{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,23]],"date-time":"2025-10-23T05:42:16Z","timestamp":1761198136364,"version":"build-2065373602"},"reference-count":42,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2025,10,23]],"date-time":"2025-10-23T00:00:00Z","timestamp":1761177600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Aging"],"abstract":"<jats:sec>\n                    <jats:title>Objectives<\/jats:title>\n                    <jats:p>This study aims to investigate age-related alterations of lung structure.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>We retrospectively collected 928 male subjects from an annual lung nodule screening cohort. The quantitative parameters included lung volume (LV), mean lesion density (MLD), emphysema indexes (LAA-910, LAA-910%, LAA-950 and LAA-950%), number of bronchi (NB) and volume of bronchi (VB), as well as ratio of airway to the lung (ALR). The quantitative parameters were calculated for total lung, right lung, left lung, and the individual lobes.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      LV and VB peaked in the group of 51\u201360\u00a0years-old and 61\u201370\u00a0years-old, respectively. MLD decreased with age, while LAA-910, LAA-950, LAA-910%, LAA-950%, and ALR all showed an increasing trend with age. LV, NB, and VB of the right lung were larger than those of the left lung, while MLD, LAA-950, LAA-950%, and ALR of the right lung were lower than those of the left lung (\n                      <jats:italic>P<\/jats:italic>\n                      &amp;lt; 0.05). The LV of bilateral upper lobes increased with age, while a decline of LV of bilateral lower lobes was observed since the sixties. The MLD of the bilateral lower lobes decreased (\n                      <jats:italic>P<\/jats:italic>\n                      &amp;lt; 0.05). The LAA-910%, LAA-950%, and ALR of the 71\u201380\u00a0years-old in all five lobes were higher than those of the other four groups (\n                      <jats:italic>P<\/jats:italic>\n                      &amp;lt; 0.05). LAA-950 and LAA-950% of bilateral lower lobes displayed a steeper increase began at 60\u00a0years old. We also provide a computational formula, LungAge Score, for the assessment of the structural lung aging features.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>Lung aging is not a linear process, and the lung structural alterations in the upper and lower lobes exhibit significant heterogeneity.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fragi.2025.1624233","type":"journal-article","created":{"date-parts":[[2025,10,23]],"date-time":"2025-10-23T05:37:24Z","timestamp":1761197844000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Age-related lung structure changes by quantitative assessment: a cross-sectional study in a Chinese male cohort"],"prefix":"10.3389","volume":"6","author":[{"given":"Meijuan","family":"Shi","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Youmin","family":"Guo","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chenwang","family":"Jin","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Cong","family":"Shen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1965","published-online":{"date-parts":[[2025,10,23]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"6779","DOI":"10.1007\/s00330-020-07020-3","article-title":"Emphysema quantification using low-dose computed tomography with deep learning-based kernel conversion comparison","volume":"30","author":"Bak","year":"2020","journal-title":"Eur. 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