Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Original Article
  • Published:

Regional grey matter shrinks in hypertensive individuals despite successful lowering of blood pressure

Journal of Human Hypertension volume 26, pages 295–305 (2012)Cite this article

Abstract

The aim of the study was to determine whether the reduction in brain grey matter volume associated with hypertension persisted or was remediated among hypertensive patients newly treated over the course of a year. A total of 41 hypertensive patients were assessed over the course of a 1-year successful anti-hypertensive treatment. Brain areas identified previously in cross-sectional studies differing in volume between hypertensive and normotensive individuals were examined with a semi-automated measurement technique (automated labelling pathway). Volumes of grey matter regions were computed at baseline after a year of treatment and compared with archival data from normotensive individuals. Reductions in regional grey matter volume over the follow-up period were observed despite successful treatment of blood pressure (BP). The comparison group of older, but normotensive, individuals showed no significant changes over a year in the regions tested in the treated hypertensive group. These novel results suggest that essential hypertension is associated with regional grey matter shrinkage, and successful reduction of BP may not completely counter that trend.

This is a preview of subscription content, access via your institution

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Figure 1
Figure 2
Figure 3

Similar content being viewed by others

References

  1. Kennedy K, Raz N . Pattern of normal age-related regional differences in white matter microstructure is modified by vascular risk. Brain Research 2009; 1297: 41–56.

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  2. Burgmans S, van Boxtel MP, Gronenschild EH, Vuurman EF, Hofman P, Uylings HB et al. Multiple indicators of age-related differences in cerebral white matter and the modifying effects of hypertension. Neuroimage 2010; 49: 2083–2093.

    Article  CAS  PubMed  Google Scholar 

  3. Firbank MJ, Wiseman RM, Burton EJ, Saxby BK, O’Brien JT, Ford GA . Brain atrophy and white matter hyperintensity change in older adults and relationship to blood pressure. Brain atrophy, WMH change, and blood pressure. J Neurol 2007; 254: 713–721.

    Article  PubMed  Google Scholar 

  4. Goldstein IB, Bartzokis G, Guthrie D, Shapiro D . Ambulatory blood pressure and brain atrophy in the healthy elderly. Neurology 2002; 59: 713–719.

    Article  PubMed  Google Scholar 

  5. Longstreth Jr T, Manolio TA, Arnold A, Burke GL, Bryan N, Jungreis CA et al. Clinical correlates of white matter findings on cranial magnetic resonance imaging of 3301 elderly people. The Cardiovascular Health Study. Stroke 1996; 27: 1274–1282.

    Article  PubMed  Google Scholar 

  6. Raz N, Rodrigue KM, Haacke EM . Brain aging and its modifiers: Insights from in vivo neuromorphometry and susceptibility weighted imaging. Ann NY Acad Sci 2007; 1097: 84–93.

    Article  PubMed  Google Scholar 

  7. Raz N, Rodrigue KM, Kennedy KM, Acker JD . Vascular health and longitudinal changes in brain and cognition in middle-aged and older adults. Neuropsychology 2007; 21: 149–157.

    Article  PubMed  Google Scholar 

  8. Gianaros PJ, Greer PJ, Ryan CM, Jennings JR . Higher blood pressure predicts lower regional grey matter volume: consequences on short-term information processing. Neuroimage 2006; 31: 754–765.

    Article  PubMed  Google Scholar 

  9. Raz N, Rodrigue KM, Acker JD . Hypertension and the brain: vulnerability of the prefrontal regions and executive functions. Behav Neurosci 2003; 117: 1169–1180.

    Article  PubMed  Google Scholar 

  10. den Heijer T, Launer LJ, Prins ND, van Dijk EJ, Vermeer SE, Hofman A et al. Association between blood pressure, white matter lesions, and atrophy of the medial temporal lobe. Neurology 2005; 64: 263–267.

    Article  CAS  PubMed  Google Scholar 

  11. Korf ES, White LR, Scheltens P, Launer LJ . Midlife blood pressure and the risk of hippocampal atrophy: the Honolulu Asia Aging Study. Hypertension 2004; 44: 29–34.

    Article  CAS  PubMed  Google Scholar 

  12. Taki Y, Goto R, Evans A, Zijdenbos A, Neelin P, Lerch J et al. Voxel-based morphometry of human brain with age and cerebrovascular risk factors. Neurobiol Aging 2004; 25: 455–463.

    Article  PubMed  Google Scholar 

  13. Raz N, Lindenberger U, Rodrigue KM, Kennedy KM, Head D, Williamson A et al. Regional brain changes in aging healthy adults: general trends, individual differences and modifiers. Cereb Cortex 2005; 15: 1676–1689.

    Article  PubMed  Google Scholar 

  14. Chen X, Wen W, Anstey KJ, Sachdev PS . Effects of cerebrovascular risk factors on gray matter volume in adults aged 60–64 years: a voxel-based morphometric study. Psychiatry Res 2006; 147: 105–114.

    Article  PubMed  Google Scholar 

  15. Strassburger TL, Lee HC, Daly EM, Szczepanik J, Krasuski JS, Mentis MJ et al. Interactive effects of age and hypertension on volumes of brain structures. Stroke 1997; 28: 1410–1417.

    Article  CAS  PubMed  Google Scholar 

  16. Waldstein SR, Katzel LI . Hypertension and cognitive function. In: Waldstein SR, Katzel LI, Elias MF (eds). Neuropsychology of Cardiovascular Disease. Lawrence Erlbaum Associates Publishers: Mahwah, NJ, 2001 pp 15–36.

    Chapter  Google Scholar 

  17. Raz N . The aging brain observed in vivo: differential changes and their modifiers. In: Cabez R, Nyberg L, Park D (eds). Cognitive Neuroscience of Aging. Oxford University Press: Oxford, UK, 2005, pp 19–57.

    Google Scholar 

  18. Baumbach GL, Chillon JM . Effects of angiotensin-converting enzyme inhibitors on cerebral vascular structure in chronic hypertension. J Hypertens Suppl 2000; 18: S7–S11.

    CAS  PubMed  Google Scholar 

  19. Schiffrin EL, Park JB, Intengan HD, Touyz RM . Correction of arterial structure and endothelial dysfunction in human essential hypertension by the angiotensin receptor antagonist losartan. Circulation 2000; 101: 1653–1659.

    Article  CAS  PubMed  Google Scholar 

  20. Wu M, Rosano C, Butters M, Whyte E, Nable M, Crooks R et al. A fully automated method for quantifying and localizing white matter hyperintensities on MR images. Psychiatry Res 2006; 148: 133–142.

    Article  PubMed Central  PubMed  Google Scholar 

  21. Carmichael OT, Aizenstein HA, Davis SW, Becker JT, Thompson PM, Meltzer CC et al. Atlas-based hippocampus segmentation in Alzheimer's disease and mild cognitive impairment. Neuroimage 2005; 27: 979–990.

    Article  PubMed  Google Scholar 

  22. Chobanian AV, Bakris GL, Black HR, Cushman WC, Green LA, Izzo Jr JL, et al. Seventh report of the Joint National Committee on Prevention, Detection, Evaluation, and Treatment of High Blood Pressure. Hypertension 2003; 42: 1206–1252.

    Article  CAS  PubMed  Google Scholar 

  23. Jennings JR, Muldoon MF, Price JC, Christie IC, Meltzer CC . Cerebrovascular support for cognitive processing in hypertensive patients is altered by blood pressure treatment. Hypertension 2008; 52: 65–71.

    Article  CAS  PubMed  Google Scholar 

  24. Jack Jr CR, Fox NC, Thompson P, Alexander G, Harvey D et al. The Alzheimer's Disease Neuroimaging Initiative (ADNI): MRI methods. J Magn Reson Imaging 2008; 27: 685–691.

    Article  PubMed Central  PubMed  Google Scholar 

  25. Jennings JR, Muldoon MF, Ryan C, Price JC, Greer P, Sutton-Tyrrell K et al. Reduced cerebral blood flow response and compensation among patients with untreated hypertension. Neurology 2005; 64: 1358–1365.

    Article  CAS  PubMed  Google Scholar 

  26. Bastianello S, Bozzao A, Paolillo A, Giugni E, Gasperini C, Koudriavtseva T et al. Fast spin-echo and fast fluid-attenuated inversion-recovery versus conventional spin-echo sequences for MR quantification of multiple sclerosis lesions. Amer J Neuroradiol 1997; 18: 699–704.

    CAS  Google Scholar 

  27. Jennings JR, Muldoon MF, Whyte EM, Scanlon J, Price J, Meltzer CC . Brain imaging findings predict blood pressure response to pharmacological treatment. Hypertension 2008; 52: 1113–1119.

    Article  CAS  PubMed  Google Scholar 

  28. Andreescu C, Butters MA, Meng S, Begley A, Rajji T, Wu M et al. Gray matter changes in late life depression: a structural MRI analysis. Neuropsychopharmacology 2008; 33: 256–257.

    Article  Google Scholar 

  29. Rorden C, Brett M . Stereotaxic display of brain lesions. Behav Neurol 2000; 12: 191–200.

    Article  PubMed  Google Scholar 

  30. Smith SM . Fast robust automated brain extraction. Hum Brain Map 2002; 17: 143–155.

    Article  Google Scholar 

  31. Soreca I, Rosano C, Jennings JR, Sheu LK, Kuller LH, Matthews KA et al. Gain in adiposity across 15 years is associated with reduced gray matter volume in healthy women. Psychosom Med 2009; 71: 485–490.

    Article  PubMed Central  PubMed  Google Scholar 

  32. Thirion JP . Image matching as a diffusion process: an analogy with Maxwell's demons. Med Image Anal 1998; 2: 243–260.

    Article  CAS  PubMed  Google Scholar 

  33. Tzourio-Mazoyer N, Landeau B, Papathanassiou D, Crivello F, Etard O, Delcroix N et al. Automated anatomical labeling of activations in SPM using a macroscopic anatomical parcellation of the MNI MRI single-subject brain. Neuroimage 2002; 15: 273–289.

    Article  CAS  PubMed  Google Scholar 

  34. Wu M, Carmichael O, Lopez-Garcia P, Carter CS, Aizenstein HJ . Quantitative comparison of AIR, SPM, and the fully deformable model for atlas-based segmentation of functional and structural MR images. Hum Brain Map 2006; 27: 747–754.

    Article  Google Scholar 

  35. Wu M, Rosano C, Aizenstein HJ . A morphological method to improve skull stripping of MR brain images. 11th Annual Meeting of the Organization for Human Brain Mapping. Illinois: Chicago 2005.

  36. Holmes CJ, Hoge R, Collins L, Woods R, Toga AW, Evans AC . Enhancement of MR images using registration for signal averaging. J Comp Assist Tomogr 1998; 22: 324–333.

    Article  CAS  Google Scholar 

  37. Chen M . 3-D deformable registration using a statistical atlas with applications in medicine. Carnegie Mellon University: Pittsburgh, PA, 1999.

    Google Scholar 

  38. Yoo T . Insight into Images: Principles and Practice for Segmentation, Registration, and Image Analysis. K Peters Ltd. Wellesey: MA, 2004.

    Book  Google Scholar 

  39. Blair JR, Spreen O . Predicting premorbid IQ: a revision of the National Adult Reading Test. Clin Neuropsychol 1989; 3: 129–136.

    Article  Google Scholar 

  40. Lezak MD . europsychological Assessment. 3rd edn. University Press: New York, 1995.

    Google Scholar 

  41. Kaplan E, Fein D, Morris R, Delis D . WAIS-R as a Neurospychological Instrument. Psychological Corporation: San Antonio, TX, 1991.

    Google Scholar 

  42. Borkowski JG, Benton AL, Spreen O . Word fluency and brain damage. Neuropsychologia 1967; 5: 135–140.

    Article  Google Scholar 

  43. Thompson LI, Heaton RK . Comparison of different versions of the Boston Naming Test. Clin Neuropsychol 1989; 3: 184–192.

    Article  Google Scholar 

  44. Morrow LA, Ryan C . Normative data for a working memory test: the four word short-term memory test. Clin Neuropsychol 2002; 16: 373–380.

    Article  PubMed  Google Scholar 

  45. Lewis R, Kupke T . Intermanual differences on skilled and unskilled motor tasks in nonlateralized brain dysfunction. Clin Neuropsychol 1992; 6: 374–382.

    Article  Google Scholar 

  46. Reitan RM . Validity of the trailmaking test as an indicator of organic brain damage. Percep Motor Skills 1958; 8: 271–276.

    Article  Google Scholar 

  47. Golden CJ . Stroop Color and Word Test. Stoelting: Chicago, 1978.

    Google Scholar 

  48. Aaronson D, Watts B . Extensions of Grier's computational formulas for A’and B’to below-chance performance. Psychol Bull 1987; 102: 439–442.

    Article  CAS  PubMed  Google Scholar 

  49. Grier J . Nonparametric indexes for sensitivity and bias: computing formulas. Psychol Bull 1971; 75: 424–429.

    Article  CAS  PubMed  Google Scholar 

  50. Milliken GA, Johnson DE . Analysis of Messy Data. Van Nostrand Reinhold: New York, 1984.

    Google Scholar 

  51. Miller GM, Chapman JP . Misunderstanding analysis of covariance. J Abnorm Psychol 2001; 110: 40–48.

    Article  CAS  PubMed  Google Scholar 

  52. Leritz EC, Salat DH, Williams VJ, Schnyer DM, Rudolph JL, Lipsitz L et al. Thickness of the human cerebral cortex is associated with metrics of cerebrovascular health in a normative sample of community dwelling older adults. Neuroimage 2011; 54 (4): 2659–2671.

    Article  PubMed  Google Scholar 

  53. Raz N, Rodrigue KM . Differential aging of the brain: patterns, cognitive correlates and modifiers. Neurosci Biobehav Rev 2006; 30: 730–748.

    Article  PubMed Central  PubMed  Google Scholar 

  54. van Dijk EJ, Breteler MM, Schmidt R, Berger K, Nilsson LG, Oudkerk M et al. The association between blood pressure, hypertension, and cerebral white matter lesions: cardiovascular determinants of dementia study. Hypertension 2004; 44: 625–630.

    Article  CAS  PubMed  Google Scholar 

  55. Kraut MA, Beason-Held LL, Elkins WD, Resnick SM . The impact of magnetic resonance imaging detected white matter hyperintensities on longitudinal changes in regional cerebral blood flow. J Cereb Blood Flow Metab 2008; 28: 190–197.

    Article  PubMed  Google Scholar 

  56. Nobili F, Rodriguez G, Marenco S, De Carli F, Gambaro M, Castello C et al. Regional cerebral blood flow in chronic hypertension. a correlative study. Stroke 1993; 24: 1148–1153.

    Article  CAS  PubMed  Google Scholar 

  57. Taki Y, Kinomura S, Sato K, Goto R, Kawashima R, Fukuda H . A longitudinal study of gray matter volume decline with age and modifying factors. Neurobiol Aging 2009; (e-pub ahead of print).

  58. Meier-Ruge W, Ulrich J, Brühlmann M, Meier E . Age-related white matter atrophy in the human brain. Ann N Y Acad Sci 1992; 673: 260–269.

    Article  CAS  PubMed  Google Scholar 

  59. Elderkin-Thompson V, Hellemann G, Pham D, Kumar A . Prefrontal brain morphology and executive function in healthy and depressed elderly. Int J Geriatr Psychiatry 2009; 24: 459–468.

    Article  PubMed  Google Scholar 

  60. Kareken DA, Mosnik DM, Doty RL, Dzemidzic M, Hutchins GD . Functional anatomy of human odor sensation, discrimination, and identification in health and again. Neuropsychology 2003; 17: 482–495.

    Article  PubMed  Google Scholar 

  61. Elderkin-Thompson V, Ballmaier M, Hellemann G, Pham D, Kumar A . Executive function and MRI prefrontal volumes among healthy older adults. Neuropsychology 2008; 22: 626–637.

    Article  PubMed  Google Scholar 

  62. Resnick SM, Goldszal AF, Davatzikos C, Golski S, Kraut MA, Metter EJ et al. One-year age changes in MRI brain volumes in older adults. Cereb Cortex 2000; 10: 464–472.

    Article  CAS  PubMed  Google Scholar 

  63. Resnick SM, Pham DL, Kraut MA, Zonderman AB, Davatzikos C . Longitudinal magnetic resonance imaging studies of older adults: a shrinking brain. J Neurosci 2003; 23: 3295–3301.

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  64. Sowell ER, Thompson PM, Toga AW . Mapping changes in the human cortex throughout the span of life. Neuroscientist 2004; 10: 372–392.

    Article  PubMed  Google Scholar 

  65. Akiyama H, Meyer JS, Mortel KF, Terayama Y, Thornby JI, Konno S . Normal human aging: factors contributing to cerebral atrophy. J Neurol Sci 1997; 152: 39–49.

    Article  PubMed  Google Scholar 

  66. Duron E, Hanon O . Vascular risk factors, cognitive decline, and dementia. Vasc Health Risk Manag 2008; 4: 363–381.

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  67. Meyer JS, Rauch GM, Rauch RA, Haque A, Crawford K . Cardiovascular and other risk factors for Alzheimer's disease and vascular dementia. Ann N Y Acad Sci 2000; 903: 411–443.

    Article  CAS  PubMed  Google Scholar 

  68. Moretti R, Torre P, Antonello RM, Manganaro D, Vilotti C, Pizzolato G . Risk factors for vascular dementia: hypotension as a key point. Vas Health Risk Manag 2008; 4: 395–402.

    Article  Google Scholar 

  69. Jennings JR, Zanstra Y . Is the brain the essential in hypertension? Neuroimage 2009; 47: 914–921.

    Article  PubMed  Google Scholar 

Download references

Acknowledgements

We thank Megan Nable and Adrienne Soehner for assistance with the scoring and the NIH grant NHLBI HL57529 for support of this research and for further support of the investigators, PJG: HL-R01-089850, MH-K01-070616; NR: R37, AG011230. Normotensive comparison data collection and sharing for this project was funded by the ADNI (Principal Investigator: Michael Weiner; NIH Grant U01 AG024904). ADNI is funded by the National Institute on Aging, the National Institute of Biomedical Imaging and Bioengineering (NIBIB), and through generous contributions from the following: Pfizer Inc., Wyeth Research, Bristol-Myers Squibb, Eli Lilly and Company, GlaxoSmithKline, Merck & Co. Inc., AstraZeneca AB, Novartis Pharmaceuticals Corporation, Alzheimer's Association, Eisai Global Clinical Development, Elan Corporation Plc, Forest Laboratories and the Institute for the Study of Aging, with participation from the US Food and Drug Administration. Industry partnerships are coordinated through the Foundation for the National Institutes of Health. The grantee organization is the Northern California Institute for Research and Education, and the study is coordinated by the Alzheimer's Disease Cooperative Study at the University of California, San Diego. ADNI data are disseminated by the Laboratory of Neuro Imaging at the University of California, Los Angeles.

Author information

Author notes

  1. J R Jennings, D N Mendelson, M F Muldoon, C M Ryan, P J Gianaros, N Raz and H Aizenstein: Data used in the preparation of this article were obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database (http://www.loni.ucla.edu/ADNI). As such, the investigators within the ADNI contributed to the design and implementation of ADNI and/or provided data but did not participate in analysis or writing of this report. ADNI investigators include (complete listing available at http://www.loni.ucla.edu/ADNI/Collaboration/ADNI_Citatation.shtml).

Authors and Affiliations

  1. Department of Psychiatry and Psychology, University of Pittsburgh, Pittsburgh, PA, USA

    J R Jennings, D N Mendelson, M F Muldoon, C M Ryan, P J Gianaros & H Aizenstein

  2. Wayne State University, Detroit, MI, USA

    N Raz

Authors
  1. J R Jennings
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. D N Mendelson
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. M F Muldoon
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. C M Ryan
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. P J Gianaros
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. N Raz
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. H Aizenstein
    View author publications

    You can also search for this author in PubMed Google Scholar

Consortia

the Alzheimer's Disease Neuroimaging Initiative

Corresponding author

Correspondence to J R Jennings.

Ethics declarations

Competing interests

The authors declare no conflict of interest.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Jennings, J., Mendelson, D., Muldoon, M. et al. Regional grey matter shrinks in hypertensive individuals despite successful lowering of blood pressure. J Hum Hypertens 26, 295–305 (2012). https://doi.org/10.1038/jhh.2011.31

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/jhh.2011.31

Keywords

This article is cited by

Search

Advanced search

Quick links