s NEUROSCIENCE APPLIED 1 (2022) 100112 References [1] Jones, G., Strugnell, S.A., DeLuca, H.F., 1998. Understanding of the molecular actions of vitamin D. Physiol. Rev. 78(4), 1193-1231. [2] Marazziti, D., Parra, E., Palermo, S., Barberi, F.M., Buccianelli, B., Ricciardulli, S., Cappelli, A., Mucci, F., Dell’Osso, L., 2021. Vitamin D: a pleiotropic hormone with possible psychotropic activities. Curr. Med. Chem. 28(19), 3843-3864. No conflict of interest doi: https://doi.org/10.1016/j.nsa.2022.100440 P.0366 NEUROSCIENCE APPLIED 1 (2022) 100112 100441 The thin EEG line between depression and borderline personality disorder J. Vukojevi c , D. Mulc , J. Eda , K. Friganovi c , P. Bre ci c , M. Cifrek , D. Vidovi c . 1 University Psychiatric Hospital Vrap ce, Department for Urgent psychiatry, Zagreb, Croatia; 2 Faculty of Electrical Engineering and Computing, Department for Electronic systems and information processing, Zagreb, Croatia; 3 University Psychiatric Hospital Vrap ce, Department for Affective disorders, Zagreb, Croatia; 4 University Psychiatric Hospital Vrap ce, Department for Sleep disorders, Zagreb, Croatia In everyday clinical practice, there is an ongoing debate about the nature of the major depressive disorder (MDD) in patients with borderline personality disorder (BPD). However, the underlying research does not distinguish between those two entities. Both old and new theories suggest that depression experienced in BPD is shaped by a negative sense of the self and pronounced dependency on others. In addition, there are the facts that there is a lifetime prevalence of dysthymia ranging between 12% and 39% in BPD patients; and that BPD patients also report a history of depression in up to 83% of cases. The notion that depression can be a distinct disorder but also a symptom in other psychopathologies was the underlying hypothesis in this study as we tried to delineate those two entities using electroencephalography (EEG) recordings and advanced machine learning methods. This study used EEG recordings from adult (aged 18) patients diagnosed with major depressive disorder with or without a comorbid diagnosis of BPD, determined by an experienced psychiatrist and according to the ICD-10 criteria. The EEG recordings were taken as a part of a standardized diagnostic procedure in the first two weeks of the patient's hospitalization. For each of the five primary characteristic EEG waves (delta, theta, alpha, beta, and gamma), features were selected and extracted from each of the 19 available EEG channels, creating a total of 475 features per subject. The training set consisted of MDD patients and the same number of patients with comorbid BPD and MDD. At the same time, the remaining data were utilized to further test and validate the machine learning model's accuracy on unseen data. The used algorithms, developed solely for this purpose, could not differentiate those two entities, meaning that patients suffering from MDD did not have significantly different EEG in terms of patients diagnosed with MDD and BPD, respecting the given data and methods used. In other words, our methodological approach did not yield positive results as it could not tell apart if the depression was a part of BPD or was it a sole diagnosis. Furthermore, we did not find any specific signal characteristic that could be attributed to BPD patients with MDD. To our knowledge, this is the first study that tried to disentangle the similarities betweenMDD patients, with or without BPD, using EEG recordings and advanced machine learning techniques. Although our study did not yield any positive results, the notion that we did not find an algorithm capable of differentiating between those two diagnoses is also something to consider further when discussing these two entities. Furthermore, by increasing the data set's clinical sensitivity and the spatiotemporal specificity of the EEG recordings, one could have a more sensitive diagnostic approach when using EEG recordings. No conflict of interest doi: https://doi.org/10.1016/j.nsa.2022.100441 P.0367 NEUROSCIENCE APPLIED 1 (2022) 100112 100442 Type of pre-existing mental disorder determines changes and levels of anxiety and depressive symptoms during the COVID-19 pandemic A. Monistrol-Mula , M. F elez-Nobrega , M.V. Moneta , E. Condominas , G. Vilagut , J. Dom enech-Abella , M. Philippe , P. Crist obal-Narv aez , B. Olaya , J. Alonso , J.M. Haro . 1 Parc 170 Sanitari Sant Joan de D eu, Research and Innovation, Sant Boi de LlobregatBarcelona, Spain; 2 Instituto de Salud Carlos III, Centre for Biomedical Research on Mental Health CIBERSAM, Madrid, Spain; 3 Institut de Recerca Sant Joan de Deu IRSJD, Epidemiology of mental disorders and ageing, Barcelona, Spain; 4 Hospital del Mar Medical Research Institute IMIM, Health Services Research Unit, Barcelona, Spain; 5 Instituto de Salud Carlos III, Centre for Biomedical Research on Publich Health CIBERESP, Madrid, Spain; 6 Universitat Pompeu Fabra UPF, Department of Medicine and Life Sciences, Barcelona, Spain; 7 Universitat de Barcelona UB, Departament de Medicine, Barcelona, Spain Background: Prevalence of mental health symptoms have varied across the COVID-19 pandemic, notably increasing during the early phase of the pandemic [1] and decreasing to pre-pandemic levels by mid-2020. [2] People with preexisting mental disorders are especially vulnerable to this mental health impact, but some studies suggest that this impact might be different according to the type of pre-existing disorder. [3], [4] However, these studies are based only on the first phase of the pandemic. Thus, we aim to examine whether the type of preexisting mental disorder influenced changes and levels of anxiety and depressive symptoms from mid-2020 to early 2021. Methods: Data from two assessments (June-2020 and February/March-2021) of the MINDCOVID study (Spanish general population) were analysed. Outcome variables included anxiety and depressive symptoms, measured with GAD-7 and PHQ-8 questionnaires, respectively. Pre-existing mental conditions were assessed using the CIDI checklist, which screens for self-reported lifetime depressive disorder, bipolar disorder, anxiety disorders, panic attacks, alcohol and drug use disorders, and other mental disorders. A 4-level variable was created: no preexisting disorder, only mood disorders (depressive and/or bipolar disorders), only anxiety disorders (including crisis and/or panic attacks) and comorbid D+A (depressive and anxiety disorders). Those with other disorders were excluded due to the small sample size. The included covariates were age and sex. Changes in anxiety and depressive symptoms between baseline and follow-up were assessed with the paired samples Wilcoxon test. Tobit regression models were conducted to test associations between type of pre-existing disorder and anxiety and depressive symptoms in follow-up, adjusting for age, sex and basal levels of anxiety or depression. Results: Final sample included 1,942 participants (mean age 49.6yrs, 16.7; 51.7% females). The 3.6% of individuals reported pre-existing mood disorders, the 20.4% pre-existing anxiety and the 9% comorbid D+A. Anxiety symptoms increased in all groups except for those with pre-existing mood conditions. Depressive symptoms only increased in those without pre-existing mental disorders and in those with pre-existing anxiety. The adjusted model showed that, compared to those with no pre-existing mental conditions, having pre-existing anxiety (0.70, SE: 0.27, p1⁄40.009) and pre-existing comorbid D+A (2.00, SE: 0.37, p<0.001) was significantly associated with higher levels of anxiety symptoms. No significant associations with anxiety symptoms were found for those with a pre-existing mood disorder (1.01, SE: 0.55, p1⁄40.067). Furthermore, having pre-existing anxiety (0.94, SE: 0.28, p1⁄40.001), pre-existing mood disorders (1.60, SE: 0.59, p1⁄40.007) and comorbid D+A (1.89, SE: 0.40, p<0.001) was significantly associated with higher levels of depressive symptoms. For both outcomes, those with comorbid D+A were the group with the strongest associations. Conclusions: Changes in anxiety and depressive symptoms from mid-2020 to early 2021 are not homogeneous across type of pre-existing mental disorders, being those with comorbid D+A at a higher risk of suffering the mental health impact of the pandemic. Further longitudinal studies are needed to determine the complete courses of mental health symptoms during the whole duration of the COVID-19 pandemic, considering the pre-existing mental conditions. References [1]K. M. Schafer, A. Lieberman, A. C. Sever, and T. Joiner, 2022. Prevalence rates of anxiety, depressive, and eating pathology symptoms between the preand peri-COVID-19 eras: A meta-analysis. Journal of Affective Disorders 298, 364372. [2]E. Robinson, A. R. Sutin, M. Daly, and A. Jones, 2022. A systematic review and meta-analysis of longitudinal cohort studies comparing mental health before versus during the COVID-19 pandemic in 2020. Journal of Affective Disorders 296, 567-576. [3]V. R. Tamsyn E et al., 2020. Mental health status of individuals with a mood-disorder during the COVID-19 pandemic in Australia: Initial results from the COLLATE project. Journal of affective disorders 275, 69–77 [4]A. Bendau, J. Plag, S. Kunas, S. Wyka, A. Str€ ohle, and M. B. Petzold 2021. Longitudinal changes in anxiety and psychological distress, and associated risk and protective factors during the first three months of the COVID-19 pandemic in Germany. Brain and Behavior 11 (2) Conflict of interest Abstracts NEUROSCIENCE APPLIED 1 (2022) 100112s NEUROSCIENCE APPLIED 1 (2022) 100112 Disclosure statement: This work was supported by the Instituto de Salud Carlos III, Ministerio de Ciencia, e Innovaci on/FEDER, (grant number COV20/00711) and by the Centro de Investigaci on Biom edica Red de Salud Mental (CIBERSAM), Instituto de Salud Carlos III, Madrid, Spain. Additional support was receive

https://doi.org/10.1016/j.nsa.2022.100442

Type of pre-existing mental disorder determines changes and levels of anxiety and depressive symptoms during the COVID-19 pandemic

A better understanding of the relationship between insulin and inflammation may have important implications for the treatment and prevention of diabetes. Chronic inflammation and diabetes are associated with coronary artery disease (CAD) . That said, many studies found no correlation between insulin and C-reactive protein (CRP) , the most common marker of chronic inflammation. We hypothesize here that the association of insulin and CRP may differ in different patient groups and may be masked by factors such as age, fitness or diseases, in particular established CAD. We thus evaluated the correlates of insulin and CRP among healthy professional athletes (Group 1, mean age = 24 years (ranging from 18-35 years) , n=59) , and patients undergoing coronary angiography (27-88 years, n=1574) who were further divided into younger (<60 years) patients without sign. CAD (Group 2, mean age = 52 years, n=258) , younger patients with sign. CAD (Group 3, mean age = 52, n=287) and older (≥60 years) patients without sign. CAD (Group 4, mean age 69, n=407) as well as older patients with sign. CAD (Group 5, mean age 70, n=622) . CRP and insulin were positively correlated in athletes (r1=0.544, p<0.001) and in patients without sign. CAD (r2=0.194, p=0.002 and r4=0.134, p=0.007) but not in patients with sign. CAD (r3=-0.001, p=0.981 and r5=0.000, p=0.955) . In multivariate models including covariates BMI and age, CRP and insulin remained significantly associated in athletes (T1=4.7, p<0.001) and in patients without sign. CAD (T2+4=3.3, p=0.001) , but not in those with sign. CAD (T3+5=0.0, p=0.997) . We conclude that CRP and insulin correlate in young and fit subjects but also in older subjects who do not have sign. CAD. The presence of CAD may abrogate the association between inflammation and insulin, potentially due to the inflammatory state of atherosclerosis. This appears worthwhile to be considered in future screening and treatment approaches.
 
 
 A.Leiherer: None. P.Fraunberger: None. M.Sohm: None. M.Philippe: None. H.Drexel: None. M.Rinderer: None. A.Muendlein: None. C.H.Saely: None. A.Mader: None. L.Sprenger: None. M.Maechler: None. A.Vonbank: None. B.Larcher: None.


454-P: Association between Insulin and C-Reactive Protein: Data from Athletes and Coronary Angiography Patients

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Type of pre-existing mental disorder determines changes and levels of anxiety and depressive symptoms during the COVID-19 pandemic

454-P: Association between Insulin and C-Reactive Protein: Data from Athletes and Coronary Angiography Patients