Effect of race on the relationship between child maltreatment and obesity in Whites and Blacks.

This cross-sectional study was designed to determine what role race plays in the relationship between obesity and child maltreatment (CM), which is currently unknown. One hundred fifteen participants successfully completed the study, including Whites (n = 60) and Blacks (n = 55) of both sexes. CM was assessed using the Childhood Trauma Questionnaire. Total fat, trunk/total fat ratio, visceral adipose tissue (VAT), and VAT/trunk ratio, were measured through Dual Energy X-ray Absorptiometry (DXA) and Corescan software estimation. A significant interaction between identifying as White and having a history of CM was found to predict body mass index (BMI) (ß = 5.02, p = .025), total fat (kg) (ß = 9.81, p = .036), and VAT (kg) (ß = 0.542, p = .025), whereas race by itself was an insignificant predictor. An interaction between having history of physical abuse and identifying as White was found to predict BMI (ß = 6.993, p = .003), total fat (ß = 12.683, p = .010), and VAT (ß = 0.591, p = .018). An interaction between having multiple CM subtypes and identifying as White predicts increased total fat (ß = 5.667, p = .034) and VAT (ß = 0.335, p = .014). Our findings indicate that the relationship between CM and obesity, measured through BMI, total body fat, and VAT, is seen in Whites but not in Blacks. Future research should investigate the nature of this racial influence to guide obesity prevention and target at-risk populations.

Who Self-Weighs and What Do They Gain From It? A Retrospective Comparison Between Smart Scale Users and the General Population in England.

BACKGROUND: Digital self-monitoring, particularly of weight, is increasingly prevalent. The associated data could be reused for clinical and research purposes. OBJECTIVE: The aim was to compare participants who use connected smart scale technologies with the general population and explore how use of smart scale technology affects, or is affected by, weight change. METHODS: This was a retrospective study comparing 2 databases: (1) the longitudinal height and weight measurement database of smart scale users and (2) the Health Survey for England, a cross-sectional survey of the general population in England. Baseline comparison was of body mass index (BMI) in the 2 databases via a regression model. For exploring engagement with the technology, two analyses were performed: (1) a regression model of BMI change predicted by measures of engagement and (2) a recurrent event survival analysis with instantaneous probability of a subsequent self-weighing predicted by previous BMI change. RESULTS: Among women, users of self-weighing technology had a mean BMI of 1.62 kg/m(2) (95% CI 1.03-2.22) lower than the general population (of the same age and height) (P<.001). Among men, users had a mean BMI of 1.26 kg/m(2) (95% CI 0.84-1.69) greater than the general population (of the same age and height) (P<.001). Reduction in BMI was independently associated with greater engagement with self-weighing. Self-weighing events were more likely when users had recently reduced their BMI. CONCLUSIONS: Users of self-weighing technology are a selected sample of the general population and this must be accounted for in studies that employ these data. Engagement with self-weighing is associated with recent weight change; more research is needed to understand the extent to which weight change encourages closer monitoring versus closer monitoring driving the weight change. The concept of isolated measures needs to give way to one of connected health metrics.

Why Start Now? Retrospective Study Evaluating Baseline Screening Mammography in Patients Age 60 and Older.

PURPOSE: Extensive data exist regarding the importance of baseline mammography and screening recommendations in the age range of 40-50 years old, however, less is known about women who start screening at age 60. The purpose of this retrospective study is to assess the characteristics and outcomes of women aged 60 years and older presenting for baseline mammographic screening. METHODS: This is an IRB-approved single institution retrospective review of data from patients aged 60+ receiving baseline screening mammograms between 2010 and 2022 was obtained. Information regarding patient demographics, breast density, and BI-RADS assessment was acquired from Cerner EHR. Of patients with a BI-RADS 0 assessment, imaging, and chart review was performed. Family history, gynecologic history, prior breast biopsy or surgery, and hormone use was reviewed. For those with a category 4 or 5 assessment after diagnostic work-up, biopsy outcomes were reported. Cancer detection rate (CDR), recall rate (RR), positive predictive value 1 (PPV1), PPV2, and PPV3 were calculated. RESULTS: Data was analyzed from 1409 women over age 60 who underwent breast cancer screening. The recall rate was 29.3% (413/1409). The CDR, PPV1, PPV2, and PPV3 were calculated as 15/1000, 5.2% (21/405), 29.2% (21/72), and 31.8% (21/66), respectively. After work-up, 224 diagnostic patients had a 1-year follow-up and none were diagnosed with breast cancer. One (1.4%, 1/71) of the BI-RADS 3 lesions was malignant at 2-year follow-up. Of the patients recalled from screening, 29.6% had a family history of breast cancer, and the majority of both recalled and nonrecalled patients had Category B breast density. There was no statistically significant difference in breast density or race of patients recalled vs not recalled. 93.2% of recalled cases were given BI-RADS descriptors, with mass and focal asymmetry being the most common lesions, and 22.1% of recalled cases included more than one lesion. CONCLUSION: Initiating screening mammography for patients over 60 years old may result in higher recall rates, but also leads to a high CDR of potentially clinically relevant invasive cancers. After a diagnostic work-up, BI-RADS 3 assessments are within standard guidelines. This study provides guidance for radiologists reading baseline mammograms and clinicians making screening recommendations in patients over age 60.

Systematic review investigating the relationship between autism spectrum disorder and metabolic dysfunction.

The objective of this systematic review is to examine metabolic dysfunction, specifically metabolic syndrome and its components, as well as type 2 diabetes mellitus (T2DM) as it relates to individuals with a diagnosis of Autism Spectrum Disorder (ASD). We searched PubMed, Embase, Cochrane, PsychInfo, and Scopus from January 1, 1998 to October 12, 2018 for English, peer-reviewed, original articles containing adult and pediatric populations with any form of ASD and metabolic dysfunction, including T2DM, hyperglycemia, hypertension, dyslipidemia, or central obesity. Exclusion criteria included studies without ASD-specific results, basic science research, review papers, case studies, and medication clinical trials. Eight studies were included in this review, with a total of 70,503 participants with ASD and 2,281,891 in comparison groups. Within ASD populations, higher prevalence for metabolic syndrome components hyperglycemia, hypertension, and dyslipidemia were observed, as well as increased incidence and prevalence of T2DM. However, heterogeneity of study definitions and measurements should be noted. While there is evidence of increased prevalence of T2DM, hyperglycemia, hypertension, and dyslipidemia for those with ASD, the relationship is poorly understood. There is also lack of research investigating central obesity and risk of metabolic syndrome as a diagnosis. More research addressing these gaps is warranted to evaluate the risk of metabolic dysfunction in populations with ASD.

Associations between poor sleep and glucose intolerance in prediabetes.

OBJECTIVES: A cross-sectional study was designed to investigate the association between sleep quality and glucose metabolism among people with prediabetes, and to explore the potential pathways linking poor sleep to glucose intolerance. METHODS: One hundred fifty-five females and males, Caucasians and African Americans, aged 19-70 completed the study for data analysis. All participants were assessed for sleep quality using the Pittsburgh Sleep Quality Index (PSQI). Fasting glucose and 2-h glucose levels were collected via a 2-h oral glucose tolerance test (OGTT) and used to define prediabetes. Participants provided blood samples for measuring inflammatory markers. Associations were conducted using Pearson's correlation with adjustments for gender, age, and body mass index (BMI). Analysis of covariance (ANCOVA) was applied to compare the two groups, prediabetes group versus the control group, after controlling for gender, age, and BMI. Regression was used to investigate predictive power of sleep subscales for inflammatory factors and glucose levels. RESULTS: More people with prediabetes suffered from poor sleep than in the normal glucose group (62% vs. 46%). The OGTT measures, i.e. fasting glucose and 2-h glucose levels, correlated with PSQI measures, but these associations did not maintain statistical significance after adjusting for gender, age, and BMI. The C-reactive protein (CRP) levels were greater in the prediabetes group than the normal glucose group (0.37 ±â€¯0.07 vs. 0.18 ±â€¯0.06 mg/L). Additionally, there was a positive correlation between sleep disturbance and CRP levels (r = 0.30, p = 0.04). Regression analysis found that sleep disturbance predicted CRP levels and significance remained after adding covariates (ß = 0.20, p = 0.04). No significant difference was observed in other measured inflammatory factors, including interleukin (IL)-6, IL-8, IL-10 and tumor necrosis factor alpha (TNFα), between the two groups. CONCLUSION: Prediabetes is positively associated with poor sleep. Increased CRP levels may be a potential underlying mechanism of this association between prediabetes and poor sleep which warrants further study. Our findings highlight the importance for clinicians to evaluate sleep quality as part of preventing the onset of future diabetes in this particular population.

Sleep quality is differentially related to adiposity in adults.

OBJECTIVES: Sleep duration is associated with adiposity in adults. Abdominal adiposity specifically is strongly correlated with metabolic alterations, however, the relationships between abdominal adiposity and sleep quality are incompletely understood. The purpose of this study is to test the hypothesis that abdominal adiposity is related to poor sleep quality while total adiposity is not; and to explore whether pathways, including immune system and hypothalamic-pituitary-adrenal axis, link abdominal adiposity to poor sleep quality. METHODS: Subjects were 101 men and women aged 38.88 ± 11.96 years with body mass index between 29.35 ± 6.93 kg/m2. Subjective sleep quality was determined by the Pittsburgh Sleep Questionnaire Index (PSQI). Body composition was determined by dual energy X-ray absorptiometry. Saliva and blood samples were collected for assessment of cortisol and markers of inflammation. In a cross-sectional study design, correlation analysis was conducted to determine the relationships between poor sleep quality and adiposity. Participants were stratified based on PSQI score to evaluate differences in main outcomes between subjects with normal (NSQ; PSQI ≤ 5) vs poor sleep quality (PSQ; PSQI > 5). RESULTS: Poor sleep quality was related to greater visceral fat (r = 0.26; p < 0.05), but not total fat. The PSQ group had greater visceral fat compared to the NSQ group (1.11 ± 0.83 kg vs 0.79 ± 0.62 kg; p < 0.05), however, there was no difference in total fat mass (33.18 ± 14.21 kg vs 29.39 ± 13.03 kg; p = 0.24). The PSQ group had significantly greater leptin (1.37 ± 0.07 ng/ml vs 1.08 ± 0.08 ng/ml; p < 0.05), but hypothalamic-pituitary-adrenal axis activity did not differ between the PSQ and NSQ groups. CONCLUSIONS: Poor sleep quality is associated with greater visceral adiposity and leptin secretion. Further research is needed to probe potential cause and effect relationships among visceral adipose tissue, leptin, and sleep quality.

Home Monitoring of Blood Pressure: Short-Term Changes During Serial Measurements for 56398 Subjects.

Hypertension is one of the greatest contributors to premature morbidity and mortality worldwide. It has been demonstrated that lowering blood pressure (BP) by just a few mmHg can bring substantial clinical benefits, reducing the risk of stroke and ischemic heart disease. Properly managing high BP is one of the most pressing global health issues, but accurate methods to continuously monitoring BP at home are still under discussion. Indeed, the BP for any given individual can fluctuate significantly during intervals as short as a few minutes. In clinical settings, the guidelines suggest to wait for 5 or 10 minutes in seated rest before taking the measure, in order to alleviate the effect of the stress induced by the clinical environment. Alternatively, BP measured in the home environment is thought to provide a more accurate measure free of the stress of a clinical environment, but there is currently a lack of extensive studies on the trajectory of serial BP measurements over minutes in the home setting. In this paper, we aim at filling this gap by analyzing a large dataset of more than 16 million BP measurements taken at home with commercial BP monitoring devices. In particular, we propose new techniques to analyze this dataset, taking into account the limitations due to the uncontrolled data collection, and we study the characteristics of the BP trajectory for consecutive measures over several minutes. We show that the BP values significantly decrease after 10 minutes minutes from the initial measurement (4.1 and 6.6 mmHg for the diastolic and systolic BP, respectively), and continue to decrease for about 25 minutes. We also describe statistically the clinical relevance of this change, observing more than 50% misclassifications for measurements in the hypertension region. We then propose a model to study the inter-subject variability, showing significant variations in the expected decrease in systolic BP. These results may provide the initial evidence for future large clinical studies using participant-monitored BP.

Advanced and Accurate Mobile Health Tracking Devices Record New Cardiac Vital Signs.

Cardiovascular disease remains the leading cause of death and disease worldwide. As demands on an already resource-constrained healthcare system intensify, disease prevention in the future will likely depend on out-of-office monitoring of cardiovascular risk factors. Mobile health tracking devices that can track blood pressure and heart rate, in addition to new cardiac vital signs, such as physical activity level and pulse wave velocity (PWV), offer a promising solution. An initial barrier is the development of accurate and easily-scalable platforms. In this study, we made a customized smartphone app and used mobile health devices to track PWV, blood pressure, heart rate, physical activity, sleep duration, and multiple lifestyle risk factors in ≈250 adults for 17 continual weeks. Eligible participants were identified by a company database and then were consented and enrolled using only a smartphone app, without any special training given. Study participants reported high overall satisfaction, and 73% of participants were able to measure blood pressure and PWV, <1 hour apart, for at least 14 of 17 weeks. The study population's blood pressure, PWV, heart rate, activity levels, sleep duration, and the interrelationships among these measurements were found to closely match either population averages or values obtained from studies performed in a controlled setting. As a proof-of-concept, we demonstrated the accuracy and ease, as well as many challenges, of using mHealth technology to accurately track PWV and new cardiovascular vital signs at home.

Cross-Sectional and longitudinal associations of objectively-measured physical activity on blood pressure: evaluation in 37 countries.

Background: We examined the cross-sectional and longitudinal associations of objectively-measured physical activity (step counts) and blood pressure (BP) among adults spanning 37 countries. Methods: Across 37 countries, we used data from a pool of 9238 adult owners of Withings' Pulse activity trackers, which measures steps taken each day, and Wireless Blood Pressure Monitor, which measures BP. Analyses were adjusted on age, sex, number of days where the tracker was worn, and number of BP measurements. Data was collected from 2009 to 2013. Results: Subjects had a mean ± standard deviation (SD) age of 51.6 ± 11.3 years and a body mass index (BMI) of 28.7±5.5 kg/m2. A 1-month increase of more than 3000 steps per day was associated with a decrease of systolic BP (SBP) and diastolic BP (DBP) among the obese (1.57mm Hg and 1.29 mm Hg respectively, both P<0.001) and the overweight population (0.79 mm Hg and 0.84 mm Hg respectively, both P≤0.001), but not in the normal weight population (P=0.60 and P=0.36 respectively). Conclusion: One-month increases in daily step counts was associated with a decrease of SBP and DBP in a large obese and overweight free living population.

Seasonal weight variation patterns in seven countries located in northern and southern hemispheres.

The digital revolution of information and technology in late 20th century has led to emergence of devices that help people monitor their weight in a long-term manner. Investigation of population-level variations of body mass using smart connected weight scales enabled the health coaches acquire deeper insights about the models of people's behavior as a function of time. Typically, body mass varies when the seasons change. That is, during the warmer seasons people's body mass tend to decrease while in colder seasons it usually moves up. In this paper we study the seasonal variations of body mass in seven countries by utilization of linear regression. Deviation of monthly weight values from the starting point of astronomical years (beginning of spring) were modeled by fitting orthogonal polynomials in each country. The distinction of weight variations in southern and northern hemispheres were then investigated. The studied population involves 6429 anonymous weight scale users from:(1) Australia, (2) Brazil, (3) France, (4) Germany, (5) Great Britain, (6) Japan, and (7) United States of America. The results suggest that there are statistically significant differences between the models of weight variation in southern and northern hemispheres. In both northern and southern hemispheres the lowest weight values were observed in the summer. However, the highest weight values were noticed in the winter and in the spring for northern and southern hemispheres, respectively.