This study applies the attractor reconstruction method to photoplethysmography (PPG) signals to explore their potential in preventive health monitoring. Conventional PPG analysis methods typically rely on beat-by-beat segmentation and feature point detection, both of which are highly sensitive to noise, waveform distortion, and individual variability. To address these limitations, we applied attractor reconstruction trajectories (ACTs) as an alternative analytical approach to PPG signal analysis. ACTs utilize the entire waveform to reconstruct signal dynamics, facilitating a more holistic and noninvasive assessment of vascular health. Fifty-five participants aged 20-89 years, comprising healthy individuals and patients with chronic liver or kidney disease were studied. Three-dimensional ACTs were constructed from fingertip PPG signals and their first (DPPG) and second (SDPPG) derivatives. ACT morphologies were classified using a template-matching approach, and their distributions were compared across age groups and signal types. ACT classification revealed distinct structural characteristics across signal types. For PPG ACTs, Approximate Triangle (40.5%) and Three Points (29.5%) were the most frequent patterns, while Three Points pattern was observed only in younger and healthy middle-aged groups and exhibited the lowest uncertainty rate (14.3%). DPPG ACTs showed the greatest structural diversity, with Radiating pattern predominating in younger participants and a clear transition toward dominance of Three-lobed pattern in older groups (60.7% in participants aged 80-89 years). In contrast, SDPPG ACTs were dominated by Irregular pattern (48.6%) across all age groups and showed the highest variability and uncertainty (17.6-20.7%). These results demonstrate that ACTs can capture age-dependent differences in PPG-derived signals, with DPPG ACTs exhibiting the clearest age-related structural transitions. While limitations remain regarding sample size and classification automation, the findings support the potential of ACT-based analysis as a complementary framework for noninvasive assessment of vascular aging using PPG signals.