Late-life obesity is a protective factor for prodromal Alzheimer’s disease: a longitudinal study

Results

Demographics

The demographics, clinical data, neuropsychometric test, and other characteristics of the study population are presented in Table 1. Of the 1,212 participants included in the study, 439 individuals were in the normal weight range; 518 were overweight; and 250 were obese. The overweight group had a higher proportion of females (p <0.001). The high BMI group had slightly less education (p = 0.022) and lower frequency of the ε4 allele of APOE gene (p = 0.001). In comparison, the prevalences of hypertension (p <0.001) and diabetes (p = 0.001) were higher in higher BMI categories, but there was no difference in the prevalences of hyperlipemia, cardiovascular diseases, and depression among the BMI categories.

Table 1. Demographic and clinical characteristics of participants according to their BMI.

Participant features	Normal weight		Over weight		Obese		P value*
Age (years)	439	76.2± 5.8	518	74.9± 5.5	250	74.1± 5.5	<0.001***
Female	439	218 (49.7%)	518	340 (65.6%)	250	125 (50%)	<0.001***
Education (years)	439	16.3± 2.8	518	15.9± 2.8	250	15.8± 2.9	0.022*
APOE Ɛ4 carrier status (0/1/2)	439	244/159/36	518	298/180/40	250	174/70/6	0.001**
NC:MCI	439	178:261	518	203:315	250	115:135	0.191
Coexisting diseases
Hypertension	439	189 (43.1%)	518	254 (49.0%)	250	149 (59.6%)	<0.001***
Hyperlipemia	439	202 (46.0%)	518	255 (49.2%)	250	129 (51.6%)	0.340
DM2	439	21 (4.8%)	518	40 (7.7%)	250	31 (12.4%)	0.001**
Depression	439	83 (18.9%)	518	99 (19.1%)	250	52 (20.8%)	0.815
CVD	439	90 (20.5%)	518	121 (23.4%)	250	54 (21.6%)	0.561
CSF biomarkers (pg/ml)
CSF Aβ42	307	172.2± 53.2	369	182.4± 55.3	184	189.5 ± 50.1	0.002**
CSF t-tau	306	88.4± 50.3	363	84.5± 52.4	182	69.9± 34.0	<0.001***
CSF p-tau	307	37.4 ± 21.0	369	37.7 ± 21.4	184	34.0± 20.3	0.126
CSF t-tau/Aβ42 ratio	306	0.60 ± 0.48	363	0.56± 0.49	182	0.42 ± 0.31	<0.001***
CSF p-tau/Aβ42 ratio	307	0.26 ± 0.20	369	0.25± 0.20	184	0.21± 0.18	0.021*
Structural MRI data (mm3)
Hippocampus	380	6721± 1053	452	6999± 1055	222	7177± 1110	<0.001***
Entorhinal Cortex	373	3483± 763	454	3676± 705	214	3707± 735	<0.001***
Middle Temporal Lobe	373	19428± 2904	454	19875± 2805	214	20289± 2720	0.001**
Cognition (score)
ADNI-MEM	439	0.41± 0.74	518	0.46± 0.76	250	0.61± 0.77	0.003**
ADNI-EF	439	0.34± 0.82	518	0.38± 0.88	250	0.34± 0.85	0.645
ADAS-Cog11	439	9.06± 4.51	518	8.72± 4.51	250	7.97± 4.54	0.009**
ADAS-Cog13	439	14.54± 7.00	518	13.89± 6.78	248	12.76± 7.10	0.006**
MMSE	439	28.1± 1.81	518	28.04± 1.75	250	28.36± 1.63	0.061
Data are given as n (%) or mean ± standard deviation
*P values for continuous variables are from one-way analysis of variance (ANOVA). P values for categorical data are from chi-square test.
*P-value <0.05;**P-value <0.01; ***P-value <0.001.
Abbreviations: BMI = body mass index; NC = Normal Cognition; MCI = Mild Cognitive Impairment; DM2 = Diabetes Mellitus Type 2; CSF =Cerebrospinal Fluid; Aβ42 = amyloid beta42; t-tau = total-tau; p-tau = phosphorylated-tau; CVD = Cardiovascular Diseases; ADAS-Cog = Alzheimer Disease Assessment Scale-cognitive subscale; MMSE = Mini-mental State Examination.

BMI and CSF biomarkers

Regarding the whole population, BMI was positively correlated with the levels of CSF Aβ42 (Spearman r = 0.157), and inversely correlated with the levels of CSF t-tau, CSF p-tau, CSF t-tau/Aβ42 and CSF p-tau/Aβ42 (r = -0.152, -0.069, -0.178 and -0.119, respectively). Multiple linear regression models confirmed the Spearman’s associations of BMI categories with CSF Aβ42 (β = 0.335; p = 0.037), CSF t-tau (β =-0.023; p = 0.026) and CSF t-tau/Aβ42 (β = -0.093; p=0.008) after controlling for age, sex, education, cognitive diagnosis, ApoE ε4 status, extracted CSF volume and comorbidities (Figure 1) in the total study population. But CSF p-tau levels (p = 0.550) and CSF p-tau/Aβ42 (p = 0.180) were not significantly correlated with BMI in this model. When stratified by cognitive diagnosis, higher BMI categories were associated with lower levels of CSF t-tau (β= -0.093; p=0.004) and CSF t-tau/Aβ42 (β= -0.123; p=0.003) only among those with MCI, but were not associated with CSF Aβ42 or p-tau in any of the cognitive diagnostic groups (Figure 1).

Figure 1. The cross-sectional associations of BMI categories with CSF biomarker levels in the whole population and the subgroups stratified by cognitive diagnosis. (A) Higher late-life BMI categories were associated with a higher level of CSF Aβ42 and lower levels of CSF t-tau (B) and CSF t-tau/Aβ42 (C). All analyses were adjusted for age, sex, education, cognitive diagnosis, ApoE ε4 status, CSF volume and prevalent comorbidities (i.e., hypertension, hyperlipemia, DM2, depression and CVD). Abbreviations: BMI = body mass index; MCI = mild cognitive impairment; NC = normal cognition; Aβ42 =β-amyloid42; t-tau = total-tau.

When adjusted for age, sex, education, cognitive diagnosis, ApoE ε4 status and comorbidities, we found no statistically significant longitudinal association between baseline BMI categories and the change rates of CSF markers during a 7-year follow-up in either the whole population or in the two subgroups (MCI and NC).

BMI and Aβ PET imaging

We looked at the correlations between BMI and regional Aβ burden in different bilateral regions (hippocampus, entorhinal cortex and middle temporal) and did not detect any statistically significant correlations between them in the whole sample. After adjusting for age, sex, education, cognitive diagnosis, ApoE ε4 status and comorbidities, higher baseline BMI categories was associated with lower load of Aβ in the right hippocampus in the total population (β = -0.010, p=0.030) and NC subjects(β = -0.011, p=0.039), but not MCI subjects. However, no associations were observed in other regions of interest (ROIs) between BMI categories and regional Aβ load.

Moreover, the longitudinal analyses showed that BMI categories at baseline were not significantly associated with the change rates of cerebral Aβ deposits during a 2-year follow-up in the overall sample nor in the subgroups, after adjusting for all variables mentioned above.

BMI and brain structures on MRI

We examined the correlations of BMI with the volumes of hippocampus, entorhinal cortex, and middle temporal lobe in the total non-demented elders and significant positive correlations were observed in the total sample (hippocampus, r = 0.200; entorhinal cortex, r = 0.149; middle temporal lobe, r = 0.142). Moreover, linear regression analyses controlling for demographic characteristics, comorbidities and total intracranial volumes (ICVs) revealed that higher BMI categories were associated with larger volumes of hippocampus (β = 129.579, p<0.001), entorhinal cortex (β =74.083, p=0.009) and middle temporal lobe (β =204.24, p=0.040; Figure 2) in the whole population. ICVs in regression models were standardized to z scores to facilitate comparisons. We also tested whether the influence of BMI categories differed between diagnostic groups at baseline. Higher BMI categories were associated with larger volumes of entorhinal cortex (β=92.612, p=0.013) in NCs but not in those with MCI. However, higher BMI categories were associated with larger volumes of hippocampus (β=169.954, p=0.002) and middle temporal lobe (β =277.260, p=0.048) in those with MCI but not in NCs.

Figure 2. The cross-sectional associations of BMI categories with brain volumes in the whole population. Higher BMI categories were associated with larger volumes of hippocampus (A), entorhinal cortex (B) and middle temporal lobe (C). All analyses were adjusted for adjusting for age, sex, education, cognitive diagnosis, ApoE ε4 status, prevalent comorbidities (i.e., hypertension, hyperlipemia, DM2, depression and CVD) and total intracranial volumes. Abbreviations: BMI = body mass index;CVD = Cardiovascular Diseases;DM2 = Diabetes Mellitus Type 2.

Longitudinal studies found no statistically significant differences in the associations between BMI categories and the change rates of 3 ROIs over the 10-year follow- up in linear mixed-effects models adjusting for the same confounders in the whole population, in the NC or MCI subgroups.

BMI and cognitive measures

We found significant correlations of BMI with ADNI-MEM score, ADAS-Cog11, ADAS-Cog13 and MMSE, but not with ADNI-EF in the whole population (ADNI-MEM, r = 0.094; ADAS-Cog11, r = -0.010; ADAS-Cog13, r = -0.106; MMSE, r = 0.189). Cross-sectionally at baseline, higher BMI categories were associated with better ADNI-MEM score (β = 0.060, p = 0.008), ADAS-Cog11 (β = -0.092, p = 0.0107) and ADAS-Cog13 (β = -0.143, p = 0.017), but were not associated with other cognitive measures in the entire sample (MMSE, p = 0.579; ADNI-EF, p = 0.370), after adjusting for demographic characteristics and prevalent comorbidities. When stratified by cognitive diagnosis, there was no association between BMI and cognitive performance in both control and MCI groups.

Longitudinally, higher baseline BMI categories were associated with slower rates of cognitive decline by all measures in the whole population (ADAS-Cog11, β = -0.351, p=7.52e-05; ADAS-Cog13, β = -0.491, p = 3.47e-06; MMSE, β = 1.56e-01, p = 0.001; ADNI-EF, β = 3.15e-02, p = 2.87e-05; ADNI-MEM, β = 2.60e-02, p = 4.07e-05; Figure 3) and in the MCI group (ADAS-Cog11, β = -0.535, p <0.001; ADAS-Cog13, β = -0.751, p = 5.64e-06; MMSE, β = 0.222, p = 0.002; ADNI-EF, β = 0.046, p = 2.87e-05; ADNI-MEM, β = 0.036, p < 0.001) over the following ten years. By contrast, in the NC group, there were no statistically significant differences in the association with rates of cognitive decline among the three BMI categories.

Figure 3. The longitudinal associations of BMI categories with cognitive performance in the entire sample. Higher BMI categories were associated with slower rates of cognitive decline on ADAS-Cog11 (A), ADAS-Cog13 (B), MMSE (C), ADNI-EF (D) and ADNI-MEM (E) over the following ten years. All analyses were adjusted for age, sex, education, cognitive diagnosis, ApoE ε4 status, as well as prevalent comorbidities (i.e., hypertension, hyperlipemia, DM2, depression and CVD). Abbreviations: BMI = body mass index; ADAS-Cog = Alzheimer Disease Assessment Scale-cognitive subscale; MMSE = Mini-Mental State Examination.

BMI and AD progression

When we examined the effect of BMI on AD risk using the Kaplan–Meier test, we found that BMI was inversely associated with AD risk. Indeed, the obese elderly had a lower risk of progression to AD over the following six years, compared to overweight and normal weight individuals (p = 0.009). However, further adjustment for confounding variables made little difference to this inverse association between BMI and AD risk (p=0.160). Figure 4 shows the Kaplan-Meier curves for the baseline BMI groups.

Figure 4. Kaplan-Meier curves comparing survival free from progression to AD according to baseline body mass index (BMI) categories. Individuals with higher BMI categories had a lower risk of progression to AD over the following six years. Abbreviations: BMI = body mass index.

Materials and Methods

ADNI

Data used in the preparation for this article were obtained from the ADNI database (http://adni.loni.usc.edu). As an ongoing project, the ADNI was launched in 2003, sponsored by many co-investigators from a range of academic institutions and private corporations. The goal of ADNI is to determine biological markers of Alzheimer’s disease through neuroimaging, genetics, and neuropsychological tests as well as other measures to develop new treatments, monitor their effectiveness, and lessen the time of clinical trials. The ADNI has recruited more than 1800 adult participants (aged 55-90 years) with NC, MCI, or mild AD from over 50 sites across the United States and Canada in three phases (1, GO, and 2). The present study consisted of 1,223 non-demented elders which were ≥65 years at the time of the baseline scan. Because few subjects were in the underweight group (BMI<18.5 kg/m², n = 11), we excluded them at baseline to avoid unmeasured confounding associated with baseline severe disease leading to very low BMI. Finally, 1,212 individuals were included in our study, including 714 MCI and 498 NC (Figure 5). The study was approved by the institutional review boards of all participating centers and all participants provided informed written consent.

Figure 5. Flowchart showing the samples used in this work and the subsets utilized for analyses. Abbreviations: AD = Alzheimer's disease; CSF =Cerebrospinal Fluid.

CSF collection and quality control

The ADNI Biomarker Core has previously described the procedures for acquiring and processing CSF for biomarker analysis. Briefly, CSF samples from each participant were drawn in the morning after an overnight fast and were collected into collection tubes. The CSF was transferred to polypropylene tubes and was immediately cooled to 4°C on dry ice until processing in the ADNI Biomarker Core laboratory at the University of Pennsylvania Medical Center, which occurred within 24 hours after collection. Aliquots (0.5 ml) were prepared from these samples after 1 h of thawing at room temperature and stored in polypropylene tubes at –80°C. INNO-BIA AlzBio3 (Innogenetics, Ghent, Belgium; reagents for research use only) immunoassay kit–based reagents were used to measure Aβ42, total-tau (t-tau), and phosphorylated-tau (p-tau).

18F florbetapir AV45 PET imaging and analysis

18F florbetapir-PET was used to estimate cerebral Aβ load. Data for 18F-Florbetapir imaging was acquired from UC Berkeley-AV45 analysis database (see http://adni.loni.usc.edu/data-samples/access-data/), in which three bilateral regions (hippocampus, entorhinal cortex and middle temporal) were selected as ROIs to study. In brief, summed images 50–70 minutes postinjection were coregistered to each participant’s MRI to enable alignment of FreeSurfer ROIs. Meanwhile, whole cerebellum also defined as reference region. The institute then applied each florbetapir scan to the corresponding MRI and calculated the mean florbetapir uptake within the cortical and reference region. Finally, ROI-based AV-45 standardized uptake value ratios (SUVR) were calculated by averaging across the three bilateral regions and dividing this average by the whole cerebellum.

MRI scans and image processing

The structural volumetric MRI files were the original MPRAGE (T1-weighted) files downloaded from UCSF data in the ADNI dataset (https://ida.loni.usc.edu/pages/access/studyData.jsp). Morphometric analysis of brain structure was completed with FreeSurfer Version 5.1 (http://surfer.nmr.mgh.harvard.edu). Detailed methodology for regional and total volume derivation has been described in detail previously.

As reported in previous studies, although some progressive brain tissue atrophies occurred with normal aging, these atrophies were greatly accelerated in AD patients [41, 42]. These domains, which included the hippocampus, entorhinal cortex and middle temporal lobe were explored in this analysis.

Neuropsychological assessment

The neuropsychological battery included the Mini-mental State Examination (MMSE) and the Alzheimer Disease Assessment Scale-cognitive subscale (ADAS-Cog) as tests of general cognition, and specific cognitive tests were administered to assess executive function (ADNI-EF) and memory (ADNI-MEM). The standard 11-item version of the ADAS-cog was augmented by adding 2 additional items (delayed word recall and number cancellation), and results from both the 11-item and 13-item versions were included in the ADNI dataset. ADNI-EF and ADNI-MEM composite z-scores were calculated from the ADNI neuropsychological battery of tests and have been described in detail elsewhere.

Covariates

An extensive set of covariates for the associations of BMI with biomarkers or cognitive outcomes were evaluated at baseline in the ADNI database. They included age, sex (0=Male; 1=Female), years of education, diagnostic history of hypertension, hyperlipemia, DM2, cardiovascular disease (CVD) and depression (1 = positive diagnostic history; 0 = negative diagnostic history). Moreover, CVD status was based on a history of myocardial infarction, angina, heart failure and atrial fibrillation. APOE ε4, the best-known AD risk gene, was used as a categorical variable (number of APOE ε4 allele; 0, 1, 2).

Statistical analysis

BMI is a recognized measure of obesity that is defined as weight in kilograms divided by the square of the height in meters. The participants in our study were categorized in accordance with the WHO classification system: normal weight (BMI = 18.5~24.9 kg/m2), overweight (BMI = 25.0~29.9 kg/m2), and obese (BMI ≥ 30.0 kg/m2). Descriptive statistics for baseline clinical characteristics, including age, sex, education, cognitive diagnosis, ApoE ε4 status and prevalent comorbidities (i.e., hypertension, hyperlipemia, DM2, depression and CVD) were initially compared among baseline BMI categories for the total samples. Clinical characteristics were compared among BMI categories using Kruskal-Wallis tests (for continuous variables) and Pearson’s chi-square tests (for categorical variables).

First, correlations of continuous BMI variables with CSF biomarkers (i.e., Aβ42, t-tau, p-tau, t-tau/Aβ42 ratio and tau/Aβ42 ratio), amyloid PET, MRI measures and cognitive test findings (i.e., ADAS-Cog11, ADAS-Cog13, MMSE, ADNI-EF and ADNI-MEM) were evaluated via Spearman correlation. Next, in the cross-sectional analyses, the effects of BMI categories on those biomarkers and cognitive test were investigated in multiple linear regression models. In these analyses, continuous response variables were tested for approximate normality and variance homogeneity, and were Box-Cox transformed when required to satisfy test criteria. Longitudinally, those biomarkers and cognitive performance over time were compared among the BMI groups by linear mixed-effects models. These models had random intercepts and slopes for time and an unstructured covariance matrix for the random effects, and included the interaction between time and BMI categories as a predictor. All the above analyses were performed both across diagnostic groups and within each diagnostic group (i.e., MCI and NC). Finally, the Kaplan–Meier method was used to estimate the relative risk of progression to AD in relation to baseline BMI categories. Cox proportional hazard model was performed to further assess the effect of late-life BMI on the risk of AD, with adjustment for age, sex, education, cognitive diagnosis, ApoE ε4 status and comorbidities.

All analyses were 2-tailed with significance set at P < 0.05 for main effects. All statistical analyses were performed with R statistical software.

Abbreviations

AD: Alzheimer’s disease; BMI: Body mass index; ADNI: Alzheimer’s Disease Neuroimaging Initiative; CSF: Cerebrospinal Fluid; Aβ42: β-amyloid42; NC: Normal cognition; MCI: Mild cognitive impairment; T-tau: Total-tau; P-tau: Phosphorylated-tau; DM2: Diabetes Mellitus Type 2; CVD: Cardiovascular diseases; MMSE: Mini-mental State Examination; ADAS-Cog: Alzheimer Disease Assessment Scale-cognitive subscale; ICV: intracranial volume; Region of interest (ROI): undefined.

Conflicts of Interest

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

This study was supported by grants from the National Natural Science Foundation of China (91849126, 81571245, and 81771148), the National Key R&D Program of China (2018YFC1314700), Shanghai Municipal Science and Technology Major Project (No.2018SHZDZX01) and ZHANGJIANG LAB, Tianqiao and Chrissy Chen Institute, and the State Key Laboratory of Neurobiology and Frontiers Center for Brain Science of Ministry of Education, Fudan University. Data collection and sharing for this project was funded by the Alzheimer's Disease Neuroimaging Initiative (ADNI) (National Institutes of Health Grant U01 AG024904) and DOD ADNI (Department of Defense award number W81XWH-12-2-0012). ADNI is funded by the National Institute on Aging, the National Institute of Biomedical Imaging and Bioengineering, and through generous contributions from the following: AbbVie, Alzheimer’s Association; Alzheimer’s Drug Discovery Foundation; Araclon Biotech; BioClinica, Inc.; Biogen; Bristol-Myers Squibb Company; CereSpir, Inc.; Cogstate; Eisai Inc.; Elan Pharmaceuticals, Inc.; Eli Lilly and Company; EuroImmun; F. Hoffmann-La Roche Ltd and its affiliated company Genentech, Inc.; Fujirebio; GE Healthcare; IXICO Ltd.; Janssen Alzheimer Immunotherapy Research and Development, LLC.; Johnson and Johnson Pharmaceutical Research and Development LLC.; Lumosity; Lundbeck; Merck and Co., Inc.; Meso Scale Diagnostics, LLC.; NeuroRx Research; Neurotrack Technologies; Novartis Pharmaceuticals Corporation; Pfizer Inc.; Piramal Imaging; Servier; Takeda Pharmaceutical Company; and Transition Therapeutics. The Canadian Institutes of Health Research is providing funds to support ADNI clinical sites in Canada. Private sector contributions are facilitated by the Foundation for the National Institutes of Health (www.fnih.org). The grantee organization is the Northern California Institute for Research and Education, and the study is coordinated by the Alzheimer’s Therapeutic Research Institute at the University of Southern California. ADNI data are disseminated by the Laboratory for Neuro Imaging at the University of Southern California.

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