Effects of glucose, insulin, and insulin resistance on cerebral 18F-FDG distribution in cognitively normal older subjects
Effects of glucose, insulin, and insulin resistance on cerebral 18F-FDG distribution in cognitively normal older subjects
Kenji Ishibashi 0 1
Airin Onishi 0 1
Yoshinori Fujiwara 1
Kiichi Ishiwata 0 1
Kenji Ishii 0 1
0 Research Team for Neuroimaging, Tokyo Metropolitan Institute of Gerontology, Tokyo, Japan, 2 Research Team for Social Participation and Community Health, Tokyo Metropolitan Institute of Gerontology, Tokyo, Japan, 3 Institute of Cyclotron and Drug Discovery Research, Southern Tohoku Research Institute for Neuroscience, Koriyama, Japan, 4 Department of Biofunctional Imaging, Fukushima Medical University , Fukushima , Japan
1 Editor: Stephen D. Ginsberg, Nathan S Kline Institute , UNITED STATES
Increasing plasma glucose levels and insulin resistance can alter the distribution pattern of fluorine-18-labeled fluorodeoxyglucose (18F-FDG) in the brain and relatively reduce 18FFDG uptake in Alzheimer's disease (AD)-related hypometabolic regions, leading to the appearance of an AD-like pattern. However, its relationship with plasma insulin levels is unclear. We aimed to compare the effects of plasma glucose levels, plasma insulin levels and insulin resistance on the appearance of the AD-like pattern in 18F-FDG images.
Funding: This study was supported by internal
funds from Tokyo Metropolitan Institute of
Fifty-nine cognitively normal older subjects (age = 75.7 ± 6.4 years) underwent 18F-FDG
positron emission tomography along with measurement of plasma glucose and insulin
levels. As an index of insulin resistance, the Homeostasis model assessment of Insulin
Resistance (HOMA-IR) was calculated.
Plasma glucose levels, plasma insulin levels, and HOMA-IR were 102.2 ± 8.1 mg/dL, 4.1 ±
1.9 μU/mL, and 1.0 ± 0.5, respectively. Whole-brain voxelwise analysis showed a negative
correlation of 18F-FDG uptake with plasma glucose levels in the precuneus and lateral
parietotemporal regions (cluster-corrected p < 0.05), and no correlation with plasma insulin levels
or HOMA-IR. In the significant cluster, 18F-FDG uptake decreased by approximately 4±5%
when plasma glucose levels increased by 20 mg/dL. In the precuneus region,
volume-ofinterest analysis confirmed a negative correlation of 18F-FDG uptake with plasma glucose
levels (r = -0.376, p = 0.002), and no correlation with plasma insulin levels (r = 0.156, p =
0.12) or HOMA-IR (r = 0.096, p = 0.24).
Gerontology, and Award 2015 of Tokyo
Metropolitan Institute of Gerontology (to Ishibashi).
Competing interests: The authors have declared
that no competing interests exist.
This study suggests that, of the three parameters, plasma glucose levels have the greatest
effect on the appearance of the AD-like pattern in 18F-FDG images.
Increase of plasma glucose levels alters the distribution pattern of fluorine-18-labeled
fluorodeoxyglucose (18F-FDG) in the brain. This phenomenon was first described by Kawasaki and
colleagues in a positron emission tomography (PET) study with 19 cognitively normal elderly
subjects, where glucose loading decreased 18F-FDG uptake especially in the precuneus,
posterior cingulate, and lateral parietotemporal regions [
]. This relationship between increasing
plasma glucose levels and decreasing 18F-FDG uptake in specific regions has been confirmed
by Burns and colleagues, who showed that, in a cross-sectional PET study with 124 cognitively
normal elderly subjects, plasma glucose levels were negatively correlated with 18F-FDG uptake
in the precuneus and lateral parietal regions [
]. Interestingly, the precuneus, posterior
cingulate, and lateral parietotemporal regions are associated with the representative hypometabolic
areas preferentially observed in Alzheimer's disease (AD), suggesting that increasing plasma
glucose levels induces the appearance of an AD-like pattern in 18F-FDG images. We have, thus
far, investigated the effects of plasma glucose levels on the appearance of AD-like patterns. In
the case of a 70-year-old man who had neither Aβ deposition nor ApoE ε4 genotype, the
ADlike pattern appeared at plasma glucose levels of 162 mg/dL but disappeared at 106 mg/dL [
The AD-like pattern in 18F-FDG images can appear in cognitively normal older subjects with
plasma glucose levels of 100 to 110 mg/dL [
], and also in young healthy subjects who received
glucose loading [
]. Additionally, increasing and decreasing plasma glucose levels induce
the AD-like pattern reversibly to appear and disappear, respectively, in cognitively normal
subjects with diabetes .
Higher insulin resistance is also able to alter the distribution pattern of 18F-FDG in the
brain, leading to the appearance of the AD-like pattern in 18F-FDG images. Baker and
colleagues conducted 18F-FDG PET scanning on cognitively normal subjects with prediabetes
and early diabetes, and showed negative correlations between insulin resistance and 18F-FDG
uptake in the precuneus, posterior cingulate, and lateral parietotemporal regions, which are
the same as the AD-related hypometabolic regions [
]. Similar associations between higher
insulin resistance and lower 18F-FDG uptake in specific areas, including AD-vulnerable
regions, have been recently shown in patients with AD [
] and in middle-aged subjects at risk
for AD [
Since an index of insulin resistance, Homeostasis Model Assessment of Insulin
Resistance (HOMA-IR), is calculated using plasma glucose and insulin levels [
magnitude of insulin resistance has a close relationship with both plasma glucose and insulin
levels. Although the AD-like pattern in 18F-FDG images can be associated with increasing
plasma glucose levels and higher insulin resistance, the relationship between the AD-like
pattern and plasma insulin levels is still unclear. The goal of this study was to compare the
effects of plasma glucose levels, plasma insulin levels and insulin resistance on the cerebral
18F-FDG distribution, and to determine, of the three parameters, which one has the
greatest effect on the appearance of the AD-like pattern. For this purpose, we performed
18F-FDG PET scanning on cognitively normal subjects as well as measuring both plasma
glucose and insulin levels.
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Materials and methods
This prospective study was conducted in accordance with the Helsinki Protocol, and approved
by the Ethics Committee of Tokyo Metropolitan Institute of Gerontology. Written informed
consent was obtained from all 59 participants. Ten subjects were men and 49 were women,
and the age range was 64 to 88 years. All subjects were recruited from ongoing longitudinal
studies of cognition and aging at the institute, and completed an interview, physical and
neurological examinations, a screening test for dementia, and magnetic resonance imaging (MRI)
scanning. In an interview, all subjects were confirmed to have a conviction that they were
cognitively normal and they were living an independent life without any assistance [
were excluded if their Mini-Mental State Examination (MMSE) score was less than 27, if their
body mass index was less than 18.5 or more than 25.0, or if they had a history of diabetes. In
physical and neurological examinations and a routine mental health interview performed by a
neurologist, those with a neurological condition, a mental health condition, or any other
uncontrolled health condition were also excluded. No subjects showed significant brain
atrophy or lesions in the MRI findings. Finally, all subjects were deemed cognitively normal and
Glucose and insulin levels
After more than 5 h of fasting, each participant underwent 18F-FDG PET scanning. Plasma
glucose levels were measured twice, immediately prior to the intravenous 18F-FDG injection
and 30 min after the injection, by using a medical device (Caresist; Horiba, Kyoto, Japan), and
the two values were averaged. The measurement system for plasma glucose levels was based on
the enzyme electrode method, which integrates hydrogen peroxide electrodes with a glucose
oxidase immobilized membrane. Plasma insulin levels were measured once immediately prior
to the 18F-FDG injection using a chemiluminescent immunoassay (BML, Tokyo, Japan).
HOMA-IR was calculated as an index of insulin resistance by the following formula:
HOMA-IR = [(fasting glucose level (mg/dL) × fasting insulin level (μU/mL)) / 405].
PET scanning and image processing
The radioligand, 18F-FDG, was synthesized using a PET synthesizer (F300; Sumitomo Heavy
Industries, Tokyo, Japan). The radiochemical purity of 18F-FDG was greater than 95%. The
PET scanning was performed at the institute using the Discovery PET/CT 710 scanner (GE
Healthcare, Milwaukee, WI) in the three-dimensional mode. The in-plane and axial
resolutions of the full width at half maximum (FWHM) were 4.52 mm and 4.83 mm, respectively.
CT-transmission data were acquired for measured attenuation correction. Emission data were
acquired for 10 min starting at 40 min after an intravenous bolus injection of approximately
150 MBq (4 mCi) of 18F-FDG. Forty-seven-slice images with 2 × 2 × 3.27 mm3 voxel size and
128 × 128 matrix size were then obtained. Data were reconstructed after correction for decay,
attenuation, and scatter.
Images were processed using the FMRIB Software Library version 5.0.4 (FSL; Oxford
University, Oxford, UK) and the Statistical Parametric Mapping, version 12 (SPM12; Wellcome
Trust Center for Neuroscience, London, UK) implemented in MATLAB, version R2014a (The
MathWorks, Natick, MA). Using the in-house developed 18F-FDG template, all 18F-FDG
images were transformed into the Montreal Neurological Institute (MNI) space from native
space for voxelwise and subsequent volume-of-interest (VOI) analyses. Then, the whole
cerebral cortex in each 18F-FDG image was masked with the MNI structural atlas included in FSL
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Fig 1. Volumes-of-interest (VOIs) in the MNI space. A mask of the whole cerebral cortex (A) and the two VOIs (B and C) are displayed on the axial
sections of the MNI standard brain. The VOI for B was equal to the significant cluster detected by the voxelwise analysis. The VOI for C was placed on the
precuneus, which was sampled from the Harvard-Oxford atlas. MNI: Montreal Neurological Institute.
(Fig 1A). Using the mask as a reference region, normalized 18F-FDG images were created in
the MNI space, and normalized values of 18F-FDG uptake were expressed as FU. The mean FU
value was set as one in the whole cerebral cortex.
Data processing and statistical analysis
The relationships between the two variables from age, plasma glucose levels, plasma insulin
levels, and HOMA-IR were tested by a partial correlation analysis with gender adjustment.
Statistical significance was set at two-tailed p < 0.05.
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After applying 8 mm FWHM spatial smoothing to FU images, whole-brain voxelwise
regression analysis was performed to assess the relationships between FU values and each
of the three variables (i.e., glucose, insulin, and HOMA-IR), controlling the effects of age
and gender. The null hypothesis was that 18F-FDG uptake did not negatively correlate with
plasma glucose levels, plasma insulin levels, or HOMA-IR. First, we calculated a statistical
t map of negative contrast between FU values and each of the three variables, using age
and gender as nuisance variables. To control a type I error, the correction for multiple
comparisons was performed on the statistical map using the AFNI's 3dClustSim program,
which computes alpha levels denoting the probability of false positive clusters that are
above the minimum cluster size for a specific voxelwise p value threshold (https://afni.
nimh.nih.gov/pub/dist/doc/program_help/3dClustSim.html: accessed June 10, 2017). The
corrected significance level was set at an alpha level of 0.05 with a voxelwise height
threshold of p < 0.05.
Following the voxelwise analysis, two VOI analyses were performed in the MNI space.
First, the significant cluster, which was detected by the voxelwise analysis, was used as a VOI
(Fig 1B). Next, as a representative AD-related hypometabolic region, the precuneus VOI was
sampled from the Harvard-Oxford atlas included in the FSL (Fig 1C). In each of the two VOI
analyses, the relationships between FU values and each of the three variables (i.e., glucose,
insulin, and HOMA-IR) were then tested by a partial correlation analysis with age and gender
adjustment. The null hypothesis was that 18F-FDG uptake did not negatively correlate with
plasma glucose levels, plasma insulin levels, or HOMA-IR values. All statistical analyses were
conducted using SPSS Statistics version 22 (IBM, Armonk, NY). Statistical significance was set
at one-tailed p < 0.05.
Age, plasma glucose levels, plasma insulin levels, and HOMA-IR in all subjects were 75.7 ± 6.4
years, 102.2 ± 8.1 mg/dL, 4.1 ± 1.9 μU/mL, and 1.0 ± 0.5, respectively (mean ± standard
deviation). The relationships among the four variables are shown in Fig 2. There was no significant
correlation of age with glucose levels (partial correlation coefficient: r = -0.089, p = 0.51), insulin
levels (r = 0.015, p = 0.91) or HOMA-IR (r = 0.016, p = 0.90). In addition, there was no significant
correlation of glucose levels with insulin levels (r = 0.057, p = 0.67) or HOMA-IR (r = 0.209,
p = 0.12). However, plasma insulin levels were significantly correlated with HOMA-IR (r = 0.985,
p < 0.001).
Whole-brain voxelwise regression analysis with age and gender adjustments revealed a
cluster consisting of 13220 voxels between plasma glucose levels and FU values (Fig 3) at a
threshold of cluster-corrected p < 0.05, where 18F-FDG uptake decreased with increasing plasma
glucose levels. They extended to the precuneus and lateral parietotemporal regions. However,
voxelwise regression analysis revealed no significant correlation of FU values with plasma
insulin levels or HOMA-IR at a threshold of cluster-corrected p < 0.05.
The results from VOI analysis in the significant cluster which was detected by the
voxelwise analysis (Fig 3) are shown in Fig 4. The whole significant cluster was used as a VOI
(Fig 1B). Plasma glucose levels were significantly correlated with FU values (r = -0.652,
p < 0.001). The simple regression line show that 18F-FDG uptake in the significant cluster
decreases by approximately 4±5% when plasma glucose levels increase by 20 mg/dL. The
results from VOI analysis in the precuneus area (Fig 1C) are shown in Fig 5. Plasma
glucose levels were significantly correlated with FU values (r = -0.376, p = 0.002). However,
there was no significant correlation of FU values with insulin levels (r = 0.156, p = 0.12) or
HOMA-IR (r = 0.096, p = 0.24).
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Fig 2. Sample age, glucose and insulin measurements, and insulin resistance. Relationships between age and glucose (A), between age and insulin
(B), between age and HOMA-IR (C), between insulin and glucose (D), between HOMA-IR and glucose (E), and between HOMA-IR and insulin (F) are
displayed. HOMA-IR: Homeostasis model assessment of Insulin Resistance.
The primary objective of this study was to compare the effects of plasma glucose levels, plasma
insulin levels and HOMA-IR on the cerebral 18F-FDG distribution, leading to the appearance
of the AD-like pattern in cognitively normal healthy subjects. We showed that there was a
negative correlation between plasma glucose levels and normalized values of 18F-FDG uptake in
the precuneus and lateral parietotemporal regions, which are the main components of
ADrelated hypometabolic areas. Although we used the whole cerebral cortex as a reference region
to create normalized 18F-FDG images, the results were comparable even with the use of the
cerebellum or the global brain as a reference region. In this study, HOMA-IR was highly
correlated with plasma insulin levels but not with plasma glucose levels, probably because the
intersubject variability in plasma insulin levels was much larger than that in plasma glucose levels.
When the coefficient of variance (CV), which is calculated as (standard deviation / mean), was
used as an index of the intersubject variability, the CVs in plasma glucose levels, plasma insulin
levels and HOMA-IR were 0.08, 0.47, and 0.48, respectively. Thus, of the three parameters,
plasma insulin levels and HOMA-IR were related, but plasma glucose levels were uncorrelated
with the other two parameters.
A few studies have investigated the relationship between insulin resistance and 18F-FDG
uptake in the brain. In a PET study on 23 patients with prediabetes and early diabetes, Baker
and colleagues showed negative correlations between HOMA-IR and 18F-FDG uptake in the
precuneus, posterior cingulate, and lateral parietotemporal regions, which are also the
representative AD-related hypometabolic areas [
]. However, they could not find any significant
correlation between the two variables in six control subjects. The authors addressed that this
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Fig 3. Negative correlation between plasma glucose levels and 18F-FDG uptake in whole-brain voxelwise analysis with age and gender adjustment.
Significant clusters at a threshold of cluster-corrected p < 0.05 showing negative correlation of plasma glucose levels with FU values are displayed on the SPM
glass brain (left side) and on the standard brain (right side) in the MNI space. They extended to the precuneus and lateral parietotemporal regions. The yellow±
red scale represents the magnitude of p values. FU: normalized values of 18F-FDG uptake using the whole cerebral cortex as a reference region, R: right, L: left,
SPM: Statistical Parametric Mapping, MNI: Montreal Neurological Institute.
difference between patient and control groups might come from the difference in the range of
HOMA-IR; its range was approximately one to 12 in the patient group, while HOMA-IR was
around 2.4 in the control group. Willette and colleagues reported that higher HOMA-IR
predicted lower 18F-FDG uptake in AD-vulnerable regions for patients with AD but not for
healthy subjects [
]. Another study from Willette and colleagues showed that higher
HOMAIR was associated with lower 18F-FDG uptake in the AD-related hypometabolic regions in
cognitively normal late middle-aged adults at risk for AD whose range of HOMA-IR was 0.5 to
]. These findings suggest that the relationship between insulin resistance and 18F-FDG
uptake in the brain can be different between healthy subjects and patients with diabetes or
AD, and that our results from the healthy subjects with HOMA-IR range of 0.3 to 2.5 are not
inconsistent with these previous studies. To our knowledge, this is the first study that has
compared the effects of plasma glucose levels, plasma insulin levels, and HOMA-IR on the cerebral
18F-FDG distribution. Our study has provided evidence that, of the three parameters, plasma
glucose levels strongly influence the cerebral 18F-FDG distribution in the precuneus and lateral
parietotemporal regions, and has the greatest effect on the appearance of the AD-like pattern.
In general, as 18F-FDG competes with glucose for the glucose transporters and hexokinase
], increasing plasma glucose levels absolutely reduces 18F-FDG uptake in every tissue of the
global brain [14±17]. However, the magnitude of reduction in 18F-FDG uptake with increasing
plasma glucose levels can be different across tissues [
], and the cerebral 18F-FDG distribution
pattern can change depending on plasma glucose levels [
]. There are two possible
explanations as to why increasing plasma glucose levels can alter the cerebral 18F-FDG distribution
pattern. First, the alteration may be affected by the regional difference in the expression of
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Fig 4. Relationship between plasma glucose levels and FU values in the significant cluster obtained from
voxelwise analysis. The whole significant cluster shown in Fig 3 was used as a volume-of-interest (Fig 1B). A
solid line represents the simple regression line between the two variables. FU: normalized values of 18F-FDG
uptake using the whole cerebral cortex as a reference region.
glucose transporters and hexokinase and/or the regional difference in the magnitude of
competition between glucose and 18F-FDG. Second, the alteration may be caused by the changes in
regional neuronal activity because regional 18F-FDG uptake is associated with regional glucose
]. We have conducted two studies to answer this issue. In one study, 18F-FDG
and 15O-H2O PET scans were performed on nine young healthy volunteers in the fasting and
glucose loading conditions [
]. 15O-H2O uptake reflects regional cerebral blood flow and
regional neuronal activity, independently of glucose transporters and hexokinase. The study
Fig 5. Relationships of FU values with glucose (A), insulin (B), and insulin resistance (C) in the precuneus. The precuneus mask was used as a
volume-of-interest (Fig 1C). A solid line represents the simple regression line between FU values and glucose. HOMA-IR: Homeostasis model assessment
of Insulin Resistance, FU: normalized values of 18F-FDG uptake using the whole cerebral cortex as a reference region.
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showed that glucose loading can alter the distribution pattern of 18F-FDG as well as 15O-H2O,
and that uptake of both radioligands decrease in the AD-related hypometabolic regions. In the
other study, dynamic 18F-FDG PET scans with arterial blood sampling, which directly
measured regional glucose metabolism, were performed on 12 young healthy volunteers in the
fasting and glucose loading conditions [
]. The study showed that glucose loading can decrease
net glucose metabolism in the AD-related hypometabolic regions, although the magnitude of
its reduction is very small. These studies strongly indicate that increasing plasma glucose levels
can decrease regional neuronal activity in the AD-related hypometabolic regions.
The extension of the cluster in Fig 3 is similar to the functional anatomy of default mode
network (DMN), which is characterized by high activity when the mind is not engaged in
specific behavioral tasks, and low activity during focused attention on the external environment
]. The DMN is known to play an important role in regulating complex cognition and
behavior [21±23]. Interestingly, recent studies using functional magnetic resonance imaging
(fMRI) showed that the functional connectivity of DMN can decrease in patient with diabetes
[24±26]. On the other hand, the functional anatomy of DMN considerably overlaps with the
main components of AD-related hypometabolic regions in 18F-FDG images. Its functional
connectivity is impaired in patients with AD , and its impairment becomes worse with AD
disease progression [
]. These findings suggest that diabetes and AD share the vulnerability
of DMN, and that reduced functional connectivity of DMN is associated with reduced
neuronal activity in the AD-related hypometabolic regions. Therefore, these lines of evidence and
our findings suggest that increasing plasma glucose levels may decrease the functional
connectivity of DMN, leading to the reduced neuronal activity in the DMN-related components
followed by the appearance of the AD-like pattern in 18F-FDG images. Further investigations
with a combination of 18F-FDG PET and resting-state fMRI are required to assess this
The precuneus and lateral parietotemporal regions shown in Fig 3 are similar to the areas
identified as hypometabolic in patients with AD. However, it should be addressed that the
magnitude of reduction in 18F-FDG uptake is quite different between the two situations.
Glucose metabolism in patients with AD, whose MMSE scores were around 18, decreased by
roughly 40%, compared to controls [
]. Another study showed that 18F-FDG uptake in
patients with AD, whose MMSE scores were around 20, decreased by roughly 25%, compared
to controls [
]. On the other hand, our findings showed that 18F-FDG uptake decreased by
approximately 4±5% when plasma glucose levels increased by 20 mg/dL. Although it may be
difficult to point out a 4±5% reduction in 18F-FDG images by visual inspection, statistical VOI
or voxelwise analysis can find the difference. We therefore recommend that when statistical
analysis is performed to assess 18F-FDG uptake, plasma glucose levels should be matched
between groups or be set as a covariate to remove the effects of glucose on the cerebral
The range of fasting plasma glucose in this study was 78.5 to 117 mg/dL. According to the
criteria for diabetes, the levels of glucose < 100 mg/dL, 100 glucose < 126 mg/dL and
glucose 126 mg/dL in the fasting condition are defined as normal, impaired fasting glucose
(IFG), and diabetes, respectively, and individuals with IFG are defined as prediabetes [
Therefore, about half of the participants in this study might fall within the prediabetes range.
Additionally, there is a possibility that some participants with higher glucose levels might be
diagnosed with diabetes if we also measured values of the 2-hour oral glucose tolerance test
and HbA1c. Interestingly, individuals with higher plasma glucose levels including IFG range
are associated with cognitive decline measured by a battery of neuropsychological tests, and
have a greater risk of developing cognitive impairment, compared to individual with
non-diabetes [31±33]. Considering these known findings, some participants with higher plasma
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glucose levels might subclinically start to decrease cognitive function although all participants
were deemed as non-MCI and non-dementia. Thus, this might affect the changes in the
cerebral 18F-FDG distribution pattern in this study.
One of the limitations of this study was the lack of the MR-based partial volume correction,
which might theoretically improve the accuracy of the results. However, the accuracy of
MRbased partial volume correction is affected by several potential sources of error. These are
misregistrations between the MRI and PET datasets, inaccurate estimation of resolution effects,
missegmentation of the MRI data into brain tissue components, and potential heterogeneity of
brain white matter [
]. Because of the low quality of MRI data, we could not apply the
MRbased partial volume correction to the PET data. However, the method was not necessarily
required in this study because of the following reasons. The participants were comprised of
cognitively normal subjects who did not show the significant atrophy in the MRI findings. The
intersubject variability in age of the participants was relatively small (CV = 0.06), and this
study did not aim to compare the intergenerational differences. However, further
investigations with the MR-based partial volume correction are desired to validate our findings.
This study showed that plasma glucose levels have a negative correlation with 18F-FDG uptake
in the precuneus and lateral parietotemporal regions, which are the main component of
ADrelated hypometabolic areas, and that 18F-FDG uptake decreased by approximately 4±5%
when plasma glucose levels increased by 20 mg/dL. These findings confirmed that increasing
plasma glucose levels can induce the appearance of the AD-like pattern in 18F-FDG images.
However, there was no significant negative correlation of 18F-FDG uptake with plasma insulin
levels or insulin resistance. Thus, of the three parameters, plasma glucose levels have the
greatest effect on the appearance of the AD-like pattern in 18F-FDG images.
The authors thank Dr. Masashi Kameyama for the professional comments and the members of
the Research Team for Neuroimaging at the Tokyo Metropolitan Institute of Gerontology for
their technical assistance.
Conceptualization: Kenji Ishibashi, Yoshinori Fujiwara, Kenji Ishii.
Data curation: Kenji Ishibashi, Airin Onishi, Yoshinori Fujiwara, Kiichi Ishiwata, Kenji Ishii.
Formal analysis: Kenji Ishibashi, Airin Onishi.
Funding acquisition: Kenji Ishibashi.
Investigation: Kenji Ishibashi, Airin Onishi, Yoshinori Fujiwara, Kiichi Ishiwata, Kenji Ishii.
Methodology: Kenji Ishibashi, Kenji Ishii.
Project administration: Kenji Ishibashi, Yoshinori Fujiwara, Kenji Ishii.
Resources: Kenji Ishibashi, Yoshinori Fujiwara, Kenji Ishii.
Supervision: Kenji Ishibashi, Yoshinori Fujiwara, Kenji Ishii.
Validation: Kenji Ishibashi, Yoshinori Fujiwara, Kenji Ishii.
Visualization: Kenji Ishibashi, Yoshinori Fujiwara, Kenji Ishii.
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Writing ± original draft: Kenji Ishibashi.
Writing ± review & editing: Kenji Ishibashi, Airin Onishi, Yoshinori Fujiwara, Kiichi
Ishiwata, Kenji Ishii.
11 / 12
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