Estimated Glomerular Filtration Rate and Proteinuria Are Separately and Independently Associated with the Prevalence of Atrial Fibrillation in General Population

<div><p>Background</p><p>Both, proteinuria and a decline in glomerular filtration rate (GFR) are associated with greater cardiovascular mortality. However, few studies have explored that proteinuria and lower GFR are related with prevalent atrial fibrillation (AF).</p> <p>Methods</p><p>This cross-sectional study was based on annual health check-up program of community-based population in Gunma, Japan from April 2011 to March 2012. A total of 20,019 adult participants were included. AF was ascertained by a standard 12-lead electrocardiogram. Cross-sectional association and correlates with prevalent AF were examined using multivariable logistic regression analysis.</p> <p>Results</p><p>The overall prevalence of AF was 0.6% (2.2 % in participants with eGFR < 60 mL▪min^-1･1.73m^-2, 0.4% and 0.2% in those with eGFR 60 to 89 and ≧90 mL▪min^-1･1.73m^-2, p for trend <0.001). The multivariable odds ratio (OR) for AF was 2.86 (95 % CI 1.16 - 7.08, p<0.001) for eGFR< 60 mL▪min^-1▪1.73m^-2 versus eGFR≧ 90 mL▪min^-1▪1.73m^-2. This association remained significant with further adjustment for proteinuria. In addition, proteinuria was also strongly associated with increased prevalence of AF (OR 2.96, 95 % CI 1.55-5.68, p<0.001), an association that remained significant after adjustment for eGFR. </p> <p>Conclusions</p><p>Proteinuria and lower eGFR are separately and significantly associated with prevalence of AF independent of well-established risk factors for AF in general population.</p> </div

Odds ratios (OR) and 95% confidence intervals (CI) of atrial fibrillation (AF) according to presence of proteinuria and eGFR.

<p>Multivariable logistic regression models adjusted for age, gender, hypertension, diabetes mellitus, smoking, and cardiac disease.</p

A Risk Score with Additional Four Independent Factors to Predict the Incidence and Recovery from Metabolic Syndrome: Development and Validation in Large Japanese Cohorts

<div><p>Background</p><p>Although many risk factors for Metabolic syndrome (MetS) have been reported, there is no clinical score that predicts its incidence. The purposes of this study were to create and validate a risk score for predicting both incidence and recovery from MetS in a large cohort.</p><p>Methods</p><p>Subjects without MetS at enrollment (n = 13,634) were randomly divided into 2 groups and followed to record incidence of MetS. We also examined recovery from it in rest 2,743 individuals with prevalent MetS.</p><p>Results</p><p>During median follow-up of 3.0 years, 878 subjects in the derivation and 757 in validation cohorts developed MetS. Multiple logistic regression analysis identified 12 independent variables from the derivation cohort and initial score for subsequent MetS was created, which showed good discrimination both in the derivation (c-statistics 0.82) and validation cohorts (0.83). The predictability of the initial score for recovery from MetS was tested in the 2,743 MetS population (906 subjects recovered from MetS), where nine variables (including age, sex, γ-glutamyl transpeptidase, uric acid and five MetS diagnostic criteria constituents.) remained significant. Then, the final score was created using the nine variables. This score significantly predicted both the recovery from MetS (c-statistics 0.70, p<0.001, 78% sensitivity and 54% specificity) and incident MetS (c-statistics 0.80) with an incremental discriminative ability over the model derived from five factors used in the diagnosis of MetS (continuous net reclassification improvement: 0.35, p < 0.001 and integrated discrimination improvement: 0.01, p<0.001).</p><p>Conclusions</p><p>We identified four additional independent risk factors associated with subsequent MetS, developed and validated a risk score to predict both incident and recovery from MetS.</p></div

Angiotensin II Reduces Lipoprotein Lipase Expression in Visceral Adipose Tissue via Phospholipase C β4 Depending on Feeding but Increases Lipoprotein Lipase Expression in Subcutaneous Adipose Tissue via c-Src

<div><p>Metabolic syndrome is characterized by visceral adiposity, insulin resistance, high triglyceride (TG)- and low high-density lipoprotein cholesterol-levels, hypertension, and diabetes—all of which often cause cardiovascular and cerebrovascular diseases. It remains unclear, however, why visceral adiposity but not subcutaneous adiposity causes insulin resistance and other pathological situations. Lipoprotein lipase (LPL) catalyzes hydrolysis of TG in plasma lipoproteins. In the present study, we investigated whether the effects of angiotensin II (AngII) on TG metabolism are mediated through an effect on LPL expression. Adipose tissues were divided into visceral adipose tissue (VAT) and subcutaneous adipose tissue (SAT) for comparison. AngII accelerated LPL expression in SAT but, on the contrary, suppressed its expression in VAT. In both SAT and VAT, AngII signaled through the same type 1 receptor. In SAT, AngII increased LPL expression via c-Src and p38 MAPK signaling. In VAT, however, AngII reduced LPL expression via the G_q class of G proteins and the subsequent phospholipase C β4 (PLCβ4), protein kinase C β1, nuclear factor κB, and inducible nitric oxide synthase signaling pathways. PLCβ4 small interfering RNA experiments showed that PLCβ4 expression is important for the AngII-induced LPL reduction in VAT, in which PLCβ4 expression increases in the evening and falls at night. Interestingly, PLCβ4 expression in VAT decreased with fasting, while AngII did not decrease LPL expression in VAT in a fasting state. In conclusion, AngII reduces LPL expression through PLCβ4, the expression of which is regulated by feeding in VAT, whereas AngII increases LPL expression in SAT. The different effects of AngII on LPL expression and, hence, TG metabolism in VAT and SAT may partly explain their different contributions to the development of metabolic syndrome.</p></div

Postulated regulatory mechanism by AngII of LPL expression in VAT and SAT.

<p>AngII stimulates LPL expression in SAT and, conversely, inhibits its expression in VAT. In both cases, the AngII-induced actions are mediated by the same ATR1 but different G proteins and intracellular signaling pathways. In VAT, PLCβ4 expression is regulated by feeding/fasting cycle and is responsible for the inhibitory role of AngII on LPL expression. See text for more detail.</p

Multivariate Logistic Regression Analysis for the Recovery from Metabolic Syndrome and Risk Scoring System in individuals with Prevalent Metabolic Syndrome.

<p>*Odds ratios to predict recovery from MetS (e.g. Odds ratio<1 means less likely to be recovered subsequently).</p><p>Other Abbreviations as in Tables <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0133884#pone.0133884.t001" target="_blank">1</a> and <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0133884#pone.0133884.t002" target="_blank">2</a>.</p><p>Multivariate Logistic Regression Analysis for the Recovery from Metabolic Syndrome and Risk Scoring System in individuals with Prevalent Metabolic Syndrome.</p

Final score predicts incidence of MetS in the entire Non-MetS population.

<p>Final risk scores for incident MetS were calculated for each individual participant in the entire population without MetS at enrollment (derivation and validation cohorts combined, n = 13,634) as described in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0133884#pone.0133884.t004" target="_blank">Table 4</a>. Incidence (%) in the bottom table represents the developed MetS cases in the population (n = 1,635). I bars represent 95% confidential interval (CI). Other abbreviations as in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0133884#pone.0133884.g001" target="_blank">Fig 1</a>.</p

PKCβ1 and p38 MAP kinase activation by AngII in VAT and SAT, respectively.

<p>(A) PKCβ1 activation over time in VAT. VAT was incubated with AngII (1 μM) for the indicated times to measure PKCβ1 phosphorylation. The ratio of phospho-PKCβ1 to total PKCβ1 was calculated based on densitometric quantification of the bands. (B) p38 MAP kinase activation over time in SAT. SAT was incubated with AngII (1 μM) for the indicated times to measure p38MAP kinase. The ratio of phospho-p38 to total p38 was calculated based on densitometric quantification of the bands. Each column and bar represents the mean ± SEM for three separate experiments. An asterisk (*) indicates <i>p</i><0.05 vs. time 0.</p

PKC, NFκB, and iNOS mediate the effect of AngII on LPL expression in VAT.

<p>(A) IκBα phosphorylation over time in VAT. VAT was cultured with AngII (1 μM) for the indicated times to measure IκBα phosphorylation with western blotting. The ratio of phospho IκBα to total IκBα was calculated based on densitometric quantification of the bands. (B, C) iNOS expression in VAT and visceral adipocytes. VAT and isolated visceral adipocytes were cultured with or without AngII (1 μM) for 24 h to measure iNOS expression by western blotting. Duplicate samples in each group were processed for western blotting. The ratio of iNOS to β-actin was calculated based on densitometric quantification of the bands (B, VAT; C, visceral adipocytes). (D) PKC is upstream of iNOS in LPL regulation in VAT. VAT was pre-treated with L-N^G-nitroarginine Methyl Ester (L-NAME) (1 mM) or 1400w (10 nM) for 1 h prior to phorbol 12-myristate 13-acetate (PMA) (10 nM) addition. After 24 h of AngII (1 μM) or PMA treatment, LPL mRNA expression was measured. The mRNA levels were normalized with β-actin. Each column and bar represents the mean ± SEM for three separate experiments. An asterisk (*) indicates <i>p</i><0.05 vs. time 0 or without AngII.</p

AngII has opposite effects on LPL expression in VAT and SAT.

<p>AngII dose-response of LPL expression in adipose tissues. VAT (A1, B1, and C1) or SAT (A2, B2, and C2) was incubated with AngII (vehicle, 10, 100, or 1000 nM) for 24 h and secreted LPL activity (A), LPL protein expression (B), and LPL mRNA expression (C), respectively, were measured as described in Materials and Methods. Each column and bar represents the mean ± SEM for three separate experiments. An asterisk (*) indicates <i>p</i><0.05 vs. without AngII. The LPL activity levels were normalized with total protein, and expression levels of LPL protein and mRNA were normalized to those of β-actin.</p