C International Psychogeriatric Association 2014 International Psychogeriatrics (2015), 27:1, 121–130 doi:10.1017/S1041610214001744
Association between body mass index and cortical thickness: among elderly cognitively normal men and women ...........................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................
Hojeong Kim,1 Changsoo Kim,2 Sang Won Seo,3 Duk L. Na,3 Hee Jin Kim,3 Mira Kang,4 Hee-Young Shin,4 Seong Kyung Cho,4 Sang eon Park,1 Jeongmin Lee,1 Jung Won Hwang,1 Seun Jeon,5 Jong-Min Lee,5 Geon Ha Kim,3 Hanna Cho,3 Byoung Seok Ye,3 Young Noh,6 Cindy W. Yoon7 and Eliseo Guallar8 1
Samsung Advanced Institute for Health Sciences and Technology, Sungkyunkwan University, Seoul, South Korea Department of Preventive Medicine, Yonsei University College of Medicine, Seoul, South Korea 3 Department of Neurology, Samsung Medical Center, School of Medicine, Sungkyunkwan University, Seoul, South Korea 4 Center for Health Promotion, Samsung Medical Center, Seoul, South Korea 5 Department of Biomedical Engineering, Hanyang University, Seoul, South Korea 6 Department of Neurology, Gachon University Gil Medical Center, Incheon, South Korea 7 Department of Neurology, College of Medicine, Inha University, Incheon, South Korea 8 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 2
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. Key words: cortical thickness, BMI, underweight, overweight, dementia
Introduction 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] and 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]. Received 12 May 2014; revision requested 24 Jun 2014; revised version received 20 Jul 2014; accepted 23 Jul 2014. First published online 29 September 2014.
There is increasing evidence that both underweight status (Buchman et al., 2005; Atti et al., 2008; Burns et al., 2010; Anstey et al., 2011) and obesity are associated with an increased risk of cognitive decline and development of dementia (Gustafson et al., 2003; Anstey et al., 2011). Obesity may also increase the risk for cerebrovascular disease (Lavie et al., 2009) and consequently lead to Alzheimer’s
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disease and vascular dementia (Gustafson, 2006). Brain atrophy precedes the onset of cognitive decline and dementia (Jack et al., 2010) and is considered an important preclinical biomarker for underlying factors that may affect dementia risk. Several studies have investigated the relationship between BMI and brain atrophy in normal subjects (Gustafson et al., 2004; Ward et al., 2005; Taki et al., 2008; Burns et al., 2010; Raji et al., 2010) with mixed findings due to small sample sizes, to differences in BMI distribution across study populations, and to the use of statistical models that constrained the associations to be linear. Indeed, several studies involving mainly severely obese individuals showed inverse associations between BMI and brain volume (Gustafson et al., 2004; Ward et al., 2005; Taki et al., 2008; Raji et al., 2010), while other studies involving mainly underweight individuals showed positive associations (Burns et al., 2010). Based on clinical and epidemiological evidence (Gustafson et al., 2003; Buchman et al., 2005; Atti et al., 2008; Burns et al., 2010; Anstey et al., 2011), we hypothesized that the association between BMI and brain atrophy is non-linear with both underweight and severely obese individuals at higher risk of brain atrophy. No study seems to have investigated non-linear associations between BMI and brain atrophy. We thus investigated the association between BMI and brain volume in a large sample of cognitively normal individuals participating in a comprehensive health exam program, with a particular emphasis in identifying non-linear associations between BMI and cortical thickness.
Methods Subjects We enrolled Korean men and women who visited the Health Promotion Center at the Samsung Medical Center in Seoul, South Korea, from September 2008 to December 2011 for a medical checkup. As part of the comprehensive exam, participants could opt for a comprehensive neurological and neurocognitive screening package that included a brain magnetic resonance imaging (MRI). Among 1,681 participants who signed up for neurological testing, we excluded 15 participants under 45 years of age and 98 participants with cognitive impairment assessed by combining the findings of an interview with a qualified neurologist and a Mini-Mental State Examination (MMSE) score below the 16th percentile for age-, gender-, and education-matched norms (eTable 1, available
as supplementary material attached to the electronic version of this paper at www.journals.cambridge. org/jid_IPG). We also excluded participants with incomplete data on demographics (N = 264), anthropometric measurements (N = 3), smoking or drinking habits (N = 10), history of hypertension (N = 3), or cholesterol or fasting blood sugar levels (N = 44). From the remaining 1,244 participants, we excluded 133 participants who had errors in cortical thickness analysis. The final sample size included 1,111 participants (555 men, 556 women) (eFigure 1, available as supplementary material attached to the electronic version of this paper at www.journals.cambridge.org/jid_IPG). The clinical characteristics of included subjects were generally similar with those of excluded subjects with the exception of systolic blood pressure, hyperlipidemia, history of stroke, and drinking (eTable 2). This study was approved by the Institutional Review Board of Samsung Medical Center. The requirement for patient consent was waived since we used retrospective de-identified data collected during health exam visits.
Data collection The examinations were conducted by trained personnel following a standard protocol. The health screening program included questionnaire data, a physical exam, laboratory tests, and a number of functional and imaging tests as previously described (Park et al., 2010). Questionnaire data included questions on physician-diagnosed disease, medication history, cigarette smoking, and alcohol consumption. Smoking status was categorized into non-smokers, ex-smokers, or current smokers. Alcohol consumption was categorized into nondrinkers and current drinkers. Height and weight were measured and BMI was calculated as weight in kilogram divided by height in meter squared. BMI was categorized into five groups based on the recommendations of the World Health Organization (WHO) for Asian populations: (WHO Expert Consultation, 2004) underweight (
Association between body mass index and cortical thickness: among elderly cognitively normal men and women ...........................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................
Hojeong Kim,1 Changsoo Kim,2 Sang Won Seo,3 Duk L. Na,3 Hee Jin Kim,3 Mira Kang,4 Hee-Young Shin,4 Seong Kyung Cho,4 Sang eon Park,1 Jeongmin Lee,1 Jung Won Hwang,1 Seun Jeon,5 Jong-Min Lee,5 Geon Ha Kim,3 Hanna Cho,3 Byoung Seok Ye,3 Young Noh,6 Cindy W. Yoon7 and Eliseo Guallar8 1
Samsung Advanced Institute for Health Sciences and Technology, Sungkyunkwan University, Seoul, South Korea Department of Preventive Medicine, Yonsei University College of Medicine, Seoul, South Korea 3 Department of Neurology, Samsung Medical Center, School of Medicine, Sungkyunkwan University, Seoul, South Korea 4 Center for Health Promotion, Samsung Medical Center, Seoul, South Korea 5 Department of Biomedical Engineering, Hanyang University, Seoul, South Korea 6 Department of Neurology, Gachon University Gil Medical Center, Incheon, South Korea 7 Department of Neurology, College of Medicine, Inha University, Incheon, South Korea 8 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 2
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. Key words: cortical thickness, BMI, underweight, overweight, dementia
Introduction 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] and 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]. Received 12 May 2014; revision requested 24 Jun 2014; revised version received 20 Jul 2014; accepted 23 Jul 2014. First published online 29 September 2014.
There is increasing evidence that both underweight status (Buchman et al., 2005; Atti et al., 2008; Burns et al., 2010; Anstey et al., 2011) and obesity are associated with an increased risk of cognitive decline and development of dementia (Gustafson et al., 2003; Anstey et al., 2011). Obesity may also increase the risk for cerebrovascular disease (Lavie et al., 2009) and consequently lead to Alzheimer’s
122
H. Kim et al.
disease and vascular dementia (Gustafson, 2006). Brain atrophy precedes the onset of cognitive decline and dementia (Jack et al., 2010) and is considered an important preclinical biomarker for underlying factors that may affect dementia risk. Several studies have investigated the relationship between BMI and brain atrophy in normal subjects (Gustafson et al., 2004; Ward et al., 2005; Taki et al., 2008; Burns et al., 2010; Raji et al., 2010) with mixed findings due to small sample sizes, to differences in BMI distribution across study populations, and to the use of statistical models that constrained the associations to be linear. Indeed, several studies involving mainly severely obese individuals showed inverse associations between BMI and brain volume (Gustafson et al., 2004; Ward et al., 2005; Taki et al., 2008; Raji et al., 2010), while other studies involving mainly underweight individuals showed positive associations (Burns et al., 2010). Based on clinical and epidemiological evidence (Gustafson et al., 2003; Buchman et al., 2005; Atti et al., 2008; Burns et al., 2010; Anstey et al., 2011), we hypothesized that the association between BMI and brain atrophy is non-linear with both underweight and severely obese individuals at higher risk of brain atrophy. No study seems to have investigated non-linear associations between BMI and brain atrophy. We thus investigated the association between BMI and brain volume in a large sample of cognitively normal individuals participating in a comprehensive health exam program, with a particular emphasis in identifying non-linear associations between BMI and cortical thickness.
Methods Subjects We enrolled Korean men and women who visited the Health Promotion Center at the Samsung Medical Center in Seoul, South Korea, from September 2008 to December 2011 for a medical checkup. As part of the comprehensive exam, participants could opt for a comprehensive neurological and neurocognitive screening package that included a brain magnetic resonance imaging (MRI). Among 1,681 participants who signed up for neurological testing, we excluded 15 participants under 45 years of age and 98 participants with cognitive impairment assessed by combining the findings of an interview with a qualified neurologist and a Mini-Mental State Examination (MMSE) score below the 16th percentile for age-, gender-, and education-matched norms (eTable 1, available
as supplementary material attached to the electronic version of this paper at www.journals.cambridge. org/jid_IPG). We also excluded participants with incomplete data on demographics (N = 264), anthropometric measurements (N = 3), smoking or drinking habits (N = 10), history of hypertension (N = 3), or cholesterol or fasting blood sugar levels (N = 44). From the remaining 1,244 participants, we excluded 133 participants who had errors in cortical thickness analysis. The final sample size included 1,111 participants (555 men, 556 women) (eFigure 1, available as supplementary material attached to the electronic version of this paper at www.journals.cambridge.org/jid_IPG). The clinical characteristics of included subjects were generally similar with those of excluded subjects with the exception of systolic blood pressure, hyperlipidemia, history of stroke, and drinking (eTable 2). This study was approved by the Institutional Review Board of Samsung Medical Center. The requirement for patient consent was waived since we used retrospective de-identified data collected during health exam visits.
Data collection The examinations were conducted by trained personnel following a standard protocol. The health screening program included questionnaire data, a physical exam, laboratory tests, and a number of functional and imaging tests as previously described (Park et al., 2010). Questionnaire data included questions on physician-diagnosed disease, medication history, cigarette smoking, and alcohol consumption. Smoking status was categorized into non-smokers, ex-smokers, or current smokers. Alcohol consumption was categorized into nondrinkers and current drinkers. Height and weight were measured and BMI was calculated as weight in kilogram divided by height in meter squared. BMI was categorized into five groups based on the recommendations of the World Health Organization (WHO) for Asian populations: (WHO Expert Consultation, 2004) underweight (
