The research converted raw plank-hold times into five performance zones based on percentile boundaries adapted from the Chinese National Physical Fitness Evaluation Standard. “Very Poor” was defined as performance at or below the 10th percentile, “Poor” as the 10th to 35th percentiles, “Medium” as the 35th to 65th, “Good” as the 65th to 90th and “Excellent” as the 90th percentile or higher. Cut-points were calculated separately for each age and sex. Of the 2,970 participants, 32.7% were classified as Medium and 24.4% as Good, together accounting for 57.1%. Another 20.6% were placed in Poor, 12.4% in Very Poor and 9.9% in Excellent. These are population-relative classifications for the studied region, not diagnoses, health-risk categories or universal standards of fitness.

Students were recruited in 2020 using stratified random sampling across the Multan, Bahawalpur and D.G. Khan administrative divisions of South Punjab. The investigators randomly selected 20 schools from each division and then selected 50 students from every school, producing a final cohort of 1,477 boys and 1,493 girls, or 49.73% and 50.27% of the sample, respectively. Each whole-year age group from 12 through 16 contained approximately 590 participants. The sampling frame represented about 16.67% of the 360 eligible higher secondary schools in the region. Data were collected before nationwide COVID-19 school closures, so the findings provide a pre-pandemic baseline for this cohort rather than a current or post-pandemic description of adolescents in Pakistan.

To construct smooth percentile curves, the researchers used the Lambda-Mu-Sigma, or LMS, method within the GAMLSS framework in R. Lambda models skewness, Mu represents the median and Sigma captures the coefficient of variation, allowing the reference distribution to depart from a simple normal curve. The fits used the Box-Cox Cole-Green distribution, with each of the three parameters modelled as a quadratic function of age. Because participants’ ages were recorded in completed years rather than months, the curves were supported by only five discrete age points for each sex. Model adequacy was assessed using worm plots, quantile-quantile plots and normalized quantile residuals, which were reported to conform closely to a standard normal distribution. Within each sex, the researchers also performed 1,000 non-parametric bootstrap resamples, refitted the model and recomputed the percentiles to obtain 95% confidence intervals.

Boys recorded a higher average plank time than girls: 80.72 ± 73.48 seconds versus 69.07 ± 58.16 seconds. The overall difference was statistically detectable after correction for multiple comparisons, but its magnitude was small, with a rank-biserial correlation of 0.06. Age-specific analyses told a less uniform story. After false-discovery-rate adjustment, statistically significant differences remained at ages 13 and 15, with effect sizes of 0.14 and 0.12, respectively. No detectable difference was found at ages 12, 14 or 16, although the unadjusted result at age 12 had a p-value of 0.045 before correction. Boys also had higher mean height, weight and body mass index, but the study did not measure biological maturation and did not adjust plank performance for body size or composition, so the relative contributions of maturation, adiposity, muscle mass and other factors could not be separated.

The clearest age pattern occurred near the upper end of the boys’ fitted distribution. Their 90th-percentile hold time increased from 132 seconds at age 12 to 226 seconds at age 16, whereas their median rose by only 11 seconds, from 50 to 61 seconds. The increasingly right-skewed distribution therefore separated a subset of long-lasting performers from the typical boy at older ages. Girls showed a steadier increase through the middle of the distribution: their median climbed from 44 seconds at age 12 to 66 seconds at age 16, and their fitted median was higher than that of boys at the oldest age. The girls’ 90th percentile followed a curved path, rising from 126 seconds at age 12 to 145 seconds at age 14 and then falling to 125 seconds at age 16. Because these are cross-sectional fitted curves, neither pattern demonstrates how an individual adolescent’s performance developed over time.

Uncertainty was greatest at the age boundaries, especially among the upper percentiles. The girls’ age-16 90th percentile of 125 seconds had a bootstrap 95% confidence interval of 118–133 seconds, while the boys’ corresponding estimate of 226 seconds had a much broader interval of 200–252 seconds. The apparent downturn in the girls’ upper percentile should therefore be treated as a boundary effect rather than evidence of declining fitness among older girls: the age-16 curves were the least constrained, and the confidence intervals at ages 14 and 16 for the girls’ upper estimates were only marginally separated. The authors also warned against using the girls’ age-16 third-percentile value of 12 seconds for individual classification because its relative bootstrap interval was 92% wide, spanning 4–15 seconds. Age-16 cut-points, particularly those in the tails, consequently require more caution than the estimates for ages 12–15.

Internal consistency was evaluated through a back-substitution procedure. The researchers first fitted the LMS model to the full sample and then compared its median, or P50, estimates with those produced after fitting a random subsample of 295 boys and 299 girls—about 20% of each sex group. Agreement was quantified with mean absolute percentage error. The average error was 6.69% for boys and 6.04% for girls. All age-specific values were below 10% except the estimate for 16-year-old boys, which reached 10.21%. The subsample produced lower estimates than the full reference in nine of the ten age-by-sex cells and did not reproduce the full model’s shape of change across ages. The procedure therefore supports the internal stability of the central estimates within the sampled population, but it is not external validation: the comparison data came from the same cohort used to construct the references, and the check evaluated only the median rather than the entire distribution or health-related outcomes.

Field testing followed a standardized protocol designed for school environments. Before measurement, each participant received verbal instruction, a visual demonstration and a familiarization attempt, followed by a warm-up lasting about two and a half minutes. The warm-up included 30 seconds each of jogging in place, leg swings, arm circles, walking lunges and hip rotations. Participants then assumed a forearm plank on a mat, with elbows directly beneath the shoulders, feet hip-width apart and the body aligned from head to heels. The timer continued until volitional fatigue or a third form error, with no artificial maximum time imposed, so exceptionally long holds could be observed. Twelve research assistants completed a training workshop, repeated practice trials and supervised pilot sessions before data collection. The researchers did not directly measure test-retest or inter-rater reliability in this sample, although standardized training and clear termination rules were intended to reduce procedural variation.

Several limitations prevent the references from being treated as national or international standards. The study covered only South Punjab, school-level clustering was not modelled in the statistical analyses, and the bootstrap intervals were described as lower bounds on the true sampling uncertainty. Biological maturation was not measured, and whole-year age recording limited the resolution of the reference curves. The plank test evaluates isometric endurance of the anterior core, not lateral or posterior muscular endurance or dynamic core stability, while absolute hold times were not adjusted for body mass or body composition. Direct reliability testing was also absent. The cross-sectional design precludes causal conclusions and observations of individual trajectories. Validation in independently collected samples, replication in other regions of Pakistan, finer age measurement, maturity assessment, body-composition-adjusted references and longitudinal research are needed before these South Punjab benchmarks can be applied more broadly or linked to health outcomes.

Subject of Research: Age- and sex-specific plank-test reference values for core muscular endurance among South Punjab adolescents aged 12–16

Article Title: Age- and sex-specific normative references and performance zones for core muscular endurance in adolescents aged 12–16 years: a cross-sectional study

Article References: Yanlan, G., Liu, Y., Hadier, S. G., Hamdani, S. D., Wu, J., & Hamdani, S. M. Z. H. (2026). Age- and sex-specific normative references and performance zones for core muscular endurance in adolescents aged 12–16 years: a cross-sectional study. Frontiers in Physiology, 17, Article 1802720. https://doi.org/10.3389/fphys.2026.1802720

Image Credits: AI Generated

DOI: 10.3389/fphys.2026.1802720

Keywords: core muscular endurance, plank test, Pakistani adolescents, normative references, percentile curves, performance zones, South Punjab, school fitness, LMS method, cross-sectional study, Age-, sex-specific

The motivation behind the work lies in a diagnostic gap that has frustrated clinicians for years. Sarcopenic obesity, the coexistence of excess fat and declining muscle, is widely recognized as more dangerous than either condition alone, with documented links to falls, fractures, cardiometabolic disease, worse cancer outcomes and all-cause mortality. Yet estimates of how common it is range wildly, from 3 percent to more than 20 percent of older adults, depending entirely on which definition and cutoffs researchers choose. Most approaches simply define obesity and sarcopenia independently, for example by combining a body mass index of 30 or above with low appendicular muscle mass, even though the two conditions interact pathophysiologically in ways that independent thresholds cannot capture.

The FM/FFM ratio was first proposed as a measure of sarcopenic obesity by nutrition scientist Carla Prado and colleagues in 2012, and it rests on an elegant conceptual framework known as the load-capacity model. Fat mass represents metabolic load, the magnitude of the insult placed on the body, encompassing inflammation, lipid accumulation and heightened metabolic demands. Fat-free mass represents metabolic capacity, the machinery available to offset that insult through glucose regulation, lipid metabolism and general metabolic function. A higher ratio therefore signals a state in which load disproportionately exceeds capacity, which is precisely the imbalance at the heart of sarcopenic obesity. Because the ratio is continuous, it scales fat mass to the amount of metabolically active tissue, meaning two people with identical fat mass can carry very different metabolic risk depending on how much muscle supports them.

To build the reference curves, the research team drew on the comprehensive cohort of the Canadian Longitudinal Study on Aging, a population-based study of 51,338 adults across all ten provinces. Participants in the comprehensive cohort underwent in-person physical assessments, including whole-body dual X-ray absorptiometry, the gold-standard technique that quantifies tissue composition by measuring how low-emission X-ray beams attenuate as they pass through fat, lean tissue and bone. The analytic sample of 28,791 participants had a weighted mean age of 59.6 years and was weighted to represent roughly 3.6 million Canadian adults. Total lean mass served as a surrogate for fat-free mass, an approximation the authors justify because cellular lipid content accounts for only a small fraction of total body weight, at most 3 percent in men and 5 percent in women.

The statistical engine behind the charts is the LMS method, developed by Tim Cole and Pamela Green, which summarizes a measurement’s shifting distribution through three parameters: lambda for skewness, mu for the median and sigma for the coefficient of variation. At each single year of age, the team solved a system of twelve equations spanning the 1st through 99th percentiles, minimizing squared errors and selecting model flexibility by balancing curve smoothness against fit using the Bayesian information criterion. The output includes both percentiles and corresponding z-scores, giving clinicians the option of greater precision. Sensitivity analyses with unweighted data confirmed the results, and graphical checks of observed versus predicted percentiles and Q-Q plots of z-scores showed adequate fit and normality.

The findings reveal striking sex differences. Men’s FM/FFM values were consistently lower than women’s at every percentile examined, and men’s curves changed far less across the lifespan, never exceeding 0.75 at any age. Women’s values, by contrast, started higher and climbed more steeply, exceeding 1.1 between ages 55 and 65 before falling sharply in older age. At the 95th percentile, the maximum value reached 0.62 at age 70 for men but 0.99 at age 65 for women. At the median, the peak was 0.42 at age 86 for men versus 0.71 at age 68 for women. The overall pattern reflects the average aging trajectory: fat mass rises while muscle mass declines, pushing the ratio upward until very old age, when the trend reverses, potentially reflecting reductions in resting metabolic rate and macronutrient oxidation.

The authors attribute the sex gap to sexual dimorphism in body composition, a biological divergence that begins at birth and persists into adulthood, leaving adult men with greater total lean mass and lower fat mass than women. Sex hormones are central players: estrogen shapes adipose tissue accumulation, metabolism and distribution, while testosterone promotes lean mass. Hormonal upheaval may also explain why women’s percentiles shift more dramatically with age, since the menopausal transition triggers substantial changes in body composition, increasing fat mass while reducing fat-free mass. At the cellular level, differences in sex chromosomes are thought to drive sex-specific gene expression, and at the population level the phenomenon manifests as higher obesity prevalence among women.

The Canadian curves align closely with earlier reference charts built from United States and Chinese data. Using DXA measurements from 13,236 American adults in the 1999 to 2004 National Health and Nutrition Examination Survey, Siervo and colleagues reported a maximum median FM/FFM of 0.46 at age 74 for men and 0.76 at age 66 for women, figures remarkably similar to the Canadian values. Chinese adults from the Ningxia cohort, measured with bioelectrical impedance analysis, showed lower ratios overall, an intriguing difference given evidence that some Asian populations carry higher body fat and lower lean mass, possibly due to hormonal or genetic factors. The authors caution that differing measurement technologies, DXA versus bioimpedance, could partly explain the divergence.

The study carries notable strengths, including its large, nationally representative dataset and its use of DXA rather than surrogate measures. Importantly, the cohort spans midlife, ages 45 to 64, capturing the period when age-related body composition changes begin and when interventions might prevent sarcopenic obesity from taking hold in later life. Limitations remain: most participants identified as white, so racial and ethnic differences in FM/FFM require further study, and survivor bias cannot be fully excluded, though recruitment from age 45 mitigates the concern. The ratio also omits muscle function, such as gait speed or grip strength, which current sarcopenic obesity definitions consider essential; the authors suggest future work should explore integrating functional parameters into the percentile framework.

The practical promise of the charts is considerable. By folding age, sex, obesity and sarcopenia into one interpretable metric, FM/FFM percentiles could serve as a screening tool to flag patients who need intervention before measurable muscle loss occurs, and they give researchers a standardized way to study sarcopenic obesity-related health outcomes across populations. Future studies are expected to track how the percentile curves respond to specific interventions, from resistance training to emerging obesity therapies. For now, the work offers clinicians and public health planners something they have never had for Canadian older adults: a continuous, population-anchored yardstick for the balance between metabolic load and metabolic capacity as it evolves across the second half of life.

Subject of Research: Age- and sex-specific reference percentile curves for the fat mass to fat-free mass ratio as a measure of sarcopenic obesity in older Canadian adults

Article Title: Age‐ and Sex‐Specific Percentile Curves for the FM/FFM Ratio Using Data From the Canadian Longitudinal Study on Aging

Article References: Kim, C., Cook, C. E., & Banack, H. R. (2026). Age‐ and Sex‐Specific Percentile Curves for the FM/FFM Ratio Using Data From the Canadian Longitudinal Study on Aging. Journal of Cachexia, Sarcopenia and Muscle, 17(5), Article e70399. https://doi.org/10.1002/jcsm.70399

Image Credits: AI Generated

DOI: 10.1002/jcsm.70399

Keywords: sarcopenic obesity, fat mass to fat-free mass ratio, body composition, Canadian Longitudinal Study on Aging, DXA, aging, percentile curves, LMS method, metabolic load, metabolic capacity, sex differences, muscle mass

The technique at the center of the study, cardiovascular magnetic resonance, is widely regarded as the gold standard for measuring the heart’s structure and function. Unlike echocardiography, which relies on ultrasound beams that must be angled through the chest wall and can be distorted by poor acoustic windows, cardiac MRI captures the beating heart in full three dimensions. The researchers used a steady-state free precession sequence, often abbreviated SSFP, which produces bright blood pools against darker muscle tissue and allows the borders of the ventricles to be traced with high precision. From these tracings, the team could calculate end-diastolic volume, the amount of blood in each ventricle when it is fully relaxed; end-systolic volume, the amount remaining after contraction; stroke volume, the difference between the two; and ejection fraction, the fraction of blood expelled with each beat. Ventricular mass, essentially the weight of the heart muscle, can also be quantified directly from the images.

Establishing what is normal, however, requires more than sophisticated hardware. It requires a carefully screened cohort of healthy volunteers, and this is where the study makes its most distinctive contribution. The participants were enrolled at a single center in Lanzhou, in northwest China, with a median age of ten years and a range spanning from just three months to eighteen years. Recruiting very young children for MRI is a formidable logistical challenge, because the technique demands that subjects lie motionless inside a noisy magnet for several minutes at a time. Infants and toddlers typically require sedation or careful feeding-and-swaddling protocols, and ethical oversight is correspondingly strict. The study was conducted according to the principles of the Declaration of Helsinki, approved by the hospital’s institutional ethics committee, and written informed consent was obtained from participants or their legal guardians. All data were anonymized before analysis.

The analytical approach deserves particular attention because it shapes how the results will be used in clinics. Rather than reporting a single average value for each measurement, the researchers constructed age-related reference curves using the lambda mu sigma method, often called the LMS method. This statistical framework, originally developed for pediatric growth charts, models three parameters simultaneously: the median of the measurement at each age, the spread around that median, and the skewness of the distribution. The result is a smooth set of percentile curves, analogous to the height and weight charts pediatricians pin to their office walls, against which any individual child’s ventricular volume, mass, or wall thickness can be plotted. Because children’s bodies grow in bursts and at different rates in different tissues, such continuous curves capture physiological change far more faithfully than coarse age brackets alone.

The findings reveal a striking pattern of sex differences that emerges and disappears depending on the parameter and the age group examined. Across the entire cohort, boys showed significantly larger values than girls for the left ventricular long diameter, the anteroposterior diameter of the right atrium, the thickness of the left ventricular free wall, left ventricular mass, right ventricular end-diastolic volume, right ventricular stroke volume, and the indexed right ventricular end-systolic volume. When the researchers restricted their comparison to children aged six to twelve, the last four of these differences persisted, suggesting that the divergence in right ventricular size and pumping output becomes apparent during the early school years. Yet the picture is not uniform: in the youngest group, from birth to six years, and in the oldest group, from twelve to eighteen, biventricular functional reference values showed no statistically significant sex differences at all.

Perhaps the most clinically reassuring result concerns ejection fraction, the single most quoted number in cardiac imaging. For both the left and right ventricles, the age groups were statistically comparable, meaning that the heart’s pumping efficiency remains remarkably stable throughout childhood even as chamber volumes and muscle mass expand several-fold. This stability matters because it gives clinicians a relatively fixed target: a child whose ejection fraction drifts below the expected percentile band is signaling trouble regardless of age. By contrast, absolute volumes and masses must always be interpreted against the age- and sex-specific curves, since a right ventricular volume that is perfectly normal for a sixteen-year-old boy would be alarming in a toddler. The study’s stratified reporting, which presents values across defined age bands as well as continuous percentiles, is designed to support both styles of interpretation.

The right ventricle, in particular, stands to benefit from this new dataset. It is the ventricle most often implicated in congenital heart disease, from tetralogy of Fallot to pulmonary valve disorders, and it is also the chamber whose function is hardest to assess with ultrasound because of its crescent shape and position directly behind the sternum. Conditions such as arrhythmogenic right ventricular cardiomyopathy, a rare but serious disorder in which heart muscle is progressively replaced by fat and fibrous tissue, are diagnosed in part by detecting subtle enlargement of the right ventricle. Without pediatric reference values, distinguishing early disease from normal variation has been one of the most difficult judgment calls in pediatric cardiology. Percentile curves derived from healthy children directly address this gap, giving follow-up protocols a quantitative anchor for deciding when a dilated ventricle truly warrants intervention.

The study also fills a population-specific need. Reference values for cardiac dimensions are known to vary with body size, body composition, ethnicity, and environmental factors, and most existing pediatric MRI norms have been derived from North American or European cohorts. Chinese children have historically been underrepresented in such databases, leaving clinicians in China and in Chinese diaspora communities to apply standards that may not perfectly fit. By enrolling a healthy Chinese pediatric population and reporting both raw and indexed measurements, the Lanzhou team has created a resource that can be cross-referenced against international norms, potentially illuminating how cardiac growth differs across populations and refining the global picture of pediatric cardiovascular development.

Several limitations are worth keeping in view. The cohort was drawn from a single center, which ensures consistent imaging protocols but means the curves may need validation in multicenter samples before being adopted universally. The published version is also an early-release, peer-reviewed accepted manuscript that will be replaced by a final version of record, so some details may be refined. Nevertheless, the core contribution is unlikely to change: a systematically acquired, openly accessible set of biventricular MRI reference values spanning the entire pediatric age range. Because the article is published open access under a Creative Commons license, clinicians and researchers anywhere can consult the full percentile tables without subscription barriers.

The broader significance of the work lies in how it converts a fundamental gap in medical knowledge into a practical tool. Growth charts for height and weight transformed pediatrics in the twentieth century by making deviation from normal visible at a glance. Reference curves for the child’s heart, built from magnetic resonance images of both ventricles, promise a similar transformation for pediatric cardiology in the years ahead. For the family of a child undergoing follow-up after congenital heart surgery, or for the teenager with an incidental finding on a sports screening, the difference between anxiety and reassurance may now rest on a curve drawn from hundreds of healthy hearts, each one imaged, measured, and folded into a map of what a growing heart should look like.

Subject of Research: Cardiac MRI reference values for biventricular structure and function in healthy children aged 0 to 18 years

Article Title: Cardiac magnetic resonance imaging reference values of biventricular structural and functional parameters for children aged 0–18 years

Article References: Li, S., Zhang, X., Wang, J., Tao, J., Guo, R., Zhang, H., Ma, Y., Yue, S., Liu, G., Wang, J., & Zhang, J. (2026). Cardiac magnetic resonance imaging reference values of biventricular structural and functional parameters for children aged 0–18 years. BMC Pediatrics. https://doi.org/10.1186/s12887-026-07718-1

Image Credits: AI Generated

DOI: 10.1186/s12887-026-07718-1

Keywords: cardiac MRI, pediatrics, reference values, biventricular function, SSFP sequence, ventricular volumes, ejection fraction, LMS method, Chinese children, pediatric cardiology, growth charts, congenital heart disease