Huntington’s disease (HD) is a genetic disorder caused by a CAG trinucleotide expansion in the huntingtin (HTT) gene. Juan de Acosta, Atlántico, a city located on the Caribbean coast of Colombia, is home to the world’s second-largest HD pedigree. Here, we include 291 descendants of this pedigree with at least one family member with HD. Blood samples were collected, and genomic DNA was extracted. We quantified the HTT CAG expansion using an amplicon sequencing protocol. The genetic heterogeneity was measured as the ratio of the mosaicism allele’s read peak and the slippage ratio of the allele’s read peak from our sequence data. The statistical and bioinformatic analyses were performed with a significance threshold of p < 0.05. We found that the average HTT CAG repeat length in all participants was 21.91 (SD = 8.92). Of the 291 participants, 33 (11.3%, 18 females) had a positive molecular diagnosis for HD. Most affected individuals were adults, and the most common primary and secondary alleles were 17/7 (CAG/CCG) and 17/10 (CAG/CCG), respectively. The mosaicism increased with age in the participants with HD, while the slippage analyses revealed differences by the HD allele type only for the secondary allele. The slippage tended to increase with the HTT CAG repeat length in the participants with HD, but the increase was not statistically significant. This study analyzed the genetic and molecular features of 291 participants, including 33 with HD. We found that the mosaicism increased with age in the participants with HD, particularly for the secondary allele. The most common haplotype was 17/7_17/10. The slippage for the secondary allele varied by the HD allele type, but there was no significant difference in the slippage by sex. Our findings offer valuable insights into HD and could have implications for future research and clinical management.

Background and objectives: The premanifest phase of Huntington’s disease (HD) is characterized by the absence of motor symptoms and exhibits structural changes in imaging that precede clinical manifestation. This study aimed to analyze volumetric changes identified through brain magnetic resonance imaging (MRI) processed using artificial intelligence (AI) software in premanifest HD individuals, focusing on the relationship between CAG triplet expansion and structural biomarkers. Methods: The study included 36 individuals descending from families affected by HD in the Department of Atlántico. Sociodemographic data were collected, followed by peripheral blood sampling to extract genomic DNA for quantifying CAG trinucleotide repeats in the Huntingtin gene. Brain volumes were evaluated using AI software (Entelai/IMEXHS, v4.3.4) based on MRI volumetric images. Correlations between brain volumes and variables such as age, sex, and disease status were determined. All analyses were conducted using SPSS (v. IBM SPSS Statistics 26), with significance set at p < 0.05. Results: The analysis of brain volumes according to CAG repeat expansion shows that individuals with ≥40 repeats evidence significant increases in cerebrospinal fluid (CSF) volume and subcortical structures such as the amygdalae and left caudate nucleus, along with marked reductions in cerebral white matter, the cerebellum, brainstem, and left pallidum. In contrast, those with <40 repeats show minimal or moderate volumetric changes, primarily in white matter and CSF. Conclusions: These findings suggest that CAG expansion selectively impacts key brain regions, potentially influencing the progression of Huntington’s disease, and that AI in neuroimaging could identify structural biomarkers long before clinical symptoms appear.

Background: Chronic low back pain (CLBP) is the leading cause of years lived with disability worldwide. Sleep disturbances are highly prevalent among patients with CLBP and may contribute to pain persistence, disability, and poorer treatment outcomes. Despite this, sleep remains an underrecognized component of routine clinical assessment. Objective: To review the available evidence on the relationship between sleep and CLBP, their shared pathophysiological mechanisms, clinical relevance, and implications for assessment and management in pain units. Methods: A structured narrative review was conducted using PubMed/MEDLINE, Scopus, and Google Scholar. Systematic reviews, meta-analyses, clinical trials, observational studies, and clinical practice guidelines addressing the relationship between sleep and CLBP, underlying mechanisms, and therapeutic strategies were included. Results: Sleep disturbances affect up to 73 % of patients with CLBP. Poor sleep quality is associated with worse clinical outcomes, greater disability, and a higher likelihood of central sensitization. The relationship between sleep and pain is bidirectional and involves alterations in endogenous pain modulation, hypothalamic–pituitary–adrenal axis dysregulation, and neuroinflammatory processes. Systematic assessment of sleep using structured clinical interviews and validated questionnaires may help identify patients with more complex clinical profiles. Among available interventions, cognitive behavioral therapy for insomnia (CBT-I) has the strongest supporting evidence. Several pharmacological treatments may improve sleep-related outcomes; however, their direct analgesic effects in CLBP appear limited. Conclusions: Sleep represents a clinically relevant dimension with important prognostic and therapeutic implications in CLBP. Routine assessment of sleep may help identify patients with greater clinical complexity and support a more individualized multimodal treatment approach. Integrating sleep evaluation into the routine management of CLBP may contribute to improved clinical and functional outcomes.