The Significance of Relative Fat Mass in Chronic Obstructive Pulmonary Disease Prevalence and Severity: Evidences From Two Cohorts
Bibliographic record
Abstract
Tingting Tu,1,* Yiting Yu,2,* Yichuan Fan,3,* Xinran Li,4 Zihan Ye,4 Ruizi Xu,2 Yiran Bu,5 Xiuxiu Zhao,1 Xianjing Chen,1 Chunyan Liu,1 Beibei Yu,1 Yage Xu,1 Xiaodiao Zhang,1 Yiben Huang1 1Department of Respiratory and Critical Medicine, The Third Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, People’s Republic of China; 2Renji College, Wenzhou Medical University, Wenzhou, Zhejiang, People’s Republic of China; 3Alberta Institute, Wenzhou Medical University, Wenzhou, Zhejiang, People’s Republic of China; 4The Second School of Medicine, Wenzhou Medical University, Wenzhou, Zhejiang, People’s Republic of China; 5The First School of Medicine, School of Information and Engineering, Wenzhou Medical University, Wenzhou, Zhejiang, People’s Republic of China*These authors contributed equally to this workCorrespondence: Xiaodiao Zhang, Department of Respiratory Medicine, The Third Affiliated Hospital of Wenzhou Medical University, No. 108 Wansong Road, Wenzhou, Zhejiang, 325000, People’s Republic of China, Email xiaodiao_zhang@126.com Yiben Huang, Department of Respiratory and Critical Medicine, The Third Affiliated Hospital of Wenzhou Medical University, Wenzhou, People’s Republic of China, Email huangyiben@126.comBackground: Chronic obstructive pulmonary disease (COPD) poses a major global health burden with high morbidity. Relative fat mass (RFM), as a novel body fat measurement indicator, can reflect the distribution of body fat. This study aims to elucidate its associations with COPD prevalence and severity in two cohorts to enhance prevention and treatment strategies.Methods: We retrospectively investigated the medical records of 166 patients with COPD and the data of 2654 subjects from two cohorts. To explore the relative importance of factors in COPD prevalence and severity, we built an extreme gradient boosting (XGBoost) machine-learning model. Logistic regression models were used to assess the relationship between COPD and RFM, with subgroup analysis to clarify the difference across diverse subgroups. Furthermore, restricted cubic spline (RCS) curves were used to explore the exposure-response relationship.Results: Multivariate logistic regression analysis revealed a significant positive association between RFM and COPD prevalence (OR = 1.043, 95% CI: 1.004– 1.083, p = 0.030) and a negative association with COPD severity (OR = 0.892, 95% CI: 0.813– 0.978, p = 0.015). According to the RCS curves, there was no nonlinear association between RFM and COPD prevalence or severity (p for nonlinear = 0.703, p for nonlinear = 0.348).Conclusion: RFM was positively associated with the prevalence of COPD but inversely associated with its severity. Specifically, RFM predicted COPD prevalence more accurately in individuals aged 40– 60 and smokers, while it predicted COPD severity more effectively in those aged ≥ 60.Keywords: relative fat mass, chronic obstructive pulmonary disease, obesity, body mass index, body fat distribution
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".