Genomics (United Kingdom)

Protein-based molecular biomarkers play an important role in prognostic modeling and risk stratification in precision medicine. However, longitudinal survival studies involving high-dimensional biomarker data are frequently challenged by pervasive missingness and limited sample sizes, which can compromise model stability and interpretability. In this study, we present and evaluate a reproducible analytical pipeline for survival risk prediction that integrates established methods for missing data handling, feature selection, and time-to-event modeling. Missing values are addressed using an unsupervised random forest-based imputation approach that leverages internal covariate structure without incorporating outcome information, thereby reducing the risk of information leakage. Feature dimensionality is subsequently reduced using penalized Cox regression with the least absolute shrinkage and selection operator, followed by refinement and stability assessment using random survival forests to capture nonlinear effects and interactions. The final set of selected biomarkers is examined using univariate and multivariable Cox proportional hazards models to support clinical interpretability and risk stratification. Using a publicly available proteomic dataset from cancer patients, we demonstrate how this sequential modeling strategy can identify stable prognostic biomarkers while highlighting the challenges of overfitting in small-sample, high-dimensional survival settings. The proposed workflow serves as a practical and transparent framework for biomarker-driven survival analysis rather than a new statistical methodology.

Geriatric syndromes (GS) are complex conditions that affect older adults and often require multidisciplinary assessment. Natural language processing (NLP) has emerged as a promising tool for extracting relevant clinical information from unstructured text in electronic health records (EHRs). However, the application of NLP in detecting and monitoring GS remains an evolving area of research. This systematic review explores the role of NLP in the identification and analysis of GS, examining its applications, methodologies, and effectiveness. Furthermore, this review discusses the existing challenges, limitations, and future directions to advance NLP applications in the GS research. We conducted a systematic literature search across ten databases to identify studies that applied NLP to GS detection. Articles were screened using predefined inclusion and exclusion criteria, and relevant studies were evaluated for quality using PROBAST. Data were extracted on study characteristics, datasets, annotation processes, NLP approaches, performance metrics, population demographics, and clinical applications. A PRISMA flow diagram was used to illustrate the study selection process. A total of 65 studies were included, where the majority of the studies used traditional rule-based and machine learning approaches. Publicly available datasets were scarce, and most studies used their private dataset, leading to significant variability in data sources and formats. Annotation methodologies differed across studies, with minimal shared guidelines or standards, making direct comparisons challenging. Performance metrics varied across syndromes, with F1-score, precision, and recall as the most commonly reported. Key challenges included the lack of dataset uniformity, differences in annotation practices, and the absence of external validation. NLP has shown potential in GS analysis, particularly for the detection of syndromes and epidemiological research. However, the majority of studies only focused on one syndrome, and variability in dataset availability, annotation processes, and model performance present challenges to broader implementation. Future research should focus on improving the comprehensiveness of GS identification, dataset standardisation, enhancing model generalisability, and integrating NLP approaches into clinical workflows.

Rice endosperm protein hydrolysate B (REPH-B) is produced by enzymatic degradation of rice endosperm protein. REPH-B contains the following peptides: QAFEPIRSV, pEAFEPIRSV, and TNPWHSPRQGSF. A previous study reported that REPH-B has the ability to induce plasma acylated ghrelin secretion and increase food intake in animal models. In this study, we investigated the effect of REPH-B on appetite in healthy adults aged over 40 y who had a relatively low visual analogue scale (VAS) score for hunger. Forty-six healthy subjects who were aware that their appetite had decreased were enrolled. Subjects were randomly allocated to one of two groups. The influence of REPH-B on appetite was evaluated by VAS under two conditions: repeated intake over 2 wk (Study 1) and single intake (Study 2). Though a single intake of REPH-B had no influence on any of the parameters evaluated, repeated intake of REPH-B significantly improved the VAS score for hunger. In conclusion, repeated intake of REPH-B might increase hunger in healthy adults who suffer from a lack of appetite. This study was registered with UMIN Clinical Trials Registry as UMIN000049108.

To evaluate how UK guidelines for individual health conditions consider coexisting multiple long term conditions and to propose improvements to guideline development processes so that guidelines appropriately account for and consider coexisting multiple long term conditions. Analysis and recommendations from review of single condition guidelines. Clinical guidelines developed by the National Institute for Health and Care Excellence (NICE), UK, 1 January 2013 to 31 December 2024. 56 clinical guidelines developed by NICE covering a broad range of long term conditions. The extent to which guideline recommendations consider multiple long term conditions and coexisting conditions, distinguishing between concordant conditions (those affecting the same organ system as the index condition) and discordant conditions (those affecting different systems). All but one of the NICE guidelines (n=55, 98%) included some advice on managing the index condition in the presence of coexisting conditions, and 50 (89%) guidelines offered general guidance on tailoring care. Only 11 (20%) guidelines, however, explicitly referred to multiple long term conditions, and none included a dedicated section on multiple long term conditions or how care should be adapted in this context. 19 (34%) guidelines featured sections looking at specific coexisting conditions or coexisting conditions generally. Coverage of coexisting conditions varied widely across categories of conditions, with mental health guidelines dealing with the most coexisting conditions (median 10, interquartile range (IQR) 4.5-14.75) in contrast with guidelines on cancer and eye disease covering the fewest conditions (median 3, 1-4.5; median 3, 2.25-2.75, respectively). Of the 397 possible concordant pairings, 120 (30%) were referenced, whereas of the 3859 possible discordant pairings, 259 (7%) were referenced, indicating greater coverage of same system combinations. Data on the composition of guideline committees showed wide variation in size, disciplinary diversity, inclusion of generalist clinicians (eg, general practitioners, general physicians, or others with no single specialty focus), and public contributors, although lived experience of multiple long term conditions was rarely specified. Despite widespread acknowledgement of coexisting or multiple long term conditions, NICE guidelines are predominantly condition specific and offer limited tailored support for the care of multiple long term conditions. Recommendations rarely considered common condition clusters or the cumulative effect of multiple long term conditions. Structured improvements, such as clearer guidance on adapting care, broader cross condition referencing, and more transparent inclusion of lived experience could enhance the relevance and usability of guidelines for clinicians managing patients with multiple long term conditions.

Evidence for the net benefit of aspirin for primary prevention of cardiovascular disease (CVD) is finely balanced, leading to variation in guideline recommendations internationally. External validity of randomised clinical trial (RCT) evidence may therefore be of particular importance. The aim of this study is to characterise real-world patients according to their eligibility for guideline-cited aspirin RCTs for primary CVD prevention. Eligibility criteria from 14 RCTs were applied to a linked primary care/hospital discharge dataset of people ≥ 40 years without CVD. Proportions eligible for each trial were calculated, and characteristics of eligible and ineligible patients compared for each trial, including Cox regression analysis of event rates for major adverse cardiovascular events (MACE), major bleeding events, and non-cardiovascular mortality. Of 570,211 included patients (300,500 [52.7%] women, 336,877 [59%] < 60 years), the median proportion ineligible for 14 RCTs was 90.7% (range 42.5-99.4%) and 24.0% of patients were ineligible for all RCTs. On average, trial-ineligible populations were younger (median age trial-ineligible 57.8 vs trial-eligible 62.6 years, p = 0.008) and a lower proportion had hypertension (23.9% vs 50.9%, p = 0.004), diabetes (6.4% vs 11.5%, p = 0.015), or a regular statin prescription (11.8% vs 26.7%, p = 0.001). Trial-ineligible populations had a higher hazard of MACE compared to trial-eligible in four RCTs and lower in ten (hazard ratio [HR] range across all RCTs 0.45 [95%CI 0.40-0.51] to 2.78 [95%CI 2.61-2.96]). Hazards of bleeding events in the trial-ineligible were lower than the trial-eligible in eight RCTs and higher in four (HR range across all RCTs 0.63 [95%CI, 0.59-0.66] to 1.69 [95%CI, 1.53-1.86]), and time-varying hazards of non-CVD death were consistently lower in four RCTs and higher in five (HR range across all RCTs and time points 0.29 [95%CI 0.24-0.36] to 11.42 [95%CI 9.91-13.17]). Compared with trial-ineligible populations within the same age and sex strata, RCTs recruited people of varying CVD risk but often excluded people at high risk of bleeding or non-CVD death, highlighting that many trials may overestimate the net benefit of aspirin for primary prevention.

Association studies have linked many genetic variants to a variety of phenotypes but understanding the biological mechanisms underlying these signals remains a major challenge. Since genes operate within complex networks, statistical interactions between genetic mutations that reflect biological pathways are expected to exist. However, their discovery has been hampered by the vast search space of variant combinations and the multiplicatively small expected effect sizes of interactions. To increase power, we created a test for interaction between single-nucleotide polymorphisms (SNPs) and groups of other variants with a direct effect on a phenotype aggregated in a polygenic score (PGS) which can be performed for any quantitative trait. In realistic simulations, this method avoids false positives and is well powered to find interaction networks. We apply it to 97 quantitative phenotypes in European samples in the UK Biobank and identify 144 independent interactions affecting 52 different traits, including important disease risk variants at genes such as APOE, FTO or TCF7L2. We develop approaches to refine identified signals and detect 38 pairwise interactions between SNPs. These include known interactions between ABO, FUT2 and TREH affecting alkaline phosphatase levels, which are shown to be part of a larger network including PIGC and FUT6, as well as an interaction for eosinophil levels between IL33 and ALOX15, two genes whose functional interaction has recently been implicated in asthma. Finally, we propose a method to partition PGSs according to the binding sites of more than 1100 transcription factors using the HOCOMOCO motif database and test for interactions involving functionally partitioned scores. We identify 12 interactions affecting eight traits, two of which directly reflect known regulatory relationships such as that between TCF7L2 (a key regulator of glucose metabolism) and the transcription factor KDM2A, which are known to interact functionally within the Wnt signalling pathway, affecting glycated haemoglobin levels. This work substantially extends the set of known epistatic effects for human phenotypes and shows how statistical interactions can reflect biological interdependencies between genes.