Metabolically Healthy Obesity and the Risk of Cardiovascular Disease in the Elderly Population

BackgroundWhether being metabolically healthy obese (MHO)—defined by the presence of obesity in the absence of metabolic syndrome—is associated with subsequent cardiovascular disease (CVD) remains unclear and may depend on the participants’ age. We examined the association of being MHO with CVD risk in the elderly.ConclusionsIn our elderly population, we found that the presence of obesity without metabolic syndrome did not confer a higher CVD risk. However, metabolic syndrome was strongly associated with CVD risk, and was associated with an increased risk in all BMI categories. Therefore, preventive interventions targeting cardiometabolic risk factors could be considered in elderly, regardless of weight status.     

Genetic associations with temporal shifts in obesity and severe obesity during the obesity epidemic in Norway: A longitudinal population-based cohort (the HUNT Study)

BackgroundObesity has tripled worldwide since 1975 as environments are becoming more obesogenic. Our study investigates how changes in population weight and obesity over time are associated with genetic predisposition in the context of an obesogenic environment over 6 decades and examines the robustness of the findings using sibling design.Methods and findingsA total of 67,110 individuals aged 13–80 years in the Nord-Trøndelag region of Norway participated with repeated standardized body mass index (BMI) measurements from 1966 to 2019 and were genotyped in a longitudinal population-based health study, the Trøndelag Health Study (the HUNT Study). Genotyping required survival to and participation in the HUNT Study in the 1990s or 2000s. Linear mixed models with observations nested within individuals were used to model the association between a genome-wide polygenic score (GPS) for BMI and BMI, while generalized estimating equations were used for obesity (BMI ≥ 30 kg/m²) and severe obesity (BMI ≥ 35 kg/m²).The increase in the average BMI and prevalence of obesity was steeper among the genetically predisposed. Among 35-year-old men, the prevalence of obesity for the least predisposed tenth increased from 0.9% (95% confidence interval [CI] 0.6% to 1.2%) to 6.5% (95% CI 5.0% to 8.0%), while the most predisposed tenth increased from 14.2% (95% CI 12.6% to 15.7%) to 39.6% (95% CI 36.1% to 43.0%). Equivalently for women of the same age, the prevalence of obesity for the least predisposed tenth increased from 1.1% (95% CI 0.7% to1.5%) to 7.6% (95% CI 6.0% to 9.2%), while the most predisposed tenth increased from 15.4% (95% CI 13.7% to 17.2%) to 42.0% (95% CI 38.7% to 45.4%). Thus, for 35-year-old men and women, respectively, the absolute change in the prevalence of obesity from 1966 to 2019 was 19.8 percentage points (95% CI 16.2 to 23.5, p < 0.0001) and 20.0 percentage points (95% CI 16.4 to 23.7, p < 0.0001) greater for the most predisposed tenth compared with the least predisposed tenth, defined using the GPS for BMI. The corresponding absolute changes in the prevalence of severe obesity for men and women, respectively, were 8.5 percentage points (95% CI 6.3 to 10.7, p < 0.0001) and 12.6 percentage points (95% CI 9.6 to 15.6, p < 0.0001) greater for the most predisposed tenth. The greater increase in BMI in genetically predisposed individuals over time was apparent after adjustment for family-level confounding using a sibling design. Key limitations include a slightly lower survival to date of genetic testing for the older cohorts and that we apply a contemporary genetic score to past time periods. Future research should validate our findings using a polygenic risk score constructed from historical data.ConclusionsIn the context of increasingly obesogenic changes in our environment over 6 decades, our findings reveal a growing inequality in the risk for obesity and severe obesity across GPS tenths. Our results suggest that while obesity is a partially heritable trait, it is still modifiable by environmental factors. While it may be possible to identify those most susceptible to environmental change, who thus have the most to gain from preventive measures, efforts to reverse the obesogenic environment will benefit the whole population and help resolve the obesity epidemic.

In a longitudinal population-based cohort study in Norway, Maria Brandkvist and colleagues investigate how genetic predisposition relates to changes in BMI and obesity over the past six decades.     

Ethnic Differences in Prevalence of General Obesity and Abdominal Obesity among Low-Income Rural Kazakh and Uyghur Adults in Far Western China and Implications in Preventive Public Health

Background

The global pandemic of obesity has become a disastrous public health issue that needs urgent attention. Previous studies have concentrated in high-income urban settings and few cover low-income rural settings especially nomadic residents in mountain areas. This study focused on low-income rural and nomadic minority people residing in China’s far west and investigated their prevalence and ethnic differences of obesity.

Methods

A questionnaire-based survey and physical examination of 8,036 individuals were conducted during 2009–2010, using stratified cluster random sampling method in nomadic Kazakhs and rural Uyghur residents (≥18 years old) in 18 villages, Xinjiang, China, about 4,407 km away from capital Beijing. Obesity was defined by BMI and WC.

Results

The overall prevalence of general and abdominal obesity in Kazakh adults were 18.3% and 60.0%, respectively and in Uyghur, 7.6% and 54.5%, respectively. Female’s prevalence of obesity was higher than male’s for general obesity (45–54 age group in Uyghur, P = 0.041) and abdominal obesity (≥55 years in Kazakhs, P_55∼ = 0.010, P_65∼ =0.001; and ≥18 years in Uyghurs, P<0.001). Kazakh’s prevalence of obesity was higher than Uyghur’s (general obesity: ≥35 years, P<0.001; abdominal obesity: ≥25 years in males and ≥65 years in females, P<0.01). The prevalence of obesity increased after 18 years old and subsequently decreased after 55 years old. Meat consumption, older age, and female gender had a higher risk of obesity in these two minorities.

Conclusions

Both general and abdominal obesity were common in rural ethnic Kazakhs and Uyghurs. The prevalence rates were different in these two minorities depending on ethnicity, gender, and age. Kazakhs, females and elderly people may be prioritized in prevention of obesity in western China. Because of cost-effectiveness in measuring BMI and WC, we recommend that BMI and WC be integrated into local preventive policies in public health toward screening obesity and related diseases in low-income rural minorities.     

Polygenic Risk Predicts Obesity in Both White and Black Young Adults

Objective

To test transethnic replication of a genetic risk score for obesity in white and black young adults using a national sample with longitudinal data.

Design and Methods

A prospective longitudinal study using the National Longitudinal Study of Adolescent Health Sibling Pairs (n = 1,303). Obesity phenotypes were measured from anthropometric assessments when study members were aged 18–26 and again when they were 24–32. Genetic risk scores were computed based on published genome-wide association study discoveries for obesity. Analyses tested genetic associations with body-mass index (BMI), waist-height ratio, obesity, and change in BMI over time.

Results

White and black young adults with higher genetic risk scores had higher BMI and waist-height ratio and were more likely to be obese compared to lower genetic risk age-peers. Sibling analyses revealed that the genetic risk score was predictive of BMI net of risk factors shared by siblings. In white young adults only, higher genetic risk predicted increased risk of becoming obese during the study period. In black young adults, genetic risk scores constructed using loci identified in European and African American samples had similar predictive power.

Conclusion

Cumulative information across the human genome can be used to characterize individual level risk for obesity. Measured genetic risk accounts for only a small amount of total variation in BMI among white and black young adults. Future research is needed to identify modifiable environmental exposures that amplify or mitigate genetic risk for elevated BMI.     

Prospective Cohort Study of Central Adiposity and Risk of Death in Middle Aged and Elderly Chinese

Asians have high prevalence of central obesity despite the low prevalence of general obesity. We evaluated associations between the central obesity measure, waist-hip ratio (WHR) with total and cause-specific mortality in middle-aged and elderly Chinese participants. Data arise from two prospective population-based cohort studies: the Shanghai Men’s Health Study involves 53,425 men (participation rate = 74.0%), age 40–74 at baseline, and the Shanghai Women’s Health Study involves 63,017 women (participation rate = 92.7%), age 40–70 at baseline. Information on lifestyle factors and anthropometric measurements were taken at baseline interview. Vital status and causes of death were obtained via surveys and annual linkages to relevant Shanghai registries through December 31, 2011. After median follow-up time of 7.5 years for the Shanghai Men’s Health Study and 13.2 years for the Shanghai Women’s Health Study, there were 2,058 and 3,167 deaths, respectively. In models adjusted for BMI and other potential confounders, WHR was associated with all-cause mortality; hazard ratios (HRs) (95% confidence intervals) across the first to fifth quintile increased from 1 (Reference), 1.10 (0.95,1.27), 1.21 (1.04,1.41), 1.11 (0.96,1.30), to 1.42 (1.22,1.65) in men and from 1 (Reference), 1.10 (0.96,1.27), 1.11 (0.97,1.27), 1.20 (1.05,1.37), to 1.48 (1.30,1.69) in women. WHR had a stronger association with cardiovascular disease, with multivariate-adjusted HRs of 1.5 to 1.7 observed for the highest versus lowest quintile of WHR. Dose-response associations were also seen for cancer and other-cause deaths. Stratified analyses suggested a stronger association with mortality among normal weight (BMI <25) than over-weight (BMI ≥25) individuals. Positive associations with mortality were observed in subgroups defined by follow-up duration, comorbidity, age, smoking, and physical activity. Greater central adiposity is associated with increased mortality in Chinese adults, even among individuals with low BMI. Physicians and the public should be aware of central adiposity’s independent effects on health.     

Obesity polymorphisms identified in genome-wide association studies interact with n-3 polyunsaturated fatty acid intake and modify the genetic association with adiposity phenotypes in Yup’ik people

n-3 Polyunsaturated fatty acids (n-3 PUFAs) have anti-obesity effects that may modulate risk of obesity, in part, through interactions with genetic factors. Genome-wide association studies (GWAS) have identified genetic variants associated with body mass index (BMI); however, the extent to which these variants influence adiposity through interactions with n-3 PUFAs remains unknown. We evaluated 10 highly replicated obesity GWAS single nucleotide polymorphisms (SNPs) for individual and cumulative associations with adiposity phenotypes in a cross-sectional sample of Yup’ik people (n = 1,073) and evaluated whether genetic associations with obesity were modulated by n-3 PUFA intake. A genetic risk score (GRS) was calculated by adding the BMI-increasing alleles across all 10 SNPs. Dietary intake of n-3 PUFAs was estimated using nitrogen stable isotope ratio (δ¹⁵N) of red blood cells, and genotype–phenotype analyses were tested in linear models accounting for familial correlations. GRS was positively associated with BMI (p = 0.012), PBF (p = 0.022), ThC (p = 0.025), and waist circumference (p = 0.038). The variance in adiposity phenotypes explained by the GRS included BMI (0.7 %), PBF (0.3 %), ThC (0.7 %), and WC (0.5 %). GRS interactions with n-3 PUFAs modified the association with adiposity and accounted for more than twice the phenotypic variation (~1–2 %), relative to GRS associations alone. Obesity GWAS SNPs contribute to adiposity in this study population of Yup’ik people and interactions with n-3 PUFA intake potentiated the risk of fat accumulation among individuals with high obesity GRS. These data suggest the anti-obesity effects of n-3 PUFAs among Yup’ik people may, in part, be dependent upon an individual’s genetic predisposition to obesity.

Electronic supplementary material

The online version of this article (doi:10.1007/s12263-013-0340-z) contains supplementary material, which is available to authorized users.     

Resistance exercise,alone and in combination with aerobic exercise,and obesity in Dallas,Texas, US: A prospective cohort study

BackgroundObesity is a significant and growing public health problem in high-income countries. Little is known about the relationship between resistance exercise (RE), alone and in combination with aerobic exercise (AE), and the risk of developing obesity. The purpose of this prospective cohort study was to examine the associations between different amounts and frequencies of RE, independent of AE, and incident obesity.Methods and findingsParticipants were 11,938 healthy adults ages 18–89 years with a BMI < 30 kg/m² at baseline who completed at least 2 clinical examinations during 1987–2005 as part of the Aerobics Center Longitudinal Study. Self-reported RE participation in minutes/week and days/week was collected from a standardized questionnaire. Incident obesity was defined as a BMI ≥ 30 kg/m² at follow-up. Incident obesity was also defined by waist circumference (WC) > 102/88 cm for men/women and percent body fat (PBF) ≥ 25%/30% for men/women at follow-up in participants who were not obese by WC (n = 9,490) or PBF (n = 8,733) at baseline. During the average 6-year follow-up, 874 (7%), 726 (8%), and 1,683 (19%) developed obesity defined by BMI, WC, or PBF, respectively. Compared with no RE, 60–119 min/wk of RE was associated with 30%, 41%, and 31% reduced risk of obesity defined by BMI (hazard ratio [95% CI], 0.70 [0.54–0.92], p = 0.008), WC (0.59 [0.44–0.81], p < 0.001), and PBF (0.69 [0.57–0.83], p < 0.001), respectively, after adjusting for confounders including age, sex, examination year, smoking status, heavy alcohol consumption, hypertension, hypercholesterolemia, diabetes, and AE. Compared with not meeting the RE guidelines of ≥2 d/wk, meeting the RE guidelines was associated with 18%, 30%, and 30% reduced risk of obesity defined by BMI (hazard ratio [95% CI], 0.82 [0.69–0.97], p = 0.02), WC (0.70 [0.57–0.85], p < 0.001), and PBF (0.70 [0.62–0.79], p < 0.001), respectively. Compared with meeting neither guideline, meeting both the AE and RE guidelines was associated with the smallest hazard ratios for obesity. Limitations of this study include limited generalizability as participants were predominantly white men from middle to upper socioeconomic strata, use of self-reported RE, and lack of detailed diet data for the majority of participants.ConclusionsIn this study, we observed that RE was associated with a significantly reduced risk of obesity even after considering AE. However, meeting both the RE and AE guidelines was associated with the lowest risk of obesity.

In a prospective cohort study, Angelique G. Brellenthin and colleagues investigate associations between resistance and aerobic exercise, and incident obesity in the United States.     

Body Mass Index Genetic Risk Score and Endometrial Cancer Risk

Genome-wide association studies (GWAS) have identified common variants that predispose individuals to a higher body mass index (BMI), an independent risk factor for endometrial cancer. Composite genotype risk scores (GRS) based on the joint effect of published BMI risk loci were used to explore whether endometrial cancer shares a genetic background with obesity. Genotype and risk factor data were available on 3,376 endometrial cancer case and 3,867 control participants of European ancestry from the Epidemiology of Endometrial Cancer Consortium GWAS. A BMI GRS was calculated by summing the number of BMI risk alleles at 97 independent loci. For exploratory analyses, additional GRSs were based on subsets of risk loci within putative etiologic BMI pathways. The BMI GRS was statistically significantly associated with endometrial cancer risk (P = 0.002). For every 10 BMI risk alleles a woman had a 13% increased endometrial cancer risk (95% CI: 4%, 22%). However, after adjusting for BMI, the BMI GRS was no longer associated with risk (per 10 BMI risk alleles OR = 0.99, 95% CI: 0.91, 1.07; P = 0.78). Heterogeneity by BMI did not reach statistical significance (P = 0.06), and no effect modification was noted by age, GWAS Stage, study design or between studies (P≥0.58). In exploratory analyses, the GRS defined by variants at loci containing monogenic obesity syndrome genes was associated with reduced endometrial cancer risk independent of BMI (per BMI risk allele OR = 0.92, 95% CI: 0.88, 0.96; P = 2.1 x 10⁻⁵). Possessing a large number of BMI risk alleles does not increase endometrial cancer risk above that conferred by excess body weight among women of European descent. Thus, the GRS based on all current established BMI loci does not provide added value independent of BMI. Future studies are required to validate the unexpected observed relation between monogenic obesity syndrome genetic variants and endometrial cancer risk.     

Role of sleep timing in caloric intake and BMI

Sleep duration has been linked to obesity and there is also an emerging literature in animals demonstrating a relationship between the timing of feeding and weight regulation. However, there is a paucity of research evaluating timing of sleep and feeding on weight regulation in humans. The goal of this study was to evaluate the role of sleep timing in dietary patterns and BMI. Participants included 52 (25 females) volunteers who completed 7 days of wrist actigraphy and food logs. Fifty-six percent were "normal sleepers" (midpoint of <5:30 AM) and 44% were "late sleepers" (midpoint of sleep ≥5:30 AM). Late sleepers had shorter sleep duration, later sleep onset and sleep offset and meal times. Late sleepers consumed more calories at dinner and after 8:00 PM, had higher fast food, full-calorie soda and lower fruit and vegetable consumption. Higher BMI was associated with shorter sleep duration, later sleep timing, caloric consumption after 8:00 PM, and fast food meals. In multivariate models, sleep timing was independently associated with calories consumed after 8:00 PM and fruit and vegetable consumption but did not predict BMI after controlling for sleep duration. Calories consumed after 8:00 PM predicted BMI after controlling for sleep timing and duration. These findings indicate that caloric intake after 8:00 PM may increase the risk of obesity, independent of sleep timing and duration. Future studies should investigate the biological and social mechanisms linking timing of sleep and feeding in order to develop novel time-based interventions for weight management.     

Birth Weight,Growth and Feeding Pattern in Early Infancy Predict Overweight/Obesity Status at Two Years of Age: A Birth Cohort Study of Chinese Infants

Objectives

To investigate the early determinants of overweight and obesity status at age two years.

Methods

A total of 1098 healthy neonates (563 boys and 535 girls) were involved in this community-based prospective study in China. Data on body weight and length were collected at birth, the 3^rd and 24^th month. A self-administered questionnaire was used to collect data on social demography and feeding patterns of children, etc. Three multivariable logistic regression models were employed to make various comparisons of weight status, i.e., model 1 (obesity vs. non-obesity), model 2 (combined overweight and obesity vs. normal weight, and model 3 (obesity, overweight and normal weight).

Results

Prevalences of overweight/obesity (95^th >BMI ≥85^th p and BMI ≥95^th p, referring to WHO BMI standards) at 2 years of age are 15.8%/11.2% for boys and 12.9%/9.0% for girls, respectively. Being born with macrosomia (OR: 1.80–1.88), relatively greater BMI increment in the first 3 months (OR: 1.15–1.16) and bottle emptying by encouragement at age two (OR: 1.30–1.57) were found in all three models to be significant risk factors for higher BMI status at 2 years. Pre-pregnancy maternal BMI (OR: 1.09–1.12), paternal BMI (OR: 1.06), and mixed breastfeeding (OR: 1.54–1.57) or formula feeding (OR: 1.90–1.93) in the first month were identified as significant in models 2 and 3. Child-initiated bottle emptying at age two was observed to increase the risk of obesity by 1.31 times but only in model 1.

Conclusion

Fetal and early postnatal growth and feeding pattern appear to have significant impacts on early childhood overweight and obesity status independent of parental BMI. Policy-based and multidisciplinary approaches to promote breastfeeding and enhancement of feeding skills of care takers may be promising intervention strategies.     

Meta-analysis of genome-wide linkage studies in BMI and obesity

Objective: The objective was to provide an overall assessment of genetic linkage data of BMI and BMI‐defined obesity using a nonparametric genome scan meta‐analysis. Research Methods and Procedures: We identified 37 published studies containing data on over 31,000 individuals from more than >10,000 families and obtained genome‐wide logarithm of the odds (LOD) scores, non‐parametric linkage (NPL) scores, or maximum likelihood scores (MLS). BMI was analyzed in a pooled set of all studies, as a subgroup of 10 studies that used BMI‐defined obesity, and for subgroups ascertained through type 2 diabetes, hypertension, or subjects of European ancestry. Results: Bins at chromosome 13q13.2‐ q33.1, 12q23‐q24.3 achieved suggestive evidence of linkage to BMI in the pooled analysis and samples ascertained for hypertension. Nominal evidence of linkage to these regions and suggestive evidence for 11q13.3‐22.3 were also observed for BMI‐defined obesity. The FTO obesity gene locus at 16q12.2 also showed nominal evidence for linkage. However, overall distribution of summed rank p values <0.05 is not different from that expected by chance. The strongest evidence was obtained in the families ascertained for hypertension at 9q31.1‐qter and 12p11.21‐q23 (p < 0.01). Conclusion: Despite having substantial statistical power, we did not unequivocally implicate specific loci for BMI or obesity. This may be because genes influencing adiposity are of very small effect, with substantial genetic heterogeneity and variable dependence on environmental factors. However, the observation that the FTO gene maps to one of the highest ranking bins for obesity is interesting and, while not a validation of this approach, indicates that other potential loci identified in this study should be investigated further.     

ADRB2 and LEPR gene polymorphisms: synergistic effects on the risk of obesity in Japanese

The objective of the present study was to validate a recently reported synergistic effect between variants located in the leptin receptor (LEPR) gene and in the β-2 adrenergic receptor (ADRB2) gene on the risk of overweight/obesity. We studied a middle-aged/elderly sample of 4,193 nondiabetic Japanese subjects stratified according gender (1,911 women and 2,282 men). The LEPR Gln223Arg (rs1137101) variant as well as both ADRB2 Arg16Gly (rs1042713) and Gln27Glu (rs1042714) polymorphisms were analyzed. The primary outcome was the risk of overweight/obesity defined as BMI ≥25 kg/m(2), whereas secondary outcomes included the risk of a BMI ≥27 kg/m(2) and BMI as a continuous variable. None of the studied polymorphisms showed statistically significant individual effects, regardless of the group or phenotype studied. Haplotype analysis also did not disclose any associations of ADRB2 polymorphisms with BMI. However, dimensionality reduction-based models confirmed significant interactions among the investigated variants for BMI as a continuous variable as well as for the risk of obesity defined as BMI ≥27 kg/m(2). All disclosed interactions were found in men only. Our results provide external validation for a male specific ADRB2-LEPR interaction effect on the risk of overweight/obesity, but indicate that effect sizes associated with these interactions may be smaller in the population studied.     

The impact of weight classification on safety: timing steps to adapt to external constraints

Objectives:

The purpose of the current study was to evaluate how weight classification influences safety by examining adults’ ability to meet a timing constraint: walking to the pace of an audio metronome.

Methods:

With a cross-sectional design, walking parameters were collected as 55 adults with normal (n=30) and overweight (n=25) body mass index scores walked to slow, normal, and fast audio metronome paces.

Results:

Between group comparisons showed that at the fast pace, those with overweight body mass index (BMI) had longer double limb support and stance times and slower cadences than the normal weight group (all ps<0.05). Examinations of participants’ ability to meet the metronome paces revealed that participants who were overweight had higher cadences at the slow and fast paces (all ps<0.05).

Conclusions:

Findings suggest that those with overweight BMI alter their gait to maintain biomechanical stability. Understanding how excess weight influences gait adaptation can inform interventions to improve safety for individuals with obesity.     

Maternal Overweight and Obesity and Risks of Severe Birth-Asphyxia-Related Complications in Term Infants: A Population-Based Cohort Study in Sweden

Background

Maternal overweight and obesity increase risks of pregnancy and delivery complications and neonatal mortality, but the mechanisms are unclear. The objective of the study was to investigate associations between maternal body mass index (BMI) in early pregnancy and severe asphyxia-related outcomes in infants delivered at term (≥37 weeks).

Methods and Findings

A nation-wide Swedish cohort study based on data from the Medical Birth Register included all live singleton term births in Sweden between 1992 and 2010. Logistic regression analyses were used to obtain odds ratios (ORs) with 95% CIs for Apgar scores between 0 and 3 at 5 and 10 minutes, meconium aspiration syndrome, and neonatal seizures, adjusted for maternal height, maternal age, parity, mother''s smoking habits, education, country of birth, and year of infant birth. Among 1,764,403 term births, 86% had data on early pregnancy BMI and Apgar scores. There were 1,380 infants who had Apgar score 0–3 at 5 minutes (absolute risk  = 0.8 per 1,000) and 894 had Apgar score 0–3 at 10 minutes (absolute risk  = 0.5 per 1,000). Compared with infants of mothers with normal BMI (18.5–24.9), the adjusted ORs (95% CI) for Apgar scores 0–3 at 10 minutes were as follows: BMI 25–29.9: 1.32 (1.10–1.58); BMI 30–34.9: 1.57 (1.20–2.07); BMI 35–39.9: 1.80 (1.15–2.82); and BMI ≥40: 3.41 (1.91–6.09). The ORs for Apgar scores 0–3 at 5 minutes, meconium aspiration, and neonatal seizures increased similarly with maternal BMI. A study limitation was lack of data on effects of obstetric interventions and neonatal resuscitation efforts.

Conclusion

Risks of severe asphyxia-related outcomes in term infants increase with maternal overweight and obesity. Given the high prevalence of the exposure and the severity of the outcomes studied, the results are of potential public health relevance and should be confirmed in other populations. Prevention of overweight and obesity in women of reproductive age is important to improve perinatal health. Please see later in the article for the Editors'' Summary     

Developing Prediction Equations and a Mobile Phone Application to Identify Infants at Risk of Obesity

Background

Advancements in knowledge of obesity aetiology and mobile phone technology have created the opportunity to develop an electronic tool to predict an infant’s risk of childhood obesity. The study aims were to develop and validate equations for the prediction of childhood obesity and integrate them into a mobile phone application (App).

Methods and Findings

Anthropometry and childhood obesity risk data were obtained for 1868 UK-born White or South Asian infants in the Born in Bradford cohort. Logistic regression was used to develop prediction equations (at 6±1.5, 9±1.5 and 12±1.5 months) for risk of childhood obesity (BMI at 2 years >91^st centile and weight gain from 0–2 years >1 centile band) incorporating sex, birth weight, and weight gain as predictors. The discrimination accuracy of the equations was assessed by the area under the curve (AUC); internal validity by comparing area under the curve to those obtained in bootstrapped samples; and external validity by applying the equations to an external sample. An App was built to incorporate six final equations (two at each age, one of which included maternal BMI). The equations had good discrimination (AUCs 86–91%), with the addition of maternal BMI marginally improving prediction. The AUCs in the bootstrapped and external validation samples were similar to those obtained in the development sample. The App is user-friendly, requires a minimum amount of information, and provides a risk assessment of low, medium, or high accompanied by advice and website links to government recommendations.

Conclusions

Prediction equations for risk of childhood obesity have been developed and incorporated into a novel App, thereby providing proof of concept that childhood obesity prediction research can be integrated with advancements in technology.     

Early Life Predictors of Increased Body Mass Index among Indigenous Australian Children

Aboriginal and Torres Strait Islander Australians are more likely than non-Indigenous Australians to be obese and experience chronic disease in adulthood—conditions linked to being overweight in childhood. Birthweight and prenatal exposures are associated with increased Body Mass Index (BMI) in other populations, but the relationship is unclear for Indigenous children. The Longitudinal Study of Indigenous Children is an ongoing cohort study of up to 1,759 children across Australia. We used a multilevel model to examine the association between children’s birthweight and BMI z-score in 2011, at age 3-9 years, adjusted for sociodemographic and maternal factors. Complete data were available for 682 of the 1,264 children participating in the 2011 survey; we repeated the analyses in the full sample with BMI recorded (n=1,152) after multilevel multiple imputation. One in ten children were born large for gestational age, and 17% were born small for gestational age. Increasing birthweight predicted increasing BMI; a 1-unit increase in birthweight z-score was associated with a 0.22-unit (95% CI:0.13, 0.31) increase in childhood BMI z-score. Maternal smoking during pregnancy was associated with a significant increase (0.25; 95% CI:0.05, 0.45) in BMI z-score. The multiple imputation analysis indicated that our findings were not distorted by biases in the missing data. High birthweight may be a risk indicator for overweight and obesity among Indigenous children. National targets to reduce the incidence of low birthweight which measure progress by an increase in the population’s average birthweight may be ignoring a significant health risk; both ends of the spectrum must be considered. Interventions to improve maternal health during pregnancy are the first step to decreasing the prevalence of high BMI among the next generation of Indigenous children.     

Cardiometabolic risk factors for COVID-19 susceptibility and severity: A Mendelian randomization analysis

BackgroundEpidemiological studies report associations of diverse cardiometabolic conditions including obesity with COVID-19 illness, but causality has not been established. We sought to evaluate the associations of 17 cardiometabolic traits with COVID-19 susceptibility and severity using 2-sample Mendelian randomization (MR) analyses.Methods and findingsWe selected genetic variants associated with each exposure, including body mass index (BMI), at p < 5 × 10⁻⁸ from genome-wide association studies (GWASs). We then calculated inverse-variance-weighted averages of variant-specific estimates using summary statistics for susceptibility and severity from the COVID-19 Host Genetics Initiative GWAS meta-analyses of population-based cohorts and hospital registries comprising individuals with self-reported or genetically inferred European ancestry. Susceptibility was defined as testing positive for COVID-19 and severity was defined as hospitalization with COVID-19 versus population controls (anyone not a case in contributing cohorts). We repeated the analysis for BMI with effect estimates from the UK Biobank and performed pairwise multivariable MR to estimate the direct effects and indirect effects of BMI through obesity-related cardiometabolic diseases. Using p < 0.05/34 tests = 0.0015 to declare statistical significance, we found a nonsignificant association of genetically higher BMI with testing positive for COVID-19 (14,134 COVID-19 cases/1,284,876 controls, p = 0.002; UK Biobank: odds ratio 1.06 [95% CI 1.02, 1.10] per kg/m²; p = 0.004]) and a statistically significant association with higher risk of COVID-19 hospitalization (6,406 hospitalized COVID-19 cases/902,088 controls, p = 4.3 × 10⁻⁵; UK Biobank: odds ratio 1.14 [95% CI 1.07, 1.21] per kg/m², p = 2.1 × 10⁻⁵). The implied direct effect of BMI was abolished upon conditioning on the effect on type 2 diabetes, coronary artery disease, stroke, and chronic kidney disease. No other cardiometabolic exposures tested were associated with a higher risk of poorer COVID-19 outcomes. Small study samples and weak genetic instruments could have limited the detection of modest associations, and pleiotropy may have biased effect estimates away from the null.ConclusionsIn this study, we found genetic evidence to support higher BMI as a causal risk factor for COVID-19 susceptibility and severity. These results raise the possibility that obesity could amplify COVID-19 disease burden independently or through its cardiometabolic consequences and suggest that targeting obesity may be a strategy to reduce the risk of severe COVID-19 outcomes.

Aaron Leong and co-workers investigate causal risk factors for COVID-10 illness and severity.     

The Relationship between Native American Ancestry,Body Mass Index and Diabetes Risk among Mexican-Americans

Higher body mass index (BMI) is a well-established risk factor for type 2 diabetes, and rates of obesity and type 2 diabetes are substantially higher among Mexican-Americans relative to non-Hispanic European Americans. Mexican-Americans are genetically diverse, with a highly variable distribution of Native American, European, and African ancestries. Here, we evaluate the role of Native American ancestry on BMI and diabetes risk in a well-defined Mexican-American population. Participants were randomly selected among individuals residing in the Houston area who are enrolled in the Mexican-American Cohort study. Using a custom Illumina GoldenGate Panel, we genotyped DNA from 4,662 cohort participants for 87 Ancestry-Informative Markers. On average, the participants were of 50.2% Native American ancestry, 42.7% European ancestry and 7.1% African ancestry. Using multivariate linear regression, we found BMI and Native American ancestry were inversely correlated; individuals with <20% Native American ancestry were 2.5 times more likely to be severely obese compared to those with >80% Native American ancestry. Furthermore, we demonstrated an interaction between BMI and Native American ancestry in diabetes risk among women; Native American ancestry was a strong risk factor for diabetes only among overweight and obese women (OR = 1.190 for each 10% increase in Native American ancestry). This study offers new insight into the complex relationship between obesity, genetic ancestry, and their respective effects on diabetes risk. Findings from this study may improve the diabetes risk prediction among Mexican-American individuals thereby facilitating targeted prevention strategies.     

Association between Obesity and Cardiometabolic Health Risk in Asian-Canadian Sub-Groups

Objectives

To quantify and compare the association between the World Health Organizations’ Asian-specific trigger points for public health action [‘increased risk’: body mass index (BMI) ≥23 kg/m², and; ‘high risk’: BMI ≥27.5 kg/m²] with self-reported cardiovascular-related conditions in Asian-Canadian sub-groups.

Methods

Six cycles of the Canadian Community Health Survey (2001–2009) were pooled to examine BMI and health in Asian sub-groups (South Asians, Chinese, Filipino, Southeast Asians, Arabs, West Asians, Japanese and Korean; N = 18 794 participants, ages 18–64 y). Multivariable logistic regression, adjusting for demographic, lifestyle characteristics and acculturation measures, was used to estimate the odds of cardiovascular-related health (high blood pressure, heart disease, diabetes, ‘at least one cardiometabolic condition’) outcomes across all eight Asian sub-groups.

Results

Compared to South Asians (OR = 1.00), Filipinos had higher odds of having ‘at least one cardiometabolic condition’ (OR = 1.29, 95% CI: 1.04–1.62), whereas Chinese (0.63, 0.474–0.9) and Arab-Canadians had lower odds (0.38, 0.28–0.51). In ethnic-specific analyses (with ‘acceptable’ risk weight as the referent), ‘increased’ and ‘high’ risk weight categories were the most highly associated with ‘at least one cardiometabolic condition’ in Chinese (‘increased’: 3.6, 2.34–5.63; ‘high’: 8.9, 3.6–22.01). Compared to normal weight South Asians, being in the ‘high’ risk weight category in all but the Southeast Asian, Arab, and Japanese ethnic groups was associated with approximately 3-times the likelihood of having ‘at least one cardiometabolic condition’.

Conclusion

Differences in the association between obesity and cardiometabolic health risks were seen among Asian sub-groups in Canada. The use of WHO’s lowered Asian-specific BMI cut-offs identified obesity-related risks in South Asian, Filipino and Chinese sub-groups that would have been masked by traditional BMI categories. These findings have implications for public health messaging, especially for ethnic groups at higher odds of obesity-related health risks.     

Obesity and cardiovascular risk factors among men and women aged 40 years and older in a rural area of Japan

Obesity is one of the most common health problems, and is recognized worldwide as an "escalating epidemic." For the establishment of an obesity-prevention strategy in Japan, it is important to assess the association between obesity and cardiovascular risk factors. Therefore, we conducted anthropometric measures of obesity and investigated the association of obesity with cardiovascular risk factors such as hypertension, diabetes, and dyslipidemia among community-dwelling men (N=85) and women (N=173) aged 40 years and older. Height, weight, and waist circumference (WC) were measured, and body mass index (BMI) was calculated. Subjects with a BMI> or =25 kg/m(2) were considered obese (BMI obesity), while men with a WC> or =85 cm and women with a WC> or =90 cm were classified as obese (WC obesity). In the present study, we defined 'obesity' as a BMI> or =25 kg/m(2) or a WC> or =85 cm for men, and a BMI> or =25 kg/m(2) or a WC> or =90 cm for women. The results of an age- and sex-adjusted logistic regression analysis indicated that BMI obesity was associated with dyslipidemia (p=0.04), WC obesity was associated with dyslipidemia (p=0.07), and 'obesity' was associated with diabetes (p=0.06) and dyslipidemia (p=0.01). These results emphasize the importance of preventing obesity in Japan. Therefore, healthcare professionals should measure BMI and WC in order to enhance their assessment of cardiovascular risk.