Disease Subtypes Research Articles

Parkinson's disease subtypes and their association with probable rapid eye movement sleep behavior disorder severity: a brainstem tractography and machine learning investigation.

Rapid Eye Movement (REM) sleep behavior disorder (RBD) affects nearly half of Parkinson's disease (PD) patients. However, the structural heterogeneity within the brainstem, which regulates REM sleep, remains largely unexplored in PD. Our objective was to identify distinct PD subtypes based on microstructural characteristics in the brainstem and examine their associations with the severity of RBD. Data, including diffusion tensor imaging and REM sleep behavior disorder screening questionnaire (RBDSQ) responses, were obtained from 124 PD patients and 61 healthy controls through the Parkinson's Progression Marker Initiative database. Mean Quantitative Anisotropy (QA) values, representing axonal density, were extracted from 14 brainstem tracts and input into the semi-supervised machine learning algorithm, Heterogeneity through Discriminative Analysis (HYDRA), to cluster subtypes. Applying HYDRA, we identified two distinct PD subtypes (Subtype 1: n = 66, Subtype 2: n = 58). Subtype 2 exhibited reduced QA across assessed brainstem tracts and significantly higher RBDSQ scores than Subtype 1 and healthy controls (p < 0.001). Conversely, Subtype 1, characterized by lower RBDSQ scores, exhibited increased QA, notably in the right medial longitudinal fasciculus, when compared to Subtype 2 and controls (p < 0.001). These findings suggest that heterogeneous axonal damage in brainstem circuits correlates with variations in RBD severity, providing insights into the neurobiological underpinnings of early PD.

Association between the Amplification Parameters of the α-Synuclein Seed Amplification Assay and Clinical and Genetic Subtypes of Parkinson's Disease.

α-Synuclein seed amplification assay on cerebrospinal fluid (CSF-αSyn-SAA) has shown high accuracy for Parkinson's disease (PD) diagnosis. The analysis of CSF-αSyn-SAA parameters may provide useful insight to dissect the heterogeneity of synucleinopathies. To assess differences in CSF-αSyn-SAA amplification parameters in participants with PD stratified by rapid eye movement (REM) sleep behavior disorder (RBD), dysautonomia, GBA, and LRRK2 variants. Clinical and CSF-αSyn-SAA data from the Parkinson's Progression Marker Initiative dataset were used. CSF-αSyn-SAA parameters included maximum fluorescence (Fmax), time to reach 50% of Fmax (T50), time to threshold (TTT), slope, and area under the curve (AUC). Sporadic PD (n = 371) was stratified according to RBD and dysautonomia (DysA) symptoms. Genetic PD included carriers of pathogenic variants of GBA (GBA-PD, n = 52) and LRRK2 (LRRK2-PD, n = 124) gene. CSF-αSyn-SAA was positive in 77% of LRRK2-PD, 92.3% of GBA-PD, and 93.8% of sporadic PD. The LRRK2-PD cohort showed longer T50 and TTT, and smaller AUC than GBA-PD (P = 0.029, P = 0.029, P = 0.016, respectively) and sporadic PD (P = 0.034, P = 0.033, P = 0.014, respectively). In the sporadic cohort, CSF-αSyn-SAA parameters were similar between PD with (n = 157) and without (n = 190) RBD, whereas participants with DysA (n = 193) presented shorter T50 (P = 0.026) and larger AUC (P = 0.029) than those without (n = 150). CSF-αSyn-SAA parameters vary across genetic and non-genetic PD subtypes at the group level. These differences are mostly driven by the presence of LRRK2 variants and DysA. Significant overlaps in the amplification parameter values exist between groups and limit their use at the individual level. Further studies are necessary to understand the mechanisms of CSF-αSyn-SAA parameter differences. © 2024 International Parkinson and Movement Disorder Society.

Deep profiling of gene expression across 18 human cancers.

Clinical and biological information in large datasets of gene expression across cancers could be tapped with unsupervised deep learning. However, difficulties associated with biological interpretability and methodological robustness have made this impractical. Here we describe an unsupervised deep-learning framework for the generation of low-dimensional latent spaces for gene-expression data from 50,211 transcriptomes across 18 human cancers. The framework, which we named DeepProfile, outperformed dimensionality-reduction methods with respect to biological interpretability and allowed us to unveil that genes that are universally important in defining latent spaces across cancer types control immune cell activation, whereas cancer-type-specific genes and pathways define molecular disease subtypes. By linking latent variables in DeepProfile to secondary characteristics of tumours, we discovered that mutation burden is closely associated with the expression of cell-cycle-related genes, and that the activity of biological pathways for DNA-mismatch repair and MHC class II antigen presentation are consistently associated with patient survival. We also found that tumour-associated macrophages are a source of survival-correlated MHC class II transcripts. Unsupervised learning can facilitate the discovery of biological insight from gene-expression data.

Diverse phenotypes, consistent treatment: a study of 30,997 South Asian and White IBD patients using the UK IBD BioResource.

To evaluate South Asian (SA) and White (WH) inflammatory bowel disease (IBD) phenotypes, and to explore treatment approach variations between these cohorts in the UK using the IBD BioResource database. Differences between WH and SA IBD patients were analysed using demographic, phenotypic and outcome data. Drug utilisation patterns and surgical outcomes were assessed in propensity score-matched (PSM) cohorts with multivariable logistic regression, Cox regression and Kaplan-Meier analysis. 30,997 eligible patients were included. UC was the predominant disease subtype in SA (p<0.001). SA were younger at diagnosis (p<0.001), had a male preponderance (p<0.001), and were less likely to have a smoking history at diagnosis. The SA CD phenotype differed from WH, with less ileal (SA 30.3%, WH 38.4%, p=0.008) and stricturing (SA 16.9%, WH 25.6%, p<0.001) disease, but more perianal disease (SA 38.5%, WH 32.2%, p=0.009). More SA UC patients had extensive disease (SA 41.7%, WH 34.1%, p<0.001). In PSM cohorts, comparing treatments, there were no differences in 5-aminosalicylate, corticosteroid, thiopurine, anti-TNF or vedolizumab use. Survival analysis in matched cohorts showed no difference in time to surgery (CD) or colectomy (UC), and SA ethnicity was not associated with a difference in risk of surgery/colectomy. Demographic and phenotypic differences exist between UK SA and WH IBD patients, highlighting distinct ethnicity-related variance, and the need for a research focus on under-represented populations. In comparing matched SA and WH patients, no disparity in medical and surgical IBD therapy in UK healthcare has been demonstrated: treatment is consistent regardless of ethnicity.

Clinical and Genomic Prediction of Coronary Artery Disease Subtypes.

Coronary artery disease (CAD) is a complex, heterogeneous disease with distinct etiological mechanisms. These different etiologies may give rise to multiple subtypes of CAD that could benefit from alternative preventions and treatments. However, so far, there have been no systematic efforts to predict CAD subtypes using clinical and genetic factors. Here, we trained and applied statistical models incorporating clinical and genetic factors to predict CAD subtypes in 26 036 patients with CAD in the UK Biobank. We performed external validation of the UK Biobank models in the US-based All of Us cohort (8598 patients with CAD). Subtypes were defined as high versus normal LDL (low-density lipoprotein) levels, high versus normal Lpa (lipoprotein A) levels, ST-segment-elevation myocardial infarction versus non-ST-segment-elevation myocardial infarction, occlusive versus nonocclusive CAD, and stable versus unstable CAD. Clinical predictors included levels of ApoA, ApoB, HDL (high-density lipoprotein), triglycerides, and CRP (C-reactive protein). Genetic predictors were genome-wide and pathway-based polygenic risk scores (PRSs). Results showed that both clinical-only and genetic-only models can predict CAD subtypes, while combining clinical and genetic factors leads to greater predictive accuracy. Pathway-based PRSs had higher discriminatory power than genome-wide PRSs for the Lpa and LDL subtypes and provided insights into their etiologies. The 10-pathway PRS most predictive of the LDL subtype involved cholesterol metabolism. Pathway PRS models had poor generalizability to the All of Us cohort. In summary, we present the first systematic demonstration that CAD subtypes can be distinguished by clinical and genomic risk factors, which could have important implications for stratified cardiovascular medicine.

Use of consensus clustering to identify distinct subtypes of chronic kidney disease and associated mortality risk

BackgroundChronic kidney disease (CKD) is a complex condition with diverse etiology and outcomes. Utilizing a data-driven clustering approach holds promise in identifying distinct CKD subgroups associated with specific risk profiles for death.MethodsUnsupervised consensus clustering was utilized to classify chronic kidney disease (CKD) into subtypes based on 45 baseline characteristics in a cohort of 6,526 participants from the US National Health and Nutrition Examination Survey (NHANES) spanning the years 1999–2000 to 2017–2018.We examined the associations between CKD subgroups and clinical endpoints related to mortality, including all-cause mortality, cardiovascular disease mortality, cancer mortality, and mortality due to other causes.ResultsA total of 6,526 individuals with CKD were classified into four clusters at baseline. Cluster 1 (n = 508) comprised patients with relatively favorable levels of cardiac and kidney function markers, lower prevalence of cancer and higher prevalence of obesity, lower medication usage, and younger age. Cluster 4 (n = 2,029) comprised patients with the worst cardiac and kidney function markers. The characteristics of cluster 2 (n = 1,439) and 3 (n = 2,550) fell in between these two clusters. From cluster 1 to cluster 4, we observed a gradual increase in the hazard ratios of all-cause mortality, cardiovascular disease mortality, and mortality due to other causes. Additionally, further sensitivity analysis revealed patient heterogeneity among predefined subgroups with similar baseline kidney function and mortality risks.ConclusionsConsensus clustering integrated baseline clinical and laboratory measures, revealing distinct CKD subgroups with markedly different risks of death, suggesting that further examination of patient subgroups could advance precision medicine.

Neuroanatomical heterogeneity drives divergent cognitive and motor trajectories in Parkinson's disease subtypes

IntroductionCognitive symptoms of Parkinson's disease (PD) may initially present subtly, often overshadowed by more noticeable motor symptoms. However, as PD progresses, predicting which individuals will experience significant cognitive decline becomes challenging due to variability, suggesting distinct PD subtypes with varying cognitive trajectories. This study aimed to identify early PD subtypes based on patterns of gray matter atrophy in brain regions associated with cognition and assess their distinct patterns of cognitive change over time. Recognizing PD primarily as a movement disorder, we also evaluated their motor symptoms. MethodsWe analyzed T1-weighted MRI data, cognitive, and motor scores from 114 de novo PD patients and 120 healthy controls. Multivariate gray matter volumetric distances (MGMV) across frontal, subcortical, parietal, temporal, and occipital regions were computed, and K-means clustering was used to identify PD subtypes. Subsequently, cognitive assessments were compared between subtypes at baseline and 48 months using linear mixed-effects models and reliable change indices. Motor-symptom changes were assessed using linear mixed-effects models. ResultsTwo PD subtypes were identified from baseline MRI. Subtype 1 showed significantly higher MGMV in frontal (p < 0.001) and subcortical (p < 0.001) regions, indicating atrophy. At 48 months, subtype 1 had poorer global cognitive performance than subtype 2 (p = 0.005) and faster progression of postural instability and gait disturbance (p = 0.04). ConclusionsPD subtypes identified early by distinct frontal and subcortical atrophy patterns exhibited divergent trajectories of cognitive decline and worsening motor symptoms over time, underscoring the neuroanatomical heterogeneity that drives clinical variability in PD.

RNA methylation: where to from here for hematological malignancies?

RNA methylation and the machinery that regulates or 'reads' its expression has recently been implicated in the pathogenesis of acute myeloid leukemia (AML) and other hematological malignancies. Modulation of these epigenetic marks has started to become a reality as several companies around the world seek to leverage this knowledge therapeutically in the clinic. While the bases of observed activity in AML have been described by numerous groups, the exact context in which these therapies will ultimately be used remains to be properly determined. Although context is likely to be of great importance here, a more 'global' mechanism of action might allow for more widespread applicability to multiple diseases or disease subtypes. In other areas such as the myelodysplastic and other pre-leukemic syndromes, data remains sparse. Ongoing work is needed to determine whether therapeutic modulation of RNA modifications is a viable and biologically plausible approach in these cases. Regardless of the outcomes, this is an exciting era for 'epitranscriptomics' as we navigate a pathway forwards. Herein I describe the current knowledge around RNA methylation and hematological malignancies including some of the relevant questions that are yet to be answered.

The impact of cerebrovascular steno-occlusive disease subtype on surgical and clinical outcomes after direct STA-MCA bypass surgery.

Although well-established in moyamoya disease (MMD), the role of direct superficial temporal artery (STA) to middle cerebral artery (MCA) bypass in non-MMD (N-MMD) cerebrovascular steno-occlusive syndromes remains controversial. Nonetheless, the recurrent stroke risk in patients with N-MMD, despite best medical management, remains exceedingly high-especially for those suffering from hypoperfusion-related ischemia. The study objective was to determine the relative safety and efficacy profiles of direct STA-MCA bypass surgery for MMD and N-MMD patients in a large contemporary cohort. The authors conducted a retrospective review of all direct STA-MCA bypass cases performed between 2014 and 2023 at a high-volume center, which yielded 139 cases. Cases were excluded if they involved double-barrel bypass, an interposition graft, or if the surgical indication was not cerebral hypoperfusion. Direct bypass graft patency was serially assessed on follow-up vessel imaging. Of the 139 included cases, 88 (63.3%) were MMD and 51 (36.7%) were N-MMD cases. The mean patient age was 49.2 years and 60.4% were female. The mean follow-up duration was 18.5 months. The perioperative stroke risk within 30 days of revascularization was 6.5% for the overall cohort, with no significant difference (p = 0.725) observed between MMD (5.7%) and N-MMD (7.8%) cases. The overall postoperative ipsilateral hemispheric and MCA distribution stroke rates at last follow-up were 11.5% and 9.4%, respectively. Despite a greater medical comorbidity burden, N-MMD cases demonstrated comparable rates of direct bypass graft occlusion (21.6% N-MMD vs 28.4% MMD, p = 0.426), MCA-distribution ischemic stroke (11.8% N-MMD vs 7.9% MMD, p = 0.549), and ipsilateral ischemic stroke (15.7% N-MMD vs 9.1% MMD, p = 0.276) to patients with MMD at last follow-up. Higher preoperative total hemispheric flow on noninvasive optimal vessel analysis (NOVA) imaging was the only variable associated with prolonged direct bypass graft patency (hazard ratio [HR] 0.39, p = 0.036). Postoperative stroke-free survival was improved by performing dural synangiosis (HR 0.31, p = 0.033) and, in multivariate analysis, was reduced with direct bypass graft occlusion (HR 4.58, p = 0.009) and a preoperative diffusion-weighted imaging-Alberta Stroke Program Early CT Score (DWI-ASPECTS) < 8 (HR 3.90, p = 0.024). This robust cohort of MMD and N-MMD STA-MCA bypass cases highlights the safety and efficacy of a technically sound direct bypass across all subtypes of cerebrovascular steno-occlusive disease. Careful attention to preoperative MRI parameters, including hemispheric flow rates on NOVA imaging, may improve surgical risk stratification. Further examination of the benefits of adjunctive indirect bypass or dural synangiosis, especially for patients with N-MMD, remains warranted.

IgG4 in the gut: Gastrointestinal IgG4-related disease or a new subtype of inflammatory bowel disease.

IgG4-related disease (IgG4-RD) is a chronic inflammatory condition characterized by tissue infiltration with IgG4-positive plasma cells, leading to fibrosis and organ dysfunction. While primarily affecting the pancreas, bile ducts, and salivary glands, IgG4-RD can also involve the gastrointestinal tract, raising questions about its relationship with inflammatory bowel disease (IBD). Recent studies suggest that patients with IBD may exhibit histological and serological features consistent with IgG4-RD, such as a dense lymphoplasmacytic infiltration, a storiform-type of fibrosis and a prominent IgG4 immune response. This overlap represents a diagnostic challenge, as differentiating between primary IBD and IgG4-RD involving the gut is crucial for appropriate treatment. Further research is essential to delineate the prevalence of tissue and serum IgG4 expression in patients with IBD. This approach could classify subtypes of IBD, enabling advancements in non-invasive diagnosis and monitoring as well as personalized therapies. The purpose of this review is to summarize the available evidence regarding intestinal involvement in IgG4-RD and the role of both serum and tissue IgG4 in inflammatory bowel diseases IBD.

Polygenic and transcriptional risk scores identify chronic obstructive pulmonary disease subtypes in the COPDGene and ECLIPSE cohort studies

Genetic variants and gene expression predict risk of chronic obstructive pulmonary disease (COPD), but their effect on COPD heterogeneity is unclear. We aimed to define high-risk COPD subtypes using genetics (polygenic risk score, PRS) and blood gene expression (transcriptional risk score, TRS) and assess differences in clinical and molecular characteristics. We defined high-risk groups based on PRS and TRS quantiles by maximising differences in protein biomarkers in a COPDGene training set and identified these groups in COPDGene and ECLIPSE test sets. We tested multivariable associations of subgroups with clinical outcomes and compared protein-protein interaction networks and drug repurposing analyses between high-risk groups. We examined two high-risk omics-defined groups in non-overlapping test sets (n=1133 NHW COPDGene, n=299 African American (AA) COPDGene, n=468 ECLIPSE). We defined "high activity" (low PRS, high TRS) and "severe risk" (high PRS, high TRS) subgroups. Participants in both subgroups had lower body-mass index (BMI), lower lung function, and alterations in metabolic, growth, and immune signalling processes compared to a low-risk (low PRS, low TRS) subgroup. "High activity" but not "severe risk" participants had greater prospective FEV1 decline (COPDGene:-51mL/year; ECLIPSE:-40mL/year) and proteomic profiles were enriched in gene sets perturbed by treatment with 5-lipoxygenase inhibitors and angiotensin-converting enzyme (ACE) inhibitors. Concomitant use of polygenic and transcriptional risk scores identified clinical and molecular heterogeneity amongst high-risk individuals. Proteomic and drug repurposing analysis identified subtype-specific enrichment for therapies and suggest prior drug repurposing failures may be explained by patient selection. National Institutes of Health.

Efficacy and safety of subcutaneous abatacept + standard treatment for active idiopathic inflammatory myopathy: phase III randomised controlled trial.

To evaluate the efficacy and safety of subcutaneous (SC) abatacept and standard of care (SOC) for the treatment of idiopathic inflammatory myopathy (IIM) over 52 weeks. In this randomised, double-blind, placebo-controlled phase III trial, patients with treatment-refractory IIM received SC abatacept (125 mg weekly) + SOC (abatacept group) or placebo + SOC (placebo group) (NCT02971683). A 24-week double-blind period was followed by an open-label period to assess outcomes from continued therapy with abatacept and initiation with abatacept (placebo-to-abatacept switch group) from 24 to 52 weeks. The primary endpoint was International Myositis Assessment and Clinical Studies definition of improvement (IMACS DOI) at week 24. Secondary efficacy and safety endpoints were assessed. Overall, 148 (double-blind) and 133 (open-label) patients were treated. Baseline demographics were well-balanced between treatment groups and disease subtypes. At 24 weeks, improvement per IMACS DOI was 56.0% for the abatacept group and 42.5% for the placebo group (p=0.083); at 52 weeks, improvement was 69.8% (continued abatacept) and 69.0% (placebo-to-abatacept switch). IMACS DOI rate at 24 weeks was greater in the non-dermatomyositis (DM) group (abatacept: 57.1%; placebo: 32.3%; p=0.040) than the DM group (abatacept: 55.0%; placebo: 50.0%; p=0.679). The observed safety profile was similar in both groups. The proportion of patients who met improvement criteria after 24 weeks was similar between abatacept and placebo groups. However, analysis by IIM subtype suggested there may be sustained benefit of SC abatacept for patients with non-DM subtypes.

Enhancing MRI radiomics feature reproducibility and classification performance in Parkinson's disease: a harmonization approach to gray-level discretization variability.

This study aimed to assess the reproducibility of MRI-derived radiomic features across multiple gray-level discretization levels for classifying Parkinson's disease (PD) subtypes, and to evaluate the impact of ComBat harmonization on feature stability and machine learning performance. T1-weighted MRI scans from 140 PD patients (70 tremor-dominant, 70 postural instability gait difficulty) and 70 healthy controls were obtained from the Parkinson's progression markers initiative (PPMI) database. Radiomic features were extracted from 16 brain regions using 6 discretization levels (8, 16, 32, 64, 128, and 256 bins). ComBat harmonization was applied using a combined batch variable incorporating both scanner models and discretization levels. Intraclass correlation coefficients (ICC) and Kruskal-Wallis tests assessed feature reproducibility before and after harmonization. Support vector machine classifiers were used for PD subtype classification. ComBat harmonization significantly improved feature reproducibility across all feature groups. The percentage of features showing excellent robustness (ICC ≥ 0.90) increased substantially after harmonization. The proportion of features significantly affected by discretization levels was reduced following harmonization. Classification accuracy improved dramatically, from a range of 0.42-0.49 before harmonization to 0.86-0.96 after harmonization across most discretization levels. AUC values similarly increased from 0.60-0.67 to 0.93-0.99 after harmonization. ComBat harmonization significantly enhanced the reproducibility of radiomic features across discretization levels and improved PD subtype classification performance. This study highlights the importance of harmonization in radiomics research for PD and suggests potential clinical applications in personalized treatment planning.

Non-invasively differentiate non-alcoholic steatohepatitis by visualizing hepatic integrin αvβ3 expression with a targeted molecular imaging modality

BACKGROUND Non-invasive methods to diagnose non-alcoholic steatohepatitis (NASH), an inflammatory subtype of non-alcoholic fatty liver disease (NAFLD), are currently unavailable. AIM To develop an integrin αvβ3-targeted molecular imaging modality to differentiate NASH. METHODS Integrin αvβ3 expression was assessed in Human LO2 hepatocytes Scultured with palmitic and oleic acids (FFA). Hepatic integrin αvβ3 expression was analyzed in rabbits fed a high-fat diet (HFD) and in rats fed a high-fat, high-carbohydrate diet (HFCD). After synthesis, cyclic arginine-glycine-aspartic acid peptide (cRGD) was labeled with gadolinium (Gd) and used as a contrast agent in magnetic resonance imaging (MRI) performed on mice fed with HFCD. RESULTS Integrin αvβ3 was markedly expressed on FFA-cultured hepatocytes, unlike the control hepatocytes. Hepatic integrin αvβ3 expression significantly increased in both HFD-fed rabbits and HFCD-fed rats as simple fatty liver (FL) progressed to steatohepatitis. The distribution of integrin αvβ3 in the liver of NASH cases largely overlapped with albumin-positive staining areas. In comparison to mice with simple FL, the relative liver MRI-T1 signal value at 60 minutes post-injection of Gd-labeled cRGD was significantly increased in mice with steatohepatitis (P &lt; 0.05), showing a positive correlation with the NAFLD activity score (r = 0.945; P &lt; 0.01). Hepatic integrin αvβ3 expression was significantly upregulated during NASH development, with hepatocytes being the primary cells expressing integrin αvβ3. CONCLUSION After using Gd-labeled cRGD as a tracer, NASH was successfully distinguished by visualizing hepatic integrin αvβ3 expression with MRI.

MicroRNA Profiling to Inform Disease Classification, Severity, and Treatment Response in Pediatric Pulmonary Hypertension.

Pediatric pulmonary hypertension is a heterogeneous disease associated with significant morbidity and mortality. MicroRNAs have been implicated as both pathologic drivers of disease and potential therapeutic targets in pediatric pulmonary hypertension. We sought to characterize the circulating microRNA profiles of a diverse array of pediatric pulmonary hypertension patients using high throughput sequencing technology. Peripheral blood samples were drawn from patients recruited at the time of a clinically indicated cardiac catheterization and microRNA sequencing followed by differential expression and target/pathway enrichment analyses were performed. Among 63 pediatric pulmonary hypertension patients, we identified specific microRNA signatures that uniquely classified patients by disease subtype, correlated with indicators of disease severity including invasive hemodynamic metrics, and changed over the course of treatment for pulmonary hypertension. These microRNA profiles include a number of specific microRNA molecules known to function in signaling pathways critical to pulmonary vascular biology and disease, including TGFβ beta, VEGF, PI3K/Akt, cGMP-PKG, and HIF-1 signaling. Circulating levels of miR-122-5p, miR-124-3p, miR-204-5p, and miR-9-5p decreased over the course of treatment in a subset of patients who had multiple samples drawn during the study period. Our findings support the further investigation of specific microRNAs as mechanistic mediators, biomarkers, and therapeutic targets in pulmonary hypertension.

Clinical and biological underpinnings of longitudinal atrophy pattern progression in Alzheimer's disease.

Magnetic resonance imaging (MRI) has recently enabled to identify four distinct Alzheimer's disease (AD) subtypes: hippocampal sparing (HpSp), typical AD (tAD), limbic predominant (Lp), and minimal atrophy (MinAtr). To date, however, the natural history of these subtypes, especially regarding the presence of subjects switching to other MRI patterns and their clinical and biological differences, remains poorly understood. To investigate the clinical and biological underpinnings of longitudinal atrophy pattern progression in AD. 251 AD patients (16 with significant memory concern, 66 with early mild cognitive impairment (MCI), 125 with late MCI, and 44 with AD dementia) from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database were assigned to their baseline MRI atrophy subtype using Freesurfer-derived cortical:hippocampal volumes ratio. Switching to other MRI patterns was investigated on longitudinal scans, and patients were accordingly classified as "switching" and "stable". Logistic regression models were applied to identify predictors of switching to other MRI patterns. 40% of Lp, 26% of HpSp, and 35% of MinAtr cases switched to other MRI patterns, with tAD representing the destination subtype of all switching HpSp and Lp, and the majority of MinAtr. At baseline significant clinical, cognitive and biomarkers differences were observed across the four subtypes. Only clinical and cognitive variables, however, were significantly associated with switch to other MRI patterns. Our results suggest convergent directions of disease progression across atypical and typical AD forms, at least in a subset of AD subjects, and highlight the importance of deep-phenotyping approaches to understand AD heterogeneity.

Divergent neurodegenerative patterns: comparison of fluorodeoxyglucose-PET and MRI-based Alzheimer’s disease subtypes

Abstract [18F] fluorodeoxyglucose (FDG)-PET and MRI are key imaging markers for neurodegeneration in Alzheimer's disease. It has been well established that parieto-temporal hypometabolism on FDG-PET is closely associated with medial temporal atrophy on MRI in Alzheimer's disease. Substantial biological heterogeneity, expressed as distinct subtypes of hypometabolism or atrophy patterns, has been previously described in Alzheimer's disease using data-driven and hypothesis-driven methods. However, the link between these two imaging modalities has not yet been explored in the context of Alzheimer's disease subtypes. To investigate this link, the current study utilised FDG-PET and MRI scans from 180 amyloid-beta positive Alzheimer's disease dementia patients, 339 amyloid-beta positive mild cognitive impairment and 176 amyloid-beta negative cognitively normal controls from the Alzheimer’s Disease Neuroimaging Initiative (ADNI). Random forest hierarchical clustering, a data-driven model for identifying subtypes, was implemented in the two modalities: one with standard uptake value ratios and the other with grey matter volumes. Five hypometabolism- and atrophy-based subtypes were identified, exhibiting both cortical-predominant and limbic-predominant patterns although with differing percentages and clinical presentations. Three cortical-predominant hypometabolism subtypes found were: Cortical Predominant (32%), Cortical Predominant+ (11%), Cortical Predominant posterior (8%); and two limbic-predominant hypometabolism subtypes: Limbic Predominant (36%) and Limbic Predominant frontal (13%). In addition, little atrophy (minimal) and widespread (diffuse) neurodegeneration subtypes were observed from the MRI data. The five atrophy subtypes were found: Cortical Predominant (19%), Limbic Predominant (27%), Diffuse (29%), Diffuse+ (6%) and Minimal (19%). Inter-modality comparisons showed that all FDG-PET subtypes displayed medial temporal atrophy, whereas the distinct MRI subtypes showed topographically similar hypometabolic patterns. Further, allocations of FDG-PET and MRI subtypes were not consistent when compared at an individual-level. Additional analysis comparing the data-driven clustering model with prior hypothesis-driven methods showed only partial agreement between these subtyping methods. FDG-PET subtypes had greater differences between limbic-predominant and cortical-predominant patterns and MRI subtypes had greater differences in severity of atrophy. In conclusion, this study highlighted that Alzheimer's disease subtypes identified using both FDG-PET and MRI capture distinct pathways showing cortical- versus limbic-predominance of neurodegeneration. However, the subtypes do not share a bidirectional relationship between modalities and are thus not interchangeable.

Cognitive performance, psychiatric comorbidities, and quality of life in pediatric patients with juvenile idiopathic arthritis: a comparative analysis with healthy controls

ABSTRACT This study aimed to assess the extent of cognitive impairment in children and adolescents with Juvenile Idiopathic Arthritis (JIA). While cognitive deficits are recognized in other systemic rheumatic diseases, exploration within the pediatric JIA population remains limited. The investigation utilized a comprehensive approach to examine neuropsychological test performance. A cohort of 160 participants (79 JIA, 81 healthy controls aged 8–17) underwent evaluations using the Pediatric Quality of Life Inventory (PedsQL), Schedule for Affective Disorders and Schizophrenia for School Age Children-Present and Lifetime Version (K-SADS-PL), and the computerized neurocognitive test battery Central Nervous System Vital Signs (CNSVS). Children with JIA exhibited statistically significant cognitive deficits across various parameters (p < .05). This was associated with an increased prevalence of lifelong psychiatric illnesses and diminished overall quality of life compared to healthy counterparts (p < .05). Analysis highlighted that specific JIA subtypes, excluding Oligoarthritis, significantly elevated the risk of neurocognitive impairments, emphasizing the impact on various cognitive outcomes (OR range: 3.1–5.1, 95% CI: 1.163–19.980). Additionally, the active disease stage was identified as a specific risk factor, amplifying the likelihood of low executive functions by 4.3 times (OR: 4.363, 95% CI: 1.095–17.378). This study underscores the critical importance of recognizing and addressing neurocognitive impairments in children with JIA. Specific attention to disease subtypes and activity levels is crucial, with the potential for targeted interventions to enhance overall cognitive well-being and quality of life in this vulnerable population.

PartIES: a disease subtyping framework with Partition-level Integration using diffusion-Enhanced Similarities from multi-omics Data.

Integrating multi-omics data helps identify disease subtypes. Many similarity-based methods were developed for disease subtyping using multi-omics data, with many of them focusing on extracting common clustering structures across multiple types of omics data, but not preserving data-type-specific clustering structures. Moreover, clustering performance of similarity-based methods is affected when similarity measures are noisy. Here we proposed PartIES, a Partition-level Integration using diffusion-Enhanced Similarities to perform disease subtyping using multi-omics data. PartIES uses diffusion to reduce noises in individual similarity/kernel matrices from individual omics data types first, and then extract partition information from diffusion-enhanced similarity matrices and integrate the partition-level similarity through a weighted average iteratively. Simulation studies showed that (1) the diffusion step enhances clustering accuracy, and (2) PartIES outperforms competing methods, particularly when omics data types provide different clustering structures. Using mRNA, long noncoding RNAs, microRNAs expression data, DNA methylation data, and somatic mutation data from The Cancer Genome Atlas project, PartIES identified subtypes in bladder urothelial carcinoma, liver hepatocellular carcinoma, and thyroid carcinoma that are most significantly associated with patient survival across all methods. Further investigations suggested that among subtype-associated genes, many of those that are highly interacting with other genes are known important cancer genes. The identified cancer subtypes also have different activity levels for some known cancer-related pathways. The R code can be accessed at https://github.com/yuqimiao/PartIES.git.

Current Review of Comorbidities in Chronic Rhinosinusitis.

Chronic rhinosinusitis (CRS) is a heterogenous disease with a significant impact on patient quality of life and a substantial economic burden. CRS is associated with several systemic inflammatory conditions. We provide an updated review of CRS comorbidities as a springboard for future comorbidity mapping and potential therapeutics. The link between environmental allergies and CRS is most evident for central compartment atopic disease (CCAD) and allergic fungal rhinosinusitis (AFRS) subtypes but remains inconclusive for CRS overall. The association between asthma and CRS, reinforced by the unified airway theory, is evidenced by their response to similar biologic therapies. Another lower respiratory tract disease, COPD, has up to a 50% co-occurrence with CRS and warrants careful screening and treatment. Eosinophilic esophagitis and CRS share eosinophilic inflammation in different sites, meriting further research. Obesity not only presents physiological challenges but also correlates with a more severe subset of CRS. Diabetes mellitus is associated with CRSwNP, possibly secondary to therapeutics with steroids. Autoimmunity may contribute to nasal polyp formation through cytokines such as B-cell activating factor (BAFF), offering potential for future therapeutics. This review illustrates the need to employ a macroscopic approach in clinical decision making and treatment of CRS. Comorbidities may contribute to an overall proinflammatory state, magnify severity of symptoms, be a source of treatment resistance, and even an opportunity for future therapeutics.