Student or educator? Sign up with your school email and get 6 months of Pro free.
Cited by 404

The DOI matches its Crossref record; 10 of the 10 references it cites were found in a registry or an index.

Risk factors for progression of brain atrophy in aging: Six-year follow-up of normal subjects

Enzinger, C., Fazekas, F., Matthews, P. M., Ropele, S., Schmidt, H., Smith, S., Schmidt, R.·2005·Neurology

Abstract

OBJECTIVES: To determine the rate of brain atrophy in neurologically asymptomatic elderly and to investigate the impact of baseline variables including conventional cerebrovascular risk factors, APOE epsilon4, and white matter hyperintensity (WMH) on its progression. METHODS: We assessed the brain parenchymal fraction at baseline and subsequent annual brain volume changes over 6 years for 201 participants (F/M = 96/105; 59.8 +/- 5.9 years) in the Austrian Stroke Prevention Study from 1.5-T MRI scans using SIENA (structural image evaluation using normalization of atrophy)/SIENAX (an adaptation of SIENA for cross-sectional measurement)(www.fmrib.ox.ac.uk/fsl). Hypertension, cardiac disease, diabetes mellitus, smoking, and regular alcohol intake were present in 64 (31.8%), 60 (29.9%), 5 (2.5%), 70 (39.3%), and 40 (20.7%) subjects, respectively. Plasma levels of fasting glucose (93.7 +/- 18.6 mg/dL), glycated hemoglobin A (HbA1c; 5.6 +/- 0.7%), total cholesterol (228.3 +/- 40.3 mg/dL), and triglycerides (127.0 +/- 75.2 mg/dL) were determined. WMH was rated as absent (n = 56), punctate (n = 120), early confluent (n = 14), and confluent (n = 11). RESULTS: The baseline brain parenchymal fraction of the entire cohort was 0.80 +/- 0.02 with a mean annual brain volume change of -0.40 +/- 0.29%. Univariate analysis demonstrated a higher rate of brain atrophy in older subjects (p = 0.0001), in those with higher HbA1c (p = 0.0001), higher body mass index (p = 0.02), high alcohol intake (p = 0.04), severe WMH (p = 0.03), and in APOE epsilon4 carriers (p = 0.07). Multivariate analysis suggested that baseline brain parenchymal fraction, HbA1c, and WMH score explain a major proportion of variance in the rates of brain atrophy in the cohort (corrected R2 = 0.27; p = 0.0001). CONCLUSIONS: Neurologically asymptomatic elderly experience continuing brain volume loss, which appears to accelerate with age. Glycated hemoglobin A (HbA1c) was identified as a risk factor for a greater rate of brain atrophy. Clustering of factors associated with the so-called metabolic syndrome in subjects with high HbA1c suggests a link between this syndrome and late-life brain tissue loss.

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.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1212/01.wnl.0000161871.83614.bb

Cites

10 of 10 references verified0 citation errors · 0 not checkable

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.

A few free checks a day · papers already verified open instantly · results are public

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

Show all 10 references

Research works printed but not linked: 36

  1. 10.1007/BF00588069 · DOI
  2. Blatter D, Bigler E, Gale S, et al. Quantitative volumetric analysis of brain MR: normative database spanning 5 decades of life. Am J Neuroradiol 1995;16:241–251.
  3. 10.1093/cercor/10.5.464 · DOI · Paper page
  4. 10.1212/WNL.42.3.527 · DOI · Paper page
  5. 10.1159/000026235 · DOI
  6. 10.1161/01.STR.28.7.1410 · DOI · Paper page
  7. 10.1002/(SICI)1099-1166(199912)14:12<1050::AID-GPS56>3.0.CO;2-Z · DOI
  8. 10.2337/diabetes.53.3.687 · DOI · Paper page
  9. 10.1007/BF03348967 · DOI
  10. 10.1097/00006231-200001000-00005 · DOI
  11. 10.1161/hs0901.095723 · DOI · Paper page
  12. 10.1212/WNL.58.10.1539 · DOI
  13. 10.1002/jmri.10325 · DOI
  14. Fox NC, Schott JM. Imaging cerebral atrophy: normal ageing to Alzheimer’s disease. Lancet 2004;31:392–394.
  15. 10.1016/S0197-4580(02)00130-6 · DOI
  16. 10.1097/00004728-200105000-00022 · DOI · Paper page
  17. 10.1212/WNL.53.1.132 · DOI · Paper page
  18. 10.1161/01.STR.28.5.951 · DOI
  19. 10.2214/ajr.149.2.351 · DOI · Paper page
  20. Schmidt R, Enzinger C, Ropele S, Schmidt H, Fazekas F, the Austrian Stroke Prevention Study. Progression of cerebral white matter lesions: 6-year results of the Austrian Stroke Prevention Study. Lancet 2003;14:2046–2048.
  21. 10.1006/nimg.2002.1040 · DOI · Paper page
  22. 10.1016/S0140-6736(99)00496-1 · DOI · Paper page
  23. 10.1001/archneur.60.7.989 · DOI · Paper page
  24. Liu RSN, Lemieux L, Bell GS, et al. A longitudinal study of brain morphometrics using quantitative magnetic resonance imaging and difference image analysis. Neuroimage 2003;20:22–33.
  25. 10.1212/WNL.51.6.1555 · DOI
  26. Raz N, Gunning FM, Head D, et al. Selective aging of the human cerebral cortex observed in vivo: differential vulnerability of the prefrontal gray matter. Cereb Cortex 1997;7:268–282.
  27. 10.1002/ana.20027 · DOI
  28. Wang D, Chalk JB, Rose SE, et al. MR image-based measurement of rates of change in volumes of brain structures. Part II: application to a study of Alzheimer’s disease and normal aging. Magn Reson Imaging 2002;20:41–48.
  29. 10.1016/S0014-2999(02)01486-3 · DOI · Paper page
  30. 10.1046/j.1464-5491.2003.01065.x · DOI · Paper page
  31. 10.1161/01.CIR.0000111245.75752.C6 · DOI · Paper page
  32. 10.1016/j.ejphar.2004.02.056 · DOI · Paper page
  33. 10.1212/01.WNL.0000120663.85143.B3 · DOI
  34. Matthews FE, Chatfield M, Freeman C, McCracken C, Brayne C, MRC CFAS. Attrition and bias in the MRC cognitive function and ageing study: an epidemiological investigation. BMC Public Health 2004;27:12.
  35. 10.1093/brain/awh144 · DOI · Paper page
  36. 10.1001/archinte.164.10.1066 · DOI · Paper page

Cited by

See all 404

Metadata

Authors
Enzinger, C., Fazekas, F., Matthews, P. M., Ropele, S., Schmidt, H., Smith, S., & Schmidt, R.
Venue
Neurology
Published
2005-05-24
Volume
64
Issue
10
Pages
1704-1711
ISSN
  • 0028-3878 (ISSN-L)
  • 1526-632X
OpenAlex
W2009088633
Cited by
404
References
10
FWCI
8.98
Type
journal article
Language
en
Open access
closed

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: Risk factors for progression of brain atrophy in aging: Six-year follow-up of normal subjects. https://app.cochat.ai/papers/10.1212/01.wnl.0000161871.83614.bb
The paper
Enzinger, C., Fazekas, F., Matthews, P. M., Ropele, S., Schmidt, H., Smith, S., & Schmidt, R. (2005). Risk factors for progression of brain atrophy in aging: Six-year follow-up of normal subjects. Neurology. https://doi.org/10.1212/01.wnl.0000161871.83614.bb

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.