Seasonality of Insulin Resistance, Glucose, and Insulin Among Middle-Aged and Elderly Population: The Rotterdam Study

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.

Seasonal variability of lesions distribution in acute ischemic stroke: A retrospective study

Seasonal variability could have an impact on the incidence and outcome of stroke. However, little is known about the correlation between seasonal variability and location of acute cerebral infarction. This study aimed to explore the relationship between onset season and the lesions distribution of acute ischemic stroke (AIS). We retrospectively analysis data from 1488 AIS patients admitted to the Second Hospital of Tianjin Medical University from 2018 to 2022. All subjects completed head magnetic resonance imaging examination (MRI) and were divided into four groups according to the onset seasons. The lesions distribution of AIS was evaluated for anterior/posterior/double circulation infarction (DCI), unilateral/bilateral infarctions, and single/multiple cerebral infarctions based on MRI. Logistic regression models were employed to assess the association of season with lesions distribution of AIS. Subgroup analysis was performed in different stroke subtypes. Of 1488 patients, 387 (26.0%) AIS occurred in spring, 425 (28.6%) in summer, 331 (22.2%) in autumn and 345 (23.2%) in winter. Multivariate logistic regression demonstrated that the winter group had 2.15 times (95% CI:1.44-3.21) risk of multiple infarctions, 2.69 times (95% CI:1.80-4.02) of bilateral infarctions and 1.54 times (95% CI:1.05-2.26) of DCI compared with summer group, respectively. Subgroup analysis showed an increased risk of multiple (p < 0.01) or bilateral infarctions (p < 0.01) in small-artery occlusion (SAO) subtype, and higher risk of bilateral infarctions (p < 0.01) or DCI (p < 0.05) in large artery atherosclerosis (LAA) subtype during winter. No significant associations of season with lesions distribution in cardioembolism subtype. Our study highlighted a prominent seasonal variability in the lesions distribution of AIS, particularly in LAA and SAO subtypes. The findings could help to formulating meteorological risk warning strategies for different subtypes.

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.

Seasonal light hours modulate peripheral clocks and energy metabolism in mice

Except for latitudes close to the equator, seasonal variation in light hours can change dramatically between summer and winter. Yet investigations into the interplay between energy metabolism and circadian rhythms typically use a 12 h light:12 h dark photoperiod corresponding to the light duration at the equator. We hypothesized that altering the seasonal photoperiod affects both the rhythmicity of peripheral tissue clocks and energy homeostasis. Mice were housed at photoperiods representing either light hours in summer, winter, or the equinox. Mice housed at a winter photoperiod exhibited an increase in the amplitude of rhythmic lipid metabolism and a modest reduction in fat mass and liver triglyceride content. Comparing melatonin-proficient and -deficient mice, the effect of seasonal light on energy metabolism was largely driven by differences in the rhythmicity of food intake and not melatonin. Together, these data indicate that seasonal light impacts energy metabolism by modulating the timing of eating.

The underlying mechanisms of cold exposure-induced ischemic stroke

Growing evidence suggests that cold exposure is to some extent a potential risk factor for ischemic stroke. At present, although the mechanism by which cold exposure induces ischemic stroke is not fully understood, some potential mechanisms have been mentioned. First, the seasonal and temperature variability of cerebrovascular risk factors (hypertension, hyperglycemia, hyperlipidemia, atrial fibrillation) may be involved. Moreover, the activation of sympathetic nervous system and renin-angiotensin system and their downstream signaling pathways (pro-inflammatory AngII, activated platelets, and dysfunctional immune cells) are also major contributors. Finally, the influenza epidemics induced by cold weather are also influencing factors that cannot be ignored. This article is the first to systematically and comprehensively describe the underlying mechanism of cold-induced ischemic stroke, aiming to provide more preventive measures and medication guidance for stroke-susceptible individuals in cold season, and also provide support for the formulation of public health policies.

Development and Validation of an Insulin Resistance Predicting Model Using a Machine-Learning Approach in a Population-Based Cohort in Korea

Background: Insulin resistance is a common etiology of metabolic syndrome, but receiver operating characteristic (ROC) curve analysis shows a weak association in Koreans. Using a machine learning (ML) approach, we aimed to generate the best model for predicting insulin resistance in Korean adults aged > 40 of the Ansan/Ansung cohort using a machine learning (ML) approach. Methods: The demographic, anthropometric, biochemical, genetic, nutrient, and lifestyle variables of 8842 participants were included. The polygenetic risk scores (PRS) generated by a genome-wide association study were added to represent the genetic impact of insulin resistance. They were divided randomly into the training (n = 7037) and test (n = 1769) sets. Potentially important features were selected in the highest area under the curve (AUC) of the ROC curve from 99 features using seven different ML algorithms. The AUC target was ≥0.85 for the best prediction of insulin resistance with the lowest number of features. Results: The cutoff of insulin resistance defined with HOMA-IR was 2.31 using logistic regression before conducting ML. XGBoost and logistic regression algorithms generated the highest AUC (0.86) of the prediction models using 99 features, while the random forest algorithm generated a model with 0.82 AUC. These models showed high accuracy and k-fold values (>0.85). The prediction model containing 15 features had the highest AUC of the ROC curve in XGBoost and random forest algorithms. PRS was one of 15 features. The final prediction models for insulin resistance were generated with the same nine features in the XGBoost (AUC = 0.86), random forest (AUC = 0.84), and artificial neural network (AUC = 0.86) algorithms. The model included the fasting serum glucose, ALT, total bilirubin, HDL concentrations, waist circumference, body fat, pulse, season to enroll in the study, and gender. Conclusion: The liver function, regular pulse checking, and seasonal variation in addition to metabolic syndrome components should be considered to predict insulin resistance in Koreans aged over 40 years.

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

CONTEXT

China has experienced a rapid increase in the prevalence of diabetes.

OBJECTIVE

We assessed the prevalence of diabetes among Chinese adults who attended preventive physical examinations and analyzed geographical and gender difference in seasonal variation of fasting blood glucose (FBG).

METHODS

The study used data from 1 390 088 participants attending preventive health examination at 430 health screening centers in 220 cities. Diagnosis of diabetes and prediabetes were based on FBG and glycated hemoglobin A1c and self-reported physician's diagnosis. We calculated age- and sex-standardized prevalence of diabetes according to the sixth Chinese population census data in 2010. Geographical distribution of diabetes and prediabetes were represented on a country map. FBG levels were analyzed to detect seasonal variation adjusted for age and gender by geographic location.

RESULTS

The standardized prevalence of diabetes was 8.70% (95% CI, 8.22%-9.19%), 10.7% in men and 6.61% in women. Among those with diabetes, 43.7% (95% CI, 40.9%-46.5%) were aware of their conditions and 38.5% (95% CI, 36.0%-41.1%) were treated. Only 49.3% (95% CI, 47.0%-51.6%) of treated patients achieved glycemic control. The mean level of FBG was higher in winter than summer and in the northern than the southern region.

CONCLUSION

The prevalence of diabetes was high while the percentages of awareness, treatment, and glycemic control were low among adults. Effective measures are needed to prevent and manage diabetes in China. Geographic and seasonal variation of diabetes should be considered for its prevention and control.

The relationship between ambient temperature and fasting plasma glucose, temperature-adjusted type 2 diabetes prevalence and control rate: a series of cross-sectional studies in Guangdong Province, China

Background

There existed evidence that type 2 diabetes mellitus (T2DM) prevalence and control rate have seasonal variation. Our study aimed to examine the ambient temperature and fasting plasma glucose (FPG) association and estimate temperature-adjusted T2DM prevalence and control rate.

Methods

Four cross-sectional health surveys with 26,350 respondents were conducted in Guangdong Province from 2007 to 2015. Multistage cluster sampling was used to recruit study participants. The data of demographic characteristics, lifestyle factors, diet and use of hypoglycemic medicine, height, weight, FPG and meteorological information were collected. And an inverse distance-weighted method was employed to estimate daily temperature exposures at the individual’ s residential district/county. Base on World Health Organization 2006 criteria, participants were divided into normal fasting glucose (NFG) participants (n = 23,877), known T2DM patients (n = 916) and newly detected T2DM patients (n = 1557). Generalized additive mixed model was employed to evaluate the nonlinear associations between temperature and FPG among different T2DM subgroups. The T2DM prevalence and control rate were estimated based on temperature-FPG association.

Results

The curves of temperature and FPG were downward parabola for total, NFG and known T2DM groups, while it was “U”-shaped for newly detected T2DM patients. When temperature decreased from 30 °C to 4 °C, the FPG significantly increased 0.24 (95%CI: 0.15, 0.33) mmol/L, 0.10 (95%CI: 0.06, 0.14) mmol/L and 1.34 (95%CI: 0.56, 2.12) mmol/L in total, NFG and known T2DM groups, respectively. Compared to 19 °C, newly detected T2DM patients’ FPGs were increased 0.73 (95%CI: 0.13, 1.30) mmol/L at 4 °C and 0.53 (0.00, 1.07) mmol/L at 30 °C. The model-estimated temperature-adjusted T2DM prevalence had a down and up trend, with 9.7% at 5 °C, 8.9% at 20 °C and 9.4% at 30 °C, respectively. At 5, 10, 15, 20, 25 and 30 °C, the model-estimated temperature-adjusted T2DM control rates were 33.2, 35.4, 38.2, 43.6, 49.1 and 55.2%.

Conclusion

Temperature was negatively associated with FPG for NFG and known T2DM subgroups, while their association was U-shape for newly detected T2DM patients. Hence, the temperature-adjusted T2DM prevalence show a dip/peak pattern and T2DM control rate display a rising trend when temperature increase. Our findings suggest temperature should be considered in T2DM clinic management and epidemiological survey.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12889-021-11563-5.

Relationship between environmental temperature and the diagnosis and treatment of gestational diabetes mellitus: An observational retrospective study

INTRODUCTION

Environmental temperature has been described to affect plasma glucose levels after oral glucose tolerance testing (OGTT).

AIMS

We evaluated the relationship between seasons and environmental temperature and gestational diabetes mellitus (GDM) diagnosis and treatment.

METHODS

We analyzed data from 2374 women retrospectively. GDM was diagnosed in 473 patients by a 100-g OGTT. OGTT results and needing of insulin therapy were evaluated in relation to seasons and environmental temperature (mean temperature and temperature change) the day of the OGTT and the preceding 14 and 28 days.

RESULTS

We found significant seasonal differences in the percentage of GDM: 24.4% in summer vs. 15.6% in autumn (p < 0.01). The odds ratio (OR) for being diagnosed with GDM was 1.78 in summer relative to autumn, after controlling for age. A higher mean temperature the day of the OGTT and the preceding 14 and 28 days increased the risk of being diagnosed with GDM the months in which temperature was rising (March-August) but not the months in which temperature was decreasing (September-February). We observed a negative correlation between temperature and fasting glucose and a positive correlation with post-load glucose. Neither the season nor the environmental temperature affected the risk of requiring insulin therapy.

CONCLUSIONS

There is a higher prevalence of GDM diagnosis at warmer seasons and at rising temperatures the 2-4 weeks prior to the OGTT. The impact of temperature is different between fasting and post-load glucose.

Objectives, design and main findings until 2020 from the Rotterdam Study

The Rotterdam Study is an ongoing prospective cohort study that started in 1990 in the city of Rotterdam, The Netherlands. The study aims to unravel etiology, preclinical course, natural history and potential targets for intervention for chronic diseases in mid-life and late-life. The study focuses on cardiovascular, endocrine, hepatic, neurological, ophthalmic, psychiatric, dermatological, otolaryngological, locomotor, and respiratory diseases. As of 2008, 14,926 subjects aged 45 years or over comprise the Rotterdam Study cohort. Since 2016, the cohort is being expanded by persons aged 40 years and over. The findings of the Rotterdam Study have been presented in over 1700 research articles and reports. This article provides an update on the rationale and design of the study. It also presents a summary of the major findings from the preceding 3 years and outlines developments for the coming period.

Vascular Dysfunction and Insulin Resistance in Aging

:

Insulin was discovered in 1922 by Banting and Best. Since that time, extensive research on the mechanisms of insulin activity and action has continued. Currently, it is known that the role of insulin is much greater than simply regulating carbohydrate metabolism. Insulin in physiological concentration is also necessary to maintain normal vascular function.

:

Insulin resistance is defined as a pathological condition characterized by reduced sensitivity of skeletal muscles, liver, and adipose tissue, to insulin and its downstream metabolic effects under normal serum glucose concentrations. There are also selective forms of insulin resistance with unique features, including vascular insulin resistance. Insulin resistance, both classical and vascular, contributes to vascular impairment resulting in increased risk of cardiovascular disease. Furthermore, in the elderly population, additional factors including redistribution of fat concentrations, low-grade inflammation, and decreased self-repair capacity [or cell senescence] amplify the vascular abnormalities related to insulin resistance.

Associations of Outdoor Temperature, Bright Sunlight, and Cardiometabolic Traits in Two European Population-Based Cohorts

CONTEXT

Seasonal variation in cold and light exposure may influence metabolic health.

OBJECTIVE

We assessed the associations of bright sunlight and outdoor temperature with measures of glucose and lipid metabolism in two populations of middle-aged European subjects.

DESIGN

Cross-sectional study.

SETTING

Two population-based European cohorts.

PARTICIPANTS

Middle-aged nondiabetic subjects from the Oxford Biobank (OBB; N = 4327; mean age, 41.4 years) and the Netherlands Epidemiology of Obesity (NEO) study (N = 5899; mean age, 55.6 years).

INTERVENTIONS

Data on outdoor bright sunlight and temperature collected from local weather stations.

MAIN OUTCOME MEASURES

Insulin resistance and fasting lipid levels. Multivariable regression analyses were adjusted for age, sex, percentage body fat, season, and either outdoor temperature or bright sunlight.

RESULTS

In the OBB cohort, increased bright sunlight exposure was associated with lower fasting insulin [-1.27% (95% CI, -2.09 to -0.47%) per extra hour of bright sunlight], lower homeostatic model assessment for insulin resistance (-1.36%; 95% CI, -2.23 to -0.50), lower homeostatic model assessment for β-cell function (-0.80%; 95% CI, -1.31 to -0.30), and lower triglyceride (-1.28%; 95% CI, -2.07 to -0.50) levels. In the NEO cohort generally unidirectional but weaker associations were observed. No associations between outdoor temperature and measures of glucose or lipid metabolism were detected following adjustment for bright sunlight.

CONCLUSIONS

Bright sunlight, but not outdoor temperature, might be associated with increased insulin sensitivity and lower triglyceride levels.

Seasonality of the Cardiac Biomarker Troponin in the Eastern Croatian Population

Background: The seasonality of acute myocardial infarction and progressive heart failure has been well established so far. Cardiac troponins (cTns) are organ-specific, not disease-specific, biomarkers. The seasonality of cTns has not been reported before. Methods: Data were collected from the emergency admission unit of a community hospital in eastern Croatia for each month of the year 2014 covering the number of patients whose doctors requested high-sensitivity cTn I (hs-cTn I) testing, the number of positive test results and hospital admissions. Results: The proportion of patients with positive test results was 15.75% (350 patients out of 2221 patients referred to testing), with the males being outnumbered by the females (F: 57.15%, M: 42.85%) (p = 0.069). The month with the highest number of patients with positive test results was December, whereas the month with the lowest number of those patients was January (p < 0.001). The highest numbers of patients referred to testing (30.9%) and of those with positive test results (50.8%) were found in the oldest age group (76+). Conclusion: Tracking the results of cTns testing during patient admissions to emergency departments would be a more effective approach from a public health perspective than tracking the number of patients diagnosed with a particular cardiovascular (CV) disease and could be used as a research approach to guide a search for precipitating factors for CV disease specific to a local community.