Increasing Dietary Fat Elicits Similar Changes in Fat Oxidation and Markers of Muscle Oxidative Capacity in Lean and Obese Humans

In lean humans, increasing dietary fat intake causes an increase in whole-body fat oxidation and changes in genes that regulate fat oxidation in skeletal muscle, but whether this occurs in obese humans is not known. We compared changes in whole-body fat oxidation and markers of muscle oxidative capacity differ in lean (LN) and obese (OB) adults exposed to a 2-day high-fat (HF) diet. Ten LN (BMI = 22.5±2.5 kg/m2, age = 30±8 yrs) and nine OB (BMI = 35.9±4.93 kg/m2, 38±5 yrs, Mean±SD) were studied in a room calorimeter for 24hr while consuming isocaloric low-fat (LF, 20% of energy) and HF (50% of energy) diets. A muscle biopsy was obtained the next morning following an overnight fast. 24h respiratory quotient (RQ) did not significantly differ between groups (LN: 0.91±0.01; OB: 0.92±0.01) during LF, and similarly decreased during HF in LN (0.86±0.01) and OB (0.85±0.01). The expression of pyruvate dehydrogenase kinase 4 (PDK4) and the fatty acid transporter CD36 increased in both LN and OB during HF. No other changes in mRNA or protein were observed. However, in both LN and OB, the amounts of acetylated peroxisome proliferator-activated receptor γ coactivator-1-α (PGC1-α) significantly decreased and phosphorylated 5-AMP-activated protein kinase (AMPK) significantly increased. In response to an isoenergetic increase in dietary fat, whole-body fat oxidation similarly increases in LN and OB, in association with a shift towards oxidative metabolism in skeletal muscle, suggesting that the ability to adapt to an acute increase in dietary fat is not impaired in obesity

Human total, basal and activity energy expenditures are independent of ambient environmental temperature

ower ambient temperature (Ta) requires greater energy expenditure to sustain body temperature. However, effects of Ta on human energetics may be buffered by environmental modification and behavioral compensation. We used the IAEA DLW database for adults in the USA (n = 3213) to determine the effect of Ta (−10 to +30°C) on TEE, basal (BEE) and activity energy expenditure (AEE) and physical activity level (PAL). There were no significant relationships (p > 0.05) between maximum, minimum and average Ta and TEE, BEE, AEE and PAL. After adjustment for fat-free mass, fat mass and age, statistically significant (p < 0.01) relationships between TEE, BEE and Ta emerged in females but the effect sizes were not biologically meaningful. Temperatures inside buildings are regulated at 18–25°C independent of latitude. Hence, adults in the US modify their environments to keep TEE constant across a wide range of external ambient temperatures

Validating a clinical laboratory parameter-based deisolation algorithm for patients with COVID-19 in the intensive care unit using viability PCR: the CoLaIC multicentre cohort study protocol

INTRODUCTION: To investigate whether biochemical and haematological changes due to the patient's host response (CoLab algorithm) in combination with a SARS-CoV-2 viability PCR (v-PCR) can be used to determine when a patient with COVID-19 is no longer infectious.We hypothesise that the CoLab algorithm in combination with v-PCR can be used to determine whether or not a patient with COVID-19 is infectious to facilitate the safe release of patients with COVID-19 from isolation. METHODS AND ANALYSIS: This study consists of three parts using three different cohorts of patients. All three cohorts contain clinical, vital and laboratory parameters, as well as logistic data related to isolated patients with COVID-19, with a focus on intensive care unit (ICU) stay. The first cohort will be used to develop an algorithm for the course of the biochemical and haematological changes of the host response of the COVID-19 patient. Simultaneously, a second prospective cohort will be used to investigate the algorithm derived in the first cohort, with daily measured laboratory parameters, next to conventional SARS-CoV-2 reverse transcriptase PCRs, as well as v-PCR, to confirm the presence of intact SARS-CoV-2 particles in the patient. Finally, a third multicentre cohort, consisting of retrospectively collected data from patients with COVID-19 admitted to the ICU, will be used to validate the algorithm. ETHICS AND DISSEMINATION: This study was approved by the Medical Ethics Committee from Maastricht University Medical Centre+ (cohort I: 2020-1565/300523) and Zuyderland MC (cohorts II and III: METCZ20200057). All patients will be required to provide informed consent. Results from this study will be disseminated via peer-reviewed journals and congress/consortium presentations

Real-time monitoring of drug-laboratory test interactions with an automated decision support application

Background-aim: The lack of knowledge of the presence of Drug-Laboratory Test Interactions (DLTIs) can cause misinterpretation of laboratory test results and delayed or erroneous diagnosis with extra healthcare costs and even harm to patients. There are over 50.000 physiological and/or analytical drug-test interactions described. In this pilot study, an automated decision support application was used to detect drug laboratory test interactions in real-time. Methods: In this multicentre study, 34 clinical rules about DLTI were programmed and validated in an automated decision support application (Gaston, Medecs B.V.). The DLTIs were described in a validated database from the Dutch Society for Clinical Chemistry. The application is able to generate a DLTI-based advisory text based on predefined aberrant laboratory test results and medication data from individual patients and present this alert text to the laboratory specialist in the laboratory information system. The software application was successfully connected and installed in one hospital laboratory in 2018 with two other hospitals to follow in 2019. Generated real-time DLTI alerts were collected and monitored during 4 weeks. Results: A mean of 45 DLTI alerts were generated per day. Twenty-one out of 34 clinical rules were generated at least once in this period. The most frequently reported interactions were magnesium - proton pump inhibitors (14%), creatine kinase – statins (13%) and potassium - ACE-inhibitors (13%). Most DLTI alerts were from the internal medicine department (43%), cardiology department (22%) and the emergency department (10%). Conclusions: In this study, an automated decision support application was implemented to facilitate signalling the presence of drug laboratory test interactions. A mean of 45 DLTI alerts per day were generated in this study. The clinical relevance of the alerts for laboratory specialists and physicians will be examined