Construction Formula of Biological Age Using the Principal Component Analysis

Prediction of lifespan and assessing risk factors of large-sample implant prostheses: a multicenter study

PURPOSE

This study aimed to analyze factors influencing the success and failure of implant prostheses and to estimate the lifespan of prostheses using standardized evaluation criteria. An online survey platform was utilized to efficiently gather large samples from multiple institutions.

MATERIALS AND METHODS

During the one-year period, patients visiting 16 institutions were assessed using standardized evaluation criteria (KAP criteria). Data from these institutions were collected through an online platform, and various statistical analyses were conducted. Risk factors were assessed using both the Cox proportional hazard model and Cox regression analysis. Survival analysis was conducted using Kaplan-Meier analysis and nomogram, and lifespan prediction was performed using principal component analysis.

RESULTS

The number of patients involved in this study was 485, with a total of 841 prostheses evaluated. The median survival was estimated to be 16 years with a 95% confidence interval. Factors found to be significantly associated with implant prosthesis failure, characterized by higher hazard ratios, included the 'type of clinic', 'type of antagonist', and 'plaque index'. The lifespan of implant prostheses that did not fail was estimated to exceed the projected lifespan by approximately 1.34 years.

CONCLUSION

To ensure the success of implant prostheses, maintaining good oral hygiene is crucial. The estimated lifespan of implant prostheses is often underestimated by approximately 1.34 years. Furthermore, standardized form, online platform, and visualization tool, such as nomogram, can be effectively utilized in future follow-up studies.

Association between kidney function and biological age: a China Health and Retirement Longitudinal Study

Introduction

The chronological age (CA) cannot precisely reflect the health status. Our study aimed to establish a model of kidney biological age to evaluate kidney function more elaborately.

Methods

The modeling group was used to establish the model, consisting of 1,303 respondents of the China Health and Retirement Longitudinal Study (CHARLS). The biological age of the kidney (BA) was constructed by principal component analysis (PCA) and Klemera and Doubal's method (KDM) with the 1,303 health respondents.

Results

PCA was chosen as the best method for our research step by step. The test group was used to apply the model. (a) BA of the kidney can distinguish respondents with from without kidney disease. (b) BA of the kidney was significantly different in various levels of kidney function. The BA of the eGFR <60 group and 60 ≤ eGFR <90 group were older than GFR ≥90 group. (c) The group with younger BA of kidney at baseline had a lower risk of kidney function decreased. (d) The risk of decreased kidney function caused by increasing BA every additional year is higher than CA.

Discussion

The BA of the kidney is a parameter negatively correlated with decreased kidney function and fills the blank of evaluation among people in the middle of heathy and kidney diseases.

Age-Related Changes in the Temperature of the Lumbar Spine Measured by Passive Microwave Radiometry (MWR)

A study was conducted to determine the age dependence of the temperature of the low back in the region of the five lumbar vertebrae by using passive microwave radiometry (MWR). The rationale for the study is that the infrared brightness on which the temperature measurement is based will be dependent upon blood circulation and thus on metabolic, vascular, and other regulatory factors. The brightness and infrared temperatures were determined in five zones above each of the medial, left, and right lateral projections of the vertebrae. A total of 115 healthy subjects were recruited, aged between 18 and 84 years. No significant differences in infrared temperature were detected. As predicted, brightness temperature increased until 25 years old and then gradually decreased. In subjects over 70 years of age, compared with those aged 60-70 years, there is a significant increase in brightness temperature at the level of 3-5 lumbar vertebrae by 0.3-0.7 °C. This is interpreted as indicating that individuals who have lived to an advanced age successfully maintain metabolic and regenerative processes. The benchmark data that has been obtained can be usefully employed in future studies of the aetiology of low back pain. In particular, the prospect exists for the technology to be used to provide a non-invasive biomarker to evaluate the effectiveness of antiaging therapies.

Effects of accelerated biological age on depressive symptoms in a causal reasoning framework

BACKGROUND

Depression in middle-aged and elderly individuals is multifaceted and heterogeneous, linked to biological age (BA) based on aging-related biomarkers. However, due to confounding with chronological age and the absence of subgroup analysis and causal reasoning, the association between BA and depressive symptoms (DS) might be unstable and requires further investigation.

METHODS

We utilized data from the China Health and Retirement Longitudinal Study (N = 9478) to perform association analysis, causal inference, and subgroup analysis. BA acceleration (BAA) was derived using machine learning and adjusted for chronological age. A generalized linear mixed-effects model (GLMM) tree algorithm was employed to identify subgroups. The causal reasoning frame included propensity score matching and fast large-scale almost matching exactly.

RESULTS

In the longitudinal analysis, BAA exhibited a consistent and significant positive association with DS, even after controlling for demographic characteristics, lifestyle factors, health status, and physical functions. This association remained unchanged within the causal framework. GLMM tree analysis identified three partitioning variables (sex, satisfaction, and BMI) and five subgroups. Further subgroup analysis revealed that BAA exerted the strongest effect on DS among women with less satisfying lives.

LIMITATIONS

Depressive symptoms were evaluated through scale measurements rather than clinical diagnosis. The sample was derived from the general population, not the clinically depressed population.

CONCLUSIONS

This study provided the first longitudinal evidence that biological age acceleration increases depressive symptoms under causal reasoning and subgroup analysis, particularly among less satisfied women. And the association between BAA and DS was independent of known risk factors.

Biomarkers selection and mathematical modeling in biological age estimation

Biological age (BA) is important for clinical monitoring and preventing aging-related disorders and disabilities. Clinical and/or cellular biomarkers are measured and integrated in years using mathematical models to display an individual's BA. To date, there is not yet a single or set of biomarker(s) and technique(s) that is validated as providing the BA that reflects the best real aging status of individuals. Herein, a comprehensive overview of aging biomarkers is provided and the potential of genetic variations as proxy indicators of the aging state is highlighted. A comprehensive overview of BA estimation methods is also provided as well as a discussion of their performances, advantages, limitations, and potential approaches to overcome these limitations.

Cardiovascular events and artificial intelligence-predicted age using 12-lead electrocardiograms

Background

There is increasing evidence that 12-lead electrocardiograms (ECG) can be used to predict biological age, which is associated with cardiovascular events. However, the utility of artificial intelligence (AI)-predicted age using ECGs remains unclear.

Methods

Using a single-center database, we developed an AI-enabled ECG using 17 042 sinus rhythm ECGs (SR-ECG) to predict chronological age (CA) with a convolutional neural network that yields AI-predicted age. Using the 5-fold cross validation method, AI-predicted age deriving from the test dataset was yielded for all ECGs. The incidence by AgeDiff and the areas under the curve by receiver operating characteristic curve with AI-predicted age for cardiovascular events were analyzed.

Results

During the mean follow-up period of 460.1 days, there were 543 cardiovascular events. The annualized incidence of cardiovascular events was 2.24 %, 2.44 %, and 3.01 %/year for patients with AgeDiff < -6, -6 to ≤6, and >6 years, respectively. The areas under the curve for cardiovascular events with CA and AI-predicted age, respectively, were 0.673 and 0.679 (Delong's test, P = 0.388) for all patients; 0.642 and 0.700 (P = 0.003) for younger patients (CA < 60 years); and 0.584 and 0.570 (P = 0.268) for older patients (CA ≥ 60 years).

Conclusions

AI-predicted age using 12-lead ECGs showed superiority in predicting cardiovascular events compared with CA in younger patients, but not in older patients.

Progress in biological age research

Biological age (BA) is a common model to evaluate the function of aging individuals as it may provide a more accurate measure of the extent of human aging than chronological age (CA). Biological age is influenced by the used biomarkers and standards in selected aging biomarkers and the statistical method to construct BA. Traditional used BA estimation approaches include multiple linear regression (MLR), principal component analysis (PCA), Klemera and Doubal's method (KDM), and, in recent years, deep learning methods. This review summarizes the markers for each organ/system used to construct biological age and published literature using methods in BA research. Future research needs to explore the new aging markers and the standard in select markers and new methods in building BA models.

A machine learning-based data mining in medical examination data: a biological features-based biological age prediction model

Background

Biological age (BA) has been recognized as a more accurate indicator of aging than chronological age (CA). However, the current limitations include: insufficient attention to the incompleteness of medical data for constructing BA; Lack of machine learning-based BA (ML-BA) on the Chinese population; Neglect of the influence of model overfitting degree on the stability of the association results.

Methods and results

Based on the medical examination data of the Chinese population (45–90 years), we first evaluated the most suitable missing interpolation method, then constructed 14 ML-BAs based on biomarkers, and finally explored the associations between ML-BAs and health statuses (healthy risk indicators and disease). We found that round-robin linear regression interpolation performed best, while AutoEncoder showed the highest interpolation stability. We further illustrated the potential overfitting problem in ML-BAs, which affected the stability of ML-Bas’ associations with health statuses. We then proposed a composite ML-BA based on the Stacking method with a simple meta-model (STK-BA), which overcame the overfitting problem, and associated more strongly with CA (r = 0.66, P < 0.001), healthy risk indicators, disease counts, and six types of disease.

Conclusion

We provided an improved aging measurement method for middle-aged and elderly groups in China, which can more stably capture aging characteristics other than CA, supporting the emerging application potential of machine learning in aging research.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12859-022-04966-7.

Biological age for chronic kidney disease patients using index model

The estimation of biological age (BA) is an important asymptomatic measure that can be used to understand the physical changes and the aging process of a living being. Factors that contribute towards profiling the human biological age can be diverse. Therefore, this study focuses on developing a BA model for patients with Chronic Kidney Disease (CKD). The procedure commences with the selection of significant biomarkers using a correlation test. Appropriate weighting is then assigned to each selected biomarker using the indexing method to produce a BA index. The BA index is matched to the age variation within the sample to acquire additional terms for the chronological age leading ultimately to the estimated BA. From a sample of 190 patients (133 trained data and 57 testing data) obtained from the University of Malaya Medical Centre (UMMC), Malaysia, the intensity of the BA is found to be between three to nine years from the chronological age. Visual observations further validate the high similarities between the training and testing data sets.

Biomarkers of geroprotection and cardiovascular health: An overview of omics studies and established clinical biomarkers in the context of diet

The slowdown, inhibition, or reversal of age-related decline (as a composite of disease, dysfunction, and, ultimately, death) by diet or natural compounds can be defined as dietary geroprotection. While there is no single reliable biomarker to judge the effects of dietary geroprotection, biomarker signatures based on omics (epigenetics, gene expression, microbiome composition) are promising candidates. Recently, omic biomarkers started to supplement established clinical ones such as lipid profiles and inflammatory cytokines. In this review, we focus on human data. We first summarize the current take on genetic biomarkers based on epidemiological studies. However, most of the remaining biomarkers that we describe, whether omics-based or clinical, are related to intervention studies. Then, because of their promising potential in the context of dietary geroprotection, we focus on the effects of berry-based interventions, which up to now have been mostly described employing clinical markers. We provide an aggregation and tabulation of all the recent systematic reviews and meta-analyses that we could find related to this topic. Finally, we present evidence for the importance of the "nutribiography," that is, the influence that an individual's history of diet and natural compound consumption can have on the effects of dietary geroprotection.

Comparing Biological Age Estimates Using Domain-Specific Measures From the Canadian Longitudinal Study on Aging

Many studies have shown that estimates of biological age (BA) can predict health-related outcomes in older adults. Often, researchers employ multiple measures belonging to a variety of biological/physiological systems, and assess the validity of BA estimates by how well they approximate chronological age (CA). However, it is not clear whether this is the best approach for judging a BA estimate, or whether certain groups of measures are more informative to this end. Using data from the Canadian Longitudinal Study on Aging, we composed panels of biological measures based on the physiological systems/domains they belong to (blood, organ function, physical/cognitive performance), and also composed a panel of measures that optimized the association of BA with CA. We then compared BA estimates for each according to their association with CA and health-related outcomes, including frailty, multimorbidity, chronic condition domains, disability, and health care utilization. Although BA estimated using all 40 measures (r = 0.74) or our age-optimized panel (r = 0.77) most closely approximated CA, the strength of associations to health-related outcomes was comparable or weaker than that of our panel composed only of physical performance measures (CA r = 0.59). All BA estimates were significantly associated to the outcomes considered, with exception to the neurological and musculoskeletal disease domains, and only varied slightly by sex. In summary, while the approximation of CA is important to consider when estimating BA, the strength of associations to prospective outcomes may be of greater importance. Hence, the context in which BA is estimated should be influenced by an investigator's specific research goals.

Utilization of Time Series Tools in Life-sciences and Neuroscience

Time series tools are part and parcel of modern day research. Their usage in the biomedical field; specifically, in neuroscience, has not been previously quantified. A quantification of trends can tell about lacunae in the current uses and point towards future uses. We evaluated the principles and applications of few classical time series tools, such as Principal Component Analysis, Neural Networks, common Auto-regression Models, Markov Models, Hidden Markov Models, Fourier Analysis, Spectral Analysis, in addition to diverse work, generically lumped under time series category. We quantified the usage from two perspectives, one, information technology professionals', other, researchers utilizing these tools for biomedical and neuroscience research. For understanding trends from the information technology perspective, we evaluated two of the largest open source question and answer databases of Stack Overflow and Cross Validated. We quantified the trends in their application in the biomedical domain, and specifically neuroscience, by searching literature and application usage on PubMed. While the use of all the time series tools continues to gain popularity in general biomedical and life science research, and also neuroscience, and so have been the total number of questions asked on Stack overflow and Cross Validated, the total views to questions on these are on a decrease in recent years, indicating well established texts, algorithms, and libraries, resulting in engineers not looking for what used to be common questions a few years back. The use of these tools in neuroscience clearly leaves room for improvement.

Red blood cell distribution width predicts long-term mortality in critically ill patients with acute kidney injury: a retrospective database study

Acute kidney injury (AKI) is a serious complication in the intensive care unit (ICU), which may increase the mortality of critically ill patients. The red blood cell distribution width (RDW) has proved useful as a predictor of short-term prognosis in critically ill patients with AKI. However, it remains unknown whether RDW has a prognostic value of long-term all-cause mortality in these patients. The data of 18279 critically ill patients with AKI at first-time hospital admission were extracted from the Medical Information Mart for Intensive Care III (MIMIC-III) database. The tertiles of the RDW values were used to divide subjects into three groups, namely RDW < 13.6% for the low RDW group, 13.6% ≤ RDW < 15.2% for the middle RDW group and RDW ≥ 15.2% for the high RDW group. Demographic data, mortality, 4-year survival time and severity scale scores were compared among groups. The Kaplan-Meier analysis and the Cox regression analysis were performed to assess the impact of RDW on all-cause mortality in AKI patients. The receiver operating characteristic (ROC) curve analysis was done to evaluate the prognostic value of RDW on the long-term outcome of critically ill patients with AKI. The median age of the enrolled subjects was 65.6 years. AKI patients with a higher RDW value had significantly shorter survival time and higher death rate. By the Kaplan-Meier analysis, patients in the higher RDW group presented significantly shorter survival time and higher death rate. The Cox regression model indicated RDW as an independent risk factor of all-cause mortality of AKI patients (HR 1.219, 95% CI, 1.211 to 1.228). By the ROC analysis, RDW appeared more efficient in predicting long-term prognosis as compared with conventional severity scales. The AUC of RDW (95% CI, 0.712 to 0.725) was significantly higher than other severity scale scores. In conclusion, RDW is positively correlated to survival time of 4-year follow-up in critically ill patients with AKI, and RDW is an independent prognostic factor of long-term outcomes of these patients.

Difference between the biologic and chronologic age as an individualized indicator for the skincare intensity selection: skin cell profile and age difference studies

Background

The present research addresses the issue of skin aging and corresponding skin treatment individualization. Particular research question was on the development of a simplified criterion supporting patient-specific decisions about the necessity and intensity of skin treatment. Basing on published results and a wide pool of our own experimental data, a hypothesis is formulated that a difference between biologic and chronologic age can be used as a powerful indicator of skin aging.

Methods

In the present paper, we report the results of studies with 80 volunteers between 15 and 65 years of age linking skin cell profile parameters to biologic and chronologic age. Biologic age was calculated using the empirical expressions based on the forced vital lung capacity, systolic blood pressure, urea concentration, and blood cholesterol level. Epidermis and derma cellular structures were studied using skin biopsy samples taken from the gluteal region.

Results

The present study supports the conclusion that biologic and chronologic age difference is changing in the progress of life. Our studies are showing that time point when calculated biologic age becomes equal to the chronologic one reflecting the onset of specific changes in the age dependencies of experimentally measured skin cell profile parameters. Thus, it is feasible that a difference between chronologic and individually assessed biologic age indeed reflects the process of skin aging.

Conclusions

With all reservations to the relatively small number of study participants, it seems feasible that a difference between biologic and chronologic age can be used as an indicator of skin aging. Additional research linking blood immune profile and skin topography to the difference of biologic and chronologic age (reported in the following paper) provides further support for the formulated hypotheses. So, a difference between calculated biologic age and chronologic age can be used as an individualized criterion supporting decisions on skin treatment strategies. Further research involving larger numbers of participants aimed at optimizing the expressions for calculating biologic age could lead to reliable and easily available express criterion supporting the decision for the individualized skin treatment.

Risk stratification by means of biological age-related factors better predicts cancer-specific survival than chronological age in patients with upper tract urothelial carcinoma: a multi-institutional database study

Background:

Chronological age is an important factor in determining the treatment options and clinical response of patients with upper tract urothelial carcinoma (UTUC). Much evidence suggests that chronological age alone is an inadequate indicator to predict the clinical response to radical nephroureterectomy (RNU).

Patients and methods:

We retrospectively reviewed the data from 1510 patients with UTUC (Ta-4) treated by surgery. White blood cell (WBC) count, neutrophil-to-lymphocyte ratio, hemoglobin (Hb), platelets, albumin, alkaline phosphatase, lactate dehydrogenase, creatinine, and corrected calcium were tested by the Spearman correlation to indicate the direction of association with chronological age, which yielded significant, negative associations of Hb (p < 0.001) and WBC (p = 0.010) with chronological age. For scoring, we assigned points for these categories as follows; point ‘0’ for Hb >14 (reference) and 13–13.9 [odds ratio (OR): 1.533], point ‘1’ for 12–12.9 (OR: 2.391), point ‘2’ for 11–11.9 (OR: 3.015), and point ‘3’ for <11 (OR: 3.584). For WBC, point ‘1’ was assigned for >9200 (OR: 2.541) and ‘0’ was assigned for the rest; 9200–8500 (reference), 8499–6000 (OR: 0.873), 5999–4500 (OR: 0.772), 4499–3200 (OR: 0.486), and <3200 (OR: 1.277).

Results:

The 10-year cancer-specific survival (CSS) in the higher risk group with scores of 4 or higher in patients age <60 years was worse than a score of 0, or 1 in age >80 years [mean estimated survival 69.7 months, confidence interval (CI): 33.3–106 versus 103.5. CI: 91–115.9]. The concordance index between biological age scoring and chronological age was 0.704 for CSS and 0.798 for recurrence-free survival. The limitation of the present study is the retrospective nature of the cohort included.

Conclusions:

The biological age scoring developed for patients with UTUC undergoing RNU. It was applicable to those with localized disease and performed well in diverse age populations.

Is Sleep Duration Associated with Biological Age (BA)?: Analysis of (2010⁻2015) South Korean NHANES Dataset South Korea

(1) Background: South Korea ranked worst in sleep duration compared to other countries, but there are no clear healthcare programs to guarantee sufficient sleep. Studies are needed to suggest evidence and arouse public awareness of the negative effects of abnormal sleep duration. In this study, we investigated the relationship between biological age (BA) and sleep duration. (2) Methods: We used data from the Korea National Health and Nutrition Examination Surveys (KNHANES V-VI; 2010–2015, which is an annually cross-sectional study including 29,309 participants). We performed multiple linear regression to investigate the associations between sleep duration and differences in BA and chronological age (CA). (3) Results: A total of 14.22% of respondents had short sleep duration (less than 6 h per day) and 7.10% of respondents had long sleep duration (more than 8 h per day). People with long sleep duration had a positive correlation with difference between BA and CA (>8 h per day, β = 1.308, p-value = 0.0001; ref = 6~8 h per day, normal). Short sleep duration had an inverse trend with the difference, although the result was not statically significant. Associations were greater in vulnerable populations, such as low income, obese, or people with chronic diseases. (4) Conclusions: Excess sleep duration that is greater than the normal range was associated with increased BA. In particular, such relationships that are related to worsening BA were greater in patients with low income, obesity, and chronic diseases. Based on our findings, healthcare professionals should also consider the negative effects of excess sleep, not only insufficient sleep. Alternatives for controlling optimal sleep duration should be reviewed, especially with vulnerable populations.

Common methods of biological age estimation

At present, no single indicator could be used as a golden index to estimate aging process. The biological age (BA), which combines several important biomarkers with mathematical modeling, has been proposed for >50 years as an aging estimation method to replace chronological age (CA). The common methods used for BA estimation include the multiple linear regression (MLR), the principal component analysis (PCA), the Hochschild's method, and the Klemera and Doubal's method (KDM). The fundamental differences in these four methods are the roles of CA and the selection criteria of aging biomarkers. In MLR and PCA, CA is treated as the selection criterion and an independent index. The Hochschild's method and KDM share a similar concept, making CA an independent variable. Previous studies have either simply constructed the BA model by one or compared the four methods together. However, reviews have yet to illustrate and compare the four methods systematically. Since the BA model is a potential estimation of aging for clinical use, such as predicting onset and prognosis of diseases, improving the elderly's living qualities, and realizing successful aging, here we summarize previous BA studies, illustrate the basic statistical steps, and thoroughly discuss the comparisons among the four common BA estimation methods.