 | < 0.001 |
| ∆ FVC, ml | ||||
| High RV/TLC & high TLC | 92.65 (− 58.81, 244.12) | 0.30 (− 0.19, 0.79) | 1.20 | 0.230 |
| High RV/TLC | 89.34 (33.81, 144.87) | 0.29 (0.11, 0.47) | 3.16 | 0.002 |
| High TLC | − 99.18 (− 174.41, − 23.96) | − 0.32 (− 0.56, − 0.08) | − 2.59 | 0.010 |
| ∆ FEV1/FVC, % | ||||
| High RV/TLC & high TLC | − 2.51 (− 4.33, − 0.69) | − 0.68 (− 1.17, − 0.19) | − 2.70 | 0.007 |
| High RV/TLC | − 2.52 (− 3.19, − 1.86) | − 0.67 (− 0.85, − 0.49) | − 7.45 | < 0.001 |
| High TLC | − 0.10 (− 1.00, 0.79) | − 0.03 (− 0.27, 0.21) | − 0.23 | 0.821 |
| Adjusted model | ||||
| ∆ FEV1, ml | ||||
| High RV/TLC & high TLC | − 47.90 (− 158.44, 62.64) | − 0.21 (− 0.70, 0.28) | − 0.85 | 0.396 |
| High RV/TLC | 5.25 (− 36.44, 46.95) | 0.02 (− 0.16, 0.21) | 0.25 | 0.805 |
| High TLC | − − 106.36 (− 160.64, − 52.07) | − 0.47 (− 0.71, − 0.23) | − 3.84 | < 0.001 |
| ∆ FVC, ml | ||||
| High RV/TLC & high TLC | 108.15 (− 43.50, 259.80) | 0.35 (− − 0.14, 0.84) | 1.40 | 0.162 |
| High RV/TLC | 100.05 (43.34, 156.76) | 0.32 (0.14, 0.51) | 3.46 | < 0.001 |
| High TLC | − 97.03 (− 172.40, − 21.67) | − 0.31 (− 0.56, − 0.07) | − 2.53 | 0.012 |
| ∆ FEV1/FVC, % | ||||
| High RV/TLC & high TLC | − 2.26 (− 4.08, − 0.46) | − 0.61 (− 1.10, − 0.12) | − 2.44 | 0.014 |
| High RV/TLC | − 2.28 (− 2.95, − 1.60) | − 0.61 (− 0.79, − 0.43) | − 6.58 | < 0.001 |
| High TLC | − 0.18 (− 1.07, 0.72) | − 0.05 (− 0.29, 0.19) | − 0.39 | 0.697 |
Estimates (β) and 95% confidence intervals (CI) were obtained from linear regression models and corresponding p-values from Wald t-distribution approximation. Adjusted models additionally adjusted for age, sex, height and weight, smoking status and pack-years at baseline. RV, residual volume; TLC, total lung capacity; FEV1, forced expiratory volume in 1 s; and FVC, forced vital capacity. ∆ were values in Visit 2 minus values in Visit 1. Significance was considered where p < 0.05.
In sensitivity analyses, treating RV/TLC and TLC as both quartile-based and continuous predictors, higher TLC was significantly associated with absolute declines in FEV1 and FVC over follow-up whereas the corresponding associations with changes in FEV1/FVC were weaker and did not reach statistical significance in the continuous models (Supplementary Tables 1 and 2). By contrast, RV/TLC exhibited a more consistent and robust association with declines in FEV1/FVC across both quartile-based and continuous specifications, supporting a more pronounced impact on the FEV1/FVC ratio than observed for TLC.
Discussion
The reported prevalence of increased RV/TLC varies substantially across studies, largely depending on population characteristics and the definition used to identify volume abnormalities. In a retrospective study of ever-smokers aged > 40 years with preserved spirometry (mean age: 61 years), approximately 31% had RV/TLC > ULN, and RV/TLC varied widely from 14 to 77% of predicted values across the normal ranges of airflow indices [7]. In SPIROMICS study of smokers with preserved FEV1/FVC, radiographically-derived RV/TLC ratios also exhibited wide inter-individual variability, ranging from ~ 21% to 59% [2]. Other at-risk populations with preserved spirometry, such as persons exposed to second-hand smoke, similarly show high variability in RV/TLC ratios [8]. Arjomandi et al. previously highlighted how a high RV/TLC ratio contributes to the decline in FEV1/FVC, eventually reaching the COPD threshold characterised by a decline in FEV1 and an increase in FVC [2]. Our study suggests that reliance on spirometry alone may overlook important physiological changes, supporting the use of lung volume assessment when monitoring disease evolution in pre-COPD.
In this research letter we report that increased RV/TLC, independent of TLC, is associated with disproportionate declines in FEV1/FVC driven by opposing changes in FEV1 and FVC, underscoring the complexity of interpreting this ratio as a marker of airflow limitation. This finding is consistent with prior observations including those of Arjomandi et al. [2], which demonstrated similar patterns in smokers with preserved spirometry. However, certain longitudinal changes particularly for TLC groups appear directionally different or weaker than reported elsewhere. These differences may be attributed to variations in phenotype definitions (e.g. ULN vs. quantiles) and cohort-specific characteristics, such as differences in population demographics, smoking exposure or follow-up duration.
Quantile-based stratification and continuous modelling showed that the relationship between RV/TLC, TLC and adverse outcomes are largely continuous rather than defined by a single threshold. Individuals in the highest RV/TLC or TLC quantiles experienced the greatest declines in lung function even if their values did not exceed the ULN threshold, suggesting that the ULN cut-off may miss important signals in individuals with sub-threshold lung volumes. This is aligned with findings from the SPIROMICS and COPDGene cohorts [2, 3], which also report continuous risk gradients for lung volume abnormalities in smokers. Together, these data support the use of continuous or quantile-based approaches in risk stratification.
Our sensitivity analyses also revealed an important distinction between TLC and RV/TLC. Elevated TLC, whether modelled as quantiles or as a continuous variable, was primarily associated with parallel declines in FEV1 and FVC, with comparatively limited or non-significant effects on FEV1/FVC in continuous models. In contrast, higher RV/TLC was strongly and consistently linked to disproportionate reductions in FEV1/FVC. This pattern suggests that RV/TC may better capture air-trapping and early small airway or parenchymal abnormalities whereas increased TLC may reflect a broader hyperinflation phenotype in which FEV1 and FVC decline more proportionally.
The preservation of MMEF in the 4 groups of smokers with preserved spirometry is particularly noteworthy. However, the interpretation of MMEF as a direct measure of small airway function remains debated. MMEF is influenced by multiple factors including lung volumes, effort dependence and larger airway contributions and may therefore reflect compensatory mechanisms or limitations in detecting subtle small airway abnormalities. In this context, air-trapping as evidenced by elevated RV/TLC, may represent early small airway dysfunction or parenchymal changes not captured by MMEF alone. This aligns with prior work such as Arjomandi et al. which demonstrated progressive small airway disease and emphysema on CT-based parametric response despite preserved FEF75-25 values [2]. Zeng and colleagues reported no significant differences in FEF25-75% between groups stratified on TLC and high FRC/TLC ratio in the COPDGene cohort [3]. Since it appears that FEF25-75% is not robust, further studies are needed as to how best unravel the role of small airway dysfunction in underlying pathophysiological changes of trapped air.
Arjomandi et al. emphasized the role of CT-derived lung volumes in detecting early disease states. Although the LEAD study lacks CT data and thus the impact of RV/TLC on radiographic phenotypes could not be explored, our findings demonstrate that body plethysmographic lung volume measurements are highly effective for the detection of pre-COPD changes and interpreting longitudinal spirometric findings. Additionally, our data underscores the importance of integrated lung function assessment in smokers at risk. While plethysmographic lung volumes have previously been shown to predict morbidity and outcomes in retrospective and clinical cohorts [7, 9], our study provides prospective, population-based confirmation of these associations using standardised GLI-based spirometry definitions and local reference equations for lung volumes. This strengthens the generalisability of these findings and highlights the value of routine lung volume assessment in clinical practice. Further prospective population-based studies are needed to clarify the mechanisms driving early changes in RV/TLC, identify targets for early intervention, and validate lung volumes as predictors of subsequent lung function decline and progression to COPD.
Further research is needed to clarify the mechanisms driving early changes in RV/TLC and to identify targets for early intervention to preserve lung health. Future prospective population-based studies are needed to validate the utility of lung volumes as a predictor of future lung function decline and progression to overt COPD.
Supplementary Information
Below is the link to the electronic supplementary material.
Acknowledgements
All authors thank all participants for their willingness to contribute to this study. We would also like to acknowledge Michael Sturm and the staff of the LEAD study, Austria, for their support with recruiting and managing participants so that high quality data can be obtained. We would also like to thank Ms. Barbara Wögerbauer for her administrative support.
Author Contributions
E.F.M.W. contributed to conception, design and original draft preparation. C.J.M.L. contributed to formal analysis, data visualisation and editing of the manuscript. T.L.M., C.G.I., M.-K. B. and R.B.-K. contributed to review and editing of the manuscript. All authors had access to and participated in the interpretation of the data and provided input into the preparation and submission of the manuscript. All authors vouch for the accuracy and completeness of the data and content presented in the manuscript.
Funding
Not applicable to the manuscript. The Austrian LEAD study has unrestricted financial support from the Ludwig Boltzmann Gesellschaft, Sigmund Freud Private University Faculty of Medicine, Vienna Healthcare Group, the Lower Austrian Health and Social Fund, AstraZeneca, Chiesi Farmaceutici, GlaxoSmithKline and Menarini Pharma. None of the supporting parties has any participation in the data, nor have they contributed to the design or the content of the manuscript. All authors declare no financial/non-financial disclosures pertaining to the manuscript.
Data Availability
Data relevant to this study can be made upon request to the corresponding author.
Declarations
Conflict of interest
The authors declare no competing interests.
Ethical Approval
No clinical trial number is applicable to this study. This study was conducted within the framework of the LEAD study which has been approved by the local Ethics committee of Vienna (EK-11-117-0711) with the project identification code, NCT017257518 (ClinicalTrials.gov) in accordance with the Declaration of Helsinki. No ethics approval was required pertaining to the manuscript.
Human Ethics and Consent to Participate
Written informed consent was obtained from all individual participants included in the LEAD study. Further consent for this study was not applicable.
Footnotes
Publisher's Note
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Charmaine J. M. Lim and Emiel F. M. Wouters have contributed equally to this work as first authors.
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Supplementary Materials
Data Availability Statement
Data relevant to this study can be made upon request to the corresponding author.