Objective To classify older adults with multiple long term conditions (MLTC) in clusters based on accumulating conditions as trajectories over time, characterise clusters and quantify associations between derived clusters and all-cause mortality. Design We conducted a retrospective cohort study using the English Longitudinal Study of Ageing (ELSA) over nine years (n=15,091 aged 50 years and older). Group-based trajectory modelling was used to classify people into MLTC clusters based on accumulating conditions over time. Derived clusters were used to quantify the associations between MLTC trajectory memberships, sociodemographic characteristics, and all-cause mortality. Five distinct clusters of MLTC of trajectories were identified and characterised as: "no-LTC" (18.57%), "single-LTC" (31.21%), "evolving MLTC" (28.82%), "moderate MLTC" (17.12%) and "high-MLTC" (7.27%). Increasing age was increasingly associated with an increased number of MLTC. Female sex (aOR = 1.13; 95%CI 1.01 to 1.27) and ethnic minority (aOR = 2.04; 95%CI 1.40 to 3.00) were associated with the "moderate-MLTC" and "high-MLTC" clusters respectively. Higher education and paid employment were associated with lower likelihood of progression over time towards an increased number of MLTC. All clusters had higher all-cause mortality than the "no-LTC" cluster. Conclusions The development of MLTC and the increase in the number of conditions over time follow distinct trajectories. These are determined by non-modifiable (age, sex, ethnicity) and modifiable factors (education and employment). Stratifying risk through clustering will enable practitioners to identify older adults with a higher likelihood of worsening MLTC over time to tailor effective interventions.

The authors have declared no competing interest.

This is independent research funded by the National Institute for Health Research (Artificial Intelligence for Multiple Long Term Conditions (AIM); (NIHR 202637). The views expressed in this publication are those of the author(s) and not necessarily those of the NHS, the National Institute for Health Research or the Department of Health and Social Care.

I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained.

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The details of the IRB/oversight body that provided approval or exemption for the research described are given below:

Ethical approval was granted by the University of Southampton Faculty of Medicine Research Committee, 67953.

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I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance).

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I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable.

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ELSA data were available through the UK Data Archive and are widely available to access in this way; as such our study data will not be made available for access.