Methods and systems for predicting, diagnosing, prognosticating, and treating in applications of precision medicine in prediabetes, diabetes, and related events

Abstract

The present invention provides methods, systems, platforms, kits and computer- implemented processes for predicting, diagnosing, classifying, profiling diabetes, prediabetes (or intermediate hyperglycemia) and diabetes-related cardiometabolic disorders and complications in a subject, and for performing precision treatment selection and clinical decision-making. In some embodiments, the invention comprises individual modules, wherein each module generates outputs comprising one or more of disease risk stratification, subtype classification, complication risk prediction, pharmacogenomic predictions, and treatment recommendations. In certain embodiments, the invention further provides an integrated, multi-method, multi-functional system, platform, kit or computer-implemented method wherein two or more of the individual modules are integrated to generate individualized health data for personalized management, selection for participation in clinical trials and generation of real-world evidence to complement clinical trial evidence for evaluation of safety, tolerability, clinical effectiveness and cost-effectiveness of interventions. Other example embodiments are described herein.

Description

METHODS AND SYSTEMS FOR PREDICTING, DIAGNOSING, PROGNOSTICATING, AND TREATING IN APPLICATIONS OF PRECISION MEDICINE IN PREDIABETES, DIABETES, AND RELATED EVENTSCROSS-REFERENCE TO RELATED APPLICATION

[0001] This application claims benefit under 35 U.S.C. § 119(e) of U.S. Provisional Application having Serial No. 63 / 755,997 filed Feb. 07, 2025, the entire contents of which is / are hereby incorporated by reference herein.TECHNICAL FIELD

[0002] This application relates to methods, panels, kits, computer-implemented systems and uses for diagnosing, classifying, profiling, preventing and treating diabetes, prediabetes, and related disorders. In particular, the invention provides integrated, multi-method, multi-omics, multifunctional platforms for management of different stages of diabetes, prediabetes (or intermediate hyperglycemia), and related disorders, as well as applications in precision medicine.BACKGROUND

[0003] Diabetes affects over 500 million people worldwide, with numbers expected to rise to 640 million by 2030. Early identification of individuals at high risk is important for timely intervention to prevent progressive decline in beta-cell function and increasing insulin resistance that culminate in overt hyperglycemia and multi -organ damage. Current methods such as blood glucose tests generally indicate risk only when blood levels are already abnormal, and there is a lack of tools that identify high-risk individuals earlier, when preventive measures are most effective. Due to the silent nature and non-specific symptoms of diabetes, 30-50% of individuals remain undiagnosed. The 75 g oral glucose tolerance test (OGTT) is regarded as the gold standard for diagnosing prediabetes and diabetes, with many individuals identified solely by elevated 2-hour plasma glucose; however, recommended screening tests such as fasting plasma glucose and hemoglobin Ale have sensitivities of only about 25% and 47%, respectively, resulting in missed opportunities for early intervention. Despite evidence supporting the use of OGTT for diagnosis and prognostication, it is not routinely used as a screening test due to additional time and procedural requirements.

[0004] Diabetes is a complex and heterogeneous disease arising from disturbances in multiple biological pathways. Accurate classification of diabetes ensures that individuals with or at risk of diabetes receive appropriate therapy at the right time. Early control of glycemia is associated with 1232214-40001 (PCT)improved glycemic durability, delayed treatment escalation (including initiation of insulin), and reduced diabetes-related complications and all-cause mortality. At least four main types and several subtypes of diabetes have been described, and overlapping or similar clinical features among these subgroups make precise classification necessary to avoid misdiagnosis, miscommunication and inappropriate treatment.

[0005] More than 50% of individuals with autoimmune type 1 diabetes (T1D) develop the disease in adulthood, and the diagnosis is often missed. In both children and adults, and depending on the severity and stage of autoimmunity, affected individuals may not present with ketoacidosis and are frequently misdiagnosed as having type 2 diabetes (T2D), leading to delayed insulin treatment; compared with classical T1D with acute presentation, adults with autoimmune T1D misclassified as T2D have an approximately 2.8-fold increased risk of end-stage kidney disease. There is also a subtype of ketosis-prone diabetes, usually precipitated by acute events such as infection, characterized by severe insulin resistance and acute presentation; in some of these individuals, beta-cell function recovers after resolution of glucotoxicity, yet many receive life-long insulin therapy associated with excessive weight gain. Such overlapping presentations indicate a need for improved classification to delineate the relative contributions of insulin resistance and insulin deficiency, using both genetic and non-genetic factors to guide treatment selection.

[0006] Monogenic diabetes, including maturity onset diabetes of the young (MODY), is caused by rare mutations or low-frequency variants with Mendelian inheritance and may be present in 5-8% of people with young-onset diabetes (diagnosed before 40 years of age), depending on phenotype and family history. MODY subtypes arise from mutations affecting distinct pathways, including HNF1A, HNF4A, HNF1B, GCK, NEURODI, KCNJ11, ABCC8, and other associated genes. Up to 80% of individuals with MODY are misdiagnosed as having type 1 ortype 2 diabetes, and even different MODY subtypes have distinct clinical characteristics, trajectories and treatment responses.

[0007] Progressive decline in beta-cell function, driven by intrinsic biological defects and exacerbated by glucotoxicity with poor glycemic control, is a hallmark of diabetes. Persistent hyperglycemia may lead to failure of oral glucose-lowering drugs and eventual insulin requirement; fluctuating glycemia can cause oxidative stress, inflammation and neurovascular damage contributing to complications, the risk of which is further influenced by genetic predisposition, control of modifiable risk factors such as blood pressure, obesity and dyslipidemia, and the use of organ-protective drugs. Identifying people with genetic susceptibility to cardiovascular and kidney complications enables earlier detection, closer monitoring, intensification of cardiovascular-kidney-metabolic risk factor management, and timely use of 2232214-40001 (PCT)organ-protective therapies to delay or prevent these outcomes. Hyperlipidemia is a major modifiable risk factor for cardiovascular and kidney complications in diabetes; individuals with diabetes or prediabetes, including those with familial hypercholesterolemia, may carry combinations of common or rare variants associated with diabetes, hyperlipidemia and other cardiovascular-kidney-metabolic risks, which together elevate disease risk and require early use of lipid-lowering and other disease-modifying therapies.

[0008] The genetic makeup of an individual also significantly influences drug pharmacokinetics and pharmacodynamics, affecting absorption, distribution, metabolism and excretion through transporters and enzymes, or altering the number or responsiveness of drug targets. As a result, carriers of specific genetic variants may have reduced or enhanced drug efficacy or an increased risk of adverse drug reactions, which can be severe. Because people with diabetes often require multiple medications to control disease progression, they may be particularly exposed to adverse drug reactions, highlighting the relevance of pharmacogenomic information.

[0009] In view of these challenges, there remain unmet medical needs for methods of early risk prediction, accurate subtype classification, comprehensive profiling of diabetes, prediabetes, and diabetes-related disorders, and precision medicine.SUMMARY

[0010] Disclosed herein are novel compositions, kits, methods and uses that are useful for diagnosing, classifying, profiling, preventing and treating diabetes, prediabetes, and related disorders. In particular, the invention provides integrated, multi-method, multi-omics, multifunctional platforms for management of diabetes, prediabetes, and related disorders, as well as applications in precision medicine.

[0011] The present invention provides methods, systems, platforms, kits and computer-implemented processes for predicting, diagnosing, classifying, profiling diabetes, prediabetes (or intermediate hyperglycemia) and diabetes-related cardiometabolic disorders and complications in a subject, and for performing precision treatment selection and clinical decision-making. In some embodiments, the invention comprises individual modules, wherein each module generates outputs comprising one or more of disease risk stratification, subtype classification, complication risk prediction, pharmacogenomic predictions, and treatment recommendations. In certain embodiments, the invention further provides an integrated, multi-method, multi-functional system, platform, kit or computer-implemented method wherein two or more of the individual modules are integrated to generate individualized health data for personalized management, selection for participation in clinical trials and generation of real-world evidence to complement3232214-40001 (PCT)clinical trial evidence for evaluation of safety, tolerability, clinical effectiveness and costeffectiveness of interventions.

[0012] The present invention provides an integrated and holistic approach to genetic assessment and management of diabetes and related cardiometabolic disorders. In certain embodiments, the invention is distinguished from existing prediabetes or diabetes assessment methods, such as the oral glucose tolerance test (OGTT) and genetic tests based on isolated or limited biomarkers, by employing a comprehensive and novel clinically significant genetic variants, evaluated using novel risk calculation and stratification frameworks within a unified technology platform that integrates multiple analytical modules. In particular embodiments, the invention enables, from a single genetic profile, coordinated disease risk stratification, subtype classification, pathophysiological pathway profiling, complication risk prediction, pharmacogenomic prediction, and treatment recommendation generation. Unlike current approaches that predominantly rely on late-stage metabolic biomarkers or fragmented genetic information, the disclosed methods, systems and kits support earlier and more accurate identification of individuals at risk for, or in different stages or subtypes of, diabetes and its complications, and facilitate evidence-based, personalized intervention strategies designed to maximize therapeutic benefit and minimize harm across the diabetes continuum.

[0013] While pharmacogenomics for avoiding adverse drug reactions is established, the present invention extends precision medicine principles to proactive selection of maximally effective therapies through holistic genetic profiling that delineates diabetes risk, subtypes, pathophysiological drivers, complication susceptibilities, and longitudinal disease trajectory across the subject's life course, thereby enabling comprehensive, stage-appropriate intervention strategies beyond conventional pharmacogenetic applications.BRIEF DESCRIPTION OF THE DRAWINGS

[0014] FIG. 1A-E illustrates a plurality of genetic variants evaluated for assessing an individual’s predisposition to developing type 1 diabetes (T1D), type 2 diabetes (T2D), and gestational diabetes mellitus (GDM).

[0015] FIG. 2A-C presents a list of genomic loci corresponding to target regions analyzed for detecting monogenic forms of diabetes in a subject.

[0016] FIG. 3A-D provides a list of genetic variants implicated in various pathophysiological pathways associated with type 2 diabetes (T2D).

[0017] FIG. 4A-J depicts panels of genetic variants utilized in determining risk profdes associated with lipid traits.4232214-40001 (PCT)

[0018] FIG. 5A-C illustrates genomic loci corresponding to target regions assessed for identification of Familial Hypercholesterolemia in a subject.

[0019] FIG. 6A-E provides a list of genetic variants employed to stratify risk for developing complications associated with diabetes.

[0020] FIG. 7A-F depicts genetic variants and corresponding genomic loci associated with genes relevant to pharmacogenomic profiling.

[0021] FIG. 8A-H illustrates a lookup table representing nucleotide states across a plurality of informative loci within the SLCO1B1 genomic region.

[0022] FIG. 9A-E illustrates a lookup table associating SLCO1B1 genotypes with corresponding organic anion transporting polypeptide 1B1 (OATP1B1) function and pharmacogenomic profiles relevant to statin therapy.

[0023] FIG. 10 illustrates a lookup table of nucleotide identities at multiple informative loci within the ABCG2 genomic region.

[0024] FIG. 11 presents functional interpretations of ABCG2 transporter activity derived from genotypic data, including derived pharmacogenomic profiles for statin therapy.

[0025] FIG. 12A-T illustrates a lookup table of nucleotide identities across a plurality of informative loci within the CYP2C9 genomic region.

[0026] FIG. 13 depicts the functional interpretation of CYP2C9 genotypes and corresponding implications for pharmacogenomic profiles in statin therapy.

[0027] FIG. 14 provides a lookup table of HLA-B*58:01 genotypes for pharmacogenomic assessment regarding Allopurinol therapy.

[0028] FIG. 15 provides a lookup table of HLA-B*31:01 genotypes relevant for pharmacogenomic profiling in relation to Carbamazepine administration.

[0029] FIG. 16 provides a lookup table of HLA-B* 15:02 genotypes relevant for pharmacogenomic profiling in relation to Carbamazepine administration.

[0030] FIG. 17A-AD illustrates a lookup table of nucleotide identities at a plurality of informative loci within the CYP2C9 genomic region linked to warfarin pharmacogenomics.

[0031] FIG. 18 illustrates a lookup table of nucleotide identities at selected loci within the CYP4F2 gene linked to warfarin pharmacogenomics.

[0032] FIG. 19 presents a lookup table of nucleotide identities in the VKORC1 genomic region linked to warfarin pharmacogenomics.

[0033] FIG. 20 illustrates a lookup table of genomic variants corresponding to CDKAL1 and GLP1R involved in predicting response to dipeptidyl peptidase-4 inhibitors (DPP4-i).5232214-40001 (PCT)

[0034] FIG. 21 illustrates a lookup table of genomic variants corresponding to the GLP1R and ARRB1 genes involved in predicting response to GLP-1 receptor agonists.

[0035] FIG. 22 depicts alternative alleles at variants associated with the SLC22A1, SLC22A2, SLC6A4, and SLC29A4 genes implicated in metformin intolerance.

[0036] FIG. 23 illustrates genetic variants of the CYP2C19 gene relevant to sulfonylurea therapeutic response.

[0037] FIG. 24 illustrates variants located at positions chr2:27508073 and chr7:44189469 (based on GRCh38 coordinate system), associated with glucokinase activator response.

[0038] FIG. 25A-E presents the CYP2C19 variants located at multiple genomic positions on chromosome 10 relevant to clopidogrel metabolism.

[0039] FIG. 26A-BO provides a lookup table of CYP2C19 diplotypes, corresponding allele function annotations, and associated clinical dosing recommendations.DETAILED DESCRIPTION

[0040] As used herein and in the claims, the terms “comprising” (or any related form such as “comprise” and “comprises”), “including” (or any related forms such as “include” or “includes”), “containing” (or any related forms such as “contain” or “contains”), means including the following elements but not excluding others. It shall be understood that for every embodiment in which the term “comprising” (or any related form such as “comprise” and “comprises”), “including” (or any related forms such as “include” or “includes”), or “containing” (or any related forms such as “contain” or “contains”) is used, this disclosure / application also includes alternate embodiments where the term “comprising”, “including,” or “containing,” is replaced with “consisting essentially of’ or “consisting of’. These alternate embodiments that use “consisting of’ or “consisting essentially of’ are understood to be narrower embodiments of the “comprising”, “including,” or “containing,” embodiments.

[0041] For the sake of clarity, “comprising”, including, and “containing”, and any related forms are open-ended terms which allows for additional elements or features beyond the named essential elements, whereas “consisting of’ is a closed end term that is limited to the elements recited in the claim and excludes any element, step, or ingredient not specified in the claim.

[0042] As used herein, the singular forms “a”, “an” and “the” are intended to include the plural forms as well, unless the context clearly indicates otherwise. Where a range is referred in the specification, the range is understood to include each discrete point within the range. For example, 1-7 means 1, 2, 3, 4, 5, 6, and 7.

[0043] As used herein, the term "about" is understood as within a range of normal tolerance in the art and not more than ±10% of a stated value. By way of example only, about 50 means from 456232214-40001 (PCT)to 55 including all values in between. As used herein, the phrase "about" a specific value also includes the specific value, for example, about 50 includes 50.

[0044] As used herein and in the claims, an “effective amount” is an amount that is effective to achieve at least a measurable amount of a desired effect. For example, in some embodiments, the amount may be effective to lessen one or more symptoms or lower glycemic parameters of diabetes or diabetes-related disorders. In other embodiments, the amount may be effective to improve a parameter of prediabetes, or to delay or prevent progression to diabetes.

[0045] As used herein and in the claims, a “subject” refers to animals such as mammals, including, but not limited to, primates (e.g., humans), cows, sheep, goats, rabbits, rats, mice and the like.

[0046] As used herein, the term “pharmaceutical composition” or “composition” refers to a formulation containing one or more active pharmaceutical ingredient(s). In some examples, the pharmaceutical composition is used as a medicament that is capable of lessening one or more symptoms or lowering glycemic parameters of diabetes or diabetes-related disorders. In other examples, the pharmaceutical composition is used as a medicament that is capable of improving a parameter of prediabetes, or to delay or prevent progression to diabetes.

[0047] As used herein and in the claims, the term “prevent”, “preventing”, “preventive”, “preventative” or “prevention” refers the methods of reducing the risk of the onset, relapse or spread of a disease or disorder or one or more of their symptoms.

[0048] As used herein, the term "treat," "treating" or "treatment" refers to methods of alleviating, abating or ameliorating a disease or condition symptoms, preventing additional symptoms, ameliorating or preventing the underlying causes of symptoms, inhibiting the disease or condition, arresting the development of the disease or condition, relieving the disease or condition, causing regression of the disease or condition, relieving a condition caused by the disease or condition, or stopping the symptoms of the disease or condition either prophylactically and / or therapeutically.

[0049] As used herein and in the claims, the terms “diabetes” and “diabetes mellitus” refer to one or more diseases characterized by sustained high sugar levels (hyperglycemia), each as diagnosed according to established diagnostic standards such as of the World Health Organization, Japan Diabetes Society, American Diabetes Association, or European Association for the Study of Diabetes. For example, according to American Diabetes Association criteria, diabetes is diagnosed at hemoglobin Ale (HbAlc) > 6.5%, Fasting Plasma Glucose (FPG) > 126 mg / dL (7 mmol / L), a 2-hour plasma glucose > 200 mg / dL during OGTT, or random plasma glucose >200 mg / dL (11.1 mmol / L) in a human subject with classic symptoms of hyperglycemia. Such diseases include, but not limited to, Type 1 diabetes, Type 2 diabetes, gestational diabetes, monogenic diabetes (including MODY), latent autoimmune diabetes in adults (LADA), and ketosis-prone diabetes.7232214-40001 (PCT)Type 1 diabetes (“T1D”) (previously known as insulin-dependent diabetes, juvenile diabetes or childhood-onset diabetes) is generally characterized by beta-cell destruction or dysfunction resulting in little or no insulin secretion. Type 2 diabetes (“T2D”), previously known as non-insulin-dependent diabetes, is generally characterized by resistance to insulin with varying degree of insulin deficiency. Gestational diabetes is a form of hyperglycemia that is first diagnosed or develops during pregnancy. Maturity-onset diabetes of the young (“MODY”) is a group of monogenic diseases characterized by inheritance of an autosomal dominant gene that disrupts insulin production. MODY typically manifests in adolescence or in young adults. In a subject diagnosed with diabetes, these subtypes may coexist in different combinations, which contribute to the heterogeneity in the presentation, trajectory, outcomes and treatment responses.

[0050] As used herein and in the claims, the terms “hyperglycaemia” refers to a state when a subject has a higher than normal levels of sugar in the blood.

[0051] As used herein and in the claims, the terms “hyperlipidaemia” refers to a state when a subject has a higher than normal levels of fat and cholesterol levels in the blood.

[0052] Although the description referred to particular embodiments, the disclosure should not be construed as limited to the embodiments set forth herein.

[0053] In various embodiments, the present invention provides methods, systems, platforms, kits and computer-implemented processes for predicting, diagnosing, classifying, profiling diabetes, prediabetes and diabetes-related cardiometabolic disorders in a subject, and for performing precision treatment selection and clinical decision-making. In some embodiments, the invention comprises individual modules, wherein each module generates outputs comprising one or more of disease risk stratification, subtype classification, complication risk prediction, pharmacogenomic predictions, and treatment recommendations. In certain embodiments, the invention further provides an integrated, multi-method, multi-functional system, platform, kit or computer-implemented method wherein two or more of the individual modules are integrated to generate individualized health data.

[0054] In some embodiments, obtaining a nucleic acid sample comprises obtaining a biological sample from a subject, wherein the biological sample comprises genomic DNA. The biological sample may be any suitable human specimen, including, without limitation, whole blood, saliva, buccal swabs, hair follicles and other tissue samples. In particular embodiments, with whole blood and saliva being preferred in particular embodiments. In further embodiments, the method comprises extracting DNA from the biological sample using any nucleic acid extraction technique capable of providing nucleic acid of sufficient quality and quantity for downstream processing. By way of example, such nucleic acid extraction techniques include, without limitation,8232214-40001 (PCT)spin-column-based extraction, magnetic-bead-based extraction, phenol-chloroform extraction, silica-membrane -based kits, and automated cartridge-based extraction systems.

[0055] In some embodiments, the method optionally further comprises amplifying the extracted DNA, or a fragment thereof, using polymerase chain reaction (PCR) or any equivalent amplification technique (e.g., multiple displacement amplification, rolling circle amplification, isothermal amplification) prior to downstream processing, wherein amplification may be performed in a uniplex, multiplex or pooled manner to enrich target regions. In other embodiments, the method does not require nucleic acid amplification prior to downstream processing.

[0056] In some embodiments, performing sequence detection comprises detecting nucleotide sequence in the extracted DNA using any suitable technology, including, but not limited to, next-generation sequencing (NGS), whole-genome sequencing, whole-exome sequencing, targeted sequencing panels, mini-sequencing assays or microarray -based genotyping. In such embodiments, the sequence detection step comprises generating sequence-containing data files, such as FASTQ, BCL, ID AT or equivalent platform-specific raw data formats, which serve as inputs for subsequent bioinformatic processing.

[0057] In some embodiments, performing sequence alignment comprises aligning the sequence data to a reference human genome, such as Genome Reference Consortium Human build GRCh38.pl 4 (GCA_000001405.29), using one or more alignment tools selected from Burrows-Wheeler Aligner (BWA), Bowtie 2 or functionally equivalent software, thereby generating alignment files (for example, BAM or CRAM files).

[0058] In certain embodiments, the method further comprises performing variant calling on the alignment files. Variant calling detects sequence differences between the subject’s genome and the reference genome, including single nucleotide polymorphisms (SNPs), insertions, deletions (indels) and larger structural variants. The variant calling step employs one or more variant-calling pipelines, such as Genome Analysis Toolkit (GATK) software or similar frameworks, to produce variant data in variant call format (VCF) files or other suitable data structures. The variant data specifies the subject’s genotype at a plurality of target loci. In some embodiments, the variant data produced as described above are supplied to one of more of the modules of the invention (for example, the modules (1) Diabetes Risk Prediction and Stratification; (2) Monogenic Diabetes Subtype Detection and Classification; (3) Type 2 Diabetes Pathophysiological Pathway Profiling; (4) Hyperlipidaemia and Dyslipidaemia Risk Assessment; (5) Disease Progression and Complication Risk Stratification; and (6) Pharmacogenomic Profiling for Diabetes and Diabetes-Related Disorders.9232214-40001 (PCT)

[0059] In some embodiments, the invention is configured to generate subject-specific absolute risk estimates (for example, an estimated incidence or probability over a predetermined period) for diabetes onset, progression, complications, or therapeutic response, rather than, or in addition to, relative risk ratios (e.g., risk relative to control population). Such absolute risk quantification enhances patient health literacy, engagement and behavioral change, and facilitates informed decision-making regarding early preventive or therapeutic measures, in contrast to conventional approaches relying primarily on relative risk metric.

[0060] Module 1. Diabetes Risk Prediction and Stratification.

[0061] In some embodiments, the invention is directed to a method for predicting the risk of developing one or more types of diabetes, such as type 1 diabetes (T1D), type 2 diabetes (T2D) and gestational diabetes mellitus (GDM), in a subject, within a defined time period (for example, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 15, 20 years or longer). In certain embodiments, the method comprises generating a diabetes risk value for each diabetes type in a subject. In specific embodiments, generating the type-specific diabetes risk value comprises generating a respective genetic risk score. In some embodiments, generating the genetic risk score comprises: (i) obtaining genotyping data or sequence data for one or more of predefined genetic variants or target regions (such as the predefined genetic variants or target regions illustrated in FIG. 1A-E) relevant to a diabetes type; and (ii) calculating the type-specific genetic risk score from the genotyping data.

[0062] In some embodiments, generating the genetic risk score further comprises assigning a score to each variant or target regions. In the example embodiments, each variant corresponds to a respective diabetes type (e.g. T1D, T2D, GDM). The score for each variant is any predetermined numerical value (e.g., 1, 2, 3, 4, 5 or other suitable value) that is based at least in part on the genotype observed at that variant. In particular embodiments, the type-specific genetic risk score is determined as a total or aggregate score based on the assigned scores for the genotypes observed at the plurality of variants corresponding to each diabetes type in the subject’s data.

[0063] In particular embodiments of the present invention, the invention is directed to a method for predicting the risk of developing type 1 diabetes (T1D), type 2 diabetes (T2D) and gestational diabetes mellitus (GDM) in a subject in the next 10 years. In particular embodiments, the method comprising generating a genetic risk score, wherein the genetic risk score is obtained by (i) obtaining genotyping data or sequence data from at least a subset of the genetic variants or target regions in FIG. 1A-E; and (ii) calculating the genetic risk score therefrom, wherein FIG. 1A-E comprises a list of genetic variants associated with one or more of T1D, T2D, and GDM. In particular embodiments, a score is assigned at each variant based on the number of risk alleles present, specifically, assigning a score of 0 for a homozygous non-risk genotype, 1 for a 10232214-40001 (PCT)heterozygous genotype, and 2 for a homozygous risk genotype. The genetic risk score for a given diabetes type is calculated as a sum or other aggregate (for example, a weighted sum or scaled sum) of the assigned scores across the plurality of genetic variants associated with that diabetes type.

[0064] Table 1 below illustrates, by way of example, the calculation of a genetic risk score for a specific diabetes type (e.g. T1D) in a subject across a subset of three predefined variants. For each variant, the subject’s genotype is assigned a score according to the number of risk alleles present (0 for homozygous non-risk, 1 for heterozygous, or 2 for homozygous risk), and the genetic risk score is the sum of these values across the variants.

[0065] Table 1. Illustrative example of the calculation of a genetic risk score.

[0066] In this illustrative example, the genetic risk score is l + 0 + 2 = 3. It will be appreciated that the number of variants, their genomic coordinates, risk alleles and coding scheme may be varied according to, for example, the diabetes types or populations, without departing from the scope of the invention.

[0067] The genetic risk score may be used to classify the subject into one of a plurality of risk categories (e.g., low risk, moderate risk, high risk, or very high risk) in developing a particular diabetes type. In certain embodiments, the subject's risk is stratified for each of T1D, T2D, and GDM, based on the genetic risk score, according to the following non-limiting thresholds:Type 1 Diabetes (T1D)<>Type 2 Diabetes (T2D)11232214-40001 (PCT)<>Gestational Diabetes Mellitus (GDM)<>

[0068] Table 2. Thresholds for stratifying risk of developing type 1 diabetes (T1D), type 2 diabetes (T2D), and gestational diabetes mellitus (GDM).

[0069] In some embodiments, generating the diabetes risk value for T2D further comprises integrating the T2D genetic risk value (T2D / GRV) derived from Table 2 with a numerical assessment derived from one or more non-genetic parameters, thereby generating a T2D diabetes risk value. The non-genetic parameters may include, without limitation, age, gender, height, body weight, birth weight, waist circumference, family history of diabetes, smoking status, history of gestational diabetes, fasting or stimulated blood glucose levels, blood pressure, exercise habits, or any combination thereof. In these embodiments, the composite diabetes risk value represents a probability (P) value that is used to classify the subject into one of a plurality of risk categories (e.g., low risk, moderate risk, high risk, or very high risk) for developing diabetes within a defined time period, including but not limited to 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 15, 20 years or longer.

[0070] In a specific embodiment, the T2D diabetes risk value represented as probability P is derived via the logistic function:1P = - - V 1 + exp ( — ( / JQ + Pi • Genetic Risk Value + p2■ Age + p3■ Family History + p4■ BMI + ps■ Smoking + ■■•)) , where covariates are coded binarily or continuously as appropriate (e.g., Family History = 1 if positive, 0 otherwise; Smoking = 1 if current smoker, 0 otherwise), and coefficients [30, [31 are fitted from training data. In particular embodiments of the present invention, the composite diabetes risk value represented as probability P is derived via the logistic function:ip = - 1 + exp (—(—10.4377 + 0.2257 x Genetic Risk Value + 0.0498 x Age + 1.2505 x Family History + 0.1669 x BMI + 0.7217 x Smoking))12232214-40001 (PCT)The T2D diabetes risk value predicts the probability of a subject developing diabetes in the next 10 years.

[0071] In certain embodiments, the method further comprises generating one or more recommendations for the subject based at least in part on the subject’s predicted risk of developing one or more diabetes types, such as T1D, T2D, and / or GDM, according to their diabetes risk value and / or genetic risk scores. In some embodiments, for subjects classified as high- or very high-risk of developing any type of diabetes, such recommendations may include, for example, regular glycaemic testing (e.g. fasting plasma glucose, OGTT, and / or pregnancy-specific glucose testing at defined intervals), and / or early preventive interventions. Preventive interventions may include, for instance, structured lifestyle modification (e.g. targeted dietary changes, physical activity and weight-management programs), and / or initiation or adjustment of pharmacotherapy in accordance with applicable clinical practice guidelines.

[0072] Module 2, Monogenic Diabetes Subtype Detection and Classification.

[0073] In some embodiments, the invention is directed to a method for detecting monogenic forms of diabetes in a subject. The method comprises obtaining a biological sample from the subject, isolating nucleic acid from the sample, and detecting, or calling, in the nucleic acid, one or more pathogenic or likely pathogenic variants in one or more genes associated with a monogenic form of diabetes, including, without limitation, ABCC8, DCAF17, GATA6, GCK, HNF1A, HNF1B, HNF4A, INS, INSR, NEURODI, PCBD1, PLIN1, PPARG, SLC29A3, TRMT10A, WFS1, ZBTB20, and KCNJ1.

[0074] In example embodiments, the method comprises identifying calling one or more pathogenic or likely pathogenic variants in one or more genes associated with monogenic diabetes, selected from a group consisting of HNF1A, HNF1B, HNF4A, GCK, GATA6, NEURODI, PLIN1, PPARG, WFS1, KCNJ11 and ABCC8. FIG. 2A-C summarizes the loci of the target regions corresponding to each gene.

[0075] In certain embodiments, each called variant is evaluated for pathogenicity according to criteria defined by, or derived from, the American College of Medical Genetics and Genomics / Association for Molecular Pathology (ACMG / AMP) guidelines (e.g. Table 3A). Each criterion is identified as an alphanumeric code. In certain embodiments, PVS1 refers to Pathogenic Very Strong 1; PSI, Pathogenic Strong 1; PS2, Pathogenic Strong 2; PS3, Pathogenic Strong 3; PS4, Pathogenic Strong 4; PM1, Pathogenic Moderate 1; PM2, Pathogenic Moderate 2; PM3, Pathogenic Moderate 3; PM4, Pathogenic Moderate 4; PM5, Pathogenic Moderate 5; PM6, Pathogenic Moderate 6; PPI, Pathogenic Supporting 1; PP2, Pathogenic Supporting 2; PP3, Pathogenic Supporting 3; and PP4, Pathogenic Supporting. In further embodiments, BAI refers 13232214-40001 (PCT)to Benign stand-alone 1; BS1, Benign Strong 1; BS2, Benign Strong 2; BS3, Benign Strong 3; BS4, Benign Strong 4; BP1, Benign supporting 1; BP2, Benign supporting 2; BP3, Benign supporting 3; BP4, Benign supporting 4; BP5, Benign supporting 5; and BP7, Benign supporting 7. Each criterion comprises one or more evidentiary requirements indicative of pathogenicity or benignity of a genetic variant.14232214-40001 (PCT)15232214-40001 (PCT)

[0076] Table 3A. ACMG / AMP Pathogenicity Criteria.

[0077] In certain embodiments, satisfaction of a given pathogenicity or benignity criterion results in assignment of a numerical weight corresponding to the relative strength of evidence. The numerical weights reflect relative evidentiary strength (e.g., very strong, strong, moderate, supporting or indeterminate) and are assigned in accordance with weight formulae derived from ACMG / AMP guideline principles, such as Table 3B.

[0078] Table 3B. Numerical weights assigned evidentiary strength for each pathogenicity criterion under ACMG / AMP guidelines.

[0079] In certain embodiments, the numerical weights assigned across pathogenicity criteria for a given variant are summed to generate a cumulative monogenic diabetes pathogenicity score. The monogenic diabetes pathogenicity score is used to classify each variant into a pathogenicity 16232214-40001 (PCT)category selected from benign, likely benign, variant of uncertain significance (VUS), likely pathogenic, or pathogenic, based on predefined score thresholds such as in Table 3C.><Table 3C. Pathogenicity score ranges under ACMG / AMP guidelines.

[0080] In further embodiments, monogenic diabetes is diagnosed based at least in part on the cumulative monogenic diabetes pathogenicity score, and classified into variant-specific subtypes based on the pathogenicity score generated for that variant. Table 4 summarizes the monogenic diabetes subtypes associated with each gene.17232214-40001 (PCT)

[0081] Table 4. Gene -Monogenic Diabetes Association. MODY=Maturity-Onset Diabetes of the Young, a several hereditary form of diabetes caused by mutations in an autosomal dominant gene disrupting insulin production.

[0082] For example, a variant detected in a gene associated with monogenic diabetes (e.g. GCK), that meets or exceeds a predefined pathogenicity score threshold in Table 3C is indicative of monogenic diabetes associated with that gene (e.g. GCK-MODY2).

[0083] It will be understood that the genes, criteria, weights, score ranges and category labels of Tables 3A-C are exemplary and non-limiting; alternative gene and variant panels, modified ACMG / AMP frameworks, or other classification systems may be employed without departing from the scope of the invention.

[0084] In certain embodiments, the method further comprises generating one or more precision recommendations for the subject based at least in part on the subject's classified monogenic diabetes subtype(s). For example, upon detection of a pathogenic or likely pathogenic variant diagnostic of GCK-MODY2, the method generates a precision recommendation, which may include consideration of therapies targeting GCK function, such as glucokinase activators. In another example, for subjects identified with HNF1A-MODY3 or HNF4A-MODY1, the recommendation may include pharmacological interventions, such as sulfonylurea (e.g. glimepiride, glipizide, and gliclazide, chlorpropamide and tolbutamide). Additional recommendations applicable across monogenic subtypes may include structured lifestyle changes (for example, dietary changes, exercise, and weight management), periodic glycemic monitoring, or referral to specialized care.

[0085] Module 3, Type 2 Diabetes Pathophysiological Pathway Profiling,

[0086] In some embodiments, the invention is directed to a method for characterizing individual genetic susceptibility to one or more pathophysiological pathways implicated in type 2 diabetes (T2D). The method comprises: (a) obtaining genetic profile data from a subject; and (b) calculating, based on variants in one or more predefined sets of loci, one or more pathway-specific polygenetic risk scores. In particular embodiments, the pathway-specific polygenetic risk scores correspond to susceptibility for pathways including, without limitation, ALP negative regulation, beta-cell function (e.g., Beta Cell 1 and Beta Cell 2 subpathways), bilirubin metabolism, cholesterol metabolism, hyperinsulinemia, lipodystrophy (e.g., Lipodystrophy 1 and 2 subpathways), liver-lipid metabolism, obesity, proinsulin secretion, and Sex Hormone-Binding Globulin-Lipoprotein(a) (SHBG-LpA) regulation.

[0087] In some embodiments, obtaining genetic profile data comprises identifying risk genotypes for the predefined pathways based on a reference variant panel in FIG. 3A-D. FIG. 3A-D 18232214-40001 (PCT)comprises a list of variants associated with T2D pathophysiological pathways, includes ALP negative (“ALP Neg”), Beta Cell 1, Beta Cell 2, bilirubin, cholesterol, hyperinsulinemia (“Hyper Insulin”), Lipodystrophy 1, Lipodystrophy 2 subpathways), liver-lipid, obesity, proinsulin, and SHBG-LpA pathways. The method further comprises coding each variant as 0, 1 or 2 risk alleles according to the number of risk alleles present; and calculating a pathway-specific polygenetic risk score (PRS, or specifically “T2D / PRS) as the sum of the coded risk alleles across the variants in the panel. In certain embodiments, the genetic risk score is normalized or converted to a percentile rank by computing a z-score relative to a reference population and applying the standard normal probability density function:

[0088] wherein :Sub ject score-Population mean • ,. , . , 0089 z = - Populati : -on standard devi : —ati ■_ —on . Alternative normalization techniques, such as direct percentile ranking, logistic transformation or machine -learning calibration, may also be employed.

[0090] It will be understood that the variants, pathways, coding scheme, aggregation method and normalization exemplified herein are non-limiting; the module may utilize weighted sums, alternative pathway groupings (e.g., beta-cell function, insulin resistance, adiposity, hepatic glucose production, lipid metabolism or incretin signaling, common variants of genes encoding monogenic diabetes, haplotype and non-haplotype based genetic variants), expanded or consolidated variant panels, or other quantitative measures while remaining within the scope of the invention.

[0091] In certain embodiments, the method further comprises generating one or more pathway -tailored recommendations for the subject based at least in part on the subject's pathway-specific polygenic risk scores, including lifestyle interventions (such as dietary changes, exercise, and weight management), and / or initiation or adjustment of pharmacotherapy in accordance with applicable clinical practice guidelines. For example, for elevated risk scores for beta-cell function (Beta Cell 1 or Beta Cell 2) pathways, the recommendations for preserving betacell function may comprise early initiation of drugs that expand or proliferate beta-cells (e.g. cellular replacement therapy, MENIN inhibitor) or reduce cellular apoptosis (e.g. immunomodulating therapy such as anti-CD3 monoclonal antibody), and early initiation of glucose lowering drugs to reduce glucotoxicity without over stimulating beta-cells and causing hypoglycemia, such as metformin, glucokinase activator (e.g. dorzagliatin), SGLT2 inhibitors (sodium -glucose co-transporter 2 inhibitors, e.g. dapagliflozin, empagliflozin), glucagon like peptide 1 (GLP-1) receptor agonists (e.g. semaglutide), GLP-1 and polypeptide co-agonist (e.g.19232214-40001 (PCT)tirzepatide, mazdutide, retatrutide), dipeptidyl peptidase-4 (DPP-4) inhibitors (e.g. sitagliptin), or thiazolidinediones (e.g. pioglitazone). Quantification of pathway-specific polygenic risk scores identifies the predominant pathophysiological mechanism(s) driving disease in a given subject (e.g., beta-cell dysfunction, insulin resistance, lipid dysregulation), thereby enabling mechanism-matched therapy prioritization to achieve early glycemic control, attenuate glucose toxicity, preserve residual beta-cell function, and forestall or delay diabetes onset and progression to complications. This targeted therapeutic matching, informed by the integrated multi-pathway profiling of the invention, provides clinical advantages over conventional approaches employing aggregate or pathway-agnostic risk assessment.

[0092] Module 4, Hyperlipidaemia Risk Assessment.

[0093] In some embodiments, the invention is directed to a method for identifying a subject having an increased genetic risk for hyperlipidaemia. In particular embodiments, the method determines risk levels for a plurality of lipid phenotypes, including, for example: (1) low high-density lipoprotein (HDL) cholesterol, (2) elevated low-density lipoprotein (LDL) cholesterol, (3) elevated total cholesterol, (4) elevated triglycerides, and (5) elevated lipoprotein(a) and / or (6) familial hypercholesterolaemia.

[0094] In certain embodiments, this module hyperlipidaemia risk assessment comprises two sub-modules. The first sub-module calculates polygenic risk scores for the lipid phenotypes (1)-(5) based on genetic variants associated with increased levels of total cholesterol, HDL cholesterol, LDL cholesterol, triglycerides and lipoprotein(a) (“HLD / PRS”). The second sub-module calculates a risk score for familial hypercholesterolaemia based on detection and classification of pathogenic or likely pathogenic variants in one or more monogenic hypercholesterolaemia genes (for example, LDLR, APOB, PCSK9, LDLRAP1, APOE, SREBF2, STAP1 or other FH-associated genes).

[0095] Submodule 1: Polygenic Risk Score for Lipid Traits.

[0096] In some embodiments, risk genotypes associated with increasing levels of blood total cholesterol (“TC”), triglycerides (“TG”), HDL cholesterol (“HDL”), LDL cholesterol (“LDL”) and lipoprotein(a) (“Lpa”) are defined according to a reference variant panel, as illustrated in FIG.4A-J. For each variant in the panel, the subject’s genotype is evaluated and encoded as 0, 1 or 2 according to the number of risk alleles present (0 for no risk allele, 1 for heterozygous, 2 for homozygous risk), and a lipid-specific genetic risk score HLD / PRS is calculated as the sum (or weighted sum) of the encoded values across all variants mapped to that lipid phenotype.

[0097] In some embodiments, the invention thereby provides a method for characterizing an individual’s genetic risk for hyperlipidaemia by calculating one or more hyperlipidaemia 20232214-40001 (PCT)polygenic risk scores (HLD / PRS), each HLD / PRS being based on the number of risk alleles at a defined set of genomic loci associated with elevated blood levels of total cholesterol, LDL cholesterol, HDL cholesterol, triglycerides and / or lipoprotein(a). In particular embodiments, each genetic variant of the panel is mapped to at least one lipid trait (for example, total cholesterol, LDL cholesterol, HDL cholesterol, triglycerides or lipoprotein(a)), and trait-specific PRSs are generated which can be used singly or jointly to infer the subject’s overall genetic susceptibility to hyperlipidaemia.

[0098] In some embodiments, “elevated” lipid levels for the purposes of the present invention are defined as total cholesterol > 5.2 mmol / L, LDL cholesterol > 3.4 mmol / L, triglycerides > 1.7 mmol / L, Lp(a) > 75 nmol / L, and “low” HDL cholesterol is defined as HDL cholesterol < 1 mmol / L, consistent with clinical reference standards used in international guidelines for dyslipidaemia risk stratification and the disclosed methods are adaptable to any such standards (e.g., European Society of Cardiology (ESC) / European Association of Atherosclerosis (EAS) 2023 Guidelines or WHO dyslipidaemia frameworks) without departing from the scope of the invention.

[0099] In exemplary embodiments, for each variant in the panel, the subject’s genotype is encoded as a numeric genotype value according to the number of risk alleles present, wherein a genotype containing no copies of the risk allele is assigned a value of 0, a genotype containing one copy of the risk allele is assigned a value of 1, and a genotype containing two copies of the risk allele is assigned a value of 2; alternative or weighted coding schemes may also be used.

[0100] The genetic risk score may be used to classify the subject into one of a plurality of risk categories (e.g., low risk, moderate risk, high risk, or very high risk) in developing a lipid trait. In certain embodiments, the subject's risk is stratified for each of (1) low high-density lipoprotein (HDL) cholesterol, (2) elevated low-density lipoprotein (LDL) cholesterol, (3) elevated total cholesterol, (4) elevated triglycerides, and (5) elevated lipoprotein(a), based on the genetic risk score, according to the following non-limiting thresholds:Low HDL cholesterol < 1 mmol / L<>High Total Cholesterol > 5.2 mmol / L21232214-40001 (PCT)<>High LDL Cholesterol > 3.4 mmol / L<>High Triglyceride > 1.7 mmol / L<>High Lp(a) > 75 nmol / L<>

[0101] Table 5 Thresholds for stratifying risk of developing elevated lipid levels.

[0102] Submodule 2: Risk Score for Monogenic Familial Hypercholesterolemia.

[0103] In some embodiments, the invention is directed to a method for detecting familial hypercholesterolemia in a subject. Familial Hypercholesterolemia (FH) is a monogenic, typically autosomal dominant disorder caused by single mutations in a FH-associated gene. Such FH-related genes include, without limitation, APOB, APOE, LDLR, LDLRAP1, PCSK9, ABCG5, ABCG8, STPA1 and LIPA.22232214-40001 (PCT)

[0104] The method comprises obtaining a biological sample from the subject, isolating nucleic acid from the sample, and detecting, or calling, in the nucleic acid, one or more pathogenic or likely pathogenic variants associated with FH. In example embodiments, the called variants comprise pathogenic or likely pathogenic variants in one or more associated genes selected from a group consisting of APOB, APOE, LDLR, LDLRAP1, PCSK9, ABCG5, ABCG8, STPA1 and LIPA. FIG. 5A-C summarizes a list of the target regions to be detected in each gene.

[0105] In certain embodiments, each called variant is evaluated for pathogenicity according to criteria defined by, or derived from, the American College of Medical Genetics and Genomics / Association for Molecular Pathology (ACMG / AMP) guidelines.

[0106] In example embodiments, the called variants corresponding to each associated gene of FH are interpreted according to the criteria set forth in Table 3A. Each criterion is identified as an alphanumeric code. In certain embodiments, PVS1 refers to Pathogenic Very Strong 1; PSI, Pathogenic Strong 1; PS2, Pathogenic Strong 2; PS3, Pathogenic Strong 3; PS4, Pathogenic Strong 4; PM1, Pathogenic Moderate 1; PM2, Pathogenic Moderate 2; PM3, Pathogenic Moderate 3; PM4, Pathogenic Moderate 4; PM5, Pathogenic Moderate 5; PM6, Pathogenic Moderate 6; PPI, Pathogenic Supporting 1; PP2, Pathogenic Supporting 2; PP3, Pathogenic Supporting 3; and PP4, Pathogenic Supporting. In further embodiments, BAI refers to Benign stand-alone 1; BS1, Benign Strong 1; BS2, Benign Strong 2; BS3, Benign Strong 3; BS4, Benign Strong 4; BP1, Benign supporting 1; BP2, Benign supporting 2; BP3, Benign supporting 3; BP4, Benign supporting 4; BP5, Benign supporting 5; and BP7, Benign supporting 7. Each criterion comprises one or more evidentiary requirements indicative of pathogenicity or benignity of a genetic variant.

[0107] In certain embodiments, the numerical weights assigned to the pathogenicity criteria for a given variant are summed to generate a cumulative FH pathogenicity score. The FH pathogenicity score is used to classify each variant into a pathogenicity category selected from benign, likely benign, VUS, likely pathogenic, or pathogenic, based on predefined score thresholds. In example embodiments, FH is detected, classified, and diagnosed based at least in part on the cumulative FH pathogenicity score obtained from variant calling and scoring, based on the score threshold in Table 3C.

[0108] For example, a variant detected in a gene associated with FH, such as APOB, that meets or exceeds a predefined pathogenicity score threshold in Table 3C is indicative of FH associated with that gene.

[0109] In certain embodiments, the method further comprises generating recommendations for subjects classified as high-risk or very high-risk, the recommendations comprising: (i) lifestyle interventions, including heart-healthy diets, regular exercise, weight management, and smoking 23232214-40001 (PCT)cessation; (ii) regular lipid profile monitoring; and (iii) pharmacotherapy as clinically appropriate. For subjects with risks of pathologically low HDL cholesterol, in a clinically significant context, recommendations may include lifestyle intervention, such as dietary changes, smoking cessation, exercise, and weight control. For an elevated genetic risk for other lipid phenotypes, pharmacotherapy recommendations may include, without limitation; for increased LDL cholesterol, total cholesterol and / or FH, HMG-CoA reductase inhibitors (statins, e.g. atorvastatin, fluvastatin, lovastatin, pitavastatin, pravastatin, rosuvastatin, and simvastatin), cholesterol absorption inhibitors (e.g. ezetimibe), PCSK9 inhibitors (e.g. alirocumab, evolocumab), citrate lyase inhibitors (e.g., bempedoic acid alone or in combination with ezetimibe), bile acid sequestrants (e.g., cholestyramine, colesevelam, or colestipol), niacin, or fixed-dose combinations thereof (e.g., ezetimibe with a statin, such as simvastatin); for elevated triglyceride levels, fibric acid derivatives (fibrates, e.g. fenofibrate or gemfibrozil), omega-3 fatty acid derivatives, or niacin formulations; for elevated lipoprotein(a), therapeutic strategies may include consideration of PCSK9 inhibitors or other emerging lipid-targeted treatments clinically appropriate to reduce Lp(a) levels. Quantification of lipid trait-specific polygenic risk scores identifies the predominant dyslipidemia pathophysiology in a subject (e.g., LDL cholesterol elevation, hypertriglyceridemia, lipoprotein(a) dysregulation), thereby enabling mechanism-matched lipid-lowering therapy prioritization to achieve early dyslipidemia control and mitigate associated vascular damage, organ dysfunction, and cardiovascular-kidney complications.

[0110] Module 5, Disease Progression and Complication Risk Stratification.

[0111] In some embodiments, the invention is directed to a method for predicting the risk of developing one or more cardiovascular-kidney-metabolic (CKM) complications associated with diabetes, such as beta-cell failure, ischemic heart disease, chronic heart failure, stroke, chronic kidney disease (or end-stage kidney disease / “ESKD”), and cancers (e.g. site cancers), in a subject. In certain embodiments, the method comprises generating a complication-specific risk value for each complication. In specific embodiments, generating the risk value comprises generating a respective genetic risk score. In some embodiments, generating the genetic risk score comprises: (i) obtaining genotyping data or sequence data for one or more predefined genetic variants or target regions relevant to one or more diabetic complication; and (ii) calculating the event-specific genetic risk score from the genotyping data.

[0112] In some embodiments, generating the genetic risk score further comprises assigning a score to each variant or target region. In example embodiments, each variant corresponds to a respective complication. The score for each variant is any predetermined numerical value (e.g., 1, 2, 3, 4, 5 or another suitable value) that is based, at least in part, on the genotype observed at that 24232214-40001 (PCT)variant. In particular embodiments, the event-specific genetic risk score is determined as a total or aggregate score based on the assigned scores for the genotypes observed at the plurality of variants corresponding to each complication in the subject’s data.

[0113] In particular embodiments, the method comprises generating a genetic risk score, wherein the genetic risk score is obtained by (i) obtaining genotyping data or sequence data from at least a subset of the genetic variants or target regions in FIG. 6A-E; and (ii) calculating the risk score therefrom, wherein FIG. 6A-E comprises a list of genetic variants associated with one or more diabetic complications selected from beta-cell failure, ischemic heart disease, chronic heart failure (“CHF”), stroke (“STK), chronic kidney disease (or end-stage kidney disease) (“eGFR (ESKD)”; eGFR=estimated Glomerular Filtration Rate), and cancer. In particular embodiments, a score is assigned at each variant based on the number of risk alleles present — specifically, assigning a score of 0 for a homozygous non-risk genotype, 1 for a heterozygous genotype, and 2 for a homozygous risk genotype. The polygenetic risk score for a given complication (“complication PRS”) is calculated as a sum or other aggregate (for example, a weighted sum or scaled sum) of the assigned scores across the plurality of genetic variants associated with that specific outcome. It will be appreciated that the number of variants, their genomic coordinates, risk alleles and coding scheme may be varied according to, for example, the types of complications or populations, without departing from the scope of the invention.

[0114] The complication PRS may be used to classify the subject into one of a plurality of risk categories (e.g., low risk, moderate risk, high risk, or very high risk) in developing a particular complication. In certain embodiments, the subject’s risk is stratified for each of beta-cell failure, ischemic heart disease, chronic heart failure, stroke, and chronic kidney disease (or end-stage kidney disease), based on the complication PRS, according to the following non-limiting thresholds:Beta-Cell Failure / Disease progression to insulin requirement<>Ischemic heart disease<25232214-40001 (PCT)>Stroke<>Chronic heart failure / Myocardial infarction<>Chronic kidney disease / End-stage kidney disease<>

[0115] Table 6 Thresholds for stratifying risk of developing diabetes complications.

[0116] In certain embodiments, the method further comprises generating recommendations for subjects classified as high-risk or very high-risk according to their complication PRS. The recommendations may comprise early initiation of organ-protective agents tailored to the predicted complication profile as clinically appropriate: for beta-cell failure risk, drugs that expand or proliferate beta-cells (e.g. cellular replacement therapy, MENIN inhibitor) or reduce cellular apoptosis (e.g. immunomodulating therapy such as anti-CD3 monoclonal antibody, as well as early initiation of glucose lowering drugs to reduce glucotoxicity without over stimulating beta-cells and causing hypoglycemia, such as metformin, glucokinase activator (e.g. dorzagliatin), SGLT2 inhibitors (sodium -glucose co-transporter 2 inhibitors, e.g. dapagliflozin, empagliflozin), glucagon like peptide 1 (GLP-1) receptor agonists (e.g. semaglutide), GLP1-1 and polypeptide co-agonist (e.g. tirzepatide, mazdutide, retatrutide), dipeptidyl peptidase-4 (DPP-4) inhibitors (e.g. sitagliptin), or thiazolidinediones (e.g. pioglitazone); for ischemic heart disease risk, statins (e.g.26232214-40001 (PCT)atorvastatin), ezetimibe, PCSK9 inhibitors (e.g. evolocumab), aspirin, ACE inhibitors, Angiotensin II Receptor Blockers (ARBs), SGLT2 inhibitors, GLP-1 receptor agonists; for stroke risk, antiplatelet therapy (e.g. aspirin, clopidogrel, cilostazol), statins, ACE inhibitors, ARBs, GLP-1 receptor agonists, GLP1 and polypeptide co-agonists, SGLT2 inhibitors for vascular protection; for chronic heart failure risk, SGLT2 inhibitors (e.g. dapagliflozin), GLP-1 receptor agonists, GLP1 and polypeptide co-agonists, mineralocorticoid receptor antagonists (MRA, e.g. spironolactone), non-steroidal MRA, aldosterone synthase inhibitor (ASi, e.g. baxdrostat, vicadrostat) and beta-blockers (e.g. carvedilol); and for end-stage kidney disease risk, SGLT2 inhibitors, mineralocorticoid receptor antagonists (MRA, e.g. spironolactone), non-steroidal MRA (e.g. finerenone), ACE inhibitors, ARBs, ASi, GLP-1 receptor agonists, GLP1 and polypeptide co-agonists, and statins to mitigate proteinuria and glomerular damage, alone or in any combination consistent with contemporary CKM guidelines.

[0117] Module 6, Pharmacogenomic Profiling for Diabetes and Diabetes-Related Disorders.

[0118] In some embodiments, the invention is directed to a method for pharmacogenomic profiling of a subject for drugs used in the treatment of diabetes and cardiovascular-kidney-metabolic (CKM) disorders. The method comprises obtaining genetic profile data from the subject and detecting variants in one or more genes implicated in the pharmacokinetics and / or pharmacodynamics of therapeutics suitable for managing type 1 diabetes (T1D), type 2 diabetes (T2D), maturity-onset diabetes of the young (MODY), hyperlipidaemia, cardiovascular disease (including heart failure, ischaemic heart disease / coronary heart disease and myocardial infarction), stroke, and chronic kidney disease / end-stage kidney disease (ESKD). In certain embodiments, the drugs include, without limitation, statins, clopidogrel, allopurinol, carbamazepine, warfarin, metformin, sulfonylureas, dipeptidyl peptidase-4(DPP-4) inhibitors, glucagon-like peptide- 1 (GLP-1) receptor agonists and glucokinase activators, and the method comprises predicting the responses of the subject to the drugs based at least in part on the detected variants. In some embodiments, the method further comprises generating a treatment recommendation for the subject, including selection of one or more drugs and / or dosage regimens tailored to the predicted drug-response, optionally with suggestions for alternative agents, dose adjustment or enhanced safety monitoring.

[0119] In certain embodiments, the method comprises: (i) detecting one or more sequence variants corresponding to genes and loci summarized in PIG. 7A-F (including, but not limited to, SLCO1B1, ABCG2, CYP2C9, HLA-B, HLA-A, CYP4F2, VKORC1, CYP2C19, GLP1R, ARRB1, SLC22A1, SLC22A2, SLC6A4, SLC29A4, and other loci specified for statins, clopidogrel, allopurinol, sulfonylureas, GLP-1 receptor agonists, DPP-4 inhibitors,27232214-40001 (PCT)carbamazepine, metformin, glucokinase activators); (ii) assigning, for each gene, a diplotype -level functional status (e.g., normal function, decreased function, poor function, no function, function uncertain or unknown) according to predefined genotype-function translation tables; and (iii) applying one or more drug-specific interpretation rules to output a recommendation comprising at least one of: selection of a preferred drug within a class, adjustment of initial or maintenance dose, enhanced safety monitoring, or selection of a non-genotype-sensitive alternative.28232214-40001 (PCT)29232214-40001 (PCT)30232214-40001 (PCT)232214-40001 (PCT)32232214-40001 (PCT)Table 17. Summary of Pharmacogenomic Profiling Methods.33232214-40001 (PCT)

[0120] Table 7. SLCO1B1 recommendations with intensity and statin dose stratified by SLCO1B1 phenotype. SAMS=Statin-Associated Muscle Symptoms, which refer to muscle-related complaints like pain, stiffness, tenderness, or weakness caused or exacerbated by taking statin medications.34232214-40001 (PCT) < >

[0121] Table 8. ABCG2 recommendations with intensity and statin dose stratified by ABCG2 phenotype.< >< >0122] Table 9. CYP2C9 recommendations with intensity and statin dose stratified by CYP2C9 phenotype. AS=Activity Score.35232214-40001 (PCT)

[0123] Table 10. Allopurinol recommendation stratified by HLA-B*58:01 phenotype.""""""36232214-40001 (PCT)""

[0124] Table 11. Carbamazepine recommendation stratified by HLA-B* 15:02 and HLA-A*31:01 phenotype.37232214-40001 (PCT)

[0125] Table. 12. Warfarin Dosing Formula.

[0126] The results from the genotype results are supplied to warfarin dose formula in Table 12, which may be alternatively expressed in the formula below:5.6044 + (—0.2614 x Age in decades) + (0.0087 x Height in cm) + (0.0128 x Weight in kg) + VKORC1 term +CYP2C9 term + Race term + (1.1816 x Enzyme inducer) + (—0.5503 x Amiodarone). CYP4F2 genotype increases the weekly warfarin dose by 5-10%.

[0128] For example, consider a 60-year-old Black male (age=6 decades), height=175 cm, weight=80 kg, VKORC1 C / T (-0.8677), CYP2C9 *l / *2 intermediate metabolizer (- 0.5211), no enzyme inducers (0), no amiodarone (0), his weekly dose will be:(5.6044-1.5684+1.5225+1.024-0.8677-0.5211-0.276+0+0)2=24.18 mg38232214-40001 (PCT)39232214-40001 (PCT)

[0129] Table 13. DPP4-i recommendation stratified by CDKAL1 and GLP1R phenotype.40232214-40001 (PCT)

[0130] Table 14. GLP-1 drug response based on GLP1R and ARRB1 phenotype.

[0131] Table 15. Sulfonylurea response prediction based on CYP2C 19 phenotype.0132] Table 16. Phenotype interpretation corresponding to FIG. 26A-BO.

[0133] In certain embodiments, the methods further comprise generating one or more pharmacogenomic recommendations for the subject based at least in part on the pharmacogenomic profding results.

[0134] Integrated Diabetes Profiling and Precision Medicine Platform and Artificial Intelligence / Machine Learning Features41232214-40001 (PCT)

[0135] In various embodiments, the present invention provides methods, systems, platforms, kits and computer-implemented processes for predicting, diagnosing, classifying and profding diabetes, prediabetes and diabetes-related cardiometabolic disorders in a subject, and for performing precision treatment selection and clinical decision-making. In certain embodiments, the invention provides an integrated, multi-method, multi-functional system, platform, kit or computer- implemented method wherein two or more modules — selected from (1) Diabetes Risk Prediction and Stratification; (2) Monogenic Diabetes Subtype Detection and Classification; (3) Type 2 Diabetes Pathophysiological Pathway Profiling; (4) Hyperlipidaemia and Dyslipidaemia Risk Assessment; (5) Disease Progression and Complication Risk Stratification; and (6) Pharmacogenomic Profiling for Diabetes and Diabetes-Related Disorders — are combined to generate individualized diabetes and cardiometabolic health profiles.

[0136] In particular embodiments, the method generates an individualized diabetes profiling report that consolidates outputs from all six modules (l)-(6), The reporting may comprise the testing results across modules, alongside precision recommendations, including pharmacotherapies, dosage regimens, lifestyle interventions, monitoring protocols, and clinical trial eligibility. In example embodiments, the individualized diabetes profiling report may take form of a portal

[0137] By way of non-limiting example and as illustrated in Figure 27, the invention encompasses an integrated workflow wherein a biological sample collected from a subject using a laboratory kit is analyzed to generate genetic profile data, together with the subject's non-genetic parameters (e.g., continuous glucose monitoring (CGM) data, clinical metrics, lifestyle factors, is uploaded to a web-based platform for processing by one or more of modules ( 1 )-(6) . The platform produces individualized reports summarizing risk stratification, pathway susceptibilities, monogenic variants, pharmacogenomic profiles, and complication predictions, along with precision recommendations encompassing personalized treatment selections, dosage regimens, lifestyle interventions, monitoring schedules, and clinical trial matching. The generated individualized reports are delivered to healthcare providers to allow continuous monitoring and dynamic decision support, enabling value-based care delivery. The data further supports development of real-world evidence databases to complement results from randomized clinical trials (RCTs) for evaluation of safety, tolerability, clinical effectiveness and cost-effectiveness of drugs, devices, diagnostics, digital platforms and care programs, and for pharmaceutical industry applications such as patient stratification for clinical trials, thereby promoting a broader precision medicine ecosystem.

[0138] In particular embodiments, the invention provides a web-based platform incorporating artificial intelligence (Al) and / or machine learning (ML) capabilities, as illustrated in Figure 2842232214-40001 (PCT)wherein multimodal input data — including genetic and multiomic profiles (e.g. proteomics, metabolomics, non-coding RNA, telomeres) from the disclosed laboratory kits, biomarkers (e.g. autoantibodies, C peptide and its derives, pro-BNP, LpA), continuous physiological monitoring metrics (e.g., glucose levels from CGM devices), and personal health data (e.g., patient non-genetic parameters, patient reported outcomes / experiences such as negative emotions, quality of life, self-care practices, treatment persistence) — undergo automated feature engineering (e.g. polygenic risk score derivation via risk allele summation, ACMG / AMP variant pathogenicity classifications) to enable model training, fine-tuning, or inference using established ML architectures (e.g., latent class analysis, tree-based ensembles such as XGBoost or random forests, feed-forward / deep neural networks, logistic regression ensembles, or gradient boosting machines), optionally employing commercial / open-source pre-trained models (e.g., XGBoost library, DeepSeek variants, or GPT-4o via API) adapted from expansive longitudinal biobanked datasets (encompassing demographics, multiomics, clinical trajectories, adverse outcomes, and interventions) for generating individualized reports, which may further encompass probabilistic outputs, temporal progression simulations (e.g beta-cell decline trajectories), and counterfactual intervention analyses (e.g. hazard ratio shifts and incremental cost-effectiveness analysis (ICER) for various scenarios, e.g. SGLT2i versus GLP-1RA, single versus dual versus triple blockers of renin-angiotensin system (ACE inhibitors / ARB, nsMRA, ASi), early versus late interventions, treatment to single versus multiple targets, intensified versus standard treatment, additional behavioral care versus usual care, data-driven care versus usual care, publicly-funded care versus public-private partnerships). The platform further provides a subject / provider portal equipped with conversational Al interfaces (e.g., generative large language models (LLMs), retrieval-augmented generation (RAG) systems, or agentic frameworks interfacing commercial APIs), which synthesize responses to natural language queries (e.g. “What is the recommended therapy for my profile?” or “Interpret my risk results?”) based on outputs derived from the six modules (l)-(6), employing human-engineered prompt templates, human-in-the-loop oversight (e.g. clinician approval for high-stakes recommendations), and safety guardrails (e.g. hallucination detection via cross-verification), thereby delivering scalable, real-time precision decision support to subjects, caregivers, healthcare providers, healthcare administrators, or payors.

[0139] Kits

[0140] In certain embodiments, the invention provides kits configured for generating genetic profile data suitable for analysis by one or more, or all, of modules ( l)-(6) . Such kit may contain at least one or more of: (a) sample collection kits for biological samples, comprising a sample collection device (e.g. collection tubes), pre-printed labels, stabilizing buffers or preservatives,43232214-40001 (PCT)and / or instructions for use; and (b) laboratory kits for genetic analysis, comprising nucleic acid extraction reagents and buffers, nucleic acid amplification / sequencing reagents (e.g. primers, probes, master mixes etc), and / or instruction for use.

[0141] In certain embodiments, the invention provides integrated laboratory kits configured for sample collection, genetic analysis, computational processing, and report generation for individualized diabetes and cardiometabolic risk profiles and recommendations., wherein such kits comprise: (a) sample collection kits for biological samples, comprising a sample collection device (e.g. collection tubes), pre-printed labels, stabilizing buffers or preservatives, and / or instructions for use; (b) laboratory kits for genetic analysis, comprising nucleic acid extraction reagents and buffers, nucleic acid amplification / sequencing reagents (e.g. primers, probes, master mixes etc), and / or instruction for use; (c) software components for analysis and reporting comprising computer-executable instructions stored on non-transitory computer-readable media (such as USB drives, optical disks, and downloadable software) for variant calling, quality filtering, and multi-module processing for modules (l)-(6) to perform disease risk stratification, subtype classification, complication risk prediction, pharmacogenomic predictions, and treatment recommendations., together with artificial intelligence (AI) / machine learning (ML) models for composite risk calibration and personalized recommendations; and (4) reporting system components comprising digital templates and clinician / patient interfaces for generating consolidated reports summarizing per-module results, risk categories, and treatment recommendations for the subjects.

[0142] NUMBERED EMBODIMENT

[0143] Embodiment 1. A method for predicting, in a subject, a risk of developing at least one type of diabetes selected from type 1 diabetes (T1D), type 2 diabetes (T2D) and gestational diabetes mellitus (GDM) within a defined time period, the method comprising:obtaining genotyping data or sequence data for the subject comprising at least a subset of genetic variants or target regions corresponding to a diabetes type, the genetic variants or target regions being selected from a panel of variants associated with one or more of T1D, T2D and GDM as illustrated in FIG. 1A-E; andcalculating, from the genotyping data or sequence data, a type-specific genetic risk score for the diabetes type, the genetic risk score being determined as an aggregate of variant-level scores assigned to genotypes observed at the genetic variants or target regions corresponding to the diabetes type, and wherein optionally the type-specific genetic risk score is further integrated with numerical assessment with one or more non-genetic parameters selected from age, sex, height, body weight, birth weight, waist circumference, family history of diabetes, smoking 44232214-40001 (PCT)status, history of GDM, blood glucose measurements, blood pressure, exercise habits, or combinations thereof.

[0144] Embodiment 2. The method of embodiment 1, wherein the defined time period is 10 years.

[0145] Embodiment 3. The method of embodiment 1 or embodiment 2, wherein calculating the type-specific genetic risk score comprises, for each genetic variant or target region corresponding to the diabetes type:assigning a score of 0 when the subject has a homozygous non-risk genotype, assigning a score of 1 when the subject has a heterozygous genotype,assigning a score of 2 when the subject has a homozygous risk genotype, and determining the genetic risk score as a sum of the assigned scores across the plurality of genetic variants or target regions corresponding to the diabetes type.

[0146] Embodiment 4. The method of any preceding embodiment, wherein the aggregate is a weighted sum or scaled sum of the variant-level scores.

[0147] Embodiment 5. The method of any preceding embodiment, wherein the panel of genetic variants comprises loci associated with T1D, T2D and GDM as defined in FIG. 1A-E.

[0148] Embodiment 6. The method of any preceding embodiment, wherein the genotyping data or sequence data is obtained using a nucleic acid sequencing assay selected from next-generation sequencing, whole-genome sequencing, whole-exome sequencing, targeted sequencing, minisequencing, microarray -based genotyping, or combinations thereof.

[0149] Embodiment 7. The method of any preceding embodiment, further comprising classifying the subject into one of a plurality of risk categories for the diabetes type, the plurality of risk categories comprising low risk, moderate risk, high risk and very high risk, based on the type-specific genetic risk score and predefined score thresholds.

[0150] Embodiment 8. The method of embodiment 7, wherein for T1D, the subject is classified as:Low Risk when the genetic risk score is =£ 107,Moderate Risk when the genetic risk score is 108 to 112,High Risk when the genetic risk score is 113 to 117, andVery High Risk when the genetic risk score is > 118.

[0151] Embodiment 9. The method of embodiment 7, wherein for T2D, the subject is assigned a T2D genetic risk value (T2D / GRV) as follows:a T2D / GRV of 2 when the genetic risk score is =£ 278,a T2D / GRV of 4 when the genetic risk score is 279 to 284,45232214-40001 (PCT)a T2D / GRV of 7 when the genetic risk score is 285 to 291, anda T2D / GRV of 9 when the genetic risk score is > 292.

[0152] Embodiment 10. The method of embodiment 7, wherein for GDM, the subject is classified as:Low Risk when the genetic risk score is =£ 4,Moderate Risk when the genetic risk score is 5,High Risk when the genetic risk score is 6 to 7, andVery High Risk when the genetic risk score is 2= 8.

[0153] Embodiment 11. The method of embodiment 10, further comprising:generating, for the subject, a T2D diabetes risk value that represents a probability of developing the diabetes type within the defined time period by integrating the T2D / GRV with one or more non-genetic parameters selected from age, sex, height, body weight, birth weight, waist circumference, family history of diabetes, smoking status, history of GDM, blood glucose measurements, blood pressure, exercise habits, or combinations thereof.

[0154] Embodiment 12. The method of embodiment 11, wherein the T2D diabetes risk value is derived using a logistic function applied to a linear combination of the T2D / GRV and the one or more non-genetic parameters.

[0155] Embodiment 13. The method of any preceding embodiment, further comprising generating one or more recommendations for the subject based at least in part on the type-specific genetic risk score or the T2D diabetes risk value, the recommendations comprising at least one of:(a) a recommendation for frequency or modality of glycaemic testing,(b) a recommendation for lifestyle modification, and(c) a recommendation for pharmacotherapy initiation or adjustment.

[0156] Embodiment 14. The method of embodiment 13, wherein the recommendation for glycaemic testing comprises at least one of fasting plasma glucose testing, oral glucose tolerance testing and pregnancy-specific glucose testing at defined intervals.

[0157] Embodiment 15. The method of embodiment 14, wherein the recommendation for lifestyle modification comprises at least one of targeted dietary changes, physical activity programs and weight-management interventions.

[0158] Embodiment 16. The method of embodiment 15, wherein the recommendation for pharmacotherapy initiation or adjustment is provided for subjects classified as high-risk or very -high-risk of developing the diabetes type.46232214-40001 (PCT)

[0159] Embodiment 17. A non-transitory computer- readable medium storing instructions which, when executed by one or more processors, cause the one or more processors to perform the method of any one of embodiments 1-16.

[0160] Embodiment 18. A system comprising at least one processor and at least one memory storing instructions which, when executed by the at least one processor, cause the system to perform the method of any one of embodiments 1-17.

[0161] Embodiment 19. A method for detecting a monogenic form of diabetes in a subject, the method comprising:obtaining a biological sample from the subject;isolating nucleic acid from the biological sample;detecting, in the nucleic acid, one or more variants in one or more genes associated with monogenic diabetes; andclassifying at least one detected variant as pathogenic or likely pathogenic by:(i) evaluating the at least one detected variant based on pathogenicity phenotypes;(ii) assigning, to each pathogenicity phenotypes, a numerical weight corresponding to its evidentiary strength;(iii) summing the assigned numerical weights to generate a monogenic diabetes pathogenicity score for the variant; and(iv) classifying the variant as pathogenic or likely pathogenic when the monogenic diabetes pathogenicity score meets or exceeds a predefined threshold, and wherein optionally the pathway-specific PRS is further integrated with numerical assessment with one or more non-genetic parameters selected from age, sex, height, body weight, birth weight, waist circumference, family history of diabetes, smoking status, history of GDM, blood glucose measurements, blood pressure, exercise habits, or combinations thereof.

[0162] Embodiment 20. The method of embodiment 19, wherein the one or more genes associated with monogenic diabetes are selected from the group consisting of ABCC8, DCAF17, GATA6, GCK, HNF1A, HNF1B, HNF4A, INS, INSR, NEURODI, PCBD1, PLIN1, PPARG, SLC29A3, TRMT10A, WFS1, ZBTB20, and KCNJ11.

[0163] Embodiment 21. The method of embodiment 19 or embodiment 20, wherein the one or more genes are selected from the group consisting of HNF1A, HNF1B, HNF4A, GCK, GATA6, NEURODI, PLIN1, PPARG, WFS1, KCNJ11, and ABCC8, and the one or more variants are detected at target regions corresponding to each gene as illustrated in FIG. 2A-C.

[0164] Embodiment 22. The method of any one of embodiments 19-21, wherein detecting the one or more variants comprises performing nucleic acid sequencing selected from next- 47232214-40001 (PCT)generation sequencing, whole-genome sequencing, whole-exome sequencing, targeted sequencing panels, minisequencing assays, or microarray-based genotyping.

[0165] Embodiment 23. The method of any one of embodiments 19-21, further comprising diagnosing the subject with a monogenic form of diabetes when at least one variant in at least one gene associated with monogenic diabetes is classified as pathogenic or likely pathogenic.

[0166] Embodiment 24. The method of embodiment 23, further comprising classifying the monogenic diabetes into a gene-specific subtype based on the gene containing the classified pathogenic or likely pathogenic variant.

[0167] Embodiment 25. The method of any one of embodiments 19-24, further comprising generating one or more precision treatment recommendations for the subject based at least in part on the classified pathogenic or likely pathogenic variant(s), wherein the recommendations comprise subtype-specific pharmacotherapy.

[0168] Embodiment 26. The method of embodiment 25, wherein for a variant classified as pathogenic or likely pathogenic in GCK gene, the recommendation comprises consideration of glucokinase activators.

[0169] Embodiment 27. The method of embodiment 26, wherein for a variant classified as pathogenic or likely pathogenic in the HNF1A or HNF4A gene, the recommendation comprises sulfonylureas selected from glimepiride, glipizide, gliclazide, chlorpropamide, and tolbutamide.

[0170] Embodiment 28. The method of any one of embodiments 19-27, wherein additional recommendations applicable across monogenic subtypes comprise one or more of structured lifestyle changes, periodic glycemic monitoring, and referral to specialized care.

[0171] Embodiment 29. A non-transitory computer-readable medium storing instructions which, when executed by one or more processors, cause the one or more processors to perform the variant classification steps of any one of embodiments 19-28.

[0172] Embodiment 30. A system comprising at least one processor and at least one memory storing instructions which, when executed by the at least one processor, cause the system to perform the variant classification steps of any one of embodiments 19-28.

[0173] Embodiment 31. A method for characterizing genetic susceptibility to one or more pathophysiological pathways implicated in type 2 diabetes (T2D) in a subject, the method comprising:obtaining genetic profile data for the subject, the genetic profile data comprising genotypes at a plurality of genetic variants in one or more predefined sets of loci associated with T2D pathophysiological pathways; and48232214-40001 (PCT)calculating, from the genetic profile data, one or more pathway-specific polygenic risk scores (PRS) for the one or more T2D pathophysiological pathways, wherein each pathwayspecific PRS is determined as an aggregate of risk allele dosages coded across the plurality of genetic variants associated with that pathway, classifying the variant as pathogenic or likely pathogenic when the monogenic diabetes pathogenicity score meets or exceeds a predefined threshold, and wherein optionally the monogenic diabetes pathogenicity score is further integrated with numerical assessment with one or more non-genetic parameters selected from age, sex, height, body weight, birth weight, waist circumference, family history of diabetes, smoking status, history of GDM, blood glucose measurements, blood pressure, exercise habits, or combinations thereof.

[0174] Embodiment 32. The method of embodiment 31, wherein the one or more T2D pathophysiological pathways are selected from the group consisting of ALP negative regulation, beta-cell function (Beta Cell 1 subpathway, Beta Cell 2 subpathway), bilirubin metabolism, cholesterol metabolism, hyperinsulinemia, lipodystrophy (Lipodystrophy 1 subpathway, Lipodystrophy 2 subpathway), liver-lipid metabolism, obesity, proinsulin secretion, Maturity-Onset Diabetes of the Young (MODY) and monogenic diabetes genes, and Sex Hormone-Binding Globulin-Lipoprotein(a) (SHBG-Lp(a)) regulation.

[0175] Embodiment 33. The method of embodiment 31 or embodiment 32, wherein the plurality of genetic variants associated with the one or more T2D pathophysiological pathways corresponds to the reference variant panel illustrated in PIG. 3A-D.

[0176] Embodiment 34. The method of any one of embodiments 31-33, wherein coding each genetic variant comprises assigning:a weight of 0 for homozygous non-risk genotype,a weight of 1 for heterozygous genotype, anda weight of 2 for homozygous risk genotype;and the pathway-specific PRS is determined as a sum of the risk allele weights across the genetic variants associated with that pathway.

[0177] Embodiment 35. The method of any one of embodiments 31-34, further comprising normalizing each pathway-specific PRS to generate a normalized PRS by a normalizing technique.

[0178] Embodiment 36. The method of embodiment 35, wherein normalizing normalization comprises z-score normalization, direct percentile ranking, logistic transformation, or machine-learning calibration.49232214-40001 (PCT)

[0179] Embodiment 37. The method of any one of embodiments 31-36, further comprising classifying the subject into one or more risk categories for each T2D pathophysiological pathway based on the corresponding pathway-specific PRS or normalized PRS and predefined thresholds.

[0180] Embodiment 38. The method of any one of embodiments 31-37, wherein the aggregate for the pathway-specific PRS comprises a weighted sum of the risk allele weights.

[0181] Embodiment 39. The method of any one of embodiments 31-38, further comprising generating one or more pathway-tailored recommendations for the subject based at least in part on the one or more pathway-specific PRS, the recommendations comprising one or more of lifestyle interventions and pharmacotherapy.

[0182] Embodiment 40. The method of embodiment 39, wherein the lifestyle interventions comprise one or more of dietary changes, exercise, and weight management.

[0183] Embodiment 41. The method of any one of embodiments 31-40, wherein obtaining the genetic profile data comprises obtaining genotyping data or sequence data using nextgeneration sequencing, targeted sequencing panels, or microarray-based genotyping.

[0184] Embodiment 42. A non-transitory computer-readable medium storing instructions which, when executed by one or more processors, cause the one or more processors to perform the method of any one of embodiments 31-41.

[0185] Embodiment 43. A system comprising at least one processor and at least one memory storing instructions which, when executed by the at least one processor, cause the system to perform the method of any one of embodiments 31-41.

[0186] Embodiment 44. A method for assessing genetic risk of hyperlipidaemia in a subject, the method comprising:obtaining genotyping data for the subject at a plurality of genetic variants associated with lipid traits;calculating, from the genotyping data or sequence data, one or more lipid-specific polygenic risk scores (HLD / PRS), each HLD / PRS corresponding to a lipid phenotype selected from low high-density lipoprotein (HDL) cholesterol, elevated low-density lipoprotein (LDL) cholesterol, elevated total cholesterol, elevated triglycerides, and elevated lipoprotein(a); and classifying the subject as having an increased genetic risk for hyperlipidaemia based on at least one of the HLD / PRS, and wherein optionally the HLD / PRS is further integrated with numerical assessment with one or more non-genetic parameters selected from age, sex, height, body weight, birth weight, waist circumference, family history of diabetes, smoking status,50232214-40001 (PCT)history of GDM, blood glucose measurements, blood pressure, exercise habits, or combinations thereof.

[0187] Embodiment 45. The method of embodiment 44, wherein the lipid-specific HLD / PRS are calculated using a reference variant panel in which risk genotypes for total cholesterol, HDL cholesterol, LDL cholesterol, triglycerides and lipoprotein(a) are defined according to a panel of variants illustrated in FIG. 4A-J.

[0188] Embodiment 46. The method of embodiment 44 or embodiment 45, wherein calculating each HLD / PRS comprises, for each variant mapped to a given lipid phenotype: encoding the subject’s genotype as 0 when no risk allele is present, 1 when one risk allele is present, and 2 when two risk alleles are present; and summing the encoded values across all variants mapped to that lipid phenotype.

[0189] Embodiment 47. The method of any one of embodiments 44-46, wherein elevated lipid levels for the lipid phenotypes are defined as total cholesterol at least 5.2 mmol / L, LDL cholesterol at least 3.4 mmol / L, triglycerides at least 1.7 mmol / L, and lipoprotein(a) at least 75 nmol / L, and wherein low HDL cholesterol is defined as less than 1 mmol / L.

[0190] Embodiment 48. The method of any one of embodiments 44-47, wherein the low HDL cholesterol, elevated LDL cholesterol, elevated total cholesterol, elevated triglycerides, and elevated lipoprotein(a) are classified as Low Risk, Moderate Risk, High Risk, and Very High Risk, according to each lipid- specific HLD / PRS.

[0191] Embodiment 49. The method of any one of embodiments 44-48, further comprising a familial hypercholesterolaemia (FH), comprising the steps of:obtaining genotyping data from the subject;detecting, in the nucleic acid, one or more variants in one or more FH-associated genes selected from APOB, APOE, LDLR, LDLRAP1, PCSK9, ABCG5, ABCG8, STPA1 and LIPA in FIG. 5A-C;classifying each detected variant into a pathogenicity category based on predetermined pathogenicity phenotypes;assigning numerical weights to each pathogenicity phenotype; summing the assigned numerical weights to generate the cumulative FH pathogenicity score.

[0192] Embodiment 50. The method of embodiment 49, further comprising diagnosing FH in the subject when the cumulative FH pathogenicity score in at least one FH-associated gene meets or exceeds a predefined pathogenicity score threshold.51232214-40001 (PCT)

[0193] Embodiment 51. The method of any one of embodiments 44-50, wherein genotyping data used to calculate HLD / PRS and to detect FH-associated variants is obtained using next-generation sequencing, targeted sequencing panels, or microarray -based genotyping.

[0194] Embodiment 52. A non-transitory computer-readable medium storing instructions which, when executed by one or more processors, cause the one or more processors to perform the method of any one of embodiments 44-51.

[0195] Embodiment 53. A system comprising at least one processor and at least one memory storing instructions which, when executed by the at least one processor, cause the system to perform the method of any one of embodiments 44-51.

[0196] Embodiment 54. A method for predicting the risk of developing one or more cardiovascular-kidney -metabolic (CKM) complications associated with diabetes in a subject, the method comprising:obtaining genotyping data or sequence data for the subject at a plurality of predefined genetic variants or target regions associated with one or more diabetic complications selected from beta-cell failure, ischemic heart disease, chronic heart failure, stroke, chronic kidney disease, end-stage kidney disease, and cancer;calculating, from the genotyping data or sequence data, a complication-specific genetic risk score for each of the one or more diabetic complications; and generating, for each complication, a complication-specific risk value based at least in part on the corresponding genetic risk score, and wherein optionally the complication-specific risk value is further integrated with numerical assessment with one or more non-genetic parameters selected from age, sex, height, body weight, birth weight, waist circumference, family history of diabetes, smoking status, history of GDM, blood glucose measurements, blood pressure, exercise habits, or combinations thereof.

[0197] Embodiment 55. The method of embodiment 54, wherein calculating the complication-specific genetic risk score comprises, for each genetic variant or target region corresponding to a given complication:assigning a score of 0 when the subject has a homozygous non-risk genotype, a score of 1 when the subject has a heterozygous genotype, and a score of 2 when the subject has a homozygous risk genotype; anddetermining the complication-specific genetic risk score as a sum of the assigned scores across the plurality of genetic variants or target regions associated with that complication.52232214-40001 (PCT)

[0198] Embodiment 56. The method of embodiment 54 or embodiment 55, wherein the plurality of predefined genetic variants or target regions corresponds to a complication panel illustrated in FIG. 6A-E.

[0199] Embodiment 57. The method of any one of embodiments 54-56, wherein the complication-specific genetic risk score is calculated as a sum of the assigned scores across the plurality of genetic variants associated with that complication.

[0200] Embodiment 58. The method of any one of embodiments 54-57, further comprising classifying the subject into one of a plurality of risk categories for each complication, the plurality of risk categories comprising at least low risk, moderate risk, high risk, and very high risk, based on the corresponding complication-specific genetic risk score and predefined score thresholds.

[0201] Embodiment 59. The method of any one of embodiments 54-58, wherein separate complication-specific genetic risk scores are calculated for each of beta-cell failure, ischemic heart disease, chronic heart failure, stroke, and chronic kidney disease, end-stage kidney disease, and cancer, and the subject is independently classified into a risk category for each complication.

[0202] Embodiment 60. The method of any one of embodiments 54-59, further comprising generating one or more complication-specific recommendations for the subject based at least in part on the subject’s complication-specific genetic risk scores.

[0203] Embodiment 61. A non-transitory computer- readable medium storing instructions which, when executed by one or more processors, cause the one or more processors to perform the method of any one of embodiments 54-60.

[0204] Embodiment 62. A system comprising at least one processor and at least one memory storing instructions which, when executed by the at least one processor, cause the system to perform the method of any one of embodiments 54-60.

[0205] Embodiment 63. A method for pharmacogenomic profiling of a subject for drugs used in the treatment of diabetes and cardiovascular-kidney-metabolic (CKM) disorders, the method comprising:obtaining genetic profile data from the subject;detecting, in the genetic profile data, one or more sequence variants in one or more genes implicated in pharmacokinetics and / or pharmacodynamics of therapeutics suitable for managing one or more conditions selected from Type 1 diabetes, Type 2 diabetes, maturity-onset diabetes of the young (MODY), hyperlipidaemia, cardiovascular disease, stroke, and chronic kidney disease or end-stage renal disease; and53232214-40001 (PCT)predicting responses of the subject to one or more drugs used to treat the one or more conditions based at least in part on the detected variant, and wherein optionally the predicting responses of the subject is further integrated with numerical assessment with one or more non-genetic parameters selected from age, sex, height, body weight, birth weight, waist circumference, family history of diabetes, smoking status, history of GDM, blood glucose measurements, blood pressure, exercise habits, or combinations thereof.

[0206] Embodiment 64. The method of embodiment 63, wherein the one or more drugs comprise at least one of statins, clopidogrel, allopurinol, carbamazepine, warfarin, metformin, sulfonylureas, dipeptidyl peptidase-4 (DPP-4) inhibitors, glucagon-like peptide- 1 (GLP-1) receptor agonists, GLP-1 and polypeptide co-agonist, and glucokinase activators.

[0207] Embodiment 65. The method of embodiment 63 or embodiment 64, further comprising generating a treatment recommendation for the subject comprising selection of one or more drugs and / or dosage regimens tailored to the predicted drug responses, optionally including suggestions for alternative agents, dose adjustment, or enhanced safety monitoring.

[0208] Embodiment 66. The method of any one of embodiments 63-65, wherein detecting the one or more sequence variants comprises detecting variants in one or more genes selected from SLCO1B1, ABCG2, CYP2C9, HLA-B, HLA-A, CYP4F2, VKORC1, CYP2C19, GLP1R, ARRB1, SLC22A1, SLC22A2, SLC6A4, SLC29A4.

[0209] Embodiment 67. The method of any one of embodiments 63-66, further comprising, for each gene, assigning a diplotype-level functional status selected from normal function, decreased function, poor function, no function, or function uncertain or unknown.

[0210] Embodiment 68. The method of any one of embodiments 63-67, wherein for statin therapy, the method comprises:detecting one or more variants in SLCO1B1, ABCG2, and CYP2C9 in FIG. 8A-H, 10, and 12A-T, respectively;assigning a diplotype functional status for each gene; and,based on the diplotype functional status, recommending a specific statin and / or dose intensity.

[0211] Embodiment 69. The method of any one of embodiments 63-67, wherein for clopidogrel, the method comprises:detecting one or more variants in CYP2C19 in FIG. 25A-E;

[0212] assigning a diplotype functional status as normal, intermediate, poor, or increased function metabolizer; and,based on the functional status, recommending clopidogrel dosage.54232214-40001 (PCT)

[0213] Embodiment 70. The method of any one of embodiments 63-67, wherein for warfarin the method comprises:detecting variants in CYP2C9, VKORC1, and CYP4F2 in FIG. 17A-AD-19; and calculating a weekly dose for warfarin.

[0214] Embodiment 71. The method of any one of embodiments 63-67, wherein for carbamazepine the method comprises:detecting one or more HLA-B and / or HLA-A variants in FIG. 15-16; and, recommending carbamazepine dosage.

[0215] Embodiment 72. The method of any one of embodiments 63-67, wherein for allopurinol the method comprises:detecting one or more HLA-B in FIG. 14; and,recommending allopurinol dosage.

[0216] Embodiment 73. The method of any one of embodiments 63-67, wherein for metformin the method comprises:detecting variants in one or more transporters SLC22A1, SLC22A2, SLC29A4, and SLC6A4 in FIG. 22; anddetermining for metformin tolerance.

[0217] Embodiment 74. The method of any one of embodiments 63-67, wherein for sulfonylureas the method comprises:detecting variants in CYP2C9 in FIG. 23, andrecommending sulfonylurea dosage.

[0218] Embodiment 75. The method of any one of embodiments 63-67, wherein for GLP-1 receptor agonists the method comprises:detecting variants in GLP 1R and / or ARRB 1 in FIG. 21 ; andgenerating recommendations GLP-1 receptor agonist dosage.

[0219] Embodiment 76. The method of any one of embodiments 63-67, wherein for DDP-4 inhibitors the method comprises:detecting variants in CDKAL1 and / or GLP1R in FIG. 20; andgenerating recommendations DDP-4 inhibitors dosage.

[0220] Embodiment 77. The method of any one of embodiments 63-67, wherein for glucokinase activators the method comprises:detecting variants in GCK in FIG. 24;recommending glucokinase (GK) activators and PPAR-y agonists for chr2:27508073 allele T and chr7:44189469 allele A.55232214-40001 (PCT)

[0221] Embodiment 78. The method of any one of embodiments 63-77, wherein obtaining the genetic profile data comprises obtaining genotyping data or sequence data usingnext-generation sequencing, targeted sequencing panels, or microarray-based genotyping.

[0222] Embodiment 79. A non-transitory computer- readable medium storing instructions which, when executed by one or more processors, cause the one or more processors to perform the method of any one of embodiments 63-77.

[0223] Embodiment 80. A system comprising at least one processor and at least one memory storing instructions which, when executed by the at least one processor, cause the system to perform the method of any one of embodiments 63-77.

[0224] Embodiment 81. An integrated method for diabetes profiling and precision medicine in a subject, the method comprising performing the methods of any one of embodiments 1-80.

[0225] Embodiment 82. The integrated method of embodiment 81, further comprising generating an individualized diabetes and cardiometabolic health report.

[0226] Embodiment 83. A computer system for diabetes profiling and precision medicine in a subject, comprising:one or more processors;one or more memories storing instructions which, when executed by the one or more processors, cause the system to:receive genetic profile data and / or non-genetic data for a subject;perform the methods of any one of embodiments 1-81 using an artificial intelligence or machine learning engine; andgenerate results for diabetes profiling and precision medicine.

[0227] Embodiment 84. The computer system of embodiment 83, wherein the system further generates an individualized diabetes and cardiometabolic health report.

[0228] Embodiment 85. A kit for use comprising: a sample collecting kit, a laboratory kit, and a non-transitory computer-readable medium storing instructions which, when executed by one or more processors, cause the one or more processors to perform the methods or system of any of the preceding embodiments.

[0229] Embodiment 86. The kit of embodiments 85, wherein the instructions further cause the one or more processors to apply artificial intelligence or machine learning models to perform the methods or systems of any of the preceding embodiments.56232214-40001 (PCT)EXAMPLES

[0230] Provided herein are examples that describe in more detail certain embodiments of the present disclosure. The examples provided herein are merely for illustrative purposes and are not meant to limit the scope of the invention in any way. All references given below and elsewhere in the present application are hereby included by reference.

[0231] Example 1

[0232] Module 1. Diabetes Risk Prediction and Stratification.

[0233] Type 1 diabetes

[0234] In one validation study for T1D, a cohort comprising 262 participants with T1D, 1,080 with T2D and 208 non-diabetic controls was analysed. The genetic score was found to have strong discriminatory power, achieving an AUC of 0.859 for distinguishing T1D from controls, and an AUC of 0.869 for differentiating T1D from T2D .

[0235] Type 2 diabetes

[0236] In another validation study for T2D, multidimensional data from a prospective cohort of working-age individuals, with a mean follow-up period of 10 years, were utilized to develop proprietary algorithms incorporating genetic, modifiable, and non-modifiable risk factors. The algorithm was designed to identify the 30% of individuals with the highest risk of developing diabetes for T2D, who account for approximately 80% of individuals who subsequently develop diabetes over the next decade.

[0237] Individuals identified as high-risk are recommended to undergo annual OGTT — the current gold standard for diagnosing diabetes and prediabetes. Upon confirmation of prediabetes or diabetes, these individuals are enrolled in a technology-enhanced, multicomponent intervention involving digital tools, clinical consultations, and pharmacotherapy, with the aim of preventing or delaying disease progression and its associated complications.

[0238] In this validation study conducted within a community-based cohort of 1,204 individuals aged 18-44 years without a prior diagnosis of diabetes, participants underwent diabetes testing followed by OGTT. Using the established cutoff value of the diabetes test algorithm: (a) No individual below the cutoff were diagnosed with diabetes based on OGTT, yielding a positive predictive value (PPV) of 0.07; (b) the test demonstrated strong discriminative ability for identifying individuals with diabetes or prediabetes, with a PPV of 0.536 and a negative predictive value (NPV) of 0.862; and (c) individuals with risk scores above the cutoff exhibited a 7.2-fold higher odds of having diabetes or prediabetes upon OGTT compared to those below the cutoff. A two-tiered screening strategy using the diabetes test algorithm is proposed to optimize identification of high-risk individuals. For every 100 individuals tested with the diabetes test 57232214-40001 (PCT)algorithm, approximately 70 can be excluded from further OGTT screening for the next 5-10 years. Among the remaining 30, approximately 50% are likely to have undiagnosed diabetes or prediabetes upon OGTT and are thus appropriate candidates for annual OGTT follow-up and preventative intervention.

[0239] In contrast, population-wide OGTT screening without risk-test-based stratification would require performing 100 OGTTs to detect approximately 3 cases, without indications of which individuals require repeat testing in subsequent years. Based on these findings, the diabetes test algorithm is proposed as a novel, clinically actionable screening tool with demonstrated utility that may not be apparent through conventional screening strategies.

[0240] Gestational Diabetes Mellitus

[0241] In the validation cohort of 954 pregnant women, 145 were diagnosed with GDM, and 127 developed abnormal glucose tolerance (AGT) postpartum. The genetic risk score demonstrated high predictive performance, with a specificity of 90.1% for GDM and 90.0% for AGT.

[0242] Example 2

[0243] Module 2, Monogenic Diabetes Subtype Detection and Classification

[0244] Two independent cohorts of young-onset diabetes (Y OD) (age at diagnosis <40 years) were used for testing and validation of a panel of genes for MODY or monogenic diabetes. In the testing cohort (Cohort A, n = 1,021), 36 pathogenic variants were identified and confirmed by Sanger sequencing. In the validation cohort (Cohort B, n = 883), 34 pathogenic variants were similarly detected and confirmed. The detection rates of pathogenic variants were comparable across both cohorts and were consistent with previously reported findings.58232214-40001 (PCT)

[0245] Table 17. Distribution of pathogenic / likely pathogenic variants of genes for MODY or monogenic diabetes among testing cohorts.

[0246] In addition, 21 previously characterized cases (14 positive and 7 negative) were tested using this method, with all results demonstrating full concordance with known diagnoses.

[0247] Example 3

[0248] Module 3 , Type 2 Diabetes Pathophysiological Pathway Profiling

[0249] In a cohort of 18,217 individuals diagnosed with T2D, polygenic risk scores (PRS) corresponding to key pathophysiological pathways were evaluated for associations with disease progression, renal dysfunction, and cardiovascular complications. The PRS related to the obesity pathway was found to be associated with increased incidence of early insulin requirement, with a hazard ratio (HR) of 1.09 (95% CI: 1.05-1.13, p = 9.3 x 10fi). as well as with actual insulin initiation (HR 1.05; 95% CI: 1.01-1.08, p = 0.0050). In addition, the same score was associated with increased incidence of albuminuria (HR 1.08; 95% CI: 1.04-1.11, p = 3.1 x 105). progression to ESKD (HR 1.10; 95% CI: 1.04-1.16, p = 0.0007), and cardiovascular events (HR 1.08; 95% CI: 1.03-1.13, p = 0.0052).

[0250] For PRS directed to the beta-cell dysfunction pathway, a lower incidence of albuminuria was observed (HR 0.94; 95% CI: 0.91-0.98, p = 0.0010 and HR 0.90; 95% CI: 0.85-0.95, p = 0.0001), along with a reduced risk of atrial fibrillation (HR 0.87; 95% CI: 0.81-0.94, p = 0.0002) and heart failure (HR 0.83; 95% CI: 0.73-0.93, p = 0.0011).

[0251] Example 4

[0252] Module 4, Hyperlipidaemia and Dyslipidaemia Risk Assessment.

[0253] The validation dataset comprised 4,271 individuals. Polygenic risk scores exhibited the highest predictive accuracy for total cholesterol (p = 7.5 x 10l()). triglycerides (p = 1.3 x 1075). HDL cholesterol (p = 9.3 x 10s3). and LDL cholesterol (p = 2.4 x |()9). For predicting abnormal lipid levels — defined as TC > 5.2 mmol / L, TG > 1.7 mmol / L, and LDL > 2.6 mmol / L — the positive prediction rates were 89.3%, 84.4%, and 95.0%, respectively.

[0254] Example 559232214-40001 (PCT)

[0255] Module 5, Disease Progression and Complication Risk Stratification.

[0256] The predictive performance of the PRS for myocardial infarction, coronary heart disease, stroke, heart failure, chronic kidney disease, and ESKD was assessed using the C-index, yielding values of 0.572, 0.547, 0.521, 0.516, 0.522, and 0.526, respectively. These PRS are used to interact with other modifiable risk factors and non-modifiable non-genetic risk factors to predict future risk of complications for early and intensified treatment.

[0257] The exemplary embodiments of the present invention are thus fully described. Although the description referred to particular embodiments, it will be clear to one skilled in the art that the present invention may be practiced with variation of these specific details. Hence this invention should not be construed as limited to the embodiments set forth herein.60232214-40001 (PCT)

Claims

CLAIMSWhat is claimed is:

1. A method for predicting, in a subject, a risk of developing at least one type of diabetes selected from type 1 diabetes (T1D), type 2 diabetes (T2D) and gestational diabetes mellitus (GDM) within a defined time period, the method comprising:obtaining genotyping data or sequence data for the subject comprising at least a subset of genetic variants or target regions corresponding to a diabetes type, the genetic variants or target regions being selected from a panel of variants associated with one or more of T1D, T2D and GDM as illustrated in FIG. 1A-E; andcalculating, from the genotyping data or sequence data, a type-specific genetic risk score for the diabetes type, the genetic risk score being determined as an aggregate of variant-level scores assigned to genotypes observed at the genetic variants or target regions corresponding to the diabetes type, and wherein optionally the type-specific genetic risk score is further integrated with numerical assessment with one or more non-genetic parameters selected from age, sex, height, body weight, birth weight, waist circumference, family history of diabetes, smoking status, history of GDM, blood glucose measurements, blood pressure, exercise habits, or combinations thereof.

2. The method of claim 1, wherein the defined time period is 10 years.

3. The method of claim 1 or claim 2, wherein calculating the type-specific genetic risk score comprises, for each genetic variant or target region corresponding to the diabetes type:assigning a score of 0 when the subject has a homozygous non-risk genotype, assigning a score of 1 when the subject has a heterozygous genotype,assigning a score of 2 when the subject has a homozygous risk genotype, and determining the genetic risk score as a sum of the assigned scores across the plurality of genetic variants or target regions corresponding to the diabetes type.

4. The method of any preceding claim, wherein the aggregate is a weighted sum or scaled sum of the variant-level scores.

5. The method of any preceding claim, wherein the panel of genetic variants comprises loci associated with T1D, T2D and GDM as defined in FIG. 1A-E.

6. The method of any preceding claim, wherein the genotyping data or sequence data is obtained using a nucleic acid sequencing assay selected from next-generation sequencing, whole-genome sequencing, whole-exome sequencing, targeted sequencing, minisequencing, microarray-based genotyping, or combinations thereof.61232214-40001 (PCT)7. The method of any preceding claim, further comprising classifying the subject into one of a plurality of risk categories for the diabetes type, the plurality of risk categories comprising low risk, moderate risk, high risk and very high risk, based on the type-specific genetic risk score and predefined score thresholds.

8. The method of claim 7, wherein for T1D, the subject is classified as:Low Risk when the genetic risk score is < 107,Moderate Risk when the genetic risk score is 108 to 112,High Risk when the genetic risk score is 113 to 117, andVery High Risk when the genetic risk score is > 118.

9. The method of claim 7, wherein for T2D, the subject is assigned a T2D genetic risk value (T2D / GRV) as follows:a T2D / GRV of 2 when the genetic risk score is < 278,a T2D / GRV of 4 when the genetic risk score is 279 to 284,a T2D / GRV of 7 when the genetic risk score is 285 to 291, anda T2D / GRV of 9 when the genetic risk score is > 292.

10. The method of claim 7, wherein for GDM, the subject is classified as:Low Risk when the genetic risk score is < 4,Moderate Risk when the genetic risk score is 5,High Risk when the genetic risk score is 6 to 7, andVery High Risk when the genetic risk score is > 8.

11. The method of claim 10, further comprising:generating, for the subject, a T2D diabetes risk value that represents a probability of developing the diabetes type within the defined time period by integrating the T2D / GRV with one or more non-genetic parameters selected from age, sex, height, body weight, birth weight, waist circumference, family history of diabetes, smoking status, history of GDM, blood glucose measurements, blood pressure, exercise habits, or combinations thereof.

12. The method of claim 11, wherein the T2D diabetes risk value is derived using a logistic function applied to a linear combination of the T2D / GRV and the one or more non-genetic parameters.

13. The method of any preceding claim, further comprising generating one or more recommendations for the subject based at least in part on the type-specific genetic risk score or the T2D diabetes risk value, the recommendations comprising at least one of:(a) a recommendation for frequency or modality of glycaemic testing,(b) a recommendation for lifestyle modification, and62232214-40001 (PCT)(c) a recommendation for pharmacotherapy initiation or adjustment.

14. The method of claim 13, wherein the recommendation for glycaemic testing comprises at least one of fasting plasma glucose testing, oral glucose tolerance testing and pregnancy-specific glucose testing at defined intervals.

15. The method of claim 14, wherein the recommendation for lifestyle modification comprises at least one of targeted dietary changes, physical activity programs and weight-management interventions.

16. The method of claim 15, wherein the recommendation for pharmacotherapy initiation or adjustment is provided for subjects classified as high-risk or very-high-risk of developing the diabetes type.

17. A non-transitory computer-readable medium storing instructions which, when executed by one or more processors, cause the one or more processors to perform the method of any one of claims 1-16.

18. A system comprising at least one processor and at least one memory storing instructions which, when executed by the at least one processor, cause the system to perform the method of any one of claims 1-17.

19. A method for detecting a monogenic form of diabetes in a subject, the method comprising:obtaining a biological sample from the subject;isolating nucleic acid from the biological sample;detecting, in the nucleic acid, one or more variants in one or more genes associated with monogenic diabetes; andclassifying at least one detected variant as pathogenic or likely pathogenic by:(i) evaluating the at least one detected variant based on pathogenicity phenotypes;(ii) assigning, to each pathogenicity phenotypes, a numerical weight corresponding to its evidentiary strength;(iii) summing the assigned numerical weights to generate a monogenic diabetes pathogenicity score for the variant; and(iv) classifying the variant as pathogenic or likely pathogenic when the monogenic diabetes pathogenicity score meets or exceeds a predefined threshold, and wherein optionally the monogenic diabetes pathogenicity score is further integrated with numerical assessment with one or more non-genetic parameters selected from age, sex, height, body weight, birth weight, waist circumference, family history of diabetes, smoking status, history of GDM, blood glucose measurements, blood pressure, exercise habits, or combinations thereof.63232214-40001 (PCT)20. The method of claim 19, wherein the one or more genes associated with monogenic diabetes are selected from the group consisting of ABCC8, DCAF17, GATA6, GCK, HNF1A, HNF1B, HNF4A, INS, INSR, NEURODI, PCBD1, PLIN1, PPARG, SLC29A3, TRMT10A, WFS1, ZBTB20, and KCNJ11.

21. The method of claim 19 or claim 20, wherein the one or more genes are selected from the group consisting of HNF1A, HNF1B, HNF4A, GCK, GATA6, NEURODI, PLIN1, PPARG, WFS1, KCNJ11, and ABCC8, and the one or more variants are detected at target regions corresponding to each gene as illustrated in FIG. 2A-C.

22. The method of any one of claims 19-21, wherein detecting the one or more variants comprises performing nucleic acid sequencing selected from next-generation sequencing, wholegenome sequencing, whole-exome sequencing, targeted sequencing panels, minisequencing assays, or microarray-based genotyping.

23. The method of any one of claims 19-21, further comprising diagnosing the subject with a monogenic form of diabetes when at least one variant in at least one gene associated with monogenic diabetes is classified as pathogenic or likely pathogenic.

24. The method of claim 23, further comprising classifying the monogenic diabetes into a gene-specific subtype based on the gene containing the classified pathogenic or likely pathogenic variant.

25. The method of any one of claims 19-24, further comprising generating one or more precision treatment recommendations for the subject based at least in part on the classified pathogenic or likely pathogenic variant(s), wherein the recommendations comprise subtypespecific pharmacotherapy.

26. The method of claim 25, wherein for a variant classified as pathogenic or likely pathogenic in GCK gene, the recommendation comprises consideration of glucokinase activators.

27. The method of claim 26, wherein for a variant classified as pathogenic or likely pathogenic in the HNF1A or HNF4A gene, the recommendation comprises sulfonylureas selected from glimepiride, glipizide, gliclazide, chlorpropamide, and tolbutamide.

28. The method of any one of claims 19-27, wherein additional recommendations applicable across monogenic subtypes comprise one or more of structured lifestyle changes, periodic glycemic monitoring, and referral to specialized care.

29. A non-transitory computer-readable medium storing instructions which, when executed by one or more processors, cause the one or more processors to perform the variant classification steps of any one of claims 19-28.64232214-40001 (PCT)30. A system comprising at least one processor and at least one memory storing instructions which, when executed by the at least one processor, cause the system to perform the variant classification steps of any one of claims 19-28.

31. A method for characterizing genetic susceptibility to one or more pathophysiological pathways implicated in type 2 diabetes (T2D) in a subject, the method comprising:obtaining genetic profile data for the subject, the genetic profile data comprising genotypes at a plurality of genetic variants in one or more predefined sets of loci associated with T2D pathophysiological pathways; andcalculating, from the genetic profile data, one or more pathway-specific polygenic risk scores (PRS) for the one or more T2D pathophysiological pathways, wherein each pathwayspecific PRS is determined as an aggregate of risk allele dosages coded across the plurality of genetic variants associated with that pathway, and wherein optionally the pathway-specific PRS is further integrated with numerical assessment with one or more non-genetic parameters selected from age, sex, height, body weight, birth weight, waist circumference, family history of diabetes, smoking status, history of GDM, blood glucose measurements, blood pressure, exercise habits, or combinations thereof.

32. The method of claim 31, wherein the one or more T2D pathophysiological pathways are selected from the group consisting of ALP negative regulation, beta-cell function (Beta Cell 1 subpathway, Beta Cell 2 subpathway), bilirubin metabolism, cholesterol metabolism, hyperinsulinemia, lipodystrophy (Lipodystrophy 1 subpathway, Lipodystrophy 2 subpathway), liver-lipid metabolism, obesity, proinsulin secretion, Maturity-Onset Diabetes of the Young (MODY) and monogenic diabetes genes, and Sex Hormone -Binding Globulin-Lipoprotein(a) (SHBG-Lp(a)) regulation.

33. The method of claim 31 or claim 32, wherein the plurality of genetic variants associated with the one or more T2D pathophysiological pathways corresponds to the reference variant panel illustrated in FIG. 3A-D.

34. The method of any one of claims 31-33, wherein coding each genetic variant comprises assigning:a weight of 0 for homozygous non-risk genotype,a weight of 1 for heterozygous genotype, anda weight of 2 for homozygous risk genotype;and the pathway-specific PRS is determined as a sum of the risk allele weights across the genetic variants associated with that pathway.65232214-40001 (PCT)35. The method of any one of claims 31-34, further comprising normalizing each pathwayspecific PRS to generate a normalized PRS by a normalizing technique.

36. The method of claim 35, wherein normalizing normalization comprises z-score normalization, direct percentile ranking, logistic transformation, or machine-learning calibration.

37. The method of any one of claims 31-36, further comprising classifying the subject into one or more risk categories for each T2D pathophysiological pathway based on the corresponding pathway-specific PRS or normalized PRS and predefined thresholds.

38. The method of any one of claims 31-37, wherein the aggregate for the pathway-specific PRS comprises a weighted sum of the risk allele weights.

39. The method of any one of claims 31-38, further comprising generating one or more pathway-tailored recommendations for the subject based at least in part on the one or more pathway-specific PRS, the recommendations comprising one or more of lifestyle interventions and pharmacotherapy.

40. The method of claim 39, wherein the lifestyle interventions comprise one or more of dietary changes, exercise, and weight management.

41. The method of any one of claims 31-40, wherein obtaining the genetic profile data comprises obtaining genotyping data or sequence data using next-generation sequencing, targeted sequencing panels, or microarray-based genotyping.

42. A non-transitory computer-readable medium storing instructions which, when executed by one or more processors, cause the one or more processors to perform the method of any one of claims 31-41.

43. A system comprising at least one processor and at least one memory storing instructions which, when executed by the at least one processor, cause the system to perform the method of any one of claims 31-41.

44. A method for assessing genetic risk of hyperlipidaemia in a subject, the method comprising:obtaining genotyping data for the subject at a plurality of genetic variants associated with lipid traits;calculating, from the genotyping data or sequence data, one or more lipid-specific polygenic risk scores (HLD / PRS), each HLD / PRS corresponding to a lipid phenotype selected from low high-density lipoprotein (HDL) cholesterol, elevated low-density lipoprotein (LDL) cholesterol, elevated total cholesterol, elevated triglycerides, and elevated lipoprotein(a); and classifying the subject as having an increased genetic risk for hyperlipidaemia based on at least one of the HLD / PRS, and wherein optionally the HLD / PRS is further integrated 66232214-40001 (PCT)with numerical assessment with one or more non-genetic parameters selected from age, sex, height, body weight, birth weight, waist circumference, family history of diabetes, smoking status, history of GDM, blood glucose measurements, blood pressure, exercise habits, or combinations thereof.

45. The method of claim 44, wherein the lipid-specific HLD / PRS are calculated using a reference variant panel in which risk genotypes for total cholesterol, HDL cholesterol, LDL cholesterol, triglycerides and lipoprotein(a) are defined according to a panel of variants illustrated in FIG. 4A-J.

46. The method of claim 44 or claim 45, wherein calculating each HLD / PRS comprises, for each variant mapped to a given lipid phenotype: encoding the subject’s genotype as 0 when no risk allele is present, 1 when one risk allele is present, and 2 when two risk alleles are present; and summing the encoded values across all variants mapped to that lipid phenotype.

47. The method of any one of claims 44-46, wherein elevated lipid levels for the lipid phenotypes are defined as total cholesterol at least 5.2 mmol / L, LDL cholesterol at least 3.4 mmol / L, triglycerides at least 1.7 mmol / L, and lipoprotein(a) at least 75 nmol / L, and wherein low HDL cholesterol is defined as less than 1 mmol / L.

48. The method of any one of claims 44-47, wherein the low HDL cholesterol, elevated LDL cholesterol, elevated total cholesterol, elevated triglycerides, and elevated lipoprotein(a) are classified as Low Risk, Moderate Risk, High Risk, and Very High Risk, according to each lipid- specific HLD / PRS.

49. The method of any one of claims 44-48, further comprising a familial hypercholesterolaemia (FH), comprising the steps of:obtaining genotyping data from the subject;detecting, in the nucleic acid, one or more variants in one or more FH-associated genes selected from APOB, APOE, LDLR, LDLRAP1, PCSK9, ABCG5, ABCG8, STPA1 and LIPA in FIG. 5A-C;classifying each detected variant into a pathogenicity category based on predetermined pathogenicity phenotypes;assigning numerical weights to each pathogenicity phenotype; summing the assigned numerical weights to generate the cumulative FH pathogenicity score.

50. The method of claim 49, further comprising diagnosing FH in the subject when the cumulative FH pathogenicity score in at least one FH-associated gene meets or exceeds a predefined pathogenicity score threshold.67232214-40001 (PCT)51. The method of any one of claims 44-50, wherein genotyping data used to calculate HLD / PRS and to detect FH-associated variants is obtained using next-generation sequencing, targeted sequencing panels, or microarray -based genotyping.

52. A non-transitory computer-readable medium storing instructions which, when executed by one or more processors, cause the one or more processors to perform the method of any one of claims 44-51.

53. A system comprising at least one processor and at least one memory storing instructions which, when executed by the at least one processor, cause the system to perform the method of any one of claims 44-51.

54. A method for predicting the risk of developing one or more cardiovascular-kidney -metabolic (CKM) complications associated with diabetes in a subject, the method comprising:obtaining genotyping data or sequence data for the subject at a plurality of predefined genetic variants or target regions associated with one or more diabetic complications selected from beta-cell failure, ischemic heart disease, chronic heart failure, stroke, chronic kidney disease, end-stage kidney disease, and cancer;calculating, from the genotyping data or sequence data, a complication-specific genetic risk score for each of the one or more diabetic complications; and generating, for each complication, a complication-specific risk value based at least in part on the corresponding genetic risk score, and wherein optionally the complication-specific risk value is further integrated with numerical assessment with one or more non-genetic parameters selected from age, sex, height, body weight, birth weight, waist circumference, family history of diabetes, smoking status, history of GDM, blood glucose measurements, blood pressure, exercise habits, or combinations thereof 55. The method of claim 54, wherein calculating the complication-specific genetic risk score comprises, for each genetic variant or target region corresponding to a given complication:assigning a score of 0 when the subject has a homozygous non-risk genotype, a score of 1 when the subject has a heterozygous genotype, and a score of 2 when the subject has a homozygous risk genotype; anddetermining the complication-specific genetic risk score as a sum of the assigned scores across the plurality of genetic variants or target regions associated with that complication.

56. The method of claim 54 or claim 55, wherein the plurality of predefined genetic variants or target regions corresponds to a complication panel illustrated in FIG. 6A-E.68232214-40001 (PCT)57. The method of any one of claims 54-56, wherein the complication-specific genetic risk score is calculated as a sum of the assigned scores across the plurality of genetic variants associated with that complication.

58. The method of any one of claims 54-57, further comprising classifying the subject into one of a plurality of risk categories for each complication, the plurality of risk categories comprising at least low risk, moderate risk, high risk, and very high risk, based on the corresponding complication-specific genetic risk score and predefined score thresholds.

59. The method of any one of claims 54-58, wherein separate complication-specific genetic risk scores are calculated for each of beta-cell failure, ischemic heart disease, chronic heart failure, stroke, and chronic kidney disease, end-stage kidney disease, and cancer, and the subject is independently classified into a risk category for each complication.

60. The method of any one of claims 54-59, further comprising generating one or more complication-specific recommendations for the subject based at least in part on the subject’s complication-specific genetic risk scores.

61. A non-transitory computer-readable medium storing instructions which, when executed by one or more processors, cause the one or more processors to perform the method of any one of claims 54-60.

62. A system comprising at least one processor and at least one memory storing instructions which, when executed by the at least one processor, cause the system to perform the method of any one of claims 54-60.

63. A method for pharmacogenomic profiling of a subject for drugs used in the treatment of diabetes and cardiovascular-kidney-metabolic(CKM) disorders, the method comprising:obtaining genetic profile data from the subject;detecting, in the genetic profile data, one or more sequence variants in one or more genes implicated in pharmacokinetics and / or pharmacodynamics of therapeutics suitable for managing one or more conditions selected from Type 1 diabetes, Type 2 diabetes, maturity-onset diabetes of the young (MODY), hyperlipidaemia, cardiovascular disease, stroke, and chronic kidney disease or end-stage renal disease; andpredicting responses of the subject to one or more drugs used to treat the one or more conditions based at least in part on the detected variants, and wherein optionally the predicting responses of the subject is further integrated with numerical assessment with one or more non-genetic parameters selected from age, sex, height, body weight, birth weight, waist circumference, family history of diabetes, smoking status, history of GDM, blood glucose measurements, blood pressure, exercise habits, or combinations thereof.69232214-40001 (PCT)64. The method of claim 63, wherein the one or more drugs comprise at least one of statins, clopidogrel, allopurinol, carbamazepine, warfarin, metformin, sulfonylureas, dipeptidyl peptidase-4 (DPP-4) inhibitors, glucagon-like peptide- 1 (GLP-1) receptor agonists, GLP-1 and polypeptide co-agonist, and glucokinase activators.

65. The method of claim 63 or claim 64, further comprising generating a treatment recommendation for the subject comprising selection of one or more drugs and / or dosage regimens tailored to the predicted drug responses, optionally including suggestions for alternative agents, dose adjustment, or enhanced safety monitoring.

66. The method of any one of claims 63-65, wherein detecting the one or more sequence variants comprises detecting variants in one or more genes selected from SLCO1B1, ABCG2, CYP2C9, HLA-B, HLA-A, CYP4F2, VKORC1, CYP2C19, GLP1R, ARRB1, SLC22A1, SLC22A2, SLC6A4, SLC29A4.

67. The method of any one of claims 63-66, further comprising, for each gene, assigning a diplotype-level functional status selected from normal function, decreased function, poor function, no function, or function uncertain or unknown.

68. The method of any one of claims 63-67, wherein for statin therapy, the method comprises:detecting one or more variants in SLCO1B1, ABCG2, and CYP2C9 in FIG. SAIT, 10, and 12A-T, respectively;assigning a diplotype functional status for each gene; and,based on the diplotype functional status, recommending a specific statin and / or dose intensity.

69. The method of any one of claims 63-67, wherein for clopidogrel, the method comprises:detecting one or more variants in CYP2C19 in FIG. 25A-E;assigning a diplotype functional status as normal, intermediate, poor, or increased function metabolizer; and,based on the functional status, recommending clopidogrel dosage.

70. The method of any one of claims 63-67, wherein for warfarin the method comprises: detecting variants in CYP2C9, VKORC1, and CYP4F2 in FIG. 17A-AD-19; and calculating a weekly dose for warfarin.

71. The method of any one of claims 63-67, wherein for carbamazepine the method comprises:detecting one or more HLA-B and / or HLA-A variants in FIG. 15-16; and, recommending carbamazepine dosage.

72. The method of any one of claims 63-67, wherein for allopurinol the method comprises:70232214-40001 (PCT)detecting one or more HLA-B in FIG. 14; and,recommending allopurinol dosage.

73. The method of any one of claims 63-67, wherein for metformin the method comprises:detecting variants in one or more transporters SLC22A1, SLC22A2, SLC29A4, and SLC6A4 in FIG. 22; anddetermining for metformin tolerance.

74. The method of any one of claims 63-67, wherein for sulfonylureas the method comprises:detecting variants in CYP2C9 in FIG. 23, andrecommending sulfonylurea dosage.

75. The method of any one of claims 63-67, wherein for GLP- 1 receptor agonists the method comprises:detecting variants in GLP1R and / or ARRB1 in FIG. 21; and generating recommendations GLP-1 receptor agonist dosage.

76. The method of any one of claims 63-67, wherein for DDP-4 inhibitors the method comprises:detecting variants in CDKAL1 and / or GLP1R in FIG. 20; and generating recommendations DDP-4 inhibitors dosage.

77. The method of any one of claims 63-67, wherein for glucokinase activators the method comprises:detecting variants in GCK in FIG. 24;recommending glucokinase (GK) activators and PPAR-y agonists for chr2:27508073 allele T and chr7:44189469 allele A.

78. The method of any one of claims 63-77, wherein obtaining the genetic profde data comprises obtaining genotyping data or sequence data using next-generation sequencing, targeted sequencing panels, or microarray -based genotyping.

79. A non-transitory computer-readable medium storing instructions which, when executed by one or more processors, cause the one or more processors to perform the method of any one of claims 63-77.

80. A system comprising at least one processor and at least one memory storing instructions which, when executed by the at least one processor, cause the system to perform the method of any one of claims 63-77.

81. An integrated method for diabetes profiling and precision medicine in a subject, the method comprising performing the methods of any one of claims 1-80.71232214-40001 (PCT)82. The integrated method of claim 81, further comprising generating an individualized diabetes and cardiometabolic health report.

83. A computer system for diabetes profding and precision medicine in a subject, comprising:one or more processors;one or more memories storing instructions which, when executed by the one or more processors, cause the system to:receive genetic profde data and / or non-genetic data for a subject;perform the methods of any one of claims 1-81 using an artificial intelligence or machine learning engine; andgenerate results for diabetes profiling and precision medicine.

84. The computer system of claim 83, wherein the system further generates an individualized diabetes and cardiometabolic health report.

85. A kit for use comprising: a sample collecting kit, a laboratory kit, a non-transitory computer-readable medium storing instructions which, when executed by one or more processors, cause the one or more processors to perform the methods or system of any of the preceding claims.

86. The kit of claim 85, wherein the instructions further cause the one or more processors to apply artificial intelligence or machine learning models to perform the methods or systems of any of the preceding claims.72232214-40001 (PCT)

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