Journal of Personalized Medicine

BackgroundSchizophrenia (SCZ) and bipolar disorder (BD) are both associated with several autoimmune disorders including rheumatoid arthritis(RA). However, a causal association of SCZ and BD on RA is controversial and elusive. In the present study, we aimed to investigate the causal association of SCZ and BD with RA by using the Mendelian randomization (MR) approach. MethodsA two-sample MR (2SMR) study including the inverse-variance weighted(IVW), weighted median, simple mode, weighted mode and MR-Egger methods were performed. We used summary-level genome-wide association study(GWAS) data in which BD and SCZ are the exposure and RA the outcome. We used data from the Psychiatric Genomics Consortium(PGC) for BD(n= 41,917) and SCZ(n= 33,426) and RA GWAS dataset(n= 2,843) from the European ancestry for RA. ResultsWe found 48 and 52 independent single nucleotide polymorphisms (SNPs, r2 <0.001)) that were significant for respectively BD and SCZ (p <5x10-8). Subsequently, these SNPs were utilized as instrumental variables(IVs) in 2SMR analysis to explore the causality of BD and SCZ on RA. The two out of five MR methods showed a statistically significant inverse causal association between BD and RA: weighted median method(odds ratio (OR), 0.869, [95% CI, 0.764-0.989]; P= 0.034) and inverse-variance weighted(IVW) method (OR, 0.810, [95% CI, 0.689-0.953]; P= 0.011). However, we did not find any significant association of SCZ with RA (OR, 1.008, [95% CI, 0.931-1.092]; P= 0.829, using the IVW method). ConclusionsThese results provide support for an inverse causal association between BD and RA. Further investigation is needed to explain the underlying protective mechanisms in the development of RA. Key messagesO_LIMendelian randomization can offer strong insight into the cause-effect relationships in rheumatology. C_LIO_LIBipolar disorder had a protective effect on rheumatoid arthritis. C_LIO_LIThere is no inverse causal association between schizophrenia and rheumatoid arthritis contrary to the findings from observational studies. C_LI

BackgroundCoronary artery disease (CAD) is a leading cause of death, and depression exacerbates CAD. Antidepressants may offer therapeutic potential for CAD. MethodsWe employed Mendelian Randomization (MR), summary-based MR (SMR), colocalization, replication analysis, and single-cell RNA annotations to assess causal relationships between antidepressant targets and CAD. Safety profiles were evaluated using the Food and Drug Administrations (FDA) Adverse Event Reporting System (FAERS). ResultsFifteen proteins demonstrated significant associations with CAD. GM2A (odds ratio [OR]: 0.975, P = 4 x 10-3), PYGL, BCHE, and several others were found to reduce the risk of CAD, while PDE4A (OR: 1.183, P < 1 x 10-3) and others were associated with an increased risk. GM2A passed sensitivity analyses and exhibited strong colocalization (posterior probability of colocalization [PPH.4] > 0.8). Elevated expression of GM2A consistently showed an inverse association with CAD risk across six tissue types, with cell-type-specific patterns observed in endothelial cells and macrophages. In SMR, FOLH1 was identified as a replicable protective factor for CAD. The FAERS recorded 52,952 adverse events (AEs) related to the selected antidepressant, affecting 6,391 patients. The predominant AEs included drug withdrawal syndrome, dizziness, paresthesia, and nausea. Significant safety signals were identified for dysphoria (reporting odds ratio [ROR] 708.12) and affect lability (ROR 362.05). Additionally, unexpected events such as insomnia, anxiety, fatigue, irritability, headache, and agitation were noted. ConclusionsOur findings suggest that antidepressants may have a therapeutic role in the treatment of CAD, with GM2A identified as a promising target for therapy. While certain antidepressants can influence CAD risk, further validation is necessary to address safety concerns.

AimPersonalized medicine is part of the future frontier of public health and precision healthcare systems have been implemented for years, both within Europe and beyond. To establish the state of the art of Sino-EU science and innovation in Personalized medicine, we have mined the major dedicated databases globally. Here we present the updated mapping on the Sino-EU collaborations. Patents, scientific publications and preprints related to Personalized medicine have been mapped and analyzed after being extracted through databases mining. The integration of the previous mapping provides a more complete overview, which does not show relevant variations, confirming previous trends. In this work we complete the mapping by providing a digital tool for consulting the various data collected.

Current insights to personalize supportive care for patients with immunological disorders, especially in the context of medical treatments, remain inadequate. Delivering and guiding supportive care unquestionably contributes to a higher quality of life and better overall healthcare. The Subjective Health Experience (SHE) Model provides a general framework, comprising four segments, to differentiate supportive healthcare in a quick and practical approach. In this report both health care workers and patients tailored the unique needs of patients with immunological disorders to improve their supportive care. Employing qualitative methods, group discussions and individual interviews were conducted with 19 healthcare professionals and 18 patients suffering from Rheumatoid Arthritis/Spondylarthritis, Inflammatory Bowel Disease (Crohns disease and Ulcerative colitis), and Psoriasis/Hidradenitis Suppurativa. The aim was to ascertain nuanced insights into the behaviour, questions, and needs of patients with six common immunological conditions guided by the SHE-model, thereby refining the personalized supportive care framework. A detailed description was made for patients with immunological disorders per SHE-model segment. Based on these insights, it was determined for each segment WHAT kind of supportive care is needed and HOW it should be offered. Notably, patients emphasized the qualitative aspects of their interactions with healthcare professionals (attention, acknowledgment, and empathetic communication), contrasting with professionals focus on the treatment plan. This led to a strategic allocation of supportive care interventions across patient segments. This study has significantly advanced our understanding of appropriate supportive healthcare for patients with immunological disorders from the perspective of the SHE-model. These findings not only enrich the existing literature but also equip healthcare professionals with a concrete guide for enhancement of supportive care, as the SHE-model is easy to perform in daily clinical care. Attention, acknowledgment, and listening comprise the foundational elements for offering and guiding supportive care.

ObjectivesThe coronavirus disease 2019 (COVID-19) epidemic brings potentially impact on the care of patients with rheumatic diseases, including SAPHO syndrome. We aimed to investigate the disease status, concerns about management, and psychological stress in SAPHO patients during the COVID-19 epidemic. MethodA structured questionnaire was distributed online to patients with SAPHO syndrome enrolled in a Chinese cohort study on March 3rd, 2020. Patients were ask about the current treatments, disease status, and concerns about disease management during the epidemic. Psychologic stress (scored from 0 to 10 points) and psychological problems were reported by the patients. ResultsA total of 157 patients (mean age 38.4 {+/-} 12.3 years, 66.9% females) were included in the study. None of the patients were diagnosed with COVID-19. Sixty-five (41.4%) patients worried about their disease conditions during the epidemic with concerns including medication shortage (73.8%), delay of consultation (46.2%), and disease aggravation (61.5%). Sixty-seven (42.7%) patients had medication withdrawal or dose reduction due to lack of drugs, irregular daily schedule or subjective reasons. The most common psychological problems reported was little interest or pleasure in doing things (66.2%). Patients with progressive disease condition were more distressed and disturbed by the epidemic. Patients with nail involvement felt more worried about their disease conditions than patients without (59.6% vs 31.0%, p =0. 001). ConclusionsThe COVID-19 epidemic imposes a negative impact on the disease management and psychological stress in SAPHO patients. Patients access to specialty care and medication well as mental stress is of great concern.

Background and AimsHeart Failure is a common and serious condition that often remains undetected until a major cardio-vascular event leads to diagnosis is secondary care. Here we propose a portable artificial intelligence tool that integrates clinical guidelines with phenotypic markers to identify high-risk patients who may benefit from formal diagnosis evaluation and timely initiation of treatment. MethodsDiagnosis guidelines are first encoded using a rule-based model, which is then used to train a neural network. Relying on de-identified real-world evidence from UK primary care, transfer learning is used to train on 91,346 historical records and forecast the 6.2% patients who received a diagnosis within 3 years. Tested for portability in an independent sample consisting of 56,308 validation records, predictions are interpreted using Shapley values and individually assessed for statistical significance by comparison with matched digital twin cohorts. A Kaplan-Meier survival analysis links positive predictions to the observed excess mortality. ResultsCompared with the prevailing challenge of under-diagnosis, model predictions in the validation set (0.7% TP, 2.7% FP) demonstrate strong statistical support, with fewer than 1.5% failing to reject a null hypothesis at p=0.05. Among the TP, the likelihood of receiving a future diagnosis is over 7.6 times higher than the baseline prevalence in the validation cohort. In both TP and FP cohorts, patients aged 60-70 years exhibited mortality rates more than fivefold higher than the control population. Furthermore, variables derived from the Complete Blood Count (CBC) including white blood cell count (WBC) and red cell distribution width (RDW), contribute significant predictive value beyond established diagnosis criteria. ConclusionsWhen implemented within a clinical decision support system, predictive AI has the potential to improve patients outcomes by leveraging routinely collected phenotypic markers, which are challenging for clinicians to interpret in the context of complex decision-making pathways.1

In this study, we developed a deep neural network (DNN) model with weight correlation descent (WCD) regularization to improve polygenic risk score predictions for complex diseases, specifically gender-specific cancers, using the UK Biobank dataset. Our DNN model with WCD outperformed both conventional PRS models and DNN models without WCD, demonstrating the importance of regularization techniques in enhancing model performance and capturing non-linear effects and interactions in genomic data. These findings contribute to a better understanding of genetic architecture, facilitating personalized interventions based on individual genetic profiles and ultimately benefiting patient care and health outcomes.

Primary ciliary dyskinesia (PCD) belongs to the group of rare genetic disorders that is extremely hard to diagnose and treat. Current diagnostic modalities detect only 70% of cases and are technically demanding. It necessitates novel computational approaches for biomarker discovery and the identification of therapeutic targets. We have developed an integrative computational pipeline analysing transcriptomic data from 6 PCD patients and 9 healthy controls. We identified 1,249 differentially expressed genes (false discovery rate below 0.05, absolute log2 fold-change exceeding 1), revealing oxidative stress as a central pathophysiological mechanism, with glutathione S-transferase theta 2B (GSTT2B) emerging as a master regulatory hub. WGCNA detected 12 co-expression modules with three significantly disease-associated modules. The application of machine learning enabled outstanding diagnostic performance with a minimal 10-gene signature, maintaining an accuracy of 0.93. The Random Forest area under the receiver operating characteristic curve was estimated to be 0.96 {+/-} 0.03. This study aided in analyzing uncharacterized genes, such as FRMPD3, C1orf194, and METTL26, which were not previously associated with PCD. The methodology adopted for drug repurposing helped in the identification of FDA-approved drugs, including N-acetylcysteine, metformin, and resveratrol. They appeared as top candidates for therapeutic intervention of PCD. The age-dependent classification revealed that 156 genes exhibited significant disease progression interactions. On the other hand, gender-associated classifications precisely identified 342 sex-specific responsive genes. BackgroundPrimary ciliary dyskinesia (PCD), is considered a rare genetic disorder that arises due to ciliary dysfunction. It causes severe respiratory illness including chronic infections, bronchiectasis, and morbidity. Although more than 50 PCD genes have been identified, the molecular mechanisms underlying PCD pathophysiology remain unclear. This obscurity leads to failed therapeutic interventions, highlighting the need for robust PCD-specific molecular characterization. MethodsThis study has incorporated an integrated computational analysis of transcriptomic data obtained from the GSE25186 dataset. This dataset encompasses nasal epithelial cells samples extracted from six and nine confirmed cases of PCD and healthy controls respectively. Different approaches were undertaken in this study. These included empirical Bayes moderated t statistics, weighted gene co-expression network analysis (WGCNA) with soft threshold {beta}=6, comprehensive pathway enrichment across KEGG, Reactome, and GO databases, machine learning classification using Random Forest and Support Vector Machines, temporal trajectory inference through pseudotime analysis, and systematic drug repurposing screening against DrugBank v5.1.8 and ChEMBL v29 databases. ResultsWe identified 1,249 differentially expressed genes (adjusted p-value < 0.05, |log2FC| > 1), comprising 533 upregulated and 716 downregulated genes. The application of WGCNA identified 12 co-expression modules that were found to be associated with three different modules. These three modules were brown module: r = 0.78, p = 2x10-, blue module: r = - 0.65, p = 0.008, and green module: r = 0.82, p = 0.001). The machine learning tools yielded outstanding diagnostic performance, with a Random Forest AUC value of 0.96 {+/-} 0.03. This led to the generation of a minimal 10-gene diagnostic signature. This study identified N-acetylcysteine (NAC) as the top therapeutic candidate, with enhanced potential for treating PCD. The other candidates, metformin and resveratrol, had composite scores of 1.85 and 0.28, respectively, whereas NAC possessed a composite score of 2.46. Systems biology-based classification by age revealed progressive molecular deterioration. A total of 156 genes had a significant age x disease interaction, with a false detection rate of less than 0.05. Gender stratification located 342 genes that were differentially responsive, leading to the design of male/female-dependent therapeutic interventions. ConclusionsThe multi-omics analysis gives significant revelations onto PCD molecular pathophysiology. The oxidative stress (GSTT2B, GPX1, SOD2) mechanism and protein homeostasis disruption (HSPA8, PDIA3, CALR) served as central regulators for disease progression. This study helps to gain novel insights into reliable diagnostic markers, FDA-approved and readily available drug candidates for PCDs therapeutic interventions. Further, age and gender associated classification of biological markers in PCD offers novel path for tailored medicines. This study established a robust molecular framework for therapeutics of rare genetic diseases.

This study analyzes the specific linkages between symptoms within individual COVID patients belonging to at-risk groups. The goal was to determine how strongly linked patient symptoms are within these at-risk groups to find any associations between factors such as comorbidities and COVID symptoms. In this study, de-identified patient data from the N3C database was utilized in order to link representative immunocompromised states with specific symptoms, and non-immunocompromised state with the same, to determine if the strength of the correlation changes for these at-risk groups. Multiple autoimmune disorders resulting in immunocompromised state were analyzed, to determine if severity of immune response and inflammatory action plays a role in any potential differences. An exploratory approach using statistical methods and visualization techniques appropriate to multidimensional data sets was taken. The identified correlations may allow pattern analysis in disease presentation specific to a given population, potentially informing pattern recognition, symptom presentation, and treatment approaches in patients with immune comorbidities.

1BACKGROUNDTraditional Chinese Medicine (TCM) is increasingly integrated into healthcare and insurance systems, therefore, it is essential to understand its current status and patients perspectives. METHODSThis cross-sectional study was conducted from January 1st to December 31st, 2023, across five TCM practices in Switzerland. All patients attending their sixth therapy session were invited to complete an electronically anonymized questionnaire covering patient demographics, treatment experiences, and satisfaction. RESULTSA total of 461 patients participated in the survey, with the majority being female (60.1%) and aged 50 years or older (57.4%). Among them, 54.0% reported multiple health conditions, with 32.9% having musculoskeletal disorders and 31.8% suffering from chronic pain as the main reasons for seeking therapy. Most patients received weekly TCM treatments (91.3%), with 50.7% also undergoing conventional therapies. Of the respondents, 50.0% reported full coverage for their TCM therapy costs. TCM was mainly accessed through personal recommendations (44.5%), and 92.2% experienced wait times under 10 minutes. Acupuncture was the predominant treatment (95.7%), with 35.8% receiving additional dietary advice. Overall satisfaction reached 96.5%, and 99.5% expressed intent to continue TCM. Full compared to no TCM expense coverage was positively associated with treatment satisfaction (odds ratio = 2.42, 95% CI [1.10 to 5.31], p = 0.028), while other medical factors showed no significant impact on satisfaction. CONCLUSIONThis study indicates that women, patients over 50 years, and those with multiple health conditions, especially musculoskeletal and pain conditions, are more likely to seek regular integrated TCM treatment. Patients reported high satisfaction with TCM, and treatment satisfaction was positively associated with full compared to no health insurance coverage. Future research should include more TCM practices and patients to further generalize these findings and precisely assess the impact of insurance coverage on TCM satisfaction to meet patient needs better.

BackgroundMusic is an effective non-pharmacologic, non-invasive, safe, and low-cost intervention to enhance psychophysiological wellness and promote relaxation. This study addresses major knowledge gaps in establishing and validating a scientifically reproducible and rigorous methodology for music repertoire selection to enhance perceived relaxation. MethodsVolunteer participants (N=293) completed a web-based music-listening survey containing 16 questions on Compositional Elements of Relaxation (CER). From the unlabeled audio excerpts isolating and representing variations of each CER isolated from chronologically diverse classical music compositions spanning 400 years, respondents selected the variation perceived to be the most relaxing. Demographics including age, sex, race, education level, occupation, and level of musical training were collected. The 16 CERs identified by music experts included Accentuation, Articulation, Dynamic Range, Familiarity, Interpretive Expertise, Melodic Shape, Meter, Recording Quality, Repetition, Register, Rubato, Tempo, Texture, Timbre, Transition, and Tonality. ResultsThe web-based music-listening survey was completed by a demographically diverse cohort of 293 volunteer participants. When choosing music with a targeted outcome of relaxation, our investigation identified, evaluated, and validated variations of 16 Compositional Elements of Relaxation that enhanced perceived relaxation. Our data showed that musical compositions with the following intrinsic characteristics promoted relaxation: lack of accentuation, legato articulation, familiarity, pp-mp dynamic range (very soft to medium soft), smooth melodic shape, quadruple meter, high clarity recording, with repetition, middle register, rubato (rhythmic flexibility), medium tempo (80-100 bpm aligning with the human resting heart rate), thin texture, piano or string instrumentation, expert performance, and smooth transition. The most significant factors associated relaxation were legato (connective articulation), an absence of accentuation (strong accents), and rubato (rhythmic flexibility inimitable by computer-generated recordings.) Results from subgroup analysis revealed age, sex, race, education, and musical training differences in preferred music for relaxation. The factors most commonly associated with differences were rubato and texture. Factors that did not differ in any subgroup analysis included Accentuation, Articulation, Interpretive Expertise, Meter, Recording Quality, Repetition, Register, and Timbre. Thin texture was increasingly preferred for relaxation with increasing education and musical training level. ConclusionsOur investigation provided a reproducible theoretical framework for selecting evidence-based qualitative Compositional Elements of Relaxation (CER)--16 parameters isolated and individually assessed as correlated with perceived relaxation. This data-driven music-selecting methodology significantly increases the scientific rigor and the probability of clinical translation for music medicine research with targeted anxiolytic outcomes.

Misophonia is a disorder of decreased tolerance to specific sounds or their associated stimuli that has been characterized using different language and methodologies. The absence of a common understanding or foundational definition of misophonia hinders progress in research to understand the disorder and develop effective treatments for individuals suffering from misophonia. From June 2020 through January 2021, a project was conducted to determine whether a committee of experts with diverse expertise related to misophonia could develop a consensus definition of misophonia. An expert committee used a modified Delphi method to evaluate candidate definitional statements that were identified through a systematic review of the published literature. Over four rounds of iterative voting, revision, and exclusion, the committee made decisions to include, exclude, or revise these statements in the definition based on the currently available scientific and clinical evidence. A definitional statement was included in the final definition only after reaching consensus at 80% or more of the committee agreeing with its premise and phrasing. The results of this rigorous consensus-building process were compiled into a final definition of misophonia that is presented here. This definition will serve as an important step to bring cohesion to the growing field of researchers and clinicians who seek to better understand and support individuals experiencing misophonia.

The impact of common and rare variants in COVID-19 host genetics is widely studied in [16]. Here, common and rare variants were used to define an interpretable machine learning model for predicting COVID-19 severity. Firstly, variants were converted into sets of Boolean features, depending on the absence or the presence of variants in each gene. An ensemble of LASSO logistic regression models was used to identify the most informative Boolean features with respect to the genetic bases of severity. After that, the Boolean features, selected by these logistic models, were combined into an Integrated PolyGenic Score, the so called IPGS, which offers a very simple description of the contribution of host genetics in COVID-19 severity. IPGS leads to an accuracy of 55-60% on different cohorts and, after a logistic regression with in input both IPGS and the age, it leads to an accuracy of 75%. The goal of this paper is to improve the previous results, using the information on the host organs involved in the disease. We generalized the IPGS adding a statistical weight for each organ, through the transformation of Boolean features into "Boolean quantum features", inspired by the Quantum Mechanics. The organs coefficients were set via the application of the genetic algorithm Pygad and, after that, we defined two new Integrated PolyGenic Score ([Formula] and [Formula]). By applying a logistic regression with both [Formula] (or indifferently [Formula]) and age as input, we reach an accuracy of 84-86%, thus improving the results previously shown in [16] by a factor of 10%.

The Phenome-wide association studies (PheWAS) have become widely used for efficient, high-throughput evaluation of relationship between a genetic factor and a large number of disease phenotypes, typically extracted from a DNA biobank linked with electronic medical records (EMR). Phecodes, billing code-derived disease case-control status, are usually used as outcome variables in PheWAS and logistic regression has been the standard choice of analysis method. Since the clinical diagnoses in EMR are often inaccurate with errors which can lead to biases in the odds ratio estimates, much effort has been put to accurately define the cases and controls to ensure an accurate analysis. Specifically in order to correctly classify controls in the population, an exclusion criteria list for each Phecode was manually compiled to obtain unbiased odds ratios. However, the accuracy of the list cannot be guaranteed without extensive data curation process. The costly curation process limits the efficiency of large-scale analyses that take full advantage of all structured phenotypic information available in EMR. Here, we proposed to estimate relative risks (RR) instead. We first demonstrated the desired nature of RR that overcomes the inaccuracy in the controls via theoretical formula. With simulation and real data application, we further confirmed that RR is unbiased without compiling exclusion criteria lists. With RR as estimates, we are able to efficiently extend PheWAS to a larger-scale, phenome construction agnostic analysis of phenotypes, using ICD 9/10 codes, which preserve much more disease-related clinical information than Phecodes.

In the EU project TRAJECTOME, we used a novel methodology to identify temporal disease maps of depression and highly prevalent co-occurring disease conditions. This information was combined with disability weights established by the Global Burden of Disease Study 2019 to create a depression-related health risk assessment tool, the Multimorbidity Adjusted Disability Score (MADS). MADS was used to stratify over one million cases from three different cohorts and evaluate the impact on utilisation of healthcare resources, mortality, pharmacological burden, healthcare expenditure and multimorbidity progression. Results indicate statistically significant associations between MADS and increased mortality rate (P <.001), heightened healthcare utilization (i.e. emergency room visits P <.001; hospitalizations P <.001; pharmaceutical prescriptions P <.001; total healthcare expenditure P <.001), and a higher risk of disease progression and incidence of new depression-related comorbidities. MADS seems to be a promising approach to predict depression-related health risk and depressions impact on individuals and healthcare systems, which can be tested in other diseases; nevertheless, clinical validation is still necessary.

Hypermobile Ehlers-Danlos Syndrome (hEDS) is a genetic connective tissue disorder characterized by hypermobile joints, chronic pain, fatigue, brain fog, orthostatic intolerance, and GI symptoms and dysmotility. Its heterogeneous presentation contributes to poor quality of life, inappropriate interventions, and prolonged diagnostic delays, often up to 10 years. This study primarily aimed to determine if physiological signals captured by a medical-grade wrist wearable could characterize autonomic patterns in hEDS and relate them to symptoms. Individuals with hEDS (n=30) and healthy controls (n=28) wore a medical grade smartwatch for 30 days, collecting continuous heart rate variability, activity, oxygen saturation, and blood pressure, alongside initial baseline symptom and quality-of-life surveys. Individuals with hEDS showed greater instability and variability in both systolic and diastolic blood pressure as well as the HRV metric LF/HF ratio, in comparison to healthy controls (p-values: 0.04, 0.02, 0.02). During sleep, metrics of parasympathetic activity (HRV measures: HF power, pNN50, RMSSD) trended lower in hEDS than healthy in comparison. As expected, survey domains assessing physiologic symptoms and quality-of-life were significantly worse in the hEDS cohort (p-values < 0.05). Notably, autonomic metrics correlated with GI symptoms in the hEDS cohort (Spearman's {rho} range: 0.38-0.60), and psychological symptoms in the healthy cohort (Spearman's {rho} range: -0.47-0.41). Principal component analysis (PCA) of physiologic and symptom features clearly separated groups, supporting distinct physiologic profiles. Combination of GI symptom index and wearable monitoring show promise as a hybrid screening approach that could substantially shorten the time to diagnosis in this population.

IntroductionStatin-associated muscle symptoms (SAMS) contribute to the nonadherence to statin therapy. In a previous study, we successfully developed a pharmacological SAMS (PSAMS) phenotyping algorithm that distinguishes objective versus nocebo SAMS using structured and unstructured electronic health records (EHRs) data. Our aim in this paper was to develop a pharmacological SAMS risk stratification (PSAMS-RS) score using these same EHR data. MethodUsing our PSAMS phenotyping algorithm, SAMS cases and controls were identified using University of Minnesota (UMN) Fairview EHR data. The statin user cohort was temporally divided into derivation (1/1/2010 to 12/31/2018) and validation (1/1/2019 to 12/31/2020) cohorts. First, from a feature set of 38 variables, a Least Absolute Shrinkage and Selection Operator (LASSO) regression model was fitted to identify important features for PSAMS cases and their coefficients. A PSAMS-RS score was calculated by multiplying these coefficients by 100 and then adding together for individual integer scores. The clinical utility of PSAMS-RS in stratifying PSAMS risk was assessed by comparing the hazard ratio (HR) between 4th vs 1st score quartile. ResultsPSAMS cases were identified in 1.9% (310/16128) of the derivation and 1.5% (64/4182) of the validation cohort. After fitting LASSO regression, 16 out of 38 clinical features were determined to be significant predictors for PSAMS risk. These factors are male gender, chronic pulmonary disease, neurological disease, tobacco use, renal disease, alcohol use, ACE inhibitors, polypharmacy, cerebrovascular disease, hypothyroidism, lymphoma, peripheral vascular disease, coronary artery disease and concurrent uses of fibrates, beta blockers or ezetimibe. After adjusting for statin intensity, patients in the PSAMS score 4th quartile had an over seven-fold (derivation) (HR, 7.1; 95% CI, 4.03-12.45) and six-fold (validation) (HR, 6.1; 95% CI, 2.15-17.45) higher hazard of developing PSAMS versus those in 1st score quartile. ConclusionThe PSAMS-RS score can be a simple tool to stratify patients risk of developing PSAMS after statin initiation which can facilitate clinician-guided preemptive measures that may prevent potential PSAMS-related statin non-adherence.

Accurate disease risk prediction is essential in healthcare to provide personalized disease prevention and treatment strategies not only to the patients, but also to the general population. In addition to demographic and environmental factors, advancements in genomic research have revealed that genetics play an important role in determining the susceptibility of diseases. However, for most complex diseases, individual genetic variants are only weakly to moderately associated with the diseases. Thus, they are not clinically informative in determining disease risks. Nevertheless, recent findings suggest that the combined effects from multiple disease-associated variants, or polygenic risk score (PRS), can stratify disease risk similar to that of rare monogenic mutations. The development of polygenic risk score provides a promising tool to evaluate the genetic contribution of disease risk; however, the quality of the risk prediction depends on many contributing factors including the precision of the target phenotypes. In this study, we evaluated the impact of phenotyping errors on the accuracies of PRS risk prediction. We utilized electronic Medical Records and Genomics Network (eMERGE) data to simulate various types of disease phenotypes. For each phenotype, we quantified the impact of phenotyping errors generated from the differential and non-differential mechanism by comparing the prediction accuracies of PRS on the independent testing data. In addition, our results showed that the rate of accuracy degradation depended on both the phenotype and the mechanism of phenotyping error.

Neurological and psychiatric manifestations affect most lupus individuals and include depression, anxiety, mood disorders, and cognitive dysfunction. Although there is evidence supporting suboptimal decision-making in lupus and its association with glucocorticoids consumption, it is not clear what variables impact such decisions. The aim of this study is to explore how social, clinical, psychological, and demographic factors impact social and temporal decision-making in people with lupus. Through a within-subjects experimental-design, our participants responded to social, clinical, psychological, and demographic electronic questionnaires. Then, they participated in two behavioral economics experiments: the third-party dictator game, and the delay discounting task. Our results show that hostility, and age are essential predictors of social decisions, whereas obsessive-compulsiveness and anxiety better predict temporal decisions. These variables behave as expected, but anxiety shows unexpected results: most anxious people act patiently and prefer delayed but bigger rewards. Finally, clinical factors are critical decision predictors for social and temporal decisions. When people are in remission, they tend to impose higher punishment on those who violate the social norm, and they also tend to prefer immediate rewards. When taking glucocorticoids, they also prefer immediate rewards, and as the dosage of glucocorticoids intake increases, they tend to impose higher punishment on norm violators. Clinicians, researchers, and practitioners must consider the side effects of glucocorticoids on decision-making.

Hypermobile Ehlers-Danlos Syndrome (hEDS) is a complex, underdiagnosed connective tissue disorder characterized by widespread symptoms affecting multiple organ systems. Recent clinical observations suggest that individuals with hEDS may be at increased risk for persistent symptoms following COVID-19, commonly referred to as Long COVID. Using data from over 19 million patients across the United States, we examined associations between hEDS, COVID-19 infection, Long COVID, and related chronic conditions. We identified just over 25,000 individuals with hEDS and estimated a prevalence of approximately 1 in 800, which is higher than previously recognized. While rates of COVID-19 infection were similar between patients with hEDS and matched controls, patients with hEDS were significantly more likely to develop Long COVID. This risk was especially elevated among patients with hEDS with overlapping conditions commonly seen in post-viral syndromes, including autonomic dysfunction, immune dysregulation, and chronic fatigue. Specifically, individuals with postural orthostatic tachycardia, mast cell-related symptoms, or chronic fatigue syndrome had the highest rates of Long COVID. Temporal diagnostic analyses revealed that many patients received an hEDS diagnosis only after a COVID-19 infection, suggesting that viral illness may exacerbate or reveal previously unrecognized symptoms. Patients with hEDS also exhibited higher odds of having additional risk factors for severe or prolonged illness, including chronic lung and autoimmune conditions, depression, and cerebrovascular disease. These findings highlight a previously unrecognized vulnerability in patients with hEDS and underscore the need for greater clinical awareness of their heightened risk for persistent post-COVID illness. Improved screening, earlier diagnosis, and integrated care pathways are urgently needed to support this complex and underserved patient population. Author summaryWe studied patients with hypermobile Ehlers-Danlos Syndrome (hEDS), a connective tissue condition that affects joints, skin, and many body systems. This condition is often misunderstood or overlooked, leaving many people undiagnosed. During the COVID-19 pandemic, people with hEDS appeared to experience more long-term symptoms after infection, a condition often called Long COVID. Here, we analyzed the health records of millions of patients in the United States to better understand post-viral outcomes. Patients with hEDS were more likely to be diagnosed with Long COVID compared to similar patients without hEDS. This was especially true for those who also had conditions such as chronic fatigue, immune conditions, or issues with heart rate and blood pressure regulation. In many cases, people were diagnosed with hEDS for the first time only after they had COVID-19, suggesting the virus may worsen or reveal symptoms that had been previously missed. Our findings show that hEDS may be more common than previously thought and that such patients face higher risks after COVID-19. Greater awareness and earlier recognition of hEDS could improve care for many patients with complex, long-lasting symptoms