Growth faltering is widespread in many low- and middle-income countries, but its effects on childhood bone mass accrual are unknown. The objective of this study was to estimate associations between length (conditional length-for-age z-scores, cLAZ) and weight (conditional weight-for-age z-scores, cWAZ) gain in 3 age intervals (ages 0-6, 6-12, 12-24 months) with dual-energy x-ray absorptiometry (DEXA)-derived measures of bone mass (total-body-less-head (TBLH) bone mineral content (BMC), areal bone mineral density (aBMD), and bone area) at 4 years of age.

Associations between interval-specific growth parameters (cLAZ and cWAZ) and bone outcomes were estimated using linear regression models, adjusted for maternal, child, and household characteristics.

Data collection occurred in Dhaka, Bangladesh.

599 healthy children enrolled in the BONe and mUScle Health in Kids Study.

cLAZ in each age interval was positively associated with TBLH BMC, aBMD, and bone area at 4 years; however, associations attenuated towards null upon adjustment for concurrent height-for-age z-scores (HAZ) at age 4 years and confounders. cWAZ from 0-6 and 6-12 months was not associated with bone mass, but every SD increase in cWAZ between 12-24 months was associated with greater BMC (7.6g; 95%CI:3.2, 12.0) and aBMD (0.008g/cm2; 95%CI:0.003, 0.014) after adjusting for concurrent WAZ, HAZ, and confounders.

Associations of linear growth (birth to 2 years) with bone mass at age 4 years were explained by concurrent HAZ. Weight gain in the second year of life may increase bone mass independently of linear growth in settings where growth faltering is common.

We need to better understand the risk factors and predictors of medication-related weight gain to improve metabolic health of individuals with schizophrenia. This study explores how trajectories of antipsychotic medication (AP) use impact body weight early in the course of schizophrenia.

We recruited 92 participants with first-episode psychosis (FEP, n = 92) during their first psychiatric hospitalization. We prospectively collected weight, body mass index (BMI), metabolic markers, and exact daily medication exposure during 6-week hospitalization. We quantified the trajectory of AP medication changes and AP polypharmacy using a novel approach based on meta-analytical ranking of medications and tested it as a predictor of weight gain together with traditional risk factors.

Most people started treatment with risperidone (n = 57), followed by olanzapine (n = 29). Then, 48% of individuals remained on their first prescribed medication, while 33% of people remained on monotherapy. Almost half of the individuals (39/92) experienced escalation of medications, mostly switch to AP polypharmacy (90%). Only baseline BMI was a predictor of BMI change. Individuals in the top tercile of weight gain, compared to those in the bottom tercile, showed lower follow-up symptoms, a trend for longer prehospitalization antipsychotic treatment, and greater exposure to metabolically problematic medications.

Early in the course of illness, during inpatient treatment, baseline BMI is the strongest and earliest predictor of weight gain on APs and is a better predictor than type of medication, polypharmacy, or medication switches. Baseline BMI predicted weight change over a period of weeks, when other traditional predictors demonstrated a much smaller effect.

To examine how the associations between meal consumption and BMI over 8 years differ by weight status in a sample of adolescents.

Longitudinal, population-based study. Breakfast, lunch and dinner consumption and BMI were self-reported. Linear regressions were used to examine how the associations between meal consumption and BMI differed by weight status.

Adolescents in the Minneapolis/St. Paul metropolitan area.

Adolescents (n 1,471) were surveyed as part of the EAT 2010–2018 in 2009–2010 (Mage = 14·3 years) and 2017–2018 (Mage = 22·0 years).

The prevalence of regular breakfast, lunch and dinner consumption (≥ 5 times/week) ranged from 45 to 65 %, 75 to 89 % and 76 to 94 %, respectively, depending on weight status category. Among adolescents with a sex- and age-specific BMI < 15th percentile, regular consumptions of breakfast, lunch and dinner during adolescence were positively associated with BMI in emerging adulthood compared with irregular consumption of breakfast, lunch and dinner (<5 times/week) after adjustment for socio-demographic characteristics (β = 5·43, β = 5·39 and β = 6·46, respectively; all P-values <0·01). Among adolescents in the BMI 15–85th and 85–95th percentiles, regular consumptions of breakfast, lunch and dinner were positively associated with BMI but to a lesser extent (P-values <0·01). For participants with a BMI ≥ 95th percentile, regular consumptions of breakfast, lunch and dinner were positively associated with BMI, but the associations were not statistically significant (P-values > 0·05).

The relationship between meal consumption during adolescence and BMI in emerging adulthood differs by adolescent weight status. Future studies should investigate underlying factors related to meal consumption routines and BMI.

Depression is a risk factor for dementia and weight change can appear as a symptom of depression. However, the association between weight change after the diagnosis of depression and the risk of dementia is poorly established. This study aimed to investigate the association between weight change before and after a diagnosis of depression with the subsequent risk of dementia.

The National Health Insurance Sharing Service database was used. 1 308 730 patients aged ⩾40 years diagnosed with depression were identified to be eligible. Weight changes after their depression diagnosis were categorized and subsequent incidence of dementia was followed up.

During an average follow-up period of 5.2 years (s.d., 2.0 years), 69 373 subjects were newly diagnosed with all-cause dementia (56 351 were Alzheimer's disease and 6877 were vascular dementia). Regarding all outcomes, compared to those with a minimal weight change (−5 to 5%), all groups with weight gain or loss showed increased risks of dementia after adjusting potential risk factors for dementia, in all analysis models with a dose–response relationship, showing a U-shaped association.

Weight change as a symptom of depression could be a predictor for the future development of dementia.

To evaluate the associations of ultra-processed food (UPF) consumption and obesity indicators among individuals with and without type 1 diabetes mellitus (T1DM) from the Coronary Artery Calcification in Type 1 Diabetes cohort study.

A secondary analysis. The consumption of UPF was assessed using the dietary data collected with the Harvard FFQ, and each food item was categorised according to the NOVA food processing classification. Height, weight and waist circumference were measured at baseline and after a mean of 14·6-year follow-up. Generalised estimating equations stratified by diabetes status were used to assess the associations between UPF intake and obesity indicators over 14 years of follow-up.

USA.

A total of 600 adults (256 T1DM and 344 non-diabetic controls) aged 39 ± 9·1 years at baseline and followed up for over 14 years were included.

Participants with T1DM consumed significantly more UPF than non-diabetic controls at baseline: 7·6 ± 3·8 v. 6·6 ± 3·4 servings per day of UPF, respectively (P < 0·01). Participants with T1DM and with the highest UPF intake had the highest weight (βQ4 v. Q1 = 3·07) and BMI (βQ4 v. Q1 = 1·02, all P < 0·05) compared with those with the lowest UPF intake. Similar positive associations were observed in non-diabetic controls.

Individuals with T1DM may consume more UPF than non-diabetic controls. Positive associations between UPF consumption and obesity indicators suggest that limiting UPF can be recommended for obesity prevention and management. Further research is needed to confirm these findings.

Individuals with a first episode of psychosis (FEP) show rapid weight gain during the first months of treatment, which is associated with a reduction in general physical health. Although genetics is assumed to be a significant contributor to weight gain, its exact role is unknown.

We assembled a population-based FEP cohort of 381 individuals that was split into a Training (n = 224) set and a Validation (n = 157) set to calculate the polygenic risk score (PRS) in a two-step process. In parallel, we obtained reference genome-wide association studies for body mass index (BMI) and schizophrenia (SCZ) to examine the pleiotropic landscape between the two traits. BMI PRSs were added to linear models that included sociodemographic and clinical variables to predict BMI increase (∆BMI) in the Validation set.

The results confirmed considerable shared genetic susceptibility for the two traits involving 449 near-independent genomic loci. The inclusion of BMI PRSs significantly improved the prediction of ∆BMI at 12 months after the onset of antipsychotic treatment by 49.4% compared to a clinical model. In addition, we demonstrated that the PRS containing pleiotropic information between BMI and SCZ predicted ∆BMI better at 3 (12.2%) and 12 months (53.2%).

We prove for the first time that genetic factors play a key role in determining ∆BMI during the FEP. This finding has important clinical implications for the early identification of individuals most vulnerable to weight gain and highlights the importance of examining genetic pleiotropy in the context of medically important comorbidities for predicting future outcomes.

Previous meta-analyses have shown that almost all antipsychotics are associated with weight gain. However, mean weight gain is not informative about clinically relevant weight gain or weight loss.

To provide further insight into the more severe body weight changes associated with antipsychotic use, we assessed the proportion of patients with clinically relevant weight gain (CRWG) and clinically relevant weight loss (CRWL), defined as ≥7% weight gain and ≥7% weight loss.

We searched PubMed, Embase and PsycInfo for randomised controlled trials of antipsychotics that reported CRWG and CRWL in study populations aged 15 years or older. We conducted meta-analyses stratified by antipsychotic and study duration using a random-effects model. We performed meta-regression analyses to assess antipsychotic-naive status and psychiatric diagnosis as modifiers for CRWG. PROSPERO: CRD42020204734.

We included 202 articles (201 studies). Almost all included antipsychotics were associated with CRWG. For CRWL, available data were too limited to draw firm conclusions. For some antipsychotics, CRWG was more pronounced in individuals who were antipsychotic-naive than in individuals switching to another antipsychotic. Moreover, a longer duration of antipsychotic use was associated with more CRWG, but not CRWL. For some antipsychotics, CRWG was higher in people diagnosed with schizophrenia, but this was inconsistent.

Switching antipsychotic medication is associated with both weight gain and weight loss, but the level of CRWG is higher than CRWL in antipsychotic-switch studies. CRWG was more pronounced in antipsychotic-naive patients, highlighting their vulnerability to weight gain. The impact of diagnosis on CRWG remains inconclusive.

Many psychotropic drugs can induce weight gain with differences in their metabolic risk profiles (i.e. high, medium or low-risk).

To compare the weight evolution of patients switching versus patients keeping their psychotropic drugs with different risk-profiles.

Data for patients switching or keeping the same drug were obtained from the Psyclin (from 2007 to 2015) and Psymetab (2007- 2019) cohort studies, conducted at the Lausanne University Hospital, Switzerland. Patients either switched from a high to a low-risk, a high to a medium-risk, a medium to a low-risk drug, or for a drug with the same risk category. Patients not switching either kept a high, medium or low-risk drug. The evolution of weight is currently being analyzed using a linear mixed-effect model.

Preliminary results showed that switching from a high to low-risk molecule had the strongest impact on weight changes. The analysis being ongoing, the quantitative results will be presented at the congress.

Switching from a high-risk to a low-risk molecule is likely to have the strongest impact on weight changes.

No significant relationships.

People with psychosis are at higher risk of cardiovascular events, partly explained by a higher predisposition to gain weight. This has been observed in studies on individuals with a first-episode psychosis (FEP) at short and long term (mainly up to 1 year) and transversally at longer term in people with chronic schizophrenia. However, there is scarcity of data regarding longer-term (above 3-year follow-up) weight progression in FEP from longitudinal studies. The aim of this study is to evaluate the longer-term (10 years) progression of weight changes and related metabolic disturbances in people with FEP.

Two hundred and nine people with FEP and 57 healthy participants (controls) were evaluated at study entry and prospectively at 10-year follow-up. Anthropometric, clinical, and sociodemographic data were collected.

People with FEP presented a significant and rapid increase in mean body weight during the first year of treatment, followed by less pronounced but sustained weight gain over the study period (Δ15.2 kg; SD 12.3 kg). This early increment in weight predicted longer-term changes, which were significantly greater than in healthy controls (Δ2.9 kg; SD 7.3 kg). Weight gain correlated with alterations in lipid and glycemic variables, leading to clinical repercussion such as increments in the rates of obesity and metabolic disturbances. Sex differences were observed, with women presenting higher increments in body mass index than men.

This study confirms that the first year after initiating antipsychotic treatment is the critical one for weight gain in psychosis. Besides, it provides evidence that weight gain keep progressing even in the longer term (10 years), causing relevant metabolic disturbances.

Maternal health in pregnancy and birth outcomes were compared between pre- and post-Varzaghan earthquake.

In this retrospective descriptive study, before and after the earthquake, 550 and 450 women were enrolled respectively. Neonatal weight, height, and head circumference, as well as maternal weight gain and hemoglobin (Hb) levels were obtained using medical records at health centers. Chi-square test and Independent t-test were used to analyze differences in pregnancy outcomes. A P-value less than 0.05 was considered significant.

A significant increase in inadequate gestational weight gain (44.1% vs 58.9%) was observed (P = 0.043) before and after the earthquake. The mean hemoglobin level in the first trimester before the earthquake was significantly higher than after the earthquake (P = 0.001). Before–after earthquake comparisons showed that the mean birth weight, birth height, and birth head circumference were decreased significantly (P < 0.05). In addition, the rates of preterm birth (18.91% vs 10.90%), abortion (17.11% vs 10.54%), and stillbirth (3.78% vs 1.82%) were increased significantly after the earthquake (P < 0.05).

Earthquake causes inadequate gestational weight gain and decreased hemoglobin levels, which lead to adverse birth outcomes. More longitudinal and well-designed studies are desired to investigate the longitudinal consequences of disasters on susceptible groups.

The endogenous opioid system affects metabolism, including weight regulation. Evidence from preclinical and clinical studies provides a rationale for targeting this system to mitigate weight-related side effects of antipsychotics. This review describes the role of the opioid system in regulating weight and metabolism, examines the effects of opioid receptor antagonism on those functions, and explores the use of opioid antagonists to mitigate antipsychotic-associated weight gain and/or metabolic effects.

A PubMed literature search was conducted to identify representative opioid antagonists and associated preclinical and clinical studies examining their potential for the regulation of weight and metabolism.

The mu opioid receptor (MOR), delta opioid receptor (DOR), and kappa opioid receptor (KOR) types have overlapping but distinct patterns of central and peripheral expression, and each contributes to the regulation of body weight and metabolism. Three representative opioid antagonists (eg, naltrexone, samidorphan, and LY255582) were identified for illustration. These opioid antagonists differed in their receptor binding and pharmacokinetic profiles, including oral bioavailability, systemic clearance, and half-life, and were associated with varying effects on food intake, energy utilization, and metabolic dysregulation.

Preclinical and clinical data suggest that antagonism of the endogenous opioid system is a mechanism to address antipsychotic-associated weight gain and metabolic dysregulation. However, evidence suggests that the differing roles of MOR, DOR, and KOR in metabolism, together with the differences in receptor binding, pharmacokinetic, and functional activity profiles of the opioid receptor antagonists discussed in this review, likely contribute to their differential pharmacodynamic effects and clinical outcomes observed regarding antipsychotic-associated weight gain.

No significant relationships.

The lockdown for the COVID-19 pandemic affects lifestyle patterns globally and impacts children and adolescents. This study aims to assess the effect of the lockdown on body weight, eating habits, and physical activity of Jordanian youth (children and adolescents).

A cross-sectional study was conducted on a sample of 477 Jordanian children and adolescents aged 6-17 y. The study tool was a structured validated questionnaire. It comprised 4 parts, including a general description of the study purpose, sociodemographic and anthropometric data, physical activity data, and food intake pattern. Questions were reported before and during lockdown. Changes in outcomes over the 2 study time points were evaluated.

After the lockdown period, the mean body weight and body mass index for age Z-scores (BAZ) showed a significant increase (P < 0.001) compared with before the lockdown period. More than 50% of the subjects reported that they spent more than 3 h in front of the screen during the lockdown. The percent of subjects who watched TV for more than 3h was increased. Moreover, physical inactivity was increased significantly during the lockdown. All food groups consumption was significantly increased during the lockdown compared to before the lockdown.

COVID-19 lockdown period was characterized by an increase in the use of screen-based devices, lower physical activity, uncontrolled food intake, and weight gain.