Faculty of Physiotherapy and Nursing, Universidad de Castilla-La Mancha, Toledo, Spain; Health and Social Research Center, Universidad de Castilla La Mancha, Cuenca, Spain.
CIPER, Faculdade de Motricidade Humana, Universidade de Lisboa, Lisbon, Portugal; ESS, Instituto Politécnico de Setúbal, Setúbal, Portugal.
Clin Nutr. 2023 Jul;42(7):1151-1158. doi: 10.1016/j.clnu.2023.05.008. Epub 2023 May 17.
Sarcopenia is primarily a disease in older people characterized by reduced muscle mass and strength. Nevertheless, sarcopenia may, at least partially, have pediatric origins. The study aimed to identify risk phenotypes for sarcopenia in healthy young people using clustering analysis procedures based on body composition and musculoskeletal fitness.
We conducted a cluster cross-sectional analysis of data from 529 youth aged 10-18 yr. Body composition was assessed using whole-body dual-energy x-ray absorptiometry (DXA), determining: lean body mass index (LBMI, kg/m), fat body mass index (FBMI, kg/m), abdominal FBMI (kg/m), and lean body mass/fat body mass ratio (LBM/FBM); body mass index was also calculated (BMI, kg/m). Musculoskeletal fitness was assessed using handgrip strength (kg) and vertical jump power (W) tests. Results were presented as absolute values and adjusted by body mass. Plank endurance (s) was also assessed. All variables were sex and age in years standardized (Z-score). LBMI or LBM/FBM ratio ≤ -1 SD were used to identify participants at risk for sarcopenia. Maturity was estimated as the years of distance from the peak height velocity (PHV) age.
Using the Z-score means for body composition and musculoskeletal fitness and having LBMI or LBM/FBM ratio as the categorical variables (at risk vs. not at risk), the cluster analyses indicated three homogeneous groups (phenotypes, P): P1, risk body composition and unfit; P2, non-risk body composition and non-fit, and P3, non-risk body composition and fit. With the LBMI as a categorical variable, the ANOVA models showed that the body composition and absolute values of musculoskeletal fitness were in P1 < P2 < P3 and the estimated PHV age of P1 > P3 in both sexes (p < 0.001). Having the LBM/FBM as a categorical variable, higher values of BMI, FBMI, and abdominal FBMI, and lower values of handgrip strength and vertical jump power both adjusted for body mass and plank endurance were observed in P1 than in P2 and/or P3 and the P2 than in the P3 in boys and girls (p < 0.001).
Two risk phenotypes for sarcopenia were identified in apparently healthy young people: I. a low LBMI phenotype with low BMI and II. a low LBM to FBM phenotype with high BMI and FBMI. In both risk phenotypes I and II, musculoskeletal fitness was low. For screening, we suggest using absolute measures of handgrip strength and vertical jump power in phenotype I and body mass adjusted measures of these markers, as well as the plank endurance time in phenotype II.
肌少症主要是一种老年人疾病，其特征为肌肉质量和力量下降。然而，肌少症至少部分可能具有儿科起源。本研究旨在使用基于身体成分和肌肉骨骼健康的聚类分析程序，确定健康年轻人中肌少症的风险表型。
我们对 529 名 10-18 岁的年轻人进行了聚类横断面分析。使用全身双能 X 射线吸收法（DXA）评估身体成分，确定：瘦体重指数（LBMI，kg/m）、脂肪体重指数（FBMI，kg/m）、腹部 FBMI（kg/m）和瘦体重/脂肪体重比（LBM/FBM）；还计算了体重指数（BMI，kg/m）。使用握力（kg）和垂直跳跃力（W）测试评估肌肉骨骼健康。结果以绝对值表示，并按体重进行了调整。还评估了平板支撑耐力（s）。所有变量均按性别和年龄（Z 分数）进行标准化。使用 LBMI 或 LBM/FBM 比值≤-1 SD 来确定肌少症风险参与者。使用峰值身高速度（PHV）年龄的年距离来估计成熟度。
使用身体成分和肌肉骨骼健康的 Z 分数平均值，并将 LBMI 或 LBM/FBM 比值作为分类变量（有风险与无风险），聚类分析表明存在三个同质组（表型，P）：P1，风险身体成分和不适合；P2，非风险身体成分和不适合，以及 P3，非风险身体成分和适合。使用 LBMI 作为分类变量，方差分析模型表明，在 P1<P2<P3 中，身体成分和肌肉骨骼健康的绝对值，以及在两性中 P1 的估计 PHV 年龄均大于 P3（p<0.001）。使用 LBM/FBM 作为分类变量，在 P1 中观察到较高的 BMI、FBMI 和腹部 FBMI 值，以及较低的握力和垂直跳跃力值，均按体重进行了调整，并在 P1 中观察到比 P2 和/或 P3 更长的平板支撑耐力时间，而在 P2 中观察到比 P3 更长的平板支撑耐力时间（p<0.001）。
在明显健康的年轻人中确定了两种肌少症风险表型：I. 低 LBMI 表型，BMI 和 II. 低 LBM 至 FBM 表型，BMI 和 FBMI 较低。在这两种风险表型中，肌肉骨骼健康状况都较低。用于筛查，我们建议在表型 I 中使用握力和垂直跳跃力的绝对值，在表型 II 中使用这些标志物的体重调整值以及平板支撑耐力时间。
