Associations of objectively measured sleep characteristics and incident hypertension among police officers: The role of obesity

Claudia, C.; Gu, Ja K.; Bhandari, Ruchi; Charles, Luenda E.; Fekedulegn, Desta; Andrew, Michael E.

doi:10.1111/jsr.12988

Cited by 13 publications

(11 citation statements)

References 38 publications

(52 reference statements)

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“…[ 39 ] A study with a 7-year follow-up reported that each 10-minute increase in sleep latency was associated with an 89% increase in hypertension risk (95% CI: 1.12–3.20). [ 31 ] Another study also found that a longer sleep latency in women with hypertension. [ 40 ]…”

Section: Discussionmentioning

confidence: 98%

Association between night sleep latency and hypertension: A cross-sectional study

et al. 2022

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Sleep disorders have been shown to increase the risk of hypertension, while the relationship between night sleep latency and hypertension is less well-known. We aimed to investigate the association between night sleep latency and hypertension, as well as related sleep factors by gender in the Chinese population. We conducted a cross-sectional study of the relationship between night sleep latency and hypertension. The sample size included 619 consecutive hospitalized patients (M/F: 302/317, 64.01 ± 12.27 years). T test, Chi-square test, and ANOVA were performed to analyze baseline data and intergroup comparisons. Spearman correlation analysis was performed to find the interrelationships. Multivariate logistic regression analysis was performed to adjust for covariables. The findings showed hypertension patients had longer night sleep latency ( P < .001). After adjusting for confounding factors, night sleep latency was positively correlated with hypertension in both men and women (odds ratio: 1.065, 95% confidence interval: 1.044–1.087). Spearman correlation analysis suggested that night sleep latency was positively correlated with systolic blood pressure ( r = 0.186, P < .001), diastolic blood pressure ( r = 0.136, P < .001), sleep initiation time ( r = 0.091, P = .023), and global Pittsburg Sleep Quality Index score ( r = 0.371, P < .001), was negatively correlated with sleep duration ( r = −0.186, P < .001), sleep time on weekdays ( r = −0.183, P < .001), and sleep time on weekends ( r = −0.179, P < .001). Longer night sleep latency was associated with an increased risk of hypertension in men and women, which might involve the pathological progression of hypertension along with other sleep factors.

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Section: Discussionmentioning

confidence: 98%

Association between night sleep latency and hypertension: A cross-sectional study

et al. 2022

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“…Blood pressure measurements for all participants were taken during a single visit. Systolic blood pressure (SBP) and diastolic blood pressure (DBP) were measured at 2-min intervals, and the average of the three measurements was calculated consecutively ( 27 ). Anthropometric variables, including height (in centimeters), weight (in kilograms), waist circumference (in centimeters), and hip circumference (in centimeters) measurements of all participants were measured using standardized techniques and equipment by two trained interviewers; body mass index (BMI) was calculated by dividing weight in kilograms by height in meters squared (kg/m2) ( 28 ).…”

Section: Methodsmentioning

confidence: 99%

A risk prediction model based on machine learning for early cognitive impairment in hypertension: Development and validation study

Zhong

Jiang

et al. 2023

Front. Public Health

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BackgroundClinical practice guidelines recommend early identification of cognitive impairment in individuals with hypertension with the help of risk prediction tools based on risk factors.ObjectiveThe aim of this study was to develop a superior machine learning model based on easily collected variables to predict the risk of early cognitive impairment in hypertensive individuals, which could be used to optimize early cognitive impairment risk assessment strategies.MethodsFor this cross-sectional study, 733 patients with hypertension (aged 30–85, 48.98% male) enrolled in multi-center hospitals in China were divided into a training group (70%) and a validation group (30%). After least absolute shrinkage and selection operator (LASSO) regression analysis with 5-fold cross-validation determined the modeling variables, three machine learning classifiers, logistic regression (LR), XGBoost (XGB), and gaussian naive bayes (GNB), were developed. The area under the ROC curve (AUC), accuracy, sensitivity, specificity, and F1 score were used to evaluate the model performance. Shape Additive explanation (SHAP) analysis was performed to rank feature importance. Further decision curve analysis (DCA) assessed the clinical performance of the established model and visualized it by nomogram.ResultsHip circumference, age, education levels, and physical activity were considered significant predictors of early cognitive impairment in hypertension. The AUC (0.88), F1 score (0.59), accuracy (0.81), sensitivity (0.84), and specificity (0.80) of the XGB model were superior to LR and GNB classifiers.ConclusionThe XGB model based on hip circumference, age, educational level, and physical activity has superior predictive performance and it shows promise in predicting the risk of cognitive impairment in hypertensive clinical settings.

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“…A fisiopatologia da insônia pode ser explicada por alguns autores pelo "modelo hiperexcitação", caracterizada por um estado instável, com hipoativação do núcleo pré-óptico ventrolateral (VLPO), o que impede a ativação eficiente do sono pela via GABAérgica, e hiperativação do sistema ativador reticular ascendente (SARA), responsável pela excitação da via orexinérgica/hipocretinérgica. Apesar da coerente descrição, essa teoria é baseada em evidências empíricas e defendida por muitos autores (Young, Da;2020).…”

Section: Introductionunclassified

Diferentes relações entre hipertensão e insônia: uma revisão integrativa

Toledo

Andrade

Almeida

et al. 2022

RSD

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Introdução: A insônia e os distúrbios de sono são condições que causam danos notáveis aos indivíduos. Tais danos incluem, possivelmente, um aumento da pressão arterial média, o que pode acarretar Hipertensão Arterial Sistêmica (HAS). O objetivo desta revisão é identificar como algumas variáveis qualitativas e quantitativas do sono podem alterar a pressão arterial, sobretudo, de pessoas com insônia. Metodologia: Trata-se de uma revisão integrativa de literatura, que inclui estudos com diferentes grupos populacionais ao longo do tempo. As bases de dados utilizadas para a coleta dos artigos foram PubMed, MEDLINE e LILACS, sendo que apenas trabalhos publicados entre 2002 e 2022 foram considerados. Resultados: 12 artigos analisados (75%) corroboram a influência da insônia sobre a pressão arterial. Nesse sentido, o sono curto, a irregularidade do sono, a dificuldade de iniciar e manter o sono, a baixa proporção do sono REM em relação ao sono NREM e alterações do sistema nervoso autônomo no ciclo sono-vigília são fatores associados à insônia que se mostraram como potenciais promotores da HAS. Conclusão: Embora outras variáveis fisiológicas e sociais impactem sobre a pressão arterial, é necessário, também, observar a influência do sono sobre a HAS. Isso porque, da totalidade dos artigos incluídos nesta revisão, apenas 25% não garantem haver associação entre insônia e hipertensão. A partir desses dados, é notável que aspectos quantitativos e qualitativos do sono devem ser foco de medidas de controle da hipertensão, doença crônica que se apresenta como fator de risco para uma série de distúrbios e eventos cardiovasculares.

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Associations of objectively measured sleep characteristics and incident hypertension among police officers: The role of obesity

Cited by 13 publications

References 38 publications

Association between night sleep latency and hypertension: A cross-sectional study

Association between night sleep latency and hypertension: A cross-sectional study

A risk prediction model based on machine learning for early cognitive impairment in hypertension: Development and validation study

Diferentes relações entre hipertensão e insônia: uma revisão integrativa

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