Two-year trajectory of fall risk in people with Parkinson disease: a latent class analysis

Published in final edited form as: Arch Phys Med Rehabil. 2016 March ; 97(3): 372–379.e1. doi:10.1016/j.apmr.2015.10.105.OBJECTIVE: To examine fall risk trajectories occurring naturally in a sample of individuals with early to middle stage Parkinson disease (PD). DESIGN: Latent class analysis, specifically growth mixture modeling (GMM), of longitudinal fall risk trajectories. SETTING: Assessments were conducted at 1 of 4 universities. PARTICIPANTS: Community-dwelling participants with PD of a longitudinal cohort study who attended at least 2 of 5 assessments over a 2-year follow-up period (N=230). INTERVENTIONS: Not applicable. MAIN OUTCOME MEASURES: Fall risk trajectory (low, medium, or high risk) and stability of fall risk trajectory (stable or fluctuating). Fall risk was determined at 6 monthly intervals using a simple clinical tool based on fall history, freezing of gait, and gait speed. RESULTS: The GMM optimally grouped participants into 3 fall risk trajectories that closely mirrored baseline fall risk status (P=.001). The high fall risk trajectory was most common (42.6%) and included participants with longer and more severe disease and with higher postural instability and gait disability (PIGD) scores than the low and medium fall risk trajectories (P<.001). Fluctuating fall risk (posterior probability <0.8 of belonging to any trajectory) was found in only 22.6% of the sample, most commonly among individuals who were transitioning to PIGD predominance. CONCLUSIONS: Regardless of their baseline characteristics, most participants had clear and stable fall risk trajectories over 2 years. Further investigation is required to determine whether interventions to improve gait and balance may improve fall risk trajectories in people with PD.Supported by the Davis Phinney Foundation, the Parkinson's Disease Foundation, National Institutes of Health (NIH) (grant nos. NIH R01 NS077959 and NIH UL1 TR000448), the Massachusetts and Utah Chapters of the American Parkinson Disease Association (APDA), the Greater St Louis Chapter of the APDA, and the APDA Center for Advanced Research at Washington University. (Davis Phinney Foundation; Parkinson's Disease Foundation; NIH R01 NS077959 - National Institutes of Health (NIH); NIH UL1 TR000448 - National Institutes of Health (NIH); Utah Chapter of the American Parkinson Disease Association (APDA); Greater St Louis Chapter of the APDA; APDA Center for Advanced Research at Washington University; Massachusetts Chapter of the American Parkinson Disease Association (APDA)

External validation of a simple clinical tool used to predict falls in people with Parkinson disease

Published in final edited form as: Parkinsonism Relat Disord. 2015 August ; 21(8): 960–963. doi:10.1016/j.parkreldis.2015.05.008.BACKGROUND: Assessment of fall risk in an individual with Parkinson disease (PD) is a critical yet often time consuming component of patient care. Recently a simple clinical prediction tool based only on fall history in the previous year, freezing of gait in the past month, and gait velocity <1.1 m/s was developed and accurately predicted future falls in a sample of individuals with PD. METHODS: We sought to externally validate the utility of the tool by administering it to a different cohort of 171 individuals with PD. Falls were monitored prospectively for 6 months following predictor assessment. RESULTS: The tool accurately discriminated future fallers from non-fallers (area under the curve [AUC] = 0.83; 95% CI 0.76–0.89), comparable to the developmental study. CONCLUSION: The results validated the utility of the tool for allowing clinicians to quickly and accurately identify an individual's risk of an impending fall.Davis Phinney Foundation, Parkinson Disease Foundation, NIH, APDA. (Davis Phinney Foundation; Parkinson Disease Foundation; NIH; APDA

Sympathetic nervous dysregulation in the absence of systolic left ventricular dysfunction in a rat model of insulin resistance with hyperglycemia

<p>Abstract</p> <p>Background</p> <p>Diabetes mellitus is strongly associated with cardiovascular dysfunction, derived in part from impairment of sympathetic nervous system signaling. Glucose, insulin, and non-esterified fatty acids are potent stimulants of sympathetic activity and norepinephrine (NE) release. We hypothesized that sustained hyperglycemia in the high fat diet-fed streptozotocin (STZ) rat model of sustained hyperglycemia with insulin resistance would exhibit progressive sympathetic nervous dysfunction in parallel with deteriorating myocardial systolic and/or diastolic function.</p> <p>Methods</p> <p>Cardiac sympathetic nervous integrity was investigated <it>in vivo </it>via biodistribution of the positron emission tomography radiotracer and NE analogue [¹¹C]<it>meta-</it>hydroxyephedrine ([¹¹C]HED). Cardiac systolic and diastolic function was evaluated by echocardiography. Plasma and cardiac NE levels and NE reuptake transporter (NET) expression were evaluated as correlative measurements.</p> <p>Results</p> <p>The animal model displays insulin resistance, sustained hyperglycemia, and progressive hypoinsulinemia. After 8 weeks of persistent hyperglycemia, there was a significant 13-25% reduction in [¹¹C]HED retention in myocardium of STZ-treated hyperglycemic but not euglycemic rats as compared to controls. There was a parallel 17% reduction in immunoblot density for NE reuptake transporter, a 1.2 fold and 2.5 fold elevation of cardiac and plasma NE respectively, and no change in sympathetic nerve density. No change in ejection fraction or fractional area change was detected by echocardiography. Reduced heart rate, prolonged mitral valve deceleration time, and elevated transmitral early to atrial flow velocity ratio measured by pulse-wave Doppler in hyperglycemic rats suggest diastolic impairment of the left ventricle.</p> <p>Conclusions</p> <p>Taken together, these data suggest that sustained hyperglycemia is associated with elevated myocardial NE content and dysregulation of sympathetic nervous system signaling in the absence of systolic impairment.</p

Use of twitter data for waste minimisation in beef supply chain

Approximately one third of the food produced is discarded or lost, which accounts for 1.3 billion tons per annum. The waste is being generated throughout the supply chain viz. farmers, wholesalers/processors, logistics, retailers and consumers. The majority of waste occurs at the interface of retailers and consumers. Many global retailers are making efforts to extract intelligence from customer’s complaints left at retail store to backtrack their supply chain to mitigate the waste. However, majority of the customers don’t leave the complaints in the store because of various reasons like inconvenience, lack of time, distance, ignorance etc. In current digital world, consumers are active on social media and express their sentiments, thoughts, and opinions about a particular product freely. For example, on an average, 45,000 tweets are tweeted daily related to beef products to express their likes and dislikes. These tweets are large in volume, scattered and unstructured in nature. In this study, twitter data is utilised to develop waste minimization strategies by backtracking the supply chain. The execution process of proposed framework is demonstrated for beef supply chain. The proposed model is generic enough and can be applied to other domains as well

Storm impacts on phytoplankton community dynamics in lakes

In many regions across the globe, extreme weather events such as storms have increased in frequency, intensity, and duration due to climate change. Ecological theory predicts that such extreme events should have large impacts on ecosystem structure and function. High winds and precipitation associated with storms can affect lakes via short-term runoff events from watersheds and physical mixing of the water column. In addition, lakes connected to rivers and streams will also experience flushing due to high flow rates. Although we have a well-developed understanding of how wind and precipitation events can alter lake physical processes and some aspects of biogeochemical cycling, our mechanistic understanding of the emergent responses of phytoplankton communities is poor. Here we provide a comprehensive synthesis that identifies how storms interact with lake and watershed attributes and their antecedent conditions to generate changes in lake physical and chemical environments. Such changes can restructure phytoplankton communities and their dynamics, as well as result in altered ecological function (e.g., carbon, nutrient and energy cycling) in the short- and long-term. We summarize the current understanding of storm-induced phytoplankton dynamics, identify knowledge gaps with a systematic review of the literature, and suggest future research directions across a gradient of lake types and environmental conditions.Peer reviewe

Diel surface temperature range scales with lake size

Ecological and biogeochemical processes in lakes are strongly dependent upon water temperature. Long-term surface warming of many lakes is unequivocal, but little is known about the comparative magnitude of temperature variation at diel timescales, due to a lack of appropriately resolved data. Here we quantify the pattern and magnitude of diel temperature variability of surface waters using high-frequency data from 100 lakes. We show that the near-surface diel temperature range can be substantial in summer relative to long-term change and, for lakes smaller than 3 km2, increases sharply and predictably with decreasing lake area. Most small lakes included in this study experience average summer diel ranges in their near-surface temperatures of between 4 and 7°C. Large diel temperature fluctuations in the majority of lakes undoubtedly influence their structure, function and role in biogeochemical cycles, but the full implications remain largely unexplored

Storm impacts on phytoplankton community dynamics in lakes

In many regions across the globe, extreme weather events, such as storms, have increased in frequency, intensity and duration. Ecological theory predicts that such extreme events should have large impacts on ecosystem structure and function. For lake ecosystems, high winds and rainfall associated with storms are linked by short term runoff events from catchments and physical mixing of the water column. Although we have a well-developed understanding of how such wind and precipitation events alter lake physical processes, our mechanistic understanding of how these short-term disturbances 48 translate from physical forcing to changes in phytoplankton communities is poor. Here, we provide a conceptual model that identifies how key storm features (i.e., the frequency, intensity, and duration of wind and precipitation) interact with attributes of lakes and their watersheds to generate changes in a lake’s physical and chemical environment and subsequently phytoplankton community structure and dynamics. We summarize the current understanding of storm-phytoplankton dynamics, identify knowledge gaps with a systematic review of the literature, and suggest future research directions by generating testable hypotheses across a global gradient of lake types and environmental conditions.Fil: Stockwell, Jason D.. University of Vermont; Estados UnidosFil: Adrian, Rita. Leibniz Institute of Freshwater Ecology and Inland Fisheries; AlemaniaFil: Andersen, Mikkel. Dundalk Institute of Technology; IrlandaFil: Anneville, Orlane. Institut National de la Recherche Agronomique; FranciaFil: Bhattacharya, Ruchi. University of Missouri; Estados UnidosFil: Burns, Wilton G.. University of Vermont; Estados UnidosFil: Carey, Cayelan C.. Virginia Tech University; Estados UnidosFil: Carvalho, Laurence. Freshwater Restoration & Sustainability Group; Reino UnidoFil: Chang, ChunWei. National Taiwan University; República de ChinaFil: De Senerpont Domis, Lisette N.. Netherlands Institute of Ecology; Países BajosFil: Doubek, Jonathan P.. University of Vermont; Estados UnidosFil: Dur, Gaël. Shizuoka University; JapónFil: Frassl, Marieke A.. Griffith University; AustraliaFil: Gessner, Mark O.. Leibniz Institute of Freshwater Ecology and Inland Fisheries; AlemaniaFil: Hejzlar, Josef. Biology Centre of the Czech Academy of Sciences; República ChecaFil: Ibelings, Bas W.. University of Geneva; SuizaFil: Janatian, Nasim. Estonian University of Life Sciences; EstoniaFil: Kpodonu, Alfred T. N. K.. City University of New York; Estados UnidosFil: Lajeunesse, Marc J.. University of South Florida; Estados UnidosFil: Lewandowska, Aleksandra M.. Tvarminne Zoological Station; FinlandiaFil: Llames, Maria Eugenia del Rosario. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto de Investigaciones Biotecnológicas. Universidad Nacional de San Martín. Instituto de Investigaciones Biotecnológicas; ArgentinaFil: Matsuzaki, Shin-ichiro S.. National Institute for Environmental Studies; JapónFil: Nodine, Emily R.. Rollins College; Estados UnidosFil: Nõges, Peeter. Estonian University of Life Sciences; EstoniaFil: Park, Ho-Dong. Shinshu University; JapónFil: Patil, Vijay P.. US Geological Survey; Estados UnidosFil: Pomati, Francesco. Swiss Federal Institute of Water Science and Technology; SuizaFil: Rimmer, Alon. Kinneret Limnological Laboratory; IsraelFil: Rinke, Karsten. Helmholtz-Centre for Environmental Research; AlemaniaFil: Rudstam, Lars G.. Cornell University; Estados UnidosFil: Rusak, James A.. Ontario Ministry of the Environment and Climate Change; CanadáFil: Salmaso, Nico. Research and Innovation Centre - Fondazione Mach; ItaliaFil: Schmitt, François. Laboratoire d’Océanologie et de Géosciences; FranciaFil: Seltmann, Christian T.. Dundalk Institute of Technology; IrlandaFil: Souissi, Sami. Universite Lille; FranciaFil: Straile, Dietmar. University of Konstanz; AlemaniaFil: Thackeray, Stephen J.. Lancaster Environment Centre; Reino UnidoFil: Thiery, Wim. Vrije Unviversiteit Brussel; Bélgica. Institute for Atmospheric and Climate Science; SuizaFil: Urrutia Cordero, Pablo. Uppsala University; SueciaFil: Venail, Patrick. Universidad de Ginebra; SuizaFil: Verburg, Piet. 8National Institute of Water and Atmospheric Research; Nueva ZelandaFil: Williamson, Tanner J.. Miami University; Estados UnidosFil: Wilson, Harriet L.. Dundalk Institute of Technology; IrlandaFil: Zohary, Tamar. Israel Oceanographic & Limnological Research; IsraelGLEON 20: All Hands' MeetingRottnest IslandAustraliaUniversity of Western AustraliaUniversity of AdelaideGlobal Lake Ecological Observatory Networ

Diel surface temperature range scales with lake size

Ecological and biogeochemical processes in lakes are strongly dependent upon water temperature. Long-term surface warming of many lakes is unequivocal, but little is known about the comparative magnitude of temperature variation at Diel timescales, due to a lack of appropriately resolved data. Here we quantify the pattern and magnitude of Diel temperature variability of surface waters using high-frequency data from 100 lakes. We show that the near-surface Diel temperature range can be substantial in summer relative to long-term change and, for lakes smaller than 3 km2, increases sharply and predictably with decreasing lake area. Most small lakes included in this study experience average summer Diel ranges in their near-surface temperatures of between 4 and 7°C. Large Diel temperature fluctuations in the majority of lakes undoubtedly influence their structure, function and role in biogeochemical cycles, but the full implications remain largely unexplored

Toxic marine microalgae and shellfish poisoning in the British isles: history, review of epidemiology, and future implications

The relationship between toxic marine microalgae species and climate change has become a high profile and well discussed topic in recent years, with research focusing on the possible future impacts of changing hydrological conditions on Harmful Algal Bloom (HAB) species around the world. However, there is very little literature concerning the epidemiology of these species on marine organisms and human health. Here, we examine the current state of toxic microalgae species around the UK, in two ways: first we describe the key toxic syndromes and gather together the disparate reported data on their epidemiology from UK records and monitoring procedures. Secondly, using NHS hospital admissions and GP records from Wales, we attempt to quantify the incidence of shellfish poisoning from an independent source. We show that within the UK, outbreaks of shellfish poisoning are rare but occurring on a yearly basis in different regions and affecting a diverse range of molluscan shellfish and other marine organisms. We also show that the abundance of a species does not necessarily correlate to the rate of toxic events. Based on routine hospital records, the numbers of shellfish poisonings in the UK are very low, but the identification of the toxin involved, or even a confirmation of a poisoning event is extremely difficult to diagnose. An effective shellfish monitoring system, which shuts down aquaculture sites when toxins exceed regularity limits, has clearly prevented serious impact to human health, and remains the only viable means of monitoring the potential threat to human health. However, the closure of these sites has an adverse economic impact, and the monitoring system does not include all toxic plankton. The possible geographic spreading of toxic microalgae species is therefore a concern, as warmer waters in the Atlantic could suit several species with southern biogeographical affinities enabling them to occupy the coastal regions of the UK, but which are not yet monitored or considered to be detrimental