Cardiometabolic aspects of polycystic ovary syndrome

Polycystic ovary syndrome (PCOS) is a common endocrine disorder affecting 6-7% of the population. It is characterised by chronic anovulation and hyperandrogenism with the clinical manifestation of oligomenorrhoea, hirsutism and acne. A ten to twenty fold increased risk for type 2 diabetes in PCOS patients compared to weight matched female control subjects makes the syndrome of high socioeconomic importance. The use of differing diagnostic criteria makes the comparison of studies on PCOS difficult until harmonisation through the Rotterdam consensus in 2004. Despite being removed from the diagnostic criteria by the the Rotterdam consensus, the LH/FSH ratio is still widely used as one of the diagnostic criteria for PCOS. Therefore to determine the usefulness of the LH/FSH ratio in the diagnosis of PCOS, I have conducted a study as described in chapter two and showed that an elevated LH to FSH ratio was as commonly found in normal women as those with PCO, and therefore of no diagnostic value.In January 2004, the European Society for Human Reproduction and Embryology (ESHRE) and the American Society for Reproductive Medicine (ASRM) co-sponsored the Rotterdam polycystic ovary syndrome consensus workshop that published diagnostic guidelines, building on the consensus statement of the National Institutes of Health 1990. The Rotterdam criteria for the diagnosis of PCOS states 2 of the 3 features needs to be present to make the diagnosis, and with the exclusion of other aetiologies (congenital adrenal hyperplasia, androgen-secreting tumors, Gushing's syndrome). These features include (l)Oligo- or anovulation (2)Clinical and/or biochemical signs of hyperandrogenism and (3)Polycystic ovaries (either 12 or more follicles measuring 2-9 mm in diameter, or an ovarian volume of > 10 cm³ ). Following the introduction of this guideline, the diagnosis of PCOS in patients recruited for studies on PCOS have been standardised. However, the current biochemical test for hyperandrogenism in women is still not ideal, due to the variation in the assay at low values. All three of total testosterone, bioavailable testosterone and free androgen index (FAI) are currently used as markers of hyperandrogenism for the diagnosis of PCOS. In chapter three, I evaluated the variability of each of the three markers as well as their use in the diagnosis and monitoring patients with PCOS and found that FAI is a better diagnostic marker for hyperandrogenism in patients with PCOS, but once the diagnosis is made, all three methods are equally good in monitoring disease progression.It has now been recognised that the diagnosis of metabolic syndrome identifies patients at increased risk of developing cardiovascular disease, and attempts have been made to develop the most convenient and useful criteria for the diagnosis of this condition in clinical practice. With the pathogenesis of metabolic syndrome not well understood, central obesity and insulin resistance are acknowledged as important causative factors. Cardiovascular disease studies in PCOS had so far been inconclusive with some suggesting increased cardiac events among women with PCOS whilst other studies suggesting no increase compared with normal cycling women. This may be attributed to small sample size in studies and variation in characteristics of patients recruited as well as surrogate markers used. C-reactive protein (CRP) had been widely used as a marker of inflammation, endothelial dysfunction and risk of cardiovascular disease in general and in patients with PCOS. However, there had not been any studies on the biovailability of this marker as the potential utility of CRP as a marker of cardiovascular risk may be limited by the magnitude of this variability in both health and disease, since there can be substantial overlap between PCOS and control individuals. In chapter four, the biological variation of high sensitivity CRP in women with PCOS were compared to normal menstruating women. I found that while the mean concentration of CRP was higher in individuals with PCOS compared to healthy controls, the intraindividual variation of CRP was similarly large in both groups. Therefore, the potential utility of CRP as a marker of cardiovascular risk may be limited by the magnitude of this variability in both health and disease, since there can be substantial overlap between PCOS and control individuals. PCOS is associated with a high risk of progression to type 2 diabetes (T2DM) and impaired glucose tolerance. A history of T2DM in a first-degree relative appears to define a subset of PCOS subjects with a greater prevalence of insulin secretory defects. However, factors underlying the progression of PCOS to T2DM are unclear and may be due to either an increase in the underlying insulin resistance or the progression of beta cell failure. Chapter five described a comparison study between the insulin resistance and beta cell functions in patients with PCOS to that of diet controlled T2DM. I found that the progression from PCOS to the development of T2DM is unlikely to be due to a further increase in insulin resistance (or variability), but rather the progressive failure of pancreatic beta cells with a decrease in insulin production.Having established the effectiveness in the diagnosis of PCOS and its progression, I went on to establish the effectiveness of individual treatments of PCOS. The treatment of patients with PCOS requires that the specific goal(s) of the therapy be first established. Individual goals may include fertility, treatment for hirsutism and/or acne, achieving a regular menstrual cycle, weight reduction and the prevention of the long term consequences associated with PCOS (type 2 diabetes, dyslipidaemia and possibly cardiovascular disease) - or all of the above. Treatments aimed at modifying the long-term consequences on cardiometabolic aspect of PCOS include weight reduction strategies as well as the use of insulin sensitizers. There is currently insufficient data to suggest the superiority of one treatment over another or the use of these medications for treatment of cardiometabolic risks in patients with PCOS. Endothelial dysfunction had been recognised as an early marker for cardiovascular disease and chapter six compared the changes in endothelial function in women treated with either metformin or pioglitazone. I found that pioglitazone significantly improved endothelial function and hs-CRP whereas metformin did not produce significant improvements. Chapter seven evaluates the effects of orlistat, metformin and pioglitazone on metabolic profile and biological variability of IR in women with PCOS. The results showed that only orlistat reduced both IR and its variability significantly, though all three drugs were effective in reducing hyperandrogenism within the 12 week period of the study. These effects with orlistat were coupled with a significantly reduction in total cholesterol through a reduction in LDL.After conducting the above studies and searching through literatures, I found that studies in cardiometabolic risks in women with PCOS had so far shown conflicting results, and this may be due to the heterogeneity within the group. Chapter eight evaluates this heterogeneity particularly between anovulatory and ovulatory women with PCOS, where all subjects met the Rotterdam criteria. Women with anovulatory PCOS were found to have higher mean and biological variability of IR compared to those having an ovulatory cycle, and both were higher than women without PCOS. This suggests that the subset of patients who ovulate may be better protected against future cardiovascular consequences.In conclusion, this thesis demonstrated that LH/FSH ratio is of no use in the diagnosis of PCOS and that free androgen index appeared to be the best diagnostic marker when compared to total testosterone and bioavailable testosterone. Through the biological variability concept this thesis demonstrated the limitation of the use of hs-CRP in PCOS, and that orlistat, through weight reduction and insulin sensitization, may be a better agent in treatment of PCOS due to its multidimensional effect resulting in both the reduction in mean IR and its biological variability. Finally, this thesis also showed that women with ovulatory PCOS may be better protected against future cardiovascular consequences

Cluster-GCN: An Efficient Algorithm for Training Deep and Large Graph Convolutional Networks

Graph convolutional network (GCN) has been successfully applied to many graph-based applications; however, training a large-scale GCN remains challenging. Current SGD-based algorithms suffer from either a high computational cost that exponentially grows with number of GCN layers, or a large space requirement for keeping the entire graph and the embedding of each node in memory. In this paper, we propose Cluster-GCN, a novel GCN algorithm that is suitable for SGD-based training by exploiting the graph clustering structure. Cluster-GCN works as the following: at each step, it samples a block of nodes that associate with a dense subgraph identified by a graph clustering algorithm, and restricts the neighborhood search within this subgraph. This simple but effective strategy leads to significantly improved memory and computational efficiency while being able to achieve comparable test accuracy with previous algorithms. To test the scalability of our algorithm, we create a new Amazon2M data with 2 million nodes and 61 million edges which is more than 5 times larger than the previous largest publicly available dataset (Reddit). For training a 3-layer GCN on this data, Cluster-GCN is faster than the previous state-of-the-art VR-GCN (1523 seconds vs 1961 seconds) and using much less memory (2.2GB vs 11.2GB). Furthermore, for training 4 layer GCN on this data, our algorithm can finish in around 36 minutes while all the existing GCN training algorithms fail to train due to the out-of-memory issue. Furthermore, Cluster-GCN allows us to train much deeper GCN without much time and memory overhead, which leads to improved prediction accuracy---using a 5-layer Cluster-GCN, we achieve state-of-the-art test F1 score 99.36 on the PPI dataset, while the previous best result was 98.71 by [16]. Our codes are publicly available at https://github.com/google-research/google-research/tree/master/cluster_gcn.Comment: In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (KDD'19

A Unified Approximation Framework for Compressing and Accelerating Deep Neural Networks

Deep neural networks (DNNs) have achieved significant success in a variety of real world applications, i.e., image classification. However, tons of parameters in the networks restrict the efficiency of neural networks due to the large model size and the intensive computation. To address this issue, various approximation techniques have been investigated, which seek for a light weighted network with little performance degradation in exchange of smaller model size or faster inference. Both low-rankness and sparsity are appealing properties for the network approximation. In this paper we propose a unified framework to compress the convolutional neural networks (CNNs) by combining these two properties, while taking the nonlinear activation into consideration. Each layer in the network is approximated by the sum of a structured sparse component and a low-rank component, which is formulated as an optimization problem. Then, an extended version of alternating direction method of multipliers (ADMM) with guaranteed convergence is presented to solve the relaxed optimization problem. Experiments are carried out on VGG-16, AlexNet and GoogLeNet with large image classification datasets. The results outperform previous work in terms of accuracy degradation, compression rate and speedup ratio. The proposed method is able to remarkably compress the model (with up to 4.9x reduction of parameters) at a cost of little loss or without loss on accuracy.Comment: 8 pages, 5 figures, 6 table

Androsterone glucuronide to dehydroepiandrosterone sulphate ratio is discriminatory for obese Caucasian women with polycystic ovary syndrome

BACKGROUND: Androsterone glucuronide (ADTG) concentrations have been suggested as a marker of the effects of androgens at the target tissue level. As the mechanism for hyperandrogenemia in obese and nonobese polycystic ovary syndrome (PCOS) may differ, this study compared the different androgen parameters in non-obese compared to obese women with PCOS, and in normal subjects. METHODS: Eleven non-obese and 14 obese women with PCOS were recruited and compared to 11 control women without PCOS. Total testosterone, dehydroepiandrosterone sulphate (DHEAS), ADTG, and androstenedione were analysed using gold standard tandem mass spectrometry, and the free androgen index (FAI) was calculated. RESULTS: Total testosterone, ADTG and androstendione levels did not differ between non-obese (body mass index (BMI) ≤25 kg/m2) and obese PCOS (BMI >25 kg/m2) but all were significantly higher than for controls (p < 0.01). The ADTG to DHEAS ratio was significantly elevated 39 ± 6 (p < 0.01) in obese PCOS in comparison to non-obese PCOS and controls (28 ± 5 and 29 ± 4, respectively). The free androgen index (FAI) and insulin resistance (HOMA-IR) were significantly higher in obese PCOS compared to non-obese PCOS and controls (p < 0.01). DHEAS was significantly higher in the non-obese versus obese PCOS (p < 0.01). All androgen parameters were significantly lower and sex hormone binding globulin (SHBG) significantly higher in normal subjects compared to those with obese and non-obese PCOS. CONCLUSIONS: The ADTG:DHEAS ratio was significantly elevated in obese PCOS compared to non-obese PCOS and controls suggesting that this may be a novel biomarker discriminatory for obese PCOS subjects, perhaps being driven by higher hepatic 5α reductase activity increasing ADTG formation in these women

Reheating and Cosmic String Production

We compute the string production rate at the end of inflation, using the string spectrum obtained in \lss in a near-de Sitter space. Our result shows that highly excited strings are hardly produced, thus the simple slow-roll inflation alone does not offer a cosmic string production mechanism.Comment: 16 pages, harvmac, v2:minor change of the title v3: major change of the conclusio

The Escherichia coli RutR transcription factor binds at targets within genes as well as intergenic regions.

The Escherichia coli RutR protein is the master regulator of genes involved in pyrimidine catabolism. Here we have used chromatin immunoprecipitation in combination with DNA microarrays to measure the binding of RutR across the chromosome of exponentially growing E. coli cells. Twenty RutR-binding targets were identified and analysis of these targets generated a DNA consensus logo for RutR binding. Complementary in vitro binding assays showed high-affinity RutR binding to 16 of the 20 targets, with the four low-affinity RutR targets lacking predicted key binding determinants. Surprisingly, most of the DNA targets for RutR are located within coding segments of the genome and appear to have little or no effect on transcript levels in the conditions tested. This contrasts sharply with other E. coli transcription factors whose binding sites are primarily located in intergenic regions. We suggest that either RutR has yet undiscovered function or that evolution has been slow to eliminate non-functional DNA sites for RutR because they do not have an adverse effect on cell fitness

Methyl-CpG-Binding PCR of Bloodspots for Confirmation of Fragile X Syndrome in Males

This study demonstrates that methyl-CpG-binding PCR (MB-PCR) is a rapid and simple method for detecting fragile X syndrome (FXS) in males, which is performed by verifying the methylation status of the FMR1 promoter in bloodspots. Proteins containing methyl-CpG-binding (MB) domains can be freeze-stored and used as stocks, and the entire test requires only a few hours. The minimum amount of DNA required for the test is 0.5 ng. At this amount, detection sensitivity is not hampered, even mixing with excess unmethylated alleles up to 320 folds. We examined bloodspots from 100 males, including 24 with FXS, in a blinded manner. The results revealed that the ability of MB-PCR to detect FMR1 promoter methylation was the same as that of Southern blot hybridization. Since individuals with 2 or more X chromosomes generally have methylated FMR1 alleles, MB-PCR cannot be used to detect FXS in females