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<p>This study aimed to evaluate the diagnostic potential of combining <sup>18</sup>F-FDG PET and susceptibility-weighted imaging (SWI) to assess cerebral glucose metabolism and iron deposition patterns in Parkinson’s disease (PD), and to determine their correlations with clinical progression and diagnostic accuracy.&#xa0;Forty-nine PD patients and 70 age-/sex-matched healthy controls underwent standardized <sup>18</sup>F-FDG PET and SWI. Metabolic activity (SUVR) and SWI phase values were quantified in cortical/subcortical regions. Statistical analyses included Mann–Whitney U tests, Pearson/Spearman correlations, and ROC curve analysis to evaluate biomarker-clinical relationships and diagnostic performance.&#xa0;PD patients exhibited hypometabolism in frontal, parietal, and temporal cortices (<i>P</i> &lt; 0.05) and hypermetabolism in the putamen, globus pallidus, and cerebellum (<i>P</i> &lt; 0.05). Cortical hypometabolism correlated with Hoehn-Yahr (H-Y) stages (e.g., temporal lobe:&#xa0;<i>r</i> = − 0.405,&#xa0;<i>P</i> = 0.004) and UPDRS III scores (e.g., frontal cortex:&#xa0;<i>r</i> = − 0.364,&#xa0;<i>P</i> = 0.011). SWI revealed reduced phase values in the substantia nigra, red nucleus, and basal ganglia (<i>P</i> &lt; 0.001), with substantia nigra phase values strongly correlating with H-Y stages (<i>r</i> = −&#xa0;0.525) and UPDRS III scores (<i>r</i> = − 0.446). Multimodal integration of <sup>18</sup>F-FDG PET and SWI achieved superior diagnostic accuracy (AUC = 0.844) compared to single-modality models (PET: AUC = 0.777; SWI: AUC = 0.780,&#xa0;<i>P</i> &lt; 0.0001).&#xa0;The integration of <sup>18</sup>F-FDG PET and SWI enhances PD diagnosis by capturing complementary metabolic and iron deposition biomarkers. Cortical hypometabolism may precede subcortical iron accumulation, aligning with Braak staging theory. Limitations include cross-sectional design and technical constraints in SWI quantification. Future studies should validate these findings with longitudinal cohorts and advanced techniques like QSM.</p>

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The combination of the 18F-FDG PET and susceptibility-weighted imaging for diagnosis of cerebral glucose metabolism and iron deposition in Parkinson’s disease

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年6月6日

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  • Zhibing He,
  • Chao Yang,
  • Ling Zhou,
  • Shuang Li

摘要

This study aimed to evaluate the diagnostic potential of combining 18F-FDG PET and susceptibility-weighted imaging (SWI) to assess cerebral glucose metabolism and iron deposition patterns in Parkinson’s disease (PD), and to determine their correlations with clinical progression and diagnostic accuracy. Forty-nine PD patients and 70 age-/sex-matched healthy controls underwent standardized 18F-FDG PET and SWI. Metabolic activity (SUVR) and SWI phase values were quantified in cortical/subcortical regions. Statistical analyses included Mann–Whitney U tests, Pearson/Spearman correlations, and ROC curve analysis to evaluate biomarker-clinical relationships and diagnostic performance. PD patients exhibited hypometabolism in frontal, parietal, and temporal cortices (P < 0.05) and hypermetabolism in the putamen, globus pallidus, and cerebellum (P < 0.05). Cortical hypometabolism correlated with Hoehn-Yahr (H-Y) stages (e.g., temporal lobe: r = − 0.405, P = 0.004) and UPDRS III scores (e.g., frontal cortex: r = − 0.364, P = 0.011). SWI revealed reduced phase values in the substantia nigra, red nucleus, and basal ganglia (P < 0.001), with substantia nigra phase values strongly correlating with H-Y stages (r = − 0.525) and UPDRS III scores (r = − 0.446). Multimodal integration of 18F-FDG PET and SWI achieved superior diagnostic accuracy (AUC = 0.844) compared to single-modality models (PET: AUC = 0.777; SWI: AUC = 0.780, P < 0.0001). The integration of 18F-FDG PET and SWI enhances PD diagnosis by capturing complementary metabolic and iron deposition biomarkers. Cortical hypometabolism may precede subcortical iron accumulation, aligning with Braak staging theory. Limitations include cross-sectional design and technical constraints in SWI quantification. Future studies should validate these findings with longitudinal cohorts and advanced techniques like QSM.