Evidence suggests interplay among the three major risk factors for Alzheimer’s disease (AD): age, APOE genotype, and sex. Here, we present comprehensive datasets and analyses of brain transcriptomes ...and blood metabolomes from human apoE2-, apoE3-, and apoE4-targeted replacement mice across young, middle, and old ages with both sexes. We found that age had the greatest impact on brain transcriptomes highlighted by an immune module led by Trem2 and Tyrobp, whereas APOE4 was associated with upregulation of multiple Serpina3 genes. Importantly, these networks and gene expression changes were mostly conserved in human brains. Finally, we observed a significant interaction between age, APOE genotype, and sex on unfolded protein response pathway. In the periphery, APOE2 drove distinct blood metabolome profile highlighted by the upregulation of lipid metabolites. Our work identifies unique and interactive molecular pathways underlying AD risk factors providing valuable resources for discovery and validation research in model systems and humans.
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•Aging leads to the most profound changes in brain gene expression networks•Immune module led by Alzheimer’s risk genes Trem2/Tyrobp is upregulated with aging•Alzheimer’s risk allele APOE4 increases the expression of Serpina3 family genes•Alzheimer’s protective allele APOE2 drives unique serum metabolome profiles
Zhao et al. present comprehensive datasets and analyses of brain transcriptomes and blood metabolomes from human apoE2-, apoE3-, and apoE4-targeted replacement mice across young, middle, and old ages with both sexes. The study provides critical insight on the molecular pathways underlying three major Alzheimer’s risk factors age, APOE, and sex.
Late-onset Alzheimer's disease (AD) can, in part, be considered a metabolic disease. Besides age, female sex and APOE ε4 genotype represent strong risk factors for AD that also give rise to large ...metabolic differences. We systematically investigated group-specific metabolic alterations by conducting stratified association analyses of 139 serum metabolites in 1,517 individuals from the AD Neuroimaging Initiative with AD biomarkers. We observed substantial sex differences in effects of 15 metabolites with partially overlapping differences for APOE ε4 status groups. Several group-specific metabolic alterations were not observed in unstratified analyses using sex and APOE ε4 as covariates. Combined stratification revealed further subgroup-specific metabolic effects limited to APOE ε4+ females. The observed metabolic alterations suggest that females experience greater impairment of mitochondrial energy production than males. Dissecting metabolic heterogeneity in AD pathogenesis can therefore enable grading the biomedical relevance for specific pathways within specific subgroups, guiding the way to personalized medicine.
Abstract
Combination antidepressant pharmacotherapies are frequently used to treat major depressive disorder (MDD). However, there is no evidence that machine learning approaches combining ...multi-omics measures (e.g., genomics and plasma metabolomics) can achieve clinically meaningful predictions of outcomes to combination pharmacotherapy. This study examined data from 264 MDD outpatients treated with citalopram or escitalopram in the Mayo Clinic Pharmacogenomics Research Network Antidepressant Medication Pharmacogenomic Study (PGRN-AMPS) and 111 MDD outpatients treated with combination pharmacotherapies in the Combined Medications to Enhance Outcomes of Antidepressant Therapy (CO-MED) study to predict response to combination antidepressant therapies. To assess whether metabolomics with functionally validated single-nucleotide polymorphisms (SNPs) improves predictability over metabolomics alone, models were trained/tested with and without SNPs. Models trained with PGRN-AMPS’ and CO-MED’s escitalopram/citalopram patients predicted response in CO-MED’s combination pharmacotherapy patients with accuracies of 76.6% (
p
< 0.01; AUC: 0.85) without and 77.5% (
p
< 0.01; AUC: 0.86) with SNPs. Then, models trained solely with PGRN-AMPS’ escitalopram/citalopram patients predicted response in CO-MED’s combination pharmacotherapy patients with accuracies of 75.3% (
p
< 0.05; AUC: 0.84) without and 77.5% (
p
< 0.01; AUC: 0.86) with SNPs, demonstrating cross-trial replication of predictions. Plasma hydroxylated sphingomyelins were prominent predictors of treatment outcomes. To explore the relationship between SNPs and hydroxylated sphingomyelins, we conducted multi-omics integration network analysis. Sphingomyelins clustered with SNPs and metabolites related to monoamine neurotransmission, suggesting a potential functional relationship. These results suggest that integrating specific metabolites and SNPs achieves accurate predictions of treatment response across classes of antidepressants. Finally, these results motivate functional investigation into how sphingomyelins might influence MDD pathophysiology, antidepressant response, or both.
Background
The gut microbiome may play a role in the pathogenesis of neuropsychiatric diseases including major depressive disorder (MDD). Bile acids (BAs) are steroid acids that are synthesized in ...the liver from cholesterol and further processed by gut-bacterial enzymes, thus requiring both human and gut microbiome enzymatic processes in their metabolism. BAs participate in a range of important host functions such as lipid transport and metabolism, cellular signaling and regulation of energy homeostasis. BAs have recently been implicated in the pathophysiology of Alzheimer's and several other neuropsychiatric diseases, but the biochemical underpinnings of these gut microbiome-linked metabolites in the pathophysiology of depression and anxiety remains largely unknown.
Method
Using targeted metabolomics, we profiled primary and secondary BAs in the baseline serum samples of 208 untreated outpatients with MDD. We assessed the relationship of BA concentrations and the severity of depressive and anxiety symptoms as defined by the 17-item Hamilton Depression Rating Scale (HRSD
17
) and the 14-item Hamilton Anxiety Rating Scale (HRSA-Total), respectively. We also evaluated whether the baseline metabolic profile of BA informs about treatment outcomes.
Results
The concentration of the primary BA chenodeoxycholic acid (CDCA) was significantly lower at baseline in both severely depressed (log
2
fold difference (LFD) = −0.48;
p
= 0.021) and highly anxious (LFD = −0.43;
p
= 0.021) participants compared to participants with less severe symptoms. The gut bacteria-derived secondary BAs produced from CDCA such as lithocholic acid (LCA) and several of its metabolites, and their ratios to primary BAs, were significantly higher in the more anxious participants (LFD's range = 0.23, 1.36;
p
's range = 6.85E-6, 1.86E-2). The interaction analysis of HRSD
17
and HRSA-Total suggested that the BA concentration differences were more strongly correlated to the symptoms of anxiety than depression. Significant differences in baseline CDCA (LFD = −0.87,
p
= 0.0009), isoLCA (LFD = −1.08,
p
= 0.016) and several BA ratios (LFD's range 0.46, 1.66,
p
's range 0.0003, 0.049) differentiated treatment failures from remitters.
Conclusion
In patients with MDD, BA profiles representing changes in gut microbiome compositions are associated with higher levels of anxiety and increased probability of first-line treatment failure. If confirmed, these findings suggest the possibility of developing gut microbiome-directed therapies for MDD characterized by gut dysbiosis.
•This study was to assess whether three symptomatically defined phenotypes of MDD, (core depression, neurovegetative of melancholia and anxiety), could be differentiated based on acylcarnitine ...profiles at baseline, after eight weeks of citalopram/escitalopram treatment.•The current data demonstrated that these phenotypes have distinct patterns of acylcarnitine levels at baseline and after eight weeks of antidepressant treatment.•These findings may help to develop a metabolomic profile of MDD patients with the aim of improving subtype classification of the MDD syndrome.
Acylcarnitines have important functions in mitochondrial energetics and β-oxidation, and have been implicated to play a significant role in metabolic functions of the brain. This retrospective study examined whether plasma acylcarnitine profiles can help biochemically distinguish the three phenotypic subtypes of major depressive disorder (MDD): core depression (CD+), anxious depression (ANX+), and neurovegetative symptoms of melancholia (NVSM+).
Depressed outpatients (n = 240) from the Mayo Clinic Pharmacogenomics Research Network were treated with citalopram or escitalopram for eight weeks. Plasma samples collected at baseline and after eight weeks of treatment with citalopram or escitalopram were profiled for short-, medium- and long-chain acylcarnitine levels using AbsoluteIDQ®p180-Kit and LC-MS. Linear mixed effects models were used to examine whether acylcarnitine levels discriminated the clinical phenotypes at baseline or eight weeks post-treatment, and whether temporal changes in acylcarnitine profiles differed between groups.
Compared to ANX+, CD+ and NVSM+ had significantly lower concentrations of short- and long-chain acylcarnitines at both baseline and week 8. In NVSM+, the medium- and long-chain acylcarnitines were also significantly lower in NVSM+ compared to ANX+. Short-chain acylcarnitine levels increased significantly from baseline to week 8 in CD+ and ANX+, whereas medium- and long-chain acylcarnitines significantly decreased in CD+ and NVSM+.
In depressed patients treated with SSRIs, β-oxidation and mitochondrial energetics as evaluated by levels and changes in acylcarnitines may provide the biochemical basis of the clinical heterogeneity of MDD, especially when combined with clinical characteristics.
Selective serotonin reuptake inhibitors (SSRIs) are the first-line treatment for major depressive disorder (MDD), yet their mechanisms of action are not fully understood and their therapeutic benefit ...varies among individuals. We used a targeted metabolomics approach utilizing a panel of 180 metabolites to gain insights into mechanisms of action and response to citalopram/escitalopram. Plasma samples from 136 participants with MDD enrolled into the Mayo Pharmacogenomics Research Network Antidepressant Medication Pharmacogenomic Study (PGRN-AMPS) were profiled at baseline and after 8 weeks of treatment. After treatment, we saw increased levels of short-chain acylcarnitines and decreased levels of medium-chain and long-chain acylcarnitines, suggesting an SSRI effect on β-oxidation and mitochondrial function. Amines-including arginine, proline, and methionine sulfoxide-were upregulated while serotonin and sarcosine were downregulated, suggesting an SSRI effect on urea cycle, one-carbon metabolism, and serotonin uptake. Eighteen lipids within the phosphatidylcholine (PC aa and ae) classes were upregulated. Changes in several lipid and amine levels correlated with changes in 17-item Hamilton Rating Scale for Depression scores (HRSD
). Differences in metabolic profiles at baseline and post-treatment were noted between participants who remitted (HRSD
≤ 7) and those who gained no meaningful benefits (<30% reduction in HRSD
). Remitters exhibited (a) higher baseline levels of C3, C5, alpha-aminoadipic acid, sarcosine, and serotonin; and (b) higher week-8 levels of PC aa C34:1, PC aa C34:2, PC aa C36:2, and PC aa C36:4. These findings suggest that mitochondrial energetics-including acylcarnitine metabolism, transport, and its link to β-oxidation-and lipid membrane remodeling may play roles in SSRI treatment response.
Metabolomics provides valuable tools for the study of drug effects, unraveling the mechanism of action and variation in response due to treatment. In this study we used electrochemistry-based ...targeted metabolomics to gain insights into the mechanisms of action of escitalopram/citalopram focusing on a set of 31 metabolites from neurotransmitter-related pathways. Overall, 290 unipolar patients with major depressive disorder were profiled at baseline, after 4 and 8 weeks of drug treatment. The 17-item Hamilton Depression Rating Scale (HRSD
) scores gauged depressive symptom severity. More significant metabolic changes were found after 8 weeks than 4 weeks post baseline. Within the tryptophan pathway, we noted significant reductions in serotonin (5HT) and increases in indoles that are known to be influenced by human gut microbial cometabolism. 5HT, 5-hydroxyindoleacetate (5HIAA), and the ratio of 5HIAA/5HT showed significant correlations to temporal changes in HRSD
scores. In the tyrosine pathway, changes were observed in the end products of the catecholamines, 3-methoxy-4-hydroxyphenylethyleneglycol and vinylmandelic acid. Furthermore, two phenolic acids, 4-hydroxyphenylacetic acid and 4-hydroxybenzoic acid, produced through noncanconical pathways, were increased with drug exposure. In the purine pathway, significant reductions in hypoxanthine and xanthine levels were observed. Examination of metabolite interactions through differential partial correlation networks revealed changes in guanosine-homogentisic acid and methionine-tyrosine interactions associated with HRSD
. Genetic association studies using the ratios of these interacting pairs of metabolites highlighted two genetic loci harboring genes previously linked to depression, neurotransmission, or neurodegeneration. Overall, exposure to escitalopram/citalopram results in shifts in metabolism through noncanonical pathways, which suggest possible roles for the gut microbiome, oxidative stress, and inflammation-related mechanisms.
Major depressive disorder (MDD) is a common and disabling syndrome with multiple etiologies that is defined by clinically elicited signs and symptoms. In hopes of developing a list of candidate ...biological measures that reflect and relate closely to the severity of depressive symptoms, so-called "state-dependent" biomarkers of depression, this pilot study explored the biochemical underpinnings of treatment response to cognitive behavior therapy (CBT) in medication-freeMDD outpatients. Plasma samples were collected at baseline and week 12 from a subset of MDD patients (N=26) who completed a course of CBT treatment as part of the Predictors of Remission in Depression to Individual and Combined Treatments (PReDICT) study. Targeted metabolomic profiling using the the AbsoluteIDQ® p180 Kit and LC-MS identified eight "co-expressed" metabolomic modules. Of these eight, three were significantly associated with change in depressive symptoms over the course of the 12-weeks. Metabolites found to be most strongly correlated with change in depressive symptoms were branched chain amino acids, acylcarnitines, methionine sulfoxide, and α-aminoadipic acid (negative correlations with symptom change) as well as several lipids, particularly the phosphatidlylcholines (positive correlation). These results implicate disturbed bioenergetics as an important state marker in the pathobiology of MDD. Exploratory analyses contrasting remitters to CBT versus those who failed the treatment further suggest these metabolites may serve as mediators of recovery during CBT treatment. Larger studies examining metabolomic change patterns in patients treated with pharmacotherapy or psychotherapy will be necessary to elucidate the biological underpinnings of MDD and the -specific biologies of treatment response.
Sex disparities in serum bile acid (BA) levels and Alzheimer's disease (AD) prevalence have been established. However, the precise link between changes in serum BAs and AD development remains ...elusive. Here, authors quantitatively determined 33 serum BAs and 58 BA features in 4 219 samples collected from 1 180 participants from the Alzheimer's Disease Neuroimaging Initiative. The findings revealed that these BA features exhibited significant correlations with clinical stages, encompassing cognitively normal (CN), early and late mild cognitive impairment, and AD, as well as cognitive performance. Importantly, these associations are more pronounced in men than women. Among participants with progressive disease stages (n = 660), BAs underwent early changes in men, occurring before AD. By incorporating BA features into diagnostic and predictive models, positive enhancements are achieved for all models. The area under the receiver operating characteristic curve improved from 0.78 to 0.91 for men and from 0.76 to 0.83 for women for the differentiation of CN and AD. Additionally, the key findings are validated in a subset of participants (n = 578) with cerebrospinal fluid amyloid‐beta and tau levels. These findings underscore the role of BAs in AD progression, offering potential improvements in the accuracy of AD prediction.
There are sex differences in serum bile acid (BA) levels and Alzheimer's disease (AD) incidence. With a longitudinal ADNI study, it is found that BAs trend in relation to AD progression and the alteration is earlier in men than women. Sex‐specific features associated with disease stages, cognitive functions, and progression highlighted BA roles in AD progression.
Alzheimer's disease (AD) is the most common neurodegenerative disease presenting major health and economic challenges that continue to grow. Mechanisms of disease are poorly understood but ...significant data point to metabolic defects that might contribute to disease pathogenesis. The Alzheimer Disease Metabolomics Consortium (ADMC) in partnership with Alzheimer Disease Neuroimaging Initiative (ADNI) is creating a comprehensive biochemical database for AD. Using targeted and non- targeted metabolomics and lipidomics platforms we are mapping metabolic pathway and network failures across the trajectory of disease. In this report we present quantitative metabolomics data generated on serum from 199 control, 356 mild cognitive impairment and 175 AD subjects enrolled in ADNI1 using AbsoluteIDQ-p180 platform, along with the pipeline for data preprocessing and medication classification for confound correction. The dataset presented here is the first of eight metabolomics datasets being generated for broad biochemical investigation of the AD metabolome. We expect that these collective metabolomics datasets will provide valuable resources for researchers to identify novel molecular mechanisms contributing to AD pathogenesis and disease phenotypes.
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