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Gatwick Airport is expected to experience its busiest festive season since the pandemic, according to the UK Civil Aviation Authority. The West Sussex airport is anticipating Sunday will be its busiest day, with 769 flights. On Christmas Day it will be open and operating with an expected 228 flights. Nick Williams, head of passenger operations, said Christmas was a "busy time" and staff were "working hard to deliver the best possible experience for passengers". The most popular Christmas long-haul destinations this year from Gatwick are Dubai, Shanghai and Cancun, with Geneva, Barcelona and Milan the most popular short-haul. Last December almost 11 million passengers jetted off from UK airports for the festive season, according to the UK CAA. Gatwick was one of the busiest of those airports. A total of 1,635,732 passengers travelled through it in December 2023. A Gatwick spokesperson said its top tips for travelling over the festive period included: Follow BBC Sussex on Facebook, on X, and on Instagram. Send your story ideas to southeasttoday@bbc.co.uk or WhatsApp us on 08081 002250. The airport reopened on Monday, a day after its runway closed when a plane was damaged in a "hard landing". The number of people travelling to the island for the event remains below the levels seen in 2019. Pictures appear to the show a plane with its nose touching the runway after a wheel collapse. Some 14 million drivers are expected to hit the road in the last weekend before Christmas, according to the RAC. Ports of Jersey is replacing free viewing deck with luxury waiting area Copyright 2024 BBC. All rights reserved. The BBC is not responsible for the content of external sites.Read about our approach to external linking.
Home » AI » Edge AI: The Future of Artificial Intelligence in embedded systems The revolutionary approach of edge AI promises to transform the way embedded systems manage processing and workloads, moving AI from the cloud to the edge of the network. Artificial Intelligence (AI) has revolutionized many industries, enabling applications that seemed unlikely just a few years ago. At the same time, the exponential growth of data and the need for real-time responses have led to the emergence of a new paradigm: edge AI. This type of technology is essential for the implementation of distributed systems, where data processing must occur as close to the point of origin as possible to minimize delays and improve security and privacy. The revolutionary approach of edge AI promises to transform the way embedded systems manage processing and workloads, moving AI from the cloud to the edge of the network. Traditionally, AI has relied on the cloud to process large amounts of information, since complex models require significant computational resources that are often not available on edge devices. In a classic architecture, data collected by sensors or other embedded devices is sent directly to the cloud, where it is processed by sophisticated models. The results of this processing are then transmitted back to edge devices to make decisions or perform specific actions. This approach, while effective, also has some important limitations. First, the latency introduced by data transfer between the device and the cloud can be significant, especially in critical applications such as healthcare monitoring or autonomous driving, where every millisecond counts; second, sending data to the cloud raises privacy and security concerns, as sensitive data can be vulnerable during transfer or storage. Edge AI aims to overcome these limitations by bringing processing closer to the source, directly on embedded devices, dramatically reducing latency as data no longer has to travel back and forth between the device and the cloud, and improving privacy and security. Instead of sending large amounts of raw data to the cloud, these systems can process and analyze sensitive data locally without ever leaving the device. According to estimates, global spending on edge computing is expected to exceed $200 billion in 2024, up 15.4% from the previous year. Embedded devices like microcontrollers don’t have the computing power of a data center, but with advances in AI algorithm efficiency and specialized hardware, it’s now possible to run models on these devices. New chips designed specifically for edge AI, such as neural processing units (NPUs) integrated into microcontrollers, are making it increasingly possible to implement models in embedded systems. Edge AI not only reduces latency and improves security, but also has the potential to reduce operating costs. Cloud processing comes with significant costs associated with bandwidth, storage, and computational power. By moving some of the processing to the edge, it’s possible to reduce the load on the cloud and, therefore, costs, which is especially beneficial in applications involving large numbers of distributed devices, such as industrial sensor networks or smart cities, where the cost of sending data to the cloud can become prohibitive. Another area where edge AI is having a significant impact is the Internet of Things (IoT) where millions of interconnected devices collect and transmit data in real time. Edge AI enables these devices to make autonomous decisions without having to rely on the cloud for every single operation. For example, in an environmental monitoring system, sensors can analyze data on-site to detect anomalies or dangerous conditions and send only the relevant information to the cloud for further analysis, which benefits in terms of reducing the volume of data transmitted, but also allowing faster reactions to critical events. The automotive sector is another example where edge AI is making a difference. In autonomous vehicles, processing speed is crucial, and edge AI allows vehicles to process data from sensors, such as cameras and lidars, directly on board without having to send it to the cloud for centralized processing, thus reducing latency and allowing the vehicle to react quickly to unexpected situations. All of this significantly improves the safety and reliability of the system. In developing edge AI solutions, Broadcom focuses on developing components and infrastructure that enable data processing and analysis directly in the field, rather than sending it to a central data center, an approach essential for applications that require low latency, high responsiveness, and real-time processing, such as the Internet of Things (IoT), intelligent surveillance, robotics, and autonomous vehicles. In particular, Broadcom enables companies to support edge AI workloads by simplifying their deployment and management, and by providing embedded device solutions that integrate improved computational capabilities, such as AI-specific processors and high-performance networking chips. Broadcom devices are optimized to handle large volumes of data generated by sensors in the field, while supporting scalability and energy efficiency. Broadcom stands out for its commitment to technology innovation, with products such as the Jericho3-AI that significantly improve the network infrastructure needed to support edge AI applications. PCIM Asia 2024 to Strengthen Power Electronics Ties Across Asia Italian-French technology company STMicroelectronics (ST) develops embedded devices for edge AI, offering solutions that integrate AI capabilities directly into devices. ST develops advanced microcontrollers such as the STM32 series, which include integrated AI accelerators and are designed to run machine learning algorithms directly on the device, enabling fast processing and reducing latency. ST offers complete development platforms that include software tools to train and deploy AI models on embedded devices, such as the STM32Cube.AI library that allows developers to convert trained neural networks into code that can run directly on STM32 microcontrollers. The ST Edge AI Suite is a set of tools for integrating AI capabilities into embedded systems. It supports STM32 microcontrollers and microprocessors, Stellar automotive microcontrollers, and MEMS smart sensors, and includes resources for data management, optimization, and deployment of AI models. ST also produces intelligent sensors that integrate advanced processing capabilities that can analyze data in real time, such as speech recognition or vibration monitoring, making them ideal for applications such as predictive maintenance or voice assistance. STMicroelectronics’ approach to integrating AI directly into embedded devices enables a wide range of innovative applications, improving operational efficiency and enabling new capabilities for intelligent devices. While it is one of the most advanced frontiers of digital transformation today, edge AI also presents some significant challenges. Deploying AI models on resource-constrained devices requires significant optimization, both at the software and hardware levels. Models must be compressed without losing too much accuracy, and hardware must be powerful enough to run these models in real time, but also energy efficient, especially in battery-powered devices. Additionally, the diversity of embedded devices and their hardware configurations means that there is no one-size-fits-all solution. Developers often need to customize AI models and processing infrastructure to fit the specific needs of the device and application. Another challenge is related to data security and privacy. While edge AI can improve privacy by processing data locally, edge devices are often more vulnerable to attacks than centralized servers, requiring the implementation of robust security measures, such as end-to-end encryption and strong authentication, to protect both the data and the AI models themselves from unauthorized access. Looking ahead, it is clear that edge AI will play an increasingly central role in the world of embedded systems. As the technology evolves, we expect to see an increase in the computational capabilities of embedded devices, making it possible to run increasingly complex models directly at the edge. Additionally, the emergence of new communications technologies, such as 5G, that offer ultra-low data rates and latency will make edge AI even more efficient and widespread. Overall, edge AI represents a game-changer for embedded systems, providing a solution to the limitations of cloud computing and opening up new possibilities for autonomous and real-time applications. By reducing latency, improving security, and reducing costs, edge AI is quickly becoming a critical component in intelligent devices. While there are still challenges ahead, advances in hardware and algorithms are accelerating its adoption in embedded systems, bringing us one step closer to a future where AI is ubiquitous and seamlessly integrated into our daily lives.
Advertisement BMC Public Healthvolume 24, Article number: 3578 (2024) Cite this article Metrics details Although many studies have reported the relationship between female hormone intake and cardiovascular disease (CVD) development, their association has not been fully elucidated and defined, based on data from the Third National Health and Nutrition Examination Survey intending to assess the health and nutritional status of non-institutionalized children and adults in the United States. This study examined the relationship between female hormone intake and coronary artery disease (CVD) development in 38,745 women, averaging 38.10 ± 12.59 years in age. We explored the association between hormone intake and CVD incidence, considering various social determinants of health (SDOH) with statistical methods like Chi-square tests, logistic regression, and stratified Chi-square analysis. Our findings reveal a complex relationship between female hormone intake and CVD development. Hormones appear to reduce CVD risk in women over 60 years old. However, hormone intake correlates with increased CVD risk in highly educated women. Socioeconomic status also influences this relationship; while hormones pose a risk factor for heart failure and stroke in impoverished or wealthy women, they serve as a protective factor against CVD for middle-income women. Additionally, hormonal intake seems beneficial for women who experienced menarche between 13 and 15 years old, menopause between 30 and 49, and had 7–9 pregnancies, especially when coupled with a diet low in sugar, fat, cholesterol, and adequate folic acid intake. These results indicate that while hormones can prevent CVD under specific conditions, their impact can be detrimental in different SDOH contexts. In conclusion, while appropriate hormone intake can prevent CVD, its effects vary across different demographic and health backgrounds. This underscores the necessity for meticulous screening of SDOH factors in clinical settings to maximize the protective benefits of hormones against CVD. Peer Review reports Cardiovascular disease (CVD) is a cardiovascular disease caused by reduced blood flow in the coronary arteries, mainly from atherosclerosis. It is often classified into stable or unstable angina and myocardial infarction, which may ultimately lead to heart failure or sudden cardiac death according to the clinical symptoms, depending mainly on the degree of stenosis due to atherosclerosis. It is the leading cause of human death all over the world. In the United States, approximately 20.1 million people have a CVD, and the prevalence is up to 30% among those over 80 years old [1]. The highest percentage of prevalence is in chronic stable angina, with approximately 11.1 million people. Although mortality from CVD has declined by a relative 25% over the past decade as a result of progressive understanding of CVD and advances in medical technology, the total number of deaths has increased, and it remains the leading cause of death in the United States and worldwide [2]. Endocrine and metabolic abnormalities such as hypertension, diabetes, hyperglycemia, obesity, and dyslipidemia are the main modifiable risk factors for CVD [3,4,5]. Moreover, gender, age, and race are associated with the prevalence of CVD [6]. Epidemiological studies found a higher incidence of myocardial infarction among Blacks than Whites in the Atherosclerosis Risk in Communities (ARIC) study in patients aged 65–84 [7]. It reported that the incidence and mortality of CVD in premenopausal women are significantly lower than those of men of the same age, and the incidence and mortality of CVD in postmenopausal women are similar to those of men [8,9,10,11]. It is attributed to the difference in estrogen levels in the premenopausal or postmenopausal period in women, and the main components of estrogen include estradiol, estrone, estriol, and other lipid-soluble steroid hormones, secreted by the ovaries [12, 13]. Estrogen replacement therapy reduces the risk of coronary artery disease in postmenopausal women [14,15,16]. It protects cardiomyocytes, reduces fibroblast collagen, boosts HDL, lowers LDL, and has cardiovascular protective effects [17]. Combined with progestogen therapy to lower lipoprotein A, improve cardiac autonomic control, and prevent aging changes in endothelial function [18]. Estrogen also regulates blood vessel and cell function and is involved in inflammatory responses and metabolism. Estrogen reduces monocyte adhesion through endoplasmic reticulum mechanisms and reduces vascular inflammation [19]. It reduces endothelial cell apoptosis, inhibits smooth muscle cell proliferation and migration, and improves atherosclerosis [20]. High estrogen levels promote Th2 and Treg immune balance and exert anti-inflammatory effects. Social determinants of health (SDOH) such as race, education, socioeconomic status, housing, medical care, and food significantly impact health. [21]Coronary heart disease is closely related to SDOH and is often overlooked in female hormone and coronary artery disease (CVD) studies [21].Women often face low socioeconomic, educational, stress, diet, and access to health care, resulting in low health resources, which are major confounding factors in CVD studies [14, 15]. Studies have shown that hormone intake is associated with CVD [22], but animal studies cannot directly infer humans [23, 24]. Observational studies have shown that menopausal hormone therapy (MHT) has a positive effect on CVD risk factors and reduces morbidity. However, the results of clinical studies are uncertain due to the neglect of SDOH or the small number of women [25, 26]. Therefore, based on data from the Third National Health and Nutrition Examination Survey (NHANES III) over a 20-year period that included 38,745 cases, our study performed a stratified Chi-square test to accurately assess whether female hormone intake is associated with the development of CVD in women, which rigorously controlled for SDOH status in the included patients. And we aimed to evaluate female hormone intake’s preventive or therapeutic potential in the development of CVD. Data came from the third NHANES from 1999 to 2020. NHANES III was a cross-sectional study intended to assess the health and nutritional status of non-institutionalized children and adults in the United States. This survey recorded dietary information and behavior questionnaire for adults and children through family interviews, blood sample collection at the Mobile Examination Center, and a comprehensive physical examination. A total of 119,128 people participated in NHANES 1999 – 2020. This study excluded male participants and participants younger than 20; missing or incomplete socioeconomic factors (education, family income level, and body mass index); and incomplete, unreliable, or uncertain factors related to CVD. Finally, the study included 38,745 eligible participants. Specific study design, sampling, and exclusion criteria are described in Fig. 1. The publicly available data were used, and no ethical approval was involved in this analysis. Flow chart of the screening process for the selection of participants in NHANES1999–2020 Whether they had CVD was based on their health status in the questionnaire, which included ” Ever told you had congestive heart failure,” ” Ever told you had angina/angina pectoris,” “Ever told you that you had a heart attack,” and “Ever told you that you had a stroke”. According to the answer “yes” or “no” to these questionnaires, we determine whether they have one or more of the above-questioned four clinical features of CVD. Female participants were asked: “Ever used female hormones such as estrogen and progesterone (include any forms of female hormones, such as pills, cream, patch, and injectables, but do not include birth control methods or use for infertility)?”Additionally, many women take estrogen as a contraceptive, not for any disease treatment. Therefore, when women were asked, “Have you ever taken the pill for any reason.” Their answers were divided into “yes” or “no.” Those who answered “yes” to one or two of the above two questions were classified as taking female hormones. Those who answered “no” to both questions were classified as not taking female hormones. The Centers for Disease Control and Prevention and the World Health Organization define SDOH as the conditions in the environments where people are born, live, learn, work, play, worship, and age that affects a wide range of health, functioning, and quality of life outcomes and risks from economic, social, environmental, and psychosocial perspectives [27]. SDOH factors included in this study include age, race (Mexican Americans, Non-Hispanic White, Non-Hispanic Black, Others Hispanic or Other races), education level (High school or below, Some College, College graduate or above), family income (Poverty to income ratio ≤ 1.30, Poverty to income ratio between1.31 ~ 3.38, Poverty to income ratio ≥ 3.39), marital status (Married/living with partner or Not married). These SDOH status was based on their answers in the questionnaire, which included “Age in years of the participant at the time of screening. Individuals80 and over are topcoded at 80 years of age.” ” Recode of reported race and Hispanic origin information.” “What is the highest grade or level of school {you have/Sp has} completed or the highest degree {you have/s/he has} received?” “Marital status” and “A ratio of family income to poverty guidelines.”. The population analyzed in this study was divided into those taking female hormones and those not taking female hormones. Covariates in multivariate models include SDOH factors associated with female hormones, such as age at menarche, menopause, number of pregnancies, diet, and average sleep duration per night, are also covariates. Based on NHANES, each covariant was classified into subgroups for comparison. The detailed classification criteria are provided in Supplementary Table 1. Chi-square analysis was performed to compare the SDOH characteristics of participants with or without CVD and whether they took the female hormones during home interviews. Multivariate logistic regression was established to define the association between female hormone intake and CVD while adjusting for all covariates. The covariates considered in multivariate models include age, ethnicity, and socioeconomic indicators such as the highest level of education obtained, marital status, income level, and BMI. Moreover, we compared the risk of CVD between participants taking or not taking female hormones in different subgroups of covariates using the Stratified Chi-square test with participants not taking female hormones as reference subjects. The risk of taking female hormones on CVD development was evaluated by odds ratio (OR) and reported by 95% CI with a two-tailed significance level of 0.05. An OR < 1 indicates a protective role of female hormone intake on CVD, and OR > 1 indicates its opposite role. All statistical analyses were performed using SPSS 25.0 with appropriate sampling weights to account for stratification. We analyze 38,745 eligible female participants with an average age of 38.10 ± 12.59 years old. Table 1 shows the characteristics of all 38,745 female participants. Among them, 35,041 participants (90.44%) without CVD, 3704 participants (9.56%) have CVD, 1356 cases (3.50%) with congestive heart failure, 1278 cases (3.30%) with angina/angina pectoris, 1511 (3.90%) with heart attack, 1007 patients (2.60%) with stroke. The correlation analysis shows that the SDOH factors, including age, ethnicity, education level, poverty to income ratio, and marital status, are associated with CVD (Table 1, p-values < 0.001). Higher age is significantly associated with CVD prevalence, presented in any clinical feature ( CVD + vs. CVD-: congestive heart failure: 41.61 ± 5.99 vs. 34.94 ± 9.47; angina/angina pectoris 42.53 ± 9.61 vs. 34.96 ± 9.46; heart attack: 41.85 ± 6.84 vs. 34.95 ± 9.47; stroke:40.61 ± 8.54 vs. 34.93 ± 9.46). The participants of the non-Hispanic white race are less likely to get CVD, specifically in heart attack (31.7% vs. 34.8%) and stroke (33.3% vs 34.7%). In contrast, non-Hispanic Blacks is prone to get CVD (congestive heart failure: 31.0% vs. 27.3%; angina/angina pectoris 38.1% vs. 26.9%; heart attack: 41.5% vs. 26.5%; stroke:29.4% vs. 27.2%). The proportion of participants in poverty with the four clinical manifestations of CVD is lower than those out of poverty (p-values < 0.001). Among the factors affecting female hormone levels, those who have experienced Bilateral Ovariectomy and Arthritis have a higher prevalence of four clinically manifested symptoms of CVD. The majority of CVD is generally higher among those who have been pregnant more than six times. The age at menarche < 10 or ≥ 16 years old and at menopause < 30 years or ≥ 60 years are associated with a higher prevalence of CVD. And there are fewer participants with CVD among obese participants (BMI ≥ 30). Additionally, the proportion of participants who intake female hormones which get CVD is significantly lower than those without CVD (congestive heart failure: 63.6% vs. 73.7%; angina/angina: 59.8% vs. 73.7%%; heart attack:66.7% vs. 73.7%; stroke:73.9% vs. 74.6%). Table 2 summarizes the analysis results for the association of the other characteristics and female hormones intake status of the participants. 71.9% of adult women have taken female hormones, and compared with women who didn’t accept female hormones, the participants who take female hormones were younger (49.38 ± 9.42y vs. 51.02 ± 8.61y, p-values < 0.001) as well as high-income (34.7% vs. 19.8%, p-values < 0.001); The married women are inclined to take the female hormones (59.3% vs. 47.1%, p-values < 0.001). The multiple logistic regression analysis was used to analyze of the association between CVD and female hormone intake before and after adjustment for covariates (Fig. 2 and Table 3). In multiple regression analysis, female hormone intake was a risk factor before adjusting for covariates, for congestive heart failure (OR = 1.563, 95%CI:1.557–1.570), angina pectoris/angina pectoris (OR = 1.398, 95%CI:1.391–1.406), heart attack (OR = 1.102, 95%CI:1.097–1.107), and stroke (OR = 1.026, 95%CI: 1.022–1.029). After adjusting for all covariates we included (Model15), intake of female hormone seemed to be a protective factor of congestive heart failure (OR = 0.892, 95%CI: 0.890–0.894); Angina/angina pectoris (OR = 0.782, 95%CI:0.780–0.784); heart attack (OR = 0.963, 95%CI:0.959–0.967) and stroke (OR = 0.942, 95%CI:0.934–0.950). These results suggest that intake of female hormone plays a protective role in CVD. The logistic regression analysis of the association between CVD and female hormone intake before and after adjustment for covariates. A congestive heart failure (B) angina/angina pectoris (C) heart attack (D) stroke Additionally, we categorized 12,808 women based on their menopausal status. Table 4 details the multiple logistic regression analysis evaluating the correlation between coronary artery disease (CVD) and pre- and post-menopausal hormone intake, with adjustments for covariates. The covariates included in our multiple regression analyses were race, age, education level, marital status, poverty to income ratio, age at menarche, age at menopause, and number of pregnancies. Post-adjustment, hormone intake was found to be a protective factor against CVD, as indicated by the odds ratios for congestive heart failure (OR = 0.915, 95% CI: 0.846–0.990), angina/angina pectoris (OR = 0.915, 95% CI: 0.849–0.986), heart attack (OR = 0.757, 95% CI: 0.687–0.834), and stroke (OR = 0.832, 95% CI: 0.768–0.901). These findings underscore the protective effect of female hormone intake on the development of CVD in postmenopausal women. To accurately assess the effect of hormone-related factors on taking female hormones and the occurrence of CVD, we make a stratified analysis for the included covariates by using age, education level, household economic level, BMI, endogenous female hormone levels, and dietary and metabolic levels as stratification variables (Table 5). The number of female respondents with female hormone intake aged ≥ 60 years old who suffered from CVD is less than those who did not suffer from CVD. Conversely, women aged between 40–60 years old who take in female hormones are at an elevated risk of a heart attack. Additionally, taking female hormones poses a stroke risk related to menopause duration < 10 or ≥ 10 years. However, for women with a shorter duration of menopause (< 10), taking female hormones may make them less susceptible to developing heart failure, heart attacks and angina. In contrast, for women with a longer duration of menopause (≥ 10), the risk of these conditions is higher. Interestingly, for women who received higher levels of education (college graduates or above), the probability of female hormone intake in people with angina pectoris, heart attack and stroke is more than those without angina pectoris, heart attack and stroke. While the probability of female hormone intake in women with CVD is less than in women without CVD under lower education levels. However, women at the middle-income level who take in female hormones are prone to suffer any type of CVD. Moreover, the women with menarche at 13–15 years old, menopause at 30–49 years old, and pregnancies 7–9 times, as well as with a low-sugar (< 25 g/day), low-fat (< 50 g/day), low-cholesterol diet (< 300 mg/day) and proper folic acid intake (> 800 ug/day) intake female hormones have a protective effect on the development of CVD (p-values < 0.001). These results suggest that the intake of female hormones is a protective or risk factor for CVD, depending on the different SODH factors and the factors that affect the endogenous estrogen release level. Although the protective effect of female hormones on CVD has been demonstrated in animal experiments [23, 24], with the emergence and development of gender medicine, it has been gradually recognized that the SDOH factors exert a binding effect on many disease occurrences [28]. Therefore, the relationship between the occurrence of CVD and female hormones intake could not be accurately defined through previous clinical trials [15]. Therefore, this study assessed the relation between female hormone intake and the development of CVD in 38,745 eligible female participants, based on data from the NHANS III over 20 years, and demonstrated that intake of female hormones is a protective or risk factor for the occurrence of the CVD depending on the different SODH factor statues and the related factors that affect the endogenous estrogen release level. Correlation analysis found that the occurrence of four clinical manifestations of CVD in females is significantly related to SDOH factors (age, race, education level, family income, and marital status) and the other factors that affect endogenous hormone release levels (bilateral ovariectomy, pregnancies number, menarche age, menopause age), and exogenous hormone intake. In addition, exogenous hormone intake in women, including female hormones for therapeutic purposes and contraceptives, was associated with many demographic and endocrine factors. Thus, these factors act as confounders that influence the exact relationship between female hormones and CVD. Furthermore, logistic regression analysis showed that female hormone intake was a risk factor for CVD development without adjusting for covariates. After adjusting for race, age, education level, marital status, family economic status, age at menarche and menarche, and the number of pregnancies, the female hormone intake shifts to a protective factor for CVD development. In future clinical studies, investigators should consider various demographic and endocrine factors affecting female hormone levels. We found several interesting results in the stratified analysis of covariates. First, it showed that female hormone intake reduces the risk of the four clinical features of CVD in women aged ≥ 60 years old, but it increases the risk of a heart attack in women between 40–60 years old and angina pectoris and stroke in women aged between 20–40 years old. It suggests that exogenous estrogen supplementation can prevent CVD in old females with lower levels of estrogen (women aged ≥ 60 years old). A prospective cohort study found that bilateral oophorectomy increased the risk of CVD, while estrogen replacement therapy could reverse this condition [29].In addition, computed tomography scanning of coronary artery calcification revealed a lower prevalence of plaque in coronary arteries in patients with estrogen replacement therapy within five years of oophorectomy [30]. Interestingly, for women who received higher levels of education (college graduates or above), intake of female hormones is a risk factor for CVD, while it is a protective factor for CVD in women with lower education levels. It may attribute to females with high education levels who are often engaged in mental work and less physical activity. A possible explanation is that women with high levels of education are primarily engaged in mental work. They have much sedentary time due to their occupational characteristics (including TV watching, computer use, and driving).Studies have shown that passive activity is associated with an increased risk of cardiovascular disease and all-cause mortality [20], meaning that the reduction in physical work due to sedentary activity which is a significant factor in the incidence of CVD. Attractively, for women in poverty (poverty to income ratio ≤ 1.3) or in wealthy (poverty to income ratio ≥ 3.39), female hormone intake is a risk factor for congestive heart failure and stroke, but it is a protective factor for the four clinical features of CVD for women in middle-income level. Many studies reported that poverty and socioeconomic disadvantage in low-income communities are significant risk factors for developing coronary artery disease [31,32,33,34]. Moreover, these studies only dichotomized economic levels into the poor and non-poor groups. Adam L. Beckman et al. found an increased prevalence of dyslipidemia and obesity in the high and low PIR groups. One study classified economic levels into high, moderate, and low PIR and found an increased prevalence of dyslipidemia and obesity in the high and low PIR groups [35], and the investigated symptoms are close to CVD. In addition, studies also have suggested that low income leads to a higher prevalence of central obesity, sedentary, and smoking [36]. However, some studies have attributed this phenomenon to the psychological stress of people in poverty rather than specific income [37]. Poverty as a chronic stressor leads to physiological dysregulation of the responsiveness to stress through the hypothalamic–pituitary–adrenal (HPA) axis and the sympathetic nervous system (SNS) [38, 39], which are associated with an increased cardiometabolic risk and for affluent people, they may be equally exposed to psychological problems [40] such as obesity, sedentary lifestyle, smoking, and especially anxiety and depression [41,42,43], leading to an increased risk of CVD. Moreover, the factors affecting endogenous estrogen secretion levels can lead to the positive or negative effects of taking female hormones on the development of CVD. The women of menarche at 13–15 years old, menopause at 30–49 years old, and pregnancies 7–9 times, as well as with a low-sugar (< 25 g/day), low-fat (< 50 g/day), low-cholesterol diet (< 300 mg/day) and proper folic acid intake (> 400 ug/day) intake female hormones have a protective effect on the development of CVD. And the opposite of these factors can lead to the unbeneficial result of taking female hormones to CVD occurrence. These results suggest that an intake of female hormones is a protective or risk factor for CVD, depending on the different SODH factors that affect the endogenous estrogen release level. Mainly in women with older age (≥ 60 years old) and low educational level, in poverty or wealth, with menarche aged at 13–15 years old, menopause age at 30–49 years old, and with a low sugar and fat diet. It explains the contradictory results of many clinical studies. The limited sample size makes it impossible to stratify the study population in detail and obtain more accurate analysis results. The findings of this study hold significant implications for clinical practice and public health strategies. Initially, they underscore the necessity for personalized medicine, emphasizing the need for tailored hormone replacement therapy (HRT) based on the specific social determinants of health (SDOH) in women, which can determine the risk of CVD from taking female hormones and make individualized decisions about timing and dosage adjustments. For instance, HRT may serve as a preventative measure for CVD in women over the age of 60. Instead, Women with certain characteristics should make more cautious decisions when using exogenous hormones, such as younger age, higher education, poor or excessive economic situation, and poor dietary conditions and habits. Furthermore, considering lifestyle factors in women with higher educational attainment is also essential, as these factors may influence the cardiovascular effects of HRT. Public health professionals can utilize this information to design and implement targeted educational and preventive programs to raise awareness among women about risk factors for CVD and to encourage healthy lifestyle choices. Additionally, the study’s findings reveal the relationship between socioeconomic status and CVD risk, suggesting that policymakers should address the specific health needs of women from low-income and high-income communities, potentially providing increased support and resources for these groups. For example, the US Healthy People 2030 agenda [44] addresses SDOH factors contributing to health inequalities. Moreover, the results indicate that proper dietary and nutritional intake, such as a low-sugar, low-fat, low-cholesterol diet, and adequate folic acid intake, may be associated with the protective effects of female hormone intake. This provides clinicians with guidance on promoting healthy dietary habits, which could contribute to reducing the risk of CVD. The advantage of this study is that the NHANES III database has many nationwide cross-sectional study samples, which can represent the overall American population after weighing the samples. Therefore, the potential factors that may affect the relationship of exposure factors to outcome variables included in this study are relatively comprehensive. Additionally, we made a stratified analysis to precisely define the protective or risk role of female hormone intake for the development of CVD among subgroup populations with different SDOH backgrounds. Moreover, selection bias can be avoided as the sample data comes from community questionnaires. However, there some limitations still existed in this study. Firstly, NHANES III used a self-reported questionnaire to inquire about female hormone intake, which introduces recall and self-reported bias; Secondly, the data comes from cross-sectional studies, which means that further prospective studies are needed to verify the causal relationship between female hormones and CVD. Our study concludes that various SDOH factors that affect hormone secretion levels are significantly associated with the development of CVD. And they are also associated with whether female hormones were taken or not. Therefore, exogenous administration of female hormones has a preventive effect on the occurrence of CVD in specific groups of women. Furthermore, our results suggest that female hormone intake is a protective or risk factor for CVD, depending on the factors affecting the endogenous estrogen release level. 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Center for Regenerative and Reconstructive Medicine, Med-X Institute of Western China Science and Technology Innovation Harbour, First Affiliated Hospital of Xi’an Jiaotong University, Xi’an, Shaanxi, 710049, China Shenao Qu, Zhixuan Zhang, Ran Ju, Yi Lv & Nana Zhang National Local Joint Engineering Research Center for Precision Surgery and Regenerative Medicine, First Affiliated Hospital of Xi’an Jiaotong University, Xi’an, Shaanxi, 710061, China Shenao Qu, Zhixuan Zhang, Ran Ju, Zhuoqun Li, Xuan Han, Yi Lv & Nana Zhang Shaanxi Provincial Center for Regenerative Medicine and Surgical Engineering Research Center, First Affiliated Hospital of Xi’an Jiaotong University, No.277, West Yanta Road, Yanta District, Xian, Shaanxi Province, 710061, China Shenao Qu, Zhixuan Zhang, Ran Ju, Zhuoqun Li, Xuan Han, Yi Lv & Nana Zhang Department of Thoracic Surgery, Peking University People’s Hospital, Beijing, 100044, China Zhixuan Zhang Department of Hepatobiliary Surgery and Institute of Advanced Surgical Technology and Engineering, First Affiliated Hospital of Xi’an Jiaotong University, Xian, 710061, China Zhuoqun Li, Xuan Han & Yi Lv Xi’an Jiaotong University Health Science Center, Xi’an, 710061, China Jing Leng, Zihan Xue & Shiran Tao You can also search for this author in PubMedGoogle Scholar You can also search for this author in PubMedGoogle Scholar You can also search for this author in PubMedGoogle Scholar You can also search for this author in PubMedGoogle Scholar You can also search for this author in PubMedGoogle Scholar You can also search for this author in PubMedGoogle Scholar You can also search for this author in PubMedGoogle Scholar You can also search for this author in PubMedGoogle Scholar You can also search for this author in PubMedGoogle Scholar You can also search for this author in PubMedGoogle Scholar All authors contributed to this manuscript. SAQ and ZXZ collected and analyzed the related data and helped draft the manuscript. RJ, ZQL, and JL helped assess analytical methods and reconcile analytical results. XH, XZH, and SRT helped extract the original data. NNZ and YL designed the study, modified the manuscript, and funded this study. All authors read and approved the final manuscript. Correspondence to Yi Lv or Nana Zhang. This research analyzed de-identifed information downloaded from the National Health and Nutrition Examination Survey public database. The National Center for Health Statistics Ethics Review Committee granted ethics approval. All methods were carried out in accordance with relevant guidelines and regulations (declaration of Helsinki). All individuals provided written informed consent before participating in the study. Not applicable. The authors declare no competing interests. Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. 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To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/. Reprints and permissions Qu, S., Zhang, Z., Ju, R. et al. Association between the female hormone intake and cardiovascular disease in the women: a study based on NHANES 1999–2020. BMC Public Health24, 3578 (2024). https://doi.org/10.1186/s12889-024-21001-x Download citation Received: Accepted: Published: DOI: https://doi.org/10.1186/s12889-024-21001-x Anyone you share the following link with will be able to read this content: Sorry, a shareable link is not currently available for this article.
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