Combination protein biomarkers predict multiple sclerosis diagnosis and outcomes.

Establishing biomarkers to predict multiple sclerosis diagnosis and prognosis has been challenging using a single biomarker approach. We hypothesised that a combination of biomarkers would increase the accuracy of prediction models to differentiate multiple sclerosis from other neurological disorders and enhance prognostication for people with multiple sclerosis. We measured 24 fluid biomarkers in the blood and cerebrospinal fluid of 77 people with multiple sclerosis and 80 people with other neurological disorders, using ELISA or Single Molecule Array assays. Primary outcomes were multiple sclerosis versus any other diagnosis, time to first relapse, and time to disability milestone (Expanded Disability Status Scale 6), adjusted for age and sex. Multivariate prediction models were calculated using the area under the curve value for diagnostic prediction, and concordance statistics (the percentage of each pair of events that are correctly ordered in time for each of the Cox regression models) for prognostic predictions. Predictions using combinations of biomarkers were considerably better than single biomarker predictions. The combination of cerebrospinal fluid [chitinase-3-like-1 + TNF-receptor-1 + CD27] and serum [osteopontin + MCP-1] had an area under the curve of 0.97 for diagnosis of multiple sclerosis, compared to the best discriminative single marker in blood (osteopontin: area under the curve 0.84) and in cerebrospinal fluid (chitinase-3-like-1 area under the curve 0.84). Prediction for time to next relapse was optimal with a combination of cerebrospinal fluid[vitamin D binding protein + Factor I + C1inhibitor] + serum[Factor B + Interleukin-4 + C1inhibitor] (concordance 0.80), and time to Expanded Disability Status Scale 6 with cerebrospinal fluid [C9 + Neurofilament-light] + serum[chitinase-3-like-1 + CCL27 + vitamin D binding protein + C1inhibitor] (concordance 0.98). A combination of fluid biomarkers has a higher accuracy to differentiate multiple sclerosis from other neurological disorders and significantly improved the prediction of the development of sustained disability in multiple sclerosis. Serum models rivalled those of cerebrospinal fluid, holding promise for a non-invasive approach. The utility of our biomarker models can only be established by robust validation in different and varied cohorts.

Differential expression of haptoglobin in individuals at clinical high risk of psychosis and its association with global functioning and clinical symptoms.

BACKGROUND: Immune dysregulation has been observed in patients with schizophrenia or first-episode psychosis, but few have examined dysregulation in those at clinical high-risk (CHR) for psychosis. The aim of this study was to examine whether the peripheral blood-based proteome was dysregulated in those with CHR. Secondly, we examined whether baseline dysregulation was related to current and future functioning and clinical symptoms. METHODS: We used data from participants of the North American Prodromal Longitudinal Studies (NAPLS) 2 and 3 (n = 715) who provided blood samples (Unaffected Comparison subjects (UC) n = 223 and CHR n = 483). Baseline proteomic data was quantified from plasma samples using mass spectrometry. Differential expression was examined between CHR and UC using logistic regression. Psychosocial functioning was measured using the Global Assessment of Functioning scale (GAF). Symptoms were measured using the subscale scores from the Scale of Psychosis-risk Symptoms; positive, negative, general, and disorganised. Three measures of each outcome were included: baseline, longest available follow-up (last follow-up) and most severe follow-up (MSF). Associations between the proteomic data, GAF and symptoms were assessed using ordinal regression. RESULTS: Of the 99 proteins quantified, six were differentially expressed between UC and CHR. However, only haptoglobin (HP) survived FDR-correction (OR:1.45, 95 %CI:1.23-1.69, padj = <0.001). HP was cross-sectionally and longitudinally associated with functioning and symptoms such that higher HP values were associated with poorer functioning and more severe symptoms. Results were evident after stringent adjustment and poorer functioning was observed in both NAPLS cohort separately. CONCLUSION: We demonstrate that elevated HP is robustly observed in those at CHR for psychosis, irrespective of transition to psychosis. HP is longitudinally associated with poorer functioning and greater symptom severity. These results agree with previous reports of increased HP gene expression in individuals at-risk for psychosis and with the dysfunction of the acute phase inflammatory response seen in psychotic disorders.

Plasma complement and coagulation proteins as prognostic factors of negative symptoms: An analysis of the NAPLS 2 and 3 studies.

INTRODUCTION: Negative symptoms impact the quality of life of individuals with psychosis and current treatment options for negative symptoms have limited effectiveness. Previous studies have demonstrated that complement and coagulation pathway protein levels are related to later psychotic experiences, psychotic disorder, and functioning. However, the prognostic relationship between complement and coagulation proteins and negative symptoms is poorly characterised. METHODS: In the North American Prodrome Longitudinal Studies 2 and 3, negative symptoms in 431 individuals at clinical high-risk for psychosis (mean age: 18.2, SD 3.6; 42.5 % female) were measured at multiple visits over 2 years using the Scale of Psychosis-Risk Symptoms. Plasma proteins were quantified at baseline using mass spectrometry. Four factors were derived to represent levels of proteins involved in the activation or regulation of the complement or coagulation systems. The relationships between standardised protein group factors and serial measurements of negative symptoms over time were modelled using generalised least squares regression. Analyses were adjusted for baseline candidate prognostic factors: negative symptoms, positive symptoms, functioning, depressive symptoms, suicidal ideation, cannabis use, tobacco use, antipsychotic use, antidepressant use, age, and sex. RESULTS: Clinical and demographic prognostic factors of follow-up negative symptoms included negative, positive, and depressive symptoms, functioning, and age. Adjusting for all candidate prognostic factors, the complement regulators group and the coagulation regulators group were identified as prognostic factors of follow-up negative symptoms (ß: 0.501, 95 % CI: 0.160, 0.842; ß: 0.430, 95 % CI: 0.080, 0.780 respectively. The relationship between complement regulator levels and negative symptoms was also observed in NAPLS2 alone (ß: 0.501, 95 % CI: -0.037, 1.039) and NAPLS3 alone, additionally adjusting for BMI (ß: 0.442, 95 % CI: 0.127, 0.757). CONCLUSION: The results indicate that plasma complement and coagulation regulator levels are prognostic factors of negative symptoms, independent of clinical and demographic prognostic factors. These results suggest complement and coagulation regulator levels could have potential utility in informing treatment decisions for negative symptoms in individuals at risk.

Proteomic Biomarkers for the Prediction of Transition to Psychosis in Individuals at Clinical High Risk: A Multi-cohort Model Development Study.

Psychosis risk prediction is one of the leading challenges in psychiatry. Previous investigations have suggested that plasma proteomic data may be useful in accurately predicting transition to psychosis in individuals at clinical high risk (CHR). We hypothesized that an a priori-specified proteomic prediction model would have strong predictive accuracy for psychosis risk and aimed to replicate longitudinal associations between plasma proteins and transition to psychosis. This study used plasma samples from participants in 3 CHR cohorts: the North American Prodrome Longitudinal Studies 2 and 3, and the NEURAPRO randomized control trial (total n = 754). Plasma proteomic data were quantified using mass spectrometry. The primary outcome was transition to psychosis over the study follow-up period. Logistic regression models were internally validated, and optimism-corrected performance metrics derived with a bootstrap procedure. In the overall sample of CHR participants (age: 18.5, SD: 3.9; 51.9% male), 20.4% (n = 154) developed psychosis within 4.4 years. The a priori-specified model showed poor risk-prediction accuracy for the development of psychosis (C-statistic: 0.51 [95% CI: 0.50, 0.59], calibration slope: 0.45). At a group level, Complement C8B, C4B, C5, and leucine-rich α-2 glycoprotein 1 (LRG1) were associated with transition to psychosis but did not surpass correction for multiple comparisons. This study did not confirm the findings from a previous proteomic prediction model of transition from CHR to psychosis. Certain complement proteins may be weakly associated with transition at a group level. Previous findings, derived from small samples, should be interpreted with caution.