Association of Rest-Activity Rhythm and Risk of Developing Dementia or Mild Cognitive Impairment in the Middle-Aged and Older Population: Prospective Cohort Study.

BACKGROUND: The relationship between 24-hour rest-activity rhythms (RARs) and risk for dementia or mild cognitive impairment (MCI) remains an area of growing interest. Previous studies were often limited by small sample sizes, short follow-ups, and older participants. More studies are required to fully explore the link between disrupted RARs and dementia or MCI in middle-aged and older adults. OBJECTIVE: We leveraged the UK Biobank data to examine how RAR disturbances correlate with the risk of developing dementia and MCI in middle-aged and older adults. METHODS: We analyzed the data of 91,517 UK Biobank participants aged between 43 and 79 years. Wrist actigraphy recordings were used to derive nonparametric RAR metrics, including the activity level of the most active 10-hour period (M10) and its midpoint, the activity level of the least active 5-hour period (L5) and its midpoint, relative amplitude (RA) of the 24-hour cycle [RA=(M10-L5)/(M10+L5)], interdaily stability, and intradaily variability, as well as the amplitude and acrophase of 24-hour rhythms (cosinor analysis). We used Cox proportional hazards models to examine the associations between baseline RAR and subsequent incidence of dementia or MCI, adjusting for demographic characteristics, comorbidities, lifestyle factors, shiftwork status, and genetic risk for Alzheimer's disease. RESULTS: During the follow-up of up to 7.5 years, 555 participants developed MCI or dementia. The dementia or MCI risk increased for those with lower M10 activity (hazard ratio [HR] 1.28, 95% CI 1.14-1.44, per 1-SD decrease), higher L5 activity (HR 1.15, 95% CI 1.10-1.21, per 1-SD increase), lower RA (HR 1.23, 95% CI 1.16-1.29, per 1-SD decrease), lower amplitude (HR 1.32, 95% CI 1.17-1.49, per 1-SD decrease), and higher intradaily variability (HR 1.14, 95% CI 1.05-1.24, per 1-SD increase) as well as advanced L5 midpoint (HR 0.92, 95% CI 0.85-0.99, per 1-SD advance). These associations were similar in people aged <70 and >70 years, and in non-shift workers, and they were independent of genetic and cardiovascular risk factors. No significant associations were observed for M10 midpoint, interdaily stability, or acrophase. CONCLUSIONS: Based on findings from a large sample of middle-to-older adults with objective RAR assessment and almost 8-years of follow-up, we suggest that suppressed and fragmented daily activity rhythms precede the onset of dementia or MCI and may serve as risk biomarkers for preclinical dementia in middle-aged and older adults.

Indirect effects of the COVID-19 pandemic on diagnosing, monitoring, and prescribing in people with diabetes and strategies for diabetes service recovery internationally.

The COVID-19 pandemic has caused major disruptions in clinical services for people with chronic long-term conditions. In this narrative review, we assess the indirect impacts of the COVID-19 pandemic on diabetes services globally and the resulting adverse effects on rates of diagnosing, monitoring, and prescribing in people with type 2 diabetes. We summarise potential practical approaches that could address these issues and improve clinical services and outcomes for people living with diabetes during the recovery phase of the pandemic.

Cardiovascular and Kidney Risks in Individuals With Type 2 Diabetes: Contemporary Understanding With Greater Emphasis on Excess Adiposity.

In high-income countries, rates of atherosclerotic complications in type 2 diabetes have declined markedly over time due to better management of traditional risk factors including lipids, blood pressure, and glycemia levels. Population-wide reductions in smoking have also helped lower atherosclerotic complications and so reduce premature mortality in type 2 diabetes. However, as excess adiposity is a stronger driver for heart failure (HF), and obesity levels have remained largely unchanged, HF risks have not declined as much and may even be rising in the increasing number of people developing type 2 diabetes at younger ages. Excess weight is also an underrecognized risk factor for chronic kidney disease (CKD). Based on evidence from a range of sources, we explain how excess adiposity must be influencing most risks well before diabetes develops, particularly in younger-onset diabetes, which is linked to greater excess adiposity. We also review potential mechanisms linking excess adiposity to HF and CKD and speculate on how some of the responsible pathways-e.g., hemodynamic, cellular overnutrition, and inflammatory-could be favorably influenced by intentional weight loss (via lifestyle or drugs). On the basis of available evidence, we suggest that the cardiorenal outcome benefits seen with sodium-glucose cotransporter 2 inhibitors may partially derive from their interference of some of these same pathways. We also note that many other complications common in diabetes (e.g., hepatic, joint disease, perhaps mental health) are also variably linked to excess adiposity, the aggregated exposure to which has now increased in type 2 diabetes. All such observations suggest a greater need to tackle excess adiposity earlier in type 2 diabetes.

Genome-wide association analysis of composite sleep health scores in 413,904 individuals.

Recent genome-wide association studies (GWASs) of several individual sleep traits have identified hundreds of genetic loci, suggesting diverse mechanisms. Moreover, sleep traits are moderately correlated, and together may provide a more complete picture of sleep health, while also illuminating distinct domains. Here we construct novel sleep health scores (SHSs) incorporating five core self-report measures: sleep duration, insomnia symptoms, chronotype, snoring, and daytime sleepiness, using additive (SHS-ADD) and five principal components-based (SHS-PCs) approaches. GWASs of these six SHSs identify 28 significant novel loci adjusting for multiple testing on six traits (p<8.3e-9), along with 341 previously reported loci (p<5e-08). The heritability of the first three SHS-PCs equals or exceeds that of SHS-ADD (SNP-h2=0.094), while revealing sleep-domain-specific genetic discoveries. Significant loci enrich in multiple brain tissues and in metabolic and neuronal pathways. Post GWAS analyses uncover novel genetic mechanisms underlying sleep health and reveal connections to behavioral, psychological, and cardiometabolic traits.

Counting the lifetime cost of obesity: Analysis based on national England data.

AIM: Obesity has a significant impact on all-cause mortality rate and overall health care resource use (HCRU). These outcomes are also strongly linked to age, sex and local deprivation of the population. We aimed to establish the lifetime costs of obesity by demographic group/geographic area using published mortality rates and HCRU use for integrated care boards (ICB) in England in the context of costs of therapeutic intervention. METHODS: Population and expected mortality rates by age, sex and deprivation were obtained from national data. Obesity class prevalence was taken from the health of the nation study. The published impact of obesity by age, group, sex and deprivation on mortality and HCRU were applied to estimate life years lost and lifetime HCRU [by sex, age band and body mass index (BMI) class for each ICB]. The year 2019 was chosen as the study basis data to avoid influences of COVID-19 pandemic on obesity rates with application of 2022/23 HCRU values. Outcomes including prevalence, deaths, life years lost, HCRU and lifetime HCRU were compared by age and sex groups across four BMI classes normal/underweight (BMI <25 kg/m2 ), overweight (25-29.9 kg/m2 ), obese class I and II (30-39.9 kg/m2 ), and obese class III (≥40), with benchmarking being set against all population being BMI <25 kg/m2 overall and by each of the 42 ICBs. We also associated future life with deaths to provide an estimate of 'future life years lost' occurring each year. RESULTS: Total population aged >16 years was 45.4 million (51% female). PREVALENCE: 13.7 million (28% of the total adult population) had a BMI ≥30 mg/m2 and BMI ≥40 kg/m2 were 1.50 million (12%) of these 1.0 million (68%) were female and of these 0.6 million 40% were women aged 16-49 years. In addition, 35% of those with a BMI ≥40 kg/m2 were in the top deprivation quintile (i.e. overall 20%). Mortality was based on expected deaths of 518K/year, and modelling suggested that if a BMI <25 kg/m2 was achieved in all individuals, the death rate would fall by 63K to 455K/year for the English population (12% reduction). For those with a BMI ≥40 kg/m2 the predicted reduction was 12K deaths (54% lower); while in those aged 16-49 years with a BMI ≥40 kg/m2 72% of deaths were linked to obesity. For future life years lost, we estimated 2.5 years were lost in people with BMI 30-39.9 kg/m2 6.7 years when BMI ≥40 kg/m2 . However, for those aged 16-49 years with a BMI ≥40 kg/m2 , 8.3 years were lost. HCRU, for weight reduction, the annual HCRU decrease from BMI ≥40 kg/m2 to BMI 30-39.9 kg/m2 was £342 per person and from BMI 30-39.9 to 25-29.9 kg/m2 the reduction was £316/person. However, lifetime costs were similar because of reduced life expectancy for obese individuals. In quality adjusted life years (QALY), overall, 791 689 future life years were lost (13.1% of all) in people with BMI ≥25 kg/m2 and were related to excess weight. When the NICE £30 000 per QALY value was applied to the estimated total 791 689 future life years lost then the potential QALY value reduction lost was equivalent to £24 billion/year or £522/person in the obese population. For morbidly obese men and women the potential QALY value lost was £2864/person/year. Regarding geography, across the 42 ICBs, we observed significant variation in the prevalence of BMI ≥40 (1.8%-4.3%), excess mortality (11.6%-15.4%) and HCRU linked to higher BMI (7.2%-8.8%). The areas with the greatest impact on HCRU were in the north-west, north-east and Midlands of England, while the south shows less impact. CONCLUSION: The expected increases in annual HCRU because of obesity, when considered over a lifetime, are being mitigated by the increased mortality of obese individuals. Our data suggest that simple short-term HCRU reduction brought about through BMI reduction will be insufficient to fund additional specialist weight reduction interventions. The HRCUs associated with BMI are not in most cases related to short-term health conditions. They are a cumulative result over a number of years, so for age 16-49 years reducing BMI from ≥40 to 30-39.9 kg/m2 might show an annual decrease in HCRU/person by £325/year for women and £80/year for men but this might not have immediately occurred within that year. For those aged >70 years reducing BMI from ≥40 to 30-39.9 kg/m2 might show an annual decrease in HCRU/person by £777/year for women and £796/year for men but also may not be manifest within that year. However, for the morbidly obese men and women, the potential QALY value lost was £2864 per person per year with the potential for these funds to be applied to intensive weight management programmes, including pharmacotherapy.

Effectiveness of lifestyle interventions/culturally bespoke programmes in South Asian ethnic groups targeting weight loss for prevention and/or remission of type 2 diabetes: a systematic review and meta-analysis of intervention trials.

BACKGROUND: People from South Asian heritage are at high risk of type 2 diabetes, but there are limited specific strategies to prevent and manage this condition. The aim was to assess the effectiveness of culturally bespoke lifestyle programmes in South Asians that target weight loss for the prevention or remission of type 2 diabetes mellitus (T2DM). METHODS: We performed a systematic review and meta-analysis of intervention trials. PubMed, Scopus, MEDLINE (EBSCOhost), CINAHL, PsycINFO and CENTRAL were searched. Human intervention trials (randomised controlled trials and quasi-experimental) investigating the effect of lifestyle interventions on the prevention and remission of T2DM in South Asians were included. Studies including participants at risk of T2DM (prevention trials) and having the disease (remission trials) with duration ≥12 weeks were eligible. For prevention trials, the primary outcome was change in weight (kg) from baseline; for remission trials, it was decrease in HbA1c to non-diabetic levels (HbA1c ≤ 6.5%) without diabetes medications. Prevention trials were separated into (i) lifestyle modification advice and (ii) lifestyle modification advice including a supervised physical activity programme. RESULTS: Twenty-four trials were eligible (21 prevention trials and 3 remission trials). In T2DM prevention trials involving only lifestyle modification advice, the mean postintervention difference in weight between intervention and control groups was -0.65 kg (95% confidence interval [CI]: -1.04, -0.26; p = 0.01). Lifestyle modification advice including a physical activity programme was associated with greater decreases in weight: -1.13 kg (95% CI: -2.04, -0.21; p = 0.02). Fasting blood glucose levels were slightly lower in intervention groups for both intervention subtypes, although there was no significant change in HbA1c levels or 2-h plasma glucose levels. Diabetes remission trials showed potential acceptability but were limited in number and involved a small sample size, and some did not include a control group. CONCLUSIONS: In South Asians, lifestyle interventions for prevention of T2DM offer only modest impacts on weight and glucose control and will unlikely reduce diabetes incidence. Alternative lifestyle interventions co-designed with members of the communities and aimed at both prevention and remission of T2DM must be urgently considered. Systematic review registration number: PROSPERO CRD42022385174 https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=385174.

Sleep regularity is a stronger predictor of mortality risk than sleep duration: A prospective cohort study.

Abnormally short and long sleep are associated with premature mortality, and achieving optimal sleep duration has been the focus of sleep health guidelines. Emerging research demonstrates that sleep regularity, the day-to-day consistency of sleep-wake timing, can be a stronger predictor for some health outcomes than sleep duration. The role of sleep regularity in mortality, however, has not been investigated in a large cohort with objective data. We therefore aimed to compare how sleep regularity and duration predicted risk for all-cause and cause-specific mortality. We calculated Sleep Regularity Index (SRI) scores from > 10 million hours of accelerometer data in 60 977 UK Biobank participants (62.8 ±â€…7.8 years, 55.0% female, median[IQR] SRI: 81.0[73.8-86.3]). Mortality was reported up to 7.8 years after accelerometer recording in 1859 participants (4.84 deaths per 1000 person-years, mean (±SD) follow-up of 6.30 ±â€…0.83 years). Higher sleep regularity was associated with a 20%-48% lower risk of all-cause mortality (p < .001 to p = 0.004), a 16%-39% lower risk of cancer mortality (p < 0.001 to p = 0.017), and a 22%-57% lower risk of cardiometabolic mortality (p < 0.001 to p = 0.048), across the top four SRI quintiles compared to the least regular quintile. Results were adjusted for age, sex, ethnicity, and sociodemographic, lifestyle, and health factors. Sleep regularity was a stronger predictor of all-cause mortality than sleep duration, by comparing equivalent mortality models, and by comparing nested SRI-mortality models with and without sleep duration (p = 0.14-0.20). These findings indicate that sleep regularity is an important predictor of mortality risk and is a stronger predictor than sleep duration. Sleep regularity may be a simple, effective target for improving general health and survival.

Shift work and evening chronotype are associated with hepatic fat fraction and non-alcoholic fatty liver disease in 282,303 UK biobank participants.

Background and aims: Non-alcoholic fatty liver disease (NAFLD) has rapidly become the most common liver disease worldwide. Modern lifestyles have been linked to this rise in prevalence with changes in rhythmic human behaviour emerging as a possible mechanism. We investigated how shift working patterns and chronotype were associated with hepatic fat fraction and NAFLD in 282,303 UK Biobank participants. Methods: We stratified participants into day, irregular-shift, and permanent night-shift workers. We then utilised multiple methods of disease identification including (i) Dallas steatosis index (DSI), (ii) ICD10 codes, and (iii) hepatic proton density fat fraction (PDFF) and examined how shift work exposure impacted these variables. We further assessed the relationship of baseline chronotype with liver phenotypes using these same outcome measures. Results: Compared to day workers, irregular-shift workers were more likely to have a high DSI (OR 1.29 (1.2-1.4)) after adjusting for major covariates with some attenuation after additional adjustment for BMI (OR 1.12 (1.03-1.22)). Likelihood of high DSI was also increased in permanent night-shift workers (OR 1.08 (0.9-1.29)) in the fully adjusted model. Mediator analysis revealed that BMI was a significant mediator of the shift work effect. Compared to participants with intermediate chronotype, those with extreme late chronotype had a higher likelihood of high DSI defined NAFLD (OR 1.45 (1.34-1.56)) and a higher likelihood of NAFLD/NASH by ICD10 code (OR 1.23 (1.09-1.39)). Hepatic PDFF was elevated in irregular shift workers, but not permanent night-shift workers. Conclusions: Irregular-shift work and extreme late chronotype are associated with pathological liver fat accumulation, suggesting circadian misalignment may have an underlying pathogenic role. These findings have implications for health interventions to mitigate the detrimental effect of shift work.

Causal Association Between Subtypes of Excessive Daytime Sleepiness and Risk of Cardiovascular Diseases.

BACKGROUND: Excessive daytime sleepiness (EDS), experienced in 10% to 20% of the population, has been associated with cardiovascular disease and death. However, the condition is heterogeneous and is prevalent in individuals having short and long sleep duration. We sought to clarify the relationship between sleep duration subtypes of EDS with cardiovascular outcomes, accounting for these subtypes. METHODS AND RESULTS: We defined 3 sleep duration subtypes of excessive daytime sleepiness: normal (6-9 hours), short (<6 hours), and long (>9 hours), and compared these with a nonsleepy, normal-sleep-duration reference group. We analyzed their associations with incident myocardial infarction (MI) and stroke using medical records of 355 901 UK Biobank participants and performed 2-sample Mendelian randomization for each outcome. Compared with healthy sleep, long-sleep EDS was associated with an 83% increased rate of MI (hazard ratio, 1.83 [95% CI, 1.21-2.77]) during 8.2-year median follow-up, adjusting for multiple health and sociodemographic factors. Mendelian randomization analysis provided supporting evidence of a causal role for a genetic long-sleep EDS subtype in MI (inverse-variance weighted ß=1.995, P=0.001). In contrast, we did not find evidence that other subtypes of EDS were associated with incident MI or any associations with stroke (P>0.05). CONCLUSIONS: Our study suggests the previous evidence linking EDS with increased cardiovascular disease risk may be primarily driven by the effect of its long-sleep subtype on higher risk of MI. Underlying mechanisms remain to be investigated but may involve sleep irregularity and circadian disruption, suggesting a need for novel interventions in this population.

Correlations in sleeping patterns and circadian preference between spouses.

Spouses may affect each other's sleeping behaviour. In 47,420 spouse-pairs from the UK Biobank, we found a weak positive phenotypic correlation between spouses for self-reported sleep duration (r = 0.11; 95% CI = 0.10, 0.12) and a weak inverse correlation for chronotype (diurnal preference) (r = -0.11; -0.12, -0.10), which replicated in up to 127,035 23andMe spouse-pairs. Using accelerometer data on 3454 UK Biobank spouse-pairs, the correlation for derived sleep duration was similar to self-report (r = 0.12; 0.09, 0.15). Timing of diurnal activity was positively correlated (r = 0.24; 0.21, 0.27) in contrast to the inverse correlation for chronotype. In Mendelian randomization analysis, positive effects of sleep duration (mean difference=0.13; 0.04, 0.23 SD per SD) and diurnal activity (0.49; 0.03, 0.94) were observed, as were inverse effects of chronotype (-0.15; -0.26, -0.04) and snoring (-0.15; -0.27, -0.04). Findings support the notion that an individual's sleep may impact that of their partner, promoting opportunities for sleep interventions at the family-level.

Comorbidity clusters and in-hospital outcomes in patients admitted with acute myocardial infarction in the USA: A national population-based study.

BACKGROUND: The prevalence of multimorbidity in patients with acute myocardial infarction (AMI) is increasing. It is unclear whether comorbidities cluster into distinct phenogroups and whether are associated with clinical trajectories. METHODS: Survey-weighted analysis of the United States Nationwide Inpatient Sample (NIS) for patients admitted with a primary diagnosis of AMI in 2018. In-hospital outcomes included mortality, stroke, bleeding, and coronary revascularisation. Latent class analysis of 21 chronic conditions was used to identify comorbidity classes. Multivariable logistic and linear regressions were fitted for associations between comorbidity classes and outcomes. RESULTS: Among 416,655 AMI admissions included in the analysis, mean (±SD) age was 67 (±13) years, 38% were females, and 76% White ethnicity. Overall, hypertension, coronary heart disease (CHD), dyslipidaemia, and diabetes were common comorbidities, but each of the identified five classes (C) included ≥1 predominant comorbidities defining distinct phenogroups: cancer/coagulopathy/liver disease class (C1); least burdened (C2); CHD/dyslipidaemia (largest/referent group, (C3)); pulmonary/valvular/peripheral vascular disease (C4); diabetes/kidney disease/heart failure class (C5). Odds ratio (95% confidence interval [CI]) for mortality ranged between 2.11 (1.89-2.37) in C2 to 5.57 (4.99-6.21) in C1. For major bleeding, OR for C1 was 4.48 (3.78; 5.31); for acute stroke, ORs ranged between 0.75 (0.60; 0.94) in C2 to 2.76 (2.27; 3.35) in C1; for coronary revascularization, ORs ranged between 0.34 (0.32; 0.36) in C1 to 1.41 (1.30; 1.53) in C4. CONCLUSIONS: We identified distinct comorbidity phenogroups that predicted in-hospital outcomes in patients admitted with AMI. Some conditions overlapped across classes, driven by the high comorbidity burden. Our findings demonstrate the predictive value and potential clinical utility of identifying patients with AMI with specific comorbidity clustering.

Beta-cell death and dysfunction drives hyperglycaemia in organ donors.

BACKGROUND: Donor hyperglycaemia following brain death has been attributed to reversible insulin resistance. However, our islet and pancreas transplant data suggest that other mechanisms may be predominant. We aimed to determine the relationships between donor insulin use and markers of beta-cell death and beta-cell function in pancreas donors after brain death. METHODS: In pancreas donors after brain death, we compared clinical and biochemical data in 'insulin-treated' and 'not insulin-treated donors' (IT vs. not-IT). We measured plasma glucose, C-peptide and levels of circulating unmethylated insulin gene promoter cell-free DNA (INS-cfDNA) and microRNA-375 (miR-375), as measures of beta-cell death. Relationships between markers of beta-cell death and islet isolation outcomes and post-transplant function were also evaluated. RESULTS: Of 92 pancreas donors, 40 (43%) required insulin. Glycaemic control and beta-cell function were significantly poorer in IT donors versus not-IT donors [median (IQR) peak glucose: 8 (7-11) vs. 6 (6-8) mmol/L, p = .016; C-peptide: 3280 (3159-3386) vs. 3195 (2868-3386) pmol/L, p = .046]. IT donors had significantly higher levels of INS-cfDNA [35 (18-52) vs. 30 (8-51) copies/ml, p = .035] and miR-375 [1.050 (0.19-1.95) vs. 0.73 (0.32-1.10) copies/nl, p = .05]. Circulating donor miR-375 was highly predictive of recipient islet graft failure at 3 months [adjusted receiver operator curve (SE) = 0.813 (0.149)]. CONCLUSIONS: In pancreas donors, hyperglycaemia requiring IT is strongly associated with beta-cell death. This provides an explanation for the relationship of donor IT with post-transplant beta-cell dysfunction in transplant recipients.

Associations between sleep health and grey matter volume in the UK Biobank cohort (n = 33 356).

As suggested by previous research, sleep health is assumed to be a key determinant of future morbidity and mortality. In line with this, recent studies have found that poor sleep is associated with impaired cognitive function. However, to date, little is known about brain structural abnormalities underlying this association. Although recent findings link sleep health deficits to specific alterations in grey matter volume, evidence remains inconsistent and reliant on small sample sizes. Addressing this problem, the current preregistered study investigated associations between sleep health and grey matter volume (139 imaging-derived phenotypes) in the UK Biobank cohort (33 356 participants). Drawing on a large sample size and consistent data acquisition, sleep duration, insomnia symptoms, daytime sleepiness, chronotype, sleep medication and sleep apnoea were examined. Our main analyses revealed that long sleep duration was systematically associated with larger grey matter volume of basal ganglia substructures. Insomnia symptoms, sleep medication and sleep apnoea were not associated with any of the 139 imaging-derived phenotypes. Short sleep duration, daytime sleepiness as well as late and early chronotype were associated with solitary imaging-derived phenotypes (no recognizable pattern, small effect sizes). To our knowledge, this is the largest study to test associations between sleep health and grey matter volume. Clinical implications of the association between long sleep duration and larger grey matter volume of basal ganglia are discussed. Insomnia symptoms as operationalized in the UK Biobank do not translate into grey matter volume findings.

Impact of the COVID-19 Pandemic on Diabetes-Related Cardiovascular Mortality in the United States.

Background In the past few decades, diabetes-related cardiovascular mortality has been steadily declining. However, the impact of the COVID19 pandemic on this trend has not been previously defined. Methods and Results Diabetes-related cardiovascular mortality data were extracted for each year between 1999 and 2020 from the Centers for Disease Control and Prevention Wide-Ranging Online Data for Epidemiologic Research (WONDER) database. Regression analysis was used to calculate the trend in the 2 decades before the pandemic (1999-2019) and thereby estimate the excess cardiovascular mortality in 2020. There was a 29.2% fall in the diabetes-related cardiovascular age-adjusted mortality rate between 1999 to 2019, largely driven by a 41% decrease in ischemic heart disease deaths. In comparison to 2019, there was an overall 15.5% increase in the diabetes-related cardiovascular age-adjusted mortality rate in the first year of the pandemic, mainly due to a 14.1% rise in ischemic heart disease deaths. Younger patients (under 55 years) and the Black population experienced the greatest increase in diabetes-related cardiovascular age-adjusted mortality rate (24.0% and 25.3%, respectively). Trend analysis estimated 16 009 excess diabetes-related cardiovascular deaths in 2020, with the majority due to ischemic heart disease (8504). Black and Hispanic or Latino populations had at least one-fifth of their 2020 diabetes-related cardiovascular age-adjusted mortality rate as excess deaths (22.3% and 20.2%, respectively). Conclusions There was a sharp rise in diabetes-related cardiovascular mortality during the first pandemic year. Black, Hispanic or Latino, and young people showed the largest increases in diabetes-related cardiovascular mortality. Targeted health policies could help address the disparities observed in this analysis.

Sleep health dimensions and shift work as longitudinal predictors of cognitive performance in the UK Biobank cohort.

STUDY OBJECTIVES: The long-term effects of sleep health and shift work on cognitive performance are unclear. In addition, research has been limited by small sample sizes and short follow-up periods. We conducted one of the largest examinations of the longitudinal influence of sleep health dimensions and shift work on cognitive performance in people of middle and old age using data from the UK Biobank. The hypothesis was that poor sleep health and shift work would predict lower cognitive performance. METHODS: Self-reported sleep duration, daytime sleepiness, insomnia symptoms, chronotype, and shift work status were assessed as predictors at baseline. Cognitive performance was operationalized by a touchscreen test battery at follow-up between 7.4 ±â€…2.2 and 9.0 ±â€…0.9 years after baseline assessment, depending on the specific task. Models were performed for each cognitive domain including relevant confounders (e.g. depression). The alpha level was set at p < 0.01 for all analyzes. RESULTS: The study sample comprised 9394 participants for the reasoning task, 30 072 for the reaction time task, 30 236 for the visual memory task, 2019 for the numeric memory task, and 9476 for the prospective memory task. Shift work without night shifts (ß = -2.0 × 10-1 ± 6.5 × 10-2, p = 0.002) and with night shifts (ß = -1.9 × 10-1 ± 7.2 × 10-2, p = 0.010) predicted a significantly reduced performance in the reasoning task. Short sleep duration (ß = -2.4 × 10-1 ± 7.9 × 10-2, p = 0.003) and shift work without night shifts (ß = -3.9 × 10-1 ± 1.2 × 10-1, p = 0.002) predicted a significantly lower performance in the task probing prospective memory. CONCLUSIONS: Our results suggest that, after controlling for confounding variables, shift work, and short sleep duration are important predictors for cognitive performance in people of middle and old age. Further work is required to examine causal mechanisms of the observed associations.

Circadian Rest-Activity Rhythms, Delirium Risk, and Progression to Dementia.

OBJECTIVE: Delirium is a complex neurocognitive syndrome suspected to be bidirectionally linked to dementia. Circadian rhythm disturbances likely contribute to dementia pathogenesis, but whether these disturbances are related to delirium risk and progression to all-cause dementia is unknown. METHODS: We analyzed continuous actigraphy data from 53,417 middle-aged or older UK Biobank participants during a median 5 years of follow-up. Four measures were used to characterize the 24-hour daily rest-activity rhythms (RARs): normalized amplitude, acrophase representing the peak activity time, interdaily stability, and intradaily variability (IV) for fragmentation of the rhythm. Cox proportional hazards models examined whether RARs predicted incident delirium (n = 551) and progression to dementia (n = 61). RESULTS: Suppressed 24-hour amplitude, lowest (Q1) versus highest (Q4) quartile (hazard ratio [HR]Q1 vs Q4 = 1.94, 95% confidence interval [CI] = 1.53-2.46, p < 0.001), and more fragmented (higher IV: HRQ4 vs Q1 = 1.49, 95% CI = 1.18-1.88, p < 0.001) rhythms predicted higher delirium risk, after adjusting for age, sex, education, cognitive performance, sleep duration/disturbances, and comorbidities. In those free from dementia, each hour of delayed acrophase was associated with delirium risk (HR = 1.13, 95% CI = 1.04-1.23, p = 0.003). Suppressed 24-hour amplitude was associated with increased risk of progression from delirium to new onset dementia (HR = 1.31, 95% CI = 1.03-1.67, p = 0.03 for each 1-standard deviation decrease). INTERPRETATION: Twenty-four-hour daily RAR suppression, fragmentation, and potentially delayed acrophase were associated with delirium risk. Subsequent progression to dementia was more likely in delirium cases with suppressed rhythms. The presence of RAR disturbances before delirium and prior to progression to dementia suggests that these disturbances may predict higher risk and be involved in early disease pathogenesis. ANN NEUROL 2023;93:1145-1157.