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We cross-sectionally investigated irregular breakfast consumption and food timing patterns in relation to weight status and inflammation among 644 participants in the Cancer Prevention Study-3 Diet Assessment Sub-study. Breakfast consumption, and the individual means and the intra-individual standard deviation (iSD) of time at first intake of the day, duration of daily intake window, and midpoint of daily intake window were collected via six 24-hour recalls and examined in relation to body mass index (BMI), waist circumference (WC), and inflammation (GlycA). Compared to consuming breakfast on all six recalls, linear regression models showed those who consumed breakfast on 5 or 4 of the days had a 1.29 (95% CI: 0.19, 2.38) and 1.64 kg/m2 (95% CI: 0.12, 3.16) higher BMI; no association for consuming breakfast ≤3 days. 1-hour later in the average time of first intake was associated with a 0.44 kg/m2 higher BMI (95% CI: 0.04, 0.84). A 1-hour increase in the iSD of first intake, was associated with a 1.12 (95% CI: 0.49, 1.75) higher BMI; iSD in duration and midpoint of intake window were significant prior to additional adjustment for iSD in first intake. 1-hour increases in iSD for first intake time (β: 0.15; 95% CI: 0.04, 0.26) and midpoint of intake window (β: 0.16; 95% CI: 0.02, 0.31) were associated with higher GlycA. No associations were observed for WC independent of BMI. The results provide evidence that irregularity in breakfast consumption and daily intake timing patterns, particularly early in the day, may be related to weight status and inflammation.
Large numbers of new medical devices and diagnostics are developed and health services need to identify which ones offer real advantages. The National Institute for Health and Care Excellence (NICE) has introduced a system for assessing technologies that are often notified by companies, based on claims made for their benefits to patients, the National Health Service, and the environment.
Detailed scrutiny of claims made for the benefits of products and the corresponding evidence, seeking associations between these and the selection of products for full evaluation to produce NICE guidance.
Between 2009 and 2015 a NICE committee considered 169 technologies, of which it selected 74 (44 percent) for full evaluation, based on the claims of benefit and the evidence available. An average of 7.5 claims were made per technology; the total number did not influence selection but presence of studies supporting all the claims (p < .001) or any of the claims (p < .05) had a positive influence, as did claims for quicker patient recovery (p < .001). A greater number of studies to support the claims made selection more likely (p < .001), as did cohort studies (p < .05) and surveys (p < .05) but, unexpectedly, not randomized trials. The Medical Device Directive class had no influence.
This study presents categories of claims that may be useful to those developing new products and to others engaged in health technology assessment. It illustrates the importance of relevant evidence and of having a clear vision of the place of new products in care pathways from an early stage.
The Health Technology Assessment (HTA) of mobile health applications involves significant challenges including rapid product development cycles, sparse evidence and uncertainty over the economic impact. However apps also provide unique opportunities, such as their potential reach and use of real-world data, which will facilitate their contribution to healthcare delivery. The National Institute for Health and Care Excellence (NICE), alongside other agencies, has been piloting the development of a health app assessment programme. This presentation reports the results of a study about the development of the Health App Briefing (HAB) which is designed as the output from a rapid assessment of the effectiveness and cost-saving potential of apps to inform decision makers in the United Kingdom National Health Service.
The HAB is built on the success of the NICE Medtech Innovation Briefings programme because many of the HTA challenges are similar to those found with medical devices. HAB development is grounded in four principles: rapid assessment; transparent process; independence from industry or the health service and input from commentators. The content includes an evidence summary for effectiveness including comments from specialist experts and users; a summary of information relating to the cost saving potential and a summary of other user benefits (including issues of access and usability). Novel features are the presentation of a rating of the potential value of the app to the health system and working with commissioners of the app to obtain real-world information for a case study about the economic impact.
The development of four HABs along with a review of the learning from the piloting process will be presented. The review will include stakeholder feedback from a workshop.
We believe the evaluation of this work presented here will be of interest to other HTA agencies around the world that are deciding how to approach the issues surrounding the assessment of health apps.
The functional load hypothesis of Berinstein (1979) put forward the idea that languages which use a suprasegmental property (duration, F0) contrastively will not use it to realise stress. The functional load hypothesis is often cited when stress correlates are discussed, both when it is observed that the language under discussion follows the hypothesis and when it fails to follow it. In the absence of a more wide-ranging assessment of how frequently languages do or do not conform to the functional load hypothesis, it is unknown whether it is an absolute, a strong tendency, a weak tendency or unsupported. The results from a database of reported stress correlates and use of contrastive duration for 140 languages are presented and discussed. No support for the functional load hypothesis is found.
OBJECTIVES/SPECIFIC AIMS: Platelets govern signal-dependent inflammatory responses by leukocytes. Although dysregulated inflammation is common in older adults, platelet-leukocyte signaling events and downstream inflammatory gene synthesis in aging is not known. METHODS/STUDY POPULATION: Highly-purified platelets and monocytes were isolated from healthy older (age>60, n=27) and younger (age<45, n=36) adults and incubated together in autologous and nonautologous conditions. Inflammatory gene synthesis by monocytes, basally and in the presence of activated platelets, was examined. Next-generation RNA-sequencing allowed for unbiased profiling of the platelet transcriptome in older and younger adults. Differentially expressed candidates in aged platelets were validated and recombinant granzyme A (in the presence and absence of TLR4 and Caspase-1 inhibition) identified putative ligands controlling inflammatory gene synthesis. RESULTS/ANTICIPATED RESULTS: In unstimulated or activated conditions, monocyte chemoattractant protein 1 (MCP-1) and interleukin-8 (IL-8) synthesis by monocytes alone did not differ between older and younger adults. However, in the presence of autologous activated platelets, monocytes from older adults synthesized significantly greater MCP-1 (867.150 vs. 216.36 ng/mL, p<0.0001) and IL-8 (41.5 vs. 9.2 ng/mL, p<0.0001) than younger adults. Nonautologous, or switch experiments, demonstrated that aged platelets were sufficient for upregulating MCP-1 and IL-8 synthesis by monocytes. Surprisingly, classic platelet proteins known to signal to monocytes and induce MCP-1 synthesis (p-selectin, RANTES, and PF4) were not increased in platelets from older adults. Using RNA-seq followed by validation via RT-PCR and immunoblot, we identified candidate platelet molecules increased in aging that mediate platelet-monocyte signaling and pro-inflammatory gene synthesis. We confirmed that granzyme A (GrmA), a serine protease not previously identified in platelets, is present in human platelets at the mRNA and protein level. GrmA is secreted by activated platelets in signal-dependent fashion. Moreover, GrmA in platelets is significantly increased in aging (~9-fold vs. younger adults). Blocking GrmA inhibited MCP-1 and IL-8 synthesis in older adults. Finally, we uncovered that platelet GrmA signaling to monocytes is regulated through TLR4 and Caspase-1. DISCUSSION/SIGNIFICANCE OF IMPACT: Human aging is associated with reprogramming of the platelet transcriptome. A previously unrecognized protein in platelets, GrmA, is increased in aging and causes increased MCP-1 and IL-8 gene synthesis by target monocytes in a TLR4 and Caspase-1 dependent mechanism. Increased platelet GrmA in aging may contribute to injurious inflammatory responses common in older adults.
Objectives: Research demonstrates heterogeneous neuropsychological profiles among individuals with mild cognitive impairment (MCI). However, few studies have included visuoconstructional ability or used latent mixture modeling to statistically identify MCI subtypes. Therefore, we examined whether unique neuropsychological MCI profiles could be ascertained using latent profile analysis (LPA), and subsequently investigated cerebrospinal fluid (CSF) biomarkers, genotype, and longitudinal clinical outcomes between the empirically derived classes. Methods: A total of 806 participants diagnosed by means of the Alzheimer’s Disease Neuroimaging Initiative (ADNI) MCI criteria received a comprehensive neuropsychological battery assessing visuoconstructional ability, language, attention/executive function, and episodic memory. Test scores were adjusted for demographic characteristics using standardized regression coefficients based on “robust” normal control performance (n=260). Calculated Z-scores were subsequently used in the LPA, and CSF-derived biomarkers, genotype, and longitudinal clinical outcome were evaluated between the LPA-derived MCI classes. Results: Statistical fit indices suggested a 3-class model was the optimal LPA solution. The three-class LPA consisted of a mixed impairment MCI class (n=106), an amnestic MCI class (n=455), and an LPA-derived normal class (n=245). Additionally, the amnestic and mixed classes were more likely to be apolipoprotein e4+ and have worse Alzheimer’s disease CSF biomarkers than LPA-derived normal subjects. Conclusions: Our study supports significant heterogeneity in MCI neuropsychological profiles using LPA and extends prior work (Edmonds et al., 2015) by demonstrating a lower rate of progression in the approximately one-third of ADNI MCI individuals who may represent “false-positive” diagnoses. Our results underscore the importance of using sensitive, actuarial methods for diagnosing MCI, as current diagnostic methods may be over-inclusive. (JINS, 2017, 23, 564–576)
Fontan survivors have depressed cardiac index that worsens over time. Serum biomarker measurement is minimally invasive, rapid, widely available, and may be useful for serial monitoring. The purpose of this study was to identify biomarkers that correlate with lower cardiac index in Fontan patients.
Methods and results
This study was a multi-centre case series assessing the correlations between biomarkers and cardiac magnetic resonance-derived cardiac index in Fontan patients ⩾6 years of age with biochemical and haematopoietic biomarkers obtained ±12 months from cardiac magnetic resonance. Medical history and biomarker values were obtained by chart review. Spearman’s Rank correlation assessed associations between biomarker z-scores and cardiac index. Biomarkers with significant correlations had receiver operating characteristic curves and area under the curve estimated. In total, 97 cardiac magnetic resonances in 87 patients met inclusion criteria: median age at cardiac magnetic resonance was 15 (6–33) years. Significant correlations were found between cardiac index and total alkaline phosphatase (−0.26, p=0.04), estimated creatinine clearance (0.26, p=0.02), and mean corpuscular volume (−0.32, p<0.01). Area under the curve for the three individual biomarkers was 0.63–0.69. Area under the curve for the three-biomarker panel was 0.75. Comparison of cardiac index above and below the receiver operating characteristic curve-identified cut-off points revealed significant differences for each biomarker (p<0.01) and for the composite panel [median cardiac index for higher-risk group=2.17 L/minute/m2 versus lower-risk group=2.96 L/minute/m2, (p<0.01)].
Higher total alkaline phosphatase and mean corpuscular volume as well as lower estimated creatinine clearance identify Fontan patients with lower cardiac index. Using biomarkers to monitor haemodynamics and organ-specific effects warrants prospective investigation.
Chapter 6 has argued that workers responded to changes in real wage rates by adapting how hard they worked so as to maintain their earnings. Household incomes therefore tracked GDP per head rather than real wage rates and progressively improved over time, doubling between the early fourteenth and late seventeenth centuries and doubling again over the course of the industrial revolution. Higher incomes translated into changing patterns of consumption and the forms these consumption choices took are the subjects of this chapter. Section 7.2 reconstructs the kilocalorie value and composition of diets based on the agricultural-output estimates presented in Chapter 3, augmented by information on imported foodstuffs. Given that populations require an average daily food intake per head of 2,000 kilocalories (Livi-Bacci, 1991: 27) to provide sufficient nourishment for both economic and biological reproduction, these calculations also provide a useful cross-check on the consistency of the agricultural-output and population estimates. Section 7.3 then considers non-food consumption drawing upon early modern evidence of material culture as revealed by probate inventories. Again, these trends need to be consistent with those of industrial output reconstructed in Chapter 4.
Price, habit, fashion and status all shaped the budgetary decisions taken by households. Demand for food was inelastic up to the point where basic subsistence needs had been met, but as incomes rose there were clear trade-offs to be obtained between increasing consumption of cheap sources of kilocalories such as pottage, potatoes and salted herrings on the one hand, or indulging in more expensive refined bread, quality ale and beer, dairy produce and meat, plus the imported luxuries of wine, sugar, tea, cocoa and tobacco, on the other. In effect, higher incomes allowed more households to trade up to a respectability basket of foodstuffs providing a more varied and processed diet but not necessarily more kilocalories. The changing relative prices of arable, livestock and luxury products influenced these consumption decisions, while the relative cheapness or dearness of food determined how much disposable income could be devoted to the increasingly varied and tempting array of non-food consumer goods (Figure 5.02).
Income distribution in England between 1270 and 1870, as elsewhere in Western Europe, was profoundly unequal due to entrenched inequalities in access to the land, capital, education and political power upon which personal wealth depended. Gender, rank and servility and their differential legal rights were determined at birth. Privilege, patronage and position ensured that rent-seeking was rife, while warfare created opportunities for ransom and plunder to the enrichment of those in command and impoverishment of the vanquished. Everywhere, as a result, there were rich men in their castles and poor men at their gates. Moreover, as van Zanden (1995) and Milanovic and others (2007) have demonstrated, the effect of economic growth was to magnify rather than mitigate these inequalities and widen the income gap between those at the top and bottom of the social pyramid.
The rich became richer as average wealth grew because the more wealth there was the greater the opportunities for those with power and privilege to enrich themselves at the expense of the weak and disadvantaged majority. In Holland one legacy of the prosperity achieved during the Dutch Golden Age was a greatly increased inequality of incomes, which was more marked in towns than rural villages and greatest of all in major cities (van Zanden, 1995). In England, similarly, Milanovic and others (2007) claim that inequality rose with average incomes between 1688 and 1801/03, thereby confirming Kuznets’ (1955) observation that income inequality typically increased during the early stages of economic growth and only declined relatively late in the modernisation process. Prior to 1870, therefore, increasing inequality can be treated, like urbanisation, as a characteristic and unavoidable manifestation of economic growth.
In 1270 the agricultural sector dominated economic output, dwarfing the industrial and service sectors. By 1870, notwithstanding an eightfold expansion of agricultural output, this situation had been reversed and industry and services were the fastest-growing and largest sectors. The progress of British industry has been closely scrutinised from 1700 but less so in earlier centuries notwithstanding that the roots of Britain’s industrial rise extend back much earlier than the conventional starting date of the industrial revolution in the mid-eighteenth century. The service sector, which already by the mid-nineteenth century had overtaken industry and emerged as the dominant sector within the economy, has received far less attention and awaits systematic investigation from the bottom up. This unevenness of treatment has required adoption of a range of approaches in order to derive valid estimates of industrial and service-sector output and thereby chart these profound changes in the structure of economic activity and volumes of industrial and service-sector output across the 600 years under investigation.
From 1700 industry is the one economic sector for which annual data have previously been gathered and analysed on a national scale. Full use has therefore been made of these existing estimates. Pioneering work by Hoffmann (1955) inadvertently overstated the growth rate of industrial output during the industrial revolution as a result of the weighting procedures applied to a dataset which covered only 56 per cent of industrial output. As Harley (1982) and Crafts (1985) separately point out, the problem is that a few industries, most notably cotton and iron, grew more rapidly than the rest of manufacturing, and these atypical industries bulk disproportionately large in Hoffmann’s output series. By extrapolating total industrial output from that series he effectively doubled the weights of the most dynamic industries. Harley (1982) and Crafts and others (1989) have overcome this problem by limiting the weights applied to cotton and iron and increasing those applied to other industries, thereby arriving at lower estimates of total industrial output growth.
This chapter provides annual estimates of output in agriculture, which was the largest sector of the economy during the middle ages, and continued to play an important role throughout the period under consideration. The approach builds on the study of Overton and Campbell (1996), which tracked long-run trends in agricultural output and labour productivity, but was restricted to estimates for a small number of benchmark years. To provide annual estimates, heavy reliance has been made on three datasets assembled for the late-medieval, early modern and modern periods. For the period c.1250 to c.1500, a Medieval Accounts Database has been assembled by Campbell (2000, 2007), drawing upon the archival labours of a number of other historians, including David Farmer, John Langdon and Jan Titow. The information on arable yields and animal stocking densities is taken largely from manorial accounts, but is supplemented by information on the non-manorial sector from tithes. For the period c.1550 to c.1750, an Early Modern Probate Inventories Database has been assembled by Overton, which provides animal stocking densities and indirect estimates of arable yields from the valuation of the assets left by farmers (Overton and others, 2004). From the early eighteenth century, use is made of the Modern Farm Accounts Database assembled by Turner, Beckett and Afton (2001).
The chapter proceeds as follows. Section 3.2 provides a brief introduction to the main data sources for the three periods. Estimates of output for the arable sector are then given in Section 3.3, followed by estimates of livestock-sector output in Section 3.4. The arable and livestock outputs are combined in Section 3.5 to provide estimates of overall agricultural output, while Section 3.6 concludes.
Economic growth can be either extensive or intensive. Extensive growth arises where more output is produced in line with a growing population but living standards remain constant, while intensive growth arises where more output is produced by each person. In the former case, there is no economic development, as the economy simply reproduces itself on a larger scale: in the latter, living standards rise as the economy goes through a process of economic development. To understand the long-run growth of the British economy reaching back to the thirteenth century therefore requires knowledge of the trajectories followed by both population and GDP. Of particular interest is whether periods of intensive growth, distinguished by rising GDP per head, were accompanied by expanding or contracting population. For it is one thing for living standards to rise during a period of population decline, such as that induced by the recurrent plagues of the second half of the fourteenth century, when survivors found themselves able to add the land and capital of those who had perished to their own stocks, but quite another for living standards and population to rise together, particularly given the emphasis of Malthus  on diminishing returns. Indeed, Kuznets (1966: 34–85) identified simultaneous growth of population and income per head (i.e. the concurrence of intensive and extensive growth) as one of the key features that distinguished modern from pre-industrial economic growth.