Triglyceride profiling in adipose tissues from obese insulin sensitive, insulin resistant and type 2 diabetes mellitus individuals

Al-Sulaiti, Haya; Diboun, Ilhame; Banu, Sameem; Al-Emadi, Mohamed; Amani, Parvaneh; Harvey, T. M.; Dömling, Alex; Latiff, Aishah A.; Elrayess, Mohamed A.

doi:10.1186/s12967-018-1548-x

Cited by 58 publications

(48 citation statements)

References 40 publications

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“…Finally, plasma TAGs represent known risk factors for cardiovascular disease and coronary heart disease. A recent study identified that TAGs at the extremes of carbon atoms and saturation had differential associations with diabetes risk (35). We also observed differential associations by TAG fatty Table 3.…”

Section: Triacylglyceridessupporting

confidence: 60%

A Prospective Analysis of Circulating Plasma Metabolites Associated with Ovarian Cancer Risk

et al. 2020

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◥ Ovarian cancer has few known risk factors, hampering identification of high-risk women. We assessed the association of prediagnostic plasma metabolites (N ¼ 420) with risk of epithelial ovarian cancer, including both borderline and invasive tumors. A total of 252 cases and 252 matched controls from the Nurses' Health Studies were included. Multivariable logistic regression was used to estimate ORs and 95% confidence intervals (CI), comparing the 90th-10th percentile in metabolite levels, using the permutationbased Westfall and Young approach to account for testing multiple correlated hypotheses. Weighted gene coexpression network analysis (WGCNA; n ¼ 10 metabolite modules) and metabolite set enrichment analysis (n ¼ 23 metabolite classes) were also evaluated. An increase in pseudouridine levels from the 10th to the 90th percentile was associated with a 2.5-fold increased risk of overall ovarian cancer (OR ¼ 2.56; 95% CI, 1.48-4.45; P ¼ 0.001/adjusted P ¼ 0.15); a similar risk estimate was observed for serous/poorly differentiated tumors (n ¼ 176 cases; comparable OR ¼ 2.38; 95% CI, 1.33-4.32; P ¼ 0.004/adjusted P ¼ 0.55). For nonserous tumors (n ¼ 34 cases), pseudouridine and C36:2 phosphatidylcholine plasmalogen had the strongest statistical associations (OR ¼ 9.84; 95% CI, 2.89-37.82; P < 0.001/adjusted P ¼ 0.07; and OR ¼ 0.11; 95% CI, 0.03-0.35; P < 0.001/adjusted P ¼ 0.06, respectively). Five WGCNA modules and 9 classes were associated with risk overall at FDR ≤ 0.20. Triacylglycerols (TAG) showed heterogeneity by tumor aggressiveness (case-only heterogeneity P < 0.0001). The TAG association with risk overall and serous tumors differed by acyl carbon content and saturation. In summary, this study suggests that pseudouridine may be a novel risk factor for ovarian cancer and that TAGs may also be important, particularly for rapidly fatal tumors, with associations differing by structural features. Significance: Pseudouridine represents a potential novel risk factor for ovarian cancer and triglycerides may be important particularly in rapidly fatal ovarian tumors.

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Section: Triacylglyceridessupporting

confidence: 60%

A Prospective Analysis of Circulating Plasma Metabolites Associated with Ovarian Cancer Risk

et al. 2020

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“…Focal adhesions act as a cellular regulator of cell growth, movement, and differentiation [40,41]. FOXOs are implicated in a broad range of cellular functions, and these are cellular properties, critical to the cell biology, including cellular differentiation, apoptosis, cell proliferation, DNA damage, and repair [42,43]. Obesity leads to the progression of insulin resistance and type II diabetes mellitus [44], and studies have reported that insulin signaling pathways regulate adiposity cell differentiation [45].…”

Section: Discussionmentioning

confidence: 99%

MiRNAs and mRNAs Analysis during Abdominal Preadipocyte Differentiation in Chickens

Sun

Zhu

et al. 2020

Animals

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The excessive deposition of abdominal fat has become an important factor in restricting the production efficiency of chickens, so reducing abdominal fat deposition is important for improving growth rate. It has been proven that miRNAs play an important role in regulating many physiological processes of organisms. In this study, we constructed a model of adipogenesis by isolating preadipocytes (Ab-Pre) derived from abdominal adipose tissue and differentiated adipocytes (Ab-Ad) in vitro. Deep sequencing of miRNAs and mRNAs expressed in Ab-Pre and Ab-Ad groups was conducted to explore the effect of miRNAs and mRNAs on fat deposition. We identified 80 differentially expressed miRNAs (DEMs) candidates, 58 of which were up-regulated and 22 down-regulated. Furthermore, six miRNAs and six mRNAs were verified by qRT-PCR, and the results showed that the expression of the DEMs and differentially expressed genes (DEGs) in the two groups was consistent with our sequencing results. When target genes of miRNA were combined with mRNA transcriptome data, a total of 891 intersection genes were obtained, we predicted the signal pathways of cross genes enrichment to the MAPK signal pathway, insulin signal pathway, fatty acid metabolism, and ECM–receptor interaction. Meanwhile, we constructed miRNA and negatively correlated mRNA target networks, including 12 miRNA–mRNAs pairs, which showed a strong association with the abdominal adipocyte differentiation (miR-214−ACSBG2, NFKB2, CAMK2A, ACLY, CCND3, PLK3, ITGB2; miR-148a-5p−ROCK2; miR-10a-5p−ELOVL5; miR-146b-5p−LAMA4; miR-6615-5p−FLNB; miR-1774−COL6A1). Overall, these findings provide a background for further research on lipid metabolism. Thus, we can better understand the molecular genetic mechanism of chicken abdominal fat deposition.

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“…Advancement in metabolomic tools including mass spectrometry (MS) technologies has allowed the identification of novel metabolic mediators of disease progression, including obesity associated insulin resistance and T2DM [13]. Recent evidence showed that adipose tissue from OIS, OIR and T2DM individuals exhibit a unique lipidomic signature associated with an increased risk of obesity-associated insulin resistance [14, 15]. Furthermore, metabolomics studies in individuals with T2DM have revealed several diabetes-associated metabolites, including 1,5-anhydroglucitol (1,5-AG), mannose and glucose [16, 17].…”

Section: Introductionmentioning

confidence: 99%

Metabolic signature of obesity-associated insulin resistance and type 2 diabetes

et al. 2019

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BackgroundObesity is associated with an increased risk of insulin resistance and type 2 diabetes mellitus (T2DM). However, some obese individuals maintain their insulin sensitivity and exhibit a lower risk of associated comorbidities. The underlying metabolic pathways differentiating obese insulin sensitive (OIS) and obese insulin resistant (OIR) individuals remain unclear.MethodsIn this study, 107 subjects underwent untargeted metabolomics of serum samples using the Metabolon platform. Thirty-two subjects were lean controls whilst 75 subjects were obese including 20 OIS, 41 OIR, and 14 T2DM individuals.ResultsOur results showed that phospholipid metabolites including choline, glycerophosphoethanolamine and glycerophosphorylcholine were significantly altered from OIS when compared with OIR and T2DM individuals. Furthermore, our data confirmed changes in metabolic markers of liver disease, vascular disease and T2DM, such as 3-hydroxymyristate, dimethylarginine and 1,5-anhydroglucitol, respectively.ConclusionThis pilot data has identified phospholipid metabolites as potential novel biomarkers of obesity-associated insulin sensitivity and confirmed the association of known metabolites with increased risk of obesity-associated insulin resistance, with possible diagnostic and therapeutic applications. Further studies are warranted to confirm these associations in prospective cohorts and to investigate their functionality.

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Triglyceride profiling in adipose tissues from obese insulin sensitive, insulin resistant and type 2 diabetes mellitus individuals

Cited by 58 publications

References 40 publications

A Prospective Analysis of Circulating Plasma Metabolites Associated with Ovarian Cancer Risk

A Prospective Analysis of Circulating Plasma Metabolites Associated with Ovarian Cancer Risk

MiRNAs and mRNAs Analysis during Abdominal Preadipocyte Differentiation in Chickens

Metabolic signature of obesity-associated insulin resistance and type 2 diabetes

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