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| ORIGINAL ARTICLE |
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| Year : 2022 | Volume
: 12
| Issue : 4 | Page : 305-318 |
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Prevalence and Risk Factors of Cognitive Impairment and its Effect on Quality of Life: A Cross-Sectional Analysis of the TILDA Cohort
Ahmed Al-Hindawi1, Louai Wael Al Tabaa1, Ahmed Ali Gebril Ali1, Yousef Waly1, Mohamed Shelig1, Muhammed Hussain1, Ali Al-Sabti2
1 School of Medicine, RCSI-Bahrain, Manama, Bahrain
2 School of Medicine, University College Dublin (UCD), Dublin, Ireland
| Date of Submission | 08-Aug-2022 |
| Date of Decision | 16-Sep-2022 |
| Date of Acceptance | 06-Oct-2022 |
| Date of Web Publication | 30-Nov-2022 |
Correspondence Address:
Ahmed Al-Hindawi
School of Medicine, RCSI-Bahrain, Manama
Bahrain
 Source of Support: None, Conflict of Interest: None  | Check |
DOI: 10.4103/ijnpnd.ijnpnd_59_22
 Abstract | | |
Objectives: Examine the prevalence of cognitive impairment within Wave 1 of the Irish Longitudinal Study on Aging (TILDA) cohort and its relationship with comorbidities and lifestyle factors. The effect of cognitive impairment on quality-of-life scores was also investigated. Methods: A secondary cross-sectional analysis of data from Wave 1 of the TILDA cohort was undertaken. Results: Prevalence of cognitive impairment ranged between 5.8% and 51.2%, depending on the instrument used (Mini-Mental State Examination [MMSE] and Montreal Cognitive Assessment [MoCA], respectively). Having hypertension (odds ratio [OR] 1.68; 95% confidence interval [CI] 1.36–2.08), being a past or current smoker (OR 1.25; 95% CI 1.01–1.55) and having low physical activity (OR 2.04; 95% CI 1.64–2.53) increased the odds of being classified as cognitively impaired (MMSE <25). Similarly, being obese (OR 1.31; 95% CI 1.17–1.47), having hypertension (OR 1.42; 95% CI 1.27–1.57), and having diabetes (OR 1.71; 95% CI 1.40–2.09) increased the odds of cognitive impairment (MoCA <26). High cholesterol was associated with a protective effect (OR 0.79; 95% CI 0.63–0.98) under MMSE <25 classification while, problematic alcohol behavior reduced the odds of being classified as cognitively impaired using MoCA <26 by 35% (OR 0.65; 95% CI 0.55–0.76). Depression was not associated with increased odds of cognitive decline. Lastly, mean quality of life (QoL) scores decreases as severity of cognitive impairment increases from normal to moderate cognitive impairment (P < 0.001). Conclusions: Several modifiable risk factors for cognitive decline were identified, including smoking, low physical activity, hypertension, diabetes, and obesity. Policies aimed at reducing the prevalence of these risk factors in the population might reduce the impact of cognitive decline on public health.
Keywords: cognitive impairment, cross-sectional study, dementia, elderly, public health, the Irish longitudinal study on ageing
How to cite this article:
Al-Hindawi A, Al Tabaa LW, Gebril Ali AA, Waly Y, Shelig M, Hussain M, Al-Sabti A. Prevalence and Risk Factors of Cognitive Impairment and its Effect on Quality of Life: A Cross-Sectional Analysis of the TILDA Cohort. Int J Nutr Pharmacol Neurol Dis 2022;12:305-18 |
How to cite this URL:
Al-Hindawi A, Al Tabaa LW, Gebril Ali AA, Waly Y, Shelig M, Hussain M, Al-Sabti A. Prevalence and Risk Factors of Cognitive Impairment and its Effect on Quality of Life: A Cross-Sectional Analysis of the TILDA Cohort. Int J Nutr Pharmacol Neurol Dis [serial online] 2022 [cited 2023 Jan 26];12:305-18. Available from: https://www.ijnpnd.com/text.asp?2022/12/4/305/362419 |
| Introduction | |  |
Background
Cognitive decline refers to the decline in the higher cortical functions such as memory, language, and judgment, ranging from mild cognitive impairment (MCI) to dementia, a level of cognitive impairment severe enough to interfere with the person’s functioning.[1]
Within the elderly population, cognitive impairment has been associated with an increased risk of morbidity and mortality from physical and psychological abuse.[2],[3] Furthermore, it has been associated with increased risks of morbidity and mortality for patients with comorbidities such as hypertension[4] and diabetes[5]; associations with decreased quality of life,[6] increased risk of burnout for caregivers,[7] and increased healthcare costs to the patient and healthcare system[8] have also been documented. In Ireland, the proportion of the population aged ≥65 is projected to increase by 160% from 2011 to 2041.[9] With an aging population, prevalence of cognitive decline will likely increase.[10]
To better understand the processes involved in aging, the Irish Longitudinal Study on Aging (TILDA) was established. TILDA is a longitudinal study of a nationally representative sample held in Ireland, involving 8504 community dwelling adults (8175 aged >50).[11],[12] The obtained data would be used to inform and improve upon policies, making Ireland a better place to age.[11]
Prevalence of cognitive decline
Prevalence estimates of cognitive decline within elderly populations are highly heterogeneous.[13]Although differences between population demographics such as age composition and education levels are likely to contribute to this heterogeneity, variation in the definition of cognitive decline across different studies is likely a large contributor as well.[13] The expected prevalence of cognitive decline in elderly European individuals ranges from 5.1% to 41%.[14]
Risk factors for cognitive decline
Several risk factors have been implicated to increase the risk of cognitive decline, including both comorbidities and lifestyle factors:
Comorbidities
Hypertension
Hypertension might increase the risk of cognitive decline by causing cerebral micro-infarcts.[15] Currently, the available evidence regarding the association of hypertension with cognitive decline is inconclusive. While cohort studies show that hypertension is associated with an increased risk of cognitive decline,[15],[16] a recent Cochrane review had concluded that hypertension does not modify the risk of cognitive decline.[17]
Depression
Depression is commonly associated with cognitive decline.[18] Different hypotheses have been theorized to explain this association, including depression being a consequence of perceived cognitive impairment,[19] a risk factor for cognitive impairment,[20] and a prodrome for cognitive impairment.[21]
Diabetes
Evidence from epidemiological studies has indicated an association between diabetes and the development of cognitive decline.[22] It is proposed that this association is mediated by defects in insulin signaling in the cerebrum, neuroinflammatory pathways, and Tau signaling.[23]
Hypercholesterolemia
Hypercholesterolemia has an age-dependent relationship with cognitive decline. In middle-aged adults, it is associated with an increased risk of cognitive decline, however, in the elderly, it has no relationship with cognitive decline or that it could even be protective.[24],[25]
Obesity
Several large cohort studies have determined obesity to be a risk factor for cognitive decline in the elderly. For instance, in two large cohort studies involving cohorts with a mean age of 66 years[26] and age ranges of 70 to 79,[27] obese individuals had lower performance on cognitive tests compared to non-obese individuals.
Lifestyle factors
Physical activity
Physical activity might reduce the risk of cognitive decline in the elderly by improving cerebral blood flow.[28] Evidence from murine studies show that increased levels of cranial growth factors, potentially mediating the protective effect of physical activity on cognitive function.[29] Current clinical evidence demonstrates a protective effect of physical activity on cognitive function as well.[30]
Smoking
Studies have shown inconsistent results regarding the association of smoking with cognitive performance.[31] Older studies conducted with short follow-up times demonstrate a positive correlation between cigarette smoking and cognitive performance. This is mainly attributed to the short-term stimulant effect of nicotine in cigarettes.[31] However, studies with longer follow-up times suggest that smoking mainly increases the risk of cognitive decline.[32] This is thought to be due to the damage that prolonged smoking causes to cerebral microvasculature.[31]
Alcohol consumption
Current evidence demonstrates an association between heavy drinking and increased risk of cognitive decline.[33] However, the effect of light to moderate alcohol consumption on cognitive decline in the elderly remains uncertain with some studies suggesting that it might be protective[34],[35],[36],[37],[38],[39] while others suggest that it does not influence cognitive abilities or may increase the risk of cognitive decline.[40],[41],[42]
Cognitive decline and quality of life (QoL)
The relationship between cognitive decline and perceived QoL is not well-understood, with studies showing conflicting result. This variability is likely due to the subjective nature of perceived QoL and variation in the severity of cognitive decline within participants.[43]
Patients with MCI are more likely to be aware of their impaired executive functioning and language abilities. Consequently, such patients are more likely to report reduced perceived quality of life in comparison to patients with more severe forms of cognitive impairment who may not be aware of their impairment.[43],[44]
Study objectives
The study objectives are as follows:- Identify the prevalence of cognitive impairment in wave 1 of the TILDA cohort and stratify it according to gender and age groups.
- Identify whether there is an association between cognitive impairment and selected comorbidities (obesity, hypertension, diabetes, high cholesterol, and depression) or lifestyle factors (smoking, problematic alcohol use, and low physical activity).
- Identify whether there is an association between cognitive impairment and mean QoL scores.
| Materials and methods | | |
Design
A secondary retrospective cross-sectional analysis was conducted using the data obtained from Wave 1, with temporal coverage starting from October 2009 to February 2011, of the TILDA study.[12]
Participants and setting
TILDA was sampled to identify a population-representative cohort, starting with randomly selecting 640 clusters of 3115 nationwide clusters with size-adjusted probability, stratified by geographic and demographic factors.[12],[45] The selected cohort underwent three modes of data collection: At-home Computer-assisted personal interview; Self-Completion Questionnaire; and follow-up comprehensive Health Assessment.[12],[45] The resulting dataset was used to derive the variables and data included in this study.
In the context of this study, it is important to note that dementia was set as exclusion criteria in participant recruitment.
Variables
This study’s outcome variable is cognitive decline, quantified using the Mini-Mental State Examination (MMSE) and Montreal Cognitive Assessment (MoCA) scores. Their use for this purpose is substantiated by their universal application as tools to screen for dementia or estimate severity and progression of cognitive impairment.[46],[47] Both MMSE (7–8 min) and MoCA’s (10–12 minutes) 30-questions cover temporospatial orientation, visuospatial, attention, memory, language, and executive function domains with differing weight and rigor. Unique items include MMSE’s reading, writing & comprehension, and MoCA’s alternating trail-making, abstract thinking, and vigilance. MoCA addresses MMSE’s ceiling-effect and low sensitivity through varied and thorough assessments, like adding forward/backward digit-span to MMSE’s backward counting for memory function.[47]
The diagnostic parameters of cognitive decline were based on what is typically reported in the literature for the purpose of external validity and comparability. MMSE used a <25 cut-off for cognitive impairment while MoCA used <26 as derived from its authors’ validation study.[48] However, results from a large (n = 9350) meta-analysis revealed a MoCA <24 cut-off level presented the optimal sensitivity to specificity ratio of MCI detection through ROC-AUC analysis,[49] which this study added to serve as a midpoint between lower parameter MMSE <25 and upper parameter MoCA <26 prevalence estimates. For moderate and severe cognitive impairment, conventional cut-off parameters were used based on the recommendations of the instruments’ authors.
The study’s exposure variables were hypertension, obesity (defined as BMI >30 kg/m2), high cholesterol, diabetes, and depression as comorbidities and smoking status, problematic alcohol behavior (≥2 on CAGE questionnaire), and level of physical activity (classified using the IPAQ questionnaire) as lifestyle factors.
To examine the effect of cognitive impairment on QoL, the Control, Autonomy, Self-Realization, and Pleasure (CASP-19) scale scores were used as a measure for QoL.
Data analysis
Data collection, analysis, and interpretation were performed using IBM SPSS (V28).
Descriptive frequencies were used to outline demographic information of the cohort, prevalence of comorbidities and poor lifestyle factors, and cognitive impairment prevalence. Chi-square tests were adopted to assess the statistical significance between cognitive impairment, comorbidities (obesity, hypertension, diabetes, high cholesterol, and depression) and poor lifestyle factors (past or current smoker, problematic alcohol behavior, and low physical activity). Further, the chi-square results were supplemented with crude odds ratios using the data obtained from cross-tabulation. The mean scores of MMSE and MoCA were compared across age groups and their statistical significance was assessed using ANOVA. Similarly, mean QoL scores were evaluated across differing severities of cognitive impairment; ANOVA was used to test for their statistical significance.
Ethics
Protocol was approved by the appropriate institutional committee or that it complied with the Helsinki Declaration as revised in 1983. Consent was obtained or waived by all participants in this study. Faculty of Health Sciences, Trinity College Dublin Research Ethics Committee issued approval Study Number 0053-01.
| Results | | |
Socio-demographic and health characteristics of participants
The mean age of the cohort was 62.97 years (±9.4). The gender distribution was 44.4% male and 55.6% female. The majority of the participants identified as either married (67.6%) or widowed (14.1%). When questioned about how the participants perceived their physical health, 77.2% reported that they are physically excellent, very good, or good. Similar results were seen with self-reported mental health, where 90% of participants reported excellent, very good, or good mental well-being. A more comprehensive tabulation of demographic information can be seen in [Table 1]. | Table 1 Socio-demographic and self-rated health characteristics of participants
Click here to view |
Comorbidities and lifestyle factors
As reported in [Table 2]/[Figure 1], 37.8% of the participants reported having long-term health problems or disability. Yet, when inquired about specific comorbidities, 63.7% of the cohort was diagnosed with hypertension, 37.4% was diagnosed with high cholesterol, 7.5% was diagnosed with diabetes, and 19.5% of the participants were classified as obese. The prevalence of depression diagnosis was 5.4%. | Table 2 Prevalence of chronic illness, obesity, hypertension, diabetes, high cholesterol, and depression within the cohort
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Within the cohort, 56.2% were identified as past or current smokers. Of those who were administered the CAGE screening questionnaire, 10.2% were identified to possibly have problematic alcohol use. In terms of levels of physical activity, a relatively even spread can be appreciated, where 31.5%, 34.1%, and 33.4% of the cohort were classified into low, moderate, and high levels of physical activity, respectively. The tabulation of lifestyle factors can be seen in [Table 3]/[Figure 2]. | Table 3 Prevalence of smoking, problematic alcohol behavior, and low physical activity
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Prevalence of cognitive impairment
The mean scores for both MMSE and MoCA decrease with increasing age ranges (values portrayed in [Table 4]/[Figure 3]), where they are highest within the 25 to 49 age group and lowest in the 80 to 84 age group. Using analysis of variance (ANOVA), P-values of <0.001 were obtained, indicating that the differences between scores within age groups were statistically significant. Furthermore, when Games–Howell post hoc test was conducted, the differences in mean scores were significant when comparing the 25 to 49 age group to the 55 to 59 and subsequent age groups (Appendix 6). | Table 4 Mean MMSE/MoCA scores, prevalence of cognitive impairment, and classification of severity of cognitive impairment
Click here to view |
Classification of cognitive impairment vastly differed depending on the instrument used and its accompanying cut-off scores (values portrayed in [Figure 4]). For instance, when using the MMSE <25 cut-off, only 5.8% of participants were identified as cognitively impaired. Conversely, when adopting the MoCA <26 cut-off, 51.2% of the cohort were classed as cognitively impaired. The percentage of participants identified as cognitively impaired is reduced to 39.9% when the MoCA ≤24 cut-off was used. When assessing the severity of cognitive impairment using MMSE (values portrayed in [Figure 5]), 5.2% and 0.7% of participants were classed as mild and moderate cognitive impairment, respectively. However, when MoCA is utilized, 46.3% and 4.6% of participants were classed as mild and moderate cognitive impairment, respectively. | Figure 4 Cognitive impairment classification prevalence (%) in TILDA cohort.
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 | Figure 5 Cognitive impairment severity classifications prevalence (%) in TILDA cohort.
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Results of gender-based ([Table 5]) and age-based ([Table 6]) stratification of cognitive impairment are presented below.
rAssociations between cognitive impairment, comorbidities, and lifestyle factors
Chi-square analyses and crude odds ratio calculations were conducted to understand the relationship between cognitive impairment, comorbidities ([Table 7]), and lifestyle factors ([Table 8]). | Table 7 Statistical significance of the association between investigated comorbidities and cognitive impairment
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 | Table 8 Statistical significance of the association between investigated lifestyle factors and cognitive impairment
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Of the conducted tests, the following associations were deemed statistically significant (P < 0.05) with cognitive impairment using the MMSE <25 cut-off: hypertension, high cholesterol, past or current smoker, and low physical activity. Comparatively, apart from depression, all the tested comorbidities and lifestyle factors presented with statistically significant associations with cognitive impairment using the MoCA <26 cut-off ([Figure 6]). | Figure 6 Age-range stratification of cognitive impairment prevalence (%) in TILDA cohort.
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With odds ratios, the influence of comorbidities and poor lifestyle factors on the development of cognitive impairment can be estimated. Having hypertension, being a past or current smoker, and having low physical activity increase the odds of being classified as cognitively impaired (MMSE <25) by 68% (odds ratio [OR] 1.68; 95% confidence interval [CI] 1.36–2.08), 25% (OR 1.25; 95% CI 1.01–1.55), and 104% (OR 2.04; 95% CI 1.64–2.53), respectively. Obesity (OR 1.21; 95% CI 0.93–1.53), diabetes (OR 1.27; 95% CI 0.87–1.86), depression (OR 0.91; 95% CI 0.56–1.46), and problematic alcohol behavior (OR 0.72; 95% CI 0.48–1.08) were not associated with the MMSE <25 cognitive impairment classification.
More associations were seen when using MoCA <26 as the classification of cognitive impairment. Being obese, having hypertension, and having diabetes increased the odds of scoring <26 on MoCA by 31% (OR 1.31; 95% CI 1.17–1.47), 42% (OR 1.42; 95% CI 1.27–1.57), and 71% (OR 1.71; 95% CI 1.40–2.09), respectively. Moreover, being a past or current smoker increases odds by 13% (OR 1.13; 95% CI 1.02–1.24) and having low physical activity increases odds by 36% (OR 1.36; 95% CI 1.22–1.52).
Interestingly, having high cholesterol is associated with a protective effect, reducing the odds of being classified as cognitively impaired using MMSE <25 by 21% (OR 0.79; 95% CI 0.63–0.98). Problematic alcohol behavior seemingly reduces the odds of being classified as cognitively impaired using MoCA <26 by 35% (OR 0.65; 95% CI 0.55–0.76).
Associations between cognitive impairment and quality of life (QoL)
As seen in [Table 9]/[Figure 7], mean QoL scores decrease as severity of cognitive impairment increases for both MMSE <25 and MoCA <26 groups. Using ANOVA, the difference between the groups was deemed statistically significant, apart from severe cognitive impairment. Post hoc analysis between groups can be seen in Appendix 11 and 12. | Table 9 Association between cognitive impairment severity and mean QoL scores
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 | Figure 7 Association between cognitive impairment severity and mean QoL scores.
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To ameliorate the understanding of this relationship, gender stratification was conducted. The impact of cognitive impairment on QoL scores remains statistically significant, whether participants were male ([Table 10]) or female ([Table 11]). | Table 10 Association between cognitive impairment severity and mean QoL scores within males
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 | Table 11 Association between cognitive impairment severity and mean QoL scores within females
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| Discussion | | |
Prevalence of cognitive impairment was examined using both MoCA and MMSE questionnaires. While the MMSE questionnaire is the most used screening tool for cognitive impairment, it is well documented to have a steep ceiling effect where “easy questions” increase the likelihood of pre-dementia patients to fall under the normal category. To address this, the MoCA questionnaire was included in the analysis given its higher sensitivity albeit a lower specificity. Consequently, the dramatic discrepancy between cognitive impairment prevalence measured by MoCA (51.2%) and MMSE (5.8%) cut-offs is unsurprising, given the large difference between the instruments’ sensitivities (90% MoCA versus 18% MMSE).[47]
Regarding the investigated comorbidities, both hypertension and diabetes were associated with an increased risk of cognitive decline ([Table 8]). It is probable that this could be a causal relationship,[50],[51] yet the possibility of confounding by variables like age and socioeconomic status cannot be disregarded. The incidence of hypertension, diabetes, and cognitive decline all increase with age and lower socioeconomic status.[52],[53],[54]
Cognitive decline (MoCA <26) was associated with obesity but not hypercholesterolemia. This appears to be in agreement with previous studies where in the elderly population, obesity is associated with an increased risk of cognitive decline[55] while hypercholesterolemia is not.[25]
Depression was found to have no association with cognitive decline, in contrast to evidence from previous studies,[56] where the consensus is that depression increases the likelihood of developing cognitive impairment. A possible explanation is that the cohort did not have sufficient depression patients (n = 457 [5.4%]), lacking the statistical power required to identify this association. Although the prevalence of depression and diabetes was similar, the statistical power of this study was presumably only adequate to detect an association between cognitive impairment and diabetes but not depression. This was likely because diabetes has a stronger association with cognitive decline.[57].
Past or current smoking was associated with an increased risk of cognitive decline (MMSE <25 and MoCA <26). Similar to hypertension and diabetes, however, this association could have been instigated by a confounding variable such as years of education[58],[59] or socioeconomic status.[54],[60]
In contrast to current evidence, problematic alcohol behavior was associated with reduced odds of cognitive decline, possibly because decreased self-awareness in the cognitively impaired interferes with CAGE questionnaire self-reporting.[61] Furthermore, a large cross-sectional study revealed lower consumption and problematic behavior in ages >65 compared to 55 to 65.[62] Given the greater representation of <65-year-old participants in this study, who are less likely to have cognitive decline despite potentially having greater alcohol consumption, age is a possible confounding factor.[62]
The association between low physical activity and risk of cognitive decline in the elderly is in line with previous studies.[30] It is possible that physical activity decreases risk cognitive decline[63] but given the cross-sectional nature of this study, another possibility is reverse causality; physical activity tends to decrease in patients with cognitive decline.[64]
The quality of lives of elderly individuals with mild and moderate cognitive impairment (measured by both MMSE and MoCA) was significantly lower in comparison to individuals with normal cognitive function. However, individuals with severe cognitive impairment, as measured by MoCA, had no significant difference compared to individuals with normal cognitive function. The likely explanation for this is that there was a beta error in the result caused by the low number of individuals with severe cognitive impairment (n = 8). As such, no definite conclusions can be drawn regarding severe cognitive impairment.
| Strengths and limitations | | |
This study had several strengths. It used data from a large nationwide study with two-stage stratified cluster sampling, ensuring that the sample is representative of the elderly Irish population[65] and reducing the chance of sampling bias. Moreover, both MoCA and MMSE were used to identify cognitive impairment. MoCA with higher sensitivity detects cases of MCI that are significantly missed in MMSE. However, MMSE with higher specificity had higher positive predictive values; both instruments compensated each other’s limitations. Thirdly, data collection for TILDA was done by trained professionals with strict and thorough protocols,[65] reducing the potential for measurement errors.
However, there are some limitations to note. To begin, the analysis was cross-sectional. Results can only suggest an association but not causation, as causal relationship through temporal link cannot be established. Secondly, known/suspected dementia was an exclusion criterion in TILDA, resulting in fewer cases of severe cognitive impairment. This possibly skewed results, reducing the study’s external validity. Furthermore, the confounding of comorbidity and lifestyle factor overlap was not accounted for and may have an underlying influence on the results. Important insights on the role of multimorbidity or potential additive odds of multiple lifestyle factors were not explored.
| Relevance and implications | | |
Cognitive impairment is a well-documented consequence of aging that has significant impact on patient wellbeing and healthcare costs. Given the lack of disease-modifying medications to treat cognitive decline, identification of potential risk factors is crucial to the reduce its impact on public health. To this end, this study has identified such potential factors which may prove useful in influencing policies and practices aimed at promoting an environment that combats cognitive decline.
Furthermore, this study examined prevalence and quality of life data for cognitive impairment. This information may prove useful in calculating quality-adjusted life years and thereby cost-effectiveness, two important metrics in guiding health policy change.
| Conclusion and future research | | |
This study identified modifiable risk factors for cognitive decline, including smoking, low physical activity, hypertension, diabetes, and obesity. This suggests that policies aimed at reducing the prevalence of these risk factors might reduce the impact of cognitive decline on the public health. However, this study was limited by having a cross-sectional design and not adjusting for confounding variables. Therefore, future cohort studies and interventional studies investigating the effect of these risk factors on risk of cognitive decline are needed. Furthermore, this study provided estimates of the prevalence and effect on quality of life of cognitive decline which can be utilized in future studies estimating the burden of cognitive decline in the population.
Acknowledgments
Ethical approval for the The Irish Longitudinal study on Ageing (TILDA) study was granted by the Faculty of Health Sciences, Trinity College Dublin Research Ethics Committee. All the participants in Wave 1 (2009–2011) of TILDA used in this study have provided informed consent. The anonymized TILDA dataset analyzed in this study can be accessed via the Irish Social Science Data Archive—www.ucd.ie/issda.
Financial support and sponsorship
Nil.
Conflicts of interest
There are no conflicts of interest.
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[Figure 1], [Figure 2], [Figure 3], [Figure 4], [Figure 5], [Figure 6], [Figure 7]
[Table 1], [Table 2], [Table 3], [Table 4], [Table 5], [Table 6], [Table 7], [Table 8], [Table 9], [Table 10], [Table 11]
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