Predict Disease Severity Research Articles

The gut microbiota as an early predictor of COVID-19 severity.

Several studies reported alterations of the human gut microbiota (GM) during COVID-19. To evaluate the potential role of the GM as an early predictor of COVID-19 at disease onset, we analyzed gut microbial samples of 315 COVID-19 patients that differed in disease severity. We observed significant variations in microbial diversity and composition associated with increasing disease severity, as the reduction of short-chain fatty acid producers such as Faecalibacterium and Ruminococcus, and the growth of pathobionts as Anaerococcus and Campylobacter. Notably, we developed a multi-class machine-learning classifier, specifically a convolutional neural network, which achieved an 81.5% accuracy rate in predicting COVID-19 severity based on GM composition at disease onset. This achievement highlights its potential as a valuable early biomarker during the first week of infection. These findings offer promising insights into the intricate relationship between GM and COVID-19, providing a potential tool for optimizing patient triage and streamlining healthcare during the pandemic.IMPORTANCEEfficient patient triage for COVID-19 is vital to manage healthcare resources effectively. This study underscores the potential of gut microbiota (GM) composition as an early biomarker for COVID-19 severity. By analyzing GM samples from 315 patients, significant correlations between microbial diversity and disease severity were observed. Notably, a convolutional neural network classifier was developed, achieving an 81.5% accuracy in predicting disease severity based on GM composition at disease onset. These findings suggest that GM profiling could enhance early triage processes, offering a novel approach to optimizing patient management during the pandemic.

Serum inflammatory biomarkers associated with disease severity and response to dupilumab treatment in bullous pemphigoid: A cluster analysis

BackgroundDupilumab, a novel therapy targeting the T helper (Th) 2-mediated inflammation, is showing clinical benefits in treating bullous pemphigoid (BP). However, limited research investigated the serum biomarkers that reflect the inflammation alterations throughout the disease course. ObjectivesTo explore the changes of the serum inflammatory biomarkers under dupilumab therapy in BP and establish their correlations with disease severity and clinical outcomes. MethodsThis exploratory study evaluated serum samples from 40 patients with BP at baseline, 30 of these patients following 16-week dupilumab therapy, and 20 senior healthy controls. Serum levels of 29 cytokines and chemokines were quantified using the Magnetic Luminex Assay. ResultsTwo distinct clusters based on serum inflammatory profiles were identified. The first cluster, characterized by elevated levels of inflammatory activation, exhibited worse disease severity and poorer remission outcomes. Following the 16-week dupilumab therapy regimen, a significant suppression of Th2-mediated inflammation in the serum was observed, alongside a relative upregulation of Th1 responses. Patients treated with adjuvant systemic steroids exhibited an enhanced suppression of B cell activating factor compared to those receiving dupilumab alone. Significant correlations were unveiled between Th2 biomarkers and clinical scores, eosinophil counts, and anti-BP180 immunoglobulin G levels. Baseline levels of CCL18, Periostin, interleukin (IL)-6, and IL-16 constitute an optimal combination to distinguish between inflammatory clusters. ConclusionsCluster analysis of serum inflammatory biomarkers provided novel insights into the heterogeneity of the inflammation profiles in BP. Baseline levels of CCL18, Periostin, IL-6, IL-16 emerged as effective predictors for disease severity and therapy response to dupilumab.

Oculomic stratification of COVID-19 patients' intensive therapy unit admission status and mortality by retinal morphological findings.

To investigate if retinal thickness has predictive utility in COVID-19 outcomes by evaluating the statistical association between retinal thickness using OCT and of COVID-19-related mortality. Secondary outcomes included associations between retinal thickness and length of stay (LoS) in hospital. In this retrospective cohort study, OCT scans from 230 COVID-19 patients admitted to the Intensive Care Unit (ITU) were compared with age and gender-matched patients with pneumonia from before March 2020. Total retinal, GCL + IPL, and RNFL thicknesses were recorded, and analysed with systemic measures collected at the time of admission and mortality outcomes, using linear regression models, Pearson's R correlation, and Principal Component Analysis. Retinal thickness was significantly associated with all-time mortality on follow up in the COVID-19 group (p = 0.015), but not 28-day mortality (p = 0.151). Retinal and GCL + IPL layer thicknesses were both significantly associated with LoS in hospital for COVID-19 patients (p = 0.006 for both), but not for patients with pneumonia (p = 0.706 and 0.989 respectively). RNFL thickness was not associated with LoS in either group (COVID-19 p = 0.097, pneumonia p = 0.692). Retinal thickness associated with LoS in hospital and long-term mortality in COVID-19 patients, suggesting that retinal structure could be a surrogate marker for frailty and predictor of disease severity in this group of patients, but not in patients with pneumonia from other causes.

Deciphering the role of lactate as aprognostic indicator in pediatric diabetic ketoacidosis.

Serum lactate levels have been recognized as arobust marker for predicting disease severity and survival in many critically ill children but consensus is lacking regarding its utility in diabetic ketoacidosis. This study aimed to investigate the relationship between initial lactate levels and disease severity in pediatric patients presenting with diabetic ketoacidosis. This single-center retrospective descriptive study involved pediatric patients with diabetic ketoacidosis in the pediatric emergency department between January 2022 and April 2023. Patients were diagnosed using the International Society for Pediatric and Adolescent Diabetes 2022 guidelines. Among the 112 patients included in the study, 41 (36.6%) were classified as mild, 42 (34.8%) as moderate and 32 (28.6%) as severe acidosis. Astatistically significant difference was observed between the time to resolution and clinical severity of diabetic ketoacidosis (p < 0.001). Elevated lactate levels of 2.5 mmol/L or above were detected in 37.5% (42/112) of our patients and asignificant increase in clinical severity was observed as lactate levels increased (p < 0.001). Correlation analysis revealed no significant relationship between lactate levels and time to resolution of diabetic ketoacidosis or length of intensive care unit stay. Multivariate analysis demonstrated asignificant association between lactate levels and severity of acidosis (p: 0.046). Although there is an association between the severity of acidosis and lactate levels in diabetic ketoacidosis, contrary to expectations, this relationship was not found to be associated with adverse outcomes. An important point not to be overlooked by pediatricians is that elevated lactate levels in diabetic ketoacidosis may not always herald poor outcomes.

Left Atrial Appendage Thrombus as a Marker of Disease Severity in 500 Patients with Atrial Fibrillation on Oral Anticoagulation: A 13-Year Follow-Up Study.

Background/Objective: Whether left atrial appendage thrombus (LAAT) in patients with atrial fibrillation (AF) on chronic anticoagulation significantly increases cardiovascular risk is unknown. This study aimed to assess LAAT prevalence and its predictive role in cardiovascular events among consecutive anticoagulated patients with AF admitted for electrical cardioversion. Methods: This prospective study included 500 patients. The primary outcome was LAAT on transesophageal echocardiography. Patients were followed up for a median of 1927.5 (interquartile range 1004-2643) days to assess cardiovascular events. Results: LAAT was detected in 65 (13%) patients. No significant differences in stroke, transient ischemic attack, systemic thromboembolic events, or myocardial infarction prevalence were observed between patients with AF with and without LAAT. Hospitalization for heart failure (HF) was more frequent in patients with LAAT than in those without LAAT; however, the effect of LAAT on HF hospitalization was not statistically significant. Patients with LAAT had a significantly higher risk of cardiovascular death than those without LAAT. LAAT and greater left atrial (LA) diameter were associated with higher rates of cardiovascular death. The independent HF hospitalization predictors were greater LA diameter, lower left ventricular ejection fraction (LVEF), and estimated glomerular filtration rate (eGFR). Conclusions: Patients with AF who received anticoagulation therapy showed a high prevalence of LAAT. LAAT and greater LA diameter were associated with significantly higher rates of cardiovascular death. LAAT, greater LA diameter, lower LVEF, and lower eGFR were associated with poor prognosis in anticoagulated patients with AF and were predictors of disease severity.

Do dynamic changes in haematological and biochemical parameters predict mortality in critically ill COVID-19 patients?

BACKGROUND: Critically ill COVID-19 patients are usually subjected to clinical, laboratory, and radiological diagnostic procedures resulting in numerous findings. Utilizing these findings as indicators for disease progression or outcome prediction is particularly intriguing. OBJECTIVES: Exploring the significance of dynamic changes in haematological and biochemical parameters in predicting the mortality of critically ill COVID-19 patients. METHODS: The present study was a prospective and observational study involving mechanically ventilated 75 critically ill adult COVID-19 patients with hypoxemic respiratory failure. The collected data included baseline patient characteristics, treatment options, outcome, and laboratory findings at admission and 7 days after. The dynamics of the obtained findings were compared between survivors and non-survivors. RESULTS: The 28-day survival rate was 61.3%. In the group of non-survivors significant dynamic changes were found for C-reactive protein (p= 0.001), interleukin-6 (p&lt; 0.001), lymphocyte (p= 0.003), neutrophil-lymphocyte ratio (p= 0.003), platelets (p&lt; 0.001), haemoglobin (p&lt; 0.001), iron (p= 0.012), and total iron-binding capacity (p&lt; 0.001). Statistically significant changes over time were found for ferritin (p= 0.010), D-dimer (p&lt; 0.001), hs-troponin T (p&lt; 0.002), lactate dehydrogenase (p= 0.001), glucose (p= 0.023), unsaturated iron-binding capacity (p= 0.008), and vitamin D (p&lt; 0.001). CONCLUSION: The dynamic changes in inflammatory, haematological and biochemical parameters can predict disease severity, and outcome.

Feline respiratory disease complex: insights into the role of viral and bacterial co-infections.

Feline respiratory disease complex (FRDC) is a highly prevalent syndrome in cats that often result in fatal outcomes. FRDC etiology is complex, and often, multiple viral and bacterial pathogens are simultaneously associated with disease causation. There is limited information about the role of co-infections in pathogenesis and the current prevalence of pathogens in North America. We aimed to conduct a study using technical advances in molecular diagnosis and statistical modeling analysis to elucidate the occurrence of pathogens and how co-infections affect disease severity. We attained information from three diagnostic laboratories in North America regarding the occurrence of Bordetella bronchiseptica, Chlamydia felis, Mycoplasma, Felid alphaherpesvirus 1 (FeHV-1), feline calicivirus (FCV), and influenza A, along with age, seasonality, sex, and clinical signs. We also evaluated the role of co-infections in disease severity. These pathogens were also investigated in clinically normal cats (control). The most detected pathogens were Mycoplasma, FCV, and FeHV-1. Most pathogens were detected in the control group, highlighting the challenge of interpreting positive testing results. Co-infections of Mycoplasma and FCV, as well as Mycoplasma and FeHV-1, were important predictors of disease severity. Age, sex, and season had a minor impact on pathogen occurrence. This study provides new insights into FRDC and underlines the relevance of diagnostic panels to screen for a range of pathogens, providing knowledge for timely diagnosis and therapeutic interventions.

Utility of plasma nucleocapsid protein in predicting severity and prognosis in severe COVID-19 patients with comorbidities

ObjectivesThe COVID-19 pandemic poses ongoing challenges to global public health systems, emphasizing the critical necessity for efficient diagnostic and prognostic markers. This study evaluates the MAGLUMI® SARS-CoV-2 Ag N protein chemiluminescent immunoassay (MAG-CLIA) for its analytical performance and its role in predicting disease severity and prognosis among severe COVID-19 patients with comorbidities. MethodsAnalytical validation of plasma MAG-CLIA SARS-CoV-2 Ag N protein encompassed precision, interference, LoQ and linearity. Plasma N protein concentrations and other biomarkers were measured within 48 h of admission, tracked until discharge or death. The Mann-Whitney U test explored the association between plasma N protein and COVID-19 severity or prognosis. Longitudinal monitoring of plasma N protein dynamics was conducted in representative patients. ResultsMAG-CLIA demonstrated precise quantification of plasma N protein with a CV below 10 % and minimal interference. The LoQ was 0.88 ng/L, with a broad linear range. Plasma N protein showed high diagnostic accuracy for COVID-19, achieving 95.42 % specificity and 78.32 % sensitivity at 2.388 ng/L. Plasma N protein emerged as a valuable prognostic indicator, correlating with mechanical ventilation need and patient survival. Plasma N protein concentrations ≥ 424.3 ng/L (AUC 0.8102, sensitivity 78.38 %, specificity 85.48 %) were associated with poor prognosis in severe COVID-19 patients with comorbidities. ConclusionsMAG-CLIA’s SARS-CoV-2 N protein detection in plasma demonstrates both analytical reliability and clinical relevance in our inaugural evaluation. As a promising prognostic biomarker for severe COVID-19 patients, it offers crucial insights into disease severity and progression, emphasizing the significance of early monitoring and intervention, especially for patients with comorbidities.

CD9 and Aryl Hydrocarbon Receptor Are Markers of Human CD19+CD14+ Atypical B Cells and Are Dysregulated in Systemic Lupus Erythematous Disease.

Aryl hydrocarbon receptor (AHR) is a ligand-activated transcription factor whose expression regulates immune cell differentiation. Single-cell transcriptomic profiling was used to ascertain the heterogeneity of AHR expression in human B cell subpopulations. We identified a unique population of B cells marked by expression of AHR, CD9, and myeloid genes such as CD14 and CXCL8. Results were confirmed directly in human PBMCs and purified B cells at the protein level. TLR9 signaling induced CD14, CD9, and IL-8 protein expression in CD19+ B cells. CD14-expressing CD9+ B cells also highly expressed AHR and atypical B cell markers such as CD11c and TBET. In patients with active lupus disease, CD14+ and CD9+ B cells are dysregulated, with loss of CD9+ B cells strongly predicting disease severity and demonstrating the relevance of CD9+ B cells in systemic lupus erythematosus and autoimmune disease.

Iron Dysregulation: Unveiling Predictive Biomarkers for Severity in COVID-19 Patients

Objective: This study aimed to elucidate the association between iron dysregulation and the severity of COVID-19, examining serum levels of iron-related biomarkers in hospitalized patients. The primary objective was to identify reliable predictors for disease severity, specifically focusing on the role of ferritin, Hemoglobin (Hb), Total Iron-binding Capacity (TIBC), and iron. Materials and Methods: The study enrolled 117 consecutive COVID-19 patients, classified into mild, severe, and critical groups. Serum levels of Hb, TIBC, iron, and ferritin were measured using standardized methods. Data were analyzed using ROC analysis, logistic regression models, and correlation assessments. The study adhered to ethical guidelines, obtaining informed consent from all participants. Results: Results revealed a consistent elevation in iron and ferritin levels as disease severity increased, accompanied by a reciprocal decline in Hb and TIBC. Ferritin emerged as a robust predictor for disease prognosis, with an AUC of 0.808, providing a sensitivity of 62.50% and a specificity of 91.74%. Age and education were identified as exacerbating factors influencing patient outcomes. The findings have been found to align with previous studies linking higher ferritin levels to adverse outcomes, supporting ferritin’s utility as a prognostic marker. Conclusion: The intricate relationship between iron metabolism and COVID-19 severity has been highlighted in this study, emphasizing elevated ferritin levels as potential predictors for severe outcomes and indicators of inflammation-induced cellular damage. These findings may contribute valuable insights into personalized patient management strategies and the potential for targeted interventions in severe COVID-19 cases. Further research involving larger populations is essential to validate these observations and enhance our understanding of the complex host response to SARS-CoV-2.

Systemic immune-inflammation index combined with pediatric appendicitis score in assessing the severity and prognosis for paediatric appendicitis.

Pediatric appendicitis is a common cause of abdominal pain in children and is recognized as a significant surgical emergency. A prompt and accurate diagnosis is essential to prevent complications such as perforation and peritonitis. To investigate the predictive value of the systemic immune-inflammation index (SII) combined with the pediatric appendicitis score (PAS) for the assessment of disease severity and surgical outcomes in children aged 5 years and older with appendicitis. Clinical data of 104 children diagnosed with acute appendicitis were analyzed. The participants were categorized into the acute appendicitis group and chronic appendicitis group based on disease presentation and further stratified into the good prognosis group and poor prognosis group based on prognosis. The SII and PAS were measured, and a joint model using the combined SII and PAS was constructed to predict disease severity and surgical outcomes. Significant differences were observed in the SII and PAS parameters between the acute appendicitis group and chronic appendicitis group. Correlation analysis showed associations among the SII, PAS, and disease severity, with the combined SII and PAS model demonstrating significant predictive value for assessing disease severity [aera under the curve (AUC) = 0.914] and predicting surgical outcomes (AUC = 0.857) in children aged 5 years and older with appendicitis. The study findings support the potential of integrating the SII with the PAS for assessing disease severity and predicting surgical outcomes in pediatric appendicitis, indicating the clinical utility of the combined SII and PAS model in guiding clinical decision-making and optimizing surgical management strategies for pediatric patients with appendicitis.

Prediction of COVID-19 Hospitalization and Mortality Using Artificial Intelligence.

COVID-19 has had a substantial influence on healthcare systems, requiring early prognosis for innovative therapies and optimal results, especially in individuals with comorbidities. AI systems have been used by healthcare practitioners for investigating, anticipating, and predicting diseases, through means including medication development, clinical trial analysis, and pandemic forecasting. This study proposes the use of AI to predict disease severity in terms of hospital mortality among COVID-19 patients. A cross-sectional study was conducted at King Abdulaziz University, Saudi Arabia. Data were cleaned by encoding categorical variables and replacing missing quantitative values with their mean. The outcome variable, hospital mortality, was labeled as death = 0 or survival = 1, with all baseline investigations, clinical symptoms, and laboratory findings used as predictors. Decision trees, SVM, and random forest algorithms were employed. The training process included splitting the data set into training and testing sets, performing 5-fold cross-validation to tune hyperparameters, and evaluating performance on the test set using accuracy. The study assessed the predictive accuracy of outcomes and mortality for COVID-19 patients based on factors such as CRP, LDH, Ferritin, ALP, Bilirubin, D-Dimers, and hospital stay (p-value ≤ 0.05). The analysis revealed that hospital stay, D-Dimers, ALP, Bilirubin, LDH, CRP, and Ferritin significantly influenced hospital mortality (p ≤ 0.0001). The results demonstrated high predictive accuracy, with decision trees achieving 76%, random forest 80%, and support vector machines (SVMs) 82%. Artificial intelligence is a tool crucial for identifying early coronavirus infections and monitoring patient conditions. It improves treatment consistency and decision-making via the development of algorithms.

Subcutaneous adipose tissue radiodensity: An emerging risk factor for severe COVID-19

BackgroundAdipose tissue radiodensity and metabolic activity may influence COVID-19 outcomes. This study evaluated the association between adipose tissue characteristics and clinical outcomes in COVID-19 patients. MethodsTwo retrospective cohorts of hospitalized COVID-19 patients were analyzed. Subcutaneous adipose tissue radiodensity (SATR) and visceral adipose tissue radiodensity were assessed by computed tomography. Fluorine-18-labelled fluorodeoxyglucose PET/computed tomography measured adipose tissue metabolic activity. Associations with mortality, length of stay, ventilation requirement, and complications were examined using regression analyses. ResultsHigh SATR was independently associated with increased mortality risk (OR: 2.70; P = 0.033), longer hospitalization (P < 0.001), higher rates of mechanical ventilation (P = 0.007), and complications: acute kidney injury (P = 0.001), secondary infection (P = 0.007), shock (P = 0.010), and pulmonary embolism (P = 0.011). SATR positively correlated with SAT glucose uptake (ρ = 0.52) and negatively with leptin levels (ρ = –0.48). ConclusionsElevated SATR at COVID-19 diagnosis predicts disease severity and worse outcomes. SATR is a potential prognostic biomarker for acute and chronic inflammatory conditions.

Glycated haemoglobin is a major predictor of disease severity in patients with NAFLD

ObjectivesCurrently, non-invasive scoring systems to stage the severity of non-alcoholic fatty liver disease (NAFLD) do not consider markers of glucose control (glycated haemoglobin, HbA1c); this study aimed to define the relationship between HbA1c and NAFLD severity in patients with and without type 2 diabetes. Research design and methodsData were obtained from 857 patients with liver biopsy staged NAFLD. Generalized-linear models and binomial regression analysis were used to define the relationships between histological NAFLD severity, age, HbA1c, and BMI. Paired biopsies from interventional studies (n = 421) were used to assess the impact of change in weight, HbA1c and active vs. placebo treatment on improvements in steatosis, non-alcoholic steatohepatitis (NASH), and fibrosis. ResultsIn the discovery cohort (n = 687), risk of severe steatosis, NASH and advanced fibrosis correlated positively with HbA1c, after adjustment for obesity and age. These data were endorsed in a separate validation cohort (n = 170). Predictive modelling using HbA1c and age was non-inferior to the established non-invasive biomarker, Fib-4, and allowed the generation of HbA1c, age, and BMI adjusted risk charts to predict NAFLD severity. Following intervention, reduction in HbA1c was associated with improvements in steatosis and NASH after adjustment for weight change and treatment, whilst fibrosis change was only associated with weight change and treatment. ConclusionsHbA1c is highly informative in predicting NAFLD severity and contributes more than BMI. Assessments of HbA1c must be a fundamental part of the holistic assessment of patients with NAFLD and, alongside age, can be used to identify patients with highest risk of advanced disease.

The Dynamics of NS1 Antigen and Platelet Levels in the Acute Phase of Dengue Hemorrhagic Fever

Introduction: Dengue hemorrhagic fever (DHF) is a significant global health concern, particularly in tropical and subtropical regions. The early diagnosis and effective management of DHF are crucial to reduce morbidity and mortality. The NS1 antigen and platelet count are two key laboratory parameters used in the diagnosis and monitoring of DHF. This study aimed to investigate the dynamics of NS1 antigen and platelet levels during the acute phase of DHF and their correlation with disease severity. Methods: A prospective cohort study was conducted at a tertiary care hospital in Central Java, Indonesia. Patients presenting with suspected DHF were enrolled and followed up during their hospitalization. Blood samples were collected at admission and at regular intervals to measure NS1 antigen levels and platelet counts. Clinical and laboratory data were collected to assess disease severity. Results: A total of 150 patients were included in the study. The majority of patients were adults (median age, 32 years) with a female predominance (58%). The median duration of fever at presentation was 4 days. NS1 antigen levels were significantly higher in patients with DHF compared to those with dengue fever (DF) (p &lt; 0.001). Platelet counts were significantly lower in patients with DHF compared to those with DF (p &lt; 0.001). NS1 antigen levels showed a negative correlation with platelet counts (r = -0.62, p &lt; 0.001). The levels of NS1 antigen and platelet count were also significantly associated with disease severity. Conclusion: The dynamics of NS1 antigen and platelet levels during the acute phase of DHF provide valuable insights into disease pathogenesis and can aid in the early diagnosis and prediction of disease severity. The negative correlation between NS1 antigen and platelet levels suggests a potential role of NS1 in the development of thrombocytopenia in DHF. Further studies are needed to elucidate the exact mechanisms underlying these observations.

Prognostic Utility of dNLR, ALRI, APRI, and SII in COVID-19 Patients with Diabetes: A Cross-Sectional Study.

The coronavirus disease 2019 (COVID-19) pandemic has necessitated the identification of biomarkers that can predict disease severity, particularly in vulnerable populations such as individuals with diabetes. This study aims to evaluate the predictive value of inflammatory and liver function markers, specifically derived Neutrophil to Lymphocyte Ratio (dNLR), aspartate aminotransferase (AST)-to-lymphocyte ratio (ALRI), AST to Platelet Ratio Index (APRI), and Systemic Inflammation Index (SII), in COVID-19 patients with and without diabetes. This cross-sectional study included 336 participants, comprising 168 patients with diabetes matched with 168 without, based on gender, body mass index (BMI), and COVID-19 severity at hospitalization. The study was conducted at Victor Babes Hospital for Infectious Diseases and Pulmonology from January 2021 to December 2023. All participants had a confirmed SARS-CoV-2 infection and met the inclusion criteria of being 18 years or older with type 1 or type 2 diabetes as per American Diabetes Association guidelines. At 3 days post symptom onset, significant differences in inflammatory and liver function markers were observed between the two groups. The dNLR, ALRI, APRI, and SII were notably higher in diabetic patients. At a dNLR cutoff of 2.685, the sensitivity and specificity were 70.312% and 65.978%, respectively, with an AUC of 0.624 (p < 0.001). The ALRI showed a cutoff of 0.812, with a sensitivity of 76.429% and specificity of 69.541% (AUC 0.752, p < 0.001). These markers demonstrated statistically significant hazard ratios at both 3 and 7 days, indicating their predictive relevance for severe COVID-19 outcomes. For instance, at 7 days, SII demonstrated a hazard ratio of 2.62 (CI: 1.29-5.04, p < 0.001), highlighting its strong prognostic capability. The study successfully identified significant differences in inflammatory and liver function markers between COVID-19 patients with and without diabetes, with these markers showing good predictive value for disease severity. The results underscore the potential of these biomarkers, particularly ALRI and SII, as valuable tools in managing COVID-19, aiding in the timely identification of patients at increased risk of severe outcomes.

Multimodal, Longitudinal Profiling of SCA1 Identifies Predictors of Disease Severity and Progression.

Spinocerebellar ataxia type 1 (SCA1) is a rare autosomal dominant neurodegenerative disease. Objective surrogate markers sensitive to detect changes in disease severity are needed to reduce sample sizes in interventional trials and identification of predictors of faster disease progression would facilitate patient selection, enrichment, or stratification in such trials. We performed a prospective 1-year longitudinal, multimodal study in 34 ataxic SCA1 individuals and 21 healthy controls. We collected clinical, patient-reported outcomes, biochemical and magnetic resonance (MR) biomarkers at baseline and after 1 year. We determined 1-year progression and evaluated the potential predictive value of several baseline markers on 1-year disease progression. At baseline, multiple structural and spectroscopic MR markers in pons and cerebellum differentiated SCA1 from healthy controls and correlated with disease severity. Plasma and cerebrospinal fluid (CSF) neurofilament light (NfL) chain and CSF glial fibrillary acidic protein (GFAP) were elevated in SCA1. In longitudinal analysis, total brainstem and pontine volume change, inventory of non-ataxia signs (INAS) count, and SCA functional index (SCAFI) showed larger responsiveness compared to the Scale for Assessment and Rating of Ataxia (SARA). Longer disease duration, longer non-expanded CAG repeat length, and higher disease burden were associated with faster SARA increase after 1-year in the SCA1 group. Similarly, lower baseline brainstem, pontine, and cerebellar volumes, as well as lower levels of N-acetylaspartate and glutamate in the cerebellar white matter, were also associated with faster SARA increase. Our results guide the selection of the most sensitive measures of disease progression in SCA1 and have identified features associated with accelerated progression that could inform the design of clinical trials. ANN NEUROL 2024;96:774-787.

Aberrant migration features in primary skin fibroblasts of Huntington's disease patients hold potential for unraveling disease progression using an image based machine learning tool

Huntington's disease (HD) is a complex neurodegenerative disorder with considerable heterogeneity in clinical manifestations. While CAG repeat length is a known predictor of disease severity, this heterogeneity suggests the involvement of additional genetic and environmental factors. Previously we revealed that HD primary fibroblasts exhibit unique features, including distinct nuclear morphology and perturbed actin cap, resembling characteristics seen in Hutchinson-Gilford Progeria Syndrome (HGPS). This study establishes a link between actin cap deficiency and cell motility in HD, which correlates with the HD patient disease severity. Here, we examined single-cell motility imaging features in HD primary fibroblasts to explore in depth the relationship between cell migration patterns and their respective HD patients' clinical severity status (premanifest, mild and severe). The single-cell analysis revealed a decline in overall cell motility in correlation with HD severity, being most prominent in severe HD subgroup and HGPS. Moreover, we identified seven distinct spatial clusters of cell migration in all groups, which their proportion varies within each group becoming a significant HD severity classifier between HD subgroups. Next, we investigated the relationship between Lamin B1 expression, serving as nuclear envelope morphology marker, and cell motility finding that changes in Lamin B1 levels are associated with specific motility patterns within HD subgroups. Based on these data we present an accurate machine learning classifier offering comprehensive exploration of cellular migration patterns and disease severity markers for future accurate drug evaluation opening new opportunities for personalized treatment approaches in this challenging disorder.

Alzheimer's Disease Related Biomarkers Were Associated with Amnestic Cognitive Impairment in Parkinson's Disease: A Cross-Sectional Cohort Study.

Cognitive impairment is common in patients with Parkinson's disease (PD) and occurs through multiple mechanisms, including Alzheimer's disease (AD) pathology and the involvement of α-synucleinopathies. We aimed to investigate the pathological biomarkers of both PD and AD in plasma and neuronal extracellular vesicles (EVs) and their association with different types of cognitive impairment in PD patients. A total of 122 patients with PD and 30 healthy controls were included in this cross-sectional cohort study between March 2021 and July 2023. Non-dementia PD patients were divided into amnestic and non-amnestic groups according to the memory domain of a neuropsychological assessment. Plasma and neuronal EV biomarkers, including α-synuclein (α-syn), beta-amyloid (Aβ), total tau (T-tau), phosphorylated tau181 (p-tau181), and glial fibrillary acidic protein (GFAP), were measured using a single-molecule array and a chemiluminescence immunoassay, respectively. Neuronal EV but not plasma α-syn levels, were significantly increased in PD as compared to healthy controls, and they were positively associated with UPDRS part III scores and the severity of cognitive impairment. A lower plasma Aβ42 level and higher neuronal EV T-tau level were found in the amnestic PD group compared to the non-amnestic PD group. The results of the current study demonstrate that neuronal EV α-syn levels can be a sensitive biomarker for assisting in the diagnosis and disease severity prediction of PD. Both AD and PD pathologies are important factors in cognitive impairment associated with PD, and AD pathologies are more involved in amnestic memory deficit in PD.

Predictors of disease severity, length of hospitalization, and recurrence in inpatients with single-organ cutaneous small vessel vasculitis.

There is a lack of systematic studies on single-organ cutaneous small vessel vasculitis (SOCV). To evaluate prognostic clinical and laboratory parameters, including systemic immune-inflammation biomarkers (SIIB) in SOCV inpatients. This study investigated the clinical and laboratory data of 178 inpatients. Blood tests were performed at baseline. SIIB were assessed based on neutrophil-to-lymphocyte ratio (NLR) and pan-immune-inflammation value (PIV). Univariable and multivariable statistics were performed. Both NLR and PIV were significantly higher in SOCV patients than in healthy controls. However, the SIIB values observed in SOCV patients were as high as those in psoriasis patients. On logistic regression analysis, disease manifestation on the upper extremities strongly predicted the absence of severe disease (OR: 0.31, 95%CI: 0.13 to 0.73; p = 0.0071). Moreover, older age (OR: 2.3, 95%CI: 1.11 to 4.77; p = 0.025) and severe disease (OR: 2.4, 95%CI: 1.16 to 4.94, p = 0.018) were significant independent predictors of longer hospital stay, whereas female sex was an independent protective factor for longer hospitalization (OR: 0.52, 95%CI: 0.28 to 0.96, p = 0.038). Lower serum C3 was a strong independent predictor of disease recurrence (OR: 13.9, 95%CI 3 to 63.4; p = 0.0007). The increase in SIIB observed in patients with SOCV reflects that systemic inflammatory alterations also play a role in SOCV patients. We identified several clinical and laboratory-based independent predictors of SOCV severity, length of hospitalization, and disease recurrence that may aid prognostication of SOCV patients.