Prevalence and risk factors for dyslipidemia among South Indian adults: A community based-NCD study

Abstract

Background
Dyslipidemia is a crucial risk factor for atherosclerotic cardiovascular disease. However, there is limited data available on the differences in the prevalence of dyslipidemia between rural and urban populations in India. The aim of the present investigation is to describe the prevalence of dyslipidemia and the risk factors associated with adverse lipid profiles among adults residing in rural, sub-urban, and urban areas of India, and to assess its association with diabetes and hypertension.Methods
We enrolled adults over the age of 20 who lived in three distinct demographic areas in South India: rural, sub-urban, and urban. Data on demographics, lifestyle, disease history, family history, body weight, height, waist circumference, blood pressure, and clinical characteristics were collected for this study. We used chi-square tests and multivariable logistic regression to analyze demographic prevalence and risk factors related to lipid abnormalities among study participants.ResultsThis study enrolled 2976 randomly selected participants from rural, sub-urban, and urban communities in Tamil Nadu, India. Of these, 865 (29.1%) were rural residents, 1030 (34.6%) were sub-urban residents, and 1081 (36.3%) were urban residents. About 80% of women who lived in the suburban area had higher rates of low-HDL cholesterol. Compared to sub-urban (29.9%, 49%, and 21.1%) and rural (33.4%, 43.4%, and 24.1%) populations, urban populations had higher prevalence rates of hypercholesterolemia, hypertriglyceridemia, and elevated LDL-C (37.3%, 52.5%, and 38.6%), respectively. Men were more likely than women to develop dyslipidemia before the age of 40, but after that age, men showed a reduced risk of dyslipidemia than women, except for low HDL cholesterol. Age group, gender, current drinker, overweight, obesity, diabetes, and hypertension showed a significant association (p < 0.05) with dyslipidemia.Conclusions
The study found that more than 85% of the sub-urban and urban population had dyslipidemia (at least one lipid abnormality) compared to rural residents (78.5%). The prevalence rates were higher among those with diabetes and hypertension in urban residents.

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International Journal of Diabetes in Developing Countries
https://doi.org/10.1007/s13410-023-01202-7
ORIGINAL ARTICLE
Prevalence andrisk factors fordyslipidemia amongSouth Indian
adults: Acommunity based‑NCD study
SundaresanMohanraj1 · GanesanVelmurugan1· KrishnanSwaminathan2· ArulrajRamakrishnan3
Received: 22 October 2022 / Accepted: 14 April 2023
© The Author(s), under exclusive licence to Research Society for Study of Diabetes in India 2023
Abstract
Background Dyslipidemia is a crucial risk factor for atherosclerotic cardiovascular disease. However, there is limited data
available on the differences in the prevalence of dyslipidemia between rural and urban populations in India. The aim of the
present investigation is to describe the prevalence of dyslipidemia and the risk factors associated with adverse lipid profiles
among adults residing in rural, sub-urban, and urban areas of India, and to assess its association with diabetes and hypertension.
Methods We enrolled adults over the age of 20 who lived in three distinct demographic areas in South India: rural, sub-
urban, and urban. Data on demographics, lifestyle, disease history, family history, body weight, height, waist circumference,
blood pressure, and clinical characteristics were collected for this study. We used chi-square tests and multivariable logistic
regression to analyze demographic prevalence and risk factors related to lipid abnormalities among study participants.
Results This study enrolled 2976 randomly selected participants from rural, sub-urban, and urban communities in Tamil
Nadu, India. Of these, 865 (29.1%) were rural residents, 1030 (34.6%) were sub-urban residents, and 1081 (36.3%) were
urban residents. About 80% of women who lived in the suburban area had higher rates of low-HDL cholesterol. Compared to
sub-urban (29.9%, 49%, and 21.1%) and rural (33.4%, 43.4%, and 24.1%) populations, urban populations had higher preva-
lence rates of hypercholesterolemia, hypertriglyceridemia, and elevated LDL-C (37.3%, 52.5%, and 38.6%), respectively.
Men were more likely than women to develop dyslipidemia before the age of 40, but after that age, men showed a reduced
risk of dyslipidemia than women, except for low HDL cholesterol. Age group, gender, current drinker, overweight, obesity,
diabetes, and hypertension showed a significant association (p < 0.05) with dyslipidemia.
Conclusions The study found that more than 85% of the sub-urban and urban population had dyslipidemia (at least one
lipid abnormality) compared to rural residents (78.5%). The prevalence rates were higher among those with diabetes and
hypertension in urban residents.
Keywords Dyslipidemia· Rural–Urban population· Non-communicable disease (NCD)· Lipid abnormality·
Cardiovascular Risk
Introduction
Atherosclerotic cardiovascular disease (ASCVD) is a major
cause of morbidity and mortality, with a high incidence and
prevalence in low- and middle-income countries. South Asians,
in particular, have a higher risk of ASCVD than other ethnic
populations [1, 2]. In India, the number of prevalent cases of
cardiovascular diseases (CVD) has increased from 25.7 million
in 1990 to 54.5 million in 2016 [3]. Dyslipidemia is one of the
crucial risk factors for CVD and is characterized by any or a
combination of the following: elevated total cholesterol (TC),
raised low-density lipoprotein cholesterol (LDL-C), raised tri-
glycerides (TG), and low high-density lipoprotein cholesterol
(HDL-C) [4]. These adverse lipid profiles are closely linked
to the pathophysiology of atherosclerosis, the key underlying
process contributing to most clinical ASCVD events [5].
According to the 2016 Lipid Association of India report,
the frequency of hypercholesterolemia varies from 10 to
15% in rural to 25% to 30% in urban populations [6]. There
are very few large cohort studies on the epidemiology of
* Sundaresan Mohanraj
mohanraj@kmchrf.org
1 Department ofBiochemistry andMicrobiology, KMCH
Research Foundation, Kovai Medical Center & Hospital,
Coimbatore641014, Tamilnadu, India
2 Department ofEndocrinology, KMCH Research Foundation,
Kovai Medical Center andHospital, Coimbatore, India
3 Department ofGastroenterology, KMCH Research Foundation,
Kovai Medical Center andHospital, Coimbatore, India

International Journal of Diabetes in Developing Countries
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hypercholesterolemia in India [7–12], but these studies are
not representative of different demographic regions in India.
However, there is limited data available on the prevalence
of lipid abnormalities based on rural versus urban living
in India. Urban–rural differences in adverse lipid profiles
may be prevalent due to several factors such as less physical
activity, high consumption of fast food, and unsaturated fat
intake in urban areas, which may contribute to a higher prev-
alence of dyslipidemia compared to rural areas. The rising
rates of non-communicable diseases (NCD) in both urban
and rural India demonstrate that lifestyles and urbanization
are common but not fully accounted for. Rapid urbaniza-
tion is initiated by urban expansion into peripheral and rural
areas, largely for economic reasons. Although it is not clear
how industrialization or urbanization increases the risk of
metabolic risk factors among people who have had different
early life experiences, several regions of India experience
different climate and geographical conditions due to sub-
stantial latitude and longitude extensions across the country.
However, we could not find any data regarding the sub-
urban population compared with the urban and rural popu-
lations. Therefore, the aim of the present investigation was
to describe the prevalence of dyslipidemia and the risk fac-
tors associated with individual adverse lipid profiles among
adults living in rural, sub-urban, and urban areas in India.
Methodology
Study design A community-based cross-sectional study.
Study sites andpopulation details
Rural‑population details The Nallampatti Panchayat has
population of 3,874 of which 1,929 are males while 1,945
are females as per report released by Census India 2011.
Sub‑urban‑population details The Chinna Thadagam Pan-
chayat has population of 8,407 of which 4,152 are males while
4,255 are females as per report released by Census India 2011.
Urban‑population details The Kalapatti Town Panchayat has
population of 39,586 of which 19,936 are males while 19,650
are females as per report released by Census India 2011.
Study areas
The methodology of the study is described in our earlier
publications (13, 14). In brief, we selected three differ-
ent geographic areas based on contacts with administra-
tive heads, logistics, and the ability to conduct long-term
follow-up. Nallampatti, a typical farming village about
60km from Coimbatore, was chosen as a representative
rural area. Thadagam, an area rich in brick-kilns 15km
from Coimbatore, and Kalapatti, a city within Coimbatore,
were chosen as representative sub-urban and urban areas,
respectively. The individual studies were named Nallam-
patti-NCD study (NNCD), Thadagam-NCD study (TNCD),
and Kalapatti-NCD study (KNCD). The studies were con-
ducted in a staggered pattern over a four-week period in
each area from April 2015 to June 2016. Each study popu-
lation was informed of our visit through the distribution of
leaflets (door to door) and by "word of mouth" through the
local government administrative workers and student volun-
teers. The exclusion criteria included age less than 20years
or greater than 85years, pregnancy, and non-residents of
the selected regions. Age 20years and above and native
residents were included in this study. All three areas had
a defined geographical boundary, and the population data
was collected from the Government of India – Census 2011.
Only the residents of these regions were invited for the study.
The residence of the participants was cross-checked by veri-
fication with the government census data and the proof of
residence documents, such as Aadhar card, voter ID card,
or driving license, provided by the participants. The study
design and protocol were approved by KMCH Ethic Com-
mittee, and informed written consent was obtained from all
participants prior to participation, following the principles
of the Declaration of Helsinki. Collected data were evaluated
on a weekly basis, and feedback was sent to the study team
in the field to correct any discrepancies.
Sample anddata collection fromsub‑urban
andurban regions
We administered a detailed questionnaire, as described
in previous publications [13, 14], to document the educa-
tional status, employment, alcohol intake, smoking status,
pesticide exposure, family disease history, and past medi-
cal history.
Anthropometry
We measured body weight using an electronic weighing
scale (SECA 813), height using a stadiometer (SECA 208),
and waist circumference in centimeters using a non-stretch-
able measuring tape between the costal margins and the iliac
crest at the end of expiration. Blood pressure was recorded
using the electronic OMRON machine in the sitting position
in the right arm (Model HEM-7130, Omron healthcare, Sin-
gapore) on two occasions 15min apart. We used the mean
of the two measurements in analyses. Body mass index was
calculated using the formula weight (kg)/height (m)2.

International Journal of Diabetes in Developing Countries
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Clinical parameters
We prepared serum and plasma samples from whole blood
collected appropriately by standard protocols. HbA1c was
measured using an automated HPLC method (D-10-Biorad),
cystatin-c was determined by Nephlometric method (BN
Prospec-Siemens), glucose was measured using a Hexoki-
nase/ GOD-POD/ endpoint method, and lipid levels were
measured using an automated analyzer (Abbott Architect
ci8200). Uric acid and creatinine levels were measured by
the Endpoint method (Abbott Architect ci8200). Fasting
and post-meal glucose were not considered due to logistical
issues.
Denitions
Dyslipidemia: National Cholesterol Education Programme
(NCEP) [6] guidelines were used for definition of dyslipi-
demia as follows:
Hypercholesterolemia – serum cholesterol lev-
els ≥ 200mg/dl (≥ 5.2mmol/l).
Hypertriglyceridemia – serum triglyceride lev-
els ≥ 150mg/dl (≥ 1.7mmol/l).
Low HDL cholesterol – HDL cholesterol levels < 40mg/
dl(< 1.04mmol/l) for men and < 50mg/dl (< 1.3mmol/l)
for women.
High LDL cholesterol – LDL cholesterol lev-
els ≥ 130mg/dl (≥ 3.4 mmol/l) calculated using the
Friedewald equation.
Hypertension was defined as either having a history of
hypertension on medications or a systolic blood pressure
of ≥ 140mm Hg and/ or diastolic blood pressure ≥ 90mm
Hg on two occasions taken 15min apart, in those without a
history of hypertension.
Overweight was defined as BMI equal to or more than
25kg/m2, and ‘obesity’ as BMI equal to or more than
30kg/m2.
Diabetes was defined as either having a history of diabe-
tes on medications or HbA1c level of ≥ 6.5% in those with-
out a history of diabetes.
Statistical analysis
Data were tabulated on Microsoft Excel and transposed
to SPSS for statistical analysis (SPSS) (IBM Corporation,
Armonk, New York, USA). Data were analysed using SPSS
version 20. Chi-square tests and multivariable logistic
regression were performed to found the demographical prev-
alence and risk factors associated with lipid abnormalities
among study participants. We analysed basic characteristics
based the dyslipidemia by study sites (rural vs sub-urban vs
urban) and sex. Prevalence of dyslipidemia in both men and
women were analyzed for three demographic areas. Mean
values of BMI, HbA1c and blood pressure were analysed.
Prevalence of dyslipidemia in diabetes and hypertension
population were analyzed for three demographic areas. Both,
men and women were analysed separately as we anticipated
that there might be sex differences in location (rural, sub-
urban and urban) effects. Multivariable logistic regression
model was used to estimate odds ratios for hypercholes-
terolemia, triglyceridemia, low HDL-C and high LDL-C,
adjusting for age and BMI. A p value < 0.05 was considered
statistically significant.
Results
A total of 2976 participants were enrolled in this study from
rural, sub-urban, and urban communities in Tamil Nadu,
India. Of these, 865 (29.1%) were rural residents, 1030
(34.6%) were sub-urban residents, and 1081 (36.3%) were
urban dwellers. The general characteristics of the study
population based on dyslipidemia are shown in Table1. In
all three demographics, residents with any lipid abnormality
had significantly higher risk factors of BMI, waist circumfer-
ence, HbA1c, total cholesterol, triglycerides, low-HDL, and
high-LDL than those with no lipid abnormality. The mean
age (SD) of rural residents with any lipid abnormality was
significantly higher than that of normal residents (p < 0.05),
whereas sub-urban and urban dwellers did not differ signifi-
cantly between normal and any lipid abnormality residents.
Similarly, the mean values of systolic and diastolic blood
pressure in rural (p = 0.002 and p = 0.05, respectively) and
urban (p = 0.005 and p = 0.002, respectively) residents with
any lipid abnormality were significantly higher than those
with no lipid abnormality, while the sub-urban residents
with any lipid abnormality were not significantly differ-
ent from those with no lipid abnormality. The prevalence
of dyslipidemia (any lipid abnormality) was similar among
sub-urban and urban residents (85.9% vs 86.2%) compared
to rural participants (78.5%). Among the three demograph-
ics, 8.3% (n = 72), 9.1% (n = 94), and 16.4% (n = 177) of
the rural, sub-urban, and urban populations had four lipid
abnormalities, respectively. On the other hand, about 21.5%
(n = 186), 14.1% (n = 145), and 13.8% (n = 149) of the rural,
sub-urban, and urban populations had no lipid abnormali-
ties, respectively. Low HDL-C was the most common dys-
lipidemia, particularly among sub-urban residents (70% vs
58.2% in rural and 68.1% in urban). The dwellers with high
LDL-C were highest in urban areas (38.6%) compared to
sub-urban (21.1%) and rural (24.1%) areas.

International Journal of Diabetes in Developing Countries
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Table 1 Basic characteristics of the study population based on the dyslipidemia
The values are mean (SD) [95%CI]
(One-way ANOVA test was used to compare means of continuous variables between the normal and any lipid abnormality among the three groups (rural, sub-urban and urban))
BMI Body-mass index; SBP Systolic blood pressure; DBP Diastolic blood pressure; HbA1c Heamoglobin A1c; TC Total cholesterol; TGL Triglyceride; L-HDLC Low- high-density lipoproteins;
H-LDLC High- low-density lipoproteins
Rural (n = 865) Sub-urban (n = 1030) Urban (n = 1081) Overall
p Value
Clinical parameters Normal
(n = 186)
Any lipid abnor-
mality (n = 679)
p Value Normal
(n = 145)
Any lipid abnor-
mality (n = 885)
p Value Normal
(n = 149)
Any lipid abnor-
mality (n = 932)
p Value
Age 46.47 (15.39)
[44.25–48.70]
48.79 (13.19)
[47.80–49.79]
.04 51.6 (15.9)
[49.0–54.2]
50.1 (14.4)
[49.1–51.0]
.25 46.5 (17.1)
[43.7–49.3]
48.8 (13.3)
[47.9–49.6]
.06 .002
BMI 21.16 (3.9)
[20.59–21.72]
23.82 (3.97)
[23.53–24.12]
< .001 20.8 (4.3)
[20.1–21.5]
23.9 (5.4)
[23.6–24.3]
< .001 22.0 (4.7)
[21.3–22.8]
25.6 (7.2)
[25.2–26.1]
< .001 < .001
SBP 126.6 (18.3)
[124.0–129.3]
132.08 (21.5)
[130.46–133.7]
.002 128.3 (21.7)
[124.7–131.8]
130.5 (22.3)
[129.1–132.0]
.25 124.0 (23.2)
[120.3–127.8]
129.4 (21.6)
[128.0–130.8]
.005 .074
DBP 80.60 (11.33)
[78.96–82.24]
82.51 (11.9)
[81.61–83.41]
.05 77.7 (12.7)
[75.7–79.8]
78.4 (11.2)
[77.7–79.2)
.52 74.5 (11.8)
[72.6–76.5]
77.8 (11.5)
[77.0–78.5]
.002 < .001
Waist circumference 84.88 (8.71)
[83.6–86.1]
91.07 (9.2)
[90.37–91.7]
< .001 84.36 (11.7)
[82.4–86.3]
90.2 (10.8)
[89.5–91.0]
< .001 85.5 (12.3)
[83.5–87.5]
92.9 (10.0)
[92.3–93.6]
< .001 < .001
Creatinine 0.76 (0.23)
[0.73–0.79]
0.75 (0.18)
[0.74–0.77]
.49 0.71 (.31)
[0.6–0.7]
0.7 (0.3)
[0.6–0.7]
.92 0.8 (0.7)
[0.6–0.9]
0.7 (0.2)
[0.7–0.7]
.09 < .001
HbA1c 5.7 (0.86)
[5.65–5.8]
6.04 (1.16)
[5.95–6.13]
.003 5.8 (1.2)
[5.6–6.0]
6.2 (1.6)
[6.1–6.3]
.001 5.8 (1.1)
[5.6–6.0]
6.2 (1.5)
[6.1–6.3]
.001 < .001
Uric acid 4.4 (1.3)
[4.3–4.6]
4.8 (1.3)
[4.7–4.9]
.001 5.1 (1.5)
[4.8–5.3]
5.2 (1.5)
[5.1–5.3]
.33 4.8 (4.4)
[4.1–5.5]
4.9 (1.3)
[4.8–5.0]
.43 < .001
TC 165.2 (20.5)
[162.2–168.2]
191.2 (40.7)
[188.1–194.3]
< .001 165.3 (22.8)
[161.5–169.1]
186.3 (40.0)
[183.6–188.9]
< .001 164.5 (20.8)
[161.2–167.9]
192.9 (39.3)
[190.4–195.4]
< .001 .003
TGL 88.1 (26.18)
[84.2–91.8]
189.7 (112.1)
[181.3–198.2]
< .001 91.9 (28.4)
[87.2–96.6]
189.8 (106.5)
[182.7–196.8]
< .001 91.8 (29.6)
[87.0–96.6]
198.3 (128.0)
[190.1–206.5]
< .001 .010
L-HDLC 52.5 (7.4)
[51.4–53.6]
41.9 (8.9)
[41.2–42.5]
< .001 51.3 (7.2)
[50.1–52.5]
39.8 (8.5)
[39.2–40.3]
< .001 54.5 (9.5)
[52.9–56.1]
40.2 (9.9)
[39.5–40.8]
< .001 < .001
H-LDLC 93.2 (19.0)
[90.4–96.0]
114.2 (32.6)
[111.8–116.7]
< .001 95.6 (20.2)
[92.2–98.9)
108.6 (33.4)
[106.4–110.8]
< .001 95.9 (18.5)
[92.9–98.9]
125.4 (32.7)
[123.3–127.5]
< .001 < .001

International Journal of Diabetes in Developing Countries
1 3
As shown in Table2, all lipid abnormalities were higher
among women than among men in all three areas of resi-
dence, except for triglycerides. Regarding the four com-
ponents of dyslipidemia, raised triglycerides recorded a
higher prevalence in men compared to women in all three
areas of participants. In sub-urban residences, raised total
cholesterol had a similar prevalence in both men (29.9%;
95% CI 25.8–33.9) and women (29.9%; 95% CI 26.2–33.9),
whereas raised total cholesterol had a higher prevalence in
rural (32.5%; 95% CI 28.2–37.1 for men and 34.2%; 95%
CI 29.8–38.9 for women) and urban settings (36.8%; 95%
CI 32.4–41.6 for men and 37.8%; 95% CI 34.1–41.7 for
women). The prevalence of low levels of HDL-C was higher
in suburban (58.2% for men, 95% CI 53.7–62.7; 80% for
women, 95% CI 76.5–83.2) and urban (58.7% for men, 95%
CI 54.1–63.2; 74.7% for women, 95% CI 70.8–78.1) areas
than in rural areas (52.3% for men, 95% CI 47.7–56.6; 63.8%
for women, 95% CI 59.6–68.4). Similarly, raised LDL-C had
a higher prevalence among urban dwellers (37.9%; 95% CI
33.1–42.7 for men and 39.2%; 95% CI 35.3–42.9 for women)
compared to rural (23.6%; 95% CI 19.8–28 for men and
24.7%; 95% CI 20.9–28.7 for women) and sub-urban popu-
lations (20.5%; 95% CI 17.1–24.1 for men and 21.7%; 95%
CI 18.2–25.3 for women). The mean and standard deviation
values of all lipid abnormalities were highest among the
urban population compared to rural and sub-urban dwellers.
Figure1 presents the prevalence of the four components
of dyslipidemia according to age groups and sex in rural,
sub-urban, and urban residences. Below the age of 40years,
men were more likely than women to have dyslipidemia,
whereas after the age of 40years, men showed a lower risk
of dyslipidemia than women, except for low-HDL choles-
terol. The highest prevalence was observed at the age of
40–59years and declined afterward. In comparison to men,
sub-urban women had an 80% prevalence of low-HDL cho-
lesterol, which was higher in residents of rural and urban
areas. Overall, women had significantly higher rates of lipid
abnormalities than males, except for raised triglycerides,
which was higher in men in all three residences.
Table3 summarizes the lipid abnormalities in residents
with diabetes and hypertension according to the area of resi-
dence. Among the study participants, the prevalence of diabe-
tes was 16.1%, 25.8%, and 23% in rural, suburban, and urban
areas, respectively. Among these participants with diabetes,
the prevalence of high total cholesterol (TC), high triglyc-
erides (TG), high low-density lipoprotein cholesterol (LDL-
C), and low high-density lipoprotein cholesterol (HDL-C)
was 35.3%, 49.6%, 25.2%, and 63.3% among rural dwellers;
34.2%, 62%, 21.4%, and 72.9% among suburban residents; and
41%, 68%, 43.4%, and 72.3% among urban populations. Fur-
thermore, we found that the prevalence of hypertension was
36.4%, 47%, and 39.7% in rural, suburban, and urban areas,
respectively. Among these participants with hypertension,
Table 2 Sex specific prevalence of lipid abnormalities in study participants (Chi-square test)
L-HDLC Low- high-density lipoproteins; H-LDLC High- low-density lipoproteins
Total (n = 2976) Rural (n = 865) Sub-urban (n = 1030) Urban (n = 1081)
Lipid Profile Men
(n = 1322)
Women
(n = 1654)
p value Men
(n = 415)
Women
(n = 450)
p value Men
(n = 469)
Women
(n = 561)
p value Men
(n = 438)
Women
(n = 643)
p value
Count (%) [95% CI]
Hypercho-
lester-
olemia
436(33)
[30.6–35.6]
565(34.2)
[31.9–36.5]
0.262 135(32.5)
[28.2–37.1]
154(34.2)
[29.8–38.9]
0.325 140(29.9)
[25.8–33.9]
168(29.9)
[26.2–33.9]
0.514 161(36.8)
[32.4–41.6]
243(37.8)
[34.1–41.7]
0.390
Hypertri-
glyceri-
demia
727(55)
[52.3–57.7]
722(43.7)
[41.1–46.1]
0.000 211(50.8)
[46.3–55.4]
165(36.7)
[32.2–40.9]
0.000 256(54.6)
[49.9–58.6]
249(44.4)
[40.3–48.8]
0.001 260(59.4)
[54.8–63.7]
308(47.9)
[43.9–51.6]
0.000
L-HDLC 747(56.5)
[53.8–59.3]
1216(73.5)
[71.2–75.6]
0.000 217(52.3)
[47.7–56.6]
287(63.8)
[59.6–68.4]
0.000 273(58.2)
[53.7–62.7]
449(80)
[76.5–83.2]
0.000 257(58.7)
[54.1–63.2]
480(74.7)
[70.8–78.1]
0.000
H-LDLC 360(27.2)
[24.9–29.6]
485(29.3)
[27.1–31.6]
0.112 98(23.6)
[19.8–28]
111(24.7)
[20.928.7]
0.389 96(20.5)
[17.1–24.1]
122(21.7)
[18.2–25.3]
0.336 166(37.9)
[33.1–42.7]
252(39.2)
[35.3–42.9]
0.358

International Journal of Diabetes in Developing Countries
1 3
the prevalence of high TC, high TG, high LDL-C, and low
HDL-C was 35.9%, 54.6%, 24.1%, and 63.2% among rural
dwellers; 36.1%, 56.7%, 25.8%, and 66.8% among suburban
residents; and 43.4%, 58.3%, 44.1%, and 69.7% among urban
populations. Overall, the prevalence of dyslipidemia in par-
ticipants with diabetes and hypertension was highest in sub-
urban and urban areas compared to rural areas.
Table4 describes the results of multivariable logis-
tic regression to identify the risk factors associated with
lipid abnormalities in the study population. After adjust-
ing for age and BMI, hypercholesterolemia was associ-
ated with suburban areas, male gender, age 40–59years,
current drinking, and being underweight. Hypertriglyc-
eridemia was associated with rural areas, male gender,
age 40–59years, current smoking, being underweight,
overweight, diabetes, and hypertension. Low HDL-C was
associated with rural and suburban areas, male gender, cur-
rent smoking, current drinking, being underweight, over-
weight, obesity, and diabetes. High LDL-C was associ-
ated with rural and suburban areas, male gender, and being
underweight or overweight.
Discussion
In the current study, we investigated the prevalence and risk
factors of dyslipidemia among Indian adults aged ≥ 20 in
various demographic groups, including rural, sub-urban, and
urban populations. Our findings indicate that more than 85%
of the sub-urban and urban populations had dyslipidemia (at
Fig. 1 Age- and demographic-
specific prevalence of dyslipi-
demia in the study population
0
10
20
30
40
50
20-40 41-60 >60 20-40 41-60 >6020-40 41-60>60
RuralSub-UrbanUrban
%
Age (Years)
HypercholesterolemiaMale
Female
0
10
20
30
40
50
60
70
20-40 41-60 >6020-40 41-60>60 20-40 41-60 >60
Rural Sub-UrbanUrban
%
Age (Years
)
Hypertriglyceridemia Male
Female
0
20
40
60
80
100
20-40 41-60 >60 20-40 41-60>60 20-4041-60 >60
RuralSub-Urban Urban
%
Age (Years)
Low HDL Cholesterol Male
Female
0
10
20
30
40
50
20-4041-60 >6020-40 41-60>60 20-4041-60 >60
RuralSub-Urban Urban
%
Age (Years)
High LDL CholesterolMale
Female
Table 3 Prevalence and
association of dyslipidemia
in diabetic and hypertensive
population (Chi-square test)
*** = p ≤ .001; ** = p ≤ .01; * = p ≤ .05; NS = p > .05; L-HDLC- Low- high-density lipoproteins; H-LDLC-
High- low-density lipoproteins
Diabetic Population (Count and %)
Total
(n = 654/2976)
Rural
(n = 139/865)
Sub-urban
(n = 266/1030)
Urban
(n = 249/1081)
Hypercholesterolemia 242(37)*** 49(35.3)*** 91(34.2)*** 102(41)***
Hypertriglyceridemia 405(61.9)*** 69(49.6)** 165(62)*** 171(68.7)***
L-HDL 462(70.6)*** 88(63.3)NS 194(72.9)NS 180(72.3)**
H-LDL 200(30.6)*** 35(25.2)** 57(21.4)* 108(43.4)***
Hypertensive Population (Count and %)
Total
(n = 1229/2976)
Rural
(n = 315/865)
Sub-urban
(n = 485/1030)
Urban
(n = 429/1081)
Hypercholesterolemia 474(38.6)*** 113(35.9)NS 175(36.1)*** 186(43.4)**
Hypertriglyceridemia 697(56.7)*** 172(54.6)*** 275(56.7)*** 250(58.3)**
L-HDL 822(66.9)NS 199(63.2)* 324(66.8)* 299(69.7)*
H-LDL 390(31.7)*** 76(24.1)NS 125(25.8)*** 189(44.1)**

International Journal of Diabetes in Developing Countries
1 3
least one lipid abnormality), compared to 78.5% of those
living in rural areas. This finding is consistent with a previ-
ous ICMR-INDIAB study that found 76.9% of people in the
Tamil Nadu region had at least one abnormal lipid.
The main findings of our investigation are as follows:
first, the highest prevalence rates of hypercholesterolemia,
hypertriglyceridemia, and high LDL-C were observed in the
urban population compared to sub-urban and rural popu-
lations, respectively. Conversely, L-HDL-C was the most
common dyslipidemia, particularly in sub-urban residents.
Additionally, the middle-aged group (40–59years) had a
higher prevalence of dyslipidemia, particularly among
Table 4 Association of risk factors with dyslipidemia by multivariable logistic regression: age and BMI adjusted
L-HDLC Low- high-density lipoproteins; H-LDLC High- low-density lipoproteins; Dependent variables: Total cholesterol, Triglyceride, Low-
high-density lipoproteins and low-density lipoproteins; Controlling variables: Age and BMI
All demographic data pooled (Odd ratio (95% CI) p Value)
Risk factors Hypercholesterolemia Hypertriglyceridemia Low-HDL High-LDL
Area
Rural 1.069(0.87–1.31) p = .537 1.28(1.04–1.57)p = .017 1.3(1.1–1.65)p = .005 1.8(1.4–2.2)p < .001
Sub-urban 1.29(1.06–1.6) p = .012 1.07(0.88–1.3)p = .47 0.8(0.65–1.0)p = .023 2.1(1.7–2.6)p < .001
Urban 1(ref) 1(ref) 1(ref) 1(ref)
Sex
Male 1.3(1.01–1.6)p = .028 0.67(0.54–0.83)p < .001 2.26(1.8–2.8)p < .001 1.3(1.-1.7)p = .01
Female 1(ref) 1(ref) 1(ref) 1(ref)
Age Groups, years
20–39 1(ref) 1(ref) 1(ref) 1(ref)
40–59 0.70(0.51–1.0)p = .027 0.68(0.51–0.92)p = .013 1.07(0.79–1.5)p = .64 0.87(0.63–1.2)p = .41
≥ 60 1.23(0.72–2.1) p = .458 1.1(0.65–1.8)p = .72 0.96(0.56–1.64)p = .89 1.5(0.84–2.5)p = .17
Education
None 1(ref) 1(ref) 1(ref) 1(ref)
Primary 0.93(0.73–1.2)p = .55 1.0(0.78–1.3)p = .97 1.1(0.9–1.5)p = .20 0.89(0.69–1.14)p = .36
Secondary 1.12(0.88–1.4)p = .33 0.92(0.72–1.16)p = .45 0.87(0.67–1.1)p = .27 1.0(0.78–1.3)p = .87
Degree 0.93(0.69–1.26)p = .66 1.0(0.77–1.4)p = .82 1.12(0.82–1.5)p = .46 0.76(0.55–1.0)p = .08
Employment status
Not-working 1(ref) 1(ref) 1(ref) 1(ref)
Employed 0.94(0.76–1.16)p = .56 0.87(0.71–1.1)p = .16 1.0(0.82–1.2)p = .81 1.0(0.8–1.2)p = .80
Smoking
Non-Smoker 1(ref) 1(ref) 1(ref) 1(ref)
Current Smoker 0.95(0.73–1.23)p = .67 0.73(0.57–0.95)p = .02 0.62(0.48–0.8)p < .001 0.869(0.65–1.14)p = 0.32
Alcohol
Non-drinker 1(ref) 1(ref) 1(ref) 1(ref)
Drinker 0.71(0.55–0.92)p = .01 1.0(0.8–1.3)p = .86 1.4(1.09–1.8)p = .007 0.8(0.61–1.0)p = .11
Tobacco
Non-Chewing 1(ref) 1(ref) 1(ref) 1(ref)
Tobacco chewing 1.0(0.82–1.23)p = .94 1.14(0.94–1.4)p = .19 1.1(0.9–1.3)p = .28 0.96(0.78–1.1)p = .75
BMI Categories
Normal 1(ref) 1(ref) 1(ref) 1(ref)
Underweight 1.81(1.3–2.6)p = .008 3.2(2.3–4.4)p < .001 2.5(1.9–3.4)p < .001 1.8(1.2–2.6)p = .001
Overweight 0.77(0.6–0.98)p = .127 0.62(0.5–0.8)p < .001 0.67(0.53–0.84)p = .001 0.78(0.62–0.98)p = .03
Obesity 1.3(0.8–2.0)p = 0.46 0.9(0.6–1.3)p = .6 0.54(0.34–0.83)p = .006 0.98(0.65–1.4)p = .94
Diabetes
Normal 1(ref) 1(ref) 1(ref) 1(ref)
Diabetes 1.12(0.92–1.37)p = .268 0.66(0.54–0.8)p < .001 0.79(0.64–0.98)p = .03 1.1(0.93–1.4)p = .18
Hypertension
Hypertension 0.85(0.71–1.02)p = .087 0.74(0.62–0.88)p = .001 1.3(0.86–1.2)p = .69 0.88(0.73–1.06)p = .20
Normotensive 1(ref) 1(ref) 1(ref) 1(ref)

International Journal of Diabetes in Developing Countries
1 3
women. Furthermore, we observed higher prevalence rates
in individuals with diabetes and hypertension across all resi-
dence types. Hypercholesterolemia, hypertriglyceridemia,
and high LDL cholesterol were more prevalent in individuals
with diabetes and hypertension residing in urban areas than
in those in rural and sub-urban areas. Moreover, we found
that age group, gender, current drinking habits, overweight
and obesity, diabetes, and hypertension were significantly
associated with dyslipidemia. However, we did not find any
association between education, employment status, tobacco
chewing, and any dyslipidemia.
The prevalence rates in the present study were comparable
to those reported in previous studies. Similar prevalence rates
were reported among Asian Indian immigrants in the United
States (n = 1038) [15], including hypercholesterolemia (43.5%),
hypertriglyceridemia (42.3%), low HDL-C (26.4%), and high
LDL-C (41.4%). The India Heart Watch study (n = 6123)11
reported that the prevalence of hypercholesterolemia was 25.0%
(men 24.8%, women 25.3%), high LDL cholesterol was 15.8%
(men 16.3%, women 15.1%), hypertriglyceridemia was 36.9%
(men 41.2%, women 31.5%), and low HDL cholesterol was
42.5% (men 34.1%, women 53.0%). The recent FitHeart study
(n = 46,919) reported the prevalence of various cholesterol lipo-
protein and triglyceride levels in more than 20 states of India13.
They found that hypercholesterolemia was observed in 26.9%
(men 24.0%, women 30.8%), hypertriglyceridemia in 42.6%
(men 45.6%, women 38.6%), high LDL-C in 60.0% (men
57.6%, women 63.1%), and low HDL cholesterol in 56.0%
(men 49.9%, women 64.5%). Another larger ICMR-INDIAB
study reported that the prevalence of hypercholesterolemia was
found in 13.9%, hypertriglyceridemia in 29.5%, low HDL-C in
72.3%, and high LDL-C in 11.8% of the Indian population12.
Misra etal. [16] used different cut point values in their study
(n = 532). They used hypertriglyceridemia > 200mg/dl and low
HDL-C < 35mg/dl as cut points among adults aged > 25years
in the urban slums of northern India. They reported that the
prevalence of hypercholesterolemia (men 26.8%, women
27.5%), hypertriglyceridemia (men 16.8%, women 12.3%),
low HDL-C (men 15.8%, women 16.7%), and high LDL-C
(men 26%, women 25.4%) was observed. All these studies
reported a low prevalence of hypercholesterolemia compared
to hypertriglyceridemia and low HDL-C, which is similar to
the findings of the present study. These findings suggest that
various components of atherogenic dyslipidemia may be the
more important lipid phenotype in Asian Indians [11]. The low
HDL-C found among sub-urban and urban dwellers was con-
sistent with an earlier report [12] and showed the unhealthy
HDL-C lipid abnormality status among urban populations,
which could put them at risk of coronary artery disease (CAD)
[17]. However, the mean value of low HDL-C was lower in
urban populations compared to rural populations, whereas the
mean cholesterol level was higher in urban than in rural popu-
lations. These results were consistent with previous surveys,
which have also shown higher mean levels of total cholesterol
in urban participants compared to rural participants, with a low
mean level of low HDL cholesterol [18]. The higher prevalence
of low HDL-C may be part of the Asian Indian Phenotype,
which includes increased plasma insulin levels, insulin resist-
ance, increased waist circumference, excess visceral fat, and
low adiponectin levels, as reported by Deepa etal. [18]. Addi-
tionally, urban residence, comparatively less physical activity,
fast food culture, high intake of salt, sugar, and saturated fat
may contribute to the higher prevalence of dyslipidemia. The
prevalence of hypercholesterolemia, hypertriglyceridemia, and
high LDL levels among the rural population was higher than
the findings from an earlier report [12]. Moreover, a recent
review reported that the prevalence of hypercholesterolemia in
the 1990s was 16%, and it has increased to 25–35% among rural
India in recent years [19]. The reasons for these results could
be attributed to the higher prevalence of current smokers and
alcohol consumption among the rural population compared to
sub-urban and urban populations. A previous study estimated
that current smoking might increase the risk of dyslipidemia in
both women and men [20].
On the other hand, Singh etal. [21] reported that the
incidence of hypercholesterolemia was 59%, hypertriglyc-
eridemia was 53%, low HDL-C was 89%, and high LDL-C
was 98% among diabetes patients. However, these preva-
lence rates were higher than those of the present study.
Bali etal. [22] stated that the prevalence of dyslipidemia
in diabetes patients was 81.8%, and among these patients,
hypercholesterolemia was 36.5%, hypertriglyceridemia was
57.2%, high LDL levels were 59.3%, and low HDL was
34.4%. The high LDL-C was much higher than in the pre-
sent study. These above reports were not comparable to the
present study owing to the study population, as ours is a gen-
eral community-based population, but they investigated only
diabetes patients from a single health center. Other studies
have reported diabetes-dyslipidemia as a frequent comorbid-
ity [23–25]. Furthermore, the prevalence of dyslipidemia
was high among hypertensive subjects in our study. This
may be owing to dyslipidemia causing endothelial damage,
which can lead to a loss of physiological vasomotor activity
with consequent elevated systemic blood pressure. A previ-
ous report stated that the incidence of dyslipidemia is more
frequent in hypertensive than in normal populations [26].
Also, hypercholesterolemia and hypertension can coexist,
resulting in dyslipidemic hypertension. The above comor-
bidities constitute important risk factors for CVD. Therefore,
appropriate risk assessments and monitoring of serum lipids
among diabetes and hypertensive patients will remain crucial
to reduce the risk of CVD mortality.
In this study, we analyzed the associations between indi-
vidual dyslipidemia and risk factors. After adjusting for age
and BMI, we found that area, gender, and BMI were com-
mon risk factors for all types of dyslipidemia. Participants

International Journal of Diabetes in Developing Countries
1 3
from rural areas showed a significant association with hyper-
triglyceridemia, low HDL-C, and high LDL-C, while sub-
urban participants were associated with hypercholesterolemia,
low HDL-C, and high LDL-C. These findings indicate that
demographic areas tend to have a greater risk of some dys-
lipidemias. The reasons for these associations might be life-
style factors and environmental factors such as exposure to air
pollution [27, 28]. Furthermore, in our study, current smok-
ing, current drinking, BMI, diabetes, and hypertension were
significantly associated with low HDL-C, consistent with
our previous study [12, 29]. Overall, the higher prevalence
of dyslipidemia and its associated risk factors found in this
study suggest that these risk factors play major roles in blood
lipid pathology and must be targeted.
This study builds on previous knowledge about dyslipi-
demia patterns in the Indian population by taking advantage
of a dataset that allows comparisons according to rural, sub-
urban, and urban settings. Several researchers have reported
urban–rural differences in the prevalence of cardiovascular
risk factors and their mortality rates in India. For a country
like India, which contains a large number of small towns
called semi-urban or sub-urban or census towns, which are
very close to major cities (within 10–50km), despite rapid
urbanization, the majority of sub-urban areas in India do not
have facilities for screening and early detection of cardio-
vascular risk factors compared to urban residents. This may
be the first study to report the prevalence of dyslipidemia
in rural, sub-urban, and urban cohorts in India. This study
was based on data from the KMCH-NCD study, which was
conducted among participants from three different regions.
Therefore, its findings may be seen as representative and
convincing. However, several limitations should be consid-
ered. The main limitation of our study is that we used a con-
venience sample rather than a representative sample. In addi-
tion, sociodemographic information was obtained through a
questionnaire, which may lead to recall bias. Furthermore,
the prevalence of dyslipidemia was based on non-fasting
blood samples, and information on lipid-lowering therapy
and non-fasting blood samples was not collected in this
study, which is another limitation. Thus, it is possible that
prevalence rates may be exaggerated due to sampling bias.
However, recently, the lipid association of India reported
that non-fasting lipid concentrations might be a better indi-
cator of average lipid concentrations in the blood than fast-
ing concentrations [6].
Conclusion
This study presents the most recent prevalence of dyslipi-
demia in rural, sub-urban, and urban India, revealing a higher
prevalence of dyslipidemia in urban residents. The middle
age group (40–59years) showed a higher prevalence of
dyslipidemia, with women having a higher prevalence than
men. Hypercholesterolemia, hypertriglyceridemia, and high
LDL cholesterol were more common in urban residents with
diabetes and hypertension compared to rural and sub-urban
residents. Age group, gender, current alcohol consumption,
overweight, obesity, diabetes, and hypertension were identi-
fied as common risk factors for dyslipidemia. These findings
underscore the need for action to more effectively reduce dys-
lipidemia and prevent cardiovascular disease in India. Thus,
appropriate risk assessments and routine monitoring of serum
lipids among patients with diabetes and hypertension will
remain crucial in reducing the risk of CVD mortality.
Acknowledgement This study is funded by the KMCH Research
Foundation in India. We would like to acknowledge Professor Mrs. Dr.
S. Madhavi from the KMCH Nursing College, aswell as Dr. Sujeetha
and Dr. K. Balasubramanian from the KMCH Institute of Allied Health
Sciences, and Dr. Muthusamy (Clinical Biochemist, KMCH). We are
alsograteful to the postgraduates, social workers, and students/staff
from the KMCH Allied Health Science for their contribution to the
conduct of this study,although they had no role in the design, data
interpretation, or manuscript writing.
Data availability On reasonable request, the datasets generated during
this work are available through the correspondences.
Ethical declarations
Ethical approval The study design and protocol received approval from
the KMCH Ethics Committee (Ref. No. NNCD: EC/AP/365/02/2015
(Rural study); TNCD:EC/AP/405/09/2015 (Sub-urban study); KNCD:
EC/AP/464/07/2016 (Urban study)). The study adhered to the princi-
ples outlined in the Declaration of Helsinki.
Informed Consent All participants provided informed written consent
prior to their participation.
Conflict of interest All authors declare no competing interests.
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