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Table 1 shows the characteristics of the study participants at baseline. The mean (SD) SBP and DBP was (123.57 ± 19.77) mmHg and (78.86 ± 12.44) mmHg, respectively. At baseline, the mean average age and BMI was (46.37 ± 12.77) years and (23.55 ± 3.25) kg/m2, respectively. Overall, 60.9% of the participants were men, 86.3% were married, 48.4% received medium-level education, and 58.8% were blue-collar workers. Moreover, nearly 36.6% were current smokers, and 19.0% were current drinkers. Individuals who reported physician-diagnosed CVDs, hypertension, and diabetes accounted for 19.7%, 14.7%, and 4.0% of all participants, respectively.
Variables Baseline, Mean ± SD
or n (%)Systolic blood pressure (mmHg) 123.57 ± 19.77 Diastolic blood pressure (mmHg) 78.86 ± 12.44 Age (years) 46.37 ± 12.77 < 40 9,310 (28.5) 40–60 17,276 (52.8) ≥ 60 6,124 (18.7) Male, n (%) 19,916 (60.9) Married, n (%) 28,222 (86.3) Education level Low (Junior middle school or less) 12,393 (37.9) Medium (Senior higher school or equivalent ) 15,844 (48.4) High (College degree and more) 4,473 (13.7) Occupation Blue-collar worker 19,241 (58.8) Managerial staff 3,457 (10.6) Logistics staff 380 (1.2) Technical staff 1,338 (4.1) Retirees 8,294 (25.4) Body mass index (kg/m2) 23.55 ± 3.25 < 24 18,812 (57.5) 24–28 10,986 (33.6) ≥ 28 2,912 (8.9) Smoking Non-smoker 18,022 (55.1) Current smoker 11,968 (36.6) Former smoker 2,720 (8.3) Drinking Non-drinker 25,210 (77.1) Current drinker 6,215 (19.0) Former drinker 1,285 (3.9) Cardiovascular disease 6,442 (19.7) Hypertension 4,798 (14.7) Diabetes 1,295 (4.0) Table 1. General characteristics of the study participants at baseline
Table 2 shows the summary statistics of the meteorological and air pollution variables during the study period. The average temperature was 9.81 ℃, with a range of −18.7 ℃ to 29.6 ℃. The mean relative humidity, wind speed, and atmospheric pressure was 39.57%, 1.75 m/s, and 848.70 hpa, respectively. The average concentrations of PM10, SO2, and NO2 were 100.85, 55.71, and 22.39 μg/m3.
Variables Mean SD Min P25 P50 P75 Max Meteorological measures Average temperature (°C) 9.81 11.05 −18.70 0.50 10.95 19.33 29.60 Relative humidity (%) 39.57 14.43 2.00 26.00 38.00 50.00 98.00 Atmospheric pressure (hpa) 848.70 5.40 833.80 844.80 848.50 852.60 865.10 Mean wind speed (m/s) 1.75 0.80 0.30 1.20 1.50 2.10 5.50 Air pollutant concentrations PM10 (μg/m3) 100.85 93.92 12.42 58.37 79.65 111.43 1180.67 SO2 (μg/m3) 55.71 37.63 2.00 32.33 48.67 69.67 329.67 NO2 (μg/m3) 22.39 8.36 3.35 16.01 22.67 27.58 62.00 Note. SD, standard deviation: Px, xth percentiles; Min, minimum; Max, maximum. Table 2. Summary statistics of daily average meteorological factors and air pollution concentrations in Jinchang (2011–2015)
The Spearman’s correlations between meteorological factors and air pollutants are shown in Supplementary Table S1, available in www.besjournal.com. The average temperature had a significant negative correlation with three air pollutants and relative humidity and atmospheric pressure and was positively correlated with wind.
Variables PM10 SO2 NO2 Temperature Relative humidity Atmospheric pressure Average temperature −0.338* −0.300* −0.217* Relative humidity −0.227* −0.170* −0.064* −0.166* Atmospheric pressure 0.110* 0.056* 0.077* −0.623* 0.138* Wind 0.002 −0.154* −0.238* 0.267* −0.237* −0.071* Note. *P < 0.05 Table S1. Spearman’s correlations between daily average meteorological factors and air pollutant concentrations in Jinchang (2011–2015)
Table 3 shows the seasonal variations of BP and outdoor temperature. Both SBP and DBP differed significantly across the four seasons at baseline and follow-up. The difference in average SBP between winter and summer was 3.5 mmHg, and the difference in average DBP was 2.75 mmHg in the two-repeated measurement.
Item Spring Summer Autumn Winter P value Baseline No. 8,779 7,372 9,139 7,420 Mean temperature (°C) 12.11 ± 6.51 22.52 ± 3.22 8.76 ± 6.84 −5.41 ± 4.30 < 0.001 SBP (mmHg) 125.05 ± 20.31 120.55 ± 18.72 123.30 ± 19.54 125.15 ± 20.26 < 0.001 DBP (mmHg) 79.14 ± 12.59 76.81 ± 12.01 79.00 ± 12.20 80.37 ± 12.72 < 0.001 Follow-up No. 8,066 8,089 9,237 7,318 Mean temperature (°C) 12.23 ± 6.46 22.40 ± 3.18 8.64 ± 7.07 −4.37 ± 4.00 < 0.001 SBP (mmHg) 125.52 ± 19.63 121.80 ± 20.22 124.75 ± 20.79 124.24 ± 18.24 < 0.001 DBP (mmHg) 78.55 ± 12.43 76.66 ± 12.08 78.43 ± 12.36 78.59 ± 12.01 < 0.001 Total SBP (mmHg) 125.27 ± 19.99 121.20 ± 19.53 124.03 ± 20.19 124.70 ± 19.18 < 0.001 DBP (mmHg) 78.86 ± 12.52 76.74 ± 12.05 78.72 ± 12.28 79.49 ± 12.40 < 0.001 Note. SBP, systolic blood pressure; DBP, diastolic blood pressure. Table 3. Average temperature and BP by season
Figure 1 shows the monthly variation of BP and the average temperature. The monthly average BP was the lowest in summer and at an elevated level in spring and winter. Generally, the monthly average temperature was negatively correlated with BP.
Figure 1. Monthly variation of SBP (A), DBP (B) and the average temperature. SBP, systolic blood pressure; DBP, diastolic blood pressure.
Figure 2A shows the estimated effects of the average temperature on BP on different lag days from the mixed-effect models. Significantly inverse associations between temperature and BP were found on all lag days. The largest effect size was found for lag 06 day for SBP and lag 04 day for DBP. For a 1 °C decrease in the average temperature on lag 06 day, SBP increased by 0.28 mmHg (95% CI: 0.27–0.30), while for a 1 °C decrease in the average temperature on lag 04 day, DBP increased by 0.16 mmHg (95% CI: 0.15–0.17).
Figure 2. Changes (95% confidence interval) in BP associated with a 1 °C decrease in the average temperature on the present day and lag days in (A) full year and (B) four seasons. SBP, systolic blood pressure; DBP, diastolic blood pressure. In full year, models control for age, gender, education, marriage, occupation, smoking, drinking, BMI, atmospheric pressure, relative humidity, wind, SO2, NO2, PM10, CVDs, diabetes and season. In four seasons, models did not control for season.
Figure 2B shows the estimated effects of the average temperature on BP stratified by season. The four seasons were a significant effect modifier on the relationship between BP and average temperature. Significant associations between temperature and BP were found in spring, summer, and fall, with the largest effect size in summer, but no significant association was found in winter. In summer, for a 1 ℃ decrease in temperature on lag 06 day, SBP increased by 0.48 mmHg (95% CI: 0.35–0.62) and DBP increased by 0.35 mmHg (95% CI: 0.26–0.45). In general, the delayed effects of temperature on BP on cumulate lag days were stronger than the effects on single lag days.
Figure 3 shows the exposure-response relationships between BP and the average temperature in the full year and three seasons, after controlling for individual characteristics, meteorological factors, and air pollutants and demonstrates that the relative risk of BP increased as the daily average temperature decreased in the study population.
Figure 3. Association between daily average temperature and BP by season. SBP, systolic blood pressure; DBP, diastolic blood pressure. Lag 06 day was used for SBP and Lag 04 day was used for DBP. In full year, models control for age, gender, education, marriage, occupation, smoking, drinking, BMI, atmospheric pressure, relative humidity, wind, SO2, NO2, PM10, CVDs, diabetes and season. In the three seasons, models did not control for season.
Table 4 shows the effects of a decrease in temperature on BP stratified by different individual characteristics. Significant adverse relationships were observed between outdoor temperature and BP in all subgroups. All individual characteristics were significant effect modifiers on the relationship between BP and outdoor temperature. The effects of outdoor temperature on SBP and DBP were significantly stronger for people who were older, male, overweight or obese, smokers, drinkers, or had chronic conditions (including CVDs and diabetes), compared to their counterparts.
Variables SBPa DBPb β 95% CI β 95% CI Age (years) < 40 0.23 0.21–0.26 0.13 0.12–0.15 40–60 0.29 0.27–0.31 0.16 0.15–0.18 ≥ 60 0.39 0.34–0.43 0.18 0.15–0.21 Interaction term P < 0.001 P < 0.001 Gender Female 0.20 0.18–0.23 0.08 0.07–0.10 Male 0.33 0.31–0.35 0.20 0.19–0.21 Interaction term P < 0.001 P < 0.001 Body mass index (kg/m2) < 24 0.26 0.24–0.27 0.14 0.12–0.15 24–28 0.31 0.29–0.34 0.18 0.16–0.20 ≥ 28 0.32 0.27–0.37 0.19 0.15–0.23 Interaction term P < 0.001 P < 0.001 Smoking Non-smoker 0.24 0.22−0.26 0.12 0.10–0.13 Current smoker 0.32 0.30−0.34 0.21 0.19–0.22 Former smoker 0.37 0.32−0.42 0.18 0.15, 0.22 Interaction term P < 0.001 P < 0.001 Drinking Non-drinker 0.27 0.25–0.29 0.14 0.13–0.16 Current drinker 0.29 0.26–0.33 0.18 0.16–0.20 Former drinker 0.36 0.27−0.44 0.18 0.12–0.24 Interaction term P < 0.001 P < 0.001 Cardiovascular disease No 0.26 0.25–0.38 0.15 0.14–0.16 Yes 0.36 0.27−0.44 0.18 0.12–0.24 Interaction term P < 0.001 P < 0.001 Diabetes No 0.28 0.26–0.29 0.16 0.15–0.17 Yes 0.42 0.33–0.50 0.17 0.11–0.24 Interaction term P < 0.001 P < 0.05 Note. SBP, systolic blood pressure; DBP, diastolic blood pressure; CI, confidence interval; BMI, body mass index. All models controlled for age, gender, education, marriage, occupation, BMI, smoking, drinking, atmospheric pressure, relative humidity, wind, SO2, NO2, PM10, CVDs, diabetes, and season. a, Lag 06 day was used for SBP. b, Lag 04 day was used for DBP. Table 4. Changes (95% confidence interval) in BP associated with a 1 °C decrease in the average temperature stratified by different levels of individual characteristics
Table 5 shows the associations between outdoor temperature and BP before and after adjusting for covariates. The associations of outdoor temperature with SBP and DBP were consistently significant before and after adjusting for the covariates. The effect sizes between temperature and BP increased after adjusting for individual characteristics, season, meteorological factors, and air pollutants.
Models SBPa DBPb β 95% CI β 95% CI T 0.21 0.20–0.22 0.12 0.11−0.13 T + Individual characteristics 0.26 0.25–0.27 0.15 0.14−0.16 T + Individual characteristics + Season 0.28 0.26–0.29 0.15 0.14−0.16 T + Individual characteristics + Season+ Meteorological factors 0.28 0.26–0.29 0.15 0.14−0.16 T + Individual characteristics +Season+ Meteorological factors + Air pollutants 0.28 0.27–0.30 0.16 0.15−0.17 Note. SBP, systolic blood pressure; DBP, diastolic blood pressure; CI, confidence interval. a, Lag 06 day was used for SBP. b, Lag 04 day was used for DBP. T, mean temperature. Individual characteristics, including age, gender, education level, marital status, occupation status, BMI, smoking, drinking, CVDs, and diabetes. Meteorological factors, including mean relative humidity, wind speed, and atmospheric pressure. Air pollutants, including PM10, SO2, and NO2. Table 5. Changes (95% confidence interval) in BP associated with a 1 °C decrease in the average temperature before and after adjustment for covariates
Table 6 shows the association between average temperature and BP in hypertensive patients and non-hypertensive individuals. In non-hypertensive individuals, the effect of temperature on BP decreases after adjusting for the variable of detected hypertension. Similarly, the effect also declines after adjusting the variable of drug use in hypertensive patients. In general, the effect size of temperature on BP is higher in hypertensive patients than in non-hypertensive individuals.
Item SBP DBP Lag days β 95% CI Lag days β 95% CI Non-hypertensivea 04 0.26 0.25−0.28 04 0.15 0.14−0.16 Non-hypertensiveb 0.19 0.17−0.20 0.09 0.08−0.10 Hypertensionc 03 0.38 0.34−0.43 04 0.19 0.16−0.22 Hypertensiond 0.20 0.16−0.23 0.07 0.04−0.10 Note. SBP, systolic blood pressure; DBP, diastolic blood pressure; CI, confidence interval. a, Models controlling for age, gender, education, marriage, occupation, smoking, drinking, BMI, atmospheric pressure, relative humidity, wind, SO2, NO2, PM10, CVDs, diabetes, and season. b, a plus detected hypertension. c, Models controlling for age, gender, education, marriage, occupation, smoking, drinking, BMI, atmospheric pressure, relative humidity, wind, SO2, NO2, PM10, diabetes, and season. d, c plus drug use. Table 6. Changes (95% confidence interval) in BP associated with a 1 °C decrease in the average temperature stratified by hypertension
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In this longitudinal study, we observed a seasonal variation in BP and significant acute effects of average outdoor temperature on BP in a large cohort in Jinchang, China. The effect sizes of outdoor temperature on both SBP and DBP were larger in summer than in the other seasons. Furthermore, the association varied significantly with age, gender, BMI, unhealthy behaviors, and chronic disease status.
Previous studies have provided inconsistent results of the relationship between outdoor temperature and BP. In this study, we found a curved negative significant association between outdoor temperature and BP, which was consistent with Hurk’s study in the Netherlands[20]. Moreover, a study from Kailuan, China, reported a U-shaped association[19]. The difference in the relationship might be due to variations of the study population, geographic location, and potentially statistical methods[29]. In addition, we found short-term lag effects in both single- and cumulative-lag models in three seasons, except for winter. The coefficients for both SBP and DBP showed a decreasing trend with the increase of single lag days. Similar results were found in studys[14,18]. Temperature and atmospheric particulates have been reported to have significant lag effects on human mortality[30, 31] and morbidity[32, 33]. These effects could possibly result from the lag effect of temperature on BP. Therefore, patients with CVDs should be more cautious with sudden changes in temperature.
We found that both SBP and DBP showed seasonal fluctuations. The difference in mean temperature between summer and winter reached 27.54 ℃ during the study period, and the corresponding differences in SBP and DBP between the two seasons were 3.5 mmHg and 2.75 mmHg, respectively. Several studies have shown significant effects of the seasons on BP in adults[20, 25, 29]. In our study, the associations between temperature and BP differed by season, after controlling for the potential confounding factors. The effects of temperature on BP in summer were greater than the effects in spring and autumn. In contrast, a study conducted in Hangzhou City in southern China found larger effects of outdoor temperature on BP in spring and autumn[11]. Our study revealed a significant interaction between temperature and season on BP, which was inconsistent with previous study conducted in the US[25]. Outdoor temperature was not significantly associated with BP in winter in our study, which could be due to the fact that participants spent the majority of their time indoors due to the cold outdoor temperatures (average annual temperature was below 10 ℃), and all residences used heating in winter. One study conducted in 10 different regions in China also reported no significant association between outdoor temperature and BP among participants who reported having central heating in their homes during the time periods with lower temperatures[13]. Another study found that the use of air conditioning in warmer temperatures did not affect the relationship between season and BP[34].
Our study found that the effects of outdoor temperature on BP varied by demographic characteristics, unhealthy behaviors, and chronic disease status. Those who were older, male, overweight or obese, smokers and drinkers, or who had CVDs and diabetes, were more susceptible to the impacts of temperature changes on BP. Previous studies have found significant associations between outdoor temperature and BP in individuals with CVDs[16], type 1 or type 2 diabetes mellitus[35], those who are taking antihypertensive medications[36], and the elderly[14]. Studies have reported a stronger impact of temperature change on BP in patients with CVDs than in those without CVDs[13, 16]. Together, these results suggest that individuals with preexisting conditions (i.e., CVDs) are more susceptible to temperature changes on their BP.
While the biological mechanisms underlying the association between temperature and BP have not been fully understood, several epidemiological studies have indicated that sympathetic nervous activity[37] and endothelial dysfunction[38] might cause elevated BP after exposure to cold temperatures in certain cardiovascular events. Experimental studies have suggested that exposure to low temperatures could influence the renin-angiotensin system to elevate BP through upregulation of AngII receptors, leading to the vasoconstriction of blood vessels[39, 40]. When the temperature increases, the expansion of blood vessels and the decrease of vascular resistance may cause a decrease in blood pressure. The high temperatures in summer can cause the body to sweat more, and a decrease in blood volume may decrease blood pressure. Additionally, aging and/or disease could impact the adaption capacity of physiological responses to perturbations resulting from the environment[41].
The strengths and limitations of our study should be considered when interpreting the results. Our study was based on a prospective cohort study with repeated BP measurements, which increased our statistical power and controlled for variability between participants. Detailed information on individual factors allowed us to control for confounding effects and conduct stratified analyses to investigate potential effect modifiers. However, our study did not control for indoor temperature due to the lack of information on indoor temperature. Several studies have suggested that indoor temperature is associated with BP[42, 43] and contributes to the seasonal effects on BP[44]. Due to the significant difference in outdoor and indoor temperature during winter in this region, we performed stratified analyses by season. No significant association between temperature and BP in winter observed in our study could be due to the impact of indoor temperature. We used data from one meteorological station as a surrogate of personal exposure to outdoor temperature, which could have caused potential misclassification of individual exposure to outdoor temperature if participants traveled to other places during the study period. However, the potential misclassification was likely to be non-differential, resulting in an under-estimation of the observed association. In addition, by comparing the differences in the basic information between the selected and excluded individuals (see Supplementary Table S2 available in www.besjournal.com), it was found that the excluded individuals were generally older, less educated, retired, and had a larger proportion of smoking and drinking than the selected individuals. The differences in these characteristics may suggest that the effect of temperature on BP among the selected individuals was underestimated.
Variables Included, Mean ± SD or n (%) Excluded, Mean ± SD or n (%) P Total 32,710 (100.0) 645 (100.0) Age (years) 46.37 ± 12.77 49.28 ± 14.09 < 0.05 Male, n (%) 19,916 (60.9) 401 (62.2) > 0.05 Married, n (%) 28,222 (86.3) 562 (87.1) > 0.05 Education level < 0.05 Low (Junior middle school or less) 12,393 (37.9) 296 (45.9) Medium (senior higher school or equivalent) 15,844 (48.4) 273 (42.3) High (College degree and more) 4,473 (13.7) 76 (11.8) Occupation < 0.05 Blue collar worker 19,241 (58.8) 304 (47.1) Managerial staff 3,457 (10.6) 96 (14.9) Logistics staff 380 (1.2) 9 (1.4) Technical staff 1,338 (4.1) 13 (2.0) Retirees 8,294 (25.4) 223 (34.6) Body mass index (kg/m2) 23.55 ± 3.25 23.36 ± 3.19* > 0.05 Smoking < 0.05 Non-smoker 18,022 (55.1) 325 (50.4) Current smoker 11,968 (36.6) 251 (38.9) Former smoker 2,720 (8.3) 69 (10.7) Drinking < 0.05 Non-drinker 25,210 (77.1) 476 (73.8) Current drinker 6,215 (19.0) 128 (19.8) Former drinker 1,285 (3.9) 41 (6.4) Cardiovascular disease 6,442 (19.7) 130 (20.2) > 0.05 Hypertension 4,798 (14.7) 83 (12.9) > 0.05 Diabetes 1,295 (4.0) 26 (4.0) > 0.05 Note. *264 subjects lack height and weight data. Table S2. The differences of the basic information between the participants included and excluded
We observed a significant negative association between outdoor temperature and BP in a large cohort population of northwest China. Our results also suggested that the association between BP and temperature varied by season. Furthermore, demographic characteristics (age, gender, and BMI), unhealthy behaviors (smoking and alcohol consumption), and chronic disease status (CVDs, hypertension, and diabetes) might modify this association. Our results warrant replication in other study populations, and future mechanistic studies are needed to elucidate the underlying mechanisms.
Effects of Outdoor Temperature on Blood Pressure in a Prospective Cohort of Northwest China
doi: 10.3967/bes2021.014
- Received Date: 2020-01-20
- Accepted Date: 2020-06-29
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Key words:
- Outdoor temperature /
- Season /
- Blood pressure /
- Jinchang cohort
Abstract:
Citation: | ZHENG Shan, WANG Min Zhen, CHENG Zhi Yuan, KANG Feng, NIE Yong Hong, MI Xiu Ying, LI Hai Yan, JIN Lan, ZHANG Ya Wei, BAI Ya Na. Effects of Outdoor Temperature on Blood Pressure in a Prospective Cohort of Northwest China[J]. Biomedical and Environmental Sciences, 2021, 34(2): 89-100. doi: 10.3967/bes2021.014 |