Towards a multifactorial approach for prediction of bipolar disorder in at risk populations

Background gh prevalence, recurrence rate, chronicity, and illness burden in bipolar disorder (BD) are well documented. Moreover, insufficient response with conventional pharmacological and manual-based psychosocial interventions, as well as evidence of illness progression and acceleration, invite the need for early detection and primary prevention. s we comprehensively review extant studies reporting on a bipolar prodrome. The overarching aim is to propose a predictive algorithm (i.e. prediction of BD in at-risk populations) integrating genetic (i.e. family history), environmental (e.g. childhood maltreatment) and biological markers (i.e. BDNF, inflammatory and oxidative stress markers). Computerized databases i.e. Pubmed, PsychInfo, Cochrane Library and Scielo were searched using the followed terms: bipolar disorder cross-referenced with prodromal, preclinical, at risk mental states, clinical high risk, ultra high risk, biomarkers, brain-derived neurotrophic factor, inflammation, cytokines, oxidative stress, prediction and predictive model. s ble evidence indicates that a prodrome to bipolar disorder exists. Commonly encountered features preceding the onset of a manic episode are affective lability, irritability, anger, depression, anxiety, substance use disorders, sleep disorders, as well as disturbances in attention and cognition. Non-specificity and insufficient sensitivity have hampered the development of an adequate prediction algorithm. tions tions include biases associated with retrospective studies, poor characterization of clinical high risk, inadequacy of prospective studies regarding sample selection and absence of specificity of risk states. sion pose a hypothetical prediction algorithm that is combinatorial in approach that attempts to integrate family history, early adversity, and selected biomarkers.