Lipid Biomarkers of Psychosis in Clinically High-Risk Individuals

Post by Shireen Parimoo 

What's the science?

Psychotic disorders like schizophrenia can disrupt daily functioning and reduce quality of life, but early detection and intervention has the potential to mitigate some of these effects. Patients often experience rapid weight gain and develop insulin resistance due to changes in their metabolism, leading researchers to hypothesize that metabolic abnormalities might precede the development of psychosis. In particular, the concentration of lipids – a class of metabolites found in fats and oils – might be altered in individuals who are at risk of developing psychosis. This week in Biological Psychiatry, Dickens and colleagues performed lipidomic analysis and used machine learning to identify lipid biomarkers that predict clinical outcomes of psychosis in clinically high risk (CHR) individuals.

How did they do it?

The authors recruited 263 individuals identified as being at clinical high risk (CHR) for psychosis from a large-scale longitudinal study along with 51 healthy controls (HC). At the beginning of the study, they recorded clinical symptoms and obtained serum samples from both groups; they then followed up with the participants 1, 2, and up to 5 years later. At their follow-up clinical assessment, patients were categorized as (i) being in remission if their symptoms improved and they no longer met the diagnostic criteria for being at risk for psychosis (CHR-remission), (ii) having persistent symptoms, or (iii) having transitioned to psychosis.

The authors identified serum concentrations of lipids using a combination of liquid chromatography and mass spectrometry, which are techniques used in lipidomic analysis to separate and characterize different types of molecules based on their molecular properties. They performed a clustering analysis to identify different lipid clusters in each group and compared them between the groups at baseline. Lipids belonging to clusters that differed the most between the two groups were then used to predict clinical outcomes for HC and individuals in each CHR group. For a more fine-grained understanding of how lipid concentrations at baseline and demographic variables might be related to later outcomes, the authors used predictive logistic regression (a machine learning technique) to distinguish between the each CHR sub-group (e.g., CHR-remission vs. other CHR individuals) and between HC and all CHR individuals.

What did they find?

At baseline, CHR patients generally had higher serum lipid levels than HC. The authors identified 12 clusters of lipids differed between the HC and CHR individuals, which included triglycerols, ether phospholipids, and sphingomyelins, among others. Triglycerol levels most reliably distinguished between the two groups based on their structural properties: triglycerols with lower number of carbon atoms and fewer double bonds were higher in the CHR group, whereas HC had higher levels of triglycerols with longer, polyunsaturated fatty acid chains. Lipid profiles also differed between males and females. In both the HC and CHR groups, triglycerol levels were higher in males whereas sphingomyelin levels were higher in females. Serum lipid concentrations at baseline also predicted clinical outcomes at follow-up. Specifically, individuals who developed psychosis had lower ether phospholipids levels at baseline compared to other high-risk individuals. On the other hand, lipid profiles also differed between those who went into remission and those whose symptoms persisted. Importantly, there were no differences in the lipid profiles between HC and CHR-remission individuals. Overall, these results indicate that serum lipid levels in high-risk individuals are a sensitive marker of both the progression and regression of clinical symptoms of psychosis.

What's the impact?

This study demonstrates that serum lipid levels can be used to predict clinical outcomes in individuals at high risk of developing psychosis. Although further work needs to be done to validate the predictive model, these findings have exciting implications for predicting the symptom trajectory of high-risk individuals and for providing early and personalized intervention to mitigate the progression of symptoms.

Dickens et al. Dysregulated lipid metabolism precedes onset of psychosis. Biological Psychiatry (2020). Access the original scientific publication here.