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16/100

⚠ Preprint — not yet peer-reviewed alzhub usefulness score 16 out of 100. Cognitive tests and assessment

Topic: Brain imaging, connectivity and assessment

Background Alzheimer’s disease (AD) is a progressive neurodegenerative brain disorder characterized by cognitive decline. The Mini-Mental State Examination (MMSE) is a well-established screening tool that evaluates five cognitive domains with a total maximum score of 30. Although previous studies have suggested a genetic basis for cognitive decline in AD, inconsistent findings have been reported and may stem from variability in phenotypic definitions. Methods We conducted a simulation study using parameters informed by real-world AD cohorts to model 10-year trajectories of MMSE scores in a sample of 500 individuals. Three types of cognitive decline patterns—linear, quadratic, and sigmoid—were simulated. We incorporated realistic missing data mechanisms to reflect typical challenges in longitudinal data collection. From the simulated MMSE data, we derived seven distinct phenotypic measures of cognitive decline. Using statistical models that assumed additive genetic effects, we compared the power of these phenotypes across different missing data scenarios to identify the most effective strategies for phenotyping in genetic association studies. Results Adjusting for baseline MMSE improved the statistical power of measures that do not incorporate it in the definition. Across all simulated scenarios, simpler non-repeated measures, such as “Slope”, “dMMSEdt”, and “Time-to-MMSE10”, consistently outperformed the others. Conclusions The phenotypic measures “Slope” and “dMMSE/dt” consistently showed strong performance across a range of data distributions, while “Time-to-MMSE10” was particularly effective in cohorts with a higher proportion of individuals experiencing severe cognitive decline. These findings highlight the importance of tailoring the choice of cognitive decline measures to the specific characteristics of the dataset, such as the severity and distribution of decline, to improve the power to detect genetic factors associated with Alzheimer’s disease.

Key findings

Results Adjusting for baseline MMSE improved the statistical power of measures that do not incorporate it in the definition.
These findings highlight the importance of tailoring the choice of cognitive decline measures to the specific characteristics of the dataset, such as the severity and distribution of decline, to improve the power to detect genetic factors associated with Alzheimer’s disease.

Study details

Study designlongitudinal study
Participants500
Evidence strengthModerate

How this study scored 16/100

Preprint — not yet peer-reviewed. It scored 44 on the same rubric, but non-peer-reviewed sources are capped at 16, so it shows as 16. Scored zero for effect reported and randomised / blinded because the record does not state it. We have no data on 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 and multi-centre collaboration, so those were left out: it earned 27 of the 62 points we could judge it on, which is 44 out of 100.

Component	Points	Why
Journal tier	2 / 10	Tier 4 — not a peer-reviewed journal (bioRxiv)
Journal's citation record	0 / 4	Not a journal article, so the journal's record does not count
Study design	7 / 12	A longitudinal study following the same people over time
Strength of evidence	3 / 5	Rated “moderate” in our summary
Peer-review validation	4 / 10	Preprint — not yet peer-reviewed. Hosted on a preprint server.
How recent	5 / 5	2026 — within 1 year(s) of the newest study here
Sample size	2 / 4	500 participants
Effect reported	0 / 3	No effect size reported — scores zero (not stated in the record — scored 0)
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	1 / 2	Not in PubMed, but has a DOI
Free to read	0 / 2	Behind a paywall — scores zero
Points	27 / 62	Scored on 12 of 20 parts. We have no data for the rest, so they are left out: 27 of the 62 points we could judge it on is 44 out of 100.
Total	44 / 100	Capped to 16 because this is not a peer-reviewed article.

What each of these 20 components measures, and what this score is not →

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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. Check the citation against the original before you use it.

APA 7th

Shi, R., Feingold, E., Kamboh, M. I., & Fan, K. (2026). Optimizing Cognitive Phenotyping for Genetic Discovery in Alzheimer’s Disease: A Simulation-Based Evaluation of Mini-Mental State Examination-Based Decline Measures. bioRxiv. https://doi.org/10.64898/2026.10.16.759741

Vancouver

Shi R, Feingold E, Kamboh MI, Fan K. Optimizing Cognitive Phenotyping for Genetic Discovery in Alzheimer’s Disease: A Simulation-Based Evaluation of Mini-Mental State Examination-Based Decline Measures. bioRxiv. 2026. doi:10.64898/2026.10.16.759741.

BibTeX

@article{shi2026-aaa991b3,
  author  = {Shi, R. and Feingold, E. and Kamboh, M. I. and Fan, K.},
  title   = {{Optimizing Cognitive Phenotyping for Genetic Discovery in Alzheimer’s Disease: A Simulation-Based Evaluation of Mini-Mental State Examination-Based Decline Measures}},
  journal = {bioRxiv},
  year    = {2026},
  doi     = {10.64898/2026.10.16.759741},
  url     = {https://doi.org/10.64898/2026.10.16.759741}
}

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