Time trends in body height according to educational level. A descriptive study from the Tromsø Study 1979–2016

Arntsen, Sondre Haakonson; Wilsgaard, Tom; Njølstad, Inger; Hansen, Anne

doi:10.1371/journal.pone.0279965

Cited by 9 publications

(2 citation statements)

References 35 publications

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“…As a result, average somatic parameter values increased across generations (Baten & Blum, 2012;Bogin, 2021;Hatton, 2014;Hatton & Bray, 2010;Steckel, 2012). Similarly, recent studies have shown a comparable secular trend and intergenerational dynamics of height associated with changes in the socioeconomic and political context in Austria (Kirchengast et al, 2022), Norway (Arntsen et al, 2023), andSpain (S anchez-García et al, 2023).…”

Section: T a B L E 3 Correlations Between Anthropometric And Socioeco...mentioning

confidence: 69%

Secular trends in height, weight, and body mass index in the context of economic and political transformations in Russia from 1885 to 2021

Negasheva,

Khafizova,

Movsesian

2023

American J Hum Biol

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ObjectivesTo study and analyze the impact of socio‐economic factors on secular changes in height, weight, and body mass index (BMI) among Moscow's youth over the time interval from the late 19th–early 20th century to the present.MethodsAnthropometric data, including height, weight, and BMI, were collected through surveys conducted on youths aged 17–20 years in Moscow from the 1880s for males and from the 1920s for females to the present. The dataset includes information on 6434 individuals surveyed from 2000 to 2019, as well as previously published mean values. Economic development indicators, such as gross domestic product (GDP) per capita, monthly average income per capita, and the Gini coefficient, were examined to analyze the association between secular trends in body size and socio‐economic conditions.ResultsA positive secular trend in height and weight has been observed among Moscow's youth from the early 20th century to the present. Substantial increases in height occurred during the second half of the previous century, stabilizing in the 2000s. Over the analyzed period, both average body weight and BMI values showed a consistent rise. The pattern for BMI exhibited a U‐shaped trend, with a decline from the 1970s to the mid‐1990s, followed by a subsequent increase. Strong correlations were found between the secular changes in body size among Moscow's youth and temporal fluctuations in key socio‐economic indicators, including GDP per capita, monthly average income per capita, and the Gini coefficient.ConclusionThe study demonstrates the significant influence of socio‐economic conditions on intergenerational changes in body size, as evidenced by the positive secular trend in physique indicators (height, weight, and BMI) among Moscow's youth.

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Section: T a B L E 3 Correlations Between Anthropometric And Socioeco...mentioning

confidence: 69%

Secular trends in height, weight, and body mass index in the context of economic and political transformations in Russia from 1885 to 2021

Negasheva,

Khafizova,

Movsesian

2023

American J Hum Biol

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“…The models were built only with age and the 88 CSLV parameters. We included age because there are well known age cohort effects on height[35,36]. Older people grew up with different diet and nutrition than younger people and are shorter on average.…”

Section: Methodsmentioning

confidence: 99%

A compact encoding of the genome suitable for machine learning prediction of traits and genetic risk scores

Fatapour,

Brody

2024

Preprint

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Genotype to phenotype prediction is a central problem in biology and medicine. Machine learning is a natural tool to address this problem. However, a person’s genotype is usually represented by a few million single-nucleotide polymorphisms and most datasets only have a few thousand patients. Thus, this problem typically has many more predictors than the number of samples (patients), making it unsuitable for machine learning.The objective of this paper is to examine the efficacy of a compact genotype representation, which employs a limited number of predictors, in predicting a person’s phenotype through the application of machine learning.We characterized a person’s genotype using chromosome-scale length variation, a measure that is computed as the average value of reported log R ratios across a portion of a chromosome. We computed these numbers from data collected by the NIH All of Us program. We used the AutoML function (h2o.ai) in binary classification mode to identify the best models to differentiate between male/female, Black/white, white/Asian, and Black/Asian. We also used the AutoML function in regression mode to predict the height of people based on their age and genotype.Our results showed that we could effectively classify a person, using only information from chromosomes 1-22, as Male/Female (AUC=0.9988±0.0001), White/Black (AUC=0.970± 0.002), Asian/White (AUC=0.877± 0.002), and Black/Asian (AUC=0.966± 0.002). This approach also effectively predicted height.In conclusion, we have shown that this compact representation of a person’s genotype, along with machine learning, can effectively predict a person’s phenotype.

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A model of errors in BMI based on self-reported and measured anthropometrics with evidence from Brazilian data

Davillas,

de Oliveira,

Jones

2024

Empir Econ

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The economics of obesity literature implicitly assumes that measured anthropometrics are errorfree and they are often treated as a gold standard when compared to self-reported data. We use factor mixture models to analyse and characterize measurement error in both self-reported and measured anthropometrics with national representative data from the 2013 National Health Survey in Brazil. Indeed, a small but statistically significant fraction of measured anthropometrics are attributed to data-recording errors. The estimated mean body weight (height) for those cases that are subject to error is 10% higher (2.9% lower) than the estimated mean of latent true body weight (height). As they are imprecisely measured and due to individual's reporting behaviour, only between 10% and 24% of our self-reported anthropometrics are free from any measurement error. Postestimation analysis allows us to calculate hybrid anthropometric predictions that best approximate the true body weight and height distribution. BMI distributions based on the hybrid measures are close to those based on measured data, while BMI based on self-reported data under-estimates the true BMI distribution. Analysis of regression models for health care utilization shows little differences between the relationship with BMI when it is based on measured data or on our hybrid BMI measure, however some differences are observed when both are compared to BMI based on self-reported data.

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Time trends in body height according to educational level. A descriptive study from the Tromsø Study 1979–2016

Cited by 9 publications

References 35 publications

Secular trends in height, weight, and body mass index in the context of economic and political transformations in Russia from 1885 to 2021

Secular trends in height, weight, and body mass index in the context of economic and political transformations in Russia from 1885 to 2021

A compact encoding of the genome suitable for machine learning prediction of traits and genetic risk scores

A model of errors in BMI based on self-reported and measured anthropometrics with evidence from Brazilian data

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