Steady-state Plasma Glucose Concentrations Research Articles

Abstract 2047: Pioglitazone Improves Apolipoprotein A1-associated Proteomic Composition More Than Weight Loss In Insulin Resistance

Insulin resistance (IR) is linked to an atherogenic dyslipidemia and an increased risk of ASCVD. Apolipoprotein A1 (ApoA1)-associated lipoproteins exert varying anti-atherogenic functions and have a large diverse proteome. It is, however, unknown whether IR contributes to atherogenic perturbations in these ApoA1-associated proteins. We thus tested the hypothesis that IR is associated with an atherogenic ApoA1 proteome and that insulin-sensitizing interventions improve the proteome to a more anti-atherogenic composition. The study included 861 participants without diabetes (age 48 ± 12 years, female = 65.5 %). We measured IR directly using the steady-state plasma glucose (SSPG) concentration during the insulin suppression test, where a higher SSPG indicates greater IR. The ApoA1-associated proteome lipoproteins were measured by mass spectrometry. A subgroup of participants underwent pioglitazone treatment (PIO; n = 38) or weight loss (WL; n = 70) for 3 months. Paired t-testing was used to quantify within-subject changes in outcomes of interest pre- to post-treatment. At baseline, several components of ApoA1-associated proteome significantly correlated with SSPG concentration ( Figure ). Both PIO and WL significantly improved IR (p<0.01). PIO led to significant increases in several ApoA1-associated proteins: ApoC1; ApoC2; ApoC3; ApoC4; ApoA2; ApoA4; ApoD; ApoE; ApoM; LCAT; LpPLA2; PLTP; PON1; and PON3. WL also led to significant increases in ApoA1-associated proteins: ANGT; ApoA4; ApoD; ApoM; LpPLA2; PLTP; PON1; and PON3. In conclusion, IR correlates with an atherogenic ApoA1-associated proteome. Both PIO and WL improve IR and the composition of the ApoA1-associated proteome, but PIO has a more pronounced effect on the proteome compared to WL. These findings support further exploration into the ApoA1-associated proteome for both understanding residual lipid-related ASCVD risk and targeted therapeutics for ASCVD reduction.

Chemerin in Participants with or without Insulin Resistance and Diabetes.

Chemerin is a chemokine/adipokine, regulating inflammation, adipogenesis and energy metabolism whose activity depends on successive proteolytic cleavages at its C-terminus. Chemerin levels and processing are correlated with insulin resistance. We hypothesized that chemerin processing would be higher in individuals with type 2 diabetes (T2D) and in those who are insulin resistant (IR). This hypothesis was tested by characterizing different chemerin forms by specific ELISA in the plasma of 18 participants with T2D and 116 without T2D who also had their insulin resistance measured by steady-state plasma glucose (SSPG) concentration during an insulin suppression test. This approach enabled us to analyze the association of chemerin levels with a direct measure of insulin resistance (SSPG concentration). Participants were divided into groups based on their degree of insulin resistance using SSPG concentration tertiles: insulin sensitive (IS, SSPG ≤ 91 mg/dL), intermediate IR (IM, SSPG 92-199 mg/dL), and IR (SSPG ≥ 200 mg/dL). Levels of different chemerin forms were highest in patients with T2D, second highest in individuals without T2D who were IR, and lowest in persons without T2D who were IM or IS. In the whole group, chemerin levels positively correlated with both degree of insulin resistance (SSPG concentration) and adiposity (BMI). Participants with T2D and those without T2D who were IR had the most proteolytic processing of chemerin, resulting in higher levels of both cleaved and degraded chemerin. This suggests that increased inflammation in individuals who have T2D or are IR causes more chemerin processing.

The relationship between insulin resistance and ion mobility lipoprotein fractions

ObjectiveInsulin resistance (IR) increases risk of type 2 diabetes and atherosclerotic cardiovascular disease and is associated with lipid and lipoprotein abnormalities including high triglycerides (TG) and low high-density lipoprotein cholesterol (HDL-C). Lipoprotein size and lipoprotein subfractions (LS) have also been used to assist in identifying persons with IR. Associations of LS and IR have not been validated using both direct measures of IR and direct measures of LS. We assessed the usefulness of fasting lipoprotein subfractions (LS) by ion mobility to identify individuals with IR. MethodsLipid panel, LS by ion mobility (LS-IM), and IR by steady-state plasma glucose (SSPG) concentration were assessed in 526 adult volunteers without diabetes. IR was defined as being in the highest tertile of SSPG concentration. LS-IM score was calculated by linear combination of regression coefficients from a stepwise regression analysis with SSPG concentration as the dependent variable. Improvement in prediction of IR was evaluated after combining LS-IM score with TG/HDL-C, TG/HDL-C and BMI as well as with TG/HDL-C, BMI, sex, race and ethnicity. IR prediction was evaluated by area under the receiver operating characteristic curve (AUC) and positive predictive value (PPV) considering the highest 5% of scores as positive test. ResultsPrediction of IR was similar by LS-IM score and TG/HDL-C (AUC=0.68; PPV=0.59 and AUC=0.70; PPV=0.59, respectively) and prediction was improved when LS-IM was combined with TG/HDL-C (AUC=0.73; PPV=0.70), TG/HDL-C and BMI (AUC=0.82; PPV=0.81) and with TG/HDL-C, BMI, sex, race and ethnicity (AUC=0.84; PPV=0.89). ConclusionFor identifying individuals with IR, LS-IM score and TG/HDL-C are comparable and their combination further improves IR prediction by TG/HDL-C alone. Among patients who have undergone IM testing, the LS-IM score may assist prioritization of subjects for further evaluation and interventions to reduce IR.

OR02-2 The Relationship of Lipoprotein Fractions as Assessed by Ion Mobility and Insulin Resistance as Measured by the Insulin Suppression Test

Abstract Insulin resistance (IR)/compensatory hyperinsulinemia exert effects on the type and nature of blood lipids that contribute to the increased risk of atherosclerotic cardiovascular disease (ASCVD). Direct measurement of IR is labor-intensive and cannot be performed in a clinical setting. Previously, we modeled the utility of fasting insulin and C-peptide measurements (IR score) to inform the degree of insulin resistance in a large cohort of individuals without diabetes who had undergone direct measurement of whole-body IR. In the current study, we examined the associations of lipoprotein subfractions with IR and determined the usefulness of lipoprotein subfractions to identify insulin resistant individuals. A total of 527 persons (median age 50 years, 65% women, 93% non-Hispanic, and 70% white) underwent measurements of BMI, lipid panel, and lipoprotein subfractions by ion mobility (IM) as well as direct measurement of IR by steady-state plasma glucose (SSPG) concentration during the insulin suppression test. Individuals in the top tertile of SSPG concentration were defined as being insulin resistant. A stepwise linear regression model was used to select a combination of lipoprotein subfractions that remained associated with SSPG after adjusting for BMI, age, sex, ethnicity, race, and triglyceride to HDL cholesterol ratio (TG/HDL-C). An IM score was calculated using linear combinations of the regression coefficients for IM lipoprotein subfractions. Similarly, scores were calculated for TG/HDL-C, the full model excluding the IM lipoprotein subfractions, and the full model that included all variables. The scores were evaluated for predicting the top tertile of SSPG by using area under the receiver operator characteristic curve (AUC) analysis and the positive predictive value (PPV) calculations where the highest 5% value of a score was considered a positive test. Several of the IM lipoprotein sub-fractions were associated with SSPG in a linear regression model after adjusting for BMI, age, sex, ethnicity, race, and TG/HDL-C. When predicting individuals in the top tertile of SSPG, the IM score and TG/HDL-C were similar (AUC=0.68 and 0.70 respectively; PPV=.59 and .59 respectively); however, when used together, they significantly improved the prediction of IR (AUC=0.73; PPV=0.70). Similarly, the score derived from the full stepwise model that included the IM score significantly improved the AUC and PPV when compared with the score from the model that excluded the IM score (AUC=0.84 vs. 0.81; PPV=0.89 vs 0.85). In conclusion, the IM score and TG/HDL-C are comparable in identifying insulin resistant individuals and their combination improves prediction of IR when combined with BMI and demographic data. Among patients who have undergone ion mobility testing, the IM score may assist prioritization of subjects for further testing by the IR score and aid in identification of persons at increased risk of ASCVD associated with IR. Presentation: Saturday, June 11, 2022 11:45 a.m. - 12:00 p.m.

Lower functional hippocampal connectivity in healthy adults is jointly associated with higher levels of leptin and insulin resistance.

Metabolic dysregulation is currently considered a major risk factor for hippocampal pathology. The aim of the present study was to characterize the influence of key metabolic drivers on functional connectivity of the hippocampus in healthy adults. Insulin resistance was directly quantified by measuring steady-state plasma glucose (SSPG) concentration during the insulin suppression test and fasting levels of insulin, glucose, leptin, and cortisol, and measurements of body mass index and waist circumference were obtained in a sample of healthy cognitively intact adults (n=104). Resting-state neuroimaging data were also acquired for the quantification of hippocampal functional cohesiveness and integration with the major resting-state networks (RSNs). Data-driven analysis using unsupervised machine learning (k-means clustering) was then employed to identify clusters of individuals based on their metabolic and functional connectivity profiles. K-means clustering identified two clusters of increasing metabolic deviance evidenced by cluster differences in the plasma levels of leptin (40.36 (29.97) vs. 27.59 (25.58)μg/L) and the degree of insulin resistance (SSPG concentration: 161.63 (65.27) vs. 125.72 (66.81)mg/dL). Individuals in the cluster with higher metabolic deviance showed lower functional cohesiveness within each hippocampus and lower integration of posterior and anterior components of the left and right hippocampus with the major RSNs. The two clusters did not differ in general intellectual ability or episodic memory. We identified two clusters of individuals differentiated by abnormalities in insulin resistance, leptin levels, and hippocampal connectivity, with one of the clusters showing greater deviance. These findings support the link between metabolic dysregulation and hippocampal function even in nonclinical samples.

Hippocampal volume reduction is associated with direct measure of insulin resistance in adults

Hippocampal integrity is highly susceptible to metabolic dysfunction, yet its mechanisms are not well defined. We studied 126 healthy individuals aged 23–61 years. Insulin resistance (IR) was quantified by measuring steady-state plasma glucose (SSPG) concentration during the insulin suppression test. Body mass index (BMI), adiposity, fasting insulin, glucose, leptin as well as structural neuroimaing with automatic hippocampal subfield segmentation were performed. Data analysis using unsupervised machine learning (k-means clustering) identified two subgroups reflecting a pattern of more pronounced hippocampal volume reduction being concurrently associated with greater adiposity and insulin resistance; the hippocampal volume reductions were uniform across subfields. Individuals in the most deviant subgroup were predominantly women (79 versus 42 %) with higher BMI [27.9 (2.5) versus 30.5 (4.6) kg/m2], IR (SSPG concentration, [156 (61) versus 123 (70) mg/dL] and leptinemia [21.7 (17.0) versus 44.5 (30.4) μg/L]. The use of person-based modeling in healthy individuals suggests that adiposity, insulin resistance and compromised structural hippocampal integrity behave as a composite phenotype; female sex emerged as risk factor for this phenotype.

Effect of the glucagon-like peptide-1 analogue liraglutide versus placebo treatment on circulating proglucagon-derived peptides that mediate improvements in body weight, insulin secretion and action: A randomized controlled trial.

To examine how circulating glucagon-like peptide-1 (GLP-1) concentrations during liraglutide treatment relate to its therapeutic actions on glucose and weight, and to study the effects of liraglutide on other proglucagon-derived peptides (PGDPs), including endogenous GLP-1, glucagon-like peptide-2, glucagon, oxyntomodulin, glicentin and major proglucagon fragment, which also regulate metabolic and weight control. Adults who were overweight/obese (body mass index 27-40 kg/m2 ) with prediabetes were randomized to liraglutide (1.8 mg/day) versus placebo for 14 weeks. We used specific assays to measure exogenous (liraglutide, GLP-1 agonist [GLP-1A]) and endogenous (GLP-1E) GLP-1, alongside five other PGDP concentrations during a mixed meal tolerance test (MMTT) completed at baseline and at week 14 (liraglutide, n = 16; placebo, n = 19). Glucose during MMTT, steady-state plasma glucose (SSPG) concentration for insulin resistance and insulin secretion rate (ISR) were previously measured. MMTT area-under-the-curve (AUC) was calculated for ISR, glucose and levels of PGDPs. Participants on liraglutide versus placebo had significantly (P ≤ .004) decreased weight (mean -3.6%, 95% CI [-5.2% to -2.1%]), SSPG (-32% [-43% to -22%]) and glucose AUC (-7.0% [-11.5% to -2.5%]) and increased ISR AUC (30% [16% to 44%]). GLP-1A AUC at study end was significantly (P ≤ .04) linearly associated with % decrease in weight (r = -0.54) and SSPG (r = -0.59) and increase in ISR AUC (r = 0.51) in the liraglutide group. Treatment with liraglutide significantly (P ≤ .005) increased exogenous GLP-1A AUC (median 310 vs. 262 pg/mL × 8 hours at baseline but decreased endogenous GLP-1E AUC [13.1 vs. 24.2 pmol/L × 8 hours at baseline]), as well as the five other PGDPs. Decreases in the PGDPs processed in the intestines are independent of weight loss, indicating a probable direct effect of GLP-1 receptor agonists to decrease their endogenous production in contrast to weight loss-dependent changes in glucagon and major proglucagon fragment that are processed in pancreatic alpha cells. Circulating GLP-1A concentrations, reflecting liraglutide levels, predict improvement in weight, insulin action and secretion in a linear manner. Importantly, liraglutide also downregulates other PGDPs, normalization of the levels of which may provide additional metabolic and weight loss benefits in the future.

Long-Term Testosterone Administration on Insulin Sensitivity in Older Men With Low or Low-Normal Testosterone Levels.

Serum testosterone levels and insulin sensitivity both decrease with age. Severe testosterone deficiency is associated with the development of insulin resistance. However, the effects of long-term testosterone administration on insulin sensitivity in older men with low or low-normal testosterone levels remain unknown. The Testosterone Effects on Atherosclerosis in Aging Men Trial was a placebo-controlled, randomized, double-blind trial. The participants were 308 community-dwelling men, ≥60 years old, with total testosterone 100 to 400 ng/dL or free testosterone <50 pg/mL. A subset of 134 nondiabetic men (mean age, 66.7 ± 5.1 years) underwent an octreotide insulin suppression test at baseline and at 3 and 36 months after randomization to measure insulin sensitivity. Insulin sensitivity was estimated as the steady-state plasma glucose (SSPG) concentration at equilibrium during octreotide and insulin administration. Secondary outcomes included total lean mass (TLM) and total fat mass (TFM) by dual energy x-ray absorptiometry. There was a significant (P = 0.003) increase in SSPG in the placebo group, whereas no change was seen in testosterone-treated subjects from baseline to 36 months; however, the between-group differences in change in SSPG over 3 years were not statistically significant (+15.3 ± 6.9 mg/dL in the placebo group vs +6.2 ± 6.4 mg/dL in the testosterone group; mixed-model effect, P = 0.17). Changes in SSPG with testosterone treatment were not associated with changes in serum total or free testosterone concentrations. Changes in TFM but not TLM were associated with increases in SSPG. Stratification by age or baseline total testosterone level did not show significant intervention effects. Testosterone administration for 36 months in older men with low or low-normal testosterone levels did not improve insulin sensitivity.

Relationship among age, insulin resistance, and blood pressure

The effect of age to modify the relationship between insulin resistance and hypertension is unclear. In this retrospective, cross-sectional study, median age was used to create two age groups (<52 vs. ≥52 years), and comparisons were made of metabolic characteristics, including steady-state plasma glucose (SSPG) concentrations measured during the insulin suppression test to quantify insulin resistance. Individuals were stratified into SSPG tertiles and categorized as having normal blood pressure (BP), prehypertension, or hypertension. SSPG concentrations were similar in the two age groups (161 vs. 164 mg/dL). In the most insulin-resistant tertile, distribution of normal BP, prehypertension, and hypertension was equal in those aged <52 years, whereas in those aged ≥52 years, prevalence of hypertension was increased approximately fivefold compared with those with normal BP. Multivariate regression analysis demonstrated significant interaction between age and SSPG in predicting systolic BP (P = .023). In stratified analysis, SSPG, but not age, was an independent predictor of systolic BP and diastolic BP in ≥52 years group, whereas the reverse was true in the younger group. The adverse impact of insulin resistance on BP was accentuated in older individuals and may have a greater impact than further aging.

Hypertriglyceridemia: A simple approach to identify insulin resistance and enhanced cardio-metabolic risk in patients with prediabetes

AimsPrediabetes (PreDM) is a metabolically heterogeneous condition, differing in degree of insulin resistance and risk of type 2 diabetes mellitus and coronary heart disease (CHD). This study was initiated to evaluate the hypothesis that a fasting plasma triglyceride (TG) concentration ⩾1.7mmol/L can aid in identifying the subset of individuals with PreDM who are most insulin resistant and at greatest risk to develop CHD as well as type 2 diabetes mellitus. MethodsIn this cross-sectional study, measurements were made of: (1) steady-state plasma glucose (SSPG) concentration during the insulin suppression test to ascertain degree of insulin resistance and (2) conventional CHD risk factors in 587 apparently healthy individuals with normal fasting plasma glucose (NFG, n=370) or PreDM (n=217). ResultsSubjects with PreDM were significantly (P<0.001) more insulin resistant (higher SSPG concentrations) and had a more adverse CHD risk profile than those with NFG. A TG concentration ⩾1.7mmol/L identified a subset of individuals with PreDM (38%) who had a higher mean SSPG concentration (11.3±3.5mmol/L vs. 9.3±3.9mmol/L, P<0.001), were more likely to be insulin resistant (66% vs. 39%, P<0.001), and had a more adverse CHD risk factor profile. ConclusionsMeasurement of fasting TG concentration in individuals with PreDM may provide a simple clinical approach to identify those who are insulin resistant, at enhanced risk of CHD, and more likely to develop type 2 diabetes mellitus.

Salsalate-induced changes in lipid, lipoprotein, and apoprotein concentrations in overweight or obese, insulin-resistant, nondiabetic individuals

Although salsalate administration consistently lowers plasma triglyceride concentrations in patients with type II diabetes, prediabetes, and/or insulin resistance, changes in low-density lipoprotein cholesterol (LDL-C) concentrations have been inconsistent; varying from no change to a significant increase. To evaluate the clinical relevance of this discordance in more detail, we directly measured LDL-C and obtained a comprehensive assessment of changes in lipid, lipoprotein, and apoprotein concentrations associated with salsalate use in insulin-resistant individuals, overweight or obese, but without diabetes, using vertical auto profile method. A single-blind, randomized, placebo-controlled study was performed in volunteers who were overweight or obese, without diabetes, and insulin resistant on the basis of their steady-state plasma glucose concentration during an insulin suppression test. Participants were randomized 2:1 to receive salsalate 3.5 g/d (n = 27) or placebo (n = 14) for 4 weeks. Comprehensive lipid, lipoprotein, and apoprotein analysis by vertical auto profile was obtained after an overnight fast, before and after study intervention. There was no change in directly measured LDL-C concentration in salsalate-treated individuals. However, salsalate administration was associated with various changes considered to decrease atherogenicity; including decreases in triglyceride and total very low-density lipoprotein cholesterol (VLDL-C) concentrations, a shift from small denser LDL lipoproteins toward larger, more buoyant LDL particles, decreases in VLDL(1+2)-C and LDL(4)-C, and nonsignificant decreases in non-high-density lipoprotein cholesterol and apolipoprotein B. No significant changes occurred in the placebo-treated group. Atherogenicity of the lipid, lipoprotein, and apoprotein profile of insulin-resistant individuals who were overweight or obese improved significantly in association with salsalate treatment. The clinical importance of this finding awaits further study.

Relationship between insulin resistance and amino acids in women and men.

Insulin resistance has been associated with higher plasma amino acid (AA) concentrations, but majority of studies have used indirect measures of insulin resistance. Our main objective was to define the relationship between plasma AA concentrations and a direct measure of insulin resistance in women and men. This was a cross-sectional study of 182 nondiabetic individuals (118 women and 64 men) who had measurement of 24 AAs and steady-state plasma glucose (SSPG) concentration (insulin resistance) using the insulin suppression test. Fourteen out of 24 AA concentrations were significantly (P < 0.05) higher in men than women; only glycine was lower in men. Majority of these AAs were positively associated with SSPG; only glycine concentration was negatively associated. Glutamic acid, isoleucine, leucine, and tyrosine concentrations had the strongest correlation with SSPG (r ≥ 0.4, P < 0.001). The degree of association was similar in women and men, independent of obesity, and similar to traditional markers of insulin resistance (e.g., glucose, triglyceride, high-density lipoprotein cholesterol). Compared with women, men tended to have a more unfavorable AA profile with higher concentration of AAs associated with insulin resistance and less glycine. However, the strength of association between a direct measurement of insulin resistance and AA concentrations were similar between sexes and equivalent to several traditional markers of insulin resistance.

Abnormalities of lipoprotein concentrations in obstructive sleep apnea are related to insulin resistance.

Prevalence of cardiovascular disease (CVD) is increased in patients with obstructive sleep apnea (OSA), possibly related to dyslipidemia in these individuals. Insulin resistance is also common in OSA, but its contribution to dyslipidemia of OSA is unclear. The study's aim was to define the relationships among abnormalities of lipoprotein metabolism, clinical measures of OSA, and insulin resistance. Cross-sectional study. OSA severity was defined by the apnea-hypopnea index (AHI) during polysomnography. Hypoxia measures were expressed as minimum and mean oxygen saturation, and the oxygen desaturation index. Insulin resistance was quantified by determining steady-state plasma glucose (SSPG) concentrations during the insulin suppression test. Fasting plasma lipid/lipoprotein evaluation was performed by vertical auto profile methodology. Academic medical center. 107 nondiabetic, overweight/obese adults. Lipoprotein particles did not correlate with AHI or any hypoxia measures, nor were there differences noted by categories of OSA severity. By contrast, even after adjustment for age, sex, and BMI, SSPG was positively correlated with triglycerides (r = 0.30, P < 0.01), very low density lipoprotein (VLDL) and its subclasses (VLDL1+2) (r = 0.21-0.23, P < 0.05), and low density lipoprotein subclass 4 (LDL4) (r = 0.30, P < 0.01). SSPG was negatively correlated with high density lipoprotein (HDL) (r = -0.38, P < 0.001) and its subclasses (HDL2 and HDL3) (r = -0.32, -0.43, P < 0.01), and apolipoprotein A1 (r = -0.33, P < 0.01). Linear trends of these lipoprotein concentrations across SSPG tertiles were also significant. Pro-atherogenic lipoprotein abnormalities in obstructive sleep apnea (OSA) are related to insulin resistance, but not to OSA severity or degree of hypoxia. Insulin resistance may represent the link between OSA-related dyslipidemia and increased cardiovascular disease risk.

Usefulness of Fetuin-A to Predict Risk for Cardiovascular Disease Among Patients With Obstructive Sleep Apnea

Patients with obstructive sleep apnea (OSA) are at increased risk for cardiovascular diseases (CVDs). Fetuin-A, a novel hepatokine, has been associated with the metabolic syndrome (MetS), insulin resistance, and type 2 diabetes mellitus, all of which are highly prevalent in patients with OSA and associated with increased CVD risk. The goal of this study was to determine whether fetuin-A could be involved in the pathogenesis of CVD risk in patients with OSA, through relations of fetuin-A with MetS components and/or insulin resistance. Overweight or obese, nondiabetic volunteers (n = 120) were diagnosed with OSA by in-laboratory nocturnal polysomnography. Steady-state plasma glucose concentrations derived during the insulin suppression test were used to quantify insulin-mediated glucose uptake; higher steady-state plasma glucose concentrations indicated greater insulin resistance. Fasting plasma fetuin-A and lipoprotein concentrations were measured. Whereas neither the prevalence of MetS nor the number of MetS components was associated with tertiles of fetuin-A concentrations, the lipoprotein components of MetS, triglycerides and high-density lipoprotein cholesterol, increased (p <0.01) and decreased (p <0.05), respectively, across fetuin-A tertiles. Additionally, comprehensive lipoprotein analysis revealed that very low density lipoprotein (VLDL) particles and VLDL subfractions (VLDL1+2 and VLDL3) were increased across fetuin-A tertiles. In contrast, neither insulin resistance nor sleep measurements related to OSA were found to be modified by fetuin-A concentrations. In conclusion, abnormalities of lipoprotein metabolism, but not MetS or insulin resistance per se, may represent a mechanism by which fetuin-A contributes to increased CVD risk in patients with OSA.

Low Circulating 25-Hydroxyvitamin D Concentrations Are Associated with Defects in Insulin Action and Insulin Secretion in Persons with Prediabetes ,

Background: Individuals with prediabetes mellitus (PreDM) and low circulating 25-hydroxyvitamin D [25(OH)D] are at increased risk of type 2 diabetes mellitus (T2DM).Objective: We aimed to determine whether low 25(OH)D concentrations are associated with defects in insulin action and insulin secretion in persons with PreDM.Methods: In this cross-sectional study, we stratified 488 nondiabetic subjects as having PreDM or normal fasting glucose (NFG) and a 25(OH)D concentration ≤20 ng/mL (deficient) or >20 ng/mL (sufficient). We determined insulin resistance by steady state plasma glucose (SSPG) concentration and homeostasis model assessment of insulin resistance (HOMA-IR) and insulin secretion by homeostasis model assessment of β-cell function (HOMA-β). We compared insulin resistance and secretion measures in PreDM and NFG groups; 25(OH)D-deficient and 25(OH)D-sufficient groups; and PreDM-deficient, PreDM-sufficient, NFG-deficient, and NFG-sufficient subgroups, adjusting for age, sex, race, body mass index, multivitamin use, and season.Results: In the PreDM group, mean SSPG concentration and HOMA-IR were higher and mean HOMA-β was lower than in the NFG group (P < 0.001 for all comparisons). In the 25(OH)D-deficient group, mean SSPG concentration was higher (P < 0.001), but neither mean HOMA-IR nor HOMA-β was significantly different from that in the 25(OH)D-sufficient group. In the PreDM-deficient subgroup, mean (95% CI) SSPG concentration was higher (P < 0.01) than in the PreDM-sufficient, NFG-deficient, and NFG-sufficient subgroups [192 (177–207) mg/dL vs. 166 (155–177) mg/dL, 148 (138–159) mg/dL, and 136 (127–144) mg/dL, respectively]. Despite greater insulin resistance, mean HOMA-β was not significantly higher in the PreDM-deficient subgroup than in the PreDM-sufficient, NFG-deficient, and NFG-sufficient subgroups [98 (85–112) vs. 91 (82–101), 123 (112–136), and 115 (106–124), respectively].Conclusion: Subjects with PreDM and low circulating 25(OH)D concentrations are the subgroup of nondiabetic individuals who are the most insulin resistant and have impaired β-cell function, attributes that put them at enhanced risk of T2DM.

Modulation of coronary heart disease risk by insulin resistance in subjects with normal glucose tolerance or prediabetes.

This study was based on the hypothesis that: (1) coronary heart disease (CHD) risk is accentuated in the insulin-resistant subset of persons with normal glucose tolerance (NGT) or prediabetes (PreDM); (2) the prevalence of insulin resistance, and associated abnormalities, is greater in subjects with PreDM; and (3) insulin resistance is the major contributor to increased CHD risk in these individuals. A 75 g oral glucose challenge was used to classify volunteers as having NGT or PreDM. Steady-state plasma glucose (SSPG) concentrations during the insulin suppression test subdivided both groups into insulin sensitive (IS = SSPG < 8.4 mmol/L) or resistant (IR = SSPG ≥ 8.4 mmol/L). Measurements were made of demographic characteristics, blood pressure, and lipid and lipoprotein concentrations, and comparisons made between the subgroups. Subjects with PreDM (n = 127) were somewhat older, more likely to be non-Hispanic men, with increased adiposity than those with NGT (n = 315). In addition, they had higher FPG concentrations, were insulin resistant (SSPG concentration; 11.4 vs. 7.2 mmol/L), with higher blood pressures, and a significantly more adverse CHD risk lipid profile (p < 0.001). Twice as many subjects with PreDM were IR (72 vs. 35 %), and the CHD risk profile was significantly worse in the IR subgroups in those with either NGT or PreDM. Coronary heart disease risk profile is significantly more adverse in subjects with PreDM as compared to individuals with NGT. However, glucose tolerance status is not the only determinant of CHD risk in nondiabetic individuals, and differences in degree of insulin resistance significantly modulate CHD risk in subjects with NGT or PreDM.

Relationship among 25-hydroxyvitamin D concentrations, insulin action, and cardiovascular disease risk in patients with essential hypertension.

Although low plasma 25-hydroxyvitamin D (25(OH)D) concentrations have been shown to predict risk of hypertension and associated cardiovascular disease (CVD), vitamin D repletion has not consistently lowered blood pressure or decreased CVD. One possibility for this discrepancy is the presence of considerable metabolic heterogeneity in patients with hypertension. To evaluate this possibility, we quantified relationships among insulin resistance, 25(OH)D concentration, and CVD risk factor profile in patients with essential hypertension. Measurements were made of 25(OH)D concentrations, multiple CVD risk factors, and insulin resistance by the steady-state plasma glucose concentration during the insulin suppression test in 140 otherwise healthy patients with essential hypertension. As a group, the patients were overweight/obese and insulin resistant and had low 25(OH)D concentrations. The more insulin resistant the patients were, the worse the CVD risk profile was. In addition, the most insulin-resistant quartile had significantly lower 25(OH)D concentrations than the most insulin-sensitive quartile (20.3±1.4 vs. 25.8±1.4ng/ml; P = 0.005). In the entire group, 25(OH)D concentration significantly correlated with magnitude of insulin resistance (steady-state plasma glucose concentration; r = -0.20; P = 0.02). There was considerable metabolic heterogeneity and substantial difference in magnitude of conventional CVD risk factors in patients with similar degrees of blood pressure elevation. The most insulin-resistant quartile of subjects had the lowest 25(OH)D concentration and the most adverse CVD risk profile, and they may be the subset of patients with essential hypertension most likely to benefit from vitamin D repletion.

Subcutaneous adipose cell size and distribution: Relationship to insulin resistance and body fat

Metabolic heterogeneity among obese individuals may be attributable to differences in adipose cell size. We sought to clarify this by quantifying adipose cell-size distribution, body fat, and insulin-mediated glucose uptake in overweight/moderately-obese individuals. 148 healthy nondiabetic subjects with BMI 25–38 kg/m2 underwent subcutaneous adipose tissue biopsies and quantification of insulin-mediated glucose uptake with steady-state plasma glucose concentrations (SSPG) during the modified insulin suppression test. Cell-size distributions were obtained with Beckman Coulter Multisizer. Primary endpoints included % small adipose cells and diameter of large adipose cells. Cell-size and metabolic parameters were compared by regression for the whole group; according to IR and IS subgroups; and by body fat quintile.Both large and small adipose cells were present in nearly equal proportions. Percent small cells was associated with SSPG (r=0.26, p=0.003). Compared to BMI-matched IS individuals, IR counterparts demonstrated fewer, but larger large adipose cells, and a greater proportion of small-to-large adipose cells. Diameter of the large adipose cells was associated with %body fat (r=0.26, p=0.014), female sex (r=0.21, p=0.036), and SSPG (r=0.20, p=0.012). In the highest vs lowest % body fat quintile, adipose cell size increased by only 7% whereas adipose cell number increased by 74%.Recruitment of adipose cells is required for expansion of body fat mass beyond BMI of 25 kg/m2. Insulin resistance is associated with accumulation of small adipose cells and enlargement of large adipose cells. These data support the notion that impaired adipogenesis may underlie insulin resistance.

Accurate method to estimate insulin resistance from multiple regression models using data of metabolic syndrome and oral glucose tolerance test.

How to measure insulin resistance (IR) accurately and conveniently is a critical issue for both clinical practice and research. In the present study, we tried to modify the β-cell function, insulin sensitivity, and glucose tolerance test (BIGTT) in patients with normal glucose tolerance (NGT) and abnormal glucose tolerance (AGT) by oral glucose tolerance test (OGTT) and metabolic syndrome (MetS) components. There were 327 participants enrolled and divided into NGT or AGT. Data from 75% of the participants were used to build the models, and the remaining 25% were used for external validation. Steady-state plasma glucose (SSPG) concentration derived from the insulin suppression test was regarded as the standard measurement for IR. Five models were built from multiple regression: model 1 (MetS model with sex, age and MetS components); model 2 (simple OGTT model with sex, age, plasma glucose, and insulin concentrations at 0 and 120min during OGTT); model 3 (full OGTT model with sex, age, and plasma glucose and insulin concentrations at 0, 30, 60, 90, 120, and 180min during OGTT); model 4 (simple combined model): model 1 and model 2; and model 5 (full model): model 1 and 3. In general, our models had higher r (2) compared with surrogates derived from OGTT, such as homeostasis model assessment-insulin resistance and quantitative insulin sensitivity check index. Among them, model 5 had the highest r (2) (0.505 in NGT, 0.556 in AGT, respectively). Our modified BIGTT models proved to be accurate and easy methods for estimating IR, and can be used in clinical practice and research.

Sex Differences in Insulin Resistance and Cardiovascular Disease Risk

The possibility that differences in insulin sensitivity explain why women, especially younger women, have a lower cardiovascular disease (CVD) risk than men remains an unsettled issue. The objective of this study was to evaluate whether sex disparities in CVD risk are associated with differences in insulin resistance. This was a cross-sectional study of women (n = 468) and men (n = 354) who had the measurement of CVD risk factors and steady-state plasma glucose (SSPG) concentration (insulin resistance) using the insulin suppression test. The population was also divided by median age (51 y) to evaluate the effect of age on sex differences. In general, the SSPG concentration was similar between sexes. At higher BMI (≥30 kg/m(2)), women had significantly lower SSPG concentration than men (sex × BMI interaction, P = .001). However, sex differences in CVD risk factors were not due to differences in SSPG but accentuated by a higher degree of insulin resistance in younger (age < 51 y) but not older (≥ 51 y) individuals. In younger individuals, women had significantly (P ≤ .007) lower diastolic blood pressure and fasting glucose and triglyceride concentration compared with men in SSPG tertile 3 (most insulin resistant) but not in tertile 1 (least insulin resistant). Older women had lower diastolic blood pressure compared with men, regardless of SSPG. High-density lipoprotein cholesterol remained higher in women, regardless of age or SSPG. The female advantage is not due to a difference in insulin action but results from an attenuation of the relationship between insulin resistance and CVD risk, especially in younger individuals.