Comparison of impedance to insulin-mediated glucose uptake in normal subjects and in subjects with latent diabetes

The effect of insulin resistance in the association between obesity and hypertension incidence among Chinese middle-aged and older adults: data from China health and retirement longitudinal study (CHARLS)

Background and aims

Obesity and insulin resistance are well-known important risk factors for hypertension. This study aimed to investigate the mediating effect of the triglyceride-glucose index (TyG) in the association between Chinese visceral obesity index (CVAI) and hypertension among Chinese middle-aged and older adults.

Methods

A total of 10,322 participants aged 45 years and older from CHARLS (2011-2018) were included. Baseline data were collected in 2011 and hypertension incidence data were gathered during follow-up in 2013, 2015 and 2018. Multivariate logistic regression models were constructed to investigate the association of CVAI and TyG with the incidence of hypertension. Additionally, mediation analyses were conducted to evaluate the mediating role of the TyG index in the relationship between CVAI and hypertension. Subgroup analysis was also performed.

Results

A total of 2,802 participants developed hypertension during the follow-up period. CVAI and TyG index were independently and significantly associated with hypertension incidence. Increasing quartiles of CVAI and TyG index were associated with high hypertension incidence in middle-aged and older adults. The TyG index was identified as a mediator in the relationship between CVAI and hypertension incidence, with a mediation effect (95% confidence interval) was 12.38% (6.75, 31.81%).

Conclusion

Our study found that CVAI and TyG were independently associated with hypertension incidence. TyG played a partial mediating effect in the positive association between CVAI and hypertension incidence.

Unraveling the mysteries of hepatic insulin signaling: deconvoluting the nuclear targets of insulin

Over 100 years have passed since insulin was first administered to a diabetic patient. Since then great strides have been made in diabetes research. It has determined where insulin is secreted from, which organs it acts on, how it is transferred into the cell and is delivered to the nucleus, how it orchestrates the expression pattern of the genes, and how it works with each organ to maintain systemic metabolism. Any breakdown in this system leads to diabetes. Thanks to the numerous researchers who have dedicated their lives to cure diabetes, we now know that there are three major organs where insulin acts to maintain glucose/lipid metabolism: the liver, muscles, and fat. The failure of insulin action on these organs, such as insulin resistance, result in hyperglycemia and/or dyslipidemia. The primary trigger of this condition and its association among these tissues still remain to be uncovered. Among the major organs, the liver finely tunes the glucose/lipid metabolism to maintain metabolic flexibility, and plays a crucial role in glucose/lipid abnormality due to insulin resistance. Insulin resistance disrupts this tuning, and selective insulin resistance arises. The glucose metabolism loses its sensitivity to insulin, while the lipid metabolism maintains it. The clarification of its mechanism is warranted to reverse the metabolic abnormalities due to insulin resistance. This review will provide a brief historical review for the progress of the pathophysiology of diabetes since the discovery of insulin, followed by a review of the current research clarifying our understanding of selective insulin resistance.

Label-free optical metabolic imaging of adipose tissues for prediabetes diagnosis

Rationale: Prediabetes can be reversed through lifestyle intervention, but its main pathologic hallmark, insulin resistance (IR), cannot be detected as conveniently as blood glucose testing. In consequence, the diagnosis of prediabetes is often delayed until patients have hyperglycemia. Therefore, developing a less invasive diagnostic method for rapid IR evaluation will contribute to the prognosis of prediabetes. Adipose tissue is an endocrine organ that plays a crucial role in the development and progression of prediabetes. Label-free visualizing the prediabetic microenvironment of adipose tissues provides a less invasive alternative for the characterization of IR and inflammatory pathology. Methods: Here, we successfully identified the differentiable features of prediabetic adipose tissues by employing the metabolic imaging of three endogenous fluorophores NAD(P)H, FAD, and lipofuscin-like pigments. Results: We discovered that 1040-nm excited lipofuscin-like autofluorescence could mark the location of macrophages. This unique feature helps separate the metabolic fluorescence signals of macrophages from those of adipocytes. In prediabetes fat tissues with IR, we found only adipocytes exhibited a low redox ratio of metabolic fluorescence and high free NAD(P)H fraction a₁. This differential signature disappears for mice who quit the high-fat diet or high-fat-high-sucrose diet and recover from IR. When mice have diabetic hyperglycemia and inflamed fat tissues, both adipocytes and macrophages possess this kind of metabolic change. As confirmed with RNA-seq analysis and histopathology evidence, the change in adipocyte's metabolic fluorescence could be an indicator or risk factor of prediabetic IR. Conclusion: Our study provides an innovative approach to diagnosing prediabetes, which sheds light on the strategy for diabetes prevention.

Leveraging the future of diagnosis and management of diabetes: From old indexes to new technologies

BACKGROUND

Diabetes is a heterogeneous and multifactorial disease. However, glycemia and glycated hemoglobin have been the focus of diabetes diagnosis and management for the last decades. As diabetes management goes far beyond glucose control, it has become clear that assessment of other biochemical parameters gives a much wider view of the metabolic state of each individual, enabling a precision medicine approach.

METHODS

In this review, we summarize and discuss indexes that have been used in epidemiological studies and in the clinical practice.

RESULTS

Indexes of insulin secretion, sensitivity/resistance and metabolism have been developed and validated over the years to account also with insulin, C-peptide, triglycerides or even anthropometric measures. Nevertheless, each one has their own objective and consequently, advantages and disadvantages for specific cases. Thus, we discuss how new technologies, namely new sensors but also new softwares/applications, can improve the diagnosis and management of diabetes, both for healthcare professionals but also for caretakers and, importantly, to promote the empowerment of people living with diabetes.

CONCLUSIONS

In long-term, the solution for a better diabetes management would be a platform that allows to integrate all sorts of relevant information for the person with diabetes and for the healthcare practitioners, namely glucose, insulin and C-peptide or, in case of need, other parameters/indexes at home, sometimes more than once a day. This solution would allow a better and simpler disease management, more adequate therapeutics thereby improving patients' quality of life and reducing associated costs.

Insulin resistance and accelerated cognitive aging

Insulin resistance may be an early sign of metabolic dysfunction with the potential to lead to neuropsychiatric sequelae in the long term. In order to identify whether insulin resistance in otherwise healthy young and middle-aged adults is associated with preclinical signs of neuropsychiatric impairment, we recruited 126 overweight but nondiabetic, nondepressed individuals who completed an insulin suppression test for direct measurement of insulin resistance as well as a battery of cognitive and neuropsychiatric measures. Insulin resistance was associated with weaker performance on a fine motor task (Purdue Pegboard) as well as increases in subclinical symptoms of depression. We submit that insulin resistance in early to mid-adulthood may be an important predictor of long-term risk for metabolic, psychiatric, and neurobehavioral dysfunction.

The relationship between insulin resistance and ion mobility lipoprotein fractions

Objective

Insulin 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.

Methods

Lipid 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.

Results

Prediction 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).

Conclusion

For 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.

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

Background

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.

Methods

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.

Results

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.

Conclusions

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.

The Quantose Insulin Resistance Test for Maternal Insulin Resistance: A Pilot Study

OBJECTIVE

Insulin resistance (IR) increases during pregnancy which can lead to hyperinsulinemia, gestational diabetes mellitus (GDM), and neonatal hypoglycemia (NH), especially in obese women. Glucose tolerance testing (GTT) is used clinically to evaluate IR in pregnancy. Quantose IR score index is a novel blood screen of IR validated in nonpregnant individuals. The score is generated using an algorithm that combines insulin and three biomarkers of fatty acid pathways (α-hydroxybutyrate, oleic acid, linoleoyl-glycerophospocholine). Our objective was to determine the validity of Quantose IR test (Metabolan Inc. Morrisville, NC) in assessing IR in pregnant obese women, as compared with the homeostatic model assessment of insulin resistance (HOMA-IR), and its ability to predict GDM and NH.

STUDY DESIGN

Women between 10^0/7 and 13^6/7 weeks of gestation with a pre-pregnancy or early pregnancy body mass index more than 30 kg/m², and no pregestational diabetes, were included. Fasting blood samples were collected at 10^0/7 to 13^6/7 (T1) and 24^0/7 to 28^0/7 (T2) weeks. Quantose IR and HOMA-IR were calculated. All women underwent an early (T1; indicated for women with obesity) and a T2 glucose tolerance tests. GDM was diagnosed using the two-step approach, and NH was defined as a neonatal glucose less than 40 mg/dL in the first 24 hours of life. Linear regression and receiver operating characteristic curves were used for analysis.

RESULTS

The trial enrolled 100 patients. Ten subjects (10%) were diagnosed with GDM in the second trimester and none in the first trimester. At T1, Quantose IR (R²= 0.48), but not 1-hour glucose tolerance test (R²= 0.07), correlated with HOMA-IR. Similar correlations were observed at T2. The 1-hour glucose tolerance test followed by HOMA-IR and Quantose IR (area under the curve [AUC]: 0.82, 0.68, and 0.62, respectively) were predictors of GDM. Quantose IR (AUC: 0.74) and 1-hour glucose tolerance test (AUC: 0.72) at T1 and T2 (AUC: 0.75; AUC: 0.93; respectively) were best predictors of NH. The best cut offs, sensitivities, and specificities for prediction of NH were determined.

CONCLUSION

Similar to nonpregnant individuals, Quantose IR appears to be a valid measure of IR in obese pregnant women. First trimester Quantose IR is a predictor of GDM diagnosed in the second trimester and NH. Given that it requires a single blood draw and no glucose challenge, it may be a useful test to evaluate and monitor IR in pregnancy. Our findings may be used as pilot data to explore the potential use of Quantose IR in pregnancy further.

KEY POINTS

· Traditional testing methods for insulin resistance in pregnancy are often performed late, are time consuming, and unpleasant to patients.. · The first trimester one-step Quantose IR test reflects insulin resistance in pregnancy and predicts GDM and neonatal hypoglycemia.. · This is the first known prospective clinical study validating Quantose IR score index in an obstetrical population at risk for developing GDM..

The Physiology of Insulin Clearance

In the 1950's, Dr. I. Arthur Mirsky first recognized the possible importance of insulin degradation changes to the pathogenesis of type 2 diabetes. While this mechanism was ignored for decades, insulin degradation is now being recognized as a possible factor in diabetes risk. After Mirsky, the relative importance of defects in insulin release and insulin resistance were recognized as risk factors. The hyperbolic relationship between secretion and sensitivity was introduced, as was the relationship between them, as expressed as the disposition index (DI). The DI was shown to be affected by environmental and genetic factors, and it was shown to be differentiated among ethnic groups. However, the importance of differences in insulin degradation (clearance) on the disposition index relationship remains to be clarified. Direct measure of insulin clearance revealed it to be highly variable among even normal individuals, and to be affected by fat feeding and other physiologic factors. Insulin clearance is relatively lower in ethnic groups at high risk for diabetes such as African Americans and Hispanic Americans, compared to European Americans. These differences exist even for young children. Two possible mechanisms have been proposed for the importance of insulin clearance for diabetes risk: in one concept, insulin resistance per se leads to reduced clearance and diabetes risk. In a second and new concept, reduced degradation is a primary factor leading to diabetes risk, such that lower clearance (resulting from genetics or environment) leads to systemic hyperinsulinemia, insulin resistance, and beta-cell stress. Recent data by Chang and colleagues appear to support this latter hypothesis in Native Americans. The importance of insulin clearance as a risk factor for metabolic disease is becoming recognized and may be treatable.

Insulin Resistance Promotes the Formation of Aortic Dissection by Inducing the Phenotypic Switch of Vascular Smooth Muscle Cells

Background

Insulin resistance (IR) plays a key role in the development of type 2 diabetes mellitus (T2DM) and is one of its most important characteristics. Previous studies have shown that IR and T2DM were independent risk factors for a variety of cardiovascular and cerebrovascular diseases. However, there are few studies on the relationship between IR and aortic dissection (AD). The goal of this research was to find evidence that IR promotes the occurrence of AD.

Methods

Through the statistical analysis, we determined the proportion of glycosylated hemoglobin (HbA1c) abnormalities (HbA1c > 5.7) in people with acute thoracic aortic dissection (ATAD) and compared the difference of messenger RNA (mRNA) and protein expression of GluT1 in the thoracic aorta of normal people and those with ATAD to find evidence that IR is a causative factor in AD. The mouse model of IR and AD and the IR model of human aortic vascular smooth muscle cells (HA-VSMC) were established. Real time-PCR (RT-PCR) and Western blotting were used to study the mRNA and protein expression. Hematoxylin and eosin (H&E), Masson, and elastic fiber staining, and immunofluorescence were used to study the morphological structure.

Results

The proportion of HbA1c abnormalities in patients with ATAD was 59.37%, and the mRNA and protein expression of GluT1 were significantly lower than that in normal people. Fasting glucose concentration (FGC), serum insulin concentration (SIC), and the homeostasis model assessment of insulin resistance (HOMA-IR) of mice was obviously increased in the high-fat diet group and the protein expressions of Glut1 and GluT4 were reduced, indicating that the mouse IR model was successfully established. The incidence of AD was different between the two groups (IR: 13/14, Ctrl: 6/14), and the protein expression of MMP2, MMP9, and OPN were upregulated and SM22 and α-SMA were downregulated in mice. The expressions of mRNA and protein of GluT1 and SM22 in HA-VSMCs with IR were reduced and OPN was increased.

Conclusion

Combined results of clinical findings, mouse models, and cell experiments show that IR induced the phenotypic switching of vascular smooth muscle cells (VSMCs) from contractile to synthetic, which contributes to the occurrence of AD. It provides a basis for further research on the specific mechanism of how IR results in AD and a new approach for the prevention and treatment of AD.

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.

Using metabolic markers to identify insulin resistance in premenopausal women with and without polycystic ovary syndrome

BACKGROUND

Insulin resistance (IR) is associated with increased risk for type 2 diabetes mellitus and cardiovascular disease. Quantifying IR is invasive and time-consuming, and thus not routinely used in clinical practice. Simple metabolic markers to predict IR exist, but have not been validated in premenopausal women or women with polycystic ovary syndrome (PCOS).

OBJECTIVE

To evaluate the ability of metabolic markers to identify premenopausal women with/without PCOS who are insulin resistant.

DESIGN/SETTING

Cross-sectional analysis.

PARTICIPANTS

One hundred and seventy-one non-diabetic premenopausal overweight/obese women without PCOS and 71 women with PCOS.

METHODS

IR was quantified by the steady-state plasma glucose during the modified insulin-suppression test. Metabolic markers (BMI, lipid/lipoprotein concentrations, and fasting glucose) were evaluated for their discriminative ability to identify IR, using area under the receiver-operating-characteristic curve (AUROC) analysis. Optimal cut-points were evaluated for predictive power.

RESULTS

In the non-PCOS group, the triglyceride/HDL cholesterol ratio (TG/HDL-C) was the best marker (AUROC 0.73). Optimal diagnostic cut-point was 1.9. In the PCOS group, the TG/HDL-C ratio, cholesterol/HDL-C ratio (TC/HDL-C), and HDL-C performed well (AUROC > 0.80), with optimal cut-points for TG/HDL-C 1.3, TC/HDL-C 3.4, and HDL-C 52 mg/dL: TG/HDL-C was more sensitive, but HDL-C had a higher PPV for IR.

CONCLUSION

TG/HDL-C can identify IR in premenopausal women with and/without PCOS; diagnostic cut-points differ from those of men and postmenopausal women. HDL-C is an alternative predictor in women with PCOS. These simple metabolic markers, which are standardized between labs, inexpensive, and routinely measured, can be used to tailor lifestyle and medical interventions to improve health outcomes in insulin-resistant premenopausal women.

Assessment of Insulin Secretion and Insulin Resistance in Human

The impaired insulin secretion and increased insulin resistance (or decreased insulin sensitivity) play a major role in the pathogenesis of all types of diabetes mellitus (DM). It is very important to assess the pancreatic β-cell function and insulin resistance/ sensitivity to determine the type of DM and to plan an optimal management and prevention strategy for DM. So far, various methods and indices have been developed to assess the β-cell function and insulin resistance/sensitivity based on static, dynamic test and calculation of their results. In fact, since the metabolism of glucose and insulin is made through a complex process related with various stimuli in several tissues, it is difficult to fully reflect the real physiology. In order to solve the theoretical and practical difficulties, research on new index is still in progress. Also, it is important to select the appropriate method and index for the purpose of use and clinical situation. This review summarized a variety of traditional methods and indices to evaluate pancreatic β-cell function and insulin resistance/sensitivity and introduced novel indices.

Ovarian Hyperandrogenism and Response to Gonadotropin-releasing Hormone Analogues in Primary Severe Insulin Resistance

CONTEXT

Insulin resistance (IR) is associated with polycystic ovaries and hyperandrogenism, but underpinning mechanisms are poorly understood and therapeutic options are limited.

OBJECTIVE

To characterize hyperandrogenemia and ovarian pathology in primary severe IR (SIR), using IR of defined molecular etiology to interrogate disease mechanism. To extend evaluation of gonadotropin-releasing hormone (GnRH) analogue therapy in SIR.

METHODS

Retrospective case note review in 2 SIR national referral centers. Female patients with SIR with documented serum total testosterone (TT) concentration.

RESULTS

Among 185 patients with lipodystrophy, 65 with primary insulin signaling disorders, and 29 with idiopathic SIR, serum TT ranged from undetectable to 1562 ng/dL (54.2 nmol/L; median 40.3 ng/dL [1.40 nmol/L]; n = 279) and free testosterone (FT) from undetectable to 18.0 ng/dL (0.625 nmol/L; median 0.705 ng/dL [0.0244 nmol/L]; n = 233). Higher TT but not FT in the insulin signaling subgroup was attributable to higher serum sex hormone-binding globulin (SHBG) concentration. Insulin correlated positively with SHBG in the insulin signaling subgroup, but negatively in lipodystrophy. In 8/9 patients with available ovarian tissue, histology was consistent with polycystic ovary syndrome (PCOS). In 6/6 patients treated with GnRH analogue therapy, gonadotropin suppression improved hyperandrogenic symptoms and reduced serum TT irrespective of SIR etiology.

CONCLUSION

SIR causes severe hyperandrogenemia and PCOS-like ovarian changes whether due to proximal insulin signaling or adipose development defects. A distinct relationship between IR and FT between the groups is mediated by SHBG. GnRH analogues are beneficial in a range of SIR subphenotypes.

100 years of insulin: celebrating the past, present and future of diabetes therapy

The year 2021 marks the centennial of Banting and Best's landmark description of the discovery of insulin. This discovery and insulin's rapid clinical deployment effectively transformed type 1 diabetes from a fatal diagnosis into a medically manageable chronic condition. In this Review, we describe key accomplishments leading to and building on this momentous occasion in medical history, including advancements in our understanding of the role of insulin in diabetes pathophysiology, the molecular characterization of insulin and the clinical use of insulin. Achievements are also viewed through the lens of patients impacted by insulin therapy and the evolution of insulin pharmacokinetics and delivery over the past 100 years. Finally, we reflect on the future of insulin therapy and diabetes treatment, as well as challenges to be addressed moving forward, so that the full potential of this transformative discovery may be realized.

Measuring Insulin Resistance in Humans

BACKGROUND

Insulin resistance is a pathophysiological condition associated with diabetes and cardiometabolic diseases that is characterized by a diminished tissue response to insulin action. Our understanding of this complex phenomenon and its role in the pathogenesis of cardiometabolic diseases is rooted in the discovery of insulin, its isolation and purification, and the challenges encountered with its therapeutic use.

SUMMARY

In this historical perspective, we explore the evolution of the term "insulin resistance" and demonstrate how advances in insulin and glucose analytics contributed to the recognition and validation of this metabolic entity. We identify primary discoveries which were pivotal in expanding our knowledge of insulin resistance, the challenges in measurement and interpretation, contemporary techniques, and areas of future exploration. Key Message: Measurements of insulin resistance are important tools for defining and treating cardiometabolic diseases. Accurate quantification of this pathophysiological entity requires careful consideration of the assumptions and pitfalls of the methodological techniques and the historical and clinical context when interpreting and applying the results.

Association between insulin resistance and left ventricular hypertrophy in asymptomatic, Black, sub-Saharan African, hypertensive patients: a case-control study

Background

Conflicting information exists regarding the association between insulin resistance (IR) and left ventricular hypertrophy (LVH). We described the associations between obesity, fasting insulinemia, homeostasis model assessment of insulin resistance (HOMA-IR), and LVH in Black patients with essential hypertension.

Methods

A case–control study was conducted at the Centre Médical de Kinshasa (CMK), the Democratic Republic of the Congo, between January and December 2019. Cases and controls were hypertensive patients with and without LVH, respectively. The relationships between obesity indices, physical inactivity, glucose metabolism and lipid disorder parameters, and LVH were assessed using linear and logistic regression analyses in simple and univariate exploratory analyses, respectively. When differences were observed between LVH and independent variables, the effects of potential confounders were studied through the use of multiple linear regression and in conditional logistic regression in multivariate analyses. The coefficients of determination (R²), adjusted odds ratios (aORs), and their 95% confidence intervals (95% CIs) were calculated to determine associations between LVH and the independent variables.

Results

Eighty-eight LVH cases (52 men) were compared against 132 controls (81 men). Variation in left ventricular mass (LVM) could be predicted by the following variables: age (19%), duration of hypertension (31.3%), body mass index (BMI, 44.4%), waist circumference (WC, 42.5%), glycemia (20%), insulinemia (44.8%), and HOMA-IR (43.7%). Hypertension duration, BMI, insulinemia, and HOMA-IR explained 68.3% of LVM variability in the multiple linear regression analysis. In the logistic regression model, obesity increased the risk of LVH by threefold [aOR 2.8; 95% CI (1.06–7.4); p = 0.038], and IR increased the risk of LVH by eightfold [aOR 8.4; 95 (3.7–15.7); p < 0.001].

Conclusion

Obesity and IR appear to be the primary predictors of LVH in Black sub-Saharan African hypertensive patients. The comprehensive management of cardiovascular risk factors should be emphasized, with particular attention paid to obesity and IR. A prospective population-based study of Black sub-Saharan individuals that includes the use of serial imaging remains essential to better understand subclinical LV deterioration over time and to confirm the role played by IR in Black sub-Saharan individuals with hypertension.

Metabolically healthy obesity: facts and fantasies

Although obesity is typically associated with metabolic dysfunction and cardiometabolic diseases, some people with obesity are protected from many of the adverse metabolic effects of excess body fat and are considered "metabolically healthy." However, there is no universally accepted definition of metabolically healthy obesity (MHO). Most studies define MHO as having either 0, 1, or 2 metabolic syndrome components, whereas many others define MHO using the homeostasis model assessment of insulin resistance (HOMA-IR). Therefore, numerous people reported as having MHO are not metabolically healthy, but simply have fewer metabolic abnormalities than those with metabolically unhealthy obesity (MUO). Nonetheless, a small subset of people with obesity have a normal HOMA-IR and no metabolic syndrome components. The mechanism(s) responsible for the divergent effects of obesity on metabolic health is not clear, but studies conducted in rodent models suggest that differences in adipose tissue biology in response to weight gain can cause or prevent systemic metabolic dysfunction. In this article, we review the definition, stability over time, and clinical outcomes of MHO, and discuss the potential factors that could explain differences in metabolic health in people with MHO and MUO - specifically, modifiable lifestyle factors and adipose tissue biology. Better understanding of the factors that distinguish people with MHO and MUO can produce new insights into mechanism(s) responsible for obesity-related metabolic dysfunction and disease.

ApoA-1 improves glucose tolerance by increasing glucose uptake into heart and skeletal muscle independently of AMPKα2

OBJECTIVE

Acute administration of the main protein component of high-density lipoprotein, apolipoprotein A-I (ApoA-1), improves glucose uptake in skeletal muscle. The molecular mechanisms mediating this are not known, but in muscle cell cultures, ApoA-1 failed to increase glucose uptake when infected with a dominant-negative AMP-activated protein kinase (AMPK) virus. We therefore investigated whether AMPK is necessary for ApoA-1-stimulated glucose uptake in intact heart and skeletal muscle in vivo.

METHODS

The effect of injection with recombinant human ApoA-1 (rApoA-1) on glucose tolerance, glucose-stimulated insulin secretion, and glucose uptake into skeletal and heart muscle with and without block of insulin secretion by injection of epinephrine (0.1 mg/kg) and propranolol (5 mg/kg), were investigated in 8 weeks high-fat diet-fed (60E%) wild-type and AMPKα2 kinase-dead mice in the overnight-fasted state. In addition, the effect of rApoA-1 on glucose uptake in isolated skeletal muscle ex vivo was studied.

RESULTS

rApoA-1 lowered plasma glucose concentration by 1.7 mmol/l within 3 h (6.1 vs 4.4 mmol/l; p < 0.001). Three hours after rApoA-1 injection, glucose tolerance during a 40-min glucose tolerance test (GTT) was improved compared to control (area under the curve (AUC) reduced by 45%, p < 0.001). This was accompanied by an increased glucose clearance into skeletal (+110%; p < 0.001) and heart muscle (+100%; p < 0.001) and an increase in glucose-stimulated insulin secretion 20 min after glucose injection (+180%; p < 0.001). When insulin secretion was blocked during a GTT, rApoA-1 still enhanced glucose tolerance (AUC lowered by 20% compared to control; p < 0.001) and increased glucose clearance into skeletal (+50%; p < 0.05) and heart muscle (+270%; p < 0.001). These improvements occurred to a similar extent in both wild-type and AMPKα2 kinase-dead mice and thus independently of AMPKα2 activity in skeletal- and heart muscle. Interestingly, rApoA-1 failed to increase glucose uptake in isolated skeletal muscles ex vivo.

CONCLUSIONS

In conclusion, ApoA-1 stimulates in vivo glucose disposal into skeletal and heart muscle independently of AMPKα2. The observation that ApoA-1 fails to increase glucose uptake in isolated muscle ex vivo suggests that additional systemic effects are required.

Origins and History of the Minimal Model of Glucose Regulation

It has long been hoped that our understanding of the pathogenesis of diabetes would be helped by the use of mathematical modeling. In 1979 Richard Bergman and Claudio Cobelli worked together to find a "minimal model" based upon experimental data from Bergman's laboratory. Model was chosen as the simplest representation based upon physiology known at the time. The model itself is two quasi-linear differential equations; one representing insulin kinetics in plasma, and a second representing the effects of insulin and glucose itself on restoration of the glucose after perturbation by intravenous injection. Model would only be sufficient if it included a delay in insulin action; that is, insulin had to enter a remote compartment, which was interstitial fluid (ISF). Insulin suppressed endogenous glucose output (by liver) slowly. Delay proved to be due to initial suppression of lipolysis; resultant lowering of free fatty acids reduced liver glucose output. Modeling also demanded that normalization of glucose after injection included an effect of glucose itself on glucose disposal and endogenous glucose production - these effects were termed "glucose effectiveness." Insulin sensitivity was calculated from fitting the model to intravenous glucose tolerance test data; the resulting insulin sensitivity index, SI, was validated with the glucose clamp method in human subjects. Model allowed us to examine the relationship between insulin sensitivity and insulin secretion. Relationship was described by a rectangular hyperbola, such that Insulin Secretion x Insulin Sensitivity = Disposition Index (DI). Latter term represents ability of the pancreatic beta-cells to compensate for insulin resistance due to factors such as obesity, pregnancy, or puberty. DI has a genetic basis, and predicts the onset of Type 2 diabetes. An additional factor was clearance of insulin by the liver. Clearance varies significantly among animal or human populations; using the model, clearance was shown to be lower in African Americans than Whites (adults and children), and may be a factor accounting for greater diabetes prevalence in African Americans. The research outlined in the manuscript emphasizes the powerful approach by which hypothesis testing, experimental studies, and mathematical modeling can work together to explain the pathogenesis of metabolic disease.

Influence of Diabetogenic Factors on Fasting and Postprandial Glucose Levels in Patients with Type 2 Diabetes Mellitus

Background: This study evaluated the relative influence of insulin resistance (IR), first-phase insulin secretion (FPIS), second-phase insulin secretion (SPIS), and glucose effectiveness (GE) in determining the difference between fasting plasma glucose (FPG) and postprandial plasma glucose (PPG) (ΔPG), in a Chinese population with type 2 diabetes (T2D) mellitus. Methods: In total, we enrolled 1213 participants with T2D (479 women). IR, FPIS, SPIS, and GE were estimated by using equations we built previously. ΔPG was defined as FPG - PPG. Results: The relative contribution of the four diabetogenic factors (DFs) was analyzed by multiple linear regression, and GE was the greatest contributor in the ΔPG value (β = 0.171, P < 0.001), whereas IR had the least influence on ΔPG (β = -0.040, P = 0.439). DFs were analyzed by using binary logistic regression to ascertain if ΔPG ≥0 (high fasting plasma glucose, HFG). Three models were built: Model 0: SPIS, Model 1: SPIS + FPIS, and Model 2: Model 1 + GE. Model 2 had the most accurate predictive power; the equation for Model 2 is P = 1/(1 - e^-x), where x = -11.88 + 312.89 × (GE) -1.22 × log(SPIS) +1.63 × log(FPIS). In this equation, P refers to the risk of HFG. Conclusions: For Chinese patients, GE had the most profound effect in determining ΔPG, followed by FPIS, SPIS, and IR. The model suggested that participants with high FPIS, SPIS, and GE would have a high incidence of HFG.

Relationship between surrogate estimates and direct measurement of insulin resistance in women with polycystic ovary syndrome

PURPOSE

To evaluate the relationship between surrogate estimates of insulin resistance and a direct measurement of insulin-mediated glucose uptake women with and without PCOS.

METHODS

Retrospective cohort study of 75 PCOS and 118 controls. Fasting plasma glucose and insulin concentrations, insulin resistance as determined by the insulin suppression test, calculation of multiple surrogate estimates of insulin resistance, total and free testosterone concentrations, and correlations between the direct measure and surrogate estimates of insulin resistance were evaluated.

RESULT(S)

Surrogate markers of insulin resistance were correlated to a variable, but statistically significant degree with the direct measure of insulin resistance in control population and the women with PCOS. There was no correlation between the surrogate estimates of insulin resistance and total or free plasma testosterone concentrations.

CONCLUSION(S)

The surrogate estimates of insulin resistance evaluated were significantly related to a direct measure of insulin resistance, and this was true of both the control population and women with PCOS. The magnitude of the relationship between the surrogate estimates and the direct measurement was comparable and not significantly altered by androgen levels. Fasting plasma insulin concentration seems to be at least as accurate as any other surrogate estimate, and is by far the simplest.

Optimized fasting and OGTT-based simple surrogate methods for assessing insulin sensitivity

AIMS

Simple surrogate indices of insulin sensitivity have been conceived to deal with costly and complicated approaches, such as the hyperinsulinemic-euglycemic clamp; however, their use has not been widespread given their variabilities in different populations. In this paper, we present two simple surrogate indices, one that uses fasting glucose and insulin values and the other based on the values from the oral glucose tolerance test.

MATERIALS AND METHODS

The proposed methods integrate easy-to-obtain anthropometric measures. Evolutionary algorithms were used to optimize the proposed methods by maximizing its correlation with the Stumvoll MCR method.

RESULTS AND CONCLUSION

When the proposed indices were applied to three study groups (control subjects, metabolic syndrome, marathon runners), a reduction in the intergroup variability of the insulin sensitivity was obtained. Moreover, the proposed index based on the oral glucose tolerance test (OGTT), which considers the glucose metabolism process and the hepatic and peripheral insulin sensitivity, showed stronger correlations with the Stumvoll method and lower intergroup variability than the fasting one.

Plasma glucose concentration 60 min post oral glucose load and risk of type 2 diabetes and cardiovascular disease: Pathophysiological implications

AIM

The aim of this study was to gain insight into the pathophysiological significance of elevated plasma glucose concentrations (mmol/L) 60 min post oral glucose load in apparently healthy individuals.

METHODS

Comparison of resistance to insulin action and associated cardio-metabolic risk factors in 490 apparently healthy persons, subdivided into those with a plasma glucose concentration 60 min following a 75-g oral glucose challenge of <8.6 versus ⩾8.6.

RESULTS

Insulin resistance was significantly greater in persons with normal glucose tolerance whose 60-min glucose concentration was ⩾8.6, associated with higher blood pressure, plasma concentrations of glucose, insulin, triglyceride and lower high-density lipoprotein cholesterol concentrations. Similar differences were seen in persons with impaired fasting glucose, but not in those with impaired glucose tolerance or both impaired fasting glucose and impaired glucose tolerance. The group whose 60-min glucose was <8.6 (n = 318) contained primarily persons with normal glucose tolerance (88%), whereas the majority of those whose 60-min value was ⩾8.6 (n = 172) had prediabetes (59%) and in particular combined impaired fasting glucose and impaired glucose tolerance.

CONCLUSION

Plasma glucose concentration of ⩾8.6 mmol/L 60 min post oral glucose identifies higher proportions of combined impaired fasting glucose and impaired glucose tolerance individuals as well as normal glucose tolerance and impaired fasting glucose individuals with a more adverse cardio-metabolic profile, contributing to observed increased overall risk of type 2 diabetes and other metabolic diseases.

A Simple and Improved Predictor of Insulin Resistance Extracted From the Oral Glucose Tolerance Test: The I0*G60

OBJECTIVE

To evaluate the diagnostic performance of several biochemical predictors of insulin resistance (IR).

DESIGN

A total of 90 nondiabetic subjects were tested with both the pancreatic suppression test (PST) and the oral glucose tolerance test (OGTT). Of them, 53 were non-insulin-resistant (NIR) subjects and the remaining 37 were insulin resistant subjects.

RESULTS

All glucose and insulin values from the OGTT were positively correlated with the steady-state plasma glucose (SSPG) value of the PST. Among the OGTT values, basal insulin (I0) displayed a stronger correlation with SSPG (r = 0.604). Receiver operating characteristic analysis of the OGTT data demonstrated that I0 exhibited the highest area under the receiver operating characteristic curve (AUROC), compared with the rest of the OGTT data. However, the reduced sensitivity of this predictor precluded its clinical use.We then tested six potential predictors of IR derived from the OGTT values. Of them, the I0*G60 had a correlation coefficient of 0.697 with the SSPG and an AUROC of 0.867, surpassing the respective values of the traditional biochemical predictors of IR. Its cutoff predicting IR was >1110 mg/dL*μΙU/mL (>428 nM*pM), its sensitivity was 0.865, and its global accuracy was 0.822. We then selected the six best biochemical predictors of IR according to their posttest probability ratio. The order was as follows: I0*G60, ISI composite, AUC-Gl*In/', quantitative insulin sensitivity check index, homeostatic model assessment 1 (HOMA1), and HOMA2.

CONCLUSION

We conclude that the I0*G60 is a promising, inexpensive, and easily calculable predictor of IR that outperforms the predictive power of the traditional predictors of IR, including the insulin sensitivity index composite.

Myths about Insulin Resistance: Tribute to Gerald Reaven

Gerald Reaven was often called the "father of insulin resistance." On the 1-year anniversary of his death in 2018, we challenge three myths associated with insulin resistance: metformin improves insulin resistance; measurement of waist circumference predicts insulin resistance better than body mass index; and insulin resistance causes weight gain. In this review, we highlight Reaven's relevant research that helped to dispel these myths associated with insulin resistance.

Impact of race/ethnicity on insulin resistance and hypertriglyceridaemia

OBJECTIVE

Insulin sensitivity affects plasma triglyceride concentration and both differ by race/ethnicity. The purpose of this study was to provide a comprehensive assessment of the variation in insulin sensitivity and its relationship to hypertriglyceridaemia between five race/ethnic groups.

RESEARCH DESIGN AND METHODS

In this cross-sectional study, clinical data for 1025 healthy non-Hispanic White, Hispanic White, East Asian, South Asian and African American individuals were analysed. Insulin-mediated glucose disposal (a direct measure of peripheral insulin sensitivity) was measured using the modified insulin suppression test. Statistical analysis was performed using analysis of co-variance.

RESULTS

Of the study participants, 63% were non-Hispanic White, 9% were Hispanic White, 11% were East Asian, 11% were South Asian and 6% were African American. Overall, non-Hispanic Whites and African Americans displayed greater insulin sensitivity than East Asians and South Asians. Triglyceride concentration was positively associated with insulin resistance in all groups, including African Americans. Nevertheless, for any given level of insulin sensitivity, African Americans had the lowest triglyceride concentrations.

CONCLUSION

Insulin sensitivity, as assessed by a direct measure of insulin-mediated glucose disposal, and its relationship to triglyceride concentration vary across five race/ethnic groups. Understanding these relationships is crucial for accurate cardiovascular risk stratification and prevention.

Dietary weight loss in insulin-resistant non-obese humans: Metabolic benefits and relationship to adipose cell size

BACKGROUND AND AIMS

Overweight and obesity increase risk for diabetes and cardiovascular disease, largely through development of insulin resistance. Benefits of dietary weight loss are documented for obese individuals with insulin resistance. Similar benefits have not been shown in overweight individuals. We sought to quantify whether dietary weight loss improves metabolic risk profile in overweight insulin-resistant individuals, and evaluated potential mediators between weight loss and metabolic response.

METHODS AND RESULTS

Healthy volunteers with BMI 25-29.9 kg/m2 underwent detailed metabolic phenotyping including insulin-mediated-glucose disposal, fasting/daylong glucose, insulin, triglycerides, FFA, and cholesterol. Subcutaneous fat biopsies were performed for measurement of adipose cell size. After 14 weeks of hypocaloric diet and 2 weeks of weight maintenance, cardiometabolic measures and biopsies were repeated. Changes in weight, % body fat, waist circumference, adipose cell size and FFA were evaluated as predictors of change in insulin resistance. Weight loss (4.3 kg) yielded significant improvements in insulin resistance and all cardiovascular risk markers except glucose, HDL-C, and LDL-C. Improvement in insulin sensitivity was greater among those with <2 vs >2 cardiovascular risk factors at baseline. Decrease in adipose cell size and waist circumference, but not weight or body fat, independently predicted improvement in insulin resistance.

CONCLUSIONS

Weight loss yields metabolic health benefits in insulin-resistant overweight adults, even in the absence of classic cardiovascular risk factors. Weight loss-related improvement in insulin sensitivity may be mediated through changes in adipose cell size and/or central distribution of body fat. The insulin-resistant subgroup of overweight individuals should be identified and targeted for dietary weight loss.

CLINICAL TRIALS IDENTIFIER

NCT00186459.

Insulin Sensitivity and Testicular Function in a Cohort of Adult Males Suspected of Being Insulin-Resistant

A cohort of 141 males (18–80 yo, 42.9 ± 12.9) strongly suspected of being Insulin Resistant (IR) was prospectively studied by determining their insulin sensitivity (Pancreatic Suppression Test, PST) and testicular function (total testosterone and SHBG). The subjects were labeled as IR when the Steady State Plasma Glucose (SSPG) was ≥150 mg/dL and Non-Insulin Resistant (NIR) when SSPG was <150 mg/dl; similarly, the subjects were labeled as Hypogonadal (HYPOG) when total testosterone was ≤3.0 ng/mL and Eugonadal (EUG) when total testosterone was >3.0 ng/mL. Two out of three subjects turned out to be IR, while around one in four subjects were HYPOG. Contingency analysis indicated a significant interdependence between insulin resistance and hypogonadism (chi-square was 4.69, p = 0.0303). Age (>43 yo) predicted hypogonadism (AUROC 0.606, p = 0.0308). Twice as many HYPOG subjects were IR as compared with EUG subjects. Also, HYPOG subjects exhibited higher SSPG values as compared with EUG subjects. Statistically, neither Weight nor BMI predicted hypogonadism, while Waist Circumference (>110 cm) was only a mediocre predictor (AUROC 0.640, p = 0.009). SSPG (>224 mg/dL) on the other hand, was the best predictor of hypogonadism (AUROC 0.709, p = 0.002), outperforming Waist Circumference (half of the subjects with an SSPG >224 mg/dL were HYPOG). Age did not predict insulin resistance, while Weight (>99 kg), BMI (>29), and especially, Waist Circumference (>99 cm, AUROC 0.812, p < 0.0001) were all predictors of insulin resistance. Almost 90% of the subjects with a waist circumference >99 cm was IR. As a logical consequence of the selection criteria (various clues suggesting insulin resistance), most subjects with normal weight in this cohort were IR (53.3%) while 20% were HYPOG. On the other hand, 13.6% of the obese subjects were NIR, and 2 out of 3 of them were both NIR and EUG. In conclusion, Waist Circumference predicted both insulin resistance (>99 cm) and hypogonadism (>110 cm), suggesting that the first hit of abdominal obesity is insulin resistance and the second hit is male hypogonadism. Normal weight did not protect from IR, while a relevant proportion of obese subjects were NIR (with 2/3 being also EUG).

Integrative Personal Omics Profiles during Periods of Weight Gain and Loss

Advances in omics technologies now allow an unprecedented level of phenotyping for human diseases, including obesity, in which individual responses to excess weight are heterogeneous and unpredictable. To aid the development of better understanding of these phenotypes, we performed a controlled longitudinal weight perturbation study combining multiple omics strategies (genomics, transcriptomics, multiple proteomics assays, metabolomics, and microbiomics) during periods of weight gain and loss in humans. Results demonstrated that: (1) weight gain is associated with the activation of strong inflammatory and hypertrophic cardiomyopathy signatures in blood; (2) although weight loss reverses some changes, a number of signatures persist, indicative of long-term physiologic changes; (3) we observed omics signatures associated with insulin resistance that may serve as novel diagnostics; (4) specific biomolecules were highly individualized and stable in response to perturbations, potentially representing stable personalized markers. Most data are available open access and serve as a valuable resource for the community.

Relationship between several surrogate estimates of insulin resistance and a direct measure of insulin-mediated glucose disposal: Comparison of fasting versus post-glucose load measurements

AIMS

The study aim was to determine the correlation of several surrogate estimates of insulin resistance with a direct measure of insulin action and the ability of these estimates to identify insulin resistant persons.

METHODS

Retrospective analysis of 454 apparently healthy individuals studied in a clinical research center. The correlations between 11 surrogate estimates of insulin resistance, using either fasting or post-oral glucose challenge values, and a direct measure of insulin-mediated glucose uptake (SSPG concentration during the Insulin Suppression Test) were determined as well as the ability of the surrogate estimates to identify insulin resistant individuals.

RESULTS

All surrogate estimates were significantly (P < .001) correlated with SSPG concentrations and successfully identified insulin resistant persons. These relationships were of lesser magnitude when estimates were based on fasting data, with the exception of the McAuley index-derived from fasting data, but resembling post-glucose challenge estimates. Moreover, correlation with SSPG concentration, and positive identification of insulin resistance, varied considerably among estimates.

CONCLUSION

All 11 surrogate estimates of insulin resistance significantly correlated with insulin-mediated glucose disposal and identified insulin resistant persons with a reasonable degree of sensitivity and specificity. For identification of insulin resistant individuals, indices based on post-glucose challenge measurements performed better than those based on fasting measurements, with the exception of McAuley index. The quantitative information derived from this analysis should help investigators select the surrogate marker of insulin resistance best suited for their study.

Effect of Pioglitazone on Cardiometabolic Risk in Patients With Obstructive Sleep Apnea

Prevalence of insulin resistance is increased in patients with obstructive sleep apnea (OSA). Because insulin resistance is an independent predictor of cardiovascular disease (CVD), this study was initiated to see if pioglitazone administration would improve insulin sensitivity and thereby decrease risk of CVD in overweight/obese, nondiabetic, insulin-resistant patients with untreated OSA. Patients (n = 30) were administered pioglitazone (45 mg/day) for 8 weeks, and measurements were made before and after intervention of insulin action (insulin-mediated glucose uptake by the insulin suppression test), C-reactive protein, lipid/lipoprotein profile, and gene expression profile of periumbilical subcutaneous fat tissue. Insulin sensitivity increased 31% (p <0.001) among pioglitazone-treated subjects, associated with a decrease in C-reactive protein concentration (p ≤0.001), a decrease in plasma triglyceride, and increase in high-density lipoprotein cholesterol concentrations (p ≤0.001), accompanied by significant changes in apolipoprotein A1 and B concentrations and lipoprotein subclasses known to decrease CVD risk. In addition, subcutaneous adipose tissue gene expression profile showed a 1.6-fold (p <0.01) increase in GLUT4 expression and decreased expression in 5 of 9 inflammatory genes (p <0.05). In conclusion, enhanced insulin sensitivity can significantly decrease multiple cardiometabolic risk factors in patients with untreated OSA, consistent with the view that coexisting insulin resistance plays an important role in the association between OSA and increased risk of CVD.

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

AIMS

Prediabetes (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.

METHODS

In 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).

RESULTS

Subjects 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.

CONCLUSIONS

Measurement 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.

Temporal Changes in Skeletal Muscle Capillary Responses and Endothelial-Derived Vasodilators in Obesity-Related Insulin Resistance

The inability of insulin to increase skeletal muscle capillary blood volume (CBV) reduces glucose uptake in insulin resistance (IR). We hypothesized that abnormalities in endothelial-derived vasodilator pathways are temporally associated with the development of IR and an impaired ability to increase skeletal muscle CBV. A comprehensive metabolic and vascular screening assessment was performed on 10 adult rhesus macaques at baseline and every 4-6 months for 2 years after starting a high-fat diet supplemented with fructose. Diet changes resulted in an 80% increase in truncal fat by 4 months. Hyperinsulinemia and decreased glucose utilization were observed from 4 to 18 months. At 24 months, pancreatic secretory function and the glucose utilization rate declined. CBV at rest and during an intravenous glucose tolerance test demonstrated a sustained increase from 4 to 18 months and then abruptly fell at 24 months. Nitric oxide bioavailability progressively decreased over 2 years. Conversely, endothelial-derived vasodilators progressively increased over 18 months and then abruptly decreased at 24 months in concert with the CBV. The increase in basal and glucose-mediated CBV early in IR may represent a compensatory response through endothelial-derived vasodilator pathways. The inability to sustain a vascular compensatory response limits glucose-mediated increases in CBV, which correlates with the severity of IR.

Dual Actions of Apolipoprotein A-I on Glucose-Stimulated Insulin Secretion and Insulin-Independent Peripheral Tissue Glucose Uptake Lead to Increased Heart and Skeletal Muscle Glucose Disposal

Apolipoprotein A-I (apoA-I) of HDL is central to the transport of cholesterol in circulation. ApoA-I also provides glucose control with described in vitro effects of apoA-I on β-cell insulin secretion and muscle glucose uptake. In addition, apoA-I injections in insulin-resistant diet-induced obese (DIO) mice lead to increased glucose-stimulated insulin secretion (GSIS) and peripheral tissue glucose uptake. However, the relative contribution of apoA-I as an enhancer of GSIS in vivo and as a direct stimulator of insulin-independent glucose uptake is not known. Here, DIO mice with instant and transient blockade of insulin secretion were used in glucose tolerance tests and in positron emission tomography analyses. Data demonstrate that apoA-I to an equal extent enhances GSIS and acts as peripheral tissue activator of insulin-independent glucose uptake and verify skeletal muscle as an apoA-I target tissue. Intriguingly, our analyses also identify the heart as an important target tissue for the apoA-I-stimulated glucose uptake, with potential implications in diabetic cardiomyopathy. Explorations of apoA-I as a novel antidiabetic drug should extend to treatments of diabetic cardiomyopathy and other cardiovascular diseases in patients with diabetes.

Does enhanced insulin sensitivity improve sleep measures in patients with obstructive sleep apnea: a randomized, placebo-controlled pilot study

BACKGROUND

High fasting insulin levels have been reported to predict development of observed apneas, suggesting that insulin resistance may contribute to the pathogenesis of obstructive sleep apnea (OSA). The aim of this study was to determine whether enhancing insulin sensitivity in individuals with OSA would improve sleep measures.

PATIENTS/METHODS

Insulin-resistant, nondiabetic individuals with untreated OSA were randomized (2:1) to pioglitazone (45 mg/day) or placebo for eight weeks in this single-blind study. All individuals had repeat measurements pertaining to sleep (overnight polysomnography and functional outcomes of sleep questionnaire) and insulin action (insulin suppression test).

RESULTS

A total of 45 overweight/obese men and women with moderate/severe OSA were randomized to pioglitazone (n = 30) or placebo (n = 15). Although insulin sensitivity increased 31% among pioglitazone-treated compared with no change among individuals receiving placebo (p <0.001 for between-group difference), no improvement in quantitative or qualitative sleep measurements was observed.

CONCLUSIONS

Pioglitazone administration increased insulin sensitivity in otherwise untreated individuals with OSA, without any change in polysomnographic sleep measures over an eight-week period. These findings do not support a causal role for insulin resistance in the pathogenesis of OSA.

Adipose Cell Size and Regional Fat Deposition as Predictors of Metabolic Response to Overfeeding in Insulin-Resistant and Insulin-Sensitive Humans

Obesity is associated with insulin resistance, but significant variability exists between similarly obese individuals, pointing to qualitative characteristics of body fat as potential mediators. To test the hypothesis that obese, insulin-sensitive (IS) individuals possess adaptive adipose cell/tissue responses, we measured subcutaneous adipose cell size, insulin suppression of lipolysis, and regional fat responses to short-term overfeeding in BMI-matched overweight/obese individuals classified as IS or insulin resistant (IR). At baseline, IR subjects exhibited significantly greater visceral adipose tissue (VAT), intrahepatic lipid (IHL), plasma free fatty acids, adipose cell diameter, and percentage of small adipose cells. With weight gain (3.1 ± 1.4 kg), IR subjects demonstrated no significant change in adipose cell size, VAT, or insulin suppression of lipolysis and only 8% worsening of insulin-mediated glucose uptake (IMGU). Alternatively, IS subjects demonstrated significant adipose cell enlargement; decrease in the percentage of small adipose cells; increase in VAT, IHL, and lipolysis; 45% worsening of IMGU; and decreased expression of lipid metabolism genes. Smaller baseline adipose cell size and greater enlargement with weight gain predicted decline in IMGU, as did increase in IHL and VAT and decrease in insulin suppression of lipolysis. Weight gain in IS humans causes maladaptive changes in adipose cells, regional fat distribution, and insulin resistance. The correlation between development of insulin resistance and changes in adipose cell size, VAT, IHL, and insulin suppression of lipolysis highlight these factors as potential mediators between obesity and insulin resistance.

Association between glycemic control and morning blood surge with vascular endothelial dysfunction in type 2 diabetes mellitus patients

OBJECTIVE

Morning blood pressure surge (MBPS) is an independent predictor of cardiovascular events. However, little is known about the association between glycemic control and MBPS, and its effect on vascular injury in patients with type 2 diabetes mellitus (T2DM). The current study examined the association between glycemic control and MBPS and the involvement of MBPS in the development of vascular dysfunction in T2DM patients.

MATERIALS AND METHODS

One hundred and twenty-two consecutive T2DM outpatients from the Department of Cardiology and Endocrinology were enrolled in this study. We did MBPS in T2DM patients, 85 (male) (69.7%) patients and 37 (female) patients (30.3%); mean age 60.1 ± 9.39; (n = 122) using 24 h ambulatory blood pressure monitoring and assessed vascular function by brachial artery flow-mediated dilation (FMD) and nitroglycerin-mediated dilation (NMD).

RESULTS

The correlation between MBPS and various clinical variables were examined by single regression analysis in all subjects. MBPS showed significant and positive correlation with pulse rate (P = 0.01), fasting blood sugar (P = 0.002), and postprandial blood sugar (P = 0.05). To further confirm the association of insulin resistance (IR) with MBPS in T2DM patients, we examined the correlation between homeostasis model assessment-IR (HOMA-IR), an established marker of IR and MBPS in diabetic (DM) patients who were not taking insulin no significant association with MBPS in T2DM patients (P = 0.41), angiotensin-converting enzyme/angiotensin receptor blocker (P = 0.07). We examined the relationship between MBPS and vascular injury by measuring endothelium-dependent FMD and endothelium-independent NMD in T2DM patients. Among the various traditional risk factors for atherosclerosis such as DM duration (P = 0.04), platelet reactivity (P = 0.04) and morning surge (P = 0.002) emerged as significant factors. HOMA-IR was a negative correlation with FMD.

CONCLUSIONS

The current study demonstrated that poor glycemic control and IR have predictive value for the occurrence of MBPS in T2DM patients, which might be significantly associated with endothelial dysfunction.

Hyperinsulinemia in individuals with obesity: Role of insulin clearance

OBJECTIVE

Several studies have shown decreased insulin clearance rate (ICR) in individuals with obesity, but it remains unclear whether this is predominately due to obesity-associated insulin resistance (IR) or obesity itself. This study aimed to clarify the complex interrelationship that exists between obesity, IR, and ICR.

METHODS

Healthy volunteers (n = 277) had measurement of IR and ICR using the insulin suppression test (IST). IR was quantified by determining the steady-state plasma glucose (SSPG) during the IST. ICR was estimated by dividing the insulin infusion rate by the steady-state plasma insulin concentration. We performed our analysis by stratifying the experimental population into four dichotomous categories, varying in obesity and IR. Obesity was defined as a body mass index (BMI) ≥ 30 kg/m(2) , and IR was defined as SSPG ≥ 150 mg/dL.

RESULTS

Individuals with obesity had higher fasting insulin compared with individuals without obesity, regardless of IR. ICR was similar between individuals with and without obesity but was higher in insulin resistant individuals compared with insulin-sensitive individuals. In multivariate analysis, both fasting insulin and SSPG were significantly associated with ICR. No significant relationships were observed between BMI and ICR.

CONCLUSIONS

Reduced ICR in obesity is secondary to IR, not excess adiposity.

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

BACKGROUND AND OBJECTIVE

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.

METHODS

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.

RESULTS

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.

CONCLUSIONS

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.

Effect of Common Genetic Variants of Growth Arrest-Specific 6 Gene on Insulin Resistance, Obesity and Type 2 Diabetes in an Asian Population

Objectives

Growth arrest-specific 6 (Gas6), a vitamin K-dependent protein, has been implicated in systemic inflammation, obesity, and insulin resistance (IR). Data from recent studies suggest that polymorphisms in the Gas6 gene are associated with cardiovascular disorders and type 2 diabetes (T2D). However, the association of Gas6 gene variants with obesity, IR, and T2D development has not been explored.

Materials and Methods

Four common single nucleotide polymorphisms (SNPs) in the Gas6 gene were genotyped in 984 participants from the Stanford Asia-Pacific Program for Hypertension and Insulin Resistance (SAPPHIRe) family cohort. An insulin suppression test was performed to determine IR based on steady-state plasma glucose (SSPG). Associations between IR indices and obesity, and SNP genotypes, based on previously-reported data for this cohort (Phase I), were analyzed. In the present follow-up study (Phase II), the effects of gene variants of Gas6 on the progression to T2D were explored in individuals who were free of T2D in Phase I. The mean follow-up period for Phase II was 5.7 years.

Results

The mean age of the study population in Phase I was 49.5 years and 16.7% of individuals developed T2D during follow-up. After adjusting for covariates, three SNPs (rs8191973, rs8197974, and rs7323932) were found to be associated with SSPG levels (p = 0.007, p = 0.03, and p = 0.011, respectively). This association remained significant after multiple testing and showed a significant interaction with physical activity for SNP rs8191973. However, no other significant correlations were observed between Gas6 polymorphisms and other indices of IR or obesity. A specific haplotype, AACG (from rs8191974, rs7323932, rs7331124, and rs8191973), was positively associated with SSPG levels (p = 0.0098). None of the polymorphisms were associated with an increased risk of T2D development.

Conclusions

Our results suggest that Gas6 gene variants are associated with IR, although their effects on subsequent progression to T2D were minimal in this prospective Asian cohort.

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

STUDY OBJECTIVE

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.

DESIGN

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.

SETTING

Academic medical center.

PARTICIPANTS

107 nondiabetic, overweight/obese adults.

MEASUREMENTS AND RESULTS

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.

CONCLUSIONS

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.

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.