1
|
Deb Nath N, Odoi A. Geographic disparities and temporal changes of diabetes-related mortality risks in Florida: a retrospective study. PeerJ 2024; 12:e17408. [PMID: 38948203 PMCID: PMC11214742 DOI: 10.7717/peerj.17408] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2023] [Accepted: 04/25/2024] [Indexed: 07/02/2024] Open
Abstract
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.
Collapse
Affiliation(s)
- Nirmalendu Deb Nath
- Biomedical and Diagnostic Sciences, University of Tennessee, Knoxville, TN, United States
| | - Agricola Odoi
- Biomedical and Diagnostic Sciences, University of Tennessee, Knoxville, TN, United States
| |
Collapse
|
2
|
Sun X, Xia X, Xue J, Gu Y, Chen Z, Liu P, Wang F, Zhou X, Liu J, Wang L, Li X. Seasonal variability of lesions distribution in acute ischemic stroke: A retrospective study. Sci Rep 2024; 14:11831. [PMID: 38783036 PMCID: PMC11116500 DOI: 10.1038/s41598-024-62631-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2024] [Accepted: 05/20/2024] [Indexed: 05/25/2024] Open
Abstract
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.
Collapse
Affiliation(s)
- Xiao Sun
- Department of Neurology, The Second Hospital of Tianjin Medical University, Tianjin, China
| | - Xiaoshuang Xia
- Department of Neurology, The Second Hospital of Tianjin Medical University, Tianjin, China
| | - Juanjuan Xue
- Department of Neurology, The Second Hospital of Tianjin Medical University, Tianjin, China
| | - Yumeng Gu
- Department of Neurology, The Second Hospital of Tianjin Medical University, Tianjin, China
| | - Zhuangzhuang Chen
- Department of Neurology, The Second Hospital of Tianjin Medical University, Tianjin, China
| | - Peilin Liu
- Department of Neurology, The Second Hospital of Tianjin Medical University, Tianjin, China
| | - Fuyin Wang
- Department of Neurology, The Second Hospital of Tianjin Medical University, Tianjin, China
| | - Xiao Zhou
- Department of Neurology, The Second Hospital of Tianjin Medical University, Tianjin, China
| | - Jiaming Liu
- Department of Neurology, The Second Hospital of Tianjin Medical University, Tianjin, China
| | - Lin Wang
- Department of Geriatrics, The Second Hospital of Tianjin Medical University, Tianjin, China
| | - Xin Li
- Department of Neurology, The Second Hospital of Tianjin Medical University, Tianjin, China.
- Tianjin Center for Health and Meteorology Multidisciplinary Innovation, Tianjin, China.
| |
Collapse
|
3
|
Seto H, Toki H, Kitora S, Oyama A, Yamamoto R. Seasonal variations of the prevalence of metabolic syndrome and its markers using big-data of health check-ups. Environ Health Prev Med 2024; 29:2. [PMID: 38246652 PMCID: PMC10808004 DOI: 10.1265/ehpm.23-00216] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2023] [Accepted: 12/07/2023] [Indexed: 01/23/2024] Open
Abstract
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.
Collapse
Affiliation(s)
- Hiroe Seto
- Graduate School of Human Sciences, Osaka University, Osaka 565-0871, Japan
- Health Care Division, Health and Counseling Center, Osaka University, Osaka 560-0043, Japan
| | - Hiroshi Toki
- Health Care Division, Health and Counseling Center, Osaka University, Osaka 560-0043, Japan
- Research Center for Nuclear Physics, Osaka University, Osaka 567-0047, Japan
| | - Shuji Kitora
- Health Care Division, Health and Counseling Center, Osaka University, Osaka 560-0043, Japan
| | - Asuka Oyama
- Health Care Division, Health and Counseling Center, Osaka University, Osaka 560-0043, Japan
| | - Ryohei Yamamoto
- Health Care Division, Health and Counseling Center, Osaka University, Osaka 560-0043, Japan
- Laboratory of Behavioral Health Promotion, Department of Health Promotion, Graduate School of Medicine, Osaka University, Osaka 565-0043, Japan
| |
Collapse
|
4
|
Small L, Lundell LS, Iversen J, Ehrlich AM, Dall M, Basse AL, Dalbram E, Hansen AN, Treebak JT, Barrès R, Zierath JR. Seasonal light hours modulate peripheral clocks and energy metabolism in mice. Cell Metab 2023; 35:1722-1735.e5. [PMID: 37689069 DOI: 10.1016/j.cmet.2023.08.005] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/23/2023] [Revised: 06/16/2023] [Accepted: 08/10/2023] [Indexed: 09/11/2023]
Abstract
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.
Collapse
Affiliation(s)
- Lewin Small
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Leonidas S Lundell
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Jo Iversen
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Amy M Ehrlich
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Morten Dall
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Astrid L Basse
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Emilie Dalbram
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Ann N Hansen
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Jonas T Treebak
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Romain Barrès
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark; Institut de Pharmacologie Moléculaire et Cellulaire, Université Côte d'Azur and CNRS, Nice, France.
| | - Juleen R Zierath
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark; Department of Physiology and Pharmacology and Department of Molecular Medicine and Surgery, Karolinska Institutet, Stockholm, Sweden.
| |
Collapse
|
5
|
Chen Z, Liu P, Xia X, Wang L, Li X. The underlying mechanisms of cold exposure-induced ischemic stroke. THE SCIENCE OF THE TOTAL ENVIRONMENT 2022; 834:155514. [PMID: 35472344 DOI: 10.1016/j.scitotenv.2022.155514] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/08/2022] [Revised: 04/13/2022] [Accepted: 04/21/2022] [Indexed: 06/14/2023]
Abstract
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.
Collapse
Affiliation(s)
- Zhuangzhuang Chen
- Department of Neurology, The Second Hospital of Tianjin Medical University, Tianjin, China
| | - Peilin Liu
- Department of Neurology, The Second Hospital of Tianjin Medical University, Tianjin, China
| | - Xiaoshuang Xia
- Department of Neurology, The Second Hospital of Tianjin Medical University, Tianjin, China
| | - Lin Wang
- Department of Geriatrics, The Second Hospital of Tianjin Medical University, Tianjin, China
| | - Xin Li
- Department of Neurology, The Second Hospital of Tianjin Medical University, Tianjin, China.
| |
Collapse
|
6
|
Development and Validation of an Insulin Resistance Predicting Model Using a Machine-Learning Approach in a Population-Based Cohort in Korea. Diagnostics (Basel) 2022; 12:diagnostics12010212. [PMID: 35054379 PMCID: PMC8774355 DOI: 10.3390/diagnostics12010212] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2021] [Revised: 01/12/2022] [Accepted: 01/13/2022] [Indexed: 12/19/2022] Open
Abstract
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.
Collapse
|
7
|
Zhang Y, Tong M, Wang B, Shi Z, Wang P, Li L, Ning Y, Lu T. Geographic, Gender, and Seasonal Variation of Diabetes: A Nationwide Study With 1.4 Million Participants. J Clin Endocrinol Metab 2021; 106:e4981-e4992. [PMID: 34314489 DOI: 10.1210/clinem/dgab543] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/22/2021] [Indexed: 11/19/2022]
Abstract
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.
Collapse
Affiliation(s)
- Yuanfeng Zhang
- China Traditional Chinese Medicine Science and Technology Development Center (Talent Exchance Center of National Administration of Traditional Chinese Medicine), Beijing 100027, China
- School of Life Sciences, Beijing University of Chinese Medicine, Beijing, 100029, China
| | - Mingkun Tong
- Meinian Public Health Institute, School of Public Health, Peking University Health Science Center, Beijing, 100191, China
| | - Bo Wang
- Meinian Public Health Institute, School of Public Health, Peking University Health Science Center, Beijing, 100191, China
| | - Zumin Shi
- Human Nutrition Department, College of Health Sciences, QU Health, Qatar University, Doha, 2713, Qatar
| | - Ping Wang
- Beijing Centers for Disease Control and Preventative Medical Research, Beijing Research Center for Preventive Medicine, Beijing, 100013, China
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University Health Science Center, Beijing, 100191, China
| | - Liming Li
- Meinian Public Health Institute, School of Public Health, Peking University Health Science Center, Beijing, 100191, China
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University Health Science Center, Beijing, 100191, China
| | - Yi Ning
- Meinian Public Health Institute, School of Public Health, Peking University Health Science Center, Beijing, 100191, China
- Beijing Academy of Artificial Intelligence, Beijing, 100084, China
| | - Tao Lu
- School of Life Sciences, Beijing University of Chinese Medicine, Beijing, 100029, China
| |
Collapse
|
8
|
Luo J, He G, Xu Y, Chen Z, Xu X, Peng J, Chen S, Hu J, Ji G, Liu T, Zeng W, Li X, Xiao J, Guo L, He Q, Ma W. 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. BMC Public Health 2021; 21:1534. [PMID: 34380442 PMCID: PMC8356456 DOI: 10.1186/s12889-021-11563-5] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2020] [Accepted: 07/28/2021] [Indexed: 01/02/2023] Open
Abstract
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.
Collapse
Affiliation(s)
- Jiali Luo
- Guangdong Provincial Center for Disease Control and Prevention, Guangdong Provincial Institute of Public Health, No. 160, Qunxian Road, Panyu District, Guangzhou, 511430, Guangdong, China.,Huashan Town Health Center, Huadu District, Guangzhou, 510880, China
| | - Guanhao He
- Guangdong Provincial Center for Disease Control and Prevention, Guangdong Provincial Institute of Public Health, No. 160, Qunxian Road, Panyu District, Guangzhou, 511430, Guangdong, China.,School of Public Health, Southern Medical University, Guangzhou, 510515, China
| | - Yanjun Xu
- Guangdong Provincial Center for Disease Control and Prevention, Guangzhou, 511430, China
| | - Zihui Chen
- Guangdong Provincial Center for Disease Control and Prevention, Guangdong Provincial Institute of Public Health, No. 160, Qunxian Road, Panyu District, Guangzhou, 511430, Guangdong, China
| | - Xiaojun Xu
- Guangdong Provincial Center for Disease Control and Prevention, Guangzhou, 511430, China
| | - Jiewen Peng
- Guangdong Provincial Center for Disease Control and Prevention, Guangdong Provincial Institute of Public Health, No. 160, Qunxian Road, Panyu District, Guangzhou, 511430, Guangdong, China
| | - Shaowei Chen
- Guangdong Provincial Center for Disease Control and Prevention, Guangdong Provincial Institute of Public Health, No. 160, Qunxian Road, Panyu District, Guangzhou, 511430, Guangdong, China
| | - Jianxiong Hu
- Guangdong Provincial Center for Disease Control and Prevention, Guangdong Provincial Institute of Public Health, No. 160, Qunxian Road, Panyu District, Guangzhou, 511430, Guangdong, China
| | - Guiyuan Ji
- Guangdong Provincial Center for Disease Control and Prevention, Guangdong Provincial Institute of Public Health, No. 160, Qunxian Road, Panyu District, Guangzhou, 511430, Guangdong, China
| | - Tao Liu
- Guangdong Provincial Center for Disease Control and Prevention, Guangdong Provincial Institute of Public Health, No. 160, Qunxian Road, Panyu District, Guangzhou, 511430, Guangdong, China
| | - Weilin Zeng
- Guangdong Provincial Center for Disease Control and Prevention, Guangdong Provincial Institute of Public Health, No. 160, Qunxian Road, Panyu District, Guangzhou, 511430, Guangdong, China
| | - Xing Li
- Guangdong Provincial Center for Disease Control and Prevention, Guangdong Provincial Institute of Public Health, No. 160, Qunxian Road, Panyu District, Guangzhou, 511430, Guangdong, China
| | - Jianpeng Xiao
- Guangdong Provincial Center for Disease Control and Prevention, Guangdong Provincial Institute of Public Health, No. 160, Qunxian Road, Panyu District, Guangzhou, 511430, Guangdong, China
| | - Lingchuan Guo
- Guangdong Provincial Center for Disease Control and Prevention, Guangdong Provincial Institute of Public Health, No. 160, Qunxian Road, Panyu District, Guangzhou, 511430, Guangdong, China
| | - Qun He
- Guangdong Provincial Center for Disease Control and Prevention, Guangdong Provincial Institute of Public Health, No. 160, Qunxian Road, Panyu District, Guangzhou, 511430, Guangdong, China
| | - Wenjun Ma
- Guangdong Provincial Center for Disease Control and Prevention, Guangdong Provincial Institute of Public Health, No. 160, Qunxian Road, Panyu District, Guangzhou, 511430, Guangdong, China. .,School of Medicine, Jinan University, Guangzhou, 510632, China.
| |
Collapse
|
9
|
Molina-Vega M, Gutiérrez-Repiso C, Muñoz-Garach A, Lima-Rubio F, Morcillo S, Tinahones FJ, Picón-César MJ. Relationship between environmental temperature and the diagnosis and treatment of gestational diabetes mellitus: An observational retrospective study. THE SCIENCE OF THE TOTAL ENVIRONMENT 2020; 744:140994. [PMID: 32717465 DOI: 10.1016/j.scitotenv.2020.140994] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/20/2020] [Revised: 07/10/2020] [Accepted: 07/13/2020] [Indexed: 06/11/2023]
Abstract
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.
Collapse
Affiliation(s)
- Maria Molina-Vega
- Department of Endocrinology and Nutrition, Virgen de la Victoria Universitary Hospital, Malaga, Spain; Research Laboratory, IBIMA, Virgen de la Victoria Universitary Hospital, Malaga, Spain
| | - Carolina Gutiérrez-Repiso
- Research Laboratory, IBIMA, Virgen de la Victoria Universitary Hospital, Malaga, Spain; CIBER Physiopathology of Obesity and Nutrition (CIBERobn), Institute of Health Carlos III, Madrid, Spain
| | - Araceli Muñoz-Garach
- Research Laboratory, IBIMA, Virgen de la Victoria Universitary Hospital, Malaga, Spain
| | - Fuensanta Lima-Rubio
- Research Laboratory, IBIMA, Virgen de la Victoria Universitary Hospital, Malaga, Spain
| | - Sonsoles Morcillo
- Department of Endocrinology and Nutrition, Virgen de la Victoria Universitary Hospital, Malaga, Spain; Research Laboratory, IBIMA, Virgen de la Victoria Universitary Hospital, Malaga, Spain; CIBER Physiopathology of Obesity and Nutrition (CIBERobn), Institute of Health Carlos III, Madrid, Spain.
| | - Francisco J Tinahones
- Research Laboratory, IBIMA, Virgen de la Victoria Universitary Hospital, Malaga, Spain; CIBER Physiopathology of Obesity and Nutrition (CIBERobn), Institute of Health Carlos III, Madrid, Spain.
| | - Mª Jose Picón-César
- Department of Endocrinology and Nutrition, Virgen de la Victoria Universitary Hospital, Malaga, Spain
| |
Collapse
|
10
|
Ikram MA, Brusselle G, Ghanbari M, Goedegebure A, Ikram MK, Kavousi M, Kieboom BCT, Klaver CCW, de Knegt RJ, Luik AI, Nijsten TEC, Peeters RP, van Rooij FJA, Stricker BH, Uitterlinden AG, Vernooij MW, Voortman T. Objectives, design and main findings until 2020 from the Rotterdam Study. Eur J Epidemiol 2020; 35:483-517. [PMID: 32367290 PMCID: PMC7250962 DOI: 10.1007/s10654-020-00640-5] [Citation(s) in RCA: 298] [Impact Index Per Article: 74.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2020] [Accepted: 04/23/2020] [Indexed: 12/19/2022]
Abstract
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.
Collapse
Affiliation(s)
- M Arfan Ikram
- Department of Epidemiology, Erasmus University Medical Center, PO Box 2040, 3000 CA, Rotterdam, The Netherlands.
| | - Guy Brusselle
- Department of Epidemiology, Erasmus University Medical Center, PO Box 2040, 3000 CA, Rotterdam, The Netherlands.,Department of Respiratory Medicine, Ghent University Hospital, Ghent, Belgium
| | - Mohsen Ghanbari
- Department of Epidemiology, Erasmus University Medical Center, PO Box 2040, 3000 CA, Rotterdam, The Netherlands
| | - André Goedegebure
- Department of Otorhinolaryngology, Erasmus University Medical Center, Rotterdam, The Netherlands
| | - M Kamran Ikram
- Department of Epidemiology, Erasmus University Medical Center, PO Box 2040, 3000 CA, Rotterdam, The Netherlands.,Department of Neurology, Erasmus University Medical Center, Rotterdam, The Netherlands
| | - Maryam Kavousi
- Department of Epidemiology, Erasmus University Medical Center, PO Box 2040, 3000 CA, Rotterdam, The Netherlands
| | - Brenda C T Kieboom
- Department of Epidemiology, Erasmus University Medical Center, PO Box 2040, 3000 CA, Rotterdam, The Netherlands
| | - Caroline C W Klaver
- Department of Epidemiology, Erasmus University Medical Center, PO Box 2040, 3000 CA, Rotterdam, The Netherlands.,Department of Ophthalmology, Erasmus University Medical Center, Rotterdam, The Netherlands
| | - Robert J de Knegt
- Department of Gastroenterology, Erasmus University Medical Center, Rotterdam, The Netherlands
| | - Annemarie I Luik
- Department of Epidemiology, Erasmus University Medical Center, PO Box 2040, 3000 CA, Rotterdam, The Netherlands
| | - Tamar E C Nijsten
- Department of Dermatology, Erasmus University Medical Center, Rotterdam, The Netherlands
| | - Robin P Peeters
- Department of Internal Medicine, Erasmus University Medical Center, Rotterdam, The Netherlands
| | - Frank J A van Rooij
- Department of Epidemiology, Erasmus University Medical Center, PO Box 2040, 3000 CA, Rotterdam, The Netherlands
| | - Bruno H Stricker
- Department of Epidemiology, Erasmus University Medical Center, PO Box 2040, 3000 CA, Rotterdam, The Netherlands
| | - André G Uitterlinden
- Department of Internal Medicine, Erasmus University Medical Center, Rotterdam, The Netherlands
| | - Meike W Vernooij
- Department of Epidemiology, Erasmus University Medical Center, PO Box 2040, 3000 CA, Rotterdam, The Netherlands.,Department of Radiology and Nuclear Medicine, Erasmus University Medical Center, Rotterdam, The Netherlands
| | - Trudy Voortman
- Department of Epidemiology, Erasmus University Medical Center, PO Box 2040, 3000 CA, Rotterdam, The Netherlands
| |
Collapse
|
11
|
Baranowska-Bik A, Bik W. Vascular Dysfunction and Insulin Resistance in Aging. Curr Vasc Pharmacol 2019; 17:465-475. [DOI: 10.2174/1570161117666181129113611] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2018] [Revised: 09/10/2018] [Accepted: 11/13/2018] [Indexed: 12/17/2022]
Abstract
:
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.
Collapse
Affiliation(s)
| | - Wojciech Bik
- Department of Neuroendocrinology, Centre of Postgraduate Medical Education, Warsaw, Poland
| |
Collapse
|
12
|
Noordam R, Ramkisoensing A, Loh NY, Neville MJ, Rosendaal FR, Willems van Dijk K, van Heemst D, Karpe F, Christodoulides C, Kooijman S. Associations of Outdoor Temperature, Bright Sunlight, and Cardiometabolic Traits in Two European Population-Based Cohorts. J Clin Endocrinol Metab 2019; 104:2903-2910. [PMID: 30759251 PMCID: PMC6543772 DOI: 10.1210/jc.2018-02532] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/24/2018] [Accepted: 02/07/2019] [Indexed: 11/19/2022]
Abstract
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.
Collapse
Affiliation(s)
- Raymond Noordam
- Department of Internal Medicine, Section of Gerontology and Geriatrics, Leiden University Medical Center, Leiden, Netherlands
| | - Ashna Ramkisoensing
- Department of Molecular Cell Biology, Division of Neurophysiology, Leiden University Medical Center, Leiden, Netherlands
| | - Nellie Y Loh
- Radcliffe Department of Medicine, Oxford Centre for Diabetes, Endocrinology, and Metabolism, University of Oxford, Oxford, United Kingdom
| | - Matt J Neville
- Radcliffe Department of Medicine, Oxford Centre for Diabetes, Endocrinology, and Metabolism, University of Oxford, Oxford, United Kingdom
- National Institute for Health Research, Oxford Biomedical Research Centre, Oxford, United Kingdom
- Oxford University Hospitals Foundation Trust, Oxford, United Kingdom
| | - Frits R Rosendaal
- Department of Clinical Epidemiology, Leiden University Medical Center, Leiden, Netherlands
| | - Ko Willems van Dijk
- Department of Human Genetics, Leiden University Medical Center, Leiden, Netherlands
- Department of Internal Medicine, Division of Endocrinology, Leiden University Medical Center, Leiden, Netherlands
| | - Diana van Heemst
- Department of Internal Medicine, Section of Gerontology and Geriatrics, Leiden University Medical Center, Leiden, Netherlands
| | - Fredrik Karpe
- Radcliffe Department of Medicine, Oxford Centre for Diabetes, Endocrinology, and Metabolism, University of Oxford, Oxford, United Kingdom
- National Institute for Health Research, Oxford Biomedical Research Centre, Oxford, United Kingdom
- Oxford University Hospitals Foundation Trust, Oxford, United Kingdom
| | - Constantinos Christodoulides
- Radcliffe Department of Medicine, Oxford Centre for Diabetes, Endocrinology, and Metabolism, University of Oxford, Oxford, United Kingdom
- Oxford University Hospitals Foundation Trust, Oxford, United Kingdom
- Correspondence and Reprint Requests: Constantinos Christodoulides, MD, PhD, Radcliffe Department of Medicine, Oxford Centre for Diabetes, Endocrinology, and Metabolism, University of Oxford, Churchill Hospital, Headington OX3 7LE, United Kingdom. E-mail:
| | - Sander Kooijman
- Radcliffe Department of Medicine, Oxford Centre for Diabetes, Endocrinology, and Metabolism, University of Oxford, Oxford, United Kingdom
- Department of Internal Medicine, Division of Endocrinology, Leiden University Medical Center, Leiden, Netherlands
| |
Collapse
|
13
|
Seasonality of the Cardiac Biomarker Troponin in the Eastern Croatian Population. J Clin Med 2018; 7:jcm7120520. [PMID: 30563299 PMCID: PMC6306947 DOI: 10.3390/jcm7120520] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2018] [Revised: 11/21/2018] [Accepted: 12/03/2018] [Indexed: 12/04/2022] Open
Abstract
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.
Collapse
|
14
|
Affiliation(s)
- Christian Herder
- Institute for Clinical Diabetology, German Diabetes Center, Leibniz Center for Diabetes Research at Heinrich Heine University, Düsseldorf, Germany.
- German Center for Diabetes Research (DZD), München-Neuherberg, Germany.
- Medical Faculty, Heinrich Heine University, Düsseldorf, Germany.
| |
Collapse
|