Background Multimorbidity, the presence of two or more conditions in one person, is common but studies are often limited to observational data and single datasets. We address this gap by integrating large-scale primary-care and genetic data from multiple studies to interrogate multimorbidity patterns and producing digital resources to support future research. Methods We defined chronic, common, and heritable conditions in individuals aged >= 65 years, using two large primary-care databases [CPRD (UK) N = 2,425,014 and SIDIAP (Spain) N = 1,053,640], and estimated heritability using the same definitions in UK Biobank (N = 451,197). We used logistic regression to estimate the co- occurrence of pairs of conditions in the primary care data. Linkage disequilibrium score regression was used to estimate genetic similarity between pairs of conditions. Meta-analyses were conducted across databases, and up to three sources of genetic data, for each pair of conditions. We classified pairs of conditions as across or within- domain based on the international classification of disease. Findings We identified 72 chronic conditions, with 43.6% of 2546 pairs showing higher co-occurrence than chance in primary care and evidence of shared genetics. Many across-domain pairs exhibited substantial shared genetics (e.g., iron deficiency anaemia and peripheral arterial disease: genetic correlation Rg = 0.45 [95% Confidence Intervals 0.27:0.64]). 33 pairs displayed negative genetic correlations, such as skin cancer and rheumatoid arthritis Rg = -0.14 [-0.21:-0.06]), due to potential adverse drug effects. Discordance between genetic and primary care data was also observed, e.g., abdominal aortic aneurysm and bladder cancer co-occurred in primary care but were not genetically correlated (Odds-Ratio = 2.23 [2.09:2.37], Rg = 0.04 [-0.20:0.28]) and schizophrenia and fibromyalgia were less likely to co-occur together in primary care but were positively genetically correlated (OR = 0.84 [0.75:0.94], Rg = 0.20 [0.11:0.29]). Interpretation Most pairs of chronic conditions show evidence of shared genetics, and co-occurrence in primary care, suggesting shared mechanisms. The identified patterns of shared genetics, negative correlations and discordance between genetic and observational data provide a foundation for future multimorbidity research. Funding UK Medical Research Council [MR/W014548/1]. Copyright (c) 2025 Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http:// creativecommons.org/licenses/by-nc-nd/4.0/).

The antibody response to SARS-CoV-2 does not follow the immunoglobulin isotype pattern of primary responses, conflicting with the current interpretation of COVID-19. METHODS: Prospective cohort study of 191 SARS-CoV-2 infection cases and 44 controls from the second wave of COVID-19. The study stratified patients by severity and analyzed the trajectories of SARS-CoV-2 antibodies and multiple immune variables. RESULTS: Isotype-specific antibody time course profiles to SARS-CoV-2 revealed a pattern of recall response in 94.2 % of cases. The time course profiles of plasmablasts, B cells, cTfh high-resolution subsets, and cytokines indicated a secondary response. The transcriptomic data showed that this cohort is strictly comparable to contemporary cohorts. CONCLUSIONS: In most cases, the immune response to SARS-CoV-2 is a recall response. This constitutes a favorable scenario for most COVID-19 cases to be subjected to immune imprinting by endemic coronavirus, which, in turn, can influence the immune response to SARS-CoV-2.

Non-alcoholic fatty liver disease (NAFLD) in the pediatric population has emerged as a significant health concern due to its alarming rise in prevalence. In children, the characteristics of the disease differ from those seen in adults. NAFLD may progress to more severe liver disease in children compared to adults with similar profiles. Liver biopsy remains the gold standard for diagnosis; its invasive nature and high cost limit its use as a first-line tool. Alternatively, magnetic resonance imaging (MRI) techniques, such as magnetic resonance imaging-estimated liver proton density fat fraction (MRI-PDFF), have shown a good correlation with the degree of histological steatosis, although their use is limited by high costs and limited accessibility. Controlled attenuation parameter (CAP), integrated with vibration-controlled transient elastography (VCTE) (FibroScan(®)), is a novel non-invasive, accessible, and effective method for diagnosing hepatic steatosis. In this article, we reviewed the existing literature on the diagnostic accuracy of CAP in pediatric NAFLD. The PubMed and EMBASE databases were searched. Seven relevant studies were identified, conducted in pediatric hospital populations with specific demographic characteristics. Two of these studies compared CAP with liver biopsy, one compared CAP with liver biopsy and MRI-PDFF, and the remaining four compared CAP with MRI. Overall, CAP proved to be accurate in detecting the presence or absence of fatty infiltration, positioning it as a promising tool to simplify the diagnosis of NAFLD in children. However, further studies in larger populations are needed to confirm these findings and facilitate its implementation in routine clinical practice.

Predicting health-related outcomes can help with proactive healthcare planning and resource management. This is especially important on the older population, an age group growing in the coming decades. Considering longitudinal rather than cross-sectional information from primary care electronic health records (EHRs) can contribute to more informed predictions. In this work, we developed prediction models using longitudinal EHRs to inform resource allocation. In this study, we developed deep-learning-based prognostic models to predict 1-year and 5-year all-cause mortality, nursing home admission, and home care need in people over 65 years old using all the longitudinal information from EHRs. The models included attention mechanisms to increase their transparency. EHRs were drawn from SIDIAP (primary care, Catalonia (Spain)) from 2010-2019. Performance on the test set was compared to that from baseline models using cross-sectional one-year history only. Data from 1,456,052 individuals over 65 years old were considered. Cohen’s kappa obtained using longitudinal data was 3.4-fold (1-year all-cause mortality), 10.3-fold (5-year all-cause mortality), 1.1-fold (5-year nursing home admission), and 1.2-fold (5-year home care need) higher than that obtained by the one-year history baseline models. Our models performed better than those not considering longitudinal data, especially when predicting further into the future. However, nursing home admission and home care need in the long term were harder to predict, suggesting their dependence on more abrupt changes. The attention maps helped to understand the predictions, enhancing model transparency. These prediction models can contribute to improve resource allocation in the general population of aging adults.