Assessing and treating obesity
AssigneeMayo Foundation for Medical Education and Research
InventorsMichael L. CAMILLERI, Andres J. ACOSTA, Paul A. DECKER, Jeanette E. ECKEL PASSOW
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Abstract
The present disclosure relates to methods and materials for assessing and/or treating obesity and/or obesity-related co-morbidities in mammals (e.g., 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 co-morbidities in a mammal (e.g., a human) identified as being likely to respond to a particular intervention (e.g., a pharmacological intervention) are provided.
Claims71
A method for treating obesity and/or one or more obesity-related co-morbidities in a mammal, the method comprising:
- (a) detecting the presence of a plurality of single nucleotide polymorphisms (SNPs) in a sample obtained from a mammal suffering from obesity, wherein the plurality of SNPs is selected from the group consisting of rs1664232, rs11118997, rs9342434, rs2335852, rs11020655, rs1885034, rs7277175, rs6923761, rs7903146, rs17782313, rs3813929, rs1047776 and any combination thereof;
- and
- (b) administering a GLP-1 agonist to the subject when the plurality of SNPs are detected in the sample, thereby treating the obesity and/or the one or more obesity-related co-morbidities.
A method for assaying a sample obtained from a mammal suffering from obesity and/or one or more obesity-related co-morbidities, the method comprising detecting the presence of a plurality of single nucleotide polymorphisms (SNPs) in a sample obtained from the mammal, wherein the plurality of SNPs are selected from the group consisting of rs1664232, rs11118997, rs9342434, rs2335852, rs11020655, rs1885034, rs7277175, rs6923761, rs7903146, rs17782313, rs3813929, rs1047776 and any combination thereof.
A system for determining an obesity phenotype of a mammal suffering from obesity, the system comprising:
- (a) one or more processors;
- (b) one or more memories operatively coupled to at least one of the one or more processors and having instructions stored thereon that, when executed by at least one of the one or more processors, cause the system to
- (i) identify the presence, absence or level of a plurality of gastrointestinal (GI) peptides, a plurality of metabolites, and/or a plurality of genetic variants in a sample obtained from a mammal suffering from obesity, thereby generating an analyte signature for the sample;
- (ii) populate a predictive machine learning model with the analyte signature of step (i);
- and
- (iii) utilize the predictive machine learning model to predict an obesity phenotype of the mammal suffering from obesity based on the analyte signature of the sample;
- and
- (c) one or more instruments in communication with at least one of the one or more processors, wherein the instruments, upon receipt of instructions sent by the at least one of the one or more processors, perform steps (i)-(iii).
A method for treating obesity in a mammal, the method comprising:
- (a) identifying the presence, absence or level of a plurality of GI peptides, a plurality of metabolites, and/or a plurality of genetic variants in a sample obtained from a mammal suffering from obesity, thereby generating an analyte signature for the sample;
- (b) populating a predictive machine learning model with the analyte signature of step (a);
- (c) utilizing the predictive machine learning model to predict an obesity phenotype of the mammal based on the analyte signature of the sample obtained from the mammal, wherein the obesity phenotype is selected from the group consisting of abnormal satiation (hungry brain), abnormal satiety (hungry gut);
- hedonic eating (emotional hunger) and slow metabolism (slow burn);
- and
- (d) administering an intervention based on the obesity phenotype predicted in step (c).
Dependencies are inferred from the claim text.