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Li SJ, Tsao PN, Tu YK, Hsieh WS, Yao NJ, Wu YT, Jeng SF. Cognitive and motor development in preterm children from 6 to 36 months of age: Trajectories, risk factors and predictability. Early Hum Dev 2022; 172:105634. [PMID: 35921693 DOI: 10.1016/j.earlhumdev.2022.105634] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/24/2021] [Revised: 07/01/2022] [Accepted: 07/23/2022] [Indexed: 11/27/2022]
Abstract
BACKGROUND Although numerous studies have examined the development of preterm children born very low birth weight (VLBW, birth body weight < 1500 g), variations of developmental progress within individuals have rarely been explored. The aim of this research was to examine the cognitive and motor trajectories in preterm children born VLBW at early ages and to assess the risk factors and predictability of these trajectories. METHOD Five hundred and eighty preterm infants born VLBW from three cohort studies (2003 to 2014) were prospectively assessed their mental and motor development using the Bayley Scales at 6, 12, 24, and 36 months, and cognitive, motor and behavioral outcomes using the Movement Assessment Battery for Children and the Child Behavior Checklist for Ages 1.5-5 at 4 years of age. RESULTS Preterm children born VLBW manifested three cognitive patterns (stably normal [64.0 %], deteriorating [31.4 %], and persistently delayed [4.6 %]) and four motor patterns (above average [6.3 %], stably normal [60.0 %], deteriorating [28.5 %], and persistently delayed [5.2 %]) during 6-36 months. Low birth body weight, stage III-IV retinopathy of prematurity and low parental socio-economic status were associated with the deteriorating patterns; prolonged hospitalization and major brain damage were additionally associated with the persistently delayed patterns. Furthermore, the cognitive and motor deteriorating pattern was each predictive of cognitive and motor impairment at 4 years of age; whereas, the persistently delayed patterns were predictive of multiple impairments. CONCLUSION AND IMPLICATIONS Preterm children born VLBW display heterogeneous trajectories in early cognitive and motor development that predict subsequent developmental and behavioral outcomes.
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Affiliation(s)
- Sin-Jie Li
- School and Graduate Institute of Physical Therapy, College of Medicine, National Taiwan University, Taipei, Taiwan; Department of Rehabilitation, Fu Jen Catholic University Hospital, Fu Jen Catholic University, New Taipei City, Taiwan
| | - Po-Nien Tsao
- Division of Neonatology, Department of Pediatrics, National Taiwan University Children's Hospital, Taipei, Taiwan.
| | - Yu-Kang Tu
- Institute of Epidemiology & Preventive Medicine, College of Public Health, National Taiwan University, Taipei, Taiwan.
| | - Wu-Shiun Hsieh
- Division of Neonatology, Department of Pediatrics, Cathay General Hospital, Taipei, Taiwan.
| | - Nai-Jia Yao
- School and Graduate Institute of Physical Therapy, College of Medicine, National Taiwan University, Taipei, Taiwan
| | - Yen-Tzu Wu
- School and Graduate Institute of Physical Therapy, College of Medicine, National Taiwan University, Taipei, Taiwan.
| | - Suh-Fang Jeng
- School and Graduate Institute of Physical Therapy, College of Medicine, National Taiwan University, Taipei, Taiwan; Physical Therapy Center, National Taiwan University Hospital, Taipei, Taiwan.
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Liu W, Sun Q, Huang L, Bhattacharya A, Wang GW, Tan X, Kuban KCK, Joseph RM, O'Shea TM, Fry RC, Li Y, Santos HP. Innovative computational approaches shed light on genetic mechanisms underlying cognitive impairment among children born extremely preterm. J Neurodev Disord 2022; 14:16. [PMID: 35240980 PMCID: PMC8903548 DOI: 10.1186/s11689-022-09429-x] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/01/2021] [Accepted: 02/22/2022] [Indexed: 11/27/2022] Open
Abstract
BACKGROUND Although survival rates for infants born extremely preterm (gestation < 28 weeks) have improved significantly in recent decades, neurodevelopmental impairment remains a major concern. Children born extremely preterm remain at high risk for cognitive impairment from early childhood to adulthood. However, there is limited evidence on genetic factors associated with cognitive impairment in this population. METHODS First, we used a latent profile analysis (LPA) approach to characterize neurocognitive function at age 10 for children born extremely preterm. Children were classified into two groups: (1) no or low cognitive impairment, and (2) moderate-to-severe cognitive impairment. Second, we performed TOPMed-based genotype imputation on samples with genotype array data (n = 528). Third, we then conducted a genome-wide association study (GWAS) for LPA-inferred cognitive impairment. Finally, computational analysis was conducted to explore potential mechanisms underlying the variant x LPA association. RESULTS We identified two loci reaching genome-wide significance (p value < 5e-8): TEA domain transcription factor 4 (TEAD4 at rs11829294, p value = 2.40e-8) and syntaxin 18 (STX18 at rs79453226, p value = 1.91e-8). Integrative analysis with brain expression quantitative trait loci (eQTL), chromatin conformation, and epigenomic annotations suggests tetraspanin 9 (TSPAN9) and protein arginine methyltransferase 8 (PRMT8) as potential functional genes underlying the GWAS signal at the TEAD4 locus. CONCLUSIONS We conducted a novel computational analysis by utilizing an LPA-inferred phenotype with genetics data for the first time. This study suggests that rs11829294 and its LD buddies have potential regulatory roles on genes that could impact neurocognitive impairment for extreme preterm born children.
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Affiliation(s)
- Weifang Liu
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Quan Sun
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Le Huang
- Curriculum in Bioinformatics and Computational Biology, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Arjun Bhattacharya
- Department of Pathology and Laboratory Medicine, David Geffen School of Medicine, University of California, Los Angeles, CA, USA
| | - Geoffery W Wang
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Xianming Tan
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
- Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Karl C K Kuban
- Department of Pediatrics, Boston University, Boston, MA, USA
| | - Robert M Joseph
- Department of Anatomy & Neurobiology, Boston University, Boston, MA, USA
| | - T Michael O'Shea
- Department of Pediatrics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Rebecca C Fry
- Department of Environmental Sciences and Engineering, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Yun Li
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
- Department of Computer Science, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
| | - Hudson P Santos
- School of Nursing, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
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