Detecting neurobeachin (NBEA) polymorphisms for GLP-1ra responsiveness

Abstract

Provided herein are compositions, systems, kits, and methods for detecting, in a DNA sample from a subject (e.g., overweight or obese subject), the presence of at least one polymorphism in the Neurobeachin (NBEA) gene. In particular embodiments, once at least one such polymorphism is detected, the subject is treated with, or provided with, a Glucagon-like peptide-1 receptor agonist (GLP-1RA).

Description

[0001] Attorney Docket No.: CCF-43818.601

[0002] DETECTING NEUROBEACHIN (NBEA) POLYMORPHISMS FOR

[0003] GLP-1RA RESPONSIVENESS

[0004] The present application claims priority to U.S. Provisional application serial number 63 / 737,152, filed December 20, 2024, which is herein incorporated by reference in its entirety.

[0005] FIELD

[0006] Provided herein are compositions, systems, kits, and methods for detecting, in a DNA sample from a subject (e.g., overweight or obese subject), the presence of at least one polymorphism in the Neurobeachin (NBEA) gene selected. In particular embodiments, once at least one such polymorphism is detected, the subject is treated with, or provided with, a Glucagon- like peptide- 1 receptor agonist (GLP-1RA).

[0007] BACKGROUND

[0008] Obesity has been on the rise since 1999 in the United States and according to most recent estimates, it impacts 42.4% of adults. It is associated with an overall worse quality of life and elevated risk of developing cardiometabolic conditions (e.g., hypertension & diabetes) and certain cancers. Weight management interventions are necessary to improve the long-term quality of life of individuals with obesity. Anti-obesity medications, with the introduction of highly effective drugs (e.g., Wegovy and Zepbound) have been recently pushed to the forefront of weight management. The prescription of these expensive medications is on the rise across the world and since their introduction to market, high demand of these medications has resulted in shortages. However, these medications are very expensive and their impact on weight loss varies significantly between individuals. For example, approximately 30% of patients who were prescribed Wegovy with lifestyle changes did not experience clinically meaningful weight loss of 10% after 68 weeks of use in a clinical trial. The affordability and high demand of these medications has created a critical need for diagnostic tests that can identify individuals who are most likely to respond to treatment thereby ensuring more precise prescribing, relieving accessibility issues and reducing the burden of unnecessary costs on payors. Attorney Docket No.: CCF-43818.601

[0009] SUMMARY

[0010] Provided herein are compositions, systems, kits, and methods for detecting, in a DNA sample from a subject (e.g., overweight or obese subject), the presence of at least one polymorphism in the Neurobeachin (NBEA) gene (e.g., selected from those in Table 3, 4, 5, or 6). In particular embodiments, once at least one such polymorphism is detected, the subject is treated with, or provided with, a Glucagon-like peptide- 1 receptor agonist (GLP- 1RA).

[0011] In some embodiments, provided herein are methods comprising: performing a nucleic acid detection assay on a DNA sample (e.g., purified DNA from plasma, serum, blood, tissue, etc.) from a subject (e.g., human subject) and detecting the presence of at least one polymorphism from Table 3, 4, 5, or 6 in the human Neurobeachin (NBEA) gene (e.g., detecting 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50,

[0012] 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75,

[0013] 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, or 90), wherein the subject has a body mass index (BMI) above 25.0 kg / m2(e.g., above 25.0, 26.0, 27.0, 28.0, 29.0, 30.0, 31.0, 32.0,

[0014] 33.0, or 34.0) and / or has type 2 diabetes.

[0015] In particular embodiments, provided herein are methods of performing an activity based on the presence of at least one polymorphism in the human Neurobeachin (NBEA) gene of a subject comprising: a) performing a nucleic acid detection assay on a DNA sample from a subject, or receiving results from the assay, wherein the assay detects the presence in the human Neurobeachin (NBEA) gene of at least one polymorphism selected from: those in Table 3, 4, 5, or 6, and wherein the subject optionally has a body mass index (BMI) above 25.0 kg / m2 and / or has type 2 diabetes; and b) performing at least one of the following activities: i) treating the subject with a Glucagon-like peptide- 1 receptor agonist (GLP-1RA), ii) providing an administration device containing the GLP-1RA to the subject, iii) demonstrating how to use the administration device for the patient; iv) providing a nonfunctional administration device to the subject as a demonstrative to teach the subject how to use the administration device; v) orally informing the subject that they are a good candidate to achieve weight loss with the GLP-1RA based on the presence of the at least one polymorphism; vi) informing the patient in writing that they are a good candidate to achieve weight loss with the GLP-1RA based on the presence of the at least one polymorphism; vii) displaying and / or generating and / or transmitting a report that indicates the presence of the at least one polymorphism, and optionally that the subject should be treated with GLP-1RA; and Attorney Docket No.: CCF-43818.601 viii) updating the subject's medical records by indicating that they are not a good candidate for achieving weight loss with the GLP-1RA.

[0016] In certain embodiments, the GLP-1RA is selected from: Exenatide, Lixisenatide, Liraglutide, Dulaglutide, Albiglutide, Semaglutide, and Tirzepatide. In other embodiments, the methods further comprise: determining the subject's weight and determining the subject's height. In additional embodiments, the method further comprise calculating the subject’s BMI based on dividing the subject's weight in kilograms by the square of the subject's height in meters. In other embodiments, the at least one polymorphism is at least ten, twenty-seven and thirty-six polymorphisms.

[0017] In additional embodiments, the at least one polymorphism comprises the following ten polymorphisms: chrl3:34996464:T:TTG, chrl3:35044829:T:TAG, chrl3:35298225:A:G, chrl3:35308438:CTATATATA:C, chrl 3:35350728: A: ATGTG, chrl3:35354906:G:A, chrl3:35373707:A:AAATAATAATAATAATAATAATAAT, chrl3:35449873:C:G, chr!3:35507613:A:G, chr!3:35607758:TA:T. In other embodiments, the at least one polymorphism comprises the following twenty-seven polymorphisms: chrl3:34996464:T:TTG, chrl 3:35044829:1: TAG, chrl3:35044833:G:T, chrl3:35045707:T:TTTTTG, chrl3:35054614:G:GT, chr!3:35078954:G:A, chrl3:35110051:TA:T, chrl3:35113586:CATCT:C, chrl3:35298225:A:G, chrl3:35301170:G:GT, chrl3:35308438:CTATATATA:C, chrl3:35320575:G:T, chr!3:35350728:A:ATGTG, chrl3:35354906:G:A, chrl3:35373707:A:AAATAATAATAATAATAATAATAAT, chrl3:35389834:A:T, chrl3:35400196:TAA:T, chrl3:35433984:A:T, chrl3:35449873:O:G, chrl3:35506768:C:G, chr!3:35507613:A:G, chr!3:35526488:A:G, chrl3:35541182:GC:G, chr!3:35606914:G:A, chrl3:35607758:TA:T, chrl3:35626800:T:C, chrl3:35647444:C:T. In further embodiments, the at least one polymorphism comprises the following thirty-six polymorphisms: chr!3:34957253:C:T, chr!3:34996464:T:TTG, chrl3:35018990:T:C, chrl3:35044829:T:TAG, chrl3:35044833:G:T, chrl3:35045707:T:TTTTTG, chrl3:35054614:G:GT, chrl3:35078954:G:A, chrl3:3511OO51:TA:T, chrl3:35113586:CATCT:C, chr!3:35150793:T:TTAGAGTAGAGTAGAG, chr 1 :35150793:T:TTAGAGTAGAGTAGAGTAGAG, chrl 3 :35298225 : A:G, chrl3:35301170:G:GT, chrl3:35308438:CTATATATA:C, chrl3:35320575:G:T, chrl3:35350728:A:ATGTG, chrl3:35354906:G:A, chrl3:35373707:A:AAATAATAATAATAATAATAATAAT, chrl3:35389834:A:T, chr!3:35389970:A:AGTGTGTGT, chr!3:35400196:TAA:T, chrl3:35433984:A:T, Attorney Docket No.: CCF-43818.601 chrl3:35441111:C:T, chrl3:35447490:T:TTA, chrl3:35449873:C:G, chrl3:35452320:G:A, chrl3:35506768:C:G, chrl3:35507613:A:G, chrl3:35518902:A:G, chr!3:35526488:A:G, chrl3:35541182:GC:G, chrl3:35606914:G:A, chrl3:35607758:TA:T, chrl3:35626800:T:C, chrl3:35647444:C:T.

[0018] In other embodiments, the subject is not currently receiving treatment with a Glucagon- like peptide- 1 receptor agonist (GLP-1RA) and / or the DNA sample is free from any GLP-lRAs. In additional embodiments, the subject has expressed an interest in receiving treatment with a GLP-1RA and / or wherein the subject is a human and / or wherein the subject’s BMI is 29.0 or 30.0 or more. In certain embodiments, the nucleic acid detection assay comprises a probe-based assay or comprises sequencing, wherein the DNA sample is optionally a cell-free DNA sample. In other embodiments, step a) is receiving results from the assay, and wherein the at least one of the following activities is treating the subject with the GLP-1RA.

[0019] In some embodiments, provided herein arc kits, systems, and compositions comprising: a) PCR primers for amplifying (see Table 7), and / or nucleic acid probes (see 'fable 7) for detecting or enriching, at least one, or five, or nine, or nineteen, or twenty-nine, or forty-three (e.g., 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47,

[0020] 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59. 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72,

[0021] 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87. 88, or 89), polymorphisms in the human Neurobeachin (NBEA) gene selected from those in Table 3, 4, 5, or 6. In other embodiments, the kits, systems, and compositions further comprise a DNA sample from a subject that: is overweight, is obese, has type 2 diabetes, has expressed an interest in Glucagon-like peptide- 1 receptor agonists (GLP-lRAs), is not currently receiving treatment with a Glucagon-like peptide-1 receptor agonist (GLP-1RA).

[0022] In additional embodiments, provided herein are methods of performing an activity based detecting the lack of sufficient polymorphisms in the human Neurobeachin (NBEA) gene of a subject comprising: a) performing a nucleic acid detection assay on a DNA sample from a subject, or receiving results from the assay, wherein the assay detects the lack of at least five, or nine, or nineteen, or twenty-nine, polymorphisms in the human Neurobeachin (NBEA) gene, wherein the polymorphisms selected from: those in Table 3, 4, 5, or 6, and wherein the subject optionally has a body mass index (BMI) above 25.0 kg / m2 and / or has type 2 diabetes; and b) performing at least one of the following activities: i) orally informing the subject that they are not a good candidate to achieve weight loss with the GLP-1RA; ii) Attorney Docket No.: CCF-43818.601 informing the patient in writing that they are not a good candidate to achieve weight loss with the GLP-1RA; iii) displaying (e.g., on a digital screen) and / or generating and / or transmitting a report that indicates the presence of the at least one polymorphism, and optionally that the subject should be treated with GLP-1RA; and iv) updating the subject's medical records (e.g., by typing on a keyboard) by indicating that they are not a good candidate for achieving weight loss with the GLP-1RA.

[0023] DESCRIPTION OF THE FIGURES

[0024] Figure 1 provides the research workflow used in Example 1.

[0025] Figure 2. NBEA signature associations with weight change percentiles in the All of Us cohort. A) shows the association between response type and NBEA signature. Logistic regression models were used for estimation while adjusting for sex, genetic ancestry, prescription duration, age and BMI at the time of GLP-1 prescription. B) shows the percentages of the cohort (excluding those taking exenatide) categorized based on their NBEA signature. C) Shows the observed proportion of individuals who were highly responsive (top 20% weight loss) compared to non-responders (weight change > 0%) according to the NBEA signature.

[0026] Figure 3. NBEA signature associations with weight change percentiles in the UK Biobank Cohort. A) Shows the association between response type and NBEA signature. Logistic regression models were used for estimation while adjusting for sex, genetic ancestry, prescription duration, age and BMI at the time of GLP-1RA prescription. B) shows the percentages of the cohort (excluding those taking exenatide) categorized based on their NBEA signature. C) shows the observed proportion of individuals who were highly responsive (top 20% weight loss) compared to non-responders (weight change > 0%).

[0027] Figure 4 shows optimal threshold selection for NBEA Signature from Example 1 .

[0028] Figure 5 shows NBEA signature associations with weight change percentiles in the All of Us cohort. This signature is discussed in Example 2 and consists of 43 single nucleotide polymorphisms. A) shows the association between response type and NBEA signature. Logistic regression models were used for estimation. B) shows the percentages of the cohort (excluding those taking exenatide) categorized based on their NBEA signature. C) Shows the observed proportion of individuals who were highly responsive (top 20% weight loss) compared to non-responders (weight change > 0%) according to the NBEA signature.

[0029] Figure 6 shows NBEA signature associations with weight change percentiles in the UK Biobank Cohort. This signature is discussed in Example 2 and consists of 43 single Attorney Docket No.: CCF-43818.601 nucleotide polymorphisms. A) Shows the association between response type and NBEA signature. Logistic regression models were used for estimation. B) shows the percentages of the cohort (excluding those taking exenatide) categorized based on their NBEA signature. C) shows the observed proportion of individuals who were highly responsive (top 20% weight loss) compared to non-responders (weight change > 0%).

[0030] Figure 7 shows NBEA signature associations with weight change percentiles in the All of Us cohort. This signature is discussed in Example 3 and is composed of 29 single nucleotide polymorphisms. A) shows the association between response type and NBEA signature. Logistic regression models were used for estimation.

[0031] Figure 8 shows NBEA signature associations with weight change percentiles in the UK Biobank Cohort. This signature is discussed in Example 3 and is composed of 29 single nucleotide polymorphisms. A) Shows the association between response type and NBEA signature. Logistic regression models were used for estimation.

[0032] Figure 9 shows NBEA signature associations with weight change percentiles in the All of Us cohort. This signature is discussed in Example 4 and is composed of 19 single nucleotide polymorphisms. A) shows the association between response type and NBEA signature. Logistic regression models were used for estimation.

[0033] Figure 10 shows NBEA signature associations with weight change percentiles in the UK Biobank Cohort. This signature is discussed in Example 4 and is composed of 19 single nucleotide polymorphisms. A) Shows the association between response type and NBEA signature. Logistic regression models were used for estimation.

[0034] Figure 11 shows NBEA signature associations with weight change percentiles in the All of Us cohort. This signature is discussed in Example 5 and is composed of 9 single nucleotide polymorphisms. A) shows the association between response type and NBEA signature. Logistic regression models were used for estimation.

[0035] Figure 12 shows NBEA signature associations with weight change percentiles in the UK Biobank Cohort. This signature is discussed in Example 5 and is composed of 9 single nucleotide polymorphisms. A) Shows the association between response type and NBEA signature. Logistic regression models were used for estimation.

[0036] DETAILED DESCRIPTION

[0037] Provided herein are compositions, systems, kits, and methods for detecting, in a DNA sample from a subject (e.g., overweight or obese subject), the presence of at least one polymorphism in the Neurobeachin (NBEA) gene. In particular embodiments, once at least Attorney Docket No.: CCF-43818.601 one such polymorphism is detected, the subject is treated with, or provided with, a Glucagon- like peptide- 1 receptor agonist (GLP-1RA). In certain embodiments, the polymorphisms herein are found in Table 6 below.

[0038] TABLE 6 Attorney Docket No.: CCF-43818.601 Attorney Docket No.: CCF-43818.601 Attorney Docket No.: CCF-43818.601 Attorney Docket No.: CCF-43818.601 Attorney Docket No.: CCF-43818.601 Attorney Docket No.: CCF-43818.601 Attorney Docket No.: CCF-43818.601 Attorney Docket No.: CCF-43818.601 Attorney Docket No.: CCF-43818.601 Attorney Docket No.: CCF-43818.601 Attorney Docket No.: CCF-43818.601 Attorney Docket No.: CCF-43818.601 Attorney Docket No.: CCF-43818.601 Attorney Docket No.: CCF-43818.601 Attorney Docket No.: CCF-43818.601 Attorney Docket No.: CCF-43818.601 Attorney Docket No.: CCF-43818.601 Attorney Docket No.: CCF-43818.601 Attorney Docket No.: CCF-43818.601 Attorney Docket No.: CCF-43818.601 Attorney Docket No.: CCF-43818.601 Attorney Docket No.: CCF-43818.601 Attorney Docket No.: CCF-43818.601 Attorney Docket No.: CCF-43818.601

[0039] The present disclosure is not limited with regard to how the polymorphisms in the human Neurobeachin (NBEA) gene (from Table 3, 4, 5, or 6) are detected. In some embodiments, detection involves measurement or detection of a characteristic of a nonamplified nucleic acid, amplified nucleic acid, a component comprising amplified nucleic acid, or a byproduct of the amplification process, such as a physical, chemical, luminescence, or electrical aspect, which correlates with amplification (e.g. fluorescence, pH change, heat change, etc.). In some embodiments, fluorescence detection methods are provided for detection of amplified or non-amplified NBEA nucleic acid.

[0040] In certain embodiments, various detection reagents, such as fluorescent and non- fluorescent dyes and probes are employed. For example, the protocols may employ reagents suitable for use in a TaqMan reaction, such as a TaqMan probe: reagents suitable for use in a SYBR Green fluorescence detection; reagents suitable for use in a molecular beacon reaction, such as molecular beacon probes; reagents suitable for use in a scorpion reaction, such as a scorpion probe; reagents suitable for use in a fluorescent DNA-binding dye-type reaction, such as a fluorescent probe; and / or reagents for use in a LightUp protocol, such as a LightUp probe. In some embodiments, provided herein are methods and compositions for detecting and / or quantifying a detectable signal (e.g. fluorescence) from the NBEA gene target nucleic acid. Thus, for example, methods may employ labeling (e.g. during amplification, postamplification) amplified nucleic acids with a detectable label, exposing partitions to a light source at a wavelength selected to cause the detectable label to fluoresce, and detecting and / or measuring the resulting fluorescence. Fluorescence emitted from label can be tracked during amplification reaction to permit monitoring of the reaction (e.g., using a SYBR Greentype compound), or fluorescence can be measure post-amplification.

[0041] In some embodiments, detection of polymorphisms in the human NBEA gene employs one or more of fluorescent labeling, fluorescent intercalation dyes, FRET-based detection methods (U.S. Pat. No. 5,945,283; PCT Publication WO 97 / 22719; both of which are incorporated by reference in their entireties), quantitative PCR, real-time Anorogenic methods (U.S. Pat. Nos. 5,210,015 to Gelfand, 5,538,848 to Eivak, et al., and 5,863,736 to Haaland, as well as Heid, C. A., et al., Genome Research, 6:986-994 (1996); Gibson, U. E. M, et al., Genome Research 6:995-1001 (1996); Holland, P. M., et al., Proc. Natl. Acad. Sci. USA 88:7276-7280, (1991); and Eivak, K. J., et al., PCR Methods and Applications 357-362 (1995), each of which is incorporated by reference in its entirety), molecular beacons (Piatek, A. S„ et al., Nat. Biotechnol. 16:359-63 (1998); Tyagi, S. and Kramer, F. R., Nature Biotechnology 14:303-308 (1996); and Tyagi, S. et al., Nat. Biotechnol. 16:49-53 (1998); Attorney Docket No.: CCF-43818.601 herein incorporated by reference in their entireties), Invader assays (Third Wave Technologies, (Madison, Wis.)) (Neri, B. P., et al., Advances in Nucleic Acid and Protein Analysis 3826: 117-125, 2000; herein incorporated by reference in its entirety), nucleic acid sequence-based amplification (NASBA; (See, e.g., Compton, J. Nucleic Acid Sequencebased Amplification, Nature 350: 91-91, 1991.; herein incorporated by reference in its entirety), Scorpion probes (Thelwell, et al. Nucleic Acids Research, 28:3752-3761, 2000; herein incorporated by reference in its entirety), partially double- stranded linear probes (Luk, K.-C., et al, J. Virological Methods 144:1-1 1 , 2007; herein incorporated by reference in its entirety), capacitive DNA detection (See, e.g., Sohn, et al. (2000) Proc. Natl. Acad. Sci. U.S.A. 97: 10687-10690; herein incorporated by reference in its entirety), etc.

[0042] Target NBEA polymorphisms may be analyzed by any number of techniques to determine the presence of, amount of, or identity of the molecule. Non-limiting examples include sequencing, mass determination, and base composition determination. The analysis may identify the sequence of all or a part of the amplified nucleic acid or one or more of its properties or characteristics to reveal the desired information.

[0043] Illustrative non-limiting examples of nucleic acid sequencing techniques include, but are not limited to, chain terminator (Sanger) sequencing and dye terminator sequencing, as well as "next generation" sequencing techniques. A number of DNA sequencing techniques are known in the art, including fluorescence-based sequencing methodologies (See, e.g., Birren et al., Genome Analysis: Analyzing DNA, 1, Cold Spring Harbor, N.Y.; herein incorporated by reference in its entirety). In some embodiments, automated sequencing techniques understood in that art are utilized. In some embodiments, the systems, devices, and methods employ parallel sequencing of partitioned amplicons (PCT Publication No: W02006084132 to Kevin McKeman et al., herein incorporated by reference in its entirety). In some embodiments, DNA sequencing is achieved by parallel oligonucleotide extension (See, e.g., U.S. Pat. No. 5,750,341 to Macevicz et al., and U.S. Pat. No. 6,306,597 to Macevicz et al., both of which are herein incorporated by reference in their entireties). Additional examples of sequencing techniques include the Church polony technology (Mitra et al., 2003, Analytical Biochemistry 320, 55-65; Shendure et al., 2005 Science 309, 1728- 1732; U.S. Pat. No. 6,432,360, U.S. Pat. No. 6,485,944, U.S. Pat No. 6,511,803; herein incorporated by reference in their entireties) the 454 picotiter pyrosequencing technology (Margulies et al., 2005 Nature 437, 376-380; US 20050130173; herein incorporated by reference in their entireties), the Solexa single base addition technology (Bennett et al., 2005, Pharmacogenomics, 6, 373-382; U.S. Pat. No. 6,787,308; U.S. Pat. No. 6,833,246; herein Attorney Docket No.: CCF-43818.601 incorporated by reference in their entireties), Illumina Single base sequencing technology, the Lynx massively parallel signature sequencing technology (Brenner et al. (2000). Nat.

[0044] Biotechnol. 18:630-634; U.S. Pat. No. 5,695,934; U.S. Pat. No. 5,714,330; herein incorporated by reference in their entireties) and the Adessi PCR colony technology (Adessi et al. (2000). Nucleic Acid Res. 28, E87; WO 00018957; herein incoiporated by reference in its entirety).

[0045] TABLE 7

[0046] SNP F o r wa r d_P r ime r (Ref _p r obe ) Reverse_Primer (Alt_Probe)

[0047] Attorney Docket No.: CCF-43818.601

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[0050] Attorney Docket No.: CCF-43818.601

[0051] EXAMPLES

[0052] EXAMPLE 1

[0053] Neurobeachin (NBEA) is associated with GLP-1 receptor agonist weight loss

[0054] Obesity impacts 42% of adults in the United States and is a significant risk factor for many cardiometabolic diseases, cancers, and other comorbidities. Glucagon- like peptide- 1 receptor agonists (GLP-1RA) have emerged as promising weight loss interventions, but their efficacy varies significantly across individuals, with some experiencing minimal weight loss. This study investigates the role of genetic variation in ncurobcachin (NBEA), a gene that encodes for a protein kinase A anchor protein, on predicting weight loss response to GLP- 1RA treatment in two large, real-world cohorts.

[0055] We utilized patient data from individuals taking GLP-1RA medication in two large cohorts: the NIH "All of Us" (N=6,556) and the UK "Biobank" (N=500). A genetic signature based on NBEA variation was developed in the All of Us cohort and independently validated in the UK Biobank. Logistic regression models assessed the association between the NBEA signature and weight loss outcomes, including any weight loss and high responsiveness (top 20th percentile weight loss).

[0056] In the All of Us cohort, individuals with a positive NBEA signature were 39% more likely to lose any weight (OR=1.39; 95% CI: 1.22-1.58; FDR P=1.0xl0s) and 69% more likely to achieve high responsiveness (top 20% percent weight loss) (OR=1.69; 95% CI: 1.46-1.95; FDR P=l.lxl0-12). A strong effect was observed in patients prescribed semaglutide, where individuals with a positive NBEA signature were 63% more likely to achieve high responsiveness (OR=1.63; 95% CI: 1.21-2.20; FDR P=1.4xl03), with weight loss of >10% in the top 20th percentile. Validation in the UK Biobank also confirmed the Attorney Docket No.: CCF-43818.601

[0057] NBEA signature for non-response (OR=1.81; 90% CI: 1.04-3.21; P=0.041) and high response (OR=2.37; 90% CI: 1.32-4.30: P=0.0081).

[0058] Recent clinical trial results demonstrating significant weight loss for glucagon-like peptide-1 receptor agonists (GLP-1RA) and dual therapies (i.e., GLP-1RA and gastric inhibitory peptide (GIP)), have pushed these AOMs to the forefront of weight management [9,10], The use of these AOMs is now on the rise across the world, and their high demand has resulted in shortages

[0011] , Despite the widespread adoption and overall efficacy, the individual efficacy from AOMs on weight loss varies significantly [8]. For example, approximately 30% of patients who were prescribed Wegovy with lifestyle changes did not experience clinically meaningful weight loss of 10% after 68 weeks of use in a clinical trial[9] . The affordability and high demand of these medications has created a critical need for tests that can identify individuals who are most likely to respond to treatment, thereby ensuring more precise prescribing, relieving accessibility issues and reducing the burden of unnecessary costs on payors.

[0059] In this Example, we investigate the impact of genetic variation in a novel gene, neurobeachin (NBEA), on weight loss response to GLP-1RA. GLP-1 stimulates secretion of insulin through activation of protein kinase A (PKA)

[0012] , NBEA encodes a protein kinase A (PKA) anchor protein[12,13], which helps to drive PKA to specific targets. Although to our knowledge, NBEA has not been investigated in GLP-1 response, PKA anchor proteins have been implicated in endogenous GLP-1 response on insulin secretion

[0012] . Interestingly, NBEA has been previously implicated in dietary preference in pre-clinical models, where NBEA+ / _knockout mice consumed larger volumes of food with glucose or fructose compared to wildtype mice

[0013] We observed a similar finding in humans, where the C allele in rs57081354, a single nucleotide variant (SNV) in NBEA, was associated with increased consumption of desserts in a retrospective analysis of the Action to Control Cardiovascular Disease in Diabetes (ACCORD) clinical trial

[0014] In addition, SNVs in NBEA have been associated with increased BMI

[0013] ,

[0060] METHODS

[0061] Study Population and Data

[0062] Data was obtained from two real-world cohorts. 1) The NIH All of Us cohort is a real- world cohortfl 5], based in United States, that contains genetic data linked to electronic medical records, and was used for the discovery analysis and the development of the genetic Attorney Docket No.: CCF-43818.601 score. 2) We subsequently validated the findings using the UK Biobank, another large real- word cohort, based in the United Kingdom

[0016] . In both cohorts, participants were included if they had available electronic health record and genetic data, were prescribed GLP-1 medications, and had body mass index (BMI) of at least 27 kg / m2documented within six months prior to the first GLP-1 prescription. The BMI threshold of 27 kg / m2was chosen based on current prescription guidelines for weight loss medications

[0017] . Importantly, more recent prescription data was available for All of Us (2022) compared to UK Biobank (2017) [15,16,18], which impacted the availability of data regarding specific GLP-lRAs.

[0063] Semaglutide was only available in the All of Us data, and not in the UK Biobank. Due to the time-frame of available data, much of the data was recorded prior to weight loss approvals for GLP-lRAs and were only on-label for hyperglycemia during the time of the study record.

[0064] Weight loss response to GLP- 1 medication

[0065] Weight loss response to GLP- IRA was investigated 12 to 18 months after each patient’s first GLP1-RA prescription. Because different GLP-lRAs result in varying degrees of weight loss, we assessed weight loss in two ways: 1) percentage weight change: and 2) response type (i.e., highly responsive and non-responsive). Using percentage weight change distributions specific to each GLP- IRA (i.e., exenatide, albiglutide, lixisenatide, liraglutide, semaglutide, duraglutide), individuals were categorized as highly responsive if they were in the top 20th percentile of weight loss for that specific GLP- IRA. Individuals were categorized as non-responders if no weight loss was observed (weight change > 0%).

[0066] Development of NBEA signature in the NIH All of Us cohort

[0067] The genetic score for NBEA was developed using genetic and health record data from the NIH All of Us cohort. All of Us was used for discovery because it has more recent coverage of health records and hence, data available on more recently developed GLP-1 medications. SNVs from whole-genome sequencing (WGS), with minor allele frequencies (MAF) >2%, were retrieved for the NBEA gene (GRCh38 positions = 13:34942270- 35673022). The number of subjects (N) across all GLP-lRAs, after excluding those in the semaglutide withheld test set (30%), was 5953. PLINK vl.9 was used to fit linear regression models between percentage weight change and SNVs. All models were adjusted for sex, age at baseline, BMI at baseline, genetic ancestry, GLP- IRA medication, and duration of GLP- Attorney Docket No.: CCF-43818.601

[0068] IRA prescription between weight measurements. Genetic ancestry was captured using top ten principal components from whole genome sequencing

[0019] ,

[0069] NBEA scores were then calculated using the clumping and thresholding method implemented in PLINK vl.9. A null linear regression model was used to predict weight change based on sex, age at baseline, BMI at baseline, GLP-1RA medication, duration of GLP-1RA prescription and genetic ancestry. NBEA scores were added to null model and NBEA score with greatest improvement in R2values was selected for subsequent signature development. NBEA score thresholds were optimized to identify highly responsive and non- responsive individuals. For each response type (i.e. highly responsive, non-responsive), the following steps were taken: i) the score was partitioned at equal intervals and the likelihood of response was modeled for individuals with score below the threshold compared to individuals with score above the threshold, ii) Logistic regression models were used to test the association between type of responder and score. These models were adjusted for sex, age at baseline, BMI at baseline, duration of GLP-1RA prescription, type 2 diabetes status and genetic ancestry, iii) The threshold with lowest P value was selected as the optimal threshold (see Figure 4). Consequently, NBEA signature for the response was binary. In the case of highly responsive individuals, the score below the threshold was positive while the score above the threshold was positive for no response to GLP-lRAs.

[0070] Validation of NBEA signature in the UK Biobank

[0071] The NBEA gene signature was validated using electronic health record and genetic data in the UK Biobank. Genotype coverage of NBEA in UK Biobank was limited, so both genotyped and imputed variants were considered for score calculation. LiftOver was used to convert SNV positions from GRCh38 to GRCh37

[0020] . Logistic regression was used to test for associations with type of response (i.e., highly responsive, non-responsive) and NBEA signature, and models were adjusted for the same covariatcs as All of Us (i.e., sex, age at baseline, BMI at baseline, duration of GLP-1RA prescription, and genetic ancestry). A onesided statistical significance of P<05 was considered the threshold for successful validation.

[0072] Results

[0073] Study Population

[0074] The NIH All of Us cohort had 255,052 individuals with genotype data and linked electronic medical record data. Out of these individuals, 6,556 met our inclusion criteria for a Attorney Docket No.: CCF-43818.601 first prescription of a GLP-1RA with a recorded BMI within 6 months prior to the start of treatment, maintained the prescription for at least 12 months, and had a recorded BMI 12-18 months after initiation of treatment. Out of 198,275 individuals with available EHR data in the UK Biobank, 500 individuals met the same criteria. There was greater racial and ethnic diversity in the NIH All of Us cohort, with 1,386 (21.1%) and 955 (14.6%) individuals identifying as Black and Hispanic, respectively, compared to 10 (2.0%) and 0 (0%) in the UK Biobank (Table 1).

[0075] Table 1 Demographic and clinical characteristics of subjects prescribed GLP-1RA medications in the NIH All of Us and the UK Biobank cohort Attorney Docket No.: CCF-43818.601

[0076] 1. The NIH All of Us data does not report exact numbers when fewer than 20 patients are recorded.

[0077] There was also a greater proportion of females in the All of Us cohort (64.0%) compared to the UK Biobank (43.6%). BMI at baseline was similar between the two cohorts with 38.55 kg / m2(SD=8.1) and 37.0 kg / m2(SD=5.8) in the All of Us and UK Biobank, respectively.

[0078] Importantly, there were significant differences in the GLP-1RA medications used between the cohorts. Because semaglutide was first approved for type 2 diabetes in 2017, and the UK Biobank only contains medical record data up to 2017, data on semaglutide was not available in this cohort. A total of 2,012 individuals met our inclusion criteria and were prescribed semaglutide in the All of Us cohort. Liraglutide was the most prescribed GLP- 1RA during the time period of the available records with 2,064 individuals in the NIH All of Us cohort and 241 in the UK Biobank (Table 1). Attorney Docket No.: CCF-43818.601

[0079] Response to GLP-1 medication

[0080] Weight loss was not consistent across GLP-1RA medications. Liraglutide resulted in mean weight loss of 2.47% (SD=7.06) after 12-18 months in All of Us and 3.44% (SD=6.09) in the UK Biobank (Table 2).

[0081] Table 2 Attorney Docket No.: CCF-43818.601

[0082] Semaglutide resulted in 94% greater weight loss, with a mean of 4.87% (SD=7.53) in the All of Us cohort, and the 20th percentiles of weight loss (i.e., high response) were 7.57%, 6.38%, 7.77% and 10.9% weight on dulaglutide, exenatide, liraglutide and semaglutide, respectively. UK Biobank participants in the top 20thpercentile also lost 8.25% when prescribed liraglutide (Table 2). Attorney Docket No.: CCF-43818.601

[0083] A meaningful proportion of patients did not lose weight on GLP-lRAs. In the NIH All of Us cohort, 36.30% and 24.30% of patients did not lose any weight after 12-18 months of liraglutide and semaglutide, respectively. The percentage of patients that did not lose weight on liraglutide was slightly lower in UK Biobank, where 28.63% of patients prescribed liraglutide did not achieve any weight loss (Table 2).

[0084] NBEA Signature in All of Us

[0085] The NBEA score with highest R2improvement compared to the null model, consisted of 89 variants (Table 3) and was significantly associated with weight 12-18 month weight change (P=1.91xl019).

[0086] TABLE 3

[0087] Association results of core single nucleotide variants included in NBEA signature Attorney Docket No.: CCF-43818.601 Attorney Docket No.: CCF-43818.601

[0088] These variants were each individually associated with percentage weight change (P<.20) (Table 3). The score ranged from -0.31 to 0.17. One-tenth unit increase in score was associated with 1.44% (95% CI:.97%-1.90%) increase in weight change.

[0089] Notably, the NBEA signature was associated with increased odds of being in the top 20 percentile of weight loss on any GLP-1RA with an odds ratio (OR) of 1 .69 (95% CI: 1.46- 1.95, FDR P=l.lxl012). When stratified by specific GLP-lRAs, the NBEA signature was associated with a 1.82 (95% CI: 1.42-2.33, FDR P=1.8xl0’3) and 1.63 (95% CI: 1.21-2.20, FDR P- 001) for being in the top 20% of weight loss for liraglutide and semaglutide, respectively (Figure 2). In addition, all GLP-lRAs combined and semaglutide were significantly associated with being in the top 10% and top 15% of weight loss (FDR P<.05) (Figure 2). Interestingly, exenatide was the only GLP-1RA not associated with the NBEA signature across any threshold of weight loss (FDR P>.05). The NBEA signature was also associated with the likelihood of not losing any weight after 12-18 months of GLP-1RA treatment (Figure 2). Patients meeting this threshold of the NBEA signature were 39% more likely to not lose weight when all GLP-lRAs were combined (OR=1.39; 95% CI: 1.22-1.58, FDR P=l.lxl0’6). When stratified by specific GLP- lRAs, the NBEA signature was associated with increased odds of being non-responsive for all Attorney Docket No.: CCF-43818.601

[0090] GLP-lRAs, except for exenatide (Figure 2). The NBEA signature displayed a strong association with non-responsiveness for patients on semaglutide (OR=1.44; 95% CI: 1.07- 1.94, FDR P=.O16).

[0091] Validation of NBEA signature in the UK Biobank

[0092] Few of the variants used in the NBEA signature were directly genotyped in the UK Biobank. For the remaining variants, imputed values were used. The NBEA signature, using the same weighting developed in the All of Us data, was significantly associated with weight loss in the UK Biobank cohort (P=.O26), successfully replicating the findings in the All of Us cohort (Figure 3). Because dulaglutide and semaglutide were not available in the UK Biobank and because exenatide was not significantly associated with the NBEA signature in the All of Us cohort, we focused the validation on liraglutide and lixisenatide.

[0093] The NBEA score was significantly associated with the likelihood of being in the top 15% and 20% of weight loss for patients on liraglutide, with ORs = 2-51 (p = ■ 009) and 2- 37 (p = -008), but failed to reach significance for the top 10% of weight loss (OR = 1-28, p = -310). The NBEA score was also significantly associated with non- responsiveness for patients on liraglutide (OR = 1-81, 90% CI: 1 04-3-21, p = -040). For individuals taking semaglutide, in the withheld validation cohort, the NBEA score was associated with top 10% (OR = 2-45, p = -0002), top 15% (OR = 2-33, p = 9-4 x 10"5) and top 20% (OR = 2-21 , p = - 0001) weight loss, exhibiting a successful validation for the associations observed in the discovery cohort.

[0094] As of June 2024, it is estimated that approximately 12% of the U.S. population — over 40 million individuals — have used GLP-lRAs

[0021] Obesity is a major health concern, associated with over 200 comorbid conditions, including cardiovascular disease, diabetes, and various cancers [4-6], As clinical trials expand the potential indications for GLP-lRAs in treating obesity[9], there is increasing pressure for healthcare payors to broaden coverage for these therapies. While the long-term benefits of obesity treatment, including reductions in associated comorbidities, may potentially offset the high costs of GLP-1RA medications, the financial burden on the healthcare system remains a point of concern.

[0095] Importantly, not all patients experience significant weight loss when treated with GLP- lRAs. Clinical trial data indicates that approximately 30% of individuals do not achieve clinically meaningful weight loss with these therapies[9]. In data presented here, from real- Attorney Docket No.: CCF-43818.601 world settings, 58% of patients prescribed semaglutide failed to lose at least 5% body weight, and 24% did not experience any weight loss after 12 to 18 months of treatment. These findings highlight the need for predictive biomarkers that can identify patients that are most likely to benefit from GLP-1RA therapy, thereby optimizing clinical decision- making and avoiding ineffective treatments for non-responders.

[0096] EXAMPLE 2

[0097] This example describes accuracy of the NBEA signature using the Single Nucleotide Polymorphisms without the inclusion of clinical data.

[0098] Methods

[0099] Example 1 methods were applied except for adjustment of clinical variables in association testing between NBEA signature and response. Logistic regression models were fit using only NBEA signature to explain response to GLP-1RA medications.

[0100] Results

[0101] Application of NBEA signature in All of Us

[0102] The NBEA signature was associated with an increased offs of being in the top 20 percentile of weight loss on any GLP-1RA with an odds ratio (OR) of 1.43 (95% CI: 1.27-1.61, FDR P=6.7x10’9). When stratified by specific GLP-l RAs, the NBEA signature was associated with a 1.54 (95% CI: 1.32-2.04, FDR P=7.8xl0’6) for being in the top 20% of weight loss for liraglutide (Figure 5).

[0103] The NBEA signature was also associated with the likelihood of not losing any weight after 12-18 months of GLP-1RA treatment (Figure 5). Patients meeting this threshold of the NBEA signature were 23% more likely to not lose weight when all GLP-lRAs were combined (OR=1.23; 95% CI: 1.11-1.36, FDR P=1.3xl0’4).

[0104] Validation of NBEA signature in the UK Biobank

[0105] The NBEA signature, using the same weighting developed in the All of Us data, was significantly associated with weight loss in the UK Biobank cohort (P=.O36), successfully replicating the findings in the All of Us cohort (Figure 6). Attorney Docket No.: CCF-43818.601

[0106] The NBEA signature was significantly associated with increased odds of being in the top 20% of weight loss for semaglutide and liraglutide with ORs >1.76. The NBEA signature was statistically significant for the likelihood of patients being non-responsive to liraglutide (P=.O25).

[0107] Conclusion

[0108] These findings show that NBEA signature, without accounting for demographic and clinical data, is useful for identifying non-response and high response to GLP-lRAs.

[0109] EXAMPLE 3

[0110] This example describes accuracy of the NBEA signature using 36 most significant Single Nucleotide Polymorphisms. These 36 SNPs were as follows: chrl3:34957253:C:T chrl3:34996464:T:TTG chrl3:35018990:T:C chrl3:35044829:T:TAG chrl3:35044833:G:T chrl3:35045707:T:TTTTTG chrl3:35054614:G:GT chrl3:35078954:G:A chrl3:35110051:TA:T chrl3:35113586:CATCT:C chrl3:35150793:T:TTAGAGTAGAGTAGAG chrl3:35150793:T:TTAGAGTAGAGTAGAGTAGAG chrl3:35298225:A:G chrl3:35301170:G:GT chrl3:35308438:CTATATATA:C chrl3:35320575:G:T chrl3:35350728:A:ATGTG chrl3:35354906:G:A chrl3:35373707:A:AAATAATAATAATAATAATAATAAT chrl3:35389834:A:T chr!3:35389970:A:AGTGTGTGT chr!3:35400196:TAA:T Attorney Docket No.: CCF-43818.601 chr!3:35433984:A:T chrl3:35441111:C:T chrl3:35447490:T:TTA chrl3:35449873:C:G chrl3:35452320:G:A chrl3:35506768:C:G chrl3:35507613:A:G chrl3:35518902:A:G chrl3:35526488:A:G chrl3:35541182:GC:G chrl3:35606914:G:A chrl3:35607758:TA:T chrl3:35626800:T:C chrl3:35647444:C:T

[0111] Methods

[0112] The study population and methods from example 1 were applied, excluding NBEA score optimization and clinical variable adjustments. In example 1, the NBEA score with the greatest R2improvement was selected for signature development, while here, the score with significant R2improvement was used. This NBEA signature comprises 36 single nucleotide polymorphisms, and logistic regression models were not adjusted for demographic or clinical variables.

[0113] RESULTS

[0114] Application of NBEA signature in All of Us

[0115] The NBEA signature was associated with an increased odds of being in the top 20 percentile of weight loss on any GLP-1RA with an odds ratio (OR) of 1.50 (95% CI: 1.31- 1.71, FDR P=3.5xl0-9). When stratified by specific GLP-lRAs, the NBEA signature was associated with increased odds of high response for all GLP-lRAs except exenatide. Odds ratios of 1.76 (95% CI: 1.39-2.21, FDR P=1.8xl0’6) and 1.41 (95% CI: 1.06-1.86, FDR P=0.018) for being in the top 20% of weight loss for liraglutide and semaglutide, respectively (Figure 7). Attorney Docket No.: CCF-43818.601

[0116] The NBEA signature was also associated with the likelihood of not losing any weight after 12-18 months of GLP-1RA treatment (Figure 7). Patients meeting this threshold of the NBEA signature were 39% more likely to not lose weight when all GLP-lRAs were combined (OR=1.39; 95% CI: 1.24-1.56, FDR P=1.5xl0’8). The NBEA signature displayed the strongest association with non-responsiveness for patients on liraglutide (OR=1.69; 95% CI: 1.40-2.04, FDR P=6.2xl0’8).

[0117] Validation of NBEA signature in the UK Biobank

[0118] The NBEA signature, using the same weighting developed in the All of Us data, was significantly associated with highly responsive group in the UK Biobank cohort (P=.OO95), successfully replicating the findings in the All of Us cohort (Figure 8). While the association of the NBEA signature with any weight loss was not significant, the estimate was positive and consistent with the findings in All of Us.

[0119] Conclusion

[0120] The 36 variant NBEA signature was associated with no response and high response in All of Us. However, it was only significantly associated with no response in UK Biobank suggesting that it can be useful for prediction of patients unlikely to respond to GEP-lRAs.

[0121] EXAMPLE 4

[0122] This example describes accuracy of the NBEA signature using 27 most significant Single Nucleotide Polymorphisms. There SNPs are as follows in Table 4:

[0123] TABLE 4 chrl3:34996464:T:TTG chrl3:35044829:T:TAG chrl3:35044833:G:T chrl3:35045707:T:TTTTTG chrl3:35054614:G:GT chrl3:35078954:G:A chrl3:35110051:TA:T chrl3:35113586:CATCT:C chr!3:35298225:A:G chr!3:35301170:G:GT Attorney Docket No.: CCF-43818.601 chrl3:35308438:CTATATATA:C chrl3:35320575:G:T chrl3:35350728:A:ATGTG chrl3:35354906:G:A chrl3:35373707:A:AAATAATAATAATAATAATAATAAT chrl3:35389834:A:T chrl3:35400196:TAA:T chrl3:35433984:A:T chrl3:35449873:C:G chrl3:35506768:C:G chrl3:35507613:A:G chrl3:35526488:A:G chrl3:35541182:GC:G chrl3:35606914:G:A chrl3:35607758:TA:T chrl3:35626800:T:C chrl3:35647444:C:T

[0124] Methods

[0125] The study population and methods from example 1 were applied, excluding NBEA score optimization and clinical variable adjustments. In example 1, the NBEA score with the greatest R2improvement was selected for signature development, while here, the score with significant R2improvement was used. This NBEA signature comprises 27 single nucleotide polymorphisms, and logistic regression models were not adjusted for demographic or clinical variables.

[0126] Results

[0127] Application of NBEA signature in All of Us

[0128] The NBEA signature was associated with an increased offs of being in the top 20 percentile of weight loss on any GLP-1RA with an odds ratio (OR) of 1.72 (95% CI: 1.47- 2.02, FDR P=2.5xl011) (Figure 9). When stratified by specific GLP-lRAs, the NBEA signature was associated with being in top 20% weight loss for liraglutide and semaglutide (Figure 5). Attorney Docket No.: CCF-43818.601

[0129] The NBEA signature was also associated with the likelihood of not losing any weight after 12-18 months of GLP-1RA treatment (Figure 5). Patients meeting this threshold of the NBEA signature were 49% more likely to not lose weight when all GLP-lRAs were combined (OR=1.49; 95% CI: 1.32-1.68, FDR P=2.1xlO10).

[0130] Validation of NBEA signature in the UK Biobank

[0131] The NBEA score was significantly associated with increased odds of being in the top 20% of weight loss for patients on liraglutide with OR=1.84 (90% CI: 1.03-3.29, P=.O41) (Figure 10). The asscocoations for top 10%, 15% and 20% response were significant in the semaglutide validation set. The NBEA signature did reach statistical significance for the likelihood of patients being non -responsive to liraglutide (P=.O44).

[0132] Conclusion

[0133] This NBEA signature based on 1,082 single nucleotide polymorphisms can predict patients who are more likely experience top 10%, 15% and 20% of weight loss on GLP- lRAs.

[0134] EXAMPLE 5

[0135] This example describes accuracy of the NBEA signature using 10 most significant Single Nucleotide Polymorphisms. The 10 polymorphisms are as follows in Table 5:

[0136] TABLE 5 chrl3:34996464:T:TTG chrl3:35044829:T:TAG chrl3:35298225:A:G chrl3:35308438:CTATATATA:C chrl3:35350728:A:ATGTG chrl3:35354906:G:A chrl3:35373707:A:AAATAATAATAATAATAATAATAAT chrl3:35449873:C:G chrl3:35507613:A:G chrl3:35607758:TA:T Attorney Docket No.: CCF-43818.601

[0137] Methods

[0138] The All of US study population and methods from example 1 were applied, excluding NBEA score optimization and clinical variable adjustments. In example 1, the NBEA score with the greatest R2improvement was selected for signature development, while here, the score with significant R2improvement was used. This NBEA signature comprises 9 single nucleotide polymorphisms, and logistic regression models were not adjusted for demographic or clinical variables.

[0139] Results

[0140] Application of NBEA signature in All of Us

[0141] The NBEA signature was associated with an increased offs of being in the top 20 percentile of weight loss on any GLP-1RA with an odds ratio (OR) of 1.46 (95% CI: 1.29- 1.65, FDR P=2.6xl0-5). When stratified by specific GLP-lRAs, the NBEA signature was associated with a 1.79 (95% CI: 1.52-2.11, FDR P=.O41) for being in the top 20% of weight loss for liraglutide (Figure 11).

[0142] The NBEA signature was also associated with the likelihood of not losing any weight after 12-18 months of GLP-1RA treatment (Figure 11). Patients meeting this threshold of the NBEA signature were 46% more likely to not lose weight when all GLP-lRAs were combined (OR=1.46; 95% CI: 1.29-1.65, FDR P=2.6xl0’5). When stratified by specific GLP- l RAs, the NBEA signature was associated with increased odds of being non-responsive for liraglutide and dulaglutide (Figure 11).

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[0183] All publications and patents mentioned in the specification and / or listed below are herein incorporated by reference. Various modifications and variations of the described method and system of the invention will be apparent to those skilled in the art without departing from the scope and spirit of the invention. Although the invention has been described in connection with specific embodiments, it should be understood that the invention as claimed should not be unduly limited to such specific embodiments. Indeed, various modifications of the described modes for carrying out the invention that are obvious to those skilled in the relevant fields are intended to be within the scope described herein.

Claims

Attorney Docket No.: CCF-43818.601CLAIMSWe Claim:

1. A method comprising : performing a nucleic acid detection assay on a DNA sample from a subject and detecting the presence of at least one polymorphism from Tables 3, 4, 5, or 6 in the human Neurobeachin (NBEA) gene, wherein said subject has a body mass index (BMI) above 25.0 kg / m2and / or has type 2 diabetes.

2. The method of claim 1, further comprising: determining said subject's weight and determining said subject's height.

3. The method of claim 2, further comprising calculating said subject's BMI based on dividing said subject's weight in kilograms by the square of said subject's height in meters.

4. The method of claim 1, wherein said at least one polymorphism is at least 5, or at least 9 polymorphisms.

5. The method of claim 1, wherein said at least one polymorphism comprises the following ten polymorphisms: chrl3:34996464:T:TTG; chrl3:35044829:T:TAG; chrl3:35298225:A:G; chrl3:35308438:CTATATATA:C; chrl3:35350728:A:ATGTG chrl3:35354906:G:A; chrl3:35373707:A:AAATAATAATAATAATAATAATAAT chrl3:35449873:C:G; chrl3:35507613:A:G; and chrl3:35607758:TA:T.

6. The method of claim 1, wherein said at least one polymorphism comprises the following 27 polymorphisms: chrl3:34996464:T:TTG; chrl3:35044829:T:TAG; chrl3:35044833:G:T; chrl3:35045707:T:TTTTTG; chrl3:35054614:G:GT; chrl3:35078954:G:A; chrl3:35110051:TA:T; chrl3:35113586:CATCT:C; chrl3:35298225:A:G; chrl3:35301170:G:GT; chrl3:35308438:CTATATATA:C; chrl3:35320575:G:T; chrl3:35350728:A:ATGTG; chrl3:35354906:G:A; chrl3:35373707:A:AAATAATAATAATAATAATAATAAT; chrl3:35389834:A:T; chrl3:35400196:TAA:T; chrl3:35433984:A:T; chrl3:35449873:C:G; chrl3:35506768:C:G; chrl3:35507613:A:G; chrl3:35526488:A:G; chrl3:35541182:GC:G; chrl3:35606914:G:A; chr!3:35607758:TA:T; chr!3:35626800:T:C; and chr!3:35647444:C:T.Attorney Docket No.: CCF-43818.6017. The method of claim 1. wherein said at least one polymorphism comprises the following 36 polymorphisms: chrl3:34957253:C:T; chrl3:34996464:T:TTG; chrl3:35018990:T:C; chrl3:35044829:T:TAG; chrl3:35044833:G:T; chrl3:35045707:T:TTTTTG; chrl3:35054614:G:GT; chrl3:35078954:G:A; chrl3:35110051:TA:T; chrl3:35113586:CATCT:C; chrl3:35150793:T:TTAGAGTAGAGTAGAG; chrl3:35150793:T:TTAGAGTAGAGTAGAGTAGAG; chrl3:35298225:A:G; chrl3:35301170:G:GT; chrl3:35308438:CTATATATA:C; chrl3:35320575:G:T; chrl3:35350728:A:ATGTG; chrl3:35354906:G:A; chrl3:35373707:A:AAATAATAATAATAATAATAATAAT; chrl3:35389834:A:T; chrl3:35389970:A:AGTGTGTGT; chrl3:35400196:TAA:T; chrl3:35433984:A:T; chrl3:35441111:C:T; chrl3:35447490:T:TTA; chrl3:35449873:C:G; chrl3:35452320:G:A; chrl3:35506768:C:G; chrl3:35507613:A:G; chrl3:35518902:A:G; chrl3:35526488:A:G; chrl3:35541182:GC:G; chrl3:35606914:G:A; chrl3:35607758:TA:T; chrl3:35626800:T:C; and chrl3:35647444:C:T.

8. The method of claim 1, wherein said subject is not currently receiving treatment with a Glucagon-like peptide-1 receptor agonist (GLP-1RA) and / or said DNA sample is free from any GLP-lRAs.

9. The method of claim 1, wherein said subject has expressed an interest in receiving treatment with a GLP-1RA; and / or wherein said subject is a human and / or wherein said subject's BMI is 29.0 or more; and / or wherein said nucleic acid detection assay comprises a probe-based assay or comprises sequencing, wherein said DNA sample is optionally a cell- free DNA sample.

10. A method performing an activity based on the presence of at least one polymorphism in the human Neurobeachin (NBEA) gene of a subject comprising: a) performing a nucleic acid detection assay on a DNA sample from a subject, or receiving results from said assay, wherein said assay detects the presence in the human Neurobeachin (NBEA) gene of at least one polymorphism selected from: those Tables 3, 4, 5, or 6, and wherein said subject optionally has a body mass index (BMI) above 25.0 kg / m2and / or has type 2 diabetes; and b) performing at least one of the following activities: i) treating said subject with a Glucagon-like peptide-1 receptor agonist (GLP-1RA),Attorney Docket No.: CCF-43818.601 ii) providing an administration device containing said GLP-1RA to said subject, iii) demonstrating how to use said administration device for said patient; iv) providing a non-functional administration device to said subject as a demonstrative to teach said subject how to use said administration device; v) orally informing said subject that they are a good candidate to achieve weight loss with said GLP-1RA based on the presence of said at least one polymorphism; vi) informing said patient in writing that they are a good candidate to achieve weight loss with said GLP-1RA based on the presence of said at least one polymorphism; vii) displaying and / or generating and / or transmitting a report that indicates the presence of said at least one polymorphism, and optionally that said subject should be treated with GLP-1RA; and viii) updating said subject's medical records by indicating that they are not a good candidate for achieving weight loss with said GLP-1RA.

11. The method of claim 10, wherein said GLP-1RA is selected from: Exenatide, Lixisenatide, Liraglutide, Dulaglutide, Albiglutide, Semaglutide, and Tirzepatide.

12. The method of claim 10, further comprising: determining said subject's weight and determining said subject's height.

13. The method of claim 12, further comprising calculating said subject's BMI based on dividing said subject's weight in kilograms by the square of said subject's height in meters.

14. The method of Claim 10, wherein said at least one polymorphism is at least five, or at least nine polymorphisms.

15. The method of claim 10, wherein said at least one polymorphism comprises the following 10 polymorphisms: chrl3:34996464:T:TTG chrl3:35044829:T:TAG chrl3:35298225:A:GAttorney Docket No.: CCF-43818.601 chrl3:35308438:CTATATATA:C chrl3:35350728:A:ATGTG chrl3:35354906:G:A chrl3:35373707:A:AAATAATAATAATAATAATAATAAT chrl3:35449873:C:G chrl3:35507613:A:G chrl3:35607758:TA:T16. The method of claim 10, wherein said at least one polymorphism comprises the following 27 polymorphisms: chrl3:34996464:T:TTG chrl3:35044829:T:TAG chrl3:35044833:G:T chrl3:35045707:T:TTTTTG chrl3:35054614:G:GT chrl3:35078954:G:A chrl3:35110051:TA:T chrl3:35113586:CATCT:C chrl3:35298225:A:G chrl3:35301170:G:GT chrl3:35308438:CTATATATA:C chrl3:35320575:G:T chrl3:35350728:A:ATGTG chrl3:35354906:G:A chrl3:35373707:A:AAATAATAATAATAATAATAATAAT chrl3:35389834:A:T chrl3:35400196:TAA:T chrl3:35433984:A:T chrl3:35449873:C:G chrl3:35506768:C:G chrl3:35507613:A:G chrl3:35526488:A:G chrl3:35541182:GC:G chrl3:35606914:G:A chr!3:35607758:TA:TAttorney Docket No.: CCF-43818.601 chrl3:35626800:T:C chrl3:35647444:C:T.

17. The method of claim 10, wherein said at least one polymorphism comprises the following 36 polymorphisms: chrl3:34957253:C:T chrl3:34996464:T:TTG chrl3:35018990:T:C chrl3:35044829:T:TAG chrl3:35044833:G:T chrl3:35045707:T:TTTTTG chrl3:35054614:G:GT chrl3:35078954:G:A chrl3:35110051:TA:T chrl3:35113586:CATCT:C chrl3:35150793:T:TTAGAGTAGAGTAGAG chrl3:35150793:T:TTAGAGTAGAGTAGAGTAGAG chrl3:35298225:A:G chrl3:35301170:G:GT chrl3:35308438:CTATATATA:C chrl3:35320575:G:T chrl3:35350728:A:ATGTG chrl3:35354906:G:A chrl3:35373707:A:AAATAATAATAATAATAATAATAAT chrl3:35389834:A:T chrl3:35389970:A:AGTGTGTGT chrl3:35400196:TAA:T chrl3:35433984:A:T chrl3:35441111:C:T chrl3:35447490:T:TTA chrl3:35449873:C:G chrl3:35452320:G:A chrl3:35506768:C:G chr!3:35507613:A:GAttorney Docket No.: CCF-43818.601 chrl3:35518902:A:G chrl3:35526488:A:G chrl3:35541182:GC:G chrl3:35606914:G:A chrl3:35607758:TA:T chrl3:35626800:T:C chrl3:35647444:C:T18. The method of claim 10, wherein said subject is not currently receiving treatment with a Glucagon-like peptide-1 receptor agonist (GLP-1RA); and / or said DNA sample is free from any GLP-lRAs; and / or wherein said subject is a human and / or wherein said subject's BMI is 29.0 or more.1 . The method of claim 10, wherein said nucleic acid detection assay comprises a probebased assay or comprises sequencing, wherein said DNA sample is optionally a cell-free DNA sample.

20. The method of Claim 10, wherein step a) is receiving results from said assay, and wherein said at least one of the following activities is treating said subject with said GLP- 1RA.

21. A kit, system, or composition comprising : a) PCR primers for amplifying, and / or nucleic acid probes for detecting or enriching, at least one, five, or nine, polymorphisms in the human Neurobeachin (NBEA) gene selected from the those in Tables 3, 4, 5, or 6.

22. The kit, system, or composition of claim 21, further comprising a DNA sample from a subject that: is overweight, is obese, has type 2 diabetes, has expressed an interest in Glucagon- like peptide-1 receptor agonists (GLP-lRAs), is not currently receiving treatment with a Glucagon-like peptide-1 receptor agonist (GLP-1RA).

23. The kit, system, or composition of Claim 21 , wherein said at least one polymorphism is at least five polymoiphisms, or at least nine polymorphisms.Attorney Docket No.: CCF-43818.60124. The kit, system, or composition of claim 21, wherein said at least one polymorphism comprises the following 10 polymorphisms: chrl3:34996464:T:TTG chrl3:35044829:T:TAG chrl3:35298225:A:G chrl3:35308438:CTATATATA:C chrl3:35350728:A:ATGTG chrl3:35354906:G:A chrl3:35373707:A:AAATAATAATAATAATAATAATAAT chrl3:35449873:C:G chrl3:35507613:A:G chrl3:35607758:TA:T.

25. The kit, system, or composition of claim 21, wherein said at least one polymorphism comprises the following 27 polymorphisms: chrl3:34996464:T:TTG chrl3:35044829:T:TAG chrl3:35044833:G:T chrl3:35045707:T:TTTTTG chrl3:35054614:G:GT chrl3:35078954:G:A chrl3:35110051:TA:T chrl3:35113586:CATCT:C chrl3:35298225:A:G chrl3:35301170:G:GT chrl3:35308438:CTATATATA:C chrl3:35320575:G:T chrl3:35350728:A:ATGTG chrl3:35354906:G:A chrl3:35373707:A:AAATAATAATAATAATAATAATAAT chrl3:35389834:A:T chrl3:35400196:TAA:T chrl3:35433984:A:T chr!3:35449873:C:GAttorney Docket No.: CCF-43818.601 chrl3:35506768:C:G chrl3:35507613:A:G chrl3:35526488:A:G chrl3:35541182:GC:G chrl3:35606914:G:A chrl3:35607758:TA:T chrl3:35626800:T:C chrl3:35647444:C:T.

26. The kit, system, or composition of claim 21, wherein said at least one polymorphism comprises the following 36 polymorphisms: chrl3:34957253:C:T chrl3:34996464:T:TTG chrl3:35018990:T:C chrl3:35044829:T:TAG chrl3:35044833:G:T chrl3:35045707:T:TTTTTG chrl3:35054614:G:GT chrl3:35078954:G:A chrl3:35110051:TA:T chrl3:35113586:CATCT:C chrl3:35150793:T:TTAGAGTAGAGTAGAG chrl3:35150793:T:TTAGAGTAGAGTAGAGTAGAG chrl3:35298225:A:G chrl3:35301170:G:GT chrl3:35308438:CTATATATA:C chrl3:35320575:G:T chrl3:35350728:A:ATGTG chrl3:35354906:G:A chrl3:35373707:A:AAATAATAATAATAATAATAATAAT chrl3:35389834:A:T chrl3:35389970:A:AGTGTGTGT chrl3:35400196:TAA:T chr!3:35433984:A:TAttorney Docket No.: CCF-43818.601 chrl3:35441111:C:T chrl3:35447490:T:TTA chrl3:35449873:C:G chrl3:35452320:G:A chrl3:35506768:C:G chrl3:35507613:A:G chrl3:35518902:A:G chrl3:35526488:A:G chrl3:35541182:GC:G chrl3:35606914:G:A chrl3:35607758:TA:T chrl3:35626800:T:C chrl3:35647444:C:T27. A method performing an activity based detecting the lack of sufficient polymorphisms in the human Neurobeachin (NBEA) gene of a subject comprising: a) performing a nucleic acid detection assay on a DNA sample from a subject, or receiving results from said assay, wherein said assay detects the lack of at least nine polymorphisms in the human Neurobeachin (NBEA) gene, wherein said polymorphisms selected from: the those in Tables 3, 4, 5, or 6, and wherein said subject optionally has a body mass index (BMI) above 25.0 kg / m2and / or has type 2 diabetes; and b) performing at least one of the following activities: i) orally informing said subject that they are not a good candidate to achieve weight loss with said GLP-1RA; ii) informing said patient in writing that they are not a good candidate to achieve weight loss with said GLP-1RA; iii) displaying and / or generating and / or transmitting a report that indicates the presence of said at least one polymorphism, and optionally that said subject should be treated with GLP-1RA; and iv) updating said subject's medical records by indicating that they are not a good candidate for achieving weight loss with said GLP-1RA.

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