SETTING: Fifteen participating cardiology centres contributed to the Malaysian National Cardiovascular Disease Database-Percutaneous Coronary Intervention (NCVD-PCI) registry.
PARTICIPANTS: 28 742 patients from the NCVD-PCI registry who had their first PCI between January 2007 and December 2014 were included. Those without their BMI recorded or BMI <11 kg/m2 or >70 kg/m2 were excluded.
MAIN OUTCOME MEASURES: In-hospital death, major adverse cardiovascular events (MACEs), vascular complications between different BMI groups were examined. Multivariable-adjusted HRs for 1-year mortality after PCI among the BMI groups were also calculated.
RESULTS: The patients were divided into four groups; underweight (BMI <18.5 kg/m2), normal BMI (BMI 18.5 to <23 kg/m2), overweight (BMI 23 to <27.5 kg/m2) and obese (BMI ≥27.5 kg/m2). Comparison of their baseline characteristics showed that the obese group was younger, had lower prevalence of smoking but higher prevalence of diabetes, hypertension and dyslipidemia. There was no difference found in terms of in-hospital death, MACE and vascular complications after PCI. Multivariable Cox proportional hazard regression analysis showed that compared with normal BMI group the underweight group had a non-significant difference (HR 1.02, p=0.952), while the overweight group had significantly lower risk of 1-year mortality (HR 0.71, p=0.005). The obese group also showed lower HR but this was non-significant (HR 0.78, p=0.056).
CONCLUSIONS: Using Asian-specific BMI cut-off points, the overweight group in our study population was independently associated with lower risk of 1-year mortality after PCI compared with the normal BMI group.
METHODS: Data from the World Health Survey conducted in 2002-2004, across 70 low-, middle- and high-income countries was used. Participants aged 18 years and over were selected using multistage, stratified cluster sampling. BMI was used as outcome variable. The potential determinants of individual-level BMI were participants' sex, age, marital-status, education, occupation, household-wealth and location(rural/urban) at the individual-level. The country-level factors used were average national income (GNI-PPP) and income inequality (Gini-index). A two-level random-intercepts and fixed-slopes model structure with individuals nested within countries was fitted, treating BMI as a continuous outcome.
RESULTS: The weighted mean BMI and standard-error of the 206,266 people from 70-countries was 23.90 (4.84). All the low-income countries were below the 25.0 mean BMI level and most of the high-income countries were above. All wealthier quintiles of household-wealth had higher scores in BMI than lowest quintile. Each USD10000 increase in GNI-PPP was associated with a 0.4 unit increase in BMI. The Gini-index was not associated with BMI. All these variables explained 28.1% of country-level, 4.9% of individual-level and 7.7% of total variance in BMI. The cross-level interaction effect between GNI-PPP and household-wealth was significant. BMI increased as the GNI-PPP increased in first four quintiles of household-wealth. However, the BMI of the wealthiest people decreased as the GNI-PPP increased.
CONCLUSION: Both individual-level and country-level factors made an independent contribution to the BMI of the people. Household-wealth and national-income had significant interaction effects.
OBJECTIVES: We aimed to establish the impact of including/excluding pregnancies with adverse neonatal outcomes when constructing GWG charts.
METHODS: This is an individual participant data analysis from 31 studies from low- and middle-income countries. We created a dataset that included all participants and a dataset restricted to those with no adverse neonatal outcomes: preterm < 37 wk, small or large for gestational age, low birth weight < 2500 g, or macrosomia > 4000 g. Quantile regression models were used to create GWG curves from 9 to 40 wk, stratified by prepregnancy BMI, in each dataset.
RESULTS: The dataset without the exclusion criteria applied included 14,685 individuals with normal weight and 4831 with overweight. After removing adverse neonatal outcomes, 10,479 individuals with normal weight and 3466 individuals with overweight remained. GWG distributions at 13, 27, and 40 wk were virtually identical between the datasets with and without the exclusion criteria, except at 40 wk for normal weight and 27 wk for overweight. For the 10th and 90th percentiles, the differences between the estimated GWG were larger for overweight (∼1.5 kg) compared with normal weight (<1 kg). Removal of adverse neonatal outcomes had minimal impact on GWG trajectories of normal weight. For overweight, the percentiles estimated in the dataset without the criteria were slightly higher than those in the dataset with the criteria applied. Nevertheless, differences were <1 kg and virtually nonexistent at the end of pregnancy.
CONCLUSIONS: Removing pregnancies with adverse neonatal outcomes has little or no influence on the GWG trajectories of individuals with normal and overweight.
METHODS: A systematic review was conducted to identify all relevant studies on the specific risk factors. Findings were summarized using a narrative synthesis and meta-analysis, where possible.
RESULTS: Overall 384 studies were included, mostly using cross-sectional designs. Findings indicated significantly increased risk of tinnitus among current (based on 26 studies) and ever smokers (based on 16 studies) and among obese people (based on seven studies), but no effect of alcohol consumption (based on 11 studies). With respect to caffeine intake or coffee drinking, only three studies examined this risk factor and so we were unable to draw conclusions.
CONCLUSION: Our results contribute to quantifying the relationship between tinnitus and specific lifestyle-related risk factors, and we highlight some of the gaps and inconsistencies across published studies.
MATERIALS AND METHODS: A dose-ranging analysis using SKF7® was conducted through a randomized, double-blind, multicentre, placebo-controlled, phase 2 clinical trial involving individuals with obesity (N = 133) between January 2020 and April 2021. The potential percentage of change was assessed in relation to BW, BMI, WC and WHtR.
RESULTS: Average treatment effect estimates (treatment group vs. placebo) show a statistically significant reduction in the percentage of change for BW (mean = -2.915; CI: -4.546, -1.285), BMI (-2.921; CI: -4.551, -1.291), WC (mean = -2.187; CI: -3.784, -0.589) and WHtR (mean = -2.294, CI: -3.908, -0.681) in the group with a total of 750 mg of SKF7® (p
MATERIALS AND METHODS: A total of 385 university students including foundation and undergraduate students took part in this study. Self-administered online surveys were used to obtain sociodemographic data, and anthropometry measurements including weight and height, night eating during studying, duration of the study, opinion on eating and academic performance, sleep quality, level of depression, anxiety, and stress of the respondents. Questionnaires were validated and IBM SPSS Statistics Software version 26.0 was used to analyse categorical and continuous variables.
RESULTS: The findings showed that there was an association between night eaters and coffee consumption with BMI (p<0.001) and sleep quality (p<0.05). However, there was no association (p>0.05) found between the types of food eaten during night studying and the mean duration of the study. The results showed drinking coffee had an association with depression, anxiety, and stress (p<0.05) among Malaysian university students.
CONCLUSION: Coffee consumption was common among undergraduate students during studying. Awareness of the risk of overconsumption of caffeine intake should be implemented in the future. However, this study did not include all types of food choices and drinks. Thus, frequency of eating energy dense food during night studying among students should be conducted in the future.