Ambient temperature and risk of cardiovascular hospitalization: An updated systematic review and meta-analysis

The association between temperatures and risk of cardiovascular mortality has been recognized but the association drawn from previous meta-analysis was weak due to the lack of sufficient studies. This paper presented a review with updated reports in the literature about the risk of cardiovascular hospitalization in relation to different temperature exposures and examined the dose–response relationship of temperature-cardiovascular hospitalization by change in units of temperature, latitudes, and lag days. The pooled effect sizes were calculated for cold, heat, heatwave, and diurnal variation using random-effects meta-analysis, and the dose–response relationship of temperature-cardiovascular admission was modelled using random-effect meta-regression. The Cochrane Q-test and index of heterogeneity (I2) were used to evaluate heterogeneity, and Egger's test was used to evaluate publication bias. Sixty-four studies were included in meta-analysis. The pooled results suggest that for a change in temperature condition, the risk of cardiovascular hospitalization increased 2.8% (RR, 1.028; 95% CI, 1.021–1.035) for cold exposure, 2.2% (RR, 1.022; 95% CI, 1.006–1.039) for heatwave exposure, and 0.7% (RR, 1.007; 95% CI, 1.002–1.012) for an increase in diurnal temperature. However no association was observed for heat exposure. The significant dose–response relationship of temperature — cardiovascular admission was found with cold exposure and diurnal temperature. Increase in one-day lag caused a marginal reduction in risk of cardiovascular hospitalizations for cold exposure and diurnal variation, and increase in latitude was associated with a decrease in risk of cardiovascular hospitalizations for diurnal temperature only. There is a significant short-term effect of cold exposure, heatwave and diurnal variation on cardiovascular hospitalizations. Further research is needed to understand the temperature-cardiovascular relationship for different climate areas.

Analysis of body composition in individuals with high bone mass reveals a marked increase in fat mass in women but not men

Context: High bone mass (HBM), detected in 0.2% of dual-energy x-ray absorptiometry (DXA) scans, is characterized by raised body mass index, the basis for which is unclear. Objective: To investigate why body mass index is elevated in individuals with HBM, we characterized body composition and examined whether differences could be explained by bone phenotypes, eg, bone mass and/or bone turnover. Design, Setting, and Participants: We conducted a case-control study of 153 cases with unexplained HBM recruited from 4 UK centers by screening 219 088 DXA scans. Atotal of 138 first-degree relatives (of whom 51 had HBM) and 39 spouses were also recruited. Unaffected individuals served as controls. Main Outcome Measures: We measured fat mass, by DXA, and bone turnover markers. Results: Amongwomen, fat mass was inversely related to age in controls (P<.01), but not in HBM cases (P<.96) in whom mean fat mass was 8.9 [95% CI 4.7, 13.0] kg higher compared with controls (fully adjusted mean difference, P<.001). Increased fat mass in male HBM cases was less marked (gender interaction P = .03). Compared with controls, lean mass was also increased in female HBM cases (by 3.3 [1.2, 5.4] kg; P<.002); however, lean mass increases wereless marked than fat mass increases, resulting in 4.5% lower percentage lean mass in HBM cases (P<.001). Osteocalcin was also lower in female HBM cases compared with controls (by 2.8 [0.1, 5.5]μg/L; P = .04). Differences in fat mass were fully attenuated after hip bone mineral density (BMD) adjustment (P = .52) but unchanged after adjustment for bone turnover (P < .001), whereas the greater hip BMD in female HBM cases was minimally attenuated by fat mass adjustment (P<.001). Conclusions: HBM is characterized by a marked increase in fat mass in females, statistically explained by their greater BMD, but not by markers of bone turnover. Copyright © 2013 by The Endocrine Society.

Mobility impairment and bad feet … who'd of guessed? The Foot Disease in Inpatients Study (FDIS)

Background Foot complications have been found to be predictors of mobility impairment and falls in community dwelling elderly patients. However, fewer studies have investigated the link between foot complications and mobility impairment in hospital in patient populations. The aim of this paper was to investigate the associations between mobility impairment and various foot complications in general inpatient populations. Methods Eligible participants were all adults admitted overnight, for any reason, into five diverse hospitals on one day; excluding maternity, mental health and cognitively impaired patients. Participants underwent a foot examination to clinically diagnose different foot complications; including foot wounds, infections, deformity, peripheral arterial disease and peripheral neuropathy. They were also surveyed on social determinant, medical history, self-care, footwear, foot complication history risk factors, and, mobility impairment defined as requiring a mobility aid for mobilisation prior to hospitalisation. Results Overall, 733 participants consented; mean(±SD) age 62(±19) years, 408 (55.8%) male, 172 (23.5%) diabetes. Mobility impairment was present in 242 (33.2%) participants; diabetes populations reported more mobility impairment than non-diabetes populations (40.7% vs 30.9%, p < 0.05). In a backwards stepwise multivariate analysis, and controlling for other risk factors, those people with mobility impairment were independently associated with increasing years of age (OR = 1.04 (95% CI) (1.02-1.05)), male gender (OR = 1.7 (1.2-2.5)), being born in Australia (OR = 1.7 (1.1-2.8), vision impairment (2.0 (1.2-3.1)), peripheral neuropathy (OR = 3.1 (2.0-4.6) and foot deformity (OR = 2.0 (1.3-3.0). Conclusions These findings support the results of other large studies investigating community dwelling elderly patients that peripheral neuropathy and foot deformity are independently associated with mobility impairment and potentially falls. Furthermore the findings suggest routine clinical diagnosis of foot complications as defined by national diabetic foot guidelines were sufficient to determine these associated foot complication risk factors for mobility impairment. Further research is required to establish if these foot complication risk factors for mobility impairment are predictors of actual falls in the inpatient environment.

The tired foot. The effects of physical activity, energy expenditure and sleep on the diabetic foot population

Background The most common pathway to development of diabetes foot ulcers is repetitive daily activity stress on the plantar surface of the neuropathic foot. Studies suggest an association between different diabetic foot complications and physical activity. However, to the best of the authors knowledge the steps/day and sleep patterns of people with diabetic foot ulcers has yet to be investigated. This observational study aims to investigate the physical activity and sleep patterns of three groups of adults with type 2 diabetes and different foot complications Methods Participants with type 2 diabetes were recruited into three groups: 1. those with no reported foot complications (DNIL), 2. those with diagnosis of neuropathy (DPN) and 3. those with a neuropathic ulcer (DFU). Exclusion criteria included peripheral arterial disease and mobility aid use. Participants wore a SenseWear Pro 3 Armband continuously for 7 days and completed an Epworth Sleepiness Scale. The Armband is a validated automated measure of activity (walking steps, average Metabolic Equivalent Task (MET), physical activity (>3 METs) duration), energy expenditure(kJ) (total and physical activity (>3 METs)) and sleep (duration). Data on age, sex, BMI, diabetes duration and HbA1c were also collected. Results Sixty-Six (14 DNIL, 22 DPN and 30 DFU's participants were recruited; 71% males, mean age 61(±12) years, diabetes duration 13(±9) years, HbA1c 8.3(±2.8), BMI 32.6(±5.9), average METs 1.2(0.2). Significant differences were reported in mean(SD) steps/day (5,859(±2,381) in DNIL; 5,007(±3,349) in DPN and 3,271(±2,417) in DFU's and daily energy expenditure (10,868(±1,307)kJ in DNIL; 11,060(±1,916)kJ in DPN and 13,006(± 3,559) in DFU's(p <0.05). No significant differences were reported for average METs, physical activity duration or energy expenditure, sleep time or Epworth score (p>0.1). Conclusions Preliminary findings suggest people with diabetes are sedentary. Results indicate that patients with a diabetic foot ulcer work significantly less than those with neuropathy or nil complications and use significantly more energy to do so. Sleep Parameters showed no differences. Recruitment is still on going.

Prevalence, codetection and seasonal distribution of upper airway viruses and bacteria in children with acute respiratory illnesses with cough as a symptom

Most studies exploring the role of upper airway viruses and bacteria in paediatric acute respiratory infections (ARI) focus on specific clinicaldiagnoses and/or do not account for virus–bacteria interactions. We aimed to describe the frequency and predictors of virus and bacteria codetection in children with ARI and cough, irrespective of clinical diagnosis. Bilateral nasal swabs, demographic, clinical and risk factor data were collected at enrollment in children aged <15 years presenting to an emergency department with an ARI and where cough was a symptom. Swabs were tested by polymerase chain reaction for 17 respiratory viruses and seven respiratory bacteria. Logistic regression was used to investigate associations between child characteristics and codetection of the organisms of interest. Between December 2011 and August 2014, swabs were collected from 817 (93.3%) of 876 enrolled children, median age 27.7 months (interquartile range13.9–60.3 months). Overall, 740 (90.6%) of 817 specimens were positive for any organism. Both viruses and bacteria were detected in 423 specimens (51.8%). Factors associated with codetection were age (adjusted odds ratio (aOR) for age <12 months = 4.9, 95% confidence interval (CI) 3.0, 7.9; age 12 to <24 months = 6.0, 95% CI 3.7, 9.8; age 24 to <60 months = 2.4, 95% CI 1.5, 3.9), male gender (aOR 1.46; 95% CI 1.1, 2.0), child care attendance (aOR 2.0; 95% CI 1.4, 2.8) and winter enrollment (aOR 2.0; 95% CI 1.3, 3.0). Haemophilus influenzae dominated the virus–bacteria pairs. Virus–H. influenzae interactions in ARI should be investigated further, especially as the contribution of nontypeable H. influenzae to acute and chronic respiratory diseases is being increasingly recognized.