DSpace Collection:https://hdl.handle.net/2440/58752024-03-18T16:13:24Z2024-03-18T16:13:24ZEffects of an antenatal dietary intervention in women with obesity or overweight on child outcomes at 8–10 years of age: LIMIT randomised trial follow-upDodd, J.M.Deussen, A.R.Peña, A.S.Mitchell, M.Louise, J.https://hdl.handle.net/2440/1404152024-02-27T02:41:58Z2023-01-01T00:00:00ZTitle: Effects of an antenatal dietary intervention in women with obesity or overweight on child outcomes at 8–10 years of age: LIMIT randomised trial follow-up
Author: Dodd, J.M.; Deussen, A.R.; Peña, A.S.; Mitchell, M.; Louise, J.
Abstract: Background: The LIMIT randomised controlled trial looked at the effect of a dietary and lifestyle intervention compared with routine antenatal care for pregnant women with overweight and obesity on pregnancy outcomes. While women in the intervention group improved diet and physical activity with a reduction of high birth weight, other outcomes were similar. We have followed the children born to women in this study at birth, 6 and 18 months and 3–5 years of age and now report follow-up of children at 8–10 years of age. Methods: Children at 8–10 years of age who were born to women who participated in the LIMIT randomised trial, and whose mother provided consent to ongoing follow-up were eligible for inclusion. The primary study endpoint was the incidence of child BMI z-score>85th centile for child sex and age. Secondary study outcomes included a range of anthropometric measures, neurodevelopment, child dietary intake, and physical activity. Analyses used intention to treat principles according to the treatment group allocated in pregnancy. Outcome assessors were blinded to the allocated treatment group. Results: We assessed 1,015 (Lifestyle Advice n=510; Standard Care n=505) (48%) of the 2,121 eligible children. BMI z-score>85th percentile was similar for children of women in the dietary Lifestyle Advice Group compared with children of women in the Standard Care Group (Lifestyle Advice 479 (45%) versus Standard Care 507 (48%); adjusted RR (aRR) 0.93; 95% CI 0.82 to 1.06; p=0.302) as were secondary outcomes. We observed that more than 45% of all the children had a BMI z-score>85th percentile, consistent with findings from follow-up at earlier time-points, indicating an ongoing risk of overweight and obesity. Conclusions: Dietary and lifestyle advice for women with overweight and obesity in pregnancy has not reduced the risk of childhood obesity, with children remaining at risk of adolescent and adult obesity. Other strategies are needed to address the risk of overweight and obesity in children including investigation of preconception interventions to assess whether this can modify the effects of maternal pre-pregnancy BMI.2023-01-01T00:00:00ZCardiometabolic risks in PCOS: a review of the current state of knowledgeKakoly, N.S.Moran, L.J.Teede, H.J.Joham, A.E.https://hdl.handle.net/2440/1402122023-12-18T03:43:35Z2019-01-01T00:00:00ZTitle: Cardiometabolic risks in PCOS: a review of the current state of knowledge
Author: Kakoly, N.S.; Moran, L.J.; Teede, H.J.; Joham, A.E.
Abstract: Introduction: Polycystic ovary syndrome (PCOS) is a common endocrine disorder affecting up to 18% women of reproductive age. It is associated with a range of metabolic, reproductive, and psychological features. Current evidence indicates a role of PCOS in the development of metabolic and increased cardiovascular risk factors (CVRF) with implications for compromised cardiovascular endpoint disease, which may have a considerable impact on health and health care costs. Areas covered: Existing studies examining long-term cardiometabolic health in PCOS are heterogeneous with inconsistent findings. In the current review, we aim to explore and critically review retrospective, prospective, meta-analysis and review articles relating to PCOS on cardiometabolic risk factors and clinical consequences to summarize the evidence, note evidence gaps, and suggest implications for future research. Expert commentary: Although there is an established association between PCOS and metabolic health, implications on cardiac health are more uncertain with associations observed for CVRF and subclinical disease, yet limited and conflicting data on actual cardiovascular endpoints. There is a lack of population-based long-term studies examining cardiometabolic morbidity and mortality in PCOS with a need for further research to progress toward a better understanding of the long-term cardiometabolic impacts in women with PCOS.
Description: Published online: 15 Dec 20182019-01-01T00:00:00ZAcupuncture or auricular electro-acupuncture as adjuncts to lifestyle interventions for weight management in PCOS: Protocol for a randomised controlled feasibility studyEe, C.Smith, C.A.Costello, M.Moran, L.Steiner, G.Z.Stepto, N.Cave, A.Albrehee, A.Teede, H.https://hdl.handle.net/2440/1399742023-11-28T00:30:37Z2020-01-01T00:00:00ZTitle: Acupuncture or auricular electro-acupuncture as adjuncts to lifestyle interventions for weight management in PCOS: Protocol for a randomised controlled feasibility study
Author: Ee, C.; Smith, C.A.; Costello, M.; Moran, L.; Steiner, G.Z.; Stepto, N.; Cave, A.; Albrehee, A.; Teede, H.
Abstract: Background: Polycystic ovary syndrome (PCOS) is a prevalent women’s health condition with reproductive, metabolic, and psychological manifestations. Weight loss can improve these symptoms and is a key goal; however, many women find this difficult to achieve. Acupuncture is a Chinese medical treatment that involves insertion of very fine metal needles into specific areas of the body and has been shown to be efficacious for weight loss in non-PCOS populations. However, few studies have been conducted in women with PCOS. A variant of acupuncture, auricular electro-acupuncture (AEA), may have beneficial effects on sympathetic tone, which is associated with insulin resistance, obesity and PCOS. Methods: This prospective three-arm open label parallel randomised controlled trial will assess feasibility and acceptability of acupuncture and/or AEA for weight loss in women with PCOS. We will enrol 39 women from the community aged between 18 and 45 years, with physician diagnosis of PCOS according to the Rotterdam criteria: body mass index (BMI) between 25 and 40 kg/m2. Women will be randomly allocated to receive one of three treatments for 12 weeks duration: body electro-acupuncture + lifestyle interventions, AEA + lifestyle interventions, or lifestyle interventions alone. The lifestyle intervention in this study is telephone-based health coaching (between 4 and 13 phone calls, depending on individual need), provided by the Get Healthy Service. Primary outcomes of the study are feasibility and acceptability of trial methods as determined by recruitment and retention rates, adherence, acceptability, credibility, and safety. Secondary outcomes include anthropometric (body weight, BMI, waist and hip circumference), metabolic (glucose tolerance and insulin sensitivity obtained from a 2-h 75 g oral glucose tolerance test with area under the curve insulin calculated using the trapezoid rule), reproductive (androgen levels, menstrual cyclicity, clinical hyperandrogenism using the Ferriman-Gallwey scoring system), autonomic (heart rate variability, blood pressure), lifestyle (physical activity levels, diet quality, weight self-efficacy), quality of life, and psychological (depression and anxiety symptoms, internal health locus of control). Discussion: This study addresses the feasibility and acceptability of novel interventions to treat overweight/obesity in PCOS. Study findings have the potential to generate a new understanding of the role of acupuncture and auricular acupuncture in weight management. Trial registration: Australian New Zealand Clinical Trial Registry, 8/6/18 ACTRN12618000975291
Description: Published online: 25 April 20202020-01-01T00:00:00ZComparison of machine learning and conventional logistic regression-based prediction models for gestational diabetes in an ethnically diverse population; the Monash GDM Machine learning modelBelsti, Y.Moran, L.Du, L.Mousa, A.De Silva, K.Enticott, J.Teede, H.https://hdl.handle.net/2440/1398812023-11-19T22:49:36Z2023-01-01T00:00:00ZTitle: Comparison of machine learning and conventional logistic regression-based prediction models for gestational diabetes in an ethnically diverse population; the Monash GDM Machine learning model
Author: Belsti, Y.; Moran, L.; Du, L.; Mousa, A.; De Silva, K.; Enticott, J.; Teede, H.
Abstract: Background: Early identification of pregnant women at high risk of developing gestational diabetes (GDM) is desirable as effective lifestyle interventions are available to prevent GDM and to reduce associated adverse outcomes. Personalised probability of developing GDM during pregnancy can be determined using a risk prediction model. These models extend from traditional statistics to machine learning methods; however, accuracy remains sub-optimal. Objective: We aimed to compare multiple machine learning algorithms to develop GDM risk prediction models, then to determine the optimal model for predicting GDM. Methods: A supervised machine learning predictive analysis was performed on data from routine antenatal care at a large health service network from January 2016 to June 2021. Predictor set 1 were sourced from the existing, internationally validated Monash GDM model: GDM history, body mass index, ethnicity, age, family history of diabetes, and past poor obstetric history. New models with different predictors were developed, considering statistical principles with inclusion of more robust continuous and derivative variables. A randomly selected 80% dataset was used for model development, with 20% for validation. Performance measures, including calibration and discrimination metrics, were assessed. Decision curve analysis was performed. Results: Upon internal validation, the machine learning and logistic regression model’s area under the curve (AUC) ranged from 71% to 93% across the different algorithms, with the best being the CatBoost Classifier (CBC). Based on the default cut-off point of 0.32, the performance of CBC on predictor set 4 was: Accuracy (85%), Precision (90%), Recall (78%), F1-score (84%), Sensitivity (81%), Specificity (90%), positive predictive value (92%), negative predictive value (78%), and Brier Score (0.39). Conclusions: In this study, machine learning approaches achieved the best predictive performance over traditional statistical methods, increasing from 75 to 93%. The CatBoost classifier method achieved the best with the model including continuous variables.
Description: Available online 21 September 20232023-01-01T00:00:00Z