Long-Term Occupational Sleep Loss and Post-Retirement Cognitive Decline or Dementia

Background: A growing but contrasting evidence relates air pollution to cognitive decline. The role of cerebrovascular diseases in amplifying this risk is unclear. Objectives: 1) Investigate the association between long-term exposure to air pollution and cognitive decline; 2) Test whether cerebrovascular diseases amplify this association. Methods: We examined 2,253 participants of the Swedish National study on Aging and Care in Kungsholmen (SNAC-K). One major air pollutant (particulate matter ≤2.5μm, PM2.5) was assessed yearly from 1990, using dispersion models for outdoor levels at residential addresses. The speed of cognitive decline (Mini-Mental State Examination, MMSE) was estimated as the rate of MMSE decline (linear mixed models) and further dichotomized into the upper (25%fastest cognitive decline), versus the three lower quartiles. The cognitive scores were used to calculate the odds of fast cognitive decline per levels of PM2.5 using regression models and considering linear and restricted cubic splines of 10 years exposure before the baseline. The potential modifier effect of cerebrovascular diseases was tested by adding an interaction term in the model. Results: We observed an inverted U-shape relationship between PM2.5 and cognitive decline. The multi-adjusted piecewise regression model showed an increased OR of fast cognitive decline of 81%(95%CI = 1.2–3.2) per interquartile range difference up to mean PM2.5 level (8.6μg/m3) for individuals older than 80. Above such level we observed no further risk increase (OR = 0.89;95%CI = 0.74–1.06). The presence of cerebrovascular diseases further increased such risk by 6%. Conclusion: Low to mean PM2.5 levels were associated with higher risk of accelerated cognitive decline. Cerebrovascular diseases further amplified such risk.

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ICU admissions: Consequences after discharge

InnovAiT Education and inspiration for general practice ◽

10.1177/1755738020932613 ◽

2020 ◽

Vol 13 (9) ◽

pp. 550-556

Author(s):

Minal Karavadra ◽

Ricky Bell

Keyword(s):

Intensive Care ◽

Cognitive Decline ◽

Intensive Care Units ◽

Critically Ill ◽

Critically Ill Patients ◽

Psychiatric Disturbance ◽

Intensive Care Department ◽

Previous Level ◽

The Uk

The intensive care department may seem a long way from the GP's consulting room, but every year tens of thousands of critically ill patients are admitted to intensive care units (ICUs) across the UK. Patients are often left with long term sequelae that may require GP input. Physical weakness, psychiatric disturbance and cognitive decline are not uncommon after an illness that requires a stay in an ICU. These hinder a patient’s return to their previous level of function and impact caregivers after discharge. This article aims to highlight the chronic symptoms patients can acquire during ICU admission that may come to the attention of GPs for their advice and treatment.

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332 - Electroconvulsive therapy in older adults with major depression was not associated with cognitive decline during a 15-year follow-up

International Psychogeriatrics ◽

10.1017/s104161022000232x ◽

2020 ◽

Vol 32 (S1) ◽

pp. 91-91

Keyword(s):

Cognitive Impairment ◽

Mild Cognitive Impairment ◽

Major Depression ◽

Cognitive Decline ◽

Electroconvulsive Therapy ◽

Antidepressant Treatment ◽

Community Sample ◽

Control Group

AUTHORS:Kerstin Johansson, Karolina Thömkvist, Ingmar Skoog and Sacuiu SF* (*presenter)OBJECTIVE:To determine the effects of electroconvulsive therapy (ECT) in major depression in relation to the development of dementia during long-term follow-up.METHOD:In an observational clinical prospective study of consecutive patients 70 years and older diagnosed with major depression at baseline 2000-2004 (n=1090), who were free of dementia and received antidepressant treatment, with or without ECT, we sought to determine if cognitive decline (mild cognitive impairment and dementia) during 15 -year follow-up was associated with receiving ECT at baseline. The control group was selected among the participants in the Gothenburg H70 Birth Cohort Studies matched by age group and sex 1:1.RESULTS:Among patients with affective syndromes 7% received ECT. During follow-up, 157 patients were diagnosed with dementia, equal proportions among those who received ECT (14.5%) and those who did not receive ECT (14.5%). The relation between ECT and cognitive decline remained non-significant irrespective antidepressive medication or presence of mild cognitive impairment at baseline.CONCLUSION:Preliminary results indicate that ECT was not associated with the development of cognitive decline in the long-term in a hospital-based cohort of 70+ year-olds. The results remain to verify against controls from a representative community sample.

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137 Recovery Dynamics in a Biomathematical Model of Fatigue

SLEEP ◽

10.1093/sleep/zsab072.136 ◽

2021 ◽

Vol 44 (Supplement_2) ◽

pp. A56-A56

Author(s):

Mark McCauley ◽

Peter McCauley ◽

Hans Van Dongen

Keyword(s):

Sleep Deprivation ◽

Goodness Of Fit ◽

Sleep Loss ◽

Sleep Restriction ◽

Commercial Aviation ◽

Circadian Misalignment ◽

Total Sleep Deprivation ◽

Homeostatic Process ◽

Recovery Sleep

Abstract Introduction In commercial aviation and other operational settings where biomathematical models of fatigue are used for fatigue risk management, accurate prediction of recovery during rest periods following duty periods with sleep loss and/or circadian misalignment is critical. The recuperative potential of recovery sleep is influenced by a variety of factors, including long-term, allostatic effects of prior sleep/wake history. For example, recovery tends to be slower after sustained sleep restriction versus acute total sleep deprivation. Capturing such dynamics has proven to be challenging. Methods Here we focus on the dynamic biomathematical model of McCauley et al. (2013). In addition to a circadian process, this model features differential equations for sleep/wake regulation including a short-term sleep homeostatic process capturing change in the order of hours/days and a long-term allostatic process capturing change in the order of days/weeks. The allostatic process modulates the dynamics of the homeostatic process by shifting its equilibrium setpoint, which addresses recently observed phenomena such as reduced vulnerability to sleep loss after banking sleep. It also differentiates the build-up and recovery rates of fatigue under conditions of chronic sleep restriction versus acute total sleep deprivation; nonetheless, it does not accurately predict the disproportionately rapid recovery seen after total sleep deprivation. To improve the model, we hypothesized that the homeostatic process may also modulate the allostatic process, with the magnitude of this effect scaling as a function of time awake. Results To test our hypothesis, we added a parameter to the model to capture modulation by the homeostatic process of the allostatic process build-up during wakefulness and dissipation during sleep. Parameter estimation using previously published laboratory datasets of fatigue showed this parameter as significantly different from zero (p<0.05) and yielding a 10%–20% improvement in goodness-of-fit for recovery without adversely affecting goodness-of-fit for pre-recovery days. Conclusion Inclusion of a modulation effect of the allostatic process by the homeostatic process improved prediction accuracy in a variety of sleep loss and circadian misalignment scenarios. In addition to operational relevance for duty/rest scheduling, this finding has implications for understanding mechanisms underlying the homeostatic and allostatic processes of sleep/wake regulation. Support (if any) Federal Express Corporation

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Psychiatric comorbid disorders of cognition: a machine learning approach using 1175 UK Biobank participants

Evidence-Based Mental Health ◽

10.1136/ebmental-2020-300147 ◽

2020 ◽

Vol 23 (4) ◽

pp. 140-145

Author(s):

Chenlu Li ◽

Delia A Gheorghe ◽

John E Gallacher ◽

Sarah Bauermeister

Keyword(s):

Machine Learning ◽

Cognitive Decline ◽

Psychiatric Disorders ◽

Characteristic Curve ◽

Area Under The Curve ◽

Imaging Study ◽

Cognitive Change ◽

Poor Mental Health ◽

Uk Biobank

BackgroundConceptualising comorbidity is complex and the term is used variously. Here, it is the coexistence of two or more diagnoses which might be defined as ‘chronic’ and, although they may be pathologically related, they may also act independently. Of interest here is the comorbidity of common psychiatric disorders and impaired cognition.ObjectivesTo examine whether anxiety and/or depression are/is important longitudinal predictors of cognitive change.MethodsUK Biobank participants used at three time points (n=502 664): baseline, first follow-up (n=20 257) and first imaging study (n=40 199). Participants with no missing data were 1175 participants aged 40–70 years, 41% women. Machine learning was applied and the main outcome measure of reaction time intraindividual variability (cognition) was used.FindingsUsing the area under the receiver operating characteristic curve, the anxiety model achieves the best performance with an area under the curve (AUC) of 0.68, followed by the depression model with an AUC of 0.63. The cardiovascular and diabetes model, and the covariates model have weaker performance in predicting cognition, with an AUC of 0.60 and 0.56, respectively.ConclusionsOutcomes suggest that psychiatric disorders are more important comorbidities of long-term cognitive change than diabetes and cardiovascular disease, and demographic factors. Findings suggest that psychiatric disorders (anxiety and depression) may have a deleterious effect on long-term cognition and should be considered as an important comorbid disorder of cognitive decline.Clinical implicationsImportant predictive effects of poor mental health on longitudinal cognitive decline should be considered in secondary and also primary care.

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