Trajectories of BMI Before Diagnosis of Type 2 Diabetes: The Rotterdam Study.

OBJECTIVE People with diabetes show great variability in weight gain and duration of obesity at the time of diagnosis. BMI trajectories and other cardiometabolic risk factors prior to type 2 diabetes were investigated. METHODS A total of 6,223 participants from the Rotterdam Study cohort were included. BMI patterns before diagnosis of diabetes were identified through latent class trajectories. RESULTS During a mean follow-up of 13.7 years, 565 participants developed type 2 diabetes. Three distinct trajectories of BMI were identified, including the "progressive overweight" group (n = 481, 85.1%), "progressive weight loss" group (n = 59, 10.4%), and "persistently high BMI" group (n = 25, 4.4%). The majority, the progressive overweight group, was characterized by a steady increase of BMI in the overweight range 10 years before diabetes diagnosis. The progressive weight loss group had fluctuations of glucose and marked beta cell function loss. The persistently high BMI group was characterized by a slight increase in insulin levels and sharp increase of insulin resistance accompanied by a rapid decrease of beta cell function. CONCLUSIONS Heterogeneity of BMI changes prior to type 2 diabetes was found in a middle-aged and elderly white population. Prevention strategies should be tailored rather than focusing only on high-risk individuals

Healthy lifestyle and life expectancy with and without Alzheimer's dementia: population based cohort study.

OBJECTIVE To determine the impact of lifestyle factors on life expectancy lived with and without Alzheimer's dementia. DESIGN Prospective cohort study. SETTING The Chicago Health and Aging Project, a population based cohort study in the United States. PARTICIPANTS 2449 men and women aged 65 years and older. MAIN EXPOSURE A healthy lifestyle score was developed based on five modifiable lifestyle factors: a diet for brain health (Mediterranean-DASH Diet Intervention for Neurodegenerative Delay-MIND diet score in upper 40% of cohort distribution), late life cognitive activities (composite score in upper 40%), moderate or vigorous physical activity (≥150 min/week), no smoking, and light to moderate alcohol consumption (women 1-15 g/day; men 1-30 g/day). MAIN OUTCOME Life expectancy with and without Alzheimer's dementia in women and men. RESULTS Women aged 65 with four or five healthy factors had a life expectancy of 24.2 years (95% confidence interval 22.8 to 25.5) and lived 3.1 years longer than women aged 65 with zero or one healthy factor (life expectancy 21.1 years, 19.5 to 22.4). Of the total life expectancy at age 65, women with four or five healthy factors spent 10.8% (2.6 years, 2.0 to 3.3) of their remaining years with Alzheimer's dementia, whereas women with zero or one healthy factor spent 19.3% (4.1 years, 3.2 to 5.1) with the disease. Life expectancy for women aged 65 without Alzheimer's dementia and four or five healthy factors was 21.5 years (20.0 to 22.7), and for those with zero or one healthy factor it was 17.0 years (15.5 to 18.3). Men aged 65 with four or five healthy factors had a total life expectancy of 23.1 years (21.4 to 25.6), which is 5.7 years longer than men aged 65 with zero or one healthy factor (life expectancy 17.4 years, 15.8 to 20.1). Of the total life expectancy at age 65, men with four or five healthy factors spent 6.1% (1.4 years, 0.3 to 2.0) of their remaining years with Alzheimer's dementia, and those with zero or one healthy factor spent 12.0% (2.1 years, 0.2 to 3.0) with the disease. Life expectancy for men aged 65 without Alzheimer's dementia and four or five healthy factors was 21.7 years (19.7 to 24.9), and for those with zero or one healthy factor life expectancy was 15.3 years (13.4 to 19.1). CONCLUSION A healthy lifestyle was associated with a longer life expectancy among men and women, and they lived a larger proportion of their remaining years without Alzheimer's dementia. The life expectancy estimates might help health professionals, policy makers, and stakeholders to plan future healthcare services, costs, and needs

Metabolically healthy obesity and the risk of cardiovascular disease in the elderly population

Background Whether being metabolically healthy obese (MHO)-defined by the presence of obesity in the absence of metabolic syndrome-is associated with subsequent cardiovascular disease (CVD) remains unclear and may depend on the participants' age. We examined the association of being MHO with CVD risk in the elderly. Methods and Findings This study included 5,314 individuals (mean age 68 years) from the prospective populationbased Rotterdam Study.We categorized our population in groups according to body mass index (BMI) and presence and absence of metabolic syndrome, and estimated the hazard ratio (HR) and 95% confidence interval (95%CI) for every group by using Cox proportional hazard models. Among 1048 (19.7%) obese individuals we identified 260 (24.8%) MHO subjects. Over 14 years of follow-up there were 861 incident CVD cases. In the multivariable adjusted analysis, we did not observe an increased CVD risk in MHO individuals (HR 1.07, 95%CI 0.75-1.53), compared to normal weight individuals without metabolic syndrome. CVD risk was increased by the presence of metabolic syndrome in normal weight (HR 1.35, 95%CI 1.02-1.80), overweight (HR 1.32, 95%CI 1.09-1.60) and obese (HR 1.33, 95%CI 1.07-1.66) individuals, compared to those with normal weight without metabolic syndrome. In a mediation analysis, 71.3% of the association between BMI and CVD was explained by the presence of metabolic syndrome. Conclusions In our elderly population, we found that the presence of obesity without metabolic syndrome did not confer a higher CVD risk. However, metabolic syndrome was strongly associated with CVD risk, and was associated with an increased risk in all BMI categories. Therefore, preventive interventions targeting cardiometabolic risk factors could be considered in elderly, regardless of weight status

Risk prediction models of natural menopause onset: a systematic review [supplementary materials].

Context Predicting the onset of menopause is important for family planning and to ensure prompt intervention in women at risk of developing menopause-related diseases. Objective To summarize risk prediction models of natural menopause onset and their performance. Data Sources and Study Selection Five bibliographic databases were searched up to March 2022. We included prospective studies on perimenopausal women or women in menopausal transition, that reported either an univariable or a multivariable model for risk prediction of natural menopause onset. Data Extraction Two authors independently extracted data according to the CHARMS (critical appraisal and data extraction for systematic reviews of prediction modelling studies) checklist. Risk of bias was assessed using PROBAST (prediction model risk of bias assessment tool). Data Synthesis Of 8'132 references identified, we included 14 articles based on 8 unique studies comprising 9'588 women (mainly Caucasian) and 3'289 natural menopause events. All the included studies used onset of natural menopause (ONM) as outcome, while four studies predicted early ONM as well. Overall, there were 180 risk prediction models investigated, with age, anti-Müllerian hormone (AMH) and follicle-stimulating hormone (FSH) being the most investigated predictors. Other studies tested different hormones (Estradiol, Inhibin B), lifestyle factors (pack-years of smoking, body mass index), imaging (antral follicle count), menopause symptoms (hot flashes, night sweats) or menstrual flow variability as predictors. Estimated C-statistic for the prediction models ranged from 0.62 to 0.95. Calibration and validation were reported in five and seven articles, respectively. All studies were rated at high risk of bias mainly due to the methodological concerns related to the statistical analysis. Conclusion Applicability and generalizability of current prediction models on ONM is limited given that these models were generated from studies at high risk of bias and from specific populations/ethnicities. Although in certain settings such models may be useful, efforts to improve their performance are needed as use becomes more widespread

Levels of ambient air pollution according to mode of transport: a systematic review

Background Controversy exists about the differences in air pollution exposure and inhalation dose between mode of transport. We aimed to review air pollution exposure and inhaled dose according to mode of transport and pollutant and their effect in terms of years of life expectancy (YLE). Methods In this systematic review, we searched ten online databases from inception to April 13, 2016, without language or temporal restrictions, for cohort, cross-sectional, and experimental studies that compared exposure to carbon monoxide, black carbon, nitrogen dioxide, and fine and coarse particles in active commuters (pedestrian or cyclist) and commuters using motorised transport (car, motorcycle, bus, or massive motorised transport [MMT—ie, train, subway, or metro]). We excluded studies that measured air pollution exposure exclusively with biomarkers or on the basis of simulated dat

Impact of Healthy Lifestyle Factors on Life Expectancies in the US Population.

BACKGROUND: Americans have a shorter life expectancy compared with residents of almost all other high-income countries. We aim to estimate the impact of lifestyle factors on premature mortality and life expectancy in the US population. METHODS: Using data from the Nurses' Health Study (1980-2014; n=78 865) and the Health Professionals Follow-up Study (1986-2014, n=44 354), we defined 5 low-risk lifestyle factors as never smoking, body mass index of 18.5 to 24.9 kg/m2, ≥30 min/d of moderate to vigorous physical activity, moderate alcohol intake, and a high diet quality score (upper 40%), and estimated hazard ratios for the association of total lifestyle score (0-5 scale) with mortality. We used data from the NHANES (National Health and Nutrition Examination Surveys; 2013-2014) to estimate the distribution of the lifestyle score and the US Centers for Disease Control and Prevention WONDER database to derive the age-specific death rates of Americans. We applied the life table method to estimate life expectancy by levels of the lifestyle score. RESULTS: During up to 34 years of follow-up, we documented 42 167 deaths. The multivariable-adjusted hazard ratios for mortality in adults with 5 compared with zero low-risk factors were 0.26 (95% confidence interval [CI], 0.22-0.31) for all-cause mortality, 0.35 (95% CI, 0.27-0.45) for cancer mortality, and 0.18 (95% CI, 0.12-0.26) for cardiovascular disease mortality. The population-attributable risk of nonadherence to 5 low-risk factors was 60.7% (95% CI, 53.6-66.7) for all-cause mortality, 51.7% (95% CI, 37.1-62.9) for cancer mortality, and 71.7% (95% CI, 58.1-81.0) for cardiovascular disease mortality. We estimated that the life expectancy at age 50 years was 29.0 years (95% CI, 28.3-29.8) for women and 25.5 years (95% CI, 24.7-26.2) for men who adopted zero low-risk lifestyle factors. In contrast, for those who adopted all 5 low-risk factors, we projected a life expectancy at age 50 years of 43.1 years (95% CI, 41.3-44.9) for women and 37.6 years (95% CI, 35.8-39.4) for men. The projected life expectancy at age 50 years was on average 14.0 years (95% CI, 11.8-16.2) longer among female Americans with 5 low-risk factors compared with those with zero low-risk factors; for men, the difference was 12.2 years (95% CI, 10.1-14.2). CONCLUSIONS: Adopting a healthy lifestyle could substantially reduce premature mortality and prolong life expectancy in US adults

Epigenome-wide association study (EWAS) on lipids: the Rotterdam Study

Background: DNA methylation is a key epigenetic mechanism that is suggested to be associated with blood lipid levels. We aimed to identify CpG sites at which DNA methylation levels are associated with blood levels of triglycerides, high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), and total cholesterol in 725 participants of the Rotterdam Study, a population-based cohort study. Subsequently, we sought replication in a non-overlapping set of 760 participants. Results: Genome-wide methylation levels were measured in whole blood using the Illumina Methylation 450 array. Associations between lipid levels and DNA methylation beta values were examined using linear mixed-effect models. All models were adjusted for sex, age, smoking, white blood cell proportions, array number, and position on array. A Bonferroni-corrected p value lower than 1.08 × 10−7 was considered statistically significant. Five CpG sites annotated to genes including DHCR24, CPT1A, ABCG1, and SREBF1 were identified and replicated. Four CpG sites were associated with triglycerides, including CpG sites annotated to CPT1A (cg00574958 and cg17058475), ABCG1 (cg06500161), and SREBF1 (cg11024682). Two CpG sites were associated with HDL-C, including ABCG1 (cg06500161) and DHCR24 (cg17901584). No significant associations were observed with LDL-C or total cholesterol. Conclusions: We report an association of HDL-C levels with methylation of a CpG site near DHCR24, a protein-coding gene involved in cholesterol biosynthesis, which has previously been reported to be associated with other metabolic traits. Furthermore, we confirmed previously reported associations of methylation of CpG sites within CPT1A, ABCG1, and SREBF1 and lipids. These results provide insight in the mechanisms that are involved in lipid metabolism. Electronic supplementary material The online version of this article (doi:10.1186/s13148-016-0304-4) contains supplementary material, which is available to authorized users

Risk factors for longitudinal changes in left ventricular diastolic function among women and men

Objective To evaluate changes in left ventricular diastolic function (LVDF) parameters and their associated risk factors over a period of 11 years among communitydwelling women and men. Methods Echocardiography was performed three times among 870 women and 630 men (age 67±3 years) from the prospective population-based Rotterdam Study during a period of 11-year follow-up. Changes in six continuous LVDF parameters were correlated with cardiovascular risk factors using a linear-mixed effect model (LMM). Results In women, smoking was associated with deleterious longitudinal changes in deceleration time (DT) (Beta (β): 7.73; 95% CI 2.56 to 12.9) and highdensity lipoprotein cholesterol was associated with improvement of septal e’ (β: 0.37; 95% CI 0.13 to 0.62) and E/e’ ratio (β: −0.46; 95% CI −0.84 to –0.08) trajectories. Among men, diabetes was associated with deleterious longitudinal changes in A wave (β: 3.83; 95% CI 0.06 to 7.60), septal e’ (β: −0.40; 95% CI −0.70 to –0.09) and E/e’ ratio (β: 0.60; 95% CI 0.14 to 1.06) and body mass index was associated with deleterious longitudinal changes in A wave (β: 1.25; 95% CI 0.84 to 1.66), E/A ratio (β: −0.007; 95% CI −0.01 to –0.003), DT (β: 0.86; 95% CI 0.017 to 1.71) and E/e’ ratio (β: 0.12; 95% CI 0.06 to 0.19). Conclusions Smoking among women and metabolic factors (diabetes mellitus and body mass index) among men showed larger deleterious associations with longitudinal changes in LVDF parameters. The favourable association of HDL was mainly observed among women. This study, for the first time, evaluates risk factors associated with changes over time in continuous LVDF parameters among women and men and generates new hypothesis for further medical research

Healthy lifestyle and life expectancy free of cancer, cardiovascular disease, and type 2 diabetes: prospective cohort study

OBJECTIVE: To examine how a healthy lifestyle is related to life expectancy that is free from major chronic diseases. DESIGN: Prospective cohort study. SETTING AND PARTICIPANTS: The Nurses' Health Study (1980-2014; n=73 196) and the Health Professionals Follow-Up Study (1986-2014; n=38 366). MAIN EXPOSURES: Five low risk lifestyle factors: never smoking, body mass index 18.5-24.9, moderate to vigorous physical activity (≥30 minutes/day), moderate alcohol intake (women: 5-15 g/day; men 5-30 g/day), and a higher diet quality score (upper 40%). MAIN OUTCOME: Life expectancy free of diabetes, cardiovascular diseases, and cancer. RESULTS: The life expectancy free of diabetes, cardiovascular diseases, and cancer at age 50 was 23.7 years (95% confidence interval 22.6 to 24.7) for women who adopted no low risk lifestyle factors, in contrast to 34.4 years (33.1 to 35.5) for women who adopted four or five low risk factors. At age 50, the life expectancy free of any of these chronic diseases was 23.5 (22.3 to 24.7) years among men who adopted no low risk lifestyle factors and 31.1 (29.5 to 32.5) years in men who adopted four or five low risk lifestyle factors. For current male smokers who smoked heavily (≥15 cigarettes/day) or obese men and women (body mass index ≥30), their disease-free life expectancies accounted for the lowest proportion (≤75%) of total life expectancy at age 50. CONCLUSION: Adherence to a healthy lifestyle at mid-life is associated with a longer life expectancy free of major chronic diseases