Distribution of Glycated Haemoglobin According to Early-Life and Contemporary Characteristics in Adolescents and Adults without Diabetes:The 1982 and 1993 Pelotas Birth Cohorts

AIM:Glycated haemoglobin (HbA1c), a marker of glucose control in individuals with diabetes mellitus, is also related with the incidence of cardiometabolic risk in populations free of disease. The aim of this study was to describe the distribution of HbA1c levels according to early-life and contemporary factors in adolescents and adults without diabetes mellitus. METHODS:HbA1c was measured in adults aged 30 years and adolescents aged 18 years who are participants in the 1982 and 1993 Pelotas Birth Cohorts, respectively. Bivariate and multivariate analyses were performed to describe the HbA1c mean values according to early-life and contemporary characteristics collected prospectively since birth. RESULTS:The distribution of the HbA1c was approximately normal in both cohorts, with a mean (SD) 5.10% (0.43) in the 1982 cohort, and 4.89% (0.50) in the 1993 cohort. HbA1c mean levels were significantly higher in individuals self-reported as black/brown skin color compared to those self-reported as white in both cohorts. Parental history of diabetes was associated with higher HbA1c mean in adults, while stunting at one year old presented an inverse relation with the outcome in adolescents. No other early and contemporary factors were associated with HbA1c levels in adults or adolescents. CONCLUSIONS:We found a consistent relationship between HbA1c and skin color in both cohorts. Further research is needed to understand the role of genomic ancestry on levels of HbA1c concentrations which may inform policies and preventive actions for diabetes mellitus and cardiometabolic risk

Repositioning of the global epicentre of non-optimal cholesterol

High blood cholesterol is typically considered a feature of wealthy western countries1,2. However, dietary and behavioural determinants of blood cholesterol are changing rapidly throughout the world3 and countries are using lipid-lowering medications at varying rates. These changes can have distinct effects on the levels of high-density lipoprotein (HDL) cholesterol and non-HDL cholesterol, which have different effects on human health4,5. However, the trends of HDL and non-HDL cholesterol levels over time have not been previously reported in a global analysis. Here we pooled 1,127 population-based studies that measured blood lipids in 102.6 million individuals aged 18 years and older to estimate trends from 1980 to 2018 in mean total, non-HDL and HDL cholesterol levels for 200 countries. Globally, there was little change in total or non-HDL cholesterol from 1980 to 2018. This was a net effect of increases in low- and middle-income countries, especially in east and southeast Asia, and decreases in high-income western countries, especially those in northwestern Europe, and in central and eastern Europe. As a result, countries with the highest level of non-HDL cholesterol—which is a marker of cardiovascular risk—changed from those in western Europe such as Belgium, Finland, Greenland, Iceland, Norway, Sweden, Switzerland and Malta in 1980 to those in Asia and the Pacific, such as Tokelau, Malaysia, The Philippines and Thailand. In 2017, high non-HDL cholesterol was responsible for an estimated 3.9 million (95% credible interval 3.7 million–4.2 million) worldwide deaths, half of which occurred in east, southeast and south Asia. The global repositioning of lipid-related risk, with non-optimal cholesterol shifting from a distinct feature of high-income countries in northwestern Europe, north America and Australasia to one that affects countries in east and southeast Asia and Oceania should motivate the use of population-based policies and personal interventions to improve nutrition and enhance access to treatment throughout the world.</p

Validity Assessment Of The Nutrition Screening Initiative Checklist In Older Adults

To ascertain whether the NSI (Nutrition Screening Initiative Checklist) is a valid screening instrument for diagnosing nutritional risk in an older population from a medium-size city in the south of Brazil. Methods The study population comprised individuals aged 60 years or older selected from a population-based cross-sectional survey. Data collection was carried out at two different timepoints: first, the NSI (instrument under test) was applied and later a dietary recall (gold standard) based on the previous day. Validity analyses were performed based on sensitivity and specificity as well as the Receiver Operating Characteristic Curve. Results A total of 183 older adults were studied. Agreement between the dietary recall and the NSI for positive and negative results was 54.6%.The sensitivity and specificity values were low for all cut-off points of the instrument. The area under the curve was 0.52 (95%CI: 0.44–0.62) for the cut-off point ≥6. Conclusion Based on sensitivity and specificity values, the NSI proved ineffective for application in the population studied. © 2016 European Society for Clinical Nutrition and Metabolism15162

Heterogeneous contributions of change in population distribution of body mass index to change in obesity and underweight

From 1985 to 2016, the prevalence of underweight decreased, and that of obesity and severe obesity increased, in most regions, with significant variation in the magnitude of these changes across regions. We investigated how much change in mean body mass index (BMI) explains changes in the prevalence of underweight, obesity, and severe obesity in different regions using data from 2896 population-based studies with 187 million participants. Changes in the prevalence of underweight and total obesity, and to a lesser extent severe obesity, are largely driven by shifts in the distribution of BMI, with smaller contributions from changes in the shape of the distribution. In East and Southeast Asia and sub-Saharan Africa, the underweight tail of the BMI distribution was left behind as the distribution shifted. There is a need for policies that address all forms of malnutrition by making healthy foods accessible and affordable, while restricting unhealthy foods through fiscal and regulatory restrictions. © Copyright