# Polygenic Risk and Linked Metabolic Profile in Systemic Lupus Erythematosus: Cross-Sectional Insights

## TL;DR

This study explores how genetic risk scores relate to metabolic profiles in systemic lupus erythematosus patients, revealing distinct patterns compared to metabolic syndrome.

## Contribution

The study introduces new insights into how polygenic risk scores influence metabolic and inflammatory traits in SLE patients.

## Key findings

- SLE patients had lower BMI, visceral fat, blood pressure, glucose, and liver enzymes compared to metabolic syndrome individuals.
- Higher polygenic risk scores in SLE were inversely associated with ferritin levels.
- Most inflammatory and metabolic markers in SLE were influenced by clinical factors rather than genetic predisposition.

## Abstract

Background/Objectives: Systemic lupus erythematosus (SLE) is a complex autoimmune disease with a multifactorial origin involving genetic, epigenetic, and environmental determinants as well as some risk factors. Genetic predisposition has been quantified through polygenic risk scores (PRS), which integrate the cumulative effect of multiple single nucleotide variants (SNVs) associated with disease risk. Despite extensive research on immune and inflammatory pathways in SLE, the interplay between genetic susceptibility and metabolic dysfunction remains poorly understood. This study aimed to explore associations between SLE-related PRS and metabolic, inflammatory, and clinical parameters in adults participating in the METAINFLAMACIÓN-CM project (Hospital Universitario Puerta de Hierro Majadahonda, Madrid, Spain). Methods: Ninety-three participants were included: 56 SLE patients and 37 individuals with metabolic syndrome (MetS) as a reference group. PRS were computed based on validated lupus-associated SNVs. Results: SLE patients showed a distinct metabolic profile compared with the MetS group, characterized by lower BMI, visceral fat, blood pressure, glucose, and liver enzyme levels. Within the SLE cohort, PRS values varied markedly and correlated with specific clinical and biochemical features. Linear regression models revealed a significant inverse association between PRS in SLE and ferritin levels, whereas other metabolic and inflammatory markers (glucose, IL-6, LDL, CRP, neutrophils) were directly influenced by clinical factors. Conclusions: Polygenic predisposition contributes to variability in SLE metabolic phenotype but does not independently drive most inflammatory parameters. SLE patients displayed metabolic and inflammatory alterations relevant to cardiovascular risk, highlighting the importance of comprehensive cardiometabolic assessment. Integrating PRS with metabolic profiling may support precision personalized management and improve cardiovascular risk evaluation in SLE.