diabetes risk
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Heart & Lung ◽  
2022 ◽  
Vol 52 ◽  
pp. 42-47
Yoshimi Fukuoka ◽  
Yoo Jung Oh

2022 ◽  
Vol 21 (1) ◽  
Annie Doubleday ◽  
Catherine J. Knott ◽  
Marnie F. Hazlehurst ◽  
Alain G. Bertoni ◽  
Joel D. Kaufman ◽  

Abstract Background Neighborhood greenspaces provide opportunities for increased physical activity and social interaction, and thus may reduce the risk of Type 2 diabetes. However, there is little robust research on greenspace and diabetes. In this study, we examine the longitudinal association between neighborhood greenspace and incident diabetes in the Multi-Ethnic Study of Atherosclerosis. Methods A prospective cohort study (N = 6814; 2000-2018) was conducted to examine the association between greenspace, measured as annual and high vegetation season median greenness determined by satellite (Normalized Difference Vegetation Index) within 1000 m of participant homes, and incident diabetes assessed at clinician visits, defined as a fasting glucose level of at least 126 mg/dL, use of insulin or use of hypoglycemic medication, controlling for covariates in stages. Five thousand five hundred seventy-four participants free of prevalent diabetes at baseline were included in our analysis. Results Over the study period, 886 (15.9%) participants developed diabetes. Adjusting for individual characteristics, individual and neighborhood-scale SES, additional neighborhood factors, and diabetes risk factors, we found a 21% decrease in the risk of developing diabetes per IQR increase in greenspace (HR: 0.79; 95% CI: 0.63, 0.99). Conclusions Overall, neighborhood greenspace provides a protective influence in the development of diabetes, suggesting that neighborhood-level urban planning that supports access to greenspace--along with healthy behaviors--may aid in diabetes prevention. Additional research is needed to better understand how an area’s greenness influences diabetes risk, how to better characterize greenspace exposure and usage, and future studies should focus on robust adjustment for neighborhood-level confounders.

B. Iyen ◽  
Y. Vinogradova ◽  
R. K. Akyea ◽  
S. Weng ◽  
N. Qureshi ◽  

Abstract Purpose Ethnic variation in risk of type 2 diabetes is well established, but its impact on mortality is less well understood. This study investigated the risk of all-cause and cardiovascular mortality associated with newly diagnosed type 2 diabetes in White, Asian and Black adults who were overweight or obese. Methods This population-based cohort study used primary care records from the UK Clinical Practice Research Datalink, linked with secondary care and death registry records. A total of 193,528 obese or overweight adults (BMI of 25 or greater), with ethnicity records and no pre-existing type 2 diabetes were identified between 01 January 1995 and 20 April 2018. Multivariable Cox proportional hazards regression estimated hazards ratios (HR) for incident type 2 diabetes in different ethnic groups. Adjusted hazards ratios for all-cause and cardiovascular mortality were determined in individuals with newly diagnosed type 2 diabetes. Results During follow-up (median 9.8 years), the overall incidence rate of type 2 diabetes (per 1,000 person-years) was 20.10 (95% CI 19.90–20.30). Compared to Whites, type 2 diabetes risk was 2.2-fold higher in Asians (HR 2.19 (2.07–2.32)) and 30% higher in Blacks (HR 1.34 (1.23–1.46)). In individuals with newly diagnosed type 2 diabetes, the rates (per 1,000 person-years) of all-cause mortality and cardiovascular mortality were 24.34 (23.73–24.92) and 4.78 (4.51–5.06), respectively. Adjusted hazards ratios for mortality were significantly lower in Asians (HR 0.70 (0.55–0.90)) and Blacks (HR 0.71 (0.51–0.98)) compared to Whites, and these differences in mortality risk were not explained by differences in severity of hyperglycaemia. Conclusions/Interpretation Type 2 diabetes risk in overweight and obese adults is greater in Asian and Black compared to White ethnic populations, but mortality is significantly higher in the latter. Greater attention to optimising screening, disease and risk management appropriate to all communities with type 2 diabetes is needed.

2022 ◽  
Dominik Lutter ◽  
Stephan Sachs ◽  
Marc Walter ◽  
Leigh Perreault ◽  
Darcy Kahn ◽  

Although insulin resistance often leads to Type 2 Diabetes Mellitus (T2D), its early stages remain often unrecognized thus reducing the probability of successful prevention and intervention. Moreover, treatment efficacy is affected by the genetics of the individual patient. To identify potential candidate genes for the prediction of diabetes risk and intervention response we linked genetic expression profiles of human skeletal muscle and intermuscular adipose tissue (IMAT) to fasting glucose (FG) and glucose infusion rate (GIR). We found that genes with a strong association to these measures clustered into three distinct expression patterns. Their predictive values for insulin resistance varied strongly between muscle and IMAT. Moreover, we discovered that individual genetic expression based classifications may differ from those classifications based predominantly on clinical parameters indicating a potential incomplete patient stratification. Out of the 15 top hit candidate genes, we identified ST3GAL2, AASS, ARF1 and the transcription factor SIN3A as novel candidates for a refined diabetes risk and intervention response prediction. Our results confirm that disease progression and a successful intervention depend on individual genetics. We anticipate that our findings may lead to a better understanding and prediction of the individual diabetes risk and may help to develop individualized intervention strategies.

2022 ◽  
Vol 22 (1) ◽  
Jessica Melin ◽  
Kristian F. Lynch ◽  
Markus Lundgren ◽  
Carin Andrén Aronsson ◽  
Helena Elding Larsson ◽  

Abstract Background Participants’ study satisfaction is important for both compliance with study protocols and retention, but research on parent study satisfaction is rare. This study sought to identify factors associated with parent study satisfaction in The Environmental Determinants of Diabetes in the Young (TEDDY) study, a longitudinal, multinational (US, Finland, Germany, Sweden) study of children at risk for type 1 diabetes. The role of staff consistency to parent study satisfaction was a particular focus. Methods Parent study satisfaction was measured by questionnaire at child-age 15 months (5579 mothers, 4942 fathers) and child-age four years (4010 mothers, 3411 fathers). Multiple linear regression analyses were used to identify sociodemographic factors, parental characteristics, and study variables associated with parent study satisfaction at both time points. Results Parent study satisfaction was highest in Sweden and the US, compared to Finland. Parents who had an accurate perception of their child’s type 1 diabetes risk and those who believed they can do something to prevent type 1 diabetes were more satisfied. More educated parents and those with higher depression scores had lower study satisfaction scores. After adjusting for these factors, greater study staff change frequency was associated with lower study satisfaction in European parents (mothers at child-age 15 months: − 0.30,95% Cl − 0.36, − 0.24, p < 0.001; mothers at child-age four years: -0.41, 95% Cl − 0.53, − 0.29, p < 0.001; fathers at child-age 15 months: -0.28, 95% Cl − 0.34, − 0.21, p < 0.001; fathers at child-age four years: -0.35, 95% Cl − 0.48, − 0.21, p < 0.001). Staff consistency was not associated with parent study satisfaction in the US. However, the number of staff changes was markedly higher in the US compared to Europe. Conclusions Sociodemographic factors, parental characteristics, and study-related variables were all related to parent study satisfaction. Those that are potentially modifiable are of particular interest as possible targets of future efforts to improve parent study satisfaction. Three such factors were identified: parent accuracy about the child’s type 1 diabetes risk, parent beliefs that something can be done to reduce the child’s risk, and study staff consistency. However, staff consistency was important only for European parents. Trial registration NCT00279318.

2022 ◽  
Vol 12 (2) ◽  
pp. 632
Yaqi Tan ◽  
He Chen ◽  
Jianjun Zhang ◽  
Ruichun Tang ◽  
Peishun Liu

Early risk prediction of diabetes could help doctors and patients to pay attention to the disease and intervene as soon as possible, which can effectively reduce the risk of complications. In this paper, a GA-stacking ensemble learning model is proposed to improve the accuracy of diabetes risk prediction. Firstly, genetic algorithms (GA) based on Decision Tree (DT) is used to select individuals with high adaptability, that is, a subset of attributes suitable for diabetes risk prediction. Secondly, the optimized convolutional neural network (CNN) and support vector machine (SVM) are used as the primary learners of stacking to learn attribute subsets, respectively. Then, the output of CNN and SVM is used as the input of the mate learner, the fully connected layer, for classification. Qingdao desensitization physical examination data from 1 January 2017 to 31 December 2019 is used, which includes body temperature, BMI, waist circumference, and other indicators that may be related to early diabetes. We compared the performance of GA-stacking with K-nearest neighbor (KNN), SVM, logistic regression (LR), Naive Bayes (NB), and CNN before and after adding GA through the average prediction time, accuracy, precision, sensitivity, specificity, and F1-score. Results show that prediction efficiency can be improved by adding GA. GA-stacking has higher prediction accuracy. Moreover, the strong generalization ability and high prediction efficiency of GA-stacking have also been verified on the early-stage diabetes risk prediction dataset published by UCI.

2022 ◽  
pp. 1-22
Kirstie Canene-Adams ◽  
Ieva Laurie ◽  
Kavita Karnik ◽  
Brian Flynn ◽  
William Goodwin ◽  

Abstract For improving human health, reformulation can be a tool as it allows individuals to consume products of choice while reducing intake of less desirable nutrients, such as sugars and fats, and potentially increasing intake of beneficial nutrients such as fibre. The potential effects of reformulating foods with increased fibre on diet and on health needs to be better understood. The objective of this statistical modelling study was to understand how fibre enrichment can affect the diet and health of consumers. The UK National Diet and Nutrition Survey (NDNS) datasets from 2014 - 2015 and 2015 - 2016 were utilised to evaluate intakes of fibre and Kilocalories with a dietary intake model. Foods and beverages eligible for fibre enrichment were identified (n = 915) based on EU legislation for fibre content claims. Those people who meet Dietary Reference Values (DRVs) and fibre enrichment health outcomes such as weight, cardiovascular disease and type 2 diabetes risk reductions were quantified pre and post fibre reformulation via Reynolds et al, D’Agostino et al, and QDiabetes algorithms, respectively. The fibre enrichment intervention showed a mean fibre intake in the UK of 19.9 g/day, signifying a 2.2 g/day increase from baseline. Modelling suggested that 5.9% of subjects could achieve a weight reduction, 72.2% a reduction in cardiovascular risk, and 71.7% a reduced risk of type 2 diabetes risk with fibre fortification (all p ≤ 0.05). This study gave a good overview of the potential public health benefits of reformulating food products using a straightforward enrichment scenario.

2022 ◽  
Vol 226 (1) ◽  
pp. S46-S47
Gina Milone ◽  
Judith H. Chung ◽  
David M. Haas ◽  
Robert M. Silver ◽  
William A. Grobman ◽  

Diabetologia ◽  
2021 ◽  
Milana A. Bochkur Dratver ◽  
Juliana Arenas ◽  
Tanayott Thaweethai ◽  
Chu Yu ◽  
Kaitlyn James ◽  

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