Archana P. Lamichhane

Association of Intrauterine Exposure to Maternal Diabetes and Obesity with Type 2 Diabetes in Youth: The SEARCH Case-Control Study

OBJECTIVE—Limited data exist on the association between in utero exposure to maternal diabetes and obesity and type 2 diabetes in diverse youth. These associations were explored in African-American, Hispanic, and non-Hispanic white youth participating in the SEARCH Case-Control Study. RESEARCH DESIGN AND METHODS—A total of 79 youth with type 2 diabetes and 190 nondiabetic control youth aged 10–22 years attended a research visit. In utero exposures to maternal diabetes and obesity were recalled by biological mothers. RESULTS—Youth with type 2 diabetes were more likely to have been exposed to maternal diabetes or obesity in utero than were nondiabetic control youth (P < 0.0001 for each). After adjusting for offspring age, sex, and race/ethnicity, exposure to maternal diabetes (odds ratio [OR] 5.7 [95% CI 2.4–13.4]) and exposure to maternal obesity (2.8 [1.5–5.2]) were independently associated with type 2 diabetes. Adjustment for other perinatal and socioeconomic factors did not alter these associations. When offspring BMI was added, the OR for the association between in utero exposure to obesity and type 2 diabetes was attenuated toward the null (OR 1.1 [0.5–2.4]). Overall, 47.2% (95% CI 30.9–63.5) of type 2 diabetes in youth could be attributed to intrauterine exposure to maternal diabetes and obesity. CONCLUSIONS—Intrauterine exposures to maternal diabetes and obesity are strongly associated with type 2 diabetes in youth. Prevention efforts may need to target, in addition to childhood obesity, the increasing number of pregnancies complicated by obesity and diabetes.

Validation of 3 Food Outlet Databases: Completeness and Geospatial Accuracy in Rural and Urban Food Environments

Despite interest in the built food environment, little is known about the validity of commonly used secondary data. The authors conducted a comprehensive field census identifying the locations of all food outlets using a handheld global positioning system in 8 counties in South Carolina (2008–2009). Secondary data were obtained from 2 commercial companies, Dun & Bradstreet, Inc. (D&B) (Short Hills, New Jersey) and InfoUSA, Inc. (Omaha, Nebraska), and the South Carolina Department of Health and Environmental Control (DHEC). Sensitivity, positive predictive value, and geospatial accuracy were compared. The field census identified 2,208 food outlets, significantly more than the DHEC (n = 1,694), InfoUSA (n = 1,657), or D&B (n = 1,573). Sensitivities were moderate for DHEC (68%) and InfoUSA (65%) and fair for D&B (55%). Combining InfoUSA and D&B data would have increased sensitivity to 78%. Positive predictive values were very good for DHEC (89%) and InfoUSA (86%) and good for D&B (78%). Geospatial accuracy varied, depending on the scale: More than 80% of outlets were geocoded to the correct US Census tract, but only 29%–39% were correctly allocated within 100 m. This study suggests that the validity of common data sources used to characterize the food environment is limited. The marked undercount of food outlets and the geospatial inaccuracies observed have the potential to introduce bias into studies evaluating the impact of the built food environment.

Neighborhood level risk factors for type 1 diabetes in youth: the SEARCH case-control study.

BackgroundEuropean ecologic studies suggest higher socioeconomic status is associated with higher incidence of type 1 diabetes. Using data from a case-control study of diabetes among racially/ethnically diverse youth in the United States (U.S.), we aimed to evaluate the independent impact of neighborhood characteristics on type 1 diabetes risk. Data were available for 507 youth with type 1 diabetes and 208 healthy controls aged 10-22 years recruited in South Carolina and Colorado in 2003-2006. Home addresses were used to identify Census tracts of residence. Neighborhood-level variables were obtained from 2000 U.S. Census. Multivariate generalized linear mixed models were applied.ResultsControlling for individual risk factors (age, gender, race/ethnicity, infant feeding, birth weight, maternal age, number of household residents, parental education, income, state), higher neighborhood household income (p = 0.005), proportion of population in managerial jobs (p = 0.02), with at least high school education (p = 0.005), working outside the county (p = 0.04) and vehicle ownership (p = 0.03) were each independently associated with increased odds of type 1 diabetes. Conversely, higher percent minority population (p = 0.0003), income from social security (p = 0.002), proportion of crowded households (0.0497) and poverty (p = 0.008) were associated with a decreased odds.ConclusionsOur study suggests that neighborhood characteristics related to greater affluence, occupation, and education are associated with higher type 1 diabetes risk. Further research is needed to understand mechanisms underlying the influence of neighborhood context.

Breast-Feeding and Type 2 Diabetes in the Youth of Three Ethnic Groups The SEARCH for Diabetes in Youth Case-Control Study

OBJECTIVE—To evaluate the hypothesis that breast-feeding is associated with reduced type 2 diabetes among African-American, Hispanic, and non-Hispanic white youth, mediated in part by current weight status. RESEARCH DESIGN AND METHODS—The SEARCH Case-Control Study, an ancillary study to SEARCH for Diabetes in Youth, was conducted in two of six SEARCH clinical sites. Eighty youth with type 2 diabetes aged 10–21 years were included. Nondiabetic control participants were recruited from primary care provider offices (n = 167). Breast-feeding information was recalled by biological mothers. RESULTS—Prevalence (%) of breast-feeding (any duration) was lower among youth with type 2 diabetes than among control subjects (19.5 vs. 27.1 for African Americans, 50.0 vs. 83.8 for Hispanics, and 39.1 vs. 77.6 for non-Hispanic whites). The overall crude odds ratio for the association of breast-feeding (ever versus never) and type 2 diabetes was 0.26 (95% CI 0.15–0.46). Results were similar by race/ethnic group (P value for interaction = 0.17). The odds ratio for the association after adjusting for 12 potential confounders was 0.43 (0.19–0.99). When current BMI z-score was added to the model, the odds ratio was attenuated (0.82 [0.30–2.30]), suggesting possible mediation through current childhood weight status. Analyses that incorporated duration of breast-feeding, adjusted for potential confounders, provided evidence for dose response (test for trend, P value <0.0001), even after inclusion of BMI z-score. CONCLUSIONS—Breast-feeding appears to be protective against development of type 2 diabetes in youth, mediated in part by current weight status in childhood.

Spatial patterning of supermarkets and fast food outlets with respect to neighborhood characteristics.

A large body of literature has reported differences in exposure to environments supporting either healthy (e.g. supermarkets) or unhealthy (e.g. fast food outlets) dietary choices by neighborhood characteristics. We explored the associations of both supermarkets and fast food outlets availability with neighborhood characteristics, and clustering of these two outlet types in a largely rural state. Compared to block groups without a supermarket, those with a supermarket had a significantly higher income, higher housing value, larger population with high school education and above, lower minority population and lower population living below poverty even after controlling for urbanicity and population density of census block groups. Surprisingly, a similar relationship was found for block groups with and without fast food outlets. This was due to spatial co-occurrence and clustering of fast food outlets around supermarket locations. Hence, future studies exploring the associations of food environment with diet or diet-related health outcome should concurrently examine all aspects of food environment (healthy and unhealthy).

Characterizing the food retail environment: Impact of count, type, and geospatial error in 2 secondary data sources

OBJECTIVE Commercial listings of food retail outlets are increasingly used by community members and food policy councils and in multilevel intervention research to identify areas with limited access to healthier food. This study quantified the amount of count, type, and geospatial error in 2 commercial data sources. METHODS InfoUSA and Dun and Bradstreet were compared with a validated field census and validity statistics were calculated. RESULTS Considering only completeness, Dun and Bradstreet data undercounted 24% of existing supermarkets and grocery stores, and InfoUSA, 29%. In addition, considering accuracy of outlet type assignment increased the undercount error to 42% and 39%, respectively. Marked overcount existed as well, and only 43% of existing supermarkets were correctly identified with respect to presence, outlet type, and location. CONCLUSIONS AND IMPLICATIONS Relying exclusively on secondary data to characterize the food environment will result in substantial error. Whereas extensive data cleaning can offset some error, verification of outlets with a field census is still the method of choice.

Evaluating geographic variation in type 1 and type 2 diabetes mellitus incidence in youth in four US regions

We evaluated geographic variation in type 1 and type 2 diabetes mellitus (T1DM, T2DM) in four regions of the United States. Data on 807 incident T1DM cases diabetes and 313 T2DM cases occurring in 2002-03 in South Carolina (SC) and Colorado (CO), 5 counties in Washington (WA), and an 8 county region around Cincinnati, Ohio (OH) among youth aged 10-19 years were obtained from the SEARCH for Diabetes in Youth Study. Geographic patterns were evaluated in a Bayesian framework. Incidence rates differed between the study regions, even within race/ethnic groups. Significant small-area variation within study region was observed for T1DM and T2DM. Evidence for joint spatial correlation between T1DM and T2DM was present at the county level for SC (r(SC)=0.31) and CO non-Hispanic Whites (r(CO)=0.40) and CO Hispanics (r(CO)=0.72). At the tract level, no evidence for meaningful joint spatial correlation was observed (r(SC)=-0.02; r(CO)=-0.02; r(OH)=0.03; and r(WA=)0.09). Our study provides evidence for the presence of both regional and small area, localized variation in type 1 and type 2 incidence among youth aged 10-19 years in the United States.

Associations of built food environment with body mass index and waist circumference among youth with diabetes

BackgroundYouth with diabetes are at increased risk for obesity and cardiovascular disease complications. However, less is known about the influence of built food environment on health outcomes in this population. The aim of this study was to explore the associations of accessibility and availability of supermarkets and fast food outlets with Body Mass Index (BMI) z-score and waist circumference among youth with diabetes.MethodsInformation on residential location and adiposity measures (BMI z-score and waist circumference) for 845 youths with diabetes residing in South Carolina was obtained from the South Carolina site of the SEARCH for Diabetes in Youth study. Food outlets data obtained from the South Carolina Department of Health and Environmental Control and InfoUSA were merged based on names and addresses of the outlets. The comprehensive data on franchised supermarket and fast food outlets was then used to construct three accessibility and availability measures around each youth’s residence.ResultsIncreased number and density of chain supermarkets around residence location were associated with lower BMI z-score and waist circumference among youth with diabetes. For instance, for a female child of 10 years of age with height of 54.2 inches and weight of 70.4 pounds, lower supermarket density around residence location was associated with about 2.8–3.2 pounds higher weight, when compared to female child of same age, height and weight with highest supermarket density around residence location. Similarly, lower supermarket density around residence location was associated with a 3.5–3.7 centimeter higher waist circumference, when compared to residence location with the highest supermarket density. The associations of number and density of chain fast food outlets with adiposity measures, however, were not significant. No significant associations were observed between distance to the nearest supermarket and adiposity measures. However, contrary to our expectation, increased distance to the nearest fast food outlet was associated with higher BMI z-score, but not with waist circumference.ConclusionsFood environments conducive to healthy eating may significantly influence health behaviors and outcomes. Efforts to increase the availability of supermarkets providing options/selections for health-promoting foods may significantly improve the dietary intake and reduce adiposity among youth with diabetes.

Associations of dietary intake patterns identified using reduced rank regression with markers of arterial stiffness among youth with type 1 diabetes

Background/Objectives:Youth with type 1 diabetes (T1DM) are at substantially increased risk for adverse vascular outcomes, but little is known about the influence of dietary behavior on cardiovascular disease (CVD) risk profile. We aimed to identify dietary intake patterns associated with CVD risk factors and evaluate their impact on arterial stiffness (AS) measures collected thereafter in a cohort of youth with T1DM.Subjects/Methods:Baseline diet data from a food frequency questionnaire and CVD risk factors (triglycerides, low density lipoprotein-cholesterol, systolic blood pressure, hemoglobin A1c, C-reactive protein and waist circumference) were available for 1153 youth aged ⩾10 years with T1DM from the SEARCH for Diabetes in Youth Study. A dietary intake pattern was identified using 33 food groups as predictors and six CVD risk factors as responses in reduced rank regression (RRR) analysis. Associations of this RRR-derived dietary pattern with AS measures (augmentation index (AIx75), n=229; pulse wave velocity, n=237; and brachial distensibility, n=228) were then assessed using linear regression.Results:The RRR-derived pattern was characterized by high intakes of sugar-sweetened beverages (SSB) and diet soda, eggs, potatoes and high-fat meats and low intakes of sweets/desserts and low-fat dairy; major contributors were SSB and diet soda. This pattern captured the largest variability in adverse CVD risk profile and was subsequently associated with AIx75 (β=0.47; P<0.01). The mean difference in AIx75 concentration between the highest and the lowest dietary pattern quartiles was 4.3% in fully adjusted model.Conclusions:Intervention strategies to reduce consumption of unhealthy foods and beverages among youth with T1DM may significantly improve CVD risk profile and ultimately reduce the risk for AS.

Spatial-Temporal Modeling of Neighborhood Sociodemographic Characteristics and Food Stores

The literature on food stores, neighborhood poverty, and race/ethnicity is mixed and lacks methods of accounting for complex spatial and temporal clustering of food resources. We used quarterly data on supermarket and convenience store locations from Nielsen TDLinx (Nielsen Holdings N.V., New York, New York) spanning 7 years (2006-2012) and census tract-based neighborhood sociodemographic data from the American Community Survey (2006-2010) to assess associations between neighborhood sociodemographic characteristics and food store distributions in the Metropolitan Statistical Areas (MSAs) of 4 US cities (Birmingham, Alabama; Chicago, Illinois; Minneapolis, Minnesota; and San Francisco, California). We fitted a space-time Poisson regression model that accounted for the complex spatial-temporal correlation structure of store locations by introducing space-time random effects in an intrinsic conditionally autoregressive model within a Bayesian framework. After accounting for census tract-level area, population, their interaction, and spatial and temporal variability, census tract poverty was significantly and positively associated with increasing expected numbers of supermarkets among tracts in all 4 MSAs. A similar positive association was observed for convenience stores in Birmingham, Minneapolis, and San Francisco; in Chicago, a positive association was observed only for predominantly white and predominantly black tracts. Our findings suggest a positive association between greater numbers of food stores and higher neighborhood poverty, with implications for policy approaches related to food store access by neighborhood poverty.