Objective
To determine the effect on weight of two mobile technology‐based (mHealth) behavioral weight loss interventions in young adults.
Methods
Randomized, controlled comparative effectiveness ...trial in 18‐ to 35‐year‐olds with BMI ≥ 25 kg/m2 (overweight/obese), with participants randomized to 24 months of mHealth intervention delivered by interactive smartphone application on a cell phone (CP); personal coaching enhanced by smartphone self‐monitoring (PC); or Control.
Results
The 365 randomized participants had mean baseline BMI of 35 kg/m2. Final weight was measured in 86% of participants. CP was not superior to Control at any measurement point. PC participants lost significantly more weight than Controls at 6 months (net effect −1.92 kg CI −3.17, −0.67, P = 0.003), but not at 12 and 24 months.
Conclusions
Despite high intervention engagement and study retention, the inclusion of behavioral principles and tools in both interventions, and weight loss in all treatment groups, CP did not lead to weight loss, and PC did not lead to sustained weight loss relative to Control. Although mHealth solutions offer broad dissemination and scalability, the CITY results sound a cautionary note concerning intervention delivery by mobile applications. Effective intervention may require the efficiency of mobile technology, the social support and human interaction of personal coaching, and an adaptive approach to intervention design.
Abstract Objective To identify novel biomarkers through metabolomic profiles that distinguish metabolically well (MW) from metabolically unwell (MUW) individuals, independent of body mass index ...(BMI). Materials/Methods This study was conducted as part of the Measurement to Understand the Reclassification of Disease of Cabarrus/Kannapolis (MURDOCK) project. Individuals from 3 cohorts were classified as lean (BMI < 25 kg/m2 ), overweight (BMI ≥ 25 kg/m2 , BMI < 30 kg/m2 ) or obese (BMI ≥ 30 kg/m2 ). Cardiometabolic abnormalities were defined as: (1) impaired fasting glucose (≥ 100 mg/dL and ≤ 126 mg/dL); (2) hypertension; (3) triglycerides ≥ 150 mg/dL; (4) HDL-C < 40 mg/dL in men, < 50 mg/dL in women; and (5) insulin resistance (calculated Homeostatic Model Assessment (HOMA-IR) index of > 5.13). MW individuals were defined as having < 2 cardiometabolic abnormalities and MUW individuals had ≥ two cardiometabolic abnormalities. Targeted profiling of 55 metabolites used mass-spectroscopy-based methods. Principal components analysis (PCA) was used to reduce the large number of correlated metabolites into clusters of fewer uncorrelated factors. Results Of 1872 individuals, 410 were lean, 610 were overweight, and 852 were obese. Of lean individuals, 67% were categorized as MUW, whereas 80% of overweight and 87% of obese individuals were MUW. PCA-derived factors with levels that differed the most between MW and MUW groups were factors 4 (branched chain amino acids BCAA) p < .0001, 8 (various metabolites) p < .0001, 9 (C4/Ci4, C3, C5 acylcarnitines) p < .0001 and 10 (amino acids) p < .0002. Further, Factor 4, distinguishes MW from MUW individuals independent of BMI. Conclusion BCAA and related metabolites are promising biomarkers that may aid in understanding cardiometabolic health independent of BMI category.
There is burgeoning evidence that branch chain amino acids (BCAAs) are biomarkers of metabolic, cardiovascular, renal and cerebrovascular disease. The purpose of this review is to summarize the ...current evidence in this area.
Recent evidence demonstrates that BCAAs are associated with insulin resistance, type 2 diabetes, risk of cardiovascular disease, stage I and II chronic kidney disease and ischemic stroke. Further, circulating levels of BCAAs have the potential to predict populations at risk for cardiometabolic disease, type 2 diabetes and mortality from ischemic heart disease. Importantly, the relationship of BCAAs to insulin resistance is affected by the intake of fat in the diet as well as age.
Current evidence supports the potential use of BCAAs as biomarkers of disease. However, questions regarding the mechanisms underlying the relationship of BCAAs to disease process and severity need to be answered prior to the use of BCAAs as a biomarker in clinical practice.
To evaluate the relationship between social needs and metformin use among adults with type 2 diabetes (T2D).
In a prospective cohort study of adults with T2D (n = 722), we linked electronic health ...record (EHR) and Surescripts (Surescripts, LLC) prescription network data to abstract data on patient-reported social needs and to calculate metformin adherence based on expected refill frequency using a proportion of days covered methodology.
After adjusting for demographics and clinical complexity, two or more social needs (-0.046; 95% CI -0.089, 0.003), being uninsured (-0.052; 95% CI -0.095, -0.009) and while adjusting for other needs, being without housing (-0.069; 95% CI -0.121, -0.018) and lack of access to medicine/health care (-0.058; 95% CI -0.115, -0.000) were associated with lower use.
We found that overall social need burden and specific needs, particularly housing and health care access, were associated with clinically significant reductions in metformin adherence among patients with T2D.
This study compares the yield and characteristics of diabetes cohorts identified using heterogeneous phenotype definitions.
Inclusion criteria from seven diabetes phenotype definitions were ...translated into query algorithms and applied to a population (n=173 503) of adult patients from Duke University Health System. The numbers of patients meeting criteria for each definition and component (diagnosis, diabetes-associated medications, and laboratory results) were compared.
Three phenotype definitions based heavily on ICD-9-CM codes identified 9-11% of the patient population. A broad definition for the Durham Diabetes Coalition included additional criteria and identified 13%. The electronic medical records and genomics, NYC A1c Registry, and diabetes-associated medications definitions, which have restricted or no ICD-9-CM criteria, identified the smallest proportions of patients (7%). The demographic characteristics for all seven phenotype definitions were similar (56-57% women, mean age range 56-57 years).The NYC A1c Registry definition had higher average patient encounters (54) than the other definitions (range 44-48) and the reference population (20) over the 5-year observation period. The concordance between populations returned by different phenotype definitions ranged from 50 to 86%. Overall, more patients met ICD-9-CM and laboratory criteria than medication criteria, but the number of patients that met abnormal laboratory criteria exclusively was greater than the numbers meeting diagnostic or medication data exclusively.
Differences across phenotype definitions can potentially affect their application in healthcare organizations and the subsequent interpretation of data.
Further research focused on defining the clinical characteristics of standard diabetes cohorts is important to identify appropriate phenotype definitions for health, policy, and research.
Eradication of
reduces the risk of gastric cancer (GC). Individuals with type 2 diabetes mellitus (T2DM) are known to be at increased risk for GC. In a cohort of
-positive individuals, we assessed ...whether those with T2DM were at risk of persistent infection following
treatment compared with individuals without T2DM.
A random subset of all individuals diagnosed as having
without intestinal metaplasia at endoscopy from 2015 to 2019 were stratified evenly by race (Black and White). After excluding those with T1DM and those without eradication testing after
treatment, logistic regression analysis was used to determine the association of T2DM with the risk of persistent
infection following treatment.
In 138 patients,
eradication rates did not differ between the 27% of individuals with T2DM compared to those without (81.1% vs 81.2%). After adjusting for age, race, and insurance status, we found no significant increased risk of persistent
infection for individuals with T2DM (odds ratio 1.40; 95% confidence interval 0.49-3.99).
eradication rates do not differ by T2DM status, providing support for clinical trials of
eradication to reduce GC incidence among high-risk populations in the United States, such as individuals with T2DM.
Increased consumption of sugar-sweetened beverages (SSBs) has been associated with an elevated risk of obesity, metabolic syndrome, and type II diabetes mellitus. However, the effects of SSB ...consumption on blood pressure (BP) are uncertain. The objective of this study was to determine the relationship between changes in SSB consumption and changes in BP among adults.
This was a prospective analysis of 810 adults who participated in the PREMIER Study (an 18-month behavioral intervention trial). BP and dietary intake (by two 24-hour recalls) were measured at baseline and at 6 and 18 months. Mixed-effects models were applied to estimate the changes in BP in responding to changes in SSB consumption. At baseline, mean SSB intake was 0.9+/-1.0 servings per day (10.5+/-11.9 fl oz/d), and mean systolic BP/diastolic BP was 134.9+/-9.6/84.8+/-4.2 mm Hg. After potential confounders were controlled for, a reduction in SSB of 1 serving per day was associated with a 1.8-mm Hg (95% confidence interval, 1.2 to 2.4) reduction in systolic BP and 1.1-mm Hg (95% confidence interval, 0.7 to 1.4) reduction in diastolic BP over 18 months. After additional adjustment for weight change over the same period, a reduction in SSB intake was still significantly associated with reductions in systolic and diastolic BPs (P<0.05). Reduced intake of sugars was also significantly associated with reduced BP. No association was found for diet beverage consumption or caffeine intake and BP. These findings suggest that sugars may be the nutrients that contribute to the observed association between SSB and BP.
Reduced consumption of SSB and sugars was significantly associated with reduced BP. Reducing SSB and sugar consumption may be an important dietary strategy to lower BP.
URL: http://clinicaltrials.gov. Unique identifier: NCT00000616.
PDF
