The relationship between anxiety symptoms and disturbances in biological rhythms in patients with depression.

BACKGROUND: Biological rhythms denote the cyclical patterns of life activities anchored to a 24-hour cycle. Research shows that depression exhibits disturbances in biological rhythms. Yet, the relationship between these biological rhythms and concomitant anxiety symptoms is insufficiently investigated in structured clinical assessments. METHODS: This multicenter study, carried out in four Chinese hospitals, comprehensively examined the relationship between anxiety and disruptions in biological rhythms among patients with depression. The study encompassed 218 patients diagnosed with depression and 205 matched healthy controls. The Chinese version of the Biological Rhythms Interview of Assessment in Neuropsychiatry was utilized to evaluate the participants' biological rhythms, focusing on four dimensions: sleep, activity, social, and diet. RESULTS: In patients with depression, there is a significant positive correlation between the severity of anxiety symptoms and the disturbances in biological rhythms. The severity of anxiety and depression, along with the quality of life, are independently associated with disruptions in biological rhythms. The mediation model reveals that anxiety symptoms mediate the relationship between depressive symptoms and biological rhythms. CONCLUSION: This research highlights the role of anxiety within the spectrum of depressive disorders and the associated disturbances in biological rhythms. Our findings shed light on potential pathways towards more targeted preventive strategies and therapeutic interventions for individuals battling depression and anxiety.

Infectious disease control: from health security strengthening to health systems improvement at global level.

Since the twenty first century, the outbreaks of global infectious diseases have caused several public health emergencies of international concern, imposing an enormous impact on population health, the economy, and social development. The COVID-19 pandemic has once again exposed deficiencies in existing global health systems, emergency management, and disease surveillance, and highlighted the importance of developing effective evaluation tools. This article outlines current challenges emerging from infectious disease control from the perspective of global health, elucidated through influenza, malaria, tuberculosis, and neglected tropical diseases. The discordance among government actors and absent data sharing platforms or tools has led to unfulfilled targets in health system resilience and a capacity gap in infectious disease response. The current situation calls for urgent action to tackle these threats of global infectious diseases with joined forces through more in-depth international cooperation and breaking governance barriers from the purview of global health. Overall, a systematic redesign should be considered to enhance the resilience of health systems, which warrants a great need to sustain capacity-building efforts in emergency preparedness and response and raises an emerging concern of data integration in the concept of One Health that aims to address shared health threats at the human-animal-environment interface.

Association of Short-Term Exposure to PM2.5 with Blood Lipids and the Modification Effects of Insulin Resistance: A Panel Study in Wuhan.

Results of previous studies about the acute effects of fine particulate matter (PM2.5) on blood lipids were inconsistent. This study aimed to quantify the short-term effects of PM2.5 on blood lipids and estimate the modifying role of insulin resistance, reflected by the homeostasis model assessment of insulin resistance (HOMA-IR). From September 2019 to January 2020, the study recruited 70 healthy adults from Wuhan University for a total of eight repeated data collections. At each visit, three consecutive days were monitored for personal exposure to PM2.5, and then a physical examination was carried out on the fourth day. The linear mixed-effect models were operated to investigate the impact of PM2.5 over diverse exposure windows on blood lipids. With the median of the HOMA-IR 1.820 as the cut-off point, participants were assigned to two groups for the interaction analyses. We found the overall mean level (standard deviation, SD) of PM2.5 was 38.34 (18.33) µg/m3. Additionally, with a 10 µg/m3 rise in PM2.5, the corresponding largest responses in triglyceride (TG), total cholesterol (TC), low-density lipoprotein cholesterol (LDL-C), as well as high-density lipoprotein cholesterol (HDL-C), were −0.91% (95% confidence interval (CI): −1.63%, −0.18%), −0.33% (95% CI: −0.64%, −0.01%,), −0.94% (95% CI: −1.53%, −0.35%), and 0.67% (95% CI: 0.32%, 1.02%), respectively. The interaction analyses revealed that a significantly greater reduction in the four lipids corresponded to PM2.5 exposure when in the group with the lower HOMA-IR (<1.820). In conclusion, short-term PM2.5 exposure over specific time windows among healthy adults was associated with reduced TG, TC, as well as LDL-C levels, and elevated HDL-C. Additionally, the association of PM2.5−lipids may be modulated by insulin resistance.

Divergent and Convergent Imaging Markers Between Bipolar and Unipolar Depression Based on Machine Learning.

Distinguishing bipolar depression (BD) from unipolar depression (UD) based on symptoms only is challenging. Brain functional connectivity (FC), especially dynamic FC, has emerged as a promising approach to identify possible imaging markers for differentiating BD from UD. However, most of such studies utilized conventional FC and group-level statistical comparisons, which may not be sensitive enough to quantify subtle changes in the FC dynamics between BD and UD. In this paper, we present a more effective individualized differentiation model based on machine learning and the whole-brain "high-order functional connectivity (HOFC)" network. The HOFC, capturing temporal synchronization among the dynamic FC time series, a more complex "chronnectome" metric compared to the conventional FC, was used to classify 52 BD, 73 UD, and 76 healthycontrols (HC). We achieved a satisfactory accuracy (70.40%) in BD vs. UD differentiation. The resultant contributing features revealed the involvement of the coordinated flexible interactions among sensory (e.g., olfaction, vision, and audition), motor, and cognitive systems. Despite sharing common chronnectome of cognitive and affective impairments, BD and UD also demonstrated unique dynamic FC synchronization patterns. UD is more associated with abnormal visual-somatomotor inter-network connections, while BD is more related to impaired ventral attention-frontoparietal inter-network connections. Moreover, we found that the illness duration modulated the BD vs. UD separation, with the differentiation performance hampered by the secondary disease effects. Our findings suggest that BD and UD may have divergent and convergent neural substrates, which further expand our knowledge of the two different mental disorders.

How long-term air pollution and its metal constituents affect type 2 diabetes mellitus prevalence? Results from Wuhan Chronic Disease Cohort.

BACKGROUND: Epidemiological evidence linking type 2 diabetes mellitus (T2DM) with air pollution is discrepant and most are restricted to the influences of air-pollutant mass concentration. This study aims to explore the effects of long-term exposure to air pollution and its metal constituents on T2DM prevalence in China. METHODS: We used data on 10,253 adult residents from the baseline survey of Wuhan Chronic Disease Cohort in 2019. Ambient PM2.5, PM10 and NO2 exposure were estimated at residences based on Chinese Air Quality Reanalysis Dataset. Concentrations of 10 metal constituents were measured by 976 PM2.5 filter samples collected from four monitoring stations. Logistic regression models were employed to examine associations of T2DM prevalence with 3-year mean concentrations of each air pollutant and PM2.5 metal constituents prior to the baseline investigation. RESULTS: A total of 673 T2DM cases (6.6%) were identified. The 3-year mean exposures to PM2.5, PM10 and NO2 were 50.89 µg/m3, 82.86 µg/m3, and 39.79 µg/m3, respectively. And interquartile range (IQR) of 10 metals in PM2.5 varied from 0.03 ng/m3 to 78.30 ng/m3. For 1 µg/m3 increment in PM2.5, PM10 and NO2, the odds of T2DM increased by 7.2% (95%CI: 1.026, 1.136), 3.1% (95%CI: 1.013, 1.050), and 2.1% (95%CI: 1.005, 1.038) after adjusting for potential confounders. Cd and Sb in PM2.5 were significant risk factors to T2DM with odds ratios of 1.350 (95%CI: 1.089, 1.673) and 1.389 (95%CI: 1.164, 1.658) for per IQR increase, respectively. Stratification analyses indicated that males and those aged ≥45 years were more susceptive to long-term air pollution. CONCLUSIONS: Long-term exposure to PM2.5, PM10 and NO2 increased T2DM prevalence in a Wuhan population, especially for men and middle-aged and elderly people. Moreover, T2DM was significantly associated with Cd and Sb in PM2.5. Further research to validate these results and to clarify the underlying mechanisms is warranted.

The association between diurnal temperature range and clinic visits for upper respiratory tract infection among college students in Wuhan, China.

The effects of daily mean temperature on health outcomes have been discussed in many previous studies, but few have considered the adverse impacts on upper respiratory tract infection (URTI) due to variance of temperature in one day. Diurnal temperature range (DTR) was a novel indicator calculated as maximum temperature minus minimum temperature on the same day. In this study, generalized additive model (GAM) with quasi-Poisson distribution was used to investigate the association between DTR and the number of daily outpatient visits for URTI among college students. Data about meteorological factors and air pollutants were provided by Hubei Meteorological Bureau and Wuhan Environmental Protection Bureau, respectively. Outpatient visits data were collected from the Hospital of Wuhan University from January 1, 2016, to December 31, 2018. Short-term exposure to DTR was associated with the increased risk of outpatient for URTI among all college students. Per 1 °C increased in DTR was associated with 0.73% (95%CI: 0.24, 1.21) increased in outpatient visits of all college students for URTI at lag 0 day. The greatest effect values were observed in males [1.35% (95%CI: 0.33,2.39)] at lag 0-6 days, and in females [0.86% (95%CI: 0.24, 1.49)] at lag 0-1 days. DTR had more adverse health impact in autumn and winter. Public health departments should consider the negative effect of DTR to formulate more effective prevention and control measures for protecting vulnerable people.

Associations of long-term exposure to ambient air pollutants with metabolic syndrome: The Wuhan Chronic Disease Cohort Study (WCDCS).

BACKGROUND: Evidence on the associations between long-term exposure to ambient air pollutants (including particle with aerodynamic diameter ≤10 µm (PM10), particle with aerodynamic diameter ≤2.5 µm (PM2.5), ozone (O3), nitrogen dioxide (NO2), and sulfur dioxide (SO2)) and prevalence of metabolic syndrome (MetS) remains inconclusive. This study aimed to determine the associations based on a case-control study nested in the Wuhan Chronic Disease Cohort study (WCDCS), a population-based study with baseline survey in 2019. METHODS: A total of 10,253 residents living in Wuhan were recruited. The 3-year average concentrations of main pollutants (PM10, PM2.5, O3, NO2, and SO2) at residences prior to the survey date were estimated to evaluate the long-term exposures. The generalized linear mixed models were used to investigate the changes in MetS prevalence by an IQR increases in each air pollutant exposure concentrations. Interaction effects between air pollutants and demographic, lifestyle, and dietary factors on MetS were evaluated by including an interactive item in the main model. RESULTS: The prevalence of MetS in Wuhan was 9.8%, and the 3-year exposure concentrations of PM10, PM2.5, O3, NO2, and SO2 were 84.1 µg/m3, 50.5 µg/m3, 55.7 µg/m3, 46.0 µg/m3, and 9.4 µg/m3, respectively. Higher PM10, PM2.5 and O3 exposure concentrations were associated with an elevated MetS prevalence (e.g. an IQR increase in PM2.5, OR = 1.193, 95% confidence intervals (95%CIs): 1.028, 1.385; for O3, OR = 1.074, 95%CIs: 1.025, 1.124), whereas NO2, and SO2 were negatively or insignificant correlated with odds of Mets (e.g. an IQR increase in NO2, OR = 0.865, 95%CIs: 0.795, 0.941). Males, smokers, alcohol drinkers and individuals who intake fruits occasionally exposure to PM10 and PM2.5 were found had a higher risk of developing MetS. CONCLUSIONS: Long-term exposure to higher concentrations of ambient air pollutants may elevate the prevalence of MetS in populations in Central China. Susceptible individuals especially those with unhealthy lifestyles had a higher risk for MetS.

Short-term exposure to ambient air pollution and type 2 diabetes mortality: A population-based time series study.

Acute health effects of air pollution on diabetes risk have not been fully studied in developing countries and the results remain inconsistent. This study aimed to investigate the association between short-term exposure to ambient air pollution and Type 2 diabetes mellitus (T2DM) mortality in China. Data on T2DM mortality from 2013 to 2019 were obtained from the Cause of Death Reporting System (CDRS) of Wuhan Center for Disease Control and Prevention. Air pollution data for the same period were collected from 10 national air quality monitoring stations of Wuhan Ecology and Environment Institute, including daily average PM2.5, PM10, SO2, and NO2. Meteorological data including daily average temperature and relative humidity were collected from Wuhan Meteorological Bureau. Generalized additive models (GAM) based on quasi-Poisson distribution were applied to evaluate the association between short-term exposure to air pollution and daily T2DM deaths. A total of 9837 T2DM deaths were recorded during the study period in Wuhan. We found that short-term exposure to PM2.5, PM10, SO2, and NO2 were positively associated with T2DM mortality, and gaseous pollutants appeared to have greater effects than particulate matter (PM). For the largest effect, per 10 µg/m3 increment in PM2.5 (lag 02), PM10 (lag 02), SO2 (lag 03), and NO2 (lag 02) were significantly associated with 1.099% (95% CI: 0.451, 1.747), 1.016% (95% CI: 0.517, 1.514), 3.835% (95% CI: 1.480, 6.189), and 1.587% (95% CI: 0.646, 2.528) increase of daily T2DM deaths, respectively. Stratified analysis showed that females or elderly population aged 65 and above were more susceptible to air pollution exposure. In conclusion, short-term exposure to air pollution was significantly associated with a higher risk of T2DM mortality. Further research is required to verify our findings and elucidate the underlying mechanisms.

Acute effects of ambient air pollution on clinic visits of college students for upper respiratory tract infection in Wuhan, China.

Ambient air pollutants have been linked to adverse health outcomes, but evidence is still relatively rare in college students. Upper respiratory tract infection (URTI) is a common disease of respiratory system among college students. In this study, we assess the acute effect of air pollution on clinic visits of college students for URTI in Wuhan, China. Data on clinic visits due to URTI were collected from Wuhan University Hospital, meteorological factors (including daily temperature and relative humidity) provided by Wuhan Meteorological Bureau, and air pollutants by Wuhan Environmental Protection Bureau. In the present study, generalized additive model with a quasi-Poisson distribution link function was used to examine the association between ambient air pollutants (fine particulate matter (PM2.5), particulate matter (PM10), sulfur dioxide (SO2), nitrogen dioxide (NO2), and ozone (O3)) and the daily number of clinic visits of college students for URTI at Wuhan University Hospital in Wuhan, China. In the meantime, the model was adjusted for the confounding effects of long-term trends, seasonality, day of the week, public holidays, vacation, and meteorological factors. The best degrees of free in model were selected based on AIC (Akaike Information Criteria). The effect modification by gender was also examined. A total of 44,499 cases with principal diagnosis of URTI were included from January 1, 2016, to December 31, 2018. In single-pollutant models, the largest increment of URTI visits were found at lag 0 day in single-day lags, and the effect values in cumulative lags were greater than those in single-day lags. PM2.5 (0.74% (95%CI: 0.05, 1.44)) at lag 0 day, PM10 (0.61% (95%CI: 0.12, 1.11)) and O3 (1.01% (95%CI: 0.24, 1.79)) at lag 0-1 days, and SO2 (9.18% (95%CI: 3.27, 15.42)) and NO2 (3.40% (95% CI:1.64, 5.19)) at lag 0-3 days were observed to be strongly and significantly associated with clinic visits for URTI. PM10 and NO2 were almost still significantly associated with URTI after controlling for the other pollutants in our two-pollutant models, where the effect value of SO2 after inclusion of O3 appeared to be the largest and the effects of NO2 were also obvious compared with the other pollutants. Subgroups analysis demonstrated that males were more vulnerable to PM10 and O3, while females seemed more vulnerable to exposure to SO2 and NO2. This study implied that short-term exposure to ambient air pollution was associated with increased risk of URTI among college students at Wuhan University Hospital in Wuhan, China. And gaseous pollutants had more negative health impact than solid pollutants. SO2 and NO2 were the major air pollutants affecting the daily number of clinic visits on URTI, to which females seemed more vulnerable than males.

Ambient air pollution and cerebrovascular disease mortality: an ecological time-series study based on 7-year death records in central China.

Most studies of short-term exposure to ambient air pollution and cerebrovascular diseases focused on specific stroke-related outcomes, and results were inconsistent due to data unavailability and limited sample size. It is unclear yet how ambient air pollution contributes to the total cardiovascular mortality in central China. Daily deaths from cerebrovascular diseases were obtained from the Disease Surveillance Point System (DSPs) of Wuhan Center for Disease Control and Prevention during the period from 2013 to 2019. Air pollution data were obtained from Wuhan Ecology and Environment Institute from 10 national air quality monitoring stations, including average daily PM2.5, PM10, SO2, NO2, and O3. Average daily temperature and relative humidity were obtained from Wuhan Meteorological Bureau. We performed a Poisson regression in generalized additive models (GAM) to examine the association between ambient air pollution and cerebrovascular disease mortality. We observed a total of 84,811 deaths from cerebrovascular diseases from 1 January 2013 to 31 December 2019 in Wuhan. Short-term exposure to PM2.5, PM10, SO2, and NO2 was positively associated with daily deaths from cerebrovascular diseases, and no significant association was found for O3. The largest effect on cerebrovascular disease mortality was found at lag0 for PM2.5 (ERR: 0.927, 95% CI: 0.749-1.105 per 10 µg/m3) and lag1 for PM10 (ERR: 0.627, 95% CI: 0.493-0.761 per 10 µg/m3), SO2 (ERR: 2.518, 95% CI: 1.914, 3.122 per 10 µg/m3), and NO2 (ERR: 1.090, 95% CI: 0.822-1.358 per 10 µg/m3). The trends across lags were statistically significant. The stratified analysis demonstrated that females were more susceptible to SO2 and NO2, while elder individuals aged above 65 years old, compared with younger people, suffered more from air pollution, especially from SO2. Short-term exposure to PM2.5, PM10, SO2, and NO2 were significantly associated with a higher risk of cerebrovascular disease mortality, and elder females seemed to suffer more from air pollution. Further research is required to reveal the underlying mechanisms.

The concurrent disturbance of dynamic functional and structural brain connectome in major depressive disorder: the prefronto-insular pathway.

BACKGROUND: Robust evidence has shown that abnormal function networks, particularly the salience network (SN), are observed in depressed patients. Although white matter structural connectivity may predict time-varying functional connectivity, including symptom phenotype, in psychiatric disorders, there is still a gap in elucidating the concurrent dynamic functional and structural connectivity profiles of the SN in depressed patients. METHODS: We measured static and dynamic functional connectivity (FC) of the SN using resting-state fMRI BOLD time series in 76 subjects (21 with major depressive disorder (MDD), 27 with bipolar depression (BD), and 28 healthy controls (HC)). Hamilton Depression Scale total score was used to measure depression severity. Furthermore, we investigated the concurrent structural connectivity using diffusion kurtosis imaging (DKI)-based tractography. RESULTS: Our findings suggested that in the presence of MDD, both structural and dynamic (but not static) FC were reduced in the SN, particularly affecting the left prefronto-insular pathways (L.aPFC-L.insula). MDD patients showed decreased connectivity variability within the SN compared with HC. The aberrant dynamic FC in the prefronto-insular pathways of the SN related to severity of depressive symptoms in MDD. Furthermore, compared with BD patients, those with MDD showed significantly decreased dynamic FC in the left prefronto-parietal system (L.aPFC-lateral parietal cortex). LIMITATIONS: The generalizability of our findings is, to some extent, constrained by the small sample size. CONCLUSIONS: The integrity of SN connectivity, particularly the prefronto-insular pathway, appears to be a crucial signature of MDD. The perturbed dynamic interaction of SN with prefrontal regions may underlie the clinical severity in depressed patients.

The Metabolic Factor Kynurenic Acid of Kynurenine Pathway Predicts Major Depressive Disorder.

Background: Metabolic factors in the kynurenine pathway (KP) have been widely accepted as being a major mechanism in Major Depressive Disorder (MDD). However, the effects of these metabolites on the degree and pattern of MDD are still poorly understood, partly due to the elusiveness of the level of metabolites when diagnosing depression. This study aimed to explore a novel diagnostic method analyzing peripheral blood with mass spectrometry to assess metabolites from KP in patients with MDD and Bipolar Depression (BD). Methods: Thirty-three patients with MDD, 20 patients with BD, and 23 healthy control participants were enrolled Metabolic factors of KP from plasma including tryptophan (TRP), kynurenine (KYN), kynurenic acid (KYNA), and quinolinic acid (QUIN) were analyzed by UPLC-3Q-MS, and levels compared across three groups. Correlation between HAMD scores and metabolite levels conducted. Receiver operating characteristic (ROC) curve was used to determine the diagnostic value of metabolic factors in MDD. Results: Levels of KYNA, QUIN, KYNA/QUIN, and KYNA/KYN were statistically different across the three groups (P < 0.05); HAMD scores and TRP, KYN, KYNA/QUIN levels were negatively correlated in the MDD group (r = -0.633, -0.477, -0.418, P < 0.05); Accuracy of KYNA diagnosing MDD was 82.5% with the optimal diagnostic value being 15.48 ng/ml. Diagnostic accuracy was increased to 83.6% when KYNA and QUIN levels were used in combination. Conclusion: This results indicate that metabolic factors of KP play a crucial role in the occurrence and development of MDD, supporting the metabolic imbalance hypothesis of MDD. Furthermore, our study also provides a new diagnostic method to study MDD based on plasma KYNA level, and suggests that KYNA would be a potential biomarker in diagnosing depression patients.

Somatic symptoms vary in major depressive disorder in China.

PURPOSE: This study aimed to investigate the clinical characteristics of somatic symptoms of patients in China who suffer from major depressive disorder (MDD). METHOD: 3273 patients who met the diagnostic criteria of Diagnostic and Statistical Manual of Mental Disorders (DSM-IV) MDD were recruited from 16 general hospitals and 16 mental health centers in China. Physicians and patients completed complementary customized depression disorder symptomatology questionnaires assessing the clinical characteristics of patients with MDD. RESULT: 1. In this study we analyzed physician-recorded data. The major somatic symptoms in patients with MDD in China were insomnia (64.6%), pre-verbal physical complaints (46.9%), weight loss (38.5%), low appetite (37.6%), circulatory system complaints (31.3%), headache (31.3%), hyposexuality (31.0%), gastrointestinal symptom complaints (29.6%), and respiratory system complaints (29.6%). 2. Compared with MDD patients who sought medical help from mental health centers, MDD patients who sought medical help from general hospitals were more likely to suffer from urinary system complaints, headache, sensory system complaints, trunk pain, and nervous system complaints. A lower prevalence rate of insomnia and hyposexuality was also observed among MDD patients who visited general hospitals (p < .05). 3. Patients aged from 40 to 54 had the highest probability of pre-verbal physical complaints, respiratory system complaints, trunk pain, hyposexuality, limb pain and other pain conditions, while patients over 55 years of age had the lowest prevalence respiratory system complaints, hyposexuality, and other pain conditions, and they also had the highest rate of low appetite and insomnia. 4. Female patients appeared to exhibit higher rates of pre-verbal physical complaints, low appetite, and insomnia than male patients, but had fewer urinary systems complaints than male patients (p < .05). CONCLUSION: The major somatic symptoms in patients with MDD in China are insomnia, pre-verbal physical complaints, weight loss, low appetite, circulatory system complaints, headache, hyposexuality, gastrointestinal system complaints, and respiratory system complaints. These symptoms vary by the type of medical setting to which patients present, and well as by age, and gender.

Aberrant Neural Activity in Patients With Bipolar Depressive Disorder Distinguishing to the Unipolar Depressive Disorder: A Resting-State Functional Magnetic Resonance Imaging Study.

This study aims to explore the intrinsic patterns of spontaneous activity of bipolar depression (BD) patients by analyzing the fractional amplitude of low frequency fluctuation (fALFF) that help differentiate BD from unipolar depressive disorder(UD). Twenty eight patients with BD, 47 patients with UD and 29 healthy controls were enrolled to receive the resting-state functional magnetic resonance imaging (rs-fMRI) scans. The group differences of fALFF values were calculated among three groups. In addition, the correlations between the clinical variables and mfALFF values were estimated. The brain regions with activation discrepancies among three groups are located in precuneus, the left middle temporal gyrus (MTG) and left inferior parietal lobe (IPL) and lingual gyrus. Compared with HC group, BD group shows decreased fALFF in precuneus, the left IPL and increased fALFF in lingual gyrus remarkably; UD group shows significantly decreased fALFF in precuneus, the left MTG and the left IPL. On the contrast of patients with UD, patients with BD have significantly increased fALFF value in the left precuneus, the left MGT and lingual gyrus. Furthermore, a negative correlation is found between the mfALFF values in precuneus and the scores of cognitive impairment factor in the UD group. The similar pattern of intrinsic activity in PCC suggests depressive state-dependent change. The aberrant patterns of intrinsic activity in precuneus, the IPL and lingual gyrus might be provide quantitative nodes that help to conduct further study for better distinguishing between BD and UD.

Dysfunction of Cognition Patterns Measured by MATRICS Consensus Cognitive Battery (MCCB) among First Episode Schizophrenia Patients and Their Biological Parents.

BACKGROUND: Schizophrenia is characterized by abnormal perception, thinking, emotions, and behaviors. Cognitive dysfunction is acknowledged as one of the most pivotal symptoms in schizophrenia. In addition to positive or negative symptoms, which had been proposed by Gallhofer in the early 1970s, schizophrenia patients suffered from cognitive impairments as well. Many studies show that there is genetic susceptibility in the first grading kinship of patients with schizophrenia. Patients with schizophrenia have cognitive impairment not only in the acute phase but also in the stable phase. Studies also show that the healthy first-grading relatives of patients with schizophrenia suffer from cognitive defects. However, there is still a lack of studies about the cognitive features of biological parents of those with schizophrenia. In this study, we speculate the biological parents of schizophrenia patients have specific cognitive dysfunction. And we explore the patterns of cognition among both schizophrenia patients and their biological parents using the Chinese version of MATRICS Consensus Cognitive Battery (MCCB). AIMS: Cognitive features of patients with schizophrenia might be affected by the cognition mode of patients' biological parents. The dysfunctional cognitive patterns need to be characterized among the patients with schizophrenia and their parents. METHODS: We applied the MATRICS Consensus Cognitive Battery (MCCB, a novel measurement tool) to evaluate the cognitive function of 29 first-episode patients with schizophrenia (meeting ICD-10 diagnostic criteria for schizophrenia, aged between 17-45 years old), 58 cases of biological parents of schizophrenia patients (aged between 40-70 years old) and 46 healthy controls (aged between 40-70 years old). Furthermore, we explored the relationship between the cognitive dysfunction in patients with schizophrenia and their biological parents. All data were analyzed using SPSS18.0 statistical software. RESULTS: 1) Male patients with schizophrenia had obvious cognitive defects in six domains of cognitive function as measured by the MCCB (all except the social cognition domain) compared to their male parents. Female patients showed lower ability on both working memory and problem reasoning than their female parents. 2) The significant differences of both working memory and reasoning problems also existed between the patients' fathers and matched healthy controls. 3) Patients' mothers didn't show any significant difference on the problem reasoning domain compared with healthy controls. However, the visual learning domain appeared abnormal in patients' mothers compared with healthy controls. CONCLUSION: There are six dimensions of cognitive impairments in both first-episode schizophrenia patients and their biological parents. Compared with healthy controls, patients' biological parents have conspicuous dysfunction in domains of working memory, problem reasoning and visual learning as well. Further study is needed to explore the underlying mechanisms of similar cognitive dysfunction between first-episode schizophrenia patients and their biological parents.