Lipid ratios and obesity indices are effective predictors of metabolic syndrome in women with polycystic ovary syndrome

Comparison of atherogenic indices for predicting the risk of metabolic syndrome in Southwest Iran: results from the Hoveyzeh Cohort Study (HCS)

BACKGROUND

Metabolic syndrome (MetS) is a cluster of risk factors related to diabetes and cardiovascular disease (CVD). Given that early identification of MetS might decrease CVD risk, it is imperative to establish a simple and cost-effective method to identify individuals at risk of MetS. The purpose of this study was to explore the relationships between several atherogenic indices (including AIP, TyG index, non-HDL-C, LDL-c/HDL-c, and TC/HDL-c) and MetS, and to assess the ability of these indices to predict MetS.

METHODS

The present cross-sectional study was conducted using baseline data from 9809 participants of the Hoveyzeh Cohort Study (HCS). MetS was defined based on the International Diabetes Federation (IDF). To examine the discriminatory abilities of each atherogenic indices in the identification of MetS, a receiver-operating characteristic curve was conducted. Logistic regression analysis was also performed to evaluate the relationship between atherogenic indices and MetS.

RESULTS

All of the atherogenic indices including the TyG index, AIP, non-HDL-C, TC/HDL-c, and LDL-c/HDL-c were significantly higher in participants with MetS than in those without MetS. According to the ROC curve analysis, the TyG index revealed the highest area under the curve (0.79 and 0.85 in men and women, respectively), followed by the AIP (0.76 and 0.83 in men and women, respectively). The best cutoff values for the TyG index and AIP were 8.96 and 0.16 for men and 8.84 and 0.05 for women, respectively. The TyG index and AIP were also strongly associated with MetS.

CONCLUSION

Among the 5 atherogenic indices evaluated, the TyG index and AIP were strongly related to MetS. The TyG index also demonstrated superior discriminative ability compared to other atherogenic indices in predicting MetS.

Characteristics of Different Obesity Metabolic Indexes and their Correlation with Insulin Resistance in Patients with Polycystic Ovary Syndrome

This study is aimed to investigate the characteristics of different obesity metabolic indexes [body mass index (BMI), waist circumference (WC), waist-to-hip ratio (WHR), waist-to-height ratio (WHtR), cardiometabolic index (CMI), body roundness index (BRI), visceral adiposity index (VAI), and lipid aggregation products (LAP)] and their correlation with insulin resistance (IR) in patients with polycystic ovary syndrome (PCOS). The study was conducted on 140 subjects with PCOS and 133 control subjects aged 18-44 years. According to insulin resistance index (HOMA-IR) ≥ 2.69 and HOMA-IR < 2.69, PCOS group members were divided into insulin resistance group and non-insulin resistance group. Anthropometric and serological characteristics of the population with PCOS focused on calculating different obesity metabolic indexes and HOMA-IR. BMI, WC, WHR, WHtR, CMI, BRI, VAI, and LAP were significantly higher in PCOS patients than in the control group, and the differences were all statistically significant (P < 0.05); In the insulin resistance group of PCOS patients, BMI, WC, WHR, WHtR, CMI, BRI, VAI, and LAP were significantly higher than in the non-insulin resistance group (P < 0.05). In PCOS patients, BMI (r = 0.658, P < 0.001), WC (r = 0.0.662, P < 0.001), WHR (r = 0.377, P < 0.001), WHtR (r = 0.660, P < 0.001), CMI (r = 0.698, P < 0.001), BRI (r = 0.757, P < 0.001), VAI (r = 0.640, P < 0.001), and LAP (r = 0.767, P < 0.001) were positively correlated with IR. Obesity metabolic indexes associated with PCOS were elevated in the PCOS group compared to the control group, and in the PCOS insulin-resistant group compared to the non-insulin resistant group. Novel obesity metabolic indexes, especially CMI, BRI and LAP, might be more appropriate for evaluating the risk of concurrent IR in PCOS.

Lipid accumulation product is an effective predictor of metabolic syndrome in non-obese women with polycystic ovary syndrome

Objective

To explore the correlation of lipid accumulation product (LAP) with metabolic syndrome (MS) and to assess the predictive value of LAP for MS risk in polycystic ovary syndrome (PCOS) with different body mass index (BMI).

Methods

A total of 242 PCOS patients and 150 controls were recruited and divided into normal-weight, overweight, and obese groups, then further divided into MS and without MS subgroups. Clinical and anthropometric variables and laboratory results were recorded. LAP was calculated from waist circumference (WC) and triglyceride using sex-specific formulae. Logistic regression analysis and receiver operating characteristic (ROC) curve were applied to determine and analyze the predictive value of LAP for MS.

Results

The prevalence of MS among PCOS patients was 45.04%, which was significantly higher than that of the controls (10%). Stratified by BMI, the incidence of MS in the normal-weight, overweight, and obese PCOS groups were 15.58%, 41.43%, and 71.58%, respectively. Logistic regression analysis indicated that LAP was an independent risk factor for MS in both normal-weight and overweight groups; however, the results were not significant in the obese group. ROC curve analysis showed that LAP had an outstanding discrimination index for MS in normal-weight (AUC=0.960, cut-off value=42.5) and overweight (AUC=0.937, cut-off value=47.93) PCOS patients, with a sensitivity of 0.917/0.931 (normal-weight/overweight) and a specificity of 0.969/0.854 (normal-weight/overweight), respectively.

Conclusion

Normal-weight and overweight PCOS patients also have a fairly high incidence of MS and should receive as much attention as obese patients. Compared to applying multiple clinical indicators, LAP is more convenient and facilitates acquiring early and accurate diagnoses of MS among non-obese PCOS patients using fewer MS markers.

Utility of Visceral Adiposity Index and Lipid Accumulation Products to Define Metabolically-Unhealthy Polycystic Ovary Syndrome in Asian Indian Women - A Cross Sectional Study

Background

Polycystic ovary syndrome (PCOS) women are at risk of developing diabetes, cardiovascular disease and metabolic syndrome (MetS) due to insulin resistance (IR) and hyperandrogenism (HA). Both visceral adiposity index (VAI) and lipid accumulation product (LAP) are simple outpatient department-based metric tools that have been introduced to screen PCOS women who are metabolically unhealthy and are at risk of development of MetS.

Aims

The aim of the study was to evaluate VAI and LAP in women with PCOS and to correlate them with metabolic and endocrine markers. The study also assessed these parameters amongst different PCOS phenotypes and determined their usefulness to define metabolically healthy PCOS (MH-PCOS) and metabolically unhealthy PCOS (MU-PCOS).

Settings and Design

The design of the study was a cross-sectional study.

Materials and Methods

Two hundred PCOS women were included in the study, and all the clinical, anthropometric, hormonal, biochemical and metabolic markers were assessed. The cohort was divided into MH-PCOS and MU-PCOS by the modified National Cholesterol Education Programme criteria. VAI and LAP were calculated and correlated with clinical, endocrine and metabolic parameters.

Statistical Analysis Used

Univariate and multivariate logistic regression analysis was used to study the independent role of VAI and LAP to predict MetS. Adjusted and unadjusted odds ratios were calculated. Receiver-operating characteristic (ROC) analysis was done to define cut-offs in Asian Indian women.

Results

VAI and LAP had good ability to correctly discriminate MU-PCOS from MH-PCOS (area under the curve [AUC] [95% confidence interval (CI)]: 0.89 [0.82-0.95]) and (AUC [95% CI [0.81-0.92] =0.86) using ROC, respectively. The sensitivity of VAI and LAP corresponding to the optimal cut-off of ≥2.76 and ≥48.06 (Youden) was 84.09% and 79.55%, respectively. Similarly, the specificity of VAI and LAP was 85.26% and 79.49%, respectively. VAI has a positive predictive value of 61.7% (95% CI [23.7%-40.3%]) and a negative predictive value of 95% (95% CI [88%-99.1%]). LAP has a positive predictive value of 53% (95% CI [40.3%-65.4%]) and a negative predictive value of 93.3% (95% CI [87.6%-96.9%]). PCOS women having VAI ≥ 2.76 had 19.3 times ([95% CI: 6.50-57.70]) more chance of developing MetS. PCOS women having LAP (≥48.06) have 3.7 times ([95% CI: 1.35-10.60]) more odds. There was no difference between ROC curves of VAI and LAP (P = 0.32).

Conclusion

VAI cut-off ≥ 2.76 and LAP with a cut-off of ≥ 48.06 may be used as markers for predicting MetS amongst PCOS women.

Association of lipid profile and obesity in patients with polycystic ovary syndrome

Objective. Abnormal lipid profile and obesity increase the risk of polycystic ovary syndrome (PCOS). PCOS patients may have a greater risk of infertility, metabolic syndrome (MetS) and cardiovascular disease (CVD) due to abnormal lipid profile and obesity. The aim of the study was to find the association between abnormal lipid profile and obesity in patients with PCOS. Methods. In this case-control study, a total of 102 female subjects (51 diagnosed PCOS and 51 age-matched healthy controls) were enrolled, aged between 20-40 years. Biochemical parameters such as total cholesterol (TC), triglycerides (TG), low-density lipoprotein-cholesterol (LDL-C), high-density lipoprotein-cholesterol (HDL-C), very low-density lipoprotein-cholesterol (VLDL-C), luteinizing hormone (LH), and follicle-stimulating hormone (FSH) were estimated. Anthropometric parameters such as body mass index (BMI), waist circumference (WC), hip circumference (HC), and waist-to-hip ratio (WHR) were recorded. A p<0.05 was considered statistically significant. Results. Mean of BMI, WC, WHR, LH, FSH, TC, TG, LDL-C, and VLDL-C was found significantly elevated in patients with PCOS as compared to controls (p<0.01). However, the mean of HDL-C was found significantly reduced in patients with PCOS as compared to controls (p<0.01). BMI has shown a significant positive correlation with WC (r=0.562, p<0.01) and WHR (r=0.580, p<0.01) among PCOS patients. LH has shown a significant positive correlation with FSH (r=0.572, p<0.01) among PCOS patients. TC has shown a significant positive correlation with TG (r=0.687, p<0.01), LDL-C (r=0.917, p<0.01), and VLDL-C (r=0.726, p<0.01) among PCOS patients. Conclusion. The results showed that abnormal lipid profile and obesity have a significant association with PCOS patients. Regular monitoring and treatment of PCOS patients are required to reduce the risk of infertility, MetS, and CVD.

Insulin level, lipid profile, and HOMA index in lean and obese patients with polycystic ovary syndrome

Polycystic ovary syndrome (PCOS) is a prevalent endocrinopathy characterized by insulin resistance, hyperinsulinemia, and increased ovarian androgen production. While obesity has been linked to the pathogenesis of PCOS, the condition is also observed in individuals with normal BMI. This cross-sectional study aimed to evaluate the biochemical profile of lean and obese patients with PCOS. Fifty female patients with previously diagnosed PCOS were included in the study, attending the outpatient clinic at AL-Zahraa Teaching Hospital in Al-Najaf between September 2021 and March 2022. Blood samples were collected from each patient to assess insulin levels, lipid profiles, and fasting blood sugar. The results showed a comparable percentage of lean and obese PCOS patients, with a slightly higher proportion of obese individuals. Statistically significant differences were observed in obese patients with higher fasting blood sugar levels, insulin, HDL cholesterol, and triglycerides. Additionally, the HOMA index, an indicator of insulin resistance, was higher in obese individuals. Lean PCOS patients exhibited metabolic, hormonal, and hematopoietic dysregulations comparable to or less pronounced than those affecting the obese phenotype. Regardless of BMI, insulin resistance is part of PCOS and must be treated immediately.

Value of the triglyceride-glucose index and non-traditional blood lipid parameters in predicting metabolic syndrome in women with polycystic ovary syndrome

PURPOSE

Insulin resistance (IR) is common in patients with polycystic ovary syndrome (PCOS). Metabolic syndrome (MS) includes, inter alia, IR, hypertension, dyslipidemia, and disturbances in glucose metabolism. The triglyceride-glucose (TyG) index and non-traditional lipid parameters are strong predictors of IR and cardiovascular disease and can be considered as screening indicators for MS. This study aimed to evaluate the predictive potential of non-traditional lipid parameters and the TyG index to identify MS in PCOS.

METHODS

This cross-sectional study included 134 women diagnosed with PCOS (50 patients with comorbid MS and 84 patients without MS). Biochemical indices were collected, and triglycerides (TG)/high-density lipoprotein cholesterol (HDL-C), total cholesterol (TC)/HDL-C, low-density lipoprotein cholesterol (LDL-C)/HDL-C, non-HDL-C, TyG, and TyG-BMI indices were calculated. Logistic regression analysis was used to compare and determine the association of the six parameters with MS, and the receiver operating characteristic (ROC) curve was used to evaluate the performance of each parameter in identifying MS in the PCOS population.

RESULTS

After adjusting for age and body mass index (BMI), TG/HDL-C, TC/HDL-C, LDL-C/HDL-C, non-HDL-C, TyG, and TyG-BMI were associated with MS (all P<0.05). The odds ratios were 4.075 (0.891, 1.107), 3.121 (1.844, 5.282), 3.106 (1.734, 5.561), 2.238 (1.302, 3.848), 13.422 (4.364, 41.282), and 1.102 (1.056, 1.150), respectively. TG/HDL-C, TC/HDL-C, LDL-C/HDL-C, non-HDL-C, TyG, and TyG-BMI are effective predictors of MS in PCOS, and their cut-off values can be used for the early detection of MS. TyG-BMI had the strongest performance in predicting MS (area under the curve 0.905, 95% CI 0.855-0.956), and its optimal critical value for predicting MS was 202.542.

CONCLUSIONS

TG/HDL-C, TC/HDL-C, LDL-C/HDL-C, non-HDL-C, TyG, and TyG-BMI are novel, clinically convenient and practical markers for the early identification of MS risk in PCOS patients.