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      Association between dietary patterns and chronic kidney disease combined with hyperuricemia

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          Abstract

          Chronic kidney disease (CKD) combined with hyperuricemia is a concerning health issue, but the association between this condition and dietary patterns remains poorly understood.

          Abstract

          Background and aims: Chronic kidney disease (CKD) combined with hyperuricemia is a concerning health issue, but the association between this condition and dietary patterns remains poorly understood. The aim of this study was to assess the associations between dietary patterns and CKD combined with hyperuricemia. Methods: This cross-sectional study was conducted involving 12 318 participants aged 18–79 years during 2018–2020. Dietary intake information was collected using a validated 110-item food frequency questionnaire. Factor analysis was used to identify major dietary patterns. CKD was defined as the presence of albuminuria or an estimated glomerular filtration rate <60 mL min −1 1.73 m −2. Hyperuricemia was defined as serum uric acid levels >420 μmol L −1 both in men and women. Logistic regression models were applied to assess the association between dietary patterns and the risk of CKD combined with hyperuricemia. Results: Five major dietary patterns were identified: ‘healthy pattern’, ‘traditional pattern’, ‘animal foods pattern’, ‘sweet foods pattern’, and ‘tea–alcohol pattern’, which together explained 38.93% of the variance in the diet. After adjusting for potential confounders, participants in the highest quartile of the traditional pattern had a lower risk of CKD combined with hyperuricemia (OR = 0.49, 95% CI: 0.32–0.74, P for trend < 0.01). Conversely, participants in the highest quartile of the sweet foods pattern had a higher risk compared to those in the lowest quartile (OR = 1.69, 95% CI: 1.18–2.42, P for trend < 0.01). However, no significant association was observed between the healthy pattern, animal foods pattern and tea–alcohol pattern and the risk of CKD combined with hyperuricemia. Conclusions: Our results suggest that the traditional pattern is associated with a reduced risk of CKD combined with hyperuricemia, whereas the sweet foods pattern is associated with an increased risk.

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          A new equation to estimate glomerular filtration rate.

          Andrew Levey, Lesley Stevens, Christopher H Schmid … (2009)
          Equations to estimate glomerular filtration rate (GFR) are routinely used to assess kidney function. Current equations have limited precision and systematically underestimate measured GFR at higher values. To develop a new estimating equation for GFR: the Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) equation. Cross-sectional analysis with separate pooled data sets for equation development and validation and a representative sample of the U.S. population for prevalence estimates. Research studies and clinical populations ("studies") with measured GFR and NHANES (National Health and Nutrition Examination Survey), 1999 to 2006. 8254 participants in 10 studies (equation development data set) and 3896 participants in 16 studies (validation data set). Prevalence estimates were based on 16,032 participants in NHANES. GFR, measured as the clearance of exogenous filtration markers (iothalamate in the development data set; iothalamate and other markers in the validation data set), and linear regression to estimate the logarithm of measured GFR from standardized creatinine levels, sex, race, and age. In the validation data set, the CKD-EPI equation performed better than the Modification of Diet in Renal Disease Study equation, especially at higher GFR (P < 0.001 for all subsequent comparisons), with less bias (median difference between measured and estimated GFR, 2.5 vs. 5.5 mL/min per 1.73 m(2)), improved precision (interquartile range [IQR] of the differences, 16.6 vs. 18.3 mL/min per 1.73 m(2)), and greater accuracy (percentage of estimated GFR within 30% of measured GFR, 84.1% vs. 80.6%). In NHANES, the median estimated GFR was 94.5 mL/min per 1.73 m(2) (IQR, 79.7 to 108.1) vs. 85.0 (IQR, 72.9 to 98.5) mL/min per 1.73 m(2), and the prevalence of chronic kidney disease was 11.5% (95% CI, 10.6% to 12.4%) versus 13.1% (CI, 12.1% to 14.0%). The sample contained a limited number of elderly people and racial and ethnic minorities with measured GFR. The CKD-EPI creatinine equation is more accurate than the Modification of Diet in Renal Disease Study equation and could replace it for routine clinical use. National Institute of Diabetes and Digestive and Kidney Diseases.
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            Global, regional, and national burden of chronic kidney disease, 1990–2017: a systematic analysis for the Global Burden of Disease Study 2017

            Boris Bikbov, Caroline A. Purcell, Andrew Levey … (2021)
            Summary Background Health system planning requires careful assessment of chronic kidney disease (CKD) epidemiology, but data for morbidity and mortality of this disease are scarce or non-existent in many countries. We estimated the global, regional, and national burden of CKD, as well as the burden of cardiovascular disease and gout attributable to impaired kidney function, for the Global Burden of Diseases, Injuries, and Risk Factors Study 2017. We use the term CKD to refer to the morbidity and mortality that can be directly attributed to all stages of CKD, and we use the term impaired kidney function to refer to the additional risk of CKD from cardiovascular disease and gout. Methods The main data sources we used were published literature, vital registration systems, end-stage kidney disease registries, and household surveys. Estimates of CKD burden were produced using a Cause of Death Ensemble model and a Bayesian meta-regression analytical tool, and included incidence, prevalence, years lived with disability, mortality, years of life lost, and disability-adjusted life-years (DALYs). A comparative risk assessment approach was used to estimate the proportion of cardiovascular diseases and gout burden attributable to impaired kidney function. Findings Globally, in 2017, 1·2 million (95% uncertainty interval [UI] 1·2 to 1·3) people died from CKD. The global all-age mortality rate from CKD increased 41·5% (95% UI 35·2 to 46·5) between 1990 and 2017, although there was no significant change in the age-standardised mortality rate (2·8%, −1·5 to 6·3). In 2017, 697·5 million (95% UI 649·2 to 752·0) cases of all-stage CKD were recorded, for a global prevalence of 9·1% (8·5 to 9·8). The global all-age prevalence of CKD increased 29·3% (95% UI 26·4 to 32·6) since 1990, whereas the age-standardised prevalence remained stable (1·2%, −1·1 to 3·5). CKD resulted in 35·8 million (95% UI 33·7 to 38·0) DALYs in 2017, with diabetic nephropathy accounting for almost a third of DALYs. Most of the burden of CKD was concentrated in the three lowest quintiles of Socio-demographic Index (SDI). In several regions, particularly Oceania, sub-Saharan Africa, and Latin America, the burden of CKD was much higher than expected for the level of development, whereas the disease burden in western, eastern, and central sub-Saharan Africa, east Asia, south Asia, central and eastern Europe, Australasia, and western Europe was lower than expected. 1·4 million (95% UI 1·2 to 1·6) cardiovascular disease-related deaths and 25·3 million (22·2 to 28·9) cardiovascular disease DALYs were attributable to impaired kidney function. Interpretation Kidney disease has a major effect on global health, both as a direct cause of global morbidity and mortality and as an important risk factor for cardiovascular disease. CKD is largely preventable and treatable and deserves greater attention in global health policy decision making, particularly in locations with low and middle SDI. Funding Bill & Melinda Gates Foundation.
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              Dietary pattern analysis: a new direction in nutritional epidemiology.

              Frank Hu (2002)
              Recently, dietary pattern analysis has emerged as an alternative and complementary approach to examining the relationship between diet and the risk of chronic diseases. Instead of looking at individual nutrients or foods, pattern analysis examines the effects of overall diet. Conceptually, dietary patterns represent a broader picture of food and nutrient consumption, and may thus be more predictive of disease risk than individual foods or nutrients. Several studies have suggested that dietary patterns derived from factor or cluster analysis predict disease risk or mortality. In addition, there is growing interest in using dietary quality indices to evaluate whether adherence to a certain dietary pattern (e.g. Mediterranean pattern) or current dietary guidelines lowers the risk of disease. In this review, we describe the rationale for studying dietary patterns, and discuss quantitative methods for analysing dietary patterns and their reproducibility and validity, and the available evidence regarding the relationship between major dietary patterns and the risk of cardiovascular disease.
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                Author and article information

                Contributors
                Journal
                Journal ID (publisher-id): FFOUAI
                Title: Food & Function
                Abbreviated Title: Food Funct.
                Publisher: Royal Society of Chemistry (RSC)
                ISSN (Print): 2042-6496
                ISSN (Electronic): 2042-650X
                Publication date Created: January 02 2024
                Publication date (Electronic): 2024
                Volume: 15
                Issue: 1
                Pages: 255-264
                Affiliations
                [1 ]Department of Clinical Epidemiology, Shengjing Hospital of China Medical University, No. 36, San Hao Street, Shenyang, Liaoning, 110004, China
                [2 ]Department of Otorhinolaryngology – Head and Neck Surgery, Shengjing Hospital of China Medical University, China
                [3 ]Department of Ultrasound, Shengjing Hospital of China Medical University, China
                [4 ]Clinical Research Centre, Shengjing Hospital of China Medical University, China
                [5 ]Liaoning Key Laboratory of Precision Medical Research on Major Chronic Disease, Shengjing Hospital of China Medical University, No. 36, San Hao Street, Shenyang, Liaoning, 110004, China
                Article
                DOI: 10.1039/D3FO03354F
                PubMed ID: 38059607
                SO-VID: fe7861ba-ee68-45ca-aa73-792a5ece3b09
                Copyright © © 2024
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