Recent evidence shows that simple and inexpensive anthropometric measurements can be used to identify, at an early stage, women with obesity at increased risk of developing metabolic syndrome (MetS). Thus, the aim of this study was to compare the accuracy of five different indexes of adiposity and/or body composition in identifying MetS in a group of 876 women (mean age ± SD: 52.1 ± 13.8 years; body mass index (BMI): 43.6 ± 6.1 kg m⁻²). The following indexes were determined for each subject: waist-to-hip ratio (WHR), waist-to-height ratio (WtHR), body mass fat index (BMFI), visceral adiposity index (VAI), and cardiometabolic index (CMI). Overall, the presence of MetS was detected in 544 patients (62%). Pearson correlation coefficients were calculated to evaluate the relationships between body composition indexes and metabolic characteristics of the women. Receiver operating characteristic (ROC) analysis was used to determine the best predictor for each adiposity index among metabolic risk factors. The ROC analysis showed VAI (AUC = 0.84) and CMI (AUC = 0.86) showed the best performance in predicting MetS. Differences were found between the ROC area of CMI and VAI with all other indexes (p < 0.05). The optimal cutoff point for early diagnosis of MetS was >0.92 for WHR, >0.76 for WtHR, >30.1 kg m⁻¹ for BMFI, >1.94 for VAI, and >0.84 for CMI. In addition, VAI and CMI were the most sensitive and specific indexes compared with other indexes. In conclusion, VAI and CMI represent the most useful and reliable indexes to be used for detecting MetS in women suffering from obesity in clinical practice.

Background: The concomitant occurrence of obesity and metabolic syndrome (MetS) causes a significant worsening of a patient’s clinical condition. Indexes that employ anthropometric measurements alone or associated with blood parameters have been investigated for their ability to identify MetS. This study aimed to evaluate the diagnostic accuracy of three of these indexes, the body adiposity index (BAI), the lipid accumulation product index (LAP), and the cardiometabolic index (CMI), in a cohort of 1912 adult subjects with obesity. Methods and Results: MetS was found in 62.3% of the enrolled subjects, with a higher prevalence in males (72.5%) than females (60.9%). Receiver operating characteristic (ROC) analysis was used to define which index performed better. The BAI was found to be the lowest-performing index, with an ROC area of 0.50, a sensitivity of 30.31%, a specificity of 74.48%, and a likelihood ratio of 1.19. On the contrary, the LAP and the CMI showed a comparable ROC area of 0.82. The LAP had a sensitivity of 63.06%, a specificity of 86.55%, and a likelihood ratio of 4.69, while the CMI had a sensitivity of 67.59%, specificity of 81.55%, and a likelihood ratio of 3.66. The analysis was also performed in the group divided into males and females, with overlapping results. Conclusions: The LAP and the CMI performed better than the BAI in detecting MetS both in the general population with obesity and in the male/female subgroups. In the future, it will be important to validate these useful diagnostic tools in order to employ them in clinical practices.

Background: Metabolic syndrome (MetS) is a globally increasing pathological condition. Recent research highlighted the utility of complete blood count-derived (CBC) inflammation indexes to predict MetS in adults with obesity. Methods: This study examined CBC-derived inflammation indexes (NHR, LHR, MHR, PHR, SIRI, AISI, and SII) in 231 adults with severe obesity (88 males, 143 females; age: 52.3 [36.4–63.3] years), divided based on the presence (MetS+) or absence (MetS-) of MetS. The relationships between the indexes and the cardiometabolic risk biomarkers HOMA-IR, TG/HDL-C, and non-HDL-C were also evaluated. Results: Individuals with metabolic syndrome (MetS+) had significantly higher values of MHR, LHR, NHR, PHR, and SIRI than those without (MetS-) (MHR and NHR: p < 0.0001; LHR: p = 0.001; PHR: p = 0.011; SIRI: p = 0.021). These values were positively correlated with the degree of MetS severity. Logistic regression (MHR and NHR: p = 0.000; LHR: p = 0.002; PHR: p = 0.022; SIRI: p = 0.040) and ROC analysis (MHR: AUC = 0.6604; LHR: AUC = 0.6343; NHR: AUC = 0.6741; PHR: AUC = 0.6054; SIRI: AUC = 0.5955) confirmed the predictive potential of CBC-derived inflammation indexes for MetS in individuals with severe obesity. CBC-derived inflammation indexes also correlated with HOMA-IR (MHR, LHR, and NHR: p < 0.0001; PHR: p < 0.001; SIRI: p = 0.000) and TG/HDL-C (MHR, LHR, NHR and PHR: p < 0.0001; SIRI: p = 0.006). Conclusions: In conclusion, this study validates CBC-derived inflammation indexes for predicting MetS in individuals with severe obesity. The relationships between these indexes and cardiometabolic risk factors can enable clinicians to better grade MetS associated with obesity.

Background: Childhood obesity is a globally increasing pathological condition leading to long-term health issues such as cardiovascular diseases and metabolic syndrome (MetS). This study aimed to determine the clinical value of the Complete Blood Count-derived inflammation indexes Monocyte/HDL-C ratio (MHR), Lymphocyte/HDL-C ratio (LHR), Neutrophil/HDL-C ratio (NHR), and System Inflammation Response Index (SIRI) to predict the presence of metabolic syndrome and its association with cardiovascular risk markers (HOMA-IR, TG/HDL-C, and non-HDL-C) in children and adolescents with obesity. Methods: The study included a total of 552 children/adolescents with severe obesity (BMI: 36.4 [32.7–40.7] kg/m²; 219 males, 333 females; age: 14.8 [12.9−16.3] years), who were further subdivided based on the presence or absence of metabolic syndrome (MetS+ and MetS respectively). Results: The MHR, LHR, and NHR indexes (p < 0.0001), but not SIRI (p = 0.524), were significantly higher in the MetS+ compared to the MetS− subgroup, showing a positive correlation with the degree of MetS severity (p < 0.0001). Furthermore, MHR, LHR, and NHR were positively associated with cardiometabolic risk biomarkers (HOMA-IR: MHR p = 0.000, LHR p = 0.001, NHR p < 0.0001; TG/HDL-C: MHR, LHR, NHR p < 0.000; non-HDL-C: MHR, LHR p < 0.0001, NHR p = 0.000). Finally, the ROC curve analysis demonstrated that among the analyzed indexes, only MHR, LHR, and NHR had diagnostic value in distinguishing MetS patients among children and adolescents with obesity (MHR: AUC = 0.7045; LHR: AUC = 0.7205; NHR: AUC = 0.6934; p < 0.0001). Conclusions: In conclusion, the MHR, LHR, and NHR indexes, but not the SIRI index, can be considered useful tools for pediatricians to assess the risk of MetS and cardiometabolic diseases in children and adolescents with obesity and to develop multidisciplinary intervention strategies to counteract the widespread disease.

Few data are currently available on the reliability of the different anthropometric, instrumental and biochemical indexes in recognizing the presence of metabolic syndrome (MetS) in children and adolescents with severe obesity. Therefore, the objective of our study was to find out the simplest and most accurate predictive index of MetS in this population at-risk. In 1065 children and adolescents (563 f, 502 m), aged 14.6 ± 2.1 years (range 10–17), with severe obesity [BMI-SDS 3.50 ± 0.36 (range 3.00–5.17)], the following indexes were evaluated: BMI, BMI-SDS, Tri-Ponderal Mass Index, Waist-to-Height ratio, TG/HDL-Cholesterol ratio, Cardiometabolic Index (CMI), and Visceral Adiposity Index (VAI). For each subject, all the components of MetS, defined according to the IDF criteria, were determined. Overall, the presence of MetS was found in 324 patients (30.4%), 167 males (33.3%) and 157 females (27.9%). According to the ROC analysis, three indexes (VAI, CMI and TG/HDL-Cholesterol ratio), performed significantly better than the other ones in identifying MetS, with no difference among them. In conclusion, the TG/HDL ratio, which just needs the evaluation of two simple biochemical parameters, offers the same accuracy as other more sophisticated indexes in recognizing MetS in children and adolescents with severe obesity, thus making it the best predictor to be easily used.

Background: Metabolic-associated fatty liver disease (MASLD) and metabolic syndrome (MetS) are increasingly prevalent among children and adolescents with obesity, posing significant long-term metabolic and cardiovascular risks. Non-invasive identification of at-risk individuals is crucial for a timely intervention. This study aimed to evaluate the diagnostic performance of the triglyceride-glucose (TyG) index and its modified versions, TyG-body mass index (TyG-BMI) and TyG-waist circumference (TyG-WC), in predicting MASLD and MetS in a large cohort of children and adolescents with obesity. Methods: A total of 758 children and adolescents with obesity (454 females, 304 males; mean age 14.8 ± 2.1 years; mean BMI 37.9 ± 6.2 kg/m²) were included. MASLD was diagnosed via ultrasonography, while MetS was defined using International Diabetes Federation criteria. TyG, TyG-WC, and TyG-BMI were calculated for all participants. Receiver operating characteristic (ROC) curves were generated to assess the diagnostic accuracy of these indexes, including sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV). Results: MASLD was detected in 38.9% of participants, with a higher prevalence in males (p < 0.0001). MetS was present in 27.8% of the cohort, with higher prevalence in males (p < 0.0001). Among the indexes, TyG-WC exhibited the highest sensitivity for MASLD (77.6%), whereas TyG-BMI had the highest specificity (63.3%). In predicting MetS, all three indexes performed better than for MASLD, with TyG demonstrating the highest PPV (54.5%) and TyG-BMI the highest NPV (87.5%). Predictive performance was lower in males than females, potentially due to sex-specific differences in fat distribution and metabolic response. Conclusions: TyG, TyG-WC, and TyG-BMI are promising, non-invasive tools for identifying children and adolescents with obesity at risk for MASLD and MetS. The superior sensitivity of TyG-WC and the high specificity of TyG-BMI highlight the value of incorporating anthropometric parameters into metabolic screening. Integrating these indexes into routine clinical practice may enhance early detection, allowing for timely intervention and personalized management strategies, ultimately reducing the long-term burden of metabolic and liver diseases in pediatric populations.