Helena Jorge1, Isabel C Duarte2, Bárbara R Correia3, Luísa Barros4, Ana Paula Relvas5, Miguel Castelo-Branco2,3. 1. PIDFIF* and Coimbra Institute for Biomedical Imaging and Translational Research, CIBIT/ICNAS, University of Coimbra, Coimbra-Lisboa, Portugal. 2. Coimbra Institute for Biomedical Imaging and Translational Research, CIBIT/ICNAS, University of Coimbra, Portugal. 3. Faculty of Medicine, Laboratory of Biostatistics and Medical Informatics, University of Coimbra, Portugal. 4. Endocrinology, Diabetes and Metabolism Department (SEMD), University and Hospital Center of Coimbra, Portugal. 5. Faculty of Psychology and Educational Sciences & Center for Social Studies, University of Coimbra, Portugal.
Abstract
BACKGROUND: Neurobehavioral decision profiles have often been neglected in chronic diseases despite their direct impact on major public health issues such as treatment adherence. This remains a major concern in diabetes, despite intensive efforts and public awareness initiatives regarding its complications. We hypothesized that high rates of low adherence are related to risk-taking profiles associated with decision-making phenotypes. If this hypothesis is correct, it should be possible to define these endophenotypes independently based both on dynamic measures of metabolic control (HbA1C) and multidimensional behavioral profiles. METHODS: In this study, 91 participants with early-stage type 1 diabetes fulfilled a battery of self-reported real-world risk behaviors and they performed an experimental task, the Balloon Analogue Risk Task (BART). RESULTS: K-means and two-step cluster analysis suggest a two-cluster solution providing information of distinct decision profiles (concerning multiple domains of risk-taking behavior) which almost perfectly match the biological partition, based on the division between stable or improving metabolic control (MC, N = 49) v. unstably high or deteriorating states (NoMC, N = 42). This surprising dichotomy of behavioral phenotypes predicted by the dynamics of HbA1C was further corroborated by standard statistical testing. Finally, the BART game enabled to identify groups differences in feedback learning and consequent behavioral choices under ambiguity, showing distinct group choice behavioral patterns. CONCLUSIONS: These findings suggest that distinct biobehavioral endophenotypes can be related to the success of metabolic control. These findings also have strong implications for programs to improve patient adherence, directly addressing risk-taking profiles.
BACKGROUND: Neurobehavioral decision profiles have often been neglected in chronic diseases despite their direct impact on major public health issues such as treatment adherence. This remains a major concern in diabetes, despite intensive efforts and public awareness initiatives regarding its complications. We hypothesized that high rates of low adherence are related to risk-taking profiles associated with decision-making phenotypes. If this hypothesis is correct, it should be possible to define these endophenotypes independently based both on dynamic measures of metabolic control (HbA1C) and multidimensional behavioral profiles. METHODS: In this study, 91 participants with early-stage type 1 diabetes fulfilled a battery of self-reported real-world risk behaviors and they performed an experimental task, the Balloon Analogue Risk Task (BART). RESULTS: K-means and two-step cluster analysis suggest a two-cluster solution providing information of distinct decision profiles (concerning multiple domains of risk-taking behavior) which almost perfectly match the biological partition, based on the division between stable or improving metabolic control (MC, N = 49) v. unstably high or deteriorating states (NoMC, N = 42). This surprising dichotomy of behavioral phenotypes predicted by the dynamics of HbA1C was further corroborated by standard statistical testing. Finally, the BART game enabled to identify groups differences in feedback learning and consequent behavioral choices under ambiguity, showing distinct group choice behavioral patterns. CONCLUSIONS: These findings suggest that distinct biobehavioral endophenotypes can be related to the success of metabolic control. These findings also have strong implications for programs to improve patient adherence, directly addressing risk-taking profiles.