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Major depressive disorder (MDD) is a clinically and biologically heterogeneous syndrome. Identifying discrete subtypes of illness with distinguishing neurobiological substrates and clinical features is a promising strategy for guiding personalised therapeutics.
This study aimed to identify depression subtypes with correlated patterns of functional network connectivity and clinical symptoms by clustering patients according to a weighted linear combination of both features in a relatively large, medication-naïve depression sample.
We recruited 115 medication-naïve adults with MDD and 129 matched healthy controls, and evaluated all participants with magnetic resonance imaging. We used regularised canonical correlation analysis to identify component mapping relationships between functional network connectivity and symptom profiles, and K-means clustering was used to define distinct subtypes of patients.
Two subtypes of MDD were identified: insomnia-dominated subtype 1 and anhedonia-dominated subtype 2. Subtype 1 was characterised by abnormal hyperconnectivity within the ventral attention network and sleep maintenance insomnia. Subtype 2 was characterised by abnormal hypoconnectivity in the subcortical and dorsal attention networks, and prominent anhedonia symptoms.
Our study identified two distinct subtypes of patients with specific neurobiological and clinical symptom profiles. These findings advance understanding of the biological and clinical heterogeneity of MDD, offering a pathway for defining categorical subtypes of illness via consideration of both biological and clinical features.
Our aim is to use the growth mixture model (GMM) to distinguish different trajectories of cognitive change in Chinese geriatric population and identify risk factors for cognitive decline in each subpopulation.
We obtained data from the Chinese Longitudinal Health Longevity Survey, using the Chinese Mini-Mental State Examination (C-MMSE) as a proxy for cognitive function. We applied the GMM to identify heterogeneous subpopulations and potential risk factors.
Our sample included 2850 older adults, 1387 (48.7%) male and 1463 (51.3%) female with age range of 62 to 108 (average of 72.3). Using GMM and best fit statistics, we identified two distinct subgroups in respect to their longitudinal cognitive function: cognitively stable (91.4%) group with 0.42 C-MMSE points decline per 3 years, and cognitively declining (8.6%) group with 4.76 C-MMSE points decline per 3 years. Of note, vision impairment and hearing impairment had the highest associations with cognitive decline, with stronger association found in the cognitively declining group than the cognitively stable group. Cognitive activities were protective in both groups. Diabetes was associated with cognitive decline in cognitive declining group. Physical activities, social activities and intake of fresh vegetables, fruits, and fish products were protective in cognitive stable group.
Using GMM, we identified heterogeneity in trajectories of cognitive change in Chinese elders. Moreover, we found risk factors specific to each subgroup, which should be considered in future studies.
To compare and validate neurocognitive tests in the Harmonized Cognitive Assessment Protocol (HCAP) for the China Health and Retirement Longitudinal Study (CHARLS), and to identify appropriate tests to be administered in future waves of CHARLS.
We recruited 825 individuals from the CHARLS sample and 766 subjects from hospitals in six provinces and cities in China. All participants were administered the HCAP-neurocognitive tests, and their informants were interviewed regarding the respondents’ functional status. Trained clinicians administered the Clinical Dementia Rating scale (CDR) to assess the respondents’ cognitive status independently.
The testing protocol took an average of 58 minutes to complete. Refusal rates for tests of general cognition, episodic memory, and language were less than 10%. All neurocognitive test scores significantly correlated with the CDR global score (correlation coefficients ranged from 0.139 to 0.641). The Mini-Mental State Examination (MMSE), the Health and Retirement Study (HRS) - telephone interview for cognitive status (TICS), community screening instrument for dementia (CSI-D) for respondent, episodic memory and language tests each accounted for more than 20% of the variance in global CDR score (p < 0.001) in bivariate tests. In the CHARLS subsample, age and education were associated with neuropsychological performance across most cognitive domains, and with functional status.
A brief set of the CHARLS-HCAP neurocognitive tests are feasible and valid to be used in the CHARLS sample and hospital samples. It could be applied in the future waves of the CHARLS study, and it allows estimating the prevalence of dementia in China through the population-based CHARLS.
The prevalence and factors associated with delays in help seeking for people with dementia in China are unknown.
Within 1,010 consecutively registered participants in the Clinical Pathway for Alzheimer's Disease in China (CPAD) study (NCT01779310), 576 persons with dementia (PWDs) and their informants reported the estimated time from symptom onset to first medical visit seeking diagnosis. Univariate analysis of general linear model was used to examine the potential factors associated with the delayed diagnosis seeking.
The median duration from the first noticeable symptom to the first visit seeking diagnosis or treatment was 1.77 years. Individuals with a positive family history of dementia had longer duration (p = 0.05). Compared with other types of dementia, people with vascular dementia (VaD) were referred for diagnosis earliest, and the sequence for such delays was: VaD < Alzheimer's disease (AD) < frontotemporal dementia (FTD) (p < 0.001). Subtypes of dementia (p < 0.001), family history (p = 0.01), and education level (p = 0.03) were associated with the increased delay in help seeking.
In China, seeking diagnosis for PWDs is delayed for approximately 2 years, even in well-established memory clinics. Clinical features, family history, and less education may impede help seeking in dementia care.
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