Diagnostic accuracy of tests for type 2 diabetes and prediabetes: A systematic review and meta-analysis

Type 2 diabetes and the role of nurses in its management

Type 2 diabetes has become a significant global public health issue. Its increasing prevalence is closely linked to sedentary lifestyles, suboptimal diets and high obesity levels. This article provides an overview of type 2 diabetes epidemiology, pathophysiology, clinical presentation, diagnostic tests, risk factors, complications and management. It also describes the role of nurses, which involves: advising patients on weight management, diet, physical activity, smoking cessation and alcohol reduction; encouraging adherence to care plans and drug treatment regimens; and providing ongoing support, education and monitoring to prevent or delay the onset of complications.

Clinical value of serum miR-214-3p expression in the diagnosis of type 2 diabetes mellitus and prediction of its chronic complications

BACKGROUND

The majority of diabetes cases fall into type 2 diabetes mellitus (T2DM), which is prone to chronic complications that have a long-term impact on patients. The aim of this study was to investigate the diagnostic potential of miR-214-3p in T2DM and its predictive value for chronic complications, providing a novel biomarker for the disease.

METHODS

A total of 156 patients with T2DM and 80 non-T2DM individuals were included. Serum miR-214-3p levels were measured by real-time reverse transcription quantitative PCR (RT-qPCR). The correlation of miR-214-3p with hemoglobin A1c (HbA1c) and low-density lipoprotein cholesterol (LDL-C) was analyzed by Spearman's rank correlation. The clinical value of miR-214-3p in T2DM was evaluated using the receiver operating characteristic (ROC) curve and logistic regression analysis.

RESULTS

The serum levels of miR-214-3p were decreased in T2DM patients compared to non-T2DM individuals. A negative correlation was identified between miR-214-3p expression and the levels of HbA1c and LDL-C. miR-214-3p could effectively differentiate T2DM patients from non-T2DM individuals with the area under ROC curve (AUC) of 0.884. Patients with low miR-214-3p expression had a higher incidence of chronic complications. The AUC for miR-214-3p in differentiating between T2DM patients with and without complications was 0.832. Low expression of miR-214-3p was a risk factor linked to the development of chronic complications in patients with T2DM.

CONCLUSION

Serum miR-214-3p was downregulated in T2DM and could differentiate T2DM patients from non-T2DM individuals. miR-214-3p was a promising biomarker for predicting the chronic complications of T2DM.

Diabetes and prediabetes among women universally screened for gestational diabetes: a multi-ethnic, population-based, prospective study with eleven years follow-up

BACKGROUND

Gestational diabetes (GDM) is a strong risk factor for later development of diabetes. However, data are scarce on the long-term risk for diabetes or prediabetes diagnosed by HbA1c, in non-selected, multi-ethnic populations universally screened for GDM using the WHO2013 criteria. We aimed to investigate the development of diabetes or prediabetes eleven years after the index pregnancy and identify risk factors in pregnancy or shortly after.

METHODS

A population-based cohort study of 360 women with complete eleven years follow-up data for diabetes (HbA1c ≥ 48 mmol/mol) or prediabetesADA (HbA1c 39-47 mmol/mol). Women were enrolled in gestational week 15 and universally screened with an oral glucose tolerance test in week 28. We performed least absolute shrinkage and selection operator (LASSO) regression to identify predictors of future diabetes or prediabetesADA and constructed a nomogram to predict individual risks.

RESULTS

Diabetes or prediabetesADA combined, was found in 26.9%, and the prevalence was slightly higher in previous GDM compared with non-GDM women (35.6% versus 23.5%; p = 0.019). The relative risk (RR) for developing diabetes or prediabetesADA was moderately elevated in GDM compared with non-GDM women (1.4 [1.0, 1.9], p = 0.035). Seven women (1.9%) had diabetes and all of these except for one, had previous GDM. Hence, the crude prevalence was 5.8% among GDM women vs. 0.4% among non-GDM women. The RR for developing diabetes was substantially higher in GDM vs. non-GDM women (14.8 [2.6, 277.1], p = 0.012). PrediabetesADA was found in 25% and the RR for prediabetesADA was not significantly increased for GDM compared to non-GDM women (1.3 [0.9, 1.8], p = 0.143). Among Europeans, 17.0% had diabetes or prediabetesADA, compared to 43.0% among South Asians (p < 0.001) and 34.4% among other ethnicities (p = 0.002). The most significant predictors identified from the LASSO were HbA1c measured in early pregnancy, ethnicity, and a family history of diabetes.

CONCLUSIONS

The risk for developing diabetes was low, overall and among GDM women. Still GDM represented a strong risk for diabetes, but not for prediabetesADA. HbA1c early in pregnancy, non-European ethnicity, and a family history of diabetes were the strongest risk factors for developing diabetes or prediabetesADA.

TRIAL REGISTRATION

STORK G2 Women and Risk of Diabetes. NCT03870724 (ClinicalTrials.gov). February 27th, 2019.

Performance of fasting plasma glucose for community-based screening of undiagnosed diabetes and pre-diabetes in sub-Saharan Africa

Introduction

Early diabetes screening is critical in sub-Saharan Africa (SSA), where the prevalence is increasing, yet a large proportion of cases remain undiagnosed. This study aimed to evaluate the performance of fasting plasma glucose (FPG) in screening diabetes and/or prediabetes compared to the 2-hour plasma glucose (2-h PG)-level in SSA.

Methods

Data from a population-based, cross-sectional diabetes screening survey involving 1550 individuals in Butajira, Ethiopia, and Enugu state, Nigeria were analyzed. Fasting plasma glucose and a 2-hour 75-g oral glucose tolerance test (OGTT) were utilized for diabetes screening. In addition, we determined and plotted the receiver operating characteristic curve for FPG against the reference standard 2-h PG to evaluate the screening tool's sensitivity and specificity.

Results

The mean (SD) age of the study participants was 44.5 (± 16.43) years, with men comprising 50.4% of the cohort. Among 1550 individuals analyzed, 4.6% and 16.8% demonstrated diabetes and prediabetes, respectively, as identified by either FPG or 2-h PG. The agreement between FPG and 2-h PG in identifying diabetes and prediabetes was moderate, with kappa statistic of 0.56 (95% CI, 0.51 - 0.61; p<0.0001) for diabetes and 0.45 (95% CI, 0.40 - 0.50; p<0.0001) for prediabetes. FPG failed to detect 34.1% of all prediabetes and 44.4% of all diabetes cases. The sensitivity of FPG in identifying diabetes cases was 44.3% at a cut-off 126 mg/dL with a specificity of 99.3%. We identified the optimal FPG cut-off for detecting newly identified diabetes cases using 2-h PG to be 105 mg/dL associated with a sensitivity and specificity of 67.2% and 94.0%, respectively.

Conclusion

FPG was able to correctly identify 99.3% of individuals with no diabetes but a significant percentage of diabetes cases would have remained undiagnosed if only FPG had been utilized instead of the 2-h PG. The use of 2-h PG test is recommended to diagnose diabetes in older individuals, females and non-obese persons who would be missed if tested by only FPG. Lowering the cut-off value for FPG to 105 mg/dL substantially increases the identification of individuals with diabetes, thus improving the effectiveness of FPG as a screening test for type 2 diabetes.

Brazilian guideline for screening and diagnosis of type 2 diabetes: a position statement from the Brazilian Diabetes Society

BACKGROUND

Patients with type 2 diabetes (T2D) often experience prolonged periods of asymptomatic hyperglycemia, which significantly increases the risk of developing chronic complications related to diabetes. Screening programs for individuals at high risk for T2D provide valuable opportunities not only for early diagnosis but also for detecting intermediate hyperglycemic states, commonly referred to as prediabetes. Interventions aimed at preventing diabetes in this group can successfully delay or even avoid the onset of the disease and its associated burdens. This review is an update of the Brazilian Diabetes Society (Sociedade Brasileira de Diabetes [SBD]) evidence-based guideline for diagnosing diabetes and screening T2D.

METHODS

The methodology was previously published and defined by the internal institutional steering committee. The working group drafted the manuscript by selecting vital clinical questions for a narrative review, utilizing MEDLINE via PubMed to identify relevant studies. The review assessed the best available evidence, including randomized clinical trials (RCTs), meta-analyses, and high-quality observational studies related to the diagnosis of diabetes.

RESULTS AND CONCLUSIONS

Fifteen specific recommendations were formulated. Screening is recommended for adults aged 35 and older or younger individuals with obesity and additional risk factors. For children and adolescents, screening is recommended starting at age ten or the onset of puberty if they are overweight or obese and have additional risk factors. Fasting plasma glucose (FPG) and HbA1c are recommended as initial screening tests. The oral glucose tolerance test (OGTT) is recommended for high-risk individuals with normal HbA1c and FPG or those with prediabetes. The 1-h OGTT is preferred over the 2-h OGTT, as it is both more practical and a superior test. A structured approach to reevaluation intervals is provided.

Culturally Tailored Social Media Intervention Improves Health Outcomes in Chinese Americans with Type 2 Diabetes: Preliminary Evidence from a Pilot RCT

BACKGROUND

Minoritized populations face many barriers to accessing evidence-based diabetes intervention.

OBJECTIVES

To evaluate the feasibility, acceptability, and potential efficacy of a social media-based intervention to improve glycemic control among Chinese Americans with type 2 diabetes.

DESIGN

A pilot randomized controlled trial (RCT) with 3-month and 6-month follow-ups.

PARTICIPANTS

Chinese Americans (n = 60, mean age 54.3 years old) with limited education (70.0% with high school or less) and low income (50.0% with annual household income < $25,000), and 88.3% have limited English proficiency.

INTERVENTION

Culturally and linguistically tailored diabetes videos (two videos/week for 12 weeks) delivered via social media and support calls from community health workers.

MAIN MEASURES

Primary outcomes include feasibility (video watch rate, biweekly call completion rate, and retention rates), acceptability (patient satisfaction), and HbA1c. Secondary health-related outcomes include body weight, BMI, physical activity, and dietary intake. Video watch rate and biweekly call completion rate were assessed at baseline and 3 months, while others were measured at baseline, 3 months, and 6 months.

RESULTS

We observed high feasibility and acceptability of the intervention, with retention rates over 87%, an 89% video watch rate, 80% biweekly phone call completion, and a satisfaction rating of 9 out of 10. The intervention group showed a significantly greater increase in fruit intake compared to the control group (0.15 cups vs. - 0.44 cups, adj_p = 0.023) at 3 months. While no significant differences in other outcomes were observed between the groups, the intervention group showed significant improvements in key outcomes, including reduced HbA1c levels (- 1.08%, adj_p < 0.001), weight loss (- 5.15 lbs, adj_p = 0.004), lower BMI (- 0.83, adj_p = 0.023), and reduced starchy food intake (- 0.33 cups, adj_p = 0.033) at 6 months.

CONCLUSIONS

The observed high feasibility and acceptability suggest the intervention's feasibility. However, due to the limited sample size, a larger-scale RCT is warranted to test the efficacy of the intervention.

TRIAL REGISTRATION

ClinicalTrials.gov NCT03557697; https://clinicaltrials.gov/ct2/show/NCT03557697.

High Rates of Abnormal Glucose Metabolism Detected by 75 g Oral Glucose Tolerance Test in Major Psychiatric Patients with Normal HbA1c and Fasting Glucose Levels

Objectives: Comorbid diabetes is an important factor in the treatment of major psychiatric disorders. However, a substantial proportion of diabetic patients remain undetected by routine diabetic indices such as blood glucose and HbA1c. This study tried to estimate rates of such unidentified diabetic or prediabetic patients by using a 75 g oral glucose tolerance test (OGTT). Methods: Participants in the test were 25 patients with major depressive disorder (MDD), 28 patients with bipolar disorder (BP), 26 patients with schizophrenia, and 28 psychiatrically normal controls. They were all Japanese, and showed non-diabetic levels of blood glucose (<126 mg/dL) and HbA1c (<6.0%). Results: Relatively high rates of psychiatric patients showed diabetes mellites (DM)-type abnormality (32% of MDD, 21.4% of BP and 42.3% of schizophrenia v. 10.7% of controls). The difference in the rates between schizophrenia and control groups was statistically significant (p = 0.008). When abnormal glucose metabolism was defined as a prediabetic state (either normal high glycemia, impaired fast glycemia or impaired glucose tolerance) or DM type in OGTT, it was more frequently seen in the psychiatric patients than in controls (64% of MDD, 46.4% of BP and 46.2% of schizophrenia v. 35.7% of controls). Individuals with DM type showed higher HbA1c values compared with those with normal (p < 0.001) and prediabetic (p = 0.021) states. Conclusions: The results suggest that relatively high proportions of patients with a major psychiatric disorder remain undetected by routine indices for abnormal glucose metabolism, indicating the importance of OGTT even if the patients showed non-diabetic levels in blood glucose or HbA1c.

The Impact of Age and Sex on Fasting Plasma Glucose and Glycated Haemoglobin (HbA1c) in the Non-diabetes Population

INTRODUCTION

We previously reported sex differences in the distribution of glycated haemoglobin (HbA1c) for men/women aged < 50 years vs older individuals, with implications for delayed diabetes diagnosis. Here, we explored whether this pattern was also seen in matched fasting plasma glucose (FPG) levels.

METHODS

We extracted data on same-day, paired HbA1c and FPG levels from clinical biochemistry laboratory databases from Mersey and West Lancashire Teaching Hospitals NHS Trust (n = 10,153) and Cambridge University Hospitals NHS Foundation Trust (n = 10,022) between Jan 2019 and Dec 2023. Only cases with a single, general-practice HbA1c test were utilised to minimise the risk of including non-diagnostic tests and tests from specialist care (e.g. endocrinology, antenatal services; final dataset: n = 17,271). We examined the links of HbA1c and FPG levels to age and sex.

RESULTS

Median HbA1c levels were 1 mmol/mol lower in women aged < 45 years compared to men aged < 45 years but not in those aged ≥ 45 years. This pattern was not seen with FPG, where median levels in women were 0.1-0.2 mmol/L lower across all ages. The HbA1c:FPG ratio was significantly higher in women than men in the 45-54 and ≥ 55 years age groups (p = 0.004, Z-score = 2.9 and p =  < 0.001, Z-score = 8.9, respectively) but not in the < 45 years age group (p = 0.649, Z-score = 0.5). We confirmed our previous finding that median HbA1c levels in women aged ≥ 55 years and 45-55 years were the same as those in men (39 and 37 mmol/mol, respectively) and that for women aged < 45 years, the median HbA1c (34 mmol/mol) was 1 mol/mol lower than for men (35 mmol/mol). This is reflected in the Z-scores, which showed the largest deviation from zero in the < 45 years age group (- 9.1) and the smallest in the older age group (- 2.9).

CONCLUSION

We showed differences in HbA1c and FPG patterns with age between men and women, with implications for the diabetes diagnostic threshold for HbA1c in pre-menopausal women, the underdiagnosis of type 2 diabetes in younger women, and missed opportunities for intervention. We propose that a suggested change to HbA1c reference ranges in this group warrants serious consideration and detailed evaluation.

Artificial sweeteners and Type 2 Diabetes Mellitus: A review of current developments and future research directions

While artificial sweeteners are Generally Regarded as Safe (GRAS), the scientific community remains divided on their safety status. The previous assumption that artificial sweeteners are inert within the body is no longer valid. Artificial sweeteners, known for their high intense sweetness and low or zero calories, are extensively used today in food and beverage products as sugar substitutes and are sometimes recommended for weight management and Type 2 Diabetes Mellitus (T2DM) patients. The general omission of information about the concentration of artificial sweeteners on market product labels makes it challenging to determine the amounts of artificial sweeteners consumed by people. Despite regulatory authorization for their usage, such as from the United States Food and Drug Administration (FDA), concerns remain about their potential association with metabolic diseases, such as T2DM, which the artificial sweeteners were supposed to reduce. This review discusses the relationship between artificial sweetener consumption and the risk of developing T2DM. With the increasing number of recent scientific studies adding to the debate on this subject matter, we assessed recent literature and up-to-date evidence. Importantly, we highlight future research directions toward furthering knowledge in this field of study.

Screening for Type 2 Diabetes Mellitus: A Systematic Review of Recent Economic Evaluations

OBJECTIVES

To examine recent economic evaluations and understand whether any type 2 diabetes mellitus (T2DM) screening designs may represent better value for money and to rate their methodological qualities.

METHODS

We systematically searched 3 concepts (economic evaluations [EEs], T2DM, screening) in 5 databases (Medline, Embase, EconLit, Web of Science, and Cochrane) for EEs published between 2010 and 2023. Two independent reviewers screened for and rated their methodological quality (using the Consensus on Health Economics Criteria Checklist-Extended).

RESULTS

Of 32 EEs, a majority were from high-income countries (69%). Half used single biomarkers (50%) to screen adults ≥30 to <60 years old (60%) but did not report locations (69%), treatments for those diagnosed (66%), diagnostic methods (57%), or screening intervals (54%). Compared with no screening, T2DM screening using single biomarkers was found to be not cost-effective (23/54 comparisons), inconclusive (16/54), dominant (11/54), or cost-effective (4/54). Compared with no screening, screening with a risk score and single biomarkers was found to be cost-effective (21/40) or dominant (19/40). The risk score alone was mostly dominant (6/10). Compared with universal screening, targeted screening among obese, overweight, or older people may be cost-effective or dominant. Compared with fasting plasma glucose or fasting capillary glucose, screening using risk scores was found to be mostly dominant or cost-effective. Expanding screening locations or lowering HbA1c or fasting plasma glucose thresholds was found to be dominant or cost-effective. Each EE had 4 to 17 items (median 13/20) on Consensus on Health Economics Criteria Checklist-Extended rated "Yes/Rather Yes."

CONCLUSIONS

EE findings varied based on screening tools, intervals, locations, minimum screening age, diagnostic methods, and treatment. Future EEs should more comprehensively report screening designs and evaluate T2DM screening in low-income countries.

Robust predictive framework for diabetes classification using optimized machine learning on imbalanced datasets

Introduction

Diabetes prediction using clinical datasets is crucial for medical data analysis. However, class imbalances, where non-diabetic cases dominate, can significantly affect machine learning model performance, leading to biased predictions and reduced generalization.

Methods

A novel predictive framework employing cutting-edge machine learning algorithms and advanced imbalance handling techniques was developed. The framework integrates feature engineering and resampling strategies to enhance predictive accuracy.

Results

Rigorous testing was conducted on three datasets-PIMA, Diabetes Dataset 2019, and BIT_2019-demonstrating the robustness and adaptability of the methodology across varying data environments.

Discussion

The experimental results highlight the critical role of model selection and imbalance mitigation in achieving reliable and generalizable diabetes predictions. This study offers significant contributions to medical informatics by proposing a robust data-driven framework that addresses class imbalance challenges, thereby advancing diabetes prediction accuracy.

Prevalence and determinants of diagnosed and undiagnosed diabetes in Hungary based on the nationally representative cross-sectional H-UNCOVER study

AIMS

To estimate prevalence of diagnosed (dDM) and undiagnosed diabetes (uDM) in Hungary and investigate determinants of uDM.

METHODS

Data was obtained from the nationally representative H-UNCOVER study. As laboratory measurements were available for 11/19 Hungarian counties, n = 5,974/17,787 people were eligible. After exclusions, 5,673 (representing 4,976,097 people) were included. dDM was defined by self-reporting, while uDM as negative self-reporting and elevated fasting glucose (≥7 mmol/l) and/or HbA1c (≥48 mmol/mol). Logistic regression for complex samples was used to calculate comparisons between dDM and uDM adjusted for age and BMI.

RESULTS

Diabetes prevalence was 12.0 %/11.9 % (women/men, 95 %CI:10.7-13.4 %/10.7-13.2 %), while 2.2 %/2.8 % (1.7-2.8 %/2.2-3.6 %) of women/men were uDM. While the proportion of uDM vs. dDM was similar for women ≥ 40, men in their forties had the highest odds for uDM. Neither unemployment (women/men OR:0.58 [0.14-2.45]/0.50 [0.13-1.92]), nor education level (tertiary vs. primary; women/men OR: 1.16 [0.53-2.56]/ 0.53 [0.24-1.18]) were associated with uDM. The risk of uDM was lower in both sexes with chronic morbidities.

CONCLUSIONS

We report higher prevalence of diabetes and undiagnosed diabetes than previous Hungarian estimates. The finding that socioeconomic factors are not associated to uDM suggests that universal health care could provide equitable access to diabetes diagnosis.

Awareness, treatment, and control among adults living with arterial hypertension or diabetes mellitus in two rural districts in Lesotho

In Lesotho, the hypertension and diabetes care cascades are unknown. We measured awareness, treatment, and control of hypertension and diabetes among adults ≥18 years and identified factors associated with each step of the cascade, based on data from a population-based, cross-sectional survey in 120 randomly sampled clusters in the districts of Butha-Buthe and Mokhotlong from 1st November 2021 to 31st August 2022. We used multivariable logistic regression to assess associations. Among participants with hypertension, 69.7% (95%CI, 67.2-72.2%, 909/1305) were aware of their condition, 67.3% (95%CI 64.8-69.9%, 878/1305) took treatment, and 49.0% (95%CI 46.3-51.7%, 640/1305) were controlled. Among participants with diabetes, 48.4% (95%CI 42.0-55.0%, 111/229) were aware of their condition, 55.8% (95%CI 49.5-62.3%, 128/229) took treatment, and 41.5% (95%CI 35.1-47.9%, 95/229) were controlled. For hypertension, women had higher odds of being on treatment (adjusted odds ratio (aOR) 2.54, 95% CI 1.78-3.61) and controlled (aOR 2.44, 95%CI 1.76-3.37) than men. Participants from urban areas had lower odds of being on treatment (aOR 0.63, 95% CI 0.44-0.90) or being controlled (aOR 0.63, 95% CI 0.46-0.85). Considerable gaps along the hypertension and diabetes care cascades in Lesotho indicate that access and quality of care for these conditions are insufficient to ensure adequate long-term health outcomes.

Evaluation of a New Diabetes Mellitus Index Based on Measurements Using the Scheimpflug Analyzer Corvis ST

PURPOSE

Chronic hyperglycemia causes changes in corneal biomechanics that can be measured with the Scheimpflug Analyzer Corvis ST. The diagnostic reliability of the new diabetes mellitus (DM) index developed based on this should be evaluated.

METHODS

In a prospective cross-sectional study, the index was initially developed using data from 81 patients with DM and 75 healthy subjects based on logistic regression analysis. The reliability of the DM index was subsequently assessed using data from another 61 patients and 37 healthy individuals. In addition, the dependence of the DM index on indicators of disease severity was analyzed.

RESULTS

The index initially achieved a sensitivity of 79% and specificity of 80% with a cutoff value of 0.58. The evaluation showed a sensitivity of 67% and specificity of 76% with an optimized cutoff of 0.51 (area under the curve = 0.737, P < 0.001). The DM index correlated weakly with the severity of diabetic retinopathy (r = 0.209, P = 0.014). It was increased in the presence of diabetic maculopathy ( P = 0.037) and in type 1 DM compared with patients with type 2 disease ( P = 0.039).

CONCLUSIONS

In this first evaluation, the new DM index achieved sufficiently good sensitivity and specificity and was weakly associated with disease-specific factors. With further improvements, it could complement the diagnostic options in DM with a simple, rapid, and noninvasive assessment method.

Myths and methodologies: Assessing glycaemic control and associated regulatory mechanisms in human physiology research

Accurate measurements of glycaemic control and the underpinning regulatory mechanisms are vital in human physiology research. Glycaemic control is the maintenance of blood glucose concentrations within optimal levels and is governed by physiological variables including insulin sensitivity, glucose tolerance and β-cell function. These can be measured with a plethora of methods, all with their own benefits and limitations. Deciding on the best method to use is challenging and depends on the specific research question(s). This review therefore discusses the theory and procedure, validity and reliability and any special considerations of a range common methods used to measure glycaemic control, insulin sensitivity, glucose tolerance and β-cell function. Methods reviewed include glycosylated haemoglobin, continuous glucose monitors, the oral glucose tolerance test, mixed meal tolerance test, hyperinsulinaemic euglycaemic clamp, hyperglycaemic clamp, intravenous glucose tolerance test and indices derived from both fasting concentrations and the oral glucose tolerance test. This review aims to help direct understanding, assessment and decisions regarding which method to use based on specific physiology-related research questions.

Type 2 Diabetes

Type 2 diabetes (T2D) is a prevalent disease that increases risk for vascular, renal, and neurologic complications. Prevention and treatment of T2D and its complications are paramount. Many advancements in T2D care have emerged over the past 5 years, including increased understanding of the importance of early intensive glycemic control, mental health, social determinants of health, healthy eating patterns, continuous glucose monitoring, and the benefits of some drugs for preventing cardiorenal disease. This review summarizes the evidence supporting T2D prevention and treatment, focusing on aspects that are commonly in the purview of primary care physicians.

Comparing the accuracy of four machine learning models in predicting type 2 diabetes onset within the Chinese population: a retrospective study

OBJECTIVE

To evaluate the effectiveness of machine learning (ML) models in predicting 5-year type 2 diabetes mellitus (T2DM) risk within the Chinese population by retrospectively analyzing annual health checkup records.

METHODS

We included 46,247 patients (32,372 and 13,875 in training and validation sets, respectively) from a national health checkup center database. Univariate and multivariate Cox analyses were performed to identify factors influencing T2DM risk. Extreme Gradient Boosting (XGBoost), support vector machine (SVM), logistic regression (LR), and random forest (RF) models were trained to predict 5-year T2DM risk. Model performances were analyzed using receiver operating characteristic (ROC) curves for discrimination and calibration plots for prediction accuracy.

RESULTS

Key variables included fasting plasma glucose, age, and sedentary time. The LR model showed good accuracy with respective areas under the ROC (AUCs) of 0.914 and 0.913 in training and validation sets; the RF model exhibited favorable AUCs of 0.998 and 0.838. In calibration analysis, the LR model displayed good fit for low-risk patients; the RF model exhibited satisfactory fit for low- and high-risk patients.

CONCLUSIONS

LR and RF models can effectively predict T2DM risk in the Chinese population. These models may help identify high-risk patients and guide interventions to prevent complications and disabilities.

Predictive modeling of multi-class diabetes mellitus using machine learning and filtering iraqi diabetes data dynamics

Diabetes is a persistent metabolic disorder linked to elevated levels of blood glucose, commonly referred to as blood sugar. This condition can have detrimental effects on the heart, blood vessels, eyes, kidneys, and nerves as time passes. It is a chronic ailment that arises when the body fails to produce enough insulin or is unable to effectively use the insulin it produces. When diabetes is not properly managed, it often leads to hyperglycemia, a condition characterized by elevated blood sugar levels or impaired glucose tolerance. This can result in significant harm to various body systems, including the nerves and blood vessels. In this paper, we propose a multiclass diabetes mellitus detection and classification approach using an extremely imbalanced Laboratory of Medical City Hospital data dynamics. We also formulate a new dataset that is moderately imbalanced based on the Laboratory of Medical City Hospital data dynamics. To correctly identify the multiclass diabetes mellitus, we employ three machine learning classifiers namely support vector machine, logistic regression, and k-nearest neighbor. We also focus on dimensionality reduction (feature selection-filter, wrapper, and embedded method) to prune the unnecessary features and to scale up the classification performance. To optimize the classification performance of classifiers, we tune the model by hyperparameter optimization with 10-fold grid search cross-validation. In the case of the original extremely imbalanced dataset with 70:30 partition and support vector machine classifier, we achieved maximum accuracy of 0.964, precision of 0.968, recall of 0.964, F1-score of 0.962, Cohen kappa of 0.835, and AUC of 0.99 by using top 4 feature according to filter method. By using the top 9 features according to wrapper-based sequential feature selection, the k-nearest neighbor provides an accuracy of 0.935 and 1.0 for the other performance metrics. For our created moderately imbalanced dataset with an 80:20 partition, the SVM classifier achieves a maximum accuracy of 0.938, and 1.0 for other performance metrics. For the multiclass diabetes mellitus detection and classification, our experiments outperformed conducted research based on the Laboratory of Medical City Hospital data dynamics.

Pregestational Prediabetes Induces Maternal Hypothalamic-Pituitary-Adrenal (HPA) Axis Dysregulation and Results in Adverse Foetal Outcomes

Maternal type 2 diabetes mellitus (T2DM) has been shown to result in foetal programming of the hypothalamic-pituitary-adrenal (HPA) axis, leading to adverse foetal outcomes. T2DM is preceded by prediabetes and shares similar pathophysiological complications. However, no studies have investigated the effects of maternal prediabetes on foetal HPA axis function and postnatal offspring development. Hence, this study investigated the effects of pregestational prediabetes on maternal HPA axis function and postnatal offspring development. Pre-diabetic (PD) and non-pre-diabetic (NPD) female Sprague Dawley rats were mated with non-prediabetic males. After gestation, male pups born from the PD and NPD groups were collected. Markers of HPA axis function, adrenocorticotropin hormone (ACTH) and corticosterone, were measured in all dams and pups. Glucose tolerance, insulin and gene expressions of mineralocorticoid (MR) and glucocorticoid (GR) receptors were further measured in all pups at birth and their developmental milestones. The results demonstrated increased basal concentrations of ACTH and corticosterone in the dams from the PD group by comparison to NPD. Furthermore, the results show an increase basal ACTH and corticosterone concentrations, disturbed MR and GR gene expression, glucose intolerance and insulin resistance assessed via the Homeostasis Model Assessment (HOMA) indices in the pups born from the PD group compared to NPD group at all developmental milestones. These observations reveal that pregestational prediabetes is associated with maternal dysregulation of the HPA axis, impacting offspring HPA axis development along with impaired glucose handling.

Diabetes and prediabetes prevalence through a community-based screening initiative in Alqunfudah, Saudi Arabia

Aim: This study aimed to identify prediabetic and diabetic patients using fasting blood sugar in Alqunfudah, Saudi Arabia. Patients & methods: Multistage stratified random sampling technique was used to recruit study participants aged 18 years and older. We measured anthropometric measures like waist circumference and body mass index. Results: A total of 332 participants were included in this study, 52.4% were female, 45.2% aged >50 years, 89.8% were Saudi, and 19.0% had been diagnosed with hypertension. Nearly a third (36.1%) of the participants were diagnosed with Type 2 diabetes mellitus and 28.3% had impaired fasting glucose. Age and hypertension were significant predictors of diabetes. Conclusion: Early detection and intervention are crucial to reducing the diabetes epidemic in Saudi Arabia.

Prevalence and determinants of cardiovascular risk factors in Lesotho: a population-based survey

BACKGROUND

There are no recent data on the prevalence of cardiovascular risk factors (CVDRFs) in Lesotho. This study aims to assess the prevalence of CVDRFs and their determinants.

METHODS

We conducted a household-based, cross-sectional survey among adults ≥18 y of age in 120 randomly sampled clusters in two districts.

RESULTS

Among 6061 participants, 52.2% were female and their median age was 39 y (interquartile range 27-58). The overall prevalence of overweight, diabetes, elevated blood pressure (BP) and tobacco use was 39.9%, 5.3%, 21.6% and 24.9%, respectively. Among participants, 34.6% had none, 45.2% had one and 20.2% had two or more CVDRFs. Women were more likely to have two or more CVDRFs (20.7% vs 12.3%). Overall, 7.5% of participants had elevated total cholesterol, 52.7% had low high-density lipoprotein cholesterol and 1.6% had elevated low-density lipoprotein cholesterol. Among younger participants (18-29 y), 16.1% reported tobacco use, 28.6% were overweight, 1.5% had diabetes and 3.5% had elevated BP. Household wealth positively correlated with the prevalence of elevated BP, overweight and diabetes, whereas tobacco use was higher among people in the lowest three wealth quintiles.

CONCLUSIONS

CVDRFs are highly prevalent in Lesotho across age and sex groups, underlining the importance of strengthening prevention and care programs in Lesotho and similar settings in southern Africa.

Handgrip strength thresholds associated with metabolic syndrome risk in children and adolescents: a systematic review and meta-analysis

OBJECTIVES

Certain studies have reported that handgrip strength (HGS) is associated with metabolic health risks in children and adolescents, and some studies have suggested HGS thresholds for identifying poor metabolic health. Therefore, we aimed to determine the HGS thresholds associated with metabolic syndrome (MetS) in children and adolescents through a systematic review.

METHODS

We searched 3 electronic databases from their inception until October 2023 to identify original papers that focused on children and adolescents and assessed their risks of MetS according to specific HGS values. Studies were selected for inclusion through a planned screening process based on specific criteria. The Quality Assessment Tool for Diagnostic Accuracy Studies version 2 (QUADAS-2) was used to evaluate quality, and a meta-analysis was performed using the diagmeta R package to suggest the optimal thresholds.

RESULTS

From the search, 8 studies were selected for this systematic review. For detecting MetS risk, the optimal threshold for HGS (defined as relative HGS by adjusting for body mass) was found to be 0.422, with a sensitivity of 76.7% (95% confidence interval [CI], 64.0 to 85.8) and a specificity of 62.9% (95% CI, 56.9 to 68.5). The stratification analysis by sex resulted in optimal thresholds of 0.416 for boys and 0.376 for girls. Additionally, when the data were stratified by age, the thresholds were 0.356 for children and 0.416 for adolescents.

CONCLUSIONS

Our results provide practical information for detecting high-risk groups and encouraging strength-related activities that may reduce the risk of MetS in children and adolescents.

Diagnostic cut-off value of haemoglobin A1c for diabetes mellitus in Harare, Zimbabwe

Very little is known about the diagnostic performance of the American Diabetes Association glycated haemoglobin (HbA1c) cut-off of 6.5% in resource-limited settings. This study, conducted between February 2023 and May 2023, aimed to determine the optimal HbA1c cut-off for the diagnosis of diabetes mellitus by measuring HbA1c and fasting plasma glucose levels in 120 adults attending care at a tertiary hospital in Harare, Zimbabwe. The optimal HbA1c cut-off was 6.1% and glucose levels were strongly correlated with HbA1c values. The prevalence of diabetes mellitus was higher (28.3%) at our derived HbA1c cut-off than with the American Diabetes Association criterion (21.6%).

What this study adds

This study highlights the need for population-specific cut-off HbA1c values in the diagnosis of diabetes mellitus.

Undiagnosed type 2 diabetes is common - intensified screening of established risk groups is imperative in Sweden: the SDPP cohort

BACKGROUND

Undiagnosed type 2 diabetes (T2D) is a global problem. Current strategies for diagnosis in Sweden include screening individuals within primary healthcare who are of high risk, such as those with hypertension, obesity, prediabetes, family history of diabetes, or those who smoke daily. In this study, we aimed to estimate the proportion of individuals with undiagnosed T2D in Stockholm County and factors associated with T2D being diagnosed by healthcare. This information could improve strategies for detection.

METHODS

We used data from the Stockholm Diabetes Prevention Programme (SDPP) cohort together with information from national and regional registers. Individuals without T2D aged 35-56 years at baseline were followed up after two ten-year periods. The proportion of diagnosed T2D was based on register information for 7664 individuals during period 1 and for 5148 during period 2. Undiagnosed T2D was assessed by oral glucose tolerance tests at the end of each period. With logistic regression, we analysed factors associated with being diagnosed among individuals with T2D.

RESULTS

At the end of the first period, the proportion of individuals with T2D who had been diagnosed with T2D or not was similar (54.0% undiagnosed). At the end of the second period, the proportion of individuals with T2D was generally higher, but they were less likely to be undiagnosed (43.5%). The likelihood of being diagnosed was in adjusted analyses associated with overweight (OR=1.85; 95% CI 1.22-2.80), obesity (OR=2.73; 95% CI 1.76-4.23), higher fasting blood glucose (OR=2.11; 95% CI 1.67-2.66), and self-estimated poor general health (OR=2.42; 95% CI 1.07-5.45). Socioeconomic factors were not associated with being diagnosed among individuals with T2D. Most individuals (>71%) who developed T2D belonged to risk groups defined by having at least two of the prominent risk factors obesity, hypertension, daily smoking, prediabetes, or family history of T2D, including individuals with T2D who had not been diagnosed by healthcare.

CONCLUSIONS

Nearly half of individuals who develop T2D during 10 years in Stockholm County are undiagnosed, emphasizing a need for intensified screening of T2D within primary healthcare. Screening can be targeted to individuals who have at least two prominent risk factors.

Universal Fasting Glucose Screening Before Gastric Emptying Scintigraphy and the High Prevalence of Undiagnosed Diabetes and Prediabetes

The aim of this study was to assess the rates of undiagnosed diabetes mellitus (DM) and pre-DM in patients undergoing gastric emptying scintigraphy (GES). Diabetes is an epidemic in the United States, and the disease is associated with altered gut motility. As a result, we suspected that a significant number of patients referred for GES may have undiagnosed DM or pre-DM. Given that established procedure standards for GES require all patients to prepare with an 8-h fast, an opportunity is provided to measure the fasting blood glucose (FBG) in all individuals before they undergo the examination. Methods: The charts of patients undergoing GES were reviewed for a history of DM and correlated with FBG and GES results. FBG values, obtained by point-of-care testing, were categorized as normal, pre-DM, or DM. Results: Patients with known DM made up 23% of those referred for GES, and most (55%) had a normal FBG. In those without a history of DM, there were a significant number with undiagnosed pre-DM (12%) and DM (33%). Conclusion: Our study provides the first measure of the likely prevalence of undiagnosed DM and pre-DM and characterizes the different gastric emptying patterns among patients with normal FBG, likely undiagnosed pre-DM, likely undiagnosed DM, and known DM.

Liquid Biopsy: A Game Changer for Type 2 Diabetes

As the burden of type 2 diabetes (T2D) continues to escalate globally, there is a growing need for novel, less-invasive biomarkers capable of early diabetes detection and monitoring of disease progression. Liquid biopsy, recognized for its minimally invasive nature, is increasingly being applied beyond oncology, and nevertheless shows its potential when the collection of the tissue biopsy is not possible. This diagnostic approach involves utilizing liquid biopsy markers such as cell-free nucleic acids, extracellular vesicles, and diverse metabolites for the molecular diagnosis of T2D and its related complications. In this context, we thoroughly examine recent developments in T2D liquid biopsy research. Additionally, we discuss the primary challenges and future prospects of employing liquid biopsy in the management of T2D. Prognosis, diagnosis and monitoring of T2D through liquid biopsy could be a game-changing technique for personalized diabetes management.

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.

Acoustic Analysis and Prediction of Type 2 Diabetes Mellitus Using Smartphone-Recorded Voice Segments

Objective

To investigate the potential of voice analysis as a prescreening or monitoring tool for type 2 diabetes mellitus (T2DM) by examining the differences in voice recordings between nondiabetic and T2DM individuals.

Patients and Methods

Total 267 participants diagnosed as nondiabetic (79 women and 113 men) or T2DM (18 women and 57 men) on the basis of American Diabetes Association guidelines were recruited in India between August 30, 2021 and June 30, 2022. Using a smartphone application, participants recorded a fixed phrase up to 6 times daily for 2 weeks, resulting in 18,465 recordings. Fourteen acoustic features were extracted from each recording to analyze differences between nondiabetic and T2DM individuals and create a prediction methodology for T2DM status.

Results

Significant differences were found between voice recordings of nondiabetic and T2DM men and women, both in the entire dataset and in an age-matched and body mass index (BMI [calculated as the weight in kilograms divided by the height in meters squared])-matched sample. The highest predictive accuracy was achieved by pitch (P<.0001), pitch SD (P<.0001), and relative average pertubation jitter (P=.02) for women, and intensity (P<.0001) and 11-point amplitude perturbation quotient shimmer (apq11, P<0.0001) for men. Incorporating these features with age and BMI, the optimal prediction models achieved accuracies of 0.75±0.22 for women and 0.70±0.10 for men through 5-fold cross-validation in the age-matched and BMI-matched sample.

Conclusion

Overall, vocal changes occur in individuals with T2DM compared with those without T2DM. Voice analysis shows potential as a prescreening or monitoring tool for T2DM, particularly when combined with other risk factors associated with the condition.

Trial Registration

clinicaltrials.gov Identifier: CTRI/2021/08/035957.

Clinical Practice Guideline: Shared Decision Making, Diagnostic Evaluation, and Pharmacotherapy in Type 2 Diabetes

BACKGROUND

Type 2 diabetes is one of the most important widespread diseases worldwide. In Germany, nearly one in five persons over age 65 has type 2 diabetes. The German National Disease Management Guideline for Type 2 Diabetes (NDMG; in German: Nationale Versorgungsleitlinie, NVL) contains updated recommendations for the diagnostic evaluation and pharmacotherapy of this disease as well as information about specific groups of people for whom early detection may be useful.

METHODS

The guideline has been updated, chapter by chapter, since 2018. Its recommendations are based on systematically searched and evaluated scientific evidence, the clinical expertise of a multidisciplinary panel of experts, and patient perspectives.

RESULTS

The new chapter on shared decision making includes a description of a structured approach that can be used when individual treatment goals have not been achieved. The diagnosis of diabetes newly requires at least two abnormally elevated laboratory values: e.g., fasting plasma glucose ≥ 126 mg/dL (≥ 7.0 mmol/L), HbA1c ≥ 6.5 % (≥ 48 mmol/mol) and/or casual plasma glucose ≥ 200 mg/dL (≥ 11.1 mmol/L). Cardiovascular and renal risks are to be considered in the choice of drug. Studies have shown that, in persons with cardiovascular disease, treatment with GLP-1 receptor agonists (GLP-1, glucagon-like peptide-1) or SGLT2 inhibitors (SGLT2, sodium-glucose co-transporter-2) was less likely than the comparison intervention to lead to certain patient-relevant endpoints, including all-cause mortality (OR = 0.88 and 0.84, respectively), hospitalization for heart failure (SGLT2 inhibitors: OR = 0.65), and worsening of renal function (OR = 0.61 and 0.59, respectively).

CONCLUSION

Current evidence continues to support the recommendations on pharmacotherapy of the 2021 guideline. The Guideline Group did not find evidence of adequate certainty to inform recommendations about the screening of persons at risk, HbA1c target values, or screening for sequelae and comorbidities. Better evidence on these matters would be desirable.

Non-Invasive Glucose Sensing Technologies and Products: A Comprehensive Review for Researchers and Clinicians

Diabetes Mellitus incidence and its negative outcomes have dramatically increased worldwide and are expected to further increase in the future due to a combination of environmental and social factors. Several methods of measuring glucose concentration in various body compartments have been described in the literature over the years. Continuous advances in technology open the road to novel measuring methods and innovative measurement sites. The aim of this comprehensive review is to report all the methods and products for non-invasive glucose measurement described in the literature over the past five years that have been tested on both human subjects/samples and tissue models. A literature review was performed in the MDPI database, with 243 articles reviewed and 124 included in a narrative summary. Different comparisons of techniques focused on the mechanism of action, measurement site, and machine learning application, outlining the main advantages and disadvantages described/expected so far. This review represents a comprehensive guide for clinicians and industrial designers to sum the most recent results in non-invasive glucose sensing techniques' research and production to aid the progress in this promising field.

Global variation in diabetes diagnosis and prevalence based on fasting glucose and hemoglobin A1c

Fasting plasma glucose (FPG) and hemoglobin A1c (HbA1c) are both used to diagnose diabetes, but these measurements can identify different people as having diabetes. We used data from 117 population-based studies and quantified, in different world regions, the prevalence of diagnosed diabetes, and whether those who were previously undiagnosed and detected as having diabetes in survey screening, had elevated FPG, HbA1c or both. We developed prediction equations for estimating the probability that a person without previously diagnosed diabetes, and at a specific level of FPG, had elevated HbA1c, and vice versa. The age-standardized proportion of diabetes that was previously undiagnosed and detected in survey screening ranged from 30% in the high-income western region to 66% in south Asia. Among those with screen-detected diabetes with either test, the age-standardized proportion who had elevated levels of both FPG and HbA1c was 29-39% across regions; the remainder had discordant elevation of FPG or HbA1c. In most low- and middle-income regions, isolated elevated HbA1c was more common than isolated elevated FPG. In these regions, the use of FPG alone may delay diabetes diagnosis and underestimate diabetes prevalence. Our prediction equations help allocate finite resources for measuring HbA1c to reduce the global shortfall in diabetes diagnosis and surveillance.

Glycated Hemoglobin Is Suboptimal for the Screening of Prediabetes and Type 2 Diabetes in Adults With Nonalcoholic Fatty Liver Disease

OBJECTIVES

Nonalcoholic fatty liver disease (NAFLD) is a risk factor for type 2 diabetes (T2D), but T2D screening tests are not well validated in this population. In this study, we assessed performance of glycated hemoglobin (A1C) and fasting plasma glucose (FPG) in glucose dysmetabolism screening and aimed to optimize detection thresholds for individuals with NAFLD.

METHODS

We retrospectively included oral glucose tolerance tests (OGTTs) from consecutive patients undergoing a specialized clinic for NAFLD, if A1C and/or fasting glucose was available within 3 months of OGTT. We compared performances of A1C and fasting glucose with the "gold standard" of OGTT using thresholds from the 2018 Diabetes Canada guidelines. A1C and FPG thresholds were optimized for detection of glucose dysmetabolism using receiver operating characteristic curves.

RESULTS

We included 63 OGTTs from individuals with NAFLD (52% female, age 48 [interquartile range 35 to 63] years, body mass index 34 [interquartile range 29 to 40] kg/m2). A1C had 16% (95% confidence interval [CI] 6% to 38%) sensitivity (Se) and 97% (95% CI 85% to 100%) specificity (Sp) for T2D detection, and 45% (95% CI 30% to 62%) Se and 100% (95% CI 83% to 100%) Sp for abnormal blood glucose detection. FPG had 67% (95% CI 45% to 83%) Se and 100% (95% CI 92% to 100%) Sp for T2D detection, and 74% (95% CI 59% to 85%) Se and 92% (95% CI 74% to 99%) Sp for abnormal blood glucose detection. Optimal A1C and FPG thresholds were 5.6% and 6.3 mmol/L for T2D detection, which are lower than current recommendations.

CONCLUSIONS

A1C is less sensitive than FPG and is suboptimal for T2D detection, suggesting that OGTT may be obtained if A1C is ≥5.6% or FPG is ≥6.3 mmol/L in individuals with NAFLD, to avoid underdiagnosis and treatment inertia.

Vitamin D deficiency and hyperglycemia in male rotating shift workers: A disturbed circadian rhythms influence

BACKGROUND

Shift work is related to several negative impacts on the health of workers. This study aimed to evaluate the association between vitamin D deficiency (VDD) and hyperglycemia in shift workers.

METHODOLOGY

This cross-sectional study included male rotating shift workers in an iron ore extraction company. Participants were classified as VDD when 25(OH)D < 20 ng/mL for a healthy population and 25(OH)D < 30 ng/mL for groups at risk for VDD. Hyperglycemia was classified when fasting glucose ≥100 mg/dL or HbA1c ≥ 5.7%. Data were compared using chi-square analysis with Cramer's V as effect size, and Bonferroni correction. Multivariate logistic regression, from a model of determination, was performed to investigate whether VDD was associated with hyperglycemia.

RESULTS

The study evaluated 1411 workers, most workers were aged 30-39 years (53.2%), and 77.5% self-declared as black, brown, with up to complete high school (71.4%) and working alternate shifts for more than 5 years (76.1%). Regarding glucose and vitamin D, 32.0% and 29.1% of the workers had hyperglycemia and VDD, respectively. In multivariate analysis, controlled for confounding factors, workers with VDD had a 119% increased chance of hyperglycemia (OR: 2.19; IC95%: 1.56-3.08). Furthermore, vitamin D levels in distribution quintiles showed a dose-response gradient in relation to hyperglycemia, where increased vitamin D values were associated with a reduction in the occurrence of hyperglycemia.

CONCLUSION

Rotating shift workers with vitamin D deficiency are more likely to have hyperglycemia.

Sick and depressed? The causal impact of a diabetes diagnosis on depression

BACKGROUND

There is sparse evidence on the impact of health information on mental health as well as on the mechanisms governing this relationship. We estimate the causal impact of health information on mental health via the effect of a diabetes diagnosis on depression.

METHODS

We employ a fuzzy regression discontinuity design (RDD) exploiting the exogenous cut-off value of a biomarker used to diagnose type-2 diabetes (glycated haemoglobin, HbA1c) and information on psycometrically validated measures of diagnosed clinical depression drawn from rich administrative longitudinal individual-level data from a large municipality in Spain. This approach allows estimating the causal impact of a type-2 diabetes diagnosis on clinica ldepression.

RESULTS

We find that overall a type-2 diabetes diagnosis increases the probability of becoming depressed, however this effect appears to be driven mostly by women, and in particular those who are relatively younger and obese. Results also appear to differ by changes in lifestyle induced by the diabetes diagnosis: while women who did not lose weight are more likely to develop depression, men who did lose weight present a reduced probability of being depressed. Results are robust to alternative parametric and non-parametric specifications and placebo tests.

CONCLUSIONS

The study provides novel empirical evidence on the causal impact of health information on mental health, shedding light on gender-based differences in such effects and potential mechanisms through changes in lifestyle behaviours.

The Effectiveness of a Traditional Chinese Medicine-Based Mobile Health App for Individuals With Prediabetes: Randomized Controlled Trial

BACKGROUND

Traditional Chinese medicine (TCM) theories assert that body constitution and meridian energy lay the foundation for disease prevention. TCM-based health concepts have not yet been incorporated into mobile health (mHealth) apps for individuals with prediabetes.

OBJECTIVE

The aim of this study was to examine the effectiveness of a TCM mHealth app for individuals with prediabetes.

METHODS

This randomized controlled trial recruited 121 individuals with prediabetes at a teaching hospital in New Taipei City between February 2020 and May 2021. The participants were randomly assigned to the TCM mHealth app group (n=42), ordinary mHealth app group (n=41), or control group (n=38). All participants received the usual care that included 15-20 minutes of health education about the disease, along with healthy diet and exercise encouragement. The ordinary mHealth app included physical activity (PA), diet, and disease education, along with individual records. The TCM mHealth app additionally included qi and body constitution information, along with constitution-based PA and diet advice. The control group received the usual care alone and did not have access to any app. Data were collected at baseline, at the end of the 12-week intervention, and 1 month after the intervention. Body constitution, including yang-deficiency, yin-deficiency, and phlegm-stasis, was measured according to the Body Constitution Questionnaire, with higher scores indicating a greater deficiency. Body energy was examined using the Meridian Energy Analysis Device. The Short-Form 36 questionnaire was used to evaluate health-related quality of life (HRQOL), which yielded physical component scores and mental component scores, with higher scores indicating better physical and mental aspects of HRQOL, respectively.

RESULTS

Compared to the control group, the TCM mHealth app group showed greater improvement in hemoglobin A1c (HbA1c), yang-deficiency and phlegm-stasis body constitution, and BMI; however, no significant differences were found in these outcomes between the TCM mHealth app and ordinary mHealth app groups. The TCM mHealth app group showed better improvement in body energy and mental component scores than the ordinary mHealth app group. There were no significant differences in fasting plasma glucose, yin-deficiency body constitution, Dietary Approaches to Stop Hypertension dietary behavior, and total PA among the three groups after the intervention.

CONCLUSIONS

Use of either the ordinary or TCM mHealth app improved HRQOL among individuals with prediabetes. Compared to the outcomes of controls not using any app, use of the TCM mHealth app was effective at improving HbA1c, BMI, yang-deficiency and phlegm-stasis body constitution, and HRQOL. Moreover, using the TCM mHealth app seemed to improve the body energy and HRQOL more than when using the ordinary mHealth app. Further studies with a larger sample size and longer follow-up period may be necessary to determine whether the differences favoring the TCM app are clinically meaningful.

TRIAL REGISTRATION

ClinicalTrials.gov NCT04096989; https://clinicaltrials.gov/ct2/show/NCT04096989.

Social Return on Investment of Social Prescribing via a Diabetes Technician for Preventing Type 2 Diabetes Progression

In Wales, the prevalence of Type 2 Diabetes Mellitus (T2DM) has increased from 7.3% in 2016 to 8% in 2020, creating a major concern for the National Health Service (NHS). Social prescribing (SP) has been found to decrease T2DM prevalence and improve wellbeing. The MY LIFE programme, a scheme evaluated between June 2021 and February 2022 in the Conwy West Primary Care Cluster, aimed to prevent T2DM by referring prediabetic patients with a BMI of ≥30 to a diabetes technician (DT), who then signposted patients to community-based SP programmes, such as the National Exercise Referral Scheme (NERS), KindEating, and Slimming World. Although some patients engaged with SP, others chose to connect only with the DT. A Social Return on Investment (SROI) analysis was conducted to evaluate those patients who engaged with the DT plus SP, and those who connected solely with the DT. Relevant participant outcomes included 'mental wellbeing' and 'good overall health', which were measured at baseline (n = 54) and at the eight-week follow-up (n = 24). The estimated social value for every GBP 1 invested for participants who engaged with the 'DT only' ranged from GBP 4.67 to 4.70. The social value for participants who engaged with the 'DT plus SP programme' ranged from GBP 4.23 to 5.07. The results indicated that most of the social value generated was associated with connecting with the DT.

Carotenoids contribution in rapid diagnosis of multiple sclerosis by Raman spectroscopy

Rapid and accurate diagnosis of any illness determines the success of treatment. The same applies to multiple sclerosis (MS), chronic, inflammatory, and neurodegenerative diseases (ND) of the central nervous system (CNS). Unfortunately, the definitive diagnosis of MS is prolonged and involves mainly clinical symptoms observation and magnetic resonance imaging (MRI) of the CNS. However, as we previously reported, Attenuated Total Reflectance Fourier Transform Infrared (ATR-FTIR) spectroscopy shed new light on the minimally invasive, label-free, and rapid diagnosis of this illness through blood fraction. Herein we introduce Raman spectroscopy coupled with chemometric analysis to provide more detailed information about the biochemical changes behind MS. This pilot study demonstrates that mentioned combination may provide a new diagnostic biomarker and bring closer to rapid MS diagnosis. It has been shown that Raman spectroscopy provides lipid and carotenoid molecules as useful biomarkers which may be applied for both diagnosis and treatment monitoring.

Impact of Lockdown Measures on Health Outcomes of Adults with Type 2 Diabetes Mellitus in Bangladesh

COVID-19 lockdown measures appreciably affected patients' lifestyles, negatively impacting on their health. This includes patients with Type 2 Diabetes Mellitus (T2DM). Care of these patients was also negatively impacted due to a priority to treat patients with COVID-19, certainly initially, within hospitals and clinics in Bangladesh, combined with a lack of access to clinics and physicians due to lockdown and other measures. This is a concern in Bangladesh with growing rates of T2DM and subsequent complications. Consequently, we sought to critically analyze the situation among patients with T2DM in Bangladesh during the initial stages of the pandemic to address this information gap and provide future direction. Overall, 731 patients were recruited by a simple random sampling method among patients attending hospitals in Bangladesh, with data collected over 3 timescales: before lockdown, during the pandemic, and after lockdown. Data extracted from patients' notes included current prescribed medicines and key parameters, including blood sugar levels, blood pressure, and comorbidities. In addition, the extent of record keeping. The glycemic status of patients deteriorated during lockdown, and comorbidities as well as complications related to T2DM increased during this period. Overall, a significant proportion of key datasets were not recorded in patients' notes by their physician before and during lockdown. This started to change after lockdown measures eased. In conclusion, lockdown measures critically affected the management of patients with T2DM in Bangladesh, building on previous concerns. Extending internet coverage for telemedicine, introduction of structured guidelines, and appreciably increasing data recording during consultations is of the utmost priority to improve the care of T2DM patients in Bangladesh.

Comparison of diagnostic accuracy for diabetes diagnosis: A systematic review and network meta-analysis

Aim

Fasting Plasma Glucose (FPG) and Hemoglobin A1c (HbA1c) are used as diagnostic tests for diagnosing diabetes mellitus, but it is unclear which test has the best diagnostic accuracy. This systematic review and network meta-analysis aimed to estimate the diagnostic accuracy of HbA1c ≥ 6.5%, FPG ≥ 126 mg/dl, and the combination of HbA1c ≥ 6.5% or FPG ≥ 126 mg/dl (HbA1c| FPG), compared with Oral Glucose Tolerance Test (OGTT) ≥ 200 mg/dl for diagnosis diabetes.

Materials and methods

We performed a comprehensive search in PubMed, Embase, Cochrane Library, and Scopus from inception to September 24th, 2021. Inclusion criteria were any study design comparing HbA1c ≥ 6.5%, FPG ≥ 126 mg/dl, and HbA1c ≥ 6.5% or FPG ≥ 126 mg/dl with OGTT ≥ 200 mg/dl as the reference test. Data were independently extracted, risk of bias was assessed using QUADAS-2 by two reviewers. Network meta-analysis was done using a bivariate regression model using the Bayesian framework. The relative ranking of all tests was also assessed.

Results

Out of 5,026 studies, 73 were included. The sensitivities of HbA1c, FPG, and HbA1c| FPG were 0.51 [95% Credible Interval (CrI): 0.43, 0.58], 0.49 (95% CrI: 0.43, 0.55), and 0.64 (95% CrI: 0.51, 0.75), while the specificities were 0.96 (95% CrI: 0.94, 0.97), 0.98 (95% CrI: 0.97, 0.98), and 0.95 (95% CrI: 0.88, 0.98), respectively. The corresponding positive likelihood ratios (LR) were 13.36 (95% CrI: 8.91, 20.72), 21.94 (95% CrI: 15.04, 31.88), and 11.78 (95% CrI: 5.48, 26.56). HbA1c| FPG is superior based on sensitivity, whereas FPG is ranked best based on specificity and positive LR.

Conclusion

Our findings suggest that FPG ≥ 126 mg/dl should be recommended as the best diagnostic test for diabetes.

Systematic review registration

https://www.crd.york.ac.uk/prospero/, identifier CRD42021282856.

Preventable risk factors for type 2 diabetes can be detected using noninvasive spontaneous electroretinogram signals

Given the ever-increasing prevalence of type 2 diabetes and obesity, the pressure on global healthcare is expected to be colossal, especially in terms of blindness. Electroretinogram (ERG) has long been perceived as a first-use technique for diagnosing eye diseases, and some studies suggested its use for preventable risk factors of type 2 diabetes and thereby diabetic retinopathy (DR). Here, we show that in a non-evoked mode, ERG signals contain spontaneous oscillations that predict disease cases in rodent models of obesity and in people with overweight, obesity, and metabolic syndrome but not yet diabetes, using one single random forest-based model. Classification performance was both internally and externally validated, and correlation analysis showed that the spontaneous oscillations of the non-evoked ERG are altered before oscillatory potentials, which are the current gold-standard for early DR. Principal component and discriminant analysis suggested that the slow frequency (0.4-0.7 Hz) components are the main discriminators for our predictive model. In addition, we established that the optimal conditions to record these informative signals, are 5-minute duration recordings under daylight conditions, using any ERG sensors, including ones working with portative, non-mydriatic devices. Our study provides an early warning system with promising applications for prevention, monitoring and even the development of new therapies against type 2 diabetes.

Diagnostic accuracy of two confirmatory tests for diabetes mellitus in adult Ugandans with recently diagnosed tuberculosis

Objective

The optimal confirmatory tests for diabetes mellitus (DM) in patients with tuberculosis (TB) vary across populations. This study aimed to evaluate the performance of two confirmatory tests for DM against the oral glucose tolerance test (OGTT) as the reference test in adult Ugandans with recently diagnosed TB.

Methods

A total of 232 adult participants receiving TB treatment underwent initial screening for DM with random blood glucose (RBG) measurement. Participants with a RBG level ⩾6.1 mmol/l received additional screening with fasting blood glucose (FBG), laboratory-measured glycated haemoglobin (HbA1c) and an OGTT. Using the latter as the gold standard and reference test, we evaluated the diagnostic accuracy of laboratory-measured HbA1c and FBG.

Results

Of the 232 participants initially screened for DM using RBG measurement, 117 participants (50.4%) had RBG level ⩾6.1 mmol/l and were scheduled to return for additional blood glucose testing. Of these, 75 (64.1%) participants returned for FBG and HbA1c measurements. A diagnosis of DM was made in 32 participants, corresponding to a prevalence of 13.8% [95% CI 9.9-18.9].The areas under the curve (AUC) for FBG and laboratory-measured HbA1c were 0.69 [95% CI 0.47-0.90] and 0.65 [95% CI 0.43-0.87], respectively. The sensitivity and specificity of a FBG level of ⩾7 mmol/l were 57.1% [95% CI 18.4-90.1] and 74.6% [95% CI 62.5-84.5], respectively, whereas the sensitivity and specificity for laboratory-measured HbA1c of ⩾6.5 mmol/l (48 mmol/mol) were 14.3% [95% CI 0.40-57.9] and 95.3% (86.9-99.0%), respectively.

Conclusion

FBG may be better than laboratory-measured HbA1c in confirming DM in adult Ugandans with recently diagnosed TB. However, because of the small study sample size, larger studies evaluating the diagnostic utility of these diabetes screening tests in adult Ugandans with TB are needed to confirm these findings.

Integrated statistical and machine learning analysis provides insight into key influencing symptoms for distinguishing early-onset type 2 diabetes

Background

Being able to predict with confidence the early onset of type 2 diabetes from a suite of signs and symptoms (features) displayed by potential sufferers is desirable to commence treatment promptly. Late or inconclusive diagnosis can result in more serious health consequences for sufferers and higher costs for health care services in the long run.

Methods

A novel integrated methodology is proposed involving correlation, statistical analysis, machine learning, multi-K-fold cross-validation, and confusion matrices to provide a reliable classification of diabetes-positive and -negative individuals from a substantial suite of features. The method also identifies the relative influence of each feature on the diabetes diagnosis and highlights the most important ones. Ten statistical and machine learning methods are utilized to conduct the analysis.

Results

A published data set involving 520 individuals (Sylthet Diabetes Hospital, Bangladesh) is modeled revealing that a support vector classifier generates the most accurate early-onset type 2 diabetes status predictions with just 11 misclassifications (2.1% error). Polydipsia and polyuria are among the most influential features, whereas obesity and age are assigned low weights by the prediction models.

Conclusion

The proposed methodology can rapidly predict early-onset type 2 diabetes with high confidence while providing valuable insight into the key influential features involved in such predictions.

Criteria for prediabetes and posttransplant diabetes mellitus after kidney transplantation: A 2-year diagnostic accuracy study of participants from a randomized controlled trial

Posttransplant diabetes mellitus (PTDM) and prediabetes (impaired glucose tolerance [IGT] and impaired fasting glucose [IFG]) are associated with cardiovascular events. We assessed the diagnostic performance of fasting plasma glucose (FPG) and HbA1c as alternatives to oral glucose tolerance test (OGTT)-derived 2-hour plasma glucose (2hPG) using sensitivity and specificity in 263 kidney transplant recipients (KTRs) from a clinical trial. Between visits at 6, 12, and 24 months after transplantation, 28%-31% of patients switched glycemic category (normal glucose tolerance [NGT], IGT/IFG, PTDM). Correlations of FPG and HbA1c against 2hPG were lower at 6 months (r = 0.59 [FPG against 2hPG]; r = 0.45 [HbA1c against 2hPG]) vs. 24 months (r = 0.73 [FPG against 2hPG]; r = 0.74 [HbA1c against 2hPG]). Up to 69% of 2hPG-defined PTDM cases were missed by conventional HbA1c and FPG thresholds. For prediabetes, concordance of FPG and HbA1c with 2hPG ranged from 6%-9%. In conclusion, in our well-defined randomized trial cohort, one-third of KTRs switched glycemic category over 2 years and although the correlations of FPG and HbA1c with 2hPG improved with time, their diagnostic concordance was poor for PTDM and, especially, prediabetes. Considering posttransplant metabolic instability, FPG's and HbA1c 's diagnostic performance, the OGTT remains indispensable to diagnose PTDM and prediabetes after kidney transplantation.

Trends in Practice Among Shoulder Specialists in the Management of Frozen Shoulder: A Consensus Survey

Background:

The management of frozen shoulder (FS) differs depending on experience level and variation between scientific guidelines and actual practice.

Purpose:

To determine the current trends and practices in the management of FS among shoulder specialists and compare them with senior shoulder specialists.

Study Design:

Consensus statement.

Methods:

A team of 15 senior shoulder specialists (faculty group) prepared a questionnaire comprising 26 questions regarding the definition, terminology, clinical signs, investigations, management, and prognosis of FS. The questionnaire was mailed to all the registered shoulder specialists of Shoulder and Elbow Society, India (SESI) (specialist group; n = 230), as well as to the faculty group (n = 15). The responses of the 2 groups were compared, and levels of consensus were determined: strong (>75%), broad (60%-74.9%), inconclusive (40%-59.9%), or disagreement (<40%).

Result:

Overall, 142 of the 230 participants in the specialist group and all 15 participants in the faculty group responded to the survey. Both groups strongly agreed that plain radiographs are required to rule out a secondary cause of FS, routine magnetic resonance imaging is not indicated to confirm FS, nonsteroidal anti-inflammatory drugs should be administered at bedtime, steroid injection (triamcinolone or methylprednisolone) is the next best option if analgesics fail to provide pain relief, passive physical therapy should be avoided in the freezing phase, <10% of patients would require any surgical intervention, and patients with diabetes and thyroid dysfunction tend to fare poorly. There was broad agreement that routine thyroid dysfunction screening is unnecessary for women, a single 40-mg steroid injection via intra-articular route is preferred, and arthroscopic capsular release (ACR) results in a better outcome than manipulation under anesthesia (MUA). Agreement was inconclusive regarding the use of combined random blood sugar (RBS) and glycosylated hemoglobin versus lone RBS to screen for diabetes in patients with FS, preference of ACR versus MUA to treat resistant FS, and the timing of surgical intervention. There was disagreement over the most appropriate term for FS, the preferred physical therapy modality for pain relief, the most important movement restriction for early diagnosis of FS, and complications seen after MUA.

Conclusion:

This survey summarized the trend in prevalent practices regarding FS among the shoulder specialists and senior shoulder surgeons of SESI.

Glycated albumin in diabetes mellitus: a meta-analysis of diagnostic test accuracy

OBJECTIVES

Guidelines recommend the diagnosis of diabetes should be based on either plasma glucose or glycated hemoglobin (HbA_1C) findings. However, lately studies have advocated glycated albumin (GA) as a useful alternative to HbA_1c. We conducted a systematic review and meta-analysis to determine the overall diagnostic accuracy of GA for the diagnosis of diabetes.

CONTENT

We searched for articles of GA diabetes diagnostic accuracy that were published up to August 2021. Studies were selected if reported an oral glucose tolerance test as a reference test, measured GA levels by enzymatic methods, and had data necessary for 2 × 2 contingency tables. A bivariate model was used to calculate the pooled estimates.

SUMMARY

This meta-analysis included nine studies, totaling 10,007 individuals. Of those, 3,106 had diabetes. The studies showed substantial heterogeneity caused by a non-threshold effect and reported different GA optimal cut-offs for diagnosing diabetes. The pooled diagnostic odds ratio (DOR) was 15.93 and the area under the curve (AUC) was 0.844, indicating a good level of overall accuracy for the diagnosis of diabetes. The effect of the GA threshold on diagnostic accuracy was reported at 15.0% and 17.1%. The optimal cut-off for diagnosing diabetes with GA was estimated as 17.1% with a pooled sensitivity of 55.1% (95% CI 36.7%-72.2%) and specificity of 94.4% (95% CI 85.3%-97.9%).

OUTLOOK

GA has good diabetes diagnostic accuracy. A GA threshold of 17.1% may be considered optimal for diagnosing diabetes in previously undiagnosed individuals.

The diagnostic accuracy of HbA1c , compared to the oral glucose tolerance test, for screening for type 2 diabetes mellitus in Africa-A systematic review and meta-analysis

OBJECTIVE

To assess the diagnostic accuracy of glycated haemoglobin A_1c (HbA_1c ), compared to fasting plasma glucose (FPG) and the oral glucose tolerance test (OGTT), in screening for type 2 diabetes (T2D) in Africa.

METHODS

We systematically searched databases for studies that compared the HbA_1c to either the OGTT, or the FPG for T2D diagnosis were included. The QUADAS 2 tool was used for assessing the quality of included studies. We used the split component synthesis (SCS) method for the meta-analysis of diagnostic accuracy studies to pool the studies for meta-analysis of sensitivity and specificity, primarily at the HbA_1c ≥48 mmol/mol (6.5%) cut-off and at other cut-offs. We assessed heterogeneity using the I²  statistic and publication bias using Doi plots.

RESULTS

Eleven studies, from seven African countries, with 12,925 participants, were included. Against the OGTT, HbA_1c ≥48 mmol/mol (6.5%) had a pooled sensitivity of 57.7% (95% confidence interval [CI] 43.4-70.9) and specificity of 92.3% (95% CI 83.9-96.5). Against the FPG, HbA_1c ≥48 mmol/mol (6.5%) had a pooled sensitivity of 64.5% (95% CI 50.5-76.4) and specificity of 94.3% (95% CI 87.9-97.5). The highest sensitivity for HbA_1c , against the OGTT, was at the 42 mmol/mol (6.0%) cut-off.

CONCLUSION

In Africa, the HbA_1c ≥48 mmol/mol (6.5%) cut-off may miss almost half of the individuals with T2D based on blood glucose measures.

Cost-effectiveness of population-based screening for diabetes and hypertension in India: an economic modelling study

BACKGROUND

India faces a high burden of diabetes and hypertension. Currently, there is a dearth of economic evidence about screening programmes, affected age groups, and frequency of screening for these diseases in Indian settings. We assessed the cost effectiveness of population-based screening for diabetes and hypertension compared with current practice in India for different scenarios, according to type of screening test, population age group, and pattern of health-care use.

METHODS

We used a hybrid decision model (decision tree and Markov model) to estimate the lifetime costs and consequences from a societal perspective. A meta-analysis was done to assess the effectiveness of population-based screening. Primary data were collected from two Indian states (Haryana and Tamil Nadu) to assess the cost of screening. The data from the National Health System Cost Database and the Costing of Health Services in India study were used to determine the health system cost of diagnostic tests and cost of treating diabetes or hypertension and their complications. A total of 962 patients were recruited to assess out-of-pocket expenditure and quality of life. Parameter uncertainty was evaluated using univariate and multivariable probabilistic sensitivity analyses. Finally, we estimated the incremental cost per quality-adjusted life-year (QALY) gained with alternative scenarios of scaling up primary health care through a health and wellness centre programme for the treatment of diabetes and hypertension.

FINDINGS

The incremental cost per QALY gained across various strategies for population-based screening for diabetes and hypertension ranged from US$0·02 million to $0·03 million. At the current pattern of health services use, none of the screening strategies of annual screening, screening every 3 years, and screening every 5 years was cost-effective at a threshold of 1-time per capita gross domestic product in India. In the scenario in which health and wellness centres provided primary care to 20% of patients who were newly diagnosed with uncomplicated diabetes or hypertension, screening the group aged between 30 and 65 years every 5 years or 3 years for either diabetes, hypertension, or a comorbid state (both diabetes and hypertension) became cost-effective. If the share of treatment for patients with newly diagnosed uncomplicated diabetes or hypertension at health and wellness centres increases to 70%, from the existing 4% at subcentres and primary health centres, annual population-based screening becomes a cost saving strategy.

INTERPRETATION

Population-based screening for diabetes and hypertension in India could potentially reduce time to diagnosis and treatment and be cost-effective if it is linked to comprehensive primary health care through health and wellness centres for provision of treatment to patients who screen positive.

FUNDING

None.

HbA1c Screening for Diabetes in Patients with Acute Coronary Syndrome: A Worthwhile Test or a Pitfall?

Background: Diagnostic concordance between HbA1c and other glucose-based tests is imperfect, and data on this problem in acute coronary syndrome (ACS) are still lacking. The aim of this study was to identify undiagnosed glucose abnormalities in ACS patients, and to compare the effectiveness and consistency of the diagnostic screening based on HbA1c to the oral glucose tolerance test (OGTT). Methods: The study group consisted of 121 ACS patients, mean age 62.3 ± 11.6 years, without known glucose abnormalities. HbA1c, admission and fasting plasma glucose in the first days of hospitalization were assessed and referred to the results of OGTT performed two weeks after discharge. Results: OGTT identified normoglycemia in 45%, pre-diabetes in 39.4%, and diabetes in 15.6%, while HbA1c revealed these categories in 39.7%, 51.2%, and 9.1%, respectively. With an HbA1c cut-off ≥6.5% (48 mmol/mol) diagnostic for diabetes, the sensitivity of the method was 41%, while specificity was 98%, compared to the OGTT. The optimal HbA1c cut-off value at the crossing of sensitivity and specificity curves was 5.9%. The HbA1c value recommended for the diagnosis of pre-diabetes and optimal cut-off point were the same (5.7%). Conclusions: Using HbA1c without OGTT in an early but stable phase of ACS may result in a significant underdiagnosis of diabetes.

Accuracy of breath test for diabetes mellitus diagnosis: a systematic review and meta-analysis

The review aimed to investigate the accuracy of breath tests in the diagnosis of diabetes mellitus, identify exhaled volatile organic compounds with the most evidence as potential biomarkers, and summarize prospects and challenges in diabetic breath tests. Databases including Medline, PubMed, EMBASE, Cochrane Library and Science Citation Index Expanded were searched. Human studies describing diabetic breath analysis with more than 10 subjects as controls and patients were included. Population demographics, breath test conditions, biomarkers, analytical techniques and diagnostic accuracy were extracted. Quality assessment was performed with the Standards for Reporting Diagnostic Accuracy and a modified QUADAS-2 (Quality Assessment of Diagnostic Accuracy Studies 2). Forty-four research with 2699 patients with diabetes were included for qualitative data analysis and 14 eligible studies were used for meta-analysis. Pooled analysis of type 2 diabetes breath test exhibited sensitivity of 91.8% (95% CI 83.6% to 96.1%), specificity of 92.1% (95% CI 88.4% to 94.7%) and area under the curve (AUC) of 0.96 (95% CI 0.94 to 0.97). Isotopic carbon dioxide (CO₂) showed the best diagnostic accuracy with pooled sensitivity of 0.949 (95% CI 0.870 to 0.981), specificity of 0.946 (95% CI 0.891 to 0.975) and AUC of 0.98 (95% CI 0.97 to 0.99). As the most widely reported biomarker, acetone showed moderate diagnostic accuracy with pooled sensitivity of 0.638 (95% CI 0.511 to 0.748), specificity of 0.801 (95% CI 0.691 to 0.878) and AUC of 0.79 (95% CI 0.75 to 0.82). Our results indicate that breath test is a promising approach with acceptable diagnostic accuracy for diabetes mellitus and isotopic CO₂ is the optimal breath biomarker. Even so, further validation and standardization in subject control, breath sampling and analysis are still required.

Glycated Hemoglobin and Methods for Its Point of Care Testing

Glycated hemoglobin (HbA1c) is a product of the spontaneous reaction between hemoglobin and elevated glucose levels in the blood. It is included among the so-called advanced glycation end products, of which is the most important for the clinical diagnosis of diabetes mellitus, and it can serve as an alternative to glycemia measurement. Compared to the diagnosis of diabetes mellitus by glycemia, the HbA1c level is less influenced by a short-term problem with diabetes compensation. Mass spectroscopy and chromatographic techniques are among the standard methods of HbA1c level measurement. Compared to glycemia measurement, there is lack of simple methods for diabetes mellitus diagnosis by means of the HbA1c assay using a point-of-care test. This review article is focused on the surveying of facts about HbA1c and its importance in diabetes mellitus diagnosis, and surveying standard methods and new methods suitable for the HbA1c assay under point-of-care conditions. Various bioassays and biosensors are mentioned and their specifications are discussed.