1. Lifestyle diseases in People living with HIV
Nombulelo Magula
Nelson R Mandela School of Medicine
University of KwaZulu-Natal

2. This is a reminder to all of us of the battle that we have fought against HIV disease. Although other parts of the world struggled against the virus, our battle was different for many reasons. As such, we now as a country carry the statistics that we do. While we were debating whether AIDS was caused by HIV or poverty, scores of people died. Across the atlantic, the disease was getting under control. The bigger challenge across the atlantic was the a different three letter word: the BMI. Amagama amathathu.

3. Diseases of Lifestyle Exposure over many decades Major risk factors
Unhealthy diets
Smoking
Lack of exercise
Stress
Major risk factors
High blood pressure
High blood cholesterol
Diabetes
obesity

4. Lifestyle diseases in 2005
2/3 deaths globally were attributable to CDL.
35 million deaths from CDL recorded were double the number of deaths
for all infectious diseases (HIV/AIDS, tuberculosis, malaria),
maternal and perinatal conditions, and
nutritional deficiencies combined.

5. Approximately 4/5 CDL deaths occurred in low and middle-income countries with the main ones being
heart disease
stroke
cancer
chronic respiratory diseases and
diabetes

9. Smoking is projected to kill 50% more people in 2015 than HIV/AIDS, and
Will be responsible for 10% of all deaths globally.

10. World’s largest ART program
IHD among top 3 causes of death in HIV infected population in 2030
in low income countries
ART has resulted in a drastic reduction in mortality and morbidity due to AIDS related conditions, however, mortality due to non-AIDS conditions, in particular CVD, seems to be rising. IHD is predicted to be among the top 3 causes of death in the HIV infected population in 2030 in low income countries. Diabetes is an important risk factor for CVD, therefore we set out to determine
Mathers C, Loncar D. Plos Med. 2006; 3:e442

11. of diabetes and dysglycaemia in South African black patients.
To determine:
Prevalence
Incidence
Predictors
of diabetes and dysglycaemia in South African black patients.
With this in mind we conducted a study to evaluate cardiovascular risk factors in our population

12. Setting

14. Eligibility Cross sectional design Study group HIV infected
2nd generation Zulu
Age > 18years
ART naïve
Informed consent
Controls
1. Age, gender and ethnically matched HIV negative volunteers
Prospective design
Study group
2nd generation Zulu
Age >18 years
ART naïve
Starting ART
Informed consent

15. Study Design Flow Diagram
Step 1: Cross-sectional
Step 2: Prospective
Outcomes:
A. lipodystrophy development (by FRAM questionnaire
B. Development of:
i. Fat re-distribution (by CT scan and Dexa scan measures)
ii. Diabetes and dysglycaemia
iii. Dyslipidaemia
iv) Other metabolic changes
At 0, 3, 6, 12, 18 and 24 months
group 1
HIV negative
Volunteers
group 2
HIV +
Not starting ART
HIV+
Starting ART
Baseline, 3, 6, 12, 18 and 24 months;
1. ART outcomes
2. Examine incidence and determinants of lipodystrophy
Participants presenting at KEH and VCT centers in Durban
group 3
HIV +
Starting ART
For the cross-sectional step of the study, Participants were categorized into three groups, HIV negative for group 1, HIV infected and not starting ART for group 2 and HIV infected and starting ART for group 3. In the prospective step, group three was followed up for 24 months after initiating ART and the outcome measured was diabetes and dysglycaemia
Diabetes and dysglycaemia in HIV infected vs control (HIV-) group

16. Methods History Physical examination Anthropometry
Circumferences
Skin folds
Oral glucose tolerance test
0 hour plasma glucose (after overnight fast) and
2 hour plasma glucose (after ingestion of 75g glucose monohydrate dissolved in 250 ml water
HbA1c
Laboratory tests
Fat distribution
DXA scan
CT scan
Outcomes
Diabetes
Impaired glucose tolerance (IGT)
Impaired fasting glucose (IFG)
Dysglycaemia=
Diabetes or IGT or IFG
(Any disorder of glycaemia)

17. WHO and ADA diagnostic criteria for diabetes and other disorders of glycaemia
Category
WHO
ADA
Diabetes
HbA1c
> 6.5 % or
Plasma glucose (PG):
Fasting or
> 7.0 mmol/l
2-h post glucose load
> mmol/l
Impaired Glucose tolerance (PG)
Fasting (if measured)
and
<7.0 mmol/l
> 7.8 mmol/l
7.8 – 11.0 mmol/l
Impaired Fasting glucose (PG)
Fasting
> 6.1 and < 7.0 mmol/l
5.6 – 6.9 mmol/l
and (if measured)
<7.8 mmol/l

18. Study Enrolment Flow Chart
Not Eligible, n=204:
Failure to return after screening, n=120
Not second generation Zulu, n=16
Consent refused, n=12
History of ART, n=20
Tuberculosis, n=27
Malignancy, n=3
Died before starting ART, n=1
Pregnant, n=4
Wishes to fall pregnant, n=1
Screened, n=530
Eligible, n=326
Group 1
HIV negative
n=88
Group 2
HIV infected, not starting ART
n=88
Group 3
HIV infected, starting ART
n=150

19. Demographic characteristics at baseline*
Variable
Group 1
HIV -
n=88
Group 2
HIV + not starting ART,
Group 3
HIV + starting ART,
n=150 p
Age (yr) ns
Female
58 (65.9)
102 (68.0)
Marital status (single)
61(69.3)
67 (79.8)
112(76.7)
High school education
61(70.1)
63(74.1)
98(69.0)
Employed
19 (30.2)
24(37.5)
59(41.3)
Tobacco smoking
18 (20.7)
18 (21.2)
24 (17.9)
Alcohol
29 (34.1)
33 (38.8)
40 (27.0)
0.01
Physical activity
Occupational: moderate
32 (40.0)
26 (31.7)
37 (30.6)
Leisure: moderate
9 (10.7)
10 (12.2)
15 (10.6)
Familial diabetes
17 (19.3)
36 (24.3)
* n (%) or Mean + SD

20. Clinical and laboratory characteristics at baseline
Variable
Group 1
HIV -
n=88
Group 2
HIV + not starting ART,
Group 3
HIV + starting ART,
n=150 p
Systolic BP (mmHg)
0.02
Diastolic BP (mmHg) ns
Body mass index(kg/m2)
0.01
Plasma glucose (mM)
0 – min
120 – min
HbA1c (%)
CD4 cell count, cells/mm3 -
404.5( )
132( )
0.0001
Log HIV RNA
0.002

21. Prevalence of diabetes and dysglycaemia**
P<0.01 for diabetes, P NS for rest of categories
**p<0.01 D group 3 vs. group 1

22. Multivariate analysis
Variable
OR (95% CI) p
Systolic blood pressure
1.07 (1.02 to 1.12)
0.003
Serum triglyceride
4.5 (1.03 to 19.8)
0.04

23. Overall response rate of Group 3 (HIV infected and starting ART)
Baseline
n =150
24 Months
Follow-up complete
n = 97
(64.7%)
Not followed-up
n = 53
(35.0%)
ch5

24. Prospective - Step 2 Antiretroviral treatment allocation*
Variable
All patients
n=150
Male
n=48
Female
n=102 p
Efavirenz
76 (50.7)
40 (83.3)
36 (35.3)
<0.0001
Nevirapine
74 (49.3)
8 (16.7)
66(64.7)
Lamivudine
150 (100.0)
48 (100.0)
102 (100.0)
Tenofovir
Ch5 tab1
*n(%)

25. 24 month follow-up complete 24 month follow-up incomplete
Baseline characteristics of Group 3 subjects: completed vs. not completed 24 month visit follow-up
Variable
24 month follow-up complete
n=97
24 month follow-up incomplete
n=53 p
Age ns
Female
64 (65.98)
38 (71.7)
Marital Status: Single
71 (73.2)
41 (77.4)
High school
64 (70.3)
34 (66.7)
Employed
47 (50.5)
12 (24)
0.002
Body mass index
CD4 cell count(cells/mm3)
log HIV RNA load
Haemoglobin
Albumin
Efavirenz
49 (50.5)
27 (50.9)
Nevirapine
48 (49.5)
26 (49.1)
Tenofovir
97 (100.0)
53 (100.0) -
Lamivudine
Ch5 tab 1 -1

26. Immunological and Virological response during 24 months follow up on ART

27. Incidence of Diabetes Mellitus* during 24 months follow up on ART
PYFU
Incidence rate 5
221.9 (150)
2.3 (0.7 to 5.3)
* OGTT criteria

28. Incidence of Dysglycaemia (Diabetes or IGT or IFG)
Incidence of Dysglycaemia (Diabetes or IGT or IFG)* during 24 months follow up on ART n
PYFU
Incidence rate 16
211.6 (150)
7.6 (4.3 to 12.3)
* OGTT criteria

29. Not developed Diabetes
Baseline characteristics : Group 3 (n:150) developed diabetes vs. not developed diabetes*
Variable
Developed Diabetes
n=5
Not developed Diabetes
n= 145 p
Age (yr)
0.5
Male
4 (80.0)
44 (30.3)
0.02
Efavirenz
5 (100.0)
71 (47.9)
BP (mmHg)
Systolic
0.04
Diastolic
0.003
BMI (kg/m2)
0.6
*by OGTT

30. Developed dysglycaemia, n=16
Baseline characteristics :Group 3 (n:150): developed vs. not developed dysglycaemia
Variable
Developed dysglycaemia, n=16
Not developed
dysglycaemia, n=134 p
Age
0.03
Male
9 (56.3)
37 (28.5)
Efavirenz
12 (75.0)
64(47.8)
0.04
Nevirapine
4 (25.0)
70(52.2)
BP (mmHg)
Systolic
0.002
Diastolic
0.01

31. World’s largest ART program
Prevalence of diabetes
HbA1c criteria
HIV negative: 1.2 %
HIV infected: 0%
(glucose-based criteria)
HIV negative: 4.9%
Prevalence of dysglycaemia
HIV negative: 8.6%
HIV infected: 3.7%

32. World’s largest ART program
IR of dysglycaemia
7.6 per 100 PYFU
(95% CI [4.3 to 12.3])
IR of diabetes
(glucose-based criteria)
2.3 per 100 PYFU (95% CI[0.7 to 5.3])
IR rate of diabetes
(HbA1c criteria)
3.8 per 100 PYFU (95% CI [1.6 to 7.4])

33. Multivariate analysis
Diabetes
Dysglycaemia
Variable
HR (95% CI) p
Visceral: subcutaneous fat
2.95 (1.25 to 6.96)
0.01
Variable
HR (95% CI) p
Systolic BP
1.04 (1.01 to 1.06)
0.002
Albumin
0.84 (0.8 to 0.9)
CD 4 cell count
0.988 (0.980 to 0.997)
0.01
Efavirenz
3.98 (1.29 to 14.8)
0.02

34. SWISS: 4.4* WIHS: 2.8* APROCO-COPILOTE:1.4* MACS: 4.7* D: A:D: 0.5*
KZN: 2.3*
Justman,et.al. 2003
De Wit,et.al
Brown, et.al. 2005
Ledergerber, et.al. 2007
Capeau, et.al. 2012
*incidence rate/100
PYFU
Incidence rate studies of Diabetes or dysglycaemia
in HIV infected patients on cART

35. Conclusion Prevalence of diabetes 0% prior to cART
Incidence of diabetes and dysglycaemia on cART is high
Monitoring for diabetes and dysglycaemia in patients on cART warranted
Probably the first study reporting efavirenz as a predictive risk factor for incident dysglycaemia
Alternative to Efavirenz as the backbone of cART needs to be considered

36. Clinical and laboratory characteristics at baseline
Variable
Group 1
HIV -
n=88
Group 2
HIV + not starting ART,
Group 3
HIV + starting ART,
n=150 p
Systolic BP (mmHg)
0.02
Diastolic BP (mmHg) ns
Body mass index(kg/m2)
0.01
Plasma glucose (mM)
0 – min
120 – min
HbA1c (%)
CD4 cell count, cells/mm3 -
404.5( )
132( )
0.0001
Log HIV RNA
0.002

37. Clinical characteristics during 24 months follow-up on ART