Browsing by Author "Muniz, Graciela"

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    Allostatic load calculation in a Chilean older-adults cohort reveals the need for age-specific clinical thresholds
    (2025) Buller, Ingrid; Muniz, Graciela; Lillo, Patricia; Riquelme, Patricio; Demanet, Michele; Saguez, Rodrigo; Damm, Francisca; Salech, Felipe; Gonzalez, Christian; Court, Felipe; Thumala, Daniela; Slachevsky Chonchol, Andrea
    Only few studies of Allostatic Load (AL) have been conducted in Latin-American cohorts. Here, we replicated a recently proposed comprehensive algorithm (ALCS) to derive an AL index in a Chilean cohort of cognitive healthy adults. Using data from the GERO cohort (n = 166, age 76.92 ± 5.07 years) we generated an ALCS AL index through and compared for model fit with a purely sample distribution l algorithm (ALES). ALCS largely improved when some clinical values were corrected for ages ≥65, although failed to achieve sufficient goodness of fit. A final inclusion of an additional inflammatory biomarker resulted in further improvement of ALES, but not on ALCS. The ALES algorithm, which do not include clinical thresholds, achieved satisfactory fit after correcting for sample disparities between sexes. This suggests the need to consider the suitability of clinical thresholds developed in younger populations and the need to develop specific thresholds for assessing older adults.
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    The impacts of social determinants of health and cardiometabolic factors on cognitive and functional aging in Colombian underserved populations
    (2023) Santamaria, Hernando; Moguilner, Sebastian; Rodriguez, Odir; Botero, Felipe; Pina, Stefanie; O’Donovan, Gary; Albala, Cecilia; Matallana, Diana; Schulte, Michael; Slachevsky Chonchol, Andrea; Yokoyama, Jennifer; Possin, Katherine; Ndhlovu, Lishomwa; Al‑Rousan, Tala; Corley, Michael; Kosik, Kenneth; Muniz, Graciela; Miranda, J. Jaime; Ibanez, Agustin
    Global initiatives call for further understanding of the impact of inequity on aging across underserved populations. Previous research in low- and middle-income countries (LMICs) presents limitations in assessing combined sources of inequity and outcomes (i.e., cognition and functionality). In this study, we assessed how social determinants of health (SDH), cardiometabolic factors (CMFs), and other medical/social factors predict cognition and functionality in an aging Colombian population. We ran a cross-sectional study that combined theory- (structural equation models) and data-driven (machine learning) approaches in a population-based study (N = 23,694; M = 69.8 years) to assess the best predictors of cognition and functionality. We found that a combination of SDH and CMF accurately predicted cognition and functionality, although SDH was the stronger predictor. Cognition was predicted with the highest accuracy by SDH, followed by demographics, CMF, and other factors. A combination of SDH, age, CMF, and additional physical/psychological factors were the best predictors of functional status. Results highlight the role of inequity in predicting brain health and advancing solutions to reduce the cognitive and functional decline in LMICs.

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