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Article ; Online: The association between water intake and future cardiometabolic disease outcomes in the Malmö Diet and Cancer cardiovascular cohort.

Carroll, Harriet A / Ericson, Ulrika / Ottosson, Filip / Enhörning, Sofia / Melander, Olle

2024 Volume 19, Issue 1, Page(s) e0296778

Abstract: The aim of this study was to explore the longitudinal association between reported baseline water intake and incidence of coronary artery disease (CAD) and type 2 diabetes in the Malmö Diet and Cancer Cohort (n = 25,369). Using cox proportional hazards ... ...

Abstract	The aim of this study was to explore the longitudinal association between reported baseline water intake and incidence of coronary artery disease (CAD) and type 2 diabetes in the Malmö Diet and Cancer Cohort (n = 25,369). Using cox proportional hazards models, we separately modelled the effect of plain and total (all water, including from food) water on CAD and type 2 diabetes risk, whilst adjusting for age, sex, diet collection method, season, smoking status, alcohol intake, physical activity, education level, energy intake, energy misreporting, body mass index, hypertension, lipid lowering medication, apolipoprotein A, apolipoprotein B, and dietary variables. Sensitivity analyses were run to assess validity. After adjustment, no association was found between tertiles of plain or total water intake and type 2 diabetes risk. For CAD, no association was found comparing moderate to low intake tertiles from plain or total water, however, risk of CAD increased by 12% (95% CI 1.03, 1.21) when comparing high to low intake tertiles of plain water, and by 17% (95% CI 1.07, 1.27) for high versus low tertiles of total water. Sensitivity analyses were largely in agreement. Overall, baseline water intake was not associated with future type 2 diabetes risk, whilst CAD risk was higher with higher water intakes. Our findings are discordant with prevailing literature suggesting higher water intakes should reduce cardiometabolic risk. These findings may be an artefact of limitations within the study, but future research is needed to understand if there is a causal underpinning.
MeSH term(s)	Humans ; Diabetes Mellitus, Type 2/epidemiology ; Drinking ; Prospective Studies ; Diet ; Risk Factors ; Coronary Artery Disease/epidemiology ; Coronary Artery Disease/etiology ; Neoplasms/epidemiology ; Neoplasms/etiology ; Water ; Apolipoproteins
Chemical Substances	Water (059QF0KO0R) ; Apolipoproteins
Language	English
Publishing date	2024-01-19
Publishing country	United States
Document type	Journal Article
ZDB-ID	2267670-3
ISSN	1932-6203 ; 1932-6203
ISSN (online)	1932-6203
ISSN	1932-6203
DOI	10.1371/journal.pone.0296778
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Plasma Metabolome Predicts Aortic Stiffness and Future Risk of Coronary Artery Disease and Mortality After 23 Years of Follow-Up in the General Population.

Ottosson, Filip / Engström, Gunnar / Orho-Melander, Marju / Melander, Olle / Nilsson, Peter M / Johansson, Madeleine

Journal of the American Heart Association

2024 Volume 13, Issue 9, Page(s) e033442

Abstract: Background: Increased aortic stiffness (arteriosclerosis) is associated with early vascular aging independent of age and sex. The underlying mechanisms of early vascular aging remain largely unexplored in the general population. We aimed to investigate ... ...

Abstract	Background: Increased aortic stiffness (arteriosclerosis) is associated with early vascular aging independent of age and sex. The underlying mechanisms of early vascular aging remain largely unexplored in the general population. We aimed to investigate the plasma metabolomic profile in aortic stiffness (vascular aging) and associated risk of incident cardiovascular disease and mortality. Methods and results: We included 6865 individuals from 2 Swedish population-based cohorts. Untargeted plasma metabolomics was performed by liquid-chromatography mass spectrometry. Aortic stiffness was assessed directly by carotid-femoral pulse wave velocity (PWV) and indirectly by augmentation index (AIx@75). A least absolute shrinkage and selection operator (LASSO) regression model was created on plasma metabolites in order to predict aortic stiffness. Associations between metabolite-predicted aortic stiffness and risk of new-onset cardiovascular disease, cardiovascular mortality, and all-cause mortality were calculated. Metabolite-predicted aortic stiffness (PWV and AIx@75) was positively associated particularly with acylcarnitines, dimethylguanidino valeric acid, glutamate, and cystine. The plasma metabolome predicted aortic stiffness (PWV and AIx@75) with good accuracy (R Conclusions: Aortic stiffness is associated particularly with altered metabolism of acylcarnitines, cystine, and dimethylguanidino valeric acid. These metabolic disturbances predict increased risk of new-onset coronary artery disease, cardiovascular mortality, and all-cause mortality after more than 23 years of follow-up in the general population.
MeSH term(s)	Humans ; Vascular Stiffness ; Male ; Female ; Sweden/epidemiology ; Middle Aged ; Coronary Artery Disease/blood ; Coronary Artery Disease/mortality ; Coronary Artery Disease/physiopathology ; Metabolome ; Aged ; Follow-Up Studies ; Metabolomics/methods ; Risk Assessment/methods ; Biomarkers/blood ; Risk Factors ; Carotid-Femoral Pulse Wave Velocity ; Adult ; Time Factors ; Incidence ; Pulse Wave Analysis ; Carnitine/analogs & derivatives
Chemical Substances	Biomarkers ; acylcarnitine ; Carnitine (S7UI8SM58A)
Language	English
Publishing date	2024-04-19
Publishing country	England
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	2653953-6
ISSN	2047-9980 ; 2047-9980
ISSN (online)	2047-9980
ISSN	2047-9980
DOI	10.1161/JAHA.123.033442
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Article ; Online: The association between plasma metabolites and future risk of all-cause mortality.

Yan, Yingxiao / Smith, Einar / Melander, Olle / Ottosson, Filip

Journal of internal medicine

2022 Volume 292, Issue 5, Page(s) 804–815

Abstract: Background: Metabolite profiles provide snapshots of the overall effect of numerous exposures accumulated over life courses, which may lead to health outcomes in the future.: Objective: We hypothesized that the risk of all-cause mortality is linked ... ...

Abstract	Background: Metabolite profiles provide snapshots of the overall effect of numerous exposures accumulated over life courses, which may lead to health outcomes in the future. Objective: We hypothesized that the risk of all-cause mortality is linked to alterations in metabolism earlier in life, which are reflected in plasma metabolite profiles. We aimed to identify plasma metabolites associated with future risk of all-cause mortality. Methods: Through metabolomics, 110 metabolites were measured in 3833 individuals from the Malmö Diet and Cancer-Cardiovascular Cohort (MDC-CC). A total of 1574 deaths occurred within an average follow-up time of 22.2 years. Metabolites that were significantly associated with all-cause mortality in MDC-CC were replicated in 1500 individuals from Malmö Preventive Project re-examination (MPP), among whom 715 deaths occurred within an average follow-up time of 11.3 years. Results: Twenty two metabolites were significantly associated with all-cause mortality in MDC-CC, of which 13 were replicated in MPP. Levels of trigonelline, glutamate, dimethylglycine, C18-1-carnitine, C16-1-carnitine, C14-1-carnitine, and 1-methyladenosine were associated with an increased risk, while levels of valine, tryptophan, lysine, leucine, histidine, and 2-aminoisobutyrate were associated with a decreased risk of all-cause mortality. Conclusion: We used metabolomics in two Swedish prospective cohorts and identified replicable associations between 13 metabolites and future risk of all-cause mortality. Novel associations between five metabolites-C18-1-carnitine, C16-1-carnitine, C14-1-carnitine, trigonelline, and 2-aminoisobutyrate-and all-cause mortality were discovered. These findings suggest potential new biomarkers for the prediction of mortality and provide insights for understanding the biochemical pathways that lead to mortality.
MeSH term(s)	Biomarkers ; Carnitine/metabolism ; Glutamates ; Histidine ; Humans ; Leucine ; Lysine ; Metabolomics ; Prospective Studies ; Tryptophan ; Valine
Chemical Substances	Biomarkers ; Glutamates ; Histidine (4QD397987E) ; Tryptophan (8DUH1N11BX) ; Leucine (GMW67QNF9C) ; Valine (HG18B9YRS7) ; Lysine (K3Z4F929H6) ; Carnitine (S7UI8SM58A)
Language	English
Publishing date	2022-07-26
Publishing country	England
Document type	Journal Article
ZDB-ID	96274-0
ISSN	1365-2796 ; 0954-6820
ISSN (online)	1365-2796
ISSN	0954-6820
DOI	10.1111/joim.13540
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Article ; Online: Deep Learning Models for LC-MS Untargeted Metabolomics Data Analysis

Russo, Francesco / Ottosson, Filip / van der Hooft, Justin J.J. / Ernst, Madeleine

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) ; ISBN: 9783031552472

2024

Abstract: Metabolomics, the measurement of all metabolites in a given system, is a growing research field with great potential and manifold applications in precision medicine. However, the high dimensionality and complexity of metabolomics data requires expert ... ...

Abstract	Metabolomics, the measurement of all metabolites in a given system, is a growing research field with great potential and manifold applications in precision medicine. However, the high dimensionality and complexity of metabolomics data requires expert knowledge, the use of proper methodology, and is largely based on manual interpretation. In this book chapter, we discuss recent published approaches using deep learning to analyze untargeted metabolomics data. These approaches were applied within diverse stages of metabolomics data analysis, e.g. to improve preprocessing, feature identification, classification, and other tasks. We focus our attention on deep learning methods applied to liquid chromatography mass spectrometry (LC-MS), but these models can be extended or adjusted to other applications. We highlight current deep learning-based computational workflows that are paving the way toward high(er)-throughput use of untargeted metabolomics, making it effective for clinical, environmental and other types of applications.
Keywords	Deep learning ; Mac ; Metabolomics
Subject code	006
Language	English
Publisher	Springer
Publishing country	nl
Document type	Article ; Online
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Article: Plasma Metabolites Associate with All-Cause Mortality in Individuals with Type 2 Diabetes.

Ottosson, Filip / Smith, Einar / Fernandez, Céline / Melander, Olle

Metabolites

2020 Volume 10, Issue 8

Abstract: Alterations in the human metabolome occur years before clinical manifestation of type 2 diabetes (T2DM). By contrast, there is little knowledge of how metabolite alterations in individuals with diabetes relate to risk of diabetes complications and ... ...

Abstract	Alterations in the human metabolome occur years before clinical manifestation of type 2 diabetes (T2DM). By contrast, there is little knowledge of how metabolite alterations in individuals with diabetes relate to risk of diabetes complications and premature mortality. Metabolite profiling was performed using liquid chromatography-mass spectrometry in 743 participants with T2DM from the population-based prospective cohorts The Malmö Diet and Cancer-Cardiovascular Cohort (MDC-CC) and The Malmö Preventive Project (MPP). During follow-up, a total of 175 new-onset cases of cardiovascular disease (CVD) and 298 deaths occurred. Cox regressions were used to relate baseline levels of plasma metabolites to incident CVD and all-cause mortality. A total of 11 metabolites were significantly (false discovery rate (fdr) <0.05) associated with all-cause mortality. Acisoga, acylcarnitine C10:3, dimethylguanidino valerate, homocitrulline, N2,N2-dimethylguanosine, 1-methyladenosine and urobilin were associated with an increased risk, while hippurate, lysine, threonine and tryptophan were associated with a decreased risk. Ten out of 11 metabolites remained significantly associated after adjustments for cardiometabolic risk factors. The associations between metabolite levels and incident CVD were not as strong as for all-cause mortality, although 11 metabolites were nominally significant (
Language	English
Publishing date	2020-07-31
Publishing country	Switzerland
Document type	Journal Article
ZDB-ID	2662251-8
ISSN	2218-1989
ISSN	2218-1989
DOI	10.3390/metabo10080315
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Article ; Online: Altered Acylcarnitine Metabolism Is Associated With an Increased Risk of Atrial Fibrillation.

Smith, Einar / Fernandez, Celine / Melander, Olle / Ottosson, Filip

Journal of the American Heart Association

2020 Volume 9, Issue 21, Page(s) e016737

Abstract: Background Atrial fibrillation (AF) is the most common cardiac arrhythmia, but the pathogenesis is not completely understood. The application of metabolomics could help in discovering new metabolic pathways involved in the development of the disease. ... ...

Abstract	Background Atrial fibrillation (AF) is the most common cardiac arrhythmia, but the pathogenesis is not completely understood. The application of metabolomics could help in discovering new metabolic pathways involved in the development of the disease. Methods and Results We measured 112 baseline fasting metabolites of 3770 participants in the Malmö Diet and Cancer Study; these participants were free of prevalent AF. Incident cases of AF were ascertained through previously validated registers. The associations between baseline levels of metabolites and incident AF were investigated using Cox proportional hazard models. During 23.1 years of follow-up, 650 cases of AF were identified (incidence rate: 8.6 per 1000 person-years). In Cox regression models adjusted for AF risk factors, 7 medium- and long-chain acylcarnitines were associated with higher risk of incident AF (hazard ratio [HR] ranging from 1.09; 95% CI, 1.00-1.18 to 1.14, 95% CI, 1.05-1.24 per 1 SD increment of acylcarnitines). Furthermore, caffeine and acisoga were also associated with an increased risk (HR, 1.17; 95% CI, 1.06-1.28 and 1.08; 95% CI, 1.00-1.18, respectively), while beta carotene was associated with a lower risk (HR, 0.90; 95% CI, 0.82-0.99). Conclusions For the first time, we show associations between altered acylcarnitine metabolism and incident AF independent of traditional AF risk factors in a general population. These findings highlight metabolic alterations that precede AF diagnosis by many years and could provide insight into the pathogenesis of AF. Future studies are needed to replicate our finding in an external cohort as well as to test whether the relationship between acylcarnitines and AF is causal.
MeSH term(s)	Aged ; Atrial Fibrillation/diagnosis ; Atrial Fibrillation/epidemiology ; Atrial Fibrillation/metabolism ; Carnitine/analogs & derivatives ; Carnitine/metabolism ; Cohort Studies ; Female ; Humans ; Incidence ; Male ; Metabolomics ; Middle Aged ; Proportional Hazards Models ; Risk Factors
Chemical Substances	acylcarnitine ; Carnitine (S7UI8SM58A)
Language	English
Publishing date	2020-10-20
Publishing country	England
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	2653953-6
ISSN	2047-9980 ; 2047-9980
ISSN (online)	2047-9980
ISSN	2047-9980
DOI	10.1161/JAHA.120.016737
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Article ; Online: Lipidomic risk scores are independent of polygenic risk scores and can predict incidence of diabetes and cardiovascular disease in a large population cohort.

Lauber, Chris / Gerl, Mathias J / Klose, Christian / Ottosson, Filip / Melander, Olle / Simons, Kai

PLoS biology

2022 Volume 20, Issue 3, Page(s) e3001561

Abstract: Type 2 diabetes (T2D) and cardiovascular disease (CVD) represent significant disease burdens for most societies and susceptibility to these diseases is strongly influenced by diet and lifestyle. Physiological changes associated with T2D or CVD, such has ... ...

Abstract	Type 2 diabetes (T2D) and cardiovascular disease (CVD) represent significant disease burdens for most societies and susceptibility to these diseases is strongly influenced by diet and lifestyle. Physiological changes associated with T2D or CVD, such has high blood pressure and cholesterol and glucose levels in the blood, are often apparent prior to disease incidence. Here we integrated genetics, lipidomics, and standard clinical diagnostics to assess future T2D and CVD risk for 4,067 participants from a large prospective population-based cohort, the Malmö Diet and Cancer-Cardiovascular Cohort. By training Ridge regression-based machine learning models on the measurements obtained at baseline when the individuals were healthy, we computed several risk scores for T2D and CVD incidence during up to 23 years of follow-up. We used these scores to stratify the participants into risk groups and found that a lipidomics risk score based on the quantification of 184 plasma lipid concentrations resulted in a 168% and 84% increase of the incidence rate in the highest risk group and a 77% and 53% decrease of the incidence rate in lowest risk group for T2D and CVD, respectively, compared to the average case rates of 13.8% and 22.0%. Notably, lipidomic risk correlated only marginally with polygenic risk, indicating that the lipidome and genetic variants may constitute largely independent risk factors for T2D and CVD. Risk stratification was further improved by adding standard clinical variables to the model, resulting in a case rate of 51.0% and 53.3% in the highest risk group for T2D and CVD, respectively. The participants in the highest risk group showed significantly altered lipidome compositions affecting 167 and 157 lipid species for T2D and CVD, respectively. Our results demonstrated that a subset of individuals at high risk for developing T2D or CVD can be identified years before disease incidence. The lipidomic risk, which is derived from only one single mass spectrometric measurement that is cheap and fast, is informative and could extend traditional risk assessment based on clinical assays.
MeSH term(s)	Cardiovascular Diseases/epidemiology ; Cardiovascular Diseases/genetics ; Cardiovascular Diseases/metabolism ; Cohort Studies ; Diabetes Mellitus, Type 2/epidemiology ; Diabetes Mellitus, Type 2/genetics ; Diabetes Mellitus, Type 2/metabolism ; Female ; Genomics/methods ; Humans ; Incidence ; Lipidomics/methods ; Lipids/blood ; Male ; Middle Aged ; Multifactorial Inheritance/genetics ; Proportional Hazards Models ; Risk Assessment/methods ; Risk Assessment/statistics & numerical data ; Risk Factors ; Sweden/epidemiology
Chemical Substances	Lipids
Language	English
Publishing date	2022-03-03
Publishing country	United States
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	2126776-5
ISSN	1545-7885 ; 1544-9173
ISSN (online)	1545-7885
ISSN	1544-9173
DOI	10.1371/journal.pbio.3001561
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Article: Metabolite Profiling in a Diet-Induced Obesity Mouse Model and Individuals with Diabetes: A Combined Mass Spectrometry and Proton Nuclear Magnetic Resonance Spectroscopy Study.

Vieira, João P P / Ottosson, Filip / Jujic, Amra / Denisov, Vladimir / Magnusson, Martin / Melander, Olle / Duarte, João M N

Metabolites

2023 Volume 13, Issue 7

Abstract: Mass spectrometry (MS) and nuclear magnetic resonance (NMR) spectroscopy techniques have been used extensively for metabolite profiling. Although combining these two analytical modalities has the potential of enhancing metabolite coverage, such studies ... ...

Abstract	Mass spectrometry (MS) and nuclear magnetic resonance (NMR) spectroscopy techniques have been used extensively for metabolite profiling. Although combining these two analytical modalities has the potential of enhancing metabolite coverage, such studies are sparse. In this study we test the hypothesis that combining the metabolic information obtained using liquid chromatography (LC) MS and
Language	English
Publishing date	2023-07-23
Publishing country	Switzerland
Document type	Journal Article
ZDB-ID	2662251-8
ISSN	2218-1989
ISSN	2218-1989
DOI	10.3390/metabo13070874
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Article ; Online: A plasma lipid signature predicts incident coronary artery disease.

Ottosson, Filip / Emami Khoonsari, Payam / Gerl, Mathias J / Simons, Kai / Melander, Olle / Fernandez, Céline

International journal of cardiology

2021 Volume 331, Page(s) 249–254

Abstract: Background: Dyslipidemia is a hallmark of cardiovascular disease but is characterized by crude measurements of triglycerides, HDL- and LDL cholesterol. Lipidomics enables more detailed measurements of plasma lipids, which may help improve risk ... ...

Abstract	Background: Dyslipidemia is a hallmark of cardiovascular disease but is characterized by crude measurements of triglycerides, HDL- and LDL cholesterol. Lipidomics enables more detailed measurements of plasma lipids, which may help improve risk stratification and understand the pathophysiology of cardiovascular disease. Methods: Lipidomics was used to measure 184 lipids in plasma samples from the Malmö Diet and Cancer - Cardiovascular Cohort (N = 3865), taken at baseline examination. During an average follow-up time of 20.3 years, 536 participants developed coronary artery disease (CAD). Least absolute shrinkage and selection operator (LASSO) were applied to Cox proportional hazards models in order to identify plasma lipids that predict CAD. Results: Eight plasma lipids improved prediction of future CAD on top of traditional cardiovascular risk factors. Principal component analysis of CAD-associated lipids revealed one principal component (PC2) that was associated with risk of future CAD (HR per SD increment =1.46, C·I = 1.35-1.48, P < 0.001). The risk increase for being in the highest quartile of PC2 (HR = 2.33, P < 0.001) was higher than being in the top quartile of systolic blood pressure. Addition of PC2 to traditional risk factors achieved an improvement (2%) in the area under the ROC-curve for CAD events occurring within 10 (P = 0.03), 15 (P = 0.003) and 20 (P = 0.001) years of follow-up respectively. Conclusions: A lipid pattern improve CAD prediction above traditional risk factors, highlighting that conventional lipid-measures insufficiently describe dyslipidemia that is present years before CAD. Identifying this hidden dyslipidemia may help motivate lifestyle and pharmacological interventions early enough to reach a substantial reduction in absolute risk.
MeSH term(s)	Cholesterol, HDL ; Cholesterol, LDL ; Coronary Artery Disease/diagnosis ; Coronary Artery Disease/epidemiology ; Humans ; Lipids ; Risk Factors ; Triglycerides
Chemical Substances	Cholesterol, HDL ; Cholesterol, LDL ; Lipids ; Triglycerides
Language	English
Publishing date	2021-02-03
Publishing country	Netherlands
Document type	Journal Article
ZDB-ID	779519-1
ISSN	1874-1754 ; 0167-5273
ISSN (online)	1874-1754
ISSN	0167-5273
DOI	10.1016/j.ijcard.2021.01.059
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Article ; Online: Altered Asparagine and Glutamate Homeostasis Precede Coronary Artery Disease and Type 2 Diabetes.

Ottosson, Filip / Smith, Einar / Melander, Olle / Fernandez, Céline

The Journal of clinical endocrinology and metabolism

2018 Volume 103, Issue 8, Page(s) 3060–3069

Abstract: Context: Type 2 diabetes mellitus (T2DM) is accompanied by an increased risk for coronary artery disease (CAD), but the overlapping metabolic disturbances preceding both diseases are insufficiently described.: Objective: We hypothesized that ... ...

Abstract	Context: Type 2 diabetes mellitus (T2DM) is accompanied by an increased risk for coronary artery disease (CAD), but the overlapping metabolic disturbances preceding both diseases are insufficiently described. Objective: We hypothesized that alterations in metabolism occur years before clinical manifestation of T2DM and CAD and that these alterations are reflected in the plasma metabolome. We thus aimed to identify plasma metabolites that predict future T2DM and CAD. Design: Through use of targeted liquid chromatography-mass spectrometry, 35 plasma metabolites (amino acid metabolites and acylcarnitines) were quantified in 1049 individuals without CAD and diabetes, drawn from a population sample of 5386 in the Malmö Preventive Project (mean age, 69.5 years; 31% women). The sample included 204 individuals who developed T2DM, 384 who developed CAD, and 496 who remained T2DM and CAD free during a mean follow-up of 6.1 years. Results: In total, 16 metabolites were significantly associated with risk for developing T2DM according to logistic regression models. Glutamate (OR, 1.96; P = 5.4e-12) was the most strongly associated metabolite, followed by increased levels of branched-chain amino acids. Incident CAD was predicted by three metabolites: glutamate (OR, 1.28; P = 6.6e-4), histidine (OR, 0.76; P = 5.1e-4), and asparagine (OR, 0.80; P = 2.2e-3). Glutamate (OR, 1.48; P = 1.6e-8) and asparagine (OR, 0.75; P = 1.8e-5) were both associated with a composite endpoint of developing T2DM or CAD. Conclusion: Several plasma metabolites were associated with incidence of T2DM and CAD; elevated glutamate and reduced asparagine levels were associated with both diseases. We thus discovered associations that might help shed additional light on why T2DM and CAD commonly co-occur.
MeSH term(s)	Aged ; Aged, 80 and over ; Asparagine/blood ; Asparagine/metabolism ; Case-Control Studies ; Coronary Artery Disease/diagnosis ; Coronary Artery Disease/metabolism ; Diabetes Mellitus, Type 2/complications ; Diabetes Mellitus, Type 2/diagnosis ; Diabetes Mellitus, Type 2/metabolism ; Diabetic Angiopathies/diagnosis ; Diabetic Angiopathies/metabolism ; Female ; Follow-Up Studies ; Glutamic Acid/blood ; Glutamic Acid/metabolism ; Homeostasis ; Humans ; Male ; Middle Aged ; Prodromal Symptoms
Chemical Substances	Glutamic Acid (3KX376GY7L) ; Asparagine (7006-34-0)
Language	English
Publishing date	2018-05-22
Publishing country	United States
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	3029-6
ISSN	1945-7197 ; 0021-972X
ISSN (online)	1945-7197
ISSN	0021-972X
DOI	10.1210/jc.2018-00546
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