Correlation Between Brain 18 F-AV45 and 18F-FDG PET Distribution Characteristics and Cognitive Function in Patients with Mild and Moderate Alzheimer’s Disease

Abstract

Background:

Florbetapir (AV45) and fluorodeoxyglucose (FDG) PET imaging are valuable techniques to detect the amyloid-β (Aβ) load and brain glucose metabolism in patients with Alzheimer’s disease (AD).

Objective:

The purpose of this study is to access the characteristics of Aβ load and FDG metabolism in brain for further investigating their relationships with cognitive impairment in AD patients.

Methods:

Twenty-seven patients with AD (average 70.6 years old, N = 13 male, N = 14 female) were enrolled in this study. These AD patients underwent the standard clinical assessment and received detailed imaging examinations of the nervous system by using Mini-Mental State Examination (MMSE), Montreal Cognitive Assessment (MOCA), ¹⁸F-AV45, and ¹⁸F-FDG PET scans.

Results:

Of 27 AD patients, 22 patients (81.5%) showed significantly increases in Aβ load and 26 patients (96.3%) had significantly reductions in FDG metabolism. The moderate AD patients had more brain areas of reduced FDG metabolism and more severe reductions in some regions compared to mild AD patients, with no differences in Aβ load observed. Moreover, the range and degree of reduced FDG metabolism in several regions were positively correlated with the total score of MMSE or MOCA, whereas the range of Aβ load did not. No correlation was found between the range of Aβ load and the range of reduced FDG metabolism in this study.

Conclusion:

The reduction in FDG metabolisms captured by ¹⁸F-FDG imaging can be used as a potential biomarker for AD diagnosis in the future. ¹⁸F-AV45 imaging did not present valuable evidence for evaluating AD patient in this study.

Keywords

Alzheimer’s disease amyloid-β load AV45 glucose metabolism PET

INTRODUCTION

Alzheimer’s disease (AD) is the most common neurodegenerative disease in elderly people, accounting for 60–70%of all dementia cases worldwide. There were approximately 50 million people living with AD and other dementias in 2019, and it is projected to reach to 152 million people by 2050. This will produce financial burdens to the patients, healthcare, and the whole society [1]. The clinical characteristics of AD include general and progressive decline in memory, executive functions, and ability to perform daily activities [2]. The disease has a fairly unique neuropathology, including extracellular neurotrophic amyloid-β (Aβ) plaque deposition and intracellular neurofibrillary tangles [3]. According to the Aβ hypothesis, abnormal accumulation of Aβ in the brain constitutes the central pathological lesion and may cause synaptic dysfunction, neuronal death, and lead to decline in cognitive function in AD [4]. Aβ molecules can form both low molecular weight oligomers and high molecular weight oligomers (such as fibrils), and Aβ fibrils can directly cause neuronal dysfunction by acting on synapses, or activate astrocytes and microglia leading to an indirectly damage to the neurons [5, 6]. On the other hand, changes in brain metabolism and functions appear to precede the cognitive symptoms in AD [7]. Thus, the assessment of brain metabolism and functions by imaging may provide an underlying strategy for the early diagnosis of AD.

Imaging biomarkers including neurodegeneration markers and amyloid deposition markers have been incorporated in 2011 revision of National Institute of Neurological and Communicative Disorders and Stroke and the Alzheimer’s Disease and Related Disorders Association (NINCDS-ADRDA) criteria of AD diagnosis [8]. In addition, positron emission tomography (PET) imaging has provided more detailed and precise information for disease diagnosis in recent years. ¹⁸F-Fluorodeoxyglucose (¹⁸F-FDG) PET detects the level of glucose metabolism in specific areas in the brain and indirectly measures the activity of neurons [9]. ¹⁸F-florbetapir (¹⁸F-AV45) PET is used to estimate the amyloid load in the brain. ¹⁸F-AV45 exhibits high-affinity binding for fibrillary amyloid and has high initial brain uptake [10]. In 2012, ¹⁸F-AV45 (Amyvid) was approved by the United States Food and Drug Administration for ruling out AD when the scan was negative [11], but there is a strong argument for applying Aβ probes in AD diagnosis [12 –14]. The reasons were as follows [12]: 1) the discrepancy in the distribution of Aβ deposits in the brain between PET images produced with amyloid tracers and histopathological and immunohistochemical analyses; 2) the insufficient spatial resolution of PET system (2-3 mm for high-resolution systems and 5–7 mm for standard scans) for amyloid plaques (approximately 50μm); 3) low affinity of amyloid tracers for amorphous plaques in the cortices even if there is an ability of binding to Aβ in fibrillar plaques and cerebral arteries; 4) No clear evidence showing necessary correlation between amyloid load and cognitive decline.

In this head-to-head comparison study, we aimed to investigate the characteristics of Aβ load and FDG metabolism in the brain of AD patients by using ¹⁸F-AV45 and ¹⁸F-FDG PET imaging for a comparison of their roles in the diagnosis and the assessment of the disease progression of AD.

MATERIALS AND METHODS

Patients

Twenty-seven AD patients enrolled in this study were inpatients of the Department of Neurology of the First Affiliated Hospital of Dalian Medical University from March 2014 to December 2019. The diagnosis of AD was made according to the criteria of NINCDS-ADRDA. All subjects underwent detailed medical history collection and clinical examination of the nervous system. Patients with the following conditions were excluded: 1) Clinical conditions that could cause progressive memory decline and cognitive dysfunction including epilepsy, encephalitis, stroke, acquired immune deficiency syndrome, treponema pallidum infection, long-term use of drugs that cause cognitive dysfunction or alcoholism, vitamin B deficiency; 2) Hachinski Ischemia Score (HIS) score > 4 points, Hamilton Anxiety Rating Scale (HAMA) score ≥7 points, Hamilton Depression Rating Scale (HAMD) score ≥7 points; 3) Those with severe heart, liver, lung, kidney, and other organ diseases; 4) Those who could not cooperate to finish the clinical physical examination and cognitive assessments; 5) Left-handed; 6) Subjects who could not lie down for 40 minutes. All patients and their legal guardians understood the purpose and content of the study and voluntarily signed an informed consent form. The study protocol was approved by the Ethics Committee of the First Affiliated Hospital of Dalian Medical University.

There were 13 patients in the mild AD group and 14 patients in the moderate AD group. There was no statistically significant difference between the mild and moderate AD groups in terms of age, gender, education level, and course of disease (Supplementary Table 1, p values >0.05). The difference in the MMSE and MOCA scores between the two groups was statistically significant (Supplementary Table 1, p value <0.0001).

Neuropsychiatric assessments

The cognitive function of the AD patients was assessed with the Mini-Mental State Examination (MMSE) and the Montreal Cognitive Assessment (MOCA) scores by two neurologists. MMSE score (30 points in total) < 27 points was considered as dementia, 21 to 26 points was mild, 11 to 20 points was moderate, and 0 to 10 points was severe dementia. The MOCA score (30 points in total) < 26 points was considered as dementia.

Positron emission tomography

The PET-CT was the Biograph 64 PET/CT scanner (Siemens, Germany). The ¹⁸F-FDG imaging agent was automatically synthesized by ¹⁸F-FDG drug synthesis system Explora FDG4 module by using Eclipse RD cyclotron (Siemens, Germany) and the radiochemical purity was >95%. The ¹⁸F-AV45 imaging agent was synthesized by the Allinone module of Trasis company, prepared by the ¹⁸F labeling of its corresponding precursor by the Department of Nuclear Medicine of the First Affiliated Hospital of Dalian Medical University. The final product’s radiochemical purity was >95%, and endotoxin and bacteria tests were negative, which met the requirements of radiopharmaceuticals.

All 27 AD patients underwent the ¹⁸F-AV45 and ¹⁸F-FDG PET examination within 2 weeks. For AV45 and FDG-PET data, a professional doctor in the Department of Nuclear Medicine used a blind method to visually analyze PET images to assess the radioactive distribution of the cerebral cortex. NeuroQ™, version 3.7 (Syntermed Inc., Atlanta, GA, USA) is a commercial brain imaging analysis software that is developed primarily for SPECT imaging and can quantitatively analyze the imaging data by automatically quantifying the average pixel values in standardized brain regions. The average value between all pixels of the whole brain is used as a reference for standardization. By comparing with the 50 normal control groups without mental illness or symptoms in the standard database, the patients’ brain FDG metabolism and Aβ load changes in each brain area were evaluated. NeuroQ^TM software divided the cerebral cortex into 30 different functional brain regions according to the Anatomical Automatic Labeling (AAL) method. In the present study, we used the number of brain areas with increased AV45 retention or decreased FDG metabolism to indicate the extent of brain areas involved.

Statistical methods

SPSS25.0 software (IBM, New York, United States) was used to analyze the data. Normally dis-tributed measurement data were expressed as mean±standard deviation (x±SD), and non-normally distributed measurement data were expressed as median (quartile difference). Normally distributed data with equal variance were tested by the independent sample T test. Data not normally distributed were analyzed by the non-parametric test of two independent samples. Counting data used Fisher’s exact probability method or Poisson regression. Pearson or Spearman correlation was used for correlation analysis. p < 0.05 was considered statistically significant.

RESULTS

Comparison of distribution of Aβ load in patients with mild and moderate AD

Preliminary visual analysis revealed that among 27 AD patients, 22 patients had visible Aβ load in cerebral cortex, accounting for 81.5%in total populations; however, there were five patients with two from mild and three from moderate AD who did not have Aβ load detected (Fig. 1). The differences in range or degree of Aβ load between mild AD and moderate AD patients were not significant.

Fig. 1

Representative images of Aβ load visual analysis data in AD patients. A) AV45-PET images with no Aβ load; B) AV45-PET images with mild Aβ load; C) AV45-PET images with obvious Aβ load. Red color means more Aβ load and blue color means less Aβ load.

Moderate AD patients showed an average increase of 0.9 brain region with AV45 retention when compared to mild patients, but the difference was not statistically significant (Table 1, p = 0.25). Besides, there were no significant differences in the degree of Aβ load between patients with mild and moderate AD, either in frontal, temporal, parietal or occipital lobes, (Supplementary Table 2, p values >0.05).

Table 1

Comparison of the range of Aβ load and FDG metabolism in the brain of patients with mild and moderate AD

	Correlation coefficient [95%CI]	Standard deviation	Z	p
Aβ load	0.87 [0.68, 1.11]	0.12	–1.16	0.25
FDG metabolism	1.83 [1.40, 2.42]	0.14	4.33	0.000^***

^*** p < 0.001.

Correlation between Aβ load and cognitive impairment in AD patients

In order to explore the relationship between Aβ load and cognitive impairment in AD patients, we analyzed the correlation between Aβ load and cognitive assessment from the total score of MMSE, MOCA or the subtests scores of MOCA. Results demonstrated that no relationship was found between the range of Aβ load and total score of MMSE or MOCA in cerebral cortex (Table 2, p > 0.05), but a negative correlation was established between the degree of Aβ load and language or abstract reasoning subtest of MOCA in left superior parietal lobe, left associative visual cortex, or right Broca area (Supplementary Table 3, p values: 0.02, 0.04, and 0.04, respectively).

Table 2

Correlation between the range of Aβ load or FDG metabolism and the total score of MMSE or MOCA

	MMSE		MOCA
	Correlation coefficient	p	Correlation coefficient	p
Aβ load	0.03	0.87	–0.05	0.80
FDG metabolism	–0.74	0.000^***	–0.54	0.004^**

^** p < 0.01; ^*** p < 0.001.

Comparison of FDG metabolism reduction in patients with mild and moderate AD

According to the preliminary visual analysis, al-most all AD patients (26 out of 27) showed decreases in FDG metabolism, accounting for 96.3%in the entire populations, expect for one mild AD patient with no reduction of FDG metabolism and at the same time no Aβ load detected in any brain areas (Fig. 2). In comparison to mild AD patients, the range and degree of FDG metabolism reduction were significantly increased in moderate AD. There was an increase of 1.8 brain regions in moderate AD patients compared with those in mild AD patients and the difference between the two groups was statistically significant (Table 1, p < 0.001). Furthermore, comparing the degree of FDG metabolism, significant reductions in FDG metabolism were observed in brain functional areas in moderate AD patients, such as left Broca region, left inferior lateral posterior temporal lobe, left parieto-temporal cortex, right inferior lateral anterior temporal lobe, right inferior lateral posterior temporal lobe and right associative visual cortex (Table 3).

Fig. 2

Representative images of FDG metabolism visual analysis data in AD patients. A) FDG-PET images with no FDG metabolism reduction; B) FDG-PET images with mild FDG metabolism reduction; C) FDG-PET images with obvious FDG metabolism reduction. Red color means more reduction of FDG metabolism.

Table 3

Comparison of the degree of FDG metabolism in the brain of patients with mild and moderate AD

Brain area	The degree of FDG metabolism		p
	Mild AD	Moderate AD
Left superior frontal gyrus	0.98±0.04	1.01±0.07	0.32
Left mid-frontal gyrus	1.11±0.04	1.08±0.06	0.14
Left inferior frontal gyrus	1.06±0.03	1.05±0.08	0.62
Left medial frontal gyrus	1.00±0.06	1.02±0.08	0.61
Left Broca area	1.15±0.04	1.10±0.07	0.04^*
Left superior lateral temporal lobe	1.09±0.04	1.07±0.05	0.14
Left medial anterior temporal lobe	0.70 (0.68,0.72)	0.72 (0.69,0.73)	0.47
Left medial posterior temporal lobe	0.92±0.03	0.92±0.04	0.99
Left inferior lateral anterior temporal lobe	0.87±0.02	0.83±0.08	0.08
Left inferior lateral posterior temporal lobe	0.97 (0.94,0.99)	0.90 (0.84,0.96)	0.01^*
Left superior parietal lobe	0.97 (0.94,0.99)	0.99 (0.96,1.00)	0.29
Left inferior parietal lobe	1.06±0.05	1.06±0.11	0.98
Left parieto-temporal cortex	1.05±0.05	0.99±0.07	0.02^*
Left associative visual cortex	1.09±0.04	1.06±0.06	0.14
Left primary visual cortex	1.19±0.07	1.23±0.10	0.24
Right superior frontal gyrus	1.01±0.04	1.00±0.06	0.67
Right mid-frontal gyrus	1.12 (1.09, 1.13)	1.07 (1.04, 1.14)	0.09
Right inferior frontal gyrus	1.09±0.04	1.05±0.07	0.14
Right medial frontal gyrus	1.05 (1.01, 1.07)	1.06 (1.02, 1.08)	0.63
Right Broca area	1.12±0.04	1.07±1.00	0.09
Right superior lateral temporal lobe	1.07±0.05	1.03±0.07	0.15
Right medial anterior temporal lobe	0.70±0.03	0.70±0.04	0.93
Right medial posterior temporal lobe	0.88±0.02	0.88±0.02	0.85
Right inferior lateral anterior temporal lobe	0.87±0.04	0.82±0.07	0.03^*
Right inferior lateral posterior temporal lobe	0.94±0.05	0.86±0.07	0.004^**
Right superior parietal lobe	0.98±0.05	1.01±0.05	0.08
Right inferior parietal lobe	1.03±0.05	1.06±0.09	0.28
Right parieto-temporal cortex	1.02±0.06	0.97±0.09	0.09
Right associative visual cortex	1.06±0.02	1.00±0.07	0.01^*
Right primary visual cortex	1.15±0.07	1.17±0.09	0.58

^* p < 0.05, ^** p < 0.01.

Correlation between decreased FDG metabolism and cognitive impairment in AD patients

We analyzed the correlation between the reduced FDG metabolism in brain functional areas and the total score of MMSE, MOCA, or the subtest scores of MOCA. Results showed that the range of reduced FDG metabolism in the cerebral cortex was negatively correlated with total score of MMSE or MOCA (Table 2, p < 0.05), whereas a positive correlation was captured between the degree of FDG metabolism and the total score of MMSE in some functional area, such as left inferior lateral posterior temporal lobe, left parieto-temporal cortex, right mid-frontal gyrus, right Broca area, right inferior lateral anterior temporal lobe, right inferior lateral posterior temporal lobe, right parieto-temporal cortex, and right associative visual cortex. In addition, positive correlation between the degree of FDG metabolism and the total score of MOCA was found in left inferior lateral posterior temporal lobe, left parieto-temporal cortex, right inferior lateral posterior temporal lobe and right associative visual cortex (Table 4). The correlation between the degree of FDG metabolism and subtests scores of MOCA was shown in Supplementary Table 4.

Table 4

Correlation between the degree of FDG metabolism and the total score of MMSE or MOCA in brain functional areas

	Area	Correlation coefficient	p
FDG metabolism and MMSE score	Left inferior lateral posterior temporal lobe	0.51	0.01^*
	Left parieto-temporal cortex	0.42	0.03^*
	Right mid-frontal gyrus	0.45	0.02^*
	Right Broca area	0.39	0.046^*
	Right inferior lateral anterior temporal lobe	0.50	0.01^*
	Right inferior lateral posterior temporal lobe	0.67	0.000^***
	Right parieto-temporal cortex	0.43	0.02^*
	Right associative visual cortex	0.57	0.002^**
FDG metabolism and MOCA score	Left inferior lateral posterior temporal lobe	0.46	0.02^*
	Left parieto-temporal cortex	0.38	0.0499^*
	Right inferior lateral posterior temporal lobe	0.46	0.02^*
	Right associative visual cortex	0.50	0.01^*

^* p < 0.05, ^** p < 0.01, ^*** p < 0.001.

Correlation between Aβ load and FDG metabolic distribution in AD patients

Further, we investigated the correlation of Aβ load and the distribution of FDG metabolism in brain functional areas in frontal lobe, temporal lobe, parietal lobe, and occipital lobe. Results indicated that there was no correlation between the range of Aβ load and the range of reduced FDG metabolism (p = 0.28). However, the degree of Aβ load in either left primary visual cortex or right inferior frontal gyrus was positively correlated with the degree of FDG metabolism (p values were 0.04, 0.049, respectively).

DISCUSSION

In recent years, neuroimaging techniques including PET-CT and MRI have played important roles in the diagnosis of AD, such as amyloid tracer - Aβ PET for plaque load [15], and FDG-PET for detecting the state of glucose utilization in the brain, which indirectly reflects the neuronal function and neurodegeneration in AD patients [9]. In our present study, we applied ¹⁸F-AV45 and ¹⁸F-FDG PET to mild and moderate AD patients to exam their roles in AD diagnosis and assessment of the disease progression.

For the diagnosis of AD, Aβ is often believed to be an important biomarker both in cerebrospinal fluid and PET imaging as it is considered one of the key pathological changes in the AD brain. ¹¹C-Pittsburgh compound B (¹¹C-PIB) PET was the earliest and widely used PET imaging technology for Aβ in the past. However, ¹¹C-PIB has a short half-life and a complicated synthesis process, while ¹⁸F-AV45 has a relatively longer half-life which allows the tracer to accumulate significantly more in the brain [16, 17]. A meta-analysis of three studies showed that ¹⁸F-AV45 PET visual analysis had a sensitivity of 90%(95%CI 75–96%) and a specificity of 81%(95%CI 24–98%) in AD patients when compared with healthy controls. However, a study of ¹⁸F-AV45 PET imaging in 184 patients found that only approximately 76%of patients with AD had positive amyloid plaques, and about 38%of MCI patients and 14%of normal people had AV45 retention [18]. Applying amyloid imaging in detecting AD was still under debate. Several literatures claimed that the high degrees of sensitivity and specificity of amyloid imaging in detecting AD were not justifiable and based on circular reasoning [12, 14]. Moreover, Aβ-targeted immunotherapies and β-secretase inhibitors such as bapineuzumab [19], solanezumab [20], verubecestat [21], atabecestat [22], and lanabecestat [23] have shown a lack of efficacy in improving cognition in AD patients in recent studies, observations that, taken together, are a strong indicator of the lack of substance in the Aβ cascade hypothesis and consequently also for the use of Aβ imaging in the assessment of AD.

In contrast, the glucose metabolism PET imaging tracer ¹⁸F-FDG can indirectly measure neurons activities and reflect the decline of neuronal function in the brain. Compared with non-demented controls, ¹⁸F-FDG PET was found to have a sensitivity of 90%(95%CI 84–94%) and a specificity of 89%(95%CI 81–94%) [24]. In our study, we did not find amyloid plaques in five AD patients, one of which did not have reductions in FDG metabolism. However, all these patients met the NINCDS-ADRDA criteria for AD clinical diagnosis. Compared to ¹⁸F-AV45 PET (81.5%positive), ¹⁸F-FDG PET (96.3%positive) seemed to be more sensitive and more reliable in detecting AD. However, FDG PET was reported to have lower specificity for AD patients versus other dementias with or without MCI (∼78%, 70%respectively) [24]. Since reduced glucose metabolism can also be found in other types of dementia, Aβ PET might be more useful for ruling out AD from other dementias when the patient had a negative scan of Aβ PET.

For the assessment of disease progression, neuroimaging studies found that Aβ deposition was negatively associated with cognitive impairment or lowered brain metabolism in AD patients [25, 26]. However, recent studies showed that Aβ load was weakly correlated with cognitive impairment [27, 28]. A study with assessment of 456 brain samples addressed that the association between Aβ plaques and dementia was decreased with age [29]. Engler et al. found that PIB-PET imaging showed no significant changes of Aβ load in a 2-year follow-up, while the MMSE score decreased overall, indicating that Aβ load did not alter as cognitive function declined [30]. Hyman et al. even proposed a 2-phase revision of Aβ hypothesis: In the first phase, soluble oligomeric and fibrillar Aβ accumulated and led to neuronal dysfunction and inflammatory responses; then, the second phase consisted of further development of tangles, neuronal loss, synaptic loss, and glial responses which might be independent of Aβ [31]. Our data showed that there were no significant differences in Aβ load between mild and moderate AD patients, and the range and degree of Aβ load were not related to total score of MMSE and MOCA.

In contrast, AD patients exhibited reduced glucose metabolism in specific brain regions, and the degree and range of reductions were increased as the disease progressed. Knopman et al. also found that the decline in glucose metabolism was an independent risk factor for AD and could predict the occurrence and development of AD. By improving glucose metabolism in the brain, the incidence of AD could be reduced, and the development of the disease could be effectively delayed [32]. Typical AD patients may have lower FDG metabolism in the temporal parietal lobe and posterior cingulate gyrus; the frontal lobe may also be affected as the disease progressed [33, 34]. In addition, while it is well known that glucose metabolism in the frontal lobe and temporal parietal lobe are closely related to cognitive impairment but the same cannot be said to the spatial distribution of amyloid deposits [35]. A very recent study showed a close relationship between ¹⁸F-FDG PET and MMSE in more brain regions especially in the supra-tentorial and temporal regions when compared to ¹⁸F-AV45 PET, indicating that ¹⁸F-FDG PET can be used as a superior indicator rather than ¹⁸F-AV45 PET [36]. Our study found that patients with moderate AD had a significantly increased range of reduced FDG metabolism with clear lower glucose metabolism in some brain functional areas when compared with mild AD patients. Moreover, the degree of FDG metabolism in brain functional areas in frontal lobe, temporal lobe, and parieto-temporal cortex was positively correlated with the total score of MMSE or MOCA. Our results were consistent with previous literature.

It is notable that this study had certain limitations, including the absence of healthy control groups and a relatively small sample size. However, by using ¹⁸F-AV45 and ¹⁸F-FDG PET imaging, we investigated the relationship of Aβ load, reduced glucose metabolism, and cognitive impairment in AD patients. Our results showed that ¹⁸F-FDG imaging could detect about 96.3%of clinically diagnosed AD patients and was correlated with cognitive impairment assessed by MMSE and MOCA scores. ¹⁸F-FDG, a sensitive and reliable tool, can be used as a potential biomarker for AD diagnosis and disease progression assessment in future clinical practice.

Footnotes

ACKNOWLEDGMENTS

This work was supported in part by funding from Professor Weidong Le from the First Affiliated Hospital of Dalian Medical University.

Authors’ disclosures available online ().

The supplementary material is available in the electronic version of this article: .

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