Released this past November, the list is based on data collected from the Web of Science and highlights some of the world’s most influential scientific minds by naming the researchers whose publications over the previous decade have included a high number of Highly Cited Papers placing them among the top 1% most-cited.
\\n\\n
We wish to congratulate all of the researchers named and especially our authors on this amazing accomplishment! We are happy and proud to share in their success!
IntechOpen is proud to announce that 179 of our authors have made the Clarivate™ Highly Cited Researchers List for 2020, ranking them among the top 1% most-cited.
\n\n
Throughout the years, the list has named a total of 252 IntechOpen authors as Highly Cited. Of those researchers, 69 have been featured on the list multiple times.
\n\n\n\n
Released this past November, the list is based on data collected from the Web of Science and highlights some of the world’s most influential scientific minds by naming the researchers whose publications over the previous decade have included a high number of Highly Cited Papers placing them among the top 1% most-cited.
\n\n
We wish to congratulate all of the researchers named and especially our authors on this amazing accomplishment! We are happy and proud to share in their success!
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\r\n\tThe fuzzy set has played a vital role in the development of information science, algebra and fuzzy decision support systems. The crisp numbers have some uncertainty and vagueness in the modeling of real world problems. Industrial and Academic experts are constantly introducing and developing innovative and effective ideas in fuzzy set and systems for fuzzy decision making problems.
\r\n
\r\n\tThis book will contain some new ideas of fuzzy sets and their generalization. This book is an edited volume and is envisioned as a selection of chapter contributions organised in 4 sections. With a scope covering, but not limited to, Generalized Fuzzy Aggregation Operators, Generalization of Fuzzy Decision Making Problems, Pythagorean Fuzzy Sets and Decision Making and Spherical Fuzzy Aggregation operators and Decision Making.
\r\n
\r\n\tThe book also hopes to discuss some new approaches of hesitant fuzzy set and probabilistic hesitant fuzzy set to aggregation operators and their application in real world problems. This book will be useful for researchers and graduate students in research in the field of fuzzy decision support systems. \r\n\t
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Theophanides",coverURL:"https://cdn.intechopen.com/books/images_new/1591.jpg",editedByType:"Edited by",editors:[{id:"37194",title:"Dr.",name:"Theophanides",surname:"Theophile",slug:"theophanides-theophile",fullName:"Theophanides Theophile"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"3092",title:"Anopheles mosquitoes",subtitle:"New insights into malaria vectors",isOpenForSubmission:!1,hash:"c9e622485316d5e296288bf24d2b0d64",slug:"anopheles-mosquitoes-new-insights-into-malaria-vectors",bookSignature:"Sylvie Manguin",coverURL:"https://cdn.intechopen.com/books/images_new/3092.jpg",editedByType:"Edited by",editors:[{id:"50017",title:"Prof.",name:"Sylvie",surname:"Manguin",slug:"sylvie-manguin",fullName:"Sylvie Manguin"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}}]},chapter:{item:{type:"chapter",id:"40363",title:"Response of Soil Microbial Biomass and Enzyme Activities Under Three Temperate Tree Species to Elevated CO2 in Changbai Mountain, Northeastern China",doi:"10.5772/52837",slug:"response-of-soil-microbial-biomass-and-enzyme-activities-under-three-temperate-tree-species-to-eleva",body:'\n\t\t
\n\t\t\t
1. Introduction
\n\t\t\t
Owing to fossil fuel combustion, deforestation, and intense agriculture, the concentrations of atmospheric CO2 [CO2] has risen by 100ppm since the mid 1800s [1], and it has been predicted to double until the end of this century compared to the pre-industrial value [2]. Numerous studies have shown a greater biomass gain of plants, higher fine root and leaf litter C/N in some species under elevated CO2 condition [3−7]. Moreover, the rising CO2 also could alter litter chemistry (e.g., total N, lignin and starch content) and fine root turnover. Because microbial growth is limited by the type and amount of organic substrates entering the soil [8, 9], the changes in above- and below-ground plant input under elevated CO2 could potentially alter both the substrate availability and microbial activity. Although the effect of elevated CO2 via plants on soil microorganisms has been few studies investigated [10−12], the detailed plant-mediated effects still are unclear because of the complexity of microbial processes.
\n\t\t\t
Soil microorganisms play an important role in nutrient cycling, CO2 emission and in formation of soil total organic carbon (TOC) pool. Therefore, any effect of the rising [CO2] on soil microorganisms might in turn feedback on the response of terrestrial ecosystem to atmospheric CO2 and the sequestration of extra carbon [9]. Soil enzymes drive soil organic matter decomposition and nutrient transformations. Soil enzyme also was considered as a sensitive indicator, which could be significantly affected by temporal variability [13]. It is evident that the seasonal patterns of temperature and moisture of north temperate ecosystems can affect the activity of soil enzymes [14]. Although several studies have investigated the effects of increased CO2 on the soil microbial biomass and activity, to our knowledge, only relative few studies have measured the seasonal fluctuations of microbial biomass and soil enzyme activity under higher CO2 levels [10,11]. At the Oak Ridge FACE site, Sinsabaugh et al. (2003) found that soil extracellular enzyme activities, substrate utilization, gross N mineralization and denitrification were all not responsive to CO2 enrichment [10]. In contrast in an agroforestry ecosystem with typical Mediterranean climate, Moscatelli et al. (2005) observed that all soil biochemical properties were significantly affected by the temporal variability, and the interaction between time and CO2 level significantly influenced β-glucosidase activity and microbial respiration [11]. These studies have produced many conflicting results, with few common themes. Furthermore, the specific seasonal patterns of soil enzyme activities vary with soil properties and types of aboveground vegetations.
\n\t\t\t
Temperate forest ecosystems, which occupy much of the earth’s terrestrial surface area, have been considered as the most important C sink for sequestering the increasing atmospheric CO2 [15]. To understand elevated CO2 effects on temperate forest ecosystems, a growing number of free air CO2 enrichment (FACE) and open-top chamber (OTC) research project have been initiated throughout the world. In 1998, we began a long term CO2-enrichment experiment using open top chamber (OTC) growing three species of trees, Korean pine (Pinus koraiensis Sieb. et Zucc), Changbai pine (Pinus sylvestriformis (Takenouchi) T. Wang ex Cheng) and oak (Quercus mongolicus Fisch) in Changbai mountain, China [16]. This mountain ecosystem belongs to the temperate continental climate influenced by monsoon and has the prominent characteristics of mid-latitudinal upland climate. The soil type of this area is upland dark-brown forest volcanic soil [17]. The area has four obvious different seasons as windy spring, hot and rainy summer, cool autumn and cold winter. Our previous studies in this OTC site demonstrated that elevated CO2 increase aboveground biomass and photosynthesis of the trees (Zhou and Han, unpublished data) and decrease total soil respiration [18]. These results were consistent with other reports from temperate forest ecosystems, e.g., Duke temperate forest FACE site [19]. To date, however, there is still a poor understanding of effects of elevated CO2 on microbial activity of soil under different tree species.
\n\t\t\t
The objectives of this study were (a) to investigate effects of elevated CO2 on microbial biomass C/N and the variations in activities of various enzymes throughout the growing season, and (b) to compare the CO2 response of activities of C, N and P cycling related enzymes in the soils under three different tree species.
\n\t\t
\n\t\t
\n\t\t\t
2. Materials and methods
\n\t\t\t
\n\t\t\t\tExperimental site, design and sampling\n\t\t\t
\n\t\t\t
The experimental fields were located at Changbai Mountain in Jilin province, northeastern China (42º24´N, 128º06´E, and 738 m elevation). The soil is a dark-brown soil developed from volcanic ash. The topography is basaltic mesa, and the parent rock is loose volcanic ash sand. The mechanical composition of the soil is approximately 29% sand (20 μm − 2 mm), 40% silt (2 − 20 μm) and 31% clay (< 2 μm). Soil organic carbon in 0-10 cm layer is approximately 8.5%, and pH is 5.7 (1:2.5, soil: water). The ecosystem is temperate with a mean annual temperature of 5°C and annual average precipitation of 967.3-1400 mm. A randomized complete block design of ambient and elevated CO2 was established in an open-top chamber facility at the research station of Changbai mountain forest ecosystems, Chinese Academy of Sciences, in the spring of 1999. Eighteen open-top chambers (each 4.2m in diameter with hexagon and 4 m in height enclosed with a clear glass open-top chamber) were utilized to control CO2 levels. Korean pine (P. koraiensis Sieb. et Zucc) and Changbai pine (P. sylvestriformis) seeds were prepared and sowed in May, 1999. CO2 fumigation treatments began after seeds germination in May 1999. For oak (Q. mongolica) experiment, five-year old seedlings were transplanted into open top chambers in autumn 2004, and CO2 enrichment started at the end of April, 2005. In each year, the exposure started at the end of April and stopped at the end of October (the whole growing season). Half of the chambers were maintained at ambient atmospheric CO2 concentrations (ca. 350 ppm), others were maintained at elevated levels (ca. 500 ppm) by dispensing 100% CO2 into the blower fans only in the daytime. Elevated CO2 concentrations were maintained by continuously monitoring CO2 concentrations in elevated and ambient-level chambers with an infrared gas analyzer (A-SENSE-D, SenseAir, Sweden) by a computer control system that recorded 10-second averages of CO2 concentration every 3 minutes and then periodically adjusting the flow of 100% CO2 into the chambers.
\n\t\t\t
Soil samples were collected seven times: May, June, August and September in 2006, and May, July and September in 2007. At each sampling date, five soil cores (3 cm in diameter and 0–10 cm at deep) were collected within each chamber. The pooled samples were homogenized and roots removed by passing the soil through a 2-mm sieve. The samples used for measurements of soil enzyme activity were kept frozen (-70°C) and microbial biomass were kept cool (4°C) until analysis within 1 week after sampling.
\n\t\t\t
\n\t\t\t\tSoil microbial biomass analysis\n\t\t\t
\n\t\t\t
Subsamples of soil were dried at 105°C for 12 h to determine gravimetric water content. Soil pH was measured in solutions of 50 ml water and 10 g air-dry soil. Microbial biomasses C (Cmin) and N (Nmic) were measured by fumigation-extraction method [20]. Subsamples of sieved soil were fumigated with alcohol-free CHCl3 for 24 h, and then extracted with 0.5 M K2SO4 solution. The K2SO4 soil extract was analysed for total dissolved organic C (DOC) and total dissolved N (TDN) using a Total Organic Carbon Analyzer (multi N/C 3000, Jena, Germany). Microbial biomass C and N were calculated as differences in extractable DOC and TDN between fumigated and unfumigated soils using a correction factor (Kc) of 0.38 for Cmin and 0.54 for Nmic [20, 21].
\n\t\t\t
\n\t\t\t\tSoil enzyme activity analysis\n\t\t\t
\n\t\t\t
Enzyme activities were determined following Freeman et al. (1995) and Kang & Freeman (1999) [22, 23]. We used fluorogenic substrates, 4-methylumbelliferyl-linked (MUB) compounds as substrate analogues (Table 1). Briefly, approximately 0.1 g fresh mass, were homogenized in 1.8 ml of 50 mM tris-maleate buffer (pH 5.0) and 0.2 ml of substrate solution were combined in a 5 ml polypropylene tube. For each sample, there were four analytical replicates plus negative controls (sample plus 4-MUB and buffer, separately) for both sample and substrate color. The tubes were tumbled for 1 h at 30°C, except for phosphatase, which was incubated for 30min. The assay was terminated by adding 2 ml ice cold 96% ethanol. The tubes were centrifuged for 5 min at 4°C (6000 r.p.m.). Fluorescence was determined with a HITACHI 650-60 Fluorescence spectrophotometer at 446 nm emission and 377 nm excitation wavelength. The increase of fluorescence was converted to enzymatic activity with a standard curve was drawn using a series of known concentrations of 4-methylumbellife. Enzyme activities are expressed in units of nmol or μmol of substrate converted per hour per g soil dry mass (nmol h−1 g−1 or μmol h−1 g−1).
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\n\t\t\t\t\t\tEnzyme
\n\t\t\t\t\t\t
Abbreviation
\n\t\t\t\t\t\t
EC
\n\t\t\t\t\t\t
Substrate
\n\t\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t\t\t\t
1,4-β-glucosidase
\n\t\t\t\t\t\t
bG
\n\t\t\t\t\t\t
3.2.1.21
\n\t\t\t\t\t\t
4-MUB-β-D-glucoside
\n\t\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t\t\t\t
1,4-α-glucosidase
\n\t\t\t\t\t\t
aG
\n\t\t\t\t\t\t
3.2.1.20
\n\t\t\t\t\t\t
4-MUB-α-D-glucoside
\n\t\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t\t\t\t
1,4-β-Acetylglucosaminidase
\n\t\t\t\t\t\t
NAG (1,4-β-NAG)
\n\t\t\t\t\t\t
3.1.6.1
\n\t\t\t\t\t\t
4-MUB-N-acetyl-β-D-glucosaminide
\n\t\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t\t\t\t
Cellobiohydrolase
\n\t\t\t\t\t\t
CBH
\n\t\t\t\t\t\t
3.2.1.91
\n\t\t\t\t\t\t
4-MUB-β-D-cellobioside
\n\t\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t\t\t\t
1,4-β-xylosidase
\n\t\t\t\t\t\t
bX
\n\t\t\t\t\t\t
3.2.1.37
\n\t\t\t\t\t\t
4-MUB-β-D-xyloside
\n\t\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t\t\t\t
Phosphatase
\n\t\t\t\t\t\t
PA
\n\t\t\t\t\t\t
3.1.3.2
\n\t\t\t\t\t\t
4-MUB-phosphate
\n\t\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t\t\t\t
Phenol oxidase
\n\t\t\t\t\t\t
PPO
\n\t\t\t\t\t\t
1.10.3.2
\n\t\t\t\t\t\t
L-3,4-Dihydroxyphenylalanine
\n\t\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t\t\t\t
Peroxidase
\n\t\t\t\t\t\t
PO
\n\t\t\t\t\t\t
1.11.17
\n\t\t\t\t\t\t
L-3,4-Dihydroxyphenylalanine
\n\t\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t\t\t\t
EC: enzyme commission number; MUB: 4-methylumbelliferyl; Abbreviation: used in this article
\n\t\t\t\t\t
\n\t\t\t\t
Table 1.
Enzyme assays conducted on soil samples collected from the open top chamber in this study.
\n\t\t\t
For the phenol oxidase and peroxidase activities assay, l-3,4-dihydroxyphenylalanine (L-DOPA) was used as substrate [24]. We prepared soil slurry solutions of 5.0 g soil in 100 ml of 50 mmol l-1 acetate buffer (pH 5.0.) The reaction mixture for the phenol oxidase assay, containing 2 ml, 5 mM l-3,4-dihydroxyphenylalanine (L-DOPA) solution and 2 ml of soil slurry in 5 ml tube, was vortexed for exactly 60 min at 20°C in a shaking incubator and was centrifuged for 5 min at 4°C (6000 r.p.m.). The absorbance of the filtrate was read at 460 nm. For peroxidase activities assay, at the beginning of the incubation, were processed in the same way as phenol oxidase, with L-DOPA substrate and the addition of 200 μl of 0.3 % H2O2 [25]. Phenol oxidase activity was subtracted from peroxidase activity to calculate the net peroxidase activities.
\n\t\t\t
\n\t\t\t\tStatistical analysis\n\t\t\t
\n\t\t\t
Data were analyzed using repeated measures analysis of variance (RM-ANOVA) with CO2 treatment, plant species and their interaction as explaining variables. Prior to analysis, the data was checked for heterogeneity of variance, and when necessary, the variable was transformed to improve normality. RM-ANOVA analyses were performed with SPSS 13.0 statistical software package (SPSS Inc.). Correlation analysis was used to test for correlations between microbial biomass C, N and soil enzyme evaluated as significant at P<0.05. Correlations among enzyme activities and microbial biomass as well as dissolve organic C and N were explored using a principal components analysis on the correlation matrix. Principal component analyses (PCA) and canonical discriminate analysis were performed with the STATISTICA 6.0 software package (StatSoft Inc.).
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\n\t\t
\n\t\t\t
3. Results
\n\t\t\t\n\t\t\t
Elevated CO2 significantly decreased the activities of 1,4-β-NAG, 1,4-β-xylosidase, phosphatase, 1,4-β-glucosedase, and phenol oxidase in soil under Changbai pine in 2007 (Fig. 1). On all sampling dates, there was no CO2 main effect on soil enzyme activity across three tree species (Table 2). Time was a significant factor affecting the activities of hydrolotic enzymes and the two oxidase enzymes in soil (Fig. 1, Table 2). The activity of 1,4-β-NAG and phenol oxidase over the two years for all sample dates showed an interaction between tree species and CO2 (P< 0.01), suggesting the responses of these two enzymes to CO2 enrichment were dependent on aboveground vegetation. Interactions between CO2 treatment and sampling time were significant for CBH, peroxidase and phenol oxidase activities.
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\n\t\t\t\t\t\t
\n\t\t\t\t\t\t
\n\t\t\t\t\t\t
\n\t\t\t\t\t\tβG
\n\t\t\t\t\t\t
αG
\n\t\t\t\t\t\t
βNAG
\n\t\t\t\t\t\t
CBH
\n\t\t\t\t\t\t
βX
\n\t\t\t\t\t\t
PA
\n\t\t\t\t\t\t
PPO
\n\t\t\t\t\t\t
PO
\n\t\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t\t\t\t
Tests of Between-Subjects Effects
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\n\t\t\t\t\t\t
\n\t\t\t\t\t\t
CO2\n\t\t\t\t\t\t
\n\t\t\t\t\t\t
\n\t\t\t\t\t\t
2.69NS\n\t\t\t\t\t\t
\n\t\t\t\t\t\t
0.70 NS\n\t\t\t\t\t\t
\n\t\t\t\t\t\t
4.06 NS\n\t\t\t\t\t\t
\n\t\t\t\t\t\t
0.63 NS\n\t\t\t\t\t\t
\n\t\t\t\t\t\t
0.06 NS\n\t\t\t\t\t\t
\n\t\t\t\t\t\t
2.59 NS\n\t\t\t\t\t\t
\n\t\t\t\t\t\t
1.75 NS\n\t\t\t\t\t\t
\n\t\t\t\t\t\t
<0.01 NS\n\t\t\t\t\t\t
\n\t\t\t\t\t
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\n\t\t\t\t\t\t
Species
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\n\t\t\t\t\t\t
10.97**\n\t\t\t\t\t\t
\n\t\t\t\t\t\t
4.03 NS\n\t\t\t\t\t\t
\n\t\t\t\t\t\t
35.05***\n\t\t\t\t\t\t
\n\t\t\t\t\t\t
2.45 NS\n\t\t\t\t\t\t
\n\t\t\t\t\t\t
120.13***\n\t\t\t\t\t\t
\n\t\t\t\t\t\t
32.09***\n\t\t\t\t\t\t
\n\t\t\t\t\t\t
88.93***\n\t\t\t\t\t\t
\n\t\t\t\t\t\t
410.14***\n\t\t\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t\t\t\t
\n\t\t\t\t\t\t
Species ×CO2\n\t\t\t\t\t\t
\n\t\t\t\t\t\t
\n\t\t\t\t\t\t
1.90NS\n\t\t\t\t\t\t
\n\t\t\t\t\t\t
1.04 NS\n\t\t\t\t\t\t
\n\t\t\t\t\t\t
5.28*\n\t\t\t\t\t\t
\n\t\t\t\t\t\t
2.10 NS\n\t\t\t\t\t\t
\n\t\t\t\t\t\t
0.39 NS\n\t\t\t\t\t\t
\n\t\t\t\t\t\t
2.94 NS\n\t\t\t\t\t\t
\n\t\t\t\t\t\t
6.69*\n\t\t\t\t\t\t
\n\t\t\t\t\t\t
3.72 NS\n\t\t\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t\t\t\t
Tests of Within-Subjects Effects
\n\t\t\t\t\t\t
\n\t\t\t\t\t\t
\n\t\t\t\t\t\t
\n\t\t\t\t\t\t
\n\t\t\t\t\t\t
\n\t\t\t\t\t\t
\n\t\t\t\t\t\t
\n\t\t\t\t\t\t
\n\t\t\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t\t\t\t
\n\t\t\t\t\t\t
Time
\n\t\t\t\t\t\t
\n\t\t\t\t\t\t
8.89***\n\t\t\t\t\t\t
\n\t\t\t\t\t\t
2.92*\n\t\t\t\t\t\t
\n\t\t\t\t\t\t
1.90 NS\n\t\t\t\t\t\t
\n\t\t\t\t\t\t
4.11**\n\t\t\t\t\t\t
\n\t\t\t\t\t\t
15.19***\n\t\t\t\t\t\t
\n\t\t\t\t\t\t
5.85***\n\t\t\t\t\t\t
\n\t\t\t\t\t\t
12.65***\n\t\t\t\t\t\t
\n\t\t\t\t\t\t
37.86***\n\t\t\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t\t\t\t
\n\t\t\t\t\t\t
Time × Species
\n\t\t\t\t\t\t
\n\t\t\t\t\t\t
7.43***\n\t\t\t\t\t\t
\n\t\t\t\t\t\t
2.97*\n\t\t\t\t\t\t
\n\t\t\t\t\t\t
5.84***\n\t\t\t\t\t\t
\n\t\t\t\t\t\t
2.97**\n\t\t\t\t\t\t
\n\t\t\t\t\t\t
7.33***\n\t\t\t\t\t\t
\n\t\t\t\t\t\t
5.06***\n\t\t\t\t\t\t
\n\t\t\t\t\t\t
10.88***\n\t\t\t\t\t\t
\n\t\t\t\t\t\t
33.40***\n\t\t\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t\t\t\t
\n\t\t\t\t\t\t
Time × CO2\n\t\t\t\t\t\t
\n\t\t\t\t\t\t
\n\t\t\t\t\t\t
1.99 NS\n\t\t\t\t\t\t
\n\t\t\t\t\t\t
0.85 NS\n\t\t\t\t\t\t
\n\t\t\t\t\t\t
2.02 NS\n\t\t\t\t\t\t
\n\t\t\t\t\t\t
2.53*\n\t\t\t\t\t\t
\n\t\t\t\t\t\t
0.82 NS\n\t\t\t\t\t\t
\n\t\t\t\t\t\t
1.52 NS\n\t\t\t\t\t\t
\n\t\t\t\t\t\t
6.72***\n\t\t\t\t\t\t
\n\t\t\t\t\t\t
6.02***\n\t\t\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t\t\t\t
\n\t\t\t\t\t\t
Time ×species × CO2\n\t\t\t\t\t\t
\n\t\t\t\t\t\t
\n\t\t\t\t\t\t
5.69***\n\t\t\t\t\t\t
\n\t\t\t\t\t\t
1.16 NS\n\t\t\t\t\t\t
\n\t\t\t\t\t\t
2.66*\n\t\t\t\t\t\t
\n\t\t\t\t\t\t
3.21**\n\t\t\t\t\t\t
\n\t\t\t\t\t\t
0.60 NS\n\t\t\t\t\t\t
\n\t\t\t\t\t\t
2.19*\n\t\t\t\t\t\t
\n\t\t\t\t\t\t
1.91*\n\t\t\t\t\t\t
\n\t\t\t\t\t\t
5.03***\n\t\t\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t\t\t\t
Note: F values are displayed and *, ** and *** represent significane at P< 0.05, 0.01 and 0.001 level, respectively. NS represents no significant effect.
\n\t\t\t\t\t
\n\t\t\t\t
Table 2.
\n\t\t\t\t\t\tF-values from RM-ANOVA showing effects of elevated CO2 and time on soil enzyme activity.
\n\t\t\t
\n\t\t\t
Figure 1.
Variations in activities of soil enzymes under Korean pine, Changpai Scotch pine and oak after exposure to ambient (open symbols) and elevated (solid symbols) CO2 concentration. Error bars represent standard errors of the means; asterisks represent significant differences, e.g. * p< 0.05, ** p< 0.01.
\n\t\t\t\n\t\t\t
Correlation analysis indicated that soil moisture was positively correlated with 1,4-α-glucosidase. The TDN content under Korean pine was negatively correlated with DOC (r = -0.6166, P< 0.01) and positively correlated with phosphatase (r = 0.5300, P< 0.01) and peroxidase (r = 0.6559, P< 0.01). TDN from Changbai pine was negatively correlated with DOC (r = -0.7242, P< 0.01), peroxidase (r = -0.7274, P< 0.01) and phenol oxidase (r = -0.5289, P< 0.01), positively correlated with xylosidase (r = 0.6849, P< 0.01).
\n\t\t\t
Mean enzyme activity decreased from highest to lowest in the following order: PPO > AP > βG > βX > CBH > NAG > PO > αG in the Korean pine soil. Mean enzyme activity in the Changbai pine soil decreased from highest to lowest in a very similar order with Korean pine: PPO > AP > βG > βX > NAG > CBH > PO > αG, therefore, tree species is a important factor influencing soil enzyme activities, there were significant interactive effects between species and sampling time.
\n\t\t\t
Figure 2.
Factor plot from a principal components analysis of soil enzyme activity data for Korean red pine, Changpai Scotch pine and oak exposed to two levels of atmospheric CO2. PO: peroxidase, PPO: phenol oxidase, AP: acid phosphatase, αG: 1,4-α-glucosidase, CBH: cellobiohydrolase, bX: 1,4-β-xylosidase, NAG: 1,4-β-acetylglucosaminidase, bG: 1,4-β-glucosidase, MBC: microbial biomass C, MBN: microbial biomass N, DOC: dissolve organic C, TDN: total dissolve N.
\n\t\t\t
Soil pH was slightly acidic (5.4–6.1) and did not vary significantly between treatment or sampling dates, and varied among species. Multivariate statistics were used to assess the functional diversity and temporal vary of the soil microbial enzyme. In the principal component analysis of the data of all eight enzyme activities, PC1 and PC2 explained 20.25% and 34.73% of the total variance, respectively (Fig. 2). The first factor (PC1) appears to be associated with labile nutrient acquisition, the second factor (PC2) appears to be associated with lignocelluloses degradation. For the canonical discriminant analysis of the soils under three trees sampled in 2006 and 2007, the following variables were determined: 1,4-β-xylosidase, 1,4-α-glucosidase, 1,4-β-NAG, cellobiohydrolase, 1,4-β-xylosidase, phosphatase, phenol oxidase, peroxidase. Canonical discriminant analysis also showed that the effect of the tree species on soil enzymes during the two experimental years (Fig. 3). Root 1 seems to discriminate mostly between pines and oak (means of the canonical variables: -2.005 and 4.010, respectively). In the vertical direction (Root 2), seems to discriminate the two pines (means of the canonical variables: Korean pine -1.005, Changbai pine 1.264). Correlation analysis indicated that soil moisture was positively correlated strongly with 1,4-α-glucosidase, microbial biomass C and N across three species of tree (Table 3). Peroxidase activity showed strong correlations with Nmic (r = 0.54, P< 0.01) and Cmic (r = 0.81, P< 0.01), phenol oxidase activity showed a high correlation with Nmic (r = 0.48, P< 0.01) and Cmic (r = 0.63, P< 0.01).
\n\t\t\t
Figure 3.
Canonical variates of the microbial properties in the soils under Korean pine, Changpai Scotch pine and oak. KE, Korean pine under elevated CO2; KA, Korean pine under ambient CO2; CE, Changpai Scotch pine under elevated CO2; CA, Changpai Scotch pine under ambient CO2 ;OE, oak under elevated CO2; OA, oak under ambient CO2.
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\n\t\t\t
4. Discussioin
\n\t\t\t
\n\t\t\t\tEffect of CO2 concentrations on soil enzyme activities\n\t\t\t
\n\t\t\t
Elevated atmospheric CO2 can lead to an increase in the size of the substrate pool utilized by soil microbes and to stimulate the activities of soil enzyme [26]. For example, Larson et al. (2002) and Lipson et al. (2005) found a significant stimulation of soil extracellular enzyme activities under elevated CO2 [27, 28]. However, our results, along with other studies [29, 10] have not detected significant increase in C-cycling enzymes across the course of experiment, and only significant at specific time was observed with a higher mean value under elevated CO2. Furthermore, the significantly different sampling time point was independent among three types of trees. These results suggest that response of soil enzymes to long-term CO2 fumigation may be dependent on soil environment and aboveground vegetation type.
\n\t\t\t
1,4-β-NAG is one of the enzymes regulating nitrogen availability in soil, the activity is often used as an indicator of N demand by microbes. Its enhancement under elevated CO2 (av. +18%) in our study of Korean pine reflected a microbial demand for N nutrient. It is interesting to note that the activities of cellobiohydrolase, 1,4-β-NAG, 1,4-β-xylosidase, phenol oxidase and microbial biomass were decreased significantly in soil of Changbai pine under elevated CO2 in summer 2007, which can be observed also from mean activity of the enzymes and microbial biomass in 2007. Ebersberger et al. (2004) also found a significant reduce in proportion of fungi indicator of PLFA at elevated CO2 in comparison to ambient CO2 [30]. Finzi et al. (2006) proposed that there is increasing evidence that microbial function is progressively N limited under elevated CO2 as duration elongation of CO2 enrichment [31]. The lower microbial activity in the Changbai pine exposed to elevated CO2 may be attributed to the greater competition for N nutrient by rapidly growth of the tree [32].
\n\t\t\t
\n\t\t\t\tTree species effects on soil enzyme under elevated CO\n\t\t\t\t\n\t\t\t\t\t2\n\t\t\t\t\n\t\t\t
\n\t\t\t
The ecophysiological responses of the three species to elevated CO2 were significant different (Zhou and Han, unpublished data). The fast-growing Changbai pine tends to show larger growth increases under elevated CO2 than the slow-growing Korean pine, but the biomass was stored in stem and branch. Many studies showed that above-ground cover plant could determine the composition of the soil microbial community structure and function [33 −35]. In our study, discrimination between the pines and the oak was observed (Fig. 3), but these results must be careful explained, because of variation in duration of elevated CO2 between the pines and oak. Changbai pine tends to possess characteristics promoting rapid growth generally associated with high competitive ability. This can explain why elevated CO2 caused a decrease in microbial biomass and extracellular enzyme activity under Changbai pine (Fig. 1), due to the increase in competition for mineral nutrients with microorganisms. We speculate that fast-growing plant may be strongly affect soil N and P cycling compared to the slow-growing plant under elevated CO2 condition, contributing disadvantage to soil microbial community as well as significant declining the activities of microbial enzyme. These results also imply that tree species might also differ in their influence on soil microbial activity that in turn affects soil properties under elevated atmospheric CO2 concentration.
\n\t\t\t
Due to Korean pine has a slower growth rate and related physiological characteristics, the tree shows a narrower response to resource levels; whereas Changbai pine has a faster growth rate related to Korean pine shows a broad response to N and P nutrient. Hungate et al. (1997) observed a species-specific growth response of species to elevated atmospheric CO2, that could either increase or decrease soil-N availability [36]. Schimel and Bennett (2004) reported fast-growing species grew significantly better and took up more of the available N and P in the elevated CO2 condition [32]. Fast-growing Changbai pine required more canopy space and more soil volume than the Korean pine and showed a relative higher demand for soil N, as the high-to-low order of 1,4-β-NAG and CBH shown, compared with CBH-to-1,4-β-NAG order in Korean pine.
\n\t\t\t
\n\t\t\t\tSeasonal and temporal dynamic of the activities of soil enzymes under elevated CO\n\t\t\t\t\n\t\t\t\t\t2\n\t\t\t\t\n\t\t\t
\n\t\t\t
Temporal fluctuation of soil moisture, soil temperature, and C input from tree roots, rhizosphere products (e.g., root exudates), and tree residues can have large effects on soil microbial biomass and activity [38]. Most of the enzymes measured in our study (table 2), were significantly affected by the temporal variability, and the interaction between time and CO2 level significantly influenced CHB, phenol oxidase and peroxidase activities across the three species. Our data also revealed a strong positive correlation between soil moisture and microbial biomass or soil enzyme activity (Table 3), especially the activity of 1,4-α-glucosidase that correlated with soil moisture independent on tree species. These results are consistent with Devi and Yadava (2006), who reported that microbial biomass showed a positive significant correlation with soil moisture [39]. Soil moisture may be a master variable controlling microbial biomass and mineralization of starch (catalyzed by 1,4-α-glucosidase) in this temperate volcanic soil (Table 3).
\n\t\t\t
\n\t\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t\t\t\t
\n\t\t\t\t\t\t
\n\t\t\t\t\t\t
Korean pine
\n\t\t\t\t\t\t
\n\t\t\t\t\t\t
Changbai pine
\n\t\t\t\t\t\t
Oak
\n\t\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t\t\t\t
1,4-β-glucosidase
\n\t\t\t\t\t\t
\n\t\t\t\t\t\t
0.0993
\n\t\t\t\t\t\t
\n\t\t\t\t\t\t
\n\t\t\t\t\t\t\t0.6063*
\n\t\t\t\t\t\t
\n\t\t\t\t\t\t
0.3779
\n\t\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t\t\t\t
1,4-α-glucosidase
\n\t\t\t\t\t\t
\n\t\t\t\t\t\t
\n\t\t\t\t\t\t\t 0.5620*
\n\t\t\t\t\t\t
\n\t\t\t\t\t\t
\n\t\t\t\t\t\t\t0.6385*
\n\t\t\t\t\t\t
\n\t\t\t\t\t\t
\n\t\t\t\t\t\t\t0.6419*
\n\t\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t\t\t\t
1,4-β-NAGase
\n\t\t\t\t\t\t
\n\t\t\t\t\t\t
0.0551
\n\t\t\t\t\t\t
\n\t\t\t\t\t\t
0.3233
\n\t\t\t\t\t\t
\n\t\t\t\t\t\t
0.5305
\n\t\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t\t\t\t
Cellobiohydrolase
\n\t\t\t\t\t\t
\n\t\t\t\t\t\t
0.4239
\n\t\t\t\t\t\t
\n\t\t\t\t\t\t
0.3061
\n\t\t\t\t\t\t
\n\t\t\t\t\t\t
\n\t\t\t\t\t\t\t0.5417*
\n\t\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t\t\t\t
1,4-β-xylosidase
\n\t\t\t\t\t\t
\n\t\t\t\t\t\t
0.2733
\n\t\t\t\t\t\t
\n\t\t\t\t\t\t
0.2320
\n\t\t\t\t\t\t
\n\t\t\t\t\t\t
\n\t\t\t\t\t\t\t0.5856*
\n\t\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t\t\t\t
Phosphatase
\n\t\t\t\t\t\t
\n\t\t\t\t\t\t
\n\t\t\t\t\t\t\t0.6050*
\n\t\t\t\t\t\t
\n\t\t\t\t\t\t
0.1812
\n\t\t\t\t\t\t
\n\t\t\t\t\t\t
0.3672
\n\t\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t\t\t\t
Phenol oxidase
\n\t\t\t\t\t\t
\n\t\t\t\t\t\t
0.4411
\n\t\t\t\t\t\t
\n\t\t\t\t\t\t
0.2139
\n\t\t\t\t\t\t
\n\t\t\t\t\t\t
0.2976
\n\t\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t\t\t\t
Peroxidase
\n\t\t\t\t\t\t
\n\t\t\t\t\t\t
0.2568
\n\t\t\t\t\t\t
\n\t\t\t\t\t\t
-0.2690
\n\t\t\t\t\t\t
\n\t\t\t\t\t\t
-0.1960
\n\t\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t\t\t\t
Microbial biomass C
\n\t\t\t\t\t\t
\n\t\t\t\t\t\t
0.5843
\n\t\t\t\t\t\t
\n\t\t\t\t\t\t
\n\t\t\t\t\t\t\t0.7042*
\n\t\t\t\t\t\t
\n\t\t\t\t\t\t
0.5018
\n\t\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t\t\t\t
Microbial biomass N
\n\t\t\t\t\t\t
\n\t\t\t\t\t\t
0.4207
\n\t\t\t\t\t\t
\n\t\t\t\t\t\t
\n\t\t\t\t\t\t\t0.9528**
\n\t\t\t\t\t\t
\n\t\t\t\t\t\t
0.6033
\n\t\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t\t\t\t
Levels of significance of the coefficients are indicated for P < 0.05* or P < 0.01**.
\n\t\t\t\t\t
\n\t\t\t\t
Table 3.
Correlation analysis of extracellular enzyme and moisture of the soil under Korean pine, Changbai pine and oak after fumigation with elevated CO2 concentration in 2006 and 2007.
\n\t\t\t
Tree growth often stimulates an increase in the size of microbial biomass during the growing season [40, 41]. The seasonal variations in 1,4-β-glucosidase of Changbai pine we observed were consistent with the seasonal variations in 1,4-α-glucosidase, 1,4-β-NAG, and cellobiohydrodase activity (e.g., greatest activity in spring), which were in agreement with a report of study in a oak forest in USA [42]. Seasonal fluctuations of these enzymes might be strongly influenced by life cycle of Changbai pine. Because spring and early summer is the fast-growing period of the pine, the developing root system could provide enough of easily mineralizable substrate to microorganisms and exhibited a positive effect on their activities. So for Changbai pine, the peak activity of soil microbial community was detected in spring. Seasonal patterns of soil enzyme were controlled by growth also observed from oak, which growth began from mid of May to end of October. This indicates that the activity of soil enzyme may be concomitantly controlled by plant seasonal growth.
\n\t\t\t
In addition, differently seasonal pattern of identical enzyme from elevated and ambient CO2 was also found, e.g., cellobiohydrolase from Korean pine, 1,4-β-NAG from Changbai pine, 1,4-β-glucosidase and 1,4-β-NAG from oak. This may be due to soil extracellular enzymes can be either induced by the substrate [43], which are often altered by elevated CO2 [44], and influenced by soil moisture, or controlled by combine effect of there factors and/or other environmental factors [13], e.g., tree species, duration of CO2 enrichment. Differences between the two coniferous pine and the broadleaf oak results from OTC may be best accounted for by the different seasonal patterns of soil microbial biomass and enzyme in respond to tree species and elevated CO2. So our results confirm the importance of taking into account the seasonal variation of biochemical parameters when these are used as indicators of soil ecosystem in response to elevated CO2.
\n\t\t\t
In conclusion, seasonal variations are the factor mostly affecting soil biological properties and nutrients availability in Changbai mountain forest ecosystem. Long-term exposure of elevated CO2 can alter microbial biomass and the production of enzymes, but the effects are always detectable at specific times and are closely linked to plant processes, soil moisture and aboveground vegetations. Hence, a single sampling of the soil may not fully reveal its response to prolonged elevated atmospheric CO2. We proposed that it is imperative to assess microbial function for soil ecosystem with one or two year-round sampling regime.
\n\t\t
\n\t
Acknowledgments
\n\t\t\t
We thank XiuXiu Wang, ShiFu Meng, LiHua Niu and GuoZheng Song for their assistance in the open top chamber, and LiHua Xin for help with soil biochemical property analysis. This research was supported by the National Natural Science Foundation of China (NSFC).
\n\t\t
\n',keywords:null,chapterPDFUrl:"https://cdn.intechopen.com/pdfs/40363.pdf",chapterXML:"https://mts.intechopen.com/source/xml/40363.xml",downloadPdfUrl:"/chapter/pdf-download/40363",previewPdfUrl:"/chapter/pdf-preview/40363",totalDownloads:2320,totalViews:165,totalCrossrefCites:1,totalDimensionsCites:1,hasAltmetrics:0,dateSubmitted:"April 4th 2012",dateReviewed:"August 29th 2012",datePrePublished:null,datePublished:"February 27th 2013",dateFinished:null,readingETA:"0",abstract:null,reviewType:"peer-reviewed",bibtexUrl:"/chapter/bibtex/40363",risUrl:"/chapter/ris/40363",book:{slug:"soil-processes-and-current-trends-in-quality-assessment"},signatures:"Jun Qiang Zheng, Ying Wang and Shi Jie Han",authors:[{id:"77736",title:"Dr",name:null,middleName:null,surname:"Han",fullName:"Han",slug:"han",email:"zhjq79@yahoo.com",position:null,institution:null}],sections:[{id:"sec_1",title:"1. Introduction",level:"1"},{id:"sec_2",title:"2. Materials and methods",level:"1"},{id:"sec_3",title:"3. 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P.\n\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\tSadowsky\n\t\t\t\t\t\t\tMJ\n\t\t\t\t\t\t\n\t\t\t\t\t\n\t\t\t\t\t2002\n\t\t\t\t\tChanges in microbial activity and composition in a pasture ecosystem exposed to elevated atmospheric carbon dioxide.\n\t\t\t\t\tPlant Soil\n\t\t\t\t\t243\n\t\t\t\t\t197\n\t\t\t\t\t207\n\t\t\t\t\n\t\t\t'},{id:"B36",body:'\n\t\t\t\t\n\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\tHungate\n\t\t\t\t\t\t\tB. A.\n\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\tHolland\n\t\t\t\t\t\t\tE. A.\n\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\tJackson\n\t\t\t\t\t\t\tR. B.\n\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\tChapin\n\t\t\t\t\t\t\tF. S.\n\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\tField\n\t\t\t\t\t\t\tC. B.\n\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\tMooney\n\t\t\t\t\t\t\tH. A.\n\t\t\t\t\t\t\n\t\t\t\t\t\n\t\t\t\t\t1997\n\t\t\t\t\tThe fate of carbon in grasslands under carbon dioxide enrichment.\n\t\t\t\t\tNature\n\t\t\t\t\t388\n\t\t\t\t\t576\n\t\t\t\t\t579\n\t\t\t\t\n\t\t\t'},{id:"B37",body:'\n\t\t\t\t\n\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\tMadeira\n\t\t\t\t\t\t\tM.\n\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\tFüleky\n\t\t\t\t\t\t\tG.\n\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\tAuxtero\n\t\t\t\t\t\t\tE.\n\t\t\t\t\t\t\n\t\t\t\t\t\n\t\t\t\t\t2007\n\t\t\t\t\tPhosphate sorption of European volcanic soils.\n\t\t\t\t\tIn Arnalds O, Bartoli F, Buurman P, Oskarsson H, Stoops G, Garcia-Rodeja E, editors. Soils of Volcanic Regions in Europe. Springer, Berlin.\n\t\t\t\t\t354\n\t\t\t\t\n\t\t\t'},{id:"B38",body:'\n\t\t\t\t\n\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\tMoscatelli\n\t\t\t\t\t\t\tM. C.\n\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\tFonck\n\t\t\t\t\t\t\tM.\n\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\tDe Angelis\n\t\t\t\t\t\t\tP.\n\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\tLarbi\n\t\t\t\t\t\t\tH.\n\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\tMacuz\n\t\t\t\t\t\t\tA.\n\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\tRambelli\n\t\t\t\t\t\t\tA.\n\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\tGrego\n\t\t\t\t\t\t\tS.\n\t\t\t\t\t\t\n\t\t\t\t\t\n\t\t\t\t\t2001\n\t\t\t\t\tMediterranean natural forest living at elevated carbon dioxide: soil biological properties and plant biomass growth.\n\t\t\t\t\tSoil Manag\n\t\t\t\t\t17\n\t\t\t\t\t195\n\t\t\t\t\t202\n\t\t\t\t\n\t\t\t'},{id:"B39",body:'\n\t\t\t\t\n\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\tDevi\n\t\t\t\t\t\t\tN. B.\n\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\tYadava\n\t\t\t\t\t\t\tP. S.\n\t\t\t\t\t\t\n\t\t\t\t\t\n\t\t\t\t\t2006\n\t\t\t\t\tSeasonal dynamics in soil microbial biomass C, N and P in a mixed-oak forest ecosystem of Manipur, North-east India.\n\t\t\t\t\tAppl Soil Ecol\n\t\t\t\t\t31\n\t\t\t\t\t220\n\t\t\t\t\t227\n\t\t\t\t\n\t\t\t'},{id:"B40",body:'\n\t\t\t\t\n\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\tKaiser\n\t\t\t\t\t\t\tE. A.\n\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\tHeinemeyer\n\t\t\t\t\t\t\tO.\n\t\t\t\t\t\t\n\t\t\t\t\t\n\t\t\t\t\t1993\n\t\t\t\t\tSeasonal variations of soil microbial biomass carbon within plough layer.\n\t\t\t\t\tSoil Biol Biochem\n\t\t\t\t\t25\n\t\t\t\t\t1649\n\t\t\t\t\t1655\n\t\t\t\t\n\t\t\t'},{id:"B41",body:'\n\t\t\t\t\n\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\tDebosz\n\t\t\t\t\t\t\tK.\n\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\tRasmussen\n\t\t\t\t\t\t\tP. H.\n\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\tPedersen\n\t\t\t\t\t\t\tA. R.\n\t\t\t\t\t\t\n\t\t\t\t\t\n\t\t\t\t\t1999\n\t\t\t\t\tTemporal variations in microbial biomass C and cellulolytic enzyme activity in arable soils: effects of organic matter input.\n\t\t\t\t\tAppl Soil Ecol\n\t\t\t\t\t13\n\t\t\t\t\t209\n\t\t\t\t\t218\n\t\t\t\t\n\t\t\t'},{id:"B42",body:'\n\t\t\t\t\n\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\tBoerner\n\t\t\t\t\t\t\tR. E. J.\n\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\tBrinkman\n\t\t\t\t\t\t\tJ. A.\n\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\tSmith\n\t\t\t\t\t\t\tA.\n\t\t\t\t\t\t\n\t\t\t\t\t\n\t\t\t\t\t2005\n\t\t\t\t\tSeasonal variations in enzyme activity and organic carbon in soil of a burned and unburned hardwood forest.\n\t\t\t\t\tSoil Biol Biochem\n\t\t\t\t\t37\n\t\t\t\t\t1419\n\t\t\t\t\t1426\n\t\t\t\t\n\t\t\t'},{id:"B43",body:'\n\t\t\t\t\n\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\tAllison\n\t\t\t\t\t\t\tS. D.\n\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\tVitousek\n\t\t\t\t\t\t\tP. M.\n\t\t\t\t\t\t\n\t\t\t\t\t\n\t\t\t\t\t2005\n\t\t\t\t\tResponses of extracellular enzymes to simple and complex nutrient inputs.\n\t\t\t\t\tSoil Biol Biochem\n\t\t\t\t\t37\n\t\t\t\t\t937\n\t\t\t\t\t944\n\t\t\t\t\n\t\t\t'},{id:"B44",body:'\n\t\t\t\t\n\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\tBillings\n\t\t\t\t\t\t\tS.\n\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\tSchaeffer\n\t\t\t\t\t\t\tS.\n\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\tZitzer\n\t\t\t\t\t\t\tS.\n\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\tCharlet\n\t\t\t\t\t\t\tT.\n\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\tSmith\n\t\t\t\t\t\t\tS.\n\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\tEvans\n\t\t\t\t\t\t\tR.\n\t\t\t\t\t\t\n\t\t\t\t\t\n\t\t\t\t\t2002\n\t\t\t\t\tAlterations of nitrogen dynamics under elevated carbon dioxide in an intact Mojave Desert ecosystem: evidence from nitrogen-15 natural abundance.\n\t\t\t\t\tOecologia\n\t\t\t\t\t131\n\t\t\t\t\t463\n\t\t\t\t\t467\n\t\t\t\t\n\t\t\t'}],footnotes:[],contributors:[{corresp:null,contributorFullName:"Jun Qiang Zheng",address:null,affiliation:'
Institute of Applied Ecology, Chinese Academy of Sciences, China
State Key Laboratory of Forest and Soil Ecology, Institute of Applied Ecology, Chinese Academy of Sciences, China
Institute of Applied Ecology, Chinese Academy of Sciences, China
'},{corresp:"yes",contributorFullName:"Shi Jie Han",address:"zhjq79@yahoo.com",affiliation:'
Institute of Applied Ecology, Chinese Academy of Sciences, China
State Key Laboratory of Forest and Soil Ecology, Institute of Applied Ecology, Chinese Academy of Sciences, China
'}],corrections:null},book:{id:"3224",title:"Soil Processes and Current Trends in Quality Assessment",subtitle:null,fullTitle:"Soil Processes and Current Trends in Quality Assessment",slug:"soil-processes-and-current-trends-in-quality-assessment",publishedDate:"February 27th 2013",bookSignature:"Maria C. Hernandez Soriano",coverURL:"https://cdn.intechopen.com/books/images_new/3224.jpg",licenceType:"CC BY 3.0",editedByType:"Edited by",editors:[{id:"116131",title:"Dr.",name:"Maria C.",middleName:null,surname:"Hernandez Soriano",slug:"maria-c.-hernandez-soriano",fullName:"Maria C. 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1. Introduction
1.1 Anticholinergic agents in nature
1.1.1 Hyoscyamine
1.1.2 Belladonna
Anticholinergic agents are substances that antagonize the effects of acetylcholine, a neurotransmitter formed by an ester of choline and acetic acid, that facilitates nerve impulses in both the central (CNS) and peripheral nervous systems. Acetylcholine is the main neurotransmitter of the parasympathetic system. It is also located within parts of the autonomic nervous system. [1] Anticholinergic agents are present in both pharmaceuticals and in nature. Substances like hyoscyamine and belladonna are naturally occurring anticholinergics which have been used medicinally to control gastric secretions, for irritable bowel symptoms, and for urinary spasms. [2] However, despite their natural origins, the use of these drugs is not without consequence and should be avoided in older adults.
2. Anticholinergics physiological effects
2.1 Mechanisms of action
2.1.1 Cellular effects
2.1.2 Organ system involvement
2.1.2.1. Brain
2.1.2.2. Eyes
2.1.2.3. Glands (salivary, sweat)
2.1.2.4. Heart
2.1.2.5. Lungs
2.1.2.6. Gastrointestinal tract
2.1.2.7. Bladder/genitourinary system
2.1.2.8. Skin
The cholinergic receptors are divided into muscarinic receptors or nicotinic receptors. There are five subtypes of muscarinic receptors, M1-M5 and two types of nicotinic receptors, NM (skeletal muscle) and NN (neuronal). M1 are found in the CNS (i.e., the cerebral cortex, hippocampus, striatum and thalamus), autonomic ganglia, gastric and salivary glands and the enteric nerves of the GI tract. M2 are located in the CNS (i.e., the hindbrain, thalamus, cerebral cortex, hippocampus, striatum, heart, smooth muscle, and autonomic nerve terminals). M3 receptors have less of a presence in the CNS although they are found in the cerebral cortex and hippocampus. They are abundant in smooth muscle and glands and the heart. M4 is preferentially expressed in the CNS (i.e., forebrain, striatum, cerebral cortex and hippocampus) while M5 are only expressed in low levels in the CNS and periphery and are found primarily in the substantia nigra and ventral tegmentum area. NM are located at the neuromuscular junction and are involved in muscle contraction. Nn are found in the autonomic ganglia and adrenal medulla. [3]
At the cellular level, anticholinergic agents act by opposing the effects of acetylcholine either at the muscarinic or nicotinic receptors. In the geriatric population, most anticholinergic drugs affect the muscarinic receptors. On the organ system level, these compounds have varying effects. Although some effects are therapeutic, it’s their toxic effects that are most worrisome, especially in geriatric patients.
While M1-M5 receptors are found in the brain, most of the deleterious effects on the CNS come from antagonizing M1 receptors. This can lead to delirium, cognitive impairment, dizziness, sedation and confusion. The predominant form of muscarinic receptors in the eyes are M3 although M1-M5 receptors are also present. Blocking these receptors leads to mydriasis and blurred vision. It is for this reason that ophthalmic atropine, a potent anticholinergic agent, is used to produce pupillary dilation and/or cycloplegia. Blocking of M1 and M3 receptors in salivary glands leads to dry mouth and difficulty swallowing whereas opposing the effects of M3 receptors in sweat glands leads to the inability to dissipate heat and can result in overheating, especially during the warmer months. The heart is primarily composed of M2 receptors and antagonizing these receptors leads to sinus tachycardia and increased contractility. Systemic atropine is used in the management of symptomatic sinus bradycardia and atrioventricular nodal block. The lungs primarily contain M1-M4 receptors and blocking these receptors results in bronchodilation. The use of long- and short-acting inhalation antimuscarinic agents in chronic obstructive pulmonary disease (COPD) takes advantage of this beneficial effect. Opposing M2 and M3 receptors in the GI tract can lead to gastric stasis and constipation. On the other hand, dicyclomine is an anticholinergic drug that is used for abdominal pain associated with irritable bowel syndrome. Antagonism of M3 receptors in the bladder inhibits detrusor and bladder contractions and is used therapeutically for urinary incontinence. This blockade can also lead to urinary retention. The role of cholinergic agents in the skin, which contains primarily M3 receptors, is complex resulting in increased nitric oxide production and vasodilation and it also involves interplay with nicotinic receptors. [4]
Nicotinic agents act either as neuromuscular blockers (i.e., atracurium, vecuronium, tubocurarine, pancuronium) or ganglionic blockers (i.e., mecamylamine). Succinylcholine is a NM receptor agonist. [4]
3. 3. Assessment of anticholinergic burden
3.1 Tune
3.2 Anticholinergic cognitive burden scale
3.3 Anticholinergic risk scale
3.4 Drug burden index
The effects of anticholinergic agents are cumulative and there are various tools available to help evaluate the degree of ‘cholinergic burden’ in an older adult’s drug regimen. Early work by Tune et al. resulted in the development of the serum anticholinergic assay, a biologic measure intended to quantify anticholinergic drug burden. [5] Using this assay, it has been shown that many drugs taken by older adults have high serum anticholinergic activity Table 1 [6].
Medications
Anticholinergic Drug Level (ng/mL Atropine
Cimetidine
0.86
Prednisolone
0.55
Theophylline
0.44
Digoxin
0.25
Furosemide
0.22
Nifedipine
0.22
Ranitidine
0.22
Isosorbide dinitrate
0.15
Warfarin
0.12
Codeine
0.11
Triamterene/HCTZ
0.08
Captopril
0.02
Table 1.
Anticholinergic drug level of medications commonly used by older adults.
Over 600 medications have some degree of anticholinergic activity. [7] One drawback of using the serum anticholinergic assay is that it may not be readily available in clinical settings and even if available, there can be a delay in care pending interpretation of results. [8]
As a result of these limitations, scales have been developed to easily calculate the cumulative anticholinergic burden. The Anticholinergic Cognitive Burden Scale (ACBS) rates drugs on a scale of 0 (no anticholinergic effect) to 3 with 1 representing a possible anticholinergic effect based on laboratory tests but no evidence of clinically relevant cognitive effects and scores of 2 or 3 indicating definite anticholinergic effects. Higher scores indicate greater anticholinergic burden and warrant a re-evaluation of the drug regimen. The presence of a drug scoring 2 or 3 can increase the risk of cognitive impairment by 46% over 6 years. Further, each point increase in the ACBS has been associated with a decrease in the Mini-Mental Examination Score of 0.33 points over the course of 2 years. This point increase has also been associated with a 26% increase in the risk of death. [9]
Another tool is the Anticholinergic Risk Scale (ARS). Similar to ACBS, the ARS is also a 3-point scale. The developers of the scale assessed whether the ARS could predict the risk of anticholinergic adverse effects in a geriatric evaluation and management (GEM) group and in a primary care (PC) group. The investigators found that in the GEM group, older adults experienced more adverse CNS effects whereas in the PC group, more elderly had peripheral adverse effects. [10]
The Anticholinergic Drug Scale (ADS), which was previously known as the Clinician-Rated Anticholinergic Scale, is another 3-point scale. It includes the largest number of anticholinergic agents. A score of 0 indicates no known anticholinergic properties; a score of 1 means that the drug has the potential for anticholinergic activity as evidenced by receptor binding sites; a score of 2 represents a drug that causes anticholinergic adverse effects at higher doses; and a score of 3 represents a drug with marked anticholinergic activity. [11]
The Anticholinergic Burden Classification (ABC) measures serum anticholinergic activity but takes into account the duration of exposure, adjusts for mode of administration (i.e., topical, nasal, oral, etc.), assesses for possible drug–drug interactions and for the ability of drugs to cross the blood brain barrier. [12]
The Anticholinergic Activity Scale (AAS) is based on in vivo radioreceptor assay determinations and ranks drugs on five levels: 0 (no anticholinergic activity); 0/+ (no or minimal anticholinergic activity); + (low anticholinergic activity), ++ (moderate anticholinergic activity), and +++ (high anticholinergic activity). [13]
The Anticholinergic Loading Scale (ALS) is a tool used in the Australian Imaging, Biomarkers and Lifestyle (AIBL) study that calculates anticholinergic load. Anticholinergic load was found to have an adverse effect on psychomotor speed and executive function in healthy controls. [14]
The Anticholinergic Effect on Cognition Scale (AECS) is another 3-point scale that uses in vitro anticholinergic potency as well as a drug’s ability to cross the blood brain barrier. [15]
Unlike the previous scales, which only focus on the anticholinergic potential of a drug regimen, the Drug Burden Index (DBI) takes into account anticholinergic effects, sedative effects of medications, and the total number of medications. It measures the effect of cumulative exposure to both anticholinergic and sedative medications on physical and cognitive function in older adults. This scale is based on the minimum recommended daily doses of each drug. Drugs that have both anticholinergic and sedative properties are classified based on their anticholinergic effects. [16] The developers studied this scale in over 3000 healthy community-dwelling older adults aged 70–79 years. They found that the use of anticholinergic and sedative medications was associated with poorer physical performance and cognitive performance. Each unit of drug burden on physical function was equal to having three additional physical comorbidities whereas each unit of drug burden on cognition was similar to having four additional physical comorbidities or about half of the effects of anxiety, depression or cognitive impairment. [17]
A recent publication compared several of the anticholinergic scales providing a description of the tool and listing the number of drugs with anticholinergic activity that are included in the scale. [18] However, while many of these scales have shown a significant correlation between anticholinergic burden assessment and serum anticholinergic drug levels, they have limitations. There is currently no ‘gold standard’ to identify an anticholinergic drug. Only parent compounds are included in these scales, therefore, there is no information on active metabolites that may also contribute to the anticholinergic burden. While some scales do take dose into the account, this is not consistently done in all tools. These scales also assume that there is a linear relationship between anticholinergic levels and toxicity. Lastly, serum anticholinergic activity assays do not distinguish between agonist versus antagonist binding of the cholinergic receptors. [19]
4. Medication classes with anticholinergic effects- the Beer’s list of potentially inappropriate medications in older adults
4.1 Antiarrhythmics
4.2 Antidepressants
4.3 Antiemetics
4.4 First generation antihistamines
4.5 Urinary incontinence antimuscarinics
4.6 Antiparkinsonian agents
4.7 Antipsychotics
4.8 Antispasmodics
4.9 Skeletal muscle relaxants
In 1991, Dr. Mark Beers published explicit criteria for the use of ‘potentially inappropriate medications’ or PIMS in older nursing home residents. [20] The Beer’s criteria, which were developed using a two-stage Delphi survey, defined inappropriate prescribing as the use of a medication where the potential risks outweigh the potential benefits. These initial criteria included 30 therapeutic classes/medications that should be avoided in elderly nursing home residents. [21] This list is updated every 3 years by the American Geriatrics Society. At the time of this writing, the latest Beer’s List was published in 2019. The criteria identifies PIMs, drugs that may be PIMS because they may exacerbate disease states or geriatric syndromes, drugs that should be used with caution, drugs that should be avoided, drugs that should be used in reduced doses (if at all) based on renal function, and drugs with strong anticholinergic properties Table 2 [22].
5.10 Use of anticholinergic PIMs in older adults with dementia and delirium
5.11 Use of anticholinergic PIMS in older adults resulting in falls/fracture
5.12 Use of anticholinergic PIMs in older adults and mortality
As mentioned previously, cholinergic receptors are found in various organ systems throughout the body. Blocking these receptors can have both therapeutic and toxic effects. The mnemonic “red as a beet, dry as a bone, blind as a bat, mad as a hatter, hot as a hare, full as a flask” reflects the classic signs and symptoms of anticholinergic poisoning. [23] However, adverse events in older adults may be more subtle. These can include drowsiness, sedation, cognitive impairment, confusion, delirium, hallucinations, blurred vision, dizziness, falls/fractures, urinary retention, constipation, tachycardia, and xerostomia. [3, 22].
Adverse CNS effects can be particularly burdensome among older adults. A recent study examining the effects of PIMs in patients with dementia found that almost one-quarter of adults aged ≥65 with cognitive impairment used drugs with clinically significant anticholinergic effects. This study measured anticholinergic burden using the ADS. [11] It found that the level 2 drugs that were most prescribed were ranitidine and cyclobenzaprine and the most commonly prescribed level 3 drugs were meclizine, tolterodine and oxybutynin. [24] A systematic review examining drug-induced delirium found that ARS scores were consistently associated with delirium. [25] A recent retrospective analysis found that if older hospitalized adults scored 3 or higher on the ACBS, they had a 3–6 fold increased risk of developing delirium compared to those who score < 3 on this tool. [26] In older adults with mild to moderate Alzheimer’s disease who were APOE-ε4 carriers, there was a positive correlation between greater progression of dementia severity and continued use of anticholinergic medications. [27]
In addition to the CNS effects of anticholinergic agents in older adults, another concern is the risk of falls and fracture. After following women with a mean age of 55 years for approximately 24 months, the APOS (Aberdeen Prospective Osteoporosis Screening Study) found that those with a ACB of ≥2 had a 2.34-fold increased odds of having had recurrent falls in ‘later life’ with ‘later life’ referring to 12 months prior to follow-up; there was a 2-year follow-up period. They postulated that anticholinergic agents may contribute to falls by causing vision problems secondary to pupillary dilation, dizziness, slowed reflexes and/or cognitive impairment. [28]
Death, the most significant anticholinergic adverse event, was observed in a systematic review of studies examining the association between anticholinergic burden and mortality in older adults. Of the 27 studies included in this systematic review, 63% of studies found a positive relationship between anticholinergic drug burden and mortality in older adults. When solely analyzing those studies that were deemed to be of the highest quality, the association between anticholinergic drug use and death rose to 80% in the elderly. [29]
6. Drug interactions involving anticholinergic agents
6.1 Acetylcholinesterase inhibitors
6.2 Potassium
6.3 Carbonic anhydrase inhibitors
6.4 Opioids
6.5 Other “moderate” anticholinergic drug-drug interactions
Besides the anticholinergic drug–drug interactions that lead to an increased anticholinergic burden, anticholinergic agents are involved in other potential drug–drug interactions.
The use of an anticholinergic agent with an acetylcholinesterase inhibitor (i.e., donepezil, galantamine and rivastigmine) results in opposing pharmacodynamic effects and can negate any small, positive benefits seen with the Alzheimer’s disease agents. [30] Conversely, acetylcholinesterase inhibitors have the potential to interfere with the therapeutic effects of anticholinergic agents. Concomitant use is not recommended. [31, 32, 33]
The concomitant administration of an anticholinergic agent and an oral solid dosage form of potassium supplement can increase the risk of GI bleeding. A liquid formulation of potassium supplement should be utilized instead if concurrent therapy is required. [34, 35, 36]
Anticholinergic agents also interact with carbonic anhydrase inhibitors such as topiramate and zonisamide potentiating the risk of oligohidrosis and hyperthermia. Patients should be monitored for decreased sweating and increases in body temperature. These combinations should be avoided. [37, 38]
Concurrent use of opioids and anticholinergics can lead to severe constipation (resulting in paralytic ileus), sedation, dizziness, confusion, cognitive and psychomotor impairment, dry mouth and urinary retention. Caution is advised. [39]
7. Contraindications to the use of anticholinergic agents
7.1 Narrow angle glaucoma
7.2 Obstructive uropathy
7.3 Myasthenia gravis
7.4 Obstructive gastrointestinal tract disease
7.5 Myocardial ischemia
Anticholinergic agents cause pupillary dilation, which is detrimental in patients with narrow angle or primary angle closure glaucoma. When the pupils dilate, this increases pressure within the eye. This increase in pressure prevents drainage of aqueous humor from the eye resulting in marked increases in ocular pressure and acute pain. If left untreated, this can lead to optic nerve damage and vision loss. The use of anticholinergics is contraindicated in patients with this type of glaucoma [41].
In overactive bladder, there are excessive contractions of the detrusor muscle producing incomplete emptying of the bladder. By blocking M3 receptors in the genitourinary tract, this causes smooth muscle relaxation and detrusor underactivity, which can lead to urinary retention. In the presence of benign prostate hyperplasia, there is compression of the urethra, which blocks the flow of urine. Anticholinergic agents are contraindicated in patients with urinary retention and bladder neck obstruction caused by prostatic hypertrophy since the use of these agents can result in an increased risk of developing an obstructive uropathy. [42]
Myasthenia gravis is an autoimmune disorder of the postsynaptic neuromuscular junction caused by antibody-mediated blockade of neuromuscular transmission that results in skeletal muscle weakness. Autoimmune antibodies form at the neuromuscular junction against nicotinic acetylcholine postsynaptic receptors. Anticholinergics, especially agents that block nicotinic cholinergic receptors, are contraindicated because they exacerbate muscle weakness. Further, acetylcholine esterase inhibitors such as pyridostigmine are considered the mainstay of treatment. The use of anticholinergic agents would antagonize the effects of these drugs. [42, 43]
Stimulation of M1 and M2 receptors in the GI tract increases GI motility. Anticholinergics block these receptors resulting in slowed GI motility. Ogilive’s syndrome or colonic pseudo-obstruction, which is massive dilation of the colon without underlying mechanical obstruction or other organic causes, can be due to the use of anticholinergic agents. These drugs can lead to an adynamic colon. Anticholinergic drugs are contraindicated in patients with achalasia, esophageal stricture or stenosis, pyloroduodenal stenosing peptic ulcer disease, pyloric obstruction and paralytic ileus. [42, 44]
Stimulation of M2 receptors in the heart slow pacemaker activity and atrioventricular (AV) conduction, which decreases contractility. Blocking these receptors leads to sinus tachycardia and increased oxygen demand. [45] Analyses of data from the EPIC (European Prospective Investigation into Cancer)-Norfolk Population Study, which was a longitudinal, observation, community cohort study, found that among the 21,000 study participants there was an increase in total anticholinergic burden and subsequent risk of all-cause mortality and incident cardiovascular disease during the follow up period. ACBS scores of ≥3 were associated with a hazard ratio of 2.17 (p < 0.00001) for cardiovascular disease incidence and higher mortality. It was thought that this was a dose-dependent, class effect for the anticholinergic agents. Potential mechanisms of this effect could be a pro-arrhythmic or pro-ischemic effect, increased hemodynamic lability, cardiac ischemia, cardiac dysrhythmias in the presence of ischemia, decrease heart rate variability, or an inflammatory response resulting in an increased risk of mortality. [46] Studies of the effects of inhalation antimuscarinics on cardiovascular status have been mixed. [47] Inhalation anticholinergic agents used in chronic obstructive pulmonary disease have been found to aggravate the balance of the autonomic nervous system leading to significantly reduced heart rate recovery following maximal cardiopulmonary exercise. [48] In a longitudinal study of over 3700 nursing home residents with coronary artery disease, the use of anticholinergics was associated with an increased risk of hospitalization and all-cause mortality (hazard ratio 1.71 if the ACB score ≥ 2). [49] Further, the use of antimuscarinics for urinary incontinence may also be associated with drug-dependent cardiovascular risk. Among these agents, darifenacin has not been associated with an increase in heart rate or QT prolongation because it is M3 selective and it appears to have the best cardiovascular safety profile. Tropsium, on the other hand, may have the highest risk of adverse cardiovascular events. On the basis of these drugs’ physiological effects and clinical trials showing increased cardiovascular risk associated with their use, it is best to avoid anticholinergics during the post-myocardial infarction period. [50]
8. Practical considerations for the use of anticholinergic agents in older agents
8.1 Nonprescription drug availability
8.2 End-of-life
8.3 Deprescribing
Anticholinergic medications are readily available over-the-counter. First generation antihistamines are available as single ingredients or in multiple symptom cough and cold products. Anticholinergics are marketed as over-the-counter sleep aids and for urinary incontinence. [51, 52, 53, 54] A recent study examining older adults’ medication decision making and behavior in regards to the use of anticholinergic over-the-counter medications found that while seniors were concerned about adverse drug events, they were not aware of age-related risk associated with the use of anticholinergic medications. [55]
Anticholinergic agents are used at end-of-life (EOL) for relief of nausea in those with a vestibular component and more commonly, to provide symptomatic relief of excessive secretions. However, data is lacking to support the use of these drugs for this latter indication. [56, 57]
Given the poor risk versus benefit of anticholinergics in older adults, there has been a movement to deprescribe these medications in the elderly. The DEFEAT-polypharmacy was a deprescribing feasibility trial conducted among 46 residential care residents in New Zealand that targeted the use of anticholinergic and sedative medications in older adults. Utilizing peer-reviewed deprescribing guidelines and a collaborative pharmacist-led medication review approach, investigators were able to demonstrate a 0.34 decrease in DBI scores at 6 months. The total number of medications were reduced by 2.13 medications per patient. There was a statistically significant reduction in the number of falls in the past 90 days. There was also a significant improvement in frailty scores. A significant decline was also observed in psychiatric, neurological, autonomic and other adverse events with a decrease in psychiatric adverse events of 1.8 three months after deprescribing and increasing to 2.24 after 6 months; other potential adverse events fell by 2.8 at the end of three months and 4.24 at 6 months post initiation of the deprescribing intervention. Participants also reported lower depression scores after six months. Cognition and quality of life were unchanged. [58]
Unfortunately, anticholinergics are sometimes prescribed as part of the prescribing cascade to manage urinary incontinence associated with the use of acetylcholinesterase inhibitors. A population-based retrospective cohort study of 44,884 older adults with dementia conducted in Canada found that there was an increased risk (adjusted hazard ratio 1.55) of subsequently receiving an anticholinergic agent following the initiation of acetylcholinesterase inhibitors. [59] The Choosing Wisely campaign, an initiative of the American Board of Internal Medicine Foundation, is designed to promote conversations between clinicians and patients by helping patients choose care that is supported by evidence, not duplicative of other tests or procedures already received, free from harm, and truly necessary. Dialog has started about the use of anticholinergic agents in older adults. In June 2016, the American Academy of Nursing made the following recommendation: “Don’t administer “prn” (i.e., as needed) sedative, antipsychotic or hypnotic medications to prevent and/or treat delirium without first assessing for, removing and treating the underlying causes of delirium and using nonpharmacologic delirium prevention and treatment approaches”. Anticholinergics are clearly identified as deliriogenic medications. [60]
In June 2020, the American Urogynecologic Society (AUS) issued a recommendation stating to “avoid using anticholinergic medication to treat overactive bladder in women older than 70”. This recommendation was based on the AUS’s concern over the ability of anticholinergic drugs to impair cognition, increase the risk of developing dementia and cause drowsiness and constipation, all potentially detrimental adverse effects in older adults. [61]
9. Conclusions
Cholinergic receptors are found throughout the body, but most especially in the CNS. Substances that block these receptors are available both naturally and pharmaceutically. Depending on the location of cholinergic receptors and their subtype, use of anticholinergic medications can result in adverse drug effects in the CNS, the eyes, the exocrine glands, the heart, the GI tract and genitourinary systems and in the skin. Older adults are especially prone to developing adverse drug events from the use of anticholinergics. Easy to use scales are available to assess the burden of anticholinergic agents in the drug regimen. The Beer’s List includes drugs that are potentially inappropriate for use in older adults because of their strong anticholinergic properties. Adverse events associated with the use of anticholinergic agents include drowsiness, sedation, cognitive impairment, confusion, delirium, hallucinations, blurred vision, dizziness, falls/fractures, urinary retention, constipation, tachycardia and xerostomia. Anticholinergic drugs have also been associated with an increase in mortality. These agents are involved with numerous drug–drug interactions adding to the anticholinergic burden. They can antagonize the effects of acetylcholinesterase inhibitors, contribute to the development of GI bleeding in patients on oral, solid forms of potassium supplementation and lead to hyperthermia in patients concomitantly receiving a carbonic anhydrase inhibitor. Anticholinergics should not be used by older adults, especially those with narrow angle glaucoma, obstructive uropathy, myasthenia gravis, obstructive GI tract disease and myocardial ischemia. Avoiding prescribing these agents whenever possible is the first step. If they are utilized, it is important for health care professionals to use the lowest doses possible, closely monitor for signs and symptoms of anticholinergic adverse events and to deprescribe as tolerated.
Conflict of interest
The author declares no conflict of interest.
\n',keywords:"anticholinergic medications, Beer’s criteria, adverse drug events, older adults, geriatrics",chapterPDFUrl:"https://cdn.intechopen.com/pdfs/74017.pdf",chapterXML:"https://mts.intechopen.com/source/xml/74017.xml",downloadPdfUrl:"/chapter/pdf-download/74017",previewPdfUrl:"/chapter/pdf-preview/74017",totalDownloads:68,totalViews:0,totalCrossrefCites:0,dateSubmitted:"June 19th 2020",dateReviewed:"October 19th 2020",datePrePublished:"November 11th 2020",datePublished:null,dateFinished:null,readingETA:"0",abstract:"Anticholinergic medications are widely used in older adults and are a common source of adverse events in this population. Common drug classes include antiarrhythmics, antidepressants, antiemetics, first generation antihistamines, urinary incontinence antimuscarinic agents, antiparkinsonian agents, antipsychotics, antispasmodics, and skeletal muscle relaxants. These drugs have been associated with delirium, cognitive impairment, sedation, dizziness, falls, fracture, constipation, urinary retention, blurred vision, tachycardia and dry mouth. If possible, these drugs should be avoided in older adults or less toxic agents within the class should be utilized. This chapter will explore the mechanism of action of anticholinergic drugs at both the cellular and organ system level; discuss how to assess for anticholinergic drug burden; list medications with anticholinergic effects as identified in the Beer’s criteria on potentially inappropriate medication use in older adults; review anticholinergic drug–drug interactions; describe contraindications to the use of anticholinergic agents; and explore practical considerations such as the availability of these substances in nonprescription medications, their use at end of life and deprescribing.",reviewType:"peer-reviewed",bibtexUrl:"/chapter/bibtex/74017",risUrl:"/chapter/ris/74017",signatures:"Donna M. Lisi",book:{id:"9515",title:"Geriatrics",subtitle:null,fullTitle:"Geriatrics",slug:null,publishedDate:null,bookSignature:"Dr. Somchai Amornyotin",coverURL:"https://cdn.intechopen.com/books/images_new/9515.jpg",licenceType:"CC BY 3.0",editedByType:null,editors:[{id:"185484",title:"Dr.",name:"Somchai",middleName:null,surname:"Amornyotin",slug:"somchai-amornyotin",fullName:"Somchai Amornyotin"}],productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"}},authors:null,sections:[{id:"sec_1",title:"1. Introduction",level:"1"},{id:"sec_1_2",title:"1.1 Anticholinergic agents in nature",level:"2"},{id:"sec_1_3",title:"1.1.1 Hyoscyamine",level:"3"},{id:"sec_2_3",title:"1.1.2 Belladonna",level:"3"},{id:"sec_5",title:"2. Anticholinergics physiological effects",level:"1"},{id:"sec_5_2",title:"2.1 Mechanisms of action",level:"2"},{id:"sec_5_3",title:"2.1.1 Cellular effects",level:"3"},{id:"sec_6_3",title:"2.1.2 Organ system involvement",level:"3"},{id:"sec_6_4",title:"2.1.2.1. Brain",level:"4"},{id:"sec_7_4",title:"2.1.2.2. Eyes",level:"4"},{id:"sec_8_4",title:"2.1.2.3. Glands (salivary, sweat)",level:"4"},{id:"sec_9_4",title:"2.1.2.4. Heart",level:"4"},{id:"sec_10_4",title:"2.1.2.5. Lungs",level:"4"},{id:"sec_11_4",title:"2.1.2.6. Gastrointestinal tract",level:"4"},{id:"sec_12_4",title:"2.1.2.7. Bladder/genitourinary system",level:"4"},{id:"sec_13_4",title:"2.1.2.8. Skin",level:"4"},{id:"sec_17",title:"3. 3. Assessment of anticholinergic burden",level:"1"},{id:"sec_17_2",title:"3.1 Tune",level:"2"},{id:"sec_18_2",title:"3.2 Anticholinergic cognitive burden scale",level:"2"},{id:"sec_19_2",title:"3.3 Anticholinergic risk scale",level:"2"},{id:"sec_20_2",title:"3.4 Drug burden index",level:"2"},{id:"sec_22",title:"4. Medication classes with anticholinergic effects- the Beer’s list of potentially inappropriate medications in older adults",level:"1"},{id:"sec_22_2",title:"4.1 Antiarrhythmics",level:"2"},{id:"sec_23_2",title:"4.2 Antidepressants",level:"2"},{id:"sec_24_2",title:"4.3 Antiemetics",level:"2"},{id:"sec_25_2",title:"4.4 First generation antihistamines",level:"2"},{id:"sec_26_2",title:"4.5 Urinary incontinence antimuscarinics",level:"2"},{id:"sec_27_2",title:"4.6 Antiparkinsonian agents",level:"2"},{id:"sec_28_2",title:"4.7 Antipsychotics",level:"2"},{id:"sec_29_2",title:"4.8 Antispasmodics",level:"2"},{id:"sec_30_2",title:"4.9 Skeletal muscle relaxants",level:"2"},{id:"sec_32",title:"5. 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Drug interactions involving anticholinergic agents",level:"1"},{id:"sec_45_2",title:"6.1 Acetylcholinesterase inhibitors",level:"2"},{id:"sec_46_2",title:"6.2 Potassium",level:"2"},{id:"sec_47_2",title:"6.3 Carbonic anhydrase inhibitors",level:"2"},{id:"sec_48_2",title:"6.4 Opioids",level:"2"},{id:"sec_49_2",title:"6.5 Other “moderate” anticholinergic drug-drug interactions",level:"2"},{id:"sec_51",title:"7. Contraindications to the use of anticholinergic agents",level:"1"},{id:"sec_51_2",title:"7.1 Narrow angle glaucoma",level:"2"},{id:"sec_52_2",title:"7.2 Obstructive uropathy",level:"2"},{id:"sec_53_2",title:"7.3 Myasthenia gravis",level:"2"},{id:"sec_54_2",title:"7.4 Obstructive gastrointestinal tract disease",level:"2"},{id:"sec_55_2",title:"7.5 Myocardial ischemia",level:"2"},{id:"sec_57",title:"8. 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Relation between delirium and Anticholinergic Drug Burden in a cohort of hospitalized older patients: an observational study. Drugs Aging. 2019;36(1):85-91. doi:10.1007/s40266-018-0612-9'},{id:"B27",body:'Dyer AH, Murphy C, Segurado R, et al. for the NILVAD Study Group. Is ongoing anticholinergic burden associated with greater cognitive decline and dementia severity in mild to moderate Alzheimer’s disease?. J Gerontol A Biol Sci Med Sci. 2020;75(5):987-994. doi:10.1093/gerona/glz244'},{id:"B28",body:'Neal SR, Wood AD, Ablett AD, et al. Anticholinergic burden in middle-aged women and recurrent falls in later life: findings from the Aberdeen prospective osteoporosis screening study (APOSS). Ther Adv Drug Saf. 2020;11:2042098620929852. Published 2020 May 27. doi:10.1177/2042098620929852 Available from: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7273562/pdf/10.1177_2042098620929852.pdf Accessed 6/20/20'},{id:"B29",body:'Ali, S., Peterson, G.M., Bereznicki, L.R. et al. Association between anticholinergic drug burden and mortality in older people: a systematic review. Eur J Clin Pharmacol . 2020; 76: 319-335. Available from: https://doi.org/10.1007/s00228-019-02795-x Accessed 6/20/20'},{id:"B30",body:'Horn JP, Hansten P. Cholinergic-anticholinergic drug interactions. Pharm Times. 2005; 71(8): 46'},{id:"B31",body:'Donepezil (Aricept®) Prescribing Information. Woodcliff Lakes, NJ; Eisai Pharmaceutical. December 2018'},{id:"B32",body:'Galantamine (Razadyne®) Prescribing Information. Titusville, NJ; Janssen Pharmaceutical. March 2020'},{id:"B33",body:'Rivastigmine (Exelon®) Prescribing Information. East Hanover, NJ; Novartis Pharmaceutical. December 2018'},{id:"B34",body:'Atropine and potassium chloride. Drugs.com Drug Interactions Checker. Available from: https://www.drugs.com/drug-interactions/atropine-with-potassium-chloride-280-0-1912-0.html?professional=1 Accessed 8/15/20'},{id:"B35",body:'Atropine and potassium citrate. Drugs.com Drug Interactions Checker. Available from: https://www.drugs.com/drug-interactions/atropine-with-potassium-citrate-280-0-1914-0.html?professional=1 Accessed 8/15/20'},{id:"B36",body:'Zarowitz BJ. Oral solid potassium chloride and anticholinergic medications: a new drug interaction for an old drug?. Geriatr Nurs. 2006;27(6):329-333. doi:10.1016/j.gerinurse.2006.10.015'},{id:"B37",body:'Atropine and topiramate. Drugs.com Drug Interactions Checker. Available from: https://www.drugs.com/drug-interactions/atropine-with-topiramate-280-0-2216-0.html?professional=1 Accessed 8/15/20'},{id:"B38",body:'Atropine and zonisamide. Drugs.com Drug Interactions Checker. Available from: https://www.drugs.com/drug-interactions/atropine-with-zonisamide-280-0-2334-0.html?professional=1 Accessed 8/15/20'},{id:"B39",body:'Atropine and morphine. Drugs.com Drug Interactions Checker. Available from: https://www.drugs.com/drug-interactions/atropine-with-morphine-280-0-1656-0.html?professional=1 Accessed 8/15/20'},{id:"B40",body:'Atropine drug interactions. Drugs.com Drug Interactions Checker. Available from: https://www.drugs.com/drug-interactions/atropine-index.html?filter=2 Accessed 8/15/20'},{id:"B41",body:'Wu A, Khawaja AP, Pasquale LR, Stein JD. A review of systemic medications that may modulate the risk of glaucoma. Eye (Lond). 2020;34(1):12-28. doi:10.1038/s41433-019-0603-z'},{id:"B42",body:'Atropine disease interactions. Drugs.com Drug Interactions Checker. Available from: https://www.drugs.com/disease-interactions/atropine.html#:~:text=Anticholinergics%20(Includes%20atropine)%20%E2%86%94%20obstructive%20uropathy&text=In%20general%2C%20the%20use%20of,occur%20and%20may%20require%20catheterization. Accessed 8/15/20'},{id:"B43",body:'Jowkar A. Myasthenia Gravis. Medscape. Available from: https://emedicine.medscape.com/article/1171206-overview Accessed 6/15/20'},{id:"B44",body:'Anonymous. Learning Radiology. Colonic pseudo-obstruction. Available from: http://www.learningradiology.com/archives2011/COW%20465-Ogilvies/ogilviescorrect.htm Accessed 6/15/20'},{id:"B45",body:'Klabunde RA. Cardiovascular pharmacology concepts- Atropine (muscarinic receptor antagonist). Available from: https://www.cvpharmacology.com/antiarrhy/atropine Accessed 6/15/20'},{id:"B46",body:'Myint PK, Fox C, Kwok CS, Luben RN, Wareham NJ, Khaw KT. Total anticholinergic burden and risk of mortality and cardiovascular disease over 10 years in 21,636 middle-aged and older men and women of EPIC-Norfolk prospective population study. Age Ageing. 2015;44(2):219-225. doi:10.1093/ageing/afu185'},{id:"B47",body:'Dweik RA. Role of anticholinergic therapy in COPD. Up-to- Date.com Available from: https://www.uptodate.com/contents/role-of-anticholinergic-therapy-in-copd?search=Anticholinergic%20therapy%20in%20copd&source=search_result&selectedTitle=1~150&usage_type=default&display_rank=1#H91301605 Literature review current through: Jul 2020. | This topic last updated: Jun 06, 2019. Accessed 8/15/20'},{id:"B48",body:'Yuan W, Nie S, Wang H, Xu Q , Jia N. Anticholinergics aggravate the imbalance of the autonomic nervous system in stable chronic obstructive pulmonary disease. BMC Pulm Med. 2019;19(1):88. Published 2019 May 9. doi:10.1186/s12890-019-0848-0 Available from: https://bmcpulmmed.biomedcentral.com/track/pdf/10.1186/s12890-019-0848-0 Accessed 8/15/20'},{id:"B49",body:'Vetrano DL, La Carpia D, Grande G, et al. Anticholinergic medication burden and 5-Year risk of hospitalization and death in nursing home elderly residents with coronary artery disease. J Am Med Dir Assoc. 2016;17(11):1056-1059. doi:10.1016/j.jamda.2016.07.012'},{id:"B50",body:'Anonymous. Antimuscarinic therapy and cardiac safety in older patients with overactive bladder: what clinicians need to Kkow. Consultant360. 2007 February; 15 Supplement 02A. Available from: https://www.consultant360.com/articles/antimuscarinic-therapy-and-cardiac-safety-older-patients-overactive-bladder-what-clinicians Accessed 6/20/20'},{id:"B51",body:'Rochon PA. Drug prescribing for older adults. Up-to-Date. https://www.uptodate.com/contents/drug-prescribing-for-older-adults#H6.Literature review current through: Jul 2020. | This topic last updated: Jun 08, 2020. Available from: https://www.uptodate.com/contents/drug-prescribing-for-older-adults Accessed 8/1/20'},{id:"B52",body:'Gray SL, Anderson ML, Dublin S, et al. Cumulative use of strong anticholinergics and incident dementia: a prospective cohort study. JAMA Intern Med. 2015;175(3):401-407. doi:10.1001/jamainternmed.2014.7663'},{id:"B53",body:'Coggins MD. Antihistamine risks. Today’s Geriatric Medicine. 2013; 6(2): 6. Available from: https://www.todaysgeriatricmedicine.com/archive/0313p6.shtml Accessed 8/1/20'},{id:"B54",body:'Albert SM, Roth T, Vitiello M, et al. Over-the-counter sleep aids. Today’s Geriatric Medicine. 2016. 9(2): 20. Available from: https://www.todaysgeriatricmedicine.com/archive/MA16p20.shtml Accessed 8/1/20'},{id:"B55",body:'Holden RJ, Srinivas P, Campbell NL, et al. Understanding older adults’ medication decision making and behavior: A study on over-the-counter (OTC) anticholinergic medications. Res Social Adm Pharm. 2019;15(1):53-60. doi:10.1016/j.sapharm.2018.03.002'},{id:"B56",body:'Albert RH. End-of-Life Care: common symptoms. Am Fam Physician. 2017;95(6):356-361'},{id:"B57",body:'Kolb H, Snowden A, Stevens E. Systematic review and narrative summary: Treatments for and risk factors associated with respiratory tract secretions (death rattle) in the dying adult. J Adv Nurs. 2018;74(7):1446-1462. doi:10.1111/jan.13557'},{id:"B58",body:'Ailabouni N, Mangin D, Nishtala PS. DEFEAT-polypharmacy: deprescribing anticholinergic and sedative medicines feasibility trial in residential aged care facilities. Int J Clin Pharm. 2019;41(1):167-178. doi:10.1007/s11096-019-00784-9'},{id:"B59",body:'Gill SS, Mamdani M, Naglie G, et al. A prescribing cascade involving cholinesterase inhibitors and anticholinergic drugs. Arch Intern Med. 2005;165(7):808-813. doi:10.1001/archinte.165.7.808'},{id:"B60",body:'American Academy of Nursing. Don’t administer “prn” (i.e., as needed) sedative, antipsychotic or hypnotic medications to prevent and/or treat delirium without first assessing for, removing and treating the underlying causes of delirium and using nonpharmacologic delirium prevention and treatment approaches. Choosing Wisely. June 12, 2016. Available from: https://www.choosingwisely.org/clinician-lists/nursing-medications-to-prevent-or-treat-delirium/ Accessed 8/15/20'},{id:"B61",body:'American Urogynecologic Society. Avoid using anticholinergic medication to treat overactive bladder in women older than 70. Choosing Wisely. June 24, 2020. Available from: https://www.choosingwisely.org/clinician-lists/augs-avoid-using-anticholinergic-medication-to-treat-overactive-bladder-in-women-older-than-70/ Accessed 8/15/20'}],footnotes:[],contributors:[{corresp:"yes",contributorFullName:"Donna M. Lisi",address:"drdonnalisi@gmail.com",affiliation:'
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XML Typesetting and pagination - web (PDF, HTML) and print files preparation
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