Biomarkers for diagnosing preeclampsia

A method of detecting the presence of one of a plurality of states associated with preeclampsia in a subject, the method comprising:

• receiving peptide structure data corresponding to a set of glycoproteins in a biological sample obtained from a subject, wherein the peptide structure data comprises at least one peptide structure from Table 3;
• inputting quantification data identified from the peptide structure data for a set of peptide structures into a machine-learning model trained to identify a disease indicator based on the quantification data;
• and
• detecting the presence of a corresponding state of the plurality of states associated with preeclampsia in response to a determination that the identified disease indicator falls within a selected range associated with the corresponding state.

A method of treating preeclampsia in a subject comprising

• receiving peptide structure data corresponding to a set of glycoproteins in the biological sample obtained from a subject, wherein the peptide structure data comprises at least one peptide structure from Table 3;
• inputting quantification data for the at least one peptide structure into a machine-learning model trained to generate a risk score indicative of a risk for developing preeclampsia based on the quantification data;
• outputting, by the machine-learning model, the quantification data using the machine learning model to generate a risk score,
• administering an effective amount of an antihypertensive, a corticosteroid, a beta blocker, or an anticonvulsant to treat preeclampsia based upon the risk score.

A method of determining a risk for developing preeclampsia in a subject comprising:

• receiving peptide structure data corresponding to a set of glycoproteins in a biological sample;
• inputting quantification data identified from the peptide structure data for a set of peptide structures into a machine-learning model trained to identify a disease indicator based on the quantification data, wherein the set of peptide structure data comprises at least one peptide structure identified from a plurality of peptide structures in Table 3;
• identifying, by the machine-learning model, the disease indicator;
• and
• determining a risk for preeclampsia based upon the identified disease indicator.

A method of treating preeclampsia in a subject comprising:

• receiving peptide structure data corresponding to a set of glycoproteins in the biological sample;
• inputting quantification data identified from the peptide structure data for a set of peptide structures into a machine-learning model trained to identify a disease indicator based on the quantification data, wherein the peptide structure data comprises at least one peptide structure identified from a plurality of peptide structures in Table 3;
• identifying, by the machine-learning model, the disease indicator;
• determining a risk for preeclampsia based upon the identified disease indicator;
• and
• administering an effective amount of an antihypertensive, a corticosteroid, a beta blocker, or an anticonvulsant to treat preeclampsia based upon the risk score.

A method of diagnosing an individual with preeclampsia comprising

• detecting the presence or amount of at least one peptide structure structures from Table 3;
• inputting a quantification of the detected at least one peptide structure into a machine-learning model trained to generate a class label,
• determining if the class label is above or below a threshold for a classification;
• identifying a diagnostic classification for the individual based on whether the class label is above or below a threshold for the classification;
• and
• diagnosing the individual as having preeclampsia based on the diagnostic classification.