Regional differences in absolute metabolite level couplings in a longitudinal study of children
Martha J Holmes1, Frances C Robertson1, Francesca Little2, Mark F Cotton3, Els Dobbels3, Andre JW van der Kouwe4,5, Barbara Laughton3, and Ernesta M Meintjes1

1MRC/UCT Medical Imaging Research Unit, Department of Human Biology, University of Cape Town, Cape Town, South Africa, 2Department of Statistical Sciences, University of Cape Town, Cape Town, South Africa, 3Children’s Infectious Diseases Clinical Research Unit, Department of Paediatrics & Child Health, Tygerberg Children’s Hospital and Faculty of Medicine and Health Sciences, Stellenbosch University, Cape Town, South Africa, 4A.A. Martinos Centre for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, United States, 5Department of Radiology, Harvard Medical School, Boston, MA, United States

Synopsis

1H-MRS non-invasively quantifies metabolites that play important roles in neurodevelopment. The physiological functions of these metabolites, however, are still debated. Examining the regional intercorrelations between metabolites such as NAA, creatine, choline and glutamate provides insight about the role of individual and coupled biochemicals in the developing brain. We examined correlations between pairs of metabolites in the midfrontal gray matter (MFGM), peritrigonal white matter (PWM), basal ganglia (BG) at 5, 7 and 9 years in a cohort of South African children. We found significant metabolite couplings in both the MFGM and PWM, however no significant couplings were observed in the BG.

Purpose

1H-MRS measures localized brain metabolism. Throughout childhood the brain develops biochemically, and different metabolites may develop in tandem depending on the brain region. Understanding how metabolites are inter-related may provide insight into healthy brain growth, and create norms to aid in identifying pathology and abnormal development.

Relationships between individual metabolites in different brain regions may also provide physiological insight. For example, even though much literature exists on the role of N-acetyl-aspartate (NAA) in the brain, debate still surrounds its precise function. NAA is primarily interpreted as a marker of neuronal integrity. However, additional proposed roles of NAA include facilitating energy metabolism in neuronal mitochondria1 and a reservoir for glutamate.2

An MRS study in healthy adults examined intra-regional metabolite correlations.3 In the anterior cingulate, NAA-glutamate levels were coupled, whereas in the cerebellar vermis multiple correlations were reported including NAA-glutamate, NAA-choline (GPC+PCh) and glutamate-choline. In schizophrenia4,5, NAA and glutamate+glutamine levels are regionally decoupled in patients compared to control subjects.

Here we identify couplings of typically measured metabolites - NAA, creatine (Cr+PCr), glutamate, choline - in a representative pediatric population in South Africa (SA). Since 95% of HIV-positive pregnant women and 68% of HIV-exposed infants in SA receive antiretroviral therapy (ART)6,7, the population includes HIV-exposed, uninfected (HEU) children. Although the risks of ART and HIV in utero exposure to brain development are expected to be minimal, long-term consequences are not well-established8,9.

We hypothesize that within a South African pediatric population (followed longitudinally from 5 - 9 years) NAA-glutamate levels will be coupled across three different brain regions, independent of age and HIV exposure.

Methods

MRI. A high-resolution T1-weighted acquisition and single voxel 1H-MRS in the midfrontal gray matter (MFGM), peritrigonal white matter (PWM) and basal ganglia (BG) were performed on a Siemens 3T Allegra Head Scanner (Siemens, Erlangen, Germany) in Cape Town, South Africa. MRS used a real-time motion and B0 corrected10 PRESS (TR/TE 2000/30 ms, 64 averages, 2:16 min). SPM12 was used for voxel segmentation to determine tissue type percentages for water concentration and partial volume calculations. Eddy current compensation and frequency/phase correction were performed. Absolute metabolite levels calculated with LCModel.

Subjects. Children studied were a subset of a longitudinal neuroimaging study, including HEU and HIV-unexposed, uninfected (HUU) children. Twenty-nine 5-year-old, forty-nine 7-year-old, and twenty-nine 9-year-old children were successfully scanned. All HEU children were exposed to ART for prevention of mother-to-child transmission. Table 1 summarizes the demographics of children included in analysis. The R-progamming language was used for statistical analysis. We used a linear mixed effects regression model to account for repeated measures in some children. Gender, HIV-exposure, ethnicity, and signal-to-noise ratio were included as confounds. The threshold for significance was Bonferroni corrected for 6 comparisons (p < 0.006).

Results

BG. No statistically significant associations were observed.

MFGM. We found significant couplings between NAA-creatine (slope = 0.68, p < 0.0001), choline-creatine (slope = 0.12, p = 0.0002), NAA-glutamate (slope = 0.26, p < 0.0001) and creatine-glutamate (slope = 1.2, p < 0.0001). (Figures 1 - 4).

PWM. We found significant couplings between NAA-creatine (slope = 0.86; p < 0.0001), NAA-glutamate (slope = 0.20; p = 0.004) and creatine-glutamate (slope = 1.3; p = 0.0001). (Figures 1, 3 and 4).

Discussion

Surprisingly, we found couplings between NAA, creatine and glutamate in MFGM and PWM. The overarching relations of NAA, glutamate and creatine suggest these metabolites are metabolically interconnected, however these relationships may be indirectly connected. Choline-creatine coupling was only observed in the MFGM indicating the relationship is region specific. The absence of metabolite couplings in the BG was unexpected, revealing different regional roles for individual metabolites. Our results support previous findings3-5 of glutamate-NAA couplings in healthy adults, extending this result to different regions and a pediatric population.

Gender, HIV exposure, ethnicity and age did not significantly confound the reported couplings, implying these results can be applied to a wide pediatric population. However, ART exposure has subsequently increased in duration and from 2 to 3 drugs in utero.

Lastly, since the observed couplings have regional dependence, are observed mostly between metabolites without spectral overlap and in metabolites with low standard deviations, we believe these relationships are not related to data collection or analysis.

Conclusion

A better understanding of the interdependence of metabolite levels in different brain regions allows for a better description of healthy brain growth, and provides additional norms against which to judge pathology and abnormal growth. Studies often report and interpret metabolite ratios in terms of creatine levels because of its expected stability. Our results indicate caution is required in interpreting ratios in pediatric populations.

Acknowledgements

Support for this study was provided by NRF/DST South African Research Chairs Initiative; US National Institute of Allergy and Infectious Diseases (NIAID) through the CIPRA network, Grant U19 AI53217; NIH grants R01HD071664 and R21MH096559; NRF grant CPR20110614000019421, and the Medical Research Council (MRC). We thank the CUBIC radiographers (Marie-Louise de Villiers, Nailah Maroof and Alison Siljeur), our research staff (Thandiwe Hamana and Rosy Khethelo), and Shabir A. Madhi for access to control participants on the CIPRA-SA04 trial.

References

[1] Moffett JR, Ross B, Arun P, et al. N-Acetylaspartate in the CNS: from neurodiagnostics to neurobiology. Prog Neurobiol. 2007; 81(2):89–131.

[2] Clark JF, Doepke A, Filosa JA, et al. N-acetylaspartate as a reservor for glutamate. Med Hypotheses. 2006;67: 506–512.

[3] Waddell KW, Zanjanipour P, Pradhan S, et al. Anterior cingulate and cerebellar GABA and Glu correlations measured by (1)H J-difference spectroscopy. Magn Reson Imaging. 2011; 29(1):19– 24.

[4] Decoupling of N-acetyl-aspartate and glutamate within the dorsolateral prefrontal cortex in schizophrenia.

[5] Kraguljac NV, Reid MA, White DM, et al. Regional decoupling of N-acetyl- aspartate and glutamate in schizophrenia. Neuropsychopharmacology. 2012; 37(12):2635–42.

[6] World Health Organization, Joint United Nations Programme on HIV/AIDS, United Nations Children’s Fund. 2011. Towards Universal Access: Scaling up Priority HIV/AIDS Interventions in the Health Sector. Progress report 2011.

[7] Joint United Nations Programme on HIV/AIDS. Together We Will End AIDS, WHO Library Cataloguing-in-Publication Data. 2012.

[8] Heidari, S, Mofenson L, Cotton MF, et al. Antiretroviral drugs for preventing mother-to-child transmission of HIV: A review of potential effects on HIV-exposed but uninfected children. J Acquir Immune Defic Syndr. 2011;57:290-296.

[9] Sugandhi, N, Rodrigues J, Kem M, et al. HIV Exposed Infants: Rethinking care for a lifelong condition. AIDS. 2013;27(2):997-1003.

[10] Hess AT, Tisdall MD, Andronesi, et al. 2011. Real-time motion and B0 corrected single voxel spectroscopy using volumetric navigators. Mag Reson Med. 2011;66:314-323.

Figures

Table 1. Demographics of children included in statistical analysis (inclusion criteria: SNR ≥ 7 and FWHM < 0.075). In the midfrontal gray matter, nine children had data at all ages and fourteen children at two time points. In the peritrigonal white matter three children had data at all ages and fourteen children at two time points. In the basal ganglia three children had data at all ages and sixteen children at two time points.

HEU = HIV-exposed, uninfected. HUU = HIV-unexposed, uninfected.


Figure 1. (A) Relationship between NAA and creatine levels in the midfrontal gray matter. NAA levels increased significantly with creatine levels (slope = 0.68; p < 0.0001). (B) Relationship between NAA and creatine levels in the peritrigonal white matter. NAA levels increased significantly with creatine levels (slope = 0.86; p < 0.0001). Gray band represents confidence interval.

Figure 2. Relationship between choline and creatine levels in the midfrontal gray matter. Gray band represents confidence interval. Choline levels increased significantly with creatine levels (slope = 0.12, p < 0.005).

Figure 3. (A) Relationship between NAA and glutamate levels in the midfrontal gray matter. NAA levels increased significantly with glutamate levels (slope = 0.12, p < 0.005). (B) Relationship between NAA and glutamate levels in the peritrigonal white matter. NAA levels increased significantly with glutamate levels (slope = 0.20, p < 0.05). Gray band represents confidence interval.

Figure 4. (A) Relationship between Glutamate and creatine levels in the midfrontal gray matter. Glutamate levels increased significantly with creatine levels (slope = 1.2; p < 0.0001). (B) Relationship between Glutamate and creatine levels in the peritrigonal white matter. Glutamate levels increased significantly with creatine levels (slope = 1.3; p = 0.0001). Gray band represents confidence interval.



Proc. Intl. Soc. Mag. Reson. Med. 24 (2016)
0425