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Strengths and limitations of this study This is the first cross-sectional analysis of vitamin D status to be determined in patients with chronic fatigue syndrome/myalgic encephalomyelitis (CFS/ME). Patients were referred through a single National Health Service (NHS) CFS services unit at the Royal Liverpool and Broadgreen University NHS trust and diagnosed with CFS/ME using the National Institute for Health and Care Excellence guidelines ensuring diagnostic uniformity. Serum 25-hydroxyvitamin D, the main marker of vitamin D status, was determined using liquid chromatography tandem mass spectrometry and comprehensive dietary/supplement analyses were undertaken to support vitamin D serum data. Recruitment included a broad spectrum of ambulatory patients with CFS/ME, age and sex matched to health controls, but excluded the severest, non-ambulatory patients with CFS/ME.

Serum 25-hydroxyvitamin D, the main marker of vitamin D status, was determined using liquid chromatography tandem mass spectrometry and comprehensive dietary/supplement analyses were undertaken to support vitamin D serum data. Recruitment included a broad spectrum of ambulatory patients with CFS/ME, age and sex matched to health controls, but excluded the severest, non-ambulatory patients with CFS/ME. Introduction Chronic fatigue syndrome (CFS), also referred to as myalgic encephalomyelitis (ME), is a debilitating and complex disease characterised by prolonged and disabling fatigue.1 Patients with CFS/ME suffer from a range of symptoms including headache, painful lymph nodes, sore throat, difficulty in sleeping or insomnia, poor cognitive function, muscle/joint pain and postexertional malaise. CFS/ME can affect individuals of all ages, races and socioeconomic status. Prevalence of the disease has been estimated between 0.2% and 1%.2–4 Despite the severity of CFS/ME and the prevalence of the disease, the aetiology and pathophysiology of CFS/ME is yet to be determined due to a lack of robust evidence within current literature to confirm any specific biological abnormalities. Current areas of investigation include infection, endocrine dysfunction, autonomic nervous system imbalance and altered immunity.

the disease, the aetiology and pathophysiology of CFS/ME is yet to be determined due to a lack of robust evidence within current literature to confirm any specific biological abnormalities. Current areas of investigation include infection, endocrine dysfunction, autonomic nervous system imbalance and altered immunity. Vitamin D regulation has become a leading focus of health professionals following its implication in health and disease.5–7 Vitamin D metabolites are regarded as vital endocrine regulators of bone health via their role in calcium and phosphate homeostasis.8 9 Evidence has shown that vitamin D deficiency (25(OH)D<30 nmol/L)10 is associated with numerous adverse health outcomes, including risk of rickets in children or osteomalacia in adults, increased risk of fractures, falls, infectious disease, type 1 and type 2 diabetes, cancer, autoimmune disease, multiple sclerosis, hypertension and heart disease.11 Since the identification of the vitamin D receptor in almost all tissues of the human body including skeletal muscle,12 evidence suggests that vitamin D metabolites are also implicated in skeletal muscle function and repair.13–16 Clinical trials undertaken in geriatric and clinical populations13 17 18 have established a potential link between vitamin D sufficiency, as assessed by circulating total 25-hydroxyvitamin D (25(OH)D) and physical performance including measures such as balance, gait speed, hand grip and lower limb strength, highlighting the role of vitamin D status in muscle function. In support of these findings, longitudinal studies undertaken in vitamin D-deficient adults showed that correcting vitamin D deficiency with use of dietary supplement interventions resulted in improved muscle mitochondrial function, associated with an improvement in symptoms of myopathy and fatigue.19 The potential importance of vitamin D signalling is also highlighted by the ability of vitamin D signalling to modulate the activation of nuclear factor kappa B, a pleiotropic transcription factor involved in many biological and pathological processes including inflammation and immunity.20 Indeed, 1,25(OH)2D is also proposed to be a potent regulator of innate and acquired immunity.21

ed by the ability of vitamin D signalling to modulate the activation of nuclear factor kappa B, a pleiotropic transcription factor involved in many biological and pathological processes including inflammation and immunity.20 Indeed, 1,25(OH)2D is also proposed to be a potent regulator of innate and acquired immunity.21 Due to the pleiotropic functions of vitamin D metabolites, the sterols have been examined in the context of disease and have consequently been correlated with a number of pathologies including tuberculosis, motor neuron and autoimmune diseases22–24 and in clinical populations including patients with osteomalacia, who display skeletal muscle myopathy and weakness that is responsive to vitamin D supplementation.25 Despite non-scientific media interest, few data exist examining circulating concentration of vitamin D metabolites in patients with CFS/ME. There has been only one retrospective study that suggests that patients with CFS/ME may experience suboptimal concentrations of circulating 25(OH)D,26 hence the assumption that low circulating 25(OH)D concentrations in CFS/ME may be responsible for the altered immune profiles27 28 and fatigue. The objective of the current study was to determine whether vitamin D status, as assessed by circulating 25(OH)D, is a contributing factor to the pathophysiology of CFS/ME. To meet this objective, we investigated whether CFS/ME was associated with altered serum concentrations of vitamin D metabolites.

7 28 and fatigue. The objective of the current study was to determine whether vitamin D status, as assessed by circulating 25(OH)D, is a contributing factor to the pathophysiology of CFS/ME. To meet this objective, we investigated whether CFS/ME was associated with altered serum concentrations of vitamin D metabolites. Methods Recruitment Male and female patients with CFS/ME (n=92) were recruited through the regional National Health Service CFS services based in Liverpool at the Royal Liverpool and Broadgreen University Hospital Trust, Merseyside, UK. All patients were newly diagnosed by clinicians in accordance with the recommended National Institute for Health and Care Excellence (NICE) guidelines 200729 and covered a wide spectrum of disease severity from mild, through to moderate and severe CFS/ME. All patients routinely completed the Chalder Fatigue Questionnaire. Bed-bound/non-ambulatory patients were not enrolled in the study. Ninety-four age-matched and sex-matched healthy controls (HCs) (with exclusions of smoking or substance misuse, medication including anti-inflammatory medication or non-ambulatory subjects) were also recruited through local advertisement in the city of Liverpool and during CFS/ME recruitment sessions and served as the control group. This group also completed the Chalder Fatigue Questionnaire. Sample size was determined according to power calculations to allow for a significant difference in serum 25(OH)D to be detected between the two cohorts. Individuals were excluded from the study if they smoked, had a history of substance misuse or had any underlying inflammatory-based medical conditions or treatments. This study conformed to the standards set by the Declaration of Helsinki, and the procedures were approved by the local University of Liverpool peer review committee and multicentre research ethics committee. All participants gave informed written consent for their participation.

based medical conditions or treatments. This study conformed to the standards set by the Declaration of Helsinki, and the procedures were approved by the local University of Liverpool peer review committee and multicentre research ethics committee. All participants gave informed written consent for their participation. Sample collection For the analysis of vitamin D metabolites including 25(OH)D2 and 25(OH)D3, participants provided a venous blood sample collected from the antecubital vein into two serum separator (4.7 mL) vacutainers. Blood samples were incubated for 45 min at room temperature to induce clotting, followed by centrifugation at 1500 g for 15 min at 4°C. Serum was aspirated from the samples and stored at −80°C until analysis. All serum samples were given an anonymous coded identification number, which was sequentially generated by the study team.

es were incubated for 45 min at room temperature to induce clotting, followed by centrifugation at 1500 g for 15 min at 4°C. Serum was aspirated from the samples and stored at −80°C until analysis. All serum samples were given an anonymous coded identification number, which was sequentially generated by the study team. Analysis of vitamin D Vitamin D metabolites 25(OH)D2 and 25(OH)D3 were analysed as previously described.8 25(OH)D2 and 25(OH)D3 were extracted from serum samples, following zinc sulfate protein precipitation, using Isolute C18 solid phase extraction cartridges. Potential interfering compounds were removed by initial elution with 50% methanol followed by elution of the vitamins using 10% tetrahydrofuran in acetonitrile. Dried extracts were reconstituted prior to injection into a high-performance liquid chromatography tandem mass spectrometry (LC–MS/MS) in the multiple reaction mode. The multiple reaction mode transitions (m/z) used were 413.2>395.3, 401.1>383.3 and 407.5>107.2 for 25(OH)D2 and 25(OH)D3, and hexadeuterated (OH)D3, respectively. The assay was validated against published acceptance criteria (FDA 2001). Assay sensitivity was determined by the lower limit of quantification: 25(OH)D3=2.5 nmol/L and 25(OH)D2=2.5 nmol/L. Coefficients of variation for the assay were 10% across a working range of 2.5–625 nmol/L for both 25(OH)D2 and 25(OH)D3.

ctively. The assay was validated against published acceptance criteria (FDA 2001). Assay sensitivity was determined by the lower limit of quantification: 25(OH)D3=2.5 nmol/L and 25(OH)D2=2.5 nmol/L. Coefficients of variation for the assay were 10% across a working range of 2.5–625 nmol/L for both 25(OH)D2 and 25(OH)D3. Intra-assay precision was assessed by running quality control materials (Chromsystems Instruments and Chemicals, Gräfelfing, Germany) and commercially bought material (calf serum) 10 times within a single run, and separately over 15 runs for interassay assessment. Extraction recovery was assessed by determining the amount of vitamin D3/D2 recovered from the amount spiked prior to extraction. The percentage recovery was calculated by the measured value against the sum of endogenous value and spiking concentration. Spiked recovery is determined by adding known quantity of 25(OH)D3/D2 to serum samples with different concentrations of endogenous 25(OH)D. MassCheck calibration materials and controls are traceable against NIST 972 reference material. The LC–MS/MS method used in the present study has previously been validated against other commercially available assays and is regarded as the most valid and reliable technique for the assessment of vitamin D metabolites including 25(OH)D2 and 25(OH)D3.30

Intra-assay precision was assessed by running quality control materials (Chromsystems Instruments and Chemicals, Gräfelfing, Germany) and commercially bought material (calf serum) 10 times within a single run, and separately over 15 runs for interassay assessment. Extraction recovery was assessed by determining the amount of vitamin D3/D2 recovered from the amount spiked prior to extraction. The percentage recovery was calculated by the measured value against the sum of endogenous value and spiking concentration. Spiked recovery is determined by adding known quantity of 25(OH)D3/D2 to serum samples with different concentrations of endogenous 25(OH)D. MassCheck calibration materials and controls are traceable against NIST 972 reference material. The LC–MS/MS method used in the present study has previously been validated against other commercially available assays and is regarded as the most valid and reliable technique for the assessment of vitamin D metabolites including 25(OH)D2 and 25(OH)D3.30 Dietary/supplement analyses Structured open-ended food/supplement diaries were provided to all patients and control. All participants were informed on how to complete the food record. The diary also contained a written example for reference. During a 7-day period, all consumed foods/drinks and supplements had to be reported with notification of time of consumption, estimated consumed quantity expressed as a household measure, unit or weight, specification and if present, a brand name. Food diaries were analysed using WinDiets Research Version (2010) software, written by Dr Alan Wise, The Robert Gordon University, Aberdeen.

to be reported with notification of time of consumption, estimated consumed quantity expressed as a household measure, unit or weight, specification and if present, a brand name. Food diaries were analysed using WinDiets Research Version (2010) software, written by Dr Alan Wise, The Robert Gordon University, Aberdeen. Patient involvement The development of both the study research question and outcome measures took into careful consideration the needs and impact on the patient cohort particularly given the highly sensitive nature of this group. Careful consideration was taken over the execution of study visits and the impact of the study protocol and sample collection. The study was designed to reduce the impact and disturbance to the patient’s condition. Senior specialists in CFS/ME provided advice with regard to study design. No patients were involved in setting the research question, nor were they involved in developing plans for design or implementation of the study. No patients were asked to advise on interpretation or writing up of results. Registered patient support groups within the local area of Merseyside including patients with CFS/ME and their carers provided support in the recruitment of patients into the study through the dissemination of study information and recruitment. Results will be disseminated by presentation to local and national research and patient groups.

upport groups within the local area of Merseyside including patients with CFS/ME and their carers provided support in the recruitment of patients into the study through the dissemination of study information and recruitment. Results will be disseminated by presentation to local and national research and patient groups. Statistics Data are presented as mean±SD throughout. The sample distribution was determined using the Pearson’s coefficient indicating a predominantly parametric distribution. Single comparisons between two groups of normally distributed data were undertaken using independent Student’s t-tests. For multiple comparison analyses to examine the effect of season, one-way analysis of variance followed by post hoc Tukey’s LSD test was used. A p value <0.05 was considered to be statistically significant. SPSS V.22 was used to analyse all data. Figures were constructed with GraphPad Prism V.6. Results Details of the study population are presented in table 1. Epidemiological studies have reported that the prevalence of CFS/ME is significantly higher in women than in men31 and the majority of patients with CFS/ME who took part in the present study were female (66 women and 26 men). Body mass index analyses revealed no significant differences between patients with CFS/ME and HCs (26.0 and 24.4 kg/m2, respectively) (table 1). Average duration of illness in the patients with CFS/ME was 36±4.8 months. Table 1 Baseline characteristics of study participants

Results Details of the study population are presented in table 1. Epidemiological studies have reported that the prevalence of CFS/ME is significantly higher in women than in men31 and the majority of patients with CFS/ME who took part in the present study were female (66 women and 26 men). Body mass index analyses revealed no significant differences between patients with CFS/ME and HCs (26.0 and 24.4 kg/m2, respectively) (table 1). Average duration of illness in the patients with CFS/ME was 36±4.8 months. Table 1 Baseline characteristics of study participants HC CFS/ME Women (n) 58 66 Men (n) 36 26 Caucasian (n) 89 91 Age (years) 33±10 40±10 BMI (kg/m2) 24.4±3.4 26.0±4.2 Illness duration (months) N/A 36±4.8 Sample acquisition 94 92 Data are presented as mean±SD unless otherwise stated. BMI, body mass index; CFS/ME, chronic fatigue syndrome/myalgic encephalomyelitis; HC, healthy control. Total serum 25(OH)D was assessed by measuring the sum of 25(OH)D2 and 25(OH)D3 metabolites. Circulating 25(OH)D2 was undetectable in 86% and 89% of CFS/ME and HC individuals, respectively (data not shown). Undetectable concentrations of circulating 25(OH)D2 in human samples have previously been reported in a number of studies.32 Total serum 25(OH)D concentration was significantly higher (p=0.001) in patients with CFS/ME compared with HCs (60.2 and 47.3 nmol/L, respectively) (figure 1A).

ls, respectively (data not shown). Undetectable concentrations of circulating 25(OH)D2 in human samples have previously been reported in a number of studies.32 Total serum 25(OH)D concentration was significantly higher (p=0.001) in patients with CFS/ME compared with HCs (60.2 and 47.3 nmol/L, respectively) (figure 1A). Figure 1 (A) Distribution plots of individual circulating total 25(OH)D concentrations (nmol/L) assessed in patients with chronic fatigue syndrome/myalgic encephalomyelitis (CFS/ME) and healthy controls (HCs) using liquid chromatography tandem mass spectrometry. *p<0.05 compared with values from the HC. (B) Total serum 25(OH)D concentration in patients with CFS/ME and HCs recruited during the spring–summer months (1 March31 August) and autumn–winter months (1 September–28 February). *p<0.05 compared with values from the HC group recruited during autumn/winter. #p<0.05 compared with values from the HC group recruited during the Spring/Summer months. Close grey circles denote HC and closed black boxes denote CFS/ME. Each data point refers to an individual participant. Solid black bars represent the mean±SD.

compared with values from the HC group recruited during autumn/winter. #p<0.05 compared with values from the HC group recruited during the Spring/Summer months. Close grey circles denote HC and closed black boxes denote CFS/ME. Each data point refers to an individual participant. Solid black bars represent the mean±SD. To further examine the observed difference in vitamin D status between patients with CFS/ME and HC, the effects of seasonal variation in circulating concentrations of 25(OH)D were determined. Statistical analyses revealed a significant interaction between group and season for total serum 25(OH)D (figure 1B). Patients with CFS/ME demonstrated significantly higher (p=0.01) total serum 25(OH)D concentrations during the autumn and winter months (autumn/winter) compared with HC individuals (59.1±24.1 and 45.6±25.9 nmol/L, respectively), with no significant difference evident during the spring and summer months (spring/summer) (figure 1B). The average total serum 25(OH)D concentration from HCs recruited during spring/summer tended to be ~30% higher (p=0.08). Although this did not reach statistical significance in comparison to individuals recruited in autumn/winter (60.2±22.7 and 45.6±25.9 nmol/L, respectively), (figure 1B), these data are in agreement with previous clinical studies demonstrating that vitamin D status varies seasonally, with concentrations of 25(OH)D increasing in spring and peaking at summer.33

significance in comparison to individuals recruited in autumn/winter (60.2±22.7 and 45.6±25.9 nmol/L, respectively), (figure 1B), these data are in agreement with previous clinical studies demonstrating that vitamin D status varies seasonally, with concentrations of 25(OH)D increasing in spring and peaking at summer.33 The higher total serum 25(OH)D concentrations in patients with CFS/ME during the autumn/winter months is likely to be a result of increased vitamin D intake. To address this possibility, dietary/supplement assessment of vitamin D intake from both diet and in the form of supplements was undertaken although, due to compliance issues, these data were only provided by 21 HCs and 24 patients with CFS/ME (table 2). Dietary intake of vitamin D did not differ significantly between patients with CFS/ME and HC individuals. However, more patients with CFS/ME reported multivitamin supplement and/or vitamin D supplement consumption than HCs. Because of the increased use of supplements by patients with CFS/ME, the cohort of patients with CFS/ME showed higher (p=0.05) average vitamin D intake through use of supplements compared with the cohort of HCs (4.9±9.7 and 0.6±1.7 µg/day, respectively) (table 2). When we limit the analysis to those individuals taking vitamin D supplements, the average intake for HCs was 4.2 µg/day compared with 14.1 µg/day for patients with CFS/ME. In addition, analysis revealed no differences in total serum 25(OH)D in the patients with CFS/ME compared with HC individuals reporting no vitamin D supplement intake (figure 2). Overall these findings suggest that the higher serum 25(OH)D concentrations observed in patients with CFS/ME during the autumn and winter months was likely to be due to increased vitamin D intake through use of supplements. Individuals with CFS/ME reported significantly higher levels of fatigue than the HC group in the Chalder Fatigue Questionnaire; a widely used and validated health questionnaire (data not shown in detail). To assess whether serum concentrations of total 25(OH)D were a contributing factor to the level of fatigue in CFS/ME, 25(OH)D values from the CFS/ME group were ordered from lowest to highest and split equally into an upper and lower range (figure 3).

y used and validated health questionnaire (data not shown in detail). To assess whether serum concentrations of total 25(OH)D were a contributing factor to the level of fatigue in CFS/ME, 25(OH)D values from the CFS/ME group were ordered from lowest to highest and split equally into an upper and lower range (figure 3). The data presented show no evidence of a difference in mean or distribution of Chalder Fatigue Questionnaire Score between the two groups and so suggest that total serum 25(OH)D concentration does not appear to be a major contributing factor to the level of fatigue in patients with CFS/ME. Figure 2 Total serum 25(OH)D concentration in patients with chronic fatigue syndrome/myalgic encephalomyelitis (CFS/ME) and healthy controls (HCs) who reported no intake of vitamin D supplements. Each data point refers to an individual participant. Solid black bars represent the mean±SD. Figure 3 Chalder Fatigue Scores in patients with chronic fatigue syndrome/myalgic encephalomyelitis (CFS/ME). 25(OH)D values from the CFS/ME group were ordered from lowest to highest and split equally into an upper and lower range. Each data point refers to an individual participant. Solid black bars represent the mean±SD. Table 2 Dietary/supplement assessment of average vitamin D intake by patients with chronic fatigue syndrome/myalgic encephalomyelitis (CFS/ME) compared with healthy control (HC) individuals HC CFS/ME p Value Average vitamin D intake—diet (µg/day) 2.9±2.1 2.4±1.6 0.6 Average vitamin D intake—supplements (µg/day) 0.6±1.7 4.9±9.7 0.05 Data are presented as mean ±SD.

Table 2 Dietary/supplement assessment of average vitamin D intake by patients with chronic fatigue syndrome/myalgic encephalomyelitis (CFS/ME) compared with healthy control (HC) individuals HC CFS/ME p Value Average vitamin D intake—diet (µg/day) 2.9±2.1 2.4±1.6 0.6 Average vitamin D intake—supplements (µg/day) 0.6±1.7 4.9±9.7 0.05 Data are presented as mean ±SD. Discussion The present findings suggest that patients with CFS/ME do not exhibit insufficient concentrations of circulating total 25(OH)D, but the values are significantly higher compared with HCs. The average concentration of serum total 25(OH)D in patients with CFS/ME was 60.2 nmol/L and was greater than the sufficient cut-off of ≥50 nmol/L set by the UK and US Institutes of Health.33 Scores from the Chalder Questionnaire revealed no differences between individuals with CFS/ME with high or low total 25(OH)D concentrations. This finding implies that vitamin D deficiency is not a major contributing factor to the pathophysiology of CFS/ME. The data from the present study further demonstrated that the average concentration of circulating total 25(OH)D in the HC group was lower (47.3 nmol/L) than the suggested sufficient concentrations (>50 nmol/L), in agreement with recent reports from the NICE that insufficient serum vitamin D concentrations are common in the UK. The present findings suggest individuals with CFS/ME have higher total 25(OH)D than HCs, particularly in the spring/summer months, due to the consumption of vitamin D supplements. In contrast, further analysis revealed no significant differences in total 25(OH)D between individuals with CFS/ME and HCs who reported no supplement intake. Overall these findings imply that the higher total 25(OH)D in the CFS/ME group is likely attributed to the overall higher supplemental vitamin D intake.

plements. In contrast, further analysis revealed no significant differences in total 25(OH)D between individuals with CFS/ME and HCs who reported no supplement intake. Overall these findings imply that the higher total 25(OH)D in the CFS/ME group is likely attributed to the overall higher supplemental vitamin D intake. Individuals with circulating 25(OH)D concentrations >50 nmol/L are likely to have sufficient concentrations to maintain normal biological functions. However, concentrations between 30 and 50 nmol/L are considered to be insufficient and <25 nmol/L are likely to lead to the pathology of muscle and bone disorders such as osteomalacia and osteoporosis and to reduce overall health.34 Previous research has been undertaken to determine changes in total 25(OH)D throughout the year as the intensity and hours of sunlight change.35 The current study further sought to examine the changes that occur in vitamin D status throughout the year in both patients with CFS/ME and age-matched/sex-matched HC individuals. Seasonal variation analyses revealed that HC individuals recruited during autumn/winter exhibited insufficient levels of circulating 25(OH)D (<50 nmol/L) with concentrations rising significantly to normal values (>50 nmol/L) in individuals recruited during spring/summer. In contrast, there was a lack of difference in circulating 25(OH)D between autumn/winter and spring/summer in patients with CFS/ME. Data showed an ~50% reduction in vitamin D supplement intake in the patient cohort recruited during the summer months. Caution is required when interpreting these findings since the study is cross-sectional and so represents different patients and these changes in supplement intake did not reach significance (data not shown), but may suggest that patients with CFS/ME reduce their supplement intake of vitamin D during the summer months. Alternatively, the patients may not have been exposed to increased sunlight during the summer months due to reduced mobility and being housebound as a result of the symptoms associated with CFS/ME. The observed suboptimal circulating 25(OH)D concentration in healthy individuals during the autumn and winter months was likely due to insufficient vitamin D intake and/or lack of adequate exposure to sunlight.13 The higher prevalence of sufficient 25(OH)D concentrations observed in patients with CFS/ME during autumn/winter were associated with higher vitamin D intake through use of supplements when compared with HCs.

d winter months was likely due to insufficient vitamin D intake and/or lack of adequate exposure to sunlight.13 The higher prevalence of sufficient 25(OH)D concentrations observed in patients with CFS/ME during autumn/winter were associated with higher vitamin D intake through use of supplements when compared with HCs. Patients with CFS/ME suffer from a wide range of debilitating symptoms and, depending on these symptoms, there may be reduction in exposure to the sun. The current study included patients across the disease spectrum from mild severity through to moderate and severe, although excluding bed-bound/non-ambulatory patients. As a result, it is plausible to assume that there may be a subgroup of patients with CFS/ME, who may be at greater risk for deficiency. One limitation of the present work was that the majority of participants who took part in the study, including CFS/ME and HC volunteers, were Caucasians who lived in the North-West of England hence the findings from the present study might not reflect all CFS/ME and HC populations. It is noteworthy to mention that there is evidence that vitamin D metabolism varies in different ethnic groups.34 Future studies comparing CFS/ME including non-ambulatory patients and HC individuals from different origin, race and disease duration are also warranted. A further limitation to the study was that only the main marker of vitamin D status, that is, 25(OH)D, was measured. It has recently been shown that there is a need to assess all of the vitamin D metabolites.35 It may be more appropriate to measure the amount of serum-free and bioavailable 25(OH)D as a predictor of vitamin D status than serum total 25(OH)D, while controlling for vitamin D binding protein phenotype.36 Future studies assessing these aspects of vitamin D metabolism in patients with CFS/ME are warranted.

lites.35 It may be more appropriate to measure the amount of serum-free and bioavailable 25(OH)D as a predictor of vitamin D status than serum total 25(OH)D, while controlling for vitamin D binding protein phenotype.36 Future studies assessing these aspects of vitamin D metabolism in patients with CFS/ME are warranted. In conclusion, patients with CFS/ME do not exhibit insufficient concentrations of circulating 25(OH)D3 at any time of the year. Given that this cohort of patients with CFS/ME clearly displays symptoms, regardless of serum 25(OH)D concentration, these data indicate that vitamin D status may not be a major contributing factor to the pathophysiology of patient with CFS/ME symptoms, although further analyses of those individuals with lower circulating 25(OH)D and more detailed investigation into vitamin D metabolism is still warranted. Current health guidelines in the UK state supplementation of vitamin D should be recommended to individuals with increased risk of deficiency and the findings from the present study suggest, in agreement with other studies, that a significant percentage of healthy individuals exhibit insufficient concentrations of circulating vitamin D during the autumn and winter months. The current findings do not support the notion that vitamin D concentration contributes to the continuing symptoms of CFS/ME. However, it may be important for patients with CFS/ME to supplement with vitamin D during the winter months as it is well documented that non-supplemented individuals may typically present with insufficient serum 25(OH)D during the winter season.

vitamin D concentration contributes to the continuing symptoms of CFS/ME. However, it may be important for patients with CFS/ME to supplement with vitamin D during the winter months as it is well documented that non-supplemented individuals may typically present with insufficient serum 25(OH)D during the winter season. Supplementary Material Reviewer comments Author's manuscript We thank Colette Foster, Emmanuel Nsutebu and Libbe Ratcliffe, the Liverpool CFS/ME Therapy Service and the Royal Liverpool Clinical Research Unit for their support. Contributors: AM, MBJB, CL, LD and GLC conceived and planned the research. KEE, GKS, MS, MF, FC, DJO and JT carried out the research, supervised by AM and MBJB and funded by grants awarded to AM, CL, LD and MBJB. KEE and FC performed the analysis of the intake diaries. KEE, GKS and JT analysed and presented the data. KEE and GKS wrote the paper. All authors revised the paper. Funding: This study was supported by grants from the British Medical Research Council, the ME Association and The University of Liverpool. Competing interests: None declared. Patient consent: Obtained. Ethics approval: This study conformed to the standards set by the Declaration of Helsinki, and the procedures were approved by the local University of Liverpool peer review committee and multi-centre research ethics committee. Provenance and peer review: Not commissioned; externally peer reviewed. Data sharing statement: No additional data are available.

Article summary Article focus The reported rates of disability are considerably higher among the older population of Northern Ireland (NI) than in the Republic of Ireland (RoI), but the reasons for this are not known. Multimorbidity is common in older people, and disability, poor self-rated health and poor quality of life are more common among those with multiple chronic conditions. We aimed to explore the effects of particular combinations of chronic conditions on disability, quality of life and self-rated health across Ireland and to test whether higher rates of chronic disease or multimorbidity explained the difference in disability rates across the island of Ireland. Key messages There are no substantial interactions between cardiovascular disease, lung disease, diabetes and chronic pain with respect to their effects on disability, poor self-rated health and poor quality of life. This means that the sum of the main effects of each condition is sufficient to explain the poor health outcomes associated with multimorbidity and that no synergistic ‘multimorbidity’ effect is needed. Middle-aged and older people in NI are far more likely to report limitations in daily activities (OR 2.8, 95% CI 2.5 to 3.2) than those in RoI even after adjusting for the moderately higher rates of chronic disease in NI compared to RoI.

Key messages There are no substantial interactions between cardiovascular disease, lung disease, diabetes and chronic pain with respect to their effects on disability, poor self-rated health and poor quality of life. This means that the sum of the main effects of each condition is sufficient to explain the poor health outcomes associated with multimorbidity and that no synergistic ‘multimorbidity’ effect is needed. Middle-aged and older people in NI are far more likely to report limitations in daily activities (OR 2.8, 95% CI 2.5 to 3.2) than those in RoI even after adjusting for the moderately higher rates of chronic disease in NI compared to RoI. Strengths and limitations of this study This study uses two large population representative datasets conducted at a similar time and using similar methodology to ascertain chronic disease and other outcomes and covariates, and so the results are applicable to the national population and are comparable between regions. However, some of the questions regarding chronic diseases and outcomes are not exactly identical across the studies, and the limited number of diseases ascertained in both studies meant that an exhaustive account of chronic disease multimorbidity was not possible. Disability, self-rated health and quality of life were each ascertained by a single question.

ng chronic diseases and outcomes are not exactly identical across the studies, and the limited number of diseases ascertained in both studies meant that an exhaustive account of chronic disease multimorbidity was not possible. Disability, self-rated health and quality of life were each ascertained by a single question. Introduction Trends in the health of ageing populations are complex. While there is evidence of a compression of morbidity and serious disability in developed countries including Ireland,1–5 the proportion of the older population suffering from chronic conditions is expected to increase substantially in the years ahead.6–8 Multimorbidity, defined as two or more chronic conditions, is common in older people9 10 and is associated with increased healthcare utilisation,11 12 greater levels of disability, dependency and a diminished quality of life (QoL).11 13–16 Although they have larger proportions of younger people than in the rest of the British Isles, both Northern Ireland (NI) and the Republic of Ireland (RoI) are experiencing the same demographic shift towards an older population, and so the expected increase in multimorbidity and the associated disability burden presents a significant challenge for health professionals and policymakers across the island.5 Previous research has suggested that chronic conditions, poor self-rated health (SRH) and functional impairment are more common in NI than in the RoI and in those from lower socioeconomic groups in both regions.8 17 18

burden presents a significant challenge for health professionals and policymakers across the island.5 Previous research has suggested that chronic conditions, poor self-rated health (SRH) and functional impairment are more common in NI than in the RoI and in those from lower socioeconomic groups in both regions.8 17 18 The effect of multiple chronic diseases on the functional ability and well-being of older people is not well understood. While multimorbidity has been shown to lead to disability,11 19 poor SRH20 and diminished QoL,21–23 little is known about how specific combinations of conditions lead to adverse outcomes. Previous works have suggested synergistic effects between some specific pairs of physical and mental chronic conditions24 25 but not others with respect to the risk of functional ability, but how specific pairs of conditions affect QoL or SRH is largely unexplored. Here, we estimate the prevalence of four groups of chronic conditions and their combined effects on SRH, disability and QoL using data from two studies representative of the older population across the island of Ireland. We consider both ‘multimorbidity’, defined by the number of groups of chronic conditions present, and the prevalence and effects of specific combinations of conditions. Our aim is to better understand the interactions between chronic conditions with respect to adverse outcomes in the older population and to test whether differences in the chronic disease profile can account for the previously reported differences in the rate of disability between NI and RoI.

ombinations of conditions. Our aim is to better understand the interactions between chronic conditions with respect to adverse outcomes in the older population and to test whether differences in the chronic disease profile can account for the previously reported differences in the rate of disability between NI and RoI. Methods Study sample This investigation was based on harmonisation of data from two population-representative studies of health: the Survey of Lifestyle, Attitudes and Nutrition in Ireland (SLAN) 2007, which conducted face-to-face interviews with 10 364 adults aged 18 years and over in RoI and the 2005 Northern Ireland Health and Social Wellbeing Survey (NIHSWS), which gathered data from 4245 individuals aged 16 years and above in NI. These data sources have previously been combined to compare health and lifestyle factors across the island of Ireland17 and the design of both surveys are described in full elsewhere.17 26 The present analysis included all participants of both studies aged 50 years and over, giving a total sample size of 6159 (1904 from NI and 4255 from RoI).

previously been combined to compare health and lifestyle factors across the island of Ireland17 and the design of both surveys are described in full elsewhere.17 26 The present analysis included all participants of both studies aged 50 years and over, giving a total sample size of 6159 (1904 from NI and 4255 from RoI). Measures of chronic conditions and health outcomes The harmonised dataset included indicators for the presence of seven key chronic conditions: heart attack, angina, stroke, asthma, chronic obstructive pulmonary disease (COPD), diabetes, musculoskeletal pain (including rheumatism, arthritis and back pain) and cancer. Online supplementary appendix 1 lists all questions used to ascertain the presence or absence of each condition. For all conditions except chronic pain, ‘presence’ was defined by a self-report of a doctor's diagnosis and the condition being present within the previous year. These were recoded into four organ system groups: cardiovascular disease (CVD, defined as the presence of a heart attack, angina or stroke), respiratory disease (COPD or asthma), diabetes and musculoskeletal pain. Disability is measured by a self-report of limited activity due to an illness or health problem. SLAN participants were asked “Is your daily activity limited by a long-term illness, health problem or disability?” NIHSWS participants were asked “Do you have any long-standing illness, disability or infirmity? By long-standing I mean anything that has troubled you over a period of time and, if yes, does this illness or disability limit your activities in any way?”

vity limited by a long-term illness, health problem or disability?” NIHSWS participants were asked “Do you have any long-standing illness, disability or infirmity? By long-standing I mean anything that has troubled you over a period of time and, if yes, does this illness or disability limit your activities in any way?” QoL was determined by the question “How would you rate your quality of life.” Responses of ‘poor’ or ‘very poor’ were considered ‘poor’ QoL, whereas all other responses (very good, good, neither good nor poor) were considered ‘good’. SRH was measured by a single question “In general would you say your health is” in SLAN and “How is your health in general, would you say it was...” in NIHSWS. A response of excellent, very good or good was considered ‘good’ SRH, whereas a response of fair, poor or very poor was considered ‘poor’ SRH.

onsidered ‘good’. SRH was measured by a single question “In general would you say your health is” in SLAN and “How is your health in general, would you say it was...” in NIHSWS. A response of excellent, very good or good was considered ‘good’ SRH, whereas a response of fair, poor or very poor was considered ‘poor’ SRH. Covariates Age was considered in three groups: 50–64, 65–74 and 75+ years. Marital status was dichotomised as living alone (single, widowed, separated or divorced) or living with a partner (married or cohabiting). Socioeconomic status (SES) was measured by current occupation (previous if retired). The Central Statistical Office (CSO) 1996 classification of occupations27 was used to characterise individuals as high (professional/managerial/technical), mid (non-manual/skilled manual) or low (semiskilled/unskilled). Respondents who were unemployed or did not provide enough information to be classified were included as a separate group in multivariate analyses. Owing to the slight variation in questions and because differences between regions are of direct interest, the source of the data, that is, SLAN versus NIHSWS, was also included as a covariate in all multivariate analyses.

e enough information to be classified were included as a separate group in multivariate analyses. Owing to the slight variation in questions and because differences between regions are of direct interest, the source of the data, that is, SLAN versus NIHSWS, was also included as a covariate in all multivariate analyses. Statistical analysis The association between conditions was measured using logistic regression, both univariately and after adjusting for all potential confounders. The number in the sample with each number and with each combination of conditions was found and prevalence was estimated by applying the weights supplied with each dataset to the sample proportions. The prevalence of disability, poor SRH and poor QoL was found within each group. Logistic regression was used to examine how the cooccurrence of diseases affected each of the outcomes. First, the effect of the number of chronic conditions was estimated after adjusting for all potential confounders. Finally, the effect of each condition and the additional effect of each pair of conditions were examined by estimating a model, including the main effect of each condition and the interactions between each pair as well as all covariates. All analyses were conducted using Stata V.12.0.

ing for all potential confounders. Finally, the effect of each condition and the additional effect of each pair of conditions were examined by estimating a model, including the main effect of each condition and the interactions between each pair as well as all covariates. All analyses were conducted using Stata V.12.0. In SLAN, 139 participants (3%) had missing data for one or more chronic conditions. In our primary analysis, such conditions were assumed to be absent, in line with the NIHSWS protocol. We conducted a sensitivity analysis excluding these cases with no substantial impact on results. To assess the effect of limiting our analysis to dichotomous outcomes, secondary analyses were conducted using ordinal logistic regression to model the full range of responses to the QoL and SRH questions. Again, there was no substantial impact on results and so, for ease of interpretation, the primary analysis is presented. Results Table 1 shows the characteristics of our sample. The demographic profiles of the two samples are similar, but all the adverse outcomes that we considered were more commonly reported in NI than in RoI. Table 1 Demographic profile of SLAN 2007 and NIHSWS 2005 samples and the numbers with disability, poor self-rated health and poor quality of life

Results Table 1 shows the characteristics of our sample. The demographic profiles of the two samples are similar, but all the adverse outcomes that we considered were more commonly reported in NI than in RoI. Table 1 Demographic profile of SLAN 2007 and NIHSWS 2005 samples and the numbers with disability, poor self-rated health and poor quality of life SLAN 2007 NIHSWS 2005 N Per cent N Per cent Sex Male 1838 43 818 43 Female 2417 57 1086 57 Age 50–64 2286 54 952 50 65–74 1150 27 549 29 75+ 819 19 403 21 Marital status Married 2427 57 1180 62 Single 1815 43 724 38 SES High 1216 29 374 20 Mid 1577 37 788 41 Low 868 20 537 28 Missing 594 14 205 11 Chronic conditions Cardiovascular disease 297 6 223 11 Lung disease 376 9 151 8 Diabetes 256 6 154 8 Chronic pain 1540 35 1050 55 Outcomes Limited daily activity 881 21 856 45 Poor self-rated health 1060 25 784 41 Poor quality of life 636 15 384 20 NIHSWS, Northern Ireland Health and Social Wellbeing Survey; SES, socioeconomic status; SLAN, Survey of Lifestyle, Attitudes and Nutrition. Table 2 shows ORs corresponding to the pairwise associations between each group of conditions, both univariately and after adjusting for age, sex, marital status, socioeconomic group and region. In multivariate analysis, every condition is positively correlated with each of the others. The strongest association is observed between diabetes and CVD, the weakest between diabetes and pain. Table 2 Pairwise associations between chronic conditions, measured by ORs reflecting the increased prevalence of one condition in the presence of the other

Table 2 shows ORs corresponding to the pairwise associations between each group of conditions, both univariately and after adjusting for age, sex, marital status, socioeconomic group and region. In multivariate analysis, every condition is positively correlated with each of the others. The strongest association is observed between diabetes and CVD, the weakest between diabetes and pain. Table 2 Pairwise associations between chronic conditions, measured by ORs reflecting the increased prevalence of one condition in the presence of the other Cardiovascular disease Lung disease Diabetes Chronic pain Cardiovascular disease – 2.46 (1.92 to 3.15) 3.01 (2.32 to 3.91) 2.18 (1.81 to 2.61) Lung disease 2.41 (1.86 to 3.11) – 2.17 (1.64 to 2.88) 2.15 (1.79 to 2.58) Diabetes 2.43 (1.85 to 3.19) 2.09 (1.57 to 2.78) – 1.43 (1.17 to 1.75) Chronic pain 1.95 (1.61 to .36) 2.17 (1.80 to 2.62) 1.33 (1.08 to 1.64) – Univariate associations are shown above the diagonal, and ORs estimated by logistic regression adjusted for age, sex, SES, marital status and the source of the data are shown below the diagonal. SES, socioeconomic status.

Cardiovascular disease Lung disease Diabetes Chronic pain Cardiovascular disease – 2.46 (1.92 to 3.15) 3.01 (2.32 to 3.91) 2.18 (1.81 to 2.61) Lung disease 2.41 (1.86 to 3.11) – 2.17 (1.64 to 2.88) 2.15 (1.79 to 2.58) Diabetes 2.43 (1.85 to 3.19) 2.09 (1.57 to 2.78) – 1.43 (1.17 to 1.75) Chronic pain 1.95 (1.61 to .36) 2.17 (1.80 to 2.62) 1.33 (1.08 to 1.64) – Univariate associations are shown above the diagonal, and ORs estimated by logistic regression adjusted for age, sex, SES, marital status and the source of the data are shown below the diagonal. SES, socioeconomic status. Table 3 shows the distribution of combinations of conditions and the proportions with disability, poor SRH and poor QoL in each group. Just over half of the older population of Ireland did not report any of the conditions we measured, and these individuals rarely reported disability (9.2%), poor health (11.9%) or poor QoL (8.9%). Around 30% reported only musculoskeletal pain with a significant effect on the prevalence of each outcome. Multimorbidity was reported by 733 individuals representing 11.3% of the population aged over 50 years in Ireland, with each of the combinations of two or more conditions being reported in 3% or fewer of the sample. Only 17 participants reported chronic conditions from all four groups, representing 0.3% of the population. CVD with chronic pain appears to be a particularly problematic combination of the conditions. Each of the subgroups including both these conditions has particularly high rates of disability, poor SRH and poor QoL, although all of those with multimorbidity do appear to have a high risk of disability, poor SRH and poor QoL.

with chronic pain appears to be a particularly problematic combination of the conditions. Each of the subgroups including both these conditions has particularly high rates of disability, poor SRH and poor QoL, although all of those with multimorbidity do appear to have a high risk of disability, poor SRH and poor QoL. Table 3 Pattern of chronic disease cooccurrence in the Irish population, and the proportion among those with each combination who suffer from limitations in daily activities, poor self-rated health and poor quality of life Cardiovascular disease Lung disease Diabetes Musculoskeletal pain Number in sample Population (%) LDA (%) Poor SRH (%) Poor QoL (%) – – – – 2947 51.5 9.2 11.9 8.9 – – – 1 1849 29.1 36.3 34.3 17.7 – 1 – 1 212 3.4 60.8 64.8 31.4 1 – – 1 201 3.1 78.8 73.5 42.9 – 1 – – 175 3.1 36.2 39.1 22.6 1 – – – 163 2.4 54.9 57.8 31.9 – – 1 – 151 2.6 24.2 36.9 12.7 – – 1 1 126 1.9 52.7 67.1 26.9 1 1 – 1 53 0.7 89.2 91.8 45.9 1 – 1 1 35 0.5 91.5 80.2 50.8 1 – 1 – 26 0.4 65.9 74.1 26.6 – 1 1 1 25 0.4 76.3 91.2 38.4 – 1 1 – 19 0.3 63.4 69.8 33.9 1 1 1 1 17 0.3 81.7 89.5 47.2 1 1 – – 15 0.2 52.5 43.9 20.2 1 1 1 – 4 0.1 62.4 62.4 50.6 Each row corresponds to a particular combination of chronic diseases, and these are sorted by their frequency in the combined sample. Prevalences are weighted to the population aged 50 years and over of the island of Ireland. LDA, limitation in daily activities; QoL, quality of life; SRH, self-rated health.

Cardiovascular disease Lung disease Diabetes Musculoskeletal pain Number in sample Population (%) LDA (%) Poor SRH (%) Poor QoL (%) – – – – 2947 51.5 9.2 11.9 8.9 – – – 1 1849 29.1 36.3 34.3 17.7 – 1 – 1 212 3.4 60.8 64.8 31.4 1 – – 1 201 3.1 78.8 73.5 42.9 – 1 – – 175 3.1 36.2 39.1 22.6 1 – – – 163 2.4 54.9 57.8 31.9 – – 1 – 151 2.6 24.2 36.9 12.7 – – 1 1 126 1.9 52.7 67.1 26.9 1 1 – 1 53 0.7 89.2 91.8 45.9 1 – 1 1 35 0.5 91.5 80.2 50.8 1 – 1 – 26 0.4 65.9 74.1 26.6 – 1 1 1 25 0.4 76.3 91.2 38.4 – 1 1 – 19 0.3 63.4 69.8 33.9 1 1 1 1 17 0.3 81.7 89.5 47.2 1 1 – – 15 0.2 52.5 43.9 20.2 1 1 1 – 4 0.1 62.4 62.4 50.6 Each row corresponds to a particular combination of chronic diseases, and these are sorted by their frequency in the combined sample. Prevalences are weighted to the population aged 50 years and over of the island of Ireland. LDA, limitation in daily activities; QoL, quality of life; SRH, self-rated health. The effect of increasing levels of multimorbidity on each of the three health outcomes (disability, poor health and poor QoL) is shown in table 4. After adjusting for age, sex, marital status, socioeconomic position and the source of the data, there is a clear increase in the risk of each outcome for each of the first three chronic conditions. Moreover, the increase in odds of all outcomes appears roughly similar (on a multiplicative scale) for each of the first three conditions added. Those with three or more chronic conditions are at extremely high risk of disability (80–90%), and unsurprisingly, the majority of these rate their health as poor, while just under half rate their QoL as poor. The number of individuals with four chronic conditions is too small for meaningful analysis, but their profile of outcomes seems similar to that of the group with three conditions.

of disability (80–90%), and unsurprisingly, the majority of these rate their health as poor, while just under half rate their QoL as poor. The number of individuals with four chronic conditions is too small for meaningful analysis, but their profile of outcomes seems similar to that of the group with three conditions. Table 4 Number in the sample with 0, 1, 2, 3 or 4 chronic conditions and prevalence in the population, and the proportion of the population in each group with limitation in daily activities, self-rated health and poor quality of life Number of conditions N Prevalence (%) Limitation in daily activities Self-rated health Poor quality of life Per cent OR 95% CI Per cent OR 95% CI Per cent OR 95% CI 0 2947 51.5 9.2 Ref 11.9 Ref 8.9 Ref 1 2338 37.2 36.6 5.08*** 4.21 to 6.12 36.4 3.83*** 3.23 to 4.53 18.7 2.17*** 1.77 to 2.67 2 599 9.3 65 15.53*** 11.81 to 20.42 68.1 13.76*** 10.62 to 17.84 33.9 4.59*** 3.50 to 6.01 3 117 1.8 85.6 47.46*** 24.44 to 92.16 86.8 38.26*** 20.02 to 73.13 45.8 7.01*** 4.36 to 11.27 4 17 0.3 81.7 31.48*** 7.43 to 133.4 89.5 45.21*** 10.79 to 189.4 47.2 6.55*** 1.64 to 26.17 ***p Value <0.001. ORs for each outcome with respect to the group with 0 chronic conditions are adjusted for age, sex, marital status, source of the data and SES. SES, socioeconomic status.

Number of conditions N Prevalence (%) Limitation in daily activities Self-rated health Poor quality of life Per cent OR 95% CI Per cent OR 95% CI Per cent OR 95% CI 0 2947 51.5 9.2 Ref 11.9 Ref 8.9 Ref 1 2338 37.2 36.6 5.08*** 4.21 to 6.12 36.4 3.83*** 3.23 to 4.53 18.7 2.17*** 1.77 to 2.67 2 599 9.3 65 15.53*** 11.81 to 20.42 68.1 13.76*** 10.62 to 17.84 33.9 4.59*** 3.50 to 6.01 3 117 1.8 85.6 47.46*** 24.44 to 92.16 86.8 38.26*** 20.02 to 73.13 45.8 7.01*** 4.36 to 11.27 4 17 0.3 81.7 31.48*** 7.43 to 133.4 89.5 45.21*** 10.79 to 189.4 47.2 6.55*** 1.64 to 26.17 ***p Value <0.001. ORs for each outcome with respect to the group with 0 chronic conditions are adjusted for age, sex, marital status, source of the data and SES. SES, socioeconomic status. Table 5 shows the independent effects of each disease and each pair of diseases on disability, SRH and QoL after adjusting for age, sex, SES, marital status and region. The main effects indicate the effect of each disease in a person suffering none of the other conditions. The contribution of the second comorbid condition can be found by multiplying the main effect of that condition by the interaction between the first and the second. An interaction less than one therefore indicates that the effect of the two diseases is less than the multiplicative effect of each one individually, while an interaction of more than one indicates a synergistic effect. In brief, what table 5 shows is that while each of the conditions had a significant effect on each of the outcomes, there were no significant positive interaction effects between pairs of conditions.

he multiplicative effect of each one individually, while an interaction of more than one indicates a synergistic effect. In brief, what table 5 shows is that while each of the conditions had a significant effect on each of the outcomes, there were no significant positive interaction effects between pairs of conditions. Table 5 Logistic regression showing the effects of combinations of chronic diseases on disability (defined by a self-report of limitations in daily activities), poor self-rated health and poor quality of life

he multiplicative effect of each one individually, while an interaction of more than one indicates a synergistic effect. In brief, what table 5 shows is that while each of the conditions had a significant effect on each of the outcomes, there were no significant positive interaction effects between pairs of conditions. Table 5 Logistic regression showing the effects of combinations of chronic diseases on disability (defined by a self-report of limitations in daily activities), poor self-rated health and poor quality of life Limitation in daily activities Poor self-rated health Poor quality of life OR 95% CI OR 95% CI OR 95% CI Main effects CVD 8.95*** 6.43 to 12.45 7.41*** 5.40 to 10.17 3.32*** 2.36 to 4.68 Respiratory disease 5.40*** 3.89 to 7.49 4.58*** 3.35 to 6.26 2.32*** 1.62 to 3.33 Diabetes 3.85*** 2.72 to 5.46 4.09*** 2.95 to 5.68 1.87** 1.27 to 2.77 Musculoskeletal pain 4.91*** 4.20 to 5.74 3.16*** 2.73 to 3.66 2.06*** 1.74 to 2.45 Two-way interactions CVD×respiratory disease 0.72 0.36 to 1.45 0.49* 0.25 to 0.97 0.62 0.36 to 1.07 CVD×diabetes 0.48* 0.25 to 0.93 0.34*** 0.18 to 0.65 0.6 0.34 to 1.06 CVD×pain 0.51** 0.32 to 0.80 0.69 0.45 to 1.06 0.96 0.63 to 1.45 Respiratory disease×diabetes 0.7 0.35 to 1.37 0.86 0.41 to 1.80 1.13 0.61 to 2.09 Respiratory disease×pain 0.54** 0.36 to 0.83 0.87 0.58 to 1.32 0.86 0.55 to 1.33 Diabetes×pain 0.64 0.40 to 1.03 0.95 0.60 to 1.51 0.86 0.53 to 1.40 Covariates Male Ref – Ref – Ref – Female 0.81** 0.71 to 0.93 0.84* 0.74 to 0.96 0.87 0.75 to 1.01 Age 50–64 years Ref – Ref – Ref – Age 65–74 years 1.14 0.97 to 1.33 1.19* 1.03 to 1.38 1.01 0.86 to 1.20 Age 75+ years 1.52*** 1.27 to 1.81 1.39*** 1.18 to 1.64 0.95 0.79 to 1.16 Republic of Ireland Ref – Ref – Ref – Northern Ireland 2.81*** 2.45 to 3.22 1.84*** 1.61 to 2.10 1.22* 1.05 to 1.42 High SES Ref – Ref – Ref – Mid SES 1.13 0.95 to 1.35 1.33** 1.12 to 1.58 1.33** 1.09 to 1.62 Low SES 1.21 1.00 to 1.47 2.02*** 1.68 to 2.43 1.63*** 1.32 to 2.02 Missing SES 1.48*** 1.17 to 1.86 1.73*** 1.38 to 2.16 1.61*** 1.25 to 2.08 Married Ref – Ref – Ref – Single or widowed 1.26** 1.09 to 1.45 1.32*** 1.16 to 1.51 1.60*** 1.38 to 1.86 *p Value <0.05; **p value <0.01; ***p value <0.001.

1.33** 1.09 to 1.62 Low SES 1.21 1.00 to 1.47 2.02*** 1.68 to 2.43 1.63*** 1.32 to 2.02 Missing SES 1.48*** 1.17 to 1.86 1.73*** 1.38 to 2.16 1.61*** 1.25 to 2.08 Married Ref – Ref – Ref – Single or widowed 1.26** 1.09 to 1.45 1.32*** 1.16 to 1.51 1.60*** 1.38 to 1.86 *p Value <0.05; **p value <0.01; ***p value <0.001. CVD, cardiovascular disease; SES, socioeconomic status. With respect to disability, all the estimates of interactions are less than one, with the interactions between CVD and diabetes, CVD and chronic pain and lung disease and chronic pain all statistically significant and around 0.5. In other words, in each of these cases, the effect of the second disease on the chance of reporting a limitation in daily activities (as measured by OR) in the presence of the first disease is about half of what the effect would be in the absence of the first disease. A similar pattern is seen with respect to SRH. Each condition alone has a statistically significant positive effect on the odds of reporting poor SRH, but there are no statistically significant positive interactions. The interactions between CVD and lung disease and between CVD and diabetes are statistically significant and less than 0.5 (table 5). The main effects of chronic conditions on QoL are lower than on disability or SRH, although each is statistically significant. No interactions were statistically significant, and estimates of interaction effects tended to be around 1 or less than 1.

A similar pattern is seen with respect to SRH. Each condition alone has a statistically significant positive effect on the odds of reporting poor SRH, but there are no statistically significant positive interactions. The interactions between CVD and lung disease and between CVD and diabetes are statistically significant and less than 0.5 (table 5). The main effects of chronic conditions on QoL are lower than on disability or SRH, although each is statistically significant. No interactions were statistically significant, and estimates of interaction effects tended to be around 1 or less than 1. In an additional model including all possible third and fourth order interactions, the higher order interactions were not statisticially significant and other effects were not changed, and so the model including only second order interactions is shown. The much higher prevalence of disability seen in NI compared to RoI is reflected in the multivariate regression (OR 2.8, 95% CI 2.5 to 3.2), with a corresponding increase in the prevalence of poor SRH (OR 1.8, 95% CI 1.6 to 2.1) and a smaller but still statistically significantly higher prevalence of reported low QoL in NI (OR 1.2, 95% CI 1.1 to 1.4). Those in lower socioeconomic groups reported poorer SRH and QoL, but the effect of SES on disability after adjusting for all other factors was not statistically significant in the multivariate model.

a smaller but still statistically significantly higher prevalence of reported low QoL in NI (OR 1.2, 95% CI 1.1 to 1.4). Those in lower socioeconomic groups reported poorer SRH and QoL, but the effect of SES on disability after adjusting for all other factors was not statistically significant in the multivariate model. Discussion Summary of findings We have explored the relationships between chronic conditions in the older population of Ireland and how different combinations of chronic conditions interact to determine a range of health outcomes, including disability, SRH and QoL.

a smaller but still statistically significantly higher prevalence of reported low QoL in NI (OR 1.2, 95% CI 1.1 to 1.4). Those in lower socioeconomic groups reported poorer SRH and QoL, but the effect of SES on disability after adjusting for all other factors was not statistically significant in the multivariate model. Discussion Summary of findings We have explored the relationships between chronic conditions in the older population of Ireland and how different combinations of chronic conditions interact to determine a range of health outcomes, including disability, SRH and QoL. Each group of chronic conditions was positively associated with each of the others after adjusting for sociodemographic variables, reflecting the probable common risk factors and clustering of chronic conditions in susceptible individuals. Approximately 50% of the population aged 50 years and over reported no morbidity from the four groups of chronic conditions included in the analysis. Thirty per cent reported only musculoskeletal pain. With respect to their effects on disability, SRH and QoL, the majority of two-way interactions between condition groups were not statistically significant, and estimates of effects were small in magnitude, suggesting that most conditions affect the health-related outcomes we examined independently of each other. Interactions between CVD and other disease groups with respect to disability and SRH were less than one, suggesting that CVD in the presence of any of the other chronic conditions has a smaller effect on these outcomes than CVD alone. Nevertheless, those with a chronic condition from more than one group are at very high risk of disability, poor SRH and poor QoL.

roups with respect to disability and SRH were less than one, suggesting that CVD in the presence of any of the other chronic conditions has a smaller effect on these outcomes than CVD alone. Nevertheless, those with a chronic condition from more than one group are at very high risk of disability, poor SRH and poor QoL. Patterns of multimorbidity The different multimorbidity indices used previously make comparison of the prevalence of multimorbidity across populations difficult.10 28 The reported prevalence of multimorbidity in the older population is typically between 50% and 90%, but this is determined primarily by the range of conditions and the population being studied.11 In our middle-aged and older population, 11% reported a chronic condition from more than one of the four groups we investigated, although the proportion with more than one chronic disease of any kind is likely to be much higher. The pattern of chronic condition cooccurrence that we describe is similar to that found among the population aged 65 years and over in the USA using data from the National Health and Nutrition Examination Survey (NHANES),29 where arthritis was by far the most prevalent condition leading to a high prevalence of multimorbidity including arthritis. A high prevalence of multimorbidity including a rheumatic disease (17%) was also reported in a sample of 2998 representative of the Spanish population aged 20 years and over,30 and ‘painful condition’ was the most commonly recorded comorbidity among the primary care population of Scotland.9

rbidity including arthritis. A high prevalence of multimorbidity including a rheumatic disease (17%) was also reported in a sample of 2998 representative of the Spanish population aged 20 years and over,30 and ‘painful condition’ was the most commonly recorded comorbidity among the primary care population of Scotland.9 Synergistic effects of disease combinations Each chronic condition had a statistically significant independent effect on disability, poor SRH and poor QoL. Where significant interactions between diseases existed, they were negative, suggesting that the effect of multiple chronic diseases is less than or equal to the effects of each combined. That the effects of comorbid conditions appear to be less than the product of their individual effects (table 5) can seem at odds with the finding that the worst outcomes are seen in those with many chronic diseases (tables 3 and 4). Much of the literature has suggested that multimorbidity is problematic for older people31 32 and our finding of very high rates of adverse outcomes in groups with multiple chronic conditions supports this idea.

odds with the finding that the worst outcomes are seen in those with many chronic diseases (tables 3 and 4). Much of the literature has suggested that multimorbidity is problematic for older people31 32 and our finding of very high rates of adverse outcomes in groups with multiple chronic conditions supports this idea. Previous results on the joint effects of diseases with respect to adverse health outcomes have been mixed. Fried et al24 in a study of the Women's Health and Ageing Study discovered several synergistic interactions between chronic conditions with respect to the risk of disability that we did not observe; however, the main effects of diseases were less than those found in our study. Data from the Kungsholmen study have shown variations in the rate of disability among individuals suffering from particular combinations of comorbid conditions, with particularly high rates seen in disease pairs, including dementia, suggesting that the specific conditions suffered are more important than the count of diseases.33

the Kungsholmen study have shown variations in the rate of disability among individuals suffering from particular combinations of comorbid conditions, with particularly high rates seen in disease pairs, including dementia, suggesting that the specific conditions suffered are more important than the count of diseases.33 On the other hand, our results suggest that the effects of multiple diseases with respect to disability are equal to or less than the combined effects of each individual disease on the individual, rather than there being any synergistic statistical interaction between them. This finding supports previous evidence derived from the development of the sickness impact profile, which suggests that a person's overall level of dysfunction is reflected not by the sum of their dysfunction across domains but by the impact of disease on the maximally affected domain and the number of domains affected.34 One possible explanation for this discrepancy across studies is the differing age range of the populations used; our population was predominantly middle–aged, whereas those of the Women's Health and Ageing Study and the Kungsholmen studies were older. It is possible that in older cohorts the main effects of single diseases with respect to disabilities are less, which would lead to apparently higher interactions in these groups. Another explanation is that some of the disease pairs previously seen to cause synergistic effects were not examined in our study; for example, owing to limitations of our dataset, we did not specifically explore the effects of stroke, visual impairment or dementia.

ad to apparently higher interactions in these groups. Another explanation is that some of the disease pairs previously seen to cause synergistic effects were not examined in our study; for example, owing to limitations of our dataset, we did not specifically explore the effects of stroke, visual impairment or dementia. Data from the Longitudinal Aging Study Amsterdam found that, similar to our own study, the effect of a single chronic disease on SRH was greater than the effect of subsequent diseases.20 With respect to QoL, our finding of no synergistic effect is supported by a previous study of 238 individuals recruited from a primary care setting35 where only two statistically significant interactions between 14 chronic conditions were detected. A study of 4656 participants aged 65 years and older of the KORA-age study discovered only two synergistic interactions between six chronic diseases with respect to health-related QoL, specifically the interactions between coronary disorders and stroke, and coronary disorders and diabetes.16

c conditions were detected. A study of 4656 participants aged 65 years and older of the KORA-age study discovered only two synergistic interactions between six chronic diseases with respect to health-related QoL, specifically the interactions between coronary disorders and stroke, and coronary disorders and diabetes.16 International comparisons International comparisons can yield important insights into determinants of health36 and the large disparity between the levels of reported disability and poor SRH in NI and RoI even after adjusting for chronic disease prevalence also warrants further investigation. Several explanations have been proposed for this difference,17 which confirms a similar observation in a smaller cross-border study of approximately 2000 individuals aged 65 years and over.18 A higher prevalence of risk factors has been reported in the UK (which includes NI) compared to that in RoI; however, this cannot explain the wide discrepancy between the disability rates. Another possibility is the influence of two different healthcare systems. While older people are more likely to visit their GP in RoI, older people in NI are significantly more likely to use a wider range of primary care services, including home help and meals on wheels,17 and it may be that the increased level of service provision increases the likelihood that a disability is reported. It is of note that the reported disability prevalence in an English population (with a similar healthcare system to that in NI), 34% for men and 35% for women,37 is closer to the rates reported here for NI (44%) than RoI (20%). The prolonged period of civil unrest experienced by the older population of NI may also have contributed to poorer health outcomes.38

y prevalence in an English population (with a similar healthcare system to that in NI), 34% for men and 35% for women,37 is closer to the rates reported here for NI (44%) than RoI (20%). The prolonged period of civil unrest experienced by the older population of NI may also have contributed to poorer health outcomes.38 Strengths and weaknesses of the study The major strength of our study is the large sample, representative of two national populations. A diverse range of health-related outcomes was explored. However, neither study was specifically designed for the analysis of older populations, and so the measures of multimorbidity and health-related outcome are limited. In particular, comparable questions on mental and cognitive health could not be derived, and disability is represented by a single question. Our multimorbidity index includes major chronic condition groups, but it is likely that many participants also suffered other conditions not included in our analysis. Nevertheless, the list of diseases we used is similar to those employed in other population-based studies, considering the distribution and effect of chronic conditions,29 39 and represents the chronic diseases with the greatest impact on the older population. Grouping chronic diseases into four groups will have masked some potential interactions between diseases within groups, in particular within the respiratory and cardiovascular groups; however, if synergistic effects between chronic diseases were generally present, we would also expect to see them across groups.

lation. Grouping chronic diseases into four groups will have masked some potential interactions between diseases within groups, in particular within the respiratory and cardiovascular groups; however, if synergistic effects between chronic diseases were generally present, we would also expect to see them across groups. We dichotomised our outcome measures for ease of interpretation, but the results were not substantively different when the full range of outcome responses for SRH and QoL was explored. The single questions we used to measure SRH and QoL are widely used and have face validity; however, it is possible that they are subject to adaptation, whereby those with chronic disease recalibrate their expectations, making comparisons with those without chronic disease problematic.40 This would have the effect of attenuating estimates of the effect of diseases. It is unlikely that the question regarding disability would be affected in this way, which may explain why the effects of disease on QoL appear to be less than the effects on disability or SRH.

th those without chronic disease problematic.40 This would have the effect of attenuating estimates of the effect of diseases. It is unlikely that the question regarding disability would be affected in this way, which may explain why the effects of disease on QoL appear to be less than the effects on disability or SRH. While it is likely that severity of conditions is an important factor in the disease burden of respondents, assessment of self-reported condition severity in community surveys is contentious, and measures of multimorbidity based on simple counts of conditions in a population setting are supported in the literature.41 Questions on musculoskeletal pain were not identical in SLAN and NIHSWS, although the results of this study are consistent when both regional datasets are analysed individually. Occupational social class measures can be problematic in postretirement age groups, although our results are consistent with previous studies showing the social gradient in health and disability.39 42

AN and NIHSWS, although the results of this study are consistent when both regional datasets are analysed individually. Occupational social class measures can be problematic in postretirement age groups, although our results are consistent with previous studies showing the social gradient in health and disability.39 42 Conclusion In the middle-aged population of Ireland, chronic conditions have an equal or reduced impact on the likelihood of disability, poor SRH and poor QoL when they co-occur in the presence of other conditions. It is broadly acceptable to consider conditions independently when estimating their effects on these outcomes in the older population. However, this does not diminish the importance of multimorbidity for the individual. A significant proportion of the population aged 50 years and over across the island of Ireland lives with multimorbidity, and this group is at the highest risk of disability, poor SRH and poor QoL. Supplementary Material Author's manuscript Reviewer comments The Northern Ireland Health and Social Wellbeing Survey (NIHSWS) data were supplied by the UK Data Archive. The Survey of Lifestyle, Attitudes and Nutrition (SLAN) data were supplied by the Irish Social Science Data Archive and is copyrighted by SLAN and the Health Promotion Unit, University College Dublin. Contributors: GMS, KR, FK and RAK conceived the study. GMS and MJH conducted the analysis. OMD, GMS and MJH interpreted the results and prepared the manuscript. All authors contributed to the final draft of the manuscript.

Supplementary Material Author's manuscript Reviewer comments The Northern Ireland Health and Social Wellbeing Survey (NIHSWS) data were supplied by the UK Data Archive. The Survey of Lifestyle, Attitudes and Nutrition (SLAN) data were supplied by the Irish Social Science Data Archive and is copyrighted by SLAN and the Health Promotion Unit, University College Dublin. Contributors: GMS, KR, FK and RAK conceived the study. GMS and MJH conducted the analysis. OMD, GMS and MJH interpreted the results and prepared the manuscript. All authors contributed to the final draft of the manuscript. Funding: This work was supported by the Centre for Ageing Research and Development in Ireland (CARDI) data mining grants programme (grant number 22-301-10). Competing interests: None. Provenance and peer review: Not commissioned; externally peer reviewed. Data sharing statement: This paper is a secondary analysis and all data are already publicly available.