Displaying publications 21 - 23 of 23 in total

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  1. Fulltext Intermittent Fasting and Obesity-Related Health Outcomes: An Umbrella Review of Meta-analyses of Randomized Clinical Trials
    Patikorn C, Roubal K, Veettil SK, Chandran V, Pham T, Lee YY, et al.
    JAMA Netw Open, 2021 12 01;4(12):e2139558.
    PMID: 34919135 DOI: 10.1001/jamanetworkopen.2021.39558
    Importance: Several meta-analyses of randomized clinical trials (RCTs) have demonstrated the many health benefits of intermittent fasting (IF). However, there has been little synthesis of the strength and quality of this evidence in aggregate to date.

    Objective: To grade the evidence from published meta-analyses of RCTs that assessed the associations of IF (zero-calorie alternate-day fasting, modified alternate-day fasting, the 5:2 diet, and time-restricted eating) with obesity-related health outcomes.

    Evidence Review: PubMed, Embase, and Cochrane database of systematic reviews were searched from database inception to January 12, 2021. Data analysis was conducted from April 2021 through July 2021. Meta-analyses of RCTs investigating effects of IF in adults were included. The effect sizes of IF were recalculated using a random-effects model. We assessed the quality of evidence per association by applying the GRADE criteria (Grading of Recommendations, Assessment, Development, and Evaluations) as high, moderate, low, and very low.

    Findings: A total of 11 meta-analyses comprising 130 RCTs (median [IQR] sample size, 38 [24-69] participants; median [IQR] follow-up period, 3 [2-5] months) were included describing 104 unique associations of different types of IF with obesity-related health outcomes (median [IQR] studies per association, 4 [3-5]). There were 28 statistically significant associations (27%) that demonstrated the beneficial outcomes for body mass index, body weight, fat mass, low-density lipoprotein cholesterol, total cholesterol, triglycerides, fasting plasma glucose, fasting insulin, homeostatic model assessment of insulin resistance, and blood pressure. IF was found to be associated with reduced fat-free mass. One significant association (1%) supported by high-quality evidence was modified alternate-day fasting for 1 to 2 months, which was associated with moderate reduction in body mass index in healthy adults and adults with overweight, obesity, or nonalcoholic fatty liver disease compared with regular diet. Six associations (6%) were supported by moderate quality evidence. The remaining associations found to be significant were supported by very low (75 associations [72%]) to low (22 associations [21%]) quality evidence.

    Conclusions and Relevance: In this umbrella review, we found beneficial associations of IF with anthropometric and cardiometabolic outcomes supported by moderate to high quality of evidence, which supports the role of IF, especially modified alternate-day fasting, as a weight loss approach for adults with overweight or obesity. More clinical trials with long-term follow-up are needed to investigate the effects of IF on clinical outcomes such as cardiovascular events and mortality.

  2. Fulltext Neurodevelopmental outcome of Malaysian very low birth weight infants: predictive value of cranial ultrasound appearances
    Ong LC, Boo NY, Chandran V, Zulfiqar A, Zamratol SM, Allison L, et al.
    Singapore Med J, 1997 Mar;38(3):108-11.
    PMID: 9269376
    The aim of the study was to determine the predictive value of cranial ultrasound scans done in the neonatal period for neurodevelopmental outcome of the Malaysian very low birthweight (VLBW, < 1500 grams) infants assessed at 12 months of corrected age. Of the 101 infants studied, 68 (67.3%) were neurodevelopmentally normal at one year of age, 18 (17.8%) had major and 15 (14.9%) had minor neurodevelopmental impairment. Neurodevelopmental outcome was normal in 66/88 (75.0%) infants who did not have severe intraventricular haemorrhage (IVH) or periventricular intraparenchymal echo densities (PVE) in the first week of life, and in 57/73 (78.1%) with uncomplicated scans at discharge. In contrast, 11/13 (84.6%) with parenchymal echo densities or severe intraventricular bleed in the early neonatal period and 17/28 (60.7%) with complicated scans at discharge had adverse sequelae. There was a significant association between lesions seen on cranial ultrasound in the neonatal period and subsequent neurodevelopmental impairment. Late neonatal ultrasound scans appear to be a better predictor of short-term neurodevelopmental outcome than early scans.
  3. Automated diagnosis of Age-related Macular Degeneration using greyscale features from digital fundus images
    Mookiah MR, Acharya UR, Koh JE, Chandran V, Chua CK, Tan JH, et al.
    Comput Biol Med, 2014 Oct;53:55-64.
    PMID: 25127409 DOI: 10.1016/j.compbiomed.2014.07.015
    Age-related Macular Degeneration (AMD) is one of the major causes of vision loss and blindness in ageing population. Currently, there is no cure for AMD, however early detection and subsequent treatment may prevent the severe vision loss or slow the progression of the disease. AMD can be classified into two types: dry and wet AMDs. The people with macular degeneration are mostly affected by dry AMD. Early symptoms of AMD are formation of drusen and yellow pigmentation. These lesions are identified by manual inspection of fundus images by the ophthalmologists. It is a time consuming, tiresome process, and hence an automated diagnosis of AMD screening tool can aid clinicians in their diagnosis significantly. This study proposes an automated dry AMD detection system using various entropies (Shannon, Kapur, Renyi and Yager), Higher Order Spectra (HOS) bispectra features, Fractional Dimension (FD), and Gabor wavelet features extracted from greyscale fundus images. The features are ranked using t-test, Kullback-Lieber Divergence (KLD), Chernoff Bound and Bhattacharyya Distance (CBBD), Receiver Operating Characteristics (ROC) curve-based and Wilcoxon ranking methods in order to select optimum features and classified into normal and AMD classes using Naive Bayes (NB), k-Nearest Neighbour (k-NN), Probabilistic Neural Network (PNN), Decision Tree (DT) and Support Vector Machine (SVM) classifiers. The performance of the proposed system is evaluated using private (Kasturba Medical Hospital, Manipal, India), Automated Retinal Image Analysis (ARIA) and STructured Analysis of the Retina (STARE) datasets. The proposed system yielded the highest average classification accuracies of 90.19%, 95.07% and 95% with 42, 54 and 38 optimal ranked features using SVM classifier for private, ARIA and STARE datasets respectively. This automated AMD detection system can be used for mass fundus image screening and aid clinicians by making better use of their expertise on selected images that require further examination.
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