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      Relationship Between Dyslipidemia and Albuminuria in Hypertensive Adults : A Nationwide Population-Based Study

      research-article
      , , MD, PhD, , MD, PhD, , MD, PhD, , MD, PhD, , MD, , MD, PhD, , MD, , MD, , MD, , , PhD
      Medicine
      Wolters Kluwer Health

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          Abstract

          This study aimed to estimate the relationship between various lipid abnormalities and albuminuria in hypertensive Korean adults. Data obtained from the Korea National Health and Nutrition Examination Survey in 2011 to 2012 were analyzed. The study included 2330 hypertensive participants. Total cholesterol (TC), triglyceride (TG), high-density lipoprotein cholesterol (HDL-C), and low-density lipoprotein cholesterol (LDL-C) levels were measured. Dyslipidemia parameters were defined as high TG ≥200 mg/dL, low HDL-C as HDL-C <40 mg/dL, high TC/HDL-C as TC/HDL-C ratio ≥4, high TG/HDL-C as TG/HDL-C ratio ≥3.8, and high LDL-C/HDL-C as LDL-C/HDL-C ratio ≥2.5. Albuminuria was defined as a urine albumin to creatinine ratio (ACR) ≥30 mg/g. Women with albuminuria showed significantly higher levels of TG, TC/HDL-C, and TG/HDL-C and a lower level of HDL-C than women without albuminuria (all P < 0.05). LogTG, TC/HDL-C, and logTG/HDL-C were positively correlated with ACR in both men and women; however, HDL-C was negatively correlated with ACR in women and non-HDL-C was positively correlated with ACR in men. In men, there was no association between ACR and lipid parameters. However, in women, higher values for logTG, TC/HDL-C, and logTG/HDL-C were associated with an increased odds ratio (OR) for albuminuria (OR [95% confidence interval]: 1.53 [1.06–2.21], 1.21 [1.02–1.45], and 1.78 [1.21–2.63], respectively) and HDL-C with a decreased OR for albuminuria (0.78 [0.67–0.92]) after adjusting for all covariates. LogTG, TC/HDL-C, and logTG/HDL-C were associated with an increased prevalence of albuminuria in hypertensive women. Screening and treatment for dyslipidemia may be necessary for hypertensive women to address potential albuminuria.

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          Use of metabolic markers to identify overweight individuals who are insulin resistant.

          Insulin resistance is more common in overweight individuals and is associated with increased risk for type 2 diabetes mellitus and cardiovascular disease. Given the current epidemic of obesity and the fact that lifestyle interventions, such as weight loss and exercise, decrease insulin resistance, a relatively simple means to identify overweight individuals who are insulin resistant would be clinically useful. To evaluate the ability of metabolic markers associated with insulin resistance and increased risk for cardiovascular disease to identify the subset of overweight individuals who are insulin resistant. Cross-sectional study. General clinical research center. 258 nondiabetic, overweight volunteers. Body mass index; fasting glucose, insulin, lipid and lipoprotein concentrations; and insulin-mediated glucose disposal as quantified by the steady-state plasma glucose concentration during the insulin suppression test. Overweight was defined as body mass index of 25 kg/m2 or greater, and insulin resistance was defined as being in the top tertile of steady-state plasma glucose concentrations. Receiver-operating characteristic curve analysis was used to identify the best markers of insulin resistance; optimal cut-points were identified and analyzed for predictive power. Plasma triglyceride concentration, ratio of triglyceride to high-density lipoprotein cholesterol concentrations, and insulin concentration were the most useful metabolic markers in identifying insulin-resistant individuals. The optimal cut-points were 1.47 mmol/L (130 mg/dL) for triglyceride, 1.8 in SI units (3.0 in traditional units) for the triglyceride-high-density lipoprotein cholesterol ratio, and 109 pmol/L for insulin. Respective sensitivity and specificity for these cut-points were 67%, 64%, and 57% and 71%, 68%, and 85%. Their ability to identify insulin-resistant individuals was similar to the ability of the criteria proposed by the Adult Treatment Panel III to diagnose the metabolic syndrome (sensitivity, 52%, and specificity, 85%). Three relatively simple metabolic markers can help identify overweight individuals who are sufficiently insulin resistant to be at increased risk for various adverse outcomes. In the absence of a standardized insulin assay, we suggest that the most practical approach to identify overweight individuals who are insulin resistant is to use the cut-points for either triglyceride concentration or the triglyceride-high-density lipoprotein cholesterol concentration ratio.
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            K/DOQI clinical practice guidelines on hypertension and antihypertensive agents in chronic kidney disease.

            (2004)
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              Albuminuria reflects widespread vascular damage. The Steno hypothesis.

              Albuminuria in Type 1 (insulin-dependent) diabetes is not only an indication of renal disease, but a new, independent risk-marker of proliferative retinopathy and macroangiopathy. The coincidence of generalised vascular dysfunction and albuminuria, advanced mesangial expansion, proliferative retinopathy, and severe macroangiopathy suggests a common cause of albuminuria and the severe renal and extrarenal complications associated with it. Enzymes involved in the metabolism of anionic components of the extracellular matrix (e.g. heparan sulphate proteoglycan) vulnerable to hyperglycaemia, seem to constitute the primary cause of albuminuria and the associated complications. Genetic polymorphism of such enzymes is possibly the main reason for variation in susceptibility.
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                Author and article information

                Journal
                Medicine (Baltimore)
                Medicine (Baltimore)
                MEDI
                Medicine
                Wolters Kluwer Health
                0025-7974
                1536-5964
                April 2016
                22 April 2016
                : 95
                : 16
                : e3224
                Affiliations
                From the Department of Family Medicine, Korea University, College of Medicine, Seoul (S-HL, DHK, Y-HK, H-YN, G-EN, J-SC, J-EL, J-ES); Department of Family Medicine, Hallym University, College of Medicine, Chunchon (YKR); Department of Family Medicine, Catholic University (SYJ); and Department of Biostatistics, The Catholic University of Korea, College of Medicine, Seoul (KDH, Y-GP), Republic of Korea.
                Author notes
                Correspondence: Do-Hoon Kim, Department of Family Medicine, Korea University Ansan Hospital, College of Medicine, Korea University, 516, Gojan1-Dong, Danwon-Gu, Ansan-Si, Gyeonggi-Do 425-707, Republic of Korea (e-mail: kmcfm@ 123456hanmail.net ).
                Yang-Hyun Kim, Department of Family Medicine, Korea University Anam Hospital, College of Medicine, Korea University, 73 Inchon-ro, Seongbuk-gu, Seoul 136-705, Republic of Korea (e-mail: mrchir@ 123456naver.com ).
                Article
                03224
                10.1097/MD.0000000000003224
                4845816
                27100412
                baee129c-7187-42ef-97d4-e6ce79ff1593
                Copyright © 2016 Wolters Kluwer Health, Inc. All rights reserved.

                This is an open access article distributed under the terms of the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 License, which allows others to remix, tweak, and build upon the work non-commercially, as long as the author is credited and the new creations are licensed under the identical terms. http://creativecommons.org/licenses/by-nc-sa/4.0

                History
                : 12 November 2015
                : 3 March 2016
                : 9 March 2016
                Categories
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                Research Article
                Observational Study
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