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Lipidomic Signature of Progression of Chronic Kidney Disease in the Chronic Renal Insufficiency Cohort.

Kidney international reports

Afshinnia F, Rajendiran TM, Karnovsky A, Soni T, Wang X, Xie D, Yang W, Shafi T, Weir MR, He J, Brecklin CS, Rhee EP, Schelling JR, Ojo A, Feldman H, Michailidis G, Pennathur S.
PMID: 28451650
Kidney Int Rep. 2016 Nov;1(4):256-268. doi: 10.1016/j.ekir.2016.08.007. Epub 2016 Aug 18.

INTRODUCTION: Human studies report conflicting results on the predictive power of serum lipids on progression of chronic kidney disease (CKD). We aimed to systematically identify the lipids that predict progression to end-stage kidney disease.METHODS: From the Chronic Renal Insufficiency...

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Afshinnia F, Rajendiran TM, Karnovsky A, et al. Lipidomic Signature of Progression of Chronic Kidney Disease in the Chronic Renal Insufficiency Cohort. Kidney Int Rep. 2016;1(4):256-268doi: 10.1016/j.ekir.2016.08.007.
Afshinnia, F., Rajendiran, T. M., Karnovsky, A., Soni, T., Wang, X., Xie, D., Yang, W., Shafi, T., Weir, M. R., He, J., Brecklin, C. S., Rhee, E. P., Schelling, J. R., Ojo, A., Feldman, H., Michailidis, G., & Pennathur, S. (2016). Lipidomic Signature of Progression of Chronic Kidney Disease in the Chronic Renal Insufficiency Cohort. Kidney international reports, 1(4), 256-268. https://doi.org/10.1016/j.ekir.2016.08.007
Afshinnia, Farsad, et al. "Lipidomic Signature of Progression of Chronic Kidney Disease in the Chronic Renal Insufficiency Cohort." Kidney international reports vol. 1,4 (2016): 256-268. doi: https://doi.org/10.1016/j.ekir.2016.08.007
Afshinnia F, Rajendiran TM, Karnovsky A, Soni T, Wang X, Xie D, Yang W, Shafi T, Weir MR, He J, Brecklin CS, Rhee EP, Schelling JR, Ojo A, Feldman H, Michailidis G, Pennathur S. Lipidomic Signature of Progression of Chronic Kidney Disease in the Chronic Renal Insufficiency Cohort. Kidney Int Rep. 2016 Nov;1(4):256-268. doi: 10.1016/j.ekir.2016.08.007. Epub 2016 Aug 18. PMID: 28451650; PMCID: PMC5402253.
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Targeted Lipidomic and Transcriptomic Analysis Identifies Dysregulated Renal Ceramide Metabolism in a Mouse Model of Diabetic Kidney Disease.

Journal of proteomics & bioinformatics

Sas KM, Nair V, Byun J, Kayampilly P, Zhang H, Saha J, Brosius FC, Kretzler M, Pennathur S.
PMID: 26778897
J Proteomics Bioinform. 2015 Oct; doi: 10.4172/jpb.S14-002. Epub 2015 May 18.

Both type 1 and type 2 diabetes are associated with altered lipid metabolism, which might in part contribute to debilitating complications such as diabetic kidney disease (DKD). Ceramides are bioactive sphingolipids that have been implicated in a variety of...

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Sas KM, Nair V, Byun J, et al. Targeted Lipidomic and Transcriptomic Analysis Identifies Dysregulated Renal Ceramide Metabolism in a Mouse Model of Diabetic Kidney Disease. J Proteomics Bioinform. 2015;doi: 10.4172/jpb.S14-002.
Sas, K. M., Nair, V., Byun, J., Kayampilly, P., Zhang, H., Saha, J., Brosius, F. C., Kretzler, M., & Pennathur, S. (2015). Targeted Lipidomic and Transcriptomic Analysis Identifies Dysregulated Renal Ceramide Metabolism in a Mouse Model of Diabetic Kidney Disease. Journal of proteomics & bioinformatics, . https://doi.org/10.4172/jpb.S14-002
Sas, Kelli M, et al. "Targeted Lipidomic and Transcriptomic Analysis Identifies Dysregulated Renal Ceramide Metabolism in a Mouse Model of Diabetic Kidney Disease." Journal of proteomics & bioinformatics vol. (2015). doi: https://doi.org/10.4172/jpb.S14-002
Sas KM, Nair V, Byun J, Kayampilly P, Zhang H, Saha J, Brosius FC, Kretzler M, Pennathur S. Targeted Lipidomic and Transcriptomic Analysis Identifies Dysregulated Renal Ceramide Metabolism in a Mouse Model of Diabetic Kidney Disease. J Proteomics Bioinform. 2015 Oct; doi: 10.4172/jpb.S14-002. Epub 2015 May 18. PMID: 26778897; PMCID: PMC4712744.
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Integrative Biology of Diabetic Kidney Disease.

Kidney diseases (Basel, Switzerland)

Harder JL, Hodgin JB, Kretzler M.
PMID: 26929927
Kidney Dis (Basel). 2015 Dec;1(3):194-203. doi: 10.1159/000439196. Epub 2015 Sep 23.

BACKGROUND: The leading cause of ESRD in the U.S. is diabetic kidney disease (DKD). Despite significant efforts to improve outcomes in DKD, the impact on disease progression has been disappointing. This has prompted clinicians and researchers to search for...

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Harder JL, Hodgin JB, Kretzler M. Integrative Biology of Diabetic Kidney Disease. Kidney Dis (Basel). 2015;1(3):194-203doi: 10.1159/000439196.
Harder, J. L., Hodgin, J. B., & Kretzler, M. (2015). Integrative Biology of Diabetic Kidney Disease. Kidney diseases (Basel, Switzerland), 1(3), 194-203. https://doi.org/10.1159/000439196
Harder, Jennifer L, et al. "Integrative Biology of Diabetic Kidney Disease." Kidney diseases (Basel, Switzerland) vol. 1,3 (2015): 194-203. doi: https://doi.org/10.1159/000439196
Harder JL, Hodgin JB, Kretzler M. Integrative Biology of Diabetic Kidney Disease. Kidney Dis (Basel). 2015 Dec;1(3):194-203. doi: 10.1159/000439196. Epub 2015 Sep 23. PMID: 26929927; PMCID: PMC4768943.
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A metabolite-GWAS (mGWAS) approach to unveil chronic kidney disease progression.

Kidney international

Zhang G, Saito R, Sharma K.
PMID: 28501300
Kidney Int. 2017 Jun;91(6):1274-1276. doi: 10.1016/j.kint.2017.03.022.

In this issue, McMahon et al. report that, by combining phenotypic, metabolomic, and genetic data, they could better detect chronic kidney disease at the early stages and provide insight into its pathobiology. The most significant findings of the study...

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Zhang G, Saito R, Sharma K. A metabolite-GWAS (mGWAS) approach to unveil chronic kidney disease progression. Kidney Int. 2017;91(6):1274-1276doi: 10.1016/j.kint.2017.03.022.
Zhang, G., Saito, R., & Sharma, K. (2017). A metabolite-GWAS (mGWAS) approach to unveil chronic kidney disease progression. Kidney international, 91(6), 1274-1276. https://doi.org/10.1016/j.kint.2017.03.022
Zhang, Guanshi, et al. "A metabolite-GWAS (mGWAS) approach to unveil chronic kidney disease progression." Kidney international vol. 91,6 (2017): 1274-1276. doi: https://doi.org/10.1016/j.kint.2017.03.022
Zhang G, Saito R, Sharma K. A metabolite-GWAS (mGWAS) approach to unveil chronic kidney disease progression. Kidney Int. 2017 Jun;91(6):1274-1276. doi: 10.1016/j.kint.2017.03.022. PMID: 28501300; PMCID: PMC5989707.
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Quantitative analysis of γ-glutamylisoleucine, γ-glutamylthreonine, and γ-glutamylvaline in HeLa cells using UHPLC-MS/MS.

Journal of separation science

Thacker JB, He C, Pennathur S.
PMID: 34042281
J Sep Sci. 2021 Aug;44(15):2898-2907. doi: 10.1002/jssc.202001266. Epub 2021 Jun 13.

γ-Glutamylpeptides have been identified as potential biomarkers for a number of diseases including cancer, diabetes, and liver disease. In this study, we developed and validated a novel quantitative analytical strategy for measuring γ-glutamylisoleucine, γ-glutamylthreonine, and γ-glutamylvaline, all of which...

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Thacker JB, He C, Pennathur S. Quantitative analysis of γ-glutamylisoleucine, γ-glutamylthreonine, and γ-glutamylvaline in HeLa cells using UHPLC-MS/MS. J Sep Sci. 2021;44(15):2898-2907doi: 10.1002/jssc.202001266.
Thacker, J. B., He, C., & Pennathur, S. (2021). Quantitative analysis of γ-glutamylisoleucine, γ-glutamylthreonine, and γ-glutamylvaline in HeLa cells using UHPLC-MS/MS. Journal of separation science, 44(15), 2898-2907. https://doi.org/10.1002/jssc.202001266
Thacker, Jonathan B, et al. "Quantitative analysis of γ-glutamylisoleucine, γ-glutamylthreonine, and γ-glutamylvaline in HeLa cells using UHPLC-MS/MS." Journal of separation science vol. 44,15 (2021): 2898-2907. doi: https://doi.org/10.1002/jssc.202001266
Thacker JB, He C, Pennathur S. Quantitative analysis of γ-glutamylisoleucine, γ-glutamylthreonine, and γ-glutamylvaline in HeLa cells using UHPLC-MS/MS. J Sep Sci. 2021 Aug;44(15):2898-2907. doi: 10.1002/jssc.202001266. Epub 2021 Jun 13. PMID: 34042281; PMCID: PMC8363571.
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Diabetic retinopathy: research to clinical practice.

Clinical diabetes and endocrinology

Shah AR, Gardner TW.
PMID: 29075511
Clin Diabetes Endocrinol. 2017 Oct 19;3:9. doi: 10.1186/s40842-017-0047-y. eCollection 2017.

BACKGROUND: Diabetic Retinopathy (DR) is a leading cause of visual impairment in the United States. The CDC estimates that the prevalence of DR will triple from 2005 to 2050.MAIN BODY: The report summarizes major past advances in diabetes research...

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Shah AR, Gardner TW. Diabetic retinopathy: research to clinical practice. Clin Diabetes Endocrinol. 2017;3:9doi: 10.1186/s40842-017-0047-y.
Shah, A. R., & Gardner, T. W. (2017). Diabetic retinopathy: research to clinical practice. Clinical diabetes and endocrinology, 39. https://doi.org/10.1186/s40842-017-0047-y
Shah, Anjali R, and Gardner, Thomas W. "Diabetic retinopathy: research to clinical practice." Clinical diabetes and endocrinology vol. 3 (2017): 9. doi: https://doi.org/10.1186/s40842-017-0047-y
Shah AR, Gardner TW. Diabetic retinopathy: research to clinical practice. Clin Diabetes Endocrinol. 2017 Oct 19;3:9. doi: 10.1186/s40842-017-0047-y. eCollection 2017. PMID: 29075511; PMCID: PMC5648499.
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Abnormalities in signaling pathways in diabetic nephropathy.

Expert review of endocrinology & metabolism

Brosius FC, Khoury CC, Buller CL, Chen S.
PMID: 20224802
Expert Rev Endocrinol Metab. 2010;5(1):51-64. doi: 10.1586/eem.09.70.

Diabetic nephropathy (DN) is characterized by a plethora of signaling abnormalities that together ultimately result in the clinical and pathologic hallmarks of DN, namely progressive albuminuria followed by a gradual decline in glomerular filtration rate leading to kidney failure,...

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Brosius FC, Khoury CC, Buller CL, et al. Abnormalities in signaling pathways in diabetic nephropathy. Expert Rev Endocrinol Metab. 2010;5(1):51-64doi: 10.1586/eem.09.70.
Brosius, F. C., Khoury, C. C., Buller, C. L., & Chen, S. (2010). Abnormalities in signaling pathways in diabetic nephropathy. Expert review of endocrinology & metabolism, 5(1), 51-64. https://doi.org/10.1586/eem.09.70
Brosius, Frank C, et al. "Abnormalities in signaling pathways in diabetic nephropathy." Expert review of endocrinology & metabolism vol. 5,1 (2010): 51-64. doi: https://doi.org/10.1586/eem.09.70
Brosius FC, Khoury CC, Buller CL, Chen S. Abnormalities in signaling pathways in diabetic nephropathy. Expert Rev Endocrinol Metab. 2010;5(1):51-64. doi: 10.1586/eem.09.70. PMID: 20224802; PMCID: PMC2834210.
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Integrating automated workflows, human intelligence and collaboration.

Summit on translational bioinformatics

Mirel B, Eichinger F, Nair V, Kretzler M.
PMID: 21347175
Summit Transl Bioinform. 2009 Mar 01;2009:79-83.

Many methods and tools have evolved for microarray analysis such as single probe evaluation, promoter module modeling and pathway analysis. Little is known, however, about optimizing this flow of analysis for the flexible reasoning biomedical researchers need for hypothesizing...

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Mirel B, Eichinger F, Nair V, et al. Integrating automated workflows, human intelligence and collaboration. Summit Transl Bioinform. 2009;2009:79-83
Mirel, B., Eichinger, F., Nair, V., & Kretzler, M. (2009). Integrating automated workflows, human intelligence and collaboration. Summit on translational bioinformatics, 200979-83.
Mirel, Barbara, et al. "Integrating automated workflows, human intelligence and collaboration." Summit on translational bioinformatics vol. 2009 (2009): 79-83.
Mirel B, Eichinger F, Nair V, Kretzler M. Integrating automated workflows, human intelligence and collaboration. Summit Transl Bioinform. 2009 Mar 01;2009:79-83. PMID: 21347175; PMCID: PMC3041562.
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Advances in diet-induced rodent models of metabolically acquired peripheral neuropathy.

Disease models & mechanisms

Eid SA, Feldman EL.
PMID: 34762126
Dis Model Mech. 2021 Nov 01;14(11). doi: 10.1242/dmm.049337. Epub 2021 Nov 11.

Peripheral neuropathy (PN) is a severe complication that affects over 30% of prediabetic and 60% of type 2 diabetic (T2D) patients. The metabolic syndrome is increasingly recognized as a major driver of PN. However, basic and translational research is...

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Eid SA, Feldman EL. Advances in diet-induced rodent models of metabolically acquired peripheral neuropathy. Dis Model Mech. 2021;14(11)doi: 10.1242/dmm.049337.
Eid, S. A., & Feldman, E. L. (2021). Advances in diet-induced rodent models of metabolically acquired peripheral neuropathy. Disease models & mechanisms, 14(11), . https://doi.org/10.1242/dmm.049337
Eid, Stéphanie A, and Feldman, Eva L. "Advances in diet-induced rodent models of metabolically acquired peripheral neuropathy." Disease models & mechanisms vol. 14,11 (2021). doi: https://doi.org/10.1242/dmm.049337
Eid SA, Feldman EL. Advances in diet-induced rodent models of metabolically acquired peripheral neuropathy. Dis Model Mech. 2021 Nov 01;14(11). doi: 10.1242/dmm.049337. Epub 2021 Nov 11. PMID: 34762126; PMCID: PMC8592018.
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Advances in diet-induced rodent models of metabolically acquired peripheral neuropathy.

Disease models & mechanisms

Eid SA, Feldman EL.
PMID: 34762126
Dis Model Mech. 2021 Nov 01;14(11). doi: 10.1242/dmm.049337. Epub 2021 Nov 11.

Peripheral neuropathy (PN) is a severe complication that affects over 30% of prediabetic and 60% of type 2 diabetic (T2D) patients. The metabolic syndrome is increasingly recognized as a major driver of PN. However, basic and translational research is...

Cite
Eid SA, Feldman EL. Advances in diet-induced rodent models of metabolically acquired peripheral neuropathy. Dis Model Mech. 2021;14(11)doi: 10.1242/dmm.049337.
Eid, S. A., & Feldman, E. L. (2021). Advances in diet-induced rodent models of metabolically acquired peripheral neuropathy. Disease models & mechanisms, 14(11), . https://doi.org/10.1242/dmm.049337
Eid, Stéphanie A, and Feldman, Eva L. "Advances in diet-induced rodent models of metabolically acquired peripheral neuropathy." Disease models & mechanisms vol. 14,11 (2021). doi: https://doi.org/10.1242/dmm.049337
Eid SA, Feldman EL. Advances in diet-induced rodent models of metabolically acquired peripheral neuropathy. Dis Model Mech. 2021 Nov 01;14(11). doi: 10.1242/dmm.049337. Epub 2021 Nov 11. PMID: 34762126; PMCID: PMC8592018.
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The determinants of complication trajectories in American Indians with type 2 diabetes.

JCI insight

Reynolds EL, Akinci G, Banerjee M, Looker HC, Patterson A, Nelson RG, Feldman EL, Callaghan BC.
PMID: 34027894
JCI Insight. 2021 May 24;6(10). doi: 10.1172/jci.insight.146849.

BACKGROUNDWe aimed to determine whether metabolic syndrome (MetS) affects longitudinal trajectories of diabetic complications, including neuropathy, cardiovascular autonomic neuropathy (CAN), and kidney disease in American Indians with type 2 diabetes.METHODSWe performed a prospective study where participants underwent annual metabolic...

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Reynolds EL, Akinci G, Banerjee M, et al. The determinants of complication trajectories in American Indians with type 2 diabetes. JCI Insight. 2021;6(10)doi: 10.1172/jci.insight.146849.
Reynolds, E. L., Akinci, G., Banerjee, M., Looker, H. C., Patterson, A., Nelson, R. G., Feldman, E. L., & Callaghan, B. C. (2021). The determinants of complication trajectories in American Indians with type 2 diabetes. JCI insight, 6(10), . https://doi.org/10.1172/jci.insight.146849
Reynolds, Evan L, et al. "The determinants of complication trajectories in American Indians with type 2 diabetes." JCI insight vol. 6,10 (2021). doi: https://doi.org/10.1172/jci.insight.146849
Reynolds EL, Akinci G, Banerjee M, Looker HC, Patterson A, Nelson RG, Feldman EL, Callaghan BC. The determinants of complication trajectories in American Indians with type 2 diabetes. JCI Insight. 2021 May 24;6(10). doi: 10.1172/jci.insight.146849. PMID: 34027894; PMCID: PMC8262294.
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Renin-angiotensin system inhibition reverses the altered triacylglycerol metabolic network in diabetic kidney disease.

Metabolomics : Official journal of the Metabolomic Society

Sas KM, Lin J, Wang CH, Zhang H, Saha J, Rajendiran TM, Soni T, Nair V, Eichinger F, Kretzler M, Brosius FC, Michailidis G, Pennathur S.
PMID: 34219205
Metabolomics. 2021 Jul 04;17(7):65. doi: 10.1007/s11306-021-01816-0.

OBJECTIVE: Dyslipidemia is a significant risk factor for progression of diabetic kidney disease (DKD). Determining the changes in individual lipids and lipid networks across a spectrum of DKD severity may identify lipids that are pathogenic to DKD progression.METHODS: We...

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Sas KM, Lin J, Wang CH, et al. Renin-angiotensin system inhibition reverses the altered triacylglycerol metabolic network in diabetic kidney disease. Metabolomics. 2021;17(7):65doi: 10.1007/s11306-021-01816-0.
Sas, K. M., Lin, J., Wang, C. H., Zhang, H., Saha, J., Rajendiran, T. M., Soni, T., Nair, V., Eichinger, F., Kretzler, M., Brosius, F. C., Michailidis, G., & Pennathur, S. (2021). Renin-angiotensin system inhibition reverses the altered triacylglycerol metabolic network in diabetic kidney disease. Metabolomics : Official journal of the Metabolomic Society, 17(7), 65. https://doi.org/10.1007/s11306-021-01816-0
Sas, Kelli M, et al. "Renin-angiotensin system inhibition reverses the altered triacylglycerol metabolic network in diabetic kidney disease." Metabolomics : Official journal of the Metabolomic Society vol. 17,7 (2021): 65. doi: https://doi.org/10.1007/s11306-021-01816-0
Sas KM, Lin J, Wang CH, Zhang H, Saha J, Rajendiran TM, Soni T, Nair V, Eichinger F, Kretzler M, Brosius FC, Michailidis G, Pennathur S. Renin-angiotensin system inhibition reverses the altered triacylglycerol metabolic network in diabetic kidney disease. Metabolomics. 2021 Jul 04;17(7):65. doi: 10.1007/s11306-021-01816-0. PMID: 34219205; PMCID: PMC8312633.
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Showing 1 to 12 of 22 entries