Literature DB >> 31573977

Increased lipogenesis and impaired β-oxidation predict type 2 diabetic kidney disease progression in American Indians.

Farsad Afshinnia1, Viji Nair1, Jiahe Lin2, Thekkelnaycke M Rajendiran3,4, Tanu Soni3, Jaeman Byun1, Kumar Sharma5, Patrice E Fort6, Thomas W Gardner6, Helen C Looker7, Robert G Nelson7, Frank C Brosius8, Eva L Feldman9, George Michailidis10,11, Matthias Kretzler1, Subramaniam Pennathur1,3,12.   

Abstract

BACKGROUNDIn this study, we identified the lipidomic predictors of early type 2 diabetic kidney disease (DKD) progression, which are currently undefined.METHODSThis longitudinal study included 92 American Indians with type 2 diabetes. Serum lipids (406 from 18 classes) were quantified using mass spectrometry from baseline samples when iothalamate-based glomerular filtration rate (GFR) was at least 90 mL/min. Affymetrix GeneChip Array was used to measure renal transcript expression. DKD progression was defined as at least 40% decline in GFR during follow-up.RESULTSParticipants had a mean age of 45 ± 9 years and median urine albumin/creatinine ratio of 43 (interquartile range 11-144). The 32 progressors had significantly higher relative abundance of polyunsaturated triacylglycerols (TAGs) and a lower abundance of C16-C20 acylcarnitines (ACs) (P < 0.001). In a Cox regression model, the main effect terms of unsaturated free fatty acids and phosphatidylethanolamines and the interaction terms of C16-C20 ACs and short-low-double-bond TAGs by categories of albuminuria independently predicted DKD progression. Renal expression of acetyl-CoA carboxylase-encoding gene (ACACA) correlated with serum diacylglycerols in the glomerular compartment (r = 0.36, and P = 0.006) and with low-double-bond TAGs in the tubulointerstitial compartment (r = 0.52, and P < 0.001).CONCLUSIONCollectively, the findings reveal a previously unrecognized link between lipid markers of impaired mitochondrial β-oxidation and enhanced lipogenesis and DKD progression in individuals with preserved GFR. Renal acetyl-CoA carboxylase activation accompanies these lipidomic changes and suggests that it may be the underlying mechanism linking lipid abnormalities to DKD progression.TRIAL REGISTRATIONClinicalTrials.gov, NCT00340678.FUNDINGNIH R24DK082841, K08DK106523, R03DK121941, P30DK089503, P30DK081943, and P30DK020572.

Entities:  

Keywords:  Chronic kidney disease; Diabetes; Fatty acid oxidation; Metabolism; Nephrology

Year:  2019        PMID: 31573977      PMCID: PMC6948762          DOI: 10.1172/jci.insight.130317

Source DB:  PubMed          Journal:  JCI Insight        ISSN: 2379-3708


  65 in total

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2.  Modular activation of nuclear factor-kappaB transcriptional programs in human diabetic nephropathy.

Authors:  Holger Schmid; Anissa Boucherot; Yoshinari Yasuda; Anna Henger; Bodo Brunner; Felix Eichinger; Almut Nitsche; Eva Kiss; Markus Bleich; Hermann-Josef Gröne; Peter J Nelson; Detlef Schlöndorff; Clemens D Cohen; Matthias Kretzler
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Authors:  Ana Reis; Alisa Rudnitskaya; Pajaree Chariyavilaskul; Neeraj Dhaun; Vanessa Melville; Jane Goddard; David J Webb; Andrew R Pitt; Corinne M Spickett
Journal:  J Lipid Res       Date:  2014-11-25       Impact factor: 5.922

4.  Association of Circulating Biomarkers (Adrenomedullin, TNFR1, and NT-proBNP) With Renal Function Decline in Patients With Type 2 Diabetes: A French Prospective Cohort.

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Journal:  Diabetes Care       Date:  2016-12-20       Impact factor: 19.112

5.  Long-term Effect of Losartan on Kidney Disease in American Indians With Type 2 Diabetes: A Follow-up Analysis of a Randomized Clinical Trial.

Authors:  Stephanie K Tanamas; Pierre-Jean Saulnier; Gudeta D Fufaa; Kevin M Wheelock; E Jennifer Weil; Robert L Hanson; William C Knowler; Peter H Bennett; Robert G Nelson
Journal:  Diabetes Care       Date:  2016-09-09       Impact factor: 19.112

Review 6.  Phosphatidylethanolamine Metabolism in Health and Disease.

Authors:  Elizabeth Calzada; Ouma Onguka; Steven M Claypool
Journal:  Int Rev Cell Mol Biol       Date:  2015-10-31       Impact factor: 6.813

7.  Sparse network modeling and metscape-based visualization methods for the analysis of large-scale metabolomics data.

Authors:  Sumanta Basu; William Duren; Charles R Evans; Charles F Burant; George Michailidis; Alla Karnovsky
Journal:  Bioinformatics       Date:  2017-05-15       Impact factor: 6.937

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

Authors:  Farsad Afshinnia; Thekkelnaycke M Rajendiran; Alla Karnovsky; Tanu Soni; Xue Wang; Dawei Xie; Wei Yang; Tariq Shafi; Matthew R Weir; Jiang He; Carolyn S Brecklin; Eugene P Rhee; Jeffrey R Schelling; Akinlolu Ojo; Harold Feldman; George Michailidis; Subramaniam Pennathur
Journal:  Kidney Int Rep       Date:  2016-08-18

9.  Top-down lipidomics reveals ether lipid deficiency in blood plasma of hypertensive patients.

Authors:  Juergen Graessler; Dominik Schwudke; Peter E H Schwarz; Ronny Herzog; Andrej Shevchenko; Stefan R Bornstein
Journal:  PLoS One       Date:  2009-07-15       Impact factor: 3.240

10.  Uremic solutes and risk of end-stage renal disease in type 2 diabetes: metabolomic study.

Authors:  Monika A Niewczas; Tammy L Sirich; Anna V Mathew; Jan Skupien; Robert P Mohney; James H Warram; Adam Smiles; Xiaoping Huang; Walker Walker; Jaeman Byun; Edward D Karoly; Elizabeth M Kensicki; Gerard T Berry; Joseph V Bonventre; Subramaniam Pennathur; Timothy W Meyer; Andrzej S Krolewski
Journal:  Kidney Int       Date:  2014-01-15       Impact factor: 10.612

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  24 in total

1.  Lipids and Cardiovascular Risk with CKD.

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2.  A Targeted Multiomics Approach to Identify Biomarkers Associated with Rapid eGFR Decline in Type 1 Diabetes.

Authors:  Christine P Limonte; Erkka Valo; Daniel Montemayor; Farsad Afshinnia; Tarunveer S Ahluwalia; Tina Costacou; Manjula Darshi; Carol Forsblom; Andrew N Hoofnagle; Per-Henrik Groop; Rachel G Miller; Trevor J Orchard; Subramaniam Pennathur; Peter Rossing; Niina Sandholm; Janet K Snell-Bergeon; Hongping Ye; Jing Zhang; Loki Natarajan; Ian H de Boer; Kumar Sharma
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4.  High-Throughput Metabolomics and Diabetic Kidney Disease Progression: Evidence from the Chronic Renal Insufficiency (CRIC) Study.

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Review 5.  Lipidomic approaches to dissect dysregulated lipid metabolism in kidney disease.

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6.  Plasma lipid metabolites associate with diabetic polyneuropathy in a cohort with type 2 diabetes.

Authors:  Amy E Rumora; Kai Guo; Fadhl M Alakwaa; Signe T Andersen; Evan L Reynolds; Marit E Jørgensen; Daniel R Witte; Hatice Tankisi; Morten Charles; Masha G Savelieff; Brian C Callaghan; Troels S Jensen; Eva L Feldman
Journal:  Ann Clin Transl Neurol       Date:  2021-05-06       Impact factor: 4.511

7.  Renin-angiotensin system inhibition reverses the altered triacylglycerol metabolic network in diabetic kidney disease.

Authors:  Kelli M Sas; Jiahe Lin; Chih-Hong Wang; Hongyu Zhang; Jharna Saha; Thekkelnaycke M Rajendiran; Tanu Soni; Viji Nair; Felix Eichinger; Matthias Kretzler; Frank C Brosius; George Michailidis; Subramaniam Pennathur
Journal:  Metabolomics       Date:  2021-07-04       Impact factor: 4.747

8.  Circulating Free Fatty Acid and Phospholipid Signature Predicts Early Rapid Kidney Function Decline in Patients With Type 1 Diabetes.

Authors:  Farsad Afshinnia; Thekkelnaycke M Rajendiran; Chenchen He; Jaeman Byun; Daniel Montemayor; Manjula Darshi; Jana Tumova; Jiwan Kim; Christine P Limonte; Rachel G Miller; Tina Costacou; Trevor J Orchard; Tarunveer S Ahluwalia; Peter Rossing; Janet K Snell-Bergeon; Ian H de Boer; Loki Natarajan; George Michailidis; Kumar Sharma; Subramaniam Pennathur
Journal:  Diabetes Care       Date:  2021-07-08       Impact factor: 17.152

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Authors:  Brian M Schmidt; Michael E Munson; Gary M Rothenberg; Crystal M Holmes; Rodica Pop-Busui
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Review 10.  Lipotoxicity and Diabetic Nephropathy: Novel Mechanistic Insights and Therapeutic Opportunities.

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