Gold(I) Carbenoids: On-Demand Access to Gold(I) Carbenes in Solution

Chloromethylgold(I) complexes of phosphine, phosphite, and N-heterocyclic carbene ligands are easily synthesized by reaction of trimethylsilyldiazomethane with the corresponding gold chloride precursors. Activation of these gold(I) carbenoids with a variety of chloride scavengers promotes reactivity typical of metallocarbenes in solution, namely homocoupling to ethylene, olefin cyclopropanation, and Buchner ring expansion of benzene

Sleep characteristics modify the association between genetic predisposition to obesity and anthropometric measurements in 119,679 UK Biobank participants

Background - Obesity is a multifactorial condition influenced by genetics, lifestyle and environment. Objective - To investigate whether the association between a validated genetic profile risk score for obesity (GPRS-obesity) with body mass index (BMI) and waist circumference (WC) was modified by sleep characteristics. Design - This study included cross-sectional data from 119,859 white European adults, aged 37-73 years, participating on the UK Biobank. Interactions between GPRS-obesity, and sleep characteristics (sleep duration, chronotype, day napping, and shift work) in their effects on BMI and WC were investigated. Results - The GPRS-obesity was associated with BMI (β:0.57 kg.m-2 per standard deviation (SD) increase in GPRS, [95%CI:0.55, 0.60]; P=6.3x10-207) and WC (β:1.21 cm, [1.15, 1.28]; P=4.2x10-289). There were significant interactions between GPRS-obesity and a variety of sleep characteristics in their relationship with BMI (P-interaction <0.05). In participants who slept <7 hrs or >9 hrs daily, the effect of GPRS-obesity on BMI was stronger (β:0.60 [0.54, 0.65] and 0.73 [0.49, 0.97] kg.m-2 per SD increase in GPRS, respectively) than in normal length sleepers (7-9 hours; β:0.52 [0.49, 0.55] kg.m-2 per SD). A similar pattern was observed for shiftworkers (β:0.68 [0.59, 0.77] versus 0.54 [0.51, 0.58] kg.m-2 for non-shiftworkers) and for night-shiftworkers (β:0.69 [0.56, 0.82] versus 0.55 [0.51, 0.58] kg.m-2 for non-night- shiftworkers), for those taking naps during the day (β:0.65 [0.52, 0.78] versus 0.51 [0.48, 0.55] kg.m-2 for those who never/rarely had naps) and for those with a self-reported evening chronotype (β:0.72 [0.61, 0.82] versus β:0.52 [0.47, 0.57] kg.m-2 for morning chronotype). Similar findings were obtained using WC as the outcome. Conclusions – This study shows that the association between genetic risk for obesity and phenotypic adiposity measures is exacerbated by adverse sleeping characteristics

Dietary fat and total energy intake modifies the association of genetic profile risk score on obesity: evidence from 48 170 UK Biobank participants

Background: Obesity is a multifactorial condition influenced by both genetics and lifestyle. The aim of this study was to investigate whether the association between a validated genetic profile risk score for obesity (GPRS-obesity) and body mass index (BMI) or waist circumference (WC) was modified by macronutrient intake in a large general population study. Methods: This study included cross-sectional data from 48 170 white European adults, aged 37–73 years, participating on the UK Biobank. Interactions between GPRS-obesity, and macronutrient intake (including total energy, protein, fat, carbohydrate and dietary fibre intake) and its effects on BMI and WC were investigated. Results: The 93-SNPs genetic profile risk score was associated with a higher BMI (β:0.57 kg.m−2 per standard deviation (s.d.) increase in GPRS, [95%CI:0.53–0.60]; P=1.9 × 10−183) independent of major confounding factors. There was a significant interaction between GPRS and total fat intake (P[interaction]=0.007). Among high fat intake individuals, BMI was higher by 0.60 [0.52, 0.67] kg.m−2 per s.d. increase in GPRS-obesity; the change in BMI with GPRS was lower among low fat intake individuals (β:0.50 [0.44, 0.57] kg.m-2). Significant interactions with similar patterns were observed for saturated fat intake (High β:0.66 [0.59, 0.73] versus Low β:0.49 [0.42, 0.55] kg.m-2, P-interaction=2 × 10-4), and total energy intake (High β:0.58 [0.51, 0.64] versus Low β:0.49 [0.42, 0.56] kg.m−2, P-interaction=0.019), but not for protein intake, carbohydrate intake and fiber intake (P-interaction >0.05). The findings were broadly similar using WC as the outcome. Conclusions: These data suggest that the benefits of reducing the intake of fats and total energy intake, may be more important in individuals with high genetic risk for obesity

Tobacco exposure and sleep disturbance in 498 208 UK Biobank participants

Background: The prevalence of sleep disturbance is high and increasing. The study investigated whether active, former and passive smoking were associated with sleep disturbance. Methods: This cross-sectional study used data from the UK Biobank: a cohort study of 502 655 participants, of whom 498 208 provided self-reported data on smoking and sleep characteristics. Multivariable multinomial and logistic regression models were used to examine the associations between smoking and sleep disturbance. Results: Long-sleep duration (>9 h) was more common among current smokers [odds ratio (OR): 1.47; 95% confidence interval (CI): 1.17–1.85; probability value (P) = 0.001] than never smokers, especially heavy (>20/day) smokers (OR: 2.85; 95% CI: 1.66–4.89; P < 0.001). Former heavy (>20/day) smokers were also more likely to report short (<6 h) sleep duration (OR: 1.41; 95% CI: 1.25–1.60; P < 0.001), long-sleep duration (OR: 1.99; 95% CI: 1.47–2.71; P < 0.001) and sleeplessness (OR: 1.47; 95% CI: 1.38–1.57; P < 0.001) than never smokers. Among never smokers, those who lived with more than one smoker had higher odds of long-sleep duration than those not cohabitating with a smoker (OR: 2.71; 95% CI: 1.26–5.82; P = 0.011). Conclusions: Active and passive exposure to high levels of tobacco smoke are associated with sleep disturbance. Existing global tobacco control interventions need to be enforced

Adverse metabolic and mental health outcomes associated with shiftwork in a population-based study of 277,168 workers in UK biobank

Background: Reported associations between shiftwork and health have largely been based on occupation-specific, or single sex studies that might not be generalizable to the entire working population. The objective of this study was to investigate whether shiftwork was independently associated with obesity, diabetes, poor sleep, and well-being in a large, UK general population cohort. Methods: Participants of the UK Biobank study who were employed at the time of assessment were included. Exposure variables were self-reported shiftwork (any shiftwork and night shiftwork); and outcomes were objectively measured obesity, inflammation and physical activity and self-reported lifestyle, sleep and well-being variables, including mental health. Results: Shiftwork was reported by 17% of the 277,168 employed participants. Shiftworkers were more likely to be male, socioeconomically deprived and smokers, and to have higher levels of physical activity. Univariately, and following adjustment for lifestyle and work-related confounders, shiftworkers were more likely to be obese, depressed, to report disturbed sleep, and to have neurotic traits. Conclusions: Shiftwork was independently associated with multiple indicators of poor health and wellbeing, despite higher physical activity, and even in shiftworkers that did not work nights. Shiftwork is an emerging social factor that contributes to disease in the urban environment across the working population

The combination of physical activity and sedentary behaviors modifies the genetic predisposition to obesity

Objective: This study aimed to investigate whether the association between a validated genetic profile risk score for BMI (GPRS‐BMI) (based on 93 single‐nucleotide polymorphisms) and phenotypic obesity (BMI) was modified by the combined categories of physical activity (PA) and sedentary behaviors in a large population‐based study. Methods: This study included cross‐sectional baseline data from 338,216 white European adult men and women aged 37 to 73 years. Interaction effects of GPRS‐BMI with the combined categories of PA and sedentary behaviors on BMI were investigated. Results: There was a significant interaction between GPRS‐BMI and the combined categories of objectively measured PA and total sedentary behavior (P[interaction]  =  3.5 × 10−6); among physically inactive and highly sedentary individuals, BMI was higher by 0.60 kg/m2 per 1‐SD increase in GPRS‐obesity (P  =  8.9 × 10−50), whereas the relevant BMI difference was 38% lower among physically active individuals and those with low sedentary time (β: 0.37 kg/m2; P  =  2.3 × 10−51). A similar pattern was observed for the combined categories of objective PA and TV viewing (inactive/high TV viewing β: 0.60 vs. active/low TV viewing β: 0.40 kg/m2; P[interaction]  =  2.9 × 10−6). Conclusions: This study provides evidence that combined categories of PA and sedentary behaviors modify the extent to which genetic predisposition to obesity results in higher BMI

Do physical activity, commuting mode, cardiorespiratory fitness and sedentary behaviours modify the genetic predisposition to higher BMI? Findings from a UK Biobank study

Objective: To investigate whether the association between a genetic profile risk score for obesity (GPRS-obesity) (based on 93 SNPs) and body mass index (BMI) was modified by physical activity (PA), cardiorespiratory fitness, commuting mode, walking pace and sedentary behaviours. Methods: For the analyses we used cross-sectional baseline data from 310,652 participants in the UK Biobank study. We investigated interaction effects of GPRS-obesity with objectively measured and self-reported PA, cardiorespiratory fitness, commuting mode, walking pace, TV viewing, playing computer games, PC-screen time and total sedentary behaviour on BMI. Body mass index (BMI) was the main outcome measure. Results: GPRS-obesity was associated with BMI (β:0.54 kg.m−2 per standard deviation (SD) increase in GPRS, [95% CI: 0.53; 0.56]; P = 2.1 × 10−241). There was a significant interaction between GPRS-obesity and objectively measured PA (P[interaction] = 3.3 × 10−11): among inactive individuals, BMI was higher by 0.58 kg.m−2 per SD increase in GPRS-obesity (p = 1.3 × 10−70) whereas among active individuals the relevant BMI difference was less (β:0.33 kg.m−2, p = 6.4 × 10−41). We observed similar patterns for fitness (Unfit β:0.72 versus Fit β:0.36 kg.m−2, P[interaction] = 1.4 × 10−11), walking pace (Slow β:0.91 versus Brisk β:0.38 kg.m−2, P[interaction] = 8.1 × 10−27), discretionary sedentary behaviour (High β:0.64 versus Low β:0.48 kg.m−2, P[interaction] = 9.1 × 10−12), TV viewing (High β:0.62 versus Low β:0.47 kg.m−2, P[interaction] = 1.7 × 10−11), PC-screen time (High β:0.82 versus Low β:0.54 kg.m−2, P[interaction] = 0.0004) and playing computer games (Often β:0.69 versus Low β:0.52 kg.m−2, P[interaction] = 8.9 × 10−10). No significant interactions were found for commuting mode (car, public transport, active commuters). Conclusions: Physical activity, sedentary behaviours and fitness modify the extent to which a set of the most important known adiposity variants affect BMI. This suggests that the adiposity benefits of high PA and low sedentary behaviour may be particularly important in individuals with high genetic risk for obesity

Seasonality of depressive symptoms in women but not in men: a cross-sectional study in the UK Biobank cohort

Background: We examined whether seasonal variations in depressive symptoms occurred independently of demographic and lifestyle factors, and were related to change in day length and/or outdoor temperature. Methods: In a cross-sectional analysis of >150,000 participants of the UK Biobank cohort, we used the cosinor method to assess evidence of seasonality of a total depressive symptoms score and of low mood, anhedonia, tenseness and tiredness scores in women and men. Associations of depressive symptoms with day length and mean outdoor temperature were then examined. Results: Seasonality of total depressive symptom scores, anhedonia and tiredness scores was observed in women but not men, with peaks in winter. In women, increased day length was associated with reduced low mood and anhedonia scores, independent of demographic and lifestyle factors. For women, longer day length was associated with increased tiredness. Associations with day length were not independent of the average outdoor temperature preceding assessment. Limitations: This was a cross-sectional investigation – longitudinal studies of within-subject seasonal variation in mood are necessary. Outcome measures relied on self-report and measured only a subset of depressive symptoms. Conclusion: This large, population-based study provides evidence of seasonal variation in depressive symptoms in women. Shorter days were associated with increased feelings of low mood and anhedonia in women. Clinicians should be aware of these population-level sex differences in seasonal mood variations in order to aid recognition and treatment of depression and subclinical depressive symptoms

Association of disrupted circadian rhythmicity with mood disorders, subjective wellbeing, and cognitive function: a cross-sectional study of 91 105 participants from the UK Biobank

Background: Disruption of sleep and circadian rhythmicity is a core feature of mood disorders and might be associated with increased susceptibility to such disorders. Previous studies in this area have used subjective reports of activity and sleep patterns, but the availability of accelerometer-based data from UK Biobank participants permits the derivation and analysis of new, objectively ascertained circadian rhythmicity parameters. We examined associations between objectively assessed circadian rhythmicity and mental health and wellbeing phenotypes, including lifetime history of mood disorder. Methods: UK residents aged 37–73 years were recruited into the UK Biobank general population cohort from 2006 to 2010. We used data from a subset of participants whose activity levels were recorded by wearing a wrist-worn accelerometer for 7 days. From these data, we derived a circadian relative amplitude variable, which is a measure of the extent to which circadian rhythmicity of rest–activity cycles is disrupted. In the same sample, we examined cross-sectional associations between low relative amplitude and mood disorder, wellbeing, and cognitive variables using a series of regression models. Our final model adjusted for age and season at the time that accelerometry started, sex, ethnic origin, Townsend deprivation score, smoking status, alcohol intake, educational attainment, overall mean acceleration recorded by accelerometry, body-mass index, and a binary measure of childhood trauma. Findings: We included 91 105 participants with accelerometery data collected between 2013 and 2015 in our analyses. A one-quintile reduction in relative amplitude was associated with increased risk of lifetime major depressive disorder (odds ratio [OR] 1·06, 95% CI 1·04–1·08) and lifetime bipolar disorder (1·11, 1·03–1·20), as well as with greater mood instability (1·02, 1·01–1·04), higher neuroticism scores (incident rate ratio 1·01, 1·01–1·02), more subjective loneliness (OR 1·09, 1·07–1·11), lower happiness (0·91, 0·90–0·93), lower health satisfaction (0·90, 0·89–0·91), and slower reaction times (linear regression coefficient 1·75, 1·05–2·45). These associations were independent of demographic, lifestyle, education, and overall activity confounders. Interpretation: Circadian disruption is reliably associated with various adverse mental health and wellbeing outcomes, including major depressive disorder and bipolar disorder. Lower relative amplitude might be linked to increased susceptibility to mood disorders