“…This study has been described previously (Baylin et al, 2003; Campos and Siles, 2000). Eligible cases were diagnosed with a first MI at one of the three hospitals in the study catchment area.…”
Objective
To examine whether total physical activity or activity patterns are associated with metabolic syndrome and its components.
Methods
Participants include 1,994 controls from a case-control study of non-fatal myocardial infarction in Costa Rica (1994–2004). Physical activity was assessed via self-administered questionnaire and patterns were identified using principal components analysis. Metabolic syndrome was assessed via blood samples and anthropometry measurements from in-home study visits. Prevalence ratios (PR) and 95% confidence intervals (CI) were calculated using log binomial regression. Adjusted least squares means of metabolic syndrome components were calculated by quintile of total activity and pattern scores.
Results
Four activity patterns were identified: rest/sleep, agricultural, light indoor activity, and manual labor. Total activity was not associated with metabolic syndrome. Metabolic syndrome prevalence was 20% lower in participants with the highest scores on the agricultural job pattern compared to those with the lowest (PR: 0.80, 95% CI: 0.68–0.94). Higher total activity was associated with lower triglycerides and lower HDL cholesterol. Higher scores on each pattern were inversely associated with metabolic syndrome components, particularly waist circumference and fasting blood glucose.
Conclusions
Patterns or types of physical activity may be more strongly associated with metabolic syndrome and its components than total activity levels.
“…This study has been described previously (Baylin et al, 2003; Campos and Siles, 2000). Eligible cases were diagnosed with a first MI at one of the three hospitals in the study catchment area.…”
Objective
To examine whether total physical activity or activity patterns are associated with metabolic syndrome and its components.
Methods
Participants include 1,994 controls from a case-control study of non-fatal myocardial infarction in Costa Rica (1994–2004). Physical activity was assessed via self-administered questionnaire and patterns were identified using principal components analysis. Metabolic syndrome was assessed via blood samples and anthropometry measurements from in-home study visits. Prevalence ratios (PR) and 95% confidence intervals (CI) were calculated using log binomial regression. Adjusted least squares means of metabolic syndrome components were calculated by quintile of total activity and pattern scores.
Results
Four activity patterns were identified: rest/sleep, agricultural, light indoor activity, and manual labor. Total activity was not associated with metabolic syndrome. Metabolic syndrome prevalence was 20% lower in participants with the highest scores on the agricultural job pattern compared to those with the lowest (PR: 0.80, 95% CI: 0.68–0.94). Higher total activity was associated with lower triglycerides and lower HDL cholesterol. Higher scores on each pattern were inversely associated with metabolic syndrome components, particularly waist circumference and fasting blood glucose.
Conclusions
Patterns or types of physical activity may be more strongly associated with metabolic syndrome and its components than total activity levels.
“…In other healthy, adult samples, 60–75% of adults nap at least one time in a 7-day week with average nap durations of about 70 minutes (Dinges, 1992; Pilcher, Michalowski, & Carrigan, 2001). Moreover, the relationship between napping and cardiovascular risk is quite controversial, with epidemiological studies reporting that frequent napping is associated with both increased (Jung, Song, Ancoli-Israel, & Barrett-Connor, 2013; Leng et al, 2014) and decreased (Campos & Siles, 2000) risk for coronary heart disease.…”
In healthy individuals, a reduction in cardiovascular output and a shift to parasympathetic/vagal dominant activity is observed across nocturnal sleep. This cardiac autonomic profile, often measured by heart rate variability (HRV), has been associated with significant benefits for the cardiovascular system. However, little is known about the autonomic profile during daytime sleep. Here we investigated the autonomic profile and the short-term reliability of HRV during daytime naps in 66 healthy young adults. Participants took an 80–120 min polysomnographically-recorded nap at 1:30 PM. Beat-by-beat RR interval values (RR), high (HF) and low frequency (LF) power, total power (TP), HF normalized units and the LF/HF ratio were obtained for 5 min during pre-sleep wakefulness and during nap sleep stages (N2, N3, REM). A subsample of 37 participants took 2 additional naps with two weeks between recordings. We observed lengthening of the RR, higher HF and HFnu and lower LF/HF during NREM, compared with REM and wake, and a marked reduction of LF and TP during N3. Short-term stability of RR and HF ranged across sleep stages between 0.52–0.76 and 0.52–0.80 respectively. Our results suggest that daytime napping in healthy young adults is associated with dynamic changes in the autonomic profile, similar to those seen during nocturnal sleep. Moreover, a reliable intra-individual measure of autonomic cardiac activity can be obtained by just a single daytime nap depending on specific parameters and recording purposes. Nap methodology may be a new and promising tool to explore sleep-dependent, autonomic fluctuations in healthy and at-risk populations.
“…Recently, a number of cross-sectional studies and prospective studies with follow-up periods of 2–10 years have concluded that daytime napping was associated with a higher risk of diabetes [7]–[9]. Evidence from the Guangzhou Burbank [10] and the Dongfeng–Tongji cohort of retired workers [6] also demonstrated that the duration of day napping was positively associated with an increased risk for type 2 diabetes among residents aged 45 years or older in China.…”
ContextBoth longer habitual day napping and Non-Alcoholic Fatty Liver Disease (NAFLD) are associated with diabetes and inflammation, but the association between day napping and NAFLD remains unexplored.ObjectiveTo investigate the association between the duration of habitual day napping and NAFLD in an elderly Chinese population and to gain insight into the role of inflammatory cytokines in this association.Design and SettingWe conducted a series of cross-sectional studies of the community population in Chongqing, China, from 2011 to 2012.ParticipantsAmong 6998 participants aged 40 to 75 years, 6438 eligible participants were included in the first study and analyzed to observe the association between day napping duration and NAFLD. In a separate study, 80 non-nappers and 90 nappers were selected to identify the role of inflammatory cytokines in this association. Logistic regression models were used to examine the odds ratios (ORs) of day nap duration with NAFLD.ResultsDay nappers had a significantly higher prevalence of NAFLD (P<0.001). Longer day napping duration was associated in a dose-dependent manner with NAFLD (P trend <0.001). After adjustment for potential confounders, the ORs were 1.67 (95% CI 1.13–2.46) for those reporting 0.5–1 h and 1.49 (95% CI 1.01–2.19) for those reporting >1 h of day napping compared with individuals who did not take day naps (all P<0.05). Longer-duration day nappers had higher levels of IL-6 and progranulin (PGRN) but lower levels of Secreted frizzled-related protein-5 (SFRP5, all P trend <0.001). After adjusting for IL-6, PGRN, and SFRP5, the association between day napping duration and NAFLD disappeared (all P>0.05).ConclusionLonger day napping duration is associated with a higher prevalence of NAFLD, and inflammatory cytokines may be an essential link between day napping and NAFLD.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.