Skip to main content Accessibility help
×
Home

Impact of schizophrenia genetic liability on the association between schizophrenia and physical illness: data-linkage study

Published online by Cambridge University Press:  10 November 2020

Kimberley M. Kendall
Affiliation:
MRC Centre for Neuropsychiatric Genetics and Genomics, Division of Psychological Medicine and Clinical Neurosciences, Cardiff University, UK
Ann John
Affiliation:
Health Data Research UK, Swansea University Medical School, Swansea University, UK
Sze Chim Lee
Affiliation:
Health Data Research UK, Swansea University Medical School, Swansea University, UK
Elliott Rees
Affiliation:
MRC Centre for Neuropsychiatric Genetics and Genomics, Division of Psychological Medicine and Clinical Neurosciences, Cardiff University, UK
Antonio F. Pardiñas
Affiliation:
MRC Centre for Neuropsychiatric Genetics and Genomics, Division of Psychological Medicine and Clinical Neurosciences, Cardiff University, UK
Marcos Del Pozo Banos
Affiliation:
Health Data Research UK, Swansea University Medical School, Swansea University, UK
Michael J. Owen
Affiliation:
MRC Centre for Neuropsychiatric Genetics and Genomics, Division of Psychological Medicine and Clinical Neurosciences, Cardiff University, UK
Michael C. O'Donovan
Affiliation:
MRC Centre for Neuropsychiatric Genetics and Genomics, Division of Psychological Medicine and Clinical Neurosciences, Cardiff University, UK
George Kirov
Affiliation:
MRC Centre for Neuropsychiatric Genetics and Genomics, Division of Psychological Medicine and Clinical Neurosciences, Cardiff University, UK
Keith Lloyd
Affiliation:
Health Data Research UK, Swansea University Medical School, Swansea University, UK
Ian Jones
Affiliation:
National Centre for Mental Health, MRC Centre for Neuropsychiatric Genetics and Genomics, Division of Psychological Medicine and Clinical Neurosciences, Cardiff University, UK
Sophie E. Legge
Affiliation:
MRC Centre for Neuropsychiatric Genetics and Genomics, Division of Psychological Medicine and Clinical Neurosciences, Cardiff University, UK
James T. R. Walters
Affiliation:
MRC Centre for Neuropsychiatric Genetics and Genomics, Division of Psychological Medicine and Clinical Neurosciences, Cardiff University, UK
Corresponding
E-mail address:
Rights & Permissions[Opens in a new window]

Abstract

Background

Individuals with schizophrenia are at higher risk of physical illnesses, which are a major contributor to their 20-year reduced life expectancy. It is currently unknown what causes the increased risk of physical illness in schizophrenia.

Aims

To link genetic data from a clinically ascertained sample of individuals with schizophrenia to anonymised National Health Service (NHS) records. To assess (a) rates of physical illness in those with schizophrenia, and (b) whether physical illness in schizophrenia is associated with genetic liability.

Method

We linked genetic data from a clinically ascertained sample of individuals with schizophrenia (Cardiff Cognition in Schizophrenia participants, n = 896) 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.

Results

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 comparison with the general SAIL population. In those with schizophrenia, carrier status for schizophrenia-associated CNVs and neurodevelopmental disorder-associated CNVs was associated with height (P = 0.015–0.017), with carriers being 7.5–7.7 cm shorter than non-carriers. We did not find evidence that the increased rates of poor physical health outcomes in schizophrenia were associated with genetic liability for the disorder.

Conclusions

This study demonstrates the value of and potential for linking genetic data from clinically ascertained research studies to anonymised health records. The increased risk for physical illness in schizophrenia is not caused by genetic liability for the disorder.

Type
Papers
Creative Commons
This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted re-use, distribution, and reproduction in any medium, provided the original work is properly cited.
Copyright
Copyright © The Author(s) 2020. Published by Cambridge University Press on behalf of the Royal College of Psychiatrists

Background

Individuals with schizophrenia have a 20-year reduced life expectancy.Reference Crump, Winkleby, Sundquist and Sundquist1 A major contributing factor is the increased rate of poor physical health outcomes in individuals with schizophrenia, related to conditions such as metabolic, cardiovascular and respiratory disease.Reference Smith, Langan, McLean, Guthrie and Mercer2 Identifying the underlying reasons for these health disparities will provide a first step towards closing this health gap and thus has become a priority in schizophrenia research and clinical care in recent years.3 It is unclear whether the poorer physical health outcomes in schizophrenia (a) arise from the pleiotropic action of schizophrenia risk factors or (b) are secondary to illness-related factors such as negative symptoms, adverse effects of treatment (particularly antipsychotic medication) or poorer access to healthcare.

Genetic factors make major contributions to schizophrenia risk, arising from both common genetic variation and rare variants such as copy number variants (CNVs), the latter of these also being associated with physical health consequences in population samples.Reference Crawford, Bracher-Smith, Kendall, Rees, Pardinas and Einon4,Reference Owen, Bracher-Smith, Kendall, Rees, Einon and Escott-Price5 A recent study, in which genetic and electronic health record data were linked for 106 160 individuals, reported associations between polygenic risk scores for schizophrenia and several physical health phenotypes including smoking and reduced rates of obesity.Reference Zheutlin, Dennis, Linnér, Moscati, Restrepo and Straub6 At present, it is not known whether these genetic risk factors for schizophrenia are also associated with physical ill health in those with the disorder.

One reason for the dearth of research examining genetics and physical health outcomes in those with mental health disorders is likely to be the lack of available physical health data in existing psychiatric cohorts. In contrast, linkage of national registry data to anonymised mental and physical healthcare records, as exemplified in Nordic countries, has provided a rich resource for mental health research and led to important insights including into physical health outcomes in schizophrenia.Reference Cederlöf, Bergen, Larsson, Landén and Lichtenstein7,Reference Cesta, Månsson, Palm, Lichtenstein, Iliadou and Landén8 The use of anonymised National Health Service (NHS) records and linkage of patient data to national registries in the UK is gaining momentumReference Lyons, Jones, John, Brooks, Verplancke and Ford9,Reference Stewart, Soremekun, Perera, Broadbent, Callard and Denis10 and this approach has enabled researchers to investigate healthcare-related outcomes for individuals with major mental health disorders such as schizophrenia.Reference Smith, Langan, McLean, Guthrie and Mercer2 The amalgamation and linkage of health record and genomic data-sets offers further potential and initial reports describing linkage are emerging.Reference Zheutlin, Dennis, Linnér, Moscati, Restrepo and Straub6,Reference Kerr, Campbell, Marten, Vitart, McIntosh and Porteous11Reference Crawford, Crosslin, Tromp, Kullo, Kuivaniemi and Hayes13 This type of approach has the potential to facilitate the collection of large-scale phenotypic data without the need to burden patients with extensive assessments. This may have particular advantage for physical health outcomes, which could be tracked longitudinally with updated health record linkage.

Study aims

In this study, we aimed to link genetic data from a clinically ascertained sample of individuals with schizophrenia to anonymised NHS and administrative data-sets in the Secure Anonymised Information Linkage (SAIL) databank in Wales,Reference Lyons, Jones, John, Brooks, Verplancke and Ford9 with a focus on the rich physical health outcome data held in primary care electronic health resources. We then aimed to examine the association between physical health outcomes and genetic liability for schizophrenia as indexed by (a) rare (frequency <1%) CNVs, and (b) polygenic risk scores.

Method

Participants

Study individuals (n = 958, aged 17–84 years, 41% female) from the Cardiff Cognition in Schizophrenia (CardiffCOGS) sample were recruited from community, in-patient and voluntary sector mental health services in the UK and underwent detailed phenotypic assessment including a Schedules for Clinical Assessment in Neuropsychiatry (SCAN) interview.Reference Wing, Babor, Brugha, Burke, Cooper and Giel14 Interview data and clinical case-note vignettes were then used to arrive at a best estimate lifetime diagnosis according to DSM-IV criteria.15 Ethical approval was obtained from relevant NHS multisite research ethics committees and written informed consent was obtained for all study participants for genetic research and linkage of their genetic information to the SAIL databank. Further information on the CardiffCOGS sample has been published elsewhere.Reference Rees, Walters, Georgieva, Isles, Chambert and Richards16

Electronic cohort from SAIL

Data source and data linkage

The SAIL databank (http://www.saildatabank.com) is a national data repository containing anonymised, person-based, linkable data in Wales for over 3 million people. The procedure for linking research study data to SAIL has been described elsewhere.Reference Lyons, Jones, John, Brooks, Verplancke and Ford9,Reference Rapsomaniki, Timmis, George, Pujades-Rodriguez, Shah and Denaxas17,Reference Lloyd, McGregor, John, Craddock, Walters and Linden18 In brief, data from CardiffCOGS study individuals who had consented to linkage were imported into SAIL in line with permissions already granted to SAIL relating to good practice in research governance and privacy protection. We adopted a split file approach to separate individual identifiers from the interview data. Identity matching and creation of pseudonymised linkage keys (anonymised linking fields) were performed by a trusted third party prior to linkage and further encryption of data sets using deterministic matching based on NHS number or probabilistic matching using available demographics based on the Welsh Demographic Service data-set (all individuals registered with a general practice (GP) surgery). We included participants whose data were probabilistically linked with an adequate level of matching accuracy (matching score ≥0.9).Reference Lloyd, McGregor, John, Craddock, Walters and Linden18

We used the General Practice Database (GPD), containing diagnoses, symptoms, investigations, prescribed medication, referrals, coded hospital contacts and test results. At time of analysis, 77% (333/432) of GP surgeries in Wales supplied their data to SAIL. We also extracted data from the Patient Episode Database for Wales (PEDW), an NHS Wales hospital admissions data-set consisting of clinical information from all hospital admissions (in-patient and day cases) covering the entire population of Wales.

Measures for health outcomes

We used ICD-10 and Read codes for GDP and PEDW data-sets, respectively, to ascertain health outcomes.19 For schizophrenia, we adopted the codes that were validated and used in previous studies.Reference Lloyd, McGregor, John, Craddock, Walters and Linden18,Reference John, McGregor, Jones, Lee, Walters and Owen20,Reference Economou, Grey, McGregor, Craddock, Lyons and Owen21 We selected smoking, type 2 diabetes mellitus, ischaemic heart disease and body mass index (BMI) because of their high frequency in clinical samples of individuals with schizophrenia and their potential to contribute to increased mortality either directly, or via phenomena such as metabolic syndrome. We selected congenital disorders, intellectual disability and epilepsy as they are neurodevelopmental phenotypes with direct relevance to CNV carrier status. All of these variables also had either established SAIL algorithms or were considered to have high-quality data available in SAIL. We identified individuals with intellectual disability,Reference Rahman, Todd, John, Tan, Kerr and Potter22,Reference Brophy, Kennedy, Fernandez-Gutierrez, John, Potter and Linehan23 ischaemic heart disease,Reference Rapsomaniki, Timmis, George, Pujades-Rodriguez, Shah and Denaxas17 epilepsy,Reference Pickrell, Lacey, Bodger, Demmler, Thomas and Lyons24 diabetes mellitusReference Eastwood, Mathur, Atkinson, Brophy, Sudlow and Flaig25 and determined smoking statusReference Atkinson, Kennedy, John, Lewis, Lyons and Brophy26 based on previously published works.

The list of codes used for extracting height, BMI and for identifying individuals with congenital disorders are given in supplementary Table 1 available at https://doi.org/10.1192/bjo.2020.42. For identifying schizophrenia, intellectual disability, ischaemic heart disease, epilepsy, diabetes mellitus and congenital disorders, we combined both the GPD and PEDW data-sets. We additionally extracted individuals’ height, BMI and smoking status from the GPD where available.

For comparison, we extracted lifetime diagnoses and estimated crude rates, as well as age- and gender-standardised rates, of health outcomes for the whole population and those diagnosed with schizophrenia between the ages of 17 and 84 years (as at 30 June 2016). For estimating standardised rates from SAIL, crude rates were first computed for five age groups (17–34, 35–44, 45–54, 55–64 and 65–84 years) for both genders. Standardised rates were then estimated based on the age and gender distribution in the clinical cohort. Linked data in SAIL were interrogated using structured query language (SQL DB2).

Genetic data

Genotyping and CNV calling

Samples were genotyped on OmniCombo or OmniExpress arrays (Illumina).Reference Rees, Walters, Georgieva, Isles, Chambert and Richards16 After standard quality control, imputation was performed using IMPUTE2Reference Howie, Donnelly and Marchini27 and the 1000 genomes (phase 3) and UK10 K reference panels.Reference Huang, Howie, McCarthy, Memari, Walter and Min28 Best-guess genotypes were generated with the following thresholds: minimal genotypic confidence >90%, INFO-score >0.9, minor allele frequency (MAF) >1%, and Hardy-Weinberg equilibrium P-value <1 × 10−10.

Full details of the CNV calling methods and quality controls measures used have been published elsewhere.Reference Rees, Kendall, Pardiñas, Legge, Pocklington and Escott-Price29 Illumina Genome Studio (version 2011.1) was used to process raw intensity data into log R ratios (LRR) and B allele frequencies. PennCNV (version 1.0.3.18) was then used to call CNVs based on 666 868 probes common to all Illumina arrays used.Reference Wang, Li, Hadley, Liu, Glessner and Grant30 CNVs were joined if separated by less than 50% of their combined length. CNVs were excluded if they were (a) called with fewer than ten probes, (b) overlapped low copy repeats by more than 50% of their length, (c) had a probe density of less than 1 probe/20 kb, or (d) had a frequency of >1%.Reference Purcell, Neale, Todd-Brown, Thomas, Ferreira and Bender31

CNVs

To examine for enrichment of rare, pathogenic CNVs in physical health comorbidity, we analysed the presence of 12 CNVs robustly associated with the risk of schizophrenia,Reference Rees, Walters, Georgieva, Isles, Chambert and Richards16,Reference Rees, Kendall, Pardiñas, Legge, Pocklington and Escott-Price29 and 54 CNVs nominally associated (P < 0.05) with intellectual disability, autism spectrum disorder or schizophrenia (‘neurodevelopmental CNVs’).Reference Coe, Witherspoon, Rosenfeld, van Bon, Vulto-van Silfhout and Bosco32 Following the approach taken in our previous work, 15q11.2 duplications were excluded because of their high frequency.Reference Kendall, Rees, Escott-Price, Einon, Thomas and Hewitt33 CNV burden analyses were carried out using PLINK on regions of variable copy number at two size thresholds (a) ≥500 KB and (b) ≥1 MB and converted into carrier status for the purpose of regression analyses.

Polygenic risk scores

Polygenic risk scores were calculated using the largest published schizophrenia genome-wide association study meta-analysis (39 915 individuals with schizophrenia, 64 639 controls) as a training set and using the established method described by Wray et al.Reference Pardiñas, Holmans, Pocklington, Escott-Price, Ripke and Carrera34,Reference Wray, Lee, Mehta, Vinkhuyzen, Dudbridge and Middeldorp35 All study individuals were excluded from the training set. scores were generated using the –score function in PLINKReference Purcell, Neale, Todd-Brown, Thomas, Ferreira and Bender31 for Single nucleotide polymorphisms with MAF >10%, INFO score >0.9, a low linkage disequilibrium to each other and excluding indels and the extended major histocompatibility complex region. Polygenic risk scores were calculated at nine P-value thresholds; 1 × 10−8, 1 × 10−6, 1 × 10−4, 1 × 10−3, 0.01, 0.05, 0.1, 0.2 and 0.5.

Analysis

Rates of physical illness

Linked data in SAIL were interrogated using structured query language (SQL DB2). All crude rates and standardised rates of health outcomes were expressed as a percentage of population affected (lifetime prevalence). All standardised rate ratios (SRRs) and their 95% confidence intervals were calculated as previously described.Reference Armitage, Berry and Matthews36Reference Newman38

Ascertainment rates of behaviours and diagnoses

We evaluated the agreement on diagnoses between the clinical and electronic cohorts for each health outcome by constructing two × two contingency tables based on the paired responses from the interview and SAIL. Level of agreement was then assessed by unweighted Cohen's kappa coefficientReference Cohen39 and Gwet's AC1.Reference Gwet40 The 95% confidence intervals of Cohen's kappa and Gwet's AC1 were estimated as described in Fleiss, Cohen & EverittReference Fleiss, Cohen and Everitt41 and Gwet,Reference Gwet40 respectively. Strength of agreement metrics was categorised according to previously described criteria.Reference Landis and Koch42

CNV analyses

Association analyses were carried out using linear regression for average BMI (normalised using log10 transformation) and average height, and Firth logistic regression for all binary traits (type 2 diabetes mellitus, smoking, ischaemic heart disease, congenital disorders, epilepsy, intellectual disability). Firth logistic regression allows for the analysis of low numbers in addition to the inclusion of covariates. All analyses included age and gender as covariates.Reference Wang43

Polygenic risk analyses

We regressed a model for each polygenic risk score created from various training P-value thresholds against a base model including age, gender, the first five principal components and any additional principal components from the first 20 that were nominally associated with the phenotype of interest. We repeated these analyses with the addition of covariates reflecting symptom severity, non-response to antipsychotics, antipsychotic exposure, smoking status and genotyping platform (defined in supplementary Table 2). All statistical analyses were carried out in R and results were subject to Bonferroni correction for the eight phenotypes examined (P-value threshold 0.0063).

Results

A total of 896 (93.5%) study individuals from CardiffCOGS 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.

Physical health outcomes in CardiffCOGS and SAIL

Table 1 and Fig. 1 outlines the frequencies of the physical health outcomes in the CardiffCOGS sample and SAIL. Within the SAIL population, individuals with schizophrenia had increased rates of epilepsy (SRR = 5.34, 95% CI 5.11–5.57), intellectual disability (SRR = 3.11, 95% CI 3.06–3.11), type 2 diabetes (SRR = 2.45, 95% CI 2.38–2.53), congenital disorders (SRR = 1.77, 95% CI 1.57–1.99), ischaemic heart disease (SRR = 1.57, 95% CI 1.51– 1.63) and smoking (SRR = 1.44, 95% CI 1.42–1.46). Individuals within CardiffCOGS had higher rates of type 2 diabetes (SRR = 1.29, 95% CI 1.10–1.52) compared with the population with schizophrenia in SAIL. Crude unadjusted population rates are given in supplementary Table 3.

Fig. 1 Rates of physical health phenotypes in the Cardiff Cognition in Schizophrenia (CardiffCOG) schizophrenia sample, the schizophrenia population in Secure Anonymised Information Linkage (SAIL) and the general population in SAIL.

T2DM, type 2 diabetes mellitus.

Table 1 Rates of physical health phenotypes in Cardiff Cognition in Schizophrenia (CardiffCOG) sample compared with the population rates in Secure Anonymised Information Linkage (SAIL)a

sam – sample (CardiffCOGS), sch – schizophrenia, gen – general population.

a. SRRsam, sch represents the standardised rate ratio (SRR) of the clinical cohort to the schizophrenia population ascertained in SAIL. SRRsch, gen represents the standardised rate ratio of the schizophrenia population ascertained in SAIL to the general population ascertained in SAIL. SAIL rates were standardised using the age and gender distribution from the clinical cohort as reference. Crude unadjusted population rates are given in supplementary Table 3. Numbers stated are out of 895 for the CardiffCOGS sample (except for smoking which was out of 792). Numbers stated for the population rate are out of 3 852 471 (except for smoking which was out of 2 958 064) and for schizophrenia population are out of 35 944 (26 588 for smoking). Rates given in parentheses in SAIL columns are standardised to account for differences in age and sex distribution between the SAIL and CardiffCOGS cohorts.

Comparison of ascertainment rates of behaviours and diagnoses in CardiffCOGS and SAIL

For physical health outcomes that were also reported at interview (smoking, type 2 diabetes mellitus, ischaemic heart disease, and epilepsy), we compared agreement with their health records (Table 2). Both Cohen's κ and Gwet's AC1 for all physical conditions ranged from 0.502 to 0.936. These show that the agreement of the rates ascertained from the interview and health records were moderate to high.Reference Landis and Koch42 The highest agreements were observed for ischaemic heart disease and epilepsy. The strength of agreement was lowest for smoking behaviour (Cohen's κ = 0.380 and Gwet's AC1 = 0.621), reflecting only fair to substantial agreement.

Table 2 Interrater agreement of the rates of ascertainment between the Cardiff Cognition in Schizophrenia (CardiffCOG) interview and linkage to National Health Service recordsa

SAIL, Secure Anonymised Information Linkage.

a. The smoking variable compares results for current/ex smoking status in SAIL and ever a regular smoker at interview. Cohen's κ refers to Cohen's kappa statistic,45 Gwet's AC1 refers to the first order agreement coefficient.46 Both values range between 0 (chance, or no agreement) and 1 (perfect agreement). Values below 0.4 indicate poor agreement, values of 0.4–0.59 indicate moderate agreement and values above 0.6 indicate substantial agreement.

CNV

A total of 2.1% (n = 15) of the CardiffCOGS sample carried a schizophrenia-associated CNV, 4.9% (n = 32) carried a neurodevelopmental CNV, 9.5% carried a chromosomal duplication or deletion ≥500 KB (19 deletions, 52 duplications, 2 both) and 3.2% carried a duplication or deletion ≥1 MB (6 deletions, 17 duplications). We found no evidence that CNV carriers had increased rates of poor physical health outcomes (Table 3). However, average height was nominally associated with carrier status for CNVs associated with schizophrenia (beta = −0.075, 95% CI −0.14 to −0.01, P = 0.017) and neurodevelopmental disorders (beta = −0.077, 95% CI −0.64 to −0.07, P = 0.015) (Table 3). CNV carriers were on average 7.5–7.7 cm shorter than non-carriers. There was no evidence for association between rare CNVs of 500 KB or greater and any of the phenotypes examined (supplementary Table 4).

Table 3 Association analysis results for physical health phenotypes and two groups of copy number variants (CNVs, 12 schizophrenia CNVs and 53 neurodevelopmental CNVs)a

P, uncorrected P-value; GP, general practice.

a. General practice/hospital/both in parentheses in the phenotype column refers to the source of diagnostic information used. Effect size refers to odds ratio in all cases except average height and average body mass index for which the effect size is standardised Beta. Counts below five are masked to preserve participant anonymity.

Polygenic risk for schizophrenia

We found no evidence for an association between polygenic risk scores for schizophrenia and the physical health outcomes studied (Fig. 2, supplementary Table 5), although there was weak evidence for an association with ischaemic heart disease at the genome-wide P-value threshold (odds ratio (OR) = 1.65, 95% CI 1.22–2.24; adjusted R 2 = 0.035, P = 0.001). The lack of association between schizophrenia polygenic risk scores and physical health outcomes remained in sensitivity analyses covarying for symptom severity, non-response to antipsychotics, antipsychotic exposure, smoking status and genotyping array (supplementary Table 6). However, we did identify significant associations between non-response to antipsychotics and type 2 diabetes (OR = 2.94, 95% CI 1.79–4.85, P ≤ 0.0001) and an association between symptom severity and intellectual disability (OR = 1.24, 95% CI 1.05–1.46, P = 0.0012). There were additional nominal associations (P < 0.05) between non-response to antipsychotics and epilepsy, and smoking and ischaemic heart disease (supplementary Table 6).

Fig. 2 Graphs of the results from regression models for the association between polygenic risk for schizophrenia and physical health outcomes.

(a) Odds ratio; (b) Beta. Odds ratios are shown for the P-value thresholds at which markers were selected.

Discussion

In this study, we report the linkage of genetic data from a clinically ascertained sample of individuals with schizophrenia to anonymised NHS health records in the SAIL databank. Consistent with data collected at interview, we found that individuals with schizophrenia in Wales had increased rates of neurodevelopmental disorders (epilepsy, intellectual disability and congenital disorders), smoking, type 2 diabetes mellitus and ischaemic heart disease compared with the general population. However, the results of our genetic analyses suggest that these increased rates are not because of genetic liability to schizophrenia; we found no evidence for an association between genetic risk for schizophrenia indexed by CNVs or polygenic risk scores and physical health outcomes.

These findings are supported by a recent population-based study that linked genetic and health record data for 106 160 individuals and found an inverse association between increased schizophrenia polygenic risk scores and obesity and an inverse association with diabetes when controlling for schizophrenia diagnosis or for antipsychotic medication history.Reference Zheutlin, Dennis, Linnér, Moscati, Restrepo and Straub6 No association was found between schizophrenia polygenic risk scores and the other physical health outcomes found in this study.Reference Zheutlin, Dennis, Linnér, Moscati, Restrepo and Straub6 Thus, the evidence available indicates that increased rates of poor physical health observed in patients with schizophrenia is unlikely to be driven by the genetic liability for the disorder.

There may be many other factors contributing to an increased rate of the physical health outcomes examined. For the non-neurodevelopmental outcomes, these include, but are not limited to, medication side-effects and lifestyle choices such as smoking and diet. For example, weight gain caused by antipsychotic medication, poor diet and smoking may all contribute to the risk of type 2 diabetes mellitus and ischaemic heart disease. This is supported by our study; we found significant associations between non-response to antipsychotics and type 2 diabetes mellitus and nominal associations between non-response to antipsychotics and epilepsy, and smoking and ischaemic heart disease. The associations with non-response 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. Our additional analyses also found that intellectual disability was significantly associated with a greater symptom severity. The reasons for this association are not yet clear. Importantly, several of these non-genetic factors are amenable to targeted intervention, which may reduce the risk of their consequent physical health outcomes.

Our finding that carriers of rare neurodevelopmental CNVs tend to be shorter than CNV non-carriers is in keeping with previous work on CNV associations with anthropometric traits in a sample of 191 161 adults.Reference Mace, Tuke, Deelen, Kristiansson, Mattsson and Nõukas44 Macé et al report associations between both total CNV burden, several individual CNV loci (also examined in our study) and alterations in height. Additional findings from Macé et al of a lack of association between anthropomorphic traits and schizophrenia suggest that the CNVs affect height independent of disease.Reference Mace, Tuke, Deelen, Kristiansson, Mattsson and Nõukas44

The rates of smoking ascertained in our study (87.0%) and in those with schizophrenia in SAIL (83.9%) appear high but are comparable with several studies also examining ‘ever’ smoking in individuals with schizophrenia.Reference de Leon and Diaz45 Similarly, the rates of ischaemic heart disease in the CardiffCOGS and SAIL schizophrenia population groups (both 6.9%) are comparable with rates established in other studies.43,44 CardiffCOGS had a higher rate of type 2 diabetes mellitus (17.4%) than the schizophrenia population in SAIL (13.5%) and other study samples such as a Swedish cohort study of individuals with schizophrenia (11–12.5%).Reference Crump, Winkleby, Sundquist and Sundquist1 This may reflect the higher than average proportion of individuals with chronic schizophrenia and long-term antipsychotic medication use in the CardiffCOGS sample.

Study limitations

The main limitation of this study was related to sample size; it is possible that we were 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. Nonetheless, our plan for future work is to link genetic data for a far greater number of individuals to their health records. This study does provide an important exemplar for the value of linking genetic data to routinely collected health-related data. Such an approach has great potential to generate a wealth of evidence, which can be translated into improved health outcomes for patients.

Supplementary material

Supplementary material is available online at https://doi.org/10.1192/bjo.2020.42

Data availability

All data requests should be submitted to the corresponding authors for consideration.

Acknowledgements

We thank the participants, clinicians, field team and MRC Centre for Neuropsychiatric Genetics and Genomics laboratory staff for their help with the CardiffCOGS study. We thank the staff at deCODE Genetics for sample genotyping.

Author contributions

K.K., A.J., S.C.L., S.E.L. and J.T.R.W. made substantial contributions to the conception and design of the study. All authors contributed either to the acquisition or analysis of the data. K.K., S.C.L. and S.L. carried out the data analysis. K.K., A.J., S.C.L., S.E.L. and J.T.R.W. drafted the manuscript. All authors contributed to the critical revision of the manuscript for important intellectual content and gave final approval of the version to be published.

Funding

This project was funded by a Wellcome Trust Institutional Strategic Support Fund (ISSF) Populations Pilot Award. This project was supported also by the following grants: Medical Research Council Centre (MR/L010305/1), Program (G0800509) and Project (MC_PC_17212) grants to Cardiff University, Medical Research Council Pathfinder (MR_PC_17211) to Swansea University, an MQ grant (MQDS16/36) to Cardiff University and the Health and Care Research Wales National Centre for Mental Health (155838) at Swansea University. K.K. was funded by a Wellcome Trust Clinical Research Training Fellowship.

Declaration of interest

The authors declare no conflicts of interest.

Footnotes

*

Joint last authors.

References

Crump, C, Winkleby, MA, Sundquist, K, Sundquist, J. Comorbidities and mortality in persons with schizophrenia: a Swedish national cohort study. Am J Psychiatry 2013; 170: 324–33.CrossRefGoogle ScholarPubMed
Smith, DJ, Langan, J, McLean, G, Guthrie, B, Mercer, SW. Schizophrenia is associated with excess multiple physical-health comorbidities but low levels of recorded cardiovascular disease in primary care: cross-sectional study. BMJ Open 2013; 3: e002808.CrossRefGoogle ScholarPubMed
National Institute for Health and Care Excellence. Schizophrenia: Core Interventions in the Treatment and Management of Schizophrenia in Adults in Primary and Secondary Care. National Institute for Health and Care Excellence, 2009.Google Scholar
Crawford, K, Bracher-Smith, M, Kendall, KM, Rees, E, Pardinas, AF, Einon, M, et al. Medical consequences of pathogenic CNVs in adults: analysis of the UK Biobank. J Med Genet 2019; 56: 131–8.CrossRefGoogle ScholarPubMed
Owen, D, Bracher-Smith, M, Kendall, K, Rees, E, Einon, M, Escott-Price, V, et al. Effects of pathogenic CNVs on physical traits in participants of the UK Biobank. BMC Genomics 2018; 19: 867.CrossRefGoogle ScholarPubMed
Zheutlin, AB, Dennis, J, Linnér, R, Moscati, A, Restrepo, N, Straub, P, et al. Penetrance and pleiotropy of polygenic risk scores for schizophrenia in 106,160 patients across four health care systems. Am J Psychiatry 2019; 176(10): 846–55.CrossRefGoogle ScholarPubMed
Cederlöf, M, Bergen, SE, Larsson, H, Landén, M, Lichtenstein, P. Acute intermittent porphyria: comorbidity and shared familial risks with schizophrenia and bipolar disorder in Sweden. Br J Psychiatry 2015; 207: 556–7.CrossRefGoogle ScholarPubMed
Cesta, CE, Månsson, M, Palm, C, Lichtenstein, P, Iliadou, AN, Landén, M. Polycystic ovary syndrome and psychiatric disorders: co-morbidity and heritability in a nationwide Swedish cohort. Psychoneuroendocrinology 2016; 73: 196203.CrossRefGoogle Scholar
Lyons, RA, Jones, KH, John, G, Brooks, CJ, Verplancke, JP, Ford, DV, et al. The SAIL databank: linking multiple health and social care datasets. BMC Med Inf Decis Mak 2009; 9: 3.CrossRefGoogle ScholarPubMed
Stewart, R, Soremekun, M, Perera, G, Broadbent, M, Callard, F, Denis, M, et al. The South London and Maudsley NHS Foundation Trust Biomedical Research Centre (SLAM BRC) case register: development and descriptive data. BMC Psychiatry 2009; 9: 51.CrossRefGoogle ScholarPubMed
Kerr, SM, Campbell, A, Marten, J, Vitart, V, McIntosh, AM, Porteous, DJ, et al. Electronic health record and genome-wide genetic data in Generation Scotland participants. Wellcome Open Res 2017; 2: 85.CrossRefGoogle Scholar
Stahl, EA, Breen, G, Forstner, AJ, McQuillin, A, Ripke, S, Trubetskoy, V, et al. Genome-wide association study identifies 30 loci associated with bipolar disorder. Nat Genet 2019; 51: 793803.CrossRefGoogle ScholarPubMed
Crawford, DC, Crosslin, DR, Tromp, G, Kullo, IJ, Kuivaniemi, H, Hayes, MG, et al. eMERGEing progress in genomics-the first seven years. Front Genet 2014; 5: 184.CrossRefGoogle ScholarPubMed
Wing, JK, Babor, T, Brugha, T, Burke, J, Cooper, JE, Giel, R, et al. SCAN. Schedules for Clinical Assessment in Neuropsychiatry. Arch Gen Psychiatry 1990; 47: 589–93.CrossRefGoogle ScholarPubMed
American Psychiatric Association. Diagnostic and statistical manual of mental disorders 4th edn, 2000.Google Scholar
Rees, E, Walters, JT, Georgieva, L, Isles, AR, Chambert, KD, Richards, AL, et al. Analysis of copy number variations at 15 schizophrenia-associated loci. Br J Psychiatry 2014; 204: 108–14.CrossRefGoogle ScholarPubMed
Rapsomaniki, E, Timmis, A, George, J, Pujades-Rodriguez, M, Shah, AD, Denaxas, S, et al. Blood pressure and incidence of twelve cardiovascular diseases: lifetime risks, healthy life-years lost, and age-specific associations in 1·25 million people. Lancet 2014; 383: 1899–911.CrossRefGoogle ScholarPubMed
Lloyd, K, McGregor, J, John, A, Craddock, N, Walters, JT, Linden, D, et al. A national population-based e-cohort of people with psychosis (PsyCymru) linking prospectively ascertained phenotypically rich and genetic data to routinely collected records: overview, recruitment and linkage. Schizophr Res 2015; 166: 131–6.CrossRefGoogle ScholarPubMed
World Health Organization. The ICD-10 classification of mental and behavioural disorders: Clinical descriptions and diagnostic guidelines. World Health Organization, 1992.Google Scholar
John, A, McGregor, J, Jones, I, Lee, SC, Walters, JTR, Owen, MJ, et al. Premature mortality among people with severe mental illness - new evidence from linked primary care data. Schizophr Res 2018; 199: 154–62.CrossRefGoogle ScholarPubMed
Economou, A, Grey, M, McGregor, J, Craddock, N, Lyons, RA, Owen, MJ, et al. The health informatics cohort enhancement project (HICE): using routinely collected primary care data to identify people with a lifetime diagnosis of psychotic disorder. BMC Res Notes 2012; 5: 95.CrossRefGoogle ScholarPubMed
Rahman, MA, Todd, C, John, A, Tan, J, Kerr, M, Potter, R, et al. School achievement as a predictor of depression and self-harm in adolescence: linked education and health record study. Br J Psychiatry. 2018; 212: 215221.CrossRefGoogle ScholarPubMed
Brophy, S, Kennedy, J, Fernandez-Gutierrez, F, John, A, Potter, R, Linehan, C, et al. Characteristics of children prescribed antipsychotics: analysis of routinely collected data. J Child Adolesc Psychopharmacol. 2018; 28(3): 180191.CrossRefGoogle ScholarPubMed
Pickrell, WO, Lacey, AS, Bodger, OG, Demmler, JC, Thomas, RH, Lyons, RA, et al. Epilepsy and deprivation, a data linkage study. Epilepsia 2015; 56: 585–91.CrossRefGoogle ScholarPubMed
Eastwood, SV, Mathur, R, Atkinson, M, Brophy, S, Sudlow, C, Flaig, R, et al. Algorithms for the capture and adjudication of prevalent and incident diabetes in UK Biobank. PLoS One 2016; 11: e0162388.CrossRefGoogle ScholarPubMed
Atkinson, MD, Kennedy, JI, John, A, Lewis, KE, Lyons, RA, Brophy, ST, et al. Development of an algorithm for determining smoking status and behaviour over the life course from UK electronic primary care records. BMC Med Inf Decis Mak 2017; 17: 2.CrossRefGoogle ScholarPubMed
Howie, BN, Donnelly, P, Marchini, J. A flexible and accurate genotype imputation method for the next generation of genome-wide association studies. PLoS Genet 2009; 5: e1000529.CrossRefGoogle ScholarPubMed
Huang, J, Howie, B, McCarthy, S, Memari, Y, Walter, K, Min, JL, et al. Improved imputation of low-frequency and rare variants using the UK10K haplotype reference panel. Nat Commun 2015; 6: 8111.CrossRefGoogle ScholarPubMed
Rees, E, Kendall, K, Pardiñas, AFF, Legge, SEE, Pocklington, A, Escott-Price, V, et al. Analysis of intellectual disability copy number variants for association with schizophrenia. JAMA Psychiatry 2016; 73: 963–9.CrossRefGoogle ScholarPubMed
Wang, K, Li, M, Hadley, D, Liu, R, Glessner, J, Grant, SF, et al. PennCNV: an integrated hidden Markov model designed for high-resolution copy number variation detection in whole-genome SNP genotyping data. Genome Res 2007; 17: 1665–74.CrossRefGoogle ScholarPubMed
Purcell, S, Neale, B, Todd-Brown, K, Thomas, L, Ferreira, MA, Bender, D, et al. PLINK: a tool set for whole-genome association and population-based linkage analyses. Am J Hum Genet 2007; 81: 559–75.CrossRefGoogle ScholarPubMed
Coe, BP, Witherspoon, K, Rosenfeld, JA, van Bon, BW, Vulto-van Silfhout, AT, Bosco, P, et al. Refining analyses of copy number variation identifies specific genes associated with developmental delay. Nat Genet 2014; 46: 1063–71.CrossRefGoogle ScholarPubMed
Kendall, KM, Rees, E, Escott-Price, V, Einon, M, Thomas, R, Hewitt, J, et al. Cognitive performance among carriers of pathogenic copy number variants: analysis of 152,000 UK Biobank subjects. Biol Psychiatry 2017; 82: 103–10.CrossRefGoogle ScholarPubMed
Pardiñas, AF, Holmans, P, Pocklington, AJ, Escott-Price, V, Ripke, S, Carrera, N, et al. Common schizophrenia alleles are enriched in mutation-intolerant genes and in regions under strong background selection. Nat Genet 2018; 50: 381–9.CrossRefGoogle ScholarPubMed
Wray, NR, Lee, SH, Mehta, D, Vinkhuyzen, AAE, Dudbridge, F, Middeldorp, CM. Research review: polygenic methods and their application to psychiatric traits. 2014; 55: 1068–87.CrossRefGoogle Scholar
Armitage, P, Berry, G, Matthews, J. Statistical methods in medical research. In Statistical Methods in Medical Research (4th edn). John Wiley and Sons, 2002.CrossRefGoogle Scholar
Flanders, WD. Approximate variance formulas for standardized rate ratios. J Chronic Dis 1984; 37: 449–53.CrossRefGoogle ScholarPubMed
Newman, S.Biostatistical Methods in Epidemiology. John Wiley and Sons, 2002.CrossRefGoogle Scholar
Cohen, J. A coefficient of agreement for nominal scales. Educ Psychol Meas 1960; 20: 3746.CrossRefGoogle Scholar
Gwet, KL. Computing inter-rater reliability and its variance in the presence of high agreement. Br J Math Stat Psychol 2008; 61: 2948.CrossRefGoogle ScholarPubMed
Fleiss, J, Cohen, J, Everitt, B. Large sample standard errors of kappa and weighted kappa. Psychol Bull 1969; 72: 323–7.CrossRefGoogle Scholar
Landis, JR, Koch, GG. The measurement of observer agreement for categorical data. Biometrics 1977; 33: 159–74.CrossRefGoogle ScholarPubMed
Wang, X. Firth logistic regression for rare variant association tests. Front Genet 2014; 5: 187.CrossRefGoogle ScholarPubMed
Mace, A, Tuke, MA, Deelen, P Kristiansson, K, Mattsson, H, Nõukas, M, et al. 2017. CNV-association meta-analysis in 191,161 European adults reveals new loci associated with anthropometric traits. Nat Commun 2017; 8: 744.CrossRefGoogle ScholarPubMed
de Leon, J, Diaz, FJ. A meta-analysis of worldwide studies demonstrates an association between schizophrenia and tobacco smoking behaviors. Schizophr Res 2005; 76: 135–57.CrossRefGoogle ScholarPubMed
Ford, DV, Jones, KH, Verplancke, JP, Lyons, RA, John, G, Brown, G, et al. The SAIL Databank: building a national architecture for e-health research and evaluation. BMC Health Serv Res 2009; 9: 157.CrossRefGoogle ScholarPubMed
Goff, DC, Sullivan, LM, McEvoy, JP, Meyer, JM, Nasrallah, HA, Daumit, GL, et al. A comparison of ten-year cardiac risk estimates in schizophrenia patients from the CATIE study and matched controls. Schizophr Res 2005 Dec; 80: 4553.CrossRefGoogle Scholar
Cohn, T, Prud'homme, D, Streiner, D, Kameh, H, Remington, G. Characterizing coronary heart disease risk in chronic schizophrenia: high prevalence of the metabolic syndrome. Can J Psychiatry 2004 Nov; 49: 753–60.CrossRefGoogle Scholar

Kendall et al. supplementary material

Table S1-S6

File 63 KB
Submit a response

eLetters

No eLetters have been published for this article.

Altmetric attention score

Full text views

Full text views reflects PDF downloads, PDFs sent to Google Drive, Dropbox and Kindle and HTML full text views.

Total number of HTML views: 215
Total number of PDF views: 214 *
View data table for this chart

* Views captured on Cambridge Core between 10th November 2020 - 22nd January 2021. This data will be updated every 24 hours.

Access
Open access
Hostname: page-component-76cb886bbf-cdxmh Total loading time: 0.566 Render date: 2021-01-22T23:28:39.959Z Query parameters: { "hasAccess": "1", "openAccess": "1", "isLogged": "0", "lang": "en" } Feature Flags: { "shouldUseShareProductTool": true, "shouldUseHypothesis": true, "isUnsiloEnabled": true, "metricsAbstractViews": false, "figures": false, "newCiteModal": false }

Send article to Kindle

To send this article to your Kindle, first ensure no-reply@cambridge.org is added to your Approved Personal Document E-mail List under your Personal Document Settings on the Manage Your Content and Devices page of your Amazon account. Then enter the ‘name’ part of your Kindle email address below. Find out more about sending to your Kindle. Find out more about sending to your Kindle.

Note you can select to send to either the @free.kindle.com or @kindle.com variations. ‘@free.kindle.com’ emails are free but can only be sent to your device when it is connected to wi-fi. ‘@kindle.com’ emails can be delivered even when you are not connected to wi-fi, but note that service fees apply.

Find out more about the Kindle Personal Document Service.

Impact of schizophrenia genetic liability on the association between schizophrenia and physical illness: data-linkage study
Available formats
×

Send article to Dropbox

To send this article to your Dropbox account, please select one or more formats and confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your <service> account. Find out more about sending content to Dropbox.

Impact of schizophrenia genetic liability on the association between schizophrenia and physical illness: data-linkage study
Available formats
×

Send article to Google Drive

To send this article to your Google Drive account, please select one or more formats and confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your <service> account. Find out more about sending content to Google Drive.

Impact of schizophrenia genetic liability on the association between schizophrenia and physical illness: data-linkage study
Available formats
×
×

Reply to: Submit a response


Your details


Conflicting interests

Do you have any conflicting interests? *