85 research outputs found

    A closer look at weight loss interventions in primary care: a systematic review and meta-analysis

    Get PDF
    PurposeThe major aims were to quantify patient weight loss using various approaches adminstered by a primary care provider for at least 6 months and to unveil relevant contextual factors that could improve patient weight loss on a long-term basis.MethodsA systematic review and meta-analysis was conducted using Cochrane Central Register of Controlled Trials, MEDLINE, EMBASE, Scopus, and Web of Science from inception to December 5, 2022. COVIDENCE systematic review software was used to identify and abstract data, as well as assess data quality and risk of bias.ResultsSeven studies included 2,187 people with obesity testing (1) anti-obesity medication (AOM), (2) AOM, intensive lifestyle counseling + meal replacements, and (3) physician training to better counsel patients on intensive lifestyle modification. Substantial heterogeneity in the outcomes was observed, as well as bias toward lack of published studies showing no effect. The random effect model estimated a treatment effect for the aggregate efficacy of primary care interventions −3.54 kg (95% CI: −5.61 kg to −1.47 kg). Interventions that included a medication component (alone or as part of a multipronged intervention) achieved a greater weight reduction by −2.94 kg (p < 0.0001). In all interventions, efficacy declined with time (reduction in weight loss by 0.53 kg per 6 months, 95% CI: 0.04–1.0 kg).ConclusionWeight loss interventions administered by a primary care provider can lead to modest weight loss. Weight loss is approximately doubled if anti-obesity medication is part of the treatment. Nevertheless, attenuated weight loss over time underscores the need for long-term treatment.Systematic review registration[https://www.crd.york.ac.uk/prospero/ CRD4202121242344], identifier (CRD42021242344)

    Metabolite Profiles of Incident Diabetes and Heterogeneity of Treatment Effect in the Diabetes Prevention Program

    Get PDF
    Novel biomarkers of type 2 diabetes (T2D) and response to preventative treatment in individuals with similar clinical risk may highlight metabolic pathways that are important in disease development. We profiled 331 metabolites in 2,015 baseline plasma samples from the Diabetes Prevention Program (DPP). Cox models were used to determine associations between metabolites and incident T2D, as well as whether associations differed by treatment group (i.e., lifestyle [ILS], metformin [MET], or placebo [PLA]), over an average of 3.2 years of follow-up. We found 69 metabolites associated with incident T2D regardless of treatment randomization. In particular, cytosine was novel and associated with the lowest risk. In an exploratory analysis, 35 baseline metabolite associations with incident T2D differed across the treatment groups. Stratification by baseline levels of several of these metabolites, including specific phospholipids and AMP, modified the effect that ILS or MET had on diabetes development. Our findings highlight novel markers of diabetes risk and preventative treatment effect in individuals who are clinically at high risk and motivate further studies to validate these interactions

    Does insulin resistance drive the association between hyperglycemia and cardiovascular risk?

    Get PDF
    Several studies have shown associations between hyperglycemia and risk of cardiovascular disease (CVD) and mortality, yet glucose-lowering treatment does little to mitigate this risk. We examined whether associations between hyperglycemia and CVD risk were explained by underlying insulin resistance.In 60 middle-aged individuals without diabetes we studied the associations of fasting plasma glucose, 2-hour post oral glucose tolerance test plasma glucose, insulin sensitivity as well as body fat percentage with CVD risk. Insulin sensitivity was measured as the glucose infusion rate during a euglycemic hyperinsulinemic clamp, body fat percentage was measured by dual X-ray absorptiometry, and CVD risk was estimated using the Framingham risk score. Associations of fasting plasma glucose, 2-hour plasma glucose, insulin sensitivity and body fat percentage with the Framingham risk score were assessed in linear regression models.Both fasting and 2-hour plasma glucose levels were associated with higher Framingham risk score (fasting glucose: r(2) = 0.21; 2-hour glucose: r(2) = 0.24; P<0.001 for both), and insulin sensitivity with lower Framingham risk score (r(2) = 0.36; P<0.001). However, adjustment for insulin sensitivity and 2-hour glucose made the effect of fasting glucose non-significant (P = 0.060). Likewise, when adjusting for insulin sensitivity and fasting glucose, the association between 2-hour glucose and Framingham risk score disappeared (P = 0.143). In contrast, insulin sensitivity was still associated with Framingham risk score after adjusting for glucose levels (P<0.001). Body fat was not associated with Framingham risk score when taking insulin sensitivity into account (P = 0.550).The association between plasma glucose levels and CVD risk is mainly explained by insulin resistance, which raises the question of whether glucose lowering per se without changes in the processes that underlie hyperglycemia should be the sole clinical paradigm in the treatment of type 2 diabetes or its prevention

    A Breakthrough Series Collaborative to Support Trauma-Informed Practice in Early Care & Education Programs

    Get PDF
    The Breakthrough Series Collaborative to supports trauma-informed practice in early care & education programs and aims to close the gap between theory and practice by creating collaborative learning opportunities that use emerging science to address barriers and promote quality improvements in focus area
    • …
    corecore