A preliminary study on the effect of renal function on the metabolism of ¹⁸F-FDG in the human cerebellum

Introduction

Positron emission tomography/computed tomography (PET/CT) is a diagnostic method commonly used in oncology, cardiovascular disease or neurological disease with applications ranging from disease diagnosis to treatment planning and monitoring of treatment response (1). Chronic kidney disease is a major public health threat, and its incidence rate in cancer patients is higher than that in non-cancer patients. The study of Renal Insufficiency and Anticancer Medications conducted by Launay-Vacher and colleagues showed that 50–60% of cancer patients had lower renal function than normal, or estimated glomerular filtration rate (eGFR) <90 mL/min/1.73 m² (2). In addition, anticancer drugs carry the risk of renal dysfunction during treatment (3).

At present, the most widely used positron tracer is 2-deoxy-2-[¹⁸F]fluoro-D-glucose (¹⁸F-FDG). Although several factors including blood glucose, sex and body mass index (BMI) will affect the accuracy of ¹⁸F-FDG standardized uptake values (SUV) measurement (4), it is well known that renal clearance is an important component of ¹⁸F-FDG metabolism, and Kobayashi et al. confirmed the partial reabsorption of FDG by the proximal tubules (5). Some researchers believe that renal function affects ¹⁸F-FDG uptake (6,7); however, there are papers that do not support this view (8,9). Of course, it is generally believed that the kidney clears the ¹⁸F-FDG activity from the blood pool, which in turn clears the background activity in normal tissues. Most previous studies have been based on the effect of renal function or eGFR on ¹⁸F-FDG uptake, and there is limited information in the literature on the effect of ¹⁸F-FDG uptake directly based on independent creatinine (CRE) or blood urea nitrogen (BUN) levels. Minamimoto et al. (6) found that ¹⁸F-FDG brain accumulation decreased and blood pool accumulation increased in patients with high plasma CRE level. However, there is no report on the effect of serum CRE reduction on ¹⁸F-FDG metabolism in the literature. Since the possible relationship between low serum CRE and type 2 diabetes has been confirmed (10), low serum CRE may affect the metabolism of ¹⁸F-FDG. In addition, the cerebellum is less affected by normal aging or neurodegenerative diseases than the cerebral cortex, and its metabolism is relatively stable (11).

In this study, we believe that low serum CRE is also a state of renal function and attempt to elucidate the effect of renal function on ¹⁸F-FDG uptake in the cerebellum or other normal tissues based on independent CRE or BUN levels.

Methods

Patients selection

The study was conducted in accordance with the Declaration of Helsinki (as revised in 2013). The study was approved by the Institutional Review Board of the First Affiliated Hospital, Dalian Medical University (No. PJ-KY-2021-280) and individual consent for this retrospective analysis was waived. The anonymity and confidentiality of patients were maintained. The inclusion/exclusion criteria were as follows: (I) No signs suggestive of any inflammatory disease on ¹⁸F-FDG PET/CT imaging. (II) Blood sugar level at acceptable range (3.9–9.0 mmol/L) for ¹⁸F-FDG PET/CT examination. (III) Measurement of CRE and BUN within two weeks of ¹⁸F-FDG PET/CT. (IV) Ability to empty the bladder prior to PET/CT scan. This study initially enrolled 283 patients who underwent standard protocol ¹⁸F-FDG PET/CT. A total of 30 patients were excluded, including one patient with kidney transplant, two patients from injection to PET/CT scan for more than 70 minutes, two patients with diffuse brain injury, two patients with cerebrovascular disease, five patients with diffuse liver metastasis, one patient with biliary dilation, three patients with lung infection, two patients with fibrotic lung disease, four patients with diffuse bone malignant tumor, two patients with diffuse inflammatory disease and six patients with lesions on the selected area of interest (ROI) during the image analysis. Finally, a total of 253 patients met the criteria and were included in the analysis. Several factors including blood glucose, sex and BMI would affect the accuracy of SUV measurement, so the basic data were collected including gender, weight, BMI and laboratory tests including blood glucose, CRE, and BUN level.

Patient preparation

On the day of the examination, all patients fasted for more than six hours prior to receiving the radiopharmaceutical injection. The alcohol, caffeine, and sweets were avoided, blood glucose was within the acceptable range. Patients were injected 5.55 MBq/kg (0.15 mCi/kg) ¹⁸F-FDG intravenously. During the uptake phase prior to ¹⁸F-FDG PET/CT scan, the patient sat in a quiet room without speaking and avoided other physical exercise for 55–68 minutes.

PET/CT scanning equipment and image reconstruction

The experiment utilized a Siemens Biograph true point PET/CT scanner (Siemens Molecular Imaging, healthcare China), with lutetium oxyorthosilicate crystal, and 64-slice CT scanner.¹⁸F-FDG was synthesized by a cyclotron (Siemens Molecular Imaging, China) and an automated synthesizer (PET Biotechnology, China). Radiochemical purity was >95%. The scanning parameters were as follows: voltage 120 kV, tube current automatic mA/s adjustment (preset: 170 mA/s), slice thickness 2 mm, the average scanning time per bed was 1 min. After reconstructing CT images, the PET images attenuated by CT data were reconstructed by the sequential subset thickness (true x) algorithm, repeated three times, with 21 subsets and 3-D Gaussian filtering. Finally, the reconstructed PET images were fused with CT images.

PET/CT image analysis

¹⁸F-FDG PET/CT images were evaluated on the Siemens workstation. On the fused PET/CT images, a 20 mm circular ROI was used to record the SUVmax from right hepatic lobe, right lung (upper lobe), bone marrow and psoas muscle at the level of the fourth lumbar vertebra, and a 10 mm circular ROI was placed at the right cerebellum. For lung, the ROI was placed in area without nodules, major blood vessels, or inflammatory changes; for liver, the ROI was placed on the right lobe dome with the largest cross-sectional area orther than CT abnormalities or large vessels. Quantitative analysis of the data was performed to obtain the SUVmax normalized by body weight. A representative image illustrating the ROI is shown in Figure 1. Each ROI was carefully placed so as not to include any disease or adjacent organs. Finally, the results of SUVmax of each group based on CRE or BUN levels were obtained. To rule out other possible confounding factors affecting ¹⁸F-FDG uptake, in the normal group, the patients with the same blood glucose, age and BMI as those in the corresponding groups were selected for one-to-one matching, then the age, gender, BMI and glucose levels pair-matched groups were analyzed.

Figure 1 2-deoxy-2-[¹⁸F]fluoro-D-glucose (¹⁸F-FDG) positron emission tomography/computed tomography (PET/CT) image ROI analysis. Circular regions of interest (ROIs, red circles) for measuring the standardized uptake value (SUV) were placed, 10 mm ROIs on cerebellum (A); 20 mm ROIs on psoas muscle, bone marrow at the level of the L4 vertebral body (B), lungs (upper lobe) (C) and liver (D), as shown.

Statistical analysis

All statistical analyses were performed using SPSS-26 (IBM SPPS, Chicago, IL, USA). Data were checked for normality using the Shapiro-Wilkes test. Descriptive analysis was used to obtain sample characteristics, including gender, age and diagnosis. Continuous data were presented as mean and standard deviation. Based on the CRE level, the multiple independent samples Kruskal-Wallis test was used to analyze the SUV values of the three groups. If there was a significant difference, post-hock testing was performed on the specific tissue. Based on the BUN level, the Mann-Whitney U test was used. For matched-pair analysis, the explore command of SPSS tested for normality of data in each group, the data were normally distributed and were analyzed using the independent samples t-test.

A P value less than 0.05 was considered statistical significance. All P values were two-sided.

Results

Clinical features

A total of 253 patients were finally included, 125 males and 128 females, with an age range of 27–84 years (mean 54 years). Among them, breast cancer patients dominated the most, 18.20% (46/253); followed by gastrointestinal cancer with 17.80% (45/253) and lung cancer with 13.8% (35/253). The remaining 50.19% (127/253) of patients had one of the following diagnoses: liver cancer, gallbladder cancer, gynecological cancer, pancreatic cancer, urinary tract cancer, lymphoma, head and neck cancer, prostate cancer, malignant fibroids, soft tissue tumors, sarcoidosis, malignant melanoma, plasmacytoma, liposarcoma, and other undiagnosed diseases. The CRE levels were normal in 201 cases, decrease in 36 cases, and increase in 16 cases (gender, age, blood glucose, and BMI are shown in Table 1). Based on BUN, patients were divided into normal group (n=222, male/female: 109/113) and abnormal group (n=31, male/female: 16/15), overall age: 27–84 years (mean 56).

In our institutional laboratory, the normal range of CRE was 47–110 µmol/L, regardless of gender. The CRE reduction level in this study ranged from 35–47 µmol/L, and the CRE level greater than 110 µmol/L was considered to be abnormally high. The normal range of BUN was 2.90–8.20 mmol/L.

Comparison of ¹⁸F-FDG uptake based on CRE or BUN levels

Based on CRE level, the SUVmax of the cerebellum was lower in the group with decrease CRE level, 7.85±2.19, while it was 9.01±1.75 and 8.63±1.18 in the group with normal and increase level, respectively. The multiple independent samples Kruskal-Wallis test showed statistically significant differences in cerebellum (Test Statistic H(2)=12.86, P=0.002). Among other groups, there was no statistical difference in the SUVmax of cerebellum, lung, liver, lumbar muscle and bone marrow (Table 2). Bonferroni-corrected post hoc test was performed on the SUVmax of cerebellum in three groups, and showed a significant difference in cerebellar SUVmax between the CRE-reduced group and the normal group (P=0.001), but not the other two groups (P value 0.420 for decrease level vs. increase level; P value 1.000 for increase level vs. normal level). When classifying patients according to BUN level, there was no significant difference between normal and abnormal group (Table 3).

Matched-pair analysis

To rule out other possible confounding factors affecting ¹⁸F-FDG uptake, such as a range of age, gender, BMI and glucose levels, the selected normal CRE groups were pair-matched with CRE decrease level group with respect to age, sex, BMI and blood glucose, respectively (1:1 nearest-neighbor matching). A comparison of matching variables between the controls and CRE decrease level group showed no significantly changed from the results before matching (Table 4).

Discussion

Among several factors that may affect SUV measurement, the blood glucose and renal function may be two important factors affecting ¹⁸F-FDG uptake. After controlling blood glucose level to a certain range, the importance of renal function status on ¹⁸F-FDG uptake was highlighted.

Unlike glucose, a high portion of ¹⁸F-FDG was excreted through the urine. In dogs, 60 min and 135 min after intravenous injection, the excretion rate of ¹⁸F-FDG through urine was 16% and 50% of each injection dose, respectively (12). Kobayashi et al. confirmed the partial reabsorption of FDG by the proximal tubules (5). These indicate that renal function has an impact on the metabolism of ¹⁸F-FDG. For the evaluation of renal function, CRE and BUN are two important indicators in clinical practice. Akers et al. reported the study of ¹⁸F-FDG PET/CT in patients with renal function impairment classified according to the level of eGFR (8). However, it is worth noting that eGFR is not a perfect test, the calculation formula of eGFR usually needs to be revised and adjusted, several factors affect its accuracy. For example, the eGFR calculation based on CRE levels are only accurate if a person’s kidney function is stable, however, the test is generally not accurate enough for acutely ill patients, including those with severe comorbidities (13).

Amyloidosis is one of the earliest changes in patients with Alzheimer’s disease (AD). Its AD neuropathological level is at intermediate or high level, and more than 15 years before the occurrence of cognitive impairment (14). Amyloid-β (Aβ) is the major component of senile plaques in the brain of AD. The senile plaques are usually observed in the cerebral cortex of AD patients rather than in the cerebellum (15); and from the biological point of view, cerebellum is a suitable reference area for PET quantification. Therefore, the cerebellum was selected as the main research area of interest in this study.

The SUVmax of cerebellum, lung, liver, psoas major muscle and bone marrow of each group were compared, and there was no statistical difference in ¹⁸F-FDG uptake between groups with elevated CRE level and abnormal BUN level, which was consistent with the previous report (8). In contrast to glomerular filtration rate and CRE clearance, it is generally accepted that blood BUN levels are not a specific indicator of impaired renal function. Therefore, we would not further discuss its impact on ¹⁸F-FDG uptake here.

At the same time, we noticed a statistically significant difference in the SUV of the cerebellum, i.e. patients with reduced CRE had lower cerebellar SUV than those with normal CRE. These results suggest that a reduction in CRE may affect ¹⁸F-FDG PET quantitative analysis in cerebellum. However, we have not yet retrieved reports of reduced ¹⁸F-FDG uptake in patients with reduced CRE levels.

In this study, some patients with fever and special tumors that may affect the liver and bone marrow SUV value were excluded, the vast majority of patients enrolled were cancer patients. In some of these patients, the disease was associated with low muscle quality due to infrequent exercise and muscle atrophy. This could partly explain the decline of CRE level. In addition, CRE, a marker of kidney function, is a chemical waste product of creatine phosphate, and creatine is a ubiquitous non-protein amino acid that is synthesized mainly in the liver (16). In chronic liver disease, the decrease of serum creatinine pool is due to the reduction of liver creatine production by 50%; generally, the baseline serum creatinine concentration of patients with chronic liver disease is significantly lower than that of the general population (17,18). Liver metastases have been reported in approximately 5% of cancer patients at diagnosis; at the same time, cancer treatments, such as certain types of chemotherapy, radiation therapy, and immunotherapy, may also lead to liver problems (19-21). All of these factors may reduce creatine production, then resulting in lower CRE.

Importantly, patients with liver damage have significant insulin resistance (22). Insulin promotes microvascular blood flow, glucose uptake, and glucose oxidation to adenosine-5'-triphosphate (23,24). Insulin resistance is defined as decrease tissue responsiveness to insulin. Furthermore, a possible relationship between low serum CRE and type 2 diabetes has been demonstrated (10,25). Several studies have shown a link between reduced ¹⁸F-FDG metabolism in the brain and insulin resistance (26,27). In addition, in this study, the majority of the patients in the group with decrease CRE level were aged and had a BMI below the normal range, and it is generally believed that renal function gradually declines with age (28), and decreased ¹⁸F-FDG metabolism in the brain was observed in normal aging or neurodegenerative diseases (AD or dementias) (29,30).

Taken together, the decrease of CRE level may be directly or indirectly related to insulin resistance and impaired glucose metabolism, and the cerebellum is relatively less affected by neurodegenerative diseases or normal aging. These pathophysiological processes can partly explain the reason for the decrease of cerebellar SUV in the group with CRE-reduced level in this study.

Sprinz et al. reported that higher blood glucose levels were significantly associated with lower brain ¹⁸F-FDG uptake (31). By controlling the influence of all other characteristics, matching samples can obtain better and accurate output when determining significant differences (32). Our matched-pair analysis showed that there was no significant difference in the outcomes between the group with CRE decrease level and the age-, sex-, BMI- and glucose-matched control group compared to the results before matching.

At present, to the best of our knowledge, there is no report to evaluate ¹⁸F-FDG uptake in patients with reduced CRE level. The limitation of this paper is that it is a single-center study, and the inclusion conditions are relatively strict, resulting in a relatively small sample size of this study. More data accumulation and analysis are needed in the future. On another hand, the reasons why renal function status affects ¹⁸F-FDG uptake may be quite complex and further studies are needed to deepen the understanding and consensus on its molecular mechanisms.

Conclusions

In patients with normal renal function and reduced CRE concentration, decrease cerebellar glucose metabolism was observed; however, no abnormal uptake of ¹⁸F-FDG was found in the cerebellum and other normal tissues of patients with impaired renal function. Consequently, in the study of cerebellar ¹⁸F-FDG metabolism, it may be necessary to consider the influence of blood CRE level.

Acknowledgments

We would like to thank the nursing and technical team of the Department of Nuclear Medicine, First Affiliated Hospital of Dalian Medical University for their care for the patients and the professional reviewers for this journal.

Funding: None.

Footnote

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://qims.amegroups.com/article/view/10.21037/qims-22-917/coif). The authors have no conflicts of interest to declare.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. The study was conducted in accordance with the Declaration of Helsinki (as revised in 2013). The study was approved by the Institutional Review Board of The First affiliated Hospital, Dalian Medical University (No. PJ-KY-2021-280) and individual consent for this retrospective analysis was waived.

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.

References

1. Li XF, Shi YM, Niu R, Shao XN, Wang JF, Shao XL, Zhang FF, Wang YT. Risk analysis in peripheral clinical T1 non-small cell lung cancer correlations between tumor-to-blood standardized uptake ratio on (18)F-FDG PET-CT and primary tumor pathological invasiveness: a real-world observational study. Quant Imaging Med Surg 2022;12:159-71. [Crossref] [PubMed]
2. Launay-Vacher V, Oudard S, Janus N, Gligorov J, Pourrat X, Rixe O, Morere JF, Beuzeboc P, Deray GRenal Insufficiency and Cancer Medications (IRMA) Study Group. Prevalence of Renal Insufficiency in cancer patients and implications for anticancer drug management: the renal insufficiency and anticancer medications (IRMA) study. Cancer 2007;110:1376-84. [Crossref] [PubMed]
3. Malyszko J, Tesarova P, Capasso G, Capasso A. The link between kidney disease and cancer: complications and treatment. Lancet 2020;396:277-87. [Crossref] [PubMed]
4. Jacene HA, Cohade CC, Zhang Z, Wahl RL. The relationship between patients' serum glucose levels and metabolically active brown adipose tissue detected by PET/CT. Mol Imaging Biol 2011;13:1278-83. [Crossref] [PubMed]
5. Kobayashi M, Shikano N, Nishii R, Kiyono Y, Araki H, Nishi K, Oh M, Okudaira H, Ogura M, Yoshimoto M, Okazawa H, Fujibayashi Y, Kawai K. Comparison of the transcellular transport of FDG and D-glucose by the kidney epithelial cell line, LLC-PK1. Nucl Med Commun 2010;31:141-6. [Crossref] [PubMed]
6. Minamimoto R, Takahashi N, Inoue T. FDG-PET of patients with suspected renal failure: standardized uptake values in normal tissues. Ann Nucl Med 2007;21:217-22. [Crossref] [PubMed]
7. Laffon E, Cazeau AL, Monet A, de Clermont H, Fernandez P, Marthan R, Ducassou D. The effect of renal failure on 18F-FDG uptake: a theoretic assessment. J Nucl Med Technol 2008;36:200-2. [Crossref] [PubMed]
8. Akers SR, Werner TJ, Rubello D, Alavi A, Cheng G. 18F-FDG uptake and clearance in patients with compromised renal function. Nucl Med Commun 2016;37:825-32. [Crossref] [PubMed]
9. Kode V, Karsch H, Osman MM, Muzaffar R. Impact of Renal Failure on 18F-FDG PET/CT Scans. Front Oncol 2017;7:155. [Crossref] [PubMed]
10. Hjelmesæth J, Røislien J, Nordstrand N, Hofsø D, Hager H, Hartmann A. Low serum creatinine is associated with type 2 diabetes in morbidly obese women and men: a cross-sectional study. BMC Endocr Disord 2010;10:6. [Crossref] [PubMed]
11. Berti V, Mosconi L, Pupi A. Brain: normal variations and benign findings in fluorodeoxyglucose-PET/computed tomography imaging. PET Clin 2014;9:129-40. [Crossref] [PubMed]
12. Gallagher BM, Ansari A, Atkins H, Casella V, Christman DR, Fowler JS, Ido T, MacGregor RR, Som P, Wan CN, Wolf AP, Kuhl DE, Reivich M. Radiopharmaceuticals XXVII. 18F-labeled 2-deoxy-2-fluoro-d-glucose as a radiopharmaceutical for measuring regional myocardial glucose metabolism in vivo: tissue distribution and imaging studies in animals. J Nucl Med 1977;18:990-6. [PubMed]
13. Levey AS, Becker C, Inker LA. Glomerular filtration rate and albuminuria for detection and staging of acute and chronic kidney disease in adults: a systematic review. JAMA 2015;313:837-46. [Crossref] [PubMed]
14. Bateman RJ, Xiong C, Benzinger TL, Fagan AM, Goate A, Fox NC, et al. Clinical and biomarker changes in dominantly inherited Alzheimer's disease. N Engl J Med 2012;367:795-804. [Crossref] [PubMed]
15. Shahnur A, Nakano M, Ishihara S, Kakuda N, Miyasaka T, Uchiyama H, Shirai Y, Moniruzzaman M, Saito T, Saido TC, Nishimura M, Funamoto S. A potential defense mechanism against amyloid deposition in cerebellum. Biochem Biophys Res Commun 2021;535:25-32. [Crossref] [PubMed]
16. Bonilla DA, Kreider RB, Stout JR, Forero DA, Kerksick CM, Roberts MD, Rawson ES. Metabolic Basis of Creatine in Health and Disease: A Bioinformatics-Assisted Review. Nutrients 2021;13:1238. [Crossref] [PubMed]
17. Takabatake T, Ohta H, Ishida Y, Hara H, Ushiogi Y, Hattori N. Low serum creatinine levels in severe hepatic disease. Arch Intern Med 1988;148:1313-5. [Crossref] [PubMed]
18. Slack A, Yeoman A, Wendon J. Renal dysfunction in chronic liver disease. Crit Care 2010;14:214. [Crossref] [PubMed]
19. Horn SR, Stoltzfus KC, Lehrer EJ, Dawson LA, Tchelebi L, Gusani NJ, Sharma NK, Chen H, Trifiletti DM, Zaorsky NG. Epidemiology of liver metastases. Cancer Epidemiol 2020;67:101760. [Crossref] [PubMed]
20. Vincenzi B, Armento G, Spalato Ceruso M, Catania G, Leakos M, Santini D, Minotti G, Tonini G. Drug-induced hepatotoxicity in cancer patients - implication for treatment. Expert Opin Drug Saf 2016;15:1219-38. [Crossref] [PubMed]
21. Zen Y, Yeh MM. Checkpoint inhibitor-induced liver injury: A novel form of liver disease emerging in the era of cancer immunotherapy. Semin Diagn Pathol 2019;36:434-40. [Crossref] [PubMed]
22. Collins JR, Lacy WW, Stiel JN, Crofford OB. Glucose intolerance and insulin resistance in patients with liver disease. II. A study of etiologic factors and evaluation of insulin actions. Arch Intern Med 1970;126:608-14. [Crossref] [PubMed]
23. Rahman MS, Hossain KS, Das S, Kundu S, Adegoke EO, Rahman MA, Hannan MA, Uddin MJ, Pang MG. Role of Insulin in Health and Disease: An Update. Int J Mol Sci Int J Mol Sci 2021;22:6403. [Crossref] [PubMed]
24. Tokarz VL, MacDonald PE, Klip A. The cell biology of systemic insulin function. J Cell Biol 2018;217:2273-89. [Crossref] [PubMed]
25. Harita N, Hayashi T, Sato KK, Nakamura Y, Yoneda T, Endo G, Kambe H. Lower serum creatinine is a new risk factor of type 2 diabetes: the Kansai healthcare study. Diabetes Care 2009;32:424-6. [Crossref] [PubMed]
26. Baker LD, Cross DJ, Minoshima S, Belongia D, Watson GS, Craft S. Insulin resistance and Alzheimer-like reductions in regional cerebral glucose metabolism for cognitively normal adults with prediabetes or early type 2 diabetes. Arch Neurol 2011;68:51-7. [Crossref] [PubMed]
27. Willette AA, Modanlo N, Kapogiannis DAlzheimer’s Disease Neuroimaging Initiative. Insulin resistance predicts medial temporal hypermetabolism in mild cognitive impairment conversion to Alzheimer disease. Diabetes 2015;64:1933-40. [Crossref] [PubMed]
28. Glassock RJ, Winearls C. Ageing and the glomerular filtration rate: truths and consequences. Trans Am Clin Climatol Assoc 2009;120:419-28. [PubMed]
29. Pourhassan Shamchi S, Khosravi M, Taghvaei R, Zirakchian Zadeh M, Paydary K, Emamzadehfard S, Werner TJ, Høilund-Carlsen PF, Alavi A. Normal patterns of regional brain (18)F-FDG uptake in normal aging. Hell J Nucl Med 2018;21:175-80. [PubMed]
30. Chételat G, Arbizu J, Barthel H, Garibotto V, Law I, Morbelli S, et al. Amyloid-PET and (18)F-FDG-PET in the diagnostic investigation of Alzheimer's disease and other dementias. Lancet Neurol 2020;19:951-62. [Crossref] [PubMed]
31. Sprinz C, Zanon M, Altmayer S, Watte G, Irion K, Marchiori E, Hochhegger B. Effects of blood glucose level on 18F fluorodeoxyglucose (18F-FDG) uptake for PET/CT in normal organs: an analysis on 5623 patients. Sci Rep 2018;8:2126. [Crossref] [PubMed]
32. Mansournia MA, Jewell NP, Greenland S. Case-control matching: effects, misconceptions, and recommendations. Eur J Epidemiol 2018;33:5-14. [Crossref] [PubMed]