|Year : 2019 | Volume
| Issue : 1 | Page : 32-38
Arterial stiffness parameters derived by oscillometric pulse wave analysis are related to estimated glomerular filtration rate but not proteinuria in Gujarati diabetics
Jayesh Dalpatbhai Solanki1, Rajkumar Bharatkumar Patel2, Ila N Hadiyel3, Hemant B Mehta1, Hirava B Munshi3, Param J Kakadia2
1 Department of Physiology, Government Medical College, Bhavnagar, Gujarat, India
2 Department of Undergraduate Medical Student, Government Medical College, Bhavnagar, Gujarat, India
3 Department of Medicine, Government Medical College, Bhavnagar, Gujarat, India
|Date of Web Publication||10-May-2019|
Dr. Ila N Hadiyel
Department of Medicine, Sir T General Hospital, Jail Road, Bhavnagar - 364 001, Gujarat
Source of Support: None, Conflict of Interest: None
Introduction: Diabetes mellitus (DM) imposes significant cardiovascular risk beyond raised blood pressure. Pulse wave analysis (PWA) infers directly about the same by arterial stiffness (AS) assessment. We studied the relation between AS and diabetic nephropathy (DN) in type 2 diabetics (T2D). Materials and Methods: We evaluated 164 T2Ds in a cross-sectional study. Oscillometric PWA performed by Mobil-o-Graph (IEM, Germany) reported AS parameters such as augmentation pressure (AP), augmentation index at heart rate 72, reflection magnitude, and aortic pulse wave velocity (aPWV). DN was evaluated by creatinine, proteinuria, and estimated glomerular filtration rate (eGFR). Parameters were further analyzed for the effect of gender, proteinuria, and grades of DN by eGFR. Multiple linear regressions were used to find significant predictors. P <0.05 was taken as statistical significance. Results: Case group constituted 91 males, with mean age 56 years, with mean duration 4.48 years, 70% prevalence of hypertension and poor glycemic control. There was mild to moderate DN and 34% prevalence of proteinuria. AS parameters were not affected significantly by proteinuria, but by increasing grades of DN. aPWV and AP were predictors of eGFR, whereas AP was a predictor of creatinine. Conclusions: AS was related with estimated GFR but not with proteinuria in Gujarati diabetics with high co-existing hypertension and predominantly mild-to-moderate grade nephropathy.
Keywords: Arterial stiffness, blood pressure, diabetics, estimated glomerular filtration rate, proteinuria, pulse wave velocity
|How to cite this article:|
Solanki JD, Patel RB, Hadiyel IN, Mehta HB, Munshi HB, Kakadia PJ. Arterial stiffness parameters derived by oscillometric pulse wave analysis are related to estimated glomerular filtration rate but not proteinuria in Gujarati diabetics. J Indian coll cardiol 2019;9:32-8
|How to cite this URL:|
Solanki JD, Patel RB, Hadiyel IN, Mehta HB, Munshi HB, Kakadia PJ. Arterial stiffness parameters derived by oscillometric pulse wave analysis are related to estimated glomerular filtration rate but not proteinuria in Gujarati diabetics. J Indian coll cardiol [serial online] 2019 [cited 2020 Feb 22];9:32-8. Available from: http://www.joicc.org/text.asp?2019/9/1/32/257961
| Introduction|| |
There is an ever-increasing burden of diabetes mellitus (DM) in India. DM itself is a risk for cardiovascular disease (CVD) and in majority, it manifests as hypertension (HTN) that co-exists in more than half of the diabetics. Blood pressure (BP) is the variable that is routinely measured for CVD status in diabetics, but it has its limitations. Arterial stiffness (AS) is a better, direct, discrete, and not completely BP-dependent parameter of vascular aging in diabetes that can be measured noninvasively by pulse wave analysis (PWA). PWA-based studies are recently published in normal controls, diabetics, and hypertensives of our region.,, However, the relation between PWA-derived AS and its correlation with various microvascular complications of diabetes is unknown in Indian population. Diabetic nephropathy (DN) is a very common and neglected microvascular complication of DM, and its correlation with AS derived by PWA has been tested in this study.
| Materials and Methods|| |
Study design and participants
This research protocol was approved by the Institutional Review Board of our medical college, and it was registered in the Clinical Trials Registry of India prospectively. We conducted a cross-sectional study on diabetic patients of medicine outpatient department of a tertiary care teaching government hospital, affiliated to a government medical college.
Inclusion and exclusion criteria
Ambulatory, nonathletic, type 2 diabetics taking antidiabetics regularly, with or without HTN, with current serum creatinine and proteinuria reports available, of either sex, nonsmoking, nonalcoholic, not known to have any acute or chronic systemic disease, and ready for written informed consent were included in the study. Apart from these criteria, we excluded individuals with estimated glomerular filtration rate (eGFR) <15, individuals using any alternative system of medicines, and pregnant women.
Sample size was calculated by Raosoft software (Raosoft Inc., free online software, Seattle, WA, USA). To have 95% confidence level and 5% precision, and considering diabetes prevalence at 7.4%, a sample size of 148 was adequate for the study population. We screened and enrolled 178 diabetics meeting inclusion criteria from general medicine outpatient department by simple random sampling. We excluded six patients due to arm circumference beyond the available cuff size, 7 patients due to poor quality of record, and one due to irregular pulse wave rhythm. Hence, the case group finally had 164 cases.
Subject assessment and definitions
Demographic characteristics, risk factors, self-reported moderate physical activity, relevant disease history, and detailed history of pharmacotherapy were carefully noted. Systolic BP (SBP) ≥140 mmHg and diastolic BP (DBP) ≥90 mmHg or use of antihypertensive medication was defined as HTN. SBP <140 mmHg and DBP <90 mmHg were taken as factors indicating BP control. Glycemic control was defined as per the American Diabetes Association guidelines 2018 based on fasting plasma glucose (<130 mg/dl) and 2-h plasma glucose (<180 mg/dl). Current reports regarding proteinuria and serum creatinine were sought. eGFR was calculated by the Modification of Diet in Renal Disease (MDRD) formula as follows: eGFR = 186 × serum creatinine−1.154 × age−0.203 × 1.212 (if patient is black) × 0.742 (if female). Based on eGFR, patients were graded for chronic kidney disease (CKD) into Grade I – eGFR ≥90, Grade II – eGFR: 60–89, Grade III – eGFR: 30–59, Grade IV – eGFR: 15–29, and Grade V – eGFR <15.
Pulse wave analysis
We used a portable, personal computer-attached, calibrated, and validated instrument namely Mobil-o-Graph (IEM GMBH, Stolberg, Germany) of physiology department. It undergoes oscillometric pressure PWA as per protocol designed by the European Society of Hypertension. Pressure oscillations are generated by brachial arterial pulsation which are transmitted to brachial BP cuff and measured by transducer to be fed into microprocessor. Computerized software records brachial pulse wave and by a validated generalized transfer factor, derives central aortic pulse wave. It further undergoes point-based and area-based analyses by computer software to derive various cardiovascular parameters.
Based on the measured mid-arm circumference, a BP cuff was chosen and applied to the left arm using standard protocol. All readings were taken after rest for 10 min, in postabsorptive phase in a calm room without external influences or avoiding arm movement.
Parameters measured and derived
- Heart rate (HR), body mass index (BMI), and body surface area
- Brachial BP, brachial SBP, brachial DBP, brachial pulse pressure (PP), and brachial mean BP
- AS-augmentation pressure (AP), augmentation index at HR 75 per minute (AIxat75), reflection magnitude percentage (Ref %), and aortic pulse wave velocity (aPWV)
- Rate pressure product (RPP) – HR per minute × SBP × 10−2
- Total AS-PP/stroke volume.,
The study data were entered into and sorted by Excel Spreadsheet. Numerical data were expressed as mean ± standard deviation until specifically indicated, and qualitative data were expressed as number (percentage). Statistical calculations were done by GraphPad InStat 3 software (free version of GraphPad software, GraphPad Inc., California, USA). Comparison of quantitative data was done by unpaired t-test, Mann–Whitney U–test, or simple ANOVA test, depending on parametric or nonparametric distribution. We compared difference in the distribution of qualitative data by normality test or Chi-square test. Multiple linear regressions, after adjusting for confounders, were applied to find the major and significant predictors of the study outcomes from AS parameters. Statistical significance level was kept at P < 0.05.
| Results|| |
[Table 1] shows the baseline and AS parameters of the study group as a whole and that of male and female subgroups. The study group overall had a mean age of 56 years, mean duration of diabetes of 4.48 years, representation of both sexes, high mean BMI, low physical activity, high co-existence of HTN, poor BP control, mean serum creatinine of 1.1, mean eGFR of 88.35, and one-third prevalence of proteinuria. Angiotensin-converting enzyme inhibitor and calcium channel blocker were the most common antihypertensives used with lesser use of beta-blockers, statins, aspirin, and diuretics. The values of the parameters of AS were higher. Male and female subgroups were comparable except height and weight values which were lesser in females. Females showed significantly higher eGFR, SBP, PP, and most of the AS parameters except aPWV.
[Table 2] shows the comparison of diabetics with proteinuria and without proteinuria (selected from the remaining patients by matching age and gender). The groups had comparable parameters except higher physical activity, higher serum creatinine, and lower eGFR in group with proteinuria than the group without proteinuria. HR, BP, and AS parameters were higher in the former group, but there was statistical significance only for reflection magnitude.
|Table 2: Baseline and arterial stiffness parameters in subgroups with proteinuria or without proteinuria (matched by total number, age, and gender)|
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[Table 3] shows the comparison of subgroups of diabetics stratified by eGFR into grades of DN (G1, G2, and G3/4). There were no statistically significant differences in subgroups for confounders except higher number of females in G3/4. HR, BP, and RPP were not significantly different between the three groups. Serum creatinine and the prevalence of proteinuria significantly increased from G1 to G3/4. AS parameters showed a trend of increase from G1 to G3/4, but statistical significance was evident only for aPWV value, AP, and qualitative distribution of high versus normal aPWV.
|Table 3: Baseline and arterial stiffness parameters in subgroups – G1, G2, G3/4 based on estimated glomerular filtration rate|
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[Table 4] shows predictors for serum creatinine and eGFR from various AS parameters after adjusting for age, height, weight, BMI, duration of diabetes, HR, and BP. Serum creatinine was significantly predicted by AP only. AP and aPWV were significant negative predictors of eGFR.
|Table 4: Correlation between diabetic nephropathy parameters and arterial stiffness parameters by multiple linear regressions after adjusting age, height, weight, body mass index, duration, brachial blood pressure, and heart rate|
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| Discussion|| |
The study of AS is very important in type 2 DM (T2DM) and so as its correlation with various T2DM complications. CKD is an important aftermath of T2DM, and we studied the same in relation to discrete AS parameters derived by noninvasive PWA. AS was raised in our diabetics as evidenced by raised PWV and AIx, in line with our previous studies done in diabetics and hypertensives., Proteinuria was present in every third diabetic as the prevalence of macroalbuminuria and CKD was 16% as defined as GFR ≤60 ml/min. This is higher than CKD prevalence in other studies done from India. It can be due to poor glycemic control, high co-existence of T2DM with HTN, mean duration 5 years, ethnic risk, and, perhaps, delayed diagnosis. Mean eGFR was 88, with predominance of Grade 1 and Grade 2 DN that led us to study AS in type 2 diabetics with mild-to-moderate renal damage.
Females had significantly higher AS than males, in line with our previous studies done in postmenopausal age groups., This gender factor, apart from the effect of age, must be accounted while drawing any conclusion of AS. We found no relation between proteinuria and AS parameters. This is in contrast to most other studies done on diabetics, hypertensives, CKD patients, or normal elderly population. The possible reasons can be: (1) high prevalence of HTN with the use of RAAS blockers in majority which have differential di-stiffening and renoprotective effects, (2) consideration of macroproteinuria rather than 24-h microproteinuria which is used in most studies, (3) Asian ethnicity which has a different risk factor profile that is not studied much in detail, (4) mean age 60 years and few elderly diabetics only unlike many other studies, and (5) mean diabetes duration 5 years with no baseline data available. Microalbuminuria has been accepted as the earliest marker for the development of DN. However, it has been reported that a large proportion of renal impairment ensue even before the appearance of microalbuminuria. Albuminuria has few confounding issues with it such as exercise, acute illness, urinary tract infection, and cardiac failure. Furthermore, it is known to occur in the urine of nondiabetics, indicating its nonspecificity for accurate prediction of diabetic kidney disease. This insignificant relation needs to be further confirmed by a cohort study starting with baseline data and use of microproteinuria rather than macroproteinuria.
Unlike proteinuria, eGFR was significantly related to and was a negative predictor of AS in our diabetics. AS showed a worsening trend with an increasing grade of DN. Similar to this study, Fountoulakis et al. prospectively investigated type 2 diabetics with eGFR ≥45 mL/min (n = 211, mean age = 60.1 years). They concluded that aortic PWV predicts eGFR decline, before the onset of advanced renal dysfunction, and is a potential target for renoprotection in younger patients with type 2 DM aged <60 years. A study of Yoon et al. in patients with early stages of CKD showed that brachial ankle PWV was independently associated with renal function decline and short-term cardiovascular events. However, the relationship between aortic stiffness and GFR is not so obvious and found to be more consistent with less severe CKD grades. In the Renal Research Institute-CKD study, after adjusting for age and SBP, there was an independent association between carotid-femoral PWV and eGFR only in the higher GFR group (like most of our study participants) but not in the lower GFR group. Similarly, in the NephroTest study, eGFR was an independent PWV determinant only in the whole population, including CKD patients and hypertensives with eGFR >60 ml/min/1.73 m2 (n = 216). In a Scottish study on CKD patients with minimal comorbidities, eGFR and aortic PWV had significant association in univariate analyses that disappeared after adjustment for cardiovascular risk factors. This emphasizes the effect of comorbidities on arterial stiffening in CKD patients, like T2DM and HTN, which were present in most of the study participants and most of our diabetics.
Proteinuria showed correlation with eGFR and creatinine level. Similarly, with decreased eGFR from Grade G1 to G4, the prevalence of proteinuria increased significantly, in line with literature. Yet, AS correlated with only eGFR and not with proteinuria. This is in contrast to REBOUND study, Framingham Heart study, and a study by Kalaitzidis et al. where they found AS to be related more with albuminuria than with eGFR. There are some differences in these studies than our study as they: (1) used regional PWV and not aortic PWV; (2) used 24-h urine albumin rather than macro-albuminuria; (3) had higher mean duration; (4) studied different ethnic groups; (5) had low prevalence of co-existing T2DM and HTN than our study; and (6) had better glycemic control in the study group than ours. eGFR by MDRD formula has found to be better predictor of CKD in Indian population as studied by Deepika et al., and our study re-affirms the same. Twenty-hour albumin measurement is not feasible in our resource-limited setups, and even serum creatinine can yield eGFR that relates to AS, a novel cardiovascular parameter. Similarly, oscillometric PWA provides a reliable estimate of AS like aortic PWV that can be used on a large scale.
AS is found to be a forerunner of HTN and significantly raised even in young individuals with familial risk of diabetes and HTN, without incident T2DM or HTN. AS is also found to be BP independent, not related to current glycemic control, and not affected much by class difference of antihypertensives. aPWV is a predictor of mortality in CKD stages 2, 3, and 4 and an indicator of progression of CKD. AS is a target for delaying decline in kidney function, and our study supports the same due to the negative association between eGFR and AS. This baseline study hints a vertical study to assess the exact cause–effect relationship between CKD and AS in our population as well as the role of glycemic control, BP control, antihypertensive pharmacotherapy, lifestyle modifications, and baseline AS level. Similarly, the association of AS with other complications of T2DM needs to be studied. Considering all these, more research is needed to ascertain the role of AS as a tool to better understand T2DM, HTN, and various aftermaths of this deadly duo.
We had some limitations such as lack of baseline data, cross-sectional study nature, nonavailability of HbA1c and various biomarkers of vascular aging, nonuse of 24-h proteinuria, and nonuse of albumin-to-creatinine ratio.
| Conclusions|| |
AS correlated with and predicted eGFR but not with proteinuria in Gujarati diabetics with high co-existing HTN and predominantly mild-to-moderate grade nephropathy. It indicates the potential importance of eGFR as a measure of microvascular complication to refer for studies focusing on AS in our diabetics.
Financial support and sponsorship
Conflicts of interest
There are no conflicts of interest.
| References|| |
Tandon N, Anjana RM, Mohan V, Kaur T, Afshin A, Ong K, et al
. The increasing burden of diabetes and variations among the states of India: The Global Burden of Disease Study 1990–2016. Lancet Glob Health 2018;6: e1352-62.
Leon BM, Maddox TM. Diabetes and cardiovascular disease: Epidemiology, biological mechanisms, treatment recommendations and future research. World J Diabetes 2015;6:1246-58.
Solanki JD, Patel KJ, Lalwani N, Mehta HB, Shah CJ, Lakhtaria MN. QT corrected for heart rate and QTC dispersion in Gujarati type 2 diabetics predominantly using preventive pharmacotherapy and with very low electrocardiogram left ventricular hypertrophy. J Diabetol 2017;8:86-91. [Full text]
Solanki JD, Munshi HB, Mehta HB, Shah CJ. Oscillometric Pulse Wave Analysis in Gujarati diabetics not receiving any anti-hypertensive: a case control study. J Family Med Prim Care 2019. [Epub of Print].
Solanki JD, Mehta HB, Shah CJ. Aortic pulse wave velocity and augmentation index@75 measured by oscillometric pulse wave analysis in Gujarati nonhypertensives. Vasc Invest Ther 2018;1:50-5
Chawla A, Chawla R, Jaggi S. Microvascular and macrovascular complications in diabetes mellitus: Distinct or continuum? Indian J Endocrinol Metab 2016;20:546-51.
Weldegiorgis M, de Zeeuw D, Li L, Parving HH, Hou FF, Remuzzi G, et al.
Longitudinal estimated GFR trajectories in patients with and without type 2 diabetes and nephropathy. Am J Kidney Dis 2018;71:91-101.
American Diabetes Association 6. Glycemic targets: Standards of medical care in diabetes-2018. Diabetes Care 2018;41:S55-64.
Tuomilehto J, Rastenyte D, Qiao Q, Barengo NC, Matz K. Epidemiology of macrovascular disease and hypertension in diabetes mellitus. International Textbook of Diabetes Mellitus. 4th
edi. Hoboken:Wiley; 2015. p. 1005-30.
Weiss W, Gohlisch C, Harsch-Gladisch C, Tölle M, Zidek W, van der Giet M, et al.
Oscillometric estimation of central blood pressure: Validation of the Mobil-O-Graph in comparison with the SphygmoCor device. Blood Press Monit 2012;17:128-31.
Weber T, Wassertheurer S, Rammer M, Maurer E, Hametner B, Mayer CC, et al.
Validation of a brachial cuff-based method for estimating central systolic blood pressure. Hypertension 2011;58:825-32.
Solanki JD, Mehta HB, Shah CJ. Pulse wave analyzed cardiovascular parameters in young first degree relatives of type 2 diabetics – A cross-sectional study. Indian Heart J 2018;70:341-5.
Solanki JD, Mehta HB, Shah CJ. Pulse wave analyzed cardiovascular parameters in young first degree relatives of hypertensives. J Res Med Sci 2018;23:72.
] [Full text]
Solanki JD, Mehta HB, Shah CJ. Oscillometric pulse wave analysis in newly diagnosed never treated Gujarati hypertensives. Vasc Invest Ther 2018;1:62-7. [Full text]
Varma PP. Prevalence of chronic kidney disease in India – Where are we heading? Indian J Nephrol 2015;25:133-5.
] [Full text]
Agrawal V, Marinescu V, Agarwal M, McCullough PA. Cardiovascular implications of proteinuria: An indicator of chronic kidney disease. Nat Rev Cardiol 2009;6:301-11.
Dash SC, Agarwal SK, Panigrahi A, Mishra J, Dash D. Diabetes, hypertension and kidney disease combination “DHKD syndrome” is common in India. J Assoc Physicians India 2018;66:30-3.
Kalaitzidis RG, Karasavvidou DP, Tatsioni A, Pappas K, Katatsis G, Liontos A, et al.
Albuminuria as a marker of arterial stiffness in chronic kidney disease patients. World J Nephrol 2015;4:406-14.
Townsend RR. Arterial stiffness and chronic kidney disease: Lessons from the chronic renal insufficiency cohort study. Curr Opin Nephrol Hypertens 2015;24:47-53.
Upadhyay A, Hwang SJ, Mitchell GF, Vasan RS, Vita JA, Stantchev PI, et al.
Arterial stiffness in mild-to-moderate CKD. J Am Soc Nephrol 2009;20:2044-53.
Vejakama P, Ingsathit A, McKay GJ, Maxwell AP, McEvoy M, Attia J, et al.
Treatment effects of renin-angiotensin aldosterone system blockade on kidney failure and mortality in chronic kidney disease patients. BMC Nephrol 2017;18:342.
Rigalleau V, Lasseur C, Raffaitin C, Beauvieux MC, Barthe N, Chauveau P, et al.
Normoalbuminuric renal-insufficient diabetic patients: A lower-risk group. Diabetes Care 2007;30:2034-9.
Jain S, Rajput A, Kumar Y, Uppuluri N, Arvind AS, Tatu U, et al.
Proteomic analysis of urinary protein markers for accurate prediction of diabetic kidney disorder. J Assoc Physicians India 2005;53:513-20.
Fountoulakis N, Thakrar C, Patel K, Viberti G, Gnudi L, Karalliedde J, et al.
Increased arterial stiffness is an independent predictor of renal function decline in patients with type 2 diabetes mellitus younger than 60 years. J Am Heart Assoc 2017;6. pii: e004934.
Yoon HE, Shin DI, Kim SJ, Koh ES, Hwang HS, Chung S, et al.
Brachial-ankle pulse wave velocity predicts decline in renal function and cardiovascular events in early stages of chronic kidney disease. Int J Med Sci 2013;10:1430-6.
Sengstock D, Sands RL, Gillespie BW, Zhang X, Kiser M, Eisele G, et al.
Dominance of traditional cardiovascular risk factors over renal function in predicting arterial stiffness in subjects with chronic kidney disease. Nephrol Dial Transplant 2010;25:853-61.
Briet M, Bozec E, Laurent S, Fassot C, London GM, Jacquot C, et al.
Arterial stiffness and enlargement in mild-to-moderate chronic kidney disease. Kidney Int 2006;69:350-7.
Solanki JD, Basida SD, Mehta HB, Panjwani SJ, Gadhavi BP, Patel P, et al.
Impact of disease control and co-existing risk factors on heart rate variability in Gujarati type 2 diabetics: An observational study. J Family Med Prim Care 2016;5:393-8.
] [Full text]
Nakao K, Uzu T, Araki S, Kume S, Deji N, Chin-Kanasaki M, et al.
Arterial stiffness and renal impairment in non-proteinuric type 2 diabetic patients. J Diabetes Investig 2012;3:86-91.
Kim JH, Kim SS, Kim IJ, Kim BH, Park JY, Lee CW, et al.
Arterial stiffness is more associated with albuminuria than decreased glomerular filtration rate in patients with type 2 diabetes mellitus: The REBOUND study. J Diabetes Res 2017;2017:7047909.
Deepika G, Veeraiah N, Shri Krishna K, Sastry BS, Mohant P. eGFR-the better predictor in CKD in Indian population. Int J Res Dev Pharm Life Sci 2016;5:2226-31.
Solanki JD, Mehta HB, Panjwani SJ, Munshi HB, Shah CJ. Effect of antihypertensive pharmacotherapy on oscillometric pulse wave analysis parameters in treated Gujarati hypertensives: A crosssectional study. J Pharmacol Pharmacother 2018;9:153-9. [Full text]
Baumann M, Wassertheurer S, Suttmann Y, Burkhardt K, Heemann U. Aortic pulse wave velocity predicts mortality in chronic kidney disease stages 2-4. J Hypertens 2014;32:899-903.
[Table 1], [Table 2], [Table 3], [Table 4]