The Link Between Inflammation and Cognitive Impairment in Severe Mental Illness

 Post by Lani Cupo

The takeaway

A large, multivariate investigation of inflammation and cognition in severe mental illness reveals patterns of immune activation associated with poor verbal learning and processing speed in individuals across diagnoses.

What's the science?

The link between inflammation and mental illness has been investigated, however, the associations between immune activation and cognitive ability in severe mental illnesses, like schizophrenia and bipolar disorder, have often glossed over individual differences in both domains. To help clarify the relationship, variability amongst both healthy individuals and those with severe mental illness must be examined. This week in Molecular Psychiatry, Saether and colleagues attempt to capture individual variance between cognitive ability and inflammation in health and severe mental illness with a multivariate analysis technique, canonical correlation analysis (CCA).

How did they do it?

The authors had access to a large pool of participants (770 healthy controls [HC], 343 with schizophrenia, and 289 with bipolar disorder). Diagnoses were determined by clinical assessments conducted by trained psychologists and medical doctors. Clinical psychologists and trained researchers administered cognitive assessments, recording variables including fine-motor speed, psychomotor processing speed, mental processing speed, verbal learning, attention, verbal memory, semantic fluency, working memory, and cognitive control. To quantify inflammation, the authors included 22 markers of neuroinflamation from a blood sample. CCA is a technique that relates two independent set of variables (each called a variate) to one another, generating new linear combinations of variables (canonical variate pairs) reflecting patterns of covariation between variates. The significance of each resulting variate pair is assessed with permutation testing, allowing the authors to determine whether each pattern of covariance is likely to arise due to chance. The participant demographics can be mapped onto the correlations to examine subgroups within the patterns. The authors organized these “loading scores” (how individual participant scores mapped onto the canonical variates) into clusters using a clustering procedure (hierarchical clustering) to establish subgroups. They confirmed the stability of the identified clusters with a resampling technique.

What did they find?

From the CCA, the authors derived two significant canonical variate pairs representing relationships of covariance among the variables. Of these, the first canonical variate pair was determined to be more reproducible in a set of un-tested samples and, therefore, further examined. This canonical variate pair captured a correlation between poor verbal learning and psychomotor processing speed with an increased presence of 4 markers of innate immune activation. Mapping diagnoses onto the correlation revealed the potential for subgroups.

The clustering analysis revealed two subgroups; Cluster 1 included worse scores on cognitive domains and increased inflammatory markers, compared to Cluster 2. The majority of HCs were in Cluster 2, while the majority of participants with severe mental illness were in Cluster 1. Of the participants with severe mental illness, those in Cluster 1 exhibited worse functioning and symptomatology and higher pharmaceutical usage than those in Cluster 2. Overall, Cluster 1 represented a group that captured most of the participants with severe mental illness and exhibited decreased cognitive ability and increased inflammation. However, group assignments did transcend diagnosis.

What's the impact?

This study is one of the first to show how the relationship between cognitive dysfunction and inflammation transcends diagnosis. Regardless of diagnosis or impairment, there is a relationship between increased innate inflammation and cognitive dysfunction, emphasizing the importance of examining heterogeneity between individuals. In a subset of individuals with severe mental illness, this may provide an important link for future investigation and treatment approaches.

Access the original scientific publication here