Associations of age, gender and body mass with 1H MR-observed brain metabolites and tissue distributions

Abstract

Recent reports have indicated that a measure of adiposity, the body mass index (BMI), is associated with MR-observed brain metabolite concentrations and tissue volume measures. In addition to indicating possible associations between brain metabolism, BMI and cognitive function, the inclusion of BMI as an additional subject selection criterion could potentially improve the detection of metabolic and structural differences between subjects and study groups. In this study, a retrospective analysis of 140 volumetric MRSI datasets was carried out to investigate the value of including BMI in the subject selection relative to age and gender. The findings replicate earlier reports of strong associations of N-acetylaspartate, creatine, choline and gray matter with age and gender, with additional observations of slightly increased spectral linewidth with age and in female relative to male subjects. Associations of metabolite levels, linewidth and gray matter volume with BMI were also observed, although only in some regions. Using voxel-based analyses, it was also observed that the patterns of the relative changes of metabolites with BMI matched those of linewidth with BMI or weight, and that residual magnetic field inhomogeneity and measures of spectral quality were influenced by body weight. It is concluded that, although associations of metabolite levels and tissue distributions with BMI occur, these may be attributable to issues associated with data acquisition and analysis; however, an organic origin for these findings cannot be specifically excluded. There is, however, sufficient evidence to warrant the inclusion of body weight as a subject selection parameter, secondary to age, and as a factor in data analysis for MRS studies of some brain regions.

Figure 1

Age and body mass index (BMI) distribution of the subject group for males (triangles) and females (circles), with the regression line generated for all subjects.

Figure 2

Representative plots showing the distributions of the mean parameter values in occipital white matter (WM) across all subjects. Examples are shown for N-acetylaspartate (NAA) (a–c) and creatine (Cre) (d–f) as a function of age, body mass index (BMI) and body weight. (g, h) Distributions of linewidth for age and BMI. (i, j) Distributions of the mean signal-to-noise ratio (SNR) (for the peak NAA value) (i) and the NAA Cramer–Rao bound (CRB) (j) as a function of BMI.

Figure 3

Example axial and coronal images generated from voxel-based linear regression analyses. (a) Axial slices from the spatial reference MRI at a slice spacing of 8 mm. (b) Creatine (Cre) intercept (for age of 20 years). (c) Change in Cre as a function of age (scale: ±5%/decade). (d) Change in Cre as a function of body mass index (BMI) (scale: ±14 i.u./kg/m²). (e) Change in spectral linewidth with BMI (scale: ±0.09 Hz/kg/m²). (f) Coronal images from the reference MRI corresponding to Fig. 2g–i at a spacing of 14 mm. (g) Change in Cre with BMI (scale: ±14 i.u./kg/m²). (h) Change in spectral linewidth with BMI (scale: ±0.09 Hz/kg/m²). (i) Change in spectral linewidth with body weight (scale: ±0.025 Hz/kg).

Figure 4

Parameter maps generated for different subject groups, as detailed in the text. (a) Reference MRI at a slice spacing of 10 mm. Mean B₀ for the low-body mass index (low-BMI) (b) and high-BMI (c) groups. (d) Slope of the regression of B₀ against BMI for all subjects. Mean spectral linewidth for the low-BMI (e) and high-BMI (f) groups. N-Acetylaspartate Cramer–Rao bound (NAA CRB) for the low-BMI (g) and high-BMI (h) groups. Mean B₀ for female (i) and male (j) subject groups (with same scale as in c).

Figure 5

Plots of total cerebral tissue fractions relative to total brain volume (TBV) as a function of age (a–c) and body mass index (BMI) (d–f) for gray matter (GM) (a, d), white matter (WM) (b, e) and cerebrospinal fluid (CSF) (c, f). The results of a linear regression for each are also shown.