# Patent

## Title
Methods and materials for assessing and treating obesity

## Abstract

This document relates to methods and materials for assessing and/or treating obese mammals (e.g., obese humans). For example, methods and materials for using one or more interventions (e.g., one or more pharmacological interventions) to treat obesity and/or obesity-related comorbidities in a mammal (e.g., a human) identified as being likely to respond to a particular intervention (e.g., a pharmacological intervention) are provided.

## Background
CROSS-REFERENCE TO RELATED APPLICATIONS
This application is a National Stage application under 35 U.S.C. § 371 of International Application No. PCT/US2018/062217, having an International Filing Date of Nov. 21, 2018, which claims the benefit of U.S. Patent Application Ser. No. 62/589,915, filed on Nov. 22, 2017. The disclosure of the prior application is considered part of (and is incorporated by reference in) the disclosure of this application.


STATEMENT REGARDING FEDERAL FUNDING
This invention was made with government support under DK067071 and DK084567 awarded by the National Institutes of Health. The government has certain rights in the invention.


BACKGROUND
1. Technical Field
This document relates to methods and materials for assessing and/or treating obesity in mammals (e.g., humans). For example, this document provides methods and materials for determining an obesity analyte signature of a mammal. For example, this document provides methods and materials for determining an obesity phenotype of a mammal. For example, this document provides methods and materials for using one or more interventions (e.g., one or more pharmacological interventions) to treat obesity and/or obesity-related comorbidities in a mammal (e.g., a human) identified as being likely to respond to a particular intervention (e.g., a pharmacological intervention).
2. Background Information
Obesity prevalence continues to increase worldwide (Ng et al., 2014 Lancet 384:766-81) and, in the United States, 69% of adults are overweight or obese (Flegal et al., 2012 JAMA 307:491-497). Estimated costs to the healthcare system are more than $550 billion annually. Increased severity of obesity correlates with a higher prevalence of the associated co-morbidities. Likewise, obesity increases the risk of premature mortality (Hensrud et al., 2006 Mayo Clinic Proceedings 81(10 Suppl):S5-10). Obesity affects almost every organ system in the body and increases the risk of numerous diseases including type 2 diabetes mellitus, hypertension, dyslipidemia, cardiovascular disease, and cancer. It is estimated that a man in his twenties with a BMI over 45 will have a 22% reduction (13 years) in life expectancy.
SUMMARY
Despite advances in understanding aspects of obesity pathophysiology, weight loss with current treatments including diet, exercise, medications, endoscopy, and surgery is highly variable (Acosta et al., 2014 Gut 63:687-95). For example, some obese patients specifically respond to particular medications, and can lose as much weight and with fewer side effects than bariatric surgery. There is a need to be able to identify which intervention(s) an obese patient is likely to respond to in order to be able to select the right intervention for the right patient based on his/her pathophysiology.
This document provides methods and materials for assessing and/or treating obesity in mammals (e.g., humans). In some cases, this document provides methods and materials for identifying an obese mammal as being responsive to a pharmacological intervention (e.g., by identifying the mammal as having a pharmacotherapy responsive obesity analyte signature), and administering one or more interventions (e.g., pharmacological interventions) to treat the mammal. For example, a sample obtained from an obese mammal can be assessed to determine if the obese mammal is likely to be responsive to pharmacological intervention based, at least in part, on an obesity phenotype, which is based, at least in part, on an obesity analyte signature in the sample. As demonstrated herein, a distinct obesity analyte signature is present in each of six main obesity phenotype groups: 1) low satiation, 2) low satiety (e.g., rapid return to hunger), 3) behavioral eating (identified by questionnaire), 4) large fasting gastric volume, 5) mixed, and 6) low resting energy expenditure group; and each obesity phenotype is likely to be responsive to one or more particular interventions (e.g., pharmacological intervention, surgical intervention, weight loss device, diet intervention, behavior intervention, and/or microbiome intervention).
Having the ability to identify which intervention(s) an obese patient is likely to respond to provides a unique and unrealized opportunity to provide an individualized approach in selecting obesity treatments.
In general, one aspect of this document features a method for treating obesity in a mammal. The method includes, or consists essentially of, identifying the mammal as having an intervention responsive obesity analyte signature in a sample obtained from the mammal; and administering an intervention to the mammal. The sample can be a blood sample, a saliva sample, a urine sample, a breath sample, or a stool sample. For example, the sample can be a breath sample. For example, the method sample can be a stool sample. The mammal can be a human. In some cases, the obesity analyte signature can include 1-methylhistine, serotonin, glutamine, gamma-amino-n-butyric-acid, isocaproic, allo-isoleucine, hydroxyproline, beta-aminoisobutyric-acid, alanine, hexanoic, tyrosine, phenylalanine, ghrelin, and peptide tyrosine tyrosine (PYY). The intervention can be effective to reduce the total body weight of said mammal by at least 4%. The intervention can be effective to reduce the total body weight of said mammal by from about 3 kg to about 100 kg. The intervention can be effective to reduce the waist circumference of said mammal by from about 1 inches to about 10 inches. The identifying step also can include obtaining results from a Hospital Anxiety and Depression Scale (HADS) questionnaire and/or a Three Factor Eating questionnaire (TFEQ). In some cases, the obesity analyte signature can include a presence of serotonin, glutamine, isocaproic, allo-isoleucine, hydroxyproline, beta-aminoisobutyric-acid, alanine, hexanoic, tyrosine, and PYY, and an absence of (e.g., lacks the presence of) 1-methylhistine, gamma-amino-n-butyric-acid, phenylalanine, ghrelin; the HADS questionnaire result does not indicate an anxiety subscale; and the mammal can be responsive to intervention with phentermine-topiramate pharmacotherapy and/or lorcaserin pharmacotherapy. In some cases, the obesity analyte signature can include a presence of 1-methylhistine, allo-isoleucine, hydroxyproline, beta-aminoisobutyric-acid, alanine, and phenylalanine, and an absence of serotonin, glutamine, gamma-amino-n-butyric-acid, isocaproic, hexanoic, tyrosine, ghrelin, and PYY; the HADS questionnaire result not indicate an anxiety subscale; and the mammal can be responsive to intervention with liraglutide pharmacotherapy. In some cases, the obesity analyte signature can include a presence of serotonin, and an absence of 1-methylhistine, glutamine, gamma-amino-n-butyric-acid, isocaproic, allo-isoleucine, hydroxyproline, beta-aminoisobutyric-acid, alanine, hexanoic, tyrosine, phenylalanine, ghrelin, and PYY; the HADS questionnaire result indicates an anxiety subscale; and the mammal can be responsive to intervention with naltrexone-bupropion pharmacotherapy. In some cases, the obesity analyte signature can include a presence of 1-methylhistine, glutamine, gamma-amino-n-butyric-acid, isocaproic, allo-isoleucine, beta-aminoisobutyric-acid, alanine, hexanoic, tyrosine, phenylalanine, PYY, and an absence of serotonin, hydroxyproline, and ghrelin; the HADS questionnaire result indicates an anxiety subscale; and the mammal can be responsive to intervention with naltrexone-bupropion pharmacotherapy. In some cases, the obesity analyte signature can include a presence of 1-methylhistine, serotonin, glutamine, gamma-amino-n-butyric-acid, isocaproic, allo-isoleucine, alanine, tyrosine, ghrelin, PYY, and an absence of hydroxyproline, beta-aminoisobutyric-acid, hexanoic, and phenylalanine; the HADS questionnaire result indicates an anxiety subscale; and the mammal can be responsive to intervention with phentermine pharmacotherapy. In some cases, the obesity analyte signature can include HTR2C, GNB3, FTO, iso-caproic acid, beta-aminoisobutyricacid, butyric, allo-isoleucine, tryptophan, and glutamine. The identifying step also can include obtaining results from a HADS questionnaire. In some cases, the obesity analyte signature can include the presence of a single nucleotide polymorphism (SNP) in HTR2C, POMC, NPY, AGRP, MC4R, GNB3, SERT, and/or BDNF; the HADS questionnaire result does not indicate an anxiety subscale; and the mammal can be responsive to intervention with phentermine-topiramate pharmacotherapy and/or lorcaserin pharmacotherapy. The SNP can be rs1414334. In some cases, the obesity analyte signature can include the presence of a SNP in PYY, GLP-1, MC4R, GPBAR1, TCF7L2, ADRA2A,PCSK, and/or TMEM18; the HADS questionnaire result not indicate an anxiety subscale; and the mammal can be responsive to intervention with liraglutide pharmacotherapy. The SNP can be rs7903146. In some cases, the obesity analyte signature can include presence of a SNP in SLC6A4/SERT, and/or DRD2; the HADS questionnaire result indicates an anxiety subscale; and the mammal can be responsive to intervention with naltrexone-bupropion pharmacotherapy. The SNP can be rs4795541. In some cases, the obesity analyte signature can include the presence of a SNP in TCF7L2, UCP3, and/or ADRA2A; the HADS questionnaire result indicates an anxiety subscale; and the mammal can be responsive to intervention with naltrexone-bupropion pharmacotherapy. The SNP can be rs1626521. In some cases, the obesity analyte signature can include the presence of a SNP in FTO, LEP, LEPR, UCP1, UCP2, UCP3, ADRA2, KLF14, NPC1, LYPLAL1, ADRB2, ADRB3, and/or BBS1; the HADS questionnaire result indicates an anxiety subscale; and the mammal can be responsive to intervention with phentermine pharmacotherapy. The SNP can be rs2075577.
In another aspect, this document features a method for treating obesity in a mammal. The method includes, or consists essentially of, administering an intervention to a mammal that was identified as having an intervention responsive obesity analyte signature. The mammal can be a human. The obesity analyte signature can include 1-methylhistine, serotonin, glutamine, gamma-amino-n-butyric-acid, isocaproic, allo-isoleucine, hydroxyproline, beta-aminoisobutyric-acid, alanine, hexanoic, tyrosine, phenylalanine, ghrelin, and peptide tyrosine tyrosine (PYY). The intervention can be effective to reduce the total body weight of said mammal by at least 4%. The intervention can be effective to reduce the total body weight of said mammal by from about 3 kg to about 100 kg. The intervention can be effective to reduce the waist circumference of said mammal by from about 1 inches to about 10 inches. The identifying step also can include obtaining results from a Hospital Anxiety and Depression Scale (HADS) questionnaire. In some cases, the obesity analyte signature can include a presence of serotonin, glutamine, isocaproic, allo-isoleucine, hydroxyproline, beta-aminoisobutyric-acid, alanine, hexanoic, tyrosine, and PYY, and an absence of (e.g., lacks the presence of) 1-methylhistine, gamma-amino-n-butyric-acid, phenylalanine, ghrelin; the HADS questionnaire result does not indicate an anxiety subscale; and the mammal can be responsive to intervention with phentermine-topiramate pharmacotherapy and/or lorcaserin pharmacotherapy. In some cases, the obesity analyte signature can include a presence of 1-methylhistine, allo-isoleucine, hydroxyproline, beta-aminoisobutyric-acid, alanine, and phenylalanine, and an absence of serotonin, glutamine, gamma-amino-n-butyric-acid, isocaproic, hexanoic, tyrosine, ghrelin, and PYY; the HADS questionnaire result not indicate an anxiety subscale; and the mammal can be responsive to intervention with liraglutide pharmacotherapy. In some cases, the obesity analyte signature can include a presence of serotonin, and an absence of 1-methylhistine, glutamine, gamma-amino-n-butyric-acid, isocaproic, allo-isoleucine, hydroxyproline, beta-aminoisobutyric-acid, alanine, hexanoic, tyrosine, phenylalanine, ghrelin, and PYY; the HADS questionnaire result indicates an anxiety subscale; and the mammal can be responsive to intervention with naltrexone-bupropion pharmacotherapy. In some cases, the obesity analyte signature can include a presence of 1-methylhistine, glutamine, gamma-amino-n-butyric-acid, isocaproic, allo-isoleucine, beta-aminoisobutyric-acid, alanine, hexanoic, tyrosine, phenylalanine, PYY, and an absence of serotonin, hydroxyproline, and ghrelin; the HADS questionnaire result indicates an anxiety subscale; and the mammal can be responsive to intervention with naltrexone-bupropion pharmacotherapy. In some cases, the obesity analyte signature can include a presence of 1-methylhistine, serotonin, glutamine, gamma-amino-n-butyric-acid, isocaproic, allo-isoleucine, alanine, tyrosine, ghrelin, PYY, and an absence of hydroxyproline, beta-aminoisobutyric-acid, hexanoic, and phenylalanine; the HADS questionnaire result indicates an anxiety subscale; and the mammal can be responsive to intervention with phentermine pharmacotherapy.
In another aspect, this document features a method for identifying an obese mammal as being responsive to treatment with an intervention. The method includes, or consists essentially of, determining an obesity analyte signature in a sample obtained from a mammal, where the obesity analyte signature can include 1-methylhistine, serotonin, glutamine, gamma-amino-n-butyric-acid, isocaproic, allo-isoleucine, hydroxyproline, beta-aminoisobutyric-acid, alanine, hexanoic, tyrosine, phenylalanine, ghrelin, and PYY; and classifying the mammal as having an intervention responsive obesity analyte signature based upon the presence and absence of analytes in the obesity analyte signature. The mammal can be a human. The sample can be a blood sample, a saliva sample, a urine sample, a breath sample, or a stool sample. For example, the sample can be a breath sample. For example, the method sample can be a stool sample. The method also can include obtaining results from a HADS questionnaire. In some cases, the obesity analyte signature can include a presence of serotonin, glutamine, isocaproic, allo-isoleucine, hydroxyproline, beta-aminoisobutyric-acid, alanine, hexanoic, tyrosine, and PYY, and an absence of (e.g., lacks the presence of) 1-methylhistine, gamma-amino-n-butyric-acid, phenylalanine, ghrelin; the HADS questionnaire result does not indicate an anxiety subscale; and the mammal can be responsive to intervention with phentermine-topiramate pharmacotherapy and/or lorcaserin pharmacotherapy. In some cases, the obesity analyte signature can include a presence of 1-methylhistine, allo-isoleucine, hydroxyproline, beta-aminoisobutyric-acid, alanine, and phenylalanine, and an absence of serotonin, glutamine, gamma-amino-n-butyric-acid, isocaproic, hexanoic, tyrosine, ghrelin, and PYY; the HADS questionnaire result not indicate an anxiety subscale; and the mammal can be responsive to intervention with liraglutide pharmacotherapy. In some cases, the obesity analyte signature can include a presence of serotonin, and an absence of 1-methylhistine, glutamine, gamma-amino-n-butyric-acid, isocaproic, allo-isoleucine, hydroxyproline, beta-aminoisobutyric-acid, alanine, hexanoic, tyrosine, phenylalanine, ghrelin, and PYY; the HADS questionnaire result indicates an anxiety subscale; and the mammal can be responsive to intervention with naltrexone-bupropion pharmacotherapy. In some cases, the obesity analyte signature can include a presence of 1-methylhistine, glutamine, gamma-amino-n-butyric-acid, isocaproic, allo-isoleucine, beta-aminoisobutyric-acid, alanine, hexanoic, tyrosine, phenylalanine, PYY, and an absence of serotonin, hydroxyproline, and ghrelin; the HADS questionnaire result indicates an anxiety subscale; and the mammal can be responsive to intervention with naltrexone-bupropion pharmacotherapy. In some cases, the obesity analyte signature can include a presence of 1-methylhistine, serotonin, glutamine, gamma-amino-n-butyric-acid, isocaproic, allo-isoleucine, alanine, tyrosine, ghrelin, PYY, and an absence of hydroxyproline, beta-aminoisobutyric-acid, hexanoic, and phenylalanine; the HADS questionnaire result indicates an anxiety subscale; and the mammal can be responsive to intervention with phentermine pharmacotherapy.
In another aspect, this document features a identifying an obese mammal as being responsive to treatment with an intervention. The method includes, or consists essentially of, determining an obesity analyte signature in a sample obtained from an obese mammal mammal, where the obesity analyte signature includes HTR2C, GNB3, FTO, iso-caproic acid, beta-aminoisobutyricacid, butyric, allo-isoleucine, tryptophan, and glutamine; obtaining results from a HADS questionnaire; and classifying the mammal as having a intervention responsive obesity analyte signature based upon the presence and absence of analytes in the obesity analyte signature. The mammal can be a human. The sample can be a blood sample, a saliva sample, a urine sample, a breath sample, or a stool sample. In some cases, the sample can be a breath sample. In some cases, the sample can be a stool sample. In some cases, the obesity analyte signature can include the presence of a SNP in HTR2C, POMC, NPY, AGRP, MC4R, GNB3, SERT, and/or BDNF; the HADS questionnaire result can not indicate an anxiety subscale; and the mammal can be classified as being responsive to intervention with phentermine-topiramate pharmacotherapy and/or lorcaserin pharmacotherapy. The SNP can be rs1414334. In some cases, the obesity analyte signature can include the presence of a SNP in PYY, GLP-1, MC4R, GPBAR1, TCF7L2, ADRA2A,PCSK, and/or TMEM18; the HADS questionnaire result can not indicate an anxiety subscale; and the mammal can be classified as being responsive to intervention with liraglutide pharmacotherapy. The SNP can be rs7903146. In some cases, the obesity analyte signature can include the presence of a SNP in SLC6A4/SERT, and/or DRD2; the HADS questionnaire result can indicate an anxiety subscale; and the mammal can be classified as being responsive to intervention with naltrexone-bupropion pharmacotherapy. The SNP can be rs4795541. In some cases, the obesity analyte signature can include the presence of a SNP in TCF7L2, UCP3, and/or ADRA2A; the HADS questionnaire result can indicate an anxiety subscale; and the mammal can be classified as being responsive to intervention with naltrexone-bupropion pharmacotherapy. The SNP can be rs1626521. In some cases, the obesity analyte signature can include the presence of a SNP in FTO, LEP, LEPR, UCP1, UCP2, UCP3, ADRA2, KLF14, NPC1, LYPLAL1, ADRB2, ADRB3, and/or BBS1; the HADS questionnaire result can indicate an anxiety subscale; and the mammal can be classified as being responsive to intervention with phentermine pharmacotherapy. The SNP can be rs2075577.
Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. Although methods and materials similar or equivalent to those described herein can be used to practice the invention, suitable methods and materials are described below. All publications, patent applications, patents, and other references mentioned herein are incorporated by reference in their entirety. In case of conflict, the present specification, including definitions, will control. In addition, the materials, methods, and examples are illustrative only and not intended to be limiting.
The details of one or more embodiments of the invention are set forth in the accompanying drawings and the description below. Other features, objects, and advantages of the invention will be apparent from the description and drawings, and from the claims.



DESCRIPTION OF THE DRAWINGS
FIGS. 1A-1E shows classifications of obesity. A) 180 Caucasian participants with obesity (BMI>30 kg·m2) were sub classified into a) abnormal satiation (16%), abnormal satiety (16%), abnormal hedonic/behavior (19%), slow metabolism (32%) and mixed group (17%). The subgroups have unique characteristics as shown for food intake until reaching fullness tested in a nutrient drink test (B), gastric emptying rate, surrogate of satiety (C) and anxiety levels, surrogate of hedonic (D), and slow metabolism (E) based on the subgroups and gender (blue=females, red=males).
FIGS. 2A and 2B show biomarker discovery. A) Venn Diagrams of unique metabolites per obesity phenotype identified using positive-HILIC untargeted metabolomics. GP1—satiation; GP2—satiety (rapid return to hunger); Gp3—hedonic; and Gp4—energy expenditure. B) A score plot of a principal component analysis (PCA) of obesity phenotypes showing that obesity phenotype groups can be separated based on metabolic differences.
FIG. 3 is a receiver operating characteristic (ROC) curve showing the sensitivity and specificity of determining an obesity phenotype based on metabolic signature.
FIG. 4 is a ROC curve using Bayesian covariate predictors for low satiation, behavioral eating, and low resting energy expenditure.
FIG. 5 is a ROC curve showing the sensitivity and specificity of determining an obesity phenotype based on metabolic signature.
FIG. 6 shows food intake meal paradigms measuring ‘maximal’ fullness (MTV), ‘usual’ fullness (VTF) in a nutrient drink test and ‘usual’ fullness to mixed meal (solids) in an ad libitum buffet meal.
FIGS. 7A-7D shows abnormal satiety deeper phenotypes. A) Gastric emptying (GE) of solid T1/2 and T1/4, GE of liquids T1/5 for females and males. B) Fasting and postprandial gastric volume for females and males. C) Postprandial PYY3-36 and GLP-1 at 90 minutes. D) correlation of Postprandial PYY3-36 at 90 minutes and food intake by a nutrient drink test.
FIGS. 8A-8B show hedonic group deeper phenotypes. A) Anxiety, depression and self-esteem levels and B) fasting serum tryptophan levels in patients with hedonic obesity compared to normal.
FIGS. 9A-9D show slow metabolism deeper phenotypes. A) Predicted resting energy expenditure in patients with normal metabolism (other) compared to slow metabolism by gender (data in percentage). B) Resting energy expenditure in patients with normal metabolism (other) compared to slow metabolism (data in kcal/day). C) Body composition in different obesity-related phenotypes measured by DEXA. Top row is calculated BMI, med-row is total body fat and lower row is total lean mass. D) Levels of metabolites in patients with slow metabolism compared to normal metabolism (other or rest). Metabolites describes are Alanine, isocaproic acid, phosphoetahnolamine, phenylalanine, tyrosine, alpha-amino-N-butyric acid, sarcasine, and 1-methylhistidine.
FIG. 10 is a bar graph showing body weight change in response to treatment with placebo or a combination of phentermine and topiramate (PhenTop) and kcal intake at prior ad-libitum meal (satiation test).
FIG. 11 is a bar graph showing body weight change in response to treatment with placebo or exenatide in patients with a particular obesity phenotype.
FIGS. 12A-12C are a flow charts showing exemplary treatment interventions for obesity groups identified based, at least in part, on a patient's obesity analyte signature.
FIG. 13 is a bar graph showing total body weight loss (TBWL) in response to individualized intervention based on pre-selecting the specific individual patient's obesity analyte signature.
FIG. 14 is a line graph showing TBWL in response to individualized intervention over time.



DETAILED DESCRIPTION
This document provides methods and materials for assessing and/or treating obesity in mammals (e.g., humans). In some cases, this document provides methods and materials for identifying an obese mammal as being responsive to a pharmacological intervention, and administering one or more pharmacological interventions to treat the mammal. For example, a sample obtained from an obese mammal can be assessed to determine if the obese mammal is likely to be responsive to intervention (e.g., pharmacological intervention, surgical intervention, weight loss device, diet intervention, behavior intervention, and/or microbiome intervention) based, at least in part, on an obesity phenotype, which is based, at least in part, on an obesity analyte signature in the sample. An obesity analyte signature can include the presence, absence, or level (e.g., concentration) of two or more (e.g., three, four, five, six, seven, eight, nine, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, or more) obesity analytes (e.g., biomarkers associated with obesity). In some cases, an obesity analyte signature can include 14 obesity analytes. For example, a pharmacotherapy responsive obesity analyte signature can be based, at least in part, on the presence, absence, or level of 14 obesity analytes. In some cases, an obesity analyte signature can include 9 obesity analytes. For example, a pharmacotherapy responsive obesity analyte signature can be based, at least in part, on the presence, absence, or level of 9 obesity analytes. In some cases, the methods and materials described herein can be used to predict further weight loss response (e.g., during the course of an obesity treatment). In some cases, the methods and materials described herein can be used to prevent plateaus (e.g., during the course of an obesity treatment). In some cases, the methods and materials described herein can be used to enhance weight loss maintenance (e.g., during the course of an obesity treatment). In some cases, the methods and materials described herein can be used to treat patients unable to lose and maintain weight with diet and exercise alone.
As described herein, a distinct obesity analyte signature can be present in each of six main obesity phenotypes: Group 1) low satiation, Group 2) low satiety (e.g., rapid return to hunger), Group 3) behavioral eating (e.g., as identified by questionnaire), Group 4) large fasting gastric volume, Group 5) mixed, and Group 6) low resting energy expenditure group. Also described herein, the obesity analyte signature in sample obtained from an obese mammal (and thus the obesity phenotype) can be used to predict intervention responsiveness. In some cases, obesity phenotype groups can be simplified as: 1) high energy intake, 2) behavioral/emotional eating, and 3) low energy expenditure; or can be simplified as 1) low satiation (fullness), 2) low satiety (return to hunger), 3) behavioral/emotional eating, 4) low energy expenditure, 5) mixed, and 6) other.
When treating obesity in a mammal (e.g., a human) as described herein, the mammal can also have one or more obesity-related (e.g., weight-related) co-morbidities. Examples of weight-related co-morbidities include, without limitation, hypertension, type 2 diabetes, dyslipidemia, obstructive sleep apnea, gastroesophageal reflux disease, weight baring joint arthritis, cancer, non-alcoholic fatty liver disease, nonalcoholic steatohepatitis, depression, anxiety, and atherosclerosis (coronary artery disease and/or cerebrovascular disease). In some cases, the methods and materials described herein can be used to treat one or more obesity-related co-morbidities.
When treating obesity in a mammal (e.g., a human) as described herein, the treatment can be effective to reduce the weight, reduce the waist circumference, slow or prevent weight gain of the mammal, improve the hemoglobin A1c, and/or improve the fasting glucose. For example, treatment described herein can be effective to reduce the weight (e.g., the total body weight) of an obese mammal by at least 3% (e.g., at least 5%, at least 8%, at least 10%, at least 12%, at least 15%, at least 18%, at least 20%, at least 22%, at least 25%, at least 28%, at least 30%, at least 33%, at least 36%, at least 39%, or at least 40%). For example, treatment described herein can be effective to reduce the weight (e.g., the total body weight) of an obese mammal by from about 3% to about 40% (e.g., from about 3% to about 35%, from about 3% to about 30%, from about 3% to about 25%, from about 3% to about 20%, from about 3% to about 15%, from about 3% to about 10%, from about 3% to about 5%, from about 5% to about 40%, from about 10% to about 40%, from about 15% to about 40%, from about 20% to about 40%, from about 25% to about 40%, from about 35% to about 40%, from about 5% to about 35%, from about 10% to about 30%, from about 15% to about 25%, or from about 18% to about 22%). For example, treatment described herein can be effective to reduce the weight (e.g., the total body weight) of an obese mammal by from about 3 kg to about 100 kg (e.g., about 5 kg to about 100 kg, about 8 kg to about 100 kg, about 10 kg to about 100 kg, about 15 kg to about 100 kg, about 20 kg to about 100 kg, about 30 kg to about 100 kg, about 40 kg to about 100 kg, about 50 kg to about 100 kg, about 60 kg to about 100 kg, about 70 kg to about 100 kg, about 80 kg to about 100 kg, about 90 kg to about 100 kg, about 3 kg to about 90 kg, about 3 kg to about 80 kg, about 3 kg to about 70 kg, about 3 kg to about 60 kg, about 3 kg to about 50 kg, about 3 kg to about 40 kg, about 3 kg to about 30 kg, about 3 kg to about 20 kg, about 3 kg to about 10 kg, about 5 kg to about 90 kg, about 10 kg to about 75 kg, about 15 kg to about 50 kg, about 20 kg to about 40 kg, or about 25 kg to about 30 kg). For example, treatment described herein can be effective to reduce the waist circumference of an obese mammal by from about 1 inches to about 10 inches (e.g., about 1 inches to about 9 inches, about 1 inches to about 8 inches, about 1 inches to about 7 inches, about 1 inches to about 6 inches, about 1 inches to about 5 inches, about 1 inches to about 4 inches, about 1 inches to about 3 inches, about 1 inches to about 2 inches, about 2 inches to about 10 inches, about 3 inches to about 10 inches, about 4 inches to about 10 inches, about 5 inches to about 10 inches, about 6 inches to about 10 inches, about 7 inches to about 10 inches, about 8 inches to about 10 inches, about 9 inches to about 10 inches, about 2 inches to about 9 inches, about 3 inches to about 8 inches, about 4 inches to about 7 inches, or about 5 inches to about 7 inches). In some cases, the methods and materials described herein can be used to improve (e.g., increase or decrease) the hemoglobin A1c of an obese mammal (e.g., an obese mammal having type 2 diabetes mellitus) to from about 0.4% to about 3% (e.g., from about 0.5% to about 3%, from about 1% to about 3%, from about 1.5% to about 3%, from about 2% to about 3%, from about 2.5% to about 3%, from about 0.4% to about 2.5%, from about 0.4% to about 2%, from about 0.4% to about 1.5%, from about 0.4% to about 1%, from about 0.5% to about 2.5%, or from about 1% to about 2%) hemoglobin A1c. In some cases, the methods and materials described herein can be used to improve (e.g., increase or decrease) the fasting glucose of an obese mammal (e.g., an obese mammal having type 2 diabetes mellitus) to from about 10 mg/dl to about 200 mg/dl (e.g., from about 15 mg/dl to about 200 mg/dl, from about 25 mg/dl to about 200 mg/dl, from about 50 mg/dl to about 200 mg/dl, from about 75 mg/dl to about 200 mg/dl, from about 100 mg/dl to about 200 mg/dl, from about 125 mg/dl to about 200 mg/dl, from about 150 mg/dl to about 200 mg/dl, from about 175 mg/dl to about 200 mg/dl, from about 190 mg/dl to about 200 mg/dl, from about 10 mg/dl to about 175 mg/dl, from about 10 mg/dl to about 150 mg/dl, from about 10 mg/dl to about 125 mg/dl, from about 10 mg/dl to about 100 mg/dl, from about 10 mg/dl to about 75 mg/dl, from about 10 mg/dl to about 50 mg/dl, from about 10 mg/dl to about 25 mg/dl, or from about 10 mg/dl to about 20 mg/dl) glucose.
Any type of mammal can be assessed and/or treated as described herein. Examples of mammals that can be assessed and/or treated as described herein include, without limitation, primates (e.g., humans and monkeys), dogs, cats, horses, cows, pigs, sheep, rabbits, mice, and rats. In some cases, the mammal can a human. In some cases, a mammal can be an obese mammal. For example, obese humans can be assessed for intervention (e.g., a pharmacological intervention) responsiveness, and treated with one or more interventions as described herein. In cases where mammal is a human, the human can be of any race. For example, a human can be Caucasian or Asian.
Any appropriate method can be used to identify a mammal as being overweight (e.g., as being obese). In some cases, calculating body mass index (BMI), measuring waist and/or hip circumference, health history (e.g., weight history, weight-loss efforts, exercise habits, eating patterns, other medical conditions, medications, stress levels, and/or family health history), physical examination (e.g., measuring your height, checking vital signs such as heart rate blood pressure, listening to your heart and lungs, and examining your abdomen), percentage of body fat and distribution, percentage of visceral and organs fat, metabolic syndrome, and/or obesity related comorbidities can be used to identify mammals (e.g., humans) as being obese. For example, a BMI of greater than about 30 kg/m2 can be used to identify mammals (e.g., Caucasian humans) as being obese. For example, a BMI of greater than about 27 kg/m2 with a co-morbidity can be used to identify mammals (e.g., Asian humans) as being obese.
Once identified as being obese, a mammal can be assessed to determine whether or not it is likely to respond to one or more interventions (e.g., pharmacological intervention, surgical intervention, weight loss device, diet intervention, behavior intervention, and/or microbiome intervention). For example, a sample obtained from the mammal can be assessed for pharmacological intervention responsiveness. As described herein, a panel of obesity analytes in a sample obtained from an obese mammal can be used to determine an obesity analyte signature of the mammal, and can be used in to determine an obesity phenotype of the mammal.
Any appropriate sample from a mammal (e.g., a human) having obesity can be assessed as described herein. In some cases, a sample can be a biological sample. In some cases, a sample can contain obesity analytes (e.g., DNA, RNA, proteins, peptides, metabolites, hormones, and/or exogenous compounds (e.g. medications)). Examples of samples that can be assessed as described herein include, without limitation, fluid samples (e.g., blood, serum, plasma, urine, saliva, sweat, or tears), breath samples, cellular samples (e.g., buccal samples), tissue samples (e.g., adipose samples), stool samples, gastro samples, and intestinal mucosa samples. In some cases, a sample (e.g., a blood sample) can be collected while the mammal is fasting (e.g., a fasting sample such as a fasting blood sample). In some cases, a sample can be processed (e.g., to extract and/or isolate obesity analytes). For example, a serum sample can be obtained from an obese mammal and can be assessed to determine if the obese mammal is likely to be responsive to one or more interventions (e.g., pharmacological intervention, surgical intervention, weight loss device, diet intervention, behavior intervention, and/or microbiome intervention) based, at least in part, on an obesity phenotype, which is based, at least in part, on an obesity analyte signature in the sample. For example, a urine sample can be obtained from an obese mammal and can be assessed to determine if the obese mammal is likely to be responsive to pharmacological intervention based, at least in part, on an obesity phenotype, which is based, at least in part, on an obesity analyte signature in the sample.
An obesity analyte signature can include any appropriate analyte. Examples of analytes that can be included in an obesity analyte signature described herein include, without limitation, DNA, RNA, proteins, peptides, metabolites, hormones, and exogenous compounds (e.g. medications). An obesity analyte signature can be evaluated using any appropriate methods. For example, metabolomics, genomics, microbiome, proteomic, peptidomics, and behavioral questionnaires can be used to evaluate and/or identify an obesity analyte signature described herein.
Any appropriate method can be used to identify an obesity phenotype as described herein. In some cases, the obesity phenotype can be identified as described in the Examples. For example, the obesity phenotype can be identified by determining the obesity analyte signature in a sample (e.g., in a sample obtained from an obese mammal). In some cases, the obesity analyte signature can be obtained by detecting the presence, absence, or level of one or more metabolites, detecting the presence, r absence, or level one or more peptides (e.g., gastrointestinal peptides), and/or detecting the presence, absence, or level of one or more single nucleotide polymorphisms (SNPs).
A metabolite can be any metabolite that is associated with obesity. In some cases, a metabolite can be an amino-compound. In some cases, a metabolite can be a neurotransmitter. In some cases, a metabolite can be a fatty acid (e.g., a short chain fatty acid). In some cases, a metabolite can be an amino compound. In some cases, a metabolite can be a bile acid. In some cases, a metabolite can be a compound shown in Table 2. Examples of metabolites that can be used to determine the obesity analyte signature in a sample (e.g., in a sample obtained from an obese mammal) include, without limitation, 1-methylhistine, serotonin, glutamine, gamma-amino-n-butyric-acid, isocaproic, allo-isoleucine, hydroxyproline, beta-aminoisobutyric-acid, alanine, hexanoic, tyrosine, phenylalanine γ-aminobutyric acid, acetic, histidine, LCA, ghrelin, ADRA2A, cholesterol, glucose, acetylcholine, propionic, CDCA, PYY, ADRA2C, insulin, adenosine, isobutyric, 1-methylhistidine, DCA, CCK, GNB3, glucagon, aspartate, butyric, 3-methylhistidine, UDCA, GLP-1, FTO, leptin, dopamine, valeric, asparagine, HDCA, GLP-2, MC4R, adiponectin, D-serine, isovaleric, phosphoethanolamine, CA, glucagon, TCF7L2, glutamate, hexanoic, arginine, GLCA, oxyntomodulin, 5-HTTLPR, glycine, octanoic, carnosine, GCDCA, neurotensin, HTR2C, myristic, taurine, GDCA, FGF, UCP2, norepinephrine, palmitic, anserine, GUDCA, GIP, UCP3, serotonin, palmitoleic, serine, GHDCA, OXM, GPBAR1, taurine, palmitelaidic, glutamine, GCA, FGF19, NR1H4, stearic, ethanolamine, TLCA, FGF21, FGFR4, oleic, glycine, TCDCA, LDL, elaidic, aspartic acid, TDCA, insulin, GLP-1, linoleic, sarcosine, TUDCA, glucagon, CCK, a-linolenic, proline, THDCA, amylin, arachidonic, alpha-aminoadipic-acid, TCA, pancreatic polypeptide, eicosapentaenoic, DHCA, neurotensin, docosahexaenoic, alpha-amino-N-butyric-acid, THCA, ornithine, GLP-1 receptor, triglycerides, cystathionine 1, GOAT, cystine, DPP4, lysine, methionine, valine, isoleucine, leucine, homocystine, tryptophan, citrulline, glutamic acid, beta-alanine, threonine, hydroxylysine 1, acetone, and acetoacetic acid. In some cases, an obesity analyte signature can include 1-methylhistine, serotonin, glutamine, gamma-amino-n-butyric-acid, isocaproic, allo-isoleucine, hydroxyproline, beta-aminoisobutyric-acid, alanine, hexanoic, tyrosine, and phenylalanine.
A gastrointestinal peptide can be any gastrointestinal peptide that is associated with obesity. In some cases, a gastrointestinal peptide can be a peptide hormone. In some cases, a gastrointestinal peptide can be released from gastrointestinal cells in response to feeding. In some cases, a gastrointestinal peptide can be a peptide shown in Table 2. Examples of gastrointestinal peptides that can be used to determine the obesity analyte signature in a sample (e.g., in a sample obtained from an obese mammal) include, without limitation, ghrelin, peptide tyrosine tyrosine (PYY), cholecystokinin (CCK), glucagon-like peptide-1 (GLP-1), GLP-2, glucagon, oxyntomodulin, neurotensin, fibroblast growth factor (FGF), GIP, OXM, FGF19, FGF19, and pancreatic polypeptide.
A SNP can be any SNP that is associate with obesity. A SNP can be in a coding sequence (e.g., in a gene) or a non-coding sequence. For example, in cases where a SNP is in a coding sequence, the coding sequence can be any appropriate coding sequence. In some cases, a coding sequence that can include a SNP associated with obesity can be a gene shown in Table 2. Examples of coding sequences that a SNP associated with obesity can be in or near include, without limitation, ADRA2A, ADRA2C, GNB3, FTO, MC4R, TCF7L2, 5-HTTLPR, HTR2C, UCP2, UCP3, GPBAR1, NR1H4, FGFR4, PYY, GLP-1, CCK, leptin, adiponectin, neurotensin, ghrelin, GLP-1 receptor, GOAT, DPP4, POMC, NPY, AGRP, SERT, BDNF, SLC6A4, DRD2, LEP, LEPR, UCP1, KLF14, NPC1, LYPLAL1, ADRB2, ADRB3, BBS1, ACSL6, ADARB2, ADCY8, ADH1B, AJAP1, ATP2C2, ATP6V0D2, C21orf7, CAMKMT, CAP2, CASC4, CD48, CDC42SE2, CDYL, CES5AP1, CLMN, CNPY4, COL19A1, COL27A1, COL4A3, CORO1C, CPZ, CTIF, DAAM2, DCHS2, DOCKS, EGFLAM, FAM125B, FAM71E2, FRMD3, GALNTL4, GLT1D1, HHAT, KRT23, LHPP, LINC00578, LINC00620, LIPC, LOC100128714, LOC100287160, LOC100289473, LOC100293612|LINC00620, LOC100506869, LOC100507053, LOC100507053|ADH1A, LOC100507053|ADH, LOC100507443, LOC100996571|CYYR1, LOC152225, LOC255130, LPAR1, LUZP2, MCM7, MICAL3, MMS19, MYBPC1, NR2F2-AS1, NSMCE2, NTN1, O3FAR1, OAZ2, OSBP2, P4HA2, PADI1, PARD3B, PARK2, PCDH15, PIEZO2, PKIB, PRH1-PRR4, PTPRD, RALGPS1|ANGPTL2, RPS24P10, RTN4RL1, RYR2, SCN2A, SEMA3C, SEMA5A, SFMBT2, SGCG, SLC22A15, SLC2A2, SLCO1B1, SMOC2, SNCAIP, SNX18, SRRM4, SUSD1, TBC1D16, TCERG1L, TENM3, TJP3, TLL1, TMEM9B, TPM1, VTI1A, VWF, WWOX, WWTR1, ZFYVE28, ZNF3, ZNF609, and ZSCAN21. In some cases, a SNP can be a SNP shown in Table 3. Examples of SNPS that can be used to determine the obesity analyte signature in a sample (e.g., in a sample obtained from an obese mammal) include, without limitation, rs657452, rs11583200, rs2820292, rs11126666, rs11688816, rs1528435, rs7599312, rs6804842, rs2365389, rs3849570, rs16851483, rs17001654, rs11727676, rs2033529, rs9400239, rs13191362, rs1167827, rs2245368, rs2033732, rs4740619, rs6477694, rs1928295, rs10733682, rs7899106, rs17094222, rs11191560, rs7903146, rs2176598, rs12286929, rs11057405, rs10132280, rs12885454, rs3736485, rs758747, rs2650492, rs9925964, rs1000940, rs1808579, rs7243357, rs17724992, rs977747, rs1460676, rs17203016, rs13201877, rs1441264, rs7164727, rs2080454, rs9914578, rs2836754, rs492400, rs16907751, rs9374842, rs9641123, rs9540493, rs4787491, rs6465468, rs7239883, rs3101336, rs12566985, rs12401738, rs11165643, rs17024393, rs543874, rs13021737, rs10182181, rs1016287, rs2121279, rs13078960, rs1516725, rs10938397, rs13107325, rs2112347, rs205262, rs2207139, rs17405819, rs10968576, rs4256980, rs11030104, rs3817334, rs7138803, rs12016871, rs12429545, rs11847697, rs7141420, rs16951275, rs12446632, rs3888190, rs1558902, rs12940622, rs6567160, rs29941, rs2075650, rs2287019, rs3810291, rs7715256, rs2176040, rs6091540, rs1800544, Ins-Del-322, rs5443, rs1129649, rs1047776, rs9939609, rs17782313, rs7903146, rs4795541, rs3813929, rs518147, rs1414334, rs659366, -3474, rs2075577, rs15763, rs1626521, rs11554825, rs4764980, rs434434, rs351855, and rs2234888.
An obesity analyte signature described herein can include any appropriate combination of analytes. For example, when an obesity analyte signature includes 14 analytes, the analytes can include 1-methylhistine, serotonin, glutamine, gamma-amino-n-butyric-acid, isocaproic, allo-isoleucine, hydroxyproline, beta-aminoisobutyric-acid, alanine, hexanoic, tyrosine, phenylalanine, ghrelin, and PYY. For example, when an obesity analyte signature includes 9 analytes, the analytes can include HTR2C, GNB3, FTO, isocaproic, beta-aminoisobutyric-acid, butyric, allo-isoleucine, tryptophan, and glutamine.
Any appropriate method can be used to detect the presence, absence, or level of an obesity analyte within a sample. For example, mass spectrometry (e.g., triple-stage quadrupole mass spectrometry coupled with ultra-performance liquid chromatography (UPLC)), radioimmuno assays, and enzyme-linked immunosorbent assays can be used to determine the presence, absence, or level of one or more analyte in a sample.
In some cases, identifying the obesity phenotype can include obtaining results from all or part of one or more questionnaires. A questionnaire can be associated with obesity. In some cases, a questionnaire can be answered the time of the assessment. In some cases, a questionnaire can be answered prior to the time of assessment. For example, when a questionnaire is answered prior to the time of the assessment, the questionnaire results can be obtained by reviewing a patient history (e.g., a medical chart). A questionnaire can be a behavioral questionnaire (e.g., psychological welfare questionnaires, alcohol use questionnaires, eating behavior questionnaires, body image questionnaires, physical activity level questionnaire, and weight management questionnaires. Examples of questionnaires that can be used to determine the obesity phenotype of a mammal (e.g., an obese mammal) include, without limitation, The Hospital Anxiety and Depression Scale (HADS) questionnaire, The Hospital Anxiety and Depression Inventory questionnaire, The Questionnaire on Eating and Weight Patterns, The Weight Efficacy Life-Style (WEL) Questionnaire, The Multidimensional Body-Self Relations Questionnaire, The Questionnaire on Eating and Weight Patterns-Revised, The Weight Efficacy Life-Style, Physical Activity Level-item Physical Activity Stages of Change Questionnaire, The Exercise Regulations Questionnaire (BREQ-3), Barriers to Being Active Quiz, and The Three Factor Eating Questionnaire (TFEQ). For example, a questionnaire can be a HADS questionnaire. For example, a questionnaire can be a TFEQ.
In some cases, an obesity analyte signature can include the presence of serotonin, glutamine, isocaproic, allo-isoleucine, hydroxyproline, beta-aminoisobutyric-acid, alanine, hexanoic, tyrosine, and PYY. For example, an obesity phenotype Group 1 can have an obesity analyte signature that includes the presence of serotonin, glutamine, isocaproic, allo-isoleucine, hydroxyproline, beta-aminoisobutyric-acid, alanine, hexanoic, tyrosine, and PYY. For example, an obesity phenotype Group 1 can have an obesity analyte signature that has an absence of (e.g., lacks the presence of) 1-methylhistine, gamma-amino-n-butyric-acid, phenylalanine, ghrelin, and includes a HADS questionnaire result that does not indicate an anxiety subscale (HADS-A; e.g., includes a HADS-A questionnaire result).
In some cases, an obesity analyte signature can include the presence of 1-methylhistine, allo-isoleucine, hydroxyproline, beta-aminoisobutyric-acid, alanine, and phenylalanine. For example, an obesity phenotype Group 2 can have an obesity analyte signature that includes the presence of -methylhistine, allo-isoleucine, hydroxyproline, beta-aminoisobutyric-acid, alanine, and phenylalanine. For example, an obesity phenotype Group 2 can have an obesity analyte signature that has an absence of (e.g., lacks the presence of) serotonin, glutamine, gamma-amino-n-butyric-acid, isocaproic, hexanoic, tyrosine, ghrelin, PYY, and does not include a HADS questionnaire result that indicates an anxiety subscale (e.g., does not include a HADS-A questionnaire result)
In some cases, an obesity analyte signature can include the presence of serotonin, and can include a HADS-A questionnaire. For example, an obesity phenotype Group 3 can have an obesity analyte signature that includes serotonin and includes a HADS-A questionnaire result. For example, an obesity phenotype Group 3 can have an obesity analyte signature that has an absence of (e.g., lacks the presence of) 1-methylhistine, glutamine, gamma-amino-n-butyric-acid, isocaproic, allo-isoleucine, hydroxyproline, beta-aminoisobutyric-acid, alanine, hexanoic, tyrosine, phenylalanine, ghrelin, and PYY.
In some cases, an obesity analyte signature can include the presence of 1-methylhistine, glutamine, gamma-amino-n-butyric-acid, isocaproic, allo-isoleucine, beta-aminoisobutyric-acid, alanine, hexanoic, tyrosine, phenylalanine, PYY, and includes a HADS-A questionnaire result. For example, an obesity phenotype Group 4 can have an obesity analyte signature that includes 1-methylhistine, glutamine, gamma-amino-n-butyric-acid, isocaproic, allo-isoleucine, beta-aminoisobutyric-acid, alanine, hexanoic, tyrosine, phenylalanine, PYY, and includes a HADS-A questionnaire result. For example, an obesity phenotype Group 4 can have an obesity analyte signature that has an absence of (e.g., lacks the presence of) serotonin, hydroxyproline, and ghrelin.
In some cases, an obesity analyte signature can include the presence of serotonin, beta-aminoisobutyric-acid, alanine, hexanoic, phenylalanine, and includes a HADS-A questionnaire. For example, an obesity phenotype Group 5 can have an obesity analyte signature that includes the presence of serotonin, beta-aminoisobutyric-acid, alanine, hexanoic, phenylalanine, and includes a HADS-A questionnaire result. For example, an obesity phenotype Group 5 can have an obesity analyte signature that has an absence of (e.g., lacks the presence of) 1-methylhistine, glutamine, gamma-amino-n-butyric-acid, isocaproic, allo-isoleucine, and hydroxyproline.
In some cases, an obesity analyte signature can include the presence of 1-methylhistine, serotonin, glutamine, gamma-amino-n-butyric-acid, isocaproic, allo-isoleucine, alanine, tyrosine, ghrelin, PYY, and includes a HADS-A questionnaire result. For example, an obesity phenotype Group 6 can have an obesity analyte signature that includes the presence of 1-methylhistine, serotonin, glutamine, gamma-amino-n-butyric-acid, isocaproic, allo-isoleucine, alanine, tyrosine, ghrelin, PYY, and includes a HADS-A questionnaire result. For example, an obesity phenotype Group 6 can have an obesity analyte signature that has an absence of (e.g., lacks the presence of) hydroxyproline, beta-aminoisobutyric-acid, hexanoic, and phenylalanine.
In some cases, identifying the obesity phenotype also can include identifying one or more additional variables and/or one or more additional assessments. For example, identifying the obesity phenotype also can include assessing the microbiome of a mammal (e.g., an obese mammal). For example, identifying the obesity phenotype also can include assessing leptin levels. For example, identifying the obesity phenotype also can include assessing the metabolome of a mammal (e.g., an obese mammal). For example, identifying the obesity phenotype also can include assessing the genome of a mammal (e.g., an obese mammal). For example, identifying the obesity phenotype also can include assessing the proteome of a mammal (e.g., an obese mammal). For example, identifying the obesity phenotype also can include assessing the peptidome of a mammal (e.g., an obese mammal).
Once the obesity phenotype of the mammal has been identified, the mammal can be assessed to determine intervention (e.g., pharmacological intervention, surgical intervention, weight loss device, diet intervention, behavior intervention, and/or microbiome intervention) responsiveness, and a treatment option for the mammal can be selected. In some cases, the obesity phenotype of a mammal can be used to select a treatment options as shown in FIG. 12, and as set forth in Table 1.







TABLE 1





Treatment options.

















Obesity Phenotype Group
Pharmacotherapy
Exemplary Intervention










Pharmacotherapy Intervention: FDA approved medications









1: low satiation
appetite suppressant in combination
phentermine-topiramate



with an anticonvuslant



appetite suppressant
lorcaserin, desvenlafaxine


2: low satiety
GLP-1 analog, GLP-1 receptor
liraglutide, exenatide, metformin,



agonist, amylin analogs
pramlitide


3: behavioral eating
antidepressant in combination with
naltrexone-bupropion



an opioid antagonist


4: large fasting gastric
antidepressant in combination with
naltrexone-bupropion


volume
an opioid antagonist


5: mixed
combination based on the combination



of phenotypes


6: low resting energy
appetite suppressant in combination with
phentermine + increased physical


expenditure
physical activity
activity







Pharmacotherapy Intervention: medications not-FDA approved









1: low satiation
melanocortin receptor
MK-0493




RM-493



appetite suppressants



CCK analogs


2: low satiety
GLP-1 analog - GLP-1
semaglutide,



receptor agonists - GLP-1/glucagon



coagonists



PYY analogs - Y receptors
velnerperit (s-2367)



agonists/antagonists
obinepitide



Oxyntomodulin analogs



Ghrelin antagonists



TGR5 agonists
Conjugated bile acids



FGF-19/21 analogs



FXR agonists



GRP-119



GRP-120



Combinations of these meds


3: behavioral eating
antidepressant
bupropion + zonisamide



opioid antagonist
Tesofensine



anti-anxiety
Buspirone



cannabionids antagonists
rimonabant


4: large fasting gastric
ghrelin antagonist


volume


5: mixed
combination based on the



combination of phenotypes


6: low resting
leptin modulators
metroleptin


energy expenditure
MetAP2 inhibitors
ZGN-1061, beloranib



B3 agonists
mirabegron










Weight Loss Devices









Obesity Phenotype Group
Surgical procedure and devices
Exemplary Intervention





1: low satiation
vagal stimulant
V-bloc



mouth occupying devices
Retrograde gastric pacing



intra-gastric space occupying
Smartbyte TM



devices
gastric balloon



sleeve gastroplasty


2: low satiety
duodenal bypass or mucosal
Endobarrier



resurfacing (example: ablation)



intra-gastric space occupying
gastric balloon



devices



malabsorptive procedures
transpyloric shuttle


3: behavioral eating
gastric emptying devices
Aspire assist


4: large fasting gastric
gastric emptying devices
Aspire assist


volume
intra-gastric space occupying
gastric balloon



devices



sleeve gastroplasty


5: mixed
combination based on the



combination of phenotypes


6: low resting energy

phentermine + increased physical


expenditure

activity


1: low satiation
Gastric occupying space
Transoral endoscopic restrictive




implant system



Brain stimulant
deep transcranial magnetic




stimulation


2: low satiety
Duodenal bypass or mucosal
Fractyl - duodenal ablation



resurfacing (example: ablation)



Intra-gastric space occupying
Intragastric balloons - adjustable



devices



Malabsorptive procedures
Magnet therapy (Incision-less




Anastomosis System)


3: behavioral eating


4: large fasting gastric
Intra-gastric space occupying
Intragastric balloons - adjustable


volume
devices



Gastric plications
POSE


5: mixed
combination based on the



combination of phenotypes


6: low resting energy
Muscle stimulants
Pulse muscle stimulator


expenditure
Energy trackers



Cold inducers (stimulates BAT)
Cold vests











Diet Intervention










Obesity Phenotype Group
Diet
Exemplary Intervention





1: low satiation
Slow eating



volumetric diet
Legumes, fruits, beans, whole grains



high fat - high protein - low carb
Atkins diet




Keto diet


2: low satiety
High protein - low carb- average
Paleo-diet



fat
Mediterranean diet


3: behavioral eating
Schedule 2-3 meals daily. No snacks
Crash diet


4: large fasting gastric
High soluble fiber
Fiber supplements,


volume


5: mixed


6: low resting energy
Low fat - Average protein, average
13-day Metabolism diet


expenditure
carbs










Surgical Intervention











Obesity Phenotype Group
Surgical procedure
Exemplary Intervention







1: low satiation
Restrictive procedures
Sleeve





RYGB





Lap-band



2: low satiety
Malabsorptive procedures
RYGB - Sleeve plus duodenal





switch



3: behavioral eating



4: large fasting gastric
Restrictive procedures
Sleeve



volume

RYGB



5: mixed



6: low resting energy
Malabsorptive procedures
RYGB - duodenal switch



expenditure











Microbiome Intervention









Obesity Phenotype Group
Microbiome status
Exemplary Intervention





1: low satiation
Microbiota inflammatory inducing
Reduce microbiome LPS induction


2: low satiety
Low microbiome richness
Increase richness of microbiota




(probiotic mix) to increase SCFA in




GI lumen


3: behavioral eating
Serotonin producing bacteria
Reduced serotonin producing




bacteria: restore Bacteroides spp


4: large fasting gastric
Low microbiome richness
Increase primary BA microbiota


volume


5: mixed


6: low resting energy
Low fatty acids producing bacteria
Increase fatty acid metabolism


expenditure

producing bacteria









Individualized pharmacological interventions for the treatment of obesity (e.g., based on the obesity phenotypes as described herein) can include any one or more (e.g., 1, 2, 3, 4, 5, 6, or more) pharmacotherapies (e.g., individualized pharmacotherapies). A pharmacotherapy can include any appropriate pharmacotherapy. In some cases, a pharmacotherapy can be an obesity pharmacotherapy. In some cases, a pharmacotherapy can be an appetite suppressant. In some cases, a pharmacotherapy can be an anticonvulsant. In some cases, a pharmacotherapy can be a GLP-1 agonist. In some cases, a pharmacotherapy can be an antidepressant. In some cases, a pharmacotherapy can be an opioid antagonist. In some cases, a pharmacotherapy can be a controlled release pharmacotherapy. For example, a controlled release pharmacotherapy can be an extended release (ER) and/or a slow release (SR) pharmacotherapy. In some cases, a pharmacotherapy can be a lipase inhibitor. In some cases, a pharmacotherapy can be a DPP4 inhibitor. In some cases, a pharmacotherapy can be a SGLT2 inhibitor. In some cases, a pharmacotherapy can be a dietary supplement. Examples of pharmacotherapies that can be used in an individualized pharmacological intervention as described herein include, without limitation, orlistat, phentermine, topiramate, lorcaserin, naltrexone, bupropion, liraglutide, exenatide, metformin, pramlitide, Januvia, canagliflozin, dexamphetamines, prebiotics, probiotics, Ginkgo biloba, and combinations thereof. For example, combination pharmacological interventions for the treatment of obesity (e.g., based on the obesity phenotypes as described herein) can include phentermine-topiramate ER, naltrexone-bupropion SR, phentermine-lorcaserin, lorcaserin-liraglutide, and lorcarserin-januvia. A pharmacotherapy can be administered using any appropriate methods. In some cases, pharmacotherapy can be administered by continuous pump, slow release implant, intra-nasal administered, intra-oral administered, and/or topical administered. In some cases, a pharmacotherapy can be administered as described elsewhere (see, e.g., Sjostrom et al., 1998 Lancet 352:167-72; Hollander et al., 1998 Diabetes Care 21:1288-94; Davidson et al., 1999 JAMA 281:235-42; Gadde et al., 2011 Lancet 377:1341-52; Smith et al., 2010 New Engl. J Med. 363:245-256; Apovian et al., 2013 Obesity 21:935-43; Pi-Sunyer et al., 2015 New Engl. J Med. 373:11-22; and Acosta et al., 2015 Clin Gastroenterol Hepatol. 13:2312-9).
Once a mammal is identified as being responsive to one or more interventions (e.g., pharmacological intervention, surgical intervention, weight loss device, diet intervention, behavior intervention, and/or microbiome intervention) based, at least in part, on an obesity phenotype, which is based, at least in part, on an obesity analyte signature in the sample, the mammal can be administered or instructed to self-administer one or more individualized pharmacotherapies.
When a mammal is identified as having an obesity phenotype that is responsive to treatment with one or more pharmacotherapies, the mammal can be administered or instructed to self-administer one or more pharmacotherapies. For example, when a mammal is identified as having a low satiation (Group 1) phenotype, based, at least in part, on an obesity analyte signature, the mammal can be administered or instructed to self-administer phentermine-topiramate (e.g., phentermine-topiramate ER) to treat the obesity. For example, when a mammal is identified as having a low satiation (Group 1) phenotype, based, at least in part, on an obesity analyte signature, the mammal can be administered or instructed to self-administer lorcaserin to treat the obesity. For example, when a mammal is identified as having a low satiety (Group 2) phenotype, based, at least in part, on an obesity analyte signature, the mammal can be administered or instructed to self-administer liraglutide to treat the obesity. For example, when a mammal is identified as having a behavioral eating (Group 3) phenotype, based, at least in part, on an obesity analyte signature, the mammal can be administered or instructed to self-administer naltrexone-bupropion (e.g., naltrexone-bupropion SR) to treat the obesity. For example, when a mammal is identified as having a large fasting gastric volume (Group 4) phenotype, based, at least in part, on an obesity analyte signature, the mammal can be administered or instructed to self-administer naltrexone-bupropion (e.g., naltrexone-bupropion SR) to treat the obesity. For example, when a mammal is identified as having a low resting energy expenditure (Group 6) phenotype, based, at least in part, on an obesity analyte signature, the mammal can be administered or instructed to self-administer phentermine, and can be instructed to increase physical activity to treat the obesity.
In some cases, one or more pharmacotherapies described herein can be administered to an obese mammal as a combination therapy with one or more additional agents/therapies used to treat obesity. For example, a combination therapy used to treat an obese mammal (e.g., an obese human) can include administering to the mammal one or more pharmacotherapies described herein and one or more obesity treatments such as weight-loss surgeries (e.g., gastric bypass surgery, laparoscopic adjustable gastric banding (LAGB), biliopancreatic diversion with duodenal switch, and a gastric sleeve), vagal nerve blockade, endoscopic devices (e.g. intragastric balloons or endoliners, magnets), endoscopic sleeve gastroplasty, and/or gastric or duodenal ablations. For example, a combination therapy used to treat an obese mammal (e.g., an obese human) can include administering to the mammal one or more pharmacotherapies described herein and one or more obesity therapies such as exercise modifications (e.g., increased physical activity), dietary modifications (e.g., reduced-calorie diet), behavioral modifications, commercial weight loss programs, wellness programs, and/or wellness devices (e.g. dietary tracking devices and/or physical activity tracking devices). In cases where one or more pharmacotherapies described herein are used in combination with one or more additional agents/therapies used to treat obesity, the one or more additional agents/therapies used to treat obesity can be administered/performed at the same time or independently. For example, the one or more pharmacotherapies described herein can be administered first, and the one or more additional agents/therapies used to treat obesity can be administered/performed second, or vice versa.
This document provides methods and materials for identifying one or more analytes associated with obesity. In some cases, analytes associated with obesity can be used in an obesity analyte signature as described herein. For example, one or more analytes associated with obesity can be identified by using a combined logit regression model. In some cases, a combined logit regression model can include stepwise variable selection (e.g., to identify variables significantly associated with a specific obesity phenotype). For example, one or more analytes associated with obesity can be identified as described in, for example, the Examples section provided herein.
The invention will be further described in the following examples, which do not limit the scope of the invention described in the claims.
EXAMPLES
Example 1: Identification of Obesity Biomarkers
Obesity phenotypes were associated with higher BMI, distinguish obesity phenotypes, and can be used to predict responsiveness to obesity pharmacotherapy and endoscopic devices (see, e.g., Acosta et al., 2015 Gastroenterology 148:537-546). In this study, biomarkers specific to each obesity phenotype were identified using metabolomics.
The overall cohort demographics [median (IQR)] were age 36 (28-46) years, BMI 35 (32-38) kg/m2, 75% females, 100% Caucasians. The groups based on phenotype > or <75% ile were not statistically different for body weight, waist circumference, hip circumference, fasting glucose. The group distribution in this cohort was: abnormal satiation (16%), abnormal satiety (16%), abnormal hedonic/psych (19%), slow metabolism/energy expenditure (32%), and mixed group (17%) (FIG. 1A). FIGS. 1B-E illustrate summarize characteristics of the quantitative changes in the subgroups: the satiation group consumed 591 (60%) more calories prior to reaching fullness; the satiety group emptied half of the solid 300 kcal meal 34 min (30%) faster; the hedonic group reported 2.8 times higher levels of anxiety; the slow metabolism group has 10% decreased predicted resting energy expenditure than other groups. These average differences were in comparison to the other groups, but excluding the group with participants with a mixed or overlapping phenotype.
Gastrointestinal Traits (Phenotypes) Associated with Obesity
Gastrointestinal functions, satiation, and satiety were characterized in 509 participants across the normal weight to obesity spectrum. Obesity was associated with decreased satiation (higher caloric intake before feeling full, measure by volume to fullness [VTF] p=0.038), large fasting gastric volume (GV, p=0.03), accelerated gastric emptying (GE) T1/2 (solids: p<0.001; liquids: p=0.011), and lower postprandial peak plasma levels of PYY (p=0.003). In addition, principal components (PC) analysis identified latent dimensions (LDs) accounting for  ̃81% of OW-OB variation and sub-classifies obesity in satiation (21%), gastric capacity (15%), behavioral (13%), gastric sensorimotor (11%) factors, GLP-1 levels (9%), and others (31%) (Acosta et al., 2015 Gastroenterology 148:537-546).
Identification of Biomarkers
An analysis of 102 patients with obesity, matched for gender, age and BMI was done. These individuals were non-diabetic and were in not medications for weight loss. Based on the profile of each patient we were able to validate the main groups in obesity in 1) low satiation, 2) rapid return to hunger, 3) behavioral eating (identified by questionnaire), 4) large fasting gastric volume, 5) mixed, and 6) low resting energy expenditure group.
A combined logit regression model using stepwise variable selection was created to identify variables that are significantly associated with each of the phenotypic classes. Untargeted metabolomics identified unique metabolites in each group (FIG. 2A). Each of these metabolites is independent from the other groups (FIG. 2B). From these metabolites, a “VIP” (variable of importance) was identified for each group. Then, a targeted metabolomics was done with the VIP as well as neurotransmitters, amino compounds, fatty acids, and short chain fatty acids. Examples variables are as shown in Tables 2-5. For example, targeted metabolites, peptides, and SNPS analyzed are as shown in Table 2, other obesity related gene variants are as shown in in Table 3, targeted peptides are as shown in in Table 4, and targeted genes are as shown in in Table 5.







TABLE 2







Analytes Examined using SNPs, Hormones, Peptides and Targeted Metabolomics.



















SNP-




Neuro
Fatty acids



containing


transmitters
and Lipid
Amino Compounds
Bile acids
Peptides
Genes
Hormones
carbohydrates





γ-aminobutyric
acetic
Histidine
LCA
ghrelin
ADRA2A
cholesterol
glucose


acid


Acetylcholine
propionic
Hydroxyproline
CDCA
PYY
ADRA2C
insulin


Adenosine
isobutyric
1-Methylhistidine
DCA
CCK
GNB3
glucagon


aspartate
butyric
3-Methylhistidine
UDCA
GLP-1
FTO
leptin


Dopamine
valeric
Asparagine
HDCA
GLP-2
MC4R
adiponectin


D-serine
isovaleric
Phosphoethanolamine
CA
glucagon
TCF7L2


Glutamate
hexanoic
Arginine
GLCA
oxyntomodulin
5-HTTLPR


Glycine
octanoic
Carnosine
GCDCA
neurotensin
HTR2C


Histidine
myristic
Taurine
GDCA
FGF
UCP2


Norepinephrine
palmitic
Anserine
GUDCA
GIP
UCP3


Serotonin
palmitoleic
Serine
GHDCA
OXM
GPBAR1


Taurine
palmitelaidic
Glutamine
GCA
FGF19
NR1H4



stearic
Ethanolamine
TLCA
FGF21
FGFR4



oleic
Glycine
TCDCA
LDL
PYY



elaidic
Aspartic Acid
TDCA
insulin
GLP-1



linoleic
Sarcosine
TUDCA
glucagon
CCK



a-linolenic
Proline
THDCA
amylin
Leptin



arachidonic
alpha-
TCA
pancreatic
Adiponectin




Aminoadipic-

polypeptide




acid



eicosapentaenoic
beta-
DHCA
leptin
Neurotensin




Aminoisobutyric-




acid



docosahexaenoic
alpha-Amino-
THCA
adiponectin
Ghrelin




N-butyric-acid



LDL
Ornithine


GLP-1







receptor



triglycerides
Cystathionine 1


GOAT




Cystine


DPP4




Lysine




Tyrosine




Methionine




Valine




Isoleucine




Leucine




Homocystine




Phenylalanine




Tryptophan




Citrulline




Glutamic Acid




beta-Alanine




Threonine




Alanine




Hydroxylysine 1




Acetone




Acetoacetic




Acid
















TABLE 3





SNPs associated with obesity [Is this title accurate?]




















SNP
Chr.
Position (bp)
Nearest Gene







rs657452
1
49,362,434
AGBL4



rs11583200
1
50,332,407
ELAVL4



rs2820292
1
200,050,910
NAV1



rs11126666
2
26,782,315
KCNK3



rs11688816
2
62,906,552
EHBP1



rs1528435
2
181,259,207
UBE2E3



rs7599312
2
213,121,476
ERBB4



rs6804842
3
25,081,441
RARB



rs2365389
3
61,211,502
FHIT



rs3849570
3
81,874,802
GBE1



rs16851483
3
142,758,126
RASA2



rs17001654
4
77,348,592
SCARB2



rs11727676
4
145,878,514
HHIP



rs2033529
6
40,456,631
TDRG1



rs9400239
6
109,084,356
FOXO3



rs13191362
6
162,953,340
PARK2



rs1167827
7
75,001,105
HIP1



rs2245368
7
76,446,079
DTX2P1



rs2033732
8
85,242,264
RALYL



rs4740619
9
15,624,326
C9orf93



rs6477694
9
110,972,163
EPB41L4B



rs1928295
9
119,418,304
TLR4



rs10733682
9
128,500,735
LMX1B



rs7899106
10
87,400,884
GRID1



rs17094222
10
102,385,430
HIF1AN



rs11191560
10
104,859,028
NT5C2



rs7903146
10
114,748,339
TCF7L2



rs2176598
11
43,820,854
HSD17B12



rs12286929
11
114,527,614
CADM1



rs11057405
12
121,347,850
CLIP1



rs10132280
14
24,998,019
STXBP6



rs12885454
14
28,806,589
PRKD1



rs3736485
15
49,535,902
DMXL2



rs758747
16
3,567,359
NLRC3



rs2650492
16
28,240,912
SBK1



rs9925964
16
31,037,396
KAT8



rs1000940
17
5,223,976
RABEP1



rs1808579
18
19,358,886
C18orf8



rs7243357
18
55,034,299
GRP



rs17724992
19
18,315,825
PGPEP1



rs977747
1
47,457,264
TAL1



rs1460676
2
164,275,935
FIGN



rs17203016
2
207,963,763
CREB1



rs13201877
6
137,717,234
IFNGR1



rs1441264
13
78,478,920
MIR548A2



rs7164727
15
70,881,044
LOC100287559



rs2080454
16
47,620,091
CBLN1



rs9914578
17
1,951,886
SMG6



rs2836754
21
39,213,610
ETS2



rs492400
2
219,057,996
USP37



rs16907751
8
81,538,012
ZBTB10



rs9374842
6
120,227,364
LOC285762



rs9641123
7
93,035,668
CALCR



rs9540493
13
65,103,705
MIR548X2



rs4787491
16
29,922,838
INO80E



rs6465468
7
95,007,450
ASB4



rs7239883
18
38,401,669
LOC284260



rs3101336
1
72,523,773
NEGR1



rs12566985
1
74,774,781
FPGT



rs12401738
1
78,219,349
FUBP1



rs11165643
1
96,696,685
PTBP2



rs17024393
1
109,956,211
GNAT2



rs543874
1
176,156,103
SEC16B



rs13021737
2
622,348
TMEM18



rs10182181
2
25,003,800
ADCY3



rs1016287
2
59,159,129
LINC01122



rs2121279
2
142,759,755
LRP1B



rs13078960
3
85,890,280
CADM2



rs1516725
3
187,306,698
ETV5



rs10938397
4
44,877,284
GNPDA2



rs13107325
4
103,407,732
SLC39A8



rs2112347
5
75,050,998
POC5



rs205262
6
34,671,142
C6orf106



rs2207139
6
50,953,449
TFAP2B



rs17405819
8
76,969,139
HNF4G



rs10968576
9
28,404,339
LINGO2



rs4256980
11
8,630,515
TRIM66



rs11030104
11
27,641,093
BDNF



rs3817334
11
47,607,569
MTCH2



rs7138803
12
48,533,735
BCDIN3D



rs12016871
13
26,915,782
MTIF3



rs12429545
13
53,000,207
OLFM4



rs11847697
14
29,584,863
PRKD1



rs7141420
14
78,969,207
NRXN3



rs16951275
15
65,864,222
MAP2K5



rs12446632
16
19,842,890
GPRC5B



rs3888190
16
28,796,987
ATP2A1



rs1558902
16
52,361,075
FTO



rs12940622
17
76,230,166
RPTOR



rs6567160
18
55,980,115
MC4R



rs29941
19
39,001,372
KCTD15



rs2075650
19
50,087,459
TOMM40



rs2287019
19
50,894,012
QPCTL



rs3810291
19
52,260,843
ZC3H4



rs7715256
5
153,518,086
GALNT10



rs2176040
2
226,801,046
LOC646736



rs6091540
20
50,521,269
ZFP64
















SNP
Chr.
Position (bp)
Genes







rs1800544


ADRA2A



Ins-Del-322


ADRA2C



rs5443


GNB3



rs1129649


GNB3



rs1047776


GNB3



rs9939609


FTO



rs17782313


MC4R



rs7903146


TCF7L2



rs4795541


5-HTTLPR



rs3813929


HTR2C



rs518147


HTR2C



rs1414334


HTR2C



rs659366


UCP2



−3474,


UCP2



rs2075577


UCP3



rs15763


UCP3



rs1626521


UCP3



rs11554825


GPBAR1



rs4764980


NR1H4



rs434434


FGFR4



rs351855


FGFR4














RSID
Gene_Symbol







exm2261885
.



exm2264702
.



kgp10003923
.



kgp10360658
.



kgp10374580
.



kgp1093561
.



kgp11089754
.



kgp11154375
.



kgp11564777
.



kgp11808957
.



kgp11836456
.



kgp11902597
.














SNP
Chr.
Position (bp)
Nearest Genes





kgp12031075


.


kgp12088423


.


kgp1283935


.


kgp1287405


.


kgp129784


.


kgp1371036


.


kgp1419661


.


kgp1612367


.


kgp16387096


.


kgp16914214


.


kgp2241756


.


kgp2251945


.


kgp238191


.


kgp2727759


.


kgp2735253


.


kgp2925720


.


kgp3186084


.


kgp3371090


.


kgp3712407


.


kgp3846165


.


kgp3847753


.


kgp429141


.


kgp4433253


.


kgp447667


.


kgp4725781


.


kgp5201059


.


kgp5201171


.


kgp5269120


.


kgp5471252


.


kgp5829795


.


kgp599811


.


kgp6037240


.


kgp6508014


.


kgp6615769


.


kgp6816777


.


kgp7069937


.


kgp7157564


.


kgp7328604


.


kgp7496475


.


kgp7707096


.


kgp7798504


.


kgp8018963


.


kgp8206543


.


kgp8818851


.


kgp8860587


.


kgp9190754


.


kgp9456377


.


kgp9526272


.


kgp9629679


.


rs10489944


.


rs10504589


.


rs10808295


.


rs11060968


.


rs11225943


.


rs11720464


.


rs12354667


.


rs12427263


.


rs13130205


.


rs1372851


.


rs1493716


.


rs1541616


.


rs1674070


.


rs1873367


.


rs1889757


.


rs2470000


.


rs2470029


.


rs2647979


.


rs2720400


.


rs2851820


.


rs2851836


.


rs288756


.


rs348337


.


rs4707490


.


rs6005420


.


rs6008618


.


rs6472339


.


rs6776731


.


rs6828992


.


rs6888630


.


rs6957234


.


rs7082638


.


rs7297442


.


rs7658020


.


rs7803317


.


rs8141901


.


rs849309


.


rs9511655


.


rs9810198


.


rs9860734


.


exm2264762


.


exm2271737


.


exm2272553


.


kgp10285805


.


kgp10360658


.


kgp10548537


.


kgp10901790


.


kgp11044637


.


kgp11343144


.


kgp11430653


.


kgp11530429


.


kgp11836456


.


kgp11960081


.


kgp11974172


.


kgp12031075


.


kgp12088423


.


kgp12289889


.


kgp127695


.


kgp1278486


.


kgp1586406


.


kgp16387096


.


kgp1727603


.


kgp1887803


.


kgp1939387


.


kgp2241672


.


kgp22776953


.


kgp227938


.


kgp2369570


.


kgp3371090


.


kgp3406296


.


kgp3660486


.


kgp3662728


.


kgp3712407


.


kgp374568


.


kgp3846165


.


kgp4074864


.


kgp429141


.


kgp447667


.


kgp4534617


.


kgp4799975


.


kgp4944907


.


kgp5201171


.


kgp5329941


.


kgp5471252


.


kgp563498


.


kgp5671927


.


kgp5780899


.


kgp5829795


.


kgp6037240


.


kgp6688816


.


kgp6827318


.


kgp7048855


.


kgp7069937


.


kgp7235499


.


kgp7945681


.


kgp8628976


.


kgp8860587


.


kgp9190754


.


kgp9231149


.


kgp9578092


.


kgp965777


.


rs10150519


.


rs10161070


.


rs10451103


.


rs10742039


.


rs10877143


.


rs11720464


.


rs12506204


.


rs12593784


.


rs12937299


.


rs13338004


.


rs1372851


.


rs1493716


.


rs1512840


.


rs1541616


.


rs16822391


.


rs17076260


.


rs17453871


.


rs1873367


.


rs201607


.


rs202558


.


rs2169564


.


rs2647979


.


rs2720400


.


rs2761413


.


rs2957787


.


rs3762535


.


rs4313958


.


rs4461665


.


rs4543516


.


rs4707490


.


rs4964150


.


rs6008618


.


rs6448182


.


rs6585563


.


rs6828992


.


rs6957234


.


rs7297442


.


rs7658020


.


rs768969


.


rs7846145


.


rs7981554


.


rs8002390


.


rs8141901


.


rs935201


.


rs9378848


.


rs9810198


.


rs9860734


.


rs9864846


.


kgp2297621


ACSL6


rs440970


ACSL6


kgp10461170


ADARB2


kgp7332119


ADARB2


rs12415114


ADARB2


kgp9064589


ADCY8


rs13133908


ADH1B


kgp12414761


ADH1B


rs13133908


ADH1B


rs2075633


ADH1B


rs7518469


AJAP1


rs429790


ATP2C2


kgp3288649


ATP6V0D2


rs2832231


C21orf7


kgp10136381


CAMKMT


kgp3161157


CAMKMT


kgp3203202


CAMKMT


kgp3968222


CAMKMT


kgp4140267


CAMKMT


rs13406580


CAMKMT


rs1551882


CAMKMT


rs17032193


CAMKMT


rs7593926


CAMKMT


kgp4005992


CAP2


kgp7298922


CASC4


kgp1789974


CD48


kgp5511006


CD48


kgp1789974


CD48


kgp5511006


CD48


kgp7256435


CDC42SE2


rs4706020


CDC42SE2


rs3812178


CDYL


rs3812179


CDYL


kgp3731792


CES5AP1


kgp6395031


CLMN


kgp6395031


CLMN


kgp4873414


CNPY4


rs3806043


COL19A1


kgp3071123


COL27A1


kgp6064462


COL27A1


kgp6796371


COL27A1


rs1249745


COL27A1


kgp5314602


COL4A3


kgp11506369


COROIC


kgp6473219


COROIC


kgp10992142


CPZ


rs8087866


CTIF


kgp7710562


DAAM2


exm430196


DCHS2


exm430197


DCHS2


kgp49288


DCHS2


kgp766527


DCHS2


rs4696584


DCHS2


kgp3890072


DOCK8


rs1980876


DOCK8


exm451231


EGFLAM


rs6897179


EGFLAM


kgp10622968


FAM125B


kgp5732367


FAM71E2


kgp10294313


FRMD3


kgp2344514


FRMD3


kgp7900743


FRMD3


kgp3392580


GALNTL4


kgp4456104


GLT1D1


kgp5287249


HHAT


exm1319778


KRT23


rs8037


KRT23


rs9257


KRT23


kgp10012744


LHPP


kgp1057196


LINC00578


kgp8853148


LINC00578


rs6799682


LINC00578


rs7632844


LINC00578


kgp11567842


LINC00620


rs12495328


LINC00620


kgp4159029


LIPC


kgp1640513


LOC100128714


kgp4598936


LOC100128714


kgp5262759


LOC100128714


rs11635697


LOC100128714


rs12593847


LOC100128714


rs8023270


LOC100128714


kgp1743339


LOC100287160


kgp7667092


LOC100289473


rs6135960


LOC100289473


rs6135960


LOC100289473


kgp5351206


LOC100293612|LINC00620


kgp10995216


LOC100506869


kgp22804264


LOC100506869


rs4760137


LOC100506869


rs1566141


LOC100507053


kgp10134243


LOC100507053


rs10008281


LOC100507053


rs1229966


LOC100507053


rs1566141


LOC100507053


rs2051428


LOC100507053


rs3819197


LOC100507053|ADH1A


rs3819197


LOC100507053|ADH1A


rs9995799


LOC100507053|ADH6


kgp1289034


LOC100507443


rs9981988


LOC100996571|CYYR1


kgp258053


LOC152225


kgp6272649


LOC152225


kgp2759189


LOC255130


kgp2759189


LOC255130


rs10980642


LPAR1


kgp5423754


LUZP2


kgp8988372


LUZP2


kgp9462081


MCM7


rs2261360


MCM7


kgp8065051


MICAL3


kgp4439669


MMS19


kgp18459


MYBPC1


kgp1800707


NR2F2-AS1


rs7831515


NSMCE2


rs16958048


NTN1


kgp7166603


OAZ2


kgp7077044


O3FAR1


kgp2414524


OSBP2


kgp2580452


OSBP2


kgp7020841


OSBP2


rs4820897


OSBP2


kgp7020841


OSBP2


kgp7082195


P4HA2


rs6667138


PADI1


rs6667138


PADI1


kgp7908292


PARD3B


rs7558785


PARD3B


kgp11077304


PARK2


exm-rs2795918


PCDH15


kgp1058322


PCDH15


kgp11029138


PCDH15


kgp11410092


PCDH15


kgp2961930


PCDH15


kgp5544438


PCDH15


kgp9631691


PCDH15


rs4082042


PCDH15


kgp9916431


PKIB


rs13218313


PKIB


kgp4769029


PRH1-PRR4


kgp9716281


PTPRD


rs7045790


RALGPS1|ANGPTL2


rs17081778


RPS24P10


kgp10074267


RTN4RL1


kgp11049623


RYR2


kgp8225782


SCN2A


rs2075703


SCN2A


rs6744911


SCN2A


rs12706974


SEMA3C


rs1358340


SEMA3C


kgp7788385


SEMA5A


rs3822799


SEMA5A


kgp3608544


SFMBT2


rs1887757


SGCG


rs9580573


SGCG


kgp390881


SLC22A15


kgp2776219


SLC2A2


exm988933


SLCO1B1


rs2306283


SLCO1B1


rs4149040


SLCO1B1


exm988933


SLCO1B1


rs2306283


SLCO1B1


rs4149040


SLCO1B1


kgp8338369


SMOC2


kgp12080543


SNCAIP


kgp4607090


SNCAIP


rs4895350


SNCAIP


kgp1043980


SNX18


kgp2071353


SRRM4


kgp7567091


SUSD1


kgp7567091


SUSD1


kgp12526521


TBC1D16


kgp5557307


TCERG1L


rs13340295


TENM3


kgp3414710


TJP3


kgp10345778


TLL1


rs4690833


TLL1


rs2568085


TMEM9B


rs1071646


TPM1


rs6738


TPM1


rs1071646


TPM1


rs6738


TPM1


rs1408817


VTI1A


rs216905


VWF


kgp2039705


WWOX


rs6804325


WWTR1


exm382632


ZFYVE28


rs12532238


ZNF3


rs6592


ZNF3


kgp6175568


ZNF609


rs11558476


ZSCAN21
















TABLE 4







Gastrointestinal peptides associated with obesity.









Hormone
Source
Normal function





Cholecystokinin (CCK)
Duodenum
Increase satiation


Ghrelin
Gastric fundus
Stimulate appetite


Glucagon-like
Distal small
Increase satiety


peptide 1 (GLP-1)
intestine and colon


Peptide YY (PYY)
Distal small
Increase satiety



intestine and colon





* Low calorie diet can alter peptide concentrations.













TABLE 5







Genes associated with obesity.











Genotype potentially




affecting organ or


Primary

mechanisms associated


Endpoints
Quantitative Trait
with trait





Satiety
Postprandial GLP-1
TCF7L2, GNB3, MC4R



and PYY


Satiation
VTF, MTV (kcal)
MC4R, GNB3, HTR2C, UCP3


Gastric
GE (solids)
TCF7L2, ADRA2A, UCP3


Emptying


Appetite
Fasting Ghrelin
MC4R, FTO, GNB3





* Gene variants were selected based on association with BMI and mechanism of action.






Table 6 summarizes the variables that were significantly associated with each of the phenotypic groups vs the rest of the groups.







TABLE 6







SNPs present in each obesity group









Obesity

Exemplary


Phenotype Group
Gene
SNP





1: low satiation
HTR2C, POMC, NPY,
rs1414334



AGRP, MC4R, GNB3,



SERT, BDNF


2: low satiety
PYY, GLP-1, MC4R,
rs7903146



GPBAR1, TCF7L2,



ADRA2A, PCSK,



TMEM18


3: behavioral eating
SLC6A4/SERT, DRD2
rs4795541


4: large fasting gastric volume
TCF7L2, UCP3,
rs1626521



ADRA2A,


5: mixed


6: low resting energy expenditure
FTO, LEP, LEPR,
rs2075577



UCP1, UCP2, UCP3,



ADRA2, KLF14,



NPC1, LYPLAL1,



ADRB2, ADRB3,



BBS1









Combinations of compounds (amino-compounds, neurotransmitters fatty acids, metabolic peptides, and metabolic gene) were identified as significantly associated with each of the obesity phenotypic groups. The variables that were significantly associated with each of the phenotypic groups included the following:
Questionnaire results:

    
    
        hospital anxiety and depression scale-anxiety subscale (HADS-A),
    
    


Metabolites:

    
    
        1-methylhistine
        seratonin
        glutamine
        gamma-amino-n-butyric-acid
        isocaproic
        allo-isoleucine
        hydroxyproline
        beta-aminoisobutyric-acid
        alanine
        hexanoic
        tyrosine
        phenylalanine
    
    


Gastrointestinal Peptides:

    
    
        fasting ghrelin
        fasting PYY

Algorithm

    
    


The following formulas were used to identify the obesity phenotype of a patient based upon the signature of the 14 compounds identified as being significantly associated with each of the obesity phenotypic groups. The formulas predicted the phenotypes with a r2 of 0.90 and a probability Chi-square of less than 0.0001.
Lin[1]


    (−1552.38148595936)
    +40.797700201235 *:Name(“HADS-A”)
    +1.32549623006262 *:Glutamine
    +−0.111622239757052 *:Alanine
    +−616.954862561479 *:Name(“gamma-Amino-N-butyric-acid”)
    +−89.402967640225 *:Name(“beta-Aminoisobutyric-acid”)
    +−1.73891527871898 *:Tyrosine
    +6.24138513457712 *:Phenylalanine
    +2148.66822848398 *:isocaproic
    +−20.6187102527618 *:hexanoic
    +56.3110714341266 *:Name(“Log(Hydroxyproline)”)
    +82.3818646650792 *:Name(“Log(1-Methylhistine)”)
    +−26.8826686365131 *:Name(“Log(seratonin)”)
    +0.245926705626903 *:Name(“PYY_-15”)
    +1.89180999803712 *:Name(“Ghrelin_-15”)
    +75.8755521857061 *:Name(“allo-Isoleucine”)

Lin [2]

    (−2031.26556804871)
    +56.9736558824775 *:Name(“HADS-A”)
    +−0.0118072070103887 *:Glutamine
    +−0.0995668418558728 *:Alanine
    +1609.83650774629 *:Name(“gamma-Amino-N-butyric-acid”)
    +123.106026249695 *:Name(“beta-Aminoisobutyric-acid”)
    +13.0377088181536 *:Tyrosine
    +−2.42979784589652 *:Phenylalanine
    +3057.74326808551 *:isocaproic
    +63.6119366218627 *:hexanoic
    +99.2853520251878 *:Name(“Log(Hydroxyproline)”)
    +0.166314503531418 *:Name(“Log(1-Methylhistine)”)
    +6.21451740476229 *:Name(“Log(seratonin)”)
    +−0.696742681406157 *:Name(“PYY_-15”)
    +2.30188885859994 *:Name(“Ghrelin_-15”)
    +220.083419205279 *:Name(“allo-Isoleucine”)

Lin [3]

    (−735.067323742327)
    +84.6709055694921 *:Name(“HADS-A”)
    +0.739638607406857 *:Glutamine
    +0.0161670919675227 *:Alanine
    +1.70702352345921 *:Name(“gamma-Amino-N-butyric-acid”)
    +4.08385430756663 *:Name(“beta-Aminoisobutyric-acid”)
    +4.83658065569896 *:Tyrosine
    +−7.4973831454893 *:Phenylalanine
    +1467.49860590747 *:isocaproic
    +51.4109043756237 *:hexanoic
    +−56.3364437814115 *:Name(“Log(Hydroxyproline)”)
    +45.3693267895892 *:Name(“Log(1-Methylhistine)”)
    +24.1167481430051 *:Name(“Log(seratonin)”)
    +−1.56458536889981 *:Name(“PYY_-15”)
    +2.15880622406247 *:Name(“Ghrelin_-15”)
    +72.4632042822316 *:Name(“allo-Isoleucine”)

Lin[4]

    (−38.8679541168302)
    +1.54112014174663 *:Name(“HADS-A”)
    +0.00119976598048842 *:Glutamine
    +0.056518755537321 *:Alanine
    +34.9734228686154 *:Name(“gamma-Amino-N-butyric-acid”)
    +2.64367056830481 *:Name(“beta-Aminoisobutyric-acid”)
    +0.0996495148185086 *:Tyrosine
    +−0.14869421223421 *:Phenylalanine
    +8.69300091428836 *:isocaproic
    +1.77363291550863 *:hexanoic
    +−1.58953123143685 *:Name(“Log(Hydroxyproline)”)
    +0.127307799711255 *:Name(“Log(1-Methylhistine)”)
    +−3.33170879355105 *:Name(“Log(seratonin)”)
    +0.0387731073018872 *:Name(“PYY_-15”)
    +0.0662851699121999 *:Name(“Ghrelin_-15”)
    +−1.4086102207227 *:Name(“allo-Isoleucine”)

1/(1+Exp(−(“Lin[1]”))+Exp((“Lin[2]”)−(“Lin[1]”))+Exp((“Lin[3]”)−(“Lin[1]”))+Exp((“Lin[4]”)−(“Lin[1]”)))  Prob[1]

1/(1+Exp((“Lin[1]”)−(“Lin[2]”))+Exp(−(“Lin[2]”))+Exp((“Lin[3]”)−(“Lin[2]”))+Exp((“Lin[4]”)−(“Lin[2]”)))  Prob[2]

1/(1+Exp((“Lin[1]”)−(“Lin[3]”))+Exp((“Lin[2]”)−(“Lin[3]”))+Exp(−(“Lin[3]”))+Exp((“Lin[4]”)−(“Lin[3]”)))  Prob[3]

1/(1+Exp((“Lin[1]”)−(“Lin[4]”))+Exp((“Lin[2]”)−(“Lin[4]”))+Exp((“Lin[3]”)−(“Lin[4]”))+Exp(−(“Lin[4]”)))  Prob[4]

1/(1+Exp((“Lin[1]”))+Exp((“Lin[2]”))+Exp((“Lin[3]”))+Exp((“Lin[4]”)))  Prob[6]

Table 7 summarizes variables (14 analytes and a questionnaire) that were significantly associated with each of the phenotypic groups with the ROC of the group vs the rest of the groups.










TABLE 7







Compounds present in each obesity group













Source
Group 1
Group 2
Group 3
Group 4
Group 5
Group 6
















1-Methylhistine

+

+

+


seratonin
+

+

+
+


Glutamine
+


+

+


gamma-Amino-



+

+


N-butyric-acid


isocaproic
+


+

+


allo-Isoleucine
+
+

+

+


Hydroxyproline
+
+


beta-Amino-
+
+

+
+


isobutyric-acid


Alanine
+
+

+
+
+


hexanoic
+


+
+


Tyrosine
+


+

+


Phenylalanine

+

+
+


Ghrelin





+


PYY
+


+

+


HADS-A


+
+
+
+


p value of
0.006
<0.001
<0.001
<0.001
0.01
<0.001


whole model


test


ROC value
0.90
0.86
0.91
0.89
0.86
0.96


(group vs rest)









One multinomial logistic model contained 14 compounds and one questionnaire, and the obesity phenotypes were predicted with more than 97% sensitivity and specificity (group 1=1, group 2=1, group 3=1, group 4=0.97 and group 6=0.96). When a mixed group is added to the equation, the obesity phenotypes can be predicted with more than 91% sensitivity and specificity (group 1=0.95, group 2=0.92, group 3=1, group 4=0.96, group 5=0.96 and group 6=0.97). When group 4 and mixed are removed from the equation, the obesity phenotypes can be predicted with 100% sensitivity and specificity.
Another multinomial logistic model contained 1 behavioral assessment, 3 germline variants, and 6 fasting targeted metabolomics. The variables can be as shown in Table 8 plus questionnaire(s) (e.g., HADS and/or TEFQ21).







TABLE 8







Variables (9 analytes and a questionnaire) that were significantly


associated with each of the phenotypic groups with the


ROC of the group vs the rest of the groups.











SNP
Gene
Name
Panel
Peptide





rs9939609.n
FTO
cystathionine1
amino compound
fasting






pyy


rs1626521.n
UCP3
glycine
amino compound
fasting






ghrelin


rs3813929.n
5-HT2CR
valine
amino compound


rs5443.n
GNB3
serotonin
neurotransmitter


rs1800544.n
ADRA2a
glutamic acid
amino compound


rs2234888.n
ADRA2c
tryptophan
amino compound


rs17782313
MC4R
histidine
amino compound




methylhistidine1
amino compound




methionine
amino compound




isocaproic
amino compound




hydroxylysine2
amino compound




ethanolamine
amino compound




hydroxyproline
amino compound




gamma-amino-
neurotransmitter




n-butyric acid




threonine
amino compound




alpha-
amino compound




aminoadipic acid




sarcosine
amino compound




arginine
amino compound




histidine
neurotransmitter




proline
amino compound









Obesity Phenotypes Biomarker

Simple-blood test biomarkers were identified that can classify obese patients into their related phenotypes. To achieve this, 25 individuals with unique obesity phenotypes were selected from the cohort of 180 participants and an untargeted metabolomics study was performed using their fasting blood samples. Thus, average of 3331 unique metabolites that are associated with each obesity-related phenotype were observed and this is illustrated through the VennDiagrams of Unique Metabolites per group using Positive-HILIC Untargeted Metabolomics (FIG. 2A). These data supported the application of a targeted metabolomics approach, hypothesis-driven, to identify and quantify associated metabolites. A two-stage design was used to develop the composition of the blood test; the training and validation cohorts consisted of 102 and 78 obese patients, respectively. Based on the profile of each patient, we were able to validate the main groups in obesity cohorts, that is 1) abnormal satiation, 2) rapid return to hunger, 3) behavioral eating; 4) abnormal energy expenditure; 5) a “mixed” group. Using a multinomial logistic regression was used to develop a classification model using elastic net shrinkage for variable selection. Discrimination was evaluated using concordance index (c-index). Receiving operating characteristic curves (ROC) for the models were constructed and area under the curve (AUC) estimated. The variables were applied to a prediction model or algorithm diagnostic (patent submitted) to identify the phenotypes. The model predicted the phenotypes with an ROC of 0.91 (AUC) for the training cohort and 0.71 for the validation cohort (FIG. 3 and FIG. 5). The accuracy of the model was 86% in the whole cohort. When the model was applied to the two previously completed placebo-controlled, randomized trials, the weight loss after 2 weeks of phentermine-topiramate ER was 58% higher in the predicted group (n=3, 2.4±0.4 kg) compared to the other groups (n=9, 1.4±0.2 kg), and after 4 weeks of exenatide, weight loss was 65% higher in the predicted group (n=6, 1.5±0.6 kg) compared to the other groups (n=4, 0.9±0.7 kg).
In summary, using this actionable classification decreases obesity heterogeneity, and facilitate our understanding of human obesity. Furthermore, we have developed and validated a novel, first-of-its-kind, simple, fasting, blood-based biomarker for obesity phenotypes.
Sensitivity and Specificity of Biomarkers
To confirm the sensitivity and specificity of the biomarkers significantly associated with each of the obesity phenotypic groups, a receiver operating characteristic (ROC) analysis was done.
FIG. 3 shows the sub-classification prediction accuracy of this combined model and an ROC analysis showed that this model has >0.90 area under the curve (AUC) for all six classes.
Next, binary classification models were derived that can predict whether a patient belongs to one group over the others. Bayesian covariate predictors were derived for low satiation, behavioral eating, and low resting energy expenditure. These models yielded an ROC AUC of 1 (FIG. 4). These data suggested that the serum metabolite levels hold all the information needed to predict obesity subclasses.
Validation of Biomarkers
To further validate the ability to phenotype obesity based on variables significantly associated with each of the phenotypic groups, the formula was applied to 60 new participants with obesity, and a ROC analysis was done.
FIG. 5 shows that the formula predicted the sub-groups with over 90% sensitivity and specificity.
Summary
These results demonstrate that serum biomarkers can be used to classify obesity patients into obesity phenotype groups.
Example 2: Obesity Phenotypes and Intervention Responsiveness
Obesity is a chronic, relapsing, multifactorial, heterogeneous disease. The heterogeneity within obesity is most evident when assessing treatment response to obesity interventions, which are generally selected based on BMI. These standard approaches fail to address the heterogeneity of obesity. As described in Example 1, obesity phenotypes were associated with higher BMI, distinguish obesity phenotypes. This Example shows that obesity phenotypes respond differently to specific interventions (e.g., pharmacological interventions). Obesity-related phenotypes were evaluated to facilitate the understanding of obesity pathophysiology, and identify sub-groups within the complex and heterogeneous obese population. A novel classification based on identifying actionable traits in the brain-gut axis in humans (see, e.g., Acosta et al., 2015 Gastroenterology 148:537-546 e534; and Camilleri et al., 2016 Gastrointest Endosc. 83:48-56) was applied to understand, in a more homogenous, phenotype-defined population, the unique or specific characteristics within each sub-group of obesity.
The specific characteristics of 180 participants with obesity (defined as BMI>30 kg/m2) were grouped based on their predominant obesity-related phenotype, based on a multiple step process (in addition to gender) to generate a homogeneous populations based on the 75th percentile within the obese group for each well-validated variable: a) satiation [studied by nutrient drink test (maximal tolerated volume, 1 kcal/ml)], b) satiety [studied by gastric emptying (T1/2, min)], c) hedonic (hospital anxiety and depression score [HADS] questionnaire), d) other (none of the above) and e) mixed (two or more criteria met).
The overall cohort demographics were as described in Example 1. Then, with the intention to validate further the applicability of the obesity phenotypes, the fact that each sub-group may have unique abnormalities compared to the other groups when tested with previously validated or reported findings in common obesity was interrogated.
Abnormal Satiation Group
Individuals with obesity typically consume more calories prior to reach ‘usual’ fullness—for every 5 kg/m2 of BMI increase, participants consumed 50 calories more (see, e.g., Acosta et al., 2015 Gastroenterology 148:537-546). Here, participants with obesity and abnormal satiation were compared to the other groups with the same validated two food intake (meal) paradigms test to measure satiation (FIG. 6A). During a nutrient drink test, females with abnormal satiation consumed 235 calories more prior to reach ‘usual’ fullness (p<0.001) and 600 calories more prior to reach ‘maximal’ fullness (p<0.001); males with abnormal satiation consumed 514 calories more prior to reach ‘usual’ fullness (p<0.001) and 752 calories more prior to reach ‘maximal’ fullness (p<0.001) compared to individuals with obesity and normal satiation. During the ad libitum buffet meal, females with abnormal satiation consumed 287 calories more prior to reach fullness (p<0.001) and males consumed 159 calories more prior to reach fullness (p=0.03) compared to individuals with obesity and normal satiation. Within obesity the sub-group with abnormal—or lack of—satiation consumed significant more calories in one meal, suggesting a deficiency in the stop signals and a hungry brain phenotype.
Abnormal Satiety Group
Accelerated gastric emptying was chosen as a surrogate for abnormal satiety based on the main fact that is an objective, reproducible test, whiles other tests, such as visual analog scores are subjective sensations of satiety. In female participants with obesity and abnormal satiety, their gastric emptying (GE) was 40% GE solids T1/4 (p<0.001), 30% GE solids T1/2 (p<0.001) and 22% GE liquids T1/5 (p=0.01) faster compared to normal satiety. In male participants with obesity and abnormal satiety, their gastric emptying was 44% GE solids T1/4(p=0.005), 38% GE solids T1/2 (p<0.001) and 33% GE liquids T1/2 (p=0.05) faster compared to normal satiety (FIG. 7A). The gastric volume fasting and postprandial is smaller in participants with abnormal satiety compared to those with normal satiety when measured by SPECT (FIG. 7B). Additionally, individuals with abnormal satiety have lower levels of gastrointestinal satiety hormones, GLP-1 (p=0.005) and PYY3-36 (p=0.01) at 90 minutes after a meal. Individuals with abnormal satiation have gastrointestinal satiety hormones similar to historical controls with normal weight (see, e.g., Acosta et al., 2015 Gastroenterology 148:537-546); and individuals in the ‘other’ group also have very low levels of these hormones, despite of having normal gastric emptying. However, the correlation of food intake when reach ‘usual’ fullness in the nutrient drink test to the secretion of PYY3-36 is linear (r=0.42, p<0.001) and significant in individuals with normal satiety and this correlation disappear in individuals with normal satiety, suggesting an inadequate response of the PYY3-36 secreting enteroendocrine (EE) cells to the meal challenge. Enteroendocrine (EE) cells are real-time nutrient, bile and microbiota sensors that regulate food intake, brain-gut communication, gastrointestinal motility, and glucose metabolism. EE cell function can be studied indirectly by measuring plasma levels of hormones such as GLP-1 or PYY, and less frequently EE cells are studied as part of whole intestinal tissue. These results suggested a hungry gut phenotype.
Hedonic Group
There is a sub-group within participants with obesity which have a very strong psychological component that may predispose them to obesity, labeled here as a ‘hedonic’ sub-group. Likely this group is acquiring most of their calories from emotional eating, cravings and reward-seeking behaviors while having appropriate sensations of satiation and satiety. Individuals in the hedonic group have higher levels of anxiety (p<0.001) and depression (p<0.001); and lower levels of self-esteem (p=0.002) when compared to other individuals with obesity. The hedonic group has a lower level of serum fasting tryptophan compared to the other groups (p=0.004, FIG. 8). Tryptophan is a precursor of serotonin and melatonin, which has been associated with depression, cravings and obesity.
Slow Metabolism Group
A sub-cohort of our population who completed indirect calorimetry testing was studied and individuals in the slow metabolism group have significant lower resting energy expenditure (90% of predicted) compared to the other groups of obesity (100% of predicted, p=0.032) were identified (FIG. 9A). The slow metabolism group have significant lower measured resting energy expenditure (kcal/day) that other groups (p<0.05) (FIG. 9B). Individuals with slow metabolism have lower systolic blood pressure (p=0.019), higher heart rate (p=0.05) higher self-steem (p=0.004). When body composition was measured using a dexa scan, there was not difference in calculated BMI or measured total fat mass among the obesity groups, however, individuals with slow metabolism had lower muscle (lean) mass compared to the other obesity groups (ANOVA p<0.05), FIG. 9C). Metabolites in patients with slow metabolism compared to normal metabolism (other or rest) were significant different (p<0.05): higher than other groups: alanine, isocaproic acid, phosphoetahnolamine, phenylalanine, tyrosine, alpha-amino-N-butyric acid, sarcasine, and lower than other groups: 1-methylhistidine (FIG. 9D).
Obesity Phenotypes Biomarker
The applicability of obesity-related phenotypes as actionable biomarkers was tested in three pilot, proof of concept studies (see, e.g., Acosta et al., 2015 Gastroenterology 148:537-546; Acosta et al., 2015 Physiol Rep 3; and Halawi et al., 2017 Lancet Gastroenterol Hepatol 2:890-899). First, in a single-center, randomized, parallel-group, double-blind, placebo-controlled, 14-day study, the effects of Phentermine-topiramate-ER (PhenTop) (7.5/46 mg, orally, daily) on gastric emptying (GE) and volume, satiation, satiety, and fasting and postprandial GI hormones was evaluated in 24 obese adults. Patients with an abnormal baseline satiation test had greater mean weight loss to PhenTop ER compared to those with normal satiation (p=0.03). In a second placebo-controlled trial, the effect of exenatide was studied, 5 μg SQ, twice daily for 30 days, on GE, satiety, satiation and weight loss in 20 obese participants with obesity and abnormal satiety. The average weight loss was 1.3 kg for exenatide and 0.5 kg for the placebo group (p=0.06), suggesting that patients with abnormal satiety may be good candidates for weight loss with a GLP-1 receptor agonist). Subsequently, in a prospective, NIH-funded, randomized, placebo controlled clinical trial to study the effects of liraglutide 3 mg, SQ, over 16 weeks on obesity phenotypes and weight in 40 obese patients. Compared to placebo, liraglutide delayed GE of solids at 5 (p<0.0001) and 16 (p=0.025) weeks, caused significant weight loss and increased satiation. At 5 and 16 weeks, GE T1/2 correlated with change in weight loss on liraglutide (all p<0.02). These results demonstrate that obesity-related phenotypes can predict response to obesity pharmacotherapy.
Phentermine-Topiramate and Obesity Phenotypes
The effects of phentermine-topiramate-ER (PhenTop) (7.5/46 mg, orally, daily) was evaluated on GE, GV, satiation, satiety, and fasting and postprandial gut hormones as described elsewhere (see, e.g., Acosta et al., 2015 Gastroenterology 148:537-546). PhenTop was associated with reduced food intake at buffet meal (mean Δ260 kcal, p=0.032) and delayed GE solids (mean ΔGE4h 6%, p=0.03; and ΔGE T1⁄2 19 min, p=0.057). There were no significant differences in GV, satiation, GE of liquids and GI hormones. Patients on PhenTop had greater mean weight loss of 1.4 kg than placebo (p=0.03). Weight loss on PhenTop was significantly associated with kcal intake at a prior satiety test. These results demonstrate that PhenTop reduces food intake and delays GE of solids, indicating that patients having an obesity phenotype of Group 1 (low satiation), are likely responsive to treatment with PhenTop.
Exenatide and Obesity Phenotypes
The effects of exenatide (5 μg, SQ, twice daily for 30 days) was evaluated on GE, satiety, and weight loss as described elsewhere (see, e.g., Acosta et al., 2015 Physiological Rep. 3(11)). Exenatide, a glucagon-like peptide-1 (GLP-1) agonist, had a very significant effect on GE of solids (p<0.001) and reduced calorie intake at a buffet meal by an average 130 kcal compared to placebo. The average weight loss was 1.3 kg for exenatide and 0.5 kg for the placebo group (FIG. 11). These results demonstrate that exenatide reduces food intake and delays GE of solids, indicating that a prior accelerated gastric emptying test predicts weight loss with exenatide; see, also, Acosta et al., 2015 Physiological Rep. 3(11)).
Surgery and Obesity Phenotypes
The best responders to the intragastric balloon therapy were identified as described elsewhere (see, e.g., Abu Dayyeh et al., “Baseline Gastric Emptying and its Change in Response to Diverse Endoscopic Bariatric TherapiesGastric Emptying Predict Weight Change Response to Endoscopic Bariatric Therapies in a Large Cohort,” IFSO annual meeting, 2015) as individuals with an accelerated gastric emptying (p<0.001) and the greater delay in gastric emptying after intragastric balloon placement (p<0.001).
Liraglutide and Obesity Phenotypes
A prospective, randomized clinical trial with liraglutide, a long-acting GLP-1 receptor agonist, was completed. The effects of liraglutide and placebo were compared over 16 weeks on gastric motor functions, satiation, satiety and weight in obese patients. The study was a randomized, double-blind, placebo-controlled trial of subcutaneous liraglutide, 3 mg, with standardized nutritional and behavioral counseling. Forty adult, otherwise healthy local residents with BMI≥30 kg/m2 were randomized. Liraglutide or placebo was escalated by 0.6 mg/day each week for 5 weeks and continued until week 16. At baseline and after 16 weeks' treatment, weight, gastric emptying of solids (GES, primary endpoint), large fasting gastric volumes, satiation, and satiety were measured. GES was also measured at 5 weeks. Statistical analysis compared treatment effects using ANCOVA (with baseline measurement as covariate). Effect of liraglutide on GES T1/2 at 5 and 16 weeks in the liraglutide group was analyzed by paired t-test. Seventeen participants were analyzed in the liraglutide group (n=19 randomized) and 18 in the placebo group (n=21 randomized).
Compared to placebo, liraglutide retarded GES at 5 (p<0.0001) and 16 (p=0.025) weeks, caused significant weight loss and increased satiation. In 16 weeks, the total body weight loss for the liraglutide group was 6.1±2.8 kg (SD) compared to 2.2±5 kg control group (p=0.0096). There was tachyphylaxis to GES effects of liraglutide from 5 to 16 weeks' treatment. At 5 and 16 weeks, GES T1/2 correlated with Δ weight loss on liraglutide (all p<0.02). Nausea was the most common adverse event in the liraglutide group (63.2%) compared to placebo (9.5%). Liraglutide, 3.0 mg, significantly delays GES after 5 and 16 weeks' treatment; effects on weight loss are associated with absolute value of GES T1/2 on liraglutide.
These results demonstrate that liraglutide significant weight loss and increased satiation, indicating that a prior low satiety test predicts weight loss with liraglutide.
Individualized Therapy
The identification of obesity-related phenotypes based on an ‘actionable’ classification and potential applicability for management of obesity could have a significant impact on the obesity epidemic.
FIG. 12 shows exemplary individualized obesity interventions based upon obesity phenotypes.
The algorithm described in Example 1 was applied to 29 new patients with obesity (Table 9). Data from (intervention) pharmacotherapy and controls were acquired retrospectively. Groups were matched for age, gender and BMI. Results were compared the outcome to 66 patients previously treated by obesity experts.







TABLE 9







Obesity Patient Characteristics.












Historical
P


Demographics
Cases
Controls
value





N
55
175 



Age
  46 ± 1.8
50 ± 1
0.03


Gender (F)
67%
73%


Race (W)
93%
89%


Weight (kg)
115 ± 3
 116 ± 1.8


BMI
39.8 ± 1 
41.6 ± 0.6


Co-morbidities %
47/41/45/49/36
43/33/35/36/30


(DM/HTN/DJD/OSA/HLD)*


MEDS %
24
22


Phentermine
25
23


Phen-Top ER
 6
10


Lorcaserin
19
25


Liraglutide 3 mg
12
 4


Bupropion-Naltrexone SR
 4
 6


Other





Ns: not statistical significant difference






The algorithm predicted the obesity group and intervention responsiveness of the new participants with over 90% sensitivity and specificity (FIG. 13, FIG. 14, and Table 10). The controls were seen in the weight management clinic by a physician expert of obesity and offered standard of care for obesity management and pharmacotherapy. The current standard of care suggests that pharmacotherapy needs to be selected based on patient-physician preference, mainly driven by side effects and other comorbidities. The cases were seen in the weight management clinic by a physician expert of obesity and offered obesity-phenotype guided pharmacotherapy for obesity management. The phenotypes seen were abnormal satiation (25%), abnormal satiety (20%), abnormal behavior (20%) and other (35%).







TABLE 10







Total body weight in response to individualized intervention.










Total Body Weight Loss, %
Controls
Cases
P value





3 months (# patients)
2.7 ± 0.5 (66)
 6.1 ± 0.8 (29)
0.0008


6 months (# patients)
4.7 ± 0.7 (57)
10.7 ± 1.2 (22)
0.0001


9-12 months (# patients)
5.7 ± 1.2 (36)
12.9 ± 1.9 (15)
0.0025









The intervention group had 74% responders (defined as those who loss more than 3% in the first month) compared to 33% in the control group. The control group number of responders was similar to the published in the current obesity literature. The significant improvement of responders resulted in a total body weight loss of 12.9 kg in the intervention group compared to 5.8 kg in the control group at 9 months.
The algorithm was also applied to 12 patients with obesity, who saw their weight loss plateau during the treatment for obesity with an intragastric balloon. These individuals saw weight loss plateau during month 3 and 6 of treatment with the balloon. At month 6, the algorithm was applied to the intervention group compared to the controls.
Summary
It was found that obesity can be sub-grouped in: abnormal satiation (16%), abnormal satiety (16%), hedonic (19%), slow metabolism (32%) and mixed group (17%). Deeper characterization within each subgroup identifies specific disturbances of function. Thus, in the group with abnormal satiation measured by two different feeding paradigms (ad libitum buffet meal and nutrient drink test), compared to lean controls and other groups of obesity; this is summarized as “hungry brain phenotype”. In the group with abnormal satiety, there is a suboptimal response of the gut to food intake, manifested as accelerated gastric emptying and decrease in peak postprandial levels of satiety hormones suggesting a “hungry gut phenotype”. In the hedonic group, there are increased levels of anxiety, depression, and cravings with low levels of serum tryptophan compared to the other groups. The slow metabolism group has decreased resting energy expenditure compared to other groups. Since identifying the obesity subgroups by deep phenotyping is limited to few academic centers, a fasting blood multi-omic test was developed and validated that predict the obesity subgroups (ROC>90% AUC). This blood test provides segmentation of diverse sub-phenotypes of obesity, has the potential to select patients for individualized treatment from the sea of obesity heterogeneity, facilitates our understanding of human obesity, and may lead to future treatment based on actionable biomarkers.
These results demonstrate that obesity phenotype groups can be used to predict treatment response, and can be used to guide individualized treatment strategies (e.g., pharmacotherapy and/or bariatric endoscopy). The obesity phenotype guided intervention doubled the weight loss in patients with obesity.
Example 3: Obesity Phenotypes and Patient Sub Populations
To validate further the applicability of the obesity phenotypes, the fact that each sub-group may have unique abnormalities compared to the other groups when tested with previously validated or reported findings in common obesity was interrogated.
The model described above was run independently for female and male sub-populations of patients. Characteristics of a complete population is as denoted in Table 11.







TABLE 11







Whole Cohort (181 patients).























#pts w/


#pts w/



Trait
Mean + SD
>2 SD
# pts
Median
>90%
trait
% pts
>75%
trait
% pts




















HADS-A
3.4 + 2.5
9
17
3
7
29
16%
6
46
25%


HADS-D
1.54 + 1.74
5
20
1
4
25
14%
3
24
13%


VTF
706 + 296
1337
9
630
1080
21
12%
900
44
24%


MTV
1286 + 417 
2142
13
1272
1896
24
13%
1539
46
25%


VAS - Full
70 + 14
−40
2
72
48
18
10%
61
44
24%


SGE T1/2
99.5 + 25.8
−47
5
98
70.8
17
 9%
81
43
24%


Buffett
917 + 295
1604
16
916
1357
23
13%
1184
44
24%









Unique analytes were identified when this cohort was separated into female and male sub-populations as shown in Table 12 and Table 13.







TABLE 12







Female sub-population (134 patients).























#pts w/


#pts w/



Trait
Mean + SD
>2 SD
# pts
Median
>90%
trait
% pts
>75%
trait
% pts




















HADS-A
4
9

3
7
20
15%
6
34
25%


HADS-D
2
5

1
4
13
10%
2
30
22%


VTF
632
1123

600
990
14
10%
750
40
30%


MTV
1174
1879

1185
1618
13
10%
1422
35
26%


VAS - Full
70
39

72
49
14
10%
61
34
25%


SGE T1/2
105
56

102
77
14
10%
90
32
24%


Buffett
896
1394

848
1279
13
10%
1028
34
25%
















TABLE 13







Male sub-population (47 patients).























#pts w/


#pts w/



Trait
Mean + SD
>2 SD
# pts
Median
>90%
trait
% pts
>75%
trait
% pts




















HADS-A
4
9

4
7
8
17% 
6
12
26%


HADS-D
2
7

2
5
6
13% 
3
17
36%


VTF
959
1659

900
1524
4
9%
1125
11
23%


MTV
1626
2517

1659
2180
4
9%
1951
11
23%


VAS - Full
69
41

72
47
4
9%
63
11
23%


SGE T1/2
83
34

78
56
6
13% 
68
11
23%


Buffett
1248
1893

1222
1693
4
9%
1469
11
23%









When analytes were identified in female and male sub-populations, the concentration of metabolites differed. See the concentrations as shown in Table 14.







TABLE 14







Concentrations of analytes in targeted metabolites.











highest
lower limit of
Coefficient



standard
quantification
of variation


analyte
concentration
(LOQ)
(CV)













Histidine
930
0.155
1.2%


Hydroxyproline
930
0.155
1.5%


1-Methylhistidine
930
0.155
0.5%


3-Methylhistidine
930
0.155
0.8%


Asparagine
1000
0.167
0.7%


Phosphoethanolamine
1000
0.167
0.4%


Arginine
930
0.155
0.5%


Carnosine
930
0.155
2.0%


Taurine
930
0.155
1.5%


Anserine
930
0.155
2.2%


Serine
930
0.155
0.7%


Glutamine
4000
0.667
0.2%


Ethanolamine
930
0.155
2.0%


Glycine
930
0.155
1.6%


Aspartic Acid
930
0.155
0.7%


Sarcosine
930
0.155
0.4%


Citrulline
930
0.155
1.6%


Glutamic Acid
930
0.155
0.6%


beta-Alanine
930
0.155
0.5%


Threonine
930
0.155
1.0%


Alanine
930
0.155
0.5%


gamma-Amino-
930
0.155
1.3%


N-butyric-acid


alpha-Aminoadipic-
930
0.155
0.0%


acid


beta-Aminoisobutyric-
930
0.155
0.4%


acid


Proline
930
0.155
1.0%


Hydroxylysine 1
930
0.155
0.6%


Hydroxylysine 2
930
0.155
0.3%


alpha-Amino-
930
0.155
0.0%


N-butyric-acid


Ornithine
930
0.155
0.9%


Cystathionine 1
930
0.155
1.0%


Cystathionine 2
930
0.155
0.6%


Lysine
930
0.155
0.2%


Cystine
930
0.155
1.7%


Tyrosine
930
0.155
0.4%


Methionine
930
0.155
1.2%


Valine
930
0.155
1.7%


Isoleucine
930
0.155
2.3%


allo-Isoleucine
930
0.155
0.9%


Homocystine
930
0.155
1.0%


Leucine
930
0.155
0.5%


Phenylalanine
930
0.155
1.6%


Tryptophan
930
0.155
1.1%


Acetylcholine
1600
0.10
5.6%


Adenosine
1600
0.10
0.5%


Norepinephrine
1600
0.10
1.0%


Dopamine
1600
0.10
0.7%


Serotonin
1600
0.10
1.0%


acetic acid
6651
5.5
 25%


propionic acid
4955
1.03
 7%


isobutyric acid
4792
1.00
 2%


butyric acid
5379
1.12
 4%


isovaleric acid
4030
0.84
 15%


valeric acid
5393
1.12
 5%


isocaproic acid
3532
0.74
 12%


caproic acid
2901
0.60
 8%









Unique targets identified in a sub-population can serve to find a unique treatment: for example TCF7L/2 genetic variant can be used to identify a group with abnormal satiety; or a simple test such as gastric emptying can be used to define abnormal satiety.
The model described above is also run independently for additional sub-populations of patients. For example, the model can be run on patients of specific ages (e.g., youth such as people from birth to about 18, adults such as people 18 or older), and specific life stages (e.g., perimenopausal women, menopausal women, post-menopausal women, and andropausal men).
Sub-populations of patients demonstrated analyte differences between obesity groups that were not seen in a full population of patients.
Example 4: Selecting Treatment(s) for Obesity Therapy
When an individual is treated with any weight loss intervention, his/her phenotype can assist in selecting a treatment.
Study Design
In a 12 week, randomized, double-blinded, active controlled trial, with 9 month open-label extension of 200 participants with obesity; the weight loss response rate to obesity-phenotype-guided pharmacotherapy (intervention) vs. non-phenotype guided (randomly selected) pharmacotherapy (control) in patients with obesity is compared. All 200 participants are phenotyped and the medication selection is randomly and double blinded (to physician, study team, and participant) to the FDA-approved medicine suggested by the phenotype or to another FDA-approved medicine not suggested by the phenotype.
All participants receive a standard intense lifestyle intervention, which consists of 2 visits with registered dietitian. The phenotypic studies include (all performed in same day in the following order): fasting blood collection, resting energy expenditure, gastric emptying with meal for breakfast, behavioral questionnaires, and buffet meal test for lunch. Blood is collected for assessment of metabolomic biomarkers, gastrointestinal hormones, DNA (blood and buccal swab), and pharmacogenomics. Stool samples are collected for microbiome and bile acid. Participants return to the CRTU to pick up medication based on the randomization and discuss the pharmacogenomics results. All participants are contacted at 4 and seen at 12 weeks (current standard in practice). A stool sample and a fasting blood sample are collected at the 12-week visit. At the 12-week visit, participants will be unblinded to their “obesity-related phenotype” and they could contact their physician to continue a FDA-approved medication as part of clinical care. Study team will prospectively follow the patients' weight, waist circumference and use of obesity medications every 3 months for 1 year.
Randomization and Allocation
A computer generated randomization is based on guiding pharmacotherapy based on the phenotype or randomly as current standard of care. Allocations are concealed.
Participants
A study cohort includes 200 patients with obesity (BMI>30 kg/m2). Participants that agree to pharmacotherapy treatment are invited to participate in the phenotypic assessment of their obesity that will guide (or not) the pharmacotherapy.
Inclusion Criteria


    
    
        a) Adults with obesity (BMI>30 Kg/m2); these are otherwise healthy individuals with no unstable psychiatric disease and controlled comorbidities or other diseases.
        b) Age: 18-75 years.
        c) Gender: Men or women. Women of childbearing potential have negative pregnancy tests within 48 hours of enrolment and before each radiation exposure.

Exclusion Criteria

        a) Abdominal bariatric surgery
        b) Positive history of chronic gastrointestinal diseases, or systemic disease that could affect gastrointestinal motility, or use of medications that may alter gastrointestinal motility, appetite or absorption, e.g., orlistat, within the last 6 months.
        c) Significant untreated psychiatric dysfunction based upon screening with the Hospital Anxiety and Depression Inventory (HAD), and the Questionnaire on Eating and Weight Patterns (binge eating disorders and bulimia). If such a dysfunction is identified by an anxiety or depression score>11 or difficulties with substance or eating disorders, the participant will be excluded and given a referral letter to his/her primary care doctor for further appraisal and follow-up.
        d) Hypersensitivity to any of the study medications.
        e) No contraindications to all FDA-approved medications

Anthropometrics and Phenotype Studies

    
    


Anthropometrics Measurements: are taken of hip-waist ratio, height, weight, blood pressure, pulse at baseline, randomization day and week 12.
Phenotype studies at baseline: After an 8-hour fasting period, and the following validated quantitative traits (phenotypes) are measured at baseline:

    
    
        a) The DEXA scan (dual energy x-ray absorptiometry) measures body composition.
        b) Resting energy expenditure: is assessed by indirect calorimetry with a ventilated hood.
        c) Gastric emptying (GE) of solids by scintigraphy: The primary endpoint is gastric half-emptying time (GE t1/2) as described elsewhere (see, e.g., Acosta et al., 2015 Gastroenterology 148:537-546; Vazquez et al., 2006 Gastroenterology 131:1717-24; and Camilleri et al., 2012 Neurogastroenterology and Motility 24:1076).
        d) Appetite (hunger level) by visual analog score fasting and after standard meal for GE and prior to the Satiation test as described elsewhere (see, e.g., Acosta et al., 2015 Gastroenterology 148:537-546).
        e) Satiation is measured by ad-libitum buffet meal to measure total caloric intake and macronutrient distribution in the chosen food. Satiation is reported in calories consumed at fullness (satiation) as described elsewhere (see, e.g., Acosta et al., 2015 Gastroenterology 148:537-546).
        f) Satiety by visual analog score postprandial after standard meal for GE and after to the Ad-libitum meal test for every 30 minutes for 2 hours as described elsewhere (see, e.g., Acosta et al., 2015 Gastroenterology 148:537-546). Satiety is measured in length of time of fullness.
        g) Self-administered questionnaires assessing affect, physical activity levels, attitudes, body image, and eating behavior; details of each questionnaire are provided below.
        h) Sample collection, handling and storage: Samples are collected after an overnight fast (of at least 8 hours) in the morning. Plasma was preserved following standard guidelines and protein degradation inhibitors, kalikrein and DPP-IV inhibitors are added to preserve the samples. Samples are stored at −80° C.
        
            a. Plasma gastrointestinal hormones (Total and active Ghrelin, GLP-1, CCK, PYY and bile acids) by radioimmunoassay, measured fasting, and 15, 45, and 90 minutes postprandial, with the primary endpoint being the peak postprandial level (test should be done simultaneously to GE).
            b. Targeted Metabolomics: Targeted metabolomics of salient classes of compounds in plasma samples are performed using mass spectrometry. Amino acids plus amino metabolites are quantified in plasma by derivatizing with 6-aminoquinolyl-N-hydroxysuccinimidyl carbamate according to Waters MassTrak kit. A 10-point calibration standard curve is used for quantification of unknowns using a triple-stage quadrupole mass spectrometer (Thermo Scientific TSQ Quantum Ultra) coupled with an ultra-performance liquid chromatography (UPLC) system (Waters Acquity UPLC). Data acquisition is performed using multiple-reaction monitoring (MRM). Concentrations of 42 analytes in each sample are calculated against their respective calibration curves with a measurement precision of <5%. Essential nonesterified fatty acid (NEFA) concentrations, such as myristic, palmitic, palmetoleic palmitoelaidic, stearic, oleic, elaidic, linoleic, linolenic and arachidonic, are measured against a six-point standard curve by LC/MS/MS, underivatized after extraction from plasma via negative electrospray ionization (ESI) and multiple reaction monitoring conditions. This technique was developed to replace the GC/MS method where NEFAs required methylation before analysis. This technique reduces the uncertainty as to whether the methylation step increases FFA concentrations by inadvertently hydrolyzing other lipid classes. Intra CV is <3% for all analytes.
            c. Blood DNA.
            d. Buccal Swab DNA for OneOme pharmacogenomics testing.
            
                i. Pharmacogenomics: Patients who have met the inclusion and exclusion criteria provide a one-time buccal scraping. 72 variants in 22 pharmacogenes, with seven cytochrome P450 enzymes (CYP1A2, CYP2B6, CYP2C9, CYP2C19, CYP2D6, CYP3A4, and CYP3A5) covering approximately 90 percent of human drug oxidation and nearly 50 percent of commonly used medications, and 15 genes related to drug action or metabolism (COMT, DPYD, DRD2, F2, F5, GRIK4, HTR2A, HTR2C, IL28B, NUDT15, OPRM1, SLCO1B1, TPMT, UGT1A1, and VKORC1) are assessed. Results for the patient are placed into the patient EHR to be utilized for clinical treatment decisions. Through chart review, including the patient's current medication list as stated in the EHR, previously reported medication inefficacy and intolerance is documented. This data is entered into a database.
            
            
        
        
        i) Stool is collected and stored to study microbiome, short chain fatty acids, and bile acids.
    
    


Studies at 12-Week Visit:

    
    
        j) Stool and fasting blood sample are collected and stored. Stool is used to measure microbiome, short chain fatty acids and bile acids (as above). Fasting blood will be used to GI hormones and metabolomics (as above).
    
    


Questionnaires to Assess GI Symptoms and Behavioral Disorders
Participants complete a series of questionnaires: Weight management Questionnaire (Mayo Clinic®), the and the Hospital Anxiety and Depression Inventory [HAD (see, e.g., Zigmond et al., 1983 Acta Psychiatrica Scandinavica 67:361-70)] to appraise the contribution of affective disorder.
Behavioural Questionnaires
a. AUDIT-C Alcoholism Screening Test—This score is used in screening by the study physician/nurse coordinator.
b. Eating Disorders Questionnaire—The Questionnaire on Eating and Weight Patterns-Revised, is a valid measure of screening for eating disorders which has been used in several national multi-site field trials. Respondents are classified as binge eating disorder, purging bulimia nervosa, non-purging bulimia nervosa, or anorexia nervosa.
c. Body Image Satisfaction—The Multidimensional Body-Self Relations Questionnaire provides a standardized attitudinal assessment of body image, normed from a national body-image survey. Items are rated on a 5-point scale, ranging from 1=Definitely Disagree to 5=Definitely Agree. A sub-scale, the Body Areas Satisfaction Scale, is used to measure feelings of satisfaction with discrete aspects of physical appearance (e.g., face, weight, hair). Cronbach's a values range from 0.70 to 0.89.
d. Eating Behaviors—The Weight Efficacy Life-Style Questionnaire [WEL] is a 20-item eating self-efficacy scale consisting of a total score and five situational factors: negative emotions, availability, social pressure, physical discomfort, and positive activities. Subjects are asked to rate their confidence about being able to successfully resist the urge to eat using a 10-point scale ranging from 0=not confident to 9=very confident.
e. Physical Activity Level—The four-item Physical Activity Stages of Change Questionnaire will be utilized to assess the physical activity level of participants.
Standard of Care:
All participants receive standard of care which consists of 1) Intense lifestyle intervention, behavioral evaluation and treatment, and a medication as part of the regular clinic management for obesity.
Intense Lifestyle Intervention and Behavioral Treatment
All the participants will meet the multidisciplinary team which consists of an Obesity Expert physician a registered dietitian nutritionist as standard of care in our clinical practice. These appointments will be schedule in the clinic and will not be covered by the current protocol. All participants are guided to 1) Nutrition: Reduce dietary intake below that required for energy balance by consuming 1200-1500 calories per day for women and 1500-1800 calories per day for men; 2) Physical Activity: reach the goal of 10,000 steps or more per day; 3) Exercise: reach the goal of 150 minutes or more of cardiovascular exercise/week; 4) Limit consumption of liquid calories (i.e. sodas, juices, alcohol, etc.).
Pharmacotherapy for Obesity
Pharmacotherapy for the treatment of obesity can be considered if a patient has a body mass index (BMI)≥30 kg/m2 or BMI>27 kg/m2 with a comorbidity such as hypertension, type 2 diabetes, dyslipidemia and obstructive sleep apnea. Medical therapy should be initiated with dose escalation based on efficacy and tolerability to the recommended dose. An assessment of efficacy and safety at 4 weeks is done. In both groups, medications are assessed for drug interactions and potential side effects as standard of care.
Medication selection: Once the phenotype tests are completed the results are filled in an algorithm to assist on the decision of the medication selection as described elsewhere (see, e.g., Acosta et al., 2015 Gastroenterology 148:537-546; Camilleri et al., 2016 Gastrointest. Endosc. 83:48-56; and Acosta et al., 2015 Physiological Rep. 3(11)). An example is below:















TEST
Abnormal result
Example 1
Example 2
Example 3
Example 4
























Satiation
>1139 kcal
1400
kcal
1000
kcal
1100
kcal
1050
kcal


(Ad libitum Buffet


Meal)


Satiety
SGE T1/2 <85
102
min
80
min
105
min
110
min


(Gastric emptying)
min or GE



1 hr >35%












Behavioral Traits
HADS A&D >6 points
5
4
9
3


(Questionnaires)


Energy Expenditure
<85% predicted
92%
93%
95%
82%


(Resting EE)


Phenotype
—
Ab Satiation
Ab Satiety
Ab Psych
Ab E.E.









Once the decision is made on the “phenotype-guided” medication, pharmacy will assess whether patient is randomized to “intervention” or “control”. Based on the randomization, patient picks up the prescription for 3 months. During the 3-month visit, participants are offered a prescription to continue the medication (if randomized to the intervention group) or to switch to the phenotype guided medication (if randomized to the control group). Patients who continue obesity pharmacotherapy are contact every three months for one year to monitor their weight and comorbidities.
Control Group: Pharmacotherapy for Obesity
Standard of care pharmacotherapy for obesity recommends the following doses and regimen for weight loss:

    
    
        Phentermine: 15-37.5 mg oral daily
        Phentermine-Topiramate Extended Release (Qsymia®) at dose of 7.5/46 mg oral daily
        Oral naltrexone extended-release/bupropion extended-release (NBSR; Contrave®) at dose of 32/360 mg oral daily (divided in 2 tables in morning and 2 tablets in evening)
        Liraglutide (Saxenda®) at dose of 3 mg subcutaneous daily

Intervention Group: By Obesity Phenotype Guided Pharmacotherapy

    
    


Participants in the intervention group will have 4 tests to assess 1) satiation, 2) satiety/return to hunger, 3) behavioral, or 4) energy expenditure. As described in FIG. 12 pharmacotherapy will by guide based on the phenotype. In case of a mixed pattern or multiple abnormal phenotypes, the most prominent phenotype is tackled.
Algorithm diagnostic:

    
    
        1. satiation: Phentermine-Topiramate Extended Release (Qsymia®) at dose of 7.5/46 mg oral daily
        2. Satiety/return to hunger: Liraglutide 3 mg SQ daily
        3. Behavioral/Psychological: Oral naltrexone extended-release/bupropion extended-release (NBSR; Contrave®) at dose of 32/360 mg oral daily (divided in 2 tables in morning and 2 tablets in evening); or
        4. Energy expenditure: Phentermine 15 mg daily plus increase physical activity.

Statistical Analyses

    
    


Primary endpoint: Total Body Weight Loss, kg (defined as weight changed from baseline to 12 weeks) in the obesity phenotype-guided pharmacotherapy (intervention) vs. the randomly assigned pharmacotherapy (control) group.
The secondary end points will be percentage of responders (defined as number of participants who loss 5% or more of total body weight) compared to baseline in the obesity phenotype guided pharmacotherapy (intervention) group vs. standard of care at 4 and 12 weeks; percentage of responders with at least 10 and 15% at 12 weeks, and 10% at 6 months and 12 months; percentage of responders at 5%, 10% and 15%; percentage of responders within each obesity-phenotype group at 4 and 12 weeks; and side effects of medications. In the open-label extension, the total body weight loss is assessed at 24 and 52 weeks in both groups.
Statistical Analyses: A randomized, double-blinded, active controlled trial of 200 participants with obesity to compare effects of intervention compared to controls in weight loss. The analysis involves an ANCOVA models, with the response being actual weight change; the covariates to be considered include gender, and BMI (at baseline) at baseline.
Sample size assessment and power calculation: The detectable effect size in weight loss between groups of interest (intervention vs. control) is given in Table 15. Using a SD for the overall weight change (pre-post) of 2.8 kg, the differences between groups that could be detected with approximately 80% power (2-sided a level of 0.05) for main effects are estimated. Thus, the sample size needed is 87 participants per group. In order to account for dropout, 100 participants per group are randomized.









TABLE 15





Mean difference (Δ) of total body
Intervention
Control


weight loss in controls group (mean
(# of
(# of


average 6.1 kg) vs. intervention group.
participants)
participants)

















Mean difference of 10% [6.7 vs. 6.1 kg)
343
343


Mean difference of 20% [7.3 vs. 6.1 kg)
87
87


Mean difference of 30% [7.9 vs. 6.1 kg)
39
39









As each 50 patients complete the 12-week treatment phase, an interim analysis is conducted by the study statistician for the purpose of ensuring (based on the observed coefficient of variation in the primary responses such as the proportion of weight difference of 20%) that the study still has sufficient power based on the sample size proposed in the study.
OTHER EMBODIMENTS
It is to be understood that while the invention has been described in conjunction with the detailed description thereof, the foregoing description is intended to illustrate and not limit the scope of the invention, which is defined by the scope of the appended claims. Other aspects, advantages, and modifications are within the scope of the following claims.

## Claims

1. A method for treating obesity in a mammal in need thereof, wherein said method comprises:
(a) determining an obesity analyte signature unique to a body mass index (BMI) of >30 kg/m2 or a BMI>27 kg/m2 with an obesity-related comorbidity in a sample obtained from said mammal, wherein said obesity analyte signature comprises a behavioral assessment and a plurality of analytes selected from the group consisting of metabolites, gastrointestinal peptides, single nucleotide polymorphisms and any combination of distinct analytes thereof; and
(b) administering an intervention to said mammal based on said obesity analyte signature determined in step (a), wherein said obesity analyte signature is obtained from a regression model of one or more variables comprising:
(i) serotonin, glutamine, isocaproic acid, allo-isoleucine, hydroxyproline, beta-aminoisobutyric-acid, alanine, hexanoic acid, tyrosine, and PYY, and excluding 1-methylhistine, gamma-amino-n-butyric-acid, phenylalanine, and ghrelin, and said behavioral assessment does not indicate anxiety on a behavioral questionnaire, and wherein said intervention is a pharmacotherapy selected from the group consisting of an appetite suppressant, an appetite suppressant in combination with an anticonvulsant a melanocortin receptor agonist, CCK analogs and any combination of distinct pharmacotherapies thereof; or
(ii) 1-methylhistine, allo-isoleucine, hydroxyproline, beta-aminoisobutyric-acid, alanine, and phenylalanine, and excluding serotonin, glutamine, gamma-amino-n-butyric-acid, isocaproic acid, hexanoic acid, tyrosine, ghrelin, and PYY, and said behavioral assessment does not indicate anxiety on a behavioral questionnaire, and wherein said intervention is a pharmacotherapy selected from the group consisting of a GLP-1 analog, GLP-1 receptor agonist, amylin analogs, PYY analogs, Y receptor agonists or antagonists, oxyntomodulin analogs, ghrelin antagonists, TGR5 agonists, FGF-19/21analogs, FXR agonists, GRP-119, GRP-120 and any combination of distinct pharmacotherapies thereof; or
(iii) serotonin, and excluding 1-methylhistine, glutamine, gamma-amino-n-butyric-acid, isocaproic acid, allo-isoleucine, hydroxyproline, beta-aminoisobutyric-acid, alanine, hexanoic acid, tyrosine, phenylalanine, ghrelin, and PYY, and said behavioral assessment does indicate anxiety on a behavioral questionnaire, and wherein said intervention is a pharmacotherapy selected from the group consisting of an anti-depressant, an opioid antagonist, an anti-anxiety agent, a cannabinoid antagonist and any combination of distinct pharmacotherapies thereof; or
(iv) 1-methylhistine, glutamine, gamma-amino-n-butyric-acid, isocaproic acid, allo-isoleucine, beta-aminoisobutyric-acid, alanine, hexanoic acid, tyrosine, phenylalanine, and PYY, and excluding serotonin, hydroxyproline, and ghrelin, and said behavioral assessment does indicate anxiety on a behavioral questionnaire, and wherein said intervention is a pharmacotherapy selected from the group consisting of an anti-depressant, an opioid antagonist, an anti-anxiety agent, a cannabinoid antagonist and any combination of distinct pharmacotherapies thereof; or
(v) 1-methylhistine, serotonin, glutamine, gamma-amino-n-butyric-acid, isocaproic acid, allo-isoleucine, alanine, tyrosine, ghrelin, and PYY, and excluding hydroxyproline, beta-aminoisobutyric-acid, hexanoic acid, and phenylalanine, and said behavioral assessment does indicate anxiety on a behavioral questionnaire, and wherein said intervention is a pharmacotherapy selected from the group consisting of an appetite suppressant, leptin modulators, MetAP2 inhibitors, B3 agonists and any combination of distinct pharmacotherapies thereof.

2. The method of claim 1, wherein said sample is selected from the group consisting of a blood sample, a saliva sample, a urine sample, a breath sample, and a stool sample.

3. The method of claim 2, wherein said sample is a breath sample.

4. The method of claim 2, wherein said sample is a stool sample.

5. A method for treating obesity in a mammal in need thereof, wherein said method comprises administering an intervention to a mammal identified as having an obesity analyte signature unique to a body mass index (BMI) of >30 kg/m2 or a BMI>27 kg/m2 with an obesity-related comorbidity in a sample obtained from said mammal, wherein said obesity analyte signature comprises a behavioral assessment and a plurality of analytes selected from the group consisting of metabolites, gastrointestinal peptides, single nucleotide polymorphisms and any combination thereof, wherein said obesity analyte signature is obtained from a regression model of one or more variables comprising:
(i) serotonin, glutamine, isocaproic acid, allo-isoleucine, hydroxyproline, beta-aminoisobutyric-acid, alanine, hexanoic acid, tyrosine, and PYY, and excluding 1-methylhistine, gamma-amino-n-butyric-acid, phenylalanine, and ghrelin, and said behavioral assessment does not indicate anxiety on a behavioral questionnaire, and wherein said intervention is a pharmacotherapy selected from the group consisting of an appetite suppressant, an appetite suppressant in combination with an anticonvulsant a melanocortin receptor agonist, CCK analogs and any combination of distinct pharmacotherapies thereof; or
(ii) 1-methylhistine, allo-isoleucine, hydroxyproline, beta-aminoisobutyric-acid, alanine, and phenylalanine, and excluding serotonin, glutamine, gamma-amino-n-butyric-acid, isocaproic acid, hexanoic acid, tyrosine, ghrelin, and PYY, and said behavioral assessment does not indicate anxiety on a behavioral questionnaire, and wherein said intervention is a pharmacotherapy selected from the group consisting of a GLP-1 analog, GLP-1 receptor agonist, amylin analogs, PYY analogs, Y receptor agonists or antagonists, oxyntomodulin analogs, ghrelin antagonists, TGR5 agonists, FGF-19/21analogs, FXR agonists, GRP-119, GRP-120 and any combination of distinct pharmacotherapies thereof; or
(iii) serotonin, and excluding 1-methylhistine, glutamine, gamma-amino-n-butyric-acid, isocaproic acid, allo-isoleucine, hydroxyproline, beta-aminoisobutyric-acid, alanine, hexanoic acid, tyrosine, phenylalanine, ghrelin, and PYY, and said behavioral assessment does indicate anxiety on a behavioral questionnaire, and wherein said intervention is a pharmacotherapy selected from the group consisting of an anti-depressant, an opioid antagonist, an anti-anxiety agent, a cannabinoid antagonist and any combination of distinct pharmacotherapies thereof; or
(iv) 1-methylhistine, glutamine, gamma-amino-n-butyric-acid, isocaproic acid, allo-isoleucine, beta-aminoisobutyric-acid, alanine, hexanoic acid, tyrosine, phenylalanine, and PYY, and excluding serotonin, hydroxyproline, and ghrelin, and said behavioral assessment does indicate anxiety on a behavioral questionnaire, and wherein said intervention is a pharmacotherapy selected from the group consisting of an anti-depressant, an opioid antagonist, an anti-anxiety agent, a cannabinoid antagonist and any combination of distinct pharmacotherapies thereof; or
(v) 1-methylhistine, serotonin, glutamine, gamma-amino-n-butyric-acid, isocaproic acid, allo-isoleucine, alanine, tyrosine, ghrelin, and PYY, and excluding hydroxyproline, beta-aminoisobutyric-acid, hexanoic acid, and phenylalanine, and said behavioral assessment does indicate anxiety on a behavioral questionnaire, and wherein said intervention is a pharmacotherapy selected from the group consisting of an appetite suppressant, leptin modulators, MetAP2 inhibitors, B3 agonists and any combination of distinct pharmacotherapies thereof.

6. The method of claim 1, wherein said mammal is a human.

7. The method of claim 1, wherein said intervention is effective to reduce the total body weight of said mammal by at least 4%.

8. The method of claim 1, wherein said intervention is effective to reduce the total body weight of said mammal by from about 3 kg to about 100 kg.

9. The method of claim 1, wherein said intervention is effective to reduce the waist circumference of said mammal by from about 1 inches to about 10 inches.

10. The method of claim 1, wherein said behavioral questionnaire is a Hospital Anxiety and Depression Scale (HADS) questionnaire.

11. The method of claim 10, wherein said obesity analyte signature is obtained from a regression model of one or more variables comprising serotonin, glutamine, isocaproic acid, allo-isoleucine, hydroxyproline, beta-aminoisobutyric-acid, alanine, hexanoic acid, tyrosine, and PYY, and excluding of 1-methylhistine, gamma-amino-n-butyric-acid, phenylalanine, and ghrelin; wherein said HADS questionnaire result does not indicate anxiety; and wherein said pharmacotherapy is an appetite suppressant selected from the group consisting of phentermine-topiramate pharmacotherapy, desvenlafaxine and lorcaserin pharmacotherapy.

12. The method of claim 10, wherein said obesity analyte signature is obtained from a regression model of one or more variables comprising 1-methylhistine, allo-isoleucine, hydroxyproline, beta-aminoisobutyric-acid, alanine, and phenylalanine, and excluding serotonin, glutamine, gamma-amino-n-butyric-acid, isocaproic acid, hexanoic acid, tyrosine, ghrelin, and PYY; wherein said HADS questionnaire result does not indicate anxiety; and wherein said pharmacotherapy is a GLP-1 receptor agonist.

13. The method of claim 10, wherein said obesity analyte signature is obtained from a regression model of one or more variables comprising serotonin, and excluding 1-methylhistine, glutamine, gamma-amino-n-butyric-acid, isocaproic acid, allo-isoleucine, hydroxyproline, beta-aminoisobutyric-acid, alanine, hexanoic acid, tyrosine, phenylalanine, ghrelin, and PYY; wherein said HADS questionnaire result indicates anxiety; and wherein said pharmacotherapy is naltrexone-bupropion pharmacotherapy.

14. The method of claim 10, wherein said obesity analyte signature is obtained from a regression model of one or more variables comprising 1-methylhistine, glutamine, gamma-amino-n-butyric-acid, isocaproic acid, allo-isoleucine, beta-aminoisobutyric-acid, alanine, hexanoic acid, tyrosine, phenylalanine, and PYY, and excluding serotonin, hydroxyproline, and ghrelin; wherein said HADS questionnaire result indicates anxiety; and wherein said pharmacotherapy is naltrexone-bupropion pharmacotherapy.

15. The method of claim 10, wherein said obesity analyte signature is obtained from a regression model of one or more variables comprising 1-methylhistine, serotonin, glutamine, gamma-amino-n-butyric-acid, isocaproic acid, allo-isoleucine, alanine, tyrosine, ghrelin, and PYY, and excluding hydroxyproline, beta-aminoisobutyric-acid, hexanoic acid, and phenylalanine; wherein said HADS questionnaire result indicates anxiety; and wherein said pharmacotherapy is phentermine pharmacotherapy.

16. The method of claim 12, wherein said GLP-1 receptor agonist comprises liraglutide.