Dataset Information

Clinical Characteristics and Associated Risk Factors of Prediabetes in the Southwestern Region of Korea from 2010-2019.

ABSTRACT: This study investigated the clinical characteristics and associated risk factors of prediabetes in the southwestern region of Korea. A total of 323 subjects from 13 prediabetes studies were included in the data analysis. Subjects with prediabetes were divided into the following subtypes: (1) normal glucose tolerance (NGT) with HbA1c 5.7%-6.4%; (2) isolated impaired fasting glucose (I-IFG); (3) isolated impaired glucose tolerance (I-IGT); and (4) combined I-IFG and I-IGT (C-IFG/IGT). Clinical and biochemical variables were compared among subtypes, and multivariate logistic regression analysis was used to identify risk factors for prediabetes subtypes. The overall proportion of subjects with NGT, I-IFG, I-IGT and C-IFG/IGT was 8.4%, 20.7%, 33.1% and 37.8%, respectively. In men, C-IFG/IGT was the most common subtype, while in women, I-IGT was the most common. The parameters related to dysglycemia, atherosclerosis and liver dysfunction were higher in subjects in the C-IFG/IGT subtype than in other subtypes. Multiple linear regression analysis revealed independent risk factors for increased FPG, 2h-PPG and HbA1c levels. This study identified the clinical features and independent risk factors for prediabetes subtypes.

SUBMITTER: Oh MR

PROVIDER: S-EPMC7230531 | biostudies-literature | 2020 Apr

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Clinical Characteristics and Associated Risk Factors of Prediabetes in the Southwestern Region of Korea from 2010-2019.

Oh Mi-Ra MR Jung Su-Jin SJ Bae Eun-Ju EJ Park Byung-Hyun BH Chae Soo-Wan SW

Journal of clinical medicine 20200413 4

This study investigated the clinical characteristics and associated risk factors of prediabetes in the southwestern region of Korea. A total of 323 subjects from 13 prediabetes studies were included in the data analysis. Subjects with prediabetes were divided into the following subtypes: (1) normal glucose tolerance (NGT) with HbA1c 5.7%-6.4%; (2) isolated impaired fasting glucose (I-IFG); (3) isolated impaired glucose tolerance (I-IGT); and (4) combined I-IFG and I-IGT (C-IFG/IGT). Clinical and ...[more]

PMID: 32295016

Similar Datasets

Project description:BackgroundJeju island had the seventh highest incidence rate of coronavirus disease 2019 (COVID-19) but showed the lowest case fatality rate among 17 provinces of Korea, which may be associated with comorbidities and geographic differences. This study aimed to analyze the epidemiological and clinical characteristics of patients with COVID-19 and evaluate the risk factors for severe COVID-19 in Jeju island, Korea.Materials and methodsAll patients with COVID-19 admitted between February 20, 2020, and June 19, 2021, at a single center were retrospectively enrolled in this study. The severity of illness was defined using five categories (asymptomatic, mild, moderate, severe, and critical) according to the National Institute of Health criteria. Then, patients with severe and critical illness were grouped into a severe group, whereas patients with asymptomatic, mild, and moderate illness were grouped into a non-severe group. Multivariate logistic regression analysis was performed using risk factors that were found to be significantly associated with the severe group.ResultsThis study included 348 patients with a median age was 57 years, and 37.5% were aged 60 or older. Among them, 43.4% were male and 10.9% were asymptomatic, whereas 41.4%, 33.9%, 12.9%, and 1.1% had mild, moderate, severe, and critical illness. The all-cause mortality of patients with COVID-19 was 0.28% (1/348). Among confirmed patients with COVID-19, exposure to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) was mainly within Jeju island (75.9%). The mean body mass index was 24.09 ± 4.04 kg/m², the median comorbidity index score was low in each group (0 in asymptomatic; 1 in mild; 1 in moderate; 1 in severe; and 2 in critical group, P <0.548). In the multivariable analysis, male sex [odds ratio (OR), 6.37; 95% confidence interval (CI), 2.69 - 15.13; P <0.001], ≥65 years of age (OR, 2.68; 95% CI, 1.18 - 6.10; P <0.019), chronic pulmonary disease (OR, 6.10; 95% CI, 1.40 - 26.61; P = 0.016), and length of fever duration (OR, 1.33; 95% CI, 1.19 - 1.49; P <0.001) were independently associated with severe COVID-19.ConclusionThe most relevant risk factors of COVID-19 severity were male sex, older age, underlying chronic lung diseases, and duration of fever during hospitalization. The risk factors for severe COVID-19 were not significantly different from those reported in other studies. However, a lower proportion of the older population among confirmed SARS-CoV-2 cases might contribute to the lower fatality rate than the national rate.

Project description:This is an analytical cross-sectional study of coronavirus disease 2019 (COVID-19) based on data collected between 1 November 2020 and 31 March 2021 in Casablanca focusing on the disease's epidemiological status and risk factors. A total of 4569 samples were collected and analysed by reverse-transcription polymerase chain reaction (RT-PCR); 967 patients were positive, representing a prevalence of 21.2 % for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The mean age was 47.5±18 years, and infection was more common in young adults (<60 years). However, all age groups were at risk of COVID-19, and in terms of disease severity, the elderly were at greater risk because of potential underlying health problems. Among the clinical signs reported in this study, loss of taste and/or smell, fever, cough and fatigue were highly significant predictors of a positive COVID-19 test result (P<0.001). An assessment of the reported symptoms revealed that 27 % of COVID-19-positive patients (n=261) experienced loss of taste and/or smell, whereas only 2 % (n=72) of COVID-19-negative patients did (P<0.001). This result was consistent between univariate (OR=18.125) and multivariate (adjusted OR=10.484) logistic regression analyses, indicating that loss of taste and/or smell is associated with a more than 10-fold higher multivariate adjusted probability of a positive COVID-19 test (adjusted OR=10.48; P<0.001). Binary logistic regression model analysis based on clinical signs revealed that loss of taste and/or smell had a performance index of 0.846 with a P<0.001, confirming the diagnostic utility of this symptom for the prediction of COVID-19-positive status. In conclusion, symptom evaluation and a RT-PCR [taking into account cycle threshold (C t) values of the PCR proxy] test remain the most useful screening tools for diagnosing COVID-19. However, loss of taste/smell, fatigue, fever and cough remain the strongest independent predictors of a positive COVID-19 result.

Project description:BackgroundDengue poses a large burden on the public health systems worldwide. severe dengue (SD) could lead to more serious clinical symptoms and even death. This study aimed to identify the cause of SD in a clinical trial during the dengue outbreak in Xishuangbanna in 2019, and could provide new insights into the pathogenic mechanisms of SD.MethodsMosquito-borne viral (DENV, JEV, and CHIKV) infections were identified. The epidemiological factors and clinical symptoms of inpatients in Xishuangbanna were recorded. The IgG and IgM levels in the serum of dengue inpatients were evaluated, and secondary infections were identified. Then, the structural proteins (C/PrM/E) were sequenced and compared with those of the same type of DENV in the same area as before, and their structures were predicted by the SWISS-MODEL (expasy.org). The full-length viral genomes were sequenced and aligned with representative strains by BioEidt or MEGA 5.0.ResultsIn this outbreak, the clinical symptoms were more serious in SD. The proportion of SD inpatients of male and Han nationality was larger than that of dengue fever (DF) inpatients (p < 0.05). DENV-2 infection was the majority in DF, with 45 inpatients. However, DENV-1 infection was the most common SD, with 54 inpatients. There were 3 DENV-3-positive inpatients in the DF group and 6 ZIKV-positive inpatients in the SD group. A secondary infection accounted for 76.47% (78 cases) of SD inpatients, but secondary infections were only in 20% (17 cases) of DF inpatients. In the three-dimensional structure of protein analysis, the C/PrM/E of DENV-1 and DENV-2 showed more stability than previous epidemic strains, while DENV-3 in 2019 showed a looser spatial structure. After a complete genome sequencing and analysis, all six DENV-2 strains belonged to cosmopolitan, five of which clustered into one branch. The GC/AT of the five strains decreased from 2014 to 2018. Compared with DF strains, SD strains had no mutations of commonness.ConclusionsSD may related to secondary heteromorphic dengue in Xishuangbanna in 2019. The coinfection of ZIKV could be another related factor for SD. The currently datas were very limited and only suggestive.

Project description:BackgroundNumerous patients around the globe are dying from coronavirus disease 2019 (COVID-19). While age is a known risk factor, risk analysis in the young generation is lacking. The present study aimed to evaluate the clinical features and mortality risk factors in younger patients (≤ 50 years) with a critical case of COVID-19 in comparison with those among older patients (> 50 years) in Korea.MethodsWe analyzed the data of adult patients only in critical condition (requiring high flow nasal cannula oxygen therapy or higher respiratory support) hospitalized with PCR-confirmed COVID-19 at 11 hospitals in Korea from July 1, 2021 to November 30, 2021 when the delta variant was a dominant strain. Patients' electronic medical records were reviewed to identify clinical characteristics.ResultsDuring the study period, 448 patients were enrolled. One hundred and forty-two were aged 50 years or younger (the younger group), while 306 were above 50 years of age (the older group). The most common pre-existing conditions in the younger group were diabetes mellitus and hypertension, and 69.7% of the patients had a body mass index (BMI) > 25 kg/m². Of 142 younger patients, 31 of 142 patients (21.8%, 19 women) did not have these pre-existing conditions. The overall case fatality rate among severity cases was 21.0%, and it differed according to age: 5.6% (n = 8/142) in the younger group, 28.1% in the older group, and 38% in the ≥ 65 years group. Age (odds ratio [OR], 7.902; 95% confidence interval [CI], 2.754-18.181), mechanical ventilation therapy (OR, 17.233; 95% CI, 8.439-35.192), highest creatinine > 1.5 mg/dL (OR, 17.631; 95% CI, 8.321-37.357), and combined blood stream infection (OR, 7.092; 95% CI, 1.061-18.181) were identified as independent predictors of mortality in total patients. Similar patterns were observed in age-specific analyses, but most results were statistically insignificant in multivariate analysis due to the low number of deaths in the younger group. The full vaccination rate was very low among study population (13.6%), and only three patients were fully vaccinated, with none of the patients who died having been fully vaccinated in the younger group. Seven of eight patients who died had a pre-existing condition or were obese (BMI > 25 kg/m²), and the one remaining patient died from a secondary infection.ConclusionAbout 22% of the patients in the young critical group did not have an underlying disease or obesity, but the rate of obesity (BMI > 25 kg/m²) was high, with a fatality rate of 5.6%. The full vaccination rate was extremely low compared to the general population of the same age group, showing that non-vaccination has a grave impact on the progression of COVID-19 to a critical condition. The findings of this study highlight the need for measures to prevent critical progression of COVID-19, such as vaccinations and targeting young adults especially having risk factors.

Dataset Information

Clinical Characteristics and Associated Risk Factors of Prediabetes in the Southwestern Region of Korea from 2010-2019.

Publications

Clinical Characteristics and Associated Risk Factors of Prediabetes in the Southwestern Region of Korea from 2010-2019.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets