ISGlobal, Institute for Global Health Barcelona, C/ Doctor Aiguader 88, 08003, Barcelona, Spain.
Center for Environmental Sciences, Hasselt University, Martelarenlaan 42, 3500, Hasselt, Belgium.
Sci Rep. 2019 Dec 10;9(1):18739. doi: 10.1038/s41598-019-55283-8.
Telomere length is considered a biomarker of biological aging. Shorter telomeres and obesity have both been associated with age-related diseases. To evaluate the association between various indices of obesity with leukocyte telomere length (LTL) in childhood, data from 1,396 mother-child pairs of the multi-centre European birth cohort study HELIX were used. Maternal pre-pregnancy body mass index (BMI) and 4 adiposity markers in children at age 8 (6-11) years were assessed: BMI, fat mass, waist circumference, and skinfold thickness. Relative LTL was obtained. Associations of LTL with each adiposity marker were calculated using linear mixed models with a random cohort effect. For each 1 kg/m² increment in maternal pre-pregnancy BMI, the child's LTL was 0.23% shorter (95%CI: 0.01,0.46%). Each unit increase in child BMI z-score was associated with 1.21% (95%CI: 0.30,2.11%) shorter LTL. Inverse associations were observed between waist circumference and LTL (-0.96% per z-score unit; 95%CI: -2.06,0.16%), and skinfold thickness and LTL (-0.10% per z-score unit; 95%CI: -0.23,0.02%). In conclusion, this large multicentric study suggests that higher child adiposity indicators are associated with short telomeres in children, and that associations are stronger for child BMI than for maternal pre-pregnancy BMI.
端粒长度被认为是生物衰老的生物标志物。较短的端粒和肥胖都与与年龄相关的疾病有关。为了评估儿童肥胖的各种指标与白细胞端粒长度（LTL）之间的关联，使用了来自多中心欧洲出生队列研究 HELIX 的 1396 对母婴对的数据。评估了母亲孕前体重指数（BMI）和 8 岁（6-11 岁）儿童的 4 种肥胖指标：BMI、体脂肪量、腰围和皮褶厚度。获得相对 LTL。使用具有随机队列效应的线性混合模型计算 LTL 与每个肥胖指标的关联。母亲孕前 BMI 每增加 1kg/m²，儿童的 LTL 缩短 0.23%（95%CI：0.01，0.46%）。儿童 BMI z 分数每增加 1 个单位，LTL 缩短 1.21%（95%CI：0.30，2.11%）。腰围与 LTL 呈负相关（每单位 z 分数减少 0.96%；95%CI：-2.06，0.16%），皮褶厚度与 LTL 呈负相关（每单位 z 分数减少 0.10%；95%CI：-0.23，0.02%）。总之，这项大型多中心研究表明，较高的儿童肥胖指标与儿童的短端粒有关，且儿童 BMI 与母亲孕前 BMI 相比，相关性更强。
