Early Detection Of Chronic Disease Research Articles

ADC-RBK: a multimodal approach for early detection of chronic diseases and focusing on Alzheimer’s

ADC-RBK: a multimodal approach for early detection of chronic diseases and focusing on Alzheimer’s

Janus Membrane-Based Wearable pH Sensor with Sweat Absorption, Gas Permeability, and Self-Adhesiveness.

Wearable biomedical sensors have enabled noninvasive and continuous physiological monitoring for daily health management and early detection of chronic diseases. Among biomedical sensors, wearable pH sensors attracted significant interest, as pH influences most biological reactions. However, conformable pH sensors that have sweat absorption ability, are self-adhesive to the skin, and are gas permeable remain largely unexplored. In this study, we present a pioneering approach to this problem by developing a Janus membrane-based pH sensor with self-adhesiveness on the skin. The sensor is composed of a hydrophobic polyurethane-polydimethylsiloxane porous hundreds nanometer-thick substrate and a hydrophilic poly(vinyl alcohol)-poly(acrylic acid) porous nanofiber layer. This Janus membrane exhibits a thickness of around 10 μm, providing a conformable adhesion to the skin. The simultaneous realization of solution absorption, gas permeability, and self-adhesiveness makes it suitable for long-term continuous monitoring without compromising the comfort of the wearer. The pH sensor was tested successfully for continuous monitoring for 7.5 h, demonstrating its potential for stable analysis of skin health conditions. The Janus membrane-based pH sensor holds significant promise for comprehensive skin health monitoring and wearable biomedical applications.

Quality and Safety Standards in Health Services: Development and Implementation

Aim. This research work aims to identify the characteristic features of the development and implementation of quality standards for patient monitoring and safety services in medical institutions, in order to improve quality based on the developed digital model, and describe the requirements for the implementation of quality control according to ISO 9001:2015 and ISO 15189:2022 Standards. Methods. The work uses a digital model of the Traumatology Center, built using the AnyLogic programming environment. This model is designed to identify and analyze effective solutions for implementation, such as patient registration processes, staffing, scheduling of specialized departments, identification of clinical characteristics and early symptoms of diseases to prevent complications during the incubation period. Results. It was discovered that modern wireless technologies and data analytics can be used to develop a remote health monitoring system, enabling early detection of chronic diseases to prevent complications. The scientific novelty of the study consists in the development of a sequence for the implementation of documentation on standardization and quality of service in medical institutions to prevent harm to patients, and improve their safety with the help of a complex of measuring devices and technologies. of the information.

Edge AI for Early Detection of Chronic Diseases and the Spread of Infectious Diseases: Opportunities, Challenges, and Future Directions

Edge AI, an interdisciplinary technology that enables distributed intelligence with edge devices, is quickly becoming a critical component in early health prediction. Edge AI encompasses data analytics and artificial intelligence (AI) using machine learning, deep learning, and federated learning models deployed and executed at the edge of the network, far from centralized data centers. AI enables the careful analysis of large datasets derived from multiple sources, including electronic health records, wearable devices, and demographic information, making it possible to identify intricate patterns and predict a person’s future health. Federated learning, a novel approach in AI, further enhances this prediction by enabling collaborative training of AI models on distributed edge devices while maintaining privacy. Using edge computing, data can be processed and analyzed locally, reducing latency and enabling instant decision making. This article reviews the role of Edge AI in early health prediction and highlights its potential to improve public health. Topics covered include the use of AI algorithms for early detection of chronic diseases such as diabetes and cancer and the use of edge computing in wearable devices to detect the spread of infectious diseases. In addition to discussing the challenges and limitations of Edge AI in early health prediction, this article emphasizes future research directions to address these concerns and the integration with existing healthcare systems and explore the full potential of these technologies in improving public health.

Health Screening Among a Rural, Hourly Workforce: North Carolina SERVIRE Project

This study demonstrates the feasibility of a novel, business-partnered, and worksite-based approach to healthcare access to facilitate chronic disease screening and diagnosis among rural hourly workers. The prevalence of undiagnosed and untreated diabetes and hypertension among screening participants was determined. From February 2021 to June 2023, investigators partnered with 29 businesses to screen 1,114 workers. Health screenings included a demographic questionnaire, A1c testing for prediabetes (A1c of 5.7-6.4) and diabetes (A1c≥6.5), hypertension (Stage 1: systolic blood pressure of 130-139 mmHg; Stage 2: systolic blood pressure ≥140 mmHg), kidney disease (estimated glomerular filtration rate <60; urine protein ≥1+), and questionnaire assessment of stroke (CHA2DS2-VASc) and sleep apnea (STOP-bang) risk. Of the 1,114 individuals screened (n=632, 56.7% male; n=497, 44.6% Black)), 388 (36%) screened positive for prediabetes or diabetes. Diabetes was previously undiagnosed in 273 (70.4%) of these participants. More than half of the participants (n=680, 62.4%) had an elevated blood pressure reading during the screening, and the majority of these participants (n=445, 65.4%) had not been previously diagnosed with hypertension. In addition, 241 (21.6%) participants were at an increased risk of stroke (CHA2DS2-VASc≥2), and 182 (23.7%) had a STOP-Bang score ≥4, indicating an increased risk of obstructive sleep apnea. By partnering with local businesses to deliver worksite-based health screenings, high rates of undiagnosed and uncontrolled diabetes and hypertension were identified among the rural, hourly workforce. This worksite-based approach to healthcare access could facilitate early detection of chronic disease, improve patient engagement in the healthcare system, and ultimately yield better long-term public health outcomes.

Conceptualised psycho-medical footprint for health status outcomes and the potential impacts for early detection and prevention of chronic diseases in the context of 3P medicine

BackgroundThe Suboptimal Health Status Questionnaire-25 (SHSQ-25) is a distinctive medical psychometric diagnostic tool designed for the early detection of chronic diseases. However, the synaptic connections between the 25 symptomatic items and their relevance in supporting the monitoring of suboptimal health outcomes, which are precursors for chronic diseases, have not been thoroughly evaluated within the framework of predictive, preventive, and personalised medicine (PPPM/3PM). This baseline study explores the internal structure of the SHSQ-25 and demonstrates its discriminatory power to predict optimal and suboptimal health status (SHS) and develop photogenic representations of their distinct relationship patterns.MethodsThe cross-sectional study involved healthy Ghanaian participants (n = 217; aged 30–80 years; ~ 61% female), who responded to the SHSQ-25. The median SHS score was used to categorise the population into optimal and SHS. Graphical LASSO model and multi-dimensional scaling configuration methods were employed to describe the network structures for the two populations.ResultsWe observed differences in the structural, node placement and node distance of the synaptic networks for the optimal and suboptimal populations. A statistically significant variance in connectivity levels was noted between the optimal (58 non-zero edges) and suboptimal (43 non-zero edges) networks (p = 0.024). Fatigue emerged as a prominently central subclinical condition within the suboptimal population, whilst the cardiovascular system domain had the greatest relevance for the optimal population. The contrast in connectivity levels and the divergent prominence of specific subclinical conditions across domain networks shed light on potential health distinctions.ConclusionsWe have demonstrated the feasibility of creating dynamic visualizers of the evolutionary trends in the relationships between the domains of SHSQ-25 relative to health status outcomes. This will provide in-depth comprehension of the conceptual model to inform personalised strategies to circumvent SHS. Additionally, the findings have implications for both health care and disease prevention because at-risk individuals can be predicted and prioritised for monitoring, and targeted intervention can begin before their symptoms reach an irreversible stage.

Staging of Alzheimer's disease based on MRI using CNN

Early detection of chronic diseases and determining the stages of damage to the patient is considered one of the most important stages of treatment, as it helps doctors take important remedial measures that help the patient recover or reduce the risk of the disease to a minimum. Alzheimer's disease is one of the neurological diseases that lead to brain atrophy, which leads to the loss of its functions. MRI images of the brain are used to detect Alzheimer's disease, but it is difficult to determine both the stages of the disease and the amount of damage in a patient using this MRI technique. In this research, we aim to detect Alzheimer's disease in addition to determining its stage based on deep learning techniques by using a classifier that uses the convolutional neural network (CNN). In the research, magnetic resonance images of the brain were used, and the hippocampus region was extracted in assessing the amount of damage because it is the most important region in diagnosing damage to the disease and reduce the amount of data entered into the neural network, our results show an accuracy of 95% in estimating brain damage. The results of the classifier used were able to determine the amount of damage according to four stages of the disease.

Understanding the implementation of health checks in the prevention and early detection of chronic diseases among Aboriginal and Torres Strait Islander people in Australia: a realist review protocol

IntroductionChronic disease remains the leading cause of morbidity and mortality among Aboriginal and Torres Strait Islander peoples in Australia. Regular structured, comprehensive health assessments are available to Aboriginal and Torres...

Profiling of Targeted miRNAs (8-nt) for the Genes Involved in Type 2 Diabetes Mellitus and Cardiac Hypertrophy

Type 2 Diabetes Mellitus (T2DM) and cardiac hypertrophy (CH) are among the top ten leading cause of deaths, worldwide. T2DM and cardiac hypertrophy are the chronic diseases, have close association and direct life-threatening complications like stroke, myocardial infarction, retinopathy, nephropathy, and limb amputation. In addition to other medical approaches, miRNAs-based strategy is considered most efficient for early detection of chronic diseases and also has potential for the treatment of T2DM and cardiac hypertrophy like it is being used for cancer in clinical trials. MicroRNAs (miRNAs) are single stranded (non-coding) of 20 to 22 nucleotides sequences which bind to their target mRNA upon the complimentary basis, to silence the protein expression at post transcriptional level. Bioinformatic databases are used like online mendelian inheritance in man (OMIM), gene testing registry (GTR), TargetScan and ShinyGO for validation of disease linked genes and sorting the common miRNAs in both diseases, such as miR-30-5p/ 101-3p.2/ 190-5p/ 506-3p/ 9-5p/ 128-3p/ 137/ 96-5p/ 7-5p/ 107/ 101-3p.1/ 98-5p/ 124-3p.2/ 124-3p.1/ 16-5p/ 15-5p/ 497-5p/ 424-5p/ 195-5p/ 1271-5p, let-7-5p. Aforementioned databases were also used for the miRNAs which have more than one disease linked genes target in each pathological condition. Such miRNAs for cardiac hypertrophy are: miR-19-3p/ 183-5p.2/ 153-3p/ 372-3p/ 302-3p/ 520-3p/ 373-3p/ 129-5p/ 144-3p/ 139-5p and for T2DM are: miR-27-3p/ 206/ 1-3p/ 181-5p. This finding would be helpful for the appropriate selection of miRNAs and to design applicable research project in future. It will require more validation by using the miRNAs expression analysis, mimic, and anti-miRNA approach to check their potential against cardiac hypertrophy and T2DM.

Profiling of Targeted miRNAs (8-nt) for the Genes Involved in Type 2 Diabetes Mellitus and Cardiac Hypertrophy

Type 2 Diabetes Mellitus (T2DM) and cardiac hypertrophy (CH) are among the top ten leading cause of deaths, worldwide. T2DM and cardiac hypertrophy are the chronic diseases, have close association and direct life-threatening complications like stroke, myocardial infarction, retinopathy, nephropathy, and limb amputation. In addition to other medical approaches, miRNAs-based strategy is considered most efficient for early detection of chronic diseases and also has potential for the treatment of T2DM and cardiac hypertrophy like it is being used for cancer in clinical trials. MicroRNAs (miRNAs) are single stranded (non-coding) of 20 to 22 nucleotides sequences which bind to their target mRNA upon the complimentary basis, to silence the protein expression at post transcriptional level. Bioinformatic databases are used like online mendelian inheritance in man (OMIM), gene testing registry (GTR), TargetScan and ShinyGO for validation of disease linked genes and sorting the common miRNAs in both diseases, such as miR-30-5p/101-3p.2/190-5p/506-3p/9-5p/128-3p/137/96-5p/7-5p/107/101-3p.1/98-5p/124-3p.2/124-3p.116-5p/15-5p/497-5p/ 424-5p/195-5p/1271-5p, let-7-5p. Aforementioned databases were also used for the miRNAs which have more than one disease linked genes target in each pathological condition. Such miRNAs for cardiac hypertrophy are: miR-19-3p/183-5p.2/153-3p/372-3p/302-3p/520-3p/373-3p/129-5p/144-3p/139-5p and for T2DM are: miR-27-3p/206/1-3p/181-5p. This finding would be helpful for the appropriate selection of miRNAs and to design applicable research project in future. It will require more validation by using the miRNAs expression analysis, mimic, and anti-miRNA approach to check their potential against cardiac hypertrophy and T2DM.

Early Detection and Diagnosis of Chronic Kidney Disease Based on Selected Predominant Features.

In numerous perilous cases, a quick medical decision is needed for the early detection of chronic diseases to avoid austere consequences that may be fatal. Chronic kidney disease (CKD) is a prevalent disease that presents a variety of challenges, including soaring costs for intervention, urgency, and, more importantly, difficulty in early detection of the disease. The current study carries out a prediction-based method that helps in detecting and diagnosing CKD patients which enables a fast and accurate decision-making process at the early stage. A combination of preprocessing and feature selection methods was developed; additionally, several prediction models, such as K-nearest neighbor (KNN), support vector machine (SVM), random forest (RF), and bagging, were trained based on the processed dataset. The performance evaluation shows higher reliability of all models in terms of accuracy, precision, sensitivity, F-measure, specificity, and area under the curve (AUC) score. Specifically, KNN outperformed with an accuracy of 99.50%, sensitivity of 99.2%, precision of 100%, specificity of 98.7%, and F-measure and AUC score of 99.6%. The experimental results of KNN show the best fitted model compared to the existing state-of-the-art methods. Moreover, the reduced feature set proves that just a few clinical tests are enough to detect CKD, resulting in diagnosis cost reduction.

An Intelligent Approach for Accurate Prediction of Chronic Diseases

Around the globe, chronic diseases pose a serious hazard to healthcare communities. The majority of the deaths are due to chronic diseases, and it causes burdens across the world. Through analyzing healthcare data and extracting patterns healthcare administrators, victims, and healthcare communities will get an advantage if the diseases are early predicted. The majority of the existing works focused on increasing the accuracy of the techniques but didn’t concentrate on other performance measures. Thus, the proposed work improves the early detection of chronic disease and safeguards the lives of the patients by increasing the specificity and sensitivity of the classifiers along with the accuracy. The proposed work used a hybrid optimization algorithm called the Hybrid Gravitational Search algorithm and Particle Swarm Optimization algorithm (HGSAPSO) to upgrade the detection of chronic diseases. Existing classifier parameters with their optimized parameters are compared and evaluated. Classifiers such as Artificial Neural Network (ANN), Support Vector Machines (SVM), K-Nearest Neighbor (Knn), and Decision tree (DT) are used. Health care data are obtained from the UCI machine learning repository to evaluate the proposed work. The proposed work is assessed on 6 benchmark datasets and the performance metrics such as Accuracy, Specificity, Sensitivity, F-measure, Recall, and Precision are compared. The experimental results exhibit that the proposed work attains better accuracy on Artificial Neural Network-Hybrid Gravitational Search algorithm and Particle Swarm Optimization algorithm (ANN-HGSAPSO) classifier compared to other classifiers. ANN-HGSAPSO provides 93% accuracy for Chronic Kidney Disease (CKD), Cardio Vascular Disease (CVD) 96%, Diabetes 82%, Hepatitis 94%, Wisconsin Breast Cancer (WBC) 91%, and for Liver disease dataset 96%.

The Impact of Community Health Coalitions On Preventative Health Outcomes: A Systematic Review

Background/Objective: Six out of ten deaths in the United States are caused by chronic diseases. There are multiple approaches to prevention and/or early detection of chronic diseases through screenings, nutrition, physical activity, and more. Unfortunately, a large portion of the US population have low rates of preventative care. Community health coalitions have the potential to improve preventative health outcomes by partnering with key members of communities to design programs that best fit community needs. In this systematic review, we examine evidence that community health coalitions can improve preventative health outcomes.&#x0D; Methods: PubMed, WebofScience, CINAHL, and PsycINFO were searched for (outcome OR health outcome) AND (prevention) AND (diabetes OR hypertension OR cancer OR prenatal) AND (community-based participatory research OR community coalition OR coalition OR health coalition). After screening 490 articles, 30 full-text, peer-reviewed, English articles meeting inclusion criteria of health coalition, prevention, health outcomes reported, and conducted in the United States were reviewed.&#x0D; Results: 27 of the 30 studies showed community health coalitions having a positive impact. Roughly half of the articles were randomized control trials. Community members and academia were represented in 80% and 77%, respectively, of the coalitions described, while other representations included faith-based organizations, health care, public health, industry, and education. Diabetes and cancer represented 74% of the targeted preventions addressed. 90% of the interventions focused on educating the community, and 33% focused on increasing health screening rates. 60% utilized trained community members to deliver interventions. 60% reported health measurements, 33% reported screening rates, and 30% reported knowledge and awareness.&#x0D; Conclusion, Impact, &amp; Implications: While the evidence suggests community health coalitions are effective in improving preventative health outcomes, the quality of the studies remain low. There is a need for increased rigor of study design and assessment in order to determine the true efficacy of community health coalitions. The majority of the randomized-control trials in this review were rated as low-quality, highlighting the difficulty of designing a randomized, controlled study in a community. Inadequate funding, lack of rigorous design, or ethical challenges can limit the quality of the assessment. Further, although prenatal care was included as a search term, there were no coalitions found that addressed this type of preventative care. This points to a future need for the creation of or increased publications from prenatal health coalitions.

The effect of COVID-19 pandemic and lockdown on consultation numbers, consultation reasons and performed services in primary care: results of a longitudinal observational study

ObjectivesThe aims of our study were to describe the effect of the COVID-19 pandemic and lockdown on primary care in Germany regarding the number of consultations, the prevalence of specific reasons for consultation presented by the patients, and the frequency of specific services performed by the GP.MethodsWe conducted a longitudinal observational study based on standardised GP interviews in a quota sampling design comparing the time before the COVID-19 pandemic (12 June 2015 to 27 April 2017) with the time during lockdown (21 April to 14 July 2020). The sample included GPs in urban and rural areas 120 km around Hamburg, Germany, and was stratified by region type and administrative districts. Differences in the consultation numbers were analysed by multivariate linear regressions in mixed models adjusted for random effects on the levels of the administrative districts and GP practices.ResultsOne hundred ten GPs participated in the follow-up, corresponding to 52.1% of the baseline. Primary care practices in 32 of the 37 selected administrative districts (86.5%) could be represented in both assessments. At baseline, GPs reported 199.6 ± 96.9 consultations per week, which was significantly reduced during COVID-19 lockdown by 49.0% to 101.8 ± 67.6 consultations per week (p < 0.001). During lockdown, the frequency of five reasons for consultation (-43.0% to -31.5%) and eleven services (-56.6% to -33.5%) had significantly decreased. The multilevel, multivariable analyses showed an average reduction of 94.6 consultations per week (p < 0.001).ConclusionsWe observed a dramatic reduction of the number of consultations in primary care. This effect was independent of age, sex and specialty of the GP and independent of the practice location in urban or rural areas. Consultations for complaints like low back pain, gastrointestinal complaints, vertigo or fatigue and services like house calls/calls at nursing homes, wound treatments, pain therapy or screening examinations for the early detection of chronic diseases were particularly affected.

‘Hippocrates’ – support system for medical decision-making for primary care for early detection of socially significant diseases

Within the framework of the Federal Project ‘Creation of a unified digital contour in health care based on the unified state information system of health care’ of the National Project ‘Healthcare’, employees of the Moscow State Medical and Dental University n.a. A. I. Evdokimov (Department of Therapy, Clinical Pharmacology and Emergency Medicine) with the assistance of RPO ‘Outpatient Doctor’ in 2020, a system for supporting medical decisions was created. The program ‘Hippocrates’ works in the testing mode, but now it is fulfilling the tasks assigned to it to increase the early detection of chronic diseases, and it also plays an important role in teaching doctors to take action based on clinical guidelines.

Value of Urine Peptides in Assessing Kidney and Cardiovascular Disease.

Urinary peptides gained significant attention as potential biomarkers especially in the context of kidney and cardiovascular disease. In this manuscript the recent literature since 2015 on urinary peptide investigation in human kidney and cardiovascular disease is reviewed. The technology most commonly used in this context is capillary electrophoresis coupled mass spectrometry, in part owed to the large database available and the well-defined dataspace. Several studies based on over 1000 subjects are reported in the recent past, especially examining CKD273, a classifier for assessment of chronic kidney disease based on 273 urine peptides. Interestingly, the most abundant urinary peptides are generally collagen fragments, which may have gone undetected for some time as they are typically modified via proline hydroxylation. The data available suggest that urinary peptides specifically depict inflammation and fibrosis, and may serve as a non-invasive tool to assess fibrosis, which appears to be a key driver in kidney and cardiovascular disease. The recent successful completion of the first urinary peptide guided intervention trial, PRIORITY, is expected to further spur clinical application of urinary peptidomics, aiming especially at early detection of chronic diseases, prediction of progression, and prognosis of drug response.

The use of functional performance tests and simple anthropomorphic measures to screen for comorbidity in primary care.

Many older adults are unaware that they have comorbid diseases. Increased adiposity and reduced muscle mass are identified as key contributors to many chronic diseases in older adults. Understanding the role they play in the development of comorbidities in older populations is of prime importance. To identify the optimal body shape associated with three common functional performance tests and to determine which anthropometric and functional performance test best explains comorbidity in a sample of older adults in Hong Kong. A total of 432 older adults participated in this cross-sectional study. Researchers assessed their body height, body mass index, waist circumference, waist-to-hip ratio, handgrip strength (kg), functional reach (cm) and results in the timed-up-and-go (TUG) test (seconds). The Charlson Comorbidity Index was used to assess comorbidity. Allometric modelling indicated that the optimal body shape associated with all functional performance tests would have required the participants to be taller and leaner. The only variable that predicted comorbidity was the TUG test. The inclusion of body size/shape variables did not improve the prediction model. Performance in the TUG test alone was found to be capable of identifying participants at risk of developing comorbidities. The TUG test has potential as a screening tool for the early detection of chronic diseases in older adults. Many older people are unaware of their own co-existing illnesses when they consult physicians for a medical condition. TUG can be a quick and useful screening measure to alert nurses in primary care to the need to proceed with more detailed assessments. It is an especially useful screening measure in settings with high patient volumes and fiscal constraints. TUG is low cost and easy to learn and is therefore also relevant for nurses and health workers in low-resource, low-income countries.

Toward Early Severity Assessment of Obstructive Lung Disease Using Multi-Modal Wearable Sensor Data Fusion During Walking.

Early detection of chronic diseases helps to minimize the disease impact on patient's health and reduce the economic burden. Continuous monitoring of such diseases helps in the evaluation of rehabilitation program effectiveness as well as in the detection of exacerbation. The use of everyday wearables i.e. chest band, smartwatch and smart band equipped with good quality sensor and light weight machine learning algorithm for the early detection of diseases is very promising and holds tremendous potential as they are widely used. In this study, we have investigated the use of acceleration, electrocardiogram, and respiration sensor data from a chest band for the evaluation of obstructive lung disease severity. Recursive feature elimination technique has been used to identity top 15 features from a set of 62 features including gait characteristics, respiration pattern and heart rate variability. A precision of 0.93, recall of 0.91 and F-1 score of 0.92 have been achieved with a support vector machine for the classification of severe patients from the non-severe patients in a data set of 60 patients. In addition, the selected features showed significant correlation with the percentage of predicted FEV1.Clinical Relevance- The study result indicates that wearable sensor data collected during natural walk can be used in the early evaluation of pulmonary patients thus enabling them to seek medical attention and avoid exacerbation. In addition, it may serve as a complementary tool for pulmonary patient evaluation during a 6-minute walk test.

Comparative Study on Suicidal Ideation Factors between the Elderlies Living Alone and the Elderlies Cohabiting

The purpose of this study is to identify factors influencing suicidal ideation for the elderly living alone. Utilizing the identified factors as the base data, the study aims to develop community programs for suicide prevention. This research is a secondary analysis of the 2017 national survey, ‘Survey on the Elderly,' conducting logistics regression analysis on the data collected on 10,299 elderly aged 65 or older living in 17 general residential facilities as of 2017. The analysis reveals that 10.5% of the elderly living alone have contemplated suicide. The frequency of suicidal ideation was attributed to such factors as gender, age, chronic disease, depression and experiences of abuse and discrimination. To prevent suicide among the elderly, therefore, active management from both public and private sectors are needed for early detection of chronic diseases and depression.

Analysis of clinical characteristics of 2243 with positive anti-gastric parietal cell antibody.

BackgroundTo facilitate the early detection of chronic diseases, we analyzed the clinical characteristics of anti‐gastric parietal cell antibody (PCA)‐positive population, revealed the early characteristics of the population.MethodsAccording to the retrospective analysis, current situation investigation and comparative analysis of the clinical characteristics and medical history of the subjects, the comparison between the groups was performed.Result(a) The positive rate of PCA detection in department of gastroenterology in our hospital was 35.80%. Among the individuals who underwent PCA, esophagogastroduodenoscopy (EGD) and pathological examination at the same time, 33.59% of the patients with PCA positive were diagnosed as atrophic gastritis by gastroscopy, which was much higher than 9.09% of the patients with PCA negative. (b) The incidence of gastroesophageal reflux, hypertension, ischemic heart disease (IHD) and cerebral ischemia in PCA‐positive population were 65.45%, 81.63%, 15.43%, and 31.61%, respectively, which were significantly higher than those in the control group. (c) The incidence rates of decreased red blood cells (RBC) and increased homocysteine (HCY) in laboratory‐related tests were 38.30% and 69.15%, respectively, which were much higher than those in control group.ConclusionPCA has predictive value for a variety of chronic diseases and timely detection is of great significance.