Jujiao Kang

BACKGROUND AND OBJECTIVES: To investigate the independent associations of social isolation and loneliness with incident dementia and to explore the potential neurobiological mechanisms. METHODS: genotype, diabetes, cancer, cardiovascular disease, and other), cognitive (speed of processing and visual memory), behavioral (current smoker, alcohol intake, and physical activity), and psychological (social isolation or loneliness, depressive symptoms, and neuroticism) factors measured at baseline were adjusted. Then, voxel-wise brainwide association analyses were used to identify gray matter volumes (GMVs) associated with social isolation and with loneliness. Partial least squares regression was performed to test the spatial correlation of GMV differences and gene expression using the Allen Human Brain Atlas. RESULTS: We included 462,619 participants (mean age at baseline 57.0 years [SD 8.1]). With a mean follow-up of 11.7 years (SD 1.7), 4,998 developed all-cause dementia. Social isolation was associated with a 1.26-fold increased risk of dementia (95% CI, 1.15-1.37) independently of various risk factors including loneliness and depression (i.e., full adjustment). However, the fully adjusted hazard ratio for dementia related to loneliness was 1.04 (95% CI, 0.94-1.16) and 75% of this relationship was attributable to depressive symptoms. Structural MRI data were obtained from 32,263 participants (mean age 63.5 years [SD 7.5]). Socially isolated individuals had lower GMVs in temporal, frontal, and other (e.g., hippocampal) regions. Mediation analysis showed that the identified GMVs partly mediated the association between social isolation at baseline and cognitive function at follow-up. Social isolation-related lower GMVs were related to underexpression of genes that are downregulated in Alzheimer disease and to genes that are involved in mitochondrial dysfunction and oxidative phosphorylation. DISCUSSION: Social isolation is a risk factor for dementia that is independent of loneliness and many other covariates. Social isolation-related brain structural differences coupled with different molecular functions also support the associations of social isolation with cognition and dementia. Social isolation may thus be an early indicator of an increased risk of dementia.

Developing a single-domain assay to identify individuals at high risk of future events is a priority for multi-disease and mortality prevention. By training a neural network, we developed a disease/mortality-specific proteomic risk score (ProRS) based on 1461 Olink plasma proteins measured in 52,006 UK Biobank participants. This integrative score markedly stratified the risk for 45 common conditions, including infectious, hematological, endocrine, psychiatric, neurological, sensory, circulatory, respiratory, digestive, cutaneous, musculoskeletal, and genitourinary diseases, cancers, and mortality. The discriminations witnessed high accuracies achieved by ProRS for 10 endpoints (e.g., cancer, dementia, and death), with C-indexes exceeding 0.80. Notably, ProRS produced much better or equivalent predictive performance than established clinical indicators for almost all endpoints. Incorporating clinical predictors with ProRS enhanced predictive power for most endpoints, but this combination only exhibited limited improvement when compared to ProRS alone. Some proteins, e.g., GDF15, exhibited important discriminative values for various diseases. We also showed that the good discriminative performance observed could be largely translated into practical clinical utility. Taken together, proteomic profiles may serve as a replacement for complex laboratory tests or clinical measures to refine the comprehensive risk assessments of multiple diseases and mortalities simultaneously. Our models were internally validated in the UK Biobank; thus, further independent external validations are necessary to confirm our findings before application in clinical settings.

BACKGROUND: Hearing impairment was recently identified as the most prominent risk factor for dementia. However, the mechanisms underlying the link between hearing impairment and dementia are still unclear. METHODS: We investigated the association of hearing performance with cognitive function, brain structure and cerebrospinal fluid (CSF) proteins in cross-sectional, longitudinal, mediation and genetic association analyses across the UK Biobank (N = 165,550), the Chinese Alzheimer's Biomarker and Lifestyle (CABLE, N = 863) study, and the Alzheimer's Disease Neuroimaging Initiative (ADNI, N = 1770) database. FINDINGS: Poor hearing performance was associated with worse cognitive function in the UK Biobank and in the CABLE study. Hearing impairment was significantly related to lower volume of temporal cortex, hippocampus, inferior parietal lobe, precuneus, etc., and to lower integrity of white matter (WM) tracts. Furthermore, a higher polygenic risk score (PRS) for hearing impairment was strongly associated with lower cognitive function, lower volume of gray matter, and lower integrity of WM tracts. Moreover, hearing impairment was correlated with a high level of CSF tau protein in the CABLE study and in the ADNI database. Finally, mediation analyses showed that brain atrophy and tau pathology partly mediated the association between hearing impairment and cognitive decline. INTERPRETATION: Hearing impairment is associated with cognitive decline, brain atrophy and tau pathology, and hearing impairment may reflect the risk for cognitive decline and dementia as it is related to bran atrophy and tau accumulation in brain. However, it is necessary to assess the mechanism in future animal studies. FUNDING: A full list of funding bodies that supported this study can be found in the Acknowledgements section.

Converging evidence increasingly implicates shared etiologic and pathophysiological characteristics among major psychiatric disorders (MPDs), such as schizophrenia (SZ), bipolar disorder (BD), and major depressive disorder (MDD). Examining the neurobiology of the psychotic-affective spectrum may greatly advance biological determination of psychiatric diagnosis, which is critical for the development of more effective treatments. In this study, ensemble clustering was developed to identify subtypes within a trans-diagnostic sample of MPDs. Whole brain amplitude of low-frequency fluctuations (ALFF) was used to extract the low-dimensional features for clustering in a total of 944 participants: 581 psychiatric patients (193 with SZ, 171 with BD, and 217 with MDD) and 363 healthy controls (HC). We identified two subtypes with differentiating patterns of functional imbalance between frontal and posterior brain regions, as compared to HC: (1) Archetypal MPDs (60% of MPDs) had increased frontal and decreased posterior ALFF, and decreased cortical thickness and white matter integrity in multiple brain regions that were associated with increased polygenic risk scores and enriched risk gene expression in brain tissues; (2) Atypical MPDs (40% of MPDs) had decreased frontal and increased posterior ALFF with no associated alterations in validity measures. Medicated Archetypal MPDs had lower symptom severity than their unmedicated counterparts; whereas medicated and unmedicated Atypical MPDs had no differences in symptom scores. Our findings suggest that frontal versus posterior functional imbalance as measured by ALFF is a novel putative trans-diagnostic biomarker differentiating subtypes of MPDs that could have implications for precision medicine.