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Background <p>The recommended dementia diagnostic pathway comprises non-specialist assessment followed by specialist diagnosis. Given increasing resource constraints and existing inequalities in accessing specialist care, more accurate assessment in non-specialist settings may improve dementia management. This study assessed the diagnostic accuracy of blood biomarkers of Alzheimer’s disease (AD) and neurodegeneration for detecting probable AD (PAD) and mild cognitive impairment (MCI) with amyloid positivity (AP), particularly when they supplement current non-specialist practice of administering Mini-Mental State Examination (MMSE).</p> Methods <p>We accessed data from the Bio-Hermes study which grouped participants as cognitively normal (n=417), MCI (n=312), and PAD (n=272). Blood biomarkers of AD and neurodegeneration included: amyloid-beta 42/40; phosphorylated-tau 181 (p-tau181); p-tau217; glial fibrillary acidic protein (GFAP); and neurofilament light (NfL). Biomarkers were added individually or as panel to MMSE to predict the following diagnostic outcomes: PAD; MCI or PAD (MCI-PAD); PAD with AP measured by positron emission tomography/cerebrospinal fluid (PAD-AP); and MCI-PAD with AP (MCI-PAD-AP). Accuracy was assessed using receiver operating characteristic (ROC) curve and area under ROC curve (AUC) following logistic regression, adjusted for covariates observable in general clinical setting (e.g., alcohol, smoking, functional impairment) and apolipoprotein E ε4 carrier status. Statistically significant differences in AUC were estimated by DeLong test. Subgroup analyses were conducted by age and race/ethnicity.</p> Results <p>MMSE plus individual biomarkers or panels significantly improved accuracy to detect PAD-AP and MCI-PAD-AP versus MMSE alone: e.g., AUC for MMSE+p-tau217, adjusted for covariates, to detect MCI-PAD-AP was 0.928 versus 0.844 for MMSE alone (DeLong test for significance P&lt;0.001); MMSE plus optimal panel comprising all five biomarkers achieved AUC of 0.939 (DeLong P&lt;0.001 versus MMSE alone). AUC improvements from biomarker addition were smaller, sometimes not statistically significant, for PAD and MCI-PAD. Composition of optimal panel varied across subgroups: e.g., p-tau217 was included in the optimal panel for non-Hispanic White, while p-tau181 was included in the panel instead for non-White race/ethnicity.</p> Conclusions <p>Blood biomarker supplementation of cognitive testing can improve detection of amyloid-positive MCI and dementia. This potentially supports an efficient and equitable dementia diagnostic pathway which contributes to the sustainable delivery of prospective amyloid-targeting therapies with proven safety, effectiveness and cost-effectiveness.</p>

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Blood biomarkers to improve dementia diagnostic accuracy: a cross-sectional analysis

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年5月1日

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  • Joseph Kwon,
  • Megan Kirk Chang,
  • Adam Gordon-Boyle,
  • Sam Creavin,
  • Lynne Hughes,
  • Vanessa Raymont,
  • Kamaldeep Bhui,
  • Apostolos Tsiachristas

摘要

Background

The recommended dementia diagnostic pathway comprises non-specialist assessment followed by specialist diagnosis. Given increasing resource constraints and existing inequalities in accessing specialist care, more accurate assessment in non-specialist settings may improve dementia management. This study assessed the diagnostic accuracy of blood biomarkers of Alzheimer’s disease (AD) and neurodegeneration for detecting probable AD (PAD) and mild cognitive impairment (MCI) with amyloid positivity (AP), particularly when they supplement current non-specialist practice of administering Mini-Mental State Examination (MMSE).

Methods

We accessed data from the Bio-Hermes study which grouped participants as cognitively normal (n=417), MCI (n=312), and PAD (n=272). Blood biomarkers of AD and neurodegeneration included: amyloid-beta 42/40; phosphorylated-tau 181 (p-tau181); p-tau217; glial fibrillary acidic protein (GFAP); and neurofilament light (NfL). Biomarkers were added individually or as panel to MMSE to predict the following diagnostic outcomes: PAD; MCI or PAD (MCI-PAD); PAD with AP measured by positron emission tomography/cerebrospinal fluid (PAD-AP); and MCI-PAD with AP (MCI-PAD-AP). Accuracy was assessed using receiver operating characteristic (ROC) curve and area under ROC curve (AUC) following logistic regression, adjusted for covariates observable in general clinical setting (e.g., alcohol, smoking, functional impairment) and apolipoprotein E ε4 carrier status. Statistically significant differences in AUC were estimated by DeLong test. Subgroup analyses were conducted by age and race/ethnicity.

Results

MMSE plus individual biomarkers or panels significantly improved accuracy to detect PAD-AP and MCI-PAD-AP versus MMSE alone: e.g., AUC for MMSE+p-tau217, adjusted for covariates, to detect MCI-PAD-AP was 0.928 versus 0.844 for MMSE alone (DeLong test for significance P<0.001); MMSE plus optimal panel comprising all five biomarkers achieved AUC of 0.939 (DeLong P<0.001 versus MMSE alone). AUC improvements from biomarker addition were smaller, sometimes not statistically significant, for PAD and MCI-PAD. Composition of optimal panel varied across subgroups: e.g., p-tau217 was included in the optimal panel for non-Hispanic White, while p-tau181 was included in the panel instead for non-White race/ethnicity.

Conclusions

Blood biomarker supplementation of cognitive testing can improve detection of amyloid-positive MCI and dementia. This potentially supports an efficient and equitable dementia diagnostic pathway which contributes to the sustainable delivery of prospective amyloid-targeting therapies with proven safety, effectiveness and cost-effectiveness.