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.

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.

Figure 9

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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.

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

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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

   "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

   "This ORCID iD identifies the author of this article:" 0000-0002-0842-0398

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