Prediction of type-2 diabetes based on several element levels in blood and chemometrics

Impact of essential metals on insulin sensitivity and fetuin in obesity-related type 2 diabetes pathogenesis

Purpose

Essential metals may be crucial in obesity and type 2 diabetes (T2DM); diabesity pathogenesis and consequences. This study aimed to determine the metal levels in obese and non-obese patients with and without T2DM and their relationships with fetuin-A(Fet-A) levels, insulin sensitivity, and insulin resistance.

Methods

A total of 314 participants were enrolled, with 160 newly diagnosed T2DM patients and 154 non-T2DM subjects categorized into diabetic obese (n = 57), diabetic non-obese (n = 103), non-diabetic obese (n = 48), and non-diabetic non-obese (n = 106) subgroups. Fet-A, insulin sensitivity (QUCKI)/resistance (HOMA-IR), fasting glucose, and body mass index (BMI) were assessed. The essential metals were measured using inductively coupled plasma mass spectroscopy (ICP-MS).

Results

Fet-A levels were 3-fold higher (1391.4 ± 839.8 ng/ml) in T2DM patients than in non-T2DM (2165.6 ± 651.9 vs. 424.3 ± 219.1 ng/ml, p < 0.0001). Fet-A levels were 2.3-fold higher in the diabetic obese group than in the diabetic non-obese group (p < 0.0001). Fet-A levels were 2.0-fold higher in the diabetic non-obese group than in the non-diabetic obese group (p < 0.0001). Fet-A levels were positively correlated with insulin resistance (HOMA-IR) (r = 0.34, p < 0.0001) and negatively correlated with insulin sensitivity (QUIKI) (r = -0.41, p < 0.0001).Cu, Se, Zn, and Fe levels were significantly lower in diabetic patients than in non-diabetic patients (p < 0.05). Se and Zn were significantly correlated with Fet-A (r = -0.41, p = 0.049 and r = -0.42, p = 0.001, respectively). Se and Zn were also correlated with insulin resistance (HOMA-IR) (r = -0.45, p = 0.049 and r = -0.36, p = 0.012, respectively) and insulin sensitivity (QUIKI) (r = 0.49, p = 0.042 and r = 0.30, p = 0.003, respectively). Similarly, Fe was negatively correlated with insulin levels (r = -0.33, p = 0.04) and insulin sensitivity (r = -0.34, p = 0.30). However, Mn was significantly correlated with Fet-A (r = 0.37, p = 0.001) and insulin resistance/sensitivity (r = 0.24, p = 0.026 and r = -0.24, p = 0.041) respectively in the diabetic obese group. Mg was an independent predictor of diabesity.

Conclusions

Mg play a significant role in obesity-related T2DM pathogenesis and complications via Fet-A, insulin sensitivity, and resistance modifications.

Recent advances in the application of ionomics in metabolic diseases

Trace elements and minerals play a significant role in human health and diseases. In recent years, ionomics has been rapidly and widely applied to explore the distribution, regulation, and crosstalk of different elements in various physiological and pathological processes. On the basis of multi-elemental analytical techniques and bioinformatics methods, it is possible to elucidate the relationship between the metabolism and homeostasis of diverse elements and common diseases. The current review aims to provide an overview of recent advances in the application of ionomics in metabolic disease research. We mainly focuses on the studies about ionomic or multi-elemental profiling of different biological samples for several major types of metabolic diseases, such as diabetes mellitus, obesity, and metabolic syndrome, which reveal distinct and dynamic patterns of ion contents and their potential benefits in the detection and prognosis of these illnesses. Accumulation of copper, selenium, and environmental toxic metals as well as deficiency of zinc and magnesium appear to be the most significant risk factors for the majority of metabolic diseases, suggesting that imbalance of these elements may be involved in the pathogenesis of these diseases. Moreover, each type of metabolic diseases has shown a relatively unique distribution of ions in biofluids and hair/nails from patients, which might serve as potential indicators for the respective disease. Overall, ionomics not only improves our understanding of the association between elemental dyshomeostasis and the development of metabolic disease but also assists in the identification of new potential diagnostic and prognostic markers in translational medicine.

The association between urine elements and fasting glucose levels in a community-based elderly people in Beijing

Epidemiological studies have demonstrated that various kinds of urinary element concentrations were different between healthy, prediabetes, and diabetes patients. Meanwhile, many studies have explored the relationship between element concentration and fasting blood glucose (FBG), but the association between joint exposure to co-existing elements and FBG level has not been well understood. The study explored the associations of joint exposure to co-existing urinary elements with FBG level in a cross-sectional design. 275 retired elderly people were recruited from Beijing, China. The questionnaire survey was conducted, and biological samples were collected. The generalized linear model (GLM) and two-phase Bayesian kernel machine regression (BKMR) model were used to perform in-depth association analysis between urinary elements and FBG. The GLM analysis showed that Zn, Sr, and Cd were significantly correlated with the FBG level, under control potential confounding factors. The BKMR analysis demonstrated 8 elements (Zn, Se, Fe, Cr, Ni, Cd, Mn, and Al) had a higher influence on FBG (posterior inclusion probabilities > 0.1). Further intensive analyses result of the BKMR model indicated that the overall estimated exposure of 8 elements was positively correlated with the FBG level and was statistically significant when all creatinine-adjusted element concentrations were at their 65th percentile. Meanwhile, the BKMR analysis showed that Cd and Zn had a statistically significant association with FBG levels when other co-existing elements were controlled at different levels (25th, 50th, or 75th percentile), respectively. The results of the GLM and BKMR model were inconsistent. The BKMR model could flexibly calculate the joint exposure to co-existing elements, evaluate the possible interaction effects and nonlinear correlations. The meaningful conclusions were found that it was difficult to get by traditional methods. This study will provide methodological reference and experimental evidence for the association between joint exposure to co-existing elements and FBG in elderly people.

Artificial Intelligence in Nutrients Science Research: A Review

Artificial intelligence (AI) as a branch of computer science, the purpose of which is to imitate thought processes, learning abilities and knowledge management, finds more and more applications in experimental and clinical medicine. In recent decades, there has been an expansion of AI applications in biomedical sciences. The possibilities of artificial intelligence in the field of medical diagnostics, risk prediction and support of therapeutic techniques are growing rapidly. The aim of the article is to analyze the current use of AI in nutrients science research. The literature review was conducted in PubMed. A total of 399 records published between 1987 and 2020 were obtained, of which, after analyzing the titles and abstracts, 261 were rejected. In the next stages, the remaining records were analyzed using the full-text versions and, finally, 55 papers were selected. These papers were divided into three areas: AI in biomedical nutrients research (20 studies), AI in clinical nutrients research (22 studies) and AI in nutritional epidemiology (13 studies). It was found that the artificial neural network (ANN) methodology was dominant in the group of research on food composition study and production of nutrients. However, machine learning (ML) algorithms were widely used in studies on the influence of nutrients on the functioning of the human body in health and disease and in studies on the gut microbiota. Deep learning (DL) algorithms prevailed in a group of research works on clinical nutrients intake. The development of dietary systems using AI technology may lead to the creation of a global network that will be able to both actively support and monitor the personalized supply of nutrients.

Diabetes mellitus type 2: Exploratory data analysis based on clinical reading

Diabetes mellitus type 2 (DMT2) is a severe and complex health problem. It is the most common type of diabetes. DMT2 is a chronic metabolic disorder that affects the way your body metabolizes sugar. With DMT2, your body either resists the effects of insulin or does not produce sufficient insulin to continue normal glucose levels. DMT2 is a disease that requires a multifactorial approach of controlling that includes lifestyle change and pharmacotherapy. Less than ideal management increases the risk of developing complications and comorbidities such as cardiovascular disease and numerous social and economic penalties. That is why the studies dedicated to the pathophysiological mechanisms and the treatment of DMT2 are extremely numerous and diverse. In this study, exploratory data analysis approaches are applied for the treatment of clinical and anthropometric readings of patients with DMT2. Since multivariate statistics is a well-known method for classification, modeling and interpretation of large collections of data, the major aim of the present study was to reveal latent relations between the objects of the investigation (group of patients and control group) and the variables describing the objects (clinical and anthropometric parameters). In the proposed method by the application of hierarchical cluster analysis and principal component analysis it is possible to identify reduced number of parameters which appear to be the most significant discriminant parameters to distinguish between four patterns of patients with DMT2. However, there is still lack of multivariate statistical studies using DMT2 data sets to assess different aspects of the problem like optimal rapid monitoring of the patients or specific separation of patients into patterns of similarity related to their health status which could be of help in preparation of data bases for DMT2 patients. The outcome from the study could be of custom for the selection of significant tests for rapid monitoring of patients and more detailed approach to the health status of DMT2 patients.

Overview of dietary supplements on patients with type 2 diabetes

BACKGROUND AND AIMS

The primary approach for managing type 2 diabetes mellitus (T2DM) involves lifestyle modification and diet therapy along with pharmacologic interventions. Many patients are interested to identify nutritional supplements that may provide benefit in prevention and management of diabetes. However, the efficacy and safety of nutritional supplements such as chromium, n-3 polyunsaturated fatty acids (PUFAs), vitamin D, zinc and magnesium in disease treatment is a worrying and controversial matter. In this narrative review, patients and health care providers are introduced to the effects of mentioned dietary supplements that may help in choosing or not choosing these supplements in treatment of diabetes.

METHODS

This review was carried out using comprehensive and systematic literature reports on the dietary supplements in the management of diabetes. Empirical searches were conducted using Google Scholar, Science Direct and PubMed databases. Searches were also undertaken using keywords, in English, such as "chromium" OR "vitamin D" OR "omega-3 fatty acids" OR "zinc" OR "magnesium" in combination with "type 2 diabetes".

RESULTS

The available evidence is insufficient to create a definite conclusion that nutritional supplements including chromium, n-3 PUFAs, vitamin D, zinc and magnesium might be beneficial for the prevention and treatment of T2DM and therefore, the general recommendation to use these supplements in the management of diabetes cannot be justified. The results of most studies lack uniformity across multiple aspects, including different dose and formation of supplements, duration, and subjects under intervention.

CONCLUSIONS

There is a need for well-designed, high quality, large and long-term studies to strengthen the available evidence and ensure the safety and efficacy of products.

Multiple Cytokine Profiling: A New Model to Predict Response to Tumor Necrosis Factor Antagonists in Ulcerative Colitis Patients

BACKGROUND AND AIMS

Ulcerative colitis (UC) is a form of inflammatory bowel disease, and antibodies against tumor necrosis factor (anti-TNF) are used for treatment. Many patients are refractory or lose response to anti-TNF, and predicting response would be an extremely valuable clinical tool. Unlike most biomarkers, cytokines directly mediate inflammation, and their measurement may predict the likelihood of response or no response.

METHODS

Serum samples were obtained from 49 UC patients before infliximab infusions, and levels of 17 cytokines were measured using a multiplex assay. The Fisher linear discriminant analysis (FLDA) was applied to the cytokine values to predict which patients would respond to infliximab. "Response" was defined as clinical remission after the third infusion, and "no response" was defined as lack of remission after the third infusion.

RESULTS

The Fisher linear discriminant analysis model identified a subset of seven predictor cytokines: TNF-α, IL-12, IL-8, IL-2, IL-5, IL1-β, and IFN-γ. The obtained canonical coefficients enabled to calculate discriminant scores as linear combinations of the cytokines; model classified thepatients as responders and nonresponders with a sensitivity of 84.2% and a specificity of 93.3%. Overall, the yield of the FLDA model was 89.8% of the total 49 patients.

CONCLUSIONS

An unbiased, statistically derived, predictive model based on measurement of serum cytokines before therapy may predict a positive or negative outcome from the administration of anti-TNF to UC patients. Because accurately measuring cytokines is simple and inexpensive, the model may be a valuable new tool to complement other laboratory parameters used in the management of IBD patients.

Dietary zinc intake and whole blood zinc concentration in subjects with type 2 diabetes versus healthy subjects: A systematic review, meta-analysis and meta-regression

The aim of this systematic review, meta-analysis and meta-regression was to examine the relationship between type 2 diabetes mellitus (T2DM) and concentration of zinc in whole blood, as well as dietary zinc intake. Searches were performed using Ovid MEDLINE, Embase (Ovid) and The Cochrane Library (CENTRAL). Observational studies conducted on diabetic and healthy adults, with data on dietary zinc intake and/or concentration of zinc in whole blood, were selected. The search strategy yielded 11,150 publications and the manual search 6, of which 11 were included in the meta-analyses. Mean difference (MD) and 95% confidence interval (CI), were calculated using the generic inverse-variance method with random-effects models. Heterogeneity was assessed by the Cochran Q-statistic and quantified by the I² statistic. Meta-regressions and stratified analysis were used to examine whether any covariate had influence on the results. The pooled MD for the dietary zinc intake meta-analysis was -0.40 (95% CI: -1.59 to 0.79; I² = 61.0%). Differences between diabetic and non-diabetic subjects became significant in the presence of complications associated with diabetes (MD = -2.26; 95% CI: -3.49 to -1.02; I² = 11.9%). Meta-regression showed that for each year since the diagnosis of diabetes the concentration of zinc in whole blood decreased in diabetic patients regarding healthy controls [MD (concentration of zinc in blood) = 732.61 + (-77.88303) × (duration of diabetes in years)], which is not generally explained by a lower intake of zinc.

A Hybrid Predictive Model Integrating C4.5 and Decision Table Classifiers for Medical Data Sets

This article describes how, recently, data mining has been in great use for extracting meaningful patterns from medical domain data sets, and these patterns are then applied for clinical diagnosis. Truly, any accurate, precise and reliable classification models significantly assist the medical practitioners to improve diagnosis, prognosis and treatment processes of individual diseases. However, numerous intelligent models have been proposed in this respect but still they have several drawbacks like, disease specificity, class imbalance, conflicting and lack adequacy for dimensionality of patient's data. The present study has attempted to design a hybrid prediction model for medical domain data sets by combining the decision tree based classifier (mainly C4.5) and the decision table based classifier (DT). The experimental results validate in favour of the claims.

Alterations of serum trace elements in patients with type 2 diabetes

OBJECTIVE

The aim of the present study is to investigate the association of trace elements and the risk of type 2 diabetes mellitus.

DESIGN AND METHODS

The 1837 participants (637 men and 1200 women) aged 40-70 were from a cross-sectional community-based study performed in downtown Shanghai. All the participants without diabetes mellitus history underwent a 75-g OGTT. The participants with diabetes mellitus took 100g steamed bread as the substitute. The fasting and OGTT 2h or postprandial 2h venous blood samples were collected. Blood glucose levels, fasting serum insulin concentrations, lipid profiles, HbA1C and 19 trace elements including magnesium, copper, zinc and selenium and so on were assayed.

RESULTS

Of all the 1837 studied subjects, 510 subjects had diabetes mellitus (191 male, 319 female). Serum magnesium levels were decreased statistically (p<0.05), but serum copper, zinc and selenium levels were significantly increased in subjects with diabetes mellitus compared to non-diabetic subjects (p<0.01 for copper, p<0.001 for zinc and selenium). Logistic regression analysis showed that serum magnesium was negatively associated with diabetes (p<0.05) and serum copper, zinc, and selenium were all positively associated with diabetes (p<0.05 for copper, p<0.001 for both zinc and selenium). Correlation analysis showed a remarkable correlation between blood glucose, HbA1C and serum magnesium, copper, zinc, and selenium (p<0.01 for zinc, p<0.001 for copper, zinc and selenium).

CONCLUSIONS

Serum magnesium levels are decreased and serum copper, zinc and selenium levels are elevated in patients with type 2 diabetes mellitus.

H2RM: A Hybrid Rough Set Reasoning Model for Prediction and Management of Diabetes Mellitus

Diabetes is a chronic disease characterized by high blood glucose level that results either from a deficiency of insulin produced by the body, or the body’s resistance to the effects of insulin. Accurate and precise reasoning and prediction models greatly help physicians to improve diagnosis, prognosis and treatment procedures of different diseases. Though numerous models have been proposed to solve issues of diagnosis and management of diabetes, they have the following drawbacks: (1) restricted one type of diabetes; (2) lack understandability and explanatory power of the techniques and decision; (3) limited either to prediction purpose or management over the structured contents; and (4) lack competence for dimensionality and vagueness of patient’s data. To overcome these issues, this paper proposes a novel hybrid rough set reasoning model (H2RM) that resolves problems of inaccurate prediction and management of type-1 diabetes mellitus (T1DM) and type-2 diabetes mellitus (T2DM). For verification of the proposed model, experimental data from fifty patients, acquired from a local hospital in semi-structured format, is used. First, the data is transformed into structured format and then used for mining prediction rules. Rough set theory (RST) based techniques and algorithms are used to mine the prediction rules. During the online execution phase of the model, these rules are used to predict T1DM and T2DM for new patients. Furthermore, the proposed model assists physicians to manage diabetes using knowledge extracted from online diabetes guidelines. Correlation-based trend analysis techniques are used to manage diabetic observations. Experimental results demonstrate that the proposed model outperforms the existing methods with 95.9% average and balanced accuracies.

Is serum zinc associated with pancreatic beta cell function and insulin sensitivity in pre-diabetic and normal individuals? Findings from the Hunter Community Study

Aim

To determine if there is a difference in serum zinc concentration between normoglycaemic, pre-diabetic and type-2 diabetic groups and if this is associated with pancreatic beta cell function and insulin sensitivity in the former 2 groups.

Method

Cross sectional study of a random sample of older community-dwelling men and women in Newcastle, New South Wales, Australia. Beta cell function, insulin sensitivity and insulin resistance were calculated for normoglycaemic and prediabetes participants using the Homeostasis Model Assessment (HOMA-2) calculator.

Result

A total of 452 participants were recruited for this study. Approximately 33% (N = 149) had diabetes, 33% (N = 151) had prediabetes and 34% (N = 152) were normoglycaemic. Homeostasis Model Assessment (HOMA) parameters were found to be significantly different between normoglycaemic and prediabetes groups (p<0.001). In adjusted linear regression, higher serum zinc concentration was associated with increased insulin sensitivity (p = 0.01) in the prediabetic group. There was also a significant association between smoking and worse insulin sensitivity.

Conclusion

Higher serum zinc concentration is associated with increased insulin sensitivity. Longitudinal studies are required to determine if low serum zinc concentration plays a role in progression from pre-diabetes to diabetes.

Blood metals concentration in type 1 and type 2 diabetics

Mechanisms for the onset of diabetes and the development of diabetic complications remain under extensive investigations. One of these mechanisms is abnormal homeostasis of metals, as either deficiency or excess of metals, can contribute to certain diabetic outcomes. Therefore, this paper will report the blood levels of chromium (Cr), copper (Cu), iron (Fe), manganese (Mn), mercury (Hg), nickel (Ni), lead (Pb), selenium (Se), and zinc (Zn) in subjects with type 1 diabetes (n = 192, mean age 48.8 years, mean disease duration 20.6 years), type 2 diabetes (n = 68, mean age 68.4 years, mean disease duration 10.2 years), and in control subjects (n = 59, mean age 57.2 years), and discuss the results indicating their possible role in diabetes. The metal concentrations were measured by sector field inductively coupled plasma mass spectrometry after microwave-induced acid digestion of blood samples. The accuracy was checked using a blood-based certified reference material, and recoveries of all elements were in the range of 92-101 % of certified values. Type 1 diabetes was found to be associated with Cr (p = 0.02), Mn (p < 0.001), Ni (p < 0.001), Pb (p = 0.02), and Zn (p < 0.001) deficiency, and type 2 diabetes with Cr (p = 0.014), Mn (p < 0.001), and Ni (p < 0.001) deficiency. These deficiencies were appreciated also subdividing the understudied patients for gender and age groups. Furthermore, in type 1 diabetes, there was a positive correlation between Pb and age (p < 0.001, ρ = 0.400) and Pb and BMI (p < 0.001, ρ = 0.309), while a negative correlation between Fe and age (p = 0.002, ρ = -0.218). In type 2 diabetes, there was a negative correlation between Fe and age (p = 0.017, ρ = -0.294) and Fe and BMI (p = 0.026, ρ = -0.301). Thus, these elements may play a role in both forms of diabetes and combined mineral supplementations could have beneficial effects.