Effect of Competition and Seed Rate on Productivity of Haemanthus Multifloras Plant in Central Darfur State, Sudan

Introduction

Weeds compete with crops when they remove a portion of a resource from a shared resource pool, leaving the crop with less of the resource than is needed for optimum growth [1-3]. Competition may occur for water, creating or exacerbating water stress. It may occur for nutrients such as nitrogen, leading to chlorosis, leaf senescence and reduced yields. These concepts also resemble those believed by Wortmann, et al. [4,5] who reported that the effect of seeding rate on yield in sorghum have been inconsistent, where higher seeding rates have been shown to increase dry matter productivity in some instances, and to have no effect on yield in others. Until recently weeds have been controlled by ploughing and disking prior to crop sowing and repeated hand weeding operations carried out by casual labour, the farmer and his family. Sowing and weeding significantly affected number of leaves per plant of Blepharis linariifolia after from 30 days from sowing [6]. In Fairhope site (USDA-ARS / UNL Faculty) plant height increased with increasing seeding rate Snider, et al. [7]. Seeding rate did not affect any of the seed yield and yield components measured Yunhua Han, et al. [8]. Higher plant densities can sometimes stimulate increases in plant height due to inter-node elongation Snider, et al. [7]. In the United States, average crop yields were depressed by 12% due to weeds USBC [9]; these results were similar to those achieved by who stated that there were more tillers per plant in the 6 and 12 kg/ha treatments than the other treatments (18 and 24 kg/ha), but mean tiller weight was similar for all treatments.

Materials and Methods

The Study Area

The study was carried out at the experimental field of Agricultural Research Corporation (Nertiti station) –over two seasons (July-October 2016 andJuly-October2017) in (WJML) Central Darfur State, Sudan. The area is located in the western part of Jebel Marra massive and extends between latitudes 12°57´ and 13° 00´ N and longitudes 24° 02´and24°04´ E. The altitude at Nertiti is 600 m above sea level (m.a.s.l.) DRCO [10]. Due to the influence of elevation, Jebel Marra climatic conditions resemble those of the Mediterranean region. Rainfall in the western slopes ranges between 420 mm/annum at Golol, Murtagello and Nertiti (1000 m.a.s.l.), and 1200 mm / annum at the upper slopes (2500 – 3000 m.a.s.l.). The minimum temperature ranges between 6°Cand 10°C FAO [10] Figure 1.

Figure 1: Central Darfur State Map in Republic of Sudan- Nertiti is the head quarter of WJML.

Land Preparation

The land was disc ploughed and then followed by harrowing and leveling. The area of the experiment was divided into 24 plots 2 × 2 meter each.

Competition and Reseeding Experiment

The experimental layout was a split plot arrangement with four replications. Weeds control was the main plot, while seed rates represented the sub-plot. Weeds control was practised via hand weeding and carried out through the experiment whenever necessary to evaluate the effect of competition reduction on growth attributes, where mowing method was used for that process. Unweeded plots were left un-touched. Seeds were broadcasted under rain fed irrigation, three seed rates were used 4, 8 and 12kg/ha. Method of sowing was broadcasting seeds on flat and then covering by rake at a depth of about one (cm). The parameters investigated were plant height(m), number of tillers /plants, number of leaves / plant and dry matter production (kg DM/ha) (Tables 1 & 2).

Table 1: Temperature (°C) and Rainfall (mm) during 2013- 2017 in WJML.

Note: Source: Jebel Marra Rural Development Project Meteorological Section (2018).

Table 2: Rainfall (mm) distribution during 2013- 2017 in WJML.

Note: Source: Jebel Marra Rural Development Project Meteorological Section (2018).

Data Analysis

Data were analyzed using statistix program version 9.0 and mean comparisons were made using the F-protected least significant difference for separation at 5% level of significance [11].

Results and Discussion

Effect of Competition on Growth and Yield Attributes of Haemanthus Multiflorus

No significant effect for hand weed control on Haemanthus multiflorus plant height during the two seasons, while differences were found in number of tillers, number of leaves per plant and dry matter yield (Table 3). Hand mowing of weeds had a positive effect on number of plant tillers or branches per plant and also on number of leaves per plant in 2016 which reflect significant differences among weeded and un-weeded treatments, since these parameters were higher in weeds controlling treatment. These results resemble those achieved by [6] who stated that sowing and weeding significantly affected number of leaves per plant of Blepharis linariifolia after from 30 days from sowing. No statistical differences were found between weeded and un-weeded treatments on the same parameters in 2017 (Tables 4-6). Forage yield (kg DM/ha) was greater in weeded pattern than un-weeded treatment in 2017, which reached1449 and 1042 kg DM/ha respectively resulting in significant differences. This finding is in line with Walter, et al. [5]. Who reported that until recently weeds have been controlled by ploughing and disking prior to crop sowing and repeated hand weeding operations carried out by casual labour, the farmer and his family?

Table 3: Competition and Seed rates experiment layout for one replication.

Note: UW= un-weeded, W= mow weeded, Sr1= seed rate1 (4 kg/ha), Sr2= seed rate2 (8 kg/ha) and Sr3= seed rate3 (12 kg/ha).

Table 4: Effect of competition on growth and yield attributes of Haemanthus multiflorus during seasons 2016 and 2017.

Note: S.L= significant level, Ns= not significant at P> 0.05, * = significant differences at P ≤0.05.

Table 5: Effect of seed rates on growth and yield attributes of Haemanthus multiflorus during seasons 2016 and 2017.

Note: S.L= significant level, Ns= not significant at P> 0.05, * = significant differences at P ≤0.05.

Table 6: Effect of competition and seed rates on growth and yield attributes of Haemanthis multifolorus during seasons 2016 and 2017.

Note: Ns= not significant at P> 0.05, * = significant at P <0.05, **= high significant at P <0.01

Effect of Seed Rates on Growth and Yield Attributes of Haemanthus Multiflorus

Seed rates treatments had an influence just on plant height in season 2016 while no effect was found in season 2017. On the other hand, no significant differences were found on tillers/ plant or number of leaves per plant according to different seed rates treatments over two seasons. Also seed rates did not affect forage productivity (Table 4). The hypothesis that plant height is increased at high seeding rates was confirmed in season 2016. These findings are consistent with Snider, et al. [7], who reported that in Fairhope site (USDA-ARS / UNL Faculty) plant height increased with increasing seeding rate. In this study seed rates had no effect on number of tillers, number of leaves per plant and dry matter production over the two seasons of the study. These results agree with Yunhua Han, et al. [8] who stated that seeding rate did not affect any of the seed yield and yield components measured.

Effect of Competition and Seed Rates on Growth and Yield Attributes of Haemanthis Multifolorus

The interaction between the different treatments namely: hand weeds control, un-weeded and seed rates had no effect on Haemanthis multifolorus height in the first season (2016) while significant differences were found among un-weeded ×4kg/ha seed rate and un-weeded ×8kg/ha seed rates treatment on plant height in season (2017). Also, significant differences were found between treatments on number of tillers per plant (Table 5). Moreover, significant differences were found on number of leaves per plant and forage productivity due to effect of treatments. The study revealed that none of the treatments had an effect on plant height in season (2016), while un-weeded involved 4 kg/ha seed rate had a significant effect on plant height in season (2017) which caused differences among it and un-weeded with 8 kg/ha seed rates treatment (0.74 and 0.53m respectively). This result differed from that obtained by Snider, et al. [7] who reported that higher plant densities can sometimes stimulate increases in plant height due to inter-node elongation. Regarding number of tillers or branches per plant; weeded ×12 kg/ha seed rate treatment demonstrated significant differences from un-weeded ×8 kg/ha seed rate and unweeded ×12 kg/ha seed rate treatments in season (2016) which reach (7.78, 5.38 and 5.03 tillers per plant respectively). This may be attributed to hand weed control more than seed rates. This finding is in line with USBC [9] who reported that in the United States, average crop yields were depressed by 12% due to weeds. Also, these results were similar to those achieved by (Julia M Lee, et al.) who stated that there were more tillers per plant in the 6 and 12 kg/ha treatments than the other treatments (18 and 24 kg/ha), but mean tiller weight was similar for all treatments. On the other hand, no effect was found for all treatments on tillers number in the second season (2017).

Weeded ×8 kg/ha seed rate treatment had a positive effect on number of leaves per plant more than other treatments in season (2016), which showed significant between this treatment and weeded ×4 kg/ha, un-weeded×4 kg/ha, un-weeded×8 kg/ha and un-weeded×12 kg/ha seed rates respectively (187.53, 122.25, 93.25, 96.65 and 77.1 leaves per plant respectively). These results agree with [6] who stated that sowing and weeding significantly affected number of leaves per plant of Blelpharislinarifolia30 days after sowing. No statistically significant effect was found among all treatments on number of leavesS per plant in second season (2017). Weeded ×12 kg/ha seed rate treatment have shown superior results on productivity than other treatments which caused differences among this treatment and un-weeded ×4 kg/ha and un-weeded ×12 kg/ha seed rates treatments in the first season which reached 846.3, 371.5 and 328.5 kg DM/ha respectively. In the second season the same treatment (Weeded ×12 kg/ha) obtained superiority on forage yield and revealed highly significant effect between it and un-weeded ×4 kg/ha treatment. There were also significant differences among this treatment and un-weeded ×8 kg/ ha and un-weeded ×12 kg/ha seed rates treatments which reach 1537.3, 881.3, 1119.2 and 1128.0 kg DM/ha respectively. These results resembled those achieved by [1-3].

Who stated that weeds compete with crops when they remove a portion of a resource from a shared resource pool, leaving the crop with less of the resource than is needed for optimum growth? Competition may occur for water, creating or exacerbating water stress. It may occur for nutrients such as nitrogen, leading to chlorosis, leaf senescence and reduced yields. These results also resemble those achieved by Wortmann. et al. [4,5] who reported that the effect of seeding rate on yield in sorghum have been inconsistent, where higher seeding rates have been shown to increase dry matter productivity in some instances, and to have no effect on yield in others [12].

Conclusion

This study concluded that weeded ×12 kg/ha seed rate treatment demonstrated significant differences; since it increased number of tillers per plant in season (2016) as well as it was shown superior results on forage production for Haemanthus multiflorus

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COVID-19 Morbidity and Mortality and its Association with HIV and Health System Factors in India

Introduction

The COVID-19 pandemic has already infected over 494 million people worldwide, leading to over 6 million deaths [1]. With about one-third of confirmed cases in Asia, India witnessed around 43 million infected cases with over 500,000 deaths as of April 2022 [2]. Ever since the first outbreak in December 2019, researchers have been reporting significant associations between mortality and morbidity due to severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), causing the coronavirus disease 2019 (COVID-19) and various clinical and non-clinical factors, including Human Immunodeficiency Virus (HIV) infection, non-communicable diseases (NCDs), socioeconomic, demographic and health system factors [3-6]. Generation of context and geographic-specific evidence related to the factors associated with the SARS-CoV-2 infection and mortality is critical for policymakers to develop evidence-based strategies and targeted interventions such as age and vulnerability-specific prevention and control programmes. This study presents an analysis of the association between COVID-19 incidence, mortality and clinical and non-clinical factors, intending to provide an insight to support and design public health strategies for its response to the pandemic.

Methods

We compiled the data for two time periods, March to August 2020 and March to June 2021 from multiple secondary data sources for 26 states of India. Data related to COVID-19 incidence and mortality were obtained from the Ministry of Health and Family Welfare (MoHFW) website https://www.mohfw.gov. in/ and https://api.covid19india.org/csv/latest/state_wise.csv (last accessed on 1st July 2021). HIV related data were obtained from various technical reports published by the National AIDS Control Organization (NACO), Ministry of Health and Family Welfare (MoHFW), Government of India [7]. Data related to sociodemographic, economic characteristics, vaccination coverage as a measure for health system performance and average genderspecific body mass index (BMI) were obtained from National Family Health Survey (NFHS-4) factsheets [4,8,9]. As complete information on all variables could be obtained only for 26 states and UTs in the country, they were included in the analysis. We considered outcome variables such as cumulative confirmed COVID-19 incidence (burden) and mortality for the two different periods. HIV incidence, HIV prevalence, HIV mortality, PMTCT needs, general vaccination coverage, Sex-ratio, child sex ratio, the proportion of people in poverty, BMI and air-travel density (risk score) were considered as potential risk factors.

Statistical Analysis

Binary associations between the independent and outcome variables were first explored using scatterplots. The associations were then quantified using spearman’s rank correlation to assess the relationship between the independent variables and outcomes variables such as cumulative COVID-19 incidence and mortality. All correlations were reported and factors that were significant at p-value of 0.1 were included in the ecological analysis. Linear regression models were fitted to assess the quantum of association of the covariates on the COVID-19 incidence and mortality. Separate models were fitted for the two time periods, March- August 2020 and March-June 2021 as there were differences in case identification strategies, reporting and other programmatic efforts. The covariates included in the regression model were: general vaccine coverage (as a measure of health systems performance), sex ratio, percentage below poverty line (as a measure of social determinant of health), BMI (as a measure of nutritional status, obesity), HIV prevalence, HIV incidence, HIV related mortality, PMTCT needs (as measures of population highly vulnerable to infection) and air travel risk. All variables in the regression models were standardized to convert them to the same scale.

Results

Association between COVID-19 Incidence and HIV, Health System Performance and Socio-Demographic, Economic Factors

The correlation analyses (Figures 1 & 2) indicated strong positive association between COVID-19 incidence and HIV prevalence (r=0.9, p<0.001), HIV incidence (r=0.8, p<0.001), HIV related mortality (r=0.8, p<0.001) and Prevention of mother-tochild transmission (PMTCT) needs (r=0.8, p<0.001), during the time period from March to August 2020. Similarly, during the time period from March to June 2021, COVID-19 incidence was positively associated with HIV prevalence (r=0.8, p<0.001), incidence (r=0.7, p<0.001), AIDS mortality (r=0.8, p<0.001) and PMTCT needs (r=0.8, p<0.001). States with higher HIV incidence and AIDS related mortality showed a corresponding higher COVID-19 burden. The general vaccination coverage in urban areas, which is considered as a measure of health system performance indicated an inverse, albeit weak, association with COVID-19 burden (r=-0.2, p=0.4) indicating states with lower vaccination rates may have a higher number of cases in 2020. However, vaccination coverage in rural areas and overall vaccination coverage did not indicate any significant association with COVID-19 case burden in both periods. In terms of the socio-demographic, and economic situation, poverty rate did not indicate any significant association with COVID-19 incidence in both the time periods. However, sex ratio (r =-0.2, r=0) and child sex ratio (r=-0.3, r=-0.2) in both the periods of the analysis showed a weak negative association with COVID-19 incidence. Overall BMI in both the period of analysis (r=0.2, r=0.1) indicated a weak positive association with COVID-19 incidence. However, air travel risk ratio (r=0.7; r=0.7) indicated a strong positive association with COVID-19 incidence.

Figure 1: Correlation plot of COVID-19 incidence and mortality with independent variables for the year 2020.

Figure 2: Correlation plot of COVID-19 incidence and mortality with independent variables for the year 2021.

Association between COVID Mortality and HIV, Health System Performance and Socio-Demographic, Economic Factors

The magnitude of association between COVID-19 mortality and HIV related indicators that are HIV prevalence, HIV incidence, HIV related mortality and PMTCT needs were almost the same as COVID-19 incidence. COVID-19 mortality did not indicate any significant correlation with general vaccination coverage in both urban and rural areas. Poverty rate (r=-0.1; r=-0.1) indicated a very weak negative association with COVID-19 mortality in both periods. Similarly, sex ratio (r =-0.3, r= -0.2) and child sex ratio (r=-0.5, r=-0.3) showed a weak negative association in both the time periods with COVID 19 mortality. The analysis indicated a very weak association between BMI (r=0.2; r=0.1) and COVID-19 mortality in both the time periods However, air travel risk ratio (r=0.7, r=0.6) indicated a strong positive association with COVID 19 mortality. Some of the correlations, although not statistically significant, hint at important directional relationships between state-level COVID-19 disease burden, mortality and the covariates.

Factors Associated with COVID-19 Mortality

The regression analysis for the year 2020, indicated that for every additional case of PLHIV in the state, there is an increase of 0.77 units of COVID-19 mortality which is statistically significant. Similarly, new HIV infections, PMTCT needs, AIDS deaths and air travel risk scores of the states were found to be the significant predictors of COVID-19 mortality. Health systems performance, socio-demographic and economic factors did not show any significant association with COVID-19 mortality. Similar trends were observed for the year 2021, where the number of PLHIV, new HIV infections, PMTCT needs, AIDS deaths and air travel risk score of the states were found to be significant predictors of COVID-19 mortality (Table 1). A correlation plot of COVID-19 incidence and mortality with independent variables has been depicted in the choropleth map (Figures 1 & 2).

Table 1: Regression analysis to quantify change in COVID 19 mortality associated with the covariates.

Note: *Percentage of population below poverty line as defined for that year.

Discussion

The study highlights the significant relationship between HIV prevalence, HIV incidence, HIV related mortality, PMTCT needs and COVID-19 incidence and mortality. The findings corroborate with a population-based cohort analysis that indicated 2.9 times (95 CI: 1·96–4·30) higher risk for PLHIV dying from COVID-19 after adjusting for age and sex, compared to the general population [4]. A meta-analysis indicated a higher mortality rate due to COVID-19 among PLHIV (3.44%) compared to COVID-19 patients without HIV infection (0.42%)[10]. In addition, cohorts of hospitalized PLHIV with COVID-19 in London and New York have revealed higher rates of severe disease requiring hospitalization relative to those without an HIV diagnosis and higher mortality even with a suppressed viral load on ART [9,11-14]. The increased risk assumption for adverse COVID-19 outcomes among PLHIV is found to be based on their immunosuppressed clinical status since, HIV infection is long associated with increased susceptibility to opportunistic infections because of the abnormal humoral and T-cell mediated immune responses [15,16].

On the other hand, HIV/SARS‐CoV‐2 co-infected patients may have mortality benefits from the immunosuppressive state [17,18]. Though concerns were raised by the World Health Organization (WHO) and Center for Disease Control and Prevention (CDC) for population at high risk including PLHIV for severe health outcomes due to COVID-19, factors like immunological and virological status of PLHIV with consumption of antiretroviral treatment (ART) might play a role in the outcome of COVID-19 infection [19,20]. The high prevalence of critical underlying co-infections among PLHIVs, in comparison to HIV-negative individuals, is found responsible for higher mortality rates due to COVID-19 and not only the HIV positive status of individuals [21]. However, adequate caution and care are required while managing COVID-19 patients with immunosuppressive conditions [15,22,23]. This study indicates protective effect of general vaccination in the reduction of COVID-19 deaths. According to literature, universal Bacillus Calmette-Guerin (BCG) vaccination policy in countries, and the rate of BCG vaccination are correlated with reduced mortality rates due to COVID-19 [24-26]. Further, it has shown to produce broad protection against viral infections and sepsis [27]. Studies have also highlighted reduced COVID-19 infection, severity and death rates among patients vaccinated with measles mumps rubella (MMR) compared to the population in the same age range without vaccination [28-31]. Supporting these findings, another study indicated higher death rates due to COVID-19 with delayed MMR vaccination programs [32].

In terms of socio-demographic and economic variables, sex ratio and child sex ratio were found to be negatively associated with COVID-19 mortality. These findings are in corroboration with a few studies indicating that infectious disease threats and deaths including COVID-19 disproportionately affect the population from less developed geographies [33-35]. According to the study, a very weak positive association was found between BMI which is a proxy for co-morbidity of obesity, and mortality due to COVID-19. However, several studies indicated higher mortality among older, people with obesity and diabetes with complications [8,36]. According to a study, frequency of obesity (BMI > 30kg/m2, 47.6%) and severe obesity (BMI > 35 kg/m2, 28.2%) was found to be higher among patients with COVID-19 infection compared to non-SARSCov- 2 respiratory disease patients (25.2% and 10.8%, respectively) [37]. Similarly, another study demonstrated higher mean values of BMI of COVID-19 infected patients who needed ICU care (25.5kg/ m2), compared with the general group (22.0kg/m2) [38]. Another systematic review suggested obesity as a prospective predictor of poor outcomes in patients with COVID-19, in all continents [39]. Data from China’s Centers for Disease Control shows that 7.3% of those with diabetes who were later diagnosed with COVID-19 died and for those with no other co-morbidity, the mortality rate was lower at 0.9% [40]. Similar studies in Italy documented that 99% of deaths were among those with at least one or other health condition, showing the highest rates among patients with three or more illnesses [41].

Conclusion

The study gained insights on the associated factors that could increase the risk of COVID-19 incidence and mortality. The study provided evidence that PLHIVs are at higher risk for COVID-19 incidence and mortality, which suggests the need for focused interventions among PLHIV, especially during a pandemic of this kind. It is also critical that there is uninterrupted access to treatment services such as ART and treatments for co-infections and comorbidities to mitigate the impact of COVID -19. As there is a higher risk for COVID-19 incidence and mortality among people with underlying chronic illness and comorbidities, public health strategies should focus on early detection, diagnosis and timely initiation of treatment.

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Efficacy of Treatment Regimens for Sodium-Glucose Counter Transporter 2 Inhibitor (Emaglyf) with Metformin and DPP-4 Inhibitor (Januvia) in Patients with Type 2 Diabetes Mellitus with Stage 1-3 Chronic Kidney Disease

Background

As is known, the general goals of the treatment of type 2 diabetes mellitus (DM 2) are to avoid acute metabolic decompensation, prevent or delay complications, reduce premature mortality and maintain quality of life [1]. Pharmacological treatment options for T2DM are divided into:

a) Non-insulin therapies, including

1) Insulin sensitizers (metformin, thiazolidinediones [TZDs]).

2) Secretion stimulants (sulfonylureas [SUs]).

3) Incretin-based therapies (receptor agonists glucagon-like peptide-1 [RAs GLP-1], dipeptidyl peptidase-4 inhibitors [DPP4-is]), and

4) Insulin-sparing agents such as α-glucosidase inhibitors (AGis) and sodium glucose cotransporter-2 inhibitors (SGLT-2is); and

b) Insulin therapy. Until recently, stepwise and combination therapy were the two guidelines for pharmacological approaches in T2DM [2-5]. Due to the lack of sufficient data on the use of early combination therapy, stepwise treatment intensification has been the standard approach to achieve glycemic control, as recommended by the ADA/EASD consensus treatment algorithm. Asia, China, Hong Kong, Taiwan, Korea and Japan follow similar rules [6].

The AACE and ADA/EASD guidelines recommend intensifying treatment with an additional drug if monotherapy fails to achieve or maintain the target HbA1c level after 3 months. Preferred third-line therapy includes insulin or a triple combination of oral antidiabetic drugs [5,6]. The AACE treatment algorithm recommends that patients with an HbA1c level of 7.5% or higher (≥59 mmol/mol) be started on combination therapy with metformin plus an additional antidiabetic agent [5]. The 2018 ADA/EASD Position Statement recommends combination treatment only if HbA1c is more than 17 mmol/mol (1.5%) above an individual’s target [7]. In line with the latest data, the 2019 update recommends early recruitment of patients with newly diagnosed T2DM to start combination therapy through shared decision making. [four]. In Taiwan, combination therapy with metformin and another antidiabetic drug is recommended for patients with an HbA1c level of 8.5% or higher (≥69 mmol/mol) at the time of diagnosis [8]. In Hong Kong and Korea, combination therapy with metformin is recommended for patients with HbA1c 7.5% or higher (≥59 mmol/mol) [9,10].

Almost all classes of hypoglycemic drugs, such as metformin, SU, AGi, GLP-1 RA, DPP4-i, and SGLT2-i, can be used in combination. Most early combination therapies use metformin as baseline therapy. The efficacy and safety of various combination therapies have been reviewed and evaluated in detail in meta-analyses [11,12]. Positive Effects of SGLT2 on the kidneys was first shown in the EMPAREG, CANVAS and DECLARE cardiovascular trials (CVOT). These studies initially focused on assessing cardiovascular safety in patients with type 2 diabetes with renal outcomes as a secondary endpoint [Barnett AH, et al. 2014]. The efficacy and renal outcomes of SGLT2 inhibitors in patients with type 2 diabetes mellitus and chronic kidney disease were studied in a 2019 US multicenter study [Michael S, et al. 2019]. However, the effectiveness remains unexplored. SGLT2 in combination with other drugs at the stage before hemodialysis in patients with DM 2 and CKD. The above was the reason for the present study.

Purpose of the Study

To study the effectiveness of combination therapy of sodiumglucose counter transporter 2 inhibitor -SGLT-2- (Emaglyf) with metformin and DPP-4 inhibitor (Januvia) in patients with stage 1-3 chronic kidney disease associated with DM2.

Material and Research Methods

A total of 40 patients with type 2 diabetes and CKD grades 1-4 were selected. To study the effect of various schemes of nephroprotective therapy on the functional state of the kidneys in DM2, patients were divided into 2 therapeutic groups:

• Group 1 consisted of 20 patients with DM 2 and CKD 1-3 tbsp. receivingSGLT-2 (emoglyph) + metformin.

• Group 2 consisted of 20 patients with DM 2 and CKD 1-3 tbsp. receivingSGLT-2 (Emoglyph) + DPP 4 (Januvia)

In the work, general clinical, clinical and biochemical (AL, AST, bilirubin, PTI, urea, creatinine, GFR, C-reactive protein, etc.), hormonal (insulin, C-peptide), immunological (uromodulin) methods of blood tests, as well as instrumental methods of examination - ultrasound of internal organs, Ultrasound and dopplerography of renal vessels, as well as statistical methods. We also evaluated the results of ECG in 12 conventional leads and echocardiography (EchoCG) (dimensions of the chambers of the heart, the thickness of its walls and myocardial contractility). The control group consisted of 20 healthy individuals. For kidney ultrasound, an Aloka ultrasound machine with a 4L convex probe (2–5 MHz) was used. The renal resistive index in segmental arteries was assessed as described by the authors. The average value of RI was calculated from 2-3 measurements in the upper, middle and lower sections of the renal sinus. Renal perfusion was assessed using the DTPM method.

The renal artery was assessed at seven points: at the exit from the aorta, in the proximal, middle and distal segments, as well as the apical, middle and inferior segmental arteries. Peak systolic (PSV) and end diastolic (EDV) blood flow velocities, resistivity index (RI), acceleration time (AT), acceleration index (PSV/AT) were calculated. Statistical processing was carried out on a personal computer using the Microsoft Excel-2019 software package using the methods of parametric and non-parametric statistics. With mild renal failure (GFR> 50 ml / min, approximately corresponding to the content of serum creatinine <1.7 mg / dl in men, <1.5 mg / dl in women) Januvia dose adjustment is not required. In moderate renal failure (GFR >30 mL/min but <50 mL/min, roughly corresponding to serum creatinine >1.7 mg/dL but <3 mg/dL in men, >1.5 mg/ dL, but <2.5 mg/dl in women) the dose of Januvia is 50 mg 1 time per day. When taking Emaglif, it is recommended to monitor kidney function before starting treatment (at least once a year), as well as before prescribing concomitant therapy that may adversely affect kidney function. Patients with renal insufficiency less than 45 ml / min / 1.73 m2) receive Emaglyf is contraindicated.

Research Results and Discussion

Table 1 shows the distribution of patients by sex and age. As can be seen from Table 1, patients in the age group from 45 to 74 years old both among men and women predominated - 25/15 cases, respectively. Table 2 gives general characteristics of patients included in the study in groups. As can be seen from Table 2, there were no significant differences in the general characteristics of the initial indicators in the studied groups (p>0.05). The mean glomerular filtration rate (GFR) was significantly lower in all groups. Next, we studied the biochemical parameters by groups before treatment (Table 2). As can be seen from Table 2, the initial data on carbohydrate metabolism indicated its decompensation in the studied groups. The next step was to conduct dopplerography of the renal arteries before and after treatment (Table 3). As seen from the data shown in Table 3 showed significant differences between the Doppler values of the renal arteries in the groups compared to the control. After 6 months of treatment according to the above schemes, we studied the effectiveness of therapy in the study groups, for which we studied the dynamics of biochemical and Doppler parameters (Tables 4 & 5). As can be seen from Table 4, after 6 months of therapy, the indicators of carbohydrate metabolism reached normalization in both groups, while the best results were observed in group 2 patients. The next step was to conduct dopplerography of the renal arteries before and after treatment (Table 5).

Table 1: Distribution of patients by sex and age.

Table 2: Mean biochemical blood parameters of patients by groups before treatment

Note: P - significance of differences compared with control data, where * - p <0.05.

As can be seen from the data, given in Table 5, after 6 months of treatment, between the Doppler values of the renal arteries in the groups, a significant improvement in the parameters was revealed.peak systolic (PSV) and end-diastolic (EDV) blood flow velocity, resistivity index (RI), acceleration time (AT), acceleration index (PSV/AT),namely, in group 2, the best results were obtained (in comparison with control data p>0.05). Thus, our study showed nephroprotective effect of both schemes. Our results confirm the literature data. Thus, according to Italian authors, antidiabetic drugs with potential nephroprotective effects, namely DPP-4 inhibitors, incretin analogues and SGLT-2 inhibitors, can have a nephroprotective effect regardless of glycemic control. Sodiumglucose co-transporter (SGLT) 2 inhibitors act at multiple sites that may affect kidney function, according to other sources. The canagliflozin Cardiovascular Assessment Study (CANVAS) showed a 27% reduction in albuminuria progression, a 40% reduction in eGFR, need for renal replacement therapy, or death from renal causes associated with canagliflozin use. All of the above confirms the high relevance of this study and dictates the need for its further continuation.

Table 3: Doppler parameters of the kidneys in patients included in the study (M ± m).

Table 4: Mean biochemical blood parameters of patients by groups after 6 months of treatment.

Note: P - significance of differences compared with control data, where * - p <0.05. , **-p<0.001 after 6 months of treatment.

Table 5: Doppler parameters of the kidneys in patients included in the study (M ± m).

Conclusion

1) After 6 months of therapy, the indicators of carbohydrate metabolism reached normalization in both groups, while the best results were observed when using the SGLT-2 + DPP4 regimen.

2) After 6 months of treatment, significant differences were found between the Doppler values of the renal arteries in the groups, namely, when using the SGLT-2 + DPP4 scheme.

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