DR WAFULA Z NALWA, MBCHB

GLYCEMIC CONTROL, CARDIOVASCULAR RISK FACTOR PROFILE AND THE THERAPEUTIC INTEREVENTIONS IN TYPE 2 DIABETES MELLITUS PATIENTS AT THE NEW NYANZA PROVINCIAL GENERAL HOSPITAL KISUMU DR WAFULA Z NALWA, MBCHB

Supervisors 1. DR MARK JOSHI 2. DR KIRTIDA ACHARYA 3. DR ANGELA AMAYO 4. DR G P O OGUTU

Introduction Epidemiology Classification Diagnosis Treatment Complications CAD risk factors Local data Results Discussion

Introduction The number of people with type 2 diabetes around the world is estimated to rise from 151 million in 2000 to 350 million by 2025 1. This is expected to affect healthcare and social support systems 2-3. Intensive therapy and cardiovascular risk reduction are beneficial in diabetes mellitus 4-6. A comprehensive therapeutic strategy to mitigate against their effects is required.

Introduction Type 2 Diabetes mellitus is due to defects in insulin secretion, insulin action, or both. Cardiovascular risk factors in these patients occur in clusters of obesity, dyslipidemia, hypertension and or microalbuminuria. It is associated with long-term damage, dysfunction, and failure of various organs through varied mechanisms 6-7

THE CLASSIFICATION OF DIABETES MELLITUS The etiopathogenic classification of diabetes includes 8 a) Type 1 diabetes results from β-cell destruction b) Type 2 diabetes results from a progressive insulin secretory defect on the background of insulin resistance. c) Other specific types of diabetes: d) Gestational diabetes mellitus (GDM) diagnosed during pregnancy.

DIAGNOSIS OF DIABETES MELLITUS Diabetes Care 2003; 26:3160. A1C > 6.5% OR FPG of >7.0mmol/l. Fasting- no caloric intake for at least 8 h. OR Symptoms of hyperglycemia and random blood glucose of 11.1Mmol/l. OR 2-hr plasma glucose of 11.1mmol/l during an OGTT.

THE COMPLICATIONS OF DIABETES MELLITUS These are divided into acute and chronic complications. The chronic complications are in turn divided into: a) Vascular complications: a) The microvascular complications are retinopathy, neuropathy and nephropathy. b) The macrovascular complications include coronary heart disease, cerebrovascular disease and peripheral vascular disease. b) Nonvascular complications a) GI tract, genitourinary tract, cataracts, glaucoma and skin.

PATHOGENESIS OF MICROVASCULAR COMPLICATIONS: a) Formation of advanced glycosylation end products 10 b) Increased glucose metabolism, via the sorbitol pathway 11 c) Increased formation of diacylglycerol leading to activation of protein kinase C (PKC): d) Increased the glucose flux through the hexosamine pathway. e) Growth factors : VEGF, TGF-β, PDGF, EGF, IGF-I, GH, BFGF, and even insulin.

CAD risk factors Diabetes patients have a greater burden of atherogenic risk factors than non diabetics. They often occur in clusters and may be divided into: a) Non-modifiable- Age, sex, family history. b) Modifiable- hypertension, hyper-glycemia, hyperinsulinemia, obesity, dyslipidemia, cigarette smoking, microalbuminuria, obesity, lack of exercise, alcohol intake, haematological, dietary.

THERAPEUTIC STRATEGIES IN TYPE 2 DIABETE MELLITUS Therapeutic strategies must be multi-pronged. a) Diabetes education b) Evaluation for microvascular, macrovascular and neurologic complications. c) Normalization of glycemia d) Minimization or elimination of cardiovascular risk factors. Eg blood pressure, dyslipidemia, obesity, cigarette smoking, excess alcohol use etc a) Avoidance of drugs that can aggravate abnormalities of insulin or lipid metabolism.

Drugs Summary of glucose lowering interventions Greatest effect Metformin 1.0-2.0 Sulfonylurea 1.0-2.0 Decrease in A1C Insulin 1.5-3.5 Moderate effectiveness Thiazolidinediones 0.5 1.4 GLP 1 Agonist 0.5-1.0 Alpha glucosidase inhibitors 0.6-0.9 Glinides 0.5-1.5 Pramlintide 0.5-1.0 Dipeptidyl peptyidase 4P4 inhibitor 0.5-0.8

Therapeutic strategies Tier 1 Good Evidence/experience Lifestyle At diagnosis Metformin Lifestyle + Metformin Inadequate response Add Basal Insulin/Sulfonylurea Lifestyle + Metformin Inadequate control Add Intensive Insulin Therapy

Therapeutic strategies Tier 2 Limited experience and evidence At diagnosis Lifestyle + Metformin Lifestyle, metformin, Pioglitazone Lifestyle, metformin, Glucagon-like Peptide-1 Agonist Inadequate control Add Sulfonylurea Add insulin Inadequate control Lifestyle + Metformin + Intensive Insulin

TREATMENT GOALS FOR ADULT PATIENTS DIABETES MELLITUS Parameter Level HB A1C < 7.0% * Pre-prandial capillary plasma glucose Peak postprandial capillary plasma glucose Blood pressure LDL cholesterol HDL cholesterol Triglycerides (3.9 7.2 mmol/l) (<10.0 mmol/l) <130/80mmHg <2.6mmol/l >1.1mmo/l <1.7mmol/l Key concepts in setting glycemic goals: A1C is the primary target for glycemic control. Postprandial glucose may be targeted if A1C goals are not met despite reaching pre-prandial glucose goals

Cardiovascular risk profile and the interventions in 2001 and 2007 studies: Variable Vaghela, 2001 Mwendwa, 2001 Nguchu, 2007 Wafula, 2009 Obesity by WHR 49.1% - 86.3% Obesity by WC 65.7% - 62.1% Obesity by BMI 66.9% 66.1% Hypertension 64.8% 50% 60% Elevated LDL 88% 35% 73.7% Smoking 31.5% 73% 8.4% Family h/o DM 77.8% 48% Family h/o HTN 66.7% 24% Drugs OHA only 48.1% 74% 43.2% Insulin only 5.6% 13% 42.1% OHA, insulin, 46.3% 11% 1.1% Diet only 2% 5.3% Statin < 1% - 8.4% Aspirin 9.3% - 14.7% ARB/ACE I - - -

Justification of the study Chronic illnesses may soon surpass infectious diseases as the leading cause of morbidity and mortality in the developing world. The outlying hospitals will bear most of this burden. Provincial (level 5) hospitals. The New Nyanza Provincial General Hospital is on such hospital. There is no data from the western region of this country. This study was therefore designed to characterize the patients and evaluate the care accorded to these patients in this setting. Passionate desire to stimulate interest in research beyond the confines and comfort of the Kenyatta National Hospital

RESEARCH QUESTION How well is diabetes being controlled at a provincial (level 5) hospital, what are the prevalent coronary artery disease risk factors and what pharmaco-therapeutic interventions are being utilised?

BROAD OBJECTIVE To determine the patient characteristics, level of glycemic control and pharmaco-therapeutic strategies utilised for both glycemic control and cardiovascular risk reduction in type 2 diabetes mellitus patients at the NNPGH.

SPECIFIC OBJECTIVES 1. To describe demographics of type 2 diabetes mellitus: Age, gender, educational attainment, employment status, and residence. 2. To determine the level of glycemic control. (Using; HBA1C, FPG, 1hr PPG ) and the predictors of glycemic control. 3. To determine the pharmacologic regimens used in glycemic control. 4. To describe the cardiovascular risk factors: Family history of diabetes mellitus or hypertension, obesity, hypertension, alcohol use &smoking. Lipid profile, Estimated GFR, Urine albumin to creatinine ratio. 5. To determine the utilization of pharmacotherapeutic interventions for risk reduction: Statins, anti-platelets & anti-hypertensive

METHODS AND MATERIALS. Study design Study site Study population Inclusion criteria Exclusion criteria Sample size estimation=118

RESULTS MMED thesis,2009

800 patients were screened 100- newly diagnosed 90- type 1 610 eligible for the study 400 - did not return 57- did not consent 153 enrolled 29-did not complete study 5- had incomplete data 119 analysed History, Physical findings: weight, height, BP, WC, HC. Lab results: HBA1C, FPG,PPG, serum lipid profile, serum creatinine, UACR

Socio-demographics of the study population Characteristic Frequency (%) Sex Marital status Male Female Married Widowed Divorced 49 41.2 70 58.8 95 79.8 22 18.5 2 1.7 Residence Employment status Urban Rural Employed Never employed Retired Unemployed 41 35.5 78 65.5 23 19.3 20 16.8 53 44.5 23 19.3 Level of education None Primary Secondary Tertiary 17 14.3 45 37.8 43 36.1 14 11.8

Distribution by Sex Total (%). n-119(100) Female (%) n-70 (58.8) Male (%) n-49(41.2) Male :female ratio 1:1.7

Distribution by Age Variable Study Range Male Female P Value mean Age 58.4 (8.9) 32 87 59.3 57.8 0.365 Age Sex P value Female Male 30-39 40-49 50-59 60-69 70-79 80-89 2 (2.9%) 10 (14.3%) 24 (34.3%) 30 (42.9%) 4 (5.7%) 0 (0.0%) 1 (2.0%) 4 (8.2%) 22 (44.9%) 17 (34.7%) 4 (8.2%) 1 (2.0%) 0.549

Distribution by Residence

Educational Attainment

Glycemic control

Glycemic control Variable Mean Range HBA1C 5.46 2.4-15.1 F PG 8.8 (4.0) 2.6 26.4 1HR PPG 15.5 (4.9) 6.7-29.2 Variable Control Category Frequency % HBA1C Good (<7%) Poor (>=7%) 104 15 87.4 12.6 FPG (8.8(4.0)mmol/L <5.9 6.0-10.9 >11 23 66 30 19.3 55.5 35.2 1HR PPG 15.5(0.95)mmol/L <10mmol/l >=10mmol/l 13 106 10.9 89.1

Categorization of glycemic control using HBA1C % 13% HBA1C <7 HBA1C >7 87%

Prevalent Drugs used for glycemic control

CV risk factors

CV risk factors Family history Variable Characteristic Number Frequency% Relative with diabetes (n=119) Relative specified (diabetes) (n=56) Relative with hypertension (n=119) Relative specified (hypertension) (n=43) Yes 56 47.1 1st degree 43 76.8 Yes 43 36.1 1 st degree 35 81.5 Smoking Smoking status EVER smoked 19 16.0 (male-14) Alcohol use Drink alcohol Yes (male-5) 8 6.7 Quantity of alcohol (n=8) 2units/day 3units/day 2 1 25.0 12.5

CVD risk factors Variable BMI Categorization N=119 Underweight Normal weight Overweight Class I Obesity Class II Obesity Class III Obesity Frequency % 6 31 58 14 7 3 5.0 26.1 48.7 (68.9%) 11.8 5.9 2.5 WC High 71 59.7 M>102; F=88 Waist hip ratio High 107 89.9

CVD risk factors-biochemical Variable Category No. of patients Frequency % Total cholesterol Desirable 67 56.3 16.8 LDL <2.6mmol/l 46 38.7 HDL >=1.1 84 70.6 TG <1.7mmol/l 89 74.8 Microalbuminuria Microalbuminuria 44 37.0 Estimated GFR Stage 0 Stage 1 Stage 2 Stage 3 Stage 4 Stage 5 14 8 55 38 2 2 11.8 6.7 46.2 31.9 1.7 1.7

Categorization of obesity

Categorization of BP Variable Categorization Total (n=119) On treatment (n=95) BP <130/80 >130/80 66 53 55.5% 44.5% 45 50 47.4% 52.6%

Categorization by FPG and 1HR PPG PPG FPG

Lipids at target 120 At Target Not at target 100 80 60 40 20 0 LDL HDL TG

Clustering of lipid abnormalities Variable Frequency Number Frequency Lipids 0 1 2 3 30 49 32 8 25.2% 41.2% 26.9% 6.7% One lipid High LDL Low HDL High TG 35 9 5 71.4% 18.4% 10.2% Two lipids LDL+TG LDL+HDL HDL+TG 14 15 3 43.8% 46.9% 9.4%

Microalbuminuria

Categorization by estimated GFR

CVD Risk factor interventions

CV risk factor clustering RF combination Frequency Percent 0 1 2 3 4 5 6 3 17 28 37 26 5 3 2.5% 14.3% 23.5% 31.1% 21.8% 4.2% 2.5%

Prevalent agents for CVD risk reduction 79.8 Yes No 9.2 7.6 Antihypertensives Antiplatelet Statins -20.2-90.8-92.4 Characteristic Number Frequency (%) BP drugs Yes No 95 24 79.8 20.2 a. RAS I 68 57.1 b. Others 51 42. 9

Prevalent anti-hypertensives, BP and HBA1C at target in patients with microalbuminuria 60 50 40 30 20 Other ACE/ARB+other ACE/ARB 10 0 Microalbuminuria Normal-urine P=0.011

At target: Therapeutic interventions Variable Target level On treatment Number Proportion HBA1C (n=119) <7 % 102 87.2% BP (n=95) <130/80mmHg 45 47.3% LDL (n=9) <2.6mmol/l 5 55.5%

There was no correlation between sociodemographic characteristics and glycemic control(chi-square)

Comparisons between the well and the poorly controlled diabetes (student-test) Mean s of variables HBA1C category P value <7% (n=104) >=7% (n=15) Age 58.6 57.5 0.677 Waist circumference (cm) 97.1 85.3 0.001* Waist-to-hip ratio 0.96 0.93 0.232 Systolic BP(mmHg) 131.2 119.0 0.027* Diastolic BP (mmhg)) 78.7 70.7 0.008* BMI (Kg/m 2) 27.6 23.3 0.006* Fasting glucose (mmol/l) 8.3 12.7 0.000* Postprandial glucose (mmol/l) 15.0 18.7 0.009* Total cholesterol 5.0 4.5 0.174 HDL 1.4 1.4 0.795 TG 1.6 1.1 0.086 LDL (mmol/l) 3.0 2.7 0.390 Albumin/creatinine ratio 30.0 73.3 0.001* GFR 70.7 68.7 0.792 Age 58.6 57.5 0.677

There was no definite predictor of glycemic control on Logistic regression analysis

Discussion The mean age for the study was 58.4yrs, 93(78.15%)were between 50 to 69 yrs of age- Elderly population according to our life expectancy in Kenya This is 10 or so years lower than the west. There were more females than males (58.8% vs 41.2%) This is in tandem with the different health seeking behaviour between the sexes. It is replicated at KNH The study population was rural in keeping with the catchment area of the Hospital and city KNH mainly urban-78% Mwendwa. Most patients 88(73.9%) had basic education and were 53 (44.5%) retired Primary and secondary education-in keeping with literacy levels in most rural settings in Kenya.

Risk factors-family history There was a modest risk in terms of family history of: Diabetes- 47.1% Hypertension -36.1% Vaghela at KNH found 77.8% and 66.7% for diabetes and hypertension respectively. What factors contribute to increased risk in rural areas other than family history? Prevalence of affected First degree relative: 76.8%-diabetes 81.5 %-hypertension. This is in keeping with the polygenic nature of inheritance of this two conditions. An affected patient likely has an affected 1 st degree relative

Risk factors-duration of disease The median duration of disease was 6.0 years; This may be reflected in better glycemic control due to residual pancreatic reserve. Longer duration is associated with poor glycemic control, poor response to OHAs and higher change over to insulin This finding may be an under estimate of the duration of disease. to the long latent period of asymptomatic diabetes, This was shorter than that seen at KNH Vaghela 2001: found a mean duration of disease of 7.6 (6.4-8.5)yrs Nturibi 2007: Mean duration of diabetes 15.9 +/- 4.23yrs

Risk factors-bmi, WC, WHR Using BMI, our pick-up rate for obesity was (20.2%). This was lower than that at KNH-Mwendwa-66% and Vaghela 66.7%. Obesity was picked better using WC-59.7% and WHR-89.9%. Thus WHR was a much more sensitive measure than BMI and WC in this population. The caveat to this observation is the limited utility of WC,WHR in patients with BMIs above 35.

Risk factors-hypertension The prevalence of hypertension was 78.9% with no significant gender difference. 47.4% (45) of the patients were well controlled on antihypertensive treatment. This is in keeping with the 50% observation in the management of hypertension. The mean BP was lower at 129/77mmHg compared to 143/87.1mmHg found at KNH by Vaghela This should translate to lower CAD, CV, CCF and even PAD through a lower atherosclerosis rate and microvascular disease rate? Role of hospital setting

Risk factors-dyslipidemia LDL was elevated in 72(60.5%); HDL low in 35(29.4%) and TG elevated in 30 (25.2%) of patients. 37(31%) of patients had 2 or more lipid abnormalities. There were various combinations of raised LDL, low HDL or raised TG. This means at least a third of the patients are at high risk for CV and require statin therapy, yet only 9% were on statins. Often TG are elevated with decreased HDL -? Dietary factors, low alcohol use, genetic factors; in our study, a minority had these combination

Risk factors-alcohol, cigarette smoking There was a low prevalence of cigarette smoking and alcohol utilization. The quantities involved were not sufficient to constitute significant risk.? Effect of socio-economics-little money as retirees to spend. This low rate augers well with reduced CVD risk. Smoking hampers glycemic control, promotes dyslipidemia and accelerates atherosclerosis in addition to an increased event and mortality rate

Risk factors-microalbuminuria and CKD 44(37%) of patients had microalbuminuria Only 18.5% of patients had a normal GFR. Most of the rest were in stage 2 and 3 of CKD. It may suggest disease duration of more than 10 years. 1. It is not surprising given the low uptake of Reno-protective medications 2. Without intervention, the risk of progression to ESRD and subsequent mortality is high. 3. There is need to increase the uptake of interventions to prevent or slow down the progression to ERSD.

Cardiovascular risk interventions in 2001, 2007, 2009 studies Variable Vaghela, 2001 Mwendwa, 2001 Nguchu, 2007 Wafula, 2009 Drugs OHA only 48.1% 74% 43.2% 83 (69.7%) Insulin only 5.6% 13% 42.1% 26 (21.8%) OHA, insulin, 46.3% 11% 1.1% 8 (6.7%) Diet only 2% 5.3% - Statin < 1% - 8.4% 9 (7.6%) Aspirin 9.3% - 14.7% 11 (9.2%) ARB/ACE I - - - 68 (57.1%) These patients were generally >40yrs with more than 2 CV risk factors. They required a comprehensive approach to manage the CV risk factors beyond Blood pressure and glycemic control

Glycemic control, therapies and the predictors There was good glycemic control with 104 (87.4%) of patients having HBA1c < 7.0% Vaghela and Nyamu had 30% and 18% good control respectively. Most patients were on OHAs +/-Insulin-91 (76.4%);Insulin 34 (28.5%). What are the determinants of glycemic control in this population? Diet, lifestyle and genetics What is the magnitude of the effect of closer clinic visits? 2-3 monthly at Nyanza vs KNH 6/12 to 1year There was concordance between FPG and HBA 1C. This provides internal validity to the results It suggests that FPG can and should be used in resource limited settings as we await cheaper HBA1C kits to monitor control.

PHARMACOTHERAPEUTIC INTERVENTIONS FOR CAD RISK REDUCTION 1. ACE-I/ARBs were used in 68 (57.1%) of patients on antihypertensives. Microalbuminuria was found in 44 (37.0%) of patients. There was no difference between those with or without; 52.3% vs 61.3% (p=0.334). Microalbuminuria was not the driver of RAAS Inhibitor prescription 2. LDL was elevated in 72 (60.5%) of patients yet only 9 (7.6%) were on statins. Serum lipid levels were not the driver of prescription 3. Platelet agents were used in only 11 (9.2%) of the patients. This should have been higher with the risk factor clustering seen earlier

PHARMACOTHERAPEUTIC INTERVENTIONS FOR CAD RISK REDUCTION Overall the following proportions of patients were on various forms of treatment: a) 99% - glycemic control agents b) 79.8%-on anti hypertensive treatment c) 9.2% -on antiplatelet agents d) 7.6% -on statins

Predictors/associations of glycemic control There was NO independent predictor of glycemic control. There was good correlation between A1C and FPG There may be a role for reverse causation in that the abnormalities seen in the patients with poor control were in themselves a result of the poor control rather than the causes of poor control

Study Limitations Selection bias may have influenced the results by selecting, willing, highly motivated and financially able patients. Single HBA1c values may not accurately reflect overall glycemic control over many years. Single spot urine may have been affected by the day to day variability. The non-probability sampling technique did not control for confounders. Hospital based study may not reflect disease in general population No immunological studies to rule out LADA. Hemoglobinopathies not tested for, for exclusion purposes. The definition of type 2 diabetes was by phenotype.

Conclusion 1 1. This study was the first of its kind in the Western Kenya. 2. Glycemic control can be achieved in technologically challenged environments. 3. Hypertension was a common problem 4. There was a significant familial component in these patients 5. Obesity was common in this rural population

Conclusion 2 6. Dyslipidemia, particularly raised LDL cholesterol was common. 7. Renal dysfunction was very common. 8. In general, the uptake of evidence based Interventions for cardiovascular risk factor reduction was low. 9. There is need for better designed studies to verify the various determinants of glycemic control

Recommendations Encourage use of FPG in resource limited settings. WHR can be used to effectively monitor central obesity. Provide facilities to measure biochemical RF for CAD to facilitate the necessary interventions. Study on prevalence of complications in this population in view of the results found in this study. Study the predictors of glycemic control Encourage evidence based practice with adequate senior supervision and instruction

Asante!