TL;DR
The findings do not support the use of SII as a diagnostic or screening biomarker for dysglycemia and neither index correlated with OGTT-derived insulin sensitivity or early insulin secretion, limiting their clinical utility for detecting dysglycemia.
bjective: Low-grade chronic inflammation contributes to residual cardiometabolic risk in type 2 diabetes (T2DM), even among patients who achieve glycemic targets. Dietary Inflammatory Index (DII) quantifies the inflammatory potential of habitual diet; however, its association with systemic inflammatory markers in well-controlled T2DM remains unclear. This study aimed to investigate the association between DII scores and circulating inflammatory markers in adults with well-controlled T2DM.Material and Methods: This cross-sectional study included 120 adults with T2DM and 30 healthy controls. Dietary intake was assessed using a validated 7-day food frequency questionnaire, and DII scores were calculated. Patients with T2DM were stratified into DII quartiles. Serum levels of interleukin-6 (IL-6), tumor necrosis factor-alpha (TNF-α), and high-sensitive C-reactive protein (hs-CRP), along with metabolic parameters, were measured. Associations were evaluated using correlation and regression analyses, and effect sizes were estimated using Cohen’s d.Results: DII scores were positively correlated with IL-6 (r=0.421, p<0.001) and hs–CRP (r=0.312, p=0.002). IL 6 levels increased progressively across DII quartiles (p for trend <0.001), with a large effect size between the lowest and highest quartiles (Cohen’s d=0.84). Higher DII scores were associated with lower intakes of dietary fiber, polyunsaturated fatty acids, magnesium, and vitamin C (all p<0.05). Serum uric acid levels also increased across DII quartiles (p=0.044). No significant association was observed between DII and TNF-α.Conclusions: In adults with well-controlled T2DM, a more proinflammatory diet is associated with higher systemic inflammation, particularly elevated IL 6 levels. These findings suggest that dietary inflammatory potential may contribute to residual cardiometabolic risk beyond glycemic control.
Obesity-associated cardiometabolic risk involves a complex interplay of systemic inflammation and atherogenic dyslipidemia that escalates with increasing adiposity. This study investigated the relationship between obesity severity and established hematological inflammatory indices and lipid-based metabolic markers, and derived a composite score (CORI_Ridge) as a proof of concept for integrating these routine parameters. In this retrospective multicenter cohort study, 1,107 adults with obesity were stratified by BMI into Grade I ( n = 475), Grade II ( n = 396), and Grade III ( n = 236) categories. Inflammatory indices (NLR, PLR, MLR, SII, SIRI, AISI) and metabolic indices (Castelli Risk Index-1 and − 2, AIP) were calculated from routine complete blood count and fasting lipid parameters. CORI_Ridge was derived via Ridge regression with continuous BMI as the dependent variable. Because the score was derived in the same cohort, performance was evaluated not only apparently but with a strict out-of-sample framework: 50 repeated stratified 70:30 holdout splits in which z-score standardization, λ selection and weight derivation were repeated from scratch within each training partition. Progressive and significant increases were observed across obesity grades in neutrophil counts, platelet counts, SII, AISI, Castelli Risk Index-1, Castelli Risk Index-2, and AIP (all p < 0.001); metabolic indices showed a true dose-response gradient, whereas inflammatory indices escalated predominantly at Grade III. CORI_Ridge correlated most strongly with obesity severity (Spearman ρ = 0.420, p < 0.001) and achieved the highest apparent discrimination of Grade III obesity (AUC 0.796, 95% CI 0.763–0.829), significantly above every individual index (all DeLong p < 0.05). Out-of-sample, CORI_Ridge achieved a median AUC of 0.779 (IQR 0.763–0.793). A simple reference model of waist circumference, fasting glucose and triglycerides performed better (median AUC 0.827), and adding CORI_Ridge to it yielded a small increment (median AUC 0.845) that reached statistical significance in only 6 of 50 splits (median DeLong p = 0.38). Systemic inflammatory and atherogenic burden escalates progressively with obesity severity. CORI_Ridge — derived from universally available routine laboratory parameters — integrates this signal into a single graded score with moderate out-of-sample discrimination for Grade III obesity; it does not, however, exceed the discriminative performance of a simple three-variable clinical model and should be regarded strictly as a proof of concept. External validation against clinical outcomes is required before any clinical application can be considered.
BACKGROUND Childhood obesity is strongly associated with insulin resistance (IR) and related metabolic complications. However, the optimal surrogate marker for identifying IR in pediatric populations remains unclear. OBJECTIVE To evaluate the diagnostic performance of commonly used IR indices and to investigate their association with metabolic outcomes in children and adolescents with obesity. METHODS This retrospective study included 899 children and adolescents with obesity followed at a tertiary pediatric endocrinology center over a 10-year period. IR was defined based on oral glucose tolerance test (OGTT) findings. The diagnostic performance of the fasting glucose-to-insulin ratio (FGIR), quantitative insulin sensitivity check index (QUICKI) and the homeostatic model assessment for insulin resistance (HOMA-IR) was assessed using receiver operating characteristic (ROC) curve analysis. Associations between IR, metabolic syndrome (MS), dysglycemia, hepatic steatosis, and biochemical parameters were analyzed. Multivariate logistic regression was performed to identify independent predictors. RESULTS HOMA-IR demonstrated the highest diagnostic performance for identifying IR (AUC: 0.711 in girls and 0.700 in boys), outperforming FGIR and QUICKI. Among pubertal participants, the optimal HOMA-IR cut-off values for predicting OGTT-defined insulin resistance were 4.22 in girls and 4.18 in boys. IR was present in 76.3% of participants and was associated with an unfavorable metabolic profile, including higher rates of dysglycemia and hepatic steatosis. Hepatic steatosis was detected in 63.5% of children and was significantly more common in boys. In multivariate analysis, male sex, BMI-SDS, and ALT were independent predictors of hepatic steatosis, whereas IR parameters were not independently associated. TSH levels were higher in IR and MS groups but remained within the normal range. CONCLUSION HOMA-IR is a reliable and practical marker for identifying IR in children with obesity. While IR is associated with multiple metabolic disturbances, adiposity and liver-related markers appear to play a more dominant role in predicting hepatic steatosis. A comprehensive metabolic evaluation, including both fasting indices and dynamic tests, is essential for early risk stratification in pediatric obesity.
Insulin resistance (IR) is a key pathogenic mechanism in Type 2 Diabetes Mellitus, often linked to obesity-related inflammation. However, IR can also occur in individuals with normal weight, a condition known as the metabolically obese normal-weight phenotype. This study assesses the predictive value of routine hematological parameters compared to combined inflammation indices for detecting IR in non-obese populations. This retrospective cross-sectional study included 305 normal-weight adults (body mass index < 25 kg/m²). IR was defined by a Homeostasis Model Assessment of Insulin Resistance (HOMA-IR) threshold of 2.5. Parameters were analyzed using univariate and hierarchical multivariable logistic regression. Parallel models, adjusted for age, sex, and body mass index (BMI), were used to assess individual leukocyte subtypes. Discriminatory performance was evaluated through Receiver Operating Characteristic (ROC) curve analysis. IR was found in 93 (30.5%) participants. The IR+ group exhibited significantly higher BMI, glucose, triglycerides, HbA1c, and white blood cell (WBC) counts (p < 0.05). Multivariable analysis identified WBC count as the only independent predictor of inflammation (OR = 1.946, p < 0.001). WBC count showed better explanatory power (Nagelkerke R2 = 0.196) and discriminatory ability (AUC = 0.725) compared to individual subtypes and composite indices, like the panimmune inflammation value (AUC = 0.510). The final multivariable model achieved an AUC of 0.736. In normal-weight adults, IR appears to exhibit a more pronounced association with generalized systemic inflammation reflected by absolute WBC counts rather than ratio-based indices. WBC count serves as a practical marker for early metabolic risk identification in non-obese individuals.
Background: Childhood obesity is a growing public health concern worldwide and is strongly associated with insulin resistance, impaired glucose metabolism, and cardiometabolic complications. Data regarding glycemic status and insulin resistance among obese Bangladeshi children remain limited. Objective: This study aimed to evaluate the demographic, clinical and laboratory parameters of obese children according to glycemic status. Methods: This cross-sectional study was conducted in the Paediatric Endocrinology Division, Department of Paediadtrics, Bangladesh Medical University (former BSMMU), from 2021 to 2022. A total of 150 newly diagnosed obese children aged 6–18 years were enrolled using purposive sampling. Participants were categorized into euglycemia, prediabetes, and diabetes mellitus (DM) groups. Statistical analyses were performed using ANOVA and Chi-square tests, with p<0.05 considered significant. Results: Among 150 obese children, 100 (66.7%) had euglycemia, 43 (28.7%) had prediabetes, and 7 (4.7%) had diabetes mellitus. Mean BMI increased significantly from the euglycemia group (26.4±2.6 kg/m²) to the prediabetes group (28.1±2.9 kg/m²) and DM group (32.3±3.4 kg/m²) (p<0.001). Waist circumference was significantly higher among children with abnormal glycemic status (p=0.015). Laboratory parameters demonstrated progressive metabolic deterioration across glycemic categories. Mean fasting insulin levels were 15.86±5.07, 23.91±6.62, and 35.96±11.36 μU/mL in the euglycemia, prediabetes, and DM groups, respectively (p<0.001). Similarly, mean HOMA-IR values increased significantly from 3.32±0.93 in the euglycemia group to 4.86±1.69 in the prediabetes group and 13.71±5.44 in the DM group (p<0.001). Significant differences were also observed in FBS, OGTT glucose level, and HbA1c among the groups (all p<0.001). Conclusion: Obese children with prediabetes and diabetes exhibit significantly higher BMI, waist circumference, fasting insulin levels, and HOMA-IR values than euglycemic obese children.
Introduction: The aim of this study was to compare metabolic parameters, inflammatory markers, indicators of iron metabolism and micronutrient levels among groups of obese individuals categorised according to the Triglyceride-Glucose (TyG) index, and to assess the relationship between the TyG index and these parameters. Methods: The data of 110 obese individuals with a BMI ≥ 30 kg/m² who presented at Eskişehir City Hospital were retrospectively analysed. Patients were divided into two groups according to the TyG index (Group 1: TyG < 8.85, n=48; Group 2: TyG ≥ 8.85, n=62). Demographic, anthropometric and laboratory data were compared. The diagnostic performance of the TyG index in predicting insulin resistance (HOMA-IR > 2.5) was assessed using ROC analysis. Results: In Group 2, BMI (37.8±4.5 vs. 32.4±2.1 kg/m²), waist circumference (114.2±12.6 vs. 98.6±8.4 cm), HOMA-IR (5.12±1.64 vs. 2.38±0.72), CRP (5.9±1.8 vs. 2.2±0.9 mg/L), ferritin (128±34 vs. 45±12 ng/mL) and uric acid (6.4±1.2 vs. 4.8±0.8 mg/dL) were significantly higher; serum iron (58±22 vs. 85±18 μg/dL), vitamin D (14.2±6.1 vs. 22.4±8.6 ng/mL), vitamin B12 (228±74 vs. 312±88 pg/mL), folate (7.2±2.6 vs. 9.8±3.1 ng/mL) and magnesium (1.75±0.3 vs. 2.08±0.2 mg/dL) were found to be significantly low. The strongest positive correlations with TyG were observed with HOMA-IR (r=+0.67) and CRP (r=+0.61). In the ROC analysis, the AUC value of the TyG index for predicting insulin resistance was 0.85 (p<0.001), and the optimal cut-off value was determined to be 8.85 (sensitivity 76.8%, specificity 81.2%). In the gender-based subgroup analysis, elevated uric acid levels were more pronounced in men (p=0.003 for interaction). Conclusion: The TyG index is a comprehensive metabolic marker that reflects insulin resistance, alongside inflammation, iron metabolism disorders and micronutrient deficiencies, in obese individuals. The use of the TyG index in the metabolic risk assessment of obese individuals may provide clinical benefits, particularly in the early detection of functional iron deficiency and micronutrient deficiencies.
We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.
