WO2021061314A1 - Dynamic metabolic rate estimation - Google Patents

Abstract

The present disclosure provides a computer-implemented method for determining a metabolic rate of a subject, comprising: (a) obtaining a data set comprising vital sign data and activity data, wherein the vital sign data corresponds to one or more vital signs of the subject, and wherein the activity data corresponds to one or more activities of the subject; (b) computer processing the data set using a trained machine learning algorithm to determine the metabolic rate; and (c) electronically outputting a report indicative of the metabolic rate of the subject determined in (b).

Description

DYNAMIC METABOLIC RATE ESTIMATION

CROSS-REFERENCE

[0001] This application claims the benefit of U.S. Patent Application No. 62/906,565, filed September 26, 2019, which is entirely incorporated by reference herein.

BACKGROUND

[0002] A metabolic rate may refer to a rate at which a body of a subject performs a metabolic function, such as, for example, a rate of tissue oxidation of a nutrient(s) in the body of subject. The metabolic rate of a subject at a given time may depend on multiple factors, such as age, gender, muscle-to-fat ratio, and hormonal function. The metabolic rate may also vary when a subject is engaged in different activities. For example, high-intensity activities such as running may require more energy, therefore the metabolic rate while performing such activities may be higher. Conversely, low-intensity activities such as sleeping may require less energy and therefore the metabolic rate may be lower during such activities. In some cases, an organ such as liver or pancreas may not function properly leading to an abnormal metabolism and/or a metabolic disorder. A metabolic disorder occurs when abnormal chemical reactions in the body disrupt the normal process of metabolism. This may lead to too much of some substances or too little of other ones that the body requires to stay healthy. There are different groups of metabolic disorders. Some affect the breakdown of amino acids, carbohydrates, or lipids. Another group, for example mitochondrial diseases, affects the parts of the cells that produce the energy (e.g., mitochondria).

SUMMARY

[0003] The present disclosure provides methods and systems for determining a metabolic rate of a subject, determining that a subject has or is at risk of having a metabolic disease or disorder, or a combination thereof. In some embodiments, a clinical intervention may be determined to treat a metabolic disease or disorder of a subject. The methods, systems, and media as disclosed herein may determine or improve upon existing methods for determining a metabolic rate of a subject. For example, methods and systems provided herein may use machine learning methods to determine a metabolic rate of a subject (e.g., a basal metabolic rate, a standard metabolic rate, a resting metabolic rate, or a glucose metabolic rate) which may lead to fast, real time, and/or remote analysis of the subject’s metabolic rate. The methods and systems provided herein can help reduce or eliminate the need for need for in person measuring of the metabolic rate using measuring equipment such as a metabolic chamber or gas analysis. The machine learning approach may be trained using large datasets in order to gain new insights into determining a subject’s metabolic rate (e.g., a basal metabolic rate, a standard metabolic rate, a resting metabolic rate, or a glucose metabolic rate). The machine learning approach may obtain data comprising one or more vital signs and/or one or more activities of the subject to analyze temporal changes in the subject’s metabolic rate.

[0004] In an aspect, the present disclosure provides a computer-implemented method for determining a metabolic rate of a subject, comprising: (a) obtaining a data set comprising vital sign data and activity data, wherein the vital sign data corresponds to one or more vital signs of the subject, and wherein the activity data corresponds to one or more activities of the subject; (b) computer processing the data set using a trained machine learning algorithm to determine the metabolic rate; and (c) electronically outputting a report indicative of the metabolic rate of the subject determined in (b).

[0005] In some embodiments, the data set further comprises one or more demographic or clinical attributes of the subject. In some embodiments, the one or more demographic or clinical attributes comprise at least one of age, race, ethnicity, gender, weight, body mass index, lean body mass, body fat percentage, body surface area, height, history of medication or other clinical treatment, history of tobacco use, history of alcohol consumption, daily activity or fitness level, genetic test results, blood test results, imaging results, and a combination thereof.

[0006] In some embodiments, the one or more vital signs comprise at least one of heart rate, respiratory rate, systolic blood pressure, diastolic blood pressure, blood oxygen concentration (Sp02), carbon dioxide concentration in respiratory gases, hormone level, sweat analysis, blood glucose, body temperature, bioimpedance, conductivity, capacitance, resistivity, galvanic skin response, electromyography, electroencephalography, electrocardiography, magnetoencephalography, magnetocardiography, immunology markers, and a combination thereof. In some embodiments, the one or more vital signs comprise at least one vital sign measured using a portable vital sign monitor.

[0007] In some embodiments, the portable vital sign monitor is selected from the group consisting of a heart rate monitor, a respiratory rate monitor, a blood pressure monitor, a blood oxygen concentration monitor, a blood glucose monitor, a body temperature monitor, a bioimpedance monitor, an electromyography monitor, an electroencephalography monitor, an electrocardiography monitor, a magnetoencephalography monitor, a magnetocardiography monitor, a smart phone, a smart watch, an activity or exercise monitor, a contactless wearable health monitoring device, and any combination thereof.

[0008] In some embodiments, the one or more activities comprise at least one of walking, jogging, running, bicycle riding, performing push-ups, performing sit-ups, performing pull- ups, exercise, performing aerobic exercise, performing anaerobic exercise, playing a sport, lifting weights, swimming, sitting, standing, talking, eating, lying down, sleeping, and a combination thereof.

[0009] In some embodiments, the data is obtained without (i) performing a gas analysis or (ii) using a metabolic chamber. In some embodiments, the metabolic rate comprises at least one of a basal metabolic rate, a standard metabolic rate, a resting metabolic rate, a glucose metabolic rate, and a combination thereof. In some embodiments, the method further comprises determining a recommended activity or lifestyle modification for the subject based at least in part on the determined metabolic rate.

[0010] In some embodiments, the method further comprises determining that the subject has or is at risk of having a metabolic disease or disorder based at least in part on the determined metabolic rate of the subject. In some embodiments, the method further comprises determining that the subject has or is at risk of having the metabolic disease or disorder at an accuracy of at least about 50%, at least about 55%, at least about 60%, at least about 65%, at least about 70%, at least about 75%, at least about 80%, at least about 85%, at least about 90%, at least about 91%, at least about 92%, at least about 93%, at least about 94%, at least about 95%, at least about 96%, at least about 97%, at least about 98%, or at least about 99%. In some embodiments, the method further comprises determining that the subject has or is at risk of having the metabolic disease or disorder at a sensitivity of at least about 50%, at least about 55%, at least about 60%, at least about 65%, at least about 70%, at least about 75%, at least about 80%, at least about 85%, at least about 90%, at least about 91%, at least about 92%, at least about 93%, at least about 94%, at least about 95%, at least about 96%, at least about 97%, at least about 98%, or at least about 99%. In some embodiments, the method further comprises determining that the subject has or is at risk of having the metabolic disease or disorder at a specificity of at least about 50%, at least about 55%, at least about 60%, at least about 65%, at least about 70%, at least about 75%, at least about 80%, at least about 85%, at least about 90%, at least about 91%, at least about 92%, at least about 93%, at least about 94%, at least about 95%, at least about 96%, at least about 97%, at least about 98%, or at least about 99%. In some embodiments, the method further comprises determining that the subject has or is at risk of having the metabolic disease or disorder at a positive predictive value of at least about 50%, at least about 55%, at least about 60%, at least about 65%, at least about 70%, at least about 75%, at least about 80%, at least about 85%, at least about 90%, at least about 91%, at least about 92%, at least about 93%, at least about 94%, at least about 95%, at least about 96%, at least about 97%, at least about 98%, or at least about 99%. In some embodiments, the method further comprises determining that the subject has or is at risk of having the metabolic disease or disorder at a negative predictive value of at least about 50%, at least about 55%, at least about 60%, at least about 65%, at least about 70%, at least about 75%, at least about 80%, at least about 85%, at least about 90%, at least about 91%, at least about 92%, at least about 93%, at least about 94%, at least about 95%, at least about 96%, at least about 97%, at least about 98%, or at least about 99%. In some embodiments, the method further comprises determining that the subject has or is at risk of having the metabolic disease or disorder with an Area Under Curve (AUC) of at least about 0.50, at least about 0.55, at least about 0.60, at least about 0.65, at least about 0.70, at least about 0.75, at least about 0.80, at least about 0.81, at least about 0.82, at least about 0.83, at least about 0.84, at least about 0.85, at least about 0.86, at least about 0.87, at least about 0.88, at least about 0.89, at least about 0.90, at least about 0.91, at least about 0.92, at least about 0.93, at least about 0.94, at least about 0.95, at least about 0.96, at least about 0.97, at least about 0.98, or at least about 0.99.

[0011] In some embodiments, the metabolic disease or disorder is selected from the group consisting of 17-alpha-hydroxylase deficiency, 17-beta hydroxy steroid dehydrogenase 3 deficiency, 18 Hydroxylase deficiency, 2-Hydroxyglutaric aciduria, 2-methylbutyryl-CoA dehydrogenase deficiency, 3 -alpha hydroxyacyl-CoA dehydrogenase deficiency, 3- Hydroxyisobutyric aciduria, 3-methylcrotonyl-CoA carboxylase deficiency, 3- methylglutaconyl-CoA hydratase deficiency (AUH defect), 5-oxoprolinase deficiency, 6- pyruvoyl-tetrahydropterin synthase deficiency, Abdominal obesity metabolic syndrome, Abetalipoproteinemia, Acatalasemia, Aceruloplasminemia, Acetyl CoA acetyltransferase 2 deficiency, Acetyl-carnitine deficiency, Acrodermatitis enteropathica, Acromegaly, Acute intermittent porphyria, Adenine phosphoribosyltransferase deficiency, Adenosine deaminase deficiency, Adenosine monophosphate deaminase 1 deficiency, Adenylosuccinase deficiency, Adrenomyeloneuropathy, Adult polyglucosan body disease, Albinism deafness syndrome, Albinism ocular late onset sensorineural deafness, ALG1-CDG (CDG-Ik), ALGl 1-CDG (CDG-Ip), ALG12-CDG (CDG-Ig), ALG13-CDG, ALG2-CDG (CDG-Ii), ALG3-CDG (CDG-Id), ALG6-CDG (CDG-Ic), ALG8-CDG (CDG-Ih), ALG9-CDG (CDG-IL), Alkaptonuria, Alpers syndrome, Alpha- 1 antitrypsin deficiency, Alpha-ketoglutarate dehydrogenase deficiency, Alpha-mannosidosis, Aminoacylase 1 deficiency, Anemia due to Adenosine triphosphatase deficiency, Anemia sideroblastic and spinocerebellar ataxia, Apparent mineralocorticoid excess, Arginase deficiency, Argininosuccinic aciduria, Aromatic L-amino acid decarboxylase deficiency, Arthrogryposis renal dysfunction cholestasis syndrome, Arts syndrome, Aspartylglycosaminuria, Ataxia with oculomotor apraxia type 1, Ataxia with vitamin E deficiency, Atransferrinemia, Atypical Gaucher disease due to saposin C deficiency, Autoimmune polyglandular syndrome type 2, Autosomal dominant neuronal ceroid lipofuscinosis 4B, Autosomal dominant optic atrophy and cataract, Autosomal dominant optic atrophy plus syndrome, Autosomal erythropoietic protoporphyria, Autosomal recessive neuronal ceroid lipofuscinosis 4A, Autosomal recessive spastic ataxia 4, Autosomal recessive spinocerebellar ataxia 9, B4GALT1-CDG (CDG-IId), Bantu siderosis, Barth syndrome, Barrier syndrome, Barrier syndrome antenatal type 1, Barrier syndrome antenatal type 2, Barrier syndrome type 3, Barrier syndrome type 4, Beta ketothiolase deficiency, Biotin-thiamine-responsive basal ganglia disease, Biotinidase deficiency, Bjornstad syndrome, Blue diaper syndrome, Carbamoyl phosphate synthetase 1 deficiency, Carnitine palmitoyl transferase 1 A deficiency, Camitine-acylcamitine translocase deficiency, Camosinemia, Central diabetes insipidus, Cerebral folate deficiency, Cerebrotendinous xanthomatosis, Ceroid lipofuscinosis neuronal 1, Chanarin-Dorfman syndrome, Chediak- Higashi syndrome, CHILD syndrome, Childhood hypophosphatasia, Cholesteryl ester storage disease, Chondrocalcinosis 1, Chondrocalcinosis 2, Chondrocalcinosis due to apatite crystal deposition, Chondrodysplasia punctata 1, X-linked recessive, Chronic progressive external ophthalmoplegia, Chylomicron retention disease, Citrulline transport defect, Citrullinemia type II, COG1-CDG (CDG-IIg), COG4-CDG (CDG-IIj), COG5-CDG (CDG-IIi), COG7- CDG (CDG-IIe), COG8-CDG (CDG-IIh), Combined oxidative phosphorylation deficiency 16, Congenital bile acid synthesis defect, type 1, Congenital bile acid synthesis defect, type 2, Congenital disorder of glycosylation type I/IIX, Congenital dyserythropoietic anemia type 2, Congenital erythropoietic porphyria, Congenital lactase deficiency, Congenital muscular dystrophy-dystroglycanopathy with or without intellectual disability (type B), Copper deficiency, familial benign, CoQ-responsive OXPHOS deficiency, Crigler Najjar syndrome, type 1, Crigler-Najjar syndrome type 2, Cystinosis, Cytochrome c oxidase deficiency, D-2- hydroxyglutaric aciduria, D-bifunctional protein deficiency, D-glycericacidemia, Danon disease, DCMA syndrome, DDOST-CDG (CDG-Ir), Deafness, dystonia, and cerebral hypomyelination, Dentatorubral-pallidoluysian atrophy, Desmosterolosis, Diamond-Blackfan anemia, Dicarboxylic aminoaciduria, Dihydrolipoamide dehydrogenase deficiency, Dihydropteridine reductase deficiency, Dihydropyrimidinase deficiency, Dihydropyrimidine dehydrogenase deficiency - Not a rare disease, Dipsogenic diabetes insipidus, DOLK-CDG (CDG-Im), Dopa-responsive dystonia, Dopamine beta hydroxylase deficiency, Dowling- Degos disease, DPAGT1-CDG (CDG-Ij), DPM1-CDG (CDG-Ie), DPM2-CDG, DPM3-CDG (CDG-Io), Dubin-Johnson syndrome, Encephalopathy due to prosaposin deficiency, Erythropoietic uroporphyria associated with myeloid malignancy, Ethylmalonic encephalopathy, Fabry disease, Familial chylomicronemia syndrome, Familial HDL deficiency, Familial hypocalciuric hypercalcemia type 1, Familial hypocalciuric hypercalcemia type 2, Familial hypocalciuric hypercalcemia type 3, Familial LCAT deficiency, Familial partial lipodystrophy type 2, Fanconi Bickel syndrome, Farber disease, Fatal infantile encephalomyopathy, Fatty acid hydroxylase-associated neurodegeneration, Fish-eye disease, Fructose- 1,6-bisphosphatase deficiency, Fucosidosis, Fukuyama type muscular dystrophy, Fumarase deficiency, Galactokinase deficiency, Galactosialidosis, Gamma aminobutyric acid transaminase deficiency, Gamma-cystathionase deficiency, Gaucher disease, Gaucher disease - ophthalmoplegia - cardiovascular calcification, Gaucher disease perinatal lethal, Gaucher disease type 1, Gaucher disease type 2, Gaucher disease type 3, Gestational diabetes insipidus, Gilbert syndrome - Not a rare disease, Gitelman syndrome, Glucose transporter type 1 deficiency syndrome, Glucose-galactose malabsorption,

Glutamate formiminotransferase deficiency, Glutamine deficiency, congenital, Glutaric acidemia type I, Glutaric acidemia type II, Glutaric acidemia type III, Glutathione synthetase deficiency, Glutathionuria, Glycine N-methyltransferase deficiency, Glycogen storage disease 8, Glycogen storage disease type 0, liver, Glycogen storage disease type 12,

Glycogen storage disease type 13, Glycogen storage disease type 1 A, Glycogen storage disease type IB, Glycogen storage disease type 3, Glycogen storage disease type 5, Glycogen storage disease type 6, Glycogen storage disease type 7, Glycoproteinosis, GM1 gangliosidosis type 1, GM1 gangliosidosis type 2, GM1 gangliosidosis type 3, GM3 synthase deficiency, GRACILE syndrome, Greenberg dysplasia, GTP cyclohydrolase I deficiency, Guanidinoacetate methyltransferase deficiency, Gyrate atrophy of choroid and retina, Haim- Munk syndrome, Hartnup disease, Hawkinsinuria, Hemochromatosis type 2, Hemochromatosis type 3, Hemochromatosis type 4, Hepatic lipase deficiency, Hepatoerythropoietic porphyria, Hereditary amyloidosis, Hereditary coproporphyria, Hereditary folate malabsorption, Hereditary fructose intolerance, Hereditary hyperekplexia, Hereditary multiple osteochondromas, Hereditary sensory and autonomic neuropathy type IE, Hereditary sensory neuropathy type 1, Hermansky Pudlak syndrome 2, Histidinemia, HMG CoA lyase deficiency, Homocarnosinosis, Homocysteinemia, Homocystinuria due to CBS deficiency, Homocystinuria due to MTHFR deficiency, HSD10 disease, Hurler syndrome, Hurler-Scheie syndrome, Hydroxykynureninuria, Hyper-IgD syndrome, Hyperbetaalaninemia, Hypercoagulability syndrome due to glycosylphosphatidylinositol deficiency, Hyperglycerolemia, Hyperinsulinism due to glucokinase deficiency, Hyperinsulinism-hyperammonemia syndrome, Hyperlipidemia type 3, Hyperlipoproteinemia type 5, Hyperlysinemia, Hyperphenylalaninemia due to dehydratase deficiency, Hyperprolinemia, Hyperprolinemia type 2, Hypertryptophanemia, Hypolipoproteinemia, Hypophosphatasia, I cell disease, Imerslund-Grasbeck syndrome, Iminoglycinuria, Inclusion body myopathy 2, Inclusion body myopathy 3, Infantile free sialic acid storage disease, Infantile neuroaxonal dystrophy, Infantile onset spinocerebellar ataxia, Insulin-like growth factor I deficiency, Intrinsic factor deficiency, Isobutyryl-CoA dehydrogenase deficiency, Isovaleric acidemia, Kanzaki disease, Kearns-Sayre syndrome, Krabbe disease atypical due to Saposin A deficiency, L-2-hydroxyglutaric aciduria, L-arginine: glycine amidinotransferase deficiency, Lactate dehydrogenase A deficiency, Lactate dehydrogenase deficiency, Lathosterolosis, LCHAD deficiency, Leber hereditary optic neuropathy, Leigh syndrome, French Canadian type, Lesch Nyhan syndrome, Leucine-sensitive hypoglycemia of infancy, Leukoencephalopathy - dystonia - motor neuropathy, Leukoencephalopathy with brain stem and spinal cord involvement and lactate elevation, Limb-girdle muscular dystrophy type 21, Limb-girdle muscular dystrophy type 2K, Limb-girdle muscular dystrophy type 2M, Limb- girdle muscular dystrophy type 2N, Limb-girdle muscular dystrophy type 20, Limb-girdle muscular dystrophy type 2T, Limb-girdle muscular dystrophy, type 2C, Lipase deficiency combined, Lipoic acid synthetase deficiency, Lipoid proteinosis of Urbach and Wiethe, Lowe oculocerebrorenal syndrome, Lysinuric protein intolerance, Malonyl-CoA decarboxylase deficiency, MAN1B1-CDG, Mannose-binding lectin protein deficiency - Not a rare disease, Mannosidosis, beta A, lysosomal, Maternal hyperphenylalaninemia, Maternally inherited diabetes and deafness, Medium-chain acyl-coenzyme A dehydrogenase deficiency, Megaloblastic anemia due to dihydrofolate reductase deficiency, Menkes disease, Metachromatic leukodystrophy, Metachromatic leukodystrophy due to saposin B deficiency, Methionine adenosyltransferase deficiency, Methylcobalamin deficiency cbl G type, Methylmalonic acidemia with homocystinuria type cblC, Methylmalonic acidemia with homocystinuria type cblD, Methylmalonic acidemia with homocystinuria type cblF, Methylmalonic acidemia with homocystinuria type cblJ, Methylmalonic aciduria, cblA type, Methylmalonic aciduria, cblB type, Mevalonic aciduria, MGAT2-CDG (CDG-IIa), Mild phenylketonuria, Mitochondrial complex I deficiency, Mitochondrial complex II deficiency, Mitochondrial complex III deficiency, Mitochondrial DNA depletion syndrome, encephalomyopathic form with methylmalonic aciduria, Mitochondrial DNA-associated Leigh syndrome, Mitochondrial encephalomyopathy lactic acidosis and stroke-like episodes, Mitochondrial myopathy and sideroblastic anemia, Mitochondrial myopathy with diabetes, Mitochondrial myopathy with lactic acidosis, Mitochondrial neurogastrointestinal encephalopathy syndrome, Mitochondrial trifunctional protein deficiency, MOGS-CDG (CDG-IIb), Mohr-Tranebjaerg syndrome, Molybdenum cofactor deficiency, Monogenic diabetes - Not a rare disease, Morquio syndrome B, MPDU1-CDG (CDG-If), MPI-CDG (CDG-Ib), MPV17-related hepatocerebral mitochondrial DNA depletion syndrome, Mucolipidosis III alpha/beta, Mucolipidosis type 4, Mucopolysaccharidosis type II, Mucopolysaccharidosis type III, Mucopolysaccharidosis type IIIA, Mucopolysaccharidosis type MB, Mucopolysaccharidosis type IIIC, Mucopolysaccharidosis type HID, Mucopolysaccharidosis type IV A, Mucopolysaccharidosis type VI, Mucopolysaccharidosis type VII, Multiple congenital anomalies-hypotonia-seizures syndrome, Multiple congenital anomalies-hypotonia-seizures syndrome type 2, Multiple endocrine neoplasia type 2B, Multiple sulfatase deficiency, Multiple symmetric lipomatosis, Muscle eye brain disease, Muscular dystrophy, congenital, megaconial type, Muscular phosphorylase kinase deficiency, Musculocontractural Ehlers-Danlos syndrome, Myoclonic epilepsy with ragged red fibers, Myoglobinuria recurrent, N acetyltransferase deficiency, N-acetyl-alpha-D- galactosaminidase deficiency type III, N-acetylglutamate synthase deficiency, NBIA/DYT/PARK-PLA2G6, Neonatal adrenoleukodystrophy, Neonatal hemochromatosis, Neonatal intrahepatic cholestasis caused by citrin deficiency, Nephrogenic diabetes insipidus, Neu Laxova syndrome, Neuroferritinopathy, Neuronal ceroid lipofuscinosis 10, Neuronal ceroid lipofuscinosis 2, Neuronal ceroid lipofuscinosis 3, Neuronal ceroid lipofuscinosis 5, Neuronal ceroid lipofuscinosis 6, Neuronal ceroid lipofuscinosis 7, Neuronal ceroid lipofuscinosis 9, Neuropathy ataxia retinitis pigmentosa syndrome, Neutral lipid storage disease with myopathy, Niemann-Pick disease type A, Niemann-Pick disease type B, Niemann-Pick disease type Cl, Niemann-Pick disease type C2, Northern epilepsy, Not otherwise specified 3-MGA-uria type, Occipital horn syndrome, Ocular albinism type 1, Oculocutaneous albinism type 1, Oculocutaneous albinism type IB, Oculocutaneous albinism type 2, Oculocutaneous albinism type 3, OP A3 defect, Optic atrophy 1, Ornithine transcarbamylase deficiency, Ornithine translocase deficiency syndrome, Orotic aciduria type 1, Papillon Lefevre syndrome, Parkinson disease type 9, Paroxysmal nocturnal hemoglobinuria, Pearson syndrome, Pentosuria, Permanent neonatal diabetes mellitus, Peroxisomal biogenesis disorders, Peroxisome disorders - Not a rare disease, Perrault syndrome, Peters plus syndrome, PGM1-CDG, Phosphoglycerate kinase deficiency, Phosphoglycerate mutase deficiency, Phosphoribosylpyrophosphate synthetase superactivity, PMM2-CDG (CDG-Ia), Pontocerebellar hypoplasia type 6, Porphyria cutanea tarda, Primary carnitine deficiency, Primary hyperoxaluria type 1, Primary hyperoxaluria type 2, Primary hyperoxaluria type 3, Primary hypomagnesemia with secondary hypocalcemia, Progressive external ophthalmoplegia, autosomal recessive 1, Progressive familial intrahepatic cholestasis 1, Progressive familial intrahepatic cholestasis type 2, Progressive familial intrahepatic cholestasis type 3, Prolidase deficiency, Propionic acidemia, Pseudocholinesterase deficiency, Pseudoneonatal adrenoleukodystrophy, Purine nucleoside phosphorylase deficiency, Pycnodysostosis, Pyridoxal 5'-phosphate-dependent epilepsy, Pyridoxine- dependent epilepsy, Pyruvate carboxylase deficiency, Pyruvate dehydrogenase complex deficiency, Pyruvate dehydrogenase phosphatase deficiency, Pyruvate kinase deficiency, Refsum disease, Refsum disease with increased pipecolic acidemia, Refsum disease, infantile form, Renal glycosuria, Renal hypomagnesemia 2, Renal hypomagnesemia-6, Renal tubulopathy, diabetes mellitus, and cerebellar ataxia due to duplication of mitochondrial DNA, RFT1-CDG (CDG-In), Rhizomelic chondrodysplasia punctata type 3, Rotor syndrome, Saccharopinuria, Salla disease, Sarcosinemia, Scheie syndrome, Schimke immunoosseous dysplasia, Schindler disease type 1, Schneckenbecken dysplasia, SCOT deficiency, Sea-Blue histiocytosis, Sengers syndrome, Sensory ataxic neuropathy, dysarthria, and ophthalmoparesis, Sepiapterin reductase deficiency, Severe combined immunodeficiency, Short-chain acyl-CoA dehydrogenase deficiency, Sialidosis type I, Sialidosis, type II,

Sialuria, French type, Sitosterolemia, Sjogren-Larsson syndrome, SLC35A1-CDG (CDG-IIf), SLC35A2-CDG, SLC35C1-CDG (CDG-IIc), Smith-Lemli-Opitz syndrome, Spastic paraplegia 7, Spinocerebellar ataxia 28, Spinocerebellar ataxia autosomal recessive 3, Spondylocostal dysostosis 1, Spondylocostal dysostosis 2, Spondylocostal dysostosis 3, Spondylocostal dysostosis 4, Spondylocostal dysostosis 6, Spondylodysplastic Ehlers-Danlos syndrome, Spondyloepimetaphyseal dysplasia joint laxity, Spondylothoracic dysostosis, SRD5A3-CDG (CDG-Iq), SSR4-CDG, Succinic semialdehyde dehydrogenase deficiency, Tangier disease, Tay-Sachs disease, Thiamine responsive megaloblastic anemia syndrome, Thiopurine S methyltranferase deficiency, Tiglic acidemia, TMEM165-CDG (CDG-IIk), Transaldolase deficiency, Transcobalamin 1 deficiency, Transient neonatal diabetes mellitus, Trehalase deficiency, Trimethylaminuria, Triosephosphate isomerase deficiency, Tyrosine hydroxylase deficiency, Tyrosine-oxidase temporary deficiency, Tyrosinemia type 1, Tyrosinemia type 2, Tyrosinemia type 3, Urea cycle disorders, Valinemia, Variegate porphyria, VLCAD deficiency, Walker-Warburg syndrome, Wilson disease, Wolfram syndrome, Wolman disease, Wrinkly skin syndrome, X-linked adrenoleukodystrophy, X- linked cerebral adrenoleukodystrophy, X-linked Charcot-Marie-Tooth disease type 5, X- linked creatine deficiency, X-linked dominant chondrodysplasia punctata 2, X-linked sideroblastic anemia, Xanthinuria type 1, Xanthinuria type 2, Zellweger syndrome, acid-base imbalance, metabolic brain disease, disorders of calcium metabolism, DNA repair-deficiency disorder, glucose metabolism disorder, hyperlactatemia, iron metabolism disorder, lipid metabolism disorder, malabsorption syndrome, metabolic syndrome X, inborn error of metabolism, mitochondrial disease, phosphorus metabolism disorder, Porphyrias, Proteostasis deficiency, metabolic skin disease, wasting syndrome, water-electrolyte imbalance, and any combination thereof.

[0012] In some embodiments, the method further comprises determining a clinical intervention to treat the metabolic disease or disorder of the subject. In some embodiments, the clinical intervention is selected from the group consisting of exercise regimen, diet regimen, blood pressure medication, cholesterol medication, diabetes medication, aspirin, other medication, weight loss, smoking cessation, and any combination thereof. In some embodiments, the subject is asymptomatic for the metabolic disease or disorder. In some embodiments, the method further comprises administering the clinical intervention to the subject. In some embodiments, the clinical intervention is selected from among a plurality of clinical interventions.

[0013] In some embodiments, the method further comprises computer processing the data and the determined metabolic rate to determine that the subject has or is at risk of having the metabolic disease or disorder.

[0014] In some embodiments, the method further comprises using a second trained machine learning algorithm to determine that the subject has or is at risk of having the metabolic disease or disorder. In some embodiments, the second trained algorithm determines that the subject has or is at risk of having the metabolic disease or disorder at a accuracy of at least about 50%, at least about 55%, at least about 60%, at least about 65%, at least about 70%, at least about 75%, at least about 80%, at least about 85%, at least about 90%, at least about 91%, at least about 92%, at least about 93%, at least about 94%, at least about 95%, at least about 96%, at least about 97%, at least about 98%, or at least about 99%. In some embodiments, the second trained algorithm determines that the subject has or is at risk of having the metabolic disease or disorder at a sensitivity of at least about 50%, at least about 55%, at least about 60%, at least about 65%, at least about 70%, at least about 75%, at least about 80%, at least about 85%, at least about 90%, at least about 91%, at least about 92%, at least about 93%, at least about 94%, at least about 95%, at least about 96%, at least about 97%, at least about 98%, or at least about 99%. In some embodiments, the second trained algorithm determines that the subject has or is at risk of having the metabolic disease or disorder of the subject at a specificity of at least about 50%, at least about 55%, at least about 60%, at least about 65%, at least about 70%, at least about 75%, at least about 80%, at least about 85%, at least about 90%, at least about 91%, at least about 92%, at least about 93%, at least about 94%, at least about 95%, at least about 96%, at least about 97%, at least about 98%, or at least about 99%. In some embodiments, the second trained algorithm determines that the subject has or is at risk of having the metabolic disease or disorder of the subject at a positive predictive value of at least about 50%, at least about 55%, at least about 60%, at least about 65%, at least about 70%, at least about 75%, at least about 80%, at least about 85%, at least about 90%, at least about 91%, at least about 92%, at least about 93%, at least about 94%, at least about 95%, at least about 96%, at least about 97%, at least about 98%, or at least about 99%. In some embodiments, the second trained algorithm determines that the subject has or is at risk of having the metabolic disease or disorder of the subject at a negative predictive value of at least about 50%, at least about 55%, at least about 60%, at least about 65%, at least about 70%, at least about 75%, at least about 80%, at least about 85%, at least about 90%, at least about 91%, at least about 92%, at least about 93%, at least about 94%, at least about 95%, at least about 96%, at least about 97%, at least about 98%, or at least about 99%.

[0015] In some embodiments, the second trained algorithm is trained using a plurality of independent training samples associated with a presence of the metabolic disease or disorder. In some embodiments, the second trained algorithm is trained using a plurality of independent training samples associated with an absence or normal risk of the metabolic disease or disorder.

[0016] In some embodiments, the report is presented on a graphical user interface of an electronic device of a user. In some embodiments, the user is the subject or a health care provider of the subject. In some embodiments, the method further comprises determining a likelihood of the metabolic disease or disorder.

[0017] In some embodiments, the trained machine learning algorithm comprises a supervised machine learning algorithm. In some embodiments, the supervised machine learning algorithm comprises a linear regression, a logistic regression, a deep learning algorithm, a support vector machine (SVM), a neural network, a Random Forest, a boosted regression, a gradient boosting algorithm, an AdaBoost algorithm, or a combination thereof. [0018] In some embodiments, the second trained machine learning algorithm comprises a second supervised machine learning algorithm. In some embodiments, the second supervised machine learning algorithm comprises a linear regression, a logistic regression, a deep learning algorithm, a support vector machine (SVM), a neural network, a Random Forest, a boosted regression, a gradient boosting algorithm, an AdaBoost algorithm, or a combination thereof.

[0019] In some embodiments, the method further comprises obtaining the data set at a plurality of time points, computer processing the data set using a trained machine learning algorithm to determine the metabolic rate at the plurality of time points, and electronically outputting the report indicative of the determined metabolic rate of the subject at the plurality of time points.

[0020] In some embodiments, the method further comprises determining the one or more activities of the subject based at least in part on one or more acquired audio or video data of the subject. In some embodiments, the method further comprises convolving a time window of the one or more acquired audio or video data of the subject to determine the one or more activities of the subject. In some embodiments, the method further comprises performing batch normalization of the data. In some embodiments, computer processing the data set using the trained machine learning algorithm comprises using an activation function. In some embodiments, the activation function is a rectified linear unit (ReLU) function or sigmoid function. In some embodiments, the method further comprises pooling the data. In some embodiments, the method further comprises concatenating a first dataset associated with the one or more vital signs and a second dataset associated with the one or more activities of the subject to produce a concatenated dataset. In some embodiments, the method further comprises performing a regression on the concatenated dataset to determine the metabolic rate.

[0021] In some embodiments, the method further comprises monitoring the metabolic disease or disorder of the subject, wherein the monitoring comprises determining that the subject has or is at risk of having the metabolic disease or disorder of the subject at a plurality of time points. In some embodiments, a difference in the determined metabolic disease or disorder of the subject among the plurality of time points is indicative of one or more clinical indications selected from the group consisting of: (i) a diagnosis of the metabolic disease or disorder of the subject, (ii) a prognosis of the metabolic disease or disorder of the subject, and (iii) an efficacy or non-efficacy of a therapeutic intervention for treating the metabolic disease or disorder of the subject.

[0022] In some embodiments, the method further comprises performing one or more clinical tests for the subject based at least in part on the determined metabolic rate of the subject. In some embodiments, the method further comprises performing one or more clinical tests for the subject based at least in part on the determined metabolic rate of the subject or the determination that the subject has or is at risk of having the metabolic disease or disorder. In some embodiments, the one or more clinical tests comprise a genetic test, a blood test, a urine test, a stool test, a metabolite test, a hormone test, or a combination thereof.

[0023] In some embodiments, the metabolic rate is selected from the group consisting of a basal metabolic rate, a standard metabolic rate, a resting metabolic rate, a glucose metabolic rate, and any combination thereof.

[0024] In some embodiments, (a) and (b) are performed substantially in real time. In some embodiments, the metabolic rate of the subject is determined within about 10 minutes, about 9 minutes, about 8 minutes, about 7 minutes, about 6 minutes, about 5 minutes, about 4 minutes, about 3 minutes, about 2 minutes, about 1 minute, about 50 seconds, about 40 seconds, about 30 seconds, about 20 seconds, about 10 seconds, about 9 seconds, about 8 seconds, about 7 seconds, about 6 seconds, about 5 seconds, about 4 seconds, about 3 seconds, about 2 seconds, or about 1 second of the data set being obtained.

[0025] In another aspect, the present disclosure provides a computer system for determining a metabolic rate of a subject, comprising: a database that is configured to store a data set comprising vital sign data and activity data, wherein the vital sign data corresponds to one or more vital signs of the subject, and wherein the activity data corresponds to one or more activities of the subject; and one or more computer processors operatively coupled to the database, wherein the one or more computer processors are individually or collectively programmed to: process the data set using a trained machine learning algorithm to determine the metabolic rate; and electronically output a report indicative of the metabolic rate of the subject determined in (i).

[0026] In some embodiments, the computer system further comprises an electronic display operatively coupled to the one or more computer processors, wherein the electronic display comprises a graphical user interface that is configured to display the report.

[0027] In another aspect, the present disclosure provides a non-transitory computer- readable medium comprising machine-executable code that, upon execution by one or more computer processors, implements a method for determining a metabolic rate of a subject, the method comprising: (a) obtaining a data set comprising vital sign data and activity data, wherein the vital sign data corresponds to one or more vital signs of the subject, and wherein the activity data corresponds to one or more activities of the subject; (b) computer processing the data set using a trained machine learning algorithm to determine the metabolic rate; and (c) electronically outputting a report indicative of the metabolic rate of the subject determined in (b).

[0028] Another aspect of the present disclosure provides a non-transitory computer- readable medium comprising machine executable code that, upon execution by one or more computer processors, implements any of the methods above or elsewhere herein.

[0029] Another aspect of the present disclosure provides a system comprising one or more computer processors and computer memory coupled thereto. The computer memory comprises machine executable code that, upon execution by the one or more computer processors, implements any of the methods above or elsewhere herein.

[0030] Additional aspects and advantages of the present disclosure will become readily apparent to those skilled in this art from the following detailed description, wherein only illustrative embodiments of the present disclosure are shown and described. As will be realized, the present disclosure is capable of other and different embodiments, and its several details are capable of modifications in various obvious respects, all without departing from the disclosure. Accordingly, the drawings and description are to be regarded as illustrative in nature, and not as restrictive.

INCORPORATION BY REFERENCE

[0031] All publications, patents, and patent applications mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent, or patent application was specifically and individually indicated to be incorporated by reference. To the extent publications and patents or patent applications incorporated by reference contradict the disclosure contained in the specification, the specification is intended to supersede and/or take precedence over any such contradictory material.

BRIEF DESCRIPTION OF THE DRAWINGS [0032] The novel features of the invention are set forth with particularity in the appended claims. A better understanding of the features and advantages of the present invention will be obtained by reference to the following detailed description that sets forth illustrative embodiments, in which the principles of the invention are utilized, and the accompanying drawings (also “Figure” and “FIG.” herein), of which:

[0033] FIG. 1 illustrates a flow chart of an example method for determining a metabolic rate of a subject.

[0034] FIG. 2 illustrates an example schematic of a system for determining a metabolic rate of a subject.

[0035] FIG. 3 illustrates a flow chart of an example computer-implemented method for determining a metabolic rate of a subject.

[0036] FIG. 4 shows a computer system that is programmed or otherwise configured to implement methods provided herein.

[0037] FIGs. 5A-5B illustrate an example of determining a basal metabolic rate of a subject in real-time, based on an input data set obtained by acquiring a set of vital sign measurements and a posture of the subject at one-minute intervals (FIG. 5A). The output data set includes a determined metabolic rate of the subject at one-minute intervals (FIG. 5B).

INCORPORATION BY REFERENCE

[0038] All publications, patents, and patent applications mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent, or patent application was specifically and individually indicated to be incorporated by reference. To the extent publications and patents or patent applications incorporated by reference contradict the disclosure contained in the specification, the specification is intended to supersede and/or take precedence over any such contradictory material.

PET ATT /ED DESCRIPTION

[0039] While various embodiments of the invention have been shown and described herein, it will be obvious to those skilled in the art that such embodiments are provided by way of example only. Numerous variations, changes, and substitutions may occur to those skilled in the art without departing from the invention. It should be understood that various alternatives to the embodiments of the invention described herein may be employed.

[0040] Whenever the term “at least,” “greater than,” or “greater than or equal to” precedes the first numerical value in a series of two or more numerical values, the term “at least,” “greater than” or “greater than or equal to” applies to each of the numerical values in that series of numerical values. For example, greater than or equal to 1, 2, or 3 is equivalent to greater than or equal to 1, greater than or equal to 2, or greater than or equal to 3. [0041] Whenever the term “no more than,” “less than,” or “less than or equal to” precedes the first numerical value in a series of two or more numerical values, the term “no more than,” “less than,” or “less than or equal to” applies to each of the numerical values in that series of numerical values. For example, less than or equal to 3, 2, or 1 is equivalent to less than or equal to 3, less than or equal to 2, or less than or equal to 1.

[0042] As used herein, the term “subject” generally refers to an entity or a medium that has or may have testable or detectable information. A subject can be a person, individual, or patient. A subject can be a vertebrate, such as, for example, a mammal. Non-limiting examples of mammals include humans, simians, farm animals, sport animals, rodents, and pets. The subject can be a person that has or is suspected of having a disease or disorder (e.g., a metabolic disease or disorder). The subject may be displaying a symptom(s) indicative of a health or physiological state or condition of the subject, such as a metabolic disease or disorder. As an alternative, the subject can be asymptomatic with respect to such health or physiological state or condition.

[0043] The term “metabolic rate,” as used herein, generally refers to a rate at which a body of a subject performs a metabolic function, such as, for example, a rate of tissue oxidation of a nutrient(s) in the body of subject. The metabolic rate of a subject at a given time may depend on multiple factors, such as age, gender, muscle-to-fat ratio, and hormonal function. The metabolic rate may also vary when a subject is engaged in different activities. For example, high-intensity activities such as running may require more energy, therefore the metabolic rate while performing such activities may be higher. Conversely, low-intensity activities such as sleeping may require less energy and therefore the metabolic rate may be lower during such activities. In some cases, an organ such as liver or pancreas may not function properly leading to an abnormal metabolism and/or a metabolic disorder. A metabolic disorder occurs when abnormal chemical reactions in the body disrupt the normal process of metabolism. This may lead to too much of some substances or too little of other ones that the body requires to stay healthy. There are different groups of metabolic disorders. Some affect the breakdown of amino acids, carbohydrates, or lipids. Another group, for example mitochondrial diseases, affects the parts of the cells that produce the energy (e.g., mitochondria).

[0044] A rate of a subject’s metabolism may be indirectly measured in vivo. Measures for metabolic rate may include oxygen (02) uptake and heat production. Measurements of 02 uptake may be commonly performed; however, 02 uptake rate measurements only provide insight into metabolic rate under fully aerobic conditions where all of an organism’s energy is provided by mitochondrial oxidative phosphorylation and 02 use. Therefore, this method may be blind to anaerobic metabolism because this form of energy production is not linked with 02 consumption.

[0045] Metabolic heat may be an inevitable product of energy transduction during adenosine tri-phosphate (ATP) turnover, and the rate of heat production may be directly proportional to ATP turnover rate and hence metabolic rate. Changes in heat production may be interpreted as a change in metabolic rate (increased heat production indicates an increase in metabolic rate and vice versa), and unlike 02 uptake rates, these measurements may not be affected by the mode of cellular energy production, and therefore detect changes in metabolic rate almost agnostic to whether they are fueled aerobically or anaerobically. These methods may require special equipment and devices such as metabolic chambers, calorimeters, etc. These methods may also be limited to measuring static metabolic rate (e.g., basal metabolic rate). One the other hand, a method capable of measuring a dynamic metabolic rate may be used to monitor a subject’s metabolic rate changes under different conditions (e.g., drugs or exercise). This may be instrumental in providing insights into a subject’s health condition, determining a risk or probability of a metabolic disorder or disease in a subject, and/or provide optimized and/or personalized intervention regiments for a subject with a metabolic disorder or disease (e.g., diet, exercise, drugs, or a combination thereof.)

[0046] Methods, systems, and media as disclosed herein may determine or improve upon existing methods for determining a metabolic rate of a subject. For example, methods and systems provided herein may use machine learning methods to determine a metabolic rate of a subject (e.g., a basal metabolic rate, a standard metabolic rate, a resting metabolic rate, or a glucose metabolic rate) which may lead to fast, real time, and/or remote analysis of the subject’s metabolic rate. The methods and systems provided herein may be advantageously performed using lower device requirements than other methods, in order to help reduce or eliminate the need for need for in person measuring of the metabolic rate using complex measuring equipment, such as a metabolic chamber or gas analysis, which may be uncomfortable for the subject (e.g., with limited movement space) and expensive. Instead, methods and systems provided herein may use wearable and contactless devices to measure data that is subsequently analyzed to determine or estimate the metabolic rate of a subject, which may be comfortable for the subject (e.g., with increased freedom of movement) and cheap. The machine learning approach may be trained using large datasets in order to gain new insights into determining a subject’s metabolic rate (e.g., a basal metabolic rate, a standard metabolic rate, a resting metabolic rate, or a glucose metabolic rate). The machine learning approach may obtain data comprising one or more vital signs and/or one or more activities of the subject to analyze temporal changes in the subject’s metabolic rate.

[0047] Methods and systems of the present disclosure may collect both vital sign data and behavior/activity data of a subject, analyze the data to learn underlying relationships between the vital sign and behavior/activity data of a subject and the corresponding dynamic metabolic rate of the subject, and using measured vital sign and behavior/activity data of a subject to determine or estimate (e.g., by classification or regression) the metabolic rate of the subject.

[0048] In some embodiments, a method of the present disclosure comprises determining a dynamic metabolic rate of a subject (e.g., in clinical or home setting) based at least in part on monitoring changes in a subject’s (e.g., patient’s) metabolic rate (e.g., of a patient with chronic disease) while the subject is performing various operations (e.g., taking drugs, exercising, resting, or sleeping). In some embodiments, both dynamic vital signs and behavior posture may be processed to estimate the metabolic rate of the subject. There may be strong relationships between a subject’s temporal vital signs, behavior posture, and metabolic rate; therefore, in some embodiments, methods and systems use a machine learning model that is constructed to dynamically estimate the metabolic rate of a subject based at least in part on computer processing a set of vital signs and a behavior posture of the subject. In some embodiments, the metabolic rate measurement is dynamic, which is different from the basal metabolic rate, which is a relative static value that does not change by time. Thus, methods and systems of the present disclosure may determine or estimate a dynamic metabolic rate, which may be used toward clinical real- time monitoring and other clinical or research applications.

[0049] Some methods and systems for determining or estimating a metabolic rate of a subject may be limited to using only a set of vital signs without considering the activities that the subject is performing, thereby limiting the precision and accuracy of the metabolic rate measurement. For instance, the metabolic rate differences between a subject who is exercising and a subject who is sleeping may be significant. In contrast, methods and systems of the present disclosure may use sensors and sensed data to analyze the behavior and posture of a subject, and for each specific behavior and/posture, a metabolic rate model is constructed to estimate the metabolic rate with high precision and accuracy.

[0050] In some embodiments, the method for determining or estimating a metabolic rate of a subject may comprise performing a regression (such as linear regression, lasso regression) or a classification (e.g., by categorizing the metabolic rate into one category from among multiple categories or types); thus regression or classification models may also be applied (e.g., support vector machine (SVM), deep learning, etc.). The methods may comprise obtaining or analyzing a set of vital signs of a subject. The vital signs may vary temporally (such as heart rate, respiratory rate, body temperature, etc.) or the vital signs may be static (such as weight and height). The methods may comprise obtaining or analyzing a behavior and/or posture of a subject. The behavior and/posture may comprise any behaviors, postures, or combinations thereof. In some embodiments, the methods may be applied to human subjects (e.g., for clinical or home health monitoring applications) or animal subjects (e.g., for agricultural applications).

[0051] The methods, systems, and media as disclosed herein may detect, determine, estimate, and/or predict the subject’s activity. For example, the methods and systems provided herein may comprise obtaining and/or analyzing input data (e.g., pictures, video stream, sensor data from wearable devices) to detect an activity that is being performed by the subject. The detection method may comprise automatically predicting the activity from a change in the input data. The detection method may comprise a way of communication with the subject (e.g., a text message, notification on an electronic device, such as a smartphone or a handheld computer) to determine the activity. The detection method may comprise analyzing the input data for routine activities (e.g., sleeping, working, eating, walking, or exercising). The activity detection or prediction method may comprise a machine learning method to construct a trained classifier to classify an activity. The machine learning method may leverage large datasets in order to gain insights into new datasets to classify an activity of the subject.

[0052] The methods, systems, and media as disclosed herein may improve upon existing methods for detecting or classifying metabolic disorders by determining or estimating a metabolic rate for a subject (e.g., a basal metabolic rate, a standard metabolic rate, a resting metabolic rate, or a glucose metabolic rate). For example, the methods and systems provided herein may use machine learning to construct a classifier to detect a metabolic disorder in a subject based on the subject’s metabolic rate. For example, the subject’s determined dynamic metabolic rate may differ from a normal dynamic metabolic rate, signaling a disease or a disorder such as a lipid metabolic disorder or a calcium metabolic disorder. The machine learning method may comprise estimating a risk of developing a metabolic disease or disorder in a subject. The method may comprise reporting the estimated risk to a user.

[0053] The methods, systems, and media as disclosed herein may improve upon existing methods for treating a metabolic condition such as a metabolic disease or disorder. For example, the methods and systems provided herein may use a machine learning method to determine a customized intervention based at least partially on the metabolic rate of the subject (e.g., a basal metabolic rate, a standard metabolic rate, a resting metabolic rate, or a glucose metabolic rate) and/or one or more data points from the input data (e.g., activity data, vital signs, or medical history of a subject). The methods described herein may provide a report comprising one or more intervention regimens customized or optimized for the subject. [0054] FIG. 1 illustrates a flow chart of an example method 100 for determining a metabolic rate in accordance with some embodiments. The method 100 may comprise obtaining one or more datasets relating to a subject. The one or more datasets may comprise a set of vital signs 101 (e.g., heart rate, temperature, or respiratory rate) or a subject’s behavior posture 102 (e.g., posture, movement, distance traveled, travel time, travel speed, etc.) The data relating to a subject may be received from one or more monitoring devices such as a sensor (e.g., a heart rate monitor, a skin conductivity sensor, a GPS) or a camera (e.g., a video camera). The method 100 may use a machine learning model 103 to determine or classify a behavior or activity of the subject 104 (e.g., sleeping, walking, cycling, swimming, etc.) based at least partially on the behavior posture data 102 received from a monitoring device. The method 100 may comprise applying a machine learning algorithm 105 (e.g., comprising one or more models) to determine a metabolic rate based at least partially on the behavior posture data, the activity of the subject 104, and/or the set of vital signs 101. The metabolic rate determined by the method 100 may be a dynamic metabolic rate (e.g., temporally varying). The dynamic metabolic rate may be determined in real-time or substantially real time.

[0055] Although the above flow chart shows a method 100 of determining a metabolic rate, in accordance with some embodiments, many variations of such a method may be performed. The operations may be completed in any order. Operations may be added or deleted. Some of the Operations may comprise sub-operations. Many of the operations may be repeated as often as beneficial or needed in the method, for example to improve an accuracy or performance of the method.

[0056] FIG. 2 illustrates an example schematic of a system for determining a metabolic rate of a subject. The system may comprise contactless and/or wearable devices obtaining datasets of a subject, such as vital signs and behavior or posture data of the subject. The method may comprise processing the vital signs of the subject using a vital signs machine learning model (e.g., classification or regression), which may extract a set of features from the vital signs for further analysis. The method may comprise processing the behavior or posture data of the subject using a behavior learning model (e.g., classification or regression), which may extract a set of features from the behavior or posture data for further analysis. The outputs of the vital signs machine learning model and/or the behavior learning model may be further analyzed by metabolic learning model (e.g., classification or regression) in order to determine a metabolic rate of the subject (e.g., basal metabolic rate, glucose metabolism rate, etc.).

[0057] The datasets relating to a subject may comprise one or more demographic or clinical attributes comprise at least one of age, race, ethnicity, gender, weight, body mass index, lean body mass, body fat percentage, body surface area, height, history of medication or other clinical treatment, history of tobacco use, history of alcohol consumption, daily activity or fitness level, genetic test results, blood test results, imaging results, and/or a combination thereof. The datasets relating to a subject may comprise one or more vital signs comprising at least one of heart rate, respiratory rate, systolic blood pressure, diastolic blood pressure, blood oxygen concentration (SpCb), carbon dioxide concentration in respiratory gases, hormone level, sweat analysis, blood glucose, body temperature, bioimpedance, conductivity, capacitance, resistivity, galvanic skin response, electromyography, el ectroencephal ography , el ectrocardi ography , magnetoencephal ography, magnetocardiography, immunology markers, and/or a combination thereof.

[0058] The set of vital signs may be measured using a monitoring device. The monitoring device may be a portable vital sign monitoring device. The portable vital sign monitor may be selected from the group consisting of a heart rate monitor, a respiratory rate monitor, a blood pressure monitor, a blood oxygen concentration monitor, a blood glucose monitor, a body temperature monitor, a bioimpedance monitor, an electromyography monitor, an electroencephalography monitor, an electrocardiography monitor, a magnetoencephalography monitor, a magnetocardiography monitor, a smart phone, a smart watch, an activity or exercise monitor, a contactless wearable health monitoring device, and/or any combination thereof.

[0059] The set of monitoring devices as disclosed herein may be remote from a computer processing system to perform machine learning disclosed herein. A monitoring device such as a sensor (e.g., a heart rate monitor, a skin conductivity sensor, a GPS) or a camera (e.g., a video camera) may collect one or more datasets (e.g. data), which may be sent to a computer processing system to perform machine learning (e.g., classification or regression) as disclosed herein. In some cases, one or more datasets may be received from a remote server. However, in some cases, one or more monitoring devices may be local to a computer processing system to perform machine learning (e.g., classification or regression) as disclosed herein. For example, a computer processing system to perform machine learning (e.g., classification or regression) may be a part of an onboard logic on a processor of a monitoring device, such as on a logic within a smartphone.

[0060] The datasets relating to a subject may comprise behavior posture data of one or more activities comprising at least one of walkingjogging, running, bicycle riding, performing push-ups, performing sit-ups, performing pull-ups, exercise, performing aerobic exercise, performing anaerobic exercise, playing a sport, lifting weights, swimming, sitting, standing, talking, eating, lying down, sleeping, and/or a combination thereof. The behavior posture data may be a video or audio data recorded from a subject. The data of one or more activities may be collected using a monitoring device. The monitoring device may be a portable camera or a wearable device. The wearable device may comprise an activity tracker, a GPS, a tachymeter, a movement sensor. The methods and systems described herein may classify an activity based at least partially on the behavior posture data and/or the one or more vital signs data using a trained machine learning algorithm.

[0061] In some cases, the datasets relating to a subject may be received from a monitoring device at least once, twice, three times, or more. For example, to calculate a metabolic rate the datasets may be obtained from the monitoring device to be analyzed one or more times per second, per minute, per hour, per day, per week, or per month. In some cases, the datasets may be obtained from a monitoring device 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 30, 40, 50, 60 times or more every minute, every hour, every day. In some cases, the data may be obtained and analyzed substantially continuously for a predefined period of time. The predefined period of time may be at least 1,

2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 30, 40, 50, 60 seconds, minutes, hours, or days.

[0062] FIG. 3 illustrates a flow chart of an example computer-implemented method for determining a metabolic rate of a subject. The computer-implemented method may comprise obtaining or analyzing datasets relating to the subject. The datasets relating to the subject may comprise a set of vital signs and/or a set of behaviors or activities of the subject. The computer-implemented method may comprise performing a time alignment of the datasets relating to the subject. The computer-implemented method may comprise performing a data de-noising of the datasets relating to the subject. The computer-implemented method may comprise performing a convolution of the datasets relating to the subject. The computer- implemented method may comprise performing a batch normalization of the datasets relating to the subject. The computer-implemented method may comprise applying an activation function to the datasets relating to the subject. The computer-implemented method may comprise pooling the datasets relating to the subject. The computer-implemented method may comprise The computer-implemented method may comprise linking together the datasets relating to a subject, such as by performing concatenation. For example, a first dataset comprising a set of vital signs of a subject and a second dataset associated with the set of activities of the subject (e.g., behavior posture data) may be concatenated to produce a concatenated dataset. The concatenated dataset may be used to make inferences using the methods and systems described herein. For example, concatenated dataset may be processed or analyzed using a machine learning classification or regression model to determine or estimate a metabolic rate of the subject.

[0063] Classifiers

[0064] Methods and systems of the present disclosure may output a classification (e.g., an output) of the subject’s metabolic rate. The classification may be based on a classifier model as disclosed herein, which classifier may be a first trained machine learning algorithm. The one or more behavior posture data and/or the one or more vital signs may be used as inputs into the first trained machine learning algorithm. The first trained machine learning algorithm may first classify one or more activities of the subject based at least in part on the one or more behavior posture data. The first trained machine learning algorithm may further comprise one or more models (e.g., trained machine learning algorithms) associated with the one or more classified activities.

[0065] For example, an activity-specific model may be used to determine a metabolic rate of a subject based at least in part on the one or more vital signs associated with the behavior posture data and/or the activity. For example, the behavior posture data may first be classified as an activity (e.g., walking, running, swimming); then a metabolic rate may be determined based on the one or more vital signs using a classification model, wherein the classification model may be specifically trained for the activity identified in the first step. For example, an activity of the subject may be classified as walking in one instance or swimming in another instance. Although, one or more vital signs from the subject collected while walking or swimming may be similar (e.g., similar body temperature, or similar heart rate), a metabolic rate of the subject may be classified differently. For example, a similar vital sign may be associated with a higher metabolic rate when obtained while swimming compared to walking. [0066] As another example, a cohort-specific model may be used to determine a metabolic rate of a subject based at least in part on the one or more vital signs associated with the behavior posture data and/or the activity. One or more cohort-specific models may be constructed based on data of the training subjects (e.g., demographic or clinical attributes). In some embodiments, the one or more demographic or clinical attributes comprise at least one of age, race, ethnicity, gender, weight, body mass index, lean body mass, body fat percentage, body surface area, height, history of medication or other clinical treatment, history of tobacco use, history of alcohol consumption, daily activity or fitness level, genetic test results, blood test results, imaging results, and a combination thereof.

[0067] The first trained machine learning algorithm may comprise a trained machine learning classifier. The classifier may comprise a supervised machine learning algorithm or an unsupervised machine learning algorithm. The supervised machine learning algorithm may comprise a linear regression, a logistic regression, a deep learning algorithm, a support vector machine (SVM), a neural network, a Random Forest, a boosted regression, a gradient boosting algorithm, an AdaBoost algorithm, or a combination thereof. The classifier may comprise an unsupervised machine learning algorithm, e.g., clustering analysis (e.g., k-means clustering, hierarchical clustering, mixture models, DBSCAN, OPTICS algorithm), principal component analysis, independent component analysis, non-negative matrix factorization, singular value decomposition, anomaly detection (e.g., local outlier factor), neural network (e.g., autoencoder, deep belief network, Hebbian learning, generative adversarial network, self-organizing map), expectation-maximization algorithm, and method of moments.

[0068] A first machine learning classifier may have one or more possible output values indicating a classification of (i) an activity of the subject, (ii) a metabolic rate of the subject, or (iii) a combination thereof. The output values may comprise descriptive labels, numerical values, or a combination thereof. The descriptive labels may comprise values comprising one of two values (e.g., (0, 1 }, (positive, negative}) or one of more than two values (e.g., (0, 1, 2}, (positive, negative, or indeterminate}, or(very low, low, medium, high, very high}). For example, an activity can be classified as one of low intensity, medium intensity or high intensity. Alternatively, a metabolic rate may be classified as a very low, low, medium, high, or very high metabolic rate.

[0069] The descriptive labels may provide an identification of a treatment for the subject’s metabolic disease or disorder, and may comprise, for example, a therapeutic intervention, a duration of the therapeutic intervention, and/or a dosage of the therapeutic intervention suitable to treat a metabolic disease or disorder. Such descriptive labels may provide an identification of secondary clinical tests that may be appropriate to perform on the subject (e.g., in order to confirm or refute the diagnosis of the metabolic disease or disorder), and may comprise, for example, an imaging test, a blood test, a genetic test, a metabolic test, a computed tomography (CT) scan, a magnetic resonance imaging (MRI) scan, an ultrasound scan, a chest X-ray, a positron emission tomography (PET) scan, a PET-CT scan, or any combination thereof. For example, such descriptive labels may provide a prognosis of the metabolic disease or disorder of the subject.

[0070] Some of the output values may comprise numerical values, such as binary, integer, continuous values, or an arbitrary number of tags. Such binary output values may comprise, for example, (0, 1}. Such integer output values may comprise, for example, (0, 1, 2}. Such continuous output values may comprise, for example, a relative number from 0 - 1,

0 - 100, or a number between a minimum and a maximum number. An output may be normalized or unnormalized. An output may be a percentage compared to a maximum and a minimum amount. For example, a metabolic rate of a subject may be classified as a percentage between about 0.1% - 100% of a maximum metabolic rate of the subject, wherein the maximum (e.g., 100%) metabolic rate may be associated with a very high intensity activity. As another example, a metabolic rate of a subject may be classified as a multiple of a basal metabolic rate of the subject. In some cases, some of the output values may be assigned based on one or more cutoff values. For example, a maximum metabolic rate may be a associated with a heart rate cut off value of at most about 150 beats per minute. An output may comprise a set of arbitrary number of tags. For example, when the predicted metabolic rate reaches a corresponding threshold, the tags may be assigned and outputted (e.g., in the form of a notification or an alert), such as a lipid metabolic disorder or a calcium metabolic disorder.

[0071] Some of the output values may be assigned based on one or more cutoff values.

For example, a binary classification of subjects may assign an output value of “positive” or 1 if the data analysis indicates that the subject has at least a 50% probability of having a metabolic disease or disorder. For example, a binary classification of subjects may assign an output value of “negative” or 0 if the data analysis indicates that the subject has less than a 50% probability of having a metabolic disease or disorder. In this case, a single cutoff value of 50% is used to classify subjects into one of the two possible binary output values.

Examples of single cutoff values may include about 1%, about 2%, about 5%, about 10%, about 15%, about 20%, about 25%, about 30%, about 35%, about 40%, about 45%, about 50%, about 55%, about 60%, about 65%, about 70%, about 75%, about 80%, about 85%, about 90%, about 91%, about 92%, about 93%, about 94%, about 95%, about 96%, about 97%, about 98%, and about 99%.

[0072] As another example, a classification of subjects may assign an output value of “positive” or 1 if the data analysis indicates that the subject has a probability of having a metabolic disease or disorder of at least about 50%, at least about 55%, at least about 60%, at least about 65%, at least about 70%, at least about 75%, at least about 80%, at least about 85%, at least about 90%, at least about 91%, at least about 92%, at least about 93%, at least about 94%, at least about 95%, at least about 96%, at least about 97%, at least about 98%, at least about 99%, or more. The classification of subjects may assign an output value of “positive” or 1 if the data analysis indicates that the subject has a probability of having a metabolic disease or disorder of more than about 50%, more than about 55%, more than about 60%, more than about 65%, more than about 70%, more than about 75%, more than about 80%, more than about 85%, more than about 90%, more than about 91%, more than about 92%, more than about 93%, more than about 94%, more than about 95%, more than about 96%, more than about 97%, more than about 98%, or more than about 99%.

[0073] The classification of subjects may assign an output value of “negative” or 0 if the data analysis indicates that the subject has a probability of having a metabolic disease or disorder of less than about 50%, less than about 45%, less than about 40%, less than about 35%, less than about 30%, less than about 25%, less than about 20%, less than about 15%, less than about 10%, less than about 9%, less than about 8%, less than about 7%, less than about 6%, less than about 5%, less than about 4%, less than about 3%, less than about 2%, or less than about 1%. The classification of subjects may assign an output value of “negative” or 0 if the data analysis indicates that the subject has a probability of having a metabolic disease or disorder of no more than about 50%, no more than about 45%, no more than about 40%, no more than about 35%, no more than about 30%, no more than about 25%, no more than about 20%, no more than about 15%, no more than about 10%, no more than about 9%, no more than about 8%, no more than about 7%, no more than about 6%, no more than about 5%, no more than about 4%, no more than about 3%, no more than about 2%, or no more than about 1%.

[0074] The classification of subject may assign an output value of “indeterminate” or 2 if the subject is not classified as “positive”, “negative”, 1, or 0. In this case, a set of two cutoff values is used to classify subjects into one of the three possible output values. Examples of sets of cutoff values may include (1%, 99%}, (2%, 98%}, (5%, 95%}, (10%, 90%}, (15%, 85%}, (20%, 80%}, (25%, 75%}, (30%, 70%}, (35%, 65%}, (40%, 60%}, and (45%, 55%}. Similarly, sets of n cutoff values may be used to classify subjects into one of //+ 1 possible output values, where n is any positive integer.

[0075] The first machine learning classifier may be trained with a plurality of independent training sets. Each of the independent training sets may comprise a list of metabolic rates associated with behavior postures, activities, vital signs, and/or other data from a plurality of subjects. Moreover, all the above mentioned data may be collected in the form of a time-series, which is associated with a time stamp corresponding to the collected time of the data.

[0076] The first machine learning classifier may be trained with at least about 50, at least about 100, at least about 150, at least about 200, at least about 250, at least about 300, at least about 350, at least about 400, at least about 450, or at least about 500 independent training sets. The classifier may be trained with no more than 500, no more than 450, no more than 400, no more than 350, no more than 300, no more than 250, no more than 200, no more than 150, no more than 100, or no more than 50 independent training sets.

[0077] Methods and systems of the present disclosure may comprise a second machine learning classifier to classify a metabolic disorder or disease in a subject. The second machine learning may be used to determine a presence or a susceptibility of a metabolic disease or disorder of the subject based at least in part on the metabolic rate of the subject. An output from the first machine learning algorithm comprising one or more metabolic rates of a subject (e.g., a basal metabolic rate, a standard metabolic rate, a resting metabolic rate, or a glucose metabolic rate) may be used as inputs into the second machine learning algorithm. The data from the datasets relating to the subject comprising one or more demographic or clinical attributes comprise at least one of age, race, ethnicity, gender, weight, body mass index, lean body mass, body fat percentage, body surface area, height, history of medication or other clinical treatment, history of tobacco use, history of alcohol consumption, daily activity or fitness level, genetic test results, blood test results, imaging results may also be used as inputs into the second machine learning algorithm.

[0078] The second machine learning algorithm may comprise a trained machine learning classifier. The classifier may comprise a supervised machine learning algorithm or an unsupervised machine learning algorithm. The supervised machine learning algorithm may comprise a linear regression, a logistic regression, a deep learning algorithm, a support vector machine (SVM), a neural network, a Random Forest, a boosted regression, a gradient boosting algorithm, an AdaBoost algorithm, or a combination thereof. The classifier may comprise an unsupervised machine learning algorithm, e.g., clustering analysis (e.g., k-means clustering, hierarchical clustering, mixture models, DBSCAN, OPTICS algorithm), principal component analysis, independent component analysis, non-negative matrix factorization, singular value decomposition, anomaly detection (e.g., local outlier factor), neural network (e.g., autoencoder, deep belief network, Hebbian learning, generative adversarial network, self-organizing map), expectation-maximization algorithm, and method of moments.

[0079] The second machine learning classifier may have one or more possible output values, each comprising one of a fixed number of possible values indicating a classification of a metabolic disease or disorder in a subject. The output may comprise a presence, an absence, or a susceptibility (e.g., elevated risk, normal or average risk, or lowered risk) of the metabolic disease or disorder of the subject. The output may also comprise a likelihood of the determined presence or susceptibility of the metabolic disease or disorder of the subject. The output values may comprise descriptive labels, numerical values, or a combination thereof. [0080] The classifier may comprise a binary classifier, such that each of the one or more output values comprises one of two values (e.g., (0, 1}, (positive, negative}, (presence, absence} or (likely, unlikely}) indicating a classification of a metabolic disease or disorder in a subject. For example, the third machine learning algorithm, described herein, may determine that a subject may be likely or unlikely to be susceptible to a metabolic disease or disorder based at least in part on the metabolic rate of the subject. The classifier may be another type of classifier, such that each of the one or more output values comprises one of more than two values (e.g., (0, 1, 2}, (positive, negative, or indeterminate}, or (highly unlikely, unlikely, likely, highly likely}) indicating a classification of a metabolic disease or disorder in a subject.

[0081] The descriptive labels may provide an identification or indication of a level of a metabolic disease or disorder (e.g., a stage of a disease, or an extent of severity) or in some cases a level of susceptibility to the disease or disorder in a subject. For example, based on a metabolic rate of a subject associated with one or more activities and/or data related to the subject comprising demographic and/or clinical attributes a subject may be identified as highly unlikely, unlikely, likely, or highly likely to develop a metabolic disorder using the third machine learning algorithm.

[0082] Some of the output values of the second machine learning algorithm may comprise numerical values, such as binary, integer, or continuous values. Such binary output values may comprise, for example, (0, 1}. Such integer output values may comprise, for example, (0, 1, 2}. Such continuous output values may comprise, for example, a number from a range of numbers. Such continuous output values may comprise, for example, an un- normalized probability value of at least 0. Such continuous output values may comprise, for example, an un-normalized probability value of at most 100. Such continuous output values may comprise, for example, a susceptibility of a subject to develop a metabolic disease or disorder. Some numerical values may be mapped to descriptive labels, for example, by mapping 1 to “susceptible” or “maximum risk”, and 0 to “not susceptible” or “no risk”.

[0083] Some of the output values may be assigned based on one or more cutoff values. For example, a subject may be classified as having a metabolic disease or disorder when a susceptibility of the subject to the disease is at least about 60%. One or more outputs from the second machine learning may be reported to a user. The report may be presented to the user on a graphical user interface of an electronic device (e.g., a smartphone, a smart watch, a personal computer, etc.) The user may be the subject or a health care provider of the subject. [0084] The second machine learning classifier may be trained with a plurality of independent training sets. Each of the independent training sets may comprise a list of presence or susceptibility to metabolic disease or disorders associated with metabolic rates, activities, vital signs, and/or other data comprising demographics and/or clinical attributes from a plurality of subjects. The training datasets may comprise labeled data. The second trained machine learning algorithm may be trained using a plurality of independent training samples associated with the absence or normal risk of the metabolic disease or disorder.

[0085] The second machine learning classifier may be trained with at least about 50, at least about 100, at least about 150, at least about 200, at least about 250, at least about 300, at least about 350, at least about 400, at least about 450, or at least about 500 independent training sets. The classifier may be trained with no more than 500, no more than 450, no more than 400, no more than 350, no more than 300, no more than 250, no more than 200, no more than 150, no more than 100, or no more than 50 independent training sets.

[0086] In the second machine learning classifier algorithm, the accuracy of classifying the degree of completion may be calculated as the percentage of independent data points that are correctly identified or classified. For example, percentage of classification outputs that matched labeled data showing a subject having/susceptible to or not having/not susceptible to a metabolic disease.

[0087] The second machine learning classifier may be configured to have an accuracy (e.g., of detecting a metabolic disease or disorder) of at least about 50%, at least about 55%, at least about 60%, at least about 65%, at least about 70%, at least about 75%, at least about 80%, at least about 81%, at least about 82%, at least about 83%, at least about 84%, at least about 85%, at least about 86%, at least about 87%, at least about 88%, at least about 89%, at least about 90%, at least about 91%, at least about 92%, at least about 93%, at least about 94%, at least about 95%, at least about 96%, at least about 97%, at least about 98%, at least about 99%, or more than about 99%; when performed on at least about 50, at least about 100, at least about 150, at least about 200, at least about 250, at least about 300, or more than about 300 independent datasets. The second trained machine learning algorithm can determine the presence or the susceptibility of the metabolic disease or disorder of the subject at an accuracy of at least about 5%, at least about 10%, at least about 15%, at least about 20%, at least about 25%, at least about 30%, at least about 35%, at least about 40%, at least about 50%, at least about 55%, at least about 60%, at least about 65%, at least about 70%, at least about 75%, at least about 80%, at least about 81%, at least about 82%, at least about 83%, at least about 84%, at least about 85%, at least about 86%, at least about 87%, at least about 88%, at least about 89%, at least about 90%, at least about 91%, at least about 92%, at least about 93%, at least about 94%, at least about 95%, at least about 96%, at least about 97%, at least about 98%, at least about 99%, or more than about 99%.

[0088] In the second machine learning classifier algorithm, the precision or positive predictive value (PPV) of classifying the presence or susceptibility of the metabolic disease or disorder of the subject may be calculated as the percentage of subjects identified as positive (e.g., having or susceptible to a metabolic disease or disorder) that truly have or be susceptible a metabolic disease or disorder.

[0089] The second machine learning classifier may be configured to have a PPV (e.g., of detecting a metabolic disease or disorder) of at least about 5%, at least about 10%, at least about 15%, at least about 20%, at least about 25%, at least about 30%, at least about 35%, at least about 40%, at least about 50%, at least about 55%, at least about 60%, at least about 65%, at least about 70%, at least about 75%, at least about 80%, at least about 81%, at least about 82%, at least about 83%, at least about 84%, at least about 85%, at least about 86%, at least about 87%, at least about 88%, at least about 89%, at least about 90%, at least about 91%, at least about 92%, at least about 93%, at least about 94%, at least about 95%, at least about 96%, at least about 97%, at least about 98%, at least about 99%, or more than about 99%.

[0090] The negative predictive value (NPV) of classifying the presence or susceptibility of the metabolic disease or disorder of the subject may be calculated as the percentage of subject identified as negative (e.g., not having or not susceptible to a metabolic disease or disorder) that truly did not have or were not susceptible to a metabolic disease or disorder. [0091] The second machine learning classifier may be configured to have an NPV (e.g., of detecting a metabolic disease or disorder) of at least about 5%, at least about 10%, at least about 15%, at least about 20%, at least about 25%, at least about 30%, at least about 35%, at least about 40%, at least about 50%, at least about 55%, at least about 60%, at least about 65%, at least about 70%, at least about 75%, at least about 80%, at least about 81%, at least about 82%, at least about 83%, at least about 84%, at least about 85%, at least about 86%, at least about 87%, at least about 88%, at least about 89%, at least about 90%, at least about 91%, at least about 92%, at least about 93%, at least about 94%, at least about 95%, at least about 96%, at least about 97%, at least about 98%, at least about 99%, or more than about 99%.

[0092] The second machine learning classifier may be configured to have a sensitivity (e.g., of detecting a metabolic disease or disorder) of at least about 5%, at least about 10%, at least about 15%, at least about 20%, at least about 25%, at least about 30%, at least about 35%, at least about 40%, at least about 50%, at least about 55%, at least about 60%, at least about 65%, at least about 70%, at least about 75%, at least about 80%, at least about 81%, at least about 82%, at least about 83%, at least about 84%, at least about 85%, at least about 86%, at least about 87%, at least about 88%, at least about 89%, at least about 90%, at least about 91%, at least about 92%, at least about 93%, at least about 94%, at least about 95%, at least about 96%, at least about 97%, at least about 98%, at least about 99%, or more than about 99%.

[0093] The second machine learning classifier may be configured to have a specificity (e.g., of detecting a metabolic disease or disorder) of at least about 5%, at least about 10%, at least about 15%, at least about 20%, at least about 25%, at least about 30%, at least about 35%, at least about 40%, at least about 50%, at least about 55%, at least about 60%, at least about 65%, at least about 70%, at least about 75%, at least about 80%, at least about 81%, at least about 82%, at least about 83%, at least about 84%, at least about 85%, at least about 86%, at least about 87%, at least about 88%, at least about 89%, at least about 90%, at least about 91%, at least about 92%, at least about 93%, at least about 94%, at least about 95%, at least about 96%, at least about 97%, at least about 98%, at least about 99%, or more than about 99%.

[0094] The second machine learning classifier may be configured to have an Area-Under- Curve (AUC) (e.g., of detecting a metabolic disease or disorder) of at least about 0.50, at least about 0.55, at least about 0.60, at least about 0.65, at least about 0.70, at least about 0.75, at least about 0.80, at least about 0.81, at least about 0.82, at least about 0.83, at least about 0.84, at least about 0.85, at least about 0.86, at least about 0.87, at least about 0.88, at least about 0.89, at least about 0.90, at least about 0.91, at least about 0.92, at least about 0.93, at least about 0.94, at least about 0.95, at least about 0.96, at least about 0.97, at least about 0.98, at least about 0.99, or more.

[0095] The AUC may be calculated as an integral of the Receiver Operator Characteristic (ROC) curve (e.g., the area under the ROC curve) associated with the trained algorithm in classifying subjects as having or not having a metabolic disease or disorder.

[0096] The trained classifier may be adjusted or tuned to improve one or more of the performance, accuracy, PPV, NPV, clinical sensitivity, clinical specificity, or AUC of identifying the metabolic disease or disorder. The trained algorithm may be adjusted or tuned by adjusting parameters of the trained algorithm (e.g., a set of cutoff values used to classify a subject as described elsewhere herein, or weights of a neural network). The trained algorithm may be adjusted or tuned continuously during the training process or after the training process has completed.

[0097] After the trained algorithm is initially trained, a subset of the inputs may be identified as most influential or most important to be included for making high-quality classifications. For example, a subset of the plurality of input data may be identified as most influential or most important to be included for making high-quality classifications or identifications of metabolic disease or disorder. The input features may be ranked based on classification metrics indicative of each individual input feature’s influence or importance toward making high-quality classifications or identifications of metabolic disease or disorder. Such metrics may be used to reduce, in some cases significantly, the number of input variables (e.g., predictor variables) that may be used to train the trained algorithm to a desired performance level (e.g., based on a desired minimum accuracy, PPV, NPV, clinical sensitivity, clinical specificity, AUC, or a combination thereof). For example, if training the trained algorithm with a plurality comprising several dozen or hundreds of input variables in the trained algorithm results in an accuracy of classification of more than 99%, then training the trained algorithm instead with only a selected subset of no more than about 5, no more than about 10, no more than about 15, no more than about 20, no more than about 25, no more than about 30, no more than about 35, no more than about 40, no more than about 45, no more than about 50, or no more than about 100 such most influential or most important input variables among the plurality can yield decreased but still acceptable accuracy of classification (e.g., at least about 50%, at least about 55%, at least about 60%, at least about 65%, at least about 70%, at least about 75%, at least about 80%, at least about 81%, at least about 82%, at least about 83%, at least about 84%, at least about 85%, at least about 86%, at least about 87%, at least about 88%, at least about 89%, at least about 90%, at least about 91%, at least about 92%, at least about 93%, at least about 94%, at least about 95%, at least about 96%, at least about 97%, at least about 98%, or at least about 99%). The subset may be selected by rank-ordering the entire plurality of input variables and selecting a predetermined number (e.g., no more than about 5, no more than about 10, no more than about 15, no more than about 20, no more than about 25, no more than about 30, no more than about 35, no more than about 40, no more than about 45, no more than about 50, or no more than about 100) of input variables with the best classification metrics.

[0098] Methods and systems of the present disclosure may comprise a third machine learning algorithm to determine a recommended activity or lifestyle modification for the subject. The recommended activity may be determined based at least in part on the determined metabolic rate of the subject. The recommended activity may be an intervention (e.g., a clinical intervention or lifestyle recommendation) related to the presence or the susceptibility of the metabolic disease or disorder of the subject. An output from the first machine learning algorithm comprising one or more metabolic rates of a subject (e.g., a basal metabolic rate, a standard metabolic rate, a resting metabolic rate, or a glucose metabolic rate) and/or an output from the second machine learning algorithm comprising a presence or a susceptibility of the metabolic disease or disorder of the subject may be used as inputs into the third machine learning algorithm. The data from the datasets relating to the subject comprising one or more demographic or clinical attributes comprise at least one of age, race, ethnicity, gender, weight, body mass index, lean body mass, body fat percentage, body surface area, height, history of medication or other clinical treatment, history of tobacco use, history of alcohol consumption, daily activity or fitness level, genetic test results, blood test results, imaging results may also be used as inputs into the third machine learning algorithm. The outputs of the third machine learning algorithm may comprise an activity, a lifestyle change, a medical intervention, and/or a combination thereof.

[0099] The third machine learning algorithm may comprise a trained machine learning classifier. The classifier may comprise a supervised machine learning algorithm or an unsupervised machine learning algorithm. The supervised machine learning algorithm may comprise a linear regression, a logistic regression, a deep learning algorithm, a support vector machine (SVM), a neural network, a Random Forest, a boosted regression, a gradient boosting algorithm, an AdaBoost algorithm, or a combination thereof. The classifier may comprise an unsupervised machine learning algorithm, e.g., clustering analysis (e.g., k-means clustering, hierarchical clustering, mixture models, DBSCAN, OPTICS algorithm), principal component analysis, independent component analysis, non-negative matrix factorization, singular value decomposition, anomaly detection (e.g., local outlier factor), neural network (e.g., autoencoder, deep belief network, Hebbian learning, generative adversarial network, self-organizing map), expectation-maximization algorithm, and method of moments.

[00100] Upon identifying the subject as having the metabolic disease or disorder, the subject may be optionally provided with a therapeutic intervention (e.g., prescribing an appropriate course of treatment to treat the metabolic disease or disorder of the subject). The therapeutic intervention may comprise a prescription of an effective dose of a drug, a further testing or evaluation of the metabolic disease or disorder, a further monitoring of the metabolic disease or disorder, or a combination thereof. If the subject is currently being treated for the metabolic disease or disorder with a course of treatment, the therapeutic intervention may comprise a subsequent different course of treatment (e.g., to increase treatment efficacy due to non-efficacy of the current course of treatment).

[00101] The therapeutic intervention may comprise recommending the subject for a secondary clinical test to confirm a diagnosis of the metabolic disease or disorder. This secondary clinical test may comprise an imaging test, a blood test, a genetic test, a metabolic test, a computed tomography (CT) scan, a magnetic resonance imaging (MRI) scan, an ultrasound scan, a chest X-ray, a positron emission tomography (PET) scan, a PET-CT scan, or any combination thereof.

[00102] A metabolic rate of a subject may be monitored over a period of time and/or under various conditions. For example, a dynamic metabolic rate of a subject may be determined before and/ or after using a drug (e.g., blood pressure medicine, metabolic drug, etc) and/or while performing various activities (e.g., sleeping, walking, running, swimming, eating, etc.) The metabolic rate of the subject may be monitored for 1 day, 2 days, 3 days, 5 days, 1 week, 2 weeks, 3 weeks, 1 month, 2 months, 3 months, 4 months, 5 months, 6 months, one year, 2 years, 5 years, or a longer period of time. The metabolic rate of the subject may be monitored consecutively or periodically. For example, the monitoring may be performed once every 1 day, 2 days, 3 days, 5 days, 1 week, 2 weeks, 3 weeks, 1 month, 2 months, 3 months, 4 months, 5 months, 6 months, one year, 2 years, 5 years, or more. At one or more timepoints, when the metabolic rate of the subject is determined, the methods described herein may also determine a presence or susceptibility of the metabolic disease or disorder of the subject. The methods described herein my further determine a change or a difference in the determined presence or susceptibility of the metabolic disease or disorder of the subject among two or more metabolic rate monitoring time points. The difference or change in the presence or susceptibility of the metabolic disease or disorder of the subject among different timepoints may indicate one or more clinical indications. The clinical indications may comprise (i) a diagnosis of the metabolic disease or disorder of the subject, (ii) a prognosis of the metabolic disease or disorder of the subject, and (iii) an efficacy or non-efficacy of a therapeutic intervention for treating the metabolic disease or disorder of the subject.

[00103] The input data relating to the subject may be analyzed and assessed over a duration of time to monitor a patient (e.g., subject who has metabolic disease or disorder or who is being treated for metabolic disease or disorder). In such cases, the quantitative measures of the dataset of the patient may change during the course of treatment. For example, the quantitative measures of the dataset of a patient with decreasing risk of the metabolic disease or disorder due to an effective treatment may shift toward the profile or distribution of a healthy subject (e.g., a subject without a metabolic disease or disorder). Conversely, for example, the quantitative measures of the dataset of a patient with increasing risk of the metabolic disease or disorder due to an ineffective treatment may shift toward the profile or distribution of a subject with higher risk of the metabolic disease or disorder or a more advanced metabolic disease or disorder.

[00104] The metabolic disease or disorder of the subject may be monitored by monitoring a course of treatment for treating the metabolic disease or disorder of the subject. The monitoring may comprise assessing the metabolic disease or disorder of the subject at two or more time points. The assessing may be based at least on the quantitative measures of sequence reads of the dataset at a panel of metabolic disease or disorder-associated genomic loci (e.g., quantitative measures of RNA transcripts or DNA at the metabolic disease or disorder-associated genomic loci), proteomic data comprising quantitative measures of proteins of the dataset at a panel of metabolic disease or disorder-associated proteins, and/or metabolome data comprising quantitative measures of a panel of metabolic disease or disorder-associated metabolites determined at each of the two or more time points.

[00105] In some embodiments, a difference in the metabolic rate (e.g., dynamic metabolic rate) determined between the two or more time points may be indicative of one or more clinical indications, such as (i) a diagnosis of the metabolic disease or disorder of the subject, (ii) a prognosis of the metabolic disease or disorder of the subject, (iii) an increased risk of the metabolic disease or disorder of the subject, (iv) a decreased risk of the metabolic disease or disorder of the subject, (v) an efficacy of the course of treatment for treating the metabolic disease or disorder of the subject, and (vi) a non-efficacy of the course of treatment for treating the metabolic disease or disorder of the subject.

[00106] In some embodiments, a difference in the metabolic rate (e.g., dynamic metabolic rate) determined between the two or more time points may be indicative of a diagnosis of the metabolic disease or disorder of the subject. For example, if the metabolic disease or disorder was not detected in the subject at an earlier time point but was detected in the subject at a later time point, then the difference is indicative of a diagnosis of the metabolic disease or disorder of the subject. A clinical action or decision may be made based on this indication of diagnosis of the metabolic disease or disorder of the subject, such as, for example, prescribing a new therapeutic intervention for the subject. The clinical action or decision may comprise recommending the subject for a secondary clinical test to confirm the diagnosis of the metabolic disease or disorder. This secondary clinical test may comprise an imaging test, a blood test, a genetic test, a metabolic test, a computed tomography (CT) scan, a magnetic resonance imaging (MRI) scan, an ultrasound scan, a chest X-ray, a positron emission tomography (PET) scan, a PET-CT scan, or any combination thereof.

[00107] In some embodiments, a difference in the metabolic rate (e.g., dynamic metabolic rate)determined between the two or more time points may be indicative of a prognosis of the metabolic disease or disorder of the subject.

[00108] In some embodiments, a difference in the metabolic rate (e.g., dynamic metabolic rate) determined between the two or more time points may be indicative of the subject having an increased risk of the metabolic disease or disorder. For example, if the metabolic disease or disorder was detected in the subject both at an earlier time point and at a later time point, and if the difference is a positive difference (e.g., the metabolic rate increased from the earlier time point to the later time point), then the difference may be indicative of the subject having an increased risk of the metabolic disease or disorder. A clinical action or decision may be made based on this indication of the increased risk of the metabolic disease or disorder, e.g., prescribing a new therapeutic intervention or switching therapeutic interventions (e.g., ending a current treatment and prescribing a new treatment) for the subject. The clinical action or decision may comprise recommending the subject for a secondary clinical test to confirm the increased risk of the metabolic disease or disorder. This secondary clinical test may comprise an imaging test, a blood test, a genetic test, a metabolic test, a computed tomography (CT) scan, a magnetic resonance imaging (MRI) scan, an ultrasound scan, a chest X-ray, a positron emission tomography (PET) scan, a PET-CT scan, or any combination thereof. [00109] In some embodiments, a difference in the metabolic rate (e.g., dynamic metabolic rate) determined between the two or more time points may be indicative of the subject having a decreased risk of the metabolic disease or disorder. For example, if the metabolic disease or disorder was detected in the subject both at an earlier time point and at a later time point, and if the difference is a negative difference (e.g., the metabolic rate decreased from the earlier time point to the later time point), then the difference may be indicative of the subject having a decreased risk of the metabolic disease or disorder. A clinical action or decision may be made based on this indication of the decreased risk of the metabolic disease or disorder (e.g., continuing or ending a current therapeutic intervention) for the subject. The clinical action or decision may comprise recommending the subject for a secondary clinical test to confirm the decreased risk of the metabolic disease or disorder. This secondary clinical test may comprise an imaging test, a blood test, a genetic test, a metabolic test, a computed tomography (CT) scan, a magnetic resonance imaging (MRI) scan, an ultrasound scan, a chest X-ray, a positron emission tomography (PET) scan, a PET-CT scan, or any combination thereof.

[00110] In some embodiments, a difference in the metabolic rate (e.g., dynamic metabolic rate) determined between the two or more time points may be indicative of an efficacy of the course of treatment for treating the metabolic disease or disorder of the subject. For example, if the metabolic disease or disorder was detected in the subject at an earlier time point but was not detected in the subject at a later time point, then the difference may be indicative of an efficacy of the course of treatment for treating the metabolic disease or disorder of the subject. A clinical action or decision may be made based on this indication of the efficacy of the course of treatment for treating the metabolic disease or disorder of the subject, e.g., continuing or ending a current therapeutic intervention for the subject. The clinical action or decision may comprise recommending the subject for a secondary clinical test to confirm the efficacy of the course of treatment for treating the metabolic disease or disorder. This secondary clinical test may comprise an imaging test, a blood test, a genetic test, a metabolic test, a computed tomography (CT) scan, a magnetic resonance imaging (MRI) scan, an ultrasound scan, a chest X-ray, a positron emission tomography (PET) scan, a PET-CT scan, or any combination thereof.

[00111] In some embodiments, a difference in the metabolic rate (e.g., dynamic metabolic rate) determined between the two or more time points may be indicative of a non-efficacy of the course of treatment for treating the metabolic disease or disorder of the subject. For example, if the metabolic disease or disorder was detected in the subject both at an earlier time point and at a later time point, and if the difference is a positive or zero difference (e.g., the metabolic rate increased or remained at a constant level from the earlier time point to the later time point), and if an efficacious treatment was indicated at an earlier time point, then the difference may be indicative of a non-efficacy of the course of treatment for treating the metabolic disease or disorder of the subject. A clinical action or decision may be made based on this indication of the non-efficacy of the course of treatment for treating the metabolic disease or disorder of the subject, e.g., ending a current therapeutic intervention and/or switching to (e.g., prescribing) a different new therapeutic intervention for the subject. The clinical action or decision may comprise recommending the subject for a secondary clinical test to confirm the non-efficacy of the course of treatment for treating the metabolic disease or disorder. This secondary clinical test may comprise an imaging test, a blood test, a genetic test, a metabolic test, a computed tomography (CT) scan, a magnetic resonance imaging (MRI) scan, an ultrasound scan, a chest X-ray, a positron emission tomography (PET) scan, a PET-CT scan, or any combination thereof.

[00112] In some cases, a report may be generated for a user based at least in part on a metabolic rate of a subject, a presence or susceptibility of the metabolic disease or disorder of the subject, a change in the presence or susceptibility of the metabolic disease or disorder of the subject when monitored over time, and/or a combination thereof. The report may comprise recommending to a user to perform or to have performed one or more clinical tests for the subject. The one or more clinical tests may help identify or categorize the metabolic disease or disorder, a cause of the metabolic disease or disorder, a treatment regimen for the disease or disorder, or a confirmation or refutation of the diagnosis of the metabolic disease or disorder. The one or more clinical tests may be used to identify a health condition of a subject and not a disorder or a disease to be used, for example, in optimized or personalized athletic training. The one or more clinical tests may comprise a genetic test, a blood test, a urine test, a stool test, a metabolite test, a hormone test, or a combination thereof.

[00113] In some embodiments, the first, second, and/or third machine learning algorithms may comprise artificial neural networks. The methods described herein may further comprise performing batch normalization of the data to improve the speed, performance, and stability of the artificial neural networks. In the methods described herein, the step of computer processing the data using the trained machine learning algorithm may further comprise using an activation function. The activation function may be a rectified linear unit (ReLU) function, a hyperbolic function, a sigmoid function, or a combination thereof.

[00114] Computer systems [00115] The present disclosure provides computer systems that are programmed to implement methods of the disclosure. FIG. 4 shows a computer system 401 that is programmed or otherwise configured to perform one or more functions or operations of the present disclosure. The computer system 401 can regulate various aspects of the present disclosure, such as, for example, obtaining data comprising one or more vital signs and one or more activities of the subject; computer processing data using a trained machine learning algorithm to determine a metabolic rate; electronically outputting a report indicative of a metabolic rate of the subject; determining that a subject has or is at risk of having a metabolic disease or disorder; determining one or more activities of a subject based on acquired data of the subject; and monitoring a metabolic disease or disorder of a subject. The computer system 401 can be an electronic device of a user or a computer system that is remotely located with respect to the electronic device. The electronic device can be a mobile electronic device.

[00116] The computer system 401 includes a central processing unit (CPU, also “processor” and “computer processor” herein) 405, which can be a single core or multi core processor, or a plurality of processors for parallel processing. The computer system 401 also includes memory or memory location 410 (e.g., random-access memory, read-only memory, flash memory), electronic storage unit 415 (e.g., hard disk), communication interface 420 (e.g., network adapter) for communicating with one or more other systems, and peripheral devices 425, such as cache, other memory, data storage and/or electronic display adapters.

The memory 410, storage unit 415, interface 420 and peripheral devices 425 are in communication with the CPU 405 through a communication bus (solid lines), such as a motherboard. The storage unit 415 can be a data storage unit (or data repository) for storing data. The computer system 401 can be operatively coupled to a computer network (“network”) 430 with the aid of the communication interface 420. The network 430 can be the Internet, an internet and/or extranet, or an intranet and/or extranet that is in communication with the Internet. The network 430 in some cases is a telecommunication and/or data network. The network 430 can include one or more computer servers, which can enable distributed computing, such as cloud computing. The network 430, in some cases with the aid of the computer system 401, can implement a peer-to-peer network, which may enable devices coupled to the computer system 401 to behave as a client or a server.

[00117] The CPU 405 can execute a sequence of machine-readable instructions, which can be embodied in a program or software. The instructions may be stored in a memory location, such as the memory 410. The instructions can be directed to the CPU 405, which can subsequently program or otherwise configure the CPU 405 to implement methods of the present disclosure. Examples of operations performed by the CPU 405 can include fetch, decode, execute, and writeback.

[00118] The CPU 405 can be part of a circuit, such as an integrated circuit. One or more other components of the system 401 can be included in the circuit. In some cases, the circuit is an application specific integrated circuit (ASIC).

[00119] The storage unit 415 can store files, such as drivers, libraries and saved programs. The storage unit 415 can store user data, e.g., user preferences and user programs. The computer system 401 in some cases can include one or more additional data storage units that are external to the computer system 401, such as located on a remote server that is in communication with the computer system 401 through an intranet or the Internet.

[00120] The computer system 401 can communicate with one or more remote computer systems through the network 430. For instance, the computer system 401 can communicate with a remote computer system of a user (e.g., mobile device of a subject or a healthcare provider). Examples of remote computer systems include personal computers (e.g., portable PC), slate or tablet PC’s (e.g., Apple® iPad, Samsung® Galaxy Tab), telephones, Smart phones (e.g., Apple® iPhone, Android-enabled device, Blackberry®), or personal digital assistants. The user can access the computer system 401 via the network 430.

[00121] Methods as described herein can be implemented by way of machine (e.g., computer processor) executable code stored on an electronic storage location of the computer system 401, such as, for example, on the memory 410 or electronic storage unit 415. The machine executable or machine-readable code can be provided in the form of software. During use, the code can be executed by the processor 405. In some cases, the code can be retrieved from the storage unit 415 and stored on the memory 410 for ready access by the processor 405. In some situations, the electronic storage unit 415 can be precluded, and machine-executable instructions are stored on memory 410.

[00122] The code can be pre-compiled and configured for use with a machine having a processer adapted to execute the code or can be compiled during runtime. The code can be supplied in a programming language that can be selected to enable the code to execute in a pre-compiled or as-compiled fashion.

[00123] Aspects of the systems and methods provided herein, such as the computer system 401, can be embodied in programming. Various aspects of the technology may be thought of as “products” or “articles of manufacture” typically in the form of machine (or processor) executable code and/or associated data that is carried on or embodied in a type of machine- readable medium. Machine-executable code can be stored on an electronic storage unit, such as memory (e.g., read-only memory, random-access memory, flash memory) or a hard disk. “Storage” type media can include any or all of the tangible memory of the computers, processors or the like, or associated modules thereof, such as various semiconductor memories, tape drives, disk drives and the like, which may provide non-transitory storage at any time for the software programming. All or portions of the software may at times be communicated through the Internet or various other telecommunication networks. Such communications, for example, may enable loading of the software from one computer or processor into another, for example, from a management server or host computer into the computer platform of an application server. Thus, another type of media that may bear the software elements includes optical, electrical and electromagnetic waves, such as used across physical interfaces between local devices, through wired and optical landline networks and over various air-links. The physical elements that carry such waves, such as wired or wireless links, optical links or the like, also may be considered as media bearing the software. As used herein, unless restricted to non-transitory, tangible “storage” media, terms such as computer or machine “readable medium” refer to any medium that participates in providing instructions to a processor for execution.

[00124] Hence, a machine-readable medium, such as computer-executable code, may take many forms, including but not limited to, a tangible storage medium, a carrier wave medium or physical transmission medium. Non-volatile storage media include, for example, optical or magnetic disks, such as any of the storage devices in any computer(s) or the like, such as may be used to implement the databases, etc. shown in the drawings. Volatile storage media include dynamic memory, such as main memory of such a computer platform. Tangible transmission media include coaxial cables; copper wire and fiber optics, including the wires that comprise a bus within a computer system. Carrier-wave transmission media may take the form of electric or electromagnetic signals, or acoustic or light waves such as those generated during radio frequency (RF) and infrared (IR) data communications. Common forms of computer-readable media therefore include for example: a floppy disk, a flexible disk, hard disk, magnetic tape, any other magnetic medium, a CD-ROM, DVD or DVD- ROM, any other optical medium, punch cards paper tape, any other physical storage medium with patterns of holes, a RAM, a ROM, a PROM and EPROM, a FLASH-EPROM, any other memory chip or cartridge, a carrier wave transporting data or instructions, cables or links transporting such a carrier wave, or any other medium from which a computer may read programming code and/or data. Many of these forms of computer-readable media may be involved in carrying one or more sequences of one or more instructions to a processor for execution.

[00125] The computer system 401 can include or be in communication with an electronic display 435 that comprises a user interface (UI) 440 for providing, for example, a feedback portal for a subject and/or a user. Examples of UTs include, without limitation, a graphical user interface (GUI) and web-based user interface.

[00126] Methods and systems of the present disclosure can be implemented by way of one or more algorithms. An algorithm can be implemented by way of software upon execution by the central processing unit 405. The algorithm can, for example, obtain data comprising one or more vital signs and one or more activities of the subject; process data using a trained machine learning algorithm to determine a metabolic rate; electronically output a report indicative of a metabolic rate of the subject; determine that a subject has or is at risk of having a metabolic disease or disorder; determine one or more activities of a subject based on acquired data of the subject; and monitor a metabolic disease or disorder of a subject.

EXAMPLES

[00127] Example 1: Real-time determination of basal metabolic rate [00128] Using methods and systems of the present disclosure, the basal metabolic rate of a subject is determined in real-time. The input data set is obtained by acquiring a set of vital sign measurements and a posture of the subject at one-minute intervals, as shown in FIG. 5A. [00129] The set of vital sign measurements includes systolic blood pressure, diastolic blood pressure, average pressure of the pulmonary artery, heart rate, and respiratory rate of the subject. The posture of the subject includes, for example, sitting or standing. The input data set is processed by a linear regression in order to produce an output data set.

[00130] The output data set includes a determined metabolic rate of the subject at one- minute intervals, as shown in FIG. 5B. For example, the subject is determined to have a basal metabolic rate of 1.7 calories per minute (cal/minute) at 1:00 am (while having a sitting posture), a basal metabolic rate of 1.8 calories per minute (cal/minute) at 1:01 am (while having a sitting posture), a basal metabolic rate of 1.5 calories per minute (cal/minute) at 1 :02 am (while having a sitting posture),, a basal metabolic rate of 2.2 calories per minute (cal/minute) at 1:03 am (while having a standing posture), etc. The real-time basal metabolic rate of the subject may be stored or displayed to a user (e.g., the subject or a health care provider or caretaker of the subject).

[00131] While preferred embodiments of the present invention have been shown and described herein, it will be obvious to those skilled in the art that such embodiments are provided by way of example only. It is not intended that the invention be limited by the specific examples provided within the specification. While the invention has been described with reference to the aforementioned specification, the descriptions and illustrations of the embodiments herein are not meant to be construed in a limiting sense. Numerous variations, changes, and substitutions will now occur to those skilled in the art without departing from the invention. Furthermore, it shall be understood that all aspects of the invention are not limited to the specific depictions, configurations or relative proportions set forth herein which depend upon a variety of conditions and variables. It should be understood that various alternatives to the embodiments of the invention described herein may be employed in practicing the invention. It is therefore contemplated that the invention shall also cover any such alternatives, modifications, variations or equivalents. It is intended that the following claims define the scope of the invention and that methods and structures within the scope of these claims and their equivalents be covered thereby.

Claims

CLAIMS WHAT IS CLAIMED IS:

1. A computer-implemented method for determining a metabolic rate of a subject, comprising:

(a) obtaining a data set comprising vital sign data and activity data, wherein the vital sign data corresponds to one or more vital signs of the subject, and wherein the activity data corresponds to one or more activities of the subject;

(b) computer processing the data set using a trained machine learning algorithm to determine the metabolic rate; and

(c) electronically outputting a report indicative of the metabolic rate of the subject determined in (b).

2. The method of claim 1, wherein the data set further comprises one or more demographic or clinical attributes of the subject.

3. The method of claim 2, wherein the one or more demographic or clinical attributes comprise at least one of age, race, ethnicity, gender, weight, body mass index, lean body mass, body fat percentage, body surface area, height, history of medication or other clinical treatment, history of tobacco use, history of alcohol consumption, daily activity or fitness level, genetic test results, blood test results, imaging results, and a combination thereof.

4. The method of claim 1, wherein the one or more vital signs comprise at least one of heart rate, respiratory rate, systolic blood pressure, diastolic blood pressure, blood oxygen concentration (SpCb), carbon dioxide concentration in respiratory gases, hormone level, sweat analysis, blood glucose, body temperature, bioimpedance, conductivity, capacitance, resistivity, galvanic skin response, electromyography, electroencephalography, electrocardiography, magnetoencephalography, magnetocardiography, immunology markers, and a combination thereof.

5. The method of claim 1, wherein the one or more vital signs comprise at least one vital sign measured using a portable vital sign monitor.

6. The method of claim 5, wherein the portable vital sign monitor is selected from the group consisting of a heart rate monitor, a respiratory rate monitor, a blood pressure monitor, a blood oxygen concentration monitor, a blood glucose monitor, a body temperature monitor, a bioimpedance monitor, an electromyography monitor, an electroencephalography monitor, an electrocardiography monitor, a magnetoencephalography monitor, a magnetocardiography monitor, a smart phone, a smart watch, an activity or exercise monitor, a contactless wearable health monitoring device, and any combination thereof.

7. The method of claim 1, wherein the one or more activities comprise at least one of walking Jogging, running, bicycle riding, performing push-ups, performing sit-ups, performing pull-ups, exercise, performing aerobic exercise, performing anaerobic exercise, playing a sport, lifting weights, swimming, sitting, standing, talking, eating, lying down, sleeping, and a combination thereof.

8. The method of claim 1, wherein the data is obtained without (i) performing a gas analysis or (ii) using a metabolic chamber.

9. The method of claim 1, wherein the metabolic rate comprises at least one of a basal metabolic rate, a standard metabolic rate, a resting metabolic rate, a glucose metabolic rate, and a combination thereof.

10. The method of claim 1, further comprising determining a recommended activity or lifestyle modification for the subject based at least in part on the determined metabolic rate.

11. The method of claim 1, further comprising determining that the subject has or is at risk of having a metabolic disease or disorder based at least in part on the determined metabolic rate of the subject.

12. The method of claim 11, further comprising determining that the subject has or is at risk of having the metabolic disease or disorder at an accuracy of at least about 80%.

13. The method of claim 11, further comprising determining that the subject has or is at risk of having the metabolic disease or disorder at a sensitivity of at least about 80%.

14. The method of claim 11, further comprising determining that the subject has or is at risk of having the metabolic disease or disorder at a specificity of at least about 80%.

15. The method of claim 11, further comprising determining that the subject has or is at risk of having the metabolic disease or disorder at a positive predictive value of at least about 80%.

16. The method of claim 11, further comprising determining that the subject has or is at risk of having the metabolic disease or disorder at a negative predictive value of at least about 80%.

17. The method of claim 11, further comprising determining that the subject has or is at risk of having the metabolic disease or disorder with an Area Under Curve (AUC) of at least about 0.80.

18. The method of any one of claims 11-17, wherein the metabolic disease or disorder is selected from the group consisting of 17-alpha-hydroxylase deficiency, 17-beta hydroxysteroid dehydrogenase 3 deficiency, 18 Hydroxylase deficiency, 2-Hydroxyglutaric aciduria, 2-methylbutyryl-CoA dehydrogenase deficiency, 3-alpha hydroxyacyl-CoA dehydrogenase deficiency, 3-Hydroxyisobutyric aciduria, 3-methylcrotonyl-CoA carboxylase deficiency, 3-methylglutaconyl-CoA hydratase deficiency (AUH defect), 5-oxoprolinase deficiency, 6-pyruvoyl-tetrahydropterin synthase deficiency, Abdominal obesity metabolic syndrome, Abetalipoproteinemia, Acatalasemia, Aceruloplasminemia, Acetyl CoA acetyltransferase 2 deficiency, Acetyl-carnitine deficiency, Acrodermatitis enteropathica, Acromegaly, Acute intermittent porphyria, Adenine phosphoribosyltransferase deficiency, Adenosine deaminase deficiency, Adenosine monophosphate deaminase 1 deficiency, Adenylosuccinase deficiency, Adrenomyeloneuropathy, Adult polyglucosan body disease, Albinism deafness syndrome, Albinism ocular late onset sensorineural deafness, ALG1-CDG (CDG-Ik), ALGll-CDG (CDG-Ip), ALG12-CDG (CDG-Ig), ALG13-CDG, ALG2-CDG (CDG-Ii), ALG3-CDG (CDG-Id), ALG6-CDG (CDG-Ic), ALG8-CDG (CDG-Ih), ALG9- CDG (CDG-IL), Alkaptonuria, Alpers syndrome, Alpha- 1 antitrypsin deficiency, Alpha- ketoglutarate dehydrogenase deficiency, Alpha-mannosidosis, Aminoacylase 1 deficiency, Anemia due to Adenosine triphosphatase deficiency, Anemia sideroblastic and spinocerebellar ataxia, Apparent mineralocorticoid excess, Arginase deficiency, Argininosuccinic aciduria, Aromatic L-amino acid decarboxylase deficiency, Arthrogryposis renal dysfunction cholestasis syndrome, Arts syndrome, Aspartylglycosaminuria, Ataxia with oculomotor apraxia type 1, Ataxia with vitamin E deficiency, Atransferrinemia, Atypical Gaucher disease due to saposin C deficiency, Autoimmune polyglandular syndrome type 2, Autosomal dominant neuronal ceroid lipofuscinosis 4B, Autosomal dominant optic atrophy and cataract, Autosomal dominant optic atrophy plus syndrome, Autosomal erythropoietic protoporphyria, Autosomal recessive neuronal ceroid lipofuscinosis 4A, Autosomal recessive spastic ataxia 4, Autosomal recessive spinocerebellar ataxia 9, B4GALT1-CDG (CDG-IId), Bantu siderosis, Barth syndrome, Bartter syndrome, Bartter syndrome antenatal type 1, Bartter syndrome antenatal type 2, Bartter syndrome type 3, Bartter syndrome type 4, Beta ketothiolase deficiency, Biotin-thiamine-responsive basal ganglia disease, Biotinidase deficiency, Bjomstad syndrome, Blue diaper syndrome, Carbamoyl phosphate synthetase 1 deficiency, Carnitine palmitoyl transferase 1 A deficiency, Carnitine-acylcamitine translocase deficiency, Carnosinemia, Central diabetes insipidus, Cerebral folate deficiency, Cerebrotendinous xanthomatosis, Ceroid lipofuscinosis neuronal 1, Chanarin-Dorfman syndrome, Chediak-Higashi syndrome, CHILD syndrome, Childhood hypophosphatasia, Cholesteryl ester storage disease, Chondrocalcinosis 1, Chondrocalcinosis 2, Chondrocalcinosis due to apatite crystal deposition, Chondrodysplasia punctata 1, X-linked recessive, Chronic progressive external ophthalmoplegia, Chylomicron retention disease, Citrulline transport defect, Citrullinemia type II, COG1-CDG (CDG-IIg), COG4-CDG (CDG-IIj), COG5-CDG (CDG-IIi), COG7-CDG (CDG-IIe), COG8-CDG (CDG-IIh), Combined oxidative phosphorylation deficiency 16, Congenital bile acid synthesis defect, type 1, Congenital bile acid synthesis defect, type 2, Congenital disorder of glycosylation type I/IIX, Congenital dyserythropoietic anemia type 2, Congenital erythropoietic porphyria, Congenital lactase deficiency, Congenital muscular dystrophy-dystroglycanopathy with or without intellectual disability (type B), Copper deficiency, familial benign, CoQ-responsive OXPHOS deficiency, Crigler Najjar syndrome, type 1, Crigler-Najjar syndrome type 2, Cystinosis, Cytochrome c oxidase deficiency, D-2-hydroxyglutaric aciduria, D-bifunctional protein deficiency, D-glycericacidemia, Danon disease, DCMA syndrome, DDOST-CDG (CDG-Ir), Deafness, dystonia, and cerebral hypomyelination, Dentatorubral-pallidoluysian atrophy, Desmosterolosis, Diamond-Blackfan anemia, Dicarboxylic aminoaciduria, Dihydrolipoamide dehydrogenase deficiency, Dihydropteridine reductase deficiency, Dihydropyrimidinase deficiency, Dihydropyrimidine dehydrogenase deficiency - Not a rare disease, Dipsogenic diabetes insipidus, DOLK-CDG (CDG-Im), Dopa-responsive dystonia, Dopamine beta hydroxylase deficiency, Dowling-Degos disease, DPAGT1-CDG (CDG-Ij), DPM1-CDG (CDG-Ie), DPM2-CDG, DPM3-CDG (CDG-Io), Dubin- Johnson syndrome, Encephalopathy due to prosaposin deficiency, Erythropoietic uroporphyria associated with myeloid malignancy, Ethylmalonic encephalopathy, Fabry disease, Familial chylomicronemia syndrome, Familial HDL deficiency, Familial hypocalciuric hypercalcemia type 1, Familial hypocalciuric hypercalcemia type 2, Familial hypocalciuric hypercalcemia type 3, Familial LCAT deficiency, Familial partial lipodystrophy type 2, Fanconi Bickel syndrome, Farber disease, Fatal infantile encephalomyopathy, Fatty acid hydroxylase- associated neurodegeneration, Fish-eye disease, Fructose- 1,6-bisphosphatase deficiency, Fucosidosis, Fukuyama type muscular dystrophy, Fumarase deficiency, Galactokinase deficiency, Galactosialidosis, Gamma aminobutyric acid transaminase deficiency, Gamma- cystathionase deficiency, Gaucher disease, Gaucher disease - ophthalmoplegia - cardiovascular calcification, Gaucher disease perinatal lethal, Gaucher disease type 1, Gaucher disease type 2, Gaucher disease type 3, Gestational diabetes insipidus, Gilbert syndrome - Not a rare disease, Gitelman syndrome, Glucose transporter type 1 deficiency syndrome, Glucose-galactose malabsorption, Glutamate formiminotransferase deficiency, Glutamine deficiency, congenital, Glutaric acidemia type I, Glutaric acidemia type II, Glutaric acidemia type III, Glutathione synthetase deficiency, Glutathionuria, Glycine N- methyltransferase deficiency, Glycogen storage disease 8, Glycogen storage disease type 0, liver, Glycogen storage disease type 12, Glycogen storage disease type 13, Glycogen storage disease type 1 A, Glycogen storage disease type IB, Glycogen storage disease type 3, Glycogen storage disease type 5, Glycogen storage disease type 6, Glycogen storage disease type 7, Glycoproteinosis, GM1 gangliosidosis type 1, GM1 gangliosidosis type 2, GM1 gangliosidosis type 3, GM3 synthase deficiency, GRACILE syndrome, Greenberg dysplasia, GTP cyclohydrolase I deficiency, Guanidinoacetate methyltransferase deficiency, Gyrate atrophy of choroid and retina, Haim-Munk syndrome, Hartnup disease, Hawkinsinuria, Hemochromatosis type 2, Hemochromatosis type 3, Hemochromatosis type 4, Hepatic lipase deficiency, Hepatoerythropoietic porphyria, Hereditary amyloidosis, Hereditary coproporphyria, Hereditary folate malabsorption, Hereditary fructose intolerance, Hereditary hyperekplexia, Hereditary multiple osteochondromas, Hereditary sensory and autonomic neuropathy type IE, Hereditary sensory neuropathy type 1, Hermansky Pudlak syndrome 2, Histidinemia, HMG CoA lyase deficiency, Homocarnosinosis, Homocysteinemia, Homocystinuria due to CBS deficiency, Homocystinuria due to MTHFR deficiency, HSD10 disease, Hurler syndrome, Hurler-Scheie syndrome, Hydroxykynureninuria, Hyper-IgD syndrome, Hyperbetaalaninemia, Hypercoagulability syndrome due to glycosylphosphatidylinositol deficiency, Hyperglycerolemia, Hyperinsulinism due to glucokinase deficiency, Hyperinsulinism-hyperammonemia syndrome, Hyperlipidemia type 3, Hyperlipoproteinemia type 5, Hyperlysinemia, Hyperphenylalaninemia due to dehydratase deficiency, Hyperprolinemia, Hyperprolinemia type 2, Hypertryptophanemia, Hypolipoproteinemia, Hypophosphatasia, I cell disease, Imerslund-Grasbeck syndrome, Iminoglycinuria, Inclusion body myopathy 2, Inclusion body myopathy 3, Infantile free sialic acid storage disease, Infantile neuroaxonal dystrophy, Infantile onset spinocerebellar ataxia, Insulin-like growth factor I deficiency, Intrinsic factor deficiency, Isobutyryl-CoA dehydrogenase deficiency, Isovaleric acidemia, Kanzaki disease, Kearns-Sayre syndrome, Krabbe disease atypical due to Saposin A deficiency, L-2-hydroxyglutaric aciduria, L- arginine: glycine amidinotransferase deficiency, Lactate dehydrogenase A deficiency, Lactate dehydrogenase deficiency, Lathosterolosis, LCHAD deficiency, Leber hereditary optic neuropathy, Leigh syndrome, French Canadian type, Lesch Nyhan syndrome, Leucine- sensitive hypoglycemia of infancy, Leukoencephalopathy - dystonia - motor neuropathy, Leukoencephalopathy with brain stem and spinal cord involvement and lactate elevation, Limb-girdle muscular dystrophy type 21, Limb-girdle muscular dystrophy type 2K, Limb- girdle muscular dystrophy type 2M, Limb-girdle muscular dystrophy type 2N, Limb-girdle muscular dystrophy type 20, Limb-girdle muscular dystrophy type 2T, Limb-girdle muscular dystrophy, type 2C, Lipase deficiency combined, Lipoic acid synthetase deficiency, Lipoid proteinosis of Urbach and Wiethe, Lowe oculocerebrorenal syndrome, Lysinuric protein intolerance, Malonyl-CoA decarboxylase deficiency, MAN1B1-CDG, Mannose-binding lectin protein deficiency - Not a rare disease, Mannosidosis, beta A, lysosomal, Maternal hyperphenylalaninemia, Maternally inherited diabetes and deafness, Medium-chain acyl- coenzyme A dehydrogenase deficiency, Megaloblastic anemia due to dihydrofolate reductase deficiency, Menkes disease, Metachromatic leukodystrophy, Metachromatic leukodystrophy due to saposin B deficiency, Methionine adenosyltransferase deficiency, Methylcobalamin deficiency cbl G type, Methylmalonic acidemia with homocystinuria type cblC, Methylmalonic acidemia with homocystinuria type cblD, Methylmalonic acidemia with homocystinuria type cblF, Methylmalonic acidemia with homocystinuria type cblJ, Methylmalonic aciduria, cblA type, Methylmalonic aciduria, cblB type, Mevalonic aciduria, MGAT2-CDG (CDG-IIa), Mild phenylketonuria, Mitochondrial complex I deficiency, Mitochondrial complex II deficiency, Mitochondrial complex III deficiency, Mitochondrial DNA depletion syndrome, encephalomyopathic form with methylmalonic aciduria, Mitochondrial DNA-associated Leigh syndrome, Mitochondrial encephalomyopathy lactic acidosis and stroke-like episodes, Mitochondrial myopathy and sideroblastic anemia, Mitochondrial myopathy with diabetes, Mitochondrial myopathy with lactic acidosis, Mitochondrial neurogastrointestinal encephalopathy syndrome, Mitochondrial trifunctional protein deficiency, MOGS-CDG (CDG-IIb), Mohr-Tranebjaerg syndrome, Molybdenum cofactor deficiency, Monogenic diabetes - Not a rare disease, Morquio syndrome B, MPDU1- CDG (CDG-If), MPI-CDG (CDG-Ib), MPV17-related hepatocerebral mitochondrial DNA depletion syndrome, Mucolipidosis III alpha/beta, Mucolipidosis type 4, Mucopolysaccharidosis type II, Mucopolysaccharidosis type III, Mucopolysaccharidosis type IIIA, Mucopolysaccharidosis type IIIB, Mucopolysaccharidosis type IIIC, Mucopolysaccharidosis type HID, Mucopolysaccharidosis type IV A, Mucopolysaccharidosis type VI, Mucopolysaccharidosis type VII, Multiple congenital anomalies-hypotonia-seizures syndrome, Multiple congenital anomalies-hypotonia-seizures syndrome type 2, Multiple endocrine neoplasia type 2B, Multiple sulfatase deficiency, Multiple symmetric lipomatosis, Muscle eye brain disease, Muscular dystrophy, congenital, megaconial type, Muscular phosphorylase kinase deficiency, Musculocontractural Ehlers-Danlos syndrome, Myoclonic epilepsy with ragged red fibers, Myoglobinuria recurrent, N acetyltransferase deficiency, N- acetyl-alpha-D-galactosaminidase deficiency type III, N-acetylglutamate synthase deficiency, NBIA/DYT/PARK-PLA2G6, Neonatal adrenoleukodystrophy, Neonatal hemochromatosis, Neonatal intrahepatic cholestasis caused by citrin deficiency, Nephrogenic diabetes insipidus, Neu Laxova syndrome, Neuroferritinopathy, Neuronal ceroid lipofuscinosis 10, Neuronal ceroid lipofuscinosis 2, Neuronal ceroid lipofuscinosis 3, Neuronal ceroid lipofuscinosis 5, Neuronal ceroid lipofuscinosis 6, Neuronal ceroid lipofuscinosis 7, Neuronal ceroid lipofuscinosis 9, Neuropathy ataxia retinitis pigmentosa syndrome, Neutral lipid storage disease with myopathy, Niemann-Pick disease type A, Niemann-Pick disease type B, Niemann-Pick disease type Cl, Niemann-Pick disease type C2, Northern epilepsy, Not otherwise specified 3-MGA-uria type, Occipital horn syndrome, Ocular albinism type 1, Oculocutaneous albinism type 1, Oculocutaneous albinism type IB, Oculocutaneous albinism type 2, Oculocutaneous albinism type 3, OP A3 defect, Optic atrophy 1, Ornithine transcarbamylase deficiency, Ornithine translocase deficiency syndrome, Orotic aciduria type 1, Papillon Lefevre syndrome, Parkinson disease type 9, Paroxysmal nocturnal hemoglobinuria, Pearson syndrome, Pentosuria, Permanent neonatal diabetes mellitus, Peroxisomal biogenesis disorders, Peroxisome disorders - Not a rare disease, Perrault syndrome, Peters plus syndrome, PGM1-CDG, Phosphoglycerate kinase deficiency, Phosphoglycerate mutase deficiency, Phosphoribosylpyrophosphate synthetase superactivity, PMM2-CDG (CDG-Ia), Pontocerebellar hypoplasia type 6, Porphyria cutanea tarda, Primary carnitine deficiency, Primary hyperoxaluria type 1, Primary hyperoxaluria type 2, Primary hyperoxaluria type 3, Primary hypomagnesemia with secondary hypocalcemia, Progressive external ophthalmoplegia, autosomal recessive 1, Progressive familial intrahepatic cholestasis 1, Progressive familial intrahepatic cholestasis type 2, Progressive familial intrahepatic cholestasis type 3, Prolidase deficiency, Propionic acidemia, Pseudocholinesterase deficiency, Pseudoneonatal adrenoleukodystrophy, Purine nucleoside phosphorylase deficiency, Pycnodysostosis, Pyridoxal 5'-phosphate-dependent epilepsy, Pyridoxine- dependent epilepsy, Pyruvate carboxylase deficiency, Pyruvate dehydrogenase complex deficiency, Pyruvate dehydrogenase phosphatase deficiency, Pyruvate kinase deficiency, Refsum disease, Refsum disease with increased pipecolic acidemia, Refsum disease, infantile form, Renal glycosuria, Renal hypomagnesemia 2, Renal hypomagnesemia-6, Renal tubulopathy, diabetes mellitus, and cerebellar ataxia due to duplication of mitochondrial DNA, RFT1-CDG (CDG-In), Rhizomelic chondrodysplasia punctata type 3, Rotor syndrome, Saccharopinuria, Salla disease, Sarcosinemia, Scheie syndrome, Schimke immunoosseous dysplasia, Schindler disease type 1, Schneckenbecken dysplasia, SCOT deficiency, Sea-Blue histiocytosis, Sengers syndrome, Sensory ataxic neuropathy, dysarthria, and ophthalmoparesis, Sepiapterin reductase deficiency, Severe combined immunodeficiency, Short-chain acyl-CoA dehydrogenase deficiency, Sialidosis type I, Sialidosis, type II,

Sialuria, French type, Sitosterolemia, Sjogren-Larsson syndrome, SLC35A1-CDG (CDG-IIf), SLC35A2-CDG, SLC35C1-CDG (CDG-IIc), Smith-Lemli-Opitz syndrome, Spastic paraplegia 7, Spinocerebellar ataxia 28, Spinocerebellar ataxia autosomal recessive 3, Spondylocostal dysostosis 1, Spondylocostal dysostosis 2, Spondylocostal dysostosis 3, Spondylocostal dysostosis 4, Spondylocostal dysostosis 6, Spondylodysplastic Ehlers-Danlos syndrome, Spondyloepimetaphyseal dysplasia joint laxity, Spondylothoracic dysostosis, SRD5A3-CDG (CDG-Iq), SSR4-CDG, Succinic semialdehyde dehydrogenase deficiency, Tangier disease, Tay-Sachs disease, Thiamine responsive megaloblastic anemia syndrome, Thiopurine S methyltranferase deficiency, Tiglic acidemia, TMEM165-CDG (CDG-IIk), Transaldolase deficiency, Transcobalamin 1 deficiency, Transient neonatal diabetes mellitus, Trehalase deficiency, Trimethylaminuria, Triosephosphate isomerase deficiency, Tyrosine hydroxylase deficiency, Tyrosine-oxidase temporary deficiency, Tyrosinemia type 1, Tyrosinemia type 2, Tyrosinemia type 3, Urea cycle disorders, Valinemia, Variegate porphyria, VLCAD deficiency, Walker-Warburg syndrome, Wilson disease, Wolfram syndrome, Wolman disease, Wrinkly skin syndrome, X-linked adrenoleukodystrophy, X- linked cerebral adrenoleukodystrophy, X-linked Charcot-Marie-Tooth disease type 5, X- linked creatine deficiency, X-linked dominant chondrodysplasia punctata 2, X-linked sideroblastic anemia, Xanthinuria type 1, Xanthinuria type 2, Zellweger syndrome, acid-base imbalance, metabolic brain disease, disorders of calcium metabolism, DNA repair-deficiency disorder, glucose metabolism disorder, hyperlactatemia, iron metabolism disorder, lipid metabolism disorder, malabsorption syndrome, metabolic syndrome X, inborn error of metabolism, mitochondrial disease, phosphorus metabolism disorder, Porphyrias, Proteostasis deficiency, metabolic skin disease, wasting syndrome, water-electrolyte imbalance, and any combination thereof.

19. The method of any one of claims 11-18, further comprising determining a clinical intervention to treat the metabolic disease or disorder of the subject.

20. The method of claim 19, wherein the clinical intervention is selected from the group consisting of exercise regimen, diet regimen, blood pressure medication, cholesterol medication, diabetes medication, aspirin, other medication, weight loss, smoking cessation, and any combination thereof.

21. The method of claim 18, wherein the subject is asymptomatic for the metabolic disease or disorder.

22. The method of claim 19, further comprising administering the clinical intervention to the subject.

23. The method of claim 19, wherein the clinical intervention is selected from among a plurality of clinical interventions.

24. The method of any one of claims 11-23, further comprising computer processing the data and the determined metabolic rate to determine that the subject has or is at risk of having the metabolic disease or disorder.

25. The method of claim 24, further comprising using a second trained machine learning algorithm to determine that the subject has or is at risk of having the metabolic disease or disorder.

26. The method of claim 25, wherein the second trained algorithm determines that the subject has or is at risk of having the metabolic disease or disorder at a accuracy of at least about 80%.

27. The method of claim 25, wherein the second trained algorithm determines that the subject has or is at risk of having the metabolic disease or disorder at a sensitivity of at least about 80%.

28. The method of claim 25, wherein the second trained algorithm determines that the subject has or is at risk of having the metabolic disease or disorder of the subject at a specificity of at least about 80%.

29. The method of claim 25, wherein the second trained algorithm determines that the subject has or is at risk of having the metabolic disease or disorder of the subject at a positive predictive value of at least about 80%.

30. The method of claim 25, wherein the second trained algorithm determines that the subject has or is at risk of having the metabolic disease or disorder of the subject at a negative predictive value of at least about 80%.

31. The method of claim 25, wherein the second trained algorithm is trained using a plurality of independent training samples associated with a presence of the metabolic disease or disorder.

32. The method of claim 25, wherein the second trained algorithm is trained using a plurality of independent training samples associated with an absence or normal risk of the metabolic disease or disorder.

33. The method of claim 1, wherein the report is presented on a graphical user interface of an electronic device of a user.

34. The method of claim 33, wherein the user is the subject or a health care provider of the subject.

35. The method of claim 25, further comprising determining a likelihood of the metabolic disease or disorder.

36. The method of claim 1, wherein the trained machine learning algorithm comprises a supervised machine learning algorithm.

37. The method of claim 36, wherein the supervised machine learning algorithm comprises a linear regression, a logistic regression, a deep learning algorithm, a support vector machine (SVM), a neural network, a Random Forest, a boosted regression, a gradient boosting algorithm, an AdaBoost algorithm, or a combination thereof.

38. The method of claim 25, wherein the second trained machine learning algorithm comprises a second supervised machine learning algorithm.

39. The method of claim 38, wherein the second supervised machine learning algorithm comprises a linear regression, a logistic regression, a deep learning algorithm, a support vector machine (SVM), a neural network, a Random Forest, a boosted regression, a gradient boosting algorithm, an AdaBoost algorithm, or a combination thereof.

40. The method of claim 1, further comprising obtaining the data set at a plurality of time points, computer processing the data set using a trained machine learning algorithm to determine the metabolic rate at the plurality of time points, and electronically outputting the report indicative of the determined metabolic rate of the subject at the plurality of time points.

41. The method of claim 1, further comprising determining the one or more activities of the subject based at least in part on one or more acquired audio or video data of the subject.

42. The method of claim 41, further comprising convolving a time window of the one or more acquired audio or video data of the subject to determine the one or more activities of the subject.

43. The method of claim 1, further comprising performing batch normalization of the data.

44. The method of claim 1, wherein computer processing the data set using the trained machine learning algorithm comprises using an activation function.

45. The method of claim 44, wherein the activation function is a rectified linear unit (ReLU) function or sigmoid function.

46. The method of claim 1, further comprising pooling the data.

47. The method of claim 1, further comprising concatenating a first dataset associated with the one or more vital signs and a second dataset associated with the one or more activities of the subject to produce a concatenated dataset.

48. The method of claim 47, further comprising performing a regression on the concatenated dataset to determine the metabolic rate.

49. The method of claim 11, further comprising monitoring the metabolic disease or disorder of the subject, wherein the monitoring comprises determining that the subject has or is at risk of having the metabolic disease or disorder of the subject at a plurality of time points.

50. The method of claim 49, wherein a difference in the determined metabolic disease or disorder of the subject among the plurality of time points is indicative of one or more clinical indications selected from the group consisting of: (i) a diagnosis of the metabolic disease or disorder of the subject, (ii) a prognosis of the metabolic disease or disorder of the subject, and (iii) an efficacy or non-efficacy of a therapeutic intervention for treating the metabolic disease or disorder of the subject.

51. The method of claim 1, further comprising performing one or more clinical tests for the subject based at least in part on the determined metabolic rate of the subject.

52. The method of claim 11, further comprising performing one or more clinical tests for the subject based at least in part on the determined metabolic rate of the subject or the determination that the subject has or is at risk of having the metabolic disease or disorder.

53. The method of claim 51 or 52, wherein said one or more clinical tests comprise a genetic test, a blood test, a urine test, a stool test, a metabolite test, a hormone test, or a combination thereof.

54. The method of claim 1, wherein (a) and (b) are performed substantially in real time.

55. A computer system for determining a metabolic rate of a subject, comprising: a database that is configured to store a data set comprising vital sign data and activity data, wherein the vital sign data corresponds to one or more vital signs of the subject, and wherein the activity data corresponds to one or more activities of the subject; and one or more computer processors operatively coupled to the database, wherein the one or more computer processors are individually or collectively programmed to:

(i) process the data set using a trained machine learning algorithm to determine the metabolic rate; and

(ii) electronically output a report indicative of the metabolic rate of the subject determined in (i).

56. The computer system of claim 55, further comprising an electronic display operatively coupled to the one or more computer processors, wherein the electronic display comprises a graphical user interface that is configured to display the report.

57. A non-transitory computer-readable medium comprising machine-executable code that, upon execution by one or more computer processors, implements a method for determining a metabolic rate of a subject, the method comprising:

(a) obtaining a data set comprising vital sign data and activity data, wherein the vital sign data corresponds to one or more vital signs of the subject, and wherein the activity data corresponds to one or more activities of the subject;

(b) computer processing the data set using a trained machine learning algorithm to determine the metabolic rate; and

(c) electronically outputting a report indicative of the metabolic rate of the subject determined in (b).