Significance of HbA1c Test in Diagnosis and Prognosis of Diabetic Patients

Label-free electrochemical detection of glycated hemoglobin (HbA1c) and C-reactive protein (CRP) to predict the maturation of coronary heart disease due to diabetes

The pathophysiological link between diabetes and heightened propensity for the development of coronary heart disease (CHD) is well-established. Prevailing evidence confirms that small increases in low concentrations of high-sensitivity C reactive protein (hs-CRP) in the human body can determine the tendency of developing CHD. Additionally, glycated hemoglobin (HbA1c) is a well-recognized biomarker to evaluate diabetes progression. Given the positive correlation between diabetes and CHD, this research presents a notably unprecedented label-free electrochemical approach for the dual detection of %HbA1c regarding Total Hb and hs-CRP, facilitating early CHD prediction and cost-effective point-of-care diagnostics. Furthermore, a novel redox probe O-(4-Nitrophenylphosphoryl)choline (C11H17N2O6P) was used for the electrochemical detection of CRP, a method not documented in scientific literature before. The calibration curves demonstrate a limit of detection (LOD) of 5 mg/mL in PBS (pH 8) and 6 mg/mL in simulated blood (SB) for a linear range of 0-30 mg/mL of HbA1c. Conjointly, a LOD of 0.007 mg/mL and 0.008 mg/mL for measurement in PBS (pH 7.4) and SB are reported for a linear range of 0-0.05 mg/mL of CRP. The electrochemical systems presented could accurately quantify HbA1c and CRP in mixed samples, demonstrating reasonable specificity and practical applicability for complex biological samples.

Protein fibril aggregation on red blood cells: a potential biomarker to distinguish neurodegenerative diseases from healthy aging

Neurodegenerative diseases like Alzheimer's disease are characterized by the accumulation of misfolded proteins into fibrils in the brain. Atomic force microscopy is a nanoscale imaging technique that can be used to resolve and quantify protein aggregates from oligomers to fibrils. Recently, we characterized protein fibrillar aggregates adsorbed on the surface of red blood cells with atomic force microscopy from patients with neurocognitive disorders, suggesting a novel Alzheimer's disease biomarker. However, the age association of fibril deposits on red blood cells has not yet been studied in detail in healthy adults. Here, we used atomic force microscopy to visualize and quantify fibril coverage on red blood cells in 50 healthy adults and 37 memory clinic patients. Fibrillar protein deposits sporadically appeared in healthy individuals but were much more prevalent in patients with neurodegenerative disease, especially those with Alzheimer's disease as confirmed by positive CSF amyloid beta 1-42/1-40 ratios. The prevalence of fibrils on the red blood cell surface did not significantly correlate with age in either healthy individuals or Alzheimer's disease patients. The overlap in fibril prevalence on red blood cells between Alzheimer's disease and amyloid-negative patients suggests that fibril deposition on red blood cells could occur in various neurodegenerative diseases. Quantifying red blood cell protein fibril morphology and prevalence on red blood cells could serve as a sensitive biomarker for neurodegeneration, distinguishing between healthy individuals and those with neurodegenerative diseases. Future studies that combine atomic force microscopy with immunofluorescence techniques in larger-scale studies could further identify the chemical nature of these fibrils, paving the way for a comprehensive, non-invasive biomarker platform for neurodegenerative diseases.

The effectiveness of social prescribing in the management of long-term conditions in community-based adults: A systematic review and meta-analysis

OBJECTIVE

The objective of this systematic review and meta-analysis was to evaluate the effectiveness of social prescribing interventions in the management of long-term conditions in adults.

DATA SOURCES

Eleven electronic databases were searched for randomised and quasi-randomised controlled trials.

REVIEW METHODS

Outcomes of interest were quality of life, physical activity, psychological well-being and disease-specific measures. Bias was assessed with the Cochrane Risk of Bias 2 tool. A narrative synthesis and meta-analysis were performed.

RESULTS

Twelve studies (n = 3566) were included in this review. Social prescribing interventions were heterogeneous and the most common risks of bias were poor blinding and high attrition. Social prescribing interventions designed to target specific long-term conditions i.e., cancer and diabetes demonstrated significant improvements in quality of life (n = 2 studies) and disease-specific psychological outcomes respectively (n = 3 studies). There was some evidence for improvement in physical activity (n = 2 studies) but most changes were within group only (n = 4 studies). Social prescribing interventions did not demonstrate any significant changes in general psychological well-being.

CONCLUSION

Social prescribing interventions demonstrated some improvements across a range of outcomes although the quality of evidence remains poor.

Relationship of device measured physical activity type and posture with cardiometabolic health markers: pooled dose-response associations from the Prospective Physical Activity, Sitting and Sleep Consortium

AIMS/HYPOTHESIS

The aim of this study was to examine the dose-response associations of device-measured physical activity types and postures (sitting and standing time) with cardiometabolic health.

METHODS

We conducted an individual participant harmonised meta-analysis of 12,095 adults (mean ± SD age 54.5±9.6 years; female participants 54.8%) from six cohorts with thigh-worn accelerometry data from the Prospective Physical Activity, Sitting and Sleep (ProPASS) Consortium. Associations of daily walking, stair climbing, running, standing and sitting time with a composite cardiometabolic health score (based on standardised z scores) and individual cardiometabolic markers (BMI, waist circumference, triglycerides, HDL-cholesterol, HbA1c and total cholesterol) were examined cross-sectionally using generalised linear modelling and cubic splines.

RESULTS

We observed more favourable composite cardiometabolic health (i.e. z score <0) with approximately 64 min/day walking (z score [95% CI] -0.14 [-0.25, -0.02]) and 5 min/day stair climbing (-0.14 [-0.24, -0.03]). We observed an equivalent magnitude of association at 2.6 h/day standing. Any amount of running was associated with better composite cardiometabolic health. We did not observe an upper limit to the magnitude of the dose-response associations for any activity type or standing. There was an inverse dose-response association between sitting time and composite cardiometabolic health that became markedly less favourable when daily durations exceeded 12.1 h/day. Associations for sitting time were no longer significant after excluding participants with prevalent CVD or medication use. The dose-response pattern was generally consistent between activity and posture types and individual cardiometabolic health markers.

CONCLUSIONS/INTERPRETATION

In this first activity type-specific analysis of device-based physical activity, ~64 min/day of walking and ~5.0 min/day of stair climbing were associated with a favourable cardiometabolic risk profile. The deleterious associations of sitting time were fully attenuated after exclusion of participants with prevalent CVD and medication use. Our findings on cardiometabolic health and durations of different activities of daily living and posture may guide future interventions involving lifestyle modification.

Explainable hypoglycemia prediction models through dynamic structured grammatical evolution

Effective blood glucose management is crucial for people with diabetes to avoid acute complications. Predicting extreme values accurately and in a timely manner is of vital importance to them. People with diabetes are particularly concerned about suffering a hypoglycemia (low value) event and, moreover, that the event will be prolonged in time. It is crucial to predict hyperglycemia (high value) and hypoglycemia events that may cause health damages in the short term and potential permanent damages in the long term. This paper describes our research on predicting hypoglycemia events at 30, 60, 90, and 120 minutes using machine learning methods. We propose using structured Grammatical Evolution and dynamic structured Grammatical Evolution to produce interpretable mathematical expressions that predict a hypoglycemia event. Our proposal generates white-box models induced by a grammar based on if-then-else conditions using blood glucose, heart rate, number of steps, and burned calories as the inputs for the machine learning technique. We apply these techniques to create three types of models: individualized, cluster, and population-based. They all are then compared with the predictions of eleven machine learning techniques. We apply these techniques to a dataset of 24 real patients of the Hospital Universitario Principe de Asturias, Madrid, Spain. The resulting models, presented as if-then-else statements that incorporate numeric, relational, and logical operations between variables and constants, are inherently interpretable. The True Positive Rate and True Negative Rate metrics are above 0.90 for 30-minute predictions, 0.80 for 60 min, and 0.70 for 90 min and 120 min for the three types of models. Individualized models exhibit the best metrics, while cluster and population-based models perform similarly. Structured and dynamic structured grammatical evolution techniques perform similarly for all forecasting horizons. Regarding the comparison of different machine learning techniques, on the shorter forecasting horizons, our proposals have a high probability of winning, a probability that diminishes on the longer time horizons. Structured grammatical evolution provides advanced forecasting models that facilitate model explanation, modification, and retesting, offering flexibility for refining solutions post-creation and a deeper understanding of blood glucose behavior. These models have been integrated into the glUCModel application, designed to serve people with diabetes.

The impact of major depressive disorder on glycaemic control in type 2 diabetes: a longitudinal cohort study using UK Biobank primary care records

BACKGROUND

This study evaluates longitudinal associations between glycaemic control, measured by mean and within-patient variability of glycated haemaglobin (HbA1c) levels, and major depressive disorder (MDD) in individuals with type 2 diabetes (T2D), focusing on the timings of these diagnoses.

METHODS

In UK Biobank, T2D was defined using self-report and linked health outcome data, then validated using polygenic scores. Repeated HbA1c measurements (mmol/mol) over the 10 years following T2D diagnosis were outcomes in mixed effects models, with disease duration included using restricted cubic splines. Four MDD exposures were considered: MDD diagnosis prior to T2D diagnosis (pre-T2D MDD), time between pre-T2D MDD diagnosis and T2D, new MDD diagnosis during follow-up (post-T2D MDD) and time since post-T2D MDD diagnosis. Models with and without covariate adjustment were considered.

RESULTS

T2D diagnostic criteria were robustly associated with T2D polygenic scores. In 11,837 T2D cases (6.9 years median follow-up), pre-T2D MDD was associated with a 0.92 increase in HbA1c (95% CI: [0.00, 1.84]), but earlier pre-T2D MDD diagnosis correlated with lower HbA1c. These pre-T2D MDD effects became non-significant after covariate adjustment. Post-T2D MDD individuals demonstrated increasing HbA1c with years since MDD diagnosis ( β = 0.51 , 95% CI: [0.17, 0.86]). Retrospectively, across study follow-up, within-patient variability in HbA1c was 1.16 (95% CI: 1.13-1.19) times higher in post-T2D MDD individuals.

CONCLUSIONS

The timing of MDD diagnosis is important for understanding glycaemic control in T2D. Poorer control was observed in MDD diagnosed post-T2D, highlighting the importance of depression screening in T2D, and closer monitoring for individuals who develop MDD after T2D.

OMEF biochip for evaluating red blood cell deformability using dielectrophoresis as a diagnostic tool for type 2 diabetes mellitus

Type 2 diabetes mellitus (T2DM) is a prevalent and debilitating disease with numerous health risks, including cardiovascular diseases, kidney dysfunction, and nerve damage. One important aspect of T2DM is its association with the abnormal morphology of red blood cells (RBCs), which leads to increased blood viscosity and impaired blood flow. Therefore, evaluating the mechanical properties of RBCs is crucial for understanding the role of T2DM in cellular deformability. This provides valuable insights into disease progression and potential diagnostic applications. In this study, we developed an open micro-electro-fluidic (OMEF) biochip technology based on dielectrophoresis (DEP) to assess the deformability of RBCs in T2DM. The biochip facilitates high-throughput single-cell RBC stretching experiments, enabling quantitative measurements of the cell size, strain, stretch factor, and post-stretching relaxation time. Our results confirm the significant impact of T2DM on the deformability of RBCs. Compared to their healthy counterparts, diabetic RBCs exhibit ∼27% increased size and ∼29% reduced stretch factor, suggesting potential biomarkers for monitoring T2DM. The observed dynamic behaviors emphasize the contrast between the mechanical characteristics, where healthy RBCs demonstrate notable elasticity and diabetic RBCs exhibit plastic behavior. These differences highlight the significance of mechanical characteristics in understanding the implications for RBCs in T2DM. With its ∼90% sensitivity and rapid readout (ultimately within a few minutes), the OMEF biochip holds potential as an effective point-of-care diagnostic tool for evaluating the deformability of RBCs in individuals with T2DM and tracking disease progression.

Unraveling the cognitive implications among individuals with co-occurring chronic periodontitis and type 2 diabetes mellitus: a cross-sectional study

OBJECTIVES

Chronic periodontitis and type 2 diabetes mellitus (T2DM) are associated with cognitive decline when examined individually. To gain deeper insight into the combined effects of these conditions on cognitive decline, the present study aimed to examine the cognitive status of individuals with co-occurring T2DM and chronic periodontitis.

METHODS

We recruited 220 participants categorized into four groups: Group I, healthy subjects; Group II, individuals with chronic periodontitis; Group III, individuals with T2DM; and Group IV, individuals with both T2DM and chronic periodontitis. Medical histories were recorded for all participants, followed by periodontal examination and evaluation of cognitive status using the Montreal Cognitive Assessment (MoCA) scale. Finger dexterity was assessed using the nine-hole peg test.

RESULTS

A statistically significant increase in the proportion of mild cognitive impairment (MCI) was observed between groups I and IV (p<0.001). Logistic regression analysis revealed that, among the parameters assessed in this study, the adjusted odds ratio (OR) was significant for age, finger dexterity scores, and co-occurrence of T2DM and periodontitis.

CONCLUSIONS

The findings of this study suggest that the co-occurrence of chronic periodontitis and T2DM can have a detrimental effect on the cognitive abilities of an individual. Subsequent research should include longitudinal monitoring of the cognitive status in patients with concurrent conditions during treatment to gain deeper prognostic insights into the relationship between these co-occurring conditions and cognitive decline.

A Novel Gene ARHGAP44 for Longitudinal Changes in Glycated Hemoglobin (HbA1c) in Subjects without Type 2 Diabetes: Evidence from the Long Life Family Study (LLFS) and the Framingham Offspring Study (FOS)

Glycated hemoglobin (HbA1c) indicates average glucose levels over three months and is associated with insulin resistance and type 2 diabetes (T2D). Longitudinal changes in HbA1c (ΔHbA1c) are also associated with aging processes, cognitive performance, and mortality. We analyzed ΔHbA1c in 1,886 non-diabetic Europeans from the Long Life Family Study to uncover gene variants influencing ΔHbA1c. Using growth curve modeling adjusted for multiple covariates, we derived ΔHbA1c and conducted linkage-guided sequence analysis. Our genome-wide linkage scan identified a significant locus on 17p12. In-depth analysis of this locus revealed a variant rs56340929 (explaining 27% of the linkage peak) in the ARHGAP44 gene that was significantly associated with ΔHbA1c. RNA transcription of ARHGAP44 was associated with ΔHbA1c. The Framingham Offspring Study data further supported these findings on the gene level. Together, we found a novel gene ARHGAP44 for ΔHbA1c in family members without T2D. Follow-up studies using longitudinal omics data in large independent cohorts are warranted.

The potential impact and diagnostic value of inflammatory markers on diabetic foot progression in type II diabetes mellitus: A case-control study

BACKGROUND

The wound-healing process in diabetic foot is affected by pro and anti-inflammatory markers, and any disruption in the inflammatory reaction interferes with tissue homeostasis, leading to chronic non-wound healing.

AIM

This study aimed to determine the diagnostic value and effect of CRP, IL-6, TNF, and HbA1c on initiation the and progression of diabetic foot ulcers.

METHOD

ELISA was used to quantify IL-6, TNF, CRP, and HbA1c in 205 patients with diabetes, and 105 were diabetic foot free. The prevalence and progression of diabetic foot were also evaluated. The area under the curve (AUC) was calculated using the receiver operating characteristic (ROC) curve to analyze the predictive values. Forward stepwise logistic regression analysis was used to compute the odds ratio (OR) and the corresponding 95% confidence intervals (CIs).

RESULTS

CRP, IL-6, and FBS were found to be significant predictors of diabetic foot (OR=1.717, 95% CI=1.250-2.358, P=0.001; OR=1.434, 95% CI=1.142-1.802, P=0.002; and OR=1.040, 95% CI=1.002-1.080, P=0.037), respectively. The AUCs for CRP, IL-6, and HbA1c in predicting diabetic foot were 0.839, 0.728, and 0.834, respectively, demonstrating a good predictive value for each diagnostic marker.

CONCLUSION

The current study demonstrated that IL-6, CRP, and HbA1c may be useful biomarkers to indicate diabetic foot progression. Furthermore, our findings showed a substantial relationship between CRP and HbA1c in individuals with diabetic foot conditions.

Taurine reduces the risk for metabolic syndrome: a systematic review and meta-analysis of randomized controlled trials

BACKGROUND

Metabolic syndrome (MetS) is a cluster of interconnected risk factors that significantly increase the likelihood of cardiovascular disease and type 2 diabetes. Taurine has emerged as a potential therapeutic agent for MetS. This meta-analysis of randomized controlled trials (RCTs) aimed to evaluate the effects of taurine supplementation on MetS-related parameters.

METHODS

We conducted electronic searches through databases like Embase, PubMed, Web of Science, Cochrane CENTRAL, and ClinicalTrials.gov, encompassing publications up to December 1, 2023. Our analysis focused on established MetS diagnostic criteria, including systolic blood pressure (SBP), diastolic blood pressure (DBP), fasting blood glucose (FBG), triglyceride (TG), and high-density lipoprotein cholesterol (HDL-C). Meta-regression explored potential dose-dependent relationships based on the total taurine dose administered during the treatment period. We also assessed secondary outcomes like body composition, lipid profile, and glycemic control.

RESULTS

Our analysis included 1024 participants from 25 RCTs. The daily dosage of taurine in the studies ranged from 0.5 g/day to 6 g/day, with follow-up periods varying between 5 and 365 days. Compared to control groups, taurine supplementation demonstrated statistically significant reductions in SBP (weighted mean difference [WMD] = -3.999 mmHg, 95% confidence interval [CI] = -7.293 to -0.706, p = 0.017), DBP (WMD = -1.509 mmHg, 95% CI = -2.479 to -0.539, p = 0.002), FBG (WMD: -5.882 mg/dL, 95% CI: -10.747 to -1.018, p = 0.018), TG (WMD: -18.315 mg/dL, 95% CI: -25.628 to -11.002, p < 0.001), but not in HDL-C (WMD: 0.644 mg/dl, 95% CI: -0.244 to 1.532, p = 0.155). Meta-regression analysis revealed a dose-dependent reduction in DBP (coefficient = -0.0108 mmHg per g, p = 0.0297) and FBG (coefficient = -0.0445 mg/dL per g, p = 0.0273). No significant adverse effects were observed compared to the control group.

CONCLUSION

Taurine supplementation exhibits positive effects on multiple MetS-related factors, making it a potential dietary addition for individuals at risk of or already experiencing MetS. Future research may explore dose-optimization strategies and potential long-term benefits of taurine for MetS management.

The Effectiveness of Clinician-Led Community-Based Group Exercise Interventions on Health Outcomes in Adults with Type 2 Diabetes Mellitus: A Systematic Review and Meta-Analysis

This systematic review and meta-analysis evaluated the combined effects of clinician-led and community-based group exercise interventions on a range of health outcomes in adults with type 2 diabetes mellitus. Our literature search spanned Medline, Scopus, PubMed, Embase, and CINAHL databases, focusing on peer-reviewed studies published between January 2003 and January 2023. We included studies involving participants aged 18 years and older and articles published in English, resulting in a dataset of eight studies with 938 participants. Spanning eight peer-reviewed studies with 938 participants, the analysis focused on the interventions' impact on glycemic control, physical fitness, and anthropometric and hematological measurements. Outcomes related to physical fitness, assessed through the six-minute walk test, the 30 s sit-to-stand test, and the chair sit-and-reach test, were extracted from five studies, all of which reported improvements. Anthropometric outcomes from seven studies highlighted positive changes in waist circumference and diastolic blood pressure; however, measures such as body mass index, systolic blood pressure, weight, and resting heart rate did not exhibit significant changes. Hematological outcomes, reviewed in four studies, showed significant improvements in fasting blood glucose, triglycerides, and total cholesterol, with glycemic control evidenced by reductions in HbA1c levels, yet LDL and HDL cholesterol levels remained unaffected. Ten of the fifteen outcome measures assessed showed significant enhancement, indicating that the intervention strategies implemented may offer substantial health benefits for managing key type 2 diabetes mellitus-related health parameters. These findings in combination with further research, could inform the refinement of physical activity guidelines for individuals with type 2 diabetes mellitus, advocating for supervised group exercise in community settings.

Dietary patterns and the risk of diabetes in Korean adults: A cross-sectional and prospective cohort study

OBJECTIVE

The aim of this study was to identify dietary patterns associated with diabetes in Korean adults and to investigate their association with diabetes risk in both a cross-sectional and prospective study.

METHODS

Predefined food groups collected by the Korea National Health and Nutrition Examination Survey (KNHANES 2015-2018, n = 19 721) were entered in a reduced rank regression (RRR) model, followed by stepwise linear regression analyses to identify the most predictive dietary patterns. We evaluated the construct validity of dietary patterns in two independent samples from KNHANES 2019 to 2021 (n = 14 223) and the Health Examinees (HEXA) cohort study (n = 30 013). Associations between dietary patterns and diabetes risk were examined using multivariable regression and multivariable-adjusted Cox proportional hazard models, respectively.

RESULTS

A dietary pattern was identified with high positive loadings for refined white rice, kimchi and salted vegetables, wheat flour and bread, and seasonings, and high negative loadings for whole grains, legumes with tofu and soymilk, poultry, eggs, and plant oils. The higher pattern scores were significantly associated with diabetes risk in KNHANES 2015 to 2018 (male: odds ratio [OR]: 1.59; 95% confidence interval [CI]: 1.35, 1.88; female: OR: 1.37; 95% CI: 1.18, 1.52), KNHANES 2019 to 2021 (male: OR: 1.47; 95% CI: 1.01, 1.69; female: OR: 1.37; 95% CI: 1.18, 1.54), and HEXA study (male: hazard ratio [HR]: 1.10; 95% CI: 1.01, 1.34; female: HR: 1.24; 95% CI: 1.02, 1.52).

CONCLUSIONS

Dietary patterns derived by RRR followed by stepwise linear regression analyses were associated with increased risks of diabetes among Korean adults.

Emerging biosensor probes for glycated hemoglobin (HbA1c) detection

Glycated hemoglobin (HbA1c), originating from the non-enzymatic glycosylation of βVal1 residues in hemoglobin (Hb), is an essential biomarker indicating average blood glucose levels over a period of 2 to 3 months without external environmental disturbances, thereby serving as the gold standard in the management of diabetes instead of blood glucose testing. The emergence of HbA1c biosensors presents affordable, readily available options for glycemic monitoring, offering significant benefits to small-scale laboratories and clinics. Utilizing nanomaterials coupled with high-specificity probes as integral components for recognition, labeling, and signal transduction, these sensors demonstrate exceptional sensitivity and selectivity in HbA1c detection. This review mainly focuses on the emerging probes and strategies integral to HbA1c sensor development. We discussed the advantages and limitations of various probes in sensor construction as well as recent advances in diverse sensing strategies for HbA1c measurement and their potential clinical applications, highlighting the critical gaps in current technologies and future needs in this evolving field.

Comparative Analysis of Biomarkers in Type 2 Diabetes Patients With and Without Comorbidities: Insights Into the Role of Hypertension and Cardiovascular Disease

Background

Type 2 diabetes mellitus (T2DM) are 90% of diabetes cases, and its prevalence and incidence, including comorbidities, are rising worldwide. Clinically, diabetes and associated comorbidities are identified by biochemical and physical characteristics including glycemia, glycated hemoglobin (HbA1c), and tests for cardiovascular, eye and kidney disease.

Objectives

Diabetes may have a common etiology based on inflammation and oxidative stress that may provide additional information about disease progression and treatment options. Thus, identifying high-risk individuals can delay or prevent diabetes and its complications.

Design

In patients with or without hypertension and cardiovascular disease, as part of progression from no diabetes to T2DM, this research studied the changes in biomarkers between control and prediabetes, prediabetes to T2DM, and control to T2DM, and classified patients based on first-attendance data. Control patients and patients with hypertension, cardiovascular, and with both hypertension and cardiovascular diseases are 156, 148, 61, and 216, respectively.

Methods

Linear discriminant analysis is used for classification method and feature importance, This study examined the relationship between Humanin and mitochondrial protein (MOTSc), mitochondrial peptides associated with oxidative stress, diabetes progression, and associated complications.

Results

MOTSc, reduced glutathione and glutathione disulfide ratio (GSH/GSSG), interleukin-1β (IL-1β), and 8-isoprostane were significant (P < .05) for the transition from prediabetes to t2dm, highlighting importance of mitochondrial involvement. complement component 5a (c5a) is a biomarker associated with disease progression and comorbidities, gsh gssg, monocyte chemoattractant protein-1 (mcp-1), 8-isoprostane being most important biomarkers.

Conclusions

Comorbidities affect the hypothesized biomarkers as diabetes progresses. Mitochondrial oxidative stress indicators, coagulation, and inflammatory markers help assess diabetes disease development and provide appropriate medications. Future studies will examine longitudinal biomarker evolution.

Glycated hemoglobin (HbA1c) is independently associated with the bioelectrical impedance phase angle in junior sumo wrestlers: A pilot study

The study explores the relationship between phase angle (PhA), an indicator of cellular health, and metabolic health parameters among junior sumo wrestlers in Japan. Given the demanding lifestyle and high-energy diets of sumo wrestlers that predispose them to metabolic syndrome post-retirement, this study focuses on a younger cohort. The primary aim is to evaluate if PhA could serve as an early indicator of metabolic health issues within this unique demographic. A total of 14 sumo wrestlers aged 9-17 years were assessed to determine the relationship between PhA and various metabolic markers, including glycated hemoglobin (HbA1c), using a TANITA MC-780A-N body composition analyzer and standard blood tests. Bivariate regression analysis and Pearson's correlation revealed a negative relationship between PhA and HbA1c even after adjusting for age and weight (ß = -0.496, r2 = 0.776, r = -0.756, p = 0.004). The results indicate a significant negative relationship between PhA and HbA1c levels, suggesting that lower PhA values, which indicate poorer cellular integrity, are associated with higher HbA1c levels, signifying impaired glycemic control. These findings underscore the potential of PhA as a valuable biomarker for monitoring metabolic health in young sumo wrestlers, with implications for early intervention and management strategies.

Baseline glycated albumin level and risk of type 2 diabetes mellitus in Healthy individuals: a retrospective longitudinal observation in Korea

Glycated albumin (GA) reflects glycemic status for the past three weeks. GA level demonstrates a strong correlation with HbA1c level and is used as an adjunctive biomarker for diagnosis and monitoring of type 2 diabetes mellitus (T2DM). In this study, we validated the predictive performance of baseline GA for development of T2DM in healthy individuals in Korea. From August 2013 to September 2014, the medical records of 3,771 healthy Koreans were retrospectively reviewed. Each participant was categorized into tertiles based on initial GA level. During the follow-up period through May 2020, study participants were evaluated for T2DM using HbA1c, fasting glucose level, and a self-reported diagnosis history. Baseline GA level by tertile (T1 to T3) was 10.4 ± 0.8% (mean ± SD), 12.1 ± 0.3%, and 13.7 ± 0.9%, respectively. The median follow-up was 5.97 years, during which 4.9% (186 of 3,771) of the participants developed T2DM. After adjusting for confounding factors, the hazard ratio for the development of T2DM in the highest GA level group (T3) compared to the reference group (T1) was 2.46 (95% CI, 1.7 to 3.58, p < 0.001 for trend) with a Harrell's C index of 0.80 (95% CI, 0.76 to 0.83). Also, within highest group of baseline HbA1c and FG levels, higher GA levels were associated with an increased HRs for T2DM. In conclusion, Our study confirms that the risk of T2DM increases with baseline GA level. Additional follow-up of the cohort is warranted to investigate the correlations between GA and other clinical indicators including diabetic complications.

Association between Diabetes and Exfoliation Syndrome

Topic

This systematic review and meta-analysis summarizes the existing evidence for the association of diabetes mellitus (DM) and exfoliation syndrome (XFS).

Clinical Relevance

Understanding and quantifying these associations may aid clinical guidelines or treatment strategies and shed light on disease pathogenesis. The role of DM in determining XFS risk may also be of interest from an individual or public health perspective.

Methods

The study protocol was preregistered on the International Prospective Register of Systematic Reviews with registration number CRD42023429771. We systematically searched PubMed and Embase from inception to June 15, 2023. Screening and full-text review were conducted by 2 independent reviewers. All observational studies reporting an age-adjusted odds ratio (OR) and 95% confidence interval (CI) for the association between DM and XFS among adults were included. Quantitative synthesis involved a random-effects meta-analysis using the DerSimonian-Laird method to generate a pooled OR. Risk of bias was evaluated using the Newcastle-Ottawa Scale.

Results

Fourteen studies (9 cross-sectional and 5 case-control) comprising 47 853 participants were included in the systematic review and meta-analysis. Random-effects meta-analysis indicated no overall association between DM and XFS (OR 0.94; 95% CI, 0.73–1.21; I 2  = 68.5%). However, subgroup analysis revealed a significant inverse association among individuals ≥ 65 years (OR 0.71; 95% CI, 0.54–0.93) versus individuals < 65 years (OR 1.22; 95% CI, 0.80–1.87; P effect modification  = 0.04). The relation between DM and XFS was also inverse in case-control studies (OR 0.75; 95% CI, 0.58–0.97) but was nonsignificant in cross-sectional studies (OR 1.17; 95% CI, 0.83–1.66; P effect modification  = 0.04). Overall risk of bias was low, with tests for publication bias showing P  ≥ 0.06.

Conclusion

This meta-analysis suggests no association between DM and XFS overall, with possible inverse associations of DM with XFS in older populations. However, given the substantial heterogeneity and borderline significance for publication bias, these findings should be interpreted with caution. Our results give insight into the unique etiology and clinical relevance of XFS while proposing the need for larger longitudinal and genetic biomarker studies.

Financial Disclosure(s)

Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.

Impartial feature selection using multi-agent reinforcement learning for adverse glycemic event prediction

We developed an attention model to predict future adverse glycemic events 30 min in advance based on the observation of past glycemic values over a 35 min period. The proposed model effectively encodes insulin administration and meal intake time using Time2Vec (T2V) for glucose prediction. The proposed impartial feature selection algorithm is designed to distribute rewards proportionally according to agent contributions. Agent contributions are calculated by a step-by-step negation of updated agents. Thus, the proposed feature selection algorithm optimizes features from electronic medical records to improve performance. For evaluation, we collected continuous glucose monitoring data from 102 patients with type 2 diabetes admitted to Cheonan Hospital, Soonchunhyang University. Using our proposed model, we achieved F1-scores of 89.0%, 60.6%, and 89.8% for normoglycemia, hypoglycemia, and hyperglycemia, respectively.

Use of Online Communities among People with Type 2 Diabetes: A Scoping Review

PURPOSE OF REVIEW

People with diabetes require continuous self-monitoring and face numerous decisions in their day-to-day lives. Therefore, on many occasions, they need more support than that provided by health professionals. In this context, peer support in online diabetes communities could be a useful tool. The purpose of the review is to describe, analyze and synthesize the available evidence on the use and health out-comes of online communities for people with type 2 diabetes mellitus. A scoping review was conducted in accordance with the Joanna Briggs Institute guidelines. Searches were performed PubMed, Web of Science, CINHAL, Scopus and Cochrane databases.

RECENT FINDINGS

From 1821 identified documents, 6 articles were included. These studies explored the characteristics of diabetes online communities and the population features. Besides, the results were classified according to whether they were clinical, psychosocial, or addressed people's experiences with the online community. The analysis underscores their value in facilitating communication, improving diabetes management, and enhancing psychosocial well-being. Future investigations should prioritize longitudinal assessments to elucidate the sustained impact of community engagement and optimize user participation for enhanced patient outcomes. The growing relevance of new technologies has led to a significant number of individuals with chronic illnesses seeking peer support. Online health communities have emerged as virtual spaces where individuals with shared health interests interact and form relationships. Within these digital spaces, individuals can engage in peer interaction, observe behaviors, and mutually benefit, potentially leading to improved attitudes toward the disease.

Indicators of Dietary Behavior and Physical Activity Change Associated with Metabolic Control of Obesity, Hypertension, and Type 2 Diabetes Mellitus in Mexican Adults: National Nutrition and Health Survey in Mexico, 2016

Introduction: Obesity (OB), type 2 diabetes mellitus (T2D), and hypertension (HTN) are health issues in Mexico linked to unhealthy behaviors. This study investigates the relationship between behavior change indicators and metabolic control in Mexican adults with OB, T2D, and HTN. Methods: We used data from the 2016 National Health and Nutrition Survey Midway (ENSANUT MC-2016), representing ∼59.5 million Mexican adults aged 20-59 with these conditions. We assessed behavior change indicators, including stages of change, self-efficacy, and perceptions of benefits and barriers. In addition, we conducted descriptive analyses and used statistical tests, such as Pearson's chi-squared test and logistic regression models, adjusted for multiple variables. Results: We found that adults in the action and maintenance stages of physical activity (PA) were four times more likely to have adequate HTN control than those in the precontemplation stage. Self-efficacy for PA was related to better control in T2D and HTN. Self-efficacy for reducing the consumption of sugary beverages was positively associated with control in OB and T2D. No significant association was observed with self-efficacy for consuming fruits and vegetables. Conclusion: Behavior-change indicators are significantly linked to metabolic control in adults with HTN. These results support the importance of these indicators in managing chronic diseases such as HTN and their potential use in public health strategies.

Identification of potential serum biomarkers associated with HbA1c levels in Indian type 2 diabetic subjects using NMR-based metabolomics

BACKGROUND

The prevalence of type 2 diabetes mellitus (T2DM), a progressive metabolic disorder characterized by chronic hyperglycemia and the development of insulin resistance, has increased globally, with worrying statistics coming from children, adolescents, and young adults from developing countries like India. Here, we investigated unique circulating metabolic signatures associated with prediabetes and T2DM in an Indian cohort using NMR-based metabolomics.

MATERIALS AND METHODS

The study subjects included healthy volunteers (N = 101), prediabetic subjects (N = 75), and T2DM patients (N = 108). Serum metabolic profiling was performed using 1H NMR spectroscopy and major perturbed metabolites were identified by multivariate analysis and receiver operating characteristic (ROC) modules.

RESULTS

Of the 36 aqueous abundant metabolites, 24 showed a statistically significant difference between healthy volunteers, prediabetics, and established T2DM subjects. On performing multivariate ROC curve analysis with 5 commonly dysregulated metabolites (namely, glucose, pyroglutamate, o-phosphocholine, serine, and methionine) in prediabetes and T2DM, AUC values obtained were 0.96 (95 % confidence interval (CI) = 0.93, 0.98) for T2DM; and 0.88 (95 % CI = 0.81, 0.93) for prediabetic subjects, respectively.

CONCLUSION

We propose that the identified metabolite panel can be used in the future as a biomarker for clinical diagnosis, patient surveillance, and for predicting individuals at risk for developing diabetes.

Probiotics and synbiotics for glycemic control in diabetes: A systematic review and meta-analysis of randomized controlled trials

BACKGROUND & AIMS

The escalating prevalence of diabetes mellitus may benefit from add-on therapeutic approaches. Given the recognized need for an updated synthesis of the literature, this systematic review and meta-analysis aimed to synthesize and critically assess the available randomized controlled trials (RCTs) that investigate the efficacy of probiotics and synbiotics on glycemic control in patients with Type 1 (T1DM) and Type 2 (T2DM) diabetes mellitus.

METHODS

Comprehensive searches were conducted on PubMed, Embase, CINAHL, Scopus, and Web of Science, focusing on adults with T1DM or T2DM. All comparators were deemed eligible. Primary outcomes included changes in glycated hemoglobin (HbA1c), fasting plasma glucose (FPG), and insulin levels. Only RCTs were included, and the Cochrane RoB2 tool assessed the risk of bias. Random-effect models facilitated data analysis, supplemented by sensitivity, subgroup analyses, and meta-regressions.

RESULTS

A total of 537 records were screened, resulting in 41 RCTs for analysis, which comprises 2991 (54% females) patients with diabetes. The meta-analysis revealed statistically significant improvements in HbA1c (standardized mean difference (SMD) = -0.282, 95% CI: [-0.37, -0.19], p < 0.001), FPG (SMD = -0.175, 95% CI: [-0.26, -0.09], p < 0.001), and insulin levels (SMD = -0.273, 95% CI: [-0.35, -0.20], p < 0.001). A medium degree of heterogeneity between studies was found in HbA1c (I2 = 62.5%), FPG (I2 = 71.5%), and insulin levels (I2 = 66.4%) analyses. Subgroup analyses indicated that the efficacy varied based on the type of strains used and the country. Multispecies strains were particularly effective in improving HbA1c levels.

CONCLUSION

The study findings suggest that probiotics and synbiotics may be effective as complementary therapies for managing diabetes. Additionally, the study underscores the need for further tailored research that considers variables such as strain types and geographical factors to deepen the understanding of the role of these interventions in diabetes care.

REVIEW REGISTRATION NUMBER

PROSPERO (CRD42023396348).

Linking elevated HbA1c with atherogenic lipid profile among high risk cardiovascular patients at Qassim, Saudi Arabia

The relationship between glycated hemoglobin (HbA1c) and an atherogenic lipid profile which is associated with a higher risk of cardiovascular disease is of interest. A retrospective cross-sectional study was conducted on 83 participants aged between 14 and 77 years. Their venous blood was drawn to determine the HbA1c and fasting lipid profile including total cholesterol triglycerides and high-density lipoprotein cholesterol (HDL-C) low-density lipoprotein cholesterol (LDL-C) non-HDL cholesterol and the LDL/HDL ratio were also calculated. The correlations between HbA1c levels and these lipid profile parameters were analyzed. The study showed a significant correlation between HbA1c and LDL-C non-HDL-C and the LDL/HDL ratio. Although there was no significant difference in total cholesterol levels among all groups the levels of total cholesterol and HbA1c were positively correlated. HDL-C exhibited direct correlations with HbA1c there was no correlation between HbA1c and clinical characteristics except for age. Data shows that HbA1c can be used as a predictor of dyslipidemia in diabetic patients there is a significant correlation between HbA1c and an atherogenic lipid profile which highlights the importance of glycemic control in reducing the risk of cardiovascular disease.

Establishment of reference interval for hemoglobin A1C and other hemoglobin subfractions for healthy Saudi adults

BACKGROUND

The establishment of Reference Intervals (RIs) for Hemoglobin A1C and other hemoglobin subfractions (A1A, A1B, F, LA1C, A0) is of utmost importance in screening, diagnosing, and monitoring diabetes and other hemoglobin abnormalities through the application of high-pressure liquid chromatography (HPLC) technique. Because there are no locally established RIs for these parameters, it is essential to establish RIs specific to the Saudi population to accurately diagnose and monitor diabetic individuals and identify abnormal levels in hemoglobin subfractions.

METHODS

As part of the IFCC global multicenter study of laboratory reference values, a cross-sectional study was conducted in Saudi Arabia. The study involved recruiting a total of 381 healthy adult subjects (>18 years, BMI 28.3 ± 6 kg/m2). Blood samples were analyzed for A1C, biochemical and other immunoassay parameters. The need for RIs based on sex, age, and BMI was determined using the standard deviation ratio (SDR) through a 3-level nested ANOVA.

RESULTS

Based on the threshold of SDR≥0.4, RIs for A1C and other Hb subfractions were not partitioned by sex or BMI, but partitioned by age (<45 & ≥45 years) for A1C, LA1C, A0 and F. Spearman's correlation between glucose, insulin, and C-peptide showed a positive association with different hemoglobin subtractions of A1B, F, A1C, and LA1C. The RIs were obtained by using the parametric method and the latent abnormal values exclusion (LAVE) principle was applied on A1C.

CONCLUSION

This study established RIs for A1C and other Hb subfractions for healthy adult Saudis. Age was found to be an important source of variation for most of the parameters including A1C. These findings will enhance the understanding and clinical decision-making concerning A1C and other hemoglobin subfractions. The elevated upper limit of RIs for A1C reflects the high prevalence of diabetes in the Saudi population specially in those with increased age.

The Key Digital Tool Features of Complex Telehealth Interventions Used for Type 2 Diabetes Self-Management and Monitoring With Health Professional Involvement: Scoping Review

BACKGROUND

Therapeutic education and patient self-management are crucial in diabetes prevention and treatment. Improving diabetes self-management requires multidisciplinary team intervention, nutrition education that facilitates self-management, informed decision-making, and the organization and delivery of appropriate health care services. The emergence of telehealth services has provided the public with various tools for educating themselves and for evaluating, monitoring, and improving their health and nutrition-related behaviors. Combining health technologies with clinical expertise, social support, and health professional involvement could help persons living with diabetes improve their disease self-management skills and prevent its long-term consequences.

OBJECTIVE

This scoping review's primary objective was to identify the key digital tool features of complex telehealth interventions used for type 2 diabetes or prediabetes self-management and monitoring with health professional involvement that help improve health outcomes. A secondary objective was to identify how these key features are developed and combined.

METHODS

A 5-step scoping review methodology was used to map relevant literature published between January 1, 2010 and March 31, 2022. Electronic searches were performed in the MEDLINE, CINAHL, and Embase databases. The searches were limited to scientific publications in English and French that either described the conceptual development of a complex telehealth intervention that combined self-management and monitoring with health professional involvement or evaluated its effects on the therapeutic management of patients with type 2 diabetes or prediabetes. Three reviewers independently identified the articles and extracted the data.

RESULTS

The results of 42 studies on complex telehealth interventions combining diabetes self-management and monitoring with the involvement of at least 1 health professional were synthesized. The health professionals participating in these studies were physicians, dietitians, nurses, and psychologists. The digital tools involved were smartphone apps or web-based interfaces that could be used with medical devices. We classified the features of these technologies into eight categories, depending on the intervention objective: (1) monitoring of glycemia levels, (2) physical activity monitoring, (3) medication monitoring, (4) diet monitoring, (5) therapeutic education, (6) health professional support, (7) other health data monitoring, and (8) health care management. The patient-logged data revealed behavior patterns that should be modified to improve health outcomes. These technologies, used with health professional involvement, patient self-management, and therapeutic education, translate into better control of glycemia levels and the adoption of healthier lifestyles. Likewise, they seem to improve monitoring by health professionals and foster multidisciplinary collaboration through data sharing and the development of more concise automatically generated reports.

CONCLUSIONS

This scoping review synthesizes multiple studies that describe the development and evaluation of complex telehealth interventions used in combination with health professional support. It suggests that combining different digital tools that incorporate diabetes self-management and monitoring features with a health professional's advice and interaction results in more effective interventions and outcomes.

Taking appreciation to heart: appreciation at work and cardiovascular risk in male employees

Introduction

While perceived appreciation at work has been associated with self-reported health and wellbeing, studies considering biological health markers are lacking. In this study, we investigated whether appreciation at work would relate to coronary heart disease (CHD) risk as well as the specificity of this proposed association.

Methods

Our study comprised a total of 103 male participants, including apparently healthy, medication-free, non-smoking men in the normotensive to hypertensive range (n = 70) as well as medicated hypertensive and CHD patients (n = 33). CHD risk was assessed by blood pressure [mean arterial pressure (MAP)], the diabetes marker glycated hemoglobin A1c (HbA1c), blood lipids [total cholesterol (TC)/high-density lipoprotein-cholesterol (HDL-C) ratio], coagulation activity (D-dimer and fibrinogen), and inflammation [interleukin (IL)-6, tumor necrosis factor-alpha (TNF-α), and C-reactive protein (CRP)]. Perceived appreciation at work, as well as potentially confounding psychological factors (social support, self-esteem, and work strain due to a lack of appreciation), were measured by self-report questionnaires.

Results

We found higher appreciation at work to relate to lower overall composite CHD risk (p's ≤ 0.011) and, in particular, to lower MAP (p's ≤ 0.007) and lower blood lipids (p's ≤ 0.031) in medication-free participants as well as all participants. This overall association was independent of confounding factors, including related psychological factors (p's ≤ 0.049).

Discussion

Our findings indicate that appreciation at work might be an independent health-promoting resource in terms of CHD risk. Implications include that encouraging appreciation at work may help reduce the development and progression of CHD.

Exploring metformin monotherapy response in Type-2 diabetes: Computational insights through clinical, genomic, and proteomic markers using machine learning algorithms

BACKGROUND

In 2016, the UK had 4.5 million people with diabetes, predominantly Type-2 Diabetes Mellitus (T2DM). The NHS allocates £10 billion (9% of its budget) to manage diabetes. Metformin is the primary treatment for T2DM, but 35% of patients don't benefit from it, leading to complications. This study aims to delve into metformin's efficacy using clinical, genomic, and proteomic data to uncover new biomarkers and build a Machine Learning predictor for early metformin response detection.

METHODS

Here we report analysis from a T2DM dataset of individuals prescribed metformin monotherapy from the Diastrat cohort recruited at the Altnagelvin Area Hospital, Northern Ireland.

RESULTS

In the clinical data analysis, comparing responders (those achieving HbA1c ≤ 48 mmol/mol) to non-responders (with HbA1c > 48 mmol/mol), we identified that creatinine levels and bodyweight were more negatively correlated with response than non-response. In genomic analysis, we identified statistically significant (p-value <0.05) variants rs6551649 (LPHN3), rs6551654 (LPHN3), rs4495065 (LPHN3) and rs7940817 (TRPC6) which appear to differentiate the responders and non-responders. In proteomic analysis, we identified 15 statistically significant (p-value <0.05, q-value <0.05) proteomic markers that differentiate controls, responders, non-responders and treatment groups, out of which the most significant were HAOX1, CCL17 and PAI that had fold change ∼2. A machine learning model was build; the best model predicted non-responders with 83% classification accuracy.

CONCLUSION

Further testing in prospective validation cohorts is required to determine the clinical utility of the proposed model.

Identifying Ontarians with Type 2 Diabetes Mellitus in Administrative Data: A Comparison of Two Case Definitions

INTRODUCTION

This study compared two previously validated sensitive and specific diabetes case definitions to explore the impact of different classification methods in Ontario ICES administrative data.

METHODS

This study included patients captured by the Ontario Diabetes Database with type 2 diabetes using either the sensitive cohort definition (≥ 2 physician visits for diabetes within 1 year or ≥ 1 drug claim for diabetes or ≥ 1 hospitalization with diabetes), or the specific cohort definition (≥ 3 physician visits for diabetes within 1 year), between October 1, 2013 to September 30, 2015. Each cohort's demographic and clinical features were described using descriptive analysis.

RESULTS

Using sensitive and specific definitions, 1,093,812 and 783,228 patients with type 2 diabetes were identified, respectively. Overall, the demographic and clinical characteristics were similar between cohorts. Patients in the sensitive cohort had mean age of 64.1 years and were 52.4% male, compared to 64.8 years and 53.6% male in the specific cohort. In the sensitive and specific cohorts respectively, 64.4% and 55.7% of patients reported one-year mean HbA1c of < 7% (53 mmol/mol) and 25.3% and 31.5% reported levels between 7.0-8.5% (53-69 mmol/mol).

CONCLUSIONS

Although sample sizes were different between sensitive and specific cohorts, demographic and clinical characteristics were similar.

Effect of advanced glycation end-products in a wide range of medical problems including COVID-19

Glycation is a physiological process that determines the aging of the organism, while in states of metabolic disorders it is significantly intensified. High concentrations of compounds such as reducing sugars or reactive aldehydes derived from lipid oxidation, occurring for example in diabetes, atherosclerosis, dyslipidemia, obesity or metabolic syndrome, lead to increased glycation of proteins, lipids and nucleic acids. The level of advanced glycation end-products (AGEs) in the body depends on rapidity of their production and the rate of their removal by the urinary system. AGEs, accumulated in the extracellular matrix of the blood vessels and other organs, cause irreversible changes in the biochemical and biomechanical properties of tissues. As a consequence, micro- and macroangiopathies appear in the system, and may contribute to the organ failure, like kidneys and heart. Elevated levels of AGEs also increase the risk of Alzheimer's disease and various cancers. In this paper, we propose a new classification due to modified amino acid residues: arginyl-AGEs, monolysyl-AGEs and lysyl-arginyl-AGEs and dilysyl-AGEs. Furthermore, we describe in detail the effect of AGEs on the pathogenesis of metabolic and old age diseases, such as diabetic complications, atherosclerosis and neurodegenerative diseases. We summarize the currently available data on the diagnostic value of AGEs and present the AGEs as a therapeutic goal in a wide range of medical problems, including SARS-CoV-2 infection and so-called long COVID.

Risk of major congenital heart disease in pregestational maternal diabetes is modified by hemoglobin A1c

OBJECTIVES

The association between pregestational diabetes mellitus (PDM) and risk of congenital heart disease (CHD) is well recognized; however, the importance of glycemic control and other coexisting risk factors during pregnancy is less clear. We sought to determine the relative risk (RR) of major CHD (mCHD) among offspring from pregnancies complicated by PDM and the effect of first-trimester glycemic control on mCHD risk.

METHODS

We determined the incidence of mCHD (requiring surgery within 1 year of birth or resulting in pregnancy termination or fetal demise) among registered births in Alberta, Canada. Linkage of diabetes status, maximum hemoglobin A1c (HbA1c) at < 16 weeks' gestation and other covariates was performed using data from the Alberta Perinatal Health Program registry. Risk of mCHD according to HbA1c was estimated as an adjusted RR (aRR), calculated using log-binomial modeling.

RESULTS

Of 1412 cases of mCHD in 594 773 (2.37/1000) births in the study period, mCHD was present in 48/7497 with PDM (6.4/1000; RR, 2.8 (95% CI, 2.1-3.7); P < 0.0001). In the entire cohort, increased maternal age (aRR, 1.03 (95% CI, 1.02-1.04); P < 0.0001) and multiple gestation (aRR, 1.37 (95% CI, 1.1-1.8); P = 0.02) were also associated with mCHD risk, whereas maternal prepregnancy weight > 91 kg was not. The stratified risk for mCHD associated with HbA1c ≤ 6.1%, > 6.1-8.0% and > 8.0% was 4.2/1000, 6.8/1000 and 17.1/1000 PDM/gestational diabetes mellitus births, respectively; the aRR of mCHD associated with PDM and HbA1c > 8.0% was 8.5 (95% CI, 5.0-14.4) compared to those without diabetes and 5.5 (95% CI, 1.6-19.4) compared to PDM with normal HbA1c (≤ 6.1%).

CONCLUSIONS

PDM is associated with a RR of 2.8 for mCHD, increasing to 8.5 in those with HbA1c > 8%. These data should facilitate refinement of referral indications for high-risk pregnancy screening. © 2023 The Authors. Ultrasound in Obstetrics & Gynecology published by John Wiley & Sons Ltd on behalf of International Society of Ultrasound in Obstetrics and Gynecology.

Vitamin D supplementation modulates glycated hemoglobin (HBA1c) in diabetes mellitus

Diabetes is a metabolic illness that increases protein glycosylation in hyperglycemic conditions, which can have an impact on almost every organ system in the body. The role of vitamin D in the etiology of diabetes under RAGE (receptor for advanced glycation end products) stress has recently received some attention on a global scale. Vitamin D's other skeletal benefits have generated a great deal of research. Vitamin D's function in the development of type 1 and type 2 diabetes is supported by the discovery of 1,25 (OH)2D3 and 1-Alpha-Hydroylase expression in immune cells, pancreatic beta cells, and several other organs besides the bone system. A lower HBA1c level, metabolic syndrome, and diabetes mellitus all seems to be associated with vitamin D insufficiency. Most of the cross-sectional and prospective observational studies that were used to gather human evidence revealed an inverse relationship between vitamin D level and the prevalence or incidence of elevated HBA1c in type 2 diabetes. Several trials have reported on the impact of vitamin D supplementation for glycemia or incidence of type 2 diabetes, with varying degrees of success. The current paper examines the available data for a relationship between vitamin D supplementation and HBA1c level in diabetes and discusses the biological plausibility of such a relationship.

Ensemble machine learning reveals key features for diabetes duration from electronic health records

Diabetes is a metabolic disorder that affects more than 420 million of people worldwide, and it is caused by the presence of a high level of sugar in blood for a long period. Diabetes can have serious long-term health consequences, such as cardiovascular diseases, strokes, chronic kidney diseases, foot ulcers, retinopathy, and others. Even if common, this disease is uneasy to spot, because it often comes with no symptoms. Especially for diabetes type 2, that happens mainly in the adults, knowing how long the diabetes has been present for a patient can have a strong impact on the treatment they can receive. This information, although pivotal, might be absent: for some patients, in fact, the year when they received the diabetes diagnosis might be well-known, but the year of the disease unset might be unknown. In this context, machine learning applied to electronic health records can be an effective tool to predict the past duration of diabetes for a patient. In this study, we applied a regression analysis based on several computational intelligence methods to a dataset of electronic health records of 73 patients with diabetes type 1 with 20 variables and another dataset of records of 400 patients of diabetes type 2 with 49 variables. Among the algorithms applied, Random Forests was able to outperform the other ones and to efficiently predict diabetes duration for both the cohorts, with the regression performances measured through the coefficient of determination R2. Afterwards, we applied the same method for feature ranking, and we detected the most relevant factors of the clinical records correlated with past diabetes duration: age, insulin intake, and body-mass index. Our study discoveries can have profound impact on clinical practice: when the information about the duration of diabetes of patient is missing, medical doctors can use our tool and focus on age, insulin intake, and body-mass index to infer this important aspect. Regarding limitations, unfortunately we were unable to find additional dataset of EHRs of patients with diabetes having the same variables of the two analyzed here, so we could not verify our findings on a validation cohort.

Social determinants of health and diabetes: using a nationally representative sample to determine which social determinant of health model best predicts diabetes risk

OBJECTIVES

Social determinants of health (SDOH) research demonstrates poverty, access to healthcare, discrimination, and environmental factors influence health outcomes. Several models are commonly used to assess SDOH, yet there is limited understanding of how these models differ regarding their ability to predict the influence of social determinants on diabetes risk. This study compares the utility of four SDOH models for predicting diabetes disparities.

STUDY DESIGN

We utilized The National Longitudinal Study of Adolescent to Adulthood (Add Health) to compare SDOH models and their ability to predict risk of diabetes and obesity.

METHODS

Previous literature has identified the World Health Organization (WHO), Healthy People, County Health Rankings, and Kaiser Family Foundation as the conventional SDOH models. We used these models to operationalize SDOH using the Add Health dataset. Add Health data were used to perform logistic regressions for HbA1c and linear regressions for body mass index (BMI).

RESULTS

The Kaiser model accounted for the largest proportion of variance (19%) in BMI. Race/ethnicity was a consistent factor predicting BMI across models. Regarding HbA1c, the Kaiser model also accounted for the largest proportion of variance (17%). Race/ethnicity and wealth was a consistent factor predicting HbA1c across models.

CONCLUSION

Policy and practice interventions should consider these factors when screening for and addressing the effects of SDOH on diabetes risk. Specific SDOH models can be constructed for diabetes based on which determinants have the largest predictive value.

Effects of energy-restricted diets with or without nuts on weight, body composition and glycaemic control in adults: a scoping review

Energy-restricted (ER) diets promote weight loss and improve body composition and glycaemic control. Nut consumption also improves these parameters. However, less is known about the combined benefit of these two strategies. This scoping review implemented a systematic search of Medline, Embase and Scopus to identify randomised controlled trials evaluating the effect of ER diets with or without nuts on body mass, body composition and glycaemic control in adults. After reviewing titles and abstracts, twenty-nine full-text articles were screened, resulting in seven studies reported in eight papers that met the inclusion criteria. Energy restriction was achieved by prescribing a set energy target or reducing intake by 1000-4200 kJ from daily energy requirements. Interventions ranged from 4 to 52 weeks in duration and contained 42-84 g/d of almonds, peanuts, pistachios or walnuts. While all studies reported that energy restriction resulted in significant weight loss, the addition of nuts to ER diets demonstrated significantly greater weight loss in only approximately half of the included studies (4/7 studies). There was limited evidence to support additional benefits from nuts for body composition measures or glycaemic control. Although improvements in weight loss and glycaemia were not consistent when nuts were included in ER diets, no study revealed an adverse effect of nut consumption on health outcomes. Future studies could explore the effect of consuming different types and amounts of nuts, combined with various levels of energy restriction on weight, body composition and glycaemic control.

Evaluating the coding accuracy of type 2 diabetes mellitus among patients with non-alcoholic fatty liver disease

BACKGROUND

Non-alcoholic fatty liver disease (NAFLD) describes a spectrum of chronic fattening of liver that can lead to fibrosis and cirrhosis. Diabetes has been identified as a major comorbidity that contributes to NAFLD progression. Health systems around the world make use of administrative data to conduct population-based prevalence studies. To that end, we sought to assess the accuracy of diabetes International Classification of Diseases (ICD) coding in administrative databases among a cohort of confirmed NAFLD patients in Calgary, Alberta, Canada.

METHODS

The Calgary NAFLD Pathway Database was linked to the following databases: Physician Claims, Discharge Abstract Database, National Ambulatory Care Reporting System, Pharmaceutical Information Network database, Laboratory, and Electronic Medical Records. Hemoglobin A1c and diabetes medication details were used to classify diabetes groups into absent, prediabetes, meeting glycemic targets, and not meeting glycemic targets. The performance of ICD codes among these groups was compared to this standard. Within each group, the total numbers of true positives, false positives, false negatives, and true negatives were calculated. Descriptive statistics and bivariate analysis were conducted on identified covariates, including demographics and types of interacted physicians.

RESULTS

A total of 12,012 NAFLD patients were registered through the Calgary NAFLD Pathway Database and 100% were successfully linked to the administrative databases. Overall, diabetes coding showed a sensitivity of 0.81 and a positive predictive value of 0.87. False negative rates in the absent and not meeting glycemic control groups were 4.5% and 6.4%, respectively, whereas the meeting glycemic control group had a 42.2% coding error. Visits to primary and outpatient services were associated with most encounters.

CONCLUSION

Diabetes ICD coding in administrative databases can accurately detect true diabetic cases. However, patients with diabetes who meets glycemic control targets are less likely to be coded in administrative databases. A detailed understanding of the clinical context will require additional data linkage from primary care settings.

Simultaneous electrochemical detection of glycated and human serum albumin for diabetes management

Developing highly selective and sensitive biosensors for diabetes management blood glucose monitoring is essential to reduce the health risks associated with diabetes. Assessing the glycation (GA) of human serum albumin (HSA) serves as an indicator for medium-term glycemic control, making it suitable for assessing the efficacy of blood glucose management protocols. However, most biosensors are not capable of simultaneous detection of the relative fraction of GA to HSA in a clinically relevant range. Here, we report an effective miniaturised biosensor architecture for simultaneous electrochemical detection of HSA and GA across relevant concentration ranges. We immobilise DNA aptamers specific for the detection of HSA and GA on gold nanoislands (Au NIs) decorated screen-printed carbon electrodes (SPCEs), and effectively passivate the residual surface sites. We achieve a dynamic detection range between 20 and 60 mg/mL for HSA and 1-40 mg/mL for GA in buffer solutions. The analytical utility of our HSA and GA biosensor architectures are validated in mice serum indicating immediate potential for clinical applications. Since HSA and GA have similar structures, we extensively assess our sensor specificity, observing high selectivity of the HSA and GA sensors against each other and other commonly present interfering molecules in blood such as glucose, glycine, ampicillin, and insulin. Additionally, we determine the glycation ratio, which is a crucial metric for assessing blood glucose management efficacy, in an extensive range representing healthy and poor blood glucose management profiles. These findings provide strong evidence for the clinical potential of our biosensor architecture for point-of-care and self-assessment of diabetes management protocols.

Exploration and Application of DNA-Binding Proteins to Make a Versatile DNA-Protein Covalent-Linking Patch (D-Pclip): The Case of a Biosensing Element

DNA-protein complexes are attractive components with broad applications in various research fields, such as DNA aptamer-enzyme complexes as biosensing elements. However, noncovalent DNA-protein complexes often decrease detection sensitivity because they are highly susceptible to environmental conditions. In this study, we developed a versatile DNA-protein covalent-linking patch (D-Pclip) for fabricating covalent and stoichiometric DNA-protein complexes. We comprehensively explored the database to determine the DNA-binding ability of the candidates and selected UdgX as the only uracil-DNA glycosylase known to form covalent bonds with DNA via uracil, with a binding efficiency >90%. We integrated a SpyTag/SpyCatcher protein-coupling system into UdgX to create a universal and convenient D-Pclip. The usability of D-Pclip was shown by preparing a stoichiometric model complex of a hemoglobin (Hb)-binding aptamer and glucose oxidase (GOx) by mixing at 4 °C. The prepared aptamer-GOx complexes detected Hb in a dose-dependent manner within the clinically required detection range in buffer and human serum without any washing procedures. D-Pclip covalently connects any uracil-inserted DNA sequence and any SpyCatcher-fused protein stoichiometrically; therefore, it has a high potential for various applications.

Injecting drug use and hepatitis C virus infection independently increase biomarkers of inflammatory disease risk which are incompletely restored by curative direct-acting antiviral therapy

Background

Hepatitis C virus (HCV) infections are more prevalent in people who inject drugs (PWID) who often experience additional health risks. HCV induces inflammation and immune alterations that contribute to hepatic and non-hepatic morbidities. It remains unclear whether curative direct acting antiviral (DAA) therapy completely reverses immune alterations in PWID.

Methods

Plasma biomarkers of immune activation associated with chronic disease risk were measured in HCV-seronegative (n=24) and HCV RNA+ (n=32) PWID at baseline and longitudinally after DAA therapy. Adjusted generalised estimating equations were used to assess longitudinal changes in biomarker levels. Comparisons between community controls (n=29) and HCV-seronegative PWID were made using adjusted multiple regression modelling.

Results

HCV-seronegative PWID exhibited significantly increased levels of inflammatory biomarkers including soluble (s) TNF-RII, IL-6, sCD14 and sCD163 and the diabetes index HbA1c as compared to community controls. CXCL10, sTNF-RII, vascular cell adhesion molecule-1 and lipopolysaccharide binding protein (LBP) were additionally elevated in PWID with viremic HCV infection as compared to HCV- PWID. Whilst curative DAA therapy reversed some biomarkers, others including LBP and sTNF-RII remained elevated 48 weeks after HCV cure.

Conclusion

Elevated levels of inflammatory and chronic disease biomarkers in PWID suggest an increased risk of chronic morbidities such as diabetes and cardiovascular disease. HCV infection in PWID poses an additional disease burden, amplified by the incomplete reversal of immune dysfunction following DAA therapy. These findings highlight the need for heightened clinical surveillance of PWID for chronic inflammatory diseases, particularly those with a history of HCV infection.

Association of Hematological Parameters and Diabetic Neuropathy: A Retrospective Study

Background

Diabetic neuropathy (DN) is a common complication of type 2 diabetes (T2DM) and is characterized by persistent inflammation. Hematological parameters have emerged as a novel marker for detecting chronic inflammatory conditions, including diabetes.

Aim

We aim to examine the association between HbA1c levels, which can indicate the presence of diabetic neuropathy, and hematological parameters to explore the possibility of using hematological parameters as a new indicator for DN in T2DM patients.

Methods

This was a retrospective study of 768 (483 males and 284 females) medical records of adult T2DM patients with or without neuropathy who attending the outpatient neuromuscular clinic at King Abdul-Aziz University Hospital from January 2016 to December 2021.

Results

The results showed significant increases in HbA1c levels (p=0.000), lymphocyte levels (p=0.028), and the neutrophil-lymphocyte ratio (NLR) (p=0.011). In the T2DM group, HBA1C levels were found to be positively correlated with age (r=0.306, p=0.000), neutrophil (NEUT) (r=0. 287, p=0.000), platelet (PLT) (r=0. 148, p=0.039), and neutrophil-lymphocyte ratio (NLR) (r=0.306193, p=0.0007), and negatively correlated with gender (r=-0.306193, p=0.0007). In the T2DMN group, HBA1C levels showed a positive correlation with hemoglobin (HB) (r=0.084, p=0.045), PLT (r=0.087, p=0.037), and PLT/mean corpuscular hemoglobin (MCH) ratio (PLT/MCH ratio) (r=0.12, p=0.004), and a negative correlation with age (r=-0.204, p=0.000), gender (r=-0.086, p=0.041), weight (WT) (r=-0.113, p=0.007), Body Surface Area (BSA) (r=-0.09, p=0.031), mean corpuscular volume (MCV) (r=-0.292, p=0.000), and MCH (r=-0.186, p=0.000).

Conclusion

Our study found a significant association between HbA1c, a biomarker for diabetic neuropathy, and various hematological parameters (HB, MCV, MCH, PLT, PLT/MCH ratio) in T2DMN patients. By effectively controlling and monitoring these variables, it may be feasible to prevent or delay the progression of peripheral neuropathy in diabetic patients. However, further research is needed to validate these findings.

Associations between food insecurity and diabetes risk factors in US adolescents in the National Health and Nutrition Examination Survey (NHANES) 2007-2016

OBJECTIVE

To evaluate the associations between household food insecurity and diabetes risk factors among lower-income US adolescents.

DESIGN

Cross-sectional analysis. Household food security status was measured using the 18-item Food Security Survey Module. Simple and multivariable linear and logistic regressions were used to assess the association between food security status and fasting plasma glucose (FPG), oral glucose tolerance test (OGTT), HbA1C and homoeostatic model assessment - insulin resistance (HOMA-IR). The analyses were adjusted for household and adolescent demographic and health characteristics.

SETTING

USA.

PARTICIPANTS

3412 US adolescents aged 12-19 years with household incomes ≤300 % of the federal poverty line from the National Health and Nutrition Examination Survey cycles 2007-2016.

RESULTS

The weighted prevalence of marginal food security was 15·4 % and of food insecurity was 32·9 %. After multivariate adjustment, adolescents with food insecurity had a 0·04 % higher HbA1C (95 % CI 0·00, 0·09, P-value = 0·04) than adolescents with food security. There was also a significant overall trend between severity of food insecurity and higher HbA1C (Ptrend = 0·045). There were no significant mean differences in adolescents' FPG, OGTT or HOMA-IR by household food security.

CONCLUSIONS

Food insecurity was associated with slightly higher HbA1c in a 10-year sample of lower-income US adolescents aged 12-19 years; however, other associations with diabetes risk factors were not significant. Overall, this suggests slight evidence for an association between food insecurity and diabetes risk in US adolescents. Further investigation is warranted to examine this association over time.

The Effects of Stretching Exercise on Levels of Blood Glucose: A Systematic Review with Meta-Analysis

BACKGROUND

Physical activity plays an important role in the management of blood glucose levels. However, compelling evidence exists only for aerobic and resistance training. In this review, we aimed to identify the potential effects of stretching exercises on blood glucose levels.

METHODS

A systematic literature search was performed using the following databases: Scopus, NLM PubMed, and Web of Science. Studies regarding the effects of stretching exercise in humans on blood glucose or any related variable were included. Further inclusion criteria were: (1) original articles (published from database inception to October 2022), (2) applying stretching as a unique exercise modality, (3) having either longitudinal or acute interventions, (4) including healthy and pathological populations, and (5) having within each study a pre- and post-intervention measure. Quality assessment of the studies was conducted using the Downs and Black checklist.

RESULTS

A total of 13 articles were included. The quality assessment revealed an overall moderate quality of the included records. Ten articles included patients with type 2 diabetes (T2D), whereas the remaining three included at-risk populations. A total of 731 people with a mean age of 56.7 ± 6.1 years old were analysed. Fasting blood glucose, 2 h post-oral glucose uptake, post-stretching intervention blood glucose levels, and HbA1c were identified as variables related to blood glucose within the studies. After the stretching interventions, a significant reduction was observed in either blood glucose (ES = - 0.79; p = 0.0174) or HbA1c (ES = - 1.11; p =  < 0.0001). Meta-analytic results highlighted greater effects in T2D patients (ES = - 1.15; p = 0.02) and for studies applying stretching as an exercise intervention (ES = - 1.27; p = 0.006) rather than considering stretching as a control exercise modality.

CONCLUSION

The results of this systematic review highlight the potential of stretching exercises to reduce blood glucose levels. In particular, if stretching is applied as a specific form of exercise intervention in patients with T2D greater effects are observed. However, further studies with more solid research designs are required, therefore, caution is needed before prescribing stretching as an exercise intervention for glycaemic management.

Discordance between Glucose Management Indicator and Glycated Hemoglobin in a Pediatric Cohort with Type 1 Diabetes: A Real-World Study

The introduction of continuous glucose monitoring (CGM) systems in clinical practice has allowed a more detailed picture of the intra- and interdaily glycemic fluctuations of individuals with type 1 diabetes (T1D). However, CGM-measured glucose control indicators may be occasionally inaccurate. This study aims to assess the discrepancy between the glucose management indicator (GMI) and glycated hemoglobin (HbA1c) (ΔGMI-HbA1c) within a cohort of children and adolescents with T1D, exploring its correlation with other CGM metrics and blood count parameters. In this single-center, cross-sectional study, we gathered demographic and clinical data, including blood count parameters, HbA1c values, and CGM metrics, from 128 pediatric subjects with T1D (43% female; mean age, 13.4 ± 3.6 years). Our findings revealed higher levels of the coefficient of variation (CV) (p < 0.001) and time above range > 250 mg/dL (p = 0.033) among subjects with ΔGMI-HbA1c > 0.3%. No association was observed between blood count parameters and ΔGMI-HbA1c. In conclusion, despite the advancements and the widespread adoption of CGM systems, HbA1c remains an essential parameter for the assessment of glycemic control, especially in individuals with suboptimal metabolic control and extreme glycemic variability.

Validation of the Framingham Diabetes Risk Model Using Community-Based KoGES Data

BACKGROUND

An 8-year prediction of the Framingham Diabetes Risk Model (FDRM) was proposed, but the predictor has a gap with current clinical standards. Therefore, we evaluated the validity of the original FDRM in Korean population data, developed a modified FDRM by redefining the predictors based on current knowledge, and evaluated the internal and external validity.

METHODS

Using data from a community-based cohort in Korea (n = 5,409), we calculated the probability of diabetes through FDRM, and developed a modified FDRM based on modified definitions of hypertension (HTN) and diabetes. We also added clinical features related to diabetes to the predictive model. Model performance was evaluated and compared by area under the curve (AUC).

RESULTS

During the 8-year follow-up, the cumulative incidence of diabetes was 8.5%. The modified FDRM consisted of age, obesity, HTN, hypo-high-density lipoprotein cholesterol, elevated triglyceride, fasting glucose, and hemoglobin A1c. The expanded clinical model added γ-glutamyl transpeptidase to the modified FDRM. The FDRM showed an estimated AUC of 0.71, and the model's performance improved to an AUC of 0.82 after applying the redefined predictor. Adding clinical features (AUC = 0.83) to the modified FDRM further improved in discrimination, but this was not maintained in the validation data set. External validation was evaluated on population-based cohort data and both modified models performed well, with AUC above 0.82.

CONCLUSION

The performance of FDRM in the Korean population was found to be acceptable for predicting diabetes, but it was improved when corrected with redefined predictors. The validity of the modified model needs to be further evaluated.

Structural determinants and cardiometabolic typologies related to frailty in community-dwelling older adults

Frailty is a geriatric syndrome linked to adverse outcomes. Co-occurring cardiometabolic factors increase frailty risk; however, their distinct combinations (typologies) associated with frailty are unclear. We aimed to identify subgroups of older adults with distinct cardiometabolic typologies and characterize their relationship with structural determinants and frailty to inform tailored approaches to prevent and delay frailty. This study was cross-sectional design and included 7984 community-dwelling older adults (65+ years) enrolled in the Health and Retirement Study (2006 and 2008). Latent class analysis was performed using seven cardiometabolic indicators (abdominal obesity, obesity, low high-density lipoprotein; and elevated blood pressure, blood sugar, total cholesterol, C-reactive protein). Frailty was indicated by ≥3 features (weakness, slowness, fatigue, low physical activity, unintentional weight loss). Logistic regression was used to examine the relationship between structural determinants (gender, race/ethnicity, and education), cardiometabolic typologies, and frailty. Three cardiometabolic subgroups were identified: insulin-resistant (n = 3547), hypertensive dyslipidemia (n = 1246), and hypertensive (n = 3191). Insulin-resistant subgroup members were more likely to be female, non-Hispanic Black, and college non-graduates; hypertensive dyslipidemia subgroup members were more likely to be non-Hispanic Others and report high school education; and hypertensive subgroup members were more likely to be male and college educated (p≤.05). Frailty risk was higher for females, Hispanic or Non-Hispanic Black older adults, and those with lower education (p≤.001). Frailty risk was greater in the insulin-resistant compared to the other subgroups (both aOR=2.0, both p<.001). Findings highlight a need to design tailored interventions targeting cardiometabolic typologies to prevent and delay frailty.

Glycated Hemoglobin Trajectories and Their Association With Treatment Outcomes Among Patients With Pulmonary TB in India: A Prospective Cohort Study

BACKGROUND

Transient hyperglycemia is seen commonly during TB treatment, yet its association with unfavorable treatment outcomes is unclear.

RESEARCH QUESTION

Does an association exist between glycated hemoglobin (HbA1c) trajectories and TB treatment outcomes?

STUDY DESIGN AND METHODS

Adults with pulmonary TB were evaluated prospectively for 18 months after the second HbA1c measurement. HbA1c trajectories during the initial 3 months of treatment were defined as follows: persistent euglycemia, HbA1c < 6.5% at baseline and 3-month follow-up; persistent hyperglycemia, HbA1c ≥ 6.5% at baseline and 3-month follow-up; transient hyperglycemia, HbA1c ≥ 6.5% at baseline and < 6.5% at 3-month follow-up; incident hyperglycemia, HbA1c < 6.5% at baseline and ≥ 6.5% at 3-month follow-up. Multivariable Poisson regression was used to measure the association between HbA1c trajectories and unfavorable treatment outcomes of failure, recurrence, and all-cause mortality.

RESULTS

Of the 587 participants, 443 participants (76%) had persistent euglycemia, 118 participants (20%) had persistent hyperglycemia, and 26 participants (4%) had transient hyperglycemia. One participant had incident hyperglycemia and was excluded. Compared with participants with persistent euglycemia, those with transient hyperglycemia showed a twofold higher risk of experiencing an unfavorable treatment outcome (adjusted incidence rate ratio [aIRR], 2.07; 95% CI, 1.04-4.15) after adjusting for confounders including diabetes treatment, and BMI; we did not find a significant association with persistent hyperglycemia (aIRR, 1.64; 95% CI, 0.71-3.79). Diabetes treatment was associated with a significantly lower risk of unfavorable treatment outcomes (aIRR, 0.38; 95% CI, 0.15-0.95).

INTERPRETATION

Transient hyperglycemia and lack of diabetes treatment was associated with a higher risk of unfavorable treatment outcomes in adults with pulmonary TB.

HbA1c-dependent projection of long-term renal outcomes

BACKGROUND

Diabetes mellitus is a major risk factor for the development of chronic kidney disease (CKD). There is limited data addressing the value of glycated hemoglobin (HbA1c) to predict renal outcomes independent of diabetes status.

METHODS

This single-center retrospective observational study presents data of 19,285 subjects, irrespective of initial CKD or diabetes status. The primary endpoint was defined as the time to manifestation of moderate CKD (estimated glomerular filtration rate [eGFR] <60 mL/min/1.73 m2 ) in subjects with eGFR ≥60 mL/min/1.73 m2 at baseline. The secondary endpoint was defined as time to progression of CKD (eGFR <30 mL/min/1.73 m2 ) in subjects with eGFR 30-60 mL/min/1.73 m2 . Multivariate time-to-event and logistic regression models were applied to estimate the influences of HbA1c, sex, age, eGFR, triglycerides, and cholesterol on both endpoints.

RESULTS

Lowest baseline HbA1c levels were associated with the slowest decline of kidney function (median time to manifestation of moderate CKD for HbA1c <5.7%: 15.9 years [95% confidence interval (CI): 15.2-16.7]; for HbA1c 5.7%-6.5%: 14.5 years [95% CI: 14.0-15.1]; for HbA1c 6.5%-8.5%: 11.1 years [95% CI: 10.4-11.7]; for HbA1c >8.5%: 8.3 years [95% CI: 7.8-9.2]; p < 0.001). Similar results were observed for the secondary endpoint. Covariate-adjusted time-to-event analysis demonstrated an almost linear correlation between continuous baseline HbA1c levels and the probabilities of reaching both endpoints.

CONCLUSIONS

HbA1c levels are a strong predictor for eGFR decline, irrespective of diabetes status or CKD stage, demonstrating a tight concentration-dependent relationship. This association becomes apparent in the prediabetic HbA1c range and remains constant over the entire HbA1c spectrum.

Prevalence of Non-diabetic Hyperglycemia in Young Adults and Its Impact on Periodontal Health

Background Non-diabetic hyperglycemia is a transitional phase of hyperglycemia that poses a hidden risk for the development of diabetes mellitus and related complications, including periodontal destruction. The current study sought to determine the prevalence of non-diabetic hyperglycemia in young adults and any possible links to periodontal health. Methods A total of 400 participants in this cross-sectional study were evaluated for non-diabetic hyperglycemia between the ages of 18 and 35 years. Group I consisted of non-diabetic hyperglycemic participants. Group II comprised an equal number of matched, healthy subjects. The groups' hyperglycemic and clinical periodontal characteristics were contrasted. Using a one-sample t-test and logistic regression analysis, the acquired data were subjected to statistical analysis. Results The prevalence of non-diabetic hyperglycemia was 19%, with men (13%) having a higher prevalence than women (6%). The mean fasting plasma glucose and hemoglobin A1c (HbA1c) levels were 114.47 ± 6.40 mg/dL and 6.10 ± 0.21%, respectively, for group I, and 85.72 ± 7.24 mg/dL and 4.38 ± 0.70% for group II. When compared to healthy controls, all periodontal parameters, including plaque index, gingival index, bleeding on probing, probing depth, and clinical attachment loss, were significantly higher in group I non-diabetic hyperglycemic patients. The regression analysis revealed statistically significant links between hyperglycemic and periodontal parameters. Conclusion The prevalence of non-diabetic hyperglycemia among young adults is a serious concern similar to that of older adults with the risk for periodontal diseases. Non-diabetic hyperglycemic considerations in young adults should be emphasized in dental and medical clinics to reduce the risk of developing diabetes mellitus and to avoid irreversible periodontal tissue damage.