Schizophrenia is associated with premature death (read our mortality gap blogs for the evidence of this). Most people in developed countries live into their late 70s or early 80s, but individuals with schizophrenia die around 20 years earlier. The majority of these deaths are due to preventable physical diseases, in particular, cardiovascular, metabolic, and respiratory disease. A better understanding of the underlying reasons for this will help clinicians to address the risks, identify potential solutions and work towards bridging this health disparity. This has become a clinical and research priority, while there has long been a question about possible underlying genetic causes.
Genetic factors contribute to the risk of schizophrenia; both common genetic variation and rare variants, such as copy number variants (CNVs). CNVs are also associated with physical health consequences in population samples. Research has been conducted by linking genetic and electronic health record data and reported associations between polygenic risk scores for schizophrenia and smoking and reduced rates of obesity (Zheutlin et al., 2019).
In this study, Kendall et al. (2020) sought to explore whether the increased risk for physical illness in schizophrenia is caused by genetic liability for the disorder.
958 individuals with schizophrenia were recruited in Wales from the community, in-patient, and voluntary sector mental health services in the UK. They were aged from 17-84 years and 41% of these individuals were female. Detailed phenotypic assessment was conducted including a Schedules for Clinical Assessment in Neuropsychiatry (SCAN) interview. Interview data and clinical notes were then used to arrive at the best estimate lifetime diagnosis according to DSM-IV criteria.
Genetic data from these individuals with schizophrenia were linked to anonymised NHS records held in the Secure Anonymised Information Linkage (SAIL) databank. Physical illnesses were defined from the General Practice Database and Patient Episode Database for Wales. Genetic liability for schizophrenia was indexed by (a) rare copy number variants (CNVs), and (b) polygenic risk scores.
- Rates of physical illness – Linked data in SAIL were analysed to find crude rates and standardised rates of health outcomes. These were expressed as a lifetime prevalence.
- Ascertainment rates of behaviours and diagnoses – Agreement on diagnoses between the clinical and electronic cohorts for each health outcome was determined based on the paired responses from the interview and SAIL.
- CNV analyses – Association analyses were carried out for average BMI and average height, and each of the following: type 2 diabetes mellitus, smoking, ischaemic heart disease, congenital disorders, epilepsy, intellectual disability. All analyses took into account age and gender.
- Polygenic risk analyses – The research team determined a model for each polygenic risk score including age, gender. These analyses were repeated taking into account symptom severity, nonresponse to antipsychotics, antipsychotic exposure and smoking status.
Appropriate statistical methods were applied for each.
A total of 896 (93.5%) study individuals were linked to health records held in the SAIL databank. Linked study individuals had an age range of 17–84 years (mean 44 years), 371 (41%) were female and 724 (81%) had genetic data available.
When compared to the general population, individuals with schizophrenia in SAIL had increased rates of:
- Epilepsy (standardised rate ratio (SRR) = 5.34)
- Intellectual disability (SRR = 3.11)
- Type 2 diabetes (SRR = 2.45)
- Congenital disorders (SRR = 1.77)
- Ischaemic heart disease (SRR = 1.57) and
- Smoking (SRR = 1.44)
In those with schizophrenia, carrier status for schizophrenia-associated CNVs and neurodevelopmental disorder-associated CNVs was associated with height (P = 0.015 to 0.017), with carriers being 7.5 to 7.7 cm shorter than non-carriers.
No evidence was found that increased rates of poor physical health outcomes in schizophrenia were associated with genetic liability for the disorder. This lack of association remained in sensitivity analyses covarying for symptom severity, non-response to antipsychotics, antipsychotic exposure, smoking status and genotyping array.
There were significant associations between:
- Non-response to antipsychotics and type-2 diabetes (OR = 2.94, 95% CI 1.79 to 4.85, p≤0.0001)
- Symptom severity and intellectual disability (OR = 1.24, 95% CI 1.05 to 1.46, p≤0.0012)
The associations with nonresponse to antipsychotics may reflect the frequent use of clozapine in this patient group, an antipsychotic known to cause weight gain and increase the risk of type 2 diabetes mellitus.
The increased risk for physical illness in schizophrenia is not caused by genetic liability for the disorder.
Strengths and limitations
It has been said that the value of healthcare data is often untapped and trapped as
different kinds of individual-patient data reside in disparate, unlinked silos (Blasimme et al., 2018).
The value of a dataset is increased when it is linked with another. For example, by combining different datasets on different disease types – as can be seen here new patterns of comorbidity can be discovered. It is recognised that the capacity to link genomic, clinical and diagnostic, medicines, and lifestyle data
forms the powerhouse for personalised medicine. (NHS England. Improving Outcomes through Personalised Medicine., 2016)
This study provides an important exemplar of the value of linking genetic data to routinely collected health-related data. This approach has enormous potential to generate a wealth of evidence, which can be translated into clinical practice to improve health outcomes for patients. The authors of the study say that their plan for future work is to link genetic data for a far greater number of individuals to their health records. I’ll definitely be keeping an eye out for this!
The main limitation of this study was related to sample size; it is possible that it was underpowered to detect genetic associations with small effect sizes. However, it can still be concluded that genetic liability to schizophrenia does not have a large or significant impact on the occurrence of physical comorbidity. The study authors’ plan is to link genetic data for a larger number of individuals to their health records.
Implications for practice
The evidence indicates that increased rates of poor physical health in patients with schizophrenia is unlikely to be driven by the genetic liability for the disorder. It is therefore likely that there are other factors contributing to an increased rate of physical health outcomes.
The findings from this study point to the need to focus on genetics and that it is very likely there is much more that is modifiable to improve physical health for patients with schizophrenia than we previously thought.
A greater emphasis must be placed on clinical practice and research on other aspects of care. There are broader issues at the health system and socio-environmental level beyond those that occur at an individual level, not mentioned in this paper, that need addressing (Liu et al., 2017). These issues have become a central part of my own PhD research exploring the lived experience of physical illness in individuals who have a diagnosis of a severe mental illness, such as schizophrenia.
Risk factors at the individual level include characteristics inherent to schizophrenia or an individual’s health‐related behaviours. These can be related to the severity of schizophrenia, affect the engagement or interaction of the person with the health care system, or include behaviours that lead to or exacerbate health problems e.g., smoking. Health system factors include treatment e.g., antipsychotic medication, delivery of services, and organisational characteristics such as the workforce or information systems infrastructure. Social determinants of health include, but are not limited to, public policies, an individual’s socioeconomic position, cultural and societal values, environmental vulnerabilities, and social support. There are lots of great Elf blogs that cover some of these in more detail written by experts (Haddison and Tracey, 2017; Mishu, 2020; Hassan, 2019).
Statement of interests
Kendall, K., John, A., Lee, S., Rees, E., Pardiñas, A., Banos, M., . . . Walters, J. (2020). Impact of schizophrenia genetic liability on the association between schizophrenia and physical illness: Data-linkage study. BJPsych Open, 6(6), E139. doi:10.1192/bjo.2020.42
Blasimme, A., Vayena, E., & Hafen, E. (2018). Democratizing Health Research Through Data Cooperatives. In Philosophy and Technology (Vol. 31, Issue 3).
Liu, N. H., Daumit, G. L., Dua, T., Aquila, R., Charlson, F., Cuijpers, P., Druss, B., Dudek, K., & Freeman, M. (2017). Excess mortality in persons with severe mental disorders: a multilevel intervention framework and priorities for clinical practice, policy and research agendas. World Psychiatry, 16(1), 30–40.
NHS England. Improving Outcomes through Personalised Medicine. (2016).
Zheutlin, A. B., Dennis, J., Linnér, R. K., Moscati, A., Restrepo, N., Straub, P., Ruderfer, D., Castro, V. M., Chen, C. Y., Ge, T., Huckins, L. M., Charney, A., Kirchner, H. L., Stahl, E. A., Chabris, C. F., Davis, L. K., & Smoller, J. W. (2019). Penetrance and pleiotropy of polygenic risk scores for schizophrenia in 106,160 patients across four health care systems. American Journal of Psychiatry, 176(10).