Ethnic and region-specific genetic risk variants of stroke and its comorbid conditions can define the variations in the burden of stroke and its phenotypic traits

  1. Rashmi Sukumaran
  2. Achuthsankar S Nair
  3. Moinak Banerjee  Is a corresponding author
  1. Human Molecular Genetics Laboratory, Rajiv Gandhi Centre for Biotechnology, India
  2. Department of Computational Biology and Bioinformatics, University of Kerala, India

Abstract

Burden of stroke differs by region, which could be attributed to differences in comorbid conditions and ethnicity. Genomewide variation acts as a proxy marker for ethnicity, and comorbid conditions. We present an integrated approach to understand this variation by considering prevalence and mortality rates of stroke and its comorbid risk for 204 countries from 2009 to 2019, and Genome-wide association studies (GWAS) risk variant for all these conditions. Global and regional trend analysis of rates using linear regression, correlation, and proportion analysis, signifies ethnogeographic differences. Interestingly, the comorbid conditions that act as risk drivers for stroke differed by regions, with more of metabolic risk in America and Europe, in contrast to high systolic blood pressure in Asian and African regions. GWAS risk loci of stroke and its comorbid conditions indicate distinct population stratification for each of these conditions, signifying for population-specific risk. Unique and shared genetic risk variants for stroke, and its comorbid and followed up with ethnic-specific variation can help in determining regional risk drivers for stroke. Unique ethnic-specific risk variants and their distinct patterns of linkage disequilibrium further uncover the drivers for phenotypic variation. Therefore, identifying population- and comorbidity-specific risk variants might help in defining the threshold for risk, and aid in developing population-specific prevention strategies for stroke.

eLife assessment

This paper provides a useful analysis of the variation of the burden of strokes across geographic regions, finding differences in the relationship between strokes and their comorbidities. This dataset and the correlations found within will be a resource for directing the focus of future investigations. The results are technically solid, but there are cases where statistical analyses are yet to be carried out to support statements of statistical significance.

https://doi.org/10.7554/eLife.94088.3.sa0

Introduction

Stroke affects over 101 million people worldwide and is ranked the second most fatal disease in the world, with 6.5 million deaths in 2019 (GBD 2019 Stroke Collaborators, 2021). Comorbid conditions of stroke are critical contributors to burden of stroke and the duration of the comorbid conditions can further determine the severity of stroke risk or mortality. Prevalence for comorbid conditions range from 43% to 94% and estimates can go as high as 99% above 66 years of age (Gallacher et al., 2019). Prevalence and mortality risk in stroke have often been evaluated from socio-economic viewpoint, but it is also critical to understand the differences in drivers such as comorbid conditions. It is the accumulated risk of comorbid conditions that enhance the risk of stroke further. Are these comorbid conditions differentially impacted by socio-economic factors and ethnogeographic factors. This was clearly evident in COVID era, when COVID-19 differentially impacted the risk of stroke, possibly due to its differential influence on the comorbidities of stroke.

Mortality in stroke, its subtypes, and their comorbid conditions have a strong ethnic bias (Tarko et al., 2022; Gardener et al., 2020; Mkoma et al., 2020). Genetics act as surrogate markers for ethnogeographic indices. It is important to understand which comorbid conditions are influenced by socio-economic indices, and how they impact the risk of stroke and their underlying genetic basis. A Danish study reported the effect sizes of association with comorbid conditions for stroke to have 15% higher mortality risk in presence of diabetes mellitus with end-organ damage, 20% for peripheral vascular disease, 25% for chronic pulmonary disease, 35% for congestive heart failure and atrial flutter, 45% for moderate to severe renal disease, and 1.8- to 2.4-fold for mild-to-severe liver disease (Schmidt et al., 2014). A UK Biobank study on stroke multimorbidity reported 1.5× higher risk of mortality in those with two additional comorbidities and a ≈2.5× higher risk of mortality in those with ≥5 comorbidities over 7 years (Gallacher et al., 2018). Thus, the differential impact of comorbid or multimorbid conditions contributing to the additive effect of illness burden needs to be addressed from an ethnogenetic perspective. Devising an appropriate strategy for prevention of stroke burden, needs a careful evaluation of the underlying genetic signature for each of these comorbid conditions and distinguishing their ethnic bias.

The objective of the study was to understand what determines the differences in stroke burden around the globe. Variations in burden of stroke could be influenced by comorbid conditions, and incidentally both stroke and its comorbid conditions can be influenced by ethnogeographic factors and genetics can act as a stable proxy marker for all. To resolve this, we considered the prevalence and mortality of a total of 11 disease conditions, consisting of stroke and its comorbid conditions, across different continents and ethnicities from 2009 to 2019. The disease conditions were further stratified as per their ethnogeographic locations and their genetic risk variants extrapolated from GWAS data. This study would provide insights on the regional patterns of the burden of stroke and its comorbid conditions, and help in resolving it from an ethnic and genetic viewpoint. These insights would further aid in developing and strategizing regional- and ethnic-specific needs for prevention of the risk of comorbid conditions and stroke.

Results

Global mortality, incidence and prevalence rates of stroke and its comorbid conditions

Globally, stroke ranks as the second most fatal disease in 2019 (84.2/100,000, 95%UI 76.8–90.2) among the eight diseases analyzed, preceded by ischemic heart disease (IHD; 117.9/100,000, 95%UI 107.8–125.9) as shown in Figure 1—figure supplement 1 and Supplementary file 1. High systolic blood pressure (high SBP) ranks as the most fatal comorbid disease condition with an age-standardized mortality rate (ASMR) of 138.9/100,000 [95%UI 121.3–155.7] among all conditions. Within stroke subtypes, ischemic stroke ranks highest globally with ASMR of 43.5/100,000 [95%UI 39.08–46.8], followed by intracerebral hemorrhage (ICH) at 36.0/100,000 [95%UI 32.9–38.7] and subarachnoid hemorrhage (SAH) at 4.7/100,000 [95%UI 4.1–5.2]. The ranking of the ASMRs of the diseases follows the same trend throughout the last decade with minor exceptions, where high body mass index (high BMI) improved its rank in 2014 by swapping with high low-density lipoprotein cholesterol (high LDL), and type 1 diabetes (T1D) dethroned chronic kidney disease (CKD) in 2019.

Global trends in crude and age-standardized incidence rates (ASIRs) show that stroke incidence ranks fourth, while IHD is on top followed by type 2 diabetes (T2D) and CKD (Figure 1—figure supplement 1, Supplementary file 1). Crude incidence rates of stroke and its subtypes increased in the last decade but ASIRs decreased, with exception of ischemic stroke, where an increase in ASIR was observed. Among other diseases, IHD, T2D, and T1D show a continuous increase in the last decade, with T2D (280.1/100,000, 95%UI 258.8–303.9) surpassing IHD (274.0/100,000, 95%UI 242.9–306.4) in 2019 in crude rates.

The global trend between crude and age-standardized prevalence rates (ASPRs) revealed that the ranking of stroke remains at sixth position throughout the years (Figure 1—figure supplement 1, Supplementary file 1). Among stroke subtypes, ischemic stroke ranks highest. The comorbid conditions, high SBP and high BMI, ranks first and second followed by CKD and T2D, and interestingly all rank above stroke. We also observe that though there is a continuous increase in prevalence rates in the last decade, the ranking of stroke or its comorbid conditions does not change over the years, with the exception of T1D ASPRs overtaking ICH. We were keen to understand if these global trends of ASMR and ASPR are influenced by region and ethnicity.

Ethno-regional differences in mortality and prevalence of stroke and its major comorbid conditions

We observed interesting patterns of ASMRs of stroke, its subtypes, and its major comorbidities across different regions over the years as shown in Figure 1a and Figure 1—figure supplement 2, Table 1 and Supplementary file 1. When assessed in terms of ranks, high SBP is the most fatal condition followed by IHD in all regions, except Oceania where IHD and high SBP swap ranks. Africa (206.2/100,000, 95%UI 177.4–234.2) and Middle East (198.6/100,000, 95%UI 162.8–234.4) have the highest ASMR for high SBP, even though they rank as only the third and sixth most populous continents (Figure 1—figure supplement 3), respectively. Both high SBP (−0.64% to −2.25% estimated annual percentage change [EAPC]) and IHD (−0.45% to −1.17% EAPC) show a decreasing trend for ASMRs in all regions. However, only Europe shows a significant decrease for high SBP (−2.26%; p = 0.009) and IHD (−2.37%; p = 0.006) in the decade. Stroke has a decreasing trend for ASMRs with East Asia and Europe showing a significant decrease of −2.2% (p = 0.021) and −2.6% (p = 0.03), respectively. Stroke has the highest mortality in East Asia (127.1/100,000, 95%UI 104.9–150.5 in 2019), and is the only region that ranks stroke higher than IHD. Though Europe, Middle East, and Central and South Asia have ASMRs similar to global rates for stroke, Central and South Asia ranks stroke as the third most fatal factor, while America, Europe, and Middle East ranks it fifth. Oceania (62.1/100,000, 95%UI 34.1–90.2) and America (40.3/100,000, 95%UI 36.2–43.1) have lowest rates for stroke in 2019.

Figure 1 with 4 supplements see all
Regional (a) mortality rates and (b) prevalence rates for stroke, its subtypes, and comorbid factors.

The figure shows the age-standardized mortality and prevalence rates per 100,000 people for stroke, its subtypes, and its comorbid factors in 2009, 2014, and 2019 in the geographical regions. The size of the points indicates the rate and position indicates rank.

Table 1
Estimated annual percentage change (EAPC) from 2009 to 2019 of age-standardized mortality rates (ASMRs) of stroke and its comorbid conditions.

ASMR per 100,000 for stroke and its comorbid conditions for 2009 and 2019, as well as EAPC from 2009 to 2019 is shown. 95% uncertainty interval is shown in parenthesis, statistically significant intervals are highlighted in bold.

Chronic kidney diseaseHigh body mass index (BMI)
Region201920092009–2019
EAPC
201920092009–2019
EAPC
Global18.29
(19.6, 16.72)
18.41
(19.22, 17.1)
–0.07
(–4.32, 4.37)
62.59
(89.13, 39.92)
61.66
(90.2, 37.44)
0.16
(–2.2, 2.57)
Africa30.58
(27.11, 34.05)
31.18
(28.37, 33.99)
–0.12
(–3.41, 3.27)
98.83
(64.77, 136.6)
91.64
(58.6, 130.04)
0.88
(–1.02, 2.82)
Central and South Asia22.74
(17.68, 27.81)
23.72
(18.93, 28.5)
0.24
(–4.07, 3.73)
62.07
(40.97, 83.17)
51.31
(30.2, 72.47)
2.0
(–0.5, 4.56)
East Asia15.8
(11.17, 20.42)
16.22
(12.1, 20.34)
–0.38
(–4.9, 4.36)
46.41
(36.68, 56.14)
39.76
(32.9, 46.61)
1.45
(–1.39, 4.38)
Europe8.18
(7.06, 9.3)
8.19
(7.1, 9.29)
0.11
(–6.17, 6.81)
73.34
(60.31, 86.36)
84.45
(69.2, 99.71)
–1.42
(–3.49, 0.7)
Middle East28.68
(22.76, 34.59)
29.75
(22.4, 37.14)
–0.46
(–3.85, 3.06)
120.14 (100.3, 139.9)122.6
(98.3, 146.9)
–0.23
(–1.91, 1.48)
Oceania13.71
(9.67, 17.74)
13.6
(9.16, 18.04)
0.17
(–4.76, 5.36)
67.52
(41.1, 93.93)
69.2
(42.2, 96.19)
–0.28
(–2.53, 2.02)
America24.95
(22.85, 26.81)
23.83
(22.22, 24.68)
0.48
(–3.24, 4.34)
72.83
(48.62, 97.9)
73.34
(49.02, 99.92)
–0.01
(–2.17, 2.23)
High low-density lipoprotein (LDL)High systolic blood pressure (SBP)
Region201920092009–2019
EAPC
201920092009–2019
EAPC
Global56.51
(73.62, 41.83)
63.9
(82.9, 47.71)
–1.29
(–3.65, 1.13)
138.88
(155.7, 121.3)
156.22
(174.7, 137.3)
–1.23
(–2.74, 0.31)
Africa64.39
(45.03, 86.13)
67.15
(48.42, 88.17)
–0.34
(–2.6, 1.98)
206.18 (177.4, 234.2)221.26 (196.2, 247.2)–0.64
(–1.9, 0.63)
Central and South Asia68.91
(45.31, 92.51)
72.3
(45.6, 98.99)
–0.46
(–2.64, 1.77)
167.69
(129.1, 206.3)
183.48 (140.3, 226.6)–0.84
(–2.23, 0.57)
East Asia53.44
(46.16, 60.71)
56.11
(48.82, 63.4)
–0.68
(–3.15, 1.82)
160.36
(130.6, 190.1)
176.4
(146.7, 206.1)
–1.1
(–2.51, 0.33)
Europe75.53
(57.71, 93.34)
96.66
(74.4, 119.0)
–2.53
(−4.51, –0.51)
138.88
(111.6, 166.2)
173.48
(139.1, 207.9)
–2.26
(−3.73, −0.76)
Middle East94.43
(75.03, 113.8)
107.43
(88.6, 126.3)
–1.3
(–3.14, 0.57)
198.61
(162.8, 234.4)
222.34
(187.2, 257.5)
–1.16
(–2.43, 0.13)
Oceania48.92
(33.56, 64.27)
53.34
(39.4, 67.27)
–0.92
(–3.51, 1.74)
94.63
(62.2, 127.06)
102.14
(70.5, 133.76)
–0.81
(–2.68, 1.09)
America40.44
(30.0, 52.53)
45.86
(34.22, 59.38)
–1.24
(–4.05, 1.64)
93.91
(80.49,105.8)
101.2
(87.28, 114.1)
–0.7
(–2.57, 1.21)
Ischemic heart diseaseIntracerebral hemorrhage
Region201920092009–2019
EAPC
201920092009–2019
EAPC
Global117.95
(125.9, 107.8)
131.77
(138.3, 122.4)
–1.17
(–2.81, 0.5)
36.04
(38.67, 32.98)
45.92
(48.45, 43.07)
–2.53
(–5.37, 0.39)
Africa154.04 (132.6, 175.4)162.34
(146.4, 180.7)
–0.45
(–1.91, 1.04)
49.82
(43.07, 57.02)
58.06
(51.19, 65.11)
–1.45
(–3.93, 1.09)
Central and South Asia167.39
(115.8, 219.0)
176.75 (118.3, 235.2)–0.51
(–1.91, 0.92)
41.45
(32.34, 50.55)
51.57
(37.19, 65.94)
–2.13
(–4.8, 0.62)
East Asia110.93 (94.7, 127.17)117.17
(100.7, 133.7)
–0.76
(–2.47, 0.97)
61.2
(49.06, 73.33)
85.51
(70.7, 100.37)
–3.54
(−5.65, –1.38)
Europe138.19
(105.5, 170.8)
174.31
(134.8, 213.8)
–2.37
(−3.85, –0.88)
15.7
(12.25, 19.15)
20.31
(15.85, 24.77)
–2.46
(–6.77, 2.04)
Middle East197.4
(154.9,239.9)
221.61
(178.7, 264.5)
–1.14
(–2.41, 0.16)
24.94
(15.45, 34.42)
31.66
(19.43, 43.89)
–2.445
(–5.8, 1.03)
Oceania100.75
(65.96, 135.6)
109.14
(77.5, 140.7)
–0.86
(–2.67, 0.99)
34.32
(12.57, 56.07)
34.25
(12.46, 56.04)
–0.056
(–3.2, 3.19)
America87.51
(79.07, 93.31)
97.5
(89.02, 102.0)
–1.06
(–2.98, 0.91)
14.06
(12.97, 15.03)
15.95
(15.05, 16.68)
–1.21
(–5.93, 3.74)
Ischemic strokeSubarachnoid hemorrhage
Region201920092009–2019
EAPC
201920092009–2019
EAPC
Global43.5
(46.8, 39.08)
49.24
(52.21, 44.75)
–1.3
(–3.99, 1.47)
4.66
(5.17, 4.13)
5.34
(6.2, 4.57)
–1.29
(–9.21, 7.33)
Africa52.05
(45.89, 58.59)
54.74
(48.9, 60.76)
–0.43
(–2.94, 2.14)
2.73
(1.62, 4.97)
3.27
(1.9, 5.95)
–1.71
(–11.75, 9.48)
Central and South Asia39.2
(28.7, 49.75)
44.32
(31.11, 57.53)
–0.99
(–3.88, 1.95)
5.77
(4.42, 7.12)
7.15
(5.05, 9.26)
–2.16
(–9.13, 5.34)
East Asia61.33
(50.37, 72.28)
64.96
(54.8, 75.11)
–0.734
(–3.04, 1.61)
5.14
(4.66, 5.62)
6.05
(5.45, 6.65)
–1.66
(–9.11, 6.4)
Europe50.36
(38.22, 62.51)
65.67
(49.49, 81.85)
–2.72
(−5.14,–0.25)
4.16
(3.55, 4.78)
4.84
(4.08, 5.6)
–1.3
(–9.74, 7.93)
Middle East61.43
(49.71, 73.15)
68.72
(57.62, 79.81)
–1.17
(–3.44, 1.15)
2.91
(2.0, 3.83)
3.74
(2.82, 4.67)
–2.39
(–11.73, 8.14)
Oceania22.56
(17.61, 27.52)
24.92
(20.87, 28.98)
–1.04
(–4.81, 2.89)
5.27
(3.32, 7.21)
5.61
(3.57, 7.64)
–0.64
(–8.31, 7.68)
America21.69
(18.8, 23.4)
23.96
(21.31, 25.49)
–0.99
(–4.83, 2.99)
4.57
(4.16, 4.9)
4.81
(4.47, 5)
–0.45
(–8.72, 8.55)
StrokeType 2 diabetes
Region201920092009–2019
EAPC
201920092009–2019
EAPC
Global84.19
(90.15, 76.76)
100.5 (105.4, 93.24)–1.85
(–3.76, 0.1)
18.49
(19.66, 17.18)
18.23
(18.97, 17.12)
0.21
(–4.07, 4.69)
Africa104.6
(93.07, 116.9)
116.07
(104.7, 127.9)
–0.97
(–2.71, 0.81)
36.45
(33.0, 40.01)
37.5
(34.54, 40.3)
–0.24
(–3.24, 2.85)
Central and South Asia86.42
(67.06, 105.8)
103.04
(75.1, 130.94)
–1.63
(–3.53, 0.3)
29.61
(24.29, 34.93)
28.54
(23.45, 33.63)
0.62
(–2.83, 4.17)
East Asia127.66
(104.9, 150.5)
156.52
(132.0, 181.0)
–2.2
(−3.73, –0.64)
17.45
(9.53, 25.37)
16.52
(9.67, 23.37)
0.59
(–3.85, 5.24)
Europe70.22
(54.79, 85.65)
90.82
(70.3, 111.34)
–2.6
(−4.65, –0.5)
8.26
(7.03, 9.48)
8.46
(6.95, 9.96)
0.09
(–6.19, 6.78)
Middle East89.28
(70.7, 107.87)
104.12
(84.6, 123.63)
–1.59
(–3.44, 0.3)
27.43
(19.25, 35.6)
28.38
(18.53, 38.22)
–0.38
(–3.86, 3.22)
Oceania62.15
(34.12, 90.18)
64.78
(37.63, 91.92)
–0.47
(–2.79, 1.9)
41.29
(15.02, 67.55)
40.0
(14.19, 65.81)
0.27
(–2.65, 3.27)
America40.33
(36.24, 43.1)
44.72
(40.98, 46.98)
–1.01
(–3.83, 1.9)
22.55
(20.65, 24.08)
22.89
(21.35, 23.69)
–0.08
(–3.96, 3.97)
Type 1 diabetes
Region201920092009–2019
EAPC
---
Global0.98
(1.17, 0.85)
1.03
(1.22, 0.88)
–0.45
(–17.5, 20.07)
---
Africa1.59
(1.24, 1.92)
1.69
(1.29, 2.14)
–0.57
(–14.01, 14.9)
---
Central and South Asia1.34
(1.09, 1.6)
1.4
(1.09, 1.71)
–0.33
(–14.93, 16.8)
---
East Asia0.91
(0.33, 1.49)
0.98
(0.39, 1.56)
–1.17
(–18.6, 20.01)
---
Europe0.51
(0.42, 0.61)
0.59
(0.49, 0.69)
–1.57
(–24.09, 27.6)
---
Middle East1.05
(0.8, 1.3)
1.27
(0.97, 1.57)
–2.26
(–17.89, 16.3)
---
Oceania1.06
(0.52, 1.6)
1.06
(0.53, 1.59)
–0.26
(–16.9, 19.74)
---
America1.04
(0.8, 1.29)
1.1
(0.87, 1.37)
–0.27
(–16.89, 19.7)
---

Among the stroke subtypes, ICH and ischemic stroke show maximum ethnogenetic differences in mortality rates (14.1/100,000–61.4/100,000) and ranking (4th to 9th) in 2019. While ICH shows a significant decrease in ASMR in East Asia (–3.53%, p = 0.009), ischemic stroke shows a significant decrease in Europe (–2.37%, p = 0.06). High BMI and high LDL rank in the top 5 but their mortality rates differed across all regions, with the highest rates for both in the Middle East. Only Europe shows a significant decrease in high LDL (–2.53%, p = 0.03) over the decade. The Middle East has the highest ASMRs due to IHD and high SBP, followed by Africa, Central and South Asia, East Asia, and Europe, all having rates higher than global. All continents have similar mortality rates for T2D and CKD across the years, except Oceania, where the T2D rate is nearly three times CKD rate. Africa has the highest mortality rate for T1D (1.59/100,000, 95%UI 1.2–1.9).

ASPRs also showed an interesting pattern of distribution, and, in contrast to mortality, showed an increase over the decade (Figure 1b and Figure 1—figure supplement 4, Table 2 and Supplementary file 1). Highest ASPRs were observed for high SBP across all regions, except America, Middle East, and Oceania, where high BMI has most prevalence. While EAPC of prevalence of high SBP showed significant decrease in all regions (−0.38% to −1.77%), except Central and South Asia, high BMI showed a significant increase (1.77–5.6%) in all (Table 2). The ASPR ranking of CKD and T2D rose to top 5, in sharp contrast to their ASMR rankings. Prevalence of CKD (EAPC 0.24– 0.7%) and T2D (EAPC 0.6–2.18%) is significantly increasing in all regions. For all other diseases, the pattern of ranking and rates across regions were stable with minor exceptions. Stroke ranks sixth for ASPRs in all regions, and it is interesting to note that ASPRs of all the comorbid conditions, except T1D, rank above stroke. While Europe shows a significant decline in ASPRs of stroke (–0.9%, p = 0.008) and ischemic stroke (–0.85%, p = 0.03) across the years, East Asia shows a significant increase for stroke (0.7%, p = 0.02) and ischemic stroke (1.09%, p = <0.001). Globally, T1D swapped its prevalence ranking with ICH in 2014, largely influenced by the significant increase in prevalence in Middle East (2.83%, p = <0.001). However, the highest prevalence of T1D is in Europe and Oceania. The prevalence of IHD has remained nearly constant in all continents in the last decade, except Oceania (–0.43%), America (–0.49%), and Middle East (–0.27%) which shows a significant decrease. In 2019, Middle East has the highest prevalence for IHD (4843.02/100,000, 95%UI 4243.02–5442.58), while America (1695.6/100,000, 95%UI 1530.8–1871.9) has the lowest ASPR, less than half the rate of the Middle East. We were further keen to understand if these regional differences in mortality and prevalence rates also reflect a socio-economic bias and if so, does it reflect in a category of comorbid conditions.

Table 2
Estimated annual percentage change (EAPC) from 2009 to 2019 of age-standardized prevalence rates (ASPRs) of stroke and its comorbid conditions.

ASPR per 100,000 for stroke and its comorbid conditions for 2009 and 2019, as well as EAPC from 2009 to 2019 is shown. 95% uncertainty interval is shown in parenthesis, statistically significant intervals are highlighted in bold.

Chronic kidney diseaseHigh body mass index (BMI)
Region201920092009–2019
EAPC
201620092009–2016
EAPC
Global8596.21 (8015.74, 9125.28)8340.38 (7792.87, 8846.68)0.34
(0.13, 0.54)
13627.48 (14742.38, 12594.1)11360.96 (11989.37, 10745.18)2.63
(2.35, 2.91)
Africa7548.86 (7063.27, 8012.72)7037.32 (6575.34, 7477.98)0.71
(0.49, 0.93)
12418.08 (9882.53, 14953.64)10068.8 (7764.52, 12373.08)3.04
(2.75, 3.34)
Central and South Asia8900.17 (8429.97, 9370.37)8656.77 (8113.18, 9200.36)0.33
(0.13, 0.54)
5191.21 (3289.67, 7092.74)3639.99 (2090.94, 5189.04)5.2
(4.72, 5.69)
East Asia8868.46 (8227.29, 9509.63)8655.32 (8120.05, 9190.6)0.27
(0.07, 0.47)
6471.83 (5527.54, 7416.12)4416.3 (3734.86, 5097.74)5.6
(5.17, 6.04)
Europe7277.72 (6612.97, 7942.47)7096.87 (6502.14, 7691.6)0.25
(0.03, 0.47)
23696.85 (23004.53, 24389.17)20946.03 (20353.15, 21538.91)1.78
(1.57, 1.98)
Middle East10589.31 (9997.33, 11181.29)9904.13 (9402.68, 10405.57)0.69
(0.51, 0.88)
29703.7 (26783.51, 32623.9)25320.15 (22564.8, 28075.51)2.36
(2.17, 2.55)
Oceania7282.16 (6638.89, 7925.43)7025.88 (6430.44, 7621.32)0.39
(0.17, 0.62)
28663.27 (26270.75, 31055.79)24822.02 (22441.51, 27202.53)2.07
(1.89, 2.26)
America8795.43 (8257.8, 9295.57)8394.13 (7864.71, 8889.84)0.52
(0.31, 0.72)
29195.14 (27054.8, 31335.48)25317.62 (23333.24, 27302.01)2.05
(1.87, 2.24)
High systolic blood pressure (SBP)Ischemic heart disease
Region201520092009–2015
EAPC
201920092009–2019
EAPC
Global22078.6 (24798.17, 19573.65)23078.95 (24774.74, 21431.11)–0.74
(−0.98, –0.49)
2421.02 (2180.5, 2692.65)2422.21 (2185.13, 2688.5)–0.01
(–0.39, 0.37)
Africa27269.91 (26515.46, 28024.36)27910.66 (27337.87, 28483.45)–0.39
(−0.61, –0.16)
2819.42 (2580.09, 3098.08)2814.78 (2584.93, 3072.69)0.02
(–0.33, 0.37)
Central and South Asia26256.46 (25139.35, 27373.58)26062.04 (25049.67, 27074.4)0.12
(–0.11, 0.35)
3577.7 (3326.28, 3829.11)3510.51 (3256.78, 3764.25)0.18
(–0.13, 0.49)
East Asia19997.47 (18656.73, 21338.2)20935.67 (19829.41, 22041.94)–0.76
(−1.02, –0.5)
2052.09 (1817.64, 2286.54)2056.98 (1821.18, 2292.77)–0.01
(–0.43, 0.4)
Europe23278.69 (21812.71, 24744.67)25915.06 (24642.41, 27187.72)–1.77
(−2, –1.54)
2476.75 (2203.31, 2750.18)2548.31 (2267.8, 2828.82)–0.3
(–0.67, 0.08)
Middle East22293.46 (20587.65, 23999.28)24327.72 (23042.57, 25612.87)–1.48
(−1.72, –1.24)
4843.02 (4243.46, 5442.58)4949.35 (4386.28, 5512.41)–0.27
(−0.54, –0.003)
Oceania18081.14 (15851.15, 20311.13)19610.27 (18278.28, 20942.26)–1.34
(−1.61, –1.08)
2734.98 (2611.03, 2858.93)2843.93 (2697.13, 2990.73)–0.43
(−0.79, –0.08)
America18041.91 (16571.13, 19512.69)19505.67 (17874.04, 21137.3)–1.29
(−1.56, –1.02)
1695.56 (1530.76, 1871.92)1770.96 (1602.8, 1955.71)–0.46
(−0.91, –0.01)
Intracerebral hemorrhageIschemic stroke
Region201920092009–2019
EAPC
201920092009–2019
EAPC
Global248.77 (217.09, 281.43)257.65 (226.36, 289.73)–0.4
(–1.57, 0.79)
950.97 (849.82, 1064.06)916.46 (827.21, 1011.45)0.43
(–0.18, 1.05)
Africa314.25 (281.2, 349.75)340.25 (305.47, 377.42)–0.88
(–1.9, 0.15)
1089.33 (984.73, 1198.86)1071.4 (971.23, 1174.89)0.18
(–0.39, 0.75)
Central and South Asia267.03 (223.81, 310.25)268.32 (214.19, 322.45)–0.05
(–1.19, 1.1)
570.43 (480.32, 660.53)533.37 (437.92, 628.82)0.76
(–0.04, 1.57)
East Asia302.65 (221.3, 384.01)319.25 (236.89, 401.6)–0.58
(–1.64, 0.48)
1183.05 (1086.71, 1279.4)1074.86 (1001.51, 1148.22)1.09
(0.53, 1.65)
Europe139.23 (120.98, 157.49)158.58 (135.99, 181.17)–1.44
(–2.94, 0.09)
753.15 (676.12, 830.17)813.8 (730.34, 897.26)–0.85
(−1.51, –0.19)
Middle East255.65 (199.55, 311.75)268.51 (211.97, 325.06)–0.7
(–1.85, 0.46)
1218.88 (1060.37, 1377.4)1220.26 (1066.3, 1374.22)–0.07
(–0.61, 0.47)
Oceania264.45 (113.71, 415.19)258.3 (103.93, 412.68)0.17
(–0.98, 1.34)
650.92 (557.27, 744.58)655.29 (569.12, 741.47)–0.09
(–0.82, 0.64)
America200.52 (176.38, 226.00)212.00
(188.08, 237.72)
–0.58
(–1.87, 0.73)
932.69 (829.99, 1041.00)977.11 (888.38, 1068.44)–0.4
(–1.00, 0.21)
Subarachnoid hemorrhageStroke
Region201920092009–2019
EAPC
201920092009–2019
EAPC
Global101.57 (87.13, 118.54)103.6
(89.2, 120.53)
–0.22
(–2.05, 1.64)
1240.26 (1139.71, 1352.99)1217.36 (1126.81, 1313.52)0.22
(–0.31, 0.76)
Africa52.47
(44.76,61.46)
53.58
(45.9, 62.63)
–0.2
(–2.74, 2.39)
1393.27 (1288.31, 1503.93)1402.18 (1303.49, 1509.44)–0.08
(–0.57, 0.43)
Central and South Asia65.01
(56.51,73.52)
64.76
(54.84,74.67)
0.05
(–2.25, 2.41)
858.5 (737.58, 979.42)822.67 (682.76, 962.57)0.48
(–0.16, 1.14)
East Asia107.87 (72.53, 143.2)107.03 (72.44, 141.62)0.12
(–1.67, 1.95)
1513.1 (1390.7, 1635.49)1425.39 (1298.41, 1552.38)0.69
(0.2, 1.18)
Europe118.34 (108.49, 128.2)124.24 (112.18, 136.31)–0.52
(–2.19, 1.18)
960.86 (861.79, 1059.92)1042.89 (932.11, 1153.68)–0.9
(−1.49, –0.31)
Middle East66.69
(58.12,75.25)
67.19
(57.51,76.87)
–0.03
(–2.27, 2.26)
1471.6 (1292.03, 1651.17)1485.6 (1313.34, 1657.86)–0.18
(–0.66, 0.31)
Oceania105.79 (85.55, 126.03)104.66 (83.96, 125.35)0.13
(–1.68, 1.97)
970.31 (719.9, 1220.73)967.25 (721.78, 1212.72)–0.0006
(–0.6, 0.6)
America138.9 (119.01, 162.64)127.3 (110.52, 146.72)0.75
(–0.87, 2.4)
1215.6 (1113.69, 1328.23)1259.25 (1171.81, 1351.47)–0.32
(–0.85, 0.22)
Type 1 diabetesType 2 diabetes
Region201920092009–2019
EAPC
201920092009–2019
EAPC
Global272.54 (216.98, 336.95)250.88 (203.63, 307.25)0.71
(–0.45, 1.89)
5282.85 (4853.59, 5752.09)4646.97 (4341.45, 4960.45)1.24
(0.97, 1.51)
Africa255.51 (201.1, 318.05)227.8 (179.02, 286.18)1.16
(–0.06, 2.39)
4516.3 (4096.49, 4976.22)3939.47 (3629.16, 4276.48)1.35
(1.06, 1.64)
Central and South Asia250.87 (235.66, 266.09)222.68 (211.11, 234.25)1.12
(–0.1, 2.36)
6401.5 (5931.41, 6871.58)5054.2 (4714.43, 5393.97)2.18
(1.93, 2.44)
East Asia132.14 (92.27, 172)117.02 (75.77, 158.27)0.97
(–0.72, 2.7)
4477.09 (4223.96, 4730.21)4237.06 (3842.25, 4631.87)0.68
(0.4, 0.97)
Europe499.9 (451.46, 548.34)458.58 (394.14, 523.02)0.69
(–0.17, 1.56)
4691.56 (4113.82, 5269.3)4044.18 (3599.81, 4488.54)1.45
(1.16, 1.74)
Middle East394.02 (354.45, 433.59)297
(246.27, 347.72)
2.84
(1.79, 3.89)
6796.8 (5773.01, 7820.59)5937.92 (5063.13, 6812.7)1.3
(1.06, 1.53)
Oceania459.51 (335.35, 583.67)441.02 (327.93, 554.11)0.4
(–0.48, 1.29)
5606.6 (3790.83, 7422.38)4732.14 (3129.63, 6334.64)1.87
(1.61, 2.14)
America417.47 (337.43, 511.23)408.22 (340.36, 487.84)0.14
(–0.79, 1.07)
6563.01 (6071.37, 7036.07)5862.85 (5515.86, 6198.91)1.09
(0.85, 1.33)

Contribution of metabolic risk and hypertension in stroke based on ethnogenetic locations

When the prevalence and mortality rates of stroke and its comorbidities were grouped into three groups, namely strokes, metabolic disorders, and high SBP, we find that out of the three proportional mortalities shown in Figure 2, strokes group has the highest proportion (37.1–47.2%) across all years and regions, except Oceania and America, where instead, metabolic disorders have the highest proportion (39.1–42.2%) that is significantly higher compared to global proportion (Supplementary file 1). East Asia has the highest proportional mortality for strokes among all regions in all 3 years (44.7–47.2%). This was in sharp contrast to the prevalence proportion of strokes (4.6–9.1%), which was the least among the three groups, with the highest proportional prevalence for strokes being 8.9–9.1% in Central and South Asia. The proportional mortality for high SBP (22.0–30.5%) is very similar across the regions. Metabolic disorders have significantly higher proportional prevalence compared to global in Middle East, Oceania, and America, the highest being in America (63.9%, Supplementary file 1). Asian and African regions have lowest proportional prevalence for metabolic disorders, with Central and South Asia having significantly lower proportions. However, these regions have the highest prevalence proportion for high SBP (46.6–54.3%), with Central and South Asia having a significantly higher proportion compared to global. On the other hand, the Middle East, Oceania, and America have significantly lower proportional prevalence compared to global. We were further keen to understand the correlation among ASMR and ASPR of comorbid conditions among ethnogeographic regions.

Proportional (a) mortality rates and (b) prevalence rates for strokes, high systolic blood pressure (SBP), and metabolic disorders.

The figure shows the proportional mortality and prevalence for all strokes in yellow (ischemic stroke, intracerebral hemorrhage, subarachnoid hemorrhage, and ischemic heart disease), high SBP in red, and metabolic disorders in green (high body mass index [BMI], high low-density lipoprotein [LDL], diabetes mellitus type 1 and 2, chronic kidney disorder). Proportions significantly different from global proportions are marked with asteriks. CSA – Central and South Asia, AFR – Africa, EAS – East Asia, EUR – Europe, MDE – Middle East, OCE – Oceania, AMR – America.

Correlation among prevalence and mortality rates based on ethnogeographic region

Correlation between ASMRs and ASPRs for stroke and all comorbid conditions across each ethnogeographic location is shown in Figure 3. High SBP prevalence and mortality show a strong positive correlation in Central and South Asia but a strong negative correlation in the Middle East. The prevalence and mortality of high BMI have a strong negative correlation in Central and South Asia and Middle East populations, but a strong positive correlation in East Asia. Prevalence of CKD has negative correlation with mortality rates in East Asia. Prevalence of T2D has negative correlation with mortality rates in Oceania and positive correlation in America. It is interesting to note that there was not much of a correlation in mortality and prevalence rates for most of the conditions. For overall stroke, though minor correlations between various ethnicities are seen, this becomes alarmingly clear in the stroke subtypes. The correlation matrix of mortality and prevalence rates of stroke and its comorbidities does reflect strong ethnogeographic distinctions, which formed the basis of further investigation on the genetic basis of stroke and its comorbidities.

Correlation of mortality versus prevalence rates of stroke, its subtypes, and comorbid factors.

The figure shows the Pearson correlation coefficients of age-standardized mortality rates versus prevalence rates across different continents for 2014. CSA – Central and South Asia, AFR – Africa, EAS – East Asia, EUR – Europe, MDE – Middle East, OCE – Oceania, AMR – America.

Ethnogeographic stratification of stroke and its comorbidities based on GWAS data

To resolve the ethnogeographic distinctions for stroke and its major comorbid conditions based on their genetic risk, we considered all GWAS loci for stroke and its major comorbid conditions, and subjected it to stratification analysis. From the GWAS loci, we observed a distinct population structure that distinguished ethnogeographic populations based on their genetic signatures (Figure 4). For all diseases, except high BMI, the individuals clustered into five groups, each corresponding with the five super-populations from 1000 Genome project namely, African, East Asian, South Asian, European, and American. For high BMI, the individuals clustered into three groups corresponding to African, East Asian, and European. Though broad clustering of ancestral populations among the diseases looks similar, the proportions of ancestral populations in certain diseases vary greatly. Among stroke, IHD, and T2D risk variants, the populations structured in a similar way, while for T1D, CKD, and LDL the patterns were slightly different in European and South Asian ancestry. Whereas for high SBP the major fluctuations were observed in South Asian and East Asian populations. This stratification was further explored using population-based clustering, where a similar pattern was observed in the principal component analysis (PCA) plots for stroke and its comorbid conditions (Figure 5). African population seems to be a distinct outlier for most diseases, and the East Asian comes a distant second in the cluster pattern.

Figure 4 with 1 supplement see all
Population structure of risk variants for stroke and its comorbid factors using a model-based clustering.

The proportion of ancestral populations was estimated from the genotype of risk variants of each disease in unrelated individuals from the 1000 Genomes project using a model-based clustering approach. The individuals were represented by their super-population in 1000 Genomes (African, East Asian, South Asian, European, and American). The estimated ancestral populations clustered into either five or three clusters (in case of body mass index [BMI]) represented by the different colors – red (population cluster 1), green (population cluster 2), yellow (population cluster 3), purple (population cluster 4), and pink (population cluster 5).

Clustering of risk variants for stroke and its comorbid factors using a PCA.

The eigenvectors were estimated from the genotype of risk variants of each disease in unrelated individuals from the 1000 Genomes project using PCA clustering. The individuals were represented by their super-population in 1000 Genomes (African, East Asian, South Asian, European, and American) shown in five different colors.

Similar patterns were observed in stratification analysis after grouping the GWAS loci of stroke and its comorbidities into three groups as before, namely strokes, metabolic disorders, and high SBP. The African, East Asian, and South Asian populations had distinct structure in all three groups (Figure 4—figure supplement 1). These observations do indicate that the underlying genetic factors of stroke and its comorbid factors can be the real indicators of ethnogeographic patterns of risk for stroke and its comorbid conditions. However, we were further keen to understand the extent of shared and unique individual risk variants across stroke and its comorbid condition and how these unique or shared variants can help in distinguishing their relevance across ethnicities.

Shared and unique risk variants of stroke among the different ethnogenetic regions

The unique and shared individual variants across stroke and its comorbid conditions were identified from the GWAS data irrespective of ethnicity. We find the majority of the risk variants were unique to a disease condition, however, several risk variants were also seen to be shared with stroke and other comorbid conditions as seen in Figure 6. We were further keen to have a deeper insight into the distribution of risk variants in stroke across ethnicities. Stroke has only 55% of the risk variants common to all the five populations as seen in Figure 7 and Supplementary file 1. Two groups of populations share the most number of variants, namely, the Africa–America–Europe–South Asia (6% of variants shared) group and the East Asia–America–Europe–South Asia (4%) group. Africa has the highest number of unique variants for stroke (6%), followed by Europe (3%).

Distribution of unique and shared risk variants in stroke and its comorbid conditions.

The bar plot shows the number of GWAS risk variants unique for stroke and its comorbidities, as well as number of GWAS risk variants shared among the different diseases. The intersection of diseases is indicated below. Intersections containing stroke are highlighted in red. The total number of GWAS risk variants considered is stroke (366), ischemic heart disease (IHD) (1137), high systolic blood pressure (SBP) (419), type 2 diabetes (T2D) (2227), type 1 diabetes (T1D) (269), chronic kidney disease (CKD) (125), high body mass index (BMI) (27), and high low-density lipoprotein (LDL) (1734).

Risk variants of stroke shared among super-populations.

The GWAS risk variants for stroke (total 291) shared among the five super-populations in 1000 Genomes (African, East Asian, South Asian, European, and American). A risk variant was considered to be present in a population if the alternate allele frequency in 1000 Genomes was greater than or equal to 0.05.

South Asia has two unique variants for stroke, rs528002287 and rs148010464, which maps to genes PCSK6 and the intergenic region of PLA2G4A/LINC01036, respectively. The variants rs528002287 and rs148010464 are low-frequency variants in South Asia with a minor allele frequency (MAF) of 0.053 and 0.054, respectively. We were further keen to understand how these unique variants in South Asia are tagged to the nearby variants of different frequencies in the different ethnicities. Can the linkage disequilibrium (LD) patterns of rare allele and common allele help in distinguishing the ethnogeographic distinction in phenotype variation. While comparing, the LD pattern of low-frequency variants and common variants across ethnicities, we observe contrasting patterns. The LD plots between the unique variants and low-frequency variants, that are tagged to it, clearly demonstrated a unique LD pattern in South Asia, compared to other populations (Figure 8). Contrastingly, the LD plots of common variants tagged to the risk variant of stroke unique to South Asia showed similar LD patterns among all populations (Figure 8—figure supplement 1). These differences might also reflect unique or distinct phenotypic differences among ethnicities for risk in stroke.

Figure 8 with 1 supplement see all
Linkage disequilibrium (LD) of low-frequency variants proxy to risk variants of stroke unique in South Asian population.

Proxy variants with frequency less than 0.1 in 1 MB region flanking the stroke risk variants unique in South Asia rs528002287 and rs148010464. The risk variants are marked in blue box. LD plots of variants in different 1000 Genome super-populations (AFR – Africa, EAS – East Asia, SAS – South Asia, AMR – America, EUR – Europe) are shown.

Discussion

To the best of our knowledge, this is the first study that explored the burden of stroke and its comorbid conditions across regions, stratifying and distinguishing their unique features based on their genetic background extrapolated from the GWAS risk loci. The dynamics of different rates of stroke, its subtypes, and comorbid factors do reflect ethnogeographic differences. Globally, the prevalence and incidence rates of stroke have increased, while mortality rates decreased with minor shifts in ranking in the last decade. Interestingly, the incidence and prevalence rank of stroke rates remain the same globally, but for mortality it ranks third, preceded only by IHD and high SBP. While the global stroke prevalence is nearly 15 times its mortality rate, prevalence of comorbid conditions such as high SBP, high BMI, CKD, and T2D are alarmingly 150- to 500-fold higher than their mortality rates. These comorbid conditions can drastically affect the outcome of stroke. Interestingly, these disparities in rates get further widened when evaluated from an ethnogeographic perspective. The age-standardized prevalence of stroke in 2019 ranges from lowest in Central and South Asia (858.5/100,000, 95%UI 737.6–979.4) to highest in East Asia (1513.1/100,000, 95%UI 1390.7–1635.5), in contrast to mortality rates, lowest in America (40.3/100,000, 95%UI 36.2–43.1) and highest in East Asia (127.7/100,000, 95%UI 104.9–150.5).

The rates of stroke, its subtypes, and comorbid conditions do correlate to some extent but their ranking varies significantly. In terms of ranking, stroke ranks sixth in prevalence across all ethnogeographic locations, but ranks second in mortality in East Asia and third in Central and South Asia and Africa. We find that among the considered comorbid conditions, some rank above stroke in both incidence and prevalence, however in terms of mortality, stroke ranks highest with exception to high SBP. Similarly, the ranking of the comorbid conditions also varies when the global population is stratified based on ethnogeographic locations. In the last decade, there has been tremendous development in the healthcare industry globally, but this is not reflected in the mortality or prevalence data from 2009 to 2019. Ranking of comorbid conditions by the rates is very crucial to identify ethnic-specific comorbid risk that can be helpful in guiding and managing stroke risk.

The changing dynamics of stroke or its comorbid conditions can be attributed to a multitude of factors. Often the global burden of stroke has been discussed from the point of view of socio-economic parameters. Studies indicate that half of the stroke-related deaths are attributable to poor management of modifiable risk factors (Avan et al., 2019; Baatiema et al., 2020). However, we observe that different socio-economic regions are driven by different risk factors. Considering that Europe, America, Oceania, and the Middle East represent high socio-economic regions, the comorbid conditions that drive prevalence and mortality rates here seem to be more of metabolic in nature, while for South Asians, high SBP is the prominent factor. It is evident from the correlation between prevalence and mortality for stroke, its subtypes, and comorbid conditions that there is an ethnic- and comorbid-specific correlation, which possibly does not reflect a clear socio-economic distinction. The comorbid conditions for stroke subtypes also differ to a large extent. Diabetes is a comorbid condition for stroke but not for SAH, and this risk is ethnic-specific (Lindgren et al., 2013; Koshy et al., 2010). Therefore, it is very pertinent to understand the stroke risk from an ethnic view point, beyond the boundaries of socio-economic criteria, as the drivers of comorbid risk and ethnicity rely on genetic and epigenetic components. Studies reported reduction in life expectancy in 31 of 37 high-income countries, deduced to be due to COVID-19 (Islam et al., 2021). However, it would be unfair to ignore the comorbid conditions which could also be the critical determinants for reduced life expectancy in these countries.

Stroke has a complex etiology, which is further influenced by its comorbid conditions and this impacts its phenotypic variability. A strong genetic risk drives both stroke and its comorbid conditions. Genetic risk variants for diabetes, cardiovascular disease, diabetic retinopathies and nephropathies, hypertension, inflammation, and kidney diseases have been reportedly shown to have strong ethnogenetic variation (Shoily et al., 2021). Implications of ethnogenetic differences were evident when GWAS genes for stroke and all studies of comorbid conditions were used to stratify the 1000 genome super-populations. We observed that the GWAS risk loci for stroke and its comorbid conditions like high BMI, high LDL, high SBP, T2D, T1D, and CKD could stratify the super-populations based on its ethnogenetic considerations. Fluctuations in genetic structure of stroke and its comorbid conditions signify the impact of ethnic variations on mortality and prevalence rates. Stroke accounts for approximately 20% of deaths in diabetics (Banerjee et al., 2012; Boehme et al., 2017). Diabetics and pre-diabetics, and the duration of diabetes have been reported to have increased risk of stroke, which gets aggravated in African-Americans (Banerjee et al., 2012; Boehme et al., 2017). As stroke and its comorbid conditions are heavily influenced by lifestyle, high-income countries showed evidence of metabolic disorders being the major cause of concern for both prevalence and mortality. Similar observation in a UK biobank cohort study on stroke suggests that genetic and lifestyle factors were independently associated with incident stroke, which emphasizes the benefit of entire populations adhering to a healthy lifestyle, independent of genetic risk (Rutten-Jacobs et al., 2018).

Metabolic risk variations could also be a reflection of their underlying genetic differences. Significant differences among ethnicities in metabolism of various macromolecules have been reported (Vasishta et al., 2022). Genetic variations demonstrate inter-ethnic differences in LDL levels resulting in differential impact on dyslipidemia (Klarin et al., 2018). A meta-analysis on European, East Asian, and African-American ethnicities revealed that common variants of CDH13 and ADIPOQ regulate adiponectin levels, an important component of BMI indicator (Dastani et al., 2012). ALDH2*504Lys allele has been reported to be associated with high BMI, increased tolerance of alcohol, high SBP, and decreased high-density lipoprotein in East Asians (Takeuchi et al., 2011; Xu et al., 2010). MEGASTROKE consortium on stroke and its comorbid factors reported five variants associated with blood pressure, two with LDL cholesterol and reported that all stroke subtypes were associated with a Genetic Risk Score for high SBP (Malik et al., 2018). A study on IHD, T2D in European populations with different healthcare systems, and local population substructures reported Polygenic risk score with similar accuracy across Europeans, to a lesser extent to South and East Asian populations, and very poor transferability for Africans (Mars et al., 2022).

Even stroke subtypes show a strong ethnic variation (Malik et al., 2018; Hu et al., 2022). While COL4A1 and COL4A2 were common denominators for most of the stroke subtypes, high-density lipoprotein was inversely associated with small vessel stroke (Meschia, 2020). The INTERSTROKE study on stroke subtypes reported that among comorbid factors, high SBP was significantly associated with ICH and diabetes, cardiac issues, and apolipoproteins with ischemic stroke (O’Donnell et al., 2016). Ischemic stroke associated with ApoB/ApoA1 ratio had higher (67.6%) population attributable fraction (PAF) in Southeast Asia compared to Western Europe, North America, and Australia (24.8%), while ischemic stroke associated with atrial fibrillation had lower PAF in South Asia (3.1%) compared to the rest (17.1%) (O’Donnell et al., 2016). Emerging studies like novel ethnic-specific genetic variants, such as SUMOylation pathway in Indians (Kumar et al., 2021), SFXN4 and TMEM108 in Africans (Keene et al., 2020) indicate the involvement of different pathways among different ethnicities in stroke. The A allele of c.*84G>A loci in CETP gene was found to be a risk factor for IHD in South Asians (Ganesan et al., 2016). High SBP was found to be a risk factor in all major stroke subtypes except lobar ICH (Georgakis et al., 2020). Therefore, identifying differential risk in different ethnicities for stroke and its subtypes, and its impact on comorbid conditions might also indicate different treatment modalities which can minimize adverse metabolic side effects.

Identifying the pattern of genetic variation is critical in distinguishing stroke and its endophenotypic variations. The risk variants rs528002287 (locus 15q26.3) in PCSK6 and rs148010464 (locus 1q31.1) an intergenic variant in PLA2G4A/LINC01036 for stroke were unique to South Asia, and were found to be associated with cardioembolic stroke and small vessel stroke in South Asians (Kumar et al., 2021). A recent INTERSTROKE study reported the association of short sleep duration with increased risk for stroke to be highest in the South Asian ethnicity (Odds Ratio 9.13, 95% Confidence Interval 5.86–14.66) (Mc Carthy et al., 2023). Decreased sleep quantity and quality have been reported to increase blood pressure (Sabanayagam and Shankar, 2010), prevalence for which was found to be highest for South Asians in our study. These observations are interesting as PCSK6 is known to regulate sodium homeostasis and thereby maintain diastolic blood pressure (Li et al., 2004; Chen et al., 2015). Reports also indicate 2.34-fold difference in the expression of PCSK6 during maintenance phase of hypertension (Marques et al., 2010). PCSK6 has also been reported to be involved in processing of precursors of Melanin Concentrating hormone (MCH) under certain conditions (Viale et al., 1999). MCH is known to play a central role in promoting and stabilizing sleep (Jego et al., 2013; Konadhode et al., 2013). In insomnia patients, PLA2G4A was reported to be upregulated by 1.88-fold after improvement in sleep (Livingston et al., 2015). In sleep deprived mice, glycolytic pathway and lipid metabolism were upregulated and expression of PLA2G4A was downregulated (Hinard et al., 2012). These observations are interesting as PLA2G4A is known to play a role in the metabolism of phospholipids, production of lipid mediators, and the release of arachidonic acid (Burke and Dennis, 2009; Shimizu et al., 2006). Arachidonic acid is involved in signaling pathways of metabolic processes like release of insulin and glucose disposal (Chen et al., 1996; Nugent et al., 2001; Wolford et al., 2003). PLA2G4A also plays a role in the production of pro-thrombotic TXA2 and thus, inhibition of PLA2G4A can reduce platelet aggregation and thromboembolism (Murakami et al., 2011). Thus, the risk of unique genetic variants in PCSK6 and PLA2G4A in South Asian ancestry may indicate a unique endophenotype for stroke, which might also indicate the influence of underlying risk variants for comorbid conditions, for example PLA2G4A in metabolic processes.

GWAS has yielded numerous common risk alleles that are associated with various human phenotypes (Manolio et al., 2009; McCarthy and Hirschhorn, 2008). Since the rationale for GWAS is the ‘common disease, common variant’ hypothesis, it has been able to identify only those common variants with a moderate effect on the associated trait and thus, these identified variants only explain a small proportion of the heritability of the trait. One of the major conclusions from the 1000 Genomes Project was that most variations in the human genome are rare and unique to specific subpopulations (Abecasis et al., 2010; Abecasis et al., 2012). From an evolutionary point of view, alleles with strong effects that are detrimental will be controlled by purifying selection keeping its frequency low. Hence, rare and low-frequency variants might be variants with large effects on traits (Bodmer and Bonilla, 2008). Examples of genes like ABCA1, PCSK9, and LDLR (Kathiresan et al., 2009; Lusis and Pajukanta, 2008), which carries both common variants with moderate effects as well as rare variants with large effect for lipid levels indicate that genes can contain both types of variants associated with a complex trait. Using this logic, we were keen to identify the influence of common and rare variants in stroke and its comorbid conditions and their pattern of LD in distinguishing ethnic-specific risk.

While we observe the majority of the GWAS variants associated with stroke are common variants, a minority of these are low-frequency variants with below 10% frequency. The alternate alleles of these variants were found to be present only in specific super-populations of 1000 Genomes, while the variants are monoallelic in the other super-populations. This difference among populations gets further enhanced when we look at the LD patterns of these low-frequency variants with other low-frequency variants which are in proxy. Distinct LD blocks with these unique rare variants present in one super-population were seen to be absent in other populations. On the other hand, LD patterns of common variants (frequency greater than 10%) in the same regions show similar LD patterns for all populations. Thus, the rare variant hypothesis could explain a significant proportion of the differences in burden of stroke seen across populations. Such genes identified could be possible candidate genes for identifying rare and low-frequency variants that could play a role in the heritability of stroke and its comorbid factors.

Conclusion

The dynamics of incidence, prevalence, and mortality rates of stroke and its subtypes along with its comorbid risk factors reflect strong ethnogeographic differences. Our work highlights that these ethnogeographic differences for stroke and its comorbid conditions need to be evaluated and stratified based on their ethnogenetic background. Genetic variables should be considered as primary evidence as they define the threshold for biochemical, metabolomic, or epigenetic variables. The different socio-economic regions are driven by different risk factors of stroke and low-frequency variants could be playing a role in the differences in burden of stroke seen in the different regions as shown in Figure 9. Identifying population-specific unique variants for stroke and its comorbid conditions might refine the drivers for endophenotypic variations for stroke risk. We would like to suggest that integrating public health genomics and articulating it with comorbid conditions for stroke should be considered crucial irrespective of the economic status, as both lower and higher socio-economic regions have different drivers of stroke risk.

Summary of the work highlighting that the burden of stroke is driven by different risk factors in different regions.

The different socio-economic regions are driven by different risk factors of stroke and low-frequency genetic variants could be playing a role in the differences in burden of stroke seen in the different regions.

Methods

Data sources

We obtained ASIRs, ASPRs, and ASMRs for a total of eight diseases and three disease conditions in 204 countries, for the years 2009–2019 using the GBD Results Tool (vizhub.healthdata.org/gbd-results/) (Global Burden of Disease Collaborative Network, 2020) and from NCD Risk Factor Collaboration (NCD-RisC, ncdrisc.org/) study (Zhou et al., 2017; NCD Risk Factor Collaboration NCD-RisC, 2017) in November 2021. GBD codes for the selected diseases were B.2.2 (Ischemic heart disease, IHD), B.2.3 (Stroke), B.2.3.1 (Ischemic stroke), B.2.3.2 (Intracerebral hemorrhage, ICH), B.2.3.3 (Subarachnoid hemorrhage, SAH), B.8.1.1 (Diabetes mellitus type 1, T1D), B.8.1.2 (Diabetes mellitus type 2, T2D), and B.8.2 (Chronic kidney disease, CKD). Three disease conditions, which are also comorbid factors of stroke, were also selected, high SBP (>110–115 mmHg), high BMI (>23.0 kg/m2), and high LDL cholesterol. For these, ASMRs in 204 countries, as well as global rates for 2009–2019 were obtained using the GBD Results Tool, and age-standardized prevalence percentages in 204 countries for the years 2009–2016 (data for 2017–2019 was not available) were obtained using NCD-Risc website (ncdrisc.org/) (Zhou et al., 2017; NCD Risk Factor Collaboration NCD-RisC, 2017). Global crude rates and age-standardized incidence, prevalence, and mortality rates were obtained from GBD and NCD for all the diseases and disease conditions (Figure 1—figure supplement 1, Supplementary file 1). GBD 2019 and NCD-RisC study compiled with the GATHER Guidelines.

Spatio-temporal trend analysis and estimated annual percent change

The 204 countries were grouped into eight geographic regions namely Global, America, Europe, Middle East, Africa, Central Asia and South Asia, East Asia, and Oceania, based on ethnicity (Supplementary file 1, Figure 1—figure supplement 3). The prevalence percentages were converted into ASPRs for each disease condition. ASMRs and ASPRs for each region were obtained using Bayesian model averaging of linear regression models with Markov chain Monte Carlo sampling. Bayesian Information Criterion (BIC) was the model selection criteria. Poisson distribution with global rates as lambda was the prior distribution on the models. The population of each country was used as weights. 10,000 draws from the posterior distribution of model parameters were used to obtain the point estimates (mean of the draws) and 95% uncertainty intervals (2.5th to 97.5th percentiles of the posterior distributions) of mortality and prevalence rates for each region for years 2009–2019. Rate estimates presented in this paper are age-standardized rates per 100,000 population.

The ASMRs and ASPRs thus obtained for the different diseases in each region were subjected to a temporal rank analysis for 2009, 2014, and 2019 using custom Perl scripts and change in the trend was plotted as a bump plot. The size and position of the points indicates the rate and rank, respectively. To quantify the temporal trends, EAPC was modeled using Poisson regression using a generalized linear model for the log-transformed rates: log(y) = β0 + β1x1 + β2x2 + … + βpxp, where y is the age-standardized rate, xi are the calendar years, βi are the rate trends. Under the assumption of linearity of log of age-standardized rate with time, EAPC = 100 * exp(βˆ) – 1. The 95% uncertainty interval is calculated as CI(EAPC) = β + (Z(1−α)/2) × SE, where α is the confidence level, SE is the standard error of βˆ.

EAPC for ASMRs and ASPRs was calculated for the time period 2009–2019 (Tables 1 and 2). EAPC for high SBP prevalence was calculated from 2009–2015 and for high BMI prevalence from 2009 to 2016. EAPC was considered statistically significant if the uncertainty interval of EAPC did not cross zero. Statistical significance of spatio-temporal difference in rates was calculated using chi-square test by assuming the rate to be under Poisson distribution. 2019 ASMRs (or ASPRs) in each region were compared with global ASMR (or ASPR) to compare change over locations and with 2009 ASMRs (ASPRs) to compare change over time. 2014 ASPRs were used when 2019 data were not available. p-values (two-sided) for ASMRs and ASPRs are shown in Supplementary file 1. Chi-square tests were done using Open Source Epidemiologic Statistics for Public Health (https://www.openepi.com/Menu/OE_Menu.htm). The relation between ASMRs and ASPRs of each region was measured using Pearson correlation. For correlation, we considered the data of 2014 due to the complete spectrum of data availability. All analysis, unless specified, was done using R Statistical Software (version 4.1.2) (R Development Core Team, 2021) with packages BAS (Clyde, 2022), Rcan (Laversanne and Vignat, 2020), corrplot (Wei and Simko, 2021), dplyr, and ggplot2.

Proportional mortality and prevalence

The diseases were classified into three categories as stroke (ischemic stroke, ICH, SAH, and IHD), metabolic disorders (high BMI, high LDL, T2D, CKD, and T1D) and high SBP. To determine the proportion of each category in a region, total ASMRs were scaled to 100 for all 3 years. The same was done for ASPRs (high LDL cholesterol data were not available). Statistical significance of difference in proportions was calculated using a one-sample test for binomial proportion using normal-theory method. The proportion mortality in each category was compared in a pairwise manner with global as well other regional proportions to calculate the two-sided p-value. The same was done for proportional prevalence. P-values for proportional mortality and prevalence are shown in Supplementary file 1. Proportion comparisons were done using Open Source Epidemiologic Statistics for Public Health (https://www.openepi.com/Menu/OE_Menu.htm).

Population structure analysis

For evaluating the ethnogenetic perspective of stroke and its comorbid conditions we considered the risk variants associated with each disease. The risk variants were obtained from GWAS Catalog (https://www.ebi.ac.uk/gwas/home), during the period November 2021 to August 2022. The trait IDs used to retrieve data from GWAS Catalog for the different diseases were EFO_0000712 (stroke), EFO_0001645 (IHD), EFO_0000537 (high SBP), MONDO_0005148 (T2D), MONDO_0005147 (T1D), EFO_0003884 (CKD), EFO_0007041 (high BMI), and EFO_0004611 (high LDL). The position of the risk variants in GRCh37 assembly was obtained, and variants less than 10,000 bp apart were excluded using custom Perl scripts. The total number of risk variants for each disease thus obtained are shown in Supplementary file 1. The genotype of identified biallelic autosomal SNPs in unrelated individuals was extracted from the 1000 Genome VCF files (https://www.internationalgenome.org/; Lusis and Pajukanta, 2008). The proportion of ancestral populations in each individual was estimated from their genotype using a model-based clustering approach (Pritchard et al., 2000). The admixture model with correlated allele frequencies was specified to cluster the individuals into either five or three clusters (in case of BMI). The genotype was converted to eigenvectors using principal component analysis (Purcell and Chang, 2023; Chang et al., 2015).

Shared and unique risk variants among ethnicities

The number of GWAS risk variants for stroke shared among, as well as, unique to the five super-populations in 1000 Genomes (African, East Asian, South Asian, European, and American) was obtained (Figure 6 and Supplementary file 1). A risk variant was considered to be present in a population if the alternate allele frequency in 1000 Genomes was greater than or equal to 0.05. For each GWAS risk variant, the gene variant map was obtained, and the genes shared among the populations was estimated (Supplementary file 1).

Calculation of LD of risk variants of stroke in South Asia

Proxy variants (R2 > 0.01) in the region ±500 kb of the risk variant of stroke present in the South Asian population was obtained from LDlink (Machiela and Chanock, 2015). Among the proxy variants, variants with MAF less than 0.1 were selected along with the risk variant as low-frequency variants, and variants with MAF greater than 0.1 were termed as common variants. LD between two alleles A and B is quantified using the coefficient of LD DAB calculated using the equation DAB = pABpApB, where pi represents the frequency of the allele i or haplotype i. To be able to compare the level of LD between different pairs of alleles, D is normalized as follows: D′ = D/Dmax, where Dmax = max{−pApB, −(1 pA)(1 − pB)} when D < 0 and Dmax = min{pB(1 pA), pA(1 − pB)} when D > 0 (Lewontin, 1964). Estimates of D′ were calculated separately for low-frequency variants and common variants, and plotted using the R library gaston (Perdry and Dandine-Roulland, 2022).

Materials and correspondence

The datasets analyzed during the current study are available in the GBD database, at https://vizhub.healthdata.org/gbd-results/?params=gbd-api-2019-permalink/fe8b05e8222bcf3ec3af555762006f2a.

All other materials including computer code will be available upon request. Please contact the corresponding author for the same.

Data availability

The datasets analyzed during the current study are available in the GBD database.

References

  1. Software
    1. Clyde M
    (2022)
    BAS: bayesian variable selection and model averaging using bayesian adaptive sampling, version R package version 1.6.4
    BAS: Bayesian Variable Selection and Model Averaging Using Bayesian Adaptive Sampling.
    1. Dastani Z
    2. Hivert MF
    3. Timpson N
    4. Perry JRB
    5. Yuan X
    6. Scott RA
    7. Henneman P
    8. Heid IM
    9. Kizer JR
    10. Lyytikäinen LP
    11. Fuchsberger C
    12. Tanaka T
    13. Morris AP
    14. Small K
    15. Isaacs A
    16. Beekman M
    17. Coassin S
    18. Lohman K
    19. Qi L
    20. Kanoni S
    21. Pankow JS
    22. Uh HW
    23. Wu Y
    24. Bidulescu A
    25. Rasmussen-Torvik LJ
    26. Greenwood CMT
    27. Ladouceur M
    28. Grimsby J
    29. Manning AK
    30. Liu CT
    31. Kooner J
    32. Mooser VE
    33. Vollenweider P
    34. Kapur KA
    35. Chambers J
    36. Wareham NJ
    37. Langenberg C
    38. Frants R
    39. Willems-Vandijk K
    40. Oostra BA
    41. Willems SM
    42. Lamina C
    43. Winkler TW
    44. Psaty BM
    45. Tracy RP
    46. Brody J
    47. Chen I
    48. Viikari J
    49. Kähönen M
    50. Pramstaller PP
    51. Evans DM
    52. St Pourcain B
    53. Sattar N
    54. Wood AR
    55. Bandinelli S
    56. Carlson OD
    57. Egan JM
    58. Böhringer S
    59. van Heemst D
    60. Kedenko L
    61. Kristiansson K
    62. Nuotio ML
    63. Loo BM
    64. Harris T
    65. Garcia M
    66. Kanaya A
    67. Haun M
    68. Klopp N
    69. Wichmann HE
    70. Deloukas P
    71. Katsareli E
    72. Couper DJ
    73. Duncan BB
    74. Kloppenburg M
    75. Adair LS
    76. Borja JB
    77. DIAGRAM+ Consortium
    78. MAGIC Consortium
    79. GLGC Investigators
    80. MuTHER Consortium
    81. Wilson JG
    82. Musani S
    83. Guo X
    84. Johnson T
    85. Semple R
    86. Teslovich TM
    87. Allison MA
    88. Redline S
    89. Buxbaum SG
    90. Mohlke KL
    91. Meulenbelt I
    92. Ballantyne CM
    93. Dedoussis GV
    94. Hu FB
    95. Liu Y
    96. Paulweber B
    97. Spector TD
    98. Slagboom PE
    99. Ferrucci L
    100. Jula A
    101. Perola M
    102. Raitakari O
    103. Florez JC
    104. Salomaa V
    105. Eriksson JG
    106. Frayling TM
    107. Hicks AA
    108. Lehtimäki T
    109. Smith GD
    110. Siscovick DS
    111. Kronenberg F
    112. van Duijn C
    113. Loos RJF
    114. Waterworth DM
    115. Meigs JB
    116. Dupuis J
    117. Richards JB
    118. Voight BF
    119. Scott LJ
    120. Steinthorsdottir V
    121. Dina C
    122. Welch RP
    123. Zeggini E
    124. Huth C
    125. Aulchenko YS
    126. Thorleifsson G
    127. McCulloch LJ
    128. Ferreira T
    129. Grallert H
    130. Amin N
    131. Wu G
    132. Willer CJ
    133. Raychaudhuri S
    134. McCarroll SA
    135. Hofmann OM
    136. Segrè AV
    137. van Hoek M
    138. Navarro P
    139. Ardlie K
    140. Balkau B
    141. Benediktsson R
    142. Bennett AJ
    143. Blagieva R
    144. Boerwinkle E
    145. Bonnycastle LL
    146. Boström KB
    147. Bravenboer B
    148. Bumpstead S
    149. Burtt NP
    150. Charpentier G
    151. Chines PS
    152. Cornelis M
    153. Crawford G
    154. Doney ASF
    155. Elliott KS
    156. Elliott AL
    157. Erdos MR
    158. Fox CS
    159. Franklin CS
    160. Ganser M
    161. Gieger C
    162. Grarup N
    163. Green T
    164. Griffin S
    165. Groves CJ
    166. Guiducci C
    167. Hadjadj S
    168. Hassanali N
    169. Herder C
    170. Isomaa B
    171. Jackson AU
    172. Johnson PRV
    173. Jørgensen T
    174. Kao WHL
    175. Kong A
    176. Kraft P
    177. Kuusisto J
    178. Lauritzen T
    179. Li M
    180. Lieverse A
    181. Lindgren CM
    182. Lyssenko V
    183. Marre M
    184. Meitinger T
    185. Midthjell K
    186. Morken MA
    187. Narisu N
    188. Nilsson P
    189. Owen KR
    190. Payne F
    191. Petersen AK
    192. Platou C
    193. Proença C
    194. Prokopenko I
    195. Rathmann W
    196. Rayner NW
    197. Robertson NR
    198. Rocheleau G
    199. Roden M
    200. Sampson MJ
    201. Saxena R
    202. Shields BM
    203. Shrader P
    204. Sigurdsson G
    205. Sparsø T
    206. Strassburger K
    207. Stringham HM
    208. Sun Q
    209. Swift AJ
    210. Thorand B
    211. Tichet J
    212. Tuomi T
    213. van Dam RM
    214. van Haeften TW
    215. van Herpt T
    216. van Vliet-Ostaptchouk JV
    217. Walters GB
    218. Weedon MN
    219. Wijmenga C
    220. Witteman J
    221. Bergman RN
    222. Cauchi S
    223. Collins FS
    224. Gloyn AL
    225. Gyllensten U
    226. Hansen T
    227. Hide WA
    228. Hitman GA
    229. Hofman A
    230. Hunter DJ
    231. Hveem K
    232. Laakso M
    233. Morris AD
    234. Palmer CNA
    235. Rudan I
    236. Sijbrands E
    237. Stein LD
    238. Tuomilehto J
    239. Uitterlinden A
    240. Walker M
    241. Watanabe RM
    242. Abecasis GR
    243. Boehm BO
    244. Campbell H
    245. Daly MJ
    246. Hattersley AT
    247. Pedersen O
    248. Barroso I
    249. Groop L
    250. Sladek R
    251. Thorsteinsdottir U
    252. Wilson JF
    253. Illig T
    254. Froguel P
    255. van Duijn CM
    256. Stefansson K
    257. Altshuler D
    258. Boehnke M
    259. McCarthy MI
    260. Soranzo N
    261. Wheeler E
    262. Glazer NL
    263. Bouatia-Naji N
    264. Mägi R
    265. Randall J
    266. Elliott P
    267. Rybin D
    268. Dehghan A
    269. Hottenga JJ
    270. Song K
    271. Goel A
    272. Lajunen T
    273. Doney A
    274. Cavalcanti-Proença C
    275. Kumari M
    276. Timpson NJ
    277. Zabena C
    278. Ingelsson E
    279. An P
    280. O’Connell J
    281. Luan J
    282. Elliott A
    283. McCarroll SA
    284. Roccasecca RM
    285. Pattou F
    286. Sethupathy P
    287. Ariyurek Y
    288. Barter P
    289. Beilby JP
    290. Ben-Shlomo Y
    291. Bergmann S
    292. Bochud M
    293. Bonnefond A
    294. Borch-Johnsen K
    295. Böttcher Y
    296. Brunner E
    297. Bumpstead SJ
    298. Chen YDI
    299. Chines P
    300. Clarke R
    301. Coin LJM
    302. Cooper MN
    303. Crisponi L
    304. Day INM
    305. de Geus EJC
    306. Delplanque J
    307. Fedson AC
    308. Fischer-Rosinsky A
    309. Forouhi NG
    310. Franzosi MG
    311. Galan P
    312. Goodarzi MO
    313. Graessler J
    314. Grundy S
    315. Gwilliam R
    316. Hallmans G
    317. Hammond N
    318. Han X
    319. Hartikainen AL
    320. Hayward C
    321. Heath SC
    322. Hercberg S
    323. Hillman DR
    324. Hingorani AD
    325. Hui J
    326. Hung J
    327. Kaakinen M
    328. Kaprio J
    329. Kesaniemi YA
    330. Kivimaki M
    331. Knight B
    332. Koskinen S
    333. Kovacs P
    334. Kyvik KO
    335. Lathrop GM
    336. Lawlor DA
    337. Le Bacquer O
    338. Lecoeur C
    339. Li Y
    340. Mahley R
    341. Mangino M
    342. Martínez-Larrad MT
    343. McAteer JB
    344. McPherson R
    345. Meisinger C
    346. Melzer D
    347. Meyre D
    348. Mitchell BD
    349. Mukherjee S
    350. Naitza S
    351. Neville MJ
    352. Orrù M
    353. Pakyz R
    354. Paolisso G
    355. Pattaro C
    356. Pearson D
    357. Peden JF
    358. Pedersen NL
    359. Pfeiffer AFH
    360. Pichler I
    361. Polasek O
    362. Posthuma D
    363. Potter SC
    364. Pouta A
    365. Province MA
    366. Rayner NW
    367. Rice K
    368. Ripatti S
    369. Rivadeneira F
    370. Rolandsson O
    371. Sandbaek A
    372. Sandhu M
    373. Sanna S
    374. Sayer AA
    375. Scheet P
    376. Seedorf U
    377. Sharp SJ
    378. Shields B
    379. Sigurðsson G
    380. Sijbrands EJG
    381. Silveira A
    382. Simpson L
    383. Singleton A
    384. Smith NL
    385. Sovio U
    386. Swift A
    387. Syddall H
    388. Syvänen AC
    389. Tönjes A
    390. Uitterlinden AG
    391. van Dijk KW
    392. Varma D
    393. Visvikis-Siest S
    394. Vitart V
    395. Vogelzangs N
    396. Waeber G
    397. Wagner PJ
    398. Walley A
    399. Ward KL
    400. Watkins H
    401. Wild SH
    402. Willemsen G
    403. Witteman JCM
    404. Yarnell JWG
    405. Zelenika D
    406. Zethelius B
    407. Zhai G
    408. Zhao JH
    409. Zillikens MC
    410. DIAGRAM Consortium
    411. GIANT Consortium
    412. Global B Pgen Consortium
    413. Borecki IB
    414. Meneton P
    415. Magnusson PKE
    416. Nathan DM
    417. Williams GH
    418. Silander K
    419. Bornstein SR
    420. Schwarz P
    421. Spranger J
    422. Karpe F
    423. Shuldiner AR
    424. Cooper C
    425. Serrano-Ríos M
    426. Lind L
    427. Palmer LJ
    428. Hu FB
    429. Franks PW
    430. Ebrahim S
    431. Marmot M
    432. Kao WHL
    433. Pramstaller PP
    434. Wright AF
    435. Stumvoll M
    436. Hamsten A
    437. Procardis Consortium
    438. Buchanan TA
    439. Valle TT
    440. Rotter JI
    441. Penninx B
    442. Boomsma DI
    443. Cao A
    444. Scuteri A
    445. Schlessinger D
    446. Uda M
    447. Ruokonen A
    448. Jarvelin MR
    449. Peltonen L
    450. Mooser V
    451. Sladek R
    452. MAGIC investigators
    453. GLGC Consortium
    454. Musunuru K
    455. Smith AV
    456. Edmondson AC
    457. Stylianou IM
    458. Koseki M
    459. Pirruccello JP
    460. Chasman DI
    461. Johansen CT
    462. Fouchier SW
    463. Peloso GM
    464. Barbalic M
    465. Ricketts SL
    466. Bis JC
    467. Feitosa MF
    468. Orho-Melander M
    469. Melander O
    470. Li X
    471. Li M
    472. Cho YS
    473. Go MJ
    474. Kim YJ
    475. Lee JY
    476. Park T
    477. Kim K
    478. Sim X
    479. Ong RTH
    480. Croteau-Chonka DC
    481. Lange LA
    482. Smith JD
    483. Ziegler A
    484. Zhang W
    485. Zee RYL
    486. Whitfield JB
    487. Thompson JR
    488. Surakka I
    489. Spector TD
    490. Smit JH
    491. Sinisalo J
    492. Scott J
    493. Saharinen J
    494. Sabatti C
    495. Rose LM
    496. Roberts R
    497. Rieder M
    498. Parker AN
    499. Pare G
    500. O’Donnell CJ
    501. Nieminen MS
    502. Nickerson DA
    503. Montgomery GW
    504. McArdle W
    505. Masson D
    506. Martin NG
    507. Marroni F
    508. Lucas G
    509. Luben R
    510. Lokki ML
    511. Lettre G
    512. Launer LJ
    513. Lakatta EG
    514. Laaksonen R
    515. Kyvik KO
    516. König IR
    517. Khaw KT
    518. Kaplan LM
    519. Johansson Å
    520. Janssens A
    521. Igl W
    522. Hovingh GK
    523. Hengstenberg C
    524. Havulinna AS
    525. Hastie ND
    526. Harris TB
    527. Haritunians T
    528. Hall AS
    529. Groop LC
    530. Gonzalez E
    531. Freimer NB
    532. Erdmann J
    533. Ejebe KG
    534. Döring A
    535. Dominiczak AF
    536. Demissie S
    537. Deloukas P
    538. de Faire U
    539. Crawford G
    540. Chen YI
    541. Caulfield MJ
    542. Boekholdt SM
    543. Assimes TL
    544. Quertermous T
    545. Seielstad M
    546. Wong TY
    547. Tai ES
    548. Feranil AB
    549. Kuzawa CW
    550. Taylor HA
    551. Gabriel SB
    552. Holm H
    553. Gudnason V
    554. Krauss RM
    555. Ordovas JM
    556. Munroe PB
    557. Kooner JS
    558. Tall AR
    559. Hegele RA
    560. Kastelein JJP
    561. Schadt EE
    562. Strachan DP
    563. Reilly MP
    564. Samani NJ
    565. Schunkert H
    566. Cupples LA
    567. Sandhu MS
    568. Ridker PM
    569. Rader DJ
    570. Kathiresan S
    (2012) Novel loci for adiponectin levels and their influence on type 2 diabetes and metabolic traits: a multi-ethnic meta-analysis of 45,891 individuals
    PLOS Genetics 8:e1002607.
    https://doi.org/10.1371/journal.pgen.1002607
    1. Li J
    2. Wang X
    3. Chen C
    4. Xu X
    5. Hong X
    6. Xu X
    7. Gao W
    8. Huo Y
    (2004)
    The association between paired basic amino acid cleaving enzyme 4 gene haplotype and diastolic blood pressure
    Chinese Medical Journal 117:382–388.
    1. Malik R
    2. Chauhan G
    3. Traylor M
    4. Sargurupremraj M
    5. Okada Y
    6. Mishra A
    7. Rutten-Jacobs L
    8. Giese AK
    9. van der Laan SW
    10. Gretarsdottir S
    11. Anderson CD
    12. Chong M
    13. Adams HHH
    14. Ago T
    15. Almgren P
    16. Amouyel P
    17. Ay H
    18. Bartz TM
    19. Benavente OR
    20. Bevan S
    21. Boncoraglio GB
    22. Brown RD
    23. Butterworth AS
    24. Carrera C
    25. Carty CL
    26. Chasman DI
    27. Chen WM
    28. Cole JW
    29. Correa A
    30. Cotlarciuc I
    31. Cruchaga C
    32. Danesh J
    33. de Bakker PIW
    34. DeStefano AL
    35. den Hoed M
    36. Duan Q
    37. Engelter ST
    38. Falcone GJ
    39. Gottesman RF
    40. Grewal RP
    41. Gudnason V
    42. Gustafsson S
    43. Haessler J
    44. Harris TB
    45. Hassan A
    46. Havulinna AS
    47. Heckbert SR
    48. Holliday EG
    49. Howard G
    50. Hsu FC
    51. Hyacinth HI
    52. Ikram MA
    53. Ingelsson E
    54. Irvin MR
    55. Jian X
    56. Jiménez-Conde J
    57. Johnson JA
    58. Jukema JW
    59. Kanai M
    60. Keene KL
    61. Kissela BM
    62. Kleindorfer DO
    63. Kooperberg C
    64. Kubo M
    65. Lange LA
    66. Langefeld CD
    67. Langenberg C
    68. Launer LJ
    69. Lee JM
    70. Lemmens R
    71. Leys D
    72. Lewis CM
    73. Lin WY
    74. Lindgren AG
    75. Lorentzen E
    76. Magnusson PK
    77. Maguire J
    78. Manichaikul A
    79. McArdle PF
    80. Meschia JF
    81. Mitchell BD
    82. Mosley TH
    83. Nalls MA
    84. Ninomiya T
    85. O’Donnell MJ
    86. Psaty BM
    87. Pulit SL
    88. Rannikmäe K
    89. Reiner AP
    90. Rexrode KM
    91. Rice K
    92. Rich SS
    93. Ridker PM
    94. Rost NS
    95. Rothwell PM
    96. Rotter JI
    97. Rundek T
    98. Sacco RL
    99. Sakaue S
    100. Sale MM
    101. Salomaa V
    102. Sapkota BR
    103. Schmidt R
    104. Schmidt CO
    105. Schminke U
    106. Sharma P
    107. Slowik A
    108. Sudlow CLM
    109. Tanislav C
    110. Tatlisumak T
    111. Taylor KD
    112. Thijs VNS
    113. Thorleifsson G
    114. Thorsteinsdottir U
    115. Tiedt S
    116. Trompet S
    117. Tzourio C
    118. van Duijn CM
    119. Walters M
    120. Wareham NJ
    121. Wassertheil-Smoller S
    122. Wilson JG
    123. Wiggins KL
    124. Yang Q
    125. Yusuf S
    126. Bis JC
    127. Pastinen T
    128. Ruusalepp A
    129. Schadt EE
    130. Koplev S
    131. Björkegren JLM
    132. Codoni V
    133. Civelek M
    134. Smith NL
    135. Trégouët DA
    136. Christophersen IE
    137. Roselli C
    138. Lubitz SA
    139. Ellinor PT
    140. Tai ES
    141. Kooner JS
    142. Kato N
    143. He J
    144. van der Harst P
    145. Elliott P
    146. Chambers JC
    147. Takeuchi F
    148. Johnson AD
    149. Sanghera DK
    150. Melander O
    151. Jern C
    152. Strbian D
    153. Fernandez-Cadenas I
    154. Longstreth WT
    155. Rolfs A
    156. Hata J
    157. Woo D
    158. Rosand J
    159. Pare G
    160. Hopewell JC
    161. Saleheen D
    162. Stefansson K
    163. Worrall BB
    164. Kittner SJ
    165. Seshadri S
    166. Fornage M
    167. Markus HS
    168. Howson JMM
    169. Kamatani Y
    170. Debette S
    171. Dichgans M
    172. AFGen Consortium
    173. INVENT Consortium
    174. STARNET
    175. BioBank Japan Cooperative Hospital Group
    176. COMPASS Consortium
    177. EPIC-CVD Consortium
    178. EPIC-InterAct Consortium
    179. METASTROKE Consortium
    180. Neurology Working Group of the CHARGE Consortium
    181. UK Young Lacunar DNA Study
    182. MEGASTROKE Consortium
    (2018) Multiancestry genome-wide association study of 520,000 subjects identifies 32 loci associated with stroke and stroke subtypes
    Nature Genetics 50:524–537.
    https://doi.org/10.1038/s41588-018-0058-3
  2. Software
    1. R Development Core Team
    (2021) R: A language and environment for statistical computing
    R Foundation for Statistical Computing, Vienna, Austria.
    1. Zhou B
    2. Bentham J
    3. Di Cesare M
    4. Bixby H
    5. Danaei G
    6. Cowan MJ
    7. Paciorek CJ
    8. Singh G
    9. Hajifathalian K
    10. Bennett JE
    11. Taddei C
    12. Bilano V
    13. Carrillo-Larco RM
    14. Djalalinia S
    15. Khatibzadeh S
    16. Lugero C
    17. Peykari N
    18. Zhang WZ
    19. Lu Y
    20. Stevens GA
    21. Riley LM
    22. Bovet P
    23. Elliott P
    24. Gu D
    25. Ikeda N
    26. Jackson RT
    27. Joffres M
    28. Kengne AP
    29. Laatikainen T
    30. Lam TH
    31. Laxmaiah A
    32. Liu J
    33. Miranda JJ
    34. Mondo CK
    35. Neuhauser HK
    36. Sundström J
    37. Smeeth L
    38. Soric M
    39. Woodward M
    40. Ezzati M
    41. Abarca-Gómez L
    42. Abdeen ZA
    43. Rahim HA
    44. Abu-Rmeileh NM
    45. Acosta-Cazares B
    46. Adams R
    47. Aekplakorn W
    48. Afsana K
    49. Aguilar-Salinas CA
    50. Agyemang C
    51. Ahmadvand A
    52. Ahrens W
    53. Al Raddadi R
    54. Al Woyatan R
    55. Ali MM
    56. Alkerwi A
    57. Aly E
    58. Amouyel P
    59. Amuzu A
    60. Andersen LB
    61. Anderssen SA
    62. Ängquist L
    63. Anjana RM
    64. Ansong D
    65. Aounallah-Skhiri H
    66. Araújo J
    67. Ariansen I
    68. Aris T
    69. Arlappa N
    70. Aryal K
    71. Arveiler D
    72. Assah FK
    73. Assunção MCF
    74. Avdicová M
    75. Azevedo A
    76. Azizi F
    77. Babu BV
    78. Bahijri S
    79. Balakrishna N
    80. Bandosz P
    81. Banegas JR
    82. Barbagallo CM
    83. Barceló A
    84. Barkat A
    85. Barros AJD
    86. Barros MV
    87. Bata I
    88. Batieha AM
    89. Baur LA
    90. Beaglehole R
    91. Romdhane HB
    92. Benet M
    93. Benson LS
    94. Bernabe-Ortiz A
    95. Bernotiene G
    96. Bettiol H
    97. Bhagyalaxmi A
    98. Bharadwaj S
    99. Bhargava SK
    100. Bi Y
    101. Bikbov M
    102. Bjerregaard P
    103. Bjertness E
    104. Björkelund C
    105. Blokstra A
    106. Bo S
    107. Bobak M
    108. Boeing H
    109. Boggia JG
    110. Boissonnet CP
    111. Bongard V
    112. Braeckman L
    113. Brajkovich I
    114. Branca F
    115. Breckenkamp J
    116. Brenner H
    117. Brewster LM
    118. Bruno G
    119. Bueno-de-Mesquita HB
    120. Bugge A
    121. Burns C
    122. Bursztyn M
    123. de León AC
    124. Cacciottolo J
    125. Cameron C
    126. Can G
    127. Cândido APC
    128. Capuano V
    129. Cardoso VC
    130. Carlsson AC
    131. Carvalho MJ
    132. Casanueva FF
    133. Casas J-P
    134. Caserta CA
    135. Chamukuttan S
    136. Chan AW
    137. Chan Q
    138. Chaturvedi HK
    139. Chaturvedi N
    140. Chen C-J
    141. Chen F
    142. Chen H
    143. Chen S
    144. Chen Z
    145. Cheng C-Y
    146. Dekkaki IC
    147. Chetrit A
    148. Chiolero A
    149. Chiou S-T
    150. Chirita-Emandi A
    151. Cho B
    152. Cho Y
    153. Chudek J
    154. Cifkova R
    155. Claessens F
    156. Clays E
    157. Concin H
    158. Cooper C
    159. Cooper R
    160. Coppinger TC
    161. Costanzo S
    162. Cottel D
    163. Cowell C
    164. Craig CL
    165. Crujeiras AB
    166. Cruz JJ
    167. D’Arrigo G
    168. d’Orsi E
    169. Dallongeville J
    170. Damasceno A
    171. Dankner R
    172. Dantoft TM
    173. Dauchet L
    174. De Backer G
    175. De Bacquer D
    176. de Gaetano G
    177. De Henauw S
    178. De Smedt D
    179. Deepa M
    180. Dehghan A
    181. Delisle H
    182. Deschamps V
    183. Dhana K
    184. Di Castelnuovo AF
    185. Dias-da-Costa JS
    186. Diaz A
    187. Dickerson TT
    188. Do HTP
    189. Dobson AJ
    190. Donfrancesco C
    191. Donoso SP
    192. Döring A
    193. Doua K
    194. Drygas W
    195. Dulskiene V
    196. Džakula A
    197. Dzerve V
    198. Dziankowska-Zaborszczyk E
    199. Eggertsen R
    200. Ekelund U
    201. El Ati J
    202. Ellert U
    203. Elliott P
    204. Elosua R
    205. Erasmus RT
    206. Erem C
    207. Eriksen L
    208. de la Peña JE
    209. Evans A
    210. Faeh D
    211. Fall CH
    212. Farzadfar F
    213. Felix-Redondo FJ
    214. Ferguson TS
    215. Fernández-Bergés D
    216. Ferrante D
    217. Ferrari M
    218. Ferreccio C
    219. Ferrieres J
    220. Finn JD
    221. Fischer K
    222. Föger B
    223. Foo LH
    224. Forslund A-S
    225. Forsner M
    226. Fortmann SP
    227. Fouad HM
    228. Francis DK
    229. do C Franco M
    230. Franco OH
    231. Frontera G
    232. Fuchs FD
    233. Fuchs SC
    234. Fujita Y
    235. Furusawa T
    236. Gaciong Z
    237. Gareta D
    238. Garnett SP
    239. Gaspoz J-M
    240. Gasull M
    241. Gates L
    242. Gavrila D
    243. Geleijnse JM
    244. Ghasemian A
    245. Ghimire A
    246. Giampaoli S
    247. Gianfagna F
    248. Giovannelli J
    249. Goldsmith RA
    250. Gonçalves H
    251. Gross MG
    252. Rivas JPG
    253. Gottrand F
    254. Graff-Iversen S
    255. Grafnetter D
    256. Grajda A
    257. Gregor RD
    258. Grodzicki T
    259. Grøntved A
    260. Gruden G
    261. Grujic V
    262. Gu D
    263. Guan OP
    264. Gudnason V
    265. Guerrero R
    266. Guessous I
    267. Guimaraes AL
    268. Gulliford MC
    269. Gunnlaugsdottir J
    270. Gunter M
    271. Gupta PC
    272. Gureje O
    273. Gurzkowska B
    274. Gutierrez L
    275. Gutzwiller F
    276. Hadaegh F
    277. Halkjær J
    278. Hambleton IR
    279. Hardy R
    280. Harikumar R
    281. Hata J
    282. Hayes AJ
    283. He J
    284. Hendriks ME
    285. Henriques A
    286. Cadena LH
    287. Herrala S
    288. Heshmat R
    289. Hihtaniemi IT
    290. Ho SY
    291. Ho SC
    292. Hobbs M
    293. Hofman A
    294. Dinc GH
    295. Hormiga CM
    296. Horta BL
    297. Houti L
    298. Howitt C
    299. Htay TT
    300. Htet AS
    301. Hu Y
    302. Huerta JM
    303. Husseini AS
    304. Huybrechts I
    305. Hwalla N
    306. Iacoviello L
    307. Iannone AG
    308. Ibrahim MM
    309. Ikram MA
    310. Irazola VE
    311. Islam M
    312. Ivkovic V
    313. Iwasaki M
    314. Jackson RT
    315. Jacobs JM
    316. Jafar T
    317. Jamrozik K
    318. Janszky I
    319. Jasienska G
    320. Jelakovic B
    321. Jiang CQ
    322. Joffres M
    323. Johansson M
    324. Jonas JB
    325. Jørgensen T
    326. Joshi P
    327. Juolevi A
    328. Jurak G
    329. Jureša V
    330. Kaaks R
    331. Kafatos A
    332. Kalter-Leibovici O
    333. Kamaruddin NA
    334. Kasaeian A
    335. Katz J
    336. Kauhanen J
    337. Kaur P
    338. Kavousi M
    339. Kazakbaeva G
    340. Keil U
    341. Boker LK
    342. Keinänen-Kiukaanniemi S
    343. Kelishadi R
    344. Kemper HCG
    345. Kengne AP
    346. Kersting M
    347. Key T
    348. Khader YS
    349. Khalili D
    350. Khang Y-H
    351. Khaw K-T
    352. Kiechl S
    353. Killewo J
    354. Kim J
    355. Klumbiene J
    356. Kolle E
    357. Kolsteren P
    358. Korrovits P
    359. Koskinen S
    360. Kouda K
    361. Koziel S
    362. Kristensen PL
    363. Krokstad S
    364. Kromhout D
    365. Kruger HS
    366. Kubinova R
    367. Kuciene R
    368. Kuh D
    369. Kujala UM
    370. Kula K
    371. Kulaga Z
    372. Kumar RK
    373. Kurjata P
    374. Kusuma YS
    375. Kuulasmaa K
    376. Kyobutungi C
    377. Laatikainen T
    378. Lachat C
    379. Lam TH
    380. Landrove O
    381. Lanska V
    382. Lappas G
    383. Larijani B
    384. Laugsand LE
    385. Laxmaiah A
    386. Bao KLN
    387. Le TD
    388. Leclercq C
    389. Lee J
    390. Lee J
    391. Lehtimäki T
    392. Lekhraj R
    393. León-Muñoz LM
    394. Levitt NS
    395. Li Y
    396. Lilly CL
    397. Lim W-Y
    398. Lima-Costa MF
    399. Lin H-H
    400. Lin X
    401. Linneberg A
    402. Lissner L
    403. Litwin M
    404. Lorbeer R
    405. Lotufo PA
    406. Lozano JE
    407. Luksiene D
    408. Lundqvist A
    409. Lunet N
    410. Lytsy P
    411. Ma G
    412. Ma J
    413. Machado-Coelho GLL
    414. Machi S
    415. Maggi S
    416. Magliano DJ
    417. Majer M
    418. Makdisse M
    419. Malekzadeh R
    420. Malhotra R
    421. Rao KM
    422. Malyutina S
    423. Manios Y
    424. Mann JI
    425. Manzato E
    426. Margozzini P
    427. Marques-Vidal P
    428. Marrugat J
    429. Martorell R
    430. Mathiesen EB
    431. Matijasevich A
    432. Matsha TE
    433. Mbanya JCN
    434. Posso AJMD
    435. McFarlane SR
    436. McGarvey ST
    437. McLachlan S
    438. McLean RM
    439. McNulty BA
    440. Khir ASM
    441. Mediene-Benchekor S
    442. Medzioniene J
    443. Meirhaeghe A
    444. Meisinger C
    445. Menezes AMB
    446. Menon GR
    447. Meshram II
    448. Metspalu A
    449. Mi J
    450. Mikkel K
    451. Miller JC
    452. Miquel JF
    453. Mišigoj-Durakovic M
    454. Mohamed MK
    455. Mohammad K
    456. Mohammadifard N
    457. Mohan V
    458. Yusoff MFM
    459. Møller NC
    460. Molnár D
    461. Momenan A
    462. Mondo CK
    463. Monyeki KDK
    464. Moreira LB
    465. Morejon A
    466. Moreno LA
    467. Morgan K
    468. Moschonis G
    469. Mossakowska M
    470. Mostafa A
    471. Mota J
    472. Motlagh ME
    473. Motta J
    474. Muiesan ML
    475. Müller-Nurasyid M
    476. Murphy N
    477. Mursu J
    478. Musil V
    479. Nagel G
    480. Naidu BM
    481. Nakamura H
    482. Námešná J
    483. Nang EEK
    484. Nangia VB
    485. Narake S
    486. Navarrete-Muñoz EM
    487. Ndiaye NC
    488. Neal WA
    489. Nenko I
    490. Nervi F
    491. Nguyen ND
    492. Nguyen QN
    493. Nieto-Martínez RE
    494. Niiranen TJ
    495. Ning G
    496. Ninomiya T
    497. Nishtar S
    498. Noale M
    499. Noboa OA
    500. Noorbala AA
    501. Noorbala T
    502. Noto D
    503. Al Nsour M
    504. O’Reilly D
    505. Oh K
    506. Olinto MTA
    507. Oliveira IO
    508. Omar MA
    509. Onat A
    510. Ordunez P
    511. Osmond C
    512. Ostojic SM
    513. Otero JA
    514. Overvad K
    515. Owusu-Dabo E
    516. Paccaud FM
    517. Padez C
    518. Pahomova E
    519. Pajak A
    520. Palli D
    521. Palmieri L
    522. Panda-Jonas S
    523. Panza F
    524. Papandreou D
    525. Parnell WR
    526. Parsaeian M
    527. Pecin I
    528. Pednekar MS
    529. Peer N
    530. Peeters PH
    531. Peixoto SV
    532. Pelletier C
    533. Peltonen M
    534. Pereira AC
    535. Pérez RM
    536. Peters A
    537. Petkeviciene J
    538. Pham ST
    539. Pigeot I
    540. Pikhart H
    541. Pilav A
    542. Pilotto L
    543. Pitakaka F
    544. Plans-Rubió P
    545. Polakowska M
    546. Polašek O
    547. Porta M
    548. Portegies ML
    549. Pourshams A
    550. Pradeepa R
    551. Prashant M
    552. Price JF
    553. Puiu M
    554. Punab M
    555. Qasrawi RF
    556. Qorbani M
    557. Radic I
    558. Radisauskas R
    559. Rahman M
    560. Raitakari O
    561. Raj M
    562. Rao SR
    563. Ramachandran A
    564. Ramos E
    565. Rampal S
    566. Reina DAR
    567. Rasmussen F
    568. Redon J
    569. Reganit PFM
    570. Ribeiro R
    571. Riboli E
    572. Rigo F
    573. de Wit TFR
    574. Ritti-Dias RM
    575. Robinson SM
    576. Robitaille C
    577. Rodríguez-Artalejo F
    578. Rodriguez-Perez del Cristo M
    579. Rodríguez-Villamizar LA
    580. Rojas-Martinez R
    581. Rosengren A
    582. Rubinstein A
    583. Rui O
    584. Ruiz-Betancourt BS
    585. Horimoto ARVR
    586. Rutkowski M
    587. Sabanayagam C
    588. Sachdev HS
    589. Saidi O
    590. Sakarya S
    591. Salanave B
    592. Salazar Martinez E
    593. Salmerón D
    594. Salomaa V
    595. Salonen JT
    596. Salvetti M
    597. Sánchez-Abanto J
    598. Sans S
    599. Santos D
    600. Santos IS
    601. dos Santos RN
    602. Santos R
    603. Saramies JL
    604. Sardinha LB
    605. Margolis GS
    606. Sarrafzadegan N
    607. Saum K-U
    608. Savva SC
    609. Scazufca M
    610. Schargrodsky H
    611. Schneider IJ
    612. Schultsz C
    613. Schutte AE
    614. Sen A
    615. Senbanjo IO
    616. Sepanlou SG
    617. Sharma SK
    618. Shaw JE
    619. Shibuya K
    620. Shin DW
    621. Shin Y
    622. Siantar R
    623. Sibai AM
    624. Silva DAS
    625. Simon M
    626. Simons J
    627. Simons LA
    628. Sjöström M
    629. Skovbjerg S
    630. Slowikowska-Hilczer J
    631. Slusarczyk P
    632. Smeeth L
    633. Smith MC
    634. Snijder MB
    635. So H-K
    636. Sobngwi E
    637. Söderberg S
    638. Solfrizzi V
    639. Sonestedt E
    640. Song Y
    641. Sørensen TI
    642. Jérome CS
    643. Soumare A
    644. Staessen JA
    645. Starc G
    646. Stathopoulou MG
    647. Stavreski B
    648. Steene-Johannessen J
    649. Stehle P
    650. Stein AD
    651. Stergiou GS
    652. Stessman J
    653. Stieber J
    654. Stöckl D
    655. Stocks T
    656. Stokwiszewski J
    657. Stronks K
    658. Strufaldi MW
    659. Sun C-A
    660. Sundström J
    661. Sung Y-T
    662. Suriyawongpaisal P
    663. Sy RG
    664. Tai ES
    665. Tammesoo M-L
    666. Tamosiunas A
    667. Tang L
    668. Tang X
    669. Tanser F
    670. Tao Y
    671. Tarawneh MR
    672. Tarqui-Mamani CB
    673. Taylor A
    674. Theobald H
    675. Thijs L
    676. Thuesen BH
    677. Tjonneland A
    678. Tolonen HK
    679. Tolstrup JS
    680. Topbas M
    681. Topór-Madry R
    682. Tormo MJ
    683. Torrent M
    684. Traissac P
    685. Trichopoulos D
    686. Trichopoulou A
    687. Trinh OTH
    688. Trivedi A
    689. Tshepo L
    690. Tulloch-Reid MK
    691. Tuomainen T-P
    692. Tuomilehto J
    693. Turley ML
    694. Tynelius P
    695. Tzourio C
    696. Ueda P
    697. Ugel E
    698. Ulmer H
    699. Uusitalo HMT
    700. Valdivia G
    701. Valvi D
    702. van der Schouw YT
    703. Van Herck K
    704. van Rossem L
    705. van Valkengoed IG
    706. Vanderschueren D
    707. Vanuzzo D
    708. Vatten L
    709. Vega T
    710. Velasquez-Melendez G
    711. Veronesi G
    712. Verschuren WMM
    713. Verstraeten R
    714. Victora CG
    715. Viet L
    716. Viikari-Juntura E
    717. Vineis P
    718. Vioque J
    719. Virtanen JK
    720. Visvikis-Siest S
    721. Viswanathan B
    722. Vollenweider P
    723. Voutilainen S
    724. Vrdoljak A
    725. Vrijheid M
    726. Wade AN
    727. Wagner A
    728. Walton J
    729. Mohamud WNW
    730. Wang M-D
    731. Wang Q
    732. Wang YX
    733. Wannamethee SG
    734. Wareham N
    735. Wedderkopp N
    736. Weerasekera D
    737. Whincup PH
    738. Widhalm K
    739. Widyahening IS
    740. Wiecek A
    741. Wijga AH
    742. Wilks RJ
    743. Willeit J
    744. Willeit P
    745. Williams EA
    746. Wilsgaard T
    747. Wojtyniak B
    748. Wong TY
    749. Wong-McClure RA
    750. Woo J
    751. Woodward M
    752. Wu AG
    753. Wu FC
    754. Wu SL
    755. Xu H
    756. Yan W
    757. Yang X
    758. Ye X
    759. Yiallouros PK
    760. Yoshihara A
    761. Younger-Coleman NO
    762. Yusoff AF
    763. Yusoff MFM
    764. Zambon S
    765. Zdrojewski T
    766. Zeng Y
    767. Zhao D
    768. Zhao W
    769. Zheng Y
    770. Zhu D
    771. Zimmermann E
    772. Zuñiga Cisneros J
    (2017) Worldwide trends in blood pressure from 1975 to 2015: a pooled analysis of 1479 population-based measurement studies with 19·1 million participants
    The Lancet 389:37–55.
    https://doi.org/10.1016/S0140-6736(16)31919-5

Article and author information

Author details

  1. Rashmi Sukumaran

    1. Human Molecular Genetics Laboratory, Rajiv Gandhi Centre for Biotechnology, Thiruvananthapuram, India
    2. Department of Computational Biology and Bioinformatics, University of Kerala, Thiruvananthapuram, India
    Contribution
    Resources, Data curation, Software, Formal analysis, Validation, Investigation, Visualization, Methodology, Writing – original draft
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-7951-3495
  2. Achuthsankar S Nair

    Department of Computational Biology and Bioinformatics, University of Kerala, Thiruvananthapuram, India
    Contribution
    Conceptualization, Supervision, Investigation, Visualization, Project administration, Writing - review and editing
    Competing interests
    No competing interests declared
  3. Moinak Banerjee

    Human Molecular Genetics Laboratory, Rajiv Gandhi Centre for Biotechnology, Thiruvananthapuram, India
    Contribution
    Conceptualization, Resources, Data curation, Software, Formal analysis, Supervision, Funding acquisition, Validation, Investigation, Visualization, Methodology, Writing – original draft, Project administration, Writing - review and editing
    For correspondence
    mbanerjee@rgcb.res.in
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-0842-0398

Funding

Kerala State Council for Science, Technology and Environment

  • Rashmi Sukumaran

Rajiv Gandhi Centre for Biotechnology, Department of Biotechnology, Ministry of Science and Technology, India

  • Moinak Banerjee

The funders had no role in study design, data collection, and interpretation, or the decision to submit the work for publication.

Acknowledgements

RS acknowledges the support of Kerala State Council for Science, Technology and Environment (KSCSTE) for providing the research fellowship. RS and ASN acknowledge the SIUCEB support at the Department of Computational Biology and Bioinformatics, University of Kerala for providing the necessary facilities to carry out the work. MB acknowledges the Department of Biotechnology for providing intra-mural support to carry out the work.

Version history

  1. Sent for peer review:
  2. Preprint posted:
  3. Reviewed Preprint version 1:
  4. Reviewed Preprint version 2:
  5. Version of Record published:

Cite all versions

You can cite all versions using the DOI https://doi.org/10.7554/eLife.94088. This DOI represents all versions, and will always resolve to the latest one.

Copyright

© 2024, Sukumaran et al.

This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited.

Metrics

  • 515
    views
  • 28
    downloads
  • 0
    citations

Views, downloads and citations are aggregated across all versions of this paper published by eLife.

Download links

A two-part list of links to download the article, or parts of the article, in various formats.

Downloads (link to download the article as PDF)

Open citations (links to open the citations from this article in various online reference manager services)

Cite this article (links to download the citations from this article in formats compatible with various reference manager tools)

  1. Rashmi Sukumaran
  2. Achuthsankar S Nair
  3. Moinak Banerjee
(2024)
Ethnic and region-specific genetic risk variants of stroke and its comorbid conditions can define the variations in the burden of stroke and its phenotypic traits
eLife 13:RP94088.
https://doi.org/10.7554/eLife.94088.3

Share this article

https://doi.org/10.7554/eLife.94088

Further reading

    1. Epidemiology and Global Health
    2. Microbiology and Infectious Disease
    Amanda C Perofsky, John Huddleston ... Cécile Viboud
    Research Article

    Influenza viruses continually evolve new antigenic variants, through mutations in epitopes of their major surface proteins, hemagglutinin (HA) and neuraminidase (NA). Antigenic drift potentiates the reinfection of previously infected individuals, but the contribution of this process to variability in annual epidemics is not well understood. Here, we link influenza A(H3N2) virus evolution to regional epidemic dynamics in the United States during 1997—2019. We integrate phenotypic measures of HA antigenic drift and sequence-based measures of HA and NA fitness to infer antigenic and genetic distances between viruses circulating in successive seasons. We estimate the magnitude, severity, timing, transmission rate, age-specific patterns, and subtype dominance of each regional outbreak and find that genetic distance based on broad sets of epitope sites is the strongest evolutionary predictor of A(H3N2) virus epidemiology. Increased HA and NA epitope distance between seasons correlates with larger, more intense epidemics, higher transmission, greater A(H3N2) subtype dominance, and a greater proportion of cases in adults relative to children, consistent with increased population susceptibility. Based on random forest models, A(H1N1) incidence impacts A(H3N2) epidemics to a greater extent than viral evolution, suggesting that subtype interference is a major driver of influenza A virus infection ynamics, presumably via heterosubtypic cross-immunity.

    1. Epidemiology and Global Health
    2. Evolutionary Biology
    Renan Maestri, Benoît Perez-Lamarque ... Hélène Morlon
    Research Article

    Several coronaviruses infect humans, with three, including the SARS-CoV2, causing diseases. While coronaviruses are especially prone to induce pandemics, we know little about their evolutionary history, host-to-host transmissions, and biogeography. One of the difficulties lies in dating the origination of the family, a particularly challenging task for RNA viruses in general. Previous cophylogenetic tests of virus-host associations, including in the Coronaviridae family, have suggested a virus-host codiversification history stretching many millions of years. Here, we establish a framework for robustly testing scenarios of ancient origination and codiversification versus recent origination and diversification by host switches. Applied to coronaviruses and their mammalian hosts, our results support a scenario of recent origination of coronaviruses in bats and diversification by host switches, with preferential host switches within mammalian orders. Hotspots of coronavirus diversity, concentrated in East Asia and Europe, are consistent with this scenario of relatively recent origination and localized host switches. Spillovers from bats to other species are rare, but have the highest probability to be towards humans than to any other mammal species, implicating humans as the evolutionary intermediate host. The high host-switching rates within orders, as well as between humans, domesticated mammals, and non-flying wild mammals, indicates the potential for rapid additional spreading of coronaviruses across the world. Our results suggest that the evolutionary history of extant mammalian coronaviruses is recent, and that cases of long-term virus–host codiversification have been largely over-estimated.