Sodsri, Tulaton

Abstract Background Short sleep is linked to increased acute myocardial infarction (MI) risk.However, the association of weekend-to-weekday sleep patterns with MI in a nationally representative population remains unknown. The goal is to investigate how weekday and weekend sleep durations affect myocardial infarction (MI) in the general population, and if longer weekend sleep can offset insufficient weekday sleep's effects. Methods A cross-sectional study utilized data from the 2017-2020 NHANES to examine the relationship between sleep patterns and MI. Participants self-reported their average sleep durations on weekdays (1) and weekends (2), along with the calculated weekend-to-weekday sleep duration ratio (SDR) (3). Logistic regression models, adjusting for various factors, including demographics and comorbidities, were employed to analyze the association. Those with SDR > 1 had more weekend sleep, while an SDR = 1 indicated equal sleep durations. Results The study involved 9,205 adult participants, with a mean ± SD age of 68.8 ± 10.8 years and 30.7% being female. Among them, 4.65% (428 patients) had a history of MI. The mean ± SD of weekday and weekend sleep hours was 7.6 ± 1.7 and 8.3 ± 1.8 hours, respectively. For weekday sleep duration, after univariate analysis, individuals compared to those in Q1 were statistically significant 0.72 times less likely in Q2 and 1.4 times more likely in Q4 to experience MI . While MI was 0.76 times less likely to occur among participants in Q3, it was not statistically significant (95% CI Q2: 0.54, 0.96; Q4: 1.05, 1.76; Q3:0.57, 1.00). After adjusting, participants only in Q3 were statistically significant 0.71 times less likely to develop MI.(Q3: 95% CI 0.51, 0.99). For weekend sleep duration, the univariate analysis showed that individuals in Q2, Q3, and Q4 were significantly less likely to experience MI (OR = 0.63, 0.67, and 0.69; 95% CIs: 0.48-0.82, 0.51-0.87, 0.53-0.90, respectively) After adjusting, only participants in Q2 were statistically significant less likely to develop MI (OR = 0.71; 95% CI 0.52 - 0.96). For the weekend-to-weekday sleep duration ratio (SDR), after univariate analysis, individuals were statistically significant 0.54 times less likely, and 1.5 times more likely to experience MI in those whose SDR is more than 1, and those whose SDR equals 1, respectively (95% CI SDR > 1: 1.14, 1.98; SDR = 1: 0.39, 0.72). After adjusting, participants with SDR more than 1 were statistically significant 0.69 times less likely to develop MI. Conclusions Moderate sleep durations on weekdays (in Q3), weekends (in Q2), along with SDR more than 1, were negatively linked with MI in a general population. This implies that sleep durations on both weekends and weekdays have comparable associations with MI. Additionally, full recovery sleep on weekends following sleep-deprived weekdays, might reduce MI risk. Further studies are needed to explore impact of weekend and weekday sleep patterns on MI.Histogram and Margins plotTable of Odd ratios

Background: Although BP is associated with kidney outcomes, its clinical significance in living kidney donors (LKD) is unknown. We aim to examine the association between pre-donation BP and the risk of kidney function decline in LKD. Methods: A retrospective cohort study using OPTN/SRTR including adult LKD, who underwent donation between 6/1972 and 9/2022, was utilized to examine the association of quartile (Q) of pre-donation SBP and DBP with time-to-event of ≥35% decline in post-donation eGFR from pre-donation eGFR by multiple Cox regression. Results: Of 174,311 adult LKD, mean±SD age was 41±12 years and 60% were female. Mean pre-donation SBP and DBP were 122±13 and 74±9 mmHg, respectively. Mean pre-donation SBP in Q 1, 2, 3, and 4 were 106±6, 117±2, 126±3, and 140±9 mmHg, respectively and the corresponding DBP were 62±5, 71±2, 78±2, and 86±5 mmHg, respectively. The median (IQR) of pre-donation eGFR was 91 (75, 111) mL/min/1.73 m2. Median(IQR) eGFR at immediate post-donation, 6-, 12-, and 24-months post-donation were 50 (40, 62), 54 (44, 67), 55 (45, 68), and 57 (47, 70) mL/min/1.73 m2, respectively. Out of 38,780 LKD with post-donation eGFR data at 6, 12, or 24 months, 31,054 had the event of ≥35% decline in post-donation eGFR from pre-donation eGFR with a median time to follow-up of 6.77 months(IQR 5.93, 12.67). The incidence rate of the event was 0.08 person-months. Compared to patients with SBP in Q 1, those with SBP in Q 2, 3, and 4 had a significantly increased risk for the event(HRSBP Q2,3,4 1.05, 1.08, and 1.15; P <0.001 – 0.003) but only patients with DBP in Q 3 and 4 had a significantly increased risk for the event (HRSBP Q3,4 1.04 and 1.09). After adjusting for age, gender, race/ethnicity, U.S. citizenship, education level, history of pre-donation HTN, diabetes, pre-donation BMI, DBP, and urine protein:creatinine ratio, there was a J-shaped association between pre-donation SBP and post-donation eGFR decline and only patients in the Q 2 became significantly associated with lower the risk for the event (HR 0.87, 95%CI 0.76, 0.99; Fig1). The pre-donation DBP – post-donation eGFR association was similar to the SBP – eGFR association but there was no statistically significant in all Q (Fig2). There was no effect-measured modifier for all covariates. Conclusions: Pre-donation SBP in a normal range(113 - 120 mmHg) is associated with the lowest risk for a post-donation eGFR decline. BP control in the normal range should be achieved for potential LKD.

Background: Hypertension is well known for its association with cardiovascular events and arthritis. However, epidemiological data on the direct relationship between heart failure and arthritis in hypertensive patients is limited. This study examines the hypothesis that arthritis increases the risk of heart failure among patients with hypertension. Additionally, the effect of gender on these outcomes was assessed. Method: In our study, we analyzed data from 6,789 adults with hypertension aged 18 to 80 years, sourced from the National Health and Nutrition Examination Survey (NHANES) for the years 2017-2018. Arthritis was defined based on participants’ self-reported diagnosis by a doctor. We utilized logistic regression to explore the relationship between arthritis and the presence of heart failure across three different models. The first model included heart failure alone, the second model included age, gender, education, race, body mass index (BMI), smoking, and drinking, and the third model additionally included coronary artery disease and stroke. Result: The prevalence of heart failure among individuals with both arthritis and hypertension stands at 6.0%. Investigation into gender reveals a notable prevalence in males at 8.2% compared to 4.5% in females [Fig 1]. The analysis revealed a positive correlation between the two conditions across the entire population, with correlation coefficients of 2.38 (95% CI: 1.68-3.37), 1.61 (95% CI: 1.11-2.34), and 1.51 (95% CI: 1.01-2.25) for the three models, respectively. Specifically, in males, there was a notably increased association in the first and second models, with correlation coefficients of 2.77 (95% CI: 1.76-4.35) and 1.69 (95% CI: 1.05-2.74), respectively. Conversely, among females, a significant association was observed only in the first model, with a correlation coefficient of 2.24 (95% CI: 1.29-3.89) [Fig 2]. Conclusion: A robust positive correlation between arthritis and heart failure was observed in hypertensive U.S. patients. Gender was found to be a factor influencing this relationship, with males having a higher risk and prevalence of heart failure compared to the overall population.