Demographics will reverse three multi-decade global trends

Between the 1980s and the 2000s, the largest ever positive labour supply shock occurred, resulting from demographic trends and from the inclusion of China and eastern Europe into the World Trade Organization. This led to a shift in manufacturing to Asia, especially China; a stagnation in real wages; a collapse in the power of private sector trade unions; increasing inequality within countries, but less inequality between countries; deflationary pressures; and falling interest rates. This shock is now reversing. As the world ages, real interest rates will rise, inflation and wage growth will pick up and inequality will fall. What is the biggest challenge to our thesis? The hardest prior trend to reverse will be that of low interest rates, which have resulted in a huge and persistent debt overhang, apart from some deleveraging in advanced economy banks. Future problems may now intensify as the demographic structure worsens, growth slows, and there is little stomach for major inflation. Are we in a trap where the debt overhang enforces continuing low interest rates, and those low interest rates encourage yet more debt finance? There is no silver bullet, but we recommend policy measures to switch from debt to equity finance

Associations of plasma ferritin and indices of body fat distribution.

<p>*Adjusted for age and residence (urban/rural).</p>†<p>Adjusted for age, residence (urban/rural), alcohol drinking, smoking, education attainment, physical activity, self-reported CVD, and family history of diabetes and CVD and menopause status (in women participants); plasma ferritin concentrations were nature log-transformed.</p>‡<p>Additionally adjusted for waist circumference or hip circumference (each other).</p>§<p>Additionally adjusted for trunk fat mass or leg fat mass (each other).</p

Characteristics of study participants^*.

<p>*Data are mean ± SD, median (interquartile range) or number (%); <i>P</i> value was calculated after adjusted for age and urban/rural residence (where appropriate).</p>‡<p>Self-reported CVD including stroke and coronary heart disease.</p>§<p>Parents or siblings had a history of diabetes or CVD.</p><p>|| This variable was log-transformed before analysis.</p

Additional file 1 of Construction of a prognostic model for hepatocellular carcinoma patients receiving transarterial chemoembolization treatment based on the Tumor Burden Score

Supplementary Material

Characteristics of the study population.

<p>Data are n (%), means±SD or medians (interquartile range).</p><p>BMI, body mass index; HOMA-B, homeostasis model assessment of pancreatic beta-cell function; HOMA-S, homeostasis model assessment of insulin sensitivity; IFG, impaired fasting glucose.</p>a<p>Body fat percentage (%) was assessed using the dual-energy X-ray absorptiometry (DEXA) among 1,634 participants (711 men and 923 women) from Shanghai.</p

Case-control analyses of <i>PCSK1</i> rs6234 with obesity and overweight.

<p>The ORs are odds ratios that represent the effects of risk allele (G-allele) based on an additive model, in which individuals homozygous for CC were coded as 0, heterozygous individuals CG were coded as 1, and individuals homozygous for GG were coded as 2; The ORs and <i>P</i> values were adjusted for age, region and sex (where appropriate).</p><p>MAF, minor allele frequency.</p

Associations between Ionomic Profile and Metabolic Abnormalities in Human Population

<div><h3>Background</h3><p>Few studies assessed effects of individual and multiple ions simultaneously on metabolic outcomes, due to methodological limitation.</p> <h3>Methodology/Principal Findings</h3><p>By combining advanced ionomics and mutual information, a quantifying measurement for mutual dependence between two random variables, we investigated associations of ion modules/networks with overweight/obesity, metabolic syndrome (MetS) and type 2 diabetes (T2DM) in 976 middle-aged Chinese men and women. Fasting plasma ions were measured by inductively coupled plasma mass spectroscopy. Significant ion modules were selected by mutual information to construct disease related ion networks. Plasma copper and phosphorus always ranked the first two among three specific ion networks associated with overweight/obesity, MetS and T2DM. Comparing the ranking of ion individually and in networks, three patterns were observed (1) “Individual ion,” such as potassium and chrome, which tends to work alone; (2) “Module ion,” such as iron in T2DM, which tends to act in modules/network; and (3) “Module-individual ion,” such as copper in overweight/obesity, which seems to work equivalently in either way.</p> <h3>Conclusions</h3><p>In conclusion, by using the novel approach of the ionomics strategy and the information theory, we observed potential associations of ions individually or as modules/networks with metabolic disorders. Certainly, these findings need to be confirmed in future biological studies.</p> </div

The severity of MetS according to amylin quartiles.

<p><i>P</i><0.001 for trend.</p

The overweight/obesity, metabolic syndrome and type 2 diabetes related ion network. A.

<p>The overweight/obesity related ion network; <b>B.</b> The metabolic syndrome related ion network; <b>C.</b> The type 2 diabetes related ion network. The size of node represents the combined Fisher score of significant combinations involve a specific ion, which indicates the strength of ion module in association with metabolic disorders. The width of the edge represents the Fisher score of edge between connected ions, which indicates the possibility of forming an ion module associated with metabolic disorders. Three ion patterns were postulated when comparing the rank of ion effect in individual and in network associated with metabolic disorders. “Individual ion” was defined as the rank of ion in the network posterior to that of single ion. “Module ion” was defined as the rank of ion in network prior to that of single ion. “Module-individual ion” was defined as the rank of ion in network equivalent to that of single ion.</p

Characteristics of participants according to amylin quartiles (<i>n</i> = 1011)^a.

a<p>Data are arithmetic mean (SD) or geometric mean (95% confidence interval) if not specified. Percentages may not sum to 100 because of rounding. <i>P</i> for trend was calculated after adjustment for age and sex.</p>b<p>Not adjusted for itself.</p