Ravagnani Salto Ana Beatriz, Salum Giovanni A, Hoffmann Maurício Scopel, Santoro Marcos L, Zugman André, Pan Pedro M, Belangero Sintia I, Ito Lucas Toshio, Doretto Victoria Fogaça, Croci Marcos S, Brañas Marcelo J A A, de Giusti Carina, Silva-Jr Francisco Da, Ribeiro Sahâmia Martins, Miguel Euripedes Constantino, Leckman James F
Department & Institute of Psychiatry Faculdade de Medicina Universidade de São Paulo (USP) São Paulo São Paulo Brazil.
Department of Psychiatry Universidade Federal do Rio Grande do Sul (UFRGS) Porto Alegre RS Brazil.
JCPP Adv. 2024 Jul 17;5(1):e12268. doi: 10.1002/jcv2.12268. eCollection 2025 Mar.
Understanding the factors that determine distinct courses of anxiety symptoms throughout development will better guide interventions. There are scarce data-driven longitudinal studies, using multi-modal predictors, investigating the chronicity of anxiety symptoms from childhood to young adulthood, particularly in a middle-income country.
2033 youths (ages 6-14 years [Mean age = 10.4 ± 1.94) at Baseline] were enrolled in the Brazilian High-Risk Cohort for Mental Conditions longitudinal study, and assessed at three timepoints, between 2010 and 2019, using the Screen for Child Anxiety Related Disorders. Confirmatory Factor Analysis provided input to Growth Mixture Models to identify the best fitting trajectory model. Multinomial logistic regression analyses tested the effects of intelligence quotient (IQ), environmental factors and polygenic risk scores on internalizing symptomatology within trajectory class membership.
The best model solution identified three classes: high-decreasing, moderate/low-stable and low-increasing symptoms over time. The high-decreasing class showed a higher incidence of anxiety symptoms at the second time point (Mean age = 13.8 ± 1.93); while anxiety symptoms were highest in the low-increasing class at the third timepoint (Mean age = 18.35 ± 2.03). Further, lower IQ predicted membership in the high-decreasing trajectory class (OR = 0.68, 95% CI [0.55, 0.85]), while higher IQ predicted membership in the low-increasing trajectory class (OR = 1.95, 95% CI [1.42, 2.67]). Finally, females were more likely than males to be in the low-increasing trajectory class. Polygenic risk scores were not associated with anxiety trajectory class membership.
Recognizing that anxiety symptoms follow diverse paths over time will allow for more effective intervention strategies. Specifically, interventions could accommodate children for greater anxiety risk in early childhood (i.e., lower IQ) versus late adolescence (i.e., higher IQ). That said, the emotional needs of girls in late adolescence should be monitored, regardless of their cognitive abilities or high achievements.
了解在整个发育过程中决定焦虑症状不同病程的因素，将能更好地指导干预措施。目前缺乏使用多模式预测指标、从儿童期到青年期对焦虑症状的慢性病程进行研究的数据驱动型纵向研究，尤其是在中等收入国家。
2033名青少年（基线时年龄为6 - 14岁[平均年龄 = 10.4 ± 1.94]）参与了巴西精神疾病高风险队列纵向研究，并在2010年至2019年期间的三个时间点使用儿童焦虑相关障碍筛查量表进行评估。验证性因素分析为生长混合模型提供输入，以确定最佳拟合轨迹模型。多项逻辑回归分析测试了智商（IQ）、环境因素和多基因风险评分对轨迹类别成员内化症状的影响。
最佳模型解决方案确定了三个类别：随着时间推移症状高下降、中度/低度稳定和低上升。高下降类别在第二个时间点焦虑症状发生率更高（平均年龄 = 13.8 ± 1.93）；而低上升类别在第三个时间点焦虑症状最高（平均年龄 = 18.35 ± 2.03）。此外，较低的智商预测了高下降轨迹类别的成员身份（OR = 0.68，95% CI [0.55, 0.85]），而较高的智商预测了低上升轨迹类别的成员身份（OR = 1.95，95% CI [1.42, 2.67]）。最后，女性比男性更有可能处于低上升轨迹类别。多基因风险评分与焦虑轨迹类别成员身份无关。
认识到焦虑症状随时间遵循不同路径，将有助于制定更有效的干预策略。具体而言，干预措施可以针对幼儿期焦虑风险较高（即智商较低）与青少年晚期焦虑风险较高（即智商较高）的儿童。也就是说，无论少女的认知能力或学业成绩如何，都应监测她们在青少年晚期的情感需求。
