Sex differences in the link between blood cobalt concentrations and insulin resistance in adults without diabetes

Abstract Background Little is known about the effects of environmental cobalt exposure on insulin resistance (IR) in the general adult population. We investigated the association between cobalt concentration and IR. Methods A total of 1281 subjects aged more than 20 years with complete blood cobalt data were identified from the National Health and Nutrition Examination Survey (NHANES) 2015–2016 cycle. Blood cobalt levels were analyzed for their association with IR among all populations and subgroups by sex. Regression coefficients and 95% confidence intervals (CIs) of blood cobalt concentrations in association with fasting glucose, insulin and homeostatic model assessment of insulin resistance (HOMA-IR) were estimated using multivariate linear regression after adjusting for age, sex, ethnicity, alcohol consumption, body mass index, education level, and household income. A multivariate generalized linear regression analysis was further carried out to explore the association between cobalt exposure and IR. Results A negative association between blood cobalt concentration (coefficient = − 0.125, 95% CI − 0.234, − 0.015; P = 0.026) and HOMA-IR in female adults in the age- and sex-adjusted model was observed. However, no associations with HOMA-IR, fasting glucose, or insulin were found in the overall population. In the generalized linear models, participants with the lowest cobalt levels had a 2.74% (95% CI 0.04%, 5.50%) increase in HOMA-IR (P for trend = 0.031) compared with subjects with the highest cobalt levels. Restricted cubic spline regression suggested that a non-linear relationship may exist between blood cobalt and HOMA-IR. Conclusions These results provide epidemiological evidence that low levels of blood cobalt are negatively associated with HOMA-IR in female adults.

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Fetuin-A as a Marker of Insulin Resistance

Serbian Journal of Experimental and Clinical Research ◽

10.2478/sjecr-2021-0042 ◽

2021 ◽

Vol 0 (0) ◽

Author(s):

Ganavi P Yamasandhi ◽

Mala Dharmalingam

Keyword(s):

Insulin Resistance ◽

Linear Regression ◽

Linear Regression Analysis ◽

Predictive Biomarker ◽

Multiple Linear Regression Analysis ◽

P Value ◽

Model Assessment ◽

Fetuin A ◽

Homeostatic Model Assessment ◽

Relationship Of

Abstract Fetuin–A is a glycoprotein which helps in the regulation of metabolism. It is an early marker of insulin resistance (IR). The aim of this study was to evaluate the role of Fetuin–A as a predictive biomarker in cases of newly detected type 2 diabetes (NDD). The study involved 60 NDD and 60 Normal Healthy Controls (NHC). All the demographics and anthropological characteristics were noted. Fasting blood samples were drawn and various biochemical parameters were analyzed. The homeostatic model assessment of insulin resistance (HOMA-IR) and the quantitative insulin sensitivity check index (QUICKI) score was calculated. Chisquare, student T-test and Mann Whitney U tests were employed to associate and compare the mean and median between the NDD and NHC groups. Pearson's and Spearman’s correlation analysis were employed to examine the relationship of Fetuin–A levels with parametric and nonparametric variables. The independent predictors of Fetuin–A was determined by employing multiple forward linear regression. Fetuin–A was significantly high in NDD compared to NHC (1323 vs. 306.98 mcg/mL; p<0.001). Majority of NDD patients demonstrated IR based on the HOMA-IR (88.33% vs. 66.67%) and QUICKI score (96.67% vs. 85%). The multiple linear regression analysis showed that systolic blood pressure, age and QUICKI score were independently associated with Fetuin–A (p value <0.01). Fetuin–A may be used as a biomarker to detect NDD. Therefore, early detection of Fetuin–A levels in NDD gives an opportunity for suitable patient management.

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Association of Serum Levels of Zinc, Copper, and Iron with Risk of Metabolic Syndrome

Nutrients ◽

10.3390/nu13020548 ◽

2021 ◽

Vol 13 (2) ◽

pp. 548

Author(s):

Chia-Wen Lu ◽

Yi-Chen Lee ◽

Chia-Sheng Kuo ◽

Chien-Hsieh Chiang ◽

Hao-Hsiang Chang ◽

...

Keyword(s):

Insulin Resistance ◽

Metabolic Syndrome ◽

Linear Models ◽

Serum Zinc ◽

Serum Levels ◽

Least Square ◽

Model Assessment ◽

Serum Concentrations ◽

Metabolic Factors ◽

Homeostatic Model Assessment

The association between serum concentrations of zinc, copper, or iron and the risk of metabolic syndrome are inconclusive. Therefore, we conduct a case-control study to explore the relationship between serum levels of zinc, copper, or iron and metabolic syndrome as well as each metabolic factor and insulin resistance. We enrolled 1165 adults, aged ≥ 40 (65.8 ± 10) years in a hospital-based population to compare the serum levels of zinc, copper, and iron between subjects with and without metabolic syndrome by using multivariate logistic regression analyses. The least square means were computed by general linear models to compare serum concentrations of zinc, copper, and iron in relation to the number of metabolic factors. The mean serum concentrations of zinc, copper, and iron were 941.91 ± 333.63 μg/L, 1043.45 ± 306.36 μg/L, and 1246.83 ± 538.13 μg/L, respectively. The odds ratios (ORs) of metabolic syndrome for the highest versus the lowest quartile were 5.83 (95% CI: 3.35–10.12; p for trend < 0.001) for zinc, 2.02 (95% CI: 1.25–3.25; p for trend: 0.013) for copper, and 2.11 (95% CI: 1.24–3.62; p for trend: 0.021) for iron after adjusting for age, sex, personal habits, body mass index, and homeostatic model assessment insulin resistance. Additionally, the serum zinc, copper, and iron concentrations increased as the number of metabolic factors rose (p for trend < 0.001). This was the first study to clearly demonstrate that higher serum levels of zinc, copper, and iron were associated with the risk of metabolic syndrome and the number of metabolic factors independent of BMI and insulin resistance.

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Breath biomarkers of insulin resistance in pre-diabetic Hispanic adolescents with obesity

Scientific Reports ◽

10.1038/s41598-021-04072-3 ◽

2022 ◽

Vol 12 (1) ◽

Author(s):

Mohammad S. Khan ◽

Suzanne Cuda ◽

Genesio M. Karere ◽

Laura A. Cox ◽

Andrew C. Bishop

Keyword(s):

Insulin Resistance ◽

Metabolic Regulation ◽

Adult Population ◽

Exhaled Breath ◽

Model Assessment ◽

Hispanic Adolescents ◽

Metabolic Markers ◽

Non Invasive ◽

Glycemic Profile ◽

Homeostatic Model Assessment

AbstractInsulin resistance (IR) affects a quarter of the world’s adult population and is a major factor in the pathogenesis of cardio-metabolic disease. In this pilot study, we implemented a non-invasive breathomics approach, combined with random forest machine learning, to investigate metabolic markers from obese pre-diabetic Hispanic adolescents as indicators of abnormal metabolic regulation. Using the ReCIVA breathalyzer device for breath collection, we have identified a signature of 10 breath metabolites (breath-IR model), which correlates with Homeostatic Model Assessment for Insulin Resistance (HOMA-IR) (R = 0.95, p < 0.001). A strong correlation was also observed between the breath-IR model and the blood glycemic profile (fasting insulin R = 0.91, p < 0.001 and fasting glucose R = 0.80, p < 0.001). Among tentatively identified metabolites, limonene, undecane, and 2,7-dimethyl-undecane, significantly cluster individuals based on HOMA-IR (p = 0.003, p = 0.002, and p < 0.001, respectively). Our breath-IR model differentiates between adolescents with and without IR with an AUC-ROC curve of 0.87, after cross-validation. Identification of a breath signature indicative of IR shows utility of exhaled breath metabolomics for assessing systemic metabolic dysregulation. A simple and non-invasive breath-based test has potential as a diagnostic tool for monitoring IR progression, allowing for earlier detection of IR and implementation of early interventions to prevent onset of type 2 diabetes mellitus.

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Cumulative soft drink consumption is associated with insulin resistance in Mexican adults

American Journal of Clinical Nutrition ◽

10.1093/ajcn/nqaa169 ◽

2020 ◽

Vol 112 (3) ◽

pp. 661-668 ◽

Cited By ~ 1

Author(s):

Berenice Rivera-Paredez ◽

Leticia Torres-Ibarra ◽

Romina González-Morales ◽

Tonatiuh Barrientos-Gutiérrez ◽

Rubí Hernández-López ◽

...

Keyword(s):

Insulin Resistance ◽

Linear Regression ◽

Fasting Glucose ◽

Soft Drink ◽

Linear Regression Analysis ◽

Colorimetric Method ◽

Soft Drinks ◽

Soft Drink Consumption ◽

Mexican Adults

ABSTRACT Background Insulin resistance (IR) is an important risk factor for type 2 diabetes (T2D) and other cardiometabolic diseases. Recent studies suggest that soft drink consumption could increase IR. However, inconsistent findings have been observed. Objective The aim was to estimate the association between the cumulative consumption of soft drinks and IR by means of the HOMA-IR in Mexican adults. Methods We analyzed the association between cumulative consumption of soft drinks and HOMA-IR change after 7 y of follow-up in participants (n = 1073) of the Health Workers Cohort Study. Soft drink consumption was estimated by food-frequency questionnaires. Insulin was measured by chemiluminescence, and fasting glucose was measured with the enzymatic colorimetric method. HOMA-IR was computed as fasting insulin (mIU/L) × fasting glucose (mmol/L)/22.5. To assess the relation between cumulative soft drink consumption and HOMA-IR change, we performed robust linear regression models. Additionally, we used fixed-effects models to estimate the association between changes in soft drink consumption and change in HOMA-IR. Results At baseline, the average age was 44 y. Mean cumulative soft drink consumption was 0.42 servings/d. Median HOMA-IR increased from 1.5 at baseline to 2.0 at follow-up. Soft drink consumption was positively associated with HOMA-IR change. In the multiple linear regression analysis, for each increase in the consumption of 2 (355 mL) soft drinks/d, the average change between baseline and follow-up HOMA-IR showed an increase of 1.11 units (95% CI: 0.74, 1.48). Conclusions Our data support the hypothesis that, in Mexican adults, a higher soft drink consumption is associated with an increase in HOMA-IR, despite known risk factors. These findings support the need for reinforcing policies to reduce soft drink consumption in our population.

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Quantile-Dependent Heritability of Glucose, Insulin, Proinsulin, Insulin Resistance, and Glycated Hemoglobin

Lifestyle Genomics ◽

10.1159/000519382 ◽

2021 ◽

pp. 1-25

Author(s):

Paul T. Williams

Keyword(s):

Insulin Resistance ◽

Fasting Glucose ◽

Tolerance Test ◽

Alternative Interpretation ◽

Model Assessment ◽

Gene Environment ◽

Heart Study ◽

Glucose Ratio ◽

Homeostatic Model Assessment ◽

Regression Slopes

Background: “Quantile-dependent expressivity” is a dependence of genetic effects on whether the phenotype (e.g., insulin resistance) is high or low relative to its distribution. Methods: Quantile-specific offspring-parent regression slopes (βOP) were estimated by quantile regression for fasting glucose concentrations in 6,453 offspring-parent pairs from the Framingham Heart Study. Results: Quantile-specific heritability (h2), estimated by 2βOP/(1 + rspouse), increased 0.0045 ± 0.0007 (p = 8.8 × 10−14) for each 1% increment in the fasting glucose distribution, that is, h2 ± SE were 0.057 ± 0.021, 0.095 ± 0.024, 0.146 ± 0.019, 0.293 ± 0.038, and 0.456 ± 0.061 at the 10th, 25th, 50th, 75th, and 90th percentiles of the fasting glucose distribution, respectively. Significant increases in quantile-specific heritability were also suggested for fasting insulin (p = 1.2 × 10−6), homeostatic model assessment of insulin resistance (HOMA-IR, p = 5.3 × 10−5), insulin/glucose ratio (p = 3.9 × 10−5), proinsulin (p = 1.4 × 10−6), proinsulin/insulin ratio (p = 2.7 × 10−5), and glucose concentrations during a glucose tolerance test (p = 0.001), and their logarithmically transformed values. Discussion/Conclusion: These findings suggest alternative interpretations to precision medicine and gene-environment interactions, including alternative interpretation of reported synergisms between ACE, ADRB3, PPAR-γ2, and TNF-α polymorphisms and being born small for gestational age on adult insulin resistance (fetal origin theory), and gene-adiposity (APOE, ENPP1, GCKR, IGF2BP2, IL-6, IRS-1, KIAA0280, LEPR, MFHAS1, RETN, TCF7L2), gene-exercise (INS), gene-diet (ACSL1, ELOVL6, IRS-1, PLIN, S100A9), and gene-socioeconomic interactions.

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Association between the Time of Length since Smoking Cessation and Insulin Resistance in Asymptomatic Korean Male Ex-Smokers

Journal of Diabetes Research ◽

10.1155/2017/6074760 ◽

2017 ◽

Vol 2017 ◽

pp. 1-7 ◽

Cited By ~ 2

Author(s):

Ko-Woon Kim ◽

Sung-Goo Kang ◽

Sang-Wook Song ◽

Na-Rae Kim ◽

Jun-Seung Rho ◽

...

Keyword(s):

Insulin Resistance ◽

Smoking Cessation ◽

Regression Analysis ◽

Linear Regression ◽

Multiple Linear Regression ◽

Linear Regression Analysis ◽

Resistance Index ◽

Significant Negative Correlation ◽

Multiple Linear Regression Analysis ◽

Model Assessment

Aim.Smoking is a major risk factor for diabetes mellitus, mainly due to decreased insulin secretion and increased insulin resistance. However, there has been little research on the effects of smoking cessation period on changes in insulin resistance. In this study, we investigated the relationships between the length of time since smoking cessation period and insulin resistance in asymptomatic Korean male ex-smokers.Methods.A total of 851 male adults were included in this study. We considered several factors that can affect insulin resistance, and through multiple linear regression analysis, we assessed the effect the length of time since smoking cessation on insulin resistance in ex-smokers. Insulin resistance was represented as the insulin resistance index estimated by homeostasis model assessment.Results. HOMA-IR values showed a statistically significant negative correlation with the length of time since smoking cessation (p=0.009) in ex-smokers. After performing multiple linear regression analysis using factors that could potentially influence insulin resistance, we found that waist circumference (p=0.026) and the length of time since smoking cessation (p=0.039) were independent predictors of HOMA-IR in asymptomatic male ex-smokers.Conclusion. The longer the smoking cessation period, the more the insulin resistance tended to decrease in asymptomatic Korean male ex-smokers.

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Breath biomarkers of insulin resistance in pre-diabetic Hispanic adolescents with obesity

10.1101/2021.06.23.21259399 ◽

2021 ◽

Author(s):

Mohammad S Khan ◽

Suzanna Cuda ◽

Genesio M Karere ◽

Laura A Cox ◽

Andrew C Bishop

Keyword(s):

Insulin Resistance ◽

Metabolic Regulation ◽

Adult Population ◽

Characteristic Curve ◽

Exhaled Breath ◽

Model Assessment ◽

Hispanic Adolescents ◽

Metabolic Markers ◽

Non Invasive ◽

Homeostatic Model Assessment

ABSTRACT Background: Insulin Resistance (IR) affects a quarter of the world's adult population and is a major factor in the pathogenesis of cardio-metabolic disease. Non-invasive sampling of exhaled breath contains metabolic markers indicative of underlying systemic metabolic abnormality. Method: In this pilot study, we implemented a non-invasive breathomics approach, combined with random forest machine learning, to investigate metabolic markers from pre-diabetic Hispanic adolescents with obesity as indicators of abnormal metabolic regulation. Findings: Exhaled breath collection using the ReCIVA breathalyzer is feasible in an adolescent population. We have identified a signature of breath metabolites (breath-IR model) which correlates with Homeostatic Model Assessment of Insulin Resistance (HOMA-IR) (R = 0.95, p <0.001). A strong correlation was also observed between the breath-IR model and the blood glycemic profile (fasting insulin R=0.91, p <0.001 and fasting glucose R=0.80, p <0.001). Among tentatively identified metabolites, limonene, undecane, and 2,7-dimethyl-undecane, significantly cluster individuals based on HOMA-IR (p =0.003, p =0.002, and p<0.001, respectively). Our breath-IR model differentiates between adolescents with and without IR with an area under the receiver operating characteristic curve of 0.87, after cross-validation. Interpretation: Identification of a breath metabolite signature indicative of IR in prediabetic Hispanic adolescents with obesity provides evidence of the utility of exhaled breath metabolomics for assessing systemic metabolic dysregulation. A simple and non-invasive breath-based test has utility as a diagnostic tool for monitoring IR progression, potentially allowing for earlier detection of IR and implementation of early interventions to prevent onset of type 2 diabetes mellitus.

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Identification of neck circumference cut-off points for insulin resistance as a marker in adolescents

Revista de Nutrição ◽

10.1590/1678-9865202134e200190 ◽

2021 ◽

Vol 34 ◽

Author(s):

Ingrid Ribeiro da Cruz MELO ◽

Márcia Ferreira Cândido de SOUZA ◽

Íkaro Daniel de Carvalho BARRETO ◽

Danielle Góes da SILVA ◽

Ricardo Queiroz GURGEL

Keyword(s):

Insulin Resistance ◽

Linear Regression ◽

Multiple Linear Regression ◽

Characteristic Curve ◽

Linear Regression Analysis ◽

Neck Circumference ◽

Model Assessment ◽

Cardiovascular Risks ◽

Homeostasis Model Assessment ◽

Cross Sectional

ABSTRACT Objective To identify cut-off points of neck circumference measurement to predict insulin resistance in adolescents. Methods Cross-sectional analysis with data derived from the Study of Cardiovascular Risks in Adolescents, nationwide, multicenter, school-based survey. We evaluated 901 adolescents, aged 12 to 17, from public and private schools in two cities of Sergipe state in Brazil. We measured demographic, anthropometric, and biochemical data, and insulin resistance using Homeostasis Model Assessment-Insulin Resistance. We used multiple linear regression and logistic analysis to evaluate the association between dependent variables (biochemical) and independent variables (anthropometric) controlled by body mass index, age, gender, and Tanner’s stage. We used the Receiver operating characteristic curve to determine cut-off points of neck circumference that can identify insulin resistance. Results The multiple linear regression analysis showed a positive association between neck circumference measurement with fasting glycemia and glycated hemoglobin (p<0.001) and a negative association with insulin (p<0.024). Furthermore, in logistic regression, the measurement of neck circumference was the only anthropometric indicator positively correlated with homeostasis model assessment-insulin resistance. The cut-off points of neck circumference for predicting insulin resistance were: 30.55cm for female pubertal and 32.10cm for post-pubertal adolescents; 35.90cm for male pubertal adolescents and 36.65cm for post-pubertal adolescents. Conclusions The measurement of neck circumference is a simple, practical anthropometric indicator and can be used as a screening tool to identify insulin resistance in adolescents.

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Development and validation of dietary and lifestyle insulinemic indices among Iranian adult population

Nutrition & Metabolism ◽

10.1186/s12986-021-00640-6 ◽

2022 ◽

Vol 19 (1) ◽

Author(s):

Ebrahim Mokhtari ◽

Farshad Teymoori ◽

Hossein Farhadnejad ◽

Parvin Mirmiran ◽

Fereidoun Azizi

Keyword(s):

Insulin Resistance ◽

Dietary Habits ◽

Serum Insulin ◽

Adult Population ◽

Linear Regression Analysis ◽

Iranian Population ◽

Model Assessment ◽

Dietary Intakes ◽

Dietary Index ◽

Diet And Lifestyle

Abstract Background There is no study regarding developing a valid index to predict insulin-related disorders in the Iranian population based on their dietary habits and lifestyle. In the current study, we aimed to develop and validate insulinemic potential indices of diet and lifestyle in Iranian adults. Methods In this cross-sectional study, we analysed data of 1063 men and women aged ≥ 25 years among participants of the examination three of Tehran lipid and glucose study (TLGS) (2006–2008). Dietary intakes were assessed using a valid semi-quantitative food frequency questionnaire. Dietary and lifestyle indices were developed using stepwise linear regression analysis based on dietary intakes, body mass index, and physical activity data. Fasting serum insulin concentration and homeostatic model assessment for insulin resistance (HOMA-IR) were used as biomarkers of hyperinsulinemia (HI) and insulin resistance (IR). Validation analyses were performed in examination four of TLGS. Results We developed four indices related to insulin homeostasis, including the dietary index for HI (DIH), the dietary index for IR (DIR), the lifestyle index for HI (LIH), and the lifestyle index for IR (LIR). Based on multivariable-adjusted models, the relative values of the biomarker in subjects in the highest quartile of indices were 45% for LIH (95% CI 1.36–1.55, Ptrend < 0.001), 28% for DIR (95% CI 1.13–1.42, Ptrend = 0.019), and 51% for LIR (95% CI 1.41–1.61, Ptrend < 0.001), higher than those in the reference quartile, respectively. Conclusion We designed and validated indices to determine the insulin potential of diet and lifestyle for the Iranian population, according to Iran’s demographic and dietary intake characteristics.

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Role of serum adropin measurement in the assessment of insulin resistance in obesity

Journal of Investigative Medicine ◽

10.1136/jim-2021-001796 ◽

2021 ◽

pp. jim-2021-001796

Author(s):

Hande Erman ◽

Ali Ozdemir ◽

Mustafa Erinc Sitar ◽

Seher Irem Cetin ◽

Banu Boyuk

Keyword(s):

Insulin Resistance ◽

Regression Analysis ◽

Metabolic Regulation ◽

Fasting Glucose ◽

Linear Regression Analysis ◽

Density Lipoprotein ◽

Model Assessment ◽

Obese Patients ◽

Factors Affecting ◽

Glycolipid Metabolism

Obesity has recently been mentioned as a metabolic pandemic in developed and developing countries and is an important known risk factor for type 2 diabetes and cardiovascular diseases. The main mechanism responsible for obesity is insulin resistance. Adropin is a peptide-structured regulatory hormone that is suggested to play a role in insulin resistance and metabolic regulation. We aimed to evaluate the associations of serum adropin with insulin resistance and clarify the factors affecting serum adropin concentrations. The study included 50 obese patients and 22 healthy controls. Patients with chronic disease and drug use history were excluded. Serum adropin and other metabolic parameters were obtained after overnight fasting. ELISA was used to measure serum adropin concentrations. The homeostatic model assessment-insulin resistance (HOMA-IR) index was used to calculate insulin resistance. Insulin resistance was defined as HOMA-IR >2.5. Serum adropin values were found to be low in the obese otherwise healthy patient group (p<0.001). Linear regression analysis revealed that age, body mass index (BMI), waist circumference (WC), high-density lipoprotein cholesterol, fasting glucose, and HOMA-IR affect serum adropin level. In multiple regression analysis, age is the most significant factor affecting serum adropin concentration. Serum adropin concentrations were negatively correlated with BMI, WC, diastolic blood pressure, fasting glucose, and insulin. Serum adropin concentrations were low in obese patients and the optimum cut-off point for adropin to indicate HOMA-IR at 2.5 is 216.7 ng/L. The findings suggest that serum adropin may contribute to the regulation of glycolipid metabolism and insulin resistance in obese patients.

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