Distress symptoms of old age and mild cognitive impairment are two distinct dimensions in older adults without major depression: effects of adverse childhood experiences and negative life events
Tran-Chi, V.-L.; Maes, M.; Nantachai, G.; Hemrungrojn, S.; Solmi, M.; Tunvirachaisakul, C.
Show abstract
BackgroundStudies in old adults showed bidirectional interconnections between amnestic mild cognitive impairment (aMCI) and affective symptoms and that adverse childhood experiences (ACE) may affect both factors. Nevertheless, these associations may be confined to older adults with clinical depression. AimsTo delineate the relationship between clinical symptoms of aMCI and affective symptoms in older adults without major depression (MDD) or dysfunctions in activities of daily living (ADL). MethodsThis case-control study recruited 61 participants with aMCI (diagnosed using Petersens criteria) and 59 older adults without aMCI and excluded subjects with MDD and ADL dysfunctions. ResultsWe uncovered 2 distinct dimensions, namely distress symptoms of old age (DSOA) comprising anxiety, depression, perceived stress and neuroticism scores, and mild cognitive dysfunctions (mCoDy) comprising episodic memory test scores, and the total Mini-Mental State Examination (MMSE) and Montreal Cognitive Assessment (MoCA) scores. A large part of the variance (37.9%) in DSOA scores was explained by ACE, negative life events (health and financial problems), a subjective feeling of cognitive decline, and education (all positively). While ACE and NLE have a highly significant impact on the DSOA, they are not associated with the mCoDy scores. Cluster analysis showed that the diagnosis of aMCI is overinclusive because some subjects with DSOA symptoms may be incorrectly classified as aMCI. ConclusionsThe clinical impact is that clinicians should carefully screen older adults for DSOA after excluding MDD. DSOA might be misinterpreted as aMCI.
Matching journals
The top 5 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Impact of the COVID-19 pandemic on cognitive function in Japanese community-dwelling older adults in a class for preventing cognitive decline 96%
- Large household reduces dementia mortality 95%
- The Healthy Brain 9 (HB9): A New Instrument to Characterize Subjective Cognitive Decline, and Detect Anosognosia in Mild Cognitive Impairment 95%
Similar papers in this journal
- Self-reported word-finding complaints are associated with cerebrospinal fluid beta-amyloid and atrophy in cognitively normal older adults 96%
- Clinical Validation and Machine Learning Optimization of MyCog: A Self-Administered Cognitive Screener for Primary Care Settings 95%
- Association of Item-Level Responses to Cognitive Function Index with Tau Pathology and Hippocampal volume in The A4 Study 94%
Similar papers in this journal
- Validation of the German version of the Life-Space Assessment LSA-D 96%
- The Longitudinal Aging Study Amsterdam COVID-19 exposure index: a cross-sectional analysis of the impact of the pandemic on daily functioning of older adults 95%
- The New Therapeutics in Alzheimer’s Disease Longitudinal Cohort study (NTAD): study protocol 94%
Similar papers in this journal
- Cardiometabolic Indicators of Cognitive Impairment in The Cameron County Hispanic Cohort 94%
- Towards the development of a management protocol for Subjective Cognitive Decline: insights from a cross-sectional and longitudinal analyses of multimodal clinical data 94%
- Sex Differences in Cognitive Performance in Alzheimer's Disease: Insights from the ADAS-Cog-13 94%
Similar papers in this journal
- The impact of the COVID-19 pandemic on wellbeing and cognitive functioning of older adults 94%
- Dementia Risk and Dynamic Response to Exercise: Methodology for an Acute Exericise Clinical Trial 92%
- Effect of MIND Diet Intervention on Cognitive Performance and Brain Structure in Healthy Obese Women: A Randomized Controlled Trial 92%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.