Diabetes Risk Score Research Articles

Serum metabolomic profiling of incident type 2 diabetes mellitus in the Multi-Ethnic Study of Atherosclerosis and Rotterdam Study.

This study aimed to investigate serum metabolomic biomarkers associated with incident type 2 diabetes mellitus (T2DM) and evaluate their performance in improving T2DM risk prediction. Untargeted proton nuclear magnetic resonance (1H NMR) spectroscopy-based metabolomics analyses were conducted in the Multi-Ethnic Study of Atherosclerosis (MESA; n=3460; discovery cohort) and Rotterdam Study (RS; n=1556; replication cohort). Multivariable cause-specific hazards models were used to analyze the associations between 23,571 serum metabolomic spectral variables and incident T2DM. Replicated metabolites required an FDR-adjusted P<0.01 in MESA, P<0.05 in RS, and consistent direction of association. Pathway and network analyses were conducted to elucidate biological mechanisms underlying T2DM development. Utility of the replicated metabolites in improving T2DM risk prediction was assessed based on the Framingham Diabetes Risk Score. A 2-sample Mendelian randomization was conducted to assess causal associations. Nineteen metabolites were significantly associated with incident T2DM. Pathway analyses revealed disturbances in aminoacyl-tRNA biosynthesis, metabolism of branched-chain amino acids (BCAAs), glycolysis/gluconeogenesis, and glycerolipid metabolism. Network analyses identified interactions with upstream regulators including p38 MAPK, c-JNK, and mTOR signaling pathways. Adding replicated metabolites to the Framingham Diabetes Risk Score showed modest to moderate improvements in prediction performance in MESA and RS, with Δ c-statistic of 0.05 (95% CI, 0.04-0.07) in MESA and 0.03 (95% CI, 0.01-0.05) in RS. Genetically increased BCAAs and mannose were associated with T2DM. 1H NMR measured metabolites involved in aminoacyl-tRNA biosynthesis, BCAA metabolism, glycolysis/gluconeogenesis, and glycerolipid metabolism were significantly associated with incident T2DM and provided modest to moderate predictive utility beyond traditional risk factors.

Efficacy and safety analysis of Kaiyu Jiangzhuo Tongzhi prescription for the treatment of pre-diabetic overweight/obese patients: a multicentre randomised controlled clinical study protocol and statistical analysis plan

The Chinese Academy of Traditional Chinese Medicine's academician Xiaolin Tong developed the Kaiyu Jiangzhuo Tongzhi prescription (KYJZ) in response to prediabetes's "yu" mechanism. This study plans to thoroughly evaluate the safety and effectiveness of KYJZ in treating prediabetes and preventing diabetes mellitus. This study is a multicenter, randomized, double-blind, placebo-controlled trial in which 598 patients with pre-diabetes were randomly assigned in a 1:1 ratio to either the KYJZ or the placebo group. The primary outcome was to assess the incidence of diabetes at the end of 48 weeks. Secondary outcomes included the rate of normal conversion to glucose tolerance at the end of 48 weeks, indicators of glucose-fat metabolism, body mass index (BMI), waist circumference (WC), and Diabetes Symptom Rating Scale (DSRS), as well as body composition analysis (BCA) and diabetes risk score (DRS). Record all adverse events that occur and analyze the data collected. The results of this study will provide strong evidence for the efficacy and safety of KYJZ in reducing the incidence of diabetes mellitus in overweight/obese patients with prediabetes.

Assessment of risk of developing diabetes mellitus using Indian diabetes risk score (IDRS) among adult population living in an urban field practice area

ABSTRACT Background: The Indian Diabetes Risk Score (IDRS) is a simple and cost-effective tool for the early detection of undiagnosed cases in the community and is most suited for the Indian population to assess the risk of developing diabetes. Aim and Objectives: To assess the risk of Type 2 Diabetes Mellitus among the adult population living in an urban area by using the Indian Diabetes Risk Score (IDRS). To determine associated risk factors among the study population. Materials and Methods: A community-based cross-sectional study was done among 145 adults in an Urban field practice area under the Department of Community Medicine, Andhra, Medical College, Visakhapatnam. All males and females aged between 20 and 60 years living in the urban field practice area for the past year or more. The known diabetics and severely ill patients were excluded. Ethical permission was obtained and informed consent was taken from all the study participants. Results: Out of 145 study population, 66 people (45.5%) were found to be at the low-risk category, 41 people (28.3%) were in the medium-risk category and 38 people (26.2%) were at high risk based on their IDRS scores. Conclusion: The study showed a statistically significant relationship between factors such as gender, age, increased waist circumference, family history of diabetes mellitus, physical activity, and increased risk score.

Prediabetes and cardiovascular risk factors: the effectiveness of a guided self-determination counselling approach in primary health care, a randomized controlled trial

BackgroundIdentify individuals who are at risk of Type 2 diabetes, who also are at a greater risk of developing cardiovascular disease is important. The rapid worldwide increase in diabetes prevalence call for Primary Health Care to find feasible prevention strategies, to reduce patient risk factors and promote lifestyle changes. Aim of this randomized controlled trial was to investigate how a nurse-lead Guided Self-Determination counselling approach can assist people at risk of type 2 diabetes to lower their coronary heart disease risk.MethodsIn this randomized controlled study, 81 people at risk of developing type 2 diabetes were assigned into an intervention group (n = 39) receiving Guided Self-Determination counselling from Primary Health Care nurses over three months and a control group (n = 42) that received a diet leaflet only. Measurements included the Finnish Diabetes Risk Score questionnaire and biological measurements of Hemoglobin A1c protein, Body Mass Index, fasting blood glucose, Blood pressure, Cholesterol, High-density lipoprotein, and triglycerides, at baseline (time1), 6 (time2) and 9 months (time 3).ResultsA total of 56 participants, equal number in intervention and control groups, completed all measurements. A significant difference between the intervention and control groups, in coronary heart disease risk was not found at 6 nor 9-months. However, within-group data demonstrated that 55.4% of the participants had lower coronary heart disease risk in the next ten years at the 9-month measurement. Indicating an overall 18% relative risk reduction of coronary heart disease risk by participating in the trial, with the number needed to treat for one to lower their risk to be nine. Within the intervention group a significant difference was found between time 1 and 3 in lower body mass index (p = 0.046), hemoglobin A1c level (p = 0.018) and diastolic blood pressure (p = 0.03).ConclusionsAlthough unable to show significant group differences in change of coronary heart disease risk by this 12-weeks intervention, the process of regular measurements and the guided self-determination counselling seem to be beneficial for within-group measures and the overall reduction of coronary heart disease risk factors.Trial registrationThis study is a part of the registered study ‘Effectiveness of Nurse-coordinated Follow-Up Programme in Primary Care for People at Risk of T2DM’ at www.ClinicalTrials.gov (NCT04688359) (accessed on 30 December 2020).

Type 2 diabetes risk is associated with poorer general health and mental health in women and men

Abstract Background Increasing evidence points to a relation between cardiometabolic and mental health, but population-based data are scarce. We investigated the association of type 2 diabetes (T2D) risk to general and mental health with a focus on potential differences between women and men. Methods The study population comprised 5,192 adults (18+ years) without known diabetes based on data of the German Health Update (GEDA) 2022. T2D risk was assessed by the German Diabetes Risk Score (GDRS), that estimates the 5-year probability of developing T2D and was categorized into low, still low, elevated, and high risk. Sex-specific Poisson regression analyses were conducted including self-rated health (SRH), self-rated mental health (SRMH), depressive symptoms (PHQ-2) and anxiety symptoms (GAD-2) as dichotomous dependent variables and age, educational level, living alone, region of residence, and social support as covariables. Results Women had a higher prevalence of low T2D risk (66.1%) and a lower prevalence of high T2D risk (9.4 %) than men (55.3% vs. 16.3%). Compared to those with low T2D risk, women and men with a high T2D risk were less likely to report very good/good SRH (women: prevalence ratio: 0.63; 95% confidence interval: 0.51-0.79; men: 0.69; 0.57-0.84) or excellent/very good SRMH (women: 0.53, 0.39-0.73; men: 0.85, 0.63-1.15). Further, those with a high T2D risk had a higher risk of depressive symptoms (women: 1.71, 1.05-2.81; men: 2.23, 1.22-4.05) and anxiety symptoms (women: 1.84, 1.03-3.29; men: 2.73, 1.09-6.81). No statistically significant interactions between sex and T2D risk were observed regarding the included health outcomes. Conclusions A high T2D risk is associated with a lower probability of a favourable SRH and SRMH and a higher risk of depressive and anxiety symptoms in men and women. The underlying mechanisms need further identification, in order to design synergistically beneficial health promotion for cardiometabolic and mental health. Key messages • No sex-specific differences in general and mental health were found in people at high T2D risk. • Further research is needed to identify the underlying mechanism to improve general and mental health.

The Effect of Health Literacy in Women on Their Level Of Knowledge About Cardiovascular Disease Risk Factors and Their Risk of Diabetes

Background: Health literacy is important not only for reading efficiency but also for early diagnosis of diseases and risk awareness. Objective: This study was conducted to determine the effect of health literacy on the level of knowledge of women regarding cardiovascular disease risk factors and their risk of developing diabetes. Method: The study employs a cross-sectional design. The research sample consists of 300 women. The data was collected by face-to-face survey method between January and July 2021. A Personal Information Form, the Turkish Health Literacy Scale-32 (THLS-32), the Cardiovascular Disease Risk Factors Knowledge Level (CARRF-KL) Scale and the Finnish Diabetes Risk Score (FINDRISC) Questionnaire were used in the study. Mann-Whitney U test and Kruskal-Wallis test were used to evaluate the data. Results: It was determined that one third of the women had inadequate THLS-32 scores and that their mean CARRF-KL and FINDRISC scores were moderate. The mean THLS-32 scale scores of those with a low education level and poor health perception were found to be lower (p

Indirect estimation of the prevalence of type 2 diabetes mellitus in the sub-population of Tehran: using non-laboratory risk-score models in Iran

BackgroundThe prevalence of type 2 diabetes mellitus (T2DM) in the population covered by the Tehran University of Medical Sciences is unclear but crucial for healthcare programs. This study aims to validate four non-laboratory risk-score models, the American Diabetes Association (ADA) Risk Score, Australian Type 2 Diabetes Risk Assessment Tool (AUSDRISK), Finnish Diabetes Risk Score (FINDRISC), and TOPICS Diabetes Screening Score, for identifying undiagnosed diabetes and indirectly estimate the prevalence of T2DM in a subset of the Tehranian population using the selected model.MethodsThis research consisted of two main parts. In the first part, non-laboratory risk-score models to identify undiagnosed T2DM were validated using Iranian data from STEPs 2016 survey. The model performance was evaluated through the Area Under the Curve (AUC) and calibration via the observed-to-expected (O/E) ratio. Additional independent data from STEPs 2011 survey in Iran were utilized to test the model results by comparing indirect prevalence estimates with observed estimates. In the second part, the prevalence of T2DM was estimated indirectly by applying the selected model to a representative random sample from a Tehranian population telephone survey conducted in 2023.ResultsAmong the different models used, AUSDRISK showed the best performance in both discrimination (AUC (95% confidence interval (CI)): 0.80 (0.78, 0.81)) and calibration (O/E ratio = 1.01). After updating the original model, there was no change in the AUC value or calibration. Additionally, our findings indicate that the indirect estimates are nearly identical to the observed values in STEPs 2011 survey. In the second part of the study, by applying the recalibrated model to a subsample, the indirect prevalence of undiagnosed diabetes and T2DM (95% CI) were estimated at 4.18% (3.87, 4.49) and 11.1% (9.34, 13.1), respectively.ConclusionGiven the strong performance of the model, it appears that indirect method can provide a cost-effective and simple approach to assess disease prevalence and intervention effectiveness.Graphical

HbA1c comparable to fasting glucose in the external validation of the African Diabetes Risk Score and other established risk prediction models in Black South Africans

BackgroundThe use of non-invasive risk scores to detect undiagnosed type 2 diabetes (T2D) ensures the restriction of invasive and costly blood tests to those most likely to be diagnosed with the disease. This study assessed and compared the performance of the African Diabetes Risk Score (ADRS) with three other diabetes risk prediction models for identifying screen-detected diabetes based on fasting plasma glucose (FPG) or glycated haemoglobin (HBA1c).MethodsAge, sex, waist circumference, body mass index, blood pressure, history of diabetes and physical activity levels from the SA-NW-PURE study were used to externally validate the ADRS and other established risk prediction models. Discrimination was assessed and compared using C-statistics and nonparametric methods. Calibration was assessed using calibration plots, before and after recalibration.ResultsNine hundred and thirty-seven participants were included; 14% had prevalent undiagnosed T2D according to FPG and 26% according to HbA1c. Discrimination was acceptable and was mostly similar between models for both diagnostic measures. The C-statistics for diagnosis by FPG ranged from 0.69 for the Simplified FINDRISC model to 0.77 for the ADRS model and 0.77 for the Simplified FINDRISC model to 0.79 for the ADRS model for diagnosis by HbA1c. Calibration ranged from acceptable to good, though over- and underestimation were present. All models improved significantly following recalibration.ConclusionsThe models performed comparably, with the ADRS offering a non-invasive way to identify up to 79% of cases. Based on its ease of use and performance, the ADRS is recommended for screening for T2D in certain Black population groups in South Africa. HbA1c as a means of diagnosis also showed comparable performance with FPG. Therefore, further validation studies can potentially use HbA1c as the standard to compare to.

Assessment of 1-Hour Postload Plasma Glucose, the Metabolic Syndrome, and the Finish Diabetes Risk Score in the Prediction of Type 2 Diabetes

ObjectiveTo compare the 1-hour postload glucose (1h-PG) value of an oral glucose tolerance test (OGTT) with the metabolic syndrome (MetS) and the Finish Diabetes Risk Score (FINDRISC) in patients with impaired fasting glucose (IFG) to predict type 2 diabetes mellitus (T2DM). MethodsA cohort study was conducted in patients at a general hospital in Lima, Perú. An OGTT was performed in subjects with IFG who were followed-up for 7 years for T2DM development. The exposure variables were 1h-PG ≥ 155 mg/dL, MetS, and a FINDRISC ≥ 13 points, and the outcome was the presence of T2DM. The relative risk, confidence interval, and area under the curve (AUROC) were also estimated. ResultsAmong 324 subjects with IFG, 218 completed the 7-year follow-up. The mean age was 56.2 ± 11.5 years, 64.0% were woman, and 63.8% were overweight/obese. Of these, 36.8% had 1h-PG ≥ 155 mg/dL and normal glucose tolerance, 66.8% had MetS, and 64.5% had FINDRISC ≥ 13 points. After 7 years, 21.1% of participants developed T2DM, with 68.8% of them who had 1h-PG ≥ 155 mg/dL (P < .001), 62.2% had MetS (P = .013), and 67.9% had FINDRISC ≥ 13 (P = .68). After adjusting by age, sex, and body mass index, the relative risk was 3.52 (1.64-7.54; 95% CI), 1.81 (0.96-3.38; 95% CI), and 1.17 (0.51-2.70; 95% CI) for each exposure variable, respectively. Also, the AUROC was 0.72 (0.60-0.83), 0.63 (0.51-0.75), and 0.51 (0.38-0.63) (P = .01), respectively. ConclusionBy performing an OGTT in patients with IFG, an 1h-PG ≥ 155 mg/dL value may be helpful to predict T2DM at 7 years better than the use of MetS or the FINDRISC.

Predicting the risk of type 2 diabetes: Standardized diabetes risk score among the Khmer ethnic minority in Vietnam.

Predicting the risk of progression to type 2 diabetes, as well as identifying the factors that increase this risk, helps the population adjust the modifiable risk factors, improve quality of life, and reduce the disease burden. A cross-sectional study was conducted on 918 ethnic Khmer minority people aged 40 and above in Vietnam who had never been diagnosed with type 2 diabetes. To predict the 10-year risk of type 2 diabetes, the Finnish Diabetes Risk Scoring Scale, adjusted for the Asian population with modification of the waist circumpherence and Body Mass Index Cut-Offs, was used. The 10-year predicted risk of progression to type 2 diabetes in ethnic Khmer people aged 40 years and older in southern Vietnam, using the Asian-modified Finnish Diabetes Risk Scoring Scale, resulted 10.54% in the total population study, females have a higher risk at 12.62% compared to 8.01% of males. Among the items that make up the Finnish Diabetes Risk Scoring Scale, age, waist circumference, BMI, family history of diabetes, history of high blood glucose, and use of blood pressure medication were the most accurate predictors, with the area under the Receiver operating characteristic (ROC) curve at 0.83, 0.81, 0.77, 0.75, 0.74 and 0.73 respectively. The optimal cut-off score to identify progression to tipe 2 diabetes was 13.5 points (Se = 1.00, Sp = 1.00, p < 0.001). The multivariable logistic regression model shows that factors associated with high risk of type 2 diabetes progression in 10 years are age, gender, occupation, economic status, education level and regular alcohol consumption (p < 0.05). The study results provide a basis for proposing potential solutions to reduce modifiable risk factors for type 2 diabetes in the population. These include providing culturally appropriate health education and changing behavior to address alcohol consumption. The use of the Asian-modified Finnish Diabetes Risk Scoring Scale to predict the risk of progression to type 2 diabetes and as a screening tool for undiagnosed type 2 diabetes is appropriate for the Vietnamese Khmer population.

Psychosis metabolic risk calculator (PsyMetRiC) in early psychosis: External validation study in Finland.

Accurate detection of cardiometabolic risk in early psychosis is crucial to reducing somatic morbidity and mortality in people with psychotic disorders. We conducted an external validation of the psychosis metabolic risk calculator (PsyMetRiC), a cardiometabolic risk prediction tool developed in the UK and tailored for young people with psychosis. We compared the predictive accuracy and clinical usefulness of PsyMetRiC and a general population-based risk prediction tool for type 2 diabetes, the Finnish Diabetes Risk Score (FINDRISC). We included first-episode psychosis and ultra-high-risk for psychosis patients without metabolic syndrome aged 18-35 years from the Helsinki Early Psychosis and Turku Early Psychosis Study cohorts. We tested two versions of PsyMetRiC: the full model including age, sex, ethnicity, body-mass index, smoking status, prescription of metabolically-active antipsychotic medication, high-density lipoprotein, and triglyceride concentrations, and the partial-model excluding biochemical predictors, and the simplified FINDRISC including BMI, sex, systolic blood pressure, and fasting glucose. Discrimination, calibration, and decision curve analyses were used to assess the predictive performance and clinical usefulness of both PsyMetRiC and FINDRISC. We performed a site-specific re-calibration of PsyMetRiC (PsyMetRiC-Fi). The study sample consisted of 278 individuals (all White European ethnicity, 58.6% male, mean age 24.8 years, 37.8% smoking, mean BMI 23.5). Discrimination was marginally better in the PsyMetRiC full model (C = 0.72, 95% CI, 0.59-0.82) compared with partial model (C = 0.70, 95% CI 0.59-0.80) or FINDRISC (C = 0.63, 95% CI 0.54-0.71). Calibration plots displayed evidence of minor miscalibration for PsyMetRiC, which corrected following recalibration. Miscalibration was more pronounced for FINDRISC. Decision curve analysis showed that PsyMetRiC offers likely clinical usefulness in improving cardiometabolic risk management in early psychosis compared with giving everyone or no one an intervention. PsyMetRiC has utility in predicting cardiometabolic risk in Finnish patients with early psychosis. It has better discriminatory accuracy and offers more accurate risk prediction compared to other available strategies.

Prevalence of Type 2 Diabetes Risk in a Higher Education Community of North-Eastern Portugal: a Prospective Cross-sectional Observational Analysis

Objectives: Type 2 diabetes (T2D) is considered one of the most prevalent diseases worldwide. The aim of this study was to analyse the risk of developing T2D in a higher education community of Bragança, North-Eastern Portugal.Methods: The observational, cross-sectional and prospective sample comprised 3021 individuals, aged between 18 and 64 years old, of whom 1759 were women (mean age 24.16 ± 8.80 years) and 1263 were men (mean age 23.37 ± 8.70 years). The Finnish Diabetes Risk Score (FINDRISC) questionnaire was employed to estimate the risk of developing T2D over the next 10 years.Results: The results indicated that approximately 37 (1%) participants in the sample exhibited a high risk of developing T2D, 117 (4%) participants exhibited a moderate risk, 660 (22%) participants exhibited a slight risk, and 2206 (73%) participants exhibited a low risk. A higher T2D risk was observed in the staff group in comparison with the other groups (p &lt; 0.001). Furthermore, body mass index (BMI) and waist circumference (WC) were the variables that most contributed to the increased risk of developing T2D (X2 = 1034.5; p &lt; 0.001).Conclusions: In conclusion, the study reported a low T2D risk for the higher education community of Bragança in the Portuguese North-Eastern region. However, the risk of T2D was found to be higher in the staff group than in the student group. The FINDRISK questionnaire may be applied for the early diagnosis of T2D risk factors.

External validation of Finnish diabetes risk score (FINDRISC) and Latin American FINDRISC for screening of undiagnosed dysglycemia: Analysis in a Peruvian hospital health care workers sample.

To evaluate the external validity of Finnish diabetes risk score (FINDRISC) and Latin American FINDRISC (LAFINDRISC) for undiagnosed dysglycemia in hospital health care workers. We carried out a cross-sectional study on health workers without a prior history of diabetes mellitus (DM). Undiagnosed dysglycemia (prediabetes or diabetes mellitus) was defined using fasting glucose and two-hour oral glucose tolerance test. LAFINDRISC is an adapted version of FINDRISC with different waist circumference cut-off points. We calculated the area under the receptor operational characteristic curve (AUROC) and explored the best cut-off point. We included 549 participants in the analysis. The frequency of undiagnosed dysglycemia was 17.8%. The AUROC of LAFINDRISC and FINDRISC were 71.5% and 69.2%; p = 0.007, respectively. The optimal cut-off for undiagnosed dysglycemiaaccording to Index Youden was ≥ 11 in LAFINDRISC (Sensitivity: 78.6%; Specificity: 51.7%) and ≥12 in FINDRISC (Sensitivity: 70.4%; Specificity: 53.9%). The discriminative capacity of both questionnaires is good for the diagnosis of dysglycemia in the healthcare personnel of the María Auxiliadora hospital. The LAFINDRISC presented a small statistical difference, nontheless clinically similar, since there was no difference by age or sex. Further studies in the general population are required to validate these results.

Risk assessment of diabetes among undergraduate students of Northern Maharashtra using Indian Diabetes Risk Score and its correlation with fasting blood glucose

Abstract Background: Diabetes, once primarily associated with old age, is increasingly prevalent among young adults. The Indian Diabetes Risk Score (IDRS) employs four parameters—age, waist circumference, family history of diabetes, and physical activity—to screen for diabetes. This study aims to evaluate the risk and prevalence of diabetes and prediabetes in undergraduate medical students and to correlate these findings with their fasting glucose levels. Materials and Methods: A cross-sectional descriptive study was carried out among 336 undergraduate medical students in the Jalgaon district of Maharashtra. Data on socio-demographic and anthropometric factors were gathered using a structured, pre-tested questionnaire. Each participant’s IDRS score was calculated. Students were then invited to have their fasting blood glucose levels measured after an overnight fast. Results: The study surveyed 336 medical students, comprising 185 males (55.06%) and 151 females (44.94%). According to the IDRS, 5 students (1.49%) were categorized as high risk, 202 students (60.1%) as moderate risk, and 129 students (38.4%) as low risk for diabetes. Fasting blood glucose tests indicated that 3 students (0.90%) had impaired glucose tolerance, and 52 (15.48%) students were pre-diabetic. There was a statistically significant correlation between fasting blood glucose levels and IDRS (R = 0.466, P &lt; 0.001). Conclusion: The risk of diabetes among the participating undergraduate students is alarmingly high, according to the IDRS score. The strong correlation between the IDRS and the blood glucose levels of the study participants demonstrates that the IDRS is a highly effective, cost-efficient tool for predicting diabetes risk.

Assessment of Risk of Type 2 Diabetes using Indian Diabetes Risk Score: A Community-Based Cross-Sectional Study in Marathwada region of Maharashtra

Background: Diabetes mellitus is one of the non-communicable diseases which have become a major global health problem whose prevalence is increasing worldwide and will reach to 12.2% (783 million) by 2045. The risk of diabetes escalates with increase in the number of risk factors and their duration as well. The Indian Diabetic Risk Score (IDRS) is a simple, low cost, feasible tool for mass screening programme for diabetes at the community level. Hence the present study was carried out to assess the level of risk of developing type 2 diabetes using IDRS. Methods: A cross-sectional study was carried out amongst 380 participants aged 20 years and above during the period of July 2018 to June 2019 in the field practice area of Rural Health Training Centre of IIMSR Medical College in Jalna, Maharashtra. A semi-structured interview schedule consisting of socio-demographic characteristics, risk factor profile and Indian Diabetes Risk Score was used. Data was analysed by using appropriate statistical test whenever necessary. Results: In our study, 52% are moderately at risk and 2% are at high risk for developing diabetes mellitus. About 62.1% participants belonged to nuclear family, 42.6% had family history of diabetes mellitus, 76.3% carried out moderate physical activity and 50.3% were overweight / obese. Family history of diabetes, lack of physical activity and overweight / obesity were found to be potential risk factors for developing diabetes mellitus. Conclusion: More than half i.e. 52% of the study subjects were at risk of having diabetes, hence screening is of utmost importance so that interventions can be initiated at an early stage.

Development and validation of a machine learning-based model to predict isolated post-challenge hyperglycemia in middle-aged and elder adults: Analysis from a multicentric study.

Due to the high cost and complexity, the oral glucose tolerance test is not adopted as the screening method for identifying diabetes patients, which leads to the misdiagnosis of patients with isolated post-challenge hyperglycemia (IPH), that is., patients with normal fasting plasma glucose (<7.0mmoL/L) and abnormal 2-h postprandial blood glucose (≥11.1mmoL/L). We aimed to develop a model to differentiate individuals with IPH from the normal population. Data from 54301 eligible participants were obtained from the Risk Evaluation of Cancers in Chinese Diabetic Individuals: a longitudinal (REACTION) study in China. Data from 37740 participants were used to develop the diagnostic system. External validation was performed among 16561 participants. Three machine learning algorithms were used to create the predictive models, which were further evaluated by various classification algorithms to establish the best predictive model. Ten features were selected to develop an IPH diagnosis system (IPHDS) based on an artificial neural network. In external validation, the AUC of the IPHDS was 0.823 (95% CI 0.811-0.836), which was significantly higher than the AUC of the Taiwan model [0.799 (0.786-0.813)] and that of the Chinese Diabetes Risk Score model [0.648 (0.635-0.662)]. The IPHDS model had a sensitivity of 75.6% and a specificity of 74.6%. This model outperformed the Taiwan and CDRS models in subgroup analyses. An online site with instant predictions was deployed at https://app-iphds-e1fc405c8a69.herokuapp.com/. The proposed IPHDS could be a convenient and user-friendly screening tool for diabetes during health examinations in a large general population.

The Relationship Between the Risk of Type 2 Diabetes and Insomnia Severity and Sleep Duration in Academicians

Introduction: Academicians, who frequently spend long hours in front of computer screens, may have lifestyles and sleep habits that differ from those of other professional groups and could entail a higher risk of diabetes. Aim: This study aimed to ascertain the association between insomnia severity, sleep duration among academicians, and the risk of type 2 diabetes. Method: This cross-sectional study was conducted with 125 individuals working as academicians at a state university between January and March 2020. Data collection tools included a Survey form, the Finnish Diabetes Risk Score, and the Insomnia Severity Index. Data analysis involved percentages, means, standard deviations, Pearson correlation, and regression tests. Results: Among the academicians, 23.2% were at moderate risk and 12.0% were at high risk of diabetes. Additionally, 39.2% were below the insomnia threshold. Significant positive correlations were found between the Finnish Diabetes Risk Score and variables such as Insomnia Severity Index, age, body mass index, waist circumference, and daily sleep hours; a significant negative correlation was noted with daily sleep hours (p &lt; 0.05). Regression analysis revealed a significant negative relationship between daily sleep hours and type 2 diabetes Risk (β = -1.25, p &lt; 0.001). Conclusion: The results demonstrated a clear and significant link between the severity of sleep deprivation, sleep duration, and diabetes risk among academicians. Therefore, adopting preventive strategies that emphasize lifestyle changes, including adequate sleep, is crucial for preventing diabetes development among academicians

Screen Time and Hours of Sleep Influence the Estimate Risk of Diabetes Mellitus and Metabolic Syndrome in Healthy Young Males.

Objective: Screen time (ST) has shown negative effects on physical and mental health, with an increase in the prevalence of overweight, metabolic syndrome (MetS), and obesity. The time spent in front of the screens was also associated with higher odds of selecting indicators of cardiometabolic disease in adulthood. In view of this, the aim of this study was to identify the risk of MetS and type 2 diabetes mellitus (T2DM) in healthy young males and relate it to ST and sleep time. Methods: We evaluated physical and laboratory characteristics, dichotomous diagnosis criteria, and continuous scores to assess MetS and Finnish Diabetes Risk Score questionnaire to measure the T2DM risk. Results: The means of MetS dichotomous and continuous severity criteria, among individuals with <7 hr of sleep, were higher than those with adequate sleep. We did not observe a direct impact of ST on the risk of MetS; nevertheless, >8 hr of ST increased 1.22 points in the T2DM risk. Conclusion: Excessive ST increased the risk of T2DM, but not of MetS. Moreover, sleeping <7 hr was associated with a higher mean of dichotomous and continuous severity criteria for MetS.

External validation of three diabetes prediction scores in a Spanish cohort: does adding high risk for depression improve the validation of the FINDRISC score (FINDRISC-MOOD)?

ObjectivesTo evaluate the external validity of the FINDRISC, DESIR and ADA risk scores for the prediction of diabetes in a Spanish population aged >45 years and to test the possible...

Utility of the ACD-GENE-CLI Score in Asian Patients with Critical Limb Ischemia Undergoing Endovascular Interventions.

Critical limb ischemia (CLI) is an emerging public health threat and lacks a reliable score for predicting the outcomes. The Age, Body Mass Index, Chronic Kidney Disease, Diabetes, and Genotyping (ABCD-GENE) risk score helps identify patients with coronary artery disease who have cytochrome P450 2C19 (CYP2C19) polymorphism-related drug resistance and are at risk for cardiovascular adverse events. However, its application to CLI remains unknown. In this study, we aim to validate a modified ACD-GENE-CLI score to improve the prediction of major adverse limb events (MALEs) in patients with CLI receiving clopidogrel. Patients with CLI receiving clopidogrel post-endovascular intervention were enrolled prospectively in two medical centers. Amputation and revascularization as MALEs were regarded as the outcomes. A total of 473 patients were recruited, with a mean follow-up duration of 25 months. Except for obesity, old age, diabetes, chronic kidney disease (CKD), and CYP2C19 polymorphisms were significantly associated with MALEs. Using bootstrap regression analysis, we established a modified risk score (ACD-GENE-CLI) that included old age (≥ 65 years), diabetes, CKD, and CYP2C19 polymorphisms. At a cutoff value of 8, the ACD-GENE-CLI score was superior to the CYP2C19 deficiency only, and the conventional ABCD-GENE score in predicting MALEs (area under the curve: 0.69 vs. 0.59 vs. 0.67, p=0.01). The diagnostic ability of the ACD-GENE-CLI score was consistent in the external validation. Also, Kaplan-Meier curves showed that in CYP2C19 deficiency, the ABCD-GENE and ACD-GENE-CLI scores could all differentiate patients with CLI who are free from MALEs. The modified ACD-GENE-CLI score could differentiate patients with CLI receiving clopidogrel who are at risk of MALEs. Further studies are required to generalize the utility of the score.