Identifying the high-benefit population for weight management-based cardiovascular disease prevention in Japan.

Background: Cardiovascular-disease (CVD) is the leading cause of death, and the association between obesity and CVD is particularly significant among women. Given the evidence highlighting the significance of weight-gain velosity, we aimed to elucidate its influence on cardio-ankle vascular index (CAVI), a reliable surrogate marker of CVD, and identify the high-benefit population where this influence is most pronounced. Methods: This multicenter retrospective study used electronic data from annual health checkups for workers in Japan. Individuals who voluntarily measured CAVI in 2019 were included, and weight-gain velosity was defined as the mean BMI gain from 2015 to 2019. Our primary outcome was the relationship between weight-gain velosity and CAVI. Results: Among 459 individuals, 53 had CAVI ≥ 9. Random forest analysis revealed that age was the most important factor, followed by lipid metabolism, weight-gain velosity, and glucose metabolism, with sex being the least important. Non-linear regression analysis of the effect of age on CAVI ≥ 9 showed the effect was pronounced after age 60, and the trend was greater in women. Among individuals aged 60 or younger, the aOR of weight-gain velosity for CAVI ≥ 9 was significantly positive (aOR 11.95, 95 %CI 1.13-126.27), while it was not significant for those older than 60. The relationship between weight-gain velosity and CAVI provides a new perspective on CVD risk factors. The effects of age, especially after 60, and weight-gain velosity in early- to middle-adulthood on arterial stiffness are emphasized. Conclusions: These findings underscore the importance of weight management under age 60, especially in women.

Lipid metabolism mediates the association between body mass index change and bone mineral density: The Taizhou imaging study.

BACKGROUND: Limited research explores the impact of body mass index (BMI) change on osteoporosis, regarding the role of lipid metabolism. We aimed to cross-sectionally investigate these relationships in 820 Chinese participants aged 55-65 from the Taizhou Imaging Study. METHODS: We used the baseline data collected between 2013 and 2018. T-score was calculated by standardizing bone mineral density and was used for osteoporosis and osteopenia diagnosis. Multinomial logistic regression was used to examine the effect of BMI change on bone health status. Multivariable linear regression was employed to identify the metabolites corrected with BMI change and T-score. Exploratory factor analysis (EFA) and mediation analysis were conducted to ascertain the involvement of the metabolites. RESULTS: BMI increase served as a protective factor against osteoporosis (OR = 0.79[0.71-0.88], P-value<0.001) and osteopenia (OR = 0.88[0.82-0.95], P-value<0.001). Eighteen serum metabolites were associated with both BMI change and T-score. Specifically, high-density lipoprotein (HDL) substructures demonstrated negative correlations (ß = -0.08 to -0.06 and - 0.12 to -0.08, respectively), while very low-density lipoprotein (VLDL) substructions showed positive correlations (ß = 0.09 to 0.10 and 0.10 to 0.11, respectively). The two lipid factors (HDL and VLDL) extracted by EFA acted as mediators between BMI change and T-score (Prop. Mediated = 8.16% and 10.51%, all P-value<0.01). CONCLUSION: BMI gain among Chinese aged 55-65 is beneficial for reducing the risk of osteoporosis. The metabolism of HDL and VLDL partially mediates the effect of BMI change on bone loss. Our research offers novel insights into the prevention of osteoporosis, approached from the perspective of weight management and lipid metabolomics.

Longitudinal analysis of epigenome-wide DNA methylation reveals novel loci associated with BMI change in East Asians.

BACKGROUND: Obesity is a global public health concern linked to chronic diseases such as cardiovascular disease and type 2 diabetes (T2D). Emerging evidence suggests that epigenetic modifications, particularly DNA methylation, may contribute to obesity. However, the molecular mechanism underlying the longitudinal change of BMI has not been well-explored, especially in East Asian populations. METHODS: This study performed a longitudinal epigenome-wide association analysis of DNA methylation to uncover novel loci associated with BMI change in 533 individuals across two Chinese cohorts with repeated DNA methylation and BMI measurements over four years. RESULTS: We identified three novel CpG sites (cg14671384, cg25540824, and cg10848724) significantly associated with BMI change. Two of the identified CpG sites were located in regions previously associated with body shape and basal metabolic rate. Annotation of the top 20 BMI change-associated CpGs revealed strong connections to obesity and T2D. Notably, these CpGs exhibited active regulatory roles and located in genes with high expression in the liver and digestive tract, suggesting a potential regulatory pathway from genome to phenotypes of energy metabolism and absorption via DNA methylation. Cross-sectional and longitudinal EWAS comparisons indicated different mechanisms between CpGs related to BMI and BMI change. CONCLUSION: This study enhances our understanding of the epigenetic dynamics underlying BMI change and emphasizes the value of longitudinal analyses in deciphering the complex interplay between epigenetics and obesity.

Non-Interventional Weight Changes Are Associated with Alterations in Lipid Profiles and in the Triglyceride-to-HDL Cholesterol Ratio.

BACKGROUND: Obesity is associated with dyslipidemia, and weight loss can improve obese patients' lipid profile. Here, we assessed whether non-interventional weight changes are associated with alterations in lipid profile, particularly the triglyceride (TG)-to-high-density lipoprotein cholesterol (HDL-C) ratio (TG/HDL-C). METHODS: In this retrospective analysis of subjects referred to medical screening, body mass index (BMI), low-density lipoprotein cholesterol (LDL-C), TG, and HDL-C levels were measured annually. Patients were divided according to BMI changes between visits. The primary outcomes were the changes in LDL-C, TG, HDL-C, and the TG/HDL-C ratio between visits. RESULTS: The final analysis included 18,828 subjects. During the year of follow-up, 9.3% of the study population lost more than 5% of their weight and 9.2% gained more than 5% of their weight. The effect of weight changes on TG and on the TG/HDL-C ratio was remarkable. Patients with greater BMI increases showed greater increases in their TG/HDL-C ratio, and conversely, a decreased BMI level had lower TG/HDL-C ratios. This is true even for moderate changes of more than 2.5% in BMI. CONCLUSIONS: Non-interventional weight changes, even modest ones, are associated with significant alterations in the lipid profile. Understanding that modest, non-interventional weight changes are associated with alterations in the TG/HDL-C ratio may aid in better risk stratification and primary prevention of CV morbidity and mortality.

A study to investigate genetic factors associated with weight gain in people with diabetes: analysis of polymorphisms in four relevant genes.

BACKGROUND: Weight change is often seen in people with diabetes. We investigated the effects of genes associated with weight change/glucose handling/insulin-signalling. MATERIALS/METHODS: DNA from diabetes individuals and non-diabetes individuals, plus clinical data, were available from the DARE study (n = 379 individuals: T1D n = 111; T2D n = 222; controls n = 46). Weight gain was assessed by temporal change of Body Mass Index (BMI). Genotyping was performed for CAV1rs926198, LEPRrs1137101, BDNFrs6265 and FTOrs9939609. RESULTS: No differences in genotype distributions were observed for the four SNPs in all groups un-stratified by weight gain. Following stratification differences in genotype distribution were observed. For those BMI relatively stable; controls showed a difference in genotype distributions versus T1D (CAV1rs926198, LEPRrs1137101). In T2D vs controls, significant differences were observed in genotype distribution for all four genes. For BMI increase, the only difference by category was LEPRrs1137101 (bothT1D/T2D vs controls). In BMI-stable groups, CAV1rs926198, T1D individuals showed lower T allele frequency (p=0.004) vs non-diabetes and for LEPRrs1137101 a higher G allele frequency versus controls (p=0.002). For T2D, CAV1rs926198, T allele frequency was lower in T2D than controls (p=0.005). For LEPR rs1137101, the G allele frequency was higher than in controls (p=0.004). In those with BMI increase, LEPRrs1137101 T1D individuals had higher G allele frequency versus controls (p=0.002) as did T2D vs controls (p=0.03). CONCLUSION: Differences in allele frequency were seen between diabetes individuals and non-diabetes diagnosed at baseline in relation to the likelihood of BMI increase of >10%. It is established that the G allele of LEPRrs1137101 is associated with weight gain/obesity. However, this is the first report of CAV1rs926198 polymorphism being associated with weight stability/gain in diabetes.

Maternal BMI changes from the prepregnancy to postpartum period are associated with postpartum cardiometabolic risk factors: a longitudinal study.

PURPOSE: This study aimed at investigating the associations between the total body mass index (BMI) change at 3 or 4 years postpartum compared to the prepregnancy and cardiometabolic risk factors. METHODS: This longitudinal study included 1305 participants. Based on the total postpartum BMI changes, they were divided into < 0 units, 0-1.7 units, and > 1.7 units groups using the interquartile range. Multiple linear regression models were used to analyze the associations. RESULTS: Compared to the reference group, there was a progressive increase in the ßcoefficient (ßcoef) of homeostasis model assessment of insulin resistance (HOMA-IR) of cardiometabolic risk in the following groups: the '0-1.7 units' group with the 'overweight traj' [ßcoef 0.33; 95% confidence intervals (CI) 0.22, 0.44)] or the 'obesity traj' [0.66; (0.45, 0.88)] and the '> 1.7 units' group with the 'normal traj' [0.33; (0.22, 0.44)], the 'overweight traj' [0.54; (0.41, 0.67)] or the 'obesity traj' [0.97; (0.79, 1.15)]. The same increasing trend of ßcoef was also found in DBP, FPG, LDL, WHR, BF%. However, the '< 0 units' group with the 'low traj' [0.13; (0.06, 0.21)] and the '0-1.7 units' group with the 'low traj' [0.08; (0.03, 0.13)] had higher high-density lipoprotein cholesterol (HDL-C) level than the reference group. CONCLUSION: Women with a postpartum BMI gain > 1.7 units are positively associated with cardiometabolic risk factors, especially for those in the 'obesity traj' or 'traj D'. Conversely, women with a postpartum BMI loss > 0 units have negative association with cardiometabolic risk factors, especially for those in the 'low traj' or 'traj B'.

Impact of Surgical Postponements due to Covid-19 on Weight and Metabolic Parameters in Bariatric Surgery in Germany: Monocentric Experiences and Considerations in a National Context.

BACKGROUND: Due to Covid-19, elective medical procedures were partly postponed to reduce the burden on the medical system. The impact of these effects in bariatric surgery and their individual consequences remain unknown. MATERIALS/METHODS: In a retrospective monocentric analysis, all bariatric patients at our centre between 01/2020 and 12/2021 were investigated. All patients with postponed surgery due to pandemic were analysed regarding weight change and metabolic parameters. In addition, we performed a nationwide cohort study of all bariatric patients in 2020 using billing data provided by the Federal Statistical Office. Population adjusted procedure rates of 2020 were compared to 2018/2019. RESULTS: Seventy-four patients (42.5%) out of 174 scheduled for bariatric surgery were postponed due to pandemic-related limitations, and 47 (63.5%) patients waited longer than 3 months. Mean postponement was 147.7 days. Apart from outliers (6.8% of all patients), mean weight (+0.9 kg) and body mass index (+0.3 kg/m2) remained stable. HbA1c increased significantly in patients with a postponement longer than 6 months (p = 0.024) and in diabetic patients (+0.18% vs -0.11 in non-diabetic, p = 0.042). In the Germany-wide cohort, the overall reduction of bariatric procedures in the first lockdown (04-06/2020) was -13.4% (p = 0.589). In the second lockdown (10-12/2020), there was no nationwide detectable reduction (+3.5%, p = 0.843) but inter-state differences. There was a catch-up in the interim months (+24.9%, p = 0.002). CONCLUSION: For future lockdowns or other healthcare bottleneck circumstances, the impact of postponement in bariatric patients has to be addressed and prioritization of vulnerable patients (e.g. diabetics) should be considered.

Associations of life course obesity with endometrial cancer in the Epidemiology of Endometrial Cancer Consortium (E2C2).

BACKGROUND: Adult obesity is a strong risk factor for endometrial cancer (EC); however, associations of early life obesity with EC are inconclusive. We evaluated associations of young adulthood (18-21 years) and adulthood (at enrolment) body mass index (BMI) and weight change with EC risk in the Epidemiology of Endometrial Cancer Consortium (E2C2). METHODS: We pooled data from nine case-control and 11 cohort studies in E2C2. We performed multivariable logistic regression analyses to estimate odds ratios (OR) and 95% confidence intervals (95% CI) for BMI (kg/m2) in young adulthood and adulthood, with adjustment for BMI in adulthood and young adulthood, respectively. We evaluated categorical changes in weight (5-kg increments) and BMI from young adulthood to adulthood, and stratified analyses by histology, menopausal status, race and ethnicity, hormone replacement therapy (HRT) use and diabetes. RESULTS: We included 14 859 cases and 40 859 controls. Obesity in adulthood (OR = 2.85, 95% CI = 2.47-3.29) and young adulthood (OR = 1.26, 95% CI = 1.06-1.50) were positively associated with EC risk. Weight gain and BMI gain were positively associated with EC; weight loss was inversely associated with EC. Young adulthood obesity was more strongly associated with EC among cases diagnosed with endometrioid histology, those who were pre/perimenopausal, non-Hispanic White and non-Hispanic Black, among never HRT users and non-diabetics. CONCLUSIONS: Young adulthood obesity is associated with EC risk, even after accounting for BMI in adulthood. Weight gain is also associated with EC risk, whereas weight loss is inversely associated. Achieving and maintaining a healthy weight over the life course is important for EC prevention efforts.

Overweight in childhood and young adulthood increases the risk for adult thromboembolic events.

BACKGROUND: Approximately one third of thromboembolic (TE) events are related to obesity, but to which extent elevated body mass index (BMI) during the distinct periods of childhood and puberty contributes is not known. We aimed to evaluate the impact of high BMI during childhood and puberty for the risk of adult venous and arterial thromboembolic events (VTE, ATE, respectively) in men. METHODS: We included 37,672 men from the BMI Epidemiology Study (BEST) Gothenburg with data on weight and height in childhood, young adult age, and on pubertal BMI change. Information on outcomes (VTE [n = 1683], ATE [n = 144], or any first TE event [VTE or ATE; n = 1780]) was retrieved from Swedish national registers. Hazard ratios (HR) and 95% confidence intervals (CI) were estimated by Cox regressions. RESULTS: Both BMI at 8 years of age and the pubertal BMI change were associated with VTE, independently of each other (BMI at 8: HR 1.06 per standard deviation [SD] increase, 95% CI, 1.01;1.11; pubertal BMI change: HR 1.11 per SD increase, 95% CI, 1.06;1.16). Individuals with normal weight during childhood followed by young adult overweight (HR 1.40, 95% CI, 1.15;1.72), and individuals with overweight at both childhood and young adult age (HR 1.48, 95% CI, 1.14;1.92), had a significantly increased risk of VTE in adult life, compared with the normal weight reference group. Individuals with overweight in childhood and in young adult age had increased risk of ATE and TE. CONCLUSION: Young adult overweight was a strong determinant, and childhood overweight a moderate determinant, of the risk of VTE in adult men.

Machine-learning prediction of BMI change among doctors and nurses in North China during the COVID-19 pandemic.

Objective: The COVID-19 pandemic has become a major public health concern over the past 3 years, leading to adverse effects on front-line healthcare workers. This study aimed to develop a Body Mass Index (BMI) change prediction model among doctors and nurses in North China during the COVID-19 pandemic, and further identified the predicting effects of lifestyles, sleep quality, work-related conditions, and personality traits on BMI change. Methods: The present study was a cross-sectional study conducted in North China, during May-August 2022. A total of 5,400 doctors and nurses were randomly recruited from 39 COVID-19 designated hospitals and 5,271 participants provided valid responses. Participants' data related to social-demographics, dietary behavior, lifestyle, sleep, personality, and work-related conflicts were collected with questionnaires. Deep Neural Network (DNN) was applied to develop a BMI change prediction model among doctors and nurses during the COVID-19 pandemic. Results: Of participants, only 2,216 (42.0%) individuals kept a stable BMI. Results showed that personality traits, dietary behaviors, lifestyles, sleep quality, burnout, and work-related conditions had effects on the BMI change among doctors and nurses. The prediction model for BMI change was developed with a 33-26-20-1 network framework. The DNN model achieved high prediction efficacy, and values of R 2, MAE, MSE, and RMSE for the model were 0.940, 0.027, 0.002, and 0.038, respectively. Among doctors and nurses, the top five predictors in the BMI change prediction model were unbalanced nutritional diet, poor sleep quality, work-family conflict, lack of exercise, and soft drinks consumption. Conclusion: During the COVID-19 pandemic, BMI change was highly prevalent among doctors and nurses in North China. Machine learning models can provide an automated identification mechanism for the prediction of BMI change. Personality traits, dietary behaviors, lifestyles, sleep quality, burnout, and work-related conditions have contributed to the BMI change prediction. Integrated treatment measures should be taken in the management of weight and BMI by policymakers, hospital administrators, and healthcare workers.

Effect of cumulative body mass index exposure and long-term related change on incident non-alcoholic fatty liver disease.

BACKGROUND: To evaluate the association between cumulative body mass index (BMI) and long-term BMI change with non-alcoholic fatty liver disease (NAFLD). METHODS: We included 19 477 adult participants (12 556 men and 6921 women) from the Kailuan study from January 2006 to December 2013. Cumulative BMI was assessed using a quadratic mixed-effects method by sex before the index year; then, the NAFLD outcome was followed till December 2019. The long-term BMI change was calculated as the percentage change in average cumulative BMI from the baseline BMI. RESULTS: During a median follow-up of 5.63 years, 6229 individuals developed incident NAFLD. Independent of baseline BMI, the NAFLD risk escalated with the cumulative BMI with adjusted hazard ratios (HRs) (95% confidence interval [CI]) of 1.60 (1.48-1.73) and 2.28 (2.06-2.53) for the intermediate tertile and the highest tertile (Ptrend <0.001). The association is amplified in women and the young. Compared to a stable weight (BMI change: -3% to 3%), NAFLD risk increased in the baseline BMI < 24 kg/m2 group with weight gain (BMI change: >3%) and decreased in BMI ≥24 kg/m2 group with weight loss (BMI change: <-3%) for men and women. However, we only observed a decreased NAFLD risk in men (HR: 0.82, 95% CI: 0.69-0.97) with BMI < 24 kg/m2 and weight loss. CONCLUSIONS: Monitoring cumulative BMI may help to identify high-risk NAFLD populations. The association between weight gain or loss varies by sex and baseline BMI, suggesting the importance of individualized weight management for NAFLD prevention.

Frequency of working at home, body mass index, and productivity in Japanese office workers: A cohort study.

BACKGROUND: Since the start of the COVID-19 pandemic, the number of people working at home has grown significantly. OBJECTIVE: This cohort study aimed to investigate whether the frequency of working at home was associated with changes in body mass index (BMI) and productivity from 2018 to 2020. METHOD: Frequency of working at home was self-reported. Participants were classified into three groups based on mean frequency of working at home per week (no working at home: ≦ 0.5 days, low frequency: 0.5 to 2.5 days, and high frequency: >2.5 days). Productivity was measured using a numerical rating scale, and BMI was calculated using height and weight. Changes in BMI and productivity were calculated by subtracting 2018 data from 2020 data. Linear regression analysis was performed by considering BMI and productivity change as outcomes and frequency of working at home as explanatory variable. RESULTS: BMI change in the high frequency group was significantly smaller than in the group that did not work at home (crude; coefficient: -0.27; 95% CI: -0.55--0.01, adjusted; coefficient: -0.30; 95% CI: -0.60--0.01). Frequency of working at home was not associated with productivity change. CONCLUSION: Working at home may be a solution for preventing BMI from increasing significantly.

Independent effect of body mass index variation on amyloid-ß positivity.

Objectives: The relationship of body mass index (BMI) changes and variability with amyloid-ß (Aß) deposition remained unclear, although there were growing evidence that BMI is associated with the risk of developing cognitive impairment or AD dementia. To determine whether BMI changes and BMI variability affected Aß positivity, we investigated the association of BMI changes and BMI variability with Aß positivity, as assessed by PET in a non-demented population. Methods: We retrospectively recruited 1,035 non-demented participants ≥50 years of age who underwent Aß PET and had at least three BMI measurements in the memory clinic at Samsung Medical Center. To investigate the association between BMI change and variability with Aß deposition, we performed multivariable logistic regression. Further distinctive underlying features of BMI subgroups were examined by employing a cluster analysis model. Results: Decreased (odds ratio [OR] = 1.68, 95% confidence interval [CI] 1.16-2.42) or increased BMI (OR = 1.60, 95% CI 1.11-2.32) was associated with a greater risk of Aß positivity after controlling for age, sex, APOE e4 genotype, years of education, hypertension, diabetes, baseline BMI, and BMI variability. A greater BMI variability (OR = 1.73, 95% CI 1.07-2.80) was associated with a greater risk of Aß positivity after controlling for age, sex, APOE e4 genotype, years of education, hypertension, diabetes, baseline BMI, and BMI change. We also identified BMI subgroups showing a greater risk of Aß positivity. Conclusion: Our findings suggest that participants with BMI change, especially those with greater BMI variability, are more vulnerable to Aß deposition regardless of baseline BMI. Furthermore, our results may contribute to the design of strategies to prevent Aß deposition with respect to weight control.

The usage of different types of food outlets was not significantly associated with body mass index during the third COVID-19 national lockdown in the United Kingdom.

Background: The United Kingdom (UK) implemented several national lockdowns during the coronavirus pandemic during which restaurants were closed and people were advised to stay at home if possible. These restrictions were eased and reapplied multiple times between March 2020 and May 2021. The change in restaurant access and prolonged restriction of activity may have an impact on body weight. Aim: The aim of this study was to examine the impact of multiple lockdowns on body mass index (BMI) change from pre-pandemic till during the third lockdown and on the use of different types of food outlets and their association with BMI change. Materials and Method: Surveys of usage of different types of food outlets were distributed online before the lockdown between 06 January and 12 December 2019 and during the third national lockdown between 29 March and 25 April 2021. The food outlet usage surveys were filled out for seven consecutive days. Self-reported BMI was reported before the pandemic and during the third phase of the lockdown. The total number of individuals who started the study before the pandemic was 681, and 60 participants completed the surveys during the third phase of lockdown. Results: For the 60 participants in both surveys mean BMI was significantly higher during the third lockdown (28.6 ± 5.9 kg.m2) in comparison with the mean BMI before the pandemic 2019 (28.0 ± 5.5 kg.m2) (paired T = 3.09, p < 0.003). There was a significant positive association between BMI change, total number of days spent in lockdown (ß = 0.05, p < 0.01, R 2 = 9.99), and age (ß = 0.06, p < 0.007, R 2 = 11.8). There was no significant association between change in BMI and change in the frequency of using fast food restaurants (FFRs), full-service restaurants (FSRs), and delivery and takeaways. Conclusion: BMI was increased significantly during the lockdown in comparison with prior to the pandemic. Individuals gained more weight the longer they stayed at home during lockdowns, and physical activity was reduced to approximately half. However, the BMI change was not related to the change in use of different types of food outlets. This pattern does not support the widespread belief that visiting restaurants or using delivery and takeaway services has a significant impact on body weight.

Obesity and Psychological Factors Associated with Weight Loss after Bariatric Surgery: A Longitudinal Study.

Bariatric surgery is indicated for treatment of severely obese people and can lead to significant weight loss as well as reduction of comorbidities associated with obesity. The present study aims to investigate the relationship between different psychological factors (e.g., tendency to binge eating, impulsivity, alexithymia), adherence to the nutritional plan, and weight loss after bariatric surgery. Forty-five candidates for bariatric surgery accessing a center for the care of obesity were assessed at T0 (pre-surgery) and T1 (6 months post-surgery) through anthropometric and psychometric measures. Simple linear correlations and linear regressions were conducted to evaluate the relationship between the psychological variables, adherence to nutritional plan, and weight loss 6 months after bariatric surgery. Non-planning impulsivity was the principal factor that succeeded in explaining adherence to the diet plan among all the variables considered. Adherence to the nutritional plan and non-planning impulsivity were considered reliable short-term predictors of weight loss after bariatric surgery. This evidence explains the usefulness of promoting research on psychological predictors of outcome in bariatric surgery. Mid- and long-term weight maintenance and quality of life need to be investigated through further follow-up.

Changes in adiposity over the life course and gene expression in postmenopausal women.

BACKGROUND: Early life adiposity and changes in adiposity over the life course are associated with mammographic breast density among postmenopausal women. However, the underlying mechanisms are unknown; therefore, we comprehensively examined the associations of early life body mass index (BMI) and changes in BMI from ages 10, 18 to age at mammogram with growth factor, RANK pathway, and sex hormone gene expression in 372 postmenopausal women. METHODS: We estimated early life BMI at age 10 using the validated 9-level Stunkard pictogram. We calculated BMI at other ages (18, 30, and current age at mammogram) by dividing weight in kilograms at these ages with height in meters squared. Sequencing for gene expression was performed using the NanoString nCounter system. After adjusting for confounders, we estimated associations using multivariable linear regressions. RESULTS: A 10 kg/m2 increase in early life BMI at age 10 was associated with a 17.2% decrease in RANKL gene expression (95% confidence interval [CI] = -30.8, -0.9) but was not associated with changes in other markers. BMI changes from ages 10, 18 to age at mammogram were associated with an increase in BMP2 and decreases in RANK, RANKL, and TNFRSF13B gene expression but were not associated with gene expression of other markers. A 10 kg/m2 increase in early life BMI from age 10 to current age was associated with a 7.8% increase in BMP2 (95% CI = -1.4, 17.8), an 8.5% decrease in RANK (95% CI = -13.9, -2.8), a 10.4% decrease in RANKL (95% CI = -16.9, -3.3), and an 8.5% decrease in TNFRSF13B gene expression (95% CI = -13.8, -2.8). CONCLUSION: The results provide new insights into the biological mechanisms underlying the associations of adiposity changes from early life to adulthood and early life adiposity with mammographic breast density in postmenopausal women.

Sex-specific association of BMI change with stroke in middle-aged and older adults with type 2 diabetes.

BACKGROUND AND AIMS: We aimed to evaluate the association between BMI change and stroke in middle-aged and older adults with type 2 diabetes and identify sex differences. METHODS AND RESULTS: The China Health and Retirement Longitudinal Study is an ongoing national population-based cohort study. Participants aged 45 or above with type 2 diabetes were enrolled and followed for stroke incidence. BMI change was defined as BMI at 2013-BMI at 2011. Of 1774 participants (mean [SD] age in 2011, 60.23 [8.88] years), 795 (44.8 %) were men. A total of 112 incident stroke cases were confirmed up to 2018. The incidence rate of stroke was similar between men and women (6.79 % vs 5.92 %, P = 0.516). BMI increase was independently associated with an increased stroke risk (adjusted odds ratio, 1.15; 95 % CI, 1.05-1.31) in men, while this positive association was not significant in women (adjusted odds ratio, 1.12; 95 % CI, 0.98-1.29). In addition, the positive dose-response relationship between BMI increase and stroke was observed only in men. CONCLUSION: Among middle-aged and older adults with type 2 diabetes, there is a sex-specific association of BMI change with stroke. An increase in BMI could result in a higher risk of incident stroke in men.

Not All Type-2-Diabetes Patients Increase Body Mass Index After Initiating Insulin: Results of Latent Class Analysis from the DPV Registry.

Background: Is insulin initiation linked to increasing body mass index (BMI) in all patients with type-2-diabetes (T2D)? To determine distinct longitudinal patterns of BMI change over time. Materials and Methods: 5057 patients with T2D (55% males, median BMI [IQR]: 30.0 [26.9-33.3] kg/m2) aged ≥40 years at diabetes diagnosis and with ≥2 years of follow-up after insulin initiation irrespective of previous or concurrent use of metformin/dipeptidyl peptidase-4-inhibitor from the multicenter prospective diabetes registry DPV were studied. To identify subgroups following a similar pattern of BMI change after insulin initiation, longitudinal group-based trajectory modeling was applied. Multinomial logistic regression was then used to analyze covariates associated with group membership. Results: Three heterogeneous groups with either relevant BMI increase (delta-BMI: +4.0 kg/m2 after 2 years; 12% of patients); slight BMI increase (+0.4 kg/m2; 80%); or BMI decrease (-3.2 kg/m2; 8%) were identified. Patients with older age [OR (95% CI): 1.37 (1.11-1.69)] and obesity [2.05 (1.65-2.55)] before insulin start were more often in the BMI decreasing group, and less often in the BMI increasing class [0.80 (0.67-0.95); 0.82 (0.69-0.98)]. A worse HbA1c both at insulin start and during follow-up [1.90 (1.60-2.26); 1.17 (1.07-1.27)], a higher insulin dose [1.67 (1.33-2.10)], and severe hypoglycemic events [2.38 (1.60-3.53)] after insulin initiation were all linked with higher odds of belonging to the BMI increasing trajectory. Conclusions: Patient heterogeneity with respect to weight gain after initiation of insulin therapy in adult T2D was detected by an objective computer algorithm. Older people with obesity should not defer from insulin use due to fear of weight gain.

Association between body mass index, its change and cognitive impairment among Chinese older adults: a community-based, 9-year prospective cohort study.

To examine the association of baseline body mass index (BMI) and BMI change with cognitive impairment among older adults in China. The study included data from the Chinese Longitudinal Healthy Longevity Study, a national community-based prospective cohort study from 2002 to 2018. Baseline BMI and BMI change were available for 12,027 adults aged older than 65 years. Cognitive impairment was defined as Chinese version of the Mini Mental State Examination score lower than 18. Multivariable Cox proportional hazard model was used. Among 12,027 participants (mean age was 81.23 years old and 47.48% were male), the proportion of underweight, normal, overweight and obese at baseline was 33.87%, 51.39%, 11.39% and 3.34%, respectively. During an average of 5.9 years' follow-up, 3086 participants (4.35 per 100 person-years) with incident cognitive impairment were identified. Compared with normal weight group, adjusted hazard ratio (AHR) for cognitive impairment was 0.86 (95% CI 0.75-0.99) among overweight group, whereas corresponding AHR was 1.02 (95% CI 0.94-1.10) in underweight and 1.01 (95% CI 0.80-1.28) in obese participants. Large weight loss (< -10%) was significantly associated with an increased risk of cognitive impairment (AHR, 1.42, 95% CI 1.29-1.56), compared to stable weight status group (-5% ~ 5%). In the restricted cubic spline models, BMI change showed a reverse J-shaped association with cognitive impairment. BMI-defined overweight, but not obesity, was associated with a lower risk of cognitive impairment among elderly Chinese adults, while large weight loss was associated with an increased risk. These findings are consistent with weight loss in the prodromal phase of dementia.