Olof Akre

This project explores the bidirectional relationship between psychiatric disorders (ADHD, OCD, anxiety) and urinary problems in children. We aim to: (1) identify genetic factors in neuroendocrine pathways contributing to this co-occurrence

(2) determine how these genetic factors influence gene expression in relevant tissues

and (3) track how these conditions develop over time and are affected by environmental factors.Spanning five years, this project brings together experts in urology, psychiatry, and genetics from Karolinska Institutet and Mount Sinai. We will analyze data from Swedish registers, international cohorts (All of Us, ABCD Study), and Swedish Twin Registry. The first two years will focus on genetic analysis, the third year on gene expression, and the final two years on developmental trajectories and data integration. The project employs pathway-specific genetic risk scores, sex-stratified analyses, and longitudinal modeling.This research reframes urinary symptoms as potential markers of broader neuroendocrine dysfunction affecting both body and brain. By understanding how stress-response systems connect these conditions, we can identify children at highest risk and determine optimal timing for intervention. This approach targeting shared biological mechanisms will improve outcomes for these vulnerable children.

A main problem in the treatment of prostate cancer is that today's investigations do not identify who has a disease requiring treatment, which leads to extensive over- and under-treatment. We need better prognostic markers to better tailor treatments to patients. The problem is that prostate cancer often grows slowly and it is difficult to study prognosis over a long period of time. Therefore, we have used the National Prostate Cancer Registry to follow up 140,000 men for up to 20 years and were able to identify 1,500 men (cases) who died of their prostate cancer and 1,500 control subjects. We have requisitioned the diagnostic pathology slides of the cases and controls, scanned and reviewed them using modern guidelines. We have then extracted genetic material from the tissue from the biopsy samples and shown in pilot studies that genetic material can be studied in such old tissue. Now, throughout the material, we will identify molecular markers in the genome that can provide prognostic information. We will also use artificial intelligence to 'train' computers to recognize patterns in the pathology images associated with death. We then use mathematical models to develop a model that better distinguishes high-risk men from low-risk men. The aim of the project is to develop a new prognostic tool so that we can give the right treatment to the right patient. If we succeed in finding better prognostic markers, then with the help of the patient's various tumor data, we will be able to make a more accurate prediction of the risk of an individual patient dying from the disease during a certain period of time, and thus it will be easier to advise the patient on which treatment strategy is most suitable .