A diabetes risk score in Korean adults was developed and validated.
This study used the National Health Insurance Service-National Health Screening Cohort (NHIS-HEALS) of 359,349 people without diabetes at baseline to derive an equation for predicting the risk of developing diabetes, using Cox proportional hazards regression models. External validation was conducted using data from the Korean Genome and Epidemiology Study. Calibration and discrimination analyses were performed separately for men and women in the development and validation datasets.
During a median follow-up of 10.8 years, 37,678 cases (event rate=10.4 per 1,000 person-years) of diabetes were identified in the development cohort. The risk score included age, family history of diabetes, alcohol intake (only in men), smoking status, physical activity, use of antihypertensive therapy, use of statin therapy, body mass index, systolic blood pressure, total cholesterol, fasting glucose, and γ glutamyl transferase (only in women). The C-statistics for the models for risk at 10 years were 0.71 (95% confidence interval [CI], 0.70 to 0.73) for the men and 0.76 (95% CI, 0.75 to 0.78) for the women in the development dataset. In the validation dataset, the C-statistics were 0.63 (95% CI, 0.53 to 0.73) for men and 0.66 (95% CI, 0.55 to 0.76) for women.
The Korean Diabetes Risk Score may identify people at high risk of developing diabetes and may be an effective tool for delaying or preventing the onset of condition as risk management strategies involving modifiable risk factors can be recommended to those identified as at high risk.
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To estimate and compare the trends of all-cause and cause-specific mortality rates for subjects with and without diabetes in South Korea, from 2003 to 2013.
Using a population-based cohort (2003 to 2013), we evaluated annual mortality rates in adults (≥30 years) with and without diabetes. The number of subjects in this analysis ranged from 585,795 in 2003 to 670,020 in 2013.
Age- and sex-adjusted all-cause mortality rates decreased consistently in both groups from 2003 to 2013 (from 14.4 to 9.3/1,000 persons in subjects with diabetes and from 7.9 to 4.4/1,000 persons in those without diabetes). The difference in mortality rates between groups also decreased (6.61 per 1,000 persons in 2003 to 4.98 per 1,000 persons in 2013). The slope associated with the mortality rate exhibited a steeper decrease in subjects with diabetes than those without diabetes (regression coefficients of time: −0.50 and −0.33, respectively;
All-cause and cardiovascular mortality rates decreased substantially from 2003 to 2013, and the decline in ischemic stroke mortality mainly contributed to the decreased cardiovascular mortality in Korean people with diabetes.
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Air pollution causes many diseases and deaths. It is important to see how air pollution affects obesity, which is common worldwide. Therefore, we analyzed data from a smartphone application for intentional weight loss, and then we validated them.
Our analysis was structured in two parts. We analyzed data from a cohort registered to a smartphone application in 10 large cities of the world and matched it with the annual pollution values. We validated these results using daily pollution data in United States and matching them with user information. Body mass index (BMI) variation between final and initial login time was considered as outcome in the first part, and daily BMI in the validation. We analyzed: daily calories intake, daily weight, daily physical activity, geographical coordinates, seasons, age, gender. Weather Underground application programming interface provided daily climatic values. Annual and daily values of particulate matter PM10 and PM2.5 were extracted. In the first part of the analysis, we used 2,608 users and then 995 users located in United States.
Air pollution was highest in Seoul and lowest in Detroit. Users decreased BMI by 2.14 kg/m2 in average (95% confidence interval, −2.26 to −2.04). From a multilevel model, PM10 (β=0.04,
This is the first study that shows how air pollution affects intentional weight loss applied on wider area of the world.
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Hepatic steatosis is caused by metabolic stress associated with a positive lipid balance, such as insulin resistance and obesity. Previously we have shown the anti-obesity effects of inhibiting serotonin synthesis, which eventually improved insulin sensitivity and hepatic steatosis. However, it is not clear whether serotonin has direct effect on hepatic lipid accumulation. Here, we showed the possibility of direct action of serotonin on hepatic steatosis.
Mice were treated with para-chlorophenylalanine (PCPA) or LP-533401 to inhibit serotonin synthesis and fed with high fat diet (HFD) or high carbohydrate diet (HCD) to induce hepatic steatosis. Hepatic triglyceride content and gene expression profiles were analyzed.
Pharmacological and genetic inhibition of serotonin synthesis reduced HFD-induced hepatic lipid accumulation. Furthermore, short-term PCPA treatment prevented HCD-induced hepatic steatosis without affecting glucose tolerance and browning of subcutaneous adipose tissue. Gene expression analysis revealed that the expressions of genes involved in
Short-term inhibition of serotonin synthesis prevented hepatic lipid accumulation without affecting systemic insulin sensitivity and energy expenditure, suggesting the direct steatogenic effect of serotonin in liver.
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Betatrophin is a newly identified hormone derived from the liver and adipose tissue, which has been suggested to regulate glucose and lipid metabolism. Circulating levels of betatrophin are altered in various metabolic diseases, although the results are inconsistent. We aimed to examine whether betatrophin is a useful biomarker in predicting the development of diabetes.
A nested case-control study was performed using a prospective Chungju Metabolic disease Cohort Study. During a 4-year follow-up period, we analyzed 167 individuals who converted to diabetes and 167 non-converters, who were matched by age, sex, and body mass index. Serum betatrophin levels were measured by an ELISA (enzyme-linked immunosorbent assay).
Baseline serum betatrophin levels were significantly higher in the converter group compared to the non-converter group (1,315±598 pg/mL vs. 1,072±446 pg/mL,
Circulating levels of betatrophin could be a potential biomarker for predicting new-onset diabetes. Further studies are needed to understand the underlying mechanism of this association.
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