Assessing improved risk prediction of rheumatoid arthritis by environmental, genetic, and metabolomic factors

Application of metabolomics in the classification of traditional Chinese medicine syndromes in rheumatoid arthritis

OBJECTIVE

Rheumatoid arthritis (RA) is frequently treated with traditional Chinese medicine (TCM), where patients are classified into distinct syndromes, such as heat-dampness syndrome (HD) and kidney-liver deficiency syndrome (KLD). However, an objective and systematic approach to differentiate these TCM syndromes remains lacking. This study is aimed at analyzing serum metabolomics to identify differential metabolites and pathways associated with HD and GS syndromes in RA patients and at evaluating their potential as diagnostic biomarkers.

METHODS

Serum samples from RA patients classified into HD and KLD groups were analyzed using metabolomics. Partial least squares discriminant analysis was employed to identify significant metabolites, while pathway analysis was conducted using the Kyoto Encyclopedia of Genes and Genomes database. Receiver operating characteristic (ROC) curve analysis was performed to assess the diagnostic potential of key metabolites.

RESULTS

Fifteen differential metabolites and two perturbed pathways-sphingolipid and D-amino acid metabolism-were identified between the KLD and HD groups. Notably, several metabolites, including C17-sphinganine and leucyl-alanine, demonstrated high diagnostic efficacy, with area under the curve (AUC) values exceeding 0.90. Correlation analysis revealed significant associations between certain metabolites and clinical indices, further substantiating their role in syndrome differentiation.

CONCLUSION

This study presents a comprehensive analysis of serum metabolites in RA patients under different TCM syndromes. The identified metabolites hold potential as biomarkers for distinguishing HD and KLD groups, paving the way for more objective and evidence-based diagnostic approaches in TCM. Key Points • Differential metabolites were identified in the serum of RA patients with heat-dampness syndrome and kidney-liver deficiency syndrome, with their metabolic pathways primarily involving sphingolipid metabolism and D-amino acid metabolism. • Serum metabolites demonstrate high efficacy in distinguishing RA patients with different TCM syndromes. • Significant correlations were observed between serum differential metabolites and clinical indicators in RA patients with varying TCM syndromes.

Integrating genetics, metabolites, and clinical characteristics in predicting cardiometabolic health outcomes using machine learning algorithms - A systematic review

BACKGROUND

Machine learning (ML) integration of clinical, metabolite, and genetic data reveals variable results in predicting cardiometabolic health (CMH) outcomes. Therefore, we aim to (1) evaluate whether a multi-modal approach incorporating all three data types using ML algorithms can improve CMH outcome prediction compared to single-modal or paired-modal models, and (2) compare the methodologies used in existing prediction models.

METHODS

We systematically searched five databases from 1998 to 2024 for ML predictive modelling studies using the multi-modal approach for CMH outcomes. Risk-of-bias assessment tools were used to assess methodological quality. Study characteristics, ML algorithms, data preprocessing, evaluation methods and metrics, feature selections, and feature importance parameters were synthesized narratively to show methodological heterogeneity.

RESULTS

Of the four included studies (3 ML algorithms), three were at low risk of bias, and one was at high risk. The multi-modal approach consistently improved T2D and BP prediction compared to single-modal or paired-modal models. Genetics showed the lowest predictive performance in three studies. Logistic regression (n = 2 studies) and random forest (n = 1) were used in T2D studies, while XGBoost was used in one BP study. One study with missing data and variations in feature selection across all studies hindered a comprehensive comparison of feature importance.

CONCLUSIONS

Our review emphasizes the potential improvement in T2D and BP prediction using ML algorithms with the multi-modal approach. However, further studies using diverse ML algorithms with optimized methodologies on single-modal, paired-modal, and multi-modal models are needed to gain insights into biomarker selection for predicting CMH outcomes.

Biomechanical determinants of rheumatoid arthritis severity and excess cardiovascular disease: common origins of two complex diseases

OBJECTIVES

The determinants of rheumatoid arthritis (RA) severity and excess cardiovascular disease (CVD) are incompletely understood. Biomechanical factors are known to influence RA severity. Articular stiffness correlates with arterial and skin stiffness. This study explored the hypothesis that constitutional stiffness is a common determinant of RA severity and excess CVD.

METHODS

Fifty-eight patients with anti-CCP antibody (ACPA) positive RA and 57 controls were enrolled noting age, sex, body mass index, alcohol and tobacco exposure, Shared Epitope status and in RA disease duration, disease activity, ACPA titre and radiographic damage. Severe RA was defined as radiographic progression >1.3 mSharp points/year or requiring biological disease-modifying antirheumatic drugs (bDMARDs). Articular stiffness (Beighton Score and right 5th metacarpophalangeal (MCP) joint stress-strain responses), carotid-femoral pulse wave velocity and skin extensibility (percent increase distance two dots with manual traction dorsum right hand) were assessed.

RESULTS

Right 5th MCP stiffness correlated with Beighton Score and with arterial and skin stiffness. High radiographic rate was associated with greater MCP articular (t test p 0.014), arterial (p 0.044) and, in RA <5 years duration, greater skin stiffness (p 0.002) with similar trends in subjects requiring bDMARDs. In RA, arterial stiffness correlated with age (ß p<0.005), articular (ß p<0.001) and skin stiffness (ß p 0.037) and inversely with alcohol consumption (p 0.035).

CONCLUSIONS

Articular, arterial and skin stiffness correlated with each other and with RA severity. As skin is not affected by RA, this association suggests that constitutional stiffness might be a common determinant of RA and CVD. Prospective studies of at-risk preclinical and early RA are required to determine if this relationship is causal.

TRIALS REGISTRATION NUMBER

ACTRN12617000170325.

The Contribution of Macrophage Plasticity to Inflammatory Arthritis and Their Potential as Therapeutic Targets

Inflammatory arthritis are common chronic inflammatory autoimmune diseases characterised by progressive, destructive inflammation of the joints leading to a loss of function and significant comorbidities; importantly, there are no cures and only 20% of patients achieve drug-free remission for over 2 years. Macrophages play a vital role in maintaining homeostasis, however, under the wrong environmental cues, become drivers of chronic synovial inflammation. Based on the current "dogma", M1 macrophages secrete pro-inflammatory cytokines and chemokines, promoting tissue degradation and joint and bone erosion which over time lead to accelerated disease progression. On the other hand, M2 macrophages secrete anti-inflammatory mediators associated with wound healing, tissue remodelling and the resolution of inflammation. Currently, four subtypes of M2 macrophages have been identified, namely M2a, M2b, M2c and M2d. However, more subtypes may exist due to macrophage plasticity and the ability for repolarisation. Macrophages are highly plastic, and polarisation exists as a continuum with diverse intermediate phenotypes. This plasticity is achieved by a highly amenable epigenome in response to environmental stimuli and shifts in metabolism. Initiating treatment during the early stages of disease is important for improved prognosis and patient outcomes. Currently, no treatment targeting macrophages specifically is available. Such therapeutics are being investigated in ongoing clinical trials. The repolarisation of pro-inflammatory macrophages towards the anti-inflammatory phenotype has been proposed as an effective approach in targeting the M1/M2 imbalance, and in turn is a potential therapeutic strategy for IA diseases. Therefore, elucidating the mechanisms that govern macrophage plasticity is fundamental for the success of novel macrophage targeting therapeutics.

Metabolic signature of healthy lifestyle and risk of rheumatoid arthritis: observational and Mendelian randomization study

BACKGROUND

While substantial evidence reveals that healthy lifestyle behaviors are associated with a lower risk of rheumatoid arthritis (RA), the underlying metabolic mechanisms remain unclear.

OBJECTIVES

This study aimed to identify the metabolic signature reflecting a healthy lifestyle and investigate its observational and genetic linkage with RA risk.

METHODS

This study included 87,258 UK Biobank participants (557 cases of incident RA) aged 37 to 73 years with complete lifestyle, genotyping and nuclear magnetic resonance (NMR) metabolomics data. A healthy lifestyle was assessed based on five factors: healthy diet, regular exercise, not smoking, moderate alcohol consumption, and normal body mass index. The metabolic signature was developed by summing selected metabolites' concentrations weighted by the coefficients using elastic net regression. We used multivariate Cox model to assess the associations between metabolic signatures and RA risk, and examined the mediating role of the metabolic signature in the impact of a healthy lifestyle on RA. We performed genome-wide association analysis (GWAS) to obtain genetic variants associated with the metabolic signature, then conducted Mendelian randomization (MR) analyses to detect causality.

RESULTS

The metabolic signature comprised of 81 metabolites, robustly correlated with healthy lifestyle ( r = 0.45, P = 4.2 × 10^-15). The metabolic signature was inversely associated with RA risk (HR per SD increment: 0.76, 95% CI: 0.70-0.83), and largely explained protective effects of healthy lifestyle on RA with 64% (95%CI: 50.4-83.3) mediation proportion. One and two-sample MR analyses also consistently showed the associations of genetically inferred per SD increment in metabolic signature with a reduction in RA risk (HR: 0.84, 95% CI: 0.75-0.94, P = 0.002 and OR: 0.84, 95% CI: 0.73-0.97, P = 0.02 respectively).

CONCLUSION

Our findings implicate the metabolic signature reflecting healthy lifestyle as a potential causal mediator in the development of RA, highlighting the importance of early lifestyle intervention and metabolic tracking for precise prevention of RA.

Metabolomics in rheumatoid arthritis: Advances and review

Rheumatoid arthritis (RA) is an autoimmune disease accompanied by metabolic alterations. The metabolic profiles of patients with RA can be determined using targeted and non-targeted metabolomics technology. Metabolic changes in glucose, lipid, and amino acid levels are involved in glycolysis, the tricarboxylic acid cycle, the pentose phosphate pathway, the arachidonic acid metabolic pathway, and amino acid metabolism. These alterations in metabolic pathways and metabolites can fulfill bio-energetic requirements, promote cell proliferation, drive inflammatory mediator secretion, mediate leukocyte infiltration, induce joint destruction and muscle atrophy, and regulate cell proliferation, which may reflect the etiologies of RA. Differential metabolites can be used as biomarkers for the diagnosis, prognosis, and risk prediction, improving the specificity and accuracy of diagnostics and prognosis prediction. Additionally, metabolic changes associated with therapeutic responses can improve the understanding of drug mechanism. Metabolic homeostasis and regulation are new therapeutic strategies for RA. In this review, we provide a comprehensive overview of advances in metabolomics for RA.

Epigenome-wide gene–age interaction study reveals reversed effects of MORN1 DNA methylation on survival between young and elderly oral squamous cell carcinoma patients

DNA methylation serves as a reversible and prognostic biomarker for oral squamous cell carcinoma (OSCC) patients. It is unclear whether the effect of DNA methylation on OSCC overall survival varies with age. As a result, we performed a two-phase gene–age interaction study of OSCC prognosis on an epigenome-wide scale using the Cox proportional hazards model. We identified one CpG probe, cg11676291_MORN1, whose effect was significantly modified by age (HR_discovery = 1.018, p = 4.07 × 10⁻⁰⁷, FDR-q = 3.67 × 10⁻⁰²; HR_validation = 1.058, p = 8.09 × 10⁻⁰³; HR_combined = 1.019, p = 7.36 × 10⁻¹⁰). Moreover, there was an antagonistic interaction between hypomethylation of cg11676291_MORN1 and age (HR_interaction = 0.284; 95% CI, 0.135–0.597; p = 9.04 × 10⁻⁰⁴). The prognosis of OSCC patients was well discriminated by the prognostic score incorporating cg11676291_MORN1–age interaction (HR_{high vs. low} = 3.66, 95% CI: 2.40–5.60, p = 1.93 × 10⁻⁰⁹). By adding 24 significant gene–age interactions using a looser criterion, we significantly improved the area under the receiver operating characteristic curve (AUC) of the model at 3- and 5-year prognostic prediction (AUC_3-year = 0.80, AUC_5-year = 0.79, C-index = 0.75). Our study identified a significant interaction between cg11676291_MORN1 and age on OSCC survival, providing a potential therapeutic target for OSCC patients.

A Roadmap for Investigating Preclinical Autoimmunity Using Patient-Oriented and Epidemiologic Study Designs: Example of Rheumatoid Arthritis

Background & Aims

Rheumatoid arthritis (RA) is a prototypic autoimmune disease causing inflammatory polyarthritis that affects nearly 1% of the population. RA can lead to joint destruction and disability along with increased morbidity and mortality. Similar to other autoimmune diseases, RA has distinct preclinical phases corresponding to genetic risk, lifestyle risk factors, autoantibody development, and non-specific symptoms prior to clinical diagnosis. This narrative review will detail observational studies for RA risk and clinical trials for RA prevention as a roadmap to investigating preclinical autoimmunity that could be applied to other diseases.

Methods

In this narrative review, we summarized previous and ongoing research studies investigating RA risk and prevention, categorizing them related to their design and preclinical phases.

Results

We detailed the following types of studies investigating RA risk and prevention: retrospective population-based and administrative datasets; prospective studies (case-control and cohort; some enrolling based on genetics, first-degree relative status, elevated biomarkers, or early symptoms/arthritis); and randomized clinical trials. These correspond to all preclinical RA phases (genetic, lifestyle, autoimmunity, early signs/symptoms). Previous and ongoing randomized controlled trials have enrolled individuals at very elevated risk for RA based on biomarkers, symptoms, imaging abnormalities, or early signs/symptoms.

Conclusion

We detailed the rich variety of study designs that is necessary to investigate distinct preclinical phases of an autoimmune disease such as RA. However, further progress is needed to fully elucidate the pathogenesis of RA that may ultimately lead to prevention or delay of disease onset.