Trends in Risk Factor Control and Treatment Among Patients With Non-Alcoholic Fatty Liver Disease and Type 2 Diabetes Between 2000 and 2020

A Territory-Wide Study

Xinrong Zhang; Terry Cheuk-Fung Yip; Yee-Kit Tse; Vicki Wing-Ki Hui; Guanlin Li; Huapeng Lin; Lilian Yan Liang; Jimmy Che-To Lai; Mandy Sze-Man Lai; Johnny T. K. Cheung; Henry Lik-Yuen Chan; Stephen Lam Chan; Alice Pik-Shan Kong; Grace Lai-Hung Wong; Vincent Wai-Sun Wong

Disclosures

Aliment Pharmacol Ther. 2023;57(10):1103-1116.

Abstract and Introduction

Abstract

Background & Aims: We aimed to determine the trends in risk factor control and treatment among patients with non-alcoholic fatty liver disease (NAFLD) and type 2 diabetes (T2D) in 2000–2020.

Methods: We conducted a territory-wide cohort study of adult patients with NAFLD and T2D diagnosed between 1 January 2000 and 31 July 2021 in Hong Kong. T2D was defined by use of any anti-diabetic agents, laboratory tests and/or diagnosis codes.

Results: This study included 16,084 patients with NAFLD and T2D (mean age, 54.8 ± 12.0 years; 7124 male [44.3%]). The percentage of patients achieving individualised haemoglobin A_1c (HbA_1c) targets increased from 44.5% (95% confidence interval [CI], 42.9–46.1) to 64.8% (95% CI, 64.1–65.5), and percentage of patients achieving individualised low-density lipoprotein-cholesterol (LDL-C) targets increased from 23.3% (95% CI, 21.9–24.7) to 54.3% (95% CI, 53.5–55.1) from 2000–2005 to 2016–2020, whereas percentage of patients achieving blood pressure control (<140/90 mm Hg) remained static at 53.1–57.2%. Combination therapy for diabetes increased, especially among those with poor glycaemic control, but there was no increase in combination therapy for hypertension. Fewer cirrhotic patients achieved blood pressure control and individualised LDL-C targets, but they were more likely to achieve individualised HbA_1c targets than non-cirrhotics. Metformin and statins were underused in cirrhotic patients. Younger patients (18–44 years) were less likely to achieve individualised HbA_1c targets than middle-aged (45–64 years) and older ones (≥65 years).

Conclusions: From 2000 to 2020, glycaemic and lipid control improved significantly, whereas blood pressure control remained static among patients with NAFLD and T2D.

Introduction

Non-alcoholic fatty liver disease (NAFLD) is rapidly growing as the most prevalent chronic liver disease worldwide, with the pooled global prevalence of NAFLD is 30.9% in 2019.^[1] NAFLD is closely associated with type 2 diabetes (T2D), and both conditions synergistically increase the risk of adverse clinical events.^[2] An umbrella review of meta-analyses showed that subjects with NAFLD had 1.9-fold risk of diabetes compared with those without the disease.^[3] T2D is also a well-established risk factor for the progression of NAFLD to non-alcoholic steatohepatitis (NASH), cirrhosis and hepatocellular carcinoma (HCC).^[4,5]

The global prevalence of T2D has been rapidly increasing over the past few decades.^[6] It can result in microvascular and macrovascular complications and represents a huge healthcare burden. Good glycaemic, blood pressure and lipid control can lower the risk of diabetic complications and liver-related outcomes, especially when all three risk factor control goals are achieved.^[7,8] However, data on the trends of risk factor control are still contradictory. The proportion of US adults who achieved the targets of risk factor control increased from 1999 to 2010,^[9] whereas another US study indicated that there was no significant improvement in risk factor control between 2005 and 2016.^[10]

Knowledge on risk factor control can shed light on areas of deficiency where interventions can make a difference. In the past few decades, novel drugs and combination therapy have allowed clinicians to manage the risk factors better.^[11] Evolving clinical guidelines also recommend the use of combination therapy for risk factor control in clinical practice.^[12]

There are a number of reasons why risk factor control in patients with NAFLD and T2D should be examined specifically. Some classes of anti-diabetic drugs such as thiazolidinediones (TZD), glucagon-like peptide-1 receptor agonists (GLP-1As) and sodium-glucose co-transporter-2 (SGLT2) inhibitors may improve hepatic steatosis and necroinflammation in patients with NASH.^[13–15] A number of metabolic drugs such as metformin, statins and aspirin may also reduce the risk of HCC and hepatic decompensation in patients with chronic liver disease.^[16–18] On the flip side, misconceptions among clinicians and patients often prevent the use of beneficial metabolic treatments in patients with chronic liver disease. One notable example is the underuse of statins in patients with NAFLD due to the overstated risk of hepatotoxicity despite overwhelming evidence of their cardiovascular benefits.^[19,20] Therefore, determining trends in risk factor control and treatment is critical for the management of NAFLD and T2D. However, few studies have reported the status of risk factor control and treatment in this special population.

Using a territory-wide cohort from Hong Kong, we aimed to study the secular trends in risk factor control and treatment among patients with NAFLD and T2D from 2000 through 2020. Our central hypothesis was that with the availability of new treatments and increased disease awareness, risk factor control would improve among patients with NAFLD and T2D from 2000 through 2020.

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Abstract and Introduction
Methods
Results
Discussion
References

Table 1. Clinical characteristics of patients with NAFLD and type 2 diabetes, 2000–2005 to 2016–2020.
Characteristics	2000–2005 N = 3692	2006–2010 N = 7075	2011–2015 N = 11,552	2016–2020 N = 16,084	p value for linear trend
Age at T2D diagnosis (years)	54.5 ± 12.4	54.1 ± 12.2	54.4 ± 12.0	54.8 ± 12.0	0.080
Male gender, n (%)	1581 (42.8)	3093 (43.7)	5097 (44.1)	7124 (44.3)	0.111
BMI, kg/m²	27.6 ± 4.3	28.0 ± 4.4	28.2 ± 4.5	28.2 ± 4.6	<0.001
Blood pressure (mm Hg)
Systolic	138 (126–151)	136 (124–148)	134 (123–145)	146 (132–160)	<0.001
Diastolic	79 (70–85)	78 (70–85)	77 (70–84)	82 (74–92)	<0.001
Educational level, n (%)					<0.001
Missing, n (%)	1834 (49.7)	3758 (53.1)	6505 (56.3)	9398 (58.4)
No schooling	252 (6.8)	402 (5.7)	561 (4.9)	652 (4.1)
Primary education	728 (19.7)	1229 (17.4)	1741 (15.1)	2161 (13.4)
Secondary education	723 (19.6)	1366 (19.3)	2201 (19.1)	3091 (19.2)
Tertiary education	118 (3.2)	259 (3.7)	439 (3.8)	647 (4.0)
Unknown	37 (1.0)	61 (0.9)	105 (0.9)	135 (0.8)
Laboratory parameters^a
Albumin (g/L)	42.1 ± 4.3	42.4 ± 4.6	42.7 ± 4.4	42.6 ± 4.2	<0.001
Total bilirubin (μmol/L)	10.0 (8.0–14.5)	11.0 (8.1–15.3)	11.0 (8.1–15.0)	10.9 (8.0–14.0)	0.229
ALT (IU/L)	38 (23–63)	39 (24–65)	38 (24–61)	40 (25–63)	0.002
AST (IU/L)	30 (22–45)	32 (22–49)	31 (22–47)	34 (23–51)	0.018
Fasting glucose (mmol/L)	8.6 ± 3.1	8.1 ± 2.8	7.6 ± 2.5	7.6 ± 2.4	<0.001
HbA_1c (%)	7.9 ± 1.8	7.6 ± 1.8	7.4 ± 1.5	7.2 ± 1.4	<0.001
Total cholesterol (mmol/L)	5.4 ± 1.2	5.2 ± 1.1	5.1 ± 1.1	4.6 ± 1.1	<0.001
HDL-cholesterol (mmol/L)	1.2 ± 0.3	1.2 ± 0.3	1.2 ± 0.3	1.2 ± 0.3	0.357
LDL-cholesterol (mmol/L)	3.2 ± 1.0	3.1 ± 1.0	3.1 ± 0.9	2.5 ± 0.9	<0.001
Triglycerides (mmol/L)	1.9 (1.4–2.7)	1.8 (1.3–2.6)	1.7 (1.2–2.3)	1.6 (1.2–2.3)	<0.001
Platelet count (×10⁹/L)	257 ± 77	257 ± 76	241 ± 71	245 ± 73	<0.001
Creatinine (μmol/L)	81.0 (69.0–96.0)	76.0 (63.0–91.0)	73.0 (61.0–88.0)	72.0 (61.0–88.0)	0.008
Comorbidities, n (%)^b
Hypertension^c	2795 (75.7)	5649 (79.8)	9321 (80.7)	13,657 (84.9)	<0.001
Dyslipidaemia^d	2560 (69.3)	5392 (76.2)	9840 (85.2)	14,504 (90.2)	<0.001
Coronary heart disease	322 (8.7)	759 (10.7)	1293 (11.2)	2075 (12.9)	<0.001
Congestive heart failure	133 (3.6)	395 (5.6)	729 (6.3)	1037 (6.4)	<0.001
Cerebrovascular events
Ischaemic	136 (3.7)	382 (5.4)	688 (6.0)	926 (5.8)	<0.001
Haemorrhagic	15 (0.4)	57 (0.8)	132 (1.1)	181 (1.1)	<0.001
Cirrhosis^e	68 (1.8)	215 (3.0)	423 (3.7)	724 (4.5)	<0.001
Chronic kidney disease^f	583 (15.8)	872 (12.3)	1131 (9.8)	1345 (8.4)	<0.001
Duration of NAFLD (years)	2.1 (1.3–3.5)	2.9 (1.2–6.3)	5.7 (3.7–8.5)	1.4 (1.0–10.2)	<0.001

Abbreviations: ALT, alanine aminotransferase; AST, aspartate aminotransferase; BMI, body mass index; eGFR, estimated glomerular filtration rate; HbA_1c, haemoglobin A_1c; HDL, high-density lipoprotein; LDL, low-density lipoprotein; NAFLD, non-alcoholic fatty liver disease.
^aLaboratory parameters were based on the first occurrence values in different time periods from 2000–2005 to 2016–2020, respectively.
^bThe presence of comorbidities were based on the first occurrence in different time periods from 2000–2005 to 2016–2020, respectively. If a patient had comorbidities first time in one period, this patient would be considered to have these comorbidities in the following period until the last follow-up year 2020.
^cHypertension was defined by the use of anti-hypertensive drugs, a resting blood pressure of ≥140/90 mmHg and/or ICD-9-CM diagnosis codes.
^dDyslipidaemia was defined by the use of lipid lowering drugs, triglyceride ≥1.7 mmol/L, low high-density lipoprotein (HDL) <1.03 mmol/L in men or <1.29 mmol/L in women, high low-density lipoprotein (LDL) ≥4.1 mmol/L and/or ICD-9-CM diagnosis codes for hyperlipidaemia.
^eCirrhosis was defined by ICD-9-CM diagnosis codes for cirrhosis and its related complications, a fibrosis-4 index >3.25 and/or an aspartate aminotransferase-to-platelet ratio index >2.
^fChronic kidney disease was defined as eGFR <60 mL/min/1.73 m².

Table 2. Factors associated with achieving selected risk factor control goals among patients with NAFLD and type 2 diabetes from 2000 to 2020.
	Individualised HbA_1c targets^a		BP control target (<140/90 mmHg)^b		Individualised LDL-C targets^c		All three risk factor control target^d
	Adjusted OR (95% CI)	p value	Adjusted OR (95% CI)	p value	Adjusted OR (95% CI)	p value	Adjusted OR (95% CI)	p value
Age group (years)
18–44	1 (ref)		1 (ref)		1 (ref)		1 (ref)
45–64	4.97 (4.51–5.47)	<0.001	0.76 (0.70–0.83)	<0.001	1.17 (1.07–1.28)	0.001	3.11 (2.69–3.60)	<0.001
≥65	15.76 (13.53–18.37)	<0.001	0.50 (0.45–0.57)	<0.001	1.05 (0.93–1.18)	0.429	3.37 (2.84–4.00)	<0.001
Sex
Female	1 (ref)		1 (ref)		1 (ref)		1 (ref)
Male	1.12 (1.04–1.22)	0.004	1.03 (0.96–1.10)	0.418	1.12 (1.04–1.20)	0.002	1.14 (1.05–1.25)	0.003
BMI (kg/m²)
<23	1 (ref)		1 (ref)		1 (ref)		1 (ref)
≥23 to <25	1.05 (0.89–1.25)	0.565	0.89 (0.77–1.03)	0.114	0.99 (0.86–1.15)	0.894	0.98 (0.83–1.17)	0.850
≥25	0.99 (0.85–1.14)	0.839	0.74 (0.66–0.84)	<0.001	0.93 (0.83–1.05)	0.265	0.85 (0.74–0.99)	0.035
Fasting glucose (mmol/L)	0.82 (0.81–0.83)	<0.001	0.99 (0.98–1.00)	0.148	1.01 (0.99–1.02)	0.340	0.92 (0.90–0.94)	<0.001
Time-average HbA_1c (%)	N/A	N/A	1.02 (0.99–1.05)	0.151	1.01 (0.98–1.04)	0.620	N/A	N/A
Hypertension	0.68 (0.61–0.77)	<0.001	N/A	N/A	1.73 (1.57–1.91)	<0.001	N/A	N/A
Dyslipidaemia	0.85 (0.74–0.97)	0.017	1.16 (1.03–1.29)	0.011	N/A	N/A	N/A	N/A
Coronary heart disease	1.77 (1.55–2.02)	<0.001	0.80 (0.73–0.89)	<0.001	0.43 (0.38–0.47)	<0.001	0.64 (0.56–0.74)	<0.001
Stroke	1.32 (1.11–1.57)	0.002	0.69 (0.61–0.79)	<0.001	0.35 (0.30–0.41)	<0.001	0.51 (0.41–0.63)	<0.001
Cirrhosis	0.87 (0.71–1.06)	0.157	0.94 (0.80–1.10)	0.454	1.06 (0.90–1.25)	0.490	0.94 (0.76–1.15)	0.530
CKD	1.88 (1.56–2.26)	<0.001	0.68 (0.60–0.77)	<0.001	0.32 (0.28–0.36)	<0.001	0.43 (0.35–0.52)	<0.001
Duration of NAFLD (year)	1.01 (1.01–1.02)	<0.001	1.03 (1.02–1.03)	<0.001	1.03 (1.02–1.03)	<0.001	1.04 (1.04–1.05)	<0.001

Note: Analysis was by binary logistic regression in patients with NAFLD and T2D from 2000–2020. Before performing binary logistic regression, missing data were assumed missing at random and were replaced with substituted values by multiple imputations. Odds ratio with 95% confidence intervals were adjusted for age, sex, BMI, fasting glucose, time-average HbA_1c, hypertension, dyslipidaemia, coronary heart disease, stroke, cirrhosis, CKD and duration of NAFLD. Educational level was not adjusted due to high percentage of missing values.
In the glycaemic control analysis, time-average HbA_1c was not adjusted to avoid overlap. In the BP control analysis, hypertension was not adjusted to avoid overlap. In the lipid control analysis, dyslipidaemia was not adjusted to avoid overlap. In achieving all three risk factor control analysis, time-average HbA_1c, hypertension and dyslipidaemia were all not adjusted to avoid overlap.
Abbreviations: BMI, body mass index; BP, blood pressure; CKD, chronic kidney disease; HbA_1c, haemoglobin A_1c; LDL-C, low-density lipoprotein-cholesterol; NAFLD, non-alcoholic fatty liver disease.
^aIndividualised HbA_1c targets were specified as follows: time-average HbA_1c less than 6.5% for young adults aged 18–44 years without complications, less than 7.0% for both young adults with complications and middle-aged adults aged 45–64 years without complications, less than 8.0% for both middle-aged adults and older adults aged 65 years or older with complications, and less than 7.5% for older adults without complications. Complications were defined as any of the following: having cardiovascular disease (congestive heart failure, coronary heart disease, heart attack, or stroke), peripheral vascular disease, retinopathy, a spot urine albumin-creatinine ratio >3.2 mg/mmol or 24-h urine albumin levels >20 mg/days, or estimated glomerular filtration rate less than 60 mL/min/1.73 m².
^bBlood pressure control was defined as mean systolic and diastolic blood pressures of less than 140/90 mmHg.
^cIndividualised LDL-C targets were defined as patients with T2D with any complications or target organ damage to have time-average LDL-C target <1.8 mmol/L; patients with T2D without any complication or target organ damage to have time-average LDL-C target <2.6 mmol/L.
^dAll three risk factor control target was defined by achieving a composite of individualised HbA_1c and LDL-C targets and BP control target (<140/90 mmHg).

Authors and Disclosures

Xinrong Zhang^1–3, Terry Cheuk-Fung Yip^1–3, Yee-Kit Tse^1–3, Vicki Wing-Ki Hui^1–3, Guanlin Li^1–3, Huapeng Lin^1–3, Lilian Yan Liang^1–3, Jimmy Che-To Lai^1–3, Mandy Sze-Man Lai^1–3, Johnny T. K. Cheung¹, Henry Lik-Yuen Chan^2,4, Stephen Lam Chan^5,6, Alice Pik-Shan Kong¹, Grace Lai-Hung Wong^1–3 and Vincent Wai-Sun Wong^1–3

¹Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Hong Kong, China
²Medical Data Analytics Centre, The Chinese University of Hong Kong, Hong Kong, China
³State Key Laboratory of Digestive Disease, The Chinese University of Hong Kong, Hong Kong, China
⁴Department of Internal Medicine, Union Hospital, Hong Kong, China
⁵Department of Clinical Oncology, The Chinese University of Hong Kong, Hong Kong, China
⁶State Key Laboratory of Translational Oncology, The Chinese University of Hong Kong, Hong Kong, China

Correspondence
Grace Lai-Hung Wong, Department of Medicine and Therapeutics, 9/F, Clinical Sciences Building, Prince of Wales Hospital, 30–32 Ngan Shing Street, Shatin, Hong Kong, China. Email: wonglaihung@cuhk.edu.hk
Vincent Wai-Sun Wong, Department of Medicine and Therapeutics, 9/F, Clinical Sciences Building, Prince of Wales Hospital, 30–32 Ngan Shing Street, Shatin, Hong Kong, China. Email: wongv@cuhk.edu.hk

Xinrong Zhang and Terry Cheuk-Fung Yip contributed equally to this work.

Author Contributions
Xinrong Zhang: Conceptualization (equal); data curation (equal); formal analysis (equal); investigation (equal); methodology (equal); writing – original draft (lead); writing – review and editing (equal). Terry Cheuk-Fung Yip: Data curation (equal); formal analysis (equal); funding acquisition (equal); methodology (equal); writing – review and editing (equal). Yee-Kit Tse: Formal analysis (equal); writing – review and editing (equal). Vicki Wing-Ki Hui: Data curation (equal); writing – review and editing (equal). Guanlin Li: Writing – review and editing (equal). Huapeng Lin: Writing – review and editing (equal). Lilian Yan Liang: Writing – review and editing (equal). Jimmy Che-To Lai: Writing – review and editing (equal). Mandy Sze-Man Lai: Methodology (supporting). Johnny T. K. Cheung: Writing – review and editing (equal). Henry Lik-Yuen Chan: Supervision (equal); writing – review and editing (equal). Stephen Lam Chan: Writing – review and editing (equal). Alice Pik-Shan Kong: Supervision (equal); writing – review and editing (equal). Grace Lai-Hung Wong: Data curation (equal); project administration (equal); supervision (equal); writing – original draft (equal); writing – review and editing (equal). Vincent Wai-Sun Wong: Conceptualization (equal); data curation (equal); formal analysis (equal); funding acquisition (equal); project administration (equal); supervision (lead); writing – original draft (supporting); writing – review and editing (equal).

Conflict of Interest Statement
Terry Yip has served as an advisory committee member and a speaker for Gilead Sciences. Henry Chan has served as an Independent Non-Executive Director for Shanghai Henlius Biotech Inc; as an advisory board member for Aligos, Aptorum, Arbutus, Hepion, Janssen, Gilead, Glaxo-Smith-Kline, Roche, Vaccitech, Vir Biotechnology and Virion Therapeutics; and as a speaker for Gilead, Roche and Viatris. Grace Wong has served as an advisory committee member for Gilead Sciences and Janssen, and as a speaker for Abbott, Abbvie, Ascletis, Bristol-Myers Squibb, Echosens, Gilead Sciences, Janssen and Roche. She has also received a research grant from Gilead Sciences. Vincent Wong has served as an advisory committee member for AbbVie, Boehringer Ingelheim, Echosens, Gilead Sciences, Inventiva, Merck, Novo Nordisk, Pfizer, ProSciento, Sagimet Biosciences and TARGET PharmaSolutions; and a speaker for Abbott, AbbVie, Echosens, Gilead Sciences and Novo Nordisk. He has also received a research grant from Gilead Sciences and is a co-founder of Illuminatio Medical Technology Limited. Stephen L. Chan has served as an advisory committee member for Astra-Zeneca, Eisai, Ipsen, MSD and Novartis, and a speaker for Astra-Zeneca, BMS, Eisai, Ipsen and MSD. Alice Kong has received honorarium for consultancy or giving lectures from Abbott, Astra Zeneca, Bayer, Boehringer Ingelheim, Eli-Lilly, Kyowa Kirin, Merck Serono, Nestle, Novo-Nordisk, Pfizer and Sanofi.

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Trends in Risk Factor Control and Treatment Among Patients With Non-Alcoholic Fatty Liver Disease and Type 2 Diabetes Between 2000 and 2020

A Territory-Wide Study

Abstract and Introduction

Abstract

Introduction

Tables

Tables

References

Authors and Disclosures

Authors and Disclosures

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