Dietary copper and selenium are associated with insulin resistance in overweight and obese Malaysian adults

Tan, Pui Yee; Roy, Mitra Soma

doi:10.1016/j.nutres.2021.06.008

Cited by 15 publications

(16 citation statements)

References 44 publications

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“…Dietary copper is a redox active metal and has been shown to cause impaired glucose metabolism in mechanistic and observational studies [22][23][24][25]. Our results conform to another NHANES report in approximately 5000 US adults showing higher serum copper, selenium, and zinc to be associated with abnormal glucose metabolism [26].…”

Section: Discussionsupporting

confidence: 86%

Associations of Dietary Antioxidants with Glycated Hemoglobin and Insulin Sensitivity in Adults with and without Type 1 Diabetes

Basu

Alman

Snell‐Bergeon

2022

Journal of Diabetes Research

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Type 1 diabetes (T1D) has been associated with increased risks of atherosclerotic cardiovascular disease, and poor glycemic control and oxidative stress play a major role in its pathology. There is a lack of data on the role of dietary antioxidant micronutrients, including vitamins and trace elements, in glycemic control in T1D. The aim of this study is to examine associations of dietary intakes of micronutrients with glycemic status. We report data from a cross-sectional analysis from the coronary artery calcification in type 1 diabetes (CACTI) study ( n = 1257 ; T1D: n = 568 ; nondiabetic controls: n = 689 ) collected between the years 2000 and 2002. Participants completed a validated food frequency questionnaire, a physical examination, and biochemical analyses. Linear regression was used to examine the associations of dietary antioxidant micronutrients with HbA1c and estimated insulin sensitivity (eIS) in models adjusted for relevant covariates and stratified by diabetes status. In adults with T1D, we observed higher dietary manganese intake associated with higher eIS in the model adjusted for age, sex, diabetes duration, and total calories. In nondiabetic controls, higher intake of manganese associated with lower HbA1c and higher eIS values that persisted in models adjusted for all relevant covariates. On the other hand, dietary copper revealed a positive association with HbA1c in models adjusted for all covariates, except BMI and plasma lipids. No associations were noted for vitamins C and E and dietary carotenoids in either group. These findings reveal dietary antioxidant micronutrients, especially trace elements such as copper and manganese deserve special attention in glycemic control in adults with T1D as well as in nondiabetic controls.This trial is register with NCT00005754.

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Section: Discussionsupporting

confidence: 86%

Associations of Dietary Antioxidants with Glycated Hemoglobin and Insulin Sensitivity in Adults with and without Type 1 Diabetes

Basu

Alman

Snell‐Bergeon

2022

Journal of Diabetes Research

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“…The study showed that as the intake of copper increases, the risk for diabetes nephropathy decreases, with a pooled odds ratio of 0.67 (95% CI: 0.54–0.84) [ 20 ]. A second cross-sectional study by Tan et al into 128 overweight and obese Malaysian adults tested the association between dietary copper intake and insulin resistance [ 21 ]. All subjects had no diabetes, cardiovascular diseases, stroke, renal or endocrinal diseases.…”

Section: Resultsmentioning

confidence: 99%

“…The study found that there is a noticeable and beneficial relationship between dietary copper and HOMA-IR, but only if the intake of copper is equal to or greater than 13.4 μg/kg/day, with an odds ratio of 0.276 (95%CI = 0.025–0.527, p value for trend = 0.033). Interestingly, the study found that the insulin-resistant group had significantly higher total cholesterol/HDL cholesterol and triglyceride and lower HDL cholesterol compared to the non-insulin-resistant group [ 21 ]. Another study by Norbitt et al used the data from the ANDROMEDA project to assess dietary intake [ 22 ].…”

Section: Resultsmentioning

confidence: 99%

The Role of Copper Intake in the Development and Management of Type 2 Diabetes: A Systematic Review

et al. 2023

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Diabetes mellitus is a worldwide public health issue with numerous complications. Several risk factors are associated with diabetes, mainly due to patients following an unhealthy lifestyle. Copper is a crucial trace element, with various physiological actions. Different intake levels of copper might contribute to diabetes development due to its dual action as both an anti- and pro-oxidant. Aim: Due to the inconclusive findings regarding the relationship between copper consumption and the management of diabetes, we decided to conduct this extensive systematic review. Up to this date, no similar study has been available in the literature. In this review, we used the preferred reporting items for systematic reviews and meta-analysis (PRISMA) guidelines. Relevant articles were identified by searching the electronic databases CINAHL, EMBASE and Medline from their respective index dates to September 2022 using keywords such as “Copper Intake” and “Type 2 Diabetes”. Any paper that has investigated copper exposure through supplementation or any other method that indicates copper intake in human subjects with type 2 diabetes and measures at least one of the outcomes of interest related to diabetes was included in this review. This review is comprised of 4 cross-sectional studies, 3 cohort studies, 2 RCTs, and 2 interventional studies. Two cohort studies found positive associations between copper intake and the risk of developing T2DM, while no significant association was found in the third study. Regarding diabetes outcomes in the four cross-sectional studies, two found inverse associations, one found a positive relationship, and one found no significant association. In interventional studies, all studies found a protective effect of copper, including the RCT, while one found no significant association. The results are inconsistent concerning the association between copper consumption and the likelihood of developing diabetes are inconsistent. Individuals should receive an adequate dietary amount of copper that is within the RDA levels (900 µg/day) to avoid copper deficiency or toxicity. Further studies, especially RCTs, are strongly needed to enable researchers to elucidate more robust conclusions regarding this association.

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“…The greater the intake of copper (>1 mg/day), the more significantly IR increased (SHAP value of RF in Supplementary Figure S2 ). A study on obese Malaysian adults [ 67 ] reported a significant positive association between dietary copper and HOMA-IR with intakes of Cu ≥ 13.4 µg/kg/day, 0.276 (CI = 0.025–0.526; p = 0.033). Excess copper intake might create oxidative stress, which further favors the progression of T2DM [ 68 ].…”

Section: Discussionmentioning

confidence: 99%

Development and Validation of an Insulin Resistance Model for a Population with Chronic Kidney Disease Using a Machine Learning Approach

2022

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Background: Chronic kidney disease (CKD) is a complex syndrome without a definitive treatment. For these patients, insulin resistance (IR) is associated with worse renal and patient outcomes. Until now, no predictive model using machine learning (ML) has been reported on IR in CKD patients. Methods: The CKD population studied was based on results from the National Health and Nutrition Examination Survey (NHANES) of the USA from 1999 to 2012. The homeostasis model assessment of IR (HOMA-IR) was used to assess insulin resistance. We began the model building process via the ML algorithm (random forest (RF), eXtreme Gradient Boosting (XGboost), logistic regression algorithms, and deep neural learning (DNN)). We compared different receiver operating characteristic (ROC) curves from different algorithms. Finally, we used SHAP values (SHapley Additive exPlanations) to explain how the different ML models worked. Results: In this study population, 71,916 participants were enrolled. Finally, we analyzed 1,229 of these participants. Their data were segregated into the IR group (HOMA IR > 3, n = 572) or non-IR group (HOMR IR ≤ 3, n = 657). In the validation group, RF had a higher accuracy (0.77), specificity (0.81), PPV (0.77), and NPV (0.77). In the test group, XGboost had a higher AUC of ROC (0.78). In addition, XGBoost also had a higher accuracy (0.7) and NPV (0.71). RF had a higher accuracy (0.7), specificity (0.78), and PPV (0.7). In the RF algorithm, the body mass index had a much larger impact on IR (0.1654), followed by triglyceride (0.0117), the daily calorie intake (0.0602), blood HDL value (0.0587), and age (0.0446). As for the SHAP value, in the RF algorithm, almost all features were well separated to show a positive or negative association with IR. Conclusion: This was the first study using ML to predict IR in patients with CKD. Our results showed that the RF algorithm had the best AUC of ROC and the best SHAP value differentiation. This was also the first study that included both macronutrients and micronutrients. We concluded that ML algorithms, particularly RF, can help determine risk factors and predict IR in patients with CKD.

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Dietary copper and selenium are associated with insulin resistance in overweight and obese Malaysian adults

Cited by 15 publications

References 44 publications

Associations of Dietary Antioxidants with Glycated Hemoglobin and Insulin Sensitivity in Adults with and without Type 1 Diabetes

Associations of Dietary Antioxidants with Glycated Hemoglobin and Insulin Sensitivity in Adults with and without Type 1 Diabetes

The Role of Copper Intake in the Development and Management of Type 2 Diabetes: A Systematic Review

Development and Validation of an Insulin Resistance Model for a Population with Chronic Kidney Disease Using a Machine Learning Approach

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