Manolis Kogevinas’s research while affiliated with Ministry of Health, Madrid and other places

Background Long-COVID has mostly been investigated in clinical settings. We aimed to assess the risk, subtypes, persistence, and determinants of long-COVID in a prospective population-based study of adults with a history of SARS-CoV-2 infection in Catalonia. Methods We examined 2764 infected individuals from a population-based cohort (COVICAT) established before the pandemic and followed up three times across the pandemic (2020, 2021, 2023). We assessed immunoglobulin (Ig)G levels against SARS-CoV-2, clinical, vaccination, sociodemographic, and lifestyle factors. Long-COVID risk and subtypes were defined based on participant-reported symptoms and electronic health records. We identified a total of 647 long-COVID cases and compared them with 2117 infected individuals without the condition. Results Between 2021 and 2023, 23% of infected subjects developed long-COVID symptoms. In 56% of long-COVID cases in 2021, symptoms persisted for 2 years. Long-COVID presented clinically in three subtypes, mild neuromuscular, mild respiratory, and severe multi-organ. The latter was associated with persistent long-COVID. Risk was higher among females, participants under 50 years, of low socioeconomic status, severe COVID-19 infection, elevated pre-vaccination IgG levels, obesity, and prior chronic disease, particularly asthma/chronic obstructive pulmonary disease and mental health conditions. A lower risk was associated to pre-infection vaccination, infection after omicron became the dominant variant, higher physical activity levels, and sleeping 6–8 h. Vaccination during the 3 months post-infection was also protective against long-COVID. Conclusions Long-COVID persisted for up to 2 years in half of the cases, and risk was influenced by multiple factors.

Bipolar disorder is a leading contributor to the global burden of disease¹. Despite high heritability (60–80%), the majority of the underlying genetic determinants remain unknown². We analysed data from participants of European, East Asian, African American and Latino ancestries (n = 158,036 cases with bipolar disorder, 2.8 million controls), combining clinical, community and self-reported samples. We identified 298 genome-wide significant loci in the multi-ancestry meta-analysis, a fourfold increase over previous findings³, and identified an ancestry-specific association in the East Asian cohort. Integrating results from fine-mapping and other variant-to-gene mapping approaches identified 36 credible genes in the aetiology of bipolar disorder. Genes prioritized through fine-mapping were enriched for ultra-rare damaging missense and protein-truncating variations in cases with bipolar disorder⁴, highlighting convergence of common and rare variant signals. We report differences in the genetic architecture of bipolar disorder depending on the source of patient ascertainment and on bipolar disorder subtype (type I or type II). Several analyses implicate specific cell types in the pathophysiology of bipolar disorder, including GABAergic interneurons and medium spiny neurons. Together, these analyses provide additional insights into the genetic architecture and biological underpinnings of bipolar disorder.

We explored predictors of shift work adaptation and how it relates to disease risk biomarker levels. These analyses included 38 male, rotating shift workers, sampled twice at the end of a 3‐week night shift and a 3‐week day shift rotation. Participants collected all 24‐h urine voids, wore activity sensors, and responded to questionnaires during each shift. Using cosinor analysis, we derived the main period of urinary 6‐sulfatoxymelatonin (aMT6s) production. Adaptation was defined as the overlap between the main aMT6s production period and sleep period assessed with actigraphy. We used linear models to identify predictors of adaptation to each shift and assessed associations between adaptation profiles and hormone, cytokine, and metabolite biomarker levels. The median duration of overlap (adaptation) was 3.85 h (IQR 2.59–5.03) in the night and 2.98 (IQR 2.17–4.11) in the day shift. In the night shift, a later chronotype (coeff: −1.16, 95% CI −1.87, −0.45) and increased light at night (coeff: −0.97, 95% CI −1.76, −0.18) were associated with poorer adaptation, while longer sleep duration was associated with better adaptation (coeff: 0.46, 95% CI 0.04, 0.88). In the day shift, later sleep onset was associated with worse adaptation (coeff: −0.06, 95% CI −0.12, −0.01), while longer sleep duration was associated with better adaptation (coeff: 0.54, 0.26, 0.81). Results suggest higher androgen and inflammatory marker levels and lower levels of several metabolite markers among less adapted individuals. Chronotype, sleep, and light at night were all associated with night or day shift adaptation. Given the small sample size, results should be viewed as exploratory, but may inform interventions to optimize adaptation of rotating shift workers.

Genetic variation at the 19q13.3 KLK locus is linked with prostate cancer susceptibility in men. The non-synonymous KLK3 single nucleotide polymorphism (SNP), rs17632542 (c.536 T > C; Ile163Thr-substitution in PSA) is associated with reduced prostate cancer risk, however, the functional relevance is unknown. Here, we identify that the SNP variant-induced change in PSA biochemical activity mediates prostate cancer pathogenesis. The ‘Thr’ PSA variant leads to small subcutaneous tumours, supporting reduced prostate cancer risk. However, ‘Thr’ PSA also displays higher metastatic potential with pronounced osteolytic activity in an experimental metastasis in-vivo model. Biochemical characterisation of this PSA variant demonstrates markedly reduced proteolytic activity that correlates with differences in in-vivo tumour burden. The SNP is associated with increased risk for aggressive disease and prostate cancer-specific mortality in three independent cohorts, highlighting its critical function in mediating metastasis. Carriers of this SNP allele have reduced serum total PSA and a higher free/total PSA ratio that could contribute to late biopsy decisions and delay in diagnosis. Our results provide a molecular explanation for the prominent 19q13.3 KLK locus, rs17632542 SNP, association with a spectrum of prostate cancer clinical outcomes.

Cancer development is influenced by genetic factors and modifiable exposures. GWAS has identified genetic variants and developed of prediction models through Polygenic Risk Scores (PRS), but PRS alone has limitations for estimating cancer risk. This study assesses a novel PRS constellation approach that integrates Polygenic Risk Scores (PRS) from both lifestyle and genetic traits to enhance prediction models for colorectal, breast, and prostate cancers. The approach was developed using the UK Biobank dataset and validated in the independent GenRisk cohort. The model, incorporating sex and age, achieved AUCs of 0.74 for CRC, 0.65 for BC, and 0.75 for PC in the UK Biobank. Including tumor-related PRSs improved PC prediction but had limited impact on CRC and BC. Age and sex inclusion boosted CRC and PC model accuracy. However, GenRisk validation showed reduced AUCs and limited utility of lifestyle PRSs, with CRC and BC models achieving 0.62 and PC 0.56. Integrating lifestyle-related characteristics into PRS does not significantly enhance cancer-specific PRS prediction. However, PRSs for these traits show independent predictive power, highlighting the importance of considering lifestyle in cancer risk and the need for precision medicine to improve early detection.

Background 10% of postmenopausal breast cancer cases are attributed to a high body mass index (BMI). BMI underestimates body fat, particularly in older women, and therefore the cancer burden attributable to obesity may be even higher. However, this is not clear. CUN-BAE (Clínica Universidad de Navarra–Body Adiposity Estimator) is an accurate validated estimator of body fat, taking into account sex and age. The objective of this study was to compare the burden of postmenopausal breast cancer attributable to excess body fat calculated using BMI and CUN-BAE. Methods This case–control study included 1033 cases of breast cancer and 1143 postmenopausal population controls from the multicase–control MCC-Spain study. Logistic regression models were used to calculate odds ratios (ORs). The population attributable fraction (PAF) of excess weight related to breast cancer was estimated with both anthropometric measures. Stratified analyses were carried out for hormone receptor type. Results Excess body weight attributable to the risk of breast cancer was 23.0% when assessed using a BMI value ≥30 kg/m ² and 38.0% when assessed using a CUN-BAE value of ≥40% body fat. Hormone receptor stratification showed that these differences in PAFs were only observed in hormone receptor positive cases, with an estimated burden of 19.9% for BMI and 41.9% for CUN-BAE. Conclusion These findings suggest that the significance of excess body fat in postmenopausal hormone receptor positive breast cancer could be underestimated when assessed using only BMI. Accurate estimation of the cancer burden attributable to obesity is crucial for planning effective prevention initiatives.