Functional Clustering of Systolic Blood Pressure and Frontal Brain Oxygenation During an Active Stand Test in the Irish Longitudinal Study on Aging (TILDA): A Comparison of Tissue Saturation Index Versus Absolute Oxygenated Hemoglobin Concentration Approaches

Understanding the clinical implications of combined patterns of blood pressure and brain oxygenation dynamics during the transition from lying to standing remains limited. These dynamics can be investigated noninvasively in humans through a standardized active stand (AS) test, which involves continuous monitoring of blood pressure using digital artery photoplethysmography and frontal brain oxygenation using near-infrared spectroscopy (NIRS). While NIRS output is commonly assessed in terms of the tissue saturation index (TSI), it can also be quantified by the absolute concentration of oxygenated hemoglobin (O2Hb). This study aimed to explore functional clustering of systolic blood pressure (sBP) and frontal brain oxygenation during the AS test within the Irish Longitudinal Study on Aging (TILDA), comparing the health correlates of TSI and O2Hb clustered with the K-shape algorithm. A total of 2793 participants from TILDA wave three were included, with a mean age of 64.5 years and 46.9% being male. Both clustering methodologies revealed statistically significant associations with participants’ characteristics, including age, sex, body mass index (BMI), pulse wave velocity, cardiovascular medication usage, and usual gait speed. Notably, the TSI_sBP clustering approach uniquely captured variations in the mini-mental state examination (MMSE) cognitive score and history of cardiovascular disease, whereas the O2Hb_sBP clustering method specifically identified variations in poststand orthostatic intolerance (OI) symptoms and future mortality. It was revealed that, when coupled with sBP signal, O2Hb information was associated with a clinically important longitudinal outcome, whereas TSI was not. However, external validation of this finding is warranted to confirm its robustness and generalizability.