Association of Glycemic Variability With Imaging Markers of Vascular Burden, β-Amyloid, Brain Atrophy, and Cognitive Impairment

BACKGROUND AND OBJECTIVE

We aimed to investigate the association between glycemic variability (GV) and neuroimaging markers of white matter hyperintensities (WMH), beta-amyloid (Aβ), brain atrophy, and cognitive impairment.

METHODS

This was a retrospective cohort study that included participants without dementia from a memory clinic. They all had Aβ PET, brain MRI, and standardized neuropsychological tests and had fasting glucose (FG) levels tested more than twice during the study period. We defined GV as the intraindividual visit-to-visit variability in FG levels. Multivariable linear regression and logistic regression were used to identify whether GV was associated with the presence of severe WMH and Aβ uptake with DM, mean FG levels, age, sex, hypertension, and presence of APOE4 allele as covariates. Mediation analyses were used to investigate the mediating effect of WMH and Aβ uptake on the relationship between GV and brain atrophy and cognition.

RESULTS

Among the 688 participants, the mean age was 72.2 years, and the proportion of female participants was 51.9%. Increase in GV was predictive of the presence of severe WMH (coefficient [95% CI] 1.032 [1.012-1.054]; p = 0.002) and increased Aβ uptake (1.005 [1.001-1.008]; p = 0.007). Both WMH and increased Aβ uptake partially mediated the relationship between GV and frontal-executive dysfunction (GV → WMH → frontal-executive; direct effect, -0.319 [-0.557 to -0.080]; indirect effect, -0.050 [-0.091 to -0.008]) and memory dysfunction (GV → Aβ → memory; direct effect, -0.182 [-0.338 to -0.026]; indirect effect, -0.067 [-0.119 to -0.015]), respectively. In addition, increased Aβ uptake completely mediated the relationship between GV and hippocampal volume (indirect effect, -1.091 [-2.078 to -0.103]) and partially mediated the relationship between GV and parietal thickness (direct effect, -0.00101 [-0.00185 to -0.00016]; indirect effect, -0.00016 [-0.00032 to -0.000002]).

DISCUSSION

Our findings suggest that increased GV is related to vascular and Alzheimer risk factors and neurodegenerative markers, which in turn leads to subsequent cognitive impairment. Furthermore, GV can be considered a potentially modifiable risk factor for dementia prevention.

Distinct effects of cholesterol profile components on amyloid and vascular burdens

BACKGROUND

Cholesterol plays important roles in β-amyloid (Aβ) metabolism and atherosclerosis. However, the relationships of plasma cholesterol levels with Aβ and cerebral small vessel disease (CSVD) burdens are not fully understood in Asians. Herein, we investigated the relationships between plasma cholesterol profile components and Aβ and CSVD burdens in a large, non-demented Korean cohort.

METHODS

We enrolled 1,175 non-demented participants (456 with unimpaired cognition [CU] and 719 with mild cognitive impairment [MCI]) aged ≥ 45 years who underwent Aβ PET at the Samsung Medical Center in Korea. We performed linear regression analyses with each cholesterol (low-density lipoprotein cholesterol [LDL-c], high-density lipoprotein cholesterol [HDL-c], and triglyceride) level as a predictor and each image marker (Aβ uptake on PET, white matter hyperintensity [WMH] volume, and hippocampal volume) as an outcome after controlling for potential confounders.

RESULTS

Increased LDL-c levels (β = 0.014 to 0.115, p = 0.013) were associated with greater Aβ uptake, independent of the APOE e4 allele genotype and lipid-lowering medication. Decreased HDL-c levels (β =  - 0.133 to - 0.006, p = 0.032) were predictive of higher WMH volumes. Increased LDL-c levels were also associated with decreased hippocampal volume (direct effect β =  - 0.053, p = 0.040), which was partially mediated by Aβ uptake (indirect effect β =  - 0.018, p = 0.006).

CONCLUSIONS

Our findings highlight that increased LDL-c and decreased HDL-c levels are important risk factors for Aβ and CSVD burdens, respectively. Furthermore, considering that plasma cholesterol profile components are potentially modified by diet, exercise, and pharmacological agents, our results provide evidence that regulating LDL-c and HDL-c levels is a potential strategy to prevent dementia.

Sex-specific relationship between non-alcoholic fatty liver disease and amyloid-β in cognitively unimpaired individuals

Background

Non-alcoholic fatty liver disease (NAFLD) is known to be associated with a high risk of clinically diagnosed Alzheimer's disease (AD). Additionally, the prevalence of NAFLD and AD is higher in elderly females than in males. However, a sex-specific association between NAFLD and amyloid-beta (Aβ) deposition remains unclear. Therefore, we investigated the sex-specific relationship between NAFLD and Aβ deposition in a large-sized cohort of cognitively unimpaired (CU) individuals.

Methods

We enrolled 673 (410 [60.9%] females and 263 [39.1%] males) CU individuals aged ≥45 years who underwent Aβ positron emission tomography (PET). The presence of NAFLD, assessed using the hepatic steatosis index, and the severity of NAFLD, assessed using the Fibrosis-4 index, were considered predictors. Aβ deposition on PET was considered as an outcome.

Results

Females had a higher frequency of NAFLD than males (48 and 23.2%, p < 0.001). Among females, the presence of NAFLD (β = 0.216, p < 0.001) was predictive of increased Aβ deposition, whereas among males, the presence of NAFLD (β = 0.191, p = 0.064) was not associated with Aβ deposition. Among females, the presence of NAFLD with low (β = 0.254, p = 0.039), intermediate (β = 0.201, p = 0.006), and high fibrosis (β = 0.257, p = 0.027) was predictive of increased Aβ deposition. Aβ deposition also increased as the severity of NAFLD increased in females (p for trend = 0.001).

Conclusion

We highlight the marked influence of NAFLD and its severity on the risk of Aβ deposition in relation to sex. Furthermore, our findings suggest that sex-specific strategies regarding the management of NAFLD are necessary for the prevention of Aβ deposition.

Independent effect of body mass index variation on amyloid-β positivity

Objectives

The relationship of body mass index (BMI) changes and variability with amyloid-β (Aβ) deposition remained unclear, although there were growing evidence that BMI is associated with the risk of developing cognitive impairment or AD dementia. To determine whether BMI changes and BMI variability affected Aβ positivity, we investigated the association of BMI changes and BMI variability with Aβ positivity, as assessed by PET in a non-demented population.

Methods

We retrospectively recruited 1,035 non-demented participants ≥50 years of age who underwent Aβ PET and had at least three BMI measurements in the memory clinic at Samsung Medical Center. To investigate the association between BMI change and variability with Aβ deposition, we performed multivariable logistic regression. Further distinctive underlying features of BMI subgroups were examined by employing a cluster analysis model.

Results

Decreased (odds ratio [OR] = 1.68, 95% confidence interval [CI] 1.16–2.42) or increased BMI (OR = 1.60, 95% CI 1.11–2.32) was associated with a greater risk of Aβ positivity after controlling for age, sex, APOE e4 genotype, years of education, hypertension, diabetes, baseline BMI, and BMI variability. A greater BMI variability (OR = 1.73, 95% CI 1.07–2.80) was associated with a greater risk of Aβ positivity after controlling for age, sex, APOE e4 genotype, years of education, hypertension, diabetes, baseline BMI, and BMI change. We also identified BMI subgroups showing a greater risk of Aβ positivity.

Conclusion

Our findings suggest that participants with BMI change, especially those with greater BMI variability, are more vulnerable to Aβ deposition regardless of baseline BMI. Furthermore, our results may contribute to the design of strategies to prevent Aβ deposition with respect to weight control.

Developing a Dementia Platform Databank Using Multiple Existing Cohorts

This study was conducted as a pilot project to evaluate the feasibility of building an integrate dementia platform converging preexisting dementia cohorts from several variable levels. The following four cohorts were used to develop this pilot platform: 1) Clinical Research Center for Dementia of South Korea (CREDOS), 2) Korean Brain Aging Study for Early Diagnosis and Prediction of Alzheimer's disease (K-BASE), 3) Environmental Pollution-induced Neurological Effects (EPINEF) study, and 4) a prospective registry in Dementia Platform Korea project (DPKR). A total of 29916 patients were included in the platform with 348 integrated variables. Among participants, 13.9%, 31.5%, and 44.2% of patients had normal cognition, mild cognitive impairment, and dementia, respectively. The mean age was 72.4 years. Females accounted for 65.7% of all patients. Those with college or higher education and those without problems in reading or writing accounted for 12.3% and 46.8%, respectively. Marital status, cohabitation, family history of Parkinson's disease, smoking and drinking status, physical activity, sleep status, and nutrition status had rates of missing information of 50% or more. Although individual cohorts were of the same domain and of high quality, we found there were several barriers to integrating individual cohorts, including variability in study variables and measurements. Although many researchers are trying to combine pre-existing cohorts, the process of integrating past data has not been easy. Therefore, it is necessary to establish a protocol with considerations for data integration at the cohort establishment stage.

Machine Learning for the Prediction of Amyloid Positivity in Amnestic Mild Cognitive Impairment

BACKGROUND

Amyloid-β (Aβ) evaluation in amnestic mild cognitive impairment (aMCI) patients is important for predicting conversion to Alzheimer's disease. However, Aβ evaluation through Aβ positron emission tomography (PET) is limited due to high cost and safety issues.

OBJECTIVE

We therefore aimed to develop and validate prediction models of Aβ positivity for aMCI using optimal interpretable machine learning (ML) approaches utilizing multimodal markers.

METHODS

We recruited 529 aMCI patients from multiple centers who underwent Aβ PET. We trained ML algorithms using a training cohort (324 aMCI from Samsung medical center) with two-phase modelling: model 1 included age, gender, education, diabetes, hypertension, apolipoprotein E genotype, and neuropsychological test scores; model 2 included the same variables as model 1 with additional MRI features. We used four-fold cross-validation during the modelling and evaluated the models on an external validation cohort (187 aMCI from the other centers).

RESULTS

Model 1 showed good accuracy (area under the receiver operating characteristic curve [AUROC] 0.837) in cross-validation, and fair accuracy (AUROC 0.765) in external validation. Model 2 led to improvement in the prediction performance with good accuracy (AUROC 0.892) in cross validation compared to model 1. Apolipoprotein E genotype, delayed recall task scores, and interaction between cortical thickness in the temporal region and hippocampal volume were the most important predictors of Aβ positivity.

CONCLUSION

Our results suggest that ML models are effective in predicting Aβ positivity at the individual level and could help the biomarker-guided diagnosis of prodromal AD.

Association between APOE ε2 and Aβ burden in patients with Alzheimer- and vascular-type cognitive impairment

OBJECTIVE

To investigate the association between APOE genotype and β-amyloid (Aβ) burden, as measured by PET in patients with subcortical vascular cognitive impairment (SVCI) and those with Alzheimer disease-related cognitive impairment (ADCI).

METHODS

This was a cross-sectional study of 310 patients with SVCI and 999 with ADCI. To evaluate the effects of APOE genotype or diagnostic group on Aβ positivity, we performed multivariate logistic regression analyses. Further distinctive underlying features of latent subgroups were examined by employing a latent class cluster analysis approach.

RESULTS

In comparison with ε3 homozygotes, in the ADCI group, ε2 carriers showed a lower frequency of Aβ positivity (odds ratio [OR] 0.43, 95% confidence interval [CI] 0.23-0.79), while in the SVCI group, ε2 carriers showed a higher frequency of Aβ positivity (OR 2.26, 95% CI 1.02-5.01). In particular, we observed an interaction effect of ε2 carrier status and diagnostic group on Aβ positivity (OR 5.12, 95% CI 1.93-13.56), in that relative to ε3 homozygotes, there were more Aβ-positive ε2 carriers in the SVCI group than in the ADCI group. We also identified latent subgroups of Aβ-positive APOE ε2 carriers with SVCI and Aβ-positive APOE ε4 carriers with ADCI.

CONCLUSIONS

Our findings suggest that APOE ε2 is distinctly associated with Aβ deposition in patients with SVCI and those with ADCI. Our findings further suggest that there is a distinctive subgroup of Aβ-positive APOE ε2 carriers with SVCI among patients with cognitive impairment.

Distinct Brain Regions in Physiological and Pathological Brain Aging

Background

Studying structural brain aging is important to understand age-related pathologies, as well as to identify the early manifestations of the Alzheimer’s disease (AD) continuum. In this study, we investigated the long-term trajectory of physiological and pathological brain aging in a large number of participants ranging from the 50s to over 80 years of age.

Objective

To explore the distinct brain regions that distinguish pathological brain aging from physiological brain aging using sophisticated measurements of cortical thickness.

Methods

A total of 2,823 cognitively normal (CN) individuals and 2,675 patients with AD continuum [874 with subjective memory impairment (SMI), 954 with amnestic mild cognitive impairment (aMCI), and 847 with AD dementia] who underwent a high-resolution 3.0-tesla MRI were included in this study. To investigate pathological brain aging, we further classified patients with aMCI and AD according to the severity of cognitive impairment. Cortical thickness was measured using a surface-based method. Multiple linear regression analyses were performed to evaluate age, diagnostic groups, and cortical thickness.

Results

Aging extensively affected cortical thickness not only in CN individuals but also in AD continuum patients; however, the precuneus and inferior temporal regions were relatively preserved against age-related cortical thinning. Compared to CN individuals, AD continuum patients including those with SMI showed a decreased cortical thickness in the perisylvian region. However, widespread cortical thinning including the precuneus and inferior temporal regions were found from the late-stage aMCI to the moderate to severe AD. Unlike the other age groups, AD continuum patients aged over 80 years showed prominent cortical thinning in the medial temporal region with relative sparing of the precuneus.

Conclusion

Our findings suggested that the precuneus and inferior temporal regions are the key regions in distinguishing between physiological and pathological brain aging. Attempts to differentiate age-related pathology from physiological brain aging at a very early stage would be important in terms of establishing new strategies for preventing accelerated pathological brain aging.

Low subjective socioeconomic status stimulates orexigenic hormone ghrelin - A randomised trial

Recent evidence suggests that lower perceived socioeconomic status is linked to increased appetite and intake of greater calories. Yet, whether insecurity of socioeconomic resources directly influences regulatory systems of appetite and energy intake is not known. Considering psychological states, mindsets and beliefs have shown to meaningfully affect physiological responses to food, the present study tested the hypothesis that low subjective socioeconomic status (SSS) will have a direct influence on physiological responses, such as appetite-related hormones (ghrelin, pancreatic polypeptide and insulin). Forty-eight healthy males were randomly (crossover, counterbalanced) assigned, to two experimental conditions where participants were either experimentally induced to feel low SSS or not (control; CON). Feelings of low SSS resulted in an increase in active ghrelin (an orexigenic hormone) following the SSS manipulation compared with baseline, while no change in active ghrelin was observed in CON. Furthermore, participants reported lower fullness and satiety following low SSS compared with CON. Our findings demonstrate that SSS may influence hunger regulation and appetite, and suggest that physiological systems regulating energy balance (i.e. caloric resources) may also be sensitive to perceived deprivation or imbalances in critical non-food resources (socioeconomic resources).