Benefit and safety of antibiotics for Alzheimer’s disease: Protocol for a systematic review and meta-analysis

Gao, Lin; Shuai, Yinqi; Wen, Lihong; Zhang, Hong; Zhang, Yi; Zhang, Xiaoyun

doi:10.1097/md.0000000000031637

Cited by 4 publications

(2 citation statements)

References 50 publications

(62 reference statements)

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“…The most frequently appearing node in scenario 3 is “quinolone antibiotics.” According to the study by Pham et al [ 34 ], it is a class of medication commonly prescribed to treat various bacterial infections and is primarily used for its antimicrobial properties [ 34 ]. Additionally from a study by Gao et al [ 35 ], their review study indicates that the brain inflammation caused by microbial infections may be one of the etiologies of ADRD, and antibiotics as novel treatments may be beneficial for delaying the development of ADRD. Quinolones exhibit a distinct pharmacokinetic profile characterized by a higher cerebrospinal fluid to serum concentration ratio compared to other commonly prescribed antibiotics [ 36 ].…”

Section: Discussionmentioning

confidence: 99%

Self-Explainable Graph Neural Network for Alzheimer Disease and Related Dementias Risk Prediction: Algorithm Development and Validation Study

Hu,

Sun,

Nian

et al. 2024

JMIR Aging

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Background: Alzheimer's disease and related dementias (ADRD) ranks as the sixth leading cause of death in the US, underlining the importance of accurate ADRD risk prediction. While recent advancement in ADRD risk prediction have primarily relied on imaging analysis, yet not all patients undergo medical imaging before an ADRD diagnosis. Merging machine learning with claims data can reveal additional risk factors and uncover interconnections among diverse medical codes.Objective: Our goal is to utilize Graph Neural Networks (GNNs) with claims data for ADRD risk prediction. Addressing the lack of human-interpretable reasons behind these predictions, we introduce an innovative method to evaluate relationship importance and its influence on ADRD risk prediction, ensuring comprehensive interpretation. Methods:We employed Variationally Regularized Encoder-decoder Graph Neural Network (VGNN) for estimating ADRD likelihood. We created three scenarios to assess the model's efficiency, using Random Forest and Light Gradient Boost Machine as baselines. We further used our relation importance method to clarify the key relationships for ADRD risk prediction.Results: VGNN surpassed other baseline models by 10% in the area under the receiver operating characteristic. The integration of the GNN model and relation importance interpretation could potentially play an essential role in providing valuable insight into factors that may contribute to or delay ADRD progression.Conclusions: Employing a GNN approach with claims data enhances ADRD risk prediction and provides insights into the impact of interconnected medical code relationships. This methodology not only enables ADRD risk modeling but also shows potential for other image analysis predictions using claims data.

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Section: Discussionmentioning

confidence: 99%

Self-Explainable Graph Neural Network for Alzheimer Disease and Related Dementias Risk Prediction: Algorithm Development and Validation Study

Hu,

Sun,

Nian

et al. 2024

JMIR Aging

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“…In light of the association between alterations in the oral microbiome and AD, the administration of antibiotics has emerged as a potential avenue to ameliorate AD symptoms ( 60 , 61 ). Despite achieving a substantial reduction in bacterial populations through gingival curettage and subgingival scaling, the primary challenge for long-term effectiveness in managing periodontitis lies in controlling the regrowth of oral microbiota ( 62 ).…”

Section: The Potential Application Of Oral Microbiota In the Preventi...mentioning

confidence: 99%

Advances in the prevention and treatment of Alzheimer’s disease based on oral bacteria

Zhang,

Mi,

Ying

et al. 2023

Front. Psychiatry

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With the global population undergoing demographic shift towards aging, the prevalence of Alzheimer’s disease (AD), a prominent neurodegenerative disorder that primarily afflicts individuals aged 65 and above, has increased across various geographical regions. This phenomenon is accompanied by a concomitant decline in immune functionality and oral hygiene capacity among the elderly, precipitating compromised oral functionality and an augmented burden of dental plaque. Accordingly, oral afflictions, including dental caries and periodontal disease, manifest with frequency among the geriatric population worldwide. Recent scientific investigations have unveiled the potential role of oral bacteria in instigating both local and systemic chronic inflammation, thereby delineating a putative nexus between oral health and the genesis and progression of AD. They further proposed the oral microbiome as a potentially modifiable risk factor in AD development, although the precise pathological mechanisms and degree of association have yet to be fully elucidated. This review summarizes current research on the relationship between oral bacteria and AD, describing the epidemiological and pathological mechanisms that may potentially link them. The purpose is to enrich early diagnostic approaches by incorporating emerging biomarkers, offering novel insights for clinicians in the early detection of AD. Additionally, it explores the potential of vaccination strategies and guidance for clinical pharmacotherapy. It proposes the development of maintenance measures specifically targeting oral health in older adults and advocates for guiding elderly patients in adopting healthy lifestyle habits, ultimately aiming to indirectly mitigate the progression of AD while promoting oral health in the elderly.

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Essential considerations towards development of effective nasal antibiotic formulation: features, strategies, and future directions

Mardikasari,

Katona,

Sipos

et al. 2024

Expert Opinion on Drug Delivery

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Benefit and safety of antibiotics for Alzheimer’s disease: Protocol for a systematic review and meta-analysis

Cited by 4 publications

References 50 publications

Self-Explainable Graph Neural Network for Alzheimer Disease and Related Dementias Risk Prediction: Algorithm Development and Validation Study

Self-Explainable Graph Neural Network for Alzheimer Disease and Related Dementias Risk Prediction: Algorithm Development and Validation Study

Advances in the prevention and treatment of Alzheimer’s disease based on oral bacteria

Essential considerations towards development of effective nasal antibiotic formulation: features, strategies, and future directions

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