Literature DB >> 23279644

Triglyceride-cholesterol imbalance across lipoprotein subclasses predicts diabetic kidney disease and mortality in type 1 diabetes: the FinnDiane Study.

V-P Mäkinen1, P Soininen, A J Kangas, C Forsblom, N Tolonen, L M Thorn, J Viikari, O T Raitakari, M Savolainen, P-H Groop, M Ala-Korpela.   

Abstract

BACKGROUND: Circulating cholesterol (C) and triglyceride (TG) levels are associated with vascular injury in type 1 diabetes (T1DM). Lipoproteins are responsible for transporting lipids, and alterations in their subclass distributions may partly explain the increased mortality in individuals with T1DM. DESIGN AND
SUBJECTS: A cohort of 3544 individuals with T1DM was recruited by the nationwide multicentre FinnDiane Study Group. At baseline, six very low-density lipoprotein VLDL, one intermediate-density lipoprotein IDL, three low-density lipoprotein LDL and four higher high-density lipoprotein HDL subclasses were quantified by proton nuclear magnetic resonance spectroscopy. At follow-up, the baseline data were analysed for incident micro- or macroalbuminuria (117 cases in 5.3 years), progression from microalbuminuria (63 cases in 6.1 years), progression from macroalbuminuria (109 cases in 5.9 years) and mortality (385 deaths in 9.4 years). Univariate associations were tested by age-matched cases and controls and multivariate lipoprotein profiles were analysed using the self-organizing map (SOM).
RESULTS: TG and C levels in large VLDL were associated with incident albuminuria, TG and C in medium VLDL were associated with progression from microalbuminuria, and TG and C in all VLDL subclasses were associated with mortality. Large HDL-C was inversely associated with mortality. Three extreme phenotypes emerged from SOM analysis: (i) low C (<3% mortality), (ii) low TG/C ratio (6% mortality), and (iii) high TG/C ratio (40% mortality) in all subclasses.
CONCLUSIONS: TG-C imbalance is a general lipoprotein characteristic in individuals with T1DM and high vascular disease risk.
© 2012 The Association for the Publication of the Journal of Internal Medicine.

Entities:  

Mesh:

Substances:

Year:  2013        PMID: 23279644     DOI: 10.1111/joim.12026

Source DB:  PubMed          Journal:  J Intern Med        ISSN: 0954-6820            Impact factor:   8.989


  15 in total

1.  Nuclear magnetic resonance-determined lipoprotein subclasses and carotid intima-media thickness in type 1 diabetes.

Authors:  Arpita Basu; Alicia J Jenkins; Ying Zhang; Julie A Stoner; Richard L Klein; Maria F Lopes-Virella; W Timothy Garvey; Timothy J Lyons
Journal:  Atherosclerosis       Date:  2015-10-31       Impact factor: 5.162

Review 2.  Hyperlipoproteinemia type 3: the forgotten phenotype.

Authors:  Paul N Hopkins; Eliot A Brinton; M Nazeem Nanjee
Journal:  Curr Atheroscler Rep       Date:  2014-09       Impact factor: 5.113

Review 3.  The emergence of proton nuclear magnetic resonance metabolomics in the cardiovascular arena as viewed from a clinical perspective.

Authors:  Naomi J Rankin; David Preiss; Paul Welsh; Karl E V Burgess; Scott M Nelson; Debbie A Lawlor; Naveed Sattar
Journal:  Atherosclerosis       Date:  2014-09-30       Impact factor: 5.162

4.  Data on carotid intima-media thickness and lipoprotein subclasses in type 1 diabetes from the Diabetes Control and Complications Trial and the Epidemiology of Diabetes Interventions and Complications (DCCT/EDIC).

Authors:  Arpita Basu; Alicia J Jenkins; Ying Zhang; Julie A Stoner; Richard L Klein; Maria F Lopes-Virella; W Timothy Garvey; Timothy J Lyons
Journal:  Data Brief       Date:  2015-11-22

5.  Metabolic profiling-multitude of technologies with great research potential, but (when) will translation emerge?

Authors:  Mika Ala-Korpela; George Davey Smith
Journal:  Int J Epidemiol       Date:  2016-10-27       Impact factor: 7.196

6.  Circulating metabolic biomarkers of renal function in diabetic and non-diabetic populations.

Authors:  Clara Barrios; Jonas Zierer; Peter Würtz; Toomas Haller; Andres Metspalu; Christian Gieger; Barbara Thorand; Christa Meisinger; Melanie Waldenberger; Olli Raitakari; Terho Lehtimäki; Sol Otero; Eva Rodríguez; Juan Pedro-Botet; Mika Kähönen; Mika Ala-Korpela; Gabi Kastenmüller; Tim D Spector; Julio Pascual; Cristina Menni
Journal:  Sci Rep       Date:  2018-10-15       Impact factor: 4.379

7.  Clinical Profiles of Selected Biomarkers Identifying Infection and Cancer Patients: A Gorzów Hospital Example.

Authors:  Katarzyna Brzeźniakiewicz-Janus; Marcus Daniel Lancé; Andrzej Tukiendorf; Mirosław Franków; Joanna Rupa-Matysek; Edyta Wolny-Rokicka; Lidia Gil
Journal:  Dis Markers       Date:  2019-09-02       Impact factor: 3.434

8.  Metabonomic profiling of serum and urine by (1)H NMR-based spectroscopy discriminates patients with chronic obstructive pulmonary disease and healthy individuals.

Authors:  Lingling Wang; Yufu Tang; Shuo Liu; Shitao Mao; Yuan Ling; Dan Liu; Xiaoyu He; Xiaoge Wang
Journal:  PLoS One       Date:  2013-06-06       Impact factor: 3.240

Review 9.  Clinical predictive factors in diabetic kidney disease progression.

Authors:  Nicholas J Radcliffe; Jas-Mine Seah; Michele Clarke; Richard J MacIsaac; George Jerums; Elif I Ekinci
Journal:  J Diabetes Investig       Date:  2016-06-08       Impact factor: 4.232

10.  Advanced lipoprotein profile disturbances in type 1 diabetes mellitus: a focus on LDL particles.

Authors:  Antonio J Amor; Esmeralda Castelblanco; Marta Hernández; Marga Gimenez; Minerva Granado-Casas; Jesús Blanco; Berta Soldevila; Enric Esmatjes; Ignacio Conget; Nuria Alonso; Emilio Ortega; Didac Mauricio
Journal:  Cardiovasc Diabetol       Date:  2020-08-09       Impact factor: 9.951

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.