Risk Factors For Prediabetes Research Articles

Prevalence, Characteristics and Risk Factors Analysis of Prediabetes: A Cross-Sectional Study

Background Prediabetes, a reversible condition before the onset of diabetes, is a significant concern in healthcare globally. The global prediabetes epidemic has emerged and has considerably impacted health expenditures. Various risk factors play important roles in the progression of prediabetes to diabetes. Intensive lifestyle and pharmacological interventions can significantly reduce the risk of diabetes progression. Objective This study aimed to determine the prevalence, characteristics, and risk factors of prediabetes state of Medan in August 2023. Methods The sample consisted of 89 participants. This was an analytical cross-sectional study in the community that met the inclusion and exclusion criteria. The determination of prediabetes is based on the results of blood tests, namely, the examination of fasting blood sugar levels (FBGL), 2-hour postprandial oral glucose tolerance test (OGTT), and hemoglobin A1c (HbA1C). Other examinations included lipid profiling (total cholesterol, HDL-C, LDL-C, and triglycerides). Data processing was performed using SPSS via univariate and bivariate analyses (chi-square test). Results Of the 89 participants, the prevalence of prediabetes based on HbA1c, FBGL and 2-hours OGTT levels was 28.1%, 50.6%, and 28.1%, respectively. 82% of the participants were female, and 53.9% were overweight or obese based on body mass index (BMI). The risk factors for prediabetes were age >64 years, female, physical inactivity, and diastolic blood pressure ≥90 mmHg (p<0.05). Other risk factors such age <45-64 years, consumption of vegetables/fruits, BMI, HDL, LDL, trygliceride, total cholesterol, systolic blood pressure, achantosis nigricans, and waist-hip circumference did not associate significantly (p>0.05). Conclusion This study found that the prevalence of prediabetes was 67.4% in Medan. Age >64 years, female, physical inactivity, and diastolic blood pressure ≥90 mmHg were the most important risk factors for prediabetes.

RISK FACTORS INFLUENCING THE INCIDENCE OF PREDIABETES BASED ON SCREENING ACTIVITIES AT THE INTEGRATED NON-COMMUNICABLE DISEASES DEVELOPMENT POST (POSBINDU PTM) IN MEDAN KOTA DISTRICT HEALTH CENTER

The risk factors for prediabetes are the same as those for T2DM, such as a family history of Diabetes, female gender, obesity, and so on. Early detection of prediabetes can open the door for T2DM prevention interventions. This type of research is descriptive-analytic with a cross-sectional design. Data collection methods include medical records and field observation. Based on the results of the Spearman test, it was found that there was no significant relationship between gender (p-value=0.609, Correlation Coefficient=-0.079, opposite relationship), hereditary history of Diabetes (p-value=0.298, Correlation Coefficient=0.160, unidirectional relationship), and obesity (p-value=0.382, Correlation Coefficient=-0.135, opposite relationship) with the incidence of prediabetes based on screening at Posbindu PTM activities at Simpang Limun Health Center and Teladan Health Center. There is no significant relationship between gender, family history of Diabetes, and obesity with the incidence of prediabetes based on screening at Posbindu PTM activities at the Simpang Limun Health Center and Teladan Health Center.

Predictors of Prediabetes Among Young Adults in East Java, Indonesia: A Cross-sectional Study

Background: Prediabetes is a condition that can be controlled and managed to prevent the occurrence of type-2 diabetes mellitus (T2DM). This condition can occur at all ages, especially in young adults. However, little is known about what factors increase the risk of prediabetes in young adults in East Java, Indonesia.Purpose: This study aimed to estimate the prevalence and the influential risk factors of prediabetes among young adults in East Java, Indonesia.Methods: This study used a cross-sectional design. The purposive sampling technique was used to recruit young adults in Surabaya, East Java Province, Indonesia. International physical activity questionnaire short-form version questionnaire (IPAQ-SF) and physical indicators for anthropometry were used to obtain data on sociodemographic characteristics, prediabetes knowledge, and physical activity. In addition, Blood pressure, impaired fasting glucose (IFG), and body mass index (BMI) were measured. Multivariable logistic regression analysis was performed to determine risk factors associated with prediabetesResults: There were 126 participants recruited with 69 (54.8%) had prediabetes based on impaired fasting glucose (IFG) levels. Age (p=0.035), routine exercise (p=0.015), activity level (p=0.026), body weight (p=<0.001), waist circumference (p=0.002), BMI (p=<0.001) and obesity (p=<0.001) were significant factors associated with prediabetes.Conclusion: The high prevalence of prediabetes in young adults is associated with age, routine exercise, activity level, body weight, waist circumference), BMI and obesity. It is crucial to implement strategies, for example through regular IFG testing to identify young adults with these risk factors for prediabetes screening.

Relationship between Marriage and Prediabetes among Healthcare Workers: Mediating Effect of Triglycerides.

Background and Objectives: In the literature, relationships between being married and having prediabetes or diabetes are inconsistent. We aimed to investigate whether marriage is a protective or risk factor for prediabetes and to uncover new insights into its impact on prediabetes. Materials and Methods: In this cross-sectional observational study, questionnaires were distributed by email to 1039 staff members who participated in an employee health check from a hospital affiliated with a medical university in Taiwan. Fasting blood glucose and triglyceride (TG) levels were checked and the questionnaires elicited basic demographic characteristics and included the Copenhagen Burnout Inventory and Nordic Musculoskeletal Questionnaire. The chi-square test or Fisher's exact test, logistic regression, and mediation analysis were conducted for statistical analysis. Results: Among the group aged 20-37 years, married (OR = 1.89, 95%CI: 1.08, 3.33), obesity (OR = 2.95, 95%CI: 1.49, 5.83), neck and shoulder pain (OR = 1.31, 95%CI: 1.01, 1.69), and elevated TG levels (OR = 1.01, 95%CI: 1.00, 1.01) were independent risk factors for prediabetes (impaired fasting glucose). For those >38 years old, overweight (OR = 2.08, 95%CI: 1.27, 3.43), obesity (OR = 4.30, 95%CI: 2.38, 7.79), and elevated triglyceride (TG) (OR = 1.003, 95%CI: 1.00, 1.01) were the independent risk factors for impaired fasting glucose. Increased TG levels serve as a mediating factor (Zm = 2.64, p < 0.01) linking marriage to an increased risk of prediabetes for the group aged 20-37 years. Conclusions: TGs play a significant role in the association between marriage and prediabetes among the group aged 20-37 years. Therefore, dietary habits, especially those of young adult couples should be considered. Our findings connect marital status to prediabetes, facilitating advances in diabetes prevention.

Examination of nonlinear associations between pulse pressure index and incident prediabetes susceptibility: a 5-year retrospective cohort investigation

Prediabetes and related complications constitute significant public health burdens globally. As an indicator closely associated with abnormal glucose metabolism and atherosclerosis, the utility of Pulse Pressure Index (PPI) as a prediabetes risk marker has not been explored. We performed a retrospective cohort analysis to investigate this putative association between PPI and prediabetes hazard. Our analysis encompassed 183,517 Chinese adults ≥ 20 years registered within the Rich Healthcare Group 2010–2016. PPI was defined as (systolic blood pressure − diastolic blood pressure)/systolic blood pressure. The relationship between PPI and prediabetes risk was assessed via Cox proportional hazards regression modeling. Non-linearity evaluations applied cubic spline fitting approaches alongside smooth curve analysis. Inflection points of PPI concerning prediabetes hazard were determined using two-piecewise Cox models. During a median follow-up of 3 years (2.17–3.96 years), new-onset prediabetes was documented in 20,607 patients (11.23%). Multivariate regression analysis showed that PPI was an independent risk factor for prediabetes, and the risk of prediabetes increased by 0.6% for every 1% increase in PPI (Hazard Ratio [HR]: 1.006, 95% Confidence Interval [CI] 1.004–1.008, P < 0.001). This association was non-significant for PPI ≤ 37.41% yet exhibited a sharp upsurge when PPI surpassed 37.41% (HR: 1.013, 95% CI 1.005–1.021, P = 0.0029). Our analysis unveils a positive, non-linear association between PPI and future prediabetes risk. Within defined PPI ranges, this relationship is negligible but dramatically elevates beyond identified thresholds.

Association between cardiovascular health and serum vitamin D and its interaction with prediabetes and diabetes

BackgroundPrediabetes and diabetes are common and serious public health problems, and high blood glucose can lead to serious cardiovascular complications. The purpose of this article was to explore the link between CVH levels and the incidence of prediabetes and diabetes in people over 20 years old, and whether serum vitamin D status could alter this relationship. Materials and MethodsData, from six consecutive cycles of the NHANES database from 2007 to 2018 were combined, eligible participants were aged ≥20 years. After excluding missing data, a total of 19,992 subjects were enrolled in the study. Significant risk factors for prediabetes and diabetes were analyzed using univariate and multivariate logistic regression. Exploring the interaction of VD and CVH on prediabetes and diabetes based on multifactorial regression analysis. ResultsThe prevalence of prediabetes among all participants was 36.15% and the prevalence of diabetes was 16.39%. CVH and vitamin D levels are influential factors in prediabetes and diabetes, and are negatively associated with the risk of developing prediabetes and diabetes. Compared with normoglycemia, poorer CVH and vitamin D deficiency only had a synergistic multiplicative interaction on the development of diabetes, and no significant interaction was observed for the development of prediabetes. Compared with prediabetes, poorer CVH and vitamin D deficiency still had a synergistic additive interaction on the development of diabetes. ConclusionsAlthough the cross-sectional study only determine the association and do not prove causality, the current results can be used to prompt people to improve their lifestyle and risk factors to prevent prediabetes or diabetes through higher CVH and adequate Vitamin D.

Prevalence, Characteristics and Risk Factors Analysis of Prediabetes: A Cross-Sectional Study

Background Prediabetes, a reversible condition before the onset of diabetes, is a significant concern in healthcare globally. The global prediabetes epidemic has emerged and has considerably impacted health expenditures. Various risk factors play important roles in the progression of prediabetes to diabetes. Intensive lifestyle and pharmacological interventions can significantly reduce the risk of diabetes progression. Objective This study aimed to determine the prevalence, characteristics, and risk factors of prediabetes state of Medan in August 2023. Methods The sample consisted of 89 participants. This was an analytical cross-sectional study in the community that met the inclusion and exclusion criteria. The determination of prediabetes is based on the results of blood tests, namely, the examination of fasting blood sugar levels (FBGL), 2-hour postprandial oral glucose tolerance test (OGTT), and hemoglobin A1c (HbA1C). Other examinations included lipid profiling (total cholesterol, HDL-C, LDL-C, and triglycerides). Data processing was performed using SPSS via univariate and bivariate analyses (chi-square test). Results Of the 89 participants, the prevalence of prediabetes based on HbA1c, FBGL and 2-hours OGTT levels was 28.1%, 50.6%, and 28.1%, respectively. 82% of the participants were female, and 53.9% were overweight or obese based on body mass index (BMI). The risk factors related to the prevalence of prediabetes were HbA1c level, impaired FBGL, and impaired 2-hours OGTT. Other risk factors such as age, sex, daily exercise, diet, BMI, waist-hip ratio, acanthosis nigricans, lipid profile, and blood pressure did not correlate significantly with the risk factors (p&gt;0.05). Conclusion This study found that the prevalence of prediabetes was 67.4% in Medan, 82% of the participants were female, and more than 50% of participants were overweight or obese. HbA1c, FBGL, and 2-hour postprandial OGTT were the most important risk factors for prediabetes.

Risk factors for prediabetes in community-dwelling adults: A generalized estimating equation logistic regression approach with natural language processing insights.

The global prevalence of prediabetes is expected to reach 8.3% (587 million people) by 2045, with 70% of people with prediabetes developing diabetes during their lifetimes. We aimed to classify community-dwelling adults with a high risk for prediabetes based on prediabetes-related symptoms and to identify their characteristics, which might be factors associated with prediabetes. We analyzed homecare nursing records (n = 26,840) of 1628 patients aged over 20 years. Using a natural language processing algorithm, we classified each nursing episode as either low-risk or high-risk for prediabetes based on the detected number and category of prediabetes-symptom words. To identify differences between the risk groups, we employed t-tests, chi-square tests, and data visualization. Risk factors for prediabetes were identified using multiple logistic regression models with generalized estimating equations. A total of 3270 episodes (12.18%) were classified as potentially high-risk for prediabetes. There were significant differences in the personal, social, and clinical factors between groups. Results revealed that female sex, age, cancer coverage as part of homecare insurance coverage, and family caregivers were significantly associated with an increased risk of prediabetes. Although prediabetes is not a life-threatening disease, uncontrolled blood glucose can cause unfavorable outcomes for other major diseases. Thus, medical professionals should consider the associated symptoms and risk factors of prediabetes. Moreover, the proposed algorithm may support the detection of individuals at a high risk for prediabetes. Implementing this approach could facilitate proactive monitoring and early intervention, leading to reduced healthcare expenses and better health outcomes for community-dwelling adults.

Central Obesity as A Risk Factor For Prediabetes

Central obesity has become a health problem and is associated with the incidence of prediabetes. Prediabetes is characterized by impaired fasting blood glucose (GDPT) or impaired glucose tolerance (TGT) or both without any complaints and symptoms, and is a condition that precedes the occurrence of type 2 diabetes mellitus. This study aims to prove central obesity as a risk factor for prediabetes by calculating the prevalence ratio. The method of this research is observational analysis with a cross-sectional design approach. The respondents of the study were the community around the work area of the Jati Ranggon health center who met the inclusion and exclusion criteria, totaling 1241 people, 523 men and 718 women. The prevalence of central obesity in this study was 39.6%. The prevalence of prediabetes is 18.8%, prediabetes with central obesity is 27.9% and non-central obesity prediabetes is 12.8%. Bivariate analysis with chi-square showed a relationship between age and the incidence of prediabetes (RP=1.62, 95% CI 1.24-2.11 and p= &lt;0.001), hypertension with the incidence of prediabetes (RP=1.58CI 95% 1.22-2.04, p=&lt;0.001), gender with prediabetes (RP=1.47, 95% CI 1.12-1.94, and p=0.005), general obesity with prediabetes (RP=1.87, 95% CI 1.44-2.42 and p=&lt;0.001), smoking with the incidence of prediabetes (RP= 0.54, 95% CI 0.33-0.88 and p=0.013). The results of multivariate analysis with cox regretion showed that the variables that affected prediabetes were central obesity (RP=1.87, CI95% 1.40-2.50, p=&lt;0.001) and general obesity (RP=1.42, CI95% 1.06-1.89, p=0.017). Central obesity is a risk factor for prediabetes with a prevalence ratio of 2.18.

The relationship between adipose tissue RAAS activity and the risk factors of prediabetes in different ethnicities: A protocol for a systematic review and meta-analysis.

The incidence and prevalence of prediabetes has become a global concern. The risk factors of prediabetes, such as insulin resistance, adiposity, lipotoxicity and obesity, in conjunction with the alteration of the renin-angiotensin-aldosterone system (RAAS), have been positively correlated with the high morbidity and mortality rate. Thus, this systematic review seeks to establish the relationship between the risk factors of prediabetes, namely insulin resistance adiposity, lipotoxicity, obesity and the RAAS. Therefore, a synthesis of these risk factors, their clinical indicators and the RAAS components will be compiled in order to establish the association between the RAAS alteration and obesity in prediabetic patients. This protocol for a systematic review was developed in compliance with the Preferred Reporting Items for Systematic Review and Meta-Analysis Protocols (PRISMA-P) standards. This will be accomplished by searching clinical Medical Subject Headings categories in MEDLINE with full texts, EMBASE, Web of Science, PubMed, Cochrane Library, Academic Search Complete, ICTRP and ClinicalTrial.gov. Reviewers will examine all of the findings and select the studies that meet the qualifying criteria. To check for bias, the Downs and Black Checklist will be used, followed by a Review Manager v5. A Forrest plot will be used for the meta-analysis and sensitivity analysis. Furthermore, the strength of the evidence will be assessed utilizing the Grading of Recommendations Assessment, Development, and Evaluation procedure (GRADE). The protocol has been registered with PROSPERO CRD42022320252. This systematic review and meta-analysis will include published randomized clinical trials, observational studies and case-control studies from the years 2000 to 2022.

The Effect of Childhood Nutritional Status Changes with Pre-Diabetes And Diabetes Risk among Adolescents: A Retrospective Cohort Study

The first 1000 days of life and before the age of five years is a critical period of nutritional opportunity and vulnerabilitya. Only few have studies that trace individuals from childhood to adolescence examined the influence of childhood undernutrition on diabetes risk. This research was a retrospective cohort study using secondary data from IFLS (Indonesian Family Life Survey) involving 373 adolescents at IFLS-5 (2014) that folllowed at IFLS-3 (2000) and at IFLS-2 (1997). The effect of childhood undernutrition on the risk of pre-diabetes and diabetes in adolescents was analyzed using multinomial logistic regression. The results of this study showed that the prevalence of pre-diabetes was 12.6% and diabetes was 15.0%. The risk factor for pre-diabetes is frequent consumption of fast food (aOR=4.38). Meanwhile, recovering from stunting was a protective factor in adolescents (aOR=0.05). Risk factors for diabetes were stunting (aOR=2.71), deteriorated nutritional status during childhood (aOR=3.85), experienced wasting during childhood (aOR=3.55), and frequent fast food consumption (aOR=3.11). Meanwhile, vegetable consumption was a protective factor of diabetes (aOR=0.37). In conclusion, this study showed that childhood undernutrition on the risk of diabetes. Improvement point is through nutrition programs in childhood and adolescence.

Diagnosis and Risk Factors of Prediabetes and Diabetes in People Living With Human Immunodeficiency Virus: Evaluation of Clinical and Microbiome Parameters.

Diabetes mellitus (DM) is more common among people living with human immunodeficiency virus (PLWH) compared with healthy individuals. In a prospective multicenter study (N = 248), we identified normoglycemic (48.7%), prediabetic (44.4%), and diabetic (6.9%) PLWH. Glycosylated hemoglobin (HbA1c) and fasting blood glucose (FBG) sensitivity in defining dysglycemia was 96.8%, while addition of oral glucose tolerance test led to reclassification of only 4 patients. Inclusion of 93 additional PLWH with known DM enabled identification of multiple independent predictors of dysglycemia or diabetes: older age, higher body mass index, Ethiopian origin, HIV duration, lower integrase inhibitor exposure, and advanced disease at diagnosis. Shotgun metagenomic microbiome analysis revealed 4 species that were significantly expanded with hyperglycemia/hyperinsulinemia, and 2 species that were differentially more prevalent in prediabetic/diabetic PLWH. Collectively, we uncover multiple potential host and microbiome predictors of altered glycemic status in PLWH, while demonstrating that FBG and HbA1c likely suffice for diabetes screening. These potential diabetic predictors merit future prospective validation.

Abstract 66: US Asian and Pacific Islander Adolescents With Obesity Have Triple the Risk of Prediabetes When Compared to Non Hispanic White Children

Introduction: Race and ethnicity and elevated body mass index (BMI) are risk factors for pre-diabetes (preDM) among adolescents. We recently observed high preDM prevalence among aggregated US Asian and Pacific Islander (API) children with obesity, but data pertaining to disaggregated API subgroups remain limited. Aim: To examine the risk of preDM among API subgroups compared to non-Hispanic White (NHW) adolescents with obesity. Methods: This retrospective study included 3233 API and 4150 NHW children age 13-17y with obesity and without DM who were identified at preventive health well-child visits in 2016-2019 and had hemoglobin A1c (HA1c) measured within 1 year of the visit. Visit data included self-reported diet/lifestyle behaviors and BMI measurement. PreDM (outcome) was defined by HA1c 5.7-6.4%. Obesity was categorized as Class 1-3 based on BMI percent of the 95th BMI percentile (obesity threshold). Four obesogenic behaviors were ascertained from clinic data: Fruit/vegetable consumption &lt;5 servings/day; juice/sugary beverage intake &gt;12 ounces/day; exercising &lt;60 minutes/day; and screen time 2+ hours/day. A neighborhood deprivation index (NDI) was calculated using area of residence and 2010 US Census data. Log binomial regression was used to examine the association of API ethnicity and preDM, reporting relative risk (RR) and 95% confidence intervals [CI]. Results: The cohort included 210 Chinese, 900 Filipino, 200 South Asian, 132 Southeast Asian, 283 Native Hawaiian/Pacific Islander (NHPI), 1508 other/unspecified API ethnicity, and 4150 NHW children. Overall, 22.4% of API vs 9.4% NHW (p&lt;0.001) children had preDM. PreDM prevalence ranged from 19.1% for Chinese, 21.7% for Filipino, 22.5% for South Asian, 24.2% for Southeast Asian, to 26.5% for NHPI subgroups. After adjusting for age, sex, NDI, obesity class, and obesogenic health behaviors, risk of preDM was about 3-fold higher (RR 2.9 [2.6-3.4] for API compared to NHW adolescents, ranging from RR 2.5 [1.8-3.6] for Chinese, RR 2.8 [2.3-3.4] for Filipino, RR 3.1 [2.2- 4.4] for South Asian, RR 3.2 [2.1-4.9] for Southeast Asian, RR 3.3 [2.5-4.4] for NHPI, and RR 3.0 [2.5-3.5] for other/unspecified API children. Obesity severity (RR 1.4 [1.2-1.6] for Class 2 and RR 2.1 [1.7-2.6] for Class 3 compared to Class 1 obesity) was also associated with preDM risk. Conclusion: PreDM risk was about 3-fold higher for API compared to NHW children with obesity even after adjusting for sociodemographic, lifestyle, and BMI differences. The risk of preDM ranged from 2.5-fold to 3.3-fold higher for the major API subgroups, especially Southeast Asian and Filipino groups compared to NHW children. These findings highlight the importance of preDM screening among API children and demonstrate heterogeneity of risk among API subgroups. Future efforts should focus on effective screening and monitoring of at-risk populations to prevent progression to Type 2 DM.

Metabolic disorders in prediabetes: From mechanisms to therapeutic management.

Diabetes, one of the world's top ten diseases, is known for its high mortality and complication rates and low cure rate. Prediabetes precedes the onset of diabetes, during which effective treatment can reduce diabetes risk. Prediabetes risk factors include high-calorie and high-fat diets, sedentary lifestyles, and stress. Consequences may include considerable damage to vital organs, including the retina, liver, and kidneys. Interventions for treating prediabetes include a healthy lifestyle diet and pharmacological treatments. However, while these options are effective in the short term, they may fail due to the difficulty of long-term implementation. Medications may also be used to treat prediabetes. This review examines prediabetic treatments, particularly metformin, glucagon-like peptide-1 receptor agonists, sodium glucose cotransporter 2 inhibitors, vitamin D, and herbal medicines. Given the remarkable impact of prediabetes on the progression of diabetes mellitus, it is crucial to intervene promptly and effectively to regulate prediabetes. However, the current body of research on prediabetes is limited, and there is considerable confusion surrounding clinically relevant medications. This paper aims to provide a comprehensive summary of the pathogenesis of pre-diabetes mellitus and its associated therapeutic drugs. The ultimate goal is to facilitate the clinical utilization of medications and achieve efficient and timely control of diabetes mellitus.

A genome-wide association study identifies 25(OH)D3-associated genetic variants in the prediabetic Chinese population.

Diabetes mellitus has been a significant public health problem, associated with high rates of morbidity, disability, and mortality. Prediabetes is a crucial period for preventing and managing diabetes. 25(OH)D3 is an important risk factor for prediabetes. However, there is limited genetic knowledge of 25(OH)D3 in the Chinese population. This study was designed to identify genetic variants associated with 25(OH)D3 and explore the potential pathogenesis of prediabetes. In this study, 451 individuals with prediabetes were recruited to determine the genetic variants associated with 25(OH)D3 through a genome-wide association study (GWAS). Gene mapping and overrepresentation analysis (ORA) were further performed to explore the candidate genes and their biological mechanisms. In this study, we identified two independent significant loci (rs9457733 and rs11243373, p < 5 × 10-6 and r2 < 0.6) and 37 candidate genes associated with 25(OH)D3 in prediabetes. Furthermore, the ORA analysis revealed that two genes in the gene sets, SLC22A1 and SLC22A3, were found to be significantly enriched in monoamine transmembrane transporter activity and quaternary ammonium group transmembrane transporter activity, as determined by WebGestalt and g:Profiler (padj < 0.05). The identification of potential genes associated with 25(OH)D3 provides a foundation for a better understanding of the pathogenesis, diagnosis, and treatment of prediabetes.

Self-Efficacy Weight Management and Family Support on Risk Factors of Prediabetes Patient

The shift in the pattern of non-communicable diseases to a younger age makes prediabetes undetected and tends to be ignored. Monitoring weight is an essential behavior for individuals to reduce the risk of obesity as a predictor of prediabetes. In addition, family support plays a crucial role in maintaining each individual's health in deciding his lifestyle. This study aimed to determine the relationship between self-efficacy weight management (SEWM) and family support on risk factors for prediabetes at the Community Health Center, Batam. The research design is a descriptive correlation with the cross-sectional approach. Fifty respondents were chosen using a purposive sampling technique based on the inclusion and exclusion criteria. Instrument of INA-PRISC questionnaire, Weight Efficacy Lifestyle Short Form Questionnaire, and Perceived Social Support from Family (PSS- Fa) utilized in this study. Based on univariate results, the majority of respondents aged 26-35 years (30%), female (68%), higher education background (78%), obesity (&gt;25) (80%), had a family history of DM (60%), not smoking (70%), physical activity &lt;150 minutes/week (94%), hypertension &gt;160/100 mmHg (44%), moderate risk of prediabetes (48%), low SEWM (70%), and good family support (38%). A bivariate statistical test using Kendall's Tau B revealed there was a significant relationship between SEWM (&lt; 0.05) and family support for risk factors for prediabetes (&lt; 0.05). It is suggested that respondents increase self-awareness in the early detection of prediabetes risk and the family's active role in assisting family members in starting an active and healthy lifestyle.

Identification of pivotal nutritional factors to reduce risk of prediabetes by machine learning

Type 2 Diabetes is a major concern in healthcare worldwide with increasingly public health burden. Prediabetes has been considered as a critical stage to reduce the risk of Type 2 Diabetes since its highly correlated with lifestyle changes. The present study explored the risk factor for prediabetes and built a machine learning model to prioritize the important factors for effective prevention. In this study, data was extracted from National Health and Nutrition Examination Survey (NHANES), including over 36,000 people classified to diabetes, prediabetes and healthy. Six machine learning models, namely, random forest, k-Nearest Neighbor (KNN), support vector machine (SVM), gradient boosting, LDA, and Xgboosting were trained for prediabetes risk factor prediction. Random forest eventually outperformed the other four models with an area under curve(AUC) score of 0.71. Also, explorations on the top features that are highly correlated with prediabetes were made. Multiple nutritional factors were identified, including thiamin, folic acid, and caffeine, which shows significant difference between healthy control and prediabetes and the lifestyles regarding these nutrition are recommended to change accordingly.

THU251 Diagnostic Performance For Predicting Hepatic And Peripheral Tissue Insulin Resistance In Young Adult Men Using 1Hour Post Load Serum Glucose And Insulin Levels A Pilot Study

Abstract Disclosure: A.J. Ledezma: None. C.F. Espinel Pachón: None. S.S. Perea: None. J.E. Caminos: None. Insulin resistance (RI) is a risk factor for prediabetes and DM2, and other non communicable diseases. Different studies have demonstrated that 1h post load serum glucose levels during an oral glucose test tolerance OGTT seem to be a better predictor of peripheral RI than 2h post load serum glucose among individuals without DM2. On the other hand, HOMA IR reflects primarily hepatic sensitivity/resistance, meanwhile, Matsuda index, may reflect both hepatic and peripheral tissue insulin sensitivity/resistance. Thus, this study aimed to evaluate the diagnostic performance, to identify hepatic and peripheral RI, of 1h post load serum glucose and insulin levels during an OGTT in young adult men, using ROC AUC analysis. This cross-sectional study was conducted on a total of 91 young adult men (18-31-year-olds), 47 healthy lean (BMI &amp;lt;25 kg/m2), and 44 obese nondiabetic subjects (BMI ≥30 kg/m2). Subjects underwent a standard 75g OGTT and blood samples were drawn at 0, 30, 60, and 120 min to obtain serum glucose and insulin levels. Clinical and anthropometric and biochemical variables were determined. Participants were classified as RI based on a predefined Matsuda index cutoff 4.3 value. Cut-offs, sensitivity, and specificity for glucose, insulin, and anthropometrics parameters were determined by AUC ROC analysis for predicting RI diagnosis, and Spearman´s correlation was established between biochemical variables and waist/height ratio and Delong test analysis. Our results showed that 1h post load glucose levels had higher sensitivity 84.1% vs 47.7% and AUC 83.9% [CI 95% 75.6; 92.1%] vs 72.4% [CI 95% 61.8%; 83.0%] than 2h post load during OGTT, however lower specificity was found 70.2% vs 89.4%. Additionally, 1h post load insulin levels had higher sensitivity 90.9% vs 77% and AUC 97.1% [CI 95% 94; 100%] vs 91.9% [CI 95% 86.3; 97.5%] than 2h post load during OGTT, without changes in specificity 97.9% vs 97.8%. It is important to highlight that waist/height ratio reached a 100% AUC 95% CI diagnostic performance for RI in our sample. Additionally, HOMA IR index 99.8% [CI 95% 99.1; 100%], 1/fasting insulin 100% [CI 95% 0.99; 100%], and fasting glucose/insulin ratio 99.7% [CI 95% 99.2; 100%], showed a high diagnostic performance for RI. In the Spearman correlation, Waist/height ratio presented a high correlation with respect to Matsuda -0.8672; p&amp;lt;0.01 G/I1H -0.7454; p&amp;lt;0.01, 1/I1H -0.7783; p&amp;lt; 0.01; Delong test, the best performance comparing 1H vs 2H, G1H/G2H with Z value 2.0668 and P 0.03 In conclusion, 1h post load glucose and insulin levels and the waist/height ratio show greater diagnostic performance than 2h post load for detecting peripheral tissue and hepatic RI in young men. Therefore, Cut-offs values of glucose and insulin levels 1h post load provide simple but useful test for early detection of different prevalent diseases related to RI. Presentation: Thursday, June 15, 2023

Inflammation on the Waiting List Is a Risk Factor for New-Onset Prediabetes and Post-Transplant Diabetes Mellitus: A Prospective Study

Introduction: Inflammation is a risk factor for diabetes in the general population. The role of inflammation in prediabetes or post-transplant diabetes mellitus (PTDM) is not clear. We evaluated the association between inflammatory markers in patients on the waiting list for renal transplantation and the onset of prediabetes and PTDM 12 months after transplantation. Methods: This is a post hoc analysis of a prospective study that included nondiabetic patients on the waiting list for kidney transplantation who underwent an oral glucose tolerance test (OGTT) and were followed up to 12 months after transplantation. At this time, those patients without PTDM underwent another OGTT. At pre-transplantation, five cytokines: TNFα, IL6, IL1β, CRP, MCP1 were determined. The association between inflammation and prediabetes/PTDM was evaluated using multiple regression models. Results: 110 patients on the waiting list were enrolled: 74 had normal glucose metabolism and 36 had prediabetes or occult diabetes. At 12 months, 53 patients had normal glucose metabolism, 25 prediabetes, and 32 PTDM. In multiple regression analysis, pre-transplant inflammation was not a risk factor for prediabetes or PTDM. This was attributed to the high interrelation between obesity, prediabetes, and inflammation: about 75% of the cases had these conditions. In a sub-analysis, we analyzed only patients without prediabetes and occult diabetes on the waiting list and found that TNFα levels and BMI at pre-transplantation were independently associated with the onset of prediabetes or PTDM 1 year after transplantation. Conclusions: Pre-transplant inflammation and BMI are risk factors for prediabetes and PTDM in patients without glucose metabolism alterations.

Association between Muscle Mass, Body Fat Mass, and Abdominal Circumstances with Insulin Resistance among Young Adult Population with Prediabetes Risk

Prediabetesis is associated with an increase in plasma insulin concentration due to a decrease in insulin sensitivity in insulin target organs. Central obesity is a risk factor for prediabetes. To determine the relationship between muscle mass, body fat mass, and abdominal circumference with insulin resistance. The study was involving 50 young adult subjects aged 15-35 years, 50 subjects who met the inclusion and exclusion criteria. Muscle mass and body fat mass were measured using Bioelectrical Impedance Analysis (BIA). Abdominal circumference was measured using a tapemeter. Hours of sleep were measured using a questionnaire, while physical activity was measured based on the IPAQ-short form. Insulin resistances were measured using HOMA-IR score. Data were analyzed using spearman correlation. The correlation between abdominal circumference using two kinds of measurement and HOMA IR was found in all subjects (r = 0.691 and r 0.659; p = 0.000). After being analyzed separately by gender, it was found that there are positive correlation between body fat mass and HOMA-IR (male r = 0.672 p 0.001 female r = 0.582 p 0.001). There were a negative correlation between skeletal muscle mass and HOMA-IR (male r= -0.653 p 0.002, female r= -0.424, p 0.019), but there was no relationship between physical activity and sleep hours with insulin resistance. There is a relationship between skeletal muscle mass, fat mass, and abdominal circumference on insulin resistance regardless of gender. Further study is needed to determine the cutoff point of HOMA-IR as the predictor of insulin resistance.