Cohort Profile: Updating the cohort profile for the MRC National Survey of Health and Development: a new clinic-based data collection for ageing research

Kuh, Diana; Pierce, Mary; Adams, Judith E.; Deanfield, John; Ekelund, Ulf; Friberg, Peter; Ghosh, Ashoke Kumar; Harwood, Nikki; Hughes, Alun D.; Macfarlane, Peter W.; Mishra, Gita D.; Pellerin, Denis; Wong, Andrew; Stephen, Alison M.; Richards, Marcus; Hardy, Rebecca

doi:10.1093/ije/dyq231

Cited by 269 publications

(338 citation statements)

References 15 publications

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“…Further details can be found on the study website and data set doi's (https://www.nshd.mrc.ac.uk/). 28 The MRC NSHD is a socially stratified British cohort of 5362 males and females followed up at regular intervals since birth during 1 week in March 1946 29, 30. Between 2006 and 2010 (at age 60–64), a total of 2856 eligible study members (those still alive and with a known address in England, Scotland, or Wales) were invited for an assessment at 1 of 6 clinical research facilities, or to be visited by a research nurse at home.…”

Section: Methodsmentioning

confidence: 99%

Physical Activity, Sedentary Time, and Cardiovascular Disease Biomarkers at Age 60 to 64 Years

Elhakeem

Cooper

Whincup

et al. 2018

JAHA

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BackgroundWe examined associations of objectively measured physical activity (PA) and sedentary time with cardiovascular disease biomarkers at age 60 to 64 years. This included investigation of sex differences and the extent to which associations may be mediated by adiposity.Methods and ResultsParticipants were 795 men and 827 women aged 60 to 64 years from the Medical Research Council National Survey of Health and Development. Combined heart rate and movement sensors worn for 5 consecutive days were used to derive overall PA energy expenditure, kJ/kg per day) and time spent sedentary (<1.5 metabolic equivalent of tasks), in light PA (1.5–3 metabolic equivalent of tasks) and moderate‐to‐vigorous intensity PA (>3 metabolic equivalent of tasks). Linear regression models were used to relate each PA parameter to inflammatory (C‐reactive protein, interleukin‐6), endothelial (tissue‐plasminogen activator, E‐selectin) and adipokine (leptin, adiponectin) markers extracted from fasting blood samples. Greater time in light PA and moderate‐to‐vigorous intensity PA and less sedentary time were associated with more favorable biomarker levels. For C‐reactive protein, interleukin‐6, and leptin, these differences were greater among women than men. For example, % differences (95% confidence intervals) in leptin for men and women per SD increases in sedentary time: 7.9 (2.7, 13.0) and 20.6 (15.3, 25.8); moderate‐to‐vigorous intensity PA: −3.8 (−8.9, 12.7) and −17.7 (−23.1, −12.4), moderate‐to‐vigorous intensity PA: −12.9 (−17.9, −8.0) and −18.3 (−23.4, −13.1). Fat mass mediated a greater proportion of these associations in women than men.ConclusionsGreater light PA and moderate‐to‐vigorous intensity PA and less sedentary time in early old age were associated with more favorable cardiovascular biomarker profiles. Fat mass partially mediated these associations but more strongly in women than men, which explained sex differences.

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Section: Methodsmentioning

confidence: 99%

Physical Activity, Sedentary Time, and Cardiovascular Disease Biomarkers at Age 60 to 64 Years

Elhakeem

Cooper

Whincup

et al. 2018

JAHA

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“…For the last time point, weight and height were measured at the time of bone mineral measurement. Information on prescribed oral glucocorticoids, aromatase inhibitors, and all medications taken for osteoporosis was obtained at age 60 to 64 years 31. Time of natural menopause or hysterectomy/bilateral oophorectomy was determined using data obtained annually from ages 47 to 54, and at ages 57 and 60 to 64 years ( n = 709) 45.…”

Section: Methodsmentioning

confidence: 99%

Life Course Dietary Patterns and Bone Health in Later Life in a British Birth Cohort Study

Ward

Prentice

Kuh

et al. 2016

Journal of Bone and Mineral Research

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Evidence for the contribution of individual foods and nutrients to bone health is weak. Few studies have considered hypothesis‐based dietary patterns and bone health. We investigated whether a protein‐calcium‐potassium–rich (PrCaK‐rich) dietary pattern over the adult life course, was positively associated with bone outcomes at 60 to 64 years of age. Diet diaries were collected at ages 36, 46, 53, and 60 to 64 years in 1263 participants (661 women) from the MRC National Survey of Health and Development. DXA and pQCT measurements were obtained at age 60 to 64 years, including size‐adjusted bone mineral content (SA‐BMC) and volumetric bone mineral density (vBMD). A food‐based dietary pattern best explaining dietary calcium, potassium, and protein intakes (g/1000 kcal) was identified using reduced rank regression. Dietary pattern Z‐scores were calculated for each individual, at each time point. Individual trajectories in dietary pattern Z‐scores were modeled to summarize changes in Z‐scores over the study period. Regression models examined associations between these trajectories and bone outcomes at age 60 to 64 years, adjusting for baseline dietary pattern Z‐score and other confounders. A consistent PrCaK‐rich dietary pattern was identified within the population, over time. Mean ± SD dietary pattern Z‐scores at age 36 years and age 60 to 64 years were –0.32 ± 0.97 and 2.2 ± 1.5 (women) and –0.35 ± 0.98 and 1.7 ± 1.6 (men), respectively. Mean trajectory in dietary pattern Z‐scores ± SD was 0.07 ± 0.02 units/year. Among women, a 0.02‐SD unit/year higher trajectory in dietary pattern Z‐score over time was associated with higher SA‐BMC (spine 1.40% [95% CI, 0.30 to 2.51]; hip 1.35% [95% CI, 0.48 to 2.23]), and vBMD (radius 1.81% [95% CI, 0.13 to 3.50]) at age 60 to 64 years. No statistically significant associations were found in men. During adulthood, an increasing score for a dietary pattern rich in protein, calcium, and potassium was associated with greater SA‐BMC at fracture‐prone sites in women. This study emphasizes the importance of these nutrients, within the context of the whole diet, to bone health. © 2016 The Authors. Journal of Bone and Mineral Research published by Wiley Periodicals, Inc. on behalf of American Society for Bone and Mineral Research (ASBMR).

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“…We combined data from six studies comprising data from up to 37,874 individuals of European origin in meta-analyses: the European Prospective Investigation into Cancer and Nutrition (EPIC)-Norfolk Cohort study (n=22,887) [19]; the LifeLines cohort study (n=7,846) [20]; the Fenland study, samples 1 (n=1,402) and 2 (n=3,186) [21,22]; the Medical Research Council (MRC) Ely study (n=1,602) [23]; and the MRC National Survey of Health and Development (NSHD) study (n=951) [24,25] (electronic supplementary material [ESM] Table 1). This sample represents nondiabetic individuals only, i.e.…”

Section: Study Populationsmentioning

confidence: 99%

Pleiotropic effects of obesity-susceptibility loci on metabolic traits: a meta-analysis of up to 37,874 individuals

et al. 2013

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Aims/hypothesis Genetic pleiotropy may contribute to the clustering of obesity and metabolic conditions. We assessed whether genetic variants that are robustly associated with BMI and waist-to-hip ratio (WHR) also influence metabolic and cardiovascular traits, independently of obesity-related traits, in meta-analyses of up to 37,874 individuals from six European population-based studies. Methods We examined associations of 32 BMI and 14 WHR loci, individually and combined in two genetic predisposition scores (GPSs), with glycaemic traits, blood lipids and BP, with and without adjusting for BMI and/or WHR. Results We observed significant associations of BMIincreasing alleles at five BMI loci with lower levels of 2 h glucose (RBJ [also known as DNAJC27], QPTCL: effect sizes −0.068 and −0.107 SD, respectively), HDL-cholesterol (SLC39A8: −0.065 SD, MTCH2: −0.039 SD), and diastolic BP (SLC39A8: −0.069 SD), and higher and lower levels of LDL-and total cholesterol (QPTCL: 0.041 and 0.042 SDs, respectively, FLJ35779 [also known as POC5]: −0.042 and −0.041 SDs, respectively) (all p<2.4×10 −4 ), independent of BMI. The WHR-increasing alleles at two WHR loci were significantly associated with higher proinsulin (GRB14: 0.069 SD) and lower fasting glucose levels (CPEB4: −0.049 SD), independent of BMI and WHR. A higher GPS-BMI was associated with lower systolic BP (−0.005 SD), diastolic BP (−0.006 SD) and 2 h glucose (−0.013 SD), while a higher GPS-WHR was associated with lower HDL-cholesterol Electronic supplementary material The online version of this article (doi:10.1007/s00125-013-2985-y) contains peer-reviewed but unedited supplementary material, which is available to authorised users. J. V. van Vliet-Ostaptchouk : M. M. van der Klauw :

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Cohort Profile: Updating the cohort profile for the MRC National Survey of Health and Development: a new clinic-based data collection for ageing research

Cited by 269 publications

References 15 publications

Physical Activity, Sedentary Time, and Cardiovascular Disease Biomarkers at Age 60 to 64 Years

Physical Activity, Sedentary Time, and Cardiovascular Disease Biomarkers at Age 60 to 64 Years

Life Course Dietary Patterns and Bone Health in Later Life in a British Birth Cohort Study

Pleiotropic effects of obesity-susceptibility loci on metabolic traits: a meta-analysis of up to 37,874 individuals

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