Plasma MIA, CRP, and Albumin Predict Cognitive Decline in Parkinson's Disease

Parkinson's disease is characterized by vitamin B6-dependent inflammatory kynurenine pathway dysfunction

Recent studies demonstrate that Parkinson's disease (PD) is associated with dysregulated metabolic flux through the kynurenine pathway (KP), in which tryptophan is converted to kynurenine (KYN), and KYN is subsequently metabolized to neuroactive compounds quinolinic acid (QA) and kynurenic acid (KA). Here, we used mass-spectrometry to compare blood and cerebral spinal fluid (CSF) KP metabolites between 158 unimpaired older adults and 177 participants with PD. We found increased neuroexcitatory QA/KA ratio in both plasma and CSF of PD participants associated with peripheral and cerebral inflammation and vitamin B6 deficiency. Furthermore, increased QA tracked with CSF tau, CSF soluble TREM2 (sTREM2) and severity of both motor and non-motor PD clinical symptoms. Finally, PD patient subgroups with distinct KP profiles displayed distinct PD clinical features. These data validate the KP as a site of brain and periphery crosstalk, integrating B-vitamin status, inflammation and metabolism to ultimately influence PD clinical manifestation.

A Multi-omics Framework Based on Machine Learning as a Predictor of Cognitive Impairment Progression in Early Parkinson's Disease

INTRODUCTION

Cognitive impairment (CI) is a common non-motor symptom of Parkinson's disease (PD). However, the diagnosis and prediction of CI progression in PD remain challenging. We aimed to explore a multi-omics framework based on machine learning integrating comprehensive radiomics, cerebrospinal fluid biomarkers, and genetics information to identify CI progression in early PD.

METHODS

Patients were first diagnosed with PD without CI at baseline. According to whether CI progressed within 5 years, patients were divided into two groups: PD without CI and PD with CI. Radiomics signatures were extracted from patients' T1-weighted MRI. We used machine learning methods to construct radiomics, hybrid, and multi-omics models in the training set and validated the models in the testing set.

RESULT

In the two groups, we found 7, 23, and 25 radiomics signatures with significant differences in the parietal, temporal, and frontal lobes, respectively. The radiomics model using the 25 signatures of the frontal lobe had an accuracy of 0.833 and an AUC (area under the curve) of 0.879 to predict CI progression. In addition, the hybrid model fused with the cerebrospinal fluid Aβ level had an accuracy of 0.867 and an AUC of 0.916. In our study, the multi-omics model showed the best predictive performance. The accuracy of the multi-omics model was 0.900, and the average AUC value after five-fold cross-validation was 0.928.

CONCLUSION

Radiomics signatures have a recognition effect in the CI progression in early PD. Multi-omics frameworks combining radiomics, cerebrospinal fluid biomarkers, and genetic information may be a potential predictor of CI progression in PD.

Red blood cell distribution width to albumin ratio (RAR) is associated with low cognitive performance in American older adults: NHANES 2011-2014

BACKGROUND

The red blood cell distribution width to albumin ratio (RAR) is a novel comprehensive biomarker of inflammation and nutrition, which has emerged as a reliable prognostic indicator for adverse outcomes and mortality in patients with various diseases. However, the association between RAR and low cognitive performance in older adults remains unclear. This study aims to investigate the relationship between RAR and low cognitive performance among older adults in the United States.

METHODS

This study, a retrospective analysis, included 2,765 participants aged 60 years and older from the National Health and Nutrition Examination Survey (NHANES) conducted between 2011 and 2014. Low cognitive performance was assessed using word learning subset from the Consortium to Establish a Registry for Alzheimer's Disease (CERAD), the Digit Symbol Substitution Test (DSST), and the Animal Fluency Test (AFT). Low cognitive performance was defined as scores below the lowest quartile in each cognitive test. The association between RAR and low cognitive performance was evaluated using weighted multivariable logistic regression, restricted cubic splines (RCS), and subgroup analyses.

RESULTS

After adjusting for all potential confounders, RAR was independently and linearly positively associated with both low DSST performance and low AFT performance. Specifically, compared to participants in the first quartile of RAR, those in the fourth quartile had adjusted ORs (95% CIs) of 1.81 (1.03, 3.20) for low DSST performance and 1.68 (1.05, 2.67) for low AFT performance. Subgroup analysis did not reveal significant interactions between stratification variables.

CONCLUSION

RAR is significantly linearly positively associated with low cognitive performance. Maintaining a lower RAR may be a crucial strategy for mitigating the risk of cognitive decline in the elderly population.

Associations of frailty and cognitive impairment with all-cause and cardiovascular mortality in older adults: a prospective cohort study from NHANES 2011-2014

BACKGROUND

The global aging trend exacerbates the challenge of frailty and cognitive impairment in older adults, yet their combined impact on health outcomes remains under-investigated. This study aims to explore how frailty and psychometric mild cognitive impairment (pMCI) jointly affect all-cause and cardiovascular disease (CVD) mortality.

METHODS

The cohort study we examined 2,442 participants aged ≥ 60, is the secondary analysis from the National Health and Nutrition Examination Survey (NHANES) 2011-2014. Frailty was quantified using a 49-item frailty index, while pMCI was determined by three composite cognition scores one standard deviation (SD) below the mean. The associations between frailty, pMCI, comorbidity, and mortality were assessed using weighted Cox proportional hazards models.

RESULTS

Of the participants, 31.37% were frail, 17.2% had pMCI, and 8.64% exhibited both conditions. The cohort was stratified into four groups based on frailty and pMCI status. After a median follow-up period of 6.5 years, frail individuals with pMCI had the highest all-cause (75.23 per 1,000 person-years) and CVD (32.97 per 1,000 person-years) mortality rates. Adjusted hazard ratios (HRs) for all-cause (3.06; 95% CI, 2.05-4.56) and CVD (3.8; 95% CI, 2.07-6.96) mortality were highest in frail older adults with pMCI compared to those who were non-frail without pMCI.

CONCLUSION

Our study highlights the ubiquity of frailty and cognitive impairment in older adults and underscores the heightened risk of mortality associated with their coexistence. These findings suggest the critical need for proactive screening and management of frailty and cognitive function in clinical practice to improve outcomes for the older adults.

Plasma fibronectin is a prognostic biomarker of disability in Parkinson's disease: a prospective, multicenter cohort study

In a prospective longitudinal study with 218 Parkinson's disease (PD) patients in the discovery cohort and 84 in the validation cohort, we aimed to identify novel blood biomarkers predicting disability milestones in PD. Through Least Absolute Shrinkage and Selection Operator-Cox (Lasso-Cox) regression, developed nomogram predictive model and Linear mixed-effects models, we identified low level of plasma fibronectin (pFN) as one of the best-performing risk markers in predicting disability milestones. A low level of pFN was associated with a short milestone-free survival period in PD. Longitudinal analysis showed an annual decline in the rate of pFN was significantly associated with the annual elevation rate in the Hoehn-Yahr stage. Moreover, pFN level was negatively correlated with phosphorylated α-synuclein, and a low level of pFN was associated with BBB disruption in the striatum on neuroimaging, providing evidence for pFN's role in PD progression. We finally identified pFN as a novel blood biomarker that predicted first-milestone disability in PD.

Potential biofluid markers for cognitive impairment in Parkinson's disease

Cognitive impairment is a particularly severe non-motor symptom of Parkinson's disease that significantly diminishes the quality of life of affected individuals. Identifying reliable biomarkers for cognitive impairment in Parkinson's disease is essential for early diagnosis, prognostic assessments, and the development of targeted therapies. This review aims to summarize recent advancements in biofluid biomarkers for cognitive impairment in Parkinson's disease, focusing on the detection of specific proteins, metabolites, and other biomarkers in blood, cerebrospinal fluid, and saliva. These biomarkers can shed light on the multifaceted etiology of cognitive impairment in Parkinson's disease, which includes protein misfolding, neurodegeneration, inflammation, and oxidative stress. The integration of biofluid biomarkers with neuroimaging and clinical data can facilitate the development of predictive models to enhance early diagnosis and monitor the progression of cognitive impairment in patients with Parkinson's disease. This comprehensive approach can improve the existing understanding of the mechanisms driving cognitive decline and support the development of targeted therapeutic strategies aimed at modifying the course of cognitive impairment in Parkinson's disease. Despite the promise of these biomarkers in characterizing the mechanisms underlying cognitive decline in Parkinson's disease, further research is necessary to validate their clinical utility and establish a standardized framework for early detection and monitoring of cognitive impairment in Parkinson's disease.

Machine Learning for Early Detection of Cognitive Decline in Parkinson's Disease Using Multimodal Biomarker and Clinical Data

Background: Parkinson's disease (PD) is the second most common neurodegenerative disease, primarily affecting the middle-aged to elderly population. Among its nonmotor symptoms, cognitive decline (CD) is a precursor to dementia and represents a critical target for early risk assessment and diagnosis. Accurate CD prediction is crucial for timely intervention and tailored management of at-risk patients. This study used machine learning (ML) techniques to predict the CD risk over five-year in early-stage PD. Methods: Data from the Early Parkinson's Disease Longitudinal Singapore (2014 to 2018) was used to predict CD defined as a one-unit annual decrease or a one-unit decline in Montreal Cognitive Assessment over two consecutive years. Four ML methods-AutoScore, Random Forest, K-Nearest Neighbors and Neural Network-were applied using baseline demographics, clinical assessments and blood biomarkers. Results: Variable selection identified key predictors of CD, including education year, diastolic lying blood pressure, diastolic standing blood pressure, systolic lying blood pressure, Hoehn and Yahr scale, body mass index, phosphorylated tau at threonine 181, total tau, Neurofilament light chain and suppression of tumorigenicity 2. Random Forest was the most effective, achieving an AUC of 0.93 (95% CI: 0.89, 0.97), using 10-fold cross-validation. Conclusions: Here, we demonstrate that ML-based models can identify early-stage PD patients at high risk for CD, supporting targeted interventions and improved PD management.

Parkinson's disease is characterized by vitamin B6-dependent inflammatory kynurenine pathway dysfunction

Parkinson's disease (PD) is a complex multisystem disorder clinically characterized by motor, non-motor, and premotor manifestations. Pathologically, PD involves neuronal loss in the substantia nigra, striatal dopamine deficiency, and accumulation of intracellular inclusions containing aggregates of α-synuclein. Recent studies demonstrate that PD is associated with dysregulated metabolic flux through the kynurenine pathway (KP), in which tryptophan is converted to kynurenine (KYN), and KYN is subsequently metabolized to neuroactive compounds quinolinic acid (QA) and kynurenic acid (KA). This multicenter study used highly sensitive liquid chromatography-tandem mass-spectrometry to compare blood and cerebral spinal fluid (CSF) KP metabolites between 158 unimpaired older adults and 177 participants with PD. Results indicate that increased neuroexcitatory QA/KA ratio in both plasma and CSF of PD participants associated with peripheral and cerebral inflammation and vitamin B6 deficiency. Furthermore, increased QA tracked with CSF tau and severity of both motor and non-motor PD clinical dysfunction. Importantly, plasma and CSF kynurenine metabolites classified PD participants with a high degree of accuracy (AUC = 0.897). Finally, analysis of metabolite data revealed subgroups with distinct KP profiles, and these were subsequently found to display distinct PD clinical features. Together, these data further support the hypothesis that the KP serves as a site of brain and periphery crosstalk, integrating B-vitamin status, inflammation and metabolism to ultimately influence PD clinical manifestation.

Association between geriatric nutritional risk index and cognitive function in older adults with/without chronic kidney disease

BACKGROUND

Cognitive impairment is highly prevalent among patients with chronic kidney disease, who face an increased risk of cognitive decline. The aim of this study was to investigate the relationship between the Geriatric Nutritional Risk Index (GNRI) and cognitive function in older individuals, both with and without chronic kidney disease (CKD).

METHODS

In this study, we analyzed data from 2728 participants in the 2011-2014 National Health and Nutrition Examination Survey (NHANES). Cognitive function was measured using the Consortium to Establish a Registry for the Alzheimer's Disease Word Learning subtest (CERAD W-L), the animal fluency test (AFT), the digit symbol substitution test (DSST), and the global cognitive z-score. The GNRI, representing whole-body nutritional status, was calculated based on serum albumin, body weight, and ideal body weight. We employed weighted multiple linear regression analyses and subgroup analyses to assess the independent association of GNRI with cognitive function in CKD and non-CKD populations. Smoothing techniques were used to fit curves, and interaction tests were used to assess the robustness and specificity of the findings.

RESULTS

Our analyses revealed a significant positive association between higher GNRI levels and cognitive function in the older US population (for global z-score: β = 0.01; 95% confidence interval [CI] = 0.00, 0.01). This association remained consistent across various subgroup analyses, including those for different gender groups, age groups, smoking statuses, diabetes statuses, hypertension statuses, individuals with a BMI below 25, individuals who consumed alcohol, and non-Hispanic white individuals. Smoothed curve-fitting analyses indicated that the GNRI was linearly related to cognitive function. No statistically significant interactions were detected among these variables.

CONCLUSION

Our findings emphasize the positive association between GNRI and cognitive health in individuals with or without CKD, especially when combined with other risk factors. Consequently, enhancing the nutritional status of the elderly may serve as a viable strategy to thwart the onset of cognitive decline.

Serum albumin and white matter hyperintensities

People living with HIV and those diagnosed with alcohol use disorders (AUD) relative to healthy individuals commonly have low levels of serum albumin, substantiated as an independent predictor of cardiovascular events. White matter hyperintensities (WMH)-a neuroimaging feature of cerebral small vessel disease-are also related to cardiovascular disease. Despite consensus regarding associations between high levels of urine albumin and WMH prevalence, and low serum albumin levels and impaired cognitive functioning, relations between serum albumin and WMH burdens have rarely been evaluated. Here, a sample including 160 individuals with AUD, 142 living with HIV, and 102 healthy controls was used to test the hypothesis that serum albumin would be inversely related to WMH volumes and directly related to cognitive performance in the two diagnostic groups. Although serum albumin and periventricular WMH volumes showed an inverse relationship in both AUD and HIV groups, this relationship persisted only in the HIV group after consideration of traditional cardiovascular (i.e., age, sex, body mass index (BMI), nicotine use, hypertension, diabetes), study-relevant (i.e., race, socioeconomic status, hepatitis C virus status), and disease-specific (i.e., CD4 nadir, HIV viral load, HIV duration) factors. Further, serum albumin contributed more significantly than periventricular WMH volume to variance in performance on a verbal learning and memory composite score in the HIV group only. Relations in both HIV and AUD groups between albumin and hematological red blood cell markers (e.g., hemoglobin, hematocrit) suggest that in this sample, serum albumin reflects hematological abnormalities. Albumin, a simple serum biomarker available in most clinical settings, may therefore help identify periventricular WMH burden and performance levels in specific cognitive domains in people living with HIV. Whether serum albumin contributes mechanistically to periventricular WMH in HIV will require additional investigation.

Interplay of human gastrointestinal microbiota metabolites: Short-chain fatty acids and their correlation with Parkinson's disease

Short-chain fatty acids (SCFAs) are, the metabolic byproducts of intestinal microbiota that, are generated through anaerobic fermentation of undigested dietary fibers. SCFAs play a pivotal role in numerous physiological functions within the human body, including maintaining intestinal mucosal health, modulating immune functions, and regulating energy metabolism. In recent years, extensive research evidence has indicated that SCFAs are significantly involved in the onset and progression of Parkinson disease (PD). However, the precise mechanisms remain elusive. This review comprehensively summarizes the progress in understanding how SCFAs impact PD pathogenesis and the underlying mechanisms. Primarily, we delve into the synthesis, metabolism, and signal transduction of SCFAs within the human body. Subsequently, an analysis of SCFA levels in patients with PD is presented. Furthermore, we expound upon the mechanisms through which SCFAs induce inflammatory responses, oxidative stress, abnormal aggregation of alpha-synuclein, and the intricacies of the gut-brain axis. Finally, we provide a critical analysis and explore the potential therapeutic role of SCFAs as promising targets for treating PD.

Adipokines, Vitamin D, and Selected Inflammatory Biomarkers among Parkinson's Disease Patients with and without Dyskinesia: A Preliminary Examination

Parkinson's disease (PD), a widely recognized neurodegenerative disorder, is characterized by a spectrum of symptoms including motor fluctuations and dyskinesia. Neuroinflammation and dysregulation of adipokines are increasingly implicated in the progression of PD. This preliminary study investigated the levels of inflammatory biomarkers and adipokines, namely interleukin-6 (IL-6), tumor necrosis factor α (TNF-α), C-reactive protein (CRP), visfatin, progranulin, and 25(OH)-vitamin D in 52 PD patients, divided equally between those with and without dyskinesia and 26 healthy controls. Significant differences in the levels of IL-6, TNF-α, visfatin, and progranulin were noted between the groups. Patients with dyskinesia exhibited notably higher IL-6 levels compared to controls, and TNF-α was significantly elevated in both PD patient groups relative to the control group. Additionally, visfatin levels were higher in PD patients without dyskinesia as opposed to those with dyskinesia, and progranulin levels were elevated in the non-dyskinetic PD group compared to controls. The findings highlight the potential role of the examined biomarkers in the pathophysiology of PD. Changes in levels of the tested inflammatory biomarkers and adipokines might be associated with Parkinson's disease and its symptoms such as dyskinesia.

Serum albumin and white matter hyperintensities

Urine albumin, high in kidney disease, predicts cardiovascular incidents and CNS white matter hyperintensity (WMH) burdens. Serum albumin - a more general biomarker which can be low in several disorders - including kidney and liver disease, malnutrition, and inflammation - also predicts cardiovascular events and is associated with cognitive impairment in several clinical populations; relations between serum albumin and WMH prevalence, however, have rarely been evaluated. In a sample of 160 individuals with alcohol use disorder (AUD), 142 infected with HIV, and 102 healthy controls, the hypothesis was tested that lower serum albumin levels would predict larger WMH volumes and worse cognitive performance irrespective of diagnosis. After considering traditional cardiovascular risk factors (e.g., age, sex, body mass index (BMI), nicotine use, hypertension, diabetes) and study-relevant variables (i.e., primary diagnoses, race, socioeconomic status, hepatitis C virus status), serum albumin survived false discovery rate (FDR)-correction in contributing variance to larger periventricular but not deep WMH volumes. This relationship was salient in the AUD and HIV groups, but not the control group. In secondary analyses, serum albumin and periventricular WMH along with age, sex, diagnoses, BMI, and hypertension were considered for hierarchical contribution to variance in performance in 4 cognitive domains. Albumin survived FDR-correction for significantly contributing to visual and verbal learning and memory performance after accounting for diagnosis. Relations between albumin and markers of liver integrity [e.g., aspartate transaminase (AST)] and blood status (e.g., hemoglobin, red blood cell count, red cell distribution width) suggest that in this sample, albumin reflects both liver dysfunction and hematological abnormalities. The current results suggest that albumin, a simple serum biomarker available in most clinical settings, can predict variance in periventricular WMH volumes and performance in visual and verbal learning and memory cognitive domains. Whether serum albumin contributes mechanistically to periventricular WMH prevalence will require additional investigation.

An interpretable multiparametric radiomics model of basal ganglia to predict dementia conversion in Parkinson's disease

Cognitive impairment in Parkinson's disease (PD) severely affects patients' prognosis, and early detection of patients at high risk of dementia conversion is important for establishing treatment strategies. We aimed to investigate whether multiparametric MRI radiomics from basal ganglia can improve the prediction of dementia development in PD when integrated with clinical profiles. In this retrospective study, 262 patients with newly diagnosed PD (June 2008-July 2017, follow-up >5 years) were included. MRI radiomic features (n = 1284) were extracted from bilateral caudate and putamen. Two models were developed to predict dementia development: (1) a clinical model-age, disease duration, and cognitive composite scores, and (2) a combined clinical and radiomics model. The area under the receiver operating characteristic curve (AUC) were calculated for each model. The models' interpretabilities were studied. Among total 262 PD patients (mean age, 68 years ± 8 [standard deviation]; 134 men), 51 (30.4%), and 24 (25.5%) patients developed dementia within 5 years of PD diagnosis in the training (n = 168) and test sets (n = 94), respectively. The combined model achieved superior predictive performance compared to the clinical model in training (AUCs 0.928 vs. 0.894, P = 0.284) and test set (AUCs 0.889 vs. 0.722, P = 0.016). The cognitive composite scores of the frontal/executive function domain contributed most to predicting dementia. Radiomics derived from the caudate were also highly associated with cognitive decline. Multiparametric MRI radiomics may have an incremental prognostic value when integrated with clinical profiles to predict future cognitive decline in PD.

Single nucleotide polymorphisms in C-reactive protein (CRP) predict response to adjunctive celecoxib treatment of resistant bipolar depression

Background

Affective illness has been associated with a proinflammatory state, and it is generally accepted that the immune system plays a key role in the pathophysiology of mood disorders. Since inflammatory biomarkers are elevated in bipolar disorder, anti-inflammatory combination therapies may enhance response and reverse treatment resistance.

Purpose

In the present study we investigated the possible impact of single nucleotide polymorphisms (SNPs) within the CRP gene on CRP blood levels, treatment response and level-of-stress perception in our cohort of treatment-resistant bipolar-depressed patients receiving escitalopram and celecoxib, or escitalopram and placebo, as previously reported (Halaris et al., 2020).

Methods

Study design, clinical findings, and CRP blood levels have been reported previously (Halaris et al., 2020; Edberg et al., 2018). In this follow-up study we extracted DNA from blood cells collected at baseline. Genome-wide genotyping was performed for all subjects using the Infinium Multi-Ethnic Global-8 v1.0 Kit. Based on reports in the literature indicating possible associations with psychiatric conditions, ten previously reported CRP gene polymorphisms were evaluated in a preliminary analysis. We focused on rs3093059 and rs3093077 were in complete LD. Carriers were defined as those possessing at least one C allele for rs3093059, or at least one G allele for rs3093077. Additionally, we determined blood levels of the medications administered.

Results

Non-carriers of rs3093059 and rs3093077 had significantly lower baseline CRP blood levels than carriers (p = 0.03). Increased rates of HAM-D17 response (p = 0.21) and remission (p = 0.13) and lower PSS-14 scores (p = 0.13) were observed in non-carriers among subjects receiving celecoxib but they did not reach statistical significance. When examining all subjects, nominally significant associations between carrier-status and remission (p = 0.04) and PSS-14 scores (p = 0.04) were observed after correcting for treatment arm. Non-carriers receiving celecoxib had the highest rates of response and remission, and the lowest stress scores.

Conclusions

Carriers of the CRP SNPs may have higher baseline CRP levels, although non-carriers appear to benefit more from celecoxib co-therapy. Determination of the carrier status in conjunction with pretreatment blood CRP level measurement may contribute to personalized psychiatric practice, but replication of the present findings is needed.

Cognitive heterogeneity in Parkinson's disease: A mechanistic view

Cognitive impairment occurs in most individuals with Parkinson's disease (PD), exacting a high toll on patients, their caregivers, and the healthcare system. In this review, we begin by summarizing the current clinical landscape surrounding cognition in PD. We then discuss how cognitive impairment and dementia may develop in PD based on the spread of the pathological protein alpha-synuclein (aSyn) from neurons in brainstem regions to those in the cortical regions of the brain responsible for higher cognitive functions, as first proposed in the Braak hypothesis. We appraise the Braak hypothesis from molecular (conformations of aSyn), cell biological (cell-to-cell spread of pathological aSyn), and organ-level (region-to-region spread of aSyn pathology at the whole brain level) viewpoints. Finally, we argue that individual host factors may be the most poorly understood aspect of this pathological process, accounting for substantial heterogeneity in the pattern and pace of cognitive decline in PD.

A prediction model of clinical outcomes in prolonged disorders of consciousness: A prospective cohort study

Objective

This study aimed to establish and validate a prediction model for clinical outcomes in patients with prolonged disorders of consciousness (pDOC).

Methods

A total of 170 patients with pDOC enrolled in our rehabilitation unit were included and divided into training (n = 119) and validation sets (n = 51). Independent predictors for improved clinical outcomes were identified by univariate and multivariate logistic regression analyses, and a nomogram model was established. The nomogram performance was quantified using receiver operating curve (ROC) and calibration curves in the training and validated sets. A decision curve analysis (DCA) was performed to evaluate the clinical usefulness of this nomogram model.

Results

Univariate and multivariate logistic regression analyses indicated that age, diagnosis at entry, serum albumin (g/L), and pupillary reflex were the independent prognostic factors that were used to construct the nomogram. The area under the curve in the training and validation sets was 0.845 and 0.801, respectively. This nomogram model showed good calibration with good consistency between the actual and predicted probabilities of improved outcomes. The DCA demonstrated a higher net benefit in clinical decision-making compared to treating all or none.

Conclusion

Several feasible, cost-effective prognostic variables that are widely available in hospitals can provide an efficient and accurate prediction model for improved clinical outcomes and support clinicians to offer suitable clinical care and decision-making to patients with pDOC and their family members.