Association of heart rate and diabetes among 0.5 million adults in the China Kadoorie biobank: Results from observational and Mendelian randomization analyses

Background and aimsObservational studies have associated resting heart rate with incident diabetes. Whether the associations are causal remains unclear. We aimed to examine the shape and strength of the associations and assessed the causal relevance of such associations in Chinese adults.Methods and resultsThe China Kadoorie Biobank enrolled 512,891 adults in China. Cox proportional hazard regression models was conducted to estimate hazard ratios (HRs) for the associations of resting heart rate with type 2 diabetes and total diabetes. Among 92,724 participants, 36 single-nucleotide polymorphisms (SNPs) related to resting heart rate were used to construct genetic risk score. We used Mendelian randomization analyses to make the causal inferences. During a median follow-up of 9 years, 7872 incident type 2 diabetes and 13,349 incident total diabetes were documented. After regression dilution bias adjustment, each 10 bpm higher heart rate was associated with about a 26% higher risk of type 2 diabetes (HR, 1.26 [95% CI, 1.23, 1.29]) and 23% higher risk of total diabetes (HR, 1.23 [95% CI, 1.20, 1.26]). Instrumental variable analyses showed participants at top quintile compared with those at bottom quintile had 30% higher risk for type 2 diabetes (HR, 1.30 [95% CI, 1.17, 1.43]), and 10% higher risk for total diabetes (HR, 1.10 [95% CI, 1.02, 1.20]).ConclusionsThis study provides evidence that resting heart rate is an important risk factor for diabetes risk. The results suggest that novel treatment approaches targeting reduction of high heart rate for incidence of diabetes may be worth further investigation.     

Validity,potential clinical utility and comparison of a consumer activity tracker and a research‐grade activity tracker in insomnia disorder II: Outside the laboratory

Accurate assessment of sleep can be fundamental for monitoring, managing and evaluating treatment outcomes within diseases. A proliferation of consumer activity trackers gives easy access to objective sleep. We evaluated the performance of a commercial device (Fitbit Alta HR) relative to a research‐grade actigraph (Actiwatch Spectrum Pro) in measuring sleep before and after a cognitive behavioural intervention in insomnia disorder. Twenty‐five individuals with DSM‐5 insomnia disorder (M = 50.6 ± 15.9 years) wore Fitbit and Actiwatch and completed a sleep diary during an in‐laboratory polysomnogram, and for 1 week preceding and following seven weekly sessions of cognitive‐behavioural intervention for insomnia. Device performance was compared for sleep outcomes (total sleep time, sleep latency, sleep efficiency and wake after sleep onset). The analyses assessed (a) agreement between devices across days and pre‐ to post‐treatment, and (b) whether pre‐ to post‐treatment changes in sleep assessed by devices correlated with clinical measures of change. Devices generally did not significantly differ from each other on sleep variable estimates, either night to night, in response to sleep manipulation (pre‐ to post‐treatment) or in response to changes in environment (in the laboratory versus at home). Change in sleep measures across time from each device showed some correlation with common clinical measures of change in insomnia, but not insomnia diagnosis as a categorical variable. Overall, the Fitbit provides similar estimates of sleep outside the laboratory to a research grade actigraph. Despite the similarity between Fitbit and Actiwatch performance, the use of consumer technology is still in its infancy and caution should be taken in its interpretation.     

Predictive modeling of schizophrenia from genomic data: Comparison of polygenic risk score with kernel support vector machines approach

A major controversy in psychiatric genetics is whether nonadditive genetic interaction effects contribute to the risk of highly polygenic disorders. We applied a support vector machines (SVMs) approach, which is capable of building linear and nonlinear models using kernel methods, to classify cases from controls in a large schizophrenia case–control sample of 11,853 subjects (5,554 cases and 6,299 controls) and compared its prediction accuracy with the polygenic risk score (PRS) approach. We also investigated whether SVMs are a suitable approach to detecting nonlinear genetic effects, that is, interactions. We found that PRS provided more accurate case/control classification than either linear or nonlinear SVMs, and give a tentative explanation why PRS outperforms both multivariate regression and linear kernel SVMs. In addition, we observe that nonlinear kernel SVMs showed higher classification accuracy than linear SVMs when a large number of SNPs are entered into the model. We conclude that SVMs are a potential tool for assessing the presence of interactions, prior to searching for them explicitly.     

Population‐based identity‐by‐descent mapping combined with exome sequencing to detect rare risk variants for schizophrenia

Genome‐wide association studies (GWASs) are highly effective at identifying common risk variants for schizophrenia. Rare risk variants are also important contributors to schizophrenia etiology but, with the exception of large copy number variants, are difficult to detect with GWAS. Exome and genome sequencing, which have accelerated the study of rare variants, are expensive so alternative methods are needed to aid detection of rare variants. Here we re‐analyze an Irish schizophrenia GWAS dataset (n = 3,473) by performing identity‐by‐descent (IBD) mapping followed by exome sequencing of individuals identified as sharing risk haplotypes to search for rare risk variants in coding regions. We identified 45 rare haplotypes (>1 cM) that were significantly more common in cases than controls. By exome sequencing 105 haplotype carriers, we investigated these haplotypes for functional coding variants that could be tested for association in independent GWAS samples. We identified one rare missense variant in PCNT but did not find statistical support for an association with schizophrenia in a replication analysis. However, IBD mapping can prioritize both individual samples and genomic regions for follow‐up analysis but genome rather than exome sequencing may be more effective at detecting risk variants on rare haplotypes.     

Growing from Our Roots: Strategies for Developing Culturally Grounded Health Promotion Interventions in American Indian,Alaska Native,and Native Hawaiian Communities

Given the paucity of empirically based health promotion interventions designed by and for American Indian, Alaska Native, and Native Hawaiian (i.e., Native) communities, researchers and partnering communities have had to rely on the adaptation of evidence-based interventions (EBIs) designed for non-Native populations, a decidedly sub-optimal approach. Native communities have called for development of Indigenous health promotion programs in which their cultural worldviews and protocols are prioritized in the design, development, testing, and implementation. There is limited information regarding how Native communities and scholars have successfully collaborated to design and implement culturally based prevention efforts “from the ground up.” Drawing on five diverse community-based Native health intervention studies, we describe strategies for designing and implementing culturally grounded models of health promotion developed in partnership with Native communities. Additionally, we highlight indigenist worldviews and protocols that undergird Native health interventions with an emphasis on the incorporation of (1) original instructions, (2) relational restoration, (3) narrative-[em]bodied transformation, and (4) indigenist community-based participatory research (ICBPR) processes. Finally, we demonstrate how culturally grounded interventions can improve population health when they prioritize local Indigenous knowledge and health-positive messages for individual to multi-level community interventions.