The DOI matches its Crossref record; 10 of the 10 references it cites were found in a registry or an index.
Insulin Resistance, Brain Atrophy, and Cognitive Performance in Late Middle–Aged Adults
Open accessAbstract
OBJECTIVE: Insulin resistance dysregulates glucose uptake and other functions in brain areas affected by Alzheimer disease. Insulin resistance may play a role in Alzheimer disease etiopathogenesis. This longitudinal study examined whether insulin resistance among late middle–aged, cognitively healthy individuals was associated with 1) less gray matter in Alzheimer disease–sensitive brain regions and 2) worse cognitive performance. RESEARCH DESIGN AND METHODS: Homeostasis model assessment of insulin resistance, gray matter volume, and the Rey Auditory Verbal Learning Test (RAVLT) were acquired in 372 participants at baseline and a consecutive subset of 121 individuals ~4 years later. Voxel-based morphometry and tensor-based morphometry were used, respectively, to test the association of insulin resistance with baseline brain volume and progressive gray matter atrophy. RESULTS: Higher insulin resistance predicted less gray matter at baseline and 4 years later in medial temporal lobe, prefrontal cortices, precuneus, and other parietal gyri. A region-of-interest analysis, independent of the voxel-wise analyses, confirmed that higher insulin resistance was related to medial temporal lobe atrophy. Atrophy itself corresponded to cognitive deficits in the RAVLT. Temporal lobe atrophy that was predicted by higher insulin resistance significantly mediated worse RAVLT encoding performance. CONCLUSIONS: These results suggest that insulin resistance in an asymptomatic, late middle–aged cohort is associated with progressive atrophy in regions affected by early Alzheimer disease. Insulin resistance may also affect the ability to encode episodic information by negatively influencing gray matter volume in medial temporal lobe.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Matthews, D. R., Hosker, J. P., Rudenski, A. S., Naylor, B. A., Treacher, D. F., & Turner, R. C. (1985) · Diabetologia · doi:10.1007/bf00280883 · Cited by 31,502
Voxel-Based Morphometry—The Methods
Ashburner, J., & Friston, K. J. (2000) · NeuroImage · doi:10.1006/nimg.2000.0582 · Cited by 8,676
A Voxel-Based Morphometric Study of Ageing in 465 Normal Adult Human Brains
Good, C. D., Johnsrude, I. S., Ashburner, J., Henson, R. N. A., Friston, K. J., & Frackowiak, R. S. J. (2001) · NeuroImage · doi:10.1006/nimg.2001.0786 · Cited by 4,561
Stages of the Pathologic Process in Alzheimer Disease: Age Categories From 1 to 100 Years
Braak, H., Thal, D. R., Ghebremedhin, E., & Del Tredici, K. (2011) · Journal of Neuropathology & Experimental Neurology · doi:10.1097/nen.0b013e318232a379 · Cited by 2,194
Metabolic reduction in the posterior cingulate cortex in very early Alzheimer's disease
Minoshima, S., Giordani, B., Berent, S., Frey, K. A., Foster, N. L., & Kuhl, D. E. (1997) · Annals of Neurology · doi:10.1002/ana.410420114 · Cited by 1,802
Prediction of AD with MRI-based hippocampal volume in mild cognitive impairment
Jack, C. R., Petersen, R. C., Xu, Y. C., O’Brien, P. C., Smith, G. E., Ivnik, R. J., Boeve, B. F., Waring, S. C., Tangalos, E. G., & Kokmen, E. (1999) · Neurology · doi:10.1212/wnl.52.7.1397 · Cited by 1,404
Haroon, E., Raison, C. L., & Miller, A. H. (2012) · Neuropsychopharmacology · doi:10.1038/npp.2011.205 · Cited by 1,000
Insulin and neurodegenerative disease: shared and specific mechanisms
Craft, S., & Stennis Watson, G. (2004) · The Lancet Neurology · doi:10.1016/s1474-4422(04)00681-7 · Cited by 830
Insulin metabolism and the risk of Alzheimer disease: The Rotterdam Study
Schrijvers, E. M. C., Witteman, J. C. M., Sijbrands, E. J. G., Hofman, A., Koudstaal, P. J., & Breteler, M. M. B. (2010) · Neurology · doi:10.1212/wnl.0b013e3181ffe4f6 · Cited by 328
Johnson, S. C., La Rue, A., Hermann, B. P., Xu, G., Koscik, R. L., Jonaitis, E. M., Bendlin, B. B., Hogan, K. J., Roses, A. D., Saunders, A. M., et al. (2011) · Alzheimer's & Dementia · doi:10.1016/j.jalz.2010.11.012 · Cited by 112
Self-citation
Cited by
Brain insulin resistance in type 2 diabetes and Alzheimer disease: concepts and conundrums
Arnold, S. E., Arvanitakis, Z., Macauley-Rambach, S. L., Koenig, A. M., Wang, H.-Y., Ahima, R. S., Craft, S., Gandy, S., Buettner, C., Stoeckel, L. E., et al. (2018) · Nature Reviews Neurology · doi:10.1038/nrneurol.2017.185 · Cited by 1,597
Type 2 diabetes in adolescents and young adults
Lascar, N., Brown, J., Pattison, H., Barnett, A. H., Bailey, C. J., & Bellary, S. (2018) · The Lancet Diabetes & Endocrinology · doi:10.1016/s2213-8587(17)30186-9 · Cited by 824
Iadecola, C., Yaffe, K., Biller, J., Bratzke, L. C., Faraci, F. M., Gorelick, P. B., Gulati, M., Kamel, H., Knopman, D. S., Launer, L. J., et al. (2016) · Hypertension · doi:10.1161/hyp.0000000000000053 · Cited by 746
Brain Insulin Resistance at the Crossroads of Metabolic and Cognitive Disorders in Humans
Kullmann, S., Heni, M., Hallschmid, M., Fritsche, A., Preissl, H., & Häring, H.-U. (2016) · Physiological Reviews · doi:10.1152/physrev.00032.2015 · Cited by 622
Brain Atrophy in Type 2 Diabetes
Moran, C., Phan, T. G., Chen, J., Blizzard, L., Beare, R., Venn, A., Münch, G., Wood, A. G., Forbes, J., Greenaway, T. M., et al. (2013) · Diabetes Care · doi:10.2337/dc13-0143 · Cited by 517
Metadata
- Authors
- Willette, A. A., Xu, G., Johnson, S. C., Birdsill, A. C., Jonaitis, E. M., Sager, M. A., Hermann, B. P., La Rue, A., Asthana, S., & Bendlin, B. B.
- Venue
- Diabetes Care
- Publisher
- American Diabetes Association
- Published
- 2013-01-17
- Volume
- 36
- Issue
- 2
- Pages
- 443-449
- ISSN
- 0149-5992 (ISSN-L)
- 1935-5548
- OpenAlex
- W2137566817
- Cited by
- 219
- References
- 10
- FWCI
- 7.58
- Type
- journal article
- Language
- en
- Open access
- hybrid
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Insulin Resistance, Brain Atrophy, and Cognitive Performance in Late Middle–Aged Adults. https://app.cochat.ai/papers/10.2337/dc12-0922
- The paper
- Willette, A. A., Xu, G., Johnson, S. C., Birdsill, A. C., Jonaitis, E. M., Sager, M. A., Hermann, B. P., La Rue, A., Asthana, S., & Bendlin, B. B. (2013). Insulin Resistance, Brain Atrophy, and Cognitive Performance in Late Middle–Aged Adults. Diabetes Care. https://doi.org/10.2337/dc12-0922
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.