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

Non-Interventional Weight Changes Are Associated with Alterations in Lipid Profiles and in the Triglyceride-to-HDL Cholesterol Ratio

BACKGROUND

Obesity is associated with dyslipidemia, and weight loss can improve obese patients' lipid profile. Here, we assessed whether non-interventional weight changes are associated with alterations in lipid profile, particularly the triglyceride (TG)-to-high-density lipoprotein cholesterol (HDL-C) ratio (TG/HDL-C).

METHODS

In this retrospective analysis of subjects referred to medical screening, body mass index (BMI), low-density lipoprotein cholesterol (LDL-C), TG, and HDL-C levels were measured annually. Patients were divided according to BMI changes between visits. The primary outcomes were the changes in LDL-C, TG, HDL-C, and the TG/HDL-C ratio between visits.

RESULTS

The final analysis included 18,828 subjects. During the year of follow-up, 9.3% of the study population lost more than 5% of their weight and 9.2% gained more than 5% of their weight. The effect of weight changes on TG and on the TG/HDL-C ratio was remarkable. Patients with greater BMI increases showed greater increases in their TG/HDL-C ratio, and conversely, a decreased BMI level had lower TG/HDL-C ratios. This is true even for moderate changes of more than 2.5% in BMI.

CONCLUSIONS

Non-interventional weight changes, even modest ones, are associated with significant alterations in the lipid profile. Understanding that modest, non-interventional weight changes are associated with alterations in the TG/HDL-C ratio may aid in better risk stratification and primary prevention of CV morbidity and mortality.

Development and validation of machine learning-augmented algorithm for insulin sensitivity assessment in the community and primary care settings: a population-based study in China

Objective

Insulin plays a central role in the regulation of energy and glucose homeostasis, and insulin resistance (IR) is widely considered as the "common soil" of a cluster of cardiometabolic disorders. Assessment of insulin sensitivity is very important in preventing and treating IR-related disease. This study aims to develop and validate machine learning (ML)-augmented algorithms for insulin sensitivity assessment in the community and primary care settings.

Methods

We analyzed the data of 9358 participants over 40 years old who participated in the population-based cohort of the Hubei center of the REACTION study (Risk Evaluation of Cancers in Chinese Diabetic Individuals). Three non-ensemble algorithms and four ensemble algorithms were used to develop the models with 70 non-laboratory variables for the community and 87 (70 non-laboratory and 17 laboratory) variables for the primary care settings to screen the classifier of the state-of-the-art. The models with the best performance were further streamlined using top-ranked 5, 8, 10, 13, 15, and 20 features. Performances of these ML models were evaluated using the area under the receiver operating characteristic curve (AUROC), the area under the precision-recall curve (AUPR), and the Brier score. The Shapley additive explanation (SHAP) analysis was employed to evaluate the importance of features and interpret the models.

Results

The LightGBM models developed for the community (AUROC 0.794, AUPR 0.575, Brier score 0.145) and primary care settings (AUROC 0.867, AUPR 0.705, Brier score 0.119) achieved higher performance than the models constructed by the other six algorithms. The streamlined LightGBM models for the community (AUROC 0.791, AUPR 0.563, Brier score 0.146) and primary care settings (AUROC 0.863, AUPR 0.692, Brier score 0.124) using the 20 top-ranked variables also showed excellent performance. SHAP analysis indicated that the top-ranked features included fasting plasma glucose (FPG), waist circumference (WC), body mass index (BMI), triglycerides (TG), gender, waist-to-height ratio (WHtR), the number of daughters born, resting pulse rate (RPR), etc.

Conclusion

The ML models using the LightGBM algorithm are efficient to predict insulin sensitivity in the community and primary care settings accurately and might potentially become an efficient and practical tool for insulin sensitivity assessment in these settings.

Development and validation of an age-sex-ethnicity-specific metabolic syndrome score in the Chinese adults

Metabolic syndrome (MetS) is characterized by metabolic dysfunctions and could predict future risk for cardiovascular diseases (CVDs). However, the traditionally defined dichotomous MetS neither reflected MetS severity nor considered demographic variations. Here we develop a continuous, age-sex-ethnicity-specific MetS score based on continuous measures of the five metabolic dysfunctions (waist circumference [WC], triglycerides [TG], high-density lipoprotein cholesterol [HDL-C], mean arterial pressure [MAP], and fasting blood glucose [FBG]). We find that the weights of metabolic dysfunctions in the score vary across age-sex-ethnicity-specific subgroups, with higher weights for TG, HDL-C, and WC. Each unit increase in the score is associated with increased risks for hyperlipidemia, diabetes, and hypertension, and elevated levels of HbA1c, cholesterol, body mass index, and serum uric acid. The score shows high sensitivity and accuracy for detecting CVD-related risk factors and is validated in different geographical regions. Our study would advance early identification of CVD risks and, more broadly, preventive medicine and sustainable development goals.

Glycemic and metabolic sub-classification of prediabetes and risk factors for cardiovascular disease in the D2d cohort

Objectives

Prediabetes represents a spectrum of metabolic abnormalities, including insulin resistance and secretory impairment, that carries increased cardiovascular disease (CVD) risk. It is unclear whether specific glycemic and metabolic sub-classifications are associated with CVD risk. This cross-sectional analysis of 3946 participants from the Vitamin D and Type 2 Diabetes (D2d) study cohort aimed to determine the associations between various baseline CVD risk factors, glycemic sub-classifications of prediabetes (FPG, 2hPG, and HbA1c), and measures of insulin sensitivity and secretion from an OGTT.

Methods

The metabolic syndrome and atherosclerotic cardiovascular disease (ASCVD) risk scores were determined for tertiles of insulin sensitivity (HOMA2S) and insulinogenic index (IGI). Unadjusted analyses showed elevated CVD risk factors in the lowest tertile for both IGI and HOMA2S.

Results

After adjustment for age, gender, race, obesity, and smoking status, the association remained between HOMA2S and ASCVD score (r = -0.11, p< 0.001) but not for IGI. Those who met at least 2 diagnosic criteria for prediabetes had the largest proportion (> 40%) of participants with high ASCVD risk score >20. A higher percentage of individuals that met all 3 criteria for prediabetes had metabolic syndrome and ASCVD risk score >20 (87.2% and 15.3%, respectively) than those who only met 1 prediabetes criterion (51.6% and 7.1%, respectively).

Conclusions

In conclusion, multiple metabolic (HOMA2S, IGI) and glycemic criteria of prediabetes (FPG, 2hPG, & HbA1c) are needed to fully recognize the elevated CVD risk profile that can manifest in prediabetes.

The relationship and interaction between triglyceride glucose index and obesity in the risk of prehypertension population: a cross-sectional study from a survey in Anhui, Eastern China

BACKGROUND

The triglyceride glucose (TyG) index has been regarded as an effective proxy of Insulin resistance (IR). Studies on the TyG index, obesity and the risk of prehypertension (PHT) in elderly people are not apparent currently. The study sought to investigate the predictive value of TyG index and the associations with PHT risk and obesity.

METHODS

A community-based cross-sectional study was conducted in Bengbu City, Anhui province, China. Participants older than 65 years accepted questionnaire surveys, physical examinations and blood biochemistry tests. Based on the testing results, indicators including BMI (body mass index), WC (waist circumference), WHtR (waist-to-height-ratio), LAP(Lipid accumulation products) and TyG were calculated. Residents were classified into quartiles by their TyG indexes. Receiver operating characteristic curve (ROC) analysis was carried out to predict obesity indices for PHT. The three additive interaction indicators, RERI (relative excess risk due to interaction), AP (attributable proportion due to interaction) and S (synergy index) were used to assess the interaction impacts.

RESULTS

Two thousand six hundred sixty-six eligible elderly people were included in study and the prevalence of PHT was 71.04% (n = 1894). With increasing TyG index quartile, PHT became more prevalent. After adjusting for confounding factors, the prevalence of PHT risk with TyG levels in the fourth quartile (Q4, male: 2.83, 95%CI: 1.77-4.54; female: 2.75, 95%CI:1.91-3.97) was greater than that in the first quartile (Q1:ref). TyG index (AUC: 0.626, 95%CI: 0.602 to 0.650) was superior than BMI (AUC: 0.609, 95%CI: 0.584 to 0.633) in predicting PHT among females. Eventually, there were significant interactions of TyG index with obesity in males (General obesity: AP = 0.87, 95%CI: 0.72 to 1.02, S = 10.48, 95%CI: 3.43 to 31.97; Abdominal obesity: AP = 0.60,95%CI: 0.38 to 0.83, S = 3.53, 95%CI: 1.99 to 6.26) and females (General obesity: AP = 0.89, 95%CI: 0.79 to 0.98, S = 12.46, 95%CI: 5.61 to 27.69; Abdominal obesity: AP = 0.66, 95%CI: 0.51 to 0.82, S = 3.89, 95%CI:2.54 to 5.98).

CONCLUSION

TyG index and PHT risk are tightly correlated. The risk of chronic disease in the elderly can be decreased by early detection of PHT utilizing the TyG index. In this research, the TyG index was more predictable than other indicators of obesity.

Reference values for the triglyceride to high-density lipoprotein ratio and its association with cardiometabolic diseases in a mixed adult population: The ELSA-Brasil study

BACKGROUND

Among several lipid ratios available, the triglyceride/HDL-cholesterol (TG/HDL-C) may detect individuals at risk of cardiometabolic diseases. However, its reference values for different ethnicities are not well established.

OBJECTIVE

To define sex- and ethnicity-specific reference values for TG/HDL-C ratio in a large sample of healthy multiethnic adults and test its association with cardiometabolic conditions.

METHODS

An apparently healthy sample (n = 2,472), aged 35-74, free of major cardiovascular risk factors, was used to generate the reference values for the TG/HDL-C. Exclusion criteria were diabetes, elevated blood pressure, obesity, hypercholesterolemia, severe hypertriglyceridemia, and smoking history. Cut-offs based on the reference values were tested in the whole ELSA Brasil study (n = 13,245), stratified by sex and ethnicity, to identify cardiometabolic conditions.

RESULTS

TG/HDL-C ratio was higher in men than women, and did not change significantly with age, regardless of sex and ethnicity. Also, black individuals showed lower levels of TG/HDL-C as compared to other ethnic groups. ROC curve showed that the cut-off based on the 75th percentile displayed better sensitivities and specificities for men and women, regardless of ethnicity. Also, the sex- and ethnicity-specific cut-offs based on the 75th percentile were significantly associated with all tested cardiometabolic conditions (hypertension, diabetes, obesity, metabolic syndrome, and insulin resistance). Also, we observed that the use of a single sex-specific cut-off (men: 2.6; women: 1.7) could be used for the different ethnicities with good reliability.

CONCLUSION

The defined TG/HDL-C cut-offs (men: 2.6; women: 1.7) are reliable and showed good clinical applicability to detect cardiometabolic conditions in a multiethnic population.

Cardiorespiratory Fitness, Body Mass Index, and Markers of Insulin Resistance in Apparently Healthy Women and Men

BACKGROUND

Insulin resistance may be present in healthy adults and is associated with poor health outcomes. Obesity is a risk factor for insulin resistance, but most obese adults do not have insulin resistance. Fitness may be protective, but the association between fitness, weight, and insulin resistance has not been studied in a large population of healthy adults.

METHODS

A cross-sectional analysis of cardiorespiratory fitness, body mass index, and markers of insulin resistance was performed. Study participants were enrolled at the Cooper Clinic in Dallas, Texas. The analysis included 19,263 women and 48,433 men with no history of diabetes or cardiovascular disease. Cardiorespiratory fitness was measured using exercise treadmill testing. Impaired fasting glucose (100-125 mg/dL) and elevated fasting triglycerides (≥150 mg/dL) were used as a markers of insulin resistance.

RESULTS

Among individuals with normal weight, poor fitness was associated with 2.2-fold higher odds of insulin resistance in women (1.4-3.6; P = .001) and 2.8-fold higher odds in men (2.1-3.6; P <.001). The impact of fitness remained significant for overweight and obese individuals, with the highest risk group being the unfit obese. Among obese women, the odds ratio for insulin resistance was 11.0 for fit women (8.7-13.9; P <.001) and 20.3 for unfit women (15.5-26.5; P <.001). Among obese men, the odds ratio for insulin resistance was 7.4 for fit men (6.7-8.2; P < .001) and 12.9 for unfit men (11.4-14.6; P < .001).

CONCLUSIONS

Independent of weight, poor fitness is associated with risk of insulin resistance. Obese individuals, particularly women, may benefit from the greatest absolute risk reduction by achieving moderate fitness.

Antidiabetic Activity and Potential Mechanism of Amentoflavone in Diabetic Mice

Aim: To investigate the anti-diabetic activity of amentoflavone (AME) in diabetic mice, and to explore the potential mechanisms. Methods: Diabetic mice induced by high fat diet and streptozotocin were administered with amentoflavone for 8 weeks. Biochemical indexes were tested to evaluate its anti-diabetic effect. Hepatic steatosis, the histopathology change of the pancreas was evaluated. The activity of glucose metabolic enzymes, the expression of Akt and pAkt, and the glucose transporter type 4 (GLUT4) immunoreactivity were detected. Results: AME decreased the level of glucose, total cholesterol (TC), triglyceride (TG), low density lipoprotein cholesterol (LDL-C) and glucagon, and increased the levels of high density lipoprotein cholesterol (HDL-C) and insulin. Additionally, AME increased the activity of glucokinase (GCK), phosphofructokinase-1 (PFK-1), and pyruvate kinase (PK), and inhibited the activity of glycogen synthase kinase-3 (GSK-3), phosphoenolpyruvate carboxykinase (PEPCK), and glucose-6-phosphatase (G-6-Pase). Mechanistically, AME increased superoxide dismutase (SOD), decreased malondialdehyde (MDA), activation of several key signaling molecules including pAkt (Ser473), and increased the translocation to the sedimenting membranes of GLUT4 in skeletal muscle tissue. Conclusions: AME exerted anti-diabetic effects by regulating glucose and lipid metabolism, perhaps via anti-oxidant effects and activating the PI3K/Akt pathway. Our study provided novel insight into the role and underlying mechanisms of AME in diabetes.

Dietary patterns interact with chromosome 9p21 rs1333048 polymorphism on the risk of obesity and cardiovascular risk factors in apparently healthy Tehrani adults

PURPOSE

Gene-dietary patterns may contribute to determining body composition and related biochemical indices. The aim of this study was to evaluate interactions between rs1333048 polymorphism and major dietary patterns on body fat percentage, general and central obesity, and related biochemical measurements.

METHODS

This cross-sectional study was conducted on 265 healthy Tehrani adults with mean age of 35 years (47.5% men, 52.5% women). Dietary patterns (DPs) were extracted by factor analysis. Bioelectrical impedance analysis was used for body analysis and rs1333048 was genotyped by the restriction fragment length polymorphism (PCR-RFLP) method.

RESULTS

Three DPs were extracted: restricted refined grains DP, legumes DP and healthy DP. AA genotype compared to CC genotype had greater odds for general obesity before (OR 3.14; 95% CI 1.008-9.60, P = 0.045) and after (OR 3.11; 95% CI 1.008-9.60, P = 0.048) adjusting for potential confounders. Individuals with AA genotype were more likely to be centrally obese before (OR 2.09; 95% CI 1.006-4.35, P = 0.048) and after (OR 2.63; 95% CI 1.12-6.17, P = 0.026) controlling for potential confounders. Significant interactions were observed between Legumes DP and rs1333048 SNP on waist circumference (P = 0.047), body fat % (BFP) (P = 0.048), hs-Crp (P = 0.042), BMI (P = 0.073), WHtR (P = 0.063) and odds for general obesity (P = 0.051). Following this DP reduced all these items for individuals with CC genotype, whereas increased them for people who carry CA or AA genotypes.

CONCLUSIONS

The findings indicate that there are significant associations between AA genotype of rs1333048 SNP and general and central obesity, and significant interaction between alleles of this SNP and major dietary patterns on the odds of general obesity, BFP, waist circumference, BMI, WHtR and hs-Crp.

Validation of a single factor representing the indicators of metabolic syndrome as a continuous measure of metabolic load and its association with health and cognitive function

The metabolic syndrome (MetS) is a cluster of risk factors for cardiovascular disease associated with reduced physical fitness, higher disease burden, and impaired cognitive functions. Little is known about the operation of these risk factors in older adults when considered comprehensively without relying on the cut-off values of the single MetS components. The three main aims of the current study were to: (i) establish a latent metabolic load factor (MetL), using confirmatory factor analysis (CFA), and representing a continuous measure of MetL, defined by indicators that are commonly used to separate MetS groups from healthy individuals; (ii) examine the associations of this MetL factor with objective health, and cognitive function in men and women; (iii) compare the magnitude of these associations to those observed for the individual indicators used to define the MetL factor as well to the classical categorized MetS vs. non-MetS groups. The current analysis is based on cross-sectional data from 1,609 participants of the Berlin Aging Study II (mean age = 68.5 years, SD (3.7); 50.1% female). We applied structural equation modeling (SEM) to establish a latent MetL factor defined by the five indicators commonly used to diagnose MetS. The latent MetL factor was associated with physician-assessed morbidity and kidney function (estimated glomerular filtration rate, eGFR) in both men and women, but not with hand grip strength and lung function (Forced Expiratory Volume in 1 Second (FEV1)). In addition, we found a negative association between MetL and fluid intelligence among men. A continuous latent variable approach representing the common variance of MetS indicators is well suited to foster our understanding of human aging as a systemic phenomenon in which risk factors are operating on either side of the normal versus pathological divide.

Clustering Effects of Metabolic Factors and the Risk of Metabolic Syndrome

Background

Metabolic syndrome is a major risk factor for cardiovascular disease. Clustering of a combination of individual factors that increase the actual rather than the expected prevalence might be helpful in understanding the pathophysiology of metabolic syndrome. The aim of this study was to analyze the most influential factors for metabolic syndrome to assess clustering factors of metabolic syndrome.

Methods

Subjects from the Korea National Health and Nutrition Examination Survey (KNHANES) VI were included in the present study. The status of health behaviors was obtained using the questionnaires included in the KNHANES VI. A complex, stratified, and multistage sampling design was used to analyze the data according to statistics from the Korea Centers for Disease Control and Prevention.

Results

A total of 2,101 men and 2,831 women aged older than 20 years were included in this study. In men, drinking alcohol more than twice per week was related with the prevalence of metabolic syndrome; while, in women, exercise was related with the prevalence of metabolic syndrome. The clustering effect was observed for more than three metabolic factors. In men, the clustering effect was strongest for the combination of hypertension, hyperglycemia, and hypertriglyceridemia. In women, the strongest clustering effect was observed for the combination of abdominal obesity, hypertriglyceridemia, and low high-density lipoprotein cholesterol concentration.

Conclusion

The health behaviors affecting metabolic syndrome in men and women included drinking alcohol more than twice a week and exercising more than four times a week, respectively; in addition, hypertriglyceridemia most significantly influenced the clustering effect of metabolic syndrome.

The changes of immunoglobulin G N-glycosylation in blood lipids and dyslipidaemia

BACKGROUND

Alternative N-glycosylation has significant structural and functional consequences on immunoglobulin G (IgG) and can affect immune responses, acting as a switch between pro- and anti-inflammatory IgG functionality. Studies have demonstrated that IgG N-glycosylation is associated with ageing, body mass index, type 2 diabetes and hypertension.

METHODS

Herein, we have demonstrated patterns of IgG glycosylation that are associated with blood lipids in a cross-sectional study including 598 Han Chinese aged 20-68 years. The IgG glycome composition was analysed by ultra-performance liquid chromatography.

RESULTS

Blood lipids were positively correlated with glycan peak GP6, whereas they were negatively correlated with GP18 (P < 0.05/57). The canonical correlation analysis indicated that initial N-glycan structures, including GP4, GP6, GP9-12, GP14, GP17, GP18 and GP23, were significantly correlated with blood lipids, including total cholesterol, total triglycerides (TG) and low-density lipoprotein (r = 0.390, P < 0.001). IgG glycans patterns were able to distinguish patients with dyslipidaemia from the controls, with an area under the curve of 0.692 (95% confidence interval 0.644-0.740).

CONCLUSIONS

Our findings indicated that a possible association between blood lipids and the observed loss of galactose and sialic acid, as well as the addition of bisecting GlcNAcs, which might be related to the chronic inflammation accompanying with the development and procession of dyslipidaemia.

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.

TG/HDL-C ratio and visceral adiposity index may be useful in assessment of insulin resistance in adults with type 1 diabetes in clinical practice

BACKGROUND

Insulin resistance (IR) is an important clinical issue in patients with type 1 diabetes due to worse metabolic control and risk of development of chronic complications.

OBJECTIVE

The aim of the study was to evaluate simple and easily available parameters as indirect markers of IR in adults with type 1 diabetes and correlate it with the results of hyperinsulinemic-euglycemic clamp.

METHODS

The study included 88 patients (62 men), aged 34.1 ± 6.5 years, with type 1 diabetes with a median disease duration of 8 (7-13) years and mean HbA1c of 7.6 ± 1.5%. Tissue sensitivity to insulin was assessed on the basis of glucose distribution rate (GDR) obtained in the course of hyperinsulinemic-euglycemic clamp. In addition, indirect markers of IR, such as estimated GDR, presence of features of metabolic syndrome, visceral adiposity index (VAI), and the triglyceride (TG)/high-density lipoprotein cholesterol (HDL-C) ratio, were evaluated.

RESULTS

In the study group, IR defined as GDR <4 mg/kg/min was observed in 33 (37.5%) patients. Group with IR had significantly higher postprandial glycemia (9.1 ± 2.0 vs 8.4 ± 1.1 mmol/L, P = .04), serum TG level (1.11 [0.75-1.92] vs 0.85 [0.60-1.08] mmol/L, P = .001), lower HDL-C level (1.59 ± 0.38 vs 1.8 ± 0.5 mmol/L, P = .02), higher TG/HDL-C ratio (1.60 [1.00-3.13] vs 1.05 [0.62-1.53], P = .001), and higher VAI (2.61 [1.31-4.25] vs 1.56 [0.96-2.25], P = .002). Significant relationship between GDR and TG/HDL-C ratio and VAI, adjusted for age, sex, HbA1c, and duration of diabetes was revealed (respectively, odds ratio 1.90 [95% confidence interval 1.15-3.15], P = .01 and odds ratio 1.47 [95% confidence interval 1.06-2.04], P = .01).

CONCLUSIONS

TG/HDL-C ratio and VAI appear to be clinically useful tools to assess IR in adults with type 1 diabetes.

Association and interaction between triglyceride-glucose index and obesity on risk of hypertension in middle-aged and elderly adults

AIMS

To investigate the association between triglyceride-glucose(TyG) index and the risk of hypertension.

METHOD

A cross-sectional study was conducted in Bengbu, China. The participants received relevant questionnaire survey, anthropometric tests, and laboratory examination. Multivariate logistic regression analysis was performed to estimate the possible association between TyG index and hypertension risk. The additive interaction evaluated by the relative excess risk due to interaction (RERI), attributable proportion due to interaction (AP), and synergy index(SI) was calculated.

RESULTS

A total of 1777 participants (748 men and 1029 women) were investigated. There was a significant increase in the risk of hypertension and isolated systolic hypertension (ISH) when comparing the highest TyG index (the fourth quartile) to the lowest TyG index (the first quartile) and corresponding ORs were 2.446 (95% CI: 1.746-3.426) and 2.621(95%CI: 1.627-4.224), respectively. However, no significant relationship was observed between TyG index and isolated diastolic hypertension (IDH). In males, significant interactions between TyG index and WHtR (RERI:1.978, 95%CI: 0.162-3.792; AP: 0.359, 0.113-0.605; SI:1.782, 1.017-3.122), smoking (AP: 0.437, 95%CI: 0.048-0.825), family history of hypertension (AP:0.433, 95%CI: 0.203-0.662; SI:2.248, 95%CI: 1.333-3.791) were observed. As for females, there were also significant interactions between TyG index and WHtR (RERI:1.415, 95%CI: 0.693-2.136; AP: 0.198, 95%CI: 0.104-0.291; SI:1.298, 95%CI:1.101-1.530), family history of hypertension (RERI:1.744, 95%CI: 0.221-3.267; AP:0.405, 95%CI: 0.113-0.697) on risk of hypertension.

CONCLUSIONS

Increased TyG index was significantly associated with higher risk of hypertension and ISH, but not for IDH in middle-aged and elderly adults. Our results also demonstrated interactions of TyG index and abdominal obesity and family history of hypertension on hypertension risk.

Relationship between simple markers of insulin resistance and coronary artery calcification

BACKGROUND

Insulin resistance in apparently healthy persons is associated with a cluster of metabolic abnormalities that promote coronary atherosclerosis. Identifying these individuals before manifest disease would provide useful clinical information.

OBJECTIVE

We hypothesized that combining 2 simple markers of insulin resistance, prediabetes (PreDM) and triglyceride (TG) concentration ≥150 mg/dL, would identify apparently healthy persons with adverse cardiometabolic risk profiles and increased coronary artery calcium (CAC) compared with those with neither or only 1 abnormality.

METHODS

A cross-sectional analysis was performed using data from 25,886 apparently healthy individuals (18,453 men and 7433 women) evaluated at the Cooper Clinic from 1998 to 2015. Participants were divided into those with a normal fasting glucose concentrations (<100 mg/dL = normal fasting glucose) or PreDM (fasting plasma glucose ≥100 and <126 mg/dL) and further subdivided into those with a plasma TG concentration <150 or ≥150 mg/dL. These 4 groups were compared on the basis of multiple coronary artery disease risk factors and the presence of CAC determined during their evaluation.

RESULTS

Participants with PreDM and a TG concentration ≥150 mg/dL had a significantly more adverse coronary artery disease risk profile than individuals with either abnormality or only 1 abnormality (PreDM or TG concentration ≥150 mg/dL). Furthermore, the odds of detectable CAC were higher in participants with PreDM and a TG ≥ 150 mg/dL than in participants with neither or only 1 abnormality.

CONCLUSION

The presence of 2 markers of insulin resistance, PreDM and TG concentration ≥150 mg/dL, is associated with increased cardiometabolic risk and detectable CAC within a population of apparently healthy individuals.