Quantifying the effects of aging and urbanization on major gastrointestinal diseases to guide preventative strategies

Sensitivity of major chronic diseases and patients of different ages to the collapse of the healthcare system during the COVID-19 pandemic in China

This study evaluates the sensitivity of major chronic diseases to the collapse of the healthcare system for developing prevention and control strategies under normal and emergency conditions. Data for the years 2018, 2019, and 2020 (coronavirus disease 2019 [COVID-19] pandemic) were curated from the National Disease Mortality Surveillance System, Chinese Center for Disease Control and Prevention for diseases such as cancer, heart disease (HD), cerebrovascular disease (CVD), and chronic obstructive pulmonary disease (COPD). The yearly death rate change for 2018, 2019, and 2020 were calculated. Similarly, expected and observed death cases, 95% confidence intervals, and Z-score were calculated for the year 2020 (COVID-19 pandemic). Furthermore, linear regression analysis was performed to analyze a correlation between the median age of various groups and the mortality rate. The observed death cases for cerebrovascular, heart, and other chronic diseases, were more than the expected death cases (430,007 vs 421,317, 369,684 vs 368,957, and 302,974 vs 300,366) as well as an upper limit of 95% confidence interval. The observed death cases for COPD and cancer are less than the expected death cases (127,786 vs 140,524, 450,346 vs 463,961) and lower limit of the 95% confidence interval. The highest Z-score was noted for cerebrovascular disease (105.14). The disease impact of severity was CVD, other chronic diseases, and HD in descending order. The unexpected decline in deaths was found for COPD and cancers with Z-scores (-166.45 and -116.32). The severity of impact was CVD, other chronic diseases, HD, cancer, and COPD in descending order. The COVID-19 pandemic has also resulted in an increase in deaths of the relatively young population as shown by the difference in rate of slop. The healthcare system collapsed due to prevention, control measures and increased burden of COVID-19 patients, affected chronic disease treatment/management and as a consequence variation in death rates occurs in different chronic diseases. A marked increase in mortality was observed in cerebrovascular disease. The unexpected decline in deaths from COPD and cancers, and increase in deaths of the relatively young population suggests that there may be opportunities for improvement in chronic disease management.

Quantitative evaluation of the role of aging and non-aging factors for predicting threats from major chronic diseases and developing control strategies

Various indicators exist to assess the threat of chronic diseases. This paper presents new ones to evaluate the role of aging and non-aging factors for predicting threats from major chronic diseases. Age at zero mortality (AM0) and age at average mortality (AMa) can be calculated by regressing age and mortality (the intercept indicates AM0, the slope indicates the observed slope and r indicates random non-aging factors). A regression equation can be created using AMa at the age of 72 and mortality at the age of 82; thus, the expected slope can be obtained for the aging factor without considering non-aging factors. It is possible to distinguish between aging and non-aging factors using the observed and expected slopes, which should be multiplied by r to produce an index of aging (IA). The lower the AM0, AMa or IA of a disease is, the greater the threat it poses to a population. The AM0 and IA were calculated using data from China (2004 and 2019) for various diseases [cancer, heart disease (HD), cerebrovascular disease (CVD), and chronic obstructive pulmonary disease (COPD)]. We found the severity of threat was highest for cancer, CVD, other chronic diseases, HD and COPD in descending order in 2019. The results suggest that changes in threats may be related to socioeconomic development. Cancer was found to be the greatest threat to younger age groups, with IA<0.5, suggesting that non-aging risk factors may play an important role in cancers. Conversely, aging may play an important role in other chronic diseases, including HD, CVD, and COPD. Compared to 2004, the AM0 of cancer showed the greatest change. In conclusions, the different indicators explain different aspects of the problem and it would be beneficial to conduct in-depth research on the theoretical basis for the association of threats of disease with socioeconomic development in order to develop prevention and control strategies.

Changes in threats from chronic obstructive pulmonary disorder and lung cancer with environmental improvements in China: Quantitative evaluation and prediction based on a model with age as a probe

Various indicators can be used to assess threats from chronic diseases. This study presented new indicators of quantitative evaluation and prediction of threats from chronic obstructive pulmonary disorder (COPD) and lung cancer and assessed relevant changes in these indicators occurring with environmental improvements. Age at zero mortality (AM0) and age at average mortality (AMa) values were calculated based on the regression of the linear relationship of age with mortality for COPD or lung cancer. The lower the AM0 or AMa of a chronic disease, the greater the threats from the disease to a population were considered to be. AM0 values of both diseases were higher in 2019 than in 2004. Moreover, AM0 was lower for lung cancer than for COPD (0.365 vs. 41.643); however, lung cancer and COPD demonstrated almost identical values for age-standardized mortality. AMa values of both the diseases in 2004 and 2019 were within the range of the median age group (70-74 years). In recent years, the overall mortality risk for lung cancer and COPD has decreased with environmental improvement, and aging has played a major role in lung cancer and COPD development. AM0 and AMa values may be used as a theoretical basis for further research on chronic diseases, particularly lung cancer and COPD.

A new pathway for considering trigger factors based on parallel-serial connection models and displaying the relationships of causal factors in low-probability events

BACKGROUND

To determine the effect size of observed factors considering trigger factors based on parallel-serial models and to explore how multiple factors can be related to the result of complex events for low-probability events with binary outcomes.

METHODS

A low-probability event with a true binary outcome can be explained by a trigger factor. The models were based on the parallel-serial connection of switches; causal factors, including trigger factors, were simplified as switches. Effect size values of an observed factor for an outcome were calculated as SAR = (Pe-Pn)/(Pe + Pn), where Pe and Pn represent percentages in the exposed and nonexposed groups, respectively, and SAR represents standardized absolute risk. The influence of trigger factors is eliminated by SAR. Actual data were collected to obtain a deeper understanding of the system.

RESULTS

SAR values of < 0.25, 0.25-0.50, and > 0.50 indicate low, medium, and high effect sizes, respectively. The system of data visualization based on the parallel-serial connection model revealed that at least 7 predictors with SAR > 0.50, including a trigger factor, were needed to predict schizophrenia. The SAR of the HLADQB1*03 gene was 0.22 for schizophrenia.

CONCLUSIONS

It is likely that the trigger factors and observed factors had a cumulative effect, as indicated by the parallel-serial connection model for binary outcomes. SAR may allow better evaluation of the effect size of a factor in complex events by eliminating the influence of trigger factors. The efficiency and efficacy of observational research could be increased if we are able to clarify how multiple factors can be related to a result in a pragmatic manner.

Overall mortality risk analysis for rectal cancer using deep learning-based fuzzy systems

Colorectal cancer is a leading cause of cancer mortality worldwide, with an increasing incidence rate in developing countries. Integration of genetic information with cancer therapy guidance has shown promise in cancer treatment, indicating its potential as an essential tool in translation oncology. However, the high-throughput analysis and variability of genomic data poses a major challenge to conventional analytic approaches. In this study, we propose an advanced analytic approach, named "Fuzzy-based RNNCoxPH," incorporated fuzzy logic, recurrent neural networks (RNNs), and Cox proportional hazards regression (CoxPH) for detecting missense variants associated with high-risk of all-cause mortality in rectum adenocarcinoma. The test data set was downloaded from "Rectum adenocarcinoma, TCGA-READ" the Genomic Data Commons (GDC) portal. In this study, four model-based risk score models were derived using RNN, CoxPH, RNNCoxPH_Addition, and RNNCoxPH_{Multiplication}. The RNNCoxPH_Addition and RNNCoxPH_{Multiplication} models were obtained as the sum and product of the RNN risk degree matrix and the CoxPH risk degree matrix, respectively. Moreover, the fuzzy logic system was used to calculate the survival risk values of missense variants and classified their membership grade to improve the identification of high-risk gene variation locations associated with cancer mortality. The four models were integrated to develop an advanced risk estimation model. There were 20 028 variants associated with survival status, amongst 17 638 variants were associated with survival and 2390 variants associated with mortality. The proposed Fuzzy-based RNNCoxPH model obtained a balanced accuracy of 93.7%, which was significantly higher than that of the other four test methods. In particular, the CoxPH model is commonly used in medical researches and the XGBoost model is famous for its high accuracy in machine learning. The results suggest that the Fuzzy-based RNNCoxPH model exhibits a higher efficacy in identifying and classifying the missense variants related to mortality risk in rectum adenocarcinoma.

Is it about time to develop social surgery?

The need for social surgery is nowadays more dire than ever, due to the exponential increase of the driving factors of surgical diseases in modern societies. During the last decades, high rates of urbanization have been linked to increased incidence of colon, liver, pancreas cancer and appendicitis.⁹ Sedentary lifestyle and obesity have translated into higher orthopedic surgical volumes.¹⁰ Climate change has jeopardized surgical supply chains, influenced surgery and pregnancy complications and increased the risk for massive trauma in the context of natural disaster.¹¹ Finally, the COVID-19 pandemic has put a strain on surgical care and training, prolonged surgical waiting lists, hindered operable non - communicable diseases' (NCDs) screening and early diagnosis.¹² Even though innovation in surgery has made big leaps in personalizing surgical planning, advancing minimally invasive approaches and improving surgical outcomes, the risk of losing the race to the mounting drivers of surgical morbidity is significant. This sets a reminder of the hippocratic doctrine of prevention over treatment, in the sense that preventing surgical disease and their complications can safeguard the access of those with non - preventable disease to better surgical care.

Life Extension and Overpopulation: Demography, Morals, and the Malthusian Objection

One of the main objections to life extension is that life extension will cause severe overpopulation. This objection presents both moral and demographic issues. To explore the demographic issue, we present an updated and improved version of the formula in chapter six of New Methuselahs for projecting the demographic impact of life extension. The new version includes additional demographical factors such as non-aging related causes of death. According to projections generated with this revised formula, moderate life extension (a life expectancy of 120 years) will not significantly increase population at the fertility rates current in the developed world, but radical life expectancy (halting aging completely, leading to an average life expectancy of 1000 years) can lead to severe overpopulation even at very low fertility rates. This formula also enables us to ascertain what fertility rate and birth spacing will prevent life extension from causing severe overpopulation. The moral issues arise if radical life extension causes overpopulation severe enough to outweigh the benefits it brings. New Methuselahs proposed a reproductive policy for avoiding severe overpopulation by limiting reproduction for those who use life extension. We then consider a moral objection to this policy that was not discussed in New Methuselahs: it is not likely that society will succeed in imposing limits to reproduction, therefore, it is likely that radical life extension is morally wrong. We respond to this objection and defend our response against two further objections.

Urban living and chronic diseases in the presence of economic growth: Evidence from a long-term study in southeastern China

High-speed urban development has brought about an increase in per capita income in low- and middle-income countries (LMICs) as well as the high prevalence rate of chronic diseases. Based on a study of chronic diseases from 2011 to 2021 in southeastern China, we used multivariate adjusted logistic regression method to analyze the effect of urban living on the incidence of typical chronic diseases and the trend of such effect with the improvement of public healthcare system. We adopted potential mediating risk factors of urban lifestyles including body mass index (BMI), frequency of dining out, sedentary time, and psychological distress in the adjusted estimation. Baseline results indicate a positive relationship between living in urban areas and the prevalence of type 2 diabetes, hyperlipidemia, and hypertension. Regarding the mediating factors, psychological distress had the highest positive coefficient (Cr) on type 2 diabetes, hyperlipidemia, and hypertension (Cr: 0.4881-0.7084), followed by BMI (Cr: 0.1042-0.1617) and frequency of dining out (Cr: 0.0311-0.0478), and finally, sedentary time (Cr: 0.0103-0.0147). However, regression results on the follow-up survey reveal that trend in the impact of living in urban areas on chronic disease diminished as the level of the healthcare system improved. Additionally, urban living was more positively correlated with the incidence of metabolic disease than with the incidence of cardiovascular disease and cancer. Our findings provide empirical evidence that future urban health planning in LMICs should pay sustained attention to upgrading the level of public health infrastructure covering urban residents as well as rural-to-urban migrants, constructing a long-term dynamic system of chronic disease prevention and control, and regularly monitoring the mental health problems of residents in order to interrupt the process of urban chronic disease prevalence in an early stage.

Quantificational evaluation of the resolving power of qualitative biomarkers with different cardinal numbers based on a magnitude-standardized index

Biomarkers are used for clinical diagnostic purposes, but existing indexes exhibit limitations in terms of the resolving power of biomarkers. This paper proposes a new index, the magnitude-standardized index (MSI), to describe the quantitative variations and resolving powers of different biomarkers. In MSI analysis models, variation scales for ratios and differences are considered simultaneously, and a higher MSI value implies a stronger risk or effect for a biological factor. We explain the rationale for the MSI via hybrid and geometric methods and verify its efficacy through simulation experiments. Our results indicate that the MSI is superior to the Youden index and odds ratio for describing resolving power. When two biomarkers with similar Youden index values, odds ratios, or MSI values but different positive test rates (or cardinal numbers) were combined, all three index values increased; however, only the MSI value remained relatively stable. For a very small cardinal number, such as that of a single nucleotide polymorphism, the MSI value is at most half of the maximum value (0.5), allowing comparisons between MSI values for biomarkers with different cardinal numbers. The MSI can thus provide a better quantifiable evaluation of the resolving power of biomarkers with different cardinal numbers.

The Liquid Biopsy in the Management of Colorectal Cancer: An Overview

Currently, there is a crucial need for novel diagnostic and prognostic biomarkers with high specificity and sensitivity in patients with colorectal cancer. A "liquid biopsy" is characterized by the isolation of cancer-derived components, such as circulating tumor cells, circulating tumor DNA, microRNAs, long non-coding RNAs, and proteins, from peripheral blood or other body fluids and their genomic or proteomic assessment. The liquid biopsy is a minimally invasive and repeatable technique that could play a significant role in screening and diagnosis, and predict relapse and metastasis, as well as monitoring minimal residual disease and chemotherapy resistance in colorectal cancer patients. However, there are still some practical issues that need to be addressed before liquid biopsy can be widely used in clinical practice. Potential challenges may include low amounts of circulating tumor cells and circulating tumor DNA in samples, lack of pre-analytical and analytical consensus, clinical validation, and regulatory endorsement. The aim of this review was to summarize the current knowledge of the role of liquid biopsy in the management of colorectal cancer.

A grading system from health to death using routine experimental indicators based on the pre-chronic disease status theory

BACKGROUND

To establish a system for assessing pre-chronic disease status (PCDS) whereby changes in biomolecule levels occur before the appearance of physical damage to body organs. We based our study on the common biomarkers of aging, disease and end-of-life processes.

METHODS

The red blood cell count as well as levels of albumin, creatinine and aspartate aminotransferase were used as indicators for measurement. The basic premise for determining PCDS was that the measured value was outside the reference range for a healthy individual. A binary outcome was determined according to reference range given by the laboratory undertaking the measurements. The Biological Age Index (BAI) was used to ascertain PCDS.

RESULTS

The four indictors that we chose were sensitive for end-of-life and aging. The BAI score for each age group increased significantly with increasing age. The BAI score of patients with cardiac disease, cerebrovascular disease, cancer or chronic obstructive pulmonary disease were mostly higher than those in healthy age-matched people.

CONCLUSION

A system for assessing PCDS centered on biomolecular detection and independent of the pathologic diagnosis could be effective.

Two Time Point Analysis of the Change in Risk and Aging Factors for Major Cancers: A 10-Year Longitudinal Study in China

OBJECTIVE

To quantify the change in risk and aging factors with a two time point analysis for major cancers to assess supportive strategies.

METHODS

The 2004 and 2015 mortality statistics in China were accessed. The standardized mortality rates of the two periods were used to calculate the ratio of change (RC) value to assess the risk of death associated with time (social development with time) for cancers. The role of age in mortality with time was evaluated by the interaction between time and age using a Poisson regression.

RESULTS

In ascending order of RC, the factors were uterus; other malignant neoplasms; esophagus; stomach; skin; liver; leukemia; "lip, oral cavity, and pharynx"; bladder; "colon and rectum"; breast; prostate; lung; ovary; pancreas; "lymphoid, hematopoietic, and related tissue"; and cervix cancers. According to their location on the scatter diagram, the 17 neoplasms could be divided into three groups, comprising undeveloped cancers (including four cancers), developed cancers (including three cancers), and cancers insensitive to social development. Unexpectedly, about 60% (as assessed by type of cancer) and two-thirds (as assessed by constituent ratio of death from all cancers) of cancers did not change with time.

CONCLUSIONS

Most cancers may be insensitive to social development. Internal factors, including aging, may be a key factor for the occurrence of cancer.

Heredity Indexes for Estimating Heritability Using Known and Unknown Family Data Based on the Model of Polygenic Inheritance

Objective

To establish a model for estimating genetic risk using known and unknown family data.

Methods

Four simulated datasets were generated for four paternal and maternal chromosomes. The simulated data for children were generated from the parental data according to the Mendelian law. The correlation coefficient between the children's and paternal data was calculated, and 2R was defined as the heredity index for continuous data (HIC). The simulated continuous data were transformed into binary data according to the gene accumulation threshold (incidence); the incidences of children in the parental no-disease group and the disease onset group were obtained; the correlation coefficient (R) was calculated as expected R (Re). The ratio of observed R (Ro) and Re was defined as the Heredity index for binary data (HIB).

Results

Different actual pedigree data (lunula and holding a hammer in the right or left hand) were successfully used to verify the accuracy of the model. The genetic risk was estimated according to the incidence in a population using a lookup table.

Conclusion

Our findings indicate the reliability of the model based on the fact that the multigene effect constitutes the normal distribution. Thus, this model can be used for comprehensive analysis of the influence of genetic and nongenetic factors on the genetic phenotype and to estimate genetic risk using known and unknown family data.

Analyzing the Relationship between Cohort and Case-Control Study Results Based on Model for Multiple Pathogenic Factors

Objective

Although the relative risk from a prospective cohort study is numerically approximate to the odds ratio from a case-control study for a low-probability event, a definite relationship between case-control and cohort studies cannot be confirmed. In this study, we established a different model to determine the relationship between case-control and cohort studies.

Methods

Two analysis models (the cross-sectional model and multiple pathogenic factor model) were established. Incidences in both the exposure group and the nonexposure group in a cohort study were compared with the frequency of the observed factor in each group (diseased and nondiseased) in a case-control study.

Results

The relationship between the results of a case-control study and a cohort study is as follows: Pe=(Pd∗m)/(Pc∗m)/(Pd∗m)/(Pn=(m)/(∗PdPc∗m)/(Pd∗m)/(Pe and Pn represent the incidence in the exposed group and nonexposed group, respectively, from the cohort study, while Pd and Pc represent the observed frequencies in the disease group and the control group, respectively, for the case-control study; finally, m)/(

Conclusions

There is a definite relationship between the results of case-control and cohort studies assessing the same exposure. The outcomes of case-control studies can be translated into cohort study data.