Obesity, dyslipidemia and brain age in first-episode psychosis
Introduction
Schizophrenia is among the most disabling psychiatric disorders (Whiteford et al., 2013). It is frequently associated with neuroanatomical alterations (Vita et al., 2006, Fornito et al., 2009), which may contribute to impaired functioning (Dazzan et al., 2015). Yet, the origins of brain changes in schizophrenia remain poorly understood and there is no treatment for them. One potential source of neuroimaging abnormalities in psychotic disorders is the comorbidity with medical conditions known to affect the brain.
Almost 1 in 2 participants with schizophrenia are overweight, and at least 2 in 5 suffer from dyslipidemia (Mitchell et al., 2013b). Increased rates of metabolic alterations are found already early in the course of illness. Overweight/obesity, low HDL-cholesterol and hypertryglyceridaemia each affect about 20% of participants with first-episode psychosis (FEP) (Mitchell et al., 2013a). The early onset and lifelong presence of metabolic alterations contribute to poor medical and psychiatric outcomes in schizophrenia (Saha et al., 2007, Brown et al., 2010, Bora et al., 2017) and other psychotic disorders (Bora et al., 2017, Hajek et al., 2016). Among metabolic alterations, obesity and dyslipidemia are the strongest contributors to cognitive impairment and functional decline in psychotic disorders (Bora et al., 2017), which may be related to the negative effects of these comorbidites on the brain.
Neurostructural alterations are frequently reported in participants with obesity (Debette et al., 2010, Tiehuis et al., 2014, Sala et al., 2014, Cherbuin et al., 2015, Masouleh et al., 2016), even in absence of other pathology (Yau et al., 2014, Alosco et al., 2014, Ou et al., 2015). These changes manifest already in adolescence (Mueller et al., 2012, Alosco et al., 2014, Ross et al., 2015, Yokum and Stice, 2017) and tend to be most pronounced in frontal lobes and limbic regions, including insula and hippocampus (Willette and Kapogiannis, 2015). Dyslipidemia may contribute to neurostructural alterations in obesity, with which it often clusters (Friedman et al., 2014, Schwarz et al., 2018). Furthermore, obesity enhances the negative effects of psychiatric morbidity on the brain (Bond et al., 2011, Bond et al., 2014), which may in turn yield adverse psychiatric outcomes (Opel et al., 2015). Interestingly, brain alterations in obesity or dyslipidemia may be preventable or treatable (Mueller et al., 2015, Tuulari et al., 2016, Mansur et al., 2017b) and resemble some of the most replicated neurostructural findings in schizophrenia and related disorders.
Longitudinal studies have shown accelerated age related loss of fronto-limbic volumes, already early in the course of schizophrenia (Paus et al., 2008, Gogtay, 2008, Shaw et al., 2010, Gogtay and Thompson, 2010). Consequently, participants with FEP typically demonstrate smaller volumes of frontal lobes, hippocampus and insula (Ganzola et al., 2014, Torres et al., 2016, Lee et al., 2016, Dietsche et al., 2017). Many of these changes progressively worsen, especially in the first years of illness (van Haren et al., 2007, Andreasen et al., 2011, Vita et al., 2012, Lee et al., 2016). This is likely related to accumulation of certain clinical variables (Zipursky et al., 2013, Zipursky, 2014). Thus, studying the interplay between obesity, dyslipidemia and brain health could help identify preventable risk factors for neuroimaging abnormalities in schizophrenia and related disorders and could provide insight into their pathophysiology and treatment.
Access to large databases of brain scans and advances in neuroimaging analyses involving machine learning, allow us to estimate the biological age of the brain from MRI (Franke et al., 2013, Koutsouleris et al., 2014). The difference between brain age and chronological age captures diffuse, multivariate neurostructural alterations into a single number. This mitigates the problem of multiple comparisons and yields relatively unbiased estimates of effect size (Reddan et al., 2017). Aside from age related changes, this measure is sensitive to brain alterations in schizophrenia (Koutsouleris et al., 2014, Schnack et al., 2016, Nenadic et al., 2017) or obesity (Ronan et al., 2016), which typically show greater brain than chronological age.
Here, we used this novel technology to study the effects of metabolic markers on brain structure in FEP. We expected, that participants with FEP, obesity and possibly dyslipidemia would demonstrate neurostructural alterations, which would make their brains appear older than their chronological age and that the effects of these factors on brain structure would be additive.
Section snippets
Materials and methods
This was a part of the Early Stages of Schizophrenia study (Spaniel et al., 2016). To ensure generalizability, we recruited participants during their first hospitalization in a large general psychiatry hospital (1200 beds), which serves the Prague and part of Central Bohemia regions - catchment area of over 1.5 million subjects. We focused on individuals with FEP, who met the following inclusion criteria: 1) were undergoing their first psychiatric hospitalization, 2) had the ICD-10 diagnosis of
Statistical analyses
Our analytical plan included the following steps: 1) we investigated associations between individual metabolic variables and BrainAGE scores, using correlation coefficients for continuous and two-sample t-tests for categorical variables; 2) we explored the effects of age and sex on BrainAGE scores, to select, which nuisance variables to control for in multivariate analyses; 3) the variables significantly associated with BrainAGE scores in step 1, were entered into multiple linear regression
Results
We recruited 120 participants with FEP and 114 controls, Table 1.
BrainAGE scores were significantly associated with diagnosis of FEP (t(232) = 2.82, p = 0.005), overweight/obesity category (t(232) = 2.74, p = 0.007) and BMI (r(232) = 0.15, p = 0.02, Fig. 1A–C). The association between BrainAGE score and BMI was related to association between BrainAGE score and weight (r(232) = 0.17, p = 0.009), not height (r(232) = 0.10, p = 0.13).
BrainAGE scores were not associated with LDL-cholesterol
Discussion
This study suggests that overweight/obesity, which is frequent in schizophrenia, may contribute to neurostructural changes already early in the course of illness. Specifically, the brains of participants with FEP appeared on average 2.64 years older than their chronological age. Weight and BMI were also significantly positively associated with advanced brain age. Consequently, BrainAGE scores were highest in participants with a combination of FEP and overweight/obesity, where the average
Study concept and design
Hajek, Spaniel, Kolenic.
Acquisition, analysis, or interpretation of data
All authors.
Drafting of the manuscript
Hajek.
Critical revision of the manuscript for important intellectual content
All authors.
Statistical analysis
Hajek, Franke, Alda, Uher, Kolenic, Hlinka.
Obtaining funding
Hajek, Spaniel, Pausova, Alda, Franke.
Administrative, technical, or material support
Franke, Kolenic, Capkova, Matejka, Spaniel.
Study supervision
Hajek, Spaniel.
Conflict of interest disclosures
None of the authors has any conflict of interest to disclose.
Funding/support
Dr. Hajek received funding for this study from Canadian Institute of Health Research (grant #341717), Brain and Behavior Research Foundation (Independent Investigator Award # 23412), the Ministry of Health of the Czech Republic (F.S., grant
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2022, NeuroImageCitation Excerpt :However, no significant association was observed with changes in LDL-cholesterol or triglycerides levels. This is consistent with the study from Kolenic et al. that did not find significant associations between delta age and lipid profiles (Kolenic et al., 2018). Our results are also consistent with our previous findings showing that the increase in grey matter and white matter densities after bariatric surgery are correlated with weight loss and improvement of the metabolic/inflammatory profiles (Michaud et al., 2020).
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These authors contributed equally.