System and computer-implemented method for determining biological age of user

Abstract

Disclosed is a system for determining a biological age of a user. The system comprises a user interface configured to receive data related to at least one biomarker of the user; and a processing arran

Description

TECHNICAL FIELD The present disclosure relates to systems for determining biological ages of users. Moreover, the present disclosure relates to computer-implemented methods for determining biological ages of users. Furthermore, the present disclosure relates to computer-readable storage mediums comprising instructions for determining biological ages of users. BACKGROUND The field of personalised healthcare has gained significant attention in recent years. The personalised healthcare focuses on two main aspects: preventative medicine and early detection and treatment of diseases, as well as optimisation of health, fitness, and nutrition. In this regard, the personalised healthcare involves measuring multiple biological parameters, which, when combined with bioinformatics, enable healthcare professionals and individuals to accurately assess an individual's current health status, disease risk, fitness level, and determine methods to mitigate said risks. Traditionally, there exist some building models that use biological data (such as DNA methylation patterns, blood biomarkers) to predict a chronological age of the individual. However, the chronological age is not the desired outcome in such research since the pace of ageing between individuals differs and this variation is the desired effect to be expressed by a biological age metric. However, such building models lack accuracy due to inappropriate design of studies to fit the purpose. There exist some methods that incorporate prediction of a better proxy of biological age such as the all-cause mortality statistic. However, such predictions are performed on a single biological data source and lack accuracy since such methods do not capture the complex interactions of different contributors (such as parts of a human body) to the biological □ no ay c. Moreover, there exists some systems that fall into a category of a black box model, which means that data obtained from analysing a biological sample is used to calculate biological age with no explanation on how this calculation was conducted. In such a case, besides being imperative to lead change from a pathophysiological perspective, understanding of what needs to change and why a parameter needs to change is an essential part of the motivation for implementing the behavioural change to achieve the desired (health) results. Therefore, the black box models lack pathophysiological actionability as well as basic motivational aspects to implement the behavioural change and achieve the desired health results. Additionally, various biomarkers such as blood pressure, waist circumference, cholesterol levels, blood glucose levels, and so forth have been used to estimate the biological age. However, such tests only covered a limited number of factors and did not provide a comprehensive assessment of the biological age. Moreover, telomeres, the protective caps have been implemented at the ends of chromosomes, shorten with each cell division and are considered a marker of cellular ageing / biological age. However, the telomere length fails to provide the comprehensive assessment of the biological age. Moreover, the telomere length provides a very tissue-dependent analysis, thus making it cumbersome for analysing each tissue separately. There exist some epigenetic age clocks that estimate biological age based on patterns of DNA methylation. However, the epigenetic age clocks fail to incorporate other important factors, such as microbiome composition / diversity, value of blood parameters, and so forth. Furthermore, the epigenetic age assessment is an example of a "black box model" where the model may not be fully interpretable and thus of limited actionability. Generally, the personalised healthcare includes genetic counselling services that involve a subject consulting with a certified genetic counsellor (namely, a healthcare professional) to discuss test results and receive personalised guidance therefrom. However, the genetic counselling services face scalability challenges due to manual interpretation of the genomic data by the genetic counsellors. Moreover, the genetic counselling services are time-consuming. Notably, the personalised healthcare includes online or web-based tools that aggregate the genetic and genomic data from multiple sources and provide annotations and classifications to help interpret the genetic and genomic data. However, such online tools require the subjects to have a certain level of expertise to be effectively used, which may not be accessible to all the subjects. Typically, the personalised healthcare includes direct-to-consumer genetic testing tools. In this regard, the genetic testing tool provides reports on ancestry, health risks, and traits based on an analysis of specific genetic markers. However, the genetic testing provides limited insights, focusing on specific markers and interpreting only one modality of biological data (such as genomic testing), thereby failing to consider the complex interconnectedness of multiple systems within the human body and lacking a holistic view of human health. Similarly, there exists a direct-to-consumer transcriptomic and microbiome testing tool that provides reports based on the transcriptomic and microbiome modalities only. Therefore, in light of the foregoing discussion, there exists a need to overcome the aforementioned drawbacks. SUMMARY The aim of the present disclosure is to provide a system and a computer-implemented method to accurately determine a biological age of an individual. The aim of the present disclosure is achieved by the system and the computer-implemented method for determining a biological age of a user and the corresponding key drivers thereof, as defined in the appended independent claims to which reference is made to. Advantageous features are set out in the appended dependent claims. Throughout the description and claims of this specification, the words "comprise", "include", "have", and "contain" and variations of these words, for example "comprising" and "comprises", mean "including but not limited to", and do not exclude other components, items, integers or steps not explicitly disclosed also to be present. Moreover, the singular encompasses the plural unless the context otherwise requires. In particular, where the indefinite article is used, the specification is to be understood as contemplating plurality as well as singularity, unless the context requires otherwise. BRIEF DESCRIPTION OF THE DRAWINGS FIG. 1 is an illustration of a system for determining a biological age of a user, in accordance with an embodiment of the present disclosure; FIG. 2 is an illustration of a flowchart of steps of a computer-implemented method for determining a biological age of a user, in accordance with an embodiment of the present disclosure; FIG. 3A is an illustration of an exemplary implementation depicting integration of various age clocks from different analyses, in accordance with an embodiment of the present disclosure; FIG. 3B is an illustration of an exemplary implementation depicting a granulated view of a microbiome age clock with regards to the specific biomarkers and their contribution, in accordance with an embodiment of the present disclosure; and FIG. 4 is an illustration of an exemplary implementation depicting a blood age clock with contribution from a specific biomarker, in accordance with an embodiment of the present disclosure. DETAILED DESCRIPTION OF EMBODIMENTS The following detailed description illustrates embodiments of the present disclosure and ways in which they can be implemented. Although some modes of carrying out the present disclosure have been disclosed, those skilled in the art would recognise that other embodiments for carrying out or practising the present disclosure are also possible. In a first aspect, the present disclosure provides a system for determining a biological age of a user, wherein the system comprises: - a user interface configured to receive data related to at least one biomarker of the user; and - a processing arrangement communicably coupled to the user interface, and configured to: - receive, from the user interface, the data related to the at least one biomarker of the user, - normalise the data related to the at least one biomarker of the user, - apply at least one algorithm on the normalised data to determine the biological age of the user and at least one key driver from amongst the at least one biomarker associated with the determined biological age, and - display, via the user interface, the determined biological age and the corresponding at least one key driver. Beneficially, the system, which may be implemented as a biological age clock invention, combines teachings in the field of biotechnology (i.e., data acquisition from different sources) and advanced data (i.e., biological data) processing, biomedical literature scrapping, a knowledge base generation, public health and preventive medicine database and the field of ageing research to determine the biological age of a user. Moreover, the system addresses the problem of accurately assessing and visually representation of key drivers contributing to individual's biological age, by integrating different biological data modalities and using advanced data processing and normalisation algorithms to populational distribution (age and gender stratified) of the biomarker. As disclosed later, the state-of-the-art of biological age calculation is achieved by maintaining up-to-date knowledge base derived by manually curating the results obtained by scrapping medical literature data bases. Furthermore, the system is trained using machine learning and artificial intelligence for adapting to the developments in the aforementioned fields for accurately determining the biological age of a user. Furthermore, the disclosed system provides a comprehensive systems biology health testing platform that is also compatible with other testing platforms allowing users to upload third-party data for holistic aggregation and integration of system biology &health data (including personal goals, medical history, nutrition, lifestyle and environment) useable for continuous monitoring and preventive care with retesting and wearable devices. Therefore, the disclosed system finds application from sick care to preventive, personalised and regenerative health care and longevity solutions that are driven or enhanced by individual's biology data. In a second aspect, the present disclosure provides a computer-implemented method for determining a biological age of a user, the computer-implemented method comprising: - receiving data related to at least one biomarker of the user; - normalising the data related to the at least one biomarker of the user, - applying at least one algorithm on the normalised data for determining the biological age of the user and at least one key driver from amongst the at least one biomarker associated with the determined biological age, and - displaying the determined biological age and the corresponding at least one key driver. Beneficially, the computer-implemented method employs normalising the data to ensure that biomarker data from different individuals or experiments can be compared meaningfully. Moreover, by normalising data and applying at least one algorithm thereon to determine the at least one key driver associated with the biological age of an individual, enables generalising the data and more accurately assessing the individual's health status and response to treatments. In a third aspect, the present disclosure provides a computer-readable storage medium comprising instructions for determining a biological age of a user, which when executed by a data processing system, cause the data processing system to carry out the computer-implemented method of the second aspect. The terms "biological age" and "chronological age" as used herein refers to refer to two distinct concepts used to describe different aspects of ageing, such as health or well-being, genetics, lifestyle, environmental influence. While chronological age is the simplest and most commonly used way to measure an individual's age, i.e., the number of years the individual has lived since his / her birthdate, biological age (or physiological age) refers to an individual's age as determined by his / her physical condition and overall health. It may be appreciated that an individual's chronological age and biological age may not always align, wherein in some cases the biological age may be older or younger that the chronological age due to the aspects of ageing. For example, lifestyle habits such as unhealthy diet, lack of (or no) exercise (physical activity), smoking, alcohol consumption, and stress; presence of chronic diseases or genetic disorders; and / or exposure to toxins and pollutants can accelerate ageing in an individual, thus, the biological age of such individual is older that their chronological age. In contrast, an individual who adopts healthy lifestyles, maintains a balanced diet, exercises regularly, and avoids harmful habits like smoking and excessive alcohol consumption may have a biological age that is younger than their chronological age. Notably, biological age is a more accurate indicator of health than chronological age, that infers how well an individual is ageing and enables predicting the individual's risk of age-related diseases. Typically, the biological age may be determined by employing biomarker assessments (e.g., blood pressure, cholesterol levels, DNA methylation patterns), functional tests (e.g., grip strength, lung function), imaging (e.g., brain scans), and so on. The system comprises the user interface configured to receive data related to at least one biomarker of the user. The term "user interface" (III) as used herein refers to a space where interaction between the user and the system or a software application occurs. Typically, the UI presents one or more spatially distributed elements or features, such as visual elements (e.g., text, graphics, layout), input methods, and feedback mechanisms, that enable the user to interact with the system. Beneficially, the interaction between the user and the system allows effective operation and control therebetween. Moreover, the UI is designed to allow the user to interact easily, efficiently, and in a user-friendly manner with the system to provide maximum usability, thereby reducing the amount of user input to achieve a desired output. Typically, the UI may be composed of one or more layers, including a human-machine interface (HMI) that interfaces machines with physical input hardware such as keyboards, a mouse, a touch screen or a game pad; and output hardware such as a computer monitor, a speaker, a printer, and the like. Moreover, a III layer may interact with one or more human senses, and may comprise: visual UI (sight), auditory GI (sound), tactile UI (touch), to display graphics, provide audio message and receive user inputs (such as images, sound, touch input), respectively. Hence, the aforementioned user interface is a multimedia user interface (MUI). In an implementation, the UI is rendered on a display screen of the system to display graphics to the user. It will be appreciated that UI is designed to also display the received data for the user's reference and further action (such as correction or deletion, as required) as well as a final output after the received data is processed by the system for the user's reference and further action (such as more data (such as a change incorporated in user's routine corresponding to the final output) to be provided for further processing, as required). Herein, the user interface is configured to receive data related to the at least one biomarker of the user via a manual input or an automated input. In an implementation, the user interface receives the data via a manual input of the data by the user. Optionally, the manual input of data involves filling out forms or providing information through text fields. Optionally, the display screen is in the form of a touch screen to receive a user input for example via a tap of finger over the display screen. Alternatively, the user input is specified by the user may be via a mouse click. In another implementation, the user input is received from an automated software application. In this regard, optionally, the automated input is achieved via one or more wearable devices and / or sensors for acquiring real-time data by syncing therewith, Internet-of-Things (loT) devices, Application Programming Interfaces (APIs), cloud data, and so on. It will be appreciated that the user interface of the system receiving the data related to the at least one biomarker of the user is designed such that it is compliant with data protection regulations. The term "at least one biomarker" as used herein refers to measurable indicators of biological states or conditions. Notably, one or more biological or physiological indicators, found in various biological samples of the user, can be used to provide insights into how an individual is ageing at a cellular or molecular level. Beneficially, a combination of such biomarkers provides a comprehensive view of a user's health and wellness. In particular, the at least one biomarker enables the user to monitor their current biomarker data, track changes over time, and make informed decisions about their health and lifestyle. In an embodiment, the data related to at least one biomarker of the user comprises at least one of: the biological data, the sensor data, a health data from a third-party source, a pre-stored biological data and a pre-stored sensor data from a third-party source. Herein, the biological data refers to information about an individual's physiological characteristics such as blood pressure, heart rate, cholesterol levels, glucose levels, body temperature, individual's DNA sequences, microbial DNA sequences within the individual's body, and epigenetic markers (DNA methylation, histone modification, and non-coding R.NA molecules modification), clinical blood biomarkers. The biological data is typically collected through direct measurements or lab tests conducted on various biological samples including saliva, blood, serum, stool, urine, and the like. Sensor data is collected by various sensors and monitoring devices including wearable, portable or fixed monitoring devices. Examples sensor data from various monitoring devices include data from wearable fitness trackers, smartwatches, medical sensors, and home health devices, that employ sensor for capturing data related to heart rate, sleep patterns, activity levels, oxygen saturation, and more. Health data from third-party sources can be obtained from external healthcare providers, medical facilities, or electronic health records (EHRs). The health data from third-party sources typically includes medical history, diagnostic reports, medication records, and treatment plans. The pre-stored biological data and the pre-stored sensor data from third-party sources refer to historical biomarker data (such as user's historical blood pressure readings or cholesterol levels) and historical sensor readings (e.g., past activity tracking, sleep patterns, or fitness metrics), that has been collected and stored in a database or electronic health record system, for comparison and trend analysis in future. It will be appreciated that the user interface is designed so as to be compatible with formats of the data being collected and the various external devices or platforms collecting said data. Beneficially, integrating the various types of biomarker data can provide a comprehensive view of a user's health and wellness and a means for monitoring the current biomarker data and a change therein over time to make informed decisions about their health and lifestyle. In an embodiment, the at least one biomarker comprises at least one of: blood biomarkers, gut microbiome composition and functional biomarkers, epigenomic biomarkers, telomeric biomarkers, proteomic biomarkers, lipidomic data, glycan data, biomarkers based on glycosylation patterns of the immune system, metabolomic biomarkers, transcriptome signatures, anthropometric measurements, physiological phenotypes, functional and wearable measurements of exercise and sleep. The above-exemplified at least one biomarker encompasses a wide range of biological or physiological indicators associated with an individual's health and ageing. Blood biomarkers include a wide range of substances found in the blood, such as glucose, cholesterol, hormones, and proteins, indicative of various health conditions and metabolic processes. Notably, changes in hormone levels, such as a decrease in growth hormone or sex hormones like oestrogen and testosterone, can be indicative of ageing. The gut microbiome consists of trillions of microorganisms in the digestive tract, analysis of which can provide insights into digestive health, immune function, and influence thereof on overall well-being of an individual. The human gut microbiome consists of at least 1500 species comprising about 100 trillion bacteria, archaea, fungi and viruses. Optionally, the gut microbiome comprises genus selected from at least one of: Bacteroides, Escherichia, Blautia, Coprococcus, Phascolarctobacterium, Bifidobacterium, Ruminococcus, Faecalibacterium, Prevotella, Parabacteroides, AUstipes, Clostridium, Dorea, Roseburia, Collinsella, Lachnospira, Oscillospira, Bilophila, Streptococcus, Butyricicoccus, Butyricimonas, Odoribacter, Lactobacillus, Lactococcus, Adlercreutzia, Subdoligranulum, Dialister, Anaerofilum, Actinomyces, Anaerotruncus, Christensenella, Akkermansia, Anaerostipes, Barnesiella, Coprobacillus, Haemophilus, Leuconostoc, Ralstonia, Rhodococcus, Staphylococcus. Epigenomic biomarkers are used for analysing gene expression resulting from chemical modifications to genetic element (such as DNA and histones), for example, changes in DNA methylation patterns over time which occur with ageing and various diseases. Telomeric biomarkers are related to the length and integrity of telomeres (protective structures at the ends of chromosomes). Telomeres shorten as cells divide and shortening of telomeres is associated with cellular ageing. Longer telomeres are often linked to a more youthful biological age. Transcriptomic biomarkers (or transcriptome signatures) involve the study of gene expression patterns, a change in which can provide insights into cellular processes and disease pathways. Proteomic biomarkers are used for analysing changes in protein expression or levels that can be indicative of various diseases and ageing-related processes. Lipidomic data pertains to the study of lipids (fats) in the body. It can provide insights into lipid metabolism, which is crucial for cardiovascular health. Glycan data relates to the study of glycans (sugar molecules attached to proteins and lipids), which are important for various cellular processes. Immune system glycosylation patterns can provide information about immune function and responses, which are critical for overall health. In this regard, biomarkers related to immune function, such as the number and activity of immune cells, can provide insights into the body's ability to defend against infections and diseases. Metabolomic biomarkers involve the analysis of small molecules (metabolites) in biological samples, changes in which can reflect metabolic processes and disease states. Moreover, biomarkers related to metabolism, including insulin sensitivity, cholesterol levels, and glucose regulation, can influence biological age. Anthropometric measurements include physical characteristics such as height, weight, body mass index (BMI), waist circumference, and body fat percentage for assessing body composition and overall health. Physiological phenotypes refer to observable characteristics related to an individual's physiological function, such as blood pressure, heart rate, lung capacity, and muscle strength. Moreover, biomarkers related to physical fitness, such as muscle mass, bone density, and aerobic capacity, can be used to assess the physiological age of an individual. Furthermore, biomarkers related to bone density and muscle strength can reflect the musculoskeletal health of an individual as they age. Functional and wearable measurements include data from devices like fitness trackers and smartwatches, which can monitor exercise levels, sleep patterns, and other health-related activities. Moreover, inflammatory biomarkers like C-reactive protein (CRP) and interleukin-6 (IL-6) can indicate the level of inflammation in the body, and chronic inflammation is associated with ageing and age-related diseases. Furthermore, oxidative stress, caused by an imbalance between antioxidants and free radicals, also contributes to ageing. Biomarkers such as malondialdehyde (MDA) and 8-hydroxy-2'-deoxyguanosine (8-OHdG) can reflect oxidative damage. Furthermore, cognitive decline with ageing can be assessed using biomarkers such as brain structure and function, as well as cognitive test scores. Beneficially, by analysing a combination of these biomarkers, researchers and healthcare providers can estimate an individual's biological age and gain insights into their overall well-being. It's important to note that the accuracy of these estimates may vary, and no single biomarker can provide a complete picture of biological age on its own. Therefore, different combinations of biomarkers are analysed to offer a comprehensive understanding of an individual's health status, ageing process, and susceptibility to various diseases. In the nutshell, analysing a combination of biomarker is a novel aspect that provides a holistic multi-omics validation for accurately determining the biological age of an individual. In an exemplary implementation, the system gathers comprehensive data from various biological tests including blood biomarkers (e.g., 50+ blood biomarkers), epigenome testing (by analysis of over 800,000 epigenetic markers), microbiome testing (by analysis of the entire gut metagenome, including bacteria, fungi, viruses, and archaea), and / or telomere length testing (from quantitative polymerase chain reaction (qPCR) analysis of telomere lengths). Referring to the above example, the blood sugar levels, the cholesterol levels, the ECG data, the lifestyle data and a past chest pain experience may be collated to determine if a subject is at a potential risk of a cardiac ailment, which helps in determining a biological age of the subject. The system comprises the processing arrangement communicably coupled to the user interface. Generally, as used herein, the term "processing arrangement" refers to a computational element that is operable to respond to and processes instructions that drive the system. Optionally, the processing arrangement includes, but is not limited to, a microprocessor, a microcontroller, a complex instruction set computing (CISC) microprocessor, a reduced instruction set (RISC) microprocessor, a very long instruction word (VLIW) microprocessor, or any other type of processing circuit. Furthermore, the term "processing arrangement" may refer to one or more individual processors, processing devices and various elements associated with a processing device that may be shared by other processing devices. Additionally, the one or more individual processors, processing devices and elements are arranged in various architectures for responding to and processing the instructions that drive the system. Moreover, the processing arrangement is communicably coupled to the user interface via a data communication network. The data communication network allows for communication between the processing arrangement and the user interface, in order to receive and subsequently process the data related to at least one biomarker of the user. Notably, the data communication network refers to an arrangement of interconnected, programmable and / or non-programmable components that, when in operation, facilitate data communication between one or more electronic devices and / or databases. Furthermore, the data communication network may include, but is not limited to, a peer-to-peer (P2P) network, a hybrid peer-to-peer network, local area networks (LANs), radio access networks (RANs), metropolitan area networks (MANs), wide area networks (WANs), all of or a portion of a public network such as global computer network known as the Internet , a private network, a cellular network and any other communication system. Additionally, the data communication network employs wired or wireless communication that can be carried out via one or more known protocols. The processing arrangement is configured to receive, from the user interface, the data related to the at least one biomarker of the user. The UI facilitates transmission of the data related to the at least one biomarker to the processing arrangement via the data communication network, as described above. It may be appreciated that the processing arrangement is configured to actively and efficiently receive data from the user interface, allowing for seamless transfer of data for subsequent processing and analysis of the data by the processing arrangement. As mentioned above, there may be different input means for data input and thus the processing arrangement should be configured to receive and process multiple data formats for a desired end application thereof. For example, if the user input is received via a touch-based UI on a touchscreen, then the touchscreen of the system is configured to be coupled to the processor to receive the data from the UI, via one or more sensors, controllers, operating system and application software. Moreover, the processing arrangement is configured to normalise the data related to the at least one biomarker of the user. Herein, normalising typically refers to a process of transforming data into a standardized format, to make the data comparable and easier to work with. Beneficially, normalisation of data ensures that the measurements from different sources or measurement techniques are consistent and can be compared accurately between different individuals or groups. For instance, if you're comparing cholesterol levels between individuals, normalising the data ensures a fair comparison regardless of other factors like age or sex. Moreover, normalised data is easier to analyse statistically, thereby improving the validity of the analysis. In an embodiment, the processing arrangement is configured to normalise the data related to the at least one biomarker based on a population-based normalisation technique. Typically, the population-based normalisation technique involves comparing the biomarker data of an individual to a larger population. Beneficially, the population-based normalisation technique allows for a more contextually relevant normalisation, taking into account the variability of biomarker values across a population. In this regard, the processing arrangement is configured to receive, from the system, data related to the at least one biomarker of the user from both the user (such as via the UI) and a representative population sample (such as via a pre-stored data). Herein, the population sample may be demographically similar to the user (age, sex, ethnicity, etc.) or adjusted for such factors during statistical analysis comprising mean (average), standard deviation (a measure of data dispersion), percentiles (like 25th, 50th, and 75th percentiles, also known as quartiles), etc, to be applied on the data. Subsequently, the user's at least one biomarker data is normalised by comparing it to the statistical measures obtained from the population sample to indicate a relative standing of the user's biomarker value within the population. Beneficially, the population-based normalisation technique takes into account the natural variations in biomarker levels among different people, allowing for a more accurate assessment of the user's health status concerning these biomarkers. In an embodiment, the processing arrangement is further configured to pre-process the data related to the at least one biomarker of the user prior to the step of normalisation thereof. Notably, the received data is a large set of data comprising for example 500+ biological entities obtained from more than 6000 references. However, it will be appreciated that in the forthcoming years, with more digitalization and advancement in science and data management, there will be a surge in data for use as reference, and thus the data related to the at least one biomarker of the user would then comprises biological entities ranging from a few thousands to ten thousands that may be obtained from references ranging from a few thousands to hundreds of thousands, as compared to the existing 500+ biological entities obtained from more than 6000 references, respectively. Optionally, the large set of data includes various types of biological measurements, physiological data, experimental results, observational data from scientific studies, and so on. Optionally, the large set of data is subjected to a pre-processing step for integrating similar data from different sources into a unified dataset, removing any inconsistencies, errors, or missing values from the data (or unified dataset), using a common data format and units of measurements to ensure comparability of data (or unified dataset) across different biological entities, and so on, prior to normalisation thereof. Beneficially, the at least one biomarker data is pre-processed to ensure consistency, removal of noise, and preparation of the data for downstream analysis and for (future) application of continuous learning using AI / ML algorithms to further improve biological age predictions. Furthermore, the processing arrangement is configured to apply at least one algorithm on the normalised data to determine the biological age of the user and at least one key driver from amongst the at least one biomarker associated with the determined biological age. Throughout the present disclosure, the term "at least one algorithm" refers to a set of instructions that are executed by the processing arrangement to determine the biological age of the user and at least one key driver from amongst the at least one biomarker associated with the determined biological age. The at least one algorithm analyses patterns within the biomarker data to estimate the biological age of a user based on their biomarker profile and how they align with patterns seen in the reference population data. Optionally, the at least one algorithm may include machine learning models, statistical methods, or a combination of both. More optionally, the at least one algorithm includes Common algorithms used include decision trees, random forests, support vector machines, neural networks, logistic regression, and deep learning models. Optionally, the at least one algorithm is selected from: a defined deterministic control algorithm, a neural network, a Hidden Markov Model, a Boltzmann machine, an adaptive neural network. The at least one algorithm may optionally be implemented as a simple software routine (for example, a predefined deterministic routine) that merely applies pre-defined logics; alternatively, the at least one algorithm may be implemented as a sophisticated artificial-intelligence-based software suite. It will be appreciated that the at least one algorithm is selected based on the nature of the data and the complexity of the medical condition under study. The technical effect of applying the at least one algorithm is that it analyses the biomarker data of the user and provides: (i) a prediction on a likelihood of the user being associated with a given medical condition, and (ii) insights into which biomarkers are the most influential or key drivers in making these predictions. Moreover, the technical effect of implementing the aforementioned machine learning (namely, artificial intelligence) algorithms is to improve the accuracy of determining the degree of the symptoms of the medical condition of the user. Beneficially, by adapting to the unique characteristics of each user, the at least one algorithm is able to generate more effective output for monitoring, diagnosing and addressing specific issues of the user. The term "key driver" refers to a specific biomarker or a combination of biomarkers that significantly influence the ageing process or the estimated biological age of an individual. In this regard, optionally, the processing arrangement is configured for continuous monitoring, and tracking changes in biomarker data over time, allowing for ongoing analysis and adjustment of the biological age estimation and key driver identification. In an example, the at least one algorithm may be applied on blood biomarkers to identify if cholesterol serves as key driver of cardiac abnormality in a user; and validate the results from medical professionals. Furthermore, the processing arrangement is configured to display, via the user interface, the determined biological age and the corresponding at least one key driver. Once the biological age and key drivers are determined, the system provides feedback to the user. The feedback could include information about their biological age, a comparison with their chronological age, insights into which biomarkers are significant contributors to their ageing process, personalised recommendations, or interventions, and so on, in a clear and understandable manner for the user and the medical professional to draw insights into the medical condition and the biological age of the user therefrom. Optionally, the UI displays the determined biological age and the corresponding at least one key driver using charts, graphs, textual information, audio-format, video-format, audio-video format, and so on. Beneficially, the charts and graphs enable showing the comparison between the biological and chronological ages of the user as indicated by the biological entities, impact of the key drivers to the biological age as compared to the norm. The audio, video, audio-video information provides visual information on how the internal organs and processes function in normal and in presence of key drivers. Moreover, an explanatory text information may accompany the charts, graphs, audio, video, audio-video information to signify the importance of the key drivers in context of the user's health. Optionally, the UI provides interactive elements that allows the user to get more detailed information about the key drivers by clicking on a specific part of the chart, graph, text, video, or audio-video. Optionally, the UI may provide personalised recommendations based on the key drivers determined by the processing arrangement. For example, referring to the above example, if cholesterol is a displayed as the key driver, the UI could suggest dietary changes, exercise routines, or medical consultations for the user to improve their cardiovascular health. Optionally, post displaying the determined biological age and the corresponding at least one key driver, the UI allows the user to provide feedback or ask questions by means of buttons or tactile input for further information, links to related resources, or a chatbot interface for answering specific user queries. In an embodiment, the system further comprises a testing platform communicably coupled to the user interface, wherein the testing platform is configured to: - acquire a biological sample from the user; - analyse the collected biological sample to generate a biological data related to the at least one biomarker of the user; and - share the generated biological data related to the at least one biomarker of the user with the user interface. In this regard, the testing platform functions as a kit of parts configured to acquire biological samples from the user to generate the biological data related to the at least one biomarker of the user, and subsequently sharing the biological data with the user interface for further processing thereof by the processing arrangement. Optionally, the biological samples may be collected from saliva, stool, sweat, tear fluid, blood, DNA, urine, tissue, or any other relevant biological material depending on the specific biomarkers being analysed. It may be appreciated that the saliva serves as a source of biological sample that provides biological entities ranging from genome (whole genome 30x; GSA 720k SNPs, etc.), epigenome (EPIC 850k CpGs; Mercury 30k CpGs), telomeres (QPCR. T / S ratio for length), protein levels, hormone concentrations, or other relevant indicators specific for the user's health condition. The stool sample may be used to analyse whole metagenome including bacteria, fungi, archaea and viral genomes, that may be present in the user's gut or other parts of body. The blood sample provides more than 50 longevity biomarkers (with nurse phlebotomy service). In this regard, the testing platform is configured for processing and analysing the acquired biological sample using various laboratory techniques, such as mass spectrometry, DNA sequencing, enzyme-linked immunosorbent assays (ELISA), and so on. Optionally, the biological sample may include a data collected from a third-party source, such as a database, a wearable device, and so on. Optionally, data from the third-party source may include information fed by the user, for example, via health questionnaire, diet questionnaire, lifestyle questionnaire, personal goals, and so on. It will be appreciated that the testing platform may be communicably coupled to the III via a wired or wireless communication network. In an example, the testing platform is implemented as a glucometer configured for measuring blood glucose levels, for monitoring diabetes in the user. In such case, a small blood sample (obtained by pricking a finger with a lancet) is placed on a test strip provided on the glucometer. Notably, the test strip contains chemicals (including glucose oxidase) for initiating an enzymatic reaction between the blood sample and the chemical to produce an electrical current proportional to the glucose concentration in the blood that is converted into a numerical value (mg / dL or mmol / L). The numerical value may be displayed on a screen of the glucometer and stored in a memory unit associated with the glucometer. The stored data is shared with the system via the UI over a software application, for further analysis. In an embodiment, the system further comprises a smart wearable device communicably coupled to the user interface, wherein the smart wearable device is configured to: - sense at least one parameter associated with the at least one biomarker of the user; - analyse the sensed at least one parameter to generate a sensor data related to the at least one biomarker of the user; and - share the generated sensor data related to the at least one biomarker of the user with the user interface. In this regard, the term "smart wearable device" as used herein refers to a device that is configured to collect data corresponding to or mitigate, alleviate or eliminate symptoms associated with one or more medical conditions. Optionally, the medical condition may be a neurological condition (such as Parkinson's Disease (PD), stroke, spinal cord injury, and so on), cardiac-vascular disease, respiratory disease, a genetic disorder, and so on. In this regard, the smart wearable device is beneficially arranged to make physical contact with the user's body, such as by means of a mechanical engagement means (e.g., strap, adhesive, vacuum cup, magnet, string, etc.). The smart wearable device is equipped with sensors capable of measuring and analysing specific biomarker-related parameters to generate the sensor data related to the specific biomarker(s) being monitored. Optionally, the sensor data may indicate trends, deviations from normal values, or patterns associated with the biomarkers. The sensor data is subsequently transmitted to the UI through wireless or wired technologies or cellular connections to present the sensor data to the user in a user-friendly and understandable format. In an exemplary implementation, the wearable device may optionally be used to collect blood sugar levels, heartbeat, and spO2 of the user that may be suffering from diabetes and is at a potential risk of developing a cardiac disease, and the collected data is shared with the UI for further analysis. Optionally, the smart wearable device comprises a sensor arrangement for sensing at least one parameter associated with the at least one biomarker of the user. Optionally, the sensor arrangement includes one or more biosensors and one or more motion sensors to sense biological entities and corresponding patterns of the user that are associated with the symptoms of the medical condition of the user. In this regard, the one or more biosensors may be configured for sensing at least one of: nerve signals, skin characteristics, cardiac signals, skin conductivity, skin impedance, user body temperature, blood flow rate. Moreover, the one or more motion sensors include at least one of: a temperature sensor, a skin impedance sensor, a galvanic skin response sensor, a skin conductance sensor, an EDA (Electrodermal Activity) sensor, a heart rate sensor (ECG), a microphone, an electrostatic skin contact sensor, an accelerometer, an inclinometer, a gyroscopic sensor, a flow rate sensor. In an embodiment, the system further comprises the processing arrangement is further configured to create a database for storing at least one of: a plurality of algorithms configured to determine the biological age of an individual, a plurality of published literature, a historical data of the user, a historical recommendation for the user, and wherein at least one algorithm from amongst the plurality of algorithms is applied to the normalised data to determine the biological age of the user. Herein, the database is a structured collection of data that is organized (such as into tables, fields, records) and stored electronically in a system or a server. Typically, the database is designed to efficiently manage, store, retrieve, and update large volumes of data. Beneficially, the database stores the aforementioned data to be accessed by the processing arrangement for determining the biological age and the corresponding at least one key driver. Moreover, the stored data may be used by the system as a future training dataset for use by machine learning and artificial intelligence tools. It will be appreciated that the plurality of algorithms may comprise discrete algorithms to be applied on different types of normalised data or based on the end application of the system, i.e., based on the complexity of the data and the medical condition. In an embodiment, the user interface is further configured to display a digital twin of the user along with the determined biological age and the corresponding at least one key driver. The term "digital twin" refers to a virtual model or replica of the user that is displayed on the UI (rendered on a display unit associated with the system). It will be appreciated that the digital twin is used to represent a virtual working of the various biological or biophysiological processes of the user based on the respective discrete levels of the biological entities and conformations and functioning thereof. Notably, the representation of the user as the digital twin changes in accordance with the changes in the respective levels of the biological entities and the conformations and functioning thereof. Optionally, the digital twin is generated on basis of real-time data of levels of the biological entities and the conformations and functioning thereof. Optionally, the digital twin provides a virtual model of an individual's brain, cardiovascular, inflammation, metabolism and muscles and bones arrangement and functioning. In an example, a digital twin of the user may be used to show the inflammation of the user as something that the user needs to focus on and therefore will be brought to their attention through a heat map. The heat map may be shaped as the user's body. In an embodiment, the processing arrangement is further configured to: - create a training dataset, by using the data related to at least one biomarker of the user, based on the determined biological age of the user; and - train a machine learning model, based on the training dataset, to select and apply another algorithm on the normalised data to adjust a contribution of the at least one biomarker to the determined biological age. In this regard, the processing arrangement utilizes the data related to at least one biomarker of the user, which has been normalised and processed, for creating the training dataset that is further used to train the machine learning model to predict the biological age accurately based on the biomarker data. Optionally, the data may be time-stamped for later reference. It will be appreciated that the machine learning model may implement artificial intelligence techniques that may be trained to perform a particular task. Optionally, the machine learning model employs, for example, regression algorithms (e.g., linear regression, support vector regression) or ensemble methods (e.g., random forests, gradient boosting), for predicting the biological age. It may be appreciated that during training, the machine learning model learns the patterns and relationships within the biomarker data for determining biological age. Optionally, the trained machine learning model may be validated using a separate validation dataset to ensure its accuracy and generalizability. If necessary, the machine learning model parameters may be refined or a different algorithm from the plurality of algorithms may be selected to be applied onto the normalised data to improve the machine learning model's accuracy. Upon validation, the machine learning model may be used to adjust the contribution of the at least one biomarker to the determined biological age by automatically selecting the suitable algorithm on the normalised data. Optionally, the training of the machine learning model may be achieved using algorithms such as a supervised learning algorithm, an unsupervised learning algorithm, and the like. The machine learning model may use the training dataset as examples and may learn a rule to map each captured data related to at least one biomarker of the user with its respective determined biological age of the user. The machine learning model may facilitate in automatic selection and / or application of a different (namely, another) algorithm on to the normalised data. In an implementation, a series of images, text information, and audio feed / recordings comprising information on are fed, as training datasets, into the machine learning model with one or more symptoms associated with a medical condition of the user, to train the machine learning model to pick up on these when processing the series of images, text information, audio and / or video feeds in real-time to determine the contribution of corresponding at least one biomarker as indicated in the training dataset to the determined biological age. Based on the monitored contribution of the at least one biomarker, feedback may be provided to the machine learning model in order to adapt accordingly for new algorithms. The new algorithms may be learnt by the machine learning model for a particular biomarker for a given medical condition. Beneficially, by using the data related to at least one biomarker of the user, the machine learning model identifies the relative importance of different biomarkers in predicting the biological age of the user. For example, the machine learning model can highlight which biomarkers have a higher impact on the ageing process and which ones have a lesser influence. Moreover, the processing arrangement is further configured to regularly update the machine learning model with new data (such as latest scientific insights or knowledge and data trends (i.e., user-specific health data)) to ensure that the machine learning model adapts to changes in the user's health and understands the biomarkers' roles in ageing. Additionally, beneficially, by training the machine learning model using existing or new data, a more accurate and personalised assessment of the ageing process is performed by the processing arrangement. Moreover, it may be appreciated that in applications involving machine learning, normalising the data related to at least one biomarker can improve the performance and training stability of the at least one algorithm. Notably, the normalised data prevents certain features from dominating the learning process due to their larger scales. In an embodiment, the processing arrangement is further configured to associate rank corresponding to the at least one key driver from amongst the at least one biomarker associated with the determined biological age, based on their importance in biological age determination, wherein the ranks are associated based on a correlation of the at least one biomarker with the determined biological age of the subject. In this regard, the at least one key driver is ranked based on its importance in determining biological age based on a correlation analysis (by employing such as Pearson correlation coefficient or Spearman rank correlation coefficient) between each of the biomarker and the determined biological age of the user. A positive correlation indicating an increase in the biomarker value is correlated with an increase in the biological age, while a negative correlation implies an inverse relationship, i.e., as the biomarker value increases, the biological age decreases. Thus, biomarkers with stronger correlations (closer to +1 or -1) are considered more important in determining biological age and are assigned higher ranks. For example, if Biomarker A has a correlation coefficient of 0.8 with biological age, it is ranked higher than Biomarker B, which has a correlation coefficient of 0.5. The technical effect of associating ranks with the at least one key driver based on its correlation with biological age is that it provides a clear understanding of which biomarkers play the most significant roles in the ageing process, and such understanding is valuable for personalised health interventions and enables targeted approaches to address specific factors influencing an individual's biological age. In an embodiment, the processing arrangement is further configured to associate rank corresponding to the at least one key driver from amongst the at least one biomarker associated with the determined biological age, based on a comparison of at least one biomarker from a first sample with at least one biomarker from a second sample, wherein the first sample and the second sample are obtained from different tissues of the user. In this regard, the first and second samples are collected from different tissues of the user. Notably, each sample contains specific biomarkers, associated with various physiological processes, relevant to the tissue type. For instance, the first sample may be from blood (representing systemic biomarkers) and the second sample may be from tissues like skin, hair, saliva, stool or muscle (representing localized biomarkers). For example, blood samples might include biomarkers related to cardiovascular health, while skin samples might contain biomarkers indicating oxidative stress or skin ageing. The at least one biomarker that is present in both samples is identified and levels thereof are compared across the different sample types to understand the correlation or disparity in the expression of these biomarkers between different tissues. Subsequently, the processing arrangement ranks the at least one key driver based on the comparison results. Biomarkers that show consistent patterns or significant differences between tissues are considered important key drivers in determining biological age across different parts of the body, and are therefore assigned higher ranks. Beneficially, by comparing biomarkers from different tissues and associating ranks based on these comparisons, the processing arrangement provides a more comprehensive understanding of how biomarkers vary across the body, thereby helping in tailoring interventions and recommendations specific to different physiological aspects, contributing to a holistic approach to understanding the user's biological age. Moreover, the two tissues may further allow amplifying the integration of the biological age clock calculation. Optionally, the ranked biomarkers may be displayed on the UI in the form of a chart or table, thereby enabling the users and healthcare professionals to quickly identify the most influential biomarkers associated with the user's biological age. It will be appreciated that the machine learning model continuously analyses the latest knowledge and data trends and ensures that the ranking reflects the latest understanding of the relationship between biomarkers and biological age. Optionally, the determined biological age is presented in a visually intuitive format alongside the individual's chronological age. The key drivers contributing to individuals biological age are clearly pointed out; thereby allowing users and healthcare professionals to better understand one's health status and devise personalised interventions to optimise health and wellbeing. Optionally, the processing arrangement is further configured to interpret the ranked biomarkers to provide actionable insights to users and healthcare professionals. For instance, if cholesterol is ranked as the top key driver, recommendations can be provided to manage cholesterol levels effectively. In an embodiment, the system further comprises a device integration application configured to provide an interactive user interface to its corresponding user for communicating with the system. The term "device integration application" refers to a software program for execution by a device by employing a processor of the device. Notably, the device integration application comprises a set of predefined functions that are programmed to provide instructions to hardware and / or software elements of the device. Moreover, the device integration application enables at least one compatible device to interact with the system. Furthermore, the device integration application is configured to provide the interactive user interface on the display of the device, to allow the user to perform specific associated tasks required for the interaction with the system. In an example, the device integration application is an application programming interface. In an embodiment, the device integration application is affiliated to an organization, for example a hospital or health provision service. Therefore, the device integration application functions in accordance with pre-programmed guidelines provided by the organization. Notably, the device integration application is configured to function in accordance with the pre-programmed guidelines upon installation thereof. The device integration application typically enables spatial tracking of the system, when in operation. The at least one compatible device that is configured to run the device integration application thereon includes, but is not limited to, a mobile, a laptop computer, a tablet computer, a desktop computer, a palmtop computer and a smart watch. The present disclosure also relates to the computer-implemented method as described above. Various embodiments and variants disclosed above, with respect to the aforementioned system, apply mutatis mutandis to the computer-implemented method. In an embodiment, the computer-implemented method further comprises - creating a training dataset based on the determined biological age of the user; and - training a machine learning model, based on the training dataset, to select and apply another algorithm on the normalised data to adjust a contribution of the at least one biomarker to the determined biological age. In an embodiment, the computer-implemented method further comprises correcting a chronological age of the user based on the contribution of the at least one biomarker to the determined biological age, wherein correcting the chronological age of the user comprises - positioning the received data related to at least one biomarker of the user on a population-based normalised hazard risk curve to obtain a correlation risk value corresponding to a given biomarker adjusted hazard risk value compared to a median value corresponding thereto; - translating the correlation risk value corresponding to the given biomarker to one or more years; - adding the one or more years to the determined biological age of the user, if the correlation risk value is higher than the median risk value, or - subtracting the one or more years from the determined biological age of the user, if the correlation risk value is lower than the median risk value; - adding contribution of the at least one biomarker to determine a final biological age of the user; and - adding the final biological age of the user to the user's chronological age. Herein, the correlation risk value corresponds to a value that signifies whether the user's biomarker risk value is higher or lower than the risk associated with a median population value of the biomarker. The hazard risk curve typically represents the relationship between the magnitude of a risk factor (such as a biomarker level; X-axis) and the likelihood of a specific event occurring (Y-axis) by means of a graph. The population-based normalisation graph displays how an individual's biomarker data (X-axis) compares to a normative or reference population risk (Y-axis). Moreover, the population-based normalised hazard risk curve is a graphical representation that depicts the probability of an event (such as disease occurrence or mortality) with regards to a risk factor of interest normalized to a risk associated with a populational (could be sex / age matched etc.) median value of a risk factor of interest. Notably, when the population-based normalised hazard risk curve is normalized and population-based, the hazard (or risk) is adjusted for specific factors to provide a standardized comparison across the entire population, enabling assessing the risk of an event occurring over time while accounting for various demographic or environmental factors that might influence the outcome. The correlation risk value is translated to a numerical representation in years to quantify the difference between the user's biomarker adjusted hazard risk value and the median value in terms of years. The difference in values is used to adjust the chronological age of the user, wherein the adjustment accounts for biomarkers that indicate an accelerated or decelerated ageing process compared to the median population. The contribution of the at least one biomarker includes the adjustment made based on the correlation risk value and any other factors derived from the biomarker data. Add the final biological age of the user to the user's chronological age to obtain the corrected chronological age of the user, accounting for the influence of biomarkers on the ageing process. Beneficially, this way the computer-implemented method corrects the user's chronological age based on the specific biomarkers' contribution to their biological age. This correction provides a more nuanced and personalised understanding of the user's ageing process, considering the unique impact of their biomarker profile compared to the median population. In an embodiment, the computer-implemented method further comprises creating a database for storing at least one of: a plurality of algorithms configured to determine the biological age of an individual, a plurality of published literature, a historical data of the user, a historical recommendation for the user, and wherein at least one algorithm from amongst the plurality of algorithms is applied to the normalised data to determine the biological age of the user. In an embodiment, the computer-implemented method further comprising configuring the processing arrangement to associate rank corresponding to the at least one key driver from amongst the at least one biomarker associated with the determined biological age, based on their importance in biological age determination, wherein the ranks are associated based on a correlation of the at least one biomarker with the determined biological age of the subject. In an embodiment, the computer-implemented method further comprises configuring the processing arrangement to associate rank corresponding to the at least one key driver from amongst the at least one biomarker associated with the determined biological age, based on a comparison of at least one biomarker from a first sample with at least one biomarker from a second sample, wherein the first sample and the second sample are obtained from different tissues of the user. The present disclosure also relates to the computer-readable storage medium as described above. Various embodiments and variants disclosed above, with respect to the aforementioned system and the aforementioned computer-implemented method, apply mutatis mutandis to the computer-readable storage medium. Beneficially, determining biological age based on a holistic biological age clock comprising more than one type of biomarkers (such as blood, DNA, epigenome, microbiome, telomeric, protein, lipid, etc.) is a significant improvement over existing solutions that focus only on a specific biomarker (i.e., only on blood biomarker(s), only on telomere length analysis, only on epigenetic data etc.), as it offers a more comprehensive and accurate assessment of biological age. By integrating data from a wide range of sources and a state-of-the-art knowledge base in the field of ageing research, the system can reveal a more holistic picture of an individual's biological age. Additionally, the granular representation of key drivers contributing to biological age calculation provides users with an intuitive way to understand their health status, a feature lacking in most existing solutions. The feature enables individuals to make informed decisions about their health, wellness, and lifestyle choices, leading to improved health outcomes and potentially increased longevity. Although the primary focus of the biological Age Clock invention is on biological age assessment, the underlying technology could be adapted for various other applications. For example, the system could be used to monitor the effectiveness of specific interventions or therapies, track disease progression, or even predict the onset of age-related diseases by identifying early warning signs in the data. In summary, the biological age clock is a novel and innovative solution to the problem of accurately assessing and representing an individual's biological age in a visually intuitive and actionable manner. By combining data from multiple (biological) sources, employing advanced data processing / normalisation algorithms, maintaining / utilising a state-of-the-art knowledge base in the field of ageing research, offering personalised recommendations and adopting a structure to allow for improvement of predictions via applying AI / ML algorithms, the system has the potential to significantly impact preventive medicine, ageing research, and health management of individuals. EXPERIMENTAL PART In an example, Jane, a 48-year-old woman, was concerned about her overall health and well-being. Although she tried to maintain a healthy lifestyle, she had a family history of age-related diseases, such as heart disease and diabetes. Jane used the disclosed system to better understand her biological age and make necessary lifestyle changes to optimise her health. Process: In this regard, Jane underwent the required tests, including blood, microbiome, and epigenome testing. These tests provided comprehensive data about her blood parameters, gut health, epigenetic markers, and telomere length. The biological age clock system processed and analysed the collected data and calculated Jane's biological age. The biological age clock presented the results on a clock face, comparing Jane's chronological age of 48 with her calculated biological age of 52. The visual representation clearly showed that her biological age was higher than her chronological age; furthermore, key drivers of the biological age calculation (HbAlC and total cholesterol) indicated where Jane's areas of focus lied. Based on the biological age assessment, the biological age clock system generated personalised recommendations for Jane, including lifestyle changes, dietary adjustments, exercise routines, and stress management techniques. Action: Jane took the recommendations to heart and started making changes to her lifestyle. She began a regular exercise routine, adopted a healthier diet, practiced mindfulness meditation for stress reduction, and consulted with her healthcare professional to monitor her progress. Outcome: Over time, Jane noticed improvements in her overall health and well-being. She underwent follow-up testing and used the Al-driven biological age clock system to track her progress. The updated clock face now showed a reduced biological age, closer to her chronological age, indicating that the personalised interventions have had a positive impact on her health. By utilising the disclosed biological age clock system, Jane had successfully optimised her health, reduced the risk of age-related diseases, and potentially increased her longevity. The AI / ML algorithms that was inherently applied on the data structure of the disclosed biological age clock system (biological data / intervention data / outcomes data) learned from Jane's biological data and the response to the proposed interventions, thus improving the algorithm to further boost biological age calculation / providing of recommendations in future people using the invention. In this regard, following preparations were ensured: Maintaining state-of-the-art knowledge base: To use the most up-to-date and the most suitable (for an individual) algorithms for biological age calculation, knowledge base was constantly being updated by manually curating the results retrieved from scraping relevant medical literature data bases (i.e., scraping for literature regarding blood-based biomarkers, epigenomic biomarkers, microbiome biomarkers, telomeric biomarkers, proteomic biomarkers, and so forth, and their association with all-cause mortality). The state-of-the-art knowledge base included Million Human Microbiome project, PUBMED, NHANES (USA), Lifelines (NL), UK Biobank, Gut Microbiome Health Index study (GMHI), data explorers corresponding to NHANES, Lifelines, UK Biobank and GMHI, All of Us Research Program (USA), Europe PMC, and similar other knowledge databases that provide integrated genetic, microbiome, blood-biomarker as well as other biomarker based longitudinal studies connecting the biomarker(s) of interest with the biologically relevant end-points (i.e., all cause mortality) The databases include the ones with original data (i.e. data from NHANES) as well as the data bases containing the reports of the summary-statistics of the original data (i.e. PUBMED with publications of the relevant studies). Data mining for parameters to be included in (blood) age clock: In an implementation of blood age clock, PUBMED was scraped for articles with the following search queries (using R easyPubMed module): i.) "all cause death" AND biomarker AND blood, ii.) "all cause death" AND biomarkers AND blood iii.) "all cause mortality" AND biomarker AND blood iv.) "all cause mortality" AND "biomarkers AND blood" on 19.02.2022. The search query resulted in 829 PubMed entities (PM articles) with Scimago (2020) IF >5 (Docs / 2 years column). These articles were screened for blood biomarkers associated with all-cause mortality. Of the 829 articles, 630 were excluded, since they were not conducted in populations of normal individuals (i.e., they were conducted on individuals with HIV, CHF, ACS); 56 were excluded for other reasons (i.e., not focusing on biomarkers, non-human study etc.). 143 articles that evaluated the association of blood biomarkers with all cause mortality in a normal (unselected) population were considered; of which 41 unique biomarkers namely albumin, alkaline phosphatase, ALT, apoB, B6, Bilirubin, BNP, calcitriol, creatinine / eGFR / eGFRcystatin, D-dimer, ESR, fibrinogen, folate, fasting glucose levels / Glucose, glycated haemoglobin / HbAlc / hemoglobin Ale, HDL, high-sensitivity troponin, homocysteine, hsCRP, iron, insulin, LDL-C, Leukocyte telomere length, lactate, leukocyte count, NLR, MR-proANP, n-3 PUFAs, natriuretic peptides, non-HDL, NT-proBNP, PUFA / FA, TC, testosterone, tryglicerides, TSH, urea nitrogen, Vit-C, Vit-D, Vit-E, VLDL were identified. It will be appreciated that in the forthcoming years, with more digitalization and advancement in science and data management, there will be a surge in data as well as databases which may be employed for referencing. Such databases would be scraped for data references with similar or different strategies as compatible with said databases. Selecting the (blood-based) biomarkers to be included in the age-clock (from the data-mined biomarkers): Biomarkers that had strong correlation with all-cause mortality, that were not strongly correlated with each other (r<0.3) were selected. In this regard, of the data-mined biomarkers only the biomarkers that were measured with the basic blood kit, namely, testing platform, were evaluated .The evaluated biomarkers were correlated with each other (via pairwise matrix)and the ones that did not correlate with any other with correlation coefficient r of more than 0.3 were included; for the list of biomarkers correlating with one / another with r of more than 0.3 only one (according to expert opinion) was chosen. Furthermore, RDW was included according to expert opinion. 14 biomarkers namely HDL, TC, hsCRP, HbAlc, alkaline phosphatase, triglycerides, vitamin D, RDW, TSH, albumin, urea, haematocrit, white cells, iron were identified. For each of those 14 biomarkers a study on normal population, correlating the biomarker with all-cause mortality (usually adjusted for traditional risk factors) was sought for. The quality of the study was evaluated according to the characteristics / size of the included population: • HDL: PMID: 32283957 meta-analysis (3.5 M patients) • TC: PMID: 33691735, PMID: 30733566 - the optimal point estimate was moved to the upper limit of normal TC levels (Cholesterol | Workplace Health Strategies by Condition | Workplace Health Promotion | CDC) • hsCRP: PMID: 20423919 • HbAlc: PMID: 24668104 (the outcome was CVD mortality) • AP: PMID: 33152050 • TG: PMID: 25131982, 19903920 • VitD: PMID: 32620963 -> model 1: adjusted for age at blood draw, sex, ethnicity, season of blood draw, and fasting status. • RDW: PMID: 31700048 • TSH: PMID: 32031647 • Albumin: PMID: 31431621 • BUN: PMID: 33152050 • Hematocrit: PMID: 33152050 • WhiteCells: PMID: 33152050 • Iron: PMID: 33152050 From the selected studies, hazard ratio profiles were extracted, i.e., associations between certain biomarker values and mortality risk. Calculating (blood-based) biological age: State-of-the-art algorithms (selected from the up-to-date knowledge base) use the pre-processed / normalised data to derive individual's (blood-based) biological age. In this regard, for each biomarker, the sample median values of the individual's comparable population (sex and age matched) were set as 0 (years added / subtracted). The individual's values were compared with the median of the comparable population according to the hazard risk profile (the profile was extracted from the studies as described in the previous paragraph) and years were added / subtracted according to the hazard ratio difference as described in the study by Tsai et al (PMID: 34491905). An example, for calculating an individual biomarker's contribution: o For each individual user, the median value of the biomarker in their "characteristically comparable population" (age + / -7 years, same sex) in a health database (currently NHANES) was set as baseline (zero contribution to age-clock) o The value of the user's biomarker was checked and depending on where it falls compared to the median value of their similar population, years were added or subtracted from their (blood-based) biological age. For example, if the user's biomarker value corresponded to a lower all-cause mortality hazard than the median value in the similar population, time was subtracted from the (blood-based) biological age; and if the user's biomarker value corresponded to a higher all-cause mortality hazard than the median value in the similar population, time was added to the (blood-based) biological age. o The final age clock was calculated by adding together all contributions from individual biomarkers, and then adding / subtracting the added contributions to the user's chronological age. Personalised recommendations: Based on biological age assessment and key drivers contributing to one's biological age, the system generates personalised, current state-of-the-art evidence-based recommendations for lifestyle changes, dietary adjustments, exercise routines, and stress management techniques to help users optimise their health and slow down the ageing process. Application of AI and ML algorithms on the data: The weighted biomarker-based contributions to biological age (such as used in i.e., blood-based biological age clock) can be adjusted by ML / AI algorithms, when the biological data is coupled to other outcomes data (i.e., connection of biomarker data with data derived from wearables). This furthers the accuracy of biological age calculation. Implementation of biological age clock: In an implementation the biological age clock integrated the blood age clock, the microbiome age clock and the epigenetic age clock, wherein the relative contribution of said age clocks was 1 / 2 blood age clock, 1 / 3 epigenetic age clock and 1 / 6 microbiome age clock. These ratios were determined from the fact of how much the static results from each of the analysis (blood biomarker levels, microbiome species / genera levels, methylation of CpG islands) tend to change from the same person through time (intrapersonal change) and the biggest change can be seen in the microbiome, thus trusted the least. For both the blood and microbiome age clocks, following steps were taken: 1. Find entities (blood biomarkers / species / genera) that correlate with health / disease / all-cause mortality 2. These correlations are normally found as Hazard ratio curves (depending on gender / age / ethnicity) 3. Anchor these curves to population databases -> normalise to populational average 4. Position the entity of the client on these curves 5. Translation of hazard risk to years added or subtracted 6. Add together the contributions of the various entities (the granulation also gives you actionable details to recommend and improve on) Blood age clock: As detailed above, the blood age clock comprised 14 biomarkers (correlated with mortality; 1000+ papers screened (829 with Scimago (2020) IF >5)). The 14 biomarkers included Total cholesterol, HDL, hcCRP, triglycerides, alkaline phosphatase, RDW, TSH, albumin, vitamin D, iron, urea, WBC, haematocrit, Hbalc. The contribution of biomarkers to the blood age was determined, depending on the risk curve and the comparison to the relevant population, such as by employing NHANES (60k individuals). The blood age clock represented a Lego block system where biomarkers could be added or subtracted at will. Microbiome aoe clocks: The microbiome age clock was calculated using two variables obtained from sequences of faecal samples. The genera-based taxonomy and the diversity of the microbial world in the gut (Shannon diversity) were used to calculate the microbiome age clock. The Shannon diversity risk ratio published in the 2021 study by Salosensaari et. al (PMID: 33976176) was used, which used a Finnish population cohort (n=7211), to find that some microbiome characteristics were associated with mortality risk during a 15-year follow-up period (including diversity scores). The beta value from this study was used to calculate the risk ratio. In contrast, the Gut Microbiome Health Index was published by Gupta et al. (PMID: 32934239) in 2020. They collected data, published by 34 different scientific articles and 4347 stool metagenomes. In this study, they also published a table of species found in healthy and unhealthy samples (corresponding to the 12 different diseases studied in different articles). Based on the prevalence published in this study, the natural logarithm of the risk ratios (LN_RR) for 17 different genera and 50 different species was calculated. Population statistics were performed for the genera and species present in all 4347 faecal metagenomes. Population prevalence of every genus / species multiplied by corresponding LN_RR was subtracted from the individual's product of LN_RR and that individual's genus or species prevalence and converted to added or subtracted years for a particular individual. Based on the calculated Shannon Diversity risk ratios and the 17 different genera present in healthy and non-healthy individuals, these were converted into added or subtracted years based on the work published by Tsai et al. in 2021 (PMID: 34491905). The years resulting from the transformation of the risk ratios of the diversity scores and the presence of 17 different genera were summed and subtracted from the chronological age (if the sum of genera-based or species-based and diversity years was negative) or added to the chronological age (if the sum of genera-based or species-based and diversity years was positive). DETAILED DESCRIPTION OF THE DRAWINGS Referring to FIG. 1, illustrated is a system 100 for determining a biological age of a user, in accordance with an embodiment of the present disclosure. The system 100 comprises a user interface 102 configured to receive data related to at least one biomarker of the user; and a processing arrangement 104 communicably coupled to the user interface 102. The processing arrangement 104 is configured to receive, from the user interface 102, the data related to the at least one biomarker of the user, normalise the data related to the at least one biomarker of the user, apply at least one algorithm on the normalised data to determine the biological age of the user and at least one key driver from amongst the at least one biomarker associated with the determined biological age, and display, via the user interface 102, the determined biological age and the corresponding at least one key driver. As shown, the system 100 further comprises a testing platform 106 communicably coupled to the user interface 102, wherein the testing platform 106 is configured to: acquire a biological sample from the user; analyse the collected biological sample to generate a biological data related to the at least one biomarker of the user; and share the generated biological data related to the at least one biomarker of the user with the user interface 102. Moreover, the system 100 further comprises a smart wearable device 108 communicably coupled to the user interface 102, wherein the smart wearable device 108 is configured to: sense at least one parameter associated with the at least one biomarker of the user; analyse the sensed at least one parameter to generate a sensor data related to the at least one biomarker of the user; and share the generated sensor data related to the at least one biomarker of the user with the user interface 102. The user interface 102 is further configured to display a digital twin 110 of the user along with the determined biological age and the corresponding at least one key driver. The processing arrangement 104 is further configured to create a database 112 for storing at least one of: a plurality of algorithms configured to determine the biological age of a person, a plurality of published literature, a historical data of the user, a historical recommendation for the user, and wherein at least one algorithm from amongst the plurality of algorithms is applied to the normalised data to determine the biological age of the user. Referring to FIG. 2, illustrated is a flowchart of steps of a computer-implemented method for determining a biological age of a user, in accordance with an embodiment of the present disclosure. At step 202, data related to at least one biomarker of the user is received. At step 204, the data related to the at least one biomarker of the user is normalised. At step 206, at least one algorithm is applied on the normalised data for determining the biological age of the user and at least one key driver from amongst the at least one biomarker associated with the determined biological age. At step 208, the determined biological age and the corresponding at least one key driver is displayed. Referring to FIG. 3A, illustrated is an exemplary implementation depicting integration of various age clocks from different analyses, in accordance with an embodiment of the present disclosure. As shown, a blood age clock, a microbiome age clock and an epigenetic age clock is integrated to result in a biological age clock system. As shown, the contributions of the blood age clock, the microbiome age clock and the epigenetic age clock is 1 / 2, 1 / 6 and 1 / 3, respectively. These ratios are based on the analysis of blood biomarker levels, microbiome species levels, methylation of CpG islands tend to change from the same person through time (intrapersonal change). As shown, the biggest change can be seen in the microbiome age clock, hence it is trusted the least. It may be appreciated that the individual age clocks may further be broken down to provide information on granularity of each of the corresponding biomarkers. Referring to FIG. 3B, illustrated is an exemplary implementation depicting a granulated view of a microbiome age clock with regards to the specific biomarkers and their contribution, in accordance with an embodiment of the present disclosure. As shown, the microbiome age clock is a further granulation of the biological age clock as depicted in FIG. 3A, to show the contributions of various microbes, e.g., Lactobacillus -0.7, Ruminococcus -1.0, Streptococcus -0.8, etc., to the microbiome age (as exemplified in FIG 3A) of the person. Referring to FIG. 4, illustrated is an exemplary implementation depicting a blood age clock with contribution from a specific biomarker, in accordance with an embodiment of the present disclosure. Herein the black line depicts correlation of biomarker values and years added / subtracted. The area 402 depicts age subtracted, the area 404 depicts age added under ly, and the area 406 depicts age added over ly. The Line between areas 402 and 404 denotes the median value in the user's comparable population and the dot in the area 402 depicts the user's values.

Claims

1. A system for determining a biological age of a user, wherein the system comprises:- a user interface configured to receive data related to at least one biomarker of the user; and- a processing arrangement communicably coupled to the user interface, and configured to:- receive, from the user interface, the data related to the at least one biomarker of the user,- normalise the data related to the at least one biomarker of the user,- apply at least one algorithm on the normalised data to determine the biological age of the user and at least one key driver from amongst the at least one biomarker associated with the determined biological age, and- display, via the user interface, the determined biological age and the corresponding at least one key driver.

2. A system according to claim 1, further comprising a testing platform communicably coupled to the user interface, wherein the testing platform is configured to:- acquire a biological sample from the user;- analyse the collected biological sample to generate a biological data related to the at least one biomarker of the user; and- share the generated biological data related to the at least one biomarker of the user with the user interface.

3. A system according to claim 1 or 2, further comprising a smart wearable device communicably coupled to the user interface, wherein the smart wearable device is configured to:- sense at least one parameter associated with the at least one biomarker of the user;- analyse the sensed at least one parameter to generate a sensor data related to the at least one biomarker of the user; and- share the generated sensor data related to the at least one biomarker of the user with the user interface.

4. A system according to any of the preceding claims, wherein the at least one biomarker comprises at least one of: blood biomarkers, gut microbiome composition and functional biomarkers, epigenomic biomarkers, telomeric biomarkers, proteomic biomarkers, lipidomic data, glycan data, biomarkers based on glycosylation patterns of the immune system, metabolomic biomarkers, transcriptome signatures, anthropometric measurements, physiological phenotypes, functional and wearable measurements of exercise and sleep.

5. A system according to any of the preceding claims, wherein the processing arrangement is further configured to create a database for storing at least one of: a plurality of algorithms configured to determine the biological age of an individual, a plurality of published literature, a historical data of the user, a historical recommendation for the user, and wherein at least one algorithm from amongst the plurality of algorithms is applied to the normalised data to determine the biological age of the user.

6. A system according to any of the preceding claims, wherein the data related to at least one biomarker of the user comprises at least one of: the biological data, the sensor data, a health data from a third-party source, a pre-stored biological data and a pre-stored sensor data from a third-party source.

7. A system according to claim 6, wherein the user interface is further configured to display a digital twin of the user along with the determined biological age and the corresponding at least one key driver.

8. A system according to any of the preceding claims, wherein the processing arrangement is further configured to:- create a training dataset, by using the data related to at least one biomarker of the user, based on the determined biological age of the user; and- train a machine learning model, based on the training dataset, to select and apply another algorithm on the normalised data to adjust a contribution of the at least one biomarker to the determined biological age.

9. A system according to any of the preceding claims, wherein the processing arrangement is further configured to pre-process the data related to the at least one biomarker of the user prior to the step of normalisation thereof.

10. A system according to any of the preceding claims, wherein the processing arrangement is configured to normalise the data related to the at least one biomarker based on a population-based normalisation technique.

11. A system according to claim 1, further comprising a device integration application configured to provide an interactive user interface to its corresponding user for communicating with the system.

12. A system according to claim 1, wherein the processing arrangement is further configured to associate rank corresponding to the at least one key driver from amongst the at least one biomarker associated with the determined biological age, based on their importance in biological age determination, wherein the ranks are associated based on a correlation of the at least one biomarker with the determined biological age of the subject.

13. A system according to claim 1, wherein the processing arrangement is further configured to associate rank corresponding to the at least one key driver from amongst the at least one biomarker associated with the determined biological age, based on a comparison of at least one biomarker from a first sample with at least one biomarker from a second sample, wherein the first sample and the second sample are obtained from different tissues of the user.

14. A computer-implemented method for determining a biological age of a user, the computer-implemented method comprising:- receiving data related to at least one biomarker of the user;- normalising the data related to the at least one biomarker of the user, - applying at least one algorithm on the normalised data for determining the biological age of the user and at least one key driver from amongst the at least one biomarker associated with the determined biological age, and- displaying the determined biological age and the corresponding at least one key driver.

15. A computer-implemented method according to any of the claims 14, further comprising:- creating a training dataset based on the determined biological age of the user; and- training a machine learning model, based on the training dataset, to select and apply another algorithm on the normalised data to adjust a contribution of the at least one biomarker to the determined biological age.

16. A computer-implemented method according to claim 14, further comprising correcting a chronological age of the user based on the contribution of the at least one biomarker to the determined biological age, wherein correcting the chronological age of the user comprises- positioning the received data related to at least one biomarker of the user on a population-based normalised hazard risk curve to obtain a correlation risk value corresponding to a given biomarker adjusted hazard risk value compared to a median value corresponding thereto;- translating the correlation risk value corresponding to the given biomarker to one or more years;- adding the one or more years to the determined biological age of the user, if the correlation risk value is higher than the median value, or- subtracting the one or more years from the determined biological age of the user, if the correlation risk value is higher than the median value;- adding contribution of the at least one biomarker to determine a final biological age of the user; and- adding the final biological age of the user to the user's chronological age.

17. A computer-implemented method according to claim 14, further comprising creating a database for storing at least one of: a plurality of algorithms configured to determine the biological age of an individual, a plurality of published literature, a historical data of the user, a historical recommendation for the user, and wherein at least one algorithm from amongst the plurality of algorithms is applied to the normalised data to determine the biological age of the user.

18. A computer-implemented method according to claim 14, further comprising configuring the processing arrangement to associate rank corresponding to the at least one key driver from amongst the at least one biomarker associated with the determined biological age, based on their importance in biological age determination, wherein the ranks are associated based on a correlation of the at least one biomarker with the determined biological age of the subject.

19. A computer-implemented method according to claim 14, further comprising configuring the processing arrangement to associate rankcorresponding to the at least one key driver from amongst the at least one biomarker associated with the determined biological age, based on a comparison of at least one biomarker from a first sample with at least one biomarker from a second sample, wherein the first sample and the second sample are obtained from different tissues of the user.

20. A computer-readable storage medium comprising instructions for determining a biological age of a user, which when executed by a data processing system, cause the data processing system to carry out the computer-implemented method of any one of claims 14-19.

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