Using Actiwatch to monitor circadian rhythm disturbance in Huntington' disease: A cautionary note

Huntington's disease (HD) is an inherited neurodegenerative disorder that is well recognised as producing progressive deterioration of motor function, including dyskinetic movements, as well as deterioration of cognition and ability to carry out activities of daily living. However, individuals with HD commonly suffer from a wide range of additional symptoms, including weight loss and sleep disturbance, possibly due to disruption of circadian rhythmicity. Disrupted circadian rhythms have been reported in mice models of HD and in humans with HD. One way of assessing an individual's circadian rhythmicity in a community setting is to monitor their sleep/wake cycles, and a convenient method for recording periods of wakefulness and sleep is to use accelerometers to discriminate between varied activity levels (including sleep) during daily life. Here we used Actiwatch® Activity monitors alongside ambulatory EEG and sleep diaries to record wake/sleep patterns in people with HD and normal volunteers. We report that periods of wakefulness during the night, as detected by activity monitors, agreed poorly with EEG recordings in HD subjects, and unsurprisingly sleep diary findings showed poor agreement with both EEG recordings and activity monitor derived sleep periods. One explanation for this is the occurrence of 'break through' involuntary movements during sleep in the HD patients, which are incorrectly assessed as wakeful periods by the activity monitor algorithms. Thus, care needs to be taken when using activity monitors to assess circadian activity in individuals with movement disorders

Galectin-3, osteopontin and successful aging

Background: Individuals who reach exceptional longevity (100+ years of age) free of common chronic age diseases (i.e. ''dodgers'') arguably represent the paradigm of successful aging in humans. As such, identification of potential biomarkers associated with this phenomenon is of medical interest. Methods: We measured serum levels of galectin-3 and osteopontin, both of which have been shown to be linked with major chronic or aging-related disorders in younger populations, in centenarian ''dodgers'' (n=81; 40 men; 100-104 years) and healthy controls (n=41; 24 men, 70-80 years). Results: Both biomarkers showed significantly lower values (p<0.001) in the former (galectin-3: 2.4±1.7 vs. 4.8±2.8 ng/mL; osteopontin: 38.1±27.7 vs. 72.6±33.1 µg/mL). Logistic regression analysis identified the combination of these two biomarkers as a significant predictor variable associated with successful aging regardless of sex (p<0.001). The area under the curve (AUC) classified the ability of galectin-3 and osteopontin to predict the likelihood of successful aging as ''fair'' (AUC=0.75) and ''good'' (AUC=0.80), respectively. Particularly, the combination of the two biomarkers showed good discriminatory power for successful aging (AUC=0.86), with sensitivity=83% and specificity=74%. Conclusions: Lower levels of both galectin-3 and osteopontin are associated with successful aging, representing potential biomarkers of this condition. Our cross-sectional data must be however approached with caution. Further research is necessary to replicate the present preliminary results in other cohorts and to identify the potential use of galectin-3 and osteopontin as potential targets (or at least predictors) in future personalized anti-aging therapies

Membrane permeation of arginine-rich cell-penetrating peptides independent of transmembrane potential as a function of lipid composition and membrane fluidity

Cell-penetrating peptides (CPPs) are prominent delivery vehicles to confer cellular entry of (bio-) macromolecules. Internalization efficiency and uptake mechanism depend, next to the type of CPP and cargo, also on cell type. Direct penetration of the plasma membrane is the preferred route of entry as this circumvents endolysosomal sequestration. However, the molecular parameters underlying this import mechanism are still poorly defined. Here, we make use of the frequently used HeLa and HEK cell lines to address the role of lipid composition and membrane potential. In HeLa cells, at low concentrations, the CPP nona-arginine (R9) enters cells by endocytosis. Direct membrane penetration occurs only at high peptide concentrations through a mechanism involving activation of sphingomyelinase which converts sphingomyelin into ceramide. In HEK cells, by comparison, R9 enters the cytoplasm through direct membrane permeation already at low concentrations. This direct permeation is strongly reduced at room temperature and upon cholesterol depletion, indicating a complex dependence on membrane fluidity and microdomain organisation. Lipidomic analyses show that in comparison to HeLa cells HEK cells have an endogenously low sphingomyelin content. Interestingly, direct permeation in HEK cells and also in HeLa cells treated with exogenous sphingomyelinase is independent of membrane potential. Membrane potential is only required for induction of sphingomyelinase-dependent uptake which is then associated with a strong hyperpolarization of membrane potential as shown by whole-cell patch clamp recordings. Next to providing new insights into the interplay of membrane composition and direct permeation, these results also refute the long-standing paradigm that transmembrane potential is a driving force for CPP uptake

Risk Analysis Index and Its Recalibrated Version Predict Postoperative Outcomes Better Than 5-Factor Modified Frailty Index in Traumatic Spinal Injury

Objective To assess the discriminative ability of the Risk Analysis Index-administrative (RAI-A) and its recalibrated version (RAI-Rev), compared to the 5-factor modified frailty index (mFI-5), in predicting postoperative outcomes in patients undergoing surgical intervention for traumatic spine injuries (TSIs). Methods The Current Procedural Terminology (CPT) and International Classification of Disease-9 (ICD-9) and ICD-10 codes were used to identify patients ≥ 18 years who underwent surgical intervention for TSI from National Surgical Quality Improvement Program (ACS-NSQIP) database 2015–2019 (n = 6,571). Multivariate analysis and receiver operating characteristic (ROC) curve analysis were conducted to evaluate the comparative discriminative ability of RAI-Rev, RAI-A, and mFI-5 for 30-day postoperative outcomes. Results Multivariate regression analysis showed that with all 3 frailty scores, increasing frailty tiers resulted in worse postoperative outcomes, and patients identified as frail and severely frail using RAI-Rev and RAI-A had the highest odds of poor outcomes. In the ROC curve/C-statistics analysis for prediction of 30-day mortality and morbidity, both RAI-Rev and RAI-A outperformed mFI-5, and for many outcomes, RAI-Rev showed better discriminative performance compared to RAI-A, including mortality (p = 0.0043, DeLong test), extended length of stay (p = 0.0042), readmission (p < 0.0001), reoperation (p = 0.0175), and nonhome discharge (p < 0.0001). Conclusion Both RAI-Rev and RAI-A performed better than mFI-5, and RAI-Rev was superior to RAI-A in predicting postoperative mortality and morbidity in TSI patients. RAI-based frailty indices can be used in preoperative risk assessment of spinal trauma patients

World Health Organization cardiovascular disease risk charts: revised models to estimate risk in 21 global regions

BACKGROUND: To help adapt cardiovascular disease risk prediction approaches to low-income and middle-income countries, WHO has convened an effort to develop, evaluate, and illustrate revised risk models. Here, we report the derivation, validation, and illustration of the revised WHO cardiovascular disease risk prediction charts that have been adapted to the circumstances of 21 global regions. METHODS: In this model revision initiative, we derived 10-year risk prediction models for fatal and non-fatal cardiovascular disease (ie, myocardial infarction and stroke) using individual participant data from the Emerging Risk Factors Collaboration. Models included information on age, smoking status, systolic blood pressure, history of diabetes, and total cholesterol. For derivation, we included participants aged 40-80 years without a known baseline history of cardiovascular disease, who were followed up until the first myocardial infarction, fatal coronary heart disease, or stroke event. We recalibrated models using age-specific and sex-specific incidences and risk factor values available from 21 global regions. For external validation, we analysed individual participant data from studies distinct from those used in model derivation. We illustrated models by analysing data on a further 123 743 individuals from surveys in 79 countries collected with the WHO STEPwise Approach to Surveillance. FINDINGS: Our risk model derivation involved 376 177 individuals from 85 cohorts, and 19 333 incident cardiovascular events recorded during 10 years of follow-up. The derived risk prediction models discriminated well in external validation cohorts (19 cohorts, 1 096 061 individuals, 25 950 cardiovascular disease events), with Harrell's C indices ranging from 0·685 (95% CI 0·629-0·741) to 0·833 (0·783-0·882). For a given risk factor profile, we found substantial variation across global regions in the estimated 10-year predicted risk. For example, estimated cardiovascular disease risk for a 60-year-old male smoker without diabetes and with systolic blood pressure of 140 mm Hg and total cholesterol of 5 mmol/L ranged from 11% in Andean Latin America to 30% in central Asia. When applied to data from 79 countries (mostly low-income and middle-income countries), the proportion of individuals aged 40-64 years estimated to be at greater than 20% risk ranged from less than 1% in Uganda to more than 16% in Egypt. INTERPRETATION: We have derived, calibrated, and validated new WHO risk prediction models to estimate cardiovascular disease risk in 21 Global Burden of Disease regions. The widespread use of these models could enhance the accuracy, practicability, and sustainability of efforts to reduce the burden of cardiovascular disease worldwide. FUNDING: World Health Organization, British Heart Foundation (BHF), BHF Cambridge Centre for Research Excellence, UK Medical Research Council, and National Institute for Health Research