An in vitro method for the characterization of lipoproteins in a sample, comprising the following steps: - obtaining a 2D diffusion-ordered 1 H NMR spectrum of the sample, - performing a surface fitting of a portion of the spectrum corresponding to the methyl signal using a plurality of model functions, each model function corresponding to a given particle size associated to a lipoprotein fraction and subclass and including at least one model parameter to be estimated during the fitting, the estimated model parameters being the set of model parameters for which the difference between the NMR signal and the model signal built as a linear combination of the model functions is minimized, and - identifying the lipoproteins present in the sample as those associated to the model functions contributing to the theoretical model signal resulting from the fitting, wherein each model function is a triplet of lorentzian functions having the form: Triplet = Lorentzian h 1, f j − f 0, w, D + Lorentzian h 2, f, w, D + Lorentzian h 3, f + f 0, w, D, where h (au), f (ppm), w (ppm), and D (cm 2 s -1) are, respectively, the intensity, chemical shift, width, and diffusion coefficient associated to a lipoprotein particle size, wherein the model parameters to be determined for each lipoprotein particle size are one or several from: f, f 0, h 1, h 2, h 3, w and D, wherein for each model function: h 1 = α ⋅ h 2, with 1 4 ≤ α ≤ 3 4, and h 3 = β ⋅ h 2, with 1 4 ≤ β ≤ 3 4, wherein the surface fitting is performed fixing at least one model parameter and using at least one other model parameter as a free parameter to be determined in the surface fitting, wherein the fixed model parameters are determined based on the lipoprotein particle size and on regression models, the regression models relating pairs of fixed model parameters and/or a model parameter and the lipoprotein particle size and being built according to the following steps: - obtaining a 2D diffusion-ordered 1 H NMR spectrum for a plurality of samples, - for each sample, performing a surface fitting of a portion of the spectrum corresponding to the methyl signal using a plurality of model functions, each model function being dependent on the model parameters to be fixed, wherein all the model parameters to be fixed are estimated during the surface fitting as the set of model parameters for which the difference between the NMR signal and the model signal built as a linear combination of the model functions is minimized, and - using the model parameters estimated in the previous step to build regression models relating pairs of model parameters and/or a model parameter and the lipoprotein particle size. - obtaining a 2D diffusion-ordered 1 H NMR spectrum of the sample, - performing a surface fitting of a portion of the spectrum corresponding to the methyl signal using a plurality of model functions, each model function corresponding to a given particle size associated to a lipoprotein fraction and subclass and including at least one model parameter to be estimated during the fitting, the estimated model parameters being the set of model parameters for which the difference between the NMR signal and the model signal built as a linear combination of the model functions is minimized, and - identifying the lipoproteins present in the sample as those associated to the model functions contributing to the theoretical model signal resulting from the fitting, wherein each model function is a triplet of lorentzian functions having the form: Triplet = Lorentzian h 1, f j − f 0, w, D + Lorentzian h 2, f, w, D + Lorentzian h 3, f + f 0, w, D, where h (au), f (ppm), w (ppm), and D (cm 2 s -1) are, respectively, the intensity, chemical shift, width, and diffusion coefficient associated to a lipoprotein particle size, wherein the model parameters to be determined for each lipoprotein particle size are one or several from: f, f 0, h 1, h 2, h 3, w and D, - obtaining a 2D diffusion-ordered 1 H NMR spectrum for a plurality of samples, - for each sample, performing a surface fitting of a portion of the spectrum corresponding to the methyl signal using a plurality of model functions, each model function being dependent on the model parameters to be fixed, wherein all the model parameters to be fixed are estimated during the surface fitting as the set of model parameters for which the difference between the NMR signal and the model signal built as a linear combination of the model functions is minimized, and - using the model parameters estimated in the previous step to build regression models relating pairs of model parameters and/or a model parameter and the lipoprotein particle size.