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Background: The 3PM framework revolutionizes disease management by facilitating early risk prediction, disease prevention, and personalized treatment. For isolated incomplete lateral meniscal injuries (IILMI), where early diagnosis is challenging due to non-specific symptoms, 3PM's proactive approach is beneficial in preventing knee joint disease progression and maintaining patients' quality of life. Aims: This study aimed to develop a predictive model within the 3PM framework, integrating knee MRI anatomical features with individual physical activity (PA) patterns to enhance early IILMI detection and treatment efficacy, improving patient outcomes and quality of life. Methods: The training dataset comprised 254 patients. Using logistic regression analyses and least absolute shrinkage and selection operator (LASSO), IILMI was identified among various preoperative factors containing knee MRI and PA features. A dynamic nomogram was constructed and subjected to internal and external validations (91 patients). Validation encompassed C-index, receiver operating characteristic (ROC) curves, calibration curves, decision curve analysis (DCA), and clinical impact curves. ROC analysis determined the risk stratification cut-off. Results: Six independent IILMI factors were identified, including PA intensity, PA type, degree of PA intensity, and MRI-derived anatomical parameters. The dynamic nomogram showed high predictive accuracy (C-index, 0.829 in training, 0.906 in validation). IILMI patients were divided into low-risk, medium-risk, and high-risk groups according to the cut-off value. Conclusion: In 3PM-guided management, the dynamic nomogram enables early IILMI diagnosis in patients while promoting IILMI stratification making personalized treatment feasible. With further development, it holds promise for effectively predicting IILMI risk, preventing severe knee pathologies, and enhancing the quality of life for at-risk patients. [ABSTRACT FROM AUTHOR]

Objective: Metabolomics measurements of eccrine sweat may provide novel and relevant biomedical information to support predictive, preventive and personalised medicine (3PM). However, only limited data is available regarding metabolic alterations accompanying chemotherapy of breast cancer patients related to residual cancer burden (RCB) or therapy response. Here, we have applied Metabo-Tip, a non-invasive metabolomics assay based on the analysis of eccrine sweat from the fingertips, to investigate the feasibility of such an approach, especially with respect to drug monitoring, assessing lifestyle parameters and stratification of breast cancer patients. Methods: Eccrine sweat samples were collected from breast cancer patients (n = 9) during the first cycle of neoadjuvant chemotherapy at four time points in this proof-of-concept study at a Tertiary University Hospital. Metabolites in eccrine sweat were analysed using mass spectrometry. Blood plasma samples from the same timepoints were also collected and analysed using a validated targeted metabolomics kit, in addition to proteomics and fatty acids/oxylipin analysis. Results: A total of 247 exogenous small molecules and endogenous metabolites were identified in eccrine sweat of the breast cancer patients. Cyclophosphamide and ondansetron were successfully detected and monitored in eccrine sweat of individual patients and accurately reflected the administration schedule. The non-essential amino acids asparagine, serine and proline, as well as ornithine were significantly regulated in eccrine sweat and blood plasma over the therapy cycle. However, their distinct time-dependent profiles indicated compartment-specific distributions. Indeed, the metabolite composition of eccrine sweat seems to largely resemble the composition of the interstitial fluid. Plasma proteins and fatty acids/oxylipins were not affected by the first treatment cycle. Individual smoking habit was revealed by the simultaneous detection of nicotine and its primary metabolite cotinine in eccrine sweat. Stratification according to RCB revealed pronounced differences in the metabolic composition of eccrine sweat in these patients at baseline, e.g., essential amino acids, possibly due to the systemic contribution of breast cancer and its impact on metabolic turnover. Conclusion: Mass spectrometry-based analysis of metabolites from eccrine sweat of breast cancer patients successfully qualified lifestyle parameters for risk assessment and allowed us to monitor drug treatment and systemic response to therapy. Moreover, eccrine sweat revealed a potentially predictive metabolic pattern stratifying patients by the extent of the metabolic activity of breast cancer tissue at baseline. Eccrine sweat is derived from the otherwise hardly accessible interstitial fluid and, thus, opens up a new dimension for biomonitoring of breast cancer in secondary and tertiary care. The simple sample collection without the need for trained personnel could also enable decentralised long-term biomonitoring to assess stable disease or disease progression. Eccrine sweat analysis may indeed significantly advance 3PM for the benefit of breast cancer patients. [ABSTRACT FROM AUTHOR]

Respiratory syncytial viral (RSV) infection is a leading persisting pulmonary disease-causing agent. It causes loss of lives especially among infants, old ages, and adults immunocompromised individuals. This viral pathogen infects children more especially those under the age of 2 and may lead to death. It causes 3 million hospitalizations and up to 60,000 deaths annually for under the age of 5. The most vulnerable are immunocompromised individuals and asthmatic children with suboptimal antiviral defenses. It is associated with bronchiolitis, pneumonia, and bronchopneumonia. Despite all the current interventions and clinical trials, the only available therapeutic strategies for this viral infection are palliative care. Therefore, it is imperative to understand the pathogenicity of RSV and the corresponding host immune response to depict a sort of a targeted intervention. With the increasingly cutting-edge methods in harnessing the pathogenicity of this viral infection, high throughput systems including omics technological advances are at the spotlight. For instance, the associated genes with RSV complications for the host, the set of microbiome identified as operational taxonomic unit, the upregulated or downregulated metabolites, the protein subtypes, and the small molecules can help explain the viral microenvironment. Moreover, these big data will lead to RSV patients' stratification through individualized patient profiles that will bring in targeted prevention and treatment algorithms tailored to individualized patients' profiles. Through this, the virus and host interactions based on the pathogenicity of infection will provide a strong ground for depicting the prevention, prediction, and personalized medicine (3PM) for RSV. The 3PM approach brought cutting edge functional medicine to the healthcare givers, thus conferring targeted prevention and precision medicine while observing personalized treatment as well as preventive regularities. The viral replication mechanisms against the host defense mechanisms are crucial for the development of safe and effective therapy. Integrative personal omics profiles, whose analysis is based on the combined proteomics, transcriptomics, genomics, proteoformics, metabolomics, and autoantibody profiles, are very robust for predicting the risk of RSV infection. The targeted prevention will emerge from the patient stratification when the diagnosis is accurately predicted. In addition, the personalized medical services will give an effective prognostic assessment for RSV complications. [ABSTRACT FROM AUTHOR]

Background: Stroke is a leading cause of mortality and disability worldwide. Identifying predictive biomarkers and modifiable risk factors is crucial for stroke prevention in the context of predictive, preventive, and personalized medicine (PPPM). We aimed to investigate the association of serum pyridoxal 5′-phosphate (PLP) levels with stroke prevalence in a nationally representative cohort and to assess the causal relationship using bidirectional Mendelian randomization (MR) analysis, with a focus on the implications for PPPM strategies in stroke management. Methods: We included 6839 participants aged ≥ 18 years from the National Health and Nutrition Examination Survey (NHANES) 2005–2013. Serum PLP levels were measured by high-performance liquid chromatography. Stroke prevalence was ascertained by self-report. We used generalized linear models, Kaplan–Meier curves, restricted cubic splines, stratified analysis, receiver operating characteristic (ROC) curves, and bidirectional two-sample MR to examine the association of PLP levels with stroke prevalence and assess the causal relationship. Results: In the fully adjusted model, participants with low serum PLP levels had significantly higher odds of stroke compared to those with high levels (odds ratio (OR) = 6.51e-01, 95% confidence interval (CI) 4.46e-01–9.50e-01, P = 2.74E-02). Kaplan–Meier curves showed significantly lower survival probability in the low PLP group (P < 0.05). The restricted cubic spline analysis revealed a non-linear association, with the highest stroke risk at lower PLP levels. The stratified analysis showed significant associations in several subgroups. The ROC analysis indicated good predictive performance of the fully adjusted model (area under the curve (AUC) > 0.7). The MR analysis supported a protective causal effect of PLP on stroke risk (OR = 0.7723581, 95% CI 0.6388086–0.9336201, P = 0.00345), while the reverse MR analysis did not suggest a causal effect of stroke on PLP levels. Conclusions: Low serum PLP levels are significantly associated with higher stroke prevalence in a nationally representative the United States (US) sample. Integration of observational and genetic evidence supports a protective causal role of PLP in stroke risk. Serum PLP may serve as a promising predictive biomarker for stroke risk assessment and a potential target for personalized nutritional interventions in stroke prevention, in line with PPPM strategies. Our findings highlight the importance of maintaining optimal vitamin B6 status for effective PPPM-guided stroke prevention and management. [ABSTRACT FROM AUTHOR]

Nowadays, the diagnosis of Parkinson's disease (PD) remains essentially clinical, based on the subjective observations of clinicians. In addition, misdiagnosis with other neuro disorders, such as Alzheimer's (AD), can occur. Herein, an untargeted lipidomic analysis of 75 plasma samples was performed to identify lipid species capable of discriminating between these two neuro groups. Therefore, PLS-DA and OPLS-DA analysis revealed significant differences in patient profiles in the sphingolipid and glycerophospholipid categories. As a result, a putative lipid biomarker panel was developed, which included HexCer (40:1; O2) and PC (O-32:0), with an area under the receiver operating characteristic curve (AUC) > 80, respectively. This panel was effective in discriminating between diseased and healthy subjects, but most importantly, it could discriminate between two neurodegenerative disorders that can present similar symptoms, namely PD and AD. Together, these findings suggest that the dysregulated metabolism of lipids plays a critical role in AD and PD pathology and may represent a valuable clinical tool for their diagnosis. Thus, further targeted studies are encouraged to better understand the underlying mechanisms of PD and confirm the diagnostic potency of the identified lipid metabolites. [ABSTRACT FROM AUTHOR]

Training machine learning models using data from severe COVID-19 patients admitted to a central hospital, where entire wards are specifically dedicated to COVID-19, may yield predictions that differ significantly from those generated using data collected from patients admitted to a high-volume specialized hospital for orthopedic surgery, where COVID-19 is only a secondary diagnosis. This disparity arises despite the two hospitals being geographically close (within20 kilometers). While machine learning can facilitate rapid public health responses, rigorous external validation and continuous monitoring are essential to ensure reliability and safety. [ABSTRACT FROM AUTHOR]

Purpose: Our study explores the utilization of objective tools for preoperative assessment of elderly patients by Emergency General Surgeons (EGS). Methods: A descriptive cross-sectional survey was conducted via the European Society for Trauma and Emergency Surgery (ESTES) Research Committee. EGS were invited through the ESTES members’ mailing list and social media platforms. The survey included two sections: (1) clinical scenarios involving elderly patients with varying chronic conditions, and (2) participant characteristics. Data collection lasted 12 weeks, with reminders sent every 4 weeks. Statistical analyses were performed using Microsoft Excel and EasyMedStat. Results: One hundred and seven surgeons responded to the survey. Median respondent age was 41 years, with a male prevalence (72.9%). Most participants were from Europe (85%). Key-findings included that 62.6% reported using one or more risk assessment tools (RATs), while 35.5% used one or more frailty scores. Additionally, 4.7% were unaware of any RATs, and 35.5% were unaware of any frailty scores. Decision-making strategies leveraging personal experience with minimal impact from RATs predominated. Conclusions: Preoperative risk assessment tool and frailty score use for elderly patients requiring emergency surgery remains limited among ESTES surgeons. Our study highlights the need for focused education and tool workflow integration to improve risk stratification, decision-making and outcomes. Institutional approaches coupled with targeted educational interventions using implementation science principles are recommended to bridge this knowledge-to-action gap. Future research should focus on developing comprehensive, user-friendly tools and evaluating their impact on patient-centered outcomes. [ABSTRACT FROM AUTHOR]

One of the principal goals of Precision Medicine is to stratify patients by accounting for individual variability. However, extracting meaningful information from Real-World Data, such as Electronic Health Records, still remains challenging due to methodological and computational issues. A Dynamic Time Warping-based unsupervised-clustering methodology is presented in this paper for the clustering of patient trajectories of multi-modal health data on the basis of shared temporal characteristics. Building on an earlier methodology, a new dimension of time-varying clinical and imaging features is incorporated, through an adapted cost-minimization algorithm for clustering on different, possibly overlapping, feature subsets. The model disease chosen is Huntington's disease (HD), characterized by progressive neurodegeneration. From a wide range of examined user-defined parameters, four case examples are highlighted to demonstrate the identified temporal patterns in multi-modal HD trajectories and to study how these differ due to the combined effects of feature weights and granularity threshold. For each identified cluster, polynomial fits that describe the time behavior of the assessed features are provided for an informative comparison, together with their averaged values. The proposed data-mining methodology permits the stratification of distinct time patterns of multi-modal health data in individuals that share a diagnosis, by employing user-customized criteria beyond the current clinical practice. Overall, this work bears implications for better analysis of individual variability in disease progression, opening doors to personalized preventative, diagnostic and therapeutic strategies. [ABSTRACT FROM AUTHOR]

Aging is a natural process with varying effects. As we grow older, our bodies become more susceptible to aging-associated diseases. These diseases, individually or collectively, lead to the formation of distinct aging phenotypes. Identifying these aging phenotypes and understanding the complex interplay between coexistent diseases would facilitate more personalized patient management, a better prognosis, and a prolonged lifespan. Many studies distinguish between successful aging and frailty. However, this simple distinction fails to reflect the diversity of underlying causes. In this study, we sought to establish the underlying causes of frailty and determine the patterns in which these causes converge to form aging phenotypes. We conducted a comprehensive geriatric examination, cognitive assessment, and survival analysis of 2,688 long-living adults (median age = 92 years). The obtained data were clustered and used as input data for the Aging Phenotype Calculator, a multiclass classification model validated on an independent dataset of 96 older adults. The accuracy of the model was assessed using the receiver operating characteristic curve and the area under the curve. Additionally, we analyzed socioeconomic factors that could contribute to specific aging patterns. We identified five aging phenotypes: non-frailty, multimorbid frailty, metabolic frailty, cognitive frailty, and functional frailty. For each phenotype, we determined the underlying diseases and conditions and assessed the survival rate. Additionally, we provided management recommendations for each of the five phenotypes based on their distinct features and associated challenges. The identified aging phenotypes may facilitate better-informed decision-making. The Aging Phenotype Calculator (ROC AUC = 92%) may greatly assist geriatricians in patient management. [ABSTRACT FROM AUTHOR]

Associations between magnetic resonance imaging (MRI)-detected lumbar intervertebral disc degeneration (LDD) and LBP are often of modest magnitude. This association may be larger in specific patient subgroups. To examine whether the association between LDD and LBP is modified by underlying genetic predispositions to pain. Cross-sectional study in UK Biobank (UKB) and Twins UK. A genome-wide association study (GWAS) of the number of anatomical chronic pain locations was conducted in 347,538 UKB participants. The GWAS was used to develop a genome-wide polygenic risk score (PRS) in a holdout sample of 30,000 UKB participants. The PRS model was then used in analyses of 645 TwinsUK participants with standardized LDD MRI assessments. Ever having had LBP associated with disability lasting ≥1 month (LBP1). Using the PRS as a proxy for "genetically-predicted propensity to pain", we stratified TwinsUK participants into PRS quartiles. A "basic" model examined the association between an LDD summary score (LSUM) and LBP1, adjusting for covariates. A "fully-adjusted" model also adjusted for PRS quartile and LSUM x PRS quartile interaction terms. In the basic model, the odds ratio (OR) of LBP1 was 1.8 per standard deviation of LSUM (95% confidence interval [CI] 1.4–2.3). In the fully-adjusted model, there was a statistically significant LSUM-LBP1 association in quartile 4, the highest PRS quartile (OR=2.5 [95% CI 1.7–3.7], p=2.6×10−6), and in quartile 3 (OR=2.0, [95% CI 1.3–3.0]; p=.002), with small-magnitude and/or nonsignificant associations in the lowest 2 PRS quartiles. PRS quartile was a significant effect modifier of the LSUM-LBP1 association (interaction p≤.05). Genetically-predicted propensity to pain modifies the LDD-LBP association, with the strongest association present in people with the highest genetic propensity to pain. Lumbar MRI findings may have stronger connections to LBP in specific subgroups of people. [ABSTRACT FROM AUTHOR]

Abstract The extensive heterogeneity and the limited availability of effective targeted therapies contribute to the challenging prognosis and restricted survival observed in triple-negative breast cancer (TNBC). Recent research indicates the aberrant expression of diverse tyrosine kinases (TKs) within this cancer, contributing significantly to tumor cell proliferation, survival, invasion, and migration. The contemporary paradigm shift towards precision medicine has highlighted TKs and their receptors as promising targets for pharmacotherapy against a range of malignancies, given their pivotal roles in tumor initiation, progression, and advancement. Intensive investigations have focused on various monoclonal antibodies (mAbs) and small molecule inhibitors that specifically target proteins such as epidermal growth factor receptor (EGFR), vascular endothelial growth factor (VEGF), vascular endothelial growth factor receptor (VEGFR), cellular mesenchymal-epithelial transition factor (c-MET), human epidermal growth factor receptor 2 (HER2), among others, for combating TNBC. These agents have been studied both in monotherapy and in combination with other chemotherapeutic agents. Despite these advances, a substantial terrain of unexplored potential lies within the realm of TK targeted therapeutics, which hold promise in reshaping the therapeutic landscape. This review summarizes the various TK targeted therapeutics that have undergone scrutiny as potential therapeutic interventions for TNBC, dissecting the outcomes and revelations stemming from diverse clinical investigations. A key conclusion from the umbrella clinical trials evidences the necessity for in-depth molecular characterization of TNBCs for the maximum efficiency of TK targeted therapeutics, either as standalone treatments or a combination. Moreover, our observation highlights that the outcomes of TK targeted therapeutics in TNBC are substantially influenced by the diversity of the patient cohort, emphasizing the prioritization of individual patient genetic/molecular profiles for precise TNBC patient stratification for clinical studies.

Abstract The incidence of MASLD and MASH-associated fibrosis is rapidly increasing worldwide. Drug therapy is hampered by large patient variability and partial representation of human MASH fibrosis in preclinical models. Here, we investigated the mechanisms underlying patient heterogeneity using a discovery dataset and validated in distinct human transcriptomic datasets, to improve patient stratification and translation into subgroup specific patterns. Patient stratification was performed using weighted gene co-expression network analysis (WGCNA) in a large public transcriptomic discovery dataset (n = 216). Differential expression analysis was performed using DESeq2 to obtain differentially expressed genes (DEGs). Ingenuity Pathway analysis was used for functional annotation. The discovery dataset showed relevant fibrosis-related mechanisms representative of disease heterogeneity. Biological complexity embedded in genes signature was used to stratify discovery dataset into six subgroups of various sizes. Of note, subgroup-specific DEGs show differences in directionality in canonical pathways (e.g. Collagen biosynthesis, cytokine signaling) across subgroups. Finally, a multiclass classification model was trained and validated in two datasets. In summary, our work shows a potential alternative for patient population stratification based on heterogeneity in MASLD-MASH mechanisms. Future research is warranted to further characterize patient subgroups and identify protein targets for virtual screening and/or in vitro validation in preclinical models.

The extensive heterogeneity and the limited availability of effective targeted therapies contribute to the challenging prognosis and restricted survival observed in triple-negative breast cancer (TNBC). Recent research indicates the aberrant expression of diverse tyrosine kinases (TKs) within this cancer, contributing significantly to tumor cell proliferation, survival, invasion, and migration. The contemporary paradigm shift towards precision medicine has highlighted TKs and their receptors as promising targets for pharmacotherapy against a range of malignancies, given their pivotal roles in tumor initiation, progression, and advancement. Intensive investigations have focused on various monoclonal antibodies (mAbs) and small molecule inhibitors that specifically target proteins such as epidermal growth factor receptor (EGFR), vascular endothelial growth factor (VEGF), vascular endothelial growth factor receptor (VEGFR), cellular mesenchymal-epithelial transition factor (c-MET), human epidermal growth factor receptor 2 (HER2), among others, for combating TNBC. These agents have been studied both in monotherapy and in combination with other chemotherapeutic agents. Despite these advances, a substantial terrain of unexplored potential lies within the realm of TK targeted therapeutics, which hold promise in reshaping the therapeutic landscape. This review summarizes the various TK targeted therapeutics that have undergone scrutiny as potential therapeutic interventions for TNBC, dissecting the outcomes and revelations stemming from diverse clinical investigations. A key conclusion from the umbrella clinical trials evidences the necessity for in-depth molecular characterization of TNBCs for the maximum efficiency of TK targeted therapeutics, either as standalone treatments or a combination. Moreover, our observation highlights that the outcomes of TK targeted therapeutics in TNBC are substantially influenced by the diversity of the patient cohort, emphasizing the prioritization of individual patient genetic/molecular profiles for precise TNBC patient stratification for clinical studies. [ABSTRACT FROM AUTHOR]

Acute pain is a physiologic, protective life-important warning neurological signal indicating multi-level tissue modulations caused by a broad spectrum of health adverse events such as stress overload, mechanical trauma, ischemia–reperfusion, sterile and infection-triggered inflammation, single- and multi-organ damage, acute and chronic wounds, tissue remodeling and degeneration, amongst others. On the other hand, pain chronification results in a pathologic transformation from the protective pain signaling into persistent debilitative medical condition with severe consequences including but not restricted to phenotype-specific behavioral patterns, reduced quality of life, and cognitive and mood disorders. Who is predisposed to an increased vs. decreased pain sensitivity and to the pain chronification? The motivation of personalized medicine that "same size does not fit all" is getting obvious also for an advanced approach in algesiology. Consequently, an in-depth patient stratification is essential for the paradigm change in overall pain management from currently applied reactive medical services to the cost-effective predictive, preventive, and personalized medicine (PPPM/3PM) in primary (reversible damage to health and targeted protection against health-to-disease transition) and secondary (personalized protection against disease progression) care. To this end, specifically innovative concepts of phenotyping elaborated in this study play a crucial role in patient stratification for predicting pain-associated outcomes, evidence-based targeted prevention of the pain chronification, and creation of treatment algorithms tailored to individualized patient profiles. [ABSTRACT FROM AUTHOR]

Rationale: Severe alcohol-associated hepatitis (SAH) is the most critical, acute, inflammatory phenotype within the alcohol-associated liver disease (ALD) spectrum, characterized by high 30- and 90-day mortality. Since several decades, corticosteroids (CS) are the only approved pharmacotherapy offering highly limited survival benefits. Contextually, there is an evident demand for 3PM innovation in the area meeting patients' needs and improving individual outcomes. Fecal microbiota transplantation (FMT) has emerged as one of the new potential therapeutic options. In this study, we aimed to address the crucial 3PM domains in order to assess (i) the impact of FMT on mortality in SAH patients beyond CS, (ii) to identify factors associated with the outcome to be improved (iii) the prediction of futility, (iv) prevention of suboptimal individual outcomes linked to increased mortality, and (v) personalized allocation of therapy. Methods: We conducted a prospective study (NCT04758806) in adult patients with SAH who were non-responders (NR) to or non-eligible (NE) for CS between January 2018 and August 2022. The intervention consisted of five 100 ml of FMT, prepared from 30 g stool from an unrelated healthy donor and frozen at − 80 °C, administered daily to the upper gastrointestinal (GI) tract. We evaluated the impact of FMT on 30- and 90-day mortality which we compared to the control group selected by the propensity score matching and treated by the standard of care; the control group was derived from the RH7 registry of patients hospitalized at the liver unit (NCT04767945). We have also scrutinized the FMT outcome against established and potential prognostic factors for SAH — such as the model for end-stage liver disease (MELD), Maddrey Discriminant Function (MDF), acute-on-chronic liver failure (ACLF), Liver Frailty Index (LFI), hepatic venous-portal pressure gradient (HVPG) and Alcoholic Hepatitis Histologic Score (AHHS) — to see if the 3PM method assigns them a new dimension in predicting response to therapy, prevention of suboptimal individual outcomes, and personalized patient management. Results: We enrolled 44 patients with SAH (NR or NE) on an intention-to-treat basis; we analyzed 33 patients per protocol for associated factors (after an additional 11 being excluded for receiving less than 5 doses of FMT), and 31 patients by propensity score matching for corresponding individual outcomes, respectively. The mean age was 49.6 years, 11 patients (33.3%) were females. The median MELD score was 29, and ACLF of any degree had 27 patients (81.8%). FMT improved 30-day mortality (p = 0.0204) and non-significantly improved 90-day mortality (p = 0.4386). Univariate analysis identified MELD ≥ 30, MDF ≥ 90, and ACLF grade > 1 as significant predictors of 30-day mortality, (p = 0.031; p = 0.014; p = 0.034). Survival was not associated with baseline LFI, HVPG, or AHHS. Conclusions and recommendations in the framework of 3PM: In the most difficult-to-treat sub-cohort of patients with SAH (i.e., NR/NE), FMT improved 30-day mortality. Factors associated with benefit included MELD ≤ 30, MDF ≤ 90, and ACLF < 2. These results support the potential of gut microbiome as a therapeutic target in the context of 3PM research and vice versa — to use 3PM methodology as the expedient unifying template for microbiome research. The results allow for immediate impact on the innovative concepts of (i) personalized phenotyping and stratification of the disease for the clinical research and practice, (ii) multilevel predictive diagnosis related to personalized/precise treatment allocation including evidence-based (ii) prevention of futile and sub-optimally effective therapy, as well as (iii) targeted prevention of poor individual outcomes in patients with SAH. Moreover, our results add to the existing evidence with the potential to generate new research along the SAH's pathogenetic pathways such as diverse individual susceptibility to alcohol toxicity, host-specific mitochondrial function and systemic inflammation, and the role of gut dysbiosis thereof. [ABSTRACT FROM AUTHOR]

Aims/hypothesis: Type 2 diabetes is a chronic condition that is caused by hyperglycaemia. Our aim was to characterise the metabolomics to find their association with the glycaemic spectrum and find a causal relationship between metabolites and type 2 diabetes. Methods: As part of the Innovative Medicines Initiative - Diabetes Research on Patient Stratification (IMI-DIRECT) consortium, 3000 plasma samples were measured with the Biocrates AbsoluteIDQ p150 Kit and Metabolon analytics. A total of 911 metabolites (132 targeted metabolomics, 779 untargeted metabolomics) passed the quality control. Multivariable linear and logistic regression analysis estimates were calculated from the concentration/peak areas of each metabolite as an explanatory variable and the glycaemic status as a dependent variable. This analysis was adjusted for age, sex, BMI, study centre in the basic model, and additionally for alcohol, smoking, BP, fasting HDL-cholesterol and fasting triacylglycerol in the full model. Statistical significance was Bonferroni corrected throughout. Beyond associations, we investigated the mediation effect and causal effects for which causal mediation test and two-sample Mendelian randomisation (2SMR) methods were used, respectively. Results: In the targeted metabolomics, we observed four (15), 34 (99) and 50 (108) metabolites (number of metabolites observed in untargeted metabolomics appear in parentheses) that were significantly different when comparing normal glucose regulation vs impaired glucose regulation/prediabetes, normal glucose regulation vs type 2 diabetes, and impaired glucose regulation vs type 2 diabetes, respectively. Significant metabolites were mainly branched-chain amino acids (BCAAs), with some derivatised BCAAs, lipids, xenobiotics and a few unknowns. Metabolites such as lysophosphatidylcholine a C17:0, sum of hexoses, amino acids from BCAA metabolism (including leucine, isoleucine, valine, N-lactoylvaline, N-lactoylleucine and formiminoglutamate) and lactate, as well as an unknown metabolite (X-24295), were associated with HbA1c progression rate and were significant mediators of type 2 diabetes from baseline to 18 and 48 months of follow-up. 2SMR was used to estimate the causal effect of an exposure on an outcome using summary statistics from UK Biobank genome-wide association studies. We found that type 2 diabetes had a causal effect on the levels of three metabolites (hexose, glutamate and caproate [fatty acid (FA) 6:0]), whereas lipids such as specific phosphatidylcholines (PCs) (namely PC aa C36:2, PC aa C36:5, PC ae C36:3 and PC ae C34:3) as well as the two n-3 fatty acids stearidonate (18:4n3) and docosapentaenoate (22:5n3) potentially had a causal role in the development of type 2 diabetes. Conclusions/interpretation: Our findings identify known BCAAs and lipids, along with novel N-lactoyl-amino acid metabolites, significantly associated with prediabetes and diabetes, that mediate the effect of diabetes from baseline to follow-up (18 and 48 months). Causal inference using genetic variants shows the role of lipid metabolism and n-3 fatty acids as being causal for metabolite-to-type 2 diabetes whereas the sum of hexoses is causal for type 2 diabetes-to-metabolite. Identified metabolite markers are useful for stratifying individuals based on their risk progression and should enable targeted interventions. [ABSTRACT FROM AUTHOR]

Background: While high-grade serous ovarian cancer (HGSC) has proven largely resistant to immunotherapy, sporadic incidents of partial and complete response have been observed in clinical trials and case reports. These observations suggest that a molecular basis for effective immunity may exist within a subpopulation of HGSC. Herein, we developed an algorithm, CONSTRU (Computing Prognostic Marker Dependencies by Successive Testing of Gene-Stratified Subgroups), to facilitate the discovery and characterization of molecular backgrounds of HGSC that confer resistance or susceptibility to protective anti-tumor immunity. Methods: We used CONSTRU to identify genes from tumor expression profiles that influence the prognostic power of an established immune cytolytic activity signature (CYTscore). From the identified genes, we developed a stratification signature (STRATsig) that partitioned patient populations into tertiles that varied markedly by CYTscore prognostic power. The tertile groups were then analyzed for distinguishing biological, clinical and immunological properties using integrative bioinformatics approaches. Results: Patient survival and molecular measures of immune suppression, evasion and dysfunction varied significantly across STRATsig tertiles in validation cohorts. Tumors comprising STRATsig tertile 1 (S-T1) showed no immune-survival benefit and displayed a hyper-immune suppressed state marked by activation of TGF-β, Wnt/β-catenin and adenosine-mediated immunosuppressive pathways, with concurrent T cell dysfunction, reduced potential for antigen presentation, and enrichment of cancer-associated fibroblasts. By contrast, S-T3 tumors exhibited diminished immunosuppressive signaling, heightened antigen presentation machinery, lowered T cell dysfunction, and a significant CYTscore-survival benefit that correlated with mutational burden in a manner consistent with anti-tumor immunoediting. These tumors also showed elevated activity of DNA damage/repair, cell cycle/proliferation and oxidative phosphorylation, and displayed greater proportions of Th1 CD4 + T cells. In these patients, but not those of S-T1 or S-T2, validated predictors of immunotherapy response were prognostic of longer patient survival. Further analyses showed that STRATsig tertile properties were not explained by known HGSC molecular or clinical subtypes or singular immune mechanisms. Conclusions: STRATsig is a composite of parallel immunoregulatory pathways that mirrors tumor immunogenic potential. Approximately one-third of HGSC cases classify as S-T3 and display a hypo-immunosuppressed and antigenic molecular composition that favors immunologic tumor control. These patients may show heightened responsiveness to current immunotherapies. [ABSTRACT FROM AUTHOR]

Background Staphylococcus aureus bacteremia (SAB) is a clinically heterogeneous disease. The ability to identify subgroups of patients with shared traits (subphenotypes) is an unmet need to allow patient stratification for clinical management and research. We aimed to test the hypothesis that clinically relevant subphenotypes can be reproducibly identified among patients with SAB. Methods We studied 3 cohorts of adults with monomicrobial SAB: a UK retrospective observational study (Edinburgh cohort, n = 458), the UK ARREST trial (n = 758), and the Spanish SAFO trial (n = 214). Latent class analysis was used to identify subphenotypes using routinely collected clinical data without considering outcomes. Mortality and microbiologic outcomes were then compared between subphenotypes. Results Included patients had predominantly methicillin-susceptible SAB (1366 of 1430, 95.5%). We identified 5 distinct, reproducible clinical subphenotypes: (A) SAB associated with older age and comorbidity, (B) nosocomial intravenous catheter-associated SAB in younger people without comorbidity, (C) community-acquired metastatic SAB, (D) SAB associated with chronic kidney disease, and (E) SAB associated with injection drug use. Survival and microbiologic outcomes differed between the subphenotypes. Mortality was highest in subphenotype A and lowest in subphenotypes B and E. Microbiologic outcomes were worse in subphenotype C. In a secondary analysis of the ARREST trial, adjunctive rifampicin was associated with increased mortality in subphenotype B and improved microbiologic outcomes in subphenotype C. Conclusions We have identified reproducible and clinically relevant subphenotypes within SAB and provide proof of principle of differential treatment effects. Through clinical trial enrichment and patient stratification, these subphenotypes could contribute to a personalized medicine approach to SAB. [ABSTRACT FROM AUTHOR]

Amyotrophic lateral sclerosis (ALS) is a motor neurodegenerative disease characterized by poor prognosis. Currently, screening and diagnostic methods for ALS remain challenging, often leading to diagnosis at an advanced stage of the disease. This delay hinders the timely initiation of therapy, negatively impacting patient well-being. Additionally, misdiagnosis with other neurodegenerative disorders that present similar profiles often occurs. Therefore, there is an urgent need for a cost-effective, rapid, and user-friendly tool capable of predicting ALS onset. In this pilot study, we demonstrate that infrared spectroscopy, coupled with chemometric analysis, can effectively identify and predict disease profiles from blood samples drawn from ALS patients. The selected predictive spectral markers, which are used in various discriminant models, achieved an AUROC sensitivity of almost 80% for distinguishing ALS patients from controls. Furthermore, the differentiation of ALS at both the initial and advanced stages from other neurodegenerative disorders showed even higher AUROC values, with sensitivities of 87% (AUROC: 0.70–0.97). These findings highlight the elevated potential of ATR-FTIR spectroscopy for routine clinical screening and early diagnosis of ALS. [ABSTRACT FROM AUTHOR]

The incidence of MASLD and MASH-associated fibrosis is rapidly increasing worldwide. Drug therapy is hampered by large patient variability and partial representation of human MASH fibrosis in preclinical models. Here, we investigated the mechanisms underlying patient heterogeneity using a discovery dataset and validated in distinct human transcriptomic datasets, to improve patient stratification and translation into subgroup specific patterns. Patient stratification was performed using weighted gene co-expression network analysis (WGCNA) in a large public transcriptomic discovery dataset (n = 216). Differential expression analysis was performed using DESeq2 to obtain differentially expressed genes (DEGs). Ingenuity Pathway analysis was used for functional annotation. The discovery dataset showed relevant fibrosis-related mechanisms representative of disease heterogeneity. Biological complexity embedded in genes signature was used to stratify discovery dataset into six subgroups of various sizes. Of note, subgroup-specific DEGs show differences in directionality in canonical pathways (e.g. Collagen biosynthesis, cytokine signaling) across subgroups. Finally, a multiclass classification model was trained and validated in two datasets. In summary, our work shows a potential alternative for patient population stratification based on heterogeneity in MASLD-MASH mechanisms. Future research is warranted to further characterize patient subgroups and identify protein targets for virtual screening and/or in vitro validation in preclinical models. [ABSTRACT FROM AUTHOR]

Simple Summary: Novel cancer biomarker discoveries are enabled by the application and analysis of omics technologies. This vast quantity of high-dimensional data necessitates the implementation of feature selection for analysis. The mathematical basis of selection methods varies considerably, which may influence subsequent inference. The aim of the study was to identify signature gene sets of grade 2 and 3 astrocytoma (brain cancer) and determine their impact on the classification and discovery of biological patterns. The application of feature selection methods reduced the number of genes and led to an increase in classification accuracy. Notably, no single gene was selected by all methods. Significant differences in Gene Ontology terms as well as KEGG pathways were discovered. Results demonstrated a significant difference in outcomes when classification-type algorithms were utilised compared to mixed types (selection and classification). This may result in the inadvertent omission of biological phenomena, while simultaneously achieving enhanced classification outcomes. Novel cancer biomarkers discoveries are driven by the application of omics technologies. The vast quantity of highly dimensional data necessitates the implementation of feature selection. The mathematical basis of different selection methods varies considerably, which may influence subsequent inferences. In the study, feature selection and classification methods were employed to identify six signature gene sets of grade 2 and 3 astrocytoma samples from the Rembrandt repository. Subsequently, the impact of these variables on classification and further discovery of biological patterns was analysed. Principal component analysis (PCA), uniform manifold approximation and projection (UMAP), and hierarchical clustering revealed that the data set (10,096 genes) exhibited a high degree of noise, feature redundancy, and lack of distinct patterns. The application of feature selection methods resulted in a reduction in the number of genes to between 28 and 128. Notably, no single gene was selected by all of the methods tested. Selection led to an increase in classification accuracy and noise reduction. Significant differences in the Gene Ontology terms were discovered, with only 13 terms overlapping. One selection method did not result in any enriched terms. KEGG pathway analysis revealed only one pathway in common (cell cycle), while the two methods did not yield any enriched pathways. The results demonstrated a significant difference in outcomes when classification-type algorithms were utilised in comparison to mixed types (selection and classification). This may result in the inadvertent omission of biological phenomena, while simultaneously achieving enhanced classification outcomes. [ABSTRACT FROM AUTHOR]

This study, employing an interim analysis, investigates the effects of the Dermamecum protocol, a structured educational and tailored approach that stratifies ostomy patients into risk paths (green, yellow, red) based on pre-operative and post-operative characteristics. The green path indicates a low risk of peristomal skin complications (PSCs), focusing on sustaining healthy behaviours and basic stoma care. The yellow path represents a moderate risk, emphasizing the need for patients to self-monitor and recognize early signs of complications. The red path corresponds to high risk, requiring stringent monitoring and immediate access to healthcare support. The study aims to reduce PSCs and improve patient outcomes. Methods include the stratification of 226 patients, with significant differences in gender distribution, BMI categories, and stoma types across the paths. Results show an occurrence rate of PSCs of 5.9% in all risk paths (5.7% green path, 4.7% yellow path, and 7.9% red path, p = 0.685), significantly lower than the median rate of 35% reported in the literature. Multiple correspondence analysis validated the stratification, with distinct clusters for each path. Poisson regression models in the exploratory framework of an interim analysis identified male gender as the only significant predictor of PSCs, indicating the need for gender-specific interventions. The findings suggest that the Dermamecum protocol effectively reduces early PSCs, providing a foundation for further research. [ABSTRACT FROM AUTHOR]

Multi-omics data play a crucial role in precision medicine, mainly to understand the diverse biological interaction between different omics. Machine learning approaches have been extensively employed in this context over the years. This review aims to comprehensively summarize and categorize these advancements, focusing on the integration of multi-omics data, which includes genomics, transcriptomics, proteomics and metabolomics, alongside clinical data. We discuss various machine learning techniques and computational methodologies used for integrating distinct omics datasets and provide valuable insights into their application. The review emphasizes both the challenges and opportunities present in multi-omics data integration, precision medicine and patient stratification, offering practical recommendations for method selection in various scenarios. Recent advances in deep learning and network-based approaches are also explored, highlighting their potential to harmonize diverse biological information layers. Additionally, we present a roadmap for the integration of multi-omics data in precision oncology, outlining the advantages, challenges and implementation difficulties. Hence this review offers a thorough overview of current literature, providing researchers with insights into machine learning techniques for patient stratification, particularly in precision oncology. Contact: anirban@klyuniv.ac.in [ABSTRACT FROM AUTHOR]

To propose a new gene expression signature that identifies endometrial disruptions independent of endometrial luteal phase timing and predicts if patients are at risk of endometrial failure. Multicentric, prospective study. Reproductive medicine research department in a public hospital affiliated with private fertility clinics and a reproductive genetics laboratory. Caucasian women (n = 281; 39.4 ± 4.8 years old with a body mass index of 22.9 ± 3.5 kg/m2) undergoing hormone replacement therapy between July 2018 and July 2021. Endometrial samples from 217 patients met RNA quality criteria for signature discovery and analysis. Endometrial biopsies collected in the mid-secretory phase. Endometrial luteal phase timing-corrected expression of 404 genes and reproductive outcomes of the first single embryo transfer (SET) after biopsy collection to identify prognostic biomarkers of endometrial failure. Removal of endometrial timing variation from gene expression data allowed patients to be stratified into poor (n = 137) or good (n = 49) endometrial prognosis groups on the basis of their clinical and transcriptomic profiles. Significant differences were found between endometrial prognosis groups in terms of reproductive rates: pregnancy (44.6% vs. 79.6%), live birth (25.6% vs. 77.6%), clinical miscarriage (22.2% vs. 2.6%), and biochemical miscarriage (20.4% vs. 0%). The relative risk of endometrial failure for patients predicted as a poor endometrial prognosis was 3.3 times higher than those with a good prognosis. The differences in gene expression between both profiles were proposed as a biomarker, coined the endometrial failure risk (EFR) signature. Poor prognosis profiles were characterized by 59 upregulated and 63 downregulated genes mainly involved in regulation (17.0%), metabolism (8.4%), immune response, and inflammation (7.8%). This EFR signature had a median accuracy of 0.92 (min = 0.88, max = 0.94), median sensitivity of 0.96 (min = 0.91, max = 0.98), and median specificity of 0.84 (min = 0.77, max = 0.88), positioning itself as a promising biomarker for endometrial evaluation. The EFR signature revealed a novel endometrial disruption, independent of endometrial luteal phase timing, present in 73.7% of patients. This EFR signature stratified patients into 2 significantly distinct and clinically relevant prognosis profiles providing opportunities for personalized therapy. Nevertheless, further validations are needed before implementing this gene signature as an artificial intelligence (AI)-based tool to reduce the risk of patients experiencing endometrial failure. [ABSTRACT FROM AUTHOR]

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• Neurodevelopmental disorders share a common genetic foundation with immune disorders but exhibit a higher degree of polygenicity. • This study identified thirty genomic loci with significant associations in both types of diseases, including eight novel loci. • The shared loci were mapped to genes enriched in three classes of pathways. • The pleiotropic loci show a significant association with blood cell traits, potentially serving as more accessible and feasible biomarkers for patient stratification. The co-occurrence and familial clustering of neurodevelopmental disorders and immune disorders suggest shared genetic risk factors. Based on genome-wide association summary statistics from five neurodevelopmental disorders and four immune disorders, we conducted genome-wide, local genetic correlation and polygenic overlap analysis. We further performed a cross-trait GWAS meta -analysis. Pleotropic loci shared between the two categories of diseases were mapped to candidate genes using multiple algorithms and approaches. Significant genetic correlations were observed between neurodevelopmental disorders and immune disorders, including both positive and negative correlations. Neurodevelopmental disorders exhibited higher polygenicity compared to immune disorders. Around 50%-90% of genetic variants of the immune disorders were shared with neurodevelopmental disorders. The cross-trait meta -analysis revealed 154 genome-wide significant loci, including 8 novel pleiotropic loci. Significant associations were observed for 30 loci with both types of diseases. Pathway analysis on the candidate genes at these loci revealed common pathways shared by the two types of diseases, including neural signaling, inflammatory response, and PI3K-Akt signaling pathway. In addition, 26 of the 30 lead SNPs were associated with blood cell traits. Neurodevelopmental disorders exhibit complex polygenic architecture, with a subset of individuals being at a heightened genetic risk for both neurodevelopmental and immune disorders. The identification of pleiotropic loci has important implications for exploring opportunities for drug repurposing, enabling more accurate patient stratification, and advancing genomics-informed precision in the medical field of neurodevelopmental disorders. [ABSTRACT FROM AUTHOR]

Objective: Systemic lupus erythematosus (SLE) is an autoimmune disease characterized by a variety of disease symptoms and an unpredictable clinical course. To improve treatment outcome, stratification based on immunological manifestations commonly seen in patients with SLE such as autoantibodies, type I interferon (IFN) signature and neutrophil extracellular trap (NET) release may help. It is assumed that there is an association between these immunological phenomena, since NET release induces IFN production and IFN induces autoantibody formation via B-cell activation. Here we studied the association between autoantibodies, the IFN signature, NET release, and clinical manifestations in patients with SLE. Methods: We performed principal component analysis (PCA) and hierarchical clustering of 57 SLE-related autoantibodies in 25 patients with SLE. We correlated each autoantibody to the IFN signature and NET inducing capacity. Results: We observed two distinct clusters: one cluster contained mostly patients with a high IFN signature. Patients in this cluster often present with cutaneous lupus, and have higher anti-dsDNA concentrations. Another cluster contained a mix of patients with a high and low IFN signature. Patients with high and low NET inducing capacity were equally distributed between the clusters. Variance between the clusters is mainly driven by antibodies against histones, RibP2, RibP0, EphB2, RibP1, PCNA, dsDNA, and nucleosome. In addition, we found a trend towards increased concentrations of autoantibodies against EphB2, RibP1, and RNP70 in patients with an IFN signature. We found a negative correlation of NET inducing capacity with anti-FcER (r = −0.530; p = 0.007) and anti-PmScl100 (r = −0.445; p = 0.03). Conclusion: We identified a subgroup of patients with an IFN signature that express increased concentrations of antibodies against DNA and RNA-binding proteins, which can be useful for further patient stratification and a more targeted therapy. We did not find positive associations between autoantibodies and NET inducing capacity. Our study further strengthens the evidence of a correlation between RNA-binding autoantibodies and the IFN signature.

Asthma has become one of the most serious chronic respiratory diseases threatening people's lives worldwide. The pathogenesis of asthma is complex and driven by numerous cells and their interactions, which contribute to its genetic and phenotypic heterogeneity. The clinical characteristic is insufficient for the precision of patient classification and therapies; thus, a combination of the functional or pathophysiological mechanism and clinical phenotype proposes a new concept called “asthma endophenotype” representing various patient subtypes defined by distinct pathophysiological mechanisms. High-throughput omics approaches including genomics, epigenomics, transcriptomics, proteomics, metabolomics and microbiome enable us to investigate the pathogenetic heterogeneity of diverse endophenotypes and the underlying mechanisms from different angles. In this review, we provide a comprehensive overview of the roles of diverse cell types in the pathophysiology and heterogeneity of asthma and present a current perspective on their contribution into the bidirectional interaction between airway inflammation and airway remodeling. We next discussed how integrated analysis of multi-omics data via machine learning can systematically characterize the molecular and biological profiles of genetic heterogeneity of asthma phenotype. The current application of multi-omics approaches on patient stratification and therapies will be described. Integrating multi-omics and clinical data will provide more insights into the key pathogenic mechanism in asthma heterogeneity and reshape the strategies for asthma management and treatment.

Abstract Background Patients suffering from chronic spontaneous urticaria (CSU) are typically classified as type I or type IIb autoimmune CSU, but further patient stratification is hindered by the lack of biomarkers. Objectives We investigated whether the histamine content of individual basophils differ between patient subtypes in CSU to evaluate its potential as a biomarker. Methods A total of 101 patients diagnosed with CSU were included in the study. The histamine content per circulating basophil was derived from the basophil count in peripheral blood and levels of total cellular blood histamine. These measures, together with results from the serum‐induced basophil histamine release assay (s‐BHRA), were correlated to information on demographics, clinical characteristics, patient reported outcomes and laboratory analyses. Results The histamine content per basophil was significantly different between s‐BHRA positive and ‐negative patients (0.175 vs. 1.40 pg/cell, p

Triple-negative breast cancer (TNBC) is the most challenging breast cancer subtype. Molecular stratification and target therapy bring clinical benefit for TNBC patients, but it is difficult to implement comprehensive molecular testing in clinical practice. Here, using our multi-omics TNBC cohort (N = 425), a deep learning-based framework was devised and validated for comprehensive predictions of molecular features, subtypes and prognosis from pathological whole slide images. The framework first incorporated a neural network to decompose the tissue on WSIs, followed by a second one which was trained based on certain tissue types for predicting different targets. Multi-omics molecular features were analyzed including somatic mutations, copy number alterations, germline mutations, biological pathway activities, metabolomics features and immunotherapy biomarkers. It was shown that the molecular features with therapeutic implications can be predicted including the somatic PIK3CA mutation, germline BRCA2 mutation and PD-L1 protein expression (area under the curve [AUC]: 0.78, 0.79 and 0.74 respectively). The molecular subtypes of TNBC can be identified (AUC: 0.84, 0.85, 0.93 and 0.73 for the basal-like immune-suppressed, immunomodulatory, luminal androgen receptor, and mesenchymal-like subtypes respectively) and their distinctive morphological patterns were revealed, which provided novel insights into the heterogeneity of TNBC. A neural network integrating image features and clinical covariates stratified patients into groups with different survival outcomes (log-rank P < 0.001). Our prediction framework and neural network models were externally validated on the TNBC cases from TCGA (N = 143) and appeared robust to the changes in patient population. For potential clinical translation, we built a novel online platform, where we modularized and deployed our framework along with the validated models. It can realize real-time one-stop prediction for new cases. In summary, using only pathological WSIs, our proposed framework can enable comprehensive stratifications of TNBC patients and provide valuable information for therapeutic decision-making. It had the potential to be clinically implemented and promote the personalized management of TNBC.

Background: Huntington's disease (HD) is a progressive neurodegenerative disease caused by a CAG trinucleotide expansion in the huntingtin gene. The length of the CAG repeat is inversely correlated with disease onset. HD is characterized by hyperkinetic movement disorder, psychiatric symptoms, and cognitive deficits, which greatly impact patient's quality of life. Despite this clear genetic course, high variability of HD patients' symptoms can be observed. Current clinical diagnosis of HD solely relies on the presence of motor signs, disregarding the other important aspects of the disease. By incorporating a broader approach that encompasses motor as well as non-motor aspects of HD, predictive, preventive, and personalized (3P) medicine can enhance diagnostic accuracy and improve patient care. Methods: Multisymptom disease trajectories of HD patients collected from the Enroll-HD study were first aligned on a common disease timescale to account for heterogeneity in disease symptom onset and diagnosis. Following this, the aligned disease trajectories were clustered using the previously published Variational Deep Embedding with Recurrence (VaDER) algorithm and resulting progression subtypes were clinically characterized. Lastly, an AI/ML model was learned to predict the progression subtype from only first visit data or with data from additional follow-up visits. Results: Results demonstrate two distinct subtypes, one large cluster (n = 7122) showing a relative stable disease progression and a second, smaller cluster (n = 411) showing a dramatically more progressive disease trajectory. Clinical characterization of the two subtypes correlates with CAG repeat length, as well as several neurobehavioral, psychiatric, and cognitive scores. In fact, cognitive impairment was found to be the major difference between the two subtypes. Additionally, a prognostic model shows the ability to predict HD subtypes from patients' first visit only. Conclusion: In summary, this study aims towards the paradigm shift from reactive to preventive and personalized medicine by showing that non-motor symptoms are of vital importance for predicting and categorizing each patients' disease progression pattern, as cognitive decline is oftentimes more reflective of HD progression than its motor aspects. Considering these aspects while counseling and therapy definition will personalize each individuals' treatment. The ability to provide patients with an objective assessment of their disease progression and thus a perspective for their life with HD is the key to improving their quality of life. By conducting additional analysis on biological data from both subtypes, it is possible to gain a deeper understanding of these subtypes and uncover the underlying biological factors of the disease. This greatly aligns with the goal of shifting towards 3P medicine. [ABSTRACT FROM AUTHOR]

Mesenchymal stromal cells (MSCs) are promising cell therapy candidates, but their debated efficacy in clinical trials still limits successful adoption. Here, we discuss proceedings from a roundtable session titled "Failure to Launch Mesenchymal Stromal Cells 10 Years Later: What's on the Horizon?" held at the International Society for Cell & Gene Therapy 2023 Annual Meeting. Panelists discussed recent progress toward developing patient-stratification approaches for MSC treatments, highlighting the role of baseline levels of inflammation in mediating MSC treatment efficacy. In addition, MSC critical quality attributes (CQAs) are beginning to be elucidated and applied to investigational MSC products, including immunomodulatory functional assays and other potency markers that will help to ensure product consistency and quality. Lastly, next-generation MSC products, such as culture-priming strategies, were discussed as a promising strategy to augment MSC basal fitness and therapeutic potency. Key variables that will need to be considered alongside investigations of patient stratification approaches, CQAs and next-generation MSC products include the specific disease target being evaluated, route of administration of the cells and cell manufacturing parameters; these factors will have to be matched with postulated mechanisms of action towards treatment efficacy. Taken together, patient stratification metrics paired with the selection of therapeutically potent MSCs (using rigorous CQAs and/or engineered MSC products) represent a path forward to improve clinical successes and regulatory endorsements. [Display omitted] [ABSTRACT FROM AUTHOR]

This research seeks to assess the potential of regionally integrated health management for specific sub-populations, including the incorporation of selfmanagement initiatives. It will achieve this by conducting a thorough stratification analysis of hospital data, utilizing the Adjusted Clinical Groups (ACG) classification system. The approach involves a retrospective review of healthcare data spanning five years, which includes patient demographics, health outcomes, and healthcare utilization metrics. We intend to use the ACG method to classify the patient population into pertinent groups that mirror their health requirements and resource use. The insights obtained from this analysis will be used to create a localized adaptation of the Kaiser Permanente Pyramid Model of Care. This adaptation aims to identify the distribution of costs among patients treated in the Rivierenland Hospital. We anticipate that stratifying data with the ACG method will identify distinct multimorbid subgroups. These subgroups will have unique healthcare requirements. Early interventions and customized health management strategies, based on these insights, could enhance health outcomes and resource efficiency for high-risk patients. This analysis will serve as a foundation for constructive discussions with hospital management and clinical staff, fostering a deeper comprehension of the patients' burden of disease. It might also foster multidisciplinary collaboration opportunities between medical specialties as with regional healthcare partners such as general practitioners (GPs), mental health and other long-term care organizations. Moreover, we anticipate that self-care initiatives, supported by customized health information, will encourage increased patient engagement and strategies for enhancing lifestyle improvements. This strategy is expected to enable the personalization of advanced care planning based on individual needs profiles, thereby improving the management of complex and chronic conditions, and encouraging self-care practices. Our anticipated findings highlight the potential benefits of a data-informed approach to advancing healthcare outcomes and present opportunities for future investigations to refine and implement such integrated care models across the region. [ABSTRACT FROM AUTHOR]

BackgroundDespite clinical suspicion, most non-invasive ischemia tests for coronary artery disease (CAD) reveal unremarkable results. Patients with a coronary artery calcium score (CACS) of zero rarely have an abnormal positron emission tomography (PET) and could be deferred from further testing. However, most patients have some extent of coronary calcification.ObjectivesCACS percentiles could be useful to exclude abnormal perfusion in patients with CACS >0, but data from patients with 82Rb PET are lacking. The aim of this study was to assess the diagnostic utility of CACS percentiles in comparison to zero calcium and absolute CACS classes.MethodsConsecutive patients with suspected CAD (n = 1,792) referred for 82Rb PET were included and analyzed for abnormal PET (SSS ≥4) and relevant ischemia (>10% myocardium). Test characteristics were calculated.ResultsThe mean age was 65 ± 11 years, 43% were female, and typical angina was reported in 21%. Abnormal PET/relevant ischemia (>10%) were observed in 19.8%/9.3%. Overall, the sensitivity/negative predictive value (NPV) of a 90.9% in all age groups.ConclusionIn patients >50 years, the 10%). They could be used to extend the scope of application of CACS 0 by 8%–10% to 32%–34% overall of patients who could be deferred from further testing.

BackgroundChronic systemic inflammation has been linked to premature mortality and limited somatic as well as mental health with consequences for capability to work and everyday functioning. We recently identified three biochemical clusters of endocrine and immune parameters (C-reactive protein (CRP), interleukin-6 (IL-6), fibrinogen, cortisol and creatinine) in participants, age 35-81 years, of the open access Midlife in the United States Study (MIDUS) dataset. These clusters have been validated in an independent cohort of Japanese mid-life adults. Among these clusters, the one characterized by high inflammation coupled with low cortisol and creatinine concentrations was associated with the highest disease burden, referred to as high-risk cluster in the following. The current study aims to further examine the nature of this cluster and specifically whether it predicts mortality and the reported inability to work the last 30 days 10 years after the biomarker assessment.Methods and findingsLongitudinally assessed health data from N ​= ​1234 individuals were analyzed in the current study. Logistic regression analyses were performed to predict mortality within one decade after first assessment (T0 ​= ​first assessment; T1 ​= ​second assessment). General linear models were used to predict the number of days study participants were unable to work due to health issues in the last 30 days (assessed at T1, analyses restricted to individuals