Progress in Metabonomics of Type 2 Diabetes Mellitus

Ma, Quantao; Li, Yaqi; Wang, Min; Tang, Zifan; Wang, Ting; Liu, Chenyue; Wang, Chunguo; Zhao, Baosheng

doi:10.3390/molecules23071834

Cited by 44 publications

(38 citation statements)

References 91 publications

(114 reference statements)

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“…A large number of metabolomics studies have been conducted in patients with T2DM [24][25][26][27][28] . Metabolomics analysis has, however, not been widely used to identify altered metabolism in healthy people who are at risk for T2DM 6,29 .…”

Section: Discussionmentioning

confidence: 99%

Postprandial Metabolism is Impaired in Overweight Normoglycemic Young Adults without Family History of Diabetes

Kumar

Satheesh

Vijayakumar

et al. 2020

Sci Rep

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Markers of inflammation and T2DM. Plasma levels of obesity/T2DM related markers [Interleukin 6 (IL-6), C-peptide, Glucagon, Insulin, Leptin, Plasminogen Activator Inhibitor-1 (PAI-1), Resistin, Visfatin, Ghrelin, Glucose-dependent Insulinotropic Polypeptide (GIP), Glucagon-like peptide-1 (GLP-1), and Adiponectin] were measured using Bio-Plex Pro Human Diabetes Assay panel, Bio-Rad, following the manufacturer's protocol 18. A detailed protocol is provided with supplementary materials. plasma sample preparation and metabolomics analysis. An Ultra-Performance Liquid Chromatography, ACQUITY UPLC System (Waters) coupled to a Quadrupole-Time of Flight (Q-TOF) mass spectrometer (SYNAPT-G2 HDMS, Waters) was used for untargeted metabolomics analysis. An analytical batch comprised of equal number of samples from all the study groups and their run order was randomized within a batch. The Quality control samples were prepared by pooling equal volume of aliquots from all the samples. QC samples were analyzed after every 5 th sample run. The features detected in <50% of the QC samples and <20% of the experimental samples were removed to exclude metabolites with poor repeatability in the metabolomics data. After normalization, features with a relative standard deviation of <30% in the QC samples were used for further statistical analysis. Detailed sample preparation, liquid chromatography, and tandem mass spectrometry protocol, data transformations, and metabolite identification are provided with the supplementary materials.

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Section: Discussionmentioning

confidence: 99%

Postprandial Metabolism is Impaired in Overweight Normoglycemic Young Adults without Family History of Diabetes

Kumar

Satheesh

Vijayakumar

et al. 2020

Sci Rep

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“…While T1D develops as a consequence of an auto-immune reaction against beta-cells, common causes for T2D are aging and obesity. Obesity causes hyperglycemia, elevated levels of free fatty acids, hypoxia, oxidative stress and an imbalance in many other metabolites, hormones and cytokines ( 123 – 126 ). This causes a switch in the composition and phenotype of immune cells in metabolic tissues from an anti-inflammatory to a more pro-inflammatory profile and thereby induces chronic low-grade inflammation, which ultimately drives insulin resistance ( 127 – 129 ).…”

Section: Effects Of the Metabolic Environment On Dcs In Diabetesmentioning

confidence: 99%

“…Polyunsaturated FAs (PUFAs) often have anti-inflammatory effects while many saturated fatty acids (SFAs) serve as pro-inflammatory molecules ( 140 , 141 ). Examples of the latter are palmitic acid (PA) and stearic acid (SA), which together with unsaturated oleic acid (OA) are among the most abundant dysregulated FFAs in obese and T2D patients ( 123 , 142 ). Especially PA is known for its pro-inflammatory effects and detrimental role in T2D pathogenesis ( 143 ).…”

Section: Effects Of the Metabolic Environment On Dcs In Diabetesmentioning

confidence: 99%

Shaping of Dendritic Cell Function by the Metabolic Micro-Environment

Brombacher

Everts

2020

Front. Endocrinol.

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Nutrients are required for growth and survival of all cells, but are also crucially involved in cell fate determination of many cell types, including immune cells. There is a growing appreciation that the metabolic micro-environment also plays a major role in shaping the functional properties of dendritic cells (DCs). Under pathological conditions nutrient availability can range from a very restricted supply, such as seen in a tumor micro-environment, to an overabundance of nutrients found in for example obese adipose tissue. In this review we will discuss what is currently known about the metabolic requirements for DC differentiation and immunogenicity and compare that to how function and fate of DCs under pathological conditions can be affected by alterations in environmental levels of carbohydrates, lipids and amino acids as well as by other metabolic cues, including availability of oxygen, redox homeostasis and lactate levels. Many of these insights have been generated using in vitro model systems, which have revealed highly diverse effects of different metabolic cues on DC function. However, they also stress the importance of shifting toward more physiologically relevant experimental settings to be able to fully delineate the role of the metabolic surroundings in its full complexity in shaping the functional properties of DCs in health and disease.

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“…T2D is a multi‐systemic metabolic disorder, of heterogeneous aetiology, involving biological, genetic and environmental factors 18 . Likewise, recent evidence has suggested that disturbances in the composition of the gut microbiota due to the exposure to high‐fat diets may cause of low‐grade systemic inflammation that may predispose to obesity, metabolic disorders and T2D 19 .…”

Section: Introductionmentioning

confidence: 99%

Obesity and impairment of pancreatic β‐cell function in early adulthood, independent of obesity age of onset: The Santiago Longitudinal Study

Burrows

Correa‐Burrows

Bunout

et al. 2020

Diabetes Metabolism Res

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Aim We investigated the relation of time of onset and length of obesity with biomarkers of β‐cell function in early adulthood in an infancy cohort. Material and methods In 1039 23‐year‐olds, body‐mass index (BMI) was measured at multiple time‐points from enrollment. BMI trajectories were interpolated with cubic polynomials. Fasting glucose, insulin and adiponectin were measured at 23 years. Homeostatic model assessment‐insulin resistance (HOMA‐IR), HOMA‐S, HOMA‐β, HOMA‐adiponectin (AD) and disposition index (DI) were estimated. IR and non‐alcoholic fatty liver (NAFL) were diagnosed. According to the BMI trajectory, five groups were defined: participants who were never obese (NOB); participants with obesity starting in adolescence and remained obese into adulthood (recent‐onset obesity, ROB); participants who were obese in early childhood but transitioned to non‐obesity as preadolescents (former obesity, FOB); participants who were obese in early childhood and remained obese into adulthood (persistent obesity, POB); participants with obesity starting in preadolescence and transitioned to non‐obesity as adolescents (transient obesity; TOB). Results Obesity was present in 47% of participants during at least one time‐point. ROBs and POBs had higher insulin, HOMA‐IR and HOMA‐β, lower HOMA‐S and DI, and higher prevalence of IR and NAFL at 23 years than NOBs, TOBs and FOBs. No differences were found in the β‐cell functionality of NOBs, TOBs and FOBs. Conclusions Persistent and recent obesity are both related to IR, NAFL and a decline of β‐cell function in emerging adulthood. Defeating obesity in childhood or adolescence allows reaching emerging adulthood with β‐cell functioning similar to that of subjects who were NOB.

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Progress in Metabonomics of Type 2 Diabetes Mellitus

Cited by 44 publications

References 91 publications

Postprandial Metabolism is Impaired in Overweight Normoglycemic Young Adults without Family History of Diabetes

Postprandial Metabolism is Impaired in Overweight Normoglycemic Young Adults without Family History of Diabetes

Shaping of Dendritic Cell Function by the Metabolic Micro-Environment

Obesity and impairment of pancreatic β‐cell function in early adulthood, independent of obesity age of onset: The Santiago Longitudinal Study

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