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Schizophrenia is a complex and heterogeneous syndrome with high clinical and biological stratification. Identifying distinctive subtypes can improve diagnostic accuracy and help precise therapy. A key challenge for schizophrenia subtyping is understanding the subtype-specific biological underpinnings of clinical heterogeneity. This study aimed to investigate if the machine learning (ML)-based neuroanatomical and symptomatic subtypes of schizophrenia are associated.
A total of 314 schizophrenia patients and 257 healthy controls from four sites were recruited. Gray matter volume (GMV) and Positive and Negative Syndrome Scale (PANSS) scores were employed to recognize schizophrenia neuroanatomical and symptomatic subtypes using K-means and hierarchical methods, respectively.
Patients with ML-based neuroanatomical subtype-1 had focally increased GMV, and subtype-2 had widespread reduced GMV than the healthy controls based on either K-means or Hierarchical methods. In contrast, patients with symptomatic subtype-1 had severe PANSS scores than subtype-2. No differences in PANSS scores were shown between the two neuroanatomical subtypes; similarly, no GMV differences were found between the two symptomatic subtypes. Cohen’s Kappa test further demonstrated an apparent dissociation between the ML-based neuroanatomical and symptomatic subtypes (P > 0.05). The dissociation patterns were validated in four independent sites with diverse disease progressions (chronic vs. first episodes) and ancestors (Chinese vs. Western).
These findings revealed a replicable dissociation between ML-based neuroanatomical and symptomatic subtypes of schizophrenia, which provides a new viewpoint toward understanding the heterogeneity of schizophrenia.
Lung cancer risk factors, like tobacco smoking, are highly prevalent in patients with schizophrenia. Whether these patients have a higher risk of lung cancer remains unknown.
We aimed to investigate whether patients with schizophrenia have a higher incidence of lung cancer compared with general population, in a meta-analysis.
Eligible studies were searched from PubMed and EMBASE databases to identify cases of lung cancer in patients with schizophrenia and the general population. This meta-analysis utilised the random-effects model and prediction interval was used to calculate the heterogeneity of these eligible studies. We assessed the quality of evidence with the Grading of Recommendations Assessment, Development and Evaluation (GRADE) approach.
There were 12 studies, totalling 496 265 patients, included in this meta-analysis. The data showed that the baseline schizophrenia diagnosis was not associated with any changes in lung cancer incidence in the overall population, with a standardised incidence ratio of 1.11 (95% CI 0.90–1.37; P = 0.31), although there was a significant heterogeneity among these studies (I2 = 94%). Moreover, there was also a substantial between-study variance with wide prediction interval values (0.47–2.64). The data were consistent for both males and females.
Up-to-date evidence from epidemiological studies indicates the lack of certainty about the association between schizophrenia diagnosis and lung cancer incidence.
Previous studies have reported conflicting results on the association between schizophrenia and cancer mortality.
To summarise available evidence and quantify the association between schizophrenia and cancer mortality using meta-analysis.
We systematically searched literature in the PubMed and Embase databases. Risk estimates and 95% confidence intervals reported in individual studies were pooled using the DerSimonian–Laird random-effects model.
We included 19 studies in the meta-analysis. Among them, 15 studies reported standardised mortality ratios (SMRs) comparing patients with schizophrenia with the general population, and the pooled SMR was 1.40 (95% CI 1.29–1.52, P<0.001). The other four studies reported hazard ratios (HRs) comparing individuals with schizophrenia with those without schizophrenia; the pooled HR was 1.51 (95% CI 1.13–2.03, P = 0.006).
Patients with schizophrenia are at a significantly increased risk of cancer mortality compared with the general population or individuals without schizophrenia.
Auditory verbal hallucinations (AVHs) have been associated with deficits
in auditory and speech-related networks. However, the resting-state
cerebral blood flow (CBF) alterations specific to AVHs in schizophrenia
To explore AVH-related CBF alterations in individuals with
In total, 35 individuals with schizophrenia with AVHs, 41 individuals
with schizophrenia without AVHs and 50 controls underwent arterial spin
labelling magnetic resonance imaging. The CBF differences were voxel-wise
compared across the three groups.
We found AVH-specific CBF increase in the right superior temporal gyrus
and caudate, and AVH-specific CBF decrease in the bilateral occipital and
left parietal cortices. We also observed consistent CBF changes in both
schizophrenia subgroups (i.e. those with and without AVHs) including
decreased CBF in the bilateral occipital regions, the left lateral
prefrontal and insular cortices, and the right anterior cingulate cortex
and increased CBF in the bilateral lateral temporal regions and putamen,
the left middle cingulate cortex and the right thalamus.
The AVH-specific CBF increases in the auditory and striatal areas and CBF
reductions in the visual and parietal areas suggest that there exists a
CBF redistribution associated with AVHs.
Background: Despite the magnitude of dementia, little research on survival duration and prognosis of dementia has been reported in developing countries. This study was conducted to investigate survival times, identify related prognostic factors and construct a prognostic index (PI) for community-based dementia patients in Beijing, China.
Methods: This study is part of the 10/66 Dementia Research Group study in China. One hundred and thirty-seven dementia patients identified by 10/66 dementia criteria among 2162 participants and 137 referent subjects matched by age and sex were followed up for five years.
Results: Ninety-one (66.4%) dementia patients and 51 (37.2%) referent subjects died during the 5-year follow-up (p < 0.01). The median survival time of dementia patients was 4.2 years (95% CI: 3.8–4.6). Severity of dementia (severe/mild, HR: 8.765, 95% CI: 4.436–17.163), substantial disability (HR: 5.503, 95% CI: 3.017–8.135), co-morbidity (HR: 4.149, 95% CI: 2.254–7.736) and age (HR: 1.079, 95% CI: 1.048–1.110) were independent predictors of survival for patients with dementia. Using the PI calculated for each dementia patient, all dementia patients were classified into three groups: low, medium and high risk groups. The median survival times of each group were 5.2 years, 4.4 years and 1.5 years (p < 0.01), respectively.
Conclusions: Survival times of community-based dementia patients were significantly shorter than those of referent subjects. Severity of dementia, substantial disability, co-morbidity and age were independent predictors of survival. The PI derived from the four predictors can stratify the mortality risk and predict life expectancy for community-dwelled dementia patients, although further validation is needed.
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