Angiopoietin-like protein 4 induces growth hormone variant secretion and aggravates insulin resistance during pregnancy, linking obesity to gestational diabetes mellitus.

Angiopoietin-like protein 4 (ANGPTL4) is a secretory glycoprotein involved in regulating glucose homeostasis in non-pregnant subjects. However, its role in glucose metabolism during pregnancy and the pathophysiology of gestational diabetes mellitus (GDM) remains elusive. Thus, this study aimed to clarify the relationship between ANGPTL4 and GDM and investigate the pathophysiology of placental ANGPTL4 in glucose metabolism. We investigated this issue using blood and placenta samples in 957 pregnant women, the human 3A-sub-E trophoblast cell line, and the L6 skeletal muscle cell line. We found that ANGPTL4 expression in the placenta was higher in obese pregnant women than in lean controls. Palmitic acid significantly induced ANGPTL4 expression in trophoblast cells in a dose-response manner. ANGPTL4 overexpression in trophoblast cells resulted in endoplasmic reticulum (ER) stress, which stimulated the expression and secretion of growth hormone-variant (GH2) but not human placental lactogen. In L6 skeletal muscle cells, soluble ANGPTL4 suppressed insulin-mediated glucose uptake through the epidermal growth factor receptor (EGFR)/extracellular signal-regulated kinases 1/2 (ERK 1/2) pathways. In pregnant women, plasma ANGPTL4 concentrations in the first trimester predicted the incidence of GDM and were positively associated with BMI, plasma triglyceride, and plasma GH2 in the first trimester. However, they were negatively associated with insulin sensitivity index ISI0,120 in the second trimester. Overall, placental ANGPTL4 is induced by obesity and is involved in the pathophysiology of GDM via the induction of ER stress and GH2 secretion. Soluble ANGPTL4 can lead to insulin resistance in skeletal muscle cells and is an early biomarker for predicting GDM.

The association between plasma angiopoietin-like protein 4, glucose and lipid metabolism during pregnancy, placental function, and risk of delivering large-for-gestational-age neonates.

BACKGROUND: Large-for-gestational-age (LGA) neonates have increased risk of adverse pregnancy outcomes and adult metabolic diseases. We aimed to investigate the relationship between plasma angiopoietin-like protein 4 (ANGPTL4), a protein involved in lipid and glucose metabolism during pregnancy, placental function, growth factors, and the risk of LGA. METHODS: We conducted a prospective cohort study and recruited women with singleton pregnancies at the National Taiwan University Hospital between 2013 and 2018. First trimester maternal plasma ANGPTL4 concentrations were measured. RESULTS: Among 353 pregnant women recruited, the LGA group had higher first trimester plasma ANGPTL4 concentrations than the appropriate-for-gestational-age group. Plasma ANGPTL4 was associated with hemoglobin A1c, post-load plasma glucose, plasma triglyceride, plasma free fatty acid concentrations, plasma growth hormone variant (GH-V), and birth weight, but was not associated with cord blood growth factors. After adjusting for age, body mass index, hemoglobin A1c, and plasma triglyceride concentrations, plasma ANGPTL4 concentrations were significantly associated with LGA risk, and its predictive performance, as measured by the area under the receiver operating characteristic curve, outperformed traditional risk factors for LGA. CONCLUSIONS: Plasma ANGPTL4 is associated with glucose and lipid metabolism during pregnancy, plasma GH-V, and birth weight, and is an early biomarker for predicting the risk of LGA.

Comparison of multiple linear regression and machine learning methods in predicting cognitive function in older Chinese type 2 diabetes patients.

INTRODUCTION: The prevalence of type 2 diabetes (T2D) has increased dramatically in recent decades, and there are increasing indications that dementia is related to T2D. Previous attempts to analyze such relationships principally relied on traditional multiple linear regression (MLR). However, recently developed machine learning methods (Mach-L) outperform MLR in capturing non-linear relationships. The present study applied four different Mach-L methods to analyze the relationships between risk factors and cognitive function in older T2D patients, seeking to compare the accuracy between MLR and Mach-L in predicting cognitive function and to rank the importance of risks factors for impaired cognitive function in T2D. METHODS: We recruited older T2D between 60-95 years old without other major comorbidities. Demographic factors and biochemistry data were used as independent variables and cognitive function assessment (CFA) was conducted using the Montreal Cognitive Assessment as an independent variable. In addition to traditional MLR, we applied random forest (RF), stochastic gradient boosting (SGB), Naïve Byer's classifier (NB) and eXtreme gradient boosting (XGBoost). RESULTS: Totally, the test cohort consisted of 197 T2D (98 men and 99 women). Results showed that all ML methods outperformed MLR, with symmetric mean absolute percentage errors for MLR, RF, SGB, NB and XGBoost respectively of 0.61, 0.599, 0.606, 0.599 and 0.2139. Education level, age, frailty score, fasting plasma glucose and body mass index were identified as key factors in descending order of importance. CONCLUSION: In conclusion, our study demonstrated that RF, SGB, NB and XGBoost are more accurate than MLR for predicting CFA score, and identify education level, age, frailty score, fasting plasma glucose, body fat and body mass index as important risk factors in an older Chinese T2D cohort.

Advanced maternal age-related clustering of metabolic abnormalities is associated with risks of adverse pregnancy outcomes.

AIMS: Advanced maternal age (AMA) is correlated with higher risk of adverse pregnancy outcomes while the pathophysiology remains unclear. Our study aimed to investigate whether AMA is linked to the clustering of metabolic abnormalities, which in turn is associated with an increased risk of adverse pregnancy outcomes. METHOD: A total of 857 pregnant woman were recruited in a prospective cohort at National Taiwan University Hospital, from November 2013 to April 2018. Metabolic abnormalities during pregnancy were defined as following: fasting plasma glucose ≥92 mg/dl, body mass index (BMI) ≥24 kg/m2, plasma high-density lipoprotein cholesterol <50 mg/dl, hyper-triglyceridemia (≥140 mg/dl in the first trimester or ≥220 mg/dl in the second trimester), and blood pressure ≥130/85 mmHg. RESULT: Incidence of large for gestational age (LGA), primary caesarean section (CS), and the presence of any adverse pregnancy outcome increased with age. The advanced-age group tended to have more metabolic abnormalities in both the first and the second trimesters. There was a significant association between the number of metabolic abnormalities in the first and the second trimesters and the incidence of LGA, gestational hypertension or preeclampsia, primary CS, preterm birth, and the presence of any adverse pregnancy outcome, adjusted for maternal age. CONCLUSION: AMA is associated with clustering of metabolic abnormalities during pregnancy, and clustering of metabolic abnormalities is correlated with increased risk of adverse pregnancy outcomes.

Comparison between multiple logistic regression and machine learning methods in prediction of abnormal thallium scans in type 2 diabetes.

BACKGROUND: The prevalence of type 2 diabetes (T2D) has been increasing dramatically in recent decades, and 47.5% of T2D patients will die of cardiovascular disease. Thallium-201 myocardial perfusion scan (MPS) is a precise and non-invasive method to detect coronary artery disease (CAD). Most previous studies used traditional logistic regression (LGR) to evaluate the risks for abnormal CAD. Rapidly developing machine learning (Mach-L) techniques could potentially outperform LGR in capturing non-linear relationships. AIM: To aims were: (1) Compare the accuracy of Mach-L methods and LGR; and (2) Found the most important factors for abnormal TMPS. METHODS: 556 T2D were enrolled in the study (287 men and 269 women). Demographic and biochemistry data were used as independent variables and the sum of stressed score derived from MPS scan was the dependent variable. Subjects with a MPS score ≥ 9 were defined as abnormal. In addition to traditional LGR, classification and regression tree (CART), random forest, Naïve Bayes, and eXtreme gradient boosting were also applied. Sensitivity, specificity, accuracy and area under the receiver operation curve were used to evaluate the respective accuracy of LGR and Mach-L methods. RESULTS: Except for CART, the other Mach-L methods outperformed LGR, with gender, body mass index, age, low-density lipoprotein cholesterol, glycated hemoglobin and smoking emerging as the most important factors to predict abnormal MPS. CONCLUSION: Four Mach-L methods are found to outperform LGR in predicting abnormal TMPS in Chinese T2D, with the most important risk factors being gender, body mass index, age, low-density lipoprotein cholesterol, glycated hemoglobin and smoking.

Gamma-Glutamyltransferase Is a Predictor for Future Changes of Diabetogenic Factors in Aged Chinese-A Four-Year Follow-Up Study.

Glucose homeostasis in the body is determined by four diabetes factors (DFs): insulin resistance (IR), glucose effectiveness (GE), and the two phases of insulin secretion-first phase (FPIS) and second phase (SPIS). Previous research points to a correlation between elevated levels of gamma-glutamyl transferase (γGT) and an increased risk of type 2 diabetes. This study investigates the relationship between γGT and the four DFs in older Chinese individuals. This study involved 2644 men and 2598 women, all of whom were relatively healthy Chinese individuals aged 60 years or more. The DFs were calculated using formulas developed by our research, based on demographic data and factors related to metabolic syndrome. Pearson's correlation was utilized to assess the relationship between γGT and the four DFs. The findings suggested a positive correlation between γGT and IR, FPIS, and SPIS, but a negative correlation with GE in men. Among women, only SPIS and GE were significantly correlated with γGT. The factors showed varying degrees of correlation, listed in descending order as follows: GE, SPIS, FPIS, and IR. This study confirms a significant correlation between γGT and DFs in this population, highlighting the noteworthy role of GE.

Comparison between linear regression and four different machine learning methods in selecting risk factors for osteoporosis in a Chinese female aged cohort.

BACKGROUND: Population aging is emerging as an increasingly acute challenge for countries around the world. One particular manifestation of this phenomenon is the impact of osteoporosis on individuals and national health systems. Previous studies of risk factors for osteoporosis were conducted using traditional statistical methods, but more recent efforts have turned to machine learning approaches. Most such efforts, however, treat the target variable (bone mineral density [BMD] or fracture rate) as a categorical one, which provides no quantitative information. The present study uses five different machine learning methods to analyze the risk factors for T-score of BMD, seeking to (1) compare the prediction accuracy between different machine learning methods and traditional multiple linear regression (MLR) and (2) rank the importance of 25 different risk factors. METHODS: The study sample includes 24 412 women older than 55 years with 25 related variables, applying traditional MLR and five different machine learning methods: classification and regression tree, Naïve Bayes, random forest, stochastic gradient boosting, and eXtreme gradient boosting. The metrics used for model performance comparisons are the symmetric mean absolute percentage error, relative absolute error, root relative squared error, and root mean squared error. RESULTS: Machine learning approaches outperformed MLR for all four prediction errors. The average importance ranking of each factor generated by the machine learning methods indicates that age is the most important factor determining T-score, followed by estimated glomerular filtration rate (eGFR), body mass index (BMI), uric acid (UA), and education level. CONCLUSION: In a group of women older than 55 years, we demonstrated that machine learning methods provide superior performance in estimating T-Score, with age being the most important impact factor, followed by eGFR, BMI, UA, and education level.

Aging Affects Insulin Resistance, Insulin Secretion, and Glucose Effectiveness in Subjects with Normal Blood Glucose and Body Weight.

AIM: Several studies have demonstrated that factors including diabetes, including insulin resistance (IR), glucose effectiveness (GE), and the first and second phase of insulin secretion (FPIS, SPIS) could easily be calculated using basic characteristics and biochemistry profiles. Aging is accompanied by deteriorations of insulin resistance (IR) and insulin secretion. However, little is known about the roles of aging in the different phases of insulin secretion (ISEC), i.e., the first and second phase of insulin secretion (FPIS, SPIS), and glucose effectiveness (GE). METHODS: In total, 169 individuals (43 men and 126 women) recruited from the data bank of the Meei-Jaw (MJ) Health Screening Center and Cardinal Tien Hospital Data Access Center between 1999 and 2008, with a similar fasting plasma glucose (FPG: 90 mg/dL) and BMI (men: 23 kg/m2, women 22 kg/m2) were enrolled. The IR, FPIS, SPIS, and GE were estimated using our previously developed equations shown below. Pearson correlation analysis was conducted to assess the correlations between age and four diabetes factors (DFs: IR, FPIS, SPIS, and GE). The equations that are used to calculate the DF in the present study were built and published by our group. RESULTS: The age of the participants ranged from 18 to 78 years. Men had higher FPIS but lower HDL-C levels than women (2.067 ± 0.159, 1.950 ± 0.186 µU/min and 1.130 ± 0.306, 1.348 ± 0.357 mmol/dl, accordingly). The results of the Pearson correlation revealed that age was negatively related to the IR and GE in both genders (IR: r = -0.39, p < 0.001 for men, r = -0.24, p < 0.003 for women; GE: r = 0.66, p < 0.001 for men, r = 0.78, p < 0.001 for women). At the same time, the FPIS was also only found to be negatively correlated with age in females (r = -0.238, p = 0.003), but there was no difference in the SPIS and age among both genders. CONCLUSIONS: We have found that in Chinese subjects with a normal FPG level (90 mg/dL) and body mass index (men: 23 kg/m2, women: 22: kg/m2), age is negatively related to the IR and GE among both genders. Only the FPIS was found to be negatively related to age in women. The tightness of their relationships, from the highest to the lowest, are GE, FPIS, and IR. These results should be interpreted with caution because of the small sample size.

Using Machine Learning to Predict Abnormal Carotid Intima-Media Thickness in Type 2 Diabetes.

Carotid intima-media thickness (c-IMT) is a reliable risk factor for cardiovascular disease risk in type 2 diabetes (T2D) patients. The present study aimed to compare the effectiveness of different machine learning methods and traditional multiple logistic regression in predicting c-IMT using baseline features and to establish the most significant risk factors in a T2D cohort. We followed up with 924 patients with T2D for four years, with 75% of the participants used for model development. Machine learning methods, including classification and regression tree, random forest, eXtreme gradient boosting, and Naïve Bayes classifier, were used to predict c-IMT. The results showed that all machine learning methods, except for classification and regression tree, were not inferior to multiple logistic regression in predicting c-IMT in terms of higher area under receiver operation curve. The most significant risk factors for c-IMT were age, sex, creatinine, body mass index, diastolic blood pressure, and duration of diabetes, sequentially. Conclusively, machine learning methods could improve the prediction of c-IMT in T2D patients compared to conventional logistic regression models. This could have crucial implications for the early identification and management of cardiovascular disease in T2D patients.

Inhibition of monoamine oxidase B reduces atherosclerosis and fatty liver in mice.

Oxidative stress is vital for pathophysiology of atherosclerosis and non-alcoholic fatty liver disease (NAFLD). Monoamine oxidase (MAO) is an important source of oxidative stress in the vascular system and liver. However, the effect of MAO inhibition on atherosclerosis and NAFLD has not been explored. In the present study, MAO A and B expressions were increased in atherosclerotic plaques in human and apolipoprotein E (ApoE)-deficient mice. Inhibition of MAO B (by deprenyl), but not MAO A (by clorgyline), reduced the atheroma area in the thoracic aorta and aortic sinus in ApoE-deficient mice fed the cholesterol-enriched diet for 15 weeks. MAO B inhibition attenuated oxidative stress, expression of adhesion molecules, production of inflammatory cytokines, and macrophage infiltration in atherosclerotic plaques and decreased plasma triglyceride and low-density lipoprotein (LDL) cholesterol concentrations. MAO B inhibition had no therapeutic effect on restenosis in the femoral artery wire-induced injury model in C57BL/6 mice. In the NAFLD mouse model, MAO B inhibition reduced lipid droplet deposition in the liver and hepatic total cholesterol and triglyceride levels in C57BL/6 mice fed high-fat diets for 10 weeks. Key enzymes for triglyceride and cholesterol biosynthesis (fatty acid synthase and 3-hydroxy-3-methylglutaryl-CoA reductase, HMGCR) and inflammatory markers were inhibited, and cholesterol clearance was up-regulated (increased LDL receptor expression and reduced proprotein convertase subtilisin/kexin type 9, PCSK9, expression) by MAO B inhibition in the liver. These results were also demonstrated in the HepG2 liver cell model. Our data suggest that MAO B inhibition is a potential and novel treatment for atherosclerosis and NAFLD.

Serum vascular adhesion protein-1 is associated with twelve-year risk of incident cancer, cancer mortality, and all-cause mortality: a community-based cohort study.

Background: Vascular adhesion protein-1 (VAP-1), a dual-function glycoprotein, has been reported to play a crucial role in inflammation and tumor progression. We conducted a community-based cohort study to investigate whether serum VAP-1 could be a potential biomarker for predicting incident cancers and mortality. Method: From 2006 to 2018, we enrolled 889 cancer-free subjects at baseline. Serum VAP-1 levels were measured using a time-resolved immunofluorometric assay. Cancer and vital status of the participants were obtained by linking records with the computerized cancer registry and death certificates in Taiwan. Results: During a median follow-up of 11.94 years, 69 subjects developed incident cancers and 66 subjects died, including 29 subjects who died from malignancy. Subjects in the highest tertile of serum VAP-1 had a significantly higher risk of cancer incidence (p=0.0006), cancer mortality (p=0.0001), and all-cause mortality (p=0.0002) than subjects in the other tertiles. The adjusted hazard ratios per one standard deviation increase in serum VAP-1 concentrations were 1.28 for cancer incidence (95% CI=1.01-1.62), 1.60 for cancer mortality (95% CI=1.14-2.23), and 1.38 for all-cause mortality (95% CI=1.09-1.75). The predictive performance of serum VAP-1 was better than that of gender, smoking, body mass index, hypertension, diabetes, and estimated glomerular filtration rate but lower than that of age for cancer incidence, cancer mortality, and all-cause mortality, as evidenced by higher increments in concordance statistics and area under the receiver operating characteristic curve. Conclusion: Serum VAP-1 levels are associated with a 12-year risk of incident cancer, cancer mortality, and all-cause mortality in a general population.

Comparison between Machine Learning and Multiple Linear Regression to Identify Abnormal Thallium Myocardial Perfusion Scan in Chinese Type 2 Diabetes.

Type 2 diabetes mellitus (T2DM) patients have a high risk of coronary artery disease (CAD). Thallium-201 myocardial perfusion scan (Th-201 scan) is a non-invasive and extensively used tool in recognizing CAD in clinical settings. In this study, we attempted to compare the predictive accuracy of evaluating abnormal Th-201 scans using traditional multiple linear regression (MLR) with four machine learning (ML) methods. From the study, we can determine whether ML surpasses traditional MLR and rank the clinical variables and compare them with previous reports.In total, 796 T2DM, including 368 men and 528 women, were enrolled. In addition to traditional MLR, classification and regression tree (CART), random forest (RF), stochastic gradient boosting (SGB) and eXtreme gradient boosting (XGBoost) were also used to analyze abnormal Th-201 scans. Stress sum score was used as the endpoint (dependent variable). Our findings show that all four root mean square errors of ML are smaller than with MLR, which implies that ML is more precise than MLR in determining abnormal Th-201 scans by using clinical parameters. The first seven factors, from the most important to the least are:body mass index, hemoglobin, age, glycated hemoglobin, Creatinine, systolic and diastolic blood pressure. In conclusion, ML is not inferior to traditional MLR in predicting abnormal Th-201 scans, and the most important factors are body mass index, hemoglobin, age, glycated hemoglobin, creatinine, systolic and diastolic blood pressure. ML methods are superior in these kinds of studies.

Comparing Multiple Linear Regression and Machine Learning in Predicting Diabetic Urine Albumin-Creatinine Ratio in a 4-Year Follow-Up Study.

The urine albumin-creatinine ratio (uACR) is a warning for the deterioration of renal function in type 2 diabetes (T2D). The early detection of ACR has become an important issue. Multiple linear regression (MLR) has traditionally been used to explore the relationships between risk factors and endpoints. Recently, machine learning (ML) methods have been widely applied in medicine. In the present study, four ML methods were used to predict the uACR in a T2D cohort. We hypothesized that (1) ML outperforms traditional MLR and (2) different ranks of the importance of the risk factors will be obtained. A total of 1147 patients with T2D were followed up for four years. MLR, classification and regression tree, random forest, stochastic gradient boosting, and eXtreme gradient boosting methods were used. Our findings show that the prediction errors of the ML methods are smaller than those of MLR, which indicates that ML is more accurate. The first six most important factors were baseline creatinine level, systolic and diastolic blood pressure, glycated hemoglobin, and fasting plasma glucose. In conclusion, ML might be more accurate in predicting uACR in a T2D cohort than the traditional MLR, and the baseline creatinine level is the most important predictor, which is followed by systolic and diastolic blood pressure, glycated hemoglobin, and fasting plasma glucose in Chinese patients with T2D.

Gestational hypertriglyceridemia and adverse pregnancy outcomes: A search for cutoffs using generalized additive models.

OBJECTIVE: To explore cutoffs of gestational hypertriglyceridemia based on the risk of adverse pregnancy outcomes. METHODS: Pregnant women who visited National Taiwan University Hospital for prenatal care were included. Fasting plasma TG in the first and second trimesters were measured. Adverse pregnancy outcomes, including gestational diabetes and large for gestational age, were recorded and used in simple and multiple generalized additive models (GAM) to identify cutoffs for gestational hypertriglyceridemia. RESULTS: We recruited 807 pregnant woman-newborn pairs. Using GAM analyses, we identified plasma TG at 95 or 140 mg/dL (1.07 or 1.58 mmol/L) in the first trimester, and 173 or 220 mg/dl (1.95 or 2.48 mmol/L) in the second trimester as potential cutoffs. Gestational hypertriglyceridemia defined by the higher cutoffs in both trimesters were associated with adverse pregnancy outcomes and had a more reasonable prevalence and better specificity than the lower cutoffs (First trimester plasma TG ≥ 140 mg/dL, adjusted OR 2.56, 95% CI 1.17-5.69, p = 0.019, prevalence 19%, specificity 83%; Second trimester plasma TG ≥ 220 mg/dL, adjusted OR 1.70, 95% CI 1.00-2.87, p = 0.049, prevalence 19%, specificity 81%). CONCLUSIONS: Fasting plasma TG ≥ 140 mg/dL in the first trimester and ≥ 220 mg/dL in the second trimester can be used as cutoffs of gestational hypertriglyceridemia.

Trends in epidemiology of hyperglycemia in pregnancy in Taiwan, 2008-2017.

Background: Hyperglycemia in pregnancy (HIP) increases the risk of adverse pregnancy outcomes. The increasing prevalence of overweight or obesity and the increasing proportion of pregnant women with advanced maternal age (AMA) in the recent decade may affect its prevalence. We analyzed the secular trend of HIP prevalence in 2008-2017 in Taiwan and investigated the impact of AMA in this study. Methods: This cross-sectional study used data from Health and Welfare Data Science Center. Pregnant women who registered their data in the Birth Certificate Application in 2008-2017 were recruited. Diagnosis of HIP was defined by ICD-9-CM and ICD-10-CM codes. Results: In 2008-2017, 151,306-211,768 pregnant women were recruited in different years. The proportion of women with AMA increased from 15.8% to 32.1%. Meanwhile, the prevalence increased from 0.5% to 0.9% for preexisting diabetes, 0.2% to 0.4% for undiagnosed diabetes, and 11.4% to 14.5% for GDM. Maternal age was significantly associated with the prevalence of HIP. For women aged <30 years, 30-34 years and ≥35 years, the prevalence of preexisting diabetes were 0.51%, 0.75% and 1.24%, respectively (p<0.05); the prevalence of undiagnosed diabetes were 0.18%, 0.24% and 0.37%, respectively (p<0.05); and the prevalence of GDM were 10.57%, 14.77% and 18.13%, respectively (p<0.05). In all age groups, the prevalence of HIP increased over time in 2008-2017. Conclusion: The prevalence of HIP increased in Taiwan in 2008-2017, which may result from the increasing proportion of pregnant women with AMA and the change in the diagnostic criteria for GDM.

Maternal Plasma Lipids During Pregnancy, Insulin-like Growth Factor-1, and Excess Fetal Growth.

CONTEXT: Maternal lipids during pregnancy and placental growth factors are associated with excess fetal growth. However, how these factors interact to increase the risk of delivering large-for-gestational-age (LGA) neonates remains unclear. In this study, we investigated the relationship between maternal plasma triglycerides (TGs) and free fatty acids (FFAs) during pregnancy, cord blood insulin-like growth factors (IGF), and LGA. OBJECTIVE: In a cell model, we studied the effect of different FAs on placental IGF-1 secretion. METHODS: This cohort study included pregnant women with term pregnancy and without diabetes or hypertensive disorders in pregnancy. Maternal fasting plasma TGs and FFAs were measured in the second trimester. Cord blood IGF-1, IGF-2, and IGF binding protein-1 and protein-3 were measured at the time of delivery. A human trophoblast cell line, 3A-sub-E, was used to evaluate the effect of different FFAs on placental IGF-1 secretion. RESULTS: We recruited 598 pregnant women-neonate pairs. Maternal plasma TG (180 mg/dL [152.5-185.5 mg/dL] vs 166 mg/dL [133-206 mg/dL], P = .04) and cord blood IGF-1 concentrations (72.7 ±â€…23.0 vs 54.1 ±â€…22.8 ng/mL, P < .001) were higher in the LGA group and were significantly associated with birth weight z score. Maternal plasma free palmitic acid (PA) and stearic acid (SA), but not oleic acid (OA) or linoleic acid (LA), were significantly associated with cord blood IGF-1 concentrations. In 3A-sub-E cells, treatment with PA, SA, and LA, but not OA, induced IGF-1 expression and secretion. CONCLUSION: Certain FFAs can induce placental IGF-1 secretion, which suggests a potential pathophysiology linking maternal plasma lipids and LGA.

Simplifying the screening of gestational diabetes by maternal age plus fasting plasma glucose at first prenatal visit: A prospective cohort study.

AIM: The addition of maternal age to fasting plasma glucose (FPG) at 24-28 gestational weeks improves the performance of GDM screening as maternal age increases. However, this method delays the diagnosis of GDM. Since FPG at the first prenatal visit (FPV) is a screening option for pre-existing diabetes, we evaluated the performance of age plus FPG at the FPV to reduce the need for the OGTT. METHODS: Pregnant women were recruited consecutively in 2013-2018 (the training cohort) and 2019 (the validation cohort). We excluded women with twin pregnancies, unavailable FPG at the FPV or OGTT data, pre-pregnancy diabetes, or a history of GDM. All participants underwent FPG and haemoglobin A1c (HbA1c) at the FPV and received 75-g OGTT at 24-28 gestational weeks if FPG at the FPV was <92 mg/dL. GDM was diagnosed by the IADPSG criteria. Two algorithms were developed with the cutoffs determined when the percentage requiring OGTT (OGTT%) was the lowest and the sensitivity was ≥90%. RESULTS: The incidence of GDM increased with age. The "FPG at the FPV" algorithm reduced OGTT% to 68.8% with the FPG cutoff at 79 mg/dl. The "age plus FPG at the FPV" algorithm, with the cutoff of 114, further reduced OGTT% to 58.3%, with the sensitivity of 90.7% (9.3% GDM missed) and the specificity of 100%. These findings were replicated in the validation cohort. CONCLUSIONS: Screening GDM by maternal age plus FPG at the FPV can reduce OGTT%, especially in populations with a significant proportion of pregnant women with advanced ages.

Targeting fibrinogen-like protein 1 is a novel therapeutic strategy to combat obesity.

Fibrinogen-like-protein 1 (FGL1) is a novel hepatokine that plays an important role in hepatic steatosis and insulin resistance. Although FGL1 expression can be detected in adipose tissues, the functions of FGL1 in adipose tissues are still unknown. In this study, 356 participants with (body mass index (BMI) ≥25 kg/m2 ; n = 134) or without obesity (BMI <25 kg/m2 ; n = 222) were recruited, and we found that the plasma FGL1 concentrations were significantly higher in obese group than those of in the normal weight group, and were positively correlated with age, BMI, waist circumference, fat content, plasma glucose at 2 hours during an oral glucose tolerance test, and the insulin sensitivity index. In univariate analyses, BMI, waist circumference, total fat, visceral fat, and subcutaneous fat areas were positively correlated with FGL1 levels. After adjusting for age and gender, obesity indices, including the BMI and different fat areas, remained significantly associated with FGL1 levels. In order to investigate the causal relationship between FGL1 and obesity, animal and cell models were used. Overexpression of FGL1 in epididymal adipose tissue by lentiviral vector encoding FGL1 increased the fat pad size, whereas FGL1-knockdown by lentiviral vector encoding short-hairpin RNA targeted to FGL1 decreased high-fat diet-induced adiposity. In addition, 3T3-L1 adipocytes were used to clarify the possible mechanism of FGL1-induced adipogenesis. FGL1 induced adipogenesis through an ERK1/2-C/EBPß-dependent pathway in 3T3-L1 adipocytes. These findings highlight the pathophysiological role of FGL1 in obesity, and FGL1 might be a novel therapeutic target to combat obesity.

Overweight and obesity are associated with clustering of metabolic risk factors in early pregnancy and the risk of GDM.

AIM: Overweight and obesity are important risk factors of gestational diabetes mellitus (GDM). Clustering of metabolic risk factors in early pregnancy may be a potential pathogenesis between the link of overweight/obesity and GDM. Since it remains unexplored, we investigated if overweight and obesity are associated with clustering of metabolic risk factors in early pregnancy and the risk of GDM in this cohort study. METHODS: Total 527 women who visited National Taiwan University Hospital for prenatal care in between November 2013 to April 2018 were enrolled. Risk factors of GDM in the first prenatal visit (FPV) were recorded. Overweight/obesity was defined if body mass index ≥24 kg/m2. GDM was diagnosed from the result of a 75g oral glucose tolerance test in 24-28 gestational weeks. RESULTS: Overweight/obesity was associated with clustering of metabolic risk factors of GDM, including high fasting plasma glucose, high HbA1c, insulin resistance, high plasma triglyceride and elevated blood pressure in FPV (p<0.05). There was a positive relationship between the number of metabolic risk factors and the incidence of GDM (p <0.05). The odds ratios of HbA1c and diastolic blood pressure were higher in overweight/obese women, compared with those in normal-weight women. CONCLUSIONS: Overweight/obesity is associated with clustering of metabolic risk factors in early pregnancy, which is correlated with higher risk of GDM. Our findings suggest that metabolic risk factors during early pregnancy should be evaluated in overweight/obese women.