Factors Affecting Brain Metabolism Measured with <sup>18</sup>FDG

doi:10.4236/ojmi.2012.21003

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Open Journal of Medical Imaging, 2012, 2, 19-22

http://dx.doi.org/10.4236/ojmi.2012.21003 Published Online March 2012 (http://www.SciRP.org/journal/ojmi)

Factors Affecting Brain Metabolism Measured with 18FDG

Hongyun Zhu1, Michael L. Goris2

1Department of Radiology, University of Arkansas for Medical Sciences (UAMS), Little Rock, USA

2Division of Nuclear Medicine, School of Medicine, Stanford University, Stanford, USA

Email: [email protected]

Received December 2, 2011; revised January 26, 2012; accepted February 8, 2012

ABSTRACT

An observational finding found a large variation in the brain SUV in patients with multiple myeloma undergoing

PET/CT. The first hypothesis considered a toxic effect of chemotherapeutic agents, but no correlation was found with

hematological signs of toxicity. Low brain FDG uptake has been described with anesthesia, but this was not relevant in

this case. An alternative is the presence of a large FDG avid mass, but that was excluded. Since there was a question of

chemotherapy toxicity, the metrics used for comparison were Hemoglobin levels (Hgb, g/dl), Erythrocyte count (RBC,

M/μL), Lymphocytes absolute counts (Lymph#, K/μL) and % (lymph, %), Granulocytes Neutrophils, K/μL), age and

C-reactive protein levels (CRP, g/L). The liver SUV (standardized uptake value) was included to eliminate unexpected

global effects on the SUV values, since FDG uptake is a competitive system with a single source (plasma FDG levels).

There was in fact no correlation between brain SUV and hepatic SUV, eliminating the so-called super scan effect. Fur-

ther analysis, however, revealed a strong positive correlation with hemoglobin or RBC levels, but an inverse effect with

Neutrophils, C-reactive proteins and age (in years). The results suggest that brain metabolism strongly depends on oxy-

gen supply and may be depressed by general inflammatory diseases and independently with age. If the variation of glu-

cose metabolism correlates with cognitive deficits (CD), considering general measures of good health may be a first

step for relief of age related CD.

Keywords: Brain 18-FDG Uptake; Anemia; Inflammation

1. Introduction

An observational finding found a large variation in the

brain SUV (standardized uptake value) in patients with

multiple myeloma undergoing PET/CT. The first hypo-

thesis considered a toxic effect of chemotherapeutic agents,

but no correlation was found with hematological signs of

toxicity. Low brain FDG uptake has been described with

anesthesia, but this was not relevant in this case. An al-

ternative is the presence of a large FDG avid mass, but

that was excluded.

Since there was a question of chemotherapy toxicity,

the metrics used for comparison were Hemoglobin levels

(Hgb, g/dL), Erythrocyte count (RBC M/μL), Lympho-

cytes absolute counts (Lymph#, K/μL) and % (lymph, %),

Granulocytes (Neutrophils K/μL), age (in years) and C-

reactive protein levels (CRP, g/L). The liver SUV was

included to eliminate unexpected global effects on the

SUV values, since FDG uptake is a competitive system

with a single source (plasma FDG levels).

2. Materials and Methods

There were 40 patients, (70% male), average age 60.7

(range 44-83), with 173 FDG PET/CT scans. The reason

for the study was evaluation of Multiple Myeloma ther-

apy. Patients were routinely followed with the metrics

cited above. Brain SUV was measured in the central brain

cortex, but the value was not site sensitive, since the cor-

relation between central and visual was 0.95 and between

central and nuclei was 0.96. In addition, patients with

abnormal cerebral regional FDG distributions (e.g. as in

Alzheimer’s, Fronto-parietal dementia, multi-stroke de-

mentia) were not included. The central brain activity in

SUV units was on average 4.96 ± 1.84 (range 1.5 - 9.7).

The average glucose level was 134 ± 27 mg/dl (range 104-

198).

After the FDG injection, the patients were kept in a

quiet, dimly lighted room, for 1 hr, before imaging be-

gan.

The analysis consisted first in an evaluation of the inter-

correlation of the metric listed above prior to using multi-

ple regressions. Multiple regressions were performed first

with all metrics and later only with those not positively

inter-correlated or with a significant single regression

with brain SUV.

3. Results

Central brain SUV correlated significantly negative with

age (p < 0.001), CRP (p < 0.05) and Neutrophils (p <

H. Y. Zhu ET AL.

0.01) and significantly positive with RBC (p < 0.001),

Hgb (p < 0.001), lymph% (p < 0.005) and Lymph# (p <

0.05).

There was significant negative correlation between

WBC and liver SUV (p < 0.05) and between WBC and

lymph% (p < 0.001). However, the linear regression be-

tween WBC and brain SUV was not significant (p >

0.05). Lymph% correlated positively with RBC (p <

0.001) and Hgb (p < 0.001).

The results was that multiple regressions were per-

formed only for HGB (not RBC), Age, Neutrophils,

lymph# and CRP. The liver SUV was included to test for

a super-scan effect.

The results of single regression between the various

metric and brain SUV with their p values are shown in

Table 1.

Table 2 shows serial multiple regressions in which the

significant factors from the previous multiple regressions

are withdrawn.

4. Discussion

Recent and remote chemotherapy could influence brain

uptake. However, the fact that a potential effect of che-

motherapy would vary in the subject is not relevant in

this study. First, it is unlikely to be correlated with age.

Second, the effect on bone marrow, if there was one,

would be a decrease in both red and white blood ele-

ments. What we observe is an opposite effect on brain

uptake of RBC and Neutrophils. Age by itself did not

correlate with the next 2 important factors, the level of

hemoglobin or red cell counts, or the level of neutrophils.

Diffuse low glucose metabolism measured with FDG

has been described in Halothane anesthesia [1], in low

states of consciousness [2], and paradoxically with low

blood glucose [3]. In addition it has been observed in

diffuse brain injury [4] without large focal abnormalities.

An alternative explanation is due to a sink effect, if other

structures or pathologies are very FDG avid. They would

then act as a FDG sink, since all structures in the body

accumulate FDG from the same plasma pool. This phe-

nomenon has been described as a metabolic super scan

[5].

None of those factors was present in this population.

The lack of correlation between brain and liver SUV ex-

cludes the metabolic super scan. Glucose levels in the

plasma were within limit (above 6 mM). There was no

(generalized) history of trauma or low consciousness.

5. Conclusion

Non-neurological factors (anemia, age, inflammation)

seem to adversely affect FDG uptake in the brain. Those

effects are independent of age, and, if shown to be causa-

tive, would suggest methods of therapeutic interventions.

Table 1. Negative effects are age, CRP, Neutrophils and

WBC (not significant). Except for age they are all associ-

ated with inflammation. Positive effects are Hgb and RBC,

lymph#, and lymph%. There is no significant correlation

between br ain and liver SUV.

Slope p<

Age –0.048 0.001

HgB 0.376 0.001

Lymph# 0.368 0.001

CRP –0.011 0.033

Neutro –0.077 0.018

Liver 0.244 0.600

WBC –0.061 0.059

RBC 1.093 0.001

Lymph% 0.026 0.007

Table 2. With the uncorrelated metrics if all are used in

multiple regressions, Age and Hgb are significant. If those

are eliminated, Lymph# and Neutrophils are significant.

The next significant factor is the CRP. The liver does not

reach significance. The F-ratio is the ratio of the variance in

brain SUV explained by the regression over the residual

variance in brain SUV.

Metric p value

Hemoglobin1.1E–05

Age 0.0001

Neutro 0.0760 0.0295

Lymph 0.3103 0.0362

CRP 0.950 0.0560 0.0346

Liver 0.8122 0.7997 0.5388

F-Ratio 1.72E–08 0.007189 0.089

If the low FDG uptake is associated with cognitive defi-

cit, it may be that improving those general dysfunctions

(except for age), may be the first step and easiest thera-

peutic approach.

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doi:10.3346/jkms.2010.25.8.1256

Supporting Data

Table S1. Inter-corre l ation betw een metrics and brain SUV.

Brain frontal Age Liver WBC RBC HemoglobinLymph%Lymph# CRP Neutro

Brain frontal 1

Age –0.2747 1

Liver 0.047734 0.021864 1

WBC –0.14381 0.132306 –0.1327 1

RBC 0.33053 0.03899

–0.00581 0.0525841

Hemoglobin 0.37794 0.010832 0.05235 0.0142430.917851

Lymph% 0.205287 0.079511 0.119979 –0.296810.1217820.1985461

Lymph# 0.167963 0.068117 0.029356 0.173632 0.3040250.3208820.6621591

CRP –0.16137 0.006303 –0.00727 0.050013–0.23132–0.26407–0.08514–0.05294 1

Neutro –0.17894 0.120962 –0.14047 0.981173–0.00624–0.04847–0.43118–0.01984 0.0611561

Inter-correlation of brain SUV and the metrics used in this paper. The yellow background highlights the negative correlations.

Table S2. The significance of the correlation shown in Table S1 is not shown if p > 0.05.

Brain frontal Age liver WBC RBC HemoglobinLymph%Lymph# CRP Neutro

Brain frontal

Age 0.001

liver

WBC 0.05 0.05 0.05

RBC 0.001

Hemoglobin 0.001 0.001

Lymph% 0.005

0.0010.005

Lymph# 0.05 0.050.0010.0010.001

CRP 0.05 0.005 0.001

Neutro 0.01 0.05 0.0010.001

Brain central activity correlated negatively with Age (p < 0.001), WBC (p < 0.05), CRP (p < 0.05), and Neutrophils (Neutro) (p < 0.01), but positively with

RBC (p < 0.01), Hemoglobin (Hgb) (P, 0.01), lymphocytes % (lymph%) (p < 0.005) and lymphocytes absolute numbers (Lymph#) (p < 0.05).

H. Y. Zhu ET AL.

Table S3. Multple regressions using all metrics: significance.

Metric p-values

Age 0.0002

Hemoglobin 0.0735

0.0418

RBC 0.6784 0.9175

0.0000

Lymph # 0.2992 0.1389 0.09960.0234

Lymph % 0.1201 0.4007 0.24760.48410.0251

Neutro 0.1611 0.1000 0.05990.05030.91760.0312

CRP 0.6016 0.5178 0.36770.08310.21700.32620.0415

WBC 0.1678 0.0931 0.05700.0492 0.8632 0.0317 0.0834 0.0834 0.0693

liver 0.3891 0.7606 0.5700 0.1949 0.1277 0.0420 0.7020 0.7020 0.7037

F-ratio 3.1E–09 5.1E–07 1.2E–060.002260.0090.0360.048 0.048 0.15

With all the metric included, the significant factor is age. All other metrics do not significantly contribute. If age is eliminated, the significant contribution is

from hemoglobin. Next come RBC, then lymph#, then lymph% etc. Some factors contribute negatively (gray background). WBC and liver never contribute

significantly. The F-ratio is the ratio of the contribution of the regression to the variance of the data over the residual variance. When only liver and WBC re-

main the F-ratio is not significant.

Table S4. Range of significant metrics.

Metric Means Range

Liver SUV 2.19 ± 0.36 1.5 - 3.6

Hgb 10.62 ± 1.86 6.8 - 17.2

Lymph# 1.00 ± 0.84 0.0024 - 5.44

CRP 19.05 ± 25.51 0 - 148

Neutrophils 4.11 ± 4.30 0.04 - 42.46

The table includes the metrics selected for multiple regressions.

Table S5. Individual correlations w e re : Table S5.

Slope p<

Age –0.048 0.001

HgB 0.376 0.001

Lymph# 0.368 0.001

CRP –0.011 0.033

Neutro –0.077 0.018

Liver 0.244 0.600

WBC –0.061 0.059

RBC 1.093 0.001

Lymph% 0.06 20.007

Table S6. Multiple regressions between all metrics and brain

SUV.

Metric Slopes Standard Error p-value

Hgb 0.3332 0.0735 0.00001

Age –0.0472 0.0120 0.00012

Intercept 4.2009 1.2801 0.00126

Neutro –0.0528 0.0296 0.07603

Lymph# 0.1594 0.1566 0.31030

CRP –0.0042 0.0049 0.39498

liver 0.0832 0.3496 0.81221

When those (Table S6) metrics are used with their slope and the intercept,

the predicted value BeSUV = 4.2009 – 0.0472 × age – 0.0528 × Neutrophiles

+ 0.1594 × lymphocytes – 0.0042 × CRP + 0.0832 × LiverSUV the prediction

(expected) is highly significant and not significantly different from unity

(Table S7).

Table S7. Prediction of brain SUV using all significant not

intercorrelated metrics.

Coefficients Standard

Error p-value

Intercept 0.0000 0.6781 1

Expected 1.0000 0.1343 4.4754E–12

F-ratio 4.4754E–12