Abstract
PURPOSE: The neurometabolic timecourse of healthy aging is not well-established, in part due to diversity of quantification methodology. In this study, a large structured cross-sectional cohort of male and female subjects throughout adulthood was recruited to investigate neurometabolic changes as a function of age, using consensus-recommended magnetic resonance spectroscopy quantification methods. METHODS: 102 healthy volunteers, with approximately equal numbers of male and female participants in each decade of age from the 20s, 30s, 40s, 50s, and 60s, were recruited with IRB approval. MR spectroscopic data were acquired on a 3T MRI scanner. Metabolite spectra were acquired using PRESS localization (TE=30 ms; 96 transients) in the centrum semiovale (CSO) and posterior cingulate cortex (PCC). Water-suppressed spectra were modeled using the Osprey algorithm, employing a basis set of 18 simulated metabolite basis functions and a cohort-mean measured macromolecular spectrum. Pearson correlations were conducted to assess relationships between metabolite concentrations and age for each voxel; Spearman correlations were conducted where metabolite distributions were non-normal. Paired t-tests were run to determine whether metabolite concentrations differed between the PCC and CSO. Finally, robust linear regressions were conducted to assess both age and sex as predictors of metabolite concentrations in the PCC and CSO and separately, to assess age, signal-noise ratio, and full width half maximum (FWHM) linewidth as predictors of metabolite concentrations. RESULTS: Data from four voxels were excluded (2 ethanol; 2 unacceptably large lipid signal). Statistically-significant age*metabolite Pearson correlations were observed for tCho (r(98)=0.33, p<0.001), tCr (r(98)=0.60, p<0.001), and mI (r(98)=0.32, p=0.001) in the CSO and for NAAG (r(98)=0.26, p=0.008), tCho(r(98)=0.33, p<0.001), tCr (r(98)=0.39, p<0.001), and Gln (r(98)=0.21, p=0.034) in the PCC. Spearman correlations for non-normal variables revealed a statistically significant correlation between sI and age in the CSO (r(86)=0.26, p=0.013). No significant correlations were seen between age and tNAA, NAA, Glx, Glu, GSH, PE, Lac, or Asp in either region (all p>0.20). Age associations for tCho, tCr, mI and sI in the CSO and for NAAG, tCho, and tCr in the PCC remained when controlling for sex in robust regressions. CSO NAAG and Asp, as well as PCC tNAA, sI, and Lac were higher in women; PCC Gln was higher in men. When including an age*sex interaction term in robust regression models, a significant age*sex interaction was seen for tCho (F(1,96)=11.53, p=0.001) and GSH (F(1,96)=7.15, p=0.009) in the CSO and tCho (F(1,96)=9.17, p=0.003), tCr (F(1,96)=9.59, p=0.003), mI (F(1,96)=6.48, p=0.012), and Lac (F(1,78)=6.50, p=0.016) in the PCC. In all significant interactions, metabolite levels increased with age in females, but not males. There was a significant positive correlation between linewidth and age. Age relationships with tCho, tCr, and mI in the CSO and tCho, tCr, mI, and sI in the PCC were significant after controlling for linewidth and FWHM in robust regressions. CONCLUSION: The primary (correlation) results indicated age relationships for tCho, tCr, mI, and sI in the CSO and for NAAG, tCho, tCr, and Gln in the PCC, while no age correlations were found for tNAA, NAA, Glx, Glu, GSH, PE, Lac, or Asp in either region. Our results provide a normative foundation for future work investigating the neurometabolic time course of healthy aging using MRS.