The demographics and biomarker characteristics of the study subjects are presented in Table 1. There was no difference in age among the groups. Compared with AD group, there were significantly fewer female subjects in sMCI group (p<0.01). The educational levels in AD group were lower than those in other diagnostic groups (p<0.05 for all). Compared with CN and sMCI, CSF Aβ42 levels were significantly lower in pMCI and AD, and CSF t-tau and p-tau were significantly higher in pMCI and AD, but there was no significant difference between pMCI and AD. The mean levels of MMSE, ADAS-cog 13, hippocampal volume, and FDG-PET (SUVR) were significantly different among the diagnostic groups. Ventricular volume was significantly higher in patients with AD compared with CN and sMCI, and lower in CN compared with other diagnostic groups (Table 1).
Serum valine in different diagnostic groups
Serum valine levels were significantly lower in patients with AD (278.05±50.4 μM) compared with CN (300.92±66.27 μM) (P<0.01) and sMCI (297.89±61.79 μM) (P<0.05). Lower serum valine levels were also found in pMCI (284.99±57.95 μM) compared with CN (300.92±66.27 μM) (P<0.05). However, there were no differences between CN and sMCI as well as between sMCI and pMCI, and similarly between pMCI and AD (Figure 1).
Serum valine in relation to CSF Aβ and tau
There was no significant correlation between CSF Aβ42 and serum valine in different diagnostic groups (CN, r=0.071, p=0.513; sMCI, r=0.044, p=0.717; pMCI, r=0.130, p=0.207; AD, r=0.088, p=0.406) (Figure 2A). Valine was negatively correlated with CSF t-tau (r=-0.260, p=0.01) (Figure 2B) and p-tau (r=-0.231, p=0.023) in pMCI (Figure 2C), but not in CN (r=0.075, p=0.491 for t-tau; r=0.052, p=0.637 for p-tau), sMCI (r=0.134, p=0.270 for t-tau; r=0.133, p=0.274 for p-tau), and AD (r=0.106, p=0.316 for t-tau; r=0.118, p=0.265 for p-tau) (Figure 2B and 2C).
Diagnostic accuracy of serum valine, CSF t-tau, and p-tau
ROC analyses were performed to detect serum valine, CSF t-tau, and p-tau related to clinical diagnoses in sMCI, pMCI, and AD. Compared to CN, CSF t-tau and p-tau showed significant diagnostic accuracy for sMCI (Table 2 and Figure 3A), pMCI (Table 2 and Figure 3B), and AD (Table 2 and Figure 3C). While the diagnostic accuracy of valine for sMCI (Table 2 and Figure 3A), pMCI (Table 2 and Figure 3B), and AD (Table 2 and Figure 3C) was not statistically significant. Compared to t-tau or p-tau alone, the combination of valine, t-tau, or p-tau provided a higher diagnostic accuracy for sMCI and AD, although not statistically significant (Table 2 and Figure 3A and C). The combination of valine, t-tau, and p-tau did not significantly improve diagnostic accuracy for pMCI (Table 2 and Figure 3B).
Could serum valine predict conversion from CN to MCI or AD and from MCI to AD
Among the subjects with longitudinal assessments, 44 CN individuals progressed to MCI or AD and 195 MCI participants progressed to AD during follow-up. We investigated whether serum valine predicted conversion from CN to MCI or AD and from MCI to AD. Cox proportional hazard models were established using serum valine as a continuous variable. HRs were then calculated for serum valine as a dichotomized variable using median values of serum valine as a cutoff (adjusted for age and sex). Serum valine did not predict conversion from CN to MCI or AD (P=0.12) (Figure 4A). However, MCI patients with lower valine (≤291pg/ml) progressed much more rapidly to AD than those with higher valine (>291pg/ml) (P=0.04) (Figure 4B).
Serum valine in relation to cognition
In each diagnostic group, serum valine did not correlate with baseline MMSE (CN, β=-0.00022, p=0.495; sMCI, β=0.00082, p=0.544; pMCI, β=-0.00032, p=0.727; AD, β=0.00295, p=0.053) (Figure 5A) and ADAS-cog13 (CN, β=0.00121, p=0.683; sMCIβ=-0.00140, p=0.811; pMCI, β=0.00137, p=0.793; AD, β=-0.01335, p=0.149) (Figure 5C). Similarly, it was not associated with the rates of change of MMSE (CN, β=-0.00009, p=0.256; sMCI, β=0.00051, p=0.536; pMCI, β=-0.00030, p=0.841; AD, β=0.00463, p=0.145) (Figure 5B) and ADAS-cog 13 (CN, β=0.00003, p=0.931; sMCI, β=-0.00021, p=0.918; pMCI, β=-0.00023, p=0.943; AD, β=-0.00778, p=0.131) (Figure 5D) during follow-up.
Serum valine in relation to brain structure and metabolism
Finally, we examined whether serum valine was associated with hippocampal volume, ventricular volumeas measured by MRI, and brain metabolism as measured by FDG-PET (SUVR). Serum valine did not correlate with baseline FDG-PET (CN, β=-9.3e-7, p=0.958; sMCI, β=0.00001, p=0.961; pMCI, β=-0.00008, p=0.371; AD, β=0.00012, p=0.382) (Figure 6A), ventricular volume (CN, β=-14.43002, p=0.455; sMCI, β=-11.01430, p=0.694; pMCI, β=3.14764, p=0.915; AD, β=10.54132, p=0.766) (Figure 6C), and hippocampal volume (CN, β=1.07287, p=0.119; sMCI, β=1.72319, p=0.073; pMCI, β=-0.92409, p=0.396; AD, β=-1.44042, p=0.208) (Figure 6E) in any diagnostic group. Serum valine was correlated with rates of change of FDG-PET (β=0.00004, p=0.016) (Figure 6B) in pMCI, but not in the other groups (CN, β=-4.2e-6, p=0.851; sMCI, β=0.00001, p=0.477; AD, β=0.00001, p=0.794) (Figure 6B). There was also no correlation between serum valine and the rate of change in ventricular volume (CN, β=-0.79444, p=0.505; sMCI, β=-1.93837, p=0.369; pMCI, β=1.19231, p=0.680; AD, β=-2.41099, p=0.549) (Figure 6D) and hippocampal volume (CN, β=0.02611, p=0.479; sMCI, β=-0.02792, p=0.743; pMCI, β=-0.09965, p=0.205; AD, β=0.00245, p=0.990) (Figure 6F) during follow-up.