Blood‐based biomarkers for Alzheimer's disease: a milestone guideline paving the way for scalable diagnosis

Since the landmark 2018 research framework redefined Alzheimer's disease (AD) from a biological perspective,1 clinical guidelines have shaped the use of biomarkers in AD patients. The revised 2024 diagnostic criteria by the Alzheimer's Association subsequently acknowledged blood-based biomarkers (BBMs) for clinical use.2 Despite growing research interest, the field has lacked a structured framework for the utilization of BBMs in clinical practice. In 2025, the Alzheimer's Association recently published a clinical practice guideline on the use of BBMs for suspected AD within specialized care settings.3 This guideline timely sets a new benchmark for how we interpret, apply, and contextualize BBMs. This guideline makes two major contributions. First, it provides specific recommendations for clinical use, incorporating tiered diagnostic thresholds: ≥90% sensitivity and ≥75% specificity for triaging tests and ≥90% sensitivity and ≥90% specificity for confirmatory tests. The ability to tie cut-off values to specific use of triaging and confirmatory testing lays the groundwork for scalable biomarker deployment. Second, the guideline's adoption of a systematic evidence review with Grading of Recommendations Assessment, Development, and Evaluation (GRADE) methodology ensures transparency and elevates the credibility of its recommendations. While foundational, the guideline requires further consideration in areas such as expanding the tested population and specifying clinical contexts for biomarker interpretation. The guideline currently focuses on individuals with mild cognitive impairment or dementia, where AD pathology is relatively severe. As understanding of AD evolves, there is growing recognition of the importance of earlier detection and intervention. The paradigm shift in AD care is reflected in emerging treatment approaches, including anti-amyloid monoclonal antibodies, non-pharmacological strategies, and lifestyle modifications. For instance, TRAILBLAZER-ALZ 3 and AHEAD 3-45 are ongoing trials evaluating amyloid monoclonal antibodies in preclinical patients, while Finnish Geriatric Intervention Study to Prevent Cognitive Impairment and Disability (FINGER), prevention of dementia by intensive vascular care (preDIVA), and U.S. Study to Protect Brain Health Through Lifestyle Intervention to Reduce Risk (POINTER) trials assess the efficacy of lifestyle modifications for dementia prevention in older adults without cognitive impairment. As these approaches increasingly target early disease stages, the role of BBMs should therefore expand to proactively identifying at-risk individuals.4 Screening strategies should be designed in anticipation of therapeutic advances, ensuring that eligible populations are ready for timely interventions as soon as effective options become available. This evolving landscape suggests BBMs should be explored in two key populations: (1) high-risk but asymptomatic individuals, such as those with older age, apolipoprotein E ε4 genotype, family history, and modifiable risk factors5 and (2) individuals experiencing subjective cognitive decline despite normal cognitive evaluation. Future iterations of the systematic review and meta-analyses should include these populations, with subgroup analyses stratified by disease stage and symptom severity, to ensure biomarker deployment aligns with evolving preventive and therapeutic strategies. A second issue involves the important but undefined concept of “high pretest probability.” While the guideline targets BBM use for suspected AD, it leaves the estimation of that likelihood entirely to clinicians. In typical amnestic presentations, clinicians can infer this probability from characteristic symptom patterns. But in atypical manifestations or those with comorbidities, clinical suspicion alone may be misleading.6 If blood biomarkers are to be used meaningfully in these gray zones, we need better tools to define what constitutes pretest probability. A clearer framework for operationalizing pretest probability would strengthen the applicability of BBMs. This could involve patient age, clinical phenotype,7 family history, and accessible algorithmic decision aids that account for comorbidities and risk factors to predict future risk.8 Without such tools, BBMs may be underutilized in populations where it is most needed.9 In addition to population scope and pretest probability, several methodological and implementation challenges remain. The guideline focuses on single-threshold biomarkers, but dual-threshold approaches are emerging in research contexts and may improve accuracy. Inter-platform variability for the same analyte remains a challenge for broad implementation. Though the guideline notes the importance of health economic outcomes, cost-effectiveness analyses remain insufficiently mature for inclusion. The role of BBMs in longitudinal monitoring and multi-analyte integration also warrants consideration in future frameworks. In sum, this guideline is a landmark achievement, translating biomarker science into structured clinical pathways that promote standardization, accessibility, and precision. While the current guideline limits BBM recommendations to specialized care settings, most individuals with cognitive concerns are first assessed in primary care. Bridging this gap will require streamlined protocols that support the safe and effective use of BBMs in community settings, thereby ensuring equitable access to advanced diagnostics outside specialized memory clinics. Future iterations should strive to bring BBMs closer to the populations and decisions that also greatly matter: those at risk but not yet impaired and those with complex manifestations that challenge clinical judgment. As such, BBMs can support AD diagnosis while also reshaping the timing, context, and outcomes. The authors thank all who contributed to this manuscript. The authors declare no conflicts of interest. This study was supported by the National Key R&D Program of China (2022YFC3602600, 2025YFE0206600), Capital's Funds for Health Improvement and Research (2024-2-1032), Beijing Nova Program (20240484566), Beijing Natural Science Foundation (L251023), National Natural Science Foundation of China (82201568, 82220108009), STI2030-Major Projects (2021ZD0201801), and Beijing Outstanding Young Scientist Program (JWZQ20240101023). Please note: The publisher is not responsible for the content or functionality of any supporting information supplied by the authors. Any queries (other than missing content) should be directed to the corresponding author for the article.