Association between insulin resistance and haematological parameters: A cohort study from adolescence to adulthood

Joint association of weight-adjusted-waist index and physical activity with insulin resistance in adolescents: a cross-sectional study

BACKGROUND

The weight-adjusted waist index (WWI) is a recently developed obesity metric, and the aim of this study was to investigate the relationship between physical activity (PA) and WWI and the homeostasis model assessment of insulin resistance (HOMA-IR) in adolescents, as well as the joint association of HOMA-IR.

METHODS

This study was based on the National Health and Nutrition Survey conducted between 2013 and 2016 and included 1024 adolescents whose median age was 15.4. Multivariate linear regression was used to examine the associations between HOMA-IR and PA and WWI. Using generalized additive models, a potential nonlinear link between WWI and HOMA-IR was evaluated. Subgroup analysis was also carried out.

RESULTS

The fully adjusted model revealed a positive association (β: 0.48, 95% CI: 0.43, 0.53) between the WWI and HOMA-IR. The HOMA-IR was lower in physically active (β: -0.16, 95% CI: -0.26, -0.05) participants versus inactive participants. Participants who had higher WWI and were not physically active (β: 0.69; 95% CI: 0.56, 0.82) had the highest levels of HOMA-IR compared to participants who had lower WWI and were physically active. Subgroup analysis revealed that these correlations were similar in males and females.

CONCLUSION

Our results demonstrated that higher WWI and PA were associated with a lower HOMA-IR and that WWI and PA had a combined association with HOMA-IR. The findings of this study are informative for the preventing insulin resistance in adolescents.

A high reticulocyte count is a risk factor for the onset of metabolic dysfunction-associated steatotic liver disease: Cross-sectional and prospective studies of data of 310,091 individuals from the UK Biobank

Background and Aims: Metabolic dysfunction-associated steatotic liver disease (MASLD) poses a considerable health risk. Nevertheless, its risk factors are not thoroughly comprehended, and the association between the reticulocyte count and MASLD remains uncertain. This study aimed to explore the relationship between reticulocyte count and MASLD. Methods: A total of 310,091 individuals from the UK Biobank were included in this cross-sectional study, and 7,316 individuals were included in this prospective study. The cross-sectional analysis categorized reticulocyte count into quartiles, considering the sample distribution. Logistic regression models examined the connection between reticulocyte count and MASLD. In the prospective analysis, Cox analysis was utilized to investigate the association. Results: Our study findings indicate a significant association between higher reticulocyte count and an elevated risk of MASLD in both the cross-sectional and prospective analyses. In the cross-sectional analysis, the adjusted odds ratios (ORs) of MASLD increased stepwise over reticulocyte count quartiles (quartile 2: OR 1.22, 95% CI 1.17-1.28, p < 0.001; quartile 3: OR 1.44; 95% CI 1.38-1.51, p < 0.001; quartile 4: OR 1.66, 95% CI 1.59-1.74, p < 0.001). The results of prospective analyses were similar. Conclusion: Increased reticulocyte count was independently associated with a higher risk of MASLD. This discovery offers new insights into the potential of reticulocytes as biomarkers for MASLD.

Simple Method to Predict Insulin Resistance in Children Aged 6-12 Years by Using Machine Learning

BACKGROUND

Due to the increasing insulin resistance (IR) in childhood, rates of diabetes and cardiovascular disease may rise in the future and seriously threaten the healthy development of children. Finding an easy way to predict IR in children can help pediatricians to identify these children in time and intervene appropriately, which is particularly important for practitioners in primary health care.

PATIENTS AND METHODS

Seventeen features from 503 children 6-12 years old were collected. We defined IR by HOMA-IR greater than 3.0, thus classifying children with IR and those without IR. Data were preprocessed by multivariate imputation and oversampling to resolve missing values and data imbalances; then, recursive feature elimination was applied to further select features of interest, and 5 machine learning methods-namely, logistic regression (LR), support vector machine (SVM), random forest (RF), extreme gradient boosting (XGBoost), and gradient boosting with categorical features support (CatBoost)-were used for model training. We tested the trained models on an external test set containing information from 133 children, from which performance metrics were extracted and the optimal model was selected.

RESULTS

After feature selection, the numbers of chosen features for the LR, SVM, RF, XGBoost, and CatBoost models were 6, 9, 10, 14, and 6, respectively. Among them, glucose, waist circumference, and age were chosen as predictors by most of the models. Finally, all 5 models achieved good performance on the external test set. Both XGBoost and CatBoost had the same AUC (0.85), which was highest among those of all models. Their accuracy, sensitivity, precision, and F1 scores were also close, but the specificity of XGBoost reached 0.79, which was significantly higher than that of CatBoost, so XGBoost was chosen as the optimal model.

CONCLUSION

The model developed herein has a good predictive ability for IR in children 6-12 years old and can be clinically applied to help pediatricians identify children with IR in a simple and inexpensive way.

Albuminuria Is Associated with Hepatic Iron Load in Patients with Non-Alcoholic Fatty Liver Disease and Metabolic Syndrome

Background: Increased albuminuria is associated with increased serum ferritin, insulin resistance, and non-alcoholic fatty liver disease (NAFLD). Liver iron accumulation is also related to hyperferritinemia, insulin resistance, and NAFLD; however, there is no evidence on its relationship with albuminuria. Aims: To assess the relationship between hepatic iron load and urine albumin-to-creatinine ratio (UACR) in patients with metabolic syndrome (MetS) and NAFLD. Methods: In total, 75 MetS and NAFLD patients (aged 40–60 years, BMI 27–40 kg/m²) were selected from a cohort according to available data on hepatic iron load (HepFe) by magnetic resonance imaging (MRI). Subjects underwent anthropometric measurements, biochemistry testing, and liver MRI. Increased albuminuria was defined by UACR. Results: UACR correlated with NAFLD, HepFe, triglycerides, serum ferritin, fasting insulin, insulin resistance (calculated using the homeostatic model assessment for insulin resistance—HOMA-IR- formula), and platelets (p < 0.05). Multiple regression analysis adjusted for gender, age, eGFR, HbA1c, T2DM, and stages of NAFLD, found that HepFe (p = 0.02), serum ferritin (p = 0.04), fasting insulin (p = 0.049), and platelets (p = 0.009) were associated with UACR (R² = 0.370; p = 0.007). UACR, liver fat accumulation, serum ferritin, and HOMA-IR increased across stages of HepFe (p < 0.05). Patients with severe NAFLD presented higher HepFe, fasting insulin, HOMA-IR, and systolic blood pressure as compared to patients in NAFLD stage 1 (p < 0.05). Conclusion: Hepatic iron load, serum ferritin, fasting insulin, and platelets were independently associated with albuminuria. In the context of MetS, increased stages of NAFLD presented higher levels of HepFe. Higher levels of HepFe were accompanied by increased serum ferritin, insulin resistance, and UACR. The association between iron accumulation, MetS, and NAFLD may represent a risk factor for the development of increased albuminuria.