Causation or selection – examining the relation between education and overweight/obesity in prospective observational studies: a meta‐analysis

Kim, T. J.; Roesler, N. M.; Knesebeck, Olaf von dem

doi:10.1111/obr.12537

Cited by 31 publications

(38 citation statements)

References 85 publications

(83 reference statements)

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“…Several potential explanations can be offered for the low predictive value of the regression model, but are mainly related to the fact that obesity is characterized for being a multifactorial disease. Although we have included in the model the two main factors that characterized obesity, energy intake and physical activity, there are other features that have not been taken into account such as social determinants (education level, economic status), endocrine disorders (hypothyroidism) or use of certain medications 4 , 7 , 60 , 61 . Another explanation for the low predictive value of the regression model could be related with the marginal effect sizes of the tested variants and the skewed distribution of the effect sizes.…”

Section: Discussionmentioning

confidence: 99%

“…An increase in the global burden of overweight and obesity will translate into an increase of the risk of several other health conditions, including type 2 diabetes, cardiovascular disease or certain types of cancer 1 . Although obesity is generally attributed to an imbalance between the energy consumed and the energy expenditure, it is also accepted that it is a complex and a multifactorial disease resulting from genetic, physiological, behavioral, sociocultural and environmental factors 3 – 7 .…”

Section: Introductionmentioning

confidence: 99%

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Phenotype and genotype predictors of BMI variability among European adults

et al. 2018

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Background/ObjectiveObesity is a complex and multifactorial disease resulting from the interactions among genetics, metabolic, behavioral, sociocultural and environmental factors. In this sense, the aim of the present study was to identify phenotype and genotype variables that could be relevant determinants of body mass index (BMI) variability.Subjects/MethodsIn the present study, a total of 1050 subjects (798 females; 76%) were included. Least angle regression (LARS) analysis was used as regression model selection technique, where the dependent variable was BMI and the independent variables were age, sex, energy intake, physical activity level, and 16 polymorphisms previously related to obesity and lipid metabolism.ResultsThe LARS analysis obtained the following formula for BMI explanation: (64.7 + 0.10 × age [years] + 0.42 × gender [0, men; 1, women] + −40.6 × physical activity [physical activity level] + 0.004 × energy intake [kcal] + 0.74 × rs9939609 [0 or 1–2 risk alleles] + −0.72 × rs1800206 [0 or 1–2 risk alleles] + −0.86 × rs1801282 [0 or 1–2 risk alleles] + 0.87 × rs429358 [0 or 1–2 risk alleles]. The multivariable regression model accounted for 21% of the phenotypic variance in BMI. The regression model was internally validated by the bootstrap method (r2 original data set = 0.208, mean r2 bootstrap data sets = 0.210).ConclusionIn conclusion, age, physical activity, energy intake and polymorphisms in FTO, APOE, PPARG and PPARA genes are significant predictors of the BMI trait.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Phenotype and genotype predictors of BMI variability among European adults

et al. 2018

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“…Abschließend wurden Analysen zur Kriteriumsvalidität durchgeführt und exemplarische Untersuchungen hin-sichtlich des Zusammenhangs soziodemografischer Merkmale mit der Häufigkeit von Untergewicht, Adipositas und selbst berichteter Gesundheit angestellt. Wir fanden ausgeprägte Altersund Bildungsgradienten und konnten somit bereits bekannte Gesundheitszusammenhänge mit den Daten der NAKO replizieren [24][25][26]. Dies spricht für eine inhaltliche Validität der in der NAKO erhobenen soziodemografischen und erwerbsbezogenen Instrumente.…”

Section: Diskussionunclassified

“…Abschließend sollen die eingesetzten Instrumente anhand einer exemplarischen Reproduktion bekannter Zusammenhänge mit dem Body-Mass-Index (BMI) und selbst berichteter Gesundheit validiert werden. Diese Zielgrößen wurden gewählt, weil soziodemografische Unterschiede bei Untergewicht und Adipositas [24,25] sowie schlechter selbst berichteter Gesundheit [26] in der Forschung gut belegt sind.…”

Section: Introductionunclassified

Soziodemografische und erwerbsbezogene Merkmale in der NAKO Gesundheitsstudie

Dragano

Reuter

Greiser

et al. 2020

Bundesgesundheitsbl

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Zusammenfassung Hintergrund In epidemiologischen Studien ist die standardisierte Erfassung soziodemografischer Merkmale von hoher Bedeutung, da Variablen wie Geschlecht, Alter, Bildung oder Erwerbsstatus wichtige Einflussfaktoren auf Gesundheitschancen und Krankheitsrisiken darstellen. In der NAKO Gesundheitsstudie werden zentrale Faktoren aus diesem Themenbereich berücksichtigt. Ziel der Arbeit Der Beitrag gibt einen Überblick über den wissenschaftlichen Hintergrund und die konkrete Erhebung soziodemografischer Angaben in der NAKO. Zudem werden die Verteilung einzelner Merkmale sowie Zusammenhänge mit gesundheitsassoziierten Maßen exemplarisch vorgestellt. Material und Methoden Anhand der Daten zur Halbzeit der Basiserhebung (n = 101.724) wurde die Verteilung soziodemografischer Merkmale dargestellt und Zusammenhänge mit beispielhaft ausgewählten Gesundheitsindikatoren (Body-Mass-Index, selbst berichtete Gesundheit) analysiert, um die Validität der Messung soziodemografischer Angaben zu beurteilen. Ergebnisse Das mittlere Alter der Teilnehmenden lag bei 52,0 Jahren (SD = 12,4). 53,6 % der Teilnehmenden waren Frauen, 54,3 % hatten einen hohen Bildungsabschluss, 60,1 % waren verheiratet zusammenlebend, 72,0 % erwerbstätig und 3,4 % erwerbslos. Bekannte Zusammenhänge zwischen Soziodemografie und Gesundheit konnten reproduziert werden. So waren niedrige Bildung, hohes Alter und Erwerbslosigkeit mit einer erhöhten Häufigkeit von Adipositas und schlechter selbst berichteter Gesundheit assoziiert. Diskussion Die NAKO Gesundheitsstudie erhebt viele soziodemografische Merkmale. In Kombination mit der Fülle an Gesundheitsdaten und dem Längsschnittdesign ergeben sich so neue Möglichkeiten für die gesundheitswissenschaftliche und sozialepidemiologische Forschung in Deutschland.

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“…This work builds on a former review that examined the relative importance of causation and reverse causality in the association between education and overweight/obesity. 25 Though education and income can be conceptualised under the broader term of the SES, specific dimensions of SES should not be regarded as interchangeable in their relation to obesity. 26 First, income rather influences material benefits for health, while education foremost relates to knowledge to gain or retain health.…”

Section: Introductionmentioning

confidence: 99%

Income and obesity: what is the direction of the relationship? A systematic review and meta-analysis

2018

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ObjectiveIt was repeatedly shown that lower income is associated with higher risks for subsequent obesity. However, the perspective of a potential reverse causality is often neglected, in which obesity is considered a cause for lower income, when obese people drift into lower-income jobs due to labour–market discrimination and public stigmatisation. This review was performed to explore the direction of the relation between income and obesity by specifically assessing the importance of social causation and reverse causality.DesignSystematic review and meta-analysis.MethodsA systematic literature search was conducted in January 2017. The databases Medline, PsycINFO, Sociological Abstracts, International Bibliography of Social Sciences and Sociological Index were screened to identify prospective cohort studies with quantitative data on the relation between income and obesity. Meta-analytic methods were applied using random-effect models, and the quality of studies assessed with the Newcastle-Ottawa Scale.ResultsIn total, 21 studies were eligible for meta-analysis. All included studies originated from either the USA (n=16), the UK (n=3) or Canada (n=2). From these, 14 studies on causation and 7 studies on reverse causality were found. Meta-analyses revealed that lower income is associated with subsequent obesity (OR 1.27, 95% CI 1.10 to 1.47; risk ratio 1.52, 95% CI 1.08 to 2.13), though the statistical significance vanished once adjusted for publication bias. Studies on reverse causality indicated a more consistent relation between obesity and subsequent income, even after taking publication bias into account (standardised mean difference −0.15, 95% CI −0.30 to 0.01). Sensitivity analyses implied that the association is influenced by obesity measurement, gender, length of observation and study quality.ConclusionsFindings suggest that there is more consistent evidence for reverse causality. Therefore, there is a need to examine reverse causality processes in more detail to understand the relation between income and obesity.PROSPERO registration number42016041296.

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Causation or selection – examining the relation between education and overweight/obesity in prospective observational studies: a meta‐analysis

Cited by 31 publications

References 85 publications

Phenotype and genotype predictors of BMI variability among European adults

Phenotype and genotype predictors of BMI variability among European adults

Soziodemografische und erwerbsbezogene Merkmale in der NAKO Gesundheitsstudie

Income and obesity: what is the direction of the relationship? A systematic review and meta-analysis

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