Abstract
Background
In longitudinal data, it is common to create ‘change scores’ by subtracting measurements taken at baseline from those taken at follow-up, and then to analyse the resulting ...‘change’ as the outcome variable. In observational data, this approach can produce misleading causal-effect estimates. The present article uses directed acyclic graphs (DAGs) and simple simulations to provide an accessible explanation for why change scores do not estimate causal effects in observational data.
Methods
Data were simulated to match three general scenarios in which the outcome variable at baseline was a (i) ‘competing exposure’ (i.e. a cause of the outcome that is neither caused by nor causes the exposure), (ii) confounder or (iii) mediator for the total causal effect of the exposure variable at baseline on the outcome variable at follow-up. Regression coefficients were compared between change-score analyses and the appropriate estimator(s) for the total and/or direct causal effect(s).
Results
Change-score analyses do not provide meaningful causal-effect estimates unless the baseline outcome variable is a ‘competing exposure’ for the effect of the exposure on the outcome at follow-up. Where the baseline outcome is a confounder or mediator, change-score analyses evaluate obscure estimands, which may diverge substantially in magnitude and direction from the total and direct causal effects.
Conclusion
Future observational studies that seek causal-effect estimates should avoid analysing change scores and adopt alternative analytical strategies.
Abstract
Objective
Nutrition plays a role in the development of Crohn’s disease CD and ulcerative colitis UC. However, prospective data on nutrition and disease onset are limited. Here, we analysed ...dietary patterns and scores in relation to inflammatory bowel disease IBD development in a prospective population-based cohort.
Methods
We analysed 125 445 participants of whom 224 individuals developed de novo UC and 97 CD over a maximum 14-year follow-up period. Participants answered health-related also prospectively and dietary questionnaires FFQ at baseline. Principal component analysis PCA was conducted deriving a-posteriori dietary patterns. Hypotheses-based a-priori dietary scores were also calculated, including the protein score, Healthy Eating Index, LifeLines Diet Score LLDS, and alternative Mediterranean Diet Score. Logistic regression models were performed between dietary patterns, scores, and IBD development.
Results
PCA identified five dietary patterns. A pattern characterised by high intake of snacks, prepared meals, non-alcoholic beverages, and sauces along with low vegetables and fruit consumption was associated with higher likelihood of CD development (odds ratio OR: 1.16, 95% confidence interval CI: 1.03-1.30, p = 0.013). A pattern comprising red meat, poultry, and processed meat, was associated with increased likelihood of UC development OR: 1.11, 95% CI: 1.01-1.20, p = 0.023. A high diet quality score LLDS was associated with decreased risk of CD OR: 0.95, 95% CI: 0.92-0.99, p = 0.009.
Conclusions
A Western dietary pattern was associated with a greater likelihood of CD development and a carnivorous pattern with UC development, whereas a relatively high diet quality LLDS was protective for CD development. Our study strengthens the importance of evaluating dietary patterns to aid prevention of IBD in the general population.
Researchers interested in studying change over time are often faced with an analytical conundrum: Whether a residualized change model versus a difference score model should be used to assess the ...effect of a key predictor on change that took place between two occasions. In this article, the authors pose a motivating example in which a researcher wants to investigate the effect of cohabitation on pre- to post-marriage change in relationship satisfaction. Key features of this example include the likely self-selection of dyads with lower relationship satisfaction to cohabit and the impossibility of using experimentation procedures to attain equivalent groups (i.e., cohabitants vs. not cohabitants). The authors use this example of a nonrandomized study to compare the residualized change and difference score models analytically and empirically. The authors describe the assumptions of the models to explain Lord’s paradox; that is, the fact that these models can lead to different inferences about the effect under investigation. They also provide recommendations for modeling data from nonrandomized studies using a latent change score framework.
Background: FTO and MC4R single nucleotide polymorphisms (SNPs) have been associated with obesity and hypothesized to influence BMI in part through appetite. This analysis aimed to enhance ...understanding of the influence of these risk alleles on BMI and appetitive traits in children. Methods: DNA from 248 unrelated children aged 9-12 years from two studies was genotyped, leaving 236 children after quality control. SNPs evaluated were FTO rs1421085 C/T and MC4R rs571312 A/C, with exposures defined by the number of obesity risk alleles (additive), ≤1 risk allele (dominant), or 2 risk alleles (recessive). The parent-completed Child Eating Behavior Questionnaire measured seven appetitive traits. A composite obesogenic appetite score summarized these traits as the average of food approach and reversed food avoidance traits, log-transformed. BMI was calculated based on height and weight and standardized by age- and sex-specific distributions. Linear regression evaluated the association between each SNP and the outcomes of BMI z-score and obesogenic appetite score, adjusting for age, sex, parent education, study, and ancestry. Results: In the recessive FTO model, having 2 obesity risk alleles (N = 39) was associated with a 0.44 standard deviation higher BMI than having <1 risk allele (N = 197) (95% CI: 0.12, 0.76; p = 0.007) after covariate adjustment, though this was not replicated in additive or dominant models. The recessive FTO risk genotype was also associated with an adjusted 6.9% increase in the composite obesogenic appetite score (95% CI: 2.8%, 11.2%, p = 0.001), with a similar finding in the additive model. MC4R genotype was not significantly associated with BMI or with the obesogenic appetite score. Conclusions: FTO was associated with BMI z-score and the obesogenic appetite score in children, supporting appetitive traits as possible mediators between genetic risk factors and pediatric obesity. Further research is warranted to determine if obesogenic appetitive traits precede the phenotype of pediatric obesity.
Citation scores (CS) have been traditionally used to measure the impact of scientific publications. Sourced from the Internet, Altmetric Attention Scores (AAS) are complementary metrics that assess ...how often publications are discussed and used globally. We compared by rank the top 500 papers by CS and AAS published in Clinical Nutrition with corresponding AAS and CS.
A search for all publications in Clinical Nutrition was performed on Dimensions (https://app.dimensions.ai/discover/publication) on 3rd April 2024. Outputs were ranked according to CS and then by AAS with the top 500 in each category selected. Scores, year and type of publication were recorded. Correlation was expressed as the Spearman's rank coefficient (ϱ).
We identified 18,790 outputs. Within the top 500 publications ranked by CS, there was a significant weak positive correlation (ϱ = 0.235, P < 0.0001) between CS median (IQR) 149 (116–223) and AAS 7 (3–22). Ranked by AAS, there was a non-significant very weak positive correlation (ϱ = 0.072, P = 0.106) between AAS 55.5 (36–115) and CS 42 (16.5–94.5). Trends remained similar when grouped by publication type. Guidelines, ranked by CS, had the highest CS and ranked by AAS, the highest CS and AAS. Publications per year, by year, ranked by CS, had a negatively skewed distribution peaking in 2012 and declined thereafter, but when ranked by AAS, had a moderately positive linear trend from 2001 to 2024 (ϱ = 0.513, P < 0.0001).
Correlation between CS and AAS was weak. Guidelines had the highest CS and AAS. CS are iterative taking years to mature while AAS are immediate.
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•NPS predicts all-cause and cancer mortality in NAFLD patients, similar to FIB-4 and NFS.•Combining NPS with FIB-4 or NFS improves mortality prediction.•Biological aging partially ...mediates the link between NPS and mortality.•NPS is a cost-effective and reliable prognostic tool for NAFLD.
This study aimed to evaluate the prognostic value of the Naples Prognostic Score (NPS) for predicting mortality in patients with nonalcoholic fatty liver disease (NAFLD) and compare its performance with established non-invasive fibrosis scores, including the fibrosis-4 index (FIB-4) and NAFLD fibrosis score (NFS).
Data from 10,035 NAFLD patients identified within the 1999–2018 National Health and Nutrition Examination Survey (NHANES) were analyzed. Cox regression models assessed the association between NPS and all-cause mortality, while time-dependent ROC analysis compared its predictive accuracy with FIB-4 and NFS. Mediation analysis explored the role of phenotypic age acceleration (PhenoAgeAccel).
NPS was significantly associated with all-cause mortality, with each point increase corresponding to a 26 % increased risk (HR = 1.26, 95 % CI: 1.19–1.34). NPS demonstrated comparable predictive performance to FIB-4 and NFS, with further improvement when combined with either score (HRs of 2.03 and 2.11 for NPS + FIB-4 and NPS + NFS, respectively). PhenoAgeAccel mediated 31.5 % of the effect of NPS on mortality.
This study found that NPS has the potential to be an independent, cost-effective, and reliable novel prognostic indicator for NAFLD that may complement existing tools and help improve risk stratification and management strategies for NAFLD, thereby preventing adverse outcomes.
Although norm-referenced scores are essential to the identification of disability, they possess several features which affect their sensitivity to change. Norm-referenced scores often decrease over ...time among people with neurodevelopmental disorders who exhibit slower-than-average increases in ability. Further, the reliability of norm-referenced scores is lower at the tails of the distribution, resulting in floor effects and increased measurement error for people with neurodevelopmental disorders. In contrast, the person ability scores generated during the process of constructing a standardized test with item response theory are designed to assess change. We illustrate these limitations of norm-referenced scores, and relative advantages of ability scores, using data from studies of autism spectrum disorder and creatine transporter deficiency.
Background: Vegetables tend to be under-consumed in children. Combining vegetables with another generally liked food (e.g., potatoes) could influence vegetable consumption. This study examined the ...extent that certain vegetables (mixed peas and carrots- MPAC) were consumed as a function of the type of potatoes they were served with (Potato Smiles™ vs. diced) and how they were served with potatoes (combined in same bowl vs. a separate bowl). Methods: N = 65 children (grades 1-6, 57% boys, 9.75 ± 2.06 years) participated in this study. Using a cross-over design, participants completed five lunchtime meal conditions in a cafeteria setting, each separated by 1 week. Each meal included a base (1% milk, chicken nuggets, applesauce, and ketchup) and one of the following conditions: (1) MPAC and a whole-wheat bread roll served separately (control), (2) MPAC and Potato Smiles™ served in a separate bowl, (3) MPAC and diced potatoes served in a separate bowl, (4) MPAC and diced potatoes served in the same bowl, and (5) MPAC and Potato Smiles™ served in the same bowl. The consumption of MPAC was measured by plate waste. Results: Meal condition was a significant predictor of MPAC (F = 5.20; p = 0.0005) with MPAC consumption highest when combined with Potato Smiles™ in the same bowl (+8.77 g vs. control) and lowest when combined with diced potatoes in the same bowl (-2.85 g vs. control). The best overall model of MPAC consumption (Likelihood Ratio = 50.1; p < 0.0001) included age, sex, z-score for height, z-score for body fat, and meal condition. Specifically, MPAC consumption was most highly correlated with age (r = 0.38), being male, height z-score (r = 0.30), and body fat z-score (r = -0.15). Conclusions: Combining MPAC with potatoes appears to influence MPAC consumption; however, this may depend on the type of potato. Further, individual characteristics in children were associated with higher MPAC consumption (e.g., age) or lower MPAC consumption (e.g., higher body fat z-score).
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