Browsing by Subject "Metabolomics"

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  • Prokić, Ivana; Lahousse, Lies; de Vries, Maaike; Liu, Jun; Kalaoja, Marita; Vonk, Judith M; van der Plaat, Diana A; van Diemen, Cleo C; van der Spek, Ashley; Zhernakova, Alexandra; Fu, Jingyuan; Ghanbari, Mohsen; Ala-Korpela, Mika; Kettunen, Johannes; Havulinna, Aki S; Perola, Markus; Salomaa, Veikko; Lind, Lars; Ärnlöv, Johan; Stricker, Bruno H C; Brusselle, Guy G; Boezen, H. M; van Duijn, Cornelia M; Amin, Najaf (BioMed Central, 2020)
    Abstract Background Chronic obstructive pulmonary disease (COPD) is a common lung disorder characterized by persistent and progressive airflow limitation as well as systemic changes. Metabolic changes in blood may help detect COPD in an earlier stage and predict prognosis. Methods We conducted a comprehensive study of circulating metabolites, measured by proton Nuclear Magnetic Resonance Spectroscopy, in relation with COPD and lung function. The discovery sample consisted of 5557 individuals from two large population-based studies in the Netherlands, the Rotterdam Study and the Erasmus Rucphen Family study. Significant findings were replicated in 12,205 individuals from the Lifelines-DEEP study, FINRISK and the Prospective Investigation of the Vasculature in Uppsala Seniors (PIVUS) studies. For replicated metabolites further investigation of causality was performed, utilizing genetics in the Mendelian randomization approach. Results There were 602 cases of COPD and 4955 controls used in the discovery meta-analysis. Our logistic regression results showed that higher levels of plasma Glycoprotein acetyls (GlycA) are significantly associated with COPD (OR = 1.16, P = 5.6 × 10− 4 in the discovery and OR = 1.30, P = 1.8 × 10− 6 in the replication sample). A bi-directional two-sample Mendelian randomization analysis suggested that circulating blood GlycA is not causally related to COPD, but that COPD causally increases GlycA levels. Using the prospective data of the same sample of Rotterdam Study in Cox-regression, we show that the circulating GlycA level is a predictive biomarker of COPD incidence (HR = 1.99, 95%CI 1.52–2.60, comparing those in the highest and lowest quartile of GlycA) but is not significantly associated with mortality in COPD patients (HR = 1.07, 95%CI 0.94–1.20). Conclusions Our study shows that circulating blood GlycA is a biomarker of early COPD pathology.
  • Prokic, Ivana; Lahousse, Lies; de Vries, Maaike; Liu, Jun; Kalaoja, Marita; Vonk, Judith M.; van der Plaat, Diana A.; van Diemen, Cleo C.; van der Spek, Ashley; Zhernakova, Alexandra; Fu, Jingyuan; Ghanbari, Mohsen; Ala-Korpela, Mika; Kettunen, Johannes; Havulinna, Aki S.; Perola, Markus; Salomaa, Veikko; Lind, Lars; Arnlov, Johan; Stricker, Bruno H. C.; Brusselle, Guy G.; Boezen, H. Marike; van Duijn, Cornelia M.; Amin, Najaf (2020)
    Background Chronic obstructive pulmonary disease (COPD) is a common lung disorder characterized by persistent and progressive airflow limitation as well as systemic changes. Metabolic changes in blood may help detect COPD in an earlier stage and predict prognosis. Methods We conducted a comprehensive study of circulating metabolites, measured by proton Nuclear Magnetic Resonance Spectroscopy, in relation with COPD and lung function. The discovery sample consisted of 5557 individuals from two large population-based studies in the Netherlands, the Rotterdam Study and the Erasmus Rucphen Family study. Significant findings were replicated in 12,205 individuals from the Lifelines-DEEP study, FINRISK and the Prospective Investigation of the Vasculature in Uppsala Seniors (PIVUS) studies. For replicated metabolites further investigation of causality was performed, utilizing genetics in the Mendelian randomization approach. Results There were 602 cases of COPD and 4955 controls used in the discovery meta-analysis. Our logistic regression results showed that higher levels of plasma Glycoprotein acetyls (GlycA) are significantly associated with COPD (OR = 1.16,P = 5.6 x 10(- 4)in the discovery and OR = 1.30,P = 1.8 x 10(- 6)in the replication sample). A bi-directional two-sample Mendelian randomization analysis suggested that circulating blood GlycA is not causally related to COPD, but that COPD causally increases GlycA levels. Using the prospective data of the same sample of Rotterdam Study in Cox-regression, we show that the circulating GlycA level is a predictive biomarker of COPD incidence (HR = 1.99, 95%CI 1.52-2.60, comparing those in the highest and lowest quartile of GlycA) but is not significantly associated with mortality in COPD patients (HR = 1.07, 95%CI 0.94-1.20). Conclusions Our study shows that circulating blood GlycA is a biomarker of early COPD pathology.
  • Jääskeläinen, Tiina; Kärkkäinen, Olli; Jokkala, Jenna; Klåvus, Anton; Heinonen, Seppo; Auriola, Seppo; Lehtonen, Marko; FINNPEC Core Invest Grp; Hanhineva, Kati; Laivuori, Hannele (2021)
    IntroductionMaternal metabolism changes substantially during pregnancy. However, few studies have used metabolomics technologies to characterize changes across gestation.Objectives and methodsWe applied liquid chromatography-mass spectrometry (LC-MS) based non-targeted metabolomics to determine whether the metabolic profile of serum differs throughout the pregnancy between pre-eclamptic and healthy women in the FINNPEC (Finnish Genetics of Preeclampsia Consortium) Study. Serum samples were available from early and late pregnancy.ResultsProgression of pregnancy had large-scale effects to the serum metabolite profile. Altogether 50 identified metabolites increased and 49 metabolites decreased when samples of early pregnancy were compared to samples of late pregnancy. The metabolic signatures of pregnancy were largely shared in pre-eclamptic and healthy women, only urea, monoacylglyceride 18:1 and glycerophosphocholine were identified to be increased in the pre-eclamptic women when compared to healthy controls.ConclusionsOur study highlights the need of large-scale longitudinal metabolomic studies in non-complicated pregnancies before more detailed understanding of metabolism in adverse outcomes could be provided. Our findings are one of the first steps for a broader metabolic understanding of the physiological changes caused by pregnancy per se.
  • Puurunen, Jenni; Sulkama, Sini; Tiira, Katriina; Araujo, Cesar; Lehtonen, Marko; Hanhineva, Kati; Lohi, Hannes (BioMed Central, 2016)
    Abstract Background Attention deficit hyperactivity disorder (ADHD) is a prevalent and multifactorial neuropsychiatric disorder in the human population worldwide. Complex etiology and clinical heterogeneity have challenged the research, diagnostics and treatment of the disease. Hyperactive and impulsive behaviour has also been observed in dogs, and they could offer a physiologically relevant model for human ADHD. As a part of our ongoing study to understand the molecular etiology of canine anxiety traits, this study was aimed to pilot an approach to identify metabolic biomarkers in canine ADHD-like behaviours for research, diagnostics and treatment purposes. Methods We collected fresh plasma samples from 22 German Shepherds with varying ADHD-like behaviours. All dogs were on the same controlled diet for 2 weeks prior to sampling. A liquid chromatography combined with mass spectrometry (LC–MS)-based non-targeted metabolite profiling was performed to identify plasma metabolites correlating with the ADHD-like behaviour of the dogs. Results 649 molecular features correlated with ADHD-like behavioural scores (praw < 0.05), and three of them [sn-1 LysoPC(18:3), PC(18:3/18:2) and sn-1 LysoPE(18:2)] had significant correlations also after FDR correction (pFDR < 0.05). Phospholipids were found to negatively correlate with ADHD-like behavioural scores, whereas tryptophan metabolites 3-indolepropionic acid (IPA) and kynurenic acid (KYNA) had negative and positive correlations with ADHD-like behavioural scores, respectively. Conclusions Our study identified associations between canine ADHD-like behaviours and metabolites that are involved in lipid and tryptophan metabolisms. The identified metabolites share similarity with earlier findings in human and rodent ADHD models. However, a larger replication study is warranted to validate the discoveries prior to further studies to understand the biological role of the identified metabolites in canine ADHD-like behaviours.
  • Puurunen, Jenni; Sulkama, Sini; Tiira, Katriina; Araujo, Cesar; Lehtonen, Marko; Hanhineva, Kati; Lohi, Hannes (2016)
    Background: Attention deficit hyperactivity disorder (ADHD) is a prevalent and multifactorial neuropsychiatric disorder in the human population worldwide. Complex etiology and clinical heterogeneity have challenged the research, diagnostics and treatment of the disease. Hyperactive and impulsive behaviour has also been observed in dogs, and they could offer a physiologically relevant model for human ADHD. As a part of our ongoing study to understand the molecular etiology of canine anxiety traits, this study was aimed to pilot an approach to identify metabolic biomarkers in canine ADHD-like behaviours for research, diagnostics and treatment purposes. Methods: We collected fresh plasma samples from 22 German Shepherds with varying ADHD-like behaviours. All dogs were on the same controlled diet for 2 weeks prior to sampling. A liquid chromatography combined with mass spectrometry (LC-MS)-based non-targeted metabolite profiling was performed to identify plasma metabolites correlating with the ADHD-like behaviour of the dogs. Results: 649 molecular features correlated with ADHD-like behavioural scores (p(raw) <0.05), and three of them [sn-1 LysoPC(18: 3), PC(18: 3/18: 2) and sn-1 LysoPE(18: 2)] had significant correlations also after FDR correction (pFDR <0.05). Phospholipids were found to negatively correlate with ADHD-like behavioural scores, whereas tryptophan metabolites 3-indolepropionic acid (IPA) and kynurenic acid (KYNA) had negative and positive correlations with ADHD-like behavioural scores, respectively. Conclusions: Our study identified associations between canine ADHD-like behaviours and metabolites that are involved in lipid and tryptophan metabolisms. The identified metabolites share similarity with earlier findings in human and rodent ADHD models. However, a larger replication study is warranted to validate the discoveries prior to further studies to understand the biological role of the identified metabolites in canine ADHD-like behaviours.
  • Esterhuizen, Karien; Lindeque, J. Zander; Mason, Shayne; van der Westhuizen, Francois H.; Suomalainen, Anu; Hakonen, Anna H.; Carroll, Christopher J.; Rodenburg, Richard J.; de Laat, Paul B.; Janssen, Mirian C. H.; Smeitink, Jan A. M.; Louw, Roan (2019)
    We used a comprehensive metabolomics approach to study the altered urinary metabolome of two mitochondrial myopathy, encephalopathy lactic acidosis and stroke like episodes (MELAS) cohorts carrying the m.3243A > G mutation. The first cohort were used in an exploratory phase, identifying 36 metabolites that were significantly perturbed by the disease. During the second phase, the 36 selected metabolites were able to separate a validation cohort of MELAS patients completely from their respective control group, suggesting usefulness of these 36 markers as a diagnostic set. Many of the 36 perturbed metabolites could be linked to an altered redox state, fatty acid catabolism and one-carbon metabolism. However, our evidence indicates that, of all the metabolic perturbations caused by MELAS, stalled fatty acid oxidation prevailed as being particularly disturbed. The strength of our study was the utilization of five different analytical platforms to generate the robust metabolomics data reported here. We show that urine may be a useful source for disease-specific metabolomics data, linking, amongst others, altered one-carbon metabolism to MELAS. The results reported here are important in our understanding of MELAS and might lead to better treatment options for the disease.
  • Lehikoinen, Anni I.; Kärkkäinen, Olli K.; Lehtonen, Marko A.S.; Auriola, Seppo O.K.; Hanhineva, Kati J.; Heinonen, Seppo T. (2018)
    Background: Although the effects of alcohol on metabolic processes in the body have been studied widely, there do not appear to be any previous reports clarifying how substance abuse changes metabolic profiles of pregnant women during the first trimester of pregnancy. Objective: Our aim was to evaluate the effect of substance abuse, especially alcohol use, on the metabolic profile of pregnant women during the first trimester. Study design: We applied mass spectrometry based non-targeted metabolite profiling of serum collected during routine visit to the hospital between gestational weeks 9 + 0 to 11 + 6 from controls (n = 55), alcohol users (n = 19), drug users (n = 24) and tobacco smokers (n = 40). Results: We observed statistically significantly differences among the study groups in serum levels of glutamate, glutamine, and serotonin (p-values Conclusion: The present study shows that alcohol and drug use were associated with increased glutamate, and decreased glutamine levels, and alcohol use is associated with decreased serotonin levels. This study serves as a proof-of-concept that the metabolite profile of human first trimester serum samples could be used to detect alcohol exposure during pregnancy. (C) 2018 Elsevier B.V. All rights reserved.
  • Tynkkynen, Juho; Chouraki, Vincent; van der Lee, Sven J.; Hernesniemi, Jussi; Yang, Qiong; Li, Shuo; Beiser, Alexa; Larson, Martin G.; Sääksjärvi, Katri; Shipley, Martin J.; Singh-Manoux, Archana; Gerszten, Robert E.; Wang, Thomas J.; Havulinna, Aki S.; Würtz, Peter; Fischer, Krista; Demirkan, Ayse; Ikram, M. Arfan; Amin, Najaf; Lehtimäki, Terho; Kähönen, Mika; Perola, Markus; Metspalu, Andres; Kangas, Antti J.; Soininen, Pasi; Ala-Korpela, Mika; Vasan, Ramachandran S.; Kivimäki, Mika; van Duijn, Cornelia M.; Seshadri, Sudha; Salomaa, Veikko (2018)
    Introduction: Metabolite, lipid, and lipoprotein lipid profiling can provide novel insights into mechanisms underlying incident dementia and Alzheimer's disease. Methods: We studied eight prospective cohorts with 22,623 participants profiled by nuclear magnetic resonance or mass spectrometry metabolomics. Four cohorts were used for discovery with replication undertaken in the other four to avoid false positives. For metabolites that survived replication, combined association results are presented. Results: Over 246,698 person-years, 995 and 745 cases of incident dementia and Alzheimer's disease were detected, respectively. Three branched-chain amino acids (isoleucine, leucine, and valine), creatinine and two very low density lipoprotein (VLDL)-specific lipoprotein lipid subclasses were associated with lower dementia risk. One high density lipoprotein (HDL; the concentration of cholesterol esters relative to total lipids in large HDL) and one VLDL (total cholesterol to total lipids ratio in very large VLDL) lipoprotein lipid subclass was associated with increased dementia risk. Branched-chain amino acids were also associated with decreased Alzheimer's disease risk and the concentration of cholesterol esters relative to total lipids in large HDL with increased Alzheimer's disease risk. Discussion: Further studies can clarify whether these molecules play a causal role in dementia pathogenesis or are merely markers of early pathology. (C) 2018 The Authors. Published by Elsevier Inc. on behalf of the Alzheimer's Association.
  • Mikkola, Tuija M; Salonen, Minna K; Kajantie, Eero; Kautiainen, Hannu; Eriksson, Johan G (2020)
    Circulating amino acids are potential markers of body composition. Previous studies are mainly limited to middle age and focus on either fat or lean mass, thereby ignoring overall body composition. We investigated the associations of fat and lean body mass with circulating amino acids in older men and women. We studied 594 women and 476 men from the Helsinki Birth Cohort Study (age 62–74 years). Bioelectrical impedance analysis was used to indicate two main body compartments by fat (fat mass/height2) and lean mass indices (lean mass/height2), dichotomized based on sex-specific medians. Eight serum amino acids were quantified using nuclear magnetic resonance spectroscopy. General linear models were adjusted for age, smoking, and fasting glucose. Higher lean mass index (LMI) was associated with higher concentrations of branched-chain amino acids in both sexes (p ≤ .001). In men, LMI was also positively associated with tyrosine (p = .006) and inversely with glycine (p < .001). Higher fat mass index was associated with higher concentrations of all branched-chain amino acids, aromatic amino acids (phenylalanine and tyrosine), and alanine in both sexes (p ≤ .008). Associations between body composition and amino acids are largely similar in older men and women. The associations are largely similar to those previously observed in younger adults.
  • Finndiane Study Grp; SDRN Type 1 Bioresource Collabora; Colombo, Marco; Valo, Erkka; Sandholm, Niina; Groop, Per-Henrik; Forsblom, Carol; Colhoun, Helen M. (2019)
    Aims/hypothesis We aimed to identify a sparse panel of biomarkers for improving the prediction of renal disease progression in type 1 diabetes. Methods We considered 859 individuals recruited from the Scottish Diabetes Research Network Type 1 Bioresource (SDRNT1BIO) and 315 individuals from the Finnish Diabetic Nephropathy (FinnDiane) study. All had an entry eGFR between 30 and 75 ml min(-1)[1.73 m](-2), with those from FinnDiane being oversampled for albuminuria. A total of 297 circulating biomarkers (30 proteins, 121 metabolites, 146 tryptic peptides) were measured in non-fasting serum samples using the Luminex platform and LC electrospray tandem MS (LC-MS/MS). We investigated associations with final eGFR adjusted for baseline eGFR and with rapid progression (a loss of more than 3 ml min(-1)[1.73 m](-2) year(-1)) using linear and logistic regression models. Panels of biomarkers were identified using a penalised Bayesian approach, and their performance was evaluated through 10-fold cross-validation and compared with using clinical record data alone. Results For final eGFR, 16 proteins and 30 metabolites or tryptic peptides showed significant association in SDRNT1BIO, and nine proteins and five metabolites or tryptic peptides in FinnDiane, beyond age, sex, diabetes duration, study day eGFR and length of follow-up (all at p <10(-4)). The strongest associations were with CD27 antigen (CD27), kidney injury molecule 1 (KIM-1) and alpha 1-microglobulin. Including the Luminex biomarkers on top of baseline covariates increased the r(2) for prediction of final eGFR from 0.47 to 0.58 in SDRNT1BIO and from 0.33 to 0.48 in FinnDiane. At least 75% of the increment in r(2) was attributable to CD27 and KIM-1. However, using the weighted average of historical eGFR gave similar performance to biomarkers. The LC-MS/MS platform performed less well. Conclusions/interpretation Among a large set of associated biomarkers, a sparse panel of just CD27 and KIM-1 contains most of the predictive information for eGFR progression. The increment in prediction beyond clinical data was modest but potentially useful for oversampling individuals with rapid disease progression into clinical trials, especially where there is little information on prior eGFR trajectories.
  • Forsgard, Richard A.; Marrachelli, Vannina G.; Korpela, Katri; Frias, Rafael; Carmen Collado, Maria; Korpela, Riitta; Monleon, Daniel; Spillmann, Thomas; Osterlund, Pia (2017)
    Purpose Chemotherapy-induced gastrointestinal toxicity (CIGT) is a complex process that involves multiple pathophysiological mechanisms. We have previously shown that commonly used chemotherapeutics 5-fluorouracil, oxaliplatin, and irinotecan damage the intestinal mucosa and increase intestinal permeability to iohexol. We hypothesized that CIGT is associated with alterations in fecal microbiota and metabolome. Our aim was to characterize these changes and examine how they relate to the severity of CIGT. Methods A total of 48 male Sprague-Dawley rats were injected intraperitoneally either with 5-fluorouracil (150 mg/kg), oxaliplatin (15 mg/kg), or irinotecan (200 mg/kg). Body weight change was measured daily after drug administration and the animals were euthanized after 72 h. Blood, urine, and fecal samples were collected at baseline and at the end of the experiment. The changes in the composition of fecal microbiota were analyzed with 16S rRNA gene sequencing. Metabolic changes in serum and urine metabolome were measured with 1 mm proton nuclear magnetic resonance (1H-NMR). Results Irinotecan increased the relative abundance of Fusobacteria and Proteobacteria, while 5-FU and oxaliplatin caused only minor changes in the composition of fecal microbiota. All chemotherapeutics increased the levels of serum fatty acids and N(CH3)(3) moieties and decreased the levels of Krebs cycle metabolites and free amino acids. Conclusions Chemotherapeutic drugs, 5-fluorouracil, oxaliplatin, and irinotecan, induce several microbial and metabolic changes which may play a role in the pathophysiology of CIGT. The observed changes in intestinal permeability, fecal microbiota, and metabolome suggest the activation of inflammatory processes.
  • Welsh, Paul; Rankin, Naomi; Li, Qiang; Mark, Patrick B.; Würtz, Peter; Ala-Korpela, Mika; Marre, Michel; Poulter, Neil; Hamet, Pavel; Chalmers, John; Woodward, Mark; Sattar, Naveed (2018)
    Aims/hypotheses We aimed to quantify the association of individual circulating amino acids with macrovascular disease, microvascular disease and all-cause mortality in individuals with type 2 diabetes. Methods We performed a case-cohort study (N = 3587), including 655 macrovascular events, 342 microvascular events (new or worsening nephropathy or retinopathy) and 632 all-cause mortality events during follow-up, in a secondary analysis of the Action in Diabetes and Vascular Disease: Preterax and Diamicron Modified Release Controlled Evaluation (ADVANCE) study. For this study, phenylalanine, isoleucine, glutamine, leucine, alanine, tyrosine, histidine and valine were measured in stored plasma samples by proton NMR metabolomics. Hazard ratios were modelled per SD increase in each amino acid. Results In models investigating associations and potential mechanisms, after adjusting for age, sex and randomised treatment, phenylalanine was positively, and histidine inversely, associated with macrovascular disease risk. These associations were attenuated to the null on further adjustment for extended classical risk factors (including eGFR and urinary albumin/creatinine ratio). After adjustment for extended classical risk factors, higher tyrosine and alanine levels were associated with decreased risk of microvascular disease (HR 0.78; 95% CI 0.67, 0.91 and HR 0.86; 95% CI 0.76, 0.98, respectively). Higher leucine (HR 0.79; 95% CI 0.69, 0.90), histidine (HR 0.89; 95% CI 0.81, 0.99) and valine (HR 0.79; 95% CI 0.70, 0.88) levels were associated with lower risk of mortality. Investigating the predictive ability of amino acids, addition of all amino acids to a risk score modestly improved classification of participants for macrovascular (continuous net reclassification index [NRI] +35.5%, p <0.001) and microvascular events (continuous NRI +14.4%, p = 0.012). Conclusions/interpretation We report distinct associations between circulating amino acids and risk of different major complications of diabetes. Low tyrosine appears to be a marker of microvascular risk in individuals with type 2 diabetes independently of fundamental markers of kidney function.
  • van der Lee, Sven J.; Teunissen, Charlotte E.; Pool, Rene; Shipley, Martin J.; Teumer, Alexander; Chouraki, Vincent; van Lent, Debora Melo; Tynkkynen, Juho; Fischer, Krista; Hernesniemi, Jussi; Haller, Toomas; Singh-Manoux, Archana; Verhoeven, Aswin; Willemsen, Gonneke; de Leeuw, Francisca A.; Wagner, Holger; van Dongen, Jenny; Hertel, Johannes; Budde, Kathrin; van Dijk, Ko Willems; Weinhold, Leonie; Ikram, M. Arfan; Pietzner, Maik; Perola, Markus; Wagner, Michael; Friedrich, Nele; Slagboom, P. Eline; Scheltens, Philip; Yang, Qiong; Gertzen, Robert E.; Egert, Sarah; Li, Shuo; Hankemeier, Thomas; van Beijsterveldt, Catharina E. M.; Vasan, Ramachandran S.; Maier, Wolfgang; Peeters, Carel F. W.; Grabe, Hans Joergen; Ramirez, Alfredo; Seshadri, Sudha; Metspalu, Andres; Kivimäki, Mika; Salomaa, Veikko; Demirkan, Ayse; Boomsma, Dorret I.; van der Flier, Wiesje M.; Amin, Najaf; van Duijn, Cornelia M. (2018)
    Introduction: Identifying circulating metabolites that are associated with cognition and dementia may improve our understanding of the pathogenesis of dementia and provide crucial readouts for preventive and therapeutic interventions. Methods: We studied 299 metabolites in relation to cognition (general cognitive ability) in two discovery cohorts (N total = 5658). Metabolites significantly associated with cognition after adjusting for multiple testing were replicated in four independent cohorts (N total = 6652), and the associations with dementia and Alzheimer's disease (N = 25,872) and lifestyle factors (N = 5168) were examined. Results: We discovered and replicated 15 metabolites associated with cognition including subfractions of high-density lipoprotein, docosahexaenoic acid, ornithine, glutamine, and glycoprotein acetyls. These associations were independent of classical risk factors including high-density lipoprotein cholesterol, low-density lipoprotein cholesterol, triglycerides, glucose, and apolipoprotein E (APOE) genotypes. Six of the cognition-associated metabolites were related to the risk of dementia and lifestyle factors. Discussion: Circulating metabolites were consistently associated with cognition, dementia, and lifestyle factors, opening new avenues for prevention of cognitive decline and dementia. (C) 2018 The Authors. Published by Elsevier Inc. on behalf of the Alzheimer's Association.
  • Ahola-Olli, Ari V.; Mustelin, Linda; Kalimeri, Maria; Kettunen, Johannes; Jokelainen, Jari; Auvinen, Juha; Puukka, Katri; Havulinna, Aki S.; Lehtimäki, Terho; Kähönen, Mika; Juonala, Markus; Keinänen-Kiukaanniemi, Sirkka; Salomaa, Veikko; Perola, Markus; Järvelin, Marjo-Riitta; Ala-Korpela, Mika; Raitakari, Olli; Wurtz, Peter (2019)
    Aims/hypothesis Metabolomics technologies have identified numerous blood biomarkers for type 2 diabetes risk in case-control studies of middle-aged and older individuals. We aimed to validate existing and identify novel metabolic biomarkers predictive of future diabetes in large cohorts of young adults. Methods NMR metabolomics was used to quantify 229 circulating metabolic measures in 11,896 individuals from four Finnish observational cohorts (baseline age 24-45 years). Associations between baseline metabolites and risk of developing diabetes during 8-15 years of follow-up (392 incident cases) were adjusted for sex, age, BMI and fasting glucose. Prospective metabolite associations were also tested with fasting glucose, 2 h glucose and HOMA-IR at follow-up. Results Out of 229 metabolic measures, 113 were associated with incident type 2 diabetes in meta-analysis of the four cohorts (ORs per 1 SD: 0.59-1.50; p Conclusions/interpretation Metabolic biomarkers across multiple molecular pathways are already predictive of the long-term risk of diabetes in young adults. Comprehensive metabolic profiling may help to target preventive interventions for young asymptomatic individuals at increased risk.
  • Lamichhane, Santosh; Kemppainen, Esko; Trost, Kajetan; Siljander, Heli; Hyöty, Heikki; Ilonen, Jorma; Toppari, Jorma; Veijola, Riitta; Hyötyläinen, Tuulia; Knip, Mikael; Oresic, Matej (2019)
    Aims/hypothesis Metabolic dysregulation may precede the onset of type 1 diabetes. However, these metabolic disturbances and their specific role in disease initiation remain poorly understood. In this study, we examined whether children who progress to type 1 diabetes have a circulatory polar metabolite profile distinct from that of children who later progress to islet autoimmunity but not type 1 diabetes and a matched control group. Methods We analysed polar metabolites from 415 longitudinal plasma samples in a prospective cohort of children in three study groups: those who progressed to type 1 diabetes; those who seroconverted to one islet autoantibody but not to type 1 diabetes; and an antibody-negative control group. Metabolites were measured using two-dimensional GC high-speed time of flight MS. Results In early infancy, progression to type 1 diabetes was associated with downregulated amino acids, sugar derivatives and fatty acids, including catabolites of microbial origin, compared with the control group. Methionine remained persistently upregulated in those progressing to type 1 diabetes compared with the control group and those who seroconverted to one islet autoantibody. The appearance of islet autoantibodies was associated with decreased glutamic and aspartic acids. Conclusions/interpretation Our findings suggest that children who progress to type 1 diabetes have a unique metabolic profile, which is, however, altered with the appearance of islet autoantibodies. Our findings may assist with early prediction of the disease.
  • Pöhö, Päivi; Lipponen, Katriina; Bespalov, Maxim M.; Sikanen, Tiina; Kotiaho, Tapio; Kostiainen, Risto (2019)
    In this study, the feasibility of direct infusion electrospray ionization microchip mass spectrometry (chip-MS) was compared to the commonly used liquid chromatography-mass spectrometry (LC-MS) in non-targeted metabolomics analysis of human foreskin fibroblasts (HFF) and human induced pluripotent stem cells (hiPSC) reprogrammed from HFF. The total number of the detected features with chip-MS and LC-MS were 619 and 1959, respectively. Approximately 25% of detected features showed statistically significant changes between the cell lines with both analytical methods. The results show that chip-MS is a rapid and simple method that allows high sample throughput from small sample volumes and can detect the main metabolites and classify cells based on their metabolic profiles. However, the selectivity of chip-MS is limited compared to LC-MS and chip-MS may suffer from ion suppression.
  • Peddinti, Gopal; Cobb, Jeff; Yengo, Loic; Froguel, Philippe; Kravic, Jasmina; Balkau, Beverley; Tuomi, Tiinamaija; Aittokallio, Tero; Groop, Leif (2017)
    Aims/hypothesis The aims of this study were to evaluate systematically the predictive power of comprehensive metabolomics profiles in predicting the future risk of type 2 diabetes, and to identify a panel of the most predictive metabolic markers. Methods We applied an unbiased systems medicine approach to mine metabolite combinations that provide added value in predicting the future incidence of type 2 diabetes beyond known risk factors. We performed mass spectrometry-based targeted, as well as global untargeted, metabolomics, measuring a total of 568 metabolites, in a Finnish cohort of 543 nondiabetic individuals from the Botnia Prospective Study, which included 146 individuals who progressed to type 2 diabetes by the end of a 10 year follow-up period. Multivariate logistic regression was used to assess statistical associations, and regularised least-squares modelling was used to perform machine learning-based risk classification and marker selection. The predictive performance of the machine learning models and marker panels was evaluated using repeated nested cross-validation, and replicated in an independent French cohort of 1044 individuals including 231 participants who progressed to type 2 diabetes during a 9 year follow-up period in the DESIR (Data from an Epidemiological Study on the Insulin Resistance Syndrome) study. Results Nine metabolites were negatively associated (potentially protective) and 25 were positively associated with progression to type 2 diabetes. Machine learning models based on the entire metabolome predicted progression to type 2 diabetes (area under the receiver operating characteristic curve, AUC = 0.77) significantly better than the reference model based on clinical risk factors alone (AUC = 0.68; DeLong's p = 0.0009). The panel of metabolic markers selected by the machine learning-based feature selection also significantly improved the predictive performance over the reference model (AUC = 0.78; p = 0.00019; integrated discrimination improvement, IDI = 66.7%). This approach identified novel predictive biomarkers, such as alpha-tocopherol, bradykinin hydroxyproline, X-12063 and X-13435, which showed added value in predicting progression to type 2 diabetes when combined with known biomarkers such as glucose, mannose and alpha-hydroxybutyrate and routinely used clinical risk factors. Conclusions/interpretation This study provides a panel of novel metabolic markers for future efforts aimed at the prevention of type 2 diabetes.
  • Hyotylainen, Tuulia; Ahonen, Linda; Pöhö, Paivi; Oresic, Matej (2017)
    Lipids have many central physiological roles including as structural components of cell membranes, energy storage sources and intermediates in signaling pathways. Lipid-related disturbances are known to underlie many diseases and their co-morbidities. The emergence of lipidomics has empowered researchers to study lipid metabolism at the cellular as well as physiological levels at a greater depth than was previously possible. The key challenges ahead in the field of lipidomics in medical research lie in the development of experimental protocols and in silico techniques needed to study lipidomes at the systems level. Clinical questions where lipidomics may have an impact in healthcare settings also need to be identified, both from the health outcomes and health economics perspectives. This article is part of a Special Issue entitled: BBALIP_Lipidomics Opinion Articles edited by Sepp Kohlwein.
  • Sen, Partho; Dickens, Alex M.; Lopez-Bascon, Maria Asuncion; Lindeman, Tuomas; Kemppainen, Esko; Lamichhane, Santosh; Rönkkö, Tuukka; Ilonen, Jorma; Toppari, Jorma; Veijola, Riitta; Hyöty, Heikki; Hyötyläinen, Tuulia; Knip, Mikael; Oresic, Matej (2020)
    Aims/hypothesis Previous metabolomics studies suggest that type 1 diabetes is preceded by specific metabolic disturbances. The aim of this study was to investigate whether distinct metabolic patterns occur in peripheral blood mononuclear cells (PBMCs) of children who later develop pancreatic beta cell autoimmunity or overt type 1 diabetes. Methods In a longitudinal cohort setting, PBMC metabolomic analysis was applied in children who (1) progressed to type 1 diabetes (PT1D, n = 34), (2) seroconverted to >= 1 islet autoantibody without progressing to type 1 diabetes (P1Ab, n = 27) or (3) remained autoantibody negative during follow-up (CTRL, n = 10). Results During the first year of life, levels of most lipids and polar metabolites were lower in the PT1D and P1Ab groups compared with the CTRL group. Pathway over-representation analysis suggested alanine, aspartate, glutamate, glycerophospholipid and sphingolipid metabolism were over-represented in PT1D. Genome-scale metabolic models of PBMCs during type 1 diabetes progression were developed by using publicly available transcriptomics data and constrained with metabolomics data from our study. Metabolic modelling confirmed altered ceramide pathways, known to play an important role in immune regulation, as specifically associated with type 1 diabetes progression. Conclusions/interpretation Our data suggest that systemic dysregulation of lipid metabolism, as observed in plasma, may impact the metabolism and function of immune cells during progression to overt type 1 diabetes. Data availability The GEMs for PBMCs have been submitted to BioModels (), under accession number MODEL1905270001. The metabolomics datasets and the clinical metadata generated in this study were submitted to MetaboLights (), under accession number MTBLS1015.
  • Wang, Qin; Wurtz, Peter; Auro, Kirsi; Makinen, Ville-Petteri; Kangas, Antti J.; Soininen, Pasi; Tiainen, Mika; Tynkkynen, Tuulia; Jokelainen, Jari; Santalahti, Kristiina; Salmi, Marko; Blankenberg, Stefan; Zeller, Tanja; Viikari, Jorma; Kahonen, Mika; Lehtimaki, Terho; Salomaa, Veikko; Perola, Markus; Jalkanen, Sirpa; Jarvelin, Marjo-Riitta; Raitakari, Olli T.; Kettunen, Johannes; Lawlor, Debbie A.; Ala-Korpela, Mika (2016)
    Background: Pregnancy triggers well-known alterations in maternal glucose and lipid balance but its overall effects on systemic metabolism remain incompletely understood. Methods: Detailed molecular profiles (87 metabolic measures and 37 cytokines) were measured for up to 4260 women (24-49 years, 322 pregnant) from three population-based cohorts in Finland. Circulating molecular concentrations in pregnant women were compared to those in non-pregnant women. Metabolic profiles were also reassessed for 583 women 6 years later to uncover the longitudinal metabolic changes in response to change in the pregnancy status. Results: Compared to non-pregnant women, all lipoprotein subclasses and lipids were markedly increased in pregnant women. The most pronounced differences were observed for the intermediate-density, low-density and high-density lipoprotein triglyceride concentrations. Large differences were also seen for many fatty acids and amino acids. Pregnant women also had higher concentrations of low-grade inflammatory marker glycoprotein acetyls, higher concentrations of interleukin-18 and lower concentrations of interleukin-12p70. The changes in metabolic concentrations for women who were not pregnant at baseline but pregnant 6 years later (or vice versa) matched (or were mirror-images of) the cross-sectional association pattern. Cross-sectional results were consistent across the three cohorts and similar longitudinal changes were seen for 653 women in 4-year and 497 women in 10-year follow-up. For multiple metabolic measures, the changes increased in magnitude across the three trimesters. Conclusions: Pregnancy initiates substantial metabolic and inflammatory changes in the mothers. Comprehensive characterisation of normal pregnancy is important for gaining understanding of the key nutrients for fetal growth and development. These findings also provide a valuable molecular reference in relation to studies of adverse pregnancy outcomes.