Who are the obese? A cluster analysis exploring subgroups of the obese

Association between obesity phenotypes and dietary patterns: A two-step cluster analysis based on the China multi-ethnic cohort study

OBJECTIVE

This study aimed to explore obesity phenotypes and investigate their association with dietary patterns.

METHODS

Data were obtained from the baseline survey conducted in the China Multi-Ethnic Cohort Study from July 2018 to August 2019. All participants with a body mass index of at least 24 kg/m2 were enrolled and underwent a questionnaire survey, physical examination, and clinical laboratory tests. A two-step cluster analysis was employed to classify the participants into phenotypes. Dietary information was collected using the food frequency questionnaire, and principal component analysis was conducted to identify distinct dietary patterns.

RESULTS

We analyzed the data of 8757 participants. They were categorized based on demographic characteristics, biochemical indicators, and anthropometric measurements into two distinct clusters identified as metabolically healthy obesity and metabolically unhealthy obesity (MUO). Key predictors included serum uric acid, sex, and diastolic blood pressure. Subgroup analysis by sex identified three distinct clusters within both male and female participants. The MUO group had the highest prevalence of a range of chronic noncommunicable diseases. The analysis uncovered three unique dietary patterns among participants classified as the premium protein, rice-oil-red meat, and oil-salt patterns. Notably, the MUO subgroup demonstrated significantly higher factor scores for both the rice-oil-red meat and oil-salt patterns.

CONCLUSIONS

Obesity phenotypes are closely related to metabolic and demographic characteristics, with serum uric acid being a significant factor in categorizing the metabolic states of obesity. The rice-oil-red meat and oil-salt patterns may be related to the metabolic status of individuals with obesity.

A latent profile analysis based on diet quality and eating behaviours in participants of the PREDISE study characterized by a higher BMI

The aim of this study was to identify eating-related latent profiles based on diet quality and eating behaviours within a population characterized by a body mass index (BMI) of at least 25 kg/m2, and to compare metabolic variables between profiles. This analysis was conducted in a sample of 614 adults (45.6% women; 44.8 ± 12.9 years) from the cross-sectional PREDISE study. Participants completed the Three-Factor Eating Questionnaire, the Intuitive Eating Scale-2, the Regulation of Eating Behavior Scale, and three self-administered 24 h food recalls. Waist circumference, blood lipids, blood pressure, and fasting glucose were measured to identify carriers of the metabolic syndrome. A latent profile analysis was performed, and cases of metabolic syndrome were compared between profiles. A three-profile solution was found. Profile 1 (22.8%) was characterized by lower diet quality, lower self-determined motivation for eating, lower restraint, and higher intuitive eating. Profile 2 (44.5%) was characterized by higher diet quality, higher self-determined motivation for eating, higher restraint, lower disinhibition, and higher intuitive eating. Profile 3 (32.7%) was characterized by intermediate diet quality, higher non-self-determined motivation for eating, higher restraint and disinhibition, and lower intuitive eating. We found fewer cases of metabolic syndrome among participants in profile 2 than in the other profiles (p = 0.0001). This study suggests that a profile characterized by a lower disinhibition and higher levels of restraint, intuitive eating, self-determined motivation, and diet quality is associated with a better metabolic health among individuals with a higher BMI.

Differences in Psychological Health and Weight Loss after Bariatric Metabolic Surgery between Patients with and without Pain Syndromes

PURPOSE

Chronic pain and obesity often co-occur, negatively affecting one another and psychological wellbeing. Pain and psychological wellbeing improve after bariatric metabolic surgery (BMS), however, it is unknown whether psychological wellbeing improves differently after weight loss between patients with and without chronic pain. We investigated whether weight loss is associated with greater psychological wellbeing and functioning change after BMS, comparing patients with and without preoperative pain syndromes.

METHODS

Depression, health-related quality of life, self-esteem, self-efficacy to exercise and controlling eating behaviours, physical activity, and food cravings were measured before and 24 months after BMS among 276 patients with obesity. The presence of preoperative chronic pain syndromes was examined as a moderator for the relationship between 24-month weight loss and changes in psychological outcomes.

RESULTS

Chronic pain syndromes were present among 46% of patients. Weight loss was associated with greater improvement in health-related quality of life, self-efficacy to exercise and controlling eating behaviours, self-esteem and greater amelioration in food cravings. Pain syndromes only moderated negatively the relationship between the postoperative weight loss and change in self-efficacy to control eating behaviours (b = -0.49, CI [-0.88,-0.12]).

CONCLUSION

Patients with and without chronic pain showed similar improvements in weight and psychological wellbeing and behaviours after BMS. The relationship between weight loss and the improvement of self-efficacy to control eating behaviours was weaker among patients with chronic pain syndrome. Further work, measuring pain severity over time, is needed to shed light on the mechanism underlying pain and postoperative change in psychological wellbeing and weight loss.

Deep Learning for Echocardiography: Introduction for Clinicians and Future Vision: State-of-the-Art Review

Exponential growth in data storage and computational power is rapidly narrowing the gap between translating findings from advanced clinical informatics into cardiovascular clinical practice. Specifically, cardiovascular imaging has the distinct advantage in providing a great quantity of data for potentially rich insights, but nuanced interpretation requires a high-level skillset that few individuals possess. A subset of machine learning, deep learning (DL), is a modality that has shown promise, particularly in the areas of image recognition, computer vision, and video classification. Due to a low signal-to-noise ratio, echocardiographic data tend to be challenging to classify; however, utilization of robust DL architectures may help clinicians and researchers automate conventional human tasks and catalyze the extraction of clinically useful data from the petabytes of collected imaging data. The promise is extending far and beyond towards a contactless echocardiographic exam-a dream that is much needed in this time of uncertainty and social distancing brought on by a stunning pandemic culture. In the current review, we discuss state-of-the-art DL techniques and architectures that can be used for image and video classification, and future directions in echocardiographic research in the current era.

A review on the beneficial effects of bariatric surgery in the management of obesity

INTRODUCTION

Obesity is a chronic disease with a complex interplay of multiple factors such as genetic, metabolic, behavioral, and environmental factors. The management of obesity includes; lifestyle modification, psychological therapy, pharmacological therapy, and bariatric surgery. To date, bariatric surgery is the most effective treatment for obesity by offering a long-term reduction in weight, remission of obesity-related complications, and improving quality of life. However, bariatric surgery is not equally effective in all patients. Thus, if we can predict who would benefit most, it will improve the risk versus benefit ratio of having surgery.

AREAS COVERED

In this narrative review, we explore the question on who will benefit the most from bariatric surgery by examining the recent evidence in the literature. In addition, we investigate the predisposing predictors of bariatric surgery response. Finally, we offer the best strategies in the clinic to explain the potential benefits of bariatric surgery to patients.

EXPERT OPINION

Bariatric surgery is an effective obesity management approach. Despite its efficacy, considerable variation of individual response exists. Thus, it is important to recognize patients that will benefit most, but at present very few predictors are available which can be clinically useful.

A foresight whole systems obesity classification for the English UK biobank cohort

Background

The number of people living with obesity or who are overweight presents a global challenge, and the development of effective interventions is hampered by a lack of research which takes a joined up, whole system, approach that considers multiple elements of the complex obesity system together. We need to better understand the collective characteristics and behaviours of those who are overweight or have obesity and how these differ from those who maintain a healthy weight.

Methods

Using the UK Biobank cohort we develop an obesity classification system using k-means clustering. Variable selection from the UK Biobank cohort is informed by the Foresight obesity system map across key domains (Societal Influences, Individual Psychology, Individual Physiology, Individual Physical Activity, Physical Activity Environment).

Results

Our classification identifies eight groups of people, similar in respect to their exposure to known drivers of obesity: ‘Younger, urban hard-pressed’, ‘Comfortable, fit families’, ‘Healthy, active and retirees’, ‘Content, rural and retirees’, ‘Comfortable professionals’, ‘Stressed and not in work’, ‘Deprived with less healthy lifestyles’ and ‘Active manual workers’. Pen portraits are developed to describe the characteristics of these different groups. Multinomial logistic regression is used to demonstrate that the classification can effectively detect groups of individuals more likely to be living with overweight or obesity. The group identified as ‘Comfortable, fit families’ are observed to have a higher proportion of healthy weight, while three groups have increased relative risk of being overweight or having obesity: ‘Active manual workers’, ‘Stressed and not in work’ and ‘Deprived with less healthy lifestyles’.

Conclusions

This paper presents the first study of UK Biobank participants to adopt this obesity system approach to characterising participants. It provides an innovative new approach to better understand the complex drivers of obesity which has the potential to produce meaningful tools for policy makers to better target interventions across the whole system to reduce overweight and obesity.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12889-022-12650-x.

Self-perceived health status among adults with obesity in Quebec: a cluster analysis

PURPOSE

People with obesity are a highly heterogeneous group. Characterizing this heterogeneity may help to improve public health by offering adapted interventions and treatments to more homogeneous sub-groups among obese patients. This research aims to (1) identify distinct clusters of people with obesity based on demographic, behavioural, and clinical factors in the province of Quebec (Canada) and (2) assess the association of these clusters with self-perceived health.

METHODS

We conducted a sex specific cluster analysis (multiple correspondence analysis and hierarchical clustering) of adults with body mass index (BMI) ≥ 30kg/m² from the 2015-2016 Canadian Community Health Survey in Quebec. Clusters were based on demographic, clinical, and behavioural characteristics. The clusters were tested for association with poor self-perceived health using logistic regression.

RESULTS

Three clusters of individuals with obesity were identified. These were (1) young individuals, (2) people with higher levels of depression and anxiety, and (3) older adults with high comorbidity. Those with high levels of depression and anxiety (9% of men versus 13% of women) were associated with the poorest self-perceived health.

CONCLUSION

People with obesity in Quebec can be categorized into three clusters based on demographic, clinical, and behavioural characteristics. The findings of this study draw attention to the need to examine the coexistence of obesity with depression and anxiety, particularly as it relates to self-perceived health.

Psychosocial and Diet-Related Lifestyle Clusters in Overweight and Obesity

This study explored intraindividual multidimensional profiles integrating psychosocial factors, namely, body image and satisfaction, weight-related self-stigma, positivity, and happiness, and behavioural-lifestyle factors, namely, adherence to a healthy diet, among Spanish adults with overweight or obesity. We further aimed to investigate the association of excess weight (i.e., measured body mass index, BMI) with the abovementioned multidimensional configurations. A convenience sample of 100 adult individuals (60% females) with excessive weight (69% overweight; 31% obesity) was recruited. They completed self-reports regarding the study variables, and their weight and height were measured. With a perspective centered on the individual, a cluster analysis was performed. Three distinct intraindividual psychosocial and diet-related profiles were identified: a group of healthy individuals with excess weight (46%); a group of individuals who were negatively affected by their excessive weight and showed the most distressed profile (18%); and a group of dysfunctional individuals who seemed to be excessively unrealistic and optimistic regarding their excessive weight and unhealthy lifestyles, but were troubled by their weight (36%). Furthermore, individuals in the affected cluster had higher obesity (mean BMI ± SD = 32.1 ± 3.7) than those in the clusters of healthy (28.0 ± 3.0) and dysfunctional individuals (28.1 ± 3.3) (p < 0.05). The results showed that there are specific psychosocial and lifestyle profiles in the adult population with excess weight and that there are relationships among psychological, behavioural, and body-composition factors. For clinical application purposes, it is important to account for the heterogeneity within individuals who are obese and to individualize the interventions, with a focus from weight change to the individual's overall well-being.

Association of a Mediterranean Diet and Fruit and Vegetable Consumption with Subjective Well-Being among Adults with Overweight and Obesity

Recent evidence suggests that among behavioral-lifestyle factors, adherence to a healthy dietary pattern such as the Mediterranean Diet (MedDiet) is linked not only to better psychological health and mental positive status but also to increased subjective well-being (SWB). Nevertheless, this association has been unexplored among individuals with excessive weight. This study explored whether adherence to the MedDiet and the intake of healthy foods such as fruits and vegetables (FV) are associated with increased happiness and life satisfaction among Spanish adults with overweight or obesity when weight, body image, and body satisfaction are also considered. A convenience sample of adult individuals with excessive weight completed self-reports on the study variables, and weight and BMI were measured by bioimpedance. No evidence of a relationship with SWB indicators was obtained for MedDiet global indicators, probably due to the low adherence to a healthy diet by these individuals. In contrast, FV intake, as a powerful indicator of healthy eating, was associated with life satisfaction when BMI and body image dimensions were considered, among which body satisfaction had a key role. Moreover, life satisfaction fully mediated the relationship between FV consumption and happiness. Our findings are expected to make a relevant contribution to knowledge on the positive correlates or protective factors for overall well-being in obesity, including dietary habits and body appreciation. Our results may inform obesity management actions focused on inclusive, positive aesthetic models and promoting a healthy lifestyle for happiness in obesity.

The effect of a web-based psychoeducation on emotional functioning, eating behaviors, and body image among premenopausal women with excess body weight

The aims of this study were twofold: (1) to investigate the effectiveness of web-based psychoeducation for emotional functioning, eating behaviors, and body image among premenopausal women with excess body weight, and (2) to compare the efficacy of two types of web-based psychoeducation. Three hundred individuals were asked to volunteer in the present study. All participants were recruited in Poland from September 2017 to July 2019. Finally, a total of 129 premenopausal women took part in the research and signed informed consent. Their ages ranged between 18 and 48 years old (M = 32.28, SD = 7.65). Self-reported weight and height were recorded. BMI was calculated using self-reported data. Their average body mass index was 30.54 kg/m² (SD = 3.69). In our randomized experiment, the participants were allocated into three groups: experimental group I (EG I, N = 43), experimental group II (EG II, N = 46), and wait list control group (CG, N = 40). Five questionnaires were included in the online survey at the baseline measurement (Day 0), at the end of psychoeducational intervention (Day 16) and 75 days from the start of the 15-day intervention (Day 76). Measurement tools included the Difficulties in Emotion Regulation Scale, the Positive and Negative Affect Schedule, the Mindful Eating Scale, the Three-Factor Eating Questionnaire, and the Body Attitude Test. Our eHealth web-based psychoeducation consisted of three modules: emotional functioning module (EG I: theoretically consistent approach (TCA) vs EG II: eclectic approach; EA), eating behaviors module (EG I, EG II: based on mindfulness-based eating training; MET), body image module (EG I, EG II: based on Cash's prevention of body image disturbances; CPBID). The first experimental group (EG I) had intervention containing TCA, MET, and CPBID, while the second experimental group (EG II) EA, MET, and CPBID. According to between-group comparison, both types of web-based psychoeducation led to an increase in adaptive emotion regulation (Day 16: EG I vs CG: p < 0.001, EG II vs CG: p < 0.001; Day 76: EG I vs CG: p < 0.01, EG II vs CG: p < 0.001). In EG I, the intervention resulted in a higher reduction (than in CG) in emotional eating (Day 16: p < 0.01, Day 76: p < 0.01), uncontrolled eating (Day 16: p < 0.05, Day 76: p < 0.05), and negative appreciation of body size (Day 16: p < 0.01, Day 76: p < 0.01). In EG II, a lower level of emotional eating was found on Day 76 (EG II vs CG: p < 0.05). Two months after completion of the 15-day intervention, no statistically significant reduction for BMI was observed in either experimental group (p > 0.05). The effectiveness of both types of web-based psychoeducation was also confirmed in within-group comparison (Day 0 vs Day 16 and Day 0 vs Day 76). There was a significant increase in emotion regulation and mindful eating, as well as a decrease in emotional eating, uncontrolled eating, negative appreciation of body size, lack of familiarity with one's body, and the experiencing of negative emotions in both experimental groups (EG I, EG II). Both types of web-based psychoeducation might have to be considered in creating future web-based psychoeducation among premenopausal women with excess body weight.

Machine Learning to Identify Metabolic Subtypes of Obesity: A Multi-Center Study

BACKGROUND AND OBJECTIVE

Clinical characteristics of obesity are heterogenous, but current classification for diagnosis is simply based on BMI or metabolic healthiness. The purpose of this study was to use machine learning to explore a more precise classification of obesity subgroups towards informing individualized therapy.

SUBJECTS AND METHODS

In a multi-center study (n=2495), we used unsupervised machine learning to cluster patients with obesity from Shanghai Tenth People's hospital (n=882, main cohort) based on three clinical variables (AUCs of glucose and of insulin during OGTT, and uric acid). Verification of the clustering was performed in three independent cohorts from external hospitals in China (n = 130, 137, and 289, respectively). Statistics of a healthy normal-weight cohort (n=1057) were measured as controls.

RESULTS

Machine learning revealed four stable metabolic different obese clusters on each cohort. Metabolic healthy obesity (MHO, 44% patients) was characterized by a relatively healthy-metabolic status with lowest incidents of comorbidities. Hypermetabolic obesity-hyperuricemia (HMO-U, 33% patients) was characterized by extremely high uric acid and a large increased incidence of hyperuricemia (adjusted odds ratio [AOR] 73.67 to MHO, 95%CI 35.46-153.06). Hypermetabolic obesity-hyperinsulinemia (HMO-I, 8% patients) was distinguished by overcompensated insulin secretion and a large increased incidence of polycystic ovary syndrome (AOR 14.44 to MHO, 95%CI 1.75-118.99). Hypometabolic obesity (LMO, 15% patients) was characterized by extremely high glucose, decompensated insulin secretion, and the worst glucolipid metabolism (diabetes: AOR 105.85 to MHO, 95%CI 42.00-266.74; metabolic syndrome: AOR 13.50 to MHO, 95%CI 7.34-24.83). The assignment of patients in the verification cohorts to the main model showed a mean accuracy of 0.941 in all clusters.

CONCLUSION

Machine learning automatically identified four subtypes of obesity in terms of clinical characteristics on four independent patient cohorts. This proof-of-concept study provided evidence that precise diagnosis of obesity is feasible to potentially guide therapeutic planning and decisions for different subtypes of obesity.

CLINICAL TRIAL REGISTRATION

www.ClinicalTrials.gov, NCT04282837.

Factors influencing dietary practices in a transitioning food environment: a cross-sectional exploration of four dietary typologies among rural and urban Ugandan women using Photovoice

BACKGROUND

Healthy and sustainable dietary practices offer a possible solution to competing tensions between health and environmental sustainability, particularly as global food systems transition. To encourage such dietary practices, it is imperative to understand existing dietary practices and factors influencing these dietary practices. The aim of this study was to identify multi-level factors in lived rural and urban Ugandan food environments that influence existing dietary practices among women of reproductive age (WRA).

METHODS

A mixed methods study was conducted. Multiple correspondence analysis followed by hierarchical cluster analysis were performed on dietary data collected among a sample (n = 73) of Ugandan WRA in Kampala (urban) and Wakiso (rural) districts to elicit dietary clusters. Dietary clusters, which were labelled as dietary typologies based on environmental impact and nutrition transition considerations, were reflective of dietary practices. Following this, a smaller sample of WRA (n = 18) participated in a Photovoice exercise and in-depth interviews to identify factors in their social, physical, socio-cultural and macro-level environments influencing their enactment of the identified dietary typologies, and therefore dietary practices.

RESULTS

Four dietary typologies emerged: 'urban, low-impact, early-stage transitioners', 'urban, medium-impact, mid-stage transitioners', 'rural, low-impact, early-stage transitioners' and 'rural, low-impact, traditionalists'. Although experienced somewhat differently, the physical environment (access, availability and cost), social networks (parents, other family members and friends) and socio-cultural environment (dietary norms) were cross-cutting influences among both urban and rural dietary typologies. Seasonality (macro-environment) directly influenced consumption of healthier and lower environmental impact, plant-based foods among the two rural dietary typology participants, while seasonality and transportation intersected to influence consumption of healthier and lower environmental impact, plant-based foods among participants in the two urban dietary typologies.

CONCLUSION

Participants displayed a range of dietary typologies, and therefore dietary practices. Family provides an avenue through which interventions aimed at encouraging healthier and lower environmental impact dietary practices can be targeted. Home gardens, urban farming and improved transportation could address challenges in availability and access to healthier, lower environmental impact plant-based foods among urban WRA.

Body Satisfaction, Weight Stigma, Positivity, and Happiness among Spanish Adults with Overweight and Obesity

Although previous evidence suggests that happiness is lower among individuals with obesity, research on the correlates of subjective well-being (SWB) is warranted to increase our knowledge. We aim to explore excess weight (i.e., measured and self-reported body mass index (BMI)), body image and satisfaction, self-stigma, positivity, and happiness among Spanish adults with overweight or obesity. We further aim to investigate the predictors of SWB in this sample. A convenience sample of 100 individuals with excess weight completed self-reports on the study variables and were weighed and their height measured. On average, the participants reported body perceptions revealing minor excessive weight, moderate body satisfaction, low-to-moderate weight-related stigma, and elevated positivity and happiness. BMI and gender/sex independently affected these variables, but there were no significant interaction effects. Furthermore, individuals with overweight or obesity with higher body satisfaction and elevated positivity were more likely to report being happy, independent of their age, gender/sex, weight, and weight-related stigma. Mediation effects were found for body satisfaction and positivity in the relationship between weight and happiness. Moreover, positive orientation suppressed the pervasive influence of stigma on SWB. Our findings confirm the key role of body image dimensions and weight-related stigma for happiness and add support to the relevance of positivity for overall well-being of individuals with excess weight. These results may inform obesity management actions focused on inclusive aesthetic models, combating social stigmatization and enhancing positivity for a flourishing and fulfilling life.

The atorvastatin metabolic phenotype shift is influenced by interaction of drug-transporter polymorphisms in Mexican population: results of a randomized trial

Atorvastatin (ATV) is a blood cholesterol-lowering drug used to prevent cardiovascular events, the leading cause of death worldwide. As pharmacokinetics, metabolism and response vary among individuals, we wanted to determine the most reliable metabolic ATV phenotypes and identify novel and preponderant genetic markers that affect ATV plasma levels. A controlled, randomized, crossover, single-blind, three-treatment, three-period, and six-sequence clinical study of ATV (single 80-mg oral dose) was conducted among 60 healthy Mexican men. ATV plasma levels were measured using high-performance liquid chromatography mass spectrometry. Genotyping was performed by real-time PCR with TaqMan probes. Four ATV metabolizer phenotypes were found: slow, intermediate, normal and fast. Six gene polymorphisms, SLCO1B1-rs4149056, ABCB1-rs1045642, CYP2D6-rs1135840, CYP2B6-rs3745274, NAT2-rs1208, and COMT- rs4680, had a significant effect on ATV pharmacokinetics (P < 0.05). The polymorphisms in SLCO1B1 and ABCB1 seemed to have a greater effect and were especially important for the shift from an intermediate to a normal metabolizer. This is the first study that demonstrates how the interaction of genetic variants affect metabolic phenotyping and improves understanding of how SLCO1B1 and ABCB1 variants that affect statin metabolism may partially explain the variability in drug response. Notwithstanding, the influence of other genetic and non-genetic factors is not ruled out.

Adolescents with full or subthreshold anorexia nervosa in a naturalistic sample: treatment interventions and patient satisfaction

BACKGROUND

Despite major research efforts, current recommendations of treatment interventions for adolescents with anorexia nervosa are scarce, and the importance of patient satisfaction for treatment outcome is yet to be established. The overall aim of the present study was to examine treatment interventions and patient satisfaction in a naturalistic sample of adolescents with anorexia nervosa or subthreshold anorexia nervosa and possible associations to outcome defined as being in remission or not at treatment follow-up.

METHODS

Participants were identified through the Swedish national quality register for eating disorder treatment (SwEat). The samples consisted of 1899 patients who were follow-up registered 1 year after entering treatment and 474 patients who had completed a 1-year patient satisfaction questionnaire. A two-step cluster analysis was used for identifying subgroups of patients who received certain combinations and various amounts of treatment forms.

RESULTS

Patients who received mainly family-based treatment and/or inpatient care were most likely to achieve remission at 1-year follow-up, compared to patients in the other clusters. They were also younger, in general. Individual therapy was the most common treatment form, and was most appreciated among the adolescents. At 1-year follow-up, many patients reported improvements in eating habits, but far fewer reported improvements regarding cognitive symptoms. Overall, the patients rated the therapist relationship in a rather positive way, but they gave quite low ratings to statements associated with their own participation in treatment.

CONCLUSIONS

The results indicate that young adolescents who receive mainly family-based treatment and/or inpatient care respond more rapidly to treatment compared to older adolescents who receive mainly individual therapy or mixed treatment interventions. At 1-year follow-up, the adolescents reported improvements in behavioral symptoms and seemed quite satisfied with the therapist relationship.

Prevalence of interstitial and other lung diseases on Aruba

Background: Health care management has to be based on the local prevalence of diseases and the local diagnostic and therapeutic needs. So far, no systematic registration system for various lung diseases exists on Aruba. Questions that need to be answered are: what specific lung disorders occur on Aruba, and what are the specific needs there? The aim of this study was to assess the prevalence of lung disorders and the diversity of the patient population. Methods: Retrospectively, all records (n=2352) of patients of the Department of Respiratory Diseases of the Dr. Horatio E. Oduber Hospital, Aruba, who were seen at the clinic at least once in the period between January 2010 and October 2014 were reviewed. Results: Asthma (22%) and sleeping disorders (20%) were the most prevalent diagnoses. The mean body mass index (BMI) of the overall lung patient population was 31.6 kg/m². Obstructive sleep apnea syndrome (OSAS) was the most frequently diagnosed sleeping disorder (78.4%). A diagnosis of interstitial lung diseases (ILD) was established in 4.4% of the cases. Among the ILDs, non-specific interstitial pneumonia (NSIP: 28%), sarcoidosis (18%) and idiopathic pulmonary fibrosis (IPF: 16%) were the most frequent. Conclusion: Obesity and OSAS appeared to be major problems on Aruba. Increased awareness, education, and diagnostic facilities are required to prevent and diagnose obesity and OSAS in an early stage. NSIP, sarcoidosis and IPF are the most frequently diagnosed ILDs on Aruba, and optimization of ILD management is warranted, considering new treatment options available for ILD, especially for IPF.(Sarcoidosis Vasc Diffuse Lung Dis 2017; 34: 217-225)

Obesity and pain: a systematic review

BACKGROUND/OBJECTIVES

The current systematic review considered research published within the 10 years preceding June 2019, dealing with the topic of obesity and pain. Within the context of the complex biological and behavioral interrelationships among these phenomena, we sought to identify gaps in the literature and to highlight key targets for future transdisciplinary research. The overarching inclusion criteria were that the included studies could directly contribute to our understanding of these complex phenomena.

METHODS

We searched PubMed/Medline/Cochrane databases dating back 10 years, using the primary search terms "obesity" and "pain," and for a secondary search we used the search terms "pain" and "diet quality."

RESULTS

Included studies (n = 70) are primarily human; however, some animal studies were included to enhance understanding of related basic biological phenomena and/or where human data were absent or significantly limited.

CONCLUSIONS

Our overall conclusions highlight (1) the mechanisms of obesity-related pain (i.e., mechanical, behavioral, and physiological) and potential biological and behavioral contributors (e.g., gender, distribution of body fat, and dietary factors), (2) the requirement for accurate and reliable objective measurement, (3) the need to integrate biological and behavioral contributors into comprehensive, well-controlled prospective study designs.

Pharmacogenetics of amfepramone in healthy Mexican subjects reveals potential markers for tailoring pharmacotherapy of obesity: results of a randomised trial

Amfepramone (AFP) is an appetite-suppressant drug used in the treatment of obesity. Nonetheless, studies on interindividual pharmacokinetic variability and its association with genetic variants are limited. We employed a pharmacokinetic and pharmacogenetic approach to determine possible metabolic phenotypes of AFP and identify genetic markers that could affect the pharmacokinetic variability in a Mexican population. A controlled, randomized, crossover, single-blind, two-treatment, two-period, and two sequence clinical study of AFP (a single 75 mg dose) was conducted in 36 healthy Mexican volunteers who fulfilled the study requirements. Amfepramone plasma levels were measured using high-performance liquid chromatography mass spectrometry. Genotyping was performed using real-time PCR with TaqMan probes. Four AFP metabolizer phenotypes were found in our population: slow, normal, intermediate, and fast. Additionally, two gene polymorphisms, ABCB1-rs1045642 and CYP3A4-rs2242480, had a significant effect on AFP pharmacokinetics (P < 0.05) and were the predictor factors in a log-linear regression model. The ABCB1 and CYP3A4 gene polymorphisms were associated with a fast metabolizer phenotype. These results suggest that metabolism of AFP in the Mexican population is variable. In addition, the genetic variants ABCB1-rs1045642 and CYP3A4-rs2242480 may partially explain the AFP pharmacokinetic variability.

Utility and justice in public health

Background

Many public health practitioners and organizations view themselves as engaged in the promotion or achievement of equity. However, discussions around public health frequently assume that practitioners and policy-makers take a utilitarian approach to this work.

Methods

I argue that public health is better understood as a social justice endeavor. I begin by presenting the utility view of public health and then discuss the equity view. This is a theoretical argument, which should help public health to justify interventions for communicable and non-communicable diseases equally, and which contributes to breaking down the 'old/new' public health divide.

Results

This argument captures practitioners' views of the work they are engaged in and allows for the moral and policy justification of important interventions in communicable and non-communicable diseases. Systemic interventions are necessary to remedy high rates of disease among certain groups and, generally, to improve the health of entire populations.

Conclusions

By viewing diseases as partly the result of failures of health protective systems in society, public health may justify interventions in communicable and non-communicable diseases equally. Public health holds a duty to improve the health of the worst-off in society; by prioritizing this group, the health of the whole community may improve.

Development and Usability of a Life-Logging Behavior Monitoring Application for Obese Patients

Background

Obesity is a global epidemic. Behavior change monitoring using a smartphone application (app) can support weight management in obese patients. These apps must undergo usability testing, which is an important step in mobile healthcare app development. The current study aimed to develop a mobile app for behavioral monitoring and to test its usability including technical effectiveness, user efficiency, and user satisfaction for obese adults.

Methods

Development of the Dr. Youth app components included information on behavioral monitoring indicators and their obesity subtypes. The usability of the app was tested with 50 obese adults in a university hospital. Participants were asked to complete eight tasks for evaluating the technical effectiveness of the app. The time to complete each task was measured to test user efficiency. To explore user satisfaction, each participant completed the System Usability Scale (SUS). Descriptive statistics were used to examine the mean user efficiency and SUS scores.

Results

Fifty adults (14 men and 36 women, aged 20–59 years) who are obese (body mass index ≥25 kg/m²) were recruited. The mean age of participants was 42.6 years (standard deviation [SD], 10.8 years); their mean body mass index was 29.6 kg/m² (SD, 5.7 kg/m²). The tasks were completed with a 99% success rate. The overall mean SUS score was 76.65 (SD, 15.43).

Conclusion

The Dr. Youth app shows acceptable technical effectiveness, user efficiency, and user satisfaction. Future study is warranted to establish the app’s clinical efficacy.

Adding Lithium to Drinking Water for Suicide Prevention—The Ethics

Recent observations associate naturally occurring trace levels of Lithium in ground water with significantly lower suicide rates. It has been suggested that adding trace Lithium to drinking water could be a safe and effective way to reduce suicide. This article discusses the many ethical implications of such population-wide Lithium medication. It compares this policy to more targeted solutions that introduce trace amounts of Lithium to groups at higher risk of suicide or lower risk of adverse effects. The question of mass treatment with Lithium recalls other choices in public health between population-wide and more targeted interventions. The framework we propose could be relevant to some of these other dilemmas.

Genome-wide association study of alcohol use disorder identification test (AUDIT) scores in 20 328 research participants of European ancestry

Genetic factors contribute to the risk for developing alcohol use disorder (AUD). In collaboration with the genetics company 23andMe, Inc., we performed a genome-wide association study of the alcohol use disorder identification test (AUDIT), an instrument designed to screen for alcohol misuse over the past year. Our final sample consisted of 20 328 research participants of European ancestry (55.3% females; mean age = 53.8, SD = 16.1) who reported ever using alcohol. Our results showed that the 'chip-heritability' of AUDIT score, when treated as a continuous phenotype, was 12%. No loci reached genome-wide significance. The gene ADH1C, which has been previously implicated in AUD, was among our most significant associations (4.4 × 10-7 ; rs141973904). We also detected a suggestive association on chromosome 1 (2.1 × 10-7 ; rs182344113) near the gene KCNJ9, which has been implicated in mouse models of high ethanol drinking. Using linkage disequilibrium score regression, we identified positive genetic correlations between AUDIT score, high alcohol consumption and cigarette smoking. We also observed an unexpected positive genetic correlation between AUDIT and educational attainment and additional unexpected negative correlations with body mass index/obesity and attention-deficit/hyperactivity disorder. We conclude that conducting a genetic study using responses to an online questionnaire in a population not ascertained for AUD may represent a cost-effective strategy for elucidating aspects of the etiology of AUD.

Where can obesity management policy make the largest impact? Evaluating sub-populations through a microsimulation approach

BACKGROUND

There is a critical need to focus limited resources on sub-groups of patients with obesity where we expect the largest return on investment. This paper identifies patient sub-groups where an investment may result in larger positive economic and health outcomes.

METHODS

The baseline population with obesity was derived from a public survey database and divided into sub-populations defined by demographics and disease status. In 2016, a validated model was used to simulate the incidence of diabetes, absenteeism, and direct medical cost in five care settings. Research findings were derived from the difference in population outcomes with and without weight loss over 15 years. Modeled weight loss scenarios included initial 5% or 12% reduction in body mass index followed by a gradual weight regain. Additional simulations were conducted to show alternative outcomes from different time courses and maintenance scenarios.

RESULTS

Univariate analyses showed that age 45-64, pre-diabetes, female, or obesity class III are independently predictive of larger savings. After considering the correlation between these factors, multivariate analyses projected young females with obesity class I as the optimal sub-group to control obesity-related medical expenditures. In contrast, the population aged 20-35 with obesity class III will yield the best health outcomes. Also, the sub-group aged 45-54 with obesity class I will produce the biggest productivity improvement. Each additional year of weight loss maintained showed increased financial benefits.

CONCLUSIONS

This paper studied the heterogeneity between many sub-populations affected by obesity and recommended different priorities for decision-makers in economic, productivity, and health realms.

How has big data contributed to obesity research? A review of the literature

There has been growing interest in the potential of ‘big data’ to enhance our understanding in medicine and public health. Although there is no agreed definition of big data, accepted critical components include greater volume, complexity, coverage and speed of availability. Much of these data are ‘found’ (as opposed to ‘made’), in that they have been collected for non-research purposes, but could include valuable information for research. The aim of this paper is to review the contribution of ‘found’ data to obesity research to date, and describe the benefits and challenges encountered. A narrative review was conducted to identify and collate peer-reviewed research studies. Database searches conducted up to September 2017 found original studies using a variety of data types and sources. These included: retail sales, transport, geospatial, commercial weight management data, social media, and smartphones and wearable technologies. The narrative review highlights the variety of data uses in the literature: describing the built environment, exploring social networks, estimating nutrient purchases or assessing the impact of interventions. The examples demonstrate four significant ways in which ‘found’ data can complement conventional ‘made’ data: firstly, in moving beyond constraints in scope (coverage, size and temporality); secondly, in providing objective, quantitative measures; thirdly, in reaching hard-to-access population groups; and lastly in the potential for evaluating real-world interventions. Alongside these opportunities, ‘found’ data come with distinct challenges, such as: ethical and legal questions around access and ownership; commercial sensitivities; costs; lack of control over data acquisition; validity; representativeness; finding appropriate comparators; and complexities of data processing, management and linkage. Despite widespread recognition of the opportunities, the impact of ‘found’ data on academic obesity research has been limited. The merit of such data lies not in their novelty, but in the benefits they could add over and above, or in combination with, conventionally collected data.

A review of machine learning in obesity

Rich sources of obesity-related data arising from sensors, smartphone apps, electronic medical health records and insurance data can bring new insights for understanding, preventing and treating obesity. For such large datasets, machine learning provides sophisticated and elegant tools to describe, classify and predict obesity-related risks and outcomes. Here, we review machine learning methods that predict and/or classify such as linear and logistic regression, artificial neural networks, deep learning and decision tree analysis. We also review methods that describe and characterize data such as cluster analysis, principal component analysis, network science and topological data analysis. We introduce each method with a high-level overview followed by examples of successful applications. The algorithms were then applied to National Health and Nutrition Examination Survey to demonstrate methodology, utility and outcomes. The strengths and limitations of each method were also evaluated. This summary of machine learning algorithms provides a unique overview of the state of data analysis applied specifically to obesity.

Informatics and machine learning to define the phenotype

INTRODUCTION

For the past decade, the focus of complex disease research has been the genotype. From technological advancements to the development of analysis methods, great progress has been made. However, advances in our definition of the phenotype have remained stagnant. Phenotype characterization has recently emerged as an exciting area of informatics and machine learning. The copious amounts of diverse biomedical data that have been collected may be leveraged with data-driven approaches to elucidate trait-related features and patterns. Areas covered: In this review, the authors discuss the phenotype in traditional genetic associations and the challenges this has imposed.Approaches for phenotype refinement that can aid in more accurate characterization of traits are also discussed. Further, the authors highlight promising machine learning approaches for establishing a phenotype and the challenges of electronic health record (EHR)-derived data. Expert commentary: The authors hypothesize that through unsupervised machine learning, data-driven approaches can be used to define phenotypes rather than relying on expert clinician knowledge. Through the use of machine learning and an unbiased set of features extracted from clinical repositories, researchers will have the potential to further understand complex traits and identify patient subgroups. This knowledge may lead to more preventative and precise clinical care.

A demographic, clinical, and behavioral typology of obesity in the United States: an analysis of National Health and Nutrition Examination Survey 2011-2012

PURPOSE

Public health reporting, randomized trials, and epidemiologic studies of obesity tend to consider it as a homogeneous entity. However, obesity may represent a heterogeneous condition according to demographic, clinical, and behavioral factors. We assessed the heterogeneity of individuals with obesity in the United States.

METHODS

We analyzed data from the 2011-2012 wave of the National Health and Nutrition Examination Survey, a nationally representative sample of adults in the United States with detailed physical examination and clinical data (n = 1380). We used cluster analysis to identify subgroups classified as obese according to demographic factors, clinical conditions, and behavioral characteristics.

RESULTS

We found significant heterogeneity among participants with obesity according to six distinct clusters (P < .001): affluent men with sleep disorders (16% of sample); older smokers with cardiovascular disease (16%); older women with high comorbidity (20%); healthy white women (13%); healthy non-white women (14%); and active men who drink higher amounts of alcohol (21%).

CONCLUSIONS

Obesity in the United States is not a homogeneous condition. Current research and treatment may fail to account for complex and interrelated factors, with implications for prevention strategies and diverse risks of obesity.

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.

Drinker Types, Harm, and Policy-Related Variables: Results from the 2011 International Alcohol Control Study in New Zealand

BACKGROUND

The aim was to identify a typology of drinkers in New Zealand based on alcohol consumption, beverage choice, and public versus private drinking locations and investigate the relationship between drinker types, harms experienced, and policy-related variables.

METHODS

Model-based cluster analysis of male and female drinkers including volumes of alcohol consumed in the form of beer, wine, spirits, and ready-to-drinks (RTDs) in off- and on-premise settings. Cluster membership was then related to harm measures: alcohol dependence, self-rated health; and to 3 policy-relevant variables: liking for alcohol adverts, price paid for alcohol, and time of purchase.

RESULTS

Males and females were analyzed separately. Men fell into 4 and women into 14 clearly discriminated clusters. The male clusters consumed a relatively high proportion of alcohol in the form of beer. Women had a number of small extreme clusters and some consumed mainly spirits-based RTDs, while others drank mainly wine. Those in the higher consuming clusters were more likely to have signs of alcohol dependency, to report lower satisfaction with their health, to like alcohol ads, and to have purchased late at night.

CONCLUSIONS

Consumption patterns are sufficiently distinctive to identify typologies of male and female alcohol consumers. Women drinkers are more heterogeneous than men. The clusters relate differently to policy-related variables.

A three-component cognitive behavioural lifestyle program for preconceptional weight-loss in women with polycystic ovary syndrome (PCOS): a protocol for a randomized controlled trial

Background

Obesity in women with polycystic ovary syndrome (PCOS) negatively affects all clinical features, and a 5 to 10% weight loss has shown promising results on reproductive, metabolic and psychological level. Incorporating a healthy diet, increasing physical activity and changing dysfunctional thought patterns in women with PCOS are key points in losing weight. The biggest challenge in weight management programs is to achieve a reasonable and sustainable weight loss. The aim of this study is to explore whether Cognitive Behavioural Therapy (CBT) by a mental health professional, working in a multidisciplinary team with a dietician and a physical therapist (a three-component intervention), is more effective for weight loss in the long term, within 12 months. We will also explore whether mobile phone applications are effective in supporting behavioural change and sustainable weight loss.

Methods

The present study is a longitudinal randomized controlled trial (RCT) to study the effectiveness of a three-component 1-year cognitive-behavioural lifestyle intervention in overweight/obese women with PCOS. A total of 210 participants are randomly assigned to three groups: 1) CBT provided by the multidisciplinary team or; 2) CBT provided by the multidisciplinary team and Short Message Service (SMS) or; 3) usual care: encourage weight loss through publicly available services (control group). The primary aim of the 12-month intervention is to explore whether a three-component 1-year cognitive-behavioural lifestyle intervention is effective to decrease weight, when compared to usual care. Secondary outcomes include: the effect of the intervention on the PCOS phenotype, waist circumference, waist to hip ratio, ovulation rates, total testosterone, SHBG, free androgen index (FAI), AMH, hirsutism, acne, fasting glucose, blood pressure and all psychological parameters. Additionally, we assessed time to pregnancy, ongoing pregnancies, clinical pregnancies, miscarriages and birth weight.

All outcome variables are measured at the start of the study, and again at 3 months, 6 months, nine months and 12 months.

Discussion

We expect that CBT provided by a multidisciplinary team, especially combined with SMS, is effective in developing a healthy lifestyle and achieving a long-term weight loss in women with PCOS. Losing 5– 10% body weight improves various PCOS characteristics. Consequently, we expect to show that CBT provided by a multidisciplinary team improves reproductive and metabolic outcomes, as well as quality of life, while at the same time being cost-effective.

Trial registration

Registered at the Netherlands National Trial Register with number NTR2450 on August 2nd, 2010.

Genome-wide association study of body mass index in subjects with alcohol dependence

Outcomes related to disordered metabolism are common in alcohol dependence (AD). To investigate alterations in the regulation of body mass that occur in the context of AD, we performed a genome-wide association study (GWAS) of body mass index (BMI) in African Americans (AAs) and European Americans (EAs) with AD. Subjects were recruited for genetic studies of AD or drug dependence and evaluated using the Semi-structured Assessment for Drug Dependence and Alcoholism. We investigated a total of 2587 AAs and 2959 EAs with DSM-IV AD diagnosis. In the stage 1 sample (N = 4137), we observed three genome-wide significant (GWS) single-nucleotide polymorphism associations, rs200889048 (P = 8.98 * 10-12 ) and rs12490016 (P = 1.44 * 10-8 ) in EAs and rs1630623 (P = 5.14 * 10-9 ) in AAs and EAs meta-analyzed. In the stage 2 sample (N = 1409), we replicated 278, 253 and 168 of the stage 1 suggestive loci (P < 5*10-4 ) in AAs, EAs, and AAs and EAs meta-analyzed, respectively. A meta-analysis of stage 1 and stage 2 samples (N = 5546) identified two additional GWS signals: rs28562191 in EAs (P = 4.46 * 10-8 ) and rs56950471 in AAs (P = 1.57 * 10-9 ). Three of the GWS loci identified (rs200889048, rs12490016 and rs1630623) were not previously reported by GWAS of BMI in the general population, and two of them raise interesting hypotheses: rs12490016-a regulatory variant located within LINC00880, where there are other GWAS-identified variants associated with birth size, adiposity in newborns and bulimia symptoms, which also interact with social stress in relation to birth size; rs1630623-a regulatory variant related to ALDH1A1, a gene involved in alcohol metabolism and adipocyte plasticity. These loci offer molecular insights regarding the regulatory mechanisms of body mass in the context of AD.

Crave, Like, Eat: Determinants of Food Intake in a Sample of Children and Adolescents with a Wide Range in Body Mass

Obesity is a heterogeneous condition with obese individuals displaying different eating patterns. Growing evidence suggests that there is a subgroup of obese adults that is marked by frequent and intense food cravings and addiction-like consumption of high-calorie foods. Little is known, however, about such a subgroup of obese individuals in childhood and adolescence. In the present study, a sample of children and adolescents with a wide range in body mass was investigated and trait food craving, liking for and intake of high- and low-calorie foods was measured. One-hundred and forty-two children and adolescents (51.4% female, n = 73; Mage = 13.7 years, SD = 2.25; MBMI-SDS = 1.26, SD = 1.50) completed the Food Cravings Questionnaire-Trait, then viewed pictures of high- and low-calorie foods and rated their liking for them, and subsequently consumed some of these foods in a bogus taste test. Contrary to expectations, higher body mass was associated with lower consumption of high-calorie foods. However, there was an interaction between body mass and trait food craving when predicting food consumption: in obese participants, higher trait food craving was associated with higher consumption of high-calorie foods and this association was not found in normal-weight participants. The relationship between trait food craving and high-calorie food consumption within obese individuals was mediated by higher liking for high-calorie foods (but not by liking for low-calorie foods). Thus, similar to adults, a subgroup of obese children and adolescents - characterized by high trait food craving - seems to exist, calling for specific targeted treatment strategies.

Alcohol drinking cultures of European adolescents

BACKGROUND

Adolescent alcohol use varies across Europe. Differences in use might be due to variations in social drinking norms. These norms become apparent, e.g. in different proportions of alcohol drinking types per country. This study's purpose is to cluster European countries according to prevalence rates of alcohol drinking habits among adolescents aged 12-16.

METHODS

Based on results of previously done cluster analyses regarding alcohol use patterns in Europe, a second level hierarchical cluster analysis is performed. To do so, the proportions of each drinking pattern per country (non, mild, episodic, frequent and heavy episodic use) across 25 European countries (N = 48 423, M = 13.83 years, 48.5% male) are used as classifying variables.

RESULTS

Three country clusters are extracted that differentiate between eight countries with 'mainly non-using' adolescents, six countries with adolescents who use alcohol in a 'mainly mild but frequent' way and 11 countries that show the 'highest proportions of (heavy) episodic drinking adolescents'.

CONCLUSIONS

When applying and developing intervention strategies, differences in adolescent alcohol drinking cultures (i.e. social drinking norms) within Europe should be focused on. Alcohol policies and prevention programs should take cultural aspects like social drinking norms into account.