Correlation of serum DKK1 level with skeletal phenotype in children with osteogenesis imperfecta

Subjects

Patients less than 18 years old with OI were recruited from the Department of Endocrinology, Peking Union Medical College Hospital (PUMCH), from April 2017 to October 2023. The inclusion criteria of OI patients were as follows: (1) a history of at least one fracture under minor trauma during childhood and an age- and gender-adjusted BMD Z-score less than -2.0 at lumbar spine (LS) or proximal femur before any anti-osteoporosis therapy; (2) presence of blue sclera or dentinogenesis imperfecta and a family history of OI [10, 11]. The exclusion criteria were as follows: with other genetic or metabolic bone diseases, with other disease that could affect bone metabolism, ongoing treatment with glucocorticoids, anti-epileptic drugs, bisphosphonates, denosumab, teriparatide, etc., and with liver or kidney dysfunction.

Age-matched healthy children who underwent physical examinations at PUMCH were included as control. This study was approved by the Scientific Ethics Committee of PUMCH (JS-3545D), and informed consents were obtained from legal guardian of each OI patient and healthy children.

Phenotype assessment

The following phenotypic information of OI patients was collected: age of OI onset, age of confirmed OI, frequency and sites of bone fracture, skeletal malformations and extra-skeletal manifestations, including blue sclera, dentinogenesis imperfecta, hearing loss, joint ligament laxity, and muscle atrophy. Height and weight were measured using a Harpenden measuring instrument (Seritex, Inc., East Rutherford, NJ, USA). For patient who was unable to stand, body length in the supine position was measured. Height and weight Z scores for OI patients at different ages and gender were calculated according to the normal reference values for Chinese children [12].

Clinical fractures included nonvertebral fractures and symptomatic vertebral fractures, which were reported by the patients or their legal guardians and confirmed by X-ray films. A semiquantitative assessment of vertebral compression fracture (VCFs) was performed by radiologists at PUMCH using Genant classification [13]. The semiquantitative Spinal Deformity Index (SDI) provides a comprehensive evaluation of spinal fracture status, considering both the number and severity of fractures. Each vertebra is visually graded on a scale of 0 to 3, representing no fracture, mild, moderate, or severe fracture, and SDI is calculated by summing these grades across all vertebrae from T4 to L4 [14]. Scoliosis was confirmed by posterior-anterior radiographs and defined as a Cobb angle higher than 10° [15]. The annual incidence of peripheral fractures was calculated by dividing the total number of peripheral fractures by the duration of disease. Areal BMD at the lumbar spine (LS) 1–4, the femoral neck (FN) and the total hip was measured using dual-energy X-ray absorptiometry (DXA, GE Lunar Prodigy Advance, USA) and analyzed by software compatible with pediatric data. Calibration and quality checks were completed daily using the DXA equipment. Patients with vertebral compression fractures or significantly deformation were excluded from the lumbar BMD analysis. The BMD Z scores of the LS and FN of children and adolescents were calculated according to the normal BMD reference values for Asian children [16, 17].

The disease phenotype exhibits significant heterogeneity, including the mildest form (type I), the most severe form among surviving patients (type III), an intermediate form between type I and type III (type IV), and the unique type with interosseous membrane calcification of the forearm and/or hypertrophic callus (type V) [18, 19]. The perinatal lethality (type II) OI was not included in this study.

Determination of the serum DKK1 and biochemical marker concentration

Fasting blood samples of OI patients and healthy controls were obtained at 8:00–10:00 in the morning. The serum DKK1 concentration was measured by enzyme-linked immunosorbent assay (ELISA) (Cat. No. DKK100B, R&D systems, USA), which was completed by Key Laboratory of Endocrinology, National Health and Family Planning Commission, PUMCH. The minimum detection value was 0.948 pg/mL, the intra-assay coefficients of variation (CV) was 1.8–2.9%, and the inter-assay CV was 7.7–8.7%.

The serum concentrations of osteoprotegerin (OPG) and sclerostin were measured by enzyme-linked immunosorbent assay (ELISA) (Cat. No. SEA108Hu, Cloud-Clone Corp, China and Cat. No. BI-20472, BIOMEDICA, Austria). The minimum detection value of OPG and sclerostin were 0.059 ng/mL and 1.3 pmol/L, respectively. The intra-assay CV were ≤ 10%and ≤ 1% for OPG and sclerostin measurement, respectively. The inter-assay CVs were ≤ 12% and ≤ 5% for OPG and sclerostin detection, respectively.

The serum levels of calcium (Ca), phosphorus (P) and alkaline phosphatase (ALP, a bone formation marker) were measured using an automatic analyser (ADVIA 1800, Siemens, Germany). The serum levels of β-isomerized carboxy-telopeptide of type I collagen (β-CTX, a bone resorption marker), procollagen I N-terminal peptide (P1NP, a bone formation marker), 25-hydroxyvitamin D (25OHD), and intact parathyroid hormone (PTH) were assessed using an automated electrochemiluminescence system (E170, Roche Diagnostics, Switzerland). All the biochemical indicators were detected by clinical central laboratory of PUMCH.

Detection of pathogenic mutations in OI patients

Genomic DNA was extracted from peripheral leukocytes of OI patients using a DNA extraction kit (QIAamp DNA, Qiagen, Frankfurt, Germany), which was sequenced using targeted next-generation sequencing (NGS) (Illumina HiSeq2000 platform, Illumina, Inc., San Diego, CA, USA) [20]. The targeted NGS panel included all known candidate genes of OI, including COL1A1, COL1A2, IFITM5, SERPINF1, CRTAP, P3H1, PPIB, SERPINH1, FKBP10, PLOD2, BMP1, SP7, TMEM38B, WNT1, CREB3L1, SPARC, MBTPS2, P4HB, SEC24D and PLS3, and 708 other skeletal disease-associated candidate genes [21]. The pathogenicity of the detected variants was classified according to the 2015 guidelines of the American College of Medical Genetics and Genomics/Association for Molecular Pathology (ACMG/AMP) [22]. The pathogenic mutations identified by NGS were validated by polymerase chain reaction (PCR) and Sanger sequence (3730 DNA Analyser, Applied Biosystems, Foster City, CA, USA).

According to genetic patterns, OI patients were divided into autosomal dominant inheritance (AD) and non-AD groups. The AD group included patients carrying COL1A1, COL1A2, IFITM5, and P4HB mutations, and patients with other gene mutations were classified into the non-AD group. Based on different effects of pathogenic mutations on type I collagen metabolism, the mutations causing amino acid substitutions in the triple helix domain of COL1A1 or COL1A2 were classified as collagen structural defects, and nonsense mutations or frame-shift mutations in COL1A1 or COL1A2 that led to an early stop codon were classified as collagen protein reducers [20, 23]. Other mutations, such as splicing mutations, were not included because of the difficulty in predicting their effects on type I collagen metabolism.

Statistical analysis

The Shapiro‒Wilk test and Kolmogorov‒Smirnov test were used to determine whether the data fit a normal distribution. Normally distributed data were expressed as the mean ± standard deviation, abnormally distributed data were expressed as the median (quartiles), and count data were expressed as numbers. Normally distributed data were compared between two groups and among different subgroups with independent sample t-tests and analysis of variance (ANOVA), respectively. Abnormally distributed data were compared between two groups and among more than two groups using the Mann‒Whitney U test and a nonparametric test (Kruskal‒Wallis test), respectively. The chi-square test and Fisher’s exact test were used to compare categorical variables. To explore the correlation, Pearson correlation analysis was applied for normally distributed data, while Spearman correlation analysis was used for abnormally distributed data.

A two-tailed P value less than 0.05 was considered statistically significant. Statistical analysis was performed using SPSS software version 25.0 (SPSS, Inc., Chicago, IL, USA). Graphics were drawn using GraphPad Prism software 10.0 (GraphPad, San Diego, CA, USA).

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