Identification Of Pathogenic Mutations And Application Of Polygenic Risk Scores In Early-Onset Diabetes Patients

Identification Of Pathogenic Mutations And Application Of Polygenic Risk Scores In Early-Onset Diabetes Patients | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Identification Of Pathogenic Mutations And Application Of Polygenic Risk Scores In Early-Onset Diabetes Patients Ivanna Atava, Raimonds Reščenko, Monta Brīvība, Līga Birzniece, and 9 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5361647/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background: Maturity-onset Diabetes of the Young (MODY) presents a diagnostic challenge, with a large proportion of cases lacking identifiable genetic mutations, which could lead to sub-optimal medical treatment and, subsequently, a decline in patients’ life quality. This study investigates the utility of polygenic risk score (PRS) in distinguishing monogenic diabetes from early-onset type 1 diabetes (T1D) and type 2 diabetes (T2D) cases to enhance diagnostic accuracy. Methods: We investigated the genetic basis of early-onset diabetes in a Latvian cohort comprising 66 patients, contrasted with 174 non-diabetic controls, using whole-genome sequencing (WGS). Results: We identified 22 causative mutations in three MODY genes ( GCK , HNF1A , and HNF4A ), eight of them being novel. We selected and tested the best-performing population specific T1D and T2D PRS models on the established diabetic cohort and controls. Patients without genetically confirmed MODY had a significantly higher risk for T1D compared to controls. A 75% centile of T1D-PRS included only 8.7% of the genetically confirmed MODY patients, compared to 34% of patients without mutations, providing good specificity for the identification of indicative T1D at this PRS range. While T2D-PRS was increased in the diabetic cohort, it did not demonstrate an ability to discriminate between MODY-positive and negative subgroups. Conclusions: Our study demonstrates that the application of WGS improves diagnostic accuracy and highlights the potential of T1D-PRS as a critical tool for the stratification of MODY-suspected patients. Early-onset Diabetes Maturity-Onset Diabetes Next-Generation Sequencing Rare variant Risk Scores Figures Figure 1 Figure 2 Figure 3 Figure 4 Background Diabetes mellitus, encompassing Type 1 (T1D) and Type 2 (T2D), also includes MODY, a monogenic form marked by autosomal dominant inheritance and early-onset hyperglycemia. MODY, usually diagnosed via clinical and genetic testing, is classified using the names of mutated genes (to date, 11 causal genes identified), with GCK-MODY, HNF1A-MODY, and HNF4A-MODY accounting for more than 90% of genetically confirmed MODY [1, 2]. Despite accounting for an estimated 1% of all diabetes cases, a great proportion of MODY cases remain unidentified [3], and more than 50% of clinically suspected MODY patients do not exhibit any known disease-causing mutations [4]. Recent advancements in PRS have shown promise in differentiating between types of diabetes [5]. The utility of a T1D-PRS in distinguishing between monogenic diabetes and T1D in young adults has been demonstrated before [6]. Some studies have successfully employed PRS to differentiate between T1D and MODY in diverse populations [7, 8]. In this study, we identified the inherited deleterious mutations in a cohort of early-onset diabetes patients from Latvia, using WGS. We also investigate the ability to explain early-onset diabetes by having increased T1D and T2D PRSs. Methods 1. Study subjects The early-onset diabetes cohort was recruited by the Genome Database of Latvian Population (LGDB) in cooperation with major Latvian hospital endocrinologists [9] from 2011 to 2017 according to established LGDB standard protocols. The following inclusion criteria were used: 1. Unspecified diagnosis of diabetes mellitus before age 35; 2. Absence of diabetic ketoacidosis; 3. Endogenous insulin secretion has been maintained for at least two years after diagnosis; 4. Complete clinical, anthropometric, and biochemical data. Additionally, three patients with a clinical diabetes diagnosis after 35 years of age were recruited to our cohort due to available genetic testing data, which proved them to be MODY-positive. In total, 66 index patients were selected for this study. In addition, 46 family members of probands with proven pathogenic (P)/likely pathogenic (LP) variants were available for either Sanger sequencing or WGS. A control group, composed of 174 individuals was selected from LGDB resources using the following criteria: 1. No diagnosis of diabetes mellitus at the time of application in LGDB; 2. No history of taking medications for high glucose levels; 3. No obesity (BMI20. 2. Acquisition of sequencing data. DNA was prepared and obtained according to LGDB standard procedures [9]. All probands were selected for WGS, and DNA libraries were prepared using the MGIEasy Universal DNA Library Prep Set (MGI Tech Co., Ltd., China) and the MGIEasy FS PCR-Free DNA Library Prep Set (MGI Tech Co., Ltd., China). WGS sequencing was performed for index patients and controls on MGI Tech DNBSEQ-T10 and DNBSEQ-T7 platforms (MGI Tech Co., Ltd., China), using DNBSEQ-T7RS high-throughput and DNBSEQ-T10×4RS high-throughput sequencing sets. 3. WGS data Variant Calling Data from WGS were processed using the nf-core/sarek pipeline v.2.7.1 [10], preprocessing steps based on Genome Analysis Toolkit (GATK) 4 best practices. Sequences were mapped to the reference human genome GRCh38/hg38 as part of the pipeline. For the variant calling step, the GATK HaplotypeCaller tool was selected. We combined samples using GATK CombineGVCFs and GenotypeGVCFs. Controls were added to the multi-sample variant call format (VCF) file using the BCFtools v1.10.2 “merge” command [11]. Singularity was used to install the necessary software, while Nextflow was necessary for analytic task automatization. 4. Selection of genes of interest A subset VCF file consisting of a diabetic cohort was made to detect potential deleterious variants. Only variants located within 11 MODY (Additional file 1) and 55 T2D candidate genes (Additional file 2) were selected. T2D candidate genes were selected based on genome-wide association studies (GWAS) that determined the association of these genes with the T2D phenotype and their role in glucose metabolism [12]. 5. Variant filtering and annotation The VCF file containing selected variants was annotated using ANNOVAR [13]. We filtered out variants that were not located in gene coding sequences and variants defined as SIFT and PolyPhen benign. Further, a Genome Aggregation Database (gnomAD) [14] population maximum filtering allele frequency (PopMax FAF) cutoff of 0.000108 (MODY estimated frequency among adults [4]) was used to filter out the remaining variants. 6. Variant interpretation Variants that were present in the ClinVar database [15] and had been previously classified as P in association with MODYwere assigned the appropriate pathogenicity category in our study. Variants absent from the ClinVar database were interpreted according to American College of Medical Genetics and Genomics/Association for Molecular Pathology (ACMG/AMP) guidelines [16]. 7. Polygenic risk score selection Individual PRSs were calculated using the pgscatalog/pgsc_calc v.2.0.0-alpha.3 [17] workflow with default options enabled. We retrieved the publicly available PRSs from the Polygenic Score (PGS) Catalog database [18]. To date, the PGS Catalog has gathered 12 T1D-PRSs from nine studies and 102 T2D-PRSs from 26 studies. Downloaded PRSs associated with T1D were tested on a previously established genotyped sample set from the LGDB collection [19], obtained by the Infinium Global Screening Array (Illumina, USA). The set consisted of 185 T1D cases and 2991 controls. PRSs associated with T2D followed a similar procedure on a sample set, which included 1603 T2D cases and 2345 controls. In order to evaluate the performance of all available T1D and T2D PRSs from the PGS Catalog, we calculated standard quality metrics for each score using R Statistical Software [20]. The area under the receiver operator characteristic curve (AUC) was obtained by comparing each model individually to the LGDB T1D or T2D phenotype using the “pROC” package [21]. The correlation coefficient was calculated using the R package “psych” [22]; the odds ratio (OR) over standard deviation (SD) increase from the generalized linear model was obtained using epiDisplay. These metrics were compared with analogous values reported by the PRS source study. 8. Polygenic risk score evaluation We selected the best-performing models for T1D and T2D to calculate PRS in the early-onset diabetes cohort. The Kruskal–Wallis test, followed by Dunn’s test, using R “rstatix” [23] and “dunn.test” packages [24] was used to compare individual PRS values. The “pROC” package was used to measure the discriminative power of the T1D and T2D scores. Statistical analysis was performed using R Statistical Software. Results 1. Cohort characteristics We analyzed 66 index patients and 46 family members from an early-onset diabetes cohort. Additionally, 174 individuals representing the general population were included as controls. Table 1 presents the phenotypic characteristics of these groups. We observed no significant differences in phenotypic and biochemical measures within the diabetic group. Both age and BMI were notably higher in the control group than in index patients, with p-values of 2.893*10 − 16 and 2.284*10 − 9 , respectively. Table 1 Study group characteristics. Early-onset diabetes Controls p-value n (females) 66 (42) 174 (105) Median age of enrollment; IQR 18.00; 11.0-30.5 44.00; 32.00–53.00 2.893*10 − 16 Median BMI; IQR 20.41; 16.88–22.44 24.35; 21.71–26.77 2.284*10 − 9 MODY(+) MODY() p-value n (females) 25 (16) 41 (26) Median age of enrollment; IQR 25.00; 13.50–38.00 17.00; 9.00-25.50 0.06 Median age at first hyperglycemia; IQR 14.00; 9.00–22.00 16.00; 7.00–22.00 0.55 Median BMI; IQR 21.46; 19.00-22.53 19.37; 15.96–21.77 0.07 Median HbA1c; IQR, % 6.36; 6.13–6.58 6.50 5.80–7.66 0.69 Median C-peptide level; IQR, ng/mL 1.23; 0.71–1.72 1.04; 0.64–1.61 0.50 IQR - interquartile range; BMI - body mass index; HbA1c - glycated hemoglobin; MODY(+) – genetically confirmed MODY patients; MODY() – probands with negative genetic testing for MODY Table 1 2. Identification of MODY Causative Variants WGS was successful for 61 index cases and 174 controls included in the study, with a mean sequencing depth of 30. An average of 3,996,090 single nucleotide polymorphisms (SNP) and 898,385 insertions-deletions (INDELs) per sample were detected. In the early-onset diabetes subgroup, WGS revealed 54 coding region variants in 49 index patients (Table 2 , Additional file 3). These included 11 synonymous, 20 missense, and 5 nonsense SNPs. The remaining 18 comprised 12 insertions (seven frameshifts) and six deletions (five frameshifts). Additionally, two pathogenic missense variants (p.Ser19Leu and p.Gly223Ser) were found in two patients with failed WGS, as they had data from a targeted sequencing panel. Table 2 Pathogenic variations identified in 66 probands with suspected MODY using whole genome sequencing. ClinVar ID Gene Chr:Position Variant Proband count Clinical significance 431973 GCK 7:44153403 c.106C > T (p.Arg36Trp) 3 P/LP 1 - GCK 7:44153390 c.118_119ins (p.Glu40ValfsTer34) 1 P (PVS1, PM2, PP4) 393453 GCK 7:44153381 c.128G > A (p.Arg43His) 1 P 1 76898 GCK 7:44153379 c.130G > A (p.Gly44Ser) 1 P/LP 1 36209 GCK 7:44152420 c.214G > A (p.Gly72Arg) 1 P 1 - GCK 7:44152395 c.239G > C (p.Gly80Ala) 1 LP (PM1, PM2, PM5_Supporting, PP1, PP2, PP3, PP4) 1743101 GCK 7:44150961 c.478G > C (p.Asp160His) 1 VUS 1 , LP (PS3_Supporting, PM1, PM2, PM5_Supporting, PP2, PP3, PP4) - GCK 7:44150027 c.517_520del (p.Ala173GlnfsTer30) 1 P (PVS1, PM2, PP4) 129144 GCK 7:44150004 c.544G > A (p.Val182Met) 1 P 1 426122 GCK 7:44149977 c.571C > T (p.Arg191Trp) 1 P 1 1172896 GCK 7:44149779 c.660C > A (p.Cys220Ter) 1 P/LP 1 435306 GCK 7:44149772 c.667G > A (p.Gly223Ser) 1 P 1 - GCK 7:44147832 c.680-5_681del (p.Gly227AspfsTer47) 1 P (PVS1, PM2, PP4) 16134 GCK 7:44147830 c.683C > T (p.Thr228Met) 2 P 1 - GCK 7:44146466 c.1016A > T (p.Glu339Val) 1 P (PM1, PM2, PM5_Strong, PP2, PP3, PP4) 36181 GCK 7:44145590 c.1160C > A (p.Ala387Glu) 1 LP 1 - HNF1A 12:120978812 c.44C > A (p.Ala15Asp) 1 LP (PM1, PM2, PM5_Supporting, PP3, PP4) 918071 HNF1A 12:120978824 c.56C > T (p.Ser19Leu) 1 VUS 1 , LP (PM1, PM2, PP1_Moderate, PP3, PP4) 1526021 HNF1A 12:120988845 c.339G > A (p.Trp113Ter) 1 P 1 - HNF1A 12:120993624 c.857C > T (p.Gln211Ter) 1 P (PVS1, PM2, PP4) 523856 HNF1A 12:120997542 c.1378C > T (p.Gln460Ter) 1 P 1 - HNF4A 20:44406172 c.164G > A (p.Cys55Tyr) 1 LP (PM1, PM2, PP3, PP4) P - pathogenic; LP - likely pathogenic; VUS - variant of uncertain significance. 1 ClinVar classification for this variant. Table 2 Thus, in total, 22 deleterious MODY-causing variants were found in 25 probands (Table 2 ). Of these, 14 variants were listed in the ClinVar database and were classified in relation to the MODY phenotype: eight were classified as P, three as P/LP, one as LP, and two as variants of uncertain significance (VUS). Relatives (n = 46) from 16 out of 25 families were sequenced, and in 13 families, at least one relative carrying the variant was identified (Table 3 ). Table 3 Characteristics of 12 early-onset dieabetes index cases and their family members, carrying genetic variation. Family nr. Gene, variant Family member Sex Age at first hyperglycemia BMI HbA1c, % Fasting C-peptide, ng/mL Total cholesterol, mmol/L Treatment Complications Family 1 HNF1A , c.56C > T (p.Ser19Leu) proband F 30 21.95 5.90 1.67 4.62 Insulin No father M 38 31.10 7.80 1.01 5.93 OHA, Insulin Hypoglycemic coma, Diabetic retinopathy daughter F 3 15.08 5.40 0.54 4.36 N/A N/A sister F 37 25.01 7.30 1.18 3.61 OHA, Insulin Diabetic retinopathy Family 2 GCK , c.680-5_681del (p.Gly227AspfsTer47) proband F 7 20.41 6.40 1.60 4.63 Diet No mother F 16 28.58 6.50 3.33 5.72 OHA, Insulin, Diet No Family 3 GCK , c.1160C > A (p.Ala387Glu) proband F 0 16.33 6.30 0.50 3.11 Diet No mother F 19 22.60 7.00 1.10 3.37 Diet No sister F 0 16.19 7.06 0.56 4.07 Insulin No Family 4 GCK , c.683C > T (p.Thr228Met) proband M 15 20.72 6.50 1.90 3.60 Diet No father M 49 23.89 N/A 1.47 5.82 N/A No Family 5 GCK , c.667G > A (p.Gly223Ser) proband F 21 22.39 6.70 0.12 4.66 Diet, Insulin No mother F 44 23.88 N/A 0.86 5.43 OHA, Diet No brother M 9 20.00 6.40 1.07 3.48 OHA, Diet No Family 6 GCK , c.106C > T (p.Arg36Trp) proband F 26 20.66 5.50 0.70 6.40 Insulin, Diet No father M 62 24.30 6.70 0.60 4.70 OHA, Diet No brother M 41 27.68 6.70 0.89 6.75 N/A N/A Family 7 GCK , c.239G > C (p.Gly80Ala) proband F 14 21.63 6.35 1.22 4.20 No No mother F 33 22.59 6.50 0.97 5.56 OHA, Insulin No maternal grandfather M 51 34.42 7.10 3.88 5.60 OHA, Diet No brother M 7 15.50 6.70 0.75 3.91 N/A N/A maternal uncle M 19 24.00 6.80 N/A 5.40 OHA, Diet No Family 8 HNF1A , c.44C > A (p.Ala15Asp) proband F 10 33.36 7.80 N/A 4.81 OHA, Insulin, Diet No mother F 17 29.75 8.80 0.89 4.10 Insulin, Diet Diabetic neuropathy, Diabetic retinopathy Family 9 GCK , c.106C > T (p.Arg36Trp) proband F 39 19.05 6.60 0.38 5.08 No No mother F 60 26.77 N/A N/A 4.90 OHA No Family 10 GCK , c.214G > A (p.Gly72Arg) proband M 44 26.23 7.09 N/A 4.99 Diet No son M 22 28.20 6.40 1.58 5.38 OHA, Diet No sister F 37 23.05 6.60 1.55 6.83 OHA, Diet Diabetic neuropathy Family 11 GCK , c.128G > A (p.Arg43His) proband M 6 16.12 6.13 0.80 3.44 Diet No father M N/A N/A N/A N/A N/A N/A N/A Family 12 GCK , c.106C > T (p.Arg36Trp) proband M 9 18.59 6.37 0.75 3.90 Diet No mother F 23 33.91 N/A 3.10 4.40 OHA, Diet No Family 13 HNF1A , c.857C > T (p.Gln211Ter) proband F 18 22.66 6.80 1.11 5.59 Insulin Diabetic retinopathy mother F 27 26.44 9.20 0.42 3.70 OHA, Insulin Cardiovascular diseases, Diabetic retinopathy, Diabetic nephropathy F- female, M - male; BMI - body mass index; HbA1c - glycated hemoglobin; OHA - oral hypoglycemic agents Table 3 The variant p.Asp160His, previously classified as VUS, is located in exon 4 of the GCK gene. This variant is absent from the population databases, however, another missense variation at the same residue (p.Asp160Asn) has been associated with MODY [ 25 ]. Several nearby residues have been reported as LP variations with no benign missense variation. In silico algorithms support the pathogenicity (REVEL = 0.946) of this variant. The index case harboring the p.Asp160His variant was diagnosed with a fasting blood glucose of 7.37 mmol/l and increased levels of glycated hemoglobin (HbA1c) of 6.11% (43 mmol/mol) at the age of 11. Auto-antibody testing in this patient for pancreatic islet-cell antibodies (ICA), islet cell antibodies (IAA), and anti-glutamic acid decarboxylase (GAD) was negative, and dietary management was chosen for hyperglycemia control. Another VUS, p.Ser19Leu, is located in exon 1 of the HNF1A gene and is absent from the population database. Variant p.Ser19Leu is located within the HNF1A dimerization domain [ 26 ]. In silico algorithms predict the pathogenicity of this variant (REVEL = 0.88). The index patient tested negative for T1D auto-antibodies and was treated with insulin. The patient’s relatives were enrolled in the study and underwent genetic testing. Detailed information about variant segregation in family and variant-carrying family member characteristics are shown in Additional file 4 and Table 3 (Family1), respectively. Eight novel deleterious variants were identified: p.Glu40ValfsTer34, p.Gly80Ala, p.Ala173GlnfsTer30, p.Gly227AspfsTer47, and p.Glu339Val in the GCK gene, p.Gln211Ter and p.Ala15Asp in the HNF1A gene, and p.Cys55Tyr in the HNF4A gene (Table 2 ). These variants were absent from the population database gnomAD [ 14 ]. All novel frameshift variants, p.Glu40ValfsTer34, p.Ala173GlnfsTer30, and p.Gly227AspfsTer47, as well as the nonsense variant p.Gln211Ter, were inducing premature termination codon formation and were predicted to undergo nonsense-mediated mRNA decay. Notably, the p.Gly227AspfsTer47 variant is expected to disrupt the canonical splice acceptor site in intron 6 - in silico tool SpliceAI [ 27 ] predicts a pathogenic effect with an acceptor loss Δ score of 1.00. Three of the novel missense variants were located on residues, crucial for protein structure or function. The GCK variant p.Gly80Ala was located within the glucokinase ATP binding site, where Gly80 residue plays a role in its formation, although not directly binding ATP [ 28 , 29 ]. The novel missense variant p.Ala15Asp is located within the HNF1A N-terminal dimerization domain [ 30 ], and p.Cys55Tyr - within a highly conserved HNF4A zinc-finger DNA binding domain [ 31 ]. Another novel missense variant, p.Glu339Val, is also expected to have a negative effect on gene function, as it occurs within the GCK gene mutational hot spot. All index patients, harboring one of the eight novel variants, demonstrated mild hyperglycemia. Only three index patients, carrying the variants p.Ala173GlnfsTer30, p.Gln211Ter, and p.Ala15Asp, received medication treatment before the study - all of them were treated with insulin. Other index cases were managing hyperglycemia with diet only. Four index patients carrying the novel variation had their family members included in the study; out of them, index cases carrying the HNF1A variation had a family history associated with a much more severe diabetic phenotype, including diabetic complications (Table 3 ). 3. Selection of best performing T1D and T2D PRSs We utilized a PGS Catalog [ 18 ] to identify the most effective PRS for T1D and T2D in the Latvian population. For subsequent analyses in our study on the early-onset diabetes cohort, we selected the four most effective PRSs for each type of diabetes based on their AUC values derived from the Latvian datasets (Additional file 5). For a more detailed analysis of PRS model discriminatory ability, we selected one best-performing model for each trait - PGS001296 [ 32 ] for T1D and PGS002771 [ 33 ] for T2D. 4. Selection of Study Groups for PRS Comparison In our evaluation of 66 patients with early-onset diabetes, no deleterious variants were found in known MODY genes in 41 individuals. To understand the role of T1D and T2D PRS in determining the etiology of diabetes, we compared polygenic risk values among three groups: 23 genetically confirmed MODY patients (MODY(+)), 38 diabetic patients without identified deleterious MODY variants (MODY()), and 174 non-diabetic individuals (CONTR). Five index cases were not included in the PRS evaluation due to a lack of or uneven sequencing quality of WGS. We also added two additional groups, one consisting of 163 T1D cases and a second consisting of 909 T2D cases. Individuals for these groups were selected from a previously established diabetic cohort within the LGDB collection [ 9 , 34 ]. 5. Evaluation of T1D-PRS We observed that the median T1D-PRS in the MODY() group was higher than in both MODY(+) and CONTR groups (Additional file 6). The difference was statistically significant in all four T1D-PRS models for the MODY() versus the CONTR comparison. Patients with genetically confirmed MODY exhibited similar T1D-PRS values as controls. The PGS001296 PRS value of the T1D group was significantly higher than the MODY() and MODY(+) groups (Fig. 1 A,B). The ROC curve analysis showed that the T1D-PRS was highly discriminatory between either CONTR or MODY(+) and T1D (AUC 0.8311 and 0.8079, respectively). The overall ability of T1D-PRS to discriminate between T1D and MODY () and CONTR and MODY() was low (AUC 0.6708 and 0.623, respectively), and failed to discriminate MODY(+) from MODY() (AUC 0.5881). However, after the categorization of MODY(+) and MODY() patients into the T1D-PRS quartiles, only 8.7% of the total MODY(+) patients were found in the 75th centile, compared to 34% of MODY() patients, providing a good specificity for the identification of indicative T1D at this PRS level (Fig. 2 A,B). Additionally, there was a statistically significant association (p = 0.04006, Fisher's exact test) between T1D-PRS quartiles and insulin usage. The proportion of probands who used insulin at any point after official clinical diabetes diagnosis (n = 60, as one proband had no data on insulin usage) grew according to T1D-PRS quantiles (Additional file 7A), while the proportion of those not using insulin decreased. The 75 centile mainly consisted of MODY() cases and two GCK-MODY cases, while other centiles were more heterogeneous concerning the MODY presence and type (Additional file 7B). 6. Evaluation of T2D-PRS We next tested the ability of T2D-PRS to indicate the potential type of diabetes in the MODY() group. The median T2D-PRS values were significantly higher in the MODY() group compared to the CONTR group (Supplementary Fig. 2). When adding the T2D group to the best-performing model, the difference between MODY(+) and CONTR group T2D-PRS was not significant (Fig. 3 A, B). As expected, the ROC curve analysis showed some T2D-PRS ability to discriminate CONTR and T2D groups (AUC 0.6939). However, T2D-PRS values displayed poor or no ability to discriminate between MODY(+) and T2D (AUC 0.56) and MODY() versus CONTR, T2D and MODY(+) groups (AUC 0.6626, 0.5579 and 0.5034, respectively). Unlike T1D-PRS, the distribution of MODY(+) and MODY() patients was similar across all quartiles of the T2D-PRS distribution (Fig. 4 A, B). Discussion In our study, we consistently identified and characterized 22 deleterious variants (eight of them being novel) in a retrospective cohort of early-onset diabetes patients and evaluated T1D and T2D PRS discriminative ability in the context of MODY diabetes. This, to our knowledge, is the first study in which a Latvian early-onset diabetes cohort is examined in the context of MODY. We identified that predominantly (76%) of disease-related variants in our cohort were associated with GCK-MODY, followed by 20% with HNF1A-MODY, and 4% with HNF4A-MODY. No other MODY subtypes were detected in our group. This distribution, with a higher prevalence of GCK-MODY, contrasts with the predominance of HNF1A-MODY found in other European cohorts [ 35 ] but aligns with observations in Hungary, Poland [ 36 ], and Russia [ 37 ] where GCK-MODY is similarly predominant. A significant proportion of the deleterious variants we detected (64%) were previously examined in relation to MODY. Twelve variants were confirmed as P or LP, while two variants were VUS due to insufficient prior evidence. However, given the specific phenotypes observed in two index patients carrying these VUS, we opted to reclassify these variants in line with the ACMG/AMP guidelines. The p.Asp160His variant, found in a region intolerant to benign variations and shown to inactivate the glucokinase enzyme [ 38 ], met several ACMG/AMP criteria (Table 2 ), which support its reclassification as LP. For another variant, p.Ser19Leu, we observed good segregation within a family. Notably, the index case’s father and sister (positive for this mutation) developed retinopathy, a complication often seen in patients with HNF1A-MODY, which is associated with an aggressive disease course and a higher risk of microvascular complications [ 39 ]. This variant is also located within the domain critical for HNF1A function. Based on the evidence, we believe that this variant should be reassigned as LP (Table 2 ). Another 36% of detected deleterious variations were novel. We were able to classify three of them as LP and five as P in accordance with ACMG/AMP guidelines. Our study presents a novel evaluation of both T1D and T2D PRS, exploring their potential to discriminate MODY from diabetes with uncertain etiology. We have found that the T1D-PRS in early diabetes cases of unknown origin is higher than in healthy controls, effectively distinguishing these cases from genetically confirmed MODY patients at the 75th percentile. This suggests that T1D-PRS can be a valuable tool in identifying patients with both early-onset T1D, given that about 10% of T1D patients lack islet autoantibodies at diagnosis, a proportion that increases over time [ 40 ], and also late-onset T1D. Furthermore, the possibility of T1D in MODY-negative cases with high T1D-PRS (75th percentile or higher) is supported by the higher proportion of insulin users in this group, where insulin therapy was empirically prescribed based on clinical judgment rather than definitive diagnostic markers, reflecting uncertainty in the initial diagnosis. An increased T1D-PRS in these cases could indicate a need for more frequent monitoring of autoantibodies or C-peptide levels to track potential progression to T1D. Conversely, individuals in the MODY() group with lower T1D-PRS (e.g., below the 25th percentile) could be prioritized for further investigation to identify novel monogenic causes of diabetes. However, our study did not observe T2D-PRS's ability to discriminate between MODY(+) and MODY() groups. Both groups exhibited slightly reduced T2D-PRS levels comparable to T2D patients but higher than the control group. This outcome challenges the typical expectation that T2D-PRS, at least in the MODY(+) group, would align with a population-based distribution, and there is a lack of similar studies in the existing literature that directly address this issue. This finding raises questions about the influence of common variants in MODY genes that are included in the best-performing T2D-PRS model and the need to exclude MODY genes from T2D-PRS calculation for such cases. Interestingly, we observed a bimodal distribution of T1D and T2D PRSs in both MODY subgroups. For the MODY() group, one explanation could be the cohort's composition, potentially including both MODY with yet unknown genetics and actual T1D or T2D patients, each with distinct PRS profiles. Alternatively, in the MODY(+) group, the bimodal distribution could result from the additive effects of monogenic mutations and polygenic variants, where a milder MODY-related diabetic phenotype may be accentuated by late-onset T1D or early-onset T2D. It may also result in a progressive and more severe phenotype later in adulthood. Interestingly, patients with the highest T1D-PRS among the MODY(+) group received insulin at the moment of diagnosis and had a GCK-MODY subtype, which is usually characterized as non-progressive and requiring no treatment [41]. One proband carried novel frameshift deletion p.Ala173GlnfsTer30, while another carried missense variant p.Thr228Met. Both probands reported no diabetic complications and their HbA1c levels were within the early-onset diabetic cohort HbA1c IQR and within the target range in people with diabetes. Unfortunately, in both cases, auto-antibody testing results were not available. There have been described cases of concurrent T1D in patients with GCK-MODY [42]; to note, it is not recommended to stop treatment in such patients if T1D is confirmed [43]. Unfortunately, we do not have data on patient glucose levels prior to insulin usage, thus we cannot explore if insulin treatment has a contribution to stabilizing blood glucose levels. Our study also has several limitations. First of all, the retrospective nature of this study influenced our inclusion criteria, diverging from the practice regarding auto-antibody testing as an exclusion criterion prior to genetic testing for MODY. Some studies exclude MODY suspects with positive tests for ICA, GAD, and IAA. However, during the period of our study’s inclusion, auto-antibody testing was not routinely conducted in Latvia, leaving most index patients untested. We consciously chose not to exclude patients based on auto-antibody status to minimize the risk of omitting potential MODY cases. This decision is supported by evidence suggesting the rare possibility of positive ICA and GAD in MODY patients [ 44 ] and false-positive IAA in those previously treated with insulin [ 45 ]. To fully understand the value of T1D-PRS in differentiating MODY from T1D, large prospective studies are necessary. Such studies should compare the relative impacts of clinical features, autoantibodies, and T1D-PRS in assessing the true clinical utility of PRS. Conclusions In conclusion, our study illustrates the potential of WGS and PRS in refining the diagnosis of early-onset diabetes. We identified 22 (eight novel) deleterious genetic variants in the early-onset diabetes cohort, clarifying the genetic background of MODY in the Latvian population. The discrimination achieved by the T1D-PRS between different groups of early-onset diabetics highlights its utility as a supplementary tool in differentiating monogenic diabetes from early-onset T1D cases, especially in those without identifiable MODY mutations. Our findings underscore the importance of integrating WGS and PRS into diagnostic strategies, though larger, prospective studies are required to fully establish PRS utility in clinical practice. Declarations Ethical approval Written informed consent was obtained from each subject and/or their legal representatives during the recruitment in LGDB (Approval by Central Medical Ethics Committee No. 01-29.1.2/6407). This study was conducted in accordance with the Declaration of Helsinki. The study protocol was approved by the Central Medical Ethics Committee of Latvia (Approval No. 01-29.1/2223). Consent for publication Not applicable. Availability of data and material The majority of the data generated and analyzed during this study are included in this published article and the corresponding supplementary materials. The raw genotyping data are under restricted access from the Genome Database of Latvian Population and are available for research purposes on a reasonable request. Competing interests All authors declare that they have no competing interests. Funding This study was supported by the European Regional Development Fund under project Nr. 1.1.1.1/20/A/126 “An integrated population based Latvian genome reference and its applicability to personal risk estimation for metabolic traits" and, in part, by the State Research program project in biomedical, medical technologies and pharmaceuticals–BioMedPharm (VPP-EM-BIOMEDICĪNA-2022/1-0001). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Authors’ contributions IKo, IDK, ULT, and IKi collaborated in the cohort selection, biological samples and phenotypic data acquisition and performed clinical data analysis. MB, LB, IKa, and KM performed genetic material preparation and sequencing. RR, RP, and IA performed the sequencing and genotype data preparation and analysis. LB, IKa, and IA performed genetic variation interpretation. IA performed a formal analysis. JK, IA, and MB wrote and reviewed the manuscript with input from other authors. JK, IKa, and IE conceptualized and supervised the study. JK is the guarantor of this work and, as such, had full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis. All authors have read and approved the final manuscript version. Acknowledgments We acknowledge the Genome Database of Latvian Population for maintaining and providing biological samples and associated data of study participants. References Naylor R, Knight Johnson A, del Gaudio D. Maturity-Onset Diabetes of the Young Overview. In: Adam MP, Feldman J, Mirzaa GM, Pagon RA, Wallace SE, Bean LJ, et al., editors. GeneReviews®. Seattle (WA): University of Washington, Seattle; 1993. Laver TW, Wakeling MN, Knox O, Colclough K, Wright CF, Ellard S, et al. 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Systematic comparison of family history and polygenic risk across 24 common diseases. Am J Hum Genet. 2022;109:2152–62. Brīvība M, Atava I, Pečulis R, Elbere I, Ansone L, Rozenberga M, et al. Evaluating the Efficacy of Type 2 Diabetes Polygenic Risk Scores in an Independent European Population. Int J Mol Sci. 2024;25:1151. Weinreich SS, Bosma A, Henneman L, Rigter T, Spruijt CM, Grimbergen AJ, et al. A decade of molecular genetic testing for MODY: a retrospective study of utilization in The Netherlands. Eur J Hum Genet. 2015;23:29–33. Gaál Z, Szűcs Z, Kántor I, Luczay A, Tóth-Heyn P, Benn O, et al. A Comprehensive Analysis of Hungarian MODY Patients—Part II: Glucokinase MODY Is the Most Prevalent Subtype Responsible for about 70% of Confirmed Cases. Life. 2021;11:771. Glotov OS, Serebryakova EA, Turkunova ME, Efimova OA, Glotov AS, Barbitoff YA, et al. Whole-exome sequencing in Russian children with non-type 1 diabetes mellitus reveals a wide spectrum of genetic variants in MODY-related and unrelated genes. Mol Med Rep. 2019;20:4905–14. Raimondo A, Chakera AJ, Thomsen SK, Colclough K, Barrett A, De Franco E, et al. Phenotypic severity of homozygous GCK mutations causing neonatal or childhood-onset diabetes is primarily mediated through effects on protein stability. Hum Mol Genet. 2014;23:6432–40. Szopa M, Wolkow J, Matejko B, Skupien J, Klupa T, Wybrańska I, et al. Prevalence of Retinopathy in Adult Patients with GCK-MODY and HNF1A-MODY. Exp Clin Endocrinol Diabetes Off J Ger Soc Endocrinol Ger Diabetes Assoc. 2015;123:524–8. Bingley PJ. Clinical applications of diabetes antibody testing. J Clin Endocrinol Metab. 2010;95:25–33. Delvecchio M, Pastore C, Giordano P. Treatment Options for MODY Patients: A Systematic Review of Literature. Diabetes Ther Res Treat Educ Diabetes Relat Disord. 2020;11:1667–85. Uday S, Campbell FM, Cropper J, Shepherd M. Monogenic diabetes and type 1 diabetes mellitus: a challenging combination. Pract Diabetes. 2014;31:327–30. Chakera AJ, Steele AM, Gloyn AL, Shepherd MH, Shields B, Ellard S, et al. Recognition and Management of Individuals With Hyperglycemia Because of a Heterozygous Glucokinase Mutation. Diabetes Care. 2015;38:1383–92. Amed S, Oram R. Maturity-Onset Diabetes of the Young (MODY): Making the Right Diagnosis to Optimize Treatment. Can J Diabetes. 2016;40:449–54. Broome DT, Pantalone KM, Kashyap SR, Philipson LH. Approach to the Patient with MODY-Monogenic Diabetes. J Clin Endocrinol Metab. 2021;106:237–50. Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5361647","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":374268893,"identity":"13f9b023-725b-42b1-8105-e194467e17d3","order_by":0,"name":"Ivanna 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14:23:12","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5361647/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5361647/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":70386542,"identity":"2a2d0bac-a6cf-4b29-96cc-df1efdabdfad","added_by":"auto","created_at":"2024-12-02 17:21:14","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":615758,"visible":true,"origin":"","legend":"\u003cp\u003eDistribution of PGS001296 T1D-PRS values across study groups.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(A)\u003c/strong\u003e Box-and-whisker plots for CONTR, MODY(+), MODY(‑) and T1D group PRS values. Box indicate the IQR, whiskers indicate the 1.5*IQR. Kruskal-Wallis and Dunn’s test were used to determine whether there was a difference in distribution between three groups. Significant p-values are shown in each image. \u003cstrong\u003e(B) \u003c/strong\u003eThe density curves of PRS stratified by CONTR, MODY(+), MODY(‑) and T1D groups. Dashed line represents the group's mean PRS value.\u003c/p\u003e\n\u003cp\u003eCONTR - controls, coloured purple; MODY(+) - probands with positive genetic testing for MODY, coloured blue; MODY( ) - probands with negative genetic testing for MODY, coloured green; T1D - type 1 diabetes, coloured pink; IQR - interquartile range.\u003c/p\u003e","description":"","filename":"Figure1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5361647/v1/adfc5c8ae600990509d8f8fd.jpg"},{"id":70386564,"identity":"4959e2cf-f401-4c5c-ac3e-bb4b403c4a51","added_by":"auto","created_at":"2024-12-02 17:21:37","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":415742,"visible":true,"origin":"","legend":"\u003cp\u003eThe discriminatory ability of PGS001296 T1D-PRS on proven and suspected MODY.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(A)\u003c/strong\u003e The proportion of MODY(+) (blue) and MODY(‑) (green) in low to high PRS subgroups (left to right). The subgroups are based on centiles of T1D-PRS values. \u003cstrong\u003e(B)\u003c/strong\u003e The distribution of MODY(‑) (n = 38) in low to high PRS subgroups. The subgroups are based on centiles of T1D-PRS values. The T1D-PRS below the 50th centile categorizes subgroups as probable MODY, and the PRS value above the 50th centile as probable T1D.\u003c/p\u003e","description":"","filename":"Figure2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5361647/v1/2cbfb3ea774da1c53549053d.jpg"},{"id":70386395,"identity":"650c2950-bf74-47b5-a34b-954e63988d4c","added_by":"auto","created_at":"2024-12-02 17:19:36","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":591164,"visible":true,"origin":"","legend":"\u003cp\u003eDistribution of PGS002771 T2D-PRS values across study groups.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(A)\u003c/strong\u003e Box-and-whisker plots for CONTR, MODY(+), MODY(‑) and T2D group PRS values. Box indicate the IQR, whiskers indicate the 1.5*IQR. Kruskal-Wallis and Dunn’s test were used to determine whether there was a difference in distribution between three groups. Significant p-values are shown in each image. \u003cstrong\u003e(B)\u003c/strong\u003e The density curves of PRS stratified by CONTR, MODY(+), MODY(‑) and T2D groups. Dashed line represents the group's mean PRS value.\u003c/p\u003e\n\u003cp\u003eCONTR - controls, coloured purple; MODY(+) - probands with positive genetic testing for MODY, coloured blue; MODY(-) - probands with negative genetic testing for MODY, coloured green; T2D - type 2 diabetes, coloured yellow; IQR - interquartile range.\u003c/p\u003e","description":"","filename":"Figure3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5361647/v1/69edd06469772b7fedacb972.jpg"},{"id":70386375,"identity":"112535d8-d612-4ba1-ba33-f67d8fdc921b","added_by":"auto","created_at":"2024-12-02 17:19:28","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":430856,"visible":true,"origin":"","legend":"\u003cp\u003eThe discriminatory ability of PGS002771 T2D-PRS on proven and suspected MODY.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(A) \u003c/strong\u003eThe proportion of MODY(+) (blue) and MODY(‑) (green) in low to high PRS subgroups (left to right). The subgroups are based on centiles of T2D-PRS values.\u003cstrong\u003e (B)\u003c/strong\u003eThe distribution of MODY(‑) (n = 38) in low to high PRS subgroups. The subgroups are based on centiles of T2D-PRS values. The T2D-PRS below the 50th centile categorizes subgroups as probable MODY, and the PRS value above the 50th centile as probable T2D.\u003c/p\u003e","description":"","filename":"Figure4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5361647/v1/120be642ce4fab31e2bf62ba.jpg"},{"id":72453626,"identity":"36ba3999-6825-48fb-884b-b773acbe0b22","added_by":"auto","created_at":"2024-12-27 09:09:05","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3024228,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5361647/v1/0415da54-122a-40fa-b25a-7339cf0d5e68.pdf"},{"id":70386463,"identity":"3503d281-c929-4f2b-a108-37fe4bf9ae39","added_by":"auto","created_at":"2024-12-02 17:20:04","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":10175,"visible":true,"origin":"","legend":"","description":"","filename":"Additionalfile1.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-5361647/v1/328d3606a6b273fa4036ef74.xlsx"},{"id":70386561,"identity":"aa73f5a0-ba1f-4708-867d-454486555b19","added_by":"auto","created_at":"2024-12-02 17:21:28","extension":"xlsx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":11629,"visible":true,"origin":"","legend":"","description":"","filename":"Additionalfile2.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-5361647/v1/4acd62b69fd51320988c4c16.xlsx"},{"id":70386416,"identity":"b91adbcd-3f4d-4889-a741-483a329b5f9d","added_by":"auto","created_at":"2024-12-02 17:19:53","extension":"xlsx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":12566,"visible":true,"origin":"","legend":"","description":"","filename":"Additionalfile3.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-5361647/v1/d8820e7f349ecf614bf399c8.xlsx"},{"id":70386541,"identity":"86d34d41-2e9b-4406-9dee-881be8ae7e0b","added_by":"auto","created_at":"2024-12-02 17:21:10","extension":"pdf","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":206363,"visible":true,"origin":"","legend":"","description":"","filename":"Additionalfile4.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5361647/v1/d4df9bf170a9a9410a029a07.pdf"},{"id":70386519,"identity":"4919ef7d-1322-4792-b77f-902e8e3cf0e1","added_by":"auto","created_at":"2024-12-02 17:21:08","extension":"xlsx","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":16043,"visible":true,"origin":"","legend":"","description":"","filename":"Additionalfile5.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-5361647/v1/05e5ee7098603255d54df396.xlsx"},{"id":70386503,"identity":"335b8584-af79-4142-a689-0d944039661f","added_by":"auto","created_at":"2024-12-02 17:20:16","extension":"pdf","order_by":6,"title":"","display":"","copyAsset":false,"role":"supplement","size":132785,"visible":true,"origin":"","legend":"","description":"","filename":"Additionalfile6.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5361647/v1/f78bb57e837f158e5c3f1116.pdf"},{"id":70386355,"identity":"42d0313b-516e-4d13-bcf9-b3b45360366a","added_by":"auto","created_at":"2024-12-02 17:19:21","extension":"pdf","order_by":7,"title":"","display":"","copyAsset":false,"role":"supplement","size":211711,"visible":true,"origin":"","legend":"","description":"","filename":"Additionalfile7.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5361647/v1/e84e42efbc35eaea517e2278.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Identification Of Pathogenic Mutations And Application Of Polygenic Risk Scores In Early-Onset Diabetes Patients","fulltext":[{"header":"Background","content":"\u003cp\u003eDiabetes mellitus, encompassing Type 1 (T1D) and Type 2 (T2D), also includes MODY, a monogenic form marked by autosomal dominant inheritance and early-onset hyperglycemia. MODY, usually diagnosed via clinical and genetic testing, is classified using the names of mutated genes (to date, 11 causal genes identified), with GCK-MODY, HNF1A-MODY, and HNF4A-MODY accounting for more than 90% of genetically confirmed MODY\u0026nbsp;[1, 2]. Despite accounting for an estimated 1% of all diabetes cases, a great proportion of MODY cases remain unidentified\u0026nbsp;[3], and more than 50% of clinically suspected MODY patients do not exhibit any known disease-causing mutations\u0026nbsp;[4].\u003c/p\u003e\n\u003cp\u003eRecent advancements in PRS have shown promise in differentiating between types of diabetes\u0026nbsp;[5]. The utility of a T1D-PRS in distinguishing between monogenic diabetes and T1D in young adults has been demonstrated before\u0026nbsp;[6]. Some studies have successfully employed PRS to differentiate between T1D and MODY in diverse populations\u0026nbsp;[7, 8].\u003c/p\u003e\n\u003cp\u003eIn this study, we identified the inherited deleterious mutations in a cohort of early-onset diabetes patients from Latvia, using WGS. We also investigate the ability to explain early-onset diabetes by having increased T1D and T2D PRSs.\u0026nbsp;\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cstrong\u003e1.\u0026nbsp;\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eStudy subjects\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe early-onset diabetes cohort was recruited by the Genome Database of Latvian Population (LGDB) in cooperation with major Latvian hospital endocrinologists\u0026nbsp;[9]\u0026nbsp;from 2011 to 2017 according to established LGDB standard protocols. The following inclusion criteria were used: 1. Unspecified diagnosis of diabetes mellitus before age 35; 2. Absence of diabetic ketoacidosis; 3. Endogenous insulin secretion has been maintained for at least two years after diagnosis; 4. Complete clinical, anthropometric, and biochemical data. Additionally, three patients with a clinical diabetes diagnosis after 35 years of age were recruited to our cohort due to available genetic testing data, which proved them to be MODY-positive. In total, 66 index patients were selected for this study. In addition, 46 family members of probands with proven pathogenic (P)/likely pathogenic (LP) variants were available for either Sanger sequencing or WGS.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;A control group, composed of 174 individuals was selected from LGDB resources using the following criteria: 1. No diagnosis of diabetes mellitus at the time of application in LGDB; 2. No history of taking medications for high glucose levels; 3. No obesity (BMI\u0026lt;30.0); 4. WGS data available with mean coverage depth \u0026gt;20.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.\u0026nbsp;\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eAcquisition of sequencing data.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDNA was prepared and obtained according to LGDB standard procedures\u0026nbsp;[9]. All probands were selected for WGS, and DNA libraries were prepared using the MGIEasy Universal DNA Library Prep Set (MGI Tech Co., Ltd., China) and the MGIEasy FS PCR-Free DNA Library Prep Set (MGI Tech Co., Ltd., China). WGS sequencing was performed for index patients and controls on MGI Tech DNBSEQ-T10 and DNBSEQ-T7 platforms (MGI Tech Co., Ltd., China), using DNBSEQ-T7RS high-throughput and DNBSEQ-T10×4RS high-throughput sequencing sets.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.\u0026nbsp;\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eWGS data Variant Calling\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData from WGS were processed using the nf-core/sarek pipeline v.2.7.1\u0026nbsp;[10], preprocessing steps based on Genome Analysis Toolkit (GATK) 4 best practices. Sequences were mapped to the reference human genome GRCh38/hg38 as part of the pipeline. For the variant calling step, the GATK HaplotypeCaller tool was selected. We combined samples using GATK CombineGVCFs and GenotypeGVCFs. Controls were added to the multi-sample variant call format (VCF) file using the BCFtools v1.10.2 “merge” command\u0026nbsp;[11]. Singularity was used to install the necessary software, while Nextflow was necessary for analytic task automatization.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4.\u0026nbsp; \u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eSelection of genes of interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA subset VCF file consisting of a diabetic cohort was made to detect potential deleterious variants. Only variants located within 11 MODY (Additional file 1) and 55 T2D candidate genes (Additional file 2) were selected. T2D candidate genes were selected based on genome-wide association studies (GWAS) that determined the association of these genes with the T2D phenotype and their role in glucose metabolism\u0026nbsp;[12].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e5.\u0026nbsp; \u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eVariant filtering and annotation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe VCF file containing selected variants was annotated using ANNOVAR\u0026nbsp;[13].\u003c/p\u003e\n\u003cp\u003eWe filtered out variants that were not located in gene coding sequences and variants defined as SIFT and PolyPhen benign. Further, a Genome Aggregation Database (gnomAD)\u0026nbsp;[14]\u0026nbsp;population maximum filtering allele frequency (PopMax FAF) cutoff of 0.000108 (MODY estimated frequency among adults\u0026nbsp;[4]) was used to filter out the remaining variants.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e6.\u0026nbsp;\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eVariant interpretation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eVariants that were present in the ClinVar database\u0026nbsp;[15]\u0026nbsp;and had been previously classified as P in association with MODYwere assigned the appropriate pathogenicity category in our study. Variants absent from the ClinVar database were interpreted according to American College of Medical Genetics and Genomics/Association for Molecular Pathology (ACMG/AMP) guidelines\u0026nbsp;[16].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e7.\u0026nbsp;\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003ePolygenic risk score selection\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIndividual PRSs were calculated using the pgscatalog/pgsc_calc v.2.0.0-alpha.3\u0026nbsp;[17]\u0026nbsp;workflow with default options enabled.\u003c/p\u003e\n\u003cp\u003eWe retrieved the publicly available PRSs from the Polygenic Score (PGS) Catalog database\u0026nbsp;[18]. To date, the PGS Catalog has gathered 12 T1D-PRSs from nine studies and 102 T2D-PRSs from 26 studies. Downloaded PRSs associated with T1D were tested on a previously established genotyped sample set from the LGDB collection\u0026nbsp;[19], obtained by the Infinium Global Screening Array (Illumina, USA). The set consisted of 185 T1D cases and 2991 controls. PRSs associated with T2D followed a similar procedure on a sample set, which included 1603 T2D cases and 2345 controls. In order to evaluate the performance of all available T1D and T2D PRSs from the PGS Catalog, we calculated standard quality metrics for each score using R Statistical Software\u0026nbsp;[20]. The area under the receiver operator characteristic curve (AUC) was obtained by comparing each model individually to the LGDB T1D or T2D phenotype using the “pROC” package\u0026nbsp;[21]. The correlation coefficient was calculated using the R package “psych”\u0026nbsp;[22]; the odds ratio (OR) over standard deviation (SD) increase from the generalized linear model was obtained using epiDisplay. These metrics were compared with analogous values reported by the PRS source study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e8.\u0026nbsp;\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003ePolygenic risk score evaluation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe selected the best-performing models for T1D and T2D to calculate PRS in the early-onset diabetes cohort.\u003c/p\u003e\n\u003cp\u003eThe Kruskal–Wallis test, followed by Dunn’s test, using R “rstatix” [23] and “dunn.test” packages [24] was used to compare individual PRS values. The “pROC” package was used to measure the discriminative power of the T1D and T2D scores. Statistical analysis was performed using R Statistical Software.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e1. Cohort characteristics\u003c/h2\u003e \u003cp\u003eWe analyzed 66 index patients and 46 family members from an early-onset diabetes cohort. Additionally, 174 individuals representing the general population were included as controls. Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e presents the phenotypic characteristics of these groups. We observed no significant differences in phenotypic and biochemical measures within the diabetic group. Both age and BMI were notably higher in the control group than in index patients, with p-values of 2.893*10\u003csup\u003e\u0026minus;\u0026thinsp;16\u003c/sup\u003e and 2.284*10\u003csup\u003e\u0026minus;\u0026thinsp;9\u003c/sup\u003e, respectively.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eStudy group characteristics.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEarly-onset diabetes\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eControls\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003en (females)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e66 (42)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e174 (105)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedian age of enrollment; IQR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18.00;\u003c/p\u003e \u003cp\u003e11.0-30.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e44.00;\u003c/p\u003e \u003cp\u003e32.00\u0026ndash;53.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.893*10\u003csup\u003e\u0026minus;\u0026thinsp;16\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedian BMI; IQR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20.41;\u003c/p\u003e \u003cp\u003e16.88\u0026ndash;22.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24.35;\u003c/p\u003e \u003cp\u003e21.71\u0026ndash;26.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.284*10\u003csup\u003e\u0026minus;\u0026thinsp;9\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eMODY(+)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eMODY()\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003ep-value\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003en (females)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25 (16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e41 (26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedian age of enrollment; IQR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25.00;\u003c/p\u003e \u003cp\u003e13.50\u0026ndash;38.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17.00;\u003c/p\u003e \u003cp\u003e9.00-25.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedian age at first hyperglycemia; IQR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14.00;\u003c/p\u003e \u003cp\u003e9.00\u0026ndash;22.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16.00;\u003c/p\u003e \u003cp\u003e7.00\u0026ndash;22.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.55\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedian BMI; IQR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e21.46;\u003c/p\u003e \u003cp\u003e19.00-22.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19.37;\u003c/p\u003e \u003cp\u003e15.96\u0026ndash;21.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedian HbA1c; IQR, %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.36;\u003c/p\u003e \u003cp\u003e6.13\u0026ndash;6.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.50\u003c/p\u003e \u003cp\u003e5.80\u0026ndash;7.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.69\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedian C-peptide level; IQR, ng/mL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.23;\u003c/p\u003e \u003cp\u003e0.71\u0026ndash;1.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.04;\u003c/p\u003e \u003cp\u003e0.64\u0026ndash;1.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.50\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eIQR - interquartile range; BMI - body mass index; HbA1c - glycated hemoglobin; MODY(+) \u0026ndash; genetically confirmed MODY patients; MODY() \u0026ndash; probands with negative genetic testing for MODY\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e2. Identification of MODY Causative Variants\u003c/h2\u003e \u003cp\u003eWGS was successful for 61 index cases and 174 controls included in the study, with a mean sequencing depth of 30. An average of 3,996,090 single nucleotide polymorphisms (SNP) and 898,385 insertions-deletions (INDELs) per sample were detected. In the early-onset diabetes subgroup, WGS revealed 54 coding region variants in 49 index patients (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, Additional file 3). These included 11 synonymous, 20 missense, and 5 nonsense SNPs. The remaining 18 comprised 12 insertions (seven frameshifts) and six deletions (five frameshifts). Additionally, two pathogenic missense variants (p.Ser19Leu and p.Gly223Ser) were found in two patients with failed WGS, as they had data from a targeted sequencing panel.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePathogenic variations identified in 66 probands with suspected MODY using whole genome sequencing.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eClinVar ID\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGene\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eChr:Position\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eVariant\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eProband count\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eClinical significance\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e431973\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eGCK\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7:44153403\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ec.106C\u0026thinsp;\u0026gt;\u0026thinsp;T (p.Arg36Trp)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eP/LP\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eGCK\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7:44153390\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ec.118_119ins (p.Glu40ValfsTer34)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eP (PVS1, PM2, PP4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e393453\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eGCK\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7:44153381\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ec.128G\u0026thinsp;\u0026gt;\u0026thinsp;A (p.Arg43His)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eP\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e76898\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eGCK\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7:44153379\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ec.130G\u0026thinsp;\u0026gt;\u0026thinsp;A (p.Gly44Ser)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eP/LP\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e36209\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eGCK\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7:44152420\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ec.214G\u0026thinsp;\u0026gt;\u0026thinsp;A (p.Gly72Arg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eP\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eGCK\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7:44152395\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ec.239G\u0026thinsp;\u0026gt;\u0026thinsp;C (p.Gly80Ala)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eLP (PM1, PM2, PM5_Supporting, PP1, PP2, PP3, PP4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1743101\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eGCK\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7:44150961\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ec.478G\u0026thinsp;\u0026gt;\u0026thinsp;C (p.Asp160His)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eVUS\u003csup\u003e1\u003c/sup\u003e, LP (PS3_Supporting, PM1, PM2, PM5_Supporting, PP2, PP3, PP4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eGCK\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7:44150027\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ec.517_520del (p.Ala173GlnfsTer30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eP (PVS1, PM2, PP4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e129144\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eGCK\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7:44150004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ec.544G\u0026thinsp;\u0026gt;\u0026thinsp;A (p.Val182Met)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eP\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e426122\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eGCK\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7:44149977\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ec.571C\u0026thinsp;\u0026gt;\u0026thinsp;T (p.Arg191Trp)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eP\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1172896\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eGCK\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7:44149779\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ec.660C\u0026thinsp;\u0026gt;\u0026thinsp;A (p.Cys220Ter)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eP/LP\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e435306\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eGCK\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7:44149772\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ec.667G\u0026thinsp;\u0026gt;\u0026thinsp;A (p.Gly223Ser)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eP\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eGCK\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7:44147832\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ec.680-5_681del (p.Gly227AspfsTer47)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eP (PVS1, PM2, PP4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e16134\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eGCK\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7:44147830\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ec.683C\u0026thinsp;\u0026gt;\u0026thinsp;T (p.Thr228Met)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eP\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eGCK\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7:44146466\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ec.1016A\u0026thinsp;\u0026gt;\u0026thinsp;T (p.Glu339Val)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eP (PM1, PM2, PM5_Strong, PP2, PP3, PP4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e36181\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eGCK\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7:44145590\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ec.1160C\u0026thinsp;\u0026gt;\u0026thinsp;A (p.Ala387Glu)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eLP\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eHNF1A\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12:120978812\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ec.44C\u0026thinsp;\u0026gt;\u0026thinsp;A (p.Ala15Asp)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eLP (PM1, PM2, PM5_Supporting, PP3, PP4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e918071\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eHNF1A\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12:120978824\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ec.56C\u0026thinsp;\u0026gt;\u0026thinsp;T (p.Ser19Leu)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eVUS\u003csup\u003e1\u003c/sup\u003e, LP (PM1, PM2, PP1_Moderate, PP3, PP4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1526021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eHNF1A\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12:120988845\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ec.339G\u0026thinsp;\u0026gt;\u0026thinsp;A (p.Trp113Ter)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eP\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eHNF1A\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12:120993624\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ec.857C\u0026thinsp;\u0026gt;\u0026thinsp;T (p.Gln211Ter)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eP (PVS1, PM2, PP4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e523856\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eHNF1A\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12:120997542\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ec.1378C\u0026thinsp;\u0026gt;\u0026thinsp;T (p.Gln460Ter)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eP\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eHNF4A\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20:44406172\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ec.164G\u0026thinsp;\u0026gt;\u0026thinsp;A (p.Cys55Tyr)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eLP (PM1, PM2, PP3, PP4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003eP - pathogenic; LP - likely pathogenic; VUS - variant of uncertain significance.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003e\u003csup\u003e1\u003c/sup\u003e ClinVar classification for this variant.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e\u003c/p\u003e \u003cp\u003eThus, in total, 22 deleterious MODY-causing variants were found in 25 probands (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Of these, 14 variants were listed in the ClinVar database and were classified in relation to the MODY phenotype: eight were classified as P, three as P/LP, one as LP, and two as variants of uncertain significance (VUS). Relatives (n\u0026thinsp;=\u0026thinsp;46) from 16 out of 25 families were sequenced, and in 13 families, at least one relative carrying the variant was identified (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCharacteristics of 12 early-onset dieabetes index cases and their family members, carrying genetic variation.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"11\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFamily nr.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGene, variant\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFamily member\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSex\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAge at first hyperglycemia\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eBMI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eHbA1c, %\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eFasting \u003c/p\u003e \u003cp\u003eC-peptide, ng/mL\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eTotal cholesterol, mmol/L\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eTreatment\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003eComplications\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFamily 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eHNF1A\u003c/em\u003e, c.56C\u0026thinsp;\u0026gt;\u0026thinsp;T (p.Ser19Leu)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eproband\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e21.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e5.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e4.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eInsulin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003efather\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e31.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e7.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e5.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eOHA, Insulin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eHypoglycemic coma, Diabetic retinopathy\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003edaughter\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e15.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e5.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e4.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eN/A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eN/A\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003esister\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e25.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e7.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e3.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eOHA, Insulin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eDiabetic retinopathy\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFamily 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eGCK\u003c/em\u003e, c.680-5_681del (p.Gly227AspfsTer47)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eproband\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e20.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e6.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e4.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eDiet\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003emother\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e28.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e6.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e5.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eOHA, Insulin, Diet\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFamily 3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eGCK\u003c/em\u003e, c.1160C\u0026thinsp;\u0026gt;\u0026thinsp;A (p.Ala387Glu)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eproband\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e16.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e6.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e3.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eDiet\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003emother\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e22.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e7.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e3.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eDiet\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003esister\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e16.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e7.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e4.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eInsulin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFamily 4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eGCK\u003c/em\u003e, c.683C\u0026thinsp;\u0026gt;\u0026thinsp;T (p.Thr228Met)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eproband\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e20.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e6.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e3.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eDiet\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003efather\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e23.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eN/A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e5.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eN/A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFamily 5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eGCK\u003c/em\u003e, c.667G\u0026thinsp;\u0026gt;\u0026thinsp;A (p.Gly223Ser)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eproband\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e22.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e6.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e4.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eDiet, Insulin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003emother\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e23.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eN/A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e5.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eOHA, Diet\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ebrother\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e20.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e6.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e3.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eOHA, Diet\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFamily 6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eGCK\u003c/em\u003e, c.106C\u0026thinsp;\u0026gt;\u0026thinsp;T (p.Arg36Trp)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eproband\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e20.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e5.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e6.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eInsulin, Diet\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003efather\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e24.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e6.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e4.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eOHA, Diet\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ebrother\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e27.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e6.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e6.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eN/A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eN/A\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFamily 7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eGCK\u003c/em\u003e, c.239G\u0026thinsp;\u0026gt;\u0026thinsp;C (p.Gly80Ala)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eproband\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e21.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e6.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e4.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003emother\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e22.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e6.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e5.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eOHA, Insulin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ematernal grandfather\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e34.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e7.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e5.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eOHA, Diet\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ebrother\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e15.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e6.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e3.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eN/A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eN/A\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ematernal uncle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e24.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e6.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eN/A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e5.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eOHA, Diet\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFamily 8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eHNF1A\u003c/em\u003e, c.44C\u0026thinsp;\u0026gt;\u0026thinsp;A (p.Ala15Asp)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eproband\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e33.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e7.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eN/A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e4.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eOHA, Insulin, Diet\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003emother\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e29.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e8.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e4.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eInsulin, Diet\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eDiabetic neuropathy, Diabetic retinopathy\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFamily 9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eGCK\u003c/em\u003e, c.106C\u0026thinsp;\u0026gt;\u0026thinsp;T (p.Arg36Trp)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eproband\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e19.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e6.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e5.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003emother\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e26.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eN/A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eN/A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e4.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eOHA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFamily 10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eGCK\u003c/em\u003e, c.214G\u0026thinsp;\u0026gt;\u0026thinsp;A (p.Gly72Arg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eproband\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e26.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e7.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eN/A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e4.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eDiet\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eson\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e28.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e6.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e5.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eOHA, Diet\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003esister\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e23.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e6.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e6.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eOHA, Diet\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eDiabetic neuropathy\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eFamily 11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eGCK\u003c/em\u003e, c.128G\u0026thinsp;\u0026gt;\u0026thinsp;A\u003c/p\u003e \u003cp\u003e(p.Arg43His)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eproband\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e16.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e6.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e3.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eDiet\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003efather\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eN/A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eN/A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eN/A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eN/A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eN/A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eN/A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eN/A\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFamily 12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eGCK\u003c/em\u003e, c.106C\u0026thinsp;\u0026gt;\u0026thinsp;T (p.Arg36Trp)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eproband\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e18.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e6.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e3.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eDiet\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003emother\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e33.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eN/A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e4.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eOHA, Diet\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFamily 13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eHNF1A\u003c/em\u003e, c.857C\u0026thinsp;\u0026gt;\u0026thinsp;T (p.Gln211Ter)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eproband\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e22.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e6.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e5.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eInsulin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eDiabetic retinopathy\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003emother\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e26.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e9.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e3.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eOHA, Insulin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eCardiovascular diseases, Diabetic retinopathy, Diabetic nephropathy\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"11\"\u003eF- female, M - male; BMI - body mass index; HbA1c - glycated hemoglobin; OHA - oral hypoglycemic agents\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e\u003c/p\u003e \u003cp\u003eThe variant p.Asp160His, previously classified as VUS, is located in exon 4 of the \u003cem\u003eGCK\u003c/em\u003e gene. This variant is absent from the population databases, however, another missense variation at the same residue (p.Asp160Asn) has been associated with MODY [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Several nearby residues have been reported as LP variations with no benign missense variation. \u003cem\u003eIn silico\u003c/em\u003e algorithms support the pathogenicity (REVEL\u0026thinsp;=\u0026thinsp;0.946) of this variant. The index case harboring the p.Asp160His variant was diagnosed with a fasting blood glucose of 7.37 mmol/l and increased levels of glycated hemoglobin (HbA1c) of 6.11% (43 mmol/mol) at the age of 11. Auto-antibody testing in this patient for pancreatic islet-cell antibodies (ICA), islet cell antibodies (IAA), and anti-glutamic acid decarboxylase (GAD) was negative, and dietary management was chosen for hyperglycemia control.\u003c/p\u003e \u003cp\u003eAnother VUS, p.Ser19Leu, is located in exon 1 of the \u003cem\u003eHNF1A\u003c/em\u003e gene and is absent from the population database. Variant p.Ser19Leu is located within the \u003cem\u003eHNF1A\u003c/em\u003e dimerization domain [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. \u003cem\u003eIn silico\u003c/em\u003e algorithms predict the pathogenicity of this variant (REVEL\u0026thinsp;=\u0026thinsp;0.88). The index patient tested negative for T1D auto-antibodies and was treated with insulin. The patient\u0026rsquo;s relatives were enrolled in the study and underwent genetic testing. Detailed information about variant segregation in family and variant-carrying family member characteristics are shown in Additional file 4 and Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e (Family1), respectively.\u003c/p\u003e \u003cp\u003eEight novel deleterious variants were identified: p.Glu40ValfsTer34, p.Gly80Ala, p.Ala173GlnfsTer30, p.Gly227AspfsTer47, and p.Glu339Val in the \u003cem\u003eGCK\u003c/em\u003e gene, p.Gln211Ter and p.Ala15Asp in the \u003cem\u003eHNF1A\u003c/em\u003e gene, and p.Cys55Tyr in the \u003cem\u003eHNF4A\u003c/em\u003e gene (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). These variants were absent from the population database gnomAD [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAll novel frameshift variants, p.Glu40ValfsTer34, p.Ala173GlnfsTer30, and p.Gly227AspfsTer47, as well as the nonsense variant p.Gln211Ter, were inducing premature termination codon formation and were predicted to undergo nonsense-mediated mRNA decay. Notably, the p.Gly227AspfsTer47 variant is expected to disrupt the canonical splice acceptor site in intron 6 - \u003cem\u003ein silico\u003c/em\u003e tool SpliceAI [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e] predicts a pathogenic effect with an acceptor loss Δ score of 1.00.\u003c/p\u003e \u003cp\u003eThree of the novel missense variants were located on residues, crucial for protein structure or function. The \u003cem\u003eGCK\u003c/em\u003e variant p.Gly80Ala was located within the glucokinase ATP binding site, where Gly80 residue plays a role in its formation, although not directly binding ATP [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. The novel missense variant p.Ala15Asp is located within the HNF1A N-terminal dimerization domain [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e], and p.Cys55Tyr - within a highly conserved HNF4A zinc-finger DNA binding domain [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Another novel missense variant, p.Glu339Val, is also expected to have a negative effect on gene function, as it occurs within the \u003cem\u003eGCK\u003c/em\u003e gene mutational hot spot.\u003c/p\u003e \u003cp\u003eAll index patients, harboring one of the eight novel variants, demonstrated mild hyperglycemia. Only three index patients, carrying the variants p.Ala173GlnfsTer30, p.Gln211Ter, and p.Ala15Asp, received medication treatment before the study - all of them were treated with insulin. Other index cases were managing hyperglycemia with diet only. Four index patients carrying the novel variation had their family members included in the study; out of them, index cases carrying the \u003cem\u003eHNF1A\u003c/em\u003e variation had a family history associated with a much more severe diabetic phenotype, including diabetic complications (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3. Selection of best performing T1D and T2D PRSs\u003c/h2\u003e \u003cp\u003eWe utilized a PGS Catalog [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e] to identify the most effective PRS for T1D and T2D in the Latvian population. For subsequent analyses in our study on the early-onset diabetes cohort, we selected the four most effective PRSs for each type of diabetes based on their AUC values derived from the Latvian datasets (Additional file 5). For a more detailed analysis of PRS model discriminatory ability, we selected one best-performing model for each trait - PGS001296 [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e] for T1D and PGS002771 [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e] for T2D.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e4. Selection of Study Groups for PRS Comparison\u003c/h2\u003e \u003cp\u003eIn our evaluation of 66 patients with early-onset diabetes, no deleterious variants were found in known MODY genes in 41 individuals. To understand the role of T1D and T2D PRS in determining the etiology of diabetes, we compared polygenic risk values among three groups: 23 genetically confirmed MODY patients (MODY(+)), 38 diabetic patients without identified deleterious MODY variants (MODY()), and 174 non-diabetic individuals (CONTR). Five index cases were not included in the PRS evaluation due to a lack of or uneven sequencing quality of WGS.\u003c/p\u003e \u003cp\u003eWe also added two additional groups, one consisting of 163 T1D cases and a second consisting of 909 T2D cases. Individuals for these groups were selected from a previously established diabetic cohort within the LGDB collection [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e5. Evaluation of T1D-PRS\u003c/h2\u003e \u003cp\u003eWe observed that the median T1D-PRS in the MODY() group was higher than in both MODY(+) and CONTR groups (Additional file 6). The difference was statistically significant in all four T1D-PRS models for the MODY() versus the CONTR comparison. Patients with genetically confirmed MODY exhibited similar T1D-PRS values as controls.\u003c/p\u003e \u003cp\u003eThe PGS001296 PRS value of the T1D group was significantly higher than the MODY() and MODY(+) groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA,B). The ROC curve analysis showed that the T1D-PRS was highly discriminatory between either CONTR or MODY(+) and T1D (AUC 0.8311 and 0.8079, respectively). The overall ability of T1D-PRS to discriminate between T1D and MODY () and CONTR and MODY() was low (AUC 0.6708 and 0.623, respectively), and failed to discriminate MODY(+) from MODY() (AUC 0.5881). However, after the categorization of MODY(+) and MODY() patients into the T1D-PRS quartiles, only 8.7% of the total MODY(+) patients were found in the 75th centile, compared to 34% of MODY() patients, providing a good specificity for the identification of indicative T1D at this PRS level (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA,B).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAdditionally, there was a statistically significant association (p\u0026thinsp;=\u0026thinsp;0.04006, Fisher's exact test) between T1D-PRS quartiles and insulin usage. The proportion of probands who used insulin at any point after official clinical diabetes diagnosis (n\u0026thinsp;=\u0026thinsp;60, as one proband had no data on insulin usage) grew according to T1D-PRS quantiles (Additional file 7A), while the proportion of those not using insulin decreased. The 75 centile mainly consisted of MODY() cases and two GCK-MODY cases, while other centiles were more heterogeneous concerning the MODY presence and type (Additional file 7B).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e6. Evaluation of T2D-PRS\u003c/h2\u003e \u003cp\u003eWe next tested the ability of T2D-PRS to indicate the potential type of diabetes in the MODY() group. The median T2D-PRS values were significantly higher in the MODY() group compared to the CONTR group (Supplementary Fig.\u0026nbsp;2). When adding the T2D group to the best-performing model, the difference between MODY(+) and CONTR group T2D-PRS was not significant (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA, B). As expected, the ROC curve analysis showed some T2D-PRS ability to discriminate CONTR and T2D groups (AUC 0.6939). However, T2D-PRS values displayed poor or no ability to discriminate between MODY(+) and T2D (AUC 0.56) and MODY() versus CONTR, T2D and MODY(+) groups (AUC 0.6626, 0.5579 and 0.5034, respectively). Unlike T1D-PRS, the distribution of MODY(+) and MODY() patients was similar across all quartiles of the T2D-PRS distribution (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA, B).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn our study, we consistently identified and characterized 22 deleterious variants (eight of them being novel) in a retrospective cohort of early-onset diabetes patients and evaluated T1D and T2D PRS discriminative ability in the context of MODY diabetes. This, to our knowledge, is the first study in which a Latvian early-onset diabetes cohort is examined in the context of MODY. We identified that predominantly (76%) of disease-related variants in our cohort were associated with GCK-MODY, followed by 20% with HNF1A-MODY, and 4% with HNF4A-MODY. No other MODY subtypes were detected in our group. This distribution, with a higher prevalence of GCK-MODY, contrasts with the predominance of HNF1A-MODY found in other European cohorts [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e] but aligns with observations in Hungary, Poland [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e], and Russia [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e] where GCK-MODY is similarly predominant.\u003c/p\u003e \u003cp\u003eA significant proportion of the deleterious variants we detected (64%) were previously examined in relation to MODY. Twelve variants were confirmed as P or LP, while two variants were VUS due to insufficient prior evidence. However, given the specific phenotypes observed in two index patients carrying these VUS, we opted to reclassify these variants in line with the ACMG/AMP guidelines. The p.Asp160His variant, found in a region intolerant to benign variations and shown to inactivate the glucokinase enzyme [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e], met several ACMG/AMP criteria (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e), which support its reclassification as LP. For another variant, p.Ser19Leu, we observed good segregation within a family. Notably, the index case\u0026rsquo;s father and sister (positive for this mutation) developed retinopathy, a complication often seen in patients with HNF1A-MODY, which is associated with an aggressive disease course and a higher risk of microvascular complications [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. This variant is also located within the domain critical for HNF1A function. Based on the evidence, we believe that this variant should be reassigned as LP (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Another 36% of detected deleterious variations were novel. We were able to classify three of them as LP and five as P in accordance with ACMG/AMP guidelines.\u003c/p\u003e \u003cp\u003eOur study presents a novel evaluation of both T1D and T2D PRS, exploring their potential to discriminate MODY from diabetes with uncertain etiology. We have found that the T1D-PRS in early diabetes cases of unknown origin is higher than in healthy controls, effectively distinguishing these cases from genetically confirmed MODY patients at the 75th percentile. This suggests that T1D-PRS can be a valuable tool in identifying patients with both early-onset T1D, given that about 10% of T1D patients lack islet autoantibodies at diagnosis, a proportion that increases over time [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e], and also late-onset T1D. Furthermore, the possibility of T1D in MODY-negative cases with high T1D-PRS (75th percentile or higher) is supported by the higher proportion of insulin users in this group, where insulin therapy was empirically prescribed based on clinical judgment rather than definitive diagnostic markers, reflecting uncertainty in the initial diagnosis. An increased T1D-PRS in these cases could indicate a need for more frequent monitoring of autoantibodies or C-peptide levels to track potential progression to T1D. Conversely, individuals in the MODY() group with lower T1D-PRS (e.g., below the 25th percentile) could be prioritized for further investigation to identify novel monogenic causes of diabetes.\u003c/p\u003e \u003cp\u003eHowever, our study did not observe T2D-PRS's ability to discriminate between MODY(+) and MODY() groups. Both groups exhibited slightly reduced T2D-PRS levels comparable to T2D patients but higher than the control group. This outcome challenges the typical expectation that T2D-PRS, at least in the MODY(+) group, would align with a population-based distribution, and there is a lack of similar studies in the existing literature that directly address this issue. This finding raises questions about the influence of common variants in MODY genes that are included in the best-performing T2D-PRS model and the need to exclude MODY genes from T2D-PRS calculation for such cases.\u003c/p\u003e \u003cp\u003eInterestingly, we observed a bimodal distribution of T1D and T2D PRSs in both MODY subgroups. For the MODY() group, one explanation could be the cohort's composition, potentially including both MODY with yet unknown genetics and actual T1D or T2D patients, each with distinct PRS profiles. Alternatively, in the MODY(+) group, the bimodal distribution could result from the additive effects of monogenic mutations and polygenic variants, where a milder MODY-related diabetic phenotype may be accentuated by late-onset T1D or early-onset T2D. It may also result in a progressive and more severe phenotype later in adulthood. Interestingly, patients with the highest T1D-PRS among the MODY(+) group received insulin at the moment of diagnosis and had a GCK-MODY subtype, which is usually characterized as non-progressive and requiring no treatment [41]. One proband carried novel frameshift deletion p.Ala173GlnfsTer30, while another carried missense variant p.Thr228Met. Both probands reported no diabetic complications and their HbA1c levels were within the early-onset diabetic cohort HbA1c IQR and within the target range in people with diabetes. Unfortunately, in both cases, auto-antibody testing results were not available. There have been described cases of concurrent T1D in patients with GCK-MODY [42]; to note, it is not recommended to stop treatment in such patients if T1D is confirmed [43]. Unfortunately, we do not have data on patient glucose levels prior to insulin usage, thus we cannot explore if insulin treatment has a contribution to stabilizing blood glucose levels.\u003c/p\u003e \u003cp\u003eOur study also has several limitations. First of all, the retrospective nature of this study influenced our inclusion criteria, diverging from the practice regarding auto-antibody testing as an exclusion criterion prior to genetic testing for MODY. Some studies exclude MODY suspects with positive tests for ICA, GAD, and IAA. However, during the period of our study\u0026rsquo;s inclusion, auto-antibody testing was not routinely conducted in Latvia, leaving most index patients untested. We consciously chose not to exclude patients based on auto-antibody status to minimize the risk of omitting potential MODY cases. This decision is supported by evidence suggesting the rare possibility of positive ICA and GAD in MODY patients [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e] and false-positive IAA in those previously treated with insulin [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eTo fully understand the value of T1D-PRS in differentiating MODY from T1D, large prospective studies are necessary. Such studies should compare the relative impacts of clinical features, autoantibodies, and T1D-PRS in assessing the true clinical utility of PRS.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eIn conclusion, our study illustrates the potential of WGS and PRS in refining the diagnosis of early-onset diabetes. We identified 22 (eight novel) deleterious genetic variants in the early-onset diabetes cohort, clarifying the genetic background of MODY in the Latvian population. The discrimination achieved by the T1D-PRS between different groups of early-onset diabetics highlights its utility as a supplementary tool in differentiating monogenic diabetes from early-onset T1D cases, especially in those without identifiable MODY mutations. Our findings underscore the importance of integrating WGS and PRS into diagnostic strategies, though larger, prospective studies are required to fully establish PRS utility in clinical practice.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthical approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWritten informed consent was obtained from each subject and/or their legal representatives during the recruitment in LGDB (Approval by Central Medical Ethics Committee No. 01-29.1.2/6407). This study was conducted in accordance with the Declaration of Helsinki. The study protocol was approved by the Central Medical Ethics Committee of Latvia (Approval No. 01-29.1/2223).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and material\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe majority of the data generated and analyzed during this study are included in this published article and the corresponding supplementary materials. The raw genotyping data are under restricted access from the Genome Database of Latvian Population and are available for research purposes on a reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was supported by the European Regional Development Fund under project Nr. 1.1.1.1/20/A/126 “An integrated population based Latvian genome reference and its applicability to personal risk estimation for metabolic traits\" and, in part, by the State Research program project in biomedical, medical technologies and pharmaceuticals–BioMedPharm (VPP-EM-BIOMEDICĪNA-2022/1-0001). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors’ contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIKo, IDK, ULT, and IKi collaborated in the cohort selection, biological samples and phenotypic data acquisition and performed clinical data analysis. MB, LB, IKa, and KM performed genetic material preparation and sequencing. RR, RP, and IA performed the sequencing and genotype data preparation and analysis. LB, IKa, and IA performed genetic variation interpretation. IA performed a formal analysis. JK, IA, and MB wrote and reviewed the manuscript with input from other authors. JK, IKa, and IE conceptualized and supervised the study. JK is the guarantor of this work and, as such, had full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis. All authors have read and approved the final manuscript version.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe acknowledge the Genome Database of Latvian Population for maintaining and providing biological samples and associated data of study participants.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eNaylor R, Knight Johnson A, del Gaudio D. Maturity-Onset Diabetes of the Young Overview. In: Adam MP, Feldman J, Mirzaa GM, Pagon RA, Wallace SE, Bean LJ, et al., editors. GeneReviews\u0026reg;. Seattle (WA): University of Washington, Seattle; 1993.\u003c/li\u003e\n\u003cli\u003eLaver TW, Wakeling MN, Knox O, Colclough K, Wright CF, Ellard S, et al. Evaluation of Evidence for Pathogenicity Demonstrates That BLK, KLF11, and PAX4 Should Not Be Included in Diagnostic Testing for MODY. Diabetes. 2022;71:1128\u0026ndash;36.\u003c/li\u003e\n\u003cli\u003eKleinberger JW, Pollin TI. 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ClinVar: improving access to variant interpretations and supporting evidence. Nucleic Acids Res. 2018;46:D1062\u0026ndash;7.\u003c/li\u003e\n\u003cli\u003eRichards S, Aziz N, Bale S, Bick D, Das S, Gastier-Foster J, et al. Standards and Guidelines for the Interpretation of Sequence Variants: A Joint Consensus Recommendation of the American College of Medical Genetics and Genomics and the Association for Molecular Pathology. Genet Med Off J Am Coll Med Genet. 2015;17:405.\u003c/li\u003e\n\u003cli\u003eLambert SA, Wingfield B, Gibson JT, Gil L, Ramachandran S, Yvon F, et al. The Polygenic Score Catalog: new functionality and tools to enable FAIR research. 2024;:2024.05.29.24307783.\u003c/li\u003e\n\u003cli\u003eLambert SA, Gil L, Jupp S, Ritchie SC, Xu Y, Buniello A, et al. The Polygenic Score Catalog as an open database for reproducibility and systematic evaluation. Nat Genet. 2021;53:420\u0026ndash;5.\u003c/li\u003e\n\u003cli\u003eRe\u0026scaron;čenko R, Brīvība M, Atava I, Rovīte V, Pečulis R, Silamiķelis I, et al. Whole-Genome Sequencing of 502 Individuals from Latvia: The First Step towards a Population-Specific Reference of Genetic Variation. Int J Mol Sci. 2023;24:15345.\u003c/li\u003e\n\u003cli\u003eR Core Team. R: A Language and Environment for Statistical Computing. 2023.\u003c/li\u003e\n\u003cli\u003eRobin X, Turck N, Hainard A, Tiberti N, Lisacek F, Sanchez J-C, et al. pROC: an open-source package for R and S+ to analyze and compare ROC curves. BMC Bioinformatics. 2011;12:77.\u003c/li\u003e\n\u003cli\u003eRevelle W. psych: Procedures for Psychological, Psychometric, and Personality Research. 2023.\u003c/li\u003e\n\u003cli\u003eKassambara A. rstatix: Pipe-Friendly Framework for Basic Statistical Tests. 2023.\u003c/li\u003e\n\u003cli\u003eDinno A. dunn.test: Dunn\u0026rsquo;s Test of Multiple Comparisons Using Rank Sums. 2017.\u003c/li\u003e\n\u003cli\u003eMirshahi UL, Colclough K, Wright CF, Wood AR, Beaumont RN, Tyrrell J, et al. Reduced penetrance of MODY-associated HNF1A/HNF4A variants but not GCK variants in clinically unselected cohorts. Am J Hum Genet. 2022;109:2018\u0026ndash;28.\u003c/li\u003e\n\u003cli\u003eNarayana N, Hua Q, Weiss MA. The dimerization domain of HNF-1\u0026alpha;: structure and plasticity of an intertwined four-helix bundle with application to diabetes mellitus11Edited by M. F. Summers. J Mol Biol. 2001;310:635\u0026ndash;58.\u003c/li\u003e\n\u003cli\u003eJaganathan K, Panagiotopoulou SK, McRae JF, Darbandi SF, Knowles D, Li YI, et al. Predicting Splicing from Primary Sequence with Deep Learning. Cell. 2019;176:535-548.e24.\u003c/li\u003e\n\u003cli\u003eMahalingam B, Cuesta-Munoz A, Davis EA, Matschinsky FM, Harrison RW, Weber IT. Structural model of human glucokinase in complex with glucose and ATP: implications for the mutants that cause hypo- and hyperglycemia. Diabetes. 1999;48:1698\u0026ndash;705.\u003c/li\u003e\n\u003cli\u003ePilkis SJ, Weber IT, Harrison RW, Bell GI. Glucokinase: structural analysis of a protein involved in susceptibility to diabetes. J Biol Chem. 1994;269:21925\u0026ndash;8.\u003c/li\u003e\n\u003cli\u003eMaga\u0026ntilde;a-Cerino JM, Luna-Arias JP, Labra-Barrios ML, Avenda\u0026ntilde;o-Borromeo B, Boldo-Le\u0026oacute;n XM, Mart\u0026iacute;nez-L\u0026oacute;pez MC. Identification and functional analysis of c.422_423InsT, a novel mutation of the HNF1A gene in a patient with diabetes. Mol Genet Genomic Med. 2017;5:50\u0026ndash;65.\u003c/li\u003e\n\u003cli\u003eLu P, Rha GB, Melikishvili M, Wu G, Adkins BC, Fried MG, et al. Structural basis of natural promoter recognition by a unique nuclear receptor, HNF4alpha. Diabetes gene product. J Biol Chem. 2008;283:33685\u0026ndash;97.\u003c/li\u003e\n\u003cli\u003eTanigawa Y, Qian J, Venkataraman G, Justesen JM, Li R, Tibshirani R, et al. Significant sparse polygenic risk scores across 813 traits in UK Biobank. PLOS Genet. 2022;18:e1010105.\u003c/li\u003e\n\u003cli\u003eMars N, Lindbohm JV, Parolo P della B, Wid\u0026eacute;n E, Kaprio J, Palotie A, et al. Systematic comparison of family history and polygenic risk across 24 common diseases. Am J Hum Genet. 2022;109:2152\u0026ndash;62.\u003c/li\u003e\n\u003cli\u003eBrīvība M, Atava I, Pečulis R, Elbere I, Ansone L, Rozenberga M, et al. Evaluating the Efficacy of Type 2 Diabetes Polygenic Risk Scores in an Independent European Population. Int J Mol Sci. 2024;25:1151.\u003c/li\u003e\n\u003cli\u003eWeinreich SS, Bosma A, Henneman L, Rigter T, Spruijt CM, Grimbergen AJ, et al. A decade of molecular genetic testing for MODY: a retrospective study of utilization in The Netherlands. Eur J Hum Genet. 2015;23:29\u0026ndash;33.\u003c/li\u003e\n\u003cli\u003eGa\u0026aacute;l Z, Szűcs Z, K\u0026aacute;ntor I, Luczay A, T\u0026oacute;th-Heyn P, Benn O, et al. A Comprehensive Analysis of Hungarian MODY Patients\u0026mdash;Part II: Glucokinase MODY Is the Most Prevalent Subtype Responsible for about 70% of Confirmed Cases. Life. 2021;11:771.\u003c/li\u003e\n\u003cli\u003eGlotov OS, Serebryakova EA, Turkunova ME, Efimova OA, Glotov AS, Barbitoff YA, et al. Whole-exome sequencing in Russian children with non-type 1 diabetes mellitus reveals a wide spectrum of genetic variants in MODY-related and unrelated genes. Mol Med Rep. 2019;20:4905\u0026ndash;14.\u003c/li\u003e\n\u003cli\u003eRaimondo A, Chakera AJ, Thomsen SK, Colclough K, Barrett A, De Franco E, et al. Phenotypic severity of homozygous GCK mutations causing neonatal or childhood-onset diabetes is primarily mediated through effects on protein stability. Hum Mol Genet. 2014;23:6432\u0026ndash;40.\u003c/li\u003e\n\u003cli\u003eSzopa M, Wolkow J, Matejko B, Skupien J, Klupa T, Wybrańska I, et al. Prevalence of Retinopathy in Adult Patients with GCK-MODY and HNF1A-MODY. Exp Clin Endocrinol Diabetes Off J Ger Soc Endocrinol Ger Diabetes Assoc. 2015;123:524\u0026ndash;8.\u003c/li\u003e\n\u003cli\u003eBingley PJ. Clinical applications of diabetes antibody testing. J Clin Endocrinol Metab. 2010;95:25\u0026ndash;33.\u003c/li\u003e\n\u003cli\u003eDelvecchio M, Pastore C, Giordano P. Treatment Options for MODY Patients: A Systematic Review of Literature. Diabetes Ther Res Treat Educ Diabetes Relat Disord. 2020;11:1667\u0026ndash;85.\u003c/li\u003e\n\u003cli\u003eUday S, Campbell FM, Cropper J, Shepherd M. Monogenic diabetes and type 1 diabetes mellitus: a challenging combination. Pract Diabetes. 2014;31:327\u0026ndash;30.\u003c/li\u003e\n\u003cli\u003eChakera AJ, Steele AM, Gloyn AL, Shepherd MH, Shields B, Ellard S, et al. Recognition and Management of Individuals With Hyperglycemia Because of a Heterozygous Glucokinase Mutation. Diabetes Care. 2015;38:1383\u0026ndash;92.\u003c/li\u003e\n\u003cli\u003eAmed S, Oram R. Maturity-Onset Diabetes of the Young (MODY): Making the Right Diagnosis to Optimize Treatment. Can J Diabetes. 2016;40:449\u0026ndash;54.\u003c/li\u003e\n\u003cli\u003eBroome DT, Pantalone KM, Kashyap SR, Philipson LH. Approach to the Patient with MODY-Monogenic Diabetes. J Clin Endocrinol Metab. 2021;106:237\u0026ndash;50.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Early-onset Diabetes, Maturity-Onset Diabetes, Next-Generation Sequencing, Rare variant, Risk Scores","lastPublishedDoi":"10.21203/rs.3.rs-5361647/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5361647/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground: \u003c/strong\u003eMaturity-onset Diabetes of the Young (MODY) presents a diagnostic challenge, with a large proportion of cases lacking identifiable genetic mutations, which could lead to sub-optimal medical treatment and, subsequently, a decline in patients’ life quality. This study investigates the utility of polygenic risk score (PRS) in distinguishing monogenic diabetes from early-onset type 1 diabetes (T1D) and type 2 diabetes (T2D) cases to enhance diagnostic accuracy.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods: \u003c/strong\u003eWe investigated the genetic basis of early-onset diabetes in a Latvian cohort comprising 66 patients, contrasted with 174 non-diabetic controls, using whole-genome sequencing (WGS).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults: \u003c/strong\u003eWe identified 22 causative mutations in three MODY genes (\u003cem\u003eGCK\u003c/em\u003e, \u003cem\u003eHNF1A\u003c/em\u003e,\u003cem\u003e \u003c/em\u003eand \u003cem\u003eHNF4A\u003c/em\u003e), eight of them being novel. We selected and tested the best-performing population specific T1D and T2D PRS models on the established diabetic cohort and controls. Patients without genetically confirmed MODY had a significantly higher risk for T1D compared to controls. A 75% centile of T1D-PRS included only 8.7% of the genetically confirmed MODY patients, compared to 34% of patients without mutations, providing good specificity for the identification of indicative T1D at this PRS range. While T2D-PRS was increased in the diabetic cohort, it did not demonstrate an ability to discriminate between MODY-positive and negative subgroups.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions: \u003c/strong\u003eOur study demonstrates that the application of WGS improves diagnostic accuracy and highlights the potential of T1D-PRS as a critical tool for the stratification of MODY-suspected patients.\u003c/p\u003e","manuscriptTitle":"Identification Of Pathogenic Mutations And Application Of Polygenic Risk Scores In Early-Onset Diabetes Patients","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-12-02 16:26:10","doi":"10.21203/rs.3.rs-5361647/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"ff48f236-834f-4d28-93e1-fe4b8f0f4d24","owner":[],"postedDate":"December 2nd, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-08-20T11:08:06+00:00","versionOfRecord":[],"versionCreatedAt":"2024-12-02 16:26:10","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5361647","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5361647","identity":"rs-5361647","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}