Cognitive function in childhood and lifetime cognitive change in relation to mental wellbeing in four cohorts of older people

Background: poorer cognitive ability in youth is a risk factor for later mental health problems but it is largely unknown whether cognitive ability, in youth or in later life, is predictive of mental wellbeing. The purpose of this study was to investigate whether cognitive ability at age 11 years, cognitive ability in later life, or lifetime cognitive change are associated with mental wellbeing in older people.Methods: we used data on 8191 men and women aged 50 to 87 years from four cohorts in the HALCyon collaborative research programme into healthy ageing: the Aberdeen Birth Cohort 1936, the Lothian Birth Cohort 1921, the National Child Development Survey, and the MRC National Survey for Health and Development. We used linear regression to examine associations between cognitive ability at age 11, cognitive ability in later life, and lifetime change in cognitive ability and mean score on the Warwick Edinburgh Mental Wellbeing Scale and meta-analysis to obtain an overall estimate of the effect of each.Results: people whose cognitive ability at age 11 was a standard deviation above the mean scored 0.53 points higher on the mental wellbeing scale (95% confidence interval 0.36, 0.71). The equivalent value for cognitive ability in later life was 0.89 points (0.72, 1.07). A standard deviation improvement in cognitive ability in later life relative to childhood ability was associated with 0.66 points (0.39, 0.93) advantage in wellbeing score. These effect sizes equate to around 0.1 of a standard deviation in mental wellbeing score. Adjustment for potential confounding and mediating variables, primarily the personality trait neuroticism, substantially attenuated these associations.Conclusion: associations between cognitive ability in childhood or lifetime cognitive change and mental wellbeing in older people are slight and may be confounded by personality trait difference

Blood pressure in primary school children in Uganda: a cross-sectional survey.

BACKGROUND: Non-communicable diseases are an emerging concern in sub-Saharan Africa, and risks for these conditions are often based on exposures in early life, with premonitory signs developing during childhood. The prevalence of hypertension has been reported to be high in African adults, but little is known about blood pressure in African children. We studied prevalence and risk factors for high blood pressure (HBP) among school children in central Uganda. METHODS: Two urban and five rural schools were randomly selected from government schools in Wakiso district, Uganda. Questionnaires were administered and anthropometric measures taken. Blood pressure (BP) was measured three times in one sitting (on day 1) and the average compared to internationally-used normograms. Children with BP >95th percentile were re-tested at two additional sittings (day 2 and day 3) within one week, and at two further follow up visits over a period of six months. Those with sustained HBP were referred for further investigation. RESULTS: Of 552 students included, 539 completed the initial assessments (days 1-3) of whom 92 (17.1%) had HBP at the initial sitting. Age (adjusted odds ratio (aOR) 1.29 (95% confidence interval 1.14, 1.47), p< 0.001), body mass index (1.70 (1.25-2.31) p = 0.001) and soil-transmitted helminths (2.52 (1.04-6.11), 0.04) were associated with increased prevalence of HBP at the initial sitting. After further investigation, sustained HBP was seen in 14 children, yielding an estimated prevalence of 3.8% allowing for losses to follow up. Four children required treatment. CONCLUSION: It is feasible to measure blood pressure accurately in the school setting. A high HBP prevalence on initial readings gave cause for concern, but follow up suggested a true HBP prevalence commensurate with international normograms. Extended follow up is important for accurate assessment of blood pressure among African children

HMM based scenario generation for an investment optimisation problem

This is the post-print version of the article. The official published version can be accessed from the link below - Copyright @ 2012 Springer-Verlag.The Geometric Brownian motion (GBM) is a standard method for modelling financial time series. An important criticism of this method is that the parameters of the GBM are assumed to be constants; due to this fact, important features of the time series, like extreme behaviour or volatility clustering cannot be captured. We propose an approach by which the parameters of the GBM are able to switch between regimes, more precisely they are governed by a hidden Markov chain. Thus, we model the financial time series via a hidden Markov model (HMM) with a GBM in each state. Using this approach, we generate scenarios for a financial portfolio optimisation problem in which the portfolio CVaR is minimised. Numerical results are presented.This study was funded by NET ACE at OptiRisk Systems

Predictors of low urinary quality of life in spinal cord injury patients on clean intermittent catheterization

ObjectiveClean intermittent catheterization (CIC) is a preferred method of bladder management for many patients with spinal cord injury (SCI), but long‐term adherence is low. The aim of this study is to identify factors associated with low urinary quality of life (QoL) in SCI adults performing CIC.MethodsOver 1.5 years, 1479 adults with SCI were prospectively enrolled through the Neurogenic Bladder Research Group registry, and 753 on CIC with no prior surgeries were included. Injury characteristics, complications, hand function, and Neurogenic Bladder Symptom Score (NBSS) were analyzed. The NBSS QoL question (overall satisfaction with urinary function) was dichotomized to generate comparative groups (dissatisfied vs neutral/satisfied).ResultsThe cohort was 32.9% female with a median age of 43.2 (18‐86) years, time since the injury of 9.8 (0‐48.2) years, and 69.0% had an injury at T1 or below. Overall 36.1% were dissatisfied with urinary QoL. On multivariable analysis, female gender (odds ratio [OR], 1.63; 95% confidence interval [CI], 1.15‐2.31; P = 0.016), earlier injury (OR, 0.95 per year; 95% CI, 0.93‐0.97; P < 0.001), ≥4 urinary tract infections (UTIs) per year (OR, 2.36; 95% CI, 1.47‐3.81; P = 0.001), and severe bowel dysfunction (OR, 1.42; 95% CI, 1.02‐1.98; P = 0.035) predicted dissatisfaction. Level of injury, fine motor hand function, and caregiver dependence for CIC were not associated with dissatisfaction.ConclusionsIn a mature SCI cohort, physical disability does not predict dissatisfaction with urinary QoL but severe bowel dysfunction and recurrent UTIs have a significant negative impact. With time the rates of dissatisfaction decline but women continue to be highly dissatisfied on CIC and may benefit from early intervention to minimize the burden of CIC on urinary QoL.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/149763/1/nau23983.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/149763/2/nau23983_am.pd

Predictive geochemical mapping using machine learning in western Kenya

Digital soil mapping is a cost-effective method for obtaining detailed information regarding the spatial distribution of chemical elements in soils. Machine learning (ML) algorithms such as random forest (RF) models have been developed for such tasks as they are capable of modelling non-linear relationships using a range of datasets and determining the importance of predictor variables, offering multiple benefits to traditional techniques such as kriging. In this study, we describe a framework for spatial prediction based on RF modelling where inverse distance weighted (IDW) predictors are used in conjunction with auxiliary environmental covariates. The model was applied to predict the total concentration (mg kg-1 ) of 56 elements, soil pH and organic matter content, as well as to assess prediction uncertainty using 466 soil samples in western Kenya (Watts et al 2021). The results of iodine (I), selenium (Se), zinc (Zn) and soil pH are highlighted in this work due to their contrasting biogeochemical cycles and widespread dietary deficiencies in sub-Saharan Africa, whilst soil pH was assessed as an important parameter to define soil chemical reactions. Algorithm performance was evaluated to determine the importance of each predictor variable and the model’s response using partial dependence profiles. The accuracy and precision of each RF model were assessed by evaluating the out-of-bag predicted values. The IDW predictor variables had the greatest impact on assessing the distribution of soil properties in the study area, however, the inclusion of auxiliary values did improve model performance for all soil properties. The results presented in this paper highlight the benefits of ML algorithms which can incorporate multiple layers of data for spatial prediction, uncertainty assessment and attributing variable importance. Additional research is now required to ensure health practitioners and the agricommunity utilise the geochemical maps presented here, and the webtool, for assessing the relationship between environmental geochemistry and endemic diseases and preventable micronutrient deficiency

Predictive geochemical mapping using machine learning in western Kenya

Digital soil mapping techniques represent a cost-effective method for obtaining detailed information regarding the spatial distribution of chemical elements in soils. Machine learning (ML) algorithms using random forest (RF) models have been developed for classification, pattern recognition and regression tasks, they are capable of modelling non-linear relationships using a range of datasets, identifying hierarchical relationships, and determining the importance of predictor variables. In this study, we describe a framework for spatial prediction based on RF modelling where inverse distance weighted (IDW) predictors are used in conjunction with ancillary environmental covariates. The model was applied to predict the total concentration (mg kg−1) and assess the prediction uncertainty of 56 elements, soil pH and organic matter content using 466 soil samples in western Kenya; the results of iodine (I), selenium (Se), zinc (Zn) and soil pH are highlighted in this work. These elements were selected due to contrasting biogeochemical cycles and widespread dietary deficiencies in sub-Saharan Africa, whilst soil pH is an important parameter controlling soil chemical reactions. Algorithm performance was evaluated determining the relative importance of each predictor variable and the model's response using partial dependence profiles. The accuracy and precision of each RF model were assessed by evaluating out-of-bag predicted values. The models R2 values range from 0.31 to 0.64 whilst CCC values range from 0.51 to 0.77. The IDW predictor variables had the greatest impact on assessing the distribution of soil properties in the study area, however, the inclusion of ancillary environmental data improved model performance for all soil properties. The results presented in this paper highlight the benefits of ML algorithms which can incorporate multiple layers of data for spatial prediction, uncertainty assessment and attributing variable importance. Additional research is now required to ensure health practitioners and the agri-community utilise the geochemical maps presented here for assessing the relationship between environmental geochemistry, endemic diseases and preventable micronutrient deficiency

Associations between APOE and low-density lipoprotein cholesterol genotypes and cognitive and physical capability: the HALCyon programme

The APOE ε2/3/4 genotype has been associated with low-density lipoprotein cholesterol (LDL-C) and Alzheimer disease. However, evidence for associations with measures of cognitive performance in adults without dementia has been mixed, as it is for physical performance. Associations may also be evident in other genotypes implicated in LDL-C levels. As part of the Healthy Ageing across the Life Course (HALCyon) collaborative research programme, genotypic information was obtained for APOE ε2/3/4, rs515135 (APOB), rs2228671 (LDLR) and rs629301 (SORT1) from eight cohorts of adults aged between 44 and 90+years. We investigated associations with four measures of cognitive (word recall, phonemic fluency, semantic fluency and search speed) and physical capability (grip strength, get up and go/walk speed, timed chair rises and ability to balance) using meta-analyses. Overall, little evidence for associations between any of the genotypes and measures of cognitive capability was observed (e.g. pooled beta for APOE ε4 effect on semantic fluency z score=- 0.02; 95% CI=- 0.05 to 0.02; p value=0.3; n=18,796). However, there was borderline evidence within studies that negative effects of APOE ε4 on nonverbal ability measures become more apparent with age. Few genotypic associations were observed with physical capability measures. The findings from our large investigation of middle-aged to older adults in the general population suggest that effects of APOE on cognitive capability are at most modest and are domain- and age-specific, while APOE has little influence on physical capability. In addition, other LDL-C-related genotypes have little impact on these traits. © The Author(s) 2014

New genetic loci implicated in fasting glucose homeostasis and their impact on type 2 diabetes risk.

Levels of circulating glucose are tightly regulated. To identify new loci influencing glycemic traits, we performed meta-analyses of 21 genome-wide association studies informative for fasting glucose, fasting insulin and indices of beta-cell function (HOMA-B) and insulin resistance (HOMA-IR) in up to 46,186 nondiabetic participants. Follow-up of 25 loci in up to 76,558 additional subjects identified 16 loci associated with fasting glucose and HOMA-B and two loci associated with fasting insulin and HOMA-IR. These include nine loci newly associated with fasting glucose (in or near ADCY5, MADD, ADRA2A, CRY2, FADS1, GLIS3, SLC2A2, PROX1 and C2CD4B) and one influencing fasting insulin and HOMA-IR (near IGF1). We also demonstrated association of ADCY5, PROX1, GCK, GCKR and DGKB-TMEM195 with type 2 diabetes. Within these loci, likely biological candidate genes influence signal transduction, cell proliferation, development, glucose-sensing and circadian regulation. Our results demonstrate that genetic studies of glycemic traits can identify type 2 diabetes risk loci, as well as loci containing gene variants that are associated with a modest elevation in glucose levels but are not associated with overt diabetes

Source apportionment of micronutrients in the diets of Kilimanjaro,Tanzania and Counties of Western Kenya

Soil, water and food supply composition data have been combined to primarily estimate micronutrient intakes and subsequent risk of deficiencies in each of the regions studied by generating new data to supplement and update existing food balance sheets. These data capture environmental influences, such as soil chemistry and the drinking water sources to provide spatially resolved crop and drinking water composition data, where combined information is currently limited, to better inform intervention strategies to target micronutrient deficiencies. Approximately 1500 crop samples were analysed, representing 86 food items across 50 sites in Tanzania in 2013 and >230 sites in Western Kenya between 2014 and 2018. Samples were analysed by ICP-MS for 58 elements, with this paper focussing on calcium (Ca), copper (Cu), iron (Fe), magnesium (Mg), selenium (Se), iodine (I), zinc (Zn) and molybdenum (Mo). In general, micronutrient supply from food groups was higher from Kilimanjaro,Tanzania than Counties in Western Kenya, albeit from a smaller sample. For both countries leafy vegetable and vegetable food groups consistently contained higher median micronutrient concentrations compared to other plant based food groups. Overall, calculated deficiency rates were 90% for Ca, Zn and I in both countries. For Mg, a slightly lower risk of deficiency was calculated for Tanzania at 0 to 1% across simplified soil classifications and for female/males, compared to 3 to 20% for Kenya. A significant difference was observed for Se, where a 3 to 28% risk of deficiency was calculated for Tanzania compared to 93 to 100% in Kenya. Overall, 11 soil predictor variables, including pH and organic matter accounted for a small proportion of the variance in the elemental concentration of food. Tanzanian drinking water presented several opportunities for delivering greater than 10% of the estimated average requirement (EAR) for micronutrients. For example, 1 to 56% of the EAR for I and up to 10% for Se or 37% for Zn could be contributed via drinking water