Adiposity in early, middle and later adult life and cardiometabolic risk markers in later life; findings from the British regional heart study.

OBJECTIVES: This research investigates the associations between body mass index (BMI) at 21, 40-59, 60-79 years of age on cardiometabolic risk markers at 60-79 years. METHODS: A prospective study of 3464 British men with BMI measured at 40-59 and 60-79 years, when cardiometabolic risk was assessed. BMI at 21 years was ascertained from military records, or recalled from middle-age (adjusted for reporting bias); associations between BMI at different ages and later cardiometabolic risk markers were examined using linear regression. Sensitive period, accumulation and mobility life course models were devised for high BMI (defined as BMI≥75th centile) and compared with a saturated BMI trajectory model. RESULTS: At ages 21, 40-59 and 60-79 years, prevalences of overweight (BMI≥25 kg/m2) were 12%, 53%, 70%, and obesity (≥30 kg/m2) 1.6%, 6.6%, and 17.6%, respectively. BMI at 21 years was positively associated with serum insulin, blood glucose, and HbA1c at 60-79 years, with increases of 1.5% (95%CI 0.8,2.3%), 0.4% (0.1,0.6%), 0.3% (0.1,0.4%) per 1 kg/m2, respectively, but showed no associations with blood pressure or blood cholesterol. However, these associations were modest compared to those between BMI at 60-79 years and serum insulin, blood glucose and HbA1c at 60-79 years, with increases of 8.6% (8.0,9.2%), 0.7% (0.5,0.9%), and 0.5% (0.4,0.7%) per 1 kg/m2, respectively. BMI at 60-79 years was also associated with total cholesterol and blood pressure. Associations for BMI at 40-59 years were mainly consistent with those of BMI at 60-79 years. None of the life course models fitted the data as well as the saturated model for serum insulin. A sensitive period at 50 years for glucose and HbA1c and sensitive period at 70 years for blood pressure were identified. CONCLUSIONS: In this cohort of men who were thin compared to more contemporary cohorts, BMI in later life was the dominant influence on cardiovascular and diabetes risk. BMI in early adult life may have a small long-term effect on diabetes risk

Global variations and time trends in the prevalence of childhood myopia, a systematic review and quantitative meta-analysis: implications for aetiology and early prevention.

The aim of this review was to quantify the global variation in childhood myopia prevalence over time taking account of demographic and study design factors. A systematic review identified population-based surveys with estimates of childhood myopia prevalence published by February 2015. Multilevel binomial logistic regression of log odds of myopia was used to examine the association with age, gender, urban versus rural setting and survey year, among populations of different ethnic origins, adjusting for study design factors. 143 published articles (42 countries, 374 349 subjects aged 1-18 years, 74 847 myopia cases) were included. Increase in myopia prevalence with age varied by ethnicity. East Asians showed the highest prevalence, reaching 69% (95% credible intervals (CrI) 61% to 77%) at 15 years of age (86% among Singaporean-Chinese). Blacks in Africa had the lowest prevalence; 5.5% at 15 years (95% CrI 3% to 9%). Time trends in myopia prevalence over the last decade were small in whites, increased by 23% in East Asians, with a weaker increase among South Asians. Children from urban environments have 2.6 times the odds of myopia compared with those from rural environments. In whites and East Asians sex differences emerge at about 9 years of age; by late adolescence girls are twice as likely as boys to be myopic. Marked ethnic differences in age-specific prevalence of myopia exist. Rapid increases in myopia prevalence over time, particularly in East Asians, combined with a universally higher risk of myopia in urban settings, suggest that environmental factors play an important role in myopia development, which may offer scope for prevention

Issues of air pollution in environmental impact assessment of development projects

The aim of this study is to establish the trends in approaches and techniques being used to address the air pollution issues in project-related development. The interest was to look at the overall issues of air pollution and how it was dealt with in the context of Environmental Impact Assessment (EIA). However, the review was not based upon individual Environmental Impact Assessment techniques. Twenty eight samples from four different sectors were reviewed and information pertaining to construction activities, baseline condition, impact predictions and mitigating measures were extracted and analyzed. It was established that only 17% of reports had described the existing air quality in an appropriate manner. The constructions activity was mainly confused with the description of intended development. Only 39 % had described the activities as ‘true’ construction activities. The impacts of the construction phase on air quality for all projects were mainly associated with the generation of dust and particulates and emissions from vehicles exhaust. The predictions were made through quantitative or qualitative techniques. The later were being used in most projects. Nevertheless, there are reports especially from road schemes, which did not mention, the impacts of construction phase of the projects on air quality, at all. There are common mitigating measures to all or most projects types such as; wetting of exposed earth surfaces and unpaved roads, covering transported materials which may potentially release dust and particles, imposing speed limits within construction site. In order, to ensure that the mitigation measures will be implemented, the written approval should be linked with terms and conditions, which include the implementation of all mitigation measures identifie

The environmental impact assessment directive of the European Communities

SIGLEUuStB Koeln(38)-881101443 / FIZ - Fachinformationszzentrum Karlsruhe / TIB - Technische InformationsbibliothekDEGerman

Differences in cardiovascular and diabetes risk markers at mean age 70 years for each 1 kg/m² increase in BMI separately at mean age 21, 50 and 70 years.

<p>n: Number of participants (subset of individuals with BMI available at all age periods).</p><p>Coef: regression coefficient represent difference in cardiovascular risk markers for 1 kg/m2 increase in BMI. Estimates are adjusted for age at the time when the risk markers were measured, and town as a fixed effect.</p><p>Differences in cardiovascular and diabetes risk markers at mean age 70 years for each 1 kg/m² increase in BMI separately at mean age 21, 50 and 70 years.</p

Distribution of BMI at mean age 21 years (early adulthood), 50 years (middle-age), and 70 years (late adulthood).

<p>Distribution of BMI at mean age 21 years (early adulthood), 50 years (middle-age), and 70 years (late adulthood).</p

Differences in cardiovascular and diabetes risk markers at mean age 70 years, associated with having high BMI (≥75^th centile) separately at mean ages 21, 50 and 70 years compared to men with normal BMI at all 3 ages (separate analyses based on sensitive period models).

<p>n: Number of participants (subset of individuals with BMI available at all age periods).</p><p>K: percentage of individuals who had high BMI (BMI in the top 25% of the distribution) also at mean age 70 years.</p><p>Coef: regression coefficient for the effect of high BMI (BMI in the top 25% of the distribution) at each sensitive period (mean age 21, 50 or 70 years) as compared with never having had high BMI. Estimates are adjusted for age at the time when the risk markers were measured, and town as fixed effect.</p><p>For each outcome, results are obtained from 3 models fitted separately (one for each period: mean age 21, 50 and 70 years).</p><p>Differences in cardiovascular and diabetes risk markers at mean age 70 years, associated with having high BMI (≥75^th centile) separately at mean ages 21, 50 and 70 years compared to men with normal BMI at all 3 ages (separate analyses based on sensitive period models).</p

Differences in cardiovascular and diabetes risk markers at mean age 70 years, by trajectory of BMI (having a high BMI at different points of the life course) at different ages (mean age 21, 50 and 70 years).

<p>n: Number of participants (subset of individuals with BMI available at all age periods). For BMI at mean 70 years this includes all available data.</p><p>BMI trajectories: Each triplet corresponds to a different trajectory of high BMI at mean age 21, 50 and 70 years; with 0 and 1 denoting BMI below and above the 75th percentile of the BMI distribution, respectively. For example, (0–0-0) denoted low BMI at all age periods, whilst (0–0-1) signified high BMI at mean age 70 years only.</p><p>Coef: Estimates are differences in risk marker from BMI trajectory 0–0-0. Estimates are adjusted for age at the time when the risk markers were measured, and town as fixed effect.</p><p>Differences in cardiovascular and diabetes risk markers at mean age 70 years, by trajectory of BMI (having a high BMI at different points of the life course) at different ages (mean age 21, 50 and 70 years).</p

Cohort characteristics in early, middle, and late adulthood in all men, and by quintile of BMI in early adulthood.

<p>n: Number of participants (subset of individuals with BMI available at all age periods).</p><p>SD: Standard deviation.</p><p>a: Estimates correspond to geometric mean (Q1, Q3).</p><p>Quintiles of BMI at 21 years are calculated using all available data (including individuals with missing BMI at mean age 50 or 70 years).</p><p>Cohort characteristics in early, middle, and late adulthood in all men, and by quintile of BMI in early adulthood.</p