Original Article | Published:

Clinical Studies and Practice

Body mass index is associated with cortical thinning with different patterns in mid- and late-life

International Journal of Obesity volume 42, pages 455461 (2018) | Download Citation

Abstract

Objective:

High BMI at midlife is associated with increased risk of dementia as well as faster decline in cognitive function. In late-life, however, high BMI has been found to be associated with both increased and decreased dementia risk. The objective of this study was to investigate the neural substrates of this age-related change in body mass index (BMI) risk.

Methods:

We measured longitudinal cortical thinning over the whole brain, based on magnetic resonance imaging scans for 910 individuals aged 44–66 years at baseline. Subjects were sampled from a large population study (PATH, Personality and Total Health through Life). After attrition and exclusions, the final analysis was based on 792 individuals, including 387 individuals aged 60–66 years and 405 individuals aged 44–49 years. A mixed-effects model was used to test the association between cortical thinning and baseline BMI, as well as percentage change in BMI.

Results:

Increasing BMI was associated with increased cortical thinning in posterior cingulate at midlife (0.014 mm kg−1 m−2, confidence interval; CI=0.005, 0.023, P<0.05 false discovery rate (FDR) corrected). In late-life, increasing BMI was associated with reduced cortical thickness, most prominently in the right supramarginal cortex (0.010 mm kg−1 m−2, CI=0.005–0.016, P<0.05 FDR corrected), as well as frontal regions. In late-life, decreasing BMI was also associated with increased cortical thinning, including right caudal middle frontal cortex (0.014 mm kg−1 m−2 (CI=0.006–0.023, P<0.05 FDR corrected).

Conclusions:

The pattern of cortical thinning—in association with increasing BMI at both midlife and late-life—is consistent with known obesity-related dementia risk. Increased cortical thinning in association with decreasing BMI at late-life may help explain the ‘obesity paradox’, where high BMI in midlife appears to be a risk factor for dementia, but high BMI in late-life appears, at times, to be protective.

Access optionsAccess options

Rent or Buy article

Get time limited or full article access on ReadCube.

from$8.99

All prices are NET prices.

References

  1. 1.

    , , , . Body mass index in midlife and late‐life as a risk factor for dementia: a meta‐analysis of prospective studies. Obes Rev 2011; 12: e426–e437.

  2. 2.

    , . Midlife obesity and dementia: meta‐analysis and adjusted forecast of dementia prevalence in the united states and china. Obesity 2013; 21: E51–E55.

  3. 3.

    , , , , , . Body mass index across midlife and cognitive change in late life. Int J Obes 2013; 37: 296–302.

  4. 4.

    , , , , , et al. Brain structure and obesity. Hum Brain Map 2010; 31: 353–364.

  5. 5.

    , , , , , . Brain imaging and cognitive predictors of stroke and Alzheimer disease in the Framingham Heart Study. Stroke 2013; 44: 2787–2794.

  6. 6.

    , , , , , et al. Tracking pathophysiological processes in Alzheimer's disease: an updated hypothetical model of dynamic biomarkers. Lancet Neurol 2013; 12: 207–216.

  7. 7.

    , , , . Age-related cortical thinning in cognitively healthy individuals in their 60s: the PATH Through Life study. Neurobiol Aging 2016; 39: 202–209.

  8. 8.

    , , , , . Greater cortical thinning in normal older adults predicts later cognitive impairment. Neurobiol Aging 2015; 36: 903–908.

  9. 9.

    , , , , , et al. Alzheimer-signature MRI biomarker predicts AD dementia in cognitively normal adults. Neurology 2011; 76: 1395–1402.

  10. 10.

    , , . Adipokines: a link between obesity and dementia? Lancet Neurol 2014; 13: 913–923.

  11. 11.

    , , , , . An 18-year follow-up of overweight and risk of Alzheimer disease. Arch Int Med 2003; 163: 1524–1528.

  12. 12.

    , , , , , et al. Midlife and late-life obesity and the risk of dementia: cardiovascular health study. Arch Neurol 2009; 66: 336–342.

  13. 13.

    , , , , . Overweight and obesity in old age are not associated with greater dementia risk. J Am Geriatr Soc 2008; 56: 2261–2266.

  14. 14.

    , , , , . Association between late-life body mass index and dementia The Kame Project. Neurology 2009; 72: 1741–1746.

  15. 15.

    , , , , , . Late‐life body mass index and dementia incidence: nine‐year follow‐up data from the Kungsholmen Project. J Am Geriatr Soc 2008; 56: 111–116.

  16. 16.

    , , , , , et al. Aging exacerbates obesity-induced oxidative stress and inflammation in perivascular adipose tissue in mice: a paracrine mechanism contributing to vascular redox dysregulation and inflammation. The J Gerontol A Bio Sci Med Sci 2013; 68: 780–792.

  17. 17.

    , , , , , . Incidence, reversibility, risk factors and the protective effect of high body mass index against sarcopenia in community‐dwelling older Chinese adults. Geriatr Gerontol Int 2014; 14: 15–28.

  18. 18.

    , , , , . Longitudinal changes in body composition associated with healthy ageing: men, aged 20–96 years. Br J Nutr 2012; 107: 1085–1091.

  19. 19.

    , , , . Inflammatory markers and loss of muscle mass (sarcopenia) and strength. The Am J Med 2006; 119: 526. e9–e17.

  20. 20.

    , , , , , et al. Cohort Profile: The PATH through life project. Int J Epidemiol 2012; 41: 951–960.

  21. 21.

    , , , , . Being overweight is associated with hippocampal atrophy: the PATH Through Life Study. Int J Obes 2015; 39: 1509.

  22. 22.

    , , . Metabolic equivalents (METS) in exercise testing, exercise prescription, and evaluation of functional capacity. Clin Cardiol 1990; 13: 555–565.

  23. 23.

    , , , , . Using sulcal and gyral measures of brain structure to investigate benefits of an active lifestyle. NeuroImage 2014; 91: 353–359.

  24. 24.

    . FreeSurfer. Neuroimage 2012; 62: 774–781.

  25. 25.

    , , , . Within-subject template estimation for unbiased longitudinal image analysis. Neuroimage 2012; 61: 1402–1418.

  26. 26.

    , , , , . Statistical analysis of longitudinal neuroimage data with linear mixed effects models. Neuroimage 2013; 66: 249–260.

  27. 27.

    , , , , , et al. An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest. Neuroimage 2006; 31: 968–980.

  28. 28.

    , , , , . Spatiotemporal linear mixed effects modeling for the mass-univariate analysis of longitudinal neuroimage data. NeuroImage 2013; 81: 358–370.

  29. 29.

    , , . Thresholding of statistical maps in functional neuroimaging using the false discovery rate. Neuroimage 2002; 15: 870–878.

  30. 30.

    , , , , , . Metabolic reduction in the posterior cingulate cortex in very early Alzheimer's disease. Ann Neurol 1997; 42: 85–94.

  31. 31.

    , , , , , . Abnormal connectivity in the posterior cingulate and hippocampus in early Alzheimer's disease and mild cognitive impairment. Alzheimer's Dement 2008; 4: 265–270.

  32. 32.

    , , , . Focal posterior cingulate atrophy in incipient Alzheimer's disease. Neurobiol Aging 2010; 31: 25–33.

  33. 33.

    , , , , , et al. MRI measures of entorhinal cortex vs hippocampus in preclinical AD. Neurology 2002; 58: 1188–1196.

  34. 34.

    , , , , . Sarcopenic obesity: a critical appraisal of the current evidence. Clin Nutr 2012; 31: 583–601.

  35. 35.

    , , , , , et al. The impact of glucose disorders on cognition and brain volumes in the elderly: the Sydney Memory and Ageing Study. Age 2014; 36: 977–993.

  36. 36.

    . Adiposity indices and dementia. Lancet Neurol 2006; 5: 713–720.

  37. 37.

    , . Brain changes underlying cognitive dysfunction in diabetes: what can we learn from MRI? Diabetes 2014; 63: 2244–2252.

  38. 38.

    , , , , , et al. Glucose dysregulation interacts with APOE-€ 4 to potentiate temporoparietal cortical thinning. Am J Alzheimer's Dis Dement 2016; 31: 76–86.

  39. 39.

    , , , , . Cortical thinning at midlife: the PATH through life study. Brain Topogr 2016; 29: 875–884.

Download references

Acknowledgements

We are grateful to Peter Butterworth, Simon Easteal, Helen Christensen, Patricia Jacomb, Karen Maxwell and the PATH interviewers. The study was supported by NHMRC grant No. 973302, 179805, 350833 157125, ARC grant No. 130101705 and the Dementia Collaborative Research Centres. Nicolas Cherbuin is funded by ARC Fellowship No. 12010227 and Kaarin Anstey by and NHMRC Fellowship No.1002560. This research was partly undertaken on the National Computational Infrastructure (NCI) facility in Canberra, Australia, which is supported by the Australian Commonwealth Government.

Author information

Affiliations

  1. ANU College of Engineering & Computer Science, The Australian National University, Canberra, Australia

    • M E Shaw
  2. Centre for Healthy Brain Ageing, Neuropsychiatric Institute, University of New South Wales, Sydney, Australia

    • P S Sachdev
  3. Centre for Research on Ageing, Health and Wellbeing, The Australian National University, Canberra, Australia

    • W Abhayaratna
    • , K J Anstey
    •  & N Cherbuin

Authors

  1. Search for M E Shaw in:

  2. Search for P S Sachdev in:

  3. Search for W Abhayaratna in:

  4. Search for K J Anstey in:

  5. Search for N Cherbuin in:

Competing interests

The authors declare no conflict of interest.

Corresponding author

Correspondence to M E Shaw.

About this article

Publication history

Received

Revised

Accepted

Published

DOI

https://doi.org/10.1038/ijo.2017.254