Short-term Weight Change Research Articles

Associations of short-term changes in obesity indices with all-cause mortality and cardiovascular disease.

This study aimed to investigate how short-term changes (1-, 3-, and 5-year) in obesity measures affect mortality and cardiovascular disease (CVD) risk. We analyzed longitudinal data from the MJ Health Centre (n = 43,304 for the 1-year study; 24,295 for the 3-year study; 16,138 for the 5-year study) with median follow-up periods of 15.8, 13.9, and 12.3 years, respectively. Associations of short-term obesity indices changes with mortality and Framingham Risk Score changes were explored using time-dependent coefficient Cox regression models, restricted cubic splines, and multivariable linear regression models. All-cause mortality was negatively associated with short-term weight and BMI changes, with greater reductions causing poorer outcomes. Compared with stable groups, short-term reduced weight and BMI were associated with greater risks of all-cause mortality and CVD-specific mortality (5-year study only). Also, either 1- and 3-year reducedor 3-year increased waist circumference and waist to height ratio were related to higher all-cause and CVD deaths than stable groups, respectively. Nonlinear relationships indicated lower cutoff values for short-term changes in obesity indices in predicting all-cause mortality. Decreased obesity indices significantly improved CVD profiles. Short-term changes in obesity indices show complex mortality risks, urging personalized approaches beyond a simple weight loss focus.

Short-Term Changes in Weight, Body Composition, and Metabolic Biomarkers After Laparoscopic Sleeve Gastrectomy in Patients with Obesity: A Comparative Prospective Study.

To investigate the changes in weight, body composition, and metabolic biomarkers in patients with obesity after laparoscopic sleeve gastrectomy (LSG) and compare those changes between patients with and without metabolic syndrome (MS). This retrospective longitudinal study included 76 patients who underwent LSG, among whom 32 had complete 1-year postoperative body composition and metabolic biomarkers. Body composition was measured by quantitative CT. Weight changes were compared between the MS and non-MS groups at 1-, 3-, 6-, and 12-month post-LSG in all patients; changes in body compositions and metabolic biomarkers from one day pre-LSG to 12-month post-LSG were also compared in those 32 patients. MS occurred in 46% (35/76) of all patients and 44% (14/32) of patients with complete follow-up data. Excess weight loss was lower in the MS group than that in the non-MS group at 1-, 3-, 6-, and 12-month post-LSG; the 12-month difference was significant (MS vs. non-MS: 0.91 ± 0.22 vs. 1.07 ± 0.42, P = 0.04). The greatest rate of visceral fat area (VFA) change occurred 12-month post-LSG in both the non-MS [0.62(0.55,0.7)] and MS [0.6(0.51,0.63)] groups. The most significant reduction in ectopic fat occurred in liver fat (LF) [non-MS, 0.45(0.22,0.58); MS, 0.39(0.23,0.58)]. LGS significantly improves weight, body composition, and metabolic biomarkers in populations with obesity, regardless of whether they have MS. Among the body composition, VFA and LF were the most significantly improved body composition measurements.

Association of body weight change with all-cause and cause-specific mortality: A nationwide population-based study

AimsWeight gain and loss increase the risk of all-cause mortality. This study explored the association of short-term weight change with all-cause and cause-specific mortality in middle-aged to older people. MethodsThis was a 8.4-year retrospective cohort study of 645,260 adults aged 40 to 80 who underwent health checkups twice within a 2-year interval between January 2009 and December 2012. Cox analyses were used to analyze the association between short-term weight change and all-cause and cause-specific mortality. ResultsWeight gain and loss were associated with an increased risk of all-cause mortality: the hazard ratios were 2.05 (95% confidence interval [CI], 1.93–2.16), 1.21 (95% CI, 1.16–1.25), 1.12 (95% CI 1.08–1.17), and 1.60 (95% CI, 1.49–1.70) for the severe weight loss, moderate weight loss, moderate weight gain, and severe weight gain groups, respectively. This U-shaped association was also seen between weight change and cause-specific mortality. Within the weight loss group, those who regained weight after two years had a reduced mortality risk. ConclusionsIn a middle-aged and elderly population, weight gain or loss of more than 3% during a 2-year interval was related to an elevated risk of all-cause and cause-specific mortality.

Association of Short-Term Changes in Menstrual Frequency, Medication Use, Weight and Exercise on Bone Mineral Density in College-Aged Women.

To evaluate if experiencing a short-term exposure (18-months) to factors such as menstrual irregularities, dieting, changes in exercise or body weight, and medication usage is associated with bone mineral density (BMD) in college-aged females. A retrospective survey assessing health behaviors during a recent 18-month time period and a DXA scan were completed in 641 females. A total of 45.5% of participants reported amenorrhea during the 18-month time period. Those who experienced amenorrhea had lower femoral neck BMD (p = 0.018), trochanter (p = 0.018) and spine BMD (p = 0.022) compared to eumenorrheic women. Lifetime oral contraceptive usage longer than six months was negatively associated with BMD at femoral neck (p = 0.018) and total hip (p = 0.021). Women who lost weight trended towards having the lowest BMD at all sites compared to women who gained weight. Following a very-low calorie diet during the time period was negatively correlated with spine BMD (p = 0.001). Time spent in vigorous and very vigorous activity was weakly correlated with some hip BMD sites but time spent in extremely vigorous activity was not. In conclusion, females who experienced weight loss, amenorrhea, or a very low-calorie diet within an 18-month period of time in young adulthood had lower BMD. Additionally, oral contraceptive usage for longer than six months during their lifetime was associated with lower BMD.

Breath analyzer for personalized monitoring of exercise-induced metabolic fat burning

Obesity increases the risk of chronic diseases, such as type 2 diabetes mellitus, dyslipidemia, and cardiovascular diseases. Simple anthropometric measurements have time limitations in reflecting short-term weight and body fat changes. Thus, for detecting, losing or maintaining weight in short term, it is desirable to develop portable/compact devices to monitor exercise-induced fat burn in real time. Exhaled breath acetone and blood-borne β-hydroxybutyric acid (BOHB) are both correlated biomarkers of the metabolic fat burning process that takes place in the liver, predominantly post-exercise. Here, we have fabricated a compact breath analyzer for convenient, noninvasive and personalized estimation of fat burning in real time in a highly automated manner. The analyzer collects end-tidal breath in a standardized, user-friendly manner and it is equipped with an array of four low-power MEMS sensors for enhanced accuracy; this device presents a combination of required and desirable design features in modern portable/compact breath analyzers. We analyzed the exhaled breath (with our analyzer) and the blood samples (for BOHB) in 20 participants after exercise; we estimated the values of BOHB, as indication of the fat burn, resulting in Pearson coefficient r between the actual and predicted BOHB of 0.8. The estimation uses the responses from the sensor array in our analyzer and demographic and anthropometric information from the participants as inputs to a machine learning algorithm. The system and approach herein may help guide regular exercise for weight loss and its maintenance based on individuals’ own metabolic changes.

Association Between Weight Change and Increased Likelihood of Abdominal Aortic Calcification Among Men.

Objective We aimed to explore the effect of weight change on abdominal aortic calcification (AAC) among men.MethodsData were obtained from the 2013 to 2014 National Health and Nutrition Examination Survey (NHANES). Self-reported cardiovascular disease patients were excluded. Lateral spine images were used to quantify AAC score and severe AAC was defined as a AAC score greater than 6. Weight change over a 10-year period was defined as long-term weight change, and weight change over a 1-year period was defined as short-term weight change. The relationship between long-term and short-term weight change with AAC grade was estimated by using multivariable regression analysis and subgroup analysis.ResultsAfter adjusting for covariates, weight gain, especially severe weight gain (> 10 kg), was associated with increased likelihood of AAC and severe AAC both in the short term (1 year) and long term (10 years) among men when compared to stable weight change, while long-term weight loss could also lead to an increased likelihood of AAC and severe AAC.ConclusionStable body weight might be a predictor of a lower risk of AAC and severe AAC among men in the long term and short term.

Dehydration and Rapid Weight Gain Between Weigh-in and Competition in Judo Athletes: The Differences between Women and Men

ABSTRACT This study aimed to investigate the sex differences in short-term weight change and hydration status in judo athletes. Thirty-five men and 15 women judo athletes voluntarily participated in this descriptive and repeated measures design study. Body mass, urine-specific gravity (USG), and body composition of the athletes were measured at the official weigh-in and the competition day’s morning. Body mass of the athletes increased during recovery time between official weigh-in and before the competition (time factor; F1-48 = 71.81, p < 0.001), this increase was higher in men athletes compared to women athletes (time-sex interaction; F1-48 = 6.56, p = 0.01). With RWG, USG values of the women and men athletes decreased (time factor; F1-48 = 8.53, p = 0.005). However, most of the athletes were still in significant or serious dehydration state. Unchanged values of total body water rates (TBW) supported dehydration in athletes before the competition (time factor, F1-48 = 2.9, p = 0.091; time-sex interaction; F1-48= 2.4, p = 0.122). The findings of the study indicated that RWG was higher in men athletes compared to women athletes, but hydration status was not affected by sex factor.Notwithstanding 15 hours of recovery between official weigh-in and the start of the competition, judo athletes were still in dehydrated state despite remaining within the limit set for RWG.

Machine Learning Analysis to Identify Digital Behavioral Phenotypes for Engagement and Health Outcome Efficacy of an mHealth Intervention for Obesity: Randomized Controlled Trial.

BackgroundThe digital health care community has been urged to enhance engagement and clinical outcomes by analyzing multidimensional digital phenotypes.ObjectiveThis study aims to use a machine learning approach to investigate the performance of multivariate phenotypes in predicting the engagement rate and health outcomes of digital cognitive behavioral therapy.MethodsWe leveraged both conventional phenotypes assessed by validated psychological questionnaires and multidimensional digital phenotypes within time-series data from a mobile app of 45 participants undergoing digital cognitive behavioral therapy for 8 weeks. We conducted a machine learning analysis to discriminate the important characteristics.ResultsA higher engagement rate was associated with higher weight loss at 8 weeks (r=−0.59; P<.001) and 24 weeks (r=−0.52; P=.001). Applying the machine learning approach, lower self-esteem on the conventional phenotype and higher in-app motivational measures on digital phenotypes commonly accounted for both engagement and health outcomes. In addition, 16 types of digital phenotypes (ie, lower intake of high-calorie food and evening snacks and higher interaction frequency with mentors) predicted engagement rates (mean R2 0.416, SD 0.006). The prediction of short-term weight change (mean R2 0.382, SD 0.015) was associated with 13 different digital phenotypes (ie, lower intake of high-calorie food and carbohydrate and higher intake of low-calorie food). Finally, 8 measures of digital phenotypes (ie, lower intake of carbohydrate and evening snacks and higher motivation) were associated with a long-term weight change (mean R2 0.590, SD 0.011).ConclusionsOur findings successfully demonstrated how multiple psychological constructs, such as emotional, cognitive, behavioral, and motivational phenotypes, elucidate the mechanisms and clinical efficacy of a digital intervention using the machine learning method. Accordingly, our study designed an interpretable digital phenotype model, including multiple aspects of motivation before and during the intervention, predicting both engagement and clinical efficacy. This line of research may shed light on the development of advanced prevention and personalized digital therapeutics.Trial RegistrationClinicalTrials.gov NCT03465306; https://clinicaltrials.gov/ct2/show/NCT03465306

A meta-analysis comparing short-term weight and cardiometabolic changes between olanzapine/samidorphan and olanzapine

This study compared weight and cardiometabolic changes after short-term treatment of olanzapine/samidorphan and olanzapine. Eligible criteria for an included trial were ≤ 24 weeks, randomized controlled trials (RCTs) that compared olanzapine/samidorphan and olanzapine treatments in patients/healthy volunteers and reported weight or cardiometabolic outcomes. Three databases were searched on October 31, 2020. Primary outcomes included weight changes and all-cause dropout rates. Standardized mean differences (SMDs) and risk ratios (RRs) were computed and pooled using a random-effect model. This meta-analysis included four RCTs (n = 1195). The heterogeneous data revealed that weight changes were not significantly different between olanzapine/samidorphan and olanzapine groups (4 RCTs, SDM = − 0.19, 95% CI − 0.45 to 0.07, I2 = 75%). The whole-sample, pooled RR of all-cause dropout rates (4 RCTs, RR = 1.02, 95% CI 0.84 to 1.23, I2 = 0%) was not significant different between olanzapine/samidorphan and olanzapine groups. A lower percentage of males and a lower initial body mass index were associated with the greater effect of samidorphan in preventing olanzapine-induced weight gain. Current evidence is insufficient to support the use of samidorphan to prevent olanzapine-induced weight gain and olanzapine-induced cardiometabolic abnormalities. Samidorphan is well accepted by olanzapine-treated patients.

Multiomic Predictors of Short-Term Weight Loss and Clinical Outcomes During a Behavioral-Based Weight Loss Intervention.

Identifying predictors of weight loss and clinical outcomes may increase understanding of individual variability in weight loss response. We hypothesized that baseline multiomic features, including DNA methylation (DNAme), metabolomics, and gut microbiome, would be predictive of short-term changes in body weight and other clinical outcomes within a comprehensive weight loss intervention. Healthy adults with overweight or obesity (n=62, age 18-55years, BMI 27-45kg/m2 , 75.8% female) participated in a 1-year behavioral weight loss intervention. To identify baseline omic predictors of changes in clinical outcomes at 3 and 6months, whole-blood DNAme, plasma metabolites, and gut microbial genera were analyzed. A network of multiomic relationships informed predictive models for 10 clinical outcomes (body weight, waist circumference, fat mass, hemoglobin A1c , homeostatic model assessment of insulin resistance, total cholesterol, triglycerides, C-reactive protein, leptin, and ghrelin) that changed significantly (P<0.05). For eight of these, adjusted R2 ranged from 0.34 to 0.78. Our models identified specific DNAme sites, gut microbes, and metabolites that were predictive of variability in weight loss, waist circumference, and circulating triglycerides and that are biologically relevant to obesity and metabolic pathways. These data support the feasibility of using baseline multiomic features to provide insight for precision nutrition-based weight loss interventions.

Is Activity Silent Working Memory Simply Episodic Memory?

Working memory (WM) maintains task-relevant information in a state ready for processing. While traditional theories assume that sustained neuronal activity is responsible for WM, the Activity Silent WM (ASWM) account proposes that maintenance can also be supported by short-term synaptic weight changes. Here, we argue that the evidence for ASWM can be explained more parsimoniously by the involvement of episodic memory (EM) in WM tasks. Like ASWM, EM relies on rapid synaptic modification that is also activity silent; however, while ASWM posits transient synaptic modifications, EM traces persist over longer time periods. We discuss how, despite this difference, well-established EM mechanisms can account for the key findings attributed to ASWM, and describe predictions of this account.

Combination Pharmacological Interventions for Smoking Cessation and Post-Cessation Weight Gain: A Meta-Analysis of Randomized Controlled Trials

Objective: Limited evidence exists concerning whether combined pharmacotherapy is more effective than monotherapy for increased smoking abstinence and post-cessation weight gain prevention. This research investigated the effect of combined pharmacotherapy on smoking abstinence and post-cessation weight change.Methods: A meta-analytic review of Randomized Controlled Trials (RCTs) published between January 1990 and July 2016 was conducted across PubMed, International Pharmaceutical Abstracts, Web of Science, and Cochrane Library. Aggregate fixed effects were estimated for continuous abstinence and mean post-cessation weight change. Keyword search terms included: “smoking cessation,” “naltrexone”, “varenicline”, and “bupropion”.Results: Eight RCTs with 2,513 participants were included. Aggregate fixed effect estimates revealed an increase in continuous smoking abstinence (OR = 1.81, p < .001) and mean decrease in post-cessation weight change (-.15 kg, p = .001). Decreased weight change was observed at 6-8 weeks follow-up (-.14 kg, p = .02). Increased mean weight change was observed among varenicline plus nicotine patch abstainers (.21 kg, p = .01), whereas bupropion plus NRT pharmacotherapies showed decreased mean weight change (-.15 kg, p = .01).Conclusion: Combination pharmacotherapy generates increased smoking abstinence and small short-term post-cessation weight change among abstainers, particularly among bupropion plus NRT when compared against varenicline plus nicotine patch.

Weight Change and All-Cause Mortality in Later Life: Findings From the National Survey of the Japanese Elderly

Recent studies predominantly in Western populations suggest that both weight gain and weight loss are associated with increased mortality risk in old age. However, evidence on this topic in Asian populations remains sparse. We examined the association between weight change and all-cause mortality in a nationally-representative sample of community-dwelling older Japanese. Data came from the National Survey of the Japanese Elderly (N = 4,869, age ≥ 60 years). Participants were followed for up to 30 years. Short-term (3 years) and medium-term (6 years) weight changes were classified as “loss ≥ 5%,” “loss 2.5%–4.9%,” “stable,” “gain 2.5%–4.9%,” and “gain ≥ 5%.” Cox proportional hazards models were used to assess the relative (stable weight as reference) mortality risk associated with weight change categories. Short-term weight loss ≥ 5% was associated with higher mortality compared to the stable category, after adjusting for sociodemographic factors, health behaviors, and health conditions (hazard ratio = 1.36; 95% confidence interval = 1.22–1.51). The other weight change categories had no significant association with mortality. This was observed both among males and females. Moreover, the same pattern of results was observed when we used the medium-term weight change indicator. In conclusion, we found that both short- and medium-term weight loss greater than 5% increased the risk of dying among older Japanese; however, other types of weight change did not. This finding could inform clinical and public health approaches to body-weight management aimed to improve the health and survival of older adults, particularly in Asian populations.

Short-term weight change and live birth among women with unexplained infertility and polycystic ovary syndrome undergoing ovulation induction

To determine if short-term weight change among women with unexplained infertility (UI) and polycystic ovary syndrome (PCOS) undergoing ovulation induction is associated with live birth. Secondary analysis of randomized trials. Multicenter fertility trial sites. A total of 900 women with UI and 750 women with PCOS. Live birth. Weight assessment at enrollment and start of up to 4-5 cycles of clomiphene, letrozole, or gonadotropins and intrauterine insemination for women with UI and clomiphene or letrozole with regular intercourse for women with PCOS. Weight data were available for 856 women with UI and 697 women with PCOS. Mean weight change was -0.2 ± 0.3 kg among women with UI and +2.2 ± 0.2 kg among women with PCOS and did not differ based on treatment allocation. There were 115 women with PCOS (16.4%) who gained ≥3 kg. Increased body mass index and three or more cycles were associated with weight gain in women with PCOS. There was no difference in live birth rate among women with PCOS and ≥3 kg weight gain and women with PCOS who did not gain weight. Women with PCOS gained an average of 2.2 kg regardless of the medication received, whereas women with UI experienced no short-term weight change during ovulation induction. Weight gain in women with PCOS was not associated with live birth rate.

Handling Missing Values in Interrupted Time Series Analysis of Longitudinal Individual-Level Data.

BackgroundIn the interrupted time series (ITS) approach, it is common to average the outcome of interest at each time point and then perform a segmented regression (SR) analysis. In this study, we illustrate that such ‘aggregate-level’ analysis is biased when data are missing at random (MAR) and provide alternative analysis methods.MethodsUsing electronic health records from the UK, we evaluated weight change over time induced by the initiation of antipsychotic treatment. We contrasted estimates from aggregate-level SR analysis against estimates from mixed models with and without multiple imputation of missing covariates, using individual-level data. Then, we conducted a simulation study for insight about the different results in a controlled environment.ResultsAggregate-level SR analysis suggested a substantial weight gain after initiation of treatment (average short-term weight change: 0.799kg/week) compared to mixed models (0.412kg/week). Simulation studies confirmed that aggregate-level SR analysis was biased when data were MAR. In simulations, mixed models gave less biased estimates than SR analysis and, in combination with multilevel multiple imputation, provided unbiased estimates. Mixed models with multiple imputation can be used with other types of ITS outcomes (eg, proportions). Other standard methods applied in ITS do not help to correct this bias problem.ConclusionAggregate-level SR analysis can bias the ITS estimates when individual-level data are MAR, because taking averages of individual-level data before SR means that data at the cluster level are missing not at random. Avoiding the averaging-step and using mixed models with or without multilevel multiple imputation of covariates is recommended.

The welfare impacts of mechanical and manual broiler catching and of circumstances at loading under field conditions

Loading of broilers for transport to the processing plant poses a notable injury risk for broilers. Therefore, the poultry industry has developed mechanical methods as alternatives to manual loading methods. Our objective in the present study was to compare manual loading (MAN) of broilers with the mechanical loading (MECH). We assessed the injuries of broilers of 12 MAN and 12 MECH flocks on-farm before and immediately after loading, documented the numbers of broilers dead on arrival reported by the processing plant, and assessed the circumstances at loading. A smaller number of broilers with a hematoma (≥0.5 cm in diameter) on the wing were observed after MAN compared with MECH using the examined harvester (MAN vs. MECH odds ratio: 0.16; 95% confidence interval: 0.10, 0.28). The number of broilers with severe wing injuries did not differ between the loading methods. The number of broilers dead on arrival was greater in mechanically loaded flocks (MAN vs. MECH odds ratio: 0.26; 95% confidence interval: 0.10, 0.68), but lower than in comparable studies. We observed a lower average stocking rate than targeted in the drawers of MECH containers, most likely because the used harvester can adapt to short-term changes in weight and adjust the stocking rate during the loading process. A longer total loading duration in MAN was associated with an increase of wing hematomas, and the involvement of more working people per 10,000 broilers during MAN was associated with a lower occurrence of hematomas. The total loading duration in MECH had no notable influence on the occurrence of injuries. Physical conditions of the involved personnel might play a larger role in MAN than in MECH. The harvester that was examined should be further developed to reduce the occurrence of hematomas. Our results indicate that the choice of loading method alone does not determine the injury risk, and multiple factors are associated with broiler welfare during loading. It is important that the chosen method is performed under the most adequate conditions.

Short-term weight trajectories and long-term weight outcomes from a lifestyle intervention in real-world clinical practice.

Centers for Disease Control and Prevention aligned lifestyle change programs are effective in promoting weight loss among those with elevated cardiometabolic risk; yet, variability in weight outcomes among participants is high. Little is known about heterogeneity of short-term weight changes among participants in real-world clinical practice. We sought to identify short-term weight trajectory clusters among lifestyle change program participants in real-world clinical practice and to examine the relationship between cluster membership and long-term weight outcomes. We identified participants from the electronic health records (2010-2017) with weight measured ≤30 days prior to program initiation (baseline) and in four intervals (3-week segments) in the 12 weeks after baseline. Clustering analysis was performed to identify distinct trajectories in percent weight change over 12 weeks. Cluster-specific differences in weight change at 12 and 52 weeks were assessed. Among 1,148 participants, across 18 clinic sites, three clusters were identified: minimal-to-no weight loss (MWL), delayed-minimal weight loss (DWL), and steady-moderate weight loss (SWL), corresponding to mean weight changes of 0.4%, -2.3%, and -4.8% at 12 weeks follow-up, respectively. Mean weight changes were 0.4%, -1.8%, and -5.1% for MWL, DWL, and SWL clusters, respectively, at 52 weeks follow-up, which correlated in direction and magnitude with short-term weight changes. Clustering analysis reveals heterogeneous, short-term weight trajectories among lifestyle change program participants in real-world clinical practice. Given the relationship between the magnitudes of short- and long-term weight change, individual participant weight trajectories may be useful in identifying potential non-responders in need of adjunctive or alternative therapy.

Changes in marrow adipose tissue with short-term changes in weight in premenopausal women with anorexia nervosa.

In anorexia nervosa, a psychiatric disease characterized by self-induced starvation and a model of chronic undernutrition, levels of subcutaneous (SAT) and visceral (VAT) adipose tissue are low, whereas marrow adipose tissue (MAT) levels are elevated compared to normal-weight women. The reason for this paradoxical elevation of an adipose tissue depot in starvation is not known. We sought to understand changes in MAT in response to subacute changes in weight and to compare these changes with those of other fat depots and body composition parameters. We conducted a 12-month longitudinal study including 46 premenopausal women (n = 26 with anorexia nervosa and n = 20 normal-weight controls) with a mean (s.e.m.) age of 28.2 ± 0.8 years. We measured MAT, SAT, VAT and bone mineral density (BMD) at baseline and after 12 months. At baseline, SAT (P < 0.0001), VAT (P < 0.02) and BMD of the spine and hip (P ≤ 0.0002) were significantly lower and vertebral and metaphyseal MAT (P ≤ 0.001) significantly higher in anorexia nervosa compared to controls. Weight gain over 12 months was associated with increases not only in SAT and VAT, but also epiphyseal MAT (P < 0.03). Changes in epiphyseal MAT were positively associated with changes in BMD (P < 0.03). In contrast to the steady state, in which MAT levels are higher in anorexia nervosa and MAT and BMD are inversely associated, short-term weight gain is associated with increases in both MAT and BMD. These longitudinal data demonstrate the dynamic nature of this fat depot and provide further evidence of its possible role in mineral metabolism.

Short-Term Weight Changes in Treated Primary Hypothyroid Subjects

Weight loss due to diuresis is an early clinical response of treatment with levothyroxine in primary hypothyroidism. The objective of this study was to evaluate weight changes in patients with primary hypothyroidism after 6 weeks of initiation of treatment with levothyroxine. This prospective observational follow up study included 99 newly diagnosed primary hypothyroid patients of 18-60 years of age of both sexes. The weight and height of each patient were measured and body mass index (BMI) was calculated both at the time of enrollment and at the end of 6 weeks of treatment with levothyroxine, and variables at the baseline and at follow up were compared. 93 patients out of 99 completed follow-up at 6±1 weeks. There were significant reduction in TSH level (85.1±51.6 vs. 1.87±0.9 µIU/mL, mean±SD) and increase in FT4 level (0.49±0.19 vs. 1.4±0.78 ng/dL, mean±SD) at follow up in comparison to their baseline values. Among the participants, 90.3% lost body weight while 5.4% gained weight and 4.3% of subjects didn’t show any change in their weight at the end of the study. The mean body weight and mean BMI after levothyroxine replacement were significantly lower (weight 62.2±13.7 vs. 59.0±12.1 kg, BMI 25.1±4.6 vs. 23.8±4.1 Kg/M2, mean±SD) than the pretreatment values. The mean changes in body weight and BMI were 3.19±0.32 Kg (mean±SEM) and 1.31±0.14 Kg/M2 (mean±SEM) respectively. The mean changes in body weight and BMI did not differ significantly among subjects with different TSH categories. No statistically significant effect of any individual predictors like age, gender, and socioeconomic status, weight at baseline, baseline TSH, baseline FT4 and presence of thyroid autoimmunity was observed on weight change. Levothyroxine replacement was associated with a significant reduction of mean body weight and BMI at short-term follow up in our study, though not all patients experienced weight loss.

Short-term weight gain is associated with accumulation of multimorbidity in mid-aged women: a 20-year cohort study.

Although weight change has been studied in relation to many individual chronic conditions, limited studies have focused on weight change and multimorbidity. This study examines the relationship between short-term weight change and the accumulation of multimorbidity in midlife. We used data from 7357 women aged 45-50 years without a history of any chronic conditions. The women were surveyed approximately every 3 years from 1996 to 2016. Associations between short-term weight change and accumulation of multimorbidity (two or more of nine chronic conditions) over each 3-year period, adjusting for baseline body mass index (BMI) or time-varying BMI (3-year period), were examined using repeated measures models. Short-term weight change was categorised into seven groups of annual weight change from high weight loss ( ≤ -5%) to high weight gain (> + 5%). Over 20 years, 60.4% (n = 4442) of women developed multimorbidity. Baseline BMI, time-varying BMI and short-term weight gain were all associated with the accumulation of multimorbidity. After controlling for sociodemographic, lifestyle factors and menopausal status, high weight gain was associated with a 25% increased odds of multimorbidity (odds ratio (OR) 1.25, 95% confidence interval (CI) 1.08-1.45) compared with maintaining a stable weight. The results were consistent among models adjusting for baseline BMI (OR 1.24, 95% CI 1.07-1.44) or time-varying BMI (OR 1.34, 95% CI 1.16-1.54). Weight loss was associated with increased odds of multimorbidity in women with normal BMI (baseline or time-varying). Short-term weight gain is associated with significantly increased odds of multimorbidity in mid-aged women. This association is independent from baseline BMI (at 45-50 years) and time-varying BMI. These findings support a persistent weight management regime and prevention of weight gain throughout women's midlife.