Three-dimensional facial-image analysis to predict heterogeneity of the human ageing rate and the impact of lifestyle

Comparison of facial skin ageing in healthy Asian and Caucasian females quantified by in vivo line-field confocal optical coherence tomography 3D imaging

BACKGROUND

Quantitative biomarkers of facial skin aging were investigated in 109 healthy Asian female volunteers, aged 20 to 70 years.

MATERIALS AND METHODS

In vivo 3D Line-field Confocal Optical Coherence Tomography (LC-OCT) imaging, enhanced by Artificial Intelligence (AI)-based quantification algorithms, was utilized to compute various metrics, including stratum corneum thickness (SC), viable epidermal (VE) thickness, and Dermal-Epidermal Junction (DEJ) undulation along with cellular metrics for the temple, cheekbone, and mandible.

RESULTS

Comparison with data from a cohort of healthy Caucasian volunteers revealed similarities in the variations of stratum corneum and viable epidermis layers, as well as cellular shape and size with age in both ethnic groups. However, specific findings emerged, such as larger, more heterogeneous nuclei in both layers, demonstrated by an increase in nuclei volume and their standard deviation, and increased network atypia, all showing significant age-related variations. Caucasian females exhibited a flatter and more homogeneous epidermis, evidenced by a decreased standard deviation of the number of layers, and a less dense cellular network with fewer cells per layer, indicated by a decrease in cell surface density.

CONCLUSION

Ethnicity-wise comparisons highlighted distinct biological features specific to each population. Asian individuals showed significantly higher DEJ undulation, higher compactness, and lower cell network atypia compared to their Caucasian counterparts across age groups. Differences in stratum corneum and viable epidermal thickness on the cheekbone were also significant. LC-OCT 3D imaging provides valuable insights into the aging process in different populations and underscores inherent biological differences between Caucasian and Asian female volunteers.

Prediagnosis recognition of acute ischemic stroke by artificial intelligence from facial images

Stroke is a major threat to life and health in modern society, especially in the aging population. Stroke may cause sudden death or severe sequela-like hemiplegia. Although computed tomography (CT) and magnetic resonance imaging (MRI) are standard diagnosis methods, and artificial intelligence models have been built based on these images, shortage in medical resources and the time and cost of CT/MRI imaging hamper fast detection, thus increasing the severity of stroke. Here, we developed a convolutional neural network model by integrating four networks, Xception, ResNet50, VGG19, and EfficientNetb1, to recognize stroke based on 2D facial images with a cross-validation area under curve (AUC) of 0.91 within the training set of 185 acute ischemic stroke patients and 551 age- and sex-matched controls, and AUC of 0.82 in an independent data set regardless of age and sex. The model computed stroke probability was quantitatively associated with facial features, various clinical parameters of blood clotting indicators and leukocyte counts, and, more importantly, stroke incidence in the near future. Our real-time facial image artificial intelligence model can be used to rapidly screen and prediagnose stroke before CT scanning, thus meeting the urgent need in emergency clinics, potentially translatable to routine monitoring.

Thermal facial image analyses reveal quantitative hallmarks of aging and metabolic diseases

Although human core body temperature is known to decrease with age, the age dependency of facial temperature and its potential to indicate aging rate or aging-related diseases remains uncertain. Here, we collected thermal facial images of 2,811 Han Chinese individuals 20-90 years old, developed the ThermoFace method to automatically process and analyze images, and then generated thermal age and disease prediction models. The ThermoFace deep learning model for thermal facial age has a mean absolute deviation of about 5 years in cross-validation and 5.18 years in an independent cohort. The difference between predicted and chronological age is highly associated with metabolic parameters, sleep time, and gene expression pathways like DNA repair, lipolysis, and ATPase in the blood transcriptome, and it is modifiable by exercise. Consistently, ThermoFace disease predictors forecast metabolic diseases like fatty liver with high accuracy (AUC > 0.80), with predicted disease probability correlated with metabolic parameters.

Coordinate-wise monotonic transformations enable privacy-preserving age estimation with 3D face point cloud

The human face is a valuable biomarker of aging, but the collection and use of its image raise significant privacy concerns. Here we present an approach for facial data masking that preserves age-related features using coordinate-wise monotonic transformations. We first develop a deep learning model that estimates age directly from non-registered face point clouds with high accuracy and generalizability. We show that the model learns a highly indistinguishable mapping using faces treated with coordinate-wise monotonic transformations, indicating that the relative positioning of facial information is a low-level biomarker of facial aging. Through visual perception tests and computational 3D face verification experiments, we demonstrate that transformed faces are significantly more difficult to perceive for human but not for machines, except when only the face shape information is accessible. Our study leads to a facial data protection guideline that has the potential to broaden public access to face datasets with minimized privacy risks.

Towards Healthy Longevity: Comprehensive Insights from Molecular Targets and Biomarkers to Biological Clocks

Aging is a complex and time-dependent decline in physiological function that affects most organisms, leading to increased risk of age-related diseases. Investigating the molecular underpinnings of aging is crucial to identify geroprotectors, precisely quantify biological age, and propose healthy longevity approaches. This review explores pathways that are currently being investigated as intervention targets and aging biomarkers spanning molecular, cellular, and systemic dimensions. Interventions that target these hallmarks may ameliorate the aging process, with some progressing to clinical trials. Biomarkers of these hallmarks are used to estimate biological aging and risk of aging-associated disease. Utilizing aging biomarkers, biological aging clocks can be constructed that predict a state of abnormal aging, age-related diseases, and increased mortality. Biological age estimation can therefore provide the basis for a fine-grained risk stratification by predicting all-cause mortality well ahead of the onset of specific diseases, thus offering a window for intervention. Yet, despite technological advancements, challenges persist due to individual variability and the dynamic nature of these biomarkers. Addressing this requires longitudinal studies for robust biomarker identification. Overall, utilizing the hallmarks of aging to discover new drug targets and develop new biomarkers opens new frontiers in medicine. Prospects involve multi-omics integration, machine learning, and personalized approaches for targeted interventions, promising a healthier aging population.

Determinants of Chromatin Organization in Aging and Cancer-Emerging Opportunities for Epigenetic Therapies and AI Technology

This review article critically examines the pivotal role of chromatin organization in gene regulation, cellular differentiation, disease progression and aging. It explores the dynamic between the euchromatin and heterochromatin, coded by a complex array of histone modifications that orchestrate essential cellular processes. We discuss the pathological impacts of chromatin state misregulation, particularly in cancer and accelerated aging conditions such as progeroid syndromes, and highlight the innovative role of epigenetic therapies and artificial intelligence (AI) in comprehending and harnessing the histone code toward personalized medicine. In the context of aging, this review explores the use of AI and advanced machine learning (ML) algorithms to parse vast biological datasets, leading to the development of predictive models for epigenetic modifications and providing a framework for understanding complex regulatory mechanisms, such as those governing cell identity genes. It supports innovative platforms like CEFCIG for high-accuracy predictions and tools like GridGO for tailored ChIP-Seq analysis, which are vital for deciphering the epigenetic landscape. The review also casts a vision on the prospects of AI and ML in oncology, particularly in the personalization of cancer therapy, including early diagnostics and treatment optimization for diseases like head and neck and colorectal cancers by harnessing computational methods, AI advancements and integrated clinical data for a transformative impact on healthcare outcomes.

Establishment and application of perceived age prediction model for the periocular aging research of Chinese Han women

BACKGROUND

The assessment of skin aging through skin measurements faces limitations, making perceived age evaluation a more valuable and direct tool for assessing skin aging. Given that the aging process markedly affects the appearance of the eye contour, characterizing the eye region could be beneficial for perceived age assessment. This study aimed to analyze age-correlated changes in the eye contour within the Chinese Han female population and to develop, validate, and apply a multiple linear regression model for predicting perceived age.

MATERIALS AND METHODS

A naïve panel of 107 Chinese women assessed the perceived ages of 212 Chinese Han women. Instrumental analysis evaluated periorbital parameters, including palpebral fissure width (PFW), palpebral fissure height (PFH), acclivity of palpebral fissure (AX), angle of inner canthal (AEN), and angle of outer canthal (AEX). These parameters were used to construct a multiple linear regression model for predicting the perceived ages of Chinese Han women. A combined treatment using Fotona 4D and an anti-aging eye cream, formulated with plant extracts, peptides, and antioxidants, was conducted to verify the cream's anti-aging efficacy and safety. This eye cream was then tested in a large-scale clinical trial involving 101 participants. The prediction model was employed in this trial to assess the perceived ages of the women after an 8-week application of the eye cream.

RESULTS

All parameters were observed to decrease with age. An intergroup comparison indicated that eyelid aging in Chinese Han women accelerates beyond the age of 50. Consequently, a linear regression model was constructed and validated, with the perceived age being calculated as 183.159 - 1.078 * AEN - 4.487 * PFW + 6.061 * PFH - 1.003 * AX - 0.328 * AEX. The anti-aging efficacy and safety of the eye cream were confirmed through combined treatment with Fotona 4D, showing improvements in wrinkles, elasticity, and dark circles under the eyes. In a large-scale clinical evaluation using this eye cream, a perceived age prediction model was applied, suggesting that 8 weeks of use made participants appear 2.25 years younger.

CONCLUSION

Our study developed and validated a multiple linear regression model to predict the perceived age of Chinese Han women. This model was successfully utilized in a large-scale clinical evaluation of anti-aging eye cream, revealing that 8 weeks of usage made participants appear 2.25 years younger. This method effectively bridges the gap between clinical research and consumer perceptions, explores the complex factors influencing perceived age, and aims to improve anti-aging formulations.

Application of AI in biological age prediction

The development of anti-aging interventions requires quantitative measurement of biological age. Machine learning models, known as "aging clocks," are built by leveraging diverse aging biomarkers that vary across lifespan to predict biological age. In addition to traditional aging clocks harnessing epigenetic signatures derived from bulk samples, emerging technologies allow the biological age estimating at single-cell level to dissect cellular diversity in aging tissues. Moreover, imaging-based aging clocks are increasingly employed with the advantage of non-invasive measurement, making it suitable for large-scale human cohort studies. To fully capture the features in the ever-growing multi-modal and high-dimensional aging-related data and uncover disease associations, deep-learning based approaches, which are effective to learn complex and non-linear relationships without relying on pre-defined features, are increasingly applied. The use of big data and AI-based aging clocks has achieved high accuracy, interpretability and generalizability, guiding clinical applications to delay age-related diseases and extend healthy lifespans.

Crystalline lens nuclear age prediction as a new biomarker of nucleus degeneration

BACKGROUND

The crystalline lens is a transparent structure of the eye to focus light on the retina. It becomes muddy, hard and dense with increasing age, which makes the crystalline lens gradually lose its function. We aim to develop a nuclear age predictor to reflect the degeneration of the crystalline lens nucleus.

METHODS

First we trained and internally validated the nuclear age predictor with a deep-learning algorithm, using 12 904 anterior segment optical coherence tomography (AS-OCT) images from four diverse Asian and American cohorts: Zhongshan Ophthalmic Center with Machine0 (ZOM0), Tomey Corporation (TOMEY), University of California San Francisco and the Chinese University of Hong Kong. External testing was done on three independent datasets: Tokyo University (TU), ZOM1 and Shenzhen People's Hospital (SPH). We also demonstrate the possibility of detecting nuclear cataracts (NCs) from the nuclear age gap.

FINDINGS

In the internal validation dataset, the nuclear age could be predicted with a mean absolute error (MAE) of 2.570 years (95% CI 1.886 to 2.863). Across the three external testing datasets, the algorithm achieved MAEs of 4.261 years (95% CI 3.391 to 5.094) in TU, 3.920 years (95% CI 3.332 to 4.637) in ZOM1-NonCata and 4.380 years (95% CI 3.730 to 5.061) in SPH-NonCata. The MAEs for NC eyes were 8.490 years (95% CI 7.219 to 9.766) in ZOM1-NC and 9.998 years (95% CI 5.673 to 14.642) in SPH-NC. The nuclear age gap outperformed both ophthalmologists in detecting NCs, with areas under the receiver operating characteristic curves of 0.853 years (95% CI 0.787 to 0.917) in ZOM1 and 0.909 years (95% CI 0.828 to 0.978) in SPH.

INTERPRETATION

The nuclear age predictor shows good performance, validating the feasibility of using AS-OCT images as an effective screening tool for nucleus degeneration. Our work also demonstrates the potential use of the nuclear age gap to detect NCs.

Single-worm quantitative proteomics reveals aging heterogeneity in isogenic Caenorhabditis elegans

The heterogeneity of aging has been investigated at cellular and organic levels in the mouse model and human, but the exploration of aging heterogeneity at whole-organism level is lacking. C. elegans is an ideal model organism for studying this question as they are self-fertilized and cultured in the same chamber. Despite the tremendous progress made in single-cell proteomic analysis, there is few single-worm proteomics studies about aging. Here, we apply single-worm quantitative mass spectrometry to quantify the heterogenous proteomic changes during aging across individuals, a total of 3524 proteins from 157 C. eleagns individuals were quantified. A reconstructed C. elegans aging trajectory and proteomic landscape of fast-aging individuals were used to analyze the heterogeneity of C. elegans aging. We characterized inter-individual proteomic variation during aging and revealed contributing factors that distinguish fast-aging individuals from their siblings.

Accurate estimation of biological age and its application in disease prediction using a multimodal image Transformer system

Aging in an individual refers to the temporal change, mostly decline, in the body's ability to meet physiological demands. Biological age (BA) is a biomarker of chronological aging and can be used to stratify populations to predict certain age-related chronic diseases. BA can be predicted from biomedical features such as brain MRI, retinal, or facial images, but the inherent heterogeneity in the aging process limits the usefulness of BA predicted from individual body systems. In this paper, we developed a multimodal Transformer-based architecture with cross-attention which was able to combine facial, tongue, and retinal images to estimate BA. We trained our model using facial, tongue, and retinal images from 11,223 healthy subjects and demonstrated that using a fusion of the three image modalities achieved the most accurate BA predictions. We validated our approach on a test population of 2,840 individuals with six chronic diseases and obtained significant difference between chronological age and BA (AgeDiff) than that of healthy subjects. We showed that AgeDiff has the potential to be utilized as a standalone biomarker or conjunctively alongside other known factors for risk stratification and progression prediction of chronic diseases. Our results therefore highlight the feasibility of using multimodal images to estimate and interrogate the aging process.

The ticking of aging clocks

Computational models that measure biological age and aging rate regardless of chronological age are called aging clocks. The underlying counting mechanisms of the intrinsic timers of these clocks are still unclear. Molecular mediators and determinants of aging rate point to the key roles of DNA damage, epigenetic drift, and inflammation. Persistent DNA damage leads to cellular senescence and the senescence-associated secretory phenotype (SASP), which induces cytotoxic immune cell infiltration; this further induces DNA damage through reactive oxygen and nitrogen species (RONS). I discuss the possibility that DNA damage (or the response to it, including epigenetic changes) is the fundamental counting unit of cell cycles and cellular senescence, that ultimately accounts for cell composition changes and functional decline in tissues, as well as the key intervention points.

Determining a multimodal aging clock in a cohort of Chinese women

BACKGROUND

Translating aging rejuvenation strategies into clinical practice has the potential to address the unmet needs of the global aging population. However, to successfully do so requires precise quantification of aging and its reversal in a way that encompasses the complexity and variation of aging.

METHODS

Here, in a cohort of 113 healthy women, tiled in age from young to old, we identified a repertoire of known and previously unknown markers associated with age based on multimodal measurements, including transcripts, proteins, metabolites, microbes, and clinical laboratory values, based on which an integrative aging clock and a suite of customized aging clocks were developed.

FINDINGS

A unified analysis of aging-associated traits defined four aging modalities with distinct biological functions (chronic inflammation, lipid metabolism, hormone regulation, and tissue fitness), and depicted waves of changes in distinct biological pathways peak around the third and fifth decades of life. We also demonstrated that the developed aging clocks could measure biological age and assess partial aging deceleration by hormone replacement therapy, a prevalent treatment designed to correct hormonal imbalances.

CONCLUSIONS

We established aging metrics that capture systemic physiological dysregulation, a valuable framework for monitoring the aging process and informing clinical development of aging rejuvenation strategies.

FUNDING

This work was supported by the National Natural Science Foundation of China (32121001), the National Key Research and Development Program of China (2022YFA1103700 and 2020YFA0804000), the National Natural Science Foundation of China (81502304), and the Quzhou Technology Projects (2022K46).

LensAge index as a deep learning-based biological age for self-monitoring the risks of age-related diseases and mortality

Age is closely related to human health and disease risks. However, chronologically defined age often disagrees with biological age, primarily due to genetic and environmental variables. Identifying effective indicators for biological age in clinical practice and self-monitoring is important but currently lacking. The human lens accumulates age-related changes that are amenable to rapid and objective assessment. Here, using lens photographs from 20 to 96-year-olds, we develop LensAge to reflect lens aging via deep learning. LensAge is closely correlated with chronological age of relatively healthy individuals (R2 > 0.80, mean absolute errors of 4.25 to 4.82 years). Among the general population, we calculate the LensAge index by contrasting LensAge and chronological age to reflect the aging rate relative to peers. The LensAge index effectively reveals the risks of age-related eye and systemic disease occurrence, as well as all-cause mortality. It outperforms chronological age in reflecting age-related disease risks (p < 0.001). More importantly, our models can conveniently work based on smartphone photographs, suggesting suitability for routine self-examination of aging status. Overall, our study demonstrates that the LensAge index may serve as an ideal quantitative indicator for clinically assessing and self-monitoring biological age in humans.

Central obesity and its association with retinal age gap: insights from the UK Biobank study

BACKGROUND

Conflicting evidence exists on the association between ageing and obesity. Retinal age derived from fundus images has been validated as a novel biomarker of ageing. In this study, we aim to investigate the association between different anthropometric phenotypes based on body mass index (BMI) and waist circumference (WC) and the retinal age gap (retinal age minus chronological age).

METHODS

A total of 35,550 participants with BMI, WC and qualified retinal imaging data available were included to investigate the association between anthropometric groups and retinal ageing. Participants were stratified into 7 different body composition groups based on BMI and WC (Normal-weight/Normal WC, Overweight/Normal WC, Mild obesity/Normal WC, Normal-weight/High WC, Overweight/High WC, Mild obesity/High WC, and Severe obesity/High WC). Linear regression and logistic regression models were fitted to investigate the association between the seven anthropometric groups and retinal age gap as continuous and categorical outcomes, respectively.

RESULTS

A total of 35,550 participants (55.6% females) with a mean age 56.8 ± 8.04 years were included in the study. Individuals in the Overweight/High WC, Mild obesity/High WC and Severe obesity/High WC groups were associated with an increase in the retinal age gap, compared with those in the Normal Weight/Normal WC group (β = 0.264, 95% CI: 0.105-0.424, P =0.001; β = 0.226, 95% CI: 0.082-0.371, P = 0.002; β = 0.273, 95% CI: 0.081-0.465, P = 0.005; respectively) in fully adjusted models. Similar findings were noted in the association between the anthropometric groups and retinal ageing process as a categorical outcome.

CONCLUSION

A significant positive association exists between central obesity and accelerated ageing indexed by retinal age gaps, highlighting the significance of maintaining a healthy body shape.

Decoding biological age from face photographs using deep learning

Because humans age at different rates, a person's physical appearance may yield insights into their biological age and physiological health more reliably than their chronological age. In medicine, however, appearance is incorporated into medical judgments in a subjective and non-standardized fashion. In this study, we developed and validated FaceAge, a deep learning system to estimate biological age from easily obtainable and low-cost face photographs. FaceAge was trained on data from 58,851 healthy individuals, and clinical utility was evaluated on data from 6,196 patients with cancer diagnoses from two institutions in the United States and The Netherlands. To assess the prognostic relevance of FaceAge estimation, we performed Kaplan Meier survival analysis. To test a relevant clinical application of FaceAge, we assessed the performance of FaceAge in end-of-life patients with metastatic cancer who received palliative treatment by incorporating FaceAge into clinical prediction models. We found that, on average, cancer patients look older than their chronological age, and looking older is correlated with worse overall survival. FaceAge demonstrated significant independent prognostic performance in a range of cancer types and stages. We found that FaceAge can improve physicians' survival predictions in incurable patients receiving palliative treatments, highlighting the clinical utility of the algorithm to support end-of-life decision-making. FaceAge was also significantly associated with molecular mechanisms of senescence through gene analysis, while age was not. These findings may extend to diseases beyond cancer, motivating using deep learning algorithms to translate a patient's visual appearance into objective, quantitative, and clinically useful measures.

Perturbation of 3D nuclear architecture, epigenomic dysregulation and aging, and cannabinoid synaptopathy reconfigures conceptualization of cannabinoid pathophysiology: part 1-aging and epigenomics

Much recent attention has been directed toward the spatial organization of the cell nucleus and the manner in which three-dimensional topologically associated domains and transcription factories are epigenetically coordinated to precisely bring enhancers into close proximity with promoters to control gene expression. Twenty lines of evidence robustly implicate cannabinoid exposure with accelerated organismal and cellular aging. Aging has recently been shown to be caused by increased DNA breaks. These breaks rearrange and maldistribute the epigenomic machinery to weaken and reverse cellular differentiation, cause genome-wide DNA demethylation, reduce gene transcription, and lead to the inhibition of developmental pathways, which contribute to the progressive loss of function and chronic immune stimulation that characterize cellular aging. Both cell lineage-defining superenhancers and the superanchors that control them are weakened. Cannabis exposure phenocopies the elements of this process and reproduces DNA and chromatin breakages, reduces the DNA, RNA protein and histone synthesis, interferes with the epigenomic machinery controlling both DNA and histone modifications, induces general DNA hypomethylation, and epigenomically disrupts both the critical boundary elements and the cohesin motors that create chromatin loops. This pattern of widespread interference with developmental programs and relative cellular dedifferentiation (which is pro-oncogenic) is reinforced by cannabinoid impairment of intermediate metabolism (which locks in the stem cell-like hyper-replicative state) and cannabinoid immune stimulation (which perpetuates and increases aging and senescence programs, DNA damage, DNA hypomethylation, genomic instability, and oncogenesis), which together account for the diverse pattern of teratologic and carcinogenic outcomes reported in recent large epidemiologic studies in Europe, the USA, and elsewhere. It also accounts for the prominent aging phenotype observed clinically in long-term cannabis use disorder and the 20 characteristics of aging that it manifests. Increasing daily cannabis use, increasing use in pregnancy, and exponential dose-response effects heighten the epidemiologic and clinical urgency of these findings. Together, these findings indicate that cannabinoid genotoxicity and epigenotoxicity are prominent features of cannabis dependence and strongly indicate coordinated multiomics investigations of cannabinoid genome-epigenome-transcriptome-metabolome, chromatin conformation, and 3D nuclear architecture. Considering the well-established exponential dose-response relationships, the diversity of cannabinoids, and the multigenerational nature of the implications, great caution is warranted in community cannabinoid penetration.

Geometric morphometrics and machine learning from three-dimensional facial scans for difficult mask ventilation prediction

Background

Unanticipated difficult mask ventilation (DMV) is a potentially life-threatening event in anesthesia. Nevertheless, predicting DMV currently remains a challenge. This study aimed to verify whether three dimensional (3D) facial scans could predict DMV in patients scheduled for general anesthesia.

Methods

The 3D facial scans were taken on 669 adult patients scheduled for elective surgery under general anesthesia. Clinical variables currently used as predictors of DMV were also collected. The DMV was defined as the inability to provide adequate and stable ventilation. Spatially dense landmarks were digitized on 3D scans to describe sufficient details for facial features and then processed by 3D geometric morphometrics. Ten different machine learning (ML) algorithms, varying from simple to more advanced, were introduced. The performance of ML models for DMV prediction was compared with that of the DIFFMASK score. The area under the receiver operating characteristic curves (AUC) with its 95% confidence interval (95% CI) as well as the specificity and sensitivity were used to evaluate the predictive value of the model.

Results

The incidence of DMV was 35/669 (5.23%). The logistic regression (LR) model performed best among the 10 ML models. The AUC of the LR model was 0.825 (95% CI, 0.765-0.885). The sensitivity and specificity of the model were 0.829 (95% CI, 0.629-0.914) and 0.733 (95% CI, 0.532-0.819), respectively. The LR model demonstrated better predictive performance than the DIFFMASK score, which obtained an AUC of 0.785 (95% CI, 0.710-0.860) and a sensitivity of 0.686 (95% CI, 0.578-0.847). Notably, we identified a significant morphological difference in the mandibular region between the DMV group and the easy mask ventilation group.

Conclusion

Our study indicated a distinct morphological difference in the mandibular region between the DMV group and the easy mask ventilation group. 3D geometric morphometrics with ML could be a rapid, efficient, and non-invasive tool for DMV prediction to improve anesthesia safety.

A transcriptome-based single-cell biological age model and resource for tissue-specific aging measures

Accurately measuring biological age is crucial for improving healthcare for the elderly population. However, the complexity of aging biology poses challenges in how to robustly estimate aging and interpret the biological significance of the traits used for estimation. Here we present SCALE, a statistical pipeline that quantifies biological aging in different tissues using explainable features learned from literature and single-cell transcriptomic data. Applying SCALE to the "Mouse Aging Cell Atlas" (Tabula Muris Senis) data, we identified tissue-level transcriptomic aging programs for more than 20 murine tissues and created a multitissue resource of mouse quantitative aging-associated genes. We observe that SCALE correlates well with other age indicators, such as the accumulation of somatic mutations, and can distinguish subtle differences in aging even in cells of the same chronological age. We further compared SCALE with other transcriptomic and methylation "clocks" in data from aging muscle stem cells, Alzheimer's disease, and heterochronic parabiosis. Our results confirm that SCALE is more generalizable and reliable in assessing biological aging in aging-related diseases and rejuvenating interventions. Overall, SCALE represents a valuable advancement in our ability to measure aging accurately, robustly, and interpretably in single cells.

LncRNAs: the missing link to senescence nuclear architecture

During cellular senescence and organismal aging, cells display various molecular and morphological changes. Although many aging-related long noncoding RNAs (lncRNAs) are highly associated with senescence-associated secretory phenotype, the roles of lncRNAs in senescence-associated nuclear architecture and morphological changes are just starting to emerge. Here I review lncRNAs associated with nuclear structure establishment and maintenance, their aging-related changes, and then focus on the pervasive, yet underappreciated, role of RNA double-strand DNA triplexes for lncRNAs to recognize targeted genomic regions, making lncRNAs the nexus between DNA and proteins to regulate nuclear structural changes. Finally, I discuss the future of deciphering direct links of lncRNA changes to various nuclear morphology changes assisted by artificial intelligence and genetic perturbations.

Human PBMC scRNA-seq-based aging clocks reveal ribosome to inflammation balance as a single-cell aging hallmark and super longevity

Quantifying aging rate is important for evaluating age-associated decline and mortality. A blood single-cell RNA sequencing dataset for seven supercentenarians (SCs) was recently generated. Here, we generate a reference 28-sample aging cohort to compute a single-cell level aging clock and to determine the biological age of SCs. Our clock model placed the SCs at a blood biological age to between 80.43 and 102.67 years. Compared to the model-expected aging trajectory, SCs display increased naive CD8⁺ T cells, decreased cytotoxic CD8⁺ T cells, memory CD4⁺ T cells, and megakaryocytes. As the most prominent molecular hallmarks at the single-cell level, SCs contain more cells and cell types with high ribosome level, which is associated with and, according to Bayesian network inference, contributes to a low inflammation state and slow aging of SCs. Inhibiting ribosomal activity or translation in monocytes validates such translation against inflammation balance revealed by our single-cell aging clock.

Biomarkers of aging

Aging biomarkers are a combination of biological parameters to (i) assess age-related changes, (ii) track the physiological aging process, and (iii) predict the transition into a pathological status. Although a broad spectrum of aging biomarkers has been developed, their potential uses and limitations remain poorly characterized. An immediate goal of biomarkers is to help us answer the following three fundamental questions in aging research: How old are we? Why do we get old? And how can we age slower? This review aims to address this need. Here, we summarize our current knowledge of biomarkers developed for cellular, organ, and organismal levels of aging, comprising six pillars: physiological characteristics, medical imaging, histological features, cellular alterations, molecular changes, and secretory factors. To fulfill all these requisites, we propose that aging biomarkers should qualify for being specific, systemic, and clinically relevant.

Retinal age as a predictive biomarker of the diabetic retinopathy grade

OBJECTIVE

To apply artificial intelligence (AI) techniques, through deep learning algorithms, for the development and optimization of a system for predicting the age of a person based on a color retinography and to study a possible relationship between the evolution of retinopathy diabetes and premature aging of the retina.

METHODS

A convolutional network was trained to calculate the age of a person based on a retinography. Said training was carried out on a set of retinographies of patients with diabetes previously divided into three subsets (training, validation and test). The difference between the chronological age of the patient and the biological age of the retina was defined as the retinal age gap.

RESULTS

A set of 98,400 images was used for the training phase, 1,000 images for the validation phase and 13,544 for the test phase. The retinal gap of the patients without DR was 0.609 years and that of the patients with DR was 1,905 years (p < 0.001), with the distribution by degree of DR being: mild DR: 1,541 years, moderate DR: 3,017 years, DR severe: 3,117 years and proliferative DR: 8,583 years.

CONCLUSIONS

The retinal age gap shows a positive mean difference between diabetics with DR versus those without DR, and it increases progressively, according to the degree of DR. These results could indicate the existence of a relationship between the evolution of the disease and premature aging of the retina.

Retinal age gap as a predictive biomarker for mortality risk

AIM

To develop a deep learning (DL) model that predicts age from fundus images (retinal age) and to investigate the association between retinal age gap (retinal age predicted by DL model minus chronological age) and mortality risk.

METHODS

A total of 80 169 fundus images taken from 46 969 participants in the UK Biobank with reasonable quality were included in this study. Of these, 19 200 fundus images from 11 052 participants without prior medical history at the baseline examination were used to train and validate the DL model for age prediction using fivefold cross-validation. A total of 35 913 of the remaining 35 917 participants had available mortality data and were used to investigate the association between retinal age gap and mortality.

RESULTS

The DL model achieved a strong correlation of 0.81 (p<0·001) between retinal age and chronological age, and an overall mean absolute error of 3.55 years. Cox regression models showed that each 1 year increase in the retinal age gap was associated with a 2% increase in risk of all-cause mortality (hazard ratio (HR)=1.02, 95% CI 1.00 to 1.03, p=0.020) and a 3% increase in risk of cause-specific mortality attributable to non-cardiovascular and non-cancer disease (HR=1.03, 95% CI 1.00 to 1.05, p=0.041) after multivariable adjustments. No significant association was identified between retinal age gap and cardiovascular- or cancer-related mortality.

CONCLUSIONS

Our findings indicate that retinal age gap might be a potential biomarker of ageing that is closely related to risk of mortality, implying the potential of retinal image as a screening tool for risk stratification and delivery of tailored interventions.

Advancement in Human Face Prediction Using DNA

The rapid improvements in identifying the genetic factors contributing to facial morphology have enabled the early identification of craniofacial syndromes. Similarly, this technology can be vital in forensic cases involving human identification from biological traces or human remains, especially when reference samples are not available in the deoxyribose nucleic acid (DNA) database. This review summarizes the currently used methods for predicting human phenotypes such as age, ancestry, pigmentation, and facial features based on genetic variations. To identify the facial features affected by DNA, various two-dimensional (2D)- and three-dimensional (3D)-scanning techniques and analysis tools are reviewed. A comparison between the scanning technologies is also presented in this review. Face-landmarking techniques and face-phenotyping algorithms are discussed in chronological order. Then, the latest approaches in genetic to 3D face shape analysis are emphasized. A systematic review of the current markers that passed the threshold of a genome-wide association (GWAS) of single nucleotide polymorphism (SNP)-face traits from the GWAS Catalog is also provided using the preferred reporting items for systematic reviews and meta-analyses (PRISMA), approach. Finally, the current challenges in forensic DNA phenotyping are analyzed and discussed.

Association of Retinal Age Gap and Risk of Kidney Failure: A UK Biobank Study

RATIONALE & OBJECTIVE

The incidence of kidney failure is known to increase with age. We have previously developed and validated the use of retinal age based on fundus images as a biomarker of aging. However, the association of retinal age with kidney failure is not clear. We investigated the association of retinal age gap (the difference between retinal age and chronological age) with future risk of kidney failure.

STUDY DESIGN

Prospective cohort study.

SETTING & PARTICIPANTS

11,052 UK Biobank study participants without any reported disease for characterizing retinal age in a deep learning algorithm. 35,864 other participants with retinal images and no kidney failure were followed to assess the association between retinal age gap and the risk of kidney failure.

EXPOSURE

Retinal age gap, defined as the difference between model-based retinal age and chronological age.

OUTCOME

Incident kidney failure.

ANALYTICAL APPROACH

A deep learning prediction model used to characterize retinal age based on retinal images and chronological age, and Cox proportional hazards regression models to investigate the association of retinal age gap with incident kidney failure.

RESULTS

After a median follow-up period of 11 (IQR, 10.89-11.14) years, 115 (0.32%) participants were diagnosed with incident kidney failure. Each 1-year greater retinal age gap at baseline was independently associated with a 10% increase in the risk of incident kidney failure (HR, 1.10 [95% CI, 1.03-1.17]; P=0.003). Participants with retinal age gaps in the fourth (highest) quartile had a significantly higher risk of incident kidney failure compared with those in the first quartile (HR, 2.77 [95% CI, 1.29-5.93]; P=0.009).

LIMITATIONS

Limited generalizability related to the composition of participants in the UK Biobank study.

CONCLUSIONS

Retinal age gap was significantly associated with incident kidney failure and may be a promising noninvasive predictive biomarker for incident kidney failure.

The landscape of aging

Aging is characterized by a progressive deterioration of physiological integrity, leading to impaired functional ability and ultimately increased susceptibility to death. It is a major risk factor for chronic human diseases, including cardiovascular disease, diabetes, neurological degeneration, and cancer. Therefore, the growing emphasis on "healthy aging" raises a series of important questions in life and social sciences. In recent years, there has been unprecedented progress in aging research, particularly the discovery that the rate of aging is at least partly controlled by evolutionarily conserved genetic pathways and biological processes. In an attempt to bring full-fledged understanding to both the aging process and age-associated diseases, we review the descriptive, conceptual, and interventive aspects of the landscape of aging composed of a number of layers at the cellular, tissue, organ, organ system, and organismal levels.

An evaluation of aging measures: from biomarkers to clocks

With the increasing number of aged population and growing burden of healthy aging demands, a rational standard for evaluation aging is in urgent need. The advancement of medical testing technology and the prospering of artificial intelligence make it possible to evaluate the biological status of aging from a more comprehensive view. In this review, we introduced common aging biomarkers and concluded several famous aging clocks. Aging biomarkers reflect changes in the organism at a molecular or cellular level over time while aging clocks tend to be more of a generalization of the overall state of the organism. We expect to construct a framework for aging evaluation measurement from both micro and macro perspectives. Especially, population-specific aging clocks and multi-omics aging clocks may better fit the demands to evaluate aging in a comprehensive and multidimensional manner and make a detailed classification to represent different aging rates at tissue/organ levels. This framework will promisingly provide a crucial basis for disease diagnosis and intervention assessment in geroscience.

LipidClock: A Lipid-Based Predictor of Biological Age

Complexity is a fundamental feature of biological systems. Omics techniques like lipidomics can simultaneously quantify many thousands of molecules, thereby directly capturing the underlying biological complexity. However, this approach transfers the original biological complexity to the resulting datasets, posing challenges in data reduction and analysis. Aging is a prime example of a process that exhibits complex behaviour across multiple scales of biological organisation. The aging process is characterised by slow, cumulative and detrimental changes that are driven by intrinsic biological stochasticity and mediated through non-linear interactions and feedback within and between these levels of organization (ranging from metabolites, macromolecules, organelles and cells to tissue and organs). Only collectively and over long timeframes do these changes manifest as the exponential increases in morbidity and mortality that define biological aging, making aging a problem more difficult to study than the aetiologies of specific diseases. But aging's time dependence can also be exploited to extract key insights into its underlying biology. Here we explore this idea by using data on changes in lipid composition across the lifespan of an organism to construct and test a LipidClock to predict biological age in the nematode Caenorhabdits elegans. The LipidClock consist of a feature transformation via Principal Component Analysis followed by Elastic Net regression and yields and Mean Absolute Error of 1.45 days for wild type animals and 4.13 days when applied to mutant strains with lifespans that are substantially different from that of wild type. Gompertz aging rates predicted by the LipidClock can be used to simulate survival curves that are in agreement with those from lifespan experiments.

Decreased Vessel Density in Retinal Capillary Plexus and Thinner Ganglion Cell Complex Associated With Cognitive Impairment

Background

To determine the association of the retinal capillary plexus (RCP) and ganglion cell complex (GCC) with cognitive impairment using optical coherence tomography angiography (OCTA).

Methods

A cross-sectional, community-based study utilizing data from the participants enrolled between August 2019 and January 2020 in the Jidong Eye Cohort Study. We assessed the vessel density in RCP and GCC thickness using OCTA, and cognitive testing using the Montreal Cognitive Assessment (MoCA). Cognitive impairment in this study was defined as MoCA score &lt; 24. We used multivariable analysis to evaluate the association of RCP and GCC with cognitive impairment after adjusting for confounders.

Results

This study analyzed 1555 participants. The mean age of participants was 52.3 (8.4) years, and 861 (55.4%) were women. Cognitive impairment was observed in 268 (17.2%) participants. The adjusted odds ratio (OR) with 95% confidence interval (95% CI) for parafovea vessel density in the deep RCP with cognitive impairment was 1.20 (1.03–1.39). For vessel area and length density surrounding foveal avascular zone with cognitive impairment, the ORs with 95% CIs were 1.23 (1.07–1.41) and 1.30 (1.13–1.49), respectively. For thickness in the superior GCC with cognitive impairment, the OR with 95% CI was 1.16 (1.01–1.32).

Conclusion

Lower vessel density in the RCP and thinner GCC were associated with cognitive impairment. Our results suggest that alterations in the RCP and GCC could provide further evidence when assessing the cognitive function and may even be potentially useful biomarkers in the detection of cognitive impairment.

All-Subset Analysis Improves the Predictive Accuracy of Biological Age for All-Cause Mortality in Chinese and U.S. Populations

BACKGROUND

Klemera-Doubal's method (KDM) is an advanced and widely applied algorithm for estimating biological age (BA), but it has no uniform paradigm for biomarker processing. This article proposed all subsets of biomarkers for estimating BAs and assessed their association with mortality to determine the most predictive subset and BA.

METHODS

Clinical biomarkers, including those from physical examinations and blood assays, were assessed in the China Health and Nutrition Survey (CHNS) 2009 wave. Those correlated with chronological age (CA) were combined to produce complete subsets, and BA was estimated by KDM from each subset of biomarkers. A Cox proportional hazards regression model was used to examine and compare each BA's effect size and predictive capacity for all-cause mortality. Validation analysis was performed in the Chinese Longitudinal Healthy Longevity Survey (CLHLS) and National Health and Nutrition Examination Survey (NHANES). KD-BA and Levine's BA were compared in all cohorts.

RESULTS

A total of 130 918 panels of BAs were estimated from complete subsets comprising 3-17 biomarkers, whose Pearson coefficients with CA varied from 0.39 to 1. The most predictive subset consisted of 5 biomarkers, whose estimated KD-BA had the most predictive accuracy for all-cause mortality. Compared with Levine's BA, the accuracy of the best-fitting KD-BA in predicting death varied among specific populations.

CONCLUSION

All-subset analysis could effectively reduce the number of redundant biomarkers and significantly improve the accuracy of KD-BA in predicting all-cause mortality.

Association of cigarette smoking with retinal capillary plexus: an optical coherence tomography angiography study

PURPOSE

To investigate the association between cigarette smoking and retinal capillary plexus (RCP) using optical coherence tomography angiography (OCTA) and to examine whether potential vascular risk factors could impact their association.

METHODS

This is a cross-sectional, community-based study. The Jidong Eye Cohort Study included participants aged ≥18 years in the Jidong community (Tangshan city, northern China) from August 2019 to January 2020. All participants underwent comprehensive ophthalmic examination and completed detailed smoking questionnaires. Retinal vessel density in the superficial and deep RCP was automatically measured using OCTA.

RESULTS

Of the 2598 participants included in the study, 2026 (78.0%) never smoked and 572 (22.0%) had a history of smoking (494 [19.0%] current smokers and 78 [3.0%] former smokers). The median (interquartile range) age was 41 (34-52) years for the non-smoking group and 45 (35-54.5) years for the smoking group. Multivariable analysis showed that smoking history is associated with a low deep RCP vessel density in the parafovea (β, -0.53; 95% confidence interval [CI], -0.82 to -0.24) and four quadrants. Increased smoking pack-years were associated with reduced deep RCP vessel density in the parafovea (p for trend <0.001) and four quadrants. The significant interaction between diabetes and smoking only was found for superficial RCP vessel density in the parafovea (p for interaction = 0.014) and four quadrants except for the temporal quadrants.

CONCLUSIONS

Cigarette smoking is an independent risk factor for reduced deep RCP vessel density. Our findings imply the potential detrimental effect of smoking on the occurrence of ocular diseases.

Retinal photograph-based deep learning predicts biological age, and stratifies morbidity and mortality risk

BACKGROUND

ageing is an important risk factor for a variety of human pathologies. Biological age (BA) may better capture ageing-related physiological changes compared with chronological age (CA).

OBJECTIVE

we developed a deep learning (DL) algorithm to predict BA based on retinal photographs and evaluated the performance of our new ageing marker in the risk stratification of mortality and major morbidity in general populations.

METHODS

we first trained a DL algorithm using 129,236 retinal photographs from 40,480 participants in the Korean Health Screening study to predict the probability of age being ≥65 years ('RetiAGE') and then evaluated the ability of RetiAGE to stratify the risk of mortality and major morbidity among 56,301 participants in the UK Biobank. Cox proportional hazards model was used to estimate the hazard ratios (HRs).

RESULTS

in the UK Biobank, over a 10-year follow up, 2,236 (4.0%) died; of them, 636 (28.4%) were due to cardiovascular diseases (CVDs) and 1,276 (57.1%) due to cancers. Compared with the participants in the RetiAGE first quartile, those in the RetiAGE fourth quartile had a 67% higher risk of 10-year all-cause mortality (HR = 1.67 [1.42-1.95]), a 142% higher risk of CVD mortality (HR = 2.42 [1.69-3.48]) and a 60% higher risk of cancer mortality (HR = 1.60 [1.31-1.96]), independent of CA and established ageing phenotypic biomarkers. Likewise, compared with the first quartile group, the risk of CVD and cancer events in the fourth quartile group increased by 39% (HR = 1.39 [1.14-1.69]) and 18% (HR = 1.18 [1.10-1.26]), respectively. The best discrimination ability for RetiAGE alone was found for CVD mortality (c-index = 0.70, sensitivity = 0.76, specificity = 0.55). Furthermore, adding RetiAGE increased the discrimination ability of the model beyond CA and phenotypic biomarkers (increment in c-index between 1 and 2%).

CONCLUSIONS

the DL-derived RetiAGE provides a novel, alternative approach to measure ageing.

A New Method for Syndrome Classification of Non-Small-Cell Lung Cancer Based on Data of Tongue and Pulse with Machine Learning

Objective

To explore the data characteristics of tongue and pulse of non-small-cell lung cancer with Qi deficiency syndrome and Yin deficiency syndrome, establish syndrome classification model based on data of tongue and pulse by using machine learning methods, and evaluate the feasibility of syndrome classification based on data of tongue and pulse.

Methods

We collected tongue and pulse of non-small-cell lung cancer patients with Qi deficiency syndrome (n = 163), patients with Yin deficiency syndrome (n = 174), and healthy controls (n = 185) using intelligent tongue diagnosis analysis instrument and pulse diagnosis analysis instrument, respectively. We described the characteristics and examined the correlation of data of tongue and pulse. Four machine learning methods, namely, random forest, logistic regression, support vector machine, and neural network, were used to establish the classification models based on symptom, tongue and pulse, and symptom and tongue and pulse, respectively.

Results

Significant difference indices of tongue diagnosis between Qi deficiency syndrome and Yin deficiency syndrome were TB-a, TB-S, TB-Cr, TC-a, TC-S, TC-Cr, perAll, and the tongue coating texture indices including TC-CON, TC-ASM, TC-MEAN, and TC-ENT. Significant difference indices of pulse diagnosis were t₄ and t₅. The classification performance of each model based on different datasets was as follows: tongue and pulse < symptom < symptom and tongue and pulse. The neural network model had a better classification performance for symptom and tongue and pulse datasets, with an area under the ROC curves and accuracy rate which were 0.9401 and 0.8806.

Conclusions

It was feasible to use tongue data and pulse data as one of the objective diagnostic basis in Qi deficiency syndrome and Yin deficiency syndrome of non-small-cell lung cancer.

Assessing the rate of aging to monitor aging itself

Healthy aging is the prime goal of aging research and interventions. Healthy aging or not can be quantified by biological aging rates estimated by aging clocks. Generation and accumulation of large scale high-dimensional biological data together with maturation of artificial intelligence among other machine learning techniques, have enabled and spurred the rapid development of various aging rate estimators (aging clocks). Here we review the data sources and compare the algorithms of recent human aging clocks, and the applications of these clocks in both researches and daily life. We envision that not only more and multiscale data on cross-sectional data will add momentum to the aging clock development, new longitudinal and interventional data will further raise the aging clock development to the next level to be trained by true biological age such as morbidity and mortality age.

Aging-associated lncRNAs are evolutionarily conserved and participate in NFκB signaling

The transcriptome undergoes global changes during aging, including both protein-coding and noncoding RNAs. Using comparative genomics, we identify aging-associated long noncoding RNAs (lncRNAs) that are under evolutionary constraint and are more conserved than lncRNAs that do not change with age. Aging-associated lncRNAs are enriched for functional elements, including binding sites for RNA-binding proteins and transcription factors, in particular nuclear factor kappa B (NFκB). Using CRISPR screening, we discovered that 13 of the aging-associated lncRNAs were regulators of the NFκB pathway, and we named this family 'NFκB modulating aging-related lncRNAs (NFKBMARLs)'. Further characterization of NFκBMARL-1 reveals it can be traced to 29 Ma before humans and is induced by NFκB during aging, inflammation and senescence. Reciprocally, NFκBMARL-1 directly regulates transcription of the NFκB inhibitor NFKBIZ in cis within the same topologically associated domain by binding to the NFKBIZ enhancer and recruiting RELA to the NFKBIZ promoter. These findings reveal many aging-associated lncRNAs are evolutionarily conserved components of the NFκB pathway.

A Rapidly Aging World in the 21st Century: Hopes from Glycomics and Unraveling the Biomarkers of Aging with the Sugar Code

A global rise in life expectancy comes with an increased burden of serious life-long health issues and the need for useful real-time measures of the aging processes. Studies have shown the value of biochemical signatures of immunoglobulin G (IgG) N-glycosylation as clinically relevant biomarkers to differentiate healthy from accelerated aging. Most human biological processes rely on glycosylation of proteins to regulate their function, but these events appear sensitive to environmental changes, age, and the presence of disease. Specifically, variations in N-glycosylation of IgG can adversely affect inflammatory pathways underpinning unhealthy aging and chronic disease pathogenesis. This expert review highlights the discrepancies between an organism's age in years of life (chronological age) versus age in terms of health status (biological age). The article examines and synthesizes the studies on IgG N-glycan profiles and the third alphabet of life, the sugar code, in relation to their relevance as dynamic indicators of aging, and to differentiate between normal and accelerated aging. The levels of N-glycan structures change with aging, suggesting that monitoring the alterations of serum glycan biosignatures with glycomics might allow real-time studies of human aging in the near future. Glycomics brings in yet another systems science technology platform to strengthen the emerging multiomics studies of aging and aging-related diseases.