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Insulin action in the postprandial state. As an anabolic hormone, insulin works on target tissues to promote storage of the products of digestion and absorption of a mixed meal. The target tissues referred to here are liver, muscle, and adipose. Glucose uptake into muscle and fat cells is mediated by the insulin-dependent glucose transporter GLUT 4. Glucose uptake into liver cells is promoted by insulin-stimulated glucose utilization for synthesis of glycogen and for glycolysis. The acetyl CoA resulting from glycolysis may ultimately be used for synthesis of triglyceride, a longer-term energy storage vehicle than glycogen. The actions of insulin on all three of these target tissues contribute to decreased plasma glucose in the postabsorptive state. Glucose6-P = = glucose-6-phosphate; GLUT 4 = = insulin-sensitive glucose-transporter 4; LDL = = low-density lipoprotein; LPL = = lipoprotein lipase; VLDL = = very-low-density lipoprotein.
Source publication
Insulin resistance is a component of several health disorders, most notably impaired glucose tolerance and type 2 diabetes mellitus. Insulin-resistant individuals have an impaired biological response to the usual action of insulin; that is, they have reduced insulin sensitivity. Various methods are used to assess insulin sensitivity both in individ...
Contexts in source publication
Context 1
... glucose disposal is both insulin mediated and non-insulin mediated. Although an exhaustive description of all of the factors mediating glucose homeostasis is beyond the scope of this arti- cle, a brief overview of insulin-mediated glucose dis- posal is shown in Figure 1. The primary action of insulin is to enable the rapid uptake of glucose into muscle and fat cells. ...
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Citations
... Insulin resistance (IR) is characterized as a decreased sensitivity to insulin-mediated glucose uptake and is a known primary risk factor for T2DM [2,3]. While more reliable identification of those with IR could prove useful for T2DM risk stratification, direct measures of IR remain both expensive and laborious to perform [4,5] and surrogate measures correlate only modestly with direct measures of IR [6][7][8]. Truncal adiposity and poor cardiorespiratory fitness (CRF) are two additional potentially modifiable risk factors of T2DM through their effects on IR but similar to IR, are difficult to accurately measure using gold standard approaches such as dual energy x-ray absorptiometry (DXA) scans and cardiopulmonary exercise testing with a metabolic cart [9][10][11][12]. ...
Aims/hypothesis
The plasma proteome holds promise as a diagnostic and prognostic tool that can accurately reflect complex human traits and disease processes. We assessed the ability of plasma proteins to predict type 2 diabetes mellitus (T2DM) and related traits.
Methods
Clinical, genetic, and high-throughput proteomic data from three subcohorts of UK Biobank participants were analyzed for association with dual-energy x-ray absorptiometry (DXA) derived truncal fat (in the adiposity subcohort), estimated maximum oxygen consumption (VO 2 max) (in the fitness subcohort), and incident T2DM (in the T2DM subcohort). We used least absolute shrinkage and selection operator (LASSO) regression to assess the relative ability of non-proteomic and proteomic variables to associate with each trait by comparing variance explained (R ² ) and area under the curve (AUC) statistics between data types. Stability selection with randomized LASSO regression identified the most robustly associated proteins for each trait. The benefit of proteomic signatures (PSs) over QDiabetes, a T2DM clinical risk score, was evaluated through the derivation of delta (Δ) AUC values. We also assessed the incremental gain in model performance metrics using proteomic datasets with varying numbers of proteins. A series of two-sample Mendelian randomization (MR) analyses were conducted to identify potentially causal proteins for adiposity, fitness, and T2DM.
Results
Across all three subcohorts, the mean age was 56.7 years and 54.9% were female. In the T2DM subcohort, 5.8% developed incident T2DM over a median follow-up of 7.6 years. LASSO-derived PSs increased the R ² of truncal fat and VO 2 max over clinical and genetic factors by 0.074 and 0.057, respectively. We observed a similar improvement in T2DM prediction over the QDiabetes score [Δ AUC: 0.016 (95% CI 0.008, 0.024)] when using a robust PS derived strictly from the T2DM outcome versus a model further augmented with non-overlapping proteins associated with adiposity and fitness. A small number of proteins (29 for truncal adiposity, 18 for VO2max, and 26 for T2DM) identified by stability selection algorithms offered most of the improvement in prediction of each outcome. Filtered and clustered versions of the full proteomic dataset supplied by the UK Biobank (ranging between 600-1,500 proteins) performed comparably to the full dataset for T2DM prediction. Using MR, we identified 4 proteins as potentially causal for adiposity, 1 as potentially causal for fitness, and 4 as potentially causal for T2DM.
Conclusions/Interpretation
Plasma PSs modestly improve the prediction of incident T2DM over that possible with clinical and genetic factors. Further studies are warranted to better elucidate the clinical utility of these signatures in predicting the risk of T2DM over the standard practice of using the QDiabetes score. Candidate causally associated proteins identified through MR deserve further study as potential novel therapeutic targets for T2DM.
... However, if the production of free radicals increases or the antioxidant factors decrease, the damage caused by them increases, leading to oxidative stress. This imbalance between the production of free radicals and peroxide substances and a defect in the antioxidant defense system can lead to oxidative stress (29,30). Internal sources of oxidative stress include peroxisomes and enzymes, especially detoxifying enzymes from the P450 complex, xanthine oxidase, and nicotinamide adenine dinucleotide oxidase complexes. ...
Objectives: The effectiveness of Scrophularia striata in controlling infections and promoting wound healing has been reported. This study aimed to investigate the antioxidant properties of the methanol extract from Scrophularia striata. Methods: Scrophularia striata, a perennial wild plant found in various temperate and tropical areas of Iran, underwent a methanol extraction process to obtain its active compounds. The antioxidant property of the methanol extract of Scrophularia striata was evaluated by quantifying the total antioxidant level, determining the total phenol content, and conducting DPPH radical scavenging assays. Results: As the extraction concentrations of Scrophularia striata increase, both the total antioxidant level and total phenol content rise dramatically. With the progression of time and increase in plant extract concentrations, the efficacy of DPPH radical scavenging also shows a corresponding enhancement. Moreover, the IC50% value of Scrophularia striata for DPPH radical scavenging consistently decreases over the observation period. Conclusion: The data suggest that Scrophularia striata possesses antioxidant properties. The presence of flavonoids and phenolic compounds in Scrophularia striata highlights its potential to alleviate various disorders by modulating oxidative stress levels.
... Insulin sensitivity was calculated by the quantitative insulin-sensitivity check index (QUICKI). 45 QUICKI was calculated as Q ¼ 1 ⁄ (log FPI þlog FPG), where FPI is the fasting plasma insulin (mU L)1 where FPI is the fasting plasma insulin and FPG is the fasting plasma glucose. 45 Power calculation: Given a sample of 100 average sleepers and 100 long sleepers, medium effect sizes of d!.4 are detectable with α ¼ .05 ...
... 45 QUICKI was calculated as Q ¼ 1 ⁄ (log FPI þlog FPG), where FPI is the fasting plasma insulin (mU L)1 where FPI is the fasting plasma insulin and FPG is the fasting plasma glucose. 45 Power calculation: Given a sample of 100 average sleepers and 100 long sleepers, medium effect sizes of d!.4 are detectable with α ¼ .05 (two-tailed) at !80% power. ...
Introduction Long sleep duration is associated with many health risks, particularly in older adults, but little is known about other characteristics associated with long sleep duration.
Methods Across 5 sites, adults aged 60-80 years who reported sleeping 8-9 h (“long sleepers”, n = 95) or 6-7.25 h (“average sleepers”, n = 103) were assessed for two weeks using actigraphy and sleep diary. Demographic and clinical characteristics, objective sleep apnea screening, self-reported sleep outcomes, and markers of inflammation and glucose regulation were measured.
Results Compared to average sleepers, long sleepers had a greater likelihood of being White and unemployed and/or retired. Long sleepers also reported longer time in bed, total sleep time and wake after sleep onset by sleep diary and by actigraphy. Other measures including medical co-morbidity, apnea/hypopnea index, sleep related outcomes such as sleepiness, fatigue, depressed mood, or markers of inflammation and glucose metabolism did not differ between long and average sleepers.
Conclusion Older adults with long sleep duration were more likely to be White, report unemployment and retirement suggesting the social factors or related sleep opportunity contributed to long sleep duration in the sample. Despite known health risks of long sleep duration, neither co-morbidity nor markers of inflammation or metabolism differed in older adults with long sleep duration compared with those with average sleep duration.
... Studies also have shown defects in insulin secretion in PCOS families 97 . Homeostatic Model Assessment of Insulin Resistance (HOMA-IR) is used frequently to assess insulin resistance 98 . Various studies also have shown the role of insulin in the synthesis of androgen in the ovaries 99,100 . ...
Polycystic Ovary Syndrome (PCOS) is the most common reproductive endocrine disorder in women of reproductive age. PCOS is characterized by hyperandrogenism, ovulatory dysfunction, and polycystic ovary morphology. The PCOS is known for more than 100 years; however, many areas of PCOS such as diagnosis, etiology, clinical features, and treatment are still debatable. This review aims to provide an overview of the historical evolution, diagnosis, biomarkers, and etiologic associations of PCOS as of today. A brief review of publications on PCOS and our research experience on PCOS are combined. All available biomarkers/associations implicated with PCOS, like androgens (testosterone, free androgen index, DHEAS, androstenedione, dihydrotestosterone), LH, 17-OH Progesterone, anti-Mullerian Hormone (AMH), inhibin B, leptin, insulin, interleukins, advanced glycation end product (AGE), bisphenol A (BPA), kisspeptin, melatonin, etc., besides genetic and epigenetic factors, associated with PCOS are briefed, along-with our research experience. The most acceptable consensus in naming the syndrome is Polycystic Ovary Syndrome (PCOS) and consensus diagnostic criteria presently followed are Rotterdam 2003 criteria with phenotypic classification (NIH 2012 criteria). Ideal androgen, method of estimation and its cutoff value is still a subject of controversy. DHT, an androgen, seems promising. The best available biomarker associated with PCOS could be AMH. Environmental contaminants such as bisphenol A and AGEs, and endogenous factors such as kisspeptin and melatonin have strong association with PCOS. Epigenetic alterations affecting various pathways (metabolic, steroid biosynthesis, ovarian function, AGE/RAGE, AMPK, inflammatory, etc.) and pathogenic variants of various genes (INSR,
... kg·m −2 ) or obese (body mass index ≥ 30.0 kg·m −2 ) with IR (HOMA-IR ≥ 2.5-5.0) [26,27]; (ii) be physically inactive, defined as failure to meet the World Health Organization minimum physical activity recommendations for adults (i.e., moderate aerobic physical activity for at least 150 to 300 min per week, or vigorous aerobic physical activity for at least 75 to 150 min per week, or an equivalent combination of moderate and vigorous activity throughout the week) [28]. Exclusion criteria were as follows: (i) HOMA-IR > 5.0 [27]; (ii) consumption of medication (other than metformin) [13]. ...
... [26,27]; (ii) be physically inactive, defined as failure to meet the World Health Organization minimum physical activity recommendations for adults (i.e., moderate aerobic physical activity for at least 150 to 300 min per week, or vigorous aerobic physical activity for at least 75 to 150 min per week, or an equivalent combination of moderate and vigorous activity throughout the week) [28]. Exclusion criteria were as follows: (i) HOMA-IR > 5.0 [27]; (ii) consumption of medication (other than metformin) [13]. ...
... Insulin sensitivity was assessed through the HOMA-IR [27]. The HOMA-IR was calculated as fasting insulin × fasting glucose/405 [12,31]. ...
Metformin, a drug widely used to treat insulin resistance, and training that combines aerobic and strength exercise modalities (i.e., concurrent training) may improve insulin sensitivity. However, there is a paucity of clinical trials investigating the effects of concurrent training, particularly on insulin resistance and fat oxidation in overweight and obese patients. Furthermore, only a few studies have compared the effects of concurrent training with metformin treatment. Therefore, the aim of this study was to examine the effects of a 12-week concurrent training pro-gram versus pharmaceutical treatment with metformin on maximum fat oxidation, glucose metabolism, and insulin resistance in overweight or obese adult patients. Male and female patients with insulin resistance were allocated by convenience to a concurrent training group (n=7 [2 males]; age=32.9±8.3 years; body mass index=30±4.0 kg.m-2) or a metformin group (n=7 [2 males]; age=34.4±14.0 years; body mass index=34.4±6.0 kg.m-2). Before and after the interventions, all participants were assessed for total body mass, body mass index, fat mass, fat-free mass, maxi-mum oxygen consumption, maximal fat oxidization during exercise, and fasting glucose, insulin resistance through the homeostatic model assessment (HOMA-IR). Due to non-normal distribution of the variable maximal fat oxidation, the Mann-Whitney U test was applied and revealed better maximal fat oxidization (Δ= 308%) in the exercise compared with the metformin group (Δ= -30.3%; p=0.035). All other outcome variables were normally distributed and significant group-by-time interactions were found for HOMA-IR (p<0.001, Δ= -84.5%), fasting insulin (p<0.001, Δ= -84.6%), and increased maximum oxygen consumption (p=0.046, Δ =12.3%) in favor of the exercise group. Similar changes were found in both groups for the remaining dependent variables. Concurrent training seems to be more effective compared with pharmaceutical metformin treatment to improve insulin resistance and fat oxidation in adult overweight and obese patients with insulin resistance. The rather small sample size calls for more research in this area.
... Mathematical modelling, particularly when combined with computer simulation, is increasingly favoured in the field of diabetes research 1,2 . Some of the published models may also be used for medical decision making and other diagnostic purposes 3,4 , or for prognostic assessment 5 . For example, this applies to the homeostasis model assessment/insulin resistance assessment/ß-cell function (HOMA-IR and HOMA-Beta) 6 and the quantitative insulin sensitivity check index (QUICKI) 7 . ...
Modelling insulin-glucose homeostasis may provide novel functional insights. In particular, simple models are clinically useful if they yield diagnostic methods. Examples include the homeostasis model assessment (HOMA) and the quantitative insulin sensitivity check index (QUICKI). However, limitations of these approaches have been criticised. Moreover, recent advances in physiological and biochemical research prompt further refinement in this area. We have developed a nonlinear model based on fundamental physiological motifs, including saturation kinetics, non-competitive inhibition, and pharmacokinetics. This model explains the evolution of insulin and glucose concentrations from perturbation to steady-state. Additionally, it lays the foundation of a structure parameter inference approach (SPINA), providing novel biomarkers of carbohydrate homeostasis, namely the secretory capacity of beta-cells (SPINA-GBeta) and insulin receptor gain (SPINA-GR). These markers correlate with central parameters of glucose metabolism, including average glucose infusion rate in hyperinsulinemic glucose clamp studies, response to oral glucose tolerance testing and HbA1c. Moreover, they mirror multiple measures of body composition. Compared to normal controls, SPINA-GR is significantly reduced in subjects with diabetes and prediabetes. The new model explains important physiological phenomena of insulin-glucose homeostasis. Clinical validation suggests that it may provide an efficient biomarker panel for screening purposes and clinical research.
... Blood samples are then collected at pre-determined intervals in the subsequent two or three hours. Glucose concentrations from the blood samples are used to determine the body's response to the glucose drink, i.e., glucose tolerance [29]. Continuous glucose monitoring devices could be used to measure glucose concentrations during the oral glucose tolerance test instead of collecting venous samples. ...
Continuous glucose monitoring devices measure glucose in interstitial fluid. The devices are effective when used by patients with type 1 and 2 diabetes but are increasingly being used by researchers who are interested in the effects of various behaviours of glucose concentrations in healthy participants. Despite their more frequent application in this setting, the devices have not yet been validated for use under such conditions. A total of 124 healthy participants were recruited to a ten-day laboratory study. Each participant underwent four oral glucose tolerance tests, and a total of 3315 out of a possible 4960 paired samples were included in the final analysis. Bland–Altman plots and mean absolute relative differences were used to determine the agreement between the two methods. Bland–Altman analyses revealed that the continuous glucose monitoring devices had proportional bias (R = 0.028, p < 0.001) and a mean bias of −0.048 mmol/L, and device measurements were more variable as glucose concentrations increased. Ninety-nine per cent of paired values were in Zones A and B of the Parkes Error Grid plot, and there was an overall mean absolute relative difference of 16.2% (±15.8%). There was variability in the continuous glucose monitoring devices, and this variability was higher when glucose concentrations were higher. If researchers were to use continuous glucose monitoring devices to measure glucose concentrations during an oral glucose tolerance test in healthy participants, this variability would need to be considered.
... It is ranges from 44 to 70%. [7,10] This wide variation may be due to different race/ethnicity, different selection criteria of PCOS and different methods used to detect IR. [7,32,33] [34] Hyperinsulinemic Euglycemic Clamp is considered as gold standard method for insulin sensitivity. [35,36] It is labor intensive, technically difficult to perform and expensive; so it is cannot be used practically. ...
Background: Polycystic ovary syndrome (PCOS) is a common endocrine disorder associated with various phenotypic expressions. Aims: This study was conducted to observe and compare various metabolic components in different phenotypes of PCOS and to find out the frequency of insulin resistance (IR) among them. Materials and Methods: Eighty (80) patients diagnosed as case of PCOS as per inclusion and exclusion criteria were recruited in this cross sectional observational study. Patients were categorized in 4 different phenotypes based on the presence of oligo/anovulation (O), hyperandrogenism (H) and polycystic ovarian morphology (P): (i) Phenotype A (O+ H+ P), Phenotype B (O+H), (iii) Phenotype C (H+P) and (iv) Phenotype D (O+P). Demographic, anthropometric, biochemical and metabolic parameters were recorded and compared by ANOVA & Chi Square test using SPSS software version 22.0. Results: Most prevalent phenotype was phenotype A (55%) followed by phenotype D (22.
... Alanine aminotransferase (ALT) levels were also analyzed using an enzymatic colorimetric test. e homeostasis model assessment of insulin resistance (HOMA-IR) was calculated based on fasting insulin levels and fasting glucose levels [21]. HOMA-IR was calculated using the following formula: e membrane was blocked for 2 h at room temperature with 5% skimmed milk. ...
This study investigated the hypothesis that Portulaca oleracea L. exerts antiobesity and antidiabetic effects by evaluating blood lipid profiles, blood glucose control factors, protein expression of lipid metabolism, and insulin sensitivity improvement. Three groups of high-fat diet (HFD) induced obese C57BL/6 mice (n = 8) received treatment with low (5%; HFD + PO5%) or high (10%; HFD + PO10%) concentrations of P. oleracea powder for 12 weeks or no treatment (HFD) and were compared with each other and a fourth control group. Weight gain was reduced by 34% in the HFD + PO10% group compared to the HFD group. Moreover, the perirenal and epididymal fat contents in the HFD + PO10% group were 6.3-fold and 1.5-fold, respectively, lower than those in the HFD group. The atherogenic index (AI) and cardiac risk factor (CRF) results in the P. oleracea-treated groups were significantly lower than those in the HFD group. The homeostasis model assessment of insulin resistance (HOMA-IR) levels was lower in the HFD + PO10% group than in the HFD group. The protein expression levels of the proliferator-activated receptor (PPAR)-α, glucose transporter (GLUT) 4 and PPAR-γ were upregulated in the HFD + PO10% group compared to the HFD group. However, the protein expression levels of tumor necrosis factor (TNF)-α were lower in the P. oleracea-treated groups than in the HFD group. Our results demonstrate that P. oleracea powder could be effectively used to treat and prevent obesity and diabetes-associated diseases through suppression of weight gain and reduction in body fat and blood glucose levels.
... This is a group that warrant further study to understand the key biological or behavioral differences, if any, when compared to the standard obese and insulin resistant group to enable T2DM prevention. HOMA-IR is also a useful tool in assessing IR because obtaining laboratory values for HOMA-IR requires little training and constitutes a much lower cost per subject (one fasting blood draw) compared to the euglycemic hyperinsulinemic clamp or the frequently sampled insulin glucose tolerance test [22]. But HOMA-IR thresholds are not universal, with clinically relevant threshold of HOMA-IR different across populations [11]. ...
Objective:
To determine whether insulin resistance (IR) measured by homeostasis model of insulin resistance (HOMA-IR) can further stratify diabetes risk in African Americans (AAs) beyond obesity and identify obese, low risk and non-obese, high risk individuals.
Methods:
Using the Jackson Heart Study cohort, we categorized participants without diabetes into four phenotypes: non-obese/insulin-sensitive, non-obese/IR, obese/insulin-sensitive and obese/IR. Obesity was defined as BMI ≥ 30 or BMI 25-30 plus an increased waist circumference. IR was defined as HOMA-IR ≥ 2. We used modified Poisson regression models to estimate the incident risk-ratios (IRR) of diabetes across these phenotypes adjusting for potential confounders and HbA1c.
Results:
Among 3219 AAs without diabetes, 14.0% were non-obese/insulin-sensitive, 24.6% non-obese/IR, 6.2% obese/insulin-sensitive, and 55.3% obese/IR. The overall crude incidence rate of diabetes was 29.91 cases/1000 person-years. In fully-adjusted models, compared to the non-obese/insulin-sensitive group, the relative risk of diabetes was highest in obese/IR (IRR = 2.35; 95% CI: 1.53, 3.60), followed by non-obese/IR (IRR = 1.59; 95% CI: 1.02, 2.46), and non-significant for the obese/insulin-sensitive (IRR = 1.70; 95% CI: 0.97, 2.99) group.
Conclusions:
HOMA-IR can further stratify diabetes risk in AA adults beyond obesity, identifying non-obese high-risk and lower-risk obese individuals. However, diabetes risk should still be carefully monitored in obese populations despite insulin sensitivity.
