Examination of retinal vascular trajectory in schizophrenia and bipolar disorder.
AIM
Evidence suggests microvascular dysfunction (wider retinal venules and narrower arterioles) in schizophrenia (SCZ) and bipolar disorder (BD). The vascular development is synchronous with neuronal development in the retina and brain. The retinal vessel trajectory is related to retinal nerve fiber layer thinning and cerebrovascular abnormalities in SCZ and BD and has not yet been examined. Hence, in this study we examined the retinal vascular trajectory in SCZ and BD in comparison with healthy volunteers (HV).


METHODS
Retinal images were acquired from 100 HV, SCZ patients, and BD patients, respectively, with a non-mydriatic fundus camera. Images were quantified to obtain the retinal arterial and venous trajectories using a validated, semiautomated algorithm. Analysis of covariance and regression analyses were conducted to examine group differences. A supervised machine-learning ensemble of bagged-trees method was used for automated classification of trajectory values.


RESULTS
There was a significant difference among groups in both the retinal venous trajectory (HV: 0.17 ± 0.08; SCZ: 0.25 ± 0.17; BD: 0.27 ± 0.20; P < 0.001) and the arterial trajectory (HV: 0.34 ± 0.15; SCZ: 0.29 ± 0.10; BD: 0.29 ± 0.11; P = 0.003) even after adjusting for age and sex (P < 0.001). On post-hoc analysis, the SCZ and BD groups differed from the HV on retinal venous and arterial trajectories, but there was no difference between SCZ and BD patients. The machine learning showed an accuracy of 86% and 73% for classifying HV versus SCZ and BD, respectively.


CONCLUSION
Smaller trajectories of retinal arteries indicate wider and flatter curves in SCZ and BD. Considering the relation between retinal/cerebral vasculatures and retinal nerve fiber layer thinness, the retinal vascular trajectory is a potential marker for SCZ and BD. As a relatively affordable investigation, retinal fundus photography should be further explored in SCZ and BD as a potential screening measure.
['Appaji|Abhishek|A|https://orcid.org/0000-0002-1978-6037', 'Nagendra|Bhargavi|B|', 'Chako|Dona M|DM|', 'Padmanabha|Ananth|A|', 'Jacob|Arpitha|A|', 'Hiremath|Chaitra V|CV|', 'Varambally|Shivarama|S|', 'Kesavan|Muralidharan|M|', 'Venkatasubramanian|Ganesan|G|', 'Rao|Shyam V|SV|', 'Webers|Carroll A B|CAB|', 'Berendschot|Tos T J M|TTJM|', 'Rao|Naren P|NP|https://orcid.org/0000-0001-9272-1873']
D000328:Adult D000465:Algorithms D001714:Bipolar Disorder D016022:Case-Control Studies D005260:Female D064368:Healthy Volunteers D006801:Humans D000069550:Machine Learning D008297:Male D012171:Retinal Vessels D012559:Schizophrenia D055815:Young Adult
retinal vascular trajectory schizophrenia bipolar disorder retinal nerve fiber layer machine learning