Macronutrient Intake and Insulin Resistance in 5665 Randomly Selected, Non-Diabetic U.S. Adults

Tucker, Larry A.

doi:10.3390/nu14050918

Cited by 11 publications

(15 citation statements)

References 62 publications

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“…However, HOMA-IR is susceptible to the accuracy of insulin measurements and is poorly reproducible (37). As previously mentioned, METS-IR is insulin independent and diagnostically superior to other non-insulin indexes of insulin resistance (17,38). In this study, we demonstrated that the dataset containing METS-IR had better fit efficacy for LSM and that the simpler, more accurate and more practical nature of METS-IR makes machine learning models built with METS-IR as predictor variables better applicable.…”

Section: Discussionsupporting

confidence: 59%

Machine learning models including insulin resistance indexes for predicting liver stiffness in United States population: Data from NHANES

Han

Tan

Shen

et al. 2022

Front. Public Health

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BackgroundPrevention and treatment of liver fibrosis at an early stage is of great prognostic importance, whereas changes in liver stiffness are often overlooked in patients before the onset of obvious clinical symptoms. Recognition of liver fibrosis at an early stage is therefore essential.ObjectiveAn XGBoost machine learning model was constructed to predict participants' liver stiffness measures (LSM) from general characteristic information, blood test metrics and insulin resistance-related indexes, and to compare the fit efficacy of different datasets for LSM.MethodsAll data were obtained from the National Health and Nutrition Examination Survey (NHANES) for the time interval January 2017 to March 2020. Participants' general characteristics, Liver Ultrasound Transient Elastography (LUTE) information, indicators of blood tests and insulin resistance-related indexes were collected, including homeostasis model assessment of insulin resistance (HOMA-IR) and metabolic score for insulin resistance (METS-IR). Three datasets were generated based on the above information, respectively named dataset A (without the insulin resistance-related indexes as predictor variables), dataset B (with METS-IR as a predictor variable) and dataset C (with HOMA-IR as a predictor variable). XGBoost regression was used in the three datasets to construct machine learning models to predict LSM in participants. A random split was used to divide all participants included in the study into training and validation cohorts in a 3:1 ratio, and models were developed in the training cohort and validated with the validation cohort.ResultsA total of 3,564 participants were included in this study, 2,376 in the training cohort and 1,188 in the validation cohort, and all information was not statistically significantly different between the two cohorts (p > 0.05). In the training cohort, datasets A and B both had better predictive efficacy than dataset C for participants' LSM, with dataset B having the best fitting efficacy [±1.96 standard error (SD), (-1.49,1.48) kPa], which was similarly validated in the validation cohort [±1.96 SD, (-1.56,1.56) kPa].ConclusionsXGBoost machine learning models built from general characteristic information and clinically accessible blood test indicators are practicable for predicting LSM in participants, and a dataset that included METS-IR as a predictor variable would improve the accuracy and stability of the models.

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Section: Discussionsupporting

confidence: 59%

Machine learning models including insulin resistance indexes for predicting liver stiffness in United States population: Data from NHANES

Han

Tan

Shen

et al. 2022

Front. Public Health

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“…Following previous studies, we used additional variables as covariates in our study ( 6 , 15 , 16 ). The demographic information included age, race (Mexican, White, Black, Other races), ratio of family income to poverty (PIR), education level (less than high school, high school, greater than high school).…”

Section: Methodsmentioning

confidence: 99%

“…METS-IR is a recently developed index that aims to be a practical and valid alternative biomarker for insulin resistance (IR) ( 14 ). Studies have demonstrated the diagnostic efficacy of METS-IR over other non-insulin-based insulin resistance indices ( 15 ). However, no studies correlating METS-IR with serum ferritin have yet been reported.…”

Section: Introductionmentioning

confidence: 99%

The Association Between METS-IR and Serum Ferritin Level in United States Female: A Cross-Sectional Study Based on NHANES

Han

Chen

et al. 2022

Front. Med.

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AimThe aim of this study was to investigate the association between the metabolic score for insulin resistance (METS-IR) and serum ferritin in females from the United States.MethodsWe conducted a cross-sectional study with 4,182 participants from the National Health and Nutrition Examination Survey (NHANES). We used METS-IR and serum ferritin as the independent and dependent variables in this study and investigated the relationship by using multiple linear regression and verified the non-linear relationship with a smooth curve fit and threshold effect model.ResultsThere was a positive relationship between METS-IR and serum ferritin, with an effect value of (β = 0.29, 95% CI: 0.14–0.44) in a fully adjusted model adjusted for potential confounders. This positive correlation became more significant as METS-IR increased (p for trend < 0.001). Subsequent subgroup analyses showed that sensitive cohorts were those aged ≥40 years, black, and with a body mass index (BMI) < 24.9 kg/m2. In a smoothed curve fit analysis, the correlation between METS-IR and serum ferritin was a straight linear relationship in all participants included in this study, but when stratified by age, race, and BMI, this positive correlation in the participants who were aged ≥40 years old, other race, and had a BMI < 24.9 kg/m2 was non-linear.ConclusionsThere was a positive association between METS-IR and serum ferritin in United States females, and this positive association was more pronounced in participants aged ≥40 years, black race and BMI < 24.9 kg/m2. This positive association was non-linear in the subgroups aged ≥40 years, white race and BMI < 24.9 kg/m2, with inflection points for METS-IR of 69.97, 67.84 and 35.84 in these respective subgroups.

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“…Therefore, T2D patients need to increase their daily intake of micronutrients [ 34 ]. In this context, several studies have demonstrated the strong antioxidant properties of vitamins that might help in improving the immune response and inflammatory processes [ 35 ]. A particular example would be riboflavin (Vitamin B2).…”

Section: Discussionmentioning

confidence: 99%

The Role of Dietary Intake in Type 2 Diabetes Mellitus: Importance of Macro and Micronutrients in Glucose Homeostasis

et al. 2022

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The prevalence of Type 2 diabetes (T2D) is increasing worldwide. Genetics and lifestyle, especially diet, are contributing factors. Analyses of macro- and micronutrient intake across global populations may help to explain their impact on glucose homeostasis and disease development. To this end, 420 Tunisians were enrolled in a prospective cross-sectional study of daily food consumption. Various data were collected and blood samples were drawn for biochemical assay. A 24-h recall questionnaire was obtained from participants to evaluate dietary intake. Statistical analyses were conducted using Nutrilog and R software. Biochemical analyses stratified the studied population (n = 371) into three groups: diabetics (n = 106), prediabetics (n = 192) and controls (n = 73); 49 subjects were excluded. Our results showed that Tunisians had hypercaloric diets high in carbohydrates and fat with variability in the levels of some vitamins and minerals, including riboflavin and niacin, that were statistically different among groups. The lower intake of vitamin D was associated with a greater risk of T2D. Higher vitamin A and sodium intake were associated with poor glucose homeostasis, although protein intake may improve it. In perspective, nutrigenomic studies can provide insight into problematic diets and poor eating habits and offer opportunities to analyze the effects of behavioral changes that can mitigate T2D development and progression.

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Macronutrient Intake and Insulin Resistance in 5665 Randomly Selected, Non-Diabetic U.S. Adults

Cited by 11 publications

References 62 publications

Machine learning models including insulin resistance indexes for predicting liver stiffness in United States population: Data from NHANES

Machine learning models including insulin resistance indexes for predicting liver stiffness in United States population: Data from NHANES

The Association Between METS-IR and Serum Ferritin Level in United States Female: A Cross-Sectional Study Based on NHANES

The Role of Dietary Intake in Type 2 Diabetes Mellitus: Importance of Macro and Micronutrients in Glucose Homeostasis

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