23
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: not found

      GH peak response to GHRH-arginine: relationship to insulin resistance and other cardiovascular risk factors in a population of adults aged 50–90

      research-article

      Read this article at

      ScienceOpenPublisherPMC
      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          Objective

          To assess the GH response to GHRH-arginine in apparently healthy adults in relation to cardiovascular risk factors.

          Design

          Cross-sectional.

          Patients

          Eighty-six male and female volunteers aged 50–90.

          Measurements

          GH peak response to GHRH-arginine and cardiovascular risk factors, including obesity, insulin resistance, low levels of high density lipoprotein (HDL) cholesterol, elevated triglycerides, and hypertension. The primary outcome measurement was GH response to GHRH-arginine. The relationship between GH peak responses and cardiovascular risk factors was determined after data collection.

          Results

          GH peaks were highly variable, ranging from 2·3 to 185 µg/l (14% with GH peaks < 9 µg/l). An increasing number of cardiovascular risk factors were associated with a lower mean GH peak ( P < 0·0001). By univariate analysis, fasting glucose, insulin, body mass index (BMI), HDL cholesterol and triglycerides were significantly associated with GH peak (all P < 0·0001). Multiple regression analysis revealed that fasting glucose, fasting insulin, BMI, triglycerides and sex accounted for 54% of GH peak variability. The role of abdominal fat as it relates to GH peak was explored in a subset of 45 subjects. Trunk fat and abdominal subregion fat measured by dual energy X-ray absorptiometry (DXA) were inversely related to GH peak ( P < 0·008 and 0·001, respectively). Analysis of this subgroup by multiple regression revealed that subregion abdominal fat became the significant obesity-related determinant of GH peak, but still lagged behind fasting insulin and glucose.

          Conclusions

          GH response to secretagogues was highly variable in apparently healthy adults aged 50–90 years. Peak GH was significantly related to fasting glucose, insulin, BMI, HDL cholesterol, triglycerides, trunk fat and abdominal subregion fat, with fasting glucose ranking first by multiple regression analysis. There was a strong relationship between cardiovascular risk factors and low GH, with individual risk factors being additive. Although these data do not differentiate between low GH being a cause or an effect of these cardiovascular risk factors, they indicate that the relationship between low GH and increased cardiovascular risk may be physiologically important in the absence of pituitary disease.

          Related collections

          Most cited references55

          • Record: found
          • Abstract: found
          • Article: not found

          Cardiovascular morbidity and mortality associated with the metabolic syndrome.

          To estimate the prevalence of and the cardiovascular risk associated with the metabolic syndrome using the new definition proposed by the World Health Organization A total of 4,483 subjects aged 35-70 years participating in a large family study of type 2 diabetes in Finland and Sweden (the Botnia study) were included in the analysis of cardiovascular risk associated with the metabolic syndrome. In subjects who had type 2 diabetes (n = 1,697), impaired fasting glucose (IFG)/impaired glucose tolerance (IGT) (n = 798) or insulin-resistance with normal glucose tolerance (NGT) (n = 1,988), the metabolic syndrome was defined as presence of at least two of the following risk factors: obesity, hypertension, dyslipidemia, or microalbuminuria. Cardiovascular mortality was assessed in 3,606 subjects with a median follow-up of 6.9 years. In women and men, respectively, the metabolic syndrome was seen in 10 and 15% of subjects with NGT, 42 and 64% of those with IFG/IGT, and 78 and 84% of those with type 2 diabetes. The risk for coronary heart disease and stroke was increased threefold in subjects with the syndrome (P < 0.001). Cardiovascular mortality was markedly increased in subjects with the metabolic syndrome (12.0 vs. 2.2%, P < 0.001). Of the individual components of the metabolic syndrome, microalbuminuria conferred the strongest risk of cardiovascular death (RR 2.80; P = 0.002). The WHO definition of the metabolic syndrome identifies subjects with increased cardiovascular morbidity and mortality and offers a tool for comparison of results from diferent studies.
            Bookmark
            • Record: found
            • Abstract: found
            • Article: not found

            The epidemic of obesity.

            As the obesity epidemic spreads, concern about the significant health and economic consequences has also grown. Obesity has been linked to a variety of chronic diseases, almost 300,000 deaths each year, and 117 billion dollars in direct and indirect annual costs in the United States alone. In this article we review the recent trends in overweight and obesity, summarize the lifestyle factors that influence the increasing prevalence of obesity, and discuss the health and economic impact of the obesity epidemic.
              Bookmark
              • Record: found
              • Abstract: found
              • Article: not found

              Age and relative adiposity are specific negative determinants of the frequency and amplitude of growth hormone (GH) secretory bursts and the half-life of endogenous GH in healthy men.

              Mean plasma GH concentrations are controlled by the frequency, amplitude, and duration of underlying GH secretory bursts as well as by the half-life of endogenous GH. We investigated the specific mechanisms that subserve the clinically recognized negative effects of age and adiposity on mean serum GH concentrations. To this end, 21 healthy men, aged 21-71 yr, who were of nearly normal body weight underwent blood sampling at 10-min intervals for 24 h. Deconvolution analysis was used to estimate specific features of GH secretion and clearance. Compared to younger men, the older tertile of men had significant reductions in 1) GH secretory burst frequency, 2) the half-life of endogenous GH, and 3) the daily GH secretory rate, but not 4) GH secretory burst half-duration, amplitude, or mass. Linear regression analysis disclosed that age was a major negative statistical determinant of GH secretory burst frequency (r = -0.80; P = 0.005) and endogenous GH half-life (r = -0.70; P = 0.024). Body mass index, an indicator of relative obesity, was a significant negative correlate of GH half-life (P = 0.045) and GH secretory burst amplitude (P = 0.031). Age and body mass index each correlated negatively with the daily GH secretion rate (P = 0.0031 and P = 0.027, respectively), and together accounted for more than 60% of the variability in 24-h GH production rates (r = -0.78; P = 0.00056). On the average, for a normal body mass index, each decade of increasing age attenuated the GH production rate by 14% and the GH half-life by 6%. Conversely, each unit increase in body mass index, at a given age, reduced the daily GH secretion rate by 6%. We conclude that age and relative adiposity are distinct and specific correlates of individual attributes of GH secretion and clearance in men.
                Bookmark

                Author and article information

                Journal
                Clin Endocrinol (Oxf)
                cen
                Clinical Endocrinology
                Blackwell Publishing Ltd
                0300-0664
                1365-2265
                August 2006
                : 65
                : 2
                : 169-177
                Affiliations
                [* ]Neuroendocrine Unit, Division of Endocrinology, General Clinical Research Center, New York University School of Medicine and Department of Veterans Affairs Medical Center New York
                []Nutrition, Exercise Physiology, and Sarcopenia Laboratory, Jean Mayer USDA Human Nutrition Research Center on Aging, Tufts University Boston, MA
                []Institute for Medical Research, North Shore LIJ Health System Manhasset, NY
                [§ ]Quest Diagnostics Nichols Institute, San Juan Capistrano CA, USA
                []Alzheimer's Disease Center, New York University School of Medicine New York, NY, USA
                Author notes
                Correspondence: David L. Kleinberg, VA Medical Center, 423 E. 23rd Street, 16043-West, New York, NY, 10010, USA. Tel.: 212 263 6772; Fax: 212 447 6219; E-mail: david.kleinberg@ 123456med.nyu.edu

                Re-use of this article is permitted in accordance with the Creative Commons Deed, Attribution 2.5, which does not permit commercial exploitation.

                Article
                10.1111/j.1365-2265.2006.02569.x
                1618818
                16886956
                d9668dc1-b10e-4bb1-a0a4-c346156c73b3
                © 2006 The Authors Journal compilation © 2006 Blackwell Publishing Ltd
                History
                : 10 January 2006
                : 10 February 2006
                : 04 April 2006
                : 05 April 2006
                Categories
                Original Articles

                Endocrinology & Diabetes
                Endocrinology & Diabetes

                Comments

                Comment on this article