Research article Topical Sections

Dual inoculation of soybean with Rhizophagus irregularis and commercial Bradyrhizobium japonicum increases nitrogen fixation and growth in organic and conventional soils

  • Soil amendment with beneficial microorganisms is gaining popularity among farmers to alleviate the decline of soil fertility and to increase food production and maintain environmental quality. However, farm management greatly influence soil microbial abundance and function, which overly affects crop growth and development. In this work, greenhouse experiments involving soybeans were conducted to evaluate the effects of bradyrhizobia and arbuscular mycorrhizal fungi (AMF) dual inoculation on nodulation, AMF root colonization, growth and nutrient acquisition under contrasting farming systems. The experimental treatments were AMF and/or bradyrhizobia inoculation and dual inoculation on SC squire soybean variety. The exotic AMF inoculants used were Funneliformis mosseae (BEG 12) and Rhizophagus irregularis (BEG 44) while bacteria were commercial Bradyrhizobium japonicum (USDA110) and native bradyrhizobia isolates. Experiments with soil samples from organic and conventional farms were set out using a completely randomized design with three replicates. The results demonstrated that bradyrhizobia and AMF dual inoculation consistently and significantly enhanced soybean nodule dry weight (NDW), shoot dry weight (SDW) and AMF root colonization compared with individual bradyrhizobia, AMF and non-inoculated control. Moreover, organic soil significantly (p = 0.001) increased soybean SDW, NDW and AMF root colonization compared to conventional soil. Remarkably, shoot nutrients content differed in organic and conventional farming where, shoot nitrogen, phosphorus, potassium and organic carbon were higher in organic farming than the latter. Among individual inoculants, Rhizophagus irregularis out-performed Funneliformis mosseae, while commercial Bradyrhizobium japonicum showed higher performance than native bradyrhizobia. Our results demonstrated the importance of organic farming, AMF and bradyrhizobia dual inoculation in enhancing soybean growth and nutrient acquisition. However, field trials should be assessed to determine the good performance of bradyrhizobia and AMF dual inoculation in organic farming before being popularized and adopted by farmers as a sustainable agronomical management strategy to increase soil fertility and food productivity.

    Citation: Nicholas Mawira Gitonga, Gilbert Koskey, Ezekiel Mugendi Njeru, John M. Maingi, Richard Cheruiyot. Dual inoculation of soybean with Rhizophagus irregularis and commercial Bradyrhizobium japonicum increases nitrogen fixation and growth in organic and conventional soils[J]. AIMS Agriculture and Food, 2021, 6(2): 478-495. doi: 10.3934/agrfood.2021028

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  • Soil amendment with beneficial microorganisms is gaining popularity among farmers to alleviate the decline of soil fertility and to increase food production and maintain environmental quality. However, farm management greatly influence soil microbial abundance and function, which overly affects crop growth and development. In this work, greenhouse experiments involving soybeans were conducted to evaluate the effects of bradyrhizobia and arbuscular mycorrhizal fungi (AMF) dual inoculation on nodulation, AMF root colonization, growth and nutrient acquisition under contrasting farming systems. The experimental treatments were AMF and/or bradyrhizobia inoculation and dual inoculation on SC squire soybean variety. The exotic AMF inoculants used were Funneliformis mosseae (BEG 12) and Rhizophagus irregularis (BEG 44) while bacteria were commercial Bradyrhizobium japonicum (USDA110) and native bradyrhizobia isolates. Experiments with soil samples from organic and conventional farms were set out using a completely randomized design with three replicates. The results demonstrated that bradyrhizobia and AMF dual inoculation consistently and significantly enhanced soybean nodule dry weight (NDW), shoot dry weight (SDW) and AMF root colonization compared with individual bradyrhizobia, AMF and non-inoculated control. Moreover, organic soil significantly (p = 0.001) increased soybean SDW, NDW and AMF root colonization compared to conventional soil. Remarkably, shoot nutrients content differed in organic and conventional farming where, shoot nitrogen, phosphorus, potassium and organic carbon were higher in organic farming than the latter. Among individual inoculants, Rhizophagus irregularis out-performed Funneliformis mosseae, while commercial Bradyrhizobium japonicum showed higher performance than native bradyrhizobia. Our results demonstrated the importance of organic farming, AMF and bradyrhizobia dual inoculation in enhancing soybean growth and nutrient acquisition. However, field trials should be assessed to determine the good performance of bradyrhizobia and AMF dual inoculation in organic farming before being popularized and adopted by farmers as a sustainable agronomical management strategy to increase soil fertility and food productivity.



    In recent years improvements in the diagnosis and treatment of cancer have increased survival rates. While breast cancer, the most common type of cancer [1], has seen a significantly increasing trend in age-standardized incidence rates in Chinese women [2], the 5-year relative survival rate has increased from 73.1% to 82.0% from 2003 to 2015 [3].Owing to its untreatable nature and the common long-term exposure of patients to the illness, breast cancer tends to evoke significant psychological stress and disorders in survivors, of which depression is particularly common [4]. Two studies have shown that the prevalence of depression in cancer patients is several-fold that of the general population [5],[6], while a recent meta-analysis found that the global prevalence of depression in breast cancer patients was 32.2% [7].

    Depression in female breast cancer survivors, even if it has not been properly diagnosed (e.g. the survivor has been experiencing specific depressive symptoms), may interfere with their ability to effectively cope with the disease, reduce treatment adherence, decrease quality of life, and increase the risk of recurrence and mortality [8][12]. Therefore, attention should be paid to female breast cancer survivors with depressive symptoms, regardless of a clinical diagnosis.

    Women in different age groups may face various challenges when coping with breast cancer, resulting in different depressive symptoms by age [13],[14]. For example, younger female breast cancer patients may experience psychological stress from life-stage-related needs that occur at a younger age (e.g. employment, childcare) [15] and from perceptions regarding the fact that diagnosis and treatment may cause the partial loss of their female identity, infertility, premature menopause, and sexual dysfunction [16][19]. Middle-aged breast cancer female survivors normally face psychological stress from having to take care of their parents—usually in later adulthood—and from considering the potential impact of breast cancer on their children [13]. Older female adults who incur normal ageing and comorbidities (e.g. chronic diseases, age-related diseases, and geriatric problems) tend to be affected by cancer symptoms, leading to the further decline of their bodily functions [20]. Therefore, various age-related sources of stress may lead to inconsistency and heterogeneity when comparing the depressive symptoms of female breast cancer patients by age.

    A few studies have analysed depressive symptoms in female breast cancer patients of different ages, but their sample had a limited age range (e.g. under 35 [21] or over 60 [22]), or their findings only compared the total scores for depressive symptoms between two age groups (e.g. ≤45 vs. 55–70 [23], ≤50 vs. >50 [24], or 18–39 vs. >39 [14]). A prior study corroborated this and concluded that, although research on the topic provided valuable evidence, researchers tend to only use the total or cut-off score of the depressive symptom scale, which conceals the heterogeneity of depressive symptoms in female cancer patients [25]. For example, even if two participants have the same total score on the depressive symptom scale, their scores may be based on different symptoms (e.g., suicidal ideation versus fatigue).

    Thus, we considered latent class analysis (LCA) appropriate to explore the subtypes of breast cancer related depression while considering the potential correlation between different depressive symptoms. The LCA is a type of person-centred analytical method that focuses on distinguishing a heterogeneous population that has co-occurring symptoms to reveal potential symptomatic heterogeneity [26]. It may be helpful when trying to objectively identify heterogeneous depressive subtypes in clinical practice; doing so may provide clinicians with more information on how to develop effective prevention and intervention programmes for female breast cancer patients with different depressive subtypes.

    To the best of our knowledge, no prior study to date has detailed the characteristics of depressive symptoms by multiple age groups or used depressive subtypes to explore depressive symptom heterogeneity among female breast cancer patients. Thus, this study aimed to (1) describe the characteristics of depressive symptoms, (2) identify depressive subtypes, and (3) explore the relationship between depressive subtypes and age in Chinese female breast cancer patients.

    This study applied a cross-sectional design.

    Using the convenient sampling method, we recruited female breast cancer patients by contacting three tertiary comprehensive hospitals in Shandong Province, China from April 2013 through June 2019. The inclusion criteria were as follows: (1) at least 18 years of age; (2) has been diagnosed with breast cancer; and (3) able to understand and independently answer the questionnaire. The initial sample of 573 potential participants was reduced to 566 after excluding those who did not complete a Patient Health Questionnaire-9 (PHQ-9) or indicate age data.

    First, eligible patients were identified using medical records from cancer wards. Then, they were asked by the investigators, who were graduate students in the psychology department, whether or not they were willing to participate in the study. Participants who agreed gave their written informed consent before completing the questionnaire. After receiving approval from the cancer ward, we collected cancer stage data from patients' medical records. Other questionnaire items were answered through a self-report.

    All procedures followed were in accordance with the ethical standards of the responsible committee on human experimentation (institutional and national) and the Helsinki Declaration of 1975, as revised in 2000. The survey formed part of a research project on the mental health status of cancer patients, which was approved by the ethics committee of the School of Nursing at the Shandong University (Ethical Approval Number: 2016-R020).

    We measured depressive symptoms using the Chinese version of the 9-item PHQ-9, which the American Society of Clinical Oncology recommends for the evaluation of depressive symptoms in cancer patients [27]. The PHQ-9 assesses the symptoms of participants in the 2 weeks prior to the questionnaire application [28]. It is rated on a 4-point scale ranging from 0 (not at all) to 3 (nearly every day). The nine items assess nine depressive symptoms based on the diagnostic criteria for major depressive disorder described in the DSM-IV. Participants' total score in this measure represents depressive severity, categorised as minimal (0–4), mild (5–9), moderate (10–14), and moderately severe/severe depressive symptoms (≥15). The Cronbach's alpha in this study was 0.824.

    The covariate variables were age (≤35, 36–44, 45–59, and ≥60 years old), race (Han/other minorities), marital status (single/partnered), residential area (urban/rural), education level (high school or lower/above high school), occupation (retired/unemployed/employed), mode of payment of medical care (own expense/health insurance), religion (religious/not religious), and cancer stage (0/I/II/III/IV).

    All descriptive analyses were performed using SPSS (version 26.0). This study compared participants' age by sociodemographic, clinical (e.g. cancer stage), and depressive characteristics (i.e. participants' mean scores for each symptom and depressive severity category based on the total PHQ-9 score).

    We used a nonparametric analysis to examine the differences among the nine depressive symptoms and the depressive severity by age group. Univariate analyses, including ANOVA and Chi-square test, were used to determine differences in depressive subtypes by socio-demographic and clinical characteristics. Subsequently, analysis of variance and post-hoc tests were used to assess the differences in depressive subtypes by the nine depressive symptoms. We set the statistical significance for all analyses at P < 0.05.

    We used LCA to identify the differences in depressive subtypes among female breast cancer patients, namely, whether or not there were homogeneous clusters in heterogeneous groups. We recorded the nine symptoms measured in the PHQ-9 as binary variables (1 = have depressive symptoms vs. 0 = no depressive symptoms) and included them in the model.

    We tested goodness of fit for a series of models (i.e. 1–6 classes) using Mplus (version 7.4). By analysing a combination of statistical indicators—including Akaike information criterion, Bayesian information criterion, sample size adjustment BIC, Vuong-Lo-Mendell-Rubin likelihood ratio test, bootstrap likelihood ratio test, and clinical interpretability [29]—we deemed that the four-class model showed optimal fit to the data.

    The participants had an average age of 45.6 (SD = 11.5) years; most were married (93.3%); unemployed (42.4%); and had medical insurance (95.6%). Approximately half lived in urban areas (57.6%). A majority had stage II cancer (41.3%), followed by stage IV (20.8%), III (20.7%), I (13.8%), then 0 (3.4%). The four age groups differed in many other sociodemographic and clinical characteristics, which are described in detail in Table 1.

    Table 1.  Sociodemographic and clinical characteristics of the total sample and different age groups (N = 566).
    Variables Total (n = 566) ≤35 years old (n = 95) 36–44 years old (n = 223) 45–59 years old (n = 151) ≥60 years old (n = 97) P
    Age, x(SD)/n(%) 45.6 (11.5) 95 (16.8%) 266 (47.0%) 108 (19.1%) 97 (17.1%)
    Marriage 0.022
     Single 38 (6.7%) 10 (10.5%) 15 (6.7%) 3 (2.0%) 10 (10.3%)
     With partner 528 (93.3%) 85 (89.5%) 208 (93.3%) 148 (98.0%) 87 (89.7%)
    Residence 0.112
     Urban area 326 (57.6%) 55 (57.9%) 139 (61.7%) 75 (49.7%) 57 (58.8%)
     Rural area 240 (42.4%) 40 (42.1%) 84 (38.3%) 76 (50.3%) 40 (41.2%)
    Education <0.001
     ≤High school 321 (56.7%) 31 (32.6%) 113 (53.0%) 101 (66.9%) 76 (78.4%)
     >High school 245 (43.3%) 64 (67.4%) 110 (47.0%) 50 (33.1%) 21 (21.6%)
    Occupation <0.001
     Retirement 59 (10.4%) 1 (1.3%) 0 (0.0%) 13 (8.6%) 45 (47.9%)
     Unemployed 235 (41.5%) 28 (35.4%) 88 (39.5%) 75 (49.7%) 44 (46.8%)
     Employed 207 (36.6%) 50 (63.3%) 112 (50.2%) 40 (26.5%) 5 (5.3%)
    Payment 0.390
     Own expense 15 (2.7%) 2 (2.1%) 9 (4.0%) 3 (2.0%) 1 (1.0%)
     With health care 541 (95.6%) 93 (97.9%) 210 (94.2%) 144 (95.4%) 94 (99.0%)
     Religion 77 (13.6%) 14 (14.7%) 36 (16.1%) 16 (10.6%) 11 (11.3%) 0.405
     Race (Han) 557 (98.4%) 93 (97.9%) 265 (99.6%) 103 (95.4%) 96 (99.0%) 0.167
    Cancer stage <0.001
     Stage 0 19 (3.4%) 2 (2.1%) 8 (3.6%) 1 (0.7%) 8 (8.2%)
     Stage Ⅰ 78 (13.8%) 13 (13.7%) 35 (15.7%) 9 (6.0%) 21 (21.7%)
     Stage Ⅱ 234 (41.3%) 41 (43.2%) 111 (49.8%) 38 (25.2%) 44 (45.4%)
     Stage Ⅲ 117 (20.7%) 23 (24.2%) 45 (20.2%) 36 (23.8%) 13 (13.4%)
     Stage Ⅳ 118 (20.8%) 16 (16.8%) 24 (10.8%) 67 (44.4%) 11 (11.3%)

    Note: Indicates that the numbers/percentages may not add up to the total, due to missing data.

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    Of all participants, 22.8% had moderate to severe depressive symptoms (i.e. PHQ-9 scores ≥10). The percentage for moderately severe/severe depressive symptoms (i.e. PHQ-9 scores ≥15) was 2.1% for participants aged ≥35, 9.4% for those aged 36–44, 5.3% for those aged 45–59, and 9.3% for those aged ≥60 (Figure 1).

    Figure 1.  The severity of depressive symptoms varies among breast cancer patients of different ages according to the PHQ-9 cutoff scores. Minimal depression (PHQ-9 score 0–4), mild (5–9), moderate (10–14) and moderately severe/severe (≥15). PHQ-9, Patient Health Questionnaire-9.
    Table 2.  Characteristics of depression in different age groups (N = 566).
    M (QL;QU)/n(%) Total (n = 566) ≤35 years 36–44 years 45–59 years ≥60 years P
    Anhedonia 1.0 (0.0;1.0) 1.0 (0.0;1.0) 1.0 (0.0;1.0) 1.0 (1.0;1.0) 1.0 (0.0;1.0) 0.095
    Sadness 1.0 (0.0;1.0) 1.0 (0.0;1.0) 1.0 (0.0;1.0) 1.0 (1.0;2.0) 1.0 (0.0;1.0) 0.001
    Sleep disturbances 1.0 (1.0;1.0) 1.0 (1.0;1.0) 1.0 (0.0;1.0) 1.0 (1.0;2.0) 1.0 (0.0;2.0) 0.328
    Fatigue 1.0 (1.0;1.0) 1.0 (1.0;1.0) 1.0 (1.0;2.0) 1.0 (1.0;1.0) 1.0 (0.0;1.0) 0.286
    Appetite disturbances 1.0 (0.0;1.0) 1.0 (0.0;1.0) 1.0 (0.0;1.0) 1.0 (1.0;1.0) 1.0 (0.0;1.0) 0.178
    Guilt or worthlessness 1.0 (0.0;1.0) 1.0 (0.0;1.0) 1.0 (0.0;1.0) 1.0 (0.0;1.0) 0.0 (0.0;1.0) 0.064
    Poor concentration 0.0 (0.0;1.0) 1.0 (0.0;1.0) 1.0 (0.0;1.0) 0.0 (0.0;1.0) 0.0 (0.0;1.0) 0.003
    Psychomotor agitation or retardation 0.0 (0.0;1.0) 0.0 (0.0;1.0) 0.0 (0.0;1.0) 0.0 (0.0;1.0) 0.0 (0.0;1.0) 0.003
    Suicidal ideation 0.0 (0.0;0.25) 0.0 (0.0;0.0) 0.0 (0.0;1.0) 0.0 (0.0;0.0) 0.0 (0.0;0.5) <0.001
    PHQ total 7.0 (4.0;9.0) 7.0 (4.0;9.0) 7.0 (4.0;10.0) 7.0 (4.0;9.0) 5.0 (2.0;9.0) 0.034
    PHQ average 0.8 (0.4;1.0) 0.8 (0.4;1.0) 0.8 (0.4;1.1) 0.8 (0.4;1.0) 0.6 (0.2;1.0) 0.034
    PHQ-9 (≥5) 384 (67.8%) 68 (71.6%) 153 (68.6%) 112 (74.2%) 51 (52.6%) 0.003
    PHQ (≥10) 129 (22.8%) 17 (17.9%) 63 (28.3%) 29 (19.2%) 20 (20.6%) 0.092
    PHQ degree 0.001
    Minimal 182 (32.2%) 27 (28.4%) 70 (31.4%) 39 (25.8%) 46 (47.4%)
    Mild 255 (45.1%) 51 (53.7%) 90 (40.4%) 83 (55.0%) 31 (32.0%)
    Moderate 89 (15.7%) 15 (15.8%) 42 (18.8%) 21 (13.9%) 11 (11.3%)
    Moderately severe/severe 40 (7.1%) 2 (2.1%) 21 (9.4%) 8 (5.3%) 9 (9.3%)

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    There were significant differences were found for the following symptoms: sadness, poor concentration, psychomotor agitation/retardation, and suicidal ideation. In addition, participants of different age groups differed in the severity of depressive symptoms (see Table 2).

    Considering goodness of fit and clinical interpretability, we determined four depressive subtypes: Class 1, 2, 3, and 4; these accounted for 27%, 16%, 30%, and 27% of all participants, respectively (Table 3). Figure 2 and Table 4 show the probability and descriptive data of participants' scores for the nine depressive symptoms in the PHQ-9 by depressive subtype. Class 4 represented the highest probability (i.e. probability of experiencing a symptom) and score for all nine symptoms; thus, it was named the severe symptoms group; Class 3 represented a relatively high probability and score for all symptoms—except for psychomotor agitation/retardation and suicidal ideation, which showed lower levels—and thus, it was named the relatively severe symptoms group; Class 2 represented a medium probability and score for all nine symptoms—except for psychomotor agitation/retardation and suicidal ideation, which showed higher levels—and thus, it was named the moderate symptoms group; and Class 1 represented the lowest probability and score for all nine symptoms—except for psychomotor agitation/retardation, which showed higher levels—and therefore, it was named the mild symptoms group. There were significant differences between these four classes and the nine depressive symptoms assessed using the PHQ-9 (Table 4).

    Table 3.  Model fit indices derived from latent class analysis on models with 1–6 classes.
    Model K Log (L) AIC BIC aBIC entropy LMR BLRT Class probability
    1 9 −3096.85 6211.70 6250.75 6222.18
    2 19 −2540.85 5119.70 5202.14 5141.82 0.86 0.00 0.00 368/198(0.65/0.35)
    3 29 −2441.40 4940.80 5066.62 4974.56 0.82 0.00 0.00 152/259/155(0.27/0.46/0.27)
    4 39 −2402.15 4882.30 5051.51 4927.70 0.78 0.02 0.00 155/91/170/150(0.27/0.16/0.30/0.27)
    5 49 −2368.00 4833.98 5046.57 4891.02 0.80 0.002 0.00 49/141/148/130/98(0.09/0.25/0.26/0.23/0.17)
    6 59 −2354.61 4827.22 5083.20 4895.90 0.81 0.46 0.09 107/38/146/52/77/146(0.19/0.07/0.26/0.09/0.14/0.26)

    Note: Abbreviations: AIC, Akaike information criterion; BIC, Bayesian information criterion; aBIC, sample size adjusted BIC; LMR, Vuong-Lo-Mendell-Rubin likelihood ratio test. BLRT, bootstrapped likelihood ratio test; Bold values indicates that a four-class model was determined as optimal one.

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    Figure 2.  The five-class model and probability of nine depressive symptoms within each class (n = 566). Class 4: severe symptoms group; Class 3: relatively severe group (with lower concentration, psychomotor agitation/retardation and suicidal ideation); Class 2: moderate symptoms group (with higher poor concentration, psychomotor agitation/retardation and suicidal ideation); Class 1: mild symptoms group.
    Table 4.  The descriptive scores of nine depressive symptoms, M(SD).
    Items Class 4 (n = 150) Class 3 (n = 170) Class 2 (n = 91) Class 1 (n = 155) F Posthoc comparisons
    Anhedonia 1.40 (0.63) 1.19 (0.57) 0.86 (0.68) 0.26 (0.52) 109.39* c4>c3>c2>c1
    Sadness 1.41 (0.65) 1.42 (1.62) 0.75 (0.59) 0.19 (0.43) 53.55* c3≈c4>c2>c1
    Sleep disturbances 1.49 (0.70) 1.31 (1.03) 1.04 (0.87) 0.43 (0.62) 49.75* c4>c3>c2>c1
    Fatigue 1.59 (0.71) 1.20 (0.53) 1.13 (0.70) 0.49 (0.70) 73.87* c4>c3≈c2>c1
    Appetite disturbances 1.37 (0.68) 1.06 (0.81) 0.88 (0.73) 0.21 (0.43) 81.11* c4>c3>c2>c1
    Guilt or worthlessness 1.50 (0.67) 0.76 (0.67) 0.34 (0.67) 0.07 (0.26) 163.33* c4>c3>c2>c1
    Poor concentration 1.35 (0.64) 0.34 (0.61) 1.01 (0.71) 0.07 (0.26) 159.56* c4>c2>c3>c1
    Psychomotor agitation or retardation 1.30 (0.69) 0.00 (0.00) 0.81 (0.65) 0.04 (0.22) 288.45* c4>c2>c1≈c3
    Suicidal ideation 0.89 (0.73) 0.07 (0.30) 0.31 (0.61) 0.03 (0.16) 102.77* c4>c2>c3≈c1
    PHQ-9 total score 12.29 (3.85) 7.36 (2.81) 7.13 (2.29) 1.79 (1.47) 362.34* c4>c3≈c2>c1

    Note: *P < 0.01. Class 4: severe symptoms group; Class 3: relatively severe group (with lower concentration, psychomotor agitation/retardation and suicidal ideation); Class 2: moderate symptoms group (with higher poor concentration, psychomotor agitation/retardation and suicidal ideation); Class 1: mild symptoms group.

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    Participants' depressive subtype distribution by age is presented in Figure 3. In the ≤35 age group, all four classes showed similar ratios (Class 1–4 were 28.4%, 24.2%, 24.2%, and 23.2%, respectively). In the 36–44 age group, 33.2% of the participants were categorized in Class 3, which was the highest proportion. In the 45–59 age groups, Class 4 showed the highest ratios (i.e. 50.3%). In the ≥60 age group, more than 50 percent of participants were categorized in the milder depressive subtypes, with Class 1 and 2 accounting for 12.4% percent and 42.3%, respectively.

    Figure 3.  The subtypes distribution in the four stage of age. Class 4: severe symptoms group; Class 3: relatively severe group (with lower concentration, psychomotor agitation/retardation and suicidal ideation); Class 2: moderate symptoms group (with higher poor concentration, psychomotor agitation/retardation and suicidal ideation); Class 1: mild symptoms group.

    This study aimed to analyse the depressive characteristics of breast cancer patients in different adult age groups. We were able to identify four latent depressive subtypes, and their distribution differed by age group. To the best of our knowledge, this is the first study to analyse and use potential depressive subtypes to explore the heterogeneity of depressive symptoms across various adult age groups in female breast cancer patients.

    The incidence of moderate to severe depressive symptoms in our sample was 22.8%; this number was similar to that reported in a previous study, which showed that such incidence in female breast cancer patients was 2–3 times higher than that in the general population [14]. Additionally, our analyses highlighted differences in single-symptom expression by age—specifically, such differences were observed for sadness, poor concentration, psychomotor agitation/retardation, and suicidal ideation. This result is supported by that of a previous study, which reported on individuals' symptomatology heterogeneity [25]. Another study showed that the age-related difference in depressive symptoms among female cancer patients is due to the impact of cancer and its treatment on specific areas of women's life (e.g. work, sex, and entertainment) [15]. Therefore, stakeholders involved in diminishing the risk of depression in breast cancer patients should focus on depressive symptom differences by age. Moreover, in-depth analyses on the causes of such age-related differences must be conducted and interventions aimed at groups characterised by specific depressive subtypes must be applied to reduce the risk of depression.

    We were able to identify four depressive subtypes: severe (Class 4), relatively severe (Class 3), moderate (Class 2), and mild depressive symptoms (Class 1). Differences in the severe, moderate, and mild groups were mainly because of depressive symptom severity, whereas the relatively severe group differed from the other three primarily owing to the presence of severe physio-somatic symptoms alongside lower psychomotor agitation/retardation and suicidal ideation. This suggests that, upon the application of LCA, both symptom characteristics and severity were important variables in determining depressive subtypes. Our results are somewhat consistent with those of previous studies showing symptomatic severity to be a crucial discriminating aspect of depressive subtypes [30],[31]. The relatively severe group with lower psychomotor agitation/retardation and suicidal ideation was discovered in this research, and it was also the most common subtype in our sample. A study that promoted a factorial analysis on the items of the PHQ-9 pointed out that the items on poor concentration and psychomotor agitation/retardation may neither belong to an affective-cognitive nor a somatic component [32]. This item contains two contradictory characteristics of psychological and cannot be clearly classified as one of the factors, which also confirms the heterogeneity of depressive symptoms. Previous studies have suggested that suicidal ideation in breast cancer patients is particularly linked to genetic characteristics (brain-derived neurotrophic factor methylation, BDNF met allele) [33]. Along with another study [34], these findings and our results suggest that depressive symptoms do not have a single structure but are comprised of different subtypes that seem to have different pathophysiological basis.

    The latent depressive subtypes we observed showed different distributions by age group. Specifically, patients aged 45–59 were more likely to have severe depressive subtype; those aged 36–44 were more likely to have relatively severe depressive subtype; those over 60 were more likely to have moderate symptoms group; and those under 35 were more likely to have mild depressive subtype. After a literature review, it is evident why female breast cancer patients aged 36 and over showed a greater tendency to experience relatively severe depressive symptoms. A study showed that perimenopausal women over 40 may suffer from poorer sleep quality and greater mood problems owing to fluctuating hormone levels, both of which are also depressive symptoms [35]. Another study revealed that breast cancer treatment may lead to menopause in women, while the stress caused by cancer and its treatment may influence depressive symptom severity [36]. When combined with the social responsibilities that female breast cancer survivors tend to have, such as caring for parents and children, the situation can lead them to experience the most severe depressive symptoms [13]. Therefore, stakeholders in the well-being of female breast cancer patients should place greater emphasis on their psychological status based on age. In clinical practice, the different depressive subtypes—and their relationships with specific age groups—described in this study may help stakeholders (e.g. physicians, psychologists, nurses) more accurately identify groups with similar symptoms across the cancer population. Greater accuracy could thereby facilitate the development and application of appropriate group interventions aimed at dealing with similar depressive symptoms.

    Despite the contributions highlighted above, this study had several limitations. First, the reliability and validity of individual symptom measurement tools—including those of the PHQ-9, which was utilised in this study—remain imprecise. Nonetheless, one advantage of the PHQ-9 is that all entries have the same response categories, which theoretically does not affect the comparability between different symptoms [33]. Future research should investigate scales for specific symptoms (e.g. suicidal ideation), such as the Suicide Severity Scale to assess suicidal behaviour [37]. Second, although the LCA assigns individuals to subtypes according to probability and evaluates the goodness of fit of different models based on statistical criteria, one study has shown that subjectivity in this procedure still exists [29]; therefore, we cannot exclude type I errors (i.e. false positives). Third, we applied convenient sampling and utilised a cross-sectional design, which are methodologies that limit the generalizability of our findings; thus, future research should consider larger populations, stratified random sampling, and a longitudinal design when analysing depressive symptoms in female breast cancer patients.

    This study described the characteristics of depressive symptoms in Chinese female breast cancer patients across different ages and identified four depressive subtypes. Our results support the heterogeneity of depressive symptoms; thus, we provide data on how to identify individual symptoms in different age groups and patients with similar symptoms characteristics. We hope that this study helps in identifying the potential mechanisms behind these relationships and develop targeted interventions for patients with a specific depressive subtype.



    [1] Masciarelli O, Llanes A, Luna V (2014) A new PGPR co-inoculated with Bradyrhizobium japonicum enhances soybean nodulation. Microbiol Res 169: 609-615. doi: 10.1016/j.micres.2013.10.001
    [2] Maphosa Y, Jideani VA (2017) The role of legumes in human nutrition. Functional Food-Improve Health through Adequate Food. Intech.
    [3] Medic J, Atkinson C, Hurburgh CR (2014) Current knowledge in soybean composition. J Am Oil Chem Soc 91: 363-384. doi: 10.1007/s11746-013-2407-9
    [4] Da Silva Júnior EB, Favero VO, Xavier GR, et al. (2018) Rhizobium inoculation of cowpea in Brazilian cerrado increases yields and nitrogen fixation. Agron J 110: 722-727. doi: 10.2134/agronj2017.04.0231
    [5] Jackson AS (2016) A brief history of soybean production in Kenya. Res J Agric Environ Manage 5: 58-64.
    [6] Muniu FK (2017) Characterization and evaluation of local cowpea accessions and their response to organic and inorganic nitrogen fertilizers in coastal Kenya. Doctoral dissertation, University of Nairobi.
    [7] Njeru EM, Maingi JM, Cheruiyot R, et al. (2013) Managing soybean for enhanced food production and soil bio-fertility in smallholder systems through maximized fertilizer use efficiency. Int J Agric For 3: 191-197.
    [8] Ndungu SM, Messmer MM, Ziegler D, et al. (2018) Cowpea (Vigna unguiculata L. Walp) hosts several widespread bradyrhizobial root nodule symbionts across contrasting agro-ecological production areas in Kenya. Agr Ecosyst Environ 261: 161-171.
    [9] Lobell DB, Field CB (2007) Global scale climate-crop yield relationships and the impacts of recent warming. Environ Res Lett 2: 014002. doi: 10.1088/1748-9326/2/1/014002
    [10] Ku YS, Au-Yeung WK, Yung YL, et al. (2013) Drought stress and tolerance in soybean. A Comprehensive Survey of Internaitonal Soybean Research-Genetics, Physiology, Agronomy and Nitrogen Relationships, 209-237.
    [11] Caliskan S, Ozkaya I, Caliskan ME, et al. (2008) The effects of nitrogen and iron fertilization on growth, yield and fertilizer use efficiency of soybean in a Mediterranean-type soil. Field Crop Res 108: 126-132. doi: 10.1016/j.fcr.2008.04.005
    [12] Brahim S, Niess A, Pflipsen M, et al. (2017) Effect of combined fertilization with rock phosphate and elemental sulphur on yield and nutrient uptake of soybean. Plant Soil Environ 63: 89-95. doi: 10.17221/22/2017-PSE
    [13] Samson ME, Menasseri-Aubry S, Chantigny MH, et al. (2019) Crop response to soil management practices is driven by interactions among practices, crop species and soil type. Field Crop Res 243: 107623. doi: 10.1016/j.fcr.2019.107623
    [14] Chibeba AM, Kyei-Boahen S, de Fátima Guimarã es M, et al. (2017) Isolation, characterization and selection of indigenous Bradyrhizobium strains with outstanding symbiotic performance to increase soybean yields in Mozambique. Agric Ecosyst Environ 246: 291-305. doi: 10.1016/j.agee.2017.06.017
    [15] Butler SJ, Vickery JA, Norris K (2007) Farmland biodiversity and the footprint of agriculture. Science 315: 381-384. doi: 10.1126/science.1136607
    [16] Wezel A, Casagrande M, Celette F, et al. (2014) Agroecological practices for sustainable agriculture. A review. Agron Sustain Dev 34: 1-20.
    [17] Altieri MA, Farrell JG, Hecht SB, et al. (2018) The Agroecosystem: Determinants, Resources, Processes, and Sustainability. Agroecology. CRC Press, 41-68.
    [18] Adesemoye AO, Kloepper JW (2009) Plant-Microbes interactions in enhanced fertilizer-use efficiency. Appl Microbiol Biotechnol 85: 1-12. doi: 10.1007/s00253-009-2196-0
    [19] Garnett T, Conn V, Kaiser BN (2009) Root based approaches to improving nitrogen use efficiency in plants. Plant Cell Environ 32: 1272-1283. doi: 10.1111/j.1365-3040.2009.02011.x
    [20] Reddy CA, Saravanan RS (2013) Polymicrobial multi-functional approach for enhancement of crop productivity. Adv Appl Microbiol 82: 53-113. doi: 10.1016/B978-0-12-407679-2.00003-X
    [21] Mwenda GM (2010) Diversity and symbiotic efficiency of rhizobia isolated from Embu, Kenya. Soil Sci Soc Am J 78: 1643.
    [22] Parr MC (2014) Promiscuous soybean: Impacts on Rhizobia diversity and smallholder Malawian agriculture. Available from: http://www.lib.ncsu.edu/resolver/1840.16/9425.
    [23] Meena RS, Yadav RS, Meena H, et al. (2015) Towards the current need to enhance legume productivity and soil sustainability worldwide: A book review. J Clean Prod 104: 513-515.
    [24] Xiao TJ, Yang QS, Ran W, et al. (2010) Effect of inoculation with arbuscularmycorrhizal fungus on nitrogen and phosphorus utilization in upland rice-mungbean intercropping system. Agr Sci China 9: 528-535. doi: 10.1016/S1671-2927(09)60126-7
    [25] Perez-Jaramillo JE, Mendes R, Raaijmakers JM (2016) Impact of plant domestication on rhizosphere microbiome assembly and functions. Plant Mol Biol 90: 635-644. doi: 10.1007/s11103-015-0337-7
    [26] Abd-Alla MH, El-Enany AWE, Nafady NA, et al. (2014) Synergistic interaction of Rhizobium leguminosarum bv. viciae and arbuscular mycorrhizal fungi as a plant growth promoting biofertilizers for faba bean (Vicia faba L.) in alkaline soil. Microbiol Res 169: 49-58.
    [27] Ruiz-Lozano JM, Collados C, Barea JM, et al. (2001) Arbuscular mycorrhizal symbiosis can alleviate drought-induced nodule senescence in soybean mplants. New Phytol 151: 493-502. doi: 10.1046/j.0028-646x.2001.00196.x
    [28] Okalebo JR, Gathua KW, Woomer PL (2002) Laboratory methods of soil and plant analysis: A working manual. Second Edition. TSBF-CIAT and Sacred Africa, Nairobi, Kenya.
    [29] Ashworth AJ, Allen FL, Wight JP, et al. (2014) Soil organic carbon sequestration rates under crop sequence diversity, bio-covers, and no-tillage. SSSAJ 78: 1726-1733. doi: 10.2136/sssaj2013.09.0422
    [30] Sáez-Plaza P, Navas MJ, Wybraniec S (2013) An overview of the kjeldahl method of nitrogen determination. Part Ⅱ. Sample preparation, working scale, instrumental finish, and quality control, Crit Rev Anal Chemi 43: 224-272.
    [31] Furseth BJ, Conley SP, Ané JM (2012) Soybean response to soil rhizobia and seed-applied rhizobia inoculants in Wisconsin. Crop Sci 52: 339-344. doi: 10.2135/cropsci2011.01.0041
    [32] Jaetzol R, Schimdt H, Hornetz B, et al. (2006) Farm management handbook of Kenya. Vol Ⅱ, Natural conditions and farm information. East Kenya, Ministry of Agriculture, Nairobi.
    [33] Woomer PL, Huising J, Giller K, et al. (2014) N2Africa final report of the first phase: 2009-2013.
    [34] Khalil S, Loynachan TE, Tabatabai MA (1994) Mycorrhizal dependency and nutrient uptake by improved and unimproved corn and soybean cultivars. Agron. J 86: 949-958.
    [35] Somasegaran P, Hoben HJ (1985) Available from: https: //www.ctahr.hawaii.edu/bnf/Downloads/Training/Rhizobium%20technology/Title%20Page.PDF.
    [36] Takács T, Cseresnyés I, Kovács R, et al. (2018) Symbiotic Effectivity of Dual and Tripartite Associations on Soybean (Glycine max L. Merr.) Cultivars Inoculated with Bradyrhizobium japonicum and AM Fungi. Front Plant Sci 9: 1631.
    [37] Xie MM, Zou YN, Wu QS, et al. (2020) Single or dual inoculation of arbuscular mycorrhizal fungi and rhizobia regulates plant growth and nitrogen acquisition in white clover. Plant Soil Environ 66: 287-294. doi: 10.17221/234/2020-PSE
    [38] Jaiswal SK, Anand A, Dhar B, et al. (2011) Genotypic characterization of phage-typed indigenous soybean bradyrhizobia and their host range symbiotic effectiveness. Microb Ecol 63: 116-126. doi: 10.1007/s00248-011-9950-4
    [39] Muthini M, Maingi JM, Muoma JO, et al. (2014) Morphological assessment and effectiveness of indigenous rhizobia isolates that nodulate P. vulgaris in water hyacinth compost testing field in Lake Victoria basin. Br J Appl Sci Technol 4: 718-738.
    [40] Phillips JM, Hayman DS (1970) Improved procedures for clearing roots and staining parasitic and vesicular-arbuscular mycorrhizal fungi for rapid assessment of infection. Trans Br Mycol Soc 55: 158-161. doi: 10.1016/S0007-1536(70)80110-3
    [41] Giovannetti M, Mosse B (1980) An evaluation of techniques for measuring vesicular arbuscular mycorrhizal infection in roots. New Phytol 84: 489-500. doi: 10.1111/j.1469-8137.1980.tb04556.x
    [42] Hassink J (1995) Density fractions of soil macroorganic matter and microbial biomass as predictors of C and N mineralization. Soil Biol Biochem 27: 1099-1108. doi: 10.1016/0038-0717(95)00027-C
    [43] Gajda AM, Martyniuk S, Stachyra A, et al. (2000) Relations between microbiological and biochemical properties of soil under different agrotechnical conditions and its productivity. Polish J Soil Sci 33: 55-60.
    [44] Mishra V, Ellouze W, Howard RJ (2018) Utility of arbuscular mycorrhizal fungi for improved production and disease mitigation in organic and hydroponic greenhouse crops. J Hortic 5: 1000537. doi: 10.4172/2376-0354.1000237
    [45] Karunasinghe TG, Fernando WC, Jayasekera LR (2009) The effect of poultry manure and inorganic fertilizer on the arbuscular mycorrhiza in coconut. J Natn Sci Foundation Sri Lanka 37: 277-279. doi: 10.4038/jnsfsr.v37i4.1476
    [46] Hunt ND, Hill JD, Liebman M (2019) Cropping system diversity effects on nutrient discharge, soil erosion, and agronomic performance. Environ Sci Technol 53: 1344-1352.
    [47] Liu XD, Feng ZW, Zhao ZY, et al. (2020) Acidic soil inhibits the functionality of arbuscular mycorrhizal fungi by reducing arbuscule formation in tomato roots. Soil Sci Plant Nutr 66: 275-284. doi: 10.1080/00380768.2020.1721320
    [48] Ye GP, Lin YX, Luo JF, et al. (2020) Responses of soil fungal diversity and community composition to long-term fertilization: Field experiment in an acidic Ultisol and literature synthesis. Appl Soil Ecol 145: 103305. doi: 10.1016/j.apsoil.2019.06.008
    [49] Sarr PS, Yamakawa T, Saeki Y, et al. (2011) Phylogenetic diversity of indigenous cowpea bradyrhizobia from soils in Japan based on sequence analysis of the 16S-23S rRNA internal transcribed spacer (ITS) region. Syst Appl Microbiol 34: 285-292. doi: 10.1016/j.syapm.2010.11.021
    [50] Koskey G, Mburu SW, Njeru EM, et al. (2017) Potential of native rhizobia in enhancing nitrogen fixation and yields of climbing beans (Phaseolus vulgaris L.) in contrasting environments of Eastern Kenya. Front Plant Sci 8: 443.
    [51] Simbine MG, Baijukya FP, Onwonga RN (2018) Intermediate maturing soybean produce multiple benefits at 1: 2 maize: soybean planting density. J Agric Sci 10: 29-46.
    [52] Njeru EM, Muthini M, Muindi MM, et al. (2020) Exploiting Arbuscular Mycorrhizal Fungi-Rhizobia-Legume Symbiosis to Increase Smallholder Farmers' Crop Production and Resilience Under a Changing Climate. Climate impacts on agricultural and natural resource sustainability in Africa. Springer, Cham, 471-488.
    [53] Lodwig EM, Poole PS (2003) Metabolism of Rhizobium bacteroids. Crit Rev Plant Sci 22: 37-38. doi: 10.1080/713610850
    [54] Antunes PM (2004) Determination on nutritional and signalling factors involved in the tripartite symbiosis formed by arbuscular mycorrhizal fungi, Bradyrhizobium and soybean. Doctoral dissertation, University of Guelph.
    [55] Mirdhe RM, Lakshman HC (2014) Synergistic interaction between arbuscular mycorrhizal fungi, Rhizobium and phosphate solubilising bacteria on Vigna Unguiculata (L) Verdc. Int J Bioassays 3: 2096-2099.
    [56] Antunes PM, Goss MJ (2005) Communication in the tripartite symbiosis formed by arbuscular mycorrhizal fungi, rhizobia and legume plants: a review. In: Zobel RW, Wright SF. (Eds), Roots and soil management: Interactions between roots and the soil. American Society of Agronomy, 48: 199-222.
    [57] Schneider KD, Lynch DK, Dunfielda K, et al. (2015) Farm system management affects community structure of arbuscular mycorrhizal fungi. Appl Soil Ecol 96: 192-200. doi: 10.1016/j.apsoil.2015.07.015
    [58] Abbott L, Robson A, Jasper DA, et al. (1992) What is the role of VA mycorrhizal hyphae in soil? In: Read DJ. (Ed), Mycorrhizas in ecosystems. Wallingford: CAB International, 37-41.
    [59] Thonar C, Frossard E, Smilauer P, et al. (2014) Competition and facilitation in synthetic communities of arbuscular mycorrhizal fungi. Mol Ecol 23: 733-746. doi: 10.1111/mec.12625
    [60] Tabassum B, Khan A, Tariq M, et al. (2017) Bottlenecks in commercialisation and future prospects of PGPR. Appl soil Ecol 121: 102-117. doi: 10.1016/j.apsoil.2017.09.030
    [61] de Paiva Barbosa L, Costa PF, Ribeiro PRA, et al. (2017) Symbiotic efficiency and genotypic characterization of variants of Bradyrhizobium spp. in commercial inoculants for soybeans. Rev Bras Ciênc Do Solo 41.
    [62] Oruru MB, Njeru EM, Pasquet R, et al. (2018) Response of a wild-type and modern cowpea cultivars to arbuscular mycorrhizal inoculation in sterilized and non-sterilized soil. J Plant Nutr 41: 90-101. doi: 10.1080/01904167.2017.1381728
    [63] Ouma EW, Asango AM, Maingi J, et al. (2016) Elucidating the potential of native rhizobial isolates to improve biological nitrogen fixation and growth of common bean and soybean in smallholder farming systems of Kenya. Int J Agron 2016: 4569241.
    [64] Miransari M (2011) Arbuscular mycorrhizal fungi and nitrogen uptake. Arch Microbiol 193: 77-81.
    [65] Upadhayay VK, Singh J, Khan A, et al. (2019) Mycorrhizal mediated micronutrients transportation in food based plants: A biofortification strategy. Mycorrhizosphere and pedogenesis. Singapore: Springer, 1-24.
    [66] Finlay RD (2008) Ecological aspects of mycorrhizal symbiosis: With special emphasis on the functional diversity of interactions involving the extraradical mycelium. J Exp Bot 59: 1115-1126. doi: 10.1093/jxb/ern059
    [67] Sofi MN, Bhat RA, Rashid A, et al. (2017) Rhizosphere Mycorrhizae communities an input for organic agriculture. Mycorrhiza-Nutrient Uptake, Biocontrol, Ecorestoration. DOI: 10.1007/978-3-319-68867-1.
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