Quantification of Lipoprotein Subfractions by Ion Mobility

Abstract

A method for quantifying lipoprotein subfractions by ion mobility is provided. Also provided herein is a method for diagnosing or prognosticating insulin resistance in patients in need of diagnosing or prognosticating insulin resistance (e.g., diabetic patients and / or prediabetic patients), the method comprising measuring lipoprotein subfraction levels in a patient sample by ion mobility.

Description

Technical Field

[0001] Cross - Reference to Related Applications This application claims the benefit of U.S. Provisional Patent Application No. 63 / 350789, filed on June 9, 2022, the entire disclosure of which is incorporated herein by reference.

[0002] The present invention relates to the identification and quantification of lipoprotein sub - fractions by ion mobility and the determination of the risk of insulin resistance.

Background Art

[0003] Insulin resistance (IR) is associated with abnormalities in lipids and lipoproteins such as high triglycerides (TG) and low high - density lipoprotein cholesterol (HDL - C), which contribute to an increased risk of atherosclerotic cardiovascular disease. Direct measurement of IR requires a great deal of labor and is not feasible in a clinical setting. Therefore, an effective method for determining the risk of insulin resistance is needed.

Summary of the Invention

[0004] In one aspect, a method for diagnosing or prognosticating insulin resistance in a patient (e.g., a diabetic patient and / or a pre - diabetic patient) in need of such diagnosis or prognosis is provided, the method comprising measuring lipoprotein sub - fraction levels in a patient sample by ion mobility. Also provided is a method for determining the amount of lipoprotein sub - fractions in a sample, the method comprising determining the amount of lipoprotein sub - fractions in the sample by ion mobility.

[0005] In certain embodiments, the methods provided herein include sub - fractions of lipoproteins in a sample by ion mobility.

[0006] In some embodiments, the insulin resistance score (RS) and / or the probability P(IR) of developing insulin resistance are provided herein based on the levels of lipoprotein subfractions measured by the methods provided herein.

[0007] In some embodiments, the insulin resistance score (RS) and / or the probability P(IR) of developing insulin resistance are provided herein based on the levels of lipoprotein subfractions measured by the methods provided herein, as well as the levels of triglycerides (TG) and high density lipoprotein cholesterol (HDL-C).

[0008] In some embodiments, the insulin resistance score (RS) and / or the probability P(IR) of developing insulin resistance are provided herein based on the levels of lipoprotein subfractions measured by the methods provided herein, as well as the levels of triglycerides (TG) and high density lipoprotein (HDL-C), in combination with gender, race, ethnicity, and body mass index (BMI) measured by standard methods.

[0009] In certain embodiments, insulin resistance is diagnosed by the levels of lipoprotein subfractions measured by the methods provided herein. In some embodiments, insulin resistance is diagnosed by the levels of lipoprotein subfractions measured by the methods provided herein, as well as the levels of triglycerides (TG) and high density lipoprotein cholesterol (HDL-C). In some embodiments, insulin resistance is diagnosed based on the levels of lipoprotein subfractions measured by the methods provided herein, as well as the levels of triglycerides (TG) and high density lipoprotein cholesterol (HDL-C), in combination with gender, race, ethnicity, and body mass index (BMI) measured by standard methods.

[0010] As used herein, the singular forms "a", "an", and "the" include the plural unless the context clearly dictates otherwise. Thus, for example, the expression "a protein" includes a plurality of protein molecules.

[0011] The term "purify" or "purifying" refers to a procedure that increases the amount of one or more target analytes as compared to other components in the sample that may interfere with the detection of the target analyte. Although not required, "purifying" may result in the complete removal of all interfering components, or all substances other than the target analyte of interest. Purification of a sample by various means may relatively reduce one or more interfering substances, for example, one or more substances that may or may not interfere with the detection of a selected parent or daughter ion by mass spectrometry. When the term "relatively reduce" is used, it is not required that the substances present with the target analyte in the material being purified be completely removed by purification.

[0012] The term "sample" refers to any sample that may contain the analyte of interest. As used herein, the term "body fluid" means any fluid that can be separated from an individual's body. For example, "body fluid" may include blood, plasma, serum, bile, saliva, urine, tears, sweat, and the like. In a preferred embodiment, the sample includes a body fluid sample. Preferably plasma or serum.

[0013] The "amount" of an analyte in a body fluid sample generally refers to an absolute value that reflects the mass of the analyte detectable in the volume of the sample. However, this amount also takes into account the relative amount compared to the amount of another analyte. For example, the amount of an analyte in a sample can be an amount greater than the control level or normal level of the analyte normally present in the sample.

[0014] As used herein, the term "about" when used in connection with a quantitative measurement that does not include the measurement of the mass of an ion refers to plus or minus 10% of the indicated value.

[0015] The above summary of the present invention is not limiting, and other features and advantages of the present invention will become apparent from the following detailed description of the present invention and the claims.

Brief Description of the Drawings

[0016]

Figure 1A

Figure 1B

Figure 1C

Figure 2A

Figure 2B

Figure 2C

Figure 3

Mode for Carrying Out the Invention

[0017] This specification provides a method for diagnosing or prognosticating insulin resistance in patients in need of diagnosing or prognosticating insulin resistance (e.g., diabetic patients and / or prediabetic patients). For example, without being bound by theory, an individual in the upper tertile of the steady-state plasma glucose (SSPG) concentration of a particular population can be defined as insulin resistant.

[0018] In some embodiments, when the SSPG concentration is ≧190 mg / dL, the individual is insulin resistant. In some embodiments, when the SSPG concentration is ≧195 mg / dL, such as >196 mg / dL, the individual is insulin resistant. In some embodiments, when the SSPG concentration is ≧198 mg / dL, the individual is insulin resistant. In some embodiments, when the SSPG concentration is ≧200 mg / dL, the individual is insulin resistant. In some embodiments, when the SSPG concentration is ≧205 mg / dL, the individual is insulin resistant.

[0019] Test samples suitable for use in the methods of the present invention include any test sample that may contain the analyte of interest. In some preferred embodiments, the sample is a biological sample. That is, a sample obtained from any biological source such as an animal, cell culture, organ culture, etc. In certain preferred embodiments, the sample is obtained from a mammal such as a dog, cat, horse, etc. Particularly preferred mammals are primates, most preferably male or female humans. Preferred samples include body fluids such as blood, plasma, serum, saliva, cerebrospinal fluid, tissue samples, etc., preferably plasma and serum. Such samples may be obtained, for example, from a patient. That is, a living person who appears at the clinical site for the diagnosis, prognosis, or treatment of a disease or symptom, such as a male or female.

[0020] In one embodiment, the methods provided herein include subfractions of lipoproteins in a sample by ion mobility. For example, the levels of lipoprotein subfractions (lipoprotein analysis) can be measured according to U.S. Patent Application Publication No. 2008 / 0305549 and Mora S., et al. Circulation. 2015 Dec 8;132(23):2220-9, each of which is incorporated herein by reference.

[0021] In some embodiments, determining the lipoprotein subfraction includes determining the amount of one or more or all of VLDL (very low density lipoprotein), IDL (intermediate density lipoprotein), LDL (low density lipoprotein), and HDL (high density lipoprotein). In some embodiments, determining the lipoprotein subfraction includes determining the amount of one or more or all of VLDL (very low density lipoprotein) medium, IDL (intermediate density lipoprotein) small, LDL (low density lipoprotein) large a, LDL (low density lipoprotein) medium, LDL (low density lipoprotein) extra small b, LDL (low density lipoprotein) extra small c, LDL (low density lipoprotein) extra small d, and HDL (high density lipoprotein) small.

[0022] In some embodiments, the insulin resistance score (RS) and / or the probability P(IR) of developing insulin resistance are provided herein based on the levels of lipoprotein subfractions measured by the methods provided herein. In some embodiments, the insulin resistance score (RS) can be determined from Equation A1.

[0023] Equation A1 RS = LS - IM score = -8.8 * VLDL medium - 19 * IDL small + 11 * LDL large a + 10.2 * LDL medium + 14.8 * LDL extra small b - 16.8 * LDL extra small c + 8.6 * LDL extra small d + 7.9 * HDL small

[0024] In some embodiments, the methods provided herein include measuring triglyceride (TG) levels and / or high density lipoprotein cholesterol (HDL-C) levels. Thus, in some embodiments, the insulin resistance score (RS) and / or the probability P(IR) of developing insulin resistance are provided herein based on the levels of lipoprotein subfractions measured by the methods provided herein, as well as the levels of triglyceride (TG) and high density lipoprotein cholesterol (HDL-C). In some embodiments, the insulin resistance score (RS) can be determined from Equation A2. The levels of triglyceride (TG) and high density lipoprotein cholesterol (HDL-C) can be measured according to methods known in the art.

[0025] Equation A2 RS = LS-IM score + TG / HDL-C = LS-IM score + 17.1 * TG / HDL-C = -8.8 * VLDL medium - 19 * IDL small + 11 * LDL large a + 10.2 * LDL medium + 14.8 * LDL extra small b - 16.8 * LDL extra small c + 8.6 * LDL extra small d + 7.9 * HDL small + 17.1 * TG / HDL-C

[0026] In some embodiments, the methods provided herein include measuring the body mass index (BMI), or measuring the body mass index (BMI) in combination with gender, race, and ethnicity. Thus, in some embodiments, the insulin resistance score (RS) and / or the probability P(IR) of developing insulin resistance are provided herein in combination with the levels of lipoprotein subfractions measured by the methods provided herein, and the levels of triglycerides (TG) and high density lipoprotein cholesterol (HDL-C), based on gender, race, ethnicity, and the body mass index (BMI) measured by standard methods. In some embodiments, the insulin resistance score (RS) can be determined from Equation A3. The levels of triglycerides (TG) and high density lipoprotein cholesterol (HDL-C) can be measured according to methods known in the art.

[0027] Equation A3 RS = LS-IM score + BMI + TG / HDL-C + gender + race + ethnicity = LS-IM score + 33.5*BMI + 17.1*TG / HDL-C - 12*male + 15.7*Hispanic + 5.3*Native American + 21.5*East Asian - 6*Black + 21*South Asian = -8.8*VLDL medium - 19*IDL small + 11*LDL large a + 10.2*LDL medium + 14.8*LDL extra small b - 16.8*LDL extra small c + 8.6*LDL extra small d + 7.9*HDL small + 33.5*BMI + 17.1*TG / HDL-C - 12*male + 15.7*Hispanic + 5.3*Native American + 21.5*East Asian - 6*Black + 21*South Asian Note: In Formula A3, when the subject is male, the variable Female = 0; when the subject is female, the variable Male = 0. When the subject is Hispanic, the variable Hispanic = 1; otherwise, the variable Hispanic = 0. When the subject is Native American, the variable Native American = 1; otherwise, the variable Native American = 0. When the subject is East Asian, the variable East Asian = 1; otherwise, the variable East Asian = 0. When the subject is African American in the South, the variable African American in the South = 1; otherwise, the variable African American in the South = 0. When the subject is Asian, the variable Asian = 1; otherwise, the variable Asian = 0.

[0028] In certain embodiments, insulin resistance is diagnosed by the levels of lipoprotein subfractions measured by the methods provided herein. In some embodiments, insulin resistance is diagnosed by the levels of lipoprotein subfractions measured by the methods provided herein, as well as triglyceride (TG) and high-density lipoprotein C (HDL-C) levels. In some embodiments, insulin resistance is diagnosed in combination with gender, race, ethnicity, and body mass index (BMI) measured by standard methods based on the levels of lipoprotein subfractions measured by the methods provided herein, as well as the levels of triglyceride (TG) and high-density lipoprotein C (HDL-C).

[0029] In certain embodiments, the methods described herein provide an insulin resistance score (e.g., according to Formula A1, Formula A2, or Formula A3).

[0030] In certain embodiments, the methods described herein provide the probability of developing insulin resistance.

[0031] In certain embodiments, the biological samples provided herein include plasma samples or serum samples.

Examples

[0032] Example 1: Quantification of Lipoprotein Subfractions by Ion Mobility The usefulness of fasting lipoprotein subfractions (LS) for identifying IR patients was evaluated.

[0033] Lipid panel, LS by ion mobility (LS-IM), and IR by steady-state plasma glucose (SSPG) concentration were evaluated in 526 adult volunteers without diabetes. IR was defined as the upper tertile of SSPG concentration. The LS-IM score was calculated by the linear combination of regression coefficients from a stepwise regression analysis with SSPG concentration as the dependent variable. Scores of models including the LS-IM score + TG / HDL-C score, gender, race, ethnicity, BMI, TG / HDL-C, and LS-IM score were also calculated. IR prediction was evaluated by the area under the receiver operating characteristic (AUC) curve and positive predictive value (PPV), considering the top 5% of scores as positive tests.

[0034] IR prediction was similar with the LS-IM score and TG / HDL-C (AUC = 0.68, PPV = 0.59 and AUC = 0.70, PPV = 0.59, respectively), and the prediction was improved when combining LS-IM with TG / HDL-C (AUC = 0.73, PPV = 0.70), TG / HDL-C and BMI (AUC = 0.82, PPV = 0.81), TG / HDL-C, BMI, gender, race, ethnicity (AUC = 0.84, PPV = 0.89).

[0035] When identifying patients with IR, the LS-IM score and TG / HDL-C were equivalent, and combining with gender, race, ethnicity, BMI further improved IR prediction by TG / HDL-C alone. Among patients who underwent the IM test, the LS-IM score may be useful for prioritizing subjects for further evaluation and intervention to reduce IR.

[0036] Insulin resistance (IR) increases the risk of type 2 diabetes (T2D) and atherosclerotic cardiovascular disease (ASCVD). However, since the techniques for directly measuring IR are labor-intensive and expensive, IR is rarely measured in healthy individuals in a clinical setting. Indirect methods for IR assessment have not been validated. Some patients with clear signs of T2D risk may be undergoing IR assessment by clinicians, but many other patients may be unaware of the increase in their IR measurements and the potential increased risk of T2D and ASCVD.

[0037] Various clinical means may be useful for identifying IR patients. The body mass index (BMI) is strongly associated with IR, but not all insulin-resistant patients are obese. IR is also associated with abnormalities in lipids and lipoproteins, including high triglyceride (TG) and low high-density lipoprotein cholesterol (HDL-C) concentrations and the predominance of small dense low-density lipoprotein (LDL) particles. The concentration ratio of TG to HDL-C (TG / HDL-C) can be used to identify insulin-resistant patients. Lipoprotein size and LS concentration have also been used to identify IR patients. In this case, an IR score based on lipoprotein information derived from nuclear magnetic resonance (NMR) has been shown to have a strong correlation with multiple measurements of IR. LS can also be measured by ion mobility. The LS quantified by NMR and ion mobility are correlated but not identical. The method based on ion mobility directly measures the number of lipoprotein particles according to size, while NMR is a measurement obtained by an algorithm.

[0038] The relationship between ion mobility-based LS (LS-IM) and direct measurement of IR has not been reported so far. If a strong correlation is found, it may provide additional information to patients and physicians about the risks of T2D and ASCVD due to IR. Therefore, we set out to describe the relationship between LS-IM and direct measurement of IR measured during an insulin suppression test and to determine the usefulness of LS for identifying insulin-resistant patients. Study Design and Methods

[0039] Regarding the study population: This cross-sectional analysis included 526 participants selected from 1072 apparently healthy individuals who volunteered to participate in IR research between 1999 and 2011. Participants were recruited from the San Francisco Bay Area through local newspaper advertisements. In this study, pregnant women, those over 79 years old or under 18 years old, those with a history of cardiovascular disease, and diabetic patients requiring insulin treatment were excluded. In this analysis, 149 participants with fasting plasma glucose ≥ 126 mg / dL and 397 participants with at least one missing measurement among any of the following: race, ethnicity, body mass index (BMI), TG, HDL-C, LDL cholesterol, systolic blood pressure, diastolic blood pressure, alanine transaminase, or ion mobility LS were excluded (Figure 3).

[0040] The institutional review board approved all studies, and all participants provided written informed consent. Clinical and Measurements

[0041] Study visits were conducted at the Clinical and Translational Research Unit of Stanford University. Race and ethnicity were self-reported. Height and weight were measured without shoes and in light clothing, and BMI was calculated by dividing weight (kilograms) by the square of height (meters). Blood pressure was measured using an automated blood pressure recorder after the participant sat quietly in a chair with feet on the floor and arms supported at heart level for 5 minutes. Blood pressure was measured three times at 1-minute intervals using an appropriately sized cuff, and the average was calculated. Insulin Suppression Test

[0042] The degree of IR was directly measured by a modified and validated version of the insulin suppression test (IST) that quantifies the ability of steady-state physiologic hyperinsulinemia to stimulate glucose uptake.

[0043] After an overnight fast, intravenous catheters were inserted into both arms. One arm was used for blood sample collection, and the other arm was for octreotide acetate (0.27 μg / m2 / min), insulin (32 mU / m 2 / min), and glucose (267 mg / m 2 / min) were used for continuous infusion for 180 minutes. Blood was sampled every 30 minutes for 150 minutes and then every 10 minutes, and steady-state plasma insulin (SSPI) and steady-state plasma glucose (SSPG) concentrations were measured.

[0044] During the IST, octreotide acetate inhibits endogenous insulin secretion, and insulin infusion results in similar SSPI concentrations (physiological hyperinsulinemia) in all individuals. The ability of physiological hyperinsulinemia to stimulate the absorption of infused glucose is indicated by the SSPG concentration. The higher the SSPG concentration, the lower the glucose absorption by insulin stimulation and the higher the insulin resistance. The IR measured during the IST has a high correlation with the IR measured during the euglycemic, hyperinsulinemic clamp test. Individuals in the upper tertile of the SSPG concentration were defined as insulin resistant. This determination is based on the results of a prospective study that subjects in the tertile with the highest SSPG concentration had more incident ASCVD than those in the tertile with the lowest SSPG concentration. Measurement of lipids and lipoproteins

[0045] The lipid panel was evaluated after an overnight fast managed by the Health Care Clinical Laboratory at Stanford University, and LDL cholesterol was calculated using the Friedewald equation. Measurement of ion mobility

[0046] The LS level was evaluated by ion mobility at Quest Diagnostics Nichols Institute (San Juan Capistrano, CA). [Mora S, Caulfield MP, Wohlgemuth J, Chen Z, Superko HR, Rowland CM, Glynn RJ, Ridker PM, Krauss RM: Atherogenic lipoprotein subfractions determined by ion mobility and first cardiovascular events after random allocation to high-intensity statin or placebo. 2015;132:2220-2229; and Mora S, Caulfield MP, Wohlgemuth J, Chen Z, Superko HR, Rowland CM, Glynn RJ, Ridker PM, Krauss RM. Atherogenic lipoprotein subfractions determined by ion mobility and first cardiovascular events after random allocation to high-intensity statin or placebo: the justification for the use of statins in prevention: an intervention trial evaluating rosuvastatin (JUPITER) Trial. Circulation 2015;132:2220-9]. LS and its definition are shown in Table 3. Statistical methods

[0047] Pearson's correlation coefficient (r) was used as a measure of pairwise correlation. The associations between TG / HDL-C and each LS-IM measurement and SSPG concentration were evaluated in separate linear regression models adjusted for age, sex, race, ethnicity, and BMI. To incorporate multiple ion mobility variables and covariates into a single model, a backward stepwise regression model was performed using the Akaike information criterion (AIC) as an index for comparing models. In the regression model, the candidate variables were age, sex, race, ethnicity, BMI, TG / HDL-C, and LS-IM measurements, and the dependent variable was SSPG concentration. Using the regression coefficients of the LS-IM measurements in the final stage model, an ion mobility score (LS-IM score) was calculated. The score for each subject was a linear combination of the LS-IM variables from the stepwise model calculated as B1*Var1 + B2*Var2 + … + Bp*Varp. Here, B1 to Bp are the regression coefficients, and Var1 to Varp are the subject-specific values of the p LS-IM variables in the final model. Scores were calculated in a similar manner for other combinations of the variables in the model. 1) LS-IM score + TG / HDL-C, 2) all non-ion mobility variables (sex, race, ethnicity, BMI, and TG / HDL-C), and 3) all variables in the full model (sex, race, ethnicity, BMI, TG / HDL-C, and LS-IM score). All continuous variables were standardized by conversion to standard deviation (SD) units if included in the regression model. The tertiles of the LS-IM score calculated using the ion mobility coefficients of the stepwise model were plotted against the SSPG concentration for the tertiles of BMI in women and men.

[0048] The receiver operating characteristic (ROC) curve was plotted, and the area under the curve (AUC) and 95% confidence interval were calculated for each of the above scores using DeLong's method. The difference in AUC was evaluated using DeLong's method for paired ROC curves. [DeLong ER, DeLong DM, Clarke-Pearson DL. Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 1988;44(3):837-45] The positive predictive value (PPV) for identifying individuals in the upper tertile of SSPG concentration was determined for each score by considering the top 5% of the score values as positive tests. The Wilson method was used to calculate the confidence interval of PPV. [Wilson EB. Probable Inference, the Law of Succession, and Statistical Inference. J Am Statistical Assoc 1927;22:158. https: / / doi.org / 10.1080 / 01621459.1927.10502953. 209-212]. The Bonferroni method was used to adjust for multiple comparisons and determine the significance level. [Bland JM, Altman DG. Multiple significance tests: the Bonferroni method. BMJ 1995;310:6973. https: / / doi.org / 10.1136 / bmj.310.6973.170 (Clinical research ed.) 170].

[0049] All analyses were performed using the R programming language. [Team RC: R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing. 2020] Results

[0050] The median age of the study participants was 50 years, and approximately two-thirds (65%) were women (Table 4). Most of the subjects were non-Hispanic (92%), and 70% were white. Almost half (48%) of the participants were obese (BMI ≥ 30.0 kg / m 2 ) and 38% were overweight (BMI 25.0 - 29.9 kg / m 2 ).

[0051] A positive correlation was found between SSPG concentration and both BMI and TG / HDL-C (r = 0.54 and 0.32, respectively) (Table 5).

[0052] The correlation coefficient (r) between BMI and various LS-IM measurements showed that the absolute value of all LS-IM measurements was less than 0.2. The only measurement that reached statistical significance (p-value < 0.0008) was the large HDL, which had a negative correlation with BMI (r = -0.17). Among the correlations between SSPG concentration and individual LS-IM measurements that reached statistical significance (p-value < 0.0008), there were negative correlations between SSPG concentration and LDL peak particle size (r = -0.26), small IDL (r = -0.21), large HDL (r = -0.18), and large LDL a (r = -0.17), and positive correlations between SSPG concentration and small LDL (r = 0.27), very small LDL a (r = 0.23), total LDL (r = 0.19), medium LDL (r = 0.19), and very small LDL b (r = 0.17). Stronger correlations were found between TG / HDL-C and LS-IM measurements, and significant correlations (p-value < 0.0008) were observed for the following measurements. There were negative correlations between TG / HDL-C and LDL peak particle size (r = -0.72), large LDL a (r = -0.50), small IDL (r = -0.38), large LDL b (r = -0.37), large HDL (r = -0.33), and total HDL (r = -0.18), and positive correlations between TG / HDL-C and very small LDL b (r = 0.64), very small LDL a (r = 0.62), very small LDL c (r = 0.60), small LDL (r = 0.50), large VLDL (r = 0.46), very small LDL d (r = 0.38), medium VLDL (r = 0.35), total LDL (r = 0.32), and non-HDL total (r = 0.23).

[0053] Most of the LS-IM measurements were associated with significant changes in SSPG concentration, as indicated by confidence intervals that did not reach zero (Table 6). The largest change per SD in the LS-IM measurements was for LDL peak particle size (SSPG concentration decreased by 16.7 mg / dL for each 1-SD increase in peak particle diameter) and small LDL (SSPG concentration increased by 15.8 mg / dL for each 1-SD increase in small LDL particle number). Large effect sizes were also seen for non-LS-IM metrics of TG / HDL-C and BMI. Similarly, SSPG concentration increased by 17 mg / dL for each 1-SD increase in TG / HDL-C. In these same models, SSPG concentration increased by an average of 40 mg / dL for each 1-SD increase in BMI.

[0054] Eight of the LS-IM measurements, along with BMI, TG / HDL-C, sex, ethnicity, and race, were the final variables remaining in a backward stepwise regression analysis with SSPG concentration as the dependent variable (Table 1). The tertiles of the LS-IM score showed a strong positive correlation across all tertiles of BMI in women and across the lower two tertiles of BMI in men (Figure 1A). Similar relationships were seen when plotting the tertiles of the TG / HDL-C score or the score representing the combination of LS-IM + TG / HDL-C (Figures 1B and 1C).

[0055] Receiver operating characteristic (ROC) curves for predicting IR (upper tertile SSPG concentration) were plotted for LS-IM score, TG / HDL-C, LS-IM score + TG / HDL-C (Figure 2A), BMI, BMI + TG / HDL-C, LS-IM score + BMI + TG / HDL-C (Figure 2B), BMI + TG / HDL-C + gender + race + ethnicity, LS-IM score + BMI + TG / HDL-C + gender + race + ethnicity (Figure 2C). The combination of LS-IM score + TG / HDL-C improved prediction of IR compared to TG / HDL-C alone (AUC = 0.73 vs 0.70, p-value < 0.0001 respectively) (Table 2). Similarly, LS-IM score + BMI + TG / HDL-C improved prediction compared to BMI + TG / HDL-C alone (AUC = 0.82 vs 0.79, p-value < 0.0001 respectively). The LS-IM score also improved prediction when added to BMI + TG / HDL-C + gender + race + ethnicity (AUC = 0.84 vs 0.81, p-value < 0.0001 respectively). In the subset of non-obese individuals, prediction was also improved by adding the LS-IM score to TG / HDL-C, BMI + TG / HDL-C, BMI + TG / HDL-C + gender + race + ethnicity (p-value < 0.0001) (Table 2).

[0056] Positive predictive value (PPV) was calculated to identify subjects in the upper tertile of SSPG concentration considering the top 5 percent values for each same variable group. PPV ranged from 59% when considering only TG / HDL-C or IM score to 89% when considering the full model of LS-IM score + BMI + TG / HDL-C + gender + race + ethnicity (Table 2). Summary

[0057] There were three main findings in this study regarding LS and IR. First, LS-IM had a weak to moderate correlation with the direct measure of IR (SSPG concentration). Second, prediction of IR by LS-IM was similar to that by TG / HDL-C. Third, prediction of IR was improved by combining the LS-IM score with TG / HDL-C and further improved by adding gender, race, ethnicity, and BMI.

[0058] The role of IR in the development of T2D and ASCVD and its association with metabolic abnormalities such as elevated TG and decreased HDL-C concentration were first formulated and published by Reaven in 1988. Subsequent studies by Reaven and his colleagues have demonstrated that IR is associated with various metabolic abnormalities and various clinical syndromes, such as non-alcoholic fatty liver disease, obstructive sleep apnea, polycystic ovary syndrome, and certain types of cancer that have harmful effects on health. Therefore, the identification of IR in individuals is clinically important because it may prompt changes in behavior and clinical management to reduce the risks associated with IR.

[0059] Since it is not feasible to directly measure IR in the clinical setting, a robust and validated clinical measurement of IR is needed to identify individuals at risk of the adverse effects of IR. In this study, we aimed to meet this need by examining whether LS-IM measured by ion mobility is useful for identifying individuals with IR.

[0060] IR-related dyslipidemia is characterized by elevated TG, decreased HDL-C concentration, predominance of small dense LDL, postprandial lipemia, and increased concentration of partially oxidized LDL. Some lipid and LS abnormalities measured by NMR are also seen in IR patients. Consistent with these previously reported findings, it was shown that some of the LS-IM measurements are associated with IR (SSPG concentration). Specifically, the SSPG concentration was found to be associated with an increase in the number of large VLDL, medium to very small LDL, and large HDL particles, and a decrease in the peak particle size and number of small IDL, large LDL, and large HDL particles. From a pathophysiological perspective, these associations are thought to occur in part by an increase in VLDL production in the liver and a decrease in clearance from plasma, an increase in hepatic lipase activity followed by hydrolysis of phospholipids in LDL and HDL particles, resulting in smaller and denser LDL particles, a decrease in large HDL particles, and an increase in small HDL particles.

[0061] In addition, it was also shown that incorporating LS-IM improved the prediction of IR measured by SSPG concentration. Specifically, when predicting individuals in the upper tertile of SSPG concentration, the AUC and PPV were similar for the case of LS-IM score alone and the case of TG / HDL-C alone, but were significantly improved when used in combination (Table 2). Furthermore, the scores obtained from the complete stepwise model including LS-IM score, including gender, race, ethnicity, BMI, and TG / HDL-C, were significantly improved in terms of AUC and PPV compared to the scores including only gender, race, ethnicity, BMI, and TG / HDL-C excluding the LS-IM score. It is difficult to compare the LS-IM score described here with the LP-IR score obtained from the aforementioned NMR. The LP-IR score is based on HOMA-IR as a measure of IR, while the current score is based on SSPG concentration, a direct measure of IR. Furthermore, the size range of the defined region differs between the two scores. However, both scores indicate a wide size range of particles that contribute independently to the association with IR.

[0062] Moreover, even simpler indices of IR such as TG / HDL-C and BMI have been shown to function similarly to the LS-IM score alone in predicting IR. Specifically, the TG / HDL-C ratio was found to be similar to the LS-IM score, and BMI was observed to be similar to the combination of the LS-IM score and TG / HDL-C in predicting IR. Previously, it was shown that individuals with insulin resistance could be identified using TG / HDL-C. The TG / HDL-C ratio is a simple method that can be used to identify individuals with an increased cardiometabolic risk and is recommended in clinical or research settings where LS-IM measurements cannot be performed. Also, according to the study results, the potential usefulness of the LS-IM score for identifying individuals with the highest insulin resistance and the highest degree of dyslipidemia is shown in both the obese and non-obese groups. As shown in Figures 1A, 1B, and 1C, within each tertile of BMI, individuals with higher scores (LS-IM, TG / HDL-C, LS-IM+TG / HDL-C) were generally found to have higher insulin resistance (higher SSPG concentration) than those with lower scores. As shown in Table 2, the AUC for predicting IR in non-obese individuals does not decrease with the LS-IM score, TG / HDL-C, or their combination. This observation is consistent with previous findings that, at a given BMI, individuals with insulin resistance have higher TG concentrations and lower HDL-C concentrations than insulin-sensitive individuals.

[0063] The strengths of this study include the fact that the usefulness of the LS-IM score in predicting IR was verified using the gold standard measures of IR. Furthermore, risk prediction was improved (at no additional cost) using data already available from LS-IM clinical trials where measurements were available. The limitations of this study include the fact that the study population was not typical of the group that undergoes the LS-IM test. The people studied were clearly healthy volunteers, whereas people who undergo the LS-IM test are mainly referred by clinicians to have the test for the risk assessment of ASCVD. Future studies will be needed to evaluate the LS-IM scores of patients undergoing the LS-IM test to assess how IR and related dyslipidemias contribute to the ASCVD risk.

[0064] In conclusion, in addition to TG / HDL-C and / or BMI, LS-IM measurement may improve the prediction of IR. Among people who have undergone the LS-IM test, this information may be used to prioritize lifestyle interventions to improve IR and the associated risks of T2D and ASCVD. Targeted interventions such as increased physical activity and weight loss have been shown to be particularly effective in improving IR and reducing progression to T2D. This information can also be used to identify individuals who may be candidates for additional testing by other validated measures such as fasting insulin and IR scores, and ultimately, to identify individuals who may be unaware of their high risk for IR and the associated T2D and ASCVD.

[0065]

Table 1

[0066]

Table 2-1

Table 2-2

[0067]

Table 3

[0068]

Table 4-1

Table 4-2

[0069]

Table 5

[0070]

Table 6

[0071] The content of articles, patents, patent applications, and all other documents and electronically available information referred to or cited in this specification is incorporated herein by reference in its entirety to the same extent as if each individual publication were specifically and individually indicated to be incorporated by reference. The applicant reserves the right to physically incorporate into this application any material and information from such articles, patents, patent applications, or other physical and electronic documents.

[0072] The methods exemplified in this specification can be appropriately implemented without the presence of any elements or limitations if not specifically disclosed herein. Accordingly, terms such as "comprising," "including," "containing," etc. shall be construed expansively without limitation. Further, the terms and expressions used in this specification are used as terms of explanation rather than limitation, and in the use of such terms and expressions, there is no intention to exclude equivalents of the illustrated and described features or portions thereof. It is recognized that various modifications are possible within the scope of the claims of the present invention. Accordingly, although the present invention has been specifically disclosed by preferred embodiments and any features, modifications and changes to the invention disclosed herein are executable by those skilled in the art and such modifications and changes are considered to be within the scope of the present invention.

[0073] The present invention has been described herein in a broad and general manner. Each of the narrower species and subgeneric groups included within the scope of the general disclosure also forms part of the method. This includes a general description of the method with provisos or negative limitations that exclude the subject matter from the genus, regardless of whether the deleted material is specifically described herein.

[0074] Other embodiments are included within the scope of the following claims. Further, when features or aspects of the method are described from the perspective of a Markush group, those skilled in the art will recognize that the present invention is also described from the perspective of any individual member or subgroup of members of the Markush group.

Claims

1. A method for providing information for diagnosing or prognosing insulin resistance in patients requiring diagnosis or prognosis of insulin resistance, comprising determining the amount of lipoprotein subfraction in a sample by ion mobility.

2. The method according to claim 1, further comprising measuring the triglyceride (TG) level.

3. The method according to claim 1, further comprising measuring high-density lipoprotein cholesterol (HDL-C) levels.

4. The method according to claim 1, further comprising measuring body mass index (BMI) in combination with sex, race, and ethnicity.

5. The method according to claim 1, further comprising measuring triglyceride (TG) levels and high-density lipoprotein cholesterol (HDL-C) levels.

6. The method according to claim 1, further comprising measuring triglyceride (TG) levels, high-density lipoprotein cholesterol (HDL-C) levels, and body mass index (BMI) in combination with sex, race, and ethnicity.

7. The method according to claim 1, comprising providing an insulin resistance score based on the amount of lipoprotein subfraction in the sample.

8. The method according to claim 1, comprising providing the probability of developing insulin resistance based on the amount of lipoprotein subfraction in the sample.

9. The method according to claim 1, wherein the sample comprises a plasma sample or a serum sample.

10. The method according to claim 1, wherein determining the amount of the lipoprotein subfraction includes determining the amount of one or more or all of VLDL (very low-density lipoprotein), IDL (intermediate-density lipoprotein), LDL (low-density lipoprotein), and HDL (high-density lipoprotein).

11. The method according to any one of claims 1 to 10, wherein determining the amount of the lipoprotein subfractions includes determining the amount of one or more or all of the following: VLDL (very low-density lipoprotein) medium, IDL (medium-density lipoprotein) small, LDL (low-density lipoprotein) large a, LDL (low-density lipoprotein) medium, LDL (low-density lipoprotein) very small b, LDL (low-density lipoprotein) very small c, LDL (low-density lipoprotein) very small d, and HDL (high-density lipoprotein) small.

12. A method for determining the amount of lipoprotein subfraction in a sample, the method comprising determining the amount of lipoprotein subfraction in the sample by ion mobility.

13. The method according to claim 12, further comprising measuring the triglyceride (TG) level.

14. The method according to claim 12, further comprising measuring high-density lipoprotein cholesterol (HDL-C) levels.

15. The method according to claim 12, further comprising measuring body mass index (BMI) in combination with sex, race, and ethnicity.

16. The method according to claim 12, further comprising measuring triglyceride (TG) levels and high-density lipoprotein cholesterol (HDL-C) levels.

17. The method according to claim 12, further comprising measuring triglyceride (TG) levels, high-density lipoprotein cholesterol (HDL-C) levels, and body mass index (BMI) in combination with sex, race, and ethnicity.

18. The method according to claim 12, wherein the method provides an insulin resistance score.

19. The method according to claim 12, wherein the method provides a probability of developing insulin resistance.

20. The method according to claim 12, wherein the sample comprises a plasma sample or a serum sample.

21. The method according to claim 12, wherein determining the amount of the lipoprotein subfraction includes determining the amount of one or more or all of VLDL (very low-density lipoprotein), IDL (intermediate-density lipoprotein), LDL (low-density lipoprotein), and HDL (high-density lipoprotein).

22. The method according to any one of claims 12 to 21, wherein determining the amount of the lipoprotein subfractions includes determining the amount of one or more or all of the following: VLDL (very low-density lipoprotein) medium, IDL (medium-density lipoprotein) small, LDL (low-density lipoprotein) large a, LDL (low-density lipoprotein) medium, LDL (low-density lipoprotein) very small b, LDL (low-density lipoprotein) very small c, LDL (low-density lipoprotein) very small d, and HDL (high-density lipoprotein) small.

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