MUSCLE MASS AS A POTENTIAL PREDICTOR FOR METABOLIC

SYNDROME IN NORMAL WEIGHT INDIVIDUALS

Julia Humphrey

Central Washington University

Overview

Introduction Metabolic Syndrome MONW & metabolic syndrome Sarcopenia & metabolic syndrome

Hypothesis/Purpose

Methods & Statistics

Results

Discussion

Introduction

Metabolic Syndrome

Cluster of symptoms correlated with risk of CVD and Type 2 DM 1988 termed syndrome-X by Reaven

NCEP ATP III Definition 3 or more of the following:

Abdominal obesity Elevated triglyceride Low HDL-C High blood pressure Elevated fasting glucose

Other definitions

Metabolic Syndrome

Prevalence is increasing 34% of U.S. population Younger adults & children increasingly

diagnosed Risk of developing chronic disease

2-fold increase of CVD 5-fold increase of type 2 DM

Increased risk via: Diet, physical inactivity, genetics

Metabolically Obese Normal Weight

Identification Subtype of at-risk individuals BMI within normal range have abnormal metabolic properties associated

with obesity Association with metabolic syndrome

Risk of developing CVD & type 2 DM Dangers presented

Not detected due to normal BMI & young age Increased mortality risk

Metabolically Obese Normal Weight

Body Composition Increased body fat Decreased muscle mass Decreased physical activity

Possible genetic predisposition Family history of type 2 DM & obesity Parents have triglyceridemia

Increased body fat in comparison

The Obese Without Cardiometabolic Risk Factor Clustering and the Normal Weight With Cardiometabolic Risk Factor Clustering

Wildman et al. 2008

Subjects 5440 NHANES 1999-2004 ≥ 20 years, BMI ≥ 18.5 kg/m2, fasted, no history of CVD

Methods Cardiometabolic Abnormality

Elevated BP Elevated TG Decreased HDL-C Elevated fasting glucose Insulin resistance Systemic inflammation

Normal weight metabolically abnormal BMI < 25 kg/m2 + 2 cardiometabolic abnormalities

Behavior & physical activity questionnaire

The Obese Without Cardiometabolic Risk Factor Clustering and the Normal Weight With Cardiometabolic Risk Factor Clustering

Wildman et al. 2008

23.5% of normal weight individuals

8.1% of entire population

Risk Factor Prevalence

Elevated BP 65.2 ± 3.7

Elevated TG 59.4 ± 2.8

Decreased HDL-C 46.3 ± 2.8

Elevated Fasting Glucose

54.9 ± 3.0

Insulin Resistance 6.2 ± 1.6

Systemic Inflammation

16.2 ± 2.0

The Obese Without Cardiometabolic Risk Factor Clustering and the Normal Weight With Cardiometabolic Risk Factor Clustering

Wildman et al. 2008

Mean age 54.7 years 46.6% reported no physical activity

Skeletal Muscle & Metabolic Syndrome

Skeletal muscle Primary site for glucose uptake Decreased MM = decreased

glucose uptake Can lead to insulin resistance

Sarcopenia Correlated with type 2 DM Relationship to cardiometabolic

syndrome Associated with insulin resistance

& prediabetes

Relative muscle mass is inversely associated with insulin resistance and prediabetes.

Srikanthan et al. 2011

Purpose Examine the association of skeletal muscle mass with

insulin resistance and dysglycemia in a nationally representative sample

Subjects 13,644 subjects > 20 years, not pregnant, fasted, BIA measurements, BMI

≥ 16 kg/m2, body weight > 35 kg, no self-reported cardiac failure

Methods SMI via BIA measurements Serum insulin, plasma glucose, HOMA-IR, HbA1C SMI in quartiles, unadjusted Differences between SMI quartiles & risk factors

Relative muscle mass is inversely associated with insulin resistance and prediabetes.

Srikanthan et al. 2011

National Health and Nutrition Examination Survey

Continuous program 2-year intervals

Nationally representative data

Sample of 5,000 from all age & major ethnic groups

Health examination & interview Demographic, socioeconomic,

dietary, & health related questions

Hypothesis, Purpose, & Methods

Hypothesis

MONW Decreased physical activity, Increased fat mass, & Decreased

muscle mass Sarcopenic studies

Inverse relationship between muscle mass & metabolic syndrome Studied in Korea on national scale, not in U.S.

NHANES Muscle mass & insulin resistance Metabolic syndrome in normal weight No studies looking at muscle mass & metabolic syndrome in

normal weight Purpose

To study the relationship between decreased muscle mass and metabolic syndrome in normal weight subjects using NHANES data

Methods

Subjects 1826 men and women NHANES 1999-2006 Controls

Age ≥ 20 years BMI 18.5 - 25 kg/m2 DXA - not preg or lactating, complete

Sarcopenia Definition Calculated ASM Index = ASM/ht2 via DXA measurements

Separated by gender Divided into tertiles

Based on gender Separated by age Lowest tertile = sarcopenic

Methods

Statistics Chi-square analysis

Tertiles separated by gender Differences between prevalence rates for each risk

factor within tertiles Separated by age

Odds Ratio Odds of risk for metabolic syndrome and each risk

factor Referencing highest muscle group

SAS 9.2

Results

Subject Characteristics

Results – Not Separated by Age

Results – Not Separated by Age

Results Recap

Not separating by age: Prevalence

Males & Females All significant except HDL

Odds Male & Female

2-fold increase in hyperglycemia 3-fold increase in BP 2-fold increase in TG

Metabolic Syndrome Male – 7-fold increase Female – 2-fold increase

Noticeable trends with age & decreased muscle mass Men more prevalent

Results – Separated by Age Men

Results – Separated by Age Men

Results – Separated by Age Female

Results – Separated by Age Female

Results Recap

Significant differences separating by age Prevalence:

Males 60+ = BP Females 40-59 = TG & metabolic syndrome

Odds ratio Males

5-fold increase for metabolic syndrome age 40-59 6-fold increase for metabolic syndrome age 60 + 3-fold increase for BP age 60 +

Females 2-fold increase for TG age 40-59 3-fold increase for metabolic syndrome age 40-59

Discussion

Discussion: Metabolic Syndrome & Muscle

Wildman et al. 2008 Cardiometabolic abnormality 23.5% of normal-weight

Srikanthan et al. 2011 Inverse relationship between muscle mass & insulin

resistance/dysglycemia My study

Not separated by age Men: 12.19% vs. 1.75% Women: 15.25% vs. 6.16%

Separated by age 20-39: 6.92% men, 0% women 40-59: 11.5% men, 19.48% women 60+: 20.14% men, 21.69% women

Discussion: Metabolic Syndrome Risk Factors

Not separated by age

Separated by age Men: 60+ BP Women: 40-59 High Triglycerides & Metabolic

Syndrome Most prevalent: Hyperglycemia, BP, TG

Male trends Prevalence & odds increase w/ age prevalence of all factors increased as muscle

decreased

Discussion: Sedentary Behavior

MONW Overlooked

Physical activity inversely related to metabolic syndrome Wildman et al. 46.6% MONW no physical activity Conus et al. MONW greater portion of time

watching TV Ford et al. prevalence of metabolic syndrome

increased w/ TV time

Importance of physical activity regardless of BMI

Limitations

Excluded multiple imputed subjects

Controlling for fasted subjects decreased sample sizes

Separating by age decreased sample sizes in tertiles 20-39 age category vs. 60+

Questions?

References

St-Onge MP, Janssen I, Heymsfield SB. Metabolic syndrome in normal-weight americans. Diabetes Care 2004;27;9:2222-2228.

[Ruderman NB, Schneider SH, Berchtold P. The “metabolically-obese,” normal-weight individual. The American Journal of Clinical Nutrition 1981;34:1617-1621.

Ruderman N, Christholm D, Pi-Sunyer X, Schneider S. The metabolically obese, normal-weight individual revisted. Diabetes 1998;47:699-713.

Conus F, Allison DB, Rabasa-Lhoret R, St-Onge M, St-Pierre DH, Tremblay-Lebeau A, Poehlman ET. Metabolic and behavioral characteristics of metabolically obese but normal-weight women. The Journal of Clinical Endocrinology & Metabolism 2004;89;10:5013-5020.

Ford ES, Kohl HW, Mokdad AH, Ajani UA. Sedentary behavior, physical activity, and the metabolic syndrome among U.S adults. Obesity Research 2005;13;3:608-614.

Klip A, Pâquet MR. Glucose transport and glucose transporters in muscle and their metabolic regulation. Diabetes Care 1990;13;3:228-243.

Lim KI, Yang SJ, Kim TN, Yoo HJ, Kang HJ, Song W, Baik SH, Choi DS, Choi KM. The association between the ratio of visceral fat to thigh muscle area and metabolic syndrome: the Korean Sarcopenic Obesity Study (KSOS). Clinical Endocrinology 2010;73:588-594.

Cruz-Jentoft AJ, Baeyens JP, Bauer JM, Boirie Y, Cederholm T, Landi F, Martin FC, Michel JP, Rolland Y, Schneider SM, Topinkova E, Vandewoude M, Zamboni M. Sarcopenia: European consensus on definition and diagnosis. Age and Ageing 2010;39:412-423.

Kim TN, Park MS, Yang SJ, Yoo HJ, Kang HJ, Song W. Seo JA, Kim SG

, Kim NH, Baik SH, Choi DS, Choi KM. Prevalence and determinant factors of sarcopenia in patients with type 2 diabetes. Diabetes Care 2010;33;7:1497-1499.

Park SW, Goodpaster BH, Strotmeyer ES, de Rekeneire N, Harris TB, Schwartz AV, Tylavsky FA, Newman AB. Decreased muscle strength and quality in older adults with type 2 diabetes: the health, aging, and body composition study. Diabetes 2006;55:1813-1818.

Kim J. Gender differences in association between appendicular skeletal mass and cardiometabolic abnormalities in normal-weight and obese adults: Korea National Health and Nutrition Examination Survery (KNHANES) IV-3 and V-I. Asia-Pacific Journal of Public Health 2012;1:1-8.

Srikanthan P, Karlamangla AS. Relative muscle mass is inversely associated with insulin resistance and prediabetes. Findings from the Third National Health and Nutrition Examination Survey. Journal of Clinical Endocrinology and Metabolism 2011;96;9:2898-2903.

Centers for Disease Control and Prevention, National Center for Health Statistics. National Health and Nutrition Examination Survey: Technical documentation for the 1999-2004 Dual Energy X-ray Absorptiometry (DXA) multiple imputation data files. 2008. p. 3-6.

Baumgartner RN. Body Composition in Healthy Aging. Ann N Y Acad Sci 2000;904:437-448. Kim TN, Yang SJ, Yoo HJ, Lim KI, Kang HJ, Song W, Seo JA, Kim SG, Kim NH, Baik SH, Choi DS, Choi KM. Prevalence of sarcopenia

and sarcopenic obesity in Korean adults: the Korean sarcopenic obesity study. International Journal of Obesity 2009;33;885-892. Parikh RM, Mohan V. Changing definitions of metabolic syndrome. Indian Journal of Endocrinology and Metabolism 2012;16;1:7-

12. Dominguez LJ. The cardiometabolic syndrome and sarcopenic obesity in older persons. The Journal of cardiometabolic Syndrome

2007:183-189. Conus F, Rabasa-Lhoret R, Réronnet F. Characteristics of metabolically obese normal-weight (MONW) subjects. Appl Physiol Nutr

Metab 2007;32:4-12. Hamilton MT, Hamilton DG, Zderie TW. Role of low energy expenditure and sitting in obesity, metabolic syndrome, type 2

diabetes, and cardiovascular disease. Diabetes 2007;56:2655-2667. Wildman RP, Muntner P, Reynolds K, McGinn AP, Rajpathak S, Wylie-Rosett J, Sowers MR. The obese without cardiometabolic risk

factor clustering and the normal weight with cardiometabolic risk factor clustering. Arch Intern Med 2008;168;15:1617-1624. McAuley PA, Artero EG, Sui X, Lee D, Church TS, Lavie CJ, Myers JN, España-Romero V, Blair SN. The obesity paradox,

cardiorespiratory fitness, and coronary heart disease. Mayo Clinic Proceedings 2012;87;5:443-451. Kokkinos P, Myers J, Faselis C, Doumas M, Kheirbek R, Nylen E. BMI-mortality paradox and fitness in African American and

Caucasian men with type 2 diabetes. Diabetes Care 2012;35:1021-1027.