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Purpose: To investigate metabolic exchange between (13)C(1)-pyruvate, (13)C(1)-lactate, and (13)C(1)-alanine in pre-clinical model systems using kinetic modeling of dynamic hyperpolarized (13)C spectroscopic data and to examine the relationship between fitted parameters and dose-response., Materials and Methods: Dynamic (13)C spectroscopy data were acquired in normal rats, wild type mice, and mice with transgenic prostate tumors (TRAMP) either within a single slice or using a one-dimensional echo-planar spectroscopic imaging (1D-EPSI) encoding technique. Rate constants were estimated by fitting a set of exponential equations to the dynamic data. Variations in fitted parameters were used to determine model robustness in 15 mm slices centered on normal rat kidneys. Parameter values were used to investigate differences in metabolism between and within TRAMP and wild type mice., Results: The kinetic model was shown here to be robust when fitting data from a rat given similar doses. In normal rats, Michaelis-Menten kinetics were able to describe the dose-response of the fitted exchange rate constants with a 13.65% and 16.75% scaled fitting error (SFE) for k(pyr-->lac) and k(pyr-->ala), respectively. In TRAMP mice, k(pyr-->lac) increased an average of 94% after up to 23 days of disease progression, whether the mice were untreated or treated with casodex. Parameters estimated from dynamic (13)C 1D-EPSI data were able to differentiate anatomical structures within both wild type and TRAMP mice., Conclusions: The metabolic parameters estimated using this approach may be useful for in vivo monitoring of tumor progression and treatment efficacy, as well as to distinguish between various tissues based on metabolic activity., (Copyright 2009 Elsevier Inc. All rights reserved.)

An extraordinary new technique using hyperpolarized (13)C-labeled pyruvate and taking advantage of increased glycolysis in cancer has the potential to improve the way magnetic resonance imaging is used for detection and characterization of prostate cancer. The aim of this study was to quantify, for the first time, differences in hyperpolarized [1-(13)C] pyruvate and its metabolic products between the various histologic grades of prostate cancer using the transgenic adenocarcinoma of mouse prostate (TRAMP) model. Fast spectroscopic imaging techniques were used to image lactate, alanine, and total hyperpolarized carbon (THC = lactate + pyruvate + alanine) from the entire abdomen of normal mice and TRAMP mice with low- and high-grade prostate tumors in 14 s. Within 1 week, the mice were dissected and the tumors were histologically analyzed. Hyperpolarized lactate SNR levels significantly increased (P < 0.05) with cancer development and progression (41 +/- 11, 74 +/- 17, and 154 +/- 24 in normal prostates, low-grade primary tumors, and high-grade primary tumors, respectively) and had a correlation coefficient of 0.95 with the histologic grade. In addition, there was minimal overlap in the lactate levels between the three groups with only one of the seven normal prostates overlapping with the low-grade primary tumors. The amount of THC, a possible measure of substrate uptake, and hyperpolarized alanine also increased with tumor grade but showed more overlap between the groups. In summary, elevated hyperpolarized lactate and potentially THC and alanine are noninvasive biomarkers of prostate cancer presence and histologic grade that could be used in future three-dimensional (13)C spectroscopic imaging studies of prostate cancer patients.

The development of dynamic nuclear polarization in solution has enabled in vivo 13C MR studies at high signal-to-noise ratio following injection of prepolarized 13C substrates. While prior studies have demonstrated the ability to observe metabolism following injection of hyperpolarized 13C pyruvate, the goal of this study was to develop and test a new hyperpolarized agent for investigating in vivo metabolism, [1-13C]lactate. A preparation for prepolarized 13C lactate and the requisite dissolution media were developed to investigate the feasibility for in vivo 13C MRS/MRSI studies following injection of this hyperpolarized agent. This study demonstrated, for the first time, not only the ability to detect hyperpolarized [1-13C]lactate in vivo but also the metabolic products 13C pyruvate, 13C alanine and 13C bicarbonate following injection in normal rats. The use of 13C lactate as a substrate provided the opportunity to study the conversion of lactate to pyruvate in vivo and to detect the secondary conversions to alanine and bicarbonate through pyruvate. This study also demonstrated the potential value of this hyperpolarized agent to investigate in vivo lactate uptake and metabolism in preclinical animal models.

ObjectTo establish and compare normative metabolite concentrations in 2nd and 3rd trimester human amniotic fluid samples in an effort to reveal metabolic biomarkers of fetal health and development.Materials and methodsTwenty-one metabolite concentrations were compared between 2nd (15-27 weeks gestation, N = 23) and 3rd (29-39 weeks gestation, N = 27) trimester amniotic fluid samples using (1)H high resolution magic angle spinning (HR-MAS) spectroscopy. Data were acquired using the electronic reference to access in vivo concentrations method and quantified using a modified semi-parametric quantum estimation algorithm modified for high-resolution ex vivo data.ResultsSixteen of 21 metabolite concentrations differed significantly between 2nd and 3rd trimester groups. Betaine (0.00846+/-0.00206 mmol/kg vs. 0.0133+/-0.0058 mmol/kg, P < 0.002) and creatinine (0.0124+/-0.0058 mmol/kg vs. 0.247+/-0.011 mmol/kg, P < 0.001) concentrations increased significantly, while glucose (5.96+/-1.66 mmol/kg vs. 2.41+/-1.69 mmol/kg, P < 0.001), citrate (0.740+/-0.217 mmol/kg vs. 0.399+/-0.137 mmol/kg, P < 0.001), pyruvate (0.0659+/-0.0103 mmol/kg vs. 0.0299+/-0.286 mmol/kg, P < 0.001), and numerous amino acid (e.g. alanine, glutamate, isoleucine, leucine, lysine, and valine) concentrations decreased significantly with advancing gestation. A stepwise multiple linear regression model applied to 50 samples showed that gestational age can be accurately predicted using combinations of alanine, glucose and creatinine concentrations.ConclusionThese results provide key normative data for 2nd and 3rd trimester amniotic fluid metabolite concentrations and provide the foundation for future development of magnetic resonance spectroscopy (MRS) biomarkers to evaluate fetal health and development.