To which extent are per-and poly-fluorinated substances associated to metabolic syndrome?

Exposure to per- and polyfluoroalkyl substances (PFAS), ubiquitous persistent environmental contaminants, has led to substantial global concern due to their potential environmental and human health effects. Several epidemiological studies have assessed the possible association between PFAS exposure and risk of metabolic syndrome (MetS), however, the results are ambiguous. The aim of this study was to assess the current human epidemiologic evidence on the association between exposure to PFAS and MetS. We performed a systematic search strategy using three electronic databases (PubMed, Scopus, and Web of Science) for relevant studies concerning the associations of PFAS with MetS and its clinical relevance from inception until January 2021. We undertook meta-analyses where there were five or more studies with exposure and outcomes assessments that were reasonably comparable. The pooled odd ratios (ORs) were calculated using random effects models and heterogeneity among studies was assessed by I2 index and Q test. A total of 12 cross-sectional studies (10 studies on the general population and two studies in the occupational settings) investigated the association between PFAS exposure and MetS. We pooled data from seven studies on the general population for perfluorooctanoic acid (PFOA) and perfluorooctanesulfonate (PFOS) and five studies for perfluorohexanesulfonate (PFHxS) and perfluorononanoic acid (PFNA). Predominately, most studies reported no statistically significant association between concentrations of PFAS and MetS. In the meta-analysis, the overall measure of effect was not statistically significant, showing no evidence of an association between concentrations of PFOA, PFOS, PFNA, and PFHxS and the risk of MetS. Based on the results of the meta-analysis, current small body of evidence does not support association between PFAS and MetS. However, due to limited number of studies and substantial heterogeneity, results should be interpreted with caution. Further scrutinizing cohort studies are needed to evaluate the association between various and less well-known PFAS substances and their mixture with MetS and its components in both adults and children in different settings.

Profiling Delirium Progression in Elderly Patients via Continuous-Time Markov Multi-State Transition Models.

Poor recognition of delirium among hospitalized elderlies is a typical challenge for health care professionals. Considering methodological insufficiency for assessing time-varying diseases, a continuous-time Markov multi-state transition model (CTMMTM) was used to investigate delirium evolution in elderly patients. This is a longitudinal observational study performed in September 2016 in an Italian hospital. Change of delirium states was modeled according to the 4AT score. A Cox model (CM) and a CTMMTM were used for identifying factors affecting delirium onset both with a two-state and three-state model. In this study, 78 patients were enrolled and evaluated for 5 days. Both the CM and the CTMMTM show that urine catheter (UC), aging, drugs, and invasive devices (ID) are risk factors for delirium onset. The CTMMTM model shows that transition from no-delirium/cognitive impairment to delirium was associated with aging (HR = 1.14; 95%CI, 1.05, 1.23) and neuroleptics (HR = 4.3; 1.57, 11.77), dopaminergic drugs (HR = 3.89; 1.2, 12.6), UC (HR = 2.92; 1.09, 7.79) and ID (HR = 1.67; 103, 2.71). These results are confirmed by the multivariable model. Aging, ID, antibiotics, drugs affecting the central nervous system, and absence of moving ability are identified as the significant predictors of delirium. Additionally, it seems that modeling with CTMMTM may show associations that are not directly detectable with the traditional CM.

Resting Energy Expenditure in the Elderly: Systematic Review and Comparison of Equations in an Experimental Population.

Elderly patients are at risk of malnutrition and need an appropriate assessment of energy requirements. Predictive equations are widely used to estimate resting energy expenditure (REE). In the study, we conducted a systematic review of REE predictive equations in the elderly population and compared them in an experimental population. Studies involving subjects older than 65 years of age that evaluated the performance of a predictive equation vs. a gold standard were included. The retrieved equations were then tested on a sample of 88 elderly subjects enrolled in an Italian nursing home to evaluate the agreement among the estimated REEs. The agreement was assessed using the intraclass correlation coefficient (ICC). A web application, equationer, was developed to calculate all the estimated REEs according to the available variables. The review identified 68 studies (210 different equations). The agreement among the equations in our sample was higher for equations with fewer parameters, especially those that included body weight, ICC = 0.75 (95% CI = 0.69-0.81). There is great heterogeneity among REE estimates. Such differences should be considered and evaluated when estimates are applied to particularly fragile populations since the results have the potential to impact the patient's overall clinical outcome.