Increased Risk Of Diabetes Research Articles

Association of low carbohydrate diet score with the risk of type 2 diabetes in an Australian population: A longitudinal study

AimsWe aimed to assess the association of a low carbohydrate diet score (LCD) with the incidence of type 2 diabetes (T2D) using Melbourne Collaborative Cohort Study (MCCS) data. MethodsBetween 1990 and 1994, the MCCS recruited 41,513 people aged 40–69 years. The first and second follow-ups were conducted in 1995–1998 and 2003–2007, respectively. We analyzed data from 39,185 participants. LCD score was calculated at baseline as the percentage of energy from carbohydrate, fat, and protein. The higher the score the less percentage of carbohydrates contributed to energy intake. The association of LCD quintiles with the incidence of diabetes was assessed using modified Poisson regression, adjusted for lifestyle, obesity, socioeconomic and other confounders. Mediation of the association by adiposity (BMI) was assessed. ResultsLCD was positively associated with diabetes risk. Higher LCD score (p for trend = 0.001) was associated with increased risk of T2D. Quintile 5 (38 % energy from carbohydrates) versus quintile 1 (55 % energy from carbohydrates) showed a 20 % increased diabetes risk (incidence risk ratio (IRR) = 1.20 (95 % CI: 1.05–1.37)). A further adjustment for BMI (Body Mass Index) and WHR (Waist-to-Hip-Ratio) eliminated the association. Mediation analysis demonstrated that BMI mediated 76 % of the LCD & diabetes association. ConclusionsConsuming a low carbohydrate diet, reflected as a high LCD score, may increase the risk of T2D which is largely explained by obesity. Results highlight the need for further studies, including clinical trials investigating the effects of a low carbohydrate diet in T2D.

Hyperglycemic Impact On Sleep Quality In Patients With Type 2 Diabetes Mellitus-A Cross Sectional Study

Background: Diabetes mellitus (DM) causes high morbidity and mortality and loss of quality of life significantly. It is an important public health concern. In epidemiologic studies, people with poor sleep quality are more prone to develop diabetes or metabolism disorder of glucose. Sleep has an important role for metabolic function and specifically homeostasis of glucose metabolism. Many studies have shown a correlation between increased diabetes risk and poor sleep quality or sleep deprivation. DM creates a negative effect impact on quality of sleep on patients. Prevalence of obesity and diabetes becomes greater among aged people, it is of vital importance to fix on sleep for overall medical management. This is aimed to determine hyperglycaemia with sleep quality among the patient. Methods: This was a cross sectional study carried out at Ibrahim General Hospital, National Health Care Network (NHN), Mirpur-10, Dhaka during the period of January 2014 to December 2014. Total 129 respondents were selected purposively for this study. Semi-structured questionnaire was pretested first and then it was used for data collection. Pittsburgh Sleep Quality Index score (PSQI) was used to assess sleep quality. Statistical Package for Social Sciences (SPSS) version 22.0. was used for analysis of the collected data. Results: Among 129 respondents, 98(76%) were male and 31(24%) were female. 93.8% respondents were married and rest 6.2% of them were unmarried. Mean(±SD) age was found 53.64(±9.26) years and range 35 to 70 years. 79.1% of the respondents had co-morbidity of DM, 70(54.3%) had diabetes more than 10 years duration. Mean(±SD) of overall sleep quality score was 9.04(±3.965) and range 1 to 18. Among the respondents of age group >64, 95.70% had poor sleep quality, 76.14% respondents sleep less than 6 hours in a day. So association between sleep quality and glycemic status of type 2 DM was significant(p<0.05). Conclusion: The prevalence of high blood sugar level in Type 2 DM patients have poor sleep quality. Type 2 DM patients with high blood sugar level have to overcompensate kidneys to visit washroom more frequent and lead to disturbance in sleep. Type 2 DM patients with controlled blood sugar level have good sleep quality. It is evident from the study that Type 2 DM patients with high sugar level (Hyperglycaemia) frequently experience sleep difficulties. Bangladesh Armed Forces Med J Vol 56 No (1) June 2023, pp 21-27

Incident Diabetes in Women With Patterns of Gestational Diabetes Occurrences Across 2 Pregnancies

Gestational diabetes is a type 2 diabetes risk indicator, and recurrence further augments risk. In women with a single occurrence across 2 pregnancies, it is unclear whether first- vs second-pregnancy gestational diabetes differ in terms of risk. To compare the hazards of incident diabetes among those with gestational diabetes in the first, in the second, and in both pregnancies with women without gestational diabetes in either. This was a retrospective cohort study with cohort inception from April 1, 1990, to December 31, 2012. Follow-up was April 1, 1990, to April 1, 2019. Participants were mothers with 2 singleton deliveries between April 1, 1990, and December 31, 2012, without diabetes before or between pregnancies, who were listed in public health care insurance administrative databases and birth, stillbirth, and death registries in Quebec, Canada. Data were analyzed from July to December 2023. Gestational diabetes occurrence(s) across 2 pregnancies. Incident diabetes from the second delivery until a third pregnancy, death, or the end of the follow-up period, whichever occurred first. The 431 980 women with 2 singleton deliveries studied had a mean (SD) age of 30.1 (4.5) years at second delivery, with a mean (SD) of 2.8 (1.5) years elapsed between deliveries; 373 415 (86.4%) were of European background, and 78 770 (18.2%) were at the highest quintile of material deprivation. Overall, 10 920 women (2.5%) had gestational diabetes in their first pregnancy, 16 145 (3.7%) in their second, and 8255 (1.9%) in both (12 205 incident diabetes events; median [IQR] follow-up 11.5 [5.3-19.4] years). First pregnancy-only gestational diabetes increased hazards 4.35-fold (95% CI, 4.06-4.67), second pregnancy-only increased hazards 7.68-fold (95% CI, 7.31-8.07), and gestational diabetes in both pregnancies increased hazards 15.8-fold (95% CI, 15.0-16.6). Compared with first pregnancy-only gestational diabetes, second pregnancy-only gestational diabetes increased hazards by 76% (95% CI, 1.63-1.91), while gestational diabetes in both pregnancies increased it 3.63-fold (95% CI, 3.36-3.93). In this retrospective cohort study of nearly half a million women with 2 singleton pregnancies, both the number and ordinal pregnancy of any gestational diabetes occurrence increased diabetes risk. These considerations offer greater nuance than an ever or never gestational diabetes dichotomy.

Exploring the Causal Relationship Between Modifiable Exposures and Diabetes Mellitus: A Two-Sample Mendelian Randomization Analysis.

Background Observational studies link lifestyle factors to diabetes, but confounding limits causal inference. This study employed Mendelian randomization (MR) to investigate the potential causal effects of major dietary, obesity, smoking, and physical activity exposures on diabetes risk. Methods A two-sample MR framework integrated FinnGen and United Kingdom Biobank (UKB) data. Genetic instruments for diet (fruits, vegetables, cheese), smoking (initiation, intensity, maternal), body mass index (BMI), and physical activity came from various consortia (n=64, 949-632, 802). Associations with diabetes odds were assessed using inverse-variance weighted analysis. Results Fruit and cheese intake and physical activity per standard deviation increase causally reduced diabetes risk in both cohorts. Conversely, smoking initiation, maternal smoking around birth, and BMI per standard deviation increase causally increased diabetes risk in both cohorts. Coffee increased diabetes risk only in FinnGen, whereas smoking intensity increased diabetes risk only in UKB. Conclusion This study provides robust evidence that modifiable lifestyle factors may have causal effects on diabetes risk. Fruit, cheese, and physical activity may protect against diabetes, whereas smoking, maternal smoking, and higher BMI appear to increase risk. Findings support public health interventions targeting diet, physical activity, smoking cessation, and healthy weight to combat the global diabetes epidemic.

COVID-19 infection and incident diabetes in American Indian and Alaska Native people: a retrospective cohort study

Evidence suggests an increased risk of new-onset diabetes following COVID-19 infection. American Indian/Alaska Native (AI/AN) people were disparately impacted by the COVID-19 pandemic and historically have had higher diabetes incidence than other racial/ethnic groups in the US. We measured the association between COVID-19 infection and incident diabetes in AI/AN people. We conducted a retrospective cohort study using de-identified patient data from the Indian Health Service's (IHS) National Patient Information Reporting System. We estimated age-adjusted diabetes incidence rates, incidence rate ratios, and adjusted hazard ratios among three cohorts spanning pre-pandemic (1/1/2018-2/28/2020) and pandemic (3/1/2020-12/31/2021) timeframes: 1) pre-pandemic cohort (1,503,085 individuals); 2) no-COVID-19 pandemic cohort (1,344,339 individuals); and 3) COVID-19 cohort (176,483 individuals). The COVID-19 cohort had an increased hazard of diabetes compared to the no-COVID-19 group (adjusted hazard ratio (aHR)=1.56; 95% CI: 1.50-1.62) and the pre-pandemic group (aHR=1.27; 95% CI: 1.22-1.32). The association between COVID-19 infection and new-onset diabetes was stronger in those with severe COVID-19 illness. A sensitivity analysis comparing the COVID-19 cohort to members of other cohorts that had acute upper respiratory infections showed an attenuated but higher risk of new-onset diabetes in those with COVID-19. AI/AN people diagnosed with COVID-19 had an elevated risk of a new diabetes diagnosis when compared to the no-COVID-19 group and the pre-pandemic group. The increased diabetes risk in the COVID-19 group remained in a sensitivity analysis that limited the comparator groups to individuals with an AURI diagnosis. US National Institute of Diabetes and Digestive and Kidney Diseases.

A low carbohydrate diet score is associated with a higher risk of developing type 2 diabetes in an Australian population: Melbourne Collaborative Cohort Study

Evidence suggests that low carbohydrate eating patterns are effective for rapid weight loss 1, however, little is known about their long-term effects on the risk of chronic diseases. We assessed the association of a low carbohydrate diet score (LCD) with the incidence of type 2 diabetes using Melbourne Collaborative Cohort Study (MCCS) data. Between 1990 and 1994, the MCCS recruited 41,513 people aged 40 to 69 years. The first and second follow-ups were conducted in 1994-1998 and 2003-2007, respectively2. We analysed data from 39,185 participants. LCD at baseline was calculated as the percentage of energy from carbohydrate, fat, and protein. The higher the score the less carbohydrate contributed to energy intake. The association of LCD quintiles with the incidence of diabetes was assessed using modified Poisson regression, adjusted for lifestyle, obesity, socioeconomic and other confounders. LCD was positively associated with diabetes risk. Higher LCD score (p for trend = 0.001) was associated with increased risk of type 2 diabetes. Quintile 5 (38% energy from carbohydrates) versus quintile 1 (55% energy from carbohydrates) showed a 20% increased diabetes risk (incidence risk ratio (IRR) = 1.20 (95% CI: 1.05-1.37)). A further adjustment for BMI and WHR eliminated the association. Mediation analysis demonstrated that BMI attributed 76% of the LCD & diabetes association. Consuming a low carbohydrate diet, reflected as a high LCD score, may increase the risk of type 2 diabetes which is largely explained by obesity. Results imply the need for further studies, including clinical trials investigating the effects of a low carbohydrate diet in type 2 diabetes.

Alterations in gut microbiota and bile acids by proton-pump inhibitor use and possible mediating effects on elevated glucose levels and insulin resistance.

Several observational studies have suggested that proton-pump inhibitor (PPI) use might increase diabetes risk, but the mechanism remains unclear. This study aimed to investigate the effects of PPI use on gut microbiota and bile acids (BAs) profiles, and to explore whether these changes could mediate the association of PPIs use with fasting blood glucose (FBG) levels and insulin resistance (IR) in Chinese population. A cross-sectional study was conducted in Shenzhen, China, from April to August 2021, enrolled 200 eligible patients from the local hospital. Participants completed a questionnaire and provided blood and stool samples. Gut microbiome was measured by16S rRNA gene sequencing, and bile acidswere quantified by UPLC-MS/MS. Insulin resistance (IR) was assessed using the Homeostasis Model Assessment 2 (HOMA2-IR). PPI use was positively associated with higher levels of FBG and HOMA2-IR after controlling for possible confounders. PPI users exhibited a decreased Firmicutes and an increase in Bacteroidetes phylum, alongside higher levels of glycoursodeoxycholic acid (GUDCA) and taurochenodeoxycholic acid (TCDCA). Higher abundances of Bacteroidetes and Fusobacterium as well as higher levels of TCDCA in PPI users were positively associated with elevated FBG or HOMA2-IR. Mediation analyses indicated that the elevated levels of FBG and HOMA2-IR with PPI use were partially mediated by the alterations in gut microbiota and specific BAs (i.e., Fusobacterium genera and TCDCA). Long-term PPI use may increase FBG and HOMA2-IR levels, and alterations in gut microbiota and BAs profiles may partially explain this association.

Diabetes Prediction with Machine Learning with Python

This article introduces an innovative approach leveraging a combination of machine learning techniques to enhance early diabetes detection, a crucial step given the disease's global impact. With the prevalence of sugar and fats in contemporary diets contributing to an increased diabetes risk, early identification through symptom recognition is key. The proposed method integrates Using Support Vector Machine (SVM) and Artificial Neural Network (ANN) algorithms, patient data is analyzed to classify diabetes diagnoses as either affirmative or negative. The study involves the utilization of a dataset that has been divided into 70% for training data and 30% for testing data. The outputs from the SVM and ANN models serve as inputs for a fuzzy logic system, which then makes the final diagnosis determination. This hybrid model is stored on a cloud platform for accessibility and uses real-time patient data for predictions. The combined machine learning model demonstrates superior accuracy in predicting diabetes compared to existing methods.

Intersectional analysis of social disparities in type 2 diabetes risk among adults in Germany: results from a nationwide population-based survey

BackgroundDifferences in type 2 diabetes risk have been reported for several sociodemographic determinants including sex/gender or socioeconomic status. From an intersectional perspective, it is important to not only consider the role of social dimensions individually, but also their intersections. This allows for a deeper understanding of diabetes risk and preventive needs among diverse population groups.MethodsAs an intersectionality-informed approach, multilevel analysis of individual heterogeneity and discriminatory accuracy (MAIHDA) was used in a population-based sample of adults without known diabetes in Germany from the cross-sectional survey “Disease knowledge and information needs– Diabetes mellitus (2017)”. Diabetes risk was assessed by the German Diabetes Risk Score (GDRS, range 0-122 points), estimating the individual risk of developing type 2 diabetes within the next 5 years based on established self-reported risk factors. Nesting individuals in 12 intersectional strata defined by combining sex/gender, educational level, and history of migration, we calculated measures to quantify the extent to which individual differences in diabetes risk were explained at strata level, and how much this was due to additive or multiplicative intersectional effects of social determinants.ResultsDrawing on data of 2,253 participants, we found good discriminatory accuracy of intersectional strata (variance partition coefficient = 14.00% in the simple intersectional model). Model-predicted GDRS means varied between 29.97 (corresponding to a “low risk” of < 2%) in women with high educational level and a history of migration, and 52.73 (“still low risk” of 2–5%) in men with low educational level without a history of migration. Variance in GDRS between strata was mainly explained by additive effects of social determinants (proportional change in variance to intersectional interaction model = 77.95%) with being male and having low educational level being associated with higher GDRS. There was no evidence of multiplicative effects in individual strata.ConclusionsType 2 diabetes risk differed between intersectional strata and can to some extent be explained at strata level. The role of intersectional effects was minor and needs to be further investigated. Findings suggest a need for specific preventive measures targeted at large groups with increased diabetes risk, such as men and persons with low educational level.

Metabolic dysfunction-associated fatty liver disease increases the risk of type 2 diabetes mellitus in young Korean adults

AimsTo investigate the impact of Metabolic Dysfunction-Associated Fatty Liver Disease (MAFLD) on the risk of type 2 diabetes mellitus in young Korean adults. MethodsData were sourced from the Korean National Health Insurance Service-Health Screening Cohort, comprising adults aged 20–39 who underwent health examinations between 2009 and 2012. Participants were grouped based on the presence of MAFLD and nonalcoholic fatty liver disease (NAFLD), both individually and in combination. The categorizations included Neither-FLD, NAFLD-only, MAFLD-only, or Both-FLD. Incident diabetes was identified through claims data during the follow-up period. ResultsAmong 6,232,656 participants, 676,747 (10.8 %) had MAFLD. During a median follow-up of 9.5 years, 182,291 incident diabetes cases were identified. Multivariate analysis revealed a significantly higher diabetes risk in the MAFLD group compared to those in the Non-MAFLD group (HR = 6.148, 95 % CI, 6.084–6.212). Notably, diabetes incidence was highest in FLI ≥ 60 subgroup with BMI ≥ 23 and metabolic syndrome. ConclusionsMAFLD is associated with a 6.1-fold increased diabetes risk in young adults, underscoring the urgent need for early intervention to mitigate this risk.

Optimal control of diabetes model with the impact of endocrine-disrupting chemical: an emerging increased diabetes risk factor

Diabetes, a persistent pathological condition characterized by disruptions in insulin hormone regulation, has exhibited a noteworthy escalation in its prevalence over recent decades. The surge in incidence is notably associated with the proliferation of endocrine-disrupting chemicals (EDCs), which have emerged as primary contributors to the manifestation of insulin resistance and the consequent disruption of beta cell function, ultimately culminating in the onset of diabetes. Consequently, this study endeavors to introduce a model for diabetes that aims to elucidate the ramifications of exposure to EDCs within the diabetic population. In the pursuit of mitigating the deleterious effects of EDC-induced diabetes, we propose a framework for optimal control strategies. The utilization of Pontryagin’s maximum principle serves to explicate the principles governing the optimal control mechanisms within the proposed model. Our findings underscore that heightened concentrations of EDCs play a pivotal role in exacerbating the prevalence of diabetes. To substantiate our model, we employ parameter estimation techniques utilizing a diabetes dataset specific to the demographic context of India. This research contributes valuable insights into the imperative need for proactive measures to regulate and diminish EDC exposure, thereby mitigating the escalating diabetes epidemic.

Smokeless Tobacco Use and Its Association with Type 2 Diabetes: A Case Control Study.

Type 2 diabetes mellitus (T2DM) is a prevalent metabolic disorder characterized by hyperglycemia and insulin resistance. Its incidence is increasing globally, with a significant impact on public health. Smokeless tobacco (SLT) is a form of tobacco consumption that has been associated with various health risks, including potential effects on glucose homeostasis. This case-control study aimed to investigate the association between SLT use and T2DM. Additionally, the study aimed to assess the relationship of age, gender, socioeconomic status (SES), body mass index (BMI), family history of diabetes, physical activity, and periodontal status with T2DM participants. The study was conducted over 24 months and included 82 T2DM cases and 164 non-diabetic controls. Demographic data, tobacco use, medical history, oral hygiene habits, BMI, and periodontal status were collected through a self-administered questionnaire and interviews. Statistical analyses were performed using Statistical Package for Social Sciences (SPSS) for Windows 26.0 (SPSS, Inc. Chicago, Illinois). The majority of T2DM cases were in the age group of 31-50 years, and there was a significant association between gender and T2DM, with more males being diabetic. There was no significant association between SES and diabetes. Obesity was found to be a significant risk factor for T2DM. Among SLT users, gutkha was the most commonly used product. SLT use was significantly associated with T2DM. Family history of diabetes and physical inactivity were also significantly associated with diabetes. The study suggests that SLT use is a risk factor for T2DM and may be associated with increased diabetes risk. Further research is warranted to understand the underlying mechanisms and potential interventions to reduce the impact of SLT on diabetes risk.

Association between ultra-processed food consumption and the incidence of type 2 diabetes: the ELSA-Brasil cohort

BackgroundUltra-processed food (UPF) consumption increases the risk of type 2 diabetes in various high-income countries, with some variation in the magnitude across studies. Our objective was to investigate the association of UPF consumption and specific subgroups with incident type 2 diabetes in Brazilian adults.MethodsThe Brazilian Longitudinal Study of Adult Health (ELSA-Brasil) is a multicenter cohort study of 15,105 adults (35–74 years) enrolled in public institutions in Brazil (2008–2010). We followed participants with two clinic visits (2012–2014; 2017–2019) and annual telephone surveillance. After excluding those with diabetes at baseline, who died or were lost in the follow-up, with missing data, with implausible energy food intake, or reporting bariatric surgery, there were 10,202 participants. We used the NOVA classification to assess UPF consumption based on a food frequency questionnaire. We defined type 2 diabetes by self-report, medication use, or comprehensive laboratory tests. We estimated relative risks (RR) and 95% confidence intervals (95% CI) using robust Poisson regression.ResultsMedian UPF consumption was 372 g/day. Over 8.2 (0.7) years of follow-up, we detected 1799 (17.6%) incident cases. After adjustment for socio-demographics, family history of diabetes, and behavioral risk factors, comparing the fourth (≥ 566 g/day) with the first (< 236 g/day) quartile of UPF distribution, RR was 1.24 (1.10–1.39); every 150 g/day increments in UPF consumption resulted in a RR of 1.05 (1.03–1.07). Reclassifying natural beverages with added sweeteners as UPF increased risk (RR 1.40; 1.25–1.58). Among UPF subgroupings, consumption of processed meats and sweetened beverages increased diabetes risk, while yogurt and dairy sweets decreased the risk (p < 0.05).ConclusionsUPF consumption increased the incidence of type 2 diabetes in Brazilian adults, with heterogeneity across specific food items. These findings add to previous evidence for the role of UPFs in the development of diabetes and other chronic diseases, supporting recommendations to avoid their intake in diabetes prevention and management.

Study of transcription factor 7-like 2 (TCF7L2) gene polymorphism in cirrhotic patients with diabetes

Patients with chronic liver disease (CLD) as chronic hepatitis C (CHC) are at high risk of diabetes type 2 (T2D). Genetic factors are suggested to modulate diabetes development in cirrhotic patients. TCF7L2 gene has been reported to be associated with type 2 diabetes, but the association of TCF7L2 with cirrhotic patients with diabetes is unclear. We aimed to study the TCF7L2 gene polymorphisms (rs 290487) in cirrhotic patients with diabetes.Method The study was assessed on 25 cirrhotic patients with type 2 diabetes who were compared to 25 cirrhotic HCV patients (nondiabetic), 25 diabetic type 2 patients, and 25 age- and gender-matched healthy control groups. After the collection of relevant clinical data and basic laboratory tests, single-nucleotide polymorphism (SNP) in the TCF7L2 gene (rs290487) was performed by a real-time PCR technique.Results Cirrhotic patients with diabetes presented significantly poorer liver function, higher incidence of cirrhotic complications, and higher glucose levels compared with cirrhotic nondiabetic patients. The TCF7L2 rs290487 TT variant showed significantly increased diabetes risk in cirrhotic patients compared with CC and CT genotypes.Conclusions TCF7L2 rs290487 polymorphism could be associated with increased diabetic risk in cirrhotic patients.

Increased Levels of Urine Volatile Organic Compounds Are Associated With Diabetes Risk and Impaired Glucose Homeostasis.

Volatile organic compounds (VOCs) are pervasive environmental pollutants that have been linked to various adverse health effects. However, the effect of ambient VOCs, whether individually or in mixtures, on diabetes remains uncertain and requires further investigation. This study investigates the effects of ambient VOCs exposure, whether single or mixed, on diabetes mellitus and glucose homeostasis in the general population. Urinary concentrations of VOC metabolites were obtained from the National Health and Nutrition Examination Survey. Survey-weighted logistic regression and generalized linear regression were used to explore the associations between individual VOC exposure and diabetes risk and glucose homeostasis indicators, respectively. Weighted quantile sum (WQS) regression models were applied to assess the combined effects of VOC mixtures. Out of 8468 participants, 1504 had diabetes mellitus. Eight VOC metabolites showed positive associations with diabetes mellitus (OR, 1.15-1.43; all P < .05), insulin resistance (IR) (OR, 1.02-1.06; P < .05), and other glucose homeostasis indicators (β, 0.04-2.32; all P < .05). Mixed VOC models revealed positive correlations between the WQS indices and diabetes risk (OR = 1.52; 95% CI, 1.29-1.81), IR (OR = 1.36; 95% CI, 1.14-1.62), and other glucose homeostasis indicators (β, 0.17-2.22; all P < .05). Urinary metabolites of ambient VOCs are significantly associated with an increased diabetes risk and impaired glucose homeostasis. Thus, primary prevention policies aimed at reducing ambient VOCs could attenuate diabetes burden.

PAX4 loss of function increases diabetes risk by altering human pancreatic endocrine cell development

The coding variant (p.Arg192His) in the transcription factor PAX4 is associated with an altered risk for type 2 diabetes (T2D) in East Asian populations. In mice, Pax4 is essential for beta cell formation but its role on human beta cell development and/or function is unknown. Participants carrying the PAX4 p.His192 allele exhibited decreased pancreatic beta cell function compared to homozygotes for the p.192Arg allele in a cross-sectional study in which we carried out an intravenous glucose tolerance test and an oral glucose tolerance test. In a pedigree of a patient with young onset diabetes, several members carry a newly identified p.Tyr186X allele. In the human beta cell model, EndoC-βH1, PAX4 knockdown led to impaired insulin secretion, reduced total insulin content, and altered hormone gene expression. Deletion of PAX4 in human induced pluripotent stem cell (hiPSC)-derived islet-like cells resulted in derepression of alpha cell gene expression. In vitro differentiation of hiPSCs carrying PAX4 p.His192 and p.X186 risk alleles exhibited increased polyhormonal endocrine cell formation and reduced insulin content that can be reversed with gene correction. Together, we demonstrate the role of PAX4 in human endocrine cell development, beta cell function, and its contribution to T2D-risk.

Chronotype, Unhealthy Lifestyle, and Diabetes Risk in Middle-Aged U.S. Women : A Prospective Cohort Study.

Evening chronotype may promote adherence to an unhealthy lifestyle and increase type 2 diabetes risk. To evaluate the role of modifiable lifestyle behaviors in the association between chronotype and diabetes risk. Prospective cohort study. Nurses' Health Study II. 63 676 nurses aged 45 to 62 years with no history of cancer, cardiovascular disease, or diabetes in 2009 were prospectively followed until 2017. Self-reported chronotype using a validated question from the Morningness-Eveningness Questionnaire. The lifestyle behaviors that were measured were diet quality, physical activity, alcohol intake, body mass index (BMI), smoking, and sleep duration. Incident diabetes cases were self-reported and confirmed using a supplementary questionnaire. Participants reporting a "definite evening" chronotype were 54% (95% CI, 49% to 59%) more likely to have an unhealthy lifestyle than participants reporting a "definite morning" chronotype. A total of 1925 diabetes cases were documented over 469 120 person-years of follow-up. Compared with the "definite morning" chronotype, the adjusted hazard ratio (HR) for diabetes was 1.21 (CI, 1.09 to 1.35) for the "intermediate" chronotype and 1.72 (CI, 1.50 to 1.98) for the "definite evening" chronotype after adjustment for sociodemographic factors, shift work, and family history of diabetes. Further adjustment for BMI, physical activity, and diet quality attenuated the association comparing the "definite evening" and "definite morning" chronotypes to 1.31 (CI, 1.13 to 1.50), 1.54 (CI, 1.34 to 1.77), and 1.59 (CI, 1.38 to 1.83), respectively. Accounting for all measured lifestyle and sociodemographic factors resulted in a reduced but still positive association (HR comparing "definite evening" vs. "definite morning" chronotype, 1.19 [CI, 1.03 to 1.37]). Chronotype assessment using a single question, self-reported data, and homogeneity of the study population. Middle-aged nurses with an evening chronotype were more likely to report unhealthy lifestyle behaviors and had increased diabetes risk compared with those with a morning chronotype. Accounting for BMI, physical activity, diet, and other modifiable lifestyle factors attenuated much but not all of the increased diabetes risk. National Institutes of Health.

Relationship between daytime napping with the occurrence and development of diabetes: a systematic review and meta-analysis

ObjectiveTo determine the relationship of napping with incident diabetes risk and glycaemic control in people with diabetes.DesignSystematic review and meta-analysis.Data sourcesMEDLINE (PubMed), EMBASE, Web of Science and the Cochrane Library...

Diabetes risk among US adults with different socioeconomic status and behavioral lifestyles: evidence from the National Health and Nutrition Examination Survey.

Diabetes disproportionately affects minorities and those with low socioeconomic status (SES) in the United States, and differences in behavioral lifestyles are largely responsible for the unequal distribution of diabetes among different groups. With data of 9,969 participants collected in the 2007-2008 and 2009-2010 cycles of the US National Health and Nutrition Examination Survey (NHANES), this study examined several mediators and their mediating effects in the connection between SES and the risk of diabetes. The SES is assessed by the income-to-poverty ratio (IPR), education level, and employment status. For the mediation analysis, we used health-related behaviors as mediators (smoking, alcohol use, consumption of green vegetables and fruits, physical activity and sedentary time, health insurance, and healthcare). In this study, the structural equation model was utilized to evaluate the mediating effects of behavioral lifestyle as a mediator in the relationship between SES and diabetes. A total of 9,969 participants were included in this study. We found a negative nonlinear association between IPR and diabetes risk (Poverall < 0.001; Pnon-linear = 0.46), which was independent of the majority of known or suspected risk factors and confounding variables (gender, age, race). Participants with lower SES had higher risk of diabetes compared with those with higher SES. In mediating analysis, we found alcohol intake (OR = 0.996), physical activity (OR = 0.993), health insurance (OR = 0.998), and healthcare (OR = 1.002) mediated the IPR-diabetes association. But in the relationship between education status and diabetes, the mediation effect of alcohol intake (OR = 0.995), physical activity (OR = 0.991), and health care (OR = 1.008) were obvious. Likewise, alcohol intake (OR = 0.996), fruit intake (OR = 0.998), and health care (OR = 0.975) were important mediators in the association between employment status and diabetes. This study provides critical insights on the link between SES and diabetes. Our results highlight that poor health-related behaviors and limited access to healthcare are important pathways for increased diabetes risk related to those with low SES, particularly among Mexican Americans and males. They should be top priorities for agencies and healthcare providers to develop behavior-related interventions to reduce inequalities in diabetes risk.

214-LB: Plasma Proteomic Markers of Iron and Risk of Diabetes in a Biracial Cohort

Higher levels of iron have been linked to increased diabetes risk among European and European American (EA) populations; whether this is true for other populations where anemia is more common is unknown. Ferritin is an iron storage protein linked to diabetes risk; transferrin transports catalytic iron, reducing its oxidative damage. We tested whether proteomic measurements of ferritin and transferrin were associated with diabetes risk in a biracial cohort of current and former smokers. EA and African American (AA) participants from the COPDGene Cohort who were free of diabetes (n=4,671) at study baseline were followed for the incidence of diabetes. The Somascan was used on plasma measured at followup to determine relative amounts of proteins among participants. Logistic regression was used to calculate OR (95% CIs) of the cross-sectional relationship of natural log transformed ferritin and transferrin with diabetes risk. During an average of 5.6 years of follow-up, the incidence of diabetes was 7.9%. Ferritin was associated with increased diabetes risk (OR=1.29, 1.04-1.61), while higher transferrin linked with decreased risk (OR=0.19, 0.06-0.56) controlling for age, sex, BMI, hepcidin and smoking. When stratified by race, increased risk was associated with higher ferritin only among AA (OR=1.46, 1.07-2.01) and not for EA (OR=1.11, 0.81-1.52). Stratification by sex revealed that the relationship of ferritin was similar among AA men and women and EA women, with non-significant ORs ranging from 1.49-1.55; but null association among EA men (OR = 1.00, 0.65-1.52). Similarly, ORs for transferrin non-significantly ranged from 0.24-0.29 for AA men and women and EA women, but for EA men there was a significant 93% decreased risk for each natural long increase in transferrin (OR=0.07, 0.001-0.60). Our data suggest that higher body iron stores, as measured by ferritin, increases diabetes risk among both AA and EA. Ferritin may be a better marker for AA men and women and EA women while transferrin is a better marker for EU men. Disclosure R. B. Conway: None. K. Young: None. K. A. Pratte: None. G. L. Kinney: None. E. Austin: None. Y. Li: None. R. Bowler: None. D. A. Mcclain: None. J. Crappo: None. Funding National Heart, Lung, and Blood Institute (U01HL089897, U01HL089856); AstraZeneca; Bayer Pharmaceuticals; Boehringer Ingelheim; Genentech; GlaxoSmithKline; Novartis; Pfizer Inc.; Sunovion