BACKGROUND: The variation in body mass index (BMI) of inpatients with anorexia nervosa has not been analyzed across the age span. A positive correlation between BMI and age has been reported in adolescent inpatients aged 15 years and younger that levels off at 15 to 18 years. BMIs standardized for age and sex (standard deviation scores, SDSs) were negatively correlated with age in these inpatients aged 8 to 18 years.
METHODS: The aims of the current retrospective study were threefold: first, to confirm the relationships of BMI, BMI-SDS and age in adolescent inpatients in a larger sample; second, to systematically assess the relationship of BMI, BMI-SDS, body height-SDS and age in adult inpatients at the time of referral; and third, to assess body height-SDSs and age to evaluate stunting.
RESULTS: We included 1001 girls (aged 12-17.9 years) and 1371 women (aged 18-73 years) admitted to inpatient treatment between 2014 and 2021. Mean BMI at admission was 14.95 kg/m2 (SD = 1.43; range 10.67-18.47) in adolescents and 14.63 kg/m2 (SD = 2.02; range 8.28-18.47) in adults. None of the adolescent patients but 20 adults had very low BMI values below 10 kg/m2. Adolescents showed a small but significant positive correlation between age and BMI (r = 0.12; p = 2.4 × 10-4). In adults, BMI was not correlated with age (r = -0.03; p = 0.3). BMI-SDSs was negatively correlated with age in adolescents and less so in adults (r = -0.35; p < 0.001 and r = -0.09; p = 0.001). Curve fit analyses for all patients indicated that there was a quadratic (age × age) relationship between age and BMI-SDS. Height correlated positively with BMI in adult (r = 0.1; p < 0.001) and adolescent (r = 0.09 p = 0.005) patients and we detected no evidence for stunting.
CONCLUSIONS: In conclusion, the BMI of inpatients seems to be relatively stable across the age span with mean values between 14 and 15 kg/m2. BMI values initially increase with age in younger patients, drop between ages 18 and 23 and then slowly decline with age.

This study aimed to evaluate trends in body mass index (BMI)-for-age z-scores among children and adolescents in Xinjiang.
Data were obtained for children and adolescents aged 7-18 years in Xinjiang from the Chinese National Survey on Students\' Constitution and Health (CNSSCH) in 1985 (n = 14 683), 1995 (n = 7198), 2005 (n = 10 253) and 2014 (n = 18 521).
The BMI-for-age z-score distribution of children and adolescents in Xinjiang showed an increased mean BMI-for-age z-score, dispersion and right-skewed of BMI-values, with a rapid increase in BMI with increasing BMI percentiles. The sex-based disparity in BMI-for-age z-scores became wider in the past 30 years.
Sex-based targeted public health measures and policies are urgently needed in Xinjiang. The rapid increases in the upper percentiles also implicated further efforts to prevent weight gain in those living already with overweight or obesity.

BACKGROUND: Because people care about their weight relative to peers and society, obesity inequality plays a role in explaining obesity incidence and the impacts of being obese on subjective well-being. While the increase in obesity prevalence and mean body mass index (BMI) is well documented, the measurement of distributional changes and corresponding obesity inequality is yet to be fully explored.
METHODS: The present study analyzed BMI data for adults aged 20 to 74 from the National Health and Nutritional Examination Survey (NHANES) I (1971-1974), II (1976-1980), III (1988-1994), and continuous NHANES (1999-2014). We applied tools developed to measure income inequality to analyze the inter-temporal variation in the BMI distribution among US adults. Using stochastic dominance tests, we construct partial orderings on cumulative BMI distributions during the study period. Shapley decompositions and inequality indices are employed to quantify the source and extent of temporal variation and decompose the inequality into within and between-group components considering age, gender, and race.
RESULTS: The BMI distribution of each NHANES study first-order stochastically dominated the BMI distribution of the previous wave from 1971-1974 to 2003-2006, whereas more recent comparisons failed to reject the null hypothesis of non-dominance. The Shapley decomposition analysis revealed that horizontal shifts of BMI distributions accounted for a majority of the increase in obesity prevalence since 1988-1991. Especially in recent years when the rate of obesity growth has slowed down, the contribution of the redistribution component dropped significantly and even became negative between 2007-2010 and 2011-2014. The inequality indexes consistently show a worsening of obesity inequality from the mid-1970s to the mid-2000s regardless of population subgroups, and this disproportionate shift of the BMI distribution is unlikely to be a result of a changing ethnic composition of the US population.
CONCLUSIONS: Our findings demonstrate that seemingly similar increases in obesity prevalence can be accompanied by very different patterns of distribution change. We find that the early phase of the obesity epidemic in the US was largely driven by increasing skewness, whereas more recent growth is a population-wide experience, regardless of demographic characteristics. Increasing morbid obesity certainly played an important role in the initial phase of the epidemic, but more recently the BMI distribution has largely horizontally shifted to the right.

BACKGROUND: Understanding trends in the distribution of body mass index (BMI) is a critical aspect of monitoring the global overweight and obesity epidemic. Conventional population health metrics often only focus on estimating and reporting the mean BMI and the prevalence of overweight and obesity, which do not fully characterize the distribution of BMI. In this study, we propose a novel method which allows for the estimation of the entire distribution.
METHODS: The proposed method utilizes the optimization algorithm, L-BFGS-B, to derive the distribution of BMI from three commonly available population health statistics: mean BMI, prevalence of overweight, and prevalence of obesity. We conducted a series of simulations to examine the properties, accuracy, and robustness of the method. We then illustrated the practical application of the method by applying it to the 2011-2012 US National Health and Nutrition Examination Survey (NHANES).
RESULTS: Our method performed satisfactorily across various simulation scenarios yielding empirical (estimated) distributions which aligned closely with the true distributions. Application of the method to the NHANES data also showed a high level of consistency between the empirical and true distributions. In situations where there were considerable outliers, the method was less satisfactory at capturing the extreme values. Nevertheless, it remained accurate at estimating the central tendency and quintiles.
CONCLUSIONS: The proposed method offers a tool that can efficiently estimate the entire distribution of BMI. The ability to track the distributions of BMI will improve our capacity to capture changes in the severity of overweight and obesity and enable us to better monitor the epidemic.