Parasympathetic activity as a potential biomarker of negative symptoms in patients with schizophrenia

Huang, Wei-Chia; Liu, Wei‐Shih; Chen, Tzu‐ting; Chen, Wen‐Hao; Huang, Wei‐Lieh

doi:10.1111/appy.12392

Cited by 6 publications

(3 citation statements)

References 74 publications

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“…In some cases, an inverse relationship has been found with the negative symptoms (Chung et al, 2013;Huang et al, 2020;Quintana et al, 2016) and others have noted no relationship at all (Benjamin et al, 2021). The present study also identified an inverse relationship (i.e., negative symptoms were positively correlated with heart function and negatively correlated with heart 50 structure) suggesting that increased social withdrawal related to increased left ventricular functioning and structure, as well as lower heart rate and diastolic blood pressure.…”

Section: Anterior Cingulate Cortexsupporting

confidence: 58%

Cardiac Dysfunction in People with Schizophrenia: Linking Clinical Outcome, the Heart, and the Brain

Nyveld¹

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While cardiovascular disease remains the primary cause of mortality in people with schizophrenia, the link between cardiac dysfunction and clinical outcome remains poorly understood. The current study is an exploratory analysis, where a total of thirteen participants with schizophrenia were included to examine clinical outcome, cardiac structure and function, and brain volume of the anterior cingulate cortex (ACC) as measured by magnetic resonance imaging, where those participants achieving remission (N = 7) had better global functioning, with fewer positive and negative symptoms on PANSS and SNS. Cardiac function and structure did not differ between remitted and non-remitted groups; but, those not in remission had significantly less volume in the left ACC. As a whole sample, no significant relationships were found between ACC volume and clinical symptoms; however, inverse relationships between social withdrawal and left ventricular end diastolic volume (LVEDV), left ventricular end systolic volume (LVESV), left ventricle stroke volume (LVSV), left-ventricular concentricity (a ratio of left ventricular EDV/mass), and right ventricular end systolic volume (RVESV) were observed. Moreover, bilateral ACC volume was significantly correlated with higher LVEDV, LVSV, RVSV, and lower LV Concentricity, whereas only the left ACC was significantly related to higher RVEDV. Of note, all participants had significantly increased left ventricular concentricity compared to the general population. These results support that clinical outcome is indeed related to brain volume but may not be related to cardiac measures of function and structure in people with schizophrenia, but more research is required to verify these conclusions and to further explore the mechanisms of cardiac dysfunction.

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Section: Anterior Cingulate Cortexsupporting

confidence: 58%

Cardiac Dysfunction in People with Schizophrenia: Linking Clinical Outcome, the Heart, and the Brain

Nyveld¹

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“…The negative symptom domain of PANSS, which largely focuses on distortions in emotional, social, and thinking processes, has been inversely related to HRV parameters in groups of patients with schizophrenia ( 28 , 31 , 34 ). Our study confirms this relation in long term monitoring.…”

Section: Discussionmentioning

confidence: 99%

Smartwatch digital phenotypes predict positive and negative symptom variation in a longitudinal monitoring study of patients with psychotic disorders

Kalisperakis

Karantinos²,

Lazaridi³

et al. 2023

Front. Psychiatry

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IntroductionMonitoring biometric data using smartwatches (digital phenotypes) provides a novel approach for quantifying behavior in patients with psychiatric disorders. We tested whether such digital phenotypes predict changes in psychopathology of patients with psychotic disorders.MethodsWe continuously monitored digital phenotypes from 35 patients (20 with schizophrenia and 15 with bipolar spectrum disorders) using a commercial smartwatch for a period of up to 14 months. These included 5-min measures of total motor activity from an accelerometer (TMA), average Heart Rate (HRA) and heart rate variability (HRV) from a plethysmography-based sensor, walking activity (WA) measured as number of total steps per day and sleep/wake ratio (SWR). A self-reporting questionnaire (IPAQ) assessed weekly physical activity. After pooling phenotype data, their monthly mean and variance was correlated within each patient with psychopathology scores (PANSS) assessed monthly.ResultsOur results indicate that increased HRA during wakefulness and sleep correlated with increases in positive psychopathology. Besides, decreased HRV and increase in its monthly variance correlated with increases in negative psychopathology. Self-reported physical activity did not correlate with changes in psychopathology. These effects were independent from demographic and clinical variables as well as changes in antipsychotic medication dose.DiscussionOur findings suggest that distinct digital phenotypes derived passively from a smartwatch can predict variations in positive and negative dimensions of psychopathology of patients with psychotic disorders, over time, providing ground evidence for their potential clinical use.

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“…Besides an increase in the variance of HRV was related to an increase in negative psychopathology. The negative symptom domain of PANSS, which largely focuses on distortions in emotional, social and thinking processes, has been inversely related to HRV parameters in groups of patients with schizophrenia 23,27,31 . Our study con rms this relation within each patient in long term monitoring.…”

Section: Discussionmentioning

confidence: 99%

Smartwatch digital phenotypes predict positive and negative symptom variation in a longitudinal monitoring study of patients with psychotic disorders

Smyrnis

Kalipserakis²,

karantinos³

et al. 2022

Preprint

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Monitoring biometric data using smartwatches (digital phenotypes) provides a novel approach for quantifying behavior in patients with psychiatric disorders. We tested whether such digital phenotypes predict changes in psychopathology of patients with psychotic disorders. We continuously monitored digital phenotypes from 35 patients (20 with schizophrenia and 15 with bipolar spectrum disorders) using a commercial smartwatch for a period of up to 14 months. These included 5-minute measures of fine motor activity from an accelerometer (FMA), average Heart Rate (HRA) and heart rate variability (HRV) from a plethysmography-based sensor, gross motor activity (GMA) measured as number of total steps per day and sleep/wake ratio (SWR). A self-reporting questionnaire (IPAQ) assessed weekly physical activity. After pooling phenotype data, their monthly mean and variance was correlated within each patient with psychopathology scores (PANSS) assessed monthly. Our results indicate that increased HRA during wakefulness and sleep correlated with increases in positive psychopathology. Besides, decreased HRV and increase in its monthly variance correlated with increases in negative psychopathology. Self-reported physical activity did not correlate with changes in psychopathology. These effects were independent from a series of demographic and clinical (including diagnosis) variables as well as changes in antipsychotic medication dose. Our findings suggest that distinct digital phenotypes derived passively from a smartwatch can predict variations in positive and negative dimensions of psychopathology of patients with psychotic disorders, over time, providing ground evidence for their potential clinical use.

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Parasympathetic activity as a potential biomarker of negative symptoms in patients with schizophrenia

Cited by 6 publications

References 74 publications

Cardiac Dysfunction in People with Schizophrenia: Linking Clinical Outcome, the Heart, and the Brain

Cardiac Dysfunction in People with Schizophrenia: Linking Clinical Outcome, the Heart, and the Brain

Smartwatch digital phenotypes predict positive and negative symptom variation in a longitudinal monitoring study of patients with psychotic disorders

Smartwatch digital phenotypes predict positive and negative symptom variation in a longitudinal monitoring study of patients with psychotic disorders

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