The association of body mass index variability with cardiovascular disease and mortality: a mediation analysis of pooled cohorts.

Aim: We aimed to investigate the effect of BMI variability on CVD and mortality and to explore the mediation effects of the main cardiovascular risk factors contributing to this association. Method: Participants aged 40-65 years were pooled from three cohort studies(ARIC [Atherosclerosis Risk in Communities], MESA [Multi-ethnic Study of Atherosclerosis], and TLGS [Tehran Lipid and Glucose Study]. We employed root mean squared error of the fractional mixed model to calculate BMI variability in the measurement period. In the event assessment period, the hazard ratios for CVD and mortality were estimated using Cox proportional hazard regression models. In the next step, the mediation and interaction effects of fasting plasma glucose, total cholesterol, and systolic blood pressure were determined. Results: A total of 19073 participants were included in this pooled analysis. During a median of 20.7 years of follow-up, 3900 (20.44%) CVD and 6480 (33.97%) all-cause mortality events were recorded. After adjusting for potential confounders, BMI variability was linked to the 1.3 (1.2-1.4) and 1.7 (1.6-1.8) increased risk of CVD and mortality, respectively. Fasting plasma glucose mediated approximately 24% and 8% of the effect of BMI variability on CVD and mortality, respectively. However, systolic blood pressure and total cholesterol did not have mediation effects in this association. Conclusion: High BMI variability is independently associated with the development of CVD and mortality. This association is partly mediated through fasting plasma glucose. Modern cardiometabolic therapies that lower fasting glucose may reduce the risk of future CVD and mortality in individuals with high BMI variability.

Weight Variability, Weight Gain Goals, and Biopsychosocial Factors Among Pregnant Women.

This study investigated the pattern of weight variability over 8 weeks and its associations with achieving weight gain goals and five biopsychosocial factors among pregnant women. We conducted a secondary analysis of 117 weeks of data from 16 pregnant women with a body mass index (BMI) ≥25. Weight variability was calculated from the difference of ending and beginning and maximum and minimum weights in a week and percent of each difference from baseline weight. Loess smoother, repeated measures model, and compound symmetric covariance matrix were used for analysis. The variability measure of maximum-minimum weight (overall mean: 2.1 ± 0.4 lbs.) was greater than the ending-beginning weight measure (overall mean: 0.7 ± 0.6 lbs.). Weight variability was negatively associated with achieving weight gain goals but not with biopsychosocial factors. Assessing weight variability is important during pregnancy so that preventive measures or lifestyle counseling can be instituted immediately to prevent excessive weight gain.

Differential patterns in runs of homozygosity in two mice lines under divergent selection for environmental variability for birth weight.

Runs of homozygosity (ROH) are defined as long continuous homozygous stretches in the genome which are assumed to originate from a common ancestor. It has been demonstrated that divergent selection for variability in mice is possible and that low variability in birth weight is associated with robustness. To analyse ROH patterns and ROH-based genomic inbreeding, two mouse lines that were divergently selected for birth weight variability for 26 generations were used, with: 752 individuals for the high variability line (H-Line), 766 individuals for the low variability line (L-Line) and 74 individuals as a reference population. Individuals were genotyped using the high density Affymetrix Mouse Diversity Genotyping Array. ROH were identified using both the sliding windows (SW) and the consecutive runs (CR) methods. Inbreeding coefficients were calculated based on pedigree (FPED ) information, on ROH identified using the SW method (FROHSW ) and on ROH identified using the CR method (FROHCR ). Differences in genomic inbreeding were not consistent across generations and these parameters did not show clear differences between lines. Correlations between FPED and FROH were high, particularly for FROHSW . Moreover, correlations between FROHSW and FPED were even higher when ROH were identified with no restrictions in the number of heterozygotes per ROH. The comparison of FROH estimates between either of the selected lines were based on significant differences at the chromosome level, mainly in chromosomes 3, 4, 6, 8, 11, 15 and 19. ROH-based inbreeding estimates that were computed using longer homozygous segments had a higher relationship with FPED . Differences in robustness between lines were not attributable to a higher homozygosis in the L-Line, but maybe to the different distribution of ROH at the chromosome level between lines. The analysis identified a set of genomic regions for future research to establish the genomic basis of robustness.

Association between weight variability, weight change and clinical outcomes in hypertension.

Objective: The effect of body weight variability (BWV) and body weight change (BWC) in high-risk individuals with hypertension, but without diabetes mellitus (DM) remains unclear. We examined the effect of BWV and BWC on the primary outcome [the composite of myocardial infarction (MI), other acute coronary syndromes, stroke, acute decompensated heart failure (HF), or cardiovascular (CV) death] and all-cause mortality in the Systolic Blood Pressure Intervention Trial (SPRINT). Methods: In this post-hoc analysis, we used multivariate Cox regression models to examine the risk associated with BWV and BWC for the primary outcome in SPRINT. BWV was defined as the intra-individual average successive variability (ASV). BWC was defined as baseline weight minus final weight. Results: A total of 8714 SPRINT participants (mean age 67.8 ± 9.4 years, 35.1 % women, 58.9 % Whites) with available data on body weight were included. The median follow-up was about 3.9 years (IQR, 3.3-4.4). In multivariable-adjusted Cox models, each 1 unit standard deviation (SD) of BWV was significantly associated with a higher risk for the primary outcome, all-cause mortality, HF, MI, and stroke [HR(95 % CI)]: 1.13 (1.07-1.19; p < 0.0001), 1.22 (1.14-1.30; p < 0.0001), 1.16 (1.07-1.26; p < 0.001), 1.10 (1.00-1.20; p = 0.047), and 1.15 (1.05-1.27; p = 0.005), respectively. Similarly, each 1 unit SD of BWC was significantly associated with a higher risk of the primary outcome, all-cause mortality, MI, and HF: 1.11(1.02-1.21; p = 0.017), 1.44 (1.26-1.65; p < 0.0001), 1.16 (1.01-1.32; p = 0.041) and 1.19 (1.02-1.40; p = 0.031) respectively. However, there was no significant association with CV death (for both BWV and BWC) or stroke (BWC). Conclusion: In high-risk hypertension, BWV and BWC were both associated with higher risk of the primary outcome and all-cause mortality. These results further stress the clinical importance of sustained weight loss and minimizing fluctuations in weight in hypertension.

Effects of body weight variability on risks of macro- and microvascular outcomes in individuals with type 2 diabetes: The Rio de Janeiro type 2 diabetes cohort.

AIMS: To investigate the effects of body weight variability (BWV) on macro- and microvascular outcomes in a type 2 diabetes cohort. METHODS: BWV parameters were assessed in 684 individuals. Multivariable Cox regressions examined associations between BWV parameters and cardiovascular outcomes (total cardiovascular events [CVEs], major CVEs [MACEs], cardiovascular deaths),all-cause mortality and microvascular outcomes. Interaction/subgroup analyses were performed according to being physically-active/sedentary and having/not lost ≥ 5 % of weight. RESULTS: Median follow-up was 11 years over which 194 total CVEs (174 MACEs), and 223 all-cause deaths (110 cardiovascular), occurred. There were 215 renal, 152 retinopathy and 167 peripheral neuropathy development/worsening outcomes. In general, increased BWV was associated with higher risks of CVEs, MACEs, all-cause mortality, advanced renal failure and peripheral neuropathy outcomes, but not of microalbuminuria and retinopathy outcomes. On interaction/subgroup analyses, increased BWV was associated with higher risks of outcomes in sedentary individuals and in those who did not lose ≥ 5 % of body weight. In physically-active participants or in those who lost ≥ 5 % weight, the adjusted risks were null or protective. CONCLUSIONS: Increased BWV was associated with most adverse outcomes; however, in those who were physically-active or consistently losing weight, it was not hazardous and might be even beneficial.

Long-term variability in physiological measures in relation to mortality and epigenetic aging: prospective studies in the USA and China.

BACKGROUND: Visit-to-visit body weight variability (BWV), pulse rate variability (PRV), and blood pressure variability (BPV) have been respectively linked to multiple health outcomes. The associations of the combination of long-term variability in physiological measures with mortality and epigenetic age acceleration (EAA) remain largely unknown. METHODS: We constructed a composite score of physiological variability (0-3) of large variability in BWV, PRV, and BPV (the top tertiles) in 2006/2008-2014/2016 in the Health and Retirement Study (HRS) and 2011-2015 in the China Health and Retirement Longitudinal Study (CHARLS). All-cause mortality was documented through 2018. EAA was calculated using thirteen DNA methylation-based epigenetic clocks among 1047 participants in a substudy of the HRS. We assessed the relation of the composite score to the risk of mortality among 6566 participants in the HRS and 6906 participants in the CHARLS by Cox proportional models and then investigated its association with EAA using linear regression models. RESULTS: A higher score of variability was associated with higher mortality risk in both cohorts (pooled hazard ratio [HR] per one-point increment, 1.27; 95% confidence interval [CI], 1.18, 1.39; P-heterogeneity = 0.344), after adjustment for multiple confounders and baseline physiological measures. Specifically, each SD increment in BWV, PRV, and BPV was related to 21% (95% CI: 15%, 28%), 6% (0%, 13%), and 12% (4%, 19%) higher hazard of mortality, respectively. The composite score was significantly related to EAA in second-generation clocks trained on health outcomes (e.g., standardized coefficient = 0.126 in the Levine clock, 95% CI: 0.055, 0.196) but not in most first-generation clocks trained on chronological age. CONCLUSIONS: Larger variability in physiological measures was associated with a higher risk of mortality and faster EAA.

Weight variability and cardiovascular outcomes: a systematic review and meta-analysis.

The association between body weight variability and the risk of cardiovascular disease (CVD) has been investigated previously with mixed findings. However, there has been no extensive study which systematically evaluates the current evidence. Furthermore, the impact of ethnicity and type 2 diabetes on this phenomena has not yet been investigated. Therefore, the aim of this study was to comprehensively evaluate the effect of weight variability on risk of CVD (any cardiovascular (CV) event, composite CV outcome, CV death, Stroke, Myocardial Infarction) and the influence of ethnicity and type 2 diabetes status on the observed association. A systematic review and meta-analysis was performed according to the meta-analyses of observational studies in epidemiology (MOOSE) guidelines. The electronic databases PubMed, Web of Science, and the Cochrane Library were searched for studies that investigated the relationship between body weight or BMI variability and CV diseases using Medical Subject Headings (MeSH) terms and keywords. The relative risks (RRs) for the outcomes were collected from studies, pooled, and analysed using a random-effects model to estimate the overall relative risk. Of 5645 articles screened, 23 studies with a total population of 15,382,537 fulfilled the prespecified criteria and were included. Individuals in the highest strata of body weight variability were found to have significantly increased risk of any CV event (RR = 1.27; 95% Confidence Interval (CI) 1.17-1.38; P < 0.0001; I2 = 97.28%), cardiovascular death (RR = 1.29; 95% CI 1.03-1.60; P < 0.0001; I2 = 55.16%), myocardial infarction (RR = 1.32; 95% CI 1.09-1.59; P = 0.0037; I2 = 97.14%), stroke (RR = 1.21; 95% CI 1.19-1.24; P < 0.0001; I2 = 0.06%), and compound CVD outcomes (RR = 1.36; 95% CI 1.08-1.73; P = 0.01; I2 = 92.41%). Similar RRs were observed regarding BMI variability and per unit standard deviation (SD) increase in body weight variability. Comparable effects were seen in people with and without diabetes, in White Europeans and Asians. In conclusion, body weight variability is associated with increased risk of CV diseases regardless of ethnicity or diabetes status. Future research is needed to prove a causative link between weight variability and CVD risk, as appropriate interventions to maintain stable weight could positively influence CVD.

Effect of intensive lifestyle intervention on the association between weight variability and major adverse cardiovascular events in overweight or obese adults with type 2 diabetes mellitus.

AIMS/INTRODUCTION: Weight variability is associated with cardiovascular outcomes in diabetic patients. However, whether the guideline-recommended intensive lifestyle intervention (ILI) will affect this association in overweight or obese adults with diabetes is not well established. MATERIALS AND METHODS: In 3,859 participants from the Action for Health in Diabetes (Look AHEAD) trial, the associations of 4 year weight variability measured by variability independent of the mean (VIM) with major adverse cardiovascular event (MACE) and secondary outcomes in ILI and diabetes support & education (DSE) arm were evaluated. RESULTS: During a median follow-up of 9.6 years, 255 (12.9%) participants in the ILI arm and 247 (13.2%) participants in the DSE arm developed MACE. Participants with the highest quartile of weight variability (VIM Q4) experienced a 2.23-fold higher risk of MACE compared with the lowest quartile (VIM Q1) in the DSE arm (hazard ratio [HR] 2.23; 95% CI 1.51-3.30). Compared with the lowest weight variability (VIM Q1), participants with the highest weight variability (VIM Q4) were associated with higher risks of secondary cardiovascular composite outcome (HR 1.88; 95% CI 1.20-2.95), all-cause mortality (HR 3.19; 95% CI 1.75-5.82), and myocardial infarction (HR 1.95; 95% CI 1.12-3.37) in the DSE arm. CONCLUSIONS: Among the overweight or obese individuals with type 2 diabetes mellitus, rising weight variability was independently associated with increased MACE risks in the DSE arm. Therefore, a guideline-recommended ILI strategy for weight loss should be adopted to improve cardiovascular outcomes without worrying about the effect of weight fluctuations.

High body weight variability is associated with increased risk of depression: a nationwide cohort study in South Korea.

BACKGROUND: Body weight variability (BWV) negatively affects the incidence and outcomes of various diseases, but the nature of the association between BWV and depression remains unclear. In this study, we aimed to test the hypothesis that BWV is associated with the risk of new-onset depression. METHODS: Data from a nationwide population-based cohort in the Korean National Health Insurance Service database were analyzed for 6 598 570 adults with no history of depression and reports of at least three health examinations. BWV was estimated using variability independent of the mean indices and divided into quartiles (Q1 lowest, Q4 highest BWV). Cox proportional hazard models were applied to assess the risk of depression according to the quartile of BWV. RESULTS: The incident rate for depression from Q1 to Q4 of BWV was 20.7, 20.3, 20.8, and 22.2 per 1000 person-years, respectively. BWV, especially high BWV, was associated with an increased risk of depression after adjusting for age, sex, smoking, alcohol consumption, physical activity, income, diabetes mellitus, hypertension, and dyslipidemia. The hazard ratio (HR) of new-onset depression was highest in Q4 relative to Q1 in the total population (HR 1.12, p < 0.0001) and was higher in women than in men (HR 1.72 v. 1.16, p < 0.0001). In stratified analyses, regardless of obesity or weight change status at baseline, the risk of depression was increased when bodyweight fluctuated highly during follow-up. CONCLUSIONS: High BWV was associated with an increased risk of depression. Further studies need to evaluate the role of high BWV with respect to the onset of depression.

Simultaneous modeling prediction of three key quality attributes of tablets by powder physical properties.

Tensile strength (TS), solid fraction (SF), and tablet weight variability (TWV) are all key quality factors to be considered during the tablet manufacturing process. For predicting them, a novel powder texture measurement methodology was proposed to study the texture attributes of 32 types of powders, and 10 types of unknown powders were used to validate the model. Principal component analysis (PCA) was used to classify the powders. Standard least-squares models were constructed to predict the TS and SF of tablets using texture properties as independent variables, with model R2 values of 0.9188 and 0.8672, respectively. Moreover, due to the advantages of decision trees in classification performance and computation time, multi-node decision trees were constructed, and the approximate range of the TS and SF can be quickly predicted by using only 2-4 key texture attributes. Partial least squares-discriminant method (PLS-DA) algorithm was used to determine whether TWV was qualified by using texture and other physical attributes of powders. In conclusion, the constructed models predicted TS, SF, and TWV well and provide an effective reference for facilitating the development of tablet formulation for direct compression (DC).

Body weight variability and the risk of dementia in patients with type 2 diabetes mellitus: A nationwide cohort study in Korea.

AIMS: This study aimed to examine the association between body weight variability and dementia risk using a large-scale cohort data of Korean patients with type 2 diabetes mellitus (T2DM). METHODS: A population-based cohort of 1,206,764 individuals with T2DM aged ≥ 40 years who underwent ≥ 3 Korean national health screenings were followed up until the end of 2019. Body weight variability was assessed using variability independent of the mean (VIM). A multivariate Cox proportional hazard regression was performed with calculating hazard ratios (HRs) with 95 % confidence intervals (CIs) of dementia incidence. RESULTS: During a median follow-up of 7.9 years, 162,615 (13.4 %) individuals developed dementia. Individuals with greater body weight variability tended to be associated with higher risk of all types of dementia (P for trend < 0.001). Individuals in the highest quartile of VIM showed 26 % (HR: 1.26, 95 % CI: 1.24-1.28), 33 % (HR: 1.33, 95 % CI: 1.30-1.36) and 28 % (HR: 1.28, 95 % CI: 1.23-1.33) higher risk for all-cause dementia, Alzheimer's disease, and vascular dementia, compared with those in the lowest quartile. These associations persisted in all body mass index categories (P for trend < 0.001). CONCLUSIONS: Maintaining an appropriate body weight may help mitigate dementia risk in patients with T2DM.

Association of magnitude of weight loss and weight variability with mortality and major cardiovascular events among individuals with type 2 diabetes mellitus: a systematic review and meta-analysis.

BACKGROUND: Weight management is strongly promoted for overweight or obese patients with type 2 diabetes (T2DM) by current guidelines. However, the prognostic impact of weight loss achieved without behavioural intervention on the mortality and cardiovascular (CV) outcomes in diabetic patients is still contested. METHODS: We searched the PubMed, Embase, and Cochrane Library databases for studies that investigated the association of weight loss or weight variability with mortality and CV outcomes. Results of studies that measured weight loss by percentage weight loss from baseline and stratified it as > 10% and 5-10% or studies that computed weight variability were pooled using random effects model. Study quality was evaluated using the Newcastle-Ottawa Scale. RESULTS: Thirty eligible studies were included in the systematic review and 13 of these were included in the meta-analysis. Large weight loss (> 10%) was associated with increased risk of all-cause mortality (pooled hazard ratio (HR) 2.27, 95% CI 1.51-3.42), composite of major CV events (pooled HR 1.71, 95% CI 1.38-2.12) and CV mortality (pooled HR 1.50, 95% CI 1.27-1.76) among T2DM patients. Moderate weight loss showed no significant association with all-cause mortality (pooled HR 1.17, 95% CI 0.97-1.41) or CV outcomes (pooled HR 1.12, 95% CI 0.94-1.33). Weight variability was associated with high hazard of all-cause mortality (pooled HR 1.54, 95% CI 1.52-1.56). CONCLUSIONS: Large weight loss and large fluctuations in weight are potential markers of increased risk of mortality and CV events in T2DM patients. Maintaining a stable weight may have positive impact in these patients.

Association of Long-Term Body Weight Variability With Dementia: A Prospective Study.

BACKGROUND: Body weight variability (BWV) refers to intraindividual weight loss and gain over a period. The association of long-term BWV with dementia remains unclear and whether this association is beyond body weight change is undetermined. METHODS: In the Health and Retirement Study, a total of 5 547 dementia-free participants (56.7% women; mean [SD] age, 71.1 [3.2] years) at baseline (2008) were followed up to 8 years (mean = 6.8 years) to detect incident dementia. Body weight was self-reported biennially from 1992 to 2008. BWV was measured as the coefficient of variation utilizing the body weight reported 9 times across 16 years before baseline. Cox-proportional hazard model was used to estimate the hazard ratio (HR) and 95% confidence interval (CI). RESULTS: Among the 5 547 participants, a total of 427 incident dementia cases were identified during follow-up. Greater long-term BWV was significantly associated with a higher risk of dementia (HR comparing extreme quartiles: 2.01, 95% CI: 1.48-2.72; HR of each SD increment: 1.21, 95% CI: 1.10-1.32; p-trend < .001) independent of mean body weight and body weight change. This significant association was even observed for BWV estimated approximately 15 years preceding dementia diagnosis (HR of each SD increment: 1.13, 95% CI: 1.03-1.23) and was more pronounced for that closer to diagnosis. CONCLUSION: Our prospective study suggested that greater BWV may be a novel risk factor for dementia.

Type 2 Diabetes Incidence and Mortality: Associations with Physical Activity, Fitness, Weight Loss, and Weight Cycling.

Cardiometabolic diseases, including cardiovascular disease (CVD) and type 2 diabetes (T2D), are the leading cause of death globally. Because T2D and obesity are strongly associated, weight loss is the cornerstone of treatment. However, weight loss is rarely sustained, which may lead to weight cycling, which is associated with increased mortality risk in patients with T2D. Meta-analyses show that weight loss is not generally associated with reduced mortality risk in T2D, whereas weight cycling is associated with increased all-cause and CVD mortality. This may be attributable in part to increased variability in CVD risk factors that often accompany weight cycling, which studies show is consistently associated with adverse CVD outcomes in patients with T2D. The inconsistent associations between weight loss and mortality risk in T2D, and consistent findings of elevated mortality risk associated with weight cycling, present a conundrum for a weight-loss focused T2D prevention and treatment strategy. This is further complicated by the findings that among patients with T2D, mortality risk is lowest in the body mass index (BMI) range of ~25-35 kg/ m 2 . Because this "obesity paradox" has been consistently demonstrated in 7 meta-analyses, the lower mortality risk for individuals with T2D in this BMI range may not be all that paradoxical. Physical activity (PA), cardiorespiratory fitness (CRF), and muscular fitness (MF) are all associated with reduced risk of T2D, and lower risk of CVD and all-cause mortality in individuals with T2D. Reducing sedentary behavior, independent of PA status, also is strongly associated with reduced risk of T2D. Improvements in cardiometabolic risk factors with exercise training are comparable to those observed in weight loss interventions, and are largely independent of weight loss. To minimize risks associated with weight cycling, it may be prudent to adopt a weight-neutral approach for prevention and treatment of individuals with obesity and T2D by focusing on increasing PA and improving CRF and MF without a specific weight loss goal.

High Bodyweight Variability Increases Depression Risk in Patients With Type 2 Diabetes Mellitus: A Nationwide Cohort Study in Korea.

Objectives: Although obesity is associated with increased risk for depression in patients with type 2 diabetes mellitus (DM), the relationship between body weight variability (BWV) and depression remains poorly studied. This study was to investigate the incidence of depression in patients with type 2 DM according to their BWV. Methods: Intraindividual variation in body weight were measured in the nationwide, population-based retrospective cohort of 540,293 patients with type 2 DM from the Korean national health insurance system between 2009 and 2010. The diagnoses of new-onset depression occurring until the end of 2017 were ascertained. Risk of new-onset depression was examined using multivariate-adjusted Cox proportional hazards regression analysis by BWV quartile. Results: 93,149 (17.2%) patients developed new-onset depression for the follow up. BWV was significantly associated with an increased risk of depression after adjusting for confounding factors. The highest BWV quartile group had a hazard ratio (HR) of 1.17 (95% CI 1.15-1.19) compared to the lowest BWV quartile group as a reference. Obese patients in the highest BWV quartile group showed 12% increased risk of depression (HR 1.12, 95% CI 1.09-1.15) while non-obese patients in the highest BWV quartile group showed 20% increased risk of depression (HR: 1.20, 95% CI: 1.17-1.23) compared to their respective lowest BWV quartile groups. Conclusion: A higher BWV was significantly associated with an increased risk of depression in patients with type 2 DM. Thus, BWV may serve as an indicator for early detection of depression in type 2 DM patients.

Association of Body Weight Variability with Adverse Cardiovascular Outcomes in Patients with Pre-Dialysis Chronic Kidney Disease.

To investigate the association of body weight variability (BWV) with adverse cardiovascular (CV) outcomes in patient with pre-dialysis chronic kidney disease (CKD), a total of 1867 participants with pre-dialysis CKD from Korean Cohort Study for Outcomes in Patients With Chronic Kidney Disease (KNOW-CKD) were analyzed. BWV was defined as the average absolute difference between successive values. The primary outcome was a composite of non-fatal CV events and all-cause mortality. Secondary outcomes were fatal and non-fatal CV events and all-cause mortality. High BWV was associated with increased risk of the composite outcome (adjusted hazard ratio (HR) 1.745, 95% confidence interval (CI) 1.065 to 2.847) as well as fatal and non-fatal CV events (adjusted HR 1.845, 95% CI 1.136 to 2.996) and all-cause mortality (adjusted HR 1.861, 95% CI 1.101 to 3.145). High BWV was associated with increased risk of fatal and non-fatal CV events, even in subjects without significant body weight gain or loss during follow-up periods (adjusted HR 2.755, 95% CI 1.114 to 6.813). In conclusion, high BWV is associated with adverse CV outcomes in patients with pre-dialysis CKD.

Process Model Approach to Predict Tablet Weight Variability for Direct Compression Formulations at Pilot and Production Scale.

Optimizing processing conditions to achieve a critical quality attribute (CQA) is an integral part of pharmaceutical quality by design (QbD). It identifies combinations of material and processing parameters ensuring that processing conditions achieve a targeted CQA. Optimum processing conditions are formulation and equipment-dependent. Therefore, it is challenging to translate a process design between formulations, pilot-scale and production-scale equipment. In this study, an empirical model was developed to determine optimum processing conditions for direct compression formulations with varying flow properties, across pilot- and production-scale tablet presses. The CQA of interest was tablet weight variability, expressed as percentage relative standard deviation. An experimental design was executed for three model placebo blends with varying flow properties. These blends were compacted on one pilot-scale and two production-scale presses. The process model developed enabled the optimization of processing parameters for each formulation, on each press, with respect to a target tablet weight variability of <1%RSD. The model developed was successfully validated using data for additional placebo and active formulations. Validation formulations were benchmarked to formulations used for model development, employing permeability index values to indicate blend flow.

Variability in body weight and the risk of cardiovascular complications in type 2 diabetes: results from the Swedish National Diabetes Register.

BACKGROUND: There is a high incidence of cardiovascular disease in diabetes. Weight variability has been reported as independent risk factor for cardiovascular disease in the general population and preliminarily also in people with type 2 diabetes. METHODS: Using data from the Swedish National Diabetes Register the possible link between visit-to-visit body weight variability and the risk of cardiovascular complications among people with type 2 diabetes and without prevalent cardiovascular diseases at baseline has been evaluated. Overall, 100,576 people with type 2 diabetes, with at least five measurements of body weight taken over three consecutive years, were included. Variability was expressed as quartiles of the standard deviation of the measures during the three years. The primary composite outcome included non-fatal myocardial infarction, non-fatal stroke, and all-cause mortality and was assessed during five years following the first 3 years of exposure to weight variability. RESULTS: After adjusting for known cardiovascular risk factors, the risk of the primary composite outcome significantly increased with increasing body weight variability [upper quartile HR = 1.45; 95% confidence interval 1.39-1.52]. Furthermore, elevated body weight variability was associated with almost all the other cardiovascular complications considered (non-fatal myocardial infarction, non-fatal stroke, all-cause mortality, peripheral arterial disease, peripheral vascular angioplasty, hospitalization for heart failure, foot ulcer, and all-cause mortality). CONCLUSIONS: High body weight variability predicts the development of cardiovascular complications in type 2 diabetes. These data suggest that any strategy to reduce the body weight in these subjects should be aimed at maintaining the reduction in the long-term, avoiding oscillations.

Association between body weight variability and incidence of Parkinson disease: A nationwide, population-based cohort study.

BACKGROUND AND PURPOSE: Although body weight variability has been associated with mortality, cardiovascular disease, and dementia, the relationship between body weight variability and Parkinson disease (PD) has rarely been studied. We aimed to investigate the longitudinal association between body weight variability and PD incidence. METHODS: A nationwide population-based, cohort study was conducted using the database from the Health Insurance Review and Assessment Service of the whole Korean population. We analyzed 2,815,135 participants (≥40 years old, mean age = 51.7 ± 8.6 years, 66.8% men) without a previous PD diagnosis. We determined individual body weight variability from baseline weight and follow-up visits. We used Cox proportional hazards regression models. RESULTS: The highest quartile group was associated with increased PD incidence compared with the lowest quartile group after adjustment for confounding factors (hazard ratio [HR] = 1.18, 95% confidence interval [CI] = 1.08-1.29). In contrast, baseline body mass index, baseline waist circumference, and waist circumference variability were not associated with increased PD incidence. In the body weight loss group, individuals within the quartile of the highest variation in body weight showed a higher HR of PD risk than those within other quartiles (HR = 1.41, 95% CI = 1.18-1.68). CONCLUSIONS: Body weight variability, especially weight loss, was associated with higher PD incidence. This finding has important implications for clinicians and supports the need for preventative measures and surveillance for PD in individuals with fluctuating body weight.

Do Weight trajectories influence diabetes control? A prospective study in Switzerland (CoLaus study).

OBJECTIVE: Identify anthropometric trajectories among subjects with type 2 diabetes mellitus (T2DM), and associate them with glycaemic control. METHODS: Prospective study including 268 community-dwelling participants with T2DM (34% women, mean age 68.7 ± 8.9 years) followed for 10.7 years (range: 8.8-13.6 years). T2DM control was considered for 1) fasting plasma glucose (FPG) < 7.0 mmol/L, or 2) HbA1c < 7.0% (53 nmol/mol). Changes in weight or waist and weight variability were considered. RESULTS: One half (FPG) and one third (HbA1c) of participants presented with uncontrolled T2DM. Half of the participants presented with obesity and 75% with abdominal obesity. During follow-up, half of the participants maintained their weight, 25% gained > 5 kg, and 25% lost < 5 kg; almost half increased their waist by > 5 cm. Using FPG as criterion, participants who lost > 5 cm waist were more likely to be controlled: multivariable-adjusted odds ratio (OR) and 95% confidence interval (CI): 3.10 (1.23-7.78). Participants with controlled T2DM also presented with a higher weight variability: multivariable adjusted mean ± standard error 4.8 ± 0.3 vs. 3.9 ± 0.3 kg, p = 0.028. Using HbA1c as criterion, participants who lost > 5 kg were less likely to be controlled: OR and (95% CI): 0.35 (0.18-0.66). Similar findings were obtained when restricting the analysis to participants who were diabetic throughout the whole study period. CONCLUSION: In a Swiss community-based sample of participants with T2DM, T2DM control rates could be implemented. Neither weight nor waist variability was significantly and consistently associated with T2DM control.