PAK3 Exacerbates Cardiac Lipotoxicity via SREBP1c in Obesity Cardiomyopathy.

Obesity-induced lipid overload in cardiomyocytes contributes to profound oxidative stress and cardiomyopathy, culminating in heart failure. In this study, we investigate a novel mechanism whereby lipids accumulate in cardiomyocytes and seek the relevant treatment strategies. P21-activated kinase 3 (PAK3) was elevated in obese human myocardium, and the murine hearts and cardiomyocytes upon diet- or fatty acid-induced stress, respectively. Mice with cardiac-specific overexpression of PAK3 were more susceptible to the development of cardiac dysfunction upon diet stress, at least partially, due to increased deposition of toxic lipids within the myocardium. Mechanistically, PAK3 promoted the nuclear expression of sterol regulatory element binding protein 1c (SREBP1c) through activation of mammalian target of rapamycin (mTOR) and ribosomal protein S6 kinase beta-1 (S6K1) pathway in cardiomyocytes, resulting in abnormal lipid genes profile, accumulation of excessive lipids, and oxidative stress. More importantly, PAK3 knockdown attenuated fatty acid-induced lipotoxicity and cell death in rat and human cardiomyocytes. More importantly, the S6K1 or SREBP1c inhibitor alleviated PAK3-triggered intracellular lipid overload and cardiac dysfunction under obese stress. Collectively, we have demonstrated that PAK3 impairs myocardial lipid homeostasis, while inhibition of cardiac lipotoxicity mitigates cardiac dysfunction. Our study provides a promising therapeutic strategy for ameliorating obesity cardiomyopathy.

Sleep duration irregularity and risk for incident cardiovascular disease in the UK Biobank.

Background: Emerging evidence supports a link between circadian disruption as measured by higher night-to-night variation in sleep duration and increased risk of cardiovascular disease (CVD). It remains unclear whether this association varies by CVD types or may be modified by average sleep duration and genetic risk for CVD. Methods: Our prospective analysis included 86,219 UK Biobank participants who were free from CVD when completing 7 days of accelerometer measurement in 2013-2016. Sleep irregularity was evaluated by the standard deviation (SD) of accelerometer-measured sleep duration over 7 days. Incident major CVD events, defined as fatal or nonfatal myocardial infarction (MI) and stroke, were identified through linkage to Hospital Episode Statistics data until May 31, 2022. Multivariable-adjusted Cox proportional hazard models were used to estimate hazard ratios (HRs) and 95% CIs for associations of sleep duration SD with risk for major CVD events overall and for MI and stroke separately. Results: We documented 2,310 incident cases of major CVD events (MI: 1,183, stroke: 1,175) over 636,258 person-years of follow-up. After adjusting for sociodemographic factors and family history of CVD, the HR (95% CI) associated with a 1-hour increase in sleep duration SD was 1.19 (1.10, 1.27) for CVD (p-trend<0.0001), 1.23 (1.11, 1.35) for MI (p-trend<0.0001), and 1.17 (1.05, 1.29) for stroke (p-trend=0.003). Additional adjustment for lifestyle factors, co-morbidities and sleep-related factors modestly attenuated these associations. Higher sleep irregularity was associated with higher CVD risk irrespective of genetic risk (p-interaction=0.43), but this association was stronger among individuals with longer average sleep duration >8 hours (p-interaction=0.006). Conclusions: Higher night-to-night variation in accelerometer-measured sleep duration was associated with consistently higher risks for major CVD events. The association did not seem to be modified by genetic risk for CVD and was more pronounced in long sleepers.

Personal light exposure patterns and incidence of type 2 diabetes: analysis of 13 million hours of light sensor data and 670,000 person-years of prospective observation.

Background: Light at night disrupts circadian rhythms, and circadian disruption is a risk factor for type 2 diabetes. Whether personal light exposure predicts diabetes risk has not been demonstrated in a large prospective cohort. We therefore assessed whether personal light exposure patterns predicted risk of incident type 2 diabetes in UK Biobank participants, using ∼13 million hours of light sensor data. Methods: Participants (N = 84,790, age (M ± SD) = 62.3 ± 7.9 years, 58% female) wore light sensors for one week, recording day and night light exposure. Circadian amplitude and phase were modeled from weekly light data. Incident type 2 diabetes was recorded (1997 cases; 7.9 ± 1.2 years follow-up; excluding diabetes cases prior to light-tracking). Risk of incident type 2 diabetes was assessed as a function of day and night light, circadian phase, and circadian amplitude, adjusting for age, sex, ethnicity, socioeconomic and lifestyle factors, and polygenic risk. Findings: Compared to people with dark nights (0-50th percentiles), diabetes risk was incrementally higher across brighter night light exposure percentiles (50-70th: multivariable-adjusted HR = 1.29 [1.14-1.46]; 70-90th: 1.39 [1.24-1.57]; and 90-100th: 1.53 [1.32-1.77]). Diabetes risk was higher in people with lower modeled circadian amplitude (aHR = 1.07 [1.03-1.10] per SD), and with early or late circadian phase (aHR range: 1.06-1.26). Night light and polygenic risk independently predicted higher diabetes risk. The difference in diabetes risk between people with bright and dark nights was similar to the difference between people with low and moderate genetic risk. Interpretation: Type 2 diabetes risk was higher in people exposed to brighter night light, and in people exposed to light patterns that may disrupt circadian rhythms. Avoidance of light at night could be a simple and cost-effective recommendation that mitigates risk of diabetes, even in those with high genetic risk. Funding: Australian Government Research Training Program.

Association Between Accelerometer-Measured Irregular Sleep Duration and Type 2 Diabetes Risk: A Prospective Cohort Study in the UK Biobank.

OBJECTIVE: To evaluate the association between irregular sleep duration and incident diabetes in a U.K. population over 7 years of follow-up. RESEARCH DESIGN AND METHODS: Among 84,421 UK Biobank participants (mean age 62 years) who were free of diabetes at the time of providing accelerometer data in 2013-2015 and prospectively followed until May 2022, sleep duration variability was quantified by the within-person SD of 7-night accelerometer-measured sleep duration. We used Cox proportional hazard models to estimate hazard ratios (HRs) for incident diabetes (identified from medical records, death register, and/or self-reported diagnosis) according to categories of sleep duration SD. RESULTS: There were 2,058 incident diabetes cases over 622,080 person-years of follow-up. Compared with sleep duration SD ≤ 30 min, the HR (95% CI) was 1.15 (0.99, 1.33) for 31-45 min, 1.28 (1.10, 1.48) for 46-60 min, 1.54 (1.32, 1.80) for 61-90 min, and 1.59 (1.33, 1.90) for ≥91 min, after adjusting for age, sex, and race. We found a nonlinear relationship (P nonlinearity 0.0002), with individuals with a sleep duration SD of >60 vs. ≤60 min having 34% higher diabetes risk (95% CI 1.22, 1.47). Further adjustment for lifestyle, comorbidities, environmental factors, and adiposity attenuated the association (HR comparing sleep duration SD of >60 vs. ≤60 min: 1.11; 95% CI 1.01, 1.22). The association was stronger among individuals with lower diabetes polygenic risk score (PRS; P interaction ≤ 0.0264) and longer sleep duration (P interaction ≤ 0.0009). CONCLUSIONS: Irregular sleep duration was associated with higher diabetes risk, particularly in individuals with a lower diabetes PRS and longer sleep duration.

Long-term trajectories of sleep duration are associated with incident diabetes in middle-to-older-aged Black and White Americans.

AIMS/HYPOTHESIS: Both short and long sleep durations have been linked to higher diabetes risk. However, sleep duration may vary over time, and there has been limited research focusing on individual sleep trajectories and diabetes risk. There are substantial racial disparities in both sleep health and diabetes risk in the USA. Thus, it is important to understand the role of suboptimal sleep patterns in diabetes risk in different racial groups. METHODS: We assessed long-term trajectories of sleep duration and incident diabetes in 22,285 Black adults (mean age ± SD, 51.1 ± 8.2 years; 64.8% women) and 13,737 White adults (mean age ± SD, 54.4 ± 9.0 years; 63.8% women) enrolled in the Southern Community Cohort Study. Nine sleep trajectories were derived based on self-reported sleep duration at baseline and after a mean of 5 years of follow-up: normal-normal (reference), short-normal, normal-short, short-short, long-normal, normal-long, long-long, long-short and short-long. Diabetes was reported using a validated questionnaire. Multivariable-adjusted logistic regression was used to determine relationships between sleep trajectories and incident diabetes. RESULTS: When compared with the normal-normal trajectory, suboptimal sleep trajectories were associated with higher likelihoods of developing diabetes (OR; 95% CI: short-normal 1.19; 1.09, 1.31; normal-short 1.14; 1.02, 1.27; short-short 1.17; 1.07, 1.28; long-normal 1.13; 0.98, 1.30; normal-long 1.16; 1.00, 1.34; long-long 1.23; 1.02, 1.48; long-short 1.45; 1.19, 1.77; short-long 1.51; 1.28, 1.77). Stratified analyses by race and socioeconomic status (i.e. education and household income) showed that most suboptimal sleep trajectories were consistently associated with incident diabetes in all sociodemographic subgroups. We also noted potential interaction with race and education for several sleep trajectories (i.e. short-long and normal-short with race; long-long and short-short with education). CONCLUSIONS/INTERPRETATION: Adults with suboptimal sleep duration trajectories are more likely to develop incident diabetes. Future research is needed to study how sociodemographic factors modulate this relationship.

The role of accelerometer-derived sleep traits on glycated haemoglobin and glucose levels: a Mendelian randomization study.

Self-reported shorter/longer sleep duration, insomnia, and evening preference are associated with hyperglycaemia in observational analyses, with similar observations in small studies using accelerometer-derived sleep traits. Mendelian randomization (MR) studies support an effect of self-reported insomnia, but not others, on glycated haemoglobin (HbA1c). To explore potential effects, we used MR methods to assess effects of accelerometer-derived sleep traits (duration, mid-point least active 5-h, mid-point most active 10-h, sleep fragmentation, and efficiency) on HbA1c/glucose in European adults from the UK Biobank (UKB) (n = 73,797) and the MAGIC consortium (n = 146,806). Cross-trait linkage disequilibrium score regression was applied to determine genetic correlations across accelerometer-derived, self-reported sleep traits, and HbA1c/glucose. We found no causal effect of any accelerometer-derived sleep trait on HbA1c or glucose. Similar MR results for self-reported sleep traits in the UKB sub-sample with accelerometer-derived measures suggested our results were not explained by selection bias. Phenotypic and genetic correlation analyses suggested complex relationships between self-reported and accelerometer-derived traits indicating that they may reflect different types of exposure. These findings suggested accelerometer-derived sleep traits do not affect HbA1c. Accelerometer-derived measures of sleep duration and quality might not simply be 'objective' measures of self-reported sleep duration and insomnia, but rather captured different sleep characteristics.

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.

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.

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.

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.