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Introduction: Air pollution is a significant contributor to morbidity and mortality globally and has been linked to an increased risk of dementia. Previous studies within the Betula cohort in Northern Sweden have demonstrated associations between air pollution and dementia, as well as distinctive metabolomic profiles in dementia patients compared to controls. This study aimed to investigate whether air pollution is associated with quantitative changes in metabolite levels within this cohort, and whether future dementia status would modify this association.

Methods: Both short-term and long-term exposure to air pollution were evaluated using high spatial resolution models and measured data. Air pollution from vehicle exhaust and woodsmoke were analyzed separately. Metabolomic profiling was conducted on 321 participants, including 58 serum samples from dementia patients and a control group matched for age, sex, and education level, using nuclear magnetic resonance spectroscopy.

Results: No statistically significant associations were found between any metabolites and any measures of short-term or long-term exposure to air pollution. However, there were trends potentially suggesting associations between both long-term and short-term exposure to air pollution with lactate and glucose metabolites. Notably, these associations were observed despite the lack of correlation between long-term and short-term air pollution exposure in this cohort. There were also tendencies for associations between air pollution from woodsmoke to be more pronounced in participants that would later develop dementia, suggesting a potential effect depending on urban/rural factors.

Discussion: While no significant associations were found, the trends observed in the data suggest potential links between air pollution exposure and changes in lactate and glucose metabolites. These findings provide some new insights into the link between air pollution and metabolic markers in a low-exposure setting. However, addressing existing limitations is crucial to improve the robustness and applicability of future research in this area. The pronounced associations in participants who later developed dementia may indicate an influence of urban/rural factors, warranting further investigation.

Both plasma and cerebrospinal fluid (CSF) are rich in cholesterol and its metabolites. Here we describe in detail a methodology for the identification and quantification of multiple sterols including oxysterols and sterol-acids found in these fluids. The method is translatable to any laboratory with access to liquid chromatography – tandem mass spectrometry. The method exploits isotope-dilution mass spectrometry for absolute quantification of target metabolites. The method is applicable for semi-quantification of other sterols for which isotope labelled surrogates are not available and approximate quantification of partially identified sterols. Values are reported for non-esterified sterols in the absence of saponification and total sterols following saponification. In this way absolute quantification data is reported for 17 sterols in the NIST SRM 1950 plasma along with semi-quantitative data for 8 additional sterols and approximate quantification for one further sterol. In a pooled (CSF) sample used for internal quality control, absolute quantification was performed on 10 sterols, semi-quantification on 9 sterols and approximate quantification on a further three partially identified sterols. The value of the method is illustrated by confirming the sterol phenotype of a patient suffering from ACOX2 deficiency, a rare disorder of bile acid biosynthesis, and in a plasma sample from a patient suffering from cerebrotendinous xanthomatosis, where cholesterol 27-hydroxylase is deficient.

Differentiating between Parkinson’s disease (PD) and the atypical Parkinsonian disorders of multiple system atrophy (MSA) and progressive supranuclear palsy (PSP) is difficult clinically due to overlapping symptomatology, especially at early disease stages. Consequently, there is a need to identify metabolic markers for these diseases and to develop them into viable biomarkers. In the present investigation, solution nuclear magnetic resonance and mass spectrometry metabolomics were used to quantitatively characterize the plasma metabolomes (a total of 167 metabolites) of a cohort of 94 individuals comprising 34 PD, 12 MSA, and 17 PSP patients, as well as 31 control subjects. The distinct and statistically significant differences observed in the metabolite concentrations of the different disease and control groups enabled the identification of potential plasma metabolite markers of each disorder and enabled the differentiation between the disorders. These group-specific differences further implicate disturbances in specific metabolic pathways. The two metabolites, formic acid and succinate, were altered similarly in all three disease groups when compared to the control group, where a reduced level of formic acid suggested an effect on pyruvate metabolism, methane metabolism, and/or the kynurenine pathway, and an increased succinate level suggested an effect on the citric acid cycle and mitochondrial dysfunction.

There is a great need for diagnostic biomarkers of impending dementia. Metabolite markers in blood have been investigated in several studies, but inconclusive findings encourage further investigation, particularly in the pre-diagnostic phase. In the present study, the serum metabolomes of 110 dementia or pre-diagnostic dementia individuals and 201 healthy individuals matched for age, gender, and education were analyzed by nuclear magnetic resonance spectroscopy in combination with multivariate data analysis. 58 metabolites were quantified in each of the 311 samples. Individuals with dementia were discriminated from controls using a panel of seven metabolites, while the pre-diagnostic dementia subjects were distinguished from controls using a separate set of seven metabolites, where threonine was a common significant metabolite in both panels. Metabolite and pathway alterations specific for dementia and pre-diagnostic dementia were identified, in particular a disturbed threonine catabolism at the pre-diagnostic stage that extends to several threonine-linked pathways at the dementia stage.

The neurodegenerative diseases amyotrophic lateral sclerosis (ALS) and Parkinson's disease (PD) share some common molecular deficits including disruption of protein homeostasis leading to disease-specific protein aggregation. While insoluble protein aggregates are the defining pathological confirmation of diagnosis, patient stratification based on early molecular etiologies may identify distinct subgroups within a clinical diagnosis that would respond differently in therapeutic development programs. We are developing targeted multiple reaction monitoring (MRM) mass spectrometry methods to rigorously quantify CSF proteins from known disease genes involved in lysosomal, ubiquitin-proteasomal, and autophagy pathways. Analysis of CSF from 21 PD, 21 ALS, and 25 control patients, rigorously matched for gender, age, and age of sample, revealed significant changes in peptide levels between PD, ALS, and control. In patients with PD, levels of two peptides for chromogranin B (CHGB, secretogranin 1) were significantly reduced. In CSF of patients with ALS, levels of two peptides from ubiquitin carboxy-terminal hydrolase like protein 1 (UCHL1) and one peptide each for glycoprotein non-metastatic melanoma protein B (GPNMB) and cathepsin D (CTSD) were all increased. Analysis of patients with ALS separated into two groups based on length of survival after CSF sampling revealed that the increases in GPNMB and UCHL1 were specific for short-lived ALS patients. While analysis of additional cohorts is required to validate these candidate biomarkers, this study suggests methods for stratification of ALS patients for clinical trials and identifies targets for drug efficacy measurements during therapeutic development.

Saliva is a readily available biofluid that is sensitive to metabolic changes and can be collected through rapid and non-invasive collection procedures, and it shows great promise for clinical metabolomic studies. This work studied the metabolite composition of, and the differences between, saliva samples collected by unstimulated spitting/drooling, paraffin chewing-stimulated spitting, and parotid gland suction using targeted nuclear magnetic resonance (NMR) spectroscopy and liquid chromatography coupled to tandem mass spectrometry (LC-MS/MS) for metabolite quantification. As applied here, these two analytical techniques provide complementary metabolite information and together extend the metabolome coverage with robust NMR quantification of soluble metabolites and sensitive targeted LC-MS/MS analysis of bioactive lipids in specific metabolic pathways. The NMR analysis was performed on ultrafiltrated (3kDa cutoff) saliva samples and resulted in a total of 45 quantified metabolites. The LC-MS/MS analysis was performed on both filtered and unfiltered samples and resulted in the quantification of two endocannabinoids (AEA and PEA) and 22 oxylipins, which at present is the most comprehensive targeted analysis of bioactive lipids in human saliva. Important differences in the metabolite composition were observed between the three saliva sample collection methods, which should be taken into consideration when designing metabolomic studies of saliva. Furthermore, the combined use of the two metabolomics platforms (NMR and LC-MS/MS) proved to be viable for research and clinical studies of the salivary metabolome.

Introduction: Amyotrophic lateral sclerosis (ALS) and Parkinson’s disease (PD) are two severe neurodegenerative disorders for which the disease mechanisms are poorly understood and reliable biomarkers are absent.

Objectives: To identify metabolite biomarkers for ALS and PD, and to gain insights into which metabolic pathways are involved in disease.

Methods: Nuclear magnetic resonance (NMR) metabolomics was utilized to characterize the metabolite profiles of cerebrospinal fluid (CSF) and plasma from individuals in three age, gender, and sampling-date matched groups, comprising 22 ALS, 22 PD and 28 control subjects.

Results: Multivariate analysis of NMR data generated robust discriminatory models for separation of ALS from control subjects. ALS patients showed increased concentrations of several metabolites in both CSF and plasma, these are alanine (CSF fold change = 1.22, p = 0.005), creatine (CSF-fc = 1.17, p = 0.001), glucose (CSF-fc = 1.11, p = 0.036), isoleucine (CSF-fc = 1.24, p = 0.002), and valine (CSF-fc = 1.17, p = 0.014). Additional metabolites in CSF (creatinine, dimethylamine and lactic acid) and plasma (acetic acid, glutamic acid, histidine, leucine, pyruvate and tyrosine) were also important for this discrimination. Similarly, panels of CSF-metabolites that discriminate PD from ALS and control subjects were identified.

Conclusions: The results for the ALS patients suggest an affected creatine/creatinine pathway and an altered branched chain amino acid (BCAA) metabolism, and suggest links to glucose and energy metabolism. Putative metabolic markers specific for ALS (e.g. creatinine and lactic acid) and PD (e.g. 3-hydroxyisovaleric acid and mannose) were identified, while several (e.g. creatine and BCAAs) were shared between ALS and PD, suggesting some overlap in metabolic alterations in these disorders.

Saliva is a biofluid that is sensitive to metabolic changes and is straightforward to collect in a non-invasive manner, but it is seldom used for metabolite analysis when studying neurodegenerative disorders. We present a procedure for both an untargeted and targeted analysis of the saliva metabolome in which nuclear magnetic resonance (NMR) spectroscopy is used in combination with multivariate data analysis. The applicability of this approach is demonstrated on saliva samples selected from the 25 year prospective Betula study, including samples from dementia subjects with either Alzheimer's disease (AD) or vascular dementia at the time of sampling or who developed it by the next sampling/assessment occasion five years later, and age-, gender-, and education-matched control individuals without dementia. Statistically significant multivariate models were obtained that separated patients with dementia from controls and revealed seven discriminatory metabolites. Dementia patients showed significantly increased concentrations of acetic acid (fold change (fc) = 1.25, p = 2 x 10(-5)), histamine (fc = 1.26, p = 0.019), and propionate (fc = 1.35, p = 0.002), while significantly decreased levels were observed for dimethyl sulfone (fc = 0.81, p = 0.005), glycerol (fc = 0.79, p = 0.04), taurine (fc = 0.70, p = 0.007), and succinate (fc = 0.62, p = 0.008). Histamine, succinate, and taurine are known to be important in AD, and acetic acid and glycerol are involved in related pathways. Dimethyl sulfone and propionate originate from the diet and bacterial flora and might reflect poorer periodontal status in the dementia patients. For these seven metabolites, a weak but statistically significant pre-diagnostic value was observed. Taken together, we present a robust and general NMR analysis approach for studying the saliva metabolome that has potential use for screening and early detection of dementia.

Low-molecular-weight metabolites in human milk are gaining increasing interest in studies of infant nutrition. In the present study, the milk metabolome from a single mother was explored at different stages of lactation. Metabolites were extracted from sample aliquots using either methanol water (MeOH/H2O) extraction or ultrafiltration. Nuclear magnetic resonance (NMR) spectroscopy was used for metabolite identification and quantification, and multi- and univariate statistical data analyses were used to detect changes over time of lactation. Compared to MeOH/H2O extraction, ultrafiltration more efficiently reduced the interference from lipid and protein resonances, thereby enabling the identification and quantification of 36 metabolites. The human milk metabolomes at the early (9-24 days after delivery) and late (31-87 days after delivery) stages of lactation were distinctly different according to multi- and univariate statistics. The late lactation stage was characterized by significantly elevated concentrations of lactose, choline, alanine, glutamate, and glutamine, as well as by reduced levels of citrate, phosphocholine, glycerophosphocholine, and N-acetylglucosamine. Our results indicate that there are significant compositional changes of the human milk metabolome also in different phases of the matured lactation stage. These findings complement temporal studies on the colostrum and transitional metabolome in providing a better understanding of the nutritional variations received by an infant.

This study assesses if nuclear magnetic resonance (NMR) metabonomics can discriminate between Parkinson's disease (PD) patients and control subjects, and consequently identify metabolic markers for the disease. One-dimensional H-1 NMR spectroscopy was used for quantitative analysis of metabolites in the cerebrospinal fluid (CSF) from 10 PD patients and 10 control individuals, together with uni- and multivariate statistical analysis to discriminate between the groups and to identify significantly altered metabolite concentrations. In total 60 metabolites were identified and of those 38 were quantified in all CSF samples. An overall lowering of metabolite content was observed in PD patients compared to control subjects (fold change of 0.85 +/- 0.30). Multivariate statistics reveal significant changes (vertical bar w*vertical bar>0.2) among nine metabolites (alanine, creatinine, dimethylamine, glucose, lactate, mannose, phenylalanine, 3-hydroxyisobutyric acid and 3-hydroxyisovaleric acid). Three of these (alanine, creatinine and mannose) are identified as significantly changed also by univariate statistics (p < 0.00132, Bonferroni corrected). Panels with all or a selected set of these metabolites were successfully used for discriminating between the two groups. In conclusion, NMR metabonomics can readily determine metabolite concentrations in CSF, identify putative biomarkers that distinguish between the PD patients and control subjects, and thus potentially become a tool for diagnostic purposes.