Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2017;57(3):953-968.
doi: 10.3233/JAD-161205.

Modeling the Relationships Among Late-Life Body Mass Index, Cerebrovascular Disease, and Alzheimer's Disease Neuropathology in an Autopsy Sample of 1,421 Subjects from the National Alzheimer's Coordinating Center Data Set

Affiliations

Modeling the Relationships Among Late-Life Body Mass Index, Cerebrovascular Disease, and Alzheimer's Disease Neuropathology in an Autopsy Sample of 1,421 Subjects from the National Alzheimer's Coordinating Center Data Set

Michael L Alosco et al. J Alzheimers Dis. 2017.

Abstract

The relationship between late-life body mass index (BMI) and Alzheimer's disease (AD) is poorly understood due to the lack of research in samples with autopsy-confirmed AD neuropathology (ADNP). The role of cerebrovascular disease (CVD) in the interplay between late-life BMI and ADNP is unclear. We conducted a retrospective longitudinal investigation and used joint modeling of linear mixed effects to investigate causal relationships among repeated antemortem BMI measurements, CVD (quantified neuropathologically), and ADNP in an autopsy sample of subjects across the AD clinical continuum. The sample included 1,421 subjects from the National Alzheimer's Coordinating Center's Uniform Data Set and Neuropathology Data Set with diagnoses of normal cognition (NC; n = 234), mild cognitive impairment (MCI; n = 201), or AD dementia (n = 986). ADNP was defined as moderate to frequent neuritic plaques and Braak stageIII-VI. Ischemic Injury Scale (IIS) operationalized CVD. Joint modeling examined relationships among BMI, IIS, and ADNP in the overall sample and stratified by initial visit Clinical Dementia Rating score. Subject-specific random intercept for BMI was the predictor for ADNP due to minimal BMI change (p = 0.3028). Analyses controlling for demographic variables and APOE ɛ4 showed lower late-life BMI predicted increased odds of ADNP in the overall sample (p < 0.001), and in subjects with CDR of 0 (p = 0.0021) and 0.5 (p = 0.0012), but not ≥1.0 (p = 0.2012). Although higher IIS predicted greater odds of ADNP (p < 0.0001), BMI did not predict IIS (p = 0.2814). The current findings confirm lower late-life BMI confers increased odds for ADNP. Lower late-life BMI may be a preclinical indicator of underlying ADNP.

Keywords: Alzheimer’s disease; body mass index; cerebrovascular disease; neuropathology; obesity.

PubMed Disclaimer

Conflict of interest statement

CONFLICTS OF INTEREST

For the remaining authors, there are no conflicts of interest to declare.

Figures

Figure 1
Figure 1
Sample size flow chart Abbreviations: UDS = Uniform Data Set; ADNP = Alzheimer’s disease neuropathology; BMI = body mass index; NC = normal cognition; MCI = mild cognitive impairment; AD = Alzheimer’s disease * ADNP positive = moderate to frequent neuritic plaques and Braak stage III-VI; ADNP negative = none or sparse neuritic plaques and Braak stage 0-II
Figure 2
Figure 2
Joint Modeling of the Relationships Among BMI, Cerebrovascular Disease, and Alzheimer’s Disease Neuropathology in 1,421 Subject’s from the NACC Neuropathology Data Set. Figure shows that lower BMI significantly predicted higher odds of having Alzheimer’s disease neuropathology. BMI was not associated with the Ischemic Injury Scale, however, higher scores on the Ischemic Injury Scale (worse cerebrovascular disease) predicted greater odds of having Alzheimer’s disease neuropathology. All analyses were controlled for age at death, education, race, sex, and presence of the APOE ε4 allele.

Similar articles

Cited by

References

    1. Hyman BT, Phelps CH, Beach TG, Bigio EH, Cairns NJ, Carrillo MC, Dickson DW, Duyckaerts C, Frosch MP, Masliah E, Mirra SS, Nelson PT, Schneider JA, Thal DR, Thies B, Trojanowski JQ, Vinters HV, Montine TJ. National Institute on Aging-Alzheimer’s Association guidelines for the neuropathologic assessment of Alzheimer’s disease. Alzheimers Dement. 2012;8:1–13. - PMC - PubMed
    1. Jack CR, Jr, Bennett DA, Blennow K, Carrillo MC, Feldman HH, Frisoni GB, Hampel H, Jagust WJ, Johnson KA, Knopman DS, Petersen RC, Scheltens P, Sperling RA, Dubois B. A/T/N: An unbiased descriptive classification scheme for Alzheimer disease biomarkers. Neurology. 2016;87:539–47. - PMC - PubMed
    1. Khan TK, Alkon DL. Alzheimer’s Disease Cerebrospinal Fluid and Neuroimaging Biomarkers: Diagnostic Accuracy and Relationship to Drug Efficacy. J Alzheimers Dis. 2015;46:817–36. - PubMed
    1. Lautner R, Palmqvist S, Mattsson N, Andreasson U, Wallin A, Palsson E, Jakobsson J, Herukka SK, Owenius R, Olsson B, Hampel H, Rujescu D, Ewers M, Landen M, Minthon L, Blennow K, Zetterberg H, Hansson O Alzheimer’s Disease Neuroimaging I. Apolipoprotein E genotype and the diagnostic accuracy of cerebrospinal fluid biomarkers for Alzheimer disease. JAMA Psychiatry. 2014;71:1183–91. - PubMed
    1. Palmqvist S, Zetterberg H, Mattsson N, Johansson P, Alzheimer’s Disease Neuroimaging I. Minthon L, Blennow K, Olsson M, Hansson O, Swedish Bio FSG Detailed comparison of amyloid PET and CSF biomarkers for identifying early Alzheimer disease. Neurology. 2015;85:1240–9. - PMC - PubMed

Publication types

Grants and funding