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Association between body mass index and cortical thickness: among elderly cognitively normal men and women

Published online by Cambridge University Press:  29 September 2014

Hojeong Kim
Affiliation:
Samsung Advanced Institute for Health Sciences and Technology, Sungkyunkwan University, Seoul, South Korea
Changsoo Kim*
Affiliation:
Department of Preventive Medicine, Yonsei University College of Medicine, Seoul, South Korea
Sang Won Seo*
Affiliation:
Department of Neurology, Samsung Medical Center, School of Medicine, Sungkyunkwan University, Seoul, South Korea
Duk L. Na
Affiliation:
Department of Neurology, Samsung Medical Center, School of Medicine, Sungkyunkwan University, Seoul, South Korea
Hee Jin Kim
Affiliation:
Department of Neurology, Samsung Medical Center, School of Medicine, Sungkyunkwan University, Seoul, South Korea
Mira Kang
Affiliation:
Center for Health Promotion, Samsung Medical Center, Seoul, South Korea
Hee-Young Shin
Affiliation:
Center for Health Promotion, Samsung Medical Center, Seoul, South Korea
Seong Kyung Cho
Affiliation:
Center for Health Promotion, Samsung Medical Center, Seoul, South Korea
Sang eon Park
Affiliation:
Samsung Advanced Institute for Health Sciences and Technology, Sungkyunkwan University, Seoul, South Korea
Jeongmin Lee
Affiliation:
Samsung Advanced Institute for Health Sciences and Technology, Sungkyunkwan University, Seoul, South Korea
Jung Won Hwang
Affiliation:
Samsung Advanced Institute for Health Sciences and Technology, Sungkyunkwan University, Seoul, South Korea
Seun Jeon
Affiliation:
Department of Biomedical Engineering, Hanyang University, Seoul, South Korea
Jong-Min Lee
Affiliation:
Department of Biomedical Engineering, Hanyang University, Seoul, South Korea
Geon Ha Kim
Affiliation:
Department of Neurology, Samsung Medical Center, School of Medicine, Sungkyunkwan University, Seoul, South Korea
Hanna Cho
Affiliation:
Department of Neurology, Samsung Medical Center, School of Medicine, Sungkyunkwan University, Seoul, South Korea
Byoung Seok Ye
Affiliation:
Department of Neurology, Samsung Medical Center, School of Medicine, Sungkyunkwan University, Seoul, South Korea
Young Noh
Affiliation:
Department of Neurology, Gachon University Gil Medical Center, Incheon, South Korea
Cindy W. Yoon
Affiliation:
Department of Neurology, College of Medicine, Inha University, Incheon, South Korea
Eliseo Guallar
Affiliation:
Departments of Epidemiology and Medicine and Welch Center for Prevention, Epidemiology, and Clinical Research, Johns Hopkins University Bloomberg School of Public Health, Baltimore, Maryland, USA
*
Changsoo Kim, MD, PhD, Department of Preventive Medicine, Yonsei University College of Medicine, 134 Shinchon-dong, Seodaemun-gu, Seoul 120-752, South Korea. Phone: +82-2-2228-1860; Fax: +82-2-392-8133. Email: [email protected].
Correspondence should be addressed to: Sang Won Seo, MD, PhD, Department of Neurology, Samsung Medical Center, School of Medicine, Sungkyunkwan University, 50 Irwon-dong, Gangnam-gu, Seoul 135-710, South Korea. Phone: +82-2-3410-1233; Fax: +82-2-3410-0052. Email: [email protected]
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Abstract

Background:

There is increasing evidence of a relationship between underweight or obesity and dementia risk. Several studies have investigated the relationship between body weight and brain atrophy, a pathological change preceding dementia, but their results are inconsistent. Therefore, we aimed to evaluate the relationship between body mass index (BMI) and cortical atrophy among cognitively normal participants.

Methods:

We recruited cognitively normal participants (n = 1,111) who underwent medical checkups and detailed neurologic screening, including magnetic resonance imaging (MRI) in the health screening visits between September 2008 and December 2011. The main outcome was cortical thickness measured using MRI. The number of subjects with five BMI groups in men/women was 9/9, 148/258, 185/128, 149/111, and 64/50 in underweight, normal, overweight, mild obesity, and moderate to severe obesity, respectively. Linear and non-linear relationships between BMI and cortical thickness were examined using multiple linear regression analysis and generalized additive models after adjustment for potential confounders.

Results:

Among men, underweight participants showed significant cortical thinning in the frontal and temporal regions compared to normal weight participants, while overweight and mildly obese participants had greater cortical thicknesses in the frontal region and the frontal, temporal, and occipital regions, respectively. However, cortical thickness in each brain region was not significantly different in normal weight and moderate to severe obesity groups. Among women, the association between BMI and cortical thickness was not statistically significant.

Conclusions:

Our findings suggested that underweight might be an important risk factor for pathological changes in the brain, while overweight or mild obesity may be inversely associated with cortical atrophy in cognitively normal elderly males.

Type
Research Article
Copyright
Copyright © International Psychogeriatric Association 2014 

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References

Amantea, D., Russo, R., Bagetta, G. and Corasaniti, M. T. (2005). From clinical evidence to molecular mechanisms underlying neuroprotection afforded by estrogens. Pharmacological Research, 52, 119132.CrossRefGoogle ScholarPubMed
Anstey, K. J., Cherbuin, N., Budge, M. and Young, J. (2011). Body mass index in midlife and late-life as a risk factor for dementia: a meta-analysis of prospective studies. Obesity Reviews, 12, e426–437. doi:10.1111/j.1467-789X.2010.00825.x.CrossRefGoogle ScholarPubMed
Atti, A. R., Palmer, K., Volpato, S., Winblad, B., De Ronchi, D. and Fratiglioni, L. (2008). Late-life body mass index and dementia incidence: nine-year follow-up data from the Kungsholmen Project. Journal of the American Geriatrics Society, 56, 111116. doi:10.1111/j.1532-5415.2007.01458.x.Google Scholar
Azimi, A. et al. (2013). Moderate overweight is beneficial and severe obesity detrimental for patients with documented atherosclerotic heart disease. Heart, 99, 655660. doi:10.1136/heartjnl-2012-303066.CrossRefGoogle ScholarPubMed
Behl, C. et al. (1997). Neuroprotection against oxidative stress by estrogens: structure-activity relationship. Molecular Pharmacology, 51, 535541.CrossRefGoogle ScholarPubMed
Boushey, C. J., Beresford, S. A., Omenn, G. S. and Motulsky, A. G. (1995). A quantitative assessment of plasma homocysteine as a risk factor for vascular disease. Probable benefits of increasing folic acid intakes. JAMA, 274, 10491057.CrossRefGoogle ScholarPubMed
Braak, H. and Braak, E. (1997). Staging of Alzheimer-related cortical destruction. International Psychogeriatrics, 9, 257261; discussion 269–272.Google Scholar
Buchman, A. S., Schneider, J. A., Wilson, R. S., Bienias, J. L. and Bennett, D. A. (2006). Body mass index in older persons is associated with Alzheimer disease pathology. Neurology, 67, 19491954.Google Scholar
Buchman, A. S., Wilson, R. S., Bienias, J. L., Shah, R. C., Evans, D. A. and Bennett, D. A. (2005). Change in body mass index and risk of incident Alzheimer disease. Neurology, 65, 892897.Google Scholar
Burns, J. M., Johnson, D. K., Watts, A., Swerdlow, R. H. and Brooks, W. M. (2010). Reduced lean mass in early Alzheimer disease and its association with brain atrophy. Archives of Neurology, 67, 428433. doi:10.1001/archneurol.2010.38.CrossRefGoogle ScholarPubMed
Clarke, R., Smith, A. D., Jobst, K. A., Refsum, H., Sutton, L. and Ueland, P. M. (1998). Folate, vitamin B12, and serum total homocysteine levels in confirmed Alzheimer disease. Archives of Neurology, 55, 14491455.CrossRefGoogle ScholarPubMed
Collins, D. L., Neelin, P., Peters, T. M. and Evans, A. C. (1994). Automatic 3D intersubject registration of MR volumetric data in standardized Talairach space. Journal of Computer Assisted Tomography, 18, 192205.Google Scholar
de Leon, M. J. et al. (1988). Abnormal cortisol response in Alzheimer's disease linked to hippocampal atrophy. Lancet, 2, 391392.Google Scholar
Enzi, G., Gasparo, M., Biondetti, P. R., Fiore, D., Semisa, M. and Zurlo, F. (1986). Subcutaneous and visceral fat distribution according to sex, age, and overweight, evaluated by computed tomography. American Journal of Clinical Nutrition, 44, 739746.Google Scholar
Girasole, G. et al. (1992). 17 beta-estradiol inhibits interleukin-6 production by bone marrow-derived stromal cells and osteoblasts in vitro: a potential mechanism for the antiosteoporotic effect of estrogens. Journal of Clinical Investigation, 89, 883891.Google Scholar
Goldberg, A. P., Busby-Whitehead, M. J., Katzel, L. I., Krauss, R. M., Lumpkin, M. and Hagberg, J. M. (2000). Cardiovascular fitness, body composition, and lipoprotein lipid metabolism in older men. Journals of Gerontology. Series A: Biological Sciences and Medical Sciences, 55, M342M349.Google Scholar
Gonzalez-Gross, M., Marcos, A. and Pietrzik, K. (2001). Nutrition and cognitive impairment in the elderly. British Journal of Nutrition, 86, 313321.CrossRefGoogle ScholarPubMed
Gray, S. L. et al. (2013). Frailty and incident dementia. Journals of Gerontology. Series A: Biological Sciences and Medical Sciences, 68, 10831090. doi:10.1093/gerona/glt013.Google Scholar
Greco, S. J., Sarkar, S., Johnston, J. M. and Tezapsidis, N. (2009). Leptin regulates tau phosphorylation and amyloid through AMPK in neuronal cells. Biochemical and Biophysical Research Communications, 380, 98104.Google Scholar
Gustafson, D. (2006). Adiposity indices and dementia. Lancet Neurology, 5, 713720. doi:10.1016/s1474-4422(06)70526-9.CrossRefGoogle ScholarPubMed
Gustafson, D., Lissner, L., Bengtsson, C., Bjorkelund, C. and Skoog, I. (2004). A 24-year follow-up of body mass index and cerebral atrophy. Neurology, 63, 18761881.Google Scholar
Gustafson, D., Rothenberg, E., Blennow, K., Steen, B. and Skoog, I. (2003). An 18-year follow-up of overweight and risk of Alzheimer disease. Archives of Internal Medicine, 163, 15241528.CrossRefGoogle ScholarPubMed
Harvey, J., Solovyova, N. and Irving, A. (2006). Leptin and its role in hippocampal synaptic plasticity. Progress in Lipid Research, 45, 369378. doi:10.1016/j.plipres.2006.03.001.Google Scholar
Hogervorst, E., Bandelow, S. and Moffat, S. D. (2005). Increasing testosterone levels and effects on cognitive functions in elderly men and women: a review. Current Drug Targets: CNS and Neurological Disorders, 4, 531540.Google Scholar
Jack, C. R. Jr., et al. (2010). Hypothetical model of dynamic biomarkers of the Alzheimer's pathological cascade. Lancet Neurology, 9, 119128. doi:10.1016/s1474-4422(09)70299-6.Google Scholar
Kim, B. J., Lee, S. H., Jung, K. H., Yu, K. H., Lee, B. C. and Roh, J. K. (2012). Dynamics of obesity paradox after stroke, related to time from onset, age, and causes of death. Neurology, 79, 856863. doi:10.1212/WNL.0b013e318266fad1.Google Scholar
Kim, J. S. et al. (2005). Automated 3-D extraction and evaluation of the inner and outer cortical surfaces using a Laplacian map and partial volume effect classification. NeuroImage, 27, 210221.Google Scholar
Kopelman, P. G. (2000). Obesity as a medical problem. Nature, 404, 635643.Google Scholar
Kotani, K. et al. (1994). Sexual dimorphism of age-related changes in whole-body fat distribution in the obese. International Journal of Obesity and Related Metabolic Disorders, 18, 207–202.Google ScholarPubMed
Lavie, C. J., Milani, R. V. and Ventura, H. O. (2009). Obesity and cardiovascular disease: risk factor, paradox, and impact of weight loss. Journal of the American College of Cardiology, 53, 19251932. doi:10.1016/j.jacc.2008.12.068.Google Scholar
Lyttelton, O., Boucher, M., Robbins, S. and Evans, A. (2007). An unbiased iterative group registration template for cortical surface analysis. NeuroImage, 34, 15351544.Google Scholar
Macchi, G. (1989). Anatomical substrate of emotional reactions. In Squire, L. and Gainotti, G. (eds.), Handbook of Neuropsychology (pp. 283303). Amsterdam: Elsevier.Google Scholar
Maggio, M. et al. (2005). The relationship between testosterone and molecular markers of inflammation in older men. Journal of Endocrinological Investigation, 28, 116119.Google Scholar
Morrison, C. D. (2009). Leptin signaling in brain: a link between nutrition and cognition? Biochimica et Biophysica Acta (BBA) - Bioenergetics, 1792, 401408. doi:10.1016/j.bbadis.2008.12.004.Google Scholar
O’Donovan, G. et al. (2005). Cardiovascular disease risk factors in habitual exercisers, lean sedentary men and abdominally obese sedentary men. International Journal of Obesity, 29, 10631069. doi:10.1038/sj.ijo.0803004.Google Scholar
Park, H. Y. et al. (2010). Lung function, coronary artery calcification, and metabolic syndrome in 4905 Korean males. Respiratory Medicine, 104, 13261335.Google Scholar
Raji, C. A. et al. (2010). Brain structure and obesity. Human Brain Mapping, 31, 353364.Google Scholar
Schenkeveld, L. et al. (2012). The influence of optimal medical treatment on the ‘obesity paradox’, body mass index and long-term mortality in patients treated with percutaneous coronary intervention: a prospective cohort study. BMJ Open, 2, e000535. doi:10.1136/bmjopen-2011-000535.Google Scholar
Simpson, E. R. et al. (1994). Aromatase cytochrome P450, the enzyme responsible for estrogen biosynthesis. Endocrine Reviews, 15, 342355.Google ScholarPubMed
Singh, V. K. and Guthikonda, P. (1997). Circulating cytokines in Alzheimer's disease. Journal of Psychiatric Research, 31, 657660.Google Scholar
Sled, J. G., Zijdenbos, A. P. and Evans, A. C. (1998). A nonparametric method for automatic correction of intensity nonuniformity in MRI data. IEEE Transactions on Medical Imaging, 17, 8797.Google Scholar
Taki, Y. et al. (2008). Relationship between body mass index and gray matter volume in 1,428 healthy individuals. Obesity (Silver Spring), 16, 119124. doi:10.1038/oby.2007.4.Google Scholar
Teixeira, A. L. et al. (2013). Decreased levels of circulating adiponectin in mild cognitive impairment and Alzheimer's disease. Neuromolecular Medicine, 15, 115121. doi:10.1007/s12017-012-8201-2.Google Scholar
Thomas, T., Thomas, G., McLendon, C., Sutton, T. and Mullan, M. (1996). beta-Amyloid-mediated vasoactivity and vascular endothelial damage. Nature, 380, 168171.Google Scholar
Uslu, S. et al. (2012). Levels of amyloid beta-42, interleukin-6 and tumor necrosis factor-alpha in Alzheimer's disease and vascular dementia. Neurochemical Research, 37, 15541559.Google Scholar
Vidoni, E. D., Townley, R. A., Honea, R. A. and Burns, J. M. (2011). Alzheimer disease biomarkers are associated with body mass index. Neurology, 77, 19131920.Google Scholar
Ward, M. A., Carlsson, C. M., Trivedi, M. A., Sager, M. A. and Johnson, S. C. (2005). The effect of body mass index on global brain volume in middle-aged adults: a cross sectional study. BMC Neurology, 5, 23.Google Scholar
Weiner, M. F., Vobach, S., Olsson, K., Svetlik, D. and Risser, R. C. (1997). Cortisol secretion and Alzheimer's disease progression. Biological Psychiatry, 42, 10301038.Google Scholar
WHO Expert Consultation (2004). Appropriate body-mass index for Asian populations and its implications for policy and intervention strategies. Lancet, 363, 157163.Google Scholar
Wood, S. N. (2011). Fast stable restricted maximum likelihood and marginal likelihood estimation of semiparametric generalized linear models. Journal of the Royal Statistical Society. Series B, Statistical Methodology, 73, 336.Google Scholar
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