Peripheral serum metabolomic profiles inform central cognitive impairment

Abstract

The incidence of Alzheimer's disease (AD) increases with age and is becoming a significant cause of worldwide morbidity and mortality. However, the metabolic perturbation behind the onset of AD remains unclear. In this study, we performed metabolite profiling in both brain (n = 109) and matching serum samples (n = 566) to identify differentially expressed metabolites and metabolic pathways associated with neuropathology and cognitive performance and to identify individuals at high risk of developing cognitive impairment. The abundances of 6 metabolites, glycolithocholate (GLCA), petroselinic acid, linoleic acid, myristic acid, palmitic acid, palmitoleic acid and the deoxycholate/cholate (DCA/CA) ratio, along with the dysregulation scores of 3 metabolic pathways, primary bile acid biosynthesis, fatty acid biosynthesis, and biosynthesis of unsaturated fatty acids showed significant differences across both brain and serum diagnostic groups (P-value < 0.05). Significant associations were observed between the levels of differential metabolites/pathways and cognitive performance, neurofibrillary tangles, and neuritic plaque burden. Metabolites abundances and personalized metabolic pathways scores were used to derive machine learning models, respectively, that could be used to differentiate cognitively impaired persons from those without cognitive impairment (median area under the receiver operating characteristic curve (AUC) = 0.772 for the metabolite level model; median AUC = 0.731 for the pathway level model). Utilizing these two models on the entire baseline control group, we identified those who experienced cognitive decline in the later years (AUC = 0.804, sensitivity = 0.722, specificity = 0.749 for the metabolite level model; AUC = 0.778, sensitivity = 0.633, specificity = 0.825 for the pathway level model) and demonstrated their pre-AD onset prediction potentials. Our study provides a proof-of-concept that it is possible to discriminate antecedent cognitive impairment in older adults before the onset of overt clinical symptoms using metabolomics. Our findings, if validated in future studies, could enable the earlier detection and intervention of cognitive impairment that may halt its progression.

Figure 1

Brain metabolome and serum metabolome composition and alterations. (a) Left panel: the brain metabolome composition. Right panel: – log10 (P-value) across clinical groups of brain tissues (NCI, MCI, AD). (b) Left panel: the serum metabolome composition. Right panel: – log10 (P-value) across clinical groups of serum tissues (NCI, MCI/AD).

Figure 2

Associations between metabolites level and global cognitive function. (a) Boxplots showing group differences and P values for identified metabolites across Braak groups for brain tissue abundances. (b) Boxplots showing group differences and significances for identified metabolites across CERAD groups for brain tissue abundances. ρ, correlation coefficient of Spearman’s rank correlation test.

Figure 3

The identified panel of metabolites and its predictive performance. (a) Boxplots showing group differences and P values for identified metabolites across NCI (non-converters), NCI (converters), and MCI/AD for serum abundances. (b) ROC curves of metabolite models trained on the 70% training data and tested on the 30% testing data according to 100-times randomly training–testing splitting. (c) The ROC curve of the final metabolite model on the validation data. (d) RF scores of the final metabolite model across NCI (non-converters), NCI (converters), and MCI/AD. *P-value < 0.05, **P-value < 0.01, ***P-value < 0.001, Wilcoxon rank sum test. The optimal cutoff was determined by the Youden index. AD Alzheimer’s disease, AUC area under the receiver operating characteristic curve, NCI(C) NCI (converters), NCI(N) NCI (non-converters), CIs confidence intervals, MCI mild cognitive impairment, NS not significant, SE sensitivity, SP specificity.

Figure 4

The pathway panel and its predictive performance. (a) Boxplots showing group differences and P values for identified pathways across NCI (non-converters), NCI (converters), and MCI/AD for serum abundances. (b) ROC curves of pathway models trained on the 70% training data and tested on the 30% testing data according to 100-times randomly training–testing splitting. (c) The ROC curve of the final pathway model on the validation data. (d) RF scores of the final pathway model across NCI (non-converters), NCI (converters), and MCI/AD. *P-value < 0.05, **P-value < 0.01, ***P-value < 0.001, Wilcoxon rank sum test. The optimal cutoff was determined by the Youden index. AD Alzheimer’s disease, AUC area under the receiver operating characteristic curve, NCI(C) NCI (converters), NCI(N) NCI (non-converters), CIs confidence intervals, MCI mild cognitive impairment, NS not significant, SE sensitivity, SP specificity.

Figure 5

Pathways involved in FFAs and BAs. Healthy neurons are highly glycolytic, catabolizing rate of glucose consumption through the glycolysis and TCA cycle to produce ATP. Reduced glucose utilization and metabolic dysfunction could be detected using FDG-PET and metabolomics approaches in AD patients. The metabolic instability with decreased glucose utilization in impaired neurons can cause the energy supply shift towards alternative energy sources, e.g., FFAs and ketone bodies. Primary BAs are synthesized in the liver from cholesterol. A dysfunction of the gut microbiome can cause the accumulation of cytotoxic secondary bile acids, e.g., DCA and GLCA, which can be secreted into the systemic circulation and then across the blood–brain barrier to enter the brain. ASBT apical sodium-dependent bile acid transporter, BSEP bile salt export pump, CA cholate, CDCA chenodeoxycholate, CoA coenzyme A, DCA deoxycholate, F-6-P fructose-6-phosphate, FATP fatty acid transporter, G-6-P glucose-6-phosphate, GCA glycocholate, GCDCA glycochenodeoxycholate, GDCA glycodeoxycholate, GLCA glycolithocholate, GLUT glucose transporter, GUDCA glycoursodeoxycholate, LCA lithocholate, MCT monocarboxylate transporter, NTCP sodium/taurocholate co-transporting polypeptide, OST organic solute and steroid transporter, TCA tricarboxylic acid, TCA taurocholate, TCDCA taurochenodeoxycholate, TDCA taurodeoxycholate, TLCA taurolithocholate, TUDCA tauroursodeoxycholate, TGR5 G protein–coupled bile acid receptor, UDCA ursodeoxycholate.