Geographic, Gender, and Seasonal Variation of Diabetes: A Nationwide Study With 1.4 Million Participants

Seasonal fluctuations of CGM metrics in individuals with type 1 diabetes using an intermittently scanned CGM device or sensor augmented pump

OBJECTIVE

To elucidate the fluctuations in glucose levels measured using CGM-metrics during the four distinct seasons of the year in individuals with type 1 diabetes mellitus (T1DM) using an intermittently scanned CGM (isCGM) device or sensor augmented pump (SAP).

RESEARCH DESIGN AND METHODS

This retrospective, single-center study enrolled 93 individuals with T1DM who were equipped with an isCGM device or SAP at Kobe University Hospital. The subjects had a median age of 47.0 years [interquartile range, 37.0-62.0 years], 25 individuals (26.9%) were male, median body mass index was 22.0 kg/m2 [20.8-23.8 kg/m2], and median hemoglobin A1c level was 7.4% [6.9-8.0%]. CGM data were reviewed from January to December 2019, and the mean sensor glucose (SG) value, time above range (TAR), time in range (TIR), time below range (TBR), and standard deviation (SD) of SG were calculated for each season (spring, March-May; summer, June-August; autumn, September-November; winter, December-February).

RESULTS

Seasonal fluctuations were detected for mean SG, TAR, TIR, and SD, with TIR being lower and mean SG, TAR, and SD being higher in cold seasons (spring or winter) than in warm seasons (summer or autumn).

CONCLUSION

Seasonal fluctuations in CGM metrics should be taken into account in future studies performed to evaluate the favorable impact of CGM on glycemic management in individuals with T1DM.

Geographic disparities and temporal changes of diabetes-related mortality risks in Florida: a retrospective study

Background

Over the last few decades, diabetes-related mortality risks (DRMR) have increased in Florida. Although there is evidence of geographic disparities in pre-diabetes and diabetes prevalence, little is known about disparities of DRMR in Florida. Understanding these disparities is important for guiding control programs and allocating health resources to communities most at need. Therefore, the objective of this study was to investigate geographic disparities and temporal changes of DRMR in Florida.

Methods

Retrospective mortality data for deaths that occurred from 2010 to 2019 were obtained from the Florida Department of Health. Tenth International Classification of Disease codes E10-E14 were used to identify diabetes-related deaths. County-level mortality risks were computed and presented as number of deaths per 100,000 persons. Spatial Empirical Bayesian (SEB) smoothing was performed to adjust for spatial autocorrelation and the small number problem. High-risk spatial clusters of DRMR were identified using Tango's flexible spatial scan statistics. Geographic distribution and high-risk mortality clusters were displayed using ArcGIS, whereas seasonal patterns were visually represented in Excel.

Results

A total of 54,684 deaths were reported during the study period. There was an increasing temporal trend as well as seasonal patterns in diabetes mortality risks with high risks occurring during the winter. The highest mortality risk (8.1 per 100,000 persons) was recorded during the winter of 2018, while the lowest (6.1 per 100,000 persons) was in the fall of 2010. County-level SEB smoothed mortality risks varied by geographic location, ranging from 12.6 to 81.1 deaths per 100,000 persons. Counties in the northern and central parts of the state tended to have high mortality risks, whereas southern counties consistently showed low mortality risks. Similar to the geographic distribution of DRMR, significant high-risk spatial clusters were also identified in the central and northern parts of Florida.

Conclusion

Geographic disparities of DRMR exist in Florida, with high-risk spatial clusters being observed in rural central and northern areas of the state. There is also evidence of both increasing temporal trends and Winter peaks of DRMR. These findings are helpful for guiding allocation of resources to control the disease, reduce disparities, and improve population health.

Association Between Arsenic Toxicity, AS3MT Gene Polymorphism and Onset of Type 2 Diabetes

Arsenic (As) exposure in drinking water has become a serious public health issue. AS3MT gene is involved in the metabolism of arsenic, so a single nucleotide polymorphism in this gene may lead to the development of type 2 diabetes in arsenic-exposed areas. This study aimed to evaluate the association of the AS3MT gene with the development of type 2 diabetes in highly arsenic-exposed areas of Punjab, Pakistan. Total 200 samples equal in number from high arsenic exposed-areas of Lahore (Nishtar) and Kasur (Mustafa Abad) were collected. rs11191439 was utilized as an influential variable to evaluate the association between arsenic metabolism and diabetes status to find a single nucleotide polymorphism in the AS3MT gene. We observed the arsenic level in drinking water of the arsenic-exposed selected areas 115.54 ± 1.23 µg/L and 96.88 ± 0.48 µg/L, respectively. The As level in the urine of diabetics (98.54 ± 2.63 µg/L and 56.38 ± 12.66 µg/L) was higher as compared to non-diabetics (77.58 ± 1.8 µg/L and 46.9 ± 8.95 µg/L) of both affected areas, respectively. Correspondingly, the As level in the blood of diabetics (6.48 ± 0.08 µg/L and 5.49 ± 1.43 µg/L) and non-diabetics (6.22 ± 0.12 µg/L and 5.26 ± 0.24 µg/L) in the affected areas. Genotyping showed significant differences in the frequencies of alleles among cases and controls. Nevertheless, notable disparities in genotype distribution were observed in SNPs rs11191439 (T/C) (P < 0.05) and when comparing T2D patients and non-diabetic control subjects. The AS3MT gene and clinical parameters show a significant association with the affected people with diabetes living in arsenic-exposed areas.

Seasonal variations of the prevalence of metabolic syndrome and its markers using big-data of health check-ups

BACKGROUND

It is crucial to understand the seasonal variation of Metabolic Syndrome (MetS) for the detection and management of MetS. Previous studies have demonstrated the seasonal variations in MetS prevalence and its markers, but their methods are not robust. To clarify the concrete seasonal variations in the MetS prevalence and its markers, we utilized a powerful method called Seasonal Trend Decomposition Procedure based on LOESS (STL) and a big dataset of health checkups.

METHODS

A total of 1,819,214 records of health checkups (759,839 records for men and 1,059,375 records for women) between April 2012 and December 2017 were included in this study. We examined the seasonal variations in the MetS prevalence and its markers using 5 years and 9 months health checkup data and STL analysis. MetS markers consisted of waist circumference (WC), systolic blood pressure (SBP), diastolic blood pressure (DBP), triglycerides (TG), high-density lipoprotein cholesterol (HDL-C), fasting plasma glucose (FPG).

RESULTS

We found that the MetS prevalence was high in winter and somewhat high in August. Among men, MetS prevalence was 2.64 ± 0.42 (mean ± SD) % higher in the highest month (January) than in the lowest month (June). Among women, MetS prevalence was 0.53 ± 0.24% higher in the highest month (January) than in the lowest month (June). Additionally, SBP, DBP, and HDL-C exhibited simple variations, being higher in winter and lower in summer, while WC, TG, and FPG displayed more complex variations.

CONCLUSIONS

This finding, complex seasonal variations of MetS prevalence, WC, TG, and FPG, could not be derived from previous studies using just the mean values in spring, summer, autumn and winter or the cosinor analysis. More attention should be paid to factors affecting seasonal variations of central obesity, dyslipidemia and insulin resistance.

The digitized chronic disease management model: scalable strategies for implementing standardized healthcare and big data analytics in Shanghai

Background

Chronic disease management (CDM) falls under production relations, and digital technology belongs to the realm of productivity. Production relations must adapt to the development of productivity. Simultaneously, the prevalence and burden of chronic diseases are becoming increasingly severe, leveraging digital technology to innovate chronic disease management model is essential.

Methods

The model was built to cover experts in a number of fields, including administrative officials, public health experts, information technology staff, clinical experts, general practitioners, nurses, metrologists. Integration of multiple big data platforms such as General Practitioner Contract Platform, Integrated Community Multimorbidity Management System and Municipal and District-Level Health Information Comprehensive Platform. This study fully analyzes the organizational structure, participants, service objects, facilities and equipment, digital technology, operation process, etc., required for new model in the era of big data.

Results

Based on information technology, we build Integrated Community Multimorbidity Care Model (ICMCM). This model is based on big data, is driven by "technology + mechanism," and uses digital technology as a tool to achieve the integration of services, technology integration, and data integration, thereby providing patients with comprehensive people-centered services. In order to promote the implementation of the ICMCM, Shanghai has established an integrated chronic disease management information system, clarified the role of each module and institution, and achieved horizontal and vertical integration of data and services. Moreover, we adopt standardized service processes and accurate blood pressure and blood glucose measurement equipment to provide services for patients and upload data in real time. On the basis of Integrated Community Multimorbidity Care Model, a platform and index system have been established, and the platform's multidimensional cross-evaluation and indicators are used for management and visual display.

Conclusions

The Integrated Community Multimorbidity Care Model guides chronic disease management in other countries and regions. We have utilized models to achieve a combination of services and management that provide a grip on chronic disease management.

Strategies to Improve Long-Term Outcomes for Patients with Chronic Kidney Disease in China

Background

Chronic kidney disease (CKD) is an incurable disease requiring lifelong management. China has a high prevalence of CKD, which disproportionately affects older adults and those with chronic risk factors for CKD development. The rising prevalence of CKD in China places a substantial burden on the general population and the healthcare system.

Summary

In China, there are currently many unmet needs for patients with CKD and high-risk individuals, resulting from a lack of education and support to reduce risk factors, delayed diagnoses, limited knowledge of CKD among primary-care physicians, and poor access to treatments among some patient populations. An integrated, nationwide approach is required to improve the current situation of CKD management in China. There are currently several national healthcare frameworks in place that focus on new major health policies to prevent disease and encourage people to adopt healthier lifestyles, and while they do not directly target CKD, they may have a positive indirect impact. We explore the unmet needs for patients with CKD in China and discuss the potential strategies that may be required to overcome them. Such strategies include improving physician and patient education, establishing a targeted screening programme, supporting patients to improve self-management behaviours, accelerating the creation of medical consortia and medical satellite centres, and migrating from hospital- to community-based management. In addition to policy-driven strategies, development of novel therapies will be key to providing new solutions for the long-term management of CKD.

Key Messages

An integrated, nationwide approach is required, incorporating policy-driven changes to the clinical management of CKD, as well as the development of novel CKD treatments.

Association between short-term exposure to extreme humidity and painful diabetic neuropathy: a case-crossover analysis

Painful diabetic neuropathy (PDN) is a common complication of diabetes mellitus, which reduces the quality of life. However, the association between PDN and environmental factors, especially ambient humidity, remains unclear. Therefore, this study investigated the impact of extreme humidity events on PDN. Data on PDN-related hospital admissions to two tertiary hospitals in Hefei, China (2014-2019) were obtained. A distributed lag non-linear model with a case-crossover design was used to quantitatively estimate the effects of ambient humidity on PDN, and the results were stratified by sex and age. The 1st, 10th, 90th, and 99th percentiles of relative humidity (RHU) were defined as extreme humidity, and the average relative humidity (74.94%) was set as the reference value. Non-linear exposure-response curves between the RHU and PDN cases were obtained. Extreme humidity (92%) had a significant effect on PDN with a relative risk (RR) of 1.13 (95% confidence interval (CI): 1.01-1.26) on a particular day, which increased with the RHU (RR: 1.21, 95% CI: 1.02-1.45 at 98% extreme humidity). Stratification analysis showed that women (RR: 1.38, 95% CI: 1.07-1.77) and patients aged < 65 years (RR: 1.26, 95% CI: 1.01-1.57) were highly susceptible to this effect on the same day. The results suggest that extreme humidity is a crucial trigger for PDN onset in diabetes patients. Furthermore, the effects vary with sex and age. This study provides detailed evidence of the adverse effects of extreme weather on diabetes patients.

Major osteoporosis fracture prediction in type 2 diabetes: a derivation and comparison study

The widely recommended fracture prediction tool FRAX was developed based on and for the general population. Although several adjusted FRAX methods were suggested for type 2 diabetes (T2DM), they still need to be evaluated in T2DM cohort.

INTRODUCTION

This study was undertaken to develop a prediction model for Chinese diabetes fracture risk (CDFR) and compare its performance with those of FRAX.

METHODS

In this retrospective cohort study, 1730 patients with T2DM were enrolled from 2009.08 to 2013.07. Major osteoporotic fractures (MOFs) during follow-up were collected from Electronic Health Records (EHRs) and telephone interviews. Multivariate Cox regression with backward stepwise selection was used to fit the model. The performances of the CDFR model, FRAX, and adjusted FRAX were compared in the aspects of discrimination and calibration.

RESULTS

6.3% of participants experienced MOF during a median follow-up of 10 years. The final model (CDFR) included 8 predictors: age, gender, previous fracture, insulin use, diabetic peripheral neuropathy (DPN), total cholesterol, triglycerides, and apolipoprotein A. This model had a C statistic of 0.803 (95%CI 0.761-0.844) and calibration χ² of 4.63 (p = 0.86). The unadjusted FRAX underestimated the MOF risk (calibration χ² 134.5, p < 0.001; observed/predicted ratio 2.62, 95%CI 2.17-3.08), and there was still significant underestimation after diabetes adjustments. Comparing FRAX, the CDFR had a higher AUC, lower calibration χ², and better reclassification of MOF.

CONCLUSION

The CDFR model has good performance in 10-year MOF risk prediction in T2DM, especially in patients with insulin use or DPN. Future work is needed to validate our model in external cohort(s).