Methodological challenges when estimating the effects of season and seasonal exposures on birth outcomes

Background Many previous studies have found seasonal patterns in birth outcomes, but with little agreement about which season poses the highest risk. Some of the heterogeneity between studies may be explained by a previously unknown bias. The bias occurs in retrospective cohorts which include all births occurring within a fixed start and end date, which means shorter pregnancies are missed at the start of the study, and longer pregnancies are missed at the end. Our objective was to show the potential size of this bias and how to avoid it. Methods To demonstrate the bias we simulated a retrospective birth cohort with no seasonal pattern in gestation and used a range of cohort end dates. As a real example, we used a cohort of 114,063 singleton births in Brisbane between 1 July 2005 and 30 June 2009 and examined the bias when estimating changes in gestation length associated with season (using month of conception) and a seasonal exposure (temperature). We used survival analyses with temperature as a time-dependent variable. Results We found strong artificial seasonal patterns in gestation length by month of conception, which depended on the end date of the study. The bias was avoided when the day and month of the start date was just before the day and month of the end date (regardless of year), so that the longer gestations at the start of the study were balanced by the shorter gestations at the end. After removing the fixed cohort bias there was a noticeable change in the effect of temperature on gestation length. The adjusted hazard ratios were flatter at the extremes of temperature but steeper between 15 and 25°C. Conclusions Studies using retrospective birth cohorts should account for the fixed cohort bias by removing selected births to get unbiased estimates of seasonal health effects

Sleep Disturbances and Glucose Metabolism in Older Adults: The Cardiovascular Health Study.

ObjectiveWe examined the associations of symptoms of sleep-disordered breathing (SDB), which was defined as loud snoring, stopping breathing for a while during sleep, and daytime sleepiness, and insomnia with glucose metabolism and incident type 2 diabetes in older adults.Research design and methodsBetween 1989 and 1993, the Cardiovascular Health Study recruited 5,888 participants ≥65 years of age from four U.S. communities. Participants reported SDB and insomnia symptoms yearly through 1989-1994. In 1989-1990, participants underwent an oral glucose tolerance test, from which insulin secretion and insulin sensitivity were estimated. Fasting glucose levels were measured in 1989-1990 and again in 1992-1993, 1994-1995, 1996-1997, and 1998-1999, and medication use was ascertained yearly. We determined the cross-sectional associations of sleep symptoms with fasting glucose levels, 2-h glucose levels, insulin sensitivity, and insulin secretion using generalized estimated equations and linear regression models. We determined the associations of updated and averaged sleep symptoms with incident diabetes in Cox proportional hazards models. We adjusted for sociodemographics, lifestyle factors, and medical history.ResultsObserved apnea, snoring, and daytime sleepiness were associated with higher fasting glucose levels, higher 2-h glucose levels, lower insulin sensitivity, and higher insulin secretion. The risk of the development of type 2 diabetes was positively associated with observed apnea (hazard ratio [HR] 1.84 [95% CI 1.19-2.86]), snoring (HR 1.27 [95% CI 0.95-1.71]), and daytime sleepiness (HR 1.54 [95% CI 1.13-2.12]). In contrast, we did not find consistent associations between insomnia symptoms and glucose metabolism or incident type 2 diabetes.ConclusionsEasily collected symptoms of SDB are strongly associated with insulin resistance and the incidence of type 2 diabetes in older adults. Monitoring glucose metabolism in such patients may prove useful in identifying candidates for lifestyle or pharmacological therapy. Further studies are needed to determine whether insomnia symptoms affect the risk of diabetes in younger adults

Is having asthma associated with an increased risk of dying from cardiovascular disease?:A prospective cohort study of 446 346 Taiwanese adults

Objectives A significant proportion of cardiovascular disease (CVD) cannot be explained by well-known risk factors such as high cholesterol, hypertension and diabetes. One potential novel risk factor for CVD is asthma. We aimed to investigate the association between asthma and mortality due to CVD. Design Prospective cohort study. Setting A large health check-up programme from 1994 to 2011 in Taipei, Taiwan. Participants 446 346 Taiwanese adults. Each participant answered questions regarding asthma history (yes/no) and current daily use of asthma medications (yes/no). Active asthma was defined as those using current daily medications for asthma. Outcomes The participants were followed for mortality from CVD, coronary heart disease (CHD) and stroke obtained through linkage to the cause-of-death register until 31 December 2011. Results We found an increased risk of dying from CVD in individuals with active asthma (adjusted HR (aHR) 1.32, 95% CI 1.08 to 1.62). The risk of death from CHD or stroke was increased in a similar manner (aHR 1.16, 95% CI 0.78 to 1.73 and aHR 1.23, 95% CI 0.86 to 1.74, respectively) although the HR estimates were less precise than that of CVD. For deaths from CVD, CHD and stroke, we found stronger associations with active asthma than non-active asthma, and for CVD and stroke stronger associations in men than women. Conclusion Our study suggests that asthma, particularly active asthma, may be associated with adverse cardiovascular consequences

The influence of ambient temperature on birth outcomes in Brisbane, Australia

Lately, there has been increasing interest in the association between temperature and adverse birth outcomes including preterm birth (PTB) and stillbirth. PTB is a major predictor of many diseases later in life, and stillbirth is a devastating event for parents and families. The aim of this study was to assess the seasonal pattern of adverse birth outcomes, and to examine possible associations of maternal exposure to temperature with PTB and stillbirth. We also aimed to identify if there were any periods of the pregnancy where exposure to temperature was particularly harmful. A retrospective cohort study design was used and we retrieved individual birth records from the Queensland Health Perinatal Data Collection Unit for all singleton births (excluding twins and triplets) delivered in Brisbane between 1 July 2005 and 30 June 2009. We obtained weather data (including hourly relative humidity, minimum and maximum temperature) and air-pollution data (including PM10, SO2 and O3) from the Queensland Department of Environment and Resource Management. We used survival analyses with the time-dependent variables of temperature, humidity and air pollution, and the competing risks of stillbirth and live birth. To assess the monthly pattern of the birth outcomes, we fitted month of pregnancy as a time-dependent variable. We examined the seasonal pattern of the birth outcomes and the relationship between exposure to high or low temperatures and birth outcomes over the four lag weeks before birth. We further stratified by categorisation of PTB: extreme PTB (< 28 weeks of gestation), PTB (28–36 weeks of gestation), and term birth (≥ 37 weeks of gestation). Lastly, we examined the effect of temperature variation in each week of the pregnancy on birth outcomes. There was a bimodal seasonal pattern in gestation length. After adjusting for temperature, the seasonal pattern changed from bimodal, to only one peak in winter. The risk of stillbirth was statistically significant lower in March compared with January. After adjusting for temperature, the March trough was still statistically significant and there was a peak in risk (not statistically significant) in winter. There was an acute effect of temperature on gestational age and stillbirth with a shortened gestation for increasing temperature from 15 °C to 25 °C over the last four weeks before birth. For stillbirth, we found an increasing risk with increasing temperatures from 12 °C to approximately 20 °C, and no change in risk at temperatures above 20 °C. Certain periods of the pregnancy were more vulnerable to temperature variation. The risk of PTB (28–36 weeks of gestation) increased as temperatures increased above 21 °C. For stillbirth, the fetus was most vulnerable at less than 28 weeks of gestation, but there were also effects in 28–36 weeks of gestation. For fetuses of more than 37 weeks of gestation, increasing temperatures did not increase the risk of stillbirth. We did not find any adverse affects of cold temperature on birth outcomes in this cohort. My findings contribute to knowledge of the relationship between temperature and birth outcomes. In the context of climate change, this is particularly important. The results may have implications for public health policy and planning, as they indicate that pregnant women would decrease their risk of adverse birth outcomes by avoiding exposure to high temperatures and seeking cool environments during hot days

Health-promoting behaviors in older adulthood and intrinsic capacity 10 years later: the HUNT study

Abstract Background With the global population growing older, there is a need for more knowledge of how to improve and/or maintain functional capacities to promote healthy ageing. In this study we aimed to assess the effect of several known health-promoting behaviors in old age with intrinsic capacity ten years later. Methods This was a prospective cohort study looking at participants that were ≥ 65 years at the time of the third wave of the Trøndelag Health Study (HUNT3, 2006–2008) who also took part in the 70 + sub-study of the fourth wave (HUNT4 70+, 2017–2019). Self-reported behavior data from short questionnaires, including diet and physical activity, were collected in HUNT3, and data on the five domains of intrinsic capacity defined by the World Health Organization were collected in HUNT4 70+. A composite index was created for both healthy life and intrinsic capacity, awarding points for how well participants adhered to guidelines for healthy living and their level of functional impairment, respectively. Ordinal logistic regression was used to assess the relationship between health-promoting behaviors and intrinsic capacity. Results Of 12,361 participants in HUNT3 ≥ 65 years, 4699 (56.5% women) also participated in HUNT4 70+. On the health-promoting behaviors, lowest adherence to healthy living guidelines were seen for fruit and vegetables intake (47.2%), milk intake (46.7%) and physical activity (31.1%). On intrinsic capacity domains, highest impairment was seen in the domains of locomotion (29.7%), hearing (11.1%) and vitality (8.3%). A higher adherence to guidelines for healthy living was associated with higher intrinsic capacity 10 years later. A one-point increase in the healthy life index was associated with a 1.15 (95% confidence interval 1.10–1.21) times increased odds of being in a higher intrinsic capacity category. Conclusion Health-promoting behaviors in old age are associated with better intrinsic capacity ten years later. In clinical settings assessment of health-promoting behaviors could potentially be done using short questionnaires

Additional file 1 of Health-promoting behaviors in older adulthood and intrinsic capacity 10 years later: the HUNT study

Supplementary Material

Assessing short-term risk of ischemic stroke in relation to all prescribed medications

We examined the short-term risk of stroke associated with drugs prescribed in Norway or Sweden in a comprehensive, hypothesis-free manner using comprehensive nation-wide data. We identified 27,680 and 92,561 cases with a first ischemic stroke via the patient- and the cause-of-death registers in Norway (2004–2014) and Sweden (2005–2014), respectively, and linked these data to prescription databases. A case-crossover design was used that compares the drugs dispensed within 1 to 14 days before the date of ischemic stroke occurrence with those dispensed 29 to 42 days before the index event. A Bolasso approach, a version of the Lasso regression algorithm, was used to select drugs that acutely either increase or decrease the apparent risk of ischemic stroke. Application of the Bolasso regression algorithm selected 19 drugs which were associated with increased risk for ischemic stroke and 11 drugs with decreased risk in both countries. Morphine in combination with antispasmodics was associated with a particularly high risk of stroke (odds ratio 7.09, 95% confidence intervals 4.81–10.47). Several potentially intriguing associations, both within and across pharmacological classes, merit further investigation in focused, follow-up studies.publishedVersio

Additional file 1 of Investigating the causal interplay between sleep traits and risk of acute myocardial infarction: a Mendelian randomization study

Additional file 1. Information on covariates; Supplementary figures. Figure S1. Flow chart of the participant selection process. Figure S2. 2×2 factorial Mendelian randomization Cox regression analysis assessing the joint effects of two sleep traits with risk of incident acute myocardial infarction in HUNT2 using weighted and unweighted genetic risk scores for sleep traits. Figure S3. Association of insomnia SNPs from Jansen et al., 2019 and acute myocardial infarction (AMI) within a) UK Biobank b) HUNT2. IVW, MR-Egger, simple median and weighted median estimates are indicated by the red, green, blue and purple lines respectively. Figure S4. Association of 24-hour sleep duration SNPs from Dashti et al., 2019 and acute myocardial infarction (AMI) within a) UK Biobank b) HUNT2. IVW, MR-Egger, simple median and weighted median estimates are indicated by the red, green, blue and purple lines respectively. Figure S5. Association of short sleep duration SNPs from Dashti et al., 2019 and acute myocardial infarction (AMI) within a) UK Biobank b) HUNT2. IVW, MR-Egger, simple median and weighted median estimates are indicated by the red, green, blue and purple lines respectively. Figure S6. Association of long sleep duration SNPs from Dashti et al., 2019 and acute myocardial infarction (AMI) within a) UK Biobank b) HUNT2. IVW, MR-Egger, simple median and weighted median estimates are indicated by the red, green, blue and purple lines respectively. Figure S7. Association of chronotype (morning preference) SNPs from Jones et al., 2019 and acute myocardial infarction (AMI) within UK Biobank. IVW, MR-Egger, simple median and weighted median estimates are indicated by the red, green, blue and purple lines respectively. Figure S8. Association of insomnia SNPs from Lane et al., 2019 and acute myocardial infarction (AMI) within a) UK Biobank b) HUNT2. IVW, MR-Egger, simple median and weighted median estimates are indicated by the red, green, blue and purple lines respectively. Figure S9. Continuous factorial Mendelian randomization analysis using genetic risk score as quantitative traits with their product term assessing the joint effects of two sleep traits with risk of incident acute myocardial infarction in UK Biobank and HUNT2. Figure S10. One-sample Mendelian randomization Cox regression analysis for risk of incident acute myocardial infarction associated with sleep traits in UK Biobank and HUNT2 after excluding participants who reported self-reported use of sleep medication. Figure S11. 2×2 factorial Mendelian randomization Cox regression analysis assessing the joint effects of two sleep traits with risk of incident acute myocardial infarction in UK Biobank and HUNT2 after excluding participants who reported self-reported use of sleep medication; and Supplementary tables. Table S1. Detailed summary of Mendelian randomization (MR) studies previously conducted on sleep traits and risk of coronary artery disease (CAD) or acute myocardial infarction (AMI). Table S2. Summary of genetic instruments showing their strength applying to UK Biobank and HUNT2. Table S3. Baseline characteristics of participants across groups categorized by dichotomizing to the median genetic risk scores for insomnia symptoms and short sleep in UK Biobank. Table S4. Baseline characteristics of participants across groups categorized by dichotomizing to the median genetic risk scores for insomnia symptoms and short sleep in HUNT2. Table S5. Baseline characteristics of participants across groups categorized by dichotomizing to the median genetic risk scores for insomnia symptoms and long sleep in UK Biobank. Table S6. Baseline characteristics of participants across groups categorized by dichotomizing to the median genetic risk scores for insomnia symptoms and long sleep in HUNT2. Table S7. Baseline characteristics of participants across groups categorized by dichotomizing to the median genetic risk scores for insomnia symptoms and chronotype (morning preference) in UK Biobank. Table S8. Baseline characteristics of participants across groups categorized by dichotomizing to the median genetic risk scores for short sleep and chronotype (morning preference) in UK Biobank. Table S9. Baseline characteristics of participants across groups categorized by dichotomizing to the median genetic risk scores for long sleep and chronotype (morning preference) in UK Biobank. Table S10. Statistical test of the proportional hazard assumption for one-sample Mendelian randomization (MR) Cox regression models. Table S11. Statistical test of the proportional hazard assumption for 2×2 factorial Mendelian randomization (MR) Cox regression models. Table S12. One-sample Mendelian randomization Cox regression analysis for risk of incident acute myocardial infarction associated with sleep traits in HUNT2 using weighted and unweighted genetic risk scores for sleep traits. Table S13. Associations between genetic risk scores and potential confounders in UK Biobank. Table S14. Associations between genetic risk scores and potential confounders in HUNT2. Table S15. One-sample Mendelian randomization analysis for risk of incident acute myocardial infarction associated with sleep traits with and without adjustment for potential confounders in UK Biobank and HUNT2. Table S16. Sensitivity analysis for risk of incident acute myocardial infarction associated with sleep traits in UK Biobank. Table S17. Sensitivity analysis for risk of incident acute myocardial infarction associated with sleep traits in HUNT2. Table S18. One-sample Mendelian randomization Cox regression analysis for risk of incident acute myocardial infarction associated with insomnia symptoms using instruments from Lane et al., 2019 in UK Biobank and HUNT2. Table S19. Sensitivity analysis for risk of incident acute myocardial infarction associated with insomnia symptoms and chronotype in UK Biobank using genetic variants genome-wide significant in 23andMe. Table S20. Sensitivity analysis for risk of incident acute myocardial infarction associated with insomnia symptoms in HUNT2 using genetic variants genome-wide significant in 23andMe. Table S21. List of medications used to define the sleep medication covariate in UK Biobank