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

Ferreira, Daniela; Severo, Mílton; Araújo, Joana; Barros, Henrique; Guimarães, João Tiago; Ramos, Elisabete

doi:10.1002/dmrr.3194

Cited by 7 publications

(8 citation statements)

References 39 publications

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“…Other studies demonstrated that circulating RBC could be used to re ect short-term uctuations in insulin-receptor a nity and long-term changes in receptor concentration [37]. Accumulating population-based evidence further support that high levels of RBC count and Hb are positively associated with underlying pathophysiological changes of hyperglycemia, including insulin resistance [14,15], β-cell dysfunction [15], and decreased insulin sensitivity [15]. HbA1c level is also affected by RBC lifespan and positively correlated with the RBC count since the lifespan of RBCs can directly affect the reaction time of glycosylation between Hb and blood glucose [38].…”

Section: Discussionmentioning

confidence: 95%

“…Hb is the speci c protein molecule in red blood cell (RBC) carrying almost all of oxygen molecules and is closely related to the number of RBC [13]. Previous studies have con rmed that RBC count is signi cantly associated with insulin resistance [14], β-cell dysfunction [15], and the incidence of type 2 diabetes [16], which suggest that peripheral RBC count may also play a role in GDM development. However, few studies have examined the relationship between RBC count in early pregnancy and the risk of GDM.…”

Section: Introductionmentioning

confidence: 99%

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Red Blood Cells in The First Trimester and The Risk of Gestational Diabetes: A Prospective Cohort Study

Jia

Yang

Zhang³

et al. 2021

Preprint

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Background: This research aimed to assess the potential association of gestational diabetes (GDM) with early trimester hematological parameters including hemoglobin (Hb), red blood cell (RBC), white blood cell (WBC), and platelet count (PLT) through a prospective cohort study.Methods: The prospective cohort included pregnant women subjected to prenatal examination at Shantou and Beijing hospitals in China from March 2014 to December 2015. Data were collected since the first perinatal visit in obstetrics clinics, and then participants were followed up at 24, 32, 36 gestational weeks and the time of delivery, respectively. Multivariable adjusted logistic regression models were conducted to estimate odds ratio (OR) and its corresponding 95% confidence interval (95% CI).Results: A total of 1004 pregnant women with singletons, less than 12 gestational weeks, and without history of chronic disease were eligible for analysis. The incidence of GDM was 18.82%, and the mean age was 29.50 ± 3.84 years. Total of 187 (18.63%) women who had abnormal RBC level and 222 (22.11%) had abnormal Hb in the first trimester of pregnancy. After multivariable adjustment, each unit increment in numeric RBC or Hb was associated with 177% and 4% increased risk for GDM. The risk for GDM was significantly increased with higher RBC (OR: 2.00 for RBC>4.55×1012 /L) and Hb (OR: 2.14 for Hb>139 g/L) levels in the first trimester.Conclusions: Elevated RBC and Hb in the first trimester are associated with increasing risk of GDM. Further evidence are warranted to confirm these possible causal relationships.

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Section: Discussionmentioning

confidence: 95%

Section: Introductionmentioning

confidence: 99%

Red Blood Cells in The First Trimester and The Risk of Gestational Diabetes: A Prospective Cohort Study

Jia

Yang

Zhang³

et al. 2021

Preprint

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“…On the other hand, previous studies on the association between platelets and UACR are very few; nevertheless, they show an independent relationship between mean platelet volume, increased albuminuria, and other microvascular complications such as retinopathy in T2DM [ 60 , 61 ]. Platelet activity plays a major role in the pathogenesis of endothelial injury and vascular disease [ 62 ]; moreover, it has been associated with hyperglycemia [ 63 ] and insulin resistance [ 64 , 65 , 66 ], which in turn are associated with increased albuminuria [ 11 ]. Although platelet count seems to share common pathways to other risk factors associated with increased albuminuria, in the current analysis, such association was independent of insulin resistance, diabetes, liver fibrosis, and other potential confounders.…”

Section: Discussionmentioning

confidence: 99%

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

Abbate

Montemayor

Mascaró

et al. 2021

JCM

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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.

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“…24,25 Regarding blood tests, previous studies have shown that blood cells, including leukocytes (WBC), erythrocytes (RBC), platelets (PLT), and hemoglobin (HGB), are associated with IR. [26][27][28] These are also tests that can be performed in most primary care hospitals during routine physical examinations, and even by using peripheral blood in many places, which is very child-friendly. In addition, there are also some biochemical tests and genetic tests that have been shown to correlate with IR.…”

Section: Feature Selectionmentioning

confidence: 99%

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

Zhang¹,

Wan²

2022

DMSO

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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.

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

Cited by 7 publications

References 39 publications

Red Blood Cells in The First Trimester and The Risk of Gestational Diabetes: A Prospective Cohort Study

Red Blood Cells in The First Trimester and The Risk of Gestational Diabetes: A Prospective Cohort Study

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

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

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