Abstract: Biomarkers and/or risk assessments identify patients having an increased risk of certain clinical disease states including, for example, CHD, type 2 diabetes, dementia, or all-cause death (ACD) using NMR signal to measure a level of “GlycA” in arbitrary units or in defined units (e.g., ?mol/L) that can be determined using a defined single peak region of proton NMR spectra. The GlycA measurement can be used as an inflammation biomarker for clinical disease states. The NMR signal for GlycA can include a fitting region of signal between about 2.080 ppm and 1.845 ppm of the proton NMR spectra.

Abstract: Biomarkers and/or risk assessments identify patients having an increased risk of certain clinical disease states including, for example, CHD, type 2 diabetes, dementia, or all-cause death (ACD) using NMR signal to measure a level of “GlycA” in arbitrary units or in defined units (e.g., ?mol/L) that can be determined using a defined single peak region of proton NMR spectra. The GlycA measurement can be used as an inflammation biomarker for clinical disease states. The NMR signal for GlycA can include a fitting region of signal between about 2.080 ppm and 1.845 ppm of the proton NMR spectra.

Abstract: A defined peak region residing between about 3.2 and 3.4 ppm of a proton NMR spectrum of an in vitro biosample is electronically evaluated to determine a level of trimethylamine-N-oxide (“TMAO”). The biosamples may be any suitable biosamples including human serum with a normal biologic range of between about 1-50 ?M or urine with a normal biologic range of between about 0-1000 ?M.

Abstract: A defined peak region residing between about 3.2 and 3.4 ppm of a proton NMR spectrum of an in vitro biosample is electronically evaluated to determine a level of trimethylamine-N-oxide (“TMAO”). The biosamples may be any suitable biosamples including human serum with a normal biologic range of between about 1-50 ?M or urine with a normal biologic range of between about 0-1000 ?M.

Abstract: Methods, computer program products and apparatus determine a subject's risk of having or developing CHD using a calculated HDL particle risk number and/or a mathematical model of risk associated with HDL particles that adjusts concentrations of at least one of the subclasses of small, medium and large HDL particle measurements to reflect predicted CHD risk. A calculated LDL particle risk number may also be generated as well as a lipoprotein particle index derived from the ratio of RLDL/RHDL.

Abstract: Embodiments of the invention are directed to methods, systems and computer programs that provide improved risk stratification for people having elevated large HDL-P using at least one defined HDL risk interaction parameter.

Abstract: Methods, systems and circuits evaluate a subject's risk of developing type 2 diabetes or developing or having prediabetes using at least one defined mathematical model of risk of progression that can stratify risk for patients having the same glucose measurement. The model may include NMR derived measurements of GlycA and a plurality of selected lipoprotein components of at least one biosample of the subject.

Abstract: Methods, computer program products and apparatus determine a subject's risk of having or developing CHD using a calculated HDL particle risk number and/or a mathematical model of risk associated with HDL particles that adjusts concentrations of at least one of the subclasses of small, medium and large HDL particle measurements to reflect predicted CHD risk. A calculated LDL particle risk number may also be generated as well as a lipoprotein particle index derived from the ratio of RLDL/RHDL.

Abstract: The clinical analyzers automatically electronically monitor selected parameters and automatically electronically adjust parameters to maintain the analyzer within desired operational ranges. The clinical NMR analyzers can be configured as a networked system with a plurality of clinical NMR analyzers located at different use sites.

Abstract: Embodiments of the invention are directed to methods, systems and computer programs that provide improved risk stratification for people having elevated large HDL-P using at least one defined HDL risk interaction parameter.

Abstract: Methods, systems and circuits evaluate a subject's risk of developing type 2 diabetes or developing or having prediabetes using at least one defined mathematical model of risk of progression that can stratify risk for patients having the same glucose measurement. The model may include NMR derived measurements of GlycA and a plurality of selected lipoprotein components of at least one biosample of the subject.

Abstract: The clinical analyzers automatically electronically monitor selected parameters and automatically electronically adjust parameters to maintain the analyzer within desired operational ranges. The clinical NMR analyzers can be configured as a networked system with a plurality of clinical NMR analyzers located at different use sites.

Abstract: Described herein are methods and systems for the determination of constituents in biosamples by NMR spectroscopy and more specifically for the determination of lipoprotein constituents LP-X, LP-Y, and LP-Z in blood plasma and serum.

Abstract: Disclosed are methods and systems to determine a subject's metabolic vulnerability index (MVX) score using at least one defined mathematical model of risk. The methods comprise evaluating various biomarkers to distinguish various health risks. In one embodiment, the method comprises evaluating biomarkers to determine a relative risk of premature all-cause mortality. The model may include NMR-derived measurements of GlycA, S-HDLP, branched chain amino acids (BCAAs), ketone bodies, total serum protein, and citrate in at least one biosample of the subject.

Abstract: Methods, systems and circuits evaluate a subject's CVD risk using a risk parameter that includes at least one HDL and inflammatory biomarker interaction parameter. The inflammatory biomarker may optionally comprise NMR derived measurements of GlycA from at least one biosample of the subject. The risk parameter may be gender-specific.

Abstract: Methods, systems and circuits evaluate a subject's risk of developing type 2 diabetes using defined mathematical models of short term risk (STR) and longer term risk of progression. The evaluations can stratify risk for patients having the same glucose measurement, particularly those with intermediate or low (normal) fasting plasma glucose (FPG) values. The STR or IR (insulin resistance) model(s) may include an inflammatory biomarker such as an NMR derived measurements of GlycA and a plurality of selected lipoprotein components of at least one biosample of the subject. Embodiments of the invention also provide methods, systems and circuits that generate STR scores as a marker of beta-cell dysfunction or impairment.

Abstract: Disclosed are methods and systems that uses GlycA concentration in biosamples to evaluate risks of CRC incidence and mortality.

Abstract: Methods, systems and circuits evaluate a subject's CVD risk using a risk parameter that includes at least one HDL and inflammatory biomarker interaction parameter. The inflammatory biomarker may optionally comprise NMR derived measurements of GlycA from at least one biosample of the subject. The risk parameter may be gender-specific.

Abstract: Methods, systems and circuits evaluate a subject's risk of developing type 2 diabetes using defined mathematical models of short term risk (STR) and longer term risk of progression. The evaluations can stratify risk for patients having the same glucose measurement, particularly those with intermediate or low (normal) fasting plasma glucose (FPG) values. The STR or IR (insulin resistance) model(s) may include an inflammatory biomarker such as an NMR derived measurements of GlycA and a plurality of selected lipoprotein components of at least one biosample of the subject. Embodiments of the invention also provide methods, systems and circuits that generate STR scores as a marker of beta-cell dysfunction or impairment.

Abstract: Methods, systems and circuits evaluate a subject's risk of developing type 2 diabetes or having prediabetes using at least one defined mathematical model of risk of progression that can stratify risk for patients having the same glucose measurement. The model may include NMR derived measurements of GlycA and a plurality of selected lipoprotein components of at least one biosample of the subject.