Feeds:
Posts
Comments

Archive for the ‘Population Health Management, Nutrition and Phytochemistry’ Category

Increased Consumption of Dietary Fiber is associated with a significantly Lower Risk of CVD and CHD

Reporter: Aviva Lev-Ari, PhD, RN

LEEDS, UK — Aside from keeping you “regular,” eating fiber also appears to be good for your heart. A new review has shown that increased consumption of dietary fiber is associated with a significantly lower risk of CVD and CHD.

For every 7 g of dietary fiber eaten daily—which can be achieved by eating two to four servings of fruits and vegetables or a serving of whole grains plus a portion of beans or lentils—the risks of CVD and CHD were each lowered by 9%, according to a new meta-analysis published December 19, 2013 in BMJ [1].

“Lower risk of cardiovascular disease was also seen with greater intakes of insoluble, cereal, fruit, and vegetable fiber,” write Diane Threapleton (University of Leeds, UK), a PhD student, and colleagues. “In addition, reduced risk for CHD was associated with greater intake of insoluble fiber and fiber from cereal or vegetable food.”

A cardioprotective effect of dietary fiber was first suggested in the 1970s, and numerous studies have attempted to investigate the link, including the effects of fiber on CV risk factors.

In the present meta-analysis, Threapleton et al analyzed 22 cohort studies that reported total dietary-fiber intake, fiber subtypes, and fiber from food sources and CVD or CHD events. CVD events included CHD along with fatal and incident stroke. Five studies suggested that each 7-g/day increase in insoluble fiber lowered the risk of CVD and CHD by 18%, respectively. Fiber consumption from cereals lowered the risk of CVD and CHD, as did fiber from vegetables. Fiber sourced from fruit lowered the risk of CVD only.

How much do physicians in private practice give their staff for raises and bonuses?
View Report >

Putting these results in perspective, Dr Robert Baron(University of California, San Francisco) gets straight to the point in his editorial, “Eat More Fiber”[2]. Although the study is limited by the potential for confounding—there is the possibility of an association between high fiber intake and other healthy behaviors—”clinicians should enthusiastically and skilfully recommend that patients consume more dietary fiber,” writes Baron. This includes a mix of soluble and insoluble fiber and fiber from multiple food sources, he adds.

Although the evidence for recommending higher fiber intake comes from “imperfect evidence,” including observational studies and expert opinion, the updated meta-analysis by Threapleton et al increases confidence in the recommendation, writes Baron.

The study was funded by the UK Department of Health. Threapleton reports that her PhD studentship receives funding from Kellogg’s. Baron reported no relevant financial disclosures.

References

  1. Threapleton DE, Greenwood DC, Evans CE, et al. Dietary fiber intake and risk of cardiovascular disease: systematic review and meta-analysis. BMJ 2013; 347:f6879. Available at: http://www.bmj.com.
  2. Baron RB. Eat more fiber. BMJ 2013; 347:f7401. Available at: http://www.bmj.com.

SOURCE

http://www.medscape.com/viewarticle/818385?nlid=43303_2105&src=wnl_edit_medp_card&uac=93761AJ&spon=2

 

Read Full Post »

LDL, HDL, TG, ApoA1 and ApoB: Genetic Loci Associated With Plasma Concentration of these Biomarkers – A Genome-Wide Analysis With Replication

Reporter: Aviva Lev-Ari, PhD, RN

Genetic Loci Associated With Plasma Concentration of Low-Density Lipoprotein Cholesterol, High-Density Lipoprotein Cholesterol, Triglycerides, Apolipoprotein A1, and Apolipoprotein B Among 6382 White Women in Genome-Wide Analysis With Replication

Daniel I. Chasman, PhD*, Guillaume Paré, MD, MS*, Robert Y.L. Zee, PhD, MPH, Alex N. Parker, PhD, Nancy R. Cook, ScD, Julie E. Buring, ScD, David J. Kwiatkowski, MD, PhD, Lynda M. Rose, MS, Joshua D. Smith, BS, Paul T. Williams, PhD, Mark J. Rieder, PhD, Jerome I. Rotter, MD, Deborah A. Nickerson, PhD, Ronald M. Krauss, MD,Joseph P. Miletich, MD and Paul M Ridker, MD, MPH

Author Affiliations

From the Center for Cardiovascular Disease Prevention (D.I.C., G.P., R.Y.L.Z., N.R.C., J.E.B., L.M.R., P.M.R.) and Donald W. Reynolds Center for Cardiovascular Research (D.I.C., G.P., R.Y.L.Z., N.R.C., D.J.K., P.M.R.), Brigham and Women’s Hospital, Harvard Medical School, Boston, Mass; Amgen, Inc, Cambridge, Mass (A.N.P., J.M.P.); Department of Genome Sciences, University of Washington, Seattle, Wash (J.D.S., M.J.R., D.A.N.); Life Science Division, Lawrence Berkeley National Laboratory, Berkeley, Calif (P.T.W., R.M.K.); Medical Genetics Institute, Cedars-Sinai Medical Center, Los Angeles, Calif (J.I.R.); and Children’s Hospital Oakland Research Institute, Oakland, Calif (R.M.K.).

Correspondence to Daniel I. Chasman, Center for Cardiovascular Disease Prevention, Brigham and Women’s Hospital, 900 Commonwealth Ave E, Boston, MA 02215. E-mail dchasman@rics.bwh.harvard.edu

Abstract

Background— Genome-wide genetic association analysis represents an opportunity for a comprehensive survey of the genes governing lipid metabolism, potentially revealing new insights or even therapeutic strategies for cardiovascular disease and related metabolic disorders.

Methods and Results— We have performed large-scale, genome-wide genetic analysis among 6382 white women with replication in 2 cohorts of 970 additional white men and women for associations between common single-nucleotide polymorphisms and low-density lipoprotein cholesterol, high-density lipoprotein cholesterol, triglycerides, apolipoprotein (Apo) A1, and ApoB. Genome-wide associations (P<5×10−8) were found at the PCSK9 gene, the APOB gene, the LPLgene, the APOA1-APOA5 locus, the LIPC gene, the CETP gene, the LDLR gene, and the APOE locus. In addition, genome-wide associations with triglycerides at the GCKRgene confirm and extend emerging links between glucose and lipid metabolism. Still other genome-wide associations at the 1p13.3 locus are consistent with emerging biological properties for a region of the genome, possibly related to the SORT1 gene. Below genome-wide significance, our study provides confirmatory evidence for associations at 5 novel loci with low-density lipoprotein cholesterol, high-density lipoprotein cholesterol, or triglycerides reported recently in separate genome-wide association studies. The total proportion of variance explained by common variation at the genome-wide candidate loci ranges from 4.3% for triglycerides to 12.6% for ApoB.

Conclusion— Genome-wide associations at the GCKR gene and near the SORT1gene, as well as confirmatory associations at 5 additional novel loci, suggest emerging biological pathways for lipid metabolism among white women.

 SOURCE:

Circulation: Cardiovascular Genetics.2008; 1: 21-30

doi: 10.1161/ CIRCGENETICS.108.773168

Read Full Post »

Genetics of Aortic and Carotid Calcification: The Role of Serum Lipids

Reporter: Aviva Lev-Ari, PhD, RN

Genetic Loci for Coronary Calcification and Serum Lipids Relate to Aortic and Carotid Calcification

Daniel Bos, MD, M. Arfan Ikram, MD, PhD, Aaron Isaacs, PhD, Benjamin F.J. Verhaaren, MD, Albert Hofman, MD, PhD, Cornelia M. van Duijn, PhD, Jacqueline C.M. Witteman, PhD, Aad van der Lugt, MD, PhD and Meike W. Vernooij, MD, PhD

Author Affiliations

From the Departments of Radiology (D.B., M.A.I., B.F.J.V., A.v.d.L., M.W.V), Epidemiology (D.B., M.A.I., A.I., B.F.J.V., A.H., C.M.v.D., J.C.M.W., M.W.V.), and Genetic Epidemiology Unit (A.I., C.M.v.D.), Erasmus MC, Rotterdam, the Netherlands.

Correspondence to Meike W. Vernooij, MD, PhD, Department of Radiology, Erasmus MC, Gravendijkwal 230, PO Box 2040, 3000CA Rotterdam, the Netherlands. E-mailm.vernooij@erasmusmc.nl

Abstract

Background—Atherosclerosis in different vessel beds shares lifestyle and environmental risk factors. It is unclear whether this holds for genetic risk factors. Hence, for the current study genetic loci for coronary artery calcification and serum lipid levels, one of the strongest risk factors for atherosclerosis, were used to assess their relation with atherosclerosis in different vessel beds.

Methods and Results—From 1987 persons of the population-based Rotterdam Study, 3 single-nucleotide polymorphisms (SNPs) for coronary artery calcification and 132 SNPs for total cholesterol, low-density lipoprotein cholesterol, high-density lipoprotein cholesterol or triglycerides were used. To quantify atherosclerotic calcification as a marker of atherosclerosis, all participants underwent nonenhanced computed tomography of the aortic arch and carotid arteries. Associations between genetic risk scores of the joint effect of the SNPs and of all calcification were investigated. The joint effect of coronary artery calcification–SNPs was associated with larger calcification volumes in all vessel beds (difference in calcification volume per SD increase in genetic risk score: 0.15 [95% confidence interval, 0.11–0.20] in aorta, 0.14 [95% confidence interval, 0.10–0.18] in extracranial carotids, and 0.11 [95% confidence interval, 0.07–0.16] in intracranial carotids). The joint effect of total cholesterol SNPs, low-density lipoprotein SNPs, and of all lipid SNPs together was associated with larger calcification volumes in both the aortic arch and the carotid arteries but attenuated after adjusting for the lipid fraction and lipid-lowering medication.

Conclusions—The genetic basis for aortic arch and carotid artery calcification overlaps with the most important loci of coronary artery calcification. Furthermore, serum lipids share a genetic predisposition with both calcification in the aortic arch and the carotid arteries, providing novel insights into the cause of atherosclerosis.

 SOURCE:

Circulation: Cardiovascular Genetics.2013; 6: 47-53

Published online before print December 16, 2012,

doi: 10.1161/ CIRCGENETICS.112.963934

 

Read Full Post »

Gene Study of Blood Pressure Response to Dietary Potassium Intervention: Genetic Epidemiology of Salt Sensitivity

Reporter: Aviva Lev-Ari, PhD, RN

Genome-Wide Linkage and Positional Candidate Gene Study of Blood Pressure Response to Dietary Potassium Intervention

The Genetic Epidemiology Network of Salt Sensitivity Study

Tanika N. Kelly, PhD, James E. Hixson, PhD, Dabeeru C. Rao, PhD, Hao Mei, MD, PhD,Treva K. Rice, PhD, Cashell E. Jaquish, PhD, Lawrence C. Shimmin, PhD, Karen Schwander, MS, Chung-Shuian Chen, MS, Depei Liu, PhD, Jichun Chen, MD,Concetta Bormans, PhD, Pramila Shukla, MS, Naveed Farhana, MS, Colin Stuart, BS,Paul K. Whelton, MD, MSc, Jiang He, MD, PhD and Dongfeng Gu, MD, PhD

Author Affiliations

From the Department of Epidemiology (T.N.K., H.M., C.-S.C., J.H.), Tulane University School of Public Health and Tropical Medicine, and Department of Medicine (J.H.), Tulane University School of Medicine, New Orleans, La; Department of Epidemiology (J.E.H., L.C.S., C.B., P.S., N.F., C.S.), University of Texas School of Public Health, Houston, Tex; Division of Biostatistics (D.C.R., T.K.R., K.S.), Washington University School of Medicine, St Louis, Mo; Division of Prevention and Population Sciences (C.E.J.), National Heart, Lung, Blood Institute, Bethesda, Md; National Laboratory of Medical Molecular Biology (D.L.), Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China; Cardiovascular Institute and Fuwai Hospital (J.C., D.G.), Chinese Academy of Medical Sciences and Peking Union Medical College and Chinese National Center for Cardiovascular Disease Control and Research, Beijing, China; and Office of the President (P.K.W.), Loyola University Health System and Medical Center, Maywood, Ill.

Correspondence to Dongfeng Gu, MD, PhD, Division of Population Genetics and Prevention, Cardiovascular Institute and Fuwai Hospital, 167 Beilishi Rd, Beijing 100037, China. E-mail gudongfeng@vip.sina.com

Abstract

Background— Genetic determinants of blood pressure (BP) response to potassium, or potassium sensitivity, are largely unknown. We conducted a genome-wide linkage scan and positional candidate gene analysis to identify genetic determinants of potassium sensitivity.

Conclusions— Genetic regions on chromosomes 3 and 11 may harbor important susceptibility loci for potassium sensitivity. Furthermore, the AGTR1 gene was a significant predictor of BP responses to potassium intake.

SOURCE:

Circulation: Cardiovascular Genetics. 2010; 3: 539-547

Published online before print September 22, 2010,

doi: 10.1161/ CIRCGENETICS.110.940635

Read Full Post »

Transthyretin and Lean Body Mass in Stable and Stressed State

Curator: Larry H Bernstein, MD, FCAP

Chapter 20
Plasma Transthyretin Reflects the Fluctuations
of Lean Body Mass in Health and Disease
Yves Ingenbleek
Abstract

Transthyretin (TTR) is a 55-kDa protein secreted mainly by the choroid plexus and the liver. Whereas its intracerebral production appears as a stable secretory process allowing even distribution of intrathecal thyroid hormones, its hepatic synthesis is influenced by nutritional and inflammatory circumstances working concomitantly. Both morbid conditions are governed by distinct pathogenic mechanisms leading to the reduction in size of lean body mass (LBM). The liver production of TTR integrates the dietary and stressful components of any disease spectrum, explaining why it is the sole plasma protein whose evolutionary patterns closely follow the shape outlined by LBM fluctuations. Serial measurement of TTR therefore provides unequalled information on the alterations affecting overall protein nutritional status. Recent advances in TTR physiopathology emphasize the detecting power and preventive role played by the protein in hyperhomocysteinemic states, acquired metabolic disorders currently ascribed to dietary restriction in water-soluble vitamins. Sulfur (S)-deficiency is proposed as an additional causal factor in the sizeable proportion of hyperhomocysteinemic patients characterized by adequate vitamin intake but experiencing varying degrees of nitrogen (N)-depletion. Owing to the fact that N and S coexist in plant and animal tissues within tightly related concentrations, decreasing LBM as an effect of dietary shortage and/or excessive hypercatabolic losses induces proportionate S-losses. Regardless of water-soluble vitamin status, elevation of homocysteine plasma levels is negatively correlated with LBM reduction and declining TTR plasma levels. These findings occur as the result of impaired cystathionine-b-synthase activity, an enzyme initiating the transsulfuration pathway and whose suppression promotes the upstream accumulation and remethylation of homocysteine molecules. Under conditions of N- and S-deficiencies,the maintenance of methionine homeostasis indicates high metabolic priority.
Y. Ingenbleek
Laboratory of Nutrition, University Louis Pasteur Strasbourg
e-mail: yves.ingenbleek@wanadoo.fr
S.J. Richardson and V. Cody (eds.), Recent Advances in Transthyretin Evolution, 329
Structure and Biological Functions,
DOI: 10.1007/978‐3‐642‐00646‐3_20, # Springer‐Verlag Berlin Heidelberg 2009

Read Full Post »

Effect of Dietary Magnesium Intake on Insulin Resistance

Reporter: Larry H. Bernstein, MD, FCAP

Dietary Magnesium Intake Improves Insulin Resistance among Non-Diabetic Individuals with Metabolic Syndrome Participating in a Dietary Trial

J Wang1,2,†, G Persuitte3,†, BC Olendzki2, NM Wedick2, …, and Yunsheng Ma 2,*
1 Department of Preventive Medicine, Medical School of Yangzhou University, Yangzhou 225001, China
2 Division of Preventive and Behavioral Medicine, Department of Medicine, University of Massachusetts Medical School, Worcester, MA 01655, USA
3 Division of Biostatistics and Health Services Research, Department of Quantitative Health Science, University of Massachusetts Medical School, Worcester, MA 01655, USA †

Nutrients 27 Sep 2013; 5(10):3910-3919; http://dx.doi.org/10.3390/nu5103910

Many cross-sectional studies show

  1. an inverse association between dietary magnesium and insulin resistance, but
  2. few longitudinal studies examine the ability to meet the Recommended Dietary Allowance (RDA)
  • for magnesium intake through food and
  • its effect on insulin resistance among participants with metabolic syndrome (MetS).

The dietary intervention study examined this question in 234 individuals with MetS. Magnesium intake was assessed using 24-h dietary recalls at baseline, 6, and 12 months.

  1. Fasting glucose and insulin levels were collected at each time point; and
  2. insulin resistance was estimated by the homeostasis model assessment (HOMA-IR).

The relation between magnesium intake and HOMA-IR was assessed using linear mixed models adjusted for covariates.

  • Baseline magnesium intake was 287 ± 93 mg/day (mean ± standard deviation), and
  • HOMA-IR, fasting glucose and fasting insulin were 3.7 ± 3.5, 99 ± 13 mg/dL, and 15 ± 13 μU/mL, respectively.

At baseline, 6-, and 12-months, 23.5%, 30.4%, and 27.7% met the RDA for magnesium. After multivariate adjustment,

    • magnesium intake was inversely associated with metabolic biomarkers of insulin resistance (P < 0.01).

Further, the likelihood of elevated HOMA-IR (>3.6) over time was 71% lower [odds ratio (OR): 0.29; 95% confidence interval (CI): 0.12, 0.72] in participants

  • in the highest quartile of magnesium intake than those in the lowest quartile.

For individuals meeting the RDA for magnesium,

  • the multivariate-adjusted OR for high HOMA-IR over time was 0.37 (95% CI: 0.18, 0.77).

These findings indicate that dietary magnesium intake is inadequate among non-diabetic individuals with MetS and suggest that

    • increasing dietary magnesium to meet the RDA has a protective effect on insulin resistance.

Keywords: magnesium; insulin resistance; metabolic syndrome; epidemiology

Cite This Article

Wang J, Persuitte G, Olendzki BC, Wedick NM, Zhang Z, Merriam PA, Fang H, Carmody J, Olendzki G-F, Ma Y. Dietary Magnesium Intake Improves Insulin Resistance among Non-Diabetic Individuals with Metabolic Syndrome Participating in a Dietary Trial. Nutrients. 2013; 5(10):3910-3919.

Read Full Post »

Cocoa and Heart Health

Reporter: Larry H. Bernstein, MD, FCAP

Nutrients 2013, 5(10), 3854-3870;   http://dx.doi.org/10.3390/nu5103854

Cocoa and Heart Health: A Historical Review of the Science

Deanna L. Pucciarelli

Department of Family and Consumer Sciences, Ball State University, Muncie, IN

Accepted: 11 September 2013 / Published: 26 September 2013

(This article belongs to the Special Issue Chocolate and Cocoa in Human Health)

Download PDF Full-Text[263 KB, uploaded 26 September 2013

Abstract: The medicinal use of cocoa has a long history dating back almost five hundred years when Hernán Cortés’s first experienced the drink
in Mesoamerica.  Doctors in Europe recommended the beverage to patients in the 1700s, and later American physicians followed suit  and prescribed the drink in early America—ca. 1800s.  This article delineates the historic trajectory of cocoa consumption, the linkage between cocoa’s bioactive-
mechanistic properties, paying special attention to

  • nitric oxides role in vasodilation of the arteries,
  • to the current indicators purporting the benefits of cocoa and cardiovascular health.

Keywords: cocoa; heart-health; nitric oxide; cardiovascular disease; medical history

Pucciarelli, D.L. Cocoa and Heart Health: A Historical Review of the Science. Nutrients 2013; 5(10): 3854-3870.

Comment

Aldujaili Emad

Senior Lecturer at Queen Margaret University

This review highlights the beneficial effect of dark chocolate intake on CVD risk factors. We have been doing trials
on dark chocolate
since 2007 studying its effects on several risk factors influencing the heart function such as

  • blood pressure, 
  • BMI, 
  • insulin resistancce
  • and body fat. 

It is important here to emphasize the issue of what type of dark chocolate is the most beneficial? Not every dark
chocolate in the market is useful. It depends on the processing. some methods destroy the polyphenols and therefore
reduce the benefits. Below some
of our publications using the Barry Callebaut chocolate produced by the Acticoa process:

S. Almoosawi, C. Tsang, L. M. Ostertag, L. Fyfe and E. A. S. Al-Dujaili (2012) Differential effect of polyphenol-rich
dark chocolate on
biomarkers of glucose metabolism and cardiovascular risk factors in healthy, overweight and obese subjects: a randomized clinical trial.Food and Function.
Cite this: http://dx.doi.org/10.1039/c2fo30060e. This journal is of The Royal Society of Chemistry 2012


Almoosawi, Suzana and Fyfe, Lorna and Ho, Clement and Al-Dujaili, Emad A S (2010) The effect of polyphenol-rich dark
chocolate on fasting
capillary whole blood glucose, total cholesterol, blood pressure and glucocorticoids in healthy overweight and obese subjects.
British Journal of Nutrition 2010;103(6):842-850. ISSN 0007-1145

Read Full Post »

Leptin signaling in mediating the cardiac hypertrophy associated with obesity

Curators: Larry H Bernstein, MD, FCAP and Aviva Lev-Ari, PhD, RN

 

There has been a lot of interest in leptins and both insulin resistance and obesity for the last decade.  The association between obesity and cardiac hypertrophy is also known, but what drives this association.  We have covered heart disease from many aspects in a long series of articles.  The next is a pleasure to take in.

Importance of leptin signaling and signal transducer and activator of transcription-3 activation in mediating the cardiac hypertrophy associated with obesity

Maren Leifheit-Nestler12, Nana-Maria Wagner13, Rajinikanth Gogiraju1,Michael Didié14, Stavros Konstantinides15, Gerd Hasenfuss1 and Katrin Schäfer1*

1Department of Cardiology and Pulmonary Medicine, Heart Research Center, Georg August University Medicine Goettingen, Robert Koch Strasse 40, D-37075, Göttingen, Germany

2Current address: Department of Pediatric Kidney, Liver and Metabolic Diseases, Hannover Medical School, Hannover, Germany

3Current address: Clinic for Anesthesiology and Intensive Care Medicine, University Medicine Rostock, Rostock, Germany

4Department of Pharmacology, Georg August University Medicine Goettingen, Goettingen, Germany

5Current address: Center for Thrombosis and Hemostasis, University Medicine Mainz, Mainz, Germany

J Translational Medicine: Cardiovascular, Metabolic and Lipoprotein Translation. 2013; 11:170.  http://www.translational-medicine.com/content/11/1/170

http://dx.doi.org/10.1186/1479-5876-11-170

This is an Open Access article distributed under the terms of the Creative Commons Attribution License 
http://creativecommons.org/licenses/by/2.0

Abstract

Background

The adipokine leptin and its receptor are expressed in the heart, and

  • leptin has been shown to promote cardiomyocyte hypertrophy in vitro.

Obesity is associated with

  • hyperleptinemia 
  • hypothalamic leptin resistance and
  • an increased risk to develop cardiac hypertrophy and heart failure.

However, the role of cardiac leptin signaling in mediating the cardiomyopathy associated with increased body weight is unclear, in particular, whether it develops subsequently to cardiac leptin resistance or overactivation of hypertrophic signaling pathways via elevated leptin levels.

Methods

The cardiac phenotype of high-fat diet (HFD)-induced obese wildtype (WT) mice was examined and compared to age-matched genetically obese leptin receptor (LepR)-deficient (LepRdb/db) or lean WT mice. To study the role of leptin-mediated STAT3 activation during obesity-induced cardiac remodeling,

  • mice in which tyrosine residue 1138 within LepR had been replaced with a serine (LepRS1138) were also analyzed.

Results

Obesity was associated with hyperleptinemia and elevated cardiac leptin expression in both diet-induced and genetically obese mice.

  • Enhanced LepR and STAT3 phosphorylation levels were detected in hearts of obese WT mice, but not in those with LepR mutations, and
  • exogenous leptin continued to induce cardiac STAT3 activation in diet-induced obese mice.

Although echocardiography revealed signs of cardiac hypertrophy in all obese mice,

  • the increase in left ventricular (LV) mass and diameter was significantly more pronounced in LepRS1138 animals.

LepRS1138 mice also exhibited an increased activation of signaling proteins downstream of LepR, including Jak2 (1.8-fold), Src kinase (1.7-fold), protein kinase B (1.3-fold) or C (1.6-fold). Histological analysis of hearts revealed that the inability of leptin to activate STAT3 in LepRdb/db and LepRS1138 mice

  • was associated with reduced cardiac angiogenesis as well as increased apoptosis and fibrosis.

Conclusions

Our findings suggest that hearts from obese mice continue to respond to elevated circulating or cardiac leptin, which

  • may mediate cardioprotection via LepR-induced STAT3 activation, whereas
  • signals distinct from LepR-Tyr1138 promote cardiac hypertrophy.

On the other hand, the presence of cardiac hypertrophy in obese mice with complete LepR signal disruption indicates that additional pathways also play a role.

Keywords:

Heart; Hypertrophy; Leptin; Obesity; Signal transduction; STAT3

Background

Obesity is frequently associated with elevated circulating leptin levels [1] and an increased risk to develop cardiac hypertrophy [2,3] or heart failure [4]. Clinical studies demonstrated a positive correlation between serum leptin levels and left ventricular (LV) mass or wall thickness [5,6], independent of blood pressure levels, suggesting a direct role for leptin in the pathogenesis of obesity-associated cardiomyopathy. Furthermore, leptin was shown to promote hypertrophy of isolated rat or human ventricular cardiomyocytes [7,8], and

  • this effect could be prevented using neutralizing antibodies [9].

Cardiac hypertrophy also develops in obese rodents fed high-fat diet (HFD)[10,11], and

  • studies in mice with (functional) leptin deficiency suggested that the cardiac hypertrophy developing in states of chronic hyperleptinemia
  • may result from the inability to transduce anti-hypertrophic and/or cardioprotective effects of the adipokine [12,13].

The effects of leptin on cell shortening and intracellular Ca2+ transients were abrogated in cardiomyocytes isolated from HFD-fed obese rats [14], but then others found

  • a preserved signal transduction in response to leptin in hyperleptinemic obese mice [15,16] or rats[17].

The leptin receptor (LepR) belongs to the family of cytokine type I receptors that signal via activation of

  • Janus kinase (Jak)-2 and
  • signal transducer and
  • activator of transcription (STAT)-3 [18].

Analysis of cardiomyocytes ex vivo revealed leptin promotes hypertrophy via activation of p38 and p42/44 MAP kinases as well as protein kinase B (Akt) [19,20]. But it is unknown whether STAT-3 activation downstream of LepR is required to transmit the cardiac effects of leptin and whether it may be involved in mediating protective (i.e. anti-apoptotic, anti-fibrotic or pro-angiogenic) signals, as previously reported in mice with cardiomyocyte-specific STAT-3 deletion [21,22].

In this study, we examined the cardiac phenotype of diet-induced (i.e. with hypothalamic leptin resistance) and genetically obese (i.e. with systemic leptin receptor deficiency) hyperleptinemic mice, developing with age or after continuous β-adrenergic stimulation. Moreover, we determined

  • the importance of leptin-mediated STAT-3 activation
  • for the development of cardiac hypertrophy in obesity
  • by analyzing mice with targeted mutation of the STAT3 binding site within LepR.

Methods

Animals

C57Bl6/J leptin receptor-deficient db/db (LepRdb/db; BKS.Cg Leprdb/Leprdb) mice and C57Bl6/J wildtype (WT) controls were obtained from Harlan Winkelmann, Germany. Mice heterozygous mutant for the LepRS1138 allele (on the congenic B6.129/J background; 98- > 99% homozygous for C57Bl/6; [23]) were obtained from Professor Martin Myers (University of Michigan Medical School, Ann Arbor, USA) and bred at the animal facility of the University of Goettingen, Germany, to generate homozygous mutant obese LepRS1138 mice. Age- and gender-matched WT (LepR+/+) and heterozygous (LepRS/+) littermates were used as controls. To induce obesity, 3 months-old mice were switched to high-fat diet (HFD; D12451) for 4 months, while controls were maintained on normal rodent chow (D12450B; both Research Diets Inc.). The composition of both diets is shown in Additional file 1: Table S1. To examine the cardiac response to hypertrophic stimuli other than leptin, osmotic minipumps (Alzet®; model 2002; Charles River Laboratories) were filled with isoprenaline hydrochloride (Sigma; 20 mg/kg body weight [BW] per day) and implanted for 14 days under the dorsal skinfold of 2 months-old, 2% isoflurane anesthetized mice. At the time of tissue harvest, mice were weighed followed by intraperitoneal anesthesia with a mixture of 2% xylazine (6 mg/kg BW) and 10% ketamine hydrochloride (100 mg/kg BW), and blood was drawn by cardiac puncture. Hearts were rapidly excised, the atria removed and ventricles immediately processed for protein isolation or cryoembedding, respectively. All animal care and experimental procedures had been approved by the institutional Animal Research Committee and complied with national guidelines for the care and use of laboratory animals.

Additional file 1: Table S1. Diet composition.
Format: DOC Size: 33KB

Serum analysis

Freshly drawn blood was allowed to clot at room temperature (RT) for 30 min, followed by centrifugation for 10 min at 3,000 rpm. The supernatant was stored at -80°C pending analysis of serum leptin levels using specific enzyme-linked immunoassays (ELISA; R&D Systems).

Echocardiography

Echocardiography was performed by a blinded examiner at the day before tissue harvest in mice under 1.5% isoflurane anesthesia using the VisualSonics Vevo 2100 system (Visualsonics) equipped with a 30 MHz center frequency ultrasound transducer, as previously described [24]. M-mode echocardiographical recordings were used to determine the end-diastolic and end-systolic LV diameter (EDD and ESD, respectively) and the ventricular wall thickness (WTh), corresponding to the mean of the anterior and posterior WTh. LV mass was calculated using the formula: 1.055 × ([AWTh + EDD + PWTh]3 – EDD3). Fractional shortening (FS) was calculated as (EDD – ESD)/EDD × 100. B-mode echocardiography images were used to calculate the heart weight, using the equation: 1.05 × (5/6) × ((Episyst × (Lsyst + ((AWThsyst + PWThsyst)/2))) – (Areasyst × Lsyst)).

Histology and immunohistochemistry

Histochemical analyses were performed on 5 μm-thick frozen cross sections through the LV. For each mouse, 4 sections (approx. 500 μm apart) and 4 randomly selected viewing fields (at 200-fold magnification) per section were analyzed and findings averaged. Cardiac fibrosis was determined after overnight incubation in Bouin’s fixative followed by Masson’s trichrome (MTC) stain. Monoclonal rat antibodies against mouse CD31 (Santa Cruz Biotechnology) were used to detect endothelial cells [24,25]. Their number was manually counted by a person blinded to the mouse genotype and expressed per mm2 or cardiomyocyte, respectively.

Single cardiomyocytes were visualized by incubation with fluorescein-labeled wheat germ agglutinin (WGA; Molecular Probes), followed by determination of the cardiomyocyte cross-sectional area (CSA) using image analysis software (Image ProPlus). Per cross section, at least 10 randomly selected cardiomyocytes were evaluated and results averaged. Apoptosis was analyzed using the ‘In Situ Cell Death Detection kit’ (Roche). Cell nuclei were visualized using 4′,6-diamidino-2-phenylindole (DAPI; Sigma).

Immunoprecipitation and western blot analysis

Membranes were blocked in 1% bovine serum albumine (in TBS, containing 0.1% Tween-20) prior to incubation with antibodies against phosphorylated (p)-Akt (S473) and total Akt, p-Jak2 (Y1007/1008) and total Jak2, p-p38 (T180/Y182) and total p38, p-p42/44 (T202/Y204) and total p42/44, p-Src (Y416) and total Src, p-STAT3 (Y705) and total STAT3, or p-PKC (pan), respectively (all Cell Signaling Technologies), or against leptin (R&D Systems) and GAPDH (Biotrend), respectively. Protein bands were visualized using HRP-conjugated secondary antibodies (Amersham Biosciences), followed by detection with SuperSignal® West Pico Substrate (Pierce). For the analysis of LepR phosphorylation, 100 μg total heart tissue lysates were immunoprecipitated under rotation at 4°C with 2 μg anti-LepR antibody (against an internal domain present in the short and long isoforms of murine LepR; Santa Cruz Biotechnology) plus 50 μL nProtein A Sepharose™ 4 Fast Flow beads (GE Healthcare) followed by detection of phosphorylated tyrosines (p-Tyr [PY20]; Santa Cruz Biotechnology) or LepR. For the analysis of STAT3 phosphorylation in response to acute elevations of circulating leptin, mice were fasted overnight, injected with recombinant murine leptin (1 mg/kg BW i.p.) and hearts harvested 30 min later.

Statistical analysis

Quantitative data are presented as mean ± standard error of the mean (SEM). Normal data distribution was tested using the D’Agostino & Pearson omnibus normality test. When three or more groups were compared, ANOVA was employed, if samples were normally distributed, or Kruskal-Wallis test, if not. For post-hoc comparisons, ANOVA was followed by Bonferroni’s and Kruskal-Wallis by Dunn’s multiple comparison test. Differences before and after isoprenaline infusion were tested using Student’s t-test for paired means. Statistical significance was assumed when P reached a value less than 0.05. All statistical analyses were performed using GraphPad PRISM software, version 4.01 (GraphPad Software Inc).

Results

Clinical and experimental studies revealed that obesity is associated with LV hypertrophy [10,11], an important risk factor for the development of heart failure. As shown in Tables 1 and 2, WT mice fed HFD for 4 months (WT + HFD; mean body weight [BW], 44±1.9 g) to induce obesity exhibited a non-significant trend towards an increased mean heart weight, LV mass and WTh compared to age-matched lean controls fed normal chow (BW, 29±1.0 g). Marked LV hypertrophy was observed in 7 months-old obese LepRdb/db mice (Table 1 and 2), consistent with a previous report [12]. Longitudinal sections through hearts of WT, WT + HFD and LepRdb/db mice are shown in Figure 1A, representative M-mode echocardiography recordings in Figure 1B and cardiac cross-sections after WGA staining to delineate cardiomyocyte borders in Figure 1C. Of note, adiposity in mice with LepR deficiency was more pronounced compared to age-matched WT + HFD mice (Table 1; P < 0.001), in which obesity develops as result of hypothalamic resistance to chronically elevated leptin levels[26].

1479-5876-11-170-1 F1 Cardiac phenotype of lean and obese WT

Figure 1.Cardiac phenotype of lean and obese WT, WT + HFD, LepRS1138 and LepRdb/db mice.
(A)
Representative H&E-stained longitudinal sections through hearts of 7 months-old mice are shown. Magnification, ×10.(B) Representative M-mode echocardiographic recordings.(C) Representative images of wheat germ agglutinin (WGA)-stained myocardial cross sections. The mean cardiomyocyte cross-sectional areas are given in Table 1.

Table 1.Body, visceral fat and heart weights in 7 months-old mice

Table 2.Echocardiographic parameter in 7 months-old mice

The presence of cardiac hypertrophy in LepR-deficient and, to a lesser extent also in diet-induced obese mice, suggests that it develops as a result of the heart’s inability to respond to elevated systemic (Table 1) and/or cardiac (Figure 2A) leptin levels. In this regard, Western blot analysis revealed increased levels of phosphorylated (p-) LepR (Figure 2B) and STAT-3 (Figure 2C) protein in hearts of HFD-induced obese mice (P < 0.05 vs. WT for both), whereas findings in LepRdb/db mice did not differ from those in lean controls or were reduced compared to those in WT + HFD mice (P < 0.05 for differences in LepR phosphorylation). Moreover, both lean and HFD-induced obese WT mice responded to a single i.p. injection of recombinant murine leptin with a significant increase in the cardiac STAT-3 phosphorylation (Figure 2D), suggesting a preserved cardiac leptin signal transduction in hyperleptinemic, diet-induced obese mice.

???????

Figure 2.Cardiac leptin expression and signal transduction in lean and obese mice.
Protein was extracted from hearts of 7 months-old mice (n = 8 per group) and analyzed for the expression of
(A) leptin, (B)phosphorylated LepR (using immunoprecipitation of LepR, followed by the detection of total phosphotyrosines and LepR) and (C) phosphorylated STAT3. (D) Cardiac STAT3 phosphorylation in response (30 min later) to a single injection of recombinant murine leptin (1 mg/kg BW i.p.) was examined in WT (n = 4) and WT + HFD (n = 6) mice. Results are expressed as -fold increase of controls (black bars) after normalization for total protein and GAPDH expression. The mean ± SEM as well as representative Western blot results are shown. *P < 0.05 and **P < 0.01 vs. WT mice; #P < 0.05 vs. WT + HFD mice.

To further study the role of leptin signaling in the development of cardiac hypertrophy and also to determine, whether the inability of leptin to activate STAT3 contributes to the cardiac maladaptation in obesity, we examined mice in which tyrosine (Tyr)1138 within LepR had been replaced by a serine (LepRS1138). In these mice, leptin cannot signal via STAT3, but continues to be able to activate Jak2 and SH2 domain-containing adapter proteins. Western blot analysis revealed that p-LepR (Figure 2B) and p-STAT3 (Figure 2C) levels in hearts of LepRS1138 mice did not significantly differ from those in WT and LepRdb/db mice. Similar to mice with complete LepR deficiency, lack of LepR-mediated STAT3 activation resulted in severe adiposity, although serum leptin levels were lower than those in LepRdb/db mice (P < 0.001; Table 1). Interestingly, obese LepRS1138 exhibited a more pronounced increase in mean heart weights not only compared to lean or diet-induced obese WT mice, but also compared to LepRdb/db mice (P < 0.001 for all comparisons; Table 1), and differences persisted after normalization for body weight (P < 0.001) or tibia length (P < 0.001). Echocardiography confirmed increased LV mass (P < 0.01) or heart weights (P < 0.001) in LepRS1138 mice compared to their LepRdb/dbcounterparts (Table 2; please also see Figure 1A-C). Moreover, hearts of LepRS1138 mice exhibited elevated levels of phosphorylated Jak2 (P < 0.001 vs. WT; Figure 3A), Src kinase (P < 0.05 vs. WT, WT + HFD and LepRdb/db; Figure 3B), Akt (P < 0.001 vs. LepRdb/db; Figure 3C), PKC (P < 0.05 vs. WT and LepRdb/db, P < 0.01 vs. WT + HFD; Figure 3D) and p38 MAPK (P < 0.01 vs. LepRdb/db; Figure 3E), suggesting that an intact, Tyr1138-independent LepR activation in the presence of elevated leptin levels may have contributed to the pronounced cardiac hypertrophy present in these mice. On the other hand, cardiac levels of p-p42/44 MAPK did not significantly differ between the mouse groups (Figure 3F).

Figure 3. Hypertrophic signal transduction

Figure 3.Hypertrophic signal transduction in hearts of lean and obese mice. Protein was isolated from hearts of 7 months-old WT (n = 15), WT + HFD (n = 12), LepRS1138(n = 15) and LepRdb/db (n = 15) mice and analyzed for the expression of phosphorylated Jak2 (A), Src kinase (B), Akt (C), PKC (D), p38 (E) and p42/44 MAPK (F). Results are expressed as -fold increase of lean control mice (after normalization for total protein [with the exception of PKC] and GAPDH expression). The mean ± SEM as well as representative findings are shown. *P < 0.05, **P < 0.01 and ***P < 0.001 vs. WT mice; #P < 0.05 and ##P < 0.01 vs. WT + HFD mice; §P < 0.05, §§P < 0.01 and §§§P < 0.001 for the difference between LepRdb/db and LepRS1138 mice.

M-mode echocardiography also revealed significantly increased enddiastolic LV diameters in LepRS1138 mice (P < 0.01 vs. WT and LepRdb/db mice; Table 2; representative findings are shown in Figure 1B), suggesting that the observed (over-)activation of LepR signaling together with the inability to induce STAT3 may result in augmented hypertrophy and maladaptive cardiac remodeling. Of note, fractional shortening (FS) was not significantly altered in HFD-induced obese WT mice (P = n.s. vs. WT mice), but found to be increased in both LepRdb/db (P < 0.01 vs. WT and P < 0.001 vs. WT + HFD mice) and LepRS1138mice (P < 0.05 vs. WT and P < 0.001 vs. WT + HFD mice). Histological analyses revealed significantly reduced numbers of CD31-positive capillary endothelial cells in LepRdb/db, and to a lesser extent also in LepRS1138 mice (Figure 4A), whereas the number of TUNEL-positive apoptotic cells (Figure 4B) and the fibrotic tissue area (Figure 4C) were found to be significantly increased in hearts of both LepRS1138 and LepRdb/db mice compared to lean and diet-induced obese WT mice.

Figure 4.Histological analysis of angiogenesis, apoptosis and fibrosis in hearts of lean and obese mice.
Serial cross sections through the LV of WT, WT + HFD, LepR
S1138 and LepRdb/db mice (n = 10 per group) were immunostained and the number of (A) CD31-positive endothelial cells and (B) TUNEL-positive apoptotic cell nuclei determined. Results are expressed per cardiomyocyte and/or mm2.(C) The degree of cardiac fibrosis was quantified after Masson’s trichrome (MTC) staining. Results are expressed as % of total tissue area (at 200-fold magnification). The mean ± SEM as well as representative findings are shown. **P < 0.01 and ***P < 0.001 vs. WT; #P < 0.05, ##P < 0.01 and ###P < 0.001 vs. WT + HFD mice.

Figure 4.Histological analysis  (unable to post)

To examine the specificity of leptin’s hypertrophic action in obesity, the cardiac response of young, i.e. 2 months-old WT (n = 12; body weight, 22 ± 0.9 g), LepRS1138 (n = 9; 34 ± 1.1 g, P < 0.001 vs. WT) and LepRdb/db mice (n = 7; 40 ± 1.3 g; P < 0.001 vs. WT and P < 0.01 vs. LepRS1138) to chronic isoprenaline infusion (20 mg/kg BW per day) was examined. Under basal conditions, similar findings as those in 7 months-old mice were observed, i.e. LepRS1138 mice exhibited an increased heart weight (P < 0.05 vs. LepRdb/db; Figure 5A), LV mass (P < 0.01 vs. WT; Figure 5B) and mean WTh (P < 0.05 vs. WT; Figure 5C), whereas other changes, such as differences in fractional shortening (Figure 5D), ESD (Figure 5E) and EDD (Figure 5F) were not (yet) detected. On the other hand, all mouse groups responded to chronic β-adrenergic stimulation with significant cardiac hypertrophy, and no differences (with the exception of heart weight; Figure 5A) were observed between LepRS1138 and LepRdb/db mice. Representative M-mode echocardiography tracings are shown in Figure 6 and summarized in Additional file 2: Table S2.

1479-5876-11-170-5 F5 Echocardiography findings

Figure 5.Echocardiography findings in young lean and obese mice before and after chronic β-adrenergic stimulation.
Isoprenaline-filled osmotic minipumps were subcutaneously implanted into 2 months-old WT (n = 12), LepR
S1138 (n = 9) and LepRdb/db (n = 7) mice to examine the cardiac response to a hypertrophic stimulus other than leptin. Echocardiography (A-F) was performed immediately before (open bars) as well as at the time of tissue harvest 14 days later (dotted bars). *P < 0.05, *P < 0.01 and ***P < 0.001 for differences vs. WT mice; §P < 0.05 for differences between LepRdb/db and LepRS1138 mice. Significance levels for differences before and after isoprenaline stimulation (as determined using Student’s t test for paired means) are indicated within the graph.

???????

Figure 6.Representative M-mode echocardiography recordings.

Additional file 2: Table S2. Echocardiographic parameter in 2 months-old mice before and 14 days after isoprenaline infusion.

Format: DOCX Size: 22KB

Discussion

The adipocytokine leptin may link obesity with cardiac hypertrophy, an important risk factor for the development of heart failure. Studies in humans [2,3] and rodents [10,11] have shown that obesity is associated with LV hypertrophy, and body mass index was identified as a strong and independent predictor of LV mass [2,3]. Importantly, cardiac hypertrophy is also observed in normotensive obese subjects [6], and plasma leptin levels are associated with increased myocardial wall thickness independent of BW or blood pressure elevations [5], suggesting a causal role for leptin in the pathogenesis of cardiac hypertrophy.

Although the major source of leptin is adipose tissue, cardiomyocytes are also capable of synthesizing leptin [27], and increased cardiac leptin levels have been reported in mice or rats following coronary ligation [13,18] or in patients with heart failure [28]. In this study, elevated circulating as well as cardiac leptin levels were detected in both diet-induced and genetically obese mice, which may have acted on cardiomyocytes as well as other, non-cardiomyocyte cells expressing leptin receptors [29]. Although leptin serum levels were higher than in previous publications [30], we explain this findings with the higher age of the mice, a factor previously found to be associated with increased circulating leptin levels [31]. Leptin has been shown to stimulate the hypertrophy of cardiomyocytes isolated from rats [7,20] or humans [8,19]. Moreover, chronic leptin infusion increased cardiac ANP expression after myocardial infarction (MI) in mice [32], whereas neutralizing LepR antibodies abrogated the hypertrophy of the surviving myocardium after coronary artery ligation in rats [33]. On the other hand and as confirmed in our analysis, cardiac hypertrophy also develops in leptin- and LepR-deficient mice and may be reversed by leptin substitution[12]. Caloric restriction experiments suggested that the anti-hypertrophic effects of leptin had occurred in addition to weight loss [12], which itself may preserve heart function and attenuate LV remodeling [34]. Thus, it is unclear whether the cardiac hypertrophy in obesity is the consequence of pro-hypertrophic effects of the adipokine or rather the result of a resistance towards leptin’s preventive effects on hypertrophic cardiac remodeling. Of note, since body weight is markedly elevated in the diet-induced and particularly, the genetically obese mice, the heart-to-body weight ratio decreases, even though the absolute heart weight is increased (but to a relatively lesser extent).

Obesity is associated with elevated circulating leptin levels and hypothalamic resistance to the weight-reducing effects of the adipokine, whereas the existence of a peripheral (e.g. cardiac) leptin resistance is controversial. For example, reduced cardiac LepR expression has been reported in HFD-fed rats[14], whereas others demonstrated unaltered cardiac STAT3 phosphorylation in diet-induced obese rodents following acute leptin administration [15–17]. Our findings also suggest that hearts from diet-induced obese mice continue to respond to leptin in the presence of chronically elevated leptin levels and that the observed elevation of serum and cardiac leptin may thus contribute to the development of cardiac hypertrophy in obesity. For example, hearts of hyperleptinemic obese WT mice (i.e. those with intact leptin receptors) exhibited signs of activated leptin signaling, including elevated levels of phosphorylated LepR and STAT3, while they were unchanged or reduced in mice with mutated or truncated forms of LepR (i.e. LepRS1138 or LepRdb/db mice). Moreover, both lean and obese WT mice responded to a single leptin injection with increased cardiac STAT3 phosphorylation. Of note, we could not spatially dissect the cardiac responsiveness to leptin, since whole heart homogenates were examined. Possible explanations underlying the discrepancy between the present and some previous studies include the animal species, as the absence of a response to leptin in obesity has been so far primarily observed in rats[14]. In addition, age, sex and feeding status of the animals or the time of recombinant leptin administration may have influenced the results. Of note, previous studies in humans have reported the existence of individuals (up to 40%) exhibiting a blunted response to leptin [35], although it is unknown, whether such phenomenon also occurs in rodents.

Interestingly, hearts from LepRS1138 mice exhibited a marked overactivation of STAT3-independent leptin signaling pathways, including Jak2, Src kinase, Akt or p38 MAPK, i.e. factors previously shown to mediate the pro-hypertrophic effects of the adipokine in cardiomyocytes [19,20]. Importantly, overactivation of leptin signaling in hearts of LepRS1138 mice was accompanied by a pronounced cardiac hypertrophy, both at the organ and the single cardiomyocyte level, despite similar adiposity. Although leptin levels were found to be lower in LepRS1138compared to LepRdb/db mice, as previously reported [23], leptin continues to be able to activate LepR signal transduction in these mice, for example via LepR-Tyr985. Similar echocardiographical findings were obtained in young (i.e. 2 months-old) and older (i.e. 7 months-old) mice, arguing against the development of cardiac hypertrophy secondary to hemodynamic or other metabolic changes associated with obesity, although we cannot exclude the possible contribution of a more pronounced hyperinsulinemia [23] to the development of cardiac hypertrophy in LepRS1138 mice. On the other hand, hypertension had not been observed in ob/ob mice [12], and heart weight increase and concentric LV hypertrophy in obese mice and humans also occurs without systolic and diastolic blood pressure elevations [5,6,36].

Although a predominant cardiac expression of the short (i.e. without STAT3 binding site) over the long LepR isoform has been reported [7,29], previous studies have shown that stimulation of neonatal rat cardiomyocytes with leptin increased STAT3 phosphorylation, nuclear translocation and DNA binding activity [32]. Also, cardiac STAT3 activation after MI was blunted in leptin-deficient mice [13]. The observation that increased cardiac STAT3 phosphorylation in hyperleptinemic, diet-induced obese mice was reduced or almost completely abolished in LepRS1138 or LepRdb/db mice suggests that cardiac STAT3 activation in obesity largely occurs downstream of elevated leptin levels and that other cytokines, also elevated in obesity and known to signal via Jak2-STAT3, may be of minor importance. On the other hand, the importance of leptin-mediated STAT3 activation in the heart and its contribution to cardioprotective signaling pathways in vivo have not been directly examined so far.

STAT3 has been implicated in cardioprotection after various injuries. For example, cardiomyocyte-specific STAT3 deletion results in dilatative cardiomyopathy, characterized by increased apoptosis and interstitial fibrosis as well as reduced myocardial capillary density [21,22]. Previous studies suggested that leptin may exert beneficial effects on the heart. For example, administration of leptin was associated with smaller infarct size after ischemia/reperfusion injury [37], whereas ischemic postconditioning failed to induce cardioprotection in mice lacking leptin or its receptor [38]. Also, leptin deficiency was associated with a worsened cardiac function and survival after coronary artery ligation, which could be improved by leptin repletion [13]. Regarding possible mechanisms, increased cardiac myocyte apoptosis was observed in hearts from leptin (receptor)-deficient mice [39,40]. Similar findings were obtained in vitro, showing that leptin protects cardiomyocytes against apoptotic cell death induced by serum starvation [41]. Our analyses also revealed significantly elevated numbers of apoptotic cells in hearts of obese LepRS1138 and LepRdb/dbmice, consistent with a reduced activation of STAT3-responsive anti-apoptotic genes [40]. Although findings in mice with systemic defects in leptin signal transduction may have been confounded by the concomitant presence of obesity and associated metabolic and inflammatory alterations, adverse cardiac remodeling after MI [42] or lethal heart failure [43] were recently reported in mice with cardiomyocyte-specific LepR deletion. On the other hand, the beneficial effects of leptin-mediated STAT3 activation may not be restricted to cardiomyocytes. For example, we and others have shown that leptin promotes the angiogenic properties of endothelial (progenitor) cells [25,44], and cardiac angiogenesis was reduced in LepRS1138 and LepRdb/db mice. In addition, hearts of obese LepRS1138 and LepRdb/db mice exhibited increased interstitial fibrosis, which may have occurred secondary to increased cardiomyocyte loss, although previous studies have shown that leptin may also directly influence myocardial matrix metabolism [45]. On the functional level, the enhanced activation of pro-hypertrophic signaling pathways in the absence of STAT3-mediated cardioprotection may have contributed to the echocardiographic finding of LV cavity dilation in LepRS1138 compared to LepRdb/db mice.

Conclusions

Taken together, our findings suggest that hearts from diet-induced obese mice continue to respond to chronically elevated leptin levels and that increased systemic and/or local leptin and enhanced cardiac LepR activation contribute the development of cardiac hypertrophy. On the other hand, chronic overactivation of hypertrophic signaling mediators together with an inabilitity to activate STAT3-dependent cardioprotective pathways may promote maladaptive cardiac remodeling. Of note, our findings also indicate that leptin signaling is not a prerequisite to develop cardiac hypertrophy in obesity and that additional pathways also contribute to the increase in LV mass associated with higher body weight.

References

  1. Considine RV, Sinha MK, Heiman ML, Kriauciunas A, Stephens TW, Nyce MR, Ohannesian JP, Marco CC, McKee LJ, Bauer TL: Serum immunoreactive-leptin concentrations in normal-weight and obese humans.

    N Engl J Med 1996, 334:292-295. PubMed Abstract | Publisher Full Text OpenURL

  2. Gottdiener JS, Reda DJ, Materson BJ, Massie BM, Notargiacomo A, Hamburger RJ, Williams DW, Henderson WG: Importance of obesity, race and age to the cardiac structural and functional effects of hypertension. The department of veterans affairs cooperative study group on antihypertensive agents.

    J Am Coll Cardiol 1994, 24:1492-1498. PubMed Abstract |Publisher Full Text OpenURL

  3. Lauer MS, Anderson KM, Kannel WB, Levy D: The impact of obesity on left ventricular mass and geometry. The framingham heart study.

    JAMA 1991, 266:231-236. PubMed Abstract | Publisher Full Text OpenURL

  4. Kenchaiah S, Evans JC, Levy D, Wilson PW, Benjamin EJ, Larson MG, Kannel WB, Vasan RS: Obesity and the risk of heart failure.

    N Engl J Med 2002, 347:305-313. PubMed Abstract | Publisher Full Text OpenURL

  5. Paolisso G, Tagliamonte MR, Galderisi M, Zito GA, Petrocelli A, Carella C, de Divitiis O, Varricchio M: Plasma leptin level is associated with myocardial wall thickness in hypertensive insulin-resistant men.

    Hypertension 1999, 34:1047-1052. PubMed Abstract |Publisher Full Text OpenURL

  6. Perego L, Pizzocri P, Corradi D, Maisano F, Paganelli M, Fiorina P, Barbieri M, Morabito A, Paolisso G, Folli F, Pontiroli AE: Circulating leptin correlates with left ventricular mass in morbid (grade III) obesity before and after weight loss induced by bariatric surgery: a potential role for leptin in mediating human left ventricular hypertrophy.

    J Clin Endocrinol Metab 2005, 90:4087-4093. PubMed Abstract |Publisher Full Text OpenURL

  7. Rajapurohitam V, Gan XT, Kirshenbaum LA, Karmazyn M: The obesity-associated peptide leptin induces hypertrophy in neonatal rat ventricular myocytes.

    Circ Res 2003, 93:277-279. PubMed Abstract | Publisher Full Text OpenURL

  8. Madani S, De Girolamo S, Munoz DM, Li RK, Sweeney G: Direct effects of leptin on size and extracellular matrix components of human pediatric ventricular myocytes.

    Cardiovasc Res 2006, 69:716-725. PubMed Abstract | Publisher Full Text

 

Read Full Post »

Risk of Bias in Translational Science

Author: Larry H. Bernstein, MD, FCAP

and

Curator: Aviva Lev-Ari, PhD, RN

 

Assessment of risk of bias in translational science

Andre Barkhordarian1, Peter Pellionisz2, Mona Dousti1, Vivian Lam1,Lauren Gleason1, Mahsa Dousti1, Josemar Moura3 and Francesco Chiappelli14*  

1Oral Biology & Medicine, School of Dentistry, UCLA, Evidence-Based Decisions Practice-Based Research Network, Los Angeles, USA

2Pre-medical program, UCLA, Los Angeles, CA

3School of Medicine, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil

4Evidence-Based Decisions Practice-Based Research Network, UCLA School of Dentistry, Los Angeles, CA

Journal of Translational Medicine 2013, 11:184   http://dx.doi.org/10.1186/1479-5876-11-184
http://www.translational-medicine.com/content/11/1/184

This is an Open Access article distributed under the terms of the Creative Commons Attribution License 
http://creativecommons.org/licenses/by/2.0

Abstract

Risk of bias in translational medicine may take one of three forms:

  1. a systematic error of methodology as it pertains to measurement or sampling (e.g., selection bias),
  2. a systematic defect of design that leads to estimates of experimental and control groups, and of effect sizes that substantially deviate from true values (e.g., information bias), and
  3. a systematic distortion of the analytical process, which results in a misrepresentation of the data with consequential errors of inference (e.g., inferential bias).

Risk of bias can seriously adulterate the internal and the external validity of a clinical study, and, unless it is identified and systematically evaluated, can seriously hamper the process of comparative effectiveness and efficacy research and analysis for practice. The Cochrane Group and the Agency for Healthcare Research and Quality have independently developed instruments for assessing the meta-construct of risk of bias. The present article begins to discuss this dialectic.

Background

As recently discussed in this journal [1], translational medicine is a rapidly evolving field. In its most recent conceptualization, it consists of two primary domains:

  • translational research proper and
  • translational effectiveness.

This distinction arises from a cogent articulation of the fundamental construct of translational medicine in particular, and of translational health care in general.

The Institute of Medicine’s Clinical Research Roundtable conceptualized the field as being composed by two fundamental “blocks”:

  • one translational “block” (T1) was defined as “…the transfer of new understandings of disease mechanisms gained in the laboratory into the development of new methods for diagnosis, therapy, and prevention and their first testing in humans…”, and
  • the second translational “block” (T2) was described as “…the translation of results from clinical studies into everyday clinical practice and health decision making…” [2].

These are clearly two distinct facets of one meta-construct, as outlined in Figure 1. As signaled by others, “…Referring to T1 and T2 by the same name—translational research—has become a source of some confusion. The 2 spheres are alike in name only. Their goals, settings, study designs, and investigators differ…” [3].

1479-5876-11-184-1  Fig 1. TM construct

Figure 1. Schematic representation of the meta-construct of translational health carein general, and translational medicine in particular, which consists of two fundamental constructs: the T1 “block” (as per Institute of Medicine’s Clinical Research Roundtable nomenclature), which represents the transfer of new understandings of disease mechanisms gained in the laboratory into the development of new methods for diagnosis, therapy, and prevention as well as their first testing in humans, and the T2 “block”, which pertains to translation of results from clinical studies into everyday clinical practice and health decision making [[3]]. The two “blocks” are inextricably intertwined because they jointly strive toward patient-centered research outcomes (PCOR) through the process of comparative effectiveness and efficacy research/review and analysis for clinical practice (CEERAP). The domain of each construct is distinct, since the “block” T1 is set in the context of a laboratory infrastructure within a nurturing academic institution, whereas the setting of “block” T2 is typically community-based (e.g., patient-centered medical/dental home/neighborhoods [4]; “communities of practice” [5]).

For the last five years at least, the Federal responsibilities for “block” T1 and T2 have been clearly delineated. The National Institutes of Health (NIH) predominantly concerns itself with translational research proper – the bench-to-bedside enterprise (T1); the Agency for Healthcare Research Quality (AHRQ) focuses on the result-translation enterprise (T2). Specifically: “…the ultimate goal [of AHRQ] is research translation—that is, making sure that findings from AHRQ research are widely disseminated and ready to be used in everyday health care decision-making…” [6]. The terminology of translational effectiveness has emerged as a means of distinguishing the T2 block from T1.

Therefore, the bench-to-bedside enterprise pertains to translational research, and the result-translation enterprise describes translational effectiveness. The meta-construct of translational health care (viz., translational medicine) thus consists of these two fundamental constructs:

  • translational research and
  • translational effectiveness,

which have distinct purposes, protocols and products, while both converging on the same goal of new and improved means of

  • individualized patient-centered diagnostic and prognostic care.

It is important to note that the U.S. Patient Protection and Affordable Care Act (PPACA, 23 March 2010) has created an environment that facilitates the pursuit of translational health care because it emphasizes patient-centered outcomes research (PCOR). That is to say, it fosters the transaction between translational research (i.e., “block” T1)(TR) and translational effectiveness (i.e., “block” T2)(TE), and favors the establishment of communities of practice-research interaction. The latter, now recognized as practice-based research networks, incorporate three or more clinical practices in the community into

  • a community of practices network coordinated by an academic center of research.

Practice-based research networks may be a third “block” (T3)(PBTN) in translational health care and they could be conceptualized as a stepping-stone, a go-between bench-to-bedside translational research and result-translation translational effectiveness [7]. Alternatively, practice-based research networks represent the practical entities where the transaction between

  • translational research and translational effectiveness can most optimally be undertaken.

It is within the context of the practice-based research network that the process of bench-to-bedside can best seamlessly proceed, and it is within the framework of the practice-based research network that

  • the best evidence of results can be most efficiently translated into practice and
  • be utilized in evidence-based clinical decision-making, viz. translational effectiveness.

Translational effectiveness

As noted, translational effectiveness represents the translation of the best available evidence in the clinical practice to ensure its utilization in clinical decisions. Translational effectiveness fosters evidence-based revisions of clinical practice guidelines. It also encourages

  • effectiveness-focused,
  • patient-centered and
  • evidence-based clinical decision-making.

Translational effectiveness rests not only on the expertise of the clinical staff and the empowerment of patients, caregivers and stakeholders, but also, and

  • most importantly on the best available evidence [8].

The pursuit of the best available evidence is the foundation of

  • translational effectiveness and more generally of
  • translational medicine in evidence-based health care.

The best available evidence is obtained through a systematic process driven by

  • a research question/hypothesis that is articulated about clearly stated criteria that pertain to the
  • patient (P), the interventions (I) under consideration (C), for the sought clinical outcome (O), within a given timeline (T) and clinical setting (S).

PICOTS is tested on the appropriate bibliometric sample, with tools of measurements designed to establish the level (e.g., CONSORT) and the quality of the evidence. Statistical and meta-analytical inferences, often enhanced by analyses of clinical relevance [9], converge into the formulation of the consensus of the best available evidence. Its dissemination to all stakeholders is key to increase their health literacy in order to ensure their full participation

  • in the utilization of the best available evidence in clinical decisions, viz., translational effectiveness.

To be clear, translational effectiveness – and, in the perspective discussed above, translational health care – is anchored on obtaining the best available evidence,

  • which emerges from highest quality research.
  • which is obtained when errors are minimized.

In an early conceptualization [10], errors in research were presented as

  • those situations that threaten the internal and the external validity of a research study –

that is, conditions that impede either the study’s reproducibility, or its generalization. In point of fact, threats to internal and external validity [10] represent specific aspects of systematic errors (i.e., bias) in the

  • research design,
  • methodology and
  • data analysis.

Thence emerged a branch of science that seeks to

  • understand,
  • control and
  • reduce risk of bias in research.

Risk of bias and the best available evidence

It follows that the best available evidence comes from research with the fewest threats to internal and to external validity – that is to say, the fewest systematic errors: the lowest risk of bias. Quality of research, as defined in the field of research synthesis [11], has become synonymous with

  • low bias and contained risk of bias [12–15].

Several years ago, the Cochrane group embarked on a new strategy for assessing the quality of research studies by examining potential sources of bias. Certain original areas of potential bias in research were identified, which pertain to

(a) the sampling and the sample allocation process, to measurement, and to other related sources of errors (reliability of testing),

(b) design issues, including blinding, selection and drop-out, and design-specific caveats, and

(c) analysis-related biases.

A Risk of Bias tool was created (Cochrane Risk of Bias), which covered six specific domains:

1. selection bias,

2. performance bias,

3. detection bias,

4. attrition bias,

5. reporting bias, and

6. other research protocol-related biases.

Assessments were made within each domain by one or more items specific for certain aspects of the domain. Each items was scored in two distinct steps:

1. the support for judgment was intended to provide a succinct free-text description of the domain being queried;

2. each item was scored high, low, or unclear risk of material bias (defined here as “…bias of sufficient magnitude to have a notable effect on the results or conclusions…” [16]).

It was advocated that assessments across items in the tool should be critically summarized for each outcome within each report. These critical summaries were to inform the investigator so that the primary meta-analysis could be performed either

  • only on studies at low risk of bias, or for
  • the studies stratified according to risk of bias [16].

This is a form of acceptable sampling analysis designed to yield increased homogeneity of meta-analytical outcomes [17]. Alternatively, the homogeneity of the meta-analysis can be further enhanced by means of the more direct quality-effects meta-analysis inferential model [18].

Clearly, one among the major drawbacks of the Cochrane Risk of Bias tool is

  • the subjective nature of its assessment protocol.

In an effort to correct for this inherent weakness of the instrument, the Cochrane group produced

  • detailed criteria for making judgments about the risk of bias from each individual item[16], and
  • that judgments be made independently by at least two people, with any discrepancies resolved by discussion [16].

This approach to increase the reliability of measurement in research synthesis protocols

  • is akin to that described by us [19,20] and by AHRQ [21].

In an effort to aid clinicians and patients in making effective health care related decisions, AHRQ developed an alternative Risk of Bias instrument for enabling systematical evaluation of evidence reporting [22]. The AHRQ Risk of Bias instrument was created to monitor four primary domains:

1. risk of bias: design, methodology, analysis scoring – low, medium, high

2. consistency: extent of similarity in effect sizes across studies within a bibliome scoring – consistent, inconsistent, unknown

3. directness: unidirectional link between the interventions of interest and the sought outcome, as opposed to multiple links in a casual chain scoring – direct, indirect

4. precision: extent of certainty for estimate of effect with respect to the outcome scoring – precise, imprecise In addition, four secondary domains were identified:

a. Dose response association: pattern of a larger effect with greater exposure (Present/Not Present/Not Applicable or Not Tested)

a. Confounders: consideration of confounding variables (Present/Absent)

a. Strength of association: likelihood that the observed effect is large enough that it cannot have occurred solely as a result of bias from potential confounding factors (Strong/Weak)

a. Publication bias

The AHRQ Risk of Bias instrument is also designed to yield an overall grade of the estimated risk of bias in quality reporting:

•Strength of Evidence Grades (scored as high – moderate – low – insufficient)

This global assessment, in addition to incorporating the assessments above, also rates:

–major benefit

–major harm

–jointly benefits and harms

–outcomes most relevant to patients, clinicians, and stakeholders

The AHRQ Risk of Bias instrument suffers from the same two major limitations as the Cochrane tool:

1. lack of formal psychometric validation as most other tools in the field [21], and

2. providing a subjective and not quantifiable assessment.

To begin the process of engaging in a systematic dialectic of the two instruments in terms of their respective construct and content validity, it is necessary

  • to validate each for reliability and validity either by means of the classic psychometric theory or generalizability (G) theory, which allows
  • the simultaneous estimation of multiple sources of measurement error variance (i.e., facets)
  • while generalizing the main findings across the different study facets.

G theory is particularly useful in clinical care analysis of this type, because it permits the assessment of the reliability of clinical assessment protocols.

  • the reliability and minimal detectable changes across varied combinations of these facets are then simply calculated [23], but
  • it is recommended that G theory determination follow classic theory psychometric assessment.

Therefore, we have commenced a process of revision the AHRQ Risk of Bias instrument by rendering questions in primary domains quantifiable (scaled 1–4),

  • which established the intra-rater reliability (r = 0.94, p < 0.05), and
  • the criterion validity (r = 0.96, p < 0.05) for this instrument (Figure 2).

????????????????????????????????????????

 

Figure 2. Proportion of shared variance in criterion validity (A) and inter-rater reliability (B) in the AHRQ Risk of Bias instrument revised as described.
Two raters were trained and standardized 
[20] with the revised AHRQ Risk of Bias and with the R-Wong instrument, which has been previously validated[24]. Each rater independently produced ratings on a sample of research reports with both instruments on two separate occasions, 1–2 months apart. Pearson correlation coefficient was used to compute the respective associations. The figure shows Venn diagrams to illustrate the intersection between each two sets data used in the correlations. The overlap between the sets in each panel represents the proportion of shared variance for that correlation. The percent of unexplained variance is given in the insert of each panel.

A similar revision of the Cochrane Risk of Bias tool may also yield promising validation data. G theory validation of both tools will follow. Together, these results will enable a critical and systematic dialectical comparison of the Cochrane and the AHRQ Risk of Bias measures.

Discussion

The critical evaluation of the best available evidence is critical to patient-centered care, because biased research findings are fundamentally invalid and potentially harmful to the patient. Depending upon the tool of measurement, the validity of an instrument in a study is obtained by means of criterion validity through correlation coefficients. Criterion validity refers to the extent to which one measures or predicts the value of another measure or quality based on a previously well-established criterion. There are other domains of validity such as: construct validity and content validity that are rather more descriptive than quantitative. Reliability however is used to describe the consistency of a measure, the extent to which a measurement is repeatable. It is commonly assessed quantitatively by correlation coefficients. Inter-rater reliability is rendered as a Pearson correlation coefficient between two independent readers, and establishes equivalence of ratings produced by independent observers or readers. Intra-rater reliability is determined by repeated measurement performed by the same subject (rater/reader) at two different points in time to assess the correlation or strength of association of the two sets of scores.

To establish the reliability of research quality assessment tools it is necessary, as we previously noted [20]:

•a) to train multiple readers in sharing a common view for the cognitive interpretation of each item. Readers must possess declarative knowledge a factual form of information known to be static in nature a certain depth of knowledge and understanding of the facts about which they are reviewing the literature. They must also have procedural knowledge known as imperative knowledge that can be directly applied to a task in this case a clear understanding of the fundamental concepts of research methodology, design, analysis and inference.

•b) to train the readers to read and evaluate the quality of a set of papers independently and blindly. They must also be trained to self-monitor and self-assess their skills for the purpose of insuring quality control.

•c) to refine the process until the inter-rater correlation coefficient and Cohen coefficient of agreement are about 0.9 (over 81% shared variance). This will establishes that the degree of attained agreement among well-trained readers is beyond chance.

•d) to obtain independent and blind reading assessments from readers on reports under study.

•e) to compute means and standard deviation of scores for each question across the reports, repeat process if the coefficient of variations are greater than 5% (i.e., less than 5% error among the readers across each questions).

The quantification provided by instruments validated in such a manner to assess the quality and the relative lack of bias in the research evidence allows for the analysis of the scores by means of the acceptable sampling protocol. Acceptance sampling is a statistical procedure that uses statistical sampling to determine whether a given lot, in this case evidence gathered from an identified set of published reports, should be accepted or rejected [12,25]. Acceptable sampling of the best available evidence can be obtained by:

•convention: accept the top 10 percentile of papers based on the score of the quality of the evidence (e.g., low Risk of Bias);

•confidence interval (CI95): accept the papers whose scores fall at of beyond the upper confidence limit at 95%, obtained with mean and variance of the scores of the entire bibliome;

•statistical analysis: accept the papers that sustain sequential repeated Friedman analysis.

To be clear, the Friedman test is a non-parametric equivalent of the analysis of variance for factorial designs. The process requires the 4-E process outlined below:

•establishing a significant Friedman outcome, which indicates significant differences in scores among the individual reports being tested for quality;

•examining marginal means and standard deviations to identify inconsistencies, and to identify the uniformly strong reports across all the domains tested by the quality instrument

•excluding those reports that show quality weakness or bias

•executing the Friedman analysis again, and repeating the 4-E process as many times as necessary, in a statistical process akin to hierarchical regression, to eliminate the evidence reports that exhibit egregious weakness, based on the analysis of the marginal values, and to retain only the group of report that harbor homogeneously strong evidence.

Taken together, and considering the domain and the structure of both tools, expectations are that these analyses will confirm that these instruments are two related entities, each measuring distinct aspects of bias. We anticipate that future research will establish that both tools assess complementary sub-constructs of one and the same archetype meta-construct of research quality.

References

  1. Jiang F, Zhang J, Wang X, Shen X: Important steps to improve translation from medical research to health policy.

    J Trans Med 2013, 11:33. BioMed Central Full Text OpenURL

  2. Sung NS, Crowley WF Jr, Genel M, Salber P, Sandy L, Sherwood LM, Johnson SB, Catanese V, Tilson H, Getz K, Larson EL, Scheinberg D, Reece EA, Slavkin H, Dobs A, Grebb J, Martinez RA, Korn A, Rimoin D:Central challenges facing the national clinical research enterprise.

    JAMA 2003, 289:1278-1287. PubMed Abstract | Publisher Full Text OpenURL

  3. Woolf SH: The meaning of translational research and why it matters.

    JAMA 2008, 299(2):211-213. PubMed Abstract | Publisher Full Text OpenURL

  4. Chiappelli F: From translational research to translational effectiveness: the “patient-centered dental home” model.

    Dental Hypotheses 2011, 2:105-112. Publisher Full Text OpenURL

  5. Maida C: Building communities of practice in comparative effectiveness research. In Comparative effectiveness and efficacy research and analysis for practice (CEERAP): applications for treatment options in health care. Edited by Chiappelli F, Brant X, Cajulis C. Heidelberg: Springer–Verlag; 2012.

    Chapter 1

    OpenURL

  6. Agency for Healthcare Research and Quality: Budget estimates for appropriations committees, fiscal year (FY) 2008: performance budget submission for congressional justification.

    Performance budget overview 2008.

    http://www.ahrq.gov/about/cj2008/cjweb08a.htm#Statement webcite. Accessed 11 May 2013

    OpenURL

  7. Westfall JM, Mold J, Fagnan L: Practice-based research—“blue highways” on the NIH roadmap.

    JAMA 2007, 297:403-406. PubMed Abstract | Publisher Full Text OpenURL

  8. Chiappelli F, Brant X, Cajulis C: Comparative effectiveness and efficacy research and analysis for practice (CEERAP) applications for treatment options in health care. Heidelberg: Springer–Verlag; 2012. OpenURL

  9. Dousti M, Ramchandani MH, Chiappelli F: Evidence-based clinical significance in health care: toward an inferential analysis of clinical relevance.

    Dental Hypotheses 2011, 2:165-177. Publisher Full Text OpenURL

  10. Campbell D, Stanley J: Experimental and quasi-experimental designs for research. Chicago, IL: Rand-McNally; 1963. OpenURL

  11. Littell JH, Corcoran J, Pillai V: Research synthesis reports and meta-analysis. New York, NY: Oxford Univeristy Press; 2008. OpenURL

  12. Chiappelli F: The science of research synthesis: a manual of evidence-based research for the health sciences. Hauppauge NY: NovaScience Publisher, Inc; 2008. OpenURL

  13. Higgins JPT, Green S: Cochrane handbook for systematic reviews of interventions version 5.0.1. Chichester, West Sussex, UK: John Wiley & Sons. The Cochrane collaboration; 2008. OpenURL

  14. CRD: Systematic Reviews: CRD’s guidance for undertaking reviews in health care. National Institute for Health Research (NIHR). University of York, UK: Center for reviews and dissemination; 2009. PubMed Abstract| Publisher Full Text OpenURL

  15. McDonald KM, Chang C, Schultz E: Closing the quality Gap: revisiting the state of the science. Summary report. U.S. Department of Health & Human Services. AHRQ, Rockville, MD: Summary report. AHRQ publication No. 12(13)-E017; 2013. OpenURL


Read Full Post »

Relationship between Adiposity and High Fructose Intake Revealed

Reporter: Larry H Bernstein, MD, FCAP

 

Dietary Fructose Feeding Increases Adipose Methylglyoxal Accumulation in Rats in Association with Low Expression and Activity of Glyoxalase-2

Christopher Masterjohn 1,2email, Youngki Park 1email, Jiyoung Lee 1email, Sang K. Noh 3email, Sung I. Koo 1email and Richard S. Bruno 1,4,* email
1 Department of Nutritional Sciences, University of Connecticut, Storrs, CT 06269, USA 2 Department of Comparative Biosciences, University of Illinois, Urbana, IL 61801, USA 3 Department of Food and Nutrition, Changwon National University, Changwon 641-773, Korea 4 Human Nutrition Program, Department of Human Sciences, The Ohio State University, Columbus, OH 43210, USA
Nutrients 2013, 5(8), 3311-3328;    http://dx.doi.org/10.3390/nu5083311

Abstract

  1. Methylglyoxal is a precursor to advanced glycation endproducts that may contribute to diabetes and its cardiovascular-related complications.
  2. Methylglyoxal is successively catabolized to d-lactate by glyoxalase-1 and glyoxalase-2.

The objective of this study was to determine whether dietary fructose and green tea extract (GTE) differentially regulate methylglyoxal accumulation in liver and adipose, mediated by tissue-specific differences in the glyoxalase system.

We fed six week old male Sprague-Dawley rats a low-fructose diet (10% w/w) or a high-fructose diet (60% w/w) containing no GTE or GTE at 0.5% or 1.0% for nine weeks.

Fructose-fed rats had higher (P < 0.05) adipose methylglyoxal, but GTE had no effect. Plasma and hepatic methylglyoxal were unaffected by fructose and GTE. Fructose and GTE also had no effect on the expression or activity of glyoxalase-1 and glyoxalase-2 at liver or adipose.

  • Regardless of diet, adipose glyoxalase-2 activity was 10.8-times lower (P < 0.05) than adipose glyoxalase-1 activity and 5.9-times lower than liver glyoxalase-2 activity.
  • Adipose glyoxalase-2 activity was also inversely related to adipose methylglyoxal (r = −0.61; P < 0.05).
  • These findings suggest that fructose-mediated adipose methylglyoxal accumulation is independent of GTE supplementation and that its preferential accumulation in adipose compared to liver is due to low constitutive expression of glyoxalase-2.

Keywords: fructose; glyoxalase I; glyoxalase II; pyruvaldehyde; rats; Sprague-Dawley

Masterjohn C, Park Y, Lee J, Noh SK, Koo SI, Bruno RS. Dietary Fructose Feeding Increases Adipose Methylglyoxal Accumulation in Rats in Association with Low Expression and Activity of Glyoxalase-2. Nutrients. 2013; 5(8):3311-3328. EISSN 2072-6643 Published by MDPI AG, Basel, Switzerland

Read Full Post »

« Newer Posts - Older Posts »