Biology subjects

Mattila, I.

Publications and source records attributed to Mattila, I..

3 recordsLinked to original sources

bioRxiv2021

High-throughput UHPLC-MS to screen metabolites in feces for gut metabolic health

(1) BackgroundFeces are the product of our diets and have been linked to diseases of the gut, including Chrons disease and metabolic diseases such as diabetes. For screening metabolites in heterogeneous samples such as feces, it is necessary to use fast and reproducible analytical methods that maximize metabolite detection. (2) MethodsAs sample preparation is crucial to obtain high quality data in MS-based clinical metabolomics, we developed a novel, efficient and robust method for preparing fecal samples for analysis with a focus in reducing aliquoting and detecting both polar and non-polar metabolites. Fecal samples (n= 475) from patients with alcohol-related liver disease and healthy controls were prepared according to the proposed method and analyzed in an UHPLC-QQQ targeted platform in order to obtain a quantitative profile of compounds that impact liver-gut axis metabolism. (3) ResultsMS analyses of the prepared fecal samples have shown reproducibility and coverage of n=28 metabolites, mostly comprising bile acids and amino acids. We report metabolite-wise relative standard deviation (RSD) in quality control samples, inter-day repeatability, LOD, LOQ and range of linearity. The average concen-trations for 135 healthy participants are reported here for clinical applications. (4) Conclusionsour high-throughput method provides an efficient tool for investigating gut-liver axis metabolism in liver-related diseases using a noninvasive collected sample.

biochemistry↗

bioRxiv2021

CARDIOVASCULAR AUTONOMIC NEUROPATHY IN TYPE 1 DIABETES IS ASSOCIATED WITH SEVERAL METABOLIC PATHWAYS: New Risk Markers on the horizon

ObjectiveCardiovascular autonomic neuropathy (CAN) in diabetes is associated with increased mortality and morbidity and is a non-treatable complication. We investigated associations between circulating metabolites and presence of CAN in persons with type 1 diabetes (T1D). MethodsCAN was assessed by cardiovascular reflex tests (CARTs) in 302 persons with T1D as heart rate response to: deep breathing; lying-to-standing test; and the Valsalva manoeuvre. More than 1 pathological CART defined the CAN diagnosis. Serum metabolomics and lipidomics profiles were analysed with two complementary non-targeted massspectrometry methods. Cross-sectional associations between single metabolites and CAN were assessed by linear regression. Models were fitted with and without adjustments for relevant confounders and multiple testing. ResultsParticipants were mean (IQR) aged 55(49, 63) years, 50% males, with diabetes duration 39(32, 47) years, HbA1c 63(55,69) mmol/mol and 34% had the CAN diagnosis. A total of 75 metabolites and 106 lipids were examined. In crude models, CAN diagnosis was associated with higher levels of hydroxy fatty acids (2,4- and 3,4-dihydroxybutanoic acids, 4-deoxytetronic acid), creatinine, sugar derivates (ribitol, ribonic acid, myo-inositol), citric acid, glycerol, phenols, phosphatidylcholines and lower levels of free fatty acids and amino acid methionine (p<0.05). Upon adjustment, positive associations with CAN were retained with hydroxy fatty acids, tricarboxylic acid (TCA) cycle-based sugar derivates, and citric acid and phenols (Padjusted<0.05). ConclusionsMetabolic pathways, including the TCA cycle, hydroxy fatty acids, phosphatidylcholines and sugar derivatives, were associated with CAN in T1D. These novel metabolic pathways associated with CAN could prove to be future modifiable risk factors.

neuroscience↗

bioRxiv2020

Lipidomics of human adipose tissue reveals diversity between body areas

Background and aimsAdipose tissue plays a pivotal role in storing excess fat and its composition reflects the history of persons lifestyle and metabolic health. Broad profiling of lipids with mass spectrometry has potential for uncovering new knowledge on the pathology of obesity, metabolic syndrome, diabetes and other related conditions. Here, we developed a lipidomic method for analyzing human subcutaneous adipose biopsies. We applied the method to four body areas to understand the differences in lipid composition between these areas. Materials and methodsAdipose tissue biopsies from 10 participants were analyzed using ultra-high-performance liquid chromatography coupled to quadrupole time-of-flight mass spectrometry. The method development included the optimization of the lipid extraction, the sample amount and the sample dilution factor to detect lipids in an appropriate concentration range. Lipidomic analyses were performed for adipose tissue collected from the abdomen, breast, thigh and lower back. Differences in lipid levels between tissues were visualized with heatmaps. ResultsLipidomic analysis on human adipose biopsies lead to the identification of 187 lipids in 2 mg of sample. Technical variation of the lipid-class specific internal standards were below 5 %, thus indicating acceptable repeatability. Triacylglycerols were highly represented in the adipose tissue samples, and lipids from 13 lipid classes were identified. Long polyunsaturated triacylglycerols in higher levels in thigh (q<0.05), when compared with the abdomen, breast and lower back, indicating that the lipidome was area-specific. ConclusionThe method presented here is suitable for the analysis of lipid profiles in 2 mg of adipose tissue. The amount of fat across the body is important for health but we argue that also the distribution and the particular profile of the lipidome may be relevant for metabolic outcomes. We suggest that the method presented in this paper could be useful for detecting such aberrations.

biophysics↗