Differences in Weight Gain Following Residential Relocation in the Moving to Health (M2H) Study.

BACKGROUND: Neighborhoods may play an important role in shaping long-term weight trajectory and obesity risk. Studying the impact of moving to another neighborhood may be the most efficient way to determine the impact of the built environment on health. We explored whether residential moves were associated with changes in body weight. METHODS: Kaiser Permanente Washington electronic health records were used to identify 21,502 members aged 18-64 who moved within King County, WA between 2005 and 2017. We linked body weight measures to environment measures, including population, residential, and street intersection densities (800 m and 1,600 m Euclidian buffers) and access to supermarkets and fast foods (1,600 m and 5,000 m network distances). We used linear mixed models to estimate associations between postmove changes in environment and changes in body weight. RESULTS: In general, moving from high-density to moderate- or low-density neighborhoods was associated with greater weight gain postmove. For example, those moving from high to low residential density neighborhoods (within 1,600 m) gained an average of 4.5 (95% confidence interval [CI] = 3.0, 5.9) lbs 3 years after moving, whereas those moving from low to high-density neighborhoods gained an average of 1.3 (95% CI = -0.2, 2.9) lbs. Also, those moving from neighborhoods without fast-food access (within 1600m) to other neighborhoods without fast-food access gained less weight (average 1.6 lbs [95% CI = 0.9, 2.4]) than those moving from and to neighborhoods with fast-food access (average 2.8 lbs [95% CI = 2.5, 3.2]). CONCLUSIONS: Moving to higher-density neighborhoods may be associated with reductions in adult weight gain.

Pro-inflammatory monocytes are increased in Duchenne muscular dystrophy and suppressed with omega-3 fatty acids: A double-blind, randomized, placebo-controlled pilot study.

BACKGROUND: Omega-3 long chain polyunsaturated fatty acids (LCPUFA) reduce circulating cytokines produced by monocytes. Nevertheless, whether the omega-3 LCPUFA regulate the monocytes and their cytokines in Duchenne muscular dystrophy (DMD) is unknown. The aim of this study was to evaluate whether circulating pro-inflammatory monocytes are increased and whether omega-3 LCPUFA selectively suppress these monocytes and their cytokines in patients with DMD. METHODS: This was a double-blind, randomized, placebo-controlled pilot study carried out in patients with DMD supplemented with omega-3 LCPUFA (n = 6) or sunflower oils (placebo, n = 6) for 6 months. Monocytes and their cytokines were measured at baseline and after 1, 2, 3, and 6 months of supplementation. RESULTS: The anti-inflammatory monocytes (median, [95% CI]) are increased at month 3 (-0.46 [-13.5-9.5] vs. 8.4 [5.5-12.5], p = 0.05) in the omega-3 LCPUFA group compared with the placebo group. The pro-inflammatory monocytes (-5.7 [-63.8-114.1] vs. -51.9 [-91.2 to -25.4], p = 0.026 and -16.4 [-50.8-50.6] vs. -57.9 [-86.9 to -18.5], p = 0.045 at months 3 and 6, respectively) and their cytokine interleukin 6 (-11.9 [-93.5-148.9] vs. -64.7 [-77.8 to -42.6], p = 0.019 at month 6) decreased in the omega-3 LCPUFA group compared with the placebo group. Pro-inflammatory monocytes decreased and anti-inflammatory monocytes were augmented (p < 0.05) during the 6 months of supplementation with omega-3 LCPUFA. CONCLUSIONS: This pilot study suggests that supplementation with omega-3 LCPUFA could have a selective reductive effect on pro-inflammatory monocytes and their cytokines in patients with DMD. These findings also support the performance of studies in a significant population to explore the role of omega-3 LCPUFA on monocyte populations and their cytokines in patients with DMD. This research was registered at clinicaltrials.gov (NCT018264229).

Natural history of circulating miRNAs in Duchenne disease: Association with muscle injury and metabolic parameters.

OBJECTIVES: This study aimed to evaluate whether the expression of circulating dystromiRs and a group of oxidative stress-related (OS-R) miRNAs is associated with muscle injury and circulating metabolic parameters in Duchenne muscular dystrophy (DMD) patients. METHODS: Twenty-four DMD patients were included in this cross-sectional study. Clinical scales to evaluate muscle injury (Vignos, GMFCS, Brooke, and Medical Research Council), enzymatic muscle injury parameters (CPK, ALT, and AST), anthropometry, metabolic indicators, physical activity, serum dystromiRs (miR-1-3p, miR-133a-3p, and miR-206), and OS-R miRNAs (miR-21-5p, miR-31-5p, miR-128-3p, and miR-144-3p) levels were measured in ambulatory and non-ambulatory DMD patients. RESULTS: DystromiRs (except miR-1-3p) and miRNAs OS-R levels were lower (p-value <.05) in the non-ambulatory group than the ambulatory group. The expression of those miRNAs correlated with Vignos scale score (For instance, rho = -0.567, p-value <0.05 for miR-21-5p) and with other scales scores of muscle function and strength. CPK, AST, and ALT concentration correlated with expression of all miRNAs (For instance, rho = 0.741, p-value <.05 between miR-206 level and AST concentration). MiR-21-5p level correlated with glucose concentration (rho = -0.369, p-value = .038), and the miR-1-3p level correlated with insulin concentration (rho = 0.343, p-value = .05). CONCLUSIONS: Non-ambulatory DMD patients have lower circulating dystromiRs and OS-R miRNAs levels than ambulatory DMD patients. The progressive muscle injury is associated with a decrease in the expression of those miRNAs, evidencing DMD progress. These findings add new information about the natural history of DMD.

Does the built environment have independent obesogenic power? Urban form and trajectories of weight gain.

OBJECTIVE: To determine whether selected features of the built environment can predict weight gain in a large longitudinal cohort of adults. METHODS: Weight trajectories over a 5-year period were obtained from electronic health records for 115,260 insured patients aged 18-64 years in the Kaiser Permanente Washington health care system. Home addresses were geocoded using ArcGIS. Built environment variables were population, residential unit, and road intersection densities captured using Euclidean-based SmartMaps at 800-m buffers. Counts of area supermarkets and fast food restaurants were obtained using network-based SmartMaps at 1600, and 5000-m buffers. Property values were a measure of socioeconomic status. Linear mixed effects models tested whether built environment variables at baseline were associated with long-term weight gain, adjusting for sex, age, race/ethnicity, Medicaid insurance, body weight, and residential property values. RESULTS: Built environment variables at baseline were associated with differences in baseline obesity prevalence and body mass index but had limited impact on weight trajectories. Mean weight gain for the full cohort was 0.06 kg at 1 year (95% CI: 0.03, 0.10); 0.64 kg at 3 years (95% CI: 0.59, 0.68), and 0.95 kg at 5 years (95% CI: 0.90, 1.00). In adjusted regression models, the top tertile of density metrics and frequency counts were associated with lower weight gain at 5-years follow-up compared to the bottom tertiles, though the mean differences in weight change for each follow-up year (1, 3, and 5) did not exceed 0.5 kg. CONCLUSIONS: Built environment variables that were associated with higher obesity prevalence at baseline had limited independent obesogenic power with respect to weight gain over time. Residential unit density had the strongest negative association with weight gain. Future work on the influence of built environment variables on health should also examine social context, including residential segregation and residential mobility.

Differential associations of the built environment on weight gain by sex and race/ethnicity but not age.

OBJECTIVE: To explore the built environment (BE) and weight change relationship by age, sex, and racial/ethnic subgroups in adults. METHODS: Weight trajectories were estimated using electronic health records for 115,260 insured Kaiser Permanente Washington members age 18-64 years. Member home addresses were geocoded using ArcGIS. Population, residential, and road intersection densities and counts of area supermarkets and fast food restaurants were measured with SmartMaps (800 and 5000-meter buffers) and categorized into tertiles. Linear mixed-effect models tested whether associations between BE features and weight gain at 1, 3, and 5 years differed by age, sex, and race/ethnicity, adjusting for demographics, baseline weight, and residential property values. RESULTS: Denser urban form and greater availability of supermarkets and fast food restaurants were associated with differential weight change across sex and race/ethnicity. At 5 years, the mean difference in weight change comparing the 3rd versus 1st tertile of residential density was significantly different between males (-0.49 kg, 95% CI: -0.68, -0.30) and females (-0.17 kg, 95% CI: -0.33, -0.01) (P-value for interaction = 0.011). Across race/ethnicity, the mean difference in weight change at 5 years for residential density was significantly different among non-Hispanic (NH) Whites (-0.47 kg, 95% CI: -0.61, -0.32), NH Blacks (-0.86 kg, 95% CI: -1.37, -0.36), Hispanics (0.10 kg, 95% CI: -0.46, 0.65), and NH Asians (0.44 kg, 95% CI: 0.10, 0.78) (P-value for interaction <0.001). These findings were consistent for other BE measures. CONCLUSION: The relationship between the built environment and weight change differs across demographic groups. Careful consideration of demographic differences in associations of BE and weight trajectories is warranted for investigating etiological mechanisms and guiding intervention development.

RITS: a toolbox for assessing complex interventions via interrupted time series models.

BACKGROUND: Various interacting and interdependent components comprise complex interventions. These components create difficulty in assessing the true impact of interventions designed to improve patient-centered outcomes. Interrupted time series (ITS) designs borrow from case-crossover designs and serve as quasi-experimental methodology able to retrospectively assess the impact of an intervention while accounting for temporal correlation. While ITS designs are aptly situated for studying the impacts of large-scale public health policies, existing ITS software implement rigid ITS methodology that often assume the pre- and post-intervention phases are fully differentiated (by a known change-point or set of time points) and do not allow for changes in both the mean functions and correlation structure. RESULTS: This article describes the Robust Interrupted Time Series (RITS) toolbox, a stand-alone user-friendly application researchers can use to implement flexible ITS models that estimate the lagged effect of an intervention on an outcome, level and trend changes, and post-intervention changes in the correlation structure, for single and multiple ITS. The RITS toolbox incorporates a formal test for the existence of a change in the outcome and estimates a change-point over a set of possible change-points defined by the researcher. In settings with multiple ITS, RITS provides a global over-all units change-point and allows for unit-specific changes in the mean functions and correlation structures. CONCLUSIONS: The RITS toolbox is the first piece of software that allows researchers to use flexible ITS models that test for the existence of a change-point, estimate the change-point (if estimation is desired), and allow for changes in both the mean functions and correlation structures at the change point. RITS does not require any knowledge of a statistical (or otherwise) programming language, is freely available to the community, and may be downloaded and used on a local machine to ensure data protection.

Clinical risk prediction models and informative cluster size: Assessing the performance of a suicide risk prediction algorithm.

Clinical visit data are clustered within people, which complicates prediction modeling. Cluster size is often informative because people receiving more care are less healthy and at higher risk of poor outcomes. We used data from seven health systems on 1,518,968 outpatient mental health visits from January 1, 2012 to June 30, 2015 to predict suicide attempt within 90 days. We evaluated true performance of prediction models using a prospective validation set of 4,286,495 visits from October 1, 2015 to September 30, 2017. We examined dividing clustered data on the person or visit level for model training and cross-validation and considered a within cluster resampling approach for model estimation. We evaluated optimism by comparing estimated performance from a left-out testing dataset to performance in the prospective dataset. We used two prediction methods, logistic regression with least absolute shrinkage and selection operator (LASSO) and random forest. The random forest model using a visit-level split for model training and testing was optimistic; it overestimated discrimination (area under the curve, AUC = 0.95 in testing versus 0.84 in prospective validation) and classification accuracy (sensitivity = 0.48 in testing versus 0.19 in prospective validation, 95th percentile cut-off). Logistic regression and random forest models using a person-level split performed well, accurately estimating prospective discrimination and classification: estimated AUCs ranged from 0.85 to 0.87 in testing versus 0.85 in prospective validation, and sensitivity ranged from 0.15 to 0.20 in testing versus 0.17 to 0.19 in prospective validation. Within cluster resampling did not improve performance. We recommend dividing clustered data on the person level, rather than visit level, to ensure strong performance in prospective use and accurate estimation of future performance at the time of model development.

Body composition and body mass index in Duchenne muscular dystrophy: Role of dietary intake.

INTRODUCTION: In Duchenne muscular dystrophy (DMD) muscle is replaced by adipose tissue. The role of dietary intake (DI) in DMD has not been evaluated. In this study we examined body composition, body mass index (BMI), and adequacy of DI in patients with DMD and evaluated the influence of DI on body composition. METHODS: Patients (n = 101; age 3-18 years; BMI 11.8-29.5 kg/m2 ) completed a dietary recall to determine DI and then underwent dual-energy X-ray absorptiometry to determine body composition. RESULTS: Preschool-age and school-age boys with DMD had high total energy intake. Protein intake per kilogram exceeded recommendations. As age increased, the percentage of boys with abnormal BMI and fat mass increased, while lean mass decreased. Dietary intake did not predict body fat or lean mass. DISCUSSION: Age-dependent changes in BD in boys with DMD may be due to endogenous metabolic factors related to the underlying disease process and to disease-related mobility impairments. Muscle Nerve 59:295-302, 2019.

Muscle function and age are associated with loss of bone mineral density in Duchenne muscular dystrophy.

INTRODUCTION: Patients with Duchenne muscular dystrophy (DMD) demonstrate decreased bone mineral density (BD). It is not clear which factors exert the greatest impact on BD loss in these patients. METHODS: In 63 patients with DMD, serum cytokines (interleukin [IL]-1, IL-6, and tumor necrosis factor-beta [TNF-ß]), C-reactive protein (CRP), creatine kinase (CK), muscle function (by Vignos scale), body composition, and total BD (the latter 2 measured by dual-energy X-ray absorptiometry, or DEXA) were determined. RESULTS: The main factors associated with BD loss were muscle function (34.0%; ß = -0.139; P < 0.023) and age (36.7%; ß = -0.151; P = 0.004). Cytokines, CRP, body fat mass, and CK did not contribute to BD loss. DISCUSSION: Muscle function and age contribute to BD loss in DMD. We propose that a cut-off of at least 6 points for the Vignos scale and at least 10.5 years of age predict a Z-score of less than or equal to -2.0. Muscle Nerve 59:417-421, 2019.

Assessing health care interventions via an interrupted time series model: Study power and design considerations.

The delivery and assessment of quality health care is complex with many interacting and interdependent components. In terms of research design and statistical analysis, this complexity and interdependency makes it difficult to assess the true impact of interventions designed to improve patient health care outcomes. Interrupted time series (ITS) is a quasi-experimental design developed for inferring the effectiveness of a health policy intervention while accounting for temporal dependence within a single system or unit. Current standardized ITS methods do not simultaneously analyze data for several units nor are there methods to test for the existence of a change point and to assess statistical power for study planning purposes in this context. To address this limitation, we propose the "Robust Multiple ITS" (R-MITS) model, appropriate for multiunit ITS data, that allows for inference regarding the estimation of a global change point across units in the presence of a potentially lagged (or anticipatory) treatment effect. Under the R-MITS model, one can formally test for the existence of a change point and estimate the time delay between the formal intervention implementation and the over-all-unit intervention effect. We conducted empirical simulation studies to assess the type one error rate of the testing procedure, power for detecting specified change-point alternatives, and accuracy of the proposed estimating methodology. R-MITS is illustrated by analyzing patient satisfaction data from a hospital that implemented and evaluated a new care delivery model in multiple units.

System- and Unit-Level Care Quality Outcome Improvements After Integrating Clinical Nurse Leaders Into Frontline Care Delivery.

OBJECTIVE: This study determined whether 1 health system's frontline nursing model redesign to integrate clinical nurse leaders (CNLs) improved care quality and outcome score consistency. METHODS: Interrupted time-series design was used to measure patient satisfaction with 7 metrics before and after formally integrating CNLs into a Michigan healthcare system. Analysis generated estimates of quality outcome: a) change point; b) level change; and c) variance, pre-post implementation. RESULTS: The lowest-performing unit showed significant increases in quality scores, but there were no significant increases at the hospital level. Quality metric consistency increased significantly for every indicator at the hospital and unit level. CONCLUSIONS: To our knowledge, this is the 1st study quantifying quality outcome consistency before and after nursing care delivery redesign with CNLs. The significant improvement suggests the CNL care model is associated with production of stable clinical microsystem practices that help to reduce clinical variability, thus improving care quality.

Reconstructing Native American population history.

The peopling of the Americas has been the subject of extensive genetic, archaeological and linguistic research; however, central questions remain unresolved. One contentious issue is whether the settlement occurred by means of a single migration or multiple streams of migration from Siberia. The pattern of dispersals within the Americas is also poorly understood. To address these questions at a higher resolution than was previously possible, we assembled data from 52 Native American and 17 Siberian groups genotyped at 364,470 single nucleotide polymorphisms. Here we show that Native Americans descend from at least three streams of Asian gene flow. Most descend entirely from a single ancestral population that we call 'First American'. However, speakers of Eskimo-Aleut languages from the Arctic inherit almost half their ancestry from a second stream of Asian gene flow, and the Na-Dene-speaking Chipewyan from Canada inherit roughly one-tenth of their ancestry from a third stream. We show that the initial peopling followed a southward expansion facilitated by the coast, with sequential population splits and little gene flow after divergence, especially in South America. A major exception is in Chibchan speakers on both sides of the Panama isthmus, who have ancestry from both North and South America.

Impact of Metabolic Hormones Secreted in Human Breast Milk on Nutritional Programming in Childhood Obesity.

Obesity is the most common metabolic disease whose prevalence is increasing worldwide. This condition is considered a serious public health problem due to associated comorbidities such as diabetes mellitus and hypertension. Perinatal morbidity related to obesity does not end with birth; this continues affecting the mother/infant binomial and could negatively impact on metabolism during early infant nutrition. Nutrition in early stages of growth may be essential in the development of obesity in adulthood, supporting the concept of "nutritional programming". For this reason, breastfeeding may play an important role in this programming. Breast milk is the most recommended feeding for the newborn due to the provided benefits such as protection against obesity and diabetes. Health benefits are based on milk components such as bioactive molecules, specifically hormones involved in the regulation of food intake. Identification of these molecules has increased in recent years but its action has not been fully clarified. Hormones such as leptin, insulin, ghrelin, adiponectin, resistin, obestatin and insulin-like growth factor-1 copeptin, apelin, and nesfatin, among others, have been identified in the milk of normal-weight women and may influence the energy balance because they can activate orexigenic or anorexigenic pathways depending on energy requirements and body stores. It is important to emphasize that, although the number of biomolecules identified in milk involved in regulating food intake has increased considerably, there is a lack of studies aimed at elucidating the effect these hormones may have on metabolism and development of the newborn. Therefore, we present a state-of-the-art review regarding bioactive compounds such as hormones secreted in breast milk and their possible impact on nutritional programming in the infant, analyzing their functions in appetite regulation.

A robust interrupted time series model for analyzing complex health care intervention data.

Current health policy calls for greater use of evidence-based care delivery services to improve patient quality and safety outcomes. Care delivery is complex, with interacting and interdependent components that challenge traditional statistical analytic techniques, in particular, when modeling a time series of outcomes data that might be "interrupted" by a change in a particular method of health care delivery. Interrupted time series (ITS) is a robust quasi-experimental design with the ability to infer the effectiveness of an intervention that accounts for data dependency. Current standardized methods for analyzing ITS data do not model changes in variation and correlation following the intervention. This is a key limitation since it is plausible for data variability and dependency to change because of the intervention. Moreover, present methodology either assumes a prespecified interruption time point with an instantaneous effect or removes data for which the effect of intervention is not fully realized. In this paper, we describe and develop a novel robust interrupted time series (robust-ITS) model that overcomes these omissions and limitations. The robust-ITS model formally performs inference on (1) identifying the change point; (2) differences in preintervention and postintervention correlation; (3) differences in the outcome variance preintervention and postintervention; and (4) differences in the mean preintervention and postintervention. We illustrate the proposed method by analyzing patient satisfaction data from a hospital that implemented and evaluated a new nursing care delivery model as the intervention of interest. The robust-ITS model is implemented in an R Shiny toolbox, which is freely available to the community.

Genomics of lactation: role of nutrigenomics and nutrigenetics in the fatty acid composition of human milk.

Human milk covers the infant's nutrient requirements during the first 6 months of life. The composition of human milk progressively changes during lactation and it is influenced by maternal nutritional factors. Nowadays, it is well known that nutrients have the ability to interact with genes and modulate molecular mechanisms impacting physiological functions. This has led to a growing interest among researchers in exploring nutrition at a molecular level and to the development of two fields of study: nutrigenomics, which evaluates the influence of nutrients on gene expression, and nutrigenetics, which evaluates the heterogeneous individual response to nutrients due to genetic variation. Fatty acids are one of the nutrients most studied in relation to lactation given their biologically important roles during early postnatal life. Fatty acids modulate transcription factors involved in the regulation of lipid metabolism, which in turn causes a variation in the proportion of lipids in milk. This review focuses on understanding, on the one hand, the gene transcription mechanisms activated by maternal dietary fatty acids and, on the other hand, the interaction between dietary fatty acids and genetic variation in genes involved in lipid metabolism. Both of these mechanisms affect the fatty acid composition of human milk.

Beneficial Effects of Enteral Docosahexaenoic Acid on the Markers of Inflammation and Clinical Outcomes of Neonates Undergoing Cardiovascular Surgery: An Intervention Study.

BACKGROUND: Neonates undergoing surgery are at risk for uncontrolled inflammatory response and adverse clinical outcomes. Docosahexaenoic acid (DHA) ameliorates inflammation, improving clinical outcomes. However, its effect has not been evaluated in neonates undergoing surgery. We evaluated the effect of DHA on markers of inflammation and clinical outcomes in neonates undergoing surgery. METHODS: A double-blind clinical trial evaluated the effect of enteral DHA (DHA group) versus sunflower oil (SO group) perioperatively administered in neonates scheduled for cardiovascular surgery. Inflammation was evaluated by percentage of cells+ for cytokines and CD69 in mononuclear cells at baseline, 24 h and 7 days post surgery. Clinical outcomes measured were sepsis, organ dysfunctions (ODs), length of stay in intensive care and bleeding. Repeated measures analysis of variance and logistic regression were applied. RESULTS: Sixteen neonates received DHA and 18 received SO. Cells+ from neonates in the DHA group showed an early increase in receptor antagonist of interleukin (IL)-1+ (IL-1ra+) and IL-10+ and a late decrease in IL-6+. IL-1ß+ and IL-10+ changes were different between groups. After adjusting for confounders, less cells from DHA group were IL-1ß+, IL-6+, IL-1ra+ and IL-10+. DHA group presented less sepsis, ODs and shorter stay, but no difference in CD69+CD4+ cells or bleeding between groups. CONCLUSIONS: Administration of enteral DHA ameliorates markers of inflammation and improves clinical outcomes in surgical neonates.

Enteral Docosahexaenoic Acid Reduces Analgesic Administration in Neonates Undergoing Cardiovascular Surgery.

BACKGROUND: Neonates undergoing surgery require analgesic medication to ameliorate acute pain. These medications produce negative side effects. Docosahexaenoic acid (DHA) has an antinociceptive effect in animals, but this has not been evaluated in human neonates. We evaluated the DHA effect on cumulative dose and duration of analgesics administered to neonates undergoing cardiovascular surgery. METHODS: A secondary analysis was performed with data from a clinical trial, in which enteral DHA was administered perioperatively compared with sunflower oil (SO). Present study assessed the antinociceptive effect of DHA by measuring the cumulative dose and duration of analgesics administered during postoperative stay in a neonatal intensive care unit. Multivariate linear regression models were performed. RESULTS: Seventeen neonates received DHA and 18 received SO in the control group. Compared with the control group, the DHA group received lower cumulative dose (14.6 ± 2.2 vs. 25.2 ± 4.8 µg/kg, p = 0.029) and shorter duration of buprenorphine (2 days (1-8) vs. 4.5 days (1-12); p = 0.053). After adjusting for confounders, the DHA group received significantly lesser buprenorphine (ß = -27 µg/kg, p = 0.028; R2 model = 0.90) for shorter duration (ß = -9 days, p = 0.003; R2 model = 0.94). No differences in fentanyl or ketorolac were detected. CONCLUSIONS: Buprenorphine administration was reduced in neonates who received DHA, suggesting that DHA likely has analgesic effects.

Excessive refined carbohydrates and scarce micronutrients intakes increase inflammatory mediators and insulin resistance in prepubertal and pubertal obese children independently of obesity.

BACKGROUND: Low-grade inflammation is the link between obesity and insulin resistance. Because physiologic insulin resistance occurs at puberty, obese pubertal children are at higher risk for insulin resistance. Excessive diets in refined carbohydrates and saturated fats are risk factors for insulin resistance, but calcium, magnesium, vitamin-D, and the omega-3 fatty acids likely protect against inflammation and insulin resistance. OBJECTIVE: To analyze interactions among dietary saturated fat, refined carbohydrates, calcium, magnesium, vitamin D, and omega-3 fatty acids on the risk of inflammation and insulin resistance in a sample of prepubertal and pubertal children. METHODS: A sample of 229 children from Mexico City was analyzed in a cross-sectional design. Anthropometric measurements, 24 h recall questionnaires, and blood samples were obtained. Serum insulin, glucose, calcium, magnesium, 25-OHD3, C-reactive protein, leptin, adiponectin, and erythrocytes fatty acids were measured. Parametric and nonparametric statistics were used for analysis. RESULTS: While mean macronutrients intake was excessive, micronutrients intake was deficient (P < 0.01). Inflammation determinants were central obesity and magnesium-deficient diets. Determinants of insulin resistance were carbohydrates intake and circulating magnesium and adiponectin. CONCLUSIONS: Magnesium-deficient diets are determinants of inflammation, while high intake of refined carbohydrates is a risk factor for insulin resistance, independently of central adiposity.

Stability of Suicide Risk Prediction Models During Changes in Health Care Delivery.

OBJECTIVE: The authors aimed to use health records data to examine how the accuracy of statistical models predicting self-harm or suicide changed between 2015 and 2019, as health systems implemented suicide prevention programs. METHODS: Data from four large health systems were used to identify specialty mental health visits by patients ages ≥11 years, assess 311 potential predictors of self-harm (including demographic characteristics, historical risk factors, and index visit characteristics), and ascertain fatal or nonfatal self-harm events over 90 days after each visit. New prediction models were developed with logistic regression with LASSO (least absolute shrinkage and selection operator) in random samples of visits (65%) from each calendar year and were validated in the remaining portion of the sample (35%). RESULTS: A model developed for visits from 2009 to mid-2015 showed similar classification performance and calibration accuracy in a new sample of about 13.1 million visits from late 2015 to 2019. Area under the receiver operating characteristic curve (AUC) ranged from 0.840 to 0.849 in the new sample, compared with 0.851 in the original sample. New models developed for each year for 2015-2019 had classification performance (AUC range 0.790-0.853), sensitivity, and positive predictive value similar to those of the previously developed model. Models selected similar predictors from 2015 to 2019, except for more frequent selection of depression questionnaire data in later years, when questionnaires were more frequently recorded. CONCLUSIONS: A self-harm prediction model developed with 2009-2015 visit data performed similarly when applied to 2015-2019 visits. New models did not yield superior performance or identify different predictors.

Neighborhood built and food environment in relation to glycemic control in people with type 2 diabetes in the moving to health study.

OBJECTIVE: To examine whether built environment and food metrics are associated with glycemic control in people with type 2 diabetes. RESEARCH DESIGN AND METHODS: We included 14,985 patients with type 2 diabetes using electronic health records from Kaiser Permanente Washington. Patient addresses were geocoded with ArcGIS using King County and Esri reference data. Built environment exposures estimated from geocoded locations included residential unit density, transit threshold residential unit density, park access, and having supermarkets and fast food restaurants within 1600-m Euclidean buffers. Linear mixed effects models compared mean changes of HbA1c from baseline at 1, 3 (primary) and 5 years by each built environment variable. RESULTS: Patients (mean age = 59.4 SD = 13.2, 49.5% female, 16.6% Asian, 9.8% Black, 5.5% Latino/Hispanic, 57.1% White, 20% insulin dependent, mean BMI = 32.7±7.7) had an average of 6 HbA1c measures available. Participants in the 1st tertile of residential density (lowest) had a greater decline in HbA1c (-0.42, -0.43, and -0.44 in years 1, 3, and 5 respectively) than those in the 3rd tertile (HbA1c = -0.37 at 1- and 3-years and -0.36 at 5-years; all p-values <0.05). Having any supermarkets within 1600 m of home was associated with a greater decrease in HbA1c at 1-year and 3-years compared to having none (all p-values <0.05). CONCLUSIONS: Lower residential density and better proximity to supermarkets may benefit HbA1c control in people with people with type 2 diabetes. However, effects were small and indicate limited clinical significance.