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2000
Volume 21, Issue 1
  • ISSN: 1573-4056
  • E-ISSN: 1875-6603
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Abstract

Background

Type 2 diabetes mellitus (T2DM) complicated with interstitial lung abnormalities (ILAs) is often overlooked and can progress to severe diabetes-induced pulmonary fibrosis (DiPF). Therefore, early diagnosis of T2DM complicated with ILAs is crucial. Chest computed tomography (CT) is an important method for diagnosing T2DM complicated with ILAs. Quantitative computed tomography (QCT) is more objective and accurate than visual assessment on CT. However, there are currently limited studies on T2DM complicated with ILAs based on quantitative CT.

Objective

This study aimed to explore the utility of quantitative computed tomography for early detection of lung injury in individuals with T2DM by examining CT-derived metrics in T2DM complicated with ILAs.

Methods

We collected data from 135 T2DM complicated with ILAs on chest CT scans retrospectively, alongside 135 non-diabetic controls with normal CT findings. Employing digital lung software, chest CT images were processed to extract quantitative parameters: total lung volume (TLV), emphysema index (LAA%, the percentage of lung area with attenuation < –950 Hu to total lung volume), pulmonary fibrosis index (LAA%, the percentage of lung area with attenuation from –700Hu to –200 Hu to the total lung volume), and pulmonary peripheral vascular index (ratio TAV/TNV, the number of blood vessels TNV, the cross-sectional area of blood vessels TAV). Statistical comparisons between groups utilized Mann-Whitney U or t-tests. Correlations between Hemoglobin A1c (HbA1c) levels and CT parameters were assessed via Pearson or Spearman correlations. Parameters showing statistical significance were further examined through receiver operating characteristic (ROC) analysis.

Results

The T2DM-ILAs cohort displayed a significantly higher LAA% compared to controls ( = -7.639, < 0.001), indicative of increased fibrotic changes. Conversely, TLV ( =-3.120, =0.002), TAV/TNV ( = -9.564, P< 0.001), and LAA% ( = -4.926, < 0.001) were reduced in T2DM-ILAs patients. The correlation between HbA1c and various CT quantitative indicators was not significant, HbA1c and TLV (=-0.043, =0.618), HbA1c and TAV (=0.143, =0.099), HbA1c and TNV (=0.064, =0.461), HbA1c and LAA% (=0.102, =0.239), HbA1c and LAA% (=-0.170, =0.049), HbA1c and TAV/TNV (=0.175, P=0.043). The peripheral vascular marker, TAV/TNV, excelled in distinguishing T2DM-related lung changes (AUC=0.84, <0.001), outperforming LAA% (AUC=0.77,<0.001). A composite index incorporating multiple quantitative parameters achieved the highest diagnostic accuracy (AUC = 0.91, < 0.001).

Conclusion

Quantitative CT parameters distinguish T2DM complicated with ILAs from non-diabetic individuals, suggesting a distinct pattern of lung injury. Our findings imply a particular susceptibility of small pulmonary blood vessels to injury in T2DM.

© The Author(s). Published by Bentham Science Publisher. 
This is an open access article distributed under the terms of the Creative Commons Attribution 4.0 International Public License (CC-BY 4.0), a copy of which is available at: https://creativecommons.org/licenses/by/4.0/legalcode. This license permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
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2025-06-03
2025-09-04

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References

  1. DwivediJ. WalP. DashB. OvaisM. SachanP. VermaV. Diabetic pneumopathy: A novel diabetes-associated complication: Pathophysiology, the underlying mechanism and combination medication.Endocr. Metab. Immune Disord. Drug Targets20242491027105210.2174/011871530326596023092611320137817659
     [Google Scholar]
  2. FusoL. PitoccoD. Antonelli-IncalziR. Diabetic lung, an underrated complication from restrictive functional pattern to pulmonary hypertension.Diabetes Metab. Res. Rev.2019356e315910.1002/dmrr.315930909316
     [Google Scholar]
  3. RajasuryaV. GunasekaranK. SuraniS. Interstitial lung disease and diabetes.World J. Diabetes202011835135710.4239/wjd.v11.i8.35132864047
     [Google Scholar]
  4. KopfS. KumarV. KenderZ. HanZ. FlemingT. HerzigS. NawrothP.P. Diabetic pneumopathy: A new diabetes-associated complication: Mechanisms, consequences and treatment considerations.Front. Endocrinol. (Lausanne)20211276520110.3389/fendo.2021.76520134899603
     [Google Scholar]
  5. HuW.S. LinC.L. Effect of anti-diabetic agent on interstitial lung disease in patients with diabetes mellitus.Naunyn Schmiedebergs Arch. Pharmacol.20243981581580910.1007/s00210‑024‑03296‑039031184
     [Google Scholar]
  6. TeagueT.T. PayneS.R. KellyB.T. DempseyT.M. McCoyR.G. SangaralinghamL.R. LimperA.H. Evaluation for clinical benefit of metformin in patients with idiopathic pulmonary fibrosis and type 2 diabetes mellitus: A national claims-based cohort analysis.Respir. Res.20222319110.1186/s12931‑022‑02001‑035410255
     [Google Scholar]
  7. HatabuH. HunninghakeG.M. RicheldiL. BrownK.K. WellsA.U. Remy-JardinM. VerschakelenJ. NicholsonA.G. BeasleyM.B. ChristianiD.C. San José EstéparR. SeoJ.B. JohkohT. SverzellatiN. RyersonC.J. Graham BarrR. GooJ.M. AustinJ.H.M. PowellC.A. LeeK.S. InoueY. LynchD.A. Interstitial lung abnormalities detected incidentally on CT: A position paper from the Fleischner Society.Lancet Respir. Med.20208772673710.1016/S2213‑2600(20)30168‑532649920
     [Google Scholar]
  8. TalakattaG. SarikhaniM. MuhamedJ. DhanyaK. SomashekarB.S. MaheshP.A. SundaresanN. RavindraP.V. Diabetes induces fibrotic changes in the lung through the activation of TGF-β signaling pathways.Sci. Rep.2018811192010.1038/s41598‑018‑30449‑y30093732
     [Google Scholar]
  9. AlevizosM.K. DanoffS.K. PappasD.A. LedererD.J. JohnsonC. HoffmanE.A. BernsteinE.J. BathonJ.M. GilesJ.T. Assessing predictors of rheumatoid arthritis-associated interstitial lung disease using quantitative lung densitometry.Rheumatology (Oxford)20226172792280410.1093/rheumatology/keab82834747452
     [Google Scholar]
  10. RomeiC. TavantiL.M. TalianiA. De LiperiA. KarwoskiR. CeliA. PallaA. BartholmaiB.J. FalaschiF. Automated computed tomography analysis in the assessment of idiopathic pulmonary fibrosis severity and progression.Eur. J. Radiol.202012410885210.1016/j.ejrad.2020.10885232028067
     [Google Scholar]
  11. PuD. YuanH. MaG. DuanH. ZhangM. YuN. CT quantitative analysis of pulmonary changes in rheumatoid arthritis.J. XRay Sci. Technol.202331354555310.3233/XST‑22132936847056
     [Google Scholar]
  12. Classification and diagnosis of diabetes: Standards of medical care in diabetes—2022.Diabetes Care2022451S17S3810.2337/dc22‑S00234964875
     [Google Scholar]
  13. PuJ RoosJ YiCA Adaptive border marching algorithm: Automatic lung segmentation on chest CT images.Comput. Med. Imaging. Graph.200832645210.1016/j.compmedimag.2008.04.005
     [Google Scholar]
  14. RaoofS. ShahM. MakeB. AllaqabandH. BowlerR. FernandoS. GreenbergH. HanM.K. HoggJ. HumphriesS. LeeK.S. LynchD. MachnickiS. MehtaA. MinaB. NaidichD. NaidichJ. NaqviZ. OhnoY. ReganE. TravisW.D. WashkoG. BramanS. Lung Imaging in COPD Part 1.Chest20231641698410.1016/j.chest.2023.03.00736907372
     [Google Scholar]
  15. HuangX. YinW. ShenM. WangX. RenT. WangL. LiuM. GuoY. Contributions of emphysema and functional small airway disease on intrapulmonary vascular volume in COPD.Int. J. Chron. Obstruct. Pulmon. Dis.2022171951196110.2147/COPD.S36897436045693
     [Google Scholar]
  16. Jiantao Pu Bin Zheng LeaderJ.K. FuhrmanC. KnollmannF. KlymA. GurD. Pulmonary lobe segmentation in CT examinations using implicit surface fitting.IEEE Trans. Med. Imaging200928121986199610.1109/TMI.2009.202711719628453
     [Google Scholar]
  17. Jiantao Pu LeaderJ.K. Bin Zheng KnollmannF. FuhrmanC. SciurbaF.C. GurD. A Computational geometry approach to automated pulmonary fissure segmentation in CT examinations.IEEE Trans. Med. Imaging200928571071910.1109/TMI.2008.201044119272987
     [Google Scholar]
  18. Jiantao Pu FuhrmanC. GoodW.F. SciurbaF.C. GurD. A differential geometric approach to automated segmentation of human airway tree.IEEE Trans. Med. Imaging201130226627810.1109/TMI.2010.207630020851792
     [Google Scholar]
  19. RobertsT.J. BurnsA.T. MacIsaacR.J. MacIsaacA.I. PriorD.L. La GercheA. Diagnosis and significance of pulmonary microvascular disease in diabetes.Diabetes Care201841485486110.2337/dc17‑190429351959
     [Google Scholar]
  20. ZhangQ. WangY. TianC. YuJ. LiY. YangJ. Clinical characteristics and genetic analysis of a Chinese pedigree of type 2 diabetes complicated with interstitial lung disease.Front. Endocrinol. (Lausanne)202313105020010.3389/fendo.2022.105020036733806
     [Google Scholar]
  21. ChenA. KarwoskiR.A. GieradaD.S. BartholmaiB.J. KooC.W. QuantitativeC.T. Quantitative CT analysis of diffuse lung disease.Radiographics2020401284310.1148/rg.202019009931782933
     [Google Scholar]
  22. ObertM. KampschulteM. LimburgR. BarańczukS. KrombachG.A. Quantitative computed tomography applied to interstitial lung diseases.Eur. J. Radiol.20181009910710.1016/j.ejrad.2018.01.01829496086
     [Google Scholar]
  23. ArianiA ImperatoriA CastiglioniM Quantitative computed tomography detects interstitial lung diseases proven by biopsy.Sarcoidosis. Vasc. Diffuse. Lung. Dis2018351162010.36141/svdld.v35i1.6537
     [Google Scholar]
  24. ReaG SverzellatiN BocchinoM Beyond visual interpretation: Quantitative analysis and artificial intelligence in interstitial lung disease diagnosis "Expanding Horizons in Radiology".Diagnostics20231314233310.3390/diagnostics13142333
     [Google Scholar]
  25. WangD. MaY. TongX. ZhangY. FanH. Diabetes mellitus contributes to idiopathic pulmonary fibrosis: A review from clinical appearance to possible pathogenesis.Front. Public Health2020819610.3389/fpubh.2020.0019632582606
     [Google Scholar]
  26. SonodaN. MorimotoA. TatsumiY. AsayamaK. OhkuboT. IzawaS. OhnoY. A prospective study of the impact of diabetes mellitus on restrictive and obstructive lung function impairment: The Saku study.Metabolism201882586410.1016/j.metabol.2017.12.00629288691
     [Google Scholar]
  27. YaribeygiH. SathyapalanT. AtkinS.L. SahebkarA. Molecular mechanisms linking oxidative stress and diabetes mellitus.Oxid. Med. Cell. Longev.2020202011310.1155/2020/860921332215179
     [Google Scholar]
  28. JagadapillaiR. RaneM. LinX. RobertsA. HoyleG. CaiL. GozalE. Diabetic microvascular disease and pulmonary fibrosis: The contribution of platelets and systemic inflammation.Int. J. Mol. Sci.20161711185310.3390/ijms1711185327834824
     [Google Scholar]
  29. ÖzşahinK. TuğrulA. MertS. YükselM. TuğrulG. Evaluation of pulmonary alveolo-capillary permeability in Type 2 diabetes mellitus.J. Diabetes Complications200620420520910.1016/j.jdiacomp.2005.07.00316798470
     [Google Scholar]
  30. ZhengH. WuJ. JinZ. YanL.J. Potential biochemical mechanisms of lung injury in diabetes.Aging Dis.20178171610.14336/AD.2016.062728203478
     [Google Scholar]
  31. MaanH.B. MeoS.A. Al RouqF. MeoI.M.U. GacuanM.E. AlkhalifahJ.M. Effect of Glycated Hemoglobin (HbA1c) and duration of disease on lung functions in type 2 diabetic patients.Int. J. Environ. Res. Public Health20211813697010.3390/ijerph1813697034209922
     [Google Scholar]
  32. HaraguchiM ShimuraS HidaW ShiratoK Pulmonary function and regional distribution of emphysema as determined by high-resolution computed tomography.Respiration199865212515910.1159/000029243
     [Google Scholar]
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CT Quantitative Analysis in Evaluating Type 2 Diabetes Mellitus Complicated with Interstitial Lung Abnormalities
Curr. Med. Imaging 21, E15734056343395 (2025); https://doi.org/10.2174/0115734056343395250526140343
/content/journals/cmir/10.2174/0115734056343395250526140343
/content/journals/cmir/10.2174/0115734056343395250526140343
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  • Article Type:     Research Article
Keyword(s): Computed tomography; Hemoglobin A1c; Interstitial lung abnormalities; Lung damage; Quantitative analysis; Type 2 diabetes mellitus

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