
==== Front
Sci Rep
Sci Rep
Scientific Reports
2045-2322
Nature Publishing Group UK London

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10.1038/s41598-024-72065-z
Article
Association between periodic variation of air temperature, humidity, atmospheric pressure and hospital admissions for acute occlusive mesenteric ischaemia
Chen Lin 123
Wang Jun 4
Zhuo Hongqing 5
Wang Zexin 5
Zhang Jizhun zhangjizhun2007@163.com

5
1 https://ror.org/0207yh398 grid.27255.37 0000 0004 1761 1174 Cheeloo College of Medicine, Shandong University, Jinan, China
2 grid.27255.37 0000 0004 1761 1174 Department of Breast and Thyroid Surgery, Shandong Provincial Hospital, Shandong University, Jinan, China
3 https://ror.org/01fd86n56 grid.452704.0 0000 0004 7475 0672 Department of Breast Surgery, The Second Hospital Of Shandong University, Jinan, China
4 Department of General Surgery, Zhangdian District People’s Hospital, Zibo, China
5 grid.460018.b 0000 0004 1769 9639 Department of Gastrointestinal Surgery, Shandong Provincial Hospital Affiliated to Shandong University, Shandong Provincial Hospital affiliated to Shandong First Medical University, 324 Jing 5 Rd, Jinan, 250021 Shandong Province China
13 9 2024
13 9 2024
2024
14 2142616 2 2024
3 9 2024
© The Author(s) 2024
2024
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Referring to the intestinal ischemic injury caused by sudden interruption of the blood supply, acute mesenteric ischemia (AMI) is a highly fatal emergency with mortality rates varying from 58 to 80%. The aim of this study was to explore the effect of temperature on AMI admission. This was a retrospective, multicentric study. The medical records of 1477 patients with verified AMI who were consecutively admitted to 3 hospitals anytime between January 2010 and December 2020 were included in the study. Distributed lag non-linear model was applied, the model was adjusted for temperature, atmospheric pressure, relative humidity, year, holiday, day of the week, time and seasonality. AMI exhibited obvious sex preference, AMI patients tended to be male (M/F ratio = 2.3:1) and in their late 50 s. Hospital admissions of acute mesenteric arterial thromboembolism (AMAT) increased significantly with high temperatures on day of exposure and lag 0–14 day. The effect curve of daily average temperature on acute mesenteric venous thromboembolism (AMVT) admission was J-shaped, and the duration of cold effect was longer, while the duration of heat effect was shorter. An increase in hospital admissions of AMVT was found above 20 °C at lag 0–30. For the first time, our study indicated that temperature is significantly associated with the risk of AMI. Although it is not possible to always avoid exposure to extreme temperatures, one should be aware of dramatic temperature fluctuations and take appropriate precautions.

Keywords

Acute mesenteric ischemia
DLNM
Temperature
Subject terms

Gastroenterology
Risk factors
Natural Science Foundation of Shandong Province in ChinaZR2022MH070 Zhang Jizhun Shandong Provincial Postdoctoral Foundation ProjectSDCX-ZG-202203071 Zhang Jizhun issue-copyright-statement© Springer Nature Limited 2024
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pmcIntroduction

Intestinal ischemic injury encompasses a broad spectrum of pathological types, including nonocclusive mesenteric ischemia, acute mesenteric venous thromboembolism (AMVT), and acute mesenteric arterial thromboembolism (AMAT)1. It is caused by venous obstruction, arterial insufficiency, or acute occlusive mesenteric ischaemia (AMI). Intestinal ischemic injury accounts for approximately 0.09%-0.2% of all abdominal emergencies2,3. AMI is more prevalent in older patients who smoke or are diagnosed with a combination of atrial fibrillation (AF), peripheral vascular disease, atherosclerotic occlusive disease, and hypercoagulability4–7. The time of onset quintuples the mortality rate within a short period of 12 h and is an independent prognostic factor for AMI8,9. Nevertheless, nonspecific laboratory examinations and elusive symptoms contribute to challenges in early diagnosis and prompt treatment10,11. It has been extensively recognized that timely and appropriate surgical treatment is an important hospital-based modifiable risk factor of outcome12.

Accumulating evidence indicates that the incidence of thromboembolic diseases, such as stroke and deep vein thrombosis, have obvious variations that synchronize, to some extent, with seasonal changes13–17. For instance, transient exposure to low temperature might trigger the symptom onset of pulmonary embolism18. Furthermore, seasonal preference is an environmental factor that affects the incidence of several gastrointestinal diseases19–21. It has been well recognized that the incidence of peptic ulcer and esophageal variceal bleeding peaks during winter, while the incidence of ischemic colitis, Crohn’s disease, colon cancer and diverticulitis peaks during summer22–26. In addition, previous research demonstrated that the incidence of acute intestinal vascular insufficiency, which is typified by ischemic colitis, increased through the summer and peaks in September27. The mechanisms underlying the seasonal patterns of these diseases are intriguing. One possible explanation is that temperature fluctuations can result in peripheral vascular vasomotion, changes in blood viscosity, and sympathetic nerve excitability28,29. Moreover, it has been reported that the sudden drawdown of temperature could result in stress-induced excitation of sympathetic nerve and adrenal gland marrow, as well as rapid secretion of adrenaline, hypertensin II and endothelinII22,30,31. Which further lead to contraction of duodenal mucosa and blood vessel, ultimately cause mucosa blood flow fall and mucosa damage32,33. These acute and chronic pathophysiological changes eventually lead to the onset of thromboembolic diseases. Understandably, a better understanding of the temperature-admission relationship of AMI could provide a scientific basis for formulating preventive strategies, early risk stratification and equitable medical resource allocation.

The purpose of this study was to explore the effect of temperature on AMI admission. We reported a lag-exposure response association between daily mean temperature and hospital admissions for AMI through a time-series analysis.

Methods

Patients

This was a retrospective, multicentric study. This study involved AMI patients who were consecutively admitted to the inpatient department of 3 hospitals between 1st January 2010 and 31th December 2020: Shandong Provincial Hospital (n = 743, Jinan, Shandong province, China), Qilu Hospital Of Shandong University (n = 213, Jinan, Shandong province, China), The Affiliated Hospital Of Qingdao University (n = 521, Qingdao, Shandong province, China). Informed consent was obtained from all subjects and/or their legal guardian(s). Research was performed in accordance with the Declaration of Helsinki. Ethics Committee of Shandong Provincial Hospital (SWYX: NO.2023-562) approved the study.

The diagnosis of AMI was made by at least two experienced gastroenterologists after comprehensively considering clinical symptoms, disease history, physical examination, imaging, intra-operative findings, and postoperative pathology. Patients with intestinal obstruction caused by other diseases, non-occlusive mesenteric ischemia, mechanically induced mesenteric ischemia or chronic mesenteric ischemia were excluded.

Environmental data

The meteorological station of Jinan (NO. 54823) is located at 36°36’N and longitude 117°00’E. The meteorological station of Qingdao (NO. 54857) is located at 36°04’N and longitude 120°20’E. They share the same monsoon climate of medium latitudes. The annual average temperature is 12.7–13.8 °C. Daily regional meteorological data including atmospheric pressure (hPa), humidity (percentage), and temperature (°C) were collected from the National Meteorological Information Center (http://data.cma.cn/).

Distributed lag non-linear model (DLNM)

DLNM was used to explore the non-linear and the lag-exposure–response relationship between daily mean temperature and AMAT/AMVT admission. The DLNM model has been extensively used in studies encompassing environmental factors, morbidity and mortality34,35. Daily mean temperature was considered as the main meteorological factors in this article, we also accounted for daily mean atmospheric pressure (PRESS) and relative humidity (RH) as confounding factors. When exploring the impacts of daily mean temperature on AMI admission, factors such as the day of week (DOW), long-term and seasonal trends, public holiday were introduced into the model as confounders. Yt is the daily observed counts of AMAT/AMVT hospitalizations on day t. β0 is the intercept. A maximum lag of 30 days was used to explore associations. Holiday refers to the binomial variable for public holidays and the summer or winter vacation. The model structure could be shown as the following formula:logE(yt)=βx+ns(time,df)+DOW+Season+Year+Holiday+ns(TEMP,df)+ns(PRESS,df)+ns(RH,df)+β0

Statistical analysis

Data were analyzed using SPSS 22 software (SPSS Inc., Chicago, IL, USA), Excel 2017, and R 4.3.2. The ‘dlnm’ package in R was used to fit DLNM. Baseline characteristics were analysed by t-tests and Chi-square test. Spearman’s correlation coefficients was used to evaluate the relationship between meteorological factors.

Results

Baseline patient characteristics

A total of 1477 patients with confirmed AMI from three hospitals were included in this study. Patient characteristics are described in Table 1. The incidence of AMI clearly demonstrated a sex preference. Patients with AMI tended to be male (n = 1030, 69.7%, M/F ratio = 2.3:1) and in their late 50 s (average: 57.27 ± 14.71). Of the 1477 patients, 622 had acute mesenteric venous thromboembolism (AMVT, 42.1%) and 855 had acute mesenteric arterial thromboembolism (AMAT, 57.9%).Table 1 Baseline characteristics of the study population.

Descriptions	Total	Diseases distribution	P1	In-hospital death	P2	
AMVT	AMAT	Yes	No	
No. of admission, n (%)	1477	–	–	–	–	–	–	
 AMVT	622 (42.1%)	–	–	–	–	–	–	
 AMAT	855 (57.9%)	–	–	–	–	–	–	
In hospital mortality rate, n (%)	242 (16.4%)	93 (15.0%)	149 (17.4%)	0.011	242 (16.4%)	1235 (83.6%)	–	
 AMVT	93 (15.0%)	–	–	–	93 (15.0%)	529 (85.0%)	–	
AMAT	149 (17.4%)	–	–	–	149 (17.4%)	706 (82.6%)	–	
Age, (years, mean ± SD)	57.27 ± 14.71	52.16 ± 14.23	60.98 ± 13.93	0.958	61.46 ± 14.39	56.45 ± 14.64	0.780	
Male, n (%)	1030 (69.7%)	425 (68.3%)	605 (70.8%)	0.047	164 (67.8%)	866 (70.1%)	0.162	
Length of stay, (days, mean ± SD)	12.94 ± 10.25	13.63 ± 9.58	12.45 ± 10.70	0.922	9.79 ± 12.51	13.57 ± 9.64	0.004	
The status of entrance, n (%)	
 Emergency admission	835 (56.5%)	366 (58.8%)	469 (54.9%)	0.002	164 (67.8%)	671 (54.3%)	 < 0.001	
Comorbid diseases, n (%)	
 Hypertension	429 (29.0%)	90 (14.5%)	339 (39.6%)	 < 0.001	80 (33.0%)	349 (28.3%)	0.006	
 Diabetes	150 (10.2%)	44 (7.1%)	106 (12.4%)	 < 0.001	27 (11.2%)	123 (10.0%)	0.265	
 Coronary heart disease	209 (14.2%)	44 (7.1%)	165 (19.3%)	 < 0.001	46 (19.0%)	163 (13.2%)	 < 0.001	
 Myocardial infarction	61 (4.1%)	7 (1.1%)	54 (6.3%)	 < 0.001	18 (7.4%)	43 (3.5%)	 < 0.001	
 Heart failure	22 (1.5%)	2 (0.3%)	20 (2.3%)	 < 0.001	9 (3.7%)	13 (1.1%)	 < 0.001	
 Liver cirrhosis	139 (9.4%)	105 (16.9%)	34 (4.0%)	 < 0.001	27 (11.2%)	112 (9.1%)	0.046	
 Previous operation history	451 (30.5%)	203 (32.6%)	248 (29.0%)	0.003	64 (25.4%)	387 (31.3%)	0.001	
 History of malignant tumour	122 (8.3%)	54 (8.7%)	68 (8.0%)	0.316	24 (10.0%)	98 (7.9%)	0.044	
 Renal disease	194 (13.1%)	61 (9.8%)	133 (15.6%)	 < 0.001	42 (17.4%)	152 (12.3%)	 < 0.001	
 Autoimmune disease	34 (2.3%)	7 (1.1%)	27 (3.2%)	 < 0.001	6 (2.5%)	28 (2.3%)	0.688	
 Bradycardia	9 (0.6%)	7 (1.1%)	2 (0.2%)	 < 0.001	4 (1.7%)	5 (0.4%)	 < 0.001	
 Atrial fibrillation	198 (13.4%)	27 (4.3%)	171 (20.0%)	 < 0.001	60 (24.8%)	138 (11.1%)	 < 0.001	
Complications, n (%)	
 Sepsis	41 (2.8%)	20 (3.2%)	21 (2.5%)	0.080	22 (9.1%)	19 (1.5%)	 < 0.001	
 Peritonitis	201 (13.6%)	107 (17.2%)	94 (11.0%)	 < 0.001	55 (22.7%)	146 (11.8%)	 < 0.001	
Other concomitant thromboembolic disease, n (%)	
 Cerebral infarction	110 (7.4%)	32 (5.1%)	78 (9.1%)	0.004	31 (12.8%)	79 (6.4%)	 < 0.001	
 Splenic vein thrombosis	90 (6.1%)	79 (12.7%)	11 (1.3%)	 < 0.001	21 (8.7%)	69 (5.6%)	 < 0.001	
 Portal vein thrombosis	314 (21.3%)	264 (42.4%)	50 (5.8%)	 < 0.001	53 (21.9%)	261 (21.1%)	0.597	
 Deep venous thrombosis of lower limb	333 (22.5%)	227 (36.5%)	106 (12.4%)	 < 0.001	64 (26.4%)	269 (21.8%)	0.003	
 Partial embolizalion of other artery	40 (2.7%)	4 (0.6%)	36 (4.2%)	 < 0.001	13 (5.4%)	27 (2.2%)	 < 0.001	
Emergency surgery, n (%)	908 (61.5%)	342 (55.0%)	566 (66.2%)	 < 0.001	113 (46.7%)	795 (64.4%)	 < 0.001	
 Enterectomy	655 (44.3%)	279 (44.9%)	376 (44.0%)	0.509	86 (35.5%)	569 (46.1%)	 < 0.001	
 Embolectomy/artery stenting	339 (23.0%)	74 (11.9%)	265 (31.0%)	 < 0.001	43 (17.8%)	296 (24.0%)	 < 0.001	
 Both	86 (5.8%)	11 (1.8%)	75 (8.8%)	 < 0.001	16 (6.6%)	70 (5.7%)	0.255	
AMVT Acute mesenteric venous thromboembolism, AMAT Acute mesenteric arterial thromboembolism, COPD: Chronic obstructive pulmonary disease.

Partial embolizalion of other artery: Patients also suffered from partial embolizalion of other artery such as renal artery, celiac axis, lower extremity artery.

P1-values and P2-values were calculated by t-tests, p < 0.05 was considered as statistically significant.

One hundred ten patients (7.4%) had a history of cerebral infarction. Patients with AF were five times more likely to develop AMAT than those with AMVT (p < 0.001). In total, 201 (13.6%) patients showed signs of peritonitis, and 41 (2.8%) patients had sepsis. A total of 242 patients (16.4%) died during hospitalization, most of whom were male (67.8%) with a mean age of 61.46 ± 14.39 years (Table 1). Compared with survivors, patients who died of AMI tended to have concomitant thromboembolic diseases (Splenic vein thrombosis and Deep venous thrombosis of lower limb, p < 0.001) and cardiovascular diseases (hypertension, congestive heart disease, AF, cerebral infarction, etc.; p < 0.01). The incidences of sepsis (9.1% vs. 1.5%) and peritonitis (22.7% vs. 11.8%) were significantly higher in non-survivors (p < 0.001). In addition, the rates of emergency surgery, enterectomy, and embolectomy/artery stenting were significantly lower in non-survivors with AMI than in survivors (p < 0.001), indicating that non-survivors were more likely to miss out on surgical opportunities.

DLNM model

DLNM was used to assess the cumulative and delayed effects of daily ambient temperature on AMAT and AMVT admissions. Seasonal dynamic variations in climatic parameters, number of AMI admissions, and in-hospital mortality rates are shown in Fig. 1A,B. Between 2010 and 2020, the daily average temperature, atmospheric pressure, relative humidity were 14.29°C, 1002.42 hpa, 62.24%, respectively (Table 2). During this period, the daily mean temperature, daily mean atmospheric pressure, and daily humidity exhibited seasonal changes (Fig. 2), with relatively low temperatures, low humidity, and high pressures in winter. The correlation between meteorological factors was analyzed using Spearman’s correlation analysis, which indicated a significant correlation between daily mean temperature, daily mean atmospheric pressure (rs = − 0.765, P < 0.01), and daily mean relative humidity (rs = 0.245, P < 0.01).Fig. 1 (A) The seasonal incidence of AMI from 2010 to 2020. (B) Heat map of monthly incidence of AMI.

Table 2 The characteristics of daily meteorological from 2010 to 2020.

Variables	Mean ± SD	Min	0.25	0.50	0.75	Max	
Mean temperature (°C)	14.29 ± 9.79	 − 11.5	5.55	15.6	23.2	30.95	
Mean atmospheric pressure (hpa)	1002.42 ± 9.13	977.55	994.6	1002.6	1009.6	1027	
Relative humidity (%)	62.24 ± 16.2	21.5	50	62.5	75	99	
Min: minimum value.

0.25: the lower quartile, equal to the 25% of all values in the sample in descending order.

0.50: the median, equal to the 50% number of all values in the sample in descending order.

0.75: the higher quartile, equal to the 75th digit of all values in the sample arranged from smallest to largest.

Max: maximum value.

Fig. 2 (A) Seasonal distribution of meteorological variables from 2010–2020. And Daily distribution of (B) mean temperature, (C) mean atmospheric pressure, (D) relative humidity.

The three-dimensional plots and contour plots (Fig. 3A,D) showed the relationships between daily mean temperature and admission for AMAT and AMVT over 30 lag days. The median mean temperature (15.6 °C) was set as the reference. High temperature (> 20°C) was associated with a significantly increased risk of AMAT admission at lag 0–14 d (Fig. 4), indicating an acute effect. During lag days 14–28, the effects of temperature on relative risk (RR) gradually decreased to a minimum.Fig. 3 The three-dimensional plot of the relationship between mean temperature and (A) AMAT, (C) AMVT along 30 lag days; The contour plot of exposure-lag-response risk of daily mean temperature for (B) AMAT, (D) AMVT admission. Blue area represents RR less than 1 and red area represents RR greater than 1. The redder the area, the greater the RR, indicating a higher risk of AMVT/AMAT onset. This figure was generated using R 4.3.2.

Fig. 4 Exposure–response curves of temperature and cumulative relative risk of hospitalizations for AMAT. The red lines are the relative risks, and the gray regions are the 95% confidence intervals.

According to the analysis of the DLNM model, the estimated effect of temperature on AMVT admissions was non-linear, with larger RRs at low temperatures. There was a significant delayed harmful effect of extremely cold temperatures on AMVT admission and no immediate effect on AMVT admission at lag 0 day. The effects of extreme low temperature (− 10 °C) initially increased over lag periods, peaking on lag day 10 (RR = 1.15, 95% confidence interval [CI]:1.04, 1.27). Subsequently, these effects began to decrease, reaching a minimum on lag day 30 (RR = 1.21, 95% CI 1.02, 1.44), and then sharply increased.

Figure 5 presents the overall cumulative temperature-admission association curves for patients with AMVT at specific lags (0, 3, 7, 14, 21, and 28 days) and at specific temperatures (− 10 °C, 0 °C, 10 °C, 20 °C, and 30 °C). The effect curve of the daily average temperature on AMVT admission was J-shaped. The duration of the cold effect was longer, while the duration of the heat effect was shorter. An increase in hospital admissions due to AMVT was observed on days with temperatures above 20 °C on lag 0–30. Low temperatures (≤ 20 °C) resulted in persistently increased RR over longer lag periods.Fig. 5 Exposure–response curves of temperature and cumulative relative risk of hospitalizations for AMVT. The red lines are the relative risks, and the gray regions are the 95% confidence intervals.

We studied the cumulative lag effects of different temperatures (− 10 °C, − 5 °C, 0 °C, 5 °C, 10 °C, 15 °C, 20 °C, 25 °C, 30 °C) on AMVT admissions during different lag days (lag 0, lag 3, lag 7, lag 14, lag 21, lag 28) (Table 3). For the total population, temperatures between 5 °C and 25 °C, e.g., 5 °C, 10 °C, 15 °C, 20 °C and 25 °C, were associated with increased cumulative risk in line with the lag days, with the largest cumulative RR on lag 28.Table 3 Accumulate relative risk (RR) estimated with 95% CI of the effect of different temperatures on AMVT admission.

Lag effect	Ors (95% CI)	
TEMP	 − 10	-5	0	5	10	15	20	25	30	
lag 0	1 (0.73, 1.36)	1 (0.77, 1.28)	1 (0.82, 1.21)	1 (0.87, 1.14)	1 (0.93, 1.07)	1 (0.99, 1.01)*	1 (0.95, 1.05)	1 (0.9, 1.12)	1 (0.85, 1.19)	
lag 3	1.01 (0.87, 1.18)	1.01 (0.89, 1.14)	1.01 (0.92, 1.11)	1 (0.94, 1.07)	1 (0.97, 1.04)*	1 (0.99, 1.01)*	1 (0.98, 1.02)*	1 (0.94, 1.05)	0.99 (0.92, 1.08)	
lag 7	1.12 (1.01, 1.25)	1.1 (1, 1.2)	1.07 (1, 1.15)	1.05 (1, 1.1)	1.03 (1, 1.05)*	1 (1, 1.01)*	0.98 (0.97, 1)*	0.96 (0.93, 1)*	0.94 (0.89, 1)	
lag 14	1.09 (0.99, 1.21)	1.07 (0.99, 1.16)	1.06 (1, 1.12)	1.04 (1, 1.08)*	1.02 (1, 1.04)*	1 (1, 1.01)*	0.99 (0.97, 1)*	0.97 (0.94, 1)*	0.95 (0.9, 1)	
lag 21	0.99 (0.9, 1.1)	1 (0.91, 1.08)	1 (0.93, 1.06)	1 (0.95, 1.04)*	1 (0.97, 1.02)*	1 (1, 1)*	1 (0.99, 1.02)*	1 (0.97, 1.04)*	1 (0.95, 1.06)	
lag 28	1.18 (1.02, 1.37)	1.14 (1.02, 1.29)	1.11 (1.01, 1.21)	1.07 (1.01, 1.14)	1.04 (1, 1.08)*	1.01 (1, 1.01)*	0.97 (0.95, 1)*	0.94 (0.9, 0.99)*	0.91 (0.84, 0.99)	
*p < 0.05.

Discussion

This study explored the temperature-admission relationship of AMI using a distributed lag non-linear threshold model in extratropical mainland Chinese cities. A total of 1477 patients with AMI treated in three hospitals between 2010 and 2020 were included in this study. Our analysis demonstrated that the incidence of AMI has been increasing over the past decade and eventually tended toward stability, while the in-hospital mortality rate of AMI has also leveled off dramatically after years of weak growth. We also confirmed the age- and sex-specific onset of AMI. Our study found that high temperature is a risk factor for AMAT, whereas low temperature is a risk factor for AMVT.

Caused largely by rising global temperatures, there has been a dramatic rise in the frequency and intensity of extreme weather events and consequent disease outbreaks, such as heatwaves and cold spells, over the past 20 years36–38. Recently, special attention has been drawn to the adverse effect of meteorological factors on health outcomes. Exposure to discrete extreme ambient temperatures or temperatures away from long-term averages causes needless suffering and death39. Recent studies in China focusing on cardiovascular mortality have documented that a 1 °C increase from the 75th to 99th percentiles of temperature was associated with corresponding increases of 3.02% cardiovascular mortality in 17 large Chinese cities40. To the best of our knowledge, there are few epidemiological studies on the correlation between AMI admissions and meteorological factors. In this study, we discovered the acute effect of high temperatures on AMAT admission for the first time. Extremely high temperatures can significantly increase the risk of AMAT admission, which presents as an acute effect; this risk gradually decreases with an increase in lag days. We found a nonlinear relationship between admission due to AMVT and daily mean temperature, which was strongest on lags days 10 and 30. There was a significant delayed harmful effect of extremely cold temperatures on AMVT admission and no immediate effect on AMVT admission on lag day 0. The effects of extremely low temperatures first increased over lag periods, reaching a peak on lag day 10, then began to decrease, reaching a dip on lag day 30, and then sharply increasing.

However, the mechanism underlying the effect of temperature on AMI remains unclear. Several pathophysiological hypotheses may explain why heat stroke induces AMAT41. First, previous studies have suggested that approximately half of the AMI cases are caused by acute mesenteric artery embolisms. The source of arterial emboli is mostly cardiogenic (common in cases of atrial fibrillation, valvular heart disease, bacterial endocarditis, etc.), followed by vasogenic (common in cases of atherosclerotic plaque detachment)8. Heatwave-specific associations have been widely observed in cases of stroke, morbidity associated with ischemic heart disease, cardiovascular morbidity, and cardiovascular ambulance use42–45. In addition, high temperatures produce a considerable augmentation of sweat evaporation, consequently leading to vasodilatation, decreased circulating blood volume, and increased blood viscosity and blood coagulation, which eventually increases the risk of thrombophilia during the hot summer season46–49. Lim et al. proposed that the temperature range of 22–27 °C was a flexion point of body hydration status, where levels of dehydration markers started to increase linearly with an increase in the apparent temperature50. Moreover, a higher ambient temperature was inversely associated with heart rate variability measures, indicating the activation of sympathetic activity and withdrawal of vagal activity51. In addition, the body's thermoregulatory system is stimulated in a high-temperature environment to redistribute blood flow by facilitating skin blood flow and decreasing gut blood flow52.

A prolonged blood flow reduction-induced oxidative/nitrosative stress response could further deteriorate the intestinal mucosal barrier function, allowing lipopolysaccharide (LPS), endotoxins, or endogenous bacteria to leak from the gut lumen into the systemic circulation53–55. On the other hand, based on the current literature, the mechanisms underlying the onset of AMVT in response to cold temperature challenges may involve multiple pathophysiological regulations. For example, enhanced sympathetic reactivity and sympathetic muscle nerve activity in response to cold stimuli could result in a surge in blood pressure during mornings, accompanied by significant sympathetic index fluctuation41. Moreover, cold air exposure can activate the renin-angiotensin system (represented by angiotensin II levels) and sympathetic nervous system (represented by norepinephrine levels), resulting in systemic vasoconstriction, thereby elevating blood pressure56. Decreased skin blood flow may lead to dehydration, hemoconcentration, and hyperviscosity41 and eventually contribute to increased AMVT admission during cold weather events.

This study had certain limitations. First, variations in weather conditions and meteorological characteristics of the surrounding area may not be completely represented by meteorological stations. Owing to the diversity of climatic conditions, the conclusions of this study may be limited by regional differences, and the generalizability of the findings to other climates and populations may be restricted. Second, due to the hierarchical medical system and retrospective features, we cannot get access to the patient information in local hospitals and other large and medium-sized hospitals. In addition, factors that affect temperature exposure, such as residential area and air conditioning, were not analyzed because this data were unavailable in our study. The influence of confounding factors, such as socioeconomic status, lifestyle habits, access to healthcare services, and genetic and cultural factors, was not considered as these data were also unavailable.

From a clinical perspective, our results provided novel and robust evidence on the relationship between temperature and AMI. AMI is a relatively rare but highly lethal condition that ranked as one of the most frequent causes of death among surgical diseases on a global level57. Special prevention management and targeted health policies need to be considered for vulnerable subpopulations under abrupt temperature changes. For example, reinforcement of health education for older men especially those in their 50 s, should be strengthened to reduce their exposure to seasonal and diurnal temperature variations. In addition, early warning mechanism or early warning system should be established. By setting different temperature thresholds or cold/heat wave warning classification, relevant government departments cooperated with meteorological departments should remind the crowd to reduce outdoor exposure when the warning level reaches certain level. Moreover, frontline medical workers should enhance the vigilance of AMI diagnosis during the peak period of incidence, effectively allocate medical care resources and utilize community-preventive services.

Author contributions

Conceptualization：L.C, JZ.Z Methodology: L.C Formal analysis: L.C, JZ.Z Data Curation: ZX.W, HQ.Z, J.W Writing—Review & Editing: L.C, JZ.Z.

Funding

Natural Science Foundation of Shandong Province in China (ZR2022MH070), Shandong Provincial Postdoctoral Foundation Project (Innovation Project SDCX-ZG-202203071).

Data availability

Data will be made available on request after contacting the corresponding author.

Competing interests

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