Many molecular aging biomarkers have been developed to capture heterogeneity in individual aging rates. Yet, systematic comparison of the modeling choices underlying these biomarkers has been limited. In this study, we trained aging biomarkers on the Rockwood frailty index (FI) and all-cause mortality using UK Biobank Olink proteomics and metabolomics (¹H-NMR) data (n = 40,696). We systematically established the impact of model choice, target outcome, and molecular data source on several age-related outcomes. From this, we developed two aging biomarkers, ProteinFrailty (ProtFI) and ProteinMortality (ProtMort), which are both ElasticNet models that use a minimal set of proteins to predict FI and mortality, respectively. In particular, ProtFI outperformed established aging biomarkers in relation to diverse outcomes, including incident cardiovascular disease, handgrip strength, and self-rated health, both in internal validation and two Dutch external cohorts (n = 995, n = 500). Our findings show that an efficient frailty-trained proteomic biomarker robustly predicts age-related decline.

Rheumatic diseases are chronic, immune-mediated conditions characterized by significant heterogeneity in presentation and disease course. However, current clinical approaches often rely on snapshot-based assessments that fail to capture the complex longitudinal evolution of these conditions. To address these limitations and support the implementation of precision medicine, we present the design for the Rheumatic Digital Twin, a novel, modular conceptual framework intended to integrate heterogeneous multimodal data, ranging from electronic health records and clinical notes to imaging and omics, into a dynamic, computational representation of the patient journey. Our theoretical architecture addresses challenges related to data silos and variable availability of data modalities through a multistage approach that envisions the use of domain-specific foundation models to independently process distinct data modalities. To effectively model the temporal progression inherent in chronic diseases, the proposed design utilizes Transformer architectures, leveraging self-attention mechanisms to treat patient events, such as lab results or medication changes, as sequential data tokens. We describe how these unimodal representations would subsequently be fused via joint embedding techniques to construct a shared, multimodal representational space. Envisioned to function analogously to a recommender system, the Rheumatic Digital Twin framework is modeled to map patients into a latent space where proximity reflects clinical and biological similarity. By identifying “nearest neighbors,” historical patients with comparable trajectories, the system aims to enable in silico cohorting, theoretically allowing clinicians to forecast key clinical events, predict treatment responses, and identify likely disease courses based on the outcomes of similar peers.

Objectives: To investigate the association between joint involvement pattern (JIP) subgroups and treatment responses to conventional synthetic disease-modifying antirheumatic drugs (csDMARDs) and biological disease-modifying antirheumatic drugs (bDMARDs), and to compare the impact of JIP subgroups with other clinical parameters in treatment-naïve patients with early rheumatoid arthritis (RA). Methods: An individual patient data meta-analysis was conducted using 2 randomised controlled trials, NOrdic Rheumatic Diseases Strategy Trials And Registries (NORD-STAR) and Behandel-Strategieën (BeSt), including 1250 treatment-naïve patients with early RA. JIP subgroup assignment was based on 4 previously identified subgroups defined by baseline clinical characteristics, primarily joint involvement in the 66/68 joint scheme. Treatment outcomes were measured using the longitudinal Clinical Disease Activity Index (CDAI) and other disease activity indices through week 48. Associations of the JIP subgroups and other clinical predictors were evaluated using a mixed-model analysis. Results: Patients with a hand-dominant JIP (JIP-Hand) showed significantly better CDAI scores after treatment (Beta for CDAI = −1.4 [95% CI, −2.3 to −0.55]; p = .0016), whereas those with a polyarthritis pattern (JIP-Poly) exhibited worse outcomes (Beta = 0.95 [95% CI, 0.064-1.8]; p = .035). Female sex was also associated with worse CDAI scores (Beta = 1.2 [95% CI, 0.40-2.0]; p = .0031), whereas anticitrullinated protein antibodies did not show a significant association (Beta = 0.19 [95% CI, −0.69 to 1.1]; p = .67). When compared across groups, csDMARDs and combined bDMARDs were similarly effective in the respective JIP subgroups (interaction p > .10). Conclusions: In early RA, csDMARD and bDMARD treatments resulted in the greatest improvement in disease activity in JIP-Hand and the least improvement in JIP-Poly.

Human longevity, which is coupled to compression of age-related disease, is a heritable trait. However, only few common genetic variants have been linked to longevity, suggesting that rare, family-specific variants may also play a role. We therefore investigated whole-genome sequencing data of long-lived individuals from the Leiden Longevity Study and identified family-specific variants residing in genes involved in the mitogen-activated protein kinase (MAPK) cascade, a lifespan-associated and evolutionarily conserved pathway emerging from studies in model organisms. We subsequently generated and functionally characterised mouse embryonic stem cells (mESCs) harbouring these variants. Two variants, located in NF1 (Phe1112Leu) and RAF1 (Asp633Tyr), reduce MAPK/extracellular signal-regulated kinase (ERK) signalling pathway activity in mESCs. At the proteomic and transcriptomic level, we observed prominent changes that were shared (e.g. upregulation of ribosomal proteins and Foxo3 expression) and opposing between the variants (e.g. downregulation of mTORC1 signalling-related proteins and Ets2 expression in the RAF1^Asp633Tyr variant cell line versus upregulation in the NF1^Phe1112Leu variant cell lines). These changes were accompanied by opposing effects on proliferation. Moreover, the RAF1^Asp633Tyr variant improved resistance to replication stress, while this was not the case for the NF1^Phe1112Leu variant. In conclusion, we identified two rare genetic variants in long-lived families that influence MAPK/ERK signalling in a manner that has previously been linked to increased lifespan in model organisms. Our findings suggest that mESCs offer a suitable starting point for studying rare genetic variants linked to human longevity, allowing for the identification of promising variants to pursue in in vivo studies using model organism.

Musculoskeletal complaints account for 30% of GP consultations, with many referred to rheumatology clinics via letters. This study developed a Machine Learning (ML) pipeline to prioritize referrals by identifying rheumatoid arthritis (RA), osteoarthritis, fibromyalgia, and patients requiring long-term care. Using 8044 referral letters from 5728 patients across 12 clinics, we trained and validated ML models in two large centers and tested their generalizability in the remaining ten. The models were robust, with RA achieving an AUC-ROC of 0.78 (CI: 0.74–0.83), osteoarthritis 0.71 (CI: 0.67–0.74), fibromyalgia 0.81 (CI: 0.77–0.85), and chronic follow-up 0.63 (CI: 0.61–0.66). The RA-classifier outperformed manual referral systems, as it prioritised RA over non-RA cases (P < 0.001), while the manual referral system could not differentiate between the two. The other classifiers showed similar prioritisation improvements, highlighting the potential to enhance care efficiency, reduce clinician workload, and facilitate earlier specialized care. Future work will focus on building clinical decision-support tools.

Objectives: Patients with rheumatoid arthritis (RA) display different trajectories towards improvement of disease. We aimed to disentangle the heterogeneity of RA disease trajectories from the first clinical visit onwards using graph-based pseudotime analysis. Methods: We studied early patients with RA over 1.5 years in 2 data sets: Leiden (Netherlands), n = 1237, with 5017 visits, and Towards a Cure for Early Rheumatoid Arthritis (TACERA) (United Kingdom), n = 243, with 750 visits. We created a pipeline for time-independent clustering of clinical and haematologic features to identify disease states. Sequence analyses of these states defined the trajectories. We studied the predictability of the trajectories with baseline features. Results: Clustering identified 8 disease states with localised inflammation (joints) and systemic inflammation (erythrocyte sedimentation rate [ESR] or leucocytes) as the main discriminating factors. The disease state sequences consisted of 4 trajectories, which we independently replicated in TACERA: A, high ESR; B, rapid progression from many inflamed joints towards remission; C, high leucocytes; and D, many inflamed joints with poor prognosis. Systemic vs local inflammation patterns showed moderate predictability at baseline (sensitivity of 71% and precision of 0.73 for trajectory A, although lower precision of 0.52 for trajectory B), while other trajectories were less predictable. Trajectories C and D had strong resemblance with B at baseline but deteriorated into less favourable trajectories. Patients in trajectory A were more often female and on average older. The trajectories were not explained by time till disease-modifying antirheumatic drug, baseline disease activity, or symptom duration. The suboptimal trajectories coincided with worse patient-reported outcomes, even when the inflammation was mainly systemic. Conclusions: We identified 4 distinct trajectories in early RA, differentiating RA into localised vs systemic inflammation. Our results highlight potential differences in disease pathology and opportunities for further targeted treatment. Inevitably, patterns without linkage to our selected features could not be detected.

NPM1-mutated AML is one of the largest entities in international classification systems of myeloid neoplasms, which are based on integrating morphologic and clinical data with genomic data. Previous research, however, indicates that bulk transcriptomics-based subtyping may improve prognostication and therapy guidance. Here, we characterized the heterogeneity in NPM1-mutated AML by performing single-cell RNA-sequencing and spectral flow cytometry on 16 AML belonging to three distinct subtypes previously identified by bulk transcriptomics. Using single-cell expression profiling we generated a comprehensive atlas of NPM1-mutated AML, collectively reconstituting complete myelopoiesis. The three NPM1-mutated transcriptional subtypes showed consistent differences in the proportions of myeloid cell clusters with distinct patterns in lineage commitment and maturational arrest. In all samples, leukemic cells were detected across different myeloid cell clusters, indicating that NPM1-mutated AML are heavily skewed but not fully arrested in myelopoiesis. Same-sample multi-color spectral flow cytometry recapitulated these skewing patterns, indicating that the three NPM1-mutated subtypes can be consistently identified across platforms. Moreover, our analyses highlighted differences in the abundance of rare hematopoietic stem cells suggesting that skewing occurs early in myelopoiesis. To conclude, by harnessing single-cell RNA-sequencing and spectral flow cytometry, we provide a detailed description of three distinct and reproducible patterns in lineage skewing in NPM1-mutated AML that may have potential relevance for prognosis and treatment of patients with NPM1-mutated AML. (Figure presented.)

Rheumatoid arthritis (RA) is a heterogeneous disease with variable symptoms, prognosis, and treatment response, necessitating refined patient classification. We applied multimodal deep learning and clustering to identify distinct RA phenotypes using baseline clinical data from 1,387 patients in the Leiden Rheumatology clinic. Four Joint Involvement Patterns (JIP) emerged: foot-predominant arthritis, seropositive oligoarticular disease, seronegative hand arthritis, and polyarthritis. Findings were validated in clinical trial data (n = 307) and an independent secondary care cohort (n = 515). Clusters showed high stability and significant differences in remission rates (P = 0.007) and methotrexate failure (P < 0.001). JIP-hand patients had superior outcomes (particularly in ACPA-positive patients) versus JIP-foot (HR:0.37, P < 0.001) and JIP-poly (HR:0.33, P = 0.005), independent of baseline disease activity and clinical markers. Synovial histology analysis (n = 194) revealed distinct inflammatory patterns across clusters, hinting at different underlying biological mechanisms. These validated RA phenotypes based on joint involvement patterns may enable targeted research into disease mechanisms and personalized treatment strategies.

The MetaboHealth score is an indicator of physiological frailty in middle aged and older individuals. The aim of the current study was to explore which molecular pathways co-vary with the MetaboHealth score. Using a Luminex cytokine assay and liquid chromatography-mass spectrometry-based proteomics we explored the plasma proteins associating with the difference in 100 extreme scoring individuals selected from two large population cohorts, the Leiden Longevity Study (LLS) and the Rotterdam Study (RS), and discordant monozygotic twin pairs from the Netherlands Twin Register (NTR). In addition, we estimated the heritability of the score using 726 monozygotic (MZ) and 450 dizygotic (DZ) twin pairs. In the contrasting extreme scoring individuals from LLS and RS, we uncovered significant differences in 3 (out of 15) cytokines (GDF15, IL6, and MIG), and 106 (out of 289) plasma proteins. The high, poor health related, score associated with 42 increased inflammatory and immune related protein levels (CRP, LBP, HPT) and lowered levels of 71 HDL remodeling and cholesterol transport related proteins (e.g. APOA1, APOA2, APOA4, and TETN). Using the NTR twins, we subsequently showed that the MetaboHealth score is moderately heritable (h² = 0.4). In MZ twins selected for maximal discordance within a pair we found 68 serum proteins associated with the MetaboHealth score indicating that only a minor part of the associations observed in LLS and RS is likely explained by genetic influences. Taken together, our study sheds light on the intricate interplay between the MetaboHealth score, plasma proteins, cytokines, and genetic influences, paving the way for future investigations aimed at optimizing this mortality risk indicator.

To date only a fraction of the genetic footprint of thyroid function has been clarified. We report a genome-wide association study meta-analysis of thyroid function in up to 271,040 individuals of European ancestry, including reference range thyrotropin (TSH), free thyroxine (FT4), free and total triiodothyronine (T3), proxies for metabolism (T3/FT4 ratio) as well as dichotomized high and low TSH levels. We revealed 259 independent significant associations for TSH (61% novel), 85 for FT4 (67% novel), and 62 novel signals for the T3 related traits. The loci explained 14.1%, 6.0%, 9.5% and 1.1% of the total variation in TSH, FT4, total T3 and free T3 concentrations, respectively. Genetic correlations indicate that TSH associated loci reflect the thyroid function determined by free T3, whereas the FT4 associations represent the thyroid hormone metabolism. Polygenic risk score and Mendelian randomization analyses showed the effects of genetically determined variation in thyroid function on various clinical outcomes, including cardiovascular risk factors and diseases, autoimmune diseases, and cancer. In conclusion, our results improve the understanding of thyroid hormone physiology and highlight the pleiotropic effects of thyroid function on various diseases.

Subtyping of acute myeloid leukaemia (AML) is predominantly based on recurrent genetic abnormalities, but recent literature indicates that transcriptomic phenotyping holds immense potential to further refine AML classification. Here we integrated five AML transcriptomic datasets with corresponding genetic information to provide an overview (n = 1224) of the transcriptomic AML landscape. Consensus clustering identified 17 robust patient clusters which improved identification of CEBPA-mutated patients with favourable outcomes, and uncovered transcriptomic subtypes for KMT2A rearrangements (2), NPM1 mutations (5), and AML with myelodysplasia-related changes (AML-MRC) (5). Transcriptomic subtypes of KMT2A, NPM1 and AML-MRC showed distinct mutational profiles, cell type differentiation arrests and immune properties, suggesting differences in underlying disease biology. Moreover, our transcriptomic clusters show differences in ex-vivo drug responses, even when corrected for differentiation arrest and superiorly capture differences in drug response compared to genetic classification. In conclusion, our findings underscore the importance of transcriptomics in AML subtyping and offer a basis for future research and personalised treatment strategies. Our transcriptomic compendium is publicly available and we supply an R package to project clusters to new transcriptomic studies.

Across the lifespan, diet and physical activity profiles substantially influence immunometabolic health. DNA methylation, as a tissue-specific marker sensitive to behavioral change, may mediate these effects through modulation of transcription factor binding and subsequent gene expression. Despite this, few human studies have profiled DNA methylation and gene expression simultaneously in multiple tissues or examined how molecular levels react and interact in response to lifestyle changes. The Growing Old Together (GOTO) study is a 13-week lifestyle intervention in older adults, which imparted health benefits to participants. Here, we characterize the DNA methylation response to this intervention at over 750 thousand CpGs in muscle, adipose, and blood. Differentially methylated sites are enriched for active chromatin states, located close to relevant transcription factor binding sites, and associated with changing expression of insulin sensitivity genes and health parameters. In addition, measures of biological age are consistently reduced, with decreases in grimAge associated with observed health improvements. Taken together, our results identify responsive molecular markers and demonstrate their potential to measure progression and finetune treatment of age-related risks and diseases.

Aging is a multifaceted and intricate physiological process characterized by a gradual decline in functional capacity, leading to increased susceptibility to diseases and mortality. While chronological age serves as a strong risk factor for age-related health conditions, considerable heterogeneity exists in the aging trajectories of individuals, suggesting that biological age may provide a more nuanced understanding of the aging process. However, the concept of biological age lacks a clear operationalization, leading to the development of various biological age predictors without a solid statistical foundation. This paper addresses these limitations by proposing a comprehensive operationalization of biological age, introducing the “AccelerAge” framework for predicting biological age, and introducing previously underutilized evaluation measures for assessing the performance of biological age predictors. The AccelerAge framework, based on Accelerated Failure Time (AFT) models, directly models the effect of candidate predictors of aging on an individual’s survival time, aligning with the prevalent metaphor of aging as a clock. We compare predictors based on the AccelerAge framework to a predictor based on the GrimAge predictor, which is considered one of the best-performing biological age predictors, using simulated data as well as data from the UK Biobank and the Leiden Longevity Study. Our approach seeks to establish a robust statistical foundation for biological age clocks, enabling a more accurate and interpretable assessment of an individual’s aging status.