The intergenerational cycle of diabetes and obesity.

<p>Early life undernutrition, maternal diabetes, gestational diabetes, and maternal obesity are associated with increased risk of diabetes and obesity in the offspring, as well as younger age of onset of diabetes. This risk can therefore be transmitted to subsequent generations. Some potential interventions targeting different stages of the reproductive cycle and life course are highlighted.</p

Monthly average of predicted volume purchased (g/per capita) for the post-tax taxed and untaxed food subcategories.

<p>Monthly average of predicted volume purchased (g/per capita) for the post-tax taxed and untaxed food subcategories.</p

Monthly average of predicted volume purchased (g/per capita) for taxed and untaxed food purchases.

<p>Monthly average of predicted volume purchased (g/per capita) for taxed and untaxed food purchases.</p

ENIG consumption descriptive statistics (33,284 households).

<p>ENIG consumption descriptive statistics (33,284 households).</p

Historical and forecasted SSB volume sales per year (million liters).

<p>Source: Own estimates based on sales data from Euromonitor International. ^f forecasted volume.</p

Own price elasticities by socioeconomic status.

<p>Source: Colombian Income and Expenditure Survey (ENIG) 2006–2007. SSB: sugar-sweetened beverages. p<0.1*, p<0.05**, p<0.01***.</p

Uncompensated elasticities from QUAIDS censored model (33,824 households).

<p>Uncompensated elasticities from QUAIDS censored model (33,824 households).</p

ENIG household demographics (33,824 households).

<p>ENIG household demographics (33,824 households).</p

Monetary Value of Diet Is Associated with Dietary Quality and Nutrient Adequacy among Urban Adults, Differentially by Sex, Race and Poverty Status

<div><p>Objective</p><p>The association between monetary value of the diet (MVD, $/day) with dietary quality was examined using a large sample of urban US adults, differentially by socio-demographic factors.</p><p>Methods</p><p>This was a cross-sectional study of 2,111 participants, aged 30–64y, using data from the Healthy Aging in Neighborhoods of Diversity across the Life Span Study. Dietary quality indices included Healthy Eating Index–2010 (HEI–2010) and Mean Adequacy Ratio (MAR), (two 24-hr recalls). A national food price database was used to estimate MVD. Multiple linear/logistic regression analyses were conducted stratifying separately by sex, race and poverty status.</p><p>Results</p><p>Women had significantly higher HEI-2010 scores than men (43.35 vs 41.57 out of 100, respectively), whereas MAR scores were higher for men (76.8 vs 69.9, out of 100), reflecting energy intake gender differentials. Importantly, a$3/day higher MVD (IQR: $3.70/d (Q1) to$6.62/d (Q4)) was associated with a 4.98±0.35 higher total HEI-2010 and a 3.88±0.37 higher MAR score, after energy-adjustment and control for key confounders. For HEI-2010 and MAR, stronger associations were observed among participants above poverty and among women, whilethe MVD vs. HEI-2010 association was additionally stronger among Whites. Sex and poverty status differentials were observed for many MAR and some HEI-2010 components.</p><p>Conclusions</p><p>Despite positive associations between measures of dietary quality and MVD, particularly above poverty and among women, approaching compliance with the Dietary Guidelines (80 or more for HEI-2010) requires a substantially higher MVD. Thus, nutrition education may further improve people’s decision-making regarding food venues and dietary choices.</p></div

Multi-component policy change simulation: predicted change in BMI^ab.

<div><p>^aPredicted change in BMI with increased availability of supermarkets and commercial physical activity facilities, by neighborhood poverty and availability of commercial physical activity facilities. Estimated using fixed effects linear regression modeling Body Mass Index (BMI, kg/m²) as a function of fast food restaurant, convenience store, supermarket, commercial physical activity facility, and public physical activity facility density within 3km buffers and development intensity within 1km buffers (Euclidean buffers around each respondent’s residential location); Coronary Artery Risk Development in Young Adults (CARDIA) Study (1992-2011). The fixed effects model is adjusted for time-varying age, income, marital status, children in household and proportion of persons below 150% of federal poverty level and significant (p<0.10) interactions between neighborhood measure and gender, and significant pairwise interactions among neighborhood measures; race, education, and study center are time invariant and therefore omitted from fixed effects models. Predictions apply estimated coefficients from final fixed effects model (Table <b>S5</b> in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0085141#pone.0085141.s001" target="_blank">File <b>S1</b></a>; n=12,921 person-exam observations representing 4,092 individuals). Error bars represent 95% confidence intervals.</p> <p>^bResource density is calculated as counts per 10,000 population within 3km Euclidean buffer. “High” and “low” levels correspond to 25^th and 75^th percentiles for each measure among all pooled person-exam observations.</p></div