The inflammatory path toward type 1 diabetes begins during pregnancy

Domain: biology
Published: 2026-01-07
Editorial outcome: major_revision

Abstract
Type 1 diabetes (T1D) is increasing globally, yet the earliest biological determinants remain poorly defined, particularly in general population studies. We studied the Swedish population-based ABIS birth cohort (n = 16,683) to identify early-life risk factors. Olink proteomic analysis (n = 286 controls, n = 146 cases) of inflammatory signals at birth shows differential abundance years before diagnosis (mean age 12.6 years), with proteins enriched for neutrophil migration, cytotoxicity, extracellular matrix remodeling, and immune regulation. Several markers remain significant in spite of prenatal and perinatal factors including family history of diabetes, and are associated with differences in compounds like stearic acid, lysine, glutamine, and persistent, environmental toxicants perfluorodecylethanoic acid and perfluorooctane sulfonate (PFOS). Using machine learning, we identify a protein subset that predicts T1D with high accuracy (AUC = 0.89 ± 0.02), independently of HLA genetic risk. These findings suggest that innate and tissue-remodeling pathways are perturbed at birth, possibly reflecting early β-cell vulnerability. Identifying these disruptions at birth with a non-invasive method opens a window for prevention, protecting β-cells before the inflammatory attack on islets begins.

Introduction
Type 1 diabetes (T1D) is a chronic autoimmune disease in which the β-cells of the pancreas are destroyed, ultimately leading to insulin deficiency. An estimated 8.4 million people worldwide live with T1D, with prevalence projected to increase to 13.5–17.4 million in the next two decades1. T1D can lead to acute and late complications. Environmental factors are associated with increased incidence2,3, including childhood enteroviral infections4–7, maternal respiratory or enteric infections during pregnancy4, serious life events5,6, and psychological stress7,8. The autoimmune process and risk of progression to clinical diabetes are strongly linked to genetics, including human leukocyte antigen (HLA) DR-DQ genotype. HLA genes, especially class II, contribute to 40–50% of genetic risk9,10, although less consequential if multiple autoantibodies have already developed11.

T1D etiology may be driven by autoimmunity and/or by increased insulin demand, triggering glucotoxicity and the other events that ultimately lead to β-cell destruction1,12,13 is not known. β-cells, compared to α-cells, are more sensitive to inflammatory conditions, where exposure to the immune system and cytokines causes significant gene and protein expression changes. These changes include activation of signal transducer and activator of transcription 1, interferon regulatory factor 1, and nuclear factor kappa-light-chain-enhancer of activated B cells (NF-κB) downstream pathways and adaptive, compensatory mechanisms that are activated to help deal with the inflammatory pressure12. Environmental risk factors for T1D continue to be explored, but findings often vary across observational cohorts, complicating the identification of specific triggers14. Issues such as selection bias in case-control studies and the limitations of autoantibody-based prediction further challenge interpretation15. Ultimately, current evidence suggests that immune tolerance is disrupted by varied and diverse environmental triggers and exposures, leading to β-cell stress3.

HLA genetics16 and autoantibodies are useful in prediction17. Non-invasive biomarkers, irrespective of genetic risk18 would be a major advance. Proteomic studies in high-genetic-risk children19,20 have revealed changes near seroconversion and before disease onset (after autoimmunity)21, with notable temporal shifts just before autoantibody detection. While these findings lay important groundwork and suggest contributors to beta-cell destruction, they are limited either by small sample size or a focus on later disease stages, leaving the earliest markers, such as those present at birth, unexplored22.

Hence, we investigate inflammatory birth proteomic markers in a large, general population cohort. Proteomic signatures are identified, suggesting that pathogenesis begins as early as pregnancy. Proteomics may offer significant prediction potential in the general population, without reliance on genetics screening, islet antibodies assays, or any invasive procedure.

Results
The All Babies in Southeast Sweden (ABIS)23 general population, birth cohort of 16,683 infants is used in this study and represents 78.6% of all children born in Östergötland, Småland, Blekinge, and Öland counties from Oct 1st 1997–Oct 1st 1999 (Fig. 1a). Physician diagnoses (ICD-10 code, E10) are obtained from the National Swedish Patient Register, validated by the National Swedish Drug Prescription Register, inclusive of all diagnosed cases up until Dec 2023. Cases included 167 ABIS children are diagnosed T1D up until this time, with a cumulative incidence of 1% and age at diagnosis ranging from 2 to 24.6 years (mean ± SD: 12.6 ± 6.1 years, median: 12.6 years, 95% CI [11.7, 13.5]; Fig. 1f). Here, controls are defined as ABIS individuals without any future autoimmune disease (n = 15,732).

Proteomic profiling of cord blood from 1,204 children is performed (Fig. 1b). As a number of these children had other diagnoses in this period, their samples are used here only for environmental association. Machine learning (ML) models identify predictive proteins in 432 selected children, including controls and cases with future T1D (Fig. 1c). Non-linear protein-metabolite-exogenous compound associations are determined using Shapley Additive exPlanations24 (SHAP) in 132 children with metabolomic data (Fig. 1d) and associations with environmental factors assessed, irrespective of future disease. Several key risk factors and proteins are significantly relevant to future disease on the basis of these approaches (Fig. 1e).

Prenatal risk factors associated with future T1D
Birth questionnaires are assessed for association with future T1D diagnosis, comparing 15,732 controls to 167 cases. A total of 44 variables are tested, including perinatal factors, family medical history, parent factors, prenatal stressors, prenatal diet, and medications and infections during pregnancy (Supplementary Data 1). The most significant birth factors are those related to family medical history of (e.g., T1D, T2D, and asthma). However, also significant are (1) perinatal factors (e.g., caesarean section, placement in a newborn care unit); (2) demographics; (3) stomach flu during pregnancy; (4) medications taken during pregnancy (e.g., antibiotics and psychotropics); whether time is spent with someone with insulin-dependent diabetes during pregnancy; and to a limited extent, prenatal diet (Fig. 2a–c and Supplementary Data 1).

These factors are carried into ML models for prediction. With the ABIS cohort having the expected global T1D incidence of 1%, class-weighted loss functions are employed to mitigate the effect on performance and generalizability. However, the predictive performance of logistic regression, eXtreme Gradient Boosting (XGBoost), Random Forest, and Support Vector Machine (SVM) models using these factors alone is limited, with area under the receiver operating characteristic curve (AUC-ROC) values from 0.43 to 0.62 (Fig. 2d).

Cord blood proteomic analyses
Selected inflammatory and immune markers are analyzed from cord blood samples spanning 146 cases and 286 controls, measured using OLINK Explore 384 Inflammation 1 and 2 panels and the Target Immune Response panel. Controls are defined as those individuals having no future autoimmune diagnoses, psychiatric conditions, or neurodevelopmental disorders, while cases had confirmed T1D diagnoses (up to 22 years of age). Controls are selected at random from the larger ABIS study. Given the significant class imbalance described above, the minority and majority classes are intentionally oversampled and undersampled, respectively.

Consistent with the full ABIS study, individuals in the T1D proteomic group are significantly more likely to have been placed in a pediatric ward as newborns (odds ratio, OR = 2.1, p = 0.015), with 13.9% of the T1D group experiencing this compared to 6.6% of controls. As expected, there is a higher likelihood of T1D among family members, including the mother (p < 0.001), father (p = 0.003), and grandparents (p = 0.014). Specifically, 29.5% of T1D individuals had a family history of T1D compared to only 10.5% of controls (p < 0.00001; OR = 3.6, 95% CI: 2.1–6.0, p < 0.0001). No significant differences are observed between groups in terms of biological sex, mode of delivery, gestational age, birth weight, maternal BMI (both pre- and post-pregnancy), parental age at birth, or other assessed environmental factors, including infection or smoking exposure during pregnancy and severe life events (Supplementary Data 2).

Groups are broadly representative relative to the overall ABIS cohort, showing no major evidence of selection bias (Table 1 and Supplementary Data 3). The only notable difference is a modest overrepresentation of females among controls included in the Olink subset (52.5%) versus those controls who are not (47.9%). Gestational age is slightly higher in T1D subjects selected for Olink analysis (39.7 ± 1.6 versus 38.4 ± 3.1 weeks). Nevertheless, the selection of controls seen in the Olink case/control cohort is sufficient to ensure that the Olink groups are broadly representative of the full ABIS study, with no major evidence of selection bias (Table 1). NPX levels are assessed across the entire case/control proteomics cohort (between future T1D, n = 146, and controls, n = 286; Fig. 3) and then stratified by HLA genetic risk for T1D (see below).

Global protein concentration differences by future diagnosis
Across the 386 proteins assessed (Supplementary Data 4), 32 are significantly, differentially expressed after false discovery rate (FDR) correction (Fig. 3a and Supplementary Data 5 and 6). Four proteins are significantly higher in cases: HLA class II histocompatibility antigen, DR alpha chain (HLA-DRA; p = 8.98e-19, q = 6.53e-16); iduronate 2-sulfatase (IDS; p = 6.65e-13, q = 2.42e-10); secretoglobin family 3A member 2 (SGB3A2; p = 0.00031, q = 0.014), and cathepsin C (CTSC; p = 0.00040, q = 0.017).

Among the 28 proteins significantly higher in controls after FDR correction were: (1) tissue inhibitor of metalloproteinases 3 (TIMP3; p = 3.68e-9, q = 8.91e-7); CD40 ligand (CD40LG; p = 1.44e-7, q = 2.61e-5); matrix extracellular phosphoglycoprotein (MEPE; p = 1.09e-6, q = 1.58e-4); adenosine deaminase (ADA; p = 2.51e-6, q = 2.98e-4); neurotrophin-3 (NTF3; p = 2.87e-6, q = 2.98e-4); CD84 molecule (CD84; p = 5.34e-6, q = 4.85e-4); serine protease inhibitor Kazal-type 2 (SPINT2; p = 1.28e-5, q = 0.0010); PDZ and LIM domain protein 7 (PDLIM7; p = 7.72e-5, q = 0.0056); and linker for activation of T cells (LAT; p = 8.49e-5, q = 0.0056). Strong correlations in proteins elevated in controls are observed: lymphocyte-specific protein 1 (LSP1) and ADA (R = 0.59), tissue inhibitor of metalloproteinases 3 (TIMP3) and CD40LG (R = 0.5), and PDLIM7 and TBC1 domain family member 5 (TBC1D, R = 0.79, p’s < 2.2e-16).

To ensure balanced comparison, a 1:1 selection of cases and controls is performed using propensity score matching (n = 280), accounting for significant factors including family history of T1D (in the mother), mode of delivery, sex of the child, week of delivery, vulnerability index, serious life events, and stomach flu during pregnancy. Subsequent analysis, adjusted for confounders and corrected for multiple comparisons, confirmed the same markers identified in the global analysis, demonstrating that the observed effects persist independently of family history (Fig. 2e). Notably, serine protease prostasin (PRSS8) also emerged as significant.

Gene enrichment of T1D-associated proteins
Gene Set Enrichment Analysis (GSEA) showed that the T1D-associated proteins serve as core components in pathways involving cytokine signaling and immune responses. Involved in cytokine pathways are HLA-DRA, CD40LG, and TIMP3, while others, such as CTSC, linker for activation of T cells (LAT), CD40LG, CD84, and ADA, play central roles in pathways related to immune system regulation, leukocyte function, the Reactome, and both adaptive and innate immune signaling (Fig. 3c–e). In the future T1D group, two gene sets, SWEET_LUNG_CANCER_KRAS_UP and GOCC_CELL_SURFACE, are upregulated. In contrast, nine gene sets are downregulated, reflecting disruptions in stress response, hydrolase regulation, and intracellular signaling pathways (Supplementary Data 7). The enriched gene sets are associated with notable core proteins: (1) TIMP3 and neurotrophin-3 (NTF3) with six of the nine downregulated gene sets; (2) forkhead box protein O1 (FOXO1) with five; (3) caspase-2 (CASP2) and MAP2K6 (mitogen-activated protein kinase kinase) with four (Fig. 3c); and 4) HLA-DRA shared between the two upregulated gene sets.

Protein concentration differences by age at diagnosis
Future cases are categorized by diagnosis at ≤5 years, 6–10 years, 11–17 years, and 18–24 years. Each group is compared to controls (Fig. 4a–c and Supplementary Fig. 1a), with results in Supplementary Data 8. The most significant proteomic differences are observed among individuals diagnosed by age 5, with 114 significant proteins, 64 remaining significant after FDR. Shared across at least three age groups are reduced ADA, TIMP3, CD40LG, and signaling threshold-regulating transmembrane adaptor 1 (SIT1), alongside increased HLA-DRA, IDS, secretoglobin family 3A member 2 (SCGB3A2), and CTSC. Unique to newborns who developed T1D in adulthood (18–24 years) are increased interleukin-20 (IL20) and C-C motif chemokine ligand 4 (CCL4)—also called macrophage inflammatory protein-1β, MIP-1β—and decreased Wnt family member 9A (WNT9A, Supplementary Fig. 1a).

Protein-protein association network and functional pathway enrichment analyses25 of the FDR-corrected protein differences in earliest-diagnosed cases (by age five) demonstrate significant interaction enrichment (protein-protein interaction (PPI) enrichment, p = 2.03e-07, Supplementary Fig. 1b). Significant enrichments of pathways related to toxoplasmosis; positive regulation of protein serine/threonine kinase activity, transferase activity, and MAP kinase activity; modulation of T-cell activation and inflammatory responses; vitamin D in inflammatory disease; Yersinia infection; signaling of RANKL/RANK, C-type lectin receptors, T-cell receptors, NF-κB, Ebstein-Barr virus LMP1, and cell surface receptors were found—all significant after FDR (Fig. 4d–f and Supplementary Data 9).

Protein concentration differences stratified by HLA genetic risk
Next, future T1D cases (n = 146) are compared to two subsets of controls (81 controls with genetic risk for T1D and 76 controls without risk; Supplementary Data 10). HLA risk is defined by the presence MHC Class II alleles DR4-DQ8 (DRB1-DQA1*03:01-DQB1*03:02) and/or DR3-DQ2 (HLA-DRB1*03:01-DQA1*05:01-DQB1*02:01)23,26, seen in as many as 90% of T1D patients27,28, as these represent the primary risk factor for islet autoimmunity29,30.

Comparing non-genetic risk controls (lacking DR4-DQ8 or DR3-DQ2 alleles) versus future T1D (Fig. 5a), four proteins are significant after FDR correction: HLA-DRA and IDS (higher in T1D) and TIMP3 and NTF3 (higher in low-risk controls (Fig. 5a). Consistent with global T1D results, the greatest differences are in HLA-DRA and IDS (higher in T1D) and TIMP3 and NTF3 (higher in controls without DR4-DQ8 or DR3-DQ2).

Next, future T1D cases are separated by HLA risk (decreased/neutral, increased/high) and compared to controls with similar genetics (Supplementary Data 10). Decreased/neutral HLA risk genotypes are seen in 125 controls and 18 with future T1D, with 20 significant proteins significant, with persistent higher levels of IDS (Fig. 5b). HLA-DRA is also higher in future T1D without HLA genetic risk, as is seen in global T1D comparisons (Fig. 5b), but not significant after FDR, as are CCL4, C-C motif chemokine ligand 21 (CCL21), lymphocyte antigen 9 (LY9, also called SLAMF3), lymphocyte antigen 75 (LY75, also called DEC-205 or CD205), tumor necrosis factor (TNF) receptor superfamily member 13B (TNFRSF13B, also called TACI), CD70 molecule (also called TNF ligand superfamily member 7, TNFSF7) CD70, signal regulatory protein beta 1 (SIRPB1), and Erb-B2 receptor tyrosine kinase 3 (ERBB3). Notably, TIMP3 is not significant between cases and controls both lacking high-risk HLA for T1D.

Far more differences are seen when comparing those with genetic risk, even after FDR adjustment (Fig. 5c, d). When comparing all future T1D to controls with either DR4-DQ8 or DR3-DQ2, four differential proteins are identified, after FDR, including SCGB3A2, HLA-DRA, and IDS (higher in T1D), as well as TIMP3 (higher in controls) (Fig. 5c). Next, subsets of children with high/increased risk are compared (n = 76 T1D, n = 44 controls). Eleven proteins are significantly different after FDR (Fig. 5d), including: HLA-DRA, IDS, and SCGB3A2 (higher in high-risk T1D) and TIMP3, LAT, CD40LG, ADA, PDLIM7, integral membrane protein 2A (ITM2A), and CD84 (higher in high-risk controls). In individuals lacking DR3-DQ2 (n = 53 T1D, n = 112 controls), with most T1D cases carrying DR4-DQ8, numerous differences are observed (Fig. 6a, b). The proteins most enriched in T1D after FDR are HLA-DRA, IDS, and IL20. In contrast, proteins like ADA, aldehyde dehydrogenase 3 family member A1 (ALDH3A1), CD40LG, LSP1, MEPE, NTF3, and TIMP3 are more highly expressed in controls. Conversely, in the subset lacking DR4-DQ8 (n = 24 T1D, n = 111 controls), fewer significant markers are identified, not reaching significance after FDR correction (Supplementary Fig. 2a, b).

Prediction of future T1D using proteomics at birth
By prioritizing proteins with high mutual information (MI) scores, T1D prediction models are focused on proteins with the highest relative influence on prediction, capturing meaningful and potentially complex protein relationships. The mean relative concentration levels of the top 40 proteins (Supplementary Fig. 3a), and their relative contribution to case and control distinction (Supplementary Fig. 3b), are assessed. Key contributors tested are HLA-DRA, IL16, PTH1R, GAL, and IDS. The observed differences between future T1D and controls aligned with previously identified markers such as HLA-DRA, IDS, PDLIM7, SPINT2, ADA, TIMP3, and LAT (Supplementary Fig. 3c).

Diagnostic accuracy of XGBoost, Random Forest, Logistic Regression, and SVM models is assessed using the top 40 (Supplementary Fig. 3d) and a refined set of 32 proteins (via the elbow method; Supplementary Fig. 3e), to predict future T1D using MI scoring. While Random Forest slightly outperformed in AUC-ROC, XGBoost matched closely and delivered a more robust F1 score when using 40 proteins, making it the preferred model for prediction. With fivefold cross-validation, XGBoost achieved an AUC-ROC of 0.77 ± 0.04, with precision (0.63 ± 0.05), recall (0.69 ± 0.07), and a strong F1 score (0.65 ± 0.04), all showing that the model is generalizable and not overfitting. Prediction of future T1D, using these proteins alone, achieved high accuracy (AUC = 0.77).

Given the established association of DR4-DQ8 with early-onset T1D31,32, we apply a separate predictive model in a restricted subset of children, comparing controls to individuals who later developed T1D but also carry DR4-DQ8 alleles (Supplementary Fig. 3f and Supplementary Fig. 4). Following hyperparameter tuning via GridSearch, the model achieves a mean AUC-ROC of 0.83 ± 0.12 (Supplementary Fig. 3g), further increasing prediction accuracy. It demonstrated robust performance across classification metrics, including an F1 score of 0.62 ± 0.16, precision of 0.66 ± 0.20, recall of 0.63 ± 0.20, and an overall accuracy of 0.84 ± 0.07.

Finally, we apply a supervised ML approach using recursive feature elimination (RFE) on the full Olink case/control dataset (n = 432). The data are split into training (80%) and testing (20%) sets, preserving class proportions. RFE is applied to recursively select the top 30 most predictive features, first excluding (Fig. 7a, b) and then including (Fig. 7c, d) dosage of HLA high-risk alleles DR4DQ8 and DR3DQ2. We perform fivefold stratified cross-validation using these fixed selected features within each fold to evaluate generalizability and maintain interpretability. Both models, Olink-only and Olink+HLA, perform similarly. The AUC-ROC is 0.89 ± 0.02 without HLA dosage (Fig. 7a) and 0.89 ± 0.03 with HLA dosage (Fig. 7c), indicating no improvement from adding HLA genetic information. The Olink+HLA model yields an F1 score of 0.70 ± 0.06, precision of 0.76 ± 0.10, and recall of 0.67 ± 0.08. The Olink-only model has slightly better performance by its F1 score of 0.73 ± 0.05 and recall of 0.71 ± 0.07, but precision of 0.74 ± 0.06. Feature selection by RFE demonstrates highly overlapping predictive protein sets, with 27 of the same 29 proteins (93%) shared between models (Fig. 7b, d). HLA-DRA and IDS are consistently ranked the top two proteins. The median difference in protein abundance in the RFE-selected proteins for the Olink+HLA model is presented for cases and controls in Fig. 7e. Given the similar predictive value of the models, to explore redundancy between allele dosage and HLA-DRA protein levels, an ANOVA test compares HLA-DRA abundance by dosage groups, revealing a strong association (p = 1.12e-08, Fig. 7f).

Metabolites, exogenous toxic compounds (PFOS), and T1D-linked proteins
Using non-parametric correlation methods in the 132 individuals with both metabolomic and proteomic data, several proteins enriched in controls—TIMP3 and PDLIM7—are significantly associated with cord serum metabolites. PDLIM7 showed positive correlations with multiple metabolites, including aspartic acid, deoxynivalenol (DON), fumaric acid, lysophospholipid lysophosphatidylethanolamine (lysoPE) 18:0, and malic acid (FDR-adjusted p = 0.0055–0.032). TIMP3 shows broader associations, including positive correlations with 3-indoleacetic acid, mycotoxin DON, glutamic acid, isocaproic acid, isoleucine/leucine, isovaleric acid, lysine, proline, and tyrosine (FDR-adjusted p = 0.012–0.042). Although non-parametric and less sensitive to extremes, the Spearman correlation is not entirely immune to outliers, which can still affect the rank order. To address this concern, we repeated the analysis removing the most extreme 5% of samples (lowest 2.5% and highest 2.5%), and results are largely consistent for TIMP3 (Supplementary Fig. 5; FDR-adjusted p’s = 0.006–0.03), suggesting that a small number of influential outliers did not sway the results. However, for PDLIM7, only lysoPE 18:0 remains significant (rho = 0.36, FDR-adjusted p = 0.007), along with perfluorinated compound perfluorooctanoic acid, branched isomer (PFOA br, rho = 0.32, FDR-adjusted p = 0.03).

Given that perfluorooctane sulfonate (PFOS) is a persistent environmental contaminant with immunomodulatory and metabolic effects, we next examine the correlation of circulating proteins with PFOS-L, the linear isomer that is often the dominant form in environmental samples. Using a nonparametric Spearman test, we observe directional associations of PFOS with the following immune proteins: IL18 (p = 0.003), SELPLG (p = 0.016), TNFRSF4 (p = 0.027), DAPP1 (p = 0.027), IL20 (p = 0.042), COLEC12 (p = 0.043), and ESM1 (p = 0.048) show negative correlations, while IL17A (p = 0.020) and ENPP5 (p = 0.038) are positively correlated (Fig. 8). Although these trends do not survive FDR correction due to the smaller sample size (n = 132), they highlight biologically plausible immune pathways that may be influenced by PFOS exposure for future study.

To complement this univariate analyses, SHAP (Shapley Additive exPlanations24) is applied to assess multivariate, non-linear associations between metabolites and exogenous compounds with the five proteins most predictive of future T1D: HLA-DRA, IDS, and CTSC (elevated in T1D), and ADA and TIMP3 (elevated in controls), as identified through both traditional (Figs. 3 and 4) and ML approaches (Fig. 7 and Supplementary Fig. 3). SHAP values revealed complex feature interactions and non-linear patterns for each protein (Fig. 9a), with the top metabolites identified by MI score (Fig. 9a–f). TIMP3 concentration is positively associated with lysine and tauro-β-muricholic acid (TbMCA) while negatively associated with stearic acid (Fig. 9b). For ADA concentration, arachidic acid and PFOS-L are positively and negatively associated, respectively (Fig. 9c). Stearic acid consistently demonstrates a strong positive association with IDS and HLA-DRA concentration (Fig. 6d, e), as does PFOS-L with HLA-DRA, although clustering suggests non-linearity. Examples of negative contributors to IDS and HLA-DRA concentration include LPE-16:0 and pyroglutamic acid, respectively (Fig. 9f).

Prenatal/perinatal factors and T1D-associated proteins
In the full Olink dataset (n = 1202), associations are discovered between the 65 proteins most significant across T1D and control global comparisons (from Fig. 3). Wilcoxon statistics are run across these proteins with respect to biological sex and several perinatal and prenatal factors on the birth questionnaires (Fig. 9g, h and Supplementary Data 11). After FDR correction, three proteins higher in males, corneodesmosin (CDSN), SH2 domain–containing protein 1A (SH2D1A), and cathepsin O (CTSO), remained significantly associated (Fig. 9g), and another 24 proteins are associated with mode of delivery (Fig. 9h). Protein associations with other environmental factors are found, including smoking of the mother, maternal stomach flu, and severe life events during pregnancy, but none are significant after FDR and thus are not presented.

Methods
Participants
The ABIS cohort follows, to present day, 16,683 children born between October 1997 and 1999 in Southeastern Sweden. Families are recruited from nine obstetric clinics across all hospitals in the counties of Östergötland, Småland, Blekinge, and Öland. They receive oral and written information and are invited to join the study. Out of the 21,700 children born in the region, 78.6% of families provided informed consent for their child to participate. Cord blood samples were collected from all ABIS children. Parents completed extensive questionnaires and diaries from the child’s birth through 13 years of age, with ABIS individuals themselves answering questionnaires in addition to parents at 8, 11–13, and then themselves at 17–19 and 23–26 years of ages77. Only the birth questionnaire is analyzed in the present study, focused on pregnancy and birth.

Biological sex (male/female) is collected in the ABIS study from the birth questionnaire (reported by the parent) and is included in the study design. Gender identity is not collected; therefore, all analyses pertain solely to biological sex. The ABIS cohort shows a relatively balanced distribution of biological sex (48.2% female, 51.8% male), and the distribution of biological sex in the T1D case and control groups is broadly representative, but with a slight overrepresentation of females among controls included in the Olink proteomics subset (52.5%) compared with controls not included (47.9%). No socially constructed or socially relevant variables, such as race, ethnicity, or socioeconomic status, are collected in this study. The ABIS cohort consists primarily of children born in Sweden to Swedish parents, and although data on race, ethnicity, or ancestry are not gathered, the population is expected to be relatively homogeneous. At enrollment, 89.2% of ABIS children have both parents born in Sweden, 7.7% have one parent born outside Sweden, and 3.1% have both parents born outside Sweden78. These data come from self-report, and serve only to contextualize the demographic structure of the cohort rather than classify participants into sociocultural groups. Because these variables are not collected, we do not use race or ethnicity as proxies for any other social variable, and no analyses rely on such constructs.

Diagnoses and study design
Diagnoses are obtained using International Classification of Diseases (ICD) codes as reported in the Swedish National Patient Register, which includes records from specialist consultations in outpatient care from public and private providers. Up to December 2020, a total of 167 ABIS children had received a T1D diagnosis (ICD-10 code E10), with diagnoses occurring at an average of 12.6 ± 6.1 years, ranging from 2 to 24.6 years (median: 12.6 years) and a cumulative incidence of 1%. Controls are defined as individuals without any autoimmune or neurodevelopmental diagnoses in their medical record, or according to the Swedish National Patient Register.

This study uses observational human cohort data with no experimental intervention. Group allocation (future T1D vs. control) was determined independently through clinical follow-up using predefined diagnostic criteria and not assigned by investigators. Blinding was not applicable, as predefined clinical outcomes and standardized laboratory measurements were used. Group status (future T1D vs. control) was determined independently through clinical follow-up, and all analyses were conducted using objective computational methods without subjective assessment. No formal sample size calculation was performed. This study leveraged all cord blood samples from children who later developed T1D in the ABIS cohort available at the time of analysis, along with eligible controls with sufficient material. As this was an observational cohort study using existing biobank material, sample sizes were determined by sample availability and are consistent with prior multi-omic studies of early-life T1D risk.

Birth questionnaires
The psychosocial vulnerability index, as used in Ahrens et al. 78, is derived from questions pertaining to the following: living conditions, parental education levels, employment and income, stressful life events, social support, and safety (Table 2).

Olink inflammatory and immune panels
Olink’s Explore 384 Inflammation 1 and 2 panels and Target Immune Response panel are used to analyze cord serum samples (n = 1204) at Olink’s headquarters in Uppsala, Sweden, encompassing 286 controls, 147 with future T1D, as well as samples from children with other conditions in the future. Controls are identified as individuals without any future diagnoses of autoimmune diseases, psychiatric disorders, or neurodevelopmental conditions, whereas cases consisted of individuals with confirmed T1D diagnoses by 22 years of age. Control subjects are randomly selected from the broader ABIS study population. To address the notable class imbalance, oversampling of the minority class and undersampling of the majority class are performed. Future T1D case and control groups are broadly representative of the full ABIS cohort, with no major evidence of selection bias (Table 1). The only notable difference is a modest overrepresentation of females among controls included in the Olink subset (52.5%) versus those controls who are not (47.9%).

The Olink Explore panels include 370 inflammatory protein markers, with an additional 14 proteins for quality control. These markers encompass a range of chemokines and cytokines, such as interferon gamma, IL-1α, IL-6, IL-10, IL-12, IL-13, IL-15, IL-17, and LTa. The Olink Target Immune Response panel comprises 92 protein biomarkers, focusing on adaptive immune responses, viral responses, and T-cell proliferation. The following assays do not meet Olink’s batch release quality control criteria and are therefore not included in this project: BCL2L11, BID, LTA, GZMB, MGLL, FLI1, MPI, EBI3-IL27, and ANGPTL7.

Quality control in proteomic analysis
Internal and external controls are run to ensure that the data does not suffer from batch effects of other technical variations. Three internal controls are added to each sample: the Incubation control, the Extension Control, and the Amplification control. The Extension Control is used for the generation of the NPX values. The Incubation Control and the Amplification Control are used to monitor the quality of assay performance, as well as the quality of individual samples. In addition, three external controls are included in each run, the Plate Control (healthy pooled plasma), Sample Control (healthy pooled plasma), and Negative Control. The Plate Control is used for data normalization, the Sample Control is used to assess potential variation between runs and plates, and the Negative Control is used to calculate Limit of Detection (LOD) for each assay and to assess potential contamination of assays. To pass QC, there should be at least 500 average matched counts (reads for each specific combination of sample and assay). Also, the deviation of the median of the Negative Controls must be less or equal to 5 standard deviations from the set predefined value for that assay, otherwise the assay receives a warning status. Two of the 1204 samples do not meet quality control, resulting in a final dataset of 1202 ABIS individuals.

Pre-processing of proteomic data
The proteomic data is presented as normalized protein expression (NPX) values. The NPX is Olink’s relative protein quantification unit on log2 scale. These values are calculated from the number of matched counts, using Next Generation Sequencing as readout. Data values for measurements below LOD are reported for all samples. All data are intensity-normalized, with the exception of PNLIPRP2 and FOLR3, which are plate control normalized because these assays showed a natural bimodal distribution. In >50% of the samples, 96% of the proteins from the Explore 384 Inflammation are detected.

Metabolomic data
Cord serum samples are analyzed by lipidomics and hydrophilic (water-soluble) metabolite profiling78. For lipidomic analysis, 10 μL of serum is mixed with internal standards and analyzed by ultra-high-performance liquid chromatography quadrupole time-of-flight mass spectrometry (UHPLC-QTOFMS from Agilent Technologies; Santa Clara, CA, USA). For polar metabolites and exogenous compounds, 40 μL of serum is mixed with internal standards and analyzed by the Agilent 1290 Infinity LC system coupled with 6545 Q-TOF MS interfaced with a dual jet stream electrospray (dual ESI) ion source (Agilent Technologies, Santa Clara, CA, USA). Quality controls include blanks,

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "In this manuscript Ahrens et al performed Olink-based proteomics of cord serum samples to identify possible biomarkers that\ncan predict the development of type 1 diabetes. They found inflammatory signatures that can predict the onset of the disease\nwith good accuracy. The methodology is rigorous and described with sufficient details. I believe that this is an important\nfinding and of interest for the scientific community. I only have a few very minor comments:\n1. Protein “expression” is an incorrect term as expression refers to the transcription process. I would change to protein\n“abundance”.\n2. Line 267 is missing to close the parentheses.\n3. Lines 285-286 – I think it should be a little clearer that the pathway is activated but not necessarily that there is an infection\nor that pathway is only activated by Yersinia.\n4. The fonts used in figure 3 are too small and hard to read.", "reviewer_2": "I am really grateful to review this manuscript. In my opinion, this manuscript can be published once some revision is done\nsuccessfully. I made two suggestions and I would like to ask your kind understanding.\nOverview\nThe application of statistical approaches in type-1 diabetes (T1D) centers on logistic regression with small-scale data in\nclinical, demographic and/or invasive genetic contents. Little literature is available on the application of machine learning in\nT1D with large-scale data in non-invasive genetic contents including proteomic information. For this reason, this study\nattempted to evaluate the usefulness of machine learning as a predictive and explainable statistical approach regarding T1D\nwith large-scale data in non-invasive proteomic contents. This study used proteomic data from 16683 children (78% of a\ngeneral population), applied four machine learning models and achieved the area under the curve of 83% with boosting\nand the random forest.\nThis study presented boosting Shapley Additive Explanations (SHAP) variable importance outcomes as well, centering on\n40 proteins such as HLA class II histocompatibility antigen, DR alpha chain (HLA-DRA), iduronate 2-sulfatase (IDS),\nsecretoglobin family 3A member 2 (SGB3A2), cathepsin C (CTSC), adenosine deaminase (ADA) and tissue inhibitor of\nmetalloproteinases 3 (TIMP3). In addition, this study reported that boosting and the random forest registered the areas under\nthe curves of 49%-52% based on clinical, demographic and environmental information. I would argue that this is a good\nstart.\nSuggestion 1: Boosting vs. Random Forest\nHowever, firstly, it can be noted that boosting and the random forest often register similar performance outcomes but bring\ndifferent results in permutation importance and SHAP plots. Boosting focuses on the final decision of the strongest tree with\nminimal bias whereas the random forest focuses on the majority vote of all trees with minimal variance. Both models have\ntheir own strengths and weaknesses, hence which model performs and explains better depends on various conditions. In\nthis vein, I would like to ask the authors to (1) compare their strengths and weaknesses and (2) compare their permutation\nimportance and SHAP plots in a comprehensive manner.\nSuggestion 2: Comprehensive Approaches to Integrate Various Types of Information\nSecondly, it can be noted that this study requests due attention to machine learning with multi-modal data but hasn’t outlined\ncomprehensive approaches to integrate various types of information, i.e., clinical, demographic, environmental, proteomic\nand other genetic. The area under the curve of boosting and the random forest in this study, 83%, is above the minimal\nrequirement of diagnostic criteria but still below its excellent standard. Are there some ways to improve this performance\noutcome by developing comprehensive approaches to integrate clinical, demographic, environmental, proteomic and other\ngenetic information such as microRNA? If so, are there some ways to optimize across different forms of data expression so\nas to obtain more reliable performance and explanation results? I would like to ask the authors to address this issue in\ngreater detail in the section of Discussion.", "reviewer_3": "The authors provide data on protein expression in cord blood samples obtained from a Swedish birth cohort of individuals\nwho do or do not develop T1D. The data are likely to be useful to the field, but their analysis and interpretation here could be\nbetter described.\nMajor comments:\nIn general, there is too much verbiage regarding interpretation of pathway/GSEA analysis data. Pathway analysis can be\nvery helpful for hypothesis generation but is most useful when backed up by functional or other information showing the\nvalidity of the mechanism at play. For example, much is made of a pathway associated with Yersinia; yersiniosis is relatively\nuncommon in Sweden and seems unlikely to be the direct cause of any inflammation observed here, especially in cord\nblood samples. The 7 proteins detected in the Yersinia pathway have a variety of roles in the immune system and can\nappear in multiple pathways. This is just one example of many pathways described in the manuscript. Without considerable\nfurther laboratory work to back up the pathway analysis, much of the speculation around all of the pathways discussed\nthroughout the manuscript (results and discussion) should be toned back.\nConversely, the authors do not devote enough time to why a variety of methodological decisions were made in their project.\nFor example, why did they use random selection – rather than selecting to ensure the cohorts were similar on important\nvariables - when identifying the cohort for Olink measurement? Why were not all T1D cases included in the Olink work?\nMore clearly describing who is included in each analysis is key to this work. While there are a couple of analyses that\nincluded the full ABIS cohort, the majority of this paper uses smaller numbers of future T1D cases compared to controls. A\nvisualization of cases and controls used across each analysis/figure would be helpful. All figures should include the n for\ncases and controls, and when subsetting is done (e.g. by age), the n for the subsets of interest should be provided.\nIn many cases, it is not clear how many variables were tested for a given analysis, nor what the cutoff for significance is and\nwhether it differs by figure. For example, supplemental table 1 lists 12 features that have an adjusted p <0.05. However, the\nnumber of variables tested from the birth questionnaires was not mentioned, the number of cases vs controls tested for each\nvariable is not presented, and the cutoff for which of these variables are considered important to the authors is not stated.\nThe text states that significant factors included “maternal illness in pregnancy” – but it is not clear which of these 12 features\nthe authors refer to as related to maternal illness. The authors mention a significant difference by “medications in pregnancy”\n– is that referencing the use of psychotropics, or anesthesia during pregnancy? Odds ratios and directions are not provided\nin the table for these variables, making it challenging to assess.\nThe abstract mentions “toxic chemical exposure.” There is not direct evidence of this that I can find in the manuscript.\nSimilarly, no detail on “stressful life events” or “environmental exposures” or “environmental factors.” Much more specificity is\nneeded throughout the manuscript in order to contextualize the results. The discussion in particular should be largely re-\nwritten. Elements of the introduction are not sufficiently precise – for example: a) there is no broad international requirement\nthat children be 2 years old for measurement of antibodies; b) this manuscript does not yield clear evidence that genetic or\nautoantibody screening could be avoided in our current understanding of T1D risk; c) the majority of the environmental\nfactors described in the introduction have been variably associated and unassociated with disease depending on the cohort,\nbut this nuance is not mentioned.\nSupp Table 1 – why are some of the features missing odds ratios and directionality of the effect? Please provide the n for\ncases and controls separately rather than just the total.\nSupp Table 3 – provides information on when the subjects selected for Olink differed from those not selected for Olink study,\nseparated by cases and controls. An important additional piece of information is how the selected cases may or may not\ndiffer from the selected controls.\nThe methods do not describe where the metabolomics data were obtained from, nor how they were generated.\nMinor comments:\nSupp Table 8 has a formatting issue.\nThe methods section says that variables missing 15% or more of subjects were excluded, but the main text results says\n50%."}, "author_response": "RESPONSE TO REVIEWERS' COMMENTS\nReviewer #1:\nI only have a few very minor comments:\n1. Protein “expression” is an incorrect term as expression refers to the transcription process. I would change\nto protein “abundance”.\nAn excellent point. Protein \"expression\" has been changed to protein \"concentration\" or “abundance”\nthroughout the text. In Olink proteomics, the values correspond to Normalized Protein eXpression (NPX).\nHowever, this metric really indicates a relative abundance or concentration within the sample.\n2. Line 267 is missing to close the parentheses.\nA few typographical errors, including this closed parenthesis, were noted in our revision and have been\naddressed. Thank you!\n3. Lines 285-286 – I think it should be a little clearer that the pathway is activated but not necessarily that\nthere is an infection or that pathway is only activated by Yersinia.\nAgreed. We now mention that network representation within this pathway does not necessarily translate\nto an active or past Yersinia infection. Constituents of this pathway are activated not only by Yersinia but\ncould have other origins.\n4. The fonts used in figure 3 are too small and hard to read.\nWe agree – the fonts of the enrichment plots were very small as presented originally. Thank you for noting\nthis. The fonts are now much larger in the revised version. These plots have been moved to an Extended\nData Figure (Extended Data Fig. 3) for enhanced visibility.\nReviewer #2:\nSuggestion 1: Boosting vs. Random Forest\nHowever, firstly, it can be noted that boosting and the random forest often register similar performance\noutcomes but bring different results in permutation importance and SHAP plots. Boosting focuses on the\nfinal decision of the strongest tree with minimal bias whereas the random forest focuses on the majority\nvote of all trees with minimal variance. Both models have their own strengths and weaknesses, hence which\nmodel performs and explains better depends on various conditions. In this vein, I would like to ask the\nauthors to (1) compare their strengths and weaknesses and (2) compare their permutation importance and\nSHAP plots in a comprehensive manner.\nThank you – yes, the features driving random forest and boosting methods may differ. This is an excellent\npoint. For this reason, we first performed mutual information scoring to extract relevant features (as a\ncommon ground) and then applied the comparative ML and traditional models for prediction using those\nfeatures alone in the original analysis. We hope that this point is clearer in the current draft. We have\nlargely rewritten this section of the results, reflecting improved models (addressed in other feedback).\nGiven the extensive additional machine learning models that have been added to this revision of the\nmanuscript (showing much improved performance from the previous draft), we have moved the other\nmaterial from the preliminary models to Extended Data.\nSuggestion 2: Comprehensive Approaches to Integrate Various Types of Information\nSecondly, it can be noted that this study requests due attention to machine learning with multi-modal data\nbut hasn’t outlined comprehensive approaches to integrate various types of information, i.e., clinical,\ndemographic, environmental, proteomic and other genetic. The area under the curve of boosting and the\nrandom forest in this study, 83%, is above the minimal requirement of diagnostic criteria but still below its\nexcellent standard. Are there some ways to improve this performance outcome by developing\ncomprehensive approaches to integrate clinical, demographic, environmental, proteomic and other genetic\ninformation such as microRNA? If so, are there some ways to optimize across different forms of data\nexpression so as to obtain more reliable performance and explanation results? I would like to ask the authors\nto address this issue in greater detail in the section of Discussion.\nThis was a very important suggestion. Thank you for this feedback, which prompted us to build more\ncomprehensive prediction models for the revision. In doing so, an AUC of 0.89 was achieved – much\nimproved from the original draft. The model accounts for HLA genetics, and we present the nuances of this\nin the discussion. We have not incorporated other data (such as demographics and environment), as the\ngoal of our investigation is to present the most simple, non-invasive way to predict future disease that could\nbe easily and inexpensively added to clinical practice at birth. Although these features may improve T1D\nprediction within this Swedish cohort, it would also likely reduce the generalizability to other populations,\nwhere these characteristics differ drastically due to culture or geography.\nReviewer #3:\nMajor comments:\nIn general, there is too much verbiage regarding interpretation of pathway/GSEA analysis data. Pathway\nanalysis can be very helpful for hypothesis generation but is most useful when backed up by functional or\nother information showing the validity of the mechanism at play. For example, much is made of a pathway\nassociated with Yersinia yersiniosis is relatively uncommon in Sweden and seems unlikely to be the direct\ncause of any inflammation observed here, especially in cord blood samples. The 7 proteins detected in the\nYersinia pathway have a variety of roles in the immune system and can appear in multiple pathways. This\nis just one example of many pathways described in the manuscript. Without considerable further laboratory\nwork to back up the pathway analysis, much of the speculation around all of the pathways discussed\nthroughout the manuscript (results and discussion) should be toned back.\nAgreed. The speculation on these pathways has been toned backed and the language is now more concise.\nMoreover, we have provided more extensive literature and discussion surrounding what is known about\nthe most significant proteins and how this may play a role in stress of the beta cells, ultimately leading to\nT1D. We hope that you find the discussion is much improved.\nConversely, the authors do not devote enough time to why a variety of methodological decisions were made\nin their project. For example, why did they use random selection – rather than selecting to ensure the cohorts\nwere similar on important variables - when identifying the cohort for Olink measurement? Why were not\nall T1D cases included in the Olink work?\nThe controls and T1D cases used in this study were indeed largely representative of those in the full ABIS\ncohort. We appreciate this point, as it prompted us to present a side-by-side comparison of significance\nacross important child characteristics (now Extended Data Fig. 2), comparing those for whom we have\nproteomic data vs. those for whom we do not. The only notable difference was a modest overrepresentation\nof females among controls included in the Olink subset (52.5%) versus those controls who were not\n(47.9%). As many T1D cases as was practical to process for proteomics were included in this investigation.\nMore clearly describing who is included in each analysis is key to this work. While there are a couple of\nanalyses that included the full ABIS cohort, the majority of this paper uses smaller numbers of future T1D\ncases compared to controls. A visualization of cases and controls used across each analysis/figure would\nbe helpful. All figures should include the n for cases and controls, and when subsetting is done (e.g. by\nage), the n for the subsets of interest should be provided.\nWe appreciate this comment. Transparency of the sample size is very important to us, as well. To address\nyour feedback, we have gone through ever major analysis presented in this paper and the corresponding\nfigures to ensure that the sample size is presented.\nIn many cases, it is not clear how many variables were tested for a given analysis, nor what the cutoff for\nsignificance is and whether it differs by figure. For example, supplemental table 1 lists 12 features that have\nan adjusted p <0.05. However, the number of variables tested from the birth questionnaires was not\nmentioned, the number of cases vs controls tested for each variable is not presented, and the cutoff for\nwhich of these variables are considered important to the authors is not stated. The text states that significant\nfactors included “maternal illness in pregnancy” – but it is not clear which of these 12 features the authors\nrefer to as related to maternal illness.\nThank you for bringing up this point. For transparency, we have completely reworked the original\nSupplementary Table. We have also included the overview of the items tested in the results section. In the\nnew table, the “n” is presented for both cases and controls, along with the p value. A total of 44 variables\nwere tested from the birth questionnaire, and these are presented.\nThe authors mention a significant difference by “medications in pregnancy” – is that referencing the use of\npsychotropics, or anesthesia during pregnancy?\nThere were two medications during pregnancy that were significant at this comparative level: antibiotics\nand psychotropics, so this is now indicated more clearly. To achieve greater transparency and clarity,\nSupplementary Table 1 has been completely replaced to show the original chi-square statistics.\nThe abstract mentions “toxic chemical exposure.” There is not direct evidence of this that I can find in the\nmanuscript. Similarly, no detail on “stressful life events” or “environmental exposures” or “environmental\nfactors.” Much more specificity is needed throughout the manuscript in order to contextualize the results.\nThe association of the proteins with these factors is described in the \"Metabolites, exogenous toxic\ncompounds, and T1D-linked proteins\" and “Prenatal/perinatal factors and T1D-associated proteins”\nparagraphs in the Results section. This has been revised in the abstract to maintain brevity, with more\nspecific mention of a few notable cord blood compounds associated with the proteins of interest. Extended\nData Fig. 9 addresses associations with cord blood metabolites and exogenous compounds; Figure 6 and\nExtended Data Fig. 10 address associations with prenatal and perinatal factors.\nThe discussion in particular should be largely re-written. Elements of the introduction are not sufficiently\nprecise – for example: a) there is no broad international requirement that children be 2 years old for\nmeasurement of antibodies; b) this manuscript does not yield clear evidence that genetic or\nautoantibody screening could be avoided in our current understanding of T1D risk; c) the majority of the\nenvironmental factors described in the introduction have been variably associated and unassociated with disease depending\non the cohort, but this nuance is not mentioned.\nThank you for this feedback. We have expanded (and largely rewritten) the discussion to satisfy the\nreviewers’ points on these and other important caveats. Going beyond the initial constraint of the word\nlimitations has allowed us to expand this discussion so it can be more informative. Additional citations\nhave also been added.\nSupp Table 1 – why are some of the features missing odds ratios and directionality of the effect? Please\nprovide the n for cases and controls separately rather than just the total.\nSupplementary Table 1 has been completely redone, showing now the “n” for cases and controls\nseparately, as well as the p values from the chi-square tests of association for each tested variable of\ninterest in the birth questionnaire.\nSupp Table 3 – provides information on when the subjects selected for Olink differed from those not\nselected for Olink study, separated by cases and controls. An important additional piece of information is\nhow the selected cases may or may not differ from the selected controls.\nThe controls were selected randomly from all controls with Olink data. No matching was done in the\nselection of controls. However, in paragraph two of the second “cord blood proteomic analysis”, we\noutline where the T1D subjects for Olink aligned with those of the full ABIS cohort. The slight difference\nin sex and gestational age is addressed in the text. The selected samples were broadly representative of the\nfull cohort.\nThe methods do not describe where the metabolomics data were obtained from, nor how they were\ngenerated.\nThank you for noting this. We now include a section on metabolomics data, where we present the major\ndetails for generating these data, as well as a citation of a previous paper from our team with more\nspecifics.\nMinor comments:\nSupp Table 8 has a formatting issue.\nThank you for reviewing these tables and finding this error. We have corrected Supplementary Table 8.\nThe methods section says that variables missing 15% or more of subjects were excluded, but the main text\nresults says 50%.\nThis has been rewritten and Supplementary Table 1 reanalyzed for a better representation.\nSincerely,\nEric W. Triplett\nProfessor and Chair"}, {"round": 2, "reviewer_comments": {"reviewer_1": "The authors satisfactorily addressed all my concerns.", "reviewer_2": "I am really grateful to re-review this manuscript. In my opinion, this manuscript can be published in current form.", "reviewer_3": "The authors have made many welcome changes to the manuscript, but some questions remain.\nMy previous request for numbers of cases and controls to be added to all figures and analyses was only partially considered.\nFigure 3 is the most worrisome; the legend still seems to lump cases and controls together by age group, and the youngest\nage group is only 23 subjects. I respectfully ask again that this be added to every figure, including the schematic figure 1, so\nthat readers can contextualize the findings presented. (Separately, the color scale on figure 3c and 3d make it impossible to\nsee the numbers and asterisks, particularly for the proteins that are most significant.)\nI continue to have concerns regarding the specificity of language in some aspects of the writing, including the introduction\nand discussion. An example concern is the section starting at line 81 (“Prenatal and environmental risk factors associated\nwith future T1D”). Supplemental table 1 is the place this data are presented. A reader may assume that “prenatal and\nenvironmental risk factors” would mean chemical or pollutant exposures, parental smoking, viruses, or a broad set of\npossibilities. Instead, the only environmental factor listed in the main text as significant is “whether time has been spent with\nsomeone with insulin-dependent diabetes”, which in a birth cohort study is likely correlated to the family history data also\nshown to be significant. I don’t have a problem with the fact that few to no dietary, smoking, or other “traditional”\nenvironmental factors were identified in the chi-squared analysis. However, I would ask again for language that more\nprecisely describes the manuscript’s findings to be employed throughout the manuscript. (Separately, the methods and\nsupplemental table 1 legend do not indicate that multiple testing correction was carried out for the chi squared analysis in\nthat section; if not performed this should be added.)\nThe introduction and discussion use many words to say why genetic risk and autoantibody testing are insufficient. This may\nwell be the case. However: the authors here present a single (very interesting!) study of cord blood proteomics in people at\nrisk for T1D. This work is interesting on its own – the introduction and discussion do not need to describe the costs or\ncontroversies of genetic or autoantibody testing (which is not mentioned for proteomics and metabolomics) in order for their\nfindings to be interesting. The manuscript provides no evidence that this cord blood test would be sufficient to identify\nwhen a person would get diabetes – which is information that change in autoantibodies and eventually glucose monitoring would\nprovide. To me, all of that is fine – there is no reason that this single cord blood dataset needs to improve upon decades of\ngenetic and autoantibody data in order to be interesting to this audience. However, the corresponding language should be\ntoned back.\nSimilarly, the teplizumab component of the discussion is irrelevant to the work presented here. I agree the use of this\ntherapy is rightfully controversial, as it is both expensive and imperfect. However, this study was not designed to shed new light\non when/whether/how teplizumab should be used, meaning lines 276 through 293 could all be cut from the discussion.\nFigure 2C: It is impossible to see in the figure whether some of the odds ratios cross 1. Can the actual range numbers be\nprovided as part of the image? Also, supplemental table 1 suggests that having a mother born in Sweden was more common\nin those children developing T1D vs not (~98% of cases vs ~93% of controls), similar in direction to having a mother with\nT1D (~7% of cases vs 0.7% of controls) . However, the odds ratio plot in 2c seems to suggest that having a mother born in\nSweden is protective against T1D in this study. Can the authors comment on this? Why not include all of the factors shown\nto be important (2a and 2b) as odds ratios (2c)?\nI continue to feel that much of the discussion could be reduced in word count. Many sentences explain what the proteins\nidentified as important may do when properly located on, inside, or near immune cells or beta cells. However, the\nrelationship between protein concentration in cord blood and these normal cellular functions is not demonstrated, and a role\nfor some of them as secreted or cleaved proteins (e.g. HLA-DRA) is unclear or unstated. This is ok – but substantially\nreducing the amount of words from lines 311-373 dedicated to these processes – particularly beta cell processes which may\nor may not be strongly reflected in cord blood given their small number in both mother and fetus - is warranted.\nThe non-parametric correlations between proteins and metabolites in extended data figure 9 appear driven by only a few\noutlying subjects. Do other methods of correlation (standard Spearman’s correlations which are also non-parametric,\nperhaps) show that these are significant relationships? Separately, the compounds on the X-axis of the second row of figure\npart a are cut off and not included in the legend.\nWhile mutual information scores and SHAP values for model outputs are provided for many of the metabolomic measures,\nactual raw differences between cases and controls for the variables themselves are not presented. This should be added to\ncontextualize findings.\nThe authors frequently report findings that are not significant after multiple testing correction. Given the very large number of\ntests performed in this manuscript, it seems that only features passing multiple test correction should be discussed\nthroughout the manuscript. For example, extended figure 10 – and discussion lines 400-404 – all suggest that extended\nfigure 10 identified a link between toxic exposures, protein levels, and T1D – but none of these data met false discovery\ncorrection. This should all be removed, as well as the nearby statement about dietary risk, which is in fact not backed up by\nthe environmental questionnaire data in this study.\nLine 411: please strike the word “more” before evidence of generalizability. This needs to be tested in at least one large\nindependent cohort, which is a reasonable caveat to state simply and clearly."}, "author_response": "n\nResponses to reviewers for 2nd resubmission of manuscript number NCOMMS-25-04817B:\nWe consider it particularly important that you ensure that the number of cases and controls is\nclearly reported in all figures and analyses. In addition, carefully revise the text to ensure the\nspecificity of the language throughout, as detailed by ref#3. Meanwhile, further edit the\ndiscussion section, as indicated by ref#3, and emphasize throughout the manuscript only\nfeatures that pass multiple test corrections.\nWhen evaluating your revised manuscript, we will not consider any similar papers published\nindependently in the meantime to compromise the novelty of your study. See here for more\ninformation.\nReviewer #3 points:\n Regarding sample size\no We have reviewed every figure and subpanel to ensure that the “n” is indicated.\nMany figures already had the “n” indicated, including Fig. 1, 2e, 3, 4, 5, 6a-f, and\nall Extended Data Fig. (except for Fig. 3, where the “n” is not applicable). We\nhave clarified the “n” in Fig. 2a-d, where the case and control sample sizes are\nadditionally provided in Supplementary Table 1. For Fig. 6g-h, the total “n” is\nindicated, and this reflects the size of the full Olink subset in our study.\n “Separately, the color scale on figure 3c and 3d make it impossible to see the numbers\nand asterisks, particularly for the proteins that are most significant.”\no Thank you for the note on readability. Visibility here is very important. Fig. 3c and\n3d have been generated with a new color scheme to improve readability, with\nwhite text against the darker cells (in either direction), and all labels were placed\nin bold.\n Regarding introduction and discussion (language): “Check specificity of language in some\naspects of the writing, including the introduction and discussion. An example concern is\nthe section starting at line 81 (“Prenatal and environmental risk factors associated\nwith future T1D”). Supplemental table 1 is the place this data are presented. A reader may\nassume that “prenatal and environmental risk factors” would mean chemical or\npollutant exposures, parental smoking, viruses, or a broad set of possibilities. Instead,\nthe only environmental factor listed in the main text as significant is “whether time has\nbeen spent with someone with insulin-dependent diabetes”, which in a birth cohort study\nis likely correlated to the family history data also shown to be significant. I don’t\nhave a problem with the fact that few to no dietary, smoking, or other “traditional”\nenvironmental factors were identified in the chi-squared analysis. However, I would ask\nagain for language that more precisely describes the manuscript’s findings to be\nemployed throughout the manuscript. (Separately, the methods and supplemental table\n1 legend do not indicate that multiple testing correction was carried out for the chi\nsquared analysis in that section; if not performed this should be added.”\no Our mention of “environmental factors” has been toned back or clarified,\nincluding in this paragraph of the Results. Thank you for pointing this out. The\nassociations with environmental factors in this investigation and this approach\nwere indeed rather limited (not very strong).\no We reworked Supp Table 1 for more clarity. FDR adjusted p values were provided\nin earlier draft, but now the n’s (for case and control) are clearly indicated and\ndash marks in the OR (95% CI) columns where the degrees of freedom exceeded\n1, i.e., no odds ratio calculated.\n Regarding introduction and discussion (genetic risk and autoantibodies): “introduction\nand discussion use many words to say why genetic risk and autoantibody testing are\ninsufficient. This may well be the case. However: the authors here present a single (very\ninteresting!) study of cord blood proteomics in people at risk for T1D. This work is\ninteresting on its own – the introduction and discussion do not need to describe the\ncosts or controversies of genetic or autoantibody testing (which is not mentioned for\nproteomics and metabolomics) in order for their findings to be interesting. The\nmanuscript provides no evidence that this cord blood test would be sufficient to identify\nwhen a person would get diabetes – which is information that change in autoantibodies\nand eventually glucose monitoring would\nprovide. To me, all of that is fine – there is no reason that this single cord blood dataset needs to improve upon decades of\ngenetic and autoantibody data in order to be interesting to this audience. However, the\ncorresponding language should be toned back.”\no We agree with the Reviewer and have significantly toned back this language. The\nedits are marked with track changes enabled in the revised draft.\n• Regarding Teplizumab: “irrelevant to the work presented here. I agree the use of this\ntherapy is rightfully controversial, as it is both expensive and imperfect. However, this\nstudy was not designed to shed new light on when/whether/how teplizumab should be\nused, meaning lines 276 through 293 could all be cut from the discussion.”\n• These sections have been cut accordingly, and we feel that the discussion reads\nmore focused with this change.\n• Regarding Fig. 2c and Supplemental Table 1: “”It is impossible to see in the figure\nwhether some of the odds ratios cross 1. Can the actual range numbers be provided as\npart of the image? Also, supplemental table 1 suggests that having a mother born in\nSweden was more common in those children developing T1D vs not (~98% of cases vs ~93% of controls) , similar in direction to having a mother with\nT1D (~7% of cases vs 0.7% of controls) . However, the odds ratio plot in 2c seems to suggest that having a mother born in\nSweden is protective against T1D in this study. Can the authors comment on\nthis? Why not include all of the factors shown to be important (2a and 2b) as odds ratios\n(2c)?\n• We have revised Fig. 2c accordingly. ORs and their 95% CI are now indicated in\nthe figure, as well. (Only the factors from Supplementary Table 1 with a single\ndegree of freedom are included in the forest plot.) We appreciate Reviewer 3’s\nattention to detail here so we could correct this.\n• Regarding discussion word count: “much of the discussion could be reduced in word\ncount. Many sentences explain what the proteins identified as important may do when\nproperly located on, inside, or near immune cells or beta cells. However, the relationship\nbetween protein concentration in cord blood and these normal cellular functions is not\ndemonstrated, and a role for some of them as secreted or cleaved proteins (e.g. HLA-\nDRA) is unclear or unstated. This is ok – but substantially reducing the amount of words\nfrom lines 311-373 dedicated to these processes – particularly beta cell processes which\nmay or may not be strongly reflected in cord blood given their small number in both\nmother and fetus - is warranted.”\n• We have significantly reduced the discussion to improve focus while still\nproviding biological rationale for the findings.\n• Regarding Extended Data Fig. 9: “non-parametric correlations between proteins and\nmetabolites in extended data figure 9 appear driven by only a few outlying subjects. Do\nother methods of correlation (standard Spearman’s correlations which are also non-\nparametric, perhaps) show that these are significant relationships? Separately, the\ncompounds on the X-axis of the second row of figure part a are cut off and not included\nin the legend.”\n• We appreciate the reviewer’s point. Although Spearman is nonparametric, it is\nnot immune to outliers entirely as they can still affect the rank order. To address\nthis concern, we have removed the most extreme 1-2% of samples, retested with\nthe Spearman, and compared results to the original test. We tested this by\nremoving the top 2.5% and bottom 2.5% (thus, the 5% of extremes), rather than\na 1-2.5% given the smaller sample size of the metabolomic dataset. After\nremoving the top and bottom 1% of values for each variable similar results were\nfound, confirming that our findings at least for TIMP3 were not driven by\nextreme values (although some protein x metabolite pairings did drop out once\nthe extremes were removed). For TIMP3, a few associations fell away, so we\nswapped the panel in the figure. For NTF3, the association went away, so we\nremoved the panel. For PDLIM7, most associations fell away, so we removed the\npanel. We added text to the results to contextualize this approach and the\ncorresponding results for transparency to the reader.\n• Regarding raw differences in metabolites: “mutual information scores and SHAP values\nfor model outputs are provided for many of the metabolomic measures, actual raw\ndifferences between cases and controls for the variables themselves are not presented.\nThis should be added to contextualize findings.”\n• As the raw differences in the metabolites was not the purpose of the present\nstudy, but rather published in two earlier ABIS reports, we do not present those\ndata here. If the dataset had been expanded and we could include those data\nhere, we would have, but unfortunately a more extensive sample size is not\navailable at this time. Instead, we are focused here on how these metabolites\nalign with the proteins most relevant to prediction. This is an important point\nthat Reviewer 3 raises, so now we need to refer readers to those papers to see\nthe metabolomic case/control results.\n• Regarding findings not significant after FDR: “Given the very large number of tests\nperformed in this manuscript, it seems that only features passing multiple test\ncorrection should be discussed throughout the manuscript. For example, extended\nfigure 10 – and discussion lines 400-404 – all suggest that extended figure 10 identified a\nlink between toxic exposures, protein levels, and T1D – but none of these data met false discovery\ncorrection. This should all be removed, as well as the nearby statement about dietary risk, which is in fact not backed up by\nthe environmental questionnaire data in this study.”\n• We have removed Extended Data Fig. 10 since it was only significant before FDR.\nWe also removed this in text: “Associations of smoking by the mother during\npregnancy, maternal stomach flu during pregnancy, and severe life event during\npregnancy were only significant before FDR correction.”\n• Regarding discussion sentence: “Line 411: please strike the word “more” before\nevidence of generalizability. This needs to be tested in at least one large independent\ncohort, which is a reasonable caveat to state simply and clearly.”\n• We have made this change in the present text. We agree, this is a reasonable\nconsideration.\nWe thank all Reviewers for their careful feedback, which has significantly improved the\ncontent and messaging of this paper."}, {"round": 3, "reviewer_comments": {"reviewer_3": "1)My previous request for changes regarding the specificity of language was in many places not addressed. Examples\ninclude:\na) The same section mentioned “Prenatal risk factors associated with T1D” still contains language that is unspecific – the\n“prenatal diet” aspect refers specifically to low fried potato consumption. The full list of environmental factors (n=44) studied\nis not presented in Supplemental Table 1 for the reader to understand what other factors were included – how did fried\npotato consumption end up in this list of only 44 variables? Returning to my review of the original version of the manuscript –\nodds ratios are not provided for all variables but it appears that the data suggest increased fried potato consumption is\nprotective against T1D?\nb)“Prenatal and perinatal factors” are also non-specifically discussed in the abstract.\nc) The manuscript mentions “environmental factors” throughout – including in the first and second paragraphs of the\nintroduction. The PFOS-L data are not presented sufficiently to justify the level of discussion here, and their raw values were\nnot presented in any figure though this was requested so that the reader can see how strong the association may be\nbetween PFOS and disease development. PFOS-L levels are also not shown to be associated with proteins in extended\nfigure 9.\n2) Similarly, my previous request regarding the comparison of the predictive abilities of cord blood proteomics to genetics,\nantibody testing, or other “invasive procedures” was also only partially addressed – this concept still remains as the final\nsentence of the introduction, as well as other locations including throughout the discussion. The authors have not identified\na biomarker nor anything close to as predictive as genetics or autoantibodies.\n3) Changes regarding false-discovery were welcome. However I note that the authors still state these findings in the\n“Prenatal/perinatal factors and T1D-associated proteins” section - they just then state the data are not shown. This sentence\nshould be removed."}, "author_response": "RESPONSES TO THE REVIEW\nRef#3 still raises concerns about the specificity of the language, especially regarding the link between\nenvironmental factors and T1D development and the ability to detect this relationship in cord blood.\nThe language has been revised throughout to address this point from Reviewer 3.\nReviewer #3 (Remarks to the Author)\n1)My previous request for changes regarding the specificity of language was in many places not\naddressed. Examples include:\na) The same section mentioned “Prenatal risk factors associated with T1D” still contains language that is\nunspecific – the “prenatal diet” aspect refers specifically to low fried potato consumption. The full list of\nenvironmental factors (n=44) studied is not presented in Supplemental Table 1 for the reader to\nunderstand what other factors were included – how did fried potato consumption end up in this list of\nonly 44 variables? Returning to my review of the original version of the manuscript – odds ratios are not\nprovided for all variables but it appears that the data suggest increased fried potato consumption is\nprotective against T1D?\nSupplemental Table 1 has been revised for clarity and the source data for figures is now provided as a\ncompanion to the manuscript, including odds ratios.\nb)“Prenatal and perinatal factors” are also non-specifically discussed in the abstract.\nThe abstract has been revised to specify delivery mode.\nc) The manuscript mentions “environmental factors” throughout – including in the first and second\nparagraphs of the introduction. The PFOS-L data are not presented sufficiently to justify the level of\ndiscussion here, and their raw values were not presented in any figure though this was requested so that\nthe reader can see how strong the association may be between PFOS and disease development. PFOS-L\nlevels are also not shown to be associated with proteins in extended figure 9.\nFor point 1, the language has been simplified at every point requested. Supplemental Table 1 (the\nattached excel file) has been corrected and improved as requested.\n2) Similarly, my previous request regarding the comparison of the predictive abilities of cord blood\nproteomics to genetics, antibody testing, or other “invasive procedures” was also only partially\naddressed – this concept still remains as the final sentence of the introduction, as well as other locations\nincluding throughout the discussion. The authors have not identified a biomarker nor anything close to\nas predictive as genetics or autoantibodies.\nFor point, 2, all sentences regarding replacing the biomarkers we have discovered in this cohort as a\nprevention tool over genetics and autoantibody assays have been removed from the Introduction and the\nDiscussion.\n3) Changes regarding false-discovery were welcome. However I note that the authors still state these\nfindings in the “Prenatal/perinatal factors and T1D-associated proteins” section - they just then state the\ndata are not shown. This sentence should be removed.\nFor point 3, the offending sentence was removed.\nPlease also include the reporting summary and provide a Source data file (by which we mean an Excel\ntable containing the numerical data based on which the graphs were drawn.)\nThank you for the comment. Source Data has been compiled for every figure and is now provided so that\nthe manuscript follows this requirement. A Reporting Summary file was also prepared."}, {"round": 4, "reviewer_comments": {"reviewer_3": "The data regarding PFOS-L were provided in supplemental materials. Optimally since the authors state that there is a non-\nlinear relationship, the authors would provide at least one new plot indicating the relationship between levels of ay of their\nproteins of interest and levels of PFOS-L. Optimally the authors would also provide an explanation why they focus on those\n43 variables from the ABIS questionnaire."}, "author_response": ""}]

Decision letter