Uku Vainik

Eating traits questionnaires as a continuum of a single concept. Uncontrolled eating

Research on eating behaviour has identified several potentially relevant eating-related traits captured by different questionnaires. Often, these questionnaires predict Body Mass Index (BMI), but the relationship between them has not been explicitly studied. We studied the unity and diversity of questionnaires capturing five common eating-related traits: Power of Food, Eating Impulsivity, emotional eating, Disinhibition, and binge eating in women from Estonia (n = 740) and Canada (n = 456). Using bifactor analysis, we showed that a) these questionnaires are largely explained by a single factor, and b) relative to this shared factor, only some questionnaires offered additional variance in predicting BMI. Hence, these questionnaires seemed to characterise a common factor, which we label Uncontrolled Eating. Item Response Theory techniques were then applied to demonstrate that c) within this common factor, the questionnaires could be placed on a continuum of Uncontrolled Eating. That is, Eating Impulsivity focused on the milder degree, Power of Food Scale, emotional eating scales, and Disinhibition on intermediate degrees, and the Binge Eating Scale on the most severe degrees of Uncontrolled Eating. In sum, evidence from two samples showed that questionnaires capturing five common BMI-related traits largely reflected the same underlying latent trait - Uncontrolled Eating. In Estonia, some questionnaires focused on different severities of this common construct, supporting a continuum model of Uncontrolled Eating. These findings provide a starting point for developing better questionnaires of the neurobehavioural correlates of obesity, and provide a unifying perspective from which to view the existing literature. R scripts and data used for the analysis are provided.

Are Trait–Outcome Associations Caused by Scales or Particular Items? Example Analysis of Personality Facets and BMI

In personality research, trait–outcome associations are often studied by correlating scale sum scores with an outcome. For example, an association between the NEO Impulsiveness scale and body mass index (BMI) is often interpreted to pertain to underlying trait Impulsiveness. We propose that this expectation can be corroborated by testing for Spearmans theorem of indifference of indicator. Namely, an underlying trait–outcome association should not depend on the specific items (i.e. indicators) used to measure the trait. To test this theorem, we outline an indicator exclusion procedure and demonstrate its viability using a simulation design. We then apply this procedure to test personality–BMI associations for indifference of indicator in a large population–based sample of adult Estonians (N = 2581) using self–ratings and informant ratings obtained with the NEO Personality Inventory–3. Our results show that the N5: Impulsiveness–BMI association mostly depends on two eating–related items, suggesting that the trait associated with BMI may be narrower than the trait the N5: Impulsiveness scale is supposed to measure. Associations between BMI, E3: Assertiveness and C2: Order seem to pertain to the trait. In sum, testing for indifference of indicator provides a potentially useful method to clarify trait–outcome relationships. R scripts are provided that implement the indicator exclusion procedure. Copyright

Diet misreporting can be corrected: confirmation of the association between energy intake and fat-free mass in adolescents.

Subjective energy intake (sEI) is often misreported, providing unreliable estimates of energy consumed. Therefore, relating sEI data to health outcomes is difficult. Recently, Börnhorst et al. compared various methods to correct sEI-based energy intake estimates. They criticised approaches that categorise participants as under-reporters, plausible reporters and over-reporters based on the sEI:total energy expenditure (TEE) ratio, and thereafter use these categories as statistical covariates or exclusion criteria. Instead, they recommended using external predictors of sEI misreporting as statistical covariates. We sought to confirm and extend these findings. Using a sample of 190 adolescent boys (mean age=14), we demonstrated that dual-energy X-ray absorptiometry-measured fat-free mass is strongly associated with objective energy intake data (onsite weighted breakfast), but the association with sEI (previous 3-d dietary interview) is weak. Comparing sEI with TEE revealed that sEI was mostly under-reported (74 %). Interestingly, statistically controlling for dietary reporting groups or restricting samples to plausible reporters created a stronger-than-expected association between fat-free mass and sEI. However, the association was an artifact caused by selection bias - that is, data re-sampling and simulations showed that these methods overestimated the effect size because fat-free mass was related to sEI both directly and indirectly via TEE. A more realistic association between sEI and fat-free mass was obtained when the model included common predictors of misreporting (e.g. BMI, restraint). To conclude, restricting sEI data only to plausible reporters can cause selection bias and inflated associations in later analyses. Therefore, we further support statistically correcting sEI data in nutritional analyses. The script for running simulations is provided.

Personality and Situation Predictors of Consistent Eating Patterns.

Introduction A consistent eating style might be beneficial to avoid overeating in a food-rich environment. Eating consistency entails maintaining a similar dietary pattern across different eating situations. This construct is relatively under-studied, but the available evidence suggests that eating consistency supports successful weight maintenance and decreases risk for metabolic syndrome and cardiovascular disease. Yet, personality and situation predictors of consistency have not been studied. Methods A community-based sample of 164 women completed various personality tests, and 139 of them also reported their eating behaviour 6 times/day over 10 observational days. We focused on observations with meals (breakfast, lunch, or dinner). The participants indicated if their momentary eating patterns were consistent with their own baseline eating patterns in terms of healthiness or size of the meal. Further, participants described various characteristics of each eating situation. Results Eating consistency was positively predicted by trait self-control. Eating consistency was undermined by eating in the evening, eating with others, eating away from home, having consumed alcohol and having undertaken physical exercise. Interactions emerged between personality traits and situations, including punishment sensitivity, restraint, physical activity and alcohol consumption. Conclusion Trait self-control and several eating situation variables were related to eating consistency. These findings provide a starting point for targeting interventions to improve consistency, suggesting that a focus on self-control skills, together with addressing contextual factors such as social situations and time of day, may be most promising. This work is a first step to provide people with the tools they need to maintain a consistently healthy lifestyle in a food-rich environment.

Overlapping Neural Endophenotypes in Addiction and Obesity

Impulsivity refers to a tendency to act rapidly without full consideration of consequences. The trait is thought to result from the interaction between high arousal responses to potential rewards and poor self-control. Studies have suggested that impulsivity confers vulnerability to both addiction and obesity. However, results in this area are unclear, perhaps due to the high phenotypic complexity of addictions and obesity. Focusing on impulsivity, the aim of this review is to tackle the putative overlaps between addiction and obesity in four domains: (1) personality research, (2) neurocognitive tasks, (3) brain imaging, and (4) clinical evidence. We suggest that three impulsivity-related domains are particularly relevant for our understanding of similarities between addiction and obesity: lower self-control (high Disinhibition/low Conscientiousness), reward sensitivity (high Extraversion/Positive Emotionality), and negative affect (high Neuroticism/Negative Emotionality). Neurocognitive studies have shown that obesity and addiction are both associated with increased impulsive decision-making and attention bias in response to drug or food cues, respectively. Mirroring this, obesity and different forms of addiction seem to exhibit similar alterations in functional MRI brain activity in response to reward processing and during self-control tasks. Overall, our review provides an integrative approach to understand those facets of obesity that present similarities to addictive behaviors. In addition, we suggest that therapeutic interventions targeting inhibitory control may represent a promising approach for the prevention and/or treatment of obesity.

Network connectivity determines cortical thinning in early Parkinson’s disease progression

Here we test the hypothesis that the neurodegenerative process in Parkinson’s disease (PD) moves stereotypically along neural networks, possibly reflecting the spread of toxic alpha-synuclein molecules. PD patients (n = 105) and matched controls (n = 57) underwent T1-MRI at entry and 1 year later as part of the Parkinson’s Progression Markers Initiative. Over this period, PD patients demonstrate significantly greater cortical thinning than controls in parts of the left occipital and bilateral frontal lobes and right somatomotor-sensory cortex. Cortical thinning is correlated to connectivity (measured functionally or structurally) to a “disease reservoir” evaluated by MRI at baseline. The atrophy pattern in the ventral frontal lobes resembles one described in certain cases of Alzheimer’s disease. Our findings suggest that disease propagation to the cortex in PD follows neuronal connectivity and that disease spread to the cortex may herald the onset of cognitive impairment.In Parkinson’s disease (PD), neurodegeneration spreads from the brainstem to the cerebral cortex. Here, in a longitudinal study of PD patients, the authors found that cortical thinning followed neural connectivity from a “disease reservoir”.

Educational attainment and personality are genetically intertwined

Heritable variance in psychological traits may reflect genetic and biological processes that are not necessarily specific to these particular traits but pertain to a broader range of phenotypes. We tested the possibility that the personality domains of the five-factor model and their 30 facets, as rated by people themselves and their knowledgeable informants, reflect polygenic influences that have been previously associated with educational attainment. In a sample of more than 3,000 adult Estonians, education polygenic scores (EPSs), which are interpretable as estimates of molecular-genetic propensity for education, were correlated with various personality traits, particularly from the neuroticism and openness domains. The correlations of personality traits with phenotypic educational attainment closely mirrored their correlations with EPS. Moreover, EPS predicted an aggregate personality trait tailored to capture the maximum amount of variance in educational attainment almost as strongly as it predicted the attainment itself. We discuss possible interpretations and implications of these findings.

Improving Assessment of the Spectrum of Reward-Related Eating: The RED-13

A diversity of scales capture facets of reward-related eating (RRE). These scales assess food cravings, uncontrolled eating, addictive behavior, restrained eating, binge eating, and other eating behaviors. However, these scales differ in terms of the severity of RRE they capture. We sought to incorporate the items from existing scales to broaden the 9-item Reward-based Eating Drive scale (RED-9; Epel et al., 2014), which assesses three dimensions of RRE (lack of satiety, preoccupation with food, and lack of control over eating), in order to more comprehensively assess the entire spectrum of RRE. In a series of 4 studies, we used Item Response Theory models to consider candidate items to broaden the RED-9. Studies 1 and 2 evaluated the abilities of additional items from existing scales to increase the RED-9’s coverage across the spectrum of RRE. Study 3 evaluated candidate items identified in Studies 1 and 2 in a new sample to assess the extent to which they accounted for more variance in areas less well-covered by the RED-9. Study 4 tested the ability of the RED-13 to provide consistent coverage across the range of the RRE spectrum. The resultant RED-13 accounted for greater variability than the RED-9 by reducing gaps in coverage of RRE in middle-to-low ranges. Like the RED-9, the RED-13 was positively correlated with BMI. The RED-13 was also positively related to a diagnosis of type 2 diabetes as well as cravings for sweet and savory foods. In summary, the RED-13 is a brief self-report measure that broadly captures the spectrum of RRE and may be a useful tool for identifying individuals at risk for overweight or obesity.

Comparison of IPAQ-SF and Two Other Physical Activity Questionnaires with Accelerometer in Adolescent Boys

Self-report measures of physical activity (PA) are easy to use and popular but their reliability is often questioned. Therefore, the general aim of the present study was to investigate the association of PA questionnaires with accelerometer derived PA, in a sample of adolescent boys. In total, 191 pubertal boys (mean age 14.0 years) completed three self-report questionnaires and wore an accelerometer (ActiGraph GT1M) for 7 consecutive days. The PA questionnaires were: International Physical Activity Questionnaire-Short Form (IPAQ-SF), Tartu Physical Activity Questionnaire (TPAQ), and the Inactivity subscale from Domain-Specific Impulsivity (DSI) scale. All three questionnaires were significantly correlated with accelerometer derived MVPA: the correlations were 0.31 for the IPAQ-SF MVPA, 0.34 for the TPAQ MVPA and -0.29 for the DSI Inactivity scale. Nevertheless, none of the questionnaires can be used as a reliable individual-level estimate of MVPA in male adolescents. The boys underreported their MVPA in IPAQ-SF as compared to accelerometer-derived MVPA (respective averages 43 and 56 minutes); underreporting was more marked in active boys with average daily MVPA at least 60 minutes, and was not significant in less active boys. Conversely, MVPA index from TPAQ overestimated the MVPA in less active boys but underestimated it in more active boys. The sedentary time reported in IPAQ-SF was an underestimate as compared to accelerometer-derived sedentary time (averages 519 and 545 minutes, respectively).

Neurobehavioral correlates of obesity are largely heritable

Significance Obesity is a widespread heritable health condition. Evidence from psychology, cognitive neuroscience, and genetics has proposed links between obesity and the brain. The current study tested whether the heritable variance in body mass index (BMI) is explained by brain and behavioral factors in a large brain imaging cohort that included multiple related individuals. We found that the heritable variance in BMI had genetic correlations 0.25–0.45 with cognitive tests, cortical thickness, and regional brain volume. In particular, BMI was associated with frontal lobe asymmetry and differences in temporal-parietal perceptual systems. Further, we found genetic overlap between certain brain and behavioral factors. In summary, the genetic vulnerability to BMI is expressed in the brain. This may inform intervention strategies. Recent molecular genetic studies have shown that the majority of genes associated with obesity are expressed in the central nervous system. Obesity has also been associated with neurobehavioral factors such as brain morphology, cognitive performance, and personality. Here, we tested whether these neurobehavioral factors were associated with the heritable variance in obesity measured by body mass index (BMI) in the Human Connectome Project (n = 895 siblings). Phenotypically, cortical thickness findings supported the “right brain hypothesis” for obesity. Namely, increased BMI is associated with decreased cortical thickness in right frontal lobe and increased thickness in the left frontal lobe, notably in lateral prefrontal cortex. In addition, lower thickness and volume in entorhinal-parahippocampal structures and increased thickness in parietal-occipital structures in participants with higher BMI supported the role of visuospatial function in obesity. Brain morphometry results were supported by cognitive tests, which outlined a negative association between BMI and visuospatial function, verbal episodic memory, impulsivity, and cognitive flexibility. Personality–BMI correlations were inconsistent. We then aggregated the effects for each neurobehavioral factor for a behavioral genetics analysis and estimated each factor’s genetic overlap with BMI. Cognitive test scores and brain morphometry had 0.25–0.45 genetic correlations with BMI, and the phenotypic correlations with BMI were 77–89% explained by genetic factors. Neurobehavioral factors also had some genetic overlap with each other. In summary, obesity as measured by BMI has considerable genetic overlap with brain and cognitive measures. This supports the theory that obesity is inherited via brain function and may inform intervention strategies.