Pub Date : 2025-11-24DOI: 10.1016/j.arr.2025.102964
Yoshiyasu Takefuji
Fuellen et al. (2025) highlighted the essential role of explainable AI methods, particularly principal component analysis (PCA), in evaluating interventions for aging and longevity. However, this paper raises significant concerns regarding PCA's linear and parametric nature, which can misrepresent complex, nonlinear data common in geroscience research. As biological relationships often defy simplistic interpretations, reliance on PCA may obscure vital insights, leading to potential misinterpretations of intervention effects. To enhance accuracy in analyses, this study advocates for the adoption of nonlinear and nonparametric methods, such as Spearman’s rank correlation and Kendall’s tau. By reconsidering their methodological approaches, researchers can foster more accurate and informed evaluations of aging-related interventions.
{"title":"Reevaluating principal component analysis in geroscience: A call for nonlinear approaches in AI-based evaluations","authors":"Yoshiyasu Takefuji","doi":"10.1016/j.arr.2025.102964","DOIUrl":"10.1016/j.arr.2025.102964","url":null,"abstract":"<div><div>Fuellen et al. (2025) highlighted the essential role of explainable AI methods, particularly principal component analysis (PCA), in evaluating interventions for aging and longevity. However, this paper raises significant concerns regarding PCA's linear and parametric nature, which can misrepresent complex, nonlinear data common in geroscience research. As biological relationships often defy simplistic interpretations, reliance on PCA may obscure vital insights, leading to potential misinterpretations of intervention effects. To enhance accuracy in analyses, this study advocates for the adoption of nonlinear and nonparametric methods, such as Spearman’s rank correlation and Kendall’s tau. By reconsidering their methodological approaches, researchers can foster more accurate and informed evaluations of aging-related interventions.</div></div>","PeriodicalId":55545,"journal":{"name":"Ageing Research Reviews","volume":"113 ","pages":"Article 102964"},"PeriodicalIF":12.4,"publicationDate":"2025-11-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145623651","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-11-24DOI: 10.1016/j.arr.2025.102959
Yucheng Luo , Yuang Song , Minxi Zeng , Bin Li , Ye Li , Ziqing Dong
Aging is one of the factors for the decline in adipose tissue browning and, consequently, age-related metabolic disorders. Metabolic disorders will in turn accelerate aging and lead to a vicious circle. Therefore, the research on the reduced browning of adipose tissue that occurs with aging, that is, adipose tissue browning aging, is necessary and of great significance for the development of metabolically healthy aging. In this study, we performed a bibliometric analysis of 2527 published articles on aging of adipose tissue browning and created a panorama from different levels so that readers could quickly understand the current state of the research field. The burst analysis and timeline analysis were used to identify the research frontiers in the field, and the newly published documents were discussed and summarized. In addition, we performed bioinformatic analysis on the GEO-derived dataset to identify the altered genes and enriched pathways associated with browning during aging. Combined with bibliometric analysis, the most concerned pathway was identified as adipogenesis, and the most concerned genes were PPARG, ADIPOQ and TG. In summary, this study provided a comprehensive picture of the current status of adipose tissue browning and aging research and identified research frontiers. Finally, the pathways and genes of most interest were identified by combined bioinformatic analysis.
{"title":"The past, present, and future of adipose tissue browning and aging: A review combined with bibliometrics and bioinformatics of 2527 documents published over the past four decades","authors":"Yucheng Luo , Yuang Song , Minxi Zeng , Bin Li , Ye Li , Ziqing Dong","doi":"10.1016/j.arr.2025.102959","DOIUrl":"10.1016/j.arr.2025.102959","url":null,"abstract":"<div><div>Aging is one of the factors for the decline in adipose tissue browning and, consequently, age-related metabolic disorders. Metabolic disorders will in turn accelerate aging and lead to a vicious circle. Therefore, the research on the reduced browning of adipose tissue that occurs with aging, that is, adipose tissue browning aging, is necessary and of great significance for the development of metabolically healthy aging. In this study, we performed a bibliometric analysis of 2527 published articles on aging of adipose tissue browning and created a panorama from different levels so that readers could quickly understand the current state of the research field. The burst analysis and timeline analysis were used to identify the research frontiers in the field, and the newly published documents were discussed and summarized. In addition, we performed bioinformatic analysis on the GEO-derived dataset to identify the altered genes and enriched pathways associated with browning during aging. Combined with bibliometric analysis, the most concerned pathway was identified as adipogenesis, and the most concerned genes were PPARG, ADIPOQ and TG. In summary, this study provided a comprehensive picture of the current status of adipose tissue browning and aging research and identified research frontiers. Finally, the pathways and genes of most interest were identified by combined bioinformatic analysis.</div></div>","PeriodicalId":55545,"journal":{"name":"Ageing Research Reviews","volume":"114 ","pages":"Article 102959"},"PeriodicalIF":12.4,"publicationDate":"2025-11-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145600530","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-11-22DOI: 10.1016/j.arr.2025.102953
Munabize Himwaba , Kermue Vasco Jarnda , Maxwell Tarwin Dweh , Xuan Liu , Yuyue Xiang , Xiyan Zou , Hanxiao Lv , Tianjiao Li , Xinya Tu , Jianwu Wang
Parkinson's disease is a neurodegenerative condition, marked by a progressive deterioration in both motor and non-motor abilities, which can severely affect the quality of life of the elderly population. With no cure available, innovative tools in treatment development and drug discovery are necessary. For several years, traditional models have been essential; however, they face limitations in replicating the complex setting of Parkinson's disease. In this regard, in silico and in vitro models have become essential tools in advancing Parkinson's disease drug discovery because of the potential they exhibit over traditional models. Here, we comprehensively review the latest innovations in both models and discuss their role in discovering therapeutic targets, optimizing drug candidates, promoting personalized medicine, and identifying biomarkers. While in vitro models better simulate the complicated cell interactions and increase the predictability of therapeutic effects, silico models shine in cost-effectiveness and high-throughput drug screening. Additionally, we discuss how combining advanced technologies with both models can help to overcome current constraints and problems in Parkinson's disease drug discovery, therefore facilitating the development of effective and targeted therapies in a shorter timeframe.
{"title":"The role of in silico and in vitro models in Parkinson’s disease: Drug discovery and therapy applications","authors":"Munabize Himwaba , Kermue Vasco Jarnda , Maxwell Tarwin Dweh , Xuan Liu , Yuyue Xiang , Xiyan Zou , Hanxiao Lv , Tianjiao Li , Xinya Tu , Jianwu Wang","doi":"10.1016/j.arr.2025.102953","DOIUrl":"10.1016/j.arr.2025.102953","url":null,"abstract":"<div><div>Parkinson's disease is a neurodegenerative condition, marked by a progressive deterioration in both motor and non-motor abilities, which can severely affect the quality of life of the elderly population. With no cure available, innovative tools in treatment development and drug discovery are necessary. For several years, traditional models have been essential; however, they face limitations in replicating the complex setting of Parkinson's disease. In this regard, in silico and in vitro models have become essential tools in advancing Parkinson's disease drug discovery because of the potential they exhibit over traditional models. Here, we comprehensively review the latest innovations in both models and discuss their role in discovering therapeutic targets, optimizing drug candidates, promoting personalized medicine, and identifying biomarkers. While in vitro models better simulate the complicated cell interactions and increase the predictability of therapeutic effects, silico models shine in cost-effectiveness and high-throughput drug screening. Additionally, we discuss how combining advanced technologies with both models can help to overcome current constraints and problems in Parkinson's disease drug discovery, therefore facilitating the development of effective and targeted therapies in a shorter timeframe.</div></div>","PeriodicalId":55545,"journal":{"name":"Ageing Research Reviews","volume":"113 ","pages":"Article 102953"},"PeriodicalIF":12.4,"publicationDate":"2025-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145598263","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Type 2 diabetes mellitus (T2DM) is increasingly recognized as a shared pathological substrate for both sarcopenia and cognitive decline, particularly Alzheimer’s disease (AD). This review synthesizes current evidence on the converging molecular pathways linking insulin resistance, hyperglycaemia, mitochondrial dysfunction, oxidative stress, and chronic inflammation to muscle wasting and neurodegeneration. Central to this interplay is the muscle–brain axis, a bidirectional communication network mediated by myokines, exercise-induced cytokines that influence metabolic and neural homeostasis. Key myokines such as IGF-1, irisin, BDNF, FGF21, and SPARC promote myogenesis, synaptic plasticity, and neuroprotection, while others including myostatin, IL-8, and GDF-15 exert detrimental effects. Context-dependent molecules such as IL-6, IL-15, lactate, and cathepsin-B show dual roles modulated by aging, inflammation, and metabolic state. Emerging data support that improved glycaemic control, enhanced insulin sensitivity, and sustained physical activity can attenuate both sarcopenia and cognitive decline. This review aims to summarize current evidence describing how insulin resistance, chronic hyperglycaemia, mitochondrial dysfunction, oxidative stress, and inflammation interact to promote both muscle wasting and neurodegeneration.
{"title":"The muscle-brain axis in type 2 diabetes: Molecular pathways linking sarcopenia and cognitive decline","authors":"Dionysios Xenos , Francesca Mancinetti , Patrizia Mecocci , Virginia Boccardi","doi":"10.1016/j.arr.2025.102955","DOIUrl":"10.1016/j.arr.2025.102955","url":null,"abstract":"<div><div>Type 2 diabetes mellitus (T2DM) is increasingly recognized as a shared pathological substrate for both sarcopenia and cognitive decline, particularly Alzheimer’s disease (AD). This review synthesizes current evidence on the converging molecular pathways linking insulin resistance, hyperglycaemia, mitochondrial dysfunction, oxidative stress, and chronic inflammation to muscle wasting and neurodegeneration. Central to this interplay is the muscle–brain axis, a bidirectional communication network mediated by myokines, exercise-induced cytokines that influence metabolic and neural homeostasis. Key myokines such as IGF-1, irisin, BDNF, FGF21, and SPARC promote myogenesis, synaptic plasticity, and neuroprotection, while others including myostatin, IL-8, and GDF-15 exert detrimental effects. Context-dependent molecules such as IL-6, IL-15, lactate, and cathepsin-B show dual roles modulated by aging, inflammation, and metabolic state. Emerging data support that improved glycaemic control, enhanced insulin sensitivity, and sustained physical activity can attenuate both sarcopenia and cognitive decline. This review aims to summarize current evidence describing how insulin resistance, chronic hyperglycaemia, mitochondrial dysfunction, oxidative stress, and inflammation interact to promote both muscle wasting and neurodegeneration.</div></div>","PeriodicalId":55545,"journal":{"name":"Ageing Research Reviews","volume":"113 ","pages":"Article 102955"},"PeriodicalIF":12.4,"publicationDate":"2025-11-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145579113","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-11-19DOI: 10.1016/j.arr.2025.102943
Qiye Shi , Junbao Hou , Xiaohan Peng , Ziqi Xu , Yang Wang , Danna Cao
Alzheimer’s disease(AD) is the most common cause of dementia and affects millions of people worldwide. The early and accurate diagnosis of AD is crucial for timely intervention and disease management. Magnetic resonance imaging (MRI) is a widely used noninvasive technique for assessing brain structure and function in patients with AD. However, conventional MRI analysis methods are often subjective, time-consuming, and depend on expert knowledge. Artificial intelligence (AI), particularly deep learning (DL), has emerged as a powerful tool for extracting meaningful information from large and complex MRI data and providing an automated and reliable diagnosis of AD. In this review, we summarize the recent advances and challenges in AI MRI for AD diagnosis, focusing on the following aspects: (1) types and characteristics of MRI data used for AD diagnosis; (2) the main AI models and architectures applied to MRI data analysis; (3) performance and evaluation metrics of AI models for AD diagnosis; (4) potential applications and limitations of AI models for AD diagnosis in clinical practice; and (5) future research directions for AI MRI for AD diagnosis. This review aims to provide a comprehensive and updated overview of the field and stimulate further research and advancements in AI-aided MRI for the diagnosis of AD.
阿尔茨海默病（AD）是痴呆症最常见的病因，影响着全世界数百万人。AD的早期准确诊断对于及时干预和疾病管理至关重要。磁共振成像（MRI）是一种广泛应用于评估AD患者大脑结构和功能的无创技术。然而，传统的MRI分析方法往往是主观的，耗时的，并依赖于专家知识。人工智能（AI），特别是深度学习（DL），已经成为一种强大的工具，可以从大量复杂的MRI数据中提取有意义的信息，并提供AD的自动可靠诊断。本文综述了人工智能MRI在阿尔茨海默病诊断中的最新进展和挑战，重点介绍了以下几个方面：(1)用于阿尔茨海默病诊断的MRI数据的类型和特征；(2)应用于MRI数据分析的主要AI模型和架构；(3)人工智能AD诊断模型的性能及评价指标；(4)人工智能模型在阿尔茨海默病诊断中的潜在应用和局限性；(5) AI MRI在AD诊断中的未来研究方向。本综述旨在提供该领域的全面和最新概述，并促进ai辅助MRI诊断AD的进一步研究和进展。
{"title":"Magnetic resonance imaging analysis for Alzheimer’s disease diagnosis using artificial intelligence: Methods, challenges, and opportunities","authors":"Qiye Shi , Junbao Hou , Xiaohan Peng , Ziqi Xu , Yang Wang , Danna Cao","doi":"10.1016/j.arr.2025.102943","DOIUrl":"10.1016/j.arr.2025.102943","url":null,"abstract":"<div><div>Alzheimer’s disease(AD) is the most common cause of dementia and affects millions of people worldwide. The early and accurate diagnosis of AD is crucial for timely intervention and disease management. Magnetic resonance imaging (MRI) is a widely used noninvasive technique for assessing brain structure and function in patients with AD. However, conventional MRI analysis methods are often subjective, time-consuming, and depend on expert knowledge. Artificial intelligence (AI), particularly deep learning (DL), has emerged as a powerful tool for extracting meaningful information from large and complex MRI data and providing an automated and reliable diagnosis of AD. In this review, we summarize the recent advances and challenges in AI MRI for AD diagnosis, focusing on the following aspects: (1) types and characteristics of MRI data used for AD diagnosis; (2) the main AI models and architectures applied to MRI data analysis; (3) performance and evaluation metrics of AI models for AD diagnosis; (4) potential applications and limitations of AI models for AD diagnosis in clinical practice; and (5) future research directions for AI MRI for AD diagnosis. This review aims to provide a comprehensive and updated overview of the field and stimulate further research and advancements in AI-aided MRI for the diagnosis of AD.</div></div>","PeriodicalId":55545,"journal":{"name":"Ageing Research Reviews","volume":"113 ","pages":"Article 102943"},"PeriodicalIF":12.4,"publicationDate":"2025-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145566758","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-11-19DOI: 10.1016/j.arr.2025.102947
Lining Su , Yanbing Wang
Alzheimer’s disease (AD) is a complex neurodegenerative disorder driven by multilayered molecular and cellular mechanisms that cannot be fully elucidated through single-omics approaches. Consequently, large-scale multi-omics integration-encompassing transcriptomics, epigenomics (e.g., methylation), and genetic association studies (GWAS/eQTL/mQTL)-has uncovered critical genetic and epigenetic networks underlying disease risk and progression.Based on these integrative insights, this review emphasized several genes-including KLHL21, SCN2B, ZNF415, and PITRM1-as potential contributors to AD pathogenesis. Notably, single-cell and spatial transcriptomics analyses revealed specific enrichment of these genes in astrocytes, underscoring the pivotal role of this cell type in Aβ clearance, tau propagation, and neuroinflammation. Exercise interventions were shown to selectively modulate the expression of these genes, providing molecular support for the preventive and therapeutic potential of non-pharmacological lifestyle strategies. Drug repurposing analyses using DrugBank have identified promising therapeutic candidates, including FDA-approved agents (e.g., valproic acid, raloxifene, and clomipramine) and naturally derived compounds (e.g., quercetin and fisetin), which may modulate key AD-related pathways. Furthermore, emerging evidence of miRNA-gene regulatory networks suggested an additional layer of post-transcriptional control that may regulate responses to pathological stimuli. Collectively, these integrative insights advocated for a multidimensional precision medicine framework that spans genetic, cellular,network, and lifestyle levels of regulation. This shift from single-target therapeutics to an integrated “gene-cell-network-lifestyle” paradigm open new theoretical and translational avenues for delaying or mitigating AD progression.
{"title":"From genes to lifestyle: A multi-dimensional framework for Alzheimer’s disease prevention and therapy","authors":"Lining Su , Yanbing Wang","doi":"10.1016/j.arr.2025.102947","DOIUrl":"10.1016/j.arr.2025.102947","url":null,"abstract":"<div><div>Alzheimer’s disease (AD) is a complex neurodegenerative disorder driven by multilayered molecular and cellular mechanisms that cannot be fully elucidated through single-omics approaches. Consequently, large-scale multi-omics integration-encompassing transcriptomics, epigenomics (e.g., methylation), and genetic association studies (GWAS/eQTL/mQTL)-has uncovered critical genetic and epigenetic networks underlying disease risk and progression.Based on these integrative insights, this review emphasized several genes-including KLHL21, SCN2B, ZNF415, and PITRM1-as potential contributors to AD pathogenesis. Notably, single-cell and spatial transcriptomics analyses revealed specific enrichment of these genes in astrocytes, underscoring the pivotal role of this cell type in Aβ clearance, tau propagation, and neuroinflammation. Exercise interventions were shown to selectively modulate the expression of these genes, providing molecular support for the preventive and therapeutic potential of non-pharmacological lifestyle strategies. Drug repurposing analyses using DrugBank have identified promising therapeutic candidates, including FDA-approved agents (e.g., valproic acid, raloxifene, and clomipramine) and naturally derived compounds (e.g., quercetin and fisetin), which may modulate key AD-related pathways. Furthermore, emerging evidence of miRNA-gene regulatory networks suggested an additional layer of post-transcriptional control that may regulate responses to pathological stimuli. Collectively, these integrative insights advocated for a multidimensional precision medicine framework that spans genetic, cellular,network, and lifestyle levels of regulation. This shift from single-target therapeutics to an integrated “gene-cell-network-lifestyle” paradigm open new theoretical and translational avenues for delaying or mitigating AD progression.</div></div>","PeriodicalId":55545,"journal":{"name":"Ageing Research Reviews","volume":"113 ","pages":"Article 102947"},"PeriodicalIF":12.4,"publicationDate":"2025-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145575072","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-11-19DOI: 10.1016/j.arr.2025.102950
Maria Laura Ferrando, Fabio Busonero, Francesca Crobu, Serena Sanna
Menopause is a hallmark of women's aging and is frequently portrayed as a medical issue. It also encompasses social and biological aspects often neglected and not well-understood, leaving women with insufficient support and attention. With the decline in estrogen levels, starting years before menopause is fully established, women experience various physical symptoms, and the risk of many age-related diseases increases sharply soon after these hormonal changes occur. Notably, these hormonal shifts also significantly impact the vaginal and gut microbiomes, contributing to dysbiosis and influencing the onset and progression of several diseases. Here, we examined the complex and dynamic relationship among aging, menopause, and microbiome changes with a particular focus on the vaginal and gut ecosystems. Emerging research highlights diet as a potential modulator for maintaining microbiome health during menopause. A deeper understanding of microbiome changes across life stages suggests the potential for microbiome-targeted strategies to support well-aging in women.
{"title":"Aging in women – The microbiome perspective","authors":"Maria Laura Ferrando, Fabio Busonero, Francesca Crobu, Serena Sanna","doi":"10.1016/j.arr.2025.102950","DOIUrl":"10.1016/j.arr.2025.102950","url":null,"abstract":"<div><div>Menopause is a hallmark of women's aging and is frequently portrayed as a medical issue. It also encompasses social and biological aspects often neglected and not well-understood, leaving women with insufficient support and attention. With the decline in estrogen levels, starting years before menopause is fully established, women experience various physical symptoms, and the risk of many age-related diseases increases sharply soon after these hormonal changes occur. Notably, these hormonal shifts also significantly impact the vaginal and gut microbiomes, contributing to dysbiosis and influencing the onset and progression of several diseases. Here, we examined the complex and dynamic relationship among aging, menopause, and microbiome changes with a particular focus on the vaginal and gut ecosystems. Emerging research highlights diet as a potential modulator for maintaining microbiome health during menopause. A deeper understanding of microbiome changes across life stages suggests the potential for microbiome-targeted strategies to support well-aging in women.</div></div>","PeriodicalId":55545,"journal":{"name":"Ageing Research Reviews","volume":"113 ","pages":"Article 102950"},"PeriodicalIF":12.4,"publicationDate":"2025-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145575038","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-11-19DOI: 10.1016/j.arr.2025.102954
Can Yin , Xinxin Tang , Juan Zeng , Zhengqin Wang , Jianing Mi , Ying Liang , Dalian Qin , Qitong Feng , Anguo Wu
Alzheimer’s disease (AD), the most prevalent neurodegenerative disorder, remains a global health crisis due to the lack of early diagnostic tools, dynamic monitoring strategies, and effective therapies. Current diagnostic methods such as cerebrospinal fluid (CSF) analysis and neuroimaging, while accurate, are invasive, expensive, and unsuitable for routine screening, highlighting the pressing need for alternative approaches. This review comprehensively examines the transformative role of next-generation biosensors in revolutionizing AD management. By leveraging breakthroughs in nanotechnology, materials science, and artificial intelligence (AI), modern biosensors enable ultrasensitive, non-invasive detection of AD biomarkers, including amyloid-β (Aβ), Tau proteins, and neurofilament light chain (NfL), across diverse biofluids such as blood, saliva, and tears. We critically evaluate electrochemical, optical, and acoustic biosensing platforms, highlighting their integration into wearable and portable devices for real-time disease monitoring and personalized therapeutic interventions. Emerging trends such as AI-driven analytics, CRISPR-based diagnostics, and closed-loop neuromodulation systems are explored for their potential to decode disease progression and optimize treatment responses. Challenges in clinical translation, including sensor stability, regulatory hurdles, and ethical considerations, are addressed to pave the way for scalable, patient-centric solutions. By synthesizing cutting-edge advancements and interdisciplinary insights, this review charts a roadmap for biosensor technologies to shift AD care from reactive to proactive, ultimately improving quality of life for patients and caregivers.
{"title":"Next-generation biosensor technologies for Alzheimer’s disease: Innovations in diagnosis, monitoring, and treatment","authors":"Can Yin , Xinxin Tang , Juan Zeng , Zhengqin Wang , Jianing Mi , Ying Liang , Dalian Qin , Qitong Feng , Anguo Wu","doi":"10.1016/j.arr.2025.102954","DOIUrl":"10.1016/j.arr.2025.102954","url":null,"abstract":"<div><div>Alzheimer’s disease (AD), the most prevalent neurodegenerative disorder, remains a global health crisis due to the lack of early diagnostic tools, dynamic monitoring strategies, and effective therapies. Current diagnostic methods such as cerebrospinal fluid (CSF) analysis and neuroimaging, while accurate, are invasive, expensive, and unsuitable for routine screening, highlighting the pressing need for alternative approaches. This review comprehensively examines the transformative role of next-generation biosensors in revolutionizing AD management. By leveraging breakthroughs in nanotechnology, materials science, and artificial intelligence (AI), modern biosensors enable ultrasensitive, non-invasive detection of AD biomarkers, including amyloid-<em>β</em> (A<em>β</em>), Tau proteins, and neurofilament light chain (NfL), across diverse biofluids such as blood, saliva, and tears. We critically evaluate electrochemical, optical, and acoustic biosensing platforms, highlighting their integration into wearable and portable devices for real-time disease monitoring and personalized therapeutic interventions. Emerging trends such as AI-driven analytics, CRISPR-based diagnostics, and closed-loop neuromodulation systems are explored for their potential to decode disease progression and optimize treatment responses. Challenges in clinical translation, including sensor stability, regulatory hurdles, and ethical considerations, are addressed to pave the way for scalable, patient-centric solutions. By synthesizing cutting-edge advancements and interdisciplinary insights, this review charts a roadmap for biosensor technologies to shift AD care from reactive to proactive, ultimately improving quality of life for patients and caregivers.</div></div>","PeriodicalId":55545,"journal":{"name":"Ageing Research Reviews","volume":"114 ","pages":"Article 102954"},"PeriodicalIF":12.4,"publicationDate":"2025-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145575025","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-11-19DOI: 10.1016/j.arr.2025.102951
Chanchan He , Ke Chen , Yue Yu , Yexuan Xiao , Yuhe Zhang , Xi Vivien Wu , Nan Jiang
Background
Despite growing interest in digital interventions to mitigate cognitive decline in older adults, their effectiveness remains uncertain due to heterogeneous study designs and the lack of core components for successful intervention design. This review systematically evaluated the efficacy of digital interventions through randomized controlled trials (RCTs) to identify key success factors and address gaps in effective design.
Methods
The search was conducted on CINAHL, Embase, PubMed, PsycINFO, and grey literature from 1 January 2010, to 1 June 2024, to identify RCTs that examined the effects of digital interventions on global cognitive function in individuals with MCI. The study was registered prospectively with PROSPERO under CRD42024528458.
Results
Out of 3414 studies, 21 RCTs were analyzed. Digital interventions improved global cognitive function in MCI patients (SMD (95 % CI) = 0.52(0.36–0.68)). Narrative synthesis showed that eight critical factors contributed to the success of these interventions: (1) User-friendly technology; (2) Multiple cognitive domains coverage; (3) Simulation of real-life scenarios; (4) Integration with physical exercise; (5) Real-time feedback and rewards; (6) Professional guidance and supervision; (7) Human participation; and (8) Social interaction. Additionally, methodological considerations for future RCTs on digital MCI interventions were: (1) Collecting data on support engagement and adherence throughout the intervention; (2) Using a hybrid outcome measurement approach with qualitative interviews and quantitative questionnaires; (3) Supplementing assessments with objective neurobiological evaluations; (4) More comparable and standardized control group; (5) Conducting follow-ups for at least one-year post-intervention; (6) Ensuring personalization and adaptability; (7) Incorporating social and professional support networks.
Conclusion
The insights from this systematic review and meta-analysis empower future digital MCI interventional researchers, health professionals, clinicians, and patients to design, develop, and implement successful interventions for older users.
{"title":"Effectiveness of digital interventions in middle-aged and older adults with mild cognitive impairment: A systematic review and meta-analysis of randomized controlled trials","authors":"Chanchan He , Ke Chen , Yue Yu , Yexuan Xiao , Yuhe Zhang , Xi Vivien Wu , Nan Jiang","doi":"10.1016/j.arr.2025.102951","DOIUrl":"10.1016/j.arr.2025.102951","url":null,"abstract":"<div><h3>Background</h3><div>Despite growing interest in digital interventions to mitigate cognitive decline in older adults, their effectiveness remains uncertain due to heterogeneous study designs and the lack of core components for successful intervention design. This review systematically evaluated the efficacy of digital interventions through randomized controlled trials (RCTs) to identify key success factors and address gaps in effective design.</div></div><div><h3>Methods</h3><div>The search was conducted on CINAHL, Embase, PubMed, PsycINFO, and grey literature from 1 January 2010, to 1 June 2024, to identify RCTs that examined the effects of digital interventions on global cognitive function in individuals with MCI. The study was registered prospectively with PROSPERO under CRD42024528458.</div></div><div><h3>Results</h3><div>Out of 3414 studies, 21 RCTs were analyzed. Digital interventions improved global cognitive function in MCI patients (SMD (95 % CI) = 0.52(0.36–0.68)). Narrative synthesis showed that eight critical factors contributed to the success of these interventions: (1) User-friendly technology; (2) Multiple cognitive domains coverage; (3) Simulation of real-life scenarios; (4) Integration with physical exercise; (5) Real-time feedback and rewards; (6) Professional guidance and supervision; (7) Human participation; and (8) Social interaction. Additionally, methodological considerations for future RCTs on digital MCI interventions were: (1) Collecting data on support engagement and adherence throughout the intervention; (2) Using a hybrid outcome measurement approach with qualitative interviews and quantitative questionnaires; (3) Supplementing assessments with objective neurobiological evaluations; (4) More comparable and standardized control group; (5) Conducting follow-ups for at least one-year post-intervention; (6) Ensuring personalization and adaptability; (7) Incorporating social and professional support networks.</div></div><div><h3>Conclusion</h3><div>The insights from this systematic review and meta-analysis empower future digital MCI interventional researchers, health professionals, clinicians, and patients to design, develop, and implement successful interventions for older users.</div></div>","PeriodicalId":55545,"journal":{"name":"Ageing Research Reviews","volume":"114 ","pages":"Article 102951"},"PeriodicalIF":12.4,"publicationDate":"2025-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145575017","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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