Patient-reported outcomes in axial spondyloarthritis and psoriatic arthritis patients treated with secukinumab for 24 months in daily clinical practice.

OBJECTIVES: In patients with axial spondyloarthritis (axSpA) or psoriatic arthritis (PsA) initiating secukinumab, we aimed to assess and compare the proportion of patients achieving 6-, 12- and 24-month patient-reported outcomes (PRO) remission and the 24-month retention rates. PATIENTS AND METHODS: Patients with axSpA or PsA from 16 European registries, who initiated secukinumab in routine care were included. PRO remission rates were defined as pain, fatigue, Patient Global Assessment (PGA) ≤2 (Numeric Rating Scale (NRS) 0-10) and Health Assessment Questionnaire (HAQ) ≤0.5, for both axSpA and PsA, and were calculated as crude values and adjusted for drug adherence (LUNDEX). Comparisons of axSpA and PsA remission rates were performed using logistic regression analyses (unadjusted and adjusted for multiple confounders). Kaplan-Meier plots with log-rank test and Cox regression analyses were conducted to assess and compare secukinumab retention rates. RESULTS: We included 3087 axSpA and 3246 PsA patients initiating secukinumab. Crude pain, fatigue, PGA and HAQ remission rates were higher in axSpA than in PsA patients, whereas LUNDEX-adjusted remission rates were similar. No differences were found between the patient groups after adjustment for confounders. The 24-month retention rates were similar in axSpA vs. PsA in fully adjusted analyses (HR [95 %CI] = 0.92 [0.84-1.02]). CONCLUSION: In this large European real-world study of axSpA and PsA patients treated with secukinumab, we demonstrate for the first time a comparable effectiveness in PRO remission and treatment retention rates between these two conditions when adjusted for confounders.

The evolutionary impact of childhood cancer on the human gene pool.

Germline pathogenic variants associated with increased childhood mortality must be subject to natural selection. Here, we analyze publicly available germline genetic metadata from 4,574 children with cancer [11 studies; 1,083 whole exome sequences (WES), 1,950 whole genome sequences (WGS), and 1,541 gene panel] and 141,456 adults [125,748 WES and 15,708 WGS]. We find that pediatric cancer predisposition syndrome (pCPS) genes [n = 85] are highly constrained, harboring only a quarter of the loss-of-function variants that would be expected. This strong indication of selective pressure on pCPS genes is found across multiple lines of germline genomics data from both pediatric and adult cohorts. For six genes [ELP1, GPR161, VHL and SDHA/B/C], a clear lack of mutational constraint calls the pediatric penetrance and/or severity of associated cancers into question. Conversely, out of 23 known pCPS genes associated with biallelic risk, two [9%, DIS3L2 and MSH2] show significant constraint, indicating that they may monoallelically increase childhood cancer risk. In summary, we show that population genetic data provide empirical evidence that heritable childhood cancer leads to natural selection powerful enough to have significantly impacted the present-day gene pool.

Mass spectrometry-based proteomics data from thousands of HeLa control samples.

Here we provide a curated, large scale, label free mass spectrometry-based proteomics data set derived from HeLa cell lines for general purpose machine learning and analysis. Data access and filtering is a tedious task, which takes up considerable amounts of time for researchers. Therefore we provide machine based metadata for easy selection and overview along the 7,444 raw files and MaxQuant search output. For convenience, we provide three filtered and aggregated development datasets on the protein groups, peptides and precursors level. Next to providing easy to access training data, we provide a SDRF file annotating each raw file with instrument settings allowing automated reprocessing. We encourage others to enlarge this data set by instrument runs of further HeLa samples from different machine types by providing our workflows and analysis scripts.

Patient-Reported Outcomes (PROs) and PRO Remission Rates in 12,262 Biologic-Naïve Patients With Psoriatic Arthritis Treated With Tumor Necrosis Factor Inhibitors in Routine Care.

OBJECTIVE: To evaluate patient-reported outcomes (PROs) after initiation of tumor necrosis factor inhibitor (TNFi) treatment in European real-world patients with psoriatic arthritis (PsA). Further, to investigate PRO remission rates across treatment courses, registries, disease duration, sex, and age at disease onset. METHODS: Visual analog scale or numerical rating scale scores for pain, fatigue, patient global assessment (PtGA), and the Health Assessment Questionnaire-Disability Index (HAQ-DI) from 12,262 patients with PsA initiating a TNFi in 13 registries were pooled. PRO remission rates (pain ≤ 1, fatigue ≤ 2, PtGA ≤ 2, and HAQ-DI ≤ 0.5) were calculated for patients still on the treatment. RESULTS: For the first TNFi, median pain score was reduced by approximately 50%, from 6 to 3, 3, and 2; as were fatigue scores, from 6 to 4, 4, and 3; PtGA scores, from 6 to 3, 3, and 2; and HAQ-DI scores, from 0.9 to 0.5, 0.5, and 0.4 at baseline, 6, 12, and 24 months, respectively. Six-month Lund Efficacy Index (LUNDEX)-adjusted remission rates for pain, fatigue, PtGA, and HAQ-DI scores were 24%, 31%, 36%, and 43% (first TNFi); 14%, 19%, 23%, and 29% (second TNFi); and 9%, 14%, 17%, and 20% (third TNFi), respectively. For biologic-naïve patients with disease duration < 5 years, 6-month LUNDEX-adjusted remission rates for pain, fatigue, PtGA, and HAQ-DI scores were 22%, 28%, 33%, and 42%, respectively. Corresponding rates for patients with disease duration > 10 years were 27%, 32%, 41%, and 43%, respectively. Remission rates were 33%, 40%, 45%, and 56% for men and 17%, 23%, 24%, and 32% for women, respectively. For patients aged < 45 years at diagnosis, 6-month LUNDEX-adjusted remission rate for pain was 29% vs 18% for patients ≥ 45 years. CONCLUSION: In 12,262 biologic-naïve patients with PsA, 6 months of treatment with a TNFi reduced pain by approximately 50%. Marked differences in PRO remission rates across treatment courses, registries, disease duration, sex, and age at onset of disease were observed, emphasizing the potential influence of factors other than disease activity on PROs.

FAVA: high-quality functional association networks inferred from scRNA-seq and proteomics data.

MOTIVATION: Protein networks are commonly used for understanding how proteins interact. However, they are typically biased by data availability, favoring well-studied proteins with more interactions. To uncover functions of understudied proteins, we must use data that are not affected by this literature bias, such as single-cell RNA-seq and proteomics. Due to data sparseness and redundancy, functional association analysis becomes complex. RESULTS: To address this, we have developed FAVA (Functional Associations using Variational Autoencoders), which compresses high-dimensional data into a low-dimensional space. FAVA infers networks from high-dimensional omics data with much higher accuracy than existing methods, across a diverse collection of real as well as simulated datasets. FAVA can process large datasets with over 0.5 million conditions and has predicted 4210 interactions between 1039 understudied proteins. Our findings showcase FAVA's capability to offer novel perspectives on protein interactions. FAVA functions within the scverse ecosystem, employing AnnData as its input source. AVAILABILITY AND IMPLEMENTATION: Source code, documentation, and tutorials for FAVA are accessible on GitHub at https://github.com/mikelkou/fava. FAVA can also be installed and used via pip/PyPI as well as via the scverse ecosystem https://github.com/scverse/ecosystem-packages/tree/main/packages/favapy.

Predictors of DAPSA28 remission in patients with psoriatic arthritis initiating a first TNF inhibitor: results from 13 European registries.

OBJECTIVES: In bio-naïve patients with PsA initiating a TNF inhibitor (TNFi), we aimed to identify baseline predictors of Disease Activity index for PsA in 28 joints (DAPSA28) remission (primary objective) and DAPSA28 moderate response at 6 months, as well as drug retention at 12 months across 13 European registries. METHODS: Baseline demographic and clinical characteristics were retrieved and the three outcomes investigated per registry and in pooled data, using logistic regression analyses on multiply imputed data. In the pooled cohort, selected predictors that were either consistently positive or negative across all three outcomes were defined as common predictors. RESULTS: In the pooled cohort (n = 13 369), 6-month proportions of remission, moderate response and 12-month drug retention were 25%, 34% and 63% in patients with available data (n = 6954, n = 5275 and n = 13 369, respectively). Five common baseline predictors of remission, moderate response and 12-month drug retention were identified across all three outcomes. The odds ratios (95% CIs) for DAPSA28 remission were: age, per year: 0.97 (0.96-0.98); disease duration, years (<2 years as reference): 2-3 years: 1.20 (0.89-1.60), 4-9 years: 1.42 (1.09-1.84), ≥10 years: 1.66 (1.26-2.20); men vs women: 1.85 (1.54-2.23); CRP of >10 vs ≤10 mg/l: 1.52 (1.22-1.89) and 1 mm increase in patient fatigue score: 0.99 (0.98-0.99). CONCLUSION: Baseline predictors of remission, response and adherence to TNFi therapy were identified, of which five were common for all three outcomes, indicating that the predictors emerging from our pooled cohort may be considered generalizable from country level to disease level.

Markers of inflammation predict survival in newly diagnosed cirrhosis: a prospective registry study.

The inflammatory activity in cirrhosis is often pronounced and related to episodes of decompensation. Systemic markers of inflammation may contain prognostic information, and we investigated their possible correlation with admissions and mortality among patients with newly diagnosed liver cirrhosis. We collected plasma samples from 149 patients with newly diagnosed (within the past 6 months) cirrhosis, and registered deaths and hospital admissions within 180 days. Ninety-two inflammatory markers were quantified and correlated with clinical variables, mortality, and admissions. Prediction models were calculated by logistic regression. We compared the disease courses of our cohort with a validation cohort of 86 patients with cirrhosis. Twenty of 92 markers of inflammation correlated significantly with mortality within 180 days (q-values of 0.00-0.044), whereas we found no significant correlations with liver-related admissions. The logistic regression models yielded AUROCs of 0.73 to 0.79 for mortality and 0.61 to 0.73 for liver-related admissions, based on a variety of modalities (clinical variables, inflammatory markers, clinical scores, or combinations thereof). The models performed moderately well in the validation cohort and were better able to predict mortality than liver-related admissions. In conclusion, markers of inflammation can be used to predict 180-day mortality in patients with newly diagnosed cirrhosis. Prediction models for newly diagnosed cirrhotic patients need further validation before implementation in clinical practice.Trial registration: NCT04422223 (and NCT03443934 for the validation cohort), and Scientific Ethics Committee No.: H-19024348.

Commonalities and differences in set-up and data collection across European spondyloarthritis registries - results from the EuroSpA collaboration.

BACKGROUND: In European axial spondyloarthritis (axSpA) and psoriatic arthritis (PsA) clinical registries, we aimed to investigate commonalities and differences in (1) set-up, clinical data collection; (2) data availability and completeness; and (3) wording, recall period, and scale used for selected patient-reported outcome measures (PROMs). METHODS: Data was obtained as part of the EuroSpA Research Collaboration Network and consisted of (1) an online survey and follow-up interview, (2) upload of real-world data, and (3) selected PROMs included in the online survey. RESULTS: Fifteen registries participated, contributing 33,948 patients (axSpA: 21,330 (63%), PsA: 12,618 (37%)). The reported coverage of eligible patients ranged from 0.5 to 100%. Information on age, sex, biological/targeted synthetic disease-modifying anti-rheumatic drug treatment, disease duration, and C-reactive protein was available in all registries with data completeness between 85% and 100%. All PROMs (Bath Ankylosing Spondylitis Disease Activity and Functional Indices, Health Assessment Questionnaire, and patient global, pain and fatigue assessments) were more complete after 2015 (68-86%) compared to prior (50-79%). Patient global, pain and fatigue assessments showed heterogeneity between registries in terms of wording, recall periods, and scale. CONCLUSION: Important heterogeneity in registry design and data collection across fifteen European axSpA and PsA registries was observed. Several core measures were widely available, and an increase in data completeness of PROMs in recent years was identified. This study might serve as a basis for examining how differences in data collection across registries may impact the results of collaborative research in the future.

Adversarial and variational autoencoders improve metagenomic binning.

Assembly of reads from metagenomic samples is a hard problem, often resulting in highly fragmented genome assemblies. Metagenomic binning allows us to reconstruct genomes by re-grouping the sequences by their organism of origin, thus representing a crucial processing step when exploring the biological diversity of metagenomic samples. Here we present Adversarial Autoencoders for Metagenomics Binning (AAMB), an ensemble deep learning approach that integrates sequence co-abundances and tetranucleotide frequencies into a common denoised space that enables precise clustering of sequences into microbial genomes. When benchmarked, AAMB presented similar or better results compared with the state-of-the-art reference-free binner VAMB, reconstructing ~7% more near-complete (NC) genomes across simulated and real data. In addition, genomes reconstructed using AAMB had higher completeness and greater taxonomic diversity compared with VAMB. Finally, we implemented a pipeline Integrating VAMB and AAMB that enabled improved binning, recovering 20% and 29% more simulated and real NC genomes, respectively, compared to VAMB, with moderate additional runtime.

Global within-species phylogenetics of sewage microbes suggest that local adaptation shapes geographical bacterial clustering.

Most investigations of geographical within-species differences are limited to focusing on a single species. Here, we investigate global differences for multiple bacterial species using a dataset of 757 metagenomics sewage samples from 101 countries worldwide. The within-species variations were determined by performing genome reconstructions, and the analyses were expanded by gene focused approaches. Applying these methods, we recovered 3353 near complete (NC) metagenome assembled genomes (MAGs) encompassing 1439 different MAG species and found that within-species genomic variation was in 36% of the investigated species (12/33) coherent with regional separation. Additionally, we found that variation of organelle genes correlated less with geography compared to metabolic and membrane genes, suggesting that the global differences of these species are caused by regional environmental selection rather than dissemination limitations. From the combination of the large and globally distributed dataset and in-depth analysis, we present a wide investigation of global within-species phylogeny of sewage bacteria. The global differences found here emphasize the need for worldwide data sets when making global conclusions.

Centenarians have a diverse gut virome with the potential to modulate metabolism and promote healthy lifespan.

Distinct gut microbiome ecology may be implicated in the prevention of aging-related diseases as it influences systemic immune function and resistance to infections. Yet, the viral component of the microbiome throughout different stages in life remains unexplored. Here we present a characterization of the centenarian gut virome using previously published metagenomes from 195 individuals from Japan and Sardinia. Compared with gut viromes of younger adults (>18 yr) and older individuals (>60 yr), centenarians had a more diverse virome including previously undescribed viral genera, such as viruses associated with Clostridia. A population shift towards higher lytic activity was also observed. Finally, we investigated phage-encoded auxiliary functions that influence bacterial physiology, which revealed an enrichment of genes supporting key steps in sulfate metabolic pathways. Phage and bacterial members of the centenarian microbiome displayed an increased potential for converting methionine to homocysteine, sulfate to sulfide and taurine to sulfide. A greater metabolic output of microbial hydrogen sulfide in centenarians may in turn support mucosal integrity and resistance to pathobionts.

Deep integrative models for large-scale human genomics.

Polygenic risk scores (PRSs) are expected to play a critical role in precision medicine. Currently, PRS predictors are generally based on linear models using summary statistics, and more recently individual-level data. However, these predictors mainly capture additive relationships and are limited in data modalities they can use. We developed a deep learning framework (EIR) for PRS prediction which includes a model, genome-local-net (GLN), specifically designed for large-scale genomics data. The framework supports multi-task learning, automatic integration of other clinical and biochemical data, and model explainability. When applied to individual-level data from the UK Biobank, the GLN model demonstrated a competitive performance compared to established neural network architectures, particularly for certain traits, showcasing its potential in modeling complex genetic relationships. Furthermore, the GLN model outperformed linear PRS methods for Type 1 Diabetes, likely due to modeling non-additive genetic effects and epistasis. This was supported by our identification of widespread non-additive genetic effects and epistasis in the context of T1D. Finally, we constructed PRS models that integrated genotype, blood, urine, and anthropometric data and found that this improved performance for 93% of the 290 diseases and disorders considered. EIR is available at https://github.com/arnor-sigurdsson/EIR.

Combinatorial batching of DNA for ultralow-cost detection of pathogenic variants.

BACKGROUND: Next-generation sequencing (NGS) based population screening holds great promise for disease prevention and earlier diagnosis, but the costs associated with screening millions of humans remain prohibitive. New methods for population genetic testing that lower the costs of NGS without compromising diagnostic power are needed. METHODS: We developed double batched sequencing where DNA samples are batch-sequenced twice - directly pinpointing individuals with rare variants. We sequenced batches of at-birth blood spot DNA using a commercial 113-gene panel in an explorative (n = 100) and a validation (n = 100) cohort of children who went on to develop pediatric cancers. All results were benchmarked against individual whole genome sequencing data. RESULTS: We demonstrated fully replicable detection of cancer-causing germline variants, with positive and negative predictive values of 100% (95% CI, 0.91-1.00 and 95% CI, 0.98-1.00, respectively). Pathogenic and clinically actionable variants were detected in RB1, TP53, BRCA2, APC, and 19 other genes. Analyses of larger batches indicated that our approach is highly scalable, yielding more than 95% cost reduction or less than 3 cents per gene screened for rare disease-causing mutations. We also show that double batched sequencing could cost-effectively prevent childhood cancer deaths through broad genomic testing. CONCLUSIONS: Our ultracheap genetic diagnostic method, which uses existing sequencing hardware and standard newborn blood spots, should readily open up opportunities for population-wide risk stratification using genetic screening across many fields of clinical genetics and genomics.

Mapache: a flexible pipeline to map ancient DNA.

SUMMARY: We introduce mapache, a flexible, robust and scalable pipeline to map, quantify and impute ancient and present-day DNA in a reproducible way. Mapache is implemented in the workflow manager Snakemake and is optimized for low-space consumption, allowing to efficiently (re)map large datasets-such as reference panels and multiple extracts and libraries per sample - to one or several genomes. Mapache can easily be customized or combined with other Snakemake tools. AVAILABILITY AND IMPLEMENTATION: Mapache is freely available on GitHub (https://github.com/sneuensc/mapache). An extensive manual is provided at https://github.com/sneuensc/mapache/wiki. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

The effect of antithrombotic treatment on the fecal immunochemical test for colorectal cancer screening: a nationwide cross-sectional study.

BACKGROUND : Screening for colorectal cancer (CRC) using the fecal immunochemical test (FIT) has been widely adopted. The use of antithrombotic treatment is increasing in the Western world. This study aimed to assess the effects of antithrombotic treatment on the FIT-based Danish national screening program for CRC. METHODS : This was a cross-sectional study of all individuals returning a FIT from 2014 until 2016. The effect of antithrombotic treatment on FIT positivity and the positive predictive value (PPV) were assessed using proportions and multivariable Poisson regression. RESULTS : Of 884 036 invited individuals, we identified 551 570 participants. A positive FIT was observed in 9052 of 77 007 individuals (11.8 %) receiving antithrombotic treatment compared with 28 387 of 474 587 individuals (6.0 %) receiving no treatment. The adjusted relative risk (RR) for a positive FIT was 1.59 (95 %CI 1.56-1.63) for any treatment. Nonvitamin K oral anticoagulants (NOACs) were associated with the largest increase in FIT positivity (adjusted RR 2.40, 95 %CI 2.48-2.54). The proportion of CRC detected at colonoscopy was slightly lower among patients on antithrombotic treatment (6.0 %, 95 %CI 5.5 %-6.6 %) than among treatment-naïve patients (6.4 %, 95 %CI 6.1 %-6.7 %). The PPV for CRC or high risk adenomas was decreased nearly twofold in patients treated with NOAC (adjusted RR 0.58, 95 %CI 0.51-0.66]). CONCLUSION : Antithrombotic treatment was associated with a decreased PPV in FIT-based CRC screening.

An immunogenetic investigation of 30 autoimmune and autoinflammatory diseases and their links to psychiatric disorders in a nationwide sample.

Autoimmune and autoinflammatory diseases (AIIDs) involve a deficit in an individual's immune system function, whereby the immune reaction is directed against self-antigens. Many AIIDs have a strong genetic component, but they can also be triggered by environmental factors. AIIDs often have a highly negative impact on the individual's physical and mental wellbeing. Understanding the genetic underpinning of AIIDs is thus crucial both for diagnosis and for identifying individuals at high risk of an AIID and mental illness as a result thereof. The aim of the present study was to perform systematic statistical and genetic analyses to assess the role of human leukocyte antigen (HLA) alleles in 30 AIIDs and to study the links between AIIDs and psychiatric disorders. We leveraged the Danish iPSYCH Consortium sample comprising 65 534 individuals diagnosed with psychiatric disorders or selected as part of a random population sample, for whom we also had genetic data and diagnoses of AIIDs. We employed regression analysis to examine comorbidities between AIIDs and psychiatric disorders and associations between AIIDs and HLA alleles across seven HLA genes. Our comorbidity analyses showed that overall AIID and five specific AIIDs were associated with having a psychiatric diagnosis. Our genetic analyses found 81 significant associations between HLA alleles and AIIDs. Lastly, we show connections across AIIDs, psychiatric disorders and infection susceptibility through network analysis of significant HLA associations in these disease classes. Combined, our results include both novel associations as well as replications of previously reported associations in a large sample, and highlight the genetic and epidemiological links between AIIDs and psychiatric disorders.

Deep Learning for Cross-Diagnostic Prediction of Mental Disorder Diagnosis and Prognosis Using Danish Nationwide Register and Genetic Data.

Importance: Diagnoses and treatment of mental disorders are hampered by the current lack of objective markers needed to provide a more precise diagnosis and treatment strategy. Objective: To develop deep learning models to predict mental disorder diagnosis and severity spanning multiple diagnoses using nationwide register data, family and patient-specific diagnostic history, birth-related measurement, and genetics. Design, Setting, and Participants: This study was conducted from May 1, 1981, to December 31, 2016. For the analysis, which used a Danish population-based case-cohort sample of individuals born between 1981 and 2005, genotype data and matched longitudinal health register data were taken from the longitudinal Danish population-based Integrative Psychiatric Research Consortium 2012 case-cohort study. Included were individuals with mental disorders (attention-deficit/hyperactivity disorder [ADHD]), autism spectrum disorder (ASD), major depressive disorder (MDD), bipolar disorder (BD), schizophrenia spectrum disorders (SCZ), and population controls. Data were analyzed from February 1, 2021, to January 24, 2022. Exposure: At least 1 hospital contact with diagnosis of ADHD, ASD, MDD, BD, or SCZ. Main Outcomes and Measures: The predictability of (1) mental disorder diagnosis and (2) severity trajectories (measured by future outpatient hospital contacts, admissions, and suicide attempts) were investigated using both a cross-diagnostic and single-disorder setup. Predictive power was measured by AUC, accuracy, and Matthews correlation coefficient (MCC), including an estimate of feature importance. Results: A total of 63 535 individuals (mean [SD] age, 23 [7] years; 34 944 male [55%]; 28 591 female [45%]) were included in the model. Based on data prior to diagnosis, the specific diagnosis was predicted in a multidiagnostic prediction model including the background population with an overall area under the curve (AUC) of 0.81 and MCC of 0.28, whereas the single-disorder models gave AUCs/MCCs of 0.84/0.54 for SCZ, 0.79/0.41 for BD, 0.77/0.39 for ASD, 0.74/0.38, for ADHD, and 0.74/0.38 for MDD. The most important data sets for multidiagnostic prediction were previous mental disorders and age (11%-23% reduction in prediction accuracy when removed) followed by family diagnoses, birth-related measurements, and genetic data (3%-5% reduction in prediction accuracy when removed). Furthermore, when predicting subsequent disease trajectories of the disorder, the most severe cases were the most easily predictable, with an AUC of 0.72. Conclusions and Relevance: Results of this diagnostic study suggest the possibility of combining genetics and registry data to predict both mental disorder diagnosis and disorder progression in a clinically relevant, cross-diagnostic setting prior to clinical assessment.

One-Third of European Patients With Axial Spondyloarthritis Reach Pain Remission With Routine Care Tumor Necrosis Factor Inhibitor Treatment.

OBJECTIVE: To investigate the distribution of patient-reported outcomes (PROs) in patients with axial spondyloarthritis (axSpA) initiating a tumor necrosis factor inhibitor (TNFi), to assess the proportion reaching PRO "remission" across registries and treatment series, and to compare patients registered to fulfill the modified New York (mNY) criteria for ankylosing spondylitis (AS) vs patients with nonradiographic axSpA (nr-axSpA). METHODS: Fifteen European registries contributed PRO scores for pain, fatigue, patient global assessment (PtGA), Bath Ankylosing Spondylitis (AS) Disease Activity Index (BASDAI), Bath AS Functional Index (BASFI), and Health Assessment Questionnaire (HAQ) from 19,498 patients with axSpA. Changes in PROs and PRO remission rates (definitions: ≤ 20 mm for pain, fatigue, PtGA, BASDAI, and BASFI; ≤ 0.5 for HAQ) were calculated at 6, 12, and 24 months of treatment. RESULTS: Heterogeneity in baseline characteristics and outcomes between registries were observed. In pooled data, 6 months after the start of a first TNFi, pain score was reduced by approximately 60% (median at baseline/6/12/24 months: 65/25/20/20 mm) in patients on treatment. Similar patterns were observed for fatigue (68/32/30/25 mm), PtGA (66/29/21/20 mm), BASDAI (58/26/21/19 mm), BASFI (46/20/16/16 mm), and HAQ (0.8/0.4/0.2/0.2). Patients with AS (n = 3281) had a slightly better response than patients with nr-axSpA (n = 993). The Lund Efficacy Index (LUNDEX)-adjusted remission rates at 6 months for pain/fatigue/PtGA/BASDAI/BASFI/HAQ were 39%/30%/38%/34%/35%/48% for the AS cohort and 30%/21%/26%/24%/33%/47% for the nr-axSpA cohort. Better PRO responses were seen with a first TNFi compared to a second and third TNFi. CONCLUSION: Patients with axSpA starting a TNFi achieved high PRO remission rates, most pronounced in those fulfilling the mNY criteria and for the first TNFi.