[The precision medicine for the treatment of acute respiratory distress syndrome is based on identification of phenotypes].

The acute respiratory distress syndrome (ARDS) is a heterogeneous syndrome characterized by distinct etiologies and complicated pathobiological mechanism. It is difficult to discriminate patients with unique biological features or individual response to specific therapy by traditional definition and subgrouping. Unfortunately, there are few clinical evidences supporting effective drug therapy to ARDS. The sub-phenotype or endotype of ARDS is related to potential mechanism of the syndrome, and is critical to personalized treatment of ARDS. An appropriate sub-phenotype of ARDS may be defined by data-driven assessment of the available data including clinical features, biological biomarkers and respiratory parameters of the patients. Latent class analysis or machine learning has potential to establish new sub-phenotype of ARDS stably, which is helpful to guide precision medicine approach.

Bile Acid Diarrhea: From Molecular Mechanisms to Clinical Diagnosis and Treatment in the Era of Precision Medicine.

Bile acid diarrhea (BAD) is a multifaceted intestinal disorder involving intricate molecular mechanisms, including farnesoid X receptor (FXR), fibroblast growth factor receptor 4 (FGFR4), and Takeda G protein-coupled receptor 5 (TGR5). Current diagnostic methods encompass bile acid sequestrants (BAS), 48-h fecal bile acid tests, serum 7α-hydroxy-4-cholesten-3-one (C4), fibroblast growth factor 19 (FGF19) testing, and 75Selenium HomotauroCholic acid test (75SeHCAT). Treatment primarily involves BAS and FXR agonists. However, due to the limited sensitivity and specificity of current diagnostic methods, as well as suboptimal treatment efficacy and the presence of side effects, there is an urgent need to establish new diagnostic and treatment methods. While prior literature has summarized various diagnostic and treatment methods and the pathogenesis of BAD, no previous work has linked the two. This review offers a molecular perspective on the clinical diagnosis and treatment of BAD, with a focus on FXR, FGFR4, and TGR5, emphasizing the potential for identifying additional molecular mechanisms as treatment targets and bridging the gap between diagnostic and treatment methods and molecular mechanisms for a novel approach to the clinical management of BAD.

Genetic Associations of Circulating Cardiovascular Proteins With Gestational Hypertension and Preeclampsia.

Importance: Hypertensive disorders of pregnancy (HDPs), including gestational hypertension and preeclampsia, are important contributors to maternal morbidity and mortality worldwide. In addition, women with HDPs face an elevated long-term risk of cardiovascular disease. Objective: To identify proteins in the circulation associated with HDPs. Design, Setting, and Participants: Two-sample mendelian randomization (MR) tested the associations of genetic instruments for cardiovascular disease-related proteins with gestational hypertension and preeclampsia. In downstream analyses, a systematic review of observational data was conducted to evaluate the identified proteins' dynamics across gestation in hypertensive vs normotensive pregnancies, and phenome-wide MR analyses were performed to identify potential non-HDP-related effects associated with the prioritized proteins. Genetic association data for cardiovascular disease-related proteins were obtained from the Systematic and Combined Analysis of Olink Proteins (SCALLOP) consortium. Genetic association data for the HDPs were obtained from recent European-ancestry genome-wide association study meta-analyses for gestational hypertension and preeclampsia. Study data were analyzed October 2022 to October 2023. Exposures: Genetic instruments for 90 candidate proteins implicated in cardiovascular diseases, constructed using cis-protein quantitative trait loci (cis-pQTLs). Main Outcomes and Measures: Gestational hypertension and preeclampsia. Results: Genetic association data for cardiovascular disease-related proteins were obtained from 21 758 participants from the SCALLOP consortium. Genetic association data for the HDPs were obtained from 393 238 female individuals (8636 cases and 384 602 controls) for gestational hypertension and 606 903 female individuals (16 032 cases and 590 871 controls) for preeclampsia. Seventy-five of 90 proteins (83.3%) had at least 1 valid cis-pQTL. Of those, 10 proteins (13.3%) were significantly associated with HDPs. Four were robust to sensitivity analyses for gestational hypertension (cluster of differentiation 40, eosinophil cationic protein [ECP], galectin 3, N-terminal pro-brain natriuretic peptide [NT-proBNP]), and 2 were robust for preeclampsia (cystatin B, heat shock protein 27 [HSP27]). Consistent with the MR findings, observational data revealed that lower NT-proBNP (0.76- to 0.88-fold difference vs no HDPs) and higher HSP27 (2.40-fold difference vs no HDPs) levels during the first trimester of pregnancy were associated with increased risk of HDPs, as were higher levels of ECP (1.60-fold difference vs no HDPs). Phenome-wide MR analyses identified 37 unique non-HDP-related protein-disease associations, suggesting potential on-target effects associated with interventions lowering HDP risk through the identified proteins. Conclusions and Relevance: Study findings suggest genetic associations of 4 cardiovascular disease-related proteins with gestational hypertension and 2 associated with preeclampsia. Future studies are required to test the efficacy of targeting the corresponding pathways to reduce HDP risk.

Resiniferatoxin: Nature's Precision Medicine to Silence TRPV1-Positive Afferents.

Resiniferatoxin (RTX) is an ultrapotent capsaicin analog with a unique spectrum of pharmacological actions. The therapeutic window of RTX is broad, allowing for the full desensitization of pain perception and neurogenic inflammation without causing unacceptable side effects. Intravesical RTX was shown to restore continence in a subset of patients with idiopathic and neurogenic detrusor overactivity. RTX can also ablate sensory neurons as a "molecular scalpel" to achieve permanent analgesia. This targeted (intrathecal or epidural) RTX therapy holds great promise in cancer pain management. Intra-articular RTX is undergoing clinical trials to treat moderate-to-severe knee pain in patients with osteoarthritis. Similar targeted approaches may be useful in the management of post-operative pain or pain associated with severe burn injuries. The current state of this field is reviewed, from preclinical studies through veterinary medicine to clinical trials.

Clinical and genomic characterisation of early-onset pancreatic cancer.

BACKGROUND: The incidence of early-onset pancreatic cancer (EOPC) has risen dramatically in recent years. We aimed to characterise the clinical and genomic features of EOPC and evaluate their therapeutic implications. METHODS: We performed a comparative, single-centre, retrospective analysis of clinical, germline, and genomic features in EOPC (≤50 years) patients and compared them with a control group of average-onset pancreatic cancer patients (AOPC, ≥70 years). Key molecular findings were compared with an external, publicly available cohort. RESULTS: We reviewed 336 patients who met all inclusion criteria (EOPC N = 139, AOPC N = 197). EOPC was associated with smoking status, lower prevalence of diabetes, better performance status, higher CA19.9 levels, and higher albumin levels at diagnosis. After adjustment for baseline covariates, we observed no differences in overall survival (OS). Age was associated with an increase in the incidence of KRASMUT both in our cohort and the validation cohort. EOPC were enriched in potentially actionable alterations according to ESCAT tiers I-IIIA when compared with AOPC in discovery and validation cohorts (19% versus 14% and 14% versus 8%, respectively). In the first-line metastatic setting, EOPC had a longer progression-free survival (hazard ratio [HR] 0.61, 95% confidence interval (CI) 0.43-0.87) and OS (HR 0.65, 95% CI 0.45-0.95), although there were no differences in response rate. After adjusting for the number of treatment lines, EOPC patients who did receive targeted therapies exhibited longer OS compared with EOPC who did not (HR 0.34, 95% CI 0.12-0.93). CONCLUSIONS: EOPC patients have improved outcomes in the metastatic setting when compared to AOPC and are enriched for targetable alterations that open opportunities for precision oncology-based approaches.

Precision Medicine in Neuropathic Pain.

Neuropathic pain is a common chronic pain condition that is caused by a lesion or disease of the somatosensory nervous system. The multitude of sensory negative and positive sensations and associated comorbidities have a major impact on quality of life of affected patients. Current treatment options often only lead to a partial pain relief or are even completely ineffective. In addition, many clinical trials for the development of new drugs have not met the primary endpoint. Therefore, there is still an unmet clinical need in neuropathic pain syndromes. One reason for this therapeutic dilemma could be the heterogeneity of neuropathic pain with a variety of pathophysiological pain mechanisms that are expressed differently in each patient regardless of the underlying disease etiology. Reclassification of neuropathic pain syndromes therefore focuses on the underlying mechanisms of pain development rather than the disease etiology. A priori stratification of patients based on these individual mechanisms could allow the identification of potential treatment responders and thus realize the concept of a mechanism-based treatment. As no biomarkers for pain mechanisms have been discovered yet, one has to rely on surrogate markers that are thought to be closely related to these mechanisms. In this chapter, we present promising predictive biomarkers, focusing in particular on sensory symptoms and signs assessed by patient-reported outcome measures and sensory testing, and discuss how these tools might be used in clinical trials in the future.

Precision Medicine in Pancreatitis: The Future of Acute Pancreatitis Care.

Acute pancreatitis (AP) continues to present a substantial burden to patients and healthcare personnel. Despite its occasionally severe progression and high mortality rate, there is no specific therapy that could be routinely applied in patients with AP. Here, we review treatment possibilities in AP, describe how the treatment approaches have changed in pancreatic cancer as an analogy, and point out potential causes for the failure of clinical trials on AP. We highlight that instead of attempting to discover generalized treatment options that could be used in any AP patient, it is time for a paradigm shift in the treatment of AP, which would help to focus more on individual patients or specific patient subpopulations when designing clinical trials and therapeutic approaches (similarly as in pancreatic cancer). Since the recruitment of specific patient subpopulations with AP could take excessive time if clinical centers work separately, the development of precision medicine in AP would require to establish an expert committee, eg, Pancreatitis Precision Medicine Interest Group, which could organize and coordinate the activities of the joined centers. With the joined forces of expert clinicians and leading centers, a new era could start in the treatment of AP, in which personalized treatment options could be discovered and introduced to efficiently reduce the burden of the disease on patients and healthcare workers.

Endotypes in bronchiectasis: moving towards precision medicine. A narrative review.

Bronchiectasis is a highly complex entity that can be very challenging to investigate and manage. Patients are diverse in their aetiology, symptoms, risk of complications and outcomes. "Endotypes"- subtypes of disease with distinct biological mechanisms, has been proposed as a means of better managing bronchiectasis. This review discusses the emerging field of endotyping in bronchiectasis. We searched PubMed and Google Scholar for randomized controlled trials (RCT), observational studies, systematic reviews and meta-analysis published from inception until October 2022, using the terms: "bronchiectasis", "endotypes", "biomarkers", "microbiome" and "inflammation". Exclusion criteria included commentaries and non-English language articles as well as case reports. Duplicate articles between databases were initially identified and appropriately excluded. Studies identified suggest that it is possible to classify bronchiectasis patients into multiple endotypes deriving from their co-morbidities or underlying causes to complex infective or inflammatory endotypes. Specific biomarkers closely related to a particular endotype might be used to determine response to treatment and prognosis. The most clearly defined examples of endotypes in bronchiectasis are the underlying causes such as immunodeficiency or allergic bronchopulmonary aspergillosis where the underlying causes are clearly related to a specific treatment. The heterogeneity of bronchiectasis extends, however, far beyond aetiology and it is now possible to identify subtypes of disease based on inflammatory mechanisms such airway neutrophil extracellular traps and eosinophilia. In future biomarkers of host response and infection, including the microbiome may be useful to guide treatments and to increase the success of randomized trials. Advances in the understanding the inflammatory pathways, microbiome, and genetics in bronchiectasis are key to move towards a personalized medicine in bronchiectasis.

Machine learning for post-acute pancreatitis diabetes mellitus prediction and personalized treatment recommendations.

Post-acute pancreatitis diabetes mellitus (PPDM-A) is the main component of pancreatic exocrine diabetes mellitus. Timely diagnosis of PPDM-A improves patient outcomes and the mitigation of burdens and costs. We aimed to determine risk factors prospectively and predictors of PPDM-A in China, focusing on giving personalized treatment recommendations. Here, we identify and evaluate the best set of predictors of PPDM-A prospectively using retrospective data from 820 patients with acute pancreatitis at four centers by machine learning approaches. We used the L1 regularized logistic regression model to diagnose early PPDM-A via nine clinical variables identified as the best predictors. The model performed well, obtaining the best AUC = 0.819 and F1 = 0.357 in the test set. We interpreted and personalized the model through nomograms and Shapley values. Our model can accurately predict the occurrence of PPDM-A based on just nine clinical pieces of information and allows for early intervention in potential PPDM-A patients through personalized analysis. Future retrospective and prospective studies with multicentre, large sample populations are needed to assess the actual clinical value of the model.

Advancing the precision management of inflammatory bowel disease in the era of omics approaches and new technology.

There is great heterogeneity among inflammatory bowel disease (IBD) patients in terms of pathogenesis, clinical manifestation, response to treatment, and prognosis, which requires the individualized and precision management of patients. Many studies have focused on prediction biomarkers and models for assessing IBD disease type, activity, severity, and prognosis. During the era of biologics, how to predict the response and side effects of patients to different treatments and how to quickly recognize the loss of response have also become important topics. Multiomics is a promising area for investigating the complex network of IBD pathogenesis. Integrating numerous amounts of data requires the use of artificial intelligence.

Non-viral precision T cell receptor replacement for personalized cell therapy.

T cell receptors (TCRs) enable T cells to specifically recognize mutations in cancer cells1-3. Here we developed a clinical-grade approach based on CRISPR-Cas9 non-viral precision genome-editing to simultaneously knockout the two endogenous TCR genes TRAC (which encodes TCRα) and TRBC (which encodes TCRß). We also inserted into the TRAC locus two chains of a neoantigen-specific TCR (neoTCR) isolated from circulating T cells of patients. The neoTCRs were isolated using a personalized library of soluble predicted neoantigen-HLA capture reagents. Sixteen patients with different refractory solid cancers received up to three distinct neoTCR transgenic cell products. Each product expressed a patient-specific neoTCR and was administered in a cell-dose-escalation, first-in-human phase I clinical trial ( NCT03970382 ). One patient had grade 1 cytokine release syndrome and one patient had grade 3 encephalitis. All participants had the expected side effects from the lymphodepleting chemotherapy. Five patients had stable disease and the other eleven had disease progression as the best response on the therapy. neoTCR transgenic T cells were detected in tumour biopsy samples after infusion at frequencies higher than the native TCRs before infusion. This study demonstrates the feasibility of isolating and cloning multiple TCRs that recognize mutational neoantigens. Moreover, simultaneous knockout of the endogenous TCR and knock-in of neoTCRs using single-step, non-viral precision genome-editing are achieved. The manufacture of neoTCR engineered T cells at clinical grade, the safety of infusing up to three gene-edited neoTCR T cell products and the ability of the transgenic T cells to traffic to the tumours of patients are also demonstrated.

Definitions, phenotypes, and subphenotypes in acute kidney injury-Moving towards precision medicine.

The current definition of acute kidney injury (AKI) is generic and, based only on markers of function, is unsuitable for guiding individualized treatment. AKI is a complex syndrome with multiple presentations and causes. Targeted AKI management will only be possible if different phenotypes and subphenotypes of AKI are recognised, based on causation and related pathophysiology. Molecular signatures to identify subphenotypes are being recognised, as specific biomarkers reveal activated pathways. Assessment of individual clinical risk needs wider dissemination to allow identification of patients at high risk of AKI. New and more timely markers for glomerular filtration rate (GFR) are available. However, AKI diagnosis and classification should not be limited to GFR, but include tubular function and damage. Combining damage and stress biomarkers with functional markers enhances risk prediction, and identifies a population enriched for clinical trials targeting AKI. We review novel developments and aim to encourage implementation of these new techniques into clinical practice as a strategy for individualizing AKI treatment akin to a precision medicine-based approach.

The understanding of asthma pathogenesis in the era of precision medicine.

Asthma is a syndrome with extremely diverse clinical phenotypes in which the onset, severity, and response to treatment are defined by the complex interplay of many genetic and environmental factors. Environmental factors epigenetically affect gene expression, and the disease is driven by a multidimensional dynamic network involving RNA and protein molecules derived from gene expression, as well as various metabolic products. In other words, specific pathophysiological mechanisms or endotypes are dynamic networks that arise in response to individual genotypes and the various environmental factors to which individuals have been exposed since before birth, such as diet, infection, air pollution, smoking, antibiotic use, and the bacterial flora of the intestinal tract, skin, and lungs. A key feature of asthma genome scans is their potential to reveal the molecular pathways that lead to pathogenesis. Endotypes that drive the disease have a significant impact on the phenotypes of asthma patients, including their drug responsiveness. Understanding endotypes will lead to not only the implementation of therapies that are tailored to the specific molecular network(s) underlying the patient's condition, but also to the development of therapeutic strategies that target individual endotypes, as well as to precision health, which will enable the prediction of disease onset with high accuracy from an early stage and the implementation of preventive strategies based on endotypes. Understanding of endotypes will pave the way for the practice of precision medicine in asthma care, moving away from 'one-size-fits-all' medicine and population-based prevention approaches that do not take individuals' susceptibility into account.

Perspective to precision medicine in scleroderma.

Systemic sclerosis (SSc) is a rare and heterogeneous disease with no relevant environmental trigger or significant responsible gene. It has been and will continue to be difficult to identify large enough patients to conduct classic population-based epidemiologic exposure/non-exposure studies with adequate power to ascertain environmental and genetic risk factors for these entities. The complexity of pathogenesis and heterogeneity are likely to require personalized/precision medicine for SSc. Since several potential drugs are currently available for specific patients if not whole SSc, classification of SSc seems to form the foundation for a better therapeutic strategy. To date, SSc has been classified based on the extent/severity of the affected area as well as some disease markers, including the autoantibody profile. However, such an analysis should also lead to improvements in the design of appropriately stratified clinical trials to determine the effects and prediction of targeted therapies. An approach based on drug response preclinically conducted using patients' own fibroblasts in vitro, can provide a precise disease marker/therapeutic selection for clinical practice. Because scleroderma dermal fibroblasts have a persistent hyper-productive phenotype occurring not only in person, but also in cell culture conditions. Thus, an accumulating approach based on disease markers ensures progression and de-escalation to re-establish a better life with a personally optimized drug environment after the onset of SSc.

A retrospective study of mortality for perioperative cardiac arrests toward a personalized treatment.

Perioperative cardiac arrest (POCA) is associated with a high mortality rate. This work aimed to study its prognostic factors for risk mitigation by means of care management and planning. A database of 380,919 surgeries was reviewed, and 150 POCAs were curated. The main outcome was mortality prior to hospital discharge. Patient demographic, medical history, and clinical characteristics (anesthesia and surgery) were the main features. Six machine learning (ML) algorithms, including LR, SVC, RF, GBM, AdaBoost, and VotingClassifier, were explored. The last algorithm was an ensemble of the first five algorithms. k-fold cross-validation and bootstrapping minimized the prediction bias and variance, respectively. Explainers (SHAP and LIME) were used to interpret the predictions. The ensemble provided the most accurate and robust predictions (AUC = 0.90 [95% CI, 0.78-0.98]) across various age groups. The risk factors were identified by order of importance. Surprisingly, the comorbidity of hypertension was found to have a protective effect on survival, which was reported by a recent study for the first time to our knowledge. The validated ensemble classifier in aid of the explainers improved the predictive differentiation, thereby deepening our understanding of POCA prognostication. It offers a holistic model-based approach for personalized anesthesia and surgical treatment.

Role of platelet in Parkinson's disease: Insights into pathophysiology & theranostic solutions.

Parkinson's disease (PD) is the second-most-common neurodegenerative disease characterized by motor and non-motor dysfunctions, which currently affects about 10 million people worldwide. Gradual death and progressive loss of dopaminergic neurons in the pars compacta region of substantia nigra result in striatal dopamine deficiency in PD. Specific mutation with further aggregation of α-synuclein in the intraneuronal inclusion bodies is considered the neuropathological hallmark of this disease. PD is often associated with various organelle dysfunctions inside a dopaminergic neuron, including mitochondrial damage, proteasomal impairment, and production of reactive oxygen species, thus causing subsequent neuronal death. Apart from several genetic and non-genetic risk factors, emerging research establishes an association between cardiovascular diseases, including coronary heart disease, myocardial infarction, congestive heart failure, and ischemic stroke with PD. The majority of these cardiovascular diseases have an origin from atherosclerosis, where endothelial dysfunction following thrombus formation is significantly regulated by blood platelet. This non-nucleated cell fragment expresses not only neuron-specific molecules and receptors but also several PD-specific biomarkers such as α-synuclein, parkin, PTEN-induced kinase-1, tyrosine hydroxylase, dopamine transporter, thus making platelet a suitable peripheral model for PD. Besides its similarity with a dopaminergic neuron, platelet structural alterations, as well as functional abnormalities, are also evident in PD. However, the molecular mechanism behind platelet dysfunction is still elusive and quite controversial. This state-of-the-art review describes the detailed mechanism of platelet impairment in PD, addressing the novel platelet-associated therapeutic drug candidates for plausible PD management.

Precision medicine versus standard of care for patients with myocardial infarction with non-obstructive coronary arteries (MINOCA): rationale and design of the multicentre, randomised PROMISE trial.

Myocardial infarction with non-obstructive coronary arteries (MINOCA) represents about 6-8% of patients presenting with myocardial infarction (MI), and it is associated with a significant risk of mortality, rehospitalisation, and angina burden, with high associated socioeconomic costs. It is important to note that multiple mechanisms may be responsible for MINOCA. However, to date, there are few prospective clinical trials on MINOCA and the treatment of these patients is still not defined, most likely because of the multiple underlying pathogenic mechanisms. The PROMISE trial is a randomised, multicentre, prospective, superiority, phase IV trial that will include 180 MINOCA patients randomised 1:1 to a "precision-medicine approach", consisting of a comprehensive diagnostic workup and pharmacological treatment specific for the underlying cause, versus a "standard of care" approach, consisting of routine diagnostic workup and standard medical treatment for acute coronary syndrome. The aim of this study is to evaluate if the "precision-medicine approach" will improve the angina status, evaluated using the Seattle Angina Questionnaire summary score, at 12 months (primary endpoint). Secondary endpoints include the rate of major adverse cardiovascular events at 12-month follow-up, the related primary and secondary healthcare costs, and the ability of cardiac magnetic resonance to evaluate the different mechanisms of MINOCA. Of importance, the results derived from this trial may pave the way for a new pathophysiology-driven approach with cause-target therapies personalised for the mechanisms of MINOCA (ClinicalTrials.gov: NCT05122780).

From cryptogenic to ESUS: Toward precision medicine?

Cryptogenic infarctions are infarctions without a defined cause, despite a complete work-up. They differ from infarctions of undetermined causes, which may involve overlapping causes or an incomplete investigation. It is also different from uncommon heritable and non-heritable causes. The term embolic stroke of undetermined source (ESUS) proposed in 2014 is defined as a non-lacunar brain infarct without proximal arterial stenosis or cardioembolic sources. The major advantage of this definition compared to cryptogenic definition is the proposition of a specific work-up. In a general population, frequent potential sources of embolism in patients with ESUS have been suggested since a long time and include: patent foramen ovale (PFO), covert atrial fibrillation (AF), complex aortic arch atheroma, large vessel atheroma with stenosis<50%, carotid web, atrial cardiomyopathy, thrombophilia associated with cancer. It took almost 30 years to show, in patients under 60 with a cryptogenic stroke and a PFO, that PFO occlusion was superior to medical treatment alone for recurrent stroke. PFO under 60 is therefore no longer a cryptogenic cause of infarction. The concept of cryptogenic stroke and its refinement in ESUS have been fruitful for the identification of PFO associated as a cause. Covert AF can be detected by different techniques but its risk significance for recurrent stroke might be different from the simple electrocardiographic detection of AF. With the development of direct oral anticoagulants (DOAs), randomized studies in patients with ESUS, were run for stroke prevention but no difference was observed between patients treated by DOA compared to aspirin. These studies showed however the heterogeneity of ESUS patients. Further ESUS classification should be considered as a tool to identify homogeneous groups. We propose to further split the ESUS group into different subgroups: ESU-PFO>60-year-old, ESUS-ATH with stenosis<50%, ESUS-AF (covert AF & atrial cardiomyopathy), ESUS-cancer and others. Precision medicine is the ability to make targeted healthcare decisions based on the specific risks of individual patients. One preliminary stage is therefore to identify homogeneous groups suitable in the future for new therapeutic trials and, at the end, for new specific treatments.

Precision Medicine Approach for Cardiometabolic Risk Factors in Therapeutic Apheresis.

Lipoprotein apheresis (LA) is currently the most powerful intervention possible to reach a maximal reduction of lipids in patients with familial hypercholesterolemia and lipoprotein(a) hyperlipidemia. Although LA is an invasive method, it has few side effects and the best results in preventing further major cardiovascular events. It has been suggested that the highly significant reduction of cardiovascular complications in patients with severe lipid disorders achieved by LA is mediated not only by the potent reduction of lipid levels but also by the removal of other proinflammatory and proatherogenic factors. Here we performed a comprehensive proteomic analysis of patients on LA treatment using intra-individually a set of differently sized apheresis filters with the INUSpheresis system. This study revealed that proteomic analysis correlates well with routine clinical chemistry in these patients. The method is eminently suited to discover new biomarkers and risk factors for cardiovascular disease in these patients. Different filters achieve reduction and removal of proatherogenic proteins in different quantities. This includes not only apolipoproteins, C-reactive protein, fibrinogen, and plasminogen but also proteins like complement factor B (CFAB), protein AMBP, afamin, and the low affinity immunoglobulin gamma Fc region receptor III-A (FcγRIIIa) among others that have been described as atherosclerosis and metabolic vascular diseases promoting factors. We therefore conclude that future trials should be designed to develop an individualized therapy approach for patients on LA based on their metabolic and vascular risk profile. Furthermore, the power of such cascade filter treatment protocols may improve the prevention of cardiometabolic disease and its complications.

Precision medicine in rheumatoid arthritis.

Rheumatoid arthritis is an autoimmune disease that causes significant morbidity. Application of cellular profiling techniques such as single-cell transcriptomics and spatial transcriptomics has uncovered novel pathogenic cell types in RA joint tissues and revealed marked heterogeneity in the cellular composition among RA patients. Together, these insights provide exciting opportunities to translate discoveries into precision medicine in RA. The present review aims to highlight novel insights into RA pathology and discuss key steps needed to translate these discoveries into actionable changes in clinical practice. We review the efforts to identify surrogate biomarkers that could be used to predict RA synovial tissue phenotypes and the corresponding responses to therapy. Finally, we discuss the opportunity to develop novel patient-derived organoid systems as a platform for therapeutic target validation.