Computer-implemented method and system for providing personalized nutritional recommendation for users

Abstract

Computer-implemented method for providing a personalized nutritional recommendation. Nutritional data for a food item is obtained 102. A nutritional index is calculated 104 for each nutrient component of the food item based on the nutritional data. A static food score is calculated 106 by combining the nutritional index for the nutrient components of the food item. A food index is calculated 108 based on the nutritional index. Multimodal health data related to at least one biological modality of the user is obtained 110. A personalised dynamic factor for the food item is calculated 112 based on the food index and the obtained multi-modal health data. A comprehensive food score of the food item is calculated 114 by combining the static food score and the personalized dynamic factor of the food item. A personalized nutritional recommendation is generated 116 for the user based on the comprehensive food score. Other embodiments disclose a system for providing a personalised nutritional recommendation. Optionally, multimodal health data includes health data obtained from different sources. For example, genetic data, microbiome data, biomarker data, imaging data or historical health data.

Description

The present disclosure relates to computer-implemented methods for providing personalized nutritional recommendations for users. Moreover, the present disclosure relates to computer-readable storage mediums comprising instructions for providing personalized nutritional recommendations for users. Furthermore, the present disclosure relates to systems for providing personalized nutritional recommendations for users. BACKGROUND Improper diet and malnutrition have been widely recognized as significant factors that contribute to the prevalence of non-communicable diseases worldwide. Despite the implementation of national dietary recommendations to influence the eating habits of consumers, their impact remains minimal. These recommendations typically adopt a generalized approach, employing visualizations such as "food pyramids" or "healthy plates" which depict individual meals or endorsed food groups. Moreover, such recommendations adopt a "one size fits all" approach, overlooking the diverse biological, phenotypical, and behavioural heterogeneity among individuals within the population. Therefore, the development of tailored and personalized nutritional recommendations is pertinent. Existing attempts at personalized nutrition comprises focus on multidisciplinary evidence base, encompassing in-vitro and animal studies, high-resolution studies, epidemiology, and randomized controlled trials (RCTs), data integration, and clinical translation. However, data integrating from multiple sources and extracting meaningful insights pose complex computational challenges. Moreover, translating research findings into practical clinical applications requires collaborative efforts between researchers, clinicians, and healthcare providers. Use of techniques such as machine learning, data analysis, and predictive modelling by the existing personalized nutritional approaches provide tailored nutritional plans based on individual user preferences and health profiles, however, they often lack comprehensive exploration of different biological data sources as well as the social, cultural, culinary, economic, and environmental contexts in which diets are consumed. Thus, leading to recommendations that are not feasible or acceptable for individuals, ultimately hindering their long-term adherence to personalised nutrition guidelines. Therefore, considering the foregoing reasons, there is a need to overcome the aforementioned drawbacks. SUMMARY The aim of the present disclosure is to provide a computer-implemented method, a computer-readable storage medium comprising instructions and a system to optimize individual health outcomes through precision nutrition strategies by aligning personalized dietary recommendations with an individual's unique genotypic and phenotypic characteristics, as well as their health objectives. The aim of the present disclosure is achieved by a computer-implemented method a computer-readable storage medium comprising instructions and a system for providing a personalized nutritional recommendation for a user, 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 flowchart of steps of a computer-implemented method for providing a personalized nutritional recommendation for a user, in accordance with an embodiment of the present disclosure; FIG. 2 is an illustration of a system for providing a personalized nutritional recommendation for a user, in accordance with an embodiment of the present disclosure; and FIGs. 3A, 3B and 3C are illustrations of exemplary implementations of a dashboard on a user interface of a user input device, 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 recognize that other embodiments for carrying out or practising the present disclosure are also possible. In a first aspect, the present disclosure provides a computer-implemented method for providing a personalized nutritional recommendation for a user, the computer-implemented method comprising: obtaining nutritional data related to a food item; calculating a nutritional index for each nutrient component of the food item based on the obtained nutritional data thereof; calculating a static food score by combining nutritional index for the nutrient components of the food item; calculating a food index based on the nutritional index for the nutrient components of the food item; obtaining multi-modal health data related to at least one biological modality of the user; calculating a personalized dynamic factor of the food item based on the food index and the obtained multi-modal health data of the user; calculating a comprehensive food score of the food item by combining the static food score and the personalized dynamic factor of the food item; and generating the personalized nutritional recommendation for the user based on the comprehensive food score of the food item. The present disclosure addresses the aforementioned challenges (and other challenges) associated with existing personalized nutritional approaches by providing a holistic, systems-wide approach that leverages advanced nutritional profiling coupled with comprehensive data on an individual's variation (phenotype, genotype, enterotype, personal goals, and other relevant factors such as social, cultural, culinary, economic, and environmental contexts) for creating evidence-based personalized nutritional recommendations on a population. Beneficially, personalized nutrition could reduce the risk of developing NCDs, promote weight management, enhance overall health status, empower individuals to make informed dietary choices based on their unique needs and preferences, thereby leading to better long-term adherence, and assist in shifting the focus from treatment to prevention, reducing healthcare costs and improving population health. In this regard, the present disclosure incorporates genomics data and considers a broader range of blood biomarkers, including vitamins and minerals status, as well as biomarkers used to assess the physiological state of different organ systems that can be modulated by dietary nutrients. Additionally, the present disclosure employs more precise body composition assessment, which is a primary determinant of personalized food recommendations. Additional feature of the present disclosure is advanced nutritional profiling which is factored into the recommendation engine due to the intrinsic nutritional constellation of individual foods that should not be overlooked despite heterogenous response of individuals to the same food / meal. Thus, evidence-informed interventions are ensured. In a second aspect, the present disclosure provides a computer-readable storage medium comprising instructions for providing a personalized nutritional recommendation for a user, which when executed by a processing arrangement, cause the processing arrangement to execute steps of a computer-implemented method of the aforementioned first aspect. The instructions, namely, food personalisation algorithm, are designed to provide a personalised food-based nutritional recommendations by integrating data from multiple (biological) sources and combining them with advanced nutritional profiling to yield personalised food scores and dietary recommendations. In a third aspect, the present disclosure provides a system for providing a personalized nutritional recommendation for a user, the system comprising a processing arrangement configured for: obtaining nutritional data related to a food item; calculating a nutritional index for each nutrient component of the food item based on the obtained nutritional data thereof; calculating a static food score by combining nutritional index for the nutrient components of the food item; calculating a food index based on the nutritional index for the nutrient components of the food item; obtaining multi-modal health data related to at least one biological modality of the user; calculating a personalized dynamic factor of the food item based on the food index and the obtained multi-modal health data of the user; calculating a comprehensive food score of the food item by combining the static food score and the personalized dynamic factor of the food item; and generating the personalized nutritional recommendation for the user based on the comprehensive food score of the food item. The innovative system aims to optimize individual health outcomes through precision nutrition strategies. By aligning personalized dietary recommendations with an individual's unique genotypic and phenotypic characteristics, as well as their health objectives, the present disclosure endeavours to provide a tailored and scientifically substantiated approach to nutritional guidance. The present disclosure embodies an advanced and adaptive personalized nutrition algorithm that utilizes systems biology approaches to integrate genomic, physiological, and real-time sensor data to generate tailored dietary recommendations for individuals seeking to optimize their health and longevity. The term "personalized nutritional recommendation" as used herein refers to advice associated with personalized nutrition based on the user's specific needs, preferences, and goals in achieving optimal health and well-being based on various factors such as genetics, metabolism, lifestyle, health status, and personal preferences. Typically, personalized nutritional recommendation involves the use of tools such as genetic testing, dietary assessments, and consultations with nutrition professionals to develop personalized dietary plans. Beneficially, personalized nutritional recommendation aims to optimize health outcomes, prevent chronic diseases, improve athletic performance, and promote overall well-being of the user. Notably, personalized nutritional recommendations aim to provide more effective and personalized guidance that considers the unique characteristics and preferences of each user, compared to traditional, one-size-fits-all dietary guidelines. Herein, the user is an individual person (a patient with a medical condition or a healthy individual desirous of maintaining an overall health thereof) or animal. The computer-implemented method employs a personalized nutrition algorithm (or food personalization algorithm) for providing the personalized nutritional recommendation. Typically, the personalized nutrition algorithm leverages input parameters, measurement methods, artificial intelligence (AI) driven modelling techniques, and modalities for integrating diverse data sources and providing personalized nutritional recommendation for more effective personalized nutrition strategies tailored to the user's needs and preferences. Moreover, the personalized nutrition algorithm continuously refines the personalized nutrition strategies based on real-time biological feedback. Furthermore, the advanced personalized nutrition algorithm and artificial intelligence capabilities allow generating insights and interventions that are not possible through traditional dietary recommendations. The term "food item" refers to an edible substance that is consumed to provide nourishment and sustenance to the body. Food items are essential sources of nutrients, including carbohydrates, proteins, fats, fibre, vitamins, minerals, and water, which are necessary for the body's growth, development, energy production, and overall health. Food items typically vary widely in their nutritional content, taste, texture, and preparation methods, and they can be consumed raw, cooked, or processed in various ways before being eaten. Optionally, food items may be categorised into food groups, such as vegetables, meats, legumes, fruits, dairy, and so on. The term "nutritional data" refers to information about the nutrient content of that specific food item. Nutritional data typically includes details about the various macronutrients (carbohydrates, proteins, and fats), micronutrients (vitamins and minerals), fibre, calories, and other relevant nutritional components present in the food item. Nutritional data of a given food item is often presented on food packaging labels, in food composition databases, nutrition tracking apps, or on restaurant menus to help consumers make informed choices about their dietary intake and to meet their nutritional needs. Notably, the comprehensive nutrition database encompasses detailed nutritional composition of >200 food items using validated food composition databases. Such nutritional data from a variety of sources is valuable for individuals who are managing their health, following specific dietary guidelines, or have food allergies or intolerances. Optionally, the food item comprises a diet type or a food ingredient, wherein the nutritional data includes at least one of: macronutrients, micronutrients, fibre, energy density, phytochemicals. Optionally, the food item is a diet type, such as a ketogenic (keto) diet, a Mediterranean diet, a vegetarian diet, a vegan diet, a flexitarian diet, and so on. Alternatively, optionally, the food item is an individual component (or food ingredient) of a meal or snack that is intended for consumption, such as fruits, vegetables, grains, meat, dairy products, or spices. It may be appreciated that the diet type or each food component has a specific nutrient composition and other relevant characteristics indicative of its nutritional data. Notably, macronutrients, such as carbohydrates, protein, fats, are required in large amounts for energy and other essential functions, and micronutrients, such as essential vitamins and minerals, are required by the body in smaller amounts for various physiological processes. Fibre is a type of carbohydrate that is important for digestive health and can help regulate blood sugar levels and cholesterol, and is typically found in plant-based foods like fruits, vegetables, whole grains, nuts, and seeds. Energy density is a measure of the amount of energy (calories) per gram of food. Notably, food items with high energy density provide more calories per gram, while food items with low energy density provide fewer calories per gram (thus may be helpful for weight management and overall health). Phytochemicals are bioactive compounds found in plants that have potential health benefits. Phytochemicals include antioxidants, flavonoids, carotenoids, and polyphenols, among others. They may help reduce inflammation, lower the risk of chronic diseases, and support overall health. Additionally, the nutritional data comprises information about ingredients (including any additives, preservatives, or flavourings); allergens (such as gluten, nuts, dairy, soy, or shellfish); serving size (the recommended portion size (e.g., grams, ounces, cups) for the food item), bioavailability information, protein digestibility-corrected amino acid score (PDCAAS), and so on. Optionally, the nutritional data comprises information about at least one nutritional component selected from at least one of: carbohydrates (glucose, fructose); amino acids (leucine, methionine, lysine, glycine, tryptophan); fatty acids (oleic acid, palmitic acid, docosaheaxaenoic acid (DHA), eicosapentaenoic acid (EPA), alpha linolenic acid (ALA), linoleic acid (LA)); vitamins (Vitamin A, B vitamins (thiamin(Bl), riboflavin(B2), niacin(B3), pantothenic acid(B5), pyridoxine(B6), biotin(B7), folate(B9), cyanocobalamin(B12)), Vitamin C, Vitamin D, Vitamin E, Vitamin K2); minerals (calcium, iron, potassium, magnesium, zinc, sodium, manganese, selenium, iodine, molybdenum); antioxidants (coenzyme Q10, EGCG, ferulic acid, resveratrol, beta carotene, astaxanthin, anthocyanins); and other compounds (such as indol-3 carbinol, sulforaphane, allicin, flavonoids, and cyanidin-3-O-glucoside. Beneficially, the aforementioned nutrient components play specific roles in the body, such as providing energy, supporting immune function, promoting bone health, and acting as antioxidants. Optionally, the nutritional data related to a food item is obtained from various food composition databases, nutrition analysis softwares, food manufacturers database, food labels, restaurant menus, published literatures, and so on. For example, macronutrient, micronutrient, and fibre composition may be obtained from the Swiss Food Composition database, USDA National Nutrient Database, the Canadian Nutrient File, and various online databases and apps. Moreover, PDCAAS, phytochemicals, and mineral bioavailability may be obtained from various sources and repositories and peer-reviewed publications. Notably, each nutrient component of the food item is associated with a nutritional index that is calculated based on the obtained nutritional data thereof. The term "nutritional index" refers to a numerical measurement or score that represents the nutritional value of a specific nutrient component of the food item. Notably, each nutrient component, such as carbohydrates, proteins, fats, vitamins, minerals, and other relevant nutrients, is assigned its own nutritional index value based on its content in the food item. Typically, the nutritional index for each nutrient component is calculated using a formula or scoring system that takes into account the amount of the nutrient component present in the food item, as indicated by the nutritional data of the food item, relative to recommended intake levels, dietary guidelines, or optimal values. Typically, higher nutritional index indicates higher levels of the nutrient component, suggesting better nutritional quality thereof, in the food item. Conversely, lower nutritional index is indicative of lower levels of the nutrient component which could impact the overall nutritional profile of the food item. In an example, the nutritional data is used calculate the nutritional index (partial scores) per individual nutrient component (e.g. protein quality, fat quality, phytochemical score). In this regard, nutritional data related to a given food item is combined using equations to yield the nutritional index (partial scores) per individual nutrient component. Beneficially, the nutritional indexes for each nutrient component of the food item provide a structured and quantitative way to evaluate the nutritional composition of food items, helping individuals optimize their dietary intake for better health and well-being. Optionally, the nutritional index for a given nutrient component of the food item per serving is a ratio of a health-promoting property and a nonhealth-promoting property, or aggregated score reflecting factors affecting bioavailability and a biological form, of the given nutrient component of the food item. Herein, the terms "health-promoting property" and "non-health-promoting property" refer to a characteristic or property of the nutrient component that is beneficial for health and may have neutral or potentially negative effects, respectively. The properties of individual food items are categorised into individual nutrient components that play an important role in individual's biology, such as metabolizable energy, individual macronutrients quantities, essential micronutrients quantities, and non-essential nutrients quantities. Importantly, macronutrients are divided into subgroups, with healthpromoting nutrient components increasing the score (e.g., complex carbohydrates, healthy omega-3 fats), while non-health-promoting nutrient components, such as an excess of sugars, saturated fats, and sodium, decrease the score. Micronutrients are stratified based on their essential roles in the body into essential and non-essential nutrients, and their scores are adjusted for bioavailability and dietary reference values (DRV). For example, in the case of a vitamin, a health-promoting property could be its antioxidant activity, role in metabolic processes, or contribution to immune function. For example, in the case of a nutrient like sodium, a non-health-promoting property could be its association with high blood pressure or risk of cardiovascular disease. Notably, the nutritional index takes into account various factors that affect the absorption and utilization of the nutrient component by the body of an individual (namely, user). These factors may include the bioavailability of the nutrient in different forms, its stability during processing and cooking, interactions with other nutrient components in the food, and individual differences in digestion and metabolism. Beneficially, the nutritional index provides a quantitative measure of the overall health-promoting quality or nutritional value of the nutrient component within the food item per serving, considering both its beneficial properties and potential limitations or factors affecting its bioavailability and biological form. Optionally, the nutritional index of a food item is determined from an energy index, a fat quality index, a protein quality index, a carbohydrate quality index, an essential micronutrient index, a fibre index, and a phytochemical index of the food item. Notably, energy index integrate the available energy of a serving size of an individual food item and its energy density to calculate the 'weight management' potential of that specific food item. Energy index has the highest overall contribution to static food score, reflecting the indispensable role of energy in supporting life. Moreover, fat quality index integrates major subgroups of lipids in an individual food item to assess its health-promoting potential based on the current scientific understanding of the metabolic effects of these lipid subclasses. The fat quality index of food items emphasizes favorable ratios between health-promoting fats, such as monounsaturated fatty acids and omega-3, and less health-promoting lipids, such as saturated fats, cholesterol, and an excess of omega-6 fatty acids. Protein quality index assesses protein quality in a serving of individual foods by combining protein content of that specific food and its digestibiliy and bioavailability, measured by the PDCAAS. Carbohydrate quality index assesses quality of carbohydrates in an individual serving of a food item as determined by the fiber content, total carbohydrate amounts, and the degree of processing. Essential micronutrient index represents the aggregate contribution of 22 essential vitamins and minerals to daily body needs, according to DRV, per serving of a food item. For nutrients where bioavailability is highly food group-specific, such as vitamin A, iron and zinc, additional bioavailability factors were used for adjustment. Fibre index is a coefficient that assesses the amount of fiber adjusted for energy in a single serving of food, comparing it with dietary guidelines that recommend specific amount of fiber intake per 1000 calories. Phytochemicals include thousands of bioactive non-nutrient plant components, commonly found in the human diet, that may have beneficial (or harmful) health effects. They are broadly classified into polyphenols, terpenoids, alkaloids, phytosterols, and organosulfur compounds. Polyphenols, the most studied class, alone accounts for 8,000 identified compounds. The aggregate phytochemical index sums up individual contributors adjusted for bioavailability. For example, the nutritional indexes of protein, fat and carbohydrate of a given food item are calculated as follows: Protein nutritional index (or quality score) = total protein content * Protein Digestibility-Corrected Amino Acid Score (PDCAAS) * serving size; ,-. . ... । . , (Monounsaturared fatty acids + omega-3 fatty acids) Fat nutritional index = 1-------— -------■—-——i---l * serving (Total fat + cholesterol content) size; and Carbohydrate nutritional index = -— ----* serving size. Total carbohydrate content Optionally, the computer-implemented method further comprises associating weights to the nutritional index for each nutrient component of the food item prior to calculating the static food score based thereon. Notably, associating weights to the nutritional index for each nutrient component of a food item allows for the prioritization of certain nutrient components over others based on their relative importance to overall health or specific dietary goals. Such weights reflect the significance of each nutrient component in contributing to the overall nutritional quality of the food item. In this regard, based on the relative importance or priority of each nutrient component based on dietary guidelines, nutritional recommendations, or health goals, higher weights may be assigned to nutrients that are considered essential for health or that are commonly lacking in the diet. Once weights are assigned, they are multiplied by the corresponding nutritional index values for each nutrient component, to produce weighted scores for each nutrient component. Beneficially, by associating weights to the nutritional index for each nutrient component, nutrient components may be effectively prioritized, and the nutritional value of a food item may be assessed in a comprehensive manner. This approach allows for tailored dietary recommendations and informed food choices based on specific nutrient needs, health goals, or dietary preferences. In an example, energy of the food item is associated with 20% weight based on available energy per serving and energy density (using formula 1000 / (available energy per serving (kcal / serving]*4.184) - (energy density [kcal / g] - 1.5)). Moreover, each macronutrient, i.e., fat, protein, and carbohydrate, is associated with 15% weight based on the ratio between health promoting (e.g. omega 3) and less health promoting nutrients (e.g. saturated fats) (using formula (monounsaturated fatty acids [g / lOOg] + omega-3 fatty acids [g / 100g]) / ((total fats [g / lOOg] + cholesterol [mg / lOOg] / 1000) * serving size [g / ml] / 100)). Furthermore, micronutrients are associated with 20% weight to account for an aggregate of the content of 22 essential vitamins and minerals per serving compared to EFSA recommended daily intake. For example, when bioavailability of iron, zinc and vitamin A is taken into account, the nutritional index of micronutrient component of the food item is calculated by using formula (Vitamin A + Vitamin C + Vitamin D + Vitamin E + Vitamin K + Vitamin Bl + Vitamin B2 + Vitamin B3 + Vitamin B5 + Vitamin B6 + Folate + Vitamin B12 + K + Mg + Ca + Mn + Zn + Fe + Mo + I + Se) - Na) / 22, wherein all micronutrients are expressed as % DRV per serving. Furthermore, fibre is associated with 10% weight based on the amount of fibre per serving compared to dietary guidelines (14g per 1000 calories) (using formula ((fibre [g / lOOg] * serving size [g / ml] I 100) * 1000) I (14 * available energy per serving [kcal / serv])). Furthermore, phytochemicals are associated with 5% weight based on phytochemicals content (polyphenols, phytosterols, isoflavones, organosulfur compounds and carotenoids) and adjusted for bioavailability (using formula 0.032 * (polyphenols [mg / lOOg] + beta-carotene [mg / lOOg] + other phytochemicals [mg / 100g]) * serving size [g / ml] I 100). Herein, the term "static food score" refers to a scientific method of ranking foods according to their healthfulness. The static food score represents an advanced in-house developed method of nutritional profiling that culminates in an aggregate numerical value that qualitatively evaluates the 'health-promoting' and less health-promoting properties of a food item based on its nutrients' content, including macronutrients, micronutrients, fibre, energy density, phytochemicals, as well as the degree of processing. Nutrients are grouped by their effect in the body, adjusted for bioavailability and population reference intakes, and weighted for their importance. Static food score is also adjusted for other health-promoting properties such as fermentation or unfavourable properties such as processing. In other words, the nutritional indexes for each nutrient component of the food item or weighted nutritional indexes for each nutrient component are aggregated to calculate the static food score, reflects the overall nutritional quality of the food item. Thus, there is one static food score for each food item. Normally, a higher static food score indicates better overall nutritional quality of the food item, while a lower static food score may suggest the food item to be a less nutritious option. Optionally, the static food score of the food is calculated based on the nutritional data related to a food item and a degree of processing thereof. The degree of processing of the food item is indicative of its level of refinement, modification, or addition of additives. Moreover, degree of food processing affects both qualitative properties of nutrients (biological activity and / or bioaccessibility) and digestive properties, such as the rate of absorption and subseqent metabolic response (eg postprandial glucose and triglycerdies elevation, respectively). The degree of processing can range from minimal processing (e.g., whole foods) to heavy processing (e.g., ultra-processed foods with additives, preservatives, and artificial ingredients). Based on the NOVA food classification system food items may be categorized into one of four groups based on processing-related characteristics, i.e., unprocessed (fresh foods), minimally processed, fermented foods, and ultraprocessed. Optionally, the degree of processing of a given food item is assigned a corresponding processing factor score which is usually adjusted with the nutritional index of the food item to yield a processing adjustment factor score for the given food item. For example, food items that are minimally processed may receive a higher processing adjustment factor score, while heavily processed foods may receive a lower processing adjustment factor score to account for potential nutrient loss, degradation, or addition of less desirable ingredients. Moreover, weights may be associated based on a degree of processing of the food item. Typically, the weights associated with processing factor ranges between 0.5 (ultra-processed) and 1.2 (fermented / probiotic foods). The adjusted nutritional scores or weight adjusted nutritional scores with the processing scores are combined to calculate the static food score for the food item, to provide a holistic assessment of the nutritional quality of a food item, helping individuals make informed choices about their dietary intake and promoting healthier eating habits. Optionally, individual nutritional components are weighted with factors that reflect the importance of their role in the human body and are summed to yield an aggregate static food score. The aggregate static score is calculated by summing up all aforementioned elements, weighted according to their respective contributions. This weighting scheme was based on the biological roles of various food components, ensuring that the total sum of weights equals 1 (100%). In the final step, the processing factor was applied as a multiplication factor (if the total sum >0) or a division factor (if the total sum <0) to derive the final static food score. Herein, the term "food index" refers to characteristics of a food item in terms of fat, protein and carbohydrate quality, vitamin D, A, E, K, and iron content as well as its potential to support bone health, methylation, inflammation, and thyroid function, etc. The aforementioned food indexes are integrated with biological factors associated with users' unique ability to metabolise nutrients, current health status and their overall biology. In this regard, the food index is calculated by aggregating the individual nutritional index for the nutrient components of a food item to provide an overall measure of the food item's nutritional quality or healthfulness. Typically, a higher food index value indicates better overall nutritional quality, while a lower food index value may suggest a less nutritious option. Beneficially, the food index serves as a comprehensive indicator that considers the combined impact of multiple nutrient components on the food item's overall nutritional profile. Moreover, aggregating the individual nutritional index for the nutrient components of a food item to provide a single value makes it easier to compare different foods and make informed dietary choices. Optionally, the food index of the food item includes at least one of: weight gain index, fat quality index, protein quality index, carbohydrate quality index, Vitamin A index, Vitamin D index, Vitamin E index, Vitamin K index, iron index, bone health index, methylation nutritional index, inflammation nutritional index, thyroid index, Omega-3 index. Typically, weight gain index (WGI) assesses a food item based on its energy (kcal) content per serving as well as energy density, which is the amount of energy per mass unit of food item that could contribute to weight gain or loss based on its available energy content. Fat quality index (FQI) assesses a food item based on the ratio between "good" fats, namely monounsaturated and omega-3 fatty acids, and total fats and cholesterol. Protein quality index (PQI) assesses a food item based on its total protein content and the PDCAAs, which considers both the essential amino acid content of the food item and the digestibility of the protein. Carbohydrate quality index (CQI) assesses a food item based on the amount of fibre (health-promoting) it contains compared to the total carbohydrate content. Vitamin A (VAI), D (VDI), E (VEI) and K (VKI) indexes assess a food item based on its vitamin A, D, E and K contents per serving, respectively, compared to the EFSA DRVs thereof that an individual should consume per day. For vitamin A, D, E and K the adequate intake is 750mg RE / day for males and 650mg RE / day for females, 15mg / day for adults, 13mg / day for males and llmg / day for females, and 70mg / day for adults, respectively. Iron index (II) assesses a food item based on its iron content per serving compared to the EFSA DRVs, which is 16mg / day for females and llmg / day for males. The vitamin C content of the food is also considered as it promotes iron absorption and thereby increases its bioavailability. Bone health index (BHI) assesses a food item based on its calcium and magnesium content per serving compared to the EFSA DRVs, and the protein quality index described above. For calcium the population reference intake is lOOOmg / day and for magnesium the adequate intake is 350mg / day for males and 300mg / day for females. Methylation index (MI) assesses a food item based on its vitamin B2, folate, vitamin B12 and zinc content per serving compared to the EFSA DRVs. For vitamin B2 (riboflavin) the adult population reference intake is 1.6mg / day, for folate 330mg / day, for vitamin B12 4mg / day, and for zinc 16.3mg / day for males and 12.7mg / day for females. Inflammation index (Ini) assesses a food item based on its fibre and omega-3 fatty acids content. Omega 3 fatty acids are building blocks for anti-inflammatory compounds such as prostaglandins, thromboxanes, leukotrienes and resolvins. Fibres are transformed by gut bacteria into short-chain fatty acids (SCFAs), which provide energy for gut cells and have potential anti-tumour and antiinflammatory effects. Thyroid index (TI) assesses a food item based on its protein quality index as well as iron, selenium, and iodine content per serving compared to the EFSA DRVs. For iron the population reference intake is 16mg / day for females and llmg / day for males, for selenium 70mg / day, and for iodine 150mg / day. These nutrients support thyroid function through the transport, synthesis, and conversion of thyroid hormones (T4 to T3) (4a, 4b, 4c). Additionally, selenium helps protect thyroid cells from oxidative damage. Overall, the index reflects how much a certain food can contribute to thyroid function. Table 1 below mentions the food index of the food item, associated weights, nutrient components of the food item and the formula for deriving the food index based on the nutritional index for the nutrient components of the food item. Food index Weights Components Formula (weights are highlighted in bold for the separate components when applicable) Weight gain index 0.4 Energy and energy density (0.75 * available energy per serving [kcal / serv]) + (0.25 * energy density [kcal / g]) Fat quality index 0 3 MU FA, omega-3 (monounsaturated fatty acids [g / lOOg] + omega-3 fatty acids fatty acids, total fats, cholesterol [g / 100g]) / ((total fats [g / lOOg] + cholesterol [mg / lOOg] / 1000) * serving size [g / ml] / 100) Protein quality index 0.3 Total protein, PDCAAs (total protein [g / lOOg] * PDCAAS * serving size [g / ml]) / 100 Carbohydrate quality index 0.3 Fibre, total carbohydrate (fibre [g / lOOg] / total carbohydrates [g / 100g]) * (serving size [g / ml] / 100) Vitamin D index 0.1 Vitamin D vitamin D [pg / lOOg] * serving size [g / ml] / 15 [pg / day DRV] Vitamin A index 0.05 Vitamin A (vitamin A activity [pg-RE / lOOg] * 0.25 for bioavailability) * serving size [g / ml] / 650 [pg / day DRV] Vitamin E index 0.05 Vitamin E vitamin E activity [mg-ATE / lOOg] * serving size [g / ml] / 11 [DRV mg / day DRV] Vitamin K index 0.07 Vitamin K vitamin K [pg / lOOg] * serving size [g / ml] / 70 [pg / day DRV] Iron index 0.05 Iron, vitamin C ((0.95) * (iron [mg / lOOg] * 0.25 for bioavailability * serving size [g / ml] / 16 [mg / day DRV])) + ((0.05) * (vitamin C [mg / lOOg] * serving size [g / ml] / 90 [mg / day DRV])) Bone health index 0.1 Protein quality, calcium, magnesium (0.25 * protein quality index) + ((0.5) * (calcium [mg / lOOg] * serving size [g / ml] / 1000 [mg / day DRV])) + ((0.25) * (magnesium [mg / lOOg] * serving size [g / ml] / 300 [mg / day DRV])) Methylation nutritional index 0.1 Vitamin B2, folate, vitamin B12, zinc ((0.6) * (vitamin B2 [mg / lOOg] * serving size [g / ml] / 1.6 [mg / day DRV])) + ((0.25) * (folate [pg / lOOg] * serving size [g / ml] / 330 [fig / day DRV])) + ((0.1) * (vitamin B12 [pg / lOOg] * serving size [g / ml] / 4 [pg / day DRV])) + ((0.05) * (zinc [mg / lOOg] * serving size [g / ml] / 16.3 for males For males / 12.7 [pg / day DRV])) Inflammation nutritional index 0.1 Fibre, omega-3 fatty acids ((0.6) * (((fibre [g / lOOg] * serving size [g / ml] / 100) * 1000) / (14 * available energy per serving [kcal / serv]) * processing factor)) + ((0.4) * (omega-3 fatty acids [g / lOOg] * serving size [g / ml] / 100)) Thyroid nutritional index 0.05 Protein quality, iron, iodine, selenium (0.4 * protein quality index) + ((0.2) * (iron [mg / lOOg] * 0.25 for bioavailability * serving size [g / ml] / 16 [mg / day DRV])) + ((0.2) * (iodine [pg / lOOg] * serving size [g / ml] / 150 [pg / day DRV])) + ((0.2) * (selenium [pg / lOOg] * serving size [g / ml] / 70 [pg / day DRV])) Table 1: Food index for a food item Herein, the term "multi-modalhealth data" refers to integration of health-related information obtained from different modes or sources to provide insights into disease mechanisms, personalized treatment strategies, and overall health, where each mode represents a distinct type or aspect of health data. The term "biological modality" as used herein typically refers to a specific type of biological entity that provides insights into various aspects of biological systems. Herein, multiple biological modalities are used in combination to obtain a comprehensive understanding of biological systems. For example, integrating genomics, transcriptomics, and proteomics data can provide a more holistic view of how genetic information is expressed and translated into functional proteins within an individual. In an example, the biological modality relates to cardiovascular health and the multi-modal health data relates to genetic data, blood data and / or microbiome data corresponding to the cardiovascular health. The multi-modal health data may be obtained as a real-time health data or as a historical health data. Herein, the multi-modal historical health data may be obtained from different multi-modal health data sources that store or generate multi-modal health data. Optionally, the multi-modal health data sources may include various biological age clocks (such as blood age clocks, epigenome age clocks, microbiome age clocks, telomere age clocks, and so on) platforms, hospital / clinic patient's data, data gathered as part of research initiatives, various other patient specific databases, wearable devices, and patient surveys. It may be appreciated that the multi-modal health data is obtained pursuant to ethical standards (informed consent from individuals contributing their data) and privacy regulations (anonymize or de-identify data). Optionally, the multi-modal health data may be obtained as a user input via a user interface (UI), besides the multi-modal health data obtained from other health data sources. Herein, the user interface is rendered on a display of a user input device associated with the user. The user input device refers to a communication device capable of providing user input and receiving and accessing any information and / or a notification. Optionally, the multi-modal health data comprises: a biological test data selected from at least one of: blood biomarker data, gut microbiome composition and functional biomarkers data, epigenomic biomarkers data, telomeric biomarkers data, proteomic biomarkers data, data pertaining to skin health, lipidomic data, glycan data, biomarkers based on glycosylation patterns of the immune system, transcriptome signatures data, and metabolomic biomarkers data; and a supplementary data selected from at least one of: anthropometric measurements data, medical imaging techniques, physiological phenotypes data, functional and wearable measurements data corresponding to exercise and sleep, a sensor data pertaining to a specific health entity, a health data from a third-party source, a pre-stored biological data and a pre-stored sensor data pertaining to a specific health entity from a third-party source, a dietary intake data, an environmental data. In this regard, the multi-modal health data may include, but is not limited to, a combination of multiomics data, medical imaging data (e.g., X-rays, MRIs), clinical measurements (e.g., blood pressure, heart rate), electronic health records (EHR) data, patient-reported outcomes, wearable device data (e.g., fitness trackers), and other types of health-related information. The multiomics data (or multiomics health data or biomarkers data) specifically refers to integration of data from different "omics" technologies (namely, genomics (study of entire genome), transcriptomics (study of gene expression), proteomics (study of proteins), metabolomics (study of small molecules / metabolites), and so on) that focus on molecular aspects of health and biology. Each of these multi-modal health data types provides unique insights into different aspects of an individual's health. Integrating and analyzing such diverse multi-modal health data contributes to a more comprehensive understanding of biological systems, disease mechanisms, and personalized health assessments. Notably, the blood biomarker data enables analyzing substances in the blood, such as proteins, hormones, and other molecules, to assess health and disease. Similarly, the gut microbiome composition and functional biomarkers data enables examining the types and functions of microorganisms in the gut to focus on digestion, metabolism, and immune function. The epigenomic biomarkers data enables studying chemical modifications to DNA and associated proteins that influence gene expression without altering the underlying DNA sequence. The telomeric biomarkers data enables assessing the length of telomeres which are associated with cellular aging. The proteomic biomarkers data enables investigating the presence, abundance, and modifications of proteins in a biological sample. The data pertaining to skin health focuses on skin health, potentially including texture, moisture levels, wrinkling intensity, and the presence of certain biomarkers. The lipidomic data enables analyzing the composition and changes in lipid molecules, which are crucial for cellular structure and function. The glycan data enables examining the structure and function of glycans. The biomarkers based on glycosylation patterns of the immune system enables studying how the immune system's glycosylation patterns change in response to health conditions. The transcriptome signatures data enables analyzing the complete set of RNA transcripts produced by the genome to understand gene expression patterns. The metabolomic biomarkers data enables investigating the complete set of small molecules (metabolites) in a biological sample to understand metabolic processes. Moreover, the anthropometric measurements data such as height, weight, body mass index (BMI), waist circumference, and other physical dimensions provides information about not only external appearance of an individual but may also be suggestive of the individual's internal state, similar to that provided by the physiological phenotypes data. For example, an individual with high BMI may be susceptible to a potential cardiac condition which may be confirmed from physiological phenotypes data including heart rate, blood pressure, respiratory rate, and other indicators of bodily functions. Similarly, medical imaging techniques, such as DEXA, fMRI and 3D photonic devices, provide even more detailed insights into body composition. Importantly, these techniques have the ability to differentiate between muscle mass, bone mineral density, total fat mass, and body fat distribution (ectopic vs subcutaneous fat). The functional and wearable measurements data includes data captured by wearable devices during physical activity and sleep, to focus on active and resting state of an individual. Optionally, the wearable devices comprise one or more sensors to measure the active and resting state of an individual, besides various sensors including, but not limited to environmental sensors, motion sensors, or other types of monitoring devices that sense data pertaining to the specific health entity. Herein, the term "health entity" refers to a component associated with health of a person, that may be measured by the sensors. For example, temperature sensors are configured to measure body temperature of an individual. The third-party source may be external sources, such as electronic health records (EHRs), health apps, or health monitoring platforms, that provide additional insights into an individual's medical history and conditions. The pre-stored biological or sensor data includes data from medical devices, wearables, or other sensor technologies. It may be appreciated that the multi-modal health data (the biological test data or the supplementary data) may include biology and health-related data from other data sources known to a person skilled in the art, and are not just limited to the aforementioned examples. Optionally, the at least one biological modality of the user comprises a health risk-factor or a biomarker. The term "health risk-factor" as used herein refers to characteristics or conditions that increase an individual's risk of developing certain diseases or health problems. Examples of health risk-factors may include high blood pressure, elevated cholesterol levels, obesity, insulin resistance, metabolic syndrome, family history of chronic diseases, and lifestyle factors such as smoking or sedentary behaviour. The term "biomarker" as used herein refers to measurable indicators of biological processes, health status, or disease risk within the body. They can include various physiological parameters, blood tests, imaging studies, genetic markers, or other diagnostic tools used to assess health and detect early signs of disease. Examples of biomarkers may include blood glucose levels, lipid profiles, blood pressure, body composition measurements, inflammatory markers, hormone levels, genetic variants, and functional tests of organ function. Beneficially, the personalized nutritional recommendation takes into account specific health indicators or markers of risk for the individual. These health riskfactors or biomarkers provide valuable information about the individual's current health status, potential health risks, and areas for improvement, allowing for more targeted and tailored dietary recommendations. Optionally, the at least one biological modality of the user includes at least one of: body composition (BCF), fat metabolism (FMF), protein metabolism (PMF), carbohydrate metabolism (CMF), Vitamin A metabolism (VAMF), Vitamin D metabolism (VDMF), Vitamin E metabolism (VEMF), Vitamin K metabolism (VKMF), iron metabolism (IMF), bone metabolism (BMF), methylation (MF), inflammation (IF), thyroid function (TFF). The aforementioned 13 biological modalities summarise the user's needs and ability to utilise macro- and certain micro-nutrients and support bone, methylation, inflammation, and thyroid functions based on blood, microbiome, genetic and anthropometric data. Optionally, the at least one biological modality of the user includes Omega-3 metabolism. Table 2 below provides the 5 aforementioned 13 biological modalities along with corresponding weights associated with the blood biomarkers, SNPs (genetic), microbial species (microbiome), and anthropometric measurements that are measured in a user. For example, the body composition is associated with anthropometries (BMI), genetic and microbiome data that have shown to 10 be associated with increased BMI, weight, or obesity, and is not governed by levels if blood biomarkers. Similarly, thyroid system factor is associated with blood biomarkers and not with anthropometries, genetic and microbiome data. For example, TSH blood biomarker, is associated with thyroid gland stimulation. Biological Factor Weights Blood biomarkers Genetics Microbiome Anthropometries Body composition / 0.15 0.1 0.75 Fat metabolism 0.7 0.2 0.1 / Protein metabolism 0.5 0.25 0.25 / Carbohydrate metabolism 0.7 0.1 0.2 / Vitamin metabolism D 0.875 0.075 0.05 / Vitamin metabolism A / 0.7 0.3 / Vitamin metabolism E / 0.7 0.3 / Vitamin metabolism K / 0.7 / / Iron metabolism 0.65 0.3 0.05 / Bone metabolism 0.3 0.6 0.1 / Methylation 0.45 0.45 0.1 / Inflammation 0.6 0.1 0.3 / Thyroid system 1 / / / Table 2: Biological modalities of the user Optionally, the computer-implemented method further comprises associating weights to the food index of the food item and the at least one biological modality of the user, wherein the weights of the food index and the at least one biological modality ranges between 0.05 and 1. As mentioned above, the food index of the food item and the at least biological modality of the user indicate the nutritional value of the food item and the user's health status and ability to metabolise certain nutrient components, respectively. Typically, weights determine the relative importance of each factor in the recommendation process, ensuring that both food index of the food item (dietary factors) and at least one biological modality (biological factors) are appropriately considered. In this regard, the food index is multiplied by its assigned weight and the biological modality is multiplied by its assigned weight. Optionally, the weights of the food index ranges from 0.05, 0.06, 0.07, 0.08, 0.09, 0.1, 0.2, 0.4, 0.6 or 0.8 up to 0.06, 0.07, 0.08, 0.09, 0.1, 0.2, 0.4, 0.6, 0.8 or 1. Optionally, the weights of the at least one biological modality ranges from 0.05, 0.06, 0.07, 0.08, 0.09, 0.1, 0.2, 0.4, 0.6 or 0.8 up to 0.06, 0.07, 0.08, 0.09, 0.1, 0.2, 0.4, 0.6, 0.8 or 1. Subsequently, these weighted scores are summed up to obtain an overall recommendation score indicative of appropriate personalized nutritional recommendation for the individual. A higher recommendation score indicates a stronger recommendation, with greater emphasis on either the food index or the biological modality, depending on their respective weights. Beneficially, associating weights to the food index of the food item and the at least one biological modality of the user allows for the customization of personalized nutritional recommendations based on both the nutritional quality of the food and the individual's specific health needs or biomarker .1¾ T* .1¾ Ud la. Typically, food indexes are combined with the at least biological modality to create 13 personalised dynamic factors. The term "personalised dynamic factor" as used herein refer to an adjustment or modification made to the personalized nutritional recommendation based on both the food index (reflecting the nutritional quality of the food) and the obtained multi-modal health data of the user (reflecting the individual's specific health status, preferences, and goals). The personalized dynamic factor allows for real-time adaptation of the food recommendation to better meet the individual's unique nutritional needs and health objectives. Optionally, the personalized dynamic factor enables adjusting nutritional recommendations based on factors such as nutrient deficiencies, dietary restrictions, health conditions, metabolic status, and personal preferences. For example, the protein quality index is combined with the protein metabolism biological modality to generate personalised dynamic factors. Optionally, the personalized dynamic factor of the food item is the product of the food index of the food item, the weights of the food index of the food item, the at least one biological modality of the user, and the weights of the at least one biological modality of the user. In an example, a methylation personalised dynamic factor may be calculated based on the food index for the nutrient components Vitamin B2, folate, vitamin B12, zinc, and the biological modality comprising blood biomarkers such as homocysteine; genetic biomarkers such as MTHFR. and PEMT; and microbiome biomarkers such as 84 different species / genus, selected from: Agathobaculum butyriciproducens, Akkermansia muciniphila, Alistipes finegoldii, Anaerostipes hadrus, Anaerotruncus colihominis, Bacteroides intestinalis, Bacteroides massiliensis, Bacteroides thetaiotaomicron, Bifidobacterium adolescentis, Bifidobacterium animalis, Bifidobacterium catenuiatum, Bifidobacterium iongum, Bifidobacterium pseudocatenulatum, Bilophila wadsworthia, Blautia hydrogenotrophica, Blautia obeum, Blautia wexlerae, Butyricimonas synergistica, Butyricimonas virosa, Clostridium boiteae, Clostridium citroniae, Clostridium disporicum, Clostridium innocuum, Clostridium lava lense, Clostridium leptum, Clostridium saccharolyticum, Clostridium spiroforme, Clostridium symbiosum, Collinsella intestinalis, Coprococcus catus, Coprococcus eutactus, Dielma fastidiosa, Dorea longicatena, Eisenbergiella tayi, Eubacterium eligens, Eubacterium hallii, Eubacterium ramulus, Faecalibacterium prausnitzii, Firmicutes bacterium, Flavonifractor plautii, Gordonibacter pameiaeae, Haemophilus parainfluenzae, Harryflintia acetispora, Holdemanella biformis, Holdemania filiformis, Lachnospira pectinoschiza, Lactococcus lactis, Lawsonibacter asaccharolyticus, Methanobrevibacter smithii, Olsenella scatoligenes, Odoribacter splanchnicus, Parabacteroides merdae, Proteobacteria bacterium, Pseudoflavonifractor capillosus, Roseburia faecis, Roseburia horn in is. In the above example, the personalized dynamic factor is calculated using the following formula: Personalized dynamic factor = food index score * weight of food index * biological modality score * weight of biological modality. Therefore, the personalized dynamic factor = 0.4*WGI*0.4*BCF + 0.3*FQI*FMF + 0.3*CQI*CMF + 0.3*PQI*PMF + 0.05*VAI*VAMF + 0.1*VDI*VDMF + 0.05*VEI*VEMF + 0.07*VKI*VKMF + 0.05*II*IMF + 0.1*BHI*BMF + 0.1*MI*MF + 0.1*InI*IF + 0.05*TI*TSF The term "comprehensive food score" as used herein refers to a holistic measure that combines both the static food score and the personalized dynamic factor of the food item. Notably, the comprehensive food score provides a comprehensive assessment of the food's overall nutritional quality, taking into account both its inherent nutritional profile (static food score) and its suitability for meeting the individual's specific health needs and goals (personalized dynamic factor). Optionally, the comprehensive food score is obtained by adding, multiplying, or otherwise combining the two types of scores in a way that reflects their relative importance and contribution to the overall assessment of the food's nutritional quality. Herein, optionally, the comprehensive food score is obtained by adding the two types of scores, i.e., the static food score and the personalized dynamic factor of the food item. Notably, a higher comprehensive food score indicates better overall nutritional quality and alignment with the individual's preferences and objectives, while a lower comprehensive food score may suggest areas for improvement or adjustment in the food recommendation. In the above example, the comprehensive food score is calculated using the following formula: Comprehensive food score = static food score * weight of static food score + [(food index score * weight of food index) * (biological modality score * weight of biological modality)]. Therefore, the comprehensive food score = 0.5*SFS + [(0.4*WGI+ 0.3*FQI + 0.3*CQI + 0.3*PQI + 0.05*VAI + 0.1 VDI + 0.05*VEI + 0.07*VKI + 0.05 II + 0.1*BHI + 0.1*MI + 0.1*InI + 0.05*TI) * (0.4*BCF+ 0.3*FMF + 0.3*CMF + 0.3*PMF + 0.05*VAMF + 0.1 VDMF + 0.05*VEMF + 0.07*VKMF + 0.05 IMF + 0.1*BMF + 0.1*MF + 0.1*IF + 0.05*TSF)]. = 0.5*SFS + (0.4*WGI*0.4*BCF + 0.3*FQI*FMF + 0.3*CQI*CMF + 0.3*PQI*PMF + 0.05*VAI*VAMF + 0.1*VDI*VDMF + 0.05*VEI*VEMF + 0.07*VKI*VKMF + 0.05*II*IMF + 0.1*BHI*BMF + 0.1*MI*MF + 0.1*InI*IF + 0.05*TI*TSF) Optionally, the computer-implemented method further comprises normalizing the comprehensive food score of the food item. Optionally, the comprehensive food score is normalized based on populational data. Typically, the normalized comprehensive food scores are created using data processing / analysis algorithms which assess where in the distribution of comprehensive food scores (i.e., which percentile) of the general population the comprehensive food score of the food item falls. Typically, the population-based normalisation technique involves comparing the static food score and the personalized dynamic factor of the food item to that static food score and the personalized dynamic factor of the food item present in a larger population, namely, a reference population. Herein, the population data may be demographically similar to the individual (age, sex, ethnicity, etc.) or adjusted for factors such as or individuals with similar health conditions or dietary preferences, during statistical analysis. The comprehensive food scores are normalized based on population data (such as by using data from known databases like NHANES, 1000G, GMHI, nutritional databases, etc.). Typically, the population-based normalized comprehensive food score lies between 0 and 1, allowing for comparison across different foods and populations. Typically, the population-based normalized comprehensive food score represents a quantitative measure of personalized food habits of an individual with respect to the health status of a population, where scores above 0.5 indicate that that the food item has a higher nutritional quality compared to the population average, while scores below 0.5 suggest lower nutritional quality. In an example, normalization between 0 and 1 suggests 0-0.5 are negative correlations and 0.5-1 are positive correlations. Thus, food items with stronger correlations (i.e., 0.5 to 1) are considered to have higher nutritional quality and are assigned higher comprehensive food scores. For example, if food item A has a correlation coefficient (or comprehensive food score) of 0.8 for an individual with a given multi-modal health data, it is ranked higher than food item B, which has a correlation coefficient (or comprehensive food score) of 0.5. Optionally, the comprehensive food score of the food item is normalized between 0 and 100. Optionally, the comprehensive food score of the food item is normalized in a range from 0, 10, 20, 30, 40, 50, 60, 70, 80 or 90 up to 10, 20, 30, 40, 50, 60, 70, 80, 90 or 100. Moreover, the computer-implemented method comprises generating the personalized nutritional recommendation for the user based on the comprehensive food score of the food item. The personalized nutritional recommendation report based on the comprehensive food score corresponding to each food item briefly summarizes the overall nutritional quality of the food item and highlights key drivers (or area of focus) of the health of the user allowing for personalized health management that aligns with the individual's health goals, nutritional needs, and preferences. Notably, the area of focus is highlighted in the personalized nutritional recommendation by identifying specific nutrient or dietary components that may be lacking or in excess in the user's diet. Focus on addressing nutritional gaps by recommending food items rich in deficient nutrients and reducing consumption of food items high in excess nutrient or unhealthy components. Thus, beneficially, the generated personalized nutritional recommendations based on the comprehensive food score of the food item, empowers users to make informed dietary choices that support their health goals and contribute to overall well-being. Additionally, the personalized nutritional recommendation report allows collaboration with healthcare professionals, such as registered dietitians, nutritionists, or physicians, to ensure that the personalized nutritional recommendations are evidence-based, safe, and effective for the individual's unique needs and circumstances. The generated personalized nutritional recommendation is provided to the user via a user interface rendered on a user input device associated with the user. Optionally, the personalized nutritional recommendation for the user comprises: the comprehensive food score of the food item and at least one biological modality; the comprehensive food score of the food item varying from a threshold value thereof; a recommended portion size of the food item with the nutritional index for each nutrient component of the food item; and at least one recommended recipe for the food item or a link thereto. In this regard, the personalized nutritional recommendation provides comprehensive food score of the food item, reflecting its overall nutritional quality, along with at least one biological modality of the user, such as a health risk-factor, biomarker, genetic predisposition, or other relevant health indicator, that influences the individual's dietary needs and health outcomes. Moreover, the personalized nutritional recommendation provides variation of the comprehensive food score from a threshold value that represent a target level of nutritional quality or a cut-off point for certain nutrient components. The personalized nutritional recommendation may emphasize on food items with comprehensive food scores above the threshold value while advising moderation or avoidance of those below it. Furthermore, the personalized nutritional recommendation may include suggestions for specific food choices, portion sizes, meal patterns, cooking methods, and dietary modifications to optimize nutrient intake and support overall health and well-being. Furthermore, the personalized nutritional recommendation may include at least one recommended recipe for the food item or provide a link to additional recipe resources that align with the individual's dietary preferences, health goals, and the nutritional profile of the food item. Notably, such recipes provide creative and delicious ways to incorporate the recommended food items into meals while maximizing its nutritional benefits. Optionally, the personalized nutritional recommendation suggests the best time of the day to consume the recommended food item to maximize its nutritional benefits. Moreover, such personalized nutritional recommendation is based on an individual's dietary preferences, cultural background, taste preferences, lifestyle habits, and any dietary restrictions or allergies when formulating recommendations, and offer practical and realistic suggestions that are feasible and enjoyable for the individual to incorporate into their daily routine. Moreover, users' data obtained from other data sources (such as questionnaires) may be used to exclude certain foods from the personalized nutritional recommendation report / list that are incompatible with individuals' lifestyles (e.g., vegan, vegetarian, lactose intolerance, peanut allergy), medical conditions (e.g., lactose intolerance, peanut allergy) or religious practices. Moreover, personalised nutritional recommendations are provided based on the 15 food groups (e.g., vegetables, fruits, dairy and substitutes, etc.) to ensure that each individual food group receives a list of food items that can be combined into balanced and complete meals. Furthermore, the food items may be categorized in groups ranging from "Avoid (0-24)", "Limit (25-49)", "Enjoy (50-74)", and "Prioritise (75-100)" along with their ranking. Beneficially, combination of the comprehensive food score, at least one biological modality, portion size recommendation, and recipe options ensure a holistic approach to personalized nutrition that addresses the individual's unique needs and preferences, surpassing the limitations of traditional "one-size-fits-all" nutritional guidelines that fail to account for individual heterogeneity and the need for tailored dietary advice. In an example, for personalized nutritional recommendations for users seeking remedy for their methylation-related health data, a comprehensive food score of 0.92 is indicative of a "good methylator" for User 1, while a comprehensive food score of 0.25 is indicative of a "bad methylator" for User 2. In such case, the personalized nutritional recommendations for User 1 and User 2, comprises a difference in fish and seafood scores ranks as follows: User 1 (Good Met): Mackerel, Mussel, Salmon, Sardine, Trout, Calamari, Cod, Smoked salmon; User 2 (Bad Met): Mackerel, Sardine, Salmon, Mussel, Trout, Smoked salmon, Pickled herring, Calamari. Moreover, the personalized nutritional recommendations for User 1 and User 2, comprises a difference in fruits scores ranks as follows: User 1 (Good Met): Kiwi, Strawberry, Red currant, lemon, raspberry, orange, passion fruit, pineapple, blueberry, melon (cantaloupe); User 2 (Bad Met): passion fruit, Strawberry, Kiwi, Red currant, raspberry, lemon, orange, banana, mango, melon (cantaloupe). Optionally, additionally, the personalized nutritional recommendation report comprises methods used to collect, normalize, and score data from each biological modality and food item; weighting factors or algorithms used in the aggregation process to generate the personalized nutritional recommendation corresponding to each biological modality and food item; references to scientific literature and sources used in the assessment, and so on. Optionally, the computer-implemented method further comprises creating and updating a dynamically updated knowledge base associated with the obtained nutritional data and the multi-modal health data, wherein the dynamically updated knowledge base is updated by using artificial intelligence / machine learning tools, at pre-defined time intervals, wherein the dynamically updated knowledge base comprises at least one of: food composition databases, and food databases, a plurality of published literature including scientific literature, articles, research findings, and experimental data. The term "dynamically updated knowledge base" as used herein refers to a repository of information that is continuously and automatically updated to reflect the latest information, changes, or additions. Herein, manually curated knowledge base, namely the dynamically updated knowledge base, is created using biomedical literature to identify the heterogenous response to individual nutrients due to individual's genetic and phenotypic idiosyncrasies for development of evidence-informed nutritional interventions. Advanced data processing and population-based normalisation of multifaceted biomarkers (where appropriate) is employed to effectively combine them and modulate static nutritional (food) indexes. Optionally, the computer-implemented method comprises, for creating a dynamically updated knowledge base, collecting multi-modal health data from various sources by for example web scraping, API integrations, or data feeds from reliable sources, in real-time or periodically (e.g., daily, weekly). In an example, the dynamically updated knowledge base is created by web scraping peer reviewed scientific literature (e.g., PubMed) and manually curating (using predefined rules) the peer reviewed scientific literature to gather scientific data regarding the multi-modal health data related to the at least one biological modality of the user. In this regard, the computer-implemented method comprises extracting information (such as article titles, authors, abstracts, publication dates, results, conclusions, specific biological entities or modalities, nutrition, and so on) from online sources where scholarly articles are published. Optionally, peer-reviewed scientific literature sources include publishers like PubMed, IEEE Xplore, ScienceDirect, or other academic databases. Moreover, the dynamically updated knowledge base serves as a database for storing the information and references for constructing algorithms that when implemented by at least one processor performs the method of the present disclosure. Optionally, the dynamically updated knowledge base is scalable to handle a growing volume of data and updates without affecting performance thereof. It may be appreciated that the data collected from various sources, such as online sources, peer-reviewed scientific literature sources, etc., are compatible (namely, comprises standardized data formats, units of measurement, and data storage protocols) and can be integrated to the created dynamically updated knowledge base. Beneficially, the dynamically updated knowledge base serves as a reliable and current source of information, and supports decision-making processes regarding a health state and influence dietary choices of an individual. Moreover, the dynamically updated knowledge base may be updated in real-time or at pre-defined time intervals, such as daily, weekly, biweekly, monthly, quarterly, and so on, or based on respective update periods of the multi-modal health data sources as well as various food composition databases. In this regard, the computer-implemented method employs artificial intelligence / machine learning tools / algorithms configured to detect changes or updates in the data sources by comparing a current state of the data sources with a previous version to identify additions, modifications, or deletions, and updating the dynamically updated knowledge base. Additionally, the dynamically updated knowledge base updates algorithms for performing the method of the present disclosure, based on the updated information in the dynamically updated knowledge base. In this regard, data from other data sources is integarted into the algorithms, such as comprehensive body composition reports, and real-time metabolic monitoring data, to update the dynamically updated knowledge base. Moreover, it may be appreciated that the dynamically updated knowledge base may be updated by only authorized individuals or systems. Optionally, the created and updated dynamically updated knowledge base may be regularly (namely, periodically) backed-up and / or recovered to safeguard against data loss or corruption. Furthermore, the dynamically updated knowledge base may be integrated with third-party health and wellness apps and services. Optionally, the computer-implemented method further comprises refining the comprehensive food score of the food item based on the dynamically updated knowledge base. In this regard, advanced analytics, machine learning, or artificial intelligence are employed to refine the comprehensive food score by identifying complex patterns and relationships within multi-modal health data and the nutritional data of the user. Optionally, the computer-implemented method combines comprehensive food scores with performance evaluation data (i.e., data from wearables or real-time sensor data and user feedback) by using advanced AI and machine learning algorithms to further refine comprehensive food scores and personalized nutritional recommendations based thereon. Moreover, involvement of healthcare professionals or researchers, or domain experts as well as the supplementary data associated with the user assists in the interpretation of the comprehensive food score. In an example, a blood biomarker data providing a three-month average of blood sugar (HbAlc) of 5.6 and a daily exercise and walking with a low-carb diet plan log obtained from a wearable device is usable for refining the comprehensive food score corresponding to insulin hormone sensitivity in the individual. Beneficially, collaborating the biological data and the supplementary data associated with the user, along with expert opinion ensures a comprehensive food score corresponding to each biological modality, and allows for personalized health management. Optionally, the computer-implemented method further comprises creating a database, and storing and safeguarding, in the database, the nutritional data and the nutritional index, the static food score, the food index, the multi-modal health data, the personalized dynamic factor, and the comprehensive food score of the food item, a plurality of algorithms configured to calculate the nutritional index, the static food score, the food index, the personalized dynamic factor, and the comprehensive food score of the food item, a plurality of published literature, a historical data of the user, a historical recommendation for the user. Herein, the database is a structured collection of data that is organized (such as into tables, fields, records) and stored electronically in the system or a remote (cloud) server. Typically, the database is designed to efficiently manage, store, retrieve, and update large volumes of data. The database serves as a centralized repository for storing comprehensive and up-to-date user and nutrition information, which may be accessed by the user or healthcare providers (or carers) of the user to get a unified view of a patient's medical history, diagnoses, medications, allergies, treatment plans, nutritional recommendations, and so on. Beneficially, the database stores the aforementioned data to be accessed by the processing arrangement for generating the personalized nutritional recommendations report for the user. 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 data (or normalised data) or based on the end application of the system, i.e., based on the complexity of the user's health data and their food preferences. More optionally, the algorithms include common algorithms used, such as decision trees, random forests, support vector machines, neural networks, logistic regression, and deep learning models. Optionally, the algorithm is selected from: a defined deterministic control algorithm, a neural network, a Hidden Markov Model, a Boltzmann machine, an adaptive neural network. The algorithm may optionally be implemented as a simple software routine (for example, a predefined deterministic routine) that merely applies pre-defined logics; alternatively, the algorithm may be implemented as a sophisticated artificial-intelligence-based software suite. It will be appreciated that the algorithm is selected based on the nature of the nutritional data and the complexity of the medical condition identified from the multi-modal health data 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. Optionally, the computer-implemented method further comprises providing an application programming interface to the user for presenting the generated personalized nutritional recommendation for the user in a form of at least one of: a tabular representation, a graphical representation, a textual representation. The application programming interface (API) is configured to request and exchange information or data between two or more software applications, such as the dynamically updated knowledge base and the user input device. Optionally, the user input device may be a smartphone device comprising a mobile application or a web-based application that employs an API to interact with a remote dynamically updated knowledge base. Such mobile application or webbased application enables patients to easily access and track their multimodal data and personalized nutritional recommendation reports over time. For instance, a health app may use an API to fetch user health data stored in a cloud-based database. The API allows the user to filter the personalized nutritional recommendation report based on specific criteria, such as comprehensive food score range, date, and so on. Moreover, presenting the generated personalized nutritional recommendation report in a form of at least one of: a tabular representation, a graphical representation, a textual representation, allows convenient understanding of the data by the user, such as some who is not technically-sound to understand the health data. For example, tabular representations allow for comparing the heath data values across different categories. In above example, the tabular representation of the comprehensive food scores of food items of a user and a threshold value thereof makes it easier for the user to compare the current user nutritional or dietary components values to the threshold value thereof and ascertain the variation therebetween. An additional column in such tabular representation may comprises a historical value of the user's health data, namely the at least one biological modality, which helps the user to identify if there is any improvement in their health data overtime as a result of following personalized recommendations, for example. Moreover, visual representation of data, such as by means of graphs, charts, plots, and so on, allows for faster and more user-friendly comprehension of data by a broader audience. For example, graphs may be used to plot blood pressure of a user over time, to analyse an improvement therein or not over a given period of time when they started following the personalized nutritional recommendation generated by the disclosed method. Furthermore, textual representations include explanations and interpretations for educating the user about health, medication usage, benefits and adversities of certain food items, and so on. Furthermore, a combination of such representations cater to a broader audience with different comprehension abilities. Moreover, aforementioned ways of representing data enables customisable report templates, allow healthcare professionals to choose specific insights and data points relevant to their patients' needs. Moreover, the API enables providing (presenting) the personalized nutritional recommendation reports to the users (patients and healthcare professionals) through a secure, user-friendly platform or format. Optionally, the computer-implemented method further comprises generating notifications for notifying the user about at least one of: a recent addition to the dynamically updated knowledge base corresponding to the multi-modal health data of the user, a comprehensive food score and / or a recent addition to at least one recommended recipe for the food item or a link thereto. Herein, the at least one change to the dynamically updated knowledge base or the comprehensive food score and / or the multi-modal health data due to an update of corresponding data sources may be implemented semi-autonomously (the updating requires a user input (or feedback) or opinion of a healthcare professional, for example) or autonomously (resulting from an updating of the corresponding data sources). Optionally, the notification is implemented as at least one of: a visual notification, an audio notification, a haptic notification, a text notification. It will be appreciated that the notification is sent to the user input device associated with the user, such that the user is educated health-wise and recent developments in the field. Beneficially, sending the notification in a timely manner enables a near-real time decision-making about the user's health or dietary habits, by timely resolution of issues related with the dietary choices. Beneficially, such alert notifications give time to the user or the healthcare professional to act immediately to avert issues in the health by providing timely help. 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 computer-implemented method, apply mutatis mutandis to the computer-readable storage medium. It may be appreciated that the processing arrangement execute steps of a computer-implemented method may be associated with the system or an external device operatively coupled with the system configured for providing a personalized nutritional recommendation for a user. The present disclosure also relates to the system as described above. Various embodiments and variants disclosed above, with respect to the aforementioned computer-implemented method and the aforementioned computer-readable storage medium, apply mutatis mutandis to the system. The term "processing arrangement" as used herein 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. In other words, the processing arrangement is a strategic organization and deployment of various servers and computing resources within a cloud infrastructure to support the operations of the system using a data communication network. The term "data communication network" as used herein refers to means for communication between the processing arrangement and other components of the system, in order to receive and subsequently process the data related to the functioning of the system. 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. Optionally, the system further comprises a user interface configured to receive a user input comprising the multi-modal health data; and provide the generated the personalized nutritional recommendation to the user. Optionally, the system further comprises an application programming interface associated with a user input device, configured to present the personalized nutritional recommendation for the user in a form of at least one of: a tabular representation, a graphical representation, a textual representation. Optionally, the system further comprises a database configured to store and safeguard the nutritional data and the nutritional index, the static food score, the food index, the multi-modal health data, the personalized dynamic factor, and the comprehensive food score of the food item, a plurality of algorithms configured to calculate the nutritional index, the static food score, the food index, the personalized dynamic factor, and the comprehensive food score of the food item, a plurality of published literature, a historical data of the user, a historical recommendation for the user. Optionally, the system further comprises a notification module configured to generate notifications for notifying the user about at least one of: a recent addition to the dynamically updated knowledge base corresponding to the multi-modal health data of the user, a comprehensive food score and / or a recent addition to at least one recommended recipe for the food item or a link thereto. Typically, the notification module is a software component or system designed to deliver timely and relevant messages, alerts, or updates to users based on predefined triggers or events. It serves as a communication mechanism to keep users informed about important information, changes, or actions that require their attention. Herein, if there are updates to the nutritional composition of a particular food item, improvements in its nutritional quality, changes in its recommended consumption based on the user's health status or preferences, or a new recipe in the database, the module sends notifications to inform the user. The notifications are personalized based on the user's individual characteristics, dietary preferences, health goals, and activity patterns. The notification module uses this information to prioritize and tailor the notifications, ensuring that they are relevant and useful to the user. The notification module supports multiple delivery channels to reach users through their preferred communication channels. Normally, notifications are delivered through various channels, such as mobile push notifications, email, SMS, in-app messages, pop-up alerts, or browser notifications. In the nutshell, addition to traditionally utilized genomic data, the computer-implemented method and system of the present disclosure is distinguished by its comprehensive nature, integrating insights from genomics, metagenomics, physiological states, and other factors such as lifestyle and the degree of physical activity. These factors collectively determine individualized nutritional needs. The disclosed computer-implemented method and system employ a novel nutritional recommendation engine that is based on a thorough understanding of the interactions between food components and an individual's unique characteristics, which dictate their need for specific nutrients. Moreover, the disclosed computer-implemented method and system consider the level of scientific evidence, metabolic pathways, current physiological state, personal preferences, and other customer data to generate a personalized food score, which serves as a template for creating customized meal plans, namely the personalized nutritional recommendation. Moreover, the personalized nutritional recommendation is strategized based upon five major tiers, each contributing differently to the overall impact of a particular food item on an individual. The five major tiers include personalised nutrition based on personalised dietary - and lifestyle data (activity level), personalised nutrition advice based on personalised phenotypic data (anthropometries), Personalised nutrition advice based on biochemical blood markers data (metabolic health), personalised nutrition advice based on personalised genomic data (genetic health), Personalised nutrition advice based on personalised metagenomic data (microbiome). Furthermore, the disclosed computer-implemented method and system is configured for incorporating additional real-time data along with the generation of the personalized nutritional recommendation. Said additional real-time data includes data obtained from wearable devices and / or validated data from metabolomics and metabotype levels, which are defined by a shared metabolic profile. Thus, the disclosed computer-implemented method and system establish proactive, evidence-based food-based guidelines that helps in promoting health span and lifespan of the users. EXPERIMENTAL PART Jane, a 35-year-old woman with a family history of diabetes, was concerned about her risk of developing the condition. She used the personalised nutritional recommendation system, which collected her genomic, physiological, microbiome, and real-time sensor data. The system identified that she had an increased risk for diabetes and suggested specific dietary adjustments to mitigate this risk. Jane followed the recommendations and successfully managed to lower her risk of developing diabetes. Sarah was a 28-year-old amateur level distance runner. She was already familiar with generic nutritional guidelines but still suffered from incomplete recovery and often experienced drop in performance. She used the personalised food algorithm to obtain guidance on her diet. The system considered her unique genetic makeup, microbiome, and body composition data, and provided her with specific advice on nutrition. As a result, Sarah experienced improved athletic performance and reduced recovery time. Mike, a 55-year-old man, was diagnosed with high blood pressure and high cholesterol. His doctor recommended dietary changes to improve his health. Mike used the personalised nutrition system, which analysed his genomic, physiological, microbiome, and anthropometric data. Based on this information, the system suggested a personalized diet plan tailored to Mike's specific needs. By following the recommendations, Mike effectively lowered his blood pressure and cholesterol levels, reducing his risk of heart disease. DETAILED DESCRIPTION OF THE DRAWINGS Referring to FIG. 1, illustrated is a flowchart depicting steps of a computer-implemented method for providing a personalized nutritional recommendation for a user, in accordance with an embodiment of the present disclosure. At step 102, nutritional data related to a food item is obtained. At step 104, a nutritional index for each nutrient component of the food item is calculated based on the obtained nutritional data thereof. At step 106, a static food score is calculated by combining nutritional index for the nutrient components of the food item. At step 108, a food index is calculated based on the nutritional index for the nutrient components of the food item. At step 110, multi-modal health data related to at least one biological modality of the user is obtained. At step 112, a personalized dynamic factor of the food item is calculated based on the food index and the obtained multi-modal health data of the user. At step 114, a comprehensive food score of the food item is calculated by combining the static food score and the personalized dynamic factor of the food item. At step 116, the personalized nutritional recommendation for the user is generated based on the comprehensive food score of the food item. Referring to FIG. 2, illustrated is a system 200 for providing a personalized nutritional recommendation for a user 202, in accordance with an embodiment of the present disclosure. As shown, the system 200 comprises a processing arrangement 204 configured for: obtaining nutritional data related to a food item; calculating a nutritional index for each nutrient component of the food item based on the obtained nutritional data thereof; calculating a static food score by combining nutritional index for the nutrient components of the food item; calculating a food index based on the nutritional index for the nutrient components of the food item; obtaining multi-modal health data related to at least one biological modality of the user; calculating a personalized dynamic factor of the food item based on the food index and the obtained multimodal health data of the user; calculating a comprehensive food score of the food item by combining the static food score and the personalized dynamic factor of the food item; and generating the personalized nutritional recommendation for the user based on the comprehensive food score of the food item. As shown, the system 200 further comprises a user interface 206 configured to receive a user input comprising the multi-modal health data; and provide the generated the personalized nutritional recommendation to the user 202. Moreover, the system 200 further comprises a database 208, communicably coupled to the processing arrangement 204, configured to store and safeguard the nutritional data and the nutritional index, the static food score, the food index, the multimodal health data, the personalized dynamic factor, and the comprehensive food score of the food item, a plurality of algorithms configured to calculate the nutritional index, the static food score, the food index, the personalized dynamic factor, and the comprehensive food score of the food item, a plurality of published literature, a historical data of the user 202, a historical recommendation for the user 202. Furthermore, the system 200 further comprises an application programming interface 210 (API) associated with a user input device 212, configured to present the personalized nutritional recommendation for the user 202 in a form of at least one of: a tabular representation, a graphical representation, a textual representation. The system 200 further comprises a notification module 214 configured to generate notifications for notifying the user 202 about at least one of: a recent addition to the dynamically updated knowledge base corresponding to the multi-modal health data of the user 202, a comprehensive food score and / or a recent addition to at least one recommended recipe for the food item or a link thereto. FIGs. 3A, 3B and 3C are exemplary implementations of a dashboard 300 on a user interface 302 of a user input device 304, in acoordance with an embodiment of the present disclosure. As shown in FIG. 3A, the dashboard 300 displays a first set of food items (or supplements) 306. As shown, the drop down menu 308, namely, 'Routines', 'Benefits', and 'Hallmarks', provide a list of options 310 to select one option per drop down menu 308 by scrolling up-down the list of options. As shown in FIG. 3B, when the desired option 312 is selected, a second set of food items 314, from amongst a firt set of food items 306, associated with the selected desired option 312 is displayed on the dashboard 300. As shown in FIG. 3C, upon clicking on a desired food item 316 selected from second set of food items 314, nutritional data 318 related to the selected desired food item 316 is displayed on the dashboard 300.

Claims

1. A computer-implemented method for providing a personalized nutritional recommendation for a user (202), the computer-implemented method comprising:obtaining nutritional data related to a food item;calculating a nutritional index for each nutrient component of the food item based on the obtained nutritional data thereof;calculating a static food score by combining nutritional index for the nutrient components of the food item;calculating a food index based on the nutritional index for the nutrient components of the food item;obtaining multi-modal health data related to at least one biological modality of the user;calculating a personalized dynamic factor of the food item based on the food index and the obtained multi-modal health data of the user;calculating a comprehensive food score of the food item by combining the static food score and the personalized dynamic factor of the food item; andgenerating the personalized nutritional recommendation for the user based on the comprehensive food score of the food item.

2. A computer-implemented method of claim 1, wherein the nutritional index for a given nutrient component of the food item per serving is a ratio of a health-promoting property and a non-health-promoting property, or aggregated score reflecting factors affecting bioavailability and a biological form, of the given nutrient component of the food item.

3. A computer-implemented method of claim 1 or 2, further comprising associating weights to the nutritional index for each nutrientcomponent of the food item prior to calculating the static food score based thereon.

4. A computer-implemented method of any of the preceding claims, wherein the static food score of the food is calculated based on the nutritional data related to a food item and a degree of processing thereof.

5. A computer-implemented method of any of the preceding claims, wherein the food index of the food item includes at least one of: weight gain index, fat quality index, protein quality index, carbohydrate quality index, Vitamin A index, Vitamin D index, Vitamin E index, Vitamin K index, iron index, bone health index, methylation nutritional index, inflammation nutritional index, thyroid index, Omega-3 index.

6. A computer-implemented method of any of the preceding claims, wherein the at least one biological modality of the user (202) comprises a health risk-factor or a biomarker.

7. A computer-implemented method of any of the preceding claims, wherein the at least one biological modality of the user (202) includes at least one of: body composition, fat metabolism, protein metabolism, carbohydrate metabolism, Vitamin A metabolism, Vitamin D metabolism, Vitamin E metabolism, Vitamin K metabolism, iron metabolism, bone metabolism, methylation, inflammation, thyroid function, Omega-3 metabolism.

8. A computer-implemented method of any of the preceding claims, further comprising normalizing the comprehensive food score of the food item.

9. A computer-implemented method of claim 8, wherein the comprehensive food score of the food item is normalized between 0 and 100.

10. A computer-implemented method of claim 1, further comprising associating weights to the food index of the food item and the at leastone biological modality of the user (202), wherein the weights of the food index and the at least one biological modality ranges between 0.05 and 1.

11. A computer-implemented method of claim 10, wherein the personalized dynamic factor of the food item is the product of the food index of the food item, the weights of the food index of the food item, the at least one biological modality of the user (202), and the weights of the at least one biological modality of the user.

12. A computer-implemented method of any of the preceding claims, wherein the multi-modal health data comprises:a biological test data selected from at least one of: blood biomarker data, gut microbiome composition and functional biomarkers data, epigenomic biomarkers data, telomeric biomarkers data, proteomic biomarkers data, data pertaining to skin health, lipidomic data, glycan data, biomarkers based on glycosylation patterns of the immune system, transcriptome signatures data, and metabolomic biomarkers data; anda supplementary data selected from at least one of: anthropometric measurements data, medical imaging techniques, physiological phenotypes data, functional and wearable measurements data corresponding to exercise and sleep, a sensor data pertaining to a specific health entity, a health data from a third-party source, a pre-stored biological data and a pre-stored sensor data pertaining to a specific health entity from a third-party source, a dietary intake data, an environmental data.

13. A computer-implemented method of any of the preceding claims, further comprising creating and updating a dynamically updated knowledge base associated with the obtained nutritional data and the multi-modal health data, wherein the dynamically updated knowledge base is updated by using artificial intelligence / machine learning tools, at pre-defined time intervals, wherein the dynamically updated knowledgebase comprises at least one of: food composition databases, and a plurality of published literature including scientific literature, articles, research findings, and experimental data.

14. A computer-implemented method of claim 13, further comprising refining the comprehensive food score of the food item based on the dynamically updated knowledge base.

15. A computer-implemented method of any of the preceding claims, further comprising creating a database, and storing and safeguarding, in the database, the nutritional data and the nutritional index, the static food score, the food index, the multi-modal health data, the personalized dynamic factor, and the comprehensive food score of the food item, a plurality of algorithms configured to calculate the nutritional index, the static food score, the food index, the personalized dynamic factor, and the comprehensive food score of the food item, a plurality of published literature, a historical data of the user (202), a historical recommendation for the user.

16. A computer-implemented method of any of preceding claims, further comprising providing an application programming interface to the user (202) for presenting the generated personalized nutritional recommendation for the user in a form of at least one of: a tabular representation, a graphical representation, a textual representation.

17. A computer-implemented method of any of preceding claims, wherein the personalized nutritional recommendation for the user (202) comprises:the comprehensive food score of the food item and at least one biological modality;the comprehensive food score of the food item varying from a threshold value thereof;a recommended portion size of the food item with the nutritional index for each nutrient component of the food item; andat least one recommended recipe for the food item or a link thereto.

18. A computer-implemented method of any of claims 13-17, further comprising generating notifications for notifying the user (202) about at least one of: a recent addition to the dynamically updated knowledge base corresponding to the multi-modal health data of the user, a comprehensive food score and / or a recent addition to at least one recommended recipe for the food item or a link thereto.

19. A computer-implemented method of any of the preceding claims, wherein the food item comprises a diet type or a food ingredient, and wherein the nutritional data includes at least one of: macronutrients, micronutrients, fibre, energy density, phytochemicals.

20. A computer-readable storage medium comprising instructions for providing a personalized nutritional recommendation for a user (202), which when executed by a processing arrangement, cause the processing arrangement to execute steps of a computer-implemented method of any of claims 1-19.

21. A system (200) for providing a personalized nutritional recommendation for a user (202), the system comprising a processing arrangement (204) configured for:obtaining nutritional data related to a food item;calculating a nutritional index for each nutrient component of the food item based on the obtained nutritional data thereof;calculating a static food score by combining nutritional index for the nutrient components of the food item;calculating a food index based on the nutritional index for the nutrient components of the food item;obtaining multi-modal health data related to at least one biological modality of the user;calculating a personalized dynamic factor of the food item based on the food index and the obtained multi-modal health data of the user;calculating a comprehensive food score of the food item by combining the static food score and the personalized dynamic factor of the food item; andgenerating the personalized nutritional recommendation for the user based on the comprehensive food score of the food item.

22. A system (200) of claim 21, further comprising a user interface (206, 302) configured toreceive a user input comprising the multi-modal health data; andprovide the generated the personalized nutritional recommendation to the user (202).

23. A system (200) of any of claims 21 or 22, further comprising an application programming interface (210) associated with a user input device (212), configured to present the personalized nutritional recommendation for the user (202) in a form of at least one of: a tabular representation, a graphical representation, a textual representation.

24. A system (200) of any of claims 21-23, further comprising a database (208) configured to store and safeguard the nutritional data and the nutritional index, the static food score, the food index, the multimodal health data, the personalized dynamic factor, and the comprehensive food score of the food item, a plurality of algorithms configured to calculate the nutritional index, the static food score, the food index, the personalized dynamic factor, and the comprehensive food score of the food item, a plurality of published literature, a historical data of the user (202), a historical recommendation for the user.

25. A system (200) of any of claims 21-24, further comprising a notification module (214) configured to generate notifications for notifying the user (202) about at least one of: a recent addition to the dynamically updated knowledge base corresponding to the multi-modal5 health data of the user, a comprehensive food score and / or a recent addition to at least one recommended recipe for the food item or a link thereto.

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