The Association Between Thyroid Diseases and Alzheimer's Disease in a National Health Screening Cohort in Korea

In Search for Low-Molecular-Weight Ligands of Human Serum Albumin That Affect Its Affinity for Monomeric Amyloid β Peptide

An imbalance between production and excretion of amyloid β peptide (Aβ) in the brain tissues of Alzheimer's disease (AD) patients leads to Aβ accumulation and the formation of noxious Aβ oligomers/plaques. A promising approach to AD prevention is the reduction of free Aβ levels by directed enhancement of Aβ binding to its natural depot, human serum albumin (HSA). We previously demonstrated the ability of specific low-molecular-weight ligands (LMWLs) in HSA to improve its affinity for Aβ. Here we develop this approach through a bioinformatic search for the clinically approved AD-related LMWLs in HSA, followed by classification of the candidates according to the predicted location of their binding sites on the HSA surface, ranking of the candidates, and selective experimental validation of their impact on HSA affinity for Aβ. The top 100 candidate LMWLs were classified into five clusters. The specific representatives of the different clusters exhibit dramatically different behavior, with 3- to 13-fold changes in equilibrium dissociation constants for the HSA-Aβ40 interaction: prednisone favors HSA-Aβ interaction, mefenamic acid shows the opposite effect, and levothyroxine exhibits bidirectional effects. Overall, the LMWLs in HSA chosen here provide a basis for drug repurposing for AD prevention, and for the search of medications promoting AD progression.

Comorbidities in patients with vascular dementia and Alzheimer's disease with Neuropsychiatric symptoms

INTRODUCTION

This study aimed to examine baseline risk factors in Alzheimer's Disease (AD) and Vascular dementia (VaD) patients with neuropsychiatry symptoms (NPS), and determine whether specific risk factors differ by subtypes of dementia for AD and VaD patients with NPS.

METHODS

A retrospective data analysis was conducted to evaluate similarities and differences in the risk factors for AD and VaD with NPS. The analysis included 2949 patients with VaD and 6341 patients with clinical confirmation of AD and VaD with or without NPS collected between February 2016 and August 2021. The multivariate logistic regression analysis was used to determine the risk factors associated with AD and VaD with NPS, by predicting the increasing odds (odds ratios (ORs) of an association of a specific baseline risk factor with AD or VaD with NPS. The validity of the regression models was tested using a Hosmer-Lemeshow test, while the Receiver Operating Curve (ROC) was used to test the sensitivity of the models.

RESULTS

In the adjusted analysis TSH (OR = 1.781, 95 % CI, p = 0.0025) and CHF (OR = 1.620, 95 %, p = 0.016) were associated with VaD with NPS, while a history of emergency department(ED) admission (OR = 0.277, 95 % CI, p = 0.003) likely to be associated with VaD patients without NPS. For AD patients, a history of CVA (OR = 1.395, 95 % CI, p = 0.032) and cancer (OR = 1.485, 95 % CI, p = 0.013) were associated with AD patients with NPS.

DISCUSSION

The findings of this study indicate that an abnormal thyroid gland and CHF were linked to VaD patients with behavioral disturbances, while CVA and cancer were linked to AD patients with behavioral disturbances. These findings suggest the need to develop management strategies for the care of patients with AD and VaD with NPS.

Graves disease is associated with increased risk of clinical Alzheimer's disease: evidence from the Medicare system

BACKGROUND

Identification of modifiable risk factors for Alzheimer's Disease (AD) onset is an important aspect of controlling the burden imposed by this disease on an increasing number of older U.S. adults. Graves disease (GD), the most common cause of hyperthyroidism in the U.S., has been hypothesized to be associated with increased AD risk, but there is no consensus. In this study, we explore the link between GD and risk of clinical AD.

METHODS

Cox and Fine-Grey models were applied to a retrospective propensity-score-matched cohort of 19,798 individuals with GD drawn from a nationally representative 5% sample of U.S. Medicare beneficiaries age 65 + over the 1991-2020 period.

RESULTS

Results showed that the presence of GD was associated with a higher risk of AD (Hazard Ratio [HR]:1.19; 95% Confidence Interval [CI]:1.13-1.26). Competing risk estimates were consistent with these findings (HR:1.14; CI:1.08-1.20) with the magnitude of associated risk varying across subgroups: Male (HR:1.25; CI:1.07-1.47), Female (HR:1.09; CI:1.02-1.16), White (HR:1.11; CI:1.03-1.19), and Black (HR:1.23; CI:1.02-1.49).

CONCLUSIONS

Our results indicate a robust and consistent association between a diagnosis of GD and a subsequent diagnosis of AD in later stages of life. The precise biological pathways that could potentially connect these two conditions remain unclear as is the role of treatment in this relationship. Replications of these findings on datasets with both biomarkers and laboratory test results, especially in underrepresented groups is vital.

Sex and Sleep Disruption as Contributing Factors in Alzheimer's Disease

Alzheimer's disease (AD) affects more women than men, with women throughout the menopausal transition potentially being the most under researched and at-risk group. Sleep disruptions, which are an established risk factor for AD, increase in prevalence with normal aging and are exacerbated in women during menopause. Sex differences showing more disrupted sleep patterns and increased AD pathology in women and female animal models have been established in literature, with much emphasis placed on loss of circulating gonadal hormones with age. Interestingly, increases in gonadotropins such as follicle stimulating hormone are emerging to be a major contributor to AD pathogenesis and may also play a role in sleep disruption, perhaps in combination with other lesser studied hormones. Several sleep influencing regions of the brain appear to be affected early in AD progression and some may exhibit sexual dimorphisms that may contribute to increased sleep disruptions in women with age. Additionally, some of the most common sleep disorders, as well as multiple health conditions that impair sleep quality, are more prevalent and more severe in women. These conditions are often comorbid with AD and have bi-directional relationships that contribute synergistically to cognitive decline and neuropathology. The association during aging of increased sleep disruption and sleep disorders, dramatic hormonal changes during and after menopause, and increased AD pathology may be interacting and contributing factors that lead to the increased number of women living with AD.

Low levels of free triiodothyronine are associated with risk of cognitive impairment in older euthyroid adults

Accumulated evidence showed that thyroid diseases induced cognitive decline. However, the relationship between thyroid hormones (THs) and cognition in older euthyroid people is still unclear. Our study aimed to estimate the association between THs within the euthyroid range and cognition in community-dwelling older adults in China. Data were extracted from a cohort study on the health status of rural older adults from the Guizhou province in China (HSRO). Serum thyroid-stimulating hormone (TSH), free thyroxine (FT4), and free triiodothyronine (FT3) were measured using the electrochemiluminescence immunoassay. Cognitive function was evaluated by the Mini-Mental State Examination (MMSE). Linear regression and a binary logistic regression model were used to explore the relationship between THs and cognition in euthyroidism (TSH level of 0.27 ~ 4.20mIU/L). A total of 957 euthyroidism individuals were included in this study, with a mean (SD) age of 71.34 (6.35) years. In individuals with euthyroidism, serum TSH and FT3 levels were positively associated with cognition (TSH:β = 0.06, 95% CI  0.01 ~ 0.11, P = 0.03; FT3:β = 0.07, 95% CI  0.01 ~ 0.12, P = 0.01); and serum FT3 and TSH levels were significantly associated with cognitive domains (P < 0.05). Further, euthyroid individuals in the lowest serum FT3(OR = 1.96; 95% CI 1.27 ~ 3.03) quartile had a twofold increased risk of cognitive impairment compared to those in the highest quartile after adjusting for potential confounding factors. These findings suggested that low levels of FT3 could be an independent risk factor for cognitive impairment in older euthyroid adults. Additionally, a positive linear association exists between serum FT3 levels and cognitive domains (such as immediate memory, language, and attention). Further studies are needed to determine the underlying mechanisms and the community significance of these findings.

ARCH: Large-scale Knowledge Graph via Aggregated Narrative Codified Health Records Analysis

Objective

Electronic health record (EHR) systems contain a wealth of clinical data stored as both codified data and free-text narrative notes, covering hundreds of thousands of clinical concepts available for research and clinical care. The complex, massive, heterogeneous, and noisy nature of EHR data imposes significant challenges for feature representation, information extraction, and uncertainty quantification. To address these challenges, we proposed an efficient Aggregated naRrative Codified Health (ARCH) records analysis to generate a large-scale knowledge graph (KG) for a comprehensive set of EHR codified and narrative features.

Methods

The ARCH algorithm first derives embedding vectors from a co-occurrence matrix of all EHR concepts and then generates cosine similarities along with associated p -values to measure the strength of relatedness between clinical features with statistical certainty quantification. In the final step, ARCH performs a sparse embedding regression to remove indirect linkage between entity pairs. We validated the clinical utility of the ARCH knowledge graph, generated from 12.5 million patients in the Veterans Affairs (VA) healthcare system, through downstream tasks including detecting known relationships between entity pairs, predicting drug side effects, disease phenotyping, as well as sub-typing Alzheimer's disease patients.

Results

ARCH produces high-quality clinical embeddings and KG for over 60,000 EHR concepts, as visualized in the R-shiny powered web-API (https://celehs.hms.harvard.edu/ARCH/). The ARCH embeddings attained an average area under the ROC curve (AUC) of 0.926 and 0.861 for detecting pairs of similar EHR concepts when the concepts are mapped to codified data and to NLP data; and 0.810 (codified) and 0.843 (NLP) for detecting related pairs. Based on the p -values computed by ARCH, the sensitivity of detecting similar and related entity pairs are 0.906 and 0.888 under false discovery rate (FDR) control of 5%. For detecting drug side effects, the cosine similarity based on the ARCH semantic representations achieved an AUC of 0.723 while the AUC improved to 0.826 after few-shot training via minimizing the loss function on the training data set. Incorporating NLP data substantially improved the ability to detect side effects in the EHR. For example, based on unsupervised ARCH embeddings, the power of detecting drug-side effects pairs when using codified data only was 0.15, much lower than the power of 0.51 when using both codified and NLP concepts. Compared to existing large-scale representation learning methods including PubmedBERT, BioBERT and SAPBERT, ARCH attains the most robust performance and substantially higher accuracy in detecting these relationships. Incorporating ARCH selected features in weakly supervised phenotyping algorithms can improve the robustness of algorithm performance, especially for diseases that benefit from NLP features as supporting evidence. For example, the phenotyping algorithm for depression attained an AUC of 0.927 when using ARCH selected features but only 0.857 when using codified features selected via the KESER network[1]. In addition, embeddings and knowledge graphs generated from the ARCH network were able to cluster AD patients into two subgroups, where the fast progression subgroup had a much higher mortality rate.

Conclusions

The proposed ARCH algorithm generates large-scale high-quality semantic representations and knowledge graph for both codified and NLP EHR features, useful for a wide range of predictive modeling tasks.

Early Depressive Symptoms Predict Faster Dementia Progression in Autosomal-Dominant Alzheimer's Disease

BACKGROUND

Depression is associated with Alzheimer's disease (AD).

OBJECTIVE

To evaluate the association between depressive symptoms and age of onset of cognitive decline in autosomal dominant AD, and to determine possible factors associated to early depressive symptoms in this population.

METHODS

We conducted a retrospective study to identify depressive symptoms among 190 presenilin 1 (PSEN1) E280A mutation carriers, subjected to comprehensive clinical evaluations in up to a 20-year longitudinal follow-up. We controlled for the following potential confounders: APOE, sex, hypothyroidism, education, marital status, residence, tobacco, alcohol, and drug abuse.

RESULTS

PSEN1 E280A carriers with depressive symptoms before mild cognitive impairment (MCI) develop dementia faster than E280A carriers without depressive symptoms (Hazard Ratio, HR = 1.95; 95% CI, 1.15-3.31). Not having a stable partner accelerated the onset of MCI (HR = 1.60; 95 % CI, 1.03-2.47) and dementia (HR = 1.68; 95 % CI, 1.09-2.60). E280A carriers with controlled hypothyroidism had later age of onset of depressive symptoms (HR = 0.48; 95 % CI, 0.25-0.92), dementia (HR = 0.43; 95 % CI, 0.21-0.84), and death (HR = 0.35; 95 % CI, 0.13-0.95). APOEɛ2 significantly affected AD progression in all stages. APOE polymorphisms were not associate to depressive symptoms. Women had a higher frequency and developed earlier depressive symptoms than men throughout the illness (HR = 1.63; 95 % CI, 1.14-2.32).

CONCLUSION

Depressive symptoms accelerated progress and faster cognitive decline of autosomal dominant AD. Not having a stable partner and factors associated with early depressive symptoms (e.g., in females and individuals with untreated hypothyroidism), could impact prognosis, burden, and costs.

Association of Hypothyroidism and the Risk of Cognitive Dysfunction: A Meta-Analysis

Objectives: The purpose of this meta-analysis was to assess whether there is an association between hypothyroidism and the risk of cognitive dysfunction. Methods: PubMed, Cochrane Library, and Embase were searched for relevant studies published from database inception to 4 May 2022, using medical subject headings (MeSHs) and keywords. Results: Eight studies involving 1,092,025 individuals were included, published between 2010 and 2021. The pooled analysis showed that there was no association between hypothyroidism and cognitive dysfunction (OR = 1.13, 95% CI = 0.84−1.51, p = 0.426), including both all-cause dementia (OR = 1.04, 95% CI = 0.76−1.43, p = 0.809) and cognitive impairment (OR = 1.50, 95% CI = 0.68−3.35, p = 0.318). Neither overt hypothyroidism (OR = 1.19, 95% CI = 0.70−2.02, p = 0.525) nor subclinical hypothyroidism (OR = 1.04, 95% CI = 0.73−1.48, p = 0.833) was associated with cognitive dysfunction. Neither prospective cohort (OR = 1.08, 95% CI = 0.77−1.51, p = 0.673) nor cross-sectional studies (OR = 1.23, 95% CI = 0.63−2.42, p = 0.545) had any effect on the association. Interestingly, the risk of cognitive dysfunction was significantly increased in the group not adjusted for vascular comorbidity (OR = 1.47, 95% CI = 1.07−2.01, p = 0.017), while it was reduced in the adjusted group (OR =0.82, 95% CI = 0.79−0.85, p < 0.001). Conclusions: This meta-analysis shows that hypothyroidism was associated with a reduced risk of cognitive dysfunction after adjustment for vascular-disease comorbidities. More prospective observational studies are needed in the future to investigate the relationship between hypothyroidism and cognitive dysfunction.