Associations between periodontal disease severity and selected cardiometabolic risk factors.

OBJECTIVES: To explore associations between periodontal disease (PD) severity and cardiometabolic risk factors, including body mass index (BMI), age, Type 2 Diabetes Mellitus (T2DM) risk, sex, and hypertension (HTN) in patients at an urban dental school clinic. METHODS AND MATERIALS: A cross-sectional study design was used to analyze electronic health record data, including periodontal status, demographic characteristics, cardiometabolic risk factors and the American Diabetes Association Diabetes Risk Test (DRT) Score. Chi-square tests and ordinal logistic regression were conducted using SAS 9.4. RESULTS: Of those with available data (n=6,778), 44% were male, 70.2% were overweight/obese, and the mean age was 50.9 (SD=16.6) years. Associations between PD severity and BMI, sex, age, DRT score, and HTN were statistically significant (all p<0.0001) in bivariate analyses. Using logistic regression, HTN (p=0.0006), sex (p<0.0001), and age (p<0.0001) were significant predictors of severe PD which was most common in those with HTN (35.9%), males (31.7%), those >60 years (36.6%). The odds of having severe PD for those with HTN were 1.2 times that of those without HTN. Males were 1.7 times more likely to have severe PD than females. Those aged 40-49 years, 50-59 years, and >60 years were 2.9, 4.2, and 4.3 times more likely to have severe PD than those who were 18-39 years, respectively. CONCLUSION: All cardiometabolic risk factors were associated with PD severity in bivariate analyses. In the logistic regression model, being older, male, and having HTN were significant predictors of PD severity. Future research is needed with a more diverse sample.

Investigation of the effect of personality traits on type 2 diabetes risk and eating awareness in adults.

AIMS: The study was conducted with the aim of investigating the impact of personality traits on the risk of developing type 2 diabetes and eating awareness among adult individuals. METHODS: It was planned and carried out as a descriptive-correlational study. The data collection process of the study was conducted using online communication methods and using Google Forms. These forms included Patient Identification Form, the Big Five Inventory to examine personality traits, the Mindful Eating Questionnaire to assess the level of eating awareness, and The Finnish Type 2 Diabetes Risk Assessment Questionnaire to determine the risk of type 2 diabetes. A total of 390 individuals were included in the study. RESULTS: Significant differences were found among the sub-dimensions of personality traits and the levels of eating awareness. Extraversion, agreeableness, and conscientiousness were found to affect eating awareness, but no effect of personality traits on the risk of developing type 2 diabetes was found. Increasing eating mindfulness was found to reduce the risk of diabetes. CONCLUSIONS: The study provides evidence of the relationship between personality traits and eating awareness and highlights the importance of eating awareness in reducing the risk of developing type 2 diabetes.

Diabetes risk reduction diet and risk of liver cancer and chronic liver disease mortality: A prospective cohort study.

BACKGROUND: We aimed to prospectively evaluate the association between a diabetes risk reduction diet (DRRD) score and the risk of liver cancer development and chronic liver disease-specific mortality. METHODS: We included 98,786 postmenopausal women from the Women's Health Initiative-Observational Study and the usual diet arm of the Diet Modification trial. The DRRD score was derived from eight factors: high intakes of dietary fiber, coffee, nuts, polyunsaturated fatty acids, low intakes of red and processed meat, foods with high glycemic index, sugar-sweetened beverages (SSBs), and trans fat based on a validated Food-Frequency Questionnaire administered at baseline (1993-1998). Multivariable hazard ratios (HRs) and 95% confidence intervals (CIs) for liver cancer incidence and chronic liver disease mortality were estimated using Cox proportional hazards regression models. RESULTS AND CONCLUSION: After a median follow-up of 22.0 years, 216 incident liver cancer cases and 153 chronic liver disease deaths were confirmed. A higher DRRD score was significantly associated with a reduced risk of developing liver cancer (HRTertile 3 vs. Tertile 1 = 0.69; 95% CI: 0.49-0.97; Ptrend = 0.03) and chronic liver disease mortality (HRT3 vs. T1 = 0.54; 95% CI: 0.35-0.82; Ptrend = 0.003). We further found inverse associations with dietary fiber and coffee, and positive associations with dietary glycemic index, SSBs, and trans fat. A higher DRRD score was associated with reduced risk of developing liver cancer and chronic liver disease mortality among postmenopausal women.

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

Background: 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. Subjects and methods: 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. Objective: 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. Results: 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. Discussion and conclusions: 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.

Dietary-Based Diabetes Risk Score and breast cancer: a prospective evaluation in the SUN project.

An association between type 2 diabetes (T2D) and breast cancer risk has been reported. This association can be potentially explained by alteration of the insulin/IGF system. Therefore, we aimed to prospectively investigate whether a previously reported Dietary-Based Diabetes Risk Score (DDS) inversely associated with T2D was also associated with breast cancer risk in the SUN ("Seguimiento Universidad de Navarra") cohort. We followed up 10,810 women (mean age = 35 years, SD = 11 years) for an average of 12.5 years during which 147 new cases of invasive breast cancer were diagnosed. A validated 136-item FFQ was administered at baseline and after 10 years of follow-up. The DDS (range: 11 to 55 points) positively weighted vegetables, fruit, whole cereals, nuts, coffee, low-fat dairy, fiber, PUFA; while it negatively weighted red meat, processed meats, and sugar-sweetened beverages. The DDS was categorized into tertiles. Self-reported medically diagnosed breast cancer cases were confirmed through medical records. We found a significant inverse association between the intermediate tertile of the DDS score and overall breast cancer risk (Hazard ratio, HRT2 vs. T1= 0.55; 95% CI: 0.36-0.82) and premenopausal breast cancer risk (HRT2= 0.26; 95% CI: 0.13-0.53), but not for the highest tertile. This association was stronger among women with a BMI < 25 kg/m2 (pinteraction: 0.029). In conclusion, moderate adherence to the DDS score was associated with a lower risk of breast cancer, especially among premenopausal women and women with a lower BMI. These findings underscore the importance of antidiabetic diet in reducing the risk of breast cancer.

Risk of New-Onset Diabetes Before and During the COVID-19 Pandemic: A Real-World Cohort Study.

BACKGROUND: Studies of new-onset diabetes as a post-acute sequela of SARS-CoV-2 infection are difficult to generalize to all socio-demographic subgroups. OBJECTIVE: To study the risk of new-onset diabetes after SARS-CoV-2 infection in a socio-demographically diverse sample. DESIGN: Retrospective cohort study of electronic health record (EHR) data available from the OneFlorida + clinical research network within the National Patient-Centered Clinical Research Network (PCORnet). SUBJECTS: Persons aged 18 or older were included as part of an Exposed cohort (positive SARS-CoV-2 test or COVID-19 diagnosis between 1 March 2020 and 29 January 2022; n = 43,906), a contemporary unexposed cohort (negative SARS-CoV-2 test; n = 162,683), or an age-sex matched historical control cohort (index visits between 2 Mar 2018 and 30 Jan 2020; n = 40,957). MAIN MEASURES: The primary outcome was new-onset type 2 diabetes ≥ 30 days after index visit. Hazard ratios and cases per 1000 person-years of new-onset diabetes were studied using target trial approaches for observational data. Associations were reported by sex, race/ethnicity, age, and hospitalization status subgroups. KEY RESULTS: The sample was 62% female, 21.4% non-Hispanic Black, and 21.4% Hispanic; mean age was 51.8 (SD, 18.9) years. Relative to historical controls (cases, 28.2 [26.0-30.5]), the unexposed (HR, 1.28 [95% CI, 1.18-1.39]; excess cases, [5.1-10.3]), and exposed cohorts (HR, 1.64 [95% CI, 1.50-1.80]; excess cases, 17.3 [13.7-20.8]) had higher risk of new-onset T2DM. Relative to the unexposed cohort, the exposed cohort had a higher risk (HR, 1.28 [1.19-1.37]); excess cases, 9.5 [6.4-12.7]). Findings were similar across subgroups. CONCLUSION: The pandemic period was associated with increased T2DM cases across all socio-demographic subgroups; the greatest risk was observed among individuals exposed to SARS-CoV-2.

Estimating Breakfast Characteristics Using Continuous Glucose Monitoring and Machine Learning in Adults With or at Risk of Type 2 Diabetes.

BACKGROUND: Continuous glucose monitoring (CGM) systems allow detailed assessment of postprandial glucose responses (PPGR), offering new insights into food choices' impact on dysglycemia. However, current approaches to analyze PPGR using a CGM require manual meal logging, limiting the scalability of CGM-driven applications like personalized nutrition and at-home diabetes risk assessment. OBJECTIVE: We propose a machine learning (ML) framework to automatically identify and characterize breakfast-related PPGRs from CGM profiles in adults at risk of or living with noninsulin-treated type 2 diabetes (T2D). METHODS: Our PPGR estimation framework uses a random forest ML algorithm trained on 15 adults without diabetes who wore a CGM for up to four weeks. The algorithm performance was evaluated on a held-out subset of the participants' CGM data as well as on an external validation data set of 36 individuals at risk for or with noninsulin-treated T2D. RESULTS: Our algorithm's estimations of breakfast PPGRs displayed no statistically significant differences to annotated PPGRs, in terms of incremental area under the curve and glucose rise (P > .05 for both data sets), while a small difference in prebreakfast glucose was found in the nondiabetes data set (P = .005) but not in the validation T2D data set (P = .18). CONCLUSIONS: We designed an ML framework to automatically estimate the timing of meal events from CGM data in individuals without diabetes and in individuals at risk or with T2D. This could provide a more scalable approach for analyzing postprandial glycemia, increasing the feasibility of CGM-based precision nutrition and diabetes risk assessment applications.

The effects of a six-month exercise intervention on white matter microstructure in older adults at risk for diabetes.

Older adults with prediabetes or obesity (i.e., those at risk for diabetes) exhibit impaired structural brain networks. Given findings that resistance training (RT) can combat brain impairments in many populations, this study aimed to test the effects of this type of exercise on white matter microstructure in older adults at risk for diabetes. Seventeen community-dwelling older adults (mean age 67.8 ± 5.7, 52.9 % female) with prediabetes or obesity were randomly allocated to thrice weekly RT or balance and tone training (BAT; control group) for six months. Diffusion weighted imaging via a 3T scanner was used to assess changes in white matter parameters -fractional anisotropy (FA), mean diffusivity (MD), radial diffusivity (RD) - over time. Participants in the RT group showed no significant changes in FA but had increased MD and RD in various regions related to cognitive function including the cingulate gyrus. Participants in the control group had both increased and decreased FA depending on the specific white matter tracts; increased FA was seen in areas related to motor coordination such as the middle cerebellar peduncle. The control group also exhibited decreased MD and RD in areas responsible for motor function (e.g., left anterior limb of the internal capsule). We conclude that both resistance and balance exercises result in changes in white matter microstructure albeit in divergent tracts that may be linked to the specific exercises performed.

MicroRNAs Associated with Metformin Treatment in the Diabetes Prevention Program.

The Diabetes Prevention Program (DPP) randomized controlled trial demonstrated that metformin treatment reduced progression to type 2 diabetes (T2D) by 31% compared to placebo in adults with prediabetes. Circulating micro-ribonucleic acids (miRs) are promising biomarkers of T2D risk, but little is known about their associations with metformin regimens for T2D risk reduction. We compared the change in 24 circulating miRs from baseline to 2 years in a subset from DPP metformin intervention (n = 50) and placebo (n = 50) groups using Wilcoxon signed rank tests. Spearman correlations were used to evaluate associations between miR change and baseline clinical characteristics. Multiple linear regression was used to adjust for covariates. The sample was 73% female, 17% Black, 13% Hispanic, and 50 ± 11 years. Participants were obese, normotensive, prediabetic, and dyslipidemic. Change in 12 miR levels from baseline to 2 years was significantly different in the metformin group compared with placebo after adjusting for multiple comparisons: six (let-7c-5p, miR-151a-3p, miR-17-5p, miR-20b-5p, miR-29b-3p, and miR-93-5p) were significantly upregulated and six (miR-130b-3p, miR-22-3p, miR-222-3p, miR-320a-3p, miR-320c, miR-92a-3p) were significantly downregulated in the metformin group. These miRs help to explain how metformin is linked to T2D risk reduction, which may lead to novel biomarkers, therapeutics, and precision health strategies.

Comparison of food intake pattern of diabetic patients and healthy individuals in a sample of Saudi population: a case-control study.

BACKGROUND: There has been a significant rise in the number of individuals diagnosed with type 2 diabetes mellitus (T2DM), with the condition reaching epidemic proportions globally. This study examined the dietary pattern of a sample of Saudi Arabian adults with T2DM compared to control non-diabetics. METHODS: Data from 414 participants, 207 control and 207 T2DM was analyzed. Anthropometric measurements, foods intake such as vegetables, fruits, whole grains, fried foods, sweetened juice, sweets, and pastries consumption as well as physical activity were obtained by an interview-survey. RESULTS: The consumption of vegetables, green and leafy vegetables, starchy vegetables, fruits, proteins, and milk was significantly higher in the diabetics (p< 0.0001 for all and p<0.01 for starchy vegetables). Of the case group, 79.7% of them consumed whole-wheat bread while 54.6% of them consumed low fat milk (p<0.0001). There was a significant decrease in the percentage of cases who consumed discretionary foods and sweetened juices and soft drinks (24.1%), avoided sweets (75.8%) and pastries (37.1%), (p<0.0001). There were also significant increases in the percentages of participants who use healthy fat (as olive oil) in the case group (78.7%) (p<0.001). There was a significant increase in the percentage of diabetics who followed a diet to lose weight (15%) (p<0.05). The majority of the two study groups were physically inactive (control 95.2% & case 94.2%). CONCLUSIONS: The results of this study provide insight on that diabetics generally follow a healthy diet, yet their engagement in physical activity may not be optimal.

Association of ADIPOQ Gene Polymorphisms with Type 2 Diabetes and Obesity Risk in the Kazakh Population: A Case-Control and Population-Based Study.

Type 2 diabetes mellitus (T2DM) is a socially significant disease with increasing prevalence worldwide. It is characterized by heterogeneous metabolic disorders and is associated with various risk factors, including BMI, abnormal lipid levels, hypertension, smoking, dietary preferences, physical inactivity, sedentary lifestyle, family history of diabetes, prediabetes or gestational diabetes, inflammation, intrauterine environment, age, sex, ethnicity, and socioeconomic status. Assessing the genetic risk of developing T2DM in specific populations remains relevant. The ADIPOQ gene, encoding adiponectin, is directly related to the risk of developing T2DM, obesity, and cardiovascular diseases. Our study demonstrated significant associations of ADIPOQ gene polymorphisms with the risk of developing T2DM and obesity, as well as with fasting glucose levels and BMI, in the Kazakh population. Specifically, rs266729 was significantly associated with T2DM and obesity in the Kazakh population, while other studied polymorphisms (rs1501299, rs2241766, and rs17846866) did not show a significant association. These findings suggest that ADIPOQ gene polymorphisms may influence T2DM risk factors and highlight the importance of genetic factors in T2DM development. However, further research in larger cohorts is needed to confirm these associations.

Predictive value of Systematic Coronary Risk Evaluation 2-Diabetes risk model and arterial stiffness for cardiovascular events in the Asian population with type 2 diabetes mellitus.

AIMS/INTRODUCTION: Individuals with diabetes are at high risk of developing cardiovascular events. The present study investigated the predictive value of the cardio-ankle vascular index (CAVI) when added to the Systematic Coronary Risk Evaluation 2-Diabetes (SCORE2-Diabetes) risk algorithm to predict cardiovascular events in the Asian population. MATERIALS AND METHODS: The SCORE2-Diabetes risk was assessed in 1,502 patients with diabetes, aged 40-69 years. Then, we further stratified each 10-year risk category with a CAVI value of 9.0. The primary outcomes (composite of all causes of death, myocardial infarction, stroke and hospitalization for heart failure) were assessed over 5 years. RESULTS: The mean age of the population was 59.8 ± 6.4 years. The proportion of 10-year risk according to the SCORE2-Diabetes risk of low, moderate, high and very high risk identified at 7.2, 30.0, 27.2 and 35.6%, respectively. The mean CAVI value was 8.4 ± 1.4, and approximately 35.4% of the patients had CAVI ≥9.0. The SCORE2-Diabetes risk algorithm independently predicted the primary outcomes in patients with diabetes (hazard ratio 1.18, 95% confidence interval [CI] 1.13-1.22), whereas CAVI did not (hazard ratio 1.03, 95% CI 0.89-1.18). The C-index for the primary outcomes of the SCORE2-Diabetes risk algorithm alone was 0.72 (95% CI 0.67-0.77). The combination of SCORE2-Diabetes and CAVI, both in the continuous value and risk groups, did not improve discrimination (C-index 0.72, 95% CI 0.67-0.77 and 0.68, 95% CI 0.64-0.74, respectively). CONCLUSIONS: Adding the CAVI to the SCORE2-Diabetes risk algorithm did not improve individual risk stratification in patients with diabetes.

Beyond the assembly line: An assessment of diabetes risks among Indian male industrial workers.

Background: Diabetes mellitus is a chronic non-communicable disease that imposes a significant burden on affected individuals and the community. Considerable attention has been given to industrial accidents and ergonomics, however, lifestyle-related diseases among industrial workers have often been neglected. Therefore, the present study was conducted with the aim to assess the prevalence of obesity/overweight and ascertain the risk of diabetes mellitus among male employees of an industrial unit in South Mumbai. Methods: The cross-sectional study was conducted among male employees of an industrial unit in South Mumbai. Family history, exercise patterns, anthropometric measurements and physical vital parameters were recorded. Body composition was assessed using bioelectrical impedance analysis (BIA). The Indian Diabetes Risk Score (IDRS) was employed to evaluate the risk of diabetes mellitus. Results: In total, 3791 industrial workers participated in the study and 44.5% of participants were above 40 years. Mean height, weight, body mass index (BMI), Waist Circumference (WC) and waist to hip ratio (WHR) were 1.67 m, 71.33 kg, 25.99, 90.81 cm and 0.91 respectively. 56.1% individuals had WC more than 90 cm and 79.1% had WHR more than 0.90. 1846 (53%) and 927 (26.6%) participants had moderate and high diabetes risk respectively. The relationship between age, weight, BMI, WC, WHR, body fat mass and fat percentage, and IDRS was statistically significant. Conclusion: A substantial proportion of industrial workers were identified as overweight and at high risk of diabetes mellitus. Consequently, it becomes imperative to offer health education and implement interventions to encourage regular exercise, adopt an active lifestyle, and promote healthy dietary habits among industrial workers.

Risk assessment of diabetes using the Indian Diabetes Risk Score among older adults: Secondary analysis from the Longitudinal Ageing Study in India.

BACKGROUND: The Indian Diabetes Risk Score (IDRS) is a simple tool to assess the probability of an individual having type 2 diabetes (T2DM) but its applicability in community-dwelling older adults is lacking. This study aimed to estimate the risk of T2DM and its determinants among older adults without prior diabetes (DM) using the IDRS, while also assessing its sensitivity and specificity in individuals with a history of diabetes. METHODS: We analyzed cross-sectional data from the Longitudinal Ageing Study in India (LASI) wave-1 (2017-18). IDRS was calculated amongst individuals aged ≥45 years considering waist circumference, physical activity, age and family history of DM. Risk was categorized as high (≥60), moderate (30-50), and low (<30). RESULTS: Among 64541 individuals, 7.27 % (95 % CI: 6.78, 7.80) were at low risk, 61.80 % (95 % CI: 60.99, 62.61) at moderate risk, and 30.93 % (95 % CI: 30.19, 31.67) at high risk for T2DM. Adjusted analysis showed higher risk of T2DM among men, widowed/divorced, urban residents, minority religions, overweight, obese, and individuals with hypertension. ROC curve yielded an AUC of 0.67 (95 % CI: 0.66, 0.67, P < 0.001). The IDRS cutoff ≥50 had 73.69 % sensitivity and 51.40 % specificity for T2DM detection. CONCLUSION: More than 9 in 10 older adults in India without history of DM have high-moderate risk of T2DM when assessed with the IDRS risk-prediction tool. However, the low specificity and moderate sensitivity of IDRS in existing DM cases constraints its practical utility as a decision tool for screening.

Serum concentration of polychlorinated biphenyls and the risk of type 2 diabetes: a 10-year follow-up historical cohort study.

This study investigated the association between serum concentrations of Polychlorinated Biphenyls (PCBs) and the risk of type 2 diabetes within the general population. A ten-year follow-up historical cohort study was conducted during 2009-2019 as part of the Bushehr MONICA cohort study in Iran. Of 893 non-diabetes participants at base line, 181 individuals were included in the study. The concentration of nine PCB congeners was measured in individuals' serum samples at baseline, and the risk of type 2 diabetes was determined based on fasting blood sugar at the end of follow-up. Multiple logistic regression models were used to assess the study outcomes after adjusting for covariates. This study included 59 diabetes individuals (32.6%; mean [SD] age: 58.64 [8.05]) and 122 non-diabetes individuals (67.4%; mean [SD] age: 52.75 [8.68]). Multivariable analysis revealed that a one-tertile increase (increasing from 33rd centile to 67th centile) in Σ non-dioxin-like-PCBs (OR 2.749, 95% CI 1.066-7.089), Σ dioxin-like-PCBs (OR 4.842, 95% CI 1.911-12.269), and Σ PCBs (OR 2.887, 95% CI 1.120-7.441) significantly associated with an increased risk of type 2 diabetes. The strongest association was obtained for dioxin-like PCBs. The results highlight a significant correlation between PCB exposure and an increased risk of type 2 diabetes. The evidence suggests that additional epidemiological studies are necessary to clarify the link between PCBs and diabetes.

Adverse Childhood Experiences and Diabetes Risk in Mississippi Adults.

Despite Mississippi's high diabetes prevalence and the growing literature finding significant associations between adverse childhood experiences (ACEs) and diabetes, no research has examined the relationship between ACEs and diabetes risk in Mississippi adults. This study utilized data from the 2020 Behavioral Risk Factor Surveillance System (BRFSS) to determine if such a relationship existed. Data for Mississippi respondents were weighted to account for nonresponse bias and non-coverage errors. Each respondent's total ACE exposure score was calculated based on the number of ACE categories experienced. Multivariate logistic regression was utilized to model the relationship between diabetes and ACE categories and diabetes and total ACE exposure scores. Variables that were significant at p<0.05 were retained in the final (best-fitting) models. All models were adjusted for sex, age, race, level of education, income, and body mass index (BMI). After adjusting for covariates, those experiencing physical abuse (adjusted odds ratio (AOR) 1.72, 95% CI 1.69; 1.75) or sexual abuse (AOR 1.56, 95% CI 1.53; 1.58) had the highest odds of ever being diagnosed with diabetes. Experiencing one ACE (AOR 1.02, 95% CI 1.01; 1.03) was associated with slightly higher odds of having diabetes, while experiencing seven ACE categories (AOR 2.20, 95% CI 2.10; 2.31) had the highest odds. Overall, this study shows a strong association between ACEs and a diagnosis of diabetes in the state of Mississippi. This relationship represents an important focus area for prevention efforts in legislation, public health campaigns, and universal screening procedures in primary care that may decrease the prevalence and burden of diabetes in Mississippi.

Estimating the effect of lipid-lowering agents on novel subtypes of adult-onset diabetes.

AIMS: The aims of the present study were to assess the effects of lipid-lowering drugs [HMG-CoA reductase inhibitors, proprotein convertase subtilisin/kexin type 9 inhibitors, and Niemann-Pick C1-Like 1 (NPC1L1) inhibitors] on novel subtypes of adult-onset diabetes through a Mendelian randomisation study. MATERIALS AND METHODS: We first inferred causal associations between lipid-related traits [including high-density lipoprotein cholesterol, low-density lipoprotein cholesterol (LDL-C), triglycerides (TG), apolipoproteins A-I, and apolipoproteins B] and novel subtypes of adult-onset diabetes. The expression quantitative trait loci of drug target genes for three classes of lipid-lowering drugs, as well as genetic variants within or nearby drug target genes associated with LDL-C, were then utilised as proxies for the exposure of lipid-lowering drugs. Mendelian randomisation analysis was performed using summary data from genome-wide association studies of LDL-C, severe autoimmune diabetes, severe insulin-deficient diabetes (SIDD), severe insulin-resistant diabetes (SIRD), mild obesity-related diabetes (MOD), and mild age-related diabetes. RESULTS: There was an association between HMGCR-mediated LDL-C and the risk of SIRD [odds ratio (OR) = 0.305, 95% confidence interval (CI) = 0.129-0.723; p = 0.007], and there was an association of PCSK9-mediated LDL-C with the risk of SIDD (OR = 0.253, 95% CI = 0.120-0.532; p < 0.001) and MOD (OR = 0.345, 95% CI = 0.171-0.696; p = 0.003). Moreover, NPC1L1-mediated LDL-C (OR = 0.109, 95% CI = 0.019-0.613; p = 0.012) and the increased expression of NPC1L1 gene in blood (OR = 0.727, 95% CI = 0.541-0.977; p = 0.034) both showed a significant association with SIRD. These results were further confirmed by sensitivity analyses. CONCLUSIONS: In summary, the different lipid-lowering medications have a specific effect on the increased risk of different novel subtypes of adult-onset diabetes.

Gestational diabetes and risk of future diabetes in a multi-ethnic population.

AIM: To investigate ethnic disparities in risk of gestational diabetes-mellitus (GDM) and future diabetes. METHODS: A population-based retrospective cohort study of women who underwent a 100-g oral glucose-tolerance-test (oGTT) during pregnancy between 2007 and 2017 in Clalit-Health-Services of the Jerusalem district. Univariate and multivariate logistic regression analyses were used to compare the risk of GDM in Arab versus Jewish women. Further, Cox-regression analysis was used to establish the risk of future diabetes. RESULTS: A total of 9875 women, 71 % of Jewish ethnicity and 29 % of Arab ethnicity were included. Arab women had a higher incidence of GDM compared to Jewish women (17.3 % vs. 10.6 %, p < 0.001), which persisted after adjusting for age, BMI, and metabolic profile (aOR 1.7; CI 1.48-2.0, P < 0.001). Additionally, Arab ethnicity was associated with an increased risk of future diabetes, even after adjusting for GDM status (aHR 5.9; 95 % CI 3.7-9.4, P < 0.001). CONCLUSIONS: Women of Arab ethnicity have a higher risk for both GDM and future diabetes, a risk that is beyond the initial increased risk associated with GDM. These findings highlight the need for increased focus on preventing diabetes in women of Arab ethnicity, especially those with a history of GDM.

A feature optimization study based on a diabetes risk questionnaire.

Introduction: The prevalence of diabetes, a common chronic disease, has shown a gradual increase, posing substantial burdens on both society and individuals. In order to enhance the effectiveness of diabetes risk prediction questionnaires, optimize the selection of characteristic variables, and raise awareness of diabetes risk among residents, this study utilizes survey data obtained from the risk factor monitoring system of the Centers for Disease Control and Prevention in the United States. Methods: Following univariate analysis and meticulous screening, a more refined dataset was constructed. This dataset underwent preprocessing steps, including data distribution standardization, the application of the Synthetic Minority Oversampling Technique (SMOTE) in combination with the Round function for equilibration, and data standardization. Subsequently, machine learning (ML) techniques were employed, utilizing enumerated feature variables to evaluate the strength of the correlation among diabetes risk factors. Results: The research findings effectively delineated the ranking of characteristic variables that significantly influence the risk of diabetes. Obesity emerges as the most impactful factor, overshadowing other risk factors. Additionally, psychological factors, advanced age, high cholesterol, high blood pressure, alcohol abuse, coronary heart disease or myocardial infarction, mobility difficulties, and low family income exhibit correlations with diabetes risk to varying degrees. Discussion: The experimental data in this study illustrate that, while maintaining comparable accuracy, optimization of questionnaire variables and the number of questions can significantly enhance efficiency for subsequent follow-up and precise diabetes prevention. Moreover, the research methods employed in this study offer valuable insights into studying the risk correlation of other diseases, while the research results contribute to heightened societal awareness of populations at elevated risk of diabetes.

Diabetes prediction model for unbalanced community follow-up data set based on optimal feature selection and scorecard.

Objectives: Diabetes is a metabolic disease and early detection is crucial to ensuring a healthy life for people with prediabetes. Community care plays an important role in public health, but the association between community follow-up of key life characteristics and diabetes risk remains unclear. Based on the method of optimal feature selection and risk scorecard, follow-up data of diabetes patients are modeled to assess diabetes risk. Methods: We conducted a study on the diabetes risk assessment model and risk scorecard using follow-up data from diabetes patients in Haizhu District, Guangzhou, from 2016 to 2023. The raw data underwent preprocessing and imbalance handling. Subsequently, features relevant to diabetes were selected and optimized to determine the optimal subset of features associated with community follow-up and diabetes risk. We established the diabetes risk assessment model. Furthermore, for a comprehensible and interpretable risk expression, the Weight of Evidence transformation method was applied to features. The transformed features were discretized using the quantile binning method to design the risk scorecard, mapping the model's output to five risk levels. Results: In constructing the diabetes risk assessment model, the Random Forest classifier achieved the highest accuracy. The risk scorecard obtained an accuracy of 85.16%, precision of 87.30%, recall of 80.26%, and an F1 score of 83.27% on the unbalanced research dataset. The performance loss compared to the diabetes risk assessment model was minimal, suggesting that the binning method used for constructing the diabetes risk scorecard is reasonable, with very low feature information loss. Conclusion: The methods provided in this article demonstrate effectiveness and reliability in the assessment of diabetes risk. The assessment model and scorecard can be directly applied to community doctors for large-scale risk identification and early warning and can also be used for individual self-examination to reduce risk factor levels.