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Background <p>Preserved Ratio Impaired Spirometry (PRISm) is a condition characterized by a forced expiratory volume in one second (FEV1) to forced vital capacity (FVC) ratio ≥ 0.70, yet FEV1 is below 80% of the predicted value. Recent studies suggest that visceral lipid accumulation may play a role in respiratory health, but the relationship between visceral fat indices and PRISm remains under explored.</p> <p>To investigate the relationship between indicators of visceral lipid accumulation and PRISm in US adults using data from the National Health and Nutrition Examination Survey (NHANES).</p> Methods <p>Data from three consecutive cycles of NHANES (2007-2012) were analyzed. Logistic regression and generalized additive models (GAM) were used to assess the relationship between various indices of visceral lipid accumulation and PRISm. The data was randomly divided into training (70%) and validation (30%) sets. Least Absolute Shrinkage and Selection Operator (LASSO) regression identified key factors for a nomogram model, which was evaluated using receiver operating characteristic(ROC) curves.</p> Results <p>A significant positive association was found between visceral fat indices and PRISm risk (<i>P</i> &lt; 0.05). GAM analysis revealed a nonlinear relationship, with PRISm risk initially plateauing or slightly decreasing at lower levels of visceral fat indices before rising above a certain threshold (<i>P</i> for non-linearity &lt; 0.05). The nomogram model demonstrated fair discriminative ability, with the area under the ROC curve (AUC) of 0.785 (95%CI: 0.771-0.800) in the training set and 0.769 (95%CI: 0.747-0.791) in the validation set.</p> Conclusions <p>This study establishes a robust association between visceral fat indicators and PRISm, highlighting the significance of lipid indices in evaluating respiratory health. Clinical screening for visceral fat indicators could be essential for more precise clinical interventions.</p>

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Association between visceral lipid accumulation indicator and preserved ratio impaired spirometry in US adults: a population-based study from 2007 to 2012

  • 期刊论文

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  • 发表日期：

    2025年9月29日

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  • Zhehao Xu,
  • Jia Ding,
  • Ruiyun Liang,
  • Xiwei Huang,
  • Shuangfeng Xie

摘要

Background

Preserved Ratio Impaired Spirometry (PRISm) is a condition characterized by a forced expiratory volume in one second (FEV1) to forced vital capacity (FVC) ratio ≥ 0.70, yet FEV1 is below 80% of the predicted value. Recent studies suggest that visceral lipid accumulation may play a role in respiratory health, but the relationship between visceral fat indices and PRISm remains under explored.

To investigate the relationship between indicators of visceral lipid accumulation and PRISm in US adults using data from the National Health and Nutrition Examination Survey (NHANES).

Methods

Data from three consecutive cycles of NHANES (2007-2012) were analyzed. Logistic regression and generalized additive models (GAM) were used to assess the relationship between various indices of visceral lipid accumulation and PRISm. The data was randomly divided into training (70%) and validation (30%) sets. Least Absolute Shrinkage and Selection Operator (LASSO) regression identified key factors for a nomogram model, which was evaluated using receiver operating characteristic(ROC) curves.

Results

A significant positive association was found between visceral fat indices and PRISm risk (P < 0.05). GAM analysis revealed a nonlinear relationship, with PRISm risk initially plateauing or slightly decreasing at lower levels of visceral fat indices before rising above a certain threshold (P for non-linearity < 0.05). The nomogram model demonstrated fair discriminative ability, with the area under the ROC curve (AUC) of 0.785 (95%CI: 0.771-0.800) in the training set and 0.769 (95%CI: 0.747-0.791) in the validation set.

Conclusions

This study establishes a robust association between visceral fat indicators and PRISm, highlighting the significance of lipid indices in evaluating respiratory health. Clinical screening for visceral fat indicators could be essential for more precise clinical interventions.