Background: Gestational diabetes mellitus (GDM) affects women with diverse pathological phenotypes, but little is known about the effects of this variation on perinatal outcomes. We explored the metabolic phenotypes of GDM and their impact on adverse pregnancy outcomes. Methods: Women diagnosed with gestational glucose intolerance or GDM were categorized into subgroups according to their prepregnancy body mass index (BMI) and the median values of the gestational Matsuda and Stumvoll indices. Logistic regression analysis was employed to assess the odds of adverse pregnancy outcomes, such as large-for-gestational age (LGA), small-for-gestational age, preterm birth, low Apgar score, and cesarean section. Results: A total of 309 women were included, with a median age of 31 years and a median BMI of 22.3 kg/m2. Women with a higher pre-pregnancy BMI had a higher risk of LGA newborns (adjusted odds ratio [aOR] for pre-pregnancy BMI ≥25 kg/m2 compared to 20–23 kg/m2, 4.26; 95% confidence interval [CI], 1.99 to 9.12; P<0.001; P for trend=0.001), but the risk of other adverse pregnancy outcomes did not differ according to pre-pregnancy BMI. Women with insulin resistance had a higher risk of LGA (aOR, 1.88; 95% CI, 1.02 to 3.47; P=0.043) and cesarean section (aOR, 2.12; 95% CI, 1.29 to 3.50; P=0.003) than women in the insulin-sensitive group. In contrast, defective β-cell function did not affect adverse pregnancy outcomes. Conclusion Different metabolic phenotypes of GDM were associated with heterogeneous pregnancy outcomes. Women with obesity and those with insulin resistance are at greater risk of adverse outcomes and might need strict glycemic management during pregnancy.

Background: Gestational diabetes mellitus (GDM) affects women with diverse pathological phenotypes, but little is known about the effects of this variation on perinatal outcomes. We explored the metabolic phenotypes of GDM and their impact on adverse pregnancy outcomes. Methods: Women diagnosed with gestational glucose intolerance or GDM were categorized into subgroups according to their prepregnancy body mass index (BMI) and the median values of the gestational Matsuda and Stumvoll indices. Logistic regression analysis was employed to assess the odds of adverse pregnancy outcomes, such as large-for-gestational age (LGA), small-for-gestational age, preterm birth, low Apgar score, and cesarean section. Results: A total of 309 women were included, with a median age of 31 years and a median BMI of 22.3 kg/m2. Women with a higher pre-pregnancy BMI had a higher risk of LGA newborns (adjusted odds ratio [aOR] for pre-pregnancy BMI ≥25 kg/m2 compared to 20–23 kg/m2, 4.26; 95% confidence interval [CI], 1.99 to 9.12; P<0.001; P for trend=0.001), but the risk of other adverse pregnancy outcomes did not differ according to pre-pregnancy BMI. Women with insulin resistance had a higher risk of LGA (aOR, 1.88; 95% CI, 1.02 to 3.47; P=0.043) and cesarean section (aOR, 2.12; 95% CI, 1.29 to 3.50; P=0.003) than women in the insulin-sensitive group. In contrast, defective β-cell function did not affect adverse pregnancy outcomes. Conclusion Different metabolic phenotypes of GDM were associated with heterogeneous pregnancy outcomes. Women with obesity and those with insulin resistance are at greater risk of adverse outcomes and might need strict glycemic management during pregnancy.

Background: Gestational diabetes mellitus (GDM) affects women with diverse pathological phenotypes, but little is known about the effects of this variation on perinatal outcomes. We explored the metabolic phenotypes of GDM and their impact on adverse pregnancy outcomes. Methods: Women diagnosed with gestational glucose intolerance or GDM were categorized into subgroups according to their prepregnancy body mass index (BMI) and the median values of the gestational Matsuda and Stumvoll indices. Logistic regression analysis was employed to assess the odds of adverse pregnancy outcomes, such as large-for-gestational age (LGA), small-for-gestational age, preterm birth, low Apgar score, and cesarean section. Results: A total of 309 women were included, with a median age of 31 years and a median BMI of 22.3 kg/m2. Women with a higher pre-pregnancy BMI had a higher risk of LGA newborns (adjusted odds ratio [aOR] for pre-pregnancy BMI ≥25 kg/m2 compared to 20–23 kg/m2, 4.26; 95% confidence interval [CI], 1.99 to 9.12; P<0.001; P for trend=0.001), but the risk of other adverse pregnancy outcomes did not differ according to pre-pregnancy BMI. Women with insulin resistance had a higher risk of LGA (aOR, 1.88; 95% CI, 1.02 to 3.47; P=0.043) and cesarean section (aOR, 2.12; 95% CI, 1.29 to 3.50; P=0.003) than women in the insulin-sensitive group. In contrast, defective β-cell function did not affect adverse pregnancy outcomes. Conclusion Different metabolic phenotypes of GDM were associated with heterogeneous pregnancy outcomes. Women with obesity and those with insulin resistance are at greater risk of adverse outcomes and might need strict glycemic management during pregnancy.

Background: Gestational diabetes mellitus (GDM) affects women with diverse pathological phenotypes, but little is known about the effects of this variation on perinatal outcomes. We explored the metabolic phenotypes of GDM and their impact on adverse pregnancy outcomes. Methods: Women diagnosed with gestational glucose intolerance or GDM were categorized into subgroups according to their prepregnancy body mass index (BMI) and the median values of the gestational Matsuda and Stumvoll indices. Logistic regression analysis was employed to assess the odds of adverse pregnancy outcomes, such as large-for-gestational age (LGA), small-for-gestational age, preterm birth, low Apgar score, and cesarean section. Results: A total of 309 women were included, with a median age of 31 years and a median BMI of 22.3 kg/m2. Women with a higher pre-pregnancy BMI had a higher risk of LGA newborns (adjusted odds ratio [aOR] for pre-pregnancy BMI ≥25 kg/m2 compared to 20–23 kg/m2, 4.26; 95% confidence interval [CI], 1.99 to 9.12; P<0.001; P for trend=0.001), but the risk of other adverse pregnancy outcomes did not differ according to pre-pregnancy BMI. Women with insulin resistance had a higher risk of LGA (aOR, 1.88; 95% CI, 1.02 to 3.47; P=0.043) and cesarean section (aOR, 2.12; 95% CI, 1.29 to 3.50; P=0.003) than women in the insulin-sensitive group. In contrast, defective β-cell function did not affect adverse pregnancy outcomes. Conclusion Different metabolic phenotypes of GDM were associated with heterogeneous pregnancy outcomes. Women with obesity and those with insulin resistance are at greater risk of adverse outcomes and might need strict glycemic management during pregnancy.

Purpose: Smoking cessation intervention is one of the key components of successful lung cancer screening program. We investigated the effectiveness and related factors of smoking cessation services provided to the participants in a population-based lung cancer screening trial. Materials and Methods: The Korean Lung Cancer Screening Project (K-LUCAS) is a nationwide, multi-center lung cancer screening trial that evaluates the feasibility of implementing population-based lung cancer screening. All 5,144 current smokers who participated in the K-LUCAS received a mandatory smoking cessation counseling. Changes in smoking status were followed up using a telephone survey in 6 months after lung cancer screening participation. The lung cancer screening’s impact on smoking cessation is analyzed by variations in the smoking cessation interventions provided in screening units. Results: Among 4,136 survey responders, participant’s motivation to quit smoking increased by 9.4% on average after lung cancer screening. After 6 months from the initial screening, 24.3% of participants stopped smoking, and 10.6% of participants had not smoked continuously for at least 6 months after screening. Over 80% of quitters stated that participation in lung cancer screening motivated them to quit smoking. Low-cost public smoking cessation program combined with lung cancer screening increased the abstinence rates. The smokers were three times more likely to quit smoking when the smoking cessation counseling was provided simultaneously with low-dose computed tomography screening results than when provided separately. Conclusion A mandatory smoking cessation intervention integrated with screening result counselling by a physician after participation in lung cancer screening could be effective for increasing smoking cessation attempts.

Purpose: Recently, we developed allele-discriminating priming system (ADPS) technology. This method increases the sensitivity of conventional quantitative polymerase chain reaction up to 100 folds, with limit of detection, 0.01%, with reinforced specificity. This prospective study aimed to develop and validate the accuracy of ADPS epidermal growth factor receptor (EGFR) Mutation Test Kit using clinical specimens. Materials and Methods: In total 189 formalin-fixed paraffin-embedded tumor tissues resected from patients with non–small cell lung cancer were used to perform a comparative evaluation of the ADPS EGFR Mutation Test Kit versus the cobas EGFR Mutation Test v2, which is the current gold standard. When the two methods had inconsistent results, next-generation sequencing–based CancerSCAN was utilized as a referee. Results: The overall agreement of the two methods was 97.4% (93.9%-99.1%); the positive percent agreement, 95.0% (88.7%-98.4%); and the negative percent agreement, 100.0% (95.9%-100.0%). EGFR mutations were detected at a frequency of 50.3% using the ADPS EGFR Mutation Test Kit and 52.9% using the cobas EGFR Mutation Test v2. There were 10 discrepant mutation calls between the two methods. CancerSCAN reproduced eight ADPS results. In two cases, mutant allele fraction was ultra-low at 0.02% and 0.06%, which are significantly below the limit of detection of the cobas assay and CancerSCAN. Based on the EGFR genotyping by ADPS, the treatment options could be switched in five patients. Conclusion The highly sensitive and specific ADPS EGFR Mutation Test Kit would be useful in detecting the patients who have lung cancer with EGFR mutation, and can benefit from the EGFR targeted therapy.

Background: Identifying risk factors for postpartum type 2 diabetes in women with gestational diabetes mellitus (GDM) is crucial for effective interventions. We examined whether changes in insulin sensitivity after delivery affects the risk of type 2 diabetes in women with GDM. Methods: This prospective cohort study included 347 women with GDM or gestational impaired glucose tolerance, who attended the follow-up visits at 2 months postpartum and annually thereafter. Changes in insulin sensitivity were calculated using the Matsuda index at GDM diagnosis and at 2 months postpartum (ΔMatsuda index). After excluding women with pregestational diabetes or those followed up only once, we analyzed the risk of postpartum type 2 diabetes based on the ΔMatsuda index tertiles. Results: The incidence of type 2 diabetes at the two-month postpartum visit decreased with increasing ΔMatsuda index tertiles (16.4%, 9.5%, and 1.8%, P=0.001). During a 4.1-year follow-up, 26 out of 230 women who attended more than two follow-up visits (11.3%) developed type 2 diabetes. Compared to the lowest tertile, subjects in the highest ΔMatsuda index tertile showed a significantly reduced risk of type 2 diabetes (hazard ratio, 0.33; 95% confidence interval, 0.12 to 0.93; P=0.036) after adjusting for confounders. Conclusion Improvement in insulin sensitivity after delivery is associated with a reduced risk of postpartum type 2 diabetes in women with GDM. Postpartum changes in insulin sensitivity could be a useful prediction for future type 2 diabetes development in women with GDM.