Objective To forecast dementia prevalence with a dynamic modelling approach that integrates calendar trends in dementia incidence with those for mortality and cardiovascular disease.Design Modelling ...study.Setting General adult population of England and Wales.Participants The English Longitudinal Study of Ageing (ELSA) is a representative panel study with six waves of data across 2002-13. Men and women aged 50 or more years, selected randomly, and their cohabiting partners were recruited to the first wave of ELSA (2002-03). 11392 adults participated (response rate 67%). To maintain representativeness, refreshment participants were recruited to the study at subsequent waves. The total analytical sample constituted 17 906 people. Constant objective criteria based on cognitive and functional impairment were used to ascertain dementia cases at each wave.Main outcome measures To estimate calendar trends in dementia incidence, correcting for bias due to loss to follow-up of study participants, a joint model of longitudinal and time-to-event data was fitted to ELSA data. To forecast future dementia prevalence, the probabilistic Markov model IMPACT-BAM (IMPACT-Better Ageing Model) was developed. IMPACT-BAM models transitions of the population aged 35 or more years through states of cardiovascular disease, cognitive and functional impairment, and dementia, to death. It enables prediction of dementia prevalence while accounting for the growing pool of susceptible people as a result of increased life expectancy and the competing effects due to changes in mortality, and incidence of cardiovascular disease.Results In ELSA, dementia incidence was estimated at 14.3 per 1000 person years in men and 17.0/1000 person years in women aged 50 or more in 2010. Dementia incidence declined at a relative rate of 2.7% (95% confidence interval 2.4% to 2.9%) for each year during 2002-13. Using IMPACT-BAM, we estimated there were approximately 767 000 (95% uncertainty interval 735 000 to 797 000) people with dementia in England and Wales in 2016. Despite the decrease in incidence and age specific prevalence, the number of people with dementia is projected to increase to 872 000, 1 092 000, and 1 205 000 in 2020, 2030, and 2040, respectively. A sensitivity analysis without the incidence decline gave a much larger projected growth, of more than 1.9 million people with dementia in 2040.Conclusions Age specific dementia incidence is declining. The number of people with dementia in England and Wales is likely to increase by 57% from 2016 to 2040. This increase is mainly driven by improved life expectancy.
Job strain is associated with an increased coronary heart disease risk, but few large-scale studies have examined the relationship of this psychosocial characteristic with the biological risk factors ...that potentially mediate the job strain - heart disease association.
We pooled cross-sectional, individual-level data from eight studies comprising 47,045 participants to investigate the association between job strain and the following cardiovascular disease risk factors: diabetes, blood pressure, pulse pressure, lipid fractions, smoking, alcohol consumption, physical inactivity, obesity, and overall cardiovascular disease risk as indexed by the Framingham Risk Score. In age-, sex-, and socioeconomic status-adjusted analyses, compared to those without job strain, people with job strain were more likely to have diabetes (odds ratio 1.29; 95% CI: 1.11-1.51), to smoke (1.14; 1.08-1.20), to be physically inactive (1.34; 1.26-1.41), and to be obese (1.12; 1.04-1.20). The association between job strain and elevated Framingham risk score (1.13; 1.03-1.25) was attributable to the higher prevalence of diabetes, smoking and physical inactivity among those reporting job strain.
In this meta-analysis of work-related stress and cardiovascular disease risk factors, job strain was linked to adverse lifestyle and diabetes. No association was observed between job strain, clinic blood pressure or blood lipids.
Studies of diet and depression have focused primarily on individual nutrients.
To examine the association between dietary patterns and depression using an overall diet approach.
Analyses were carried ...on data from 3486 participants (26.2% women, mean age 55.6 years) from the Whitehall II prospective cohort, in which two dietary patterns were identified: 'whole food' (heavily loaded by vegetables, fruits and fish) and 'processed food' (heavily loaded by sweetened desserts, fried food, processed meat, refined grains and high-fat dairy products). Self-reported depression was assessed 5 years later using the Center for Epidemiologic Studies - Depression (CES-D) scale.
After adjusting for potential confounders, participants in the highest tertile of the whole food pattern had lower odds of CES-D depression (OR = 0.74, 95% CI 0.56-0.99) than those in the lowest tertile. In contrast, high consumption of processed food was associated with an increased odds of CES-D depression (OR = 1.58, 95% CI 1.11-2.23).
In middle-aged participants, a processed food dietary pattern is a risk factor for CES-D depression 5 years later, whereas a whole food pattern is protective.
Although overweight and obesity have been studied in relation to individual cardiometabolic diseases, their association with risk of cardiometabolic multimorbidity is poorly understood. Here we aimed ...to establish the risk of incident cardiometabolic multimorbidity (ie, at least two from: type 2 diabetes, coronary heart disease, and stroke) in adults who are overweight and obese compared with those who are a healthy weight.
We pooled individual-participant data for BMI and incident cardiometabolic multimorbidity from 16 prospective cohort studies from the USA and Europe. Participants included in the analyses were 35 years or older and had data available for BMI at baseline and for type 2 diabetes, coronary heart disease, and stroke at baseline and follow-up. We excluded participants with a diagnosis of diabetes, coronary heart disease, or stroke at or before study baseline. According to WHO recommendations, we classified BMI into categories of healthy (20·0–24·9 kg/m2), overweight (25·0–29·9 kg/m2), class I (mild) obesity (30·0–34·9 kg/m2), and class II and III (severe) obesity (≥35·0 kg/m2). We used an inclusive definition of underweight (<20 kg/m2) to achieve sufficient case numbers for analysis. The main outcome was cardiometabolic multimorbidity (ie, developing at least two from: type 2 diabetes, coronary heart disease, and stroke). Incident cardiometabolic multimorbidity was ascertained via resurvey or linkage to electronic medical records (including hospital admissions and death). We analysed data from each cohort separately using logistic regression and then pooled cohort-specific estimates using random-effects meta-analysis.
Participants were 120 813 adults (mean age 51·4 years, range 35–103; 71 445 women) who did not have diabetes, coronary heart disease, or stroke at study baseline (1973–2012). During a mean follow-up of 10·7 years (1995–2014), we identified 1627 cases of multimorbidity. After adjustment for sociodemographic and lifestyle factors, compared with individuals with a healthy weight, the risk of developing cardiometabolic multimorbidity in overweight individuals was twice as high (odds ratio OR 2·0, 95% CI 1·7–2·4; p<0·0001), almost five times higher for individuals with class I obesity (4·5, 3·5–5·8; p<0·0001), and almost 15 times higher for individuals with classes II and III obesity combined (14·5, 10·1–21·0; p<0·0001). This association was noted in men and women, young and old, and white and non-white participants, and was not dependent on the method of exposure assessment or outcome ascertainment. In analyses of different combinations of cardiometabolic conditions, odds ratios associated with classes II and III obesity were 2·2 (95% CI 1·9–2·6) for vascular disease only (coronary heart disease or stroke), 12·0 (8·1–17·9) for vascular disease followed by diabetes, 18·6 (16·6–20·9) for diabetes only, and 29·8 (21·7–40·8) for diabetes followed by vascular disease.
The risk of cardiometabolic multimorbidity increases as BMI increases; from double in overweight people to more than ten times in severely obese people compared with individuals with a healthy BMI. Our findings highlight the need for clinicians to actively screen for diabetes in overweight and obese patients with vascular disease, and pay increased attention to prevention of vascular disease in obese individuals with diabetes.
NordForsk, Medical Research Council, Cancer Research UK, Finnish Work Environment Fund, and Academy of Finland.
Type 2 diabetes increases the risk for dementia, but whether it affects cognition before old age is unclear. We investigated whether duration of diabetes in late midlife and poor glycaemic control ...were associated with accelerated cognitive decline.
5653 participants from the Whitehall II cohort study (median age 54·4 years IQR 50·3–60·3 at first cognitive assessment), were classified into four groups: normoglycaemia, prediabetes, newly diagnosed diabetes, and known diabetes. Tests of memory, reasoning, phonemic and semantic fluency, and a global score that combined all cognitive tests, were assessed three times over 10 years (1997–99, 2002–04, and 2007–09). Mean HbA1c was used to assess glycaemic control during follow-up. Analyses were adjusted for sociodemographic characteristics, health-related behaviours, and chronic diseases.
Compared with normoglycaemic participants, those with known diabetes had a 45% faster decline in memory (10 year difference in decline −0·13 SD, 95% CI −0·26 to −0·00; p=0·046), a 29% faster decline in reasoning (−0·10 SD, −0·19 to −0·01; p=0·026), and a 24% faster decline in the global cognitive score (−0·11 SD, −0·21 to −0·02; p=0·014). Participants with prediabetes or newly diagnosed diabetes had similar rates of decline to those with normoglycaemia. Poorer glycaemic control in participants with known diabetes was associated with a significantly faster decline in memory (−0·12 –0·22 to −0·01; p=0·034) and a decline in reasoning that approached significance (−0·07 –0·15 to 0·00; p=0·052).
The risk of accelerated cognitive decline in middle-aged patients with type 2 diabetes is dependent on both disease duration and glycaemic control.
US National Institutes of Health, UK Medical Research Council.
Although it has been hypothesized that the association of physical activity with depressive and anxiety symptoms is bidirectional, few studies have examined this issue in a prospective setting. We ...studied this bidirectional association using data on physical activity and symptoms of anxiety and depression at three points in time over 8 years. A total of 9,309 participants of the British Whitehall II prospective cohort study provided data on physical activity, anxiety and depression symptoms and 10 covariates at baseline in 1985. We analysed the associations of physical activity with anxiety and/or depression symptoms using multinomial logistic regression (with anxiety and depression symptoms as dependent variables) and binary logistic regression (with physical activity as the dependent variable). There was a cross-sectional inverse association between physical activity and anxiety and/or depressive symptoms at baseline (ORs between 0.63 and 0.72). In cumulative analyses, regular physical activity across all three data waves, but not irregular physical activity, was associated with reduced likelihood of depressive symptoms at follow-up (OR = 0.71, 95 % CI 0.54, 0.99). In a converse analysis, participants with anxiety and depression symptoms at baseline had higher odds of not meeting the recommended levels of physical activity at follow-up (OR = 1.79, 95 % CI 1.17, 2.74). This was also the case in individuals with anxiety and/or depression symptoms at both baseline and follow-up (OR = 1.70, 95 % CI 1.10, 2.63). The association between physical activity and symptoms of anxiety and/or depression appears to be bidirectional.
We examine the impact of wind energy installation on the local economies of counties in the United States. Using data on the universe of commercial wind energy installations from 1995 to 2018, we ...find that wind energy installation led to economically meaningful increases in county GDP per-capita, income per-capita, median household income, and median home values. We also find evidence that while wind energy installation has little effect on total employment, the composition of local employment shifts away from farm towards non-farm employment, notably leading to an increase in construction and manufacturing employment. Finally, we show that the impact of wind energy installation on local economic development varies significantly by installed capacity and by county urban/rural status. For policymakers, our results have three important implications: (1) wind energy increases the size of the local economy and increases local incomes, but it does not stop population decline; (2) the size of these benefits increase at an increasing rate with the amount of installed generating capacity per-capita; and (3) rural communities with multiple installations and a greater amount of wind energy capacity benefit the most economically from these installations.
•Wind energy increases the size of the local economy and increases local incomes.•The benefits of wind increase at an increasing rate with installed capacity.•Rural communities with multiple installations benefit the most economically from wind.•Wind energy does not stop population decline.
Observational studies suggest that diet is linked to cognitive health. However, the duration of follow-up in many studies is not sufficient to take into account the long preclinical phase of ...dementia, and the evidence from interventional studies is not conclusive.
To examine whether midlife diet is associated with subsequent risk for dementia.
Population-based cohort study established in 1985-1988 that had dietary intake assessed in 1991-1993, 1997-1999, and 2002-2004 and follow-up for incident dementia until March 31, 2017.
Food frequency questionnaire to derive the Alternate Healthy Eating Index (AHEI), an 11-component diet quality score (score range, 0-110), with higher scores indicating a healthier diet.
Incident dementia ascertained through linkage to electronic health records.
Among 8225 participants without dementia in 1991-1993 (mean age, 50.2 years SD, 6.1 years; 5686 69.1% were men), a total of 344 cases of incident dementia were recorded during a median follow-up of 24.8 years (interquartile range, 24.2-25.1 years). No significant difference in the incidence rate for dementia was observed in tertiles of AHEI exposure during 1991-1993, 1997-1999 (median follow-up, 19.1 years), and 2002-2004 (median follow-up, 13.5 years). Compared with an incidence rate for dementia of 1.76 (95% CI, 1.47-2.12) per 1000 person-years in the worst tertile of AHEI (lowest tertile of diet quality) in 1991-1993, the absolute rate difference for the intermediate tertile was 0.03 (95% CI, -0.43 to 0.49) per 1000 person-years and for the best tertile was 0.04 (95% CI, -0.42 to 0.51) per 1000 person-years. Compared with the worst AHEI tertile in 1997-1999 (incidence rate for dementia, 2.06 95% CI, 1.62 to 2.61 per 1000 person-years), the absolute rate difference for the intermediate AHEI tertile was 0.14 (95% CI, -0.58 to 0.86) per 1000 person-years and for the best AHEI tertile was 0.14 (95% CI, -0.58 to 0.85) per 1000 person-years. Compared with the worst AHEI tertile in 2002-2004 (incidence rate for dementia, 3.12 95% CI, 2.49 to 3.92 per 1000 person-years), the absolute rate difference for the intermediate AHEI tertile was -0.61 (95% CI, -1.56 to 0.33) per 1000 person-years and for the best AHEI tertile was -0.73 (95% CI, -1.67 to 0.22) per 1000 person-years. In the multivariable analysis, the adjusted hazard ratios (HRs) for dementia per 1-SD (10-point) AHEI increment were not significant as assessed in 1991-1993 (adjusted HR, 0.97 95% CI, 0.87 to 1.08), in 1997-1999 (adjusted HR, 0.97 95% CI, 0.83 to 1.12), or in 2002-2004 (adjusted HR, 0.87 95% CI, 0.75 to 1.00).
In this long-term prospective cohort study, diet quality assessed during midlife was not significantly associated with subsequent risk for dementia.
Mexico has one of the highest rates of obesity and overweight worldwide, affecting 75% of the population. The country has experienced a dietary and food retail transition involving increased ...availability of high-calorie-dense foods and beverages. This study aimed to assess the relationship between the retail food environment and body mass index (BMI) in Mexico.
Geographical and food outlet data were obtained from official statistics; anthropometric measurements and socioeconomic characteristics of adult participants (N = 22,219) came from the nationally representative 2012 National Health and Nutrition Survey (ENSANUT). Densities (store count/census tract area (CTA)) of convenience stores, restaurants, fast-food restaurants, supermarkets and fruit and vegetable stores were calculated. The association of retail food environment variables, sociodemographic data and BMI was tested using multilevel linear regression models.
Convenience store density was high (mean (SD) = 50.0 (36.9)/CTA) compared with other food outlets in Mexico. A unit increase in density of convenience stores was associated with a 0.003 kg/m
(95% CI: 0.0006, 0.005, p = 0.011) increase in BMI, equivalent to 0.34 kg extra weight for an adult 1.60 m tall for every additional 10% store density increase (number of convenience stores per CTA (km
)). Metropolitan areas showed the highest density of food outlet concentration and the highest associations with BMI (β = 0.01, 95% CI: 0.004-0.01, p < 0.001). A 10% store density increase in these areas would represent a 1 kg increase in weight for an adult 1.60 m tall.
Convenience store density was associated with higher mean BMI in Mexican adults. An excessive convenience store availability, that offers unhealthy food options, coupled with low access to healthy food resources or stores retailing healthy food, including fruits and vegetables, may increase the risk of higher BMI. This is the first study to assess the association of the retail food environment and BMI at a national level in Mexico.
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