Hiroshi Bando, MD, PhD, FACP
Lecturer, Tokushima University and Medical Research, Nakashowa 1-61, Tokushima 770-0943, Japan; Tel. +81-90-3187-2485; E-mail: pianomed@bronze.ocn.ne.jp
As to adequate nutritional therapy with metabolic and diabetic patients, the discussion has been continued for long in the light of calorie restriction (CR) and low-carbohydrate diet (LCD).1,2 Several researcher showed the predominant efficacy of LCD compared with CR.3-8 Recently, Bernstein and Feinman have suggested adequate definition and treatment of LCD, in addition to the statement of American Diabetes Association (ADA).1
In Japan, authors and colleagues have treated lots of patients with diabetes mellitus, and investigated the efficacy of LCD.9,10,11 For medical and social development of LCD, we proposed 3 types of actual LCD in daily life, which are petit, standard and super LCD.9,12.13 We have also reported the significant role of ketone bodies in LCD and physiological role for pregnant female and fetus newborn axis.11,14
Furthermore, we investigated the clinical significance of Morbus (M) value in the treatment of LCD for the patients with type 2 diabetes mellitus (T2DM).11,12M-value is useful marker in the evaluation for blood glucose variability in diabetic patients that expresses both elevated glucose level and increased mean amplitude of glycemic excursions (MAGE).15-17 When glycemic control gets better, numerical value of M-value decreases highly, which is simple and helpful for clinical and medical practices and research.
In this study, we have treated T2DM patients for super LCD and investigated the changes of average blood glucose, M-value and renal biomarkers such as uric acid, creatinine and Cystatin C.
The subjects were 93 patients with T2DM, including 41 males and 52 females, with 58.3±13.2 years old (mean±SD), and 60 years in median value. They were admitted for 14 days, and received the same treatment protocol for endocrine, metabolic and renal examination.
The methods included formula diet and examination. On admission, CR diet was given on day 1 and 2, including 60% carbohydrates, 25% lipids and 15% protein with 1400 kcal/day. After that, LCD was given from day 3 until day 14, including 12% carbohydrates, 64% lipids and 24% protein with 1400 kcal/ day, which is so-called super LCD formula used for long years in our investigation.9-12
We measured several biomarkers on day 2, 4 and 14. On day 2, the basal biomarkers such as blood glucose/HbA1c, M-value and sera levels of uric acid, creatinine, cystatin C, and daily profile of blood glucose were measured. On day 4, daily profile of blood glucose was measured. On day 14, uric acid, creatinine and other biomarkers were measured.
One subject who is 64-years-old man with HbA1c 7.3% had 3 times of daily profile of glucose. The average glucose and M-value were calculated for that subject.
Analysis for M-value
The M-value (Morbus value) stands for the combination of two factors. One is the level of blood gluose, and another is the MAGE. It is a logarithmic transformation of the deviation of glycemia from an arbitrary assigned “ideal” glucose value.15-17 The formula is as follows: M = MBS+MW, where MW=(maximum blood glucose-minimum glucose)/20; MBS=the mean of MBSBS; MBSBS=individual M-value for each blood glucose value calculated as (absolute value of [10×log (blood glucose value/120)]3 ).
For the M-value, the standard range is <180, borderline is 180-320 and abnormal is >320. Whereas in the M-value, the standard range is <5, borderline is 5-10 and abnormal is >10. It was reported that multiple sampling and a 7-point glycemic trial per day would have yielded similar results.17
Statistical Analyses
In current study, data was represented as the mean±standard deviation, and also represented median, quartile of 25% and 75% in biomarkers. For statistical analyses, correlation coefficients were calculated using the Microsoft Excel analytical tool.18 Furthermore, we used JMP (Version 8) statistical analysis software (JMP Japan Division of SAS Institute Japan Ltd., Minato-ku, Tokyo, Japan) and Microsoft Excel analytical tool. A significance level of less than 5% obtained using a two-tailed test was considered to be statistically significant.
Ethical Considerations
Current study was conducted in compliance with the ethical principles of the Declaration of Helsinki and Japan’s Act on the Protection of Personal Information along with the Ministerial Ordinance on Good Clinical Practice (GCP) for Drug (Ordinance of Ministry of Health and Welfare No. 28 of March 27, 1997). No ethical committee meeting was held. Informed consent was obtained from the subjects related to this research. The study was registered with UMIN #R000031211.
Fundamental data
The results obtained from 93 subjects were shown in Table 1. By the level of M-value, subjects were classified into 4 groups. Group 1-4 revealed the M-value, less than 25, 26-100, 101-250, more than 251, with the number of the cases 24, 24, 24, 21, respectively.
| Table 1: General Data of the Subjects | ||||
|
Categorization |
Group 1 | Group 2 | Group 3 |
Group 4 |
| Number |
24 24 |
24 | 24 |
21 |
| Sex (male/female) |
13/11 |
8/16 | 9/15 |
11/10 |
| Age in average (y.o.) |
56.2±13.3 |
59.1±15.1 | 62.8±7.4 |
54.7±15.1 |
| Age in median (y.o.) |
57 (47-64) |
63 (50-72) | 63 (58-68) |
60 (50-65) |
| Body Mass Index (kg/m2 ) |
23.8±3.0 |
24.2±5.2 | 25.4±4.2 |
27.3±5.2 |
| M-value on day 2 |
4-25 |
26-100 | 101-250 |
251-1285 |
| The results were expressed by Mean±SD. The results of age were also expressed by median (25%-75%). |
||||
Glucose Metabolism and M-value
The results for HbA1c, fasting glucose and M-value were shown in Table 2. M-value in 4 groups decreased from day 2 to 4, which was 10.4 to 9.1, 53.5 to 7.7, 150 to 19.1 and 438 to 87, respectively (Figure 1). The average glucose and M-value of 64-years-old patient with T2DM were shown in Table 3.
| Table 2: HbA1c, Glucose and Morbus (M) Value of the Subjects. | ||||
|
Categorization |
Group 1 | Group 2 | Group 3 |
Group 4 |
| HbA1c | ||||
| HbA1c on day 2 in average (%) |
6.6±1.1 |
7.4±1.3 | 8.5±1.2 |
9.5±1.7 |
| HbA1c on day 2 in median (%) |
6.4 (6.1-6.8) |
7.3 (6.3-8.2) | 8.4 (7.6-9.4) |
9.0 (8.6-10.6) Fasting Glucose |
| Fasting Glucose | ||||
| Fasting Glucose on day 2 (mg/dL) |
117.3±20.4 |
146.5±31.3 | 183.5±42.5 |
226.5±38.5 |
| Fasting Glucose on day 4 (mg/dL) |
110.5±42.7 |
125.2±27.0 | 147.7±30.6 |
186.8±43.3 |
| Fasting Glucose on day 14 (mg/dL) |
98.9±15.0 |
110.3±21.3 | 119.2±24.4 |
133.1±42.1 |
| Average Glucose | ||||
| average glucose on day 2 (mg/dL) |
128.2±11.8 |
163.6±46.2 | 211.1±20.3 |
298.6±46.3 |
| average glucose on day 4 (mg/dL) |
111.6±19.2 |
135.5±32.2 | 159.0±21.7 |
198.2±47.7 |
| Morbus value | ||||
| M value on day 2 |
10.4 (6.2-17.7) |
53.5( 41-67) | 150 (125-194) |
438 (343-701) |
| M value on day 4 |
9.1 (4.4-14.3) |
7.7 (3.9-19.9) | 19.1 (14.0-29.9) |
87.0 (33-148) |
| The results were expressed by Mean±SD. The results of HbA1c and M value were expressed by median (25%-75%). |
||||
| Table 3: Average Glucose and M-value of Patient with T2DM. | |||||||||
|
Blood Glucose level (mg/dL) |
|||||||||
|
Time (h) |
8 | 10 | 12 | 14 | 17 | 19 | 21 | Average |
M-value |
| Day 2 |
180 |
315 | 288 | 335 | 268 | 333 | 304 | 289 |
426.0 |
| Day 4 |
140 |
191 | 185 | 180 | 152 | 166 | 160 | 168 |
29.0 |
| Day 14 |
97 |
124 | 112 | 125 | 86 | 104 | 93 | 106 |
7.4 |
| The patient was 64-years-old male with HbA1c 7.3%. He was on CR on day 1,2 and LCD on day 3-14. Blood glucose was measured 7 times a day from 08:00 h to 21:00 h. M-value stands for combined implication of both average glucose and Mean Amplitude of Glycemic Excursions (MAGE). |
|||||||||
Figure 1: Changes of M-value on Day 2 and 4 in 4 Groups.
The M-values in 4 groups were investigated on day 2 and 4. The median levels on day 2 to day 4 in 4 groups were 10.4 to 9.1, 53.5 to 7.7, 150 to 19.1 and 438 to 87, respectively. All subjects were provided calorie restricted (CR) diet on day 1 and 2, and low carbohydrate diet (LCD) on day 3 and 4. Abbreviation: 1-CR means group 1 and Calorie Restriction on day 2, and 4-LCD means group 4 and Low Carbohydrate Diet on day 4.
Renal Function
Renal biomarkers on day 2 and day 14 were shown in Table 4. The average uric acid (UA) level in each group revealed significant increase from day 2 to day 14. (Figure 2) There was significant correlation between UA increment and creatinine increment on day 2 and day 14 (Figure 3). Other markers did not show any significant difference in 4 groups.
| Table 4: Renal Function of the Subjects. | ||||
|
Categorization |
Group 1 | Group 2 | Group 3 |
Group 4 |
| Uric Acid | ||||
| Uric Acid on day 2 (mg/dL) |
5.45±1.37 |
4.90±1.46 | 5.33±1.36 |
4.89±1.40 |
| Uric Acid on day 14 (mg/dL) |
6.59±1.92 |
6.19±.2.01 | 6.18±1.29 |
5.90±1.63 |
| Uric Acid increment (mg/dL) |
1.01±1.03 |
1.12±1.02 | 1.22±1.06 |
1.12±1.03 |
| Creatinine | ||||
| Creatinine on day 2 (mg/dL) |
0.75±0.15 |
0.70±0.17 | 0.76±0.21 |
0.64±0.15 |
| Creatinine on day 14 (mg/dL) |
0.81±0.18 |
0.77±0.16 | 0.80±0.23 |
0.72±0.17 |
| Blood Urea Nitrogen | ||||
| BUN on day 2 (mg/dL) |
18.5±5.0 |
18.3±4.8 | 19.3±7.4 |
17.5±4.3 |
| BUN on day 14 (mg/dL) |
22.3±7.2 |
21.2±5.3 | 21.6±11.1 |
17.9±3.8 |
| Other markers | ||||
| Cystatine C on day 2 (mg/L) |
0.81±0.18 |
0.79±0.17 | 0.83±0.32 |
0.69±0.15 |
| CCr on day 4 (ml/min) |
99.7±25.3 |
96.3±29.7 | 95.9±27.2 |
115.3±25.3 |
| eGFR on day 4 (ml/min) |
78.6±19.9 |
75.9±23.4 | 75.6±21.4 |
90.9±19.9 |
| u-uric acid/creatinine |
0.42±0.11 |
0.52±0.18 | 0.53±0.13 |
0.61±0.22 |
| The results were expressed by Mean±SD. | ||||
Figure 2: Changes of Uric Acid on Day 2 and 14 in 4 Groups.
Uric acid (UA) levels were investigated in group 1-4 on day 4 and day 14. The average UA level in each grouprevealed significant increase from day 2 to day 14. The asterisk represents as follows: *p<0.05, **p<0.01. Gr1-d2: Group 1 day 2; Gr4-d14: Group 4 day 14.
Figure 3: Correlation between Uric Acid Increment and Creatinine Increment on Day 2 and Day 14.
There were significant correlation between both factors (p<0.05).
There was no significant correlation between UA increment from day 2 to 14 and Cystatin C. There was negative significant correlation between Creatinine Clearance (CCr) and Cystatin C. (Figure 4). There was positive significant correlation between Creatinine and Cystatin C (p<0.01) (Figure 5).
Figure 4: Correlation between Creatinine Clearance and Cystatin C.
There were negative significant correlation between both factors (p<0.01).
Figure 5: Correlation between Creatinine and Cystatin C.
There were positive significant correlation between both factors (p<0.01).
LCD was introduced by Bernstein and Atkins, and got developed for long years.19,20 Successively, the effect of LCD have been reported.21-24 In Japan, we have continued clinical study and research concerning LCD, and reported effect of weight reduction, elevated ketone bodies and its physiological role and significant evaluation using M-value.11,12
M-value is a useful biomarker to express the variability of blood glucose including glucose level and fluctuation. There were discussions concerning how many times of sampling per day is necessary. It was reported that multiple sampling and a 7-point glycemic trial per day would have yielded similar results.25-27
M-value reveals similar result for continuous glucose monitoring (CGM) for 48 hours which is considered as ideal research method.28There are rare reports concerning M-value. Patients with T1DM were investigated for blood glucose fluctuations 3 times and more per day, using CGM and self-monitoring of blood glucose (SMBG).29
In this study, the data of M-value and HbA1c in 4 groups showed parallel relation. M-value on day 4 was decreased than that on day 2, suggesting clinical short effect of LCD.
In previous reports, microalbuminuria and glomerular filtration rate (GFR) were followed 14 years, and they did not vary in relation to diabetes status.30,31Continuing LCD with 14% carbohydrate for 1 year, obese adults with T2DM had no adverse affect on clinical markers of renal function or on preexisting kidney disease.32 Similar results were obtained for creatinine, eGFR, albuminuria, or fluid and electrolyte balance.33-34
LCD is as safe as CR and Mediterranean, in preserving/improving renal function among moderately obese participants with or without type 2 diabetes.35 Potential improvement is likely to be mediated by weight loss-induced improvements in insulin sensitivity and blood pressure. For LCD for a year, patients with stage 1-3 renal disease had an improvement in renal function, whereas patients with hyperfiltration had a decrease in the GFR.36
Our current study showed that uric acid was significantly elevated from day 2 to day 14 in 4 groups. One of the causes of elevated uric acid was supposed to be some dehydrated status i.e., less water intake than expected. This is compatible with the result of significant correlation with elevated uric acid and elevated creatinine, with similar elevated tendency of BUN. Another probable cause would be the relative decrease of total calorie intake. When nutrition changes from CR to LCD, total calorie often decreases compared with that of previous status. Third possible cause would be the hyperfiltration of glomerulus in early stage of diabetic nephropathy. As the degree of hyperfiltration becomes less, creatinine and uric acid values could be increased.36 These speculation are possible because of accumulative reports for years.
In comparison with previous reports for years, our study is based on a short period of 2 weeks. Consequently, our speculation would be possibility due to limited research protocol.
In this study, Cystatin C showed significant positive correlation with creatinine and negative correlation with creatinine clearance. Serum Cystatin C alone provides GFR estimates that are nearly as accurate as serum creatinine adjusted for age, sex and race.37The chronic kidney disease-Epidemiology collaboration (CKD-EPI) creatinine equation was reported to be more accurate than the modification of diet in renal disease (MDRD) Study equation.38 Recently, the combined creatinineCystatin C equation have been precise and useful for various patho-physiological states including T2DM.39-41 Our results would become reference data among renal biomarkers in clinical terms which represents actual interrelationship on LCD in the patients with T2DM.
Our study has small and limited research situation, then further research will be necessary concerning the renal functions in LCD.1 LCD would be highly evaluated for treatment of diabetes with the clinical research of influence for renal function.
In this study, the changes of M-value on CR/LCD, creatinine, uric acid and Cystatin C were investigated in patients with T2DM. Decreased M indicates the efficacy of LCD for short period, and influence for renal biomarkers in detail relationship will be studied in the future.
The content of this article was presented at the 89th and 90th Scientific Meeting of Japan Endocrine Society (JES) Annual Congress, Kyoto, Japan, 2016 and 2017. The authors would like to thank the patients and staff for their co-operation and support.
The authors declare that they have no conflicts of interest.
1. Feinman RD, Pogozelski WK, Astrup A, et al. Dietary carbohydrate restriction asthe first approach in diabetes management: Critical review and evidence base. Nutrition. 2015; 31(1): 1-13. doi: 10.1016/j.nut.2014.06.011
2. Westman EC, Vernon MC. Has carbohydrate-restriction been forgotten as atreatment for diabetes mellitus? A perspective on the ACCORD study design. Nutr Metab (Lond). 2008; 5: 10. doi: 10.1186/1743-7075-5-10
3. Shai I, Schwarzfuchs D, Henkin Y, et al. Weight loss with a low-carbohydrate, mediterranean, or low-fat diet. N Engl J Med. 2008; 359: 229-241. doi: 10.1056/NEJMoa0708681
4. Accurso A, Bernstein RK, Dahlqvist A, et al. Dietary carbohydrate restriction in type 2 diabetes mellitus and metabolic syndrome: time for a critical appraisal. Nutr Metab (Lond). 2008; 5: 9. doi: 10.1186/1743-7075-5-9
5. Schwarzfuchs D, Golan R, Shai I. Four-year follow-up after two-year dietary interventions. N Engl J Med. 2012; 367: 1373- 1374. doi: 10.1056/NEJMc1204792
6. Atallah R, Filion KB, Wakil SM. Long-term effects of 4 popular diets on weight loss and cardiovascular risk factors: A systematic review of randomized controlled trials. Circ Cardiovasc Qual Outcomes. 2014; 7(6): 815-827. doi: 10.1161/CIRCOUTCOMES.113.000723
7. McVay MA, Voils CI, Coffman CJ, et al. Factors associated with choice of a low-fat or low-carbohydrate diet during a behavioral weight loss intervention. Appetite. 2014; 83: 117-124. doi: 10.1016/j.appet.2014.08.023
8. Gow ML, Garnett SP, Baur LA, Lister NB. The effectiveness of different diet strategies to reduce type 2 diabetes risk in youth. Nutrients. 2016; 8(8). pii: E486. doi: 10.3390/nu8080486
9. Ebe K, Ebe Y, Yokota S, et al. Low Carbohydrate diet (LCD) treated for three cases as diabetic diet therapy. Asia Pac Fam Med. 2004; 51: 125-129.
10. Bando H, Nakamura T. Carbo-count therapy and low carbohydrate diet (LCD). The Journal of the Therapy. 2008; 90: 3105-3111.
11. Bando H, Ebe K, Nakamura T, Bando M, Yonei Y. Low carbohydrate diet (LCD): Long- and short-term effects and hyperketonemia. Glycative Stress Research. 2016; 3(4): 193-204. Website. http://www.toukastress.jp/webj/article/2016/GS16-18.pdf. Accessed June 27, 2017.
12. Bando H, Ebe K, Muneta T, Bando M, Yonei Y. Effect of low carbohydrate diet on type 2 diabetic patients and usefulness of M-value. Diabetes Res Open J. 2017; 3(1): 9-16. doi: 10.17140/ DROJ-3-130
13. Bando H, Ebe K, Muneta T, Bando M, Yonei Y. Clinical effect of low carbohydrate diet (LCD): Case report. Diabetes Case Rep. 2017; 2: 124. doi: 10.4172/2572-5629.1000124
14. Muneta T, Kawaguchi E, Nagai Y, et al. Ketone body elevation in placenta, umbilical cord, newborn and mother in normal delivery. Glycative Stress Research. 2016; 3(3): 133-140. Website. http://www.toukastress.jp/webj/article/2016/GS16-10.pdf. Accessed June 27, 2017.
15. Schlichtkrull J, Munck O, Jersild M. The M-value, an index of blood sugar control in diabetics. Acta Med Scand. 1965; 177: 95-102. doi: 10.1111/j.0954-6820.1965.tb01810.x
16. Service FJ, Molnar GD, Rosevear JW, Ackerman E, Gatewood LC, Taylor WF. Mean amplitude of glycemic excursions, a measure of diabetic instability. Diabetes. 1970; 19: 644-655. doi: 10.2337/diab.19.9.644
17. Siegelaar SE, Holleman F, Hoekstra JBL, Devries JH. Glucose variability; does it matter? Endocr Rev. 2010; 31(2): 171- 182. doi: 10.1210/er.2009-0021
18. Yanai H. 4 steps Excel Statistics. 3rd ed. Tokorozawa, Japan: Seiunsha Publishing Co. Ltd.; 2014.
19. Bernstein RK. Dr. Bernstein’s DiabetesSolution: The Complete Guide to Achieving Normal Blood Sugars. New York, USA: Little, Brown US; 2011.
20. Atkins RC. Dr Atkins’ New Diet Revolution. New York, USA: Harper-Collins; 2001.
21. Hu T, Yao L, Reynolds K, et al. The effects of a low-carbohydrate diet vs. a low-fat diet on novel cardiovascular risk factors: A randomized controlled trial. Nutrients. 2015; 7(9): 7978-7994. doi: 10.3390/nu7095377
22. Chen JH, Ouyang C, Ding Q, Song J, Cao W, Mao L. A moderate low-carbohydrate low-calorie diet improves lipid profile, insulin sensitivity and adiponectin expression in rats. Nutrients. 2015; 7: 4724-4738. doi: 10.3390/nu7064724
23. Saslow LR, Kim S, Daubenmier JJ, et al. A randomized Pilot trial of a moderate carbohydrate diet compared to a very low carbohydrate diet in overweight or obese individuals with type 2 diabetes mellitus or prediabetes. PLoS ONE. 2014; 9(4): e91027. doi: 10.1371/journal.pone.0091027
24. Mansoor N, Vinknes KJ, Veierød MB, Retterstøl K. Effects of low-carbohydrate diets vs. low-fat diets on body weight and cardiovascular risk factors: A meta-analysis of randomised controlled trials. Br J Nutr. 2016; 115(3): 466-479. doi: 10.1017/ S0007114515004699
25. Monnier L, Colette C. Glycemic variability: Can we bridge the divide between controversies? Diabetes Care. 2011; 34(4): 1058-1059. doi: 10.2337/dc11-0071
26. Service FH. Glucose variability. Diabetes. 2013; 62(5): 1398-404. doi: 10.2337/db12-1396
27. Siegelaar SE, Barwari T, Kulik W, Hoekstra JB, DeVries JH. No relevant relationship between glucose variability and oxidative stress in well-regulated type 2 diabetes patients. J Diabetes Sci Technol. 2011; 5(1): 86-92. doi: 10.1177/193229681100500112
28. Baghurst P. Calculating the mean amplitude of glycemic excursion fromcontinuous glucose monitoring data: an automated algorithm. Diabetes Technol Ther. 2011; 13(3): 296-302. doi: 10.1089/dia.2010.0090
29. Kusunoki Y, Katsuno T, Nakae R, et al. Evaluation of blood glucose fluctuation in Japanese patients with type 1 diabetes mellitus by self-monitoring of blood glucose and continuous glucose monitoring. Diabetes Res Clin Pract. 2015; 108(2): 342-349. doi: 10.1016/j.diabres.2015.01.040
30. Lin J, Hu FB, Curhan GC. Associations of diet with albuminuria and kidney function decline. Clin J Am Soc Nephrol. 2010; 5: 836-843. doi: 10.2215/CJN.08001109
31. Lin J, Fung TT, Hu FB, Curhan GC. Association of dietary patterns with albuminuria and kidney function decline in older white women: A subgroup analysis from the nurses’ health study. Am J Kidney Dis. 2011; 57(2): 245-254. doi: 10.1053/j. ajkd.2010.09.027
32. Tay J, Thompson CH, Luscombe-Marsh ND, et al. Longterm effects of a very low carbohydrate compared with a high carbohydrate diet on renal function in individuals with type 2 diabetes: A randomized trial. Medicine (Baltimore). 2015; 94(47): e2181. doi: 10.1097/MD.0000000000002181
33. Brinkworth GD, Buckley JD, Noakes M, Clifton PM. Renal function following long-term weight loss in individuals with abdominal obesity on a very-low-carbohydrate diet vs. highcarbohydrate diet. J Am Diet Assoc. 2010; 110: 633-638. doi: 10.1016/j.jada.2009.12.016
34. Friedman AN, Ogden LG, Foster GD, et al. Comparative effects of low-carbohydrate high-protein versus low-fat diets on the kidney. Clin J Am Soc Nephrol. 2012; 7: 1103-1111. doi: 10.2215/CJN.11741111
35. Tirosh A, Golan R, Harman-Boehm I, et al. Renal function following three distinct weight loss dietary strategies during 2 years of a randomized controlled trial. Diabetes Care. 2013; 36: 2225-2232. doi: 10.2337/dc12-1846
36. Jesudason DR, Pedersen E, Clifton PM. Weight-loss diets in people with type 2 diabetes and renal disease: a randomized controlled trial of the effect of different dietary protein amounts. Am J Clin Nutr. 2013; 98: 494-501. doi: 10.3945/ajcn.113.060889
37. Stevens LA, Coresh J, Schmid CH, et al. Estimating GFR using serum cystatin C alone and in combination with serum creatinine: A pooled analysis of 3,418 individuals with CKD. Am J Kidney Dis. 2008; 51: 395-406. doi: 10.1053/j.ajkd.2007.11.018
38. Levey AS, Stevens LA, Schmid CH, et al. A new equation to estimate glomerular filtration rate. Ann Intern Med. 2009; 150(9): 604-612. doi: 10.7326/0003-4819-150-9-200905050- 00006
39. Inker LA, Schmid CH, Tighiouart H, et al. Estimating glomerular filtration rate from serum creatinine and cystatin C. N Engl J Med. 2012; 367(1): 20-29. doi: 10.1056/NEJMoa1114248
40. Stevens PE, Levin A, for the Kidney Disease: Improving Global Outcomes Chronic Kidney Disease Guideline Development Work Group Members. Evaluation and management of chronic kidney disease: Synopsis of the kidney disease: Improving global outcomes 2012 clinical practice guideline. Ann Intern Med. 2013; 158: 825-830. doi: 10.7326/0003-4819-158- 11-201306040-00007
41. Fan L, Inker LA, Rossert J, et al. Glomerular filtration rateestimation using cystatin C alone or combined with creatinine as aconfirmatory test. Nephrol Dial Transplant. 2014; 29: 1195- 1203. doi: 10.1093/ndt/gft509
© 2014-2024 I Openventio Publisher is licensed by Creative Commons Attribution, unless otherwise indicated.
