Anthropometrics, Metabolic Syndrome, and Mortality Hazard

Independent indices (height, body mass index, a body shape index, and hip index) derived from basic anthropometrics have been found to be powerful predictors of mortality hazard, especially when the attributable risks are summed over these indices to give an anthropometric risk index (ARI). The metabolic syndrome (MS) is defined based on the co-occurrence of anthropometric, clinical, and laboratory criteria and is also widely employed for evaluating disease risk. Here, we investigate correlations between ARI and MS in a general population sample, the United States Third National Health and Nutrition Examination Survey. Baseline values of ARI and MS were also evaluated for their association with mortality over approximately 20 years of follow-up. ARI was found to be positively correlated with each component of MS, suggesting connections between the two entities as measures of cardiometabolic risk. ARI and MS were both significant predictors of mortality hazard. Although the association of ARI with mortality hazard was stronger than that of MS, a combined model with both ARI and MS score as predictors improved predictive ability over either construct in isolation. We conclude that the combination of anthropometrics and clinical and laboratory measurements holds the potential to increase the effectiveness of risk assessment compared to using either anthropometrics or the current components of MS alone

An Anthropometric Risk Index Based on Combining Height, Weight, Waist, and Hip Measurements

Body mass index (BMI) can be considered an application of a power law model to express body weight independently of height. Based on the same power law principle, we previously introduced a body shape index (ABSI) to be independent of BMI and height. Here, we develop a new hip index (HI) whose normalized value is independent of height, BMI, and ABSI. Similar to BMI, HI demonstrates a U-shaped relationship to mortality in the Third National Health and Nutrition Examination Survey (NHANES III) population. We further develop a new anthropometric risk index (ARI) by adding log hazard ratios from separate nonlinear regressions of the four indicators, height, BMI, ABSI, and HI, against NHANES III mortality hazard. ARI far outperforms any of the individual indicators as a linear mortality predictor in NHANES III. The superior performance of ARI also holds for predicting mortality hazard in the independent Atherosclerosis Risk in Communities (ARIC) cohort.Thus,HI, along with BMI and ABSI, can capture the risk profile associated with body size and shape. These can be combined in a risk indicator that utilizes complementary information fromheight, weight, and waist and hip circumference.The combined ARI is promising for further research and clinical applications

Combining Body Mass and Shape Indices in Clinical Practice

We present preliminary clinical experience with combined consideration of the commonly used BMI (body mass index) and the newly developed ABSI (a body shape index) using a point of care anthropometric calculator for comparisons of index values and associated relative risks to population normals. In a series of 282 patients, BMI and ABSI were close to being independently distributed, supporting the value of considering both indices. Three selected cases illustrate scenarios where assessment of ABSI together with BMI could inform patient care and counseling. These data suggest that combined assessment of BMI and ABSI may prove useful in clinical practice

A New Body Shape Index Predicts Mortality Hazard Independently of Body Mass Index

Background Obesity, typically quantified in terms of Body Mass Index (BMI) exceeding threshold values, is considered a leading cause of premature death worldwide. For given body size (BMI), it is recognized that risk is also affected by body shape, particularly as a marker of abdominal fat deposits. Waist circumference (WC) is used as a risk indicator supplementary to BMI, but the high correlation of WC with BMI makes it hard to isolate the added value of WC. Methods and Findings We considered a USA population sample of 14,105 non-pregnant adults () from the National Health and Nutrition Examination Survey (NHANES) 1999–2004 with follow-up for mortality averaging 5 yr (828 deaths). We developed A Body Shape Index (ABSI) based on WC adjusted for height and weight: ABSI had little correlation with height, weight, or BMI. Death rates increased approximately exponentially with above average baseline ABSI (overall regression coefficient of per standard deviation of ABSI [95% confidence interval: –]), whereas elevated death rates were found for both high and low values of BMI and WC. (–) of the population mortality hazard was attributable to high ABSI, compared to (–) for BMI and (–) for WC. The association of death rate with ABSI held even when adjusted for other known risk factors including smoking, diabetes, blood pressure, and serum cholesterol. ABSI correlation with mortality hazard held across the range of age, sex, and BMI, and for both white and black ethnicities (but not for Mexican ethnicity), and was not weakened by excluding deaths from the first 3 yr of follow-up. Conclusions Body shape, as measured by ABSI, appears to be a substantial risk factor for premature mortality in the general population derivable from basic clinical measurements. ABSI expresses the excess risk from high WC in a convenient form that is complementary to BMI and to other known risk factors

Up-to-date probabilistic temperature climatologies

With ongoing global warming, climatologies based on average past temperatures are increasingly recognized as imperfect guides for current conditions, yet there is no consensus on alternatives. Here, we compare several approaches to deriving updated expected values of monthly mean temperatures, including moving average, exponentially weighted moving average, and piecewise linear regression. We go beyond most previous work by presenting updated climate normals as probability distributions rather than only point estimates, enabling estimation of the changing likelihood of hot and cold extremes. We show that there is a trade-off between bias and variance in these approaches, but that bias can be mitigated by an additive correction based on a global average temperature series, which has much less interannual variability than a single-station series. Using thousands of monthly temperature time series from the Global Historical Climatology Network (GHCN), we find that the exponentially weighted moving average with a timescale of 15 years and global bias correction has good overall performance in hindcasting temperatures over the last 30 years (1984–2013) compared with the other methods tested. Our results suggest that over the last 30 years, the likelihood of extremely hot months (above the 99th percentile of the temperature probability distribution as of the early 1980s) has increased more than fourfold across the GHCN stations, whereas the likelihood of very cold months (under the 1st percentile) has decreased by over two-thirds

Interannual Variability and Seasonal Predictability of Wind and Solar Resources

Solar and wind resources available for power generation are subject to variability due to meteorological factors. Here, we use a new global climate reanalysis product, Version 2 of the NASA Modern-Era Retrospective Analysis for Research and Applications (MERRA-2), to quantify interannual variability of monthly-mean solar and wind resource from 1980 to 2016 at a resolution of about 0.5 degrees. We find an average coefficient of variation (CV) of 11% for monthly-mean solar radiation and 8% for wind speed. Mean CVs were about 25% greater over ocean than over land and, for land areas, were greatest at high latitude. The correlation between solar and wind anomalies was near zero in the global mean, but markedly positive or negative in some regions. Both wind and solar variability were correlated with values of climate modes such as the Southern Oscillation Index and Arctic Oscillation, with correlations in the Northern Hemisphere generally stronger during winter. We conclude that reanalysis solar and wind fields could be helpful in assessing variability in power generation due to interannual fluctuations in the solar and wind resource. Skillful prediction of these fluctuations seems to be possible, particularly for certain regions and seasons, given the persistence or predictability of climate modes with which these fluctuations are associated

Global trends in extreme precipitation: climate models versus observations

Precipitation events are expected to become substantially more intense under global warming, but few global comparisons of observations and climate model simulations are available to constrain predictions of future changes in precipitation extremes. We present a systematic global-scale comparison of changes in historical (1901–2010) annual-maximum daily precipitation between station observations (compiled in HadEX2) and the suite of global climate models contributing to the fifth phase of the Coupled Model Intercomparison Project (CMIP5). We use both parametric and non-parametric methods to quantify the strength of trends in extreme precipitation in observations and models, taking care to sample them spatially and temporally in comparable ways. We find that both observations and models show generally increasing trends in extreme precipitation since 1901, with the largest changes in the deep tropics. Annual-maximum daily precipitation (Rx1day) has increased faster in the observations than in most of the CMIP5 models. On a global scale, the observational annual-maximum daily precipitation has increased by an average of 5.73 mm over the last 110 years, or 8.5% in relative terms. This corresponds to an increase of 10% K−1 in global warming since 1901, which is larger than the average of climate models, with 8.3% K−1. The average rate of increase in extreme precipitation per K of warming in both models and observations is higher than the rate of increase in atmospheric water vapor content per K of warming expected from the Clausius–Clapeyron equation. We expect our findings to help inform assessments of precipitation-related hazards such as flooding, droughts and storms

Estimating Climate Trends: Application to United States Plant Hardiness Zones

The United States Department of Agriculture classifies plant hardiness zones based on mean annual minimum temperatures over some past period (currently 1976–2005). Since temperatures are changing, these values may benefit from updating. I outline a multistep methodology involving imputation of missing station values, geostatistical interpolation, and time series smoothing to update a climate variable’s expected value compared to a climatology period and apply it to estimating annual minimum temperature change over the coterminous United States. I show using hindcast experiments that trend estimation gives more accurate predictions of minimum temperatures 1-2 years in advance compared to the previous 30 years’ mean alone. I find that annual minimum temperature increased roughly 2.5 times faster than mean temperature (~2.0 K versus ~0.8 K since 1970), and is already an average of 1.2  0.5 K (regionally up to ~2 K) above the 1976–2005 mean, so that much of the country belongs to warmer hardiness zones compared to the current map. The methods developed may also be applied to estimate changes in other climate variables and geographic regions

Spatiotemporal regime of climate and streamflow in the U.S. Great Lakes Basin

We analyzed interannual and seasonal regimes of river runoff, precipitation, and air temperature for three nested regions: (1) the Upper Peninsula of Michigan, (2) the U.S. portion of the Great Lakes Basin, and (3) glaciated portions of the northeastern U.S. spanning from the Dakotas to New England. Data sources included historical records of 50 to 105 years duration from 45 USGS gauging stations and 198 U.S. National Climate Network stations, and satellite-derived estimates of global monthly precipitation gridded at 2.5 resolution for 1979-2008 (partly data about precipitations were collected and passed for analysis by Glenn Hodgkins from USGS). We examined the spatiotemporal variability of climate characteristics for these regions as a multidimensional structure obtained from empirical data using factor analysis. The structure consisted of a few (2 to 7) centers of variability for each characteristic, and reflected the diversity of landscapes within the regions examined. Trends and regime shifts for mutual time intervals of river runoff, precipitation and temperature showed different direction of changes. The results obtained at the three scales were generally in agreement