Comprehensive biomarker testing of glycemia, insulin resistance, and beta cell function has greater sensitivity to detect diabetes risk than fasting glucose and HbA1c and is associated with improved glycemic control in clinical practice

Effects of a diverse prebiotic fibre supplement on HbA1c, insulin sensitivity and inflammatory biomarkers in pre-diabetes: a pilot placebo-controlled randomised clinical trial

Prebiotic fibre represents a promising and efficacious treatment to manage pre-diabetes, acting via complementary pathways involving the gut microbiome and viscosity-related properties. In this study, we evaluated the effect of using a diverse prebiotic fibre supplement on glycaemic, lipid and inflammatory biomarkers in patients with pre-diabetes. Sixty-six patients diagnosed with pre-diabetes (yet not receiving glucose-lowering medications) were randomised into treatment (thirty-three) and placebo (thirty-three) interventions. Participants in the treatment arm consumed 20 g/d of a diverse prebiotic fibre supplement, and participants in the placebo arm consumed 2 g/d of cellulose for 24 weeks. A total of fifty-one and forty-eight participants completed the week 16 and week 24 visits, respectively. The intervention was well tolerated, with a high average adherence rate across groups. Our results extend upon previous work, showing a significant change in glycated haemoglobin (HbA1c) in the treatment group but only in participants with lower baseline HbA1c levels (< 6 % HbA1c) (P = 0·05; treatment -0·17 ± 0·27 v. placebo 0·07 ± 0·29, mean ± sd). Within the whole cohort, we showed significant improvements in insulin sensitivity (P = 0·03; treatment 1·62 ± 5·79 v. placebo -0·77 ± 2·11) and C-reactive protein (P FWE = 0·03; treatment -2·02 ± 6·42 v. placebo 0·94 ± 2·28) in the treatment group compared with the placebo. Together, our results support the use of a diverse prebiotic fibre supplement for physiologically relevant biomarkers in pre-diabetes.

Untargeted approach to investigating the metabolomics profile of type 2 diabetes emiratis

Type 2 Diabetes (T2D) is expected to be the seventh most significant cause of death worldwide by 2030. Although research into its mechanism has received the attention it deserves, our understanding of T2D is still limited. This case-control study employs untargeted metabolomics to explore novel T2D plasma biomarkers in the Emirati population. Ninety-two UAE nationals were included in the cohort, with fifty T2D and forty-two non-T2D profiles. Participants were then stratified into three groups based on metabolic profiles, clinically verified diabetic status, and current HbA1c values: namely controlled diabetics, uncontrolled diabetics and prediabetics, and non-diabetics. The study identified fifteen significant differentially abundant metabolites between the uncontrolled diabetics group and the prediabetics or controlled diabetics group. Interestingly, some metabolites essential for the corticosteroid and thyroid signaling pathways were found to be significantly elevated in poorly controlled T2D, including cortisol, glycocholic acid, bile acids, thyroxine, and the tryptophan metabolite, 5-hydroxyindoleacetic acid. These findings align with those from prior western cohorts and suggest an intriguing linkage between T2D glycemic control and thyroid and adrenal signaling that may provide new diagnostic and prognostic indicators. RESEARCH SIGNIFICANCE: This study investigates the underlooked metabolomic role and correlation with T2D in the UAE population. The report indicates fifteen significant differentially abundant metabolites between on diabetics, uncontrolled diabetics and or controlled diabetics or prediabetics. This panel of metabolites such as thyroxine and corticosteroids should be considered further as potential diagnostic or prognostic biomarkers for T2D in the region.

Biomarkers of non-communicable chronic disease: an update on contemporary methods

Chronic diseases constitute a major global burden with significant impact on health systems, economies, and quality of life. Chronic diseases include a broad range of diseases that can be communicable or non-communicable. Chronic diseases are often associated with modifications of normal physiological levels of various analytes that are routinely measured in serum and other body fluids, as well as pathological findings, such as chronic inflammation, oxidative stress, and mitochondrial dysfunction. Identification of at-risk populations, early diagnosis, and prediction of prognosis play a major role in preventing or reducing the burden of chronic diseases. Biomarkers are tools that are used by health professionals to aid in the identification and management of chronic diseases. Biomarkers can be diagnostic, predictive, or prognostic. Several individual or grouped biomarkers have been used successfully in the diagnosis and prediction of certain chronic diseases, however, it is generally accepted that a more sophisticated approach to link and interpret various biomarkers involved in chronic disease is necessary to improve our current procedures. In order to ensure a comprehensive and unbiased coverage of the literature, first a primary frame of the manuscript (title, headings and subheadings) was drafted by the authors working on this paper. Second, based on the components drafted in the preliminary skeleton a comprehensive search of the literature was performed using the PubMed and Google Scholar search engines. Multiple keywords related to the topic were used. Out of screened papers, only 190 papers, which are the most relevant, and recent articles were selected to cover the topic in relation to etiological mechanisms of different chronic diseases, the most recently used biomarkers of chronic diseases and finally the advances in the applications of multivariate biomarkers of chronic diseases as statistical and clinically applied tool for the early diagnosis of chronic diseases was discussed. Recently, multivariate biomarkers analysis approach has been employed with promising prospect. A brief discussion of the multivariate approach for the early diagnosis of the most common chronic diseases was highlighted in this review. The use of diagnostic algorithms might show the way for novel criteria and enhanced diagnostic effectiveness inpatients with one or numerous non-communicable chronic diseases. The search for new relevant biomarkers for the better diagnosis of patients with non-communicable chronic diseases according to the risk of progression, sickness, and fatality is ongoing. It is important to determine whether the newly identified biomarkers are purely associations or real biomarkers of underlying pathophysiological processes. Use of multivariate analysis could be of great importance in this regard.

Videogames That Encourage Healthy Behavior Did Not Alter Fasting Insulin or Other Diabetes Risks in Children: Randomized Clinical Trial

Background: Previous research indicates games for health have substantial promise in promoting change in children's diet and physical activity (PA) behavior for obesity and diabetes prevention, but the research has generally not been rigorous. The study reported here was an efficacy trial of two role-playing videogames played in sequence, "Escape from Diab" (hereinafter called Diab) and "Nanoswarm: Invasion from Inner Space" (hereinafter called Nano), on diabetes and obesity risk factors: fasting insulin and body mass index (BMI), and risk-related behaviors: diet, PA, and sedentary behavior (SB). Design: A two-group (treatment vs. wait list control) randomized clinical trial was used with baseline, immediate postintervention (∼3 months postbaseline), and 2 months postassessments. Intervention: Diab and Nano were desktop or laptop role-playing videogames with nine sessions (each episode/session lasting ∼60 minutes). Two storylines attempted to immerse players and used ethnically diverse characters to model desired behaviors. Tailored goal setting, problem solving, and motivational statements were used. Methods: A sample of 200 overweight or obese children (ages 10-12 years from 85th to 99th BMI percentile [%ile]) was recruited, primarily using a volunteer list. Fasting insulin was the primary dependent variable. BMI, fruit, vegetable and sweetened beverage intakes, PA, and SBs were secondary outcomes. Generalized linear mixed models were used to test for the treatment effects. Results: No significant differences were detected in any of the tested outcome variables. Conclusions: The lack of differences may indicate that games cannot change dietary behaviors and thereby not change-related clinical outcomes. Alternatively, there seem to have been changes in (1) the types of videogames children expect and like to play since a pilot study was conducted, (2) productization challenges, and/or (3) problems in staff management of the trial. All may have contributed to the lack of effect.

Systematic review of the biological variation data for diabetes related analytes

BACKGROUND

Objective interpretation of laboratory test results used to diagnose and monitor diabetes mellitus in part requires the application of biological variation data (BVD). The quality of published BVD has been questioned. The aim of this study was to quality assess publications reporting BVD for diabetes-related analytes using the Biological Variation Data Critical Appraisal Checklist (BIVAC); to assess whether published BVD are fit for purpose and whether the study design and population attributes influence BVD estimates and to undertake a meta-analysis of the BVD from BIVAC-assessed publications.

METHODS

Publications reporting data for glucose, HbA_1c, adiponectin, C-peptide, fructosamine, insulin like growth factor 1 (IGF-1), insulin like growth factor binding protein 3 (IGFBP-3), insulin, lactate and pyruvate were identified using a systematic literature search. These publications were assessed using the BIVAC, receiving grades A, B, C or D, where A is of highest quality. A meta-analysis of the BVD from the assessed studies utilised weightings based upon BIVAC grades and the width of the data confidence intervals to generate global BVD estimates.

RESULTS

BIVAC assessment of 47 publications delivered 1 A, 3 B, 39C and 4 D gradings. Publications relating to adiponectin, C-peptide, IGF-1, IGFBP-3, lactate and pyruvate were all assessed as grade C. Meta-analysis enabled global BV estimates for all analytes except pyruvate, lactate and fructosamine.

CONCLUSIONS

This study delivers updated and evidence-based BV estimates for diabetes-related analytes. There remains a need for delivery of new high-quality BV studies for several clinically important analytes.

Profiling of plasma metabolites in postmenopausal women with metabolic syndrome

Objective:

The aim of the study was to investigate the associations of amino acids and other polar metabolites with metabolic syndrome (MetS) in postmenopausal women in a lean Asian population.

Methods:

The participants were 1,422 female residents enrolled in a cohort study from April to August 2012. MetS was defined according to the National Cholesterol Education Program Adult Treatment Panel III modified for Japanese women. Associations were examined between MetS and 78 metabolites assayed in fasting plasma samples using capillary electrophoresis-mass spectrometry. Replication analysis was performed to confirm the robustness of the results in a separate population created by random allocation.

Results:

Analysis was performed for 877 naturally postmenopausal women, including 594 in the original population and 283 in the replication population. The average age, body mass index, and levels of high- and low-density lipoprotein cholesterol of the entire population were 64.6 years, 23.0 kg/m², 72.1 mg/dL, and 126.1 mg/dL, respectively. There was no significant difference in low-density lipoprotein cholesterol levels between women with and without MetS. Thirteen metabolites were significantly related to MetS: multiple plasma amino acids were elevated in women with MetS, including branched-chain amino acids, alanine, glutamate, and proline; and alpha-aminoadipate, which is generated by lysine degradation, was also significantly increased.

Conclusions:

Our large-scale metabolomic profiling indicates that Japanese postmenopausal women with MetS have abnormal polar metabolites, suggesting altered catabolic pathways. These results may help to understand metabolic disturbance, including in persons with normal body mass index and relatively high levels of high-density lipoprotein cholesterol, and may have clinical utility based on further studies.

Associations of MTHFR C677T polymorphism with insulin resistance, results of NURSE Study (Nursing Unacquainted Related Stress Etiologies)

Background

The insulin resistance syndrome is one of the major contributors of metabolic syndrome, diabetes Type 2 and atherosclerotic cardiovascular disease. A common mutation (677C to T; Ala to Val) in the methylenetetrahydrofolate reductase (MTHFR) gene is associated with decreased specific MTHFR activity and elevation of the homocysteine. The aim of this study is investigation of association between MTHFR 677C > T polymorphism with insulin resistance by using HOMA (Homeostasis Model Assessment) index in nurses who are potentially prone to develop insulin resistance because of unfavorable effects of shift work.

Method

Nursing Unacquainted Related Stress Etiologies Study (Nurse Study) was conducted in five different educational hospitals of Tehran University of Medical Science (TUMS). The nurses aged 22–57 who have been referred by the matron were recruited. A self-administered questionnaire was completed. Anthropometric measurements including weight, height, waist and hip circumference in addition to blood pressure were measured. Insulin resistance and Insulin sensitivity were measured using the homeostatic model assessment (HOMA) and quantitative insulin sensitivity check index (QUICKI) respectively. The detection of MTHFR C677T polymorphism in exon four of MTHFR gene was performed by polymerase chain reaction–restriction fragment length polymorphism (PCR–RFLP) analysis using HINFI restriction enzyme digestion.

Result

A total of 273 subjects were recruited in the study. CT genotype were detected in 51.6% (129) subjects and CC and TT genotype were seen in 9.2% (25) and 35.2% (96) subjects respectively. Participants with TT genotype (9.65 ± 4.00) have significantly lower insulin level than participants with CT genotype (14.12 ± 15.34) (p-value: 0.01). The same significant difference was observed for HOMA index (p-value: 0.03). Result showed that HOMA is lower in subjects who are taking supplements.

Conclusion

Result of this study showed subjects with TT genotype had significantly lower HOMA compare to CT genotype and the same pattern was seen for insulin level. We also found subjects taking supplement have lower HOMA compared to others regardless of their genotype.

GC-MS based Gestational Diabetes Mellitus longitudinal study: Identification of 2-and 3-hydroxybutyrate as potential prognostic biomarkers

Gestational Diabetes Mellitus (GDM) causes severe short- and long-term complications for the mother, fetus and neonate, including type 2-diabetes (T2DM) later in life. In this pilot study, GC-Q/MS analysis was applied for plasma metabolomics fingerprinting of 24 healthy and 24 women with GDM at different stages of gestation (second and third trimester) and postpartum (one and three months). Multivariate (unsupervised and supervised) statistical analysis was performed to investigate variance in the data, identify outliers and for unbiased assessment of data quality. Plasma fingerprints allowed for the discrimination of GDM pregnant women from controls both in the 2nd and 3rd trimesters of gestation. However, metabolic profiles tended to be similar after delivery. Follow up of these women revealed that 4 of them developed T2DM within 2 years postpartum. Multivariate PLS-DA models limited to women with GDM showed clear separation 3 months postpartum. In the 2nd trimester of gestation there was also a clear separation between GDM women that were normoglycemic after pregnancy and those with recognized postpartum T2DM. Metabolites that had the strongest discriminative power between these groups in the 2nd trimester of gestation were 2-hydroxybutyrate, 3-hydroxybutyrate, and stearic acid. We have described, that early GDM comprises metabotypes that are associated with the risk of future complications, including postpartum T2DM. In this pilot study, we provide evidence that 2-hydroxybutyrate and 3-hydroxybutyrate may be considered as future prognostic biomarkers to predict the onset of diabetic complications in women with gestational diabetes after delivery.

Recent advances in molecular biomarkers for diabetes mellitus: a systematic review

CONTEXT

Diabetes is a growing global metabolic epidemic. Current research is focussing on exploring how the biological processes and clinical outcomes of diabetes are related and developing novel biomarkers to measure these relationships, as this can subsequently improve diagnostic, therapeutic and management capacity.

OBJECTIVE

The objective of this study is to identify the most recent advances in molecular biomarkers of diabetes and directions that warrant further research.

METHODS

Using a systematic search strategy, the MEDLINE, CINAHL and OVID MEDLINE databases were canvassed for articles that investigated molecular biomarkers for diabetes. Initial selections were made based on article title, whilst final inclusion was informed by a critical appraisal of the full text of each article.

RESULTS

The systematic search returned 246 records, of which 113 were unique. Following screening, 29 records were included in the final review. Three main research strategies (the development of novel technologies, broad biomarker panels, and targeted approaches) identified a number of potential biomarkers for diabetes including miR-126, C-reactive protein, 2-aminoadipic acid and betatrophin.

CONCLUSION

The most promising research avenue identified is the detection and quantification of micro RNA. Further, the utilisation of functionalised electrodes as a means to detect biomarker compounds also warrants attention.

Metabolomics and Type 2 Diabetes: Translating Basic Research into Clinical Application

Type 2 diabetes (T2D) and its comorbidities have reached epidemic proportions, with more than half a billion cases expected by 2030. Metabolomics is a fairly new approach for studying metabolic changes connected to disease development and progression and for finding predictive biomarkers to enable early interventions, which are most effective against T2D and its comorbidities. In metabolomics, the abundance of a comprehensive set of small biomolecules (metabolites) is measured, thus giving insight into disease-related metabolic alterations. This review shall give an overview of basic metabolomics methods and will highlight current metabolomics research successes in the prediction and diagnosis of T2D. We summarized key metabolites changing in response to T2D. Despite large variations in predictive biomarkers, many studies have replicated elevated plasma levels of branched-chain amino acids and their derivatives, aromatic amino acids and α-hydroxybutyrate ahead of T2D manifestation. In contrast, glycine levels and lysophosphatidylcholine C18:2 are depressed in both predictive studies and with overt disease. The use of metabolomics for predicting T2D comorbidities is gaining momentum, as are our approaches for translating basic metabolomics research into clinical applications. As a result, metabolomics has the potential to enable informed decision-making in the realm of personalized medicine.