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Neurometabolite Alterations Associated With Cognitive Performance in Perinatally HIV-Infected Children

View Article: PubMed Central - PubMed

ABSTRACT

Despite treatment with combination antiretroviral therapy (cART), cognitive impairment is still observed in perinatally HIV-infected children. We aimed to evaluate potential underlying cerebral injury by comparing neurometabolite levels between perinatally HIV-infected children and healthy controls. This cross-sectional study evaluated neurometabolites, as measured by Magnetic Resonance Spectroscopy (MRS), in perinatally HIV-infected children stable on cART (n = 26) and healthy controls (n = 36).

Participants were included from a cohort of perinatally HIV-infected children and healthy controls, matched group-wise for age, gender, ethnicity, and socio-economic status. N-acetylaspartate (NAA), glutamate (Glu), myo-inositol (mI), and choline (Cho) levels were studied as ratios over creatine (Cre). Group differences and associations with HIV-related parameters, cognitive functioning, and neuronal damage markers (neurofilament and total Tau proteins) were determined using age-adjusted linear regression analyses.

HIV-infected children had increased Cho:Cre in white matter (HIV-infected = 0.29 ± 0.03; controls = 0.27 ± 0.03; P value = 0.045). Lower nadir CD4+ T-cell Z-scores were associated with reduced neuronal integrity markers NAA:Cre and Glu:Cre. A Centers for Disease Control and Prevention (CDC) stage C diagnosis was associated with higher glial markers Cho:Cre and mI:Cre. Poorer cognitive performance was mainly associated with higher Cho:Cre in HIV-infected children, and with lower NAA:Cre and Glu:Cre in healthy controls. There were no associations between neurometabolites and neuronal damage markers in blood or CSF.

Compared to controls, perinatally HIV-infected children had increased Cho:Cre in white matter, suggestive of ongoing glial proliferation. Levels of several neurometabolites were associated with cognitive performance, suggesting that MRS may be a useful method to assess cerebral changes potentially linked to cognitive outcomes.

No MeSH data available.


Exemplar planning of chemical shift imaging (CSI), superimposed on a 3D-T1-weighted image. Based on the 3D-T1-weighted scan (A), gray matter (GM), and white matter (WM) segmentation was performed within the CSI field-of-view (B). Estimated metabolite levels (C) were averaged within all GM and WM voxels to obtain mean GM and WM values (D). CSI = chemical shift imaging, GM = gray matter, WM = white matter.
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Figure 1: Exemplar planning of chemical shift imaging (CSI), superimposed on a 3D-T1-weighted image. Based on the 3D-T1-weighted scan (A), gray matter (GM), and white matter (WM) segmentation was performed within the CSI field-of-view (B). Estimated metabolite levels (C) were averaged within all GM and WM voxels to obtain mean GM and WM values (D). CSI = chemical shift imaging, GM = gray matter, WM = white matter.

Mentions: Chemical Shift Imaging (CSI) H+-MRS was performed on a 3.0 Tesla (3T) MRI scanner at the Academic Medical Center (Intera, Philips Healthcare, Best, the Netherlands), equipped with a 16-channel phased array head coil. Structural 3D echo gradient T1 with multiplanar reconstruction was conducted as previously reported.19 MR spectra were acquired from 1 axial T1 slice positioned directly above the corpus callosum. Water was suppressed by applying 3 chemical shift-selective excitation pulses, using a Point Resolved Spectroscopy sequence (TE/TR = 37/2000 milliseconds). Using the advantage of CSI to cover large brain areas while accounting for varying metabolite levels across brain regions,20 we selected total grey matter (GM) and total white matter (WM) within our slice as volumes of interest (VOI). The segmentation of GM and WM was performed using a T1-weighted scan. Figure 1 illustrates the acquisition and analysis workflow. LC Model (Stephen Provencher, Oakville, Canada) was used for the quantification of the metabolite spectra.21 LC Model validated the concentration of each metabolite, adjusted the phase and ppm shift of the spectra, estimated the baseline and performed eddy current correction. Peak registration in LC Model used the most prominent peaks of NAA, Cho, and Cre for initial referencing. Spectra were generated for 33 neurometabolites. We excluded spectra with poor signal-to-noise ratios, a disproportionate water signal or other significant artifacts. Based on previous literature, we selected NAA, Glu, mI, Cho, and Cre as neurometabolites of interest, which were all good quality spectra (SD < 20%). As spectral peak areas were not directly proportional to metabolite concentrations, metabolite levels were measured as a ratio over Cre, a commonly used, relatively stable marker for energy potential in brain tissue.22 Measuring metabolites as ratios to Cre enabled correction for differences between imaging and localization methods, as well as different contributions of CSF to the VOIs.


Neurometabolite Alterations Associated With Cognitive Performance in Perinatally HIV-Infected Children
Exemplar planning of chemical shift imaging (CSI), superimposed on a 3D-T1-weighted image. Based on the 3D-T1-weighted scan (A), gray matter (GM), and white matter (WM) segmentation was performed within the CSI field-of-view (B). Estimated metabolite levels (C) were averaged within all GM and WM voxels to obtain mean GM and WM values (D). CSI = chemical shift imaging, GM = gray matter, WM = white matter.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4998374&req=5

Figure 1: Exemplar planning of chemical shift imaging (CSI), superimposed on a 3D-T1-weighted image. Based on the 3D-T1-weighted scan (A), gray matter (GM), and white matter (WM) segmentation was performed within the CSI field-of-view (B). Estimated metabolite levels (C) were averaged within all GM and WM voxels to obtain mean GM and WM values (D). CSI = chemical shift imaging, GM = gray matter, WM = white matter.
Mentions: Chemical Shift Imaging (CSI) H+-MRS was performed on a 3.0 Tesla (3T) MRI scanner at the Academic Medical Center (Intera, Philips Healthcare, Best, the Netherlands), equipped with a 16-channel phased array head coil. Structural 3D echo gradient T1 with multiplanar reconstruction was conducted as previously reported.19 MR spectra were acquired from 1 axial T1 slice positioned directly above the corpus callosum. Water was suppressed by applying 3 chemical shift-selective excitation pulses, using a Point Resolved Spectroscopy sequence (TE/TR = 37/2000 milliseconds). Using the advantage of CSI to cover large brain areas while accounting for varying metabolite levels across brain regions,20 we selected total grey matter (GM) and total white matter (WM) within our slice as volumes of interest (VOI). The segmentation of GM and WM was performed using a T1-weighted scan. Figure 1 illustrates the acquisition and analysis workflow. LC Model (Stephen Provencher, Oakville, Canada) was used for the quantification of the metabolite spectra.21 LC Model validated the concentration of each metabolite, adjusted the phase and ppm shift of the spectra, estimated the baseline and performed eddy current correction. Peak registration in LC Model used the most prominent peaks of NAA, Cho, and Cre for initial referencing. Spectra were generated for 33 neurometabolites. We excluded spectra with poor signal-to-noise ratios, a disproportionate water signal or other significant artifacts. Based on previous literature, we selected NAA, Glu, mI, Cho, and Cre as neurometabolites of interest, which were all good quality spectra (SD < 20%). As spectral peak areas were not directly proportional to metabolite concentrations, metabolite levels were measured as a ratio over Cre, a commonly used, relatively stable marker for energy potential in brain tissue.22 Measuring metabolites as ratios to Cre enabled correction for differences between imaging and localization methods, as well as different contributions of CSF to the VOIs.

View Article: PubMed Central - PubMed

ABSTRACT

Despite treatment with combination antiretroviral therapy (cART), cognitive impairment is still observed in perinatally HIV-infected children. We aimed to evaluate potential underlying cerebral injury by comparing neurometabolite levels between perinatally HIV-infected children and healthy controls. This cross-sectional study evaluated neurometabolites, as measured by Magnetic Resonance Spectroscopy (MRS), in perinatally HIV-infected children stable on cART (n&#8202;=&#8202;26) and healthy controls (n&#8202;=&#8202;36).

Participants were included from a cohort of perinatally HIV-infected children and healthy controls, matched group-wise for age, gender, ethnicity, and socio-economic status. N-acetylaspartate (NAA), glutamate (Glu), myo-inositol (mI), and choline (Cho) levels were studied as ratios over creatine (Cre). Group differences and associations with HIV-related parameters, cognitive functioning, and neuronal damage markers (neurofilament and total Tau proteins) were determined using age-adjusted linear regression analyses.

HIV-infected children had increased Cho:Cre in white matter (HIV-infected&#8202;=&#8202;0.29&#8202;&plusmn;&#8202;0.03; controls&#8202;=&#8202;0.27&#8202;&plusmn;&#8202;0.03; P value&#8202;=&#8202;0.045). Lower nadir CD4+ T-cell Z-scores were associated with reduced neuronal integrity markers NAA:Cre and Glu:Cre. A Centers for Disease Control and Prevention (CDC) stage C diagnosis was associated with higher glial markers Cho:Cre and mI:Cre. Poorer cognitive performance was mainly associated with higher Cho:Cre in HIV-infected children, and with lower NAA:Cre and Glu:Cre in healthy controls. There were no associations between neurometabolites and neuronal damage markers in blood or CSF.

Compared to controls, perinatally HIV-infected children had increased Cho:Cre in white matter, suggestive of ongoing glial proliferation. Levels of several neurometabolites were associated with cognitive performance, suggesting that MRS may be a useful method to assess cerebral changes potentially linked to cognitive outcomes.

No MeSH data available.