ABSTRACT
Chromatinopathies (CPs) are an expanding group of rare genetic disorders affecting epigenetic machinery. Besides an intricate genotypic spectrum, these conditions share overlapping phenotypes characterized by neurocognitive impairment, growth defects and distinctive, but often convergent, facial features. Although individually rare, the landscape of CPs is increasingly growing and represents an emerging and possibly underestimated cause of disability. Due to their complexity and rarity, accurate diagnosis and management pose significant difficulties. To address these challenges and gain a deeper overview of these diseases' spectrum, we retrospectively collected clinical characteristics of 239 patients diagnosed with CPs and critically analyzed their diagnostic journey, growth charts, neurological and gestaltic features. Starting from the largest collection of CPs to date, our data point to wide sequencing analyses as the best shortcut to diagnosis. We have also demonstrated the importance of growth defects in this group of disorders that require dedicated growth tables, and we have delved into the great variability of neurological and clinical burden in these conditions. This retrospective study provides a significant advance in our understanding of these rare diseases and will help to improve diagnostic, therapeutic, and clinical approaches to CPs and to develop personalized multidisciplinary care plans for affected patients.
Keywords: diagnosis, epigenomics, intellectual disability, syndrome
Chromatinopathies (CP) are a growing group of rare genetic disorders characterized by cognitive deficits and growth abnormalities. This is the largest collection of CP to date, contributing to a deeper understanding of the landscape and diagnosis of these rare diseases, strongly improved by the use of large‐scale sequencing technologies.
1. Introduction
The epigenetic machinery is an extremely tangled system finely tuned by a vast array of factors that ensure proper cellular function and development. Pathogenic variants in genes involved in this system underlie a vast spectrum of conditions known as “Chromatinopathies” (CPs) or Mendelian disorders of Epigenetic Machinery (MDEMs) [1]. Despite their individual rarity [2], collectively CPs account for almost 10% of neurodevelopmental disorders [3]. Their landscape is rapidly expanding thanks to the impressive advancements in diagnostic tools and the growing knowledge in this field. According to recent literature, nearly 179 CPs have been identified [4], a substantial increase compared to previous estimates [5, 6]. These disorders share common phenotypic features, including neurocognitive impairment, growth abnormalities, skeletal malformations, and immune dysfunctions [4]. However, their clinical presentation can be highly variable, affecting multiple body districts and posing considerable diagnostic challenges. On the other hand, the growing complexity of genetic tests together with the costs faced by public health, imposes a thoughtful approach to the diagnostic definition of syndromic cases [7]. We collected and analyzed clinical charts of a large cohort of CP patients to critically review the diagnostic approach to these conditions, to gain a deeper and comprehensive knowledge of their phenotypical spectrum and to further define diagnostic handles that may help to raise the clinical suspect for CPs. This extensive retrospective analysis enriches the phenotypic and genotypic landscape of CPs, bridging the gap toward comprehensive care for these individuals, including accurate diagnosis, genetic counseling, and tailored management strategies.
2. Materials and Methods
We conducted a retrospective cohort study at the Paediatric Genetics Outpatient Clinic of the SC Paediatrics, Clinica De Marchi, Fondazione IRCCS Ca′ Granda Ospedale Maggiore Policlinico, Milan. Clinical data were extracted from electronic and paper medical records of all patients evaluated at the Outpatient Clinic between January 1993 and January 2025. Only patients with a molecularly confirmed diagnosis of any CP were included. Collected information was de‐identified and managed in accordance with the Declaration of Helsinki. For all enrolled patients, the complete history of molecular tests performed was reviewed. All variants related to the diagnosed condition were verified and single nucleotide variants (SNV) were re‐classified according to American College of Medical Genetics (ACMG) guidelines ([8], see Supporting Information). Literature or Clinvar/LOVD references have been included, where available.
Auxological parameters recorded (weight, length/height, and occipitofrontal circumference, OFC) were those at birth, at the first and the last visits. All parameters were expressed both as absolute values and percentiles, calculated using online tools (Inescharts.com for birth, SimulConsult.com for subsequent). For analysis purposes, gestational age generically reported as “at term,” was standardized to 40 + 0 gestational weeks. When no precise data were available, we implemented a conservative approximation. For auxological parameters, values reported as < 3rd percentile were imputed as 2nd percentile. Similarly, for psychomotor milestones, for example, “first words > 24 months” was recorded as “25 months.” Neurodevelopmental diagnoses (i.e., intellectual disability, ID) were defined according to any type of targeted evaluations. Hypotonia was determined based on neurological assessments. Any health issue was reported. All reported clinical features were standardized as HPO terms (Human Phenotype Ontology, https://hpo.jax.org/). Missing data were excluded from retrospective analyses.
One‐way ANOVA test was performed to test for significant differences in mean age at diagnosis and in mean Intellective Quotient (IQ) among the three largest diagnostic groups. p values < 0.05 were considered statistically significant. For post hoc analysis of significant ANOVA results, a 2‐sample t test was conducted on group pairs (see Table S3). To adjust for multiple comparison biases in the post hoc analysis, the Bonferroni correction was applied, setting the level of statistical significance at p < 0.016.
To investigate possible predictors of delayed diagnosis and severe ID, we performed univariate and multivariate regression analyses taking into account birth date and several neurological factors, namely IQ values, hypotonia, autism spectrum disorder, seizures and brain MRI anomalies.
Analyses and graphs were performed using JMP Pro 17.2.0 software (701896).
3. Results
An overall number of 266 patients was referred to our Clinic under the suspicion of CPs. In 239 patients (115 females and 124 males), the diagnosis of CPs was molecularly confirmed. Recruited individuals were affected by 34 different CPs. For 5 patients affected by Sotos Syndrome and 1 patient with a splicing variant in the CHD7 gene, molecular diagnosis was confirmed but the exact genetic anomaly was not available. The three main sub‐populations were composed of patients affected by Sotos syndrome (SS, n = 64), Rubinstein‐Taybi syndrome (RSTS, n = 59), and Kleefstra syndrome (KLEEFS, n = 36) (see Figure 1). Patients were born between 1987 and 2023; the mean follow‐up time was 4.8 years (range: 0–24 years). A detailed description of data collected, and their analyses is provided below.
FIGURE 1.
Pie chart representing the distribution of different CPs within our cohort of patients. Each color corresponds to a specific CP subtype. Please refer to the legend for details. Details: CREBBP ex 31: CREBBP exon 31, related to Menke‐Hennekham Syndrome; del/dup: Indicates deletion or duplication of the gene, which can be related to specific conditions (e.g., NSD1 and RAI1). [Colour figure can be viewed at wileyonlinelibrary.com]
3.1. Diagnosis: Age and Odyssey
The average age at diagnosis was 5 years and 6 months (range: 0–31 years) (see Figure 2). The vast majority of pathogenic variants were sporadic events (153/159, 96%, arose de novo) in haploinsufficient genes. For the only PRMT7‐affected patient, we described a recessive transmission, with both parents confirmed as healthy carriers of the disorder (as already reported in [9]). Two familiar structural variants involving NSD1 were responsible for the few inherited cases in our cohort: 1 family with a father‐to‐daughter transmission of a deletion involving this gene, and one family of three with a mother‐to‐sons transmission of a duplication in region 5q35.3. All pathogenic variants included in this publication have been submitted on Clinvar. Based on genetic diagnosis, patients were divided into nine groups: 8 homogeneous groups for disorders with at least 5 individuals (CHD7‐related disorder (rd), MIM#608892, Coffin Siris Syndrome (CSS), KBG syndrome, MIM#611192, KLEEFS, MIM#607001, RSTS, MIM#600140 and #602700 SS, TBL1XR1 rd, MIM#608628, Wiedemann Steiner Syndrome (WDSTS), MIM#159555) and a ninth ‘Other’ group, for remaining conditions (including patients with defect in ASH1L, MIM#607999, CREBBP exon 30–31, MIM#618332, EZH2, MIM#601573, HDAC2, MIM#605164, HNRNPU, MIM#602869, KANSL1, MIM#612452, KDM6A, MIM#300128, KDM6B, MIM#611577, KMT2C, MIM#606833, KMT2D, MIM#602113, KMT2E, MIM#608444, MECP2, MIM#300005, NSD1 (MIM#606681) duplication, PRMT7, MIM#610087, RAI1 (MIM#607642) deletion or duplication, SET, MIM#600960, SETD5, MIM#615743, SMC1A, MIM#300040, SRCAP, MIM#611421, STAG1, MIM#604358, USP7, MIM#602519 ZMYM2, MIM#602221). CHD7 patients were the earliest diagnosed (mean: 1 year and 6 months, range: 1 month—5 years and 9 months) and PRMT7 the latest (only one case at the age of 23.6 years) (see Table S1). ANOVA statistical analysis revealed no significant differences in age at diagnosis among the three main groups (SS, RSTS, KLEEFS) (Table S2). Univariate analyses demonstrated a significant correlation among age at diagnosis and birth date, IQ values, hypotonia and autism. In the multivariate regression model, only birth date preserved a strong correlation to age at diagnosis (p value < 0.0001, see Table S4). The vast majority of cases were diagnosed by targeted gene sequencing or Next Generation Sequencing (NGS) (n = 175, i.e., 73%), 35 by Array‐CGH analysis (a‐CGH, 14.6% of the total), 13 by MLPA (Multiplex Ligation‐dependent Probe Amplification) analysis and 11 by FISH (Fluorescent In Situ Hybridization) (5% and 4%, respectively) (see Supporting Information). Non‐diagnostic tests included 88 a‐CGH (64% of the total performed), 87 karyotypes, 34 FISH, 31 FRAXA, 27 targeted sequencing, 19 MLPA and 19 NGS analyses, among which 2 WES trio (Whole Exome Sequencing) (Supporting Information).
FIGURE 2.
Distribution of age at diagnosis according to birth date. Red line: Cauchy estimate. Blue line: Estimated mean line. Colored isometric lines represent density for quartiles. [Colour figure can be viewed at wileyonlinelibrary.com]
3.2. Auxological Parameters
Weight, length and OFC absolute values at birth were collected for the different conditions under study, as well as and their percentiles' (pc) distribution (see Figure S1). Mean pc of all auxological parameters (weight, height, OFC) is reported in Table 1. Considering the whole cohort, mean pc values correctly align with the 50th centile. Focusing on three main groups, as predictable, SS displays an upper centiles distribution of all growth parameters; in RSTS height and OFC range lower than weight while KLEEFS mainly displays a tendency to microcephaly. Observing centiles distribution in the rarer condition of our cohort, it is notable that CHD7 patients on average assess on lower centiles both for weight and height, while TBL1XR1 individuals are strongly characterized by low height. Values collected at the first and last visits allowed us to draw general (see Figure 3A) and specific curves for the three main diseases of our cohort (KLEEFS, RSTS, SS) (see Figure 3B). These curves did not show a clear pubertal spurt, neither in the total cohort nor in the pooled ones.
TABLE 1.
Summary of auxological parameters at birth and later in life of all different conditions under study, grouped according to clinical diagnosis.
| CHD7 rd | CSS | KBG | KLEEFS | KMT2C rd | Other | RSTS | SS | TBL1XR1 rd | WDSTS | Total | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| BW mean pc (N) | 37 (7) | 23.2 (11) | 30.2 (6) | 39.6 (29) | 56.2 (6) | 39.9 (31) | 22.3 (57) | 75.4 (64) | 50.8 (6) | 34.9 (9) | 41 (226) |
| BL mean pc (N) | 25.3 (4) | 41 (9) | 59 (5) | 34.6 (26) | 37.2 (5) | 45.1 (26) | 25.1 (51) | 89.9 (53) | 9.3 (4) | 36.3 (7) | 40.3 (190) |
| BOFC mean pc (N) | 59.4 (4) | 36.7 (5) | 69.3 (4) | 35.2 (24) | 52.2 (5) | 45.1 (26) | 21.4 (47) | 89.3 (48) | 50 (4) | 34.2 (6) | 49.3 (173) |
| W mean pc (N) | 13.8 (13) | 43.2 (16) | 28.3 (8) | 72.5 (43) | 54 (4) | 44.8 (47) | 30.1 (87) | 78.7 (102) | 30 (6) | 33.1 (16) | 52.3 (342) |
| H mean pc (N) | 13.5 (12) | 20.25 (15) | 34.7 (8) | 62.0 (44) | 33.2 (4) | 32.5 (47) | 8.4 (78) | 81.3 (99) | 16.7 (6) | 24.3 (14) | 43.4 (327) |
| OFC mean pc (N) | 26.7 (7) | 47.4 (16) | 25.6 (7) | 21.2 (42) | 65.7 (3) | 51.1 (47) | 11.5 (79) | 97.1 (87) | 44.3 (6) | 34.3 (15) | 47.7 (309) |
Abbreviations: BL, birth length; BOFC, birth occipitofrontal circumference; BW, birth weight; H, height; N, number of measures available; OFC, occipitofrontal circumference; pc, percentile; W, weight.
FIGURE 3.
Distribution of Weight (W), Height (H) and OFC in the overall cohort (A) and pooled for the three main diagnostic group under study (B) (KLEEFS, blue line, RSTS, red line, SS, green line). [Colour figure can be viewed at wileyonlinelibrary.com]
3.3. Clinical Features
Neurocognitive impairment represents the most shared feature in these conditions (see Figure 4 and Table 2). Mean age at first steps was 21 months (range: 11–60), mean age at first words was 23 months and a half (range: 9–48). Most patients exhibited moderate intellectual disability (ID), as reflected in the IQ (Intellective Quotient) distribution (Figure S2). ANOVA statistical analysis was suggestive of a statistically significant difference in IQ among three main diagnostic groups. The Bonferroni corrected t test demonstrated a significant difference in IQ between KLEEFS and SS patients (Tables S2 and S3). Furthermore, in 82.7% of patients any kind of MRI abnormality was reported, 67.8% of patients displayed behavioral difficulties, 62% reported any kind of hypotonia. IQ demonstrated to strongly rely on birth date but resulted to be independent from several neurocognitive aspects at univariate analyses (p value = 0.0083, see Table S5). Figure 4 and Table 2 also summarize the systemic manifestations observed in the overall cohort and in the nine diagnostic categories. Visual and acoustic impairment are observed in 33.9% and 23.3% of patients, respectively. Cardiac abnormalities are frequently reported (54.8%) as well as genitourinary ones (41%). Constipation appears to be more frequent than gastroesophageal reflux (GERD). Recurrent infections are quite common, involving one third of this population as well as abnormalities of spine curvature (respectively 35.2% and 26.8%).
FIGURE 4.
Graphic distribution of clinical manifestations in the whole cohort, each group is represented by a specific color, as reported in the legend. abn., abnormality; ASD, autism spectrum disorder; Behav., behavioral problems; def., defect; Endoc., endocrine; GDD, global developmental delay; GI, gastrointestinal; GU, genitourinary; hyperm., hypermobility; Hypoacus., hypoacusia; Hypot., hypotonia; ID, intellectual disability; MRI, magnetic resonance imaging; Rec. Inf., recurrent infections.; Refract., refractive; Strab., strabismus. [Colour figure can be viewed at wileyonlinelibrary.com]
TABLE 2.
Clinical manifestations reported in the entire cohort and in the nine subgroups of our cohort. Percentages were calculated including only patients for whom an assessment for the specific problem was clearly available.
| CHD7 rd | CSS | KBG | KLEEFS | KMT2C rd | Other | RSTS | SS | TBL1XR1 rd | WDSTS | Total (Perc.) | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| GDD | 7 | 9 | 4 | 34 | 4 | 23 | 53 | 26 | 5 | 7 | 172 (88.2) |
| First steps (m) | 31 | 22 | 18.4 | 22.6 | 16.8 | 19.4 | 23.9 | 21.1 | 15.2 | 27.1 | 21.6 |
| First words (m) | 18.3 | 18 | 31 | 21 | 21.1 | 25 | 22.7 | 18.5 | 18.5 | 23.5 | |
| GQS (N) | 2 | 1 | 0 | 9 | 0 | 9 | 12 | 8 | 1 | 2 | 44 |
| Mean (range) | 59.5 (41–78) | 73 | 63.4 (41–90) | 65 (38–104) | 62.7 (34–95) | 72.3 (41–88.7) | 46 | 68 (53–83) | 65 (34–104) | ||
| IQ (N) | 0 | 3 | 3 | 9 | 3 | 13 | 17 | 10 | 1 | 2 | 61 |
| Mean (range) | 88.3 (48–113) | 72.7 (56–91) | 47.9 (36–78) | 68.7 (55–92) | 70.5 (32–106) | 65.5 (36–102) | 85.1 (67–115) | 32 | 72.5 (58–87) | 68.5 (32–115) | |
| B‐ID | 0 | 0 | 1 | 0 | 0 | 2 | 5 | 3 | 0 | 0 | 11 (8.4) |
| ID | 5 | 4 | 2 | 22 | 4 | 12 | 26 | 15 | 3 | 7 | 100 (76.3) |
| ASD | 0 | 0 | 0 | 3 | 1 | 1 | 3 | 7 | 1 | 4 | 20 (17.1) |
| Behavioral | 1 | 2 | 3 | 16 | 4 | 14 | 8 | 24 | 4 | 4 | 80 (67.8) |
| Hypotonia | 3 | 4 | 1 | 23 | 1 | 11 | 25 | 19 | 3 | 5 | 95 (62) |
| Seizures | 0 | 5 | 2 | 7 | 0 | 10 | 5 | 16 | 2 | 1 | 48 (30.4) |
| B‐MRI abn. | 7 | 7 | 2 | 23 | 3 | 19 | 38 | 25 | 5 | 5 | 134 (82.7) |
| Refractive def. | 1 | 4 | 1 | 19 | 1 | 10 | 27 | 14 | 1 | 2 | 80 (33.9) |
| Strabismus | 1 | 4 | 1 | 7 | 1 | 7 | 31 | 7 | 1 | 2 | 62 (26.3) |
| Hypoacusia | 7 | 3 | 1 | 16 | 1 | 1 | 16 | 2 | 1 | 0 | 48 (23.3) |
| Abn. hard palate morph. | 1 | 1 | 2 | 1 | 1 | 4 | 18 | 16 | 0 | 2 | 46 (22.5) |
| Heart abn. | 6 | 4 | 3 | 23 | 3 | 13 | 34 | 30 | 0 | 3 | 119 (54.8) |
| Recurr. inf. | 3 | 3 | 1 | 13 | 2 | 6 | 21 | 31 | 1 | 2 | 83 (35.2) |
| Stitic | 2 | 0 | 0 | 7 | 0 | 3 | 16 | 12 | 0 | 2 | 42 (28.7) |
| GERD | 2 | 0 | 0 | 4 | 2 | 2 | 17 | 3 | 1 | 2 | 33 (14.7) |
| GU abn. | 3 | 4 | 3 | 11 | 2 | 9 | 31 | 14 | 0 | 3 | 80 (41) |
| Cryptorchidism | 2 | 2 | 0 | 4 | 0 | 3 | 15 | 2 | 0 | 1 | 29 (12.2) |
| Endocrin. abn. | 5 | 2 | 0 | 6 | 2 | 5 | 8 | 19 | 2 | 4 | 53 (24.4) |
| Abn. spine curvature | 2 | 1 | 1 | 7 | 0 | 5 | 19 | 20 | 0 | 3 | 58 (26.8) |
| Joint hypermob. | 0 | 1 | 1 | 8 | 2 | 1 | 5 | 15 | 0 | 0 | 33 (15.3) |
Abbreviations: Abn., abnormalities; B‐ID, borderline intellectual disability; B‐MRI, Brain Magnetic Resonance Imaging; GDD, global developmental delay; GERD, gastroesophageal reflux disease; GQS, General Quotient Score; GU, genitourinary; ID, intellectual disability; IQ, Intellective Quotient; m, months; N, number of patients.
3.4. Facial Traits
Main dysmorphisms described in the overall patient cohort have been categorized according to HPO terms and collected in Table 3. Facial description and/or photographic documentation were only available for 194 out of 239 patients. Of the 45 patients with missing gestaltic details, 34 were affected by SS, 3 by CHD7 rd, 3 by RSTS (1 CREBBP and 2 EP300), 1 by KLEEFS; 4 further patients were excluded as only partial information regarding the phenotype was available (3 RSTS patients and 1 patient with a variant of CHD7).
TABLE 3.
Dysmorphic facial traits and extremities abnormalities reported in the whole cohort, expressed as total number and relative percentage.
| HPO | N (total: 194) | Percentage |
|---|---|---|
| Abnormal Head | 35 | 18.0 |
| Brachicefaly HP:0000248 | 17 | 8.8 |
| Dolichocephaly HP:0000268 | 11 | 5.7 |
| Hypertrychosis | 60 | 30.9 |
| Grimacing | 31 | 16.0 |
| Hairline abnormalities | 59 | 30.4 |
| Anterior low HP:0000294 | 35 | 18.0 |
| High anterior hairline HP:0009890 | 22 | 11.3 |
| Forehead | 68 | 35.1 |
| Broad forehead HP:0000337 | 25 | 12.9 |
| Prominent forehead HP:0011220 | 14 | 7.2 |
| Narrow forehead HP:0000341 | 23 | 11.9 |
| Eyebrows | 98 | 50.5 |
| Synophris HP:0000664 | 41 | 21.1 |
| Highly arched eyebrow HP:0002553 | 38 | 19.6 |
| Horizontal eyebrow HP:0011228 | 5 | 2.6 |
| Thin eyebrow HP:0045074 | 6 | 3.1 |
| Thick eyebrow HP:0000574 | 22 | 11.3 |
| Prominent eyelashes HP:0011231 | 36 | 18.6 |
| Epicanthus HP:0000286 | 40 | 20.6 |
| Eyes | 91 | 46.9 |
| Hypertelorism HP:0000316 | 57 | 29.4 |
| Deeply set eyes HP:0000490 | 38 | 19.6 |
| Palpebral fissures | 104 | 53.6 |
| Downslanted palpebral fissures HP:0000494 | 45 | 23.2 |
| Long palpebral fissure HP:0000637 | 19 | 9.8 |
| Narrow palpebral fissure HP:0045025 | 9 | 4.6 |
| Upslanted palpebral fissure HP:0000582 | 29 | 14.9 |
| Nose | 149 | 76.8 |
| Low hanging columella HP:0009765 | 60 | 30.9 |
| Bulbous nose HP:0000414 | 49 | 25.3 |
| Wide nasal root (no HPO) | 37 | 19.1 |
| Convex nasal ridge HP:0000444 | 27 | 13.9 |
| Philtrum | 85 | 43.8 |
| Deep philtrum HP:0002002 | 10 | 5.2 |
| Long philtrum HP:0000343 | 26 | 13.4 |
| Smooth philtrum HP:0000319 | 30 | 15.5 |
| Short philtrum HP:0000322 | 34 | 17.5 |
| Lips | 104 | 53.6 |
| Thin upper lip vermilion HP:0000219 | 22 | 11.3 |
| Thin vermilion border HP:0000233 | 13 | 6.7 |
| Everted lower lip vermilion HP:0000232 | 14 | 7.2 |
| Teeth | 50 | 25.8 |
| Talon cusp HP:0011088 | 6 | 3.1 |
| Widely spaced teeth HP:0000687 | 16 | 8.2 |
| Malar flattening HP:0000272 | 22 | 11.3 |
| Ears | 83 | 42.8 |
| Low‐set ears HP:0000369 | 26 | 13.4 |
| Posteriorly rotated ears HP:0000358 | 17 | 8.8 |
| Macrotia HP:0000400 | 16 | 8.2 |
| Hands | 112 | 57.7 |
| Abnormal thumb morphology HP:0001172 | 68 | 35.1 |
| Polydactyly HP:0010442 | 2 | 1.0 |
| Fetal pads | 8 | 4.1 |
| Feet | 98 | 50.5 |
| Broad distal hallux HP:0008111 | 45 | 23.2 |
| Polydactyly HP:0010442 | 4 | 2.1 |
4. Discussion
CPs include a broad spectrum of conditions characterized by strongly convergent and hardly distinguishable phenotypes. Although individually rare, they represent one main cause of neurodevelopmental defects up to date [3]. Here we present the largest cohort of patients with CPs reported in literature, providing a comprehensive overview of their clinical features, genetic diagnoses, and developmental outcomes. Our population is composed by 239 individuals affected by 31 distinct CPs (see Figure 1), mainly SS (N = 64), RSTS (N = 59) and KLEEFS (N = 36). Genes involved in epigenetic machinery are strongly intolerant to loss of function, as demonstrated by the fact that nearly all included disorders display an autosomal dominant transmission with the only exception of PRMT7 related condition [10]. New sequencing technologies, together with a deeper understanding of epigenetic mechanisms, emerge from our review as the main factor leading to identifying genetic causes of CPs at an increasingly early age. In facts, correlation analyses point out to birth date comes out as the only strong predictor of delayed diagnosis, regardless of clinical presentation (see Figure 2 and Table S4). Novel technologies have enabled to achieve a prompt diagnostic definition, particularly for CHD7, often diagnosed before age of 2 years, with significant benefits in treatment and monitoring. Furthermore, the large number of non‐diagnostic array CGH performed (64%), together with the strong clinical overlapping among these conditions arises important insights to ameliorate our diagnostic approach. Given the genotypic and phenotypic complexity of these conditions, wide sequencing analyses, such as WES/WGS, emerge as the most efficient diagnostic tool on the suspect of CPs. These data underscore the need to bypass step‐wise genetic testing to minimize diagnostic delay and healthcare costs.
Growth defects are a hallmark of CPs. Using the collected parameters, we plotted growth curves for CPs and for three main disorders under study (RSTS, KLEFS, SS, see Figure 3B). Our findings suggest that the normal pubertal peak may be absent in various CPs, as already described for two of them (RSTS, Stevens CA [11] and Kabuki Syndrome [12]). This result, if confirmed by dedicated studies, gives a glimpse into the underlying mechanisms of growth defects in CPs. A future goal will be to better define specific growth curves for each CP, similar to these available for RSTS [13] and Kabuki syndrome [12]. Personalized growth charts may help in an accurate and efficient assessment of somatic development in CPs patients.
Our findings define CPs as a spectrum of neurodevelopmental disorders, reinforcing previous case series on these conditions [14]. Although ID comes up as a leading clinical sign for CPs, it is to note that 33 patients (23.5%) in our population display a borderline ID/normal intelligence, underscoring the extreme phenotypic continuum of CPs. This broad variability challenges traditional diagnostic approaches and highlights the vital importance of well‐trained clinical geneticists for the identification of milder phenotypes. Furthermore, the unpredictable presentation of CPS, together with benign clinical pictures, also represents a central issue in genetic counseling for reproductive planning.
Hypotonia, especially during the neonatal period, stands up as a considerable feature in these conditions, affecting more than half of our cohort (62%). Furthermore, being a life‐long and basically untreatable symptom, the present review might have underestimated hypotonia prevalence, that has been reported as high as 82.6% by Harris and colleagues. This difference might be related to the different clinical setting, with American patients potentially beneficing from more thorough and timely neurological evaluations within the context of Chromatin Clinics.
Other neurocognitive impairments, as epilepsy (30.4%) and autism (17.1%), might not be a reliable clinical hallmarks to raise CPs suspect, given that prevalences widely vary among reports (e.g., in Harris 2024 respectively 8.7% and 20.9%). It is fascinating, but not unpredictable, to notice how clinical features strongly depend on sample's genotype. Our cohort represents to date the largest data collection from a CPs population. Still, the unavoidable heterogeneity of cohorts makes it difficult to compare our data to previous reports [14]. In particular, the higher prevalence of KLEEFS in our population may have driven the high distribution of epilepsy and autism [15].
Focusing on ID, most patients in our cohort had a moderate ID (N = 19, see Figure S2), mainly driven by the high number of IQ measurements in the RSTS population (17 out of 61), and in line with previous reports [16]. The average IQ in our cohort was 68, attesting a mild disability (median 65, range 32–115). We observed significantly higher IQs in SS patients (N = 10, mean 85, range: 67–115) than in KLEEFS (N = 9, mean 47.9, range: 36–78) (see Table 2 and Tables S2 and S3). As already described for other neurodevelopmental disorders, correlation analyses demonstrated a significant association between IQ and birth date (see Table S5), highlighting how effective early interventions are in substantially modify the natural course of CPs [17]. On the other hand, we demonstrated that cognitive impairment in CPs is greatly unpredictable, being independent from a number of factors such as brain anomalies, autism, hypotonia and epilepsy (see Table S5).
Regarding the remaining clinical manifestations (see Figure 4 and Table 2), we showed a significant prevalence of visual disturbances (60.2%), of abnormalities of the cardiovascular system (54.8%) and of the genitourinary system (41%). In addition, 35.2% of patients reported a history of recurrent infections. These data, consistent with existing literature [4], underline the importance of multidisciplinary care for patients with CPs. Analysis of facial features revealed a distinctive phenotypic pattern for RSTS, KLEEFS and SS; WDSTS patients present a profile strongly similar to that of RSTS patients (see Figure S3).
A primary limitation of this retrospective study is the possible selection bias in the cohort's composition, driven by collaborations with specific families associations. Furthermore, the multi‐decade collection period introduces variability in diagnostic methods and reporting standards.
Despite these limitations, the large sample size and the detailed longitudinal data provide robust evidence regarding the significant variability across CPs and the importance of individualized assessments and interventions [18, 19].
Establishing national reference centers, already active in the United States [14], would ensure optimal management of these rare diseases, improving the quality of life of these patients and their families.
5. Conclusions
The present data collection represents the largest case series of patients with CPs. This extensive data set gives a significant contribution to the understanding of these rare diseases. The vast and growing number of conditions included in the spectrum of CPs, coupled with their wide and complex phenotypic variability, requires the introduction of dedicated Outpatient Clinics for their monitoring and diagnosis. Such an approach would facilitate not only the achievement of a deeper knowledge and expertise in the recognition, treatment, and management of these disorders, but also the development of personalized multidisciplinary care for these patients. Finally, it is vital to emphasize the burden of non‐diagnostic tests performed by a large number of patients in our cohort. Today, genome‐wide technologies are widely available and may represent the best opportunity in the diagnostic approach to syndromic IDs. Such an approach could not only avoid excessive costs and multiple tests, but also allow encompassing the complex and convergent phenotypic landscape of CPs.
Author Contributions
Giulia Bruna Marchetti: conceptualization, writing original drafts, data curation, investigation, formal analysis. Erica Rosina: writing review and editing, investigation. Camilla Meossi: investigation. Michela Mura: investigation. Lidia Pezzani: investigation. Romano Tenconi: investigation. Angelo Selicorni: investigation. Maria Francesca Bedeschi: investigation. Carlo Agostoni: investigation. Palma Finelli: investigation. Sara De Matteis: writing review and editing, investigation. Valentina Massa: writing review and editing, investigation, funding acquisition, project administration. Elisabetta Di Fede: investigation. Laura Pezzoli: conceptualization, review and editing, investigation, funding acquisition, project administration. Cristina Gervasini: writing review and editing, investigation, funding acquisition, project administration. Maria Iascone: conceptualization, review and editing, investigation, funding acquisition, project administration. Donatella Milani: conceptualization, writing original drafts, investigation, funding acquisition, project administration.
Ethics Statement
The authors have nothing to report.
Conflicts of Interest
The authors declare no conflicts of interest.
Supporting information
Data S1: cge70099‐sup‐0001‐Figures.pdf.
Data S2: cge70099‐sup‐0002‐Supinfo.ods.
Data S3: cge70099‐sup‐0003‐Tables.docx.
Acknowledgments
The present study was partially funded by Regione Lombardia as FRRB Project 3441133. Part of the authors of this publication is a member of the European Reference Network on Rare Congenital Malformations and Rare Intellectual Disability ERN‐ITHACA [EU Framework Partnership Agreement ID: 3HP‐HP‐FPA ERN‐01‐2016/739516]. Open access funding provided by BIBLIOSAN.
The authors are warmly grateful to families associations (ASSIGulliver, Kleefstra Italia, Associazione RTS) for their longstanding collaboration and support to Our Center.
Marchetti G. B., Rosina E., Meossi C., et al., “Portrait of a Spectrum: Clinical and Genetic Characterization of a Large Cohort of Chromatinopathies—30 Years' Experience From a Third Level Center,” Clinical Genetics 109, no. 4 (2026): 707–716, 10.1111/cge.70099.
Funding: This work was supported by Fondazione Regionale per la Ricerca Biomedica, 3441133.
Data Availability Statement
The data that support the findings of this study are available on request from the corresponding author. The data are not publicly available due to privacy or ethical restrictions.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Data S1: cge70099‐sup‐0001‐Figures.pdf.
Data S2: cge70099‐sup‐0002‐Supinfo.ods.
Data S3: cge70099‐sup‐0003‐Tables.docx.
Data Availability Statement
The data that support the findings of this study are available on request from the corresponding author. The data are not publicly available due to privacy or ethical restrictions.