Type 2 diabetes remission trajectories and variation in risk of diabetes complications: A population-based cohort study

Hajira Dambha-Miller; Hilda O Hounkpatin; Beth Stuart; Andrew Farmer; Simon Griffin

doi:10.1371/journal.pone.0290791

. 2023 Aug 29;18(8):e0290791. doi: 10.1371/journal.pone.0290791

Type 2 diabetes remission trajectories and variation in risk of diabetes complications: A population-based cohort study

Hajira Dambha-Miller ¹, Hilda O Hounkpatin ^1,^*, Beth Stuart ^1,^¤, Andrew Farmer ², Simon Griffin ^3,⁴

Editor: Billy Morara Tsima⁵

PMCID: PMC10464964 PMID: 37643199

Abstract

Biochemical remission of type 2 diabetes is achievable through dietary changes, physical activity and subsequent weight loss. We aim to identify distinct diabetes remission trajectories in a large population-based cohort over seven-years follow-up and to examine associations between remission trajectories and diabetes complications. Group-based trajectory modelling examined longitudinal patterns of HbA_1c level (adjusting for remission status) over time. Multivariable Cox models quantified the association between each remission trajectory and microvascular complications, macrovascular complications, cardiovascular (CVD) events and all-cause mortality. Four groups were assigned. Group 1 (8,112 [13.5%]; achieving HbA_1c <48 mmol/mol (6.5%) followed by increasing HbA_1c levels); Group 2 (6,369 [10.6%]; decreasing HbA_1c levels >48 mmol/mol (6.5%)); Group 3 (36,557 [60.6%]; stable high HbA_1c levels); Group 4 (9,249 [15.3%]; stable low HbA_1c levels (<48mmol/mol or <6.5%)). Compared to Group 3, Groups 1 and 4 had lower risk of microvascular complications (aHRs (95% CI): 0.65 (0.61–0.70), p-value <0.001;0.59 (0.55–0.64) p-value<0.001, respectively)), macrovascular complications (aHRs (95% CI): 0.83 (0.75–0.92), p-value<0.001; 0.66 (0.61–0.71), p-value<0.001) and CVD events (aHRs (95% CI): 0.74(0.67–0.83), p-value<0.001; 0.67(0.61–0.73), p-vlaue<0.001). Risk of CVD outcomes were similar for Groups 2 and 3. Compared to Group 3, Group 1 (aHR: 0.82(95% CI: 0.76–0.89)) had lower risk of mortality, but Group 4 had higher risk of mortality (aHR: 1.11(95% CI: 1.03–1.19)). Risk of CVD outcomes vary by pattern of remission over time, with lowest risk for those in remission longer. People who achieve remission, even for shorter periods of time, continue to benefit from this lower exposure to hyperglycaemia, which may, in turn, lower the risk of CVD outcomes including mortality.

Introduction

Type 2 diabetes affects 463 million adults globally corresponding to 6.28% of the world’s population and total diabetes-related health expenditure is estimated to be over £570 billion [1]. This substantial economic burden is in part related to associated cardiovascular disease (CVD). People with type 2 diabetes compared to those without are more likely to have CVD including peripheral arterial disease, ischaemic stroke, stable angina, heart failure, and non-fatal myocardial infarction. Intensive multifactorial management is effective at reducing these complications, and recent evidence demonstrates that biochemical remission of the disease is achievable through dietary changes, physical activity and subsequent weight loss [2,3]. Remission is defined as a level of glycaemia below the diagnostic threshold (HbA_1c < 6.5% or 48 mmol/mol) in the absence of medication or bariatric surgery. We have previously demonstrated that ≥ 10% weight loss achieved early after diagnosis is strongly associated with remission ((RR 2.43 (95% CI 1.78 to 3.31, p<0.01)) [4]. Remission is often temporary and over the course of type 2 diabetes, individuals will move between states of remission and relapse. To date, these longitudinal patterns of remission have not been described in large population-based cohorts.

It is plausible that since remission is defined by HbA_1c level, previous studies on the association between glycaemia and CVD outcomes might be comparable. Observational studies consistently demonstrate a positive association between glycaemia and CVD [5], whereas evaluations of interventions to lower glucose report heterogeneous findings [6–8]. The Look Ahead trial of an intensive behavioural intervention was terminated due to futility in relation to the CVD endpoint [9]. The results of trials of pharmacological interventions to lower glucose have varied according to the drugs (or combination of drugs) undergoing evaluation, the speed and extent of reduction of glucose levels, participants’ existing CVD risk and their point in the disease trajectory at baseline [6–8]. Extrapolation of these findings to characterise the impact of remission on the development of CVD outcomes is therefore challenging. To our knowledge, one study has examined the impact of remission on long-term CVD outcomes, with earlier studies focusing on short-term CVD outcomes [4,10–12]. This study reported that remission was associated with lower risk of microvascular complications, macrovascular complications, and CVD events [12]. However, it is unclear whether this risk varies by different patterns of remission over time. This knowledge could inform clinical and policy initiatives which have recently been promoting biochemical remission as a target for management of type 2 diabetes. Accordingly, in this study we describe longitudinal patterns of remission in a large population-based cohort and model these into distinct groups over seven-year follow-up. We then examine risk of CVD outcomes, and all-cause mortality overall and by pattern of remission.

Materials and methods

Design

A retrospective cohort study.

Data source

The Electronic Care and Health Information Analytics (CHIA) database is a pseudo-anonymised live electronic database with routinely collected primary care data for approximately 1.5 million people from 150 primary care practices across Hampshire and the Isle of Wight (Southern England, UK) with linked clinical and biochemistry data from local hospitals.

Population

We identified a cohort of people with type 2 diabetes using the Quality and Outcomes Framework (QOF) Read code diagnosis. QOF coding is used for NHS administration and financial purposes with high levels of accuracy/completeness [13]. From 120,000 people coded with type 2 diabetes by this criteria on the 1^st January 2013, we included 60,287 in our cohort who also had linked and continuous records for seven years until 1^st April 2020 (or death) and for whom remission status could be assessed.

Exposure

Remission was defined as having two HbA_1c level < 48 mmol/mol (6.5%) measurements separated over a period of at least six months in the absence of diabetes medications or bariatric surgery [14]. Remission status was assessed for people with HbA_1c data for at least two follow-up measurements (i.e., those surviving for at least the first 12 months of follow up).

Outcomes

Macrovascular complication as a composite of stroke, myocardial infarct (MI) coronary heart disease (CHD), peripheral arterial disease (PAD), or amputation
Microvascular complications as a composite of peripheral neuropathy, retinopathy, and nephropathy
CVD events as a composite of MI, amputation, and stroke
All-cause mortality

We used QoF definitions for peripheral neuropathy, retinopathy, and nephropathy and these were captured using read codes from the primary care record. There was complete data on each outcome measure as a result of the linked data.

Covariates

Sociodemographic characteristics

Baseline data were extracted on age, sex, ethnicity (White, Black, Asian, Mixed and other) and socioeconomic status. This was defined using the 2019 Index of Multiple Deprivation (IMD) quintiles which is a small-area measure of socioeconomic status, ranked nationally and comprises seven domains: income, employment, education/skills/training, health and disability, crime, barriers to housing and services, and living environment) were available. IMD 1 represents the most deprived and IMD 5 represents the least deprived groups.

Clinical variables

Baseline comorbidities were defined from diagnostic codes from existing QOF conditions including coronary heart disease, chronic kidney disease, chronic obstructive pulmonary disease (COPD), asthma, cancer, dementia, atrial fibrillation, epilepsy, heart failure, stroke, peripheral vascular disease, hypertension, osteoporosis, osteoarthritis, and depression. Frailty was defined using the electronic frailty Index score. Latest smoking status was extracted at the start of the study (1^st January 2013). Weight, body mass index (BMI), systolic and diastolic blood pressure and biochemistry measures (including HbA_1c total cholesterol, HDL-cholesterol and eGFR) were taken between January 2013 and April 2020 in six-month intervals, where available. For baseline, we used measures recorded between 1^st January 2013-1^st April 2013.

Medication

Prescribed repeat medication data were extracted from the electronic record at 6-month intervals for the duration of the study period. We used the prescriptions between 1^st January 2013-1^st April 2013 as the baseline.

Ethics statement

CHIA is an anonymous National Health Service database and all individuals have consented for collection of their medical records for inclusion in the database (written consent). Ethical and governance approval for this study was obtained from the University of Southampton (ERGO 56127), and Care and Health Information Exchange Information Governance Group (CHIE IGG). All data were fully anonymised prior to the research team gaining access to the data.

Statistical analysis

We summarised baseline characteristics of the whole cohort. There were missing data on ethnicity (49%) and IMD (0.9%). Ethnicity is frequently missing from routinely collected primary care records and we assigned missing data into the white category in keeping with the local population and previous studies [15]. For weight and HbA_1c data that were missing (n = 29678 (49.2%) and n = 30002 (49.8%)), we assumed missing at random and imputed these in a model that included the following non-missing variables; age, sex, diabetes duration, total number of comorbidities at baseline, practice ID, and outcome variables. Data were multiply imputed using Markov Chain Monte Carlo using STATA SE 16.0. We used 10 cycles of imputation. Separate similar imputation models were applied for the remaining biochemistry data. All imputed data after patient death were recoded as missing. With a complete dataset, we summarised participant characteristics stratified by remission status.

We then modelled trajectories of HbA_1c level (as a binary measure indicating 48 mmol/mol (6.5%) and above or below 48 mmol/mol) over time and adjusting for remission status at each time point using group-based trajectory modelling in STATA (program developed by Jones and Nagin and based on imputed HbA_1c data) [16]. Group-based trajectory models (GBTMs) are mixture models that assume a population is composed of a mixture of distinct subgroups of people who have similar developmental trajectories. A series of unadjusted GBTMs were applied to fit 1 through to 6 group models. The shape of the trajectory was determined by first fitting the trajectory as a cubic function and then reducing the function (to quadratic, slope, or intercept only) if higher polynomials were not statistically significant. The number of trajectories in the model was increased by one and the steps were repeated. Participants are assigned to the group they have highest probability of belonging. We considered participants as belonging to a group if the classification probability was >0.80. The best-fitting model was selected based on 3 criteria: (1) the Bayesian Information Criterion (BIC) (where a lower BIC indicates better fit) (2) the odds of correct classification into each group and (3) the average posterior probabilities of group membership, as a measure of classification quality (>0.80 or greater in all group) [16].The best-fitting model was fitted to each imputed dataset and the classification probabilities from each dataset were saved and averaged to determine group membership. We then used descriptive statistics to compare baseline sociodemographic and clinical characteristics for each remission group. Model F statistics were used to test differences in variables across the groups. Multinomial models (unadjusted and adjusted for age, sex, ethnicity, IMD, baseline weight, diabetes duration, number of comorbidities and clustering within practices) were used to examine the association between weight change categories (no change or weight gain, weight loss (≤ 2.5–5%), (≤5–10%) and (≥10%) from baseline weight) and group membership. We examine associations between weight change categories and remission groups as previous studies have found an association with overall remission [5,17].

We fitted multivariable-adjusted Cox proportional hazards models to quantify the association between remission at any point during the follow-up for the whole cohort and the incidence of i) macrovascular complications ii) microvascular complications, iii) CVD events, and iv) all-cause mortality). We then constructed the same models with each distinct remission group. People with the event of interest before the start of study were excluded from the respective analysis. Quarter of death rather than exact date of death was available in the database therefore, the mid-point of the quarter of death was used as the date of death in the time to event analyses. For participants with multiple outcome events, we used the time the first event occurred in our time to event analyses. Multivariable models were adjusted based on a priori reasoning for age, sex, ethnicity, IMD, baseline weight, diabetes duration, number of co-morbidities and clustering within practices. Finally, we ran a sensitivity analysis to test the robustness of our imputation methods by re-running the cox models and including only those with non-missing (non-imputed) data. A p-value of <0.05 was considered as statistical significance in all analyses.

Results

Baseline population characteristics

The cohort included 60,287 people with type 2 diabetes with a mean duration of follow-up of 6.9 years. 7,312 (12.1%) people died during follow up. The mean age of the cohort was 64.6 years, most were male (n = 34,408, 57.1%), white (n = 58,148, 96.5%) and with a mean (SD) duration of diabetes of 8.1 (6.8) years. Baseline characteristics are summarised in Table 1. During the 7-year follow-up period, 11,491 (19.1%) people achieved remission at some point for at least a 6-month period. People who achieved remission compared to those who did not were older (p<0.001), more likely to be female (p<0.001), non-smokers (p<0.001), from a less deprived area (p<0.001) and with a lower baseline weight (p<0.001). Those not included in our study cohort (i.e., those diagnosed with diabetes after 1^st January 2013 or with less than seven years continuous data) were younger [mean (SD) 58.1 (14.2)], had shorter diabetes duration [mean (SD) = 5.0 (6.7)] and fewer comorbidities at baseline [mean (SD) 0.9 (1.1)], and slightly higher weight at baseline ([mean (SD) 94.2 (0.2)].

Table 1. Baseline characteristics of the type 2 diabetes cohort within the CHIA database stratified by remission status^¥.

		All (n = 60287)		Remission^¥ (n = 11335)		Non-remission^¥ (n = 48,607)
Sociodemographic
Age, years^*		64.6 (12.0)		66.3 (11.9)		64.1 (12.0)
Male gender, n (%)		34408 (57.1)		5992 (52.9)		28224 (58.1)
Ethnicity, n (%)
White		58148 (96.5)		11047 (97.5)		46767 (96.2)
Black		217 (0.4)		35 (0.3)		181 (0.4)
Asian		1514 (2.5)		190 (1.7)		1317 (2.7)
Mixed/Other		408 (0.7)		63 (0.6)		342 (0.7)
Socioeconomic Status, n (%)
Index of Multiple Deprivation quintile 1		7576 (12.6)		1173 (10.3)		6363 (13.1)
(most deprived)		7576 (12.6)		1173 (10.3)		6363 (13.1)
Index of Multiple Deprivation quintile 2		12137 (20.1)		2280 (20.1)		9788 (20.1)
Index of Multiple Deprivation quintile 3		11457 (19.0)		2018 (17.8)		9375 (19.3)
Index of Multiple Deprivation quintile 4		13028 (21.6)		2554 (22.5)		10398 (21.4)
Index of Multiple Deprivation quintile 5 (least deprived)		16089 (26.7)		3310 (29.2)		12683 (26.1)
Clinical
Diabetes duration, years (n = 60138)	60138	8.1 (6.8)	11324	5.9 (5.3)	48469	8.7 (7.0)
Frailty Index (n = 60244)		0.2 (0.1)	11316	0.2 (0.1)	48583	0.2 (0.1)
Total number baseline comorbidities n (%)		1.3 (1.2)		1.4 (1.2)		1.3 (1.2)
Hypertension, n (%)		30868 (51.2)		5968 (52.7)		24721 (50.9)
Stroke n (%)		2584 (4.3)		541 (4.8)		2027 (4.2)
Myocardial Infarction n (%)		4208 (7.0)		702 (6.2)		3483 (7.2)
Amputation n (%)		648 (1.1)		85 (0.7)		560 (1.2)
Current smoker, n (%)		6559 (10.9)		1177 (10.4)		5345 (11.0)
Weight, kg^*		90.8 (20.7)		88.7 (20.4)		91.4 (20.7)
BMI, kg/m²^*		31.5 (6.3)		30.9 (6.3)		31.7 (6.3)
Systolic blood pressure, mmHg^*		136.1 (15.4)		136.2 (15.5)		136.1 (15.4)
Diastolic blood pressure, mmHg^*		77.2 (9.4)		77.2 (9.4)		77.2 (9.4)
Total cholesterol, mmol/l^*		4.6 (1.2)		4.7 (1.2)		4.5 (1.2)
HDL cholesterol, mmol/l^*		1.2 (0.4)		1.3 (0.4)		1.2 (0.3)
HbA_1c level, mmol/mol^*		60.1 (20.4)		59.3 (19.7)		60.2 (20.6)
eGFR		73.1 (17.1)		72.6 (17.2)		73.2 (17.0)
Total number of medications prescribed^#		3.9 (2.4)		3.3 (2.4)		4.1 (2.4)
Anti-hypertensive medication, n (%)		32509 (53.9)		6071 (53.6)		26253 (54.0)
Lipid-lowering medication, n (%)		40992 (68.0)		6903 (60.9)		33862 (69.7)
Hypoglycaemic medication, n(%)		41085 (68.1)		4087(36.1)		36799 (75.7)

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*Mean (SD). Remission was defined as having two HbA_1c < 6.5% (48mmol/mol) readings separated by at least a period of 6 months and no oral hypoglycaemic medication and no history of bariatric surgery

^¥Estimation sample varies across imputations; minimum number of observations reported. Baseline biochemistry data was defined as the mean of any measurements taken between 1^st January 2013 and 31^st March 2013) ^#Medication was defined as being prescribed during the first 6 months of the follow-up year (i.e., Jan-Jul 2013).

Characteristics of groups by remission trajectory

The best fitting model identified 4 groups with varying patterns of HbA_1c level and remission: Group 1 (8,112 [13.5%]; achieving HbA_1c <48 mmol/mol (6.5%) followed by increasing HbA_1c levels); Group 2 (6,369 [10.6%]; decreasing HbA_1c levels); Group 3 (36,557 [60.6%]; stable high HbA_1c levels); Group 4 (9,249 [15.3%]; stable low HbA_1c levels (<48mmol/mol or <6.5%)). Fig 1 presents the mean HbA_1c levels overtime for each group.

The sociodemographic and clinical characteristics for each remission group are summarised in Table 2 below. There were statistically significant differences in characteristics across the four groups. Individuals in Group 2, who had decreasing HbA_1c levels, were mainly older and living in less deprived areas. Those in group 3 who had increasing HbA_1c levels were younger, more likely to be male, current smokers, and had a longer duration of diabetes. Those in Group 1 (who achieved HbA_1c levels <48mmol/mol (6.5%) but then had increased HbA_1c levels) were also younger but had shorter diabetes duration and fewer medications prescribed. Those who had low (<48 mmol/mol (6.5%)) HbA_1c levels throughout follow-up (Group 4) were likely to be older, living in less deprived areas, with the shortest duration of diabetes and a lower baseline weight (Table 2). Group 3 was selected as the reference group in regression models as this was the group with high HbA_1c levels throughout the study and therefore a useful comparator to estimate associations with the different remission trajectories. Multinomial regression models indicated that, compared to those who did not change weight or those that gained weight, patients who achieved weight loss of ≥10% at 18 months follow-up were more likely to be in group 4 compared to group 3 (unadjusted relative risk ratio [RRR] (95% CI): 1.13 (1.06–1.21); adjusted RRR (95% CI): 1.42 (1.31–1.54)) (Table 3). Patients who achieved weight loss of ≥10% at 18 months follow-up were less likely to be in Group 2 compared to Group 3 (unadjusted RRR (95% CI): 0.89 (0.81–0.98); adjusted RRR (95% CI): 0.89 (0.81–0.98). Weight loss of ≥10% at 18 months follow-up was associated with Group 1 (compared to Group 3) in adjusted models but not unadjusted models (Table 3). Weight change at 18 months follow-up was examined as this was the time point at which key differences in HbA_1c were noted in Fig 1.

Table 2. Baseline characteristics by remission group in the CHIA type 2 diabetes cohort (n = 60,287).

	Group 1 Achieving HbA_1c <48mmol/mol (6.5%) followed by increasing HbA_1c levels	Group 2 Decreasing HbA_1c levels (>48mmol/mol)	Group 3 Stable high HbA_1c levels	Group 4 Stable low HbA_1c levels	P-value^#
N (%)	8112 (13.8)	6369 (10.7)	36557 (60.0)	9249 (15.5)
Sociodemographic
Age, years^*	65.4 (11.6)	66.8 (11.1)	63.1 (12.1)	67.8 (11.3)	0.419
Male gender, n (%)	4598 (56.7)	3609 (56.7)	21302 (58.3)	4899 (53.0)	0.006
Ethnicity, n (%)
White	7772 (95.8)	6132 (96.3)	35239 (96.4)	9005 (97.4)
Black	25 (0.3)	25 (0.4)	137 (0.4)	30 (0.3)	0.858
Asian	247 (3.0)	162 (2.5)	944 (2.6)	161 (1.7)	<0.001
Mixed/Other	68 (0.8)	50 (0.8)	237 (0.6)	53 (0.6)	0.012
Index of Multiple Deprivation, n (%)
quintile 1 (most deprived)	871 (10.8)	750 (11.8)	4979 (13.6)	976 (10.5)	0.003
quintile 2	1575 (19.4)	1203 (18.9)	7631 (20.9)	1727 (18.7)
quintile 3	1487 (18.3)	1151 (18.1)	7146 (19.5)	1672 (18.1)
quintile 4	1828 (22.5)	1413 (22.2)	7702 (21.1)	2086 (22.5)
quintile 5 (least deprived)	2351 (29.0)	1852 (29.1)	9099 (24.9)	2788 (30.2)
Clinical
Diabetes duration, years	6.7 (6.2)	7.5 (6.1)	9.1 (7.2)	6.3 (5.6)	<0.001
Frailty Index	0.2 (0.1)	0.2 (0.1)	0.2 (0.1)	0.2 (0.1)	0.043
Total number baseline comorbidities n(%)	1.3 (1.2)	1.4 (1.2)	1.2 (1.2)	1.4 (1.3)	0.038
Hypertension, n (%)	4434 (54.7)	3573 (56.1)	177726 (48.6)	5089 (55.0)	<0.001
Stroke n (%)	358 (4.4)	297 (4.7)	1448 (4.0)	481 (5.2)	0.353
Myocardial Infarction n (%)	518 (6.4)	457 (7.2)	2638 (7.2)	595 (6.4)	0.537
Amputation n (%)	67 (0.8)	79 (1.2)	428 (1.2)	74 (0.8)	0.840
Current smoker, n (%)	832 (10.3)	672 (10.6)	4117 (11.3)	938 (10.1)	0.416
Weight, kg^*	89.2 (20.3)	90.5 (20.7)	92.1 (20.7)	87.8 (20.3)	0.954
BMI, kg/m²^*	31.3 (6.3)	32.0 (6.4)	31.7 (6.3)	30.7 (6.3)	<0.001
Systolic blood pressure, mmHg^*	136.0 (15.2)	136.9 (15.4)	136.1 (15.4)	135.7 (15.5)	0.128
Diastolic blood pressure, mmHg^*	77.1 (9.4)	77.0 (9.4)	77.5 (9.4)	76.4 (9.3)	0.044
Total cholesterol, mmol/l^*	4.6 (1.2)	4.5 (1.2)	4.6 (1.2)	4.6 (1.2)	0.076
HDL cholesterol, mmol/l^*	1.2 (0.4)	1.2 (0.3)	1.2 (0.3)	1.3 (0.4)	<0.001
HbA_1c level, mmol/mol^*	59.2 (19.9)	59.8 (20.1)	60.5 (20.8)	59.2 (18.9)	0.764
eGFR	72.4 (16.7)	71.9 (16.9)	73.8 (17.1)	71.5 (17.1)	0.237
Total number of medications prescribed^#	3.8 (2.4)	4.0 (2.4)	4.0 (2.4)	3.8 (2.4)	0.088
Anti-hypertensive medication, n (%)	4497 (55.4)	3717 (58.4)	18773 (51.4)	5522 (59.7)	0.950
Lipid-lowering medication, n (%)	5512 (67.9)	4458 (70.0)	24918 (68.2)	6104 (66.0)	0.002
Hypoglycaemic medication, n(%)	5272 (65.0)	4337 (68.1)	26913 (73.6)	4563 (49.3)	<0.001

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*Remission group determined for the 60,287 people who were alive at the first follow period (i.e. at 6 months following the start of the study) and therefore had follow-up data. Model F statistics from regression models reported here. Models used on imputed data were linear regression models for continuous variables, logistic regression models for binary variables, and ordered logistic regression models for ordered categorical variables (like IMD).

Table 3. Multinomial regression showing associations between weight change categories and group membership (compared to group 3).

	Unadjusted (n = 59743)				Adjusted^# (n = 59598)
	Risk	95% CI		p-value	Risk	95% CI		p-value
	ratio	95% CI		p-value	ratio	95% CI		p-value
% Weight change category
Group 1
No change or weight gain (from baseline) (n = 4360 (53.8%))	1.00				1.00
Weight loss (≥2.5% to < 5%) (n = 610 (7.5%))	1.02	0.91	1.15	0.730	1.06	0.93	1.21	0.350
Weight loss (≥5 to <10%) (n = 815 (10.1%))	1.04	0.92	1.17	0.531	1.11	1.00	1.24	0.047
Weight loss (≥10%) (n = 2322 (28.7%))	0.99	0.92	1.07	0.769	1.17	1.08	1.26	0.000
Group 2
No change or weight gain (from baseline) (n = 3573 (56.1%))	1.00				1.00
Weight loss (≥2.5% to < 5%)(n = 470 (7.4%))	0.96	0.83	1.11	0.564	0.93	0.80	1.09	0.352
Weight loss (≥5 to <10%)(n = 608 (9.6%))	0.94	0.82	1.08	0.395	0.93	0.81	1.06	0.265
Weight loss (≥10%) (n = 1718 (27.0%))	0.89	0.81	0.98	0.017	0.89	0.81	0.98	0.017
Group 4
No change or weight gain (from baseline) (n = 4700 (51.2%))	1.00				1.00
Weight loss (≥2.5% to < 5%)(n = 658 (7.2%))	1.02	0.90	1.16	0.733	1.08	0.94	1.25	0.254
Weight loss (≥5 to <10%)(n = 954 (10.4%))	1.13	1.00	1.27	0.053	1.23	1.09	1.40	0.002
Weight loss (≥10%) (n = 2859 (31.2%))	1.13	1.06	1.21	0.000	1.42	1.31	1.54	0.000

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^#Adjusted model includes baseline weight, sociodemographic variables (age, sex, ethnicity and IMD), diabetes duration, number of co-morbidities and clustering within practices.

Remission and CVD outcomes by remission trajectory

In our study cohort, 3,928 (6.5%) had a CVD event, 7,312 (12.1%) died, 4867 (8.1%) people had macrovascular complications, 15,527 (25.8%) had microvascular complications during the study period. In Cox models, people with type 2 diabetes who achieved remission at any point during the seven-year follow-up had a significantly lower risk of CVD events, macrovascular complications and microvascular complications, in both unadjusted and adjusted models. People with type 2 diabetes who achieved remission at any point during the seven-year follow-up had a significantly lower risk of all-cause mortality in adjusted models. These results are shown in S1 Table.

Group 3 (stable high HbA_1c levels) was assigned as the reference category for our Cox modelling. Compared to this group, people in all the remaining groups had a lower risk of developing microvascular complication in both unadjusted and adjusted models and those in Groups 1 and 4 also had lower risk of macrovascular complications and CVD events (in unadjusted and adjusted models). The risk of these complications was lowest for people in Group 4 who started off and remained at low HbA_1c levels (<48mmol/mol or <6.5%) for the entire seven-year follow-up period. Those in Group 1 (who had earlier decrease below HbA_1c <48mmol/mol or 6.5%, followed increasing HbA_1c levels) also had significantly lower risk of complications compared to Group 3. These results are shown in Table 4.

Table 4. Association between remission group and CVD outcomes in the CHIA type 2 diabetes cohort.

		Unadjusted model					Adjusted model*
		HR	95% CI		p-value		HR	95% CI		p-value
Macrovascular complications	N = 48,942					N = 48,829
Group 3 (ref)		1					1
Group 1		0.85	0.78	0.93	<0.001		0.83	0.75	0.92	<0.001
Group 2		0.97	0.88	1.06	0.490		0.91	0.82	1.00	0.054
Group 4		0.70	0.64	0.77	<0.001		0.66	0.61	0.71	<0.001
Microvascular complications	N = 41,609					N = 41,527
Group 3 (ref)		1					1
Group 1		0.63	0.60	0.66	<0.001		0.65	0.61	0.70	<0.001
Group 2		0.78	0.74	0.82	<0.001		0.80	0.76	0.85	<0.001
Group 4		0.56	0.53	0.59	<0.001		0.59	0.55	0.64	<0.001
CVD events	N = 53,218					N = 53,097
Group 3 (ref)		1					1
Group 1		0.76	0.69	0.84	<0.001		0.74	0.67	0.83	<0.001
Group 2		0.94	0.85	1.04	0.254		0.88	0.79	0.98	0.021
Group 4		0.71	0.64	0.78	<0.001		0.67	0.61	0.73	<0.001
Death	N = 60,287					N = 60,138
Group 3 (ref)		1					1
Group 1		0.85	0.79	0.92	<0.001		0.82	0.76	0.89	<0.001
Group 2		1.01	0.93	1.09	0.829		0.92	0.85	1.01	0.086
Group 4		1.29	1.21	1.37	<0.001		1.11	1.03	1.19	0.004

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Group 1 achieving HbA_1c <48 mmol/mol (6.5%) followed by increasing HbA_1c levels); Group 2 decreasing HbA_1c levels); Group 3 stable high HbA_1c levels); Group 4 stable low HbA_1c levels (<48mmol/mol or <6.5%). The adjusted model shown above included sociodemographic variables (age, sex, ethnicity, IMD), baseline weight, diabetes duration, number of co-morbidities and clustering within practices.

People with event of interest prior to the start of study were excluded from the analysis.

CVD events included a composite of myocardial infarction, amputation and stroke. Microvascular complications included a composite of peripheral neuropathy, retinopathy, and nephropathy. Macrovascular complications include a composite of stroke, MI, coronary heart disease peripheral arterial disease (PAD) and amputation. All-cause mortality was death from any cause.

For risk of all-cause mortality, the rate at which HbA_1c levels below 48mmol/mol or 6.5% was achieved was important. Group 2 (decreasing HbA_1c levels, though not achieving remission) had similar risk of all-cause mortality to Group 3 (stable high HbA_1c levels). Group 1 (achieving HbA_1c levels <48 mmol/mol or <6.5% followed by increasing HbA_1c levels) had lower risk of all-cause mortality than Group 3. However, Group 4 (i.e., those who started and remained with low HbA_1c levels throughout and limited variation) had higher risk of all-cause mortality.

Discussion

Main findings

In this population-based cohort of 60,287 people with type 2 diabetes, remission was common with 19% of people achieving remission at some point for at least 6 months. Achieving remission regardless of duration or pattern of HbA_1c level and remission status over time was associated with a lower risk of microvascular complications, macrovascular complications, and CVD events. However, the risk of these complications and mortality varied according to remission trajectories over time.

Comparison with existing literature

To our knowledge, this is the first study to describe long-term patterns of different type 2 diabetes remission trajectories and their associations with CVD outcomes and all-cause mortality in a population-based cohort. No previous studies have utilised group-based trajectory modelling in this way and instead have examined remission as single whole cohorts [11,12]. Many also include only limited follow-up (<12 months) and therefore have not been able to report on the risk of microvascular complications, macrovascular complication or death [11]. The findings extend our previous findings by highlighting that lower risk of CVD outcomes is achieved regardless of duration of remission, though patients with consistently low HbA_1c levels have lowest risk of CVD outcomes. Consistent with the observational studies and in contrast to some of the trials of glucose-lowering drugs [18], we observed consistent trends in unadjusted and adjusted models between remission group and a lower incidence of both macrovascular and microvascular complications. Weight loss of ≥10% was an important predictor of remission trajectory, which is consistent with previous findings on the link between weight loss and remission [4,17].

Possible explanation for our findings

Although a significant proportion of patients achieved weight loss of ≥10% across each group, Group 4 had the highest proportion of patients in this group (31.2%) suggesting that weight loss of ≥10% was more likely in this group of people who had lowest levels of HbA_1c at baseline. The proportion of patients achieving weight loss of ≥10% in Group 2 was lower than the proportion achieving weight loss ≥10% in Group 3 (stable high HbA_1c levels) which may be unexpected but may be partly due to differences in baseline characteristics such as BMI across the two groups (Table 2) as well as a slightly larger mean decrease in HbA_1c level for Group 3 compared to Group 2 (Fig 1); In terms of all-cause mortality, we found that the trajectory of remission was important. Patients in Group 1 (i.e., those achieving remission followed by increasing HbA_1c levels) had lower risk of mortality than those in Group 3 (stable high HbA_1c levels). This finding suggests that glycaemic control over a period of time may result in improved long-term health outcomes, even following subsequent increases in HbA_1c. Moreover, Group 1 may have lower risk of mortality due to having consistently lower HbA_1c levels compared to Group 3. This reflects increasing risk of mortality at higher levels of HbA_1c and is in line with some studies that have reported a linear or J shaped relationship between HbA_1c and mortality [19]. Similarly, patients in Group 4 (stable low HbA_1c levels with limited variation) had higher risk of mortality. It is possible that people in Group 4 included people who are unwell with a high risk of mortality (such as those with cancer) and therefore could be more likely to lose weight and go into remission. This unintentional weight loss could not be distinguished from intentional weight loss and might be a plausible explanation for some of the observed variations by remission group. Further research is needed to explore which patients achieving remission are at higher risk of mortality. Further work is needed in larger and longer cohorts with more ethnic and socially diverse populations to develop targeted and personalised interventions according to remission group.

Strengths and limitations

A strength of the study is our large population-based cohort of 60,287 people with type 2 diabetes across a wide geographic region of Southern England including 150 GP practices. The cohort included heterogeneity in age, sex and disease profiles but was limited to mainly people from white ethnicity. This reflects the local area but may not be generalisable to more diverse populations. We included a reasonable follow-up period of seven years with most previous studies examining remission limited to shorter durations [10]. The dataset used is from routinely collected clinical records and is dependent on clinicians accurately recording clinical events and thus is subject to error. However, we used Quality Outcome Framework measures wherever possible which are used for payment and administrative purposes. These measures have previously undergone validity testing and have high levels of completeness and accuracy [13]. We did not have exact dates for deaths in the database with only quarter of death available, so we used the mid-point of the quarter of death in the time to event analyses which may have introduced some error in our analysis. A further limitation was that we were only able to account for prescribed drugs that were captured in the electronic record; we did not have data on whether oral hypoglycaemic drugs were obtained from other sources or the exact date these drugs were prescribed in primary care. Missing data was another limitation of our study which is common with routinely collected data. Although, our sensitivity analysis demonstrated the robustness of our imputation methods as similar associations were observed in the non-imputed analysis. It is possible that some of our findings may be due to chance as we did conduct a number of hypothesis tests. Given that we observed consistent trends across all models and groups, this is less likely. Finally, we cannot rule out reverse causality.

Conclusions

Remission of type 2 diabetes at any point during the course of diabetes is common in routine clinical care but patterns of remission including maintenance, vary considerably. People who achieve remission, even for shorter periods of time, continue to benefit from this lower exposure to hyperglycaemia, which may, in turn, lower the risk of CVD outcomes including mortality.

Supporting information

S1 Table. Association between remission and incidence of CVD outcomes and mortality over seven-year follow in the CHIA type 2 diabetes cohort.

(DOCX)

Click here for additional data file.^{(16.8KB, docx)}

Data Availability

"We do not have governance permissions to share individual-level data on which these analyses were conducted since they derive from clinical record data. However, direct data requests can be made to the database Electronic Care and Health Information Analytics (CHIA) governance team, who may be contacted by email: info.chie@nhs.net or phone: +44(0)3001231519."

Funding Statement

HDM is an Associate Professor in Primary Care Research and received National Institute for Health Research School of Primary Care Research (NIHR SPCR) funding (SPCR2014-10043) for this project. The views and opinions expressed by authors in this publication are those of the authors and do not necessarily reflect those of the UK National Institute for Health Research (NIHR) or the Department of Health and Social Care. AF is a NIHR Senior Investigator and receives support from NIHR Oxford BioMedical Research Centre. The University of Cambridge has received salary support in respect of SJG from the NHS in the East of England through the Clinical Academic Reserve. The specific role of this author is articulated in the ‘author contributions’ section. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

References

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PLoS One. doi: 10.1371/journal.pone.0290791.r001

Decision Letter 0

Billy Morara Tsima

21 Jun 2023

PONE-D-23-05345Type 2 diabetes remission trajectories and variation in risk of diabetes complications: A population-based cohort studyPLOS ONE

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Reviewer #2: Partly

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Reviewer #2: Yes

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Reviewer #1: Abstract

The abstract is well written and easy to read. The fact that the researchers has found that Group 4, which was the group that had a stable low HbA1c levels (<48mmol/mol or <6.5%)), had the highest risk of mortality is a significant finding, which must form part of their conclusion. It does not suffice to say further research is needed to understand which patients achieving remission are at higher risk of mortality. Your conclusion must be guided by your study findings. Common sense may dictate that tight glycemic control is associated with low risk of complications and low risk mortality but the real world maybe different

INTRODUCTION

This not labeled in accordance with accepted journal format. Please label it at the very beginning in bold letters. The introduction is adequate. It has literature review and state the rationale and objectives of the study.

METHODS

The methods and statistical analysis are scientifically sounds. They are satisfactory

RESULTS

The results are well presented in a logical and comprehensive manner. The tables captures important outcomes and they are properly labelled.

DISCUSSIONS

The authors have not really allocated a specific discussion section for this manuscript but rather preferred to include some components of the discussion underneath the CONCLUSIONS. The Researchers has not really allocated much to discuss the findings of their study BUT rather they have chosen to place more emphasis on the strength and limitations of the study.

I will call upon the authors to discuss their findings in details and what they entail. They need to tell us more about the findings of increased mortality in Group 4 and less mortality in Group 1 and come up with possibilities for these findings, even if it maybe hypothetical discussions/reasoning. They may also consider downsizing their Strength/weakness so that they do not exceed the word limit allowed by the journal.

CONCLUSION

The Researchers need to write a brief and concise conclusion that wraps up the FINDINGS of their research.

ACKNOWLEDGEMENTS

I have nothing to add here

REFERENCES

They are adequate and appropriate

Tables and Figures

They EXCELLENT and they improve the readability of the manuscript.

Reviewer #2: In this paper “Type 2 diabetes remission trajectories and variation in risk of diabetes complications: A population-based cohort study”, the authors used Group-Based Trajectory Model to create remission groups and estimate hazard of complications in that groups. To do so, they included a cohort of 60,287 people with type 2 diabetes across a wide geographic region of Southern England. It is valuable work but there are weakness in variables selection, finding presentation, and interpretation.

Abstract:

Please present results without confidence interval in the Abstract, it is recommended give the p-value.

Introduction:

It is not necessary to narrate values of confidence intervals which estimated in other study.

Reference 1, inserted two times in a continues sentence, please correct that.

At first, authors cited reference 3 for saying the effect of interventions and then use that for futility of intervention!

Population, covariates and analysis:

Please report number of missing (and percent) for Weight and HBA1c

Authors must give information about generated imputation.

It is recommended to use SSBIC and CAIC for select better model.

Author explain about uncertainty in assignment to trajectory, how is it considered?

Please determine the significant value in Analysis method.

How authors identify the predictors for trajectory groups?

Please, list the statistical tests used for comparison of variables presented in table 2.

In table 1,

Express all percent for column; it seems that the percent for medications is wrong.

Please check numbers for Comorbidity, Medication prescribed,

Author claim that Remission are older than Non-remission, is there a statistical test for it?

Also, in results, authors say "There were statistically significant differences in characteristics across the four groups". It is recommend that explain about significant variable belong providing p-value.

Weight is a non-significant variable among 4 trajectory groups; please explain about the reason for estimating the risk ratio in different category of Loss weight.

Authors describe about considering loss weight at 18 month of fallow-up, was this only due to change in HBA1c? Authors not provided any information about number of loss weighted in each category by trajectory groups.

Please present number (and percent) for each category of Loss weight variable.

Authors estimate the risk of trajectory group, so it seems that consider group 4 as reference for comprehensibility and adherence.

In ‘Remission and CVD outcomes by remission trajectory’ section;

How set time for subject with multiple complications, please explain that.

Provided descriptions are disagreeable with table, for example there is not significant for G2.

In table 4, number of complications in each trajectory groups is necessary.

In page 10, first paragraph, what is the mean RRR? It seems that author mean was Risk Ratio.

Author must deeply, clinically, and statistically interpretation of all result specifically for table 3 and 4.

In page 17, title for figure repeated two times.

Reference need to be written in Vancouver style.

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Reviewer #1: Yes: Dr Dipesalema Joel

Reviewer #2: Yes: Hssein Ali Adineh

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PLoS One. 2023 Aug 29;18(8):e0290791. doi: 10.1371/journal.pone.0290791.r002

Author response to Decision Letter 0

24 Jul 2023

**We thank the Editor and Reviewers for their interest in our study and helpful comments

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**We have reformatted to the manuscript to meet the journal’s requirements.

** Thank you for highlighting this. We have now included an ethics section to the paper (page 7) as below:

“Ethics statement

**Please see response to Q2 above.

**Thank you for this point. Our earlier publication was peer-reviewed and has been published in Endocrinology, Diabetes & Metabolism . In the cover letter, we have now explained that although both these papers are on a similar subject, they are completely distinct in their aims, methods, analyses, and results although it is the same population being examined. No aspect of the aim, methods or results have been published elsewhere. To clarify:

• The previous publication aimed to examine the relationship between remission and CVD outcomes. The present paper expands on this work to understand the granularity of this association specifically in relation to microvascular and macrovascular complications, as well as CVD events.

• This current paper also uses different analytical methods including group-based modelling to examine whether achieving remission at different time points over the course of diabetes relates to risk of poor outcomes. Our results find that patients who consistently have low HbA1c levels (<48mmol/mol or 6.5%) have the lowest risk of CVD outcomes, though patients who achieve remission for shorter periods also have a lower risk of CVD outcomes compared to those who do not achieve remission. Additionally, we report baseline characteristics of patients in each group, which clinicians may find helpful to predict trajectories of patients. You will see that the results sections are distinct in each paper. We present and cite some of the results of earlier paper in the current paper for completeness.

• Below is a table illustrating the differences across the two papers:

Previous Publication Current paper

Aims To quantify the association between type 2 diabetes remission and 5-year incidence of cardiovascular disease outcomes, overall and in pre-defined subgroups ( age [<45, 45-54,55-64, 75-84, 85+], sex [male, female], diabetes duration [<5, 5-<10, 10-<20, 20+ years), pre-existing CVD (no/yes; defined as a composite of myocardial infarction (MI), amputation, and stroke), baseline BMI [underweight (<18.5kg/m2), normal (18.5-24.9kg/m2), overweight (25-29.9kg/m2), obese (>=30kg/m2)] (13), baseline HbA1c [<6.5%) (48mmol/mol); 6.5-8% (48-63.9 mmol/mol); 8-9% (64-74.9 mmol/mol); >9% (75mmol/mol)] and number of co-morbidities (0, 1-2, 3+) ) To identify distinct diabetes remission trajectories in a large population-based cohort over seven-years follow-up and to examine associations between remission trajectories and diabetes complications

Study sample 60,287 adults (aged 18-85 years) over 7 years who were coded for type 2 diabetes and who had continuously recorded electronic records over seven years from the 1st January 2013 to 1st April 2020 60,287 adults (aged 18-85 years) over 7 years who were coded for type 2 diabetes and who had continuously recorded electronic records over seven years from the 1st January 2013 to 1st April 2020

Study exposure Remission (at any point during the first two years of the study period: 1st January 2013-31st December 2015) Remission trajectory group; Remission at any point during 1st January 2013 to 1st April 2020 (or death)

Study outcome CVD events, microvascular and macrovascular complications All-cause mortality, CVD events, microvascular and macrovascular complications

Study time points Remission during 2013-2015 and CVD outcome between 2016-2020 2013-2020

Methodological Approach Multivariable regression models and Cox regression models Group-based trajectory models, multinomial regression models, Cox regression model

Results Baseline characteristics of overall sample;

Association between remission and incidence of CVD outcomes over five-year follow-up;

Size and statistical significance of interaction of remission status (no remission vs remission) with pre-defined subgroups (age, sex, diabetes duration, pre-existing CVD, baseline BMI, baseline HbA1c and number of comorbidities)

Association of above remission-subgroup interactions on CVD events,

microvascular complications,

macrovascular complications;

Association between remission of and incidence of microvascular complications over five-year follow-up by subgroups, Baseline characteristics of overall sample;

Derivation of remission trajectory groups (based on variation in HbA1c levels over the 7 year follow-up period, additionally adjusting for remission status)

Baseline characteristics by remission trajectory;

Association between weight change categories and remission trajectory;

Association between remission-trajectory and CVD outcomes and mortality

Conclusions Achieving remission of type 2 diabetes is associated with a lower risk of microvascular complications, particularly for younger groups and those with fewer comorbidities. Four different trajectories of remission were identified. Risk of CVD outcomes vary by pattern of remission over time, with lowest risk for those in remission longer. People who achieve remission, even for shorter periods of time, continue to benefit from this lower exposure to hyperglycaemia, which may, in turn, lower the risk of CVD outcomes including mortality

In your revised cover letter, please address the following prompts:

** Thank you. We do not have governance permissions to share individual-level data on which these analyses were conducted since they derive from clinical record data. However, direct data requests can be made to the database Electronic Care and Health Information Analytics (CHIA) governance team.

We will update your Data Availability statement on your behalf to reflect the information you provide.

**Thank you.

** We have now added this to page 7 of the manuscript.

** We have now added this to page 24 of the manuscript.

**We have checked and updated the reference list to meet the referencing style in the guidelines. References 1 and 8 have been changed as previous references (as shown in tracked changes) could no longer be accessed.

[Note: HTML markup is below. Please do not edit.]

Reviewers' comments:

Reviewer #1: Abstract

**Thank you for this important point. We agree and have now concluded that “risk of CVD outcomes vary by pattern of remission over time, with lowest risk for those in remission longer. People who achieve remission, even for shorter periods of time, continue to benefit from this lower exposure to hyperglycaemia, which may, in turn, lower the risk of CVD outcomes including mortality.”

INTRODUCTION

**This has now been corrected and appropriate labels are now used throughout the manuscript.

METHODS

The methods and statistical analysis are scientifically sounds. They are satisfactory

**Thank you.

RESULTS

The results are well presented in a logical and comprehensive manner. The tables captures important outcomes and they are properly labelled.

**Thank you.

DISCUSSIONS

**Thank you for this very helpful suggestion. We have now added a detailed discussion section to the paper which more fully discusses the findings of our study, particularly Table 3 and 4. Please see the discussion section on pages 19-22, which includes a section on ‘possible explanations for our findings’.

CONCLUSION

The Researchers need to write a brief and concise conclusion that wraps up the FINDINGS of their research.

**We have revised the conclusion so it is now succinct and focused on our study findings. Please see page 22.

ACKNOWLEDGEMENTS

I have nothing to add here

REFERENCES

They are adequate and appropriate

Tables and Figures

They EXCELLENT and they improve the readability of the manuscript.

**We thank the Reviewer for their helpful and encouraging comments and suggestions.

Abstract:

Please present results without confidence interval in the Abstract, it is recommended give the p-value.

**Thank you for this helpful comment. We feel it is helpful to have the confidence intervals and some readers may be interested in this. However, as the Reviewer suggests, we have also included the p-values in the abstract.

Introduction:

It is not necessary to narrate values of confidence intervals which estimated in other study.

**We agree and have now removed these from the introduction.

Reference 1, inserted two times in a continues sentence, please correct that.

**We have now corrected this.

At first, authors cited reference 3 for saying the effect of interventions and then use that for futility of intervention!

**Thank you for pointing this out. This has now been corrected.

Population, covariates and analysis:

Please report number of missing (and percent) for Weight and HBA1c

**We have now stated this on page 8 line 5.

Authors must give information about generated imputation.

**All data presented in the tables and text here is based on the generated imputation. We describe how data was imputed in the methods section (1st paragraph on page 8).

It is recommended to use SSBIC and CAIC for select better model.

**Thank you for this helpful suggestion. We have used the sample-size BIC (SSBIC) here as well as other criterion as these are the criteria that have been suggested to use for group-based trajectory modelling methodology as cited in the reference 16 and this is the methodology we have followed. The SSBIC considers the likelihood of the model as well as the number of parameters in the model. However, it is very often the case that both the SSBIC and cAIC select the same model.

Author explain about uncertainty in assignment to trajectory, how is it considered?

**Thank you for highlighting this. Uncertainty is always a possibility but reduced by the use of multiple selection criteria (Mesidor et al, 2022). Participants are assigned to the group they have highest probability of belonging. We considered participants as belonging to a group if the classification probability was >0.80.

Mésidor M, Rousseau MC, O'Loughlin J, Sylvestre MP. Does group-based trajectory modeling estimate spurious trajectories? BMC Med Res Methodol. 2022;22(1):194. doi: 10.1186/s12874-022-01622-9.

Please determine the significant value in Analysis method.

**We have included, on page 9, that a p-value of <0.05 was considered as statistical significance in all analyses.

How authors identify the predictors for trajectory groups?

**Predictors of trajectory group membership were assessed with the multivariable regression models. Potential predictors were selected (a priori) and included in these models based on the existing literature and discussion with the research team which included clinical academics.

Please, list the statistical tests used for comparison of variables presented in table 2.

**We report Model F statistics for all test in Table 2. This is because we use multiple imputed data which are better suited to statistical models rather than statistical tests. Regression models were used for continuous variables, logistic regression models for binary variables, and ordered logistic regression models for ordered categorical variables (like IMD). We report this on the footnote of the Table 2 on page 15.

In table 1,

Express all percent for column; it seems that the percent for medications is wrong.

**Thank you for this point and apologies for the confusion. As this is a large table, for comorbidities, smoking status, and prescribed medication we have decided to present the number and percentage for those that have, for example, the comorbidity. We would like to keep the table as it is (to avoid having a very large table), unless the journal requires us to change this.

Please check numbers for Comorbidity, Medication prescribed,

**We have checked the numbers and it is correct.

Author claim that Remission are older than Non-remission, is there a statistical test for it?

**Thanks for highlighting this. We have now fitted appropriate regression models (as described above) and these differences are statistically significant. We report this on lines 7-8 on page 10.

**Thank you for this point. In the text, we refer the reader to Table 2 where we present the p-values for each variable. Table 2 shows gender, IMD, ethnicity, diabetes duration, frailty, and baseline comorbidities amongst others vary across the groups.

Weight is a non-significant variable among 4 trajectory groups; please explain about the reason for estimating the risk ratio in different category of Loss weight.

**This is an important point. The Reviewer is right that there was no significant association between trajectory and weight change (as a continuous variable). However, we know from the literature as well as our previous work that weight change is important for remission and significant associations with remission have been shown using weight change categories (reference 6 and 17 in the paper) We have justified this on lines 11-12 on page 9.

**18 month follow up point was selected based on Figure 1 (graph showing mean HbA1c level over time for each remission group). The graph shows a decrease in HbA1c level for all groups and the largest differences in mean HbA1c level across groups at 18 months.

**We have now presented number and percentages to Table 3.

Authors estimate the risk of trajectory group, so it seems that consider group 4 as reference for comprehensibility and adherence.

**Thank you. Using Group 4 (those that remain in remission throughout study) would not allow us to estimate associations with stable remission. Therefore, we have decided to use Group 3 (the group that have high HbA1c throughout the study period) as the reference so that associations with different degrees of remission can be estimated compared to this group (never achieving remission). This is also consistent with the analyses assessing associations with remission at any time point, where ‘no remission’ is the reference (S1 Table). We have added this to lines 7-9 of page 13.

In ‘Remission and CVD outcomes by remission trajectory’ section;

How set time for subject with multiple complications, please explain that.

**CVD event, microvascular complication and macrovascular complications were each composite measures. For subjects with multiple events, we defined study period as the start of the study until the time the first event occurred. We have added this to the ‘Statistical analysis’ section on page 9. We have used a maximum follow-up of 7 years for all study participants.

Provided descriptions are disagreeable with table, for example there is not significant for G2.

**We have checked Table 3 and Table 3 shows significant association of weight loss of ≥10% with Group 2 membership RRR: 0.89(0.81-0.98), p-value=0.017, both in unadjusted and adjusted models. We have further discussed this finding in the discussion section on page 20.

In table 4, number of complications in each trajectory groups is necessary.

**Thank you for this point. We did not look at the association between number of complications and trajectories as this was not the aim of the study and may also require longer follow-up period than was available to us. Further, Table 4 reports on associations using Cox models and results would not be directly comparable with a regression model more suited to a continuous outcome (number of complications).

In page 10, first paragraph, what is the mean RRR? It seems that author mean was Risk Ratio.

**This means Relative Risk Ratio as it is a multinomial model. We have now defined on first use.

Author must deeply, clinically, and statistically interpretation of all result specifically for table 3 and 4.

** Thank you for this very helpful suggestion. We have now added a detailed discussion section to the paper which more fully discusses the findings of our study, particularly Table 3 and 4. Please see the discussion section on pages 19-22, which includes a section on ‘possible explanations for our findings’.

In page 17, title for figure repeated two times.

**This has now been corrected.

Reference need to be written in Vancouver style.

**Thank you. We have updated to the reference list to Vancouver style.

Attachment

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PLoS One. doi: 10.1371/journal.pone.0290791.r003

Decision Letter 1

Billy Morara Tsima

16 Aug 2023

Type 2 diabetes remission trajectories and variation in risk of diabetes complications: A population-based cohort study

PONE-D-23-05345R1

Dear Dr. Hounkpatin,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

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Additional Editor Comments (optional):

Reviewers' comments:

PLoS One. doi: 10.1371/journal.pone.0290791.r004

Acceptance letter

Billy Morara Tsima

21 Aug 2023

PONE-D-23-05345R1

Type 2 diabetes remission trajectories and variation in risk of diabetes complications: A population-based cohort study

Dear Dr. Hounkpatin:

I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now with our production department.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

S1 Table. Association between remission and incidence of CVD outcomes and mortality over seven-year follow in the CHIA type 2 diabetes cohort.

(DOCX)

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Attachment

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Data Availability Statement

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PERMALINK

Type 2 diabetes remission trajectories and variation in risk of diabetes complications: A population-based cohort study

Hajira Dambha-Miller

Hilda O Hounkpatin

Beth Stuart

Andrew Farmer

Simon Griffin

Roles

Abstract

Introduction

Materials and methods

Design

Data source

Population

Exposure

Outcomes

Covariates

Sociodemographic characteristics

Clinical variables

Medication

Ethics statement

Statistical analysis

Results

Baseline population characteristics

Table 1. Baseline characteristics of the type 2 diabetes cohort within the CHIA database stratified by remission status¥.

Characteristics of groups by remission trajectory

Fig 1. Mean HbA1c level for each remission group over seven-year follow-up within the CHIA type 2 diabetes cohort (n = 60,287).

Table 2. Baseline characteristics by remission group in the CHIA type 2 diabetes cohort (n = 60,287).

Table 3. Multinomial regression showing associations between weight change categories and group membership (compared to group 3).

Remission and CVD outcomes by remission trajectory

Table 4. Association between remission group and CVD outcomes in the CHIA type 2 diabetes cohort.

Discussion

Main findings

Comparison with existing literature

Possible explanation for our findings

Strengths and limitations

Conclusions

Supporting information

Data Availability

Funding Statement

References

Decision Letter 0

Billy Morara Tsima

Roles

Author response to Decision Letter 0

Decision Letter 1

Billy Morara Tsima

Roles

Acceptance letter

Billy Morara Tsima

Roles

Associated Data

Supplementary Materials

Data Availability Statement

ACTIONS

PERMALINK

RESOURCES

Similar articles

Cited by other articles

Links to NCBI Databases

Table 1. Baseline characteristics of the type 2 diabetes cohort within the CHIA database stratified by remission status^¥.

Fig 1. Mean HbA_1c level for each remission group over seven-year follow-up within the CHIA type 2 diabetes cohort (n = 60,287).