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Speech digital biomarker combined with fluid biomarkers predict cognitive impairment through machine learning
Jin-Tao Wang, Nan Zhi, Gang Xu, Jie-Li Geng, Jin-Wen Xiao, Hai-Xia Li, Jian-Ping Li, Xin-Yi Xie et al.. Alzheimer's research & therapy, 2025-10-21
Topic: Plasma and spinal-fluid biomarkers
Significant differences in AD biomarkers were observed among different groups. Notably, the speech digital biomarker percentage of silence duration (PSD) was correlated with cognitive level, serum glial fibrillary acidic protein (GFAP), neurofilament light chain (NFL), phosphorylated tau protein 217 (p-Tau217) and amyloid deposition in specific brain regions. Additionally, we discovered that as the different stages of Aβ deposition progress, PSD, p-Tau217, and GFAP exhibit a two-stage change pattern. Based on the findings, a machine learning CI diagnostic model (AUC = 0.928, 95% CI 0.897 to 0.960) incorporating PSD, APOE, GFAP, and demographic information was developed. Furthermore, an Aβ status classification model (AUC = 0.845, 95% CI 0.783 to 0.907) with PSD, APOE, p-Tau217, and demographic data was also constructed. Combining speech digital markers with serum and other biomarkers helps identify CI, representing a promising advance in AD detection. This study serves as a preliminary yet encouraging step toward applying speech digital biomarkers in AD diagnostics.
Key findings
- Significant differences in AD biomarkers were observed among different groups.
- Notably, the speech digital biomarker percentage of silence duration (PSD) was correlated with cognitive level, serum glial fibrillary acidic protein (GFAP), neurofilament light chain (NFL), phosphorylated tau protein 217 (p-Tau217) and amyloid deposition in specific brain regions.
- Additionally, we discovered that as the different stages of Aβ deposition progress, PSD, p-Tau217, and GFAP exhibit a two-stage change pattern.
- Based on the findings, a machine learning CI diagnostic model (AUC = 0.928, 95% CI 0.897 to 0.960) incorporating PSD, APOE, GFAP, and demographic information was developed.
- Furthermore, an Aβ status classification model (AUC = 0.845, 95% CI 0.783 to 0.907) with PSD, APOE, p-Tau217, and demographic data was also constructed.
Study details
- Participants238
- Effect sizeAUC = 0.928; AUC = 0.845
- Evidence strengthLow
How this study scored 57/100
Published in a peer-reviewed journal. Scored zero for study design and randomised / blinded because the record does not state it. We have no data on journal's citation record, times cited, citations per year, impact for its field and year, cited by studies here, still being cited, senior author's track record, institution's track record, multi-centre collaboration and free to read, so those were left out: it earned 32 of the 56 points we could judge it on, which is 57 out of 100.
| Component | Points | Why |
|---|---|---|
| Journal tier | 7 / 10 | Tier 3 — peer-reviewed journal (Alzheimer's research & therapy) |
| Journal's citation record | — / 4 | No journal record from OpenAlex — left out of this score |
| Study design | 0 / 12 | No study design recorded — scores zero, not guessed (not stated in the record — scored 0) |
| Strength of evidence | 1 / 5 | Rated “low” in our summary |
| Peer-review validation | 10 / 10 | Peer-reviewed. Published as an article in a named peer-reviewed journal. |
| How recent | 4 / 5 | 2025 — within 3 year(s) of the newest study here |
| Sample size | 2 / 4 | 238 participants |
| Effect reported | 3 / 3 | Effect reported with a statistic (interval, p-value, ratio or similar) |
| Studied in people | 3 / 3 | Studied in people (participant count stated) |
| Randomised / blinded | 0 / 2 | No randomisation or blinding stated — scores zero (not stated in the record — scored 0) |
| Times cited | — / 7 | No citation count (not found in OpenAlex) — left out of this score |
| Citations per year | — / 5 | No citation count or no year — left out of this score |
| Impact for its field and year | — / 6 | No field-weighted citation impact (OpenAlex has none yet) — left out of this score |
| Cited by studies here | — / 6 | Not yet checked against this library's citation graph — left out of this score |
| Still being cited | — / 4 | No yearly citation counts — left out of this score |
| Senior author's track record | — / 4 | Senior author's record not available — left out of this score |
| Institution's track record | — / 3 | No institution listed in OpenAlex — left out of this score |
| Multi-centre collaboration | — / 3 | Institutions not listed — left out of this score |
| Indexed in PubMed | 2 / 2 | Indexed in PubMed (PMID 41121436) |
| Free to read | — / 2 | Access not recorded — left out of this score |
| Points | 32 / 56 | Scored on 10 of 20 parts. We have no data for the rest, so they are left out: 32 of the 56 points we could judge it on is 57 out of 100. |
| Total | 57 / 100 | — |
What each of these 20 components measures, and what this score is not →
Read the original
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Cite this study APA, Vancouver, BibTeX · download .bib or .ris
Built from the record alzhub holds for this paper, not looked up. Volume, issue and page numbers are not held in the record. The record's author list ends in “et al.”, so not every author is named. Check the citation against the original before you use it.
APA 7th
Wang, J.-T., Zhi, N., Xu, G., Geng, J.-L., Xiao, J.-W., Li, H.-X., et al. (2025). Speech digital biomarker combined with fluid biomarkers predict cognitive impairment through machine learning. Alzheimer's research & therapy. https://doi.org/10.1186/s13195-025-01877-6
Vancouver
Wang J.-T, Zhi N, Xu G, Geng J.-L, Xiao J.-W, Li H.-X, et al. Speech digital biomarker combined with fluid biomarkers predict cognitive impairment through machine learning. Alzheimer's research & therapy. 2025. doi:10.1186/s13195-025-01877-6. PMID: 41121436.
BibTeX
@article{wang2025-41121436,
author = {Wang, Jin-Tao and Zhi, Nan and Xu, Gang and Geng, Jie-Li and Xiao, Jin-Wen and Li, Hai-Xia and Li, Jian-Ping and Xie, Xin-Yi and others},
title = {{Speech digital biomarker combined with fluid biomarkers predict cognitive impairment through machine learning}},
journal = {Alzheimer's research \& therapy},
year = {2025},
doi = {10.1186/s13195-025-01877-6},
pmid = {41121436},
url = {https://doi.org/10.1186/s13195-025-01877-6}
}
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