ARIC Manuscript Proposal # 1618

Transcription

1 ARIC Manuscript Proposal # 1618 PC Reviewed: 3/9/10 Status: A Priority: 2 SC Reviewed: Status: Priority: 1.a. Full Title: Does physical activity modify the association between plasma fatty acids and incident Type 2 diabetes.: The Atherosclerosis Risk in Communities (ARIC) study? b. Abbreviated Title (Length 26 characters): PA, fatty acids & diabetes 2. Writing Group: Writing group members: Lisa Chow Lynn Eberly James Pankow Elizabeth Seaquist John Eckfeldt Aaron Folsom Ron Hoogeveen David Couper I, the first author, confirm that all the coauthors have given their approval for this manuscript proposal. LC [please confirm with your initials electronically or in writing] First author: Address: Lisa Chow MD Division of Endocrinology, Diabetes and Metabolism University of Minnesota Medical School MMC Delaware St SE Minneapolis, MN Phone: Fax: chow0007@umn.edu

2 ARIC author to be contacted if there are questions about the manuscript and the first author does not respond or cannot be located (this must be an ARIC investigator). Name: James Pankow PhD Address: Epidemiology Room 300 WBOB 1300 S 2nd St Minneapolis, MN Phone: Fax: pankow@umn.edu 3. Timeline: Statistical analysis: March to May 2010 Manuscript preparation: May 2010 through July 2010 Manuscript revision: August 2010 Manuscript submission: September Rationale: Type 2 diabetes (T2DM) is a burgeoning public health problem that continues to impose considerable morbidity, mortality, and socioeconomic burden despite recent strides in the discovery of therapeutic options. The search for novel therapies can be accelerated by a better understanding of the mechanisms underlying the precursors of T2DM: pancreatic beta cell dysfunction and insulin resistance. Previously, data from the ARIC cohort demonstrated a positive association between fasting plasma free fatty acid (FA) 1 and proportion of plasma saturated FA 2 with incident diabetes. These observations are significant, as lipotoxicity the elevation of lipids and/or lipid metabolites within blood or tissues with subsequent metabolic derangement is a postulated mechanism for development of insulin resistance 3. As the skeletal muscle is the primary site of peripheral insulin resistance 4, 5, there is much interest in the role of skeletal muscle lipotoxicity and its contribution to insulin resistance. In untrained subjects, insulin resistance correlates strongly with intramyocellular lipid content as measured by muscle biopsy 6 or magnetic resonance imaging 7. Lipid infusion has been shown to increase intramyocellular lipid content 8, 9, and produce insulin resistance through the disruption of insulin stimulated glucose uptake in skeletal muscle 12. Yet, the association of lipotoxicity with skeletal muscle insulin resistance is more complicated than lipid exposure. Endurance athletes have intramyocellular lipid levels that are comparable with those of subjects with T2DM but are insulin sensitive 13. Various explanations for this paradox include differences in intramyocellular lipid turnover, presence of lipotoxic metabolites, or differences in intramyocellular lipid composition. The paradox of the trained subject highlights the protective aspect of skeletal muscle training on the development of lipid induced insulin resistance. Training to prevent T2DM, however, does not have to be at the level of an endurance athlete. Incidence rates of T2DM has been shown to decline with increased energy expenditure, with a 500 kcal increment in energy expenditure decreasing the age adjusted risk of T2DM by 6% (95% CI of 0.9 to 0.98) 14. A moderate 16 week progressive exercise training program using sedentary,

3 obese ( BMI: /- 0.7 kg/m 2 ) insulin resistant, older (66.4 +/- 0.8 yr) subjects improved insulin sensitivity by 21% (P = 0.02) and increased intramyocellular lipid content by 21% 15. Although the ARIC cohort did not specifically recruit trained subjects or define participant fitness using Vo2 max, it is nonetheless suitable in shedding further light on the impact of physical activity on FA content and incident diabetes. First, a positive association between fasting plasma free FA 1 and proportion of plasma saturated FA 2 with incident diabetes in ARIC has been previously described. Thus, baseline measures of diet, activity, plasma free FA, and plasma FA composition are already available. Second, the use of an older cohort (age group at baseline) with 9 years of follow up ensures a reasonable event rate of incident diabetes. Third, the effect of activity can be approximated by comparing subjects between the highest and lowest quartiles of physical activity. Thus, the resources of ARIC are uniquely suited to answer the question whether physical activity modifies the effect of plasma free FA content on incident diabetes. 5. Main Hypothesis/Study Questions: The goals of this project are to determine whether differences exist in plasma FA composition between active and sedentary subjects and to determine whether the associations between fatty acids and incident diabetes differ by physical activity status. Our primary hypothesis is that physical activity modifies the effect of plasma free FA content on incident diabetes, such that sedentary subjects will have stronger associations between free FA or saturated FA and incident diabetes than active subjects. Specific Aims: 1) To determine the extent to which active subjects will have differences in plasma levels of free FA and full FA profile compared with sedentary subjects using ARIC baseline data. 2) To determine the extent to which sedentary subjects will have stronger associations between free FA or saturated FA and incident diabetes than active subjects using ARIC incident diabetes data. 6. Design and analysis (study design, inclusion/exclusion, outcome and other variables of interest with specific reference to the time of their collection, summary of data analysis, and any anticipated methodologic limitations or challenges if present). Design and analysis: The primary outcome will be incident diabetes. We will use a nested case cohort design to evaluate the effect of plasma levels of free FA on incident diabetes, with possible effect modification by baseline physical activity. We will use a prospective cohort design (Minneapolis center only) to evaluate the effect of plasma FA composition on incident diabetes, with possible effect modification by baseline physical activity. Inclusion and exclusion criteria For analysis of plasma free FA, we will use data from the Inflammatory Precursors of Type 2 Diabetes ancillary study, which measured plasma FA on incident diabetes cases and a cohort random sample. Subjects from all four ARIC centers were included in this ancillary study. For analysis of plasma FA composition from phospholipid and cholesterol esters, we will use data from the ARIC subjects at the Minneapolis field center, which is the only center with such measures available. Subjects will be excluded from analysis if they had any of the following at baseline: diabetes, unknown diabetes status, missing FA measurements, or use of medications that may affect the lipid profile (oral contraceptives, cholesterol-lowering medication).

4 Outcomes: Diabetes We will define incident diabetes by one of the following: physician diagnosis, fasting blood glucose greater than 126, use of anti-diabetes medication, random blood glucose greater than 200 during any of the follow up ARIC visits. Exposures: Physical Activity: Physical activity will be calculated using the responses to the Baecke Questionnaire of Habitual Physical Activity.The Baecke Questionnaire was performed at the baseline during the ARIC study, 3 scores: sports, leisure, and work. The focus will be on measurement of sport activity and total activity (combination of the 3 scores). We will categorize physical activity into quartiles. We will classify subjects in the highest quartile of physical activity as active and the lowest quartile of physical activity as sedentary. We will consider further defining the active group as vigorous, activity intensity greater than 5 METS, for further comparison against the sedentary group 16. To increase statistical power, we may use the Baecke score as a continuous variable. Plasma FA composition: Plasma free FA levels will be obtained from previously measured specimens 1. We will also categorize plasma free FA acids into quintiles for comparison. The composition of plasma cholesterol esters and plasma phospholipids will be obtained from previously measured specimens 2. We will calculate percent saturated FA, monounsaturated FA, and polyunsaturated FA by summing the respective FA with carbon atoms. In additional to using fatty acids as continuous variables, we will also categorize the fatty acids (saturated FA, monounsaturated FA, and polyunsaturated FA) into quintiles for comparison. Covariates: Age Sex Ethnicity We will adjust for ethnicity in the free FA analysis since this measurement includes subjects from all centers. For FA composition which was measured only from the Minneapolis center, we will exclude the non white subjects due to small numbers. ARIC center Family history Will be considered positive if either natural mother or natural father had diabetes Anthropomorphic measures: Baseline measures of adiposity will be used as continuous variables. These will include BMI, waist circumference, hip circumference, waist hip ratio, and the sum of triceps, subscapular, and subcutaneous skin folds. Diet: Proportion of energy intake from saturated fat intake will be derived from the baseline food frequency questionnaire. Insulin Resistance: Insulin resistance will be calculated using baseline values of insulin and glucose in the homeostatic model assessment (HOMA).

5 Data analysis: Using baseline data, we will compare plasma FA levels and FA composition between active and sedentary groups using analysis of variance (ANOVA). Analysis will be performed for plasma free FA levels, with additional analysis for individual FA and FA categories (saturated FA, monounsaturated FA, polyunsaturated FA). Covariates will be incorporated using ANCOVA. Free FA: As free FA are available only in the Inflammatory Precursors of Type 2 Diabetes ancillary study, we will use a nested case cohort design to evaluate the effect of plasma levels of free FA on incident diabetes using hazard ratios for incident diabetes. After confirming the proportionalhazards assumption, proportional-hazards models will be constructed to examine the association of plasma free FA with incident diabetes with appropriate weights to account for the case-cohort design; hazard ratios will be adjusted for covariates of interest. The first model will include plasma free FA as the main predictor, with age, sex, race, and center as covariates. A second model will additionally adjust for other T2DM risk factors, including saturated fat in diet, family history and anthropomorphic measures. To assess multiplicative interaction with physical activity, an interaction term (physical activity * plasma free FA) will be added to the model. FA categories: As fatty acid composition is only available for the Minneapolis cohort, we will first use the entire Minneapolis cohort in a stratified analysis with incident rates of diabetes across quintiles of saturated FA and activity ( active vs sedentary ) and look for additive interaction and multiplicative interaction with physical activity. We may also perform a stratified analysis with incident rates of diabetes across quintiles of monounsaturated fat and polyunsaturated fat and activity ( active vs sedentary ). We will then use a nested case cohort design to evaluate the effect of the different FA categories using hazard ratios for incident diabetes. After confirming the proportional-hazards assumption, proportional-hazards models will be constructed to examine the association of percent saturated FA with incident diabetes; hazard ratios will be adjusted for covariates of interest. The first model will include percent plasma saturated FA as the main predictor, with age, and sex as covariates. A second model will additionally adjust for other T2DM risk factors, including diet and anthropomorphic measures. To assess multiplicative interaction with physical activity, an interaction term (physical activity * percent saturated FA) will be added to the model.

6 7.a.Will the data be used for non-cvd analysis in this manuscript? Yes x_ No b. If Yes, is the author aware that the file ICTDER03 must be used to exclude persons with a value RES_OTH = CVD Research for non-dna analysis, and for DNA analysis RES_DNA = CVD Research would be used? Yes _ No (This file ICTDER03 has been distributed to ARIC PIs, and contains the responses to consent updates related to stored sample use for research.) 8.a. Will the DNA data be used in this manuscript? Yes x_ No 8.b. If yes, is the author aware that either DNA data distributed by the Coordinating Center must be used, or the file ICTDER03 must be used to exclude those with value RES_DNA = No use/storage DNA? Yes No 9. The lead author of this manuscript proposal has reviewed the list of existing ARIC Study manuscript proposals and has found no overlap between this proposal and previously approved manuscript proposals either published or still in active status. ARIC Investigators have access to the publications lists under the Study Members Area of the web site at: x Yes No 10. What are the most related manuscript proposals in ARIC (authors are encouraged tocontact lead authors of these proposals for comments on the new proposal or collaboration)? Pankow JS, Duncan BB, Schmidt MI, et al. Fasting plasma free fatty acids and risk of type 2 diabetes - The atherosclerosis risk in communities study. Diabetes Care 2004;27(1): Wang L, Folsom AR, Zheng ZJ, Pankow JS, Eckfeldt JH. Plasma fatty acid composition and incidence of diabetes in middle-aged adults: the Atherosclerosis Risk in Communities (ARIC) Study. American Journal of Clinical Nutrition 2003;78(1): a. Is this manuscript proposal associated with any ARIC ancillary studies or use any ancillary study data? x_ Yes No 11.b. If yes, is the proposal x_ A. primarily the result of an ancillary study (list number* ) B. primarily based on ARIC data with ancillary data playing a minor role (usually control variables; list number(s)* ) *ancillary studies are listed by number at Manuscript preparation is expected to be completed in one to three years. If a manuscript is not submitted for ARIC review at the end of the 3-years from the date of the approval, the manuscript proposal will expire.

7 Bibilography 1. Pankow JS, Duncan BB, Schmidt MI, et al. Fasting plasma free fatty acids and risk of type 2 diabetes - The atherosclerosis risk in communities study. Diabetes Care 2004;27(1): Wang L, Folsom AR, Zheng ZJ, Pankow JS, Eckfeldt JH. Plasma fatty acid composition and incidence of diabetes in middle-aged adults: the Atherosclerosis Risk in Communities (ARIC) Study. American Journal of Clinical Nutrition 2003;78(1): Shulman GI. Cellular mechanisms of insulin resistance. Journal of Clinical Investigation 2000;106(2): Shulman GI, Rothman DL, Jue T, Stein P, Defronzo RA, Shulman RG. Quantitation of Muscle Glycogen- Synthesis in Normal Subjects and Subjects with Non-Insulin-Dependent Diabetes by C-13 Nuclear Magnetic- Resonance Spectroscopy. New England Journal of Medicine 1990;322(4): Defronzo RA, Jacot E, Jequier E, Maeder E, Wahren J, Felber JP. The Effect of Insulin on the Disposal of Intravenous Glucose - Results from Indirect Calorimetry and Hepatic and Femoral Venous Catheterization. Diabetes 1981;30(12): Pan DA, Lillioja S, Kriketos AD, et al. Skeletal muscle triglyceride levels are inversely related to insulin action. Diabetes 1997;46(6): Perseghin G, Scifo P, De Cobelli F, et al. Intramyocellular triglyceride content is a determinant of in vivo insulin resistance in humans: a 1H-13C nuclear magnetic resonance spectroscopy assessment in offspring of type 2 diabetic parents. Diabetes 1999;48(8): Brechtel K, Dahl DB, Machann J, et al. Fast elevation of the intramyocellular lipid content in the presence of circulating free fatty acids and hyperinsulinemia: A dynamic H-1-MRS study. Magnetic Resonance in Medicine 2001;45(2): Bachmann OP, Dahl DB, Brechtel K, et al. Effects of intravenous and dietary lipid challenge on intramyocellular lipid content and the relation with insulin sensitivity in humans. Diabetes 2001;50(11): Boden G, Jadali F, White J, et al. Effects of Fat on Insulin-Stimulated Carbohydrate-Metabolism in Normal Men. Journal of Clinical Investigation 1991;88(3): Boden G, Chen XH. Effects of Fat on Glucose-Uptake and Utilization in Patients with Non-Insulin- Dependent Diabetes. Journal of Clinical Investigation 1995;96(3): Belfort R, Mandarino L, Kashyap S, et al. Dose-response effect of elevated plasma free fatty acid on insulin signaling. Diabetes 2005;54(6): Goodpaster BH, He J, Watkins S, Kelley DE. Skeletal muscle lipid content and insulin resistance: evidence for a paradox in endurance-trained athletes. Journal of Clinical Endocrinology & Metabolism 2001;86(12): Helmrich SP, Ragland DR, Leung RW, Paffenbarger RS. Physical-Activity and Reduced Occurrence of Non-Insulin-Dependent Diabetes-Mellitus. New England Journal of Medicine 1991;325(3): Dube JJ, Amati F, Stefanovic-Racic M, Toledo FGS, Sauers SE, Goodpaster BH. Exercise-induced alterations in intramyocellular lipids and insulin resistance: the athlete's paradox revisited. American Journal of Physiology-Endocrinology and Metabolism 2008;294(5):E882-E Schmitz KH, Arnett DK, Bank A, et al. Arterial distensibility and physical activity in the ARIC study. Medicine and Science in Sports and Exercise 2001;33(12):