The Journal of Prevention of Alzheimer's Disease最新文献

Background

Dementia is a growing global health challenge. Quantifying the current burden and predicting the future increases of dementia-related deaths are necessary to enhance effective policy decisions and health system planning.

Methods

Data on dementia mortality was derived from the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2019 study. The 2020–2050 dementia-related deaths were forecasted using the Bayesian age-period-cohort model.

Results

Globally, the number of dementia-related death increased from 0.56 million in 1990 to 1.62 million in 2019 and were estimated to increase to 4.91 million by the year 2050. Metabolic risk factors would become the most important modifiable risk factors affecting dementia death which account for 28.10% of dementia related death by the year 2050. For different Socio-demographic Index (SDI) regions, the low SDI region would have the highest age-standardized mortality rate (ASMR) (29.16 per 100,000) by 2050. Moreover, the number of dementia-related deaths under the age of 70 years was predicted to reach 0.18 million by 2050.

Conclusions

Dementia related death remains a global health problem, and health policies targeting metabolic risk factors may be an important way to alleviate this problem.

Introduction

The Shape Trail Test (STT) was developed based upon the Trail Making Test, as a culture-neutral test for measuring processing speed and mental flexibility. This study aims to evaluate the accuracy and validity of this five-minute test for differentiating individuals with normal cognition (NC), subjective memory impairment (SMI), and mild cognitive impairment (MCI).

Method

The study included 210 participants aged 50–80 years, with 70 participants in each group matched for age, education, and gender.

Results

No significant difference in STT measures was found between the NC and SMI groups. In contrast, both the NC and SMI groups exhibited significantly better performance (shorter completion time in STT-A and STT-B and fewer STT-B errors) than the MCI group. No significant group differences were found in STT-A errors. Stepwise regression analysis identified three significant predictors for classifying the MCI group from the NC and/or SMI groups, including the STT-B completion time, the STT-A errors, and the interaction between STT-B completion time and STT-B errors. The composite score of these three predictors demonstrated good discriminatory power for classifying the MCI group from the other groups, with area under the curves (AUCs) of 0.76–0.79 (p < 0.001), sensitivities of 78.6%–80%, and specificities of 60%–61.4%. However, none of the STT measures or their interactions were significant predictors for differentiating the SMI group from the NC group. Besides, the STT measures were significantly correlated with age, education, and executive function measures.

Discussion

The STT could be a culture- and language-free, reliable test for assessing executive function and a sensitive test for predicting MCI.

Evidence This study investigated the relationship between periodontal disease (PD) and Alzheimer’s Disease (AD) through a Systematic Literature Network Analysis (SLNA), combining bibliometric analysis with a Systematic Literature Review (SLR). Analyzing 328 documents from 2000 to 2023, we utilized the Bibliometrix R-package for multiple bibliometric analysis. The SLR primarily centered on the 47 most globally cited papers, highlighting influential research. Our study reveals a positive correlation between Periodontal Disease (PD) and Alzheimer’s Disease (AD), grounded in both biological plausibility and a comprehensive review of the literature, yet the exact causal relationship remains a subject of ongoing scientific investigation. We conducted a detailed analysis of the two main pathways by which PD could contribute to brain inflammation: (a) the Inflammatory Cascade, and (b) Microbial Involvement. The results of our SLNA emphasize the importance of oral health in reducing Alzheimer’s risk, suggesting that managing periodontal health could be an integral part of Alzheimer’s prevention and treatment strategies. The insights from this SLNA pave the way for future research and clinical practices, underscoring the necessity of interdisciplinary methods in both the investigation and treatment of neurodegenerative diseases like Alzheimer’s. Furthermore, our study presents a prospective research roadmap to support ongoing advancement in this field.

Background

Resting heart rate (RHR), has been related to increased risk of dementia, but the relationship between RHR and brain age is unclear.

Objective

We aimed to investigate the association of RHR with brain age and brain age gap (BAG, the difference between predicted brain age and chronological age) assessed by multimodal Magnetic Resonance Imaging (MRI) in mid- and old-aged adults.

Design

A longitudinal study from the UK Biobank neuroimaging project where participants underwent brain MRI scans 9+ years after baseline.

Setting

A population-based study.

Participants

A total of 33,381 individuals (mean age 54.74 ± 7.49 years; 53.44% female).

Measurements

Baseline RHR was assessed by blood pressure monitor and categorized as <60, 60–69 (reference), 70–79, or ≥80 beats per minute (bpm). Brain age was predicted using LASSO through 1,079 phenotypes in six MRI modalities (including T1-weighted MRI, T2-FLAIR, T2*, diffusion-MRI, task fMRI, and resting-state fMRI). Data were analyzed using linear regression models.

Results

As a continuous variable, higher RHR was associated with older brain age (β for per 1-SD increase: 0.331, 95% [95% confidence interval, CI]: 0.265, 0.398) and larger BAG (β: 0.263, 95% CI: 0.202, 0.324). As a categorical variable, RHR 70–79 bpm and RHR ≥80 bpm were associated with older brain age (β [95% CI]: 0.361 [0.196, 0.526] / 0.737 [0.517, 0.957]) and larger BAG (0.256 [0.105, 0.407] / 0.638 [0.436, 0.839]), but RHR< 60 bpm with younger brain age (−0.324 [−0.500, −0.147]) and smaller BAG (−0.230 [−0.392, −0.067]), compared to the reference group. These associations between elevated RHR and brain age were similar in both middle-aged (<60) and older (≥60) adults, whereas the association of RHR< 60 bpm with younger brain age and larger BAG was only significant among middle-aged adults. In stratification analysis, the association between RHR ≥80 bpm and older brain age was present in people with and without CVDs, while the relation of RHR 70–79 bpm to brain age present only in people with CVD.

Conclusion

Higher RHR (>80 bpm) is associated with older brain age, even among middle-aged adults, but RHR< 60 bpm is associated with younger brain age. Greater RHR could be an indicator for accelerated brain aging.

Background

Amyloid-beta (Aβ) plaque is a neuropathological hallmark of Alzheimer’s disease (AD). As anti-amyloid monoclonal antibodies enter the market, predicting brain amyloid status is critical to determine treatment eligibility.

Objective

To predict brain amyloid status utilizing machine learning approaches in the Advancing Reliable Measurement in Alzheimer’s Disease and Cognitive Aging (ARMADA) study.

Design

ARMADA is a multisite study that implemented the National Institute of Health Toolbox for Assessment of Neurological and Behavioral Function (NIHTB) in older adults with different cognitive ability levels (normal, mild cognitive impairment, early-stage dementia of the AD type).

Setting

Participants across various sites were involved in the ARMADA study for validating the NIHTB.

Participants

199 ARMADA participants had either PET or CSF information (mean age 76.3 ± 7.7, 51.3% women, 42.3% some or complete college education, 50.3% graduate education, 88.9% White, 33.2% with positive AD biomarkers).

Measurements

We used cognition, emotion, motor, sensation scores from NIHTB, and demographics to predict amyloid status measured by PET or CSF. We applied LASSO and random forest models and used the area under the receiver operating curve (AUROC) to evaluate the ability to identify amyloid positivity.

Results

The random forest model reached AUROC of 0.74 with higher specificity than sensitivity (AUROC 95% CI:0.73 -0.76, Sensitivity 0.50, Specificity 0.88) on the held-out test set; higher than the LASSO model (0.68 (95% CI:0.68 – 0.69)). The 10 features with the highest importance from the random forest model are: picture sequence memory, cognition total composite, cognition fluid composite, list sorting working memory, words-in-noise test (hearing), pattern comparison processing speed, odor identification, 2-minutes-walk endurance, 4-meter walk gait speed, and picture vocabulary. Overall, our model revealed the validity of measurements in cognition, motor, and sensation domains, in associating with AD biomarkers.

Conclusion

Our results support the utilization of the NIH toolbox as an efficient and standardizable AD biomarker measurement that is better at identifying amyloid negative (i.e., high specificity) than positive cases (i.e., low sensitivity).

Background

Many observational studies have examined the association of disease-modifying antirheumatic drugs (DMARDs) with dementia risk, but the evidence has been mixed, possibly due to methodological reasons. This systematic review (PROSPERO: CRD42023432122) aims to assess existing observational evidence and to suggest if repurposing DMARDs for dementia prevention merits further investigation.

Methods

Four electronic databases up to October 26, 2023, were searched. Cohort or case-control studies that examined dementia risk associated with DMARDs in people with rheumatoid arthritis were included. Risk of bias was evaluated using the Cochrane Collaboration’s Risk of Bias in Nonrandomized Studies of Interventions (ROBINS-I) criteria. Findings were summarized by individual drug classes and by risk of bias.

Results

Of 12,180 unique records, 14 studies (4 case-control studies, 10 cohort studies) were included. According to the ROBINS-I criteria, there were 2 studies with low risk of bias, 1 study with moderate risk, and 11 studies with serious or critical risk. Among studies with low risk of bias, one study suggested that hydroxychloroquine versus methotrexate was associated with lower incident dementia, and the other study showed no associations of tumor necrosis factor (TNF) inhibitors, tocilizumab, and tofacitinib, compared to abatacept, with incident dementia.

Conclusion

Studies that adequately addressed important biases were limited. Studies with low risk of bias did not support repurposing TNF inhibitors, tocilizumab, abatacept or tofacitinib for dementia prevention, but hydroxychloroquine may be a potential candidate. Further studies that carefully mitigate important sources of biases are warranted, and long-term evidence will be preferred.

Aim

Alzheimer’s disease (AD) is among common cause of dementia. Complementary therapies, such as resistance exercise (RE), have been proposed as an alternative for the treatment of AD. We performed a systematic review and meta-analysis to investigate the effects of RE on the cognitive function of AD animal models and their physiological mechanisms.

Methods

This review was submitted to PROSPERO (CRD42019131266) and was done according to PRISMA checklist. Four databases were used in the search: MEDLINE/PUBMED, SCOPUS, Web of Science and Google Scholar. We used SYRCLE and CAMAREDES to assess the risk of bias and methodological quality. We calculated the standardized mean difference using 95% confidence intervals and considered the random effects model and p < 0.05 to determine significance.

Key Findings

A total of 1,807 studies were founded, and after the selection process, only 11 studies were included in this review and 8 studies were included for meta-analysis. Four studies applied RE before AD induction, 7 studies applied RE after AD induction or in the AD condition. All studies included 550 adult and older animals weighing 25–280g. Our analysis revealed that RE had a positive effect on memory in AD animal models but did not show a significant impact on anxiety.

Conclusion

RE performed four or six weeks, more than three days a week, had a significant protective effect on memory. The included studies had a high risk of bias and moderate methodological quality. Therefore, RE can be a potential strategy for preventing cognitive decline in animal models.

Background and Objectives

Diabetes and especially insulin resistance are associated with an increased risk of developing cognitive dysfunction, making anti-diabetic drugs an interesting therapeutic option for the treatment of neurodegenerative disorders. Dual amylin and calcitonin receptor agonists (DACRAs) elicit beneficial effects on glycemic control and insulin sensitivity. However, whether DACRAs affect cognition is unknown.

Design and Intervention

Zucker Diabetic Fatty rats were treated with either the DACRA KBP-336 (4.5 nmol/kg Q3D), the amylin analog AM1213 (25 nmol/kg QD), or vehicle for 18 weeks. Further, the efficacy of a late KBP-336 intervention was evaluated by including a group starting treatment on day 30. Glucose control and tolerance were evaluated throughout the study and spatial learning and memory were evaluated by Morris Water Maze after 17 weeks of treatment.

Results

When evaluating spatial learning, rats receiving KBP-336 throughout the study performed significantly better than AM1213, vehicle, and late intervention KBP-336. Both KBP-336 and AM1213 treatments improved spatial memory compared to the vehicle. The overall performance in the cognitive tests was reflected in the treatment efficacy on glycemic control, where KBP-336 was superior to AM1213.

Conclusion

In summary, the DACRA KBP-336 ameliorates diabetes-induced spatial learning and memory impairment in diabetic rats. Further, KBP-336 improves long-term glycemic control superior to the amylin analog AM1213. Taken together, KBP-336 is, due to its anti-diabetic and insulin-sensitizing properties, a promising candidate for the treatment of cognitive impairments.

Background

Recent developments in blood biomarkers (BBM) have shown promising results in diagnosing amyloid pathology in Alzheimer’s Disease (AD). However, information on how these BBMs can best be used in clinical settings to optimise clinical decision-making and long-term health outcomes for individuals with AD is still lacking.

Objectives

We aim to assess the potential value of BBM in AD diagnosis within the context of disease-modifying treatment (DMT).

Design

We developed a decision analytic model to evaluate the long-term health outcomes using BBM in AD diagnosis. We compared standard of care (SOC) diagnosis workflow to the integration of BBM as a (1) referral decision tool in primary health center (PHC) and (2) triaging tool for invasive CSF examination in specialist memory clinic (MC). We combined a decision tree and a Markov model to simulate the patient’s diagnostic journey, treatment decisions following diagnosis and long-term health outcomes. Input parameters for the model were identified from published literature and registry data analysis. We conducted a cost-utility analysis from the societal perspective using a one-year cycle length and a 30-year (lifetime) horizon.

Measurements

We reported the simulated outcomes in the percentage of correct diagnosis, costs (in 2022 Euros), quality-adjusted life year (QALY), and incremental cost-effectiveness ratios (ICER) associated with each diagnosis strategy.

Results

Compared to SOC, integrating BBM in PHC increased patient referrals by 8% and true positive AD diagnoses by 10.4%. The lifetime costs for individuals diagnosed with AD were € 249,685 and €250,287, and QALYs were 9.5 and 9.52 in SOC and PHC pathways, respectively. The cost increments were €603, and QALYs gained were 0.01, resulting in an ICER of €48,296. Using BBM in MC reduced the exposure to invasive CSF procedures and costs but also reduced true positive AD diagnoses and QALYs.

Conclusions

Using BBM at PHC to make referral decisions might increase initial diagnostic costs but can prevent high costs associated with disease progression, providing a cost-effective DMT is available, whereas using BBM in MC could reduce the initial evaluation cost but incur high costs associated with disease progression.

Background

Disease modifying therapies (DMTs) may be most beneficial in early disease, when progression is slow and changes small, with clinical relevance difficult to interpret.

Objectives

Time component tests (TCTs) translate differences between treatments from mean change, vertical distance between longitudinal trajectories, into intuitively understood time saved, horizontal distance between trajectories, which can be readily combined across endpoints in a global TCT (gTCT).

Design

The value of composites, time savings estimates, and combination scores to optimize measurement and interpretation of DMTs are demonstrated, along with construction details and simulation studies.

Setting

TCT methods were applied to a randomized phase II clinical trial.

Participants

Patients with early Alzheimer’s disease (N=332).

Intervention

Three treatment groups with AFFITOPE® AD02 and two control groups with aluminum oxyhydroxide, AD04.

Measurements

The co-primary efficacy outcomes were an adapted ADAS-Cog (aADAS) and adapted ADCS-ADL (aADL), which were optimized composite scales specific to cognitive and functional domains. A composite based on these two scores was the study’s prespecified primary outcome. The CDR-sb and standard non-adapted ADCS-ADL and ADAS-Cog scales were prespecified secondary outcomes.

Results

The AD04 2 mg group showed some statistically significant effects compared with other study arms. It is unclear whether the observed 3.8-point difference on the composite is clinically meaningful. TCT results show a time savings of 11 months in an 18-month study with AD04 2 mg.

Conclusion

The relevance of 11 months saved is more universally understood than a mean difference of 3.8 points in the composite outcome. These results suggest that a combination of a composite approach and a time savings interpretation offers a powerful approach for detecting and interpreting disease modifying effects.