This work is motivated by the following problem: Can we identify the disease-causing gene in a patient affected by a monogenic disorder? This problem is an instance of root cause discovery. Specifically, we aim to identify the intervened variable in one interventional sample using a set of observational samples as reference. We consider a linear structural equation model where the causal ordering is unknown. We begin by examining a simple method that uses squared z-scores and characterize the conditions under which this method succeeds and fails, showing it generally cannot identify the root cause. We then prove, without additional assumptions, that the root cause is identifiable even if the causal ordering is not. Two key ingredients of this identifiability result are the use of permutations and the Cholesky decomposition, which allow us to exploit an invariant property across different permutations to discover the root cause. Furthermore, we characterize permutations that yield the correct root cause and, based on this, propose a valid method for root cause discovery. We also adapt this approach to high-dimensional settings. Finally, we evaluate our methods through simulations and apply the high-dimensional method to discover disease-causing genes in the gene expression dataset that motivates this work.
Distinguishing correlation from causation is a fundamental challenge in many scientific fields, including biology, especially when interventions like randomized controlled trials are infeasible and only observational data are available. Methods based on statistical tests of conditional independence within the Mendelian Randomization framework can detect causality between two observed variables that are each associated with a third instrumental variable. However, these methods for detecting causal relationships between traits (e.g., two gene expression or clinical traits associated with a genetic variant, all observed in the same population) often assume a linear relationship, thereby hindering the discovery of causal gene networks from genomics data.We have developed NLCD, a method for NonLinear Causal Discovery from genomics data based on nonlinear regression modeling and conditional feature importance scoring. NLCD uses these techniques to extend the statistical tests in an existing linear causal discovery method called the Causal Inference Test (CIT). We benchmarked NLCD against current state-of-the-art methods: CIT, Findr, and MRPC. On simulated datasets, NLCD performs comparably to most methods in detecting linear relations (Average AUPRC (Area Under the Precision-Recall Curve) of NLCD = 0.94, CIT = 0.94, Findr = 0.94, and MRPC = 0.99), and outperforms them in detecting nonlinear (sine and sawtooth type) relations between two genes (Average AUPRC of NLCD = 0.76, CIT = 0.60, Findr = 0.56, and MRPC = 0.73). When tested on a nonlinear subset of a yeast genomic dataset to recover known causal relations involving transcription factors, NLCD and CIT performed comparable to each other and slightly better than Findr and MRPC (Average AUPRC of NLCD = 0.82, CIT = 0.81, Findr = 0.71, and MRPC = 0.54). On application to a human genomic dataset, NLCD revealed active causal gene pairs (IRF1 → PSME1 and HLA-C → HLA-T) in the muscle tissue, and clarified the promises and challenges in discovering causal gene networks in tissues under in vivo human settings.
FGFR4 signaling is an essential driver in hepatocellular carcinoma. However, traditional screening is often time-consuming, highlighting a need for efficient strategies to identify covalent chemotypes and accelerate FGFR4 drug discovery. We established an integrated AI-driven virtual screening framework to discover FGFR4 covalent inhibitors. The theoretical predictions were evaluated through a biochemical pipeline, encompassing in vitro FGFR4 kinase assays, immunoblotting of intracellular signaling cascades, and bottom-up LC-MS/MS peptide mapping. Biological validation of the computational predictions identified five distinct chemical scaffolds (hits 1, 2, 5, 7, and 8) exhibiting antiproliferative activity. The two most active candidates, hit 1 and hit 2, were selected for further mechanistic profiling. These compounds demonstrated dose-dependent FGFR4 kinase inhibition with IC50 values of 1.06 μM and 3.57 μM, respectively. Cellular assays revealed that both compounds attenuate FGFR4 autophosphorylation and its downstream FRS2/ERK1/2 signaling cascade without inducing non-specific protein degradation. Furthermore, bottom-up LC-MS/MS peptide mapping provided direct structural evidence that hit 1 and hit 2 engage the target cysteine residue via a Michael addition mechanism. Our AI-guided computational workflow identified multiple covalent FGFR4 inhibitors with measurable biological activity. Hit 1 and hit 2 represent structurally characterized covalent scaffolds. This study provides chemical starting points for targeted HCC therapy and demonstrates the integration of theoretical prediction and experimental validation in covalent drug discovery.
The Swiss Personalized Health Network (SPHN) facilitates the interoperability and secure sharing of health-related data for research in Switzerland, in line with the findable, accessible, interoperable, and reusable (FAIR) principles. Since medical datasets can be highly sensitive, access is often governed by complex legal and regulatory requirements. Enabling researchers to discover, understand, and evaluate datasets through rich, well-structured metadata is therefore essential to support informed decisions about data suitability and reuse. This study describes the design and functionality of the SPHN Metadata Catalog and its role in supporting the discovery, exploration, and reuse assessment of health-related datasets. The SPHN Metadata Catalog is a FAIR Data Point-compliant infrastructure that provides rich, structured metadata in both human and machine-readable form. Dataset descriptions are based on the HealthDCAT (Health Data Catalog Vocabulary) Application Profile, ensuring a standardized representation of health data catalogs. Beyond the descriptive metadata typically offered by other catalogs, the SPHN Metadata Catalog includes extensive dataset-level statistics expressed using the Vocabulary of Interlinked Datasets. An interactive visualization component further enables users to explore graph-based schemas and datasets, including entities, attributes, relationships, and their relative abundances. The SPHN Metadata Catalog enables users to explore the semantic structure of graph schemas and statistics of datasets prior to requesting access. Researchers can examine data structures, relationships, attributes, and the abundances of individual data elements. This functionality supports feasibility assessments and informed evaluations of dataset suitability and reuse conditions. By combining HealthDCAT Application Profile-based descriptions with rich statistical metadata and interactive exploration capabilities, the SPHN Metadata Catalog enhances dataset discoverability and supports FAIR-compliant data reuse. As a key component of Switzerland's health data research infrastructure, the SPHN Metadata Catalog provides a foundation for future interoperability initiatives, including potential alignment with emerging frameworks such as the European Health Data Space.
Duchenne muscular dystrophy (DMD), a fatal X-linked disorder, features progressive muscle fibrosis as a key driver of mortality. While CTGF represents a therapeutic target for DMD, its VWC-domain-targeting antibody (FG-3019) failed in clinical trials. Through experimental validation, we identified CT-domain as a superior target domain, as it contributed more fibrosis activity of CTGF than VWC-domain without elevating compensatory TGF-β1 level. Aptamers are synthetic oligonucleotides identified through SELEX, which can specifically bind to flexible protein domains through their unique 3D conformations. Their small molecular size enables effective tissue penetration while maintaining high target specificity, making them ideal CT-domain inhibitors. Nevertheless, conventional SELEX involves time-consuming and inefficient multiple screening rounds. Here, we employed our generative AI model, AptGEN, to rapidly discover a potent CT-domain specific aptamer within 42 days. This chemically modified aptamer (Apc003OA) distributed and remained in muscle tissues for an extended period, whereas FG-3019 could not. Importantly, it demonstrated better fibrosis inhibitory activity in vitro and in mdx mice when compared to FG-3019. Furthermore, Apc003OA demonstrated a favorable safety profile in mdx mice. Within 10 months, we progressed from target domain discovery, aptamer drug discovery, and then obtained both Orphan Drug Designation and Pediatric Rare Disease Designation by US Food and Drug Administration.
Brain age is a global measure that compares structural brain MRI with large reference datasets. Predicted age deviation (PAD) is the deviation between predicted brain age and chronological age, with positive values indicating advanced aging. Identifying blood-based biomarkers that approximate brain PAD could provide an accessible and cost-effective measure of brain health as an alternative to MRI, but no blood-based biomarkers have yet been identified. This study aimed to investigate novel blood-based biomarkers associated with accelerated PAD using an unbiased proteomics approach to discover new biomarkers. This study is a secondary analysis with a cross-sectional case-control design using the LIMBIC-CENC dataset as a discovery approach to understand novel biomarker patterns. Brain age was estimated using brainageR in 137 participants aged ≤40 years with no substantial cognitive deficits or neurological disorders. Cases (n = 76) included individuals with brain age ≥5 years older than chronological age, whereas controls (n = 61) had brain age equal to or younger than chronological age (PAD range: -1.3 to 0; mean = -0.9) and were otherwise matched on demographics and clinical features. Unbiased proteomic profiling of ∼5,400 proteins was performed using the Olink Explore platform. Differential protein expression between groups was assessed using Wilcoxon tests with Benjamini-Hochberg correction. Receiver operating characteristic (ROC) analysis was performed on probabilities derived from generalized linear models (GLMs) to identify optimal protein combinations, prioritizing maximizing both sensitivity and negative predictive value. Olink analyses identified 418 proteins that were significantly different between groups after multiple-comparison correction. Upregulated proteins in participants with PAD≥5 years included: component inhibitor-nuclear factor kappa-b kinase (CHUK), methenyltetrahydrofolate synthetase domain containing (MTHFSD), and epidermal growth factor (EGF), with log2 fold changes of 1.70-1.80. Insulin-like peptide 3 (INSL3) was the most downregulated protein (log2 fold change -2.27). Enriched pathways involved nuclear factor kappa-b (NF-κB), heat-shock protein, and Wingless/Integrated (Wnt) signaling. Models including 6-7 dysregulated proteins (e.g., CHUK and INSL3) achieved AUCs>0.9, with sensitivities >0.90 and specificities >0.70. These discovery-based findings warrant validation in larger cohorts and suggest potential for blood-based protein panel detection of early, clinically silent, pre-pathological accelerated brain aging changes when interventions may be most effective.
Acinetobacter baumannii is a Gram-negative pathogen which is widespread in nature. Although antibiotics such as carbapenems and polymyxins are used to treat A. baumannii infections, the emergence of multidrug-resistant A. baumannii has greatly limited treatment options. Thus, A. baumannii infections are regarded as a great threat to global health. Previously, we successfully introduced the laeA gene into fungal strains in order to significantly increase the production of secondary metabolites, allowing us to create an original library (the ŌmuraLaeA-Introduced Fungal (ŌLF) library) and discover novel biological activities. In this study, we aimed to discover antibacterial compounds against A. baumannii from the ŌLF library. Following the screening program, 3-chlorogentisyl alcohol (1) was isolated from the Penicillium concentricum KTF-0317 strain, which produced the compound in higher amounts than the corresponding wild-type strain. Our investigations found that 1 demonstrated strong antibacterial activity even against quinolone - resistant A. baumannii strain. However, difficulties were encountered in the purification of natural compound 1. Therefore, synthetic samples were prepared via total synthesis and supplied for biological evaluation, enabling clarification of the structure-activity relationships. Our innovative approach revealed the overlooked biological activity of 1 and showed the prolificity and usefulness of the ŌLF library.
The biggest challenge faced by classical anticancer therapy is drug resistance, which causes cancer recurrence and metastasis. Two underlying mechanisms could be responsible, including the stemness of pro-survival autophagy-associated cancer stem cells (CSCs). Background/Objectives: The relationship between CSCs and autophagy in gynecological cancer is still unknown. However, it has been shown that CSCs' in vitro self-renewal ability is decreased when autophagy is inhibited. Helping to maintain normal tissue homeostasis, autophagy is a catabolic process involved in degrading long-lived proteins and cytoplasmic organelles. Autophagy acts as a key player in the human body's self-regenerating tissues. It also has a reproductive function, contributing to decidualization for a successful pregnancy. The aim of our review is to identify similarities and differences between these processes, using these findings to discover new therapeutic strategies through nanotechnology. Method: We conducted a narrative review, identifying heterogeneity in the data in the literature, and found 153 relevant articles. Discussions: While autophagy has been proven to be capable of acting as a tumor suppressor, it also promotes tumor progression. Moreover, it has been linked to cancer stem cell regulation, therapy resistance, cancer invasion, and metastasis. Several molecular mechanisms have been linked to autophagy. Remarkably, some cellular processes required for proper placentation, including autophagy, are common between placental development and tumor growth. Just as trophoblast cells invade and migrate, so do cancer cells. While in the trophoblast, this phenomenon is programmed and controlled; in cancer, this regulation is lost. As shown, we thus observed commonalities and discrepancies in the phenotypes and underlying molecular mechanisms of autophagy regulation in preeclampsia versus cancer contexts. Translational applicability of nanomedicine research strategies and design paradigms between preeclampsia intervention and cancer therapy has been sought. Conclusions: Autophagy-based nanotechnology seems to be feasible in both placental ischemia in preeclampsia and cancers. This review draws parallels between targeted treatments in malignancies and placenta-derived PE. Comparing these diseases provides a novel molecular rationale and the possibility of identifying treatment through autophagy modulation.
Purpose: Chronic diseases contribute toward increased rates of disability, morbidity, and mortality around the world. People living in rural areas are disproportionately more likely to have chronic diseases and greater chronic disease risk from modifiable lifestyle risk factors. The purpose of this study was to explore the perspectives of rural participants of an online lifestyle medicine intervention study regarding online health program delivery format, digital data collection, and making health behavior modifications. Design: The study had a descriptive qualitative design using individual semi-structured interviews via Zoom to collect information from a subset of intervention group participants. The interviews (n = 26) were transcribed within the Zoom application and checked for accuracy. Grounded theory guided the thematic analysis of qualitative data using an iterative process to discover patterns, concepts, and central themes. Results: Four categorical themes emerged: Online and Digital Format, Health Behavior Outcomes, Health Behavior Change Barriers, and Health Behavior Change Facilitators. The findings highlighted rural perspectives about participating in online and digital intervention study modalities and making health behavior changes, including barriers and facilitators. Conclusion: The results emphasize that people living in rural areas can actively participate in online interventions, digitally complete study activities, and make healthier lifestyle choices.
Patient serum for anti-HLA antibodies are tested with purified antigens covalently affixed to beads as targets. Serologic nomenclature from the World Health Organization (WHO) is currently used to specify the reactivity of antibodies, although target antigens are defined at a finer allelic level. We investigated whether different reactivity patterns could be present at the allele level of a single WHO serologic antigen. We analyzed 2389 serum samples from 1215 patients waiting for solid-organ transplant by using class II single antigen beads for detection of HLA antibodies. We used principal component analysis to identify unique antibody reactivity patterns against the beads. We particularly focused on patterns within a WHO antigen that suggested the presence of new serologic class II HLA specificities. We used the HLA DQ7 data to illustrate the principle of the discovery methodology. Four assay target beads representing different DQA1 alleles were classified as DQ7. Two principal components accounted for about 98.6%of the DQ7 data variance. The dominant principal component, which accounted for 83.9% of the variance, represented a reaction pattern in which sera reacted equally with all 4 beads. The second principal component (14.7% of the variance) represented sera that reacted selectively with DQA1*03:01, DQB1*03:01. Four of 10 DR and 6 of 7 DQ WHO serologic specificities that were studied showed more than 5% nonconcordant patterns revealed by principal component analysis. Principal component analysis revealed differential reactivity patterns of the alleles of single WHO serological specificity. This method could be a powerful tool for identifying patterns of reactivity, which may represent new serological specificities.
The development of safe, non-opioid analgesics remains a pressing challenge. The α2A-adrenergic receptor (α2AAR) is a valid analgesic target, yet clinical translation has been hindered by dose-limiting central side effects. We discover CC10137, an orally bioavailable, peripherally restricted α2AAR agonist that produces a robust, dose-dependent anti-allodynic effect across eight murine pain models, without causing sedation, hypotension, or hypothermia. Co-administration of CC10137 (1 mg/kg, p.o.) and morphine (1 mg/kg s.c.) robustly reverses allodynia by 86% in a neuropathic pain model, surpassing either drug alone (∼45%) or a 3-fold higher morphine dose (3 mg/kg, s.c., 65%). This synergy is further replicated in a cancer pain model. Unlike morphine, CC10137 does not impair spinal nociceptive reflex in a tail-flick model, preserving an essential protective mechanism. CC10137 can thus selectively suppress allodynia under a broad spectrum of pathological pain conditions with a favorable safety profile, highlighting its potential as a standalone or combination therapeutic for chronic pain.
Background: Cyclin-dependent kinase 2 (CDK2) is a key regulator of cell cycle progression and an important therapeutic target in cancer treatment. This study aims to identify novel CDK2 inhibitors using an integrated computational approach combining machine learning and structure-based methods. Methods: A computational pipeline was developed incorporating Lipinski's Rule of Five filtering, machine learning (ML)-based activity prediction, molecular docking, and molecular dynamics simulations (MDs). A dataset of CDK2 inhibitors with IC50 values was retrieved from ChEMBL, and molecular fingerprints were generated using PaDEL. A 5-fold stratified cross-validation approach was applied to train multiple classifiers, with the random forest model showing the best performance. Predicted active compounds from the InterBioScreen database were subjected to docking against CDK2 (PDB ID: 2FVD) using PyRx, followed by 100 ns MDS for stability analysis. Results: The random forest classifier achieved an AUC-ROC of 0.90 and an accuracy of 0.84. A total of 187 compounds were predicted as active. Among these, two compounds, STOCK4S-00019 and STOCK4S-00025, demonstrated docking scores comparable to the co-crystallized reference ligand. Molecular dynamics simulations confirmed stable binding, consistent interaction patterns, and favorable conformational behavior throughout the simulation period. Conclusions: The identified compounds, STOCK4S-00019 (hit1) and STOCK4S-00025 (hit2), show strong potential as CDK2 inhibitors. These findings support their further investigation through experimental validation and highlight the effectiveness of integrated computational approaches in anticancer drug discovery.
Glycogen synthase kinase-3β (GSK-3β) is a key therapeutic target for Alzheimer's disease, but identifying safe, brain-penetrant inhibitors remains difficult. This study aimed to discover novel CNS-active GSK-3β inhibitors using a rigorous multi-tier computational pipeline. The workflow combined ligand-based and structure-based pharmacophore modeling, virtual screening of the ZINCPharmer database, AutoDock Vina docking, ADME and blood-brain barrier (BBB) filtering with SwissADME, toxicity prediction using ProTox-3.0, and validation by 100-ns molecular dynamics simulations with MM/GBSA and MM/PBSA free energy calculations. Pharmacophore screening with a ≤ 1.0 Å RMSD cutoff identified 1,085 ligand-based and 36 structure-based hits. After docking and developability filtering, two BBB-permeant candidates were prioritized: SB1, a structure-based hit (predicted LD50 = 2500 mg/kg, toxicity class 5), and LB1, a ligand-based hit (predicted LD50 = 521 mg/kg, toxicity class 4). Molecular dynamics confirmed stable binding for both compounds. MM/GBSA analysis showed favorable binding free energies for SB1 (-27.68 kcal/mol) and LB1 (-25.74 kcal/mol), both surpassing the co-crystallized reference (-8.75 kcal/mol). These findings identify SB1 and LB1 as promising, safe, and brain-penetrant GSK-3β lead compounds for experimental validation in Alzheimer's disease.
Multidrug-resistant fungal infections caused by Candida and Aspergillus species have become one of the major global health concerns, especially among immunocompromised individuals. The small number of antifungals available and the rapid emergence of resistance to azoles, echinocandins and polyenes underscore the urgent need to develop alternative therapeutic strategies with different mechanisms of action. Antifungal peptides (AFPs) have attracted increasing attention as promising candidates due to their broad-spectrum activity, multimodal mechanisms of action, and their low likelihood of resistance development. This review presents a thorough and holistic summary of the research on AFPs that target clinically significant drug-resistant fungi such as Candida auris, azole-resistant Candida albicans, and triazole-resistant Aspergillus fumigatus. We review the structural and physicochemical properties of AFPs and address their various antifungal mechanisms, which include membrane disruption, oxidative stress induction, and disruption of intracellular homeostasis, as well as biofilm inhibition. We further highlight an emerging computational-experimental pipeline to discover and optimize AFPs, combining sequence mining, machine learning-based screening, molecular docking, molecular dynamics simulations, and in vitro and in vivo validation. We also explore the major translational challenges, such as hemolytic toxicity, proteolytic instability, pharmacokinetic constraints, manufacturing complexity, regulatory concerns, and sustainable peptide manufacturing strategies, and discuss advanced delivery systems (e.g., liposomes, PLGA nanoparticles, chitosan-based systems, and hydrogels) to improve therapeutic efficacy and stability. In summary, this review proposes an integrated translational development framework that connects computational design, experimental validation, and delivery engineering, thereby positioning AFPs as a promising next-generation strategy in the fight against multidrug-resistant fungal infections.
Methyltransferase-like protein 3 (METTL3) is a promising therapeutic target for Acute Myeloid Leukemia (AML). To discover novel METTL3 inhibitors, we designed a series of compounds by hybridizing the natural product Genistein and the non-nucleoside METTL3 inhibitor STM2457. Among the 31 de novo-generated compounds subjected to molecular docking, the top-scoring hit H1 was selected for chemical synthesis and its structure was confirmed by NMR and MS. H1 showed an inhibitory activity against METTL3 with an IC50 value of 980.3 nM, significantly better than Genistein (37.31% inhibition at 40 µM). Molecular docking revealed that H1's binding mode was similar to STM2457, forming hydrogen bonds and hydrophobic interactions within the METTL3 pocket. Druglikeness prediction by SwissADMET and ADMETlab 3.0 showed that H1 exhibited to be within an acceptable range. This study validated our design strategy and provided a foundation for developing more potent and selective METTL3 inhibitors.
A novel four-step strategy was established to systematically evaluate the toxicity, chemical profiles, and biological activities of Arisaema decipiens Schott (synonym: Arisaema rhizomatum C.E.C.Fisch, AD) and its eight processed products prepared via diverse traditional methods including steaming, ginger-steaming, liquor-soaking, and lime-water treating. The results demonstrated that processing significantly mitigated toxicity by damaging calcium oxalate raphides and reducing toxic lectin levels. Conversely, bioactive constituents, particularly flavonoids and alkaloids, were enriched post-processing. Eighteen differential metabolites were identified as quality markers (Q-markers), which exhibited strong correlations with antioxidant, anti-haemolytic, and anti-inflammatory activities. Notably, the ZAD (Zheng Fa, ginger steamed) processed products displayed superior therapeutic efficacy. This study validated the attenuation of toxicity and enhancement of efficacy through processing, offering a robust scientific basis for quality control and mechanistic exploration of AD. This approach served as an auxiliary tool for achieving quality control of processed TCM and could support further exploration of processing mechanisms.
Eupolyphaga sinensis Walker (ES) is traditionally valued for its ability to invigorate blood circulation, resolve blood stasis, eliminate blood clots, and promote the healing of tendons and bones, which has a long-standing history of use and extensive clinical applications. According to the Chinese Pharmacopoeia, only the female insect is approved for medicinal use, yet distinguishing male ES from female ES remains challenging with conventional methods. This work is committed to discover peptide biomarkers to distinguish males from females using dimethyl labeling-based quantitative peptidomics strategy. The analysis revealed significant differences in peptide abundance, leading to the identification of three male ES derived characteristic peptides that could be used to distinguish males from females; these were confirmed and validated using UPLC-MS/MS and MRM mode. These peptide biomarkers could accurately detect the presence of male adulteration in female ES samples across varying mixing ratios as well. These findings provide a practical molecular tool for quality control and standardization of ES medicinal materials.
Breast cancer is one of the prominent reasons of death in women. HER2 is a promising target to counter breast cancer. In the current research, a structure-based pharmacophore model was generated to map and screen CMNPD, a comprehensive database of marine natural products. The two compounds (CMNPD30448 (hit1) and CMNPD7060 (hit2)) displayed better LibDock scores than the reference co-crystallized ligand. These compounds demonstrated stable molecular dynamics results conducted for 500 ns with stable root mean square deviation (RMSD) at 0.3 nm, stable radius of gyration (Rg) and root mean square fluctuation (RMSF). On ChEMBL compounds, different PaDEL descriptors and various machine learning (ML) and neural network (NN) methods were used. The results showed that PubChem fingerprints with random forest classification model displayed an accuracy of 0.91 and a receiver operating characteristic area under the curve (ROC-AUC) of 0.96. This model further predicted the retrieved compounds as 'active'. The explainable random forest with LIME showed that PubChem fingerprint440 [C(-C)(-O)(=O)], PubChem fingerprint452 [C(-O)(=O)], PubChem fingerprint380 [C(~O)(~O)], PubChem fingerprint566 [O-C-C-N] and PubChem fingerprint712 [C-C(C)-C(C)-C] for hit1 and PubChem fingerprint700 [O-C-C-C-C-C-O-C], PubChem fingerprint380 [C(~O)(~O)], and PubChem fingerprint712 [C-C(C)-C(C)-C] for hit2 have contributed towards plausible inhibitory potential. These findings suggest the two compounds CMNPD30448 and CMNPD7060 might serve as HER2 inhibitors. Further in vitro and in vivo analysis are required before using them.
This observational study characterized obesity care pathways and healthcare resource use in England between 2015 and 2019. Data were obtained from Discover and the Salford Integrated Record (SIR), two databases of linked primary and secondary electronic health records in England. Adults with a body mass index (BMI) ≥ 30 kg/m2 who attended obesity clinics were included over 4 years (January 1, 2015 [SIR] and January 1, 2016 [Discover] to December 31, 2018). Overall, 1698 people living with obesity were included from Discover and 561 from SIR. Most (74.9%-78.6%) received a lifestyle intervention as their first intervention, whereas 6.2%-16.3% received a pharmacological treatment or metabolic/bariatric surgical procedure. Time to first intervention was typically > 6 months, and most individuals remained in their baseline BMI group after 8 months' follow-up. Obesity-related complications resulted in high annual per-person direct healthcare costs, particularly for acute cardiovascular events such as myocardial infarction (Discover: 2285 GBP; SIR: 2194 GBP) and incident stroke (Discover: 3005 GBP; SIR: 1550 GBP). In conclusion, the time to first intervention can be considerable for people living with obesity in England and these individuals may incur high healthcare costs. There is an unmet need for timely and effective weight management in England.
Most drugs target proteins, and proteome-wide genetic analyses in diverse populations could discover potential novel and repurposed targets for improved prevention and treatment of ischemic heart disease (IHD) beyond statin therapy. The purposes of this study were to use cis-acting single nucleotide polymorphisms (cis-pQTLs) identified for plasma proteins in East Asians and Europeans to discover and validate potential drug targets for IHD. We measured plasma levels of 9,520 (Olink/SomaScan: 2,923/7,297) proteins in a case-cohort study of IHD (1,976 incident cases and 2,001 subcohort controls) in statin-free individuals in the prospective China Kadoorie Biobank (CKB). Genome-wide association studies identified 2,895 (Olink/SomaScan: 1,301/1,594) cis-pQTLs for these proteins in CKB. Two-sample Mendelian randomization (MR) and colocalization analyses assessed associations of all available cis-pQTLs for these proteins with IHD in East Asians (n = 29,319 cases), with further replication in Europeans (n = 181,522 cases) and comparison with findings in previous MR studies. In CKB observational analyses, a total of 959 (Olink/SomaScan: 426/533) proteins were associated at false discovery rate-corrected P < 0.05 with IHD after adjusting for major IHD risk factors. Two-sample MR analyses provided genetic support for 54 unique (Olink/SomaScan: 36/28) proteins in IHD etiology. Colocalization analyses confirmed shared gene-protein-IHD associations (posterior probability of hypothesis 4 [PPH4] ≥0.8) for 15 unique (Olink/SomaScan: 10/10) proteins, including 8 lipid-related, 3 inflammation-related, 1 blood pressure-related, and 3 alcohol-related proteins in East Asians. In Europeans, MR analyses of 12 non-alcohol-related proteins showed directionally concordant results for 8 proteins, with 5 having strong colocalization evidence of shared gene-protein-IHD associations (PPH4 ≥0.8), including 4 lipid-related (proprotein convertase subtilisin/kexin type 9, LPA, APOE, cadherin-1) and 1 systolic blood pressure-related (fibroblast growth factor 5) protein. However, 4 proteins showed directionally discordant MR results, including 2 lipid-related (APOA5, SORT1) and 1 inflammation-related (transforming growth factor beta 1) proteins with strong colocalization evidence of shared gene-protein-IHD associations (PPH4 ≥0.8). Comparison with previous MR studies revealed little consistency across studies in the number and identity of target proteins for IHD beyond well-established lipid-related (low-density lipoprotein cholesterol, lipoprotein(a), and triglycerides) or inflammation-related (interleukin-6) protein targets. The findings support a role for lipid-driven chronic inflammation in IHD etiology, and treatment strategies simultaneously targeting multiple lipid and inflammation pathways should be prioritized for further research to improve drug treatment of IHD beyond statin therapy.