We studied the utility of T1D-PGS and T2D-PGS in 370 women with previous GDM. We demonstrated a good ability for the T1D-PGS to predict development of type 1 diabetes, especially when combined with clinical risk factors during index pregnancy. These findings indicate that the T1D-PGS has potential to identify women with GDM at high risk of type 1 diabetes who may benefit from targeted surveillance postpartum.

The T2D-PGS was not predictive of development of type 2 diabetes, and when combined with clinical risk factors during pregnancy, it provided no substantial improvement compared with clinical risk factors alone. Overall, the variability between groups was low, which may reflect the cohort composition. Given the selective risk-factor-based screening strategy for GDM in Denmark, the GDM population probably has a higher a priori risk of diabetes than GDM populations identified through universal screening, as reflected in the relatively high prevalence of type 2 diabetes. Furthermore, temporal changes in type 2 diabetes trajectories following GDM in our cohort may also contribute to the limited predictive ability of T2D-PGS. Women diagnosed with GDM between 1987 and 1996 were older at the time of pregnancy, had a higher pregestational BMI, had more familial disposition to diabetes and progressed more rapidly to diabetes and prediabetes compared with those diagnosed earlier between 1978 and 1985 [6]. These findings suggest that the advanced maternal age and increasing prevalence of obesity over time contribute more to the development of type 2 diabetes and may thereby attenuate the relative influence of genetic factors. However, other studies have demonstrated that combining T2D-PGS with clinical risk factors in women with previous GDM improves predictive ability, with potential implications for preventive strategies [23, 35]. These discrepancies may reflect differences in clinical characteristics, including age, ethnicity, baseline metabolic risk factors and the long follow-up time among participants in our study.

To our knowledge, this is the first study to assess the utility of a T1D-PGS in women with previous GDM. Consistent with our findings, prospective studies in high-risk individuals, including children with HLA class 1 diabetes susceptibility genotypes, and individuals with a family history of type 1 diabetes who are positive for autoantibodies, have shown that higher T1D-PGS was associated with both disease progression and onset [17,18,19,20].

In this study, genetic risk was assessed using a T1D-PGS that has previously been validated in a European type 1 diabetes population within the UK Biobank, where it demonstrated strong discriminative ability of type 1 diabetes with an AUC of 0.92 and a median T1D-PGS of 14.6 among individuals with type 1 diabetes, corresponding to a sensitivity of 54% and a specificity of 97% [13]. In our study, the T1D-PGS had lower predictive performance with an AUC of 0.788. The median T1D-PGS for women who developed type 1 diabetes was 13.32, corresponding to a sensitivity of 50% and specificity of 84%. The lower median T1D-PGS and predictive capability observed in our study may reflect differences in clinical characteristics among participants. Higher genetic risk has been associated with earlier onset of type 1 diabetes [36, 37], and in our study, all participants were diagnosed in adulthood based on diagnosis codes, whereas the type 1 diabetes cases in the UK Biobank were defined using strict clinical criteria for type 1 diabetes, including development at a young age [13]. Consistently, the T1D-PGS values observed in our study align with findings from other studies of individuals with adult-onset type 1 diabetes [19, 37, 38].

In our study, the T1D-PGS, clinical risk factors during pregnancy and GADA measured at the clinical follow-up each demonstrated similar predictive ability for type 1 diabetes on their own. The highest predictive performance was achieved by combining clinical risk factors during pregnancy and GADA. It is important to note, however, that GADA were measured at the clinical follow-up, when all type 1 diabetes cases had developed, and do not necessarily reflect a prediction that would be available during pregnancy. Nevertheless, this finding underscores that if autoantibodies had been available during pregnancy, the predictive performance would probably have improved.

A major advantage of a PGS compared with clinical risk factors and biomarkers is that it remains constant throughout life and can be measured at any time point, whereas clinical predictors and biomarkers may be unavailable or vary over time. Assessing genetic risk for type 1 diabetes in women with GDM offers several clinical advantages, including targeted postpartum follow-up and more accurate disease classification. Postpartum follow-up in individuals with high T1D-PGS may include autoantibody screening, as the presence of autoantibodies has been shown to be a strong predictor of type 1 diabetes development in women with GDM [10, 11]. In other high-risk type 1 diabetes populations, autoantibody monitoring programmes have proven effective for early detection of disease progression, reducing the risk of diabetic ketoacidosis at diagnosis and allowing timely therapeutic interventions to delay the onset of overt type 1 diabetes [7, 8, 21, 39]. Another potential advantage of assessing genetic risk for type 1 diabetes is more accurate diabetes classification, as the T1D-PGS has proven effective in discriminating between type 1 and type 2 diabetes, as well as between type 1 diabetes and MODY [14, 16]. This may be particularly relevant for women with GDM, as identifying those with high risk of type 1 diabetes can be challenging due to the predominance of later-onset type 2 diabetes, combined with type 1 diabetes often being perceived as a disease occurring in childhood or adolescence [36, 40]. However, approximately half of all cases with type 1 diabetes present in adulthood, often with clinical features similar to young-onset disease, including diabetic ketoacidosis [38, 40, 41]. In this respect, the T1D-PGS may guide the diagnosis in a group with high risk of diagnostic misclassification, especially during the first decade after pregnancy, when most type 1 diabetes cases occur [5]. Consistent with this, we found that the T1D-PGS had the highest predictive performance within the first 10 years after index pregnancy.

Despite the potential advantages of using genetic screening for type 1 diabetes in women with GDM, several challenges to clinical implementation remain. A key challenge is defining clinically useful thresholds for high genetic risk. Given that type 1 diabetes is a chronic and progressive autoimmune disease with an unpredictable progression to overt disease, knowledge of high genetic risk may induce psychological stress and potentially adversely affect quality of life [42]. These considerations support the use of thresholds with high specificity to minimise false positive results. The median T1D-PGS of 13.32 among women with type 1 diabetes in our study had a moderate specificity of 84%, whereas the previously established median T1D-PGS value of 14.6 from individuals with type 1 diabetes in the UK Biobank provided a higher specificity of 93%; however, this cutoff identified only one-third of type 1 diabetes cases, highlighting the challenge of defining clinically relevant thresholds. Other challenges for clinical implementation include lack of guidelines for follow-up and monitoring of high-risk individuals. Additionally, most healthcare professionals have limited experience with interpreting and communicating PGS results, as these have primarily been used for research purposes. As such, clinical implementation would necessitate specific education and training. However, given the substantial clinical potential within this field, growing attention has been directed toward these challenges [43]. Moreover, economic considerations must be addressed, as universal genetic testing of all women with GDM would be costly. However, a more targeted approach may be warranted, focusing on those with the highest clinical risk, based on e.g. BMI and high glucose levels.

A key strength of our study is the assessment of data at multiple timepoints, including clinical risk factors during pregnancy, detailed follow-up assessments of glucose tolerance at a median of 11 years after pregnancy and registry data with ICD-10 diagnosis codes for diabetes covering up to 42 years after pregnancy. Data on clinical risk factors present at the time of pregnancy enable the assessment of the predictive ability of the T1D-PGS already at the time of pregnancy. Furthermore, the combination of the clinical examination of glucose tolerance, including measurements of C-peptide and GADA at follow-up, knowledge on MODY-predisposing genetic variants and registry data enabled thorough diagnostic assessment of diabetes development.

Our study also has limitations. A major limitation is the small number of women with type 1 diabetes in our cohort, as the condition is relatively rare. The limited sample size increases sensitivity to random variation, and validation in larger, independent cohorts is warranted. Furthermore, women were recruited based on deliveries from a single centre, and all were self-reported white Danish, which reduces the generalisability of our findings to a more unselected GDM population [44]. In addition, the T1D-PGS was developed and validated using genetic data primarily from white Europeans, and studies evaluating the utility of T1D-PGS in other ethnic groups have demonstrated varying risk estimates, raising concerns about the external validity in real-world populations of individuals with mixed ethnic backgrounds [45]. Further, as type 1 diabetes developed after pregnancy, we have no information on disease progression and cannot exclude that some women in our study may have latent autoimmune diabetes in adults (LADA), which is a slowly progressing form of autoimmune diabetes characterised by the presence of autoantibodies and a longer insulin-free period following diagnosis [46]. In Denmark, there are no specific diagnosis codes for LADA, which can result in misclassification as both type 1 and type 2 diabetes. Given that LADA typically exhibits a genetic profile intermediate between type 1 and type 2 diabetes [46, 47], classification of LADA as type 1 diabetes may contribute to a lower overall T1D-PGS in the present cohort. Furthermore, a limitation is the lack of valid time-to-diabetes information, as only secondary care diagnoses are captured in the registries, leaving prior diagnoses in primary care unobserved and thereby limiting time-to-event analyses.

Further research is necessary to validate our findings and assess the applicability of the T1D-PGS in women with GDM, ideally with prospective designs and larger cohorts comprising more women with type 1 diabetes.

In conclusion, the T1D-PGS demonstrated good predictive ability for development of type 1 diabetes in women with GDM, especially when combined with clinical risk factors during pregnancy. Furthermore, the T1D-PGS offers a possibility to distinguish between type 1 diabetes and other forms of diabetes, and thereby holds potential as a clinical tool to identify women who could benefit from targeted follow-up after pregnancy, potentially enabling therapeutic interventions to delay disease onset. However, several challenges remain for implementation into clinical practice, including defining thresholds for high risk and establishing standardised guidelines for clinical follow-up. The T2D-PGS showed no predictive ability for type 2 diabetes.