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Optic disc localization is of great diagnostic value related to retinal diseases, such as glaucoma and diabetic retinopathy. However, the detection process is quite challenging because positions of optic discs vary from image to image, and moreover, pathological changes, like hard exudates or neovascularization, may alter optic disc appearance. In this paper, we propose a robust approach to accurately detect the optic disc region and locate the optic disc center in color retinal images. The proposed technique employs a kernelized least-squares classifier to decide the area that contains optic disc. Then connected-component labeling and lumination information are used together to find the convergence of blood vessels, which is thought to be optic disc center. The proposed method has been evaluated over two datasets: the Digital Retinal Images for Vessel Extraction (DRIVE), and the Non-fluorescein Images for Vessel Extraction (NIVE) datasets. Experimental results have shown that our method outperforms existing methods, achieving a competitive accuracy (97.52 %) and efficiency (1.1577s). [ABSTRACT FROM AUTHOR]

A-FABP (adipocyte fatty acid-binding protein), one of the most abundant proteins in adipocytes, plays a key role in obesity-related insulin resistance, inflammation and atherosclerosis in animals. In the present study, we sought to investigate the association of A-FABP with HF (heart failure) in Chinese subjects. Serum A-FABP levels were measured in 252 HF patients and 261 age-, gender- and BMI (body mass index)-matched non-HF subjects. Echocardiography was performed on each patient. The severity of HF was determined by the NYHA (New York Heart Association) classification system. After adjustments for age, gender and BMI, serum A-FABP concentrations in patients with HF were significantly higher than in non-HF patients [11.17 (6.63-19.93) ng/ml compared with 5.67 (3.20-8.87) ng/ml; P<0.001] and significantly progressed with the NYHA class (P<0.001). In addition, NT-proBNP (N-terminal pro-brain natriuretic peptide) was independently and positively correlated with A-FABP (standardized ß =0.340, P<0.001) after adjusting for confounding factors. Each echocardiographic parameter, especially LVEF (left ventricular ejection fraction), was independently associated with A-FABP (all P<0.05). Multivariate logistic regression analysis demonstrated that A-FABP concentration was an independent risk factor for HF [odds ratio, 6.93 (95% confidence interval, 2.49-19.30); P<0.001]. Our results demonstrate that A-FABP is closely associated with HF, and raise the possibility that increased A-FABP may be causally related to the pathogenesis of heart dysfunction in humans. [ABSTRACT FROM AUTHOR]

The choroid layer, situated between the retina and sclera, is a tissue layer that contains blood vessels. Optical coherence tomography (OCT) is a method that utilizes light for imaging purposes to capture detailed images of this specific part of the retina. Although there have been notable advancements, the automated choroid segmentation persists difficult due to the inherently low contrast of OCT images. Handcrafted features, which provide domain-specific knowledge, and convolutional neural network (CNN) methods, which handle large sets of general features, are both employed in addressing this challenge. There is a plea to merge these two different classes of feature-generation methods. The challenge is to form a combined set of features that can outperform either feature extraction method. We proposed a cascaded method for choroid layer segmentation that logically combines a CNN feature set with handcrafted features. Our method used handcrafted features, Gabor features, Haar features, and gray-level co-occurrence features due to the robustness to segment low-contrast images. A support vector machine was independently trained using the CNN feature set and handcrafted feature set, which were then linearly combined for the final choroid segmentation. The method under consideration was assessed using a dataset comprising 525 images. Furthermore, we introduced two metrics to quantitatively evaluate the thickness of the layer: (i) the pixel-wise error in the segmentation and (ii) the average error in the generated thickness map. Through experimentation, the results demonstrated that our proposed method successfully accomplished the intended objective, a remarkable accuracy of 97 percent, with a mean error rate of 2.84. Moreover, it outperformed existing state-of-the-art segmentation methods. [ABSTRACT FROM AUTHOR]

• IL-33/IL1RL1 axis induces the activation of NF-κB in AML cells. • IL-33-dependent NF-κB activation is important for the maintenance of AML cells. • NF-κB signaling in turn causes autocrine IL-6-induced activation of AKT in AML. Despite recent advances in therapeutic regimens, the prognosis of acute myeloid leukemia (AML) remains poor. Following our previous finding that interleukin-33 (IL-33) promotes cell survival along with activated NF-κB in AML, we further investigated the role of NF-κB during leukemia development. Flow cytometry was performed to value the apoptosis and proliferation. qRT-PCR and western blot were performed to detect the expression of IL-6, active caspase 3, BIRC2, Bcl-2, and Bax, as well as activated NF-κB p65 and AKT. Finally, xenograft mouse models and AML patient samples were used to verify the findings observed in AML cell lines. IL-33-mediated NF-κB activation in AML cell lines contributes to a reduction in apoptosis, an increase in proliferation rate as well as a decrease in drug sensitivity, which were reversed by NF-κB inhibitor, Bay-117085. Moreover, IL-33 decreased the expression of active caspase-3 while increasing the levels of BIRC2, Bcl-2, and Bax, and these effects were blocked by Bay-117085. Additionally, NF-κB activation induced by IL-33 increases the production of IL-6 and autocrine activation of AKT. Co-culture of bone marrow stroma with AML cells resulted in increased IL-33 expression by leukemia cells, along with decreased apoptosis level and reduced drug sensitivity. Finally, we confirmed the in vivo pro-tumor effect mediated by IL-33/ NF-κB axis using a xenograft model of AML. Our data indicate that IL-33/IL1RL1-dependent signaling contributes to AML cell activation of NF-κB, which in turn causes autocrine IL-6-induced activation of pAKT, supporting IL-33/NF-κB/pAKT as a potential target for AML therapy. [ABSTRACT FROM AUTHOR]

Glomerulonephritis (GN) is a severe kidney disorder in which the tissues in the kidney become inflamed and have problems filtering waste from the blood. Typical approaches for GN diagnosis require a specialist’s examination of pathological glomerular features (PGF) in pathology images of a patient. These PGF are primarily analyzed via manual quantitative evaluation, which is a time-consuming, labor-intensive, and error-prone task for doctors. Thus, automatic and accurate detection of PGF is crucial for the efficient diagnosis of GN and other kidney-related diseases. Recent advances in convolutional neural network-based deep learning methods have shown the capability of learning complex structural variants with promising detection results in medical image applications. However, these methods are not directly applicable to glomerular detection due to large spatial and structural variability and inter-class imbalance. Thus, we propose an efficient glomerular detection network (EGDNet) for the first time for seven types of PGF detection. Our EGDNet consists of four modules: (i) a hybrid data augmentation strategy to resolve dataset problems, (ii) an efficient intersection over unit balancing module for uniform sampling of hard and easy samples, (iii) a feature pyramid balancing module to obtain balanced multi-scale features for robust detection, and (iv) balanced L1 regression loss which alleviates the impact of anomalous data for multi-PGF detection. We also formulated a private dataset of seven PGF from an affiliated hospital in Shanghai, China. Experiments on the dataset show that our EGDNet outperforms state-of-the-art methods by achieving superior accuracy of 91.2%\documentclass[12pt]{minimal}\usepackage{amsmath}\usepackage{wasysym}\usepackage{amsfonts}\usepackage{amssymb}\usepackage{amsbsy}\usepackage{mathrsfs}\usepackage{upgreek}\setlength{\oddsidemargin}{-69pt}\begin{document}$$\%$$\end{document}, 94.9%\documentclass[12pt]{minimal}\usepackage{amsmath}\usepackage{wasysym}\usepackage{amsfonts}\usepackage{amssymb}\usepackage{amsbsy}\usepackage{mathrsfs}\usepackage{upgreek}\setlength{\oddsidemargin}{-69pt}\begin{document}$$\%$$\end{document}, and 94.2%\documentclass[12pt]{minimal}\usepackage{amsmath}\usepackage{wasysym}\usepackage{amsfonts}\usepackage{amssymb}\usepackage{amsbsy}\usepackage{mathrsfs}\usepackage{upgreek}\setlength{\oddsidemargin}{-69pt}\begin{document}$$\%$$\end{document} on small, medium, and large pathological features, respectively. [ABSTRACT FROM AUTHOR]

Hypertensive retinopathy (HR) can potentially lead to vision loss if left untreated. Early screening and treatment are critical in reducing the risk of vision loss. The computer-aided diagnostic system presents an opportunity to improve the efficiency and reliability of HR screening and diagnosis, particularly given the shortage of specialized medical professionals and the challenges faced by primary care physicians in making precise diagnoses. A notable barrier to the development of such diagnostic algorithms is the lack of publicly available benchmarks and datasets. To address these issues, we organized a challenge named “HRDC—Hypertensive Retinopathy Diagnosis Challenge” in conjunction with the Computer Graphics International (CGI) 2023 conference. The challenge provided a fundus image dataset for two clinical tasks: hypertension classification and HR classification, with each task containing 1000 images. This paper presents a concise summary and analysis of the submitted methods and results for the two challenge tasks. For hypertension classification, the best performing algorithm achieved a Kappa score of 0.3819, an F1 score of 0.6337, and a specificity of 0.8472. For HR classification, the best performing algorithm achieved a Kappa score of 0.4154, an F1 score of 0.6122, and a specificity of 0.8444. We also explored an ensemble approach to the top-ranking methods, which further improved performance beyond the individual best performing algorithm for each task. The challenge results show that there is room for further optimization of these methods, but the insights and methodologies derived from this challenge provide valuable directions for developing more precise and reliable classification models for hypertension and HR. [ABSTRACT FROM AUTHOR]

Swept-source optical coherence tomography (SS-OCT) is widely used to diagnose high myopia due to its advantage in imaging the ocular anatomic structures. Although the scleral deformation provides information on the risk of the high myopia, further validation of these highly promising findings in clinical studies has been limited by the current semi-automated software, which requires human input, and the automatic analysis of the scleral structure is quite challenging due to the ambiguous boundaries. To address these challenges, we propose a deep contour attention network (DCANet) for automatic segmentation of scleral deformation structure. Specifically, we design a scale-aware attention feature fusion module to achieve cross-scale feature fusion, which can facilitate the network to learn complementary information from multi-scale features. In addition, we develop a pyramid feature enhancement module to allow the network to learn global contextual features through the combination of receptive field and attention mechanism, and we also propose a boundary heatmap label to enrich boundary information. We evaluate the performance of the proposed method on two in-house SS-OCT datasets. In addition to the multiple metrics that are used for evaluating the segmentation performance, including Jaccard similarity coefficient, dice similarity coefficient and boundary distance error, we also propose length similarity coefficient and angle similarity coefficient to evaluate the length estimation and angle estimation, respectively. The experimental results show that our method can effectively improve the segmentation performance, and our DCANet achieves the overall best performance on two datasets compared with other state-of-the-art networks. Our findings motivate the development of clinically applicable deep learning systems for the prediction of high myopia progression on the basis of the scleral phenotypes from SS-OCT images. [ABSTRACT FROM AUTHOR]

Primary diabetes care and diabetic retinopathy (DR) screening persist as major public health challenges due to a shortage of trained primary care physicians (PCPs), particularly in low-resource settings. Here, to bridge the gaps, we developed an integrated image-language system (DeepDR-LLM), combining a large language model (LLM module) and image-based deep learning (DeepDR-Transformer), to provide individualized diabetes management recommendations to PCPs. In a retrospective evaluation, the LLM module demonstrated comparable performance to PCPs and endocrinology residents when tested in English and outperformed PCPs and had comparable performance to endocrinology residents in Chinese. For identifying referable DR, the average PCP's accuracy was 81.0% unassisted and 92.3% assisted by DeepDR-Transformer. Furthermore, we performed a single-center real-world prospective study, deploying DeepDR-LLM. We compared diabetes management adherence of patients under the unassisted PCP arm (n = 397) with those under the PCP+DeepDR-LLM arm (n = 372). Patients with newly diagnosed diabetes in the PCP+DeepDR-LLM arm showed better self-management behaviors throughout follow-up (P < 0.05). For patients with referral DR, those in the PCP+DeepDR-LLM arm were more likely to adhere to DR referrals (P < 0.01). Additionally, DeepDR-LLM deployment improved the quality and empathy level of management recommendations. Given its multifaceted performance, DeepDR-LLM holds promise as a digital solution for enhancing primary diabetes care and DR screening., (© 2024. The Author(s).)

Importance: Myopic maculopathy (MM) is a major cause of vision impairment globally. Artificial intelligence (AI) and deep learning (DL) algorithms for detecting MM from fundus images could potentially improve diagnosis and assist screening in a variety of health care settings., Objectives: To evaluate DL algorithms for MM classification and segmentation and compare their performance with that of ophthalmologists., Design, Setting, and Participants: The Myopic Maculopathy Analysis Challenge (MMAC) was an international competition to develop automated solutions for 3 tasks: (1) MM classification, (2) segmentation of MM plus lesions, and (3) spherical equivalent (SE) prediction. Participants were provided 3 subdatasets containing 2306, 294, and 2003 fundus images, respectively, with which to build algorithms. A group of 5 ophthalmologists evaluated the same test sets for tasks 1 and 2 to ascertain performance. Results from model ensembles, which combined outcomes from multiple algorithms submitted by MMAC participants, were compared with each individual submitted algorithm. This study was conducted from March 1, 2023, to March 30, 2024, and data were analyzed from January 15, 2024, to March 30, 2024., Exposure: DL algorithms submitted as part of the MMAC competition or ophthalmologist interpretation., Main Outcomes and Measures: MM classification was evaluated by quadratic-weighted κ (QWK), F1 score, sensitivity, and specificity. MM plus lesions segmentation was evaluated by dice similarity coefficient (DSC), and SE prediction was evaluated by R2 and mean absolute error (MAE)., Results: The 3 tasks were completed by 7, 4, and 4 teams, respectively. MM classification algorithms achieved a QWK range of 0.866 to 0.901, an F1 score range of 0.675 to 0.781, a sensitivity range of 0.667 to 0.778, and a specificity range of 0.931 to 0.945. MM plus lesions segmentation algorithms achieved a DSC range of 0.664 to 0.687 for lacquer cracks (LC), 0.579 to 0.673 for choroidal neovascularization, and 0.768 to 0.841 for Fuchs spot (FS). SE prediction algorithms achieved an R2 range of 0.791 to 0.874 and an MAE range of 0.708 to 0.943. Model ensemble results achieved the best performance compared to each submitted algorithms, and the model ensemble outperformed ophthalmologists at MM classification in sensitivity (0.801; 95% CI, 0.764-0.840 vs 0.727; 95% CI, 0.684-0.768; P = .006) and specificity (0.946; 95% CI, 0.939-0.954 vs 0.933; 95% CI, 0.925-0.941; P = .009), LC segmentation (DSC, 0.698; 95% CI, 0.649-0.745 vs DSC, 0.570; 95% CI, 0.515-0.625; P < .001), and FS segmentation (DSC, 0.863; 95% CI, 0.831-0.888 vs DSC, 0.790; 95% CI, 0.742-0.830; P < .001)., Conclusions and Relevance: In this diagnostic study, 15 AI models for MM classification and segmentation on a public dataset made available for the MMAC competition were validated and evaluated, with some models achieving better diagnostic performance than ophthalmologists.

Diabetic retinopathy (DR) is a major ocular complication of diabetes and the leading cause of blindness and visual impairment, particularly among adults of working-age adults. Although the medical and economic burden of DR is significant and its global prevalence is expected to increase, particularly in low- and middle-income countries, a large portion of vision loss caused by DR remains preventable through early detection and timely intervention. This perspective reviewed the latest developments in research and innovation in three areas, first novel biomarkers (including advanced imaging modalities, serum biomarkers, and artificial intelligence technology) to predict the incidence and progression of DR, second, screening and early detection of referable DR and vision-threatening DR (VTDR), and finally, novel therapeutic strategies for VTDR, including diabetic macular oedema (DME), with the goal of reducing diabetic blindness., (Copyright © 2024. Published by Elsevier Inc.)

Artificial intelligence (AI) use in diabetes care is increasingly being explored to personalise care for people with diabetes and adapt treatments for complex presentations. However, the rapid advancement of AI also introduces challenges such as potential biases, ethical considerations, and implementation challenges in ensuring that its deployment is equitable. Ensuring inclusive and ethical developments of AI technology can empower both health-care providers and people with diabetes in managing the condition. In this Review, we explore and summarise the current and future prospects of AI across the diabetes care continuum, from enhancing screening and diagnosis to optimising treatment and predicting and managing complications., Competing Interests: Declaration of interests NS is the director of London Safety and Training Solutions. HL is supported by the National Key Research and Development Program of China (2022YFA1004804), the Excellent Young Scientists Fund of the National Natural Science Foundation of China (82022012), and the innovative research team of high-level local universities in Shanghai (SHSMU-ZDCX20212700). Y-CT is supported by the National Medical Research Council of Singapore (NMRC/MOH/ HCSAINV21nov-0001)., (Copyright © 2024 Published by Elsevier Ltd. All rights reserved, including those for text and data mining, AI training, and similar technologies.)

Understanding and addressing the dynamics of infectious diseases, such as coronavirus disease 2019, are essential for effectively managing the current situation and developing intervention strategies. Epidemiologists commonly use mathematical models, known as epidemiological equations (EE), to simulate disease spread. However, accurately estimating the parameters of these models can be challenging due to factors like variations in social distancing policies and intervention strategies. In this study, we propose a novel method called deep dynamic epidemiological modeling (DDE) to address these challenges. The DDE method combines the strengths of EE with the capabilities of deep neural networks to improve the accuracy of fitting real-world data. In DDE, we apply neural ordinary differential equations to solve variant-specific equations, ensuring a more precise fit for disease progression in different geographic regions. In the experiment, we tested the performance of the DDE method and other state-of-the-art methods using real-world data from five diverse geographic entities: the USA, Colombia, South Africa, Wuhan in China, and Piedmont in Italy. Compared to the state-of-the-art method, DDE significantly improved accuracy, with an average fitting Pearson coefficient exceeding 0.97 across the five geographic entities. In summary, the DDE method enhances the accuracy of parameter fitting in epidemiological models and provides a foundation for constructing simpler models adaptable to different geographic areas., Competing Interests: Competing InterestsThe authors declare no competing interests., (© The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.)

Obesity is closely related to non-alcoholic fatty liver disease (NAFLD). Although sex differences in body fat distribution have been well demonstrated, little is known about the sex-specific associations between adipose tissue and the development of NAFLD. Using community-based cohort data, we evaluated the associations between magnetic resonance imaging quantified areas of abdominal adipose tissue, including visceral adipose tissue (VAT) and subcutaneous adipose tissue (SAT), and incident NAFLD in 2830 participants (1205 males and 1625 females) aged 55-70 years. During a 4.6-year median follow-up, the cumulative incidence rates of NAFLD increased with areas of VAT and SAT both in males and in females. Further analyses showed that the above-mentioned positive associations were stronger in males than in females, especially in participants under 60 years old. In contrast, these sex differences disappeared in those over 60 years old. Furthermore, the risk of developing NAFLD increased non-linearly with increasing fat area in a sex-specific pattern. Additionally, sex-specific potential mediators, such as insulin resistance, lipid metabolism, inflammation, and adipokines, may exist in the associations between adipose tissue and NAFLD. This study showed that the associations between abdominal fat and the risk of NAFLD were stratified by sex and age, highlighting the potential need for sex- and age-specific management of NAFLD., (© The Author(s) (2023). Published by Oxford University Press on behalf of Journal of Molecular Cell Biology, CEMCS, CAS.)

Emerging evidence suggests that modulation of gut microbiota by dietary fibre may offer solutions for metabolic disorders. In a randomized placebo-controlled crossover design trial (ChiCTR-TTRCC-13003333) in 37 participants with overweight or obesity, we test whether resistant starch (RS) as a dietary supplement influences obesity-related outcomes. Here, we show that RS supplementation for 8 weeks can help to achieve weight loss (mean -2.8 kg) and improve insulin resistance in individuals with excess body weight. The benefits of RS are associated with changes in gut microbiota composition. Supplementation with Bifidobacterium adolescentis, a species that is markedly associated with the alleviation of obesity in the study participants, protects male mice from diet-induced obesity. Mechanistically, the RS-induced changes in the gut microbiota alter the bile acid profile, reduce inflammation by restoring the intestinal barrier and inhibit lipid absorption. We demonstrate that RS can facilitate weight loss at least partially through B. adolescentis and that the gut microbiota is essential for the action of RS., (© 2024. The Author(s).)

We described a challenge named "DRAC - Diabetic Retinopathy Analysis Challenge" in conjunction with the 25th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2022). Within this challenge, we provided the DRAC datset, an ultra-wide optical coherence tomography angiography (UW-OCTA) dataset (1,103 images), addressing three primary clinical tasks: diabetic retinopathy (DR) lesion segmentation, image quality assessment, and DR grading. The scientific community responded positively to the challenge, with 11, 12, and 13 teams submitting different solutions for these three tasks, respectively. This paper presents a concise summary and analysis of the top-performing solutions and results across all challenge tasks. These solutions could provide practical guidance for developing accurate classification and segmentation models for image quality assessment and DR diagnosis using UW-OCTA images, potentially improving the diagnostic capabilities of healthcare professionals. The dataset has been released to support the development of computer-aided diagnostic systems for DR evaluation., Competing Interests: The authors declare no competing interests., (© 2024 The Author(s).)

Diabetic retinopathy (DR) is the leading cause of preventable blindness worldwide. The risk of DR progression is highly variable among different individuals, making it difficult to predict risk and personalize screening intervals. We developed and validated a deep learning system (DeepDR Plus) to predict time to DR progression within 5 years solely from fundus images. First, we used 717,308 fundus images from 179,327 participants with diabetes to pretrain the system. Subsequently, we trained and validated the system with a multiethnic dataset comprising 118,868 images from 29,868 participants with diabetes. For predicting time to DR progression, the system achieved concordance indexes of 0.754-0.846 and integrated Brier scores of 0.153-0.241 for all times up to 5 years. Furthermore, we validated the system in real-world cohorts of participants with diabetes. The integration with clinical workflow could potentially extend the mean screening interval from 12 months to 31.97 months, and the percentage of participants recommended to be screened at 1-5 years was 30.62%, 20.00%, 19.63%, 11.85% and 17.89%, respectively, while delayed detection of progression to vision-threatening DR was 0.18%. Altogether, the DeepDR Plus system could predict individualized risk and time to DR progression over 5 years, potentially allowing personalized screening intervals., (© 2024. The Author(s).)

Lung cancer is a major cause for cancer-related deaths. The detection of pulmonary cancer in the early stages can highly increase survival rate. Manual delineation of lung nodules by radiologists is a tedious task. We developed a novel computer-aided decision support system for lung nodule detection based on a 3D Deep Convolutional Neural Network (3DDCNN) for assisting the radiologists. Our decision support system provides a second opinion to the radiologists in lung cancer diagnostic decision making. In order to leverage 3-dimensional information from Computed Tomography (CT) scans, we applied median intensity projection and multi-Region Proposal Network (mRPN) for automatic selection of potential region-of-interests. Our Computer Aided Diagnosis (CAD) system has been trained and validated using LUNA16, ANODE09, and LIDC-IDR datasets; the experiments demonstrate the superior performance of our system, attaining sensitivity, specificity, AUROC, accuracy, of 98.4%, 92%, 96% and 98.51% with 2.1 FPs per scan. We integrated cloud computing, trained and validated our Cloud-Based 3DDCNN on the datasets provided by Shanghai Sixth People's Hospital, as well as LUNA16, ANODE09, and LIDC-IDR. Our system outperformed the state-of-the-art systems and obtained an impressive 98.7% sensitivity at 1.97 FPs per scan. This shows the potentials of deep learning, in combination with cloud computing, for accurate and efficient lung nodule detection via CT imaging, which could help doctors and radiologists in treating lung cancer patients.

Background: Fibroblast growth factor 21 (FGF21) is a hormone involved in the metabolism of carbohydrates and lipids. Increased circulating FGF21 levels are closely associated with nonalcoholic fatty liver disease (NAFLD). However, the association between genetic variations of FGF21 and NAFLD remains unknown. In our study, we aimed to investigate the association of these genetic variations with serum FGF21 levels and NAFLD., Methods: We genotyped four single nucleotide polymorphisms (SNPs) in FGF21 and its flanking region in 340 nondiabetic subjects. NAFLD was defined as the presence of a specific abdominal ultrasonographic pattern. Serum FGF21 concentrations were measured using an enzyme-linked immunosorbent assay kit., Results: We found significant evidence of an association with NAFLD for rs499765 (p = 0.039). After adjusting for age and sex, the effect of rs499765 on NAFLD remained significant (p = 0.045). However, after adjusting for multiple comparisons, no association was found. Moreover, rs499765 was associated with serum FGF21 levels (p = 0.030). In addition, both rs2071699 and rs838136 showed an association with serum aspartate aminotransferase levels (p = 0.049 and p = 0.047, respectively). The SNP rs838136 also showed a correlation with serum alanine aminotransferase concentrations after adjustment for body mass index (p = 0.034). We also combined the minor group with the heterozygous genotype and observed that rs499765 had an effect on FGF21 (p = 0.031)., Conclusion: The variant rs499765 adjacent to FGF21 is associated with serum FGF21 levels and NAFLD in a Chinese nondiabetic population., (© 2014 S. Karger AG, Basel.)

Objectives: To evaluate haemoglobin A1c (HbA(1c)) in diagnosing diabetes and identify the optimal HbA(1c) threshold to be used in Chinese adults., Design: Multistage stratified cross sectional epidemiological survey., Setting: Shanghai, China, 2007-8., Participants: 4886 Chinese adults over 20 years of age with no history of diabetes., Main Outcome Measures: Performance of HbA(1c) at increasing thresholds for diagnosing diabetes., Results: The area under the receiver operating characteristics curve for detecting undiagnosed diabetes was 0.856 (95% confidence interval 0.828 to 0.883) for HbA(1c) alone and 0.920 (0.900 to 0.941) for fasting plasma glucose alone. Very high specificity (96.1%, 95% confidence interval 95.5% to 96.7%) was achieved at an HbA(1c) threshold of 6.3% (2 SD above the normal mean). Moreover, the corresponding sensitivity was 62.8% (57.1% to 68.3%), which was equivalent to that of a fasting plasma glucose threshold of 7.0 mmol/l (57.5%, 51.7% to 63.1%) in detecting undiagnosed diabetes. In participants at high risk of diabetes, the HbA(1c) threshold of 6.3% showed significantly higher sensitivity (66.9%, 61.0% to 72.5%) than both fasting plasma glucose >or=7.0 mmol/l (54.4%, 48.3% to 60.4%) and HbA(1c) >or=6.5% (53.7%, 47.6% to 59.7%) (P<0.01)., Conclusions: An HbA(1c) threshold of 6.3% was highly specific for detecting undiagnosed diabetes in Chinese adults and had sensitivity similar to that of using a fasting plasma glucose threshold of 7.0 mmol/l. This optimal HbA(1c) threshold may be suitable as a diagnostic criterion for diabetes in Chinese adults when fasting plasma glucose and oral glucose tolerance tests are not available.

Objective: Fibroblast growth factor (FGF) 21, a hormone primarily secreted by liver, has recently been shown to have beneficial effects on glucose and lipid metabolism and insulin sensitivity in animal models. This study investigated the association of serum FGF21 levels with insulin secretion and sensitivity, as well as circulating parameters of lipid metabolism and hepatic enzymes in Chinese subjects., Design: Serum FGF21 levels were determined by ELISA in 134 normal glucose tolerance (NGT), 101 isolated-impaired fasting glucose, and 118 isolated-impaired glucose tolerance (I-IGT) Chinese subjects, and their association with parameters of adiposity, glucose, and lipid profiles, and levels of liver injury markers was studied. In a subgroup of this study, the hyperglycemic clamp technique was performed in 31 NGT, 17 isolated-impaired fasting glucose, and 15 I-IGT subjects to measure insulin secretion and sensitivity to test the associations with serum FGF21., Results: The serum FGF21 levels in I-IGT were significantly higher than NGT subjects [164.6 pg/ml (89.7, 261.0) vs. 111.8 pg/ml (58.0, 198.9); P < 0.05], and correlated positively with several parameters of adiposity. Multiple stepwise regression analysis showed an independent association of serum FGF21 with serum triglycerides, total cholesterol, and gamma-glutamyltransferase (all P < 0.05). However, FGF21 did not correlate with insulin secretion and sensitivity, as measured by hyperglycemic clamp and a 75-g oral glucose tolerance test., Conclusions: Serum levels of FGF21 are closely related to adiposity, lipid metabolism, and biomarkers of liver injury but not insulin secretion and sensitivity in humans.

Background: Recent data show that blood glucose (BG) variability is an HbA1c-independent risk factor for diabetic complications. This study investigated the characteristics of BG variability in type 2 diabetic patients and the effect of intensive treatment., Material/methods: Forty-eight subjects with normal glucose regulation and 69 patients with newly diagnosed type 2 diabetes were monitored using the continuous glucose monitoring system. A subset of the type 2 diabetic patients (n=23) whose HbA1c was >8.5% was monitored a second time following 2 to 3 weeks of treatment with multiple daily injections. The mean amplitude of glycemic excursions (MAGE), mean of daily differences (MODD), and the incremental areas above preprandial glucose values (AUCpp) were used for assessing intra-day, inter-day, and postprandial BG variability., Results: In type 2 diabetic patients, the MAGE, MODD, and AUCpp levels were all higher than those of subjects with normal glucose regulation (P<0.001). Multivariant regression analysis indicated that AUCpp was the main independent factor influencing MAGE (Adjusted R2=0.56), while postprandial hyperglycemia was most prominent following breakfast and less evident during lunch and dinner. After intensive treatment, significant decreases in MAGE, MODD, and AUCpp were observed (41%, 29%, and 49%, respectively, P<0.001). AUCpp after breakfast was higher than after lunch and dinner (P<0.05). In 65.2% of the subjects, peak intra-day values occurred 103+/-30 minutes after breakfast., Conclusions: Minimizing glycemic variability in type 2 diabetic patients, especially postprandial glucose excursions following breakfast, may be an important aspect of glucose management.