Variation In Insulin Sensitivity Research Articles

Auto adaptation of closed-loop insulin delivery system using continuous reward functions and incremental discretization

Several closed or hybrid loop controllers for Blood Glucose (BG) regulation, which are also known as Artificial Pancreas (AP) Systems or Automated Insulin Delivery systems (AIDs), are in development worldwide. Most AIDs are designed and evaluated for short-term performance, with a particular emphasis on the post-meal period. However, if controllers are not adapted properly to account for variations in physiology that affect Insulin Sensitivity (IS), the AIDs may perform inadequately. In this work, the performance of two Reinforcement Learning (RL) agents trained under both piecewise and continuous reward functions is evaluated in-silico for long-term adaptation of a Fully Automated Insulin Delivery (fAID) system. An automatic adaptive discretization scheme that expands the state space as needed is also implemented to avoid disproportionate state space exploration. The proposed agents are evaluated for long-term adaptation of the Automatic Regulation of Glucose (ARG) algorithm, considering variations in IS. Results show that both RL agents have improved performance compared to a rule-based decision-making approach and the baseline controller for the majority of the adult population. Moreover, the use of a continuous shaped reward function proves to enhance the performance of the agents further than a piecewise one.

Nonequilibrium thermodynamics and mitochondrial protein content predict insulin sensitivity and fuel selection during exercise in human skeletal muscle.

Introduction: Many investigators have attempted to define the molecular nature of changes responsible for insulin resistance in muscle, but a molecular approach may not consider the overall physiological context of muscle. Because the energetic state of ATP (ΔGATP) could affect the rate of insulin-stimulated, energy-consuming processes, the present study was undertaken to determine whether the thermodynamic state of skeletal muscle can partially explain insulin sensitivity and fuel selection independently of molecular changes. Methods: 31P-MRS was used with glucose clamps, exercise studies, muscle biopsies and proteomics to measure insulin sensitivity, thermodynamic variables, mitochondrial protein content, and aerobic capacity in 16 volunteers. Results: After showing calibrated 31P-MRS measurements conformed to a linear electrical circuit model of muscle nonequilibrium thermodynamics, we used these measurements in multiple stepwise regression against rates of insulin-stimulated glucose disposal and fuel oxidation. Multiple linear regression analyses showed 53% of the variance in insulin sensitivity was explained by 1) VO2max (p = 0.001) and the 2) slope of the relationship of ΔGATP with the rate of oxidative phosphorylation (p = 0.007). This slope represents conductance in the linear model (functional content of mitochondria). Mitochondrial protein content from proteomics was an independent predictor of fractional fat oxidation during mild exercise (R2 = 0.55, p = 0.001). Conclusion: Higher mitochondrial functional content is related to the ability of skeletal muscle to maintain a greater ΔGATP, which may lead to faster rates of insulin-stimulated processes. Mitochondrial protein content per se can explain fractional fat oxidation during mild exercise.

OR26-03 Lower Serum Myostatin Levels Are Associated with Higher Insulin Sensitivity in Adults with Overweight/Obesity

In preclinical models, inhibition of the myokine myostatin prevents or improves insulin resistance (IR). However, studies investigating the association between serum myostatin levels and IR in humans are discrepant, perhaps in part because myostatin immunoassays lack specificity and sensitivity. New sensitive and specific myostatin LC-MS/MS assays make it possible to determine if higher serum myostatin levels are independently associated with greater IR in adults with overweight/obesity. If true, therapeutic manipulation of myostatin pathways may be a potential therapeutic target to prevent or treat type 2 diabetes (T2DM) in this high-risk population, in which current strategies, e.g. weight loss, are difficult to implement and maintain.We studied 75 adults (53% women), 20–65 yo, BMI ≥25 kg/m2 and generally healthy without T2DM. Serum myostatin levels (1° independent variable) were measured by LC-MS/MS (Brigham Research Assay Core, Boston, MA), with no cross-reactivity with growth differentiation factor 11 (GDF11), activins or transforming growth factor beta (TGF-β), sensitivity of 0.5 ng/mL and intra- and inter-assay coefficient of variation of 10 and 12%. Insulin sensitivity (IS) (1° dependent variable) was estimated by QUICKI, appendicular lean mass (ALM) by DXA, visceral adipose tissue (VAT) by CT and intrahepatic (IHL) and intramyocellular lipids (IMCL) by MR spectroscopy. Models were run sex- combined and stratified given sex differences in muscle mass.Mean age was 47.9±12.2 years and BMI was 33.2±5.7 kg/m2 (mean±SD). Compared to men, women had lower mean ALM (20.9±3.3 vs 29.2±3.3 kg, p<0.0001) and serum myostatin levels (7.28±1.87 vs 8.28±1.89 ng/mL, p=0.02) and similar mean IS (0.16±0.02 vs 0.15±0.02, p=0.13). Lower serum myostatin levels were associated with higher IS in the whole group (R=-0.32, p=0.008) and in women (R=-0.41, p=0.02)—both remained significant after controlling for ALM—but not in men (R=-0.16, p=0.36). In a multivariate model including VAT, IHL, IMCL and ALM, lower serum myostatin levels were associated with higher IS in the whole group (B1= -0.37, p=0.003), in women (B1= -0.43, p=0.02) and in men (B1= -0.37, p=0.05). In a stepwise regression model including VAT, IHL, IMCL and ALM, VAT explained 18%, IHL explained 10% and myostatin explained 8% of the variability in IS in the whole group; in women, myostatin explained 18% and IHL explained 12% of the variability; in men, VAT explained 26% of the variability and myostatin was not a significant determinant.In conclusion, lower serum myostatin levels were associated with greater IS in adults with overweight/obesity, independent of muscle and adipose depots known to be associated with T2DM risk. Future studies should investigate potential sex differences in the association between myostatin and IS. Therapeutic manipulation of myostatin pathways may be a potential therapeutic target to prevent or treat T2DM.

3574 Effect modification between kidney function and adiposity in the association with central and peripheral insulin sensitivity among Nondiabetic patients with moderate Chronic Kidney Disease and Healthy Controls

OBJECTIVES/SPECIFIC AIMS: The main aim of this study was to investigate the interaction between glomerular filtration rate (GFR) and body mass index (as well as serum leptin) as determinants of peripheral and central insulin sensitivity (IS). METHODS/STUDY POPULATION: This was a cross-sectional investigation of 140 nondiabetic participants – 56 with CKD (GFR = 15-59 ml/min/1.73m2) and 94 with normal GFR (≥ 60 ml/min/1.73m2) – recruited as part of the relationship of insulin sensitivity in kidney disease and vascular health (RISKD) study. Peripheral (skeletal muscle) and central (hepatic) IS were assessed with the hyperinsulinemic euglycemic glucose clamp (HEGC) and homeostasis assessment of insulin resistance (HOMA-IR) respectively. Creatinine-based estimated GFR (eGFR) was obtained using the CKD-EPI equation and body mass index (BMI) was computed from baseline weight and height measurements. Linear regression models with robust standard errors (to relax homoscedasticity assumptions) and interaction terms were used to investigate GFR and BMI as predictors of HEGC-derived insulin sensitivity index (ISI) and HOMA-IR. RESULTS/ANTICIPATED RESULTS: The mean (SD) age was 53.9 (14.5) years; 50.7% were female and 36.7% were African-American. Compared to controls, CKD patients had significantly lower mean (SD) ISI [5.4 (3.2) vs. 3.1 (1.6), p &lt; 0.0001]. Log ISI was positively correlated (r = 0.39, p &lt; 0.0001) with eGFR and inversely correlated (−0.30, p &lt; 0.0001) with BMI and log leptin (−0.42, p &lt; 0.0001). In multivariable models adjusted for age, sex and race, a 10 ml/min/1.73m2 lower eGFR was associated with a greater decrease in ISI among non-obese (0.48; 95% CI: −0.25, −0.70) compared to obese participants (−0.18; 95% CI: −0.02, −0.35) (p-interaction = 0.04). Patients with low eGFR (in particular, the lower margin of the CKD stage 3 range, 30ml/min) had lower ISI even with BMI within normal range (Figure 1a). At higher eGFR, BMI had a greater impact on ISI. P-interaction = 0.046, for differential BMI effects at lower vs. higher eGFR. Log HOMA-IR was inversely correlated with eGFR (r = - 0.49, p &lt; 0.0001) and positively correlated with BMI (r = 0.52, p &lt; 0.0001) and log leptin (0.46, p &lt; 0.0001). HOMA-IR was lower for persons with higher GFR compared to lower GFR, at any BMI value. For example, at a BMI of 30 and a GFR of 120, HOMA-IR was 1.2 compared to 4.8 at a GFR of 30 (Figure 1b). Also, persons with high GFR had low HOMA-IR even with BMI in the obese range. BMI had a greater effect on HOMA-IR at lower eGFR. P-interaction = 0.005, for differential BMI effects at lower vs. higher eGFR. Similar findings were obtained when using log leptin in lieu of BMI in models for ISI and HOMA-IR. DISCUSSION/SIGNIFICANCE OF IMPACT: Measures of adiposity (BMI and leptin) and GFR were independently predictive of insulin sensitivity (IS) but the magnitude of the effect of BMI (or leptin) on IS varied significantly across GFR levels and type of IS (peripheral versus central). The effect of BMI on central IS (HOMA-IR) was more pronounced at lower GFR with small changes in BMI translating into greater variations in IS. Conversely, at low GFR, peripheral IS (ISI) is less affected by BMI. Persons with GFR at the lower margin of the CKD stage 3 range were significantly insulin resistant (low ISI) regardless of their BMI. More studies are required to further elucidate these interaction patterns for central and peripheral IS.

The variability in beta-cell function in placebo-treated subjects with type 2 diabetes: application of the weight-HbA1c-insulin-glucose (WHIG) model.

The weight-glycosylated haemoglobin (HbA1C)-insulin-glucose (WHIG) model describes the effects of changes in weight on insulin sensitivity (IS) in newly diagnosed, obese subjects receiving placebo treatment. This model was applied to a wider population of placebo-treated subjects, to investigate factors influencing the variability in IS and β-cell function. The WHIG model was applied to the WHIG dataset (Study 1) and two other placebo datasets (Studies 2 and 3). Studies 2 and 3 consisted of nonobese subjects and subjects with advanced type 2 diabetes mellitus (T2DM). Body weight, fasting serum insulin (FSI), fasting plasma glucose (FPG) and HbA1c were used for nonlinear mixed-effects modelling (using NONMEM v7.2 software). Sources of interstudy variability (ISV) and potential covariates (age, gender, diabetes duration, ethnicity, compliance) were investigated. An ISV for baseline parameters (body weight and β-cell function) was required. The baseline β-cell function was significantly lower in subjects with advanced T2DM (median difference: Study 2: 15.6%, P < 0.001; Study 3: 22.7%, P < 0.001) than in subjects with newly diagnosed T2DM (Study 1). A reduction in the estimated insulin secretory response in subjects with advanced T2DM was observed but diabetes duration was not a significant covariate. The WHIG model can be used to describe the changes in weight, IS and β-cell function in the diabetic population. IS remained relatively stable between subjects but a large ISV in β-cell function was observed. There was a trend towards decreasing β-cell responsiveness with diabetes duration, and further studies, incorporating subjects with a longer history of diabetes, are required.

Relationships between insulin sensitivity, skeletal muscle mass and muscle quality in obese adolescent boys

We examined the relationships between insulin sensitivity (IS), skeletal muscle (SM) mass and SM quality in youth. Forty obese adolescent boys (body mass index ≥ 95 th percentile, 12-18 years) participated in this study. IS and glucose tolerance was measured by a 3 h hyperinsulinemic-euglycemic clamp and a 2 h oral glucose tolerance test (OGTT), total SM mass and intermusular adipose tissue (IMAT) by whole-body magnetic resonance imaging, and muscular strength by one-repetition maximum leg and bench press. IMAT was associated (P<0.05) with IS (r=-0.53) and OGTT-insulin area under the curve (AUC; r=0.31). Similarly, muscular strength was associated (P<0.05) with both IS (r=0.39) and OGTT-insulin AUC (r=-0.32). By contrast, total SM mass was not associated with IS or any OGTT parameters (P>0.1). After accounting for race and tanner stage, IMAT and muscular strength remained significantly associated with IS, together explaining a total of 41% of the variance in IS. Our findings suggest that SM quality, but not SM mass, is associated with IS in obese adolescent boys.

Insulin Sensitivity and Liver Fat: Role of Iron Load

Increased liver fat (LF) is associated with insulin resistance. However, a considerable individual variability between LF and insulin sensitivity (IS) is observed, and at equal levels of LF, insulin-resistant as well as insulin-sensitive individuals are found. Our objective was to study whether hepatic iron load (HIL) explains some of the variation between IS and LF. HIL was measured using a quantitative T2* magnetic resonance gradient echo imaging technique, and LF was measured by (1)H-magnetic resonance spectroscopy. Low T2* values indicate high HIL. We studied the association of LF and HIL with anthropometric data and IS. A total of 113 healthy nondiabetic subjects [69 females, 44 males; age 47 ± 1 yr; body mass index (BMI) = 28.9 ± 0.5 kg/m(2)] at increased risk for type 2 diabetes were included in the study. T2* values adjusted for age negatively associated with serum ferritin levels (P < 0.0001) and positively associated with IS (P = 0.009). In addition, T2* values associated with LF (P = 0.008) but not with BMI (P = 0.6). In a multivariate model, IS adjusted for gender, age, and BMI was associated with T2* values (P = 0.015). IS adjusted for gender and age was independently associated with LF (P = 0.033) and T2* values (P = 0.004). In a stepwise regression analysis, LF explained 13.5% (P < 0.01) of the variation in IS, and HIL explained an additional 4.1% (P = 0.03). HIL explains part of the variation between LF and IS. The mechanism by which iron load induces insulin resistance is possibly independent of the pathways involved in insulin resistance induced by fatty liver disease.

Relationships between fat deposition in the liver and skeletal muscle and insulin sensitivity in Japanese individuals: a pilot study

Purpose:To evaluate the relationships between insulin sensitivity (IS), body fat accumulation, and aerobic capacity in middle- to older-aged Japanese participants with visceral adiposity.Participants and methods:Aerobic capacity was measured during an incremental ramp exercise test. Computed tomography was used to measure visceral (VFA) and subcutaneous (SFA) fat area, the fat in liver-to-spleen ratio (L/S), and low-density skeletal muscle area (LDMA). IS was assessed using euglycemic-hyperinsulinemic clamps.Results:A total of 11 males and 9 females, age 58 ± 9 years (mean ± standard deviation), body mass index 29 ±4.1 kg/m2, and VFA 190 ±53 cm2 participated in this study. In unadjusted models, VFA, LDMA, and L/S were significantly correlated with IS, which remained in adjusted models for LDMA and L/S, but not for VFA. In multiple stepwise regression analysis including sex, age, body fat, VFA, SFA, alcohol consumption, and aerobic capacity (oxygen uptake at the lactate threshold), L/S, and LDMA accounted for 70% of the total variance in IS. Percentage body fat and SFA, but not VFA, were significantly correlated with high molecular-weight adiponectin levels (r = 0.58, P < 0.01 and r = 0.54, P < 0.05, respectively). IS and L/S were significantly and negatively correlated with tumor necrosis factor-α (r = −0.67 and −0.63, respectively; both P < 0.01) and plasminogen activator inhibitor-1 (r = −0.58, P < 0.01 and −0.52, P < 0.05, respectively), whereas LDMA was not.Conclusion:These findings indicate that ectopic fat deposition in the liver and skeletal muscle may be associated with peripheral IS independently of body fat accumulation and aerobic capacity in middle- to older-aged Japanese individuals with visceral adiposity. Because of the small sample size, additional larger studies are needed to provide further insight into these preliminary findings.

Design and Clinical Pilot Testing of the Model-Based Dynamic Insulin Sensitivity and Secretion Test (DISST)

Insulin resistance is a significant risk factor in the pathogenesis of type 2 diabetes. This article presents pilot study results of the dynamic insulin sensitivity and secretion test (DISST), a high-resolution, low-intensity test to diagnose insulin sensitivity (IS) and characterize pancreatic insulin secretion in response to a (small) glucose challenge. This pilot study examines the effect of glucose and insulin dose on the DISST, and tests its repeatability. DISST tests were performed on 16 subjects randomly allocated to low (5 g glucose, 0.5 U insulin), medium (10 g glucose, 1 U insulin) and high dose (20 g glucose, 2 U insulin) protocols. Two or three tests were performed on each subject a few days apart. Average variability in IS between low and medium dose was 10.3% (p=.50) and between medium and high dose 6.0% (p=.87). Geometric mean variability between tests was 6.0% (multiplicative standard deviation (MSD) 4.9%). Geometric mean variability in first phase endogenous insulin response was 6.8% (MSD 2.2%). Results were most consistent in subjects with low IS. These findings suggest that DISST may be an easily performed dynamic test to quantify IS with high resolution, especially among those with reduced IS.

Update on Mathematical Modeling Research to Support the Development of Automated Insulin Delivery Systems

One year after its initial meeting, the Glycemia Modeling Working Group reconvened during the 2009 Diabetes Technology Meeting in San Francisco, CA. The discussion, involving 39 scientists, again focused on the need for individual investigators to have access to the clinical data required to develop and refine models of glucose metabolism, the need to understand the differences among the distinct models and control algorithms, and the significance of day-to-day subject variability. The key conclusion was that model-based comparisons of different control algorithms, or the models themselves, are limited by the inability to access individual model-patient parameters. It was widely agreed that these parameters, as opposed to the average parameters that are typically reported, are necessary to perform such comparisons. However, the prevailing view was that, if investigators were to make the parameters available, it would limit their ability (and that of their institution) to benefit from the invested work in developing their models. A general agreement was reached regarding the importance of each model having an insulin pharmacokinetic/pharmacodynamic profile that is not different from profiles reported in the literature (88% of the respondents agreed that the model should have similar curves or be analyzed separately) and the importance of capturing intraday variance in insulin sensitivity (91% of the respondents indicated that this could result in changes in fasting glucose of >or=15%, with 52% of the respondents believing that the variability could effect changes of >or=30%). Seventy-six percent of the participants indicated that high-fat meals were thought to effect changes in other model parameters in addition to gastric emptying. There was also widespread consensus as to how a closed-loop controller should respond to day-to-day changes in model parameters (with 76% of the participants indicating that fasting glucose should be within 15% of target, with 30% of the participants believing that it should be at target). The group was evenly divided as to whether the glucose sensor per se continues to be the major obstacle in achieving closed-loop control. Finally, virtually all participants agreed that a future two-day workshop should be organized to compare, contrast, and understand the differences among the different models and control algorithms.

Heritability of insulin sensitivity and lipid profile depend on BMI: evidence for gene–obesity interaction

Aims/hypothesisEvidence from candidate gene studies suggests that obesity may modify genetic susceptibility to type 2 diabetes and dyslipidaemia. On an aggregate level, gene–obesity interactions are expected to result in different heritability estimates at different obesity levels. However, this hypothesis has never been tested.MethodThe present study included 2,180 British female twins. BMI was used as an index of general obesity. Outcome measures were insulin sensitivity (indexed by quantitative insulin-sensitivity check index [QUICKI]) and fasting plasma lipid profile. Structural equation modelling was used to test whether BMI interacted with latent genetic and environmental effects to impact on the outcome measures.ResultsGenetic influences on triacylglycerol increased with BMI (p < 0.001) whereas the unique environmental influence on QUICKI decreased with BMI (p < 0.001), resulting in a higher heritability estimate for both measures at higher BMI levels. This was further illustrated by stratified analysis in twin pairs concordant for normal weight and twin pairs concordant for overweight. Heritability was 19 percentage points higher for triacylglycerol (p < 0.001) and 31 percentage points higher for QUICKI (p < 0.01) among twins concordant for overweight than among twins concordant for normal weight. BMI had no moderator effect on the latent genetic and environmental factors for total cholesterol and HDL-cholesterol.Conclusions/interpretationOur results suggest that the expression of genes influencing triacylglycerol and insulin sensitivity can vary as a function of obesity status. The substantial increases in the genetic contribution to the total variance in insulin sensitivity and triacylglycerols at higher BMIs may prove extremely valuable in the search for candidate genes.

Identification of Intraday Metabolic Profiles during Closed-Loop Glucose Control in Individuals with Type 1 Diabetes

Algorithms for closed-loop insulin delivery can be designed and tuned empirically; however, a metabolic model that is predictive of clinical study results can potentially accelerate the process. Using data from a previously conducted closed-loop insulin delivery study, existing models of meal carbohydrate appearance, insulin pharmacokinetics, and the effect on glucose metabolism were identified for each of the 10 subjects studied. Insulin's effects to increase glucose uptake and decrease endogenous glucose production were described by the Bergman minimal model, and compartmental models were used to describe the pharmacokinetics of subcutaneous insulin absorption and glucose appearance following meals. The composite model, comprised of only five equations and eight parameters, was identified with and without intraday variance in insulin sensitivity (S(I)), glucose effectiveness at zero insulin (GEZI), and endogenous glucose production (EGP) at zero insulin. Substantial intraday variation in SI, GEZI and EGP was observed in 7 of 10 subjects (root mean square error in model fit greater than 25 mg/dl with fixed parameters and nadir and/or peak glucose levels differing more than 25 mg/dl from model predictions). With intraday variation in these three parameters, plasma glucose and insulin were well fit by the model (R(2) = 0.933 +/- 0.00971 [mean +/- standard error of the mean] ranging from 0.879-0.974 for glucose; R(2) = 0.879 +/- 0.0151, range 0.819-0.972 for insulin). Once subject parameters were identified, the original study could be reconstructed using only the initial glucose value and basal insulin rate at the time closed loop was initiated together with meal carbohydrate information (glucose, R(2) = 0.900 +/- 0.015; insulin delivery, R(2) = 0.640 +/- 0.034; and insulin concentration, R(2) = 0.717 +/- 0.041). Metabolic models used in developing and comparing closed-loop insulin delivery algorithms will need to explicitly describe intraday variation in metabolic parameters, but the model itself need not be comprised by a large number of compartments or differential equations.

Muscle mass and functional correlates of insulin sensitivity in lean young Indian men

There is a high prevalence of diabetes mellitus associated with insulin resistance in Indian adults, probably due to an inappropriately high accumulation of body fat at lower body mass indices (BMIs, kg/m(2)), as well as to a lower skeletal muscle mass. Although skeletal muscle is an important site of glucose disposal, the strength of its association with insulin sensitivity (IS) is unknown in the Indian population. This was a cross-sectional study performed to assess the association of body fat, muscle and muscle function with IS in 51 young, healthy, nonobese Indian men with BMI ranging from 15 to 25 kg/m(2), using hyperinsulinemic euglycemic clamp method. The median IS was 5.32 (mg/(kg min))/(microU/ml), lower, upper quartile, 4.03, 6.56); (SI units: 4.25, lower, upper quartile, 3.22, 5.24 (micromol/(kg min))/(pmol/l)). A multiple linear regression of the natural log transformed value of IS on BMI and body muscle mass measured as the appendicular lean soft tissue (expressed as a percentage of body weight) explained 49% of the variance in IS. Independently, body fat percent and muscle (handgrip) strength showed significant bivariate correlations with IS (rho=-0.61, P<0.001; rho=0.32, P=0.027, respectively), although these variables did not enter into the multiple regression. BMI and body fat have been shown to be inversely related to IS at higher BMIs. This study indicates that a relative increase in BMI and reduced muscle mass and possibly function are also associated with reduced IS in lean Indian men.

Association of Physical Activity and Body Composition With Insulin Sensitivity in a Community Sample of Adolescents

To examine how body composition and physical activity are related to insulin sensitivity and secretion in adolescents. Cross-sectional. Baltimore, Maryland. Fifty-six healthy adolescents (34 boys and 22 girls; mean [SD] age, 13.3 [1.3] years; 95% were African American) who had been recruited at infancy from a health care clinic serving a low-income, urban community. Main Exposures Physical activity was measured for 5 to 7 days by a uniaxial accelerometer placed on the right ankle. Proportion of time spent in play-equivalent physical activity (PEPA) was defined as 1800 or more counts per minute. Body mass index (BMI) (calculated as weight in kilograms divided by height in meters squared) was converted to an age- and sex-specific z score. Insulin sensitivity, insulin secretion, and disposition index calculated from a fasting oral glucose tolerance test. Thirty-nine percent of the adolescents had a BMI in the 85th percentile or higher; half of those were overweight (BMI > or = 95th percentile). Play-equivalent physical activity and BMI z score were not correlated. In multivariate analyses, BMI z score and time spent in PEPA together explained 21% of the variance in insulin sensitivity and 18% in insulin secretion. Independent of each other, high BMI z score and low proportion of PEPA were significantly associated with low insulin sensitivity (partial r(2) = 0.14 and 0.10, respectively) and high insulin secretion (partial r(2) = 0.10 and 0.10, respectively), but not with disposition index. In a cohort of urban, predominantly African American adolescents, both body composition and physical activity were independently associated with insulin sensitivity. At this point, insulin secretion is appropriately regulated.

Racial Differences in Adiponectin in Youth

OBJECTIVE—The purpose of this study was to investigate 1) whether adiponectin is associated with insulin sensitivity independent of visceral adipose tissue in African-American and Caucasian youth and 2) whether adiponectin is associated with racial differences in insulin sensitivity. RESEARCH DESIGN AND METHODS—Total body fat was measured by dual-energy X-ray absorptiometry and abdominal adipose tissue with computed tomography. Insulin sensitivity was measured by a 3-h hyperinsulinemic-euglycemic clamp. RESULTS—Adiponectin was inversely associated (P &amp;lt; 0.01) with visceral adipose tissue, fasting insulin, and proinsulin and was positively related (P &amp;lt; 0.01) to insulin sensitivity after controlling for Tanner stage and sex independent of race. Stepwise multiple regression revealed that adiponectin was a strong independent predictor of insulin sensitivity, explaining 27% of the variance in insulin sensitivity. When subjects were categorized into tertiles of visceral adipose tissue and further low (≤50th) and high (&amp;gt;50th) adiponectin groups, insulin sensitivity was significantly different across the visceral adipose tissue groups (main effect, P &amp;lt; 0.01) in both races. However, within each visceral adipose tissue group, subjects with high adiponectin had higher insulin sensitivity (main effect, P &amp;lt; 0.05) than subjects with low adiponectin, independent of race. Racial differences in insulin sensitivity remained significant (P &amp;lt; 0.01) after controlling for leptin and visceral adipose tissue but not (P &amp;gt; 0.05) after additional adjustment for adiponectin. CONCLUSIONS—Adiponectin is associated with insulin sensitivity independent of visceral adipose tissue in both African-American and Caucasian youth. Low adiponectin in African-American youth may be a biological marker that predisposes them to a greater risk of insulin resistance.

Does adiponectin explain the lower insulin sensitivity and hyperinsulinemia of African-American children?

Adiponectin is an adipocytokine with antidiabetogenic properties. The present study investigated: (i) the effect of race on adiponectin levels and (ii) the relationship of adiponectin levels in children to insulin sensitivity and secretion. Fasting adiponectin levels were determined in 22 healthy prepubertal black compared with 22 white children of similar body composition. We previously reported these black children to have lower insulin sensitivity and higher first-phase insulin secretion than their white peers. Fasting adiponectin levels were lower in black children (9.9 +/- 1.0 microg/mL vs. 15.7 +/- 1.1 microg/mL, p < 0.001). Adiponectin correlated positively with insulin sensitivity (r = 0.29, p = 0.06) and negatively with first-phase insulin levels (r = -0.47, p = 0.001). In a multiple regression analysis, 48% of the variance in first-phase insulin secretion was explained by the independent effects of race (p = 0.017), adiponectin (p = 0.03), and percentage of body fat (p < 0.001). Adiponectin did not contribute significantly to the variance in insulin sensitivity. In summary, black children have approximately 35% lower adiponectin levels than their white peers. Lower adiponectin does not seem to explain the racial differences in insulin sensitivity. The relationship of hyperinsulinemia and hypoadiponectinemia needs to be further explored.

VO2max and Dietary Carbohydrate Determine the Variance in Insulin Resistance Following Exercise in Obese Elderly

0150 Exercise training reverses insulin resistance in obese and elderly adults, but there is a wide variation in the magnitude of the response. The reason for this variation remains unknown. PURPOSE: To determine the contribution of altered macronutrient intake and improved aerobic fitness on the variation in improved insulin sensitivity in obese elderly men and women participating in an aerobic exercise training program. METHODS: Twenty-two obese men and women (Age = 63±1 years, BMI = 33.6±1.1 kg/m2) volunteered to participate in a 12-week supervised exercise program (5 d/wk, 60 min/day, treadmill/cycle ergometry @ 85% of heart rate max). Insulin resistance was determined before and after the 12-week program using the euglycemic (5.0 mM) hyperinsulinemic (40 mU.m2.min−1) clamp procedure. Aerobic fitness was assessed during a treadmill VO2max test and macronutrient intake was determined from multiple 3-day food records. RESULTS: After the exercise training program insulin sensitivity was increased by 36% (3.6±0.4 to 4.9±0.6 mg.kg FFM.min−1). VO2max improved 15%, from 21.1±0.8 to 24.3±1.3 ml.kg.min−1 (P<0.003). Total caloric intake and macronutrient (carbohydrate, protein, fat) content of the diet was similar before and at the end of the program. Stepwise multiple regression analysis indicated that the significant change achieved in VO2max, explained 39% of the variance in improved insulin sensitivity, while subtle increases in carbohydrate intake, (expressed per/kgdelta) accounted for an additional 13% of the variance. Together, these two variables combined, could explain 52% of the variance in insulin sensitivity (r = 0.72, P<0.001). CONCLUSION: Observing the impact of altered VO2max on insulin stimulated glucose disposal has reinforced our understanding that the metabolic effect of any given therapeutic manipulation is strongly influenced by the degree of biologic adaptation, as well as the specific dietary intake. Supported by NIH grants AG12834, RR00080 and the Diabetes Assoc. of Greater Cleveland.

Determinants of insulin sensitivity in chronic heart failure.

To describe the determinants of insulin sensitivity (IS) in chronic heart failure (CHF), we created a model in which the influence of lifestyle factors and etiology of heart failure on IS were incorporated concomitantly with age, left ventricular ejection fraction (LVEF) and parameters of body composition. Observational cohort study. Outpatient clinic for chronic heart failure. Fifty-seven male CHF patients [NYHA class II-III, age 61+/-9 years, body mass index (BMI) 26.9+/-3.3 kg/m2 (mean+/-S.D.)]. Euglycemic hyperinsulinemic clamp, cycle ergometry, anthropometric measurements, LVEF and a physical activity questionnaire. A model explaining the variance of IS in CHF. IS was 18.2+/-8.6 microg.kg(-1).min(-1).mU(-1).l(-1), fasting insulin level was 15.9+/-11.0 mU/l and fasting glucose level was 5.5+/-0.6 mmol/l. Peak VO2 was 19.1+/-4.9 ml.kg(-1).min(-1) and LVEF 26.2+/-7.1%. IS was inversely associated with fasting insulin concentration (r=-0.50, P<0.001) and BMI (r=-0.54, P<0.001). After controlling for BMI, IS also revealed a correlation with age (r=-0.36, P<0.01). The model explained 60% of variance in IS: BMI contributed 20%, smoking 17%, age 17% and physical activity in daily life (DPA) 16% (all P<0.05) to the variance of IS, whereas LVEF (9%) and etiology of heart failure (8%) contributed moderately. In CHF patients, IS is for a major part predicted by BMI, smoking, age, daily physical activity, LVEF and etiology of heart failure.

Development of a prediction equation for insulin sensitivity from anthropometry and fasting insulin in prepubertal and early pubertal children.

To test the utility of homeostasis model assessment (HOMA) in predicting insulin sensitivity [x10(- 4) min(-1)/(microIU/ml)] in children and to develop and compare two new prediction equations for insulin sensitivity in children using demographic and anthropometric measures in the presence or absence of fasting insulin. We studied 156 white and African-American children with complete data (mean age 9.7 +/- 1.8 years, 87.8% Tanner Stage 1 or 2). For development of new equations, two-thirds of the children were randomly assigned to a development group, whereas the remaining children were assigned to a cross-validation group. A modified HOMA equation accurately predicted insulin sensitivity, but its utility is similar to fasting insulin alone. Demographic and anthropometric measures alone did not predict insulin sensitivity accurately, even when precise measures of body composition were included in the prediction model. Ethnicity, calf skinfold, and fasting insulin together explained 73% of the variance in insulin sensitivity and accurately predicted insulin sensitivity. The regression of measured versus predicted insulin sensitivity in the cross-validation group was not significantly different from the line of identity (P > 0.05). Mean difference between measured and predicted insulin sensitivity was also not significant (P > 0.05). Some bias was apparent, particularly in white boys. Ethnicity, calf skinfold, and fasting insulin can accurately predict insulin sensitivity with greater precision than HOMA or fasting insulin alone (R(2) = 0.73). Future studies, however, are needed to examine whether a universal equation is possible. A cross-validated prediction equation may be useful in population-based studies when complex measures of insulin sensitivity are not available.