Globally, the incidence and prevalence of diabetes mellitus has risen dramatically, owing mainly to the increase in type 2 diabetes mellitus (T2DM). In 2021, 537 million people worldwide (11% of the ...global population) had diabetes, and this number is expected to increase to 783 million (12%) by 2045. The growing burden of T2DM is secondary to the pandemic of obesity, which in turn has been attributed to increased intake of processed food, reduced physical activity, and increased sedentary behaviour. This so-called western lifestyle is related with the global increase in urbanization and technological development. One of the most frequent and severe long-term complications of diabetes is diabetic kidney disease (DKD), defined as chronic kidney disease in a person with diabetes. Approximately 20–50% of patients with T2DM will ultimately develop DKD. Worldwide, DKD is the leading cause of chronic kidney disease and end-stage kidney disease, accounting for 50% of cases. In addition, DKD results in high cardiovascular morbidity and mortality, and decreases patients’ health-related quality of life. In this review we provide an update of the diagnosis, epidemiology, and causes of DKD.
During the first wave of COVID-19 it was hypothesized that COVID-19 is subject to multi-wave seasonality, similar to Influenza-Like Illnesses since time immemorial. One year into the pandemic, we ...aimed to test the seasonality hypothesis for COVID-19.
We calculated the average annual time-series for Influenza-Like Illnesses based on incidence data from 2016 till 2019 in the Netherlands, and compared these with two COVID-19 time-series during 2020/2021 for the Netherlands. We plotted the time-series on a standardized logarithmic infection scale. Finally, we calculated correlation coefficients and used univariate regression analysis to estimate the strength of the association between the time-series of COVID-19 and Influenza-Like Illnesses.
The time-series for COVID-19 and Influenza-Like Illnesses were strongly and highly significantly correlated. The COVID-19 peaks were all during flu season, and lows were all in the opposing period. Finally, COVID-19 meets the multi-wave characteristics of earlier flu-like pandemics, namely a short first wave at the tail-end of a flu season, and a longer and more intense second wave during the subsequent flu season.
We conclude that seasonal patterns of COVID-19 incidence and Influenza-Like Illnesses incidence are highly similar, in a country in the temperate climate zone, such as the Netherlands. Further, the COVID-19 pandemic satisfies the criteria of earlier respiratory pandemics, namely a first wave that is short-lived at the tail-end of flu season, and a second wave that is longer and more severe.
This seems to imply that the same factors that are driving the seasonality of Influenza-Like Illnesses are causing COVID-19 seasonality as well, such as solar radiation (UV), temperature, relative humidity, and subsequently seasonal allergens and allergies.
•Time-series of COVID-19 and Influenza-Like illnesses have highly similar seasonal patterns in the Netherlands.•COVID-19 satisfies the seasonal criteria of earlier flu-like pandemics.•The implication is that the seasonal factors driving flu season, are also responsible for COVID-19 seasonality.•Determined seasonality by applying comparative time-series analysis and a standardized logarithmic infection scale.
We recently showed that seasonal patterns of COVID-19 incidence and Influenza-Like Illnesses incidence are highly similar, in a country in the temperate climate zone, such as the Netherlands. We ...hypothesize that in The Netherlands the same environmental factors and mobility trends that are associated with the seasonality of flu-like illnesses are predictors of COVID-19 seasonality as well.
We used meteorological, pollen/hay fever and mobility data from the Netherlands. For the reproduction number of COVID-19 (Rt), we used daily estimates from the Dutch State Institute for Public Health. For all datasets, we selected the overlapping period of COVID-19 and the first allergy season: from February 17, 2020 till September 21, 2020 (n = 218). Backward stepwise multiple linear regression was used to develop an environmental prediction model of the Rt of COVID-19. Next, we studied whether adding mobility trends to an environmental model improved the predictive power.
Through stepwise backward multiple linear regression four highly significant (p < 0.01) predictive factors are selected in our combined model: temperature, solar radiation, hay fever incidence, and mobility to indoor recreation locations. Our combined model explains 87.5% of the variance of Rt of COVID-19 and has a good and highly significant fit: F(4, 213) = 374.2, p < 0.00001. This model had a better overall predictive performance than a solely environmental model, which explains 77.3% of the variance of Rt (F(4, 213) = 181.3, p < 0.00001).
We conclude that the combined mobility and environmental model can adequately predict the seasonality of COVID-19 in a country with a temperate climate like the Netherlands. In this model higher solar radiation, higher temperature and hay fever are related to lower COVID-19 reproduction, and higher mobility to indoor recreation locations is related to an increased COVID-19 spread.
•The seasonality of COVID-19 can be explained by environmental factors and mobility.•A combined model explains 87.5% of the variance of the reproduction number (Rt) of COVID-19.•Inhibitors of the Rt of COVID-19 are higher solar radiation, and seasonal allergens/allergies.•Mobility to indoor recreation locations increases the Rt of COVID-19.•Adding mobility trends to an environmental model improves its predictive value regarding COVID-19 seasonality.
Current models for flu-like epidemics insufficiently explain multi-cycle seasonality. Meteorological factors alone, including the associated behavior, do not predict seasonality, given substantial ...climate differences between countries that are subject to flu-like epidemics or COVID-19. Pollen is documented to be allergenic, it plays a role in immuno-activation and defense against respiratory viruses, and seems to create a bio-aerosol that lowers the reproduction number of flu-like viruses. Therefore, we hypothesize that pollen may explain the seasonality of flu-like epidemics, including COVID-19, in combination with meteorological variables.
We have tested the Pollen-Flu Seasonality Theory for 2016–2020 flu-like seasons, including COVID-19, in the Netherlands, with its 17.4 million inhabitants. We combined changes in flu-like incidence per 100 K/Dutch residents (code: ILI) with pollen concentrations and meteorological data. Finally, a predictive model was tested using pollen and meteorological threshold values, inversely correlated to flu-like incidence.
We found a highly significant inverse correlation of r(224) = −0.41 (p < 0.001) between pollen and changes in flu-like incidence, corrected for the incubation period. The correlation was stronger after taking into account the incubation time. We found that our predictive model has the highest inverse correlation with changes in flu-like incidence of r(222) = −0.48 (p < 0.001) when average thresholds of 610 total pollen grains/m3, 120 allergenic pollen grains/m3, and a solar radiation of 510 J/cm2 are passed. The passing of at least the pollen thresholds, preludes the beginning and end of flu-like seasons. Solar radiation is a co-inhibitor of flu-like incidence, while temperature makes no difference. However, higher relative humidity increases with flu-like incidence.
We conclude that pollen is a predictor of the inverse seasonality of flu-like epidemics, including COVID-19, and that solar radiation is a co-inhibitor, in the Netherlands.
Display omitted
•Testing pollen-flu seasonality theory for 2016–2020 in the Netherlands, overlapping COVID-19•Pollen have allergenic and immuno-activating properties.•Highly significant inverse correlation between pollen and flu-like incidence.•Solar radiation is a co-inhibitor of flu-like epidemics.•COVID-19 does not break with seasonality pattern, but more data are needed for conclusion.
For older patients with kidney failure, lowering symptom burden may be more important than prolonging life. Dialysis initiation may affect individual kidney failure-related symptoms differently, but ...the change in symptoms before and after start of dialysis has not been studied. Therefore, we investigated the course of total and individual symptom number and burden before and after starting dialysis in older patients.
The European Quality (EQUAL) study is an ongoing, prospective, multicenter study in patients ≥65 years with an incident eGFR ≤20 ml/min per 1.73 m
. Using the dialysis symptom index (DSI), 30 symptoms were assessed every 3-6 months between 2012 and 2021. Scores for symptom number range from zero to 30 and, for burden, from zero to 150, with higher scores indicating more severity. Using mixed effects models, we studied symptoms during the year preceding and the year after dialysis initiation.
We included 456 incident patients on dialysis who filled out at least one DSI during the year before or after dialysis. At dialysis initiation, mean (SD) participant age was 76 (6) years, 75% were men, mean (SD) eGFR was 8 (3) ml/min per 1.73 m
, 44% had diabetes, and 46% had cardiovascular disease. In the year before dialysis initiation, symptom number increased +3.6 (95% confidence interval 95% CI, +2.5 to +4.6) and symptom burden increased +13.3 (95% CI, +9.5 to +17.0). In the year after, symptom number changed -0.9 (95% CI, -3.4 to +1.5) and burden decreased -5.9 (95% CI, -14.9 to -3.0). At dialysis initiation, "fatigue," "decreased interest in sex," and "difficulty becoming sexually aroused" had the highest prevalence of 81%, 69%, and 68%, respectively, with a burden of 2.7, 2.4, and 2.3, respectively. "Fatigue" somewhat improved after dialysis initiation, whereas the prevalence and burden of sexual symptoms further increased.
Symptom burden worsened considerably before and stabilized after dialysis initiation. "Fatigue," "decreased interest in sex," and "difficulty becoming sexually aroused" were considered most burdensome, of which only "fatigue" somewhat improved after dialysis initiation.
Chronic kidney disease (CKD) is highly prevalent among older post-myocardial infarction (MI) patients. It is not known whether CKD is an independent risk factor for mortality in older post-MI ...patients with optimal cardiovascular drug-treatment. Therefore, we studied the relation between kidney function and all-cause and specific mortality among older post-MI patients, without severe heart failure, who are treated with state-of-the-art pharmacotherapy. From 2002-2006, 4,561 Dutch post-MI patients were enrolled and followed until death or January 2012. We estimated Glomerular Filtration Rate (eGFR) with cystatin C (cysC) and creatinine (cr) using the CKD-EPI equations and analyzed the relation with any and major causes of death using Cox models and restricted cubic splines. Mean (SD) for age was 69 years (5.6), 79% were men, 17% smoked, 21% had diabetes, 90% used antihypertensive drugs, 98% used antithrombotic drugs and 85% used statins. Patients were divided into four categories of baseline eGFRcysC: ≥90 (33%; reference), 60-89 (47%), 30-59 (18%), and <30 (2%) ml/min/1.73m2. Median follow-up was 6.4 years. During follow-up, 873 (19%) patients died: 370 (42%) from cardiovascular causes, 309 (35%) from cancer, and 194 (22%) from other causes. After adjustment for age, sex and classic cardiovascular risk factor, hazard ratios (95%-confidence intervals) for any death according to the four eGFRcysC categories were: 1 (reference), 1.4 (1.1-1.7), 2.9 (2.3-3.6) and 4.4 (3.0-6.4). The hazard ratios of all-cause and cause-specific mortality increased linearly below kidney functions of 80 ml/min/1.73 m2. Weaker results were obtained for eGFRcr. To conclude, we found in optimal cardiovascular drug-treated post-MI patients an inverse graded relation between kidney function and mortality for both cardiovascular as well as non-cardiovascular causes. Risk of mortality increased linearly below kidney function of about 80 ml/min/1.73 m2.
Abstract
Background
Post-myocardial infarction (MI) patients have a doubled rate of kidney function decline compared with the general population. We investigated the extent to which high intake of ...total, animal and plant protein are risk factors for accelerated kidney function decline in older stable post-MI patients.
Methods
We analysed 2255 post-MI patients (aged 60–80 years, 80% men) of the Alpha Omega Cohort. Dietary data were collected with a biomarker-validated 203-item food frequency questionnaire. At baseline and 41 months, we estimated glomerular filtration rate based on the Chronic Kidney Disease Epidemiology Collaboration equations for serum cystatin C estimated glomerular filtration rate (eGFRcysC) alone and both creatinine and cystatin C (eGFRcr–cysC).
Results
Mean standard deviation (SD) baseline eGFRcysC and eGFRcr–cysC were 82 (20) and 79 (19) mL/min/1.73 m2. Of all patients, 16% were current smokers and 19% had diabetes. Mean (SD) total protein intake was 71 (19) g/day, of which two-thirds was animal and one-third plant protein. After multivariable adjustment, including age, sex, total energy intake, smoking, diabetes, systolic blood pressure, renin–angiotensin system blocking drugs and fat intake, each incremental total daily protein intake of 0.1 g/kg ideal body weight was associated with an additional annual eGFRcysC decline of −0.12 (95% confidence interval −0.19 to −0.04) mL/min/1.73 m2, and was similar for animal and plant protein. Patients with a daily total protein intake of ≥1.20 compared with <0.80 g/kg ideal body weight had a 2-fold faster annual eGFRcysC decline of −1.60 versus −0.84 mL/min/1.73 m2. Taking eGFRcr–cysC as outcome showed similar results. Strong linear associations were confirmed by restricted cubic spline analyses.
Conclusion
A higher protein intake was significantly associated with a more rapid kidney function decline in post-MI patients.
The prevalence of obesity is increasing globally and is associated with chronic kidney disease and premature mortality. However, the impact of recipient obesity on kidney transplant outcomes remains ...unclear. This study aimed to investigate the association between recipient obesity and mortality, death-censored graft loss and delayed graft function (DGF) following kidney transplantation.
A systematic review and meta-analysis was conducted using Medline, Embase and the Cochrane Library. Observational studies or randomized controlled trials investigating the association between recipient obesity at transplantation and mortality, death-censored graft loss and DGF were included. Obesity was defined as a body mass index (BMI) of ≥30 kg/m(2). Obese recipients were compared with those with a normal BMI (18.5-24.9 kg/m(2)). Pooled estimates of hazard ratios (HRs) for patient mortality or death-censored graft loss and odds ratios (ORs) for DGF were calculated.
Seventeen studies including 138 081 patients were analysed. After adjustment, there was no significant difference in mortality risk in obese recipients HR = 1.24, 95% confidence interval (CI) = 0.90-1.70, studies = 5, n = 83 416. However, obesity was associated with an increased risk of death-censored graft loss (HR = 1.06, 95% CI = 1.01-1.12, studies = 5, n = 83 416) and an increased likelihood of DGF (OR = 1.68, 95% CI = 1.39-2.03, studies = 4, n = 28 847).
Despite having a much higher likelihood of DGF, obese transplant recipients have only a slightly increased risk of graft loss and experience similar survival to recipients with normal BMI.
Abstract Early kidney injury may be detected by urinary markers, such as beta-2 microglobulin (B2M), tissue inhibitor of metalloproteinases-2 (TIMP-2), insulin-like growth factor-binding protein 7 ...(IGFBP7), kidney injury molecule-1 (KIM-1) and/or neutrophil gelatinase-associated lipocalin (NGAL). Of these biomarkers information on pathophysiology and reference ranges in both healthy and diseased populations are scarce. Differences in urinary levels of B2M, TIMP-2, IGFBP7, KIM-1 and NGAL were compared 24 h before and after nephrectomy in 38 living kidney donors from the REnal Protection Against Ischaemia–Reperfusion in transplantation study. Linear regression was used to assess the relation between baseline biomarker concentration and kidney function 1 year after nephrectomy. Median levels of urinary creatinine and creatinine standardized B2M, TIMP-2, IGFBP7, KIM-1, NGAL, and albumin 24 h before nephrectomy in donors were 9.4 mmol/L, 14 μg/mmol, 16 pmol/mmol, 99 pmol/mmol, 63 ng/mmol, 1390 ng/mmol and 0.7 mg/mmol, with median differences 24 h after nephrectomy of − 0.9, + 1906, − 7.1, − 38.3, − 6.9, + 2378 and + 1.2, respectively. The change of donor eGFR after 12 months per SD increment at baseline of B2M, TIMP-2, IGFBP7, KIM-1 and NGAL was: − 1.1, − 2.3, − 0.7, − 1.6 and − 2.8, respectively. Urinary TIMP-2 and IGFBP7 excretion halved after nephrectomy, similar to urinary creatinine, suggesting these markers predominantly reflect glomerular filtration. B2M and NGAL excretion increased significantly, similar to albumin, indicating decreased proximal tubular reabsorption following nephrectomy. KIM-1 did not change considerably after nephrectomy. Even though none of these biomarkers showed a strong relation with long-term donor eGFR, these results provide valuable insight into the pathophysiology of these urinary biomarkers.
Previous studies showed that statins reduce the progression of kidney function decline and proteinuria, but whether specific types of statins are more beneficial than others remains unclear. We ...performed a network meta-analysis of randomized controlled trials (RCT) to investigate which statin most effectively reduces kidney function decline and proteinuria. We searched MEDLINE, Embase, Web of Science, and the Cochrane database until July 13, 2018, and included 43 RCTs (>110,000 patients). We performed a pairwise random-effects meta-analysis and a network meta-analysis according to a frequentist approach. We assessed network inconsistency, publication bias, and estimated for each statin the probability of being the best treatment. Considerable heterogeneity was present among the included studies. In pairwise meta-analyses, 1-year use of statins versus control reduced kidney function decline by 0.61 (95%-CI: 0.27; 0.95) mL/min/1.73 m
and proteinuria with a standardized mean difference of -0.58 (95%-CI:-0.88; -0.29). The network meta-analysis for the separate endpoints showed broad confidence intervals due to the small number available RCTs for each individual comparison. In conclusion, 1-year statin use versus control attenuated the progression of kidney function decline and proteinuria. Due to the imprecision of individual comparisons, results were inconclusive as to which statin performs best with regard to renal outcome.
PDF
