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Deep learning (DL) models require extensive data to achieve strong performance and generalization. Deep generative models (DGMs) offer a solution by synthesizing data. Yet current approaches for tabular data often fail to preserve feature correlations and distributions during training, struggle with multi-metric hyperparameter selection, and lack comprehensive evaluation protocols. We address this gap with a unified framework that integrates training, hyperparameter tuning, and evaluation. First, we introduce a novel correlation- and distribution-aware loss function that regularizes DGMs, enhancing their ability to generate synthetic tabular data that faithfully represents the underlying data distributions. Theoretical analysis establishes stability and consistency guarantees. To enable principled hyper-parameter search via Bayesian optimization (BO), we also propose a new multi-objective aggregation strategy based on iterative objective refinement Bayesian optimization (IORBO), along with a comprehensive statistical testing framework. We validate the proposed approach using a benchmarking framework with twenty real-world datasets and ten established tabular DGM baselines. The correlation-aware loss function significantly improves the synthetic data fidelity and downstream machine learning (ML) performance, while IORBO consistently outperforms standard Bayesian optimization (SBO) in hyper-parameter selection. The unified framework advances tabular generative modeling beyond isolated method improvements. Code is available at: https://github.com/vuhoangminh/TabGen-Framework.

Serotonin and serotonin metabolism has for decades been understood as playing a critical role in mood disorders and has more recently also been implicated in brain tumour biology. However, in part due to the lack of direct investigation of genetic and epigenetic variation affecting serotonin pathways within human brain tissue this understanding has recently been challenged. We analysed genetic and epigenetic variation in the Monoamine oxidase A (MAOA) and serotonin transporter (5HTT) genes using 232 biobanked glioma tissue samples from 216 adult patients. We further examined the association between use of antidepressants (targeting serotonergic pathways), serotonin levels and methylation. In male patients, genetic variation in the MAOA gene was significantly associated with tissue serotonin levels. Further analysis identified five single nucleotide variants (SNVs) that may contribute to this association. In contrast, 5HTT variants were not statistically associated with serotonin pathway metabolites, nor were MAOA variants in females. Increased methylation at several 5HTT CpG sites was positively correlated with serotonin levels and negatively correlated with 5-HIAA levels. In males, one CpG site in the MAOA gene was negatively associated with the 5-HIAA/serotonin ratio, suggesting reduced enzymatic degradation of serotonin due to lower MAOA activity. Patients using antidepressants had lower tissue serotonin levels. In males, genetic variation in the MAOA gene was significantly associated with tissue serotonin levels, although this association was not mediated by methylation. Our result supports the notion that the MAOA and 5HTT genes are related to serotonin metabolism and that such metabolism is related to antidepressant use.

Key features: 

• The Northern Sweden Health and Disease Study (NSHDS) was initiated in the mid-1980s. The NSHDS is a population-based prospective longitudinal cohort comprising >140 000 participants in the two northernmost regions in Sweden, Norrbotten and Västerbotten, with >240 000 blood samples and 1.5 million person-years of follow-up.
• The NSHDS includes three sub-cohorts: the Västerbotten Intervention Programme (VIP), the expanded Northern Sweden Monitoring of Trends and Determinants of Cardiovascular Disease (MONICA) Study, and the Mammography Screening Project (MSP). The VIP is both a community-based cardiometabolic intervention programme encouraging healthy lifestyle (targeting individuals 40, 50, and 60 years of age), and a corresponding research cohort. The MONICA is an observational study focusing on cardiovascular disease and its associated risk factors, recruiting individuals aged 25–74 years. The MSP recruited women attending mammography during 1995–2006. The NSHDS median participation age is 50 years (53% women).
• Most participants contribute data on health, lifestyle, anthropometric measures, blood pressure, blood lipids, and glucose tolerance, along with research blood samples that are fractionated, frozen within an hour of collection, and stored at –80°C. Linkage to registries, clinical cohorts, and biological tissue archives facilitates studies of well-characterized participants (often combined with intervention studies).
• Collaborations are encouraged. Additional information can be found at: info.brs@umu.se; https://www.umu.se/en/biobank

This review explores the pivotal role of preanalytical variables in bringing liquid biopsy approaches into the clinic for brain tumors. Preanalytical variables encompass a range of critical issues, from blood sample collection and handling to the impact of tumor heterogeneity and patient-specific factors. These variables introduce challenges such as false positives, false negatives, and variability in the analysis of tumor signals, which can hinder the diagnostic and prognostic utility of liquid biopsies. Understanding the nuances of preanalytical variables is essential for the successful implementation of liquid biopsy in clinical settings. This paper delves into strategies aimed at mitigating the influence of preanalytical variables by emphasizing the importance of standardized sample collection protocols, optimized sample processing and storage, quality control measures, and the integration of multiple liquid biopsy modalities.

Purpose: Although sociodemographic factors such as socioeconomic status (SES), travel time to health care, cohabitation status, and region of residence are observed to influence incidence and survival for several types of cancers, it is unclear whether similar effects have been observed in patients with glioma. This study investigates whether these factors affect survival for glioma patients.

Methods: In this retrospective study, the Swedish National Quality Registry for Brain Tumors was used to identify 1,276 patients with glioma WHO grade I–IV for whom data were deposited between 2009 and 2013. The RISK North database, which links data from the National Cancer Quality Register with citizen demographic data from the Longitudinal Integration Database for Health Insurance and Labor Market Studies (LISA), the Total Population Registry (TPR), and the Geography Database (GD), was utilized to assess survival in patients with glioma in relation to education level, cohabitation status, travel time to regional hospitals, and region of residence.

Results: In the multivariable analysis, longer survival was observed among WHO grade III-IV glioma patients with higher education level (middle school (ref) HR: 1, high school HR: 0.81 CI [0.67–0.98], p = 0.033; university/college HR: 0.81 CI [0.66–1.00], p = 0.048). Survival was not associated with travel time, cohabitation status, or region of residence in the multivariable survival analysis.

Conclusion: Low education level was associated with reduced survival for patients with glioma WHO grade III and IV in multivariable survival analyses, but no differences in survival were found in relation to travel time, cohabitation status, or region of residence.

The aim of the present study was to investigate if use of antidepressants is related to the risk of developing lower (WHO grade 2-3) and higher grade (WHO grade 4) glioma. A registry based case-control study was performed using 1283 glioma cases and 6400 age-, sex- and geographically matched controls, diagnosed in Sweden 2009-2013. Conditional logistic regression was used to analyze whether Selective Serotonin Reuptake Inhibitors (SSRIs) or non-SSRIs were associated with the risk of developing lower- or higher-grade glioma in the study population. Our results show that use of antidepressant medication was not associated with the risk of developing glioma. We also performed a meta-analysis in which the dataset from the present study was combined with results from two previous epidemiological studies to answer the same questions. The meta-analysis showed a modest risk reduction of developing glioma in relation to antidepressant treatment (OR 0.90 [95% CI 0.83-0.97]), when all glioma subgroups and all forms of antidepressant medications were combined. In conclusion, it remains possible that antidepressants may have common monoaminergic mechanism(s) that reduce the risk of developing glioma.

Background: Depression and treatment with antidepressant medication is common in patients with malignant glioma. However, the extent to which antidepressants may affect the disease is not fully understood. Therefore, the purpose of the present study was to investigate possible associations between treatment with antidepressant medication and survival in glioma patients.

Methods: We performed a registry-based cohort study including 1231 patients with malignant glioma (WHO grade 2, 3 and 4) having undergone surgery, and 6400 matched controls without glioma. All data was extracted from the RISK North database, which contains information from multiple national population-based registries in Sweden.

Results: Treatment with antidepressants is more common in patients with malignant glioma (27%), compared to controls (16%), p<.001. Treatment with antidepressants after surgery for glioma was significantly associated with poorer survival. These effects were observed both for selective serotonin reuptake inhibitors (SSRIs) and non-SSRIs. In grade 4 glioma, SSRI treatment was associated with a HR of 3.32 (95% CI 2.69–4.10, p<.001), and non-SSRI treatment a HR of 3.54 (95% CI 2.52–4.99, p<.001), compared to glioma patients without antidepressants. In grade 2-3 glioma, the HR for SSRI treatment was 3.26 (95% CI 2.19–4.85, p<.001), and for non-SSRI treatment 7.71 (95% CI 4.22-14.12, p<.001).

Conclusions: Our results demonstrate a negative association between antidepressant medication and survival in glioma. Further research will be needed to clarify causation.

Gliomas are highly complex and metabolically active brain tumors associated with poor prognosis. Recent reports have found altered levels of blood metabolites during early tumor development, suggesting that tumor development could be detected several years before clinical manifestation. In this study, we performed metabolite analyses of blood samples collected from healthy controls and future glioma patients, up to eight years before glioma diagnosis, and on the day of glioma surgery. We discovered that metabolites related to early glioma development were associated with an increased energy turnover, as highlighted by elevated levels of TCA-related metabolites such as fumarate, malate, lactate and pyruvate in pre-diagnostic cases. We also found that metabolites related to glioma progression at surgery were primarily high levels of amino acids and metabolites of amino acid catabolism, with elevated levels of 11 amino acids and two branched-chain alpha-ketoacids, ketoleucine and ketoisoleucine. High amino acid turnover in glioma tumor tissue is currently utilized for PET imaging, diagnosis and delineation of tumor margins. By examining blood-based metabolic progression patterns towards disease onset, we demonstrate that this high amino acid turnover is also detectable in a simple blood sample. These findings provide additional insight of metabolic alterations during glioma development and progression.

Background: Polygenic risk scores (PRS) aggregate the contribution of many risk variants to provide a personalized genetic susceptibility profile. Since sample sizes of glioma genome-wide association studies (GWAS) remain modest, there is a need to efficiently capture genetic risk using available data.

Methods: We applied a method based on continuous shrinkage priors (PRS-CS) to model the joint effects of over 1 million common variants on disease risk and compared this to an approach (PRS-CT) that only selects a limited set of independent variants that reach genome-wide significance (P < 5 x 10(-8)). PRS models were trained using GWAS stratified by histological (10 346 cases and 14 687 controls) and molecular subtype (2632 cases and 2445 controls), and validated in 2 independent cohorts.

Results: PRS-CS was generally more predictive than PRS-CT with a median increase in explained variance (R-2) of 24% (interquartile range = 11-30%) across glioma subtypes. Improvements were pronounced for glioblastoma (GBM), with PRS-CS yielding larger odds ratios (OR) per standard deviation (SD) (OR = 1.93, P = 2.0 x 10(-54) vs. OR = 1.83, P = 9.4 x 10(-50)) and higher explained variance (R-2 = 2.82% vs. R-2 = 2.56%). Individuals in the 80th percentile of the PRS-CS distribution had a significantly higher risk of GBM (0.107%) at age 60 compared to those with average PRS (0.046%, P = 2.4 x 10(-12)). Lifetime absolute risk reached 1.18% for glioma and 0.76% for IDH wildtype tumors for individuals in the 95th PRS percentile. PRS-CS augmented the classification of IDH mutation status in cases when added to demographic factors (AUC = 0.839 vs. AUC = 0.895, P-Delta AUC = 6.8 x 10(-9)).

Conclusions: Genome-wide PRS has the potential to enhance the detection of high-risk individuals and help distinguish between prognostic glioma subtypes.