Genetic predisposition and antipsychotic treatment effect on metabolic syndrome in schizophrenia: a ten-year follow-up study using the Estonian Biobank

Maris Alver; Silva Kasela; Liina Haring; Laura Birgit Luitva; Estonian Biobank Research Team; Health Informatics Research Team; Krista Fischer; Märt Möls; Lili Milani

doi:10.1016/j.lanepe.2024.100914

Genetic predisposition and antipsychotic treatment effect on metabolic syndrome in schizophrenia: a ten-year follow-up study using the Estonian Biobank

Lancet Reg Health Eur. 2024 Apr 26:41:100914. doi: 10.1016/j.lanepe.2024.100914. eCollection 2024 Jun.

Authors

Maris Alver¹, Silva Kasela¹, Liina Haring^{2

3}, Laura Birgit Luitva¹; Estonian Biobank Research Team; Health Informatics Research Team; Krista Fischer^{1

4}, Märt Möls^{1

4}, Lili Milani¹

Affiliations

¹ Estonian Genome Centre, Institute of Genomics, University of Tartu, Riia 23b, Tartu, 51010, Estonia.
² Department of Psychiatry, Institute of Clinical Medicine, University of Tartu, Raja 31, Tartu, 50417, Estonia.
³ Psychiatry Clinic of Tartu University Hospital, Raja 31, Tartu, 50417, Estonia.
⁴ Institute of Mathematics and Statistics, University of Tartu, Narva mnt 18, Tartu, 51009, Estonia.

Abstract

Background: Schizophrenia (SCZ) patients exhibit 30% higher prevalence of metabolic syndrome (MetS) compared to the general population with its suboptimal management contributing to increased mortality. Large-scale studies providing real-world evidence of the underlying causes remain limited.

Methods: To address this gap, we used real-world health data from the Estonian Biobank, spanning a median follow-up of ten years, to investigate the impact of genetic predisposition and antipsychotic treatment on the development of MetS in SCZ patients. Specifically, we set out to characterize antipsychotic treatment patterns, genetic predisposition of MetS traits, MetS prognosis, and body mass index (BMI) trajectories, comparing SCZ cases (n = 677) to age- and sex-matched controls (n = 2708).

Findings: SCZ cases exhibited higher genetic predisposition to SCZ (OR = 1.75, 95% CI 1.58-1.94), but lower polygenic burden for increased BMI (OR = 0.88, 95% CI 0.88-0.96) and C-reactive protein (OR = 0.88, 95% CI 0.81-0.97) compared to controls. While SCZ cases showed worse prognosis of MetS (HR 1.95, 95% CI 1.54-2.46), higher antipsychotic adherence within the first treatment year was associated with reduced long-term MetS incidence. Linear mixed modelling, incorporating multiple BMI timepoints, underscored the significant contribution of both, antipsychotic medication, and genetic predisposition to higher BMI, driving the substantially upward trajectory of BMI in SCZ cases.

Interpretation: These findings contribute to refining clinical risk prediction and prevention strategies for MetS among SCZ patients and emphasize the significance of incorporating genetic information, long-term patient tracking, and employing diverse perspectives when analyzing real-world health data.

Funding: EU Horizon 2020, Swedish Research Council, Estonian Research Council, Estonian Ministry of Education and Research, University of Tartu.

Keywords: Antipsychotics; Body mass index; Genetics; Metabolic syndrome; Polygenic risk scores; Real world data; Schizophrenia; Treatment.