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318 results on '"Ajnakina, Olesya"'

1. Predicting Risk of Dementia with Survival Machine Learning and Statistical Methods: Results on the English Longitudinal Study of Ageing Cohort

Author

Stamate, Daniel, Musto, Henry, Ajnakina, Olesya, and Stahl, Daniel

Subjects
Computer Science - Machine Learning
Abstract

Machine learning models that aim to predict dementia onset usually follow the classification methodology ignoring the time until an event happens. This study presents an alternative, using survival analysis within the context of machine learning techniques. Two survival method extensions based on machine learning algorithms of Random Forest and Elastic Net are applied to train, optimise, and validate predictive models based on the English Longitudinal Study of Ageing ELSA cohort. The two survival machine learning models are compared with the conventional statistical Cox proportional hazard model, proving their superior predictive capability and stability on the ELSA data, as demonstrated by computationally intensive procedures such as nested cross-validation and Monte Carlo validation. This study is the first to apply survival machine learning to the ELSA data, and demonstrates in this case the superiority of AI based predictive modelling approaches over the widely employed Cox statistical approach in survival analysis. Implications, methodological considerations, and future research directions are discussed., Comment: Henry Musto is joint first author

Published
2023

6. Resource profile and user guide of the Polygenic Index Repository

Author

Becker, Joel, Burik, Casper AP, Goldman, Grant, Wang, Nancy, Jayashankar, Hariharan, Bennett, Michael, Belsky, Daniel W, Karlsson Linnér, Richard, Ahlskog, Rafael, Kleinman, Aaron, Hinds, David A, Caspi, Avshalom, Corcoran, David L, Moffitt, Terrie E, Poulton, Richie, Sugden, Karen, Williams, Benjamin S, Harris, Kathleen Mullan, Steptoe, Andrew, Ajnakina, Olesya, Milani, Lili, Esko, Tõnu, Iacono, William G, McGue, Matt, Magnusson, Patrik KE, Mallard, Travis T, Harden, K Paige, Tucker-Drob, Elliot M, Herd, Pamela, Freese, Jeremy, Young, Alexander, Beauchamp, Jonathan P, Koellinger, Philipp D, Oskarsson, Sven, Johannesson, Magnus, Visscher, Peter M, Meyer, Michelle N, Laibson, David, Cesarini, David, Benjamin, Daniel J, Turley, Patrick, and Okbay, Aysu

Subjects
Epidemiology, Health Sciences, 2.6 Resources and infrastructure (aetiology), Aetiology, Data Analysis, Databases, Genetic, Genome-Wide Association Study, Humans, Multifactorial Inheritance, Polymorphism, Single Nucleotide, Polygenic score, Polygenic index, Repository, Measurement error, 23andMe Research Group, Biomedical and clinical sciences, Health sciences, Psychology
Abstract

Polygenic indexes (PGIs) are DNA-based predictors. Their value for research in many scientific disciplines is growing rapidly. As a resource for researchers, we used a consistent methodology to construct PGIs for 47 phenotypes in 11 datasets. To maximize the PGIs' prediction accuracies, we constructed them using genome-wide association studies-some not previously published-from multiple data sources, including 23andMe and UK Biobank. We present a theoretical framework to help interpret analyses involving PGIs. A key insight is that a PGI can be understood as an unbiased but noisy measure of a latent variable we call the 'additive SNP factor'. Regressions in which the true regressor is this factor but the PGI is used as its proxy therefore suffer from errors-in-variables bias. We derive an estimator that corrects for the bias, illustrate the correction, and make a Python tool for implementing it publicly available.

Published
2021

8. Cognitive performance at first episode of psychosis and the relationship with future treatment resistance: Evidence from an international prospective cohort study

Author

Millgate, Edward, Smart, Sophie E., Pardiñas, Antonio F., Kravariti, Eugenia, Ajnakina, Olesya, Kępińska, Adrianna P., Andreassen, Ole A., Barnes, Thomas R.E., Berardi, Domenico, Crespo-Facorro, Benedicto, D'Andrea, Giuseppe, Demjaha, Arsime, Di Forti, Marta, Doody, Gillian A., Kassoumeri, Laura, Ferchiou, Aziz, Guidi, Lorenzo, Joyce, Eileen M., Lastrina, Ornella, Melle, Ingrid, Pignon, Baptiste, Richard, Jean-Romain, Simonsen, Carmen, Szöke, Andrei, Tarricone, Ilaria, Tortelli, Andrea, Vázquez-Bourgon, Javier, Murray, Robin M., Walters, James T.R., and MacCabe, James H.

Published
2023
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9. Predicting Risk of Dementia with Survival Machine Learning and Statistical Methods: Results on the English Longitudinal Study of Ageing Cohort

Author

Stamate, Daniel, Musto, Henry, Ajnakina, Olesya, Stahl, Daniel, Rannenberg, Kai, Editor-in-Chief, Soares Barbosa, Luís, Editorial Board Member, Goedicke, Michael, Editorial Board Member, Tatnall, Arthur, Editorial Board Member, Neuhold, Erich J., Editorial Board Member, Stiller, Burkhard, Editorial Board Member, Tröltzsch, Fredi, Editorial Board Member, Pries-Heje, Jan, Editorial Board Member, Kreps, David, Editorial Board Member, Reis, Ricardo, Editorial Board Member, Furnell, Steven, Editorial Board Member, Mercier-Laurent, Eunika, Editorial Board Member, Winckler, Marco, Editorial Board Member, Malaka, Rainer, Editorial Board Member, Maglogiannis, Ilias, editor, Iliadis, Lazaros, editor, Macintyre, John, editor, and Cortez, Paulo, editor

Published
2022
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11. Clinical predictors of antipsychotic treatment resistance: Development and internal validation of a prognostic prediction model by the STRATA-G consortium

Author

Smart, Sophie E., Agbedjro, Deborah, Pardiñas, Antonio F., Ajnakina, Olesya, Alameda, Luis, Andreassen, Ole A., Barnes, Thomas R.E., Berardi, Domenico, Camporesi, Sara, Cleusix, Martine, Conus, Philippe, Crespo-Facorro, Benedicto, D'Andrea, Giuseppe, Demjaha, Arsime, Di Forti, Marta, Do, Kim, Doody, Gillian, Eap, Chin B., Ferchiou, Aziz, Guidi, Lorenzo, Homman, Lina, Jenni, Raoul, Joyce, Eileen, Kassoumeri, Laura, Lastrina, Ornella, Melle, Ingrid, Morgan, Craig, O'Neill, Francis A., Pignon, Baptiste, Restellini, Romeo, Richard, Jean-Romain, Simonsen, Carmen, Španiel, Filip, Szöke, Andrei, Tarricone, Ilaria, Tortelli, Andrea, Üçok, Alp, Vázquez-Bourgon, Javier, Murray, Robin M., Walters, James T.R., Stahl, Daniel, and MacCabe, James H.

Published
2022
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14. Development and initial evaluation of a clinical prediction model for risk of treatment resistance in first-episode psychosis: Schizophrenia Prediction of Resistance to Treatment (SPIRIT).

Author

Farooq, Saeed, Hattle, Miriam, Kingstone, Tom, Ajnakina, Olesya, Dazzan, Paola, Demjaha, Arsime, Murray, Robin M., Di Forti, Marta, Jones, Peter B., Doody, Gillian A., Shiers, David, Andrews, Gabrielle, Milner, Abbie, Nettis, Maria Antonietta, Lawrence, Andrew J., van der Windt, Danielle A., and Riley, Richard D.

Subjects
DECISION making, PROGNOSTIC models, PREDICTION models, MISSING data (Statistics), LOGISTIC regression analysis
Abstract

Background: A clinical tool to estimate the risk of treatment-resistant schizophrenia (TRS) in people with first-episode psychosis (FEP) would inform early detection of TRS and overcome the delay of up to 5 years in starting TRS medication. Aims: To develop and evaluate a model that could predict the risk of TRS in routine clinical practice. Method: We used data from two UK-based FEP cohorts (GAP and AESOP-10) to develop and internally validate a prognostic model that supports identification of patients at high-risk of TRS soon after FEP diagnosis. Using sociodemographic and clinical predictors, a model for predicting risk of TRS was developed based on penalised logistic regression, with missing data handled using multiple imputation. Internal validation was undertaken via bootstrapping, obtaining optimism-adjusted estimates of the model's performance. Interviews and focus groups with clinicians were conducted to establish clinically relevant risk thresholds and understand the acceptability and perceived utility of the model. Results: We included seven factors in the prediction model that are predominantly assessed in clinical practice in patients with FEP. The model predicted treatment resistance among the 1081 patients with reasonable accuracy; the model's C-statistic was 0.727 (95% CI 0.723–0.732) prior to shrinkage and 0.687 after adjustment for optimism. Calibration was good (expected/observed ratio: 0.999; calibration-in-the-large: 0.000584) after adjustment for optimism. Conclusions: We developed and internally validated a prediction model with reasonably good predictive metrics. Clinicians, patients and carers were involved in the development process. External validation of the tool is needed followed by co-design methodology to support implementation in early intervention services. [ABSTRACT FROM AUTHOR]

Published
2024
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15. First episode psychosis : looking backwards and forwards

Author

Ajnakina, Olesya, MacCabe, James Hunter, and Murray, Robin MacGregor

Subjects
616.89
Abstract

Introduction: Psychotic disorders are known for their wide variability in clinical and social outcomes beginning from illness onset and throughout their course. Our current lack of understanding of the origins for this heterogeneity is further compounded by dearth in knowledge on how patients come to the attention of mental health services and methodological incongruity across different studies. Employing samples of first episode psychosis (FEP) patients, the aims of this thesis were to: 1) look back on the pathways to care patients used to enter mental health services and the use of prodromal services in South London; and 2) examine trajectories of the psychotic disorders and potential predictors of their longitudinal outcomes. Methods: Two large samples of patients with FEP (i.e., GAP and EU-GEI) were utilised in this thesis. For the study 1, information on pathways to care undertaken prior to coming to generic services for FEP was extracted from the Biomedical Research Centre (BRC) Case Register Interactive Search (CRIS) system. For studies 2, 3 and 4, using electronic clinical records, extensive information in three domains-clinical, social and service uses-was collated over 4-5 years after contact with mental health services. Results: Only a small fraction of individuals (4.1%) who present with FEP to the main secondary mental health provider have previously been in contact with prodromal services and made a subsequent transition to psychotic disorder; 77% this sub-group of patients entered their pathway to care via referral from General Practice or other health professional. In contrast, 45% of FEP group without prior contact with the prodromal services made first contact with mental health services via emergency services and 18% of this group were referred by the criminal justice system. Further, combining the baseline schizophrenia diagnosis with five symptom dimensions (i.e., positive, negative, excited, disorganised/concrete and depressed dimensions) generated the best model fit for predicting time to first remission. During the 5-year follow up after first contact with mental health services, a higher proportion of Black African and Black Caribbean ethnicity had compulsory re-admissions and instances of police involvement during an admission to a psychiatric unit compared with White British ethnic group. Patients of Black African and Black Caribbean ethnicity did not differ from White British ethnic group in overall functional disability and illness severity, or frequency of remission or recovery during the follow up period. However, patients of Black ethnicity become increasing socially excluded as their illness progress. In a sample of first-episode schizophrenia spectrum patients, 35% of the sample met the criteria for treatment resistance (TR) at the end of the first 5 years of follow up. Of these TR patients, 70% of these were treatment resistant from illness onset. Those who subsequently developed TR were more likely to have an early illness onset (<20 years) compared to those with non-TR. The relationship between an early age of onset (<20 years) and TR was specific to patients of Black ethnicity and patients of male gender. Conclusions: Very few of those who come to FEP services come after being seen for an at-risk-mental state by prodromal services suggesting that the scope for reducing or delaying onset of psychosis by this means may still be limited. My results indicate that supplementing the baseline categorical schizophrenia diagnosis with ratings on five symptom dimensions improves the prediction of delayed treatment response as measured by time to first remission. Further, the longitudinal trajectory of psychosis in patients of Black ethnicity did not show greater clinical or functional deterioration than white patients. However, their course remains characterised by more compulsion, and longer periods of admission. Finally, I showed that for the majority of the TR group, lack of response to antipsychotic treatment is present from illness onset, necessitating a consideration for an earlier use of clozapine.

Published
2017

18. The genetic architecture of dog ownership : large-scale genome-wide association study in 97,552 European-ancestry individuals.

Author

Gong, Tong, Karlsson, Robert, Yao, Shuyang, Magnusson, Patrik K E, Ajnakina, Olesya, Steptoe, Andrew, Bhatta, Laxmi, Brumpton, Ben, Kumar, Ashish, Mélen, Erik, Lin, Keng-Han, Tian, Chao, Fall, Tove, Almqvist, Catarina, Gong, Tong, Karlsson, Robert, Yao, Shuyang, Magnusson, Patrik K E, Ajnakina, Olesya, Steptoe, Andrew, Bhatta, Laxmi, Brumpton, Ben, Kumar, Ashish, Mélen, Erik, Lin, Keng-Han, Tian, Chao, Fall, Tove, and Almqvist, Catarina

Abstract

Dog ownership has been associated with several complex traits and there is evidence of genetic influence. We performed a genome-wide association study of dog ownership through meta-analysis of 31,566 Swedish twins in five discovery cohorts and additional 65,986 European-ancestry individuals in three replication cohorts from Sweden, Norway, and the UK. Association test with >7.4 million single-nucleotide polymorphisms were meta-analyzed using a fixed effect model after controlling for population structure and relatedness. We identified two suggestive loci using discovery cohorts, which did not reach genome-wide significance after meta-analysis with replication cohorts. Single-nucleotide polymorphisms-based heritability of dog ownership using linkage disequilibrium score regression was estimated at 0.123 (CI 0.038-0.207) using the discovery cohorts and 0.018 (CI -0.002, 0.039) when adding in replication cohorts. Negative genetic correlation with complex traits including type 2 diabetes, depression, neuroticism and asthma was only found using discovery summary data. Furthermore, we did not identify any genes/gene-sets reaching even suggestive level of significance. This genome-wide association study does not, by itself, provide clear evidence on common genetic variants that influence the dog ownership among European-ancestry individuals.

Published
2024
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20. Personality traits and loneliness among older people in the UK: Cross-sectional and longitudinal analysis from the English Longitudinal Study of Ageing.

Author

Tapia-Munoz, Thamara, Ajnakina, Olesya, Fancourt, Daisy, and Steptoe, Andrew

Subjects
*LONELINESS, *PERSONALITY, *OLDER people, *CROSS-sectional method, *GENOME-wide association studies, *SOCIAL isolation
Abstract

Little research has analysed the relationship between personality traits and loneliness, considering polygenic risk scores (PGSs), social isolation, socioeconomic, and health factors. We used data from 4,892 older adults 52 years and older from the English Longitudinal Study of Ageing (ELSA). ELSA wave 5 (2010/2011) was our baseline, and wave 6 (2012/2013) to wave 9 (2018/2019) was the loneliness follow-up. Polygenic risk scores for loneliness were derived from genome-wide association studies. We conducted multiple linear regression and multilevel LMMs to analyse the cross-sectional and longitudinal relationship between personality traits and loneliness. Extroversion (Coef. = -.375; 95% CI:−.447 to.302), Neuroticism (Coef. =.557; 95% CI:.485–.629), Agreeableness (Coef. = −.188: 95% CI: −.273 to.103), Conscientiousness (Coef. = −.183; 95% CI: −.271 to.095), and Openness to Experience (Coef. = −.170; 95% CI: −.236 to.103) were associated with loneliness at baseline. Over eight years of follow-up, loneliness levels decreased on average. Only Extroversion was associated with the loneliness rate of decline (Coef. =.012; 95% CI:.002–.022) after potential confounders had been considered. An important task of health and social services is to identify older adults at risk of physical, mental, and cognitive health issues. The success of public health and clinical interventions to reduce loneliness could be informed by a consideration of personality profiles. [ABSTRACT FROM AUTHOR]

Published
2024
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21. Long-Term Course of Remission and Recovery in Psychotic Disorders.

Author

Tramazzo, Sara, Lian, Wenxuan, Ajnakina, Olesya, Carlson, Gabrielle, Bromet, Evelyn, Kotov, Roman, and Jonas, Katherine

Subjects
PSYCHOSES, SCHIZOPHRENIA, LONG-term health care, PEOPLE with schizophrenia, AUDITORY neuropathy, DISEASE progression
Abstract

Objective: Understanding prognosis is critical to anticipating public health needs and providing care to individuals with psychotic disorders. However, the long-term course of remission and recovery remains unclear. In this study, the most common trajectories of illness course are described for a cohort of individuals followed for 25 years since first admission for psychosis. Methods: Participants are from the Suffolk County Mental Health Project, an epidemiological study of first-admission psychosis. Data for the present study was collected from six follow-ups, with 311 individuals assessed at the 25-year follow-up. Common patterns of remission and recovery were assessed in the baseline cohort of 591 individuals and the subsample from the 25-year follow up. Results: In the baseline cohort and the 25-year subsample, the most common trajectory for individuals with schizophrenia spectrum disorders was no remission and no recovery. Among individuals with other psychotic disorders, in both the baseline and 25-year cohorts, the modal pattern was one of intermittent remission and recovery. Individuals with other psychotic disorders were more likely to experience stable remission (15.1%) and stable recovery (21.1%), outcomes that were rare among individuals with schizophrenia spectrum disorders (0% and 0.6%, respectively). Conclusions: The modal longitudinal pattern for individuals with other psychoses is one of multiple transitions into and out of symptomatic and functional recovery. Engagement in a long-term health care plan may help individuals detect and respond to these changes. Sustained remission and recovery are rare among people with schizophrenia spectrum disorders. Efforts should be directed toward developing more effective treatments for this population. [ABSTRACT FROM AUTHOR]

Published
2024
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32. Evidence-based umbrella review of 162 peripheral biomarkers for major mental disorders

Author

Carvalho, André F., Solmi, Marco, Sanches, Marcos, Machado, Myrela O., Stubbs, Brendon, Ajnakina, Olesya, Sherman, Chelsea, Sun, Yue Ran, Liu, Celina S., Brunoni, Andre R., Pigato, Giorgio, Fernandes, Brisa S., Bortolato, Beatrice, Husain, Muhammad I., Dragioti, Elena, Firth, Joseph, Cosco, Theodore D., Maes, Michael, Berk, Michael, Lanctôt, Krista L., Vieta, Eduard, Pizzagalli, Diego A., Smith, Lee, Fusar-Poli, Paolo, Kurdyak, Paul A., Fornaro, Michele, Rehm, Jürgen, and Herrmann, Nathan

Published
2020
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35. Development and Validation of Predictive Model for a Diagnosis of First Episode Psychosis Using the Multinational EU-GEI Case-control Study and Modern Statistical Learning Methods

Author

Hersenen-Medisch 1, Brain, Ajnakina, Olesya, Fadilah, Ihsan, Quattrone, Diego, Arango, Celso, Berardi, Domenico, Bernardo, Miguel, Bobes, Julio, De Haan, Lieuwe, Del-Ben, Cristina Marta, Gayer-Anderson, Charlotte, Stilo, Simona, Jongsma, Hannah E., Lasalvia, Antonio, Tosato, Sarah, Llorca, Pierre Michel, Menezes, Paulo Rossi, Rutten, Bart P., Santos, Jose Luis, Sanjuán, Julio, Selten, Jean Paul, Szöke, Andrei, Tarricone, Ilaria, D'Andrea, Giuseppe, Tortelli, Andrea, Velthorst, Eva, Jones, Peter B., Romero, Manuel Arrojo, La Cascia, Caterina, Kirkbride, James B., Van Os, Jim, O'Donovan, Michael, Morgan, Craig, Di Forti, Marta, Murray, Robin M., Hubbard, Kathryn, Stahl, Daniel, Hersenen-Medisch 1, Brain, Ajnakina, Olesya, Fadilah, Ihsan, Quattrone, Diego, Arango, Celso, Berardi, Domenico, Bernardo, Miguel, Bobes, Julio, De Haan, Lieuwe, Del-Ben, Cristina Marta, Gayer-Anderson, Charlotte, Stilo, Simona, Jongsma, Hannah E., Lasalvia, Antonio, Tosato, Sarah, Llorca, Pierre Michel, Menezes, Paulo Rossi, Rutten, Bart P., Santos, Jose Luis, Sanjuán, Julio, Selten, Jean Paul, Szöke, Andrei, Tarricone, Ilaria, D'Andrea, Giuseppe, Tortelli, Andrea, Velthorst, Eva, Jones, Peter B., Romero, Manuel Arrojo, La Cascia, Caterina, Kirkbride, James B., Van Os, Jim, O'Donovan, Michael, Morgan, Craig, Di Forti, Marta, Murray, Robin M., Hubbard, Kathryn, and Stahl, Daniel

Published
2023

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