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Zhifanggou is a first order tributary of the Xingzi River, which lies in the middle and upper reaches of the Yanhe River in Ansai County, Shaanxi Province. Zhifanggou Watershed (109°13′46″-109°16′03″E and 36°42′42″-36°46′28″N) belongs to No. 2 sub-region of the hilly and gully area in the Loess Plateau (Fig. 1). The watershed area is 8.27 km2 and the elevation is between 1040–1425 m above sea level. The relative altitude difference of the upstream and downstream river bed is 210 m and the mean river slope is 37‰. Most of the height differences between the hill top and valley are 150–200 m. Soil erosion is dominated by water erosion and gravity erosion. Sheet erosion, rills and ephemeral gullies are the main erosion forms on the hilly slopes, and gully erosion and gravity erosion are the main erosion forms on the gully slopes35.Figure 1The relative location of Zhifanggou Watershed and the Yanhe River basin, the Digital Elevation Model of Zhifanggou Watershed (Fig. 1 is generated by ArcGIS 10.2, Yanhe River is a primary tributary of the Yellow River. The Yanhe River boundary and river system are extracted by hydrological analysis tools, Zhifanggou Watershed is overlayed by the DEM map and the hillshade map. ArcGIS website: http://www.esrichina.com.cn/softwareproduct/ArcGIS/).
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The relative location of Zhifanggou Watershed and the Yanhe River basin, the Digital Elevation Model of Zhifanggou Watershed (Fig. 1 is generated by ArcGIS 10.2, Yanhe River is a primary tributary of the Yellow River. The Yanhe River boundary and river system are extracted by hydrological analysis tools, Zhifanggou Watershed is overlayed by the DEM map and the hillshade map. ArcGIS website: http://www.esrichina.com.cn/softwareproduct/ArcGIS/).
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The watershed belongs to a transition zone between semi-humid and semi-arid in the warm temperate climate, the mean annual temperature is 8.8 °C and mean annual precipitation is 482.7 mm. Rainfall mainly falls as rainstorms and is concentrated in June-September, which accounts for 73.6% of the total annual rainfall amount.
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The main soil type in the watershed is loess according to the Chinese soil classification system in 1995. The loess area accounts for 65.5% of the total soil area, following by 25.1% of Red Soil and Two Coloured Soils. Soil mechanical composition includes clay (<0.002 mm, 53.9–74.8%, silt (0.002~0.05 mm, 16–26%), and sand (0.05~2 mm, 5.88–31.8%)36. Soil has a uniform texture, with low organic matter content (6.82–34.6 g/kg) and loose structure, and can be easily dispersed and transported (Fig. 2). Before watershed governance, the watershed had sparse vegetation, high planting rates and serious soil erosion37. The mean sediment yield modulus for many years was ≤14 000 t/(km2•a). Since integrated basin management started in 1997, the watershed was vegetated by afforestation and grass planting38.Figure 2Gully erosion of slope surface (a) and flat arable land (b) in Ansai County of the Yanhe River Basin in 2013 (The pictures were taken by Lei Wu in Ansai County in November 2013).
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Seventeen typical pulsed rainstorms were observed in Zhifanggou Watershed from 1997–2010, to study rainfall, runoff and sediment relationships. The 17 rainstorms have their own local characteristics of short duration and high intensity, resulting in different interactions between runoff and sediment yield. In the 17 rainstorms, rainfall ranged from 13.4 mm on 4 May 1997 to 56.4 mm on 20 May 1998, with rainfall durations of 150 minutes and 330 minutes, respectively. In August 2010, two short high intensity rainfall events occurred, 50 and 60 minutes rainfall duration on 11 August 2010 and 18 August 2010 with 55.6 and 40.5 mm rainfall, respectively.
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The monitoring indexes of soil and water conservation in a small watershed includes rainfall, water level, flow rate and flood sediment (suspended sediment). In this study, two methods, including rain gauge and automatic rain recorder, were used to measure rainfall amount in the watershed. Three rainfall stations were established in the central, upper and lower reaches of the watershed, respectively. The simultaneous measurement of rain gauge and automatic rain recorder were checked with each other and the mean value taken as the watershed rainfall. Water-level observation is one of the basic measurements at the runoff station, and is the basis of flow estimation. Water level was measured by water gauge, and the flow rate from the watershed outlet was calculated using a flume weir flow formula39. The basic requirements of water-level observation at the basin outlet are: the daily water level was measured at 0800 and 2000 (local time) during normal water-level periods, and the flood level was measured by points of flood rising, falling and water-level changing at different short intervals40. Observation accuracy was ±1 cm. Sediment was analysed by collecting runoff samples. The observation of suspended sediment is carried out by artificial observation method. In the process of runoff yield, the turbid water samples were collected artificially by a sampling bottle at a certain time interval, the sediment content was measured by drying in the laboratory, the sediment yield of the whole basin was then calculated41. The volume of the sample bottle is 1000 mL. Sampling times were 3, 6, 12 and 30 minutes after runoff yield during one rainstorm.
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Regression analysis is an important method to study relationships between runoff and sediment yield in small watersheds42. In this study, the statistical analysis software (Excel & SPSS) was used to analyse the runoff and sediment relationships of typical rainstorm events43. Besides, Cluster Analysis (MATLAB Procedure) is an important approach of unsupervised learning44. As a data analysis tool, its importance has been widely recognized in various fields. A Cluster Analysis is performed in order to find the ‘natural grouping’ of data sets, which is also called a ‘cluster’. Generally, a cluster is a collection of similar elements. Similarity coefficient and distance are two important parameters of Cluster Analysis. The similarity coefficient is indicative of the similarity degree between different sample variables, the range is 0–1. When its value is close to 1, the similarity degree between samples is strong. If the value is 0, there is no correlation between samples. Distance represents the geometric distance between different points, and the distance is related to the attributes of the sample points. The basic idea of Cluster Analysis is: first “n” samples are classified as one class separately, and the most similar of them is classified as a new class, at this point, the number of categories becomes “n − 1”. Then, the similarity between the new class and other “n − 2” classes is calculated, and the nearest approximation is classified as another new class, the total class number becomes “n − 2”. By analogy, the process continues until all variables are classified as one category. This clustering process can be expressed by the cluster map. This study used 153 data sets of 17 sets and 9 categories as the sample data (Table 1). Firstly, these data are standardized to eliminate the dimensional differences, so that the indexes are comparable. Secondly, the distance or similarity coefficient between different variables is calculated. Then, the appropriate classification methods are selected and used to progressively cluster samples.Table 1Rainfall runoff and sediment yield parameters and Cluster Analysis results during 17 event-based rainstorms.DateCluster resultsRainfall (mm)Runoff yield time (s)Mean flow (m3/s)Peak flow (m3/s)Total flow (m3)Runoff depth (mm)Runoff coefficientMean sediment concentration (g/cm3)Suspended sediment discharge (t)Sediment yield modulus (t/km2)1997–4–5113.490000.00730.011180.510.00970.00070.48038.654.671997-6-5116.475600.00670.009448.990.00590.00040.37918.562.241997-28-7214.2162000.00960.0151141.370.01710.00120.39255.406.701998-21-5256.4198000.01060.0174310.950.03760.00070.07021.712.631998-23-6524.6162002.603412.24216051.591.94090.09270.1963140.76379.781998-12-7220.9234000.00860.014228.590.02760.00130.10423.732.871998-20-8230.6162000.25670.62082826.80.34180.01120.049139.7016.892000-7-7534.8150002.39867.481117602.542.12850.06120.2604570.58552.672000-14-7641.0176404.504713.277744026.095.32360.12980.24810920.671320.522000-26-7844.5144008.926317.295464942.417.85280.17650.25616618.762009.522000-11-8716.3144002.41285.232936247.424.38300.26970.2047399.33894.722001-26-7546.7126001.30014.727617283.952.09000.04480.1021759.51212.762001-16-8431.8136803.50627.581426541.133.20930.10090.1243296.41398.602008-28-6518.6162001.32112.021516774.842.02840.10910.0831394.09168.572009-7-7134.554000.00690.009419.110.00230.00010.0140.260.032010-11-8355.630004.00906.073112852.171.55410.03330.076970.77117.382010-18-8340.536002.29243.45778032.220.97120.02080.023185.9522.48Note: The similarity coefficient value is 0.8267 by programming calculation of Cluster Analysis.
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Figures 3–9 show the regression relationships between sediment concentrations and runoff amount under different rainstorm events. Correlations between runoff and sediment yield are diverse under different rainstorm events. Generally, sediment concentrations increase with increased flow rate, but when sediment concentration reaches a certain value, the change of sediment concentration is very small, even if the flow continuously increases. The value eventually tends to stabilize. It also shows that the influence of flow rate on sediment concentration has different effects during different rainfall erosion events. Therefore, the corresponding curve fitting can also be changed in different rainstorm events45. In all rainstorm erosion events, there is not always a strong correlation between runoff and sediment yield. Statistical analysis of 17 rainstorm events found that the regression relationships of 4 May 1997, 6 May 1997, 12 July 1998 and 26 July 2001 were weak, R2 (coefficient of determination) were all <0.5 and p values were all >0.05. The values of R2 for the rainstorm events on 18 August 2010 and 21 May 1998 are both <0.8, but there were three rainstorm events when R2 values were all >0.95 and p values were all <0.01 (23 June 1998, 26 August 2001 and 7 July 2000).Figure 3Relationships between sediment concentration (0–800 kg/m3) and runoff (0.002–0.022 m3/s) under different rainstorm events.Figure 4Relationships between sediment concentration (0–600 kg/m3) and runoff (0–18 m3/s) under different rainstorm events.Figure 5Relationships between sediment concentration (0–250 kg/m3) and runoff (0–8 m3/s) under different rainstorm events.Figure 6Relationships between sediment concentration (0–200 kg/m3) and runoff (0.004–0.016 m3/s) under different rainstorm events.Figure 7Relationships between sediment concentration (0–160 kg/m3) and runoff (0–7 m3/s) under different rainstorm events.Figure 8Relationships between sediment concentration (0–120 kg/m3) and runoff (0–0.7 m3/s) under different rainstorm events.Figure 9Relationships between sediment concentration (0–25 kg/m3) and runoff (0–0.01 m3/s) under different rainstorm events.
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In terms of the strongest correlations, 17 rainstorm erosion events include linear, power, logarithmic and exponential functions. The logarithmic function is the upper convex curve, which is characterized by sediment yield increasing sharply with increased runoff (Figs 3–9). Representative rainstorm events are 7 July 2000, 23 June 1998, and 16 August 2001. The erosion amount of the three rainstorms roughly lies between 3000–5000 t. The power function can be divided into the upper convex or lower convex type according to the size of the index. Representative rainfall events are 26 July 2000, 14 July 2000, 28 June 2008 and 21 August 1998. The sediment yield rate with increased runoff is lower than the logarithmic function during the initial runoff yield period. The sediment yield rate of the linear regression relation is between power function and logarithmic function. Representative rainfall events are 28 July 1997, 11 August 2000, 11 August 2010 and 7 July 2009. The regression relationships on 6 May 1997 and 26 July 2001 were the weakest. The 26 July 2001 was a heavy rainfall erosion event. The 6 May 1997 was a prolonged rainfall event. Because the project of returning farmland to forest in 1997 was in the primary stage, the relationship between sediment yield and runoff was unstable. In 2009, with the effectiveness of soil and water conservation measures, the sediment yield of small rainstorm events was linearly correlated with runoff. A representative rainfall event was 7 July 2009. These results also validated that the surface conditions on the loess slope significantly affected runoff and sediment yield processes46. The above analysis is of great significance for further summarizing relationships between runoff and sediment yield, and understanding the variation of sediment yield associated with different rainstorm events.
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The various regression associations between runoff and sediment yield are related to the characteristics of both rainstorms and soil erosion on the Loess Plateau. Generally, the storms in the north of Shaanxi Province mostly appear at a single station and single time, indicating that the most storms in this region are usually local ones with short duration47. Both rill and shallow gully erosion are very serious on the Loess Plateau, and gully erosion is also very active. The sediment yield of gully erosion accounts for >70% of total sediment yield in the watershed. Although the rills and shallow gullies are small, they are numerous, and dense networks cover all loess slopes. During periods of infiltration-excess rainstorms, overland flow always flows along the direction of minimum resistance, because of the spatial difference of soil erosion resistance, and forms a small stream of relatively concentrated runoff on the slope, and then develops into different erosion types. The rill, shallow gully, dissected valley and gully in Zhifanggou Watershed form a dense network, and play very important roles in the formation of pulsed runoff-erosion events. The weak erosion resistance of loess soil is the fundamental reason for the severe erosion. Under the action of water flow, the stability of sediment particles is determined by the size of sediment particles and the cohesive forces between them48. For coarse-grained sediment, size plays a major role in stability, and the larger the particle size, the greater the starting velocity. When the particle size is less than a certain value, fine particles are also difficult to move, and the smaller the particle size, the larger the starting velocity. In general, fine and silt sand particles are most easy to erode, and the content of silt sand particle is the highest and more than >50% of the loess soil, but the starting velocity of clay soil particles is slightly larger.
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For slopes without rill or shallow gully erosion, the final destination of filling water in the early excess infiltration rainstorm is infiltration. After overflow develops on slopes, sheet overflow will finally enter the channel. Therefore, in this case, the confluence speed is small, the flood duration is long, and the ratios of flood peak and discharge amount are reduced49. On the slope with rill or shallow gully erosion, the filling water in the early period of infiltration-excess rainstorm appears as a ‘dam break effect’ after appearance of the rill or gully erosion during the rainstorm. It suddenly releases by way of ‘installment time deposit’, and rapidly changes into centralized and strong channel flow50. This phenomenon reduces infiltration of rainwater into soil, increases the surface runoff coefficient, and makes the flood show features of “high peak and small volume, less runoff and much sand, peak with rising and dropping steeply, sudden coming and rapid going away”51. The characteristics of rainstorm erosion have important influences on relationships between runoff and sediment yield.
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Rainstorms on the Loess Plateau have highly variable spatio-temporal characteristics52. Heavy rainfall is concentrated in a few hours or even minutes. There are also major differences for the same rainstorm within several kilometres; it has great locality. The typical spatio-temporal characteristics of rainstorms determine the dominant position of infiltration-excess runoff on the Loess Plateau, and the flood process showed a precipitous fall with a sharp thin peak. Runoff generation was mainly driven by precipitation characteristics and the initial catchment saturation53. Because of the particularity of hydrological processes and the implementation of soil conservation measures on the Loess Plateau, the correlations of rainfall-runoff (y = 451.16e0.0598×, R2 = 0.101, n = 17, p > 0.05) and rainfall-sediment (y = 36509x − 4E + 06, R2 = 0.114, n = 17, p > 0.05) are often both scattered and very weak (Table 1), although the correlation of rainfall-sediment (mm, kg) is stronger than rainfall-runoff (mm, m3). The results are consistent with previous studies that the annual sediment yield of the Xiliugou Basin in the upper Yellow River showed a significant downward trend from 1960–2013, whereas no significant trend was detected in annual precipitation54. Furthermore, high vegetation cover and well-developed biological soil crust were the important factors in reducing runoff and erosion55,56. The results accord with Zhao et al. (2014), who showed pastures and crops were effective in decreasing runoff and erosion57. The relation of rainstorm and runoff can be expressed as the relation between total rainstorm amount and total runoff amount, or the relation between the spatio-temporal process of the rainstorm and the corresponding runoff yield.
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The loess hilly region is a typical infiltration-excess runoff area. The main factors affecting runoff and sediment yield are rainfall intensity, rainfall duration and surface conditions (slope, gully density and antecedent moisture condition). Among them, the surface condition is a more deterministic factor, while rainfall is a stochastic factor58. Rainfall intensity in the loess hilly region plays a decisive role in runoff yield amount, while antecedent soil water content takes second place. Each stage of soil loss by unit runoff for a given mean rainfall intensity was significantly different among rainstorm patterns59. Besides, because the climate of the Loess Plateau is arid and semi-arid, the thickness of loess soil is often 50–80 m and the thickest is 150–180 m, the water storage capacity of the whole vadose zone is very large, low intensity rainfall often does not produce runoff, only intense rainstorms produce surface runoff. Due to the uneven spatio-temporal distribution of rainstorms, runoff yield from catchments is local, and extensive simultaneous runoff is extremely rare. Therefore, the flood process is often sudden rise and sudden fall, and the peak volume is small. Under the same conditions, if rainfall is large, runoff is generally large, and runoff erosion capacity and sediment transport capacity are also large. The more uneven the distribution of rainfall in time and space, the more concentrated the sediment yield in different periods, the greater the amount of runoff and sediment yield in the same rainfall event60.
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Soil erosion models are an important means of soil loss prediction and water conservation assessment61. The relationship between runoff and sediment is the basis of soil erosion models62. Most of the Loess Plateau is covered by large amounts of loess soil, it has characteristics of uniform texture, loose and porous structure, vertical joint development and high water permeability. The soil is erodible.
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The unit hydrograph is an important method to analyse relationships between runoff and sediment yield of event-based rainstorms. Figure 10 shows two hourly flow-sediment hydrographs of Zhifanggou Watershed under typical rainstorm events. Figure 10(a) indicates that sediment concentrations increased along with increased flow rate during the water rising period, and the water flow fades away quickly and sediment concentration decays slowly. The results agree well with Cao et al. (2015) that the initial soil losses increased rapidly and reached the peak value nearly at the same time as the peak runoff in the Red Soil region of southern China, then soil loss decreased and runoff leveled out63. Generally, the typicality of rainstorm erosion in loess hilly region are mainly manifested in several stages: First, because of differences of soil and topography, overland flow is often collected together from sheet overland flow and small rill flow, and causes rill erosion. Second, with the continuity of the rainfall-runoff process, the small rill flow also gradually increases, it will form shallow gully, dissected valley, gully and gravity erosion on steep slopes before entering into large ditches or streams. Third, infiltration-excess runoff on slopes pours into dissected valleys through overflow, rill flow and shallow gully flow, and then flows into river tributaries, again by different levels of ditches. Fourth, due to runoff erosion, gravity erosion and the increased sediment carrying capacity, the sediment carrying flow increases sediment concentration with the rising of the channel water-level. However, the increased amplitude of sediment concentration does not often correspond to the increase in river flow, and the occurrence time of the flood peak is not always consistent with the sand peak31.Figure 10Flow and sediment hydrograph on (a) 14 July 2000 and (b) 21 August 1998.
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Figure 10(b) indicates that the sand peak was always behind the flood peak, both on 14 July 2000 and 21 August 1998. Generally, runoff erosion can be fully developed by the long runoff convergence time, the sediment carrying capacity increases with increased river flow, and peak values of sediment concentration often appear at the same time or lag behind the flood peak. However, on the loess hilly and gully region, as soil water content increases in the late period of runoff yield, the shear strength of loess decreases, runoff erosion increases, and gravity erosion also frequently occurs, which makes the probability of the sand peak lagging behind the flood peak increase. The results are consistent with Dugan et al. (2009) who demonstrated the importance of antecedent moisture condition as an important factor in the rainfall runoff and sediment transport response to precipitation events64. The fundamental cause of erosion changes lies in rainfall characteristics, the soil erosion quantity caused by high rainfall intensity at a certain stage of an event-based rainstorm accounted for most soil erosion65.
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Figure 11 shows linear regression between total suspended sediment discharge and total runoff using 17 typical observed rainstorm events. In the flood rising section of flow hydrographs, the runoff-sediment data points often fall below the regression equation line, while in the flood falling section, the data points are above the regression line, indicating that sediment transport processes lag behind flow processes (Fig. 11). Such behaviour is consistent with land cover since, although sparse, the secondary forest and shrub land promotes shading and inhibits development of an herbaceous layer, favouring the sediment detachment process and decreasing surface roughness66. Among the three relationships of rainstorm, runoff and sediment yield, runoff amount has a strong correlation with sediment yield, and they can be statistically regressed as simple linear or approximate exponential associations. The results are consistent with Guo et al. (2017) that the relationship between runoff and sediment yield under the different land disturbances could be described by an exponential function67. Therefore, the corresponding association between sediment transport rate and flow processes can be quantitatively expressed by a mathematical model. Total sediment yield may be quantitatively estimated by the runoff-sediment relationship under different rainstorm events in the Zhifanggou Watershed. However, statistics show that the particularly large sediment concentration is often caused by the collapse of steep loess slopes, due to the flow scouring effects on the gully slope control line. But there is some uncertainty regarding the collapse of steep loess slopes, so the occurrence of large high sediment content also has some randomness. Besides, Poesen et al. (2003) found that collapse of stream banks and gully formation increased specific sediment yield, which occurs more intensively when soil is unprotected upon land use changes68. Both these phenomena will cause the scattered correlation between runoff and sediment concentration.Figure 11Regression analysis curve between total suspended sediment discharge and total runoff using 17 typical observed rainstorm events.
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Soil conservation is the ultimate goal of sediment yield research69. Statistical techniques such as hierarchical cluster analysis and regression analysis with multiple grouping variables have been widely used to study soil erosion responses under different rainfall and runoff patterns70. Previous studies have classified regional rainfall events to evaluate the impact of rainfall characteristics on runoff generation, erosion and sediment processes in a semi-arid region based on rainfall depth, rainfall duration and 30-minute maximum rainfall intensity71,72. In this study, Cluster Analysis was used to assess individual rainstorm-erosion events similarities using 17 rainstorm-erosion event parameters. Figure 12 shows Cluster Analysis results for different event-based rainstorm samples in Zhifanggou Watershed. The three rainstorm events of 4 May 1997, 6 May 1997 and 7 July 2009 have similar characteristics, and they are classified into one category in Cluster Analysis. The runoff yield times of the three rainstorms are all between 83–150 minutes. The average flow is between 0.005–0.01 m3/s, peak flow is between 0.009–0.012 m3/s, and runoff depth is between 0.002–0.01 mm. Runoff coefficients were all small for these three rainstorms due to small rainfall intensity and runoff yield.Figure 12Cluster Analysis results of different event-based rainfall samples in Zhifanggou Watershed.
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Cluster Analysis identified major differences between the rainfall-runoff erosion event on 26 July 2000 and other rainstorm events. The time of runoff yield in this rainstorm event was not the longest, but the peak flow, total flow and suspended sediment transport rates were all in the largest category. Flood duration is one of the main factors affecting the type of rainstorm erosion events in the basin. Specifically, the rainstorm duration was short during this rainstorm event, and rainfall intensity changed quickly. Rainfall intensity exceeded the soil infiltration rate, which makes rainstorms generate infiltration-excess runoff and form peak flow and then cause severe soil erosion. However, the two rainstorms (23 June 1998 and 28 June 2008) both occurred in June, they have similar rainfall duration, and the total runoff amounts were 16051.6 and 16774.8 m3, respectively. The difference of these indexes is small in the two rainstorm-erosion events, but there is a major difference for the modulus of sediment yield, they are 379.8 and 168.6 t/km2, respectively. Firstly, the large-scale returning farmland project was implemented on the Loess Plateau in 1997, by 2008 the preliminary governance results were achieved, so the total runoff amount may be large, but the sediment yield decreased due to vegetation interception. This indicates that the regulation of soil and water conservation measures on spatio-temporal scale effects of the rainstorm-erosion process is the key to control sediment output from the basin73. At the same time, the increase of vegetation coverage will increase rainfall interception74, reduce the kinetic energy of raindrops75, weaken rainfall erosivity76, and reduce runoff and sediment yield77. Secondly, the main reason for the sediment yield difference between 1998 and 2008 cannot be fully attributed to afforestation and grass planting. Because in this case the amount of rainfall, rainfall intensity and peak flow may play important roles in the large difference of sediment yield modulus. The high intensity and short duration of heavy rainstorms are the main factors accelerating storm runoff erosion in this watershed. Previous studies indicate that gully erosion cannot be effectively controlled by vegetation measures in the loess hilly areas with high valley density and deep dissected valleys78. Although vegetation cover significantly increased and soil erodibility significantly decreased with vegetation development and age of restoration, soil infiltration rates decreased. Thus the runoff coefficient and the runoff volume both increased, then the watershed gully erosion and sediment yield also increased27. Similarly, Xu et al. (2017) and Wu et al. (2017) both found that soil loss increased with increased rainfall intensity79,80.
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A small watershed is both the main source of regional sediment yield and the basic unit of erosion control and ecological environment restoration81. Table 1 shows runoff and sediment parameters and cluster results of different event-based rainstorms in Zhifanggou Watershed. The cluster results indicate that 17 rainstorms can be broadly divided into two major categories: (i) The first is the strongest rainstorm event, which reflects the maximum peak discharge, and the maximum suspended sediment discharge, such as the rainstorm that occurred on 26 July 2000. The average flow rate in this rainstorm was 8.9263 m3/s, the peak flow rate was 17.2954 m3/s and the total flow was 16774.84 m3. This shows that the great rainstorm produced large runoff and very intensive erosion, and the concentrated runoff is a dynamic condition for strong erosion of loess soil. (ii) The second is the rainstorm event which reflects the individual peak discharge and suspended sediment transport, which forms the other 16 rainstorm events. The second category can be subdivided into two smaller categories: one category is the rainstorm event with average flow >1.3 m3/s and suspended sediment amount >1300 t; the other category is the rainstorm event where the suspended sediments are <1000 t. The two small categories can also be subdivided into many small categories at the next level. Therefore, Cluster Analysis can be used to measure the similarity of different rainfall erosion events. Based on regression and Cluster Analyses in Figs 3–9 and Table 1, the criterion of relationships between sediment yield and runoff under typical rainstorm events in Zhifanggou Watershed can be statistically summarized as follows. For rainstorm events of sediment modulus >1000 t/km2, the power function of the sediment-runoff fitting effect is better than the logarithmic function. For rainstorm events of sediment modulus between 300–1000 t/km2, the logarithmic function of the sediment-runoff fitting effect is superior to the power function. For rainstorm events of sediment modulus <300 t/km2, linear associations are stronger. Therefore, classifying and describing the similar characteristics of runoff erosion in different rainstorm events can both provide important data support for soil conservation planning in a watershed, and provide a strong theoretical basis for establishing soil erosion prediction models. So, Cluster Analysis is a useful tool to evaluate characteristics of different rainstorm erosion events and to understand soil erosion and sediment transport processes.
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Taking Zhifanggou Watershed of 8.27 km2 in the loess hilly and gully region as the study area, the characteristics of runoff erosion and sediment yield based on 17 observed rainstorm events from 1997–2010 were statistically evaluated. The pulsed rainstorm is both the original driving force of slope soil erosion, and one of the most important factors influencing sediment transport processes. The main power of water erosion is rainfall and surface runoff, the amounts of runoff and erosion are closely related to rainfall intensity and rainfall amount. However, correlations of rainfall-runoff and rainfall-sediment during different rainstorms are often scattered and the correlation coefficients are often weak, due to effects of the infiltration-excess runoff and soil conservation measures in the loess hilly region. Runoff has a strong correlation with sediment yield. It can be expressed by simple exponential or linear equations. The sediment transport modulus can be quantitatively estimated by the corresponding runoff-sediment correlation under different rainstorm events. The response characteristic of sediment yield simulation is variable in different levels of pulsed runoff-erosion events. Affected by hyper-concentration flows, great changes have occurred in the interactions between flow and sediment concentration for different flood events. For rainstorm events of sediment modulus >1000 t/km2, the power function of the sediment-runoff fitting effect is stronger than the logarithmic function. For rainstorm events of sediment modulus between 300–1000 t/km2, the logarithmic function of the sediment-runoff fitting effect is superior to the power function. For rainstorm events of sediment modulus <300 t/km2, linear relationships are stronger. This new overall understanding opens opportunities for decision-makers and managers to conserve soil based on observation data of rainstorm erosion in small watersheds.
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The impact of work hours on resident well-being and patient safety have long been a controversial issue. The death of Libby Zion in 1984 following her admission to the hospital less than 20 hours after her evaluation from the emergency department (ED) led to an in-depth look at resident physician work hours .
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clinical case
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In 2003, the Accreditation Council for Graduate Medical Education (ACGME) instituted the common duty hour standards, which included an 80-hour workweek averaged over four weeks and an adequate rest period, which was defined as 10 hours of rest per duty period. For emergency medicine, this was further defined such that residents may work no more than 12 continuous hours with an equivalent amount of time off between scheduled work periods. An emergency medicine resident may work no more than 60 scheduled hours in an ED and no more than 72 duty hours in total per week . What has not been considered is whether resident commuting time has any impact on a resident’s workweek, well-being, and/or rest time and if resident commuting time should be considered in calculating total resident work hours. Barger et al. found that extended duration work shifts, defined as greater than 24 hours, posed safety hazards for interns. The risk of falling asleep while driving or while stopped in traffic was also increased when working five or more extended shifts .
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There has been research looking into the effect of night-shift work and car crashes. Huffmyer et al. recently found that anesthesia residents who worked six consecutive night shifts had greater difficulty controlling speed and driving performance in a driving simulator session lasting 55 minutes . Lee et al. reported that night-shift work increases driving drowsiness and degrades driving performance . These factors increase the risk of near-crash driving events. In their study, they found that subjective reports and objective indices of drowsiness became prominent within the first 15 to 30 minutes of driving. They also found that driving for more than 45 minutes after a night shift was likely to increase drowsiness-related impairment .
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study
| 99.56 |
Steele et al. reported a higher number of motor vehicle collisions and near-crashes among emergency medicine residents driving home after a night shift . Approximately 74% of the collisions and 80% of the near-crashes occurred following a night shift. This was compared to day shifts where much lower incidences of motor vehicle collisions and near-crashes were found (12% vs. 7%). Shift time was not considered in this study .
|
study
| 99.94 |
A self-administered electronic survey was generated using SurveyMonkey (www.surveymonkey.com) and distributed to 174 allopathic emergency medicine program directors on a national academic listserv in 2016. Program directors were asked to distribute the survey link among their residents. Responses were collected for four weeks. Participation was voluntary. The study received institutional board review approval (#1210314).
|
study
| 99.94 |
All emergency medicine residents were eligible to participate. The survey was released in May 2016 to make post graduate year (PGY) one residents eligible since they would have completed 10 months of residency. The survey instrument consisted of 12 multiple-choice questions (Table 1).
|
study
| 95.9 |
“Commuter time” is defined as travel to work one way and time must be estimated as daily average time taking into account occasional prolonged time due to bad weather, rush hours, construction delays, etc. What is your commuter time from leaving home to arrival at the hospital?
|
other
| 99.94 |
Associations of shift time by commute time and commute method by commute time were studied. Associations of commute time by the incidence of falling asleep while driving and commute time by car collision incidence was calculated using a chi-square analysis using SAS version 9.4 (SAS Institute Inc., Cary NC). Association of commuter time with effect on well-being was also calculated by a chi-square analysis.
|
study
| 100.0 |
Commuter time was found to be 30 minutes or less in 70% of respondents. Sixteen percent of residents reported a commuter time of 31-45 minutes and 11% reported 46-60 minutes. Two residents (0.4%) reported a commuter time of 76-90 minutes and one resident (0.2%) had a commuter time of 91-105 minutes. None reported commuter times greater than 105 minutes. The train was the method of commuting for the resident who reported commute times of 91-105 minutes; this resident also reported working 12-hour shifts. Of the two residents with 76-90 minutes commute times, one commuted by car and the other by train. The former worked eight- and 12-hour shifts and responded yes to falling asleep. The latter worked various shift times (Table 3, Table 4).
|
study
| 99.9 |
Commuter time was defined as the time for one trip from work to home. Most concerning was the 29.3% (n=166) that reported falling asleep while driving their car home from work. Car collisions while commuting were reported in 12%, a higher incidence when compared with Steele et al., who reported only 8% .
|
other
| 99.5 |
When looking at the association between shift times and commuting times (Table 5), we found that most residents worked at least 10-hour shifts (n=313, 71.95%). Most residents had 30 minutes or shorter commute times (n=311, 71.49%). Almost half of the residents surveyed worked at least 10-hour shifts and had a 30-minute or less commute (n=205, 47.13%).
|
study
| 99.94 |
When looking at the association of commute time and incidence of falling asleep while driving, residents who drove for a longer time were more likely to fall asleep. When commute times were greater than one hour, 66% (6/9) reported they had fallen asleep while driving. When asked about car collisions (Table 6), those who drove for a longer time were more likely to have a car collision. Of those who commuted for more than one hour by car, 44% (4/9) reported they had been in a car collision.
|
study
| 99.94 |
As seen in the data presented here, most residents have a short commute time of 30 minutes or less. This is similar to the national commuting data of 25 minutes. What is more concerning is that the 30% of residents who answered this survey had an extended commute time of greater than 30 minutes and possibly up to 105 minutes. If one was working a 12-hour shift and had a 105-minute commute each way, the resident would be commuting for a total of 210 minutes per shift. Adding this on to a 12-hour shift means that the resident would have only 8.5 hours off between shifts for rest and recovery time. If one was to factor in basic daily actions, such as eating and grooming/showering, a resident working a 12-hour shift with a commute time greater than one hour each way would have reduced and possibly insufficient sleep time especially when the recommended sleep is seven to nine hours per day . Given that almost 30% of respondents reported falling asleep while driving, a potential reduction in rest/sleep and recovery time is very concerning for residents who may be commuting more than two hours/day, especially if the resident is driving. Not surprisingly, increased commuter times were associated with a statistically significant decrease in well-being.
|
study
| 99.94 |
This study highlights an area that is often not considered but could potentially be playing an important role in resident wellness. Extended commute times can affect total sleep time, non-work-related time, and the time one needs to decompress. Given the importance of commuting time and its potential implications, one questions whether residency accrediting bodies should consider this data.
|
other
| 99.9 |
Limitations in this study were similar to those in the Steele et al. study of emergency medicine residents and car collisions. These limitations are primarily selection bias and low survey response rate. Reasons for a low response rate are not clear but may have included surveys not having been forwarded to residents by their leadership team. Recall bias regarding falling asleep or having experienced a near-crash driving event is another limitation.
|
study
| 99.94 |
While the majority of emergency medicine residents surveyed have commuter times of 30 minutes or less, there is a small population of residents with commuter times of 76 to 105 minutes. Given that these residents often work 12-hours shifts, those whose commute is up to 105 minutes each way could be traveling a total round trip of more than 3.5 hours to/from work. These extended commuter times may be having detrimental effects on resident health and well-being. We believe that further research is warranted to investigate these areas and the impact of commuting on resident work hour times. Future efforts may also focus on residence location and commuting time to improve resident wellness.
|
other
| 99.9 |
Estrogen exerts potentially helpful effects on brain synapse structure and function in regions such as the prefrontal cortex and hippocampus. In women, endogenous estrogen exposure (EEE) occurs mainly during the reproductive phase. Estrogen levels rise during pregnancy, but fall postnatally, particularly with breastfeeding, and are lower after a first pregnancy than in nulliparous women. Earlier menarche and later menopause (hence longer reproductive period), nulliparity or lower parity, older age at birth of first child, and less breastfeeding are therefore proxy indicators of lifetime EEE.
|
study
| 98.06 |
The hypothesis that estrogen is neuroprotective for women is supported by inverse associations between indicators of lifetime EEE and late-life cognitive function[2–7], and prospective and historic cohort studies indicating adverse cognitive outcomes associated with premature surgically-induced menopause, and premature ovarian failure (POF). However, the evidence remains inconclusive. Only two studies of EEE were population-based, effects on cognition were small, and not always replicated. Although effect sizes linked to oopherectomy and POF are larger, associations with dementia were not replicated in a large Finland registry linkage study.
|
review
| 99.9 |
Few studies have examined the effects of EEE on cognitive decline, incident dementia or Alzheimer’s disease (AD). In the population-based Esprit study in France, EEE indicators were associated in the hypothesized direction with baseline cognitive function, but not with cognitive decline over the next four years. In case-control studies, childlessness was inversely associated with AD among women but not men, and increasing numbers of pregnancies were associated with AD, and age of onset among cases. In a nested case-control study, AD risk increased with increasing age at menarche. The largest and most definitive study to date was carried out in the population-based Rotterdam cohort; 3601 postmenopausal women aged 55 years or older were followed up for a median of 6.3 years (21,046 person years). Counter to the hypothesis, women with natural menopause and more reproductive years had an increased risk of dementia (adjusted RR for highest versus lowest quarter 1.78, 95% CI confidence interval [CI] 1.12–2.84). The association was modified by APOE genotype, with a stronger association among APOE e4 carriers, while among non-carriers no association with dementia or AD was observed.
|
study
| 99.7 |
We set out to study associations between indicators of EEE and dementia incidence in the 10/66 Dementia Research Group’s population-based cohort studies in seven urban and three rural catchment area sites in six Latin American countries, and China. Historically, these populations were characterised by higher fertility rates, and a greater variation in age at first birth and parity than in high income country populations. Contemporary market penetration data suggest very low rates of use of hormone replacement therapy (exogenous estrogen), allowing the effects of EEE to be estimated more precisely. Our primary hypothesis is that a longer reproductive period is independently associated with a lower risk of incident dementia. Our secondary hypotheses are that younger age at menarche, older age at menopause, lower parity, older age at birth of first child, and higher indices of cumulative endogenous estrogen exposure (ICEEE), are each associated with a lower risk of incident dementia. Finally, we test the hypothesis that APOE genotype modifies any effect of reproductive period on incident dementia risk.
|
study
| 99.94 |
The 10/66 population-based study protocols for baseline and incidence waves , and a full description of the cohort profile are available in open access publications. Relevant details are provided here. One-phase population-based surveys were carried out of all residents aged 65 years and over in geographically defined catchment areas (urban sites in Cuba, Dominican Republic, Puerto Rico and Venezuela, and urban and rural sites in Mexico, Peru, and China). Baseline surveys were completed between 2003 and 2007, other than in Puerto Rico (2007–2009). The target sample was 2000 for each country, and 3000 for Cuba. The baseline survey included clinical and informant interviews, and physical examination. DNA collections were carried out in the Latin American countries, and APOE genotype determined in Cuba, Dominican Republic, Venezuela and Puerto Rico. Incidence waves were subsequently completed, with a mortality screen, between 2007 and 2011 (2011–2013 in Puerto Rico) aiming for 3–4 years follow-up in each site. Assessments were identical to baseline protocols for dementia ascertainment, and similar in other respects. We revisited participants’ residences on up to five occasions. When no longer resident we sought information on their vital status and current residence, from additional contacts recorded at baseline. Where participants had moved away, we sought to re-interview them, even outside the catchment area. If deceased, we recorded the date, and completed an informant verbal autopsy, including evidence of cognitive and functional decline suggestive of dementia onset between baseline assessment and death.
|
study
| 100.0 |
The 10/66 population-based study interview covers dementia diagnosis, mental disorders, physical health, anthropometry, demographics, an extensive risk factor questionnaire, disability, health service utilisation, care arrangements and strain. Only relevant assessments are detailed here.
|
study
| 97.9 |
Reproductive history measures: Evidence suggests that longer reproductive period (age at menopause minus age at menarche), lower parity, older age at birth of first child, and greater postmenopausal body mass are indicators of higher EEE across the life course . Age at menarche, age at menopause, number of live births, and age at birth of first child were ascertained from all female participants at baseline interview, using four questions
|
study
| 99.94 |
Weight was not measured at baseline, so waist circumference (in centimetres) was used as the relevant proxy indicator instead. Following the method proposed by Smith et al each indicator was z-scored, and a composite Index of Cumulative Endogenous Estrogen Exposure (ICEEE) calculated as ((age at menopause + age at birth of first child + waist circumference)–(age at menarche + number of children).
|
study
| 100.0 |
Confounders and other covariates: Age, education, marital status, household assets, tobacco consumption (ever versus never), and hazardous drinking (>21 units per week, before the age of 65) were all ascertained in the baseline questionnaire. Height, leg length, skull circumference and waist circumference were measured in the physical examination.
|
study
| 99.94 |
Dementia: 10/66 dementia diagnosis is allocated to those scoring above a cutpoint of predicted probability for dementia, calculated using coefficients from a logistic regression equation developed, calibrated and validated cross-culturally in the 25 centre 10/66 pilot study, applied to outputs from a) a 25–40 minute structured clinical interview, the Geriatric Mental State, assessing symptoms and signs of depression, anxiety and psychosis, as well as those suggestive of organic brain disease including dementia, b) two cognitive tests; the 32-item Community Screening Instrument for Dementia (CSI-D) COGSCORE, covering memory, language, praxis, and executive function, and the modified Consortium to Establish a Registry for Alzheimer’s Disease (CERAD) 10 word list learning task with delayed recall, and c) informant reports of cognitive and functional decline from the 26-item CSI-D RELSCORE. Therefore, the assessment comprises a comprehensive clinical mental state assessment, multidomain assessment of cognitive functioning, and an informant interview. The criterion, concurrent and predictive validity of the 10/66 diagnosis were superior to that of the DSM-IV criterion in subsequent evaluations[25–28]. For those who died between baseline and follow-up we diagnosed ‘probable incident dementia’ by applying three criteria:
|
study
| 100.0 |
model the effect of ages at menarche, birth of first child, and menopause; reproductive period; parity; ICEEE; and premature ovarian failure on 10/66 dementia incidence using a competing-risks regression derived from Fine and Gray’s proportional subhazards model (Stata stcrreg command), based on a cumulative incidence function, indicating the probability of failure (dementia onset) before a given time, acknowledging the possibility of a competing event (dementia-free death). Time to death was the time from baseline interview to the exact date of death. Time to dementia onset (which could not be ascertained precisely) was the midpoint between baseline and follow-up interview. Competing risks regression keeps those who experience competing events at risk so that they can be counted as having no chance of failing. We report adjusted sub-hazard ratios (ASHR) with robust 95% confidence intervals adjusted for household clustering. We also test for modification by APOE genotype of the effect of reproductive period on dementia incidence, by extending the adjusted models described above by the appropriate interaction terms, and also by restricting the analysis to those with no APOE e4 genotype. We fit all models separately for each site and combine them using a fixed effects meta-analysis. Higgins I2 estimates the proportion of between-site variability in the estimates accounted for by heterogeneity, as opposed to sampling error; up to 40% heterogeneity is conventionally considered negligible, while up to 60% reflects moderate heterogeneity .
|
study
| 100.0 |
The study protocol and the consent procedures were approved by the King’s College London research ethics committee and in all countries where the research was carried out: 1- Medical Ethics Committee of Peking University the Sixth Hospital (Institute of Mental Health, China); 2- the Memory Institute and Related Disorders (IMEDER) Ethics Committee (Peru); 3- Finlay Albarran Medical Faculty of Havana Medical University Ethical Committee (Cuba); 4- Hospital Universitario de Caracas Ethics Committee (Venezuela); 5- Consejo Nacional de Bioética y Salud (CONABIOS, Dominican Republic); 6- Instituto Nacional de Neurología y Neurocirugía Ethics Committee (Mexico); 7- University of Puerto Rico, Medical Sciences Campus Institutional Review Board (IRB). Informed consent was documented in writing in all cases. Literate participants signed their consent. For participants who were illiterate, the information sheet was read to them in the presence of a literate independent witness, who attested by signature that this process had been completed, and that the participant had provided informed consent. For participants who lacked capacity to consent, agreement for their participation was obtained from next-of-kin. These procedures were approved by the ethics committees.
|
other
| 99.8 |
In all, 9,428 interviews were completed with women, at baseline, in the 10 sites in seven countries. Response proportions at baseline varied between 72% and 98%, and exceeded 80% in all sites other than urban China. The ‘at risk’ cohort comprised 8,466 dementia-free women (Table 1). Mean age at baseline ranged from 72.0 to 75.4 years, lower in rural than urban sites and in China than in Latin America. Educational levels were lowest in rural China (84% not completing primary education), rural Mexico (83%), Dominican Republic (73%), and urban Mexico (57%) and highest in urban Peru (11%), Puerto Rico (23%), and Cuba (26%). In other sites, between one-third and one-half of participants had not completed primary education. Seven percent of women were nulliparous (from 0.4% in rural Peru to 14.6% in Cuba), strongly associated with never having been married (34% of never married women and 5% of married women were nulliparous). Mean parity was 4.1 (SD 3.0), higher in rural than urban sites and ranging from 2.4 (urban Cuba) to 7.2 (rural Mexico). There was significant between site variation in ages at menarche and menopause, and reproductive period. Menarche (R2 = 15.7%) was earlier in Latin American and urban sites. Menopause (R2 = 4.6%) was later in Chinese sites. Reproductive period (R2 = 1.6%) showed no clear pattern of variation among sites. From the ‘at risk’ cohort, 1,451 participants (17.1%) were lost to follow-up; 473 (5.6%) had refused, 387 (4.6%) were traced but could not be contacted for interview, 336 (4.0%) could not be traced, and for 225 (3.0%) the reason for loss to follow-up was not documented (Table 1). This left 7,015 women eligible to be included in the cohort, of whom 6,007 were reinterviewed, and 1,008 had died and an informant verbal autopsy was completed. Of these, 6,854 women had baseline reproductive period data, and were followed up for 26,463 person years (Table 1).
|
study
| 99.94 |
2Competing risk refers to ‘dementia-free death’, that is those participants who had died before they could be reinterviewed, but for whom there was no evidence from informant verbal autopsy interview of their having developed probable dementia before death.
|
other
| 99.9 |
In the at risk cohort, longer reproductive period was associated, predictably, with earlier menarche and (particularly) later menopause, and with a higher ICEEE (Table 2). Longer reproductive period was also associated with older age; with being more likely to complete primary education; with lower parity; with older age at birth of first child; with a lower prevalence of hazardous drinking and stroke; with taller stature, and with better baseline cognitive function (the composite CSI-D COGSCORE, and the CERAD animal naming test, but not the CERAD 10 word list delayed recall). The effect on COGSCORE (effect size per quarter of reproductive period +0.064, 95% CI +0.021 to +0.107, r2 = 0.1%) was no longer statistically significant having adjusted for age, education, marital status, stroke, hazardous alcohol use, and height (+0.024, 95% CI -0.019 to +0.067, r2 = 0.0%). The effect on animal naming (+0.195, 95% CI +0.094 to +0.295, r2 = 0.2%) also lost statistical significance after adjusting for the same covariates (+0.092, 95% CI -0.006 to +0.190, r2 = 0.0%). Although the proportion with one or more APOE e4 alleles did decline significantly across quarters of reproductive period (p = 0.048, Table 2), neither mean reproductive period (p = 0.08), nor mean age at menarche (p = 0.38), nor mean age at menopause (p = 0.17) differed significantly across APOE genotypes.
|
study
| 99.94 |
Controlling for age, education and household assets, there was no association between reproductive period and dementia incidence, either in individual sites or after pooled metaanalysis (ASHR per year 1.001, 95% CI 0.988–1.015, I2 0.0%) (Table 3). The effect of reproductive period was unchanged after controlling additionally for marital status, hazardous alcohol use, stroke, and height (ASHR per year 1.001, 95% CI 0.987–1.015, I2 0.0%). In Cuba, Dominican Republic, Venezuela and Puerto Rico, where APOE genotype was available, there was no evidence for an interaction between APOE genotype and reproductive period (ASHR 0.993, 95% CI 0.947–1.041, I2 = 39.3%), or for an effect of reproductive period among non-carriers of the e4 allele (ASHR 1.012, 95% CI 0.991–1.034, I2 = 0.0%). Controlling for APOE genotype in addition to age, education, household assets, marital status, hazardous alcohol use, stroke and height did not affect the fully adjusted association between reproductive period and incident dementia (ASHR 1.018, 95% CI 0.998–1.038, I2 = 0.0%).
|
study
| 99.94 |
Controlling for age, education and household assets, there was no association between dementia incidence and either age at menarche (pooled ASHR per year 0.986, 95% CI 0.944–1.030, I2 0.0%), or age at menopause (ASHR per year 1.000, 95% CI 0.986–1.013, I2 0.0%) (Table 3). There was also no association between premature ovarian failure (before the age of 40 years) and incident dementia (ASHR 1.19, 95% CI 0.91–1.55, I2 0.0%). However, as hypothesized, greater parity was associated with incident dementia, controlling for marital status as well as age, education and assets (ASHR per birth 1.030, 95% CI 1.002–1.059, I2 0.0%). The effect of nulliparity, a rare exposure, was difficult to estimate with no exposed incident cases in urban and rural Peru or rural China; in the remaining sites there was no association (ASHR 1.16, 95% CI 0.86–1.56, I2 30.2%). Neither was there any evidence for an association between the ICEEE and incident dementia (ASHR per SD 0.987, 95% CI 0.951–1.025, I2 0.0%).
|
study
| 99.94 |
In the largest prospective cohort study to date we have found quite strong evidence that EEE is not importantly associated with subsequent risk of incident dementia. We also failed to replicate a previously reported interaction between APOE genotype and reproductive period. The precision of our null estimates for the hypothesised main effects of proxy indicators of EEE, observed consistently across diverse settings, exclude the possibility of other than trivial effects. The possible exception is premature ovarian failure, a rare exposure, with an upper confidence interval for the ASHR of 1.55, and with suggestive trends towards positive associations in some Latin American sites. While we did observe an association between greater parity and incident dementia, this seems unlikely to be mechanistically explained by cumulative EEE, since the impact of reproductive period on this pathway would be expected to be much greater. Of note, grand multiparity is linked to increased mortality from both diabetes and cardiovascular diseases.
|
study
| 99.94 |
In common with some other studies, we did find evidence for an association between reproductive period and baseline cognitive function, somewhat stronger for animal naming than for the CSI-D composite assessment of cognitive function. However, consistent with other studies the effect sizes were very small, and, in our study, were substantially accounted for by plausible confounders.
|
study
| 100.0 |
Strengths of this study are that associations have been assessed longitudinally, in large population-based dementia-free cohorts, encompassing rural and urban catchment area sites in the Caribbean, Latin America, and China. We used meta-analytical techniques to increase the precision of our estimates. Fixed effect meta-analysis is appropriate, given the negligible heterogeneity for all of the associations studied.
|
study
| 100.0 |
We acknowledge some limitations. First there will have been some misclassification of the recalled exposures, which, given the prospective design, is likely to have been non-differential with respect to the outcome. The effect would therefore be towards an attenuation of any genuine association towards the null. Second, we did not gather information on use of hormone replacement therapy (exogenous estrogen). However, availability, awareness and use of such medication can be safely assumed to have been negligible in these countries over the relevant period [16,32–34], particularly in the predominately socio-economically disadvantaged catchment area populations studied. Nevertheless, we cannot exclude the possibility that use of exogenous estrogen could have masked associations with proxy indicators for EEE, particularly if this was used selectively by those experiencing earlier menopause. Third, we did not enquire whether menopause was surgically induced or naturally occurring. Oopherectomy, because of the sudden fall in estrogen levels, might have a particularly marked impact on cognitive functioning. Fourth, it is likely that reproductive history predicts post-reproductive mortality, with a higher mortality risk for those with younger age at first birth, and a U-shaped relationship with parity . To the extent that such associations might selectively remove those who might be at risk for developing dementia from the at risk population, this could bias estimates of association. This possibility is addressed, in part, through our use of competing risk regression to model associations, but this would only account for selective mortality patterns over the follow-up period.
|
study
| 99.94 |
The only previous longitudinal study of these associations was in a smaller and younger cohort from Rotterdam (167 incident dementia cases, compared with 692 in our study). The greater power and precision of our study may not entirely explain the discrepancy in findings; the Dutch study reported a statistically significant increased risk of dementia concentrated in the three-quarters of the cohort with the longest reproductive periods, but only for non-carriers of the APOE e4 genotype. Although the distribution of reproductive periods was similar between the two cohorts, the reproductive history of the Dutch women was very different. Mean parity was 2.2 compared with 4.1 in our study, 11% reported ever using HRT, and 24% reported an artificial menopause from surgery, drugs or radiation therapy.
|
study
| 99.94 |
We found no evidence to support the theory that natural variation in cumulative exposure to endogenous estrogens across the reproductive period influences the incidence of dementia in late life. Any beneficial effect on cognitive reserve is likely to be very small, and may arise from confounding by shared developmental antecedents. The case for post-menopausal hormone replacement is currently controversial, with conflicting evidence, and some clear risks associated with longer-term use. Nevertheless, the concept of a ‘critical window’ in the immediate post-menopausal period has been widely discussed, during which estrogen replacement therapy may be both less risky, and more beneficial to cognition. Our study provides only indirect evidence to inform this debate, since our focus was upon pre-menopausal endogenous exposure. However, associations with indicators of endogenous exposure are routinely presented as ‘proof of concept’ for the estrogen hypothesis, and this evidence is significantly weakened by the current study.
|
study
| 99.94 |
Emotional eating is often defined as an increase in food intake in order to cope with negative emotions (Macht and Simons, 2011). This type of eating style characterizes several eating-related psychopathologies. For example, negative affect precipitates binge eating episodes in individuals with bulimia nervosa and binge eating disorder (Haedt-Matt and Keel, 2011) and increases desire to eat in obese individuals (van Strien et al., 2016). Although food intake is often used as an emotion regulation strategy, it usually does not reduce negative affect effectively and, thus, the mechanisms that generate and maintain emotional eating are far from being clear (Haedt-Matt et al., 2014). Moreover, it seems that there are numerous moderators that determine the effects of emotions on eating behavior such as the arousal level that accompanies certain emotions or individual differences in eating habits (Macht, 2008).
|
review
| 99.75 |
While the majority of studies on emotional eating have focused on eating to alleviate a negative mood, it has been shown that a positive mood can also result in increased food intake (Cardi et al., 2015). Moreover, while most research has focused on increased food intake, it has been found that experiencing emotions can also result in a decrease in food intake, depending on self-reported emotional eating tendencies (van Strien et al., 2012). A recent review of the literature on self-report measures of emotional eating criticized their lack of predictive validity, pointing to the weak and inconsistent associations with actual food intake in laboratory-based and naturalistic studies (Bongers and Jansen, 2016). One reason for this might be that they only cover a restricted range of relevant aspects of emotional eating. For example, most measures solely include negative emotions and only ask about eating more in response to these emotions, leaving out the possibility of reduced food intake (Bongers and Jansen, 2016).
|
review
| 99.9 |
A range of self-report measures are available for the assessment of emotional eating tendencies such as the Dutch Eating Behavior Questionnaire (DEBQ; van Strien et al., 1986), the Emotional Eating Scale (EES; Arnow et al., 1995), the Emotional Overeating Questionnaire (EOQ; Masheb and Grilo, 2006), the Emotional Appetite Questionnaire (EMAQ; Geliebter and Aversa, 2003), and the Positive-Negative Emotional Eating Scale (PNEES; Sultson et al., 2017). To date, the only self-report questionnaire that does differentiate between positive and negative emotions and assesses eating less or eating more in response to these emotions is the EMAQ. However, this scale was not developed via item selection based on empirical data and does not differentiate between different negative emotions (e.g., with different arousal levels; Geliebter and Aversa, 2003). Therefore, we developed a new self-report measure of emotional eating that differentiates between different emotions and between decreased or increased food consumption in response to these emotions (Salzburg Emotional Eating Scale, SEES). For this, an item pool of 40 items was created (see method section below and Table 1).
|
study
| 99.94 |
German item wording in square brackets. Five items with the highest factor loadings for each factor (printed in boldface) were selected for the Salzburg Emotional Eating Scale. For reasons of clarity, only factor loadings > 0.400 are displayed. Response categories were: I eat much less than usual (= 1), I eat less than usual (= 2), I eat just as much as usual (= 3), I eat more than usual (= 4), and I eat much more than usual (= 5).
|
study
| 99.9 |
In study 1, we explored factor structure of this item pool and items with the highest factor loadings were selected for further refining the scale. We expected that participants would report eating more than usual in response to positive emotions and negative, low arousing emotions (Cardi et al., 2015). In response to negative, high arousing emotions, however, we expected that participants would report eating less than usual (Reichenberger et al., 2018). Based on the findings with the EMAQ (Nolan et al., 2010; Bourdier et al., 2017), we expected that eating more in response to positive emotions would be associated with eating less in response to negative emotions. Furthermore, men were expected to report eating more in response to positive emotions than women, and women were expected to report eating more in response to negative emotions than men (Nolan et al., 2010). Finally, we expected that eating in response to positive emotions would be associated with lower BMI while eating in response to negative emotions would be associated with higher BMI (Nolan et al., 2010; Bourdier et al., 2017).
|
study
| 99.94 |
Studies 2 and 3 aimed at replicating findings about psychometric properties (factor structure, internal consistencies), mean score differences and associations between subscales, and correlates of the scale (sex, BMI). Study 2 also examined associations with other questionnaire measures as a preliminary indication of validity. Specifically, we expected that there would be medium-to-high correlations with similar questionnaire measures on stress- and emotional eating and small correlations with other eating-related questionnaire measures (eating disorder pathology, perceived self-regulatory success in weight regulation). Finally, we expected that there would be no or only small correlations with measures that are not directly related to eating behavior (depressiveness, impulsivity).
|
study
| 100.0 |
The study was approved by the institutional review board of the University of Salzburg. A link to the online survey at www.unipark.com was distributed via e-mail to a mailing list at the University of Salzburg addressing students and staff and via social networks. Participation was voluntary and participants did not receive any compensation. The website was visited 413 times and n = 285 participants completed the entire set of questions.
|
study
| 99.9 |
Most participants were women (78.9%, n = 225), students (74.7%, n = 213), and had German (63.9%, n = 182) or Austrian (30.5%, n = 87) citizenship. Mean age was M = 25.4 years (SD = 9.00, Range: 17–67). Mean BMI was M = 22.5 kg/m2 (SD = 4.07, Range: 15.9–43.2). Twenty-eight participants (9.80%) had underweight (BMI < 18.5 kg/m2), 208 participants (73.0%) had normal weight (BMI = 18.5–24.9 kg/m2), 32 participants (11.2%) had overweight (BMI = 25.0–29.9 kg/m2), and 15 participants (5.30%) had obesity (BMI ≥ 30.0 kg/m2; BMI data missing for two participants).
|
study
| 99.94 |
A pool of 40 items (20 positive and 20 negative emotions) was generated based on existing questionnaires of emotional eating (DEBQ, EES, EOQ, EMAQ) and the Positive and Negative Affect Schedule (Watson et al., 1988). Items are displayed in Table 1. Each item began with the stem When I am/feel…, followed by an adjective describing an emotional state. Response categories ranged from I eat much less than usual to I eat much more than usual (scored from 1 to 5). Items were presented in randomized order in the online survey.
|
study
| 100.0 |
Sample size exceeded the minimum 5:1 subjects-to-item ratio necessary for exploratory factor analysis (Costello and Osborne, 2005). Furthermore, the Kaiser-Meyer-Olkin Measure of Sampling Adequacy (KMO = 0.871) and Bartlett's Test of Sphericity [χ(780)2 = 4390, p < 0.001] indicated that data were adequate for conducting an exploratory factor analysis. The number of factors was determined by both parallel analysis and the Minimum Average Partial (MAP) test using the SPSS-syntax provided by O'Connor (2000). Principal Component Analysis was chosen as extraction method and Promax (κ = 4) was selected as rotation method. Internal consistency of factors was evaluated with Cronbach's alpha. Associations between different factor scores and between factor scores and BMI were tested with Pearson correlation coefficients. Sex differences of factor scores were tested with independent samples Mann-Whitney- U-tests.
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study
| 100.0 |
Parallel analysis (Figure 1) as well as the MAP test (averaged squared partial correlation: component 1 = 0.025, component 2 = 0.010, component 3 = 0.010, component 4 = 0.009, component 5 = 0.010) suggested extraction of four factors, which explained 43.6% of variance. The first factor included items related to positive emotions. The second factor included items related to negative, but low arousing emotions. The third and fourth factor included items related to high arousing emotions such as anger and anxiety (Table 1).
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study
| 100.0 |
In order to reduce the number of items for the SEES, five items with the highest factor loadings were selected for further analyses (Table 1). Thus, this resulted in four subscales that we termed happiness (containing the items cheerful, happy, optimistic, proud, confident), sadness (containing the items sad, depressed, bored, lonely, frustrated), anger (containing the items angry, furious, upset, irritated, jealous), and anxiety (containing the items anxious, worried, tense, uneasy, nervous). Internal consistencies of these subscales ranged between α = 0.713–0.800 (Table 2). Subscale scores of sadness, anger, and anxiety were positively correlated with each other while scores on happiness were negatively correlated with sadness and anxiety (Table 2). Mean scores on all subscales significantly differed from each other (all ts > 3.37, ps ≤ 0.001) in the following descending order: sadness > happiness > anger > anxiety (Figure 2A). Men had higher scores (M = 3.20, SD = 0.54) than women (M = 2.95, SD = 0.42) on the happiness subscale (p < 0.001) and had lower scores (M = 3.22, SD = 0.66) than women (M = 3.40, SD = 0.79) on the sadness subscale (p = 0.036). Men and women did not differ on anger and anxiety subscale scores (ps > 0.080). There were positive correlations between BMI and sadness (r = 0.256, p < 0.001), anger (r = 0.117, p = 0.05), and anxiety (r = 0.269, p < 0.001) subscale scores, but no correlation with the happiness subscale (r = 0.011, p = 0.856).
|
study
| 100.0 |
The study was approved by the institutional review board of the University of Salzburg. A link to the online survey at www.unipark.com was distributed via e-mail to student mailing lists at several German and Austrian universities, via social networks, and via a posting on the website of the German version of Psychology Today. Three × 50 € were raffled among participants who completed the survey. The website was visited 1,396 times and n = 805 participants completed the entire set of questions. Fifteen participants were excluded from analyses because they answered questions too rapidly (total completion time less than 5 min), leaving a final sample size of n = 790.
|
study
| 99.94 |
Most participants were women (82.9%, n = 655) and had German (81.3%, n = 642) or Austrian (14.2%, n = 112) citizenship. The majority of participants were students (79.6%, n = 629), employed (11.4%, n = 90), or pupils (4.70%, n = 37). Mean age was M = 24.7 years (SD = 6.79, Range: 15–65). Mean BMI was M = 22.3 kg/m2 (SD = 3.93, Range: 15.0–50.9). Seventy-six participants (9.60%) had underweight (BMI < 18.5 kg/m2), 583 participants (73.9%) had normal weight (BMI = 18.5–24.9 kg/m2), 92 participants (11.7%) had overweight (BMI = 25.0–29.9 kg/m2), and 38 participants (4.80%) had obesity (BMI ≥ 30.0 kg/m2; BMI data missing for one participant).
|
study
| 99.94 |
The SSES (Meule et al., 2018a) is a ten-item questionnaire for measuring eating in response to stress. Response categories range from I eat much less than usual to I eat much more than usual (scored from 1 to 5). Thus, higher values represent eating more when stressed while lower values indicate eating less when stressed. Internal consistency was α = 0.899 in the current study.
|
study
| 99.94 |
The emotional eating subscale of the DEBQ (van Strien et al., 1986; Grunert, 1989) is a ten-item questionnaire for measuring eating in response to negative emotions. Response categories range from never to very often (scored from 1 to 5). Thus, higher values indicate the frequency of eating more when experiencing negative emotions. Internal consistency was α = 0.909 in the current study.
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study
| 100.0 |
The PSRS (Meule et al., 2012) is a three-item questionnaire for measuring how successful individuals are in watching their weight, in losing weight, and how difficult it is for them to stay in shape. Response categories are anchored not successful/not difficult and very successful/very difficult (scored from 1 to 7). Thus, higher values indicate higher perceived self-regulatory success in weight regulation. Internal consistency was α = 0.696 in the current study.
|
study
| 100.0 |
The EDE-Q8 (Kliem et al., 2016) is an eight-item short form of the EDE-Q for measuring eating disorder psychopathology in the past 28 days. Response categories range from no days/never/not at all to every day/everytime/markedly (scored from 0 to 6). Thus, higher values indicate higher eating disorder psychopathology. Internal consistency was α = 0.913 in the current study.
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study
| 100.0 |
The short form of the CES-D (Radloff, 1977; Hautzinger et al., 2012) is a 15-item questionnaire for measuring depressive symptoms in the past seven days. Response categories range from rarely or none of the time to most or all of the time (scored from 0 to 3). Thus, higher values indicate higher depressiveness. Internal consistency was α = 0.911 in the current study.
|
study
| 99.94 |
The BIS-15 (Spinella, 2007; Meule et al., 2011) is a 15-item short form of the BIS-11 for measuring trait impulsivity. Response categories range from rarely/never to almost always/always (scored from 1 to 4). Thus, higher values indicate higher impulsivity. Internal consistencies were α = 0.670 (attentional impulsivity subscale), α = 0.734 (motor impulsivity subscale), and α = 0.794 (non-planning impulsivity subscale) in the current study.
|
study
| 100.0 |
A confirmatory factor analysis was computed with Amos 24 (IBM SPSS, Chicago) to test the four-factor structure of the SEES found in study 1. Maximum likelihood estimation was used, fixing the factor loading of the first items of every subscale to 1. According to the recommendations of Hu and Bentler (1999), model fit was evaluated by two fit indices: the comparative fit index (CFI), with 0.90 ≤ CFI < 0.95 indicating acceptable fit and CFI ≥ 0.95 indicating good fit, and the root mean square error of approximation (RMSEA), with 0.05 < RMSEA ≤ 0.08 indicating acceptable fit and RMSEA ≤ 0.05 indicating good fit. In order to evaluate whether factor structure of the SEES varied between female and male participants, we tested measurement invariance at three levels: configural, factor loading and intercept invariance. Measurement invariance across sex was evaluated according to recommendations by Chen (2007). Specifically, a χ2 difference test can be used for statistical comparison between nested models, but is almost always large and statistically significant with complex models and large samples and, thus, an impractical and unrealistic criterion for measurement invariance (Chen et al., 2005). Therefore, model fit changes were examined and decreases in CFI ≤ 0.010 or increases in RMSEA of ≤ 0.015 were considered to indicate measurement invariance (Chen, 2007). Associations between SEES subscale scores, BMI, and scores on the other questionnaires were examined with correlational analyses. Sex differences of SEES subscale scores were tested with independent samples Mann-Whitney- U-tests.
|
study
| 100.0 |
The four-factor structure had acceptable model fit (CFI = 0.932, RMSEA = 0.051) and standardized estimates are depicted in Figure 3A. Model fit changes between the configural invariance model (CFI = 0.917, RMSEA = 0.040) and the factor loading invariance model (CFI = 0.917, RMSEA = 0.039) indicated sex invariance for the factor score estimates (ΔCFI = 0.000, ΔRMSEA = 0.001). Similarly, model fit changes between the intercept invariance model (CFI = 0.906, RMSEA = 0.040) and the factor loading model (ΔCFI = 0.011, ΔRMSEA = 0.001) indicated sex invariance for the intercepts.
|
study
| 100.0 |
Subscale scores of sadness, anger, and anxiety were positively correlated with each other while scores on happiness were negatively correlated with sadness, anger, and anxiety scores (Table 3). Similar to study 1, mean scores on all subscales significantly differed from each other (all ts > 4.81, ps < 0.001) in the following descending order: sadness > happiness > anger > anxiety (Figure 2B). Similar to study 1, men had higher scores (M = 3.14, SD = 0.44) than women (M = 2.98, SD = 0.47) on the happiness subscale (p < 0.001) and had lower scores (M = 3.16, SD = 0.65) than women (M = 3.42, SD = 0.75) on the sadness subscale (p < 0.001). In addition, men had lower scores (M = 2.75, SD = 0.50) than women (M = 2.88, SD = 0.61) on the anger subscale (p = 0.023). Men and women did not differ on anxiety subscale scores (p = 0.709).
|
study
| 100.0 |
Scores on the happiness subscale were negatively correlated with BMI, stress eating, emotional eating, eating disorder pathology, depressiveness, and non-planning impulsivity, and positively correlated with perceived self-regulatory success in weight regulation (Table 4). In contrast, scores on the sadness, anger, and anxiety subscales were positively correlated with BMI, stress eating, emotional eating, and eating disorder pathology, and negatively correlated with perceived self-regulatory success in weight regulation (Table 4). In addition, scores on the sadness subscale were positively correlated with depressiveness and attentional impulsivity (Table 4).
|
study
| 100.0 |
The study was approved by the institutional review board of the University of Salzburg. A link to the online survey at www.limesurvey.org was distributed via e-mail to student mailing lists at the University of Salzburg. To broaden the age range as compared to studies 1 and 2, these included students from the university's 55-PLUS program, which is an educational opportunity for older adults at the university. Furthermore, adolescent participants were recruited at a local high school. Participation was voluntary and participants did not receive any compensation. The website was visited 623 times and n = 450 participants completed the study.
|
study
| 100.0 |
Most participants were women (74.4%, n = 335) and had Austrian (50.9%, n = 229) or German (42.7%, n = 192) citizenship. The majority of participants indicated their occupation as student (29.1%, n = 131), employed (26.4%, n = 119), or other (32.2%, n = 145). Mean age was M = 33.5 years (SD = 18.2, Range: 14–86). Mean BMI was M = 23.8 kg/m2 (SD = 4.91, Range: 15.6–50.2). Thirty-four participants (7.60%) had underweight (BMI < 18.5 kg/m2), 291 participants (64.7%) had normal weight (BMI = 18.5–24.9 kg/m2), 79 participants (17.6%) had overweight (BMI = 25.0–29.9 kg/m2), and 46 participants (10.2%) had obesity (BMI ≥ 30.0 kg/m2).
|
study
| 99.94 |
The four-factor structure had acceptable model fit (CFI = 0.917, RMSEA = 0.073) and standardized estimates are depicted in Figure 3B. Model fit changes between the configural invariance model (CFI = 0.898, RMSEA = 0.058) and the factor loading invariance model (CFI = 0.895, RMSEA = 0.058) indicated sex invariance for the factor score estimates (ΔCFI = 0.003, ΔRMSEA = 0.000). Similarly, model fit changes between the intercept invariance model (CFI = 0.877, RMSEA = 0.061) and the factor loading model (ΔCFI = 0.018, ΔRMSEA = 0.003) indicated sex invariance for the intercepts.
|
study
| 100.0 |
Similar to studies 1 and 2, subscale scores of sadness, anger, and anxiety were positively correlated with each other while scores on happiness were negatively correlated with sadness, anger, and anxiety scores (Table 5). Again, mean scores on all subscales significantly differed from each other (all ts > 3.73, ps < 0.001) in the following descending order: sadness > happiness > anger > anxiety (Figure 2C). Similar to studies 1 and 2, men had higher scores (M = 3.04, SD = 0.54) than women (M = 2.80, SD = 0.53) on the happiness subscale (p < 0.001) and lower scores (M = 3.13, SD = 0.70) than women (M = 3.34, SD = 0.87) on the sadness subscale (p = 0.011). In addition, men had higher scores (M = 2.65, SD = 0.60) than women (M = 2.44, SD = 0.71) on the anxiety subscale (p = 0.003). Men and women did not differ on anger subscale scores (p = 0.784). BMI correlated positively with sadness (r = 0.147, p = 0.002) and anxiety (r = 0.141, p = 0.003) subscale scores, negatively with happiness subscale scores (r = −0.165, p < 0.001), and did not correlate with anger subscale scores (r = 0.041, p = 0.381).
|
study
| 100.0 |
Progress in the research on the conceptual foundations of emotional eating, its potential mechanisms and its clinical correlates requires continuous refinement of the respective psychometric scales. The current studies document the development and preliminary validation of a new self-report measure of emotional eating—the SEES—which extends previous scales by detailing specific emotions and differentiating emotional over- and undereating. Item reduction from a pool of 40 emotional states resulted in a 20-item scale with four subscales, which were invariant across sex and had acceptable-to-good internal consistencies. Scores on these subscales represent self-reported eating in response to positive emotions (happiness), negative but low arousal emotions (sadness), and negative but high arousing emotions (anger and anxiety). These four affective states are consistent with the four most often measured emotions in emotion research (Weidman et al., 2017). Higher scores represent increased food intake, medium scores represent unchanged food intake, and lower scores represent decreased food intake.
|
study
| 99.94 |
Subscale scores significantly differed from each other: there was an overall tendency to report eating more than usual when experiencing sadness, eating just as much as usual when experiencing happiness, and eating less than usual when experiencing anger or anxiety (Figure 2). These differences might be attributable to different levels of bodily arousal that accompanies these emotions (Macht, 2008) and respective neuroendocrine changes (Torres and Nowson, 2007). Therefore, results point to specific mappings of emotion type on intake type (e.g., sadness to overeating, anger/anxiety to undereating) that—if collapsed by a measure—might mask or fully occlude any relationship and, thus, lead to inconsistent or contradictory findings.
|
study
| 99.94 |
In line with findings with the EMAQ, men reported to eat more when being happy whereas women reported to eat more when being sad. Moreover, eating in response to positive emotions was negatively correlated with eating in response to negative emotions and, similarly, correlates of these subscales diverged (Nolan et al., 2010; Bourdier et al., 2017). Although results of all three studies were not entirely consistent, an overall picture emerged such that eating more in response to happiness was associated with having a lower BMI, reporting lower eating pathology, and higher perceived success in weight regulation. Thus, it appears that such “happy overeating” represents a functional, healthy eating style that may reflect an intuitive change in eating behavior associated with appropriate perception of bodily signals (Herbert et al., 2013). Moreover, higher scores on the happiness subscale were associated with eating less in response to negative emotions, which may indicate that “happy overeating” and “unhappy undereating” might be two sides of the same coin and together associated with positive eating- and weight-related outcomes. This interpretation, however, needs to consider a person's body weight. For example, we have preliminary data available showing that inpatients with anorexia nervosa have significantly higher scores on the happiness subscale and lower scores on the sadness subscale than normal-weight control participants (Meule et al., 2018b). This suggests that the “happy overeating”–“unhappy undereating” combination can also reflect higher eating disorder severity in some individuals, particularly in those with underweight.
|
study
| 99.94 |
In contrast, eating more in response to negative emotions was associated with having a higher BMI, reporting higher eating pathology, and lower perceived success in weight regulation. Again, the flipside of this pattern was eating less in response to happiness, suggesting that the configuration of “unhappy overeating” and “happy undereating” is a more dysfunctional, unhealthy eating pattern that relates to unfavorable eating- and weight-related outcomes. Thus, these findings replicate and extend findings about correlates of eating in response to positive and negative emotions (Geliebter and Aversa, 2003; Nolan et al., 2010; Bourdier et al., 2017), providing preliminary support for validity and usefulness of the SEES.
|
study
| 99.94 |
Although the SEES allows for a more fine-grained analysis of emotional effects on eating, it still relies on self-report, which can potentially be biased. Specifically, the questionnaire requires that participants have significant insight of their day-to-day fluctuations in affect and its influence on food intake. For example, it has been suggested that individuals scoring high on self-report measures of emotional eating may overestimate how much they actually eat in response to certain emotions or may simply attribute their overeating to negative affect retrospectively (Royal and Kurtz, 2010; Adriaanse et al., 2016). Thus, validity of the SEES may be investigated in future studies that examine relationships with implicit measures (e.g., an emotional eating-related implicit association task; Bongers et al., 2013), with food intake after emotion induction in the laboratory, or with emotional eating assessed in daily life (e.g., with a combination of ecological momentary assessment and dietary assessment such as the Automated Self-Administered 24-h dietary assessment tool; Subar et al., 2012). Furthermore, our data are based on samples, which entail common limitations of online-based research (e.g., self-selection, completing the questionnaire multiple times). Thus, it is necessary to examine psychometric properties (e.g., measurement invariance across different age groups) and correlates of the SEES in more representative samples, which also include, for example, a higher proportion of individuals with lower education.
|
study
| 99.94 |
In accordance with other reports (Bongers and Jansen, 2016), our data suggest that focusing solely on increased food intake in response to negative emotions only covers a small proportion of what can be termed emotional eating. Specifically, different emotions have different effects on food intake and these have dissociable correlates. For example, “happy overeating” (or “unhappy undereating”) as opposed to “happy undereating” (or “unhappy overeating”) appears to be an adaptive, functional behavior in most individuals. Thus, we suggest that traditional definitions of the term emotional eating need to be opened up to all possible combinations of emotion and intake types such that emotional eating can be defined as any alteration in food intake (which can include eating less or eating more than usual) in response to affective states (which can include positive and negative emotions). By providing a fine-grained assessment of emotional eating that takes these aspects into account, the SEES opens exciting new avenues for research on the mechanisms and clinical correlates of emotional eating, not only in the psychometric domain but also in experimental and naturalistic studies.
|
review
| 99.44 |
Non-coding DNA constitutes over 98% of the human genome and harbors numerous functional elements essential for regulating gene expression and maintaining chromosomal architecture1. Mutations at non-coding regions may drive cancer by dysregulating proto-oncogenes and tumor suppressor genes, as exemplified by recent studies demonstrating recurrent point mutations at the TERT promoter in multiple cancer types2,3 and TAL1 enhancer insertions in T-cell acute lymphoblastic leukemia4. While previous pan-cancer analyses of tumor genomes have nominated regulatory driver mutations5,6, these studies have typically not been sufficiently powered to identify tissue-specific non-coding driver mutations, as hundreds of samples are usually needed to reliably identify driver mutations in individual cancer types7. Recently, the whole-genome mutational landscapes of breast8, liver9, and pancreatic10 cancer tumors have been studied to identify cancer-specific non-coding drivers. However, the prevalence and impact of non-coding driver mutations is still unknown for most cancer types.
|
review
| 99.75 |
CTCF is a DNA-binding protein essential for the maintenance of genome architecture by mediating both short and long-range chromosomal contacts11,12. Together with the cohesin complex, CTCF organizes chromatin into large topologically associating domains (TADs), insulating the local chromosomal neighborhoods from adjacent regions. Disruption of CTCF binding can therefore lead to dysregulation of gene expression11,12. In cancer, CTCF binding is disrupted through various mechanisms, such as DNA copy number alterations spanning domain boundaries13, microdeletions within CBSs13, and hypermethylation of CBSs14. These alterations at CBSs may drive cancer progression by allowing ectopic expression of oncogenes. Notably, recent studies have reported a genome-wide elevated somatic mutation rate across CBSs in several cancer types15–18. This suggests that mutational and DNA repair processes may act differently at CBSs relative to other genomic regions, thereby resulting in an overall elevated mutational burden at such sites in cancer. However, no study to date has rigorously tested the hypothesis that even amidst this elevated mutational burden, positive selection may still act on specific CBSs to drive cancer in individual tumor types. To accurately identify such genomic sites under positive selection, statistical tests must take into account regional biases in the mutation burden.
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study
| 99.94 |
Comprehensive genetic and molecular profiling have previously identified new molecular subtypes and genetic drivers of gastric adenocarcinoma19–21. Studies have also investigated the extent and impact of mutational signatures22,23 and epigenetic dysregulation in gastric cancer (GC) genomes24,25. Yet, it is unknown to what extent mutations in specific non-coding elements may drive GC, a leading cause of global cancer mortality. Here, we performed uniform and accurate identification of somatic single nucleotide variants (SNVs) and insertions/deletions (indels) in 212 GC genomes using an ensemble mutation calling approach. We present a comprehensive statistical approach, incorporating both epigenetic and sequence covariates, to identify non-coding regions with significantly higher mutation burdens over background, indicating positive selection and a role in gastric tumorigenesis. Performing an unbiased genome-wide scan of focal mutation hotspots (~20 bp, as TF binding motifs are typically <20 bp), we detect 34 significant recurring non-coding hotspots—of these, 11 overlapped CBSs. We further characterize these sites by analyzing CBS specific mutation biases, gene expression of neighboring genes, chromosomal instability, and incidence of these mutations in other cancer types. Overall, our analyses nominate these CBS hotspots as candidate drivers of GC. Furthermore, our analysis suggests a general link between CBS mutations and chromosomal instability in gastrointestinal cancers.
|
study
| 99.94 |
We analyzed the whole-genome sequences of 212 gastric adenocarcinoma tumors and matched normal samples collated from four different sources (Supplementary Data 1; Methods). All samples were uniformly processed using an accurate somatic mutation calling pipeline (Supplementary Fig. 1a–b). Briefly, we trained a random forest classifier that predicts high confidence somatic mutation calls (SNVs and indels) by combining the outputs of four independent mutation callers. This approach achieved >85% accuracy on an independent test data set of curated somatic mutations26. We excluded 20 low-quality samples with less than 400 mutation calls from the discovery cohort (Supplementary Fig. 1c). In addition, we removed five samples with strong enrichment of C>A substitutions, a sign of oxidative damage during DNA preparation27,28 (Supplementary Fig. 1c). Somatic mutations in coding (CDS) regions, immunoglobin loci, and poorly mappable regions were also removed from further analyses. After uniform processing, samples from the four cohorts showed comparable distributions of somatic mutation counts and similar mutation spectra (Fig. 1a and Supplementary Fig. 1a). The ICGC cohort had slightly fewer mutations per tumor, probably due to the lower sequencing depth of this cohort.Fig. 1Summary of the data. a Gastric tumor samples grouped by cohort and ordered by SNV count within each cohort. The panels show coverage, SNV count, indel count, mutation spectrum, molecular subtype, and Lauren’s classification of each sample. b Correlations between epigenetic features and somatic mutation rates in different tumor subtypes (error bars represent s.e.m of the correlation coefficient). c Principle component analysis of contributions of epigenetic features to the variance in the mutation rate of individual tumors. Colored stacked bars show the contribution of individual epigenetic features to the first two principal components
|
study
| 100.0 |
Summary of the data. a Gastric tumor samples grouped by cohort and ordered by SNV count within each cohort. The panels show coverage, SNV count, indel count, mutation spectrum, molecular subtype, and Lauren’s classification of each sample. b Correlations between epigenetic features and somatic mutation rates in different tumor subtypes (error bars represent s.e.m of the correlation coefficient). c Principle component analysis of contributions of epigenetic features to the variance in the mutation rate of individual tumors. Colored stacked bars show the contribution of individual epigenetic features to the first two principal components
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study
| 100.0 |
A previous study identified four molecular subtypes of gastric adenocarcinoma19: tumors that are EBV positive (EBV), tumors with high levels of microsatellite instability (MSI), tumors that exhibit copy number instability (CIN), and tumor that are genomically stable (GS). We investigated the correlations between somatic mutation rates of the four cancer subtypes and epigenetic profiles of gastric tissue obtained from the Roadmap Epigenomics project29. In general, somatic mutation rates were negatively correlated with regions of open chromatin (DNaseI hypersensitivity) and histone marks of active promoters (H3K4me3) and enhancers (H3K27ac) (Fig. 1b). The depletion of somatic mutations in regions of open chromatin is likely due to enhanced accessibility to the DNA repair machinery30–32. Notably, somatic mutations in the EBV subtype were less correlated with histone features and replication timing compared to the CIN and GS subtypes, suggesting that additional mutational biases may exist in EBV infected tumors.
|
study
| 100.0 |
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